diff --git a/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb index ac8e6a3..daa6417 100644 --- a/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb +++ b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb @@ -75,7 +75,9 @@ "misc2 = [\"cards_yellow\",\"cards_red\",\"cards_yellow_red\",\"fouls\",\"fouled\",\"offsides\",\"crosses\",\"interceptions\",\"tackles_won\",\"pens_won\",\"pens_conceded\",\"own_goals\",\"ball_recoveries\",\"aerials_won\",\"aerials_lost\",\"aerials_won_pct\"]\n", "\n", "def get_tables(url,text):\n", - " res = requests.get(url)\n", + " headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}\n", + "\n", + " res = requests.get(url, headers = headers)\n", " ## The next two lines get around the issue with comments breaking the parsing.\n", " comm = re.compile(\"\")\n", " soup = BeautifulSoup(comm.sub(\"\",res.text),'lxml')\n", @@ -281,59 +283,107 @@ " \n", " \n", " 0\n", + " Oliver Abildgaard\n", + " dk DEN\n", + " MF\n", + " Hellas Verona\n", + " 26-250\n", + " 1996\n", + " 2.0\n", + " 0.0\n", + " 37.0\n", + " 0.0\n", + " ...\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 5.0\n", + " 3.0\n", + " 1.0\n", + " 75.0\n", + " \n", + " \n", + " 1\n", " Tammy Abraham\n", " eng ENG\n", " FW\n", " Roma\n", - " 25-118\n", + " 25-136\n", " 1997\n", - " 19.0\n", - " 15.0\n", - " 1338.0\n", - " 5.0\n", + " 22.0\n", + " 18.0\n", + " 1582.0\n", + " 6.0\n", " ...\n", - " 19.0\n", - " 29.0\n", - " 8.0\n", + " 23.0\n", + " 36.0\n", + " 9.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 28.0\n", - " 35.0\n", - " 32.0\n", - " 52.2\n", + " 33.0\n", + " 45.0\n", + " 42.0\n", + " 51.7\n", " \n", " \n", - " 1\n", + " 2\n", + " Christian Acella\n", + " it ITA\n", + " MF\n", + " Cremonese\n", + " 20-223\n", + " 2002\n", + " 1.0\n", + " 0.0\n", + " 15.0\n", + " 0.0\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " 3\n", " Francesco Acerbi\n", " it ITA\n", " DF\n", " Inter\n", - " 34-352\n", + " 35-005\n", " 1988\n", - " 12.0\n", - " 10.0\n", - " 930.0\n", + " 15.0\n", + " 13.0\n", + " 1200.0\n", " 0.0\n", " ...\n", - " 7.0\n", - " 7.0\n", + " 9.0\n", + " 9.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 52.0\n", - " 25.0\n", - " 18.0\n", - " 58.1\n", + " 66.0\n", + " 38.0\n", + " 21.0\n", + " 64.4\n", " \n", " \n", - " 2\n", + " 4\n", " Yacine Adli\n", " fr FRA\n", " MF,FW\n", " Milan\n", - " 22-183\n", + " 22-201\n", " 2000\n", " 4.0\n", " 1.0\n", @@ -352,54 +402,6 @@ " 0.0\n", " \n", " \n", - " 3\n", - " Michel Aebischer\n", - " ch SUI\n", - " FW,MF\n", - " Bologna\n", - " 26-022\n", - " 1997\n", - " 17.0\n", - " 8.0\n", - " 767.0\n", - " 1.0\n", - " ...\n", - " 10.0\n", - " 14.0\n", - " 5.0\n", - " 1.0\n", - " 1.0\n", - " 0.0\n", - " 42.0\n", - " 5.0\n", - " 8.0\n", - " 38.5\n", - " \n", - " \n", - " 4\n", - " Felix Afena-Gyan\n", - " gh GHA\n", - " FW,MF\n", - " Cremonese\n", - " 20-009\n", - " 2003\n", - " 10.0\n", - " 2.0\n", - " 296.0\n", - " 0.0\n", - " ...\n", - " 7.0\n", - " 3.0\n", - " 4.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 14.0\n", - " 8.0\n", - " 14.0\n", - " 36.4\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -424,12 +426,12 @@ " ...\n", " \n", " \n", - " 514\n", + " 540\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 20-309\n", + " 20-327\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -448,12 +450,12 @@ " 0.0\n", " \n", " \n", - " 515\n", + " 541\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Fiorentina\n", - " 25-125\n", + " 25-143\n", " 1997\n", " 2.0\n", " 0.0\n", @@ -472,12 +474,12 @@ " 50.0\n", " \n", " \n", - " 516\n", + " 542\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Spezia\n", - " 25-125\n", + " 25-143\n", " 1997\n", " 1.0\n", " 0.0\n", @@ -496,112 +498,112 @@ " 0.0\n", " \n", " \n", - " 517\n", + " 543\n", " Milan Đurić\n", " ba BIH\n", " FW\n", " Hellas Verona\n", - " 32-251\n", + " 32-269\n", " 1990\n", - " 15.0\n", - " 6.0\n", - " 618.0\n", + " 16.0\n", + " 7.0\n", + " 703.0\n", " 1.0\n", " ...\n", " 15.0\n", - " 13.0\n", - " 2.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " 14.0\n", - " 103.0\n", - " 27.0\n", - " 79.2\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 16.0\n", + " 113.0\n", + " 28.0\n", + " 80.1\n", " \n", " \n", - " 518\n", + " 544\n", " Filip Đuričić\n", " rs SRB\n", " MF,FW\n", " Sampdoria\n", - " 30-363\n", + " 31-016\n", " 1992\n", + " 21.0\n", " 18.0\n", - " 15.0\n", - " 1201.0\n", + " 1397.0\n", " 2.0\n", " ...\n", - " 19.0\n", - " 32.0\n", + " 23.0\n", + " 35.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 67.0\n", - " 7.0\n", - " 11.0\n", - " 38.9\n", + " 78.0\n", + " 8.0\n", + " 13.0\n", + " 38.1\n", " \n", " \n", "\n", - "

519 rows × 120 columns

\n", + "

545 rows × 115 columns

\n", "" ], "text/plain": [ - " player nationality position team age birth_year \\\n", - "0 Tammy Abraham eng ENG FW Roma 25-118 1997 \n", - "1 Francesco Acerbi it ITA DF Inter 34-352 1988 \n", - "2 Yacine Adli fr FRA MF,FW Milan 22-183 2000 \n", - "3 Michel Aebischer ch SUI FW,MF Bologna 26-022 1997 \n", - "4 Felix Afena-Gyan gh GHA FW,MF Cremonese 20-009 2003 \n", - ".. ... ... ... ... ... ... \n", - "514 Petar Zovko ba BIH GK Spezia 20-309 2002 \n", - "515 Szymon Żurkowski pl POL MF Fiorentina 25-125 1997 \n", - "516 Szymon Żurkowski pl POL MF Spezia 25-125 1997 \n", - "517 Milan Đurić ba BIH FW Hellas Verona 32-251 1990 \n", - "518 Filip Đuričić rs SRB MF,FW Sampdoria 30-363 1992 \n", + " player nationality position team age birth_year \\\n", + "0 Oliver Abildgaard dk DEN MF Hellas Verona 26-250 1996 \n", + "1 Tammy Abraham eng ENG FW Roma 25-136 1997 \n", + "2 Christian Acella it ITA MF Cremonese 20-223 2002 \n", + "3 Francesco Acerbi it ITA DF Inter 35-005 1988 \n", + "4 Yacine Adli fr FRA MF,FW Milan 22-201 2000 \n", + ".. ... ... ... ... ... ... \n", + "540 Petar Zovko ba BIH GK Spezia 20-327 2002 \n", + "541 Szymon Żurkowski pl POL MF Fiorentina 25-143 1997 \n", + "542 Szymon Żurkowski pl POL MF Spezia 25-143 1997 \n", + "543 Milan Đurić ba BIH FW Hellas Verona 32-269 1990 \n", + "544 Filip Đuričić rs SRB MF,FW Sampdoria 31-016 1992 \n", "\n", " games games_starts minutes goals ... fouls fouled offsides \\\n", - "0 19.0 15.0 1338.0 5.0 ... 19.0 29.0 8.0 \n", - "1 12.0 10.0 930.0 0.0 ... 7.0 7.0 1.0 \n", - "2 4.0 1.0 116.0 0.0 ... 3.0 1.0 0.0 \n", - "3 17.0 8.0 767.0 1.0 ... 10.0 14.0 5.0 \n", - "4 10.0 2.0 296.0 0.0 ... 7.0 3.0 4.0 \n", + "0 2.0 0.0 37.0 0.0 ... 1.0 1.0 0.0 \n", + "1 22.0 18.0 1582.0 6.0 ... 23.0 36.0 9.0 \n", + "2 1.0 0.0 15.0 0.0 ... 0.0 0.0 0.0 \n", + "3 15.0 13.0 1200.0 0.0 ... 9.0 9.0 1.0 \n", + "4 4.0 1.0 116.0 0.0 ... 3.0 1.0 0.0 \n", ".. ... ... ... ... ... ... ... ... \n", - "514 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", - "515 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", - "516 1.0 0.0 8.0 0.0 ... 0.0 0.0 0.0 \n", - "517 15.0 6.0 618.0 1.0 ... 15.0 13.0 2.0 \n", - "518 18.0 15.0 1201.0 2.0 ... 19.0 32.0 0.0 \n", + "540 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "541 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "542 1.0 0.0 8.0 0.0 ... 0.0 0.0 0.0 \n", + "543 16.0 7.0 703.0 1.0 ... 15.0 14.0 3.0 \n", + "544 21.0 18.0 1397.0 2.0 ... 23.0 35.0 0.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 1.0 0.0 0.0 28.0 35.0 \n", - "1 0.0 0.0 0.0 52.0 25.0 \n", - "2 0.0 0.0 0.0 5.0 0.0 \n", - "3 1.0 1.0 0.0 42.0 5.0 \n", - "4 0.0 0.0 0.0 14.0 8.0 \n", + "0 0.0 0.0 0.0 5.0 3.0 \n", + "1 1.0 0.0 0.0 33.0 45.0 \n", + "2 0.0 0.0 0.0 1.0 0.0 \n", + "3 0.0 0.0 0.0 66.0 38.0 \n", + "4 0.0 0.0 0.0 5.0 0.0 \n", ".. ... ... ... ... ... \n", - "514 0.0 0.0 0.0 2.0 0.0 \n", - "515 0.0 0.0 0.0 2.0 1.0 \n", - "516 0.0 0.0 0.0 3.0 0.0 \n", - "517 0.0 0.0 0.0 14.0 103.0 \n", - "518 0.0 0.0 0.0 67.0 7.0 \n", + "540 0.0 0.0 0.0 2.0 0.0 \n", + "541 0.0 0.0 0.0 2.0 1.0 \n", + "542 0.0 0.0 0.0 3.0 0.0 \n", + "543 0.0 0.0 0.0 16.0 113.0 \n", + "544 0.0 0.0 0.0 78.0 8.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 32.0 52.2 \n", - "1 18.0 58.1 \n", - "2 3.0 0.0 \n", - "3 8.0 38.5 \n", - "4 14.0 36.4 \n", + "0 1.0 75.0 \n", + "1 42.0 51.7 \n", + "2 0.0 0.0 \n", + "3 21.0 64.4 \n", + "4 3.0 0.0 \n", ".. ... ... \n", - "514 0.0 0.0 \n", - "515 1.0 50.0 \n", - "516 0.0 0.0 \n", - "517 27.0 79.2 \n", - "518 11.0 38.9 \n", + "540 0.0 0.0 \n", + "541 1.0 50.0 \n", + "542 0.0 0.0 \n", + "543 28.0 80.1 \n", + "544 13.0 38.1 \n", "\n", - "[519 rows x 120 columns]" + "[545 rows x 115 columns]" ] }, "execution_count": 3, @@ -674,23 +676,23 @@ " it ITA\n", " GK\n", " Sampdoria\n", - " 26-010\n", + " 26-028\n", " 1997\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 32.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 36.0\n", " ...\n", - " 38.8\n", - " 142.0\n", - " 57.7\n", - " 44.5\n", - " 243.0\n", - " 15.0\n", - " 6.2\n", + " 39.4\n", + " 178.0\n", + " 57.3\n", + " 44.4\n", + " 315.0\n", + " 21.0\n", + " 6.7\n", " 20.0\n", - " 1.05\n", - " 15.4\n", + " 0.91\n", + " 14.1\n", " \n", " \n", " 1\n", @@ -698,23 +700,23 @@ " it ITA\n", " GK\n", " Cremonese\n", - " 22-211\n", + " 22-229\n", " 2000\n", - " 10.0\n", - " 10.0\n", - " 900.0\n", - " 14.0\n", - " ...\n", - " 40.0\n", - " 66.0\n", - " 59.1\n", - " 49.3\n", - " 108.0\n", - " 10.0\n", - " 9.3\n", - " 7.0\n", - " 0.70\n", " 13.0\n", + " 13.0\n", + " 1170.0\n", + " 21.0\n", + " ...\n", + " 39.3\n", + " 94.0\n", + " 56.4\n", + " 47.3\n", + " 174.0\n", + " 13.0\n", + " 7.5\n", + " 8.0\n", + " 0.62\n", + " 12.9\n", " \n", " \n", " 2\n", @@ -722,23 +724,23 @@ " it ITA\n", " GK\n", " Sassuolo\n", - " 36-001\n", + " 36-019\n", " 1987\n", - " 17.0\n", - " 17.0\n", - " 1530.0\n", - " 26.0\n", + " 20.0\n", + " 20.0\n", + " 1800.0\n", + " 30.0\n", " ...\n", - " 34.4\n", - " 157.0\n", - " 31.8\n", - " 33.3\n", - " 220.0\n", - " 13.0\n", - " 5.9\n", - " 15.0\n", - " 0.88\n", - " 14.5\n", + " 34.1\n", + " 179.0\n", + " 33.5\n", + " 34.2\n", + " 276.0\n", + " 17.0\n", + " 6.2\n", + " 23.0\n", + " 1.15\n", + " 14.9\n", " \n", " \n", " 3\n", @@ -746,23 +748,23 @@ " it ITA\n", " GK\n", " Monza\n", - " 25-185\n", + " 25-203\n", " 1997\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 28.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 30.0\n", " ...\n", " 33.0\n", - " 115.0\n", - " 31.3\n", - " 31.3\n", - " 246.0\n", + " 138.0\n", + " 36.2\n", + " 33.6\n", + " 313.0\n", " 10.0\n", - " 4.1\n", - " 12.0\n", - " 0.63\n", - " 13.4\n", + " 3.2\n", + " 16.0\n", + " 0.73\n", + " 13.0\n", " \n", " \n", " 4\n", @@ -770,23 +772,23 @@ " pl POL\n", " GK\n", " Spezia\n", - " 25-162\n", + " 25-180\n", " 1997\n", - " 18.0\n", - " 18.0\n", - " 1572.0\n", - " 29.0\n", + " 20.0\n", + " 20.0\n", + " 1752.0\n", + " 34.0\n", " ...\n", - " 34.4\n", - " 106.0\n", - " 63.2\n", - " 47.9\n", - " 235.0\n", + " 34.9\n", + " 122.0\n", + " 62.3\n", + " 47.1\n", + " 270.0\n", " 9.0\n", - " 3.8\n", + " 3.3\n", " 24.0\n", - " 1.37\n", - " 15.6\n", + " 1.23\n", + " 15.3\n", " \n", " \n", " 5\n", @@ -794,23 +796,23 @@ " it ITA\n", " GK\n", " Lecce\n", - " 27-291\n", + " 27-309\n", " 1995\n", - " 20.0\n", - " 20.0\n", - " 1800.0\n", - " 24.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 25.0\n", " ...\n", - " 40.9\n", - " 156.0\n", - " 75.0\n", - " 51.4\n", - " 304.0\n", - " 13.0\n", - " 4.3\n", - " 20.0\n", + " 41.5\n", + " 169.0\n", + " 76.3\n", + " 51.9\n", + " 327.0\n", + " 15.0\n", + " 4.6\n", + " 22.0\n", " 1.00\n", - " 12.5\n", + " 12.8\n", " \n", " \n", " 6\n", @@ -818,7 +820,7 @@ " it ITA\n", " GK\n", " Fiorentina\n", - " 27-316\n", + " 27-334\n", " 1995\n", " 3.0\n", " 3.0\n", @@ -842,7 +844,7 @@ " si SVN\n", " GK\n", " Inter\n", - " 38-198\n", + " 38-216\n", " 1984\n", " 8.0\n", " 8.0\n", @@ -866,7 +868,7 @@ " fr FRA\n", " GK\n", " Milan\n", - " 27-209\n", + " 27-227\n", " 1995\n", " 7.0\n", " 7.0\n", @@ -890,7 +892,7 @@ " pt POR\n", " GK\n", " Lazio\n", - " 24-023\n", + " 24-041\n", " 1999\n", " 1.0\n", " 1.0\n", @@ -914,22 +916,22 @@ " it ITA\n", " GK\n", " Napoli\n", - " 25-312\n", + " 25-330\n", " 1997\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 14.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 15.0\n", " ...\n", + " 26.5\n", + " 134.0\n", + " 18.7\n", " 26.6\n", - " 114.0\n", - " 20.2\n", - " 27.1\n", - " 197.0\n", + " 232.0\n", " 6.0\n", - " 3.0\n", - " 23.0\n", - " 1.21\n", + " 2.6\n", + " 24.0\n", + " 1.09\n", " 17.2\n", " \n", " \n", @@ -938,23 +940,23 @@ " rs SRB\n", " GK\n", " Torino\n", - " 25-342\n", + " 25-360\n", " 1997\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 20.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 23.0\n", " ...\n", - " 40.8\n", - " 142.0\n", - " 94.4\n", - " 72.1\n", - " 232.0\n", - " 18.0\n", - " 7.8\n", - " 33.0\n", - " 1.74\n", - " 16.5\n", + " 40.9\n", + " 160.0\n", + " 93.8\n", + " 71.4\n", + " 271.0\n", + " 19.0\n", + " 7.0\n", + " 37.0\n", + " 1.68\n", + " 16.3\n", " \n", " \n", " 12\n", @@ -962,23 +964,23 @@ " it ITA\n", " GK\n", " Hellas Verona\n", - " 26-342\n", + " 26-360\n", " 1996\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 31.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 33.0\n", " ...\n", - " 43.5\n", - " 153.0\n", - " 58.2\n", - " 44.8\n", - " 261.0\n", - " 10.0\n", - " 3.8\n", - " 46.0\n", - " 2.42\n", - " 17.8\n", + " 44.2\n", + " 175.0\n", + " 63.4\n", + " 47.5\n", + " 305.0\n", + " 15.0\n", + " 4.9\n", + " 47.0\n", + " 2.14\n", + " 16.7\n", " \n", " \n", " 13\n", @@ -986,23 +988,23 @@ " ar ARG\n", " GK\n", " Atalanta\n", - " 28-267\n", + " 28-285\n", " 1994\n", - " 13.0\n", - " 13.0\n", - " 1087.0\n", - " 14.0\n", + " 16.0\n", + " 16.0\n", + " 1357.0\n", + " 15.0\n", " ...\n", - " 33.0\n", - " 86.0\n", - " 65.1\n", - " 49.4\n", - " 147.0\n", - " 8.0\n", - " 5.4\n", + " 32.8\n", + " 101.0\n", + " 62.4\n", + " 47.9\n", + " 186.0\n", " 10.0\n", - " 0.83\n", - " 14.6\n", + " 5.4\n", + " 14.0\n", + " 0.93\n", + " 15.1\n", " \n", " \n", " 14\n", @@ -1010,23 +1012,23 @@ " mx MEX\n", " GK\n", " Salernitana\n", - " 37-199\n", + " 37-217\n", " 1985\n", - " 5.0\n", - " 5.0\n", - " 450.0\n", - " 14.0\n", + " 6.0\n", + " 6.0\n", + " 540.0\n", + " 17.0\n", " ...\n", - " 43.3\n", - " 52.0\n", - " 78.8\n", - " 54.7\n", - " 78.0\n", - " 2.0\n", - " 2.6\n", + " 41.9\n", + " 60.0\n", + " 71.7\n", + " 52.2\n", + " 86.0\n", + " 3.0\n", + " 3.5\n", " 4.0\n", - " 0.80\n", - " 16.5\n", + " 0.67\n", + " 15.7\n", " \n", " \n", " 15\n", @@ -1034,23 +1036,23 @@ " cm CMR\n", " GK\n", " Inter\n", - " 26-301\n", + " 26-319\n", " 1996\n", - " 11.0\n", - " 11.0\n", - " 990.0\n", - " 12.0\n", - " ...\n", - " 31.0\n", - " 79.0\n", - " 35.4\n", - " 37.9\n", - " 150.0\n", - " 6.0\n", - " 4.0\n", - " 3.0\n", - " 0.27\n", + " 14.0\n", + " 14.0\n", + " 1260.0\n", " 13.0\n", + " ...\n", + " 30.2\n", + " 95.0\n", + " 32.6\n", + " 36.2\n", + " 177.0\n", + " 8.0\n", + " 4.5\n", + " 4.0\n", + " 0.29\n", + " 12.7\n", " \n", " \n", " 16\n", @@ -1058,23 +1060,23 @@ " pt POR\n", " GK\n", " Roma\n", - " 34-347\n", + " 35-000\n", " 1988\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 16.0\n", " ...\n", - " 31.4\n", - " 139.0\n", - " 30.2\n", - " 32.8\n", - " 221.0\n", - " 6.0\n", + " 31.9\n", + " 166.0\n", + " 30.1\n", + " 32.4\n", + " 263.0\n", + " 7.0\n", " 2.7\n", - " 13.0\n", + " 15.0\n", " 0.68\n", - " 13.8\n", + " 13.7\n", " \n", " \n", " 17\n", @@ -1082,7 +1084,7 @@ " it ITA\n", " GK\n", " Sassuolo\n", - " 41-309\n", + " 41-327\n", " 1981\n", " 2.0\n", " 2.0\n", @@ -1106,7 +1108,7 @@ " it ITA\n", " GK\n", " Juventus\n", - " 30-079\n", + " 30-097\n", " 1992\n", " 7.0\n", " 6.0\n", @@ -1130,22 +1132,22 @@ " it ITA\n", " GK\n", " Lazio\n", - " 28-317\n", + " 28-335\n", " 1994\n", + " 22.0\n", + " 21.0\n", + " 1973.0\n", " 19.0\n", - " 18.0\n", - " 1703.0\n", - " 15.0\n", " ...\n", - " 32.5\n", - " 115.0\n", - " 34.8\n", - " 34.8\n", - " 256.0\n", - " 7.0\n", - " 2.7\n", - " 37.0\n", - " 1.96\n", + " 33.0\n", + " 123.0\n", + " 35.0\n", + " 35.0\n", + " 290.0\n", + " 8.0\n", + " 2.8\n", + " 39.0\n", + " 1.78\n", " 18.1\n", " \n", " \n", @@ -1154,7 +1156,7 @@ " ro ROU\n", " GK\n", " Cremonese\n", - " 25-245\n", + " 25-263\n", " 1997\n", " 9.0\n", " 9.0\n", @@ -1178,23 +1180,23 @@ " it ITA\n", " GK\n", " Salernitana\n", - " 31-265\n", + " 31-283\n", " 1991\n", - " 15.0\n", - " 15.0\n", - " 1350.0\n", - " 24.0\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", + " 25.0\n", " ...\n", - " 36.1\n", - " 134.0\n", - " 46.3\n", - " 40.6\n", - " 199.0\n", + " 36.5\n", + " 142.0\n", + " 47.9\n", + " 40.9\n", + " 207.0\n", " 15.0\n", - " 7.5\n", - " 11.0\n", - " 0.73\n", - " 14.2\n", + " 7.2\n", + " 12.0\n", + " 0.75\n", + " 14.4\n", " \n", " \n", " 22\n", @@ -1202,23 +1204,23 @@ " it ITA\n", " GK\n", " Udinese\n", - " 31-332\n", + " 31-350\n", " 1991\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 21.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 25.0\n", " ...\n", - " 33.5\n", - " 153.0\n", - " 39.9\n", - " 36.8\n", - " 253.0\n", - " 4.0\n", - " 1.6\n", - " 9.0\n", - " 0.47\n", - " 13.5\n", + " 33.0\n", + " 175.0\n", + " 37.1\n", + " 35.3\n", + " 293.0\n", + " 5.0\n", + " 1.7\n", + " 10.0\n", + " 0.45\n", + " 13.7\n", " \n", " \n", " 23\n", @@ -1226,23 +1228,23 @@ " pl POL\n", " GK\n", " Bologna\n", - " 31-268\n", + " 31-286\n", " 1991\n", - " 20.0\n", - " 20.0\n", - " 1800.0\n", - " 30.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 32.0\n", " ...\n", - " 32.1\n", - " 141.0\n", - " 37.6\n", - " 33.9\n", - " 280.0\n", + " 32.6\n", + " 159.0\n", + " 37.1\n", + " 33.8\n", + " 304.0\n", " 18.0\n", - " 6.4\n", - " 17.0\n", - " 0.85\n", - " 14.2\n", + " 5.9\n", + " 18.0\n", + " 0.82\n", + " 13.5\n", " \n", " \n", " 24\n", @@ -1250,7 +1252,7 @@ " it ITA\n", " GK\n", " Atalanta\n", - " 30-263\n", + " 30-281\n", " 1992\n", " 7.0\n", " 6.0\n", @@ -1274,23 +1276,23 @@ " pl POL\n", " GK\n", " Juventus\n", - " 32-285\n", + " 32-303\n", " 1990\n", + " 16.0\n", + " 16.0\n", + " 1392.0\n", " 13.0\n", - " 13.0\n", - " 1122.0\n", - " 11.0\n", " ...\n", - " 36.1\n", - " 61.0\n", - " 41.0\n", - " 39.2\n", - " 155.0\n", - " 4.0\n", - " 2.6\n", - " 7.0\n", - " 0.56\n", - " 14.2\n", + " 35.2\n", + " 82.0\n", + " 47.6\n", + " 41.8\n", + " 198.0\n", + " 5.0\n", + " 2.5\n", + " 12.0\n", + " 0.78\n", + " 15.4\n", " \n", " \n", " 26\n", @@ -1298,23 +1300,23 @@ " ro ROU\n", " GK\n", " Milan\n", - " 36-353\n", + " 37-006\n", " 1986\n", - " 12.0\n", - " 12.0\n", - " 1080.0\n", - " 16.0\n", + " 15.0\n", + " 15.0\n", + " 1350.0\n", + " 22.0\n", " ...\n", - " 32.7\n", - " 51.0\n", - " 35.3\n", - " 34.9\n", - " 125.0\n", - " 8.0\n", - " 6.4\n", + " 32.6\n", + " 74.0\n", + " 51.4\n", + " 41.1\n", + " 174.0\n", + " 9.0\n", + " 5.2\n", " 10.0\n", - " 0.83\n", - " 15.1\n", + " 0.67\n", + " 13.7\n", " \n", " \n", " 27\n", @@ -1322,23 +1324,23 @@ " it ITA\n", " GK\n", " Fiorentina\n", - " 32-326\n", + " 32-344\n", " 1990\n", - " 16.0\n", - " 16.0\n", - " 1440.0\n", - " 23.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 27.0\n", " ...\n", - " 33.3\n", - " 101.0\n", - " 43.6\n", - " 39.7\n", - " 149.0\n", - " 7.0\n", - " 4.7\n", - " 30.0\n", - " 1.88\n", - " 18.7\n", + " 32.9\n", + " 125.0\n", + " 42.4\n", + " 39.1\n", + " 174.0\n", + " 9.0\n", + " 5.2\n", + " 38.0\n", + " 2.00\n", + " 19.0\n", " \n", " \n", " 28\n", @@ -1346,23 +1348,23 @@ " it ITA\n", " GK\n", " Empoli\n", - " 26-113\n", + " 26-131\n", " 1996\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 28.0\n", " ...\n", - " 33.9\n", - " 103.0\n", - " 45.6\n", - " 41.8\n", - " 399.0\n", + " 33.6\n", + " 114.0\n", + " 44.7\n", + " 41.5\n", + " 431.0\n", " 25.0\n", - " 6.3\n", - " 8.0\n", - " 0.42\n", - " 10.5\n", + " 5.8\n", + " 12.0\n", + " 0.55\n", + " 10.9\n", " \n", " \n", " 29\n", @@ -1370,7 +1372,7 @@ " nl NED\n", " GK\n", " Spezia\n", - " 32-022\n", + " 32-040\n", " 1991\n", " 3.0\n", " 2.0\n", @@ -1394,7 +1396,7 @@ " ba BIH\n", " GK\n", " Spezia\n", - " 20-309\n", + " 20-327\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -1419,200 +1421,200 @@ ], "text/plain": [ " player nationality position team age \\\n", - "0 Emil Audero it ITA GK Sampdoria 26-010 \n", - "1 Marco Carnesecchi it ITA GK Cremonese 22-211 \n", - "2 Andrea Consigli it ITA GK Sassuolo 36-001 \n", - "3 Michele Di Gregorio it ITA GK Monza 25-185 \n", - "4 Bartłomiej Drągowski pl POL GK Spezia 25-162 \n", - "5 Wladimiro Falcone it ITA GK Lecce 27-291 \n", - "6 Pierluigi Gollini it ITA GK Fiorentina 27-316 \n", - "7 Samir Handanović si SVN GK Inter 38-198 \n", - "8 Mike Maignan fr FRA GK Milan 27-209 \n", - "9 Luís Maximiano pt POR GK Lazio 24-023 \n", - "10 Alex Meret it ITA GK Napoli 25-312 \n", - "11 Vanja Milinković-Savić rs SRB GK Torino 25-342 \n", - "12 Lorenzo Montipò it ITA GK Hellas Verona 26-342 \n", - "13 Juan Musso ar ARG GK Atalanta 28-267 \n", - "14 Guillermo Ochoa mx MEX GK Salernitana 37-199 \n", - "15 André Onana cm CMR GK Inter 26-301 \n", - "16 Rui Patrício pt POR GK Roma 34-347 \n", - "17 Gianluca Pegolo it ITA GK Sassuolo 41-309 \n", - "18 Mattia Perin it ITA GK Juventus 30-079 \n", - "19 Ivan Provedel it ITA GK Lazio 28-317 \n", - "20 Ionuț Radu ro ROU GK Cremonese 25-245 \n", - "21 Luigi Sepe it ITA GK Salernitana 31-265 \n", - "22 Marco Silvestri it ITA GK Udinese 31-332 \n", - "23 Łukasz Skorupski pl POL GK Bologna 31-268 \n", - "24 Marco Sportiello it ITA GK Atalanta 30-263 \n", - "25 Wojciech Szczęsny pl POL GK Juventus 32-285 \n", - "26 Ciprian Tătărușanu ro ROU GK Milan 36-353 \n", - "27 Pietro Terracciano it ITA GK Fiorentina 32-326 \n", - "28 Guglielmo Vicario it ITA GK Empoli 26-113 \n", - "29 Jeroen Zoet nl NED GK Spezia 32-022 \n", - "30 Petar Zovko ba BIH GK Spezia 20-309 \n", + "0 Emil Audero it ITA GK Sampdoria 26-028 \n", + "1 Marco Carnesecchi it ITA GK Cremonese 22-229 \n", + "2 Andrea Consigli it ITA GK Sassuolo 36-019 \n", + "3 Michele Di Gregorio it ITA GK Monza 25-203 \n", + "4 Bartłomiej Drągowski pl POL GK Spezia 25-180 \n", + "5 Wladimiro Falcone it ITA GK Lecce 27-309 \n", + "6 Pierluigi Gollini it ITA GK Fiorentina 27-334 \n", + "7 Samir Handanović si SVN GK Inter 38-216 \n", + "8 Mike Maignan fr FRA GK Milan 27-227 \n", + "9 Luís Maximiano pt POR GK Lazio 24-041 \n", + "10 Alex Meret it ITA GK Napoli 25-330 \n", + "11 Vanja Milinković-Savić rs SRB GK Torino 25-360 \n", + "12 Lorenzo Montipò it ITA GK Hellas Verona 26-360 \n", + "13 Juan Musso ar ARG GK Atalanta 28-285 \n", + "14 Guillermo Ochoa mx MEX GK Salernitana 37-217 \n", + "15 André Onana cm CMR GK Inter 26-319 \n", + "16 Rui Patrício pt POR GK Roma 35-000 \n", + "17 Gianluca Pegolo it ITA GK Sassuolo 41-327 \n", + "18 Mattia Perin it ITA GK Juventus 30-097 \n", + "19 Ivan Provedel it ITA GK Lazio 28-335 \n", + "20 Ionuț Radu ro ROU GK Cremonese 25-263 \n", + "21 Luigi Sepe it ITA GK Salernitana 31-283 \n", + "22 Marco Silvestri it ITA GK Udinese 31-350 \n", + "23 Łukasz Skorupski pl POL GK Bologna 31-286 \n", + "24 Marco Sportiello it ITA GK Atalanta 30-281 \n", + "25 Wojciech Szczęsny pl POL GK Juventus 32-303 \n", + "26 Ciprian Tătărușanu ro ROU GK Milan 37-006 \n", + "27 Pietro Terracciano it ITA GK Fiorentina 32-344 \n", + "28 Guglielmo Vicario it ITA GK Empoli 26-131 \n", + "29 Jeroen Zoet nl NED GK Spezia 32-040 \n", + "30 Petar Zovko ba BIH GK Spezia 20-327 \n", "\n", " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\n", - "0 1997 19.0 19.0 1710.0 32.0 ... \n", - "1 2000 10.0 10.0 900.0 14.0 ... \n", - "2 1987 17.0 17.0 1530.0 26.0 ... \n", - "3 1997 19.0 19.0 1710.0 28.0 ... \n", - "4 1997 18.0 18.0 1572.0 29.0 ... \n", - "5 1995 20.0 20.0 1800.0 24.0 ... \n", + "0 1997 22.0 22.0 1980.0 36.0 ... \n", + "1 2000 13.0 13.0 1170.0 21.0 ... \n", + "2 1987 20.0 20.0 1800.0 30.0 ... \n", + "3 1997 22.0 22.0 1980.0 30.0 ... \n", + "4 1997 20.0 20.0 1752.0 34.0 ... \n", + "5 1995 22.0 22.0 1980.0 25.0 ... \n", "6 1995 3.0 3.0 270.0 2.0 ... \n", "7 1984 8.0 8.0 720.0 13.0 ... \n", "8 1995 7.0 7.0 630.0 8.0 ... \n", "9 1999 1.0 1.0 5.0 0.0 ... \n", - "10 1997 19.0 19.0 1710.0 14.0 ... \n", - "11 1997 19.0 19.0 1710.0 20.0 ... \n", - "12 1996 19.0 19.0 1710.0 31.0 ... \n", - "13 1994 13.0 13.0 1087.0 14.0 ... \n", - "14 1985 5.0 5.0 450.0 14.0 ... \n", - "15 1996 11.0 11.0 990.0 12.0 ... \n", - "16 1988 19.0 19.0 1710.0 16.0 ... \n", + "10 1997 22.0 22.0 1980.0 15.0 ... \n", + "11 1997 22.0 22.0 1980.0 23.0 ... \n", + "12 1996 22.0 22.0 1980.0 33.0 ... \n", + "13 1994 16.0 16.0 1357.0 15.0 ... \n", + "14 1985 6.0 6.0 540.0 17.0 ... \n", + "15 1996 14.0 14.0 1260.0 13.0 ... \n", + "16 1988 22.0 22.0 1980.0 19.0 ... \n", "17 1981 2.0 2.0 180.0 3.0 ... \n", "18 1992 7.0 6.0 588.0 4.0 ... \n", - "19 1994 19.0 18.0 1703.0 15.0 ... \n", + "19 1994 22.0 21.0 1973.0 19.0 ... \n", "20 1997 9.0 9.0 810.0 19.0 ... \n", - "21 1991 15.0 15.0 1350.0 24.0 ... \n", - "22 1991 19.0 19.0 1710.0 21.0 ... \n", - "23 1991 20.0 20.0 1800.0 30.0 ... \n", + "21 1991 16.0 16.0 1440.0 25.0 ... \n", + "22 1991 22.0 22.0 1980.0 25.0 ... \n", + "23 1991 22.0 22.0 1980.0 32.0 ... \n", "24 1992 7.0 6.0 623.0 9.0 ... \n", - "25 1990 13.0 13.0 1122.0 11.0 ... \n", - "26 1986 12.0 12.0 1080.0 16.0 ... \n", - "27 1990 16.0 16.0 1440.0 23.0 ... \n", - "28 1996 19.0 19.0 1710.0 22.0 ... \n", + "25 1990 16.0 16.0 1392.0 13.0 ... \n", + "26 1986 15.0 15.0 1350.0 22.0 ... \n", + "27 1990 19.0 19.0 1710.0 27.0 ... \n", + "28 1996 22.0 22.0 1980.0 28.0 ... \n", "29 1991 3.0 2.0 154.0 1.0 ... \n", "30 2002 1.0 0.0 74.0 2.0 ... \n", "\n", " gk_passes_length_avg gk_goal_kicks gk_pct_goal_kicks_launched \\\n", - "0 38.8 142.0 57.7 \n", - "1 40.0 66.0 59.1 \n", - "2 34.4 157.0 31.8 \n", - "3 33.0 115.0 31.3 \n", - "4 34.4 106.0 63.2 \n", - "5 40.9 156.0 75.0 \n", + "0 39.4 178.0 57.3 \n", + "1 39.3 94.0 56.4 \n", + "2 34.1 179.0 33.5 \n", + "3 33.0 138.0 36.2 \n", + "4 34.9 122.0 62.3 \n", + "5 41.5 169.0 76.3 \n", "6 32.4 11.0 63.6 \n", "7 26.1 45.0 4.4 \n", "8 33.5 18.0 38.9 \n", "9 0.0 0.0 0.0 \n", - "10 26.6 114.0 20.2 \n", - "11 40.8 142.0 94.4 \n", - "12 43.5 153.0 58.2 \n", - "13 33.0 86.0 65.1 \n", - "14 43.3 52.0 78.8 \n", - "15 31.0 79.0 35.4 \n", - "16 31.4 139.0 30.2 \n", + "10 26.5 134.0 18.7 \n", + "11 40.9 160.0 93.8 \n", + "12 44.2 175.0 63.4 \n", + "13 32.8 101.0 62.4 \n", + "14 41.9 60.0 71.7 \n", + "15 30.2 95.0 32.6 \n", + "16 31.9 166.0 30.1 \n", "17 29.7 16.0 31.3 \n", "18 30.3 41.0 39.0 \n", - "19 32.5 115.0 34.8 \n", + "19 33.0 123.0 35.0 \n", "20 37.3 67.0 50.7 \n", - "21 36.1 134.0 46.3 \n", - "22 33.5 153.0 39.9 \n", - "23 32.1 141.0 37.6 \n", + "21 36.5 142.0 47.9 \n", + "22 33.0 175.0 37.1 \n", + "23 32.6 159.0 37.1 \n", "24 28.5 61.0 68.9 \n", - "25 36.1 61.0 41.0 \n", - "26 32.7 51.0 35.3 \n", - "27 33.3 101.0 43.6 \n", - "28 33.9 103.0 45.6 \n", + "25 35.2 82.0 47.6 \n", + "26 32.6 74.0 51.4 \n", + "27 32.9 125.0 42.4 \n", + "28 33.6 114.0 44.7 \n", "29 41.7 8.0 87.5 \n", "30 35.1 13.0 76.9 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 44.5 243.0 15.0 \n", - "1 49.3 108.0 10.0 \n", - "2 33.3 220.0 13.0 \n", - "3 31.3 246.0 10.0 \n", - "4 47.9 235.0 9.0 \n", - "5 51.4 304.0 13.0 \n", + "0 44.4 315.0 21.0 \n", + "1 47.3 174.0 13.0 \n", + "2 34.2 276.0 17.0 \n", + "3 33.6 313.0 10.0 \n", + "4 47.1 270.0 9.0 \n", + "5 51.9 327.0 15.0 \n", "6 49.3 24.0 1.0 \n", "7 24.0 79.0 2.0 \n", "8 40.4 65.0 5.0 \n", "9 0.0 0.0 0.0 \n", - "10 27.1 197.0 6.0 \n", - "11 72.1 232.0 18.0 \n", - "12 44.8 261.0 10.0 \n", - "13 49.4 147.0 8.0 \n", - "14 54.7 78.0 2.0 \n", - "15 37.9 150.0 6.0 \n", - "16 32.8 221.0 6.0 \n", + "10 26.6 232.0 6.0 \n", + "11 71.4 271.0 19.0 \n", + "12 47.5 305.0 15.0 \n", + "13 47.9 186.0 10.0 \n", + "14 52.2 86.0 3.0 \n", + "15 36.2 177.0 8.0 \n", + "16 32.4 263.0 7.0 \n", "17 28.3 28.0 2.0 \n", "18 35.3 99.0 2.0 \n", - "19 34.8 256.0 7.0 \n", + "19 35.0 290.0 8.0 \n", "20 46.3 107.0 6.0 \n", - "21 40.6 199.0 15.0 \n", - "22 36.8 253.0 4.0 \n", - "23 33.9 280.0 18.0 \n", + "21 40.9 207.0 15.0 \n", + "22 35.3 293.0 5.0 \n", + "23 33.8 304.0 18.0 \n", "24 48.9 86.0 7.0 \n", - "25 39.2 155.0 4.0 \n", - "26 34.9 125.0 8.0 \n", - "27 39.7 149.0 7.0 \n", - "28 41.8 399.0 25.0 \n", + "25 41.8 198.0 5.0 \n", + "26 41.1 174.0 9.0 \n", + "27 39.1 174.0 9.0 \n", + "28 41.5 431.0 25.0 \n", "29 56.6 24.0 1.0 \n", "30 51.1 24.0 1.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 6.2 20.0 \n", - "1 9.3 7.0 \n", - "2 5.9 15.0 \n", - "3 4.1 12.0 \n", - "4 3.8 24.0 \n", - "5 4.3 20.0 \n", + "0 6.7 20.0 \n", + "1 7.5 8.0 \n", + "2 6.2 23.0 \n", + "3 3.2 16.0 \n", + "4 3.3 24.0 \n", + "5 4.6 22.0 \n", "6 4.2 0.0 \n", "7 2.5 5.0 \n", "8 7.7 6.0 \n", "9 0.0 0.0 \n", - "10 3.0 23.0 \n", - "11 7.8 33.0 \n", - "12 3.8 46.0 \n", - "13 5.4 10.0 \n", - "14 2.6 4.0 \n", - "15 4.0 3.0 \n", - "16 2.7 13.0 \n", + "10 2.6 24.0 \n", + "11 7.0 37.0 \n", + "12 4.9 47.0 \n", + "13 5.4 14.0 \n", + "14 3.5 4.0 \n", + "15 4.5 4.0 \n", + "16 2.7 15.0 \n", "17 7.1 0.0 \n", "18 2.0 9.0 \n", - "19 2.7 37.0 \n", + "19 2.8 39.0 \n", "20 5.6 9.0 \n", - "21 7.5 11.0 \n", - "22 1.6 9.0 \n", - "23 6.4 17.0 \n", + "21 7.2 12.0 \n", + "22 1.7 10.0 \n", + "23 5.9 18.0 \n", "24 8.1 14.0 \n", - "25 2.6 7.0 \n", - "26 6.4 10.0 \n", - "27 4.7 30.0 \n", - "28 6.3 8.0 \n", + "25 2.5 12.0 \n", + "26 5.2 10.0 \n", + "27 5.2 38.0 \n", + "28 5.8 12.0 \n", "29 4.2 3.0 \n", "30 4.2 2.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \n", - "0 1.05 15.4 \n", - "1 0.70 13.0 \n", - "2 0.88 14.5 \n", - "3 0.63 13.4 \n", - "4 1.37 15.6 \n", - "5 1.00 12.5 \n", + "0 0.91 14.1 \n", + "1 0.62 12.9 \n", + "2 1.15 14.9 \n", + "3 0.73 13.0 \n", + "4 1.23 15.3 \n", + "5 1.00 12.8 \n", "6 0.00 9.3 \n", "7 0.63 14.6 \n", "8 0.86 12.9 \n", "9 0.00 19.0 \n", - "10 1.21 17.2 \n", - "11 1.74 16.5 \n", - "12 2.42 17.8 \n", - "13 0.83 14.6 \n", - "14 0.80 16.5 \n", - "15 0.27 13.0 \n", - "16 0.68 13.8 \n", + "10 1.09 17.2 \n", + "11 1.68 16.3 \n", + "12 2.14 16.7 \n", + "13 0.93 15.1 \n", + "14 0.67 15.7 \n", + "15 0.29 12.7 \n", + "16 0.68 13.7 \n", "17 0.00 9.0 \n", "18 1.38 17.8 \n", - "19 1.96 18.1 \n", + "19 1.78 18.1 \n", "20 1.00 14.5 \n", - "21 0.73 14.2 \n", - "22 0.47 13.5 \n", - "23 0.85 14.2 \n", + "21 0.75 14.4 \n", + "22 0.45 13.7 \n", + "23 0.82 13.5 \n", "24 2.02 19.9 \n", - "25 0.56 14.2 \n", - "26 0.83 15.1 \n", - "27 1.88 18.7 \n", - "28 0.42 10.5 \n", + "25 0.78 15.4 \n", + "26 0.67 13.7 \n", + "27 2.00 19.0 \n", + "28 0.55 10.9 \n", "29 1.74 15.7 \n", "30 2.47 17.0 \n", "\n", @@ -1687,577 +1689,577 @@ " 0\n", " Atalanta\n", " 24.0\n", - " 48.4\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 36.0\n", - " 25.0\n", + " 48.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 40.0\n", + " 28.0\n", " 6.0\n", " 8.0\n", " ...\n", - " 231.0\n", - " 196.0\n", - " 27.0\n", + " 275.0\n", + " 229.0\n", + " 30.0\n", " 6.0\n", " 1.0\n", " 1.0\n", - " 1117.0\n", - " 279.0\n", - " 239.0\n", - " 53.9\n", + " 1296.0\n", + " 328.0\n", + " 273.0\n", + " 54.6\n", " \n", " \n", " 1\n", " Bologna\n", - " 24.0\n", - " 52.6\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", " 25.0\n", - " 19.0\n", - " 3.0\n", - " 3.0\n", - " ...\n", + " 52.4\n", + " 22.0\n", " 242.0\n", - " 238.0\n", - " 41.0\n", + " 1980.0\n", + " 27.0\n", + " 20.0\n", + " 4.0\n", + " 4.0\n", + " ...\n", + " 282.0\n", + " 265.0\n", + " 42.0\n", " 3.0\n", " 5.0\n", " 2.0\n", - " 1104.0\n", - " 180.0\n", - " 224.0\n", - " 44.6\n", + " 1230.0\n", + " 209.0\n", + " 251.0\n", + " 45.4\n", " \n", " \n", " 2\n", " Cremonese\n", - " 29.0\n", - " 44.0\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 14.0\n", - " 6.0\n", + " 31.0\n", + " 43.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 15.0\n", + " 7.0\n", " 2.0\n", " 4.0\n", " ...\n", - " 257.0\n", - " 194.0\n", - " 44.0\n", + " 283.0\n", + " 225.0\n", + " 46.0\n", " 3.0\n", " 4.0\n", " 1.0\n", - " 1042.0\n", - " 267.0\n", - " 348.0\n", + " 1207.0\n", + " 314.0\n", + " 409.0\n", " 43.4\n", " \n", " \n", " 3\n", " Empoli\n", - " 26.0\n", - " 46.3\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 17.0\n", - " 9.0\n", + " 28.0\n", + " 47.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 21.0\n", + " 11.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 222.0\n", - " 237.0\n", - " 26.0\n", + " 262.0\n", + " 268.0\n", + " 30.0\n", " 0.0\n", - " 3.0\n", + " 4.0\n", " 0.0\n", - " 907.0\n", - " 189.0\n", + " 1051.0\n", " 217.0\n", - " 46.6\n", + " 263.0\n", + " 45.2\n", " \n", " \n", " 4\n", " Fiorentina\n", - " 27.0\n", + " 28.0\n", " 57.2\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 21.0\n", - " 17.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 23.0\n", + " 18.0\n", " 2.0\n", " 4.0\n", " ...\n", - " 244.0\n", - " 249.0\n", - " 25.0\n", - " 2.0\n", + " 288.0\n", + " 295.0\n", + " 32.0\n", " 2.0\n", + " 3.0\n", " 1.0\n", - " 1002.0\n", + " 1200.0\n", + " 328.0\n", " 289.0\n", - " 249.0\n", - " 53.7\n", + " 53.2\n", " \n", " \n", " 5\n", " Hellas Verona\n", - " 29.0\n", - " 43.4\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 16.0\n", - " 13.0\n", + " 34.0\n", + " 42.9\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 18.0\n", + " 15.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 285.0\n", - " 185.0\n", - " 43.0\n", + " 326.0\n", + " 220.0\n", + " 50.0\n", " 0.0\n", " 1.0\n", " 1.0\n", - " 1142.0\n", - " 388.0\n", - " 345.0\n", - " 52.9\n", + " 1344.0\n", + " 445.0\n", + " 423.0\n", + " 51.3\n", " \n", " \n", " 6\n", " Inter\n", " 23.0\n", - " 52.9\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 37.0\n", - " 25.0\n", + " 54.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 40.0\n", + " 27.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 233.0\n", - " 229.0\n", - " 32.0\n", + " 269.0\n", + " 262.0\n", + " 40.0\n", " 1.0\n", " 2.0\n", " 2.0\n", - " 960.0\n", - " 232.0\n", - " 198.0\n", - " 54.0\n", + " 1131.0\n", + " 288.0\n", + " 231.0\n", + " 55.5\n", " \n", " \n", " 7\n", " Juventus\n", " 26.0\n", - " 49.5\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 30.0\n", - " 23.0\n", - " 2.0\n", + " 49.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 34.0\n", + " 26.0\n", " 3.0\n", + " 4.0\n", " ...\n", - " 227.0\n", - " 199.0\n", - " 27.0\n", - " 3.0\n", + " 262.0\n", + " 234.0\n", + " 38.0\n", + " 4.0\n", " 2.0\n", " 0.0\n", - " 965.0\n", - " 230.0\n", - " 230.0\n", - " 50.0\n", + " 1158.0\n", + " 272.0\n", + " 258.0\n", + " 51.3\n", " \n", " \n", " 8\n", " Lazio\n", " 21.0\n", - " 51.2\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 34.0\n", - " 25.0\n", + " 51.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 36.0\n", + " 26.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 194.0\n", - " 252.0\n", - " 26.0\n", + " 230.0\n", + " 294.0\n", + " 29.0\n", " 2.0\n", " 1.0\n", " 0.0\n", - " 972.0\n", - " 181.0\n", - " 183.0\n", - " 49.7\n", + " 1186.0\n", + " 229.0\n", + " 218.0\n", + " 51.2\n", " \n", " \n", " 9\n", " Lecce\n", " 26.0\n", - " 42.5\n", + " 42.4\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 18.0\n", - " 12.0\n", + " 14.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 283.0\n", - " 254.0\n", - " 36.0\n", + " 322.0\n", + " 272.0\n", + " 39.0\n", " 2.0\n", - " 3.0\n", + " 4.0\n", " 1.0\n", - " 1097.0\n", - " 294.0\n", - " 373.0\n", - " 44.1\n", + " 1220.0\n", + " 332.0\n", + " 417.0\n", + " 44.3\n", " \n", " \n", " 10\n", " Milan\n", " 27.0\n", - " 54.5\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 33.0\n", - " 28.0\n", + " 53.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 36.0\n", + " 31.0\n", " 2.0\n", " 2.0\n", " ...\n", - 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" 33.0\n", + " 212.0\n", + " 279.0\n", + " 40.0\n", " 4.0\n", " 2.0\n", " 0.0\n", - " 1012.0\n", - " 231.0\n", - " 201.0\n", - " 53.5\n", + " 1183.0\n", + " 280.0\n", + " 232.0\n", + " 54.7\n", " \n", " \n", " 13\n", " Roma\n", - " 25.0\n", - " 49.4\n", + " 26.0\n", + " 49.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 25.0\n", - " 16.0\n", - " 3.0\n", - " 5.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 229.0\n", - " 250.0\n", - " 32.0\n", + " 261.0\n", + " 296.0\n", + " 35.0\n", " 4.0\n", " 0.0\n", - " 0.0\n", - " 1001.0\n", - " 225.0\n", - " 184.0\n", - " 55.0\n", + " 1.0\n", + " 1164.0\n", + " 266.0\n", + " 221.0\n", + " 54.6\n", " \n", " \n", " 14\n", " Salernitana\n", " 28.0\n", - " 44.8\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", + " 46.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 24.0\n", " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 242.0\n", - " 207.0\n", - " 42.0\n", + " 272.0\n", + " 231.0\n", + " 43.0\n", " 0.0\n", - " 7.0\n", + " 8.0\n", " 0.0\n", - " 1034.0\n", - " 240.0\n", - " 257.0\n", - " 48.3\n", + " 1149.0\n", + " 285.0\n", + " 291.0\n", + " 49.5\n", " \n", " \n", " 15\n", " Sampdoria\n", - " 29.0\n", - " 49.4\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", + " 31.0\n", + " 47.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 10.0\n", " 8.0\n", - " 7.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 278.0\n", - " 293.0\n", - " 39.0\n", + " 314.0\n", + " 330.0\n", + " 40.0\n", " 0.0\n", - " 5.0\n", + " 6.0\n", " 2.0\n", - " 968.0\n", - " 318.0\n", - " 300.0\n", - " 51.5\n", + " 1116.0\n", + " 349.0\n", + " 362.0\n", + " 49.1\n", " \n", " \n", " 16\n", " Sassuolo\n", - " 27.0\n", + " 29.0\n", " 48.7\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 25.0\n", " 18.0\n", - " 13.0\n", - " 3.0\n", " 4.0\n", + " 5.0\n", " ...\n", - " 199.0\n", - " 236.0\n", - " 16.0\n", - " 3.0\n", + " 227.0\n", + " 269.0\n", + " 19.0\n", + " 4.0\n", " 4.0\n", " 0.0\n", - " 868.0\n", - " 173.0\n", - " 216.0\n", - " 44.5\n", + " 994.0\n", + " 206.0\n", + " 256.0\n", + " 44.6\n", " \n", " \n", " 17\n", " Spezia\n", - " 30.0\n", - " 46.7\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 15.0\n", + " 33.0\n", + " 45.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 17.0\n", " 10.0\n", - " 2.0\n", - " 2.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 281.0\n", - " 193.0\n", - " 37.0\n", - " 1.0\n", + " 313.0\n", + " 220.0\n", + " 39.0\n", " 1.0\n", + " 2.0\n", " 0.0\n", - " 1136.0\n", - " 281.0\n", - " 322.0\n", - " 46.6\n", + " 1234.0\n", + " 306.0\n", + " 343.0\n", + " 47.1\n", " \n", " \n", " 18\n", " Torino\n", - " 23.0\n", - " 52.8\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 19.0\n", - " 14.0\n", + " 27.0\n", + " 53.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 22.0\n", + " 17.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 277.0\n", - " 195.0\n", - " 46.0\n", + " 319.0\n", + " 224.0\n", + " 50.0\n", " 1.0\n", " 4.0\n", " 0.0\n", - " 1002.0\n", - " 295.0\n", - " 323.0\n", - " 47.7\n", + " 1184.0\n", + " 333.0\n", + " 367.0\n", + " 47.6\n", " \n", " \n", " 19\n", " Udinese\n", + " 25.0\n", + " 49.9\n", " 22.0\n", - " 50.1\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 26.0\n", - " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", + " 25.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 234.0\n", - " 238.0\n", - " 33.0\n", + " 263.0\n", + " 267.0\n", + " 37.0\n", " 0.0\n", " 2.0\n", - " 0.0\n", - " 989.0\n", - " 237.0\n", - " 189.0\n", - " 55.6\n", + " 2.0\n", + " 1140.0\n", + " 277.0\n", + " 233.0\n", + " 54.3\n", " \n", " \n", "\n", - "

20 rows × 157 columns

\n", + "

20 rows × 152 columns

\n", "" ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 Atalanta 24.0 48.4 19.0 209.0 1710.0 \n", - "1 Bologna 24.0 52.6 20.0 220.0 1800.0 \n", - "2 Cremonese 29.0 44.0 19.0 209.0 1710.0 \n", - "3 Empoli 26.0 46.3 19.0 209.0 1710.0 \n", - "4 Fiorentina 27.0 57.2 19.0 209.0 1710.0 \n", - "5 Hellas Verona 29.0 43.4 19.0 209.0 1710.0 \n", - "6 Inter 23.0 52.9 19.0 209.0 1710.0 \n", - "7 Juventus 26.0 49.5 19.0 209.0 1710.0 \n", - "8 Lazio 21.0 51.2 19.0 209.0 1710.0 \n", - "9 Lecce 26.0 42.5 20.0 220.0 1800.0 \n", - "10 Milan 27.0 54.5 19.0 209.0 1710.0 \n", - "11 Monza 29.0 55.1 19.0 209.0 1710.0 \n", - "12 Napoli 24.0 61.3 19.0 209.0 1710.0 \n", - "13 Roma 25.0 49.4 19.0 209.0 1710.0 \n", - "14 Salernitana 28.0 44.8 20.0 220.0 1800.0 \n", - "15 Sampdoria 29.0 49.4 19.0 209.0 1710.0 \n", - "16 Sassuolo 27.0 48.7 19.0 209.0 1710.0 \n", - "17 Spezia 30.0 46.7 20.0 220.0 1800.0 \n", - "18 Torino 23.0 52.8 19.0 209.0 1710.0 \n", - "19 Udinese 22.0 50.1 19.0 209.0 1710.0 \n", + "0 Atalanta 24.0 48.6 22.0 242.0 1980.0 \n", + "1 Bologna 25.0 52.4 22.0 242.0 1980.0 \n", + "2 Cremonese 31.0 43.8 22.0 242.0 1980.0 \n", + "3 Empoli 28.0 47.5 22.0 242.0 1980.0 \n", + "4 Fiorentina 28.0 57.2 22.0 242.0 1980.0 \n", + "5 Hellas Verona 34.0 42.9 22.0 242.0 1980.0 \n", + "6 Inter 23.0 54.5 22.0 242.0 1980.0 \n", + "7 Juventus 26.0 49.0 22.0 242.0 1980.0 \n", + "8 Lazio 21.0 51.8 22.0 242.0 1980.0 \n", + "9 Lecce 26.0 42.4 22.0 242.0 1980.0 \n", + "10 Milan 27.0 53.5 22.0 242.0 1980.0 \n", + "11 Monza 29.0 55.0 22.0 242.0 1980.0 \n", + "12 Napoli 24.0 61.6 22.0 242.0 1980.0 \n", + "13 Roma 26.0 49.3 22.0 242.0 1980.0 \n", + "14 Salernitana 28.0 46.0 22.0 242.0 1980.0 \n", + "15 Sampdoria 31.0 47.3 22.0 242.0 1980.0 \n", + "16 Sassuolo 29.0 48.7 22.0 242.0 1980.0 \n", + "17 Spezia 33.0 45.5 22.0 242.0 1980.0 \n", + "18 Torino 27.0 53.0 22.0 242.0 1980.0 \n", + "19 Udinese 25.0 49.9 22.0 242.0 1980.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 36.0 25.0 6.0 8.0 ... 231.0 196.0 27.0 \n", - "1 25.0 19.0 3.0 3.0 ... 242.0 238.0 41.0 \n", - "2 14.0 6.0 2.0 4.0 ... 257.0 194.0 44.0 \n", - "3 17.0 9.0 0.0 0.0 ... 222.0 237.0 26.0 \n", - "4 21.0 17.0 2.0 4.0 ... 244.0 249.0 25.0 \n", - "5 16.0 13.0 0.0 0.0 ... 285.0 185.0 43.0 \n", - "6 37.0 25.0 2.0 2.0 ... 233.0 229.0 32.0 \n", - "7 30.0 23.0 2.0 3.0 ... 227.0 199.0 27.0 \n", - "8 34.0 25.0 3.0 4.0 ... 194.0 252.0 26.0 \n", - "9 18.0 12.0 1.0 2.0 ... 283.0 254.0 36.0 \n", - "10 33.0 28.0 2.0 2.0 ... 236.0 223.0 30.0 \n", - "11 22.0 13.0 3.0 3.0 ... 248.0 262.0 38.0 \n", - "12 46.0 37.0 4.0 5.0 ... 179.0 240.0 33.0 \n", - "13 25.0 16.0 3.0 5.0 ... 229.0 250.0 32.0 \n", - "14 24.0 16.0 1.0 1.0 ... 242.0 207.0 42.0 \n", - "15 8.0 7.0 0.0 0.0 ... 278.0 293.0 39.0 \n", - "16 18.0 13.0 3.0 4.0 ... 199.0 236.0 16.0 \n", - "17 15.0 10.0 2.0 2.0 ... 281.0 193.0 37.0 \n", - "18 19.0 14.0 1.0 1.0 ... 277.0 195.0 46.0 \n", - "19 26.0 22.0 0.0 0.0 ... 234.0 238.0 33.0 \n", + "0 40.0 28.0 6.0 8.0 ... 275.0 229.0 30.0 \n", + "1 27.0 20.0 4.0 4.0 ... 282.0 265.0 42.0 \n", + "2 15.0 7.0 2.0 4.0 ... 283.0 225.0 46.0 \n", + "3 21.0 11.0 0.0 0.0 ... 262.0 268.0 30.0 \n", + "4 23.0 18.0 2.0 4.0 ... 288.0 295.0 32.0 \n", + "5 18.0 15.0 0.0 0.0 ... 326.0 220.0 50.0 \n", + "6 40.0 27.0 2.0 2.0 ... 269.0 262.0 40.0 \n", + "7 34.0 26.0 3.0 4.0 ... 262.0 234.0 38.0 \n", + "8 36.0 26.0 3.0 4.0 ... 230.0 294.0 29.0 \n", + "9 20.0 14.0 1.0 2.0 ... 322.0 272.0 39.0 \n", + "10 36.0 31.0 2.0 2.0 ... 273.0 263.0 37.0 \n", + "11 27.0 17.0 4.0 4.0 ... 288.0 298.0 42.0 \n", + "12 54.0 42.0 5.0 6.0 ... 212.0 279.0 40.0 \n", + "13 29.0 19.0 4.0 6.0 ... 261.0 296.0 35.0 \n", + "14 24.0 16.0 1.0 1.0 ... 272.0 231.0 43.0 \n", + "15 10.0 8.0 0.0 0.0 ... 314.0 330.0 40.0 \n", + "16 25.0 18.0 4.0 5.0 ... 227.0 269.0 19.0 \n", + "17 17.0 10.0 3.0 3.0 ... 313.0 220.0 39.0 \n", + "18 22.0 17.0 1.0 1.0 ... 319.0 224.0 50.0 \n", + "19 29.0 25.0 0.0 0.0 ... 263.0 267.0 37.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 6.0 1.0 1.0 1117.0 279.0 \n", - "1 3.0 5.0 2.0 1104.0 180.0 \n", - "2 3.0 4.0 1.0 1042.0 267.0 \n", - "3 0.0 3.0 0.0 907.0 189.0 \n", - "4 2.0 2.0 1.0 1002.0 289.0 \n", - "5 0.0 1.0 1.0 1142.0 388.0 \n", - "6 1.0 2.0 2.0 960.0 232.0 \n", - "7 3.0 2.0 0.0 965.0 230.0 \n", - "8 2.0 1.0 0.0 972.0 181.0 \n", - "9 2.0 3.0 1.0 1097.0 294.0 \n", - "10 1.0 3.0 1.0 1015.0 275.0 \n", - "11 3.0 0.0 1.0 906.0 215.0 \n", - "12 4.0 2.0 0.0 1012.0 231.0 \n", - "13 4.0 0.0 0.0 1001.0 225.0 \n", - "14 0.0 7.0 0.0 1034.0 240.0 \n", - "15 0.0 5.0 2.0 968.0 318.0 \n", - "16 3.0 4.0 0.0 868.0 173.0 \n", - "17 1.0 1.0 0.0 1136.0 281.0 \n", - "18 1.0 4.0 0.0 1002.0 295.0 \n", - "19 0.0 2.0 0.0 989.0 237.0 \n", + "0 6.0 1.0 1.0 1296.0 328.0 \n", + "1 3.0 5.0 2.0 1230.0 209.0 \n", + "2 3.0 4.0 1.0 1207.0 314.0 \n", + "3 0.0 4.0 0.0 1051.0 217.0 \n", + "4 2.0 3.0 1.0 1200.0 328.0 \n", + "5 0.0 1.0 1.0 1344.0 445.0 \n", + "6 1.0 2.0 2.0 1131.0 288.0 \n", + "7 4.0 2.0 0.0 1158.0 272.0 \n", + "8 2.0 1.0 0.0 1186.0 229.0 \n", + "9 2.0 4.0 1.0 1220.0 332.0 \n", + "10 1.0 4.0 1.0 1178.0 325.0 \n", + "11 4.0 0.0 1.0 1076.0 252.0 \n", + "12 4.0 2.0 0.0 1183.0 280.0 \n", + "13 4.0 0.0 1.0 1164.0 266.0 \n", + "14 0.0 8.0 0.0 1149.0 285.0 \n", + "15 0.0 6.0 2.0 1116.0 349.0 \n", + "16 4.0 4.0 0.0 994.0 206.0 \n", + "17 1.0 2.0 0.0 1234.0 306.0 \n", + "18 1.0 4.0 0.0 1184.0 333.0 \n", + "19 0.0 2.0 2.0 1140.0 277.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 239.0 53.9 \n", - "1 224.0 44.6 \n", - "2 348.0 43.4 \n", - "3 217.0 46.6 \n", - "4 249.0 53.7 \n", - "5 345.0 52.9 \n", - "6 198.0 54.0 \n", - "7 230.0 50.0 \n", - "8 183.0 49.7 \n", - "9 373.0 44.1 \n", - "10 224.0 55.1 \n", - "11 197.0 52.2 \n", - "12 201.0 53.5 \n", - "13 184.0 55.0 \n", - "14 257.0 48.3 \n", - "15 300.0 51.5 \n", - "16 216.0 44.5 \n", - "17 322.0 46.6 \n", - "18 323.0 47.7 \n", - "19 189.0 55.6 \n", + "0 273.0 54.6 \n", + "1 251.0 45.4 \n", + "2 409.0 43.4 \n", + "3 263.0 45.2 \n", + "4 289.0 53.2 \n", + "5 423.0 51.3 \n", + "6 231.0 55.5 \n", + "7 258.0 51.3 \n", + "8 218.0 51.2 \n", + "9 417.0 44.3 \n", + "10 263.0 55.3 \n", + "11 243.0 50.9 \n", + "12 232.0 54.7 \n", + "13 221.0 54.6 \n", + "14 291.0 49.5 \n", + "15 362.0 49.1 \n", + "16 256.0 44.6 \n", + "17 343.0 47.1 \n", + "18 367.0 47.6 \n", + "19 233.0 54.3 \n", "\n", - "[20 rows x 157 columns]" + "[20 rows x 152 columns]" ] }, "execution_count": 5, @@ -2328,577 +2330,577 @@ " 0\n", " vs Atalanta\n", " 24.0\n", - " 51.6\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", + " 51.4\n", " 22.0\n", - " 16.0\n", + " 242.0\n", + " 1980.0\n", + " 23.0\n", + " 17.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 209.0\n", - " 218.0\n", - " 25.0\n", + " 244.0\n", + " 256.0\n", + " 26.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1165.0\n", - " 239.0\n", - " 279.0\n", - " 46.1\n", + " 1335.0\n", + " 273.0\n", + " 328.0\n", + " 45.4\n", " \n", " \n", " 1\n", " vs Bologna\n", - " 24.0\n", - " 47.4\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 28.0\n", - " 16.0\n", + " 25.0\n", + " 47.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 30.0\n", + " 18.0\n", " 5.0\n", " 5.0\n", " ...\n", - " 252.0\n", - " 229.0\n", - " 33.0\n", - " 3.0\n", + " 280.0\n", + " 268.0\n", + " 38.0\n", " 3.0\n", + " 4.0\n", " 1.0\n", - " 1081.0\n", - " 224.0\n", - " 180.0\n", - " 55.4\n", + " 1204.0\n", + " 250.0\n", + " 210.0\n", + " 54.3\n", " \n", " \n", " 2\n", " vs Cremonese\n", - " 29.0\n", - " 56.0\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 32.0\n", - " 20.0\n", + " 31.0\n", + " 56.2\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 39.0\n", + " 25.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 205.0\n", - " 247.0\n", - " 29.0\n", + " 239.0\n", + " 271.0\n", + " 36.0\n", " 3.0\n", " 4.0\n", " 0.0\n", - " 1052.0\n", - " 348.0\n", - " 267.0\n", + " 1229.0\n", + " 409.0\n", + " 314.0\n", " 56.6\n", " \n", " \n", " 3\n", " vs Empoli\n", - " 26.0\n", - " 53.7\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", + " 28.0\n", + " 52.5\n", " 22.0\n", - " 16.0\n", - " 1.0\n", - " 3.0\n", + " 242.0\n", + " 1980.0\n", + " 28.0\n", + " 20.0\n", + " 2.0\n", + " 4.0\n", " ...\n", - " 251.0\n", - " 214.0\n", - " 31.0\n", + " 283.0\n", + " 253.0\n", + " 36.0\n", " 2.0\n", " 0.0\n", " 0.0\n", - " 999.0\n", + " 1148.0\n", + " 263.0\n", " 217.0\n", - " 189.0\n", - " 53.4\n", + " 54.8\n", " \n", " \n", " 4\n", " vs Fiorentina\n", - " 27.0\n", + " 28.0\n", " 42.8\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 24.0\n", - " 18.0\n", - " 2.0\n", - " 2.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 28.0\n", + " 20.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 259.0\n", - " 229.0\n", - " 52.0\n", + " 307.0\n", + " 269.0\n", + " 59.0\n", " 1.0\n", " 4.0\n", " 0.0\n", - " 942.0\n", - " 249.0\n", + " 1130.0\n", " 289.0\n", - " 46.3\n", + " 328.0\n", + " 46.8\n", " \n", " \n", " 5\n", " vs Hellas Verona\n", - " 29.0\n", - " 56.6\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", + " 34.0\n", + " 57.1\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 32.0\n", " 30.0\n", - " 28.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 192.0\n", - " 276.0\n", - " 22.0\n", + " 234.0\n", + " 315.0\n", + " 25.0\n", " 1.0\n", " 0.0\n", - " 1.0\n", - " 1038.0\n", - " 345.0\n", - " 388.0\n", - " 47.1\n", + " 2.0\n", + " 1231.0\n", + " 423.0\n", + " 445.0\n", + " 48.7\n", " \n", " \n", " 6\n", " vs Inter\n", " 23.0\n", - " 47.1\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 23.0\n", - " 20.0\n", + " 45.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 24.0\n", + " 21.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 242.0\n", - " 214.0\n", - " 18.0\n", + " 276.0\n", + " 248.0\n", + " 19.0\n", " 2.0\n", " 2.0\n", " 1.0\n", - " 869.0\n", - " 198.0\n", - " 232.0\n", - " 46.0\n", + " 1007.0\n", + " 231.0\n", + " 288.0\n", + " 44.5\n", " \n", " \n", " 7\n", " vs Juventus\n", " 26.0\n", - " 50.5\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 15.0\n", - " 12.0\n", + " 51.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 17.0\n", + " 14.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 210.0\n", - " 210.0\n", - " 25.0\n", + " 246.0\n", + " 242.0\n", + " 29.0\n", " 0.0\n", - " 3.0\n", + " 4.0\n", " 0.0\n", - " 947.0\n", - " 230.0\n", - " 230.0\n", - " 50.0\n", + " 1134.0\n", + " 258.0\n", + " 272.0\n", + " 48.7\n", " \n", " \n", " 8\n", " vs Lazio\n", " 21.0\n", - " 48.8\n", + " 48.2\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 15.0\n", - " 11.0\n", + " 13.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 261.0\n", - " 185.0\n", - " 42.0\n", + " 308.0\n", + " 218.0\n", + " 44.0\n", " 1.0\n", " 4.0\n", " 1.0\n", - " 1021.0\n", - " 183.0\n", - " 181.0\n", - " 50.3\n", + " 1233.0\n", + " 218.0\n", + " 229.0\n", + " 48.8\n", " \n", " \n", " 9\n", " vs Lecce\n", " 26.0\n", " 57.6\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 23.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 24.0\n", " 16.0\n", - " 3.0\n", - " 3.0\n", + " 4.0\n", + " 4.0\n", " ...\n", - " 263.0\n", - " 265.0\n", - " 43.0\n", + " 283.0\n", + " 300.0\n", + " 45.0\n", " 3.0\n", " 2.0\n", - " 1.0\n", - " 1072.0\n", - " 373.0\n", - " 294.0\n", - " 55.9\n", + " 2.0\n", + " 1200.0\n", + " 417.0\n", + " 332.0\n", + " 55.7\n", " \n", " \n", " 10\n", " vs Milan\n", " 27.0\n", - " 45.5\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 23.0\n", - " 17.0\n", - " 2.0\n", + " 46.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", + " 22.0\n", " 3.0\n", + " 4.0\n", " ...\n", - " 234.0\n", - " 226.0\n", - " 20.0\n", - " 3.0\n", - " 2.0\n", - " 2.0\n", - " 964.0\n", - " 224.0\n", " 275.0\n", - " 44.9\n", + " 261.0\n", + " 25.0\n", + " 4.0\n", + " 2.0\n", + " 2.0\n", + " 1125.0\n", + " 263.0\n", + " 325.0\n", + " 44.7\n", " \n", " \n", " 11\n", " vs Monza\n", " 29.0\n", - " 44.9\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 27.0\n", - " 21.0\n", + " 45.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", + " 22.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 279.0\n", - " 242.0\n", - " 34.0\n", + " 318.0\n", + " 281.0\n", + " 37.0\n", " 0.0\n", - " 3.0\n", + " 4.0\n", " 1.0\n", - " 958.0\n", - " 197.0\n", - " 215.0\n", - " 47.8\n", + " 1145.0\n", + " 242.0\n", + " 253.0\n", + " 48.9\n", " \n", " \n", " 12\n", " vs Napoli\n", " 24.0\n", - " 38.7\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 14.0\n", - " 9.0\n", + " 38.4\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 15.0\n", + " 10.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 258.0\n", - " 167.0\n", - " 27.0\n", + " 298.0\n", + " 199.0\n", + " 28.0\n", " 1.0\n", - " 4.0\n", + " 5.0\n", " 0.0\n", - " 933.0\n", - " 201.0\n", - " 231.0\n", - " 46.5\n", + " 1100.0\n", + " 232.0\n", + " 280.0\n", + " 45.3\n", " \n", " \n", " 13\n", " vs Roma\n", - " 25.0\n", - " 50.6\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 16.0\n", - " 12.0\n", + " 26.0\n", + " 50.7\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 18.0\n", + " 14.0\n", " 0.0\n", " 0.0\n", " ...\n", + " 316.0\n", + " 246.0\n", + " 12.0\n", + " 0.0\n", + " 6.0\n", + " 0.0\n", + " 1156.0\n", + " 221.0\n", " 266.0\n", - " 214.0\n", - " 10.0\n", - " 0.0\n", - " 5.0\n", - " 0.0\n", - " 981.0\n", - " 184.0\n", - " 225.0\n", - " 45.0\n", + " 45.4\n", " \n", " \n", " 14\n", " vs Salernitana\n", " 28.0\n", - " 55.3\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 38.0\n", - " 23.0\n", - " 4.0\n", - " 7.0\n", + " 54.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 42.0\n", + " 26.0\n", + " 5.0\n", + " 8.0\n", " ...\n", - " 226.0\n", - " 233.0\n", - " 49.0\n", - " 7.0\n", + " 252.0\n", + " 259.0\n", + " 57.0\n", + " 8.0\n", " 1.0\n", " 1.0\n", - " 1084.0\n", - " 257.0\n", - " 240.0\n", - " 51.7\n", + " 1213.0\n", + " 291.0\n", + " 285.0\n", + " 50.5\n", " \n", " \n", " 15\n", " vs Sampdoria\n", - " 29.0\n", - " 50.6\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 30.0\n", - " 21.0\n", - " 4.0\n", - " 6.0\n", + " 31.0\n", + " 52.7\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 34.0\n", + " 24.0\n", + " 5.0\n", + " 7.0\n", " ...\n", - " 299.0\n", - " 266.0\n", - " 55.0\n", - " 3.0\n", - " 0.0\n", - " 0.0\n", - " 1017.0\n", + " 339.0\n", " 300.0\n", - " 318.0\n", - " 48.5\n", + " 59.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 1189.0\n", + " 362.0\n", + " 349.0\n", + " 50.9\n", " \n", " \n", " 16\n", " vs Sassuolo\n", - " 27.0\n", - " 51.3\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", " 29.0\n", - " 20.0\n", + " 51.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 33.0\n", + " 24.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 249.0\n", - " 182.0\n", - " 62.0\n", + " 287.0\n", + " 206.0\n", + " 72.0\n", " 2.0\n", - " 4.0\n", - " 0.0\n", - " 986.0\n", - " 216.0\n", - " 173.0\n", - " 55.5\n", + " 5.0\n", + " 1.0\n", + " 1131.0\n", + " 256.0\n", + " 206.0\n", + " 55.4\n", " \n", " \n", " 17\n", " vs Spezia\n", - " 30.0\n", - " 53.4\n", - " 20.0\n", - " 220.0\n", - " 1800.0\n", - " 32.0\n", - " 27.0\n", - " 0.0\n", + " 33.0\n", + " 54.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 37.0\n", + " 28.0\n", " 1.0\n", + " 2.0\n", " ...\n", - " 207.0\n", - " 261.0\n", - " 46.0\n", + " 235.0\n", + " 291.0\n", + " 53.0\n", " 1.0\n", + " 3.0\n", " 2.0\n", - " 2.0\n", - " 1182.0\n", - " 322.0\n", - " 281.0\n", - " 53.4\n", + " 1275.0\n", + " 343.0\n", + " 306.0\n", + " 52.9\n", " \n", " \n", " 18\n", " vs Torino\n", + " 27.0\n", + " 47.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 23.0\n", - " 47.2\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 20.0\n", - " 12.0\n", + " 15.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 209.0\n", - " 265.0\n", - " 21.0\n", + " 239.0\n", + " 306.0\n", + " 24.0\n", " 3.0\n", " 1.0\n", " 0.0\n", - " 998.0\n", - " 323.0\n", - " 295.0\n", - " 52.3\n", + " 1155.0\n", + " 367.0\n", + " 333.0\n", + " 52.4\n", " \n", " \n", " 19\n", " vs Udinese\n", + " 25.0\n", + " 50.1\n", " 22.0\n", - " 49.9\n", - " 19.0\n", - " 209.0\n", - " 1710.0\n", - " 21.0\n", - " 15.0\n", + " 242.0\n", + " 1980.0\n", + " 23.0\n", + " 16.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 250.0\n", - " 227.0\n", - " 29.0\n", + " 282.0\n", + " 252.0\n", + " 34.0\n", " 2.0\n", " 0.0\n", " 1.0\n", - " 950.0\n", - " 189.0\n", - " 237.0\n", - " 44.4\n", + " 1101.0\n", + " 233.0\n", + " 277.0\n", + " 45.7\n", " \n", " \n", "\n", - "

20 rows × 157 columns

\n", + "

20 rows × 152 columns

\n", "" ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 vs Atalanta 24.0 51.6 19.0 209.0 1710.0 \n", - "1 vs Bologna 24.0 47.4 20.0 220.0 1800.0 \n", - "2 vs Cremonese 29.0 56.0 19.0 209.0 1710.0 \n", - "3 vs Empoli 26.0 53.7 19.0 209.0 1710.0 \n", - "4 vs Fiorentina 27.0 42.8 19.0 209.0 1710.0 \n", - "5 vs Hellas Verona 29.0 56.6 19.0 209.0 1710.0 \n", - "6 vs Inter 23.0 47.1 19.0 209.0 1710.0 \n", - "7 vs Juventus 26.0 50.5 19.0 209.0 1710.0 \n", - "8 vs Lazio 21.0 48.8 19.0 209.0 1710.0 \n", - "9 vs Lecce 26.0 57.6 20.0 220.0 1800.0 \n", - "10 vs Milan 27.0 45.5 19.0 209.0 1710.0 \n", - "11 vs Monza 29.0 44.9 19.0 209.0 1710.0 \n", - "12 vs Napoli 24.0 38.7 19.0 209.0 1710.0 \n", - "13 vs Roma 25.0 50.6 19.0 209.0 1710.0 \n", - "14 vs Salernitana 28.0 55.3 20.0 220.0 1800.0 \n", - "15 vs Sampdoria 29.0 50.6 19.0 209.0 1710.0 \n", - "16 vs Sassuolo 27.0 51.3 19.0 209.0 1710.0 \n", - "17 vs Spezia 30.0 53.4 20.0 220.0 1800.0 \n", - "18 vs Torino 23.0 47.2 19.0 209.0 1710.0 \n", - "19 vs Udinese 22.0 49.9 19.0 209.0 1710.0 \n", + "0 vs Atalanta 24.0 51.4 22.0 242.0 1980.0 \n", + "1 vs Bologna 25.0 47.6 22.0 242.0 1980.0 \n", + "2 vs Cremonese 31.0 56.2 22.0 242.0 1980.0 \n", + "3 vs Empoli 28.0 52.5 22.0 242.0 1980.0 \n", + "4 vs Fiorentina 28.0 42.8 22.0 242.0 1980.0 \n", + "5 vs Hellas Verona 34.0 57.1 22.0 242.0 1980.0 \n", + "6 vs Inter 23.0 45.5 22.0 242.0 1980.0 \n", + "7 vs Juventus 26.0 51.0 22.0 242.0 1980.0 \n", + "8 vs Lazio 21.0 48.2 22.0 242.0 1980.0 \n", + "9 vs Lecce 26.0 57.6 22.0 242.0 1980.0 \n", + "10 vs Milan 27.0 46.5 22.0 242.0 1980.0 \n", + "11 vs Monza 29.0 45.0 22.0 242.0 1980.0 \n", + "12 vs Napoli 24.0 38.4 22.0 242.0 1980.0 \n", + "13 vs Roma 26.0 50.7 22.0 242.0 1980.0 \n", + "14 vs Salernitana 28.0 54.0 22.0 242.0 1980.0 \n", + "15 vs Sampdoria 31.0 52.7 22.0 242.0 1980.0 \n", + "16 vs Sassuolo 29.0 51.3 22.0 242.0 1980.0 \n", + "17 vs Spezia 33.0 54.5 22.0 242.0 1980.0 \n", + "18 vs Torino 27.0 47.0 22.0 242.0 1980.0 \n", + "19 vs Udinese 25.0 50.1 22.0 242.0 1980.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 22.0 16.0 1.0 1.0 ... 209.0 218.0 25.0 \n", - "1 28.0 16.0 5.0 5.0 ... 252.0 229.0 33.0 \n", - "2 32.0 20.0 4.0 4.0 ... 205.0 247.0 29.0 \n", - "3 22.0 16.0 1.0 3.0 ... 251.0 214.0 31.0 \n", - "4 24.0 18.0 2.0 2.0 ... 259.0 229.0 52.0 \n", - "5 30.0 28.0 0.0 1.0 ... 192.0 276.0 22.0 \n", - "6 23.0 20.0 2.0 2.0 ... 242.0 214.0 18.0 \n", - "7 15.0 12.0 1.0 2.0 ... 210.0 210.0 25.0 \n", - "8 15.0 11.0 1.0 1.0 ... 261.0 185.0 42.0 \n", - "9 23.0 16.0 3.0 3.0 ... 263.0 265.0 43.0 \n", - "10 23.0 17.0 2.0 3.0 ... 234.0 226.0 20.0 \n", - "11 27.0 21.0 0.0 0.0 ... 279.0 242.0 34.0 \n", - "12 14.0 9.0 1.0 2.0 ... 258.0 167.0 27.0 \n", - "13 16.0 12.0 0.0 0.0 ... 266.0 214.0 10.0 \n", - "14 38.0 23.0 4.0 7.0 ... 226.0 233.0 49.0 \n", - "15 30.0 21.0 4.0 6.0 ... 299.0 266.0 55.0 \n", - "16 29.0 20.0 4.0 4.0 ... 249.0 182.0 62.0 \n", - "17 32.0 27.0 0.0 1.0 ... 207.0 261.0 46.0 \n", - "18 20.0 12.0 3.0 4.0 ... 209.0 265.0 21.0 \n", - "19 21.0 15.0 2.0 2.0 ... 250.0 227.0 29.0 \n", + "0 23.0 17.0 1.0 1.0 ... 244.0 256.0 26.0 \n", + "1 30.0 18.0 5.0 5.0 ... 280.0 268.0 38.0 \n", + "2 39.0 25.0 4.0 4.0 ... 239.0 271.0 36.0 \n", + "3 28.0 20.0 2.0 4.0 ... 283.0 253.0 36.0 \n", + "4 28.0 20.0 3.0 3.0 ... 307.0 269.0 59.0 \n", + "5 32.0 30.0 0.0 1.0 ... 234.0 315.0 25.0 \n", + "6 24.0 21.0 2.0 2.0 ... 276.0 248.0 19.0 \n", + "7 17.0 14.0 1.0 2.0 ... 246.0 242.0 29.0 \n", + "8 19.0 13.0 1.0 1.0 ... 308.0 218.0 44.0 \n", + "9 24.0 16.0 4.0 4.0 ... 283.0 300.0 45.0 \n", + "10 29.0 22.0 3.0 4.0 ... 275.0 261.0 25.0 \n", + "11 29.0 22.0 0.0 0.0 ... 318.0 281.0 37.0 \n", + "12 15.0 10.0 1.0 2.0 ... 298.0 199.0 28.0 \n", + "13 18.0 14.0 0.0 0.0 ... 316.0 246.0 12.0 \n", + "14 42.0 26.0 5.0 8.0 ... 252.0 259.0 57.0 \n", + "15 34.0 24.0 5.0 7.0 ... 339.0 300.0 59.0 \n", + "16 33.0 24.0 4.0 4.0 ... 287.0 206.0 72.0 \n", + "17 37.0 28.0 1.0 2.0 ... 235.0 291.0 53.0 \n", + "18 23.0 15.0 3.0 4.0 ... 239.0 306.0 24.0 \n", + "19 23.0 16.0 2.0 2.0 ... 282.0 252.0 34.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 1.0 8.0 1.0 1165.0 239.0 \n", - "1 3.0 3.0 1.0 1081.0 224.0 \n", - "2 3.0 4.0 0.0 1052.0 348.0 \n", - "3 2.0 0.0 0.0 999.0 217.0 \n", - "4 1.0 4.0 0.0 942.0 249.0 \n", - "5 1.0 0.0 1.0 1038.0 345.0 \n", - "6 2.0 2.0 1.0 869.0 198.0 \n", - "7 0.0 3.0 0.0 947.0 230.0 \n", - "8 1.0 4.0 1.0 1021.0 183.0 \n", - "9 3.0 2.0 1.0 1072.0 373.0 \n", - "10 3.0 2.0 2.0 964.0 224.0 \n", - "11 0.0 3.0 1.0 958.0 197.0 \n", - "12 1.0 4.0 0.0 933.0 201.0 \n", - "13 0.0 5.0 0.0 981.0 184.0 \n", - "14 7.0 1.0 1.0 1084.0 257.0 \n", - "15 3.0 0.0 0.0 1017.0 300.0 \n", - "16 2.0 4.0 0.0 986.0 216.0 \n", - "17 1.0 2.0 2.0 1182.0 322.0 \n", - "18 3.0 1.0 0.0 998.0 323.0 \n", - "19 2.0 0.0 1.0 950.0 189.0 \n", + "0 1.0 8.0 1.0 1335.0 273.0 \n", + "1 3.0 4.0 1.0 1204.0 250.0 \n", + "2 3.0 4.0 0.0 1229.0 409.0 \n", + "3 2.0 0.0 0.0 1148.0 263.0 \n", + "4 1.0 4.0 0.0 1130.0 289.0 \n", + "5 1.0 0.0 2.0 1231.0 423.0 \n", + "6 2.0 2.0 1.0 1007.0 231.0 \n", + "7 0.0 4.0 0.0 1134.0 258.0 \n", + "8 1.0 4.0 1.0 1233.0 218.0 \n", + "9 3.0 2.0 2.0 1200.0 417.0 \n", + "10 4.0 2.0 2.0 1125.0 263.0 \n", + "11 0.0 4.0 1.0 1145.0 242.0 \n", + "12 1.0 5.0 0.0 1100.0 232.0 \n", + "13 0.0 6.0 0.0 1156.0 221.0 \n", + "14 8.0 1.0 1.0 1213.0 291.0 \n", + "15 4.0 0.0 0.0 1189.0 362.0 \n", + "16 2.0 5.0 1.0 1131.0 256.0 \n", + "17 1.0 3.0 2.0 1275.0 343.0 \n", + "18 3.0 1.0 0.0 1155.0 367.0 \n", + "19 2.0 0.0 1.0 1101.0 233.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 279.0 46.1 \n", - "1 180.0 55.4 \n", - "2 267.0 56.6 \n", - "3 189.0 53.4 \n", - "4 289.0 46.3 \n", - "5 388.0 47.1 \n", - "6 232.0 46.0 \n", - "7 230.0 50.0 \n", - "8 181.0 50.3 \n", - "9 294.0 55.9 \n", - "10 275.0 44.9 \n", - "11 215.0 47.8 \n", - "12 231.0 46.5 \n", - "13 225.0 45.0 \n", - "14 240.0 51.7 \n", - "15 318.0 48.5 \n", - "16 173.0 55.5 \n", - "17 281.0 53.4 \n", - "18 295.0 52.3 \n", - "19 237.0 44.4 \n", + "0 328.0 45.4 \n", + "1 210.0 54.3 \n", + "2 314.0 56.6 \n", + "3 217.0 54.8 \n", + "4 328.0 46.8 \n", + "5 445.0 48.7 \n", + "6 288.0 44.5 \n", + "7 272.0 48.7 \n", + "8 229.0 48.8 \n", + "9 332.0 55.7 \n", + "10 325.0 44.7 \n", + "11 253.0 48.9 \n", + "12 280.0 45.3 \n", + "13 266.0 45.4 \n", + "14 285.0 50.5 \n", + "15 349.0 50.9 \n", + "16 206.0 55.4 \n", + "17 306.0 52.9 \n", + "18 333.0 52.4 \n", + "19 277.0 45.7 \n", "\n", - "[20 rows x 157 columns]" + "[20 rows x 152 columns]" ] }, "execution_count": 6, @@ -2916,150 +2918,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "11e51337", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Outfield player columns\n", - "['player' 'nationality' 'position' 'team' 'age' 'birth_year' 'games'\n", - " 'games_starts' 'minutes' 'goals' 'assists' 'pens_made' 'pens_att'\n", - " 'cards_yellow' 'cards_red' 'goals_per90' 'assists_per90'\n", - " 'goals_assists_per90' 'goals_pens_per90' 'goals_assists_pens_per90' 'xg'\n", - " 'npxg' 'xg_per90' 'npxg_per90' 'minutes_90s' 'shots_on_target'\n", - " 'shots_free_kicks' 'shots_on_target_pct' 'shots_on_target_per90'\n", - " 'goals_per_shot' 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net'\n", - " 'npxg_net' 'passes_completed' 'passes' 'passes_pct'\n", - " 'passes_total_distance' 'passes_progressive_distance'\n", - " 'passes_completed_short' 'passes_short' 'passes_pct_short'\n", - " 'passes_completed_medium' 'passes_medium' 'passes_pct_medium'\n", - " 'passes_completed_long' 'passes_long' 'passes_pct_long' 'assisted_shots'\n", - " 'passes_into_final_third' 'passes_into_penalty_area'\n", - " 'crosses_into_penalty_area' 'progressive_passes' 'passes_live'\n", - " 'passes_dead' 'passes_free_kicks' 'through_balls' 'passes_switches'\n", - " 'crosses' 'corner_kicks' 'corner_kicks_in' 'corner_kicks_out'\n", - " 'corner_kicks_straight' 'throw_ins' 'passes_offsides' 'passes_blocked'\n", - " 'sca' 'sca_per90' 'sca_passes_live' 'sca_passes_dead' 'sca_dribbles'\n", - " 'sca_shots' 'sca_fouled' 'gca' 'gca_per90' 'gca_passes_live'\n", - " 'gca_passes_dead' 'gca_dribbles' 'gca_shots' 'gca_fouled' 'gca_defense'\n", - " 'tackles' 'tackles_won' 'tackles_def_3rd' 'tackles_mid_3rd'\n", - " 'tackles_att_3rd' 'dribble_tackles' 'dribbles_vs' 'dribble_tackles_pct'\n", - " 'dribbled_past' 'blocks' 'blocked_shots' 'blocked_passes' 'interceptions'\n", - " 'clearances' 'errors' 'touches' 'touches_def_pen_area' 'touches_def_3rd'\n", - " 'touches_mid_3rd' 'touches_att_3rd' 'touches_att_pen_area'\n", - " 'touches_live_ball' 'dribbles_completed' 'dribbles'\n", - " 'dribbles_completed_pct' 'passes_received' 'miscontrols' 'dispossessed'\n", - " 'cards_yellow_red' 'fouls' 'fouled' 'offsides' 'pens_won' 'pens_conceded'\n", - " 'own_goals' 'ball_recoveries' 'aerials_won' 'aerials_lost'\n", - " 'aerials_won_pct']\n", - "Keeper player columns\n", - "['player' 'nationality' 'position' 'team' 'age' 'birth_year' 'gk_games'\n", - " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", - " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", - " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", - " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", - " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", - " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", - " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", - " 'gk_passes_completed_launched' 'gk_passes_launched'\n", - " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", - " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", - " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", - " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", - " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", - " 'gk_avg_distance_def_actions']\n", - "Team columns\n", - "['team' 'players_used' 'possession' 'games' 'games_starts' 'minutes'\n", - " 'goals' 'assists' 'pens_made' 'pens_att' 'cards_yellow' 'cards_red'\n", - " 'goals_per90' 'assists_per90' 'goals_assists_per90' 'goals_pens_per90'\n", - " 'goals_assists_pens_per90' 'xg' 'npxg' 'xg_per90' 'npxg_per90' 'gk_games'\n", - " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", - " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", - " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", - " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", - " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", - " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", - " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", - " 'gk_passes_completed_launched' 'gk_passes_launched'\n", - " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", - " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", - " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", - " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", - " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", - " 'gk_avg_distance_def_actions' 'shots_on_target' 'shots_free_kicks'\n", - " 'shots_on_target_pct' 'shots_on_target_per90' 'goals_per_shot'\n", - " 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net' 'npxg_net'\n", - " 'passes_completed' 'passes' 'passes_pct' 'passes_total_distance'\n", - " 'passes_progressive_distance' 'passes_completed_short' 'passes_short'\n", - " 'passes_pct_short' 'passes_completed_medium' 'passes_medium'\n", - " 'passes_pct_medium' 'passes_completed_long' 'passes_long'\n", - " 'passes_pct_long' 'assisted_shots' 'passes_into_final_third'\n", - " 'passes_into_penalty_area' 'crosses_into_penalty_area'\n", - " 'progressive_passes' 'passes_live' 'passes_dead' 'passes_free_kicks'\n", - " 'through_balls' 'passes_switches' 'crosses' 'corner_kicks'\n", - " 'corner_kicks_in' 'corner_kicks_out' 'corner_kicks_straight' 'throw_ins'\n", - " 'passes_offsides' 'passes_blocked' 'sca' 'sca_per90' 'sca_passes_live'\n", - " 'sca_passes_dead' 'sca_dribbles' 'sca_shots' 'sca_fouled' 'gca'\n", - " 'gca_per90' 'gca_passes_live' 'gca_passes_dead' 'gca_dribbles'\n", - " 'gca_shots' 'gca_fouled' 'gca_defense' 'tackles' 'tackles_won'\n", - " 'tackles_def_3rd' 'tackles_mid_3rd' 'tackles_att_3rd' 'dribble_tackles'\n", - " 'dribbles_vs' 'dribble_tackles_pct' 'dribbled_past' 'blocks'\n", - " 'blocked_shots' 'blocked_passes' 'interceptions' 'clearances' 'errors'\n", - " 'touches' 'touches_def_pen_area' 'touches_def_3rd' 'touches_mid_3rd'\n", - " 'touches_att_3rd' 'touches_att_pen_area' 'touches_live_ball'\n", - " 'dribbles_completed' 'dribbles' 'dribbles_completed_pct'\n", - " 'passes_received' 'miscontrols' 'dispossessed' 'cards_yellow_red' 'fouls'\n", - " 'fouled' 'offsides' 'pens_won' 'pens_conceded' 'own_goals'\n", - " 'ball_recoveries' 'aerials_won' 'aerials_lost' 'aerials_won_pct']\n", - "Vs Team columns\n", - "['team' 'players_used' 'possession' 'games' 'games_starts' 'minutes'\n", - " 'goals' 'assists' 'pens_made' 'pens_att' 'cards_yellow' 'cards_red'\n", - " 'goals_per90' 'assists_per90' 'goals_assists_per90' 'goals_pens_per90'\n", - " 'goals_assists_pens_per90' 'xg' 'npxg' 'xg_per90' 'npxg_per90' 'gk_games'\n", - " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", - " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", - " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", - " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", - " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", - " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", - " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", - " 'gk_passes_completed_launched' 'gk_passes_launched'\n", - " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", - " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", - " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", - " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", - " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", - " 'gk_avg_distance_def_actions' 'shots_on_target' 'shots_free_kicks'\n", - " 'shots_on_target_pct' 'shots_on_target_per90' 'goals_per_shot'\n", - " 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net' 'npxg_net'\n", - " 'passes_completed' 'passes' 'passes_pct' 'passes_total_distance'\n", - " 'passes_progressive_distance' 'passes_completed_short' 'passes_short'\n", - " 'passes_pct_short' 'passes_completed_medium' 'passes_medium'\n", - " 'passes_pct_medium' 'passes_completed_long' 'passes_long'\n", - " 'passes_pct_long' 'assisted_shots' 'passes_into_final_third'\n", - " 'passes_into_penalty_area' 'crosses_into_penalty_area'\n", - " 'progressive_passes' 'passes_live' 'passes_dead' 'passes_free_kicks'\n", - " 'through_balls' 'passes_switches' 'crosses' 'corner_kicks'\n", - " 'corner_kicks_in' 'corner_kicks_out' 'corner_kicks_straight' 'throw_ins'\n", - " 'passes_offsides' 'passes_blocked' 'sca' 'sca_per90' 'sca_passes_live'\n", - " 'sca_passes_dead' 'sca_dribbles' 'sca_shots' 'sca_fouled' 'gca'\n", - " 'gca_per90' 'gca_passes_live' 'gca_passes_dead' 'gca_dribbles'\n", - " 'gca_shots' 'gca_fouled' 'gca_defense' 'tackles' 'tackles_won'\n", - " 'tackles_def_3rd' 'tackles_mid_3rd' 'tackles_att_3rd' 'dribble_tackles'\n", - " 'dribbles_vs' 'dribble_tackles_pct' 'dribbled_past' 'blocks'\n", - " 'blocked_shots' 'blocked_passes' 'interceptions' 'clearances' 'errors'\n", - " 'touches' 'touches_def_pen_area' 'touches_def_3rd' 'touches_mid_3rd'\n", - " 'touches_att_3rd' 'touches_att_pen_area' 'touches_live_ball'\n", - " 'dribbles_completed' 'dribbles' 'dribbles_completed_pct'\n", - " 'passes_received' 'miscontrols' 'dispossessed' 'cards_yellow_red' 'fouls'\n", - " 'fouled' 'offsides' 'pens_won' 'pens_conceded' 'own_goals'\n", - " 'ball_recoveries' 'aerials_won' 'aerials_lost' 'aerials_won_pct']\n" - ] - } - ], + "outputs": [], "source": [ "print('Outfield player columns')\n", "print(df_outfield.columns.values)\n", @@ -3086,9 +2948,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "2e1370cd", "metadata": {}, + "outputs": [], + "source": [ + "df_outfield = get_outfield_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats')\n", + "\n", + "df_outfield.to_csv('fbref_data/season2122/outfield_players.csv', index=False)\n", + "\n", + "df_outfield" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36441b0e", + "metadata": {}, "outputs": [ { "data": { @@ -3117,10 +2993,1565 @@ " team\n", " age\n", " birth_year\n", + " gk_games\n", + " gk_games_starts\n", + " gk_minutes\n", + " gk_goals_against\n", + " ...\n", + " gk_passes_length_avg\n", + " gk_goal_kicks\n", + " gk_pct_goal_kicks_launched\n", + " gk_goal_kick_length_avg\n", + " gk_crosses\n", + " gk_crosses_stopped\n", + " gk_crosses_stopped_pct\n", + " gk_def_actions_outside_pen_area\n", + " gk_def_actions_outside_pen_area_per90\n", + " gk_avg_distance_def_actions\n", + " \n", + " \n", + " \n", + " \n", + " 0\n", + " Emil Audero\n", + " it ITA\n", + " GK\n", + " Sampdoria\n", + " 24\n", + " 1997\n", + " 29.0\n", + " 29.0\n", + " 2560.0\n", + " 48.0\n", + " ...\n", + " 36.3\n", + " 212.0\n", + " 67.9\n", + " 47.6\n", + " 418.0\n", + " 22.0\n", + " 5.3\n", + " 25.0\n", + " 0.88\n", + " 13.5\n", + " \n", + " \n", + " 1\n", + " Nicola Bagnolini\n", + " it ITA\n", + " GK\n", + " Bologna\n", + " 17\n", + " 2004\n", + " 1.0\n", + " 0.0\n", + " 3.0\n", + " 0.0\n", + " ...\n", + " 61.0\n", + " 1.0\n", + " 100.0\n", + " 60.0\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 2\n", + " Francesco Bardi\n", + " it ITA\n", + " GK\n", + " Bologna\n", + " 29\n", + " 1992\n", + " 2.0\n", + " 2.0\n", + " 177.0\n", + " 2.0\n", + " ...\n", + " 33.0\n", + " 13.0\n", + " 38.5\n", + " 32.1\n", + " 24.0\n", + " 2.0\n", + " 8.3\n", + " 0.0\n", + " 0.00\n", + " 5.5\n", + " \n", + " \n", + " 3\n", + " Vid Belec\n", + " si SVN\n", + " GK\n", + " Salernitana\n", + " 31\n", + " 1990\n", + " 23.0\n", + " 21.0\n", + " 1938.0\n", + " 49.0\n", + " ...\n", + " 39.5\n", + " 182.0\n", + " 73.1\n", + " 49.9\n", + " 329.0\n", + " 12.0\n", + " 3.6\n", + " 14.0\n", + " 0.65\n", + " 12.7\n", + " \n", + " \n", + " 4\n", + " Alessandro Berardi\n", + " it ITA\n", + " GK\n", + " Hellas Verona\n", + " 30\n", + " 1991\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 3.0\n", + " ...\n", + " 39.7\n", + " 7.0\n", + " 100.0\n", + " 58.7\n", + " 12.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 5\n", + " Etrit Berisha\n", + " al ALB\n", + " GK\n", + " Torino\n", + " 32\n", + " 1989\n", + " 10.0\n", + " 10.0\n", + " 900.0\n", + " 8.0\n", + " ...\n", + " 39.4\n", + " 78.0\n", + " 87.2\n", + " 58.6\n", + " 148.0\n", + " 7.0\n", + " 4.7\n", + " 10.0\n", + " 1.00\n", + " 14.6\n", + " \n", + " \n", + " 6\n", + " Andrea Consigli\n", + " it ITA\n", + " GK\n", + " Sassuolo\n", + " 34\n", + " 1987\n", + " 37.0\n", + " 37.0\n", + " 3322.0\n", + " 63.0\n", + " ...\n", + " 30.9\n", + " 246.0\n", + " 31.7\n", + " 28.8\n", + " 491.0\n", + " 21.0\n", + " 4.3\n", + " 30.0\n", + " 0.81\n", + " 13.4\n", + " \n", + " \n", + " 7\n", + " Alessio Cragno\n", + " it ITA\n", + " GK\n", + " Cagliari\n", + " 27\n", + " 1994\n", + " 35.0\n", + " 35.0\n", + " 3150.0\n", + " 63.0\n", + " ...\n", + " 39.4\n", + " 316.0\n", + " 73.4\n", + " 48.2\n", + " 484.0\n", + " 22.0\n", + " 4.5\n", + " 33.0\n", + " 0.94\n", + " 15.0\n", + " \n", + " \n", + " 8\n", + " Bartłomiej Drągowski\n", + " pl POL\n", + " GK\n", + " Fiorentina\n", + " 23\n", + " 1997\n", + " 7.0\n", + " 7.0\n", + " 556.0\n", + " 8.0\n", + " ...\n", + " 30.8\n", + " 39.0\n", + " 43.6\n", + " 40.2\n", + " 61.0\n", + " 2.0\n", + " 3.3\n", + " 20.0\n", + " 3.24\n", + " 22.3\n", + " \n", + " \n", + " 9\n", + " Wladimiro Falcone\n", + " it ITA\n", + " GK\n", + " Sampdoria\n", + " 26\n", + " 1995\n", + " 10.0\n", + " 9.0\n", + " 855.0\n", + " 14.0\n", + " ...\n", + " 33.2\n", + " 74.0\n", + " 70.3\n", + " 45.0\n", + " 164.0\n", + " 7.0\n", + " 4.3\n", + " 2.0\n", + " 0.21\n", + " 10.2\n", + " \n", + " \n", + " 10\n", + " Vincenzo Fiorillo\n", + " it ITA\n", + " GK\n", + " Salernitana\n", + " 31\n", + " 1990\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 5.0\n", + " ...\n", + " 36.4\n", + " 12.0\n", + " 83.3\n", + " 50.8\n", + " 16.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 4.0\n", + " \n", + " \n", + " 11\n", + " Luca Gemello\n", + " it ITA\n", + " GK\n", + " Torino\n", + " 21\n", + " 2000\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 47.8\n", + " 3.0\n", + " 100.0\n", + " 56.3\n", + " 10.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 1.00\n", + " 21.0\n", + " \n", + " \n", + " 12\n", + " Samir Handanović\n", + " si SVN\n", + " GK\n", + " Inter\n", + " 37\n", + " 1984\n", + " 37.0\n", + " 37.0\n", + " 3330.0\n", + " 30.0\n", + " ...\n", + " 28.1\n", + " 142.0\n", + " 20.4\n", + " 26.5\n", + " 393.0\n", + " 15.0\n", + " 3.8\n", + " 10.0\n", + " 0.27\n", + " 11.2\n", + " \n", + " \n", + " 13\n", + " Luca Lezzerini\n", + " it ITA\n", + " GK\n", + " Venezia\n", + " 26\n", + " 1995\n", + " 6.0\n", + " 6.0\n", + " 495.0\n", + " 9.0\n", + " ...\n", + " 30.3\n", + " 44.0\n", + " 34.1\n", + " 31.0\n", + " 100.0\n", + " 2.0\n", + " 2.0\n", + " 3.0\n", + " 0.55\n", + " 13.8\n", + " \n", + " \n", + " 14\n", + " Niki Mäenpää\n", + " fi FIN\n", + " GK\n", + " Venezia\n", + " 36\n", + " 1985\n", + " 17.0\n", + " 16.0\n", + " 1485.0\n", + " 27.0\n", + " ...\n", + " 36.0\n", + " 143.0\n", + " 39.2\n", + " 35.4\n", + " 275.0\n", + " 6.0\n", + " 2.2\n", + " 10.0\n", + " 0.61\n", + " 10.8\n", + " \n", + " \n", + " 15\n", + " Mike Maignan\n", + " fr FRA\n", + " GK\n", + " Milan\n", + " 26\n", + " 1995\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 21.0\n", + " ...\n", + " 33.0\n", + " 149.0\n", + " 38.9\n", + " 36.5\n", + " 347.0\n", + " 25.0\n", + " 7.2\n", + " 45.0\n", + " 1.41\n", + " 16.8\n", + " \n", + " \n", + " 16\n", + " Davide Marfella\n", + " it ITA\n", + " GK\n", + " Napoli\n", + " 21\n", + " 1999\n", + " 1.0\n", + " 0.0\n", + " 11.0\n", + " 0.0\n", + " ...\n", + " 15.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 17\n", + " Alex Meret\n", + " it ITA\n", + " GK\n", + " Napoli\n", + " 24\n", + " 1997\n", + " 7.0\n", + " 7.0\n", + " 619.0\n", + " 6.0\n", + " ...\n", + " 25.1\n", + " 36.0\n", + " 25.0\n", + " 26.5\n", + " 89.0\n", + " 3.0\n", + " 3.4\n", + " 5.0\n", + " 0.73\n", + " 14.6\n", + " \n", + " \n", + " 18\n", + " Vanja Milinković-Savić\n", + " rs SRB\n", + " GK\n", + " Torino\n", + " 24\n", + " 1997\n", + " 27.0\n", + " 27.0\n", + " 2430.0\n", + " 33.0\n", + " ...\n", + " 44.4\n", + " 169.0\n", + " 91.1\n", + " 66.3\n", + " 290.0\n", + " 18.0\n", + " 6.2\n", + " 22.0\n", + " 0.81\n", + " 14.3\n", + " \n", + " \n", + " 19\n", + " Lorenzo Montipò\n", + " it ITA\n", + " GK\n", + " Hellas Verona\n", + " 25\n", + " 1996\n", + " 34.0\n", + " 34.0\n", + " 3060.0\n", + " 50.0\n", + " ...\n", + " 42.3\n", + " 231.0\n", + " 68.4\n", + " 46.5\n", + " 495.0\n", + " 21.0\n", + " 4.2\n", + " 23.0\n", + " 0.68\n", + " 13.1\n", + " \n", + " \n", + " 20\n", + " Juan Musso\n", + " ar ARG\n", + " GK\n", + " Atalanta\n", + " 27\n", + " 1994\n", + " 33.0\n", + " 33.0\n", + " 2932.0\n", + " 42.0\n", + " ...\n", + " 31.3\n", + " 208.0\n", + " 69.2\n", + " 49.7\n", + " 308.0\n", + " 28.0\n", + " 9.1\n", + " 39.0\n", + " 1.20\n", + " 16.5\n", + " \n", + " \n", + " 21\n", + " David Ospina\n", + " co COL\n", + " GK\n", + " Napoli\n", + " 32\n", + " 1988\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 25.0\n", + " ...\n", + " 28.6\n", + " 135.0\n", + " 14.1\n", + " 22.5\n", + " 353.0\n", + " 18.0\n", + " 5.1\n", + " 31.0\n", + " 1.00\n", + " 17.0\n", + " \n", + " \n", + " 22\n", + " Daniele Padelli\n", + " it ITA\n", + " GK\n", + " Udinese\n", + " 35\n", + " 1985\n", + " 3.0\n", + " 3.0\n", + " 270.0\n", + " 8.0\n", + " ...\n", + " 29.6\n", + " 22.0\n", + " 50.0\n", + " 40.9\n", + " 56.0\n", + " 1.0\n", + " 1.8\n", + " 2.0\n", + " 0.67\n", + " 13.2\n", + " \n", + " \n", + " 23\n", + " Ivor Pandur\n", + " hr CRO\n", + " GK\n", + " Hellas Verona\n", + " 21\n", + " 2000\n", + " 3.0\n", + " 3.0\n", + " 270.0\n", + " 6.0\n", + " ...\n", + " 33.1\n", + " 15.0\n", + " 53.3\n", + " 37.1\n", + " 22.0\n", + " 1.0\n", + " 4.5\n", + " 1.0\n", + " 0.33\n", + " 11.3\n", + " \n", + " \n", + " 24\n", + " Rui Patrício\n", + " pt POR\n", + " GK\n", + " Roma\n", + " 33\n", + " 1988\n", + " 38.0\n", + " 38.0\n", + " 3420.0\n", + " 43.0\n", + " ...\n", + " 34.2\n", + " 222.0\n", + " 32.0\n", + " 31.5\n", + " 393.0\n", + " 12.0\n", + " 3.1\n", + " 26.0\n", + " 0.68\n", + " 14.3\n", + " \n", + " \n", + " 25\n", + " Gianluca Pegolo\n", + " it ITA\n", + " GK\n", + " Sassuolo\n", + " 40\n", + " 1981\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 3.0\n", + " ...\n", + " 26.7\n", + " 8.0\n", + " 12.5\n", + " 19.8\n", + " 8.0\n", + " 1.0\n", + " 12.5\n", + " 0.0\n", + " 0.00\n", + " 6.0\n", + " \n", + " \n", + " 26\n", + " Mattia Perin\n", + " it ITA\n", + " GK\n", + " Juventus\n", + " 28\n", + " 1992\n", + " 5.0\n", + " 5.0\n", + " 405.0\n", + " 7.0\n", + " ...\n", + " 29.6\n", + " 26.0\n", + " 23.1\n", + " 29.2\n", + " 77.0\n", + " 1.0\n", + " 1.3\n", + " 4.0\n", + " 0.89\n", + " 12.6\n", + " \n", + " \n", + " 27\n", + " Carlo Pinsoglio\n", + " it ITA\n", + " GK\n", + " Juventus\n", + " 31\n", + " 1990\n", + " 1.0\n", + " 0.0\n", + " 45.0\n", + " 1.0\n", + " ...\n", + " 29.6\n", + " 1.0\n", + " 0.0\n", + " 37.0\n", + " 12.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 28\n", + " Ivan Provedel\n", + " it ITA\n", + " GK\n", + " Spezia\n", + " 27\n", + " 1994\n", + " 31.0\n", + " 31.0\n", + " 2761.0\n", + " 52.0\n", + " ...\n", + " 40.1\n", + " 229.0\n", + " 69.4\n", + " 51.7\n", + " 476.0\n", + " 28.0\n", + " 5.9\n", + " 25.0\n", + " 0.81\n", + " 11.8\n", + " \n", + " \n", + " 29\n", + " Ionuț Radu\n", + " ro ROU\n", + " GK\n", + " Inter\n", + " 24\n", + " 1997\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 2.0\n", + " ...\n", + " 33.3\n", + " 4.0\n", + " 25.0\n", + " 29.8\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 1.00\n", + " 16.0\n", + " \n", + " \n", + " 30\n", + " Boris Radunović\n", + " rs SRB\n", + " GK\n", + " Cagliari\n", + " 25\n", + " 1996\n", + " 3.0\n", + " 3.0\n", + " 270.0\n", + " 5.0\n", + " ...\n", + " 41.5\n", + " 28.0\n", + " 82.1\n", + " 51.1\n", + " 42.0\n", + " 1.0\n", + " 2.4\n", + " 1.0\n", + " 0.33\n", + " 13.8\n", + " \n", + " \n", + " 31\n", + " Nicola Ravaglia\n", + " it ITA\n", + " GK\n", + " Sampdoria\n", + " 32\n", + " 1988\n", + " 1.0\n", + " 0.0\n", + " 5.0\n", + " 1.0\n", + " ...\n", + " 57.0\n", + " 2.0\n", + " 100.0\n", + " 65.5\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 32\n", + " Pepe Reina\n", + " es ESP\n", + " GK\n", + " Lazio\n", + " 38\n", + " 1982\n", + " 15.0\n", + " 15.0\n", + " 1350.0\n", + " 29.0\n", + " ...\n", + " 29.8\n", + " 99.0\n", + " 29.3\n", + " 30.1\n", + " 186.0\n", + " 10.0\n", + " 5.4\n", + " 10.0\n", + " 0.67\n", + " 13.7\n", + " \n", + " \n", + " 33\n", + " Sergio Romero\n", + " ar ARG\n", + " GK\n", + " Venezia\n", + " 34\n", + " 1987\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", + " 33.0\n", + " ...\n", + " 34.6\n", + " 142.0\n", + " 45.8\n", + " 38.8\n", + " 265.0\n", + " 14.0\n", + " 5.3\n", + " 11.0\n", + " 0.69\n", + " 11.8\n", + " \n", + " \n", + " 34\n", + " Francesco Rossi\n", + " it ITA\n", + " GK\n", + " Atalanta\n", + " 30\n", + " 1991\n", + " 1.0\n", + " 0.0\n", + " 37.0\n", + " 1.0\n", + " ...\n", + " 37.8\n", + " 4.0\n", + " 25.0\n", + " 39.3\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 18.0\n", + " \n", + " \n", + " 35\n", + " Giacomo Satalino\n", + " it ITA\n", + " GK\n", + " Sassuolo\n", + " 22\n", + " 1999\n", + " 1.0\n", + " 0.0\n", + " 8.0\n", + " 0.0\n", + " ...\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", + " 22.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 36\n", + " Adrian Šemper\n", + " hr CRO\n", + " GK\n", + " Genoa\n", + " 23\n", + " 1998\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 1.0\n", + " ...\n", + " 38.5\n", + " 7.0\n", + " 85.7\n", + " 52.0\n", + " 14.0\n", + " 2.0\n", + " 14.3\n", + " 0.0\n", + " 0.00\n", + " 11.2\n", + " \n", + " \n", + " 37\n", + " Luigi Sepe\n", + " it ITA\n", + " GK\n", + " Salernitana\n", + " 30\n", + " 1991\n", + " 16.0\n", + " 16.0\n", + " 1392.0\n", + " 24.0\n", + " ...\n", + " 40.8\n", + " 130.0\n", + " 63.8\n", + " 46.4\n", + " 235.0\n", + " 10.0\n", + " 4.3\n", + " 13.0\n", + " 0.84\n", + " 16.0\n", + " \n", + " \n", + " 38\n", + " Marco Silvestri\n", + " it ITA\n", + " GK\n", + " Udinese\n", + " 30\n", + " 1991\n", + " 35.0\n", + " 35.0\n", + " 3150.0\n", + " 50.0\n", + " ...\n", + " 36.7\n", + " 252.0\n", + " 46.8\n", + " 38.4\n", + " 462.0\n", + " 16.0\n", + " 3.5\n", + " 10.0\n", + " 0.29\n", + " 11.0\n", + " \n", + " \n", + " 39\n", + " Salvatore Sirigu\n", + " it ITA\n", + " GK\n", + " Genoa\n", + " 34\n", + " 1987\n", + " 37.0\n", + " 37.0\n", + " 3330.0\n", + " 59.0\n", + " ...\n", + " 36.6\n", + " 263.0\n", + " 68.8\n", + " 45.9\n", + " 476.0\n", + " 14.0\n", + " 2.9\n", + " 20.0\n", + " 0.54\n", + " 12.9\n", + " \n", + " \n", + " 40\n", + " Łukasz Skorupski\n", + " pl POL\n", + " GK\n", + " Bologna\n", + " 30\n", + " 1991\n", + " 36.0\n", + " 36.0\n", + " 3240.0\n", + " 53.0\n", + " ...\n", + " 34.0\n", + " 294.0\n", + " 28.9\n", + " 26.6\n", + " 498.0\n", + " 27.0\n", + " 5.4\n", + " 14.0\n", + " 0.39\n", + " 11.3\n", + " \n", + " \n", + " 41\n", + " Marco Sportiello\n", + " it ITA\n", + " GK\n", + " Atalanta\n", + " 29\n", + " 1992\n", + " 5.0\n", + " 5.0\n", + " 450.0\n", + " 5.0\n", + " ...\n", + " 26.0\n", + " 28.0\n", + " 64.3\n", + " 47.0\n", + " 45.0\n", + " 1.0\n", + " 2.2\n", + " 4.0\n", + " 0.80\n", + " 13.9\n", + " \n", + " \n", + " 42\n", + " Thomas Strakosha\n", + " al ALB\n", + " GK\n", + " Lazio\n", + " 26\n", + " 1995\n", + " 23.0\n", + " 23.0\n", + " 2070.0\n", + " 29.0\n", + " ...\n", + " 28.5\n", + " 119.0\n", + " 20.2\n", + " 23.6\n", + " 283.0\n", + " 11.0\n", + " 3.9\n", + " 16.0\n", + " 0.70\n", + " 14.5\n", + " \n", + " \n", + " 43\n", + " Wojciech Szczęsny\n", + " pl POL\n", + " GK\n", + " Juventus\n", + " 31\n", + " 1990\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 29.0\n", + " ...\n", + " 30.8\n", + " 172.0\n", + " 29.7\n", + " 31.5\n", + " 481.0\n", + " 24.0\n", + " 5.0\n", + " 25.0\n", + " 0.76\n", + " 13.5\n", + " \n", + " \n", + " 44\n", + " Ciprian Tătărușanu\n", + " ro ROU\n", + " GK\n", + " Milan\n", + " 35\n", + " 1986\n", + " 6.0\n", + " 6.0\n", + " 540.0\n", + " 10.0\n", + " ...\n", + " 33.3\n", + " 36.0\n", + " 36.1\n", + " 35.0\n", + " 82.0\n", + " 6.0\n", + " 7.3\n", + " 3.0\n", + " 0.50\n", + " 12.5\n", + " \n", + " \n", + " 45\n", + " Pietro Terracciano\n", + " it ITA\n", + " GK\n", + " Fiorentina\n", + " 31\n", + " 1990\n", + " 32.0\n", + " 31.0\n", + " 2862.0\n", + " 43.0\n", + " ...\n", + " 32.2\n", + " 170.0\n", + " 35.3\n", + " 33.7\n", + " 291.0\n", + " 22.0\n", + " 7.6\n", + " 51.0\n", + " 1.60\n", + " 16.7\n", + " \n", + " \n", + " 46\n", + " Guglielmo Vicario\n", + " it ITA\n", + " GK\n", + " Empoli\n", + " 24\n", + " 1996\n", + " 38.0\n", + " 38.0\n", + " 3420.0\n", + " 70.0\n", + " ...\n", + " 29.5\n", + " 257.0\n", + " 41.6\n", + " 37.5\n", + " 602.0\n", + " 35.0\n", + " 5.8\n", + " 38.0\n", + " 1.00\n", + " 13.9\n", + " \n", + " \n", + " 47\n", + " Jeroen Zoet\n", + " nl NED\n", + " GK\n", + " Spezia\n", + " 30\n", + " 1991\n", + " 7.0\n", + " 7.0\n", + " 630.0\n", + " 19.0\n", + " ...\n", + " 30.7\n", + " 54.0\n", + " 37.0\n", + " 37.4\n", + " 95.0\n", + " 2.0\n", + " 2.1\n", + " 6.0\n", + " 0.86\n", + " 16.4\n", + " \n", + " \n", + " 48\n", + " Petar Zovko\n", + " ba BIH\n", + " GK\n", + " Spezia\n", + " 19\n", + " 2002\n", + " 1.0\n", + " 0.0\n", + " 29.0\n", + " 0.0\n", + " ...\n", + " 35.9\n", + " 2.0\n", + " 50.0\n", + " 35.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 2.0\n", + " 6.21\n", + " 28.5\n", + " \n", + " \n", + "\n", + "

49 rows × 47 columns

\n", + "" + ], + "text/plain": [ + " player nationality position team age birth_year \\\n", + "0 Emil Audero it ITA GK Sampdoria 24 1997 \n", + "1 Nicola Bagnolini it ITA GK Bologna 17 2004 \n", + "2 Francesco Bardi it ITA GK Bologna 29 1992 \n", + "3 Vid Belec si SVN GK Salernitana 31 1990 \n", + "4 Alessandro Berardi it ITA GK Hellas Verona 30 1991 \n", + "5 Etrit Berisha al ALB GK Torino 32 1989 \n", + "6 Andrea Consigli it ITA GK Sassuolo 34 1987 \n", + "7 Alessio Cragno it ITA GK Cagliari 27 1994 \n", + "8 Bartłomiej Drągowski pl POL GK Fiorentina 23 1997 \n", + "9 Wladimiro Falcone it ITA GK Sampdoria 26 1995 \n", + "10 Vincenzo Fiorillo it ITA GK Salernitana 31 1990 \n", + "11 Luca Gemello it ITA GK Torino 21 2000 \n", + "12 Samir Handanović si SVN GK Inter 37 1984 \n", + "13 Luca Lezzerini it ITA GK Venezia 26 1995 \n", + "14 Niki Mäenpää fi FIN GK Venezia 36 1985 \n", + "15 Mike Maignan fr FRA GK Milan 26 1995 \n", + "16 Davide Marfella it ITA GK Napoli 21 1999 \n", + "17 Alex Meret it ITA GK Napoli 24 1997 \n", + "18 Vanja Milinković-Savić rs SRB GK Torino 24 1997 \n", + "19 Lorenzo Montipò it ITA GK Hellas Verona 25 1996 \n", + "20 Juan Musso ar ARG GK Atalanta 27 1994 \n", + "21 David Ospina co COL GK Napoli 32 1988 \n", + "22 Daniele Padelli it ITA GK Udinese 35 1985 \n", + "23 Ivor Pandur hr CRO GK Hellas Verona 21 2000 \n", + "24 Rui Patrício pt POR GK Roma 33 1988 \n", + "25 Gianluca Pegolo it ITA GK Sassuolo 40 1981 \n", + "26 Mattia Perin it ITA GK Juventus 28 1992 \n", + "27 Carlo Pinsoglio it ITA GK Juventus 31 1990 \n", + "28 Ivan Provedel it ITA GK Spezia 27 1994 \n", + "29 Ionuț Radu ro ROU GK Inter 24 1997 \n", + "30 Boris Radunović rs SRB GK Cagliari 25 1996 \n", + "31 Nicola Ravaglia it ITA GK Sampdoria 32 1988 \n", + "32 Pepe Reina es ESP GK Lazio 38 1982 \n", + "33 Sergio Romero ar ARG GK Venezia 34 1987 \n", + "34 Francesco Rossi it ITA GK Atalanta 30 1991 \n", + "35 Giacomo Satalino it ITA GK Sassuolo 22 1999 \n", + "36 Adrian Šemper hr CRO GK Genoa 23 1998 \n", + "37 Luigi Sepe it ITA GK Salernitana 30 1991 \n", + "38 Marco Silvestri it ITA GK Udinese 30 1991 \n", + "39 Salvatore Sirigu it ITA GK Genoa 34 1987 \n", + "40 Łukasz Skorupski pl POL GK Bologna 30 1991 \n", + "41 Marco Sportiello it ITA GK Atalanta 29 1992 \n", + "42 Thomas Strakosha al ALB GK Lazio 26 1995 \n", + "43 Wojciech Szczęsny pl POL GK Juventus 31 1990 \n", + "44 Ciprian Tătărușanu ro ROU GK Milan 35 1986 \n", + "45 Pietro Terracciano it ITA GK Fiorentina 31 1990 \n", + "46 Guglielmo Vicario it ITA GK Empoli 24 1996 \n", + "47 Jeroen Zoet nl NED GK Spezia 30 1991 \n", + "48 Petar Zovko ba BIH GK Spezia 19 2002 \n", + "\n", + " gk_games gk_games_starts gk_minutes gk_goals_against ... \\\n", + "0 29.0 29.0 2560.0 48.0 ... \n", + "1 1.0 0.0 3.0 0.0 ... \n", + "2 2.0 2.0 177.0 2.0 ... \n", + "3 23.0 21.0 1938.0 49.0 ... \n", + "4 1.0 1.0 90.0 3.0 ... \n", + "5 10.0 10.0 900.0 8.0 ... \n", + "6 37.0 37.0 3322.0 63.0 ... \n", + "7 35.0 35.0 3150.0 63.0 ... \n", + "8 7.0 7.0 556.0 8.0 ... \n", + "9 10.0 9.0 855.0 14.0 ... \n", + "10 1.0 1.0 90.0 5.0 ... \n", + "11 1.0 1.0 90.0 0.0 ... \n", + "12 37.0 37.0 3330.0 30.0 ... \n", + "13 6.0 6.0 495.0 9.0 ... \n", + "14 17.0 16.0 1485.0 27.0 ... \n", + "15 32.0 32.0 2880.0 21.0 ... \n", + "16 1.0 0.0 11.0 0.0 ... \n", + "17 7.0 7.0 619.0 6.0 ... \n", + "18 27.0 27.0 2430.0 33.0 ... \n", + "19 34.0 34.0 3060.0 50.0 ... \n", + "20 33.0 33.0 2932.0 42.0 ... \n", + "21 31.0 31.0 2790.0 25.0 ... \n", + "22 3.0 3.0 270.0 8.0 ... \n", + "23 3.0 3.0 270.0 6.0 ... \n", + "24 38.0 38.0 3420.0 43.0 ... \n", + "25 1.0 1.0 90.0 3.0 ... \n", + "26 5.0 5.0 405.0 7.0 ... \n", + "27 1.0 0.0 45.0 1.0 ... \n", + "28 31.0 31.0 2761.0 52.0 ... \n", + "29 1.0 1.0 90.0 2.0 ... \n", + "30 3.0 3.0 270.0 5.0 ... \n", + "31 1.0 0.0 5.0 1.0 ... \n", + "32 15.0 15.0 1350.0 29.0 ... \n", + "33 16.0 16.0 1440.0 33.0 ... \n", + "34 1.0 0.0 37.0 1.0 ... \n", + "35 1.0 0.0 8.0 0.0 ... \n", + "36 1.0 1.0 90.0 1.0 ... \n", + "37 16.0 16.0 1392.0 24.0 ... \n", + "38 35.0 35.0 3150.0 50.0 ... \n", + "39 37.0 37.0 3330.0 59.0 ... \n", + "40 36.0 36.0 3240.0 53.0 ... \n", + "41 5.0 5.0 450.0 5.0 ... \n", + "42 23.0 23.0 2070.0 29.0 ... \n", + "43 33.0 33.0 2970.0 29.0 ... \n", + "44 6.0 6.0 540.0 10.0 ... \n", + "45 32.0 31.0 2862.0 43.0 ... \n", + "46 38.0 38.0 3420.0 70.0 ... \n", + "47 7.0 7.0 630.0 19.0 ... \n", + "48 1.0 0.0 29.0 0.0 ... \n", + "\n", + " gk_passes_length_avg gk_goal_kicks gk_pct_goal_kicks_launched \\\n", + "0 36.3 212.0 67.9 \n", + "1 61.0 1.0 100.0 \n", + "2 33.0 13.0 38.5 \n", + "3 39.5 182.0 73.1 \n", + "4 39.7 7.0 100.0 \n", + "5 39.4 78.0 87.2 \n", + "6 30.9 246.0 31.7 \n", + "7 39.4 316.0 73.4 \n", + "8 30.8 39.0 43.6 \n", + "9 33.2 74.0 70.3 \n", + "10 36.4 12.0 83.3 \n", + "11 47.8 3.0 100.0 \n", + "12 28.1 142.0 20.4 \n", + "13 30.3 44.0 34.1 \n", + "14 36.0 143.0 39.2 \n", + "15 33.0 149.0 38.9 \n", + "16 15.0 0.0 0.0 \n", + "17 25.1 36.0 25.0 \n", + "18 44.4 169.0 91.1 \n", + "19 42.3 231.0 68.4 \n", + "20 31.3 208.0 69.2 \n", + "21 28.6 135.0 14.1 \n", + "22 29.6 22.0 50.0 \n", + "23 33.1 15.0 53.3 \n", + "24 34.2 222.0 32.0 \n", + "25 26.7 8.0 12.5 \n", + "26 29.6 26.0 23.1 \n", + "27 29.6 1.0 0.0 \n", + "28 40.1 229.0 69.4 \n", + "29 33.3 4.0 25.0 \n", + "30 41.5 28.0 82.1 \n", + "31 57.0 2.0 100.0 \n", + "32 29.8 99.0 29.3 \n", + "33 34.6 142.0 45.8 \n", + "34 37.8 4.0 25.0 \n", + "35 0.0 1.0 0.0 \n", + "36 38.5 7.0 85.7 \n", + "37 40.8 130.0 63.8 \n", + "38 36.7 252.0 46.8 \n", + "39 36.6 263.0 68.8 \n", + "40 34.0 294.0 28.9 \n", + "41 26.0 28.0 64.3 \n", + "42 28.5 119.0 20.2 \n", + "43 30.8 172.0 29.7 \n", + "44 33.3 36.0 36.1 \n", + "45 32.2 170.0 35.3 \n", + "46 29.5 257.0 41.6 \n", + "47 30.7 54.0 37.0 \n", + "48 35.9 2.0 50.0 \n", + "\n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", + "0 47.6 418.0 22.0 \n", + "1 60.0 3.0 0.0 \n", + "2 32.1 24.0 2.0 \n", + "3 49.9 329.0 12.0 \n", + "4 58.7 12.0 0.0 \n", + "5 58.6 148.0 7.0 \n", + "6 28.8 491.0 21.0 \n", + "7 48.2 484.0 22.0 \n", + "8 40.2 61.0 2.0 \n", + "9 45.0 164.0 7.0 \n", + "10 50.8 16.0 0.0 \n", + "11 56.3 10.0 0.0 \n", + "12 26.5 393.0 15.0 \n", + "13 31.0 100.0 2.0 \n", + "14 35.4 275.0 6.0 \n", + "15 36.5 347.0 25.0 \n", + "16 0.0 1.0 0.0 \n", + "17 26.5 89.0 3.0 \n", + "18 66.3 290.0 18.0 \n", + "19 46.5 495.0 21.0 \n", + "20 49.7 308.0 28.0 \n", + "21 22.5 353.0 18.0 \n", + "22 40.9 56.0 1.0 \n", + "23 37.1 22.0 1.0 \n", + "24 31.5 393.0 12.0 \n", + "25 19.8 8.0 1.0 \n", + "26 29.2 77.0 1.0 \n", + "27 37.0 12.0 0.0 \n", + "28 51.7 476.0 28.0 \n", + "29 29.8 4.0 0.0 \n", + "30 51.1 42.0 1.0 \n", + "31 65.5 1.0 0.0 \n", + "32 30.1 186.0 10.0 \n", + "33 38.8 265.0 14.0 \n", + "34 39.3 3.0 0.0 \n", + "35 22.0 1.0 0.0 \n", + "36 52.0 14.0 2.0 \n", + "37 46.4 235.0 10.0 \n", + "38 38.4 462.0 16.0 \n", + "39 45.9 476.0 14.0 \n", + "40 26.6 498.0 27.0 \n", + "41 47.0 45.0 1.0 \n", + "42 23.6 283.0 11.0 \n", + "43 31.5 481.0 24.0 \n", + "44 35.0 82.0 6.0 \n", + "45 33.7 291.0 22.0 \n", + "46 37.5 602.0 35.0 \n", + "47 37.4 95.0 2.0 \n", + "48 35.0 4.0 0.0 \n", + "\n", + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "0 5.3 25.0 \n", + "1 0.0 0.0 \n", + "2 8.3 0.0 \n", + "3 3.6 14.0 \n", + "4 0.0 0.0 \n", + "5 4.7 10.0 \n", + "6 4.3 30.0 \n", + "7 4.5 33.0 \n", + "8 3.3 20.0 \n", + "9 4.3 2.0 \n", + "10 0.0 0.0 \n", + "11 0.0 1.0 \n", + "12 3.8 10.0 \n", + "13 2.0 3.0 \n", + "14 2.2 10.0 \n", + "15 7.2 45.0 \n", + "16 0.0 0.0 \n", + "17 3.4 5.0 \n", + "18 6.2 22.0 \n", + "19 4.2 23.0 \n", + "20 9.1 39.0 \n", + "21 5.1 31.0 \n", + "22 1.8 2.0 \n", + "23 4.5 1.0 \n", + "24 3.1 26.0 \n", + "25 12.5 0.0 \n", + "26 1.3 4.0 \n", + "27 0.0 0.0 \n", + "28 5.9 25.0 \n", + "29 0.0 1.0 \n", + "30 2.4 1.0 \n", + "31 0.0 0.0 \n", + "32 5.4 10.0 \n", + "33 5.3 11.0 \n", + "34 0.0 0.0 \n", + "35 0.0 0.0 \n", + "36 14.3 0.0 \n", + "37 4.3 13.0 \n", + "38 3.5 10.0 \n", + "39 2.9 20.0 \n", + "40 5.4 14.0 \n", + "41 2.2 4.0 \n", + "42 3.9 16.0 \n", + "43 5.0 25.0 \n", + "44 7.3 3.0 \n", + "45 7.6 51.0 \n", + "46 5.8 38.0 \n", + "47 2.1 6.0 \n", + "48 0.0 2.0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \n", + "0 0.88 13.5 \n", + "1 0.00 0.0 \n", + "2 0.00 5.5 \n", + "3 0.65 12.7 \n", + "4 0.00 0.0 \n", + "5 1.00 14.6 \n", + "6 0.81 13.4 \n", + "7 0.94 15.0 \n", + "8 3.24 22.3 \n", + "9 0.21 10.2 \n", + "10 0.00 4.0 \n", + "11 1.00 21.0 \n", + "12 0.27 11.2 \n", + "13 0.55 13.8 \n", + "14 0.61 10.8 \n", + "15 1.41 16.8 \n", + "16 0.00 0.0 \n", + "17 0.73 14.6 \n", + "18 0.81 14.3 \n", + "19 0.68 13.1 \n", + "20 1.20 16.5 \n", + "21 1.00 17.0 \n", + "22 0.67 13.2 \n", + "23 0.33 11.3 \n", + "24 0.68 14.3 \n", + "25 0.00 6.0 \n", + "26 0.89 12.6 \n", + "27 0.00 0.0 \n", + "28 0.81 11.8 \n", + "29 1.00 16.0 \n", + "30 0.33 13.8 \n", + "31 0.00 0.0 \n", + "32 0.67 13.7 \n", + "33 0.69 11.8 \n", + "34 0.00 18.0 \n", + "35 0.00 0.0 \n", + "36 0.00 11.2 \n", + "37 0.84 16.0 \n", + "38 0.29 11.0 \n", + "39 0.54 12.9 \n", + "40 0.39 11.3 \n", + "41 0.80 13.9 \n", + "42 0.70 14.5 \n", + "43 0.76 13.5 \n", + "44 0.50 12.5 \n", + "45 1.60 16.7 \n", + "46 1.00 13.9 \n", + "47 0.86 16.4 \n", + "48 6.21 28.5 \n", + "\n", + "[49 rows x 47 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_keeper = get_keeper_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats')\n", + "\n", + "df_keeper.to_csv('fbref_data/season2122/keepers_players.csv', index=False)\n", + "\n", + "df_keeper" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9172edae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouled
0Tammy Abrahameng ENGFWRoma23199737.036.03084.017.0...Atalanta32.055.038.049.017.01.01.00.0418.03420.062.048.05.06.0...513.0454.068.078.080.049.43.06.03.02292.0685.0518.056.9
1Francesco Acerbiit ITADFLazio33198830.029.02536.04.0...18.016.04.01.00.00.0162.069.0Bologna36.050.638.0418.03420.043.061.634.04.05.0...443.0497.056.03.09.02.01972.0492.0520.048.6
2Michel Aebischerch SUIMFBologna24199712.0Cagliari33.044.538.0418.03420.034.026.03.04.0443.00.0...13.04.00.00.00.00.027.05.04.055.6549.0490.070.03.010.01.01995.0696.0742.048.4
3Felix Afena-Gyangh GHAFW,MFRoma18200317.06.0668.02.0Empoli28.047.438.0418.03420.047.027.07.07.0...15.024.04.00.00.00.031.011.015.042.3506.0463.083.06.010.06.01998.0427.0549.043.8
4Kevin Agudeloco COLMF,FWSpezia22199823.012.01237.03.0...36.024.00.00.00.00.067.015.014.051.7
..................................................................
627Nadir Zorteait ITADF,MFSalernitana22199929.013.01410.01.0...14.06.00.00.00.00.072.08.017.032.0
628Petar Zovkoba BIHGKSpezia1920021.00.029.00.0...0.01.00.00.00.00.00.00.00.00.0
629Szymon Żurkowskipl POLMFEmpoli23199735.029.02307.06.0...41.067.00.00.01.00.0162.024.032.042.9
630Milan Đurićba BIHFWSalernitana311990Fiorentina28.057.738.0418.03420.059.033.023.02165.05.09.012.0...24.045.06.01.00.00.040.0242.083.074.5464.0565.057.08.05.02.01885.0469.0456.050.7
631Filip Đuričićrs SRBMF,FWSassuolo29199212.09.0671.02.05Genoa40.043.938.0418.03420.026.019.06.07.0...566.0499.072.04.05.02.02228.0635.0743.046.1
6Hellas Verona31.050.638.0418.03420.063.044.07.08.0...560.0429.097.06.04.02.02413.0689.0733.048.5
7Inter27.056.538.0418.03420.083.057.07.011.0...466.0412.057.07.04.01.02004.0549.0476.053.6
8Juventus32.051.538.0418.03420.056.037.05.06.0...508.0503.074.05.07.01.02008.0550.0440.055.6
9Lazio27.055.438.0418.03420.074.048.07.09.0...441.0450.044.06.06.01.01941.0411.0427.049.0
10Milan28.054.038.0418.03420.066.039.05.08.0...465.0513.080.05.05.02.02167.0521.0517.050.2
11Napoli27.058.338.0418.03420.074.046.010.014.0...460.0482.067.09.01.01.01981.0393.0403.049.4
12Roma29.051.438.0418.03420.058.031.07.09.0...500.0474.060.05.06.02.00.00.00.01980.0524.0441.054.3
13Salernitana42.041.238.0418.03420.032.019.04.05.0...494.0522.041.04.08.02.01849.0657.0506.056.5
14Sampdoria34.046.138.0418.03420.042.029.02.04.0...495.0513.082.03.04.02.02044.0609.0506.054.6
15Sassuolo29.054.938.0418.03420.060.039.07.07.0...461.0479.043.06.09.01.01926.0343.0404.045.9
16Spezia30.042.738.0418.03420.038.025.03.05.037.5...508.0439.077.04.013.03.01994.0530.0589.047.4
17Torino31.053.338.0418.03420.043.027.06.06.0...636.0460.084.04.011.00.02113.0781.0891.046.7
18Udinese29.042.738.0418.03420.058.036.03.04.0...564.0461.078.04.07.01.02027.0453.0534.045.9
19Venezia39.042.338.0418.03420.034.023.03.05.0...522.0457.061.05.012.02.01942.0564.0583.049.2
\n", - "

632 rows × 120 columns

\n", + "

20 rows × 152 columns

\n", "
" ], "text/plain": [ - " player nationality position team age birth_year games \\\n", - "0 Tammy Abraham eng ENG FW Roma 23 1997 37.0 \n", - "1 Francesco Acerbi it ITA DF Lazio 33 1988 30.0 \n", - "2 Michel Aebischer ch SUI MF Bologna 24 1997 12.0 \n", - "3 Felix Afena-Gyan gh GHA FW,MF Roma 18 2003 17.0 \n", - "4 Kevin Agudelo co COL MF,FW Spezia 22 1998 23.0 \n", - ".. ... ... ... ... .. ... ... \n", - "627 Nadir Zortea it ITA DF,MF Salernitana 22 1999 29.0 \n", - "628 Petar Zovko ba BIH GK Spezia 19 2002 1.0 \n", - "629 Szymon Żurkowski pl POL MF Empoli 23 1997 35.0 \n", - "630 Milan Đurić ba BIH FW Salernitana 31 1990 33.0 \n", - "631 Filip Đuričić rs SRB MF,FW Sassuolo 29 1992 12.0 \n", + " team players_used possession games games_starts minutes \\\n", + "0 Atalanta 32.0 55.0 38.0 418.0 3420.0 \n", + "1 Bologna 36.0 50.6 38.0 418.0 3420.0 \n", + "2 Cagliari 33.0 44.5 38.0 418.0 3420.0 \n", + "3 Empoli 28.0 47.4 38.0 418.0 3420.0 \n", + "4 Fiorentina 28.0 57.7 38.0 418.0 3420.0 \n", + "5 Genoa 40.0 43.9 38.0 418.0 3420.0 \n", + "6 Hellas Verona 31.0 50.6 38.0 418.0 3420.0 \n", + "7 Inter 27.0 56.5 38.0 418.0 3420.0 \n", + "8 Juventus 32.0 51.5 38.0 418.0 3420.0 \n", + "9 Lazio 27.0 55.4 38.0 418.0 3420.0 \n", + "10 Milan 28.0 54.0 38.0 418.0 3420.0 \n", + "11 Napoli 27.0 58.3 38.0 418.0 3420.0 \n", + "12 Roma 29.0 51.4 38.0 418.0 3420.0 \n", + "13 Salernitana 42.0 41.2 38.0 418.0 3420.0 \n", + "14 Sampdoria 34.0 46.1 38.0 418.0 3420.0 \n", + "15 Sassuolo 29.0 54.9 38.0 418.0 3420.0 \n", + "16 Spezia 30.0 42.7 38.0 418.0 3420.0 \n", + "17 Torino 31.0 53.3 38.0 418.0 3420.0 \n", + "18 Udinese 29.0 42.7 38.0 418.0 3420.0 \n", + "19 Venezia 39.0 42.3 38.0 418.0 3420.0 \n", "\n", - " games_starts minutes goals ... fouls fouled offsides pens_won \\\n", - "0 36.0 3084.0 17.0 ... 38.0 49.0 17.0 1.0 \n", - "1 29.0 2536.0 4.0 ... 18.0 16.0 4.0 1.0 \n", - "2 4.0 443.0 0.0 ... 13.0 4.0 0.0 0.0 \n", - "3 6.0 668.0 2.0 ... 15.0 24.0 4.0 0.0 \n", - "4 12.0 1237.0 3.0 ... 36.0 24.0 0.0 0.0 \n", - ".. ... ... ... ... ... ... ... ... \n", - "627 13.0 1410.0 1.0 ... 14.0 6.0 0.0 0.0 \n", - "628 0.0 29.0 0.0 ... 0.0 1.0 0.0 0.0 \n", - "629 29.0 2307.0 6.0 ... 41.0 67.0 0.0 0.0 \n", - "630 23.0 2165.0 5.0 ... 24.0 45.0 6.0 1.0 \n", - "631 9.0 671.0 2.0 ... 10.0 14.0 2.0 0.0 \n", + " goals assists pens_made pens_att ... fouls fouled offsides \\\n", + "0 62.0 48.0 5.0 6.0 ... 513.0 454.0 68.0 \n", + "1 43.0 34.0 4.0 5.0 ... 443.0 497.0 56.0 \n", + "2 34.0 26.0 3.0 4.0 ... 549.0 490.0 70.0 \n", + "3 47.0 27.0 7.0 7.0 ... 506.0 463.0 83.0 \n", + "4 59.0 33.0 9.0 12.0 ... 464.0 565.0 57.0 \n", + "5 26.0 19.0 6.0 7.0 ... 566.0 499.0 72.0 \n", + "6 63.0 44.0 7.0 8.0 ... 560.0 429.0 97.0 \n", + "7 83.0 57.0 7.0 11.0 ... 466.0 412.0 57.0 \n", + "8 56.0 37.0 5.0 6.0 ... 508.0 503.0 74.0 \n", + "9 74.0 48.0 7.0 9.0 ... 441.0 450.0 44.0 \n", + "10 66.0 39.0 5.0 8.0 ... 465.0 513.0 80.0 \n", + "11 74.0 46.0 10.0 14.0 ... 460.0 482.0 67.0 \n", + "12 58.0 31.0 7.0 9.0 ... 500.0 474.0 60.0 \n", + "13 32.0 19.0 4.0 5.0 ... 494.0 522.0 41.0 \n", + "14 42.0 29.0 2.0 4.0 ... 495.0 513.0 82.0 \n", + "15 60.0 39.0 7.0 7.0 ... 461.0 479.0 43.0 \n", + "16 38.0 25.0 3.0 5.0 ... 508.0 439.0 77.0 \n", + "17 43.0 27.0 6.0 6.0 ... 636.0 460.0 84.0 \n", + "18 58.0 36.0 3.0 4.0 ... 564.0 461.0 78.0 \n", + "19 34.0 23.0 3.0 5.0 ... 522.0 457.0 61.0 \n", "\n", - " pens_conceded own_goals ball_recoveries aerials_won aerials_lost \\\n", - "0 1.0 0.0 68.0 78.0 80.0 \n", - "1 0.0 0.0 162.0 69.0 43.0 \n", - "2 0.0 0.0 27.0 5.0 4.0 \n", - "3 0.0 0.0 31.0 11.0 15.0 \n", - "4 0.0 0.0 67.0 15.0 14.0 \n", - ".. ... ... ... ... ... \n", - "627 0.0 0.0 72.0 8.0 17.0 \n", - "628 0.0 0.0 0.0 0.0 0.0 \n", - "629 1.0 0.0 162.0 24.0 32.0 \n", - "630 0.0 0.0 40.0 242.0 83.0 \n", - "631 0.0 0.0 34.0 3.0 5.0 \n", + " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", + "0 3.0 6.0 3.0 2292.0 685.0 \n", + "1 3.0 9.0 2.0 1972.0 492.0 \n", + "2 3.0 10.0 1.0 1995.0 696.0 \n", + "3 6.0 10.0 6.0 1998.0 427.0 \n", + "4 8.0 5.0 2.0 1885.0 469.0 \n", + "5 4.0 5.0 2.0 2228.0 635.0 \n", + "6 6.0 4.0 2.0 2413.0 689.0 \n", + "7 7.0 4.0 1.0 2004.0 549.0 \n", + "8 5.0 7.0 1.0 2008.0 550.0 \n", + "9 6.0 6.0 1.0 1941.0 411.0 \n", + "10 5.0 5.0 2.0 2167.0 521.0 \n", + "11 9.0 1.0 1.0 1981.0 393.0 \n", + "12 5.0 6.0 2.0 1980.0 524.0 \n", + "13 4.0 8.0 2.0 1849.0 657.0 \n", + "14 3.0 4.0 2.0 2044.0 609.0 \n", + "15 6.0 9.0 1.0 1926.0 343.0 \n", + "16 4.0 13.0 3.0 1994.0 530.0 \n", + "17 4.0 11.0 0.0 2113.0 781.0 \n", + "18 4.0 7.0 1.0 2027.0 453.0 \n", + "19 5.0 12.0 2.0 1942.0 564.0 \n", "\n", - " aerials_won_pct \n", - "0 49.4 \n", - "1 61.6 \n", - "2 55.6 \n", - "3 42.3 \n", - "4 51.7 \n", - ".. ... \n", - "627 32.0 \n", - "628 0.0 \n", - "629 42.9 \n", - "630 74.5 \n", - "631 37.5 \n", + " aerials_lost aerials_won_pct \n", + "0 518.0 56.9 \n", + "1 520.0 48.6 \n", + "2 742.0 48.4 \n", + "3 549.0 43.8 \n", + "4 456.0 50.7 \n", + "5 743.0 46.1 \n", + "6 733.0 48.5 \n", + "7 476.0 53.6 \n", + "8 440.0 55.6 \n", + "9 427.0 49.0 \n", + "10 517.0 50.2 \n", + "11 403.0 49.4 \n", + "12 441.0 54.3 \n", + "13 506.0 56.5 \n", + "14 506.0 54.6 \n", + "15 404.0 45.9 \n", + "16 589.0 47.4 \n", + "17 891.0 46.7 \n", + "18 534.0 45.9 \n", + "19 583.0 49.2 \n", "\n", - "[632 rows x 120 columns]" + "[20 rows x 152 columns]" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "df_outfield = get_outfield_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats')\n", - "\n", - "df_outfield.to_csv('fbref_data/season2122/outfield_players.csv', index=False)\n", - "\n", - "df_outfield" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36441b0e", - "metadata": {}, - "outputs": [], - "source": [ - "df_keeper = get_keeper_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats')\n", - "\n", - "df_keeper.to_csv('fbref_data/season2122/keepers_players.csv', index=False)\n", - "\n", - "df_keeper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9172edae", - "metadata": {}, - "outputs": [], "source": [ "df_team = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'for')\n", "\n", @@ -4034,7 +5689,7 @@ " \n", " \n", "\n", - "

20 rows × 157 columns

\n", + "

20 rows × 152 columns

\n", "" ], "text/plain": [ @@ -4126,7 +5781,7 @@ "18 453.0 54.1 \n", "19 564.0 50.8 \n", "\n", - "[20 rows x 157 columns]" + "[20 rows x 152 columns]" ] }, "execution_count": 11, @@ -4144,7 +5799,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "22ad9be5", "metadata": {}, "outputs": [], diff --git a/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb b/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb index ef992e4..4f198f5 100644 --- a/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb +++ b/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "3ecf3676", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "id": "8a6867a5", "metadata": {}, "outputs": [ @@ -40,7 +40,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_5304\\552672520.py:19: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3244\\552672520.py:19: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -120,480 +120,480 @@ " Atalanta\n", " Atalanta\n", " 24.0\n", - " 49.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 38.0\n", - " 27.0\n", + " 48.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 40.0\n", + " 28.0\n", " 6.0\n", " 8.0\n", " ...\n", - " 232.0\n", " 244.0\n", + " 256.0\n", " 26.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1261.0\n", - " 260.0\n", - " 317.0\n", - " 45.1\n", + " 1335.0\n", + " 273.0\n", + " 328.0\n", + " 45.4\n", " \n", " \n", " Bologna\n", " Bologna\n", " 25.0\n", - " 51.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 52.4\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 27.0\n", " 20.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 262.0\n", - " 254.0\n", - " 36.0\n", + " 280.0\n", + " 268.0\n", + " 38.0\n", " 3.0\n", " 4.0\n", " 1.0\n", - " 1145.0\n", - " 239.0\n", - " 193.0\n", - " 55.3\n", + " 1204.0\n", + " 250.0\n", + " 210.0\n", + " 54.3\n", " \n", " \n", " Cremonese\n", " Cremonese\n", - " 30.0\n", - " 44.2\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 31.0\n", + " 43.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 15.0\n", " 7.0\n", " 2.0\n", " 4.0\n", " ...\n", - " 232.0\n", - " 264.0\n", - " 33.0\n", + " 239.0\n", + " 271.0\n", + " 36.0\n", " 3.0\n", " 4.0\n", " 0.0\n", - " 1168.0\n", - " 388.0\n", - " 296.0\n", - " 56.7\n", + " 1229.0\n", + " 409.0\n", + " 314.0\n", + " 56.6\n", " \n", " \n", " Empoli\n", " Empoli\n", - " 27.0\n", - " 46.8\n", + " 28.0\n", + " 47.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 19.0\n", " 11.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 270.0\n", - " 244.0\n", - " 35.0\n", + " 283.0\n", + " 253.0\n", + " 36.0\n", " 2.0\n", " 0.0\n", " 0.0\n", - " 1104.0\n", - " 242.0\n", - " 211.0\n", - " 53.4\n", + " 1148.0\n", + " 263.0\n", + " 217.0\n", + " 54.8\n", " \n", " \n", " Fiorentina\n", " Fiorentina\n", " 28.0\n", - " 57.3\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 57.2\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 23.0\n", " 18.0\n", " 2.0\n", " 4.0\n", " ...\n", - " 296.0\n", - " 252.0\n", - " 54.0\n", + " 307.0\n", + " 269.0\n", + " 59.0\n", " 1.0\n", " 4.0\n", " 0.0\n", - " 1061.0\n", - " 274.0\n", - " 317.0\n", - " 46.4\n", + " 1130.0\n", + " 289.0\n", + " 328.0\n", + " 46.8\n", " \n", " \n", " Verona\n", " Hellas Verona\n", " 34.0\n", - " 43.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 17.0\n", - " 14.0\n", + " 42.9\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 18.0\n", + " 15.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 213.0\n", - " 302.0\n", - " 24.0\n", + " 234.0\n", + " 315.0\n", + " 25.0\n", " 1.0\n", " 0.0\n", " 2.0\n", - " 1173.0\n", - " 385.0\n", - " 422.0\n", - " 47.7\n", + " 1231.0\n", + " 423.0\n", + " 445.0\n", + " 48.7\n", " \n", " \n", " Inter\n", " Inter\n", " 23.0\n", - " 54.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 54.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 40.0\n", " 27.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 263.0\n", - " 237.0\n", + " 276.0\n", + " 248.0\n", " 19.0\n", " 2.0\n", " 2.0\n", " 1.0\n", - " 960.0\n", - " 221.0\n", - " 266.0\n", - " 45.4\n", + " 1007.0\n", + " 231.0\n", + " 288.0\n", + " 44.5\n", " \n", " \n", " Juventus\n", " Juventus\n", " 26.0\n", - " 49.2\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 33.0\n", - " 25.0\n", + " 49.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 34.0\n", + " 26.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 229.0\n", - " 232.0\n", - " 27.0\n", + " 246.0\n", + " 242.0\n", + " 29.0\n", " 0.0\n", " 4.0\n", " 0.0\n", - " 1064.0\n", - " 247.0\n", - " 257.0\n", - " 49.0\n", + " 1134.0\n", + " 258.0\n", + " 272.0\n", + " 48.7\n", " \n", " \n", " Lazio\n", " Lazio\n", " 21.0\n", - " 51.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 51.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 36.0\n", " 26.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 294.0\n", - " 206.0\n", + " 308.0\n", + " 218.0\n", " 44.0\n", " 1.0\n", " 4.0\n", " 1.0\n", - " 1157.0\n", - " 207.0\n", - " 216.0\n", - " 48.9\n", + " 1233.0\n", + " 218.0\n", + " 229.0\n", + " 48.8\n", " \n", " \n", " Lecce\n", " Lecce\n", " 26.0\n", " 42.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 20.0\n", " 14.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 275.0\n", - " 279.0\n", + " 283.0\n", + " 300.0\n", " 45.0\n", " 3.0\n", " 2.0\n", - " 1.0\n", - " 1141.0\n", - " 396.0\n", - " 316.0\n", - " 55.6\n", + " 2.0\n", + " 1200.0\n", + " 417.0\n", + " 332.0\n", + " 55.7\n", " \n", " \n", " Milan\n", " Milan\n", " 27.0\n", - " 53.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 35.0\n", - " 30.0\n", + " 53.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 36.0\n", + " 31.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 255.0\n", - " 251.0\n", + " 275.0\n", + " 261.0\n", " 25.0\n", " 4.0\n", " 2.0\n", " 2.0\n", - " 1059.0\n", - " 249.0\n", - " 305.0\n", - " 44.9\n", + " 1125.0\n", + " 263.0\n", + " 325.0\n", + " 44.7\n", " \n", " \n", " Monza\n", " Monza\n", " 29.0\n", - " 55.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 26.0\n", - " 16.0\n", + " 55.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 27.0\n", + " 17.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 303.0\n", - " 263.0\n", + " 318.0\n", + " 281.0\n", " 37.0\n", " 0.0\n", " 4.0\n", " 1.0\n", - " 1075.0\n", - " 226.0\n", - " 241.0\n", - " 48.4\n", + " 1145.0\n", + " 242.0\n", + " 253.0\n", + " 48.9\n", " \n", " \n", " Napoli\n", " Napoli\n", " 24.0\n", - " 61.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 51.0\n", - " 40.0\n", + " 61.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 54.0\n", + " 42.0\n", " 5.0\n", " 6.0\n", " ...\n", - " 291.0\n", - " 192.0\n", + " 298.0\n", + " 199.0\n", " 28.0\n", " 1.0\n", " 5.0\n", " 0.0\n", - " 1049.0\n", - " 214.0\n", - " 259.0\n", - " 45.2\n", + " 1100.0\n", + " 232.0\n", + " 280.0\n", + " 45.3\n", " \n", " \n", " Roma\n", " Roma\n", " 26.0\n", - " 48.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 28.0\n", + " 49.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", " 19.0\n", - " 3.0\n", - " 5.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 292.0\n", - " 238.0\n", - " 10.0\n", + " 316.0\n", + " 246.0\n", + " 12.0\n", " 0.0\n", - " 5.0\n", + " 6.0\n", " 0.0\n", - " 1095.0\n", - " 205.0\n", - " 245.0\n", - " 45.6\n", + " 1156.0\n", + " 221.0\n", + " 266.0\n", + " 45.4\n", " \n", " \n", " Salernitana\n", " Salernitana\n", " 28.0\n", - " 45.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 46.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 24.0\n", " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 238.0\n", - " 242.0\n", - " 52.0\n", + " 252.0\n", + " 259.0\n", + " 57.0\n", " 8.0\n", " 1.0\n", " 1.0\n", - " 1143.0\n", - " 268.0\n", - " 247.0\n", - " 52.0\n", + " 1213.0\n", + " 291.0\n", + " 285.0\n", + " 50.5\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", " 31.0\n", - " 47.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 47.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 10.0\n", " 8.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 328.0\n", - " 288.0\n", - " 56.0\n", + " 339.0\n", + " 300.0\n", + " 59.0\n", " 4.0\n", " 0.0\n", " 0.0\n", - " 1126.0\n", - " 340.0\n", - " 339.0\n", - " 50.1\n", + " 1189.0\n", + " 362.0\n", + " 349.0\n", + " 50.9\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", " 29.0\n", - " 48.5\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 24.0\n", + " 48.7\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 25.0\n", " 18.0\n", " 4.0\n", " 5.0\n", " ...\n", - " 275.0\n", - " 200.0\n", - " 69.0\n", + " 287.0\n", + " 206.0\n", + " 72.0\n", " 2.0\n", " 5.0\n", - " 0.0\n", - " 1088.0\n", - " 243.0\n", - " 195.0\n", - " 55.5\n", + " 1.0\n", + " 1131.0\n", + " 256.0\n", + " 206.0\n", + " 55.4\n", " \n", " \n", " Spezia\n", " Spezia\n", " 33.0\n", - " 45.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 15.0\n", + " 45.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 17.0\n", " 10.0\n", - " 2.0\n", - " 2.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 225.0\n", - " 279.0\n", - " 50.0\n", + " 235.0\n", + " 291.0\n", + " 53.0\n", " 1.0\n", + " 3.0\n", " 2.0\n", - " 2.0\n", - " 1225.0\n", - " 337.0\n", - " 285.0\n", - " 54.2\n", + " 1275.0\n", + " 343.0\n", + " 306.0\n", + " 52.9\n", " \n", " \n", " Torino\n", " Torino\n", - " 25.0\n", + " 27.0\n", " 53.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 22.0\n", " 17.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 229.0\n", - " 287.0\n", - " 23.0\n", + " 239.0\n", + " 306.0\n", + " 24.0\n", " 3.0\n", " 1.0\n", " 0.0\n", - " 1095.0\n", - " 347.0\n", - " 319.0\n", - " 52.1\n", + " 1155.0\n", + " 367.0\n", + " 333.0\n", + " 52.4\n", " \n", " \n", " Udinese\n", " Udinese\n", " 25.0\n", - " 50.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 27.0\n", - " 23.0\n", + " 49.9\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", + " 25.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 274.0\n", - " 242.0\n", + " 282.0\n", + " 252.0\n", " 34.0\n", " 2.0\n", " 0.0\n", " 1.0\n", - " 1063.0\n", - " 222.0\n", - " 264.0\n", + " 1101.0\n", + " 233.0\n", + " 277.0\n", " 45.7\n", " \n", " \n", @@ -604,95 +604,95 @@ "text/plain": [ " team team_players_used team_possession team_games \\\n", "team_idx \n", - "Atalanta Atalanta 24.0 49.0 21.0 \n", - "Bologna Bologna 25.0 51.9 21.0 \n", - "Cremonese Cremonese 30.0 44.2 21.0 \n", - "Empoli Empoli 27.0 46.8 21.0 \n", - "Fiorentina Fiorentina 28.0 57.3 21.0 \n", - "Verona Hellas Verona 34.0 43.1 21.0 \n", - "Inter Inter 23.0 54.0 21.0 \n", - "Juventus Juventus 26.0 49.2 21.0 \n", - "Lazio Lazio 21.0 51.4 21.0 \n", - "Lecce Lecce 26.0 42.4 21.0 \n", - "Milan Milan 27.0 53.9 21.0 \n", - "Monza Monza 29.0 55.9 21.0 \n", - "Napoli Napoli 24.0 61.4 21.0 \n", - "Roma Roma 26.0 48.9 21.0 \n", - "Salernitana Salernitana 28.0 45.1 21.0 \n", - "Sampdoria Sampdoria 31.0 47.9 21.0 \n", - "Sassuolo Sassuolo 29.0 48.5 21.0 \n", - "Spezia Spezia 33.0 45.9 21.0 \n", - "Torino Torino 25.0 53.0 21.0 \n", - "Udinese Udinese 25.0 50.0 21.0 \n", + "Atalanta Atalanta 24.0 48.6 22.0 \n", + "Bologna Bologna 25.0 52.4 22.0 \n", + "Cremonese Cremonese 31.0 43.8 22.0 \n", + "Empoli Empoli 28.0 47.5 22.0 \n", + "Fiorentina Fiorentina 28.0 57.2 22.0 \n", + "Verona Hellas Verona 34.0 42.9 22.0 \n", + "Inter Inter 23.0 54.5 22.0 \n", + "Juventus Juventus 26.0 49.0 22.0 \n", + "Lazio Lazio 21.0 51.8 22.0 \n", + "Lecce Lecce 26.0 42.4 22.0 \n", + "Milan Milan 27.0 53.5 22.0 \n", + "Monza Monza 29.0 55.0 22.0 \n", + "Napoli Napoli 24.0 61.6 22.0 \n", + "Roma Roma 26.0 49.3 22.0 \n", + "Salernitana Salernitana 28.0 46.0 22.0 \n", + "Sampdoria Sampdoria 31.0 47.3 22.0 \n", + "Sassuolo Sassuolo 29.0 48.7 22.0 \n", + "Spezia Spezia 33.0 45.5 22.0 \n", + "Torino Torino 27.0 53.0 22.0 \n", + "Udinese Udinese 25.0 49.9 22.0 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", "team_idx \n", - "Atalanta 231.0 1890.0 38.0 27.0 \n", - "Bologna 231.0 1890.0 27.0 20.0 \n", - "Cremonese 231.0 1890.0 15.0 7.0 \n", - "Empoli 231.0 1890.0 19.0 11.0 \n", - "Fiorentina 231.0 1890.0 23.0 18.0 \n", - "Verona 231.0 1890.0 17.0 14.0 \n", - "Inter 231.0 1890.0 40.0 27.0 \n", - "Juventus 231.0 1890.0 33.0 25.0 \n", - "Lazio 231.0 1890.0 36.0 26.0 \n", - "Lecce 231.0 1890.0 20.0 14.0 \n", - "Milan 231.0 1890.0 35.0 30.0 \n", - "Monza 231.0 1890.0 26.0 16.0 \n", - "Napoli 231.0 1890.0 51.0 40.0 \n", - "Roma 231.0 1890.0 28.0 19.0 \n", - "Salernitana 231.0 1890.0 24.0 16.0 \n", - "Sampdoria 231.0 1890.0 10.0 8.0 \n", - "Sassuolo 231.0 1890.0 24.0 18.0 \n", - "Spezia 231.0 1890.0 15.0 10.0 \n", - "Torino 231.0 1890.0 22.0 17.0 \n", - "Udinese 231.0 1890.0 27.0 23.0 \n", + "Atalanta 242.0 1980.0 40.0 28.0 \n", + "Bologna 242.0 1980.0 27.0 20.0 \n", + "Cremonese 242.0 1980.0 15.0 7.0 \n", + "Empoli 242.0 1980.0 21.0 11.0 \n", + "Fiorentina 242.0 1980.0 23.0 18.0 \n", + "Verona 242.0 1980.0 18.0 15.0 \n", + "Inter 242.0 1980.0 40.0 27.0 \n", + "Juventus 242.0 1980.0 34.0 26.0 \n", + "Lazio 242.0 1980.0 36.0 26.0 \n", + "Lecce 242.0 1980.0 20.0 14.0 \n", + "Milan 242.0 1980.0 36.0 31.0 \n", + "Monza 242.0 1980.0 27.0 17.0 \n", + "Napoli 242.0 1980.0 54.0 42.0 \n", + "Roma 242.0 1980.0 29.0 19.0 \n", + "Salernitana 242.0 1980.0 24.0 16.0 \n", + "Sampdoria 242.0 1980.0 10.0 8.0 \n", + "Sassuolo 242.0 1980.0 25.0 18.0 \n", + "Spezia 242.0 1980.0 17.0 10.0 \n", + "Torino 242.0 1980.0 22.0 17.0 \n", + "Udinese 242.0 1980.0 29.0 25.0 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", "team_idx ... \n", - "Atalanta 6.0 8.0 ... 232.0 \n", - "Bologna 4.0 4.0 ... 262.0 \n", - "Cremonese 2.0 4.0 ... 232.0 \n", - "Empoli 0.0 0.0 ... 270.0 \n", - "Fiorentina 2.0 4.0 ... 296.0 \n", - "Verona 0.0 0.0 ... 213.0 \n", - "Inter 2.0 2.0 ... 263.0 \n", - "Juventus 3.0 4.0 ... 229.0 \n", - "Lazio 3.0 4.0 ... 294.0 \n", - "Lecce 1.0 2.0 ... 275.0 \n", - "Milan 2.0 2.0 ... 255.0 \n", - "Monza 4.0 4.0 ... 303.0 \n", - "Napoli 5.0 6.0 ... 291.0 \n", - "Roma 3.0 5.0 ... 292.0 \n", - "Salernitana 1.0 1.0 ... 238.0 \n", - "Sampdoria 0.0 0.0 ... 328.0 \n", - "Sassuolo 4.0 5.0 ... 275.0 \n", - "Spezia 2.0 2.0 ... 225.0 \n", - "Torino 1.0 1.0 ... 229.0 \n", - "Udinese 0.0 0.0 ... 274.0 \n", + "Atalanta 6.0 8.0 ... 244.0 \n", + "Bologna 4.0 4.0 ... 280.0 \n", + "Cremonese 2.0 4.0 ... 239.0 \n", + "Empoli 0.0 0.0 ... 283.0 \n", + "Fiorentina 2.0 4.0 ... 307.0 \n", + "Verona 0.0 0.0 ... 234.0 \n", + "Inter 2.0 2.0 ... 276.0 \n", + "Juventus 3.0 4.0 ... 246.0 \n", + "Lazio 3.0 4.0 ... 308.0 \n", + "Lecce 1.0 2.0 ... 283.0 \n", + "Milan 2.0 2.0 ... 275.0 \n", + "Monza 4.0 4.0 ... 318.0 \n", + "Napoli 5.0 6.0 ... 298.0 \n", + "Roma 4.0 6.0 ... 316.0 \n", + "Salernitana 1.0 1.0 ... 252.0 \n", + "Sampdoria 0.0 0.0 ... 339.0 \n", + "Sassuolo 4.0 5.0 ... 287.0 \n", + "Spezia 3.0 3.0 ... 235.0 \n", + "Torino 1.0 1.0 ... 239.0 \n", + "Udinese 0.0 0.0 ... 282.0 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", "team_idx \n", - "Atalanta 244.0 26.0 1.0 \n", - "Bologna 254.0 36.0 3.0 \n", - "Cremonese 264.0 33.0 3.0 \n", - "Empoli 244.0 35.0 2.0 \n", - "Fiorentina 252.0 54.0 1.0 \n", - "Verona 302.0 24.0 1.0 \n", - "Inter 237.0 19.0 2.0 \n", - "Juventus 232.0 27.0 0.0 \n", - "Lazio 206.0 44.0 1.0 \n", - "Lecce 279.0 45.0 3.0 \n", - "Milan 251.0 25.0 4.0 \n", - "Monza 263.0 37.0 0.0 \n", - "Napoli 192.0 28.0 1.0 \n", - "Roma 238.0 10.0 0.0 \n", - "Salernitana 242.0 52.0 8.0 \n", - "Sampdoria 288.0 56.0 4.0 \n", - "Sassuolo 200.0 69.0 2.0 \n", - "Spezia 279.0 50.0 1.0 \n", - "Torino 287.0 23.0 3.0 \n", - "Udinese 242.0 34.0 2.0 \n", + "Atalanta 256.0 26.0 1.0 \n", + "Bologna 268.0 38.0 3.0 \n", + "Cremonese 271.0 36.0 3.0 \n", + "Empoli 253.0 36.0 2.0 \n", + "Fiorentina 269.0 59.0 1.0 \n", + "Verona 315.0 25.0 1.0 \n", + "Inter 248.0 19.0 2.0 \n", + "Juventus 242.0 29.0 0.0 \n", + "Lazio 218.0 44.0 1.0 \n", + "Lecce 300.0 45.0 3.0 \n", + "Milan 261.0 25.0 4.0 \n", + "Monza 281.0 37.0 0.0 \n", + "Napoli 199.0 28.0 1.0 \n", + "Roma 246.0 12.0 0.0 \n", + "Salernitana 259.0 57.0 8.0 \n", + "Sampdoria 300.0 59.0 4.0 \n", + "Sassuolo 206.0 72.0 2.0 \n", + "Spezia 291.0 53.0 1.0 \n", + "Torino 306.0 24.0 3.0 \n", + "Udinese 252.0 34.0 2.0 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", "team_idx \n", @@ -705,68 +705,68 @@ "Inter 2.0 1.0 \n", "Juventus 4.0 0.0 \n", "Lazio 4.0 1.0 \n", - "Lecce 2.0 1.0 \n", + "Lecce 2.0 2.0 \n", "Milan 2.0 2.0 \n", "Monza 4.0 1.0 \n", "Napoli 5.0 0.0 \n", - "Roma 5.0 0.0 \n", + "Roma 6.0 0.0 \n", "Salernitana 1.0 1.0 \n", "Sampdoria 0.0 0.0 \n", - "Sassuolo 5.0 0.0 \n", - "Spezia 2.0 2.0 \n", + "Sassuolo 5.0 1.0 \n", + "Spezia 3.0 2.0 \n", "Torino 1.0 0.0 \n", "Udinese 0.0 1.0 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", "team_idx \n", - "Atalanta 1261.0 260.0 \n", - "Bologna 1145.0 239.0 \n", - "Cremonese 1168.0 388.0 \n", - "Empoli 1104.0 242.0 \n", - "Fiorentina 1061.0 274.0 \n", - "Verona 1173.0 385.0 \n", - "Inter 960.0 221.0 \n", - "Juventus 1064.0 247.0 \n", - "Lazio 1157.0 207.0 \n", - "Lecce 1141.0 396.0 \n", - "Milan 1059.0 249.0 \n", - "Monza 1075.0 226.0 \n", - "Napoli 1049.0 214.0 \n", - "Roma 1095.0 205.0 \n", - "Salernitana 1143.0 268.0 \n", - "Sampdoria 1126.0 340.0 \n", - "Sassuolo 1088.0 243.0 \n", - "Spezia 1225.0 337.0 \n", - "Torino 1095.0 347.0 \n", - "Udinese 1063.0 222.0 \n", + "Atalanta 1335.0 273.0 \n", + "Bologna 1204.0 250.0 \n", + "Cremonese 1229.0 409.0 \n", + "Empoli 1148.0 263.0 \n", + "Fiorentina 1130.0 289.0 \n", + "Verona 1231.0 423.0 \n", + "Inter 1007.0 231.0 \n", + "Juventus 1134.0 258.0 \n", + "Lazio 1233.0 218.0 \n", + "Lecce 1200.0 417.0 \n", + "Milan 1125.0 263.0 \n", + "Monza 1145.0 242.0 \n", + "Napoli 1100.0 232.0 \n", + "Roma 1156.0 221.0 \n", + "Salernitana 1213.0 291.0 \n", + "Sampdoria 1189.0 362.0 \n", + "Sassuolo 1131.0 256.0 \n", + "Spezia 1275.0 343.0 \n", + "Torino 1155.0 367.0 \n", + "Udinese 1101.0 233.0 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", "team_idx \n", - "Atalanta 317.0 45.1 \n", - "Bologna 193.0 55.3 \n", - "Cremonese 296.0 56.7 \n", - "Empoli 211.0 53.4 \n", - "Fiorentina 317.0 46.4 \n", - "Verona 422.0 47.7 \n", - "Inter 266.0 45.4 \n", - "Juventus 257.0 49.0 \n", - "Lazio 216.0 48.9 \n", - "Lecce 316.0 55.6 \n", - "Milan 305.0 44.9 \n", - "Monza 241.0 48.4 \n", - "Napoli 259.0 45.2 \n", - "Roma 245.0 45.6 \n", - "Salernitana 247.0 52.0 \n", - "Sampdoria 339.0 50.1 \n", - "Sassuolo 195.0 55.5 \n", - "Spezia 285.0 54.2 \n", - "Torino 319.0 52.1 \n", - "Udinese 264.0 45.7 \n", + "Atalanta 328.0 45.4 \n", + "Bologna 210.0 54.3 \n", + "Cremonese 314.0 56.6 \n", + "Empoli 217.0 54.8 \n", + "Fiorentina 328.0 46.8 \n", + "Verona 445.0 48.7 \n", + "Inter 288.0 44.5 \n", + "Juventus 272.0 48.7 \n", + "Lazio 229.0 48.8 \n", + "Lecce 332.0 55.7 \n", + "Milan 325.0 44.7 \n", + "Monza 253.0 48.9 \n", + "Napoli 280.0 45.3 \n", + "Roma 266.0 45.4 \n", + "Salernitana 285.0 50.5 \n", + "Sampdoria 349.0 50.9 \n", + "Sassuolo 206.0 55.4 \n", + "Spezia 306.0 52.9 \n", + "Torino 333.0 52.4 \n", + "Udinese 277.0 45.7 \n", "\n", "[20 rows x 303 columns]" ] }, - "execution_count": 22, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "id": "b6a3db98", "metadata": {}, "outputs": [], @@ -818,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "id": "c34eb398", "metadata": {}, "outputs": [ @@ -899,21 +899,21 @@ " Napoli\n", " Meret\n", " NaN\n", - " 551\n", - " 25-325\n", + " 555\n", + " 25-329\n", " 1997\n", " 0\n", " ...\n", - " 19.5\n", - " 27.1\n", - " 221.0\n", + " 18.7\n", + " 26.6\n", + " 232.0\n", " 6.0\n", - " 2.7\n", + " 2.6\n", " 24.0\n", - " 1.14\n", + " 1.09\n", " 17.2\n", - " 6.261905\n", - " 0.478566\n", + " 6.250000\n", + " 0.470734\n", " \n", " \n", " Provedel\n", @@ -923,21 +923,21 @@ " Lazio\n", " Provedel\n", " NaN\n", - " 560\n", - " 28-330\n", + " 564\n", + " 28-334\n", " 1994\n", " 0\n", " ...\n", - " 34.2\n", - " 34.5\n", - " 279.0\n", - " 7.0\n", - " 2.5\n", + " 35.0\n", + " 35.0\n", + " 290.0\n", + " 8.0\n", + " 2.8\n", " 39.0\n", - " 1.86\n", - " 18.2\n", - " 6.261905\n", - " 0.365769\n", + " 1.78\n", + " 18.1\n", + " 6.272727\n", + " 0.360784\n", " \n", " \n", " Vicario\n", @@ -947,21 +947,21 @@ " Empoli\n", " Vicario\n", " NaN\n", - " 569\n", - " 26-126\n", + " 573\n", + " 26-130\n", " 1996\n", " 0\n", " ...\n", - " 47.2\n", - " 42.4\n", - " 426.0\n", + " 44.7\n", + " 41.5\n", + " 431.0\n", " 25.0\n", - " 5.9\n", + " 5.8\n", " 12.0\n", - " 0.57\n", + " 0.55\n", " 10.9\n", - " 6.476190\n", - " 0.392677\n", + " 6.454545\n", + " 0.396264\n", " \n", " \n", " Szczesny\n", @@ -971,21 +971,21 @@ " Juventus\n", " Szczesny\n", " NaN\n", - " 566\n", - " 32-298\n", + " 570\n", + " 32-302\n", " 1990\n", " 0\n", " ...\n", - " 41.7\n", - " 38.7\n", - " 178.0\n", - " 4.0\n", - " 2.2\n", + " 47.6\n", + " 41.8\n", + " 198.0\n", + " 5.0\n", + " 2.5\n", " 12.0\n", - " 0.83\n", - " 15.8\n", - " 6.033333\n", - " 0.339935\n", + " 0.78\n", + " 15.4\n", + " 6.031250\n", + " 0.329239\n", " \n", " \n", " Falcone\n", @@ -995,21 +995,21 @@ " Lecce\n", " Falcone\n", " NaN\n", - " 546\n", - " 27-304\n", + " 550\n", + " 27-308\n", " 1995\n", " 0\n", " ...\n", - " 76.1\n", - " 51.7\n", - " 316.0\n", + " 76.3\n", + " 51.9\n", + " 327.0\n", " 15.0\n", - " 4.7\n", - " 20.0\n", - " 0.95\n", - " 12.5\n", - " 6.309524\n", - " 0.361089\n", + " 4.6\n", + " 22.0\n", + " 1.00\n", + " 12.8\n", + " 6.363636\n", + " 0.431220\n", " \n", " \n", " ...\n", @@ -1043,8 +1043,8 @@ " Sampdoria\n", " Luca\n", " NaN\n", - " 134\n", - " 24-208\n", + " 135\n", + " 24-212\n", " 1998\n", " 1\n", " ...\n", @@ -1056,8 +1056,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.947397\n", - " 0.490100\n", + " 6.152892\n", + " 0.608256\n", " \n", " \n", " Voelkerling Persson\n", @@ -1067,10 +1067,10 @@ " Lecce\n", " Persson\n", " NaN\n", - " 508\n", - " 20-026\n", + " 512\n", + " 20-030\n", " 2003\n", - " 3\n", + " 4\n", " ...\n", " 0.0\n", " 0.0\n", @@ -1080,8 +1080,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.694209\n", - " 0.383547\n", + " 6.117574\n", + " 0.756644\n", " \n", " \n", " Montevago\n", @@ -1091,8 +1091,8 @@ " Sampdoria\n", " Montevago\n", " NaN\n", - " 331\n", - " 19-329\n", + " 334\n", + " 19-333\n", " 2003\n", " 6\n", " ...\n", @@ -1104,8 +1104,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.623932\n", - " 0.280948\n", + " 5.803886\n", + " 0.322430\n", " \n", " \n", " Krollis\n", @@ -1115,8 +1115,8 @@ " Spezia\n", " Krollis\n", " NaN\n", - " 258\n", - " 21-105\n", + " 261\n", + " 21-109\n", " 2001\n", " 1\n", " ...\n", @@ -1128,8 +1128,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.124287\n", - " 0.483769\n", + " 5.689979\n", + " 0.321766\n", " \n", " \n", " Vivaldo\n", @@ -1152,8 +1152,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.215679\n", - " 0.378965\n", + " 5.835742\n", + " 0.342133\n", " \n", " \n", "\n", @@ -1163,39 +1163,39 @@ "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID \\\n", "name \n", - "Meret 0 572 P Napoli Meret NaN 551 \n", - "Provedel 1 2814 P Lazio Provedel NaN 560 \n", - "Vicario 2 4964 P Empoli Vicario NaN 569 \n", - "Szczesny 3 453 P Juventus Szczesny NaN 566 \n", - "Falcone 4 2134 P Lecce Falcone NaN 546 \n", + "Meret 0 572 P Napoli Meret NaN 555 \n", + "Provedel 1 2814 P Lazio Provedel NaN 564 \n", + "Vicario 2 4964 P Empoli Vicario NaN 573 \n", + "Szczesny 3 453 P Juventus Szczesny NaN 570 \n", + "Falcone 4 2134 P Lecce Falcone NaN 550 \n", "... ... ... .. ... ... ... ... \n", - "De Luca 538 5512 A Sampdoria Luca NaN 134 \n", - "Voelkerling Persson 539 5837 A Lecce Persson NaN 508 \n", - "Montevago 540 6113 A Sampdoria Montevago NaN 331 \n", - "Krollis 541 6143 A Spezia Krollis NaN 258 \n", + "De Luca 538 5512 A Sampdoria Luca NaN 135 \n", + "Voelkerling Persson 539 5837 A Lecce Persson NaN 512 \n", + "Montevago 540 6113 A Sampdoria Montevago NaN 334 \n", + "Krollis 541 6143 A Spezia Krollis NaN 261 \n", "Vivaldo 542 6160 A Udinese Vivaldo NaN -1 \n", "\n", " age birth_year games ... \\\n", "name ... \n", - "Meret 25-325 1997 0 ... \n", - "Provedel 28-330 1994 0 ... \n", - "Vicario 26-126 1996 0 ... \n", - "Szczesny 32-298 1990 0 ... \n", - "Falcone 27-304 1995 0 ... \n", + "Meret 25-329 1997 0 ... \n", + "Provedel 28-334 1994 0 ... \n", + "Vicario 26-130 1996 0 ... \n", + "Szczesny 32-302 1990 0 ... \n", + "Falcone 27-308 1995 0 ... \n", "... ... ... ... ... \n", - "De Luca 24-208 1998 1 ... \n", - "Voelkerling Persson 20-026 2003 3 ... \n", - "Montevago 19-329 2003 6 ... \n", - "Krollis 21-105 2001 1 ... \n", + "De Luca 24-212 1998 1 ... \n", + "Voelkerling Persson 20-030 2003 4 ... \n", + "Montevago 19-333 2003 6 ... \n", + "Krollis 21-109 2001 1 ... \n", "Vivaldo 0 0 0 ... \n", "\n", " gk_pct_goal_kicks_launched gk_goal_kick_length_avg \\\n", "name \n", - "Meret 19.5 27.1 \n", - "Provedel 34.2 34.5 \n", - "Vicario 47.2 42.4 \n", - "Szczesny 41.7 38.7 \n", - "Falcone 76.1 51.7 \n", + "Meret 18.7 26.6 \n", + "Provedel 35.0 35.0 \n", + "Vicario 44.7 41.5 \n", + "Szczesny 47.6 41.8 \n", + "Falcone 76.3 51.9 \n", "... ... ... \n", "De Luca 0.0 0.0 \n", "Voelkerling Persson 0.0 0.0 \n", @@ -1205,11 +1205,11 @@ "\n", " gk_crosses gk_crosses_stopped gk_crosses_stopped_pct \\\n", "name \n", - "Meret 221.0 6.0 2.7 \n", - "Provedel 279.0 7.0 2.5 \n", - "Vicario 426.0 25.0 5.9 \n", - "Szczesny 178.0 4.0 2.2 \n", - "Falcone 316.0 15.0 4.7 \n", + "Meret 232.0 6.0 2.6 \n", + "Provedel 290.0 8.0 2.8 \n", + "Vicario 431.0 25.0 5.8 \n", + "Szczesny 198.0 5.0 2.5 \n", + "Falcone 327.0 15.0 4.6 \n", "... ... ... ... \n", "De Luca 0.0 0.0 0.0 \n", "Voelkerling Persson 0.0 0.0 0.0 \n", @@ -1223,7 +1223,7 @@ "Provedel 39.0 \n", "Vicario 12.0 \n", "Szczesny 12.0 \n", - "Falcone 20.0 \n", + "Falcone 22.0 \n", "... ... \n", "De Luca 0.0 \n", "Voelkerling Persson 0.0 \n", @@ -1233,11 +1233,11 @@ "\n", " gk_def_actions_outside_pen_area_per90 \\\n", "name \n", - "Meret 1.14 \n", - "Provedel 1.86 \n", - "Vicario 0.57 \n", - "Szczesny 0.83 \n", - "Falcone 0.95 \n", + "Meret 1.09 \n", + "Provedel 1.78 \n", + "Vicario 0.55 \n", + "Szczesny 0.78 \n", + "Falcone 1.00 \n", "... ... \n", "De Luca 0.00 \n", "Voelkerling Persson 0.00 \n", @@ -1247,22 +1247,22 @@ "\n", " gk_avg_distance_def_actions vote_avg vote_std \n", "name \n", - "Meret 17.2 6.261905 0.478566 \n", - "Provedel 18.2 6.261905 0.365769 \n", - "Vicario 10.9 6.476190 0.392677 \n", - "Szczesny 15.8 6.033333 0.339935 \n", - "Falcone 12.5 6.309524 0.361089 \n", + "Meret 17.2 6.250000 0.470734 \n", + "Provedel 18.1 6.272727 0.360784 \n", + "Vicario 10.9 6.454545 0.396264 \n", + "Szczesny 15.4 6.031250 0.329239 \n", + "Falcone 12.8 6.363636 0.431220 \n", "... ... ... ... \n", - "De Luca 0.0 5.947397 0.490100 \n", - "Voelkerling Persson 0.0 5.694209 0.383547 \n", - "Montevago 0.0 5.623932 0.280948 \n", - "Krollis 0.0 6.124287 0.483769 \n", - "Vivaldo 0.0 6.215679 0.378965 \n", + "De Luca 0.0 6.152892 0.608256 \n", + "Voelkerling Persson 0.0 6.117574 0.756644 \n", + "Montevago 0.0 5.803886 0.322430 \n", + "Krollis 0.0 5.689979 0.321766 \n", + "Vivaldo 0.0 5.835742 0.342133 \n", "\n", "[543 rows x 160 columns]" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1284,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "id": "71c8804e", "metadata": {}, "outputs": [], @@ -1319,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "id": "7eb4e666", "metadata": {}, "outputs": [ @@ -1376,21 +1376,21 @@ " Sassuolo\n", " Frattesi\n", " NaN\n", - " 191\n", - " 23-111\n", + " 192\n", + " 23-115\n", " 1999\n", - " 21\n", + " 22\n", " ...\n", - " 232.0\n", " 244.0\n", + " 256.0\n", " 26.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1261.0\n", - " 260.0\n", - " 317.0\n", - " 45.1\n", + " 1335.0\n", + " 273.0\n", + " 328.0\n", + " 45.4\n", " \n", " \n", "\n", @@ -1399,10 +1399,10 @@ ], "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID age \\\n", - "Frattesi 274 2848 C Sassuolo Frattesi NaN 191 23-111 \n", + "Frattesi 274 2848 C Sassuolo Frattesi NaN 192 23-115 \n", "\n", " birth_year games ... opp_vs_team_fouls opp_vs_team_fouled \\\n", - "Frattesi 1999 21 ... 232.0 244.0 \n", + "Frattesi 1999 22 ... 244.0 256.0 \n", "\n", " opp_vs_team_offsides opp_vs_team_pens_won \\\n", "Frattesi 26.0 1.0 \n", @@ -1411,15 +1411,15 @@ "Frattesi 8.0 1.0 \n", "\n", " opp_vs_team_ball_recoveries opp_vs_team_aerials_won \\\n", - "Frattesi 1261.0 260.0 \n", + "Frattesi 1335.0 273.0 \n", "\n", " opp_vs_team_aerials_lost opp_vs_team_aerials_won_pct \n", - "Frattesi 317.0 45.1 \n", + "Frattesi 328.0 45.4 \n", "\n", "[1 rows x 766 columns]" ] }, - "execution_count": 26, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1433,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "id": "6b4c1b00", "metadata": {}, "outputs": [ @@ -1443,7 +1443,7 @@ "'Sassuolo'" ] }, - "execution_count": 27, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1456,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "id": "6ea6df1f", "metadata": {}, "outputs": [ @@ -1556,7 +1556,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1588,7 +1588,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "id": "47bc651f", "metadata": {}, "outputs": [], @@ -1742,7 +1742,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "id": "3f43d509", "metadata": {}, "outputs": [], @@ -1773,7 +1773,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "id": "930d00c7", "metadata": {}, "outputs": [ @@ -1825,26 +1825,26 @@ " \n", " Frattesi\n", " C\n", - " 21\n", - " 21\n", - " 1726\n", - " 47.4\n", + " 22\n", + " 22\n", + " 1816\n", + " 45.0\n", " 0.13\n", " 0.28\n", - " 78.6\n", - " 57.1\n", - " 48.5\n", + " 78.5\n", + " 58.6\n", + " 48.7\n", " ...\n", - " 0.01854\n", - " 0.016222\n", - " 0.015643\n", - " 0.021437\n", - " 0.00927\n", - " 0.006952\n", - " 0.275782\n", - " 0.026072\n", - " 0.020278\n", - " 0.008111\n", + " 0.018722\n", + " 0.015419\n", + " 0.015419\n", + " 0.020925\n", + " 0.009361\n", + " 0.006608\n", + " 0.278084\n", + " 0.025881\n", + " 0.020374\n", + " 0.007709\n", " \n", " \n", "\n", @@ -1853,27 +1853,27 @@ ], "text/plain": [ " r games games_starts minutes shots_on_target_pct \\\n", - "Frattesi C 21 21 1726 47.4 \n", + "Frattesi C 22 22 1816 45.0 \n", "\n", " goals_per_shot goals_per_shot_on_target passes_pct \\\n", - "Frattesi 0.13 0.28 78.6 \n", + "Frattesi 0.13 0.28 78.5 \n", "\n", " aerials_won_pct team_possession ... miscontrols dispossessed \\\n", - "Frattesi 57.1 48.5 ... 0.01854 0.016222 \n", + "Frattesi 58.6 48.7 ... 0.018722 0.015419 \n", "\n", " fouls fouled aerials_won aerials_lost carries \\\n", - "Frattesi 0.015643 0.021437 0.00927 0.006952 0.275782 \n", + "Frattesi 0.015419 0.020925 0.009361 0.006608 0.278084 \n", "\n", " progressive_carries carries_into_final_third \\\n", - "Frattesi 0.026072 0.020278 \n", + "Frattesi 0.025881 0.020374 \n", "\n", " carries_into_penalty_area \n", - "Frattesi 0.008111 \n", + "Frattesi 0.007709 \n", "\n", "[1 rows x 112 columns]" ] }, - "execution_count": 31, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1896,7 +1896,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "id": "3ae718d2", "metadata": {}, "outputs": [ @@ -2013,24 +2013,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2039,11 +2026,24 @@ " 6.0\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2052,24 +2052,24 @@ " 6.0\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2079,22 +2079,22 @@ " \n", " \n", "\n", - "

5992 rows × 10 columns

\n", + "

6268 rows × 10 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -2103,16 +2103,16 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \n", - "5987 0.5 5.5 \n", - "5988 0.0 6.0 \n", - "5989 0.0 6.0 \n", - "5990 0.0 6.0 \n", - "5991 0.0 10.0 \n", + "6263 0.0 6.0 \n", + "6264 0.0 6.0 \n", + "6265 0.0 6.0 \n", + "6266 0.0 6.5 \n", + "6267 0.0 10.0 \n", "\n", - "[5992 rows x 10 columns]" + "[6268 rows x 10 columns]" ] }, - "execution_count": 32, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2132,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "id": "ec0bc56b", "metadata": {}, "outputs": [ @@ -2196,7 +2196,10 @@ "5600\n", "5700\n", "5800\n", - "5900\n" + "5900\n", + "6000\n", + "6100\n", + "6200\n" ] } ], @@ -2224,7 +2227,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "b292f9f9", "metadata": {}, "outputs": [ @@ -2310,15 +2313,15 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.007364\n", - " 0.001473\n", - " 0.006627\n", - " 0.005891\n", - " 0.013991\n", - " 0.011046\n", - " 0.365243\n", - " 0.008100\n", - " 0.016200\n", + " 0.006906\n", + " 0.001381\n", + " 0.007597\n", + " 0.006906\n", + " 0.013812\n", + " 0.010359\n", + " 0.356354\n", + " 0.008287\n", + " 0.015884\n", " 0.000000\n", " \n", " \n", @@ -2334,15 +2337,15 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.001876\n", - " 0.007505\n", - " 0.005629\n", - " 0.005629\n", - " 0.022514\n", - " 0.015009\n", - " 0.487805\n", - " 0.003752\n", - " 0.003752\n", + " 0.003210\n", + " 0.006421\n", + " 0.004815\n", + " 0.004815\n", + " 0.022472\n", + " 0.014446\n", + " 0.462279\n", + " 0.004815\n", + " 0.004815\n", " 0.000000\n", " \n", " \n", @@ -2358,16 +2361,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.005993\n", - " 0.005243\n", - " 0.013483\n", - " 0.001498\n", - " 0.016479\n", - " 0.010487\n", - " 0.283895\n", - " 0.011985\n", - " 0.009738\n", - " 0.000749\n", + " 0.005727\n", + " 0.005011\n", + " 0.013601\n", + " 0.002147\n", + " 0.017180\n", + " 0.010021\n", + " 0.282749\n", + " 0.011453\n", + " 0.009306\n", + " 0.000716\n", " \n", " \n", " 4\n", @@ -2418,35 +2421,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.008130\n", - " 0.252033\n", - " 0.008130\n", - " 0.008130\n", - " 0.000000\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2454,23 +2433,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.012804\n", - " 0.012164\n", - " 0.011524\n", - " 0.010243\n", - " 0.010883\n", - " 0.011524\n", - " 0.213828\n", - " 0.008323\n", - " 0.014725\n", - " 0.001921\n", + " 0.013317\n", + " 0.012107\n", + " 0.011501\n", + " 0.009685\n", + " 0.011501\n", + " 0.010896\n", + " 0.213680\n", + " 0.007869\n", + " 0.013923\n", + " 0.001816\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", + " 0.027027\n", + " 0.027027\n", + " 0.081081\n", + " 0.027027\n", + " 0.108108\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2478,47 +2481,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.035842\n", - " 0.025986\n", - " 0.018817\n", - " 0.014337\n", - " 0.008961\n", - " 0.032258\n", - " 0.170251\n", - " 0.022401\n", - " 0.013441\n", - " 0.009857\n", + " 0.035398\n", + " 0.025664\n", + " 0.018584\n", + " 0.015044\n", + " 0.008850\n", + " 0.037168\n", + " 0.170796\n", + " 0.022124\n", + " 0.013274\n", + " 0.009735\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", - " 0.130435\n", - " 0.043478\n", - " 0.043478\n", + " 0.057471\n", + " 0.045977\n", + " 0.022989\n", + " 0.034483\n", + " 0.034483\n", + " 0.126437\n", + " 0.298851\n", + " 0.011494\n", " 0.000000\n", - " 0.000000\n", - " 0.130435\n", - " 0.260870\n", - " 0.000000\n", - " 0.000000\n", - " 0.043478\n", + " 0.011494\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2526,79 +2529,79 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.011765\n", - " 0.011765\n", - " 0.000000\n", - " 0.023529\n", - " 0.058824\n", - " 0.058824\n", - " 0.247059\n", - " 0.035294\n", - " 0.011765\n", - " 0.023529\n", + " 0.055901\n", + " 0.012422\n", + " 0.006211\n", + " 0.031056\n", + " 0.031056\n", + " 0.086957\n", + " 0.298137\n", + " 0.037267\n", + " 0.012422\n", + " 0.012422\n", " \n", " \n", "\n", - "

5992 rows × 122 columns

\n", + "

6268 rows × 122 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", "0 0.5 5.5 ... 0.000000 0.000000 0.000000 \n", - "1 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "2 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "3 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", + "1 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "2 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "3 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", "4 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", "... ... ... ... ... ... ... \n", - "5987 0.5 5.5 ... 0.016260 0.024390 0.016260 \n", - "5988 0.0 6.0 ... 0.012804 0.012164 0.011524 \n", - "5989 0.0 6.0 ... 0.035842 0.025986 0.018817 \n", - "5990 0.0 6.0 ... 0.130435 0.043478 0.043478 \n", - "5991 0.0 10.0 ... 0.011765 0.011765 0.000000 \n", + "6263 0.0 6.0 ... 0.013317 0.012107 0.011501 \n", + "6264 0.0 6.0 ... 0.000000 0.000000 0.027027 \n", + "6265 0.0 6.0 ... 0.035398 0.025664 0.018584 \n", + "6266 0.0 6.5 ... 0.057471 0.045977 0.022989 \n", + "6267 0.0 10.0 ... 0.055901 0.012422 0.006211 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", "0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "2 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "3 0.001498 0.016479 0.010487 0.283895 0.011985 \n", + "1 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "2 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "3 0.002147 0.017180 0.010021 0.282749 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", "... ... ... ... ... ... \n", - "5987 0.024390 0.016260 0.008130 0.252033 0.008130 \n", - "5988 0.010243 0.010883 0.011524 0.213828 0.008323 \n", - "5989 0.014337 0.008961 0.032258 0.170251 0.022401 \n", - "5990 0.000000 0.000000 0.130435 0.260870 0.000000 \n", - "5991 0.023529 0.058824 0.058824 0.247059 0.035294 \n", + "6263 0.009685 0.011501 0.010896 0.213680 0.007869 \n", + "6264 0.027027 0.081081 0.027027 0.108108 0.000000 \n", + "6265 0.015044 0.008850 0.037168 0.170796 0.022124 \n", + "6266 0.034483 0.034483 0.126437 0.298851 0.011494 \n", + "6267 0.031056 0.031056 0.086957 0.298137 0.037267 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", "0 0.000000 0.000000 \n", - "1 0.016200 0.000000 \n", - "2 0.003752 0.000000 \n", - "3 0.009738 0.000749 \n", + "1 0.015884 0.000000 \n", + "2 0.004815 0.000000 \n", + "3 0.009306 0.000716 \n", "4 0.004734 0.000000 \n", "... ... ... \n", - "5987 0.008130 0.000000 \n", - "5988 0.014725 0.001921 \n", - "5989 0.013441 0.009857 \n", - "5990 0.000000 0.043478 \n", - "5991 0.011765 0.023529 \n", + "6263 0.013923 0.001816 \n", + "6264 0.000000 0.000000 \n", + "6265 0.013274 0.009735 \n", + "6266 0.000000 0.011494 \n", + "6267 0.012422 0.012422 \n", "\n", - "[5992 rows x 122 columns]" + "[6268 rows x 122 columns]" ] }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2617,7 +2620,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "1db9f77f", "metadata": {}, "outputs": [], @@ -2629,7 +2632,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "id": "f56df3ca", "metadata": {}, "outputs": [ @@ -2691,15 +2694,15 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.007364\n", - " 0.001473\n", - " 0.006627\n", - " 0.005891\n", - " 0.013991\n", - " 0.011046\n", - " 0.365243\n", - " 0.008100\n", - " 0.016200\n", + " 0.006906\n", + " 0.001381\n", + " 0.007597\n", + " 0.006906\n", + " 0.013812\n", + " 0.010359\n", + " 0.356354\n", + " 0.008287\n", + " 0.015884\n", " 0.000000\n", " \n", " \n", @@ -2715,15 +2718,15 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.001876\n", - " 0.007505\n", - " 0.005629\n", - " 0.005629\n", - " 0.022514\n", - " 0.015009\n", - " 0.487805\n", - " 0.003752\n", - " 0.003752\n", + " 0.003210\n", + " 0.006421\n", + " 0.004815\n", + " 0.004815\n", + " 0.022472\n", + " 0.014446\n", + " 0.462279\n", + " 0.004815\n", + " 0.004815\n", " 0.000000\n", " \n", " \n", @@ -2739,16 +2742,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.005993\n", - " 0.005243\n", - " 0.013483\n", - " 0.001498\n", - " 0.016479\n", - " 0.010487\n", - " 0.283895\n", - " 0.011985\n", - " 0.009738\n", - " 0.000749\n", + " 0.005727\n", + " 0.005011\n", + " 0.013601\n", + " 0.002147\n", + " 0.017180\n", + " 0.010021\n", + " 0.282749\n", + " 0.011453\n", + " 0.009306\n", + " 0.000716\n", " \n", " \n", " 4\n", @@ -2787,15 +2790,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.000000\n", - " 0.000000\n", - " 0.022222\n", - " 0.000000\n", " 0.011111\n", + " 0.005556\n", + " 0.016667\n", " 0.022222\n", - " 0.533333\n", - " 0.066667\n", - " 0.066667\n", + " 0.016667\n", + " 0.027778\n", + " 0.561111\n", + " 0.072222\n", + " 0.055556\n", " 0.000000\n", " \n", " \n", @@ -2823,35 +2826,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.008130\n", - " 0.252033\n", - " 0.008130\n", - " 0.008130\n", - " 0.000000\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2859,23 +2838,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.012804\n", - " 0.012164\n", - " 0.011524\n", - " 0.010243\n", - " 0.010883\n", - " 0.011524\n", - " 0.213828\n", - " 0.008323\n", - " 0.014725\n", - " 0.001921\n", + " 0.013317\n", + " 0.012107\n", + " 0.011501\n", + " 0.009685\n", + " 0.011501\n", + " 0.010896\n", + " 0.213680\n", + " 0.007869\n", + " 0.013923\n", + " 0.001816\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", + " 0.027027\n", + " 0.027027\n", + " 0.081081\n", + " 0.027027\n", + " 0.108108\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2883,47 +2886,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.035842\n", - " 0.025986\n", - " 0.018817\n", - " 0.014337\n", - " 0.008961\n", - " 0.032258\n", - " 0.170251\n", - " 0.022401\n", - " 0.013441\n", - " 0.009857\n", + " 0.035398\n", + " 0.025664\n", + " 0.018584\n", + " 0.015044\n", + " 0.008850\n", + " 0.037168\n", + " 0.170796\n", + " 0.022124\n", + " 0.013274\n", + " 0.009735\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", - " 0.130435\n", - " 0.043478\n", - " 0.043478\n", + " 0.057471\n", + " 0.045977\n", + " 0.022989\n", + " 0.034483\n", + " 0.034483\n", + " 0.126437\n", + " 0.298851\n", + " 0.011494\n", " 0.000000\n", - " 0.000000\n", - " 0.130435\n", - " 0.260870\n", - " 0.000000\n", - " 0.000000\n", - " 0.043478\n", + " 0.011494\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2931,79 +2934,79 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.011765\n", - " 0.011765\n", - " 0.000000\n", - " 0.023529\n", - " 0.058824\n", - " 0.058824\n", - " 0.247059\n", - " 0.035294\n", - " 0.011765\n", - " 0.023529\n", + " 0.055901\n", + " 0.012422\n", + " 0.006211\n", + " 0.031056\n", + " 0.031056\n", + " 0.086957\n", + " 0.298137\n", + " 0.037267\n", + " 0.012422\n", + " 0.012422\n", " \n", " \n", "\n", - "

5326 rows × 122 columns

\n", + "

5581 rows × 122 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "5 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "5 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", - "1 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "2 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "3 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", + "1 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "2 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "3 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", "4 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", - "5 0.5 5.5 ... 0.000000 0.000000 0.022222 \n", + "5 0.5 5.5 ... 0.011111 0.005556 0.016667 \n", "... ... ... ... ... ... ... \n", - "5987 0.5 5.5 ... 0.016260 0.024390 0.016260 \n", - "5988 0.0 6.0 ... 0.012804 0.012164 0.011524 \n", - "5989 0.0 6.0 ... 0.035842 0.025986 0.018817 \n", - "5990 0.0 6.0 ... 0.130435 0.043478 0.043478 \n", - "5991 0.0 10.0 ... 0.011765 0.011765 0.000000 \n", + "6263 0.0 6.0 ... 0.013317 0.012107 0.011501 \n", + "6264 0.0 6.0 ... 0.000000 0.000000 0.027027 \n", + "6265 0.0 6.0 ... 0.035398 0.025664 0.018584 \n", + "6266 0.0 6.5 ... 0.057471 0.045977 0.022989 \n", + "6267 0.0 10.0 ... 0.055901 0.012422 0.006211 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", - "1 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "2 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "3 0.001498 0.016479 0.010487 0.283895 0.011985 \n", + "1 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "2 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "3 0.002147 0.017180 0.010021 0.282749 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", - "5 0.000000 0.011111 0.022222 0.533333 0.066667 \n", + "5 0.022222 0.016667 0.027778 0.561111 0.072222 \n", "... ... ... ... ... ... \n", - "5987 0.024390 0.016260 0.008130 0.252033 0.008130 \n", - "5988 0.010243 0.010883 0.011524 0.213828 0.008323 \n", - "5989 0.014337 0.008961 0.032258 0.170251 0.022401 \n", - "5990 0.000000 0.000000 0.130435 0.260870 0.000000 \n", - "5991 0.023529 0.058824 0.058824 0.247059 0.035294 \n", + "6263 0.009685 0.011501 0.010896 0.213680 0.007869 \n", + "6264 0.027027 0.081081 0.027027 0.108108 0.000000 \n", + "6265 0.015044 0.008850 0.037168 0.170796 0.022124 \n", + "6266 0.034483 0.034483 0.126437 0.298851 0.011494 \n", + "6267 0.031056 0.031056 0.086957 0.298137 0.037267 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", - "1 0.016200 0.000000 \n", - "2 0.003752 0.000000 \n", - "3 0.009738 0.000749 \n", + "1 0.015884 0.000000 \n", + "2 0.004815 0.000000 \n", + "3 0.009306 0.000716 \n", "4 0.004734 0.000000 \n", - "5 0.066667 0.000000 \n", + "5 0.055556 0.000000 \n", "... ... ... \n", - "5987 0.008130 0.000000 \n", - "5988 0.014725 0.001921 \n", - "5989 0.013441 0.009857 \n", - "5990 0.000000 0.043478 \n", - "5991 0.011765 0.023529 \n", + "6263 0.013923 0.001816 \n", + "6264 0.000000 0.000000 \n", + "6265 0.013274 0.009735 \n", + "6266 0.000000 0.011494 \n", + "6267 0.012422 0.012422 \n", "\n", - "[5326 rows x 122 columns]" + "[5581 rows x 122 columns]" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3022,7 +3025,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "id": "df26c8e0", "metadata": {}, "outputs": [], @@ -3040,7 +3043,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "id": "4d06576a", "metadata": {}, "outputs": [ @@ -3066,119 +3069,110 @@ "15 - goals_per_shot\n", "16 - goals_per_shot_on_target\n", "17 - passes_pct\n", - "18 - dribble_tackles_pct\n", - "19 - dribbles_completed_pct\n", - "20 - aerials_won_pct\n", - "21 - team_possession\n", - "22 - team_goals_assists_per90\n", - "23 - team_goals_pens_per90\n", - "24 - team_goals_assists_pens_per90\n", - "25 - team_xg_per90\n", - "26 - team_gk_goals_against_per90\n", - "27 - team_gk_save_pct\n", - "28 - team_gk_clean_sheets_pct\n", - "29 - team_passes_pct\n", - "30 - team_passes_pct_medium\n", - "31 - team_passes_pct_long\n", - "32 - team_sca_per90\n", - "33 - team_gca_per90\n", - "34 - team_dribble_tackles_pct\n", - "35 - team_aerials_won_pct\n", - "36 - vs_team_possession\n", - "37 - vs_team_goals_per90\n", - "38 - vs_team_assists_per90\n", - "39 - vs_team_xg_per90\n", - "40 - vs_team_gk_save_pct\n", - "41 - vs_team_gk_clean_sheets_pct\n", - "42 - vs_team_gk_pct_passes_launched\n", - "43 - vs_team_gk_crosses_stopped_pct\n", - "44 - vs_team_shots_on_target_per90\n", - "45 - vs_team_passes_pct\n", - "46 - vs_team_passes_pct_short\n", - "47 - vs_team_passes_pct_medium\n", - "48 - vs_team_passes_pct_long\n", - "49 - vs_team_sca_per90\n", - "50 - vs_team_gca_per90\n", - "51 - vs_team_dribble_tackles_pct\n", - "52 - vs_team_dribbles_completed_pct\n", - "53 - vs_team_aerials_won_pct\n", - "54 - opp_team_possession\n", - "55 - opp_team_goals_assists_per90\n", - "56 - opp_team_goals_pens_per90\n", - "57 - opp_team_goals_assists_pens_per90\n", - "58 - opp_team_xg_per90\n", - "59 - opp_team_gk_goals_against_per90\n", - "60 - opp_team_gk_save_pct\n", - "61 - opp_team_gk_clean_sheets_pct\n", - "62 - opp_team_passes_pct\n", - "63 - opp_team_passes_pct_medium\n", - "64 - opp_team_passes_pct_long\n", - "65 - opp_team_sca_per90\n", - "66 - opp_team_gca_per90\n", - "67 - opp_team_dribble_tackles_pct\n", - "68 - opp_team_aerials_won_pct\n", - "69 - opp_vs_team_possession\n", - "70 - opp_vs_team_goals_per90\n", - "71 - opp_vs_team_assists_per90\n", - "72 - opp_vs_team_xg_per90\n", - "73 - opp_vs_team_gk_save_pct\n", - "74 - opp_vs_team_gk_clean_sheets_pct\n", - "75 - opp_vs_team_gk_pct_passes_launched\n", - "76 - opp_vs_team_gk_crosses_stopped_pct\n", - "77 - opp_vs_team_shots_on_target_per90\n", - "78 - opp_vs_team_passes_pct\n", - "79 - opp_vs_team_passes_pct_short\n", - "80 - opp_vs_team_passes_pct_medium\n", - "81 - opp_vs_team_passes_pct_long\n", - "82 - opp_vs_team_sca_per90\n", - "83 - opp_vs_team_gca_per90\n", - "84 - opp_vs_team_dribble_tackles_pct\n", - "85 - opp_vs_team_dribbles_completed_pct\n", - "86 - opp_vs_team_aerials_won_pct\n", - "87 - vote_avg\n", - "88 - vote_std\n", - "89 - goals\n", - "90 - assists\n", - "91 - cards_yellow\n", - "92 - cards_red\n", - "93 - xg\n", - "94 - npxg\n", - "95 - shots_on_target\n", - "96 - passes_completed\n", - "97 - passes_into_final_third\n", - "98 - passes_into_penalty_area\n", - "99 - progressive_passes\n", - "100 - passes_live\n", - "101 - passes_dead\n", - "102 - through_balls\n", - "103 - passes_switches\n", - "104 - crosses\n", - "105 - corner_kicks\n", - "106 - dribble_tackles\n", - "107 - dribbles_vs\n", - "108 - dribbled_past\n", - "109 - blocks\n", - "110 - blocked_shots\n", - "111 - blocked_passes\n", - "112 - interceptions\n", - "113 - clearances\n", - "114 - errors\n", - "115 - touches\n", - "116 - touches_def_pen_area\n", - "117 - touches_def_3rd\n", - "118 - touches_mid_3rd\n", - "119 - touches_att_3rd\n", - "120 - touches_att_pen_area\n", - "121 - touches_live_ball\n", - "122 - dribbles_completed\n", - "123 - dribbles\n", - "124 - passes_received\n", - "125 - miscontrols\n", - "126 - dispossessed\n", - "127 - fouls\n", - "128 - fouled\n", - "129 - aerials_won\n", - "130 - aerials_lost\n" + "18 - aerials_won_pct\n", + "19 - team_possession\n", + "20 - team_goals_assists_per90\n", + "21 - team_goals_pens_per90\n", + "22 - team_goals_assists_pens_per90\n", + "23 - team_xg_per90\n", + "24 - team_gk_goals_against_per90\n", + "25 - team_gk_save_pct\n", + "26 - team_gk_clean_sheets_pct\n", + "27 - team_passes_pct\n", + "28 - team_passes_pct_medium\n", + "29 - team_passes_pct_long\n", + "30 - team_sca_per90\n", + "31 - team_gca_per90\n", + "32 - team_aerials_won_pct\n", + "33 - vs_team_possession\n", + "34 - vs_team_goals_per90\n", + "35 - vs_team_assists_per90\n", + "36 - vs_team_xg_per90\n", + "37 - vs_team_gk_save_pct\n", + "38 - vs_team_gk_clean_sheets_pct\n", + "39 - vs_team_gk_pct_passes_launched\n", + "40 - vs_team_gk_crosses_stopped_pct\n", + "41 - vs_team_shots_on_target_per90\n", + "42 - vs_team_passes_pct\n", + "43 - vs_team_passes_pct_short\n", + "44 - vs_team_passes_pct_medium\n", + "45 - vs_team_passes_pct_long\n", + "46 - vs_team_sca_per90\n", + "47 - vs_team_gca_per90\n", + "48 - vs_team_aerials_won_pct\n", + "49 - opp_team_possession\n", + "50 - opp_team_goals_assists_per90\n", + "51 - opp_team_goals_pens_per90\n", + "52 - opp_team_goals_assists_pens_per90\n", + "53 - opp_team_xg_per90\n", + "54 - opp_team_gk_goals_against_per90\n", + "55 - opp_team_gk_save_pct\n", + "56 - opp_team_gk_clean_sheets_pct\n", + "57 - opp_team_passes_pct\n", + "58 - opp_team_passes_pct_medium\n", + "59 - opp_team_passes_pct_long\n", + "60 - opp_team_sca_per90\n", + "61 - opp_team_gca_per90\n", + "62 - opp_team_aerials_won_pct\n", + "63 - opp_vs_team_possession\n", + "64 - opp_vs_team_goals_per90\n", + "65 - opp_vs_team_assists_per90\n", + "66 - opp_vs_team_xg_per90\n", + "67 - opp_vs_team_gk_save_pct\n", + "68 - opp_vs_team_gk_clean_sheets_pct\n", + "69 - opp_vs_team_gk_pct_passes_launched\n", + "70 - opp_vs_team_gk_crosses_stopped_pct\n", + "71 - opp_vs_team_shots_on_target_per90\n", + "72 - opp_vs_team_passes_pct\n", + "73 - opp_vs_team_passes_pct_short\n", + "74 - opp_vs_team_passes_pct_medium\n", + "75 - opp_vs_team_passes_pct_long\n", + "76 - opp_vs_team_sca_per90\n", + "77 - opp_vs_team_gca_per90\n", + "78 - opp_vs_team_aerials_won_pct\n", + "79 - vote_avg\n", + "80 - vote_std\n", + "81 - goals\n", + "82 - assists\n", + "83 - cards_yellow\n", + "84 - cards_red\n", + "85 - xg\n", + "86 - npxg\n", + "87 - shots_on_target\n", + "88 - passes_completed\n", + "89 - passes_into_final_third\n", + "90 - passes_into_penalty_area\n", + "91 - progressive_passes\n", + "92 - passes_live\n", + "93 - passes_dead\n", + "94 - through_balls\n", + "95 - passes_switches\n", + "96 - crosses\n", + "97 - corner_kicks\n", + "98 - blocks\n", + "99 - blocked_shots\n", + "100 - blocked_passes\n", + "101 - interceptions\n", + "102 - clearances\n", + "103 - errors\n", + "104 - touches\n", + "105 - touches_def_pen_area\n", + "106 - touches_def_3rd\n", + "107 - touches_mid_3rd\n", + "108 - touches_att_3rd\n", + "109 - touches_att_pen_area\n", + "110 - touches_live_ball\n", + "111 - passes_received\n", + "112 - miscontrols\n", + "113 - dispossessed\n", + "114 - fouls\n", + "115 - fouled\n", + "116 - aerials_won\n", + "117 - aerials_lost\n", + "118 - carries\n", + "119 - progressive_carries\n", + "120 - carries_into_final_third\n", + "121 - carries_into_penalty_area\n" ] } ], @@ -3189,7 +3183,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "id": "01bb7413", "metadata": {}, "outputs": [ @@ -3264,7 +3258,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 34, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -3283,7 +3277,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "id": "02f961d8", "metadata": {}, "outputs": [], @@ -3396,7 +3390,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "id": "5e3694a0", "metadata": {}, "outputs": [], @@ -3420,7 +3414,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 22, "id": "9b671646", "metadata": {}, "outputs": [ @@ -3471,27 +3465,27 @@ " \n", " \n", " Consigli\n", - " 19\n", - " 19\n", - " 1710\n", - " 1.47\n", - " 58.3\n", - " 31.6\n", - " -0.25\n", - " 41.6\n", - " 31.0\n", - " 34.3\n", + " 20\n", + " 20\n", + " 1800\n", + " 1.5\n", + " 59.7\n", + " 30.0\n", + " -0.28\n", + " 40.8\n", + " 30.5\n", + " 34.1\n", " ...\n", - " 23.3\n", - " 0.35\n", - " -4.7\n", - " 112.0\n", - " 269.0\n", - " 686.0\n", - " 99.0\n", - " 172.0\n", - " 255.0\n", - " 16.0\n", + " 24.4\n", + " 0.33\n", + " -5.6\n", + " 113.0\n", + " 277.0\n", + " 711.0\n", + " 108.0\n", + " 179.0\n", + " 276.0\n", + " 17.0\n", " \n", " \n", "\n", @@ -3500,30 +3494,30 @@ ], "text/plain": [ " gk_games gk_games_starts gk_minutes gk_goals_against_per90 \\\n", - "Consigli 19 19 1710 1.47 \n", + "Consigli 20 20 1800 1.5 \n", "\n", " gk_save_pct gk_clean_sheets_pct gk_psxg_net_per90 \\\n", - "Consigli 58.3 31.6 -0.25 \n", + "Consigli 59.7 30.0 -0.28 \n", "\n", " gk_passes_pct_launched gk_pct_passes_launched \\\n", - "Consigli 41.6 31.0 \n", + "Consigli 40.8 30.5 \n", "\n", " gk_passes_length_avg ... gk_psxg \\\n", - "Consigli 34.3 ... 23.3 \n", + "Consigli 34.1 ... 24.4 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "Consigli 0.35 -4.7 \n", + "Consigli 0.33 -5.6 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "Consigli 112.0 269.0 686.0 \n", + "Consigli 113.0 277.0 711.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "Consigli 99.0 172.0 255.0 16.0 \n", + "Consigli 108.0 179.0 276.0 17.0 \n", "\n", "[1 rows x 92 columns]" ] }, - "execution_count": 37, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3538,7 +3532,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 23, "id": "33f805a9", "metadata": {}, "outputs": [ @@ -3605,7 +3599,10 @@ "5600\n", "5700\n", "5800\n", - "5900\n" + "5900\n", + "6000\n", + "6100\n", + "6200\n" ] } ], @@ -3635,7 +3632,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 24, "id": "4d484846", "metadata": {}, "outputs": [ @@ -3697,15 +3694,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 12.7\n", - " 0.21\n", - " -1.3\n", - " 81.0\n", - " 185.0\n", - " 372.0\n", - " 107.0\n", - " 98.0\n", - " 174.0\n", + " 13.0\n", + " 0.2\n", + " -1.0\n", + " 88.0\n", + " 201.0\n", + " 397.0\n", + " 112.0\n", + " 101.0\n", + " 186.0\n", " 10.0\n", " \n", " \n", @@ -3829,35 +3826,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -3877,11 +3850,35 @@ " NaN\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -3901,17 +3898,17 @@ " NaN\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", " NaN\n", " NaN\n", @@ -3925,11 +3922,11 @@ " NaN\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -3950,79 +3947,79 @@ " \n", " \n", "\n", - "

5992 rows × 102 columns

\n", + "

6268 rows × 102 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... gk_psxg \\\n", - "0 0.5 5.5 ... 12.7 \n", + "0 0.5 5.5 ... 13.0 \n", "1 0.0 10.0 ... NaN \n", "2 0.0 6.0 ... NaN \n", "3 0.5 5.5 ... NaN \n", "4 0.5 5.0 ... NaN \n", "... ... ... ... ... \n", - "5987 0.5 5.5 ... NaN \n", - "5988 0.0 6.0 ... NaN \n", - "5989 0.0 6.0 ... NaN \n", - "5990 0.0 6.0 ... NaN \n", - "5991 0.0 10.0 ... NaN \n", + "6263 0.0 6.0 ... NaN \n", + "6264 0.0 6.0 ... NaN \n", + "6265 0.0 6.0 ... NaN \n", + "6266 0.0 6.5 ... NaN \n", + "6267 0.0 10.0 ... NaN \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.3 \n", + "0 0.2 -1.0 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "5987 NaN NaN \n", - "5988 NaN NaN \n", - "5989 NaN NaN \n", - "5990 NaN NaN \n", - "5991 NaN NaN \n", + "6263 NaN NaN \n", + "6264 NaN NaN \n", + "6265 NaN NaN \n", + "6266 NaN NaN \n", + "6267 NaN NaN \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", + "0 88.0 201.0 397.0 \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "... ... ... ... \n", - "5987 NaN NaN NaN \n", - "5988 NaN NaN NaN \n", - "5989 NaN NaN NaN \n", - "5990 NaN NaN NaN \n", - "5991 NaN NaN NaN \n", + "6263 NaN NaN NaN \n", + "6264 NaN NaN NaN \n", + "6265 NaN NaN NaN \n", + "6266 NaN NaN NaN \n", + "6267 NaN NaN NaN \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", + "0 112.0 101.0 186.0 10.0 \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "... ... ... ... ... \n", - "5987 NaN NaN NaN NaN \n", - "5988 NaN NaN NaN NaN \n", - "5989 NaN NaN NaN NaN \n", - "5990 NaN NaN NaN NaN \n", - "5991 NaN NaN NaN NaN \n", + "6263 NaN NaN NaN NaN \n", + "6264 NaN NaN NaN NaN \n", + "6265 NaN NaN NaN NaN \n", + "6266 NaN NaN NaN NaN \n", + "6267 NaN NaN NaN NaN \n", "\n", - "[5992 rows x 102 columns]" + "[6268 rows x 102 columns]" ] }, - "execution_count": 39, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -4033,7 +4030,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 25, "id": "adab3577", "metadata": {}, "outputs": [], @@ -4043,7 +4040,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 26, "id": "e7ae792d", "metadata": {}, "outputs": [ @@ -4105,15 +4102,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 12.70\n", - " 0.21\n", - " -1.30\n", - " 81.0\n", - " 185.0\n", - " 372.0\n", - " 107.0\n", - " 98.0\n", - " 174.0\n", + " 13.00\n", + " 0.20\n", + " -1.00\n", + " 88.0\n", + " 201.0\n", + " 397.0\n", + " 112.0\n", + " 101.0\n", + " 186.0\n", " 10.0\n", " \n", " \n", @@ -4129,15 +4126,15 @@ " 0.0\n", " 4.5\n", " ...\n", - " 30.80\n", - " 0.28\n", - " 1.80\n", - " 99.0\n", - " 240.0\n", - " 563.0\n", + " 32.50\n", + " 0.29\n", + " 2.50\n", + " 101.0\n", + " 242.0\n", + " 576.0\n", " 117.0\n", - " 153.0\n", - " 299.0\n", + " 159.0\n", + " 304.0\n", " 18.0\n", " \n", " \n", @@ -4153,15 +4150,15 @@ " 0.0\n", " 4.5\n", " ...\n", - " 26.40\n", - " 0.27\n", - " 0.40\n", + " 27.80\n", + " 0.26\n", + " -0.20\n", " 102.0\n", - " 310.0\n", - " 766.0\n", - " 113.0\n", - " 108.0\n", - " 426.0\n", + " 313.0\n", + " 806.0\n", + " 117.0\n", + " 114.0\n", + " 431.0\n", " 25.0\n", " \n", " \n", @@ -4177,15 +4174,15 @@ " 0.0\n", " 3.0\n", " ...\n", - " 9.35\n", + " 9.45\n", " 0.24\n", - " 0.85\n", + " 0.95\n", " 24.0\n", " 68.0\n", - " 291.5\n", - " 47.0\n", - " 69.5\n", - " 122.5\n", + " 299.5\n", + " 48.5\n", + " 72.5\n", + " 128.0\n", " 3.5\n", " \n", " \n", @@ -4237,83 +4234,59 @@ " ...\n", " \n", " \n", - " 5929\n", - " 21\n", + " 6200\n", + " 22\n", " Consigli\n", " Sassuolo\n", - " Atalanta\n", - " 1\n", - " 6.0\n", + " Udinese\n", " 0\n", + " 7.0\n", + " -2\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.0\n", " ...\n", - " 23.30\n", - " 0.35\n", - " -4.70\n", - " 112.0\n", - " 269.0\n", - " 686.0\n", - " 99.0\n", - " 172.0\n", - " 255.0\n", - " 16.0\n", + " 24.40\n", + " 0.33\n", + " -5.60\n", + " 113.0\n", + " 277.0\n", + " 711.0\n", + " 108.0\n", + " 179.0\n", + " 276.0\n", + " 17.0\n", " \n", " \n", - " 5942\n", - " 21\n", + " 6214\n", + " 22\n", " Dragowski\n", " Spezia\n", - " Napoli\n", - " 1\n", - " 5.0\n", - " -3\n", + " Empoli\n", + " 0\n", + " 6.5\n", + " -2\n", " 0\n", " 0.0\n", - " 2.0\n", + " 4.5\n", " ...\n", - " 28.10\n", + " 29.50\n", " 0.27\n", - " -3.90\n", - " 85.0\n", - " 268.0\n", - " 503.0\n", - " 92.0\n", - " 109.0\n", - " 251.0\n", + " -4.50\n", + " 93.0\n", + " 285.0\n", + " 528.0\n", + " 97.0\n", + " 122.0\n", + " 270.0\n", " 9.0\n", " \n", " \n", - " 5953\n", - " 21\n", + " 6227\n", + " 22\n", " Milinkovic-Savic V.\n", " Torino\n", - " Udinese\n", - " 1\n", - " 6.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 6.5\n", - " ...\n", - " 21.90\n", - " 0.23\n", - " -0.10\n", - " 148.0\n", - " 524.0\n", - " 788.0\n", - " 95.0\n", - " 155.0\n", - " 263.0\n", - " 18.0\n", - " \n", - " \n", - " 5966\n", - " 21\n", - " Silvestri\n", - " Udinese\n", - " Torino\n", + " Milan\n", " 0\n", " 6.5\n", " -1\n", @@ -4321,116 +4294,140 @@ " 0.0\n", " 5.5\n", " ...\n", - " 23.90\n", + " 23.10\n", + " 0.24\n", + " 0.10\n", + " 150.0\n", + " 540.0\n", + " 817.0\n", + " 97.0\n", + " 160.0\n", + " 271.0\n", + " 19.0\n", + " \n", + " \n", + " 6241\n", + " 22\n", + " Silvestri\n", + " Udinese\n", + " Sassuolo\n", + " 1\n", + " 6.0\n", + " -2\n", + " 0\n", + " 0.0\n", + " 4.0\n", + " ...\n", + " 24.00\n", " 0.31\n", - " 1.90\n", - " 90.0\n", - " 225.0\n", - " 450.0\n", - " 76.0\n", - " 164.0\n", - " 275.0\n", + " 1.00\n", + " 91.0\n", + " 227.0\n", + " 470.0\n", + " 80.0\n", + " 175.0\n", + " 293.0\n", " 5.0\n", " \n", " \n", - " 5980\n", - " 21\n", + " 6254\n", + " 22\n", " Montipo'\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", - " -1\n", + " 6.5\n", + " 0\n", " 0\n", " 0.0\n", - " 5.0\n", + " 6.5\n", " ...\n", - " 27.60\n", - " 0.25\n", - " -4.40\n", - " 206.0\n", - " 413.0\n", - " 497.0\n", - " 65.0\n", - " 168.0\n", - " 294.0\n", + " 28.70\n", + " 0.26\n", + " -3.30\n", + " 208.0\n", + " 433.0\n", + " 513.0\n", + " 67.0\n", + " 175.0\n", + " 305.0\n", " 15.0\n", " \n", " \n", "\n", - "

415 rows × 102 columns

\n", + "

435 rows × 102 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 \n", - "15 1 Skorupski Bologna Lazio 0 6.5 -2 \n", - "44 1 Vicario Empoli Spezia 0 5.5 -1 \n", - "57 1 Gollini Fiorentina Cremonese 1 5.0 -2 \n", - "71 1 Handanovic Inter Lecce 0 6.5 -1 \n", - "... ... ... ... ... ... ... ... \n", - "5929 21 Consigli Sassuolo Atalanta 1 6.0 0 \n", - "5942 21 Dragowski Spezia Napoli 1 5.0 -3 \n", - "5953 21 Milinkovic-Savic V. Torino Udinese 1 6.5 0 \n", - "5966 21 Silvestri Udinese Torino 0 6.5 -1 \n", - "5980 21 Montipo' Verona Lazio 1 6.0 -1 \n", + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "15 1 Skorupski Bologna Lazio 0 6.5 \n", + "44 1 Vicario Empoli Spezia 0 5.5 \n", + "57 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "71 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "6200 22 Consigli Sassuolo Udinese 0 7.0 \n", + "6214 22 Dragowski Spezia Empoli 0 6.5 \n", + "6227 22 Milinkovic-Savic V. Torino Milan 0 6.5 \n", + "6241 22 Silvestri Udinese Sassuolo 1 6.0 \n", + "6254 22 Montipo' Verona Salernitana 1 6.5 \n", "\n", - " assists cards_malus fantavote ... gk_psxg \\\n", - "0 0 0.5 5.5 ... 12.70 \n", - "15 0 0.0 4.5 ... 30.80 \n", - "44 0 0.0 4.5 ... 26.40 \n", - "57 0 0.0 3.0 ... 9.35 \n", - "71 0 0.0 5.5 ... 10.60 \n", - "... ... ... ... ... ... \n", - "5929 0 0.0 6.0 ... 23.30 \n", - "5942 0 0.0 2.0 ... 28.10 \n", - "5953 0 0.0 6.5 ... 21.90 \n", - "5966 0 0.0 5.5 ... 23.90 \n", - "5980 0 0.0 5.0 ... 27.60 \n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 13.00 \n", + "15 -2 0 0.0 4.5 ... 32.50 \n", + "44 -1 0 0.0 4.5 ... 27.80 \n", + "57 -2 0 0.0 3.0 ... 9.45 \n", + "71 -1 0 0.0 5.5 ... 10.60 \n", + "... ... ... ... ... ... ... \n", + "6200 -2 0 0.0 5.0 ... 24.40 \n", + "6214 -2 0 0.0 4.5 ... 29.50 \n", + "6227 -1 0 0.0 5.5 ... 23.10 \n", + "6241 -2 0 0.0 4.0 ... 24.00 \n", + "6254 0 0 0.0 6.5 ... 28.70 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.30 \n", - "15 0.28 1.80 \n", - "44 0.27 0.40 \n", - "57 0.24 0.85 \n", + "0 0.20 -1.00 \n", + "15 0.29 2.50 \n", + "44 0.26 -0.20 \n", + "57 0.24 0.95 \n", "71 0.30 -1.40 \n", "... ... ... \n", - "5929 0.35 -4.70 \n", - "5942 0.27 -3.90 \n", - "5953 0.23 -0.10 \n", - "5966 0.31 1.90 \n", - "5980 0.25 -4.40 \n", + "6200 0.33 -5.60 \n", + "6214 0.27 -4.50 \n", + "6227 0.24 0.10 \n", + "6241 0.31 1.00 \n", + "6254 0.26 -3.30 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", - "15 99.0 240.0 563.0 \n", - "44 102.0 310.0 766.0 \n", - "57 24.0 68.0 291.5 \n", + "0 88.0 201.0 397.0 \n", + "15 101.0 242.0 576.0 \n", + "44 102.0 313.0 806.0 \n", + "57 24.0 68.0 299.5 \n", "71 27.0 52.0 266.0 \n", "... ... ... ... \n", - "5929 112.0 269.0 686.0 \n", - "5942 85.0 268.0 503.0 \n", - "5953 148.0 524.0 788.0 \n", - "5966 90.0 225.0 450.0 \n", - "5980 206.0 413.0 497.0 \n", + "6200 113.0 277.0 711.0 \n", + "6214 93.0 285.0 528.0 \n", + "6227 150.0 540.0 817.0 \n", + "6241 91.0 227.0 470.0 \n", + "6254 208.0 433.0 513.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", - "15 117.0 153.0 299.0 18.0 \n", - "44 113.0 108.0 426.0 25.0 \n", - "57 47.0 69.5 122.5 3.5 \n", + "0 112.0 101.0 186.0 10.0 \n", + "15 117.0 159.0 304.0 18.0 \n", + "44 117.0 114.0 431.0 25.0 \n", + "57 48.5 72.5 128.0 3.5 \n", "71 46.0 45.0 79.0 2.0 \n", "... ... ... ... ... \n", - "5929 99.0 172.0 255.0 16.0 \n", - "5942 92.0 109.0 251.0 9.0 \n", - "5953 95.0 155.0 263.0 18.0 \n", - "5966 76.0 164.0 275.0 5.0 \n", - "5980 65.0 168.0 294.0 15.0 \n", + "6200 108.0 179.0 276.0 17.0 \n", + "6214 97.0 122.0 270.0 9.0 \n", + "6227 97.0 160.0 271.0 19.0 \n", + "6241 80.0 175.0 293.0 5.0 \n", + "6254 67.0 175.0 305.0 15.0 \n", "\n", - "[415 rows x 102 columns]" + "[435 rows x 102 columns]" ] }, - "execution_count": 41, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4449,7 +4446,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 27, "id": "d8b8b6da", "metadata": {}, "outputs": [], @@ -4459,7 +4456,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "54519b24", "metadata": {}, "outputs": [ @@ -4505,76 +4502,70 @@ "35 - team_passes_pct_long\n", "36 - team_sca_per90\n", "37 - team_gca_per90\n", - "38 - team_dribble_tackles_pct\n", - "39 - team_aerials_won_pct\n", - "40 - vs_team_possession\n", - "41 - vs_team_goals_per90\n", - "42 - vs_team_assists_per90\n", - "43 - vs_team_xg_per90\n", - "44 - vs_team_gk_save_pct\n", - "45 - vs_team_gk_clean_sheets_pct\n", - "46 - vs_team_gk_pct_passes_launched\n", - "47 - vs_team_gk_crosses_stopped_pct\n", - "48 - vs_team_shots_on_target_per90\n", - "49 - vs_team_passes_pct\n", - "50 - vs_team_passes_pct_short\n", - "51 - vs_team_passes_pct_medium\n", - "52 - vs_team_passes_pct_long\n", - "53 - vs_team_sca_per90\n", - "54 - vs_team_gca_per90\n", - "55 - vs_team_dribble_tackles_pct\n", - "56 - vs_team_dribbles_completed_pct\n", - "57 - vs_team_aerials_won_pct\n", - "58 - opp_team_possession\n", - "59 - opp_team_goals_assists_per90\n", - "60 - opp_team_goals_pens_per90\n", - "61 - opp_team_goals_assists_pens_per90\n", - "62 - opp_team_xg_per90\n", - "63 - opp_team_gk_goals_against_per90\n", - "64 - opp_team_gk_save_pct\n", - "65 - opp_team_gk_clean_sheets_pct\n", - "66 - opp_team_passes_pct\n", - "67 - opp_team_passes_pct_medium\n", - "68 - opp_team_passes_pct_long\n", - "69 - opp_team_sca_per90\n", - "70 - opp_team_gca_per90\n", - "71 - opp_team_dribble_tackles_pct\n", - "72 - opp_team_aerials_won_pct\n", - "73 - opp_vs_team_possession\n", - "74 - opp_vs_team_goals_per90\n", - "75 - opp_vs_team_assists_per90\n", - "76 - opp_vs_team_xg_per90\n", - "77 - opp_vs_team_gk_save_pct\n", - "78 - opp_vs_team_gk_clean_sheets_pct\n", - "79 - opp_vs_team_gk_pct_passes_launched\n", - "80 - opp_vs_team_gk_crosses_stopped_pct\n", - "81 - opp_vs_team_shots_on_target_per90\n", - "82 - opp_vs_team_passes_pct\n", - "83 - opp_vs_team_passes_pct_short\n", - "84 - opp_vs_team_passes_pct_medium\n", - "85 - opp_vs_team_passes_pct_long\n", - "86 - opp_vs_team_sca_per90\n", - "87 - opp_vs_team_gca_per90\n", - "88 - opp_vs_team_dribble_tackles_pct\n", - "89 - opp_vs_team_dribbles_completed_pct\n", - "90 - opp_vs_team_aerials_won_pct\n", - "91 - vote_avg\n", - "92 - vote_std\n", - "93 - gk_shots_on_target_against\n", - "94 - gk_saves\n", - "95 - gk_free_kick_goals_against\n", - "96 - gk_corner_kick_goals_against\n", - "97 - gk_own_goals_against\n", - "98 - gk_psxg\n", - "99 - gk_psnpxg_per_shot_on_target_against\n", - "100 - gk_psxg_net\n", - "101 - gk_passes_completed_launched\n", - "102 - gk_passes_launched\n", - "103 - gk_passes\n", - "104 - gk_passes_throws\n", - "105 - gk_goal_kicks\n", - "106 - gk_crosses\n", - "107 - gk_crosses_stopped\n" + "38 - team_aerials_won_pct\n", + "39 - vs_team_possession\n", + "40 - vs_team_goals_per90\n", + "41 - vs_team_assists_per90\n", + "42 - vs_team_xg_per90\n", + "43 - vs_team_gk_save_pct\n", + "44 - vs_team_gk_clean_sheets_pct\n", + "45 - vs_team_gk_pct_passes_launched\n", + "46 - vs_team_gk_crosses_stopped_pct\n", + "47 - vs_team_shots_on_target_per90\n", + "48 - vs_team_passes_pct\n", + "49 - vs_team_passes_pct_short\n", + "50 - vs_team_passes_pct_medium\n", + "51 - vs_team_passes_pct_long\n", + "52 - vs_team_sca_per90\n", + "53 - vs_team_gca_per90\n", + "54 - vs_team_aerials_won_pct\n", + "55 - opp_team_possession\n", + "56 - opp_team_goals_assists_per90\n", + "57 - opp_team_goals_pens_per90\n", + "58 - opp_team_goals_assists_pens_per90\n", + "59 - opp_team_xg_per90\n", + "60 - opp_team_gk_goals_against_per90\n", + "61 - opp_team_gk_save_pct\n", + "62 - opp_team_gk_clean_sheets_pct\n", + "63 - opp_team_passes_pct\n", + "64 - opp_team_passes_pct_medium\n", + "65 - opp_team_passes_pct_long\n", + "66 - opp_team_sca_per90\n", + "67 - opp_team_gca_per90\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_aerials_won_pct\n", + "85 - vote_avg\n", + "86 - vote_std\n", + "87 - gk_shots_on_target_against\n", + "88 - gk_saves\n", + "89 - gk_free_kick_goals_against\n", + "90 - gk_corner_kick_goals_against\n", + "91 - gk_own_goals_against\n", + "92 - gk_psxg\n", + "93 - gk_psnpxg_per_shot_on_target_against\n", + "94 - gk_psxg_net\n", + "95 - gk_passes_completed_launched\n", + "96 - gk_passes_launched\n", + "97 - gk_passes\n", + "98 - gk_passes_throws\n", + "99 - gk_goal_kicks\n", + "100 - gk_crosses\n", + "101 - gk_crosses_stopped\n" ] } ], diff --git a/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb b/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb deleted file mode 100644 index 469b3ed..0000000 --- a/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb +++ /dev/null @@ -1,9740 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4a867836", - "metadata": {}, - "source": [ - "Bayesian Neural Network model traning and prediction data generation." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "210da263", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.neural_network import MLPRegressor\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import r2_score\n", - "\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "edbf3b27", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", - "import tensorflow_datasets as tfds\n", - "import tensorflow_probability as tfp\n", - "\n", - "tfk = tf.keras\n", - "tf.keras.backend.set_floatx(\"float32\")\n", - "import tensorflow_probability as tfp\n", - "tfd = tfp.distributions\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.ensemble import IsolationForest\n", - "\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "markdown", - "id": "9dadf6ec", - "metadata": {}, - "source": [ - "Load the training databases, generated in player_match_database_creation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fa098fa4", - "metadata": {}, - "outputs": [], - "source": [ - "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", - "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", - "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f71fa9a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...miscontrolsdispossessedfoulsfouledaerials_wonaerials_lostcarriesprogressive_carriescarries_into_final_thirdcarries_into_penalty_area
01ToloiAtalantaSampdoria07.0100.010.0...0.0073640.0014730.0066270.0058910.0139910.0110460.3652430.0081000.0162000.000000
11DjimsitiAtalantaSampdoria06.0000.06.0...0.0018760.0075050.0056290.0056290.0225140.0150090.4878050.0037520.0037520.000000
21HateboerAtalantaSampdoria06.0000.55.5...0.0059930.0052430.0134830.0014980.0164790.0104870.2838950.0119850.0097380.000749
31OkoliAtalantaSampdoria05.5000.55.0...0.0130180.0035500.0153850.0082840.0473370.0272190.2698220.0023670.0047340.000000
41ZorteaAtalantaSampdoria06.0000.55.5...0.0000000.0000000.0222220.0000000.0111110.0222220.5333330.0666670.0666670.000000
..................................................................
2455138TamezeVeronaLazio05.5000.05.5...0.0198140.0101010.0128210.0132090.0236990.0213680.3372180.0213680.0128210.003885
2455238HonglaVeronaLazio07.0100.59.5...0.0184330.0122890.0230410.0076800.0230410.0261140.3410140.0092170.0153610.003072
2455338LasagnaVeronaLazio07.0100.59.5...0.0464530.0228040.0160470.0084460.0261820.0413850.1908780.0219590.0109800.005912
2455438CaprariVeronaLazio06.0000.06.0...0.0339540.0197150.0153340.0270170.0029210.0098580.3913840.0427160.0277470.018620
2455538SimeoneVeronaLazio07.0100.010.0...0.0512630.0301550.0211080.0218620.0226160.0407090.2461360.0150770.0128160.006031
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24556 rows × 122 columns

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" - ], - "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "1 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "2 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "4 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "24551 38 Tameze Verona Lazio 0 5.5 0 0 \n", - "24552 38 Hongla Verona Lazio 0 7.0 1 0 \n", - "24553 38 Lasagna Verona Lazio 0 7.0 1 0 \n", - "24554 38 Caprari Verona Lazio 0 6.0 0 0 \n", - "24555 38 Simeone Verona Lazio 0 7.0 1 0 \n", - "\n", - " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", - "0 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "1 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "2 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", - "3 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", - "4 0.5 5.5 ... 0.000000 0.000000 0.022222 \n", - "... ... ... ... ... ... ... \n", - "24551 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", - "24552 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", - "24553 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", - "24554 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", - "24555 0.0 10.0 ... 0.051263 0.030155 0.021108 \n", - "\n", - " fouled aerials_won aerials_lost carries progressive_carries \\\n", - "0 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "1 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "2 0.001498 0.016479 0.010487 0.283895 0.011985 \n", - "3 0.008284 0.047337 0.027219 0.269822 0.002367 \n", - "4 0.000000 0.011111 0.022222 0.533333 0.066667 \n", - "... ... ... ... ... ... \n", - "24551 0.013209 0.023699 0.021368 0.337218 0.021368 \n", - "24552 0.007680 0.023041 0.026114 0.341014 0.009217 \n", - "24553 0.008446 0.026182 0.041385 0.190878 0.021959 \n", - "24554 0.027017 0.002921 0.009858 0.391384 0.042716 \n", - "24555 0.021862 0.022616 0.040709 0.246136 0.015077 \n", - "\n", - " carries_into_final_third carries_into_penalty_area \n", - "0 0.016200 0.000000 \n", - "1 0.003752 0.000000 \n", - "2 0.009738 0.000749 \n", - "3 0.004734 0.000000 \n", - "4 0.066667 0.000000 \n", - "... ... ... \n", - "24551 0.012821 0.003885 \n", - "24552 0.015361 0.003072 \n", - "24553 0.010980 0.005912 \n", - "24554 0.027747 0.018620 \n", - "24555 0.012816 0.006031 \n", - "\n", - "[24556 rows x 122 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db = pd.concat([db1, db2, db3], ignore_index = True) \n", - "\n", - "db" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d024554", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...12.700.21-1.3081.0185.0372.0107.098.0174.010.0
11SkorupskiBolognaLazio06.5-200.04.5...30.800.281.8099.0240.0563.0117.0153.0299.018.0
21VicarioEmpoliSpezia05.5-100.04.5...26.400.270.40102.0310.0766.0113.0108.0426.025.0
31GolliniFiorentinaCremonese15.0-200.03.0...9.350.240.8524.068.0291.547.069.5122.53.5
41HandanovicInterLecce06.5-100.05.5...10.600.30-1.4027.052.0266.046.045.079.02.0
..................................................................
124021ConsigliSassuoloAtalanta16.0000.06.0...23.300.35-4.70112.0269.0686.099.0172.0255.016.0
124121DragowskiSpeziaNapoli15.0-300.02.0...28.100.27-3.9085.0268.0503.092.0109.0251.09.0
124221Milinkovic-Savic V.TorinoUdinese16.5000.06.5...21.900.23-0.10148.0524.0788.095.0155.0263.018.0
124321SilvestriUdineseTorino06.5-100.05.5...23.900.311.9090.0225.0450.076.0164.0275.05.0
124421Montipo'VeronaLazio16.0-100.05.0...27.600.25-4.40206.0413.0497.065.0168.0294.015.0
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1245 rows × 102 columns

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" - ], - "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 \n", - "1 1 Skorupski Bologna Lazio 0 6.5 -2 \n", - "2 1 Vicario Empoli Spezia 0 5.5 -1 \n", - "3 1 Gollini Fiorentina Cremonese 1 5.0 -2 \n", - "4 1 Handanovic Inter Lecce 0 6.5 -1 \n", - "... ... ... ... ... ... ... ... \n", - "1240 21 Consigli Sassuolo Atalanta 1 6.0 0 \n", - "1241 21 Dragowski Spezia Napoli 1 5.0 -3 \n", - "1242 21 Milinkovic-Savic V. Torino Udinese 1 6.5 0 \n", - "1243 21 Silvestri Udinese Torino 0 6.5 -1 \n", - "1244 21 Montipo' Verona Lazio 1 6.0 -1 \n", - "\n", - " assists cards_malus fantavote ... gk_psxg \\\n", - "0 0 0.5 5.5 ... 12.70 \n", - "1 0 0.0 4.5 ... 30.80 \n", - "2 0 0.0 4.5 ... 26.40 \n", - "3 0 0.0 3.0 ... 9.35 \n", - "4 0 0.0 5.5 ... 10.60 \n", - "... ... ... ... ... ... \n", - "1240 0 0.0 6.0 ... 23.30 \n", - "1241 0 0.0 2.0 ... 28.10 \n", - "1242 0 0.0 6.5 ... 21.90 \n", - "1243 0 0.0 5.5 ... 23.90 \n", - "1244 0 0.0 5.0 ... 27.60 \n", - "\n", - " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.30 \n", - "1 0.28 1.80 \n", - "2 0.27 0.40 \n", - "3 0.24 0.85 \n", - "4 0.30 -1.40 \n", - "... ... ... \n", - "1240 0.35 -4.70 \n", - "1241 0.27 -3.90 \n", - "1242 0.23 -0.10 \n", - "1243 0.31 1.90 \n", - "1244 0.25 -4.40 \n", - "\n", - " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", - "1 99.0 240.0 563.0 \n", - "2 102.0 310.0 766.0 \n", - "3 24.0 68.0 291.5 \n", - "4 27.0 52.0 266.0 \n", - "... ... ... ... \n", - "1240 112.0 269.0 686.0 \n", - "1241 85.0 268.0 503.0 \n", - "1242 148.0 524.0 788.0 \n", - "1243 90.0 225.0 450.0 \n", - "1244 206.0 413.0 497.0 \n", - "\n", - " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", - "1 117.0 153.0 299.0 18.0 \n", - "2 113.0 108.0 426.0 25.0 \n", - "3 47.0 69.5 122.5 3.5 \n", - "4 46.0 45.0 79.0 2.0 \n", - "... ... ... ... ... \n", - "1240 99.0 172.0 255.0 16.0 \n", - "1241 92.0 109.0 251.0 9.0 \n", - "1242 95.0 155.0 263.0 18.0 \n", - "1243 76.0 164.0 275.0 5.0 \n", - "1244 65.0 168.0 294.0 15.0 \n", - "\n", - "[1245 rows x 102 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", - "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", - "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", - "\n", - "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", - "\n", - "db_gk" - ] - }, - { - "cell_type": "markdown", - "id": "04df0936", - "metadata": {}, - "source": [ - "Load player stats from current season and past seasons" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bc9dae87", - "metadata": {}, - "outputs": [], - "source": [ - "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", - "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", - "\n", - "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", - "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", - "\n", - "players = players_orig" - ] - }, - { - "cell_type": "markdown", - "id": "397babf2", - "metadata": {}, - "source": [ - "Load team data from current season and add an average Serie A team row" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "493b0495", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_11840\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", - " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" - ] - }, - { - "data": { - "text/html": [ - "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0049.0021.0231.01890.038.027.06.008.0...232.0244.026.001.08.001.01261.0260.0317.045.10
BolognaBologna25.0051.9021.0231.01890.027.020.04.004.0...262.0254.036.003.04.001.01145.0239.0193.055.30
CremoneseCremonese30.0044.2021.0231.01890.015.07.02.004.0...232.0264.033.003.04.000.01168.0388.0296.056.70
EmpoliEmpoli27.0046.8021.0231.01890.019.011.00.000.0...270.0244.035.002.00.000.01104.0242.0211.053.40
FiorentinaFiorentina28.0057.3021.0231.01890.023.018.02.004.0...296.0252.054.001.04.000.01061.0274.0317.046.40
VeronaHellas Verona34.0043.1021.0231.01890.017.014.00.000.0...213.0302.024.001.00.002.01173.0385.0422.047.70
InterInter23.0054.0021.0231.01890.040.027.02.002.0...263.0237.019.002.02.001.0960.0221.0266.045.40
JuventusJuventus26.0049.2021.0231.01890.033.025.03.004.0...229.0232.027.000.04.000.01064.0247.0257.049.00
LazioLazio21.0051.4021.0231.01890.036.026.03.004.0...294.0206.044.001.04.001.01157.0207.0216.048.90
LecceLecce26.0042.4021.0231.01890.020.014.01.002.0...275.0279.045.003.02.001.01141.0396.0316.055.60
MilanMilan27.0053.9021.0231.01890.035.030.02.002.0...255.0251.025.004.02.002.01059.0249.0305.044.90
MonzaMonza29.0055.9021.0231.01890.026.016.04.004.0...303.0263.037.000.04.001.01075.0226.0241.048.40
NapoliNapoli24.0061.4021.0231.01890.051.040.05.006.0...291.0192.028.001.05.000.01049.0214.0259.045.20
RomaRoma26.0048.9021.0231.01890.028.019.03.005.0...292.0238.010.000.05.000.01095.0205.0245.045.60
SalernitanaSalernitana28.0045.1021.0231.01890.024.016.01.001.0...238.0242.052.008.01.001.01143.0268.0247.052.00
SampdoriaSampdoria31.0047.9021.0231.01890.010.08.00.000.0...328.0288.056.004.00.000.01126.0340.0339.050.10
SassuoloSassuolo29.0048.5021.0231.01890.024.018.04.005.0...275.0200.069.002.05.000.01088.0243.0195.055.50
SpeziaSpezia33.0045.9021.0231.01890.015.010.02.002.0...225.0279.050.001.02.002.01225.0337.0285.054.20
TorinoTorino25.0053.0021.0231.01890.022.017.01.001.0...229.0287.023.003.01.000.01095.0347.0319.052.10
UdineseUdinese25.0050.0021.0231.01890.027.023.00.000.0...274.0242.034.002.00.001.01063.0222.0264.045.70
AvgAvg27.0549.9921.0231.01890.026.519.32.252.9...263.8249.836.352.12.850.71112.6275.5275.549.86
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21 rows × 303 columns

\n", - "
" - ], - "text/plain": [ - " team team_players_used team_possession team_games \\\n", - "Atalanta Atalanta 24.00 49.00 21.0 \n", - "Bologna Bologna 25.00 51.90 21.0 \n", - "Cremonese Cremonese 30.00 44.20 21.0 \n", - "Empoli Empoli 27.00 46.80 21.0 \n", - "Fiorentina Fiorentina 28.00 57.30 21.0 \n", - "Verona Hellas Verona 34.00 43.10 21.0 \n", - "Inter Inter 23.00 54.00 21.0 \n", - "Juventus Juventus 26.00 49.20 21.0 \n", - "Lazio Lazio 21.00 51.40 21.0 \n", - "Lecce Lecce 26.00 42.40 21.0 \n", - "Milan Milan 27.00 53.90 21.0 \n", - "Monza Monza 29.00 55.90 21.0 \n", - "Napoli Napoli 24.00 61.40 21.0 \n", - "Roma Roma 26.00 48.90 21.0 \n", - "Salernitana Salernitana 28.00 45.10 21.0 \n", - "Sampdoria Sampdoria 31.00 47.90 21.0 \n", - "Sassuolo Sassuolo 29.00 48.50 21.0 \n", - "Spezia Spezia 33.00 45.90 21.0 \n", - "Torino Torino 25.00 53.00 21.0 \n", - "Udinese Udinese 25.00 50.00 21.0 \n", - "Avg Avg 27.05 49.99 21.0 \n", - "\n", - " team_games_starts team_minutes team_goals team_assists \\\n", - "Atalanta 231.0 1890.0 38.0 27.0 \n", - "Bologna 231.0 1890.0 27.0 20.0 \n", - "Cremonese 231.0 1890.0 15.0 7.0 \n", - "Empoli 231.0 1890.0 19.0 11.0 \n", - "Fiorentina 231.0 1890.0 23.0 18.0 \n", - "Verona 231.0 1890.0 17.0 14.0 \n", - "Inter 231.0 1890.0 40.0 27.0 \n", - "Juventus 231.0 1890.0 33.0 25.0 \n", - "Lazio 231.0 1890.0 36.0 26.0 \n", - "Lecce 231.0 1890.0 20.0 14.0 \n", - "Milan 231.0 1890.0 35.0 30.0 \n", - "Monza 231.0 1890.0 26.0 16.0 \n", - "Napoli 231.0 1890.0 51.0 40.0 \n", - "Roma 231.0 1890.0 28.0 19.0 \n", - "Salernitana 231.0 1890.0 24.0 16.0 \n", - "Sampdoria 231.0 1890.0 10.0 8.0 \n", - "Sassuolo 231.0 1890.0 24.0 18.0 \n", - "Spezia 231.0 1890.0 15.0 10.0 \n", - "Torino 231.0 1890.0 22.0 17.0 \n", - "Udinese 231.0 1890.0 27.0 23.0 \n", - "Avg 231.0 1890.0 26.5 19.3 \n", - "\n", - " team_pens_made team_pens_att ... vs_team_fouls \\\n", - "Atalanta 6.00 8.0 ... 232.0 \n", - "Bologna 4.00 4.0 ... 262.0 \n", - "Cremonese 2.00 4.0 ... 232.0 \n", - "Empoli 0.00 0.0 ... 270.0 \n", - "Fiorentina 2.00 4.0 ... 296.0 \n", - "Verona 0.00 0.0 ... 213.0 \n", - "Inter 2.00 2.0 ... 263.0 \n", - "Juventus 3.00 4.0 ... 229.0 \n", - "Lazio 3.00 4.0 ... 294.0 \n", - "Lecce 1.00 2.0 ... 275.0 \n", - "Milan 2.00 2.0 ... 255.0 \n", - "Monza 4.00 4.0 ... 303.0 \n", - "Napoli 5.00 6.0 ... 291.0 \n", - "Roma 3.00 5.0 ... 292.0 \n", - "Salernitana 1.00 1.0 ... 238.0 \n", - "Sampdoria 0.00 0.0 ... 328.0 \n", - "Sassuolo 4.00 5.0 ... 275.0 \n", - "Spezia 2.00 2.0 ... 225.0 \n", - "Torino 1.00 1.0 ... 229.0 \n", - "Udinese 0.00 0.0 ... 274.0 \n", - "Avg 2.25 2.9 ... 263.8 \n", - "\n", - " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", - "Atalanta 244.0 26.00 1.0 \n", - "Bologna 254.0 36.00 3.0 \n", - "Cremonese 264.0 33.00 3.0 \n", - "Empoli 244.0 35.00 2.0 \n", - "Fiorentina 252.0 54.00 1.0 \n", - "Verona 302.0 24.00 1.0 \n", - "Inter 237.0 19.00 2.0 \n", - "Juventus 232.0 27.00 0.0 \n", - "Lazio 206.0 44.00 1.0 \n", - "Lecce 279.0 45.00 3.0 \n", - "Milan 251.0 25.00 4.0 \n", - "Monza 263.0 37.00 0.0 \n", - "Napoli 192.0 28.00 1.0 \n", - "Roma 238.0 10.00 0.0 \n", - "Salernitana 242.0 52.00 8.0 \n", - "Sampdoria 288.0 56.00 4.0 \n", - "Sassuolo 200.0 69.00 2.0 \n", - "Spezia 279.0 50.00 1.0 \n", - "Torino 287.0 23.00 3.0 \n", - "Udinese 242.0 34.00 2.0 \n", - "Avg 249.8 36.35 2.1 \n", - "\n", - " vs_team_pens_conceded vs_team_own_goals \\\n", - "Atalanta 8.00 1.0 \n", - "Bologna 4.00 1.0 \n", - "Cremonese 4.00 0.0 \n", - "Empoli 0.00 0.0 \n", - "Fiorentina 4.00 0.0 \n", - "Verona 0.00 2.0 \n", - "Inter 2.00 1.0 \n", - "Juventus 4.00 0.0 \n", - "Lazio 4.00 1.0 \n", - "Lecce 2.00 1.0 \n", - "Milan 2.00 2.0 \n", - "Monza 4.00 1.0 \n", - "Napoli 5.00 0.0 \n", - "Roma 5.00 0.0 \n", - "Salernitana 1.00 1.0 \n", - "Sampdoria 0.00 0.0 \n", - "Sassuolo 5.00 0.0 \n", - "Spezia 2.00 2.0 \n", - "Torino 1.00 0.0 \n", - "Udinese 0.00 1.0 \n", - "Avg 2.85 0.7 \n", - "\n", - " vs_team_ball_recoveries vs_team_aerials_won \\\n", - "Atalanta 1261.0 260.0 \n", - "Bologna 1145.0 239.0 \n", - "Cremonese 1168.0 388.0 \n", - "Empoli 1104.0 242.0 \n", - "Fiorentina 1061.0 274.0 \n", - "Verona 1173.0 385.0 \n", - "Inter 960.0 221.0 \n", - "Juventus 1064.0 247.0 \n", - "Lazio 1157.0 207.0 \n", - "Lecce 1141.0 396.0 \n", - "Milan 1059.0 249.0 \n", - "Monza 1075.0 226.0 \n", - "Napoli 1049.0 214.0 \n", - "Roma 1095.0 205.0 \n", - "Salernitana 1143.0 268.0 \n", - "Sampdoria 1126.0 340.0 \n", - "Sassuolo 1088.0 243.0 \n", - "Spezia 1225.0 337.0 \n", - "Torino 1095.0 347.0 \n", - "Udinese 1063.0 222.0 \n", - "Avg 1112.6 275.5 \n", - "\n", - " vs_team_aerials_lost vs_team_aerials_won_pct \n", - "Atalanta 317.0 45.10 \n", - "Bologna 193.0 55.30 \n", - "Cremonese 296.0 56.70 \n", - "Empoli 211.0 53.40 \n", - "Fiorentina 317.0 46.40 \n", - "Verona 422.0 47.70 \n", - "Inter 266.0 45.40 \n", - "Juventus 257.0 49.00 \n", - "Lazio 216.0 48.90 \n", - "Lecce 316.0 55.60 \n", - "Milan 305.0 44.90 \n", - "Monza 241.0 48.40 \n", - "Napoli 259.0 45.20 \n", - "Roma 245.0 45.60 \n", - "Salernitana 247.0 52.00 \n", - "Sampdoria 339.0 50.10 \n", - "Sassuolo 195.0 55.50 \n", - "Spezia 285.0 54.20 \n", - "Torino 319.0 52.10 \n", - "Udinese 264.0 45.70 \n", - "Avg 275.5 49.86 \n", - "\n", - "[21 rows x 303 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", - "\n", - "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", - "avg_row['team']['Avg'] = 'Avg'\n", - "\n", - "team_data = pd.concat([team_data, avg_row])\n", - "\n", - "team_data" - ] - }, - { - "cell_type": "markdown", - "id": "cc1cd13d", - "metadata": {}, - "source": [ - "Data processing functions copied from player_match_dataset_creation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "32f56138", - "metadata": {}, - "outputs": [], - "source": [ - "features_abs = ['r',\n", - " 'games',\n", - " 'games_starts', \n", - " 'minutes',\n", - " 'shots_on_target_pct',\n", - " 'goals_per_shot',\n", - " 'goals_per_shot_on_target',\n", - " 'passes_pct',\n", - " #'dribble_tackles_pct',\n", - " #'dribbles_completed_pct',\n", - " 'aerials_won_pct',\n", - " 'team_possession',\n", - " 'team_goals_assists_per90',\n", - " 'team_goals_pens_per90',\n", - " 'team_goals_assists_pens_per90',\n", - " 'team_xg_per90',\n", - " 'team_gk_goals_against_per90',\n", - " 'team_gk_save_pct',\n", - " 'team_gk_clean_sheets_pct',\n", - " 'team_passes_pct',\n", - " 'team_passes_pct_medium',\n", - " 'team_passes_pct_long',\n", - " 'team_sca_per90',\n", - " 'team_gca_per90',\n", - " #'team_dribble_tackles_pct',\n", - " 'team_aerials_won_pct',\n", - " 'vs_team_possession',\n", - " 'vs_team_goals_per90',\n", - " 'vs_team_assists_per90',\n", - " 'vs_team_xg_per90',\n", - " 'vs_team_gk_save_pct',\n", - " 'vs_team_gk_clean_sheets_pct',\n", - " 'vs_team_gk_pct_passes_launched',\n", - " 'vs_team_gk_crosses_stopped_pct',\n", - " 'vs_team_shots_on_target_per90',\n", - " 'vs_team_passes_pct',\n", - " 'vs_team_passes_pct_short',\n", - " 'vs_team_passes_pct_medium',\n", - " 'vs_team_passes_pct_long',\n", - " 'vs_team_sca_per90',\n", - " 'vs_team_gca_per90',\n", - " #'vs_team_dribble_tackles_pct',\n", - " #'vs_team_dribbles_completed_pct',\n", - " 'vs_team_aerials_won_pct',\n", - " 'opp_team_possession',\n", - " 'opp_team_goals_assists_per90',\n", - " 'opp_team_goals_pens_per90',\n", - " 'opp_team_goals_assists_pens_per90',\n", - " 'opp_team_xg_per90',\n", - " 'opp_team_gk_goals_against_per90',\n", - " 'opp_team_gk_save_pct',\n", - " 'opp_team_gk_clean_sheets_pct',\n", - " 'opp_team_passes_pct',\n", - " 'opp_team_passes_pct_medium',\n", - " 'opp_team_passes_pct_long',\n", - " 'opp_team_sca_per90',\n", - " 'opp_team_gca_per90',\n", - " #'opp_team_dribble_tackles_pct',\n", - " 'opp_team_aerials_won_pct',\n", - " 'opp_vs_team_possession',\n", - " 'opp_vs_team_goals_per90',\n", - " 'opp_vs_team_assists_per90',\n", - " 'opp_vs_team_xg_per90',\n", - " 'opp_vs_team_gk_save_pct',\n", - " 'opp_vs_team_gk_clean_sheets_pct',\n", - " 'opp_vs_team_gk_pct_passes_launched',\n", - " 'opp_vs_team_gk_crosses_stopped_pct',\n", - " 'opp_vs_team_shots_on_target_per90',\n", - " 'opp_vs_team_passes_pct',\n", - " 'opp_vs_team_passes_pct_short',\n", - " 'opp_vs_team_passes_pct_medium',\n", - " 'opp_vs_team_passes_pct_long',\n", - " 'opp_vs_team_sca_per90',\n", - " 'opp_vs_team_gca_per90',\n", - " #'opp_vs_team_dribble_tackles_pct',\n", - " #'opp_vs_team_dribbles_completed_pct',\n", - " 'opp_vs_team_aerials_won_pct',\n", - " \n", - " 'vote_avg',\n", - " 'vote_std']\n", - "\n", - "features_rel = [\n", - " 'goals',\n", - " 'assists',\n", - " 'cards_yellow',\n", - " 'cards_red',\n", - " 'xg',\n", - " 'npxg',\n", - " 'shots_on_target',\n", - " 'passes_completed',\n", - " 'passes_into_final_third',\n", - " 'passes_into_penalty_area',\n", - " 'progressive_passes',\n", - " 'passes_live',\n", - " 'passes_dead',\n", - " 'through_balls',\n", - " 'passes_switches',\n", - " 'crosses',\n", - " 'corner_kicks',\n", - " #'dribble_tackles',\n", - " #'dribbles_vs',\n", - " #'dribbled_past',\n", - " 'blocks',\n", - " 'blocked_shots',\n", - " 'blocked_passes',\n", - " 'interceptions',\n", - " 'clearances',\n", - " 'errors',\n", - " 'touches',\n", - " 'touches_def_pen_area',\n", - " 'touches_def_3rd',\n", - " 'touches_mid_3rd',\n", - " 'touches_att_3rd',\n", - " 'touches_att_pen_area',\n", - " 'touches_live_ball',\n", - " #'dribbles_completed',\n", - " #'dribbles',\n", - " 'passes_received',\n", - " 'miscontrols',\n", - " 'dispossessed',\n", - " 'fouls',\n", - " 'fouled',\n", - " 'aerials_won',\n", - " 'aerials_lost',\n", - " 'carries',\n", - " 'progressive_carries',\n", - " 'carries_into_final_third',\n", - " 'carries_into_penalty_area']\n", - "\n", - "features_rel_gamecorr = [\n", - " 'goals',\n", - " 'assists',\n", - " 'xg',\n", - " 'npxg',\n", - " 'cards_yellow',\n", - " 'cards_red'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4001f3f8", - "metadata": {}, - "outputs": [], - "source": [ - "features_abs_gk = [\n", - " 'gk_games',\n", - " 'gk_games_starts',\n", - " 'gk_minutes',\n", - " 'gk_goals_against_per90', \n", - " 'gk_save_pct',\n", - " 'gk_clean_sheets_pct',\n", - " 'gk_psxg_net_per90',\n", - " 'gk_passes_pct_launched',\n", - " 'gk_pct_passes_launched',\n", - " 'gk_passes_length_avg',\n", - " 'gk_pct_goal_kicks_launched',\n", - " 'gk_goal_kick_length_avg',\n", - " 'gk_crosses_stopped_pct',\n", - " 'gk_def_actions_outside_pen_area_per90',\n", - " 'gk_avg_distance_def_actions',\n", - " \n", - " 'team_possession',\n", - " 'team_goals_assists_per90',\n", - " 'team_goals_pens_per90',\n", - " 'team_goals_assists_pens_per90',\n", - " 'team_xg_per90',\n", - " 'team_gk_goals_against_per90',\n", - " 'team_gk_save_pct',\n", - " 'team_gk_clean_sheets_pct',\n", - " 'team_passes_pct',\n", - " 'team_passes_pct_medium',\n", - " 'team_passes_pct_long',\n", - " 'team_sca_per90',\n", - " 'team_gca_per90',\n", - " #'team_dribble_tackles_pct',\n", - " 'team_aerials_won_pct',\n", - " 'vs_team_possession',\n", - " 'vs_team_goals_per90',\n", - " 'vs_team_assists_per90',\n", - " 'vs_team_xg_per90',\n", - " 'vs_team_gk_save_pct',\n", - " 'vs_team_gk_clean_sheets_pct',\n", - " 'vs_team_gk_pct_passes_launched',\n", - " 'vs_team_gk_crosses_stopped_pct',\n", - " 'vs_team_shots_on_target_per90',\n", - " 'vs_team_passes_pct',\n", - " 'vs_team_passes_pct_short',\n", - " 'vs_team_passes_pct_medium',\n", - " 'vs_team_passes_pct_long',\n", - " 'vs_team_sca_per90',\n", - " 'vs_team_gca_per90',\n", - " #'vs_team_dribble_tackles_pct',\n", - " #'vs_team_dribbles_completed_pct',\n", - " 'vs_team_aerials_won_pct',\n", - " 'opp_team_possession',\n", - " 'opp_team_goals_assists_per90',\n", - " 'opp_team_goals_pens_per90',\n", - " 'opp_team_goals_assists_pens_per90',\n", - " 'opp_team_xg_per90',\n", - " 'opp_team_gk_goals_against_per90',\n", - " 'opp_team_gk_save_pct',\n", - " 'opp_team_gk_clean_sheets_pct',\n", - " 'opp_team_passes_pct',\n", - " 'opp_team_passes_pct_medium',\n", - " 'opp_team_passes_pct_long',\n", - " 'opp_team_sca_per90',\n", - " 'opp_team_gca_per90',\n", - " #'opp_team_dribble_tackles_pct',\n", - " 'opp_team_aerials_won_pct',\n", - " 'opp_vs_team_possession',\n", - " 'opp_vs_team_goals_per90',\n", - " 'opp_vs_team_assists_per90',\n", - " 'opp_vs_team_xg_per90',\n", - " 'opp_vs_team_gk_save_pct',\n", - " 'opp_vs_team_gk_clean_sheets_pct',\n", - " 'opp_vs_team_gk_pct_passes_launched',\n", - " 'opp_vs_team_gk_crosses_stopped_pct',\n", - " 'opp_vs_team_shots_on_target_per90',\n", - " 'opp_vs_team_passes_pct',\n", - " 'opp_vs_team_passes_pct_short',\n", - " 'opp_vs_team_passes_pct_medium',\n", - " 'opp_vs_team_passes_pct_long',\n", - " 'opp_vs_team_sca_per90',\n", - " 'opp_vs_team_gca_per90',\n", - " #'opp_vs_team_dribble_tackles_pct',\n", - " #'opp_vs_team_dribbles_completed_pct',\n", - " 'opp_vs_team_aerials_won_pct',\n", - " \n", - " 'vote_avg',\n", - " 'vote_std']\n", - "\n", - "features_rel_gk = [\n", - " 'gk_shots_on_target_against',\n", - " 'gk_saves',\n", - " 'gk_free_kick_goals_against',\n", - " 'gk_corner_kick_goals_against',\n", - " 'gk_own_goals_against',\n", - " 'gk_psxg',\n", - " 'gk_psnpxg_per_shot_on_target_against',\n", - " 'gk_psxg_net',\n", - " 'gk_passes_completed_launched',\n", - " 'gk_passes_launched',\n", - " 'gk_passes',\n", - " 'gk_passes_throws',\n", - " 'gk_goal_kicks',\n", - " 'gk_crosses',\n", - " 'gk_crosses_stopped',\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f4017b2f", - "metadata": {}, - "outputs": [], - "source": [ - "DEL_G = False\n", - "\n", - "features_to_del = [\n", - " 'goals',\n", - " 'assists',\n", - " 'xg',\n", - " 'npxg'\n", - "]\n", - "\n", - "def player_match_data(player, pteam, oppteam, oldseason = False):\n", - " if(not(player in players.index)):\n", - " return None\n", - " \n", - " if(oldseason):\n", - " pdata = players_old.loc[[player]]\n", - " else:\n", - " pdata = players.loc[[player]]\n", - " \n", - " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", - " \n", - " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", - " \n", - " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", - " \n", - " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", - " \n", - " return(out)\n", - "\n", - "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", - " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", - " \n", - " if(not isinstance(pdata, pd.DataFrame)):\n", - " return None\n", - " \n", - " assert pdata['games'][0] > 0\n", - " \n", - " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", - " \n", - " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", - " \n", - " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", - " \n", - " if(DEL_G):\n", - " out[features_to_del] = 0\n", - " \n", - " return out\n", - "\n", - "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", - " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", - " \n", - " if(not isinstance(pdata, pd.DataFrame)):\n", - " return None\n", - " \n", - " if(pdata['gk_games'][0] <= 0):\n", - " return None\n", - " \n", - " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", - " \n", - " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", - "\n", - " return out\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "21fef3ae", - "metadata": {}, - "source": [ - "Players stats rework:\n", - "the current season stats are averaged (according to a calculated weight) with the past season data.\n", - "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", - "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", - "\n", - "These modified stats are used only for prediction, not for model traning.\n", - "\n", - "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", - "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6f8707b8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "Averaging players stats with past seasons:\n", - "Meret 1\n", - "Provedel 1\n", - "Vicario 1\n", - "Szczesny 1\n", - "Falcone 1\n", - "Silvestri 1\n", - "Rui Patricio 1\n", - "Sepe 1\n", - "Milinkovic-Savic V. 1\n", - "Musso 1\n", - "Maignan 0.7094551282051282\n", - "Audero 1\n", - "Montipo' 1\n", - "Skorupski 1\n", - "Consigli 1\n", - "Dragowski 1\n", - "Terracciano 1\n", - "Tatarusanu 1\n", - "Handanovic 0.7012375012375013\n", - "Sportiello 1\n", - "Perin 1\n", - "Zoet 1\n", - "Pegolo 1\n", - "Mirante 0.0\n", - "Ujkani 0.0\n", - "Berisha 0.0\n", - "Marchetti 0.0\n", - "Padelli 0.0\n", - "Bardi 0.0\n", - "Cordaz 0.0\n", - "Pinsoglio 0.0\n", - "Fiorillo 0.0\n", - "Cragno 0.0\n", - "Sirigu 0.0\n", - "Rossi F. 0.0\n", - "Berardi A. 0.0\n", - "Gemello 0.0\n", - "Ravaglia 0.0\n", - "Boer 0.0\n", - "Adamonis 0.0\n", - "Marfella 0.0\n", - "Zovko 1\n", - "Piana 0.0\n", - "Dimarco 1\n", - "Smalling 1\n", - "Di Lorenzo 1\n", - "Danilo 1\n", - "Hernandez T. 0.9121565934065934\n", - "Udogie 0.7876399790685507\n", - "Parisi 1\n", - "Mario Rui 0.8108058608058609\n", - "Romagnoli 1\n", - "Bastoni S. 0.6800307219662058\n", - "Mazzocchi 1\n", - "Tomori 0.9938910551813778\n", - "Scalvini 1\n", - "Toloi 1\n", - "Demiral 0.9845499738356883\n", - "Maehle 1\n", - "Dumfries 0.8845154845154845\n", - "Juan Jesus 0.5405372405372405\n", - "Depaoli 1\n", - "Mancini 1\n", - "Ibanez 0.953889248006895\n", - "Rodrigo Becao 0.6949764521193094\n", - "Ebuehi 1\n", - "Gosens 1\n", - "Darmian 1\n", - "Reca 1\n", - "Bremer 0.9015984015984014\n", - "Rrahmani 0.6879564879564879\n", - "Vojvoda 0.8387646835922699\n", - "Bastoni 0.8892709441096539\n", - "Milenkovic 0.7631113984055161\n", - "Kalulu 1\n", - "Martinez Quarta 1\n", - "Casale 0.6428062678062678\n", - "Perez N. 1\n", - "Izzo 1\n", - "Luperto 1\n", - "Skriniar 0.9266352694924124\n", - "Rodriguez R. 0.8585003232062055\n", - "Marusic 1\n", - "Lazzari 0.8892709441096539\n", - "Kyriakopoulos 0.056997600101048373\n", - "Ampadu 0.911961601616774\n", - "Ismajli 1\n", - "Llorente D. affine to Ibanez\n", - "Llorente D. 0.04769446240034476\n", - "Cambiaso 1\n", - "Hysaj 0.9505999747379058\n", - "Biraghi 0.8765468765468765\n", - "Medel 0.8353757353757354\n", - "Bonucci 0.6081043956043956\n", - "Calabria 0.8108058608058608\n", - "Acerbi 0.7713675213675213\n", - "Spinazzola 1\n", - "Lykogiannis 0.9445316588173731\n", - "Pellegrini Lu. 0.0\n", - "Djidji 1\n", - "Augello 0.9008954008954009\n", - "Singo 0.6949764521193094\n", - "Mari' 1\n", - "De Vrij 0.7026984126984127\n", - "Patric 0.6756715506715507\n", - "Faraoni 0.40540293040293046\n", - "Ceccherini 0.7154169360051713\n", - "Hateboer 1\n", - "Rogerio 1\n", - "Aina 0.9266352694924122\n", - "Ferrari G. 0.855850630850631\n", - "Fazio 1\n", - "Buongiorno 1\n", - "Gunter 0.7162698412698413\n", - "Troost-Ekong affine to Fazio\n", - "Troost-Ekong 0.20270146520146523\n", - "Soumaoro 0.8687205651491368\n", - "Ceccaroni 0.0\n", - "Soppy 0.5903322867608581\n", - "Ferrari A. 0.359332696289218\n", - "Zappacosta 0.23847231200172375\n", - "Gyomber 0.7323407775020678\n", - "Alex Sandro 0.9845499738356883\n", - "Pezzella Giu. 0.7083987441130296\n", - "Bereszynski 0.7083987441130298\n", - "Venuti 0.6345437171524128\n", - "Palomino 0.23847231200172375\n", - "Nuytinck 0.36731786731786725\n", - "Magnani 1\n", - "Colley 0.8108058608058609\n", - "Nikolaou 0.855850630850631\n", - "Terzic 1\n", - "Igor 0.8648595848595849\n", - "Toljan 1\n", - "Zortea 0.056997600101048373\n", - "Dawidowicz 1\n", - "Bellanova 0.5332033557840009\n", - "Erlic 0.7713675213675213\n", - "Ballo-Toure' affine to Calabria\n", - "Ballo-Toure' 0.1871090448013525\n", - "Stojanovic 0.8353757353757354\n", - "Amian 0.7094551282051282\n", - "Zima 0.7297252747252747\n", - "De Winter 1\n", - "Romagnoli S. 0.0\n", - "Ghiglione 0.7628909551986474\n", - "Rugani 0.4054029304029304\n", - "De Sciglio 0.8108058608058608\n", - "Djimsiti 0.47079049982275784\n", - "Caldara 0.7464846980976013\n", - "Karsdorp 0.4054029304029304\n", - "Marchizza 0.34798534798534797\n", - "Kjaer 1\n", - "Ruggeri 1\n", - "Zanoli 0.6887210012210012\n", - "Radovanovic 1\n", - "D'ambrosio 0.32432234432234436\n", - "De Silvestri 0.47079049982275784\n", - "Chiriches 0.455980800808387\n", - "Murru 0.6633866133866133\n", - "Bonifazi 0.5159673659673659\n", - "Walukiewicz 0.9445316588173731\n", - "Walukiewicz 0.36246197549180287\n", - "Ranieri L. 0.12243928910595576\n", - "Gabbia 1\n", - "Kumbulla 0.38155569920275806\n", - "Lovato 0.7231570512820512\n", - "Tuia 0.18365893365893363\n", - "Ferrer 0.12011938678605345\n", - "Antov 1\n", - "Vasquez 0.5903322867608581\n", - "Ruan 1\n", - "Ostigard 1\n", - "Coppola D. 1\n", - "Cacace 0.6486446886446887\n", - "Conti 0.2316588173731031\n", - "Conti 0.843001582405036\n", - "Marrone 0.15742194313622884\n", - "Tonelli 0.0\n", - "Radu 1\n", - "Florenzi 0.13513431013431015\n", - "Sala 0.5405372405372405\n", - "Fares 0.0\n", - "Fares 0.0\n", - "Romagna 0.0\n", - "Romagna 0.0\n", - "Muldur 0.05231005553586199\n", - "Amey 0.0\n", - "Zaccagni 1\n", - "Milinkovic-Savic 0.8765468765468765\n", - "Barella 0.9008954008954009\n", - "Zielinski 0.972967032967033\n", - "Luis Alberto 0.8585003232062055\n", - "Felipe Anderson 0.8961538461538462\n", - "Koopmeiners 1\n", - "Calhanoglu 0.953889248006895\n", - "Frattesi 0.945940170940171\n", - "Diaz B. 0.9415809996455157\n", - "Zambo Anguissa 1\n", - "Elmas 0.8765468765468765\n", - "Miranchuk 1\n", - "Samardzic 1\n", - "Pereyra 1\n", - "Politano 0.9336552336552336\n", - "Rabiot 0.8614812271062272\n", - "Lazovic 0.8108058608058609\n", - "Lobotka 1\n", - "Bonaventura 0.9415809996455157\n", - "Pessina 1\n", - "Tonali 0.8108058608058608\n", - "Pellegrini Lo. 1\n", - "El Shaarawy 0.900895400895401\n", - "Orsolini 1\n", - "Ikone' 1\n", - "Candreva 0.9182946682946682\n", - "Bennacer 0.9938910551813778\n", - "Pasalic 0.8327195327195327\n", - "Mkhitaryan 0.9597660404112016\n", - "Chiesa 0.8108058608058608\n", - "Bandinelli 0.972967032967033\n", - "Fagioli affine to Henderson L.\n", - "Fagioli 0.512087912087912\n", - "Messias 0.9355452240067625\n", - "Arslan 1\n", - "Ricci S. 1\n", - "Verdi 1\n", - "Sensi 1\n", - "Barak 0.9689592017178223\n", - "Soriano 0.9266352694924124\n", - "Dominguez 1\n", - "Brozovic 0.5096493982208269\n", - "Cristante 1\n", - "Saponara 0.9505999747379058\n", - "Vecino 1\n", - "Locatelli 0.9415809996455157\n", - "Zaniolo 0.7528911564625851\n", - "Maldini 1\n", - "Marin 0.9182946682946682\n", - "Zalewski 1\n", - "Bajrami 0.04722658294086866\n", - "Coulibaly L. 1\n", - "De Roon 1\n", - "Mandragora 1\n", - "Bourabia 1\n", - "Sottil 0.4729700854700855\n", - "Aebischer 1\n", - "Ederson D.s. 1\n", - "Miretti 1\n", - "Cataldi 0.9628319597069598\n", - "Djuricic 1\n", - "Linetty 1\n", - "Haas 1\n", - "Walace 0.945940170940171\n", - "Agudelo 1\n", - "Pobega 0.6010656010656009\n", - "Nicolussi Caviglia 1\n", - "Rovella 0.9445316588173729\n", - "Amrabat 1\n", - "Tameze 0.8534798534798534\n", - "Gyasi 0.8108058608058608\n", - "Ilic 0.5681948260073261\n", - "Matheus Henrique 0.8432380952380952\n", - "Harroui 0.9582251082251082\n", - "Volpato 1\n", - "Duncan 0.6388167388167388\n", - "Cuadrado 0.7862359862359862\n", - "Ekdal 0.8900394477317554\n", - "Schouten 1\n", - "Obiang 1\n", - "Kovalenko 0.6236968160045083\n", - "Crnigoj 0.09723120017237664\n", - "Basic 0.7828470380194518\n", - "Asllani 0.8623984710941232\n", - "Sabiri 1\n", - "Grassi 0.6060744810744811\n", - "Krunic 0.5791470434327577\n", - "Rincon 1\n", - "Miguel Veloso 1\n", - "Henderson L. 0.6401098901098902\n", - "Lopez M. 0.6486446886446886\n", - "Cuisance 0.12714849253310792\n", - "Saelemaekers 0.5855820105820106\n", - "Maggiore 0.4722658294086865\n", - "Akpa Akpro 0.0\n", - "Akpa Akpro 0.0\n", - "Maleh 0.29516614338042907\n", - "Romero L. 1\n", - "Ceide 1\n", - "Benassi 0.4132326007326007\n", - "Gagliardini 0.9008954008954009\n", - "Vieira 0.11806645735217164\n", - "Bianco 1\n", - "Galdames 0.0\n", - "Kastanos 0.7207163207163206\n", - "Vignato 0.20661630036630035\n", - "Askildsen 1\n", - "Bove 1\n", - "Bohinen 1\n", - "Bakayoko 0.0\n", - "Zurkowski 0.04722658294086866\n", - "Castrovilli 0.2115145723841376\n", - "Demme 0.256043956043956\n", - "Darboe 0.0\n", - "Darboe 0.32432234432234436\n", - "Urbanski 0.0\n", - "Yepes 1\n", - "Osimhen 1\n", - "Martinez L. 0.972967032967033\n", - "Dybala 0.8549640015157256\n", - "Rafael Leao 0.953889248006895\n", - "Immobile 0.8369608885737918\n", - "Vlahovic 1\n", - "Arnautovic 0.6879564879564879\n", - "Dzeko 0.945940170940171\n", - "Nzola 1\n", - "Beto 1\n", - "Giroud 1\n", - "Abraham 0.9203742203742203\n", - "Deulofeu 0.7631113984055161\n", - "Simeone 0.5667189952904238\n", - "Lozano 1\n", - "Correa 1\n", - "Berardi 0.6388167388167388\n", - "Pedro 0.9628319597069598\n", - "Sanabria 0.8946823291650877\n", - "Thauvin affine to Deulofeu\n", - "Thauvin 0.04769446240034476\n", - "Cabral 1\n", - "Caprari 0.9917582417582418\n", - "Piatek 1\n", - "Rebic 0.878373015873016\n", - "Bonazzoli 0.9628319597069598\n", - "Zapata D. 0.945940170940171\n", - "Gonzalez N. 0.5405372405372406\n", - "Brekalo 0.051654075091575095\n", - "Kean 0.9628319597069598\n", - "Okereke 1\n", - "Muriel 0.7807760141093475\n", - "Pinamonti 0.7805504680504681\n", - "Di Francesco F. 1\n", - "Caputo 0.27548840048840045\n", - "Boga 1\n", - "Alvarez A. affine to Raspadori\n", - "Alvarez A. 0.7207163207163207\n", - "Petagna 1\n", - "Barrow 0.7154169360051713\n", - "Djuric 1\n", - "Henry 0.8014208014208013\n", - "Success 1\n", - "Gabbiadini 1\n", - "Kallon 1\n", - "Nestorovski 1\n", - "Raspadori 0.6428062678062678\n", - "Lasagna 0.9845499738356883\n", - "Belotti 1\n", - "Pellegri 1\n", - "Verde 0.5405372405372406\n", - "Destro 0.6121964455297788\n", - "Seck 1\n", - "Sansone 0.6005969339302673\n", - "Quagliarella 0.7370962370962372\n", - "Defrel 0.4267399267399267\n", - "Pjaca 0.6887210012210012\n", - "Gaich 0.11019536019536019\n", - "Piccoli 0.3305860805860806\n", - "Piccoli 0.30394544405533425\n", - "Shomurodov 0.05903322867608582\n", - "Afena-Gyan 1\n", - "Ibrahimovic 0.0\n", - "Pussetto 0.29516614338042907\n", - "Cancellieri 1\n", - "Oddei 0.0\n", - "Oddei 0.6486446886446887\n", - "Braaf 0.41323260073260076\n", - "Raimondo 0.0\n", - "Kaio Jorge 0.0\n", - "Players with low quantity of games:\n", - "Gravillon 0.0\n", - "Masina 0.6666666666666667\n", - "Aiwu 0.0\n", - "Thiaw 0.8333333333333334\n", - "Zeefuik 0.0\n", - "Wisniewski 0.16666666666666663\n", - "Dermaku 0.16666666666666663\n", - "Donati 0.6666666666666667\n", - "Antov 0.6666666666666667\n", - "Ostigard 0.6666666666666667\n", - "Gila 0.6666666666666667\n", - "Bayeye 0.16666666666666663\n", - "Moutinho J. 0.6666666666666667\n", - "Paletta 0.0\n", - "Romagna 0.0\n", - "Cassandro 0.0\n", - "Amey 0.16666666666666663\n", - "Zanotti 0.0\n", - "Ebosele 0.8333333333333334\n", - "Buta 0.0\n", - "Abankwah 0.0\n", - "Guessand A. 0.0\n", - "Cabal 0.5\n", - "Sosa 0.8333333333333334\n", - "Guarino 0.0\n", - "Carboni F. 0.6666666666666667\n", - "Pogba 0.0\n", - "Duda 0.33333333333333337\n", - "Wijnaldum 0.16666666666666663\n", - "Nicolussi Caviglia 0.8333333333333334\n", - "Volpato 0.8333333333333334\n", - "Machin 0.0\n", - "Esposito Sa. 0.5\n", - "Akpa Akpro 0.0\n", - "Romero L. 0.8333333333333334\n", - "Bianco 0.5\n", - "Galdames 0.0\n", - "Tahirovic 0.8333333333333334\n", - "Abildgaard 0.16666666666666663\n", - "Winks 0.6666666666666667\n", - "D'andrea 0.8333333333333334\n", - "Cipot 0.33333333333333337\n", - "Gaetano 0.5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Darboe 0.6171184371184371\n", - "Urbanski 0.16666666666666663\n", - "Bertini 0.0\n", - "Yepes 0.8333333333333334\n", - "Pyyhtia 0.5\n", - "Trimboli 0.0\n", - "Pafundi 0.16666666666666663\n", - "Adli 0.6666666666666667\n", - "Vignato S. 0.5\n", - "Samek 0.0\n", - "Zerbin 0.6666666666666667\n", - "Ilkhan 0.6666666666666667\n", - "Degli Innocenti 0.16666666666666663\n", - "Acella 0.0\n", - "Carboni V. 0.33333333333333337\n", - "Paoletti 0.6666666666666667\n", - "Malagrida 0.16666666666666663\n", - "Faticanti 0.0\n", - "Solbakken 0.33333333333333337\n", - "Ngonge 0.33333333333333337\n", - "Oddei 0.5090109890109888\n", - "Braaf 0.4600503663003662\n", - "Raimondo 0.16666666666666663\n", - "De Luca 0.16666666666666663\n", - "Voelkerling Persson 0.5\n", - "Krollis 0.16666666666666663\n", - "Vivaldo 0.0\n" - ] - } - ], - "source": [ - "#for i in range(players.columns.shape[0]):\n", - "# print(str(i) + ' - ' + players.columns[i])\n", - "\n", - "cols_toadapt = players.columns[9:]\n", - "\n", - "players = players_orig.copy()\n", - "\n", - "min_games = 6\n", - "\n", - "current_season_games = max(players_orig['games'])\n", - "\n", - "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", - "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", - "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", - "WEIGHT_mul_gk = 2\n", - "\n", - "rcsv = pd.read_csv('config/affine_players.txt') \n", - "affine_players = pd.DataFrame(rcsv)\n", - "affine_players = affine_players.set_index('player')\n", - "\n", - "\n", - "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", - " if(same_team):\n", - " weight_0 = WEIGHT_0_same_team\n", - " else:\n", - " weight_0 = WEIGHT_0_different_team\n", - "\n", - " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", - " weight = min(weight, 1)\n", - "\n", - " return abs(weight)\n", - "\n", - "print(' ')\n", - "print('Averaging players stats with past seasons:')\n", - "\n", - "for i in range(players.shape[0]):\n", - " p = players.index[i]\n", - " \n", - "\n", - " if(p in players_old.index or p in affine_players.index):\n", - " p_ = p\n", - " affine = 0\n", - " \n", - " if(p in affine_players.index):\n", - " affine = 1\n", - " p_ = affine_players.loc[p]['alike']\n", - " \n", - " print(p + ' affine to ' + p_)\n", - " \n", - " if(players.loc[p]['r'] == 'P'):\n", - " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", - " weight *= WEIGHT_mul_gk\n", - " weight = min(weight, 1)\n", - " else:\n", - " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", - "\n", - " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", - " \n", - " print(p + ' ' + str(weight)) \n", - " \n", - " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", - " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", - " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", - " \n", - " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", - " \n", - " print(p + ' ' + str(weight))\n", - " \n", - " \n", - "# handle players with low quantitites of games\n", - "\n", - "print('Players with low quantity of games:')\n", - "\n", - "def calc_weight_low(current_games, min_games = min_games):\n", - " weight = 1 - (min_games - current_games)/min_games\n", - " \n", - " weight = min(weight, 1)\n", - "\n", - " return abs(weight)\n", - "\n", - "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", - "\n", - "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", - "count = 0\n", - "\n", - "for i in range(players_orig.shape[0]):\n", - " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", - " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", - " count = count + 1\n", - " \n", - "mean_players_stats /= count\n", - "\n", - "for i in range(players.shape[0]):\n", - " p = players.index[i]\n", - " \n", - " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", - " weight = calc_weight_low(players.loc[p]['games'])\n", - " \n", - " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", - " \n", - " print(p + ' ' + str(weight))\n", - " \n", - " \n", - "players_out = players.copy()\n", - "players_out = players_out.set_index(players_out.columns[0])\n", - "players_out.insert(2, 'name', players_out.index)\n", - "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "49c28b07", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", - " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", - " ...\n", - " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", - " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", - " 'gk_def_actions_outside_pen_area',\n", - " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", - " 'vote_avg', 'vote_std'],\n", - " dtype='object', length=151)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "players.columns[9:]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d29102e5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 - matchday\n", - "1 - player\n", - "2 - team\n", - "3 - oppteam\n", - "4 - home\n", - "5 - vote\n", - "6 - goals\n", - "7 - assists\n", - "8 - cards_malus\n", - "9 - fantavote\n", - "10 - r\n", - "11 - games\n", - "12 - games_starts\n", - "13 - minutes\n", - "14 - shots_on_target_pct\n", - "15 - goals_per_shot\n", - "16 - goals_per_shot_on_target\n", - "17 - passes_pct\n", - "18 - aerials_won_pct\n", - "19 - team_possession\n", - "20 - team_goals_assists_per90\n", - "21 - team_goals_pens_per90\n", - "22 - team_goals_assists_pens_per90\n", - "23 - team_xg_per90\n", - "24 - team_gk_goals_against_per90\n", - "25 - team_gk_save_pct\n", - "26 - team_gk_clean_sheets_pct\n", - "27 - team_passes_pct\n", - "28 - team_passes_pct_medium\n", - "29 - team_passes_pct_long\n", - "30 - team_sca_per90\n", - "31 - team_gca_per90\n", - "32 - team_aerials_won_pct\n", - "33 - vs_team_possession\n", - "34 - vs_team_goals_per90\n", - "35 - vs_team_assists_per90\n", - "36 - vs_team_xg_per90\n", - "37 - vs_team_gk_save_pct\n", - "38 - vs_team_gk_clean_sheets_pct\n", - "39 - vs_team_gk_pct_passes_launched\n", - "40 - vs_team_gk_crosses_stopped_pct\n", - "41 - vs_team_shots_on_target_per90\n", - "42 - vs_team_passes_pct\n", - "43 - vs_team_passes_pct_short\n", - "44 - vs_team_passes_pct_medium\n", - "45 - vs_team_passes_pct_long\n", - "46 - vs_team_sca_per90\n", - "47 - vs_team_gca_per90\n", - "48 - vs_team_aerials_won_pct\n", - "49 - opp_team_possession\n", - "50 - opp_team_goals_assists_per90\n", - "51 - opp_team_goals_pens_per90\n", - "52 - opp_team_goals_assists_pens_per90\n", - "53 - opp_team_xg_per90\n", - "54 - opp_team_gk_goals_against_per90\n", - "55 - opp_team_gk_save_pct\n", - "56 - opp_team_gk_clean_sheets_pct\n", - "57 - opp_team_passes_pct\n", - "58 - opp_team_passes_pct_medium\n", - "59 - opp_team_passes_pct_long\n", - "60 - opp_team_sca_per90\n", - "61 - opp_team_gca_per90\n", - "62 - opp_team_aerials_won_pct\n", - "63 - opp_vs_team_possession\n", - "64 - opp_vs_team_goals_per90\n", - "65 - opp_vs_team_assists_per90\n", - "66 - opp_vs_team_xg_per90\n", - "67 - opp_vs_team_gk_save_pct\n", - "68 - opp_vs_team_gk_clean_sheets_pct\n", - "69 - opp_vs_team_gk_pct_passes_launched\n", - "70 - opp_vs_team_gk_crosses_stopped_pct\n", - "71 - opp_vs_team_shots_on_target_per90\n", - "72 - opp_vs_team_passes_pct\n", - "73 - opp_vs_team_passes_pct_short\n", - "74 - opp_vs_team_passes_pct_medium\n", - "75 - opp_vs_team_passes_pct_long\n", - "76 - opp_vs_team_sca_per90\n", - "77 - opp_vs_team_gca_per90\n", - "78 - opp_vs_team_aerials_won_pct\n", - "79 - vote_avg\n", - "80 - vote_std\n", - "81 - goals.1\n", - "82 - assists.1\n", - "83 - cards_yellow\n", - "84 - cards_red\n", - "85 - xg\n", - "86 - npxg\n", - "87 - shots_on_target\n", - "88 - passes_completed\n", - "89 - passes_into_final_third\n", - "90 - passes_into_penalty_area\n", - "91 - progressive_passes\n", - "92 - passes_live\n", - "93 - passes_dead\n", - "94 - through_balls\n", - "95 - passes_switches\n", - "96 - crosses\n", - "97 - corner_kicks\n", - "98 - blocks\n", - "99 - blocked_shots\n", - "100 - blocked_passes\n", - "101 - interceptions\n", - "102 - clearances\n", - "103 - errors\n", - "104 - touches\n", - "105 - touches_def_pen_area\n", - "106 - touches_def_3rd\n", - "107 - touches_mid_3rd\n", - "108 - touches_att_3rd\n", - "109 - touches_att_pen_area\n", - "110 - touches_live_ball\n", - "111 - passes_received\n", - "112 - miscontrols\n", - "113 - dispossessed\n", - "114 - fouls\n", - "115 - fouled\n", - "116 - aerials_won\n", - "117 - aerials_lost\n", - "118 - carries\n", - "119 - progressive_carries\n", - "120 - carries_into_final_third\n", - "121 - carries_into_penalty_area\n" - ] - } - ], - "source": [ - "for i in range(db.columns.shape[0]):\n", - " print(str(i) + \" - \" + str(db.columns[i]))" - ] - }, - { - "cell_type": "markdown", - "id": "089690d6", - "metadata": {}, - "source": [ - "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", - "\n", - "For outfield players: X -> y = [vote, fantavote]\n", - "\n", - "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f19304f6", - "metadata": {}, - "outputs": [], - "source": [ - "npdb = np.array(db)\n", - "\n", - "y = npdb[:, [5,9]] # vote, fantavote\n", - "\n", - "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", - "\n", - "f_start = 14\n", - "\n", - "X = npdb[:, f_start:]\n", - "\n", - "if(DEL_G): \n", - " del_g_idx = [\n", - " list(db.columns).index('goals.1') - f_start,\n", - " list(db.columns).index('assists.1') - f_start,\n", - " list(db.columns).index('xg') - f_start,\n", - " list(db.columns).index('npxg') - f_start,\n", - " list(db.columns).index('shots_on_target') - f_start]\n", - " \n", - " X[:, del_g_idx] = 0\n", - "\n", - "\n", - "# add role and home factor\n", - "toadd = np.zeros((X.shape[0], 4))\n", - "toadd[:, 0] = npdb[:, 4] # home\n", - "\n", - "toadd[:, 1] = npdb[:, 10] == 'D'\n", - "toadd[:, 2] = npdb[:, 10] == 'C'\n", - "toadd[:, 3] = npdb[:, 10] == 'A'\n", - "\n", - "X = np.concatenate((X, toadd), axis = 1)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "370d41d2", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "scaler.fit(X)\n", - "\n", - "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", - "\n", - "X_train = scaler.transform(X_train_)\n", - "X_test = scaler.transform(X_test_)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a7b1fb52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 - matchday\n", - "1 - player\n", - "2 - team\n", - "3 - oppteam\n", - "4 - home\n", - "5 - vote\n", - "6 - goals\n", - "7 - assists\n", - "8 - cards_malus\n", - "9 - fantavote\n", - "10 - gk_games\n", - "11 - gk_games_starts\n", - "12 - gk_minutes\n", - "13 - gk_goals_against_per90\n", - "14 - gk_save_pct\n", - "15 - gk_clean_sheets_pct\n", - "16 - gk_psxg_net_per90\n", - "17 - gk_passes_pct_launched\n", - "18 - gk_pct_passes_launched\n", - "19 - gk_passes_length_avg\n", - "20 - gk_pct_goal_kicks_launched\n", - "21 - gk_goal_kick_length_avg\n", - "22 - gk_crosses_stopped_pct\n", - "23 - gk_def_actions_outside_pen_area_per90\n", - "24 - gk_avg_distance_def_actions\n", - "25 - team_possession\n", - "26 - team_goals_assists_per90\n", - "27 - team_goals_pens_per90\n", - "28 - team_goals_assists_pens_per90\n", - "29 - team_xg_per90\n", - "30 - team_gk_goals_against_per90\n", - "31 - team_gk_save_pct\n", - "32 - team_gk_clean_sheets_pct\n", - "33 - team_passes_pct\n", - "34 - team_passes_pct_medium\n", - "35 - team_passes_pct_long\n", - "36 - team_sca_per90\n", - "37 - team_gca_per90\n", - "38 - team_aerials_won_pct\n", - "39 - vs_team_possession\n", - "40 - vs_team_goals_per90\n", - "41 - vs_team_assists_per90\n", - "42 - vs_team_xg_per90\n", - "43 - vs_team_gk_save_pct\n", - "44 - vs_team_gk_clean_sheets_pct\n", - "45 - vs_team_gk_pct_passes_launched\n", - "46 - vs_team_gk_crosses_stopped_pct\n", - "47 - vs_team_shots_on_target_per90\n", - "48 - vs_team_passes_pct\n", - "49 - vs_team_passes_pct_short\n", - "50 - vs_team_passes_pct_medium\n", - "51 - vs_team_passes_pct_long\n", - "52 - vs_team_sca_per90\n", - "53 - vs_team_gca_per90\n", - "54 - vs_team_aerials_won_pct\n", - "55 - opp_team_possession\n", - "56 - opp_team_goals_assists_per90\n", - "57 - opp_team_goals_pens_per90\n", - "58 - opp_team_goals_assists_pens_per90\n", - "59 - opp_team_xg_per90\n", - "60 - opp_team_gk_goals_against_per90\n", - "61 - opp_team_gk_save_pct\n", - "62 - opp_team_gk_clean_sheets_pct\n", - "63 - opp_team_passes_pct\n", - "64 - opp_team_passes_pct_medium\n", - "65 - opp_team_passes_pct_long\n", - "66 - opp_team_sca_per90\n", - "67 - opp_team_gca_per90\n", - "68 - opp_team_aerials_won_pct\n", - "69 - opp_vs_team_possession\n", - "70 - opp_vs_team_goals_per90\n", - "71 - opp_vs_team_assists_per90\n", - "72 - opp_vs_team_xg_per90\n", - "73 - opp_vs_team_gk_save_pct\n", - "74 - opp_vs_team_gk_clean_sheets_pct\n", - "75 - opp_vs_team_gk_pct_passes_launched\n", - "76 - opp_vs_team_gk_crosses_stopped_pct\n", - "77 - opp_vs_team_shots_on_target_per90\n", - "78 - opp_vs_team_passes_pct\n", - "79 - opp_vs_team_passes_pct_short\n", - "80 - opp_vs_team_passes_pct_medium\n", - "81 - opp_vs_team_passes_pct_long\n", - "82 - opp_vs_team_sca_per90\n", - "83 - opp_vs_team_gca_per90\n", - "84 - opp_vs_team_aerials_won_pct\n", - "85 - vote_avg\n", - "86 - vote_std\n", - "87 - gk_shots_on_target_against\n", - "88 - gk_saves\n", - "89 - gk_free_kick_goals_against\n", - "90 - gk_corner_kick_goals_against\n", - "91 - gk_own_goals_against\n", - "92 - gk_psxg\n", - "93 - gk_psnpxg_per_shot_on_target_against\n", - "94 - gk_psxg_net\n", - "95 - gk_passes_completed_launched\n", - "96 - gk_passes_launched\n", - "97 - gk_passes\n", - "98 - gk_passes_throws\n", - "99 - gk_goal_kicks\n", - "100 - gk_crosses\n", - "101 - gk_crosses_stopped\n" - ] - } - ], - "source": [ - "for i in range(db_gk.columns.shape[0]):\n", - " print(str(i) + \" - \" + str(db_gk.columns[i]))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5a7cf079", - "metadata": {}, - "outputs": [], - "source": [ - "npdb_gk= np.array(db_gk)\n", - "\n", - "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", - "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", - "\n", - "f_start_gk = 13\n", - "\n", - "X_gk = npdb_gk[:, f_start_gk:]\n", - "\n", - "# add home factor\n", - "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", - "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", - "\n", - "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0bc0568b", - "metadata": {}, - "outputs": [], - "source": [ - "scaler_gk = StandardScaler()\n", - "scaler_gk.fit(X_gk)\n", - "\n", - "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", - "\n", - "X_gk_train = scaler_gk.transform(X_gk_train_)\n", - "X_gk_test = scaler_gk.transform(X_gk_test_)" - ] - }, - { - "cell_type": "markdown", - "id": "ebd27493", - "metadata": {}, - "source": [ - "MLP Regressor , to see performance of a simple neural network" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "04564bee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1569906306642661\n", - "0.19152381866355805\n" - ] - }, - { - "data": { - "image/png": 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", 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iYWIackqq4OyoxFtT+2DWkDB2AdkhBixERNTmqNUCViVfxsdJGVCpBXTzd8fKRwbirhAujmuvGLAQEVGbkl9ejefXnsDBi/kAgD/EdMI/pveFuwsvefaMZ4+IiNqMlMsFeO77NOSVVcPVSYm/3d8XD93dmV1AbQADFiIisnsqtYAVey/h0z0ZUAtAZGBHrHxkIKKCPKxdNWohDFiIiMiu5ZVVYdH3J3D4cgEA4KFBnfH2tD7o4MxLXFvCs0lERHbr0MV8LFqbhvzyGnRwdsA/pvfFAwM7W7taZAEMWIiIyO7UqdT4dM9FrNh3CYIA9Ar2wIrZA9EjsKO1q0YWwoCFiIjsSm5JFRZ+n4ZfMwsBALOGdMGbU3vD1cnByjUjS2LAQkREdmP/hTws/iEdhRU1cHd2wLIZ/XF/dKi1q0WtgAELERHZvFqVGh/tysAXyZcBAH1CPbFi9kBE+LtbuWbUWhiwEBGRTbtRfBsLEtPw27UiAMCjw8Px6pS72AXUzjBgISIim7X77E28+FM6iitr4eHiiPce7I8p/UKsXS2yAgYsRERkc2rq1Hh/x3n851AmAKB/Zy+smDUQXfw6WLlmZC0MWIiIyKZkF1ZifmIa0rOLAQCP3xOBJfG94OyotG7FyKoYsBARkc3YcToXL/2UjrKqOni6OuLDh6IR2yfY2tUiG8CAhYiIrK66ToVl287jq8NXAQAxXbzx2awYdPZhFxBpMGAhIiKrulZQgflr0nDqegkA4OnR3fBiXE84ObALiBoxYCEiIqvZcvIGlqw7hfLqOvh0cMJHCdEY3yvI2tUiG8SAhYiIWl1VrQp/33IW3x3NAgAM7uqD5bNiEOLlZuWaka0yu73twIEDmDp1KkJDQ6FQKLBx40bJ63PnzoVCoZB8DRs2rMn9rlu3Dr1794aLiwt69+6NDRs2mFs1IiKyA1duleMPnx/Gd0ezoFAA88Z1R+JTwxiskFFmBywVFRWIjo7GihUrZMtMnjwZOTk54te2bduM7jMlJQUzZ87EnDlzkJ6ejjlz5iAhIQFHjx41t3pERGTDNqZdx32fHcK5nFL4uTvj6z8NwUtxveDIfBVqgkIQBKHZGysU2LBhA6ZPny4+N3fuXBQXF+u1vBgzc+ZMlJaWYvv27eJzkydPho+PDxITE03aR2lpKby8vFBSUgJPT0+TfzcREVne7RoV3tp8BmuPZwMAhnXzxacPxyDI09XKNSNrM/X6bZGQdv/+/QgMDERUVBSeeuop5OXlGS2fkpKC2NhYyXNxcXE4fPiw7DbV1dUoLS2VfBERke25eLMM01Yewtrj2VAogOcmROK7J4cxWCGztHjSbXx8PB566CGEh4cjMzMTb7zxBsaPH4/ffvsNLi4uBrfJzc1FUJA0KzwoKAi5ubmyv2fZsmV4++23W7TuRETUsn48no2/bjqD27UqBHi44NOZAzCih7+1q0V2qMUDlpkzZ4qP+/bti7vvvhvh4eHYunUrHnjgAdntFAqF5GdBEPSe07Z06VIsXrxY/Lm0tBRhYWF3UHMiImopFdV1eGPTaaxPvQ4AGNnDH5/MHIAAD8M3rkRNsfiw5pCQEISHh+PixYuyZYKDg/VaU/Ly8vRaXbS5uLjIttgQEZH1nM8txbzvUnH5VgWUCmDxpCj8ZWwPOCjlb0KJmmLxtOyCggJkZ2cjJER+OfDhw4cjKSlJ8tyuXbswYsQIS1ePiIhaiCAI+P7XLExb8Qsu36pAkKcLEp8ahvnjIxms0B0zu4WlvLwcly5dEn/OzMzEiRMn4OvrC19fX7z11luYMWMGQkJCcPXqVbz66qvw9/fHH/7wB3GbRx99FJ06dcKyZcsAAM899xxGjx6N9957D9OmTcOmTZuwe/duHDp0qAUOkYiILK28ug6vrj+Fzek3AABjogLwcUI0/DqyJZxahtkBy/HjxzFu3Djx54Y8ksceewyrVq3CqVOn8M0336C4uBghISEYN24c1q5dCw8PD3GbrKwsKJWNjTsjRozA999/j9dffx1vvPEGunfvjrVr12Lo0KF3cmxERNQKztwowfw1acjMr4CDUoGX4nriz6O6QclWFWpBdzQPiy3hPCxERK1LEAR8ezQLf99yFjV1aoR6ueKz2TEYFO5r7aqRHTH1+s21hIiIyGylVbVYuu4Utp7KAQBMvCsQHzwYDR93ZyvXjNoqBixERGSWk78XY/6aNGQVVsJRqcCS+F54YmSE0akoiO4UAxYiIjKJIAhY/ctVLNt+DrUqAZ193LBi9kAMCPO2dtWoHWDAQkRETSqprMVLP6Vj19mbAIC4PkF4/8FoeLk5Wblm1F4wYCEiIqPSsoowf00arhffhrODEq/dexceHR7OLiBqVQxYiIjIILVawJeHMvHejvOoUwsI9+uAFbMGol9nL2tXjdohBixERKSnqKIGL/yYjr3n8wAA9/YPwbIH+sHTlV1AZB0MWIiISOL41UIsSExDTkkVnB2VeHNqb8we0oVdQGRVDFiIiAiApgvoiwOX8dGuDKjUArr5u2PF7IHoHcrJOMn6GLAQERHyy6ux+Id0HMi4BQCYPiAU//hDP3R04WWCbAM/iURE7dyRKwVYmJiGvLJquDop8bf7++KhuzuzC4hsCgMWIqJ2SqUWsHLfJfxzdwbUAtAjsCNWzh6InsEeTW9M1MoYsBARtUN5ZVV4fu0J/HKpAADw4KDO+Nu0PujgzMsC2SZ+MomI2plfLuXjue9PIL+8Gm5ODvjH9L6YMaiztatFZBQDFiKidqJOpcbyPRfx2b5LEASgZ5AHVj4yED0CO1q7akRNYsBCRNQO3CytwoLENPyaWQgAmDUkDG9O7QNXJwcr14zINAxYiIjauP0X8rD4h3QUVtTA3dkB7zzQD9MGdLJ2tYjMwoCFiKiNqlOp8VFSBlbtvwwA6B3iiZWPDESEv7uVa0ZkPgYsRERt0I3i21iYmIbj14oAAHOGheO1e+9iFxDZLQYsRERtzJ5zN/HCj+korqyFh4sj3nuwP6b0C7F2tYjuCAMWIqI2oqZOjQ92nse/D2YCAPp39sKKWQPRxa+DlWtGdOcYsBARtQHZhZVYkJiGE9nFAIA/3dMVS+J7wcWRXUDUNjBgISKyczvP5OKlH9NRWlUHT1dHfPBQNOL6BFu7WkQtigELEZGdqq5TYdm28/jq8FUAwIAwb6yYHYPOPuwCoraHAQsRkR26VlCB+WvScOp6CQDgz6O74aW4nnByUFq5ZkSWwYCFiMjObD2ZgyXrTqKsug7eHZzwcUI0xvcKsna1iCyKAQsRkZ2oqlXhH1vP4tsjWQCAu8N9sHxWDEK93axcMyLLY8BCRGQHrtwqx7w1aTiXUwoAeHZsdyyeFAVHdgFRO8GAhYjIxm06cR2vrj+FihoV/Nyd8fHMARgTFWDtahG1KgYsREQ26naNCm//fAbfH8sGAAzr5otPH45BkKerlWtG1PoYsBAR2aBLeWWY910aLtwsg0IBLBgfiecmRMJBqbB21YiswuzOzwMHDmDq1KkIDQ2FQqHAxo0bxddqa2vxyiuvoF+/fnB3d0doaCgeffRR3Lhxw+g+v/rqKygUCr2vqqoqsw+IiMje/fTb75j62S+4cLMM/h1d8O0TQ7F4UhSDFWrXzA5YKioqEB0djRUrVui9VllZidTUVLzxxhtITU3F+vXrkZGRgfvvv7/J/Xp6eiInJ0fy5erKZk8iaj8qa+rwwg/pePHHdNyuVeGeHn7Y9txI3NPD39pVI7I6s7uE4uPjER8fb/A1Ly8vJCUlSZ777LPPMGTIEGRlZaFLly6y+1UoFAgO5lTSRNQ+Xcgtw7Pf/YbLtyqgVADPT4zCs+N6sFWFqJ7Fc1hKSkqgUCjg7e1ttFx5eTnCw8OhUqkwYMAA/P3vf0dMTIxs+erqalRXV4s/l5aWtlSViYhajSAIWHssG29uPoPqOjWCPF3w6cMxGNbNz9pVI7IpFh3AX1VVhSVLlmD27Nnw9PSULderVy989dVX2Lx5MxITE+Hq6op77rkHFy9elN1m2bJl8PLyEr/CwsIscQhERBZTXl2HRWtPYMn6U6iuU2NMVAC2LRzFYIXIAIUgCEKzN1YosGHDBkyfPl3vtdraWjz00EPIysrC/v37jQYsutRqNQYOHIjRo0dj+fLlBssYamEJCwtDSUmJWb+LiMgaztwowYI1abiSXwEHpQIvxvbE06O7QckuIGpnSktL4eXl1eT12yJdQrW1tUhISEBmZib27t1rdgChVCoxePBgoy0sLi4ucHFxudOqEhG1KkEQ8O3RLPx9y1nU1KkR4uWKz2bF4O6uvtauGpFNa/GApSFYuXjxIvbt2wc/P/ObNgVBwIkTJ9CvX7+Wrh4RkdWUVtVi6fpT2HoyBwAwoVcgPnwoGj7uzlauGZHtMztgKS8vx6VLl8SfMzMzceLECfj6+iI0NBQPPvggUlNTsWXLFqhUKuTm5gIAfH194eys+aN89NFH0alTJyxbtgwA8Pbbb2PYsGGIjIxEaWkpli9fjhMnTmDlypUtcYxERFZ36vcSzFuTiqzCSjgqFVgS3wtPjIyAQsEuICJTmB2wHD9+HOPGjRN/Xrx4MQDgsccew1tvvYXNmzcDAAYMGCDZbt++fRg7diwAICsrC0plY75vcXEx/vznPyM3NxdeXl6IiYnBgQMHMGTIEHOrR0RkUwRBwNeHr+KdbedRo1Kjk7cbVsyOQUwXH2tXjciu3FHSrS0xNWmHiKi1lFTW4uV16dh55iYAILZ3ED54MBpeHZysXDMi22HVpFsiovYuLasI89ek4XrxbTg7KPHqlF54bERXdgERNRMDFiKiFiQIAv5zMBPv7TiPOrWALr4dsHL2QPTr7GXtqhHZNQYsREQtpKiiBi/+mI495/MAAPf2C8GyGf3g6couIKI7xYCFiKgFHL9aiIWJabhRUgVnRyX+el9vPDK0C7uAiFoIAxYiojugVgv44sBlfLQrAyq1gAh/d6yYHYM+oewCImpJDFiIiJqpoLwai39IR3LGLQDAtAGh+H9/6IeOLvzXStTS+FdFRNQMR68UYOH3abhZWg0XRyX+Nq0PEu4OYxcQkYUwYCEiMoNKLeDzfZfwye4MqAWgR2BHrJw9ED2DPaxdNaI2jQELEZGJbpVVY9HaNPxyqQAAMGNgZ/x9eh90cOa/UiJL418ZEZEJfrmUj+e+P4H88mq4OTng79P74sFBna1dLaJ2gwELEZERKrWAT/dcxGd7L0IQgJ5BHlj5SAx6BLILiKg1MWAhIpJxs7QKCxPTcDSzEADw8OAwvDm1D9ycHaxcM6L2hwELEZEByRm3sHjtCRRU1MDd2QHvPNAP0wZ0sna1iNotBixERFrqVGp8lJSBVfsvAwDuCvHEytkx6BbQ0co1I2rfGLAQEdW7UXwbCxPTcPxaEQBgzrBwvHbvXXB1YhcQkbUxYCEiArD3/E0s/iEdxZW18HBxxLsz+uPe/iHWrhYR1WPAQkTtWq1KjQ92XsC/DlwBAPTr5IUVs2MQ7udu5ZoRkTYGLETUbv1eVIn5a9JwIrsYADB3RFcsndILLo7sAiKyNQxYiKhd2nkmFy/9mI7Sqjp4ujrig4eiEdcn2NrVIiIZDFiIqF2prlPh3e3nsfqXqwCAAWHe+GxWDMJ8O1i3YkRkFAMWImo3sgoqMW9NKk5dLwEAPDUqAi/F9YKzo9LKNSOipjBgIaJ2YdupHLzy00mUVdfBu4MTPnooGhPuCrJ2tYjIRAxYiKhNq6pV4f9tPYf/HbkGALg73AfLZ8Ug1NvNyjUjInMwYCGiNiszvwLzvkvF2ZxSAMCzY7vj+UlRcHJgFxCRvWHAQkRt0qYT1/Hq+lOoqFHB190Zn8wcgDFRAdauFhE1EwMWImpTqmpVePvnM0j8NRsAMDTCF8tnxSDI09XKNSOiO8GAhYjajEt55Zj3XSou3CyDQgEsGNcDCydEwpFdQER2jwELEbUJ6377Ha9vPI3btSr4d3TBP2cOwMhIf2tXi4haCAMWIrJrlTV1+OumM/jpt98BAPf08MMnMwcg0INdQERtCQMWIrJbGTfLMO+7VFzMK4dSASyaGIV543rAQamwdtWIqIUxYCEiuyMIAn44no03N59BVa0agR4uWD4rBsO6+Vm7akRkIWZnoh04cABTp05FaGgoFAoFNm7cKHldEAS89dZbCA0NhZubG8aOHYszZ840ud9169ahd+/ecHFxQe/evbFhwwZzq0ZE7UB5dR2eX3sCr6w7hapaNUZHBWDbc6MYrBC1cWYHLBUVFYiOjsaKFSsMvv7+++/j448/xooVK3Ds2DEEBwdj0qRJKCsrk91nSkoKZs6ciTlz5iA9PR1z5sxBQkICjh49am71iKgNO3ujFPd/dggbT9yAg1KBlyf3xFdzB8O/o4u1q0ZEFqYQBEFo9sYKBTZs2IDp06cD0LSuhIaGYtGiRXjllVcAANXV1QgKCsJ7772Hp59+2uB+Zs6cidLSUmzfvl18bvLkyfDx8UFiYqJJdSktLYWXlxdKSkrg6enZ3EMiIhskCAK+O5qFv205i5o6NUK8XLF8VgwGd/W1dtWI6A6Zev1u0ckJMjMzkZubi9jYWPE5FxcXjBkzBocPH5bdLiUlRbINAMTFxRndprq6GqWlpZIvImp7yqpqMT8xDa9vPI2aOjUm9ArEtoWjGKwQtTMtGrDk5uYCAIKCpCugBgUFia/JbWfuNsuWLYOXl5f4FRYWdgc1JyJbdOr3Etz32SFsPZkDR6UCr025C/957G74uDtbu2pE1MosMv2jQiEdUigIgt5zd7rN0qVLUVJSIn5lZ2c3v8JEZFMEQcBXv2RixqrDuFZQiU7ebvjhmeF4anS3Jv+XEFHb1KLDmoODgwFoWkxCQkLE5/Py8vRaUHS3021NaWobFxcXuLgw0Y6orSm5XYtXfjqJHWc0/xNiewfhgwej4dXByco1IyJratEWloiICAQHByMpKUl8rqamBsnJyRgxYoTsdsOHD5dsAwC7du0yug0RtT0nsotx7/KD2HEmF04OCrw5tTf+b84gBitEZH4LS3l5OS5duiT+nJmZiRMnTsDX1xddunTBokWL8M477yAyMhKRkZF455130KFDB8yePVvc5tFHH0WnTp2wbNkyAMBzzz2H0aNH47333sO0adOwadMm7N69G4cOHWqBQyQiWycIAr48lIl3t59HnVpAF98OWDE7Bv07e1u7akRkI8wOWI4fP45x48aJPy9evBgA8Nhjj+Grr77Cyy+/jNu3b+PZZ59FUVERhg4dil27dsHDw0PcJisrC0plY+POiBEj8P333+P111/HG2+8ge7du2Pt2rUYOnTonRwbEdmB4soavPhjOnafywMATOkXjHdn9IenK1tViKjRHc3DYks4DwuR/fntWiEWrEnDjZIqODsq8cZ9vfHHoV3aRGJtRkYGLl++jB49eiAyMtLa1SGyWaZev7mWEJEWXmRah1ot4P8OXMGHuy5ApRYQ4e+OFbNj0CfUy9pVu2OFhYWY/cfZ2Ll9p/hcXHwcEr9LhI+PjxVrRmTfLDKsmcjeFBYWYvKUyejZsyemTJmCqKgoTJ4yGUVFRdauWptTUF6Nx78+hvd2nIdKLWDagFD8vGBkmwhWAGD2H2dj94HdwAMAngfwALD7wG7MemSWVeqTkZGB7du34+LFi1b5/UQthV1CRAAmT5mM3Qd2QxWnAsIBXAMcdjpg4uiJ2LFth7Wr12YcvVKAhd+n4WZpNVwclXj7/j6YOTisTXQBAZrgoGfPnppgpb/WC+kANmheb62WO7b0kL2wytT8RPYoIyMDO7fv1AQr/QF4AegPqGJV2Ll9J+9MW4BKLeCzPRcx699HcLO0Gt0D3LFp/j14eEjbyFdpcPnyZc2DcJ0Xumq+aY+wtDRba+khulMMWKjda62LTHttmr9VVo3H/vsrPkrKgFoAZgzsjJ8XjESv4LbXEtq9e3fNg2s6L1zVfOvRo0er1KO5QXh7/YySfWDAQu2epS8y7Tk/5vClfExZfhCHLuXDzckBHz4UjY8SotHB2bx8f3u5kEZFRSEuPg4OOx003UAlANIBh10OiIuPa7XuIHODcEt/Ru3l/JGNE9qIkpISAYBQUlJi7aqQHYqLjxMc3B0ETISA6RAwCYKDu4MQFx/Xcvt+AAKeh4AHWm7ftqpOpRY+2nVB6LpkixD+yhZh0sf7hYzcUrP3U1BQIMTFxwkAxK+4+DihsLDQArVuGYWFhVav84ULFzS/+wEIeEvr6w+a+mRkZEjKW+ozao/nj1qfqddvJt1Sm2fKUOXU1FQMGzEMtdW14nNOLk749civGDBgwB39bltJwmwtN0ur8Nz3aThypRAA8PDgMLw5tQ/cnB3EMqYOHxeToUeoAHcAlYDDL/aRDH3x4kVcunTJakPkxfcuVqVpWbmqaenRfe8s+RltT8nsnBKh+Zh0S+2eOc3cE2MnorauVvJcbV0txk8cb/R3NNXUbUtJmK3hQMYtTPn0II5cKYS7swM+fXgA3p3RXwxWzDknYh6GhwpIArARwC5A1dE+kqEjIyMRHx9vtYtX4neJmDh6IrABwCcANgATR09E4neJknKW+oy2l2T29tzl29oYsFCbZeooiZ07d6KosAhwgqQsnICiwiK9hTkB0/9J2UoSpqXVqdR4f8d5PPrfX1FQUYO7Qjzx84KRmDagk6ScOSNXLl++DCigyQPRPi+lABRtL9hraT4+PtixbQcyMjKwbds2ZGRkYMe2HXpDmi31GW0vwTpHY7UeBizUJplzd7d161ZN7/oUSMoiHoAAbNmyRW//4j+pSQCmA4g1/E/KVpIwLSmn5DZm/fsIPt+vuUD9cVgXbHh2BLoFdJSUM/eOW6lUas5LPIBQAHkAOgGYDEAAHB05Ubcpmur1t9RntD0E6+2lFclWMGChNsmcu7vAwECjZcXX65nbVWFq07w92nc+D1M+PYhjV4vQ0cURK2bH4B/T+8HVyUGvrLl33Gq1WvMgDcAKAN8B+AzACc3TdXV1LXIM5jBntIu1R8aY01Vhic9oewjW20srkq3gLQq1SZK7O+1Ewquab9p3d4MHDzZadsiQIZJ9i10VxdA0A9cnE2IbxK4K7X/GDU3z1k7CbEm1KjU+3HkB/3fgCgCgXycvrJgdg3A/d9ltzDknkvJZOjvKMlzeksyZNdZWZpiVtALWJyw3tALqJrxa6jOa+F0iZj0yCzs3NL4XE+PbRrAOmP+ZpjvDgIXapIa7u907d0NVqgI6AqgAHA47YGL8RMk/Y7VarQlAtkLTBdEVmn849QGI7p282FXR0IWE+u8CgA3yXRWRkZF2H6gAwO9FlViQmIa0rGIAwNwRXbF0Si+4OOq3quhRQPO+ar/P2+uf15GZmal53gnANEgDQzVw9erVVns/JXkK9fXYvdPwxd+cspbS0AqIYGhaAeupghpbAQ29dy39GW2Lwbo2yf8ZQWc0ls7/GbpzDFiozfp8xecYMmwICnYXiM95B3hj1cpVknI3btzQXEB9oGkSbxAE4CZw8+ZNSfmsrPpbfJlm4GvXdDvtNdrCsMddZ3Lx0k8nUXK7Fp6ujnj/wWhM7hts0raXL1/WvM8hkL7PEQAy9Vum9HKLAElguGXLFkyaNKkFjso48eL/ACS5NKpYFXZukF78JWW16qwS9MveSX2a+hyZ2wpoaW0lWDekrbci2RIGLNRmPfnnJ1FQVCB5rqCoAE/++UnsSdojPnf9+nXNg1kA6gAUAvCF5q/jE60Apd6hQ4c0D2SagQ8dOoQnn3xSfNpWugjuRE2dGsu2n8PqX64CAKLDvLFiVgzCfDuYvA+x+TwGwH1ofJ9/B5Cp33yuVNan2MkEhg4OJrTotAAxTyENwHqtFyI037Qv/qbkNDT3wm3O50gMwmWCPd0gnJqvrbci2RIm3VKblJGRgX1792mCDu0hsY7A3j17JYmQQ4cO1Ty4BsAPQGT996uap4cPHy7Zd1FRUWPXhlYyYUPXhm5So70Pe8wqqMSDXxwWg5WnRkXgx6eHmxWsiBret98BBNZ/l+kSEsmMMmmtOS/NyaWx5MgYcz5H6enpmgcygVNaWlqz60GGWXvenfaAAQu1ScnJyUaHKicnJ4tlIyIiGnNYtAOQ+ubzrl27SvY9dOhQzb5rIRlVgVrNvrUDHMmwR93uBDsY9rjtVA7uXX4QJ38vgXcHJ/zn0bvx2r294exo/r8OvS6hhvctBIBgeH0bY+elsLDwTg7NPA25NDrz9OgGWpYaGWPu8FlxZJtM4KQ78o3IHjBgobZN5g5Tm3ghlQlAdC+k4j/7AJ0d+dd/8/eX7huQHZprq8Meq2pVeGPjaTz7XSrKquswKNwHWxeOwsTeQc3ep9j6cFvnhUrNN93Wh5ycHM15UUB6XgBAAHJzc5tdF3OYE/wClhkibO7w2YSEBKOtgAkJCc2uC5G1MIeF2qQxY8ZoHlyDpmWjCI35Etqvo76/H9Dvlqj/2WB/v/YMrA0JjQa6Nrp37655LgcGkx+tMeyxqaTNzPwKzF+TijM3SgEAz4zpjhdio+DkcGf3N1FRUXBycUJtUa1kqC2SNes26daltLRU894pAMQC6FBf/qDmuZKSkmYdX7OFA8hH42epq+FilshpMHf4bFRUFLx9vFFcUixNcHYAvH282W1BdokBC7VJUVFRGDVmFA5uOgiotF5wAEaNGSX5h52enm50+GxaWhrmzp0rls/Ly2ucgdVAQmN+fr60MkaSH1uCqRdoU5I2N6ffwNJ1J1FRo4KvuzM+TojG2J6mdR80VY+dO3dqFpfUGWqLIKD2Zi2SkpIko348PT3132dAE+hsALy8vMw+vuYQg9tEANqNOkE6r+toyZEx5g6fzcjIQHFhsea91q6zP1B8s7hFRisRtTZ2CZFdMmUW0draWoPP19VK51UpKSkx2uRfWloqKX/+/HnNg4Y77osACiDecZ85c0Ysa8mZMM1ddO2hhIewa98uSR7Grn278GDCg6iqVWHp+lNYmJiGihoVhkT4YtvCUSYFK6bW4+jRo0bXBkpJSZGUz8vL0zyQee90W74sldwcFRUFT29P4JbOC/mAl7dXq134zelqEj93bjov1OdJ22pXJJExbGEhu2LqXXRGRgaOpBwBXKDXapKSkiK5wxRHm8g0+YtTxNfr1auX5oHMHXefPn3Ep8ShuTJN+XeyHs6DCQ9iX/I+yXM7k3biwYQHJcO2Ac37sXfPXr35QQRBwIHkDMR/vBeZRTVQKID543rguQmRcDSxC8jUidI6depktGWqS5cukv26u9fPmivTrSe+DvPmSjFXRkYGSktKDX6WSkpKWq21wpyuJklX5Aho3mMlgN9gta5Ic7SFOYuo5TFgIbti6sXxhx9+MNoV88MPP+C1114DAPj6+mpelwlAxNfrDRw4UPPAwB03AERHR4tPmTuLrqkyMjKwb88+QHcqEgHYu3uv3kVUTAzVaa1w7zYOvl2fRWZRDfw7uuCfMwdgZKQ/TGVOoBAaGmqwDg2BYVCQNKE3MjJS0+qyCXrdeoCm5aOBOXOlmMucz1JrMLmrqWHU92Gt51y0nrdBbWHOIrIcBixkN8yZRbSp7gTxdWjNq1IEaWLsVhicV0WpVBrNedFuNRGn8a+DXvLjnaw4nJycbLQOycnJkoua2H1S39KjEFzgW/sMOjpqckZCHcqw8bkJCPRwNase5gQK5rY26eao6PL09BQfWzK52ZzPkq0QZ7oVYPAzLRfAWbtlwxaWNSDbxRwWshvm5IPce++9mgfXoMkx2Q/gMsSL43333SeWPXTokOYf+72Q5rBMASBozWxb79ixY0ZzXn799VexrDhLrswQaLlp/Jty8+bNxjpotWw01EE3vyMoKEhzAdsOOJ3rguDKj9FRNQmCWoXiQ99ibtdys4MVwECg0JCXkgO9QEFsbZIZaqvb2iS2VOm2CNT/HBMTo/+8zDm5E5LPkrarmm/anyVbIc50K/OZ1v18mJsPZQnmzjVD7Q9bWMhumDO0My4uTvNgIwDtFJT6EF17NIr4T1kmENKdoCwjI8NoefF11F8YjAyBvuMp0mVaNnSNGTMGEAD3QZPg2+lpKJWuqCsrQP4vH6I6/RTGrf5b8+tg4iioXbt2See7aVDf2rRnzx7Ex8eLT586daqxFeleSFsJarRmc4V8l1fDOdFtcTJHXFwcfP19Ubi1UK9bz9fft1XWMzKXuNyEzPuhu9yEpVs2TF7/yEidW3v9I3NZu3WqPWALC9kNc2YR/fLLL43OTvrVV1+JZSXJndqjfq5qnu7YsaOkHjk5OY3ltV3Veb2BdqJpw53uZBi9829qFFR+fr7Rlg3dodXnLl6B330vwP+e56B0csXtilTkZC5E9QVNUHD16lX5yhhhTquXOApoGoAFAB6p/36/5mndlqyff/7ZaCvBzz//rF8hmXNyp47/ehx+nn6SETp+nn44/utx2W1MGclmKZLlJrRd1XyTnY25hVs2zGm5seSyBpZkC61T7QUDFrIrpg7tXLNmjdGL3f/+9z+xbFhYmObBJkhno92sebpz586SfYt3/jJdGydPnhTLioGDzAW9oEC6OKOp//wKCgqMdoFotwqdvVGKNw5VoGOfcRDUKhQlf428FW9Cvb1EnBZfd0ixqcy5yAQH16/qHA7pmk1dNU936tRJsgvxGExo+RozZozRcyI3V4qpIiIikJ+Xj127duHtt9/Grl27kJ+Xr1nWQYctXMDMWW7CkkPvzRlqbqllDSzN3tcKsyfsEiK7YurQToWifspZmX/CYgIogA4djC/ipz18FgDKy8sNJ9I6AhCAiooK8SlxSLRMN5ZKpT38xfSmeT8/P6PH5+fnB0EQsObXLLz981nUCK6oK8tHfvH7qO5xFnAFEAagHECm/gKPZmkIFLS7SwzM+nvvvfdi06ZNsu+FdncQoLUas0x57dWao6KiMHLUSE0rjfY5UQKjRo+SvdiZ24wfHh6Ouro6vfWltDWne6WluxPE5QR8IH0/ggDclHaRmTuLrqnMSZJvkPhdImY9Mgs7NzSOEpoYf2fLGlhSc46Rmo8BC9mlpoZ2enh4aB7I/BMWX29gZMSNLjEIkUkG1Q5CxGTXbdBMkNYRQAXE6eVDQkLEsuYMERbXK5I7Pt8ALEhMw5aTmu6p8b0Csem1v6D6ZrbeEGEfP59m52HoLWjYIAJApoFRQkbeC91RQiqVyuiQcN1gz9nZGQpnBYTRgjiNv+KAAk5OTnr1Nnf4rDnz/5hzAbP4MN5Z0ATWhdDMYeMITcukFnNn0TVVc3JSLLGsgSXZe96NvWnxLqGuXbtCoVDofc2bN89g+f379xssL84mSu1CS/f3Z2ZmGm0Sz8zMFMtev37daPeKmMBYr66uzmh+jPZoF3E0jxLAbmiSgJPqfxak+S5mL5Qoc3zOwd2xoaIntpzMgaNSgVen9MJ/Hr0bPSM6ay5Y2nV21JoIz4Cmzot4dx4DaV7KAM3T2nfn6enpmveiRue9qNG8F2lpaZJ9C4JgdFFKccK/+nru3b0XwhRBM1HaAAAjACFeEOel0WZuM76p5c3tXrFUd4I4Cd81SLvfrtZXL1xawXf+8Q6UdUrJ+6ysU+Ldd95tdh3uJCclMjIS8fHxNn+xt9e8G3vV4i0sx44dk9z5nD59GpMmTcJDDz1kdLsLFy5I5lUICNAdB0ptkaXuMKuqqozOf1JdXS0+detW/Qxw5syzYeLImJKSEk1goYbB+UG0F/AThwjfgMG5M7T/+QUFBRk8Po+774PPmCdQKjihk7cbPpsdg4FdfJCRkYGUX1L07vwhACkbUpp95y+5Ox+u0rSa5AEOh/XvzsVVrnW6ihp+Fl+vFxISonl/ptcfZyaAbtAEexukrVPm3Oma2wpiTnlzulcs2Z1g7oSFEyZNQG2ddCmL2rpajJswDkUFzcu9sVTLjS1pD8doS1q8hSUgIADBwcHi15YtW9C9e/cmk94CAwMl22n3T1PbZak7TN1EWV3irKsA3NzqF1yRuUvSzXERuy5kLo7aXRsnT540Ol+KdoIuAKOJwtrEO+j6ETeKP7rD/4Wl8J3wDBSOThjgr8C2haMwsIsmuDBl2K82c87L5ys+h3cHb0mriXcHb6xauUpSbvDgwUZbpoYMGSIp37NnT82DNGgSoE9Bkxh9QvO09ky3kkDBwLw72oGCua0g5pQ3J3HUksmu4oSFDTksDa1T3tCbsHDnzp0oLio2eF6Ki4qRlJSE5hI/G1p1MPTZsGfmrPFEd8aio4Rqamrw7bff4vHHH29MgpQRExODkJAQTJgwAfv27Wty39XV1SgtLZV8kX2x5HDKvn37Gr049uvXTyzr7OxstPvI2dlZsm8x4VUmwBHzSwBkZ2drHsh082jPhyEJKgwsqqgdVIjbhQPOZVEI9fsU7o73QBBqUbj7X7jX6wa8OjTmbkhmujVQZ+35YMw9L8/OfxbFlcXAJGhaQ2KB4spi/GXeXyTlzJlwD6hv2WoYuj0CwHAA90Acui22jEETKHj5eGkCpu+gCVj+B2AT4OUjXaDQ3GZ8yQy9Bsrr5t6YegGzZHeCmGc1C9Kuutmap7VbWL799luj5+Xbb79tdj3Ez0YsjH427FlD3k1GRga2bduGjIwM7Ni2g0sJWIBFk243btyI4uJizJ07V7ZMSEgI/vWvf2HQoEGorq7G//73P0yYMAH79+/H6NGjZbdbtmwZ3n77bQvUmlrLnSSs7dy5E0ePHsXw4cMNJoz++uuvRrtttC+OOTk5mudDYTBx9MaNG/oVaAhwtJNHD0Cvu6OmpqbxojsJgDuAysayBleUllnTSFvDitAeR6bB5565UDg4obY4F/nb30NN1kWcPdvNcJ23Q9pFsEO/zs3qXgmGJh+lniqoMbhpKLt//36j+923b59kTZ7U1FSj6+H89ttv4lMZGRkoKS4BnKE3yVxJsXSBwqioKKMTwel+5sTuFZn3Trd7xdTEUUt2J+h1TdXH2EjXfNMOhpoaUddcBru8AKjc2+YIGpPXeKJms2jA8uWXXyI+Pl7S/K6rZ8+ejU2/0AyvzM7Oxocffmg0YFm6dCkWL14s/lxaWto4nwbZheYMp7x8+TKGDh+KgluN85f4Bfjh2NFjkjkxzp49q3kg809YfB1a+SwxAO5D44iK3wFkSvNdgPo7fwGai9hurRfqL6TarRV1dXWast6QXNAbhpdqX+zEuUSKIA1ukqE3l0huYRkCHngdHSKHAQAq1IdQULAcws1KQKE/O6+Y8+IF/WGut6ULD5pzXsQ1a0pgcCZf7eDm999/N7pf3cCwtrbW6Ho42sGeuEBhQ3caILtAYUZGBgrzCzVBls57UXizUO9CKnav6OYo1Z9vufWgTLmANWcYrylDoCXBUE59TqECcEjXD4YeeeQRzbxEMuflj3/8o9FjkGOLI2g4G619s1jAcu3aNezevRvr169vurCOYcOGNdkM6eLiAhcXl+ZWj2xAc+4whw4fioLSAskFrGBrAQYPHYz8vMbZXcWLiMw/Ye2LjKOjI2pqazQXwinQS1DUvSCJrSYKGLxI19TUiGXFobkyF3TtgCUzM7Mx78BAcHP16lVERkbit2uFOO4/ER1CPSGoa1Ho8m+UO2wD+kIzZHkDkJur3USjFezIrDCtHQyZc17Ei3nDTL6AJFDQfu/EAESmpUI3MBRHCckEIYJWYo85CxSKXWsyQ351p/EXW1gEaLo26odMNwSSzV1xGzBvGK+5Cerv/OMd7B2xF6qUxkEQShf9kT9xcXHw9vVG8dZivRYnb1/vZg95t9T8Ls3BVaDbBosFLKtXr0ZgYGDjwmFmSEtLk4wAoLbLnDvMnTt3alpWDIx0KdhQgKSkJPGfq7+/PwoKC2S7bbRHoYkXRpkRRdrDZxs3guxFWptarTZaVsw1ALB161ajwc3PW7bgsnM3fLDzAlTOnqgtvIFbHu+i1vFK4y/sqvmmu1YMoNkHHAGMh7T1xsBcM6aeF7H+MoGC9sU8ICAAV65ckW3l0R0lJL7vMvvWPi/iQonXoOnaK0JjKxkMLJTYUNZAd4kuMSi7V2vfYdC8hxvkW1jMuZs3pTXG3AnpYifHoha1kvK1W2sxMXaiJLgHgL2792Lo8KGo3dDYauXk4oR9e5rOJ5RjSyNouAp022CRgEWtVmP16tV47LHH9P6Yly5diuvXr+Obb74BAPzzn/9E165d0adPHzFJd926dVi3bp0lqkY2xmAwIOPo0aOaBzIXsJSUFDFg6dy5My5cuKCZs0O726Z+/hPtaeAl86pEQ3Mh7wDgLAC1kTtoE/r8zbnolpSUyAY3yh2eOKTsh83bNfMTdSw4j7PfvAHhvtsG7151R0mJXSYyrTfaXSaA6Xf+krtoA4GC9l301KlTcfTXo5oyMQBuA3CD5n1WANOmTdPbv7hvA8eoTex23gS9ifEAaZeXZBp/7RYFmWn8xaAsDQYXmtT9fFjibt7cIdDmBPcAsPS1pVA7qoExEFuQ1L+oseTVJXd0QbeFmWs5G23bYZGAZffu3cjKysLjjz+u91pOTo7k7q+mpgYvvvgirl+/Djc3N/Tp0wdbt27FlClTLFE1sjHm3PlIFnQzcAHTnl7ew8PD6Eq/2jPdSoKmVK391ucoaHc9SJhwIRXnJJIpqz1nkdwKuy4RfeD/p5dR6OIHF0cl3rq/D3Z8/iPO1N6WnWdDHPZcTxxxI9N6Y3CuGTR95x8VFQUfPx8UbSoyOIOu9ra3bt1qnAguTatsfRCpu3K1v78/8gvyZY/R369xNJY4h40jpLMVbwWglgZOUVFR8PH1QVFxkV6Lmo+vj97xivuWSZzW7dqwxN28ufkg5gT3lkyOtYWZa20xl4aaxyIBS2xsrOyds/YquQDw8ssv4+WXX7ZENcjGmXvnI1nQzcAFTHt9l/PnzxvNf9CbSdlIcqfBeKUhF0O7u+mQTHljo3O0yorDdBuCG0EBz7qH4O34CBQeDlCW52HT6zPQK9gTny48L50FtkH9xb9hFFEDMelWpmtKrgu2qW6NjIwMFBUWaUbn6AQKRYVFknN48uRJo0HkqVOnJPvWm+lW5xj1/scYOd96dS4o0iTdaqf6+ANFN4sMX6AFyCZO6+7bEnfz5uaDmBPct8YF3ZojaGwpl4buDFdrJqsxd+IscUE3B0gnw6rPNdGep0Sc/0Rm3+LrDbQvdl6QnbBNUl5minmDZRvyNhrq7KlfVgzItgPK094IrHwbPnWPQqFwQPnpPehzfSt6BWtmgxYTVKVTxGiCAegPlxZbL8JhcI4X7SUCANNXHBbPicx7p31OioqKjJbV3Xdpaanm/XCGZB4POANQQDL3kjmfJbGszBwlBj93CgDFkM7pU6Kph/YxWmoyOHNXMm5IpDU0t5CPr3TtqLY+vby9rgJN+rj4IVmNZEKuJkbySEyGpvvhCiRTtWsTL+gy+66qqtLfrwk5KSIj3Q8GW1iK0didoLXgn3bZqVOnYtOmTXDt2R9+QS/CUekLdU0VCn9dhYpf9mDG6tVi2du3b2se6E7jUp9OUVlZKXlaDNBk5njRDeDEbg2tLpCGmW4NdmuY8N6Jw5Zlyhoc1gw0zhKsk+yqHZSZcxdtzhwlgNZ6UDJz+mh3ZVnybt7cfBBBEAy2TqkFaZa1JDm2VCW2GBpaXsFe2UIuDd05BixkNeZOyCUmQ2onVp6CmFipnSzp4OCAOlWd7Cghg0s/mJCTIjKx+8HR0VFzHLWQdifUtwpJpvE/dQpeI2fDa8TDUCiUqKm4hvzz76L2t2xAUb94YL2KigrNA90RPmqd1+vl5OQYneNFexi0OZPBie+5zHunfU46duyoyZWRKau7BIJSqdR8RmSSXbXPodkjUowk3erKz68fUSMTaBUUNM4JZMmRMebkg+zcuRMlRSWaliB3ANnQBHvlQMmGEr2k289XfI4hw4agYHfjsXgHtJ0p9G0hl4buHAMWspim8h+6d+9udDIz3btRcQVmmZaNhnlKAK3hxAoYnNxNL//BSG6MbLeQCa0KTc3R0fB6XmkVtlZ0g/c9motIWfpOFO3+F4S6as2oDUFrpljodJcYyAfRXlQR0FlbxkAehvbSGeZMBmfOrLH9+/fHlcwrsu+zODS5npOTE6prqjXJrgYWjtRtgRMvuhuMX3QvX76s+f0hMDizsW7OhjhKSGYklHbiNGD5u3lTRtZJkm69ANQ3/KD+Y6GddAsAT/35KRSWFUre58JthXjyz09iT9KeFqm3LeBstPaNAQu1OLNX+j2wG6pJKrG7RK4peuvWrUZbNrZs2SL+ExYnKVOgycndAEgXimtgIKlSwpwWmWnQLHqoPYtu/e86kHELz689gTq/7lDX3Ebh3pWo6L5fk1ehdYHWTqStrKw0+l7odgn16NEDKUdSZIMQ7eDwxo0bRhN0ddcdMnXWWDGJVqVT1hEGg0iVSmW0K0Y3UBDXrRlRX0YJFJ/QrFuj3Y0ldtvchlRl43ulTRwSLTNkWjdh2VJ38+YMlzYn6TYjIwN79+zVSxQWBAF7N+zlsF+yGUy6pRb3YMKD2Jm0U/LczqSdeDDhQb2y4kJxSRCTV+VWOhVbDWRaNvQWwNS+6DYkeE6GfIuJm87PHQyW0mjoytJOaDSwLo8oXOf3dgWgUMJ71Bw8tvpXFFTUoCYvEzlfL9IEKwYWoTO47pDMe6G72OiYMWOMvh/jxo0Ty8oNrzY0KZ05Cazl5eWNLWTaSbSOABT1r2sRW6dk6qHdeiUu2Oih0qw7lALgF0DVUX/BxqioKPgF+GlaSrSTaIs1yzzoXpxv3rzZWG/t8vX11k1YbhAZGYn4+PgWu9ibs4J2XFyc5hgNJN36BfhJWlfMXcmbyFrYwkItKiMjA/v27tN0vdwPSTP+3j36d2vm3I16eXlpHsjcNXp6eupvZGoibcM8Gwa6Hpoc+dOgvivLIJ1kV4cIP/jPegmuYX0hCMAjQ7tgWcIDEOpqTKqzk5OT5oJ9DZpA6zrEHAVAv7vkwIEDmgdGLkoNi5Sac3eulzitk8CqXQ+xhUW7xQQQk2hlmdCSJenG0mphQSr0urEyMjIaJ1ULBZAHTevXFM2karqfUXFCP5nWLN3uN0toznDppJ1JBmev3b1rt6ScZCVvA++z7vw4RNbCgIValDjMVaYZX3edlgam9C0HBQUZXSVZu2new8MDZWVlpgc3DXXWvoDFQ/5CauLIH5HWGj6u3e6G/73Pw6GDF9TVlVg59x5MjQ7FO3X13VQmXKDFLq2NkCbe1scPuq0x4urUMvs+cuSI+FRcXBycXZxQs7VWL9fE2cVJcncuJk7LJLBqt4KICzKakLwK1CcsNyROG8h5cXRo/PcldmMJMLiys/ZFV2wVkknm1c1hMWe2Yktpzlwpps5eK/5dyZxD7VmCiayJAQtZhqktG2bYt2+f0UTaPXv2YMmSJQDqR5AYSaQ1OEpI5gJmkADZkT96xERhB3h3exReDjMAANW5l5C/6T1M/eSGtKzc6BWtfYuLKspMwqab7Ovt7W30/fD19RXLZmRkoKa6Fo6OQJ1WwOboCNTU1Uru5sXEad3hs/XvhXY+iDiUXCZoEodq13Nzc9MEnQqdfdefb+1RRdevX298jwyMgtLuxlIqlUZb1HRbp6KiohrrbSDpVnzdgswdLi1pkdEaEm5o9lqxu1DmHOouVUBkLQxYqEWZM8zVXL///rvRGWm15xIpLi7WPJC52OlOUgYA0F0v0MD6gRJdAGQa+bmBADhMC4B/95fhqr4LAFBatBlF3/4XUNXplZUbvWJov6YMrQbqgwUBsgs8as9Lc/nyZdlUHAVkZj41klTcwMfHx+SgCagPYBoqor1Kcn2LmnZisTgpncwoKO38pqysLKOtgNeuXZPUIyEhAW+88YZs0m1CQgIszdzh0ua0IkVFRWH8xPHYu3+v9JcqgPETxzPhlmwGAxZqUeYMczXXXXfdpVnQUOYi3bt3b+kGDaOEtC92xrptzKWba3nDYCm49RgCv67Pw0HtATXKke/8KW67p0gvftpiANwH6cXfUMACmNySJV7AZAIL7aRUpVIJQQGonSDJQ1JvBwSVtAVCkrDphcYcFsfG1xvOeW5urtHRWLq5EmIrkUzOi3YrkjhXilbXm+YFzTftfUtm/dXWVb8soPlMe/t6o7iiWG84vbeXd6td0M0ZLi1Z/8hAK5Jui8y//+/fmiHhtxq75fx8/PCff/3HEodC1CwMWKhFmTPM1Vxi3onMhUZMym2gPSqmgVyCp9htg6Znrm0oL9PS01C+pk4Nn/FPwnPwdABAtSID+c7voU55U34IdEMLxBSYNh+MOUOrAdnAQntUUUMLhFpnWLNapgXC1HqIo49mQRNY/A5NorA/gE8MLJegXWdtXfWL9OrVq7GLTPscbgOgBvr06SOWFXMyZOqsm7ORkZGB4sJigysfF28obrVhv2YPlzbSiqRLHBKu9Xku3qk/JJzImhiwUIuSDHOtQ+OdvCOAT+QXUtu5cyeOHj2K4cOHS5I6tYndPDIXGjGpU5upuTRmdK+YUj67sBLz16SKwUpp6gYUdfwa6Fonm5ci7lem20aPke4V3fKSyc8MvHe6c5oAMOm969Kli9G8m/Dwxp2IicAyywPITrJnQjAUGBho9ALt79+4svOYMWOM1lm329LWVvs1JUHdnDpbasFGopbGgIValLnrtFy+fBlDhw+VNkUH+OHY0WOaxQC1dOvWzeiFxuA/VXNaIMxNFJYp7xY1HFOWH0RZVR1Ut8tQsPUT3M78VX80j1y3lAn5IAAaF2DUfk2mJaaurs5ogKMdLJiThyTOKCyTd6O9X3G5hCIYbJkSh0hrMzEJWWTCOYyKisL4CfU5G9p1dgTGT9DP2bDH1X7NqbOtBWREchiwUIsyNzlw8NDBKCqWJsAWFBZg0OBBmq4lLVeuXDF8ka6/+Iv/eBs0XKBztH7+DS3XvaI7auS6E3wmPg7PQVNRVlWHgV28sXnJn6Aqu2XeFI0y3TYG/QFNLgQJaNbxqS6olm298fDwkG6gAJTbAbXW8HHlIUCtk41rzqyx4rpKMi1TTk5O+hUXIDt6RZu5yd7m5GxYcn0gSzGnzvYYkFH7xJluqcWJs9duAPAJgA2GZ6/duXMnigqLNHkH2jOIOgFFhUVISkqSlBdzJ3SHsNT/nJmpk5na0L2SUv91uP5nue4Vc2auBTSjRlYA+A5w/F8Igj0/gOegqQCAp8d0w9qnh2uClYbcCp1jlN23borIVSN1CIcmSXdG/feuhouJLR0BOi/467yOxrV2vByhGT6+EUBS/c+CdPZac2aNFbtlZO7ktbttzNUw0kWxTSE5h4rtCoMjXcScDa0Zd4srNTkbhpj6mbYlpta5Ibhx2Okgee8cdjkgLj7OJgMyap/YwkItztTkQHFtoCnQzNaaBk0SZv2EbdprAwFaE4sZyvuATA6LqQSYN3OtVpJuh4jR8FPMh9KhA1SVJcjf+jGWvntcum9T82PMyEsBIDs3iK6amhqjCxpWV1eLZRtGmJTUQTKnSUn9CCvtO27JrLE6x6c7a+z48ePxzTffyN7Jjx8/3nDlZQJUXT/98JPeKJrY+Fi9C7TBnA0YnqOkQcNneteuXThy5IjRXCtbYU6SrqUXbCRqCQxYyCxNrcCsrakZQAMDAzUPdkDsQgAgruEjvl4vJyfH6GRpemu6GBk1onfxV8C8mWsFQHGfM3x6PgUPVTwAoKryNPJXfwBVeYF+eXOSf01NugVk5wbR5ejoaHRBQ93J0iAAai9I5jRRGwjgJPkP+WgMnLpqntbLf2hoydIOyOpbsnTXP1IqlVALas0xjYN0MjgVoFRIG4hNvUA3J2fDnIUHbY0pSbqWWrCRqCUxYCGTmPMP29SyCQkJeOOvb2guuAZmJ9WdkEscySLTWqE3ysSMYZ1iQKDdC+UC2UDB0bcTArotgbMqAgLUKHH8ASWOa4ByteENzMmPMTXp1gz+/v6aWWNlLtIBAY19RZJ1eQy0xmhf0MX8B5mRP9qtMdnZ2UZbsrRnowU0M91WVFTITgbn1kF3tUqNpi7QzcnZkCw8WP9+7N6pWXiwLQ37NSW4IbIWBixkEvEftlZg0bBSrO4/7OkPTMfBIwclZXfu3Ynpf5iO5P3JYrnMzEyjs5NevXrV8D9Pmbt5g0xt2WhoSYmBphWhA4AzMNjC4t57LHzj5kGpdIMKRch3/ghVDieanlvF1G6ecGgSTY3U2cHBASq1SrYFSXudHQAYPXq05v2WuUiPHTtWfEqpVJrcGtOQw1JQVKA38kc3h6WqqkpzzEWQtmTVz1yr3S0FaI1skgmcZIdBN8HcJFoO+yWyDQxYqEniP+xgSAILVZAKO7dL/2FnZGTgYPJBvbIIAg4kH5CUXbVqldEL0ueff244T0Dmbt4gU1s2BM3vRJrWczotLLdrVPjrptPwn/oiAKAqOx35NR9CFVZkPAgxt5vHhONTKpWaFieZFiTdIcKenp5Gu2M6duwolhXnbJEJ9rQDBXNyWMQh0KGQfjYMDIFuOEZjgZPBYdAwrdvSnJwNDvslsg0MWKhJ5nQR/PDDD41ltbt56vNBfvjhB7z22msA6lcINnJB0l5BWNRwh25khllJWVNbNhqek9lvxs0yzPsuFRfzyiGoVSj5JRElv/4A1Gl1ARmb4M0ZwChIlwiokqmHCcfn6OiomYhN5iKqu7ijOLGaTHeMdr6QpebwGD58OI4ePSq79MDIkSMlu3BxcdGsJySzb1dXV8nT5nRbmpOzwWG/RLaBw5qpSXp3ul713ycDEKRdBOKKyg0JmxsB7ALgqSm7f/9+saw4c63MBUl8XZv2iJuGekyBbGKsuG7NJ/XfvY2UvReau/88aHJI6vfr3m8i7l9xCBfzyhHg4YKb37+OksPfA/ergQUAHoHm+/0G9tuw73gAIwAMqP8+uYl6NHF8YmvENWi6xy4CKIB4EdVtrUhISJAmFk+v/14CvXwhc4a5Si7m2urroX0xj42NbQwifwcQWP+9PoicMGGCZBd33XWX0X2Lr9eT5JnUD69u6LaUExkZifj4eKMtJBz2S2Qb2MJCTTKniwCA0dYY7ZFDYhKtzJ2rbI6COTkseTo/6y6Opy0NkpVtFZGu8L33WXTsOx5VtWqMivTHJzMHIOD1U4318IJpE7yZmktjYlmxhUVmlJDeqB+gcRK2JJ3yBgInU7tMzMkHSU1NNbr4YXp6OuLj48WnhwwZgpQjKbKtZEOHDhXLWjrPhMN+iayPAQs1yZwm8fHjx2Pv3r2y3TwTJ04UyyoUCqPdNrrDXEWm5rA0DGseDekIpBoY7orRWtnWKacrAjxegZNvGAS1Ci/H98ZfxnSHUqlVJ3NG/rRw2fDwcJy/cF52wcauXbtKyotdN10gXfm5/mfdPAxLzOFx/vx5zQOZdabOnDkjKS9JyjYQ4GhPFGjpPBMO+yWyPgYs1CRz7qIHDhyoeSBz4YiOjhafElsJZC5IBlsJAP1WknyZihsZgWSw7BQA/YCOqjj4hj8NBZxRV5aP/M0fYN77p6Xlzc2PkUl2NSfvRqE1Y1pISIgmAJAZ4h0SEiLZrRh0yuSPyOVhtOQcHmPHjsW3334ru86UbpeQuPq2TIAjrt6N1ssz4bBfIuthwEIm+XzF55q1VzY0TormHeCNVStXScqZc+EQE0NlLkgGAxZzJ4OT6Zoy1A2iCHeDX+18uKvGAAAq1cdQsPoTqG+X6hc2Z+SPkWRXbR4eHpq5UhQ6ZetHK3l4Nq73079/f02+kAmBIaATdMZaZj2cpi7mTzzxBJ7885OGAzIlMHfuXEn5Rx55BP/73/9kA5w//vGPho/PTtb7ISLzMOmWTGLW2isKaC5CWgmKYqCgRRyZcg2ai1Fk/ferOq9ra2gJ0U5KjYd8oCCTKKzLKbAbQvAp3FVjIKAORY7/xa1LfzMcrDSQWZdHj/bcI9PrvxdD7/3o27dvYzA1AsDw+u/1++jTp49Y9tlnn9U8kElI/ctf9M+LtdfD2blzpyawbAjIGhKhAUANvbWj4uLi4O3rrQlwtD9L2wBvX2+9Ie/WPj4isiy2sFCTzFl7JTk5WXPBDYG0laB+ro3k5GSxbNeuXXEl84omuNG+464PbiIiIgxXqAUTWAVBQMeYe+E7/kkoHJ1QV5uHW3gfNRnnjbbGGJsATbu82GpSB4PJrtrdGlOmTEFKSopmH4e1yta3sNx77736dZDrljLA2nkYR48e1Tz4CzTdeNnQrB3lD+ATICUlRS8IST2eisFDB0ta9vwC/HDs6DG9/Vv7+IjIshiwUJOaldAokyuhzcPDw2hw4+7uLinv4OCgGVkk092kO/cIAKMJrCW3a7Fk3Un4xWpaIyqvH0HBT/+EuqpcUg+DBMhOgKZtyJAh2LN3j+YvrT803UBuAM4CUGtebyAuVdDQwiJAE3z8Br2hxw0rKiMUBt87Y0mm1srDEEf1NJyT+t7Dhi6e4cOH620TERGB/Lx8JCUlISUlxaRFB5lnQtQ2MWChJpmTlzJmzJjGLqGRADpCM7S4fuK4MWPGiGXFkSwywY1uC8vYsWM1F3+ZFplx48aJZRUKBQQIsi0QriFRuO+zg8guvA1BVYuifatRFr4ZeEq/Hrrc3d01a9zI1Fs70Bo7diz27NmjCSzStHZSH1hoT4kPoDE3RruFxUBuTHOTaK0pLi5OM43/1gK9c+IX4Gc0EJk0aZLNr45MRJbV4jksb731FhQKheQrODjY6DbJyckYNGgQXF1d0a1bN3zxxRctXS26A+ZMnBUVFYVRo0dphg7vhmbiuCQANcCo0aMkZf39/RuDG+2JxOoDEH9/aVLIuHHjNBc6b0hzILwACNJRJuLqxLU6ZWsBj0H3I3D2e8guvI0wXzfc/O4VlKVulq2H7vDqGTNmGJ0A7cEHHxTLiq0iMZBOMjdA53XUd6cBmoRiAxPSia/rnhOtOtj6ZGbHjh6Dn6ef5Jz4eRru4iEi0maRFpY+ffpg9+7d4s8Gm+rrZWZmYsqUKXjqqafw7bff4pdffsGzzz6LgIAAzYWBbII5E2c5OTlB4ayAMEUQR+coting5OQkKRcUFCQNKhrUtygEBUknWFGpVEZzR2pqasSy4joz0+vLZQPKLh3h57EIHToOAwDE9w3GuzP6o8v/y0W1ka4p7dE5gCZYELtrDIzm6dWrl6Sst683ircWa5KFu0JsVfD29ZZf3NELTU5IZ4+TmTWni4eICLBQwOLo6Nhkq0qDL774Al26dME///lPAJrpto8fP44PP/yQAYsNMTWhMSMjA3t379WbcVQQBOzdsFeSoKvdPWSI7utDhw5tDBS0c0fqAwXtHIhOnTrhypUr4sXfuWsvBNS+DEchEEJdLZQnN+LzZauhUCjw+OOPaz5/Mt0rTz31lKQe4gRoMsmj2hOgZWRkoLiwWLMYpM6w5uKbxYbfD5muN933w56TTNnFQ0Tmssiw5osXLyI0NBQRERF4+OGHNRcOGSkpKZo1RrTExcXh+PHjmknFZFRXV6O0tFTyRZbX1NorpiToNoiKisL4ieP1R7UogPETx+v9joYcCEPDfnVzIF599VXNg2sKeNY+gOCad+EoBKK25gZy/vcC3pw9Vuzq+eSTT4x28Xz44YeSeoh5J9egSRwdW//9quZp7a4p8f2YBWk3z2z590OxTSHpelNsVxh8PxqYsh4OEZG9a/GAZejQofjmm2+wc+dO/Pvf/0Zubi5GjBiBgoICg+Vzc3P1mv6DgoJQV1eH/Hy5KUyBZcuWwcvLS/wKCwtr0eOg5jFnMTwA+OmHnxA3KU7yXNykOPz0w08G9y/mQBwGkALgsOEciCeeeAJOHt4IcPwrfOoehwKOqChNRs5/noO68JreJGVbft5iMN9ly89b9OrwxBNPwMnFyeD8IE4uTpJ9S94PA3PNGHo/YsfFSuoROy5W9v0gImo3BAsrLy8XgoKChI8++sjg65GRkcI777wjee7QoUMCACEnJ0d2v1VVVUJJSYn4lZ2dLQAQSkpKWrT+ZL64+DjBwd1BwB8g4HkI+AMEB3cHIS4+TnabjIwMYdu2bUJGRoZJv2PXrl3C22+/Lezatcvg60evFAgD39omhL+yRQhbvE7oGB0nABCcXJyEtLQ02f2+8MILQv/+/YUXXnjB6O9PS0sTnFycBGjae4zuuzXeDyIie1VSUmLS9VshCFrL51rIpEmT0KNHD6xatUrvtdGjRyMmJgaffvqp+NyGDRuQkJCAyspKvURNOaWlpfDy8kJJSYlkMi5qfUVFRZpk0O2NyaBx8XFI/C4RPj4+Fv3darWAVcmX8XFSBlRqAd0C3BHrno0zv+zChAkT9FpW7tRXX32FPXv2GN23Nd8PIiJbZ+r12+LzsFRXV+PcuXMYNWqUwdeHDx+On3/+WfLcrl27cPfdd5scrJBtsVYyaH55NZ5fewIHL2q6Eh+I6YS/T+8LdxdH4Jk5Fvmdc+fObTIIsufkWCIiW9HiLSwvvvgipk6dii5duiAvLw//+Mc/kJycjFOnTiE8PBxLly7F9evX8c033wDQDGvu27cvnn76aTz11FNISUnBM888g8TERLNGCbGFpX07fDkfz31/ArfKquHqpMTfpvXFQ4M6682hQkREtsVqLSy///47Zs2ahfz8fAQEBGDYsGE4cuQIwsM1w0ZycnKQlZUllo+IiMC2bdvw/PPPY+XKlQgNDcXy5cs5pJlMolIL+GzvRSzfcxFqAYgM7IjPHxmIyCCPpjcmIiK70So5LK2BLSztT15pFRatPYHDlzUj0BLu7oy37+8LN2f5iQqJiMi22EwOC5ElHLx4C8+vPYH88hp0cHbA//tDX/whprO1q0VERBbCgIXsSp1KjX/uvoiV+y9BEIBewR5YMXsgegR2tHbViIjIghiwkN3IKbmN5xJP4NerhQCA2UO74K/39YarE7uAiIjaOgYsZBf2XcjD4rUnUFRZi44ujnjngX64PzrU2tUiIqJWwoCFbFqtSo0Pd13A/yVr1qPq28kTK2YNRFd/dyvXjIiIWhMDFrJZ14tvY8GaVKRmFQMAHhsejlfvvQsujuwCIiJqbxiwkE1KOnsTL/6YjpLbtfBwdcT7M/ojvl+ItatFRERWwoCFbEpNnRrv7TiPLw9lAgCiO3thxeyBCPPtYOWaERGRNTFgIZuRXViJ+YlpSM8uBgA8MTICr0zuBWdHpXUrRkREVseAhWzCjtM5eOmnkyirqoOXmxM+fCgak3oHWbtaRERkIxiwkFVV16nwztZz+DrlGgBgYBdvLJ8Vg84+7AIiIqJGDFjIaq7mV2B+YipOXy8FADw9phtejO0JJwd2ARERkRQDFrKKLSdvYMm6UyivroNPByd8nDAA43oFWrtaRERkoxiwUKuqqlXhb1vOYs3RLADA4K4+WD4rBiFeblauGRER2TIGLNRqLt8qx7zvUnE+twwKBTBvbA8smhgJR3YBERFRExiwUKvYkPY7XttwGpU1Kvh3dMYnMwdgVGSAtatFRER2ggELWdTtGhXe3HwaPxz/HQAwvJsfPn14AAI9Xa1cMyIisicMWMhiLt4sw7w1qci4WQ6FAnhuQiQWjI+Eg1Jh7aoREZGdYcBCFvHj8Wy8sek0qmrVCPBwwacPD8CI7v7WrhYREdkpBizUoiqq6/DGptNYn3odADAq0h8fJwxAgIeLlWtGRET2jAELtZjzuaWY910qLt+qgFIBvBDbE38Z0x1KdgEREdEdYsBCd0wQBHx/LBtvbT6D6jo1gj1dsXxWDIZE+Fq7akRE1EYwYKE7UlZVi1c3nMbP6TcAAGN7BuDjhAHwdXe2cs2IiKgtYcBCzXb6egnmr0nF1YJKOCgVeDmuJ54a1Y1dQERE1OIYsJDZBEHAt0eu4e9bzqFGpUYnbzcsnxWDQeE+1q4aERG1UQxYyCylVbVYsu4ktp3KBQBMvCsIHz7UH94d2AVERESWw4CFTJaeXYz5ianILrwNJwcFlsTfhcfv6QqFgl1ARERkWQxYqEmCIGD1L1exbPs51KoEdPZxw8rZAxEd5m3tqhERUTvBgIWMKq6swUs/nUTS2ZsAgMl9gvHeg/3h5eZk5ZoREVF7woCFZKVmFWHBmjRcL74NZwclXr/vLswZFs4uICIianUMWEiPWi3gP4eu4P0dF1CnFhDu1wErZw9E305e1q4aERG1U8qW3uGyZcswePBgeHh4IDAwENOnT8eFCxeMbrN//34oFAq9r/Pnz7d09agJhRU1ePKb43hn23nUqQXc1z8EWxaMZLBCRERW1eItLMnJyZg3bx4GDx6Muro6vPbaa4iNjcXZs2fh7u5udNsLFy7A09NT/DkgIKClq0dGHLtaiIWJacgpqYKzoxJvTe2DWUPC2AVERERW1+IBy44dOyQ/r169GoGBgfjtt98wevRoo9sGBgbC29u7patETVCrBaxKvoyPkzKgUgvoFuCOlbMH4q4Qz6Y3JiIiagUWz2EpKSkBAPj6Nr0QXkxMDKqqqtC7d2+8/vrrGDdunGzZ6upqVFdXiz+XlpbeeWXbofzyajy/9gQOXswHAPwhphP+Mb0v3F2Y3kRERLajxXNYtAmCgMWLF2PkyJHo27evbLmQkBD861//wrp167B+/Xr07NkTEyZMwIEDB2S3WbZsGby8vMSvsLAwSxxCm5ZyuQBTPj2Igxfz4eqkxPsP9sfHCdEMVoiIyOYoBEEQLLXzefPmYevWrTh06BA6d+5s1rZTp06FQqHA5s2bDb5uqIUlLCwMJSUlkjwY0qdSC1ix9xI+3ZMBtQBEBnbEykcGIirIw9pVIyKidqa0tBReXl5NXr8tdiu9YMECbN68GQcOHDA7WAGAYcOG4dtvv5V93cXFBS4uLndSxXYpr6wKi74/gcOXCwAADw3qjLen9UEHZ7aqEBGR7Wrxq5QgCFiwYAE2bNiA/fv3IyIioln7SUtLQ0hISAvXrn07dDEfi9amIb+8Bh2cHfCP6X3xwEDzg0kiIqLW1uIBy7x587BmzRps2rQJHh4eyM3VrOrr5eUFNzc3AMDSpUtx/fp1fPPNNwCAf/7zn+jatSv69OmDmpoafPvtt1i3bh3WrVvX0tVrl+pUany65yJW7LsEQQB6BXtgxeyB6BHY0dpVIyIiMkmLByyrVq0CAIwdO1by/OrVqzF37lwAQE5ODrKyssTXampq8OKLL+L69etwc3NDnz59sHXrVkyZMqWlq9fu5JZUYeH3afg1sxAAMGtIF7w5tTdcnRysXDMiIiLTWTTptjWZmrTTnuy/kIfFP6SjsKIG7s4OWDajP+6PDrV2tYiIiERWT7ol66lVqfHRrgx8kXwZANAn1BMrZg9EhL/xmYaJiIhsFQOWNuZ68W0sTEzDb9eKAACPDg/Hq1PuYhcQERHZNQYsbcjuszfx4k/pKK6shYerI96f0R/x/TjSioiI7B8Dljagpk6N93ecx38OZQIAojt74bNZA9HFr4OVa0ZERNQyGLDYuezCSsxPTEN6djEA4PF7IrAkvhecHS266gIREVGrYsBix3aczsVLP6WjrKoOnq6O+PChaMT2CbZ2tYiIiFocAxY7VF2nwrJt5/HV4asAgJgu3vhsVgw6+7ALiIiI2iYGLHbmWkEF5q9Jw6nrJQCAp0d3w4txPeHkwC4gIiJquxiw2JEtJ29gybpTKK+ug08HJ3yUEI3xvYKsXS0iIiKLY8BiB6pqVfj7lrP47qhmOYPBXX2wfFYMQrzcrFwzIiKi1sGAxcZduVWOeWvScC6nFAoF8OzY7nh+YhQc2QVERETtCAMWG7Yx7Tpe3XAKlTUq+Lk745OZAzA6KsDa1SIiImp1DFhs0O0aFd7afAZrj2cDAIZ188Xyh2MQ6Olq5ZoRERFZBwMWG3PxZhnmrUlFxs1yKBTAwvGRWDghEg5KhbWrRkREZDUMWGzIj8ez8ddNZ3C7VoUADxd8OnMARvTwt3a1iIiIrI4Biw2oqK7DG5tOY33qdQDAyB7++GTmAAR4uFi5ZkRERLaBAYuVnc8txbzvUnH5VgWUCmDxpCg8O7YHlOwCIiIiEjFgsRJBELD2WDbe3HwG1XVqBHm6YPnDMRjazc/aVSMiIrI5DFisoLy6Dq+uP4XN6TcAAGOiAvBxQjT8OrILiIiIyBAGLK3szI0SzF+Thsz8CjgoFXgprif+PKobu4CIiIiMYMDSSgRBwLdHruHvW8+hpk6NUC9XfDY7BoPCfa1dNSIiIpvHgKUVlFbVYsm6k9h2KhcAMPGuQHzwYDR83J2tXDMiIiL7wIDFwk7+Xoz5a9KQVVgJJwcFXpncC0+MjIBCwS4gIiIiUzFgsRBBELD6l6tYtv0calUCOvu4YcXsgRgQ5m3tqhEREdkdBiwWUFJZi5d+SseuszcBAJP7BOO9B/vDy83JyjUjIiKyTwxYWlhaVhHmr0nD9eLbcHZQ4rV778Kjw8PZBURERHQHGLC0ELVawJeHMvHejvOoUwsI9+uAFbMGol9nL2tXjYiIyO4xYGkBRRU1eOHHdOw9nwcAuLd/CN59oB88XNkFRERE1BIYsNyh41cLsSAxDTklVXB2VOLNqb0xe0gXdgERERG1IAYszaRWC/jiwGV8tCsDKrWAbv7uWDF7IHqHelq7akRERG0OA5ZmyC+vxuIf0nEg4xYAYPqAUPzjD/3Q0YVvJxERkSUoLbXjzz//HBEREXB1dcWgQYNw8OBBo+WTk5MxaNAguLq6olu3bvjiiy8sVbU7cuRKAaZ8ehAHMm7B1UmJ92f0xyczBzBYISIisiCLBCxr167FokWL8NprryEtLQ2jRo1CfHw8srKyDJbPzMzElClTMGrUKKSlpeHVV1/FwoULsW7dOktUr1lUagGf7r6I2f8+gryyavQI7IjN80ciYXAY81WIiIgsTCEIgtDSOx06dCgGDhyIVatWic/dddddmD59OpYtW6ZX/pVXXsHmzZtx7tw58blnnnkG6enpSElJMel3lpaWwsvLCyUlJfD0bNk8kryyKjy/9gR+uVQAAHhoUGe8Pa0POjizVYWIiOhOmHr9bvEWlpqaGvz222+IjY2VPB8bG4vDhw8b3CYlJUWvfFxcHI4fP47a2lqD21RXV6O0tFTyZQm/XMrHlE8P4ZdLBXBzcsDHCdH44KFoBitEREStqMUDlvz8fKhUKgQFBUmeDwoKQm5ursFtcnNzDZavq6tDfn6+wW2WLVsGLy8v8SssLKxlDkDL7RoVnvv+BPLLq9Er2AM/LxiJBwZ2bvHfQ0RERMZZLOlWN69DEASjuR6Gyht6vsHSpUtRUlIifmVnZ99hjfW5OTvgo4RozBrSBRvn3YMegR1b/HcQERFR01q8X8Pf3x8ODg56rSl5eXl6rSgNgoODDZZ3dHSEn5+fwW1cXFzg4uLSMpU2YkxUAMZEBVj89xAREZG8Fm9hcXZ2xqBBg5CUlCR5PikpCSNGjDC4zfDhw/XK79q1C3fffTecnDi9PRERUXtnkS6hxYsX4z//+Q/++9//4ty5c3j++eeRlZWFZ555BoCmO+fRRx8Vyz/zzDO4du0aFi9ejHPnzuG///0vvvzyS7z44ouWqB4RERHZGYsMdZk5cyYKCgrwt7/9DTk5Oejbty+2bduG8PBwAEBOTo5kTpaIiAhs27YNzz//PFauXInQ0FAsX74cM2bMsET1iIiIyM5YZB4Wa7DkPCxERERkGVabh4WIiIiopTFgISIiIpvHgIWIiIhsHgMWIiIisnkMWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyeQxYiIiIyOZZZGp+a2iYsLe0tNTKNSEiIiJTNVy3m5p4v80ELGVlZQCAsLAwK9eEiIiIzFVWVgYvLy/Z19vMWkJqtRo3btyAh4cHFApFi+23tLQUYWFhyM7ObrNrFLX1Y+Tx2b+2fow8PvvX1o/RkscnCALKysoQGhoKpVI+U6XNtLAolUp07tzZYvv39PRskx9CbW39GHl89q+tHyOPz/619WO01PEZa1lpwKRbIiIisnkMWIiIiMjmMWBpgouLC9588024uLhYuyoW09aPkcdn/9r6MfL47F9bP0ZbOL42k3RLREREbRdbWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyeQxYAHz++eeIiIiAq6srBg0ahIMHDxotn5ycjEGDBsHV1RXdunXDF1980Uo1Nd+yZcswePBgeHh4IDAwENOnT8eFCxeMbrN//34oFAq9r/Pnz7dSrU331ltv6dUzODjY6Db2dP66du1q8FzMmzfPYHl7OHcHDhzA1KlTERoaCoVCgY0bN0peFwQBb731FkJDQ+Hm5oaxY8fizJkzTe533bp16N27N1xcXNC7d29s2LDBQkdgnLHjq62txSuvvIJ+/frB3d0doaGhePTRR3Hjxg2j+/zqq68MnteqqioLH41hTZ3DuXPn6tV12LBhTe7XHs4hAIPnQqFQ4IMPPpDdpy2dQ1OuC7b4d9juA5a1a9di0aJFeO2115CWloZRo0YhPj4eWVlZBstnZmZiypQpGDVqFNLS0vDqq69i4cKFWLduXSvX3DTJycmYN28ejhw5gqSkJNTV1SE2NhYVFRVNbnvhwgXk5OSIX5GRka1QY/P16dNHUs9Tp07JlrW383fs2DHJsSUlJQEAHnroIaPb2fK5q6ioQHR0NFasWGHw9ffffx8ff/wxVqxYgWPHjiE4OBiTJk0S1wszJCUlBTNnzsScOXOQnp6OOXPmICEhAUePHrXUYcgydnyVlZVITU3FG2+8gdTUVKxfvx4ZGRm4//77m9yvp6en5Jzm5OTA1dXVEofQpKbOIQBMnjxZUtdt27YZ3ae9nEMAeufhv//9LxQKBWbMmGF0v7ZyDk25Ltjk36HQzg0ZMkR45plnJM/16tVLWLJkicHyL7/8stCrVy/Jc08//bQwbNgwi9WxJeXl5QkAhOTkZNky+/btEwAIRUVFrVexZnrzzTeF6Ohok8vb+/l77rnnhO7duwtqtdrg6/Z07gRBEAAIGzZsEH9Wq9VCcHCw8O6774rPVVVVCV5eXsIXX3whu5+EhARh8uTJkufi4uKEhx9+uMXrbA7d4zPk119/FQAI165dky2zevVqwcvLq2Ur10IMHeNjjz0mTJs2zaz92PM5nDZtmjB+/HijZWz5HOpeF2z177Bdt7DU1NTgt99+Q2xsrOT52NhYHD582OA2KSkpeuXj4uJw/Phx1NbWWqyuLaWkpAQA4Ovr22TZmJgYhISEYMKECdi3b5+lq9ZsFy9eRGhoKCIiIvDwww/jypUrsmXt+fzV1NTg22+/xeOPP97kAp/2cu50ZWZmIjc3V3KOXFxcMGbMGNm/SUD+vBrbxlaUlJRAoVDA29vbaLny8nKEh4ejc+fOuO+++5CWltY6FWym/fv3IzAwEFFRUXjqqaeQl5dntLy9nsObN29i69ateOKJJ5osa6vnUPe6YKt/h+06YMnPz4dKpUJQUJDk+aCgIOTm5hrcJjc312D5uro65OfnW6yuLUEQBCxevBgjR45E3759ZcuFhITgX//6F9atW4f169ejZ8+emDBhAg4cONCKtTXN0KFD8c0332Dnzp3497//jdzcXIwYMQIFBQUGy9vz+du4cSOKi4sxd+5c2TL2dO4Mafi7M+dvsmE7c7exBVVVVViyZAlmz55tdEG5Xr164auvvsLmzZuRmJgIV1dX3HPPPbh48WIr1tZ08fHx+O6777B371589NFHOHbsGMaPH4/q6mrZbez1HH799dfw8PDAAw88YLScrZ5DQ9cFW/07bDOrNd8J3btVQRCM3sEaKm/oeVszf/58nDx5EocOHTJarmfPnujZs6f48/Dhw5GdnY0PP/wQo0ePtnQ1zRIfHy8+7tevH4YPH47u3bvj66+/xuLFiw1uY6/n78svv0R8fDxCQ0Nly9jTuTPG3L/J5m5jTbW1tXj44YehVqvx+eefGy07bNgwSdLqPffcg4EDB+Kzzz7D8uXLLV1Vs82cOVN83LdvX9x9990IDw/H1q1bjV7Y7e0cAsB///tfPPLII03motjqOTR2XbC1v8N23cLi7+8PBwcHvegvLy9PL0psEBwcbLC8o6Mj/Pz8LFbXO7VgwQJs3rwZ+/btQ+fOnc3eftiwYVa/EzCFu7s7+vXrJ1tXez1/165dw+7du/Hkk0+ava29nDsA4ggvc/4mG7Yzdxtrqq2tRUJCAjIzM5GUlGS0dcUQpVKJwYMH2815DQkJQXh4uNH62ts5BICDBw/iwoULzfq7tIVzKHddsNW/w3YdsDg7O2PQoEHiyIsGSUlJGDFihMFthg8frld+165duPvuu+Hk5GSxujaXIAiYP38+1q9fj7179yIiIqJZ+0lLS0NISEgL167lVVdX49y5c7J1tbfz12D16tUIDAzEvffea/a29nLuACAiIgLBwcGSc1RTU4Pk5GTZv0lA/rwa28ZaGoKVixcvYvfu3c0KlAVBwIkTJ+zmvBYUFCA7O9tofe3pHDb48ssvMWjQIERHR5u9rTXPYVPXBZv9O2yR1F079v333wtOTk7Cl19+KZw9e1ZYtGiR4O7uLly9elUQBEFYsmSJMGfOHLH8lStXhA4dOgjPP/+8cPbsWeHLL78UnJychJ9++slah2DUX/7yF8HLy0vYv3+/kJOTI35VVlaKZXSP8ZNPPhE2bNggZGRkCKdPnxaWLFkiABDWrVtnjUMw6oUXXhD2798vXLlyRThy5Ihw3333CR4eHm3m/AmCIKhUKqFLly7CK6+8oveaPZ67srIyIS0tTUhLSxMACB9//LGQlpYmjpJ59913BS8vL2H9+vXCqVOnhFmzZgkhISFCaWmpuI85c+ZIRvL98ssvgoODg/Duu+8K586dE959913B0dFROHLkiE0dX21trXD//fcLnTt3Fk6cOCH5m6yurpY9vrfeekvYsWOHcPnyZSEtLU3405/+JDg6OgpHjx5t9eMTBOPHWFZWJrzwwgvC4cOHhczMTGHfvn3C8OHDhU6dOrWJc9igpKRE6NChg7Bq1SqD+7Dlc2jKdcEW/w7bfcAiCIKwcuVKITw8XHB2dhYGDhwoGfL72GOPCWPGjJGU379/vxATEyM4OzsLXbt2lf3A2gIABr9Wr14tltE9xvfee0/o3r274OrqKvj4+AgjR44Utm7d2vqVN8HMmTOFkJAQwcnJSQgNDRUeeOAB4cyZM+Lr9n7+BEEQdu7cKQAQLly4oPeaPZ67hqHXul+PPfaYIAiaIZVvvvmmEBwcLLi4uAijR48WTp06JdnHmDFjxPINfvzxR6Fnz56Ck5OT0KtXL6sFacaOLzMzU/Zvct++feI+dI9v0aJFQpcuXQRnZ2chICBAiI2NFQ4fPtz6B1fP2DFWVlYKsbGxQkBAgODk5CR06dJFeOyxx4SsrCzJPuz1HDb4v//7P8HNzU0oLi42uA9bPoemXBds8e9QUV95IiIiIpvVrnNYiIiIyD4wYCEiIiKbx4CFiIiIbB4DFiIiIrJ5DFiIiIjI5jFgISIiIpvHgIWIiIhsHgMWIiIisnkMWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyef8fer4G6mBOqusAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", - "\n", - "regr.fit(X_train, y_train)\n", - "\n", - "\n", - "y_train_predict = regr.predict(X_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", - "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", - "\n", - "\n", - "plt.show()\n", - "\n", - "y_test_predict = regr.predict(X_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", - "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f9513b25", - "metadata": {}, - "source": [ - "Train neural network for outfield players.\n", - "\n", - "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8aad9652", - "metadata": {}, - "outputs": [], - "source": [ - "load_model_of = True# load scaler and model weights for outfield player predictor\n", - "refit_model_of = True\n", - "\n", - "if(load_model_of):\n", - " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", - " \n", - " X_train = scaler.transform(X_train_)\n", - " X_test = scaler.transform(X_test_)\n", - "\n", - "\n", - "n_epochs = 1000\n", - "\n", - "n_samples = X_train.shape[0]\n", - "\n", - "batch_size = 256\n", - "\n", - "X_len = X_train.shape[1]\n", - "y_len = y_train.shape[1]\n", - "\n", - "\n", - "#tailweight_param = 1.1\n", - "\n", - "tailweight_min = 0.5\n", - "tailweight_range = 1.2\n", - "\n", - "\n", - "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", - "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", - "\n", - "\n", - "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", - "x = tfk.layers.Dropout(0.2)(inputs)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "x = tfk.layers.Dropout(0.2)(x)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "\n", - "\n", - "prob_dist_params = 4\n", - "\n", - "def prob_dist(t): \n", - " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", - " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", - " allow_nan_stats = False)\n", - "\n", - "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", - "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", - "\n", - "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", - "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", - "\n", - "\n", - "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", - "\n", - "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", - " loss=neg_log_likelihood)\n", - "\n", - "if(load_model_of):\n", - " modelb.load_weights('saves/modelb')\n", - " \n", - "if( (not load_model_of) or refit_model_of):\n", - " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", - " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", - " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "4e2bf9dc", - "metadata": {}, - "outputs": [], - "source": [ - "def sample_predict(X, iterations = 100):\n", - " y = np.zeros((2, X.shape[0]))\n", - " \n", - " dist = modelb(X)\n", - " \n", - " for i in range(iterations):\n", - " y[0, :] += dist[0].sample()\n", - " y[1, :] += dist[1].sample()\n", - " \n", - " return y.transpose() / iterations\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c2674211", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.14539483185579238\n", - "0.16229047232752447\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_train_predict = sample_predict(X_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", - "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", - "\n", - "plt.show()\n", - "\n", - "y_test_predict = sample_predict(X_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", - "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f574ffdb", - "metadata": {}, - "source": [ - "Train neural network for goalkeepers.\n", - "\n", - "For clean sheet probability prediction, a Bernoulli distribution is used." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "41e7e1ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/2500\n", - "8/8 [==============================] - 4s 92ms/step - loss: 363.3525 - distribution_lambda_26_loss: 106.5460 - distribution_lambda_27_loss: 255.9949 - distribution_lambda_28_loss: 0.8116 - val_loss: 255.7005 - val_distribution_lambda_26_loss: 95.0135 - val_distribution_lambda_27_loss: 159.8931 - val_distribution_lambda_28_loss: 0.7939\n", - "Epoch 2/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 242.5359 - distribution_lambda_26_loss: 90.2925 - distribution_lambda_27_loss: 151.4580 - distribution_lambda_28_loss: 0.7854 - val_loss: 181.1897 - val_distribution_lambda_26_loss: 80.4324 - val_distribution_lambda_27_loss: 99.9920 - val_distribution_lambda_28_loss: 0.7654\n", - "Epoch 3/2500\n", - "8/8 [==============================] - 0s 5ms/step - loss: 172.7781 - distribution_lambda_26_loss: 75.9389 - distribution_lambda_27_loss: 96.0833 - distribution_lambda_28_loss: 0.7559 - val_loss: 137.6391 - val_distribution_lambda_26_loss: 68.3221 - val_distribution_lambda_27_loss: 68.5772 - val_distribution_lambda_28_loss: 0.7398\n", - "Epoch 4/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 134.3410 - distribution_lambda_26_loss: 64.5182 - distribution_lambda_27_loss: 69.0890 - distribution_lambda_28_loss: 0.7337 - val_loss: 109.9033 - val_distribution_lambda_26_loss: 58.6612 - val_distribution_lambda_27_loss: 50.5242 - val_distribution_lambda_28_loss: 0.7179\n", - "Epoch 5/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 107.2862 - distribution_lambda_26_loss: 55.5948 - distribution_lambda_27_loss: 50.9793 - distribution_lambda_28_loss: 0.7121 - val_loss: 90.9552 - val_distribution_lambda_26_loss: 50.7684 - val_distribution_lambda_27_loss: 39.4882 - val_distribution_lambda_28_loss: 0.6985\n", - "Epoch 6/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 89.6161 - distribution_lambda_26_loss: 48.2157 - distribution_lambda_27_loss: 40.7019 - distribution_lambda_28_loss: 0.6985 - val_loss: 77.0989 - val_distribution_lambda_26_loss: 44.2830 - val_distribution_lambda_27_loss: 32.1339 - val_distribution_lambda_28_loss: 0.6820\n", - "Epoch 7/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 76.6649 - distribution_lambda_26_loss: 42.5049 - distribution_lambda_27_loss: 33.4795 - distribution_lambda_28_loss: 0.6805 - val_loss: 66.2563 - val_distribution_lambda_26_loss: 38.8386 - val_distribution_lambda_27_loss: 26.7498 - val_distribution_lambda_28_loss: 0.6679\n", - "Epoch 8/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 65.1514 - distribution_lambda_26_loss: 36.9772 - distribution_lambda_27_loss: 27.5046 - distribution_lambda_28_loss: 0.6695 - val_loss: 57.5291 - val_distribution_lambda_26_loss: 34.2010 - val_distribution_lambda_27_loss: 22.6720 - val_distribution_lambda_28_loss: 0.6561\n", - "Epoch 9/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 57.1919 - distribution_lambda_26_loss: 33.0281 - distribution_lambda_27_loss: 23.5040 - distribution_lambda_28_loss: 0.6597 - val_loss: 50.3063 - val_distribution_lambda_26_loss: 30.2402 - val_distribution_lambda_27_loss: 19.4200 - val_distribution_lambda_28_loss: 0.6460\n", - "Epoch 10/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 49.8005 - distribution_lambda_26_loss: 28.9802 - distribution_lambda_27_loss: 20.1705 - distribution_lambda_28_loss: 0.6498 - val_loss: 44.2238 - val_distribution_lambda_26_loss: 26.8281 - val_distribution_lambda_27_loss: 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val_distribution_lambda_28_loss: 0.2815\n", - "Epoch 1405/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6810 - distribution_lambda_26_loss: 0.3005 - distribution_lambda_27_loss: 1.1295 - distribution_lambda_28_loss: 0.2510 - val_loss: 1.8142 - val_distribution_lambda_26_loss: 0.4112 - val_distribution_lambda_27_loss: 1.1213 - val_distribution_lambda_28_loss: 0.2817\n", - "Epoch 1406/2500\n", - "8/8 [==============================] - 0s 7ms/step - loss: 1.6321 - distribution_lambda_26_loss: 0.2767 - distribution_lambda_27_loss: 1.1065 - distribution_lambda_28_loss: 0.2489 - val_loss: 1.8091 - val_distribution_lambda_26_loss: 0.4108 - val_distribution_lambda_27_loss: 1.1177 - val_distribution_lambda_28_loss: 0.2807\n", - "Epoch 1407/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6636 - distribution_lambda_26_loss: 0.2850 - distribution_lambda_27_loss: 1.1221 - distribution_lambda_28_loss: 0.2565 - val_loss: 1.8142 - val_distribution_lambda_26_loss: 0.4112 - val_distribution_lambda_27_loss: 1.1224 - val_distribution_lambda_28_loss: 0.2805\n", - "Epoch 1408/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6377 - distribution_lambda_26_loss: 0.2683 - distribution_lambda_27_loss: 1.1275 - distribution_lambda_28_loss: 0.2419 - val_loss: 1.8402 - val_distribution_lambda_26_loss: 0.4245 - val_distribution_lambda_27_loss: 1.1346 - val_distribution_lambda_28_loss: 0.2811\n", - "Epoch 1409/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6658 - distribution_lambda_26_loss: 0.2691 - distribution_lambda_27_loss: 1.1491 - distribution_lambda_28_loss: 0.2476 - val_loss: 1.8537 - val_distribution_lambda_26_loss: 0.4351 - val_distribution_lambda_27_loss: 1.1382 - val_distribution_lambda_28_loss: 0.2804\n", - "Epoch 1410/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6474 - distribution_lambda_26_loss: 0.2918 - distribution_lambda_27_loss: 1.1075 - distribution_lambda_28_loss: 0.2481 - val_loss: 1.8183 - val_distribution_lambda_26_loss: 0.4050 - val_distribution_lambda_27_loss: 1.1338 - val_distribution_lambda_28_loss: 0.2795\n", - "Epoch 1411/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6426 - distribution_lambda_26_loss: 0.2748 - distribution_lambda_27_loss: 1.1227 - distribution_lambda_28_loss: 0.2452 - val_loss: 1.8153 - val_distribution_lambda_26_loss: 0.4023 - val_distribution_lambda_27_loss: 1.1315 - val_distribution_lambda_28_loss: 0.2814\n", - "Epoch 1412/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6965 - distribution_lambda_26_loss: 0.2804 - distribution_lambda_27_loss: 1.1505 - distribution_lambda_28_loss: 0.2657 - val_loss: 1.8345 - val_distribution_lambda_26_loss: 0.4169 - val_distribution_lambda_27_loss: 1.1306 - val_distribution_lambda_28_loss: 0.2870\n", - "Epoch 1413/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6683 - distribution_lambda_26_loss: 0.2938 - distribution_lambda_27_loss: 1.1290 - distribution_lambda_28_loss: 0.2455 - val_loss: 1.8302 - val_distribution_lambda_26_loss: 0.4211 - val_distribution_lambda_27_loss: 1.1267 - val_distribution_lambda_28_loss: 0.2824\n", - "Epoch 1414/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6318 - distribution_lambda_26_loss: 0.2861 - distribution_lambda_27_loss: 1.1085 - distribution_lambda_28_loss: 0.2372 - val_loss: 1.8295 - val_distribution_lambda_26_loss: 0.4248 - val_distribution_lambda_27_loss: 1.1256 - val_distribution_lambda_28_loss: 0.2791\n", - "Epoch 1415/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6503 - distribution_lambda_26_loss: 0.2964 - distribution_lambda_27_loss: 1.1161 - distribution_lambda_28_loss: 0.2378 - val_loss: 1.8168 - val_distribution_lambda_26_loss: 0.4143 - val_distribution_lambda_27_loss: 1.1216 - val_distribution_lambda_28_loss: 0.2808\n", - "Epoch 1416/2500\n", - "8/8 [==============================] - 0s 7ms/step - loss: 1.6750 - distribution_lambda_26_loss: 0.2846 - distribution_lambda_27_loss: 1.1224 - distribution_lambda_28_loss: 0.2680 - val_loss: 1.8054 - val_distribution_lambda_26_loss: 0.4101 - val_distribution_lambda_27_loss: 1.1189 - val_distribution_lambda_28_loss: 0.2763\n", - "Epoch 1417/2500\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8/8 [==============================] - 0s 6ms/step - loss: 1.6381 - distribution_lambda_26_loss: 0.2740 - distribution_lambda_27_loss: 1.1117 - distribution_lambda_28_loss: 0.2524 - val_loss: 1.8139 - val_distribution_lambda_26_loss: 0.4185 - val_distribution_lambda_27_loss: 1.1185 - val_distribution_lambda_28_loss: 0.2769\n", - "Epoch 1418/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.5959 - distribution_lambda_26_loss: 0.2659 - distribution_lambda_27_loss: 1.1019 - distribution_lambda_28_loss: 0.2281 - val_loss: 1.8195 - val_distribution_lambda_26_loss: 0.4156 - val_distribution_lambda_27_loss: 1.1225 - val_distribution_lambda_28_loss: 0.2813\n", - "Epoch 1419/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6285 - distribution_lambda_26_loss: 0.2839 - distribution_lambda_27_loss: 1.1172 - distribution_lambda_28_loss: 0.2274 - val_loss: 1.8219 - val_distribution_lambda_26_loss: 0.4192 - val_distribution_lambda_27_loss: 1.1207 - val_distribution_lambda_28_loss: 0.2820\n", - "Epoch 1420/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.5751 - distribution_lambda_26_loss: 0.2813 - distribution_lambda_27_loss: 1.0689 - distribution_lambda_28_loss: 0.2249 - val_loss: 1.8140 - val_distribution_lambda_26_loss: 0.4139 - val_distribution_lambda_27_loss: 1.1180 - val_distribution_lambda_28_loss: 0.2821\n" - ] - } - ], - "source": [ - "load_model_gk = True# load scaler and model weights for goalkeeper player predictor\n", - "refit_model_gk = True\n", - "\n", - "if(load_model_gk):\n", - " scaler_gk = pickle.load(open('saves/scaler_gk.pkl', 'rb'))\n", - " \n", - " X_gk_train = scaler_gk.transform(X_gk_train_)\n", - " X_gk_test = scaler_gk.transform(X_gk_test_)\n", - " \n", - " \n", - "n_epochs = 2500\n", - "\n", - "n_samples = X_gk_train.shape[0]\n", - "\n", - "batch_size = 128\n", - "\n", - "X_gk_len = X_gk_train.shape[1]\n", - "y_gk_len = y_gk_train.shape[1]\n", - "\n", - "\n", - "#tailweight_param = 1.1\n", - "\n", - "tailweight_min = 0.5\n", - "tailweight_range = 1.1\n", - "\n", - "\n", - "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 30)\n", - "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", - "\n", - "\n", - "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (inputs)\n", - "x = tfk.layers.Dropout(0.2)(x)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "\n", - "\n", - "prob_dist_params = 4\n", - "\n", - "def prob_dist(t): \n", - " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", - " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", - " allow_nan_stats = False)\n", - "\n", - "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", - "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", - "\n", - "x2 = tfk.layers.Dense(16, activation=\"sigmoid\")(x)\n", - "\n", - "x22 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", - "out_2 = tfp.layers.DistributionLambda(prob_dist)(x22)\n", - "\n", - "x23 = tfk.layers.Dense(1, activation=\"sigmoid\")(x2)\n", - "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x23)\n", - "\n", - "\n", - "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", - "\n", - "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", - " loss=neg_log_likelihood)\n", - "\n", - "if(load_model_gk):\n", - " modelb_gk.load_weights('saves/modelb_gk')\n", - "\n", - "if( (not load_model_gk) or refit_model_gk): \n", - " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", - " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", - " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "39a9bdc6", - "metadata": {}, - "outputs": [], - "source": [ - "def sample_predict_gk(X, iterations = 100):\n", - " y = np.zeros((3, X.shape[0]))\n", - " \n", - " dist = modelb_gk(X)\n", - " \n", - " for i in range(iterations):\n", - " y[0, :] += dist[0].sample()\n", - " y[1, :] += dist[1].sample()\n", - " y[2, :] += dist[2].sample()\n", - " \n", - " return y.transpose() / iterations\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c41cf448", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.26941154847581295\n", - "0.6648856762830053\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", - "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", - "\n", - "plt.show()\n", - "\n", - "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", - "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "91869883", - "metadata": {}, - "source": [ - "Use the following codes to save the scalers and the model weights" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "cecf5392", - "metadata": {}, - "outputs": [], - "source": [ - "save_model_of = True\n", - "save_model_gk = True\n", - "\n", - "if(save_model_of):\n", - " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", - " modelb.save_weights('saves/modelb')\n", - " \n", - "if(save_model_gk):\n", - " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", - " modelb_gk.save_weights('saves/modelb_gk')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "32635a0e", - "metadata": {}, - "source": [ - "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", - "\n", - "Estimate prediction mean and sigma (using a custom definitions).\n", - "\n", - "Generate a plot.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ddf433f9", - "metadata": {}, - "outputs": [], - "source": [ - "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", - " if(players['r'][player] == 'P'):\n", - " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", - "\n", - " x_ptest = np.array(ptest)[:, 3:]\n", - " r = np.array(ptest)[0, 0]\n", - "\n", - " # add home and role\n", - " xadd = np.zeros((1, 1))\n", - " xadd[0, 0] = home\n", - "\n", - " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", - "\n", - " x_scaled = scaler_gk.transform(x_ptest)\n", - "\n", - " dist = modelb_gk(x_scaled)\n", - " \n", - " clean_shoot_prob = dist[2].probs.numpy()[0]\n", - " else:\n", - " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", - "\n", - " x_ptest = np.array(ptest)[:, 4:]\n", - " r = np.array(ptest)[0, 0]\n", - "\n", - " # add home and role\n", - " xadd = np.zeros((1, 4))\n", - " xadd[0, 0] = home\n", - " xadd[0, 1] = r == 'D'\n", - " xadd[0, 2] = r == 'C'\n", - " xadd[0, 3] = r == 'A'\n", - "\n", - " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", - "\n", - " x_scaled = scaler.transform(x_ptest)\n", - "\n", - " dist = modelb(x_scaled)\n", - " \n", - " \n", - " x = np.arange(0, 40, 0.002)\n", - "\n", - " px1 = dist[0].prob(x);\n", - " px2 = dist[1].prob(x);\n", - "\n", - " \n", - " #sample1 = dist[0].sample(10000)\n", - " #sample2 = dist[1].sample(10000)\n", - " \n", - " m1 = np.average(x, weights = px1)\n", - " m2 = np.average(x, weights = px2)\n", - " \n", - " #m1 = np.mean(sample1)\n", - " #m2 = np.mean(sample2)\n", - " \n", - " #s1 = np.std(sample1)\n", - " #s2 = np.std(sample2)\n", - " \n", - " # not standard deviation, but expected range extimated by quantile \n", - " \n", - " if(players['r'][player] == 'P'):\n", - " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", - " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", - " else:\n", - " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", - " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", - " \n", - "\n", - " \n", - " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", - " y_pred_m = np.array([m1, m2]).flatten()\n", - " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", - " y_pred_s = np.array([s1, s2]).flatten()\n", - " \n", - " clean_sheet_text = ''\n", - " if(players['r'][player] == 'P'):\n", - " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", - " \n", - " if(plot):\n", - " ax = plt.gca()\n", - " \n", - " plt.plot(x, px1, \n", - " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", - " color = 'b')\n", - " plt.plot(x, px2, \n", - " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", - " color = 'g')\n", - " \n", - " plt.fill_between(x, px1, color = 'lightblue')\n", - " plt.fill_between(x, px2, color = 'lightgreen')\n", - " \n", - " plt.legend()\n", - " \n", - " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", - " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", - " \n", - " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", - " \n", - " plt.xlim([0, 15])\n", - " \n", - " if(players['r'][player] == 'P'): \n", - " plt.ylim([0, 2.5])\n", - " else:\n", - " plt.ylim([0, 1.5])\n", - " \n", - " plt.show()\n", - " \n", - " if(log):\n", - " print(player + ': ' + \n", - " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", - " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", - " return [y_pred_m, y_pred_s, dist]\n" - ] - }, - { - "cell_type": "markdown", - "id": "98817aa0", - "metadata": {}, - "source": [ - "Load Serie A calendar. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d31bad38", - "metadata": {}, - "outputs": [], - "source": [ - "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", - "\n", - "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", - "\n", - "matchday = 0\n", - "\n", - "for i in range(cal.shape[0]):\n", - " if(cal[i, 0][0].isnumeric()):\n", - " matchday = matchday + 1\n", - " continue\n", - " \n", - " teams = cal[i, 0].split('-')\n", - " \n", - " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", - "\n", - " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "62b9f588", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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380 rows × 3 columns

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" - ], - "text/plain": [ - " matchday team1 team2\n", - "0 1 Fiorentina Cremonese\n", - "1 1 Verona Napoli\n", - "2 1 Juventus Sassuolo\n", - "3 1 Lazio Bologna\n", - "4 1 Lecce Inter\n", - ".. ... ... ...\n", - "375 38 Lecce Bologna\n", - "376 38 Sassuolo Fiorentina\n", - "377 38 Milan Verona\n", - "378 38 Torino Inter\n", - "379 38 Udinese Juventus\n", - "\n", - "[380 rows x 3 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cal_df" - ] - }, - { - "cell_type": "markdown", - "id": "62872819", - "metadata": {}, - "source": [ - "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c58ba41d", - "metadata": {}, - "outputs": [], - "source": [ - "def PlayerMatch(player, match = 0):\n", - " team = players.loc[player]['team']\n", - " \n", - " if(match == 0):\n", - " oppteam = 'Avg'\n", - " home = 1\n", - " else:\n", - " for i in range (cal_df.shape[0]):\n", - " if(cal_df['matchday'][i] == match):\n", - " if(cal_df['team1'][i] == team):\n", - " home = 1\n", - " oppteam = cal_df['team2'][i]\n", - " elif(cal_df['team2'][i] == team):\n", - " home = 0\n", - " oppteam = cal_df['team1'][i]\n", - " \n", - " return [player, team, oppteam, home]\n", - "\n", - "def predict_player(player, match = 0, plot = 0, log = 0, oldseason = False):\n", - " [player, team, oppteam, home] = PlayerMatch(player, match)\n", - " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" - ] - }, - { - "cell_type": "markdown", - "id": "e8b63a98", - "metadata": {}, - "source": [ - "Load current matchday playing probabilities for Serie A players." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f79792b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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starterpercentage
player
Tatarusanu1.0090
Thiaw0.6565
Kjaer1.0085
Kalulu1.0090
Saelemaekers0.6060
.........
Dumfries0.4560
Mkhitaryan0.4555
Asllani0.0030
Gagliardini0.0050
Lukaku0.4060
\n", - "

438 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " starter percentage\n", - "player \n", - "Tatarusanu 1.00 90\n", - "Thiaw 0.65 65\n", - "Kjaer 1.00 85\n", - "Kalulu 1.00 90\n", - "Saelemaekers 0.60 60\n", - "... ... ...\n", - "Dumfries 0.45 60\n", - "Mkhitaryan 0.45 55\n", - "Asllani 0.00 30\n", - "Gagliardini 0.00 50\n", - "Lukaku 0.40 60\n", - "\n", - "[438 rows x 2 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", - "\n", - "probables" - ] - }, - { - "cell_type": "markdown", - "id": "e4751014", - "metadata": {}, - "source": [ - "Generate prediction data for each Serie A player for the current matchday.\n", - "\n", - "Output to excel file, using a template made for data elaboration." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "5e63c2b7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret: MV 6.24 ± 0.83; FV 5.81 + 0.85 (56.5% cs)\n", - "Provedel: MV 6.24 ± 0.83; FV 4.69 + 1.04 (0.9% cs)\n", - "Vicario: MV 6.24 ± 0.83; FV 5.24 + 0.81 (22.3% cs)\n", - "Szczesny: MV 6.24 ± 0.83; FV 5.41 + 1.14 (20.8% cs)\n", - "Falcone: MV 6.24 ± 0.83; FV 5.10 + 0.81 (1.3% cs)\n", - "Silvestri: MV 6.24 ± 0.83; FV 6.32 + 0.70 (90.3% cs)\n", - "Rui Patricio: MV 6.24 ± 0.83; FV 5.21 + 1.01 (36.2% cs)\n", - "Onana: MV 6.24 ± 0.83; FV 5.92 + 0.68 (75.7% cs)\n", - "Sepe: MV 6.24 ± 0.83; FV 5.12 + 0.83 (15.6% cs)\n", - "Milinkovic-Savic V.: MV 6.24 ± 0.83; FV 5.00 + 0.95 (11.2% cs)\n", - "Musso: MV 6.24 ± 0.83; FV 3.81 + 1.49 (0.6% cs)\n", - "Maignan: MV 6.24 ± 0.83; FV 6.00 + 0.73 (73.6% cs)\n", - "Carnesecchi: MV 6.24 ± 0.83; FV 4.57 + 1.04 (0.2% cs)\n", - "Di Gregorio: MV 6.25 ± 0.82; FV 4.11 + 1.48 (1.1% cs)\n", - "Audero: MV 6.24 ± 0.83; FV 4.55 + 1.06 (0.6% cs)\n", - "Montipo': MV 6.24 ± 0.83; FV 4.98 + 0.94 (6.1% cs)\n", - "Skorupski: MV 6.24 ± 0.83; FV 4.59 + 0.97 (0.3% cs)\n", - "Consigli: MV 6.24 ± 0.83; FV 4.17 + 1.27 (0.2% cs)\n", - "Dragowski: MV 6.24 ± 0.83; FV 5.35 + 0.75 (19.2% cs)\n", - "Terracciano: MV 6.24 ± 0.83; FV 4.16 + 1.30 (0.1% cs)\n", - "Tatarusanu: MV 6.24 ± 0.83; FV 4.59 + 1.26 (1.1% cs)\n", - "Handanovic: MV 6.24 ± 0.83; FV 5.88 + 0.68 (73.4% cs)\n", - "Sportiello: MV 6.23 ± 0.83; FV 3.84 + 1.56 (0.6% cs)\n", - "Perin: MV 6.25 ± 0.83; FV 5.67 + 1.10 (36.8% cs)\n", - "Zoet: MV 6.24 ± 0.83; FV 4.94 + 0.82 (6.0% cs)\n", - "Ochoa: MV 6.24 ± 0.83; FV 4.69 + 0.90 (2.1% cs)\n", - "Pegolo: MV 6.19 ± 0.84; FV 3.81 + 1.55 (0.2% cs)\n", - "Gollini: MV 6.24 ± 0.83; FV 5.18 + 0.91 (12.0% cs)\n", - "Mirante no data\n", - "Sarr M. no data\n", - "Lamanna no data\n", - "Ujkani no data\n", - "Berisha: MV 6.24 ± 0.83; FV 5.34 + 0.97 (24.2% cs)\n", - "Marchetti no data\n", - "Perilli no data\n", - "Padelli: MV 6.24 ± 0.83; FV 4.85 + 0.92 (0.8% cs)\n", - "Perisan no data\n", - "Bardi: MV 6.24 ± 0.83; FV 4.52 + 1.11 (1.2% cs)\n", - "Cordaz no data\n", - "Pinsoglio: MV 6.24 ± 0.83; FV 4.93 + 0.88 (5.9% cs)\n", - "Fiorillo: MV 6.24 ± 0.83; FV 4.54 + 1.12 (0.1% cs)\n", - "Cragno: MV 6.24 ± 0.83; FV 4.31 + 1.32 (0.0% cs)\n", - "Sirigu: MV 6.24 ± 0.83; FV 4.39 + 1.28 (0.0% cs)\n", - "Cerofolini no data\n", - "Rossi F.: MV 6.24 ± 0.83; FV 4.15 + 1.31 (0.1% cs)\n", - "Ravaglia F. no data\n", - "Brancolini no data\n", - "Bleve no data\n", - "Berardi A.: MV 6.24 ± 0.83; FV 4.39 + 1.22 (0.1% cs)\n", - "Russo A. no data\n", - "Gemello: MV 6.24 ± 0.83; FV 6.00 + 0.89 (75.0% cs)\n", - "Ravaglia: MV 6.24 ± 0.83; FV 4.33 + 1.32 (0.0% cs)\n", - "Boer no data\n", - "Adamonis no data\n", - "Marfella: MV 6.24 ± 0.83; FV 5.68 + 0.90 (40.5% cs)\n", - "Zovko: MV 6.24 ± 0.83; FV 4.84 + 0.84 (1.3% cs)\n", - "Piana no data\n", - "Bagnolini no data\n", - "Luis Maximiano: MV 6.10 ± 0.86; FV 4.82 + 1.71 (56.1% cs)\n", - "Svilar no data\n", - "Sorrentino A. no data\n", - "Ciezkowski no data\n", - "Saro no data\n", - "Vasquez D. no data\n", - "Turk no data\n", - "Dimarco: MV 6.29 ± 0.95; FV 6.93 + 2.01\n", - "Smalling: MV 6.22 ± 1.00; FV 6.64 + 1.69\n", - "Doig: MV 6.32 ± 1.21; FV 7.07 + 2.57\n", - "Carlos Augusto: MV 6.04 ± 1.05; FV 6.52 + 2.02\n", - "Kim: MV 6.32 ± 0.94; FV 6.85 + 1.93\n", - "Posch: MV 6.17 ± 1.20; FV 6.96 + 2.72\n", - "Di Lorenzo: MV 6.24 ± 0.80; FV 6.70 + 1.63\n", - "Danilo: MV 6.27 ± 0.94; FV 6.74 + 1.77\n", - "Hernandez T.: MV 6.04 ± 1.15; FV 6.44 + 1.95\n", - "Udogie: MV 6.15 ± 1.10; FV 6.75 + 2.24\n", - "Parisi: MV 6.28 ± 0.90; FV 6.93 + 1.99\n", - "Mario Rui: MV 6.23 ± 0.79; FV 6.57 + 1.45\n", - "Romagnoli: MV 6.13 ± 0.99; FV 6.30 + 1.31\n", - "Bastoni S.: MV 6.15 ± 0.95; FV 6.54 + 1.64\n", - "Mazzocchi: MV 6.18 ± 1.05; FV 6.69 + 1.95\n", - "Valeri: MV 5.93 ± 0.81; FV 6.08 + 0.99\n", - "Tomori: MV 6.13 ± 0.91; FV 6.40 + 1.32\n", - "Scalvini: MV 5.82 ± 1.11; FV 5.97 + 1.53\n", - "Toloi: MV 5.99 ± 0.97; FV 6.18 + 1.29\n", - "Demiral: MV 5.83 ± 0.90; FV 5.92 + 1.15\n", - "Maehle: MV 5.83 ± 1.00; FV 5.94 + 1.35\n", - "Dumfries: MV 6.22 ± 1.09; FV 6.79 + 2.06\n", - "Baschirotto: MV 6.17 ± 1.06; FV 6.57 + 1.81\n", - "Bijol: MV 6.00 ± 1.01; FV 6.29 + 1.61\n", - "Schuurs: MV 6.16 ± 0.81; FV 6.36 + 1.17\n", - "Juan Jesus: MV 6.19 ± 0.70; FV 6.51 + 1.31\n", - "Depaoli: MV 6.07 ± 1.04; FV 6.54 + 1.96\n", - "Mancini: MV 6.10 ± 0.72; FV 6.14 + 0.80\n", - "Ibanez: MV 6.09 ± 1.00; FV 6.44 + 1.57\n", - "Rodrigo Becao: MV 6.16 ± 0.88; FV 6.41 + 1.29\n", - "Ebuehi: MV 6.17 ± 0.80; FV 6.55 + 1.41\n", - "Gosens: MV 6.09 ± 0.66; FV 6.41 + 1.16\n", - "Darmian: MV 6.18 ± 0.80; FV 6.68 + 1.61\n", - "Reca: MV 6.03 ± 1.02; FV 6.31 + 1.56\n", - "Bremer: MV 6.08 ± 1.02; FV 6.32 + 1.43\n", - "Sernicola: MV 5.78 ± 0.84; FV 5.86 + 1.03\n", - "Rrahmani: MV 6.30 ± 0.91; FV 6.83 + 1.87\n", - "Vojvoda: MV 6.02 ± 0.99; FV 6.26 + 1.48\n", - "Holm: MV 6.06 ± 0.87; FV 6.28 + 1.28\n", - "Bastoni: MV 6.17 ± 0.83; FV 6.30 + 1.08\n", - "Milenkovic: MV 5.84 ± 1.08; FV 5.92 + 1.41\n", - "Kalulu: MV 5.82 ± 1.04; FV 5.84 + 1.14\n", - "Martinez Quarta: MV 5.90 ± 1.03; FV 5.98 + 1.21\n", - "Casale: MV 5.94 ± 0.94; FV 6.01 + 1.20\n", - "Perez N.: MV 6.09 ± 0.87; FV 6.13 + 0.97\n", - "Olivera: MV 6.17 ± 0.70; FV 6.58 + 1.42\n", - "Izzo: MV 5.93 ± 0.79; FV 5.93 + 0.75\n", - "Luperto: MV 6.13 ± 0.93; FV 6.32 + 1.23\n", - "Skriniar: MV 6.02 ± 0.74; FV 5.97 + 0.69\n", - "Rodriguez R.: MV 5.96 ± 0.87; FV 5.98 + 0.97\n", - "Marusic: MV 5.99 ± 0.81; FV 6.02 + 0.88\n", - "Lazzari: MV 6.03 ± 0.85; FV 6.07 + 0.95\n", - "Kyriakopoulos: MV 5.99 ± 0.94; FV 6.08 + 1.15\n", - "Ampadu: MV 5.88 ± 0.88; FV 5.88 + 1.02\n", - "Ismajli: MV 6.15 ± 0.74; FV 6.16 + 0.85\n", - "Llorente D.: MV 5.98 ± 0.98; FV 6.19 + 1.35\n", - "Cambiaso: MV 6.00 ± 0.81; FV 6.02 + 0.87\n", - "Hysaj: MV 5.96 ± 0.71; FV 6.00 + 0.73\n", - "Biraghi: MV 5.96 ± 0.75; FV 5.97 + 0.72\n", - "Medel: MV 5.99 ± 0.78; FV 5.92 + 0.73\n", - "Bonucci: MV 6.20 ± 0.97; FV 6.66 + 1.78\n", - "Calabria: MV 5.95 ± 1.00; FV 6.17 + 1.48\n", - "Acerbi: MV 6.15 ± 0.74; FV 6.24 + 0.94\n", - "Spinazzola: MV 5.95 ± 0.66; FV 6.03 + 0.65\n", - "Lykogiannis: MV 6.01 ± 0.71; FV 6.08 + 0.79\n", - "Pellegrini Lu.: MV 6.02 ± 0.77; FV 6.06 + 0.86\n", - "Djidji: MV 5.87 ± 1.01; FV 5.99 + 1.42\n", - "Lazaro: MV 5.91 ± 1.06; FV 6.07 + 1.40\n", - "Augello: MV 5.68 ± 0.90; FV 5.72 + 1.07\n", - "Gallo: MV 5.96 ± 0.75; FV 5.93 + 0.72\n", - "Singo: MV 6.03 ± 0.91; FV 6.24 + 1.33\n", - "Mari': MV 5.80 ± 1.11; FV 5.90 + 1.49\n", - "Caldirola: MV 5.77 ± 0.96; FV 5.72 + 0.97\n", - "Dodo': MV 5.77 ± 0.95; FV 5.65 + 0.95\n", - "De Vrij: MV 6.05 ± 0.78; FV 6.14 + 0.89\n", - "Patric: MV 6.00 ± 0.93; FV 6.01 + 1.00\n", - "Faraoni: MV 6.04 ± 0.83; FV 6.32 + 1.33\n", - "Ceccherini: MV 5.96 ± 0.96; FV 6.12 + 1.35\n", - "Hateboer: MV 5.73 ± 0.86; FV 5.75 + 1.02\n", - "Rogerio: MV 5.77 ± 0.93; FV 5.74 + 1.09\n", - "Umtiti: MV 5.92 ± 1.03; FV 6.00 + 1.18\n", - "Aina: MV 6.00 ± 1.03; FV 6.25 + 1.51\n", - "Birindelli: MV 5.80 ± 0.74; FV 5.80 + 0.80\n", - "Lucumi': MV 5.94 ± 0.84; FV 5.91 + 0.89\n", - "Ehizibue: MV 5.93 ± 0.92; FV 6.20 + 1.55\n", - "Bianchetti: MV 5.54 ± 1.02; FV 5.60 + 1.15\n", - "Ferrari G.: MV 5.71 ± 1.10; FV 5.78 + 1.40\n", - "Fazio: MV 5.80 ± 1.31; FV 6.05 + 1.86\n", - "Gravillon: MV 5.92 ± 1.00; FV 6.06 + 1.41\n", - "Buongiorno: MV 5.88 ± 0.89; FV 5.87 + 0.97\n", - "Gunter: MV 5.66 ± 1.00; FV 5.62 + 1.15\n", - "Troost-Ekong: MV 5.86 ± 1.14; FV 5.93 + 1.39\n", - "Soumaoro: MV 5.88 ± 0.98; FV 5.86 + 1.01\n", - "Ceccaroni: MV 5.93 ± 1.03; FV 6.05 + 1.34\n", - "Pongracic: MV 5.96 ± 0.78; FV 5.93 + 0.76\n", - "Soppy: MV 5.77 ± 0.76; FV 5.81 + 0.84\n", - "Gendrey: MV 5.82 ± 0.70; FV 5.80 + 0.63\n", - "Hien: MV 5.91 ± 0.81; FV 5.87 + 0.83\n", - "Ferrari A.: MV 5.57 ± 1.18; FV 5.61 + 1.33\n", - "Masina: MV 6.10 ± 1.06; FV 6.62 + 2.09\n", - "Zappacosta: MV 5.93 ± 0.79; FV 6.08 + 1.03\n", - "Gyomber: MV 5.83 ± 0.85; FV 5.78 + 0.82\n", - "Alex Sandro: MV 5.82 ± 0.91; FV 5.76 + 0.84\n", - "Pezzella Giu.: MV 5.90 ± 0.68; FV 5.91 + 0.60\n", - "Bereszynski: MV 6.00 ± 0.65; FV 5.95 + 0.56\n", - "Venuti: MV 5.75 ± 0.83; FV 5.73 + 0.85\n", - "Palomino: MV 5.86 ± 1.01; FV 5.92 + 1.19\n", - "Nuytinck: MV 5.71 ± 1.10; FV 5.68 + 1.25\n", - "Marlon: MV 5.78 ± 0.76; FV 5.75 + 0.72\n", - "Magnani: MV 5.95 ± 0.84; FV 5.89 + 0.84\n", - "Colley: MV 5.67 ± 1.14; FV 5.68 + 1.36\n", - "Nikolaou: MV 5.74 ± 0.79; FV 5.64 + 0.77\n", - "Terzic: MV 6.00 ± 0.56; FV 5.93 + 0.40\n", - "Igor: MV 5.75 ± 0.98; FV 5.60 + 0.94\n", - "Toljan: MV 5.65 ± 0.86; FV 5.60 + 0.91\n", - "Zortea: MV 5.80 ± 0.84; FV 5.88 + 1.10\n", - "Dawidowicz: MV 5.87 ± 0.97; FV 5.90 + 1.23\n", - "Celik: MV 5.81 ± 0.74; FV 5.81 + 0.68\n", - "Bellanova: MV 6.05 ± 0.78; FV 6.18 + 0.93\n", - "Erlic: MV 5.75 ± 1.06; FV 5.71 + 1.18\n", - "Ballo-Toure': MV 6.14 ± 0.79; FV 6.51 + 1.35\n", - "Dest: MV 5.79 ± 0.83; FV 5.78 + 0.80\n", - "Stojanovic: MV 5.90 ± 0.76; FV 5.91 + 0.76\n", - "Amian: MV 5.80 ± 0.81; FV 5.74 + 0.85\n", - "Bradaric: MV 5.75 ± 0.83; FV 5.73 + 0.98\n", - "Daniliuc: MV 5.81 ± 1.08; FV 5.86 + 1.33\n", - "Zima: MV 5.87 ± 0.94; FV 5.90 + 1.12\n", - "De Winter: MV 5.94 ± 0.76; FV 5.87 + 0.67\n", - "Quagliata: MV 5.73 ± 0.71; FV 5.73 + 0.68\n", - "Ebosse: MV 5.75 ± 0.72; FV 5.69 + 0.64\n", - "Aiwu: MV 5.70 ± 1.06; FV 5.75 + 1.30\n", - "Lochoshvili: MV 5.58 ± 0.90; FV 5.54 + 0.96\n", - "Bronn: MV 5.80 ± 0.70; FV 5.78 + 0.61\n", - "Thiaw: MV 5.99 ± 0.83; FV 5.97 + 0.80\n", - "Zeefuik: MV 5.98 ± 0.86; FV 6.06 + 1.03\n", - "Romagnoli S.: MV 5.98 ± 1.15; FV 6.30 + 1.81\n", - "Ghiglione: MV 5.67 ± 0.97; FV 5.69 + 1.15\n", - "Rugani: MV 6.04 ± 0.62; FV 5.98 + 0.52\n", - "De Sciglio: MV 5.94 ± 0.66; FV 5.94 + 0.57\n", - "Djimsiti: MV 5.80 ± 0.79; FV 5.81 + 0.80\n", - "Caldara: MV 5.79 ± 0.96; FV 5.73 + 1.01\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Karsdorp: MV 5.88 ± 0.79; FV 5.90 + 0.74\n", - "Marchizza: MV 5.75 ± 0.76; FV 5.71 + 0.79\n", - "Kjaer: MV 5.87 ± 0.72; FV 5.83 + 0.63\n", - "Okoli: MV 5.59 ± 0.92; FV 5.53 + 0.89\n", - "Amione: MV 5.52 ± 0.85; FV 5.45 + 0.88\n", - "Ruggeri: MV 5.71 ± 0.65; FV 5.73 + 0.54\n", - "Zanoli: MV 5.70 ± 0.81; FV 5.63 + 0.83\n", - "Wisniewski: MV 5.95 ± 0.87; FV 6.00 + 1.03\n", - "Radovanovic: MV 5.68 ± 0.75; FV 5.60 + 0.71\n", - "Dermaku: MV 5.93 ± 0.88; FV 5.98 + 1.05\n", - "D'ambrosio: MV 6.16 ± 0.68; FV 6.39 + 1.11\n", - "De Silvestri: MV 5.95 ± 0.99; FV 6.14 + 1.46\n", - "Chiriches: MV 5.58 ± 1.22; FV 5.54 + 1.24\n", - "Murru: MV 5.50 ± 0.82; FV 5.44 + 0.84\n", - "Bonifazi: MV 5.83 ± 0.83; FV 5.74 + 0.82\n", - "Donati: MV 5.75 ± 0.89; FV 5.71 + 0.94\n", - "Walukiewicz: MV 5.91 ± 0.70; FV 5.91 + 0.62\n", - "Ranieri L.: MV 5.78 ± 0.85; FV 5.77 + 1.01\n", - "Gabbia: MV 5.65 ± 0.90; FV 5.57 + 0.87\n", - "Kumbulla: MV 5.87 ± 0.65; FV 5.90 + 0.56\n", - "Adopo: MV 5.75 ± 0.66; FV 5.66 + 0.67\n", - "Pirola: MV 5.63 ± 0.90; FV 5.54 + 0.92\n", - "Lovato: MV 5.73 ± 1.03; FV 5.63 + 1.05\n", - "Tuia: MV 5.80 ± 0.90; FV 5.82 + 1.11\n", - "Ferrer: MV 5.90 ± 0.89; FV 5.90 + 0.98\n", - "Antov: MV 5.69 ± 1.06; FV 5.66 + 1.13\n", - "Vasquez: MV 5.62 ± 1.00; FV 5.60 + 1.12\n", - "Ruan: MV 5.70 ± 1.07; FV 5.55 + 1.00\n", - "Ostigard: MV 6.13 ± 0.64; FV 6.09 + 0.68\n", - "Coppola D.: MV 5.80 ± 0.74; FV 5.71 + 0.73\n", - "Cacace: MV 5.94 ± 0.72; FV 5.90 + 0.63\n", - "Gatti: MV 5.95 ± 0.77; FV 5.93 + 0.72\n", - "Gila: MV 5.90 ± 0.88; FV 5.89 + 0.95\n", - "Bayeye: MV 5.91 ± 0.99; FV 6.03 + 1.38\n", - "Sambia: MV 5.83 ± 0.80; FV 5.83 + 0.79\n", - "Moutinho J.: MV 5.79 ± 0.81; FV 5.72 + 0.84\n", - "Conti: MV 5.79 ± 0.90; FV 5.84 + 1.12\n", - "Marrone: MV 5.55 ± 0.92; FV 5.47 + 0.95\n", - "Tonelli: MV 5.88 ± 0.76; FV 5.83 + 0.67\n", - "Murillo: MV 5.54 ± 0.73; FV 5.44 + 0.72\n", - "Radu: MV 6.05 ± 0.74; FV 5.91 + 0.63\n", - "Paletta: MV 5.84 ± 0.94; FV 5.88 + 1.14\n", - "Florenzi: MV 6.15 ± 0.83; FV 6.39 + 1.19\n", - "Sala: MV 5.93 ± 0.73; FV 5.95 + 0.78\n", - "Fares: MV 5.79 ± 0.80; FV 5.78 + 0.93\n", - "Romagna: MV 5.82 ± 0.98; FV 5.84 + 1.22\n", - "Cassandro: MV 5.92 ± 0.87; FV 5.99 + 1.06\n", - "Muldur: MV 5.69 ± 0.86; FV 5.65 + 0.94\n", - "Amey: MV 6.04 ± 0.87; FV 6.12 + 1.04\n", - "Zanotti: MV 6.10 ± 0.81; FV 6.27 + 1.07\n", - "Ebosele: MV 6.03 ± 0.66; FV 5.99 + 0.59\n", - "Buta: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Abankwah: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Guessand A.: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Cabal: MV 6.00 ± 0.89; FV 6.09 + 1.07\n", - "Sosa: MV 5.76 ± 0.96; FV 5.75 + 1.10\n", - "Guarino: MV 6.06 ± 0.81; FV 6.19 + 1.00\n", - "Carboni F.: MV 5.90 ± 0.92; FV 5.97 + 1.06\n", - "Zaccagni: MV 6.40 ± 1.33; FV 7.40 + 3.24\n", - "Kvaratskhelia: MV 6.64 ± 1.45; FV 7.60 + 3.27\n", - "Milinkovic-Savic: MV 6.28 ± 1.28; FV 7.06 + 2.80\n", - "Barella: MV 6.38 ± 1.15; FV 7.20 + 2.56\n", - "Zielinski: MV 6.41 ± 1.10; FV 7.17 + 2.38\n", - "Luis Alberto: MV 6.31 ± 1.08; FV 7.02 + 2.29\n", - "Strefezza: MV 6.29 ± 0.94; FV 7.09 + 2.28\n", - "Felipe Anderson: MV 6.19 ± 1.25; FV 7.02 + 2.81\n", - "Koopmeiners: MV 6.12 ± 1.16; FV 6.75 + 2.37\n", - "Calhanoglu: MV 6.33 ± 0.99; FV 7.01 + 2.14\n", - "Frattesi: MV 6.10 ± 1.20; FV 6.77 + 2.47\n", - "Diaz B.: MV 6.14 ± 1.19; FV 6.89 + 2.56\n", - "Vlasic: MV 6.13 ± 1.16; FV 6.76 + 2.27\n", - "Zambo Anguissa: MV 6.40 ± 1.11; FV 7.13 + 2.35\n", - "Elmas: MV 6.36 ± 1.08; FV 7.09 + 2.28\n", - "Miranchuk: MV 6.21 ± 1.21; FV 6.93 + 2.46\n", - "Samardzic: MV 6.22 ± 1.05; FV 6.86 + 2.14\n", - "Pereyra: MV 6.25 ± 1.09; FV 6.88 + 2.22\n", - "Politano: MV 6.29 ± 0.89; FV 6.98 + 2.05\n", - "Rabiot: MV 6.22 ± 1.12; FV 6.85 + 2.23\n", - "Ciurria: MV 6.04 ± 1.16; FV 6.67 + 2.38\n", - "Lazovic: MV 6.27 ± 1.15; FV 6.95 + 2.31\n", - "Lobotka: MV 6.18 ± 0.69; FV 6.65 + 1.51\n", - "Radonjic: MV 6.13 ± 1.00; FV 6.64 + 1.82\n", - "Ferguson: MV 6.23 ± 0.94; FV 6.81 + 1.94\n", - "Bonaventura: MV 6.09 ± 0.96; FV 6.40 + 1.49\n", - "Pessina: MV 6.11 ± 1.09; FV 6.64 + 2.05\n", - "Tonali: MV 6.10 ± 1.09; FV 6.54 + 1.91\n", - "Kostic: MV 6.20 ± 0.99; FV 6.77 + 1.94\n", - "Baldanzi: MV 6.40 ± 1.07; FV 7.27 + 2.53\n", - "Lovric: MV 6.13 ± 0.76; FV 6.48 + 1.30\n", - "Pellegrini Lo.: MV 6.06 ± 1.17; FV 6.66 + 2.28\n", - "El Shaarawy: MV 6.19 ± 0.97; FV 6.84 + 2.00\n", - "Orsolini: MV 6.29 ± 1.36; FV 7.26 + 3.30\n", - "Ikone': MV 6.04 ± 1.10; FV 6.46 + 1.92\n", - "Candreva: MV 6.05 ± 1.14; FV 6.45 + 1.98\n", - "Bennacer: MV 6.14 ± 0.83; FV 6.43 + 1.25\n", - "Pasalic: MV 5.91 ± 1.01; FV 6.31 + 1.73\n", - "Mkhitaryan: MV 6.16 ± 0.90; FV 6.68 + 1.69\n", - "Colpani: MV 5.96 ± 0.87; FV 6.43 + 1.75\n", - "Pogba: MV 6.04 ± 0.83; FV 6.19 + 1.04\n", - "Chiesa: MV 6.19 ± 0.98; FV 6.69 + 1.81\n", - "Bandinelli: MV 6.06 ± 0.80; FV 6.39 + 1.28\n", - "Matic: MV 6.12 ± 0.78; FV 6.38 + 1.15\n", - "Fagioli: MV 6.19 ± 0.96; FV 6.75 + 1.86\n", - "Messias: MV 5.95 ± 1.07; FV 6.32 + 1.84\n", - "Arslan: MV 6.01 ± 0.70; FV 6.10 + 0.79\n", - "Ricci S.: MV 6.17 ± 0.91; FV 6.52 + 1.49\n", - "Ranocchia F.: MV 6.01 ± 0.92; FV 6.43 + 1.65\n", - "Verdi: MV 6.17 ± 0.84; FV 6.66 + 1.63\n", - "Sensi: MV 6.00 ± 1.10; FV 6.41 + 1.95\n", - "Barak: MV 5.83 ± 0.84; FV 5.97 + 1.20\n", - "Soriano: MV 6.04 ± 0.74; FV 6.20 + 1.02\n", - "Dominguez: MV 6.23 ± 1.09; FV 6.81 + 2.17\n", - "Vilhena: MV 5.86 ± 1.00; FV 6.11 + 1.54\n", - "Brozovic: MV 6.22 ± 0.82; FV 6.67 + 1.59\n", - "Cristante: MV 5.95 ± 0.85; FV 6.09 + 1.06\n", - "Thorstvedt: MV 5.87 ± 0.89; FV 6.06 + 1.30\n", - "De Ketelaere: MV 5.82 ± 0.79; FV 5.88 + 0.90\n", - "Saponara: MV 6.09 ± 1.12; FV 6.49 + 1.89\n", - "Vecino: MV 5.90 ± 0.89; FV 6.01 + 1.22\n", - "Locatelli: MV 6.09 ± 0.70; FV 6.20 + 0.87\n", - "Zaniolo: MV 5.91 ± 1.00; FV 6.18 + 1.59\n", - "Duda: MV 6.08 ± 0.87; FV 6.26 + 1.17\n", - "Maldini: MV 5.96 ± 0.88; FV 6.24 + 1.39\n", - "Marin: MV 6.08 ± 0.99; FV 6.44 + 1.63\n", - "Zalewski: MV 6.01 ± 0.68; FV 6.11 + 0.74\n", - "Bajrami: MV 6.11 ± 1.12; FV 6.70 + 2.19\n", - "Coulibaly L.: MV 5.94 ± 1.15; FV 6.22 + 1.79\n", - "Gonzalez J.: MV 6.04 ± 0.84; FV 6.27 + 1.26\n", - "De Roon: MV 5.91 ± 0.75; FV 5.93 + 0.77\n", - "Mandragora: MV 5.91 ± 0.88; FV 5.98 + 1.16\n", - "Wijnaldum: MV 5.93 ± 0.91; FV 6.01 + 1.05\n", - "Bourabia: MV 5.96 ± 0.78; FV 5.98 + 0.84\n", - "Sottil: MV 6.07 ± 1.11; FV 6.45 + 1.84\n", - "Aebischer: MV 6.02 ± 0.85; FV 6.30 + 1.39\n", - "Ederson D.s.: MV 5.82 ± 0.79; FV 5.90 + 0.99\n", - "Miretti: MV 6.03 ± 0.72; FV 6.18 + 0.90\n", - "Blin: MV 6.01 ± 0.64; FV 5.99 + 0.59\n", - "Hjulmand: MV 5.98 ± 1.07; FV 6.04 + 1.21\n", - "Cataldi: MV 5.99 ± 0.79; FV 6.00 + 0.83\n", - "Djuricic: MV 5.72 ± 0.90; FV 5.81 + 1.17\n", - "Linetty: MV 5.96 ± 0.88; FV 6.12 + 1.28\n", - "Haas: MV 6.02 ± 0.75; FV 6.27 + 1.11\n", - "Walace: MV 5.92 ± 0.74; FV 5.88 + 0.68\n", - "Agudelo: MV 5.89 ± 0.70; FV 5.91 + 0.72\n", - "Pobega: MV 6.00 ± 0.88; FV 6.28 + 1.30\n", - "Camara Ma.: MV 6.12 ± 0.70; FV 6.17 + 0.81\n", - "Paredes: MV 5.87 ± 0.68; FV 5.86 + 0.57\n", - "Ndombele': MV 6.05 ± 0.65; FV 5.99 + 0.58\n", - "Nicolussi Caviglia: MV 5.87 ± 1.19; FV 6.24 + 1.99\n", - "Rovella: MV 5.91 ± 0.95; FV 5.92 + 0.95\n", - "Amrabat: MV 5.87 ± 0.89; FV 5.84 + 0.96\n", - "Tameze: MV 5.96 ± 0.79; FV 6.03 + 0.94\n", - "Gyasi: MV 5.89 ± 1.01; FV 6.04 + 1.49\n", - "Ilic: MV 6.00 ± 0.99; FV 6.23 + 1.48\n", - "Matheus Henrique: MV 5.89 ± 0.84; FV 6.03 + 1.16\n", - "Harroui: MV 5.84 ± 0.80; FV 6.00 + 1.16\n", - "Volpato: MV 6.02 ± 1.11; FV 6.60 + 2.21\n", - "Pickel: MV 5.67 ± 0.80; FV 5.70 + 0.89\n", - "Moro N.: MV 6.05 ± 0.66; FV 6.12 + 0.78\n", - "Duncan: MV 5.88 ± 0.76; FV 5.92 + 0.90\n", - "Machin: MV 5.86 ± 0.94; FV 5.94 + 1.23\n", - "Cuadrado: MV 6.04 ± 0.88; FV 6.19 + 1.10\n", - "Ekdal: MV 5.82 ± 0.71; FV 5.77 + 0.71\n", - "Meite': MV 5.73 ± 1.03; FV 5.76 + 1.28\n", - "Schouten: MV 6.01 ± 0.78; FV 6.02 + 0.84\n", - "Obiang: MV 5.91 ± 0.65; FV 5.93 + 0.64\n", - "Kovalenko: MV 5.97 ± 0.77; FV 6.07 + 0.95\n", - "Crnigoj: MV 5.93 ± 0.89; FV 6.10 + 1.23\n", - "Basic: MV 5.95 ± 0.67; FV 6.02 + 0.73\n", - "Asllani: MV 6.07 ± 0.63; FV 6.08 + 0.65\n", - "Sabiri: MV 5.76 ± 0.84; FV 5.80 + 1.04\n", - "Terracciano F.: MV 6.09 ± 0.63; FV 6.13 + 0.72\n", - "Castagnetti: MV 5.72 ± 0.70; FV 5.70 + 0.67\n", - "Oudin: MV 5.82 ± 0.65; FV 5.86 + 0.60\n", - "Grassi: MV 6.04 ± 0.67; FV 6.00 + 0.62\n", - "Krunic: MV 5.89 ± 0.76; FV 5.95 + 0.78\n", - "Rincon: MV 5.68 ± 0.79; FV 5.60 + 0.85\n", - "Miguel Veloso: MV 6.01 ± 0.72; FV 6.05 + 0.79\n", - "Leris: MV 5.70 ± 0.76; FV 5.66 + 0.87\n", - "Esposito Sa.: MV 5.92 ± 0.95; FV 5.97 + 1.10\n", - "Henderson L.: MV 6.01 ± 0.71; FV 6.16 + 0.90\n", - "Lopez M.: MV 5.85 ± 0.90; FV 5.84 + 1.09\n", - "Cuisance: MV 5.70 ± 0.72; FV 5.64 + 0.74\n", - "Saelemaekers: MV 5.82 ± 0.78; FV 5.89 + 0.82\n", - "Maggiore: MV 5.93 ± 0.81; FV 6.08 + 1.06\n", - "Akpa Akpro: MV 6.05 ± 0.82; FV 6.20 + 1.05\n", - "Maleh: MV 5.95 ± 0.74; FV 6.10 + 0.99\n", - "Romero L.: MV 6.14 ± 1.06; FV 6.71 + 2.08\n", - "Ceide: MV 5.80 ± 0.71; FV 5.83 + 0.73\n", - "D'alessandro: MV 6.02 ± 0.67; FV 6.10 + 0.74\n", - "Benassi: MV 5.71 ± 0.73; FV 5.75 + 0.81\n", - "Gagliardini: MV 5.97 ± 0.63; FV 6.02 + 0.62\n", - "Vieira: MV 5.87 ± 0.73; FV 5.91 + 0.98\n", - "Bianco: MV 6.00 ± 0.85; FV 6.07 + 1.01\n", - "Vranckx: MV 6.07 ± 0.74; FV 6.21 + 0.88\n", - "Galdames: MV 5.76 ± 1.04; FV 5.84 + 1.35\n", - "Marcos Antonio: MV 5.94 ± 0.61; FV 5.92 + 0.51\n", - "Fazzini: MV 5.87 ± 0.63; FV 5.86 + 0.54\n", - "Sulemana I.: MV 5.93 ± 0.64; FV 5.95 + 0.62\n", - "Tahirovic: MV 5.96 ± 0.60; FV 5.97 + 0.50\n", - "Abildgaard: MV 5.97 ± 0.85; FV 6.05 + 1.06\n", - "Barberis: MV 5.87 ± 0.98; FV 5.92 + 1.14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Kastanos: MV 5.82 ± 0.69; FV 5.83 + 0.69\n", - "Vignato: MV 6.05 ± 0.68; FV 6.09 + 0.71\n", - "Valoti: MV 5.72 ± 0.67; FV 5.66 + 0.65\n", - "Winks: MV 5.79 ± 1.00; FV 5.78 + 1.19\n", - "Askildsen: MV 5.72 ± 0.64; FV 5.69 + 0.58\n", - "Bove: MV 5.73 ± 0.74; FV 5.74 + 0.82\n", - "Bohinen: MV 5.75 ± 0.62; FV 5.73 + 0.52\n", - "D'andrea: MV 5.89 ± 0.78; FV 6.05 + 1.09\n", - "Iling-Junior: MV 6.09 ± 0.69; FV 6.25 + 0.94\n", - "Cipot: MV 5.97 ± 0.96; FV 6.08 + 1.23\n", - "Bakayoko: MV 5.82 ± 0.76; FV 5.79 + 0.68\n", - "Gaetano: MV 6.05 ± 0.74; FV 6.09 + 0.81\n", - "Zurkowski: MV 6.18 ± 1.08; FV 6.69 + 2.04\n", - "Castrovilli: MV 6.01 ± 0.90; FV 6.23 + 1.34\n", - "Demme: MV 6.07 ± 0.65; FV 6.24 + 0.90\n", - "Darboe: MV 5.95 ± 0.90; FV 5.99 + 0.94\n", - "Urbanski: MV 6.00 ± 0.88; FV 6.09 + 1.10\n", - "Bertini: MV 5.93 ± 0.93; FV 6.01 + 1.20\n", - "Yepes: MV 5.71 ± 1.15; FV 5.69 + 1.29\n", - "Pyyhtia: MV 6.02 ± 0.76; FV 6.06 + 0.87\n", - "Trimboli: MV 5.78 ± 0.94; FV 5.77 + 1.15\n", - "Pafundi: MV 5.92 ± 0.85; FV 5.93 + 0.88\n", - "Helgason: MV 5.80 ± 0.62; FV 5.80 + 0.54\n", - "Adli: MV 5.87 ± 0.77; FV 5.93 + 0.83\n", - "Vignato S.: MV 5.95 ± 0.91; FV 6.06 + 1.08\n", - "Hrustic: MV 5.80 ± 0.74; FV 5.78 + 0.72\n", - "Samek: MV 5.94 ± 0.86; FV 6.01 + 1.06\n", - "Zerbin: MV 6.10 ± 0.74; FV 6.18 + 0.90\n", - "Ilkhan: MV 5.85 ± 1.06; FV 5.92 + 1.34\n", - "Degli Innocenti: MV 6.04 ± 0.81; FV 6.17 + 0.99\n", - "Acella: MV 5.76 ± 1.04; FV 5.84 + 1.35\n", - "Carboni V.: MV 6.01 ± 0.84; FV 6.13 + 1.00\n", - "Paoletti: MV 5.97 ± 0.87; FV 6.05 + 1.08\n", - "Malagrida: MV 5.80 ± 0.95; FV 5.80 + 1.17\n", - "Faticanti: MV 5.95 ± 0.89; FV 6.06 + 1.04\n", - "Osimhen: MV 6.59 ± 1.51; FV 7.92 + 4.46\n", - "Martinez L.: MV 6.47 ± 1.44; FV 7.71 + 3.99\n", - "Dybala: MV 6.43 ± 1.34; FV 7.49 + 3.41\n", - "Rafael Leao: MV 6.29 ± 1.39; FV 7.41 + 3.60\n", - "Lookman: MV 6.25 ± 1.40; FV 7.54 + 3.85\n", - "Immobile: MV 6.29 ± 1.43; FV 7.63 + 4.01\n", - "Vlahovic: MV 6.39 ± 1.45; FV 7.75 + 4.21\n", - "Arnautovic: MV 6.28 ± 1.35; FV 7.45 + 3.62\n", - "Dia: MV 6.18 ± 1.41; FV 7.42 + 3.75\n", - "Dzeko: MV 6.30 ± 1.36; FV 7.47 + 3.65\n", - "Milik: MV 6.36 ± 1.24; FV 7.27 + 2.98\n", - "Nzola: MV 6.14 ± 1.34; FV 7.26 + 3.45\n", - "Beto: MV 6.15 ± 1.23; FV 7.09 + 2.99\n", - "Giroud: MV 6.13 ± 1.26; FV 6.97 + 2.88\n", - "Abraham: MV 6.12 ± 1.30; FV 7.28 + 3.46\n", - "Deulofeu: MV 6.38 ± 1.28; FV 7.28 + 2.98\n", - "Lauriente': MV 6.20 ± 1.35; FV 7.06 + 3.08\n", - "Simeone: MV 6.38 ± 1.43; FV 7.68 + 4.03\n", - "Lozano: MV 6.35 ± 1.22; FV 7.12 + 2.50\n", - "Correa: MV 6.26 ± 1.04; FV 7.08 + 2.46\n", - "Berardi: MV 6.24 ± 1.42; FV 7.46 + 3.80\n", - "Pedro: MV 6.15 ± 1.16; FV 6.78 + 2.36\n", - "Lukaku: MV 6.08 ± 1.19; FV 6.87 + 2.65\n", - "Sanabria: MV 6.00 ± 1.16; FV 6.52 + 2.14\n", - "Thauvin: MV 6.44 ± 1.30; FV 7.38 + 3.08\n", - "Cabral: MV 5.96 ± 1.10; FV 6.42 + 1.98\n", - "Hojlund: MV 5.98 ± 1.11; FV 6.52 + 2.13\n", - "Caprari: MV 5.95 ± 1.03; FV 6.48 + 2.03\n", - "Di Maria: MV 6.33 ± 1.32; FV 7.28 + 3.16\n", - "Piatek: MV 5.92 ± 1.16; FV 6.46 + 2.17\n", - "Rebic: MV 6.18 ± 1.33; FV 6.97 + 2.88\n", - "Bonazzoli: MV 6.05 ± 1.09; FV 6.56 + 2.03\n", - "Zapata D.: MV 5.82 ± 0.93; FV 6.17 + 1.53\n", - "Kouame': MV 5.97 ± 1.09; FV 6.37 + 1.88\n", - "Gonzalez N.: MV 6.12 ± 1.18; FV 6.71 + 2.27\n", - "Brekalo: MV 5.99 ± 1.06; FV 6.37 + 1.81\n", - "Mota: MV 6.03 ± 1.21; FV 6.83 + 2.71\n", - "Kean: MV 6.11 ± 1.22; FV 6.86 + 2.68\n", - "Okereke: MV 5.76 ± 1.10; FV 6.24 + 1.89\n", - "Ceesay: MV 5.95 ± 0.89; FV 6.32 + 1.48\n", - "Colombo: MV 5.99 ± 1.10; FV 6.48 + 2.01\n", - "Dessers: MV 5.79 ± 1.04; FV 6.25 + 1.81\n", - "Muriel: MV 6.01 ± 1.19; FV 6.41 + 2.09\n", - "Pinamonti: MV 5.88 ± 1.07; FV 6.35 + 1.94\n", - "Di Francesco F.: MV 6.04 ± 0.95; FV 6.39 + 1.56\n", - "Jovic: MV 5.90 ± 1.07; FV 6.31 + 1.88\n", - "Origi: MV 5.92 ± 1.07; FV 6.26 + 1.78\n", - "Caputo: MV 6.18 ± 1.27; FV 7.17 + 3.17\n", - "Boga: MV 6.28 ± 1.22; FV 7.01 + 2.50\n", - "Cambiaghi: MV 6.24 ± 0.95; FV 6.81 + 1.87\n", - "Alvarez A.: MV 5.98 ± 1.08; FV 6.36 + 1.87\n", - "Banda: MV 5.97 ± 0.73; FV 6.09 + 0.88\n", - "Ciofani D.: MV 5.85 ± 1.05; FV 6.29 + 1.82\n", - "Petagna: MV 5.95 ± 1.11; FV 6.48 + 2.09\n", - "Barrow: MV 6.16 ± 1.24; FV 6.74 + 2.39\n", - "Djuric: MV 6.03 ± 0.72; FV 6.23 + 0.99\n", - "Henry: MV 6.10 ± 1.23; FV 6.66 + 2.33\n", - "Success: MV 5.98 ± 0.70; FV 6.09 + 0.84\n", - "Gabbiadini: MV 5.86 ± 1.16; FV 6.39 + 2.11\n", - "Zirkzee: MV 6.13 ± 1.01; FV 6.63 + 1.86\n", - "Lammers: MV 5.82 ± 0.80; FV 5.91 + 1.07\n", - "Satriano: MV 5.92 ± 0.90; FV 6.13 + 1.35\n", - "Kallon: MV 5.89 ± 0.84; FV 6.14 + 1.29\n", - "Nestorovski: MV 6.28 ± 0.99; FV 7.11 + 2.37\n", - "Raspadori: MV 6.17 ± 1.05; FV 6.79 + 2.11\n", - "Botheim: MV 5.90 ± 1.02; FV 6.27 + 1.73\n", - "Gytkjaer: MV 5.84 ± 0.90; FV 6.11 + 1.45\n", - "Solbakken: MV 6.09 ± 0.99; FV 6.36 + 1.40\n", - "Lasagna: MV 5.81 ± 0.90; FV 5.94 + 1.23\n", - "Belotti: MV 5.70 ± 0.65; FV 5.71 + 0.63\n", - "Pellegri: MV 5.94 ± 0.98; FV 6.24 + 1.62\n", - "Buonaiuto: MV 5.90 ± 0.89; FV 6.21 + 1.42\n", - "Verde: MV 6.08 ± 0.95; FV 6.46 + 1.66\n", - "Destro: MV 6.09 ± 1.30; FV 7.00 + 3.01\n", - "Seck: MV 6.10 ± 0.75; FV 6.37 + 1.16\n", - "Sansone: MV 6.01 ± 1.03; FV 6.37 + 1.77\n", - "Quagliarella: MV 5.83 ± 0.76; FV 5.93 + 1.04\n", - "Defrel: MV 5.84 ± 0.92; FV 6.09 + 1.45\n", - "Pjaca: MV 5.98 ± 0.81; FV 6.15 + 1.00\n", - "Gaich: MV 6.01 ± 1.18; FV 6.50 + 2.17\n", - "Soule': MV 6.18 ± 0.76; FV 6.57 + 1.40\n", - "Tsadjout: MV 5.83 ± 0.83; FV 6.00 + 1.20\n", - "Piccoli: MV 6.08 ± 1.23; FV 7.02 + 2.97\n", - "Shomurodov: MV 6.03 ± 1.09; FV 6.56 + 2.07\n", - "Afena-Gyan: MV 5.61 ± 0.76; FV 5.68 + 0.90\n", - "Ngonge: MV 6.09 ± 0.88; FV 6.35 + 1.33\n", - "Karamoh: MV 5.90 ± 0.98; FV 6.26 + 1.66\n", - "Ibrahimovic: MV 6.21 ± 1.25; FV 7.21 + 3.12\n", - "Pussetto: MV 5.85 ± 0.95; FV 6.13 + 1.53\n", - "Cancellieri: MV 5.80 ± 0.59; FV 5.80 + 0.54\n", - "Valencia D.: MV 5.76 ± 0.61; FV 5.64 + 0.62\n", - "Oddei: MV 6.01 ± 0.91; FV 6.22 + 1.32\n", - "Braaf: MV 5.97 ± 0.95; FV 6.16 + 1.41\n", - "Raimondo: MV 6.05 ± 0.95; FV 6.23 + 1.31\n", - "Kaio Jorge: MV 5.96 ± 0.60; FV 5.99 + 0.54\n", - "De Luca: MV 5.78 ± 0.95; FV 5.83 + 1.22\n", - "Voelkerling Persson: MV 5.84 ± 0.77; FV 5.87 + 0.82\n", - "Montevago: MV 5.63 ± 0.63; FV 5.58 + 0.63\n", - "Krollis: MV 5.95 ± 0.90; FV 6.04 + 1.14\n", - "Vivaldo: MV 5.93 ± 0.89; FV 5.98 + 0.98\n" - ] - }, - { - "data": { - "text/html": [ - "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
Rossi F.PAtalantaLazio00.016.2400650.4138364.1523730.6566035.9964780.2503040.6213451.5898294.9945010.649063-0.8560580.9787610.057899
SportielloPAtalantaLazio00.056.2299340.4136063.8428240.7812135.9874150.2522970.6155001.5897754.6774170.889610-0.6578250.9622570.630536
MussoPAtalantaLazio01.0906.2413050.4136883.8050550.7454405.9976820.2499270.6221321.5898484.5882810.860939-0.6380860.9650510.630550
ToloiDAtalantaLazio01.0905.9941160.4856076.1789810.6445575.9571240.5640840.0483940.9989745.7783100.9544990.3019211.5998860.000000
ZappacostaDAtalantaLazio00.0505.9273810.3962256.0800740.5160575.8848010.4593640.0685731.1002375.7558980.7608670.3061171.5999050.000000
............................................................
NgongeAVeronaSalernitana10.6606.0899630.4408746.3521810.6660916.0474860.5106300.0614381.0348305.9045080.9519720.3352371.5998920.000000
DjuricAVeronaSalernitana10.006.0332070.3616166.2300600.4966365.9835210.4157870.0884291.1362815.9244020.7384490.2980061.5999130.000000
BraafAVeronaSalernitana10.0555.9723260.4767096.1579600.7055975.9506810.5584650.0286161.0069615.6948271.0201010.3246051.5998760.000000
KallonAVeronaSalernitana10.0355.8940610.4205456.1435550.6454485.8155070.4739570.1223171.0853365.6341390.8335410.4236691.5998790.000000
LasagnaAVeronaSalernitana11.0705.8089270.4520285.9439230.6160455.7213140.5079060.1271941.0593285.4754300.8179980.4002291.5998800.000000
\n", - "

520 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " role team oppteam home starter vote% MV \\\n", - "player \n", - "Rossi F. P Atalanta Lazio 0 0.0 1 6.240065 \n", - "Sportiello P Atalanta Lazio 0 0.0 5 6.229934 \n", - "Musso P Atalanta Lazio 0 1.0 90 6.241305 \n", - "Toloi D Atalanta Lazio 0 1.0 90 5.994116 \n", - "Zappacosta D Atalanta Lazio 0 0.0 50 5.927381 \n", - "... ... ... ... ... ... ... ... \n", - "Ngonge A Verona Salernitana 1 0.6 60 6.089963 \n", - "Djuric A Verona Salernitana 1 0.0 0 6.033207 \n", - "Braaf A Verona Salernitana 1 0.0 55 5.972326 \n", - "Kallon A Verona Salernitana 1 0.0 35 5.894061 \n", - "Lasagna A Verona Salernitana 1 1.0 70 5.808927 \n", - "\n", - " MV std FV FV std MV loc MV scale MV skewness \\\n", - "player \n", - "Rossi F. 0.413836 4.152373 0.656603 5.996478 0.250304 0.621345 \n", - "Sportiello 0.413606 3.842824 0.781213 5.987415 0.252297 0.615500 \n", - "Musso 0.413688 3.805055 0.745440 5.997682 0.249927 0.622132 \n", - "Toloi 0.485607 6.178981 0.644557 5.957124 0.564084 0.048394 \n", - "Zappacosta 0.396225 6.080074 0.516057 5.884801 0.459364 0.068573 \n", - "... ... ... ... ... ... ... \n", - "Ngonge 0.440874 6.352181 0.666091 6.047486 0.510630 0.061438 \n", - "Djuric 0.361616 6.230060 0.496636 5.983521 0.415787 0.088429 \n", - "Braaf 0.476709 6.157960 0.705597 5.950681 0.558465 0.028616 \n", - "Kallon 0.420545 6.143555 0.645448 5.815507 0.473957 0.122317 \n", - "Lasagna 0.452028 5.943923 0.616045 5.721314 0.507906 0.127194 \n", - "\n", - " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", - "player \n", - "Rossi F. 1.589829 4.994501 0.649063 -0.856058 0.978761 \n", - "Sportiello 1.589775 4.677417 0.889610 -0.657825 0.962257 \n", - "Musso 1.589848 4.588281 0.860939 -0.638086 0.965051 \n", - "Toloi 0.998974 5.778310 0.954499 0.301921 1.599886 \n", - "Zappacosta 1.100237 5.755898 0.760867 0.306117 1.599905 \n", - "... ... ... ... ... ... \n", - "Ngonge 1.034830 5.904508 0.951972 0.335237 1.599892 \n", - "Djuric 1.136281 5.924402 0.738449 0.298006 1.599913 \n", - "Braaf 1.006961 5.694827 1.020101 0.324605 1.599876 \n", - "Kallon 1.085336 5.634139 0.833541 0.423669 1.599879 \n", - "Lasagna 1.059328 5.475430 0.817998 0.400229 1.599880 \n", - "\n", - " Clean Sheet % \n", - "player \n", - "Rossi F. 0.057899 \n", - "Sportiello 0.630536 \n", - "Musso 0.630550 \n", - "Toloi 0.000000 \n", - "Zappacosta 0.000000 \n", - "... ... \n", - "Ngonge 0.000000 \n", - "Djuric 0.000000 \n", - "Braaf 0.000000 \n", - "Kallon 0.000000 \n", - "Lasagna 0.000000 \n", - "\n", - "[520 rows x 19 columns]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "matchday_out = 22\n", - "\n", - "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", - "\n", - "for i in range(players.shape[0]):\n", - " try:\n", - " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", - " \n", - " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", - " \n", - " role = players['r'][player] \n", - " \n", - " starter = 0\n", - " voteperc = 0\n", - " \n", - " cs = 0\n", - " if(role == 'P'):\n", - " cs = dist[2].probs.numpy()[0] * 100\n", - " \n", - " if(player in probables.index):\n", - " starter = probables['starter'][player]\n", - " voteperc = probables['percentage'][player]\n", - " \n", - " row = [player, role, team, oppteam, home, \n", - " starter, voteperc, \n", - " mean[0], std[0], \n", - " mean[1], std[1], \n", - " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", - " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", - " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", - " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", - " cs]\n", - " \n", - " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", - " \n", - " output = pd.concat([output, row_df])\n", - " \n", - " except:\n", - " print(players.index[i] + ' no data')\n", - "\n", - "output = output.set_index('player')\n", - "\n", - "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", - "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", - "\n", - "output" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "6befd611", - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "template_file = 'outputs/pred_matchday_base.xlsx'\n", - "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", - "\n", - "shutil.copyfile(template_file, dest_file)\n", - "\n", - "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", - " output.to_excel(writer, sheet_name='data')" - ] - }, - { - "cell_type": "markdown", - "id": "eed7a7ac", - "metadata": {}, - "source": [ - "Predict average Serie A performance for each player" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "2b637a15", - "metadata": {}, - "outputs": [], - "source": [ - "gk_starters = ['Maignan', 'Ochoa', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", - " 'Terracciano', 'Onana', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Carnesecchi', 'Rui Patricio',\n", - " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "60d73507", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret (6.24, 0.41); (5.58, 0.42)\n", - "Provedel (6.24, 0.41); (5.95, 0.36)\n", - "Vicario (6.24, 0.41); (5.36, 0.42)\n", - "Szczesny (6.24, 0.41); (6.01, 0.36)\n", - "Falcone (6.24, 0.41); (4.85, 0.45)\n", - "Silvestri (6.24, 0.41); (5.11, 0.44)\n", - "Rui Patricio (6.24, 0.41); (5.50, 0.38)\n", - "Onana (6.19, 0.42); (4.75, 0.59)\n", - "Sepe (6.24, 0.41); (5.10, 0.46)\n", - "Milinkovic-Savic V. (6.24, 0.41); (4.90, 0.41)\n", - "Musso (6.24, 0.41); (4.99, 0.63)\n", - "Maignan (6.24, 0.41); (5.81, 0.41)\n", - "Carnesecchi (6.24, 0.41); (5.22, 0.48)\n", - "Di Gregorio (6.25, 0.41); (5.01, 0.61)\n", - "Audero (6.24, 0.41); (4.75, 0.48)\n", - "Montipo' (6.24, 0.41); (4.72, 0.52)\n", - "Skorupski (6.24, 0.41); (4.76, 0.59)\n", - "Consigli (6.24, 0.41); (4.91, 0.50)\n", - "Dragowski (6.24, 0.41); (4.97, 0.54)\n", - "Terracciano (6.18, 0.42); (4.50, 0.65)\n", - "Tatarusanu (6.18, 0.43); (3.89, 0.76)\n", - "Handanovic (5.87, 0.50); (4.47, 0.71)\n", - "Sportiello (6.23, 0.42); (5.75, 0.47)\n", - "Perin (6.24, 0.41); (5.57, 0.41)\n", - "Zoet (6.24, 0.41); (5.55, 0.43)\n", - "Ochoa (6.23, 0.42); (4.02, 0.95)\n", - "Pegolo (6.24, 0.41); (5.55, 0.40)\n", - "Gollini (6.24, 0.41); (5.16, 0.43)\n", - "Mirante no data\n", - "Sarr M. no data\n", - "Lamanna no data\n", - "Ujkani no data\n", - "Berisha (6.24, 0.41); (5.29, 0.44)\n", - "Marchetti no data\n", - "Perilli no data\n", - "Padelli (6.24, 0.41); (4.83, 0.48)\n", - "Perisan no data\n", - "Bardi (6.24, 0.41); (5.27, 0.54)\n", - "Cordaz no data\n", - "Pinsoglio (6.24, 0.41); (5.06, 0.42)\n", - "Fiorillo (6.17, 0.43); (3.83, 0.97)\n", - "Cragno (6.24, 0.41); (4.42, 0.63)\n", - "Sirigu (6.24, 0.41); (4.39, 0.64)\n", - "Cerofolini no data\n", - "Rossi F. (6.00, 0.46); (3.42, 1.07)\n", - "Ravaglia F. no data\n", - "Brancolini no data\n", - "Bleve no data\n", - "Berardi A. (6.24, 0.41); (4.34, 0.60)\n", - "Russo A. no data\n", - "Gemello (6.24, 0.41); (5.79, 0.40)\n", - "Ravaglia (6.24, 0.41); (4.34, 0.66)\n", - "Boer no data\n", - "Adamonis no data\n", - "Marfella (6.24, 0.41); (5.13, 0.46)\n", - "Zovko (5.74, 0.52); (2.94, 1.36)\n", - "Piana no data\n", - "Bagnolini no data\n", - "Luis Maximiano (6.30, 0.40); (6.35, 0.47)\n", - "Svilar no data\n", - "Sorrentino A. no data\n", - "Ciezkowski no data\n", - "Saro no data\n", - "Vasquez D. no data\n", - "Turk no data\n", - "Dimarco (6.22, 0.50); (6.73, 0.92)\n", - "Smalling (6.24, 0.51); (6.68, 0.89)\n", - "Doig (6.21, 0.58); (6.85, 1.17)\n", - "Carlos Augusto (6.10, 0.55); (6.59, 1.02)\n", - "Kim (6.26, 0.48); (6.60, 0.80)\n", - "Posch (6.12, 0.59); (6.78, 1.23)\n", - "Di Lorenzo (6.23, 0.43); (6.55, 0.72)\n", - "Danilo (6.27, 0.51); (6.68, 0.87)\n", - "Hernandez T. (6.03, 0.60); (6.44, 1.02)\n", - "Udogie (6.09, 0.55); (6.55, 1.01)\n", - "Parisi (6.21, 0.44); (6.63, 0.79)\n", - "Mario Rui (6.22, 0.44); (6.47, 0.67)\n", - "Romagnoli (6.19, 0.47); (6.42, 0.68)\n", - "Bastoni S. (6.14, 0.50); (6.58, 0.92)\n", - "Mazzocchi (6.12, 0.50); (6.55, 0.89)\n", - "Valeri (6.08, 0.37); (6.31, 0.55)\n", - "Tomori (6.12, 0.48); (6.37, 0.69)\n", - "Scalvini (6.11, 0.54); (6.48, 0.89)\n", - "Toloi (6.18, 0.50); (6.51, 0.77)\n", - "Demiral (6.08, 0.46); (6.33, 0.68)\n", - "Maehle (5.98, 0.50); (6.22, 0.76)\n", - "Dumfries (6.04, 0.55); (6.44, 0.97)\n", - "Baschirotto (6.19, 0.54); (6.64, 0.96)\n", - "Bijol (5.92, 0.53); (6.12, 0.77)\n", - "Schuurs (6.15, 0.37); (6.23, 0.47)\n", - "Juan Jesus (6.19, 0.38); (6.45, 0.62)\n", - "Depaoli (5.96, 0.48); (6.19, 0.75)\n", - "Mancini (6.11, 0.36); (6.14, 0.40)\n", - "Ibanez (6.11, 0.52); (6.47, 0.85)\n", - "Rodrigo Becao (6.14, 0.47); (6.42, 0.71)\n", - "Ebuehi (6.09, 0.43); (6.38, 0.65)\n", - "Gosens (5.98, 0.34); (6.21, 0.50)\n", - "Darmian (6.08, 0.41); (6.46, 0.71)\n", - "Reca (5.89, 0.55); (6.13, 0.80)\n", - "Bremer (5.97, 0.54); (6.12, 0.68)\n", - "Sernicola (5.94, 0.40); (6.07, 0.55)\n", - "Rrahmani (6.26, 0.49); (6.56, 0.78)\n", - "Vojvoda (6.03, 0.46); (6.18, 0.59)\n", - "Holm (6.00, 0.44); (6.22, 0.66)\n", - "Bastoni (6.10, 0.46); (6.21, 0.56)\n", - "Milenkovic (5.91, 0.53); (6.07, 0.73)\n", - "Kalulu (5.78, 0.56); (5.83, 0.68)\n", - "Martinez Quarta (6.04, 0.52); (6.26, 0.73)\n", - "Casale (6.01, 0.45); (6.12, 0.56)\n", - "Perez N. (6.07, 0.46); (6.20, 0.57)\n", - "Olivera (6.15, 0.38); (6.48, 0.65)\n", - "Izzo (6.01, 0.39); (6.00, 0.39)\n", - "Luperto (5.82, 0.53); (5.82, 0.56)\n", - "Skriniar (5.84, 0.42); (5.81, 0.41)\n", - "Rodriguez R. (6.00, 0.39); (5.98, 0.39)\n", - "Marusic (6.03, 0.39); (6.05, 0.41)\n", - "Lazzari (6.05, 0.41); (6.08, 0.44)\n", - "Kyriakopoulos (5.91, 0.50); (6.00, 0.59)\n", - "Ampadu (5.84, 0.47); (5.86, 0.57)\n", - "Ismajli (6.06, 0.40); (6.03, 0.40)\n", - "Llorente D. (5.93, 0.52); (6.10, 0.72)\n", - "Cambiaso (5.96, 0.41); (5.99, 0.45)\n", - "Hysaj (6.00, 0.35); (6.05, 0.36)\n", - "Biraghi (6.00, 0.39); (6.05, 0.41)\n", - "Medel (5.95, 0.39); (5.91, 0.38)\n", - "Bonucci (6.21, 0.54); (6.66, 0.94)\n", - "Calabria (5.87, 0.53); (6.06, 0.77)\n", - "Acerbi (6.06, 0.41); (6.12, 0.46)\n", - "Spinazzola (6.00, 0.34); (6.05, 0.35)\n", - "Lykogiannis (5.97, 0.37); (6.04, 0.40)\n", - "Pellegrini Lu. (6.04, 0.37); (6.09, 0.41)\n", - "Djidji (5.92, 0.45); (5.99, 0.57)\n", - "Lazaro (6.02, 0.48); (6.16, 0.60)\n", - "Augello (5.83, 0.46); (5.93, 0.61)\n", - "Gallo (5.96, 0.38); (5.93, 0.36)\n", - "Singo (6.04, 0.42); (6.18, 0.53)\n", - "Mari' (5.83, 0.57); (5.94, 0.75)\n", - "Caldirola (5.81, 0.48); (5.77, 0.49)\n", - "Dodo' (5.78, 0.48); (5.70, 0.51)\n", - "De Vrij (5.87, 0.43); (5.90, 0.48)\n", - "Patric (6.04, 0.43); (6.05, 0.46)\n", - "Faraoni (5.98, 0.43); (6.16, 0.62)\n", - "Ceccherini (5.87, 0.52); (5.98, 0.71)\n", - "Hateboer (5.87, 0.48); (5.99, 0.69)\n", - "Rogerio (5.77, 0.45); (5.71, 0.49)\n", - "Umtiti (5.91, 0.50); (5.98, 0.56)\n", - "Aina (6.07, 0.53); (6.38, 0.80)\n", - "Birindelli (5.83, 0.37); (5.84, 0.40)\n", - "Lucumi' (5.91, 0.43); (5.89, 0.46)\n", - "Ehizibue (5.85, 0.45); (5.98, 0.65)\n", - "Bianchetti (5.76, 0.48); (5.78, 0.61)\n", - "Ferrari G. (5.77, 0.56); (5.83, 0.72)\n", - "Fazio (5.66, 0.63); (5.72, 0.71)\n", - "Gravillon (5.97, 0.45); (6.04, 0.54)\n", - "Buongiorno (5.95, 0.41); (5.90, 0.39)\n", - "Gunter (5.79, 0.50); (5.78, 0.62)\n", - "Troost-Ekong (5.77, 0.57); (5.78, 0.63)\n", - "Soumaoro (5.86, 0.50); (5.83, 0.52)\n", - "Ceccaroni (5.89, 0.53); (5.99, 0.66)\n", - "Pongracic (5.95, 0.40); (5.91, 0.38)\n", - "Soppy (5.85, 0.40); (5.87, 0.42)\n", - "Gendrey (5.87, 0.36); (5.83, 0.33)\n", - "Hien (5.81, 0.42); (5.74, 0.44)\n", - "Ferrari A. (5.77, 0.54); (5.80, 0.67)\n", - "Masina (6.05, 0.53); (6.51, 0.98)\n", - "Zappacosta (6.08, 0.41); (6.31, 0.59)\n", - "Gyomber (5.79, 0.46); (5.72, 0.46)\n", - "Alex Sandro (5.83, 0.49); (5.74, 0.45)\n", - "Pezzella Giu. (5.93, 0.35); (5.93, 0.32)\n", - "Bereszynski (5.89, 0.34); (5.86, 0.31)\n", - "Venuti (5.84, 0.41); (5.83, 0.43)\n", - "Palomino (6.08, 0.50); (6.25, 0.63)\n", - "Nuytinck (5.85, 0.51); (5.85, 0.56)\n", - "Marlon (5.81, 0.38); (5.77, 0.36)\n", - "Magnani (5.82, 0.47); (5.74, 0.47)\n", - "Colley (5.77, 0.57); (5.84, 0.72)\n", - "Nikolaou (5.70, 0.43); (5.60, 0.43)\n", - "Terzic (6.04, 0.28); (5.97, 0.22)\n", - "Igor (5.79, 0.48); (5.68, 0.48)\n", - "Toljan (5.71, 0.43); (5.62, 0.44)\n", - "Zortea (5.84, 0.44); (5.89, 0.56)\n", - "Dawidowicz (5.82, 0.52); (5.87, 0.68)\n", - "Celik (5.79, 0.39); (5.75, 0.39)\n", - "Bellanova (5.90, 0.42); (5.96, 0.50)\n", - "Erlic (5.82, 0.50); (5.76, 0.52)\n", - "Ballo-Toure' (6.13, 0.41); (6.50, 0.69)\n", - "Dest (5.78, 0.44); (5.77, 0.48)\n", - "Stojanovic (5.76, 0.43); (5.71, 0.47)\n", - "Amian (5.78, 0.43); (5.72, 0.47)\n", - "Bradaric (5.73, 0.44); (5.71, 0.50)\n", - "Daniliuc (5.75, 0.57); (5.77, 0.65)\n", - "Zima (5.94, 0.41); (5.94, 0.42)\n", - "De Winter (5.75, 0.43); (5.67, 0.40)\n", - "Quagliata (5.94, 0.32); (5.96, 0.29)\n", - "Ebosse (5.77, 0.36); (5.69, 0.34)\n", - "Aiwu (5.89, 0.48); (5.96, 0.64)\n", - "Lochoshvili (5.77, 0.40); (5.70, 0.43)\n", - "Bronn (5.77, 0.37); (5.72, 0.34)\n", - "Thiaw (6.00, 0.43); (6.02, 0.44)\n", - "Zeefuik (5.90, 0.45); (5.93, 0.55)\n", - "Romagnoli S. (5.98, 0.59); (6.34, 0.96)\n", - "Ghiglione (5.82, 0.45); (5.83, 0.57)\n", - "Rugani (6.00, 0.32); (5.93, 0.27)\n", - "De Sciglio (5.87, 0.34); (5.87, 0.29)\n", - "Djimsiti (5.97, 0.39); (5.99, 0.40)\n", - "Caldara (5.73, 0.52); (5.66, 0.56)\n", - "Karsdorp (5.90, 0.40); (5.90, 0.40)\n", - "Marchizza (5.84, 0.38); (5.80, 0.37)\n", - "Kjaer (5.90, 0.38); (5.84, 0.35)\n", - "Okoli (5.75, 0.46); (5.71, 0.45)\n", - "Amione (5.67, 0.42); (5.60, 0.45)\n", - "Ruggeri (5.87, 0.33); (5.85, 0.28)\n", - "Zanoli (5.81, 0.37); (5.78, 0.34)\n", - "Wisniewski (5.88, 0.47); (5.91, 0.57)\n", - "Radovanovic (5.64, 0.40); (5.53, 0.39)\n", - "Dermaku (5.94, 0.45); (5.99, 0.53)\n", - "D'ambrosio (6.13, 0.37); (6.31, 0.52)\n", - "De Silvestri (5.90, 0.51); (6.04, 0.72)\n", - "Chiriches (5.76, 0.52); (5.70, 0.54)\n", - "Murru (5.64, 0.40); (5.58, 0.44)\n", - "Bonifazi (5.80, 0.42); (5.72, 0.41)\n", - "Donati (5.79, 0.44); (5.75, 0.47)\n", - "Walukiewicz (5.78, 0.39); (5.73, 0.38)\n", - "Ranieri L. (5.83, 0.43); (5.85, 0.55)\n", - "Gabbia (5.67, 0.47); (5.56, 0.47)\n", - "Kumbulla (5.92, 0.33); (5.90, 0.29)\n", - "Adopo (5.82, 0.31); (5.78, 0.27)\n", - "Pirola (5.60, 0.49); (5.49, 0.49)\n", - "Lovato (5.67, 0.54); (5.55, 0.52)\n", - "Tuia (5.83, 0.48); (5.89, 0.63)\n", - "Ferrer (5.84, 0.48); (5.85, 0.55)\n", - "Antov (5.75, 0.53); (5.71, 0.56)\n", - "Vasquez (5.79, 0.43); (5.72, 0.47)\n", - "Ruan (5.78, 0.51); (5.63, 0.47)\n", - "Ostigard (6.10, 0.34); (6.07, 0.35)\n", - "Coppola D. (5.74, 0.38); (5.64, 0.39)\n", - "Cacace (5.78, 0.40); (5.71, 0.37)\n", - "Gatti (5.89, 0.40); (5.85, 0.38)\n", - "Gila (5.90, 0.41); (5.88, 0.41)\n", - "Bayeye (5.95, 0.44); (6.00, 0.51)\n", - "Sambia (5.75, 0.43); (5.73, 0.44)\n", - "Moutinho J. (5.76, 0.44); (5.69, 0.47)\n", - "Conti (5.96, 0.45); (6.15, 0.68)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Marrone (5.60, 0.44); (5.50, 0.45)\n", - "Tonelli (5.75, 0.45); (5.66, 0.43)\n", - "Murillo (5.63, 0.35); (5.53, 0.33)\n", - "Radu (6.08, 0.36); (5.94, 0.30)\n", - "Paletta (5.89, 0.47); (5.96, 0.59)\n", - "Florenzi (6.15, 0.44); (6.40, 0.63)\n", - "Sala (5.85, 0.37); (5.86, 0.39)\n", - "Fares (5.81, 0.39); (5.80, 0.42)\n", - "Romagna (5.88, 0.48); (5.88, 0.55)\n", - "Cassandro (5.94, 0.45); (6.00, 0.54)\n", - "Muldur (5.75, 0.43); (5.67, 0.44)\n", - "Amey (6.02, 0.45); (6.13, 0.55)\n", - "Zanotti (5.93, 0.45); (6.00, 0.54)\n", - "Ebosele (5.99, 0.34); (6.02, 0.34)\n", - "Buta (5.94, 0.45); (6.01, 0.56)\n", - "Abankwah (5.94, 0.45); (6.01, 0.56)\n", - "Guessand A. (5.94, 0.45); (6.01, 0.56)\n", - "Cabal (5.88, 0.48); (5.91, 0.57)\n", - "Sosa (5.77, 0.48); (5.76, 0.56)\n", - "Guarino (5.89, 0.46); (5.92, 0.53)\n", - "Carboni F. (5.93, 0.46); (6.01, 0.54)\n", - "Zaccagni (6.44, 0.66); (7.42, 1.59)\n", - "Kvaratskhelia (6.57, 0.72); (7.58, 1.69)\n", - "Milinkovic-Savic (6.28, 0.61); (7.10, 1.39)\n", - "Barella (6.29, 0.58); (7.02, 1.24)\n", - "Zielinski (6.34, 0.55); (7.00, 1.10)\n", - "Luis Alberto (6.33, 0.53); (7.07, 1.15)\n", - "Strefezza (6.33, 0.50); (7.15, 1.19)\n", - "Felipe Anderson (6.20, 0.61); (7.07, 1.43)\n", - "Koopmeiners (6.26, 0.60); (7.06, 1.35)\n", - "Calhanoglu (6.27, 0.50); (6.81, 0.95)\n", - "Frattesi (6.18, 0.62); (6.92, 1.32)\n", - "Diaz B. (6.21, 0.63); (6.95, 1.31)\n", - "Vlasic (6.18, 0.57); (6.83, 1.16)\n", - "Zambo Anguissa (6.32, 0.55); (6.90, 1.04)\n", - "Elmas (6.25, 0.53); (6.89, 1.06)\n", - "Miranchuk (6.27, 0.59); (7.02, 1.26)\n", - "Samardzic (6.19, 0.57); (6.84, 1.15)\n", - "Pereyra (6.18, 0.55); (6.74, 1.06)\n", - "Politano (6.24, 0.45); (6.80, 0.91)\n", - "Rabiot (6.16, 0.57); (6.71, 1.08)\n", - "Ciurria (6.07, 0.59); (6.68, 1.18)\n", - "Lazovic (6.16, 0.55); (6.71, 1.03)\n", - "Lobotka (6.20, 0.38); (6.56, 0.70)\n", - "Radonjic (6.16, 0.48); (6.65, 0.88)\n", - "Ferguson (6.20, 0.48); (6.73, 0.93)\n", - "Bonaventura (6.14, 0.50); (6.57, 0.86)\n", - "Pessina (6.17, 0.56); (6.72, 1.05)\n", - "Tonali (6.10, 0.58); (6.57, 1.05)\n", - "Kostic (6.17, 0.53); (6.69, 0.96)\n", - "Baldanzi (6.31, 0.53); (7.11, 1.20)\n", - "Lovric (6.12, 0.41); (6.50, 0.71)\n", - "Pellegrini Lo. (6.10, 0.59); (6.68, 1.17)\n", - "El Shaarawy (6.24, 0.49); (6.90, 1.04)\n", - "Orsolini (6.23, 0.67); (7.12, 1.56)\n", - "Ikone' (6.03, 0.57); (6.49, 1.02)\n", - "Candreva (5.88, 0.57); (6.14, 0.84)\n", - "Bennacer (6.12, 0.42); (6.37, 0.60)\n", - "Pasalic (6.08, 0.59); (6.68, 1.17)\n", - "Mkhitaryan (6.02, 0.46); (6.39, 0.78)\n", - "Colpani (5.99, 0.43); (6.43, 0.82)\n", - "Pogba (5.97, 0.44); (6.05, 0.52)\n", - "Chiesa (6.17, 0.53); (6.62, 0.92)\n", - "Bandinelli (5.98, 0.44); (6.19, 0.65)\n", - "Matic (6.16, 0.40); (6.48, 0.66)\n", - "Fagioli (6.18, 0.52); (6.69, 0.95)\n", - "Messias (5.97, 0.54); (6.41, 0.98)\n", - "Arslan (5.97, 0.38); (6.11, 0.48)\n", - "Ricci S. (6.17, 0.42); (6.45, 0.66)\n", - "Ranocchia F. (6.07, 0.45); (6.48, 0.79)\n", - "Verdi (6.10, 0.41); (6.45, 0.68)\n", - "Sensi (5.99, 0.58); (6.36, 0.97)\n", - "Barak (5.86, 0.45); (6.03, 0.67)\n", - "Soriano (6.02, 0.38); (6.17, 0.51)\n", - "Dominguez (6.18, 0.53); (6.69, 1.00)\n", - "Vilhena (5.87, 0.50); (6.08, 0.76)\n", - "Brozovic (6.18, 0.46); (6.56, 0.77)\n", - "Cristante (5.98, 0.43); (6.12, 0.57)\n", - "Thorstvedt (5.92, 0.46); (6.11, 0.68)\n", - "De Ketelaere (5.87, 0.40); (6.00, 0.54)\n", - "Saponara (6.07, 0.57); (6.58, 1.05)\n", - "Vecino (5.95, 0.44); (6.09, 0.59)\n", - "Locatelli (6.07, 0.37); (6.14, 0.42)\n", - "Zaniolo (5.91, 0.50); (6.16, 0.79)\n", - "Duda (6.02, 0.46); (6.15, 0.59)\n", - "Maldini (5.94, 0.45); (6.25, 0.72)\n", - "Marin (5.90, 0.53); (6.08, 0.76)\n", - "Zalewski (6.06, 0.35); (6.13, 0.40)\n", - "Bajrami (6.21, 0.58); (6.85, 1.16)\n", - "Coulibaly L. (5.83, 0.56); (6.03, 0.77)\n", - "Gonzalez J. (6.07, 0.46); (6.40, 0.76)\n", - "De Roon (6.03, 0.37); (6.08, 0.41)\n", - "Mandragora (5.93, 0.46); (6.08, 0.68)\n", - "Wijnaldum (5.91, 0.47); (5.98, 0.57)\n", - "Bourabia (5.91, 0.41); (5.94, 0.46)\n", - "Sottil (6.09, 0.58); (6.56, 1.03)\n", - "Aebischer (5.99, 0.45); (6.25, 0.70)\n", - "Ederson D.s. (5.88, 0.44); (6.04, 0.64)\n", - "Miretti (5.98, 0.38); (6.10, 0.44)\n", - "Blin (6.02, 0.33); (6.00, 0.31)\n", - "Hjulmand (5.98, 0.54); (6.09, 0.63)\n", - "Cataldi (6.02, 0.38); (6.03, 0.39)\n", - "Djuricic (5.82, 0.46); (5.94, 0.65)\n", - "Linetty (5.97, 0.43); (6.14, 0.62)\n", - "Haas (5.92, 0.41); (6.08, 0.58)\n", - "Walace (5.93, 0.38); (5.93, 0.40)\n", - "Agudelo (5.86, 0.36); (5.91, 0.42)\n", - "Pobega (5.99, 0.44); (6.28, 0.70)\n", - "Camara Ma. (6.13, 0.35); (6.18, 0.42)\n", - "Paredes (5.85, 0.35); (5.83, 0.31)\n", - "Ndombele' (5.96, 0.34); (5.94, 0.30)\n", - "Nicolussi Caviglia (5.76, 0.56); (5.92, 0.79)\n", - "Rovella (5.92, 0.48); (5.94, 0.48)\n", - "Amrabat (5.89, 0.44); (5.88, 0.48)\n", - "Tameze (5.91, 0.41); (5.94, 0.48)\n", - "Gyasi (5.88, 0.51); (6.07, 0.77)\n", - "Ilic (6.01, 0.45); (6.13, 0.56)\n", - "Matheus Henrique (5.94, 0.44); (6.07, 0.59)\n", - "Harroui (5.88, 0.42); (6.03, 0.59)\n", - "Volpato (6.05, 0.56); (6.58, 1.09)\n", - "Pickel (5.79, 0.38); (5.81, 0.47)\n", - "Moro N. (6.03, 0.34); (6.11, 0.40)\n", - "Duncan (5.92, 0.41); (6.05, 0.58)\n", - "Machin (5.91, 0.47); (6.02, 0.63)\n", - "Cuadrado (5.95, 0.48); (6.05, 0.57)\n", - "Ekdal (5.84, 0.37); (5.81, 0.38)\n", - "Meite' (5.85, 0.46); (5.88, 0.59)\n", - "Schouten (5.99, 0.40); (6.02, 0.44)\n", - "Obiang (5.98, 0.33); (5.98, 0.31)\n", - "Kovalenko (5.92, 0.39); (6.01, 0.50)\n", - "Crnigoj (5.93, 0.44); (6.13, 0.64)\n", - "Basic (5.98, 0.33); (6.05, 0.35)\n", - "Asllani (5.96, 0.33); (5.99, 0.32)\n", - "Sabiri (5.85, 0.46); (5.99, 0.66)\n", - "Terracciano F. (6.06, 0.32); (6.09, 0.34)\n", - "Castagnetti (5.87, 0.32); (5.87, 0.27)\n", - "Oudin (5.87, 0.34); (5.92, 0.33)\n", - "Grassi (5.92, 0.35); (5.91, 0.32)\n", - "Krunic (5.93, 0.40); (6.02, 0.49)\n", - "Rincon (5.80, 0.40); (5.76, 0.45)\n", - "Miguel Veloso (5.95, 0.37); (5.97, 0.38)\n", - "Leris (5.78, 0.40); (5.79, 0.51)\n", - "Esposito Sa. (5.83, 0.52); (5.90, 0.61)\n", - "Henderson L. (5.92, 0.39); (6.03, 0.49)\n", - "Lopez M. (5.90, 0.44); (5.89, 0.49)\n", - "Cuisance (5.79, 0.37); (5.75, 0.39)\n", - "Saelemaekers (5.85, 0.40); (5.97, 0.52)\n", - "Maggiore (5.93, 0.40); (6.05, 0.51)\n", - "Akpa Akpro (5.91, 0.46); (5.95, 0.56)\n", - "Maleh (5.97, 0.39); (6.17, 0.56)\n", - "Romero L. (6.20, 0.52); (6.80, 1.04)\n", - "Ceide (5.87, 0.36); (5.88, 0.36)\n", - "D'alessandro (6.06, 0.33); (6.15, 0.39)\n", - "Benassi (5.80, 0.34); (5.81, 0.35)\n", - "Gagliardini (5.84, 0.32); (5.86, 0.32)\n", - "Vieira (5.86, 0.35); (5.85, 0.42)\n", - "Bianco (6.03, 0.42); (6.15, 0.53)\n", - "Vranckx (6.07, 0.39); (6.25, 0.50)\n", - "Galdames (5.90, 0.47); (5.98, 0.64)\n", - "Marcos Antonio (5.94, 0.30); (5.91, 0.24)\n", - "Fazzini (5.79, 0.33); (5.73, 0.30)\n", - "Sulemana I. (5.87, 0.32); (5.86, 0.32)\n", - "Tahirovic (6.00, 0.30); (5.99, 0.27)\n", - "Abildgaard (5.90, 0.44); (5.93, 0.55)\n", - "Barberis (5.87, 0.49); (5.91, 0.57)\n", - "Kastanos (5.80, 0.33); (5.80, 0.32)\n", - "Vignato (5.95, 0.36); (5.98, 0.35)\n", - "Valoti (5.77, 0.33); (5.71, 0.32)\n", - "Winks (5.88, 0.45); (5.89, 0.51)\n", - "Askildsen (5.74, 0.33); (5.69, 0.32)\n", - "Bove (5.75, 0.37); (5.73, 0.41)\n", - "Bohinen (5.75, 0.31); (5.71, 0.26)\n", - "D'andrea (5.97, 0.39); (6.10, 0.50)\n", - "Iling-Junior (6.06, 0.36); (6.19, 0.45)\n", - "Cipot (5.87, 0.52); (6.00, 0.70)\n", - "Bakayoko (5.81, 0.39); (5.78, 0.36)\n", - "Gaetano (6.00, 0.42); (6.08, 0.51)\n", - "Zurkowski (6.12, 0.55); (6.66, 1.08)\n", - "Castrovilli (6.00, 0.46); (6.28, 0.72)\n", - "Demme (6.03, 0.37); (6.20, 0.48)\n", - "Darboe (5.95, 0.46); (5.98, 0.49)\n", - "Urbanski (5.98, 0.46); (6.09, 0.59)\n", - "Bertini (5.97, 0.45); (6.08, 0.56)\n", - "Yepes (5.79, 0.55); (5.79, 0.61)\n", - "Pyyhtia (6.00, 0.39); (6.07, 0.46)\n", - "Trimboli (5.88, 0.46); (5.92, 0.58)\n", - "Pafundi (5.95, 0.45); (6.04, 0.57)\n", - "Helgason (5.82, 0.32); (5.82, 0.28)\n", - "Adli (5.92, 0.40); (6.02, 0.52)\n", - "Vignato S. (6.04, 0.45); (6.18, 0.57)\n", - "Hrustic (5.75, 0.37); (5.72, 0.37)\n", - "Samek (5.96, 0.45); (6.05, 0.57)\n", - "Zerbin (6.05, 0.42); (6.19, 0.53)\n", - "Ilkhan (5.92, 0.49); (5.99, 0.57)\n", - "Degli Innocenti (5.90, 0.45); (5.94, 0.53)\n", - "Acella (5.90, 0.47); (5.98, 0.64)\n", - "Carboni V. (5.89, 0.47); (5.97, 0.59)\n", - "Paoletti (6.03, 0.40); (6.10, 0.45)\n", - "Malagrida (5.90, 0.46); (5.95, 0.58)\n", - "Faticanti (5.96, 0.45); (6.05, 0.56)\n", - "Osimhen (6.49, 0.75); (7.89, 2.22)\n", - "Martinez L. (6.32, 0.71); (7.68, 2.04)\n", - "Dybala (6.49, 0.67); (7.48, 1.61)\n", - "Rafael Leao (6.32, 0.71); (7.44, 1.82)\n", - "Lookman (6.40, 0.72); (7.68, 2.00)\n", - "Immobile (6.30, 0.71); (7.65, 2.02)\n", - "Vlahovic (6.29, 0.72); (7.73, 2.12)\n", - "Arnautovic (6.21, 0.67); (7.33, 1.73)\n", - "Dia (6.12, 0.68); (7.25, 1.73)\n", - "Dzeko (6.20, 0.67); (7.31, 1.71)\n", - "Milik (6.29, 0.62); (7.19, 1.48)\n", - "Nzola (6.11, 0.66); (7.22, 1.69)\n", - "Beto (6.06, 0.61); (6.94, 1.42)\n", - "Giroud (6.13, 0.65); (6.97, 1.45)\n", - "Abraham (6.19, 0.65); (7.28, 1.69)\n", - "Deulofeu (6.30, 0.64); (7.19, 1.48)\n", - "Lauriente' (6.28, 0.68); (7.19, 1.59)\n", - "Simeone (6.25, 0.70); (7.47, 1.86)\n", - "Lozano (6.15, 0.59); (6.89, 1.27)\n", - "Correa (6.11, 0.50); (6.81, 1.10)\n", - "Berardi (6.28, 0.71); (7.53, 1.94)\n", - "Pedro (6.20, 0.57); (6.86, 1.19)\n", - "Lukaku (5.96, 0.56); (6.48, 1.04)\n", - "Sanabria (6.01, 0.58); (6.58, 1.11)\n", - "Thauvin (6.38, 0.65); (7.30, 1.52)\n", - "Cabral (6.01, 0.57); (6.57, 1.10)\n", - "Hojlund (6.07, 0.62); (6.88, 1.38)\n", - "Caprari (5.99, 0.54); (6.51, 1.03)\n", - "Di Maria (6.26, 0.67); (7.21, 1.61)\n", - "Piatek (5.89, 0.54); (6.34, 0.96)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rebic (6.19, 0.67); (6.98, 1.46)\n", - "Bonazzoli (6.01, 0.50); (6.43, 0.88)\n", - "Zapata D. (5.89, 0.50); (6.25, 0.84)\n", - "Kouame' (6.01, 0.58); (6.51, 1.07)\n", - "Gonzalez N. (6.23, 0.62); (7.02, 1.36)\n", - "Brekalo (6.05, 0.58); (6.64, 1.14)\n", - "Mota (6.07, 0.63); (6.87, 1.38)\n", - "Kean (6.06, 0.62); (6.74, 1.27)\n", - "Okereke (5.95, 0.55); (6.41, 1.00)\n", - "Ceesay (5.99, 0.49); (6.46, 0.90)\n", - "Colombo (6.06, 0.60); (6.68, 1.20)\n", - "Dessers (5.95, 0.50); (6.38, 0.91)\n", - "Muriel (6.12, 0.63); (6.76, 1.28)\n", - "Pinamonti (5.90, 0.55); (6.37, 0.99)\n", - "Di Francesco F. (6.05, 0.52); (6.45, 0.91)\n", - "Jovic (5.96, 0.56); (6.37, 0.98)\n", - "Origi (5.96, 0.56); (6.39, 0.99)\n", - "Caputo (6.01, 0.59); (6.72, 1.26)\n", - "Boga (6.42, 0.65); (7.26, 1.40)\n", - "Cambiaghi (6.10, 0.48); (6.53, 0.84)\n", - "Alvarez A. (6.03, 0.56); (6.50, 1.03)\n", - "Banda (5.99, 0.39); (6.18, 0.53)\n", - "Ciofani D. (5.98, 0.52); (6.44, 0.96)\n", - "Petagna (5.97, 0.56); (6.46, 1.03)\n", - "Barrow (6.05, 0.60); (6.58, 1.12)\n", - "Djuric (5.98, 0.38); (6.17, 0.51)\n", - "Henry (5.89, 0.52); (6.25, 0.87)\n", - "Success (5.94, 0.37); (6.10, 0.50)\n", - "Gabbiadini (5.96, 0.58); (6.45, 1.06)\n", - "Zirkzee (6.04, 0.51); (6.50, 0.89)\n", - "Lammers (5.83, 0.41); (5.94, 0.55)\n", - "Satriano (5.86, 0.45); (6.00, 0.62)\n", - "Kallon (5.87, 0.41); (6.02, 0.58)\n", - "Nestorovski (6.23, 0.50); (7.01, 1.13)\n", - "Raspadori (6.02, 0.52); (6.50, 0.96)\n", - "Botheim (5.90, 0.49); (6.28, 0.84)\n", - "Gytkjaer (5.85, 0.44); (6.08, 0.67)\n", - "Solbakken (6.12, 0.51); (6.38, 0.73)\n", - "Lasagna (5.78, 0.43); (5.89, 0.56)\n", - "Belotti (5.69, 0.33); (5.71, 0.33)\n", - "Pellegri (5.94, 0.47); (6.20, 0.75)\n", - "Buonaiuto (5.92, 0.37); (6.05, 0.48)\n", - "Verde (6.01, 0.48); (6.45, 0.89)\n", - "Destro (5.95, 0.59); (6.47, 1.09)\n", - "Seck (6.10, 0.35); (6.23, 0.45)\n", - "Sansone (5.96, 0.50); (6.23, 0.80)\n", - "Quagliarella (5.83, 0.39); (5.91, 0.50)\n", - "Defrel (5.87, 0.48); (6.10, 0.73)\n", - "Pjaca (5.87, 0.43); (6.01, 0.56)\n", - "Gaich (5.86, 0.52); (6.23, 0.88)\n", - "Soule' (6.18, 0.40); (6.49, 0.65)\n", - "Tsadjout (5.83, 0.39); (5.91, 0.51)\n", - "Piccoli (5.96, 0.56); (6.53, 1.09)\n", - "Shomurodov (6.01, 0.53); (6.52, 1.01)\n", - "Afena-Gyan (5.68, 0.33); (5.65, 0.33)\n", - "Ngonge (6.01, 0.46); (6.19, 0.64)\n", - "Karamoh (5.92, 0.49); (6.24, 0.78)\n", - "Ibrahimovic (6.23, 0.64); (7.23, 1.56)\n", - "Pussetto (5.90, 0.51); (6.26, 0.87)\n", - "Cancellieri (5.77, 0.29); (5.79, 0.24)\n", - "Valencia D. (5.73, 0.29); (5.68, 0.30)\n", - "Oddei (6.08, 0.45); (6.28, 0.62)\n", - "Braaf (5.89, 0.50); (5.99, 0.69)\n", - "Raimondo (6.00, 0.48); (6.18, 0.66)\n", - "Kaio Jorge (5.87, 0.30); (5.93, 0.26)\n", - "De Luca (5.90, 0.47); (5.99, 0.63)\n", - "Voelkerling Persson (5.86, 0.41); (5.88, 0.45)\n", - "Montevago (5.66, 0.32); (5.61, 0.32)\n", - "Krollis (5.91, 0.49); (6.02, 0.67)\n", - "Vivaldo (5.94, 0.47); (6.07, 0.63)\n" - ] - }, - { - "data": { - "text/html": [ - "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaAvg1006.2316510.4153215.7522000.4663985.9918270.2614920.5936561.5897646.1556900.590086-0.4689421.05647451.863781
MussoPAtalantaAvg111006.2360960.4067234.9877620.6286556.0060060.2658510.5673401.5901645.4374330.847580-0.3824341.02644725.157676
Rossi F.PAtalantaAvg1006.0001380.4557283.4212531.0693725.7434560.3022200.5592171.5849534.5156150.947310-0.8582680.9713220.053009
ToloiDAtalantaAvg11766.1824710.4970416.5101970.7675266.1182150.5683530.0832370.9741075.9491161.0457150.3767681.5998780.000000
ScalviniDAtalantaAvg11806.1063350.5422636.4845680.8881506.0352030.6188260.0843940.9432025.8001931.1675020.4074051.5998530.000000
............................................................
NgongeAVeronaAvg1096.0123850.4609146.1927690.6422345.9485070.5269570.0894401.0271535.7537530.9098690.3430631.5998840.000000
DjuricAVeronaAvg10765.9832040.3825596.1697580.5139845.9213250.4355830.1049661.1182635.8214420.7313870.3390151.5999050.000000
KallonAVeronaAvg10765.8726360.4126386.0247730.5804595.8015790.4676570.1121881.0942015.5841900.7714890.3988691.5998870.000000
BraafAVeronaAvg1045.8904890.4995805.9897700.6871505.8540360.5808560.0462501.0004025.5307680.9848640.3323061.5998530.000000
LasagnaAVeronaAvg11805.7785220.4289155.8883830.5595435.7078190.4872410.1070391.0832485.4726440.7542830.3861471.5998870.000000
\n", - "

520 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " role team oppteam home starter vote% MV MV std \\\n", - "player \n", - "Sportiello P Atalanta Avg 1 0 0 6.231651 0.415321 \n", - "Musso P Atalanta Avg 1 1 100 6.236096 0.406723 \n", - "Rossi F. P Atalanta Avg 1 0 0 6.000138 0.455728 \n", - "Toloi D Atalanta Avg 1 1 76 6.182471 0.497041 \n", - "Scalvini D Atalanta Avg 1 1 80 6.106335 0.542263 \n", - "... ... ... ... ... ... ... ... ... \n", - "Ngonge A Verona Avg 1 0 9 6.012385 0.460914 \n", - "Djuric A Verona Avg 1 0 76 5.983204 0.382559 \n", - "Kallon A Verona Avg 1 0 76 5.872636 0.412638 \n", - "Braaf A Verona Avg 1 0 4 5.890489 0.499580 \n", - "Lasagna A Verona Avg 1 1 80 5.778522 0.428915 \n", - "\n", - " FV FV std MV loc MV scale MV skewness \\\n", - "player \n", - "Sportiello 5.752200 0.466398 5.991827 0.261492 0.593656 \n", - "Musso 4.987762 0.628655 6.006006 0.265851 0.567340 \n", - "Rossi F. 3.421253 1.069372 5.743456 0.302220 0.559217 \n", - "Toloi 6.510197 0.767526 6.118215 0.568353 0.083237 \n", - "Scalvini 6.484568 0.888150 6.035203 0.618826 0.084394 \n", - "... ... ... ... ... ... \n", - "Ngonge 6.192769 0.642234 5.948507 0.526957 0.089440 \n", - "Djuric 6.169758 0.513984 5.921325 0.435583 0.104966 \n", - "Kallon 6.024773 0.580459 5.801579 0.467657 0.112188 \n", - "Braaf 5.989770 0.687150 5.854036 0.580856 0.046250 \n", - "Lasagna 5.888383 0.559543 5.707819 0.487241 0.107039 \n", - "\n", - " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", - "player \n", - "Sportiello 1.589764 6.155690 0.590086 -0.468942 1.056474 \n", - "Musso 1.590164 5.437433 0.847580 -0.382434 1.026447 \n", - "Rossi F. 1.584953 4.515615 0.947310 -0.858268 0.971322 \n", - "Toloi 0.974107 5.949116 1.045715 0.376768 1.599878 \n", - "Scalvini 0.943202 5.800193 1.167502 0.407405 1.599853 \n", - "... ... ... ... ... ... \n", - "Ngonge 1.027153 5.753753 0.909869 0.343063 1.599884 \n", - "Djuric 1.118263 5.821442 0.731387 0.339015 1.599905 \n", - "Kallon 1.094201 5.584190 0.771489 0.398869 1.599887 \n", - "Braaf 1.000402 5.530768 0.984864 0.332306 1.599853 \n", - "Lasagna 1.083248 5.472644 0.754283 0.386147 1.599887 \n", - "\n", - " Clean Sheet % \n", - "player \n", - "Sportiello 51.863781 \n", - "Musso 25.157676 \n", - "Rossi F. 0.053009 \n", - "Toloi 0.000000 \n", - "Scalvini 0.000000 \n", - "... ... \n", - "Ngonge 0.000000 \n", - "Djuric 0.000000 \n", - "Kallon 0.000000 \n", - "Braaf 0.000000 \n", - "Lasagna 0.000000 \n", - "\n", - "[520 rows x 19 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", - "\n", - "tot_matches = 2 # home and not home\n", - "\n", - "current_season_games = max(players_orig['games'])\n", - "\n", - "for i in range(players.shape[0]):\n", - " try:\n", - " home = 0\n", - "\n", - " for k in range(tot_matches):\n", - " #matchday_out = k + 1\n", - " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", - "\n", - " player = players.index[i]\n", - " team = players['team'][i]\n", - " oppteam = 'Avg'\n", - " home = not home\n", - "\n", - " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", - "\n", - " role = players['r'][player] \n", - "\n", - " starter = 0\n", - " voteperc = 0\n", - "\n", - " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", - " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", - "\n", - " cs = 0\n", - " if(role == 'P'):\n", - " cs = dist[2].probs.numpy()[0] * 100\n", - "\n", - " starter = int( player in gk_starters )\n", - " if(starter):\n", - " voteperc = 100\n", - " else:\n", - " voteperc = 0\n", - " else:\n", - " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", - " voteperc = int( min( 1, games / current_season_games ) * 100) \n", - "\n", - " if(k == 0):\n", - " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", - "\n", - " numrow_ = [mean[0], std[0], \n", - " mean[1], std[1], \n", - " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", - " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", - " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", - " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", - " cs] \n", - "\n", - " if(k == 0):\n", - " numrow = numrow_\n", - " else:\n", - " for j in range(len(numrow)):\n", - " numrow[j] += numrow_[j]\n", - "\n", - " for j in range(len(numrow)):\n", - " numrow[j] /= tot_matches\n", - "\n", - " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", - " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", - "\n", - " row += numrow # list concat\n", - "\n", - " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", - "\n", - " output = pd.concat([output, row_df])\n", - " except:\n", - " print(players.index[i] + ' no data')\n", - " \n", - " \n", - "\n", - "output = output.set_index('player')\n", - "\n", - "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", - "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", - "\n", - "output" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "b47cbd63", - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", - "\n", - "template_file = 'outputs/pred_matchday_base.xlsx'\n", - "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", - "\n", - "shutil.copyfile(template_file, dest_file)\n", - "\n", - "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", - " output.to_excel(writer, sheet_name='data')" - ] - }, - { - "cell_type": "markdown", - "id": "cf9df3bb", - "metadata": {}, - "source": [ - "Various predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "7300f3c2", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret: MV 6.28 ± 0.85; FV 5.50 + 1.44 (51.2% cs)\n", - "Szczesny: MV 6.24 ± 0.82; FV 5.66 + 1.20 (64.1% cs)\n", - "Provedel: MV 6.25 ± 0.83; FV 5.65 + 1.27 (62.9% cs)\n", - "Maignan: MV 6.51 ± 0.90; FV 5.93 + 1.10 (45.0% cs)\n", - "Rui Patricio: MV 6.05 ± 0.77; FV 5.38 + 1.25 (53.8% cs)\n", - "Onana: MV 6.37 ± 0.78; FV 5.16 + 1.17 (18.4% cs)\n", - "Milinkovic-Savic V.: MV 6.20 ± 0.77; FV 5.35 + 0.89 (26.9% cs)\n", - "Musso: MV 6.36 ± 0.86; FV 5.64 + 1.24 (43.8% cs)\n", - "Vicario: MV 6.46 ± 0.87; FV 5.88 + 1.21 (60.8% cs)\n", - "Silvestri: MV 6.39 ± 0.83; FV 5.71 + 1.16 (46.2% cs)\n", - "Terracciano: MV 6.19 ± 0.77; FV 4.58 + 1.10 (4.1% cs)\n", - "Skorupski: MV 6.17 ± 0.71; FV 4.56 + 0.94 (5.2% cs)\n", - "Falcone: MV 6.37 ± 0.84; FV 5.13 + 1.02 (8.7% cs)\n", - "Di Gregorio: MV 6.42 ± 0.87; FV 5.69 + 0.99 (36.9% cs)\n", - "Consigli: MV 6.39 ± 0.78; FV 4.83 + 1.19 (7.7% cs)\n", - "Carnesecchi: MV 6.33 ± 0.82; FV 5.49 + 1.28 (37.1% cs)\n", - "Montipo': MV 6.30 ± 0.89; FV 4.39 + 1.24 (2.4% cs)\n", - "Audero: MV 6.26 ± 0.81; FV 4.60 + 1.03 (4.2% cs)\n", - "Dragowski: MV 6.37 ± 0.81; FV 4.71 + 1.13 (3.1% cs)\n", - "Ochoa: MV 6.44 ± 0.82; FV 5.12 + 1.19 (6.2% cs)\n", - "Tatarusanu: MV 5.86 ± 0.65; FV 3.31 + 2.21 (0.9% cs)\n", - "Handanovic: MV 6.09 ± 0.70; FV 4.79 + 1.18 (11.7% cs)\n", - "Sportiello: MV 6.22 ± 0.78; FV 5.37 + 1.22 (27.8% cs)\n", - "Sepe: MV 6.52 ± 0.80; FV 5.22 + 1.24 (12.7% cs)\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.51791594, 5.21583604]),\n", - " array([0.40012765, 0.61914635], dtype=float32),\n", - " [,\n", - " ,\n", - " ]]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Meret', log = 1, plot = 0)\n", - "predict_player('Szczesny', log = 1, plot = 0)\n", - "predict_player('Provedel', log = 1, plot = 0)\n", - "predict_player('Maignan', log = 1, plot = 0)\n", - "predict_player('Rui Patricio', log = 1, plot = 0)\n", - "predict_player('Onana', log = 1, plot = 0)\n", - "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", - "predict_player('Musso', log = 1, plot = 0)\n", - "predict_player('Vicario', log = 1, plot = 0)\n", - "predict_player('Silvestri', log = 1, plot = 0)\n", - "predict_player('Terracciano', log = 1, plot = 0)\n", - "predict_player('Skorupski', log = 1, plot = 0)\n", - "predict_player('Falcone', log = 1, plot = 0)\n", - "predict_player('Di Gregorio', log = 1, plot = 0)\n", - "predict_player('Consigli', log = 1, plot = 0)\n", - "predict_player('Carnesecchi', log = 1, plot = 0)\n", - "predict_player('Montipo\\'', log = 1, plot = 0)\n", - "predict_player('Audero', log = 1, plot = 0)\n", - "predict_player('Dragowski', log = 1, plot = 0)\n", - "predict_player('Ochoa', log = 1, plot = 0)\n", - "predict_player('Tatarusanu', log = 1, plot = 0)\n", - "predict_player('Handanovic', log = 1, plot = 0)\n", - "predict_player('Sportiello', log = 1, plot = 0)\n", - "predict_player('Sepe', log = 1, plot = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "bd126870", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", - "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", - "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", - "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", - "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", - "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", - "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", - "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", - "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", - "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", - "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", - "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", - "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.41176047, 8.29456946]),\n", - " array([0.70926785, 2.7289915 ], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Muriel', plot = 0, log = 1)\n", - "predict_player('Tonali', plot = 0, log = 1)\n", - "predict_player('Lobotka', plot = 0, log = 1)\n", - "predict_player('Politano', plot = 0, log = 1)\n", - "predict_player('Zapata D.', plot = 0, log = 1)\n", - "predict_player('Frattesi', plot = 0, log = 1)\n", - "predict_player('Gonzalez N.', plot = 0, log = 1)\n", - "predict_player('Abraham', plot = 0, log = 1)\n", - "predict_player('Pobega', plot = 0, log = 1)\n", - "predict_player('Mario Rui', plot = 0, log = 1)\n", - "predict_player('Cuadrado', plot = 0, log = 1)\n", - "predict_player('Skriniar', plot = 0, log = 1)\n", - "predict_player('Lukaku', plot = 0, log = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "4b9f5a7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Skriniar: MV 6.14 ± 0.82; FV 6.38 + 1.13\n", - "Cuadrado: MV 6.29 ± 0.96; FV 6.83 + 1.94\n", - "Bastoni: MV 6.13 ± 0.73; FV 6.32 + 0.98\n", - "Barak: MV 6.31 ± 1.41; FV 7.50 + 3.97\n", - "Politano: MV 6.18 ± 0.82; FV 6.57 + 1.55\n", - "Smalling: MV 6.17 ± 0.86; FV 6.56 + 1.43\n", - "Gosens: MV 6.05 ± 0.54; FV 6.11 + 0.66\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.04807256, 6.10812885]),\n", - " array([0.27137518, 0.32888246], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Skriniar', log = 1, oldseason= True)\n", - "predict_player('Cuadrado', log = 1, oldseason= True)\n", - "predict_player('Bastoni', log = 1, oldseason= True)\n", - "predict_player('Barak', log = 1, oldseason= True)\n", - "predict_player('Politano', log = 1, oldseason= True)\n", - "predict_player('Smalling', log = 1, oldseason= True)\n", - "predict_player('Gosens', log = 1, oldseason= True)\n", - "\n", - "predict_player('Skriniar', log = 1, oldseason= False)\n", - "predict_player('Cuadrado', log = 1, oldseason= False)\n", - "predict_player('Bastoni', log = 1, oldseason= False)\n", - "predict_player('Barak', log = 1, oldseason= False)\n", - "predict_player('Politano', log = 1, oldseason= False)\n", - "predict_player('Smalling', log = 1, oldseason= False)\n", - "predict_player('Gosens', log = 1, oldseason= False)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "10c7ad3e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0XLx40dyH5caNGzhy5AgcHR3x0UcfVeq+fv31V3NScbdhw4bh22+/Rd++fdG7d2+MGzcOAQEByMjIQEJCAo4fP47169cDMH3h/Pe//8WMGTPQtWtXnDlzBrNmzUJoaCj0er35nGPGjMH8+fMxduxYXLp0Cc2bN8eBAwcwe/Zs9OvXDz169Cgz1qK+IGfPnrUY1VSSyMhIREZGVuIdKW7AgAFYsWIFwsLC0KJFC8TGxmLu3LlVMp+Fm5sbBg8ejBUrVuDWrVt48803LfprnDp1Cq+88gqefvppNGjQAEqlErt27cKpU6fw7rvvWjUWmUyGOXPmYPTo0RgwYABefPFFaDQazJ07F7du3cInn3xy33N06tQJ//nPfzB+/HgcO3YMXbp0gaOjI5KTk3HgwAE0b968wgljvXr1YG9vjx9++AGNGzeGk5MT/P394e/vX9lbBVD+z3GRP//8E56enpVKqKiGkrb/LNUmRaNpjh49WmxfQUGBGBQUJDZo0EDU6/WiKIriJ598IoaEhIgqlUps3LixuGTJkhJ79Jc2miY6Olps1KiRqFKpxLp164pRUVHismXLRABiYmKiRdlNmzaJ3bp1E11cXESVSiUGBweLw4YNE//44w9zmYqOpiltKXLy5Elx+PDhore3t2hnZyf6+vqKTzzxhLho0SJzGY1GI7755ptiQECAqFarxTZt2oibNm0Sx44dW2z0Snp6ujhp0iTRz89PVCgUYnBwsDht2jSxsLDwvjFnZWWJTk5O4pw5cyy23z2apiyVHU2TmZkpTpw4UfT29hYdHBzExx9/XNy/f7/YtWtXsWvXrveNozwjdO62Y8cO87/D2bNnLfbduHFDHDdunBgWFiY6OjqKTk5OYosWLcT//e9/5s9kae43gure0TRFNm3aJLZv315Uq9Wio6Oj2L17d/HgwYMVOnd0dLTYvn170dHRUbS3txfr1asnjhkzRjx27Ji5TGk/IyV9jtasWSOGhYWJdnZ2IgBxxowZFnHcLTg4WOzfv3+x897771eRz7HRaBSDg4OLjXAj2yaIYgkzQBFRrfPqq69i586dOH36dJU/g4SoNDt37kSvXr1w+vRphIWFSR0OVRMmI0QEwDQ6o2HDhli2bNkDj5Ihqqxu3bqhfv36WLJkidShUDVinxEiAmCahvuHH34oNqSUqLpkZmaia9eu5iHuVHuwZoSIiIgkxaG9REREJCkmI0RERCQpJiNEREQkqRrRgdVoNOL69etwdnbmkEMiIqIaQhRF5OTkwN/f32KSwXvViGTk+vXrCAwMlDoMIiIiqoQrV66UObNyjUhGiqbUvnLlClxcXCSOhqjmycsDimb0vn4dcHSUNh5ryNPmwf9z001df+M6HJU2cFNENiY7OxuBgYElPhrjbjUiGSlqmnFxcWEyQlQJdz0vEC4utpGMyLVy4PYDmV1cXJiMED3E7tfFgh1YiYiISFI1omaEiB6MQgGMHXtn3RYoZAqMbTnWvE5ENVeNmIE1Ozsbrq6uyMrKYjMNERFRDVHe72/+OUFEZGMMBgN0Op3UYVAtIJfLoVAoHnjaDSYjRLWAKAL5+aZ1BwfAFqbrEUUR+TrTTTnYOXAOottyc3Nx9epV1IBKb7IRDg4O8PPzg1KprPQ5mIwQ1QL5+YCTk2k9N9c2RtPk6/LhFGW6qdxpuRxNA1ONyNWrV+Hg4IA6deowQaMqJYoitFotbt68icTERDRo0KDMic3KwmSEiMhG6HQ6iKKIOnXqwN7eXupwqBawt7eHnZ0dLl++DK1WC7VaXanzcGgvEZGNYY0IVafK1oZYnMMKcRARERFVGpMRIiIikhSTESIiIpIUkxEiIpLUuHHjIAgCJk2aVGzf5MmTIQgCxo0bBwAYOHAgevToUeJ5Dh8+DEEQcPz48VKvlZCQgCeffBKurq5wdnbGY489hqSkpFLLb9y4EeHh4XBzc4OjoyNatWqF77//vtTyUVFREAQBU6ZMKbWMNRQWFmLcuHFo3rw5FAoFBg0aVKHjNRoNWrVqBUEQEBcXZ96enp6OPn36wN/fHyqVCoGBgXjllVeQnZ1t3Ru4B5MRolpALgeGDTMtdz80ryaTy+QY1mQYhjUZBrnMRm6qFgsMDMSPP/6IgoIC87bCwkKsWbMGQUFB5m0TJ07Erl27cPny5WLniI6ORqtWrdCmTZsSr3HhwgU8/vjjCAsLw549e3Dy5El88MEHZY4A8fDwwPTp03H48GGcOnUK48ePx/jx47F9+/ZiZY8ePYrFixejRYsWFbl1AMDMmTPNCVd5GAwG2Nvb47XXXis1OSvL22+/Df+iR3nfRSaT4amnnsIvv/yCs2fPYsWKFfjjjz9KTBStiUN7iWoBtRpYv17qKKxLrVBj/dM2dlNWdvdkd9WtopPrtWnTBhcvXsTGjRsxevRoAKZaicDAQNStW9dcbsCAAfD29saKFSswY8YM8/b8/HysXbsWs2fPLvUa06dPR79+/TBnzhzztrvPXZKIiAiL16+//jpWrlyJAwcOoHfv3ubtubm5GD16NJYsWYKPP/64XPf8IBwdHbFw4UIAwMGDB3Hr1q1yH/v7779jx44d+Omnn/D7779b7HN3d8dLL71kfh0cHIzJkydj7ty5Vom7NKwZISKyUUWT3UmxVCYJGj9+PJYvX25+HR0djQkTJliUUSgUGDNmDFasWGExy+z69euh1WrNicy9jEYjtmzZgoYNG6J3797w9vZG+/btsWnTpnLHJ4oidu7ciTNnzqBLly4W+15++WX079+/UrUU1enGjRt44YUX8P3338PBweG+5a9fv46NGzeia9euVRoXkxEiInooPPfcczhw4AAuXbqEy5cv4+DBg3j22WeLlZswYQIuXbqEPXv2mLdFR0djyJAhcHd3L/HcqampyM3NxSeffII+ffpgx44dGDx4MIYMGYK9e/eWGVdWVhacnJygVCrRv39/fP311+jZs6d5/48//ojY2FhERUWV+173798PJycn8zJ79mz88MMPxbZZkyiKGDduHCZNmoTw8PAyy44cORIODg4ICAiAi4sLli5datVY7sVmGqJaIC/P9qaDz9PmcTr4+3BwMP17S3XtivLy8kL//v2xcuVKiKKI/v37w8vLq1i5sLAwdOzYEdHR0ejWrRsuXLiA/fv3Y8eOHaWe22g0AgCeeuopTJ06FQDQqlUrHDp0CIsWLSrzL39nZ2fExcUhNzcXO3fuRGRkJOrWrYuIiAhcuXIFr7/+Onbs2FGh2UfDw8MtOo5+9dVXuHbtGj799FPzNg8Pj3Kfrzy+/vprZGdnY9q0afct+7///Q8zZszAmTNn8N577yEyMhILFiywajx3q3Aysm/fPsydOxexsbFITk7Gzz//XO5evAcPHkTXrl3RrFkzi38EIiKyPkGoeYnnhAkT8MorrwAA5s+fX2q5iRMn4pVXXsH8+fOxfPlyBAcHo3v37qWW9/LygkKhQJMmTSy2N27cGAcOHCgzJplMhvr16wMwJTAJCQmIiopCREQEYmNjkZqairZt25rLGwwG7Nu3D9988w00Gg3kJfQat7e3N58TMCUe2dnZFtusbdeuXfjzzz+hUqkstoeHh2P06NFYuXKleZuvry98fX0RFhYGT09PdO7cGR988AH8/PyqJLYKN9Pk5eWhZcuW+Oabbyp0XFZWFsaMGVPmh4WIiGq3Pn36QKvVQqvVWnQQvdfw4cMhl8uxevVqrFy5EuPHjy9zGnylUol27drhzJkzFtvPnj2L4ODgCsUoiiI0Gg0AoHv37vj7778RFxdnXoq+3OPi4kpMRKTy1Vdf4eTJk+Y4t27dCgBYu3Yt/u///q/U44r65hTdc1WocM1I37590bdv3wpf6MUXX8SoUaMgl8sr1GGIiIhqD7lcjoSEBPN6aZycnDBixAi89957yMrKKtew2LfeegsjRoxAly5d0K1bN2zbtg2//vqrRd+TMWPGICAgwNz/IyoqCuHh4ahXrx60Wi22bt2K7777zjySxdnZGc2aNbO4jqOjIzw9PYttv5tWq0VGRob5ddHQ2ZSUFIt7dCpqXy1BfHy8+Tw5OTnmFodWrVoBAI4cOYIxY8Zg586dCAgIsBgiXXR+AKhXrx4eeeQRAMDWrVtx48YNtGvXDk5OToiPj8fbb7+NTp06ISQkpNRYHlS19BlZvnw5Lly4gFWrVpVryJNGo7HIwKp6shUiInp4uLi4lKvcxIkTsWzZMvTq1avYF21JBg8ejEWLFiEqKgqvvfYaGjVqhJ9++gmPP/64uUxSUpLFg9/y8vIwefJkXL16Ffb29ggLC8OqVaswYsSIit/YXQ4dOoRu3bqVWWbGjBmYOXNmqfv79etnMd9K69atAdypycjPz8eZM2eg0+nKHZe9vT2WLFmCqVOnQqPRIDAwEEOGDMG7775b7nNUhiDePTaqogcLwn37jJw7dw6PP/449u/fj4YNG2LmzJnYtGlTmX1GZs6ciY8++qjY9qysrHJ/SInoDnZgrR0KCwuRmJiI0NDQSj/KnaiiyvrcZWdnw9XV9b7f31U6tNdgMGDUqFH46KOP0LBhw3IfN23aNGRlZZmXK1euVGGUREREJKUqbabJycnBsWPHcOLECXPvaKPRCFEUoVAosGPHDjzxxBPFjlOpVMV6+xJR5cnlQL9+d9ZtgVwmR78G/czrRFRzVWky4uLigr///tti24IFC7Br1y5s2LABoaGhVXl5IrpNrQa2bJE6CutSK9TYMsrGboqolqpwMpKbm4vz58+bXycmJiIuLg4eHh4ICgrCtGnTcO3aNXz33XeQyWTFehN7e3tDrVaX2cuYiIiIao8KJyPHjh2z6AEcGRkJABg7dixWrFiB5OTkMh/HTERERHS3BxpNU13K2xuXiEqWlwd4e5vWU1NtZzSN92emm0p9M5WjacDRNCQNa4ym4bNpiGoJqR4lX5XydTZ4U0S1EJ/aS0RERJJiMkJERESSYjJCREREkmIyQkREkho3bhwEQSi2nD9/HgMHDkSPHj1KPO7w4cMQBAHHjx8vcX9J5xQEAXPnzi01Fp1Oh1mzZqFevXpQq9Vo2bIltm3bZlFm5syZxc7p6+tb+Teggs6fPw9nZ2e4ubmVWW7Pnj2lvgdHjx41l9u5cyc6duwIZ2dn+Pn54Z133oFer6/iu7DEZISIiCTXp08fJCcnWyyhoaGYOHEidu3aZfFAuCLR0dFo1aoV2rRpU+I57z1fdHQ0BEHA0KFDS43j/fffx7fffouvv/4a8fHxmDRpEgYPHowTJ05YlGvatKnFue+d4PN+Zs6cWa4nDd9Lp9Nh5MiR6Ny5833LduzYsdh78PzzzyMkJATh4eEAgFOnTqFfv37o06cPTpw4gR9//BG//PJLlT8Y714cTUNUC8hkQNeud9ZtgUyQoWtwV/M6FSeKomQjjhzsHCAIQrnLq1SqEmsXBgwYAG9vb6xYsQIzZswwb8/Pz8fatWsxe/bsUs957/k2b96Mbt26oW7duqUe8/3332P69Onod/v5CS+99BK2b9+Ozz//HKtWrTKXUygU1VobUuT9999HWFgYunfvjkOHDpVZVqlUWsSo0+nwyy+/4JVXXjH/2/z4449o0aIFPvzwQwBA/fr1ERUVhZEjR2LGjBlwdnauupu5C5MRolrA3h7Ys0fqKKzL3s4ee8btkTqMh1q+Lt/8ZOPqZq0nKSsUCowZMwYrVqzAhx9+aP4SXb9+PbRaLUaPHl2u89y4cQNbtmzBypUryyyn0WiKzZVhb2+PAwcOWGw7d+4c/P39oVKp0L59e8yePbvMJMcadu3ahfXr1yMuLg4bN26s8PG//PIL0tLSLGpkSrvfwsJCxMbGIiIi4gGjLh/+OUFERJL77bff4OTkZF6efvpp874JEybg0qVL2HNXRh0dHY0hQ4bA3d29XOdfuXIlnJ2dMWTIkDLL9e7dG/PmzcO5c+dgNBoRExODzZs3Izk52Vymffv2+O6777B9+3YsWbIEKSkp6NixI9LT0yt20xWQnp6OcePGYcWKFZWe/HPZsmXo3bs3AgMDzdt69+6NQ4cOYc2aNTAYDLh27Ro+/vhjALC456rGmhEiIhvlYOeA3Gm5kl27Irp164aFCxeaXzveNU1wWFgYOnbsiOjoaHTr1g0XLlzA/v37sWPHjnKfPzo6GqNHj77vzLRffvklXnjhBYSFhUEQBNSrVw/jx4/H8uXLzWX69u1rXm/evDk6dOiAevXqYeXKleZHpNxr//79FsdptVqIoogNGzaYt7333nt47733Sjz+hRdewKhRo9ClS5dy3e+9rl69iu3bt2PdunUW23v16oW5c+di0qRJeO6556BSqfDBBx/gwIEDkFfnI77FGiArK0sEIGZlZUkdClGNlJsril5epiU3V+porCNXkyt6zfESveZ4ibkaG7mpB1RQUCDGx8eLBQUFUodSIWPHjhWfeuqpMsssW7ZMtLe3F7OyssTp06eLISEhotFoLNf59+3bJwIQ4+Liyh1TQUGBePXqVdFoNIpvv/222KRJkzLL9+jRQ5w0aVKp+/Pz88Vz586Zl1dffVUcMmSIxbb09PRSj3d1dRXlcrl5kclkIgBRLpeLy5Ytu+/9zJo1S6xTp46o1WpL3G80GsVr166J+fn5Ynx8vAhAPHLkyH3PK4plf+7K+/3NmhGiWiItTeoIrC8t3wZviko0fPhwvP7661i9ejVWrlyJF154odwdZJctW4a2bduiZcuW5b6eWq1GQEAAdDodfvrpJwwfPrzUshqNBgkJCWWOcLG3t0f9+vXNrz08PJCdnW2xrSyHDx+GwWAwv968eTM+/fRTHDp0CAEBAWUeK4oili9fjjFjxsDOzq7EMoIgwN/fHwCwZs0aBAYGljpKqSowGSEiooeek5MTRowYgffeew9ZWVnlHhabnZ2N9evX4/PPPy9x/5gxYxAQEICoqCgAwF9//YVr166hVatWuHbtGmbOnAmj0Yi3337bfMybb76JgQMHIigoCKmpqfj444+RnZ2NsWPHPvB9lqZx48YWr48dOwaZTIZmzZqZtx05cgRjxozBzp07LRKUXbt2ITExERMnTizx3HPnzkWfPn0gk8mwceNGfPLJJ1i3bl21NtOwAysREdUIEydORGZmJnr06IGgoKByHfPjjz9CFEWMHDmyxP1JSUkWHTULCwvx/vvvo0mTJhg8eDACAgJw4MABiwnGrl69ipEjR6JRo0YYMmQIlEol/vzzTwQHBz/Q/T2o/Px8nDlzBjqdzmL7smXL0LFjx2IJTZHff/8dnTt3Rnh4OLZs2YLNmzdj0KBB1RDxHYIoimK1XrESyvsIYiIqWV4e4HR7hGduLuD44CMuJZenzTMPW7XWMNKarqxHuRNVlbI+d+X9/mbNCBEREUmKyQgRERFJih1YiWoBmQy4/SgKm5oOPtw/3LxORDUXkxGiWsDeHrjrIZ02wd7OHkdfsLGbIqql+OcEEZGNqQHjEsiGWOPzxmSEiMhGFM0LodVqJY6EapP8fNOToUubUK082ExDVAvk5wNNmpjW4+MBh4o9NuShlK/LR5P5ppuKfzm+ws9CsUUKhQIODg64efMm7OzsILOVDkL0UBJFEfn5+UhNTYWbm9sDTZLGZISoFhBF4PLlO+u2QBRFXM66bF4n05Tefn5+SExMxOWif3CiKubm5gZfX98HOgeTESIiG6JUKtGgQQM21VC1sLOzs8q08UxGiIhsjEwm4wysVKOwQZGIiIgkxWSEiIiIJMVkhIiIiCTFPiNEtYAg3BnaKwjSxmItgiCgSZ0m5nUiqrmYjBDVAg4OwOnTUkdhXQ52Djg92cZuiqiWYjMNERERSYrJCBEREUmKyQhRLZCfDzRtalpuP0aixsvX5aPpgqZouqAp8nU2clNEtRT7jBDVAqJoeiZN0botEEUR8TfjzetEVHOxZoSIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJFXhZGTfvn0YOHAg/P39IQgCNm3aVGb5jRs3omfPnqhTpw5cXFzQoUMHbN++vbLxElElCAIQHGxabGXmdEEQEOwajGDXYE4HT1TDVTgZycvLQ8uWLfHNN9+Uq/y+ffvQs2dPbN26FbGxsejWrRsGDhyIEydOVDhYIqocBwfg0iXT4uAgdTTW4WDngEtTLuHSlEtwsLORmyKqpQTxAQboC4KAn3/+GYMGDarQcU2bNsWIESPw4Ycflqt8dnY2XF1dkZWVBRcXl0pESkRERNWtvN/f1T7pmdFoRE5ODjw8PEoto9FooNFozK+zs7OrIzQiIiKSQLV3YP3888+Rl5eH4cOHl1omKioKrq6u5iUwMLAaIySyPQUFQLt2pqWgQOporKNAV4B2S9qh3ZJ2KNDZyE0R1VLVWjOyZs0azJw5E5s3b4a3t3ep5aZNm4bIyEjz6+zsbCYkRA/AaASOHbuzbguMohHHrh8zrxNRzVVtycjatWsxceJErF+/Hj169CizrEqlgkqlqqbIiIiISErV0kyzZs0ajBs3DqtXr0b//v2r45JERERUQ1S4ZiQ3Nxfnz583v05MTERcXBw8PDwQFBSEadOm4dq1a/juu+8AmBKRMWPG4Msvv8Rjjz2GlJQUAIC9vT1cXV2tdBtERERUU1W4ZuTYsWNo3bo1WrduDQCIjIxE69atzcN0k5OTkZSUZC7/7bffQq/X4+WXX4afn595ef311610C0RERFSTVbhmJCIiAmVNTbJixQqL13v27KnoJYiIiKgWqfZ5RohIGl5eUkdgfV4ONnhTRLUQkxGiWsDREbh5U+oorMtR6Yibb9nYTRHVUnxqLxEREUmKyQgRERFJiskIUS1QUABERJgWW5oOPmJFBCJWRHA6eKIajn1GiGoBoxHYu/fOui0wikbsvbzXvE5ENRdrRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUhxNQ1RLODhIHYH1OdjZ4E0R1UJMRohqAUdHIC9P6iisy1HpiLz3bOymiGopNtMQERGRpJiMEBERkaSYjBDVAoWFQP/+pqWwUOporKNQX4j+q/uj/+r+KNTbyE0R1VLsM0JUCxgMwNatd9ZtgcFowNZzW83rRFRzsWaEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxaG9RLWAoyMgilJHYV2OSkeIM2zspohqKdaMEBERkaSYjBAREZGkmIwQ1QKFhcDTT5sWW5oO/un1T+Pp9U9zOniiGo7JCFEtYDAAGzaYFluaDn5D/AZsiN/A6eCJajgmI0RERCQpJiNEREQkKSYjREREJCkmI0RERCQpJiNEREQkKSYjREREJClOB09UCzg4ALm5d9ZtgYOdA3Kn5ZrXiajmYjJCVAsIgun5NLZEEAQ4Km3spohqKTbTEBERkaSYjBDVAhoNMG6cadFopI7GOjR6DcZtGodxm8ZBo7eRmyKqpSqcjOzbtw8DBw6Ev78/BEHApk2b7nvM3r170bZtW6jVatStWxeLFi2qTKxEVEl6PbBypWnR66WOxjr0Rj1WnlyJlSdXQm+0kZsiqqUqnIzk5eWhZcuW+Oabb8pVPjExEf369UPnzp1x4sQJvPfee3jttdfw008/VThYIiIisj0V7sDat29f9O3bt9zlFy1ahKCgIHzxxRcAgMaNG+PYsWP47LPPMHTo0IpenoiIiGxMlfcZOXz4MHr16mWxrXfv3jh27Bh0Ol2Jx2g0GmRnZ1ssREREZJuqPBlJSUmBj4+PxTYfHx/o9XqkpaWVeExUVBRcXV3NS2BgYFWHSURERBKpltE0giBYvBZFscTtRaZNm4asrCzzcuXKlSqPkYiIiKRR5ZOe+fr6IiUlxWJbamoqFAoFPD09SzxGpVJBpVJVdWhERET0EKjyZKRDhw749ddfLbbt2LED4eHhsLOzq+rLExFMU8Cnpt5ZtwUOdg5IfTPVvE5ENVeFm2lyc3MRFxeHuLg4AKahu3FxcUhKSgJgamIZM2aMufykSZNw+fJlREZGIiEhAdHR0Vi2bBnefPNN69wBEd2XIAB16piWUlpHaxxBEFDHsQ7qONYptcmXiGqGCteMHDt2DN26dTO/joyMBACMHTsWK1asQHJysjkxAYDQ0FBs3boVU6dOxfz58+Hv74+vvvqKw3qJiIgIACCIRb1JH2LZ2dlwdXVFVlYWXFxcpA6HqMbRaIDbfzdg3jzAFrpkafQaRG433dS83vOgUtjATRHZmPJ+fzMZIaoF8vIAJyfTem6ubTzBN0+bB6co003lTsvlE3yJHkLl/f7mg/KIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSVT4dPBFJz94eSEy8s24L7O3skfh6onmdiGouJiNEtYBMBoSESB2FdckEGULcQqQOg4isgM00REREJCkmI0S1gFYLvPWWadFqpY7GOrQGLd7a8Rbe2vEWtAYbuSmiWorTwRPVApwOnoikwOngiYiIqEZgMkJERESSYjJCREREkmIyQkRERJJiMkJERESSYjJCREREkuIMrES1gL098M8/d9Ztgb2dPf556R/zOhHVXExGiGoBmQxo2lTqKKxLJsjQ1NvGboqolmIzDREREUmKNSNEtYBWC8yebVp/7z1AqZQ2HmvQGrSYvd90U+91fg9KuQ3cFFEtxengiWoBTgdPRFLgdPBERERUIzAZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkkxGSEiIiJJcZ4RolpArQaOHLmzbgvUCjWOPH/EvE5ENReTEaJaQC4H2rWTOgrrksvkaBdgYzdFVEuxmYaIiIgkxZoRolpAqwW+/NK0/vrrtjMd/Jd/mm7q9cde53TwRDUYp4MnqgU4HTwRSYHTwRMREVGNwGSEiIiIJMVkhIiIiCTFZISIiIgkValkZMGCBQgNDYVarUbbtm2xf//+Msv/8MMPaNmyJRwcHODn54fx48cjPT29UgETERGRbalwMrJ27VpMmTIF06dPx4kTJ9C5c2f07dsXSUlJJZY/cOAAxowZg4kTJ+L06dNYv349jh49iueff/6BgyciIqKar8JDe9u3b482bdpg4cKF5m2NGzfGoEGDEBUVVaz8Z599hoULF+LChQvmbV9//TXmzJmDK1eulOuaHNpL9GAMBqCoArNzZ9OMrDWdwWjA/iTTTXUO6gy5zAZuisjGVMnQXq1Wi9jYWPTq1ctie69evXDo0KESj+nYsSOuXr2KrVu3QhRF3LhxAxs2bED//v1LvY5Go0F2drbFQkSVJ5cDERGmxRYSEcA0HXxESAQiQiKYiBDVcBVKRtLS0mAwGODj42Ox3cfHBykpKSUe07FjR/zwww8YMWIElEolfH194ebmhq+//rrU60RFRcHV1dW8BAYGViRMIiIiqkEq1YFVEASL16IoFttWJD4+Hq+99ho+/PBDxMbGYtu2bUhMTMSkSZNKPf+0adOQlZVlXsrbnENEJdPpgPnzTYtOJ3U01qEz6DD/yHzMPzIfOoON3BRRLVWhZ9N4eXlBLpcXqwVJTU0tVltSJCoqCp06dcJbb70FAGjRogUcHR3RuXNnfPzxx/Dz8yt2jEqlgkqlqkhoRFQGrRZ45RXT+rhxgJ2dpOFYhdagxSu/m25qXKtxsJPbwE0R1VIVqhlRKpVo27YtYmJiLLbHxMSgY8eOJR6Tn58PmczyMvLbjdY14LE4REREVMUq3EwTGRmJpUuXIjo6GgkJCZg6dSqSkpLMzS7Tpk3DmDFjzOUHDhyIjRs3YuHChbh48SIOHjyI1157DY8++ij8/f2tdydERERUI1WomQYARowYgfT0dMyaNQvJyclo1qwZtm7diuDgYABAcnKyxZwj48aNQ05ODr755hu88cYbcHNzwxNPPIFPP/3UendBRERENVaF5xmRAucZIXoweXmAk5NpPTcXcHSUNh5ryNPmwSnKdFO503LhqLSBmyKyMVUyzwgRERGRtTEZISIiIklVuM8IEdU8KhXw22931m2BSqHCbyN/M68TUc3FPiNERERUJdhnhIiIiGoENtMQ1QI6HfDDD6b10aNtYwZWnUGHH/423dTo5qM5AytRDcZmGqJagEN7iUgKbKYhIiKiGoHJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJivOMENUCKhWwbt2ddVugUqiwbtg68zoR1VycZ4SIiIiqBOcZISIiohqBzTREtYBeD/z8s2l98GBAYQM/+XqjHj8nmG5qcOPBUMhs4KaIain+9BLVAhoNMHy4aT031zaSEY1eg+EbTDeVOy0XCqUN3BRRLcVmGiIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpIUkxEiIiKSFJMRIiIikhTHwhHVAkolsHz5nXVboJQrsfyp5eZ1Iqq5OB08ERERVQlOB09EREQ1AptpiGoBvR7Yvt203ru3bczAqjfqsf286aZ61+/N6eCJajD+9BLVAhoNMGCAad2WpoMfsMZ0U5wOnqhmYzMNERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJimPhiGoBpRL45ps767ZAKVfim77fmNeJqObidPBERERUJTgdPBEREdUIbKYhqgUMBmD/ftN6586AXC5tPNZgMBqwP8l0U52DOkMus4GbIqqlKlUzsmDBAoSGhkKtVqNt27bYX/RbrhQajQbTp09HcHAwVCoV6tWrh+jo6EoFTEQVV1gIdOtmWgoLpY7GOgr1hei2shu6reyGQr2N3BRRLVXhmpG1a9diypQpWLBgATp16oRvv/0Wffv2RXx8PIKCgko8Zvjw4bhx4waWLVuG+vXrIzU1FXq9/oGDJyIiopqvwh1Y27dvjzZt2mDhwoXmbY0bN8agQYMQFRVVrPy2bdvwzDPP4OLFi/Dw8CjXNTQaDTQajfl1dnY2AgMD2YGVqJLy8gAnJ9N6bi7g6ChtPNaQp82DU5TppnKn5cJRaQM3RWRjqqQDq1arRWxsLHr16mWxvVevXjh06FCJx/zyyy8IDw/HnDlzEBAQgIYNG+LNN99EQUFBqdeJioqCq6ureQkMDKxImERERFSDVKiZJi0tDQaDAT4+PhbbfXx8kJKSUuIxFy9exIEDB6BWq/Hzzz8jLS0NkydPRkZGRqn9RqZNm4bIyEjz66KaESIiIrI9lRpNIwiCxWtRFIttK2I0GiEIAn744Qe4uroCAObNm4dhw4Zh/vz5sLe3L3aMSqWCSqWqTGhERERUw1SomcbLywtyubxYLUhqamqx2pIifn5+CAgIMCcigKmPiSiKuHr1aiVCJiIiIltSoZoRpVKJtm3bIiYmBoMHDzZvj4mJwVNPPVXiMZ06dcL69euRm5sLp9s96M6ePQuZTIZHHnnkAUInovKyswPmzLmzbgvs5HaY02OOeZ2Iaq4Kj6ZZu3YtnnvuOSxatAgdOnTA4sWLsWTJEpw+fRrBwcGYNm0arl27hu+++w4AkJubi8aNG+Oxxx7DRx99hLS0NDz//PPo2rUrlixZUq5rcjp4IiKimqe8398V7jMyYsQIpKenY9asWUhOTkazZs2wdetWBAcHAwCSk5ORlJRkLu/k5ISYmBi8+uqrCA8Ph6enJ4YPH46PP/64ErdFREREtoYPyiOqBQwG4Phx03qbNrYzHfzxZNNNtfFrw+ngiR5CVVYzQkQ1T2Eh8OijpnVbmfSsUF+IR5eaboqTnhHVbHxqLxEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYpDe4lqATs7YMaMO+u2wE5uhxldZ5jXiajm4qRnRDZCFIHft4lYusKIs2cAVzegd0/g1clyuLtLHR0R1Uac9IyoFsnOBkaMNmDbb3IAd2YiPbQXmPe5EdHRwJBBbJUloocTkxGiGi4nB+jU1YB/4uRQ2Ino9Uw+WnTQIP2GHL//4ICr5+0wdDAw/QMjRo6QoXFjQGYDeYlRNCLhZgIAoHGdxpAJNnBTRLUUm2mIajCjEejRx4DdMXK4uBswfXEm6jfXmffrtED0bBfs+PHOVOm2Mh18njYPTlFOADgdPNHDqrzf3/xTgqgGm/O5Ebtj5FDZG4slIgBgpwT+MyMbPUfkmbf9/fdD//cHEdUyTEaIaqgzZ0R8+L4AABj3Tk6xRKSIIADPvZFjfj1mghFabbWESERULkxGiGqo198wQqcV0LKTBj1H5JdZVn5X77BzCXK8P9NYxdEREZUfkxEiCRy6cgitl7ZGk2+bYNOZTRU+fucuEdu3yCGTi5jwXjYEoWLHz5sr4MwZNtcQ0cOByQhRNUvKSkLPVT0Rdy0OCSkJGLp2KHYm7qzQOaa9b6rZ6DUiH4/U01fo2FaPF8KgF/DqVNaOENHDgckIUTWL3BmJfG0+Ar0D0bxucxhFIyb8OgE6Q8l9Pu61/4CIo4dNw3iHvphb4euPisyGXCEi5nc5tu9g7QgRSY/JCFE1Ss1Lxeb4zQCAoRFDMbrnaDjZOyEpMwnLTy4v1zlm/Z+pRiNiUAE8fMpXuyFXiHhyQi6enJCLoPoG9Bll6mPy1rtGPPyD+0tmJ7fDmx3exJsd3uR08EQ1HJMRomr0wz8/QG/QI8gnCCG+IVCr1OjWphsA4H9H/of7Tfvz77/AH9vkEAQRT00of62InRIY+3YOxr6dAzslMHRSLlT2Rvx9Qo7fttTMbEQpV2Jur7mY22sulHKl1OEQ0QNgMkJUjX468xMAoFX9VuZt7Zu0h0KuwL83/sWRa0fKPP6rBQYAQNsIDfxDDZWOw9XDaK4deX9Gza0dISLbwGSEqJrkaHLwV9JfAICmoU3N253sndCyfksAwNJTS0s9vrAQWL3KNGym132G8t7LaARSr8qRelUO4+2Wnacm5EFlb8Sp4zWzdsQoGnHp1iVcunUJRpGdcYlqMiYjRNXkYNJB6A16eLh4wNvd22Jf6watAQCbEjaV+sW6dr0RWZkyePnr0aqzpkLX1hYKeKmHN17q4Q1toSmhcfU0ovczpqTm/z6peV/mBboChH4ZitAvQ1GgK5A6HCJ6AExGiKrJzium4bt1/etCuGdikLCgMKiVaqTlpuFg0sESj1+wyFR70WNYAeTyEotU2ICxeZArRPx1UI5jx2pe7QgR2QYmI0TV5MCVAwCAUL/QYvsUCgWa120OAFgVv6rY/sRE4MghU8fVJ4ZWrImmLJ6+RnTqWwgAiPqs5tWOEJFtYDJCVA0MRgNOXjsJoORkBACa1W0GANh2bluxfd/9YOqs2uwxLTzLOZy3vAaMNY3K2fyTDFevWvXURETlwmSEqBrE34xHga4AKjsVfD18SyzTKLARZDIZkjKTcD7jvMW+1WtM/3+8v/X7RtRrpkfTdhoY9AI++6LyI3SIiCqLyQhRNThx4wQAIKBOAGSykn/s1Co16vnXAwBsOrvJvP3vv0WcjTfNuPpYz8IqiW/AuDwAwIpoAYVVcwkiolIxGSGqBkdSTPOH+Hv6l1muSUgTAJbJyMpVpmaZ1l00cHKtmk6mbSM08PLXIytThjVr2XeEiKoXkxGiahB3Iw4A4OflV2a5xiGNAQBHko4gT5sHUQTWrjONvOn8AE00coWIPqPy0GeUafRMsf1yoOdw0/m/WVAzRtUoZApMDp+MyeGToZAppA6HiB4Af4KJqsGZ1DMAAH+vsmtGfNx94OHigYzsDOxK3AX/3AG4ekkGlb0R4d0q335ipwRe+DC7zDLdh+Zj3TdOOH5EjpMnRbRsKZRZXmoqhQrz+8+XOgwisgLWjBBVsfT8dKTlpgEA/DzLrhkRBAFhwWEAgM3nN2PdT7ebaDproLKv2jjd6xjxaA9TwvPlfDbVEFH1YTJCVMX+Tv0bAODp4gm1Un3f8o2DTE01Oy7swCbTA37xaI+Kzbh6L1EEsjJkyMqQlfkcmqIZWdeuliEn54EuWeVEUcTNvJu4mXfzvg8YJKKHG5MRoip2KvUUAMDXs+Qhvfdq8EgDyGQyXMm8grPJlyCTi2jb9cGGuGgKBEzo6IMJHX2gKSi9+aVZey38Q/TIzxPw3aqHu3YkX5cP78+84f2ZN/J11psIjoiqH5MRoioWnxEPAPBy9SpXebVKfWditPrb0fRRbZWNormXIAA9bz+Eb/FS1jYQUfVgMkJUxc5mnAVQ/mQEABoHm5pqUG87Hn2ieif+6PpUAeQKEaeOy3HqFBMSIqp6TEaIqtjlzMsAAC+38icjgc4tTSt1d6J1t7JHwVibq8edkTuLlj7cTTVEZBuYjBBVIVEUcfWW6YEvFakZyTjSGcj1AZR5SLP7q6rCK9UTQ0xzjqz5QYBWW+2XJ6JaplLJyIIFCxAaGgq1Wo22bdti//795Tru4MGDUCgUaNWqVWUuS1TjJOcmQ6vXQibI4OHsUe7jTm+vD5zvDQA4kfJHVYVXqtadNXCrY8CtDBl+/ZVNNURUtSqcjKxduxZTpkzB9OnTceLECXTu3Bl9+/ZFUlJSmcdlZWVhzJgx6N69e6WDJappzqWfAwC4O7tDLpeX6xi9VoZz+wKBC0XJyK4qi680cgUQMchUO7KQTTVEVMUqnIzMmzcPEydOxPPPP4/GjRvjiy++QGBgIBYuXFjmcS+++CJGjRqFDh06VDpYoprmn/R/AFSsiebin/7Q5CrhmNURAgQkZZ5DRkHKA8UhV4iIGJSPiEH5JU4HX5InBptG1ezeIcP16w90+SqhkCkwtuVYjG05ltPBE9VwFUpGtFotYmNj0atXL4vtvXr1wqFDh0o9bvny5bhw4QJmzJhRrutoNBpkZ2dbLEQ10b8Z/wKoWOfVf3cGAwCadMxHoE8gACAuec8DxWGnBF79JAuvfpIFO2X5jgmoa0Cj1loYjQKWrTA80PWrgkqhwopBK7Bi0AqoFCqpwyGiB1ChZCQtLQ0GgwE+Pj4W2318fJCSUvJfbufOncO7776LH374AQpF+f56iYqKgqurq3kJDAysSJhED43zmecBVKxmJOGPEABA4+6XERZkmhr+xI3q7zcCAE8MNdWOLF+OMmduJSJ6EJXqwCoIljM4iqJYbBsAGAwGjBo1Ch999BEaNmxY7vNPmzYNWVlZ5uXKlSuVCZNIcomZiQAAT1fPcpXPvOqE5AQvCDIjGnVLMj+n5mTKARiMla+dEEWgMF9AYb5QoaSiU59CqOyNSDwvx8GDD1c2Iooi8rR5t59u/HDFRkQVU6FkxMvLC3K5vFgtSGpqarHaEgDIycnBsWPH8Morr0ChUEChUGDWrFk4efIkFAoFdu0quWOeSqWCi4uLxUJUE13LvAYAqONWp1zl/91laqIJanMDjh6FCPYNhlqpRp4mGxcyT1Y6Dk2BgNFtfDG6jW+Z08Hfy95JRIfepjlHvl32cHVkzdflwynKCU5RTpwOnqiGq1AyolQq0bZtW8TExFhsj4mJQceOHYuVd3Fxwd9//424uDjzMmnSJDRq1AhxcXFo3779g0VP9BDLKMhAriYXgOkheeVhbqLpcQkAIJfJ0SiwEQAgLnm31WMsj6I5R37eIENeniQhEJGNq3AzTWRkJJYuXYro6GgkJCRg6tSpSEpKwqRJkwCYmljGjBljOrlMhmbNmlks3t7eUKvVaNasGRwdHa17N0QPkfPppv4iLo4uUJaj16hBJ8PZvab+UY27XzZvL2qqOX4jpsTjqlrjcC18AvXIyxWwfgObQ4jI+iqcjIwYMQJffPEFZs2ahVatWmHfvn3YunUrgoNN1cvJycn3nXOEqDY4nXEaQPk7r1466ovCHBUcPfMR2PqGeXtRMnI+7R/kaDKtH+h9yGR35hxZ/JA11RCRbahUB9bJkyfj0qVL0Gg0iI2NRZcuXcz7VqxYgT179pR67MyZMxEXF1eZyxLVKPHpFXtab/ztJpqwJ5Igu+sn093ZHb4evhBFI07dKN9sx9YWMagAgiDi8H45Ll2SJAQismF8Ng1RFTEP6y3nHCN3hvReKravqHZEiqnhAcA7wIBmj5keUrM0mrUjRGRdTEaIqkjRsN7y1IxkJTvi+j91IAgiwp4o3sxpnm8kZY9kw1ifGGIasbJiJWBkPkJEVsQ5lImqyJVM0/w45UlGiob0Bra+ASevgmL76wXUg53CDrfy05CU9S+C3RpXKBaZXESH3gXm9cpo36MQDk5GXEuSYe9eEd26lX+IcFWQy+QY1mSYeZ2Iai4mI0RVIE+bh4y8DADlS0YS/jAlI2FPXC5xv53CDvUD6iPhcgJOpOyscDKiVAFvfnmrQsfcS2UPdOxbiD/WO+DbZUZ06yZtAqBWqLH+6fWSxkBE1sFmGqIqcDHzIgDAQeUAB7VDmWUNegFn9gQBuDO/SEnMQ3xTpBniC9xpqtm8UYacHMnCICIbw2SEqAoUPSCvPNPAXz7mi4IsNRzcChHc9kap5YqSkTOpJ1Cgk2b2sYatdPAP1aOwQMDatZxzhIisg8kIURVIyEgAUL6RNAk7QwAAjbpdLrM/h7ebNzxcPKA36nHqxr4KxVOYL2BomB+GhvmhML/yfT0EAeg22FQ7skTiUTV52jwIHwkQPhKQp+XUsEQ1GZMRoipwJv0MgIr1F2nco+T+IkUEQUCz0GYAgEPXf37ACCsv4qkCyGQijhyW49w5ycIgIhvCZISoClzIvADg/slITqoDrp40PWSytM6rd2tZvyUAIPbKLugMmgeMsnI8fIxo+bjp2suWc4wvET04JiNEVSAp0zRXyP2SkYRdpo6rj7RIhYvP/Z88G+ofChdHFxTo8hGXsvfBA62kboNNw4RXrgQMBsnCICIbwWSEyMp0Bh1uZJs6ot43GbnnKb33IxNkaFnPVDty6NrGSsf4oNo9UQgnVyNSrsuwc6dkYRCRjWAyQmRll7MuwygaYaewg4ujS6nljAYBZ3abakbCut+/iaZIqwatAADHru6EzqB9oFgrS6kCHu9vqh35dimbaojowTAZIbKyc+mmXp1erl4QhNJHriQd90F+pj3ULhqEtEsu9/lD/ULh4uCCfG0eTt2Qvqnmt18E3LolWRhEZAOYjBBZWdGw3vvNMZKw0zSKplHXJMgV5Z+zQyaToUX9FgCAPUk/lu8YuYg2XQvRpmthpaeDv1e9ZjoENdBBqxGwek31zzkil8nRr0E/9GvQj9PBE9VwTEaIrKxowrM6rnXKLHenv0j5m2iKhIeFAwCOXtmJfN39p0JVqoDp32Zi+reZUKoqfLkSCQLQbYipdmTJsupPRtQKNbaM2oIto7ZArVBX+/WJyHqYjBBZ2fmM8wDKrhnJTVfjyonbQ3q7X6rwNYJ9guHt7g2dQYuDSZsrFac1dBlYAJlcRFysDPHxkoVBRDUckxEiK7uUeQlA2SNpzuwOhigK8GuSBjf/is8eKggCHm38KABg9+XVlYrTGty8jGjb1TTnyFKJZ2QlopqLyQiRFRlFI67eugqg7Kngi/qLlHdIb0nCG4VDgIAzqXFIyS27qacwX8Co1j4Y1drngaaDL0m32w/PW/U9oNdb9dRlytPmwXG2IxxnO3I6eKIajskIkRVdz7kOnUEHmUwGd2f3EssYjcC/RclIBYb03svN2Q0NAxsCAHZdWnXf8poCGTQF1v+Rb9tVAxcPA26myrBtW/X2HcnX5SNfd//J4ojo4cZkhMiKLmSYpoH3cPYodYTHtVPeyE1zgMpJi9D21x/oeu2btgcA7Lz4I/RG3QOdq7IUdqa+IwCwWIKOrERU8zEZIbKiMxmmB+SV1Xm16MF4DbpcgUL5YP0sWtRrAWcHZ9zKT8eRa78/0LkeRNGomt+3CEhLkywMIqqhmIwQWdHp9NMAgDpupQ/rTdgZAuDB+osUUcgV6NC0AwBg64XFD3y+ygpppEfdJjrodQJW/cCOrERUMUxGiKzoXMad2VdLkn9LhUtHfQE8WH+Ru3Vo1gGCICAh5TiSss5Y5ZyV0W2wqe/G0mjJQiCiGorJCJEVXcy4CKD0mpGzewIhGmXwaZgOj8D7T1ZWHu7O7mhetzkAYMuFRVY5Z2V0HlAAhZ2I06dkOHlSsjCIqAZiMkJkJaIoIikzCUDpw3rjH2DW1bJ0btEZALD3ws/IKkwvtl+QiWjaToOm7TQQZFXTydTZXUS7JwoBAAsXG6rkGneTCTJ0De6KrsFdIRP4q4yoJuNPMJGVpOalokBXAEEQ4OlcvAOraUhvCADrNdEUqf9IfQT5BEFn0OK3cwuL7VepgVnfZ2DW9xlQVeHM6d2HmTqyrv5BQH4Vj7i1t7PHnnF7sGfcHtjb2VftxYioSjEZIbKSov4i7s7uUCgUxfZfjfNB9g1HqJy0qNfxmlWvLQgCurftDgDYdm5luZ5XUxVadtLA+xE9crJkWL2GHVmJqHyYjBBZydn0swBK77z6z7ZQAEBYt8tQqKzfjNG8XnN4u3sjX5uH7ReXW/385SGTAb1GmKpEvl7AOUeIqHyYjBBZSXy66UlxpXVePb3dlIw07ZNYJdeXCTJz7cgv/36LQv2ddpLCfAHjO3hjfAdvq08Hf68nhpo6sp46LkdsbNVdJ0+bhzpz66DO3DqcDp6ohmMyQmQlRROelVQzknnVCdf+9oYgiGjS81KVxRDeKByeLp7ILsjEr/f0HcnOlCM7s+RZYa3J1cOIDr1NHVm/+KZqO7Km5achLZ+zrBHVdExGiKzkQqZpKviSakZO7zDVioS0S4aTV0GVxSCXy9GvQz8AwOaERcjRZFbZtcrSe6SppmL9jzLcuiVJCERUgzAZIbICURSRlHF7WG8JNSOnt9UFUHVNNHdr3bA1/L38UaDNw09n/lfl1ytJWBsdghrooCkUEL2CHVmJqGxMRoisIC0/DXnaPAgQij2XRpOnwLn9jwAAmva+WOWxyAQZ+nfoDwDYdmYlUnOvVPk17yUIQO+Rpj4rCxYCIvuyElEZmIwQWcH5jPMAADdnN9gp7Cz2ndkTBL1GAc/gLPiGZVRLPE1CmqDBIw2gM+iw7OS71XLNe3V5sgBqByMunJVh925mI0RUOiYjRFYQn2YaSVNSE80/v99uoul9EULVDmQxEwQBQ7oOgUwmw7EruxGXsrt6LnwXBycRXZ409Y+ZM49NNURUOiYjRFZw8qbpYSw+Hj4W240GAfExVTuktzR+nn7o0rILAGDlqQ9Rt6kG9Zppq2w6+JL0f87UVLNjqwznz1v33DJBhnD/cIT7h3M6eKIajj/BRFZQVDPi426ZjFz80x+5Nx3g4FaI+p2sO+tqefR5tA9cHFyQWngeLWdOxZwN6VU6Hfy9HqmnR+vOhRBFAZ/9z7rDfO3t7HH0haM4+sJRTgdPVMMxGSGygrNpptlX760ZOflrfQBAs74XIber/qYKtUqNoRFDAQCb47/FufQT1R7DwHGmYb7freAwXyIqWaWSkQULFiA0NBRqtRpt27bF/v37Sy27ceNG9OzZE3Xq1IGLiws6dOiA7du3VzpgooeNRq/BtVumWo+7a0aMRuDU7WSk5ZNWbqOogJb1W6JNwzYwikZ8dfRlaA2F1Xr9Fh21CGqgQ0G+gEWL2XeEiIqrcDKydu1aTJkyBdOnT8eJEyfQuXNn9O3bF0lJSSWW37dvH3r27ImtW7ciNjYW3bp1w8CBA3HiRPX/hUZUFc5lnINRNEKtVMPF0cW8/fIxX2QlO0HlpEGjiJJ/PqrLwEdHQPjyEq7P2I0Vx6Kq9dqCAPQfa6od+eprQK+3znnzdfkI+SIEIV+EIF9XxY8IJqIqVeFkZN68eZg4cSKef/55NG7cGF988QUCAwOxcGHxx5YDwBdffIG3334b7dq1Q4MGDTB79mw0aNAAv/766wMHT/QwSLiZAMBUKyLcNVymqFakae9LVfJgvIpwUDlCzAwGskKw/cwP+Ovqlmq9fpeBBXDxMCD5qgwbNlinA60oiricdRmXsy5D5EQmRDVahZIRrVaL2NhY9OrVy2J7r169cOjQoXKdw2g0IicnBx4eHqWW0Wg0yM7OtliIHlZFI2m8PbzN20TxTn+RlgOla6IpzddHpiAl93K1XU+pAno/Y6q9+OQzIydBIyILFUpG0tLSYDAY4ONj2UnPx8cHKSkp5TrH559/jry8PAwfPrzUMlFRUXB1dTUvgYGBFQmTqFr9ffNvAJb9Ra6eqoOMJFcoHXRo3OOSRJGVLMgnGAXaPMw5PBYaffU1b/QZlQ+lSsTJWDl27mQ2QkR3VKoDq3DPzE2iKBbbVpI1a9Zg5syZWLt2Lby9vUstN23aNGRlZZmXK1eqfzprovI6k2Z6Wq+vh695W9zmBgCAxt0vQelgpU4SVjK612g4qh1xOf0s/vfXizCK1dOp1M3LiO7DTMnPzI/ZkZWI7qhQMuLl5QW5XF6sFiQ1NbVYbcm91q5di4kTJ2LdunXo0aNHmWVVKhVcXFwsFqKHkd6oR2KGaTKzomG9RiNwfEMjAEDrwWcli600bk5umDhgIuQyOY5e2YlVf39Ubdd+amIu5AoRB/fKcfgwa0eIyKRCyYhSqUTbtm0RExNjsT0mJgYdO3Ys9bg1a9Zg3LhxWL16Nfr371+5SIkeQufSz0Gr10JppzQ/IC/xL39kXnWBykmDJr2rd9bV8qrrXxcje4wEAGyOX4Lfzy+tluvW8Tei6+0p4lk7QkRFKtxMExkZiaVLlyI6OhoJCQmYOnUqkpKSMGnSJACmJpYxY8aYy69ZswZjxozB559/jsceewwpKSlISUlBVlaW9e6CSCJxN+IAmKZeL5qSPHZ9GACg5cALUNpLO4qmiCAAvo3S4dso3fx8nPCwcPR+tDcAYOmxD7H70o/VEsug53MhCCJ2bJXj5MnK144IgoAmdZqgSZ0m5WomJqKHl6KiB4wYMQLp6emYNWsWkpOT0axZM2zduhXBwcEAgOTkZIs5R7799lvo9Xq8/PLLePnll83bx44dixUrVjz4HRBJ6GjyUQCAv5c/AECvlSFus2kUTdthZySL615KBz3ePbyq2PY+7fugQFOAfSf3Yf5fb0ApV6NT4KAqjSWgrgGP9S7E4W32mPmxET+vl1fqPA52Djg9+bSVoyMiKQhiDRign52dDVdXV2RlZbH/CD1UIlZFYO+FvRgWMQyPt3gc/2wLxdJRT8LFJw8z/1kGmfyh//GCUTRi3c51+DP+T8gEGV55bB66Bpc+2s0aEhMUeHNwHQiCiJMngebNWbNBZIvK+/3NZ9MQPYCEVNOEZ0U1I7Hr73RcrQmJCGB6+u3wJ4YjPCzcNGX84SnYcm5xlV4ztLEe7XsWQBQFvDudfUeIajsmI0SVlFGQgdScVACAv6c/8rOU+Of3egCAtk//K2VoxWjzFfikw7P4pMOz0OYXb52VyWQY1XMUOrfsDACIjp2J1f/MrtKZTUe+buo7svVXOY4erfh18nX5aLqgKZouaMrp4IlqOCYjRJV06sYpAICniyfUKjWObwiDrlABv8ZpCGyVKnF0lkQRSDnjiZQznqXOfioTZBjSZQj6tO8DAPjpn2/w2Z8TqmxitMD6enS5PbLm7fcqXjsiiiLib8Yj/mY8p4MnquGYjBBV0uFrhwEAAXUCAAB/ft8UAPDYc6dRUwd3CIKAPu37YMQTIyCXyfHn5e14b3d/pOVdq5LrDX/FNO/Inj/k2LuXCQVRbcVkhKiSDl47CAAI8gnClZN1cPWUN+RKPcKHP1xNNJXRoVkHTB48GY5qR1xKP4M3Yrrj2PUdVr+Ob6DBPCtr5Nt8Zg1RbcVkhKiSTlw/AcCUjPz5fTMAQIsBF+DoUShlWFZTL6AeIp+JxCN1HkFuYTai9o3D0rhp0Bk0Vr3O0y/lQmVvxPEjcqz5kdkIUW3EZISoEm7m3cT1rOsAAB/nUPMomg7P2da8F54unpjy9BR0adUFAPD7vyvx9h89cSHzb6tdw8PHiMEv5AEA3nxbREGB1U5NRDUEkxGiSjhy7QgAwNvdGwm/tkJhjgqewVmo39n2HuqoUCgwpMsQPD/geTiqHZGUeR7v7uiL707NhNZgnVqgJ8fnwsvPgOSrMnz6GYf6EtU2TEaIKmH/1f0AgCCfYOz9thUAoNOEU5A9pD9RggC4B2bDPTC70p1rm9VthneffRetGrSCUTRic/xiTN3eBceTdz5wfCp74Nk3sgEAcz4RcP36/Y8RBAHBrsEIdg3mdPBENRxnYCWqhC7fd8H+i/vROWAi9r+wFEoHHWb+swwObtbtT/GwOnXhFDbs3oDsfFMC0SrgcUxoORsBLvUrfU5RBN57xhNnTyoxdLgBG9ZWbpp4Inp4cAZWoiqiN+oRezUWAHB925MAgEdHxteaRAQAWtRrgXefexcRrSMgk8kQd+0ApmzrhiUn3kFmQeXmWBEEYOL72ZDJRPy0To7tOx76v5OIyEqYjBBVUFxKHPK1+VAp7HHh534AgM7/iZM2KAk4qBwwqPMgvDP6HTQJaQKj0YBtZ77HS789iuUn30dWYXqFz1m/uQ59RpuG+r7wooh8TqxKVCswGSGqoJ2XTH0kHHLaAUYFwrpfgk+DW9IGdR/aAjk+7/4MPu/+DLQF1m3+8HH3wX+e/A9eGvQSgn2CoTNo8VtCNF76rR2Wn3y/whOmjXo9B56+Bly5JMOHM0vvzFqgK0C7Je3Qbkk7FOg4BIeoJmMyQlRBMZdiAABZh/oDALq/fkzKcMpFNAq4csIHV074QDRWTWfPRkGNMGX4FPznyf8g0DsQGn2hKSnZ8hjm/fVCuYcD2zuJeP6DLADAF/8TcOJEyc01RtGIY9eP4dj1YzCKHIFDVJMVf2IWEZXKKBpx5IppWK/xYgRC219H/U5VM1V6TSQIApqENEHj4MaIvxSP3cd34/y18ziYuAUHE7cgzKc1eoeOx2OBA6CUq0s9z6PdNWjfswB/xdjjmdFGnDwuQF16cSKq4VgzQlQBx64fQ05hDqBxAZLboNcbR2rsc2iqkiAIaBraFK8MfQVvPPMG2jZqC5kgw783TuDLP1/D87+0xNK4abiSdbbUc7w4MxuungacTZDhrXdZ80Fky5iMEFXAprObTCvneyGwRTrCul+WNJ6aINA7EM/1fg4fjvsQfdr3gZuTG/I0Ofj935WY8nsE3vzjCWw+swAZBSkWx7l6GjH5Y1NzzTdfyvDHTo6uIbJVTEaIKmDj6d9MK+f7ovfbf7FWpALcnN3Qp30ffDjuQ/znyf+ged3mkAkyJKb9i+9OfIz/bG6L9/cMwPYLK5GtMY3ECe+mQc/hpqninx0j4uZNKe+AiKoK+4wQlVNqXirOpP0DCECQa0s07X1A6pBqJJlMhiYhTdAkpAly8nMQdy4Ox88eR2JyIhJSjiMh5TiWHHsPDeo0x6P+/dDz5f44feQxXL+kwLARBuyKkUPO+dCIbAqTEaJyWrjrV0AQgeTWGDLtfI2rFXH0fPgm7XB2cEbnlp3RuWVnZGRn4PjZ44g7F4erN6/ibOopnE09BeATeE4KgnzfMOz7tw/e/qAjPp/tCADwcvCS9gaIyCo4HTxROYgi4P56b2R57oDP9Vcx7ZN6Uodk0zJzMvHPxX/wT+I/OH/1PAxGw52dehUauoRjVPue6FevH9r4tYFcxqoSoodReb+/mYwQlcPC79Iw+bwfINfjpW6z0ai5g9Qh1RqFmkL8m/QvTl86jZN/X4ZWYTndvLPaGeGPhKNbUDd0C+6GcP9wqBUcB0z0MGAyQmQlt24Bjzy1GHlPvAhnY338d8orUodUa+m1AuZPaIbEmwlQNNkGeYOd0OgtZ1+1k9uhqW9TPB70OLoHdcejAY/C39lfooiJajcmI0RWMm6iESuNTwAhe9H30YHo/Vh3qUOqMG2BHN8OHwQAeHHdJijtDWUf8BArzFbiy35PI/msI+z+0x1eja6ibVhLJKUm4eL1i8gtyC12jKejJ1r6tUR7//bo6N8R4QHh8HXylSB6otqlvN/f7MBKVIZNm0Ws/OUs8MpeCBDQvmlbqUOqFNEo4MLBR8zrNZnaRYsX127GvH79ke19GMmZQNvgN9CjHSCKItKy0pCYnIiL1y/iUvIl3Mi8gfS8dOw6vwu7zu8yn6eOUx00822Glt4tEe4bjhbeLdDIqxGUcqWEd0dUOzEZISpFaiow8XkRaPstAKBpaFO4ObtJGxQBANwCcvHCml/x+R+m10ufHYiX12+Hg6sWddzqoI5bHTza+FEAgEanwbWb13Al9QqupF7B1dSruJFxAzdzb2L3+d3YfX63+bxymRwhHiFo4t0Erbxboa1PWzSu0xihbqGwk9tJcatEtQKTEaIS6PXA0yMNyMgugNBmBUQAHZt3lDosuot3g0zgdjJy7aQPFjw1FC+u2wxnb8shzCo7Fer610Vd/7rmbRqtBtfSruHazWtITk82L4XaQlxIu4ALaRfwa/yv5vJymRwBrgGo51kPYZ5haObVDE28mqChZ0P4OflBqGnjvIkeMkxGiErw9rtG7Nslh7zrQhhUt1DHrQ7CgsKkDotK4eiZj6unvPFFn6cxacMm1KmbVWZ5lbJ4giKKIm7l3kJyejKup11HSnoKktOTkXorFTq9DkmZSUjKTLKoSQEAezt7BLoHItgtGPXc66GReyM0cG+AUPdQhLiFwMGOI6+I7ofJCNE9Vn5vxP8+lwGKAii7f4oCI9AjvAdkMj494WE1+eefEf3MCKRfcsOXfYdj/IotqNfheoXOIQgC3J3d4e7sjiYhTczbjaIR2bnZSL2VitTMVNy8dRM3b91EamYqMrIzUKArwNnUszibehYxiCl2Xg8HDzzi9giC3YJR370+6rvVR4hrCB5xeQSPuDwCd7U7a1ao1mMyQnSXLVtFTJxg+mJo/OqnSDCmw8PFA+GNwiWOjMriVfcWXt+2DotHPIWrp7wx/6kheOq/+9HlPycfeKZcmSCDm7Mb3Jzd0DCwocU+vUGP9Kx0pGWlIT07HelZ6cjIzkBGdgbSs9NRqC1ERn4GMvIzcOr6qRLPr1Ko4O3sDX9nfwS4BCDYJRghriHmhMXf2R91HOpwYjeyaUxGiG7bu0/E0KGAQS+g3aALOOX5KaAH+rbvC7kNPAxF6aCTOgSrUyrujHxx8cnHq1vWY+2UHjj+UyP8PC0Cl474Ydhnu+HorqmS6yvkCvh4+MDHw6fE/fmF+UjPTjcnJxlZGcjMzURWbhZu5d5CbkEuNHoNrmRewZXMK6VeRybI4GbvBk9HT3g7ecPXyRf+Tv4IcArAI06PwM/ZDz6OPvB18oWHvQdrWqjG4TwjRAB+2yJi2DBAUyigVecC2I19CkevxiDENwSvP/06f7nXIKII7Pu2FTZ/0BlGgwyufrl45ss/0LjHZalDK0an1yErL8ucnBQt5tc5t5CTnwMR5f81rZAp4O7gDg9HD3jae8LLwQt1HOrAx8EHvo6+8HH0gZeDl3nxtPeESqGqwruk2oyTnhGVU/QKI/7zggCDXkDrLoVo9+YCLD7+BmQyGaYOn4pA70CpQ6RKuBzrgx8m90LqOQ8AQPjwBAyccRCufnkSR1YxBqMBeQV5yM7PRk5eDnIKcpCdl42c/Bxk52cjNz/X/DpfU7mHITooHeBm72ZKYuw94OngCU+1JzzsPeCl9oKXvSlpcVO7wd3eHe5qd7jbu8PRzpGJOpWJyQjRfWi1wKtTjFi80NQx9fH+Bej3zn58tO8paPSF6N+hP3q26ylxlPQgtPkK/Pbfjti/uBVEUYDKSYuebxxBlxdOQumglzo8q9Pr9cgpyEFOfg5yC3KRV5CHvMI85BXkIbfw9uvb23ILcpFfmA+jaKz09eQyOZxUTnBRu8BF7WJKVtTucFO7mZIatacpcVG5w1XlCheVC5xVznBRuZgXlVzFhMaGMRkhKsOpUyKeG2fEqROmviDDX85BxLh/MX13P2Tmp6FhYENMemqSzYyg0RXKsXxsfwDA+JVbYKeuudPBF9HpdVi+dTkAYHy/8bBTlD4pWdIJb/z0TgQuH/MDADh75+GJV2PRafzfNpmUlJdRNKJQU2hOTu5OXgo0BcjX5CNfk48CTQEKCgvM6/mafBiNlU9i7qaQKeCocoSj0rQ4q5wtEhZXlStcla6mREflDmelM5yUTnBSOsHRznTM3etMbh4unA6eqAQ5OcD/fWLE53MF6HVyOLoY8dqnt+DX9jTe3zMUmflp8PXwxbh+42wmEQEAo0FAfEyoed0WGEUj4i/Fm9fLEtQ6Fa9vW4fY9WH4PeoxZCS5YvMHXfDHF+F47Nl4dBz3NzyDs6sj7IeKTJDBQe0AB7UD6rjVKfdxoihCq9daJChFSUqBpgD5hXe2aXQaFGoLzYtGa3qt0Zk6FeuNemQVZCGroOy5YSpyT/Z29lDbqeGgdICDnYP5/0VJS1Ey42xnSmxcVa5wtnOGo50j7O3sYa+wN//fwc6h2DaObLI+JiNUKxQUAIuXGjFrFpCRZkoy2j1RiBdnZuGs9je888drKNDmw9vdG5OemgQHFSeqsjUyGdBuxL9oM+Qsjv7YGDHz2iH9sit2fhmOXV+1RVj3y2gz9Aya9b0Iexet1OE+1ARBgMpOBZWdqtKPSDCKRmi1WstE5Z7E5e7kpWjR6rTQ6rXQaDXQ6rXQ6rTQ6DTQG/Tm8+Zp85CnzUN6XroV7/oOhUwBlUIFlUIFtZ0aaoX6zv8VaovkxcHOAQ4KB/P/He0c4WjnaContzcfo1KooJKrzP+/d5taoYZKrrLZRIjJCNm0xERgwbdGLF0C3MowJSG+wXo8OzUHDbtcwvJTH+DgpS0AgGDfYDw/4Hk4OzhLGTJVMbmdEY89dxrtRsbj9PZQHIxugTO7g5HwRwgS/giBXKlH2BNJaNLzEhpFJMEr1Dp/sZMlmSCDWqWGWqW2yvkMRgN0Oh00OlOSotFpzInK3f83r+uLb9PpdZaL4c56UbIDmGpz9Fo98rTV3xlaJsigVCihlCthJ7czr6sUKijld9bVCjWUcqU5iSnappabtqvkKvOiVqihlCnvrMuVsJPZmc9XtNjJLbeVVKayyVKlkpEFCxZg7ty5SE5ORtOmTfHFF1+gc+fOpZbfu3cvIiMjcfr0afj7++Ptt9/GpEmTKhUwUVlEETh9GtjyuxFr1oo4GSsHYEpCvAP0eGpiHsJ6x2HrpQX4cssm6AxaCBAQ0ToCAzoOsIn5RKh85AoRLfpfRIv+F3HzghtiNzTCiZ8b4MZZT5zeVhent5mmivcMzkK9jtcQ1OYGglrfgH/TNChUNb/Pja2Ry+SQq+RWS27uZTQaLZKTkhIXrU5bLIm5d9HqtdAb9OalKNEpddHrLYZ2G0UjCnWFKNQVVsl9PigBAhRyBezkdlDIFZBry/c7tcLJyNq1azFlyhQsWLAAnTp1wrfffou+ffsiPj4eQUFBxconJiaiX79+eOGFF7Bq1SocPHgQkydPRp06dTB06NCKXp7ITBSBlBTg5Eng2AkjjhwTcXCfcLsZxpSAyGQiGndKQ+NB+2AI3IWt17diScwl8zlCfEMwNGIoh+/WcnXq3UKfd/5C77f/QkqCJ/7eWhdn9gTh0lE/pF92RfplVxxZY5oiXq7Uw7v+LXjXz4RPgwx4N8iEV2gWXP1y4eKbB7nioR8TQJUgk8mgkpmapqqTKIowGo2WCYxBVyxhKTOhuSfpMRqN0Bv1MBgMMBgNMBgMZb42H2PQm18bjKZ993ZkFiGakjHD7UkWy5kzVXg0Tfv27dGmTRssXLjQvK1x48YYNGgQoqKiipV/55138MsvvyAhIcG8bdKkSTh58iQOHz5crmtyNI3tMxgAjca0FBaa+nhkZgIZGaYlPV3EzXQjkq4Cly8DSVd0uJ6iRZ6mELDLBxzSbi83IXdNhVuD81AFJUDrcB7pBddx98dcLpOjcUhjRLSKQL2AerWi570mT4F3Al8GAHx6ZT5UjjV/BIlGp8E7C98BAHz60qdV8iWhybXDhcMBuHTUF0nHfZB0wgf5mfallhdkRjh758PNPxfOdfLh4F4Ie1cNHNwL4eBm+r/aWQulvR529jrY2euhvL3Y3d6mUBohyMQHnsaeqDoYRSOMhnuSm6KExWBAXnYevm77tXVH02i1WsTGxuLdd9+12N6rVy8cOnSoxGMOHz6MXr16WWzr3bs3li1bBp1OBzu74sPxNBoNNJo70zdnZZnabL2mdIGgvF3lI9yTQ4n3bBMB0eK1UPJxJc5sWPI2sRxl7uuu64v3xlyhc5d8LYutgli8WLmvV3oZsdznKfu8onhXkbuLlnRuwQAoCgHnQqBVIdC69NETBgDpgCkjv52Vu7u4I9gnGA0CG6BxcGNzB1VNTtVME/6w0eTLAZhGixTmFEA01PxmBo1eY/73LcwphFglNRKFqNv+X9Rt/y8A02c286ozUs+7I/W8G25edEPaBTdkXnVG1g1HiAY5slOA7BQHAA/WCVqQGyBXiJApjJDJTYtcIZrWFUYICtGUtNz+4REE01L082NaBwRBNP+/aL/FPpRQhshKjIZcAF/jfvUeFUpG0tLSYDAY4ONj+RwGHx8fpKSklHhMSkpKieX1ej3S0tLg5+dX7JioqCh89NFHxbbrlp+sSLhEZpm3/4tDnNShSOgtAMCMJvcpVgPN+GSG1CFYnWgA9AYAtSNfJhuXk5MDV1fXUvdXqgPrvdXaoiiWWdVdUvmStheZNm0aIiMjza9v3bqF4OBgJCUllXkzti47OxuBgYG4cuVKrW6u4vtgwvfBhO/DHXwvTPg+mDwM74MoisjJyYG/v3+Z5SqUjHh5eUEulxerBUlNTS1W+1HE19e3xPIKhQKenp4lHqNSqaBSFW//dXV1rdUfrCIuLi58H8D3oQjfBxO+D3fwvTDh+2Ai9ftQnkqECk0xqVQq0bZtW8TExFhsj4mJQceOHUs8pkOHDsXK79ixA+Hh4SX2FyEiIqLapcLzXUdGRmLp0qWIjo5GQkICpk6diqSkJPO8IdOmTcOYMWPM5SdNmoTLly8jMjISCQkJiI6OxrJly/Dmm29a7y6IiIioxqpwn5ERI0YgPT0ds2bNQnJyMpo1a4atW7ciODgYAJCcnIykpCRz+dDQUGzduhVTp07F/Pnz4e/vj6+++qpCc4yoVCrMmDGjxKab2oTvgwnfBxO+DyZ8H+7ge2HC98GkJr0PNeKpvURERGS7bOexpERERFQjMRkhIiIiSTEZISIiIkkxGSEiIiJJMRkhIiIiST30yciCBQsQGhoKtVqNtm3bYv/+/VKHVO2ioqLQrl07ODs7w9vbG4MGDcKZM2ekDktSUVFREAQBU6ZMkToUSVy7dg3PPvssPD094eDggFatWiE2NlbqsKqVXq/H+++/j9DQUNjb26Nu3bqYNWtWsUea25p9+/Zh4MCB8Pf3hyAI2LRpk8V+URQxc+ZM+Pv7w97eHhERETh9+rQ0wVahst4HnU6Hd955B82bN4ejoyP8/f0xZswYXL9+XbqAq8j9Pg93e/HFFyEIAr744otqi6+8HupkZO3atZgyZQqmT5+OEydOoHPnzujbt6/FPCa1wd69e/Hyyy/jzz//RExMDPR6PXr16oW8vDypQ5PE0aNHsXjxYrRo0ULqUCSRmZmJTp06wc7ODr///jvi4+Px+eefw83NTerQqtWnn36KRYsW4ZtvvkFCQgLmzJmDuXPn4uuvv5Y6tCqVl5eHli1b4ptvvilx/5w5czBv3jx88803OHr0KHx9fdGzZ0/k5ORUc6RVq6z3IT8/H8ePH8cHH3yA48ePY+PGjTh79iyefPJJCSKtWvf7PBTZtGkT/vrrr/s+I0Yy4kPs0UcfFSdNmmSxLSwsTHz33XcliujhkJqaKgIQ9+7dK3Uo1S4nJ0ds0KCBGBMTI3bt2lV8/fXXpQ6p2r3zzjvi448/LnUYkuvfv784YcIEi21DhgwRn332WYkiqn4AxJ9//tn82mg0ir6+vuInn3xi3lZYWCi6urqKixYtkiDC6nHv+1CSI0eOiADEy5cvV09QEijtfbh69aoYEBAg/vPPP2JwcLD4v//9r9pju5+HtmZEq9UiNjYWvXr1stjeq1cvHDp0SKKoHg5ZWVkAAA8PD4kjqX4vv/wy+vfvjx49ekgdimR++eUXhIeH4+mnn4a3tzdat26NJUuWSB1WtXv88cexc+dOnD17FgBw8uRJHDhwAP369ZM4MukkJiYiJSXF4vemSqVC165d+XszKwuCINS6GkSj0YjnnnsOb731Fpo2bSp1OKWq8HTw1SUtLQ0Gg6HY04B9fHyKPQW4NhFFEZGRkXj88cfRrFkzqcOpVj/++CNiY2Nx7NgxqUOR1MWLF7Fw4UJERkbivffew5EjR/Daa69BpVJZPBfK1r3zzjvIyspCWFgY5HI5DAYD/u///g8jR46UOjTJFP1uLOn35uXLl6UI6aFQWFiId999F6NGjap1T/H99NNPoVAo8Nprr0kdSpke2mSkiCAIFq9FUSy2rTZ55ZVXcOrUKRw4cEDqUKrVlStX8Prrr2PHjh1Qq9VShyMpo9GI8PBwzJ49GwDQunVrnD59GgsXLqxVycjatWuxatUqrF69Gk2bNkVcXBymTJkCf39/jB07VurwJMXfm3fodDo888wzMBqNWLBggdThVKvY2Fh8+eWXOH78+EP/7//QNtN4eXlBLpcXqwVJTU0tlvXXFq+++ip++eUX7N69G4888ojU4VSr2NhYpKamom3btlAoFFAoFNi7dy+++uorKBQKGAwGqUOsNn5+fmjSpInFtsaNG9e6jt1vvfUW3n33XTzzzDNo3rw5nnvuOUydOhVRUVFShyYZX19fAODvzdt0Oh2GDx+OxMRExMTE1Lpakf379yM1NRVBQUHm35uXL1/GG2+8gZCQEKnDs/DQJiNKpRJt27ZFTEyMxfaYmBh07NhRoqikIYoiXnnlFWzcuBG7du1CaGio1CFVu+7du+Pvv/9GXFyceQkPD8fo0aMRFxcHuVwudYjVplOnTsWGdp89e9b85OzaIj8/HzKZ5a8wuVxu80N7yxIaGgpfX1+L35tarRZ79+6tdb83ixKRc+fO4Y8//oCnp6fUIVW75557DqdOnbL4venv74+33noL27dvlzo8Cw91M01kZCSee+45hIeHo0OHDli8eDGSkpIwadIkqUOrVi+//DJWr16NzZs3w9nZ2fxXj6urK+zt7SWOrno4OzsX6yPj6OgIT0/PWtd3ZurUqejYsSNmz56N4cOH48iRI1i8eDEWL14sdWjVauDAgfi///s/BAUFoWnTpjhx4gTmzZuHCRMmSB1alcrNzcX58+fNrxMTExEXFwcPDw8EBQVhypQpmD17Nho0aIAGDRpg9uzZcHBwwKhRoySM2vrKeh/8/f0xbNgwHD9+HL/99hsMBoP596aHhweUSqVUYVvd/T4P9yZhdnZ28PX1RaNGjao71LJJO5jn/ubPny8GBweLSqVSbNOmTa0czgqgxGX58uVShyap2jq0VxRF8ddffxWbNWsmqlQqMSwsTFy8eLHUIVW77Oxs8fXXXxeDgoJEtVot1q1bV5w+fbqo0WikDq1K7d69u8TfB2PHjhVF0TS8d8aMGaKvr6+oUqnELl26iH///be0QVeBst6HxMTEUn9v7t69W+rQrep+n4d7PaxDewVRFMVqynuIiIiIinlo+4wQERFR7cBkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCT1//0ceJg8nLR+AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.3442238 , 7.94607029]),\n", - " array([0.71331024, 2.395806 ], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Rafael Leao', plot = 1, log = 1)" - ] - }, - { - "cell_type": "markdown", - "id": "0a5de8a1", - "metadata": {}, - "source": [ - "Code for predicting the probability distribution of total team points" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "65f9a572", - "metadata": {}, - "outputs": [], - "source": [ - "squad = ['Szczesny',\n", - " 'Demiral',\n", - " 'Kim',\n", - " 'Di Lorenzo',\n", - " 'Valeri',\n", - " 'Kostic',\n", - " 'Frattesi',\n", - " 'Barella',\n", - " 'Strefezza',\n", - " 'Rafael Leao',\n", - " 'Hojlund']\n", - "\n", - "\n", - "dist = [None] * len(squad)\n", - "\n", - "defenders = list([0]) * len(squad)\n", - "\n", - "\n", - "for i in range(len(squad)):\n", - " [X, y, dist[i]] = predict_player(squad[i], plot = 0, log = 0) \n", - " \n", - " if(players['r'][squad[i]] == 'D'):\n", - " defenders[i] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "2e78f674", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Avg Total Points = 76.45314080810547\n", - "Avg Mod Points = 1.548\n", - "Avg Clean Sheets = 0.34\n" - ] - } - ], - "source": [ - "ITERS = 500\n", - "\n", - "MOD = True\n", - "\n", - "total_points = np.zeros(ITERS)\n", - "clean_sheets = np.zeros(ITERS)\n", - "mod_points = np.zeros(ITERS)\n", - "\n", - "mv_samples = [None] * len(squad)\n", - "fv_samples = [None] * len(squad)\n", - "\n", - "for i in range(len(squad)):\n", - " mv_samples[i] = dist[i][0].sample(ITERS)\n", - " fv_samples[i] = dist[i][1].sample(ITERS)\n", - " \n", - "cs_samples = dist[0][2].sample(ITERS)\n", - "\n", - "for k in range(ITERS):\n", - " d_points = list([0]) * len(squad)\n", - " \n", - " cleansheet = float(cs_samples[k])\n", - " total_points[k] += cleansheet\n", - " clean_sheets[k] += cleansheet\n", - " \n", - " for i in range(len(squad)): \n", - " if(defenders[i] == 1):\n", - " d_points[i] = float(mv_samples[i][k])\n", - "\n", - " total_points[k] += float(fv_samples[i][k])\n", - " \n", - " d_points.sort(reverse = True)\n", - "\n", - " if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n", - " mod_avg = 0\n", - " for j in range(3):\n", - " mod_avg += round(d_points[j] * 2) / 2\n", - " mod_avg /= 3\n", - "\n", - " if(mod_avg >= 7):\n", - " mod_points[k] = 6\n", - " elif(mod_avg >= 6.5):\n", - " mod_points[k] = 3\n", - " elif(mod_avg >= 6):\n", - " mod_points[k] = 1\n", - "\n", - " total_points[k] += mod_points[k]\n", - " \n", - " \n", - "print('Avg Total Points = ' + str(total_points.mean()))\n", - "print('Avg Mod Points = ' + str(mod_points.mean()))\n", - "print('Avg Clean Sheets = ' + str(clean_sheets.mean()))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "c5d752d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loc_0 = total_points.mean()\n", - "\n", - "squad_model = tf.keras.Sequential(\n", - " [\n", - " tf.keras.layers.Dense(3),\n", - " tfp.layers.DistributionLambda(\n", - " lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n", - " skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n", - " )\n", - " ]\n", - ")\n", - "\n", - "def negloglik(y, distr):\n", - " return -distr.log_prob(y)\n", - "\n", - "squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n", - "\n", - "dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n", - "squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "9754ec4c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n", - "\n", - "\n", - "x = np.arange(start = 0, stop = 200, step = 0.001)\n", - "prb = squad_points_dist.prob(x)\n", - "\n", - "mn = total_points.mean()\n", - "\n", - "f, ax = plt.subplots(1, 2)\n", - "\n", - "ax[0].plot(x, prb)\n", - "ax[0].fill_between(x, prb, color = 'lightblue')\n", - "ax[0].vlines(x = mn, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n", - "\n", - "ax[0].set_xlim([40, 120])\n", - "ax[0].set_ylim([0, 0.12])\n", - "\n", - "ax[0].legend()\n", - "\n", - "#ax[0].hist(total_points, bins = 20, density = True)\n", - "\n", - "\n", - "ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", - "\n", - "text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n", - " 'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n", - " 'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n", - "\n", - "ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", - "\n", - "ax[1].axis('off')\n", - "\n", - "\n", - "\n", - "plt.subplots_adjust(right=1.5)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "30744d7a", - "metadata": {}, - "source": [ - "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", - "\n", - "Here the code to generate the probability density function is reproduced." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "3ec6c3dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# custom franction to calculate the probability density function\n", - "\n", - "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", - "\n", - " mul = np.sinh( np.arcsinh(2) * delta)\n", - " \n", - " mul = 2 / mul\n", - " \n", - " sigma_corr = sigma * mul\n", - " \n", - " z = (x - mu) / sigma_corr\n", - " \n", - " \n", - " \n", - " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", - " \n", - " f = np.exp(-0.5 * S * S)\n", - "\n", - " f /= np.sqrt(2 * np.pi)\n", - " \n", - " f *= 1 / ( sigma_corr * delta )\n", - " \n", - " f *= np.sqrt(1 + S * S)\n", - " \n", - " f /= np.sqrt(1 + z * z)\n", - " \n", - " return f\n", - " \n", - "\n", - "x = np.arange(start = 0, stop = 15, step = 0.001)\n", - "\n", - "\n", - "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "605dc968", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb b/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb new file mode 100644 index 0000000..ba55a86 --- /dev/null +++ b/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb @@ -0,0 +1,7215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a867836", + "metadata": {}, + "source": [ + "Bayesian Neural Network model traning and prediction data generation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "210da263", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPRegressor\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import r2_score\n", + "\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "edbf3b27", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_probability as tfp\n", + "\n", + "tfk = tf.keras\n", + "tf.keras.backend.set_floatx(\"float32\")\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import IsolationForest\n", + "\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "markdown", + "id": "9dadf6ec", + "metadata": {}, + "source": [ + "Load the training databases, generated in player_match_database_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fa098fa4", + "metadata": {}, + "outputs": [], + "source": [ + "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", + "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", + "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f71fa9a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...miscontrolsdispossessedfoulsfouledaerials_wonaerials_lostcarriesprogressive_carriescarries_into_final_thirdcarries_into_penalty_area
01ToloiAtalantaSampdoria07.0100.010.0...0.0069060.0013810.0075970.0069060.0138120.0103590.3563540.0082870.0158840.000000
11DjimsitiAtalantaSampdoria06.0000.06.0...0.0032100.0064210.0048150.0048150.0224720.0144460.4622790.0048150.0048150.000000
21HateboerAtalantaSampdoria06.0000.55.5...0.0057270.0050110.0136010.0021470.0171800.0100210.2827490.0114530.0093060.000716
31OkoliAtalantaSampdoria05.5000.55.0...0.0130180.0035500.0153850.0082840.0473370.0272190.2698220.0023670.0047340.000000
41ZorteaAtalantaSampdoria06.0000.55.5...0.0111110.0055560.0166670.0222220.0166670.0277780.5611110.0722220.0555560.000000
..................................................................
2480638TamezeVeronaLazio05.5000.05.5...0.0198140.0101010.0128210.0132090.0236990.0213680.3372180.0213680.0128210.003885
2480738HonglaVeronaLazio07.0100.59.5...0.0184330.0122890.0230410.0076800.0230410.0261140.3410140.0092170.0153610.003072
2480838LasagnaVeronaLazio07.0100.59.5...0.0464530.0228040.0160470.0084460.0261820.0413850.1908780.0219590.0109800.005912
2480938CaprariVeronaLazio06.0000.06.0...0.0339540.0197150.0153340.0270170.0029210.0098580.3913840.0427160.0277470.018620
2481038SimeoneVeronaLazio07.0100.010.0...0.0512630.0301550.0211080.0218620.0226160.0407090.2461360.0150770.0128160.006031
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24811 rows × 122 columns

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" + ], + "text/plain": [ + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "1 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "2 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "4 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "24806 38 Tameze Verona Lazio 0 5.5 0 0 \n", + "24807 38 Hongla Verona Lazio 0 7.0 1 0 \n", + "24808 38 Lasagna Verona Lazio 0 7.0 1 0 \n", + "24809 38 Caprari Verona Lazio 0 6.0 0 0 \n", + "24810 38 Simeone Verona Lazio 0 7.0 1 0 \n", + "\n", + " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", + "0 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "1 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "2 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", + "3 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", + "4 0.5 5.5 ... 0.011111 0.005556 0.016667 \n", + "... ... ... ... ... ... ... \n", + "24806 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", + "24807 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", + "24808 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", + "24809 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", + "24810 0.0 10.0 ... 0.051263 0.030155 0.021108 \n", + "\n", + " fouled aerials_won aerials_lost carries progressive_carries \\\n", + "0 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "1 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "2 0.002147 0.017180 0.010021 0.282749 0.011453 \n", + "3 0.008284 0.047337 0.027219 0.269822 0.002367 \n", + "4 0.022222 0.016667 0.027778 0.561111 0.072222 \n", + "... ... ... ... ... ... \n", + "24806 0.013209 0.023699 0.021368 0.337218 0.021368 \n", + "24807 0.007680 0.023041 0.026114 0.341014 0.009217 \n", + "24808 0.008446 0.026182 0.041385 0.190878 0.021959 \n", + "24809 0.027017 0.002921 0.009858 0.391384 0.042716 \n", + "24810 0.021862 0.022616 0.040709 0.246136 0.015077 \n", + "\n", + " carries_into_final_third carries_into_penalty_area \n", + "0 0.015884 0.000000 \n", + "1 0.004815 0.000000 \n", + "2 0.009306 0.000716 \n", + "3 0.004734 0.000000 \n", + "4 0.055556 0.000000 \n", + "... ... ... \n", + "24806 0.012821 0.003885 \n", + "24807 0.015361 0.003072 \n", + "24808 0.010980 0.005912 \n", + "24809 0.027747 0.018620 \n", + "24810 0.012816 0.006031 \n", + "\n", + "[24811 rows x 122 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db = pd.concat([db1, db2, db3], ignore_index = True) \n", + "\n", + "db" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1d024554", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...13.000.20-1.0088.0201.0397.0112.0101.0186.010.0
11SkorupskiBolognaLazio06.5-200.04.5...32.500.292.50101.0242.0576.0117.0159.0304.018.0
21VicarioEmpoliSpezia05.5-100.04.5...27.800.26-0.20102.0313.0806.0117.0114.0431.025.0
31GolliniFiorentinaCremonese15.0-200.03.0...9.450.240.9524.068.0299.548.572.5128.03.5
41HandanovicInterLecce06.5-100.05.5...10.600.30-1.4027.052.0266.046.045.079.02.0
..................................................................
130022ConsigliSassuoloUdinese07.0-200.05.0...24.400.33-5.60113.0277.0711.0108.0179.0276.017.0
130122DragowskiSpeziaEmpoli06.5-200.04.5...29.500.27-4.5093.0285.0528.097.0122.0270.09.0
130222Milinkovic-Savic V.TorinoMilan06.5-100.05.5...23.100.240.10150.0540.0817.097.0160.0271.019.0
130322SilvestriUdineseSassuolo16.0-200.04.0...24.000.311.0091.0227.0470.080.0175.0293.05.0
130422Montipo'VeronaSalernitana16.5000.06.5...28.700.26-3.30208.0433.0513.067.0175.0305.015.0
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1305 rows × 102 columns

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" + ], + "text/plain": [ + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "1 1 Skorupski Bologna Lazio 0 6.5 \n", + "2 1 Vicario Empoli Spezia 0 5.5 \n", + "3 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "4 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "1300 22 Consigli Sassuolo Udinese 0 7.0 \n", + "1301 22 Dragowski Spezia Empoli 0 6.5 \n", + "1302 22 Milinkovic-Savic V. Torino Milan 0 6.5 \n", + "1303 22 Silvestri Udinese Sassuolo 1 6.0 \n", + "1304 22 Montipo' Verona Salernitana 1 6.5 \n", + "\n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 13.00 \n", + "1 -2 0 0.0 4.5 ... 32.50 \n", + "2 -1 0 0.0 4.5 ... 27.80 \n", + "3 -2 0 0.0 3.0 ... 9.45 \n", + "4 -1 0 0.0 5.5 ... 10.60 \n", + "... ... ... ... ... ... ... \n", + "1300 -2 0 0.0 5.0 ... 24.40 \n", + "1301 -2 0 0.0 4.5 ... 29.50 \n", + "1302 -1 0 0.0 5.5 ... 23.10 \n", + "1303 -2 0 0.0 4.0 ... 24.00 \n", + "1304 0 0 0.0 6.5 ... 28.70 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.20 -1.00 \n", + "1 0.29 2.50 \n", + "2 0.26 -0.20 \n", + "3 0.24 0.95 \n", + "4 0.30 -1.40 \n", + "... ... ... \n", + "1300 0.33 -5.60 \n", + "1301 0.27 -4.50 \n", + "1302 0.24 0.10 \n", + "1303 0.31 1.00 \n", + "1304 0.26 -3.30 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 88.0 201.0 397.0 \n", + "1 101.0 242.0 576.0 \n", + "2 102.0 313.0 806.0 \n", + "3 24.0 68.0 299.5 \n", + "4 27.0 52.0 266.0 \n", + "... ... ... ... \n", + "1300 113.0 277.0 711.0 \n", + "1301 93.0 285.0 528.0 \n", + "1302 150.0 540.0 817.0 \n", + "1303 91.0 227.0 470.0 \n", + "1304 208.0 433.0 513.0 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 112.0 101.0 186.0 10.0 \n", + "1 117.0 159.0 304.0 18.0 \n", + "2 117.0 114.0 431.0 25.0 \n", + "3 48.5 72.5 128.0 3.5 \n", + "4 46.0 45.0 79.0 2.0 \n", + "... ... ... ... ... \n", + "1300 108.0 179.0 276.0 17.0 \n", + "1301 97.0 122.0 270.0 9.0 \n", + "1302 97.0 160.0 271.0 19.0 \n", + "1303 80.0 175.0 293.0 5.0 \n", + "1304 67.0 175.0 305.0 15.0 \n", + "\n", + "[1305 rows x 102 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", + "\n", + "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", + "\n", + "db_gk" + ] + }, + { + "cell_type": "markdown", + "id": "04df0936", + "metadata": {}, + "source": [ + "Load player stats from current season and past seasons" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bc9dae87", + "metadata": {}, + "outputs": [], + "source": [ + "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", + "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", + "\n", + "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", + "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", + "\n", + "players = players_orig" + ] + }, + { + "cell_type": "markdown", + "id": "397babf2", + "metadata": {}, + "source": [ + "Load team data from current season and add an average Serie A team row" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "493b0495", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_1568\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" + ] + }, + { + "data": { + "text/html": [ + "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0048.60022.0242.01980.040.0028.006.008.0...244.00256.0026.01.08.001.01335.00273.00328.0045.40
BolognaBologna25.0052.40022.0242.01980.027.0020.004.004.0...280.00268.0038.03.04.001.01204.00250.00210.0054.30
CremoneseCremonese31.0043.80022.0242.01980.015.007.002.004.0...239.00271.0036.03.04.000.01229.00409.00314.0056.60
EmpoliEmpoli28.0047.50022.0242.01980.021.0011.000.000.0...283.00253.0036.02.00.000.01148.00263.00217.0054.80
FiorentinaFiorentina28.0057.20022.0242.01980.023.0018.002.004.0...307.00269.0059.01.04.000.01130.00289.00328.0046.80
VeronaHellas Verona34.0042.90022.0242.01980.018.0015.000.000.0...234.00315.0025.01.00.002.01231.00423.00445.0048.70
InterInter23.0054.50022.0242.01980.040.0027.002.002.0...276.00248.0019.02.02.001.01007.00231.00288.0044.50
JuventusJuventus26.0049.00022.0242.01980.034.0026.003.004.0...246.00242.0029.00.04.000.01134.00258.00272.0048.70
LazioLazio21.0051.80022.0242.01980.036.0026.003.004.0...308.00218.0044.01.04.001.01233.00218.00229.0048.80
LecceLecce26.0042.40022.0242.01980.020.0014.001.002.0...283.00300.0045.03.02.002.01200.00417.00332.0055.70
MilanMilan27.0053.50022.0242.01980.036.0031.002.002.0...275.00261.0025.04.02.002.01125.00263.00325.0044.70
MonzaMonza29.0055.00022.0242.01980.027.0017.004.004.0...318.00281.0037.00.04.001.01145.00242.00253.0048.90
NapoliNapoli24.0061.60022.0242.01980.054.0042.005.006.0...298.00199.0028.01.05.000.01100.00232.00280.0045.30
RomaRoma26.0049.30022.0242.01980.029.0019.004.006.0...316.00246.0012.00.06.000.01156.00221.00266.0045.40
SalernitanaSalernitana28.0046.00022.0242.01980.024.0016.001.001.0...252.00259.0057.08.01.001.01213.00291.00285.0050.50
SampdoriaSampdoria31.0047.30022.0242.01980.010.008.000.000.0...339.00300.0059.04.00.000.01189.00362.00349.0050.90
SassuoloSassuolo29.0048.70022.0242.01980.025.0018.004.005.0...287.00206.0072.02.05.001.01131.00256.00206.0055.40
SpeziaSpezia33.0045.50022.0242.01980.017.0010.003.003.0...235.00291.0053.01.03.002.01275.00343.00306.0052.90
TorinoTorino27.0053.00022.0242.01980.022.0017.001.001.0...239.00306.0024.03.01.000.01155.00367.00333.0052.40
UdineseUdinese25.0049.90022.0242.01980.029.0025.000.000.0...282.00252.0034.02.00.001.01101.00233.00277.0045.70
AvgAvg27.2549.99522.0242.01980.027.3519.752.353.0...277.05262.0537.92.12.950.81172.05292.05292.1549.82
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21 rows × 303 columns

\n", + "
" + ], + "text/plain": [ + " team team_players_used team_possession team_games \\\n", + "Atalanta Atalanta 24.00 48.600 22.0 \n", + "Bologna Bologna 25.00 52.400 22.0 \n", + "Cremonese Cremonese 31.00 43.800 22.0 \n", + "Empoli Empoli 28.00 47.500 22.0 \n", + "Fiorentina Fiorentina 28.00 57.200 22.0 \n", + "Verona Hellas Verona 34.00 42.900 22.0 \n", + "Inter Inter 23.00 54.500 22.0 \n", + "Juventus Juventus 26.00 49.000 22.0 \n", + "Lazio Lazio 21.00 51.800 22.0 \n", + "Lecce Lecce 26.00 42.400 22.0 \n", + "Milan Milan 27.00 53.500 22.0 \n", + "Monza Monza 29.00 55.000 22.0 \n", + "Napoli Napoli 24.00 61.600 22.0 \n", + "Roma Roma 26.00 49.300 22.0 \n", + "Salernitana Salernitana 28.00 46.000 22.0 \n", + "Sampdoria Sampdoria 31.00 47.300 22.0 \n", + "Sassuolo Sassuolo 29.00 48.700 22.0 \n", + "Spezia Spezia 33.00 45.500 22.0 \n", + "Torino Torino 27.00 53.000 22.0 \n", + "Udinese Udinese 25.00 49.900 22.0 \n", + "Avg Avg 27.25 49.995 22.0 \n", + "\n", + " team_games_starts team_minutes team_goals team_assists \\\n", + "Atalanta 242.0 1980.0 40.00 28.00 \n", + "Bologna 242.0 1980.0 27.00 20.00 \n", + "Cremonese 242.0 1980.0 15.00 7.00 \n", + "Empoli 242.0 1980.0 21.00 11.00 \n", + "Fiorentina 242.0 1980.0 23.00 18.00 \n", + "Verona 242.0 1980.0 18.00 15.00 \n", + "Inter 242.0 1980.0 40.00 27.00 \n", + "Juventus 242.0 1980.0 34.00 26.00 \n", + "Lazio 242.0 1980.0 36.00 26.00 \n", + "Lecce 242.0 1980.0 20.00 14.00 \n", + "Milan 242.0 1980.0 36.00 31.00 \n", + "Monza 242.0 1980.0 27.00 17.00 \n", + "Napoli 242.0 1980.0 54.00 42.00 \n", + "Roma 242.0 1980.0 29.00 19.00 \n", + "Salernitana 242.0 1980.0 24.00 16.00 \n", + "Sampdoria 242.0 1980.0 10.00 8.00 \n", + "Sassuolo 242.0 1980.0 25.00 18.00 \n", + "Spezia 242.0 1980.0 17.00 10.00 \n", + "Torino 242.0 1980.0 22.00 17.00 \n", + "Udinese 242.0 1980.0 29.00 25.00 \n", + "Avg 242.0 1980.0 27.35 19.75 \n", + "\n", + " team_pens_made team_pens_att ... vs_team_fouls \\\n", + "Atalanta 6.00 8.0 ... 244.00 \n", + "Bologna 4.00 4.0 ... 280.00 \n", + "Cremonese 2.00 4.0 ... 239.00 \n", + "Empoli 0.00 0.0 ... 283.00 \n", + "Fiorentina 2.00 4.0 ... 307.00 \n", + "Verona 0.00 0.0 ... 234.00 \n", + "Inter 2.00 2.0 ... 276.00 \n", + "Juventus 3.00 4.0 ... 246.00 \n", + "Lazio 3.00 4.0 ... 308.00 \n", + "Lecce 1.00 2.0 ... 283.00 \n", + "Milan 2.00 2.0 ... 275.00 \n", + "Monza 4.00 4.0 ... 318.00 \n", + "Napoli 5.00 6.0 ... 298.00 \n", + "Roma 4.00 6.0 ... 316.00 \n", + "Salernitana 1.00 1.0 ... 252.00 \n", + "Sampdoria 0.00 0.0 ... 339.00 \n", + "Sassuolo 4.00 5.0 ... 287.00 \n", + "Spezia 3.00 3.0 ... 235.00 \n", + "Torino 1.00 1.0 ... 239.00 \n", + "Udinese 0.00 0.0 ... 282.00 \n", + "Avg 2.35 3.0 ... 277.05 \n", + "\n", + " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", + "Atalanta 256.00 26.0 1.0 \n", + "Bologna 268.00 38.0 3.0 \n", + "Cremonese 271.00 36.0 3.0 \n", + "Empoli 253.00 36.0 2.0 \n", + "Fiorentina 269.00 59.0 1.0 \n", + "Verona 315.00 25.0 1.0 \n", + "Inter 248.00 19.0 2.0 \n", + "Juventus 242.00 29.0 0.0 \n", + "Lazio 218.00 44.0 1.0 \n", + "Lecce 300.00 45.0 3.0 \n", + "Milan 261.00 25.0 4.0 \n", + "Monza 281.00 37.0 0.0 \n", + "Napoli 199.00 28.0 1.0 \n", + "Roma 246.00 12.0 0.0 \n", + "Salernitana 259.00 57.0 8.0 \n", + "Sampdoria 300.00 59.0 4.0 \n", + "Sassuolo 206.00 72.0 2.0 \n", + "Spezia 291.00 53.0 1.0 \n", + "Torino 306.00 24.0 3.0 \n", + "Udinese 252.00 34.0 2.0 \n", + "Avg 262.05 37.9 2.1 \n", + "\n", + " vs_team_pens_conceded vs_team_own_goals \\\n", + "Atalanta 8.00 1.0 \n", + "Bologna 4.00 1.0 \n", + "Cremonese 4.00 0.0 \n", + "Empoli 0.00 0.0 \n", + "Fiorentina 4.00 0.0 \n", + "Verona 0.00 2.0 \n", + "Inter 2.00 1.0 \n", + "Juventus 4.00 0.0 \n", + "Lazio 4.00 1.0 \n", + "Lecce 2.00 2.0 \n", + "Milan 2.00 2.0 \n", + "Monza 4.00 1.0 \n", + "Napoli 5.00 0.0 \n", + "Roma 6.00 0.0 \n", + "Salernitana 1.00 1.0 \n", + "Sampdoria 0.00 0.0 \n", + "Sassuolo 5.00 1.0 \n", + "Spezia 3.00 2.0 \n", + "Torino 1.00 0.0 \n", + "Udinese 0.00 1.0 \n", + "Avg 2.95 0.8 \n", + "\n", + " vs_team_ball_recoveries vs_team_aerials_won \\\n", + "Atalanta 1335.00 273.00 \n", + "Bologna 1204.00 250.00 \n", + "Cremonese 1229.00 409.00 \n", + "Empoli 1148.00 263.00 \n", + "Fiorentina 1130.00 289.00 \n", + "Verona 1231.00 423.00 \n", + "Inter 1007.00 231.00 \n", + "Juventus 1134.00 258.00 \n", + "Lazio 1233.00 218.00 \n", + "Lecce 1200.00 417.00 \n", + "Milan 1125.00 263.00 \n", + "Monza 1145.00 242.00 \n", + "Napoli 1100.00 232.00 \n", + "Roma 1156.00 221.00 \n", + "Salernitana 1213.00 291.00 \n", + "Sampdoria 1189.00 362.00 \n", + "Sassuolo 1131.00 256.00 \n", + "Spezia 1275.00 343.00 \n", + "Torino 1155.00 367.00 \n", + "Udinese 1101.00 233.00 \n", + "Avg 1172.05 292.05 \n", + "\n", + " vs_team_aerials_lost vs_team_aerials_won_pct \n", + "Atalanta 328.00 45.40 \n", + "Bologna 210.00 54.30 \n", + "Cremonese 314.00 56.60 \n", + "Empoli 217.00 54.80 \n", + "Fiorentina 328.00 46.80 \n", + "Verona 445.00 48.70 \n", + "Inter 288.00 44.50 \n", + "Juventus 272.00 48.70 \n", + "Lazio 229.00 48.80 \n", + "Lecce 332.00 55.70 \n", + "Milan 325.00 44.70 \n", + "Monza 253.00 48.90 \n", + "Napoli 280.00 45.30 \n", + "Roma 266.00 45.40 \n", + "Salernitana 285.00 50.50 \n", + "Sampdoria 349.00 50.90 \n", + "Sassuolo 206.00 55.40 \n", + "Spezia 306.00 52.90 \n", + "Torino 333.00 52.40 \n", + "Udinese 277.00 45.70 \n", + "Avg 292.15 49.82 \n", + "\n", + "[21 rows x 303 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", + "\n", + "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", + "avg_row['team']['Avg'] = 'Avg'\n", + "\n", + "team_data = pd.concat([team_data, avg_row])\n", + "\n", + "team_data" + ] + }, + { + "cell_type": "markdown", + "id": "cc1cd13d", + "metadata": {}, + "source": [ + "Data processing functions copied from player_match_dataset_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "32f56138", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs = ['r',\n", + " 'games',\n", + " 'games_starts', \n", + " 'minutes',\n", + " 'shots_on_target_pct',\n", + " 'goals_per_shot',\n", + " 'goals_per_shot_on_target',\n", + " 'passes_pct',\n", + " #'dribble_tackles_pct',\n", + " #'dribbles_completed_pct',\n", + " 'aerials_won_pct',\n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " #'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " #'vs_team_dribble_tackles_pct',\n", + " #'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " #'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " #'opp_vs_team_dribble_tackles_pct',\n", + " #'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel = [\n", + " 'goals',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red',\n", + " 'xg',\n", + " 'npxg',\n", + " 'shots_on_target',\n", + " 'passes_completed',\n", + " 'passes_into_final_third',\n", + " 'passes_into_penalty_area',\n", + " 'progressive_passes',\n", + " 'passes_live',\n", + " 'passes_dead',\n", + " 'through_balls',\n", + " 'passes_switches',\n", + " 'crosses',\n", + " 'corner_kicks',\n", + " #'dribble_tackles',\n", + " #'dribbles_vs',\n", + " #'dribbled_past',\n", + " 'blocks',\n", + " 'blocked_shots',\n", + " 'blocked_passes',\n", + " 'interceptions',\n", + " 'clearances',\n", + " 'errors',\n", + " 'touches',\n", + " 'touches_def_pen_area',\n", + " 'touches_def_3rd',\n", + " 'touches_mid_3rd',\n", + " 'touches_att_3rd',\n", + " 'touches_att_pen_area',\n", + " 'touches_live_ball',\n", + " #'dribbles_completed',\n", + " #'dribbles',\n", + " 'passes_received',\n", + " 'miscontrols',\n", + " 'dispossessed',\n", + " 'fouls',\n", + " 'fouled',\n", + " 'aerials_won',\n", + " 'aerials_lost',\n", + " 'carries',\n", + " 'progressive_carries',\n", + " 'carries_into_final_third',\n", + " 'carries_into_penalty_area']\n", + "\n", + "features_rel_gamecorr = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg',\n", + " 'cards_yellow',\n", + " 'cards_red'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4001f3f8", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs_gk = [\n", + " 'gk_games',\n", + " 'gk_games_starts',\n", + " 'gk_minutes',\n", + " 'gk_goals_against_per90', \n", + " 'gk_save_pct',\n", + " 'gk_clean_sheets_pct',\n", + " 'gk_psxg_net_per90',\n", + " 'gk_passes_pct_launched',\n", + " 'gk_pct_passes_launched',\n", + " 'gk_passes_length_avg',\n", + " 'gk_pct_goal_kicks_launched',\n", + " 'gk_goal_kick_length_avg',\n", + " 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area_per90',\n", + " 'gk_avg_distance_def_actions',\n", + " \n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " #'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " #'vs_team_dribble_tackles_pct',\n", + " #'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " #'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " #'opp_vs_team_dribble_tackles_pct',\n", + " #'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel_gk = [\n", + " 'gk_shots_on_target_against',\n", + " 'gk_saves',\n", + " 'gk_free_kick_goals_against',\n", + " 'gk_corner_kick_goals_against',\n", + " 'gk_own_goals_against',\n", + " 'gk_psxg',\n", + " 'gk_psnpxg_per_shot_on_target_against',\n", + " 'gk_psxg_net',\n", + " 'gk_passes_completed_launched',\n", + " 'gk_passes_launched',\n", + " 'gk_passes',\n", + " 'gk_passes_throws',\n", + " 'gk_goal_kicks',\n", + " 'gk_crosses',\n", + " 'gk_crosses_stopped',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f4017b2f", + "metadata": {}, + "outputs": [], + "source": [ + "DEL_G = False\n", + "\n", + "features_to_del = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg'\n", + "]\n", + "\n", + "def player_match_data(player, pteam, oppteam, oldseason = False):\n", + " if(not(player in players.index)):\n", + " return None\n", + " \n", + " if(oldseason):\n", + " pdata = players_old.loc[[player]]\n", + " else:\n", + " pdata = players.loc[[player]]\n", + " \n", + " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", + " \n", + " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", + " \n", + " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", + " \n", + " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", + " \n", + " return(out)\n", + "\n", + "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " assert pdata['games'][0] > 0\n", + " \n", + " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", + " \n", + " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", + " \n", + " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", + " \n", + " if(DEL_G):\n", + " out[features_to_del] = 0\n", + " \n", + " return out\n", + "\n", + "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " if(pdata['gk_games'][0] <= 0):\n", + " return None\n", + " \n", + " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", + " \n", + " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", + "\n", + " return out\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "21fef3ae", + "metadata": {}, + "source": [ + "Players stats rework:\n", + "the current season stats are averaged (according to a calculated weight) with the past season data.\n", + "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", + "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", + "\n", + "These modified stats are used only for prediction, not for model traning.\n", + "\n", + "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", + "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f8707b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Averaging players stats with past seasons:\n", + "Meret 1\n", + "Provedel 1\n", + "Vicario 1\n", + "Szczesny 1\n", + "Falcone 1\n", + "Silvestri 1\n", + "Rui Patricio 1\n", + "Sepe 1\n", + "Milinkovic-Savic V. 1\n", + "Musso 1\n", + "Maignan 1\n", + "Audero 1\n", + "Montipo' 1\n", + "Skorupski 1\n", + "Consigli 1\n", + "Dragowski 1\n", + "Terracciano 1\n", + "Tatarusanu 1\n", + "Handanovic 0.7372677888806921\n", + "Sportiello 1\n", + "Perin 1\n", + "Zoet 1\n", + "Pegolo 1\n", + "Mirante 0.0\n", + "Ujkani 0.0\n", + "Berisha 0.0\n", + "Marchetti 1\n", + "Padelli 0.0\n", + "Bardi 1\n", + "Cordaz 0.0\n", + "Pinsoglio 0.0\n", + "Fiorillo 0.0\n", + "Cragno 0.07071960297766748\n", + "Sirigu 0.0668969217356314\n", + "Rossi F. 0.0\n", + "Berardi A. 0.0\n", + "Gemello 0.0\n", + "Ravaglia 1\n", + "Boer 0.0\n", + "Adamonis 0.0\n", + "Marfella 0.0\n", + "Zovko 1\n", + "Piana 0.0\n", + "Dimarco 1\n", + "Smalling 1\n", + "Di Lorenzo 1\n", + "Danilo 1\n", + "Hernandez T. 1\n", + "Udogie 0.9565331442750797\n", + "Parisi 1\n", + "Mario Rui 0.7658517004816814\n", + "Romagnoli 1\n", + "Bastoni S. 0.6399743856559673\n", + "Mazzocchi 1\n", + "Tomori 1\n", + "Scalvini 1\n", + "Toloi 1\n", + "Demiral 1\n", + "Maehle 1\n", + "Dumfries 1\n", + "Juan Jesus 0.649497814013943\n", + "Depaoli 1\n", + "Mancini 1\n", + "Ibanez 0.9846664720478762\n", + "Rodrigo Becao 0.8502516838000708\n", + "Ebuehi 1\n", + "Gosens 1\n", + "Darmian 1\n", + "Reca 1\n", + "Bremer 0.9750733137829912\n", + "Rrahmani 0.8642078351755771\n", + "Vojvoda 0.9834089158894498\n", + "Bastoni 0.9199631793804531\n", + "Milenkovic 0.8387899576704131\n", + "Kalulu 1\n", + "Martinez Quarta 1\n", + "Casale 0.756306865177833\n", + "Perez N. 1\n", + "Izzo 1\n", + "Luperto 1\n", + "Skriniar 0.743970223325062\n", + "Rodriguez R. 1\n", + "Marusic 1\n", + "Lazzari 0.8799647802769551\n", + "Kyriakopoulos 0.4694318473517583\n", + "Ampadu 1\n", + "Ismajli 1\n", + "Llorente D. affine to Ibanez\n", + "Llorente D. 0.2188147715661947\n", + "Cambiaso 1\n", + "Hysaj 1\n", + "Biraghi 0.9718529944336394\n", + "Medel 0.9017820888788629\n", + "Bonucci 0.6716397849462366\n", + "Calabria 0.858427180759687\n", + "Acerbi 0.9900744416873448\n", + "Spinazzola 1\n", + "Lykogiannis 0.8397952853598015\n", + "Pellegrini Lu. 0.13751033912324234\n", + "Djidji 1\n", + "Augello 1\n", + "Singo 0.8856788372917405\n", + "Mari' 1\n", + "De Vrij 0.826633581472291\n", + "Patric 0.8266335814722912\n", + "Faraoni 0.658723635235732\n", + "Ceccherini 0.6564443146985842\n", + "Hateboer 1\n", + "Rogerio 1\n", + "Aina 0.9447240931111898\n", + "Ferrari G. 0.8955197132616488\n", + "Fazio 1\n", + "Buongiorno 1\n", + "Gunter 0.1650124069478908\n", + "Troost-Ekong affine to Fazio\n", + "Troost-Ekong 0.46498138957816376\n", + "Soumaoro 0.9299627791563275\n", + "Ceccaroni 0.03535980148883374\n", + "Soppy 0.5303970223325062\n", + "Ferrari A. 0.5918923292696083\n", + "Zappacosta 0.510567800321121\n", + "Gyomber 0.9199631793804531\n", + "Alex Sandro 0.9742467210209147\n", + "Pezzella Giu. 0.7071960297766748\n", + "Bereszynski 0.5303970223325062\n", + "Venuti 0.593019743230122\n", + "Palomino 0.47409867172675524\n", + "Nuytinck 0.6417149159084642\n", + "Magnani 1\n", + "Colley 0.6199751861042183\n", + "Nikolaou 0.9988489109456851\n", + "Terzic 1\n", + "Igor 0.9092969396195203\n", + "Toljan 1\n", + "Zortea 0.2560537349191409\n", + "Dawidowicz 1\n", + "Bellanova 0.518990634755463\n", + "Erlic 0.9075682382133995\n", + "Ballo-Toure' affine to Calabria\n", + "Ballo-Toure' 0.23845199465546857\n", + "Stojanovic 0.8266335814722912\n", + "Amian 0.9299627791563275\n", + "Zima 0.5579776674937966\n", + "De Winter 1\n", + "Romagnoli S. 0.195409429280397\n", + "Ghiglione 1\n", + "Rugani 0.6199751861042183\n", + "De Sciglio 0.9919602977667494\n", + "Djimsiti 0.6799727847594653\n", + "Caldara 0.7984471303930201\n", + "Karsdorp 0.4477598566308244\n", + "Marchizza 0.521091811414392\n", + "Kjaer 1\n", + "Ruggeri 1\n", + "Zanoli 1\n", + "Radovanovic 0.8856788372917405\n", + "D'ambrosio 0.6199751861042183\n", + "De Silvestri 0.39998399103497956\n", + "Chiriches 0.4694318473517583\n", + "Murru 0.901782088878863\n", + "Bonifazi 0.39452966388450256\n", + "Walukiewicz 1\n", + "Ranieri L. 0.22918389853873725\n", + "Gabbia 1\n", + "Kumbulla 0.4376295431323894\n", + "Lovato 0.9281947890818858\n", + "Ferrer 0.13777226357871517\n", + "Vasquez 0.7955955334987593\n", + "Ruan 1\n", + "Ostigard 1\n", + "Coppola D. 1\n", + "Cacace 1\n", + "Conti 0.1771357674583481\n", + "Conti 0.6679835812950357\n", + "Marrone 0.11786600496277913\n", + "Tonelli 0.08856788372917405\n", + "Radu 1\n", + "Florenzi 0.258322994210091\n", + "Sala 0.4133167907361455\n", + "Fares 0.0\n", + "Fares 0.0\n", + "Romagna 0.0\n", + "Romagna 0.0\n", + "Muldur 0.03999839910349796\n", + "Amey 0.0\n", + "Zaccagni 1\n", + "Milinkovic-Savic 0.9718529944336394\n", + "Barella 1\n", + "Zielinski 1\n", + "Luis Alberto 1\n", + "Felipe Anderson 1\n", + "Koopmeiners 1\n", + "Calhanoglu 1\n", + "Frattesi 1\n", + "Diaz B. 1\n", + "Zambo Anguissa 1\n", + "Elmas 1\n", + "Miranchuk 1\n", + "Samardzic 1\n", + "Pereyra 1\n", + "Politano 0.9393563425821488\n", + "Rabiot 1\n", + "Lazovic 0.8752590862647788\n", + "Lobotka 1\n", + "Bonaventura 1\n", + "Pessina 1\n", + "Tonali 0.9299627791563276\n", + "Pellegrini Lo. 1\n", + "El Shaarawy 1\n", + "Orsolini 1\n", + "Ikone' 1\n", + "Candreva 1\n", + "Bennacer 0.9999599775874489\n", + "Pasalic 0.8378043055462409\n", + "Mkhitaryan 1\n", + "Chiesa 1\n", + "Bandinelli 1\n", + "Fagioli affine to Henderson L.\n", + "Fagioli 0.7178660049627792\n", + "Messias 0.9538079786218743\n", + "Arslan 1\n", + "Ricci S. 1\n", + "Verdi 1\n", + "Sensi 1\n", + "Barak 1\n", + "Soriano 0.9565331442750797\n", + "Dominguez 1\n", + "Brozovic 0.743970223325062\n", + "Cristante 1\n", + "Saponara 1\n", + "Vecino 1\n", + "Locatelli 1\n", + "Zaniolo 0.5756912442396314\n", + "Maldini 1\n", + "Marin 0.996949958643507\n", + "Zalewski 1\n", + "Bajrami 0.3889578163771712\n", + "Coulibaly L. 1\n", + "De Roon 1\n", + "Mandragora 1\n", + "Bourabia 1\n", + "Sottil 0.6716397849462366\n", + "Aebischer 1\n", + "Ederson D.s. 1\n", + "Miretti 1\n", + "Cataldi 1\n", + "Djuricic 1\n", + "Linetty 1\n", + "Haas 1\n", + "Walace 1\n", + "Agudelo 1\n", + "Pobega 0.5625422964132641\n", + "Rovella 1\n", + "Amrabat 1\n", + "Tameze 0.9789081885856079\n", + "Gyasi 0.9644058450510063\n", + "Ilic 0.27072348014888337\n", + "Matheus Henrique 1\n", + "Harroui 1\n", + "Volpato 1\n", + "Duncan 0.6763365666591473\n", + "Cuadrado 0.9393563425821488\n", + "Ekdal 1\n", + "Schouten 1\n", + "Obiang 1\n", + "Kovalenko 0.7153559839664058\n", + "Crnigoj 0.2547985695518902\n", + "Basic 0.8551381877299562\n", + "Asllani 0.8609342971194303\n", + "Sabiri 1\n", + "Grassi 0.7425558312655087\n", + "Krunic 0.7528270116979794\n", + "Rincon 1\n", + "Miguel Veloso 1\n", + "Henderson L. 0.6199751861042183\n", + "Lopez M. 0.8502516838000708\n", + "Cuisance 0.8567951899217408\n", + "Saelemaekers 0.7921905155776124\n", + "Maggiore 0.45967741935483875\n", + "Akpa Akpro 0.0\n", + "Akpa Akpro 0.0\n", + "Maleh 0.5745967741935485\n", + "Romero L. 0.9299627791563275\n", + "Ceide 1\n", + "Benassi 1\n", + "Gagliardini 0.9644058450510063\n", + "Vieira 0.08839950372208435\n", + "Bianco 1\n", + "Galdames 0.0\n", + "Kastanos 0.9644058450510062\n", + "Vignato 0.2062655086848635\n", + "Askildsen 1\n", + "Bove 1\n", + "Bohinen 1\n", + "Bakayoko 0.26570365118752215\n", + "Zurkowski 0.21215880893300246\n", + "Castrovilli 0.5391088574819289\n", + "Demme 0.32630272952853595\n", + "Darboe 0.0\n", + "Darboe 0.24799007444168736\n", + "Urbanski 0.0\n", + "Yepes 1\n", + "Osimhen 1\n", + "Martinez L. 1\n", + "Dybala 0.9815393171900401\n", + "Rafael Leao 1\n", + "Immobile 0.9599615784839509\n", + "Vlahovic 1\n", + "Arnautovic 0.6011880592525753\n", + "Dzeko 1\n", + "Nzola 1\n", + "Beto 1\n", + "Giroud 1\n", + "Abraham 1\n", + "Deulofeu 0.5835060575098525\n", + "Simeone 0.6718362282878412\n", + "Lozano 1\n", + "Correa 1\n", + "Berardi 0.7139108203624331\n", + "Pedro 1\n", + "Sanabria 1\n", + "Thauvin affine to Deulofeu\n", + "Thauvin 0.36469128594365785\n", + "Cabral 1\n", + "Caprari 1\n", + "Piatek 1\n", + "Rebic 1\n", + "Bonazzoli 0.9299627791563275\n", + "Zapata D. 1\n", + "Gonzalez N. 0.7139108203624331\n", + "Brekalo 0.15469913151364761\n", + "Kean 0.9299627791563275\n", + "Okereke 1\n", + "Muriel 1\n", + "Pinamonti 0.9281947890818859\n", + "Di Francesco F. 1\n", + "Caputo 0.5500413564929694\n", + "Boga 1\n", + "Alvarez A. affine to Raspadori\n", + "Alvarez A. 0.688861317893576\n", + "Petagna 1\n", + "Barrow 0.9117282148591446\n", + "Djuric 1\n", + "Henry 0.6000451161741484\n", + "Success 1\n", + "Gabbiadini 1\n", + "Kallon 1\n", + "Nestorovski 1\n", + "Raspadori 0.6531741108354012\n", + "Lasagna 1\n", + "Belotti 1\n", + "Pellegri 1\n", + "Verde 0.7514850740657192\n", + "Destro 0.5500413564929694\n", + "Seck 1\n", + "Sansone 0.688861317893576\n", + "Quagliarella 0.6763365666591473\n", + "Defrel 0.6526054590570719\n", + "Pjaca 0.6703629032258065\n", + "Piccoli 1\n", + "Shomurodov 0.44199751861042186\n", + "Afena-Gyan 1\n", + "Ibrahimovic 0.2156435429927716\n", + "Pussetto 0.22099875930521093\n", + "Cancellieri 1\n", + "Oddei 0.0\n", + "Oddei 0.4959801488833747\n", + "Braaf 1\n", + "Raimondo 1\n", + "Kaio Jorge 0.0\n", + "Players with low quantity of games:\n", + "Aiwu 0.0\n", + "Zeefuik 0.16666666666666663\n", + "Dermaku 0.16666666666666663\n", + "Ostigard 0.8333333333333334\n", + "Gila 0.6666666666666667\n", + "Bayeye 0.16666666666666663\n", + "Moutinho J. 0.6666666666666667\n", + "Paletta 0.0\n", + "Romagna 0.0\n", + "Cassandro 0.16666666666666663\n", + "Amey 0.16666666666666663\n", + "Zanotti 0.16666666666666663\n", + "Buta 0.0\n", + "Abankwah 0.16666666666666663\n", + "Guessand A. 0.0\n", + "Guarino 0.0\n", + "Carboni F. 0.33333333333333337\n", + "Pogba 0.5\n", + "Machin 0.0\n", + "Akpa Akpro 0.0\n", + "Bianco 0.6666666666666667\n", + "Galdames 0.0\n", + "D'andrea 0.8333333333333334\n", + "Cipot 0.8333333333333334\n", + "Gaetano 0.8333333333333334\n", + "Darboe 0.668006617038875\n", + "Urbanski 0.16666666666666663\n", + "Bertini 0.0\n", + "Yepes 0.8333333333333334\n", + "Pyyhtia 0.6666666666666667\n", + "Trimboli 0.0\n", + "Adli 0.8333333333333334\n", + "Vignato S. 0.5\n", + "Samek 0.0\n", + "Ilkhan 0.5\n", + "Degli Innocenti 0.0\n", + "Acella 0.16666666666666663\n", + "Carboni V. 0.8333333333333334\n", + "Malagrida 0.6666666666666667\n", + "Faticanti 0.0\n", + "Oddei 0.5853432588916461\n", + "Braaf 0.6666666666666667\n", + "Raimondo 0.33333333333333337\n", + "De Luca 0.33333333333333337\n", + "Krollis 0.16666666666666663\n", + "Vivaldo 0.0\n" + ] + } + ], + "source": [ + "#for i in range(players.columns.shape[0]):\n", + "# print(str(i) + ' - ' + players.columns[i])\n", + "\n", + "cols_toadapt = players.columns[9:]\n", + "\n", + "players = players_orig.copy()\n", + "\n", + "min_games = 6\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", + "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", + "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", + "WEIGHT_mul_gk = 2\n", + "\n", + "rcsv = pd.read_csv('config/affine_players.txt') \n", + "affine_players = pd.DataFrame(rcsv)\n", + "affine_players = affine_players.set_index('player')\n", + "\n", + "\n", + "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", + " if(same_team):\n", + " weight_0 = WEIGHT_0_same_team\n", + " else:\n", + " weight_0 = WEIGHT_0_different_team\n", + "\n", + " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "print(' ')\n", + "print('Averaging players stats with past seasons:')\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + "\n", + " if(p in players_old.index or p in affine_players.index):\n", + " p_ = p\n", + " affine = 0\n", + " \n", + " if(p in affine_players.index):\n", + " affine = 1\n", + " p_ = affine_players.loc[p]['alike']\n", + " \n", + " print(p + ' affine to ' + p_)\n", + " \n", + " if(players.loc[p]['r'] == 'P'):\n", + " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + " weight *= WEIGHT_mul_gk\n", + " weight = min(weight, 1)\n", + " else:\n", + " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + "\n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight)) \n", + " \n", + " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", + " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", + " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", + " \n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "# handle players with low quantitites of games\n", + "\n", + "print('Players with low quantity of games:')\n", + "\n", + "def calc_weight_low(current_games, min_games = min_games):\n", + " weight = 1 - (min_games - current_games)/min_games\n", + " \n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", + "\n", + "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", + "count = 0\n", + "\n", + "for i in range(players_orig.shape[0]):\n", + " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", + " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", + " count = count + 1\n", + " \n", + "mean_players_stats /= count\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", + " weight = calc_weight_low(players.loc[p]['games'])\n", + " \n", + " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "players_out = players.copy()\n", + "players_out = players_out.set_index(players_out.columns[0])\n", + "players_out.insert(2, 'name', players_out.index)\n", + "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "49c28b07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", + " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", + " ...\n", + " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", + " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area',\n", + " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", + " 'vote_avg', 'vote_std'],\n", + " dtype='object', length=151)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "players.columns[9:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d29102e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - r\n", + "11 - games\n", + "12 - games_starts\n", + "13 - minutes\n", + "14 - shots_on_target_pct\n", + "15 - goals_per_shot\n", + "16 - goals_per_shot_on_target\n", + "17 - passes_pct\n", + "18 - aerials_won_pct\n", + "19 - team_possession\n", + "20 - team_goals_assists_per90\n", + "21 - team_goals_pens_per90\n", + "22 - team_goals_assists_pens_per90\n", + "23 - team_xg_per90\n", + "24 - team_gk_goals_against_per90\n", + "25 - team_gk_save_pct\n", + "26 - team_gk_clean_sheets_pct\n", + "27 - team_passes_pct\n", + "28 - team_passes_pct_medium\n", + "29 - team_passes_pct_long\n", + "30 - team_sca_per90\n", + "31 - team_gca_per90\n", + "32 - team_aerials_won_pct\n", + "33 - vs_team_possession\n", + "34 - vs_team_goals_per90\n", + "35 - vs_team_assists_per90\n", + "36 - vs_team_xg_per90\n", + "37 - vs_team_gk_save_pct\n", + "38 - vs_team_gk_clean_sheets_pct\n", + "39 - vs_team_gk_pct_passes_launched\n", + "40 - vs_team_gk_crosses_stopped_pct\n", + "41 - vs_team_shots_on_target_per90\n", + "42 - vs_team_passes_pct\n", + "43 - vs_team_passes_pct_short\n", + "44 - vs_team_passes_pct_medium\n", + "45 - vs_team_passes_pct_long\n", + "46 - vs_team_sca_per90\n", + "47 - vs_team_gca_per90\n", + "48 - vs_team_aerials_won_pct\n", + "49 - opp_team_possession\n", + "50 - opp_team_goals_assists_per90\n", + "51 - opp_team_goals_pens_per90\n", + "52 - opp_team_goals_assists_pens_per90\n", + "53 - opp_team_xg_per90\n", + "54 - opp_team_gk_goals_against_per90\n", + "55 - opp_team_gk_save_pct\n", + "56 - opp_team_gk_clean_sheets_pct\n", + "57 - opp_team_passes_pct\n", + "58 - opp_team_passes_pct_medium\n", + "59 - opp_team_passes_pct_long\n", + "60 - opp_team_sca_per90\n", + "61 - opp_team_gca_per90\n", + "62 - opp_team_aerials_won_pct\n", + "63 - opp_vs_team_possession\n", + "64 - opp_vs_team_goals_per90\n", + "65 - opp_vs_team_assists_per90\n", + "66 - opp_vs_team_xg_per90\n", + "67 - opp_vs_team_gk_save_pct\n", + "68 - opp_vs_team_gk_clean_sheets_pct\n", + "69 - opp_vs_team_gk_pct_passes_launched\n", + "70 - opp_vs_team_gk_crosses_stopped_pct\n", + "71 - opp_vs_team_shots_on_target_per90\n", + "72 - opp_vs_team_passes_pct\n", + "73 - opp_vs_team_passes_pct_short\n", + "74 - opp_vs_team_passes_pct_medium\n", + "75 - opp_vs_team_passes_pct_long\n", + "76 - opp_vs_team_sca_per90\n", + "77 - opp_vs_team_gca_per90\n", + "78 - opp_vs_team_aerials_won_pct\n", + "79 - vote_avg\n", + "80 - vote_std\n", + "81 - goals.1\n", + "82 - assists.1\n", + "83 - cards_yellow\n", + "84 - cards_red\n", + "85 - xg\n", + "86 - npxg\n", + "87 - shots_on_target\n", + "88 - passes_completed\n", + "89 - passes_into_final_third\n", + "90 - passes_into_penalty_area\n", + "91 - progressive_passes\n", + "92 - passes_live\n", + "93 - passes_dead\n", + "94 - through_balls\n", + "95 - passes_switches\n", + "96 - crosses\n", + "97 - corner_kicks\n", + "98 - blocks\n", + "99 - blocked_shots\n", + "100 - blocked_passes\n", + "101 - interceptions\n", + "102 - clearances\n", + "103 - errors\n", + "104 - touches\n", + "105 - touches_def_pen_area\n", + "106 - touches_def_3rd\n", + "107 - touches_mid_3rd\n", + "108 - touches_att_3rd\n", + "109 - touches_att_pen_area\n", + "110 - touches_live_ball\n", + "111 - passes_received\n", + "112 - miscontrols\n", + "113 - dispossessed\n", + "114 - fouls\n", + "115 - fouled\n", + "116 - aerials_won\n", + "117 - aerials_lost\n", + "118 - carries\n", + "119 - progressive_carries\n", + "120 - carries_into_final_third\n", + "121 - carries_into_penalty_area\n" + ] + } + ], + "source": [ + "for i in range(db.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db.columns[i]))" + ] + }, + { + "cell_type": "markdown", + "id": "089690d6", + "metadata": {}, + "source": [ + "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", + "\n", + "For outfield players: X -> y = [vote, fantavote]\n", + "\n", + "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f19304f6", + "metadata": {}, + "outputs": [], + "source": [ + "npdb = np.array(db)\n", + "\n", + "y = npdb[:, [5,9]] # vote, fantavote\n", + "\n", + "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", + "\n", + "f_start = 14\n", + "\n", + "X = npdb[:, f_start:]\n", + "\n", + "if(DEL_G): \n", + " del_g_idx = [\n", + " list(db.columns).index('goals.1') - f_start,\n", + " list(db.columns).index('assists.1') - f_start,\n", + " list(db.columns).index('xg') - f_start,\n", + " list(db.columns).index('npxg') - f_start,\n", + " list(db.columns).index('shots_on_target') - f_start]\n", + " \n", + " X[:, del_g_idx] = 0\n", + "\n", + "\n", + "# add role and home factor\n", + "toadd = np.zeros((X.shape[0], 4))\n", + "toadd[:, 0] = npdb[:, 4] # home\n", + "\n", + "toadd[:, 1] = npdb[:, 10] == 'D'\n", + "toadd[:, 2] = npdb[:, 10] == 'C'\n", + "toadd[:, 3] = npdb[:, 10] == 'A'\n", + "\n", + "X = np.concatenate((X, toadd), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "370d41d2", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "scaler.fit(X)\n", + "\n", + "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", + "\n", + "X_train = scaler.transform(X_train_)\n", + "X_test = scaler.transform(X_test_)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a7b1fb52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - gk_games\n", + "11 - gk_games_starts\n", + "12 - gk_minutes\n", + "13 - gk_goals_against_per90\n", + "14 - gk_save_pct\n", + "15 - gk_clean_sheets_pct\n", + "16 - gk_psxg_net_per90\n", + "17 - gk_passes_pct_launched\n", + "18 - gk_pct_passes_launched\n", + "19 - gk_passes_length_avg\n", + "20 - gk_pct_goal_kicks_launched\n", + "21 - gk_goal_kick_length_avg\n", + "22 - gk_crosses_stopped_pct\n", + "23 - gk_def_actions_outside_pen_area_per90\n", + "24 - gk_avg_distance_def_actions\n", + "25 - team_possession\n", + "26 - team_goals_assists_per90\n", + "27 - team_goals_pens_per90\n", + "28 - team_goals_assists_pens_per90\n", + "29 - team_xg_per90\n", + "30 - team_gk_goals_against_per90\n", + "31 - team_gk_save_pct\n", + "32 - team_gk_clean_sheets_pct\n", + "33 - team_passes_pct\n", + "34 - team_passes_pct_medium\n", + "35 - team_passes_pct_long\n", + "36 - team_sca_per90\n", + "37 - team_gca_per90\n", + "38 - team_aerials_won_pct\n", + "39 - vs_team_possession\n", + "40 - vs_team_goals_per90\n", + "41 - vs_team_assists_per90\n", + "42 - vs_team_xg_per90\n", + "43 - vs_team_gk_save_pct\n", + "44 - vs_team_gk_clean_sheets_pct\n", + "45 - vs_team_gk_pct_passes_launched\n", + "46 - vs_team_gk_crosses_stopped_pct\n", + "47 - vs_team_shots_on_target_per90\n", + "48 - vs_team_passes_pct\n", + "49 - vs_team_passes_pct_short\n", + "50 - vs_team_passes_pct_medium\n", + "51 - vs_team_passes_pct_long\n", + "52 - vs_team_sca_per90\n", + "53 - vs_team_gca_per90\n", + "54 - vs_team_aerials_won_pct\n", + "55 - opp_team_possession\n", + "56 - opp_team_goals_assists_per90\n", + "57 - opp_team_goals_pens_per90\n", + "58 - opp_team_goals_assists_pens_per90\n", + "59 - opp_team_xg_per90\n", + "60 - opp_team_gk_goals_against_per90\n", + "61 - opp_team_gk_save_pct\n", + "62 - opp_team_gk_clean_sheets_pct\n", + "63 - opp_team_passes_pct\n", + "64 - opp_team_passes_pct_medium\n", + "65 - opp_team_passes_pct_long\n", + "66 - opp_team_sca_per90\n", + "67 - opp_team_gca_per90\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_aerials_won_pct\n", + "85 - vote_avg\n", + "86 - vote_std\n", + "87 - gk_shots_on_target_against\n", + "88 - gk_saves\n", + "89 - gk_free_kick_goals_against\n", + "90 - gk_corner_kick_goals_against\n", + "91 - gk_own_goals_against\n", + "92 - gk_psxg\n", + "93 - gk_psnpxg_per_shot_on_target_against\n", + "94 - gk_psxg_net\n", + "95 - gk_passes_completed_launched\n", + "96 - gk_passes_launched\n", + "97 - gk_passes\n", + "98 - gk_passes_throws\n", + "99 - gk_goal_kicks\n", + "100 - gk_crosses\n", + "101 - gk_crosses_stopped\n" + ] + } + ], + "source": [ + "for i in range(db_gk.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db_gk.columns[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5a7cf079", + "metadata": {}, + "outputs": [], + "source": [ + "npdb_gk= np.array(db_gk)\n", + "\n", + "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", + "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", + "\n", + "f_start_gk = 13\n", + "\n", + "X_gk = npdb_gk[:, f_start_gk:]\n", + "\n", + "# add home factor\n", + "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", + "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", + "\n", + "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0bc0568b", + "metadata": {}, + "outputs": [], + "source": [ + "scaler_gk = StandardScaler()\n", + "scaler_gk.fit(X_gk)\n", + "\n", + "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", + "\n", + "X_gk_train = scaler_gk.transform(X_gk_train_)\n", + "X_gk_test = scaler_gk.transform(X_gk_test_)" + ] + }, + { + "cell_type": "markdown", + "id": "ebd27493", + "metadata": {}, + "source": [ + "MLP Regressor , to see performance of a simple neural network" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "04564bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1569906306642661\n", + "0.19152381866355805\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", + "\n", + "regr.fit(X_train, y_train)\n", + "\n", + "\n", + "y_train_predict = regr.predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = regr.predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f9513b25", + "metadata": {}, + "source": [ + "Train neural network for outfield players.\n", + "\n", + "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8aad9652", + "metadata": {}, + "outputs": [], + "source": [ + "load_model_of = True# load scaler and model weights for outfield player predictor\n", + "refit_model_of = False\n", + "\n", + "if(load_model_of):\n", + " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", + " \n", + " X_train = scaler.transform(X_train_)\n", + " X_test = scaler.transform(X_test_)\n", + "\n", + "\n", + "n_epochs = 1000\n", + "\n", + "n_samples = X_train.shape[0]\n", + "\n", + "batch_size = 256\n", + "\n", + "X_len = X_train.shape[1]\n", + "y_len = y_train.shape[1]\n", + "\n", + "\n", + "#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.2\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", + "x = tfk.layers.Dropout(0.2)(inputs)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "x = tfk.layers.Dropout(0.2)(x)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", + "\n", + "\n", + "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", + "\n", + "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_of):\n", + " modelb.load_weights('saves/modelb')\n", + " \n", + "if( (not load_model_of) or refit_model_of):\n", + " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", + " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4e2bf9dc", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict(X, iterations = 100):\n", + " y = np.zeros((2, X.shape[0]))\n", + " \n", + " dist = modelb(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " \n", + " return y.transpose() / iterations\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "c2674211", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.14539483185579238\n", + "0.16229047232752447\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train_predict = sample_predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = sample_predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f574ffdb", + "metadata": {}, + "source": [ + "Train neural network for goalkeepers.\n", + "\n", + "For clean sheet probability prediction, a Bernoulli distribution is used." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "41e7e1ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2500\n", + "9/9 [==============================] - 4s 92ms/step - loss: 105.9979 - distribution_lambda_21_loss: 99.1596 - distribution_lambda_22_loss: 5.7268 - distribution_lambda_23_loss: 1.1114 - val_loss: 85.6839 - val_distribution_lambda_21_loss: 79.7444 - val_distribution_lambda_22_loss: 4.8681 - val_distribution_lambda_23_loss: 1.0713\n", + "Epoch 2/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 78.7734 - distribution_lambda_21_loss: 72.8187 - distribution_lambda_22_loss: 4.8669 - distribution_lambda_23_loss: 1.0879 - val_loss: 63.5727 - val_distribution_lambda_21_loss: 58.3870 - val_distribution_lambda_22_loss: 4.1357 - val_distribution_lambda_23_loss: 1.0500\n", + "Epoch 3/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 59.3165 - distribution_lambda_21_loss: 54.1085 - distribution_lambda_22_loss: 4.1424 - distribution_lambda_23_loss: 1.0655 - val_loss: 48.0409 - val_distribution_lambda_21_loss: 43.4830 - val_distribution_lambda_22_loss: 3.5318 - val_distribution_lambda_23_loss: 1.0260\n", + "Epoch 4/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 45.1089 - distribution_lambda_21_loss: 40.5643 - distribution_lambda_22_loss: 3.4981 - distribution_lambda_23_loss: 1.0466 - val_loss: 37.6652 - val_distribution_lambda_21_loss: 33.5340 - val_distribution_lambda_22_loss: 3.1345 - val_distribution_lambda_23_loss: 0.9967\n", + "Epoch 5/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 36.3207 - distribution_lambda_21_loss: 32.1626 - distribution_lambda_22_loss: 3.1592 - distribution_lambda_23_loss: 0.9990 - val_loss: 30.6755 - val_distribution_lambda_21_loss: 26.8368 - val_distribution_lambda_22_loss: 2.8735 - val_distribution_lambda_23_loss: 0.9652\n", + "Epoch 6/2500\n", + "9/9 [==============================] - 0s 7ms/step - loss: 30.0433 - distribution_lambda_21_loss: 26.1435 - distribution_lambda_22_loss: 2.9074 - distribution_lambda_23_loss: 0.9924 - val_loss: 25.8243 - val_distribution_lambda_21_loss: 22.1859 - val_distribution_lambda_22_loss: 2.7075 - val_distribution_lambda_23_loss: 0.9309\n", + "Epoch 7/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 25.5853 - distribution_lambda_21_loss: 21.8961 - distribution_lambda_22_loss: 2.7424 - distribution_lambda_23_loss: 0.9468 - val_loss: 22.3435 - val_distribution_lambda_21_loss: 18.8484 - val_distribution_lambda_22_loss: 2.5991 - val_distribution_lambda_23_loss: 0.8960\n", + "Epoch 8/2500\n", + "9/9 [==============================] - 0s 5ms/step - loss: 22.2391 - distribution_lambda_21_loss: 18.7085 - distribution_lambda_22_loss: 2.6146 - distribution_lambda_23_loss: 0.9160 - val_loss: 19.7279 - val_distribution_lambda_21_loss: 16.3430 - val_distribution_lambda_22_loss: 2.5234 - val_distribution_lambda_23_loss: 0.8615\n", + "Epoch 9/2500\n", + "9/9 [==============================] - 0s 5ms/step - loss: 19.7891 - distribution_lambda_21_loss: 16.3677 - distribution_lambda_22_loss: 2.5346 - distribution_lambda_23_loss: 0.8867 - val_loss: 17.6639 - val_distribution_lambda_21_loss: 14.3678 - val_distribution_lambda_22_loss: 2.4684 - val_distribution_lambda_23_loss: 0.8277\n", + "Epoch 10/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 17.8097 - distribution_lambda_21_loss: 14.4868 - distribution_lambda_22_loss: 2.4627 - distribution_lambda_23_loss: 0.8602 - val_loss: 16.0307 - val_distribution_lambda_21_loss: 12.8070 - val_distribution_lambda_22_loss: 2.4279 - val_distribution_lambda_23_loss: 0.7958\n", + "Epoch 11/2500\n", + 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"#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.1\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 50)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (inputs)\n", + "x = tfk.layers.Dropout(0.2)(x)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "\n", + "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", + "\n", + "x3 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x3 = tfk.layers.Dropout(0.5)(x3)\n", + "x3 = tfk.layers.Dense(1, activation=\"sigmoid\")(x3)\n", + "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x3)\n", + "\n", + "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", + "\n", + "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_gk):\n", + " modelb_gk.load_weights('saves/modelb_gk')\n", + "\n", + "if( (not load_model_gk) or refit_model_gk): \n", + " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", + " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "39a9bdc6", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict_gk(X, iterations = 100):\n", + " y = np.zeros((3, X.shape[0]))\n", + " \n", + " dist = modelb_gk(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " y[2, :] += dist[2].sample()\n", + " \n", + " return y.transpose() / iterations\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c41cf448", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.29228994199557035\n", + "0.5457850368866328\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", + "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", + "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "91869883", + "metadata": {}, + "source": [ + "Use the following codes to save the scalers and the model weights" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "cecf5392", + "metadata": {}, + "outputs": [], + "source": [ + "save_model_of = True\n", + "save_model_gk = True\n", + "\n", + "if(save_model_of):\n", + " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", + " modelb.save_weights('saves/modelb')\n", + " \n", + "if(save_model_gk):\n", + " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", + " modelb_gk.save_weights('saves/modelb_gk')\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "32635a0e", + "metadata": {}, + "source": [ + "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", + "\n", + "Estimate prediction mean and sigma (using a custom definitions).\n", + "\n", + "Generate a plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ddf433f9", + "metadata": {}, + "outputs": [], + "source": [ + "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", + " if(players['r'][player] == 'P'):\n", + " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 3:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 1))\n", + " xadd[0, 0] = home\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler_gk.transform(x_ptest)\n", + "\n", + " dist = modelb_gk(x_scaled)\n", + " \n", + " clean_shoot_prob = dist[2].probs.numpy()[0]\n", + " else:\n", + " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 4:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 4))\n", + " xadd[0, 0] = home\n", + " xadd[0, 1] = r == 'D'\n", + " xadd[0, 2] = r == 'C'\n", + " xadd[0, 3] = r == 'A'\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler.transform(x_ptest)\n", + "\n", + " dist = modelb(x_scaled)\n", + " \n", + " \n", + " x = np.arange(0, 40, 0.002)\n", + "\n", + " px1 = dist[0].prob(x);\n", + " px2 = dist[1].prob(x);\n", + "\n", + " \n", + " #sample1 = dist[0].sample(10000)\n", + " #sample2 = dist[1].sample(10000)\n", + " \n", + " m1 = np.average(x, weights = px1)\n", + " m2 = np.average(x, weights = px2)\n", + " \n", + " #m1 = np.mean(sample1)\n", + " #m2 = np.mean(sample2)\n", + " \n", + " #s1 = np.std(sample1)\n", + " #s2 = np.std(sample2)\n", + " \n", + " # not standard deviation, but expected range extimated by quantile \n", + " \n", + " if(players['r'][player] == 'P'):\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", + " else:\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", + " \n", + "\n", + " \n", + " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", + " y_pred_m = np.array([m1, m2]).flatten()\n", + " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", + " y_pred_s = np.array([s1, s2]).flatten()\n", + " \n", + " clean_sheet_text = ''\n", + " if(players['r'][player] == 'P'):\n", + " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", + " \n", + " if(plot):\n", + " ax = plt.gca()\n", + " \n", + " plt.plot(x, px1, \n", + " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", + " color = 'b')\n", + " plt.plot(x, px2, \n", + " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", + " color = 'g')\n", + " \n", + " plt.fill_between(x, px1, color = 'lightblue')\n", + " plt.fill_between(x, px2, color = 'lightgreen')\n", + " \n", + " plt.legend()\n", + " \n", + " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " \n", + " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", + " \n", + " plt.xlim([0, 15])\n", + " \n", + " if(players['r'][player] == 'P'): \n", + " plt.ylim([0, 2.5])\n", + " else:\n", + " plt.ylim([0, 1.5])\n", + " \n", + " plt.show()\n", + " \n", + " if(log):\n", + " print(player + ': ' + \n", + " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", + " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", + " return [y_pred_m, y_pred_s, dist]\n" + ] + }, + { + "cell_type": "markdown", + "id": "98817aa0", + "metadata": {}, + "source": [ + "Load Serie A calendar. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d31bad38", + "metadata": {}, + "outputs": [], + "source": [ + "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", + "\n", + "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", + "\n", + "matchday = 0\n", + "\n", + "for i in range(cal.shape[0]):\n", + " if(cal[i, 0][0].isnumeric()):\n", + " matchday = matchday + 1\n", + " continue\n", + " \n", + " teams = cal[i, 0].split('-')\n", + " \n", + " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", + "\n", + " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "62b9f588", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " matchday team1 team2\n", + "0 1 Fiorentina Cremonese\n", + "1 1 Verona Napoli\n", + "2 1 Juventus Sassuolo\n", + "3 1 Lazio Bologna\n", + "4 1 Lecce Inter\n", + ".. ... ... ...\n", + "375 38 Lecce Bologna\n", + "376 38 Sassuolo Fiorentina\n", + "377 38 Milan Verona\n", + "378 38 Torino Inter\n", + "379 38 Udinese Juventus\n", + "\n", + "[380 rows x 3 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df" + ] + }, + { + "cell_type": "markdown", + "id": "62872819", + "metadata": {}, + "source": [ + "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c58ba41d", + "metadata": {}, + "outputs": [], + "source": [ + "def PlayerMatch(player, match = 0):\n", + " team = players.loc[player]['team']\n", + " \n", + " if(match == 0):\n", + " oppteam = 'Avg'\n", + " home = 1\n", + " else:\n", + " for i in range (cal_df.shape[0]):\n", + " if(cal_df['matchday'][i] == match):\n", + " if(cal_df['team1'][i] == team):\n", + " home = 1\n", + " oppteam = cal_df['team2'][i]\n", + " elif(cal_df['team2'][i] == team):\n", + " home = 0\n", + " oppteam = cal_df['team1'][i]\n", + " \n", + " return [player, team, oppteam, home]\n", + "\n", + "def predict_player(player, match = 0, plot = 0, log = 0, oldseason = False):\n", + " [player, team, oppteam, home] = PlayerMatch(player, match)\n", + " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" + ] + }, + { + "cell_type": "markdown", + "id": "e8b63a98", + "metadata": {}, + "source": [ + "Load current matchday playing probabilities for Serie A players." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f79792b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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starterpercentage
player
Falcone1.0090
Gendrey1.0090
Baschirotto1.0090
Umtiti1.0090
Gallo0.6565
.........
Barrenechea0.0020
Pogba0.0050
Chiesa0.4055
Soule'0.0020
Milik0.4060
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474 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " starter percentage\n", + "player \n", + "Falcone 1.00 90\n", + "Gendrey 1.00 90\n", + "Baschirotto 1.00 90\n", + "Umtiti 1.00 90\n", + "Gallo 0.65 65\n", + "... ... ...\n", + "Barrenechea 0.00 20\n", + "Pogba 0.00 50\n", + "Chiesa 0.40 55\n", + "Soule' 0.00 20\n", + "Milik 0.40 60\n", + "\n", + "[474 rows x 2 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", + "\n", + "probables" + ] + }, + { + "cell_type": "markdown", + "id": "e4751014", + "metadata": {}, + "source": [ + "Generate prediction data for each Serie A player for the current matchday.\n", + "\n", + "Output to excel file, using a template made for data elaboration." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5e63c2b7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret: MV 6.24 ± 0.83; FV 5.13 + 1.01 (14.5% cs)\n", + "Provedel: MV 6.24 ± 0.83; FV 6.62 + 0.71 (96.3% cs)\n", + "Vicario: MV 6.24 ± 0.83; FV 5.09 + 0.90 (17.2% cs)\n", + "Szczesny: MV 6.24 ± 0.83; FV 6.39 + 0.69 (92.6% cs)\n", + "Falcone: MV 6.24 ± 0.83; FV 4.83 + 0.90 (1.1% cs)\n", + "Silvestri: MV 6.24 ± 0.83; FV 4.39 + 1.17 (0.9% cs)\n", + "Rui Patricio: MV 6.24 ± 0.83; FV 4.75 + 0.91 (3.2% cs)\n", + "Onana: MV 6.24 ± 0.83; FV 4.67 + 0.91 (3.5% cs)\n", + "Sepe: MV 6.24 ± 0.83; FV 5.77 + 0.83 (58.3% cs)\n", + "Milinkovic-Savic V.: MV 6.24 ± 0.83; FV 4.84 + 0.87 (6.9% cs)\n", + "Musso: MV 6.25 ± 0.82; FV 5.42 + 0.84 (25.0% cs)\n", + "Maignan: MV 6.24 ± 0.83; FV 5.36 + 0.82 (14.1% cs)\n", + "Carnesecchi: MV 6.24 ± 0.83; FV 4.39 + 1.15 (0.1% cs)\n", + "Di Gregorio: MV 6.24 ± 0.83; FV 4.55 + 1.07 (1.9% cs)\n", + "Audero: MV 6.24 ± 0.83; FV 5.17 + 0.83 (18.3% cs)\n", + "Montipo': MV 6.24 ± 0.83; FV 4.62 + 1.04 (0.1% cs)\n", + "Skorupski: MV 6.24 ± 0.83; FV 4.27 + 1.07 (1.9% cs)\n", + "Consigli: MV 6.24 ± 0.83; FV 4.27 + 1.30 (0.1% cs)\n", + "Dragowski: MV 6.24 ± 0.83; FV 4.49 + 1.03 (0.4% cs)\n", + "Terracciano: MV 6.24 ± 0.83; FV 5.35 + 0.85 (30.5% cs)\n", + "Tatarusanu: MV 6.24 ± 0.83; FV 5.44 + 0.97 (19.7% cs)\n", + "Handanovic: MV 6.24 ± 0.83; FV 4.94 + 0.89 (4.0% cs)\n", + "Sportiello: MV 6.18 ± 0.86; FV 5.72 + 1.09 (40.4% cs)\n", + "Perin: MV 6.24 ± 0.83; FV 5.31 + 0.79 (24.2% cs)\n", + "Zoet: MV 6.24 ± 0.83; FV 5.52 + 0.75 (18.9% cs)\n", + "Ochoa: MV 6.24 ± 0.83; FV 5.22 + 0.82 (15.7% cs)\n", + "Pegolo: MV 6.24 ± 0.83; FV 4.16 + 1.34 (0.1% cs)\n", + "Gollini: MV 6.25 ± 0.83; FV 4.93 + 1.34 (43.4% cs)\n", + "Mirante no data\n", + "Sarr M. no data\n", + "Lamanna no data\n", + "Ujkani no data\n", + "Berisha: MV 6.24 ± 0.83; FV 5.94 + 0.88 (65.1% cs)\n", + "Marchetti: MV 6.24 ± 0.83; FV 4.18 + 1.24 (0.1% cs)\n", + "Perilli: MV 6.24 ± 0.83; FV 4.29 + 1.29 (1.1% cs)\n", + "Padelli: MV 6.23 ± 0.83; FV 4.02 + 1.38 (0.3% cs)\n", + "Perisan: MV 6.26 ± 0.81; FV 5.53 + 0.94 (32.5% cs)\n", + "Bardi: MV 6.24 ± 0.83; FV 6.04 + 0.78 (78.7% cs)\n", + "Cordaz no data\n", + "Pinsoglio: MV 6.24 ± 0.83; FV 6.19 + 0.70 (86.7% cs)\n", + "Fiorillo: MV 6.24 ± 0.83; FV 4.96 + 0.82 (0.3% cs)\n", + "Cragno: MV 6.24 ± 0.83; FV 4.65 + 1.12 (0.1% cs)\n", + "Sirigu: MV 6.24 ± 0.83; FV 4.35 + 1.30 (0.0% cs)\n", + "Cerofolini no data\n", + "Rossi F.: MV 6.24 ± 0.83; FV 4.53 + 1.05 (0.8% cs)\n", + "Ravaglia F.: MV 6.24 ± 0.83; FV 4.70 + 0.92 (1.4% cs)\n", + "Brancolini no data\n", + "Bleve no data\n", + "Berardi A.: MV 6.23 ± 0.83; FV 3.86 + 1.53 (0.1% cs)\n", + "Russo A. no data\n", + "Gemello: MV 6.24 ± 0.83; FV 6.15 + 0.84 (78.0% cs)\n", + "Ravaglia: MV 6.24 ± 0.83; FV 5.89 + 0.71 (59.6% cs)\n", + "Boer no data\n", + "Adamonis no data\n", + "Marfella: MV 6.24 ± 0.83; FV 5.17 + 0.97 (11.7% cs)\n", + "Zovko: MV 6.24 ± 0.83; FV 4.26 + 1.19 (0.2% cs)\n", + "Piana no data\n", + "Bagnolini no data\n", + "Luis Maximiano: MV 6.24 ± 0.83; FV 6.75 + 0.74 (97.3% cs)\n", + "Svilar no data\n", + "Sorrentino A. no data\n", + "Ciezkowski no data\n", + "Saro no data\n", + "Vasquez D. no data\n", + "Turk: MV 6.24 ± 0.83; FV 5.24 + 0.78 (5.8% cs)\n", + "Dimarco: MV 6.24 ± 1.00; FV 6.79 + 1.87\n", + "Smalling: MV 6.25 ± 1.01; FV 6.66 + 1.74\n", + "Doig: MV 5.99 ± 1.03; FV 6.26 + 1.61\n", + "Carlos Augusto: MV 6.08 ± 1.06; FV 6.49 + 1.85\n", + "Kim: MV 6.23 ± 1.02; FV 6.49 + 1.50\n", + "Posch: MV 6.16 ± 1.11; FV 6.62 + 2.00\n", + "Di Lorenzo: MV 6.21 ± 0.98; FV 6.51 + 1.50\n", + "Danilo: MV 6.30 ± 0.99; FV 6.73 + 1.74\n", + "Hernandez T.: MV 6.32 ± 1.13; FV 6.96 + 2.22\n", + "Udogie: MV 5.90 ± 1.07; FV 6.07 + 1.53\n", + "Parisi: MV 6.17 ± 0.98; FV 6.54 + 1.59\n", + "Mario Rui: MV 6.14 ± 1.00; FV 6.33 + 1.32\n", + "Romagnoli: MV 6.20 ± 0.87; FV 6.37 + 1.23\n", + "Bastoni S.: MV 5.98 ± 0.94; FV 6.16 + 1.37\n", + "Mazzocchi: MV 6.13 ± 0.86; FV 6.52 + 1.49\n", + "Valeri: MV 6.08 ± 0.84; FV 6.36 + 1.28\n", + "Tomori: MV 6.17 ± 0.80; FV 6.37 + 1.17\n", + "Scalvini: MV 6.27 ± 1.07; FV 6.79 + 1.96\n", + "Toloi: MV 6.25 ± 1.02; FV 6.72 + 1.81\n", + "Demiral: MV 6.13 ± 0.80; FV 6.37 + 1.21\n", + "Maehle: MV 6.19 ± 0.99; FV 6.69 + 1.81\n", + "Dumfries: MV 6.00 ± 1.03; FV 6.30 + 1.64\n", + "Baschirotto: MV 6.06 ± 0.99; FV 6.32 + 1.46\n", + "Bijol: MV 5.76 ± 1.19; FV 5.85 + 1.55\n", + "Schuurs: MV 6.20 ± 0.82; FV 6.30 + 1.09\n", + "Juan Jesus: MV 6.15 ± 0.74; FV 6.33 + 1.06\n", + "Depaoli: MV 5.92 ± 0.87; FV 6.05 + 1.20\n", + "Mancini: MV 6.14 ± 0.86; FV 6.37 + 1.25\n", + "Ibanez: MV 5.79 ± 1.19; FV 5.92 + 1.57\n", + "Rodrigo Becao: MV 5.80 ± 1.09; FV 5.80 + 1.31\n", + "Ebuehi: MV 6.08 ± 0.82; FV 6.35 + 1.20\n", + "Gosens: MV 6.04 ± 0.75; FV 6.31 + 1.12\n", + "Darmian: MV 6.10 ± 0.76; FV 6.37 + 1.17\n", + "Reca: MV 5.88 ± 1.02; FV 6.01 + 1.44\n", + "Bremer: MV 6.20 ± 1.02; FV 6.60 + 1.66\n", + "Sernicola: MV 5.87 ± 1.07; FV 6.03 + 1.55\n", + "Rrahmani: MV 6.20 ± 1.02; FV 6.47 + 1.50\n", + "Vojvoda: MV 6.02 ± 0.79; FV 6.09 + 0.86\n", + "Holm: MV 5.89 ± 0.85; FV 6.01 + 1.17\n", + "Bastoni: MV 6.14 ± 0.84; FV 6.27 + 1.07\n", + "Milenkovic: MV 6.04 ± 0.93; FV 6.22 + 1.32\n", + "Kalulu: MV 6.06 ± 0.92; FV 6.24 + 1.17\n", + "Martinez Quarta: MV 6.06 ± 0.94; FV 6.24 + 1.29\n", + "Casale: MV 6.09 ± 0.82; FV 6.18 + 0.98\n", + "Perez N.: MV 5.79 ± 1.17; FV 5.90 + 1.55\n", + "Olivera: MV 6.10 ± 0.71; FV 6.30 + 1.00\n", + "Izzo: MV 6.04 ± 0.92; FV 6.18 + 1.19\n", + "Luperto: MV 5.90 ± 0.96; FV 5.88 + 0.92\n", + "Skriniar: MV 5.99 ± 0.79; FV 6.00 + 0.79\n", + "Rodriguez R.: MV 6.12 ± 0.69; FV 6.09 + 0.72\n", + "Marusic: MV 6.08 ± 0.79; FV 6.00 + 0.76\n", + "Lazzari: MV 6.06 ± 0.75; FV 6.02 + 0.73\n", + "Kyriakopoulos: MV 5.99 ± 0.91; FV 6.04 + 1.01\n", + "Ampadu: MV 5.77 ± 0.99; FV 5.73 + 1.14\n", + "Ismajli: MV 5.95 ± 0.85; FV 5.88 + 0.78\n", + "Llorente D.: MV 5.90 ± 1.07; FV 6.08 + 1.55\n", + "Cambiaso: MV 5.97 ± 0.77; FV 5.96 + 0.74\n", + "Hysaj: MV 6.04 ± 0.65; FV 5.95 + 0.55\n", + "Biraghi: MV 6.16 ± 0.87; FV 6.42 + 1.28\n", + "Medel: MV 5.97 ± 0.72; FV 5.91 + 0.64\n", + "Bonucci: MV 6.24 ± 1.04; FV 6.68 + 1.79\n", + "Calabria: MV 6.15 ± 0.96; FV 6.58 + 1.65\n", + "Acerbi: MV 6.06 ± 0.72; FV 6.05 + 0.71\n", + "Spinazzola: MV 6.16 ± 0.87; FV 6.53 + 1.50\n", + "Lykogiannis: MV 6.03 ± 0.81; FV 6.14 + 0.97\n", + "Pellegrini Lu.: MV 6.04 ± 0.72; FV 6.01 + 0.69\n", + "Djidji: MV 6.00 ± 0.79; FV 6.08 + 0.89\n", + "Lazaro: MV 6.16 ± 0.86; FV 6.34 + 1.16\n", + "Augello: MV 6.01 ± 0.91; FV 6.33 + 1.49\n", + "Gallo: MV 5.77 ± 0.79; FV 5.74 + 0.75\n", + "Singo: MV 6.12 ± 0.85; FV 6.39 + 1.25\n", + "Mari': MV 5.86 ± 1.07; FV 5.91 + 1.22\n", + "Caldirola: MV 5.89 ± 1.02; FV 5.99 + 1.30\n", + "Dodo': MV 5.93 ± 0.95; FV 5.98 + 1.13\n", + "De Vrij: MV 6.01 ± 0.82; FV 6.07 + 0.88\n", + "Patric: MV 6.07 ± 0.80; FV 5.96 + 0.75\n", + "Faraoni: MV 5.94 ± 0.88; FV 6.08 + 1.21\n", + "Ceccherini: MV 5.85 ± 1.07; FV 5.95 + 1.46\n", + "Hateboer: MV 6.06 ± 0.89; FV 6.34 + 1.41\n", + "Rogerio: MV 5.66 ± 0.84; FV 5.63 + 0.86\n", + "Umtiti: MV 5.80 ± 0.97; FV 5.75 + 1.00\n", + "Aina: MV 6.15 ± 0.87; FV 6.46 + 1.36\n", + "Birindelli: MV 5.80 ± 0.71; FV 5.78 + 0.74\n", + "Lucumi': MV 5.94 ± 0.81; FV 5.90 + 0.79\n", + "Ehizibue: MV 5.73 ± 1.01; FV 5.83 + 1.32\n", + "Bianchetti: MV 5.69 ± 1.03; FV 5.72 + 1.28\n", + "Ferrari G.: MV 5.65 ± 1.13; FV 5.66 + 1.29\n", + "Fazio: MV 5.65 ± 1.26; FV 5.69 + 1.40\n", + "Gravillon: MV 6.08 ± 0.79; FV 6.02 + 0.78\n", + "Buongiorno: MV 6.11 ± 0.75; FV 6.06 + 0.76\n", + "Gunter: MV 5.80 ± 0.95; FV 5.72 + 0.88\n", + "Troost-Ekong: MV 5.79 ± 0.93; FV 5.77 + 0.94\n", + "Soumaoro: MV 5.91 ± 0.92; FV 5.88 + 0.88\n", + "Ceccaroni: MV 5.81 ± 1.05; FV 5.83 + 1.21\n", + "Pongracic: MV 5.89 ± 0.79; FV 5.83 + 0.74\n", + "Soppy: MV 6.03 ± 0.73; FV 6.10 + 0.81\n", + "Gendrey: MV 5.84 ± 0.65; FV 5.83 + 0.55\n", + "Hien: MV 5.81 ± 0.94; FV 5.76 + 1.06\n", + "Ferrari A.: MV 5.69 ± 1.13; FV 5.70 + 1.35\n", + "Masina: MV 5.79 ± 0.92; FV 6.10 + 1.37\n", + "Zappacosta: MV 6.16 ± 0.81; FV 6.53 + 1.42\n", + "Gyomber: MV 5.90 ± 0.85; FV 5.88 + 0.81\n", + "Alex Sandro: MV 5.90 ± 0.87; FV 5.81 + 0.78\n", + "Pezzella Giu.: MV 5.85 ± 0.68; FV 5.84 + 0.57\n", + "Bereszynski: MV 5.82 ± 0.72; FV 5.79 + 0.74\n", + "Venuti: MV 5.90 ± 0.79; FV 5.92 + 0.85\n", + "Palomino: MV 6.17 ± 0.85; FV 6.40 + 1.26\n", + "Nuytinck: MV 5.90 ± 0.91; FV 5.88 + 0.89\n", + "Marlon: MV 5.74 ± 0.79; FV 5.67 + 0.77\n", + "Magnani: MV 5.77 ± 0.96; FV 5.71 + 1.04\n", + "Colley: MV 5.87 ± 1.02; FV 5.91 + 1.11\n", + "Nikolaou: MV 5.66 ± 0.88; FV 5.58 + 0.96\n", + "Terzic: MV 6.04 ± 0.61; FV 6.00 + 0.53\n", + "Igor: MV 5.87 ± 0.93; FV 5.84 + 1.00\n", + "Toljan: MV 5.60 ± 0.90; FV 5.55 + 0.90\n", + "Zortea: MV 5.74 ± 0.83; FV 5.77 + 0.96\n", + "Dawidowicz: MV 5.76 ± 1.00; FV 5.76 + 1.24\n", + "Celik: MV 5.77 ± 0.75; FV 5.72 + 0.74\n", + "Bellanova: MV 5.99 ± 0.87; FV 6.17 + 1.15\n", + "Erlic: MV 5.68 ± 1.06; FV 5.60 + 1.07\n", + "Ballo-Toure': MV 6.17 ± 0.74; FV 6.56 + 1.39\n", + "Dest: MV 5.93 ± 0.77; FV 5.95 + 0.74\n", + "Stojanovic: MV 5.75 ± 0.78; FV 5.70 + 0.76\n", + "Amian: MV 5.68 ± 0.82; FV 5.65 + 0.91\n", + "Bradaric: MV 5.74 ± 0.93; FV 5.73 + 1.05\n", + "Daniliuc: MV 5.75 ± 1.02; FV 5.74 + 1.15\n", + "Zima: MV 6.03 ± 0.75; FV 5.99 + 0.73\n", + "De Winter: MV 5.77 ± 0.82; FV 5.71 + 0.74\n", + "Quagliata: MV 5.88 ± 0.69; FV 5.90 + 0.70\n", + "Ebosse: MV 5.62 ± 0.82; FV 5.52 + 0.83\n", + "Aiwu: MV 5.83 ± 1.09; FV 5.93 + 1.48\n", + "Lochoshvili: MV 5.79 ± 0.99; FV 5.84 + 1.30\n", + "Bronn: MV 5.69 ± 0.80; FV 5.65 + 0.74\n", + "Thiaw: MV 6.03 ± 0.89; FV 6.05 + 0.91\n", + "Zeefuik: MV 5.86 ± 0.94; FV 5.91 + 1.23\n", + "Romagnoli S.: MV 5.84 ± 1.10; FV 5.98 + 1.54\n", + "Ghiglione: MV 5.81 ± 0.93; FV 5.92 + 1.28\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rugani: MV 6.07 ± 0.59; FV 5.97 + 0.48\n", + "De Sciglio: MV 5.91 ± 0.62; FV 5.91 + 0.48\n", + "Djimsiti: MV 6.08 ± 0.72; FV 6.07 + 0.75\n", + "Caldara: MV 5.63 ± 1.02; FV 5.56 + 1.09\n", + "Karsdorp: MV 5.88 ± 0.80; FV 5.86 + 0.79\n", + "Marchizza: MV 5.79 ± 0.70; FV 5.79 + 0.62\n", + "Kjaer: MV 6.06 ± 0.70; FV 5.98 + 0.63\n", + "Okoli: MV 5.93 ± 0.79; FV 5.89 + 0.73\n", + "Amione: MV 5.85 ± 0.96; FV 5.91 + 1.25\n", + "Ruggeri: MV 6.07 ± 0.68; FV 6.07 + 0.70\n", + "Zanoli: MV 6.08 ± 0.85; FV 6.34 + 1.26\n", + "Wisniewski: MV 5.68 ± 0.77; FV 5.59 + 0.78\n", + "Radovanovic: MV 5.63 ± 0.80; FV 5.56 + 0.77\n", + "Dermaku: MV 5.95 ± 0.86; FV 5.97 + 0.94\n", + "D'ambrosio: MV 6.09 ± 0.75; FV 6.14 + 0.85\n", + "De Silvestri: MV 5.98 ± 1.01; FV 6.16 + 1.42\n", + "Chiriches: MV 5.68 ± 1.14; FV 5.64 + 1.22\n", + "Murru: MV 5.70 ± 0.76; FV 5.63 + 0.71\n", + "Bonifazi: MV 5.78 ± 0.83; FV 5.69 + 0.80\n", + "Donati: MV 5.78 ± 1.22; FV 5.99 + 1.71\n", + "Walukiewicz: MV 6.11 ± 0.70; FV 6.08 + 0.71\n", + "Ranieri L.: MV 5.92 ± 0.82; FV 5.97 + 1.04\n", + "Gabbia: MV 5.84 ± 0.77; FV 5.80 + 0.70\n", + "Kumbulla: MV 5.74 ± 1.09; FV 5.67 + 1.12\n", + "Adopo: MV 6.10 ± 0.66; FV 6.08 + 0.67\n", + "Pirola: MV 5.89 ± 1.10; FV 6.15 + 1.72\n", + "Lovato: MV 5.65 ± 1.02; FV 5.59 + 0.96\n", + "Tuia: MV 5.99 ± 0.60; FV 5.95 + 0.49\n", + "Ferrer: MV 5.80 ± 0.95; FV 5.80 + 1.16\n", + "Antov: MV 5.68 ± 1.06; FV 5.54 + 1.00\n", + "Vasquez: MV 5.74 ± 0.96; FV 5.69 + 1.09\n", + "Ruan: MV 5.69 ± 1.07; FV 5.55 + 1.02\n", + "Ostigard: MV 6.08 ± 0.75; FV 6.10 + 0.82\n", + "Coppola D.: MV 5.74 ± 0.78; FV 5.64 + 0.82\n", + "Cacace: MV 5.82 ± 0.64; FV 5.82 + 0.51\n", + "Gatti: MV 6.10 ± 0.80; FV 6.06 + 0.82\n", + "Gila: MV 6.02 ± 0.89; FV 5.96 + 0.87\n", + "Bayeye: MV 6.08 ± 0.82; FV 6.17 + 0.97\n", + "Sambia: MV 5.83 ± 0.81; FV 5.84 + 0.78\n", + "Moutinho J.: MV 5.79 ± 0.87; FV 5.76 + 1.03\n", + "Conti: MV 5.93 ± 0.83; FV 6.07 + 1.13\n", + "Marrone: MV 5.64 ± 0.88; FV 5.53 + 0.89\n", + "Tonelli: MV 5.77 ± 0.83; FV 5.71 + 0.75\n", + "Murillo: MV 5.68 ± 0.79; FV 5.60 + 0.72\n", + "Radu: MV 6.06 ± 0.78; FV 5.83 + 0.69\n", + "Paletta: MV 5.91 ± 0.96; FV 6.00 + 1.23\n", + "Florenzi: MV 6.16 ± 0.73; FV 6.42 + 1.19\n", + "Sala: MV 5.85 ± 0.80; FV 5.90 + 0.97\n", + "Fares: MV 5.80 ± 0.76; FV 5.76 + 0.72\n", + "Romagna: MV 5.73 ± 0.96; FV 5.72 + 1.08\n", + "Cassandro: MV 5.93 ± 0.82; FV 5.94 + 0.87\n", + "Muldur: MV 5.60 ± 0.86; FV 5.56 + 0.87\n", + "Amey: MV 6.03 ± 0.89; FV 6.09 + 1.00\n", + "Zanotti: MV 6.00 ± 0.84; FV 6.11 + 1.01\n", + "Ebosele: MV 5.86 ± 0.70; FV 5.87 + 0.72\n", + "Buta: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", + "Abankwah: MV 5.74 ± 1.01; FV 5.70 + 1.16\n", + "Guessand A.: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", + "Cabal: MV 5.84 ± 0.67; FV 5.76 + 0.62\n", + "Sosa: MV 5.67 ± 0.86; FV 5.59 + 0.89\n", + "Guarino: MV 5.97 ± 0.88; FV 6.01 + 0.94\n", + "Carboni F.: MV 5.92 ± 0.90; FV 5.97 + 1.05\n", + "Zaccagni: MV 6.47 ± 1.27; FV 7.42 + 3.02\n", + "Kvaratskhelia: MV 6.49 ± 1.41; FV 7.50 + 3.35\n", + "Milinkovic-Savic: MV 6.23 ± 1.22; FV 7.11 + 2.90\n", + "Barella: MV 6.29 ± 1.16; FV 7.02 + 2.42\n", + "Zielinski: MV 6.24 ± 1.04; FV 6.74 + 1.84\n", + "Luis Alberto: MV 6.31 ± 1.01; FV 7.00 + 2.17\n", + "Strefezza: MV 6.20 ± 1.01; FV 6.77 + 1.92\n", + "Felipe Anderson: MV 6.29 ± 1.15; FV 7.10 + 2.62\n", + "Koopmeiners: MV 6.38 ± 1.16; FV 7.19 + 2.55\n", + "Calhanoglu: MV 6.31 ± 0.98; FV 6.91 + 1.98\n", + "Frattesi: MV 6.04 ± 1.06; FV 6.48 + 1.89\n", + "Diaz B.: MV 6.31 ± 1.31; FV 7.21 + 2.92\n", + "Vlasic: MV 6.30 ± 1.07; FV 6.93 + 2.11\n", + "Zambo Anguissa: MV 6.21 ± 0.98; FV 6.61 + 1.63\n", + "Elmas: MV 6.16 ± 1.00; FV 6.73 + 1.91\n", + "Miranchuk: MV 6.39 ± 1.16; FV 7.14 + 2.40\n", + "Samardzic: MV 6.04 ± 1.08; FV 6.46 + 1.90\n", + "Pereyra: MV 5.95 ± 1.20; FV 6.35 + 2.07\n", + "Politano: MV 6.18 ± 0.82; FV 6.61 + 1.51\n", + "Rabiot: MV 6.32 ± 1.23; FV 7.16 + 2.71\n", + "Ciurria: MV 6.06 ± 1.08; FV 6.51 + 1.94\n", + "Lazovic: MV 6.14 ± 1.01; FV 6.63 + 1.83\n", + "Lobotka: MV 6.18 ± 0.81; FV 6.45 + 1.26\n", + "Radonjic: MV 6.26 ± 0.96; FV 6.80 + 1.81\n", + "Ferguson: MV 6.13 ± 0.94; FV 6.46 + 1.47\n", + "Bonaventura: MV 6.20 ± 1.00; FV 6.71 + 1.83\n", + "Pessina: MV 6.09 ± 1.05; FV 6.44 + 1.70\n", + "Tonali: MV 6.29 ± 1.14; FV 6.93 + 2.19\n", + "Kostic: MV 6.24 ± 1.06; FV 6.84 + 2.01\n", + "Baldanzi: MV 6.20 ± 1.03; FV 6.80 + 2.01\n", + "Lovric: MV 6.06 ± 0.94; FV 6.40 + 1.53\n", + "Pellegrini Lo.: MV 6.09 ± 1.17; FV 6.68 + 2.34\n", + "El Shaarawy: MV 6.20 ± 0.88; FV 6.78 + 1.84\n", + "Orsolini: MV 6.15 ± 1.32; FV 7.02 + 3.02\n", + "Ikone': MV 6.02 ± 1.07; FV 6.48 + 1.91\n", + "Candreva: MV 6.15 ± 1.07; FV 6.67 + 2.01\n", + "Bennacer: MV 6.16 ± 0.75; FV 6.48 + 1.28\n", + "Pasalic: MV 6.25 ± 1.22; FV 7.05 + 2.69\n", + "Mkhitaryan: MV 6.12 ± 1.00; FV 6.63 + 1.80\n", + "Colpani: MV 6.01 ± 0.81; FV 6.37 + 1.41\n", + "Pogba: MV 6.14 ± 0.82; FV 6.23 + 1.00\n", + "Chiesa: MV 6.10 ± 0.82; FV 6.34 + 1.15\n", + "Bandinelli: MV 5.99 ± 0.80; FV 6.15 + 1.05\n", + "Matic: MV 6.16 ± 0.82; FV 6.48 + 1.35\n", + "Fagioli: MV 6.21 ± 1.05; FV 6.71 + 1.83\n", + "Messias: MV 6.13 ± 1.14; FV 6.84 + 2.39\n", + "Arslan: MV 5.86 ± 0.77; FV 5.94 + 0.94\n", + "Ricci S.: MV 6.19 ± 0.82; FV 6.46 + 1.29\n", + "Ranocchia F.: MV 6.07 ± 0.83; FV 6.39 + 1.34\n", + "Verdi: MV 6.18 ± 1.21; FV 6.73 + 2.29\n", + "Sensi: MV 6.03 ± 1.08; FV 6.39 + 1.83\n", + "Barak: MV 5.94 ± 0.96; FV 6.26 + 1.59\n", + "Soriano: MV 6.06 ± 0.88; FV 6.36 + 1.40\n", + "Dominguez: MV 6.14 ± 0.98; FV 6.50 + 1.59\n", + "Vilhena: MV 5.93 ± 0.97; FV 6.22 + 1.58\n", + "Brozovic: MV 6.13 ± 0.86; FV 6.44 + 1.34\n", + "Cristante: MV 5.99 ± 0.87; FV 6.11 + 1.16\n", + "Thorstvedt: MV 5.85 ± 0.84; FV 6.01 + 1.13\n", + "De Ketelaere: MV 5.84 ± 0.73; FV 5.96 + 0.79\n", + "Saponara: MV 6.12 ± 1.13; FV 6.67 + 2.09\n", + "Vecino: MV 6.06 ± 0.87; FV 6.26 + 1.21\n", + "Locatelli: MV 6.11 ± 0.81; FV 6.21 + 0.97\n", + "Zaniolo: MV 5.94 ± 1.02; FV 6.20 + 1.64\n", + "Duda: MV 5.81 ± 0.69; FV 5.80 + 0.78\n", + "Maldini: MV 5.95 ± 0.91; FV 6.22 + 1.46\n", + "Marin: MV 6.01 ± 0.94; FV 6.21 + 1.30\n", + "Zalewski: MV 6.04 ± 0.77; FV 6.21 + 1.04\n", + "Bajrami: MV 5.95 ± 1.00; FV 6.30 + 1.63\n", + "Coulibaly L.: MV 5.97 ± 1.04; FV 6.29 + 1.73\n", + "Gonzalez J.: MV 5.93 ± 0.80; FV 6.04 + 1.00\n", + "De Roon: MV 6.19 ± 0.87; FV 6.59 + 1.55\n", + "Mandragora: MV 6.04 ± 0.98; FV 6.35 + 1.59\n", + "Wijnaldum: MV 6.19 ± 1.10; FV 6.85 + 2.29\n", + "Bourabia: MV 5.85 ± 0.79; FV 5.87 + 0.95\n", + "Sottil: MV 6.11 ± 1.10; FV 6.57 + 1.92\n", + "Aebischer: MV 5.95 ± 0.83; FV 6.13 + 1.19\n", + "Ederson D.s.: MV 6.02 ± 0.79; FV 6.23 + 1.10\n", + "Miretti: MV 6.02 ± 0.71; FV 6.15 + 0.80\n", + "Blin: MV 5.90 ± 0.74; FV 5.96 + 0.83\n", + "Hjulmand: MV 5.87 ± 0.90; FV 5.83 + 0.90\n", + "Cataldi: MV 6.03 ± 0.74; FV 5.97 + 0.68\n", + "Djuricic: MV 5.85 ± 0.94; FV 6.01 + 1.35\n", + "Linetty: MV 6.06 ± 0.82; FV 6.25 + 1.10\n", + "Haas: MV 5.96 ± 0.77; FV 6.11 + 0.99\n", + "Walace: MV 5.85 ± 0.92; FV 5.85 + 1.07\n", + "Agudelo: MV 5.84 ± 0.75; FV 5.92 + 0.96\n", + "Pobega: MV 6.11 ± 0.88; FV 6.54 + 1.51\n", + "Camara Ma.: MV 6.09 ± 0.87; FV 6.18 + 1.02\n", + "Paredes: MV 5.92 ± 0.69; FV 5.85 + 0.58\n", + "Ndombele': MV 5.98 ± 0.76; FV 6.14 + 0.97\n", + "Nicolussi Caviglia: MV 5.90 ± 1.05; FV 6.16 + 1.68\n", + "Rovella: MV 5.97 ± 1.08; FV 6.14 + 1.36\n", + "Amrabat: MV 5.98 ± 0.90; FV 6.06 + 1.11\n", + "Tameze: MV 5.86 ± 0.78; FV 5.83 + 0.87\n", + "Gyasi: MV 5.78 ± 0.91; FV 5.89 + 1.24\n", + "Ilic: MV 6.18 ± 0.86; FV 6.51 + 1.39\n", + "Matheus Henrique: MV 5.81 ± 0.91; FV 5.94 + 1.22\n", + "Harroui: MV 5.89 ± 0.87; FV 6.10 + 1.24\n", + "Volpato: MV 6.02 ± 1.14; FV 6.66 + 2.34\n", + "Pickel: MV 5.77 ± 0.82; FV 5.78 + 0.99\n", + "Moro N.: MV 6.11 ± 0.93; FV 6.43 + 1.43\n", + "Duncan: MV 5.94 ± 0.82; FV 6.12 + 1.23\n", + "Machin: MV 5.94 ± 0.95; FV 6.05 + 1.27\n", + "Cuadrado: MV 6.06 ± 0.92; FV 6.19 + 1.09\n", + "Ekdal: MV 5.82 ± 0.78; FV 5.82 + 0.91\n", + "Meite': MV 5.85 ± 0.94; FV 5.87 + 1.21\n", + "Schouten: MV 5.98 ± 0.85; FV 5.99 + 0.89\n", + "Obiang: MV 5.78 ± 0.64; FV 5.75 + 0.57\n", + "Kovalenko: MV 5.85 ± 0.79; FV 5.92 + 1.01\n", + "Crnigoj: MV 5.95 ± 0.79; FV 6.11 + 1.04\n", + "Basic: MV 6.04 ± 0.63; FV 6.03 + 0.61\n", + "Asllani: MV 6.04 ± 0.66; FV 6.08 + 0.68\n", + "Sabiri: MV 5.91 ± 0.92; FV 6.08 + 1.35\n", + "Terracciano F.: MV 5.97 ± 0.70; FV 6.04 + 0.79\n", + "Castagnetti: MV 5.88 ± 0.79; FV 5.89 + 0.90\n", + "Oudin: MV 5.87 ± 0.62; FV 5.90 + 0.52\n", + "Grassi: MV 5.93 ± 0.66; FV 5.91 + 0.55\n", + "Krunic: MV 6.00 ± 0.70; FV 6.06 + 0.75\n", + "Rincon: MV 5.79 ± 0.76; FV 5.73 + 0.77\n", + "Miguel Veloso: MV 5.87 ± 0.73; FV 5.85 + 0.78\n", + "Leris: MV 5.87 ± 0.88; FV 5.98 + 1.24\n", + "Esposito Sa.: MV 5.74 ± 0.87; FV 5.63 + 0.91\n", + "Henderson L.: MV 5.94 ± 0.76; FV 6.04 + 0.89\n", + "Lopez M.: MV 5.80 ± 0.84; FV 5.75 + 0.89\n", + "Cuisance: MV 5.82 ± 0.63; FV 5.82 + 0.57\n", + "Saelemaekers: MV 5.98 ± 0.76; FV 6.24 + 1.05\n", + "Maggiore: MV 5.95 ± 0.77; FV 6.08 + 0.96\n", + "Akpa Akpro: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", + "Maleh: MV 5.87 ± 0.67; FV 5.94 + 0.74\n", + "Romero L.: MV 6.12 ± 0.98; FV 6.80 + 2.21\n", + "Ceide: MV 5.78 ± 0.66; FV 5.81 + 0.61\n", + "D'alessandro: MV 6.05 ± 0.65; FV 6.12 + 0.73\n", + "Benassi: MV 5.83 ± 0.80; FV 5.94 + 1.05\n", + "Gagliardini: MV 5.86 ± 0.64; FV 5.89 + 0.62\n", + "Vieira: MV 5.92 ± 0.66; FV 5.96 + 0.70\n", + "Bianco: MV 6.10 ± 0.80; FV 6.26 + 1.07\n", + "Vranckx: MV 5.85 ± 0.67; FV 5.90 + 0.63\n", + "Galdames: MV 5.87 ± 1.06; FV 5.98 + 1.47\n", + "Marcos Antonio: MV 6.11 ± 0.84; FV 6.73 + 1.90\n", + "Fazzini: MV 5.82 ± 0.64; FV 5.80 + 0.55\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sulemana I.: MV 5.83 ± 0.65; FV 5.79 + 0.65\n", + "Tahirovic: MV 6.03 ± 0.65; FV 6.02 + 0.65\n", + "Abildgaard: MV 5.89 ± 0.61; FV 5.88 + 0.55\n", + "Barberis: MV 5.76 ± 0.84; FV 5.74 + 0.90\n", + "Kastanos: MV 5.97 ± 0.86; FV 6.16 + 1.18\n", + "Vignato: MV 6.01 ± 0.76; FV 6.13 + 0.86\n", + "Valoti: MV 5.84 ± 0.68; FV 5.83 + 0.70\n", + "Winks: MV 5.92 ± 0.78; FV 5.91 + 0.71\n", + "Askildsen: MV 5.73 ± 0.62; FV 5.71 + 0.53\n", + "Bove: MV 5.98 ± 0.86; FV 6.25 + 1.38\n", + "Bohinen: MV 5.82 ± 0.61; FV 5.84 + 0.48\n", + "D'andrea: MV 5.89 ± 0.72; FV 5.98 + 0.82\n", + "Iling-Junior: MV 6.01 ± 0.64; FV 6.04 + 0.63\n", + "Cipot: MV 5.91 ± 0.80; FV 5.98 + 1.00\n", + "Bakayoko: MV 5.86 ± 0.72; FV 5.83 + 0.63\n", + "Gaetano: MV 6.15 ± 0.74; FV 6.35 + 1.10\n", + "Zurkowski: MV 5.97 ± 0.96; FV 6.25 + 1.55\n", + "Castrovilli: MV 6.00 ± 0.80; FV 6.25 + 1.25\n", + "Demme: MV 5.90 ± 0.80; FV 5.99 + 1.00\n", + "Darboe: MV 5.91 ± 0.93; FV 5.93 + 1.02\n", + "Urbanski: MV 5.99 ± 0.90; FV 6.05 + 1.04\n", + "Bertini: MV 5.99 ± 0.86; FV 6.02 + 0.91\n", + "Yepes: MV 5.75 ± 0.89; FV 5.67 + 0.92\n", + "Pyyhtia: MV 5.87 ± 0.82; FV 5.83 + 0.84\n", + "Trimboli: MV 5.97 ± 0.88; FV 6.05 + 1.04\n", + "Pafundi: MV 5.96 ± 0.74; FV 5.88 + 0.65\n", + "Helgason: MV 5.85 ± 0.62; FV 5.85 + 0.51\n", + "Adli: MV 5.94 ± 0.69; FV 6.03 + 0.75\n", + "Vignato S.: MV 5.96 ± 0.91; FV 6.03 + 1.10\n", + "Hrustic: MV 5.67 ± 0.69; FV 5.64 + 0.71\n", + "Samek: MV 5.92 ± 0.85; FV 5.96 + 0.95\n", + "Zerbin: MV 5.82 ± 0.67; FV 5.84 + 0.74\n", + "Ilkhan: MV 5.93 ± 0.78; FV 5.95 + 0.78\n", + "Degli Innocenti: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", + "Acella: MV 5.90 ± 1.05; FV 6.03 + 1.47\n", + "Carboni V.: MV 6.05 ± 0.77; FV 6.14 + 0.87\n", + "Paoletti: MV 5.92 ± 0.60; FV 5.91 + 0.45\n", + "Malagrida: MV 6.04 ± 0.96; FV 6.26 + 1.32\n", + "Faticanti: MV 5.94 ± 0.92; FV 6.02 + 1.15\n", + "Osimhen: MV 6.38 ± 1.48; FV 7.89 + 4.50\n", + "Martinez L.: MV 6.23 ± 1.44; FV 7.69 + 4.20\n", + "Dybala: MV 6.47 ± 1.37; FV 7.51 + 3.43\n", + "Rafael Leao: MV 6.44 ± 1.45; FV 7.65 + 3.92\n", + "Lookman: MV 6.46 ± 1.42; FV 7.65 + 3.83\n", + "Immobile: MV 6.30 ± 1.40; FV 7.57 + 3.90\n", + "Vlahovic: MV 6.16 ± 1.34; FV 7.32 + 3.51\n", + "Arnautovic: MV 6.16 ± 1.33; FV 7.23 + 3.36\n", + "Dia: MV 6.19 ± 1.33; FV 7.29 + 3.44\n", + "Dzeko: MV 6.16 ± 1.34; FV 7.25 + 3.36\n", + "Milik: MV 6.25 ± 1.20; FV 7.07 + 2.67\n", + "Nzola: MV 6.04 ± 1.35; FV 7.05 + 3.21\n", + "Beto: MV 5.86 ± 1.09; FV 6.38 + 2.02\n", + "Giroud: MV 6.27 ± 1.35; FV 7.38 + 3.54\n", + "Abraham: MV 6.14 ± 1.22; FV 7.03 + 2.90\n", + "Deulofeu: MV 6.15 ± 1.21; FV 6.84 + 2.47\n", + "Lauriente': MV 6.17 ± 1.34; FV 7.04 + 3.07\n", + "Simeone: MV 6.12 ± 1.32; FV 7.11 + 3.12\n", + "Lozano: MV 6.03 ± 1.04; FV 6.59 + 2.04\n", + "Correa: MV 6.05 ± 1.08; FV 6.68 + 2.23\n", + "Berardi: MV 6.17 ± 1.38; FV 7.30 + 3.51\n", + "Pedro: MV 6.20 ± 1.05; FV 6.86 + 2.24\n", + "Lukaku: MV 6.12 ± 1.34; FV 7.15 + 3.26\n", + "Sanabria: MV 6.24 ± 1.28; FV 7.22 + 3.15\n", + "Thauvin: MV 6.07 ± 1.10; FV 6.54 + 1.95\n", + "Cabral: MV 6.15 ± 1.26; FV 7.08 + 2.93\n", + "Hojlund: MV 6.25 ± 1.36; FV 7.39 + 3.56\n", + "Caprari: MV 5.97 ± 0.95; FV 6.29 + 1.53\n", + "Di Maria: MV 6.30 ± 1.26; FV 7.09 + 2.67\n", + "Piatek: MV 5.85 ± 0.99; FV 6.21 + 1.66\n", + "Rebic: MV 6.22 ± 1.36; FV 7.14 + 3.19\n", + "Bonazzoli: MV 6.01 ± 0.96; FV 6.41 + 1.71\n", + "Zapata D.: MV 6.09 ± 0.95; FV 6.49 + 1.60\n", + "Kouame': MV 6.09 ± 1.24; FV 6.84 + 2.60\n", + "Gonzalez N.: MV 6.21 ± 1.27; FV 7.05 + 2.81\n", + "Brekalo: MV 6.14 ± 1.18; FV 6.82 + 2.39\n", + "Mota: MV 5.99 ± 1.16; FV 6.55 + 2.23\n", + "Kean: MV 6.13 ± 1.30; FV 7.05 + 3.06\n", + "Okereke: MV 5.85 ± 1.05; FV 6.24 + 1.79\n", + "Ceesay: MV 5.89 ± 0.95; FV 6.21 + 1.52\n", + "Colombo: MV 5.87 ± 0.99; FV 6.24 + 1.68\n", + "Dessers: MV 5.95 ± 1.16; FV 6.53 + 2.25\n", + "Muriel: MV 6.13 ± 1.05; FV 6.51 + 1.72\n", + "Pinamonti: MV 5.77 ± 0.96; FV 6.11 + 1.58\n", + "Di Francesco F.: MV 5.92 ± 0.91; FV 6.13 + 1.31\n", + "Jovic: MV 5.97 ± 1.14; FV 6.47 + 2.12\n", + "Origi: MV 5.97 ± 1.06; FV 6.40 + 1.91\n", + "Caputo: MV 5.99 ± 1.13; FV 6.62 + 2.31\n", + "Boga: MV 6.42 ± 1.23; FV 7.25 + 2.65\n", + "Cambiaghi: MV 6.14 ± 1.04; FV 6.73 + 2.07\n", + "Alvarez A.: MV 5.88 ± 1.01; FV 6.22 + 1.64\n", + "Banda: MV 5.91 ± 0.74; FV 6.02 + 0.86\n", + "Ciofani D.: MV 6.01 ± 1.12; FV 6.48 + 2.05\n", + "Petagna: MV 5.96 ± 1.01; FV 6.32 + 1.68\n", + "Barrow: MV 5.98 ± 1.17; FV 6.42 + 2.08\n", + "Djuric: MV 5.93 ± 0.71; FV 6.06 + 0.87\n", + "Henry: MV 5.89 ± 1.06; FV 6.30 + 1.84\n", + "Success: MV 5.89 ± 0.92; FV 6.11 + 1.36\n", + "Gabbiadini: MV 5.94 ± 1.09; FV 6.43 + 2.01\n", + "Zirkzee: MV 6.01 ± 1.08; FV 6.37 + 1.77\n", + "Lammers: MV 5.82 ± 0.87; FV 6.01 + 1.26\n", + "Satriano: MV 5.89 ± 0.92; FV 6.09 + 1.33\n", + "Kallon: MV 5.87 ± 0.82; FV 6.05 + 1.18\n", + "Nestorovski: MV 6.06 ± 0.72; FV 6.52 + 1.43\n", + "Raspadori: MV 6.01 ± 1.05; FV 6.54 + 2.00\n", + "Botheim: MV 5.93 ± 0.95; FV 6.32 + 1.68\n", + "Gytkjaer: MV 5.84 ± 0.84; FV 6.05 + 1.27\n", + "Solbakken: MV 5.92 ± 1.00; FV 6.36 + 1.83\n", + "Lasagna: MV 5.78 ± 0.78; FV 5.84 + 0.98\n", + "Belotti: MV 5.78 ± 0.78; FV 5.90 + 1.01\n", + "Pellegri: MV 5.99 ± 0.89; FV 6.30 + 1.44\n", + "Buonaiuto: MV 5.91 ± 0.84; FV 6.10 + 1.17\n", + "Verde: MV 5.92 ± 1.09; FV 6.29 + 1.85\n", + "Destro: MV 6.00 ± 1.20; FV 6.61 + 2.37\n", + "Seck: MV 6.13 ± 0.66; FV 6.25 + 0.89\n", + "Sansone: MV 6.11 ± 1.16; FV 6.63 + 2.15\n", + "Quagliarella: MV 5.86 ± 0.78; FV 6.01 + 1.08\n", + "Defrel: MV 5.77 ± 0.87; FV 5.93 + 1.21\n", + "Pjaca: MV 5.92 ± 0.81; FV 6.04 + 0.93\n", + "Gaich: MV 5.87 ± 0.91; FV 6.08 + 1.39\n", + "Soule': MV 6.18 ± 0.80; FV 6.64 + 1.52\n", + "Tsadjout: MV 5.88 ± 0.98; FV 6.26 + 1.67\n", + "Piccoli: MV 5.81 ± 0.74; FV 5.86 + 0.75\n", + "Shomurodov: MV 5.88 ± 0.97; FV 6.23 + 1.63\n", + "Afena-Gyan: MV 5.69 ± 0.74; FV 5.72 + 0.87\n", + "Ngonge: MV 6.10 ± 1.16; FV 6.59 + 2.12\n", + "Karamoh: MV 6.22 ± 1.06; FV 6.92 + 2.30\n", + "Ibrahimovic: MV 6.37 ± 1.33; FV 7.38 + 3.18\n", + "Pussetto: MV 5.94 ± 1.06; FV 6.42 + 1.99\n", + "Cancellieri: MV 5.83 ± 0.62; FV 5.83 + 0.50\n", + "Valencia D.: MV 5.70 ± 0.60; FV 5.65 + 0.63\n", + "Oddei: MV 5.94 ± 0.86; FV 6.08 + 1.11\n", + "Braaf: MV 5.80 ± 0.88; FV 5.87 + 1.17\n", + "Raimondo: MV 6.00 ± 0.93; FV 6.08 + 1.07\n", + "Kaio Jorge: MV 5.95 ± 0.59; FV 5.98 + 0.50\n", + "De Luca: MV 5.97 ± 0.82; FV 6.06 + 0.93\n", + "Voelkerling Persson: MV 5.89 ± 0.65; FV 5.96 + 0.64\n", + "Montevago: MV 5.65 ± 0.64; FV 5.63 + 0.62\n", + "Krollis: MV 5.86 ± 0.95; FV 5.94 + 1.30\n", + "Vivaldo: MV 5.82 ± 1.04; FV 5.90 + 1.39\n" + ] + }, + { + "data": { + "text/html": [ + "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaSpezia11.00756.1784020.4280255.7161080.5468025.9284000.2635850.6092681.5888116.2837800.623820-0.6253401.07269240.422094
MussoPAtalantaSpezia10.0056.2468360.4124635.4185290.4188306.0055360.2527780.6122031.5899515.8132680.512082-0.5411221.03104124.998909
Rossi F.PAtalantaSpezia10.0016.2419590.4139164.5331270.5269115.9980540.2497280.6230621.5898425.0662490.630503-0.5907080.9720400.773813
ScalviniDAtalantaSpezia11.00906.2733230.5370456.7866010.9808436.1571860.5984840.1425540.9371845.9679681.2064330.4635731.5998480.000000
ToloiDAtalantaSpezia11.00906.2540100.5106116.7197040.9057506.1626700.5754460.1167880.9588885.9783641.1343930.4489291.5998580.000000
............................................................
GaichAVeronaInter10.55555.8674530.4574206.0846430.6927825.7927380.5192260.1061631.0451695.5228090.8747290.4423011.5998540.000000
DjuricAVeronaInter10.45605.9272610.3529756.0585050.4371225.8834220.4082260.0795311.1544085.8030530.6628720.2787511.5999180.000000
KallonAVeronaInter10.00405.8745990.4109656.0492300.5923005.8060450.4666890.1085031.0951995.5937080.7801420.4070941.5998840.000000
BraafAVeronaInter10.00355.7962740.4400125.8698300.5854895.7579440.5119310.0553471.0603285.5059670.8671160.3018241.5998890.000000
LasagnaAVeronaInter11.00805.7778340.3893635.8360370.4891945.7277980.4488260.0824881.1236435.5066600.6985200.3360681.5999010.000000
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525 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Sportiello P Atalanta Spezia 1 1.00 75 6.178402 0.428025 \n", + "Musso P Atalanta Spezia 1 0.00 5 6.246836 0.412463 \n", + "Rossi F. P Atalanta Spezia 1 0.00 1 6.241959 0.413916 \n", + "Scalvini D Atalanta Spezia 1 1.00 90 6.273323 0.537045 \n", + "Toloi D Atalanta Spezia 1 1.00 90 6.254010 0.510611 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Inter 1 0.55 55 5.867453 0.457420 \n", + "Djuric A Verona Inter 1 0.45 60 5.927261 0.352975 \n", + "Kallon A Verona Inter 1 0.00 40 5.874599 0.410965 \n", + "Braaf A Verona Inter 1 0.00 35 5.796274 0.440012 \n", + "Lasagna A Verona Inter 1 1.00 80 5.777834 0.389363 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Sportiello 5.716108 0.546802 5.928400 0.263585 0.609268 \n", + "Musso 5.418529 0.418830 6.005536 0.252778 0.612203 \n", + "Rossi F. 4.533127 0.526911 5.998054 0.249728 0.623062 \n", + "Scalvini 6.786601 0.980843 6.157186 0.598484 0.142554 \n", + "Toloi 6.719704 0.905750 6.162670 0.575446 0.116788 \n", + "... ... ... ... ... ... \n", + "Gaich 6.084643 0.692782 5.792738 0.519226 0.106163 \n", + "Djuric 6.058505 0.437122 5.883422 0.408226 0.079531 \n", + "Kallon 6.049230 0.592300 5.806045 0.466689 0.108503 \n", + "Braaf 5.869830 0.585489 5.757944 0.511931 0.055347 \n", + "Lasagna 5.836037 0.489194 5.727798 0.448826 0.082488 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Sportiello 1.588811 6.283780 0.623820 -0.625340 1.072692 \n", + "Musso 1.589951 5.813268 0.512082 -0.541122 1.031041 \n", + "Rossi F. 1.589842 5.066249 0.630503 -0.590708 0.972040 \n", + "Scalvini 0.937184 5.967968 1.206433 0.463573 1.599848 \n", + "Toloi 0.958888 5.978364 1.134393 0.448929 1.599858 \n", + "... ... ... ... ... ... \n", + "Gaich 1.045169 5.522809 0.874729 0.442301 1.599854 \n", + "Djuric 1.154408 5.803053 0.662872 0.278751 1.599918 \n", + "Kallon 1.095199 5.593708 0.780142 0.407094 1.599884 \n", + "Braaf 1.060328 5.505967 0.867116 0.301824 1.599889 \n", + "Lasagna 1.123643 5.506660 0.698520 0.336068 1.599901 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Sportiello 40.422094 \n", + "Musso 24.998909 \n", + "Rossi F. 0.773813 \n", + "Scalvini 0.000000 \n", + "Toloi 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Braaf 0.000000 \n", + "Lasagna 0.000000 \n", + "\n", + "[525 rows x 19 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matchday_out = 33\n", + "\n", + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " try:\n", + " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + " \n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", + " \n", + " role = players['r'][player] \n", + " \n", + " starter = 0\n", + " voteperc = 0\n", + " \n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + " \n", + " if(player in probables.index):\n", + " starter = probables['starter'][player]\n", + " voteperc = probables['percentage'][player]\n", + " \n", + " row = [player, role, team, oppteam, home, \n", + " starter, voteperc, \n", + " mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs]\n", + " \n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + " \n", + " output = pd.concat([output, row_df])\n", + " \n", + " except:\n", + " print(players.index[i] + ' no data')\n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6befd611", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "eed7a7ac", + "metadata": {}, + "source": [ + "Predict average Serie A performance for each player" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b637a15", + "metadata": {}, + "outputs": [], + "source": [ + "gk_starters = ['Maignan', 'Ochoa', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", + " 'Terracciano', 'Onana', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Carnesecchi', 'Rui Patricio',\n", + " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60d73507", + "metadata": {}, + "outputs": [], + "source": [ + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "tot_matches = 2 # home and not home\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " try:\n", + " home = 0\n", + "\n", + " for k in range(tot_matches):\n", + " #matchday_out = k + 1\n", + " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + "\n", + " player = players.index[i]\n", + " team = players['team'][i]\n", + " oppteam = 'Avg'\n", + " home = not home\n", + "\n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", + "\n", + " role = players['r'][player] \n", + "\n", + " starter = 0\n", + " voteperc = 0\n", + "\n", + " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", + " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", + "\n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + "\n", + " starter = int( player in gk_starters )\n", + " if(starter):\n", + " voteperc = 100\n", + " else:\n", + " voteperc = 0\n", + " else:\n", + " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", + " voteperc = int( min( 1, games / current_season_games ) * 100) \n", + "\n", + " if(k == 0):\n", + " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", + "\n", + " numrow_ = [mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs] \n", + "\n", + " if(k == 0):\n", + " numrow = numrow_\n", + " else:\n", + " for j in range(len(numrow)):\n", + " numrow[j] += numrow_[j]\n", + "\n", + " for j in range(len(numrow)):\n", + " numrow[j] /= tot_matches\n", + "\n", + " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", + " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", + "\n", + " row += numrow # list concat\n", + "\n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + "\n", + " output = pd.concat([output, row_df])\n", + " except:\n", + " print(players.index[i] + ' no data')\n", + " \n", + " \n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b47cbd63", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "cf9df3bb", + "metadata": {}, + "source": [ + "Various predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7300f3c2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret: MV 6.08 ± 0.61; FV 5.48 + 1.00 (64.3% cs)\n", + "Szczesny: MV 6.12 ± 0.63; FV 5.79 + 0.88 (87.3% cs)\n", + "Provedel: MV 6.18 ± 0.65; FV 5.49 + 1.00 (55.3% cs)\n", + "Maignan: MV 6.50 ± 0.78; FV 5.78 + 0.88 (47.3% cs)\n", + "Rui Patricio: MV 6.36 ± 0.72; FV 6.14 + 0.74 (82.2% cs)\n", + "Onana: MV 6.11 ± 0.62; FV 5.88 + 0.85 (82.9% cs)\n", + "Milinkovic-Savic V.: MV 6.09 ± 0.62; FV 4.87 + 1.25 (6.9% cs)\n", + "Musso: MV 6.56 ± 0.81; FV 6.04 + 0.79 (72.9% cs)\n", + "Vicario: MV 6.68 ± 0.87; FV 5.75 + 0.88 (37.2% cs)\n", + "Silvestri: MV 6.11 ± 1.17; FV 4.85 + 0.64 (13.9% cs)\n", + "Terracciano: MV 6.12 ± 0.63; FV 5.38 + 1.01 (29.3% cs)\n", + "Skorupski: MV 6.27 ± 0.68; FV 5.46 + 1.00 (47.3% cs)\n", + "Falcone: MV 6.42 ± 0.75; FV 5.29 + 1.02 (2.8% cs)\n", + "Di Gregorio: MV 6.39 ± 0.75; FV 5.73 + 0.89 (61.6% cs)\n", + "Consigli: MV 6.16 ± 0.64; FV 5.22 + 1.11 (45.3% cs)\n", + "Carnesecchi: MV 6.27 ± 0.77; FV 5.12 + 0.93 (7.8% cs)\n", + "Montipo': MV 6.47 ± 0.77; FV 5.20 + 1.05 (6.4% cs)\n", + "Audero: MV 6.48 ± 0.77; FV 5.26 + 0.95 (7.2% cs)\n", + "Dragowski: MV 6.52 ± 0.80; FV 6.02 + 0.79 (76.4% cs)\n", + "Ochoa: MV 6.46 ± 0.78; FV 5.69 + 0.79 (23.2% cs)\n", + "Tatarusanu: MV 6.04 ± 0.69; FV 4.11 + 1.16 (3.0% cs)\n", + "Handanovic: MV 6.41 ± 0.74; FV 6.19 + 0.74 (92.1% cs)\n", + "Sportiello: MV 6.39 ± 0.75; FV 5.18 + 0.93 (15.2% cs)\n", + "Sepe: MV 6.21 ± 0.71; FV 4.87 + 1.05 (4.9% cs)\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.21465971, 4.86660706]),\n", + " array([0.35387683, 0.5225544 ], dtype=float32),\n", + " [,\n", + " ,\n", + " ]]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Meret', log = 1, plot = 0)\n", + "predict_player('Szczesny', log = 1, plot = 0)\n", + "predict_player('Provedel', log = 1, plot = 0)\n", + "predict_player('Maignan', log = 1, plot = 0)\n", + "predict_player('Rui Patricio', log = 1, plot = 0)\n", + "predict_player('Onana', log = 1, plot = 0)\n", + "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", + "predict_player('Musso', log = 1, plot = 0)\n", + "predict_player('Vicario', log = 1, plot = 0)\n", + "predict_player('Silvestri', log = 1, plot = 0)\n", + "predict_player('Terracciano', log = 1, plot = 0)\n", + "predict_player('Skorupski', log = 1, plot = 0)\n", + "predict_player('Falcone', log = 1, plot = 0)\n", + "predict_player('Di Gregorio', log = 1, plot = 0)\n", + "predict_player('Consigli', log = 1, plot = 0)\n", + "predict_player('Carnesecchi', log = 1, plot = 0)\n", + "predict_player('Montipo\\'', log = 1, plot = 0)\n", + "predict_player('Audero', log = 1, plot = 0)\n", + "predict_player('Dragowski', log = 1, plot = 0)\n", + "predict_player('Ochoa', log = 1, plot = 0)\n", + "predict_player('Tatarusanu', log = 1, plot = 0)\n", + "predict_player('Handanovic', log = 1, plot = 0)\n", + "predict_player('Sportiello', log = 1, plot = 0)\n", + "predict_player('Sepe', log = 1, plot = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd126870", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", + "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", + "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", + "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", + "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", + "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", + "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", + "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", + "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", + "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", + "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", + "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", + "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.41176047, 8.29456946]),\n", + " array([0.70926785, 2.7289915 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Muriel', plot = 0, log = 1)\n", + "predict_player('Tonali', plot = 0, log = 1)\n", + "predict_player('Lobotka', plot = 0, log = 1)\n", + "predict_player('Politano', plot = 0, log = 1)\n", + "predict_player('Zapata D.', plot = 0, log = 1)\n", + "predict_player('Frattesi', plot = 0, log = 1)\n", + "predict_player('Gonzalez N.', plot = 0, log = 1)\n", + "predict_player('Abraham', plot = 0, log = 1)\n", + "predict_player('Pobega', plot = 0, log = 1)\n", + "predict_player('Mario Rui', plot = 0, log = 1)\n", + "predict_player('Cuadrado', plot = 0, log = 1)\n", + "predict_player('Skriniar', plot = 0, log = 1)\n", + "predict_player('Lukaku', plot = 0, log = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4b9f5a7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skriniar: MV 6.14 ± 0.82; FV 6.38 + 1.13\n", + "Cuadrado: MV 6.29 ± 0.96; FV 6.83 + 1.94\n", + "Bastoni: MV 6.13 ± 0.73; FV 6.32 + 0.98\n", + "Barak: MV 6.31 ± 1.41; FV 7.50 + 3.97\n", + "Politano: MV 6.18 ± 0.82; FV 6.57 + 1.55\n", + "Smalling: MV 6.17 ± 0.86; FV 6.56 + 1.43\n", + "Gosens: MV 6.05 ± 0.54; FV 6.11 + 0.66\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.04807256, 6.10812885]),\n", + " array([0.27137518, 0.32888246], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Skriniar', log = 1, oldseason= True)\n", + "predict_player('Cuadrado', log = 1, oldseason= True)\n", + "predict_player('Bastoni', log = 1, oldseason= True)\n", + "predict_player('Barak', log = 1, oldseason= True)\n", + "predict_player('Politano', log = 1, oldseason= True)\n", + "predict_player('Smalling', log = 1, oldseason= True)\n", + "predict_player('Gosens', log = 1, oldseason= True)\n", + "\n", + "predict_player('Skriniar', log = 1, oldseason= False)\n", + "predict_player('Cuadrado', log = 1, oldseason= False)\n", + "predict_player('Bastoni', log = 1, oldseason= False)\n", + "predict_player('Barak', log = 1, oldseason= False)\n", + "predict_player('Politano', log = 1, oldseason= False)\n", + "predict_player('Smalling', log = 1, oldseason= False)\n", + "predict_player('Gosens', log = 1, oldseason= False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "10c7ad3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0XLx40dyH5caNGzhy5AgcHR3x0UcfVeq+fv31V3NScbdhw4bh22+/Rd++fdG7d2+MGzcOAQEByMjIQEJCAo4fP47169cDMH3h/Pe//8WMGTPQtWtXnDlzBrNmzUJoaCj0er35nGPGjMH8+fMxduxYXLp0Cc2bN8eBAwcwe/Zs9OvXDz169Cgz1qK+IGfPnrUY1VSSyMhIREZGVuIdKW7AgAFYsWIFwsLC0KJFC8TGxmLu3LlVMp+Fm5sbBg8ejBUrVuDWrVt48803LfprnDp1Cq+88gqefvppNGjQAEqlErt27cKpU6fw7rvvWjUWmUyGOXPmYPTo0RgwYABefPFFaDQazJ07F7du3cInn3xy33N06tQJ//nPfzB+/HgcO3YMXbp0gaOjI5KTk3HgwAE0b968wgljvXr1YG9vjx9++AGNGzeGk5MT/P394e/vX9lbBVD+z3GRP//8E56enpVKqKiGkrb/LNUmRaNpjh49WmxfQUGBGBQUJDZo0EDU6/WiKIriJ598IoaEhIgqlUps3LixuGTJkhJ79Jc2miY6Olps1KiRqFKpxLp164pRUVHismXLRABiYmKiRdlNmzaJ3bp1E11cXESVSiUGBweLw4YNE//44w9zmYqOpiltKXLy5Elx+PDhore3t2hnZyf6+vqKTzzxhLho0SJzGY1GI7755ptiQECAqFarxTZt2oibNm0Sx44dW2z0Snp6ujhp0iTRz89PVCgUYnBwsDht2jSxsLDwvjFnZWWJTk5O4pw5cyy23z2apiyVHU2TmZkpTpw4UfT29hYdHBzExx9/XNy/f7/YtWtXsWvXrveNozwjdO62Y8cO87/D2bNnLfbduHFDHDdunBgWFiY6OjqKTk5OYosWLcT//e9/5s9kae43gure0TRFNm3aJLZv315Uq9Wio6Oj2L17d/HgwYMVOnd0dLTYvn170dHRUbS3txfr1asnjhkzRjx27Ji5TGk/IyV9jtasWSOGhYWJdnZ2IgBxxowZFnHcLTg4WOzfv3+x897771eRz7HRaBSDg4OLjXAj2yaIYgkzQBFRrfPqq69i586dOH36dJU/g4SoNDt37kSvXr1w+vRphIWFSR0OVRMmI0QEwDQ6o2HDhli2bNkDj5Ihqqxu3bqhfv36WLJkidShUDVinxEiAmCahvuHH34oNqSUqLpkZmaia9eu5iHuVHuwZoSIiIgkxaG9REREJCkmI0RERCQpJiNEREQkqRrRgdVoNOL69etwdnbmkEMiIqIaQhRF5OTkwN/f32KSwXvViGTk+vXrCAwMlDoMIiIiqoQrV66UObNyjUhGiqbUvnLlClxcXCSOhqjmycsDimb0vn4dcHSUNh5ryNPmwf9z001df+M6HJU2cFNENiY7OxuBgYElPhrjbjUiGSlqmnFxcWEyQlQJdz0vEC4utpGMyLVy4PYDmV1cXJiMED3E7tfFgh1YiYiISFI1omaEiB6MQgGMHXtn3RYoZAqMbTnWvE5ENVeNmIE1Ozsbrq6uyMrKYjMNERFRDVHe72/+OUFEZGMMBgN0Op3UYVAtIJfLoVAoHnjaDSYjRLWAKAL5+aZ1BwfAFqbrEUUR+TrTTTnYOXAOottyc3Nx9epV1IBKb7IRDg4O8PPzg1KprPQ5mIwQ1QL5+YCTk2k9N9c2RtPk6/LhFGW6qdxpuRxNA1ONyNWrV+Hg4IA6deowQaMqJYoitFotbt68icTERDRo0KDMic3KwmSEiMhG6HQ6iKKIOnXqwN7eXupwqBawt7eHnZ0dLl++DK1WC7VaXanzcGgvEZGNYY0IVafK1oZYnMMKcRARERFVGpMRIiIikhSTESIiIpIUkxEiIpLUuHHjIAgCJk2aVGzf5MmTIQgCxo0bBwAYOHAgevToUeJ5Dh8+DEEQcPz48VKvlZCQgCeffBKurq5wdnbGY489hqSkpFLLb9y4EeHh4XBzc4OjoyNatWqF77//vtTyUVFREAQBU6ZMKbWMNRQWFmLcuHFo3rw5FAoFBg0aVKHjNRoNWrVqBUEQEBcXZ96enp6OPn36wN/fHyqVCoGBgXjllVeQnZ1t3Ru4B5MRolpALgeGDTMtdz80ryaTy+QY1mQYhjUZBrnMRm6qFgsMDMSPP/6IgoIC87bCwkKsWbMGQUFB5m0TJ07Erl27cPny5WLniI6ORqtWrdCmTZsSr3HhwgU8/vjjCAsLw549e3Dy5El88MEHZY4A8fDwwPTp03H48GGcOnUK48ePx/jx47F9+/ZiZY8ePYrFixejRYsWFbl1AMDMmTPNCVd5GAwG2Nvb47XXXis1OSvL22+/Df+iR3nfRSaT4amnnsIvv/yCs2fPYsWKFfjjjz9KTBStiUN7iWoBtRpYv17qKKxLrVBj/dM2dlNWdvdkd9WtopPrtWnTBhcvXsTGjRsxevRoAKZaicDAQNStW9dcbsCAAfD29saKFSswY8YM8/b8/HysXbsWs2fPLvUa06dPR79+/TBnzhzztrvPXZKIiAiL16+//jpWrlyJAwcOoHfv3ubtubm5GD16NJYsWYKPP/64XPf8IBwdHbFw4UIAwMGDB3Hr1q1yH/v7779jx44d+Omnn/D7779b7HN3d8dLL71kfh0cHIzJkydj7ty5Vom7NKwZISKyUUWT3UmxVCYJGj9+PJYvX25+HR0djQkTJliUUSgUGDNmDFasWGExy+z69euh1WrNicy9jEYjtmzZgoYNG6J3797w9vZG+/btsWnTpnLHJ4oidu7ciTNnzqBLly4W+15++WX079+/UrUU1enGjRt44YUX8P3338PBweG+5a9fv46NGzeia9euVRoXkxEiInooPPfcczhw4AAuXbqEy5cv4+DBg3j22WeLlZswYQIuXbqEPXv2mLdFR0djyJAhcHd3L/HcqampyM3NxSeffII+ffpgx44dGDx4MIYMGYK9e/eWGVdWVhacnJygVCrRv39/fP311+jZs6d5/48//ojY2FhERUWV+173798PJycn8zJ79mz88MMPxbZZkyiKGDduHCZNmoTw8PAyy44cORIODg4ICAiAi4sLli5datVY7sVmGqJaIC/P9qaDz9PmcTr4+3BwMP17S3XtivLy8kL//v2xcuVKiKKI/v37w8vLq1i5sLAwdOzYEdHR0ejWrRsuXLiA/fv3Y8eOHaWe22g0AgCeeuopTJ06FQDQqlUrHDp0CIsWLSrzL39nZ2fExcUhNzcXO3fuRGRkJOrWrYuIiAhcuXIFr7/+Onbs2FGh2UfDw8MtOo5+9dVXuHbtGj799FPzNg8Pj3Kfrzy+/vprZGdnY9q0afct+7///Q8zZszAmTNn8N577yEyMhILFiywajx3q3Aysm/fPsydOxexsbFITk7Gzz//XO5evAcPHkTXrl3RrFkzi38EIiKyPkGoeYnnhAkT8MorrwAA5s+fX2q5iRMn4pVXXsH8+fOxfPlyBAcHo3v37qWW9/LygkKhQJMmTSy2N27cGAcOHCgzJplMhvr16wMwJTAJCQmIiopCREQEYmNjkZqairZt25rLGwwG7Nu3D9988w00Gg3kJfQat7e3N58TMCUe2dnZFtusbdeuXfjzzz+hUqkstoeHh2P06NFYuXKleZuvry98fX0RFhYGT09PdO7cGR988AH8/PyqJLYKN9Pk5eWhZcuW+Oabbyp0XFZWFsaMGVPmh4WIiGq3Pn36QKvVQqvVWnQQvdfw4cMhl8uxevVqrFy5EuPHjy9zGnylUol27drhzJkzFtvPnj2L4ODgCsUoiiI0Gg0AoHv37vj7778RFxdnXoq+3OPi4kpMRKTy1Vdf4eTJk+Y4t27dCgBYu3Yt/u///q/U44r65hTdc1WocM1I37590bdv3wpf6MUXX8SoUaMgl8sr1GGIiIhqD7lcjoSEBPN6aZycnDBixAi89957yMrKKtew2LfeegsjRoxAly5d0K1bN2zbtg2//vqrRd+TMWPGICAgwNz/IyoqCuHh4ahXrx60Wi22bt2K7777zjySxdnZGc2aNbO4jqOjIzw9PYttv5tWq0VGRob5ddHQ2ZSUFIt7dCpqXy1BfHy8+Tw5OTnmFodWrVoBAI4cOYIxY8Zg586dCAgIsBgiXXR+AKhXrx4eeeQRAMDWrVtx48YNtGvXDk5OToiPj8fbb7+NTp06ISQkpNRYHlS19BlZvnw5Lly4gFWrVpVryJNGo7HIwKp6shUiInp4uLi4lKvcxIkTsWzZMvTq1avYF21JBg8ejEWLFiEqKgqvvfYaGjVqhJ9++gmPP/64uUxSUpLFg9/y8vIwefJkXL16Ffb29ggLC8OqVaswYsSIit/YXQ4dOoRu3bqVWWbGjBmYOXNmqfv79etnMd9K69atAdypycjPz8eZM2eg0+nKHZe9vT2WLFmCqVOnQqPRIDAwEEOGDMG7775b7nNUhiDePTaqogcLwn37jJw7dw6PP/449u/fj4YNG2LmzJnYtGlTmX1GZs6ciY8++qjY9qysrHJ/SInoDnZgrR0KCwuRmJiI0NDQSj/KnaiiyvrcZWdnw9XV9b7f31U6tNdgMGDUqFH46KOP0LBhw3IfN23aNGRlZZmXK1euVGGUREREJKUqbabJycnBsWPHcOLECXPvaKPRCFEUoVAosGPHDjzxxBPFjlOpVMV6+xJR5cnlQL9+d9ZtgVwmR78G/czrRFRzVWky4uLigr///tti24IFC7Br1y5s2LABoaGhVXl5IrpNrQa2bJE6CutSK9TYMsrGboqolqpwMpKbm4vz58+bXycmJiIuLg4eHh4ICgrCtGnTcO3aNXz33XeQyWTFehN7e3tDrVaX2cuYiIiIao8KJyPHjh2z6AEcGRkJABg7dixWrFiB5OTkMh/HTERERHS3BxpNU13K2xuXiEqWlwd4e5vWU1NtZzSN92emm0p9M5WjacDRNCQNa4ym4bNpiGoJqR4lX5XydTZ4U0S1EJ/aS0RERJJiMkJERESSYjJCREREkmIyQkREkho3bhwEQSi2nD9/HgMHDkSPHj1KPO7w4cMQBAHHjx8vcX9J5xQEAXPnzi01Fp1Oh1mzZqFevXpQq9Vo2bIltm3bZlFm5syZxc7p6+tb+Teggs6fPw9nZ2e4ubmVWW7Pnj2lvgdHjx41l9u5cyc6duwIZ2dn+Pn54Z133oFer6/iu7DEZISIiCTXp08fJCcnWyyhoaGYOHEidu3aZfFAuCLR0dFo1aoV2rRpU+I57z1fdHQ0BEHA0KFDS43j/fffx7fffouvv/4a8fHxmDRpEgYPHowTJ05YlGvatKnFue+d4PN+Zs6cWa4nDd9Lp9Nh5MiR6Ny5833LduzYsdh78PzzzyMkJATh4eEAgFOnTqFfv37o06cPTpw4gR9//BG//PJLlT8Y714cTUNUC8hkQNeud9ZtgUyQoWtwV/M6FSeKomQjjhzsHCAIQrnLq1SqEmsXBgwYAG9vb6xYsQIzZswwb8/Pz8fatWsxe/bsUs957/k2b96Mbt26oW7duqUe8/3332P69Onod/v5CS+99BK2b9+Ozz//HKtWrTKXUygU1VobUuT9999HWFgYunfvjkOHDpVZVqlUWsSo0+nwyy+/4JVXXjH/2/z4449o0aIFPvzwQwBA/fr1ERUVhZEjR2LGjBlwdnauupu5C5MRolrA3h7Ys0fqKKzL3s4ee8btkTqMh1q+Lt/8ZOPqZq0nKSsUCowZMwYrVqzAhx9+aP4SXb9+PbRaLUaPHl2u89y4cQNbtmzBypUryyyn0WiKzZVhb2+PAwcOWGw7d+4c/P39oVKp0L59e8yePbvMJMcadu3ahfXr1yMuLg4bN26s8PG//PIL0tLSLGpkSrvfwsJCxMbGIiIi4gGjLh/+OUFERJL77bff4OTkZF6efvpp874JEybg0qVL2HNXRh0dHY0hQ4bA3d29XOdfuXIlnJ2dMWTIkDLL9e7dG/PmzcO5c+dgNBoRExODzZs3Izk52Vymffv2+O6777B9+3YsWbIEKSkp6NixI9LT0yt20xWQnp6OcePGYcWKFZWe/HPZsmXo3bs3AgMDzdt69+6NQ4cOYc2aNTAYDLh27Ro+/vhjALC456rGmhEiIhvlYOeA3Gm5kl27Irp164aFCxeaXzveNU1wWFgYOnbsiOjoaHTr1g0XLlzA/v37sWPHjnKfPzo6GqNHj77vzLRffvklXnjhBYSFhUEQBNSrVw/jx4/H8uXLzWX69u1rXm/evDk6dOiAevXqYeXKleZHpNxr//79FsdptVqIoogNGzaYt7333nt47733Sjz+hRdewKhRo9ClS5dy3e+9rl69iu3bt2PdunUW23v16oW5c+di0qRJeO6556BSqfDBBx/gwIEDkFfnI77FGiArK0sEIGZlZUkdClGNlJsril5epiU3V+porCNXkyt6zfESveZ4ibkaG7mpB1RQUCDGx8eLBQUFUodSIWPHjhWfeuqpMsssW7ZMtLe3F7OyssTp06eLISEhotFoLNf59+3bJwIQ4+Liyh1TQUGBePXqVdFoNIpvv/222KRJkzLL9+jRQ5w0aVKp+/Pz88Vz586Zl1dffVUcMmSIxbb09PRSj3d1dRXlcrl5kclkIgBRLpeLy5Ytu+/9zJo1S6xTp46o1WpL3G80GsVr166J+fn5Ynx8vAhAPHLkyH3PK4plf+7K+/3NmhGiWiItTeoIrC8t3wZviko0fPhwvP7661i9ejVWrlyJF154odwdZJctW4a2bduiZcuW5b6eWq1GQEAAdDodfvrpJwwfPrzUshqNBgkJCWWOcLG3t0f9+vXNrz08PJCdnW2xrSyHDx+GwWAwv968eTM+/fRTHDp0CAEBAWUeK4oili9fjjFjxsDOzq7EMoIgwN/fHwCwZs0aBAYGljpKqSowGSEiooeek5MTRowYgffeew9ZWVnlHhabnZ2N9evX4/PPPy9x/5gxYxAQEICoqCgAwF9//YVr166hVatWuHbtGmbOnAmj0Yi3337bfMybb76JgQMHIigoCKmpqfj444+RnZ2NsWPHPvB9lqZx48YWr48dOwaZTIZmzZqZtx05cgRjxozBzp07LRKUXbt2ITExERMnTizx3HPnzkWfPn0gk8mwceNGfPLJJ1i3bl21NtOwAysREdUIEydORGZmJnr06IGgoKByHfPjjz9CFEWMHDmyxP1JSUkWHTULCwvx/vvvo0mTJhg8eDACAgJw4MABiwnGrl69ipEjR6JRo0YYMmQIlEol/vzzTwQHBz/Q/T2o/Px8nDlzBjqdzmL7smXL0LFjx2IJTZHff/8dnTt3Rnh4OLZs2YLNmzdj0KBB1RDxHYIoimK1XrESyvsIYiIqWV4e4HR7hGduLuD44CMuJZenzTMPW7XWMNKarqxHuRNVlbI+d+X9/mbNCBEREUmKyQgRERFJih1YiWoBmQy4/SgKm5oOPtw/3LxORDUXkxGiWsDeHrjrIZ02wd7OHkdfsLGbIqql+OcEEZGNqQHjEsiGWOPzxmSEiMhGFM0LodVqJY6EapP8fNOToUubUK082ExDVAvk5wNNmpjW4+MBh4o9NuShlK/LR5P5ppuKfzm+ws9CsUUKhQIODg64efMm7OzsILOVDkL0UBJFEfn5+UhNTYWbm9sDTZLGZISoFhBF4PLlO+u2QBRFXM66bF4n05Tefn5+SExMxOWif3CiKubm5gZfX98HOgeTESIiG6JUKtGgQQM21VC1sLOzs8q08UxGiIhsjEwm4wysVKOwQZGIiIgkxWSEiIiIJMVkhIiIiCTFPiNEtYAg3BnaKwjSxmItgiCgSZ0m5nUiqrmYjBDVAg4OwOnTUkdhXQ52Djg92cZuiqiWYjMNERERSYrJCBEREUmKyQhRLZCfDzRtalpuP0aixsvX5aPpgqZouqAp8nU2clNEtRT7jBDVAqJoeiZN0botEEUR8TfjzetEVHOxZoSIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJFXhZGTfvn0YOHAg/P39IQgCNm3aVGb5jRs3omfPnqhTpw5cXFzQoUMHbN++vbLxElElCAIQHGxabGXmdEEQEOwajGDXYE4HT1TDVTgZycvLQ8uWLfHNN9+Uq/y+ffvQs2dPbN26FbGxsejWrRsGDhyIEydOVDhYIqocBwfg0iXT4uAgdTTW4WDngEtTLuHSlEtwsLORmyKqpQTxAQboC4KAn3/+GYMGDarQcU2bNsWIESPw4Ycflqt8dnY2XF1dkZWVBRcXl0pESkRERNWtvN/f1T7pmdFoRE5ODjw8PEoto9FooNFozK+zs7OrIzQiIiKSQLV3YP3888+Rl5eH4cOHl1omKioKrq6u5iUwMLAaIySyPQUFQLt2pqWgQOporKNAV4B2S9qh3ZJ2KNDZyE0R1VLVWjOyZs0azJw5E5s3b4a3t3ep5aZNm4bIyEjz6+zsbCYkRA/AaASOHbuzbguMohHHrh8zrxNRzVVtycjatWsxceJErF+/Hj169CizrEqlgkqlqqbIiIiISErV0kyzZs0ajBs3DqtXr0b//v2r45JERERUQ1S4ZiQ3Nxfnz583v05MTERcXBw8PDwQFBSEadOm4dq1a/juu+8AmBKRMWPG4Msvv8Rjjz2GlJQUAIC9vT1cXV2tdBtERERUU1W4ZuTYsWNo3bo1WrduDQCIjIxE69atzcN0k5OTkZSUZC7/7bffQq/X4+WXX4afn595ef311610C0RERFSTVbhmJCIiAmVNTbJixQqL13v27KnoJYiIiKgWqfZ5RohIGl5eUkdgfV4ONnhTRLUQkxGiWsDREbh5U+oorMtR6Yibb9nYTRHVUnxqLxEREUmKyQgRERFJiskIUS1QUABERJgWW5oOPmJFBCJWRHA6eKIajn1GiGoBoxHYu/fOui0wikbsvbzXvE5ENRdrRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUhxNQ1RLODhIHYH1OdjZ4E0R1UJMRohqAUdHIC9P6iisy1HpiLz3bOymiGopNtMQERGRpJiMEBERkaSYjBDVAoWFQP/+pqWwUOporKNQX4j+q/uj/+r+KNTbyE0R1VLsM0JUCxgMwNatd9ZtgcFowNZzW83rRFRzsWaEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxaG9RLWAoyMgilJHYV2OSkeIM2zspohqKdaMEBERkaSYjBAREZGkmIwQ1QKFhcDTT5sWW5oO/un1T+Pp9U9zOniiGo7JCFEtYDAAGzaYFluaDn5D/AZsiN/A6eCJajgmI0RERCQpJiNEREQkKSYjREREJCkmI0RERCQpJiNEREQkKSYjREREJClOB09UCzg4ALm5d9ZtgYOdA3Kn5ZrXiajmYjJCVAsIgun5NLZEEAQ4Km3spohqKTbTEBERkaSYjBDVAhoNMG6cadFopI7GOjR6DcZtGodxm8ZBo7eRmyKqpSqcjOzbtw8DBw6Ev78/BEHApk2b7nvM3r170bZtW6jVatStWxeLFi2qTKxEVEl6PbBypWnR66WOxjr0Rj1WnlyJlSdXQm+0kZsiqqUqnIzk5eWhZcuW+Oabb8pVPjExEf369UPnzp1x4sQJvPfee3jttdfw008/VThYIiIisj0V7sDat29f9O3bt9zlFy1ahKCgIHzxxRcAgMaNG+PYsWP47LPPMHTo0IpenoiIiGxMlfcZOXz4MHr16mWxrXfv3jh27Bh0Ol2Jx2g0GmRnZ1ssREREZJuqPBlJSUmBj4+PxTYfHx/o9XqkpaWVeExUVBRcXV3NS2BgYFWHSURERBKpltE0giBYvBZFscTtRaZNm4asrCzzcuXKlSqPkYiIiKRR5ZOe+fr6IiUlxWJbamoqFAoFPD09SzxGpVJBpVJVdWhERET0EKjyZKRDhw749ddfLbbt2LED4eHhsLOzq+rLExFMU8Cnpt5ZtwUOdg5IfTPVvE5ENVeFm2lyc3MRFxeHuLg4AKahu3FxcUhKSgJgamIZM2aMufykSZNw+fJlREZGIiEhAdHR0Vi2bBnefPNN69wBEd2XIAB16piWUlpHaxxBEFDHsQ7qONYptcmXiGqGCteMHDt2DN26dTO/joyMBACMHTsWK1asQHJysjkxAYDQ0FBs3boVU6dOxfz58+Hv74+vvvqKw3qJiIgIACCIRb1JH2LZ2dlwdXVFVlYWXFxcpA6HqMbRaIDbfzdg3jzAFrpkafQaRG433dS83vOgUtjATRHZmPJ+fzMZIaoF8vIAJyfTem6ubTzBN0+bB6co003lTsvlE3yJHkLl/f7mg/KIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSVT4dPBFJz94eSEy8s24L7O3skfh6onmdiGouJiNEtYBMBoSESB2FdckEGULcQqQOg4isgM00REREJCkmI0S1gFYLvPWWadFqpY7GOrQGLd7a8Rbe2vEWtAYbuSmiWorTwRPVApwOnoikwOngiYiIqEZgMkJERESSYjJCREREkmIyQkRERJJiMkJERESSYjJCREREkuIMrES1gL098M8/d9Ztgb2dPf556R/zOhHVXExGiGoBmQxo2lTqKKxLJsjQ1NvGboqolmIzDREREUmKNSNEtYBWC8yebVp/7z1AqZQ2HmvQGrSYvd90U+91fg9KuQ3cFFEtxengiWoBTgdPRFLgdPBERERUIzAZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkkxGSEiIiJJcZ4RolpArQaOHLmzbgvUCjWOPH/EvE5ENReTEaJaQC4H2rWTOgrrksvkaBdgYzdFVEuxmYaIiIgkxZoRolpAqwW+/NK0/vrrtjMd/Jd/mm7q9cde53TwRDUYp4MnqgU4HTwRSYHTwRMREVGNwGSEiIiIJMVkhIiIiCTFZISIiIgkValkZMGCBQgNDYVarUbbtm2xf//+Msv/8MMPaNmyJRwcHODn54fx48cjPT29UgETERGRbalwMrJ27VpMmTIF06dPx4kTJ9C5c2f07dsXSUlJJZY/cOAAxowZg4kTJ+L06dNYv349jh49iueff/6BgyciIqKar8JDe9u3b482bdpg4cKF5m2NGzfGoEGDEBUVVaz8Z599hoULF+LChQvmbV9//TXmzJmDK1eulOuaHNpL9GAMBqCoArNzZ9OMrDWdwWjA/iTTTXUO6gy5zAZuisjGVMnQXq1Wi9jYWPTq1ctie69evXDo0KESj+nYsSOuXr2KrVu3QhRF3LhxAxs2bED//v1LvY5Go0F2drbFQkSVJ5cDERGmxRYSEcA0HXxESAQiQiKYiBDVcBVKRtLS0mAwGODj42Ox3cfHBykpKSUe07FjR/zwww8YMWIElEolfH194ebmhq+//rrU60RFRcHV1dW8BAYGViRMIiIiqkEq1YFVEASL16IoFttWJD4+Hq+99ho+/PBDxMbGYtu2bUhMTMSkSZNKPf+0adOQlZVlXsrbnENEJdPpgPnzTYtOJ3U01qEz6DD/yHzMPzIfOoON3BRRLVWhZ9N4eXlBLpcXqwVJTU0tVltSJCoqCp06dcJbb70FAGjRogUcHR3RuXNnfPzxx/Dz8yt2jEqlgkqlqkhoRFQGrRZ45RXT+rhxgJ2dpOFYhdagxSu/m25qXKtxsJPbwE0R1VIVqhlRKpVo27YtYmJiLLbHxMSgY8eOJR6Tn58PmczyMvLbjdY14LE4REREVMUq3EwTGRmJpUuXIjo6GgkJCZg6dSqSkpLMzS7Tpk3DmDFjzOUHDhyIjRs3YuHChbh48SIOHjyI1157DY8++ij8/f2tdydERERUI1WomQYARowYgfT0dMyaNQvJyclo1qwZtm7diuDgYABAcnKyxZwj48aNQ05ODr755hu88cYbcHNzwxNPPIFPP/3UendBRERENVaF5xmRAucZIXoweXmAk5NpPTcXcHSUNh5ryNPmwSnKdFO503LhqLSBmyKyMVUyzwgRERGRtTEZISIiIklVuM8IEdU8KhXw22931m2BSqHCbyN/M68TUc3FPiNERERUJdhnhIiIiGoENtMQ1QI6HfDDD6b10aNtYwZWnUGHH/423dTo5qM5AytRDcZmGqJagEN7iUgKbKYhIiKiGoHJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJivOMENUCKhWwbt2ddVugUqiwbtg68zoR1VycZ4SIiIiqBOcZISIiohqBzTREtYBeD/z8s2l98GBAYQM/+XqjHj8nmG5qcOPBUMhs4KaIain+9BLVAhoNMHy4aT031zaSEY1eg+EbTDeVOy0XCqUN3BRRLcVmGiIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpIUkxEiIiKSFJMRIiIikhTHwhHVAkolsHz5nXVboJQrsfyp5eZ1Iqq5OB08ERERVQlOB09EREQ1AptpiGoBvR7Yvt203ru3bczAqjfqsf286aZ61+/N6eCJajD+9BLVAhoNMGCAad2WpoMfsMZ0U5wOnqhmYzMNERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJimPhiGoBpRL45ps767ZAKVfim77fmNeJqObidPBERERUJTgdPBEREdUIbKYhqgUMBmD/ftN6586AXC5tPNZgMBqwP8l0U52DOkMus4GbIqqlKlUzsmDBAoSGhkKtVqNt27bYX/RbrhQajQbTp09HcHAwVCoV6tWrh+jo6EoFTEQVV1gIdOtmWgoLpY7GOgr1hei2shu6reyGQr2N3BRRLVXhmpG1a9diypQpWLBgATp16oRvv/0Wffv2RXx8PIKCgko8Zvjw4bhx4waWLVuG+vXrIzU1FXq9/oGDJyIiopqvwh1Y27dvjzZt2mDhwoXmbY0bN8agQYMQFRVVrPy2bdvwzDPP4OLFi/Dw8CjXNTQaDTQajfl1dnY2AgMD2YGVqJLy8gAnJ9N6bi7g6ChtPNaQp82DU5TppnKn5cJRaQM3RWRjqqQDq1arRWxsLHr16mWxvVevXjh06FCJx/zyyy8IDw/HnDlzEBAQgIYNG+LNN99EQUFBqdeJioqCq6ureQkMDKxImERERFSDVKiZJi0tDQaDAT4+PhbbfXx8kJKSUuIxFy9exIEDB6BWq/Hzzz8jLS0NkydPRkZGRqn9RqZNm4bIyEjz66KaESIiIrI9lRpNIwiCxWtRFIttK2I0GiEIAn744Qe4uroCAObNm4dhw4Zh/vz5sLe3L3aMSqWCSqWqTGhERERUw1SomcbLywtyubxYLUhqamqx2pIifn5+CAgIMCcigKmPiSiKuHr1aiVCJiIiIltSoZoRpVKJtm3bIiYmBoMHDzZvj4mJwVNPPVXiMZ06dcL69euRm5sLp9s96M6ePQuZTIZHHnnkAUInovKyswPmzLmzbgvs5HaY02OOeZ2Iaq4Kj6ZZu3YtnnvuOSxatAgdOnTA4sWLsWTJEpw+fRrBwcGYNm0arl27hu+++w4AkJubi8aNG+Oxxx7DRx99hLS0NDz//PPo2rUrlixZUq5rcjp4IiKimqe8398V7jMyYsQIpKenY9asWUhOTkazZs2wdetWBAcHAwCSk5ORlJRkLu/k5ISYmBi8+uqrCA8Ph6enJ4YPH46PP/64ErdFREREtoYPyiOqBQwG4Phx03qbNrYzHfzxZNNNtfFrw+ngiR5CVVYzQkQ1T2Eh8OijpnVbmfSsUF+IR5eaboqTnhHVbHxqLxEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYpDe4lqATs7YMaMO+u2wE5uhxldZ5jXiajm4qRnRDZCFIHft4lYusKIs2cAVzegd0/g1clyuLtLHR0R1Uac9IyoFsnOBkaMNmDbb3IAd2YiPbQXmPe5EdHRwJBBbJUloocTkxGiGi4nB+jU1YB/4uRQ2Ino9Uw+WnTQIP2GHL//4ICr5+0wdDAw/QMjRo6QoXFjQGYDeYlRNCLhZgIAoHGdxpAJNnBTRLUUm2mIajCjEejRx4DdMXK4uBswfXEm6jfXmffrtED0bBfs+PHOVOm2Mh18njYPTlFOADgdPNHDqrzf3/xTgqgGm/O5Ebtj5FDZG4slIgBgpwT+MyMbPUfkmbf9/fdD//cHEdUyTEaIaqgzZ0R8+L4AABj3Tk6xRKSIIADPvZFjfj1mghFabbWESERULkxGiGqo198wQqcV0LKTBj1H5JdZVn5X77BzCXK8P9NYxdEREZUfkxEiCRy6cgitl7ZGk2+bYNOZTRU+fucuEdu3yCGTi5jwXjYEoWLHz5sr4MwZNtcQ0cOByQhRNUvKSkLPVT0Rdy0OCSkJGLp2KHYm7qzQOaa9b6rZ6DUiH4/U01fo2FaPF8KgF/DqVNaOENHDgckIUTWL3BmJfG0+Ar0D0bxucxhFIyb8OgE6Q8l9Pu61/4CIo4dNw3iHvphb4euPisyGXCEi5nc5tu9g7QgRSY/JCFE1Ss1Lxeb4zQCAoRFDMbrnaDjZOyEpMwnLTy4v1zlm/Z+pRiNiUAE8fMpXuyFXiHhyQi6enJCLoPoG9Bll6mPy1rtGPPyD+0tmJ7fDmx3exJsd3uR08EQ1HJMRomr0wz8/QG/QI8gnCCG+IVCr1OjWphsA4H9H/of7Tfvz77/AH9vkEAQRT00of62InRIY+3YOxr6dAzslMHRSLlT2Rvx9Qo7fttTMbEQpV2Jur7mY22sulHKl1OEQ0QNgMkJUjX468xMAoFX9VuZt7Zu0h0KuwL83/sWRa0fKPP6rBQYAQNsIDfxDDZWOw9XDaK4deX9Gza0dISLbwGSEqJrkaHLwV9JfAICmoU3N253sndCyfksAwNJTS0s9vrAQWL3KNGym132G8t7LaARSr8qRelUO4+2Wnacm5EFlb8Sp4zWzdsQoGnHp1iVcunUJRpGdcYlqMiYjRNXkYNJB6A16eLh4wNvd22Jf6watAQCbEjaV+sW6dr0RWZkyePnr0aqzpkLX1hYKeKmHN17q4Q1toSmhcfU0ovczpqTm/z6peV/mBboChH4ZitAvQ1GgK5A6HCJ6AExGiKrJzium4bt1/etCuGdikLCgMKiVaqTlpuFg0sESj1+wyFR70WNYAeTyEotU2ICxeZArRPx1UI5jx2pe7QgR2QYmI0TV5MCVAwCAUL/QYvsUCgWa120OAFgVv6rY/sRE4MghU8fVJ4ZWrImmLJ6+RnTqWwgAiPqs5tWOEJFtYDJCVA0MRgNOXjsJoORkBACa1W0GANh2bluxfd/9YOqs2uwxLTzLOZy3vAaMNY3K2fyTDFevWvXURETlwmSEqBrE34xHga4AKjsVfD18SyzTKLARZDIZkjKTcD7jvMW+1WtM/3+8v/X7RtRrpkfTdhoY9AI++6LyI3SIiCqLyQhRNThx4wQAIKBOAGSykn/s1Co16vnXAwBsOrvJvP3vv0WcjTfNuPpYz8IqiW/AuDwAwIpoAYVVcwkiolIxGSGqBkdSTPOH+Hv6l1muSUgTAJbJyMpVpmaZ1l00cHKtmk6mbSM08PLXIytThjVr2XeEiKoXkxGiahB3Iw4A4OflV2a5xiGNAQBHko4gT5sHUQTWrjONvOn8AE00coWIPqPy0GeUafRMsf1yoOdw0/m/WVAzRtUoZApMDp+MyeGToZAppA6HiB4Af4KJqsGZ1DMAAH+vsmtGfNx94OHigYzsDOxK3AX/3AG4ekkGlb0R4d0q335ipwRe+DC7zDLdh+Zj3TdOOH5EjpMnRbRsKZRZXmoqhQrz+8+XOgwisgLWjBBVsfT8dKTlpgEA/DzLrhkRBAFhwWEAgM3nN2PdT7ebaDproLKv2jjd6xjxaA9TwvPlfDbVEFH1YTJCVMX+Tv0bAODp4gm1Un3f8o2DTE01Oy7swCbTA37xaI+Kzbh6L1EEsjJkyMqQlfkcmqIZWdeuliEn54EuWeVEUcTNvJu4mXfzvg8YJKKHG5MRoip2KvUUAMDXs+Qhvfdq8EgDyGQyXMm8grPJlyCTi2jb9cGGuGgKBEzo6IMJHX2gKSi9+aVZey38Q/TIzxPw3aqHu3YkX5cP78+84f2ZN/J11psIjoiqH5MRoioWnxEPAPBy9SpXebVKfWditPrb0fRRbZWNormXIAA9bz+Eb/FS1jYQUfVgMkJUxc5mnAVQ/mQEABoHm5pqUG87Hn2ieif+6PpUAeQKEaeOy3HqFBMSIqp6TEaIqtjlzMsAAC+38icjgc4tTSt1d6J1t7JHwVibq8edkTuLlj7cTTVEZBuYjBBVIVEUcfWW6YEvFakZyTjSGcj1AZR5SLP7q6rCK9UTQ0xzjqz5QYBWW+2XJ6JaplLJyIIFCxAaGgq1Wo22bdti//795Tru4MGDUCgUaNWqVWUuS1TjJOcmQ6vXQibI4OHsUe7jTm+vD5zvDQA4kfJHVYVXqtadNXCrY8CtDBl+/ZVNNURUtSqcjKxduxZTpkzB9OnTceLECXTu3Bl9+/ZFUlJSmcdlZWVhzJgx6N69e6WDJappzqWfAwC4O7tDLpeX6xi9VoZz+wKBC0XJyK4qi680cgUQMchUO7KQTTVEVMUqnIzMmzcPEydOxPPPP4/GjRvjiy++QGBgIBYuXFjmcS+++CJGjRqFDh06VDpYoprmn/R/AFSsiebin/7Q5CrhmNURAgQkZZ5DRkHKA8UhV4iIGJSPiEH5JU4HX5InBptG1ezeIcP16w90+SqhkCkwtuVYjG05ltPBE9VwFUpGtFotYmNj0atXL4vtvXr1wqFDh0o9bvny5bhw4QJmzJhRrutoNBpkZ2dbLEQ10b8Z/wKoWOfVf3cGAwCadMxHoE8gACAuec8DxWGnBF79JAuvfpIFO2X5jgmoa0Cj1loYjQKWrTA80PWrgkqhwopBK7Bi0AqoFCqpwyGiB1ChZCQtLQ0GgwE+Pj4W2318fJCSUvJfbufOncO7776LH374AQpF+f56iYqKgqurq3kJDAysSJhED43zmecBVKxmJOGPEABA4+6XERZkmhr+xI3q7zcCAE8MNdWOLF+OMmduJSJ6EJXqwCoIljM4iqJYbBsAGAwGjBo1Ch999BEaNmxY7vNPmzYNWVlZ5uXKlSuVCZNIcomZiQAAT1fPcpXPvOqE5AQvCDIjGnVLMj+n5mTKARiMla+dEEWgMF9AYb5QoaSiU59CqOyNSDwvx8GDD1c2Iooi8rR5t59u/HDFRkQVU6FkxMvLC3K5vFgtSGpqarHaEgDIycnBsWPH8Morr0ChUEChUGDWrFk4efIkFAoFdu0quWOeSqWCi4uLxUJUE13LvAYAqONWp1zl/91laqIJanMDjh6FCPYNhlqpRp4mGxcyT1Y6Dk2BgNFtfDG6jW+Z08Hfy95JRIfepjlHvl32cHVkzdflwynKCU5RTpwOnqiGq1AyolQq0bZtW8TExFhsj4mJQceOHYuVd3Fxwd9//424uDjzMmnSJDRq1AhxcXFo3779g0VP9BDLKMhAriYXgOkheeVhbqLpcQkAIJfJ0SiwEQAgLnm31WMsj6I5R37eIENeniQhEJGNq3AzTWRkJJYuXYro6GgkJCRg6tSpSEpKwqRJkwCYmljGjBljOrlMhmbNmlks3t7eUKvVaNasGRwdHa17N0QPkfPppv4iLo4uUJaj16hBJ8PZvab+UY27XzZvL2qqOX4jpsTjqlrjcC18AvXIyxWwfgObQ4jI+iqcjIwYMQJffPEFZs2ahVatWmHfvn3YunUrgoNN1cvJycn3nXOEqDY4nXEaQPk7r1466ovCHBUcPfMR2PqGeXtRMnI+7R/kaDKtH+h9yGR35hxZ/JA11RCRbahUB9bJkyfj0qVL0Gg0iI2NRZcuXcz7VqxYgT179pR67MyZMxEXF1eZyxLVKPHpFXtab/ztJpqwJ5Igu+sn093ZHb4evhBFI07dKN9sx9YWMagAgiDi8H45Ll2SJAQismF8Ng1RFTEP6y3nHCN3hvReKravqHZEiqnhAcA7wIBmj5keUrM0mrUjRGRdTEaIqkjRsN7y1IxkJTvi+j91IAgiwp4o3sxpnm8kZY9kw1ifGGIasbJiJWBkPkJEVsQ5lImqyJVM0/w45UlGiob0Bra+ASevgmL76wXUg53CDrfy05CU9S+C3RpXKBaZXESH3gXm9cpo36MQDk5GXEuSYe9eEd26lX+IcFWQy+QY1mSYeZ2Iai4mI0RVIE+bh4y8DADlS0YS/jAlI2FPXC5xv53CDvUD6iPhcgJOpOyscDKiVAFvfnmrQsfcS2UPdOxbiD/WO+DbZUZ06yZtAqBWqLH+6fWSxkBE1sFmGqIqcDHzIgDAQeUAB7VDmWUNegFn9gQBuDO/SEnMQ3xTpBniC9xpqtm8UYacHMnCICIbw2SEqAoUPSCvPNPAXz7mi4IsNRzcChHc9kap5YqSkTOpJ1Cgk2b2sYatdPAP1aOwQMDatZxzhIisg8kIURVIyEgAUL6RNAk7QwAAjbpdLrM/h7ebNzxcPKA36nHqxr4KxVOYL2BomB+GhvmhML/yfT0EAeg22FQ7skTiUTV52jwIHwkQPhKQp+XUsEQ1GZMRoipwJv0MgIr1F2nco+T+IkUEQUCz0GYAgEPXf37ACCsv4qkCyGQijhyW49w5ycIgIhvCZISoClzIvADg/slITqoDrp40PWSytM6rd2tZvyUAIPbKLugMmgeMsnI8fIxo+bjp2suWc4wvET04JiNEVSAp0zRXyP2SkYRdpo6rj7RIhYvP/Z88G+ofChdHFxTo8hGXsvfBA62kboNNw4RXrgQMBsnCICIbwWSEyMp0Bh1uZJs6ot43GbnnKb33IxNkaFnPVDty6NrGSsf4oNo9UQgnVyNSrsuwc6dkYRCRjWAyQmRll7MuwygaYaewg4ujS6nljAYBZ3abakbCut+/iaZIqwatAADHru6EzqB9oFgrS6kCHu9vqh35dimbaojowTAZIbKyc+mmXp1erl4QhNJHriQd90F+pj3ULhqEtEsu9/lD/ULh4uCCfG0eTt2Qvqnmt18E3LolWRhEZAOYjBBZWdGw3vvNMZKw0zSKplHXJMgV5Z+zQyaToUX9FgCAPUk/lu8YuYg2XQvRpmthpaeDv1e9ZjoENdBBqxGwek31zzkil8nRr0E/9GvQj9PBE9VwTEaIrKxowrM6rnXKLHenv0j5m2iKhIeFAwCOXtmJfN39p0JVqoDp32Zi+reZUKoqfLkSCQLQbYipdmTJsupPRtQKNbaM2oIto7ZArVBX+/WJyHqYjBBZ2fmM8wDKrhnJTVfjyonbQ3q7X6rwNYJ9guHt7g2dQYuDSZsrFac1dBlYAJlcRFysDPHxkoVBRDUckxEiK7uUeQlA2SNpzuwOhigK8GuSBjf/is8eKggCHm38KABg9+XVlYrTGty8jGjb1TTnyFKJZ2QlopqLyQiRFRlFI67eugqg7Kngi/qLlHdIb0nCG4VDgIAzqXFIyS27qacwX8Co1j4Y1drngaaDL0m32w/PW/U9oNdb9dRlytPmwXG2IxxnO3I6eKIajskIkRVdz7kOnUEHmUwGd2f3EssYjcC/RclIBYb03svN2Q0NAxsCAHZdWnXf8poCGTQF1v+Rb9tVAxcPA26myrBtW/X2HcnX5SNfd//J4ojo4cZkhMiKLmSYpoH3cPYodYTHtVPeyE1zgMpJi9D21x/oeu2btgcA7Lz4I/RG3QOdq7IUdqa+IwCwWIKOrERU8zEZIbKiMxmmB+SV1Xm16MF4DbpcgUL5YP0sWtRrAWcHZ9zKT8eRa78/0LkeRNGomt+3CEhLkywMIqqhmIwQWdHp9NMAgDpupQ/rTdgZAuDB+osUUcgV6NC0AwBg64XFD3y+ygpppEfdJjrodQJW/cCOrERUMUxGiKzoXMad2VdLkn9LhUtHfQE8WH+Ru3Vo1gGCICAh5TiSss5Y5ZyV0W2wqe/G0mjJQiCiGorJCJEVXcy4CKD0mpGzewIhGmXwaZgOj8D7T1ZWHu7O7mhetzkAYMuFRVY5Z2V0HlAAhZ2I06dkOHlSsjCIqAZiMkJkJaIoIikzCUDpw3rjH2DW1bJ0btEZALD3ws/IKkwvtl+QiWjaToOm7TQQZFXTydTZXUS7JwoBAAsXG6rkGneTCTJ0De6KrsFdIRP4q4yoJuNPMJGVpOalokBXAEEQ4OlcvAOraUhvCADrNdEUqf9IfQT5BEFn0OK3cwuL7VepgVnfZ2DW9xlQVeHM6d2HmTqyrv5BQH4Vj7i1t7PHnnF7sGfcHtjb2VftxYioSjEZIbKSov4i7s7uUCgUxfZfjfNB9g1HqJy0qNfxmlWvLQgCurftDgDYdm5luZ5XUxVadtLA+xE9crJkWL2GHVmJqHyYjBBZydn0swBK77z6z7ZQAEBYt8tQqKzfjNG8XnN4u3sjX5uH7ReXW/385SGTAb1GmKpEvl7AOUeIqHyYjBBZSXy66UlxpXVePb3dlIw07ZNYJdeXCTJz7cgv/36LQv2ddpLCfAHjO3hjfAdvq08Hf68nhpo6sp46LkdsbNVdJ0+bhzpz66DO3DqcDp6ohmMyQmQlRROelVQzknnVCdf+9oYgiGjS81KVxRDeKByeLp7ILsjEr/f0HcnOlCM7s+RZYa3J1cOIDr1NHVm/+KZqO7Km5achLZ+zrBHVdExGiKzkQqZpKviSakZO7zDVioS0S4aTV0GVxSCXy9GvQz8AwOaERcjRZFbZtcrSe6SppmL9jzLcuiVJCERUgzAZIbICURSRlHF7WG8JNSOnt9UFUHVNNHdr3bA1/L38UaDNw09n/lfl1ytJWBsdghrooCkUEL2CHVmJqGxMRoisIC0/DXnaPAgQij2XRpOnwLn9jwAAmva+WOWxyAQZ+nfoDwDYdmYlUnOvVPk17yUIQO+Rpj4rCxYCIvuyElEZmIwQWcH5jPMAADdnN9gp7Cz2ndkTBL1GAc/gLPiGZVRLPE1CmqDBIw2gM+iw7OS71XLNe3V5sgBqByMunJVh925mI0RUOiYjRFYQn2YaSVNSE80/v99uoul9EULVDmQxEwQBQ7oOgUwmw7EruxGXsrt6LnwXBycRXZ409Y+ZM49NNURUOiYjRFZw8qbpYSw+Hj4W240GAfExVTuktzR+nn7o0rILAGDlqQ9Rt6kG9Zppq2w6+JL0f87UVLNjqwznz1v33DJBhnD/cIT7h3M6eKIajj/BRFZQVDPi426ZjFz80x+5Nx3g4FaI+p2sO+tqefR5tA9cHFyQWngeLWdOxZwN6VU6Hfy9HqmnR+vOhRBFAZ/9z7rDfO3t7HH0haM4+sJRTgdPVMMxGSGygrNpptlX760ZOflrfQBAs74XIber/qYKtUqNoRFDAQCb47/FufQT1R7DwHGmYb7freAwXyIqWaWSkQULFiA0NBRqtRpt27bF/v37Sy27ceNG9OzZE3Xq1IGLiws6dOiA7du3VzpgooeNRq/BtVumWo+7a0aMRuDU7WSk5ZNWbqOogJb1W6JNwzYwikZ8dfRlaA2F1Xr9Fh21CGqgQ0G+gEWL2XeEiIqrcDKydu1aTJkyBdOnT8eJEyfQuXNn9O3bF0lJSSWW37dvH3r27ImtW7ciNjYW3bp1w8CBA3HiRPX/hUZUFc5lnINRNEKtVMPF0cW8/fIxX2QlO0HlpEGjiJJ/PqrLwEdHQPjyEq7P2I0Vx6Kq9dqCAPQfa6od+eprQK+3znnzdfkI+SIEIV+EIF9XxY8IJqIqVeFkZN68eZg4cSKef/55NG7cGF988QUCAwOxcGHxx5YDwBdffIG3334b7dq1Q4MGDTB79mw0aNAAv/766wMHT/QwSLiZAMBUKyLcNVymqFakae9LVfJgvIpwUDlCzAwGskKw/cwP+Ovqlmq9fpeBBXDxMCD5qgwbNlinA60oiricdRmXsy5D5EQmRDVahZIRrVaL2NhY9OrVy2J7r169cOjQoXKdw2g0IicnBx4eHqWW0Wg0yM7OtliIHlZFI2m8PbzN20TxTn+RlgOla6IpzddHpiAl93K1XU+pAno/Y6q9+OQzIydBIyILFUpG0tLSYDAY4ONj2UnPx8cHKSkp5TrH559/jry8PAwfPrzUMlFRUXB1dTUvgYGBFQmTqFr9ffNvAJb9Ra6eqoOMJFcoHXRo3OOSRJGVLMgnGAXaPMw5PBYaffU1b/QZlQ+lSsTJWDl27mQ2QkR3VKoDq3DPzE2iKBbbVpI1a9Zg5syZWLt2Lby9vUstN23aNGRlZZmXK1eqfzprovI6k2Z6Wq+vh695W9zmBgCAxt0vQelgpU4SVjK612g4qh1xOf0s/vfXizCK1dOp1M3LiO7DTMnPzI/ZkZWI7qhQMuLl5QW5XF6sFiQ1NbVYbcm91q5di4kTJ2LdunXo0aNHmWVVKhVcXFwsFqKHkd6oR2KGaTKzomG9RiNwfEMjAEDrwWcli600bk5umDhgIuQyOY5e2YlVf39Ubdd+amIu5AoRB/fKcfgwa0eIyKRCyYhSqUTbtm0RExNjsT0mJgYdO3Ys9bg1a9Zg3LhxWL16Nfr371+5SIkeQufSz0Gr10JppzQ/IC/xL39kXnWBykmDJr2rd9bV8qrrXxcje4wEAGyOX4Lfzy+tluvW8Tei6+0p4lk7QkRFKtxMExkZiaVLlyI6OhoJCQmYOnUqkpKSMGnSJACmJpYxY8aYy69ZswZjxozB559/jsceewwpKSlISUlBVlaW9e6CSCJxN+IAmKZeL5qSPHZ9GACg5cALUNpLO4qmiCAAvo3S4dso3fx8nPCwcPR+tDcAYOmxD7H70o/VEsug53MhCCJ2bJXj5MnK144IgoAmdZqgSZ0m5WomJqKHl6KiB4wYMQLp6emYNWsWkpOT0axZM2zduhXBwcEAgOTkZIs5R7799lvo9Xq8/PLLePnll83bx44dixUrVjz4HRBJ6GjyUQCAv5c/AECvlSFus2kUTdthZySL615KBz3ePbyq2PY+7fugQFOAfSf3Yf5fb0ApV6NT4KAqjSWgrgGP9S7E4W32mPmxET+vl1fqPA52Djg9+bSVoyMiKQhiDRign52dDVdXV2RlZbH/CD1UIlZFYO+FvRgWMQyPt3gc/2wLxdJRT8LFJw8z/1kGmfyh//GCUTRi3c51+DP+T8gEGV55bB66Bpc+2s0aEhMUeHNwHQiCiJMngebNWbNBZIvK+/3NZ9MQPYCEVNOEZ0U1I7Hr73RcrQmJCGB6+u3wJ4YjPCzcNGX84SnYcm5xlV4ztLEe7XsWQBQFvDudfUeIajsmI0SVlFGQgdScVACAv6c/8rOU+Of3egCAtk//K2VoxWjzFfikw7P4pMOz0OYXb52VyWQY1XMUOrfsDACIjp2J1f/MrtKZTUe+buo7svVXOY4erfh18nX5aLqgKZouaMrp4IlqOCYjRJV06sYpAICniyfUKjWObwiDrlABv8ZpCGyVKnF0lkQRSDnjiZQznqXOfioTZBjSZQj6tO8DAPjpn2/w2Z8TqmxitMD6enS5PbLm7fcqXjsiiiLib8Yj/mY8p4MnquGYjBBV0uFrhwEAAXUCAAB/ft8UAPDYc6dRUwd3CIKAPu37YMQTIyCXyfHn5e14b3d/pOVdq5LrDX/FNO/Inj/k2LuXCQVRbcVkhKiSDl47CAAI8gnClZN1cPWUN+RKPcKHP1xNNJXRoVkHTB48GY5qR1xKP4M3Yrrj2PUdVr+Ob6DBPCtr5Nt8Zg1RbcVkhKiSTlw/AcCUjPz5fTMAQIsBF+DoUShlWFZTL6AeIp+JxCN1HkFuYTai9o3D0rhp0Bk0Vr3O0y/lQmVvxPEjcqz5kdkIUW3EZISoEm7m3cT1rOsAAB/nUPMomg7P2da8F54unpjy9BR0adUFAPD7vyvx9h89cSHzb6tdw8PHiMEv5AEA3nxbREGB1U5NRDUEkxGiSjhy7QgAwNvdGwm/tkJhjgqewVmo39n2HuqoUCgwpMsQPD/geTiqHZGUeR7v7uiL707NhNZgnVqgJ8fnwsvPgOSrMnz6GYf6EtU2TEaIKmH/1f0AgCCfYOz9thUAoNOEU5A9pD9RggC4B2bDPTC70p1rm9VthneffRetGrSCUTRic/xiTN3eBceTdz5wfCp74Nk3sgEAcz4RcP36/Y8RBAHBrsEIdg3mdPBENRxnYCWqhC7fd8H+i/vROWAi9r+wFEoHHWb+swwObtbtT/GwOnXhFDbs3oDsfFMC0SrgcUxoORsBLvUrfU5RBN57xhNnTyoxdLgBG9ZWbpp4Inp4cAZWoiqiN+oRezUWAHB925MAgEdHxteaRAQAWtRrgXefexcRrSMgk8kQd+0ApmzrhiUn3kFmQeXmWBEEYOL72ZDJRPy0To7tOx76v5OIyEqYjBBVUFxKHPK1+VAp7HHh534AgM7/iZM2KAk4qBwwqPMgvDP6HTQJaQKj0YBtZ77HS789iuUn30dWYXqFz1m/uQ59RpuG+r7wooh8TqxKVCswGSGqoJ2XTH0kHHLaAUYFwrpfgk+DW9IGdR/aAjk+7/4MPu/+DLQF1m3+8HH3wX+e/A9eGvQSgn2CoTNo8VtCNF76rR2Wn3y/whOmjXo9B56+Bly5JMOHM0vvzFqgK0C7Je3Qbkk7FOg4BIeoJmMyQlRBMZdiAABZh/oDALq/fkzKcMpFNAq4csIHV074QDRWTWfPRkGNMGX4FPznyf8g0DsQGn2hKSnZ8hjm/fVCuYcD2zuJeP6DLADAF/8TcOJEyc01RtGIY9eP4dj1YzCKHIFDVJMVf2IWEZXKKBpx5IppWK/xYgRC219H/U5VM1V6TSQIApqENEHj4MaIvxSP3cd34/y18ziYuAUHE7cgzKc1eoeOx2OBA6CUq0s9z6PdNWjfswB/xdjjmdFGnDwuQF16cSKq4VgzQlQBx64fQ05hDqBxAZLboNcbR2rsc2iqkiAIaBraFK8MfQVvPPMG2jZqC5kgw783TuDLP1/D87+0xNK4abiSdbbUc7w4MxuungacTZDhrXdZ80Fky5iMEFXAprObTCvneyGwRTrCul+WNJ6aINA7EM/1fg4fjvsQfdr3gZuTG/I0Ofj935WY8nsE3vzjCWw+swAZBSkWx7l6GjH5Y1NzzTdfyvDHTo6uIbJVTEaIKmDj6d9MK+f7ovfbf7FWpALcnN3Qp30ffDjuQ/znyf+ged3mkAkyJKb9i+9OfIz/bG6L9/cMwPYLK5GtMY3ECe+mQc/hpqninx0j4uZNKe+AiKoK+4wQlVNqXirOpP0DCECQa0s07X1A6pBqJJlMhiYhTdAkpAly8nMQdy4Ox88eR2JyIhJSjiMh5TiWHHsPDeo0x6P+/dDz5f44feQxXL+kwLARBuyKkUPO+dCIbAqTEaJyWrjrV0AQgeTWGDLtfI2rFXH0fPgm7XB2cEbnlp3RuWVnZGRn4PjZ44g7F4erN6/ibOopnE09BeATeE4KgnzfMOz7tw/e/qAjPp/tCADwcvCS9gaIyCo4HTxROYgi4P56b2R57oDP9Vcx7ZN6Uodk0zJzMvHPxX/wT+I/OH/1PAxGw52dehUauoRjVPue6FevH9r4tYFcxqoSoodReb+/mYwQlcPC79Iw+bwfINfjpW6z0ai5g9Qh1RqFmkL8m/QvTl86jZN/X4ZWYTndvLPaGeGPhKNbUDd0C+6GcP9wqBUcB0z0MGAyQmQlt24Bjzy1GHlPvAhnY338d8orUodUa+m1AuZPaIbEmwlQNNkGeYOd0OgtZ1+1k9uhqW9TPB70OLoHdcejAY/C39lfooiJajcmI0RWMm6iESuNTwAhe9H30YHo/Vh3qUOqMG2BHN8OHwQAeHHdJijtDWUf8BArzFbiy35PI/msI+z+0x1eja6ibVhLJKUm4eL1i8gtyC12jKejJ1r6tUR7//bo6N8R4QHh8HXylSB6otqlvN/f7MBKVIZNm0Ws/OUs8MpeCBDQvmlbqUOqFNEo4MLBR8zrNZnaRYsX127GvH79ke19GMmZQNvgN9CjHSCKItKy0pCYnIiL1y/iUvIl3Mi8gfS8dOw6vwu7zu8yn6eOUx00822Glt4tEe4bjhbeLdDIqxGUcqWEd0dUOzEZISpFaiow8XkRaPstAKBpaFO4ObtJGxQBANwCcvHCml/x+R+m10ufHYiX12+Hg6sWddzqoI5bHTza+FEAgEanwbWb13Al9QqupF7B1dSruJFxAzdzb2L3+d3YfX63+bxymRwhHiFo4t0Erbxboa1PWzSu0xihbqGwk9tJcatEtQKTEaIS6PXA0yMNyMgugNBmBUQAHZt3lDosuot3g0zgdjJy7aQPFjw1FC+u2wxnb8shzCo7Fer610Vd/7rmbRqtBtfSruHazWtITk82L4XaQlxIu4ALaRfwa/yv5vJymRwBrgGo51kPYZ5haObVDE28mqChZ0P4OflBqGnjvIkeMkxGiErw9rtG7Nslh7zrQhhUt1DHrQ7CgsKkDotK4eiZj6unvPFFn6cxacMm1KmbVWZ5lbJ4giKKIm7l3kJyejKup11HSnoKktOTkXorFTq9DkmZSUjKTLKoSQEAezt7BLoHItgtGPXc66GReyM0cG+AUPdQhLiFwMGOI6+I7ofJCNE9Vn5vxP8+lwGKAii7f4oCI9AjvAdkMj494WE1+eefEf3MCKRfcsOXfYdj/IotqNfheoXOIQgC3J3d4e7sjiYhTczbjaIR2bnZSL2VitTMVNy8dRM3b91EamYqMrIzUKArwNnUszibehYxiCl2Xg8HDzzi9giC3YJR370+6rvVR4hrCB5xeQSPuDwCd7U7a1ao1mMyQnSXLVtFTJxg+mJo/OqnSDCmw8PFA+GNwiWOjMriVfcWXt+2DotHPIWrp7wx/6kheOq/+9HlPycfeKZcmSCDm7Mb3Jzd0DCwocU+vUGP9Kx0pGWlIT07HelZ6cjIzkBGdgbSs9NRqC1ERn4GMvIzcOr6qRLPr1Ko4O3sDX9nfwS4BCDYJRghriHmhMXf2R91HOpwYjeyaUxGiG7bu0/E0KGAQS+g3aALOOX5KaAH+rbvC7kNPAxF6aCTOgSrUyrujHxx8cnHq1vWY+2UHjj+UyP8PC0Cl474Ydhnu+HorqmS6yvkCvh4+MDHw6fE/fmF+UjPTjcnJxlZGcjMzURWbhZu5d5CbkEuNHoNrmRewZXMK6VeRybI4GbvBk9HT3g7ecPXyRf+Tv4IcArAI06PwM/ZDz6OPvB18oWHvQdrWqjG4TwjRAB+2yJi2DBAUyigVecC2I19CkevxiDENwSvP/06f7nXIKII7Pu2FTZ/0BlGgwyufrl45ss/0LjHZalDK0an1yErL8ucnBQt5tc5t5CTnwMR5f81rZAp4O7gDg9HD3jae8LLwQt1HOrAx8EHvo6+8HH0gZeDl3nxtPeESqGqwruk2oyTnhGVU/QKI/7zggCDXkDrLoVo9+YCLD7+BmQyGaYOn4pA70CpQ6RKuBzrgx8m90LqOQ8AQPjwBAyccRCufnkSR1YxBqMBeQV5yM7PRk5eDnIKcpCdl42c/Bxk52cjNz/X/DpfU7mHITooHeBm72ZKYuw94OngCU+1JzzsPeCl9oKXvSlpcVO7wd3eHe5qd7jbu8PRzpGJOpWJyQjRfWi1wKtTjFi80NQx9fH+Bej3zn58tO8paPSF6N+hP3q26ylxlPQgtPkK/Pbfjti/uBVEUYDKSYuebxxBlxdOQumglzo8q9Pr9cgpyEFOfg5yC3KRV5CHvMI85BXkIbfw9uvb23ILcpFfmA+jaKz09eQyOZxUTnBRu8BF7WJKVtTucFO7mZIatacpcVG5w1XlCheVC5xVznBRuZgXlVzFhMaGMRkhKsOpUyKeG2fEqROmviDDX85BxLh/MX13P2Tmp6FhYENMemqSzYyg0RXKsXxsfwDA+JVbYKeuudPBF9HpdVi+dTkAYHy/8bBTlD4pWdIJb/z0TgQuH/MDADh75+GJV2PRafzfNpmUlJdRNKJQU2hOTu5OXgo0BcjX5CNfk48CTQEKCgvM6/mafBiNlU9i7qaQKeCocoSj0rQ4q5wtEhZXlStcla6mREflDmelM5yUTnBSOsHRznTM3etMbh4unA6eqAQ5OcD/fWLE53MF6HVyOLoY8dqnt+DX9jTe3zMUmflp8PXwxbh+42wmEQEAo0FAfEyoed0WGEUj4i/Fm9fLEtQ6Fa9vW4fY9WH4PeoxZCS5YvMHXfDHF+F47Nl4dBz3NzyDs6sj7IeKTJDBQe0AB7UD6rjVKfdxoihCq9daJChFSUqBpgD5hXe2aXQaFGoLzYtGa3qt0Zk6FeuNemQVZCGroOy5YSpyT/Z29lDbqeGgdICDnYP5/0VJS1Ey42xnSmxcVa5wtnOGo50j7O3sYa+wN//fwc6h2DaObLI+JiNUKxQUAIuXGjFrFpCRZkoy2j1RiBdnZuGs9je888drKNDmw9vdG5OemgQHFSeqsjUyGdBuxL9oM+Qsjv7YGDHz2iH9sit2fhmOXV+1RVj3y2gz9Aya9b0Iexet1OE+1ARBgMpOBZWdqtKPSDCKRmi1WstE5Z7E5e7kpWjR6rTQ6rXQaDXQ6rXQ6rTQ6DTQG/Tm8+Zp85CnzUN6XroV7/oOhUwBlUIFlUIFtZ0aaoX6zv8VaovkxcHOAQ4KB/P/He0c4WjnaContzcfo1KooJKrzP+/d5taoYZKrrLZRIjJCNm0xERgwbdGLF0C3MowJSG+wXo8OzUHDbtcwvJTH+DgpS0AgGDfYDw/4Hk4OzhLGTJVMbmdEY89dxrtRsbj9PZQHIxugTO7g5HwRwgS/giBXKlH2BNJaNLzEhpFJMEr1Dp/sZMlmSCDWqWGWqW2yvkMRgN0Oh00OlOSotFpzInK3f83r+uLb9PpdZaL4c56UbIDmGpz9Fo98rTV3xlaJsigVCihlCthJ7czr6sUKijld9bVCjWUcqU5iSnappabtqvkKvOiVqihlCnvrMuVsJPZmc9XtNjJLbeVVKayyVKlkpEFCxZg7ty5SE5ORtOmTfHFF1+gc+fOpZbfu3cvIiMjcfr0afj7++Ptt9/GpEmTKhUwUVlEETh9GtjyuxFr1oo4GSsHYEpCvAP0eGpiHsJ6x2HrpQX4cssm6AxaCBAQ0ToCAzoOsIn5RKh85AoRLfpfRIv+F3HzghtiNzTCiZ8b4MZZT5zeVhent5mmivcMzkK9jtcQ1OYGglrfgH/TNChUNb/Pja2Ry+SQq+RWS27uZTQaLZKTkhIXrU5bLIm5d9HqtdAb9OalKNEpddHrLYZ2G0UjCnWFKNQVVsl9PigBAhRyBezkdlDIFZBry/c7tcLJyNq1azFlyhQsWLAAnTp1wrfffou+ffsiPj4eQUFBxconJiaiX79+eOGFF7Bq1SocPHgQkydPRp06dTB06NCKXp7ITBSBlBTg5Eng2AkjjhwTcXCfcLsZxpSAyGQiGndKQ+NB+2AI3IWt17diScwl8zlCfEMwNGIoh+/WcnXq3UKfd/5C77f/QkqCJ/7eWhdn9gTh0lE/pF92RfplVxxZY5oiXq7Uw7v+LXjXz4RPgwx4N8iEV2gWXP1y4eKbB7nioR8TQJUgk8mgkpmapqqTKIowGo2WCYxBVyxhKTOhuSfpMRqN0Bv1MBgMMBgNMBgMZb42H2PQm18bjKZ993ZkFiGakjHD7UkWy5kzVXg0Tfv27dGmTRssXLjQvK1x48YYNGgQoqKiipV/55138MsvvyAhIcG8bdKkSTh58iQOHz5crmtyNI3tMxgAjca0FBaa+nhkZgIZGaYlPV3EzXQjkq4Cly8DSVd0uJ6iRZ6mELDLBxzSbi83IXdNhVuD81AFJUDrcB7pBddx98dcLpOjcUhjRLSKQL2AerWi570mT4F3Al8GAHx6ZT5UjjV/BIlGp8E7C98BAHz60qdV8iWhybXDhcMBuHTUF0nHfZB0wgf5mfallhdkRjh758PNPxfOdfLh4F4Ie1cNHNwL4eBm+r/aWQulvR529jrY2euhvL3Y3d6mUBohyMQHnsaeqDoYRSOMhnuSm6KExWBAXnYevm77tXVH02i1WsTGxuLdd9+12N6rVy8cOnSoxGMOHz6MXr16WWzr3bs3li1bBp1OBzu74sPxNBoNNJo70zdnZZnabL2mdIGgvF3lI9yTQ4n3bBMB0eK1UPJxJc5sWPI2sRxl7uuu64v3xlyhc5d8LYutgli8WLmvV3oZsdznKfu8onhXkbuLlnRuwQAoCgHnQqBVIdC69NETBgDpgCkjv52Vu7u4I9gnGA0CG6BxcGNzB1VNTtVME/6w0eTLAZhGixTmFEA01PxmBo1eY/73LcwphFglNRKFqNv+X9Rt/y8A02c286ozUs+7I/W8G25edEPaBTdkXnVG1g1HiAY5slOA7BQHAA/WCVqQGyBXiJApjJDJTYtcIZrWFUYICtGUtNz+4REE01L082NaBwRBNP+/aL/FPpRQhshKjIZcAF/jfvUeFUpG0tLSYDAY4ONj+RwGHx8fpKSklHhMSkpKieX1ej3S0tLg5+dX7JioqCh89NFHxbbrlp+sSLhEZpm3/4tDnNShSOgtAMCMJvcpVgPN+GSG1CFYnWgA9AYAtSNfJhuXk5MDV1fXUvdXqgPrvdXaoiiWWdVdUvmStheZNm0aIiMjza9v3bqF4OBgJCUllXkzti47OxuBgYG4cuVKrW6u4vtgwvfBhO/DHXwvTPg+mDwM74MoisjJyYG/v3+Z5SqUjHh5eUEulxerBUlNTS1W+1HE19e3xPIKhQKenp4lHqNSqaBSFW//dXV1rdUfrCIuLi58H8D3oQjfBxO+D3fwvTDh+2Ai9ftQnkqECk0xqVQq0bZtW8TExFhsj4mJQceOHUs8pkOHDsXK79ixA+Hh4SX2FyEiIqLapcLzXUdGRmLp0qWIjo5GQkICpk6diqSkJPO8IdOmTcOYMWPM5SdNmoTLly8jMjISCQkJiI6OxrJly/Dmm29a7y6IiIioxqpwn5ERI0YgPT0ds2bNQnJyMpo1a4atW7ciODgYAJCcnIykpCRz+dDQUGzduhVTp07F/Pnz4e/vj6+++qpCc4yoVCrMmDGjxKab2oTvgwnfBxO+DyZ8H+7ge2HC98GkJr0PNeKpvURERGS7bOexpERERFQjMRkhIiIiSTEZISIiIkkxGSEiIiJJMRkhIiIiST30yciCBQsQGhoKtVqNtm3bYv/+/VKHVO2ioqLQrl07ODs7w9vbG4MGDcKZM2ekDktSUVFREAQBU6ZMkToUSVy7dg3PPvssPD094eDggFatWiE2NlbqsKqVXq/H+++/j9DQUNjb26Nu3bqYNWtWsUea25p9+/Zh4MCB8Pf3hyAI2LRpk8V+URQxc+ZM+Pv7w97eHhERETh9+rQ0wVahst4HnU6Hd955B82bN4ejoyP8/f0xZswYXL9+XbqAq8j9Pg93e/HFFyEIAr744otqi6+8HupkZO3atZgyZQqmT5+OEydOoHPnzujbt6/FPCa1wd69e/Hyyy/jzz//RExMDPR6PXr16oW8vDypQ5PE0aNHsXjxYrRo0ULqUCSRmZmJTp06wc7ODr///jvi4+Px+eefw83NTerQqtWnn36KRYsW4ZtvvkFCQgLmzJmDuXPn4uuvv5Y6tCqVl5eHli1b4ptvvilx/5w5czBv3jx88803OHr0KHx9fdGzZ0/k5ORUc6RVq6z3IT8/H8ePH8cHH3yA48ePY+PGjTh79iyefPJJCSKtWvf7PBTZtGkT/vrrr/s+I0Yy4kPs0UcfFSdNmmSxLSwsTHz33XcliujhkJqaKgIQ9+7dK3Uo1S4nJ0ds0KCBGBMTI3bt2lV8/fXXpQ6p2r3zzjvi448/LnUYkuvfv784YcIEi21DhgwRn332WYkiqn4AxJ9//tn82mg0ir6+vuInn3xi3lZYWCi6urqKixYtkiDC6nHv+1CSI0eOiADEy5cvV09QEijtfbh69aoYEBAg/vPPP2JwcLD4v//9r9pju5+HtmZEq9UiNjYWvXr1stjeq1cvHDp0SKKoHg5ZWVkAAA8PD4kjqX4vv/wy+vfvjx49ekgdimR++eUXhIeH4+mnn4a3tzdat26NJUuWSB1WtXv88cexc+dOnD17FgBw8uRJHDhwAP369ZM4MukkJiYiJSXF4vemSqVC165d+XszKwuCINS6GkSj0YjnnnsOb731Fpo2bSp1OKWq8HTw1SUtLQ0Gg6HY04B9fHyKPQW4NhFFEZGRkXj88cfRrFkzqcOpVj/++CNiY2Nx7NgxqUOR1MWLF7Fw4UJERkbivffew5EjR/Daa69BpVJZPBfK1r3zzjvIyspCWFgY5HI5DAYD/u///g8jR46UOjTJFP1uLOn35uXLl6UI6aFQWFiId999F6NGjap1T/H99NNPoVAo8Nprr0kdSpke2mSkiCAIFq9FUSy2rTZ55ZVXcOrUKRw4cEDqUKrVlStX8Prrr2PHjh1Qq9VShyMpo9GI8PBwzJ49GwDQunVrnD59GgsXLqxVycjatWuxatUqrF69Gk2bNkVcXBymTJkCf39/jB07VurwJMXfm3fodDo888wzMBqNWLBggdThVKvY2Fh8+eWXOH78+EP/7//QNtN4eXlBLpcXqwVJTU0tlvXXFq+++ip++eUX7N69G4888ojU4VSr2NhYpKamom3btlAoFFAoFNi7dy+++uorKBQKGAwGqUOsNn5+fmjSpInFtsaNG9e6jt1vvfUW3n33XTzzzDNo3rw5nnvuOUydOhVRUVFShyYZX19fAODvzdt0Oh2GDx+OxMRExMTE1Lpakf379yM1NRVBQUHm35uXL1/GG2+8gZCQEKnDs/DQJiNKpRJt27ZFTEyMxfaYmBh07NhRoqikIYoiXnnlFWzcuBG7du1CaGio1CFVu+7du+Pvv/9GXFyceQkPD8fo0aMRFxcHuVwudYjVplOnTsWGdp89e9b85OzaIj8/HzKZ5a8wuVxu80N7yxIaGgpfX1+L35tarRZ79+6tdb83ixKRc+fO4Y8//oCnp6fUIVW75557DqdOnbL4venv74+33noL27dvlzo8Cw91M01kZCSee+45hIeHo0OHDli8eDGSkpIwadIkqUOrVi+//DJWr16NzZs3w9nZ2fxXj6urK+zt7SWOrno4OzsX6yPj6OgIT0/PWtd3ZurUqejYsSNmz56N4cOH48iRI1i8eDEWL14sdWjVauDAgfi///s/BAUFoWnTpjhx4gTmzZuHCRMmSB1alcrNzcX58+fNrxMTExEXFwcPDw8EBQVhypQpmD17Nho0aIAGDRpg9uzZcHBwwKhRoySM2vrKeh/8/f0xbNgwHD9+HL/99hsMBoP596aHhweUSqVUYVvd/T4P9yZhdnZ28PX1RaNGjao71LJJO5jn/ubPny8GBweLSqVSbNOmTa0czgqgxGX58uVShyap2jq0VxRF8ddffxWbNWsmqlQqMSwsTFy8eLHUIVW77Oxs8fXXXxeDgoJEtVot1q1bV5w+fbqo0WikDq1K7d69u8TfB2PHjhVF0TS8d8aMGaKvr6+oUqnELl26iH///be0QVeBst6HxMTEUn9v7t69W+rQrep+n4d7PaxDewVRFMVqynuIiIiIinlo+4wQERFR7cBkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCT1//0ceJg8nLR+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.3442238 , 7.94607029]),\n", + " array([0.71331024, 2.395806 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Rafael Leao', plot = 1, log = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "30744d7a", + "metadata": {}, + "source": [ + "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", + "\n", + "Here the code to generate the probability density function is reproduced." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "3ec6c3dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# custom franction to calculate the probability density function\n", + "\n", + "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", + "\n", + " mul = np.sinh( np.arcsinh(2) * delta)\n", + " \n", + " mul = 2 / mul\n", + " \n", + " sigma_corr = sigma * mul\n", + " \n", + " z = (x - mu) / sigma_corr\n", + " \n", + " \n", + " \n", + " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", + " \n", + " f = np.exp(-0.5 * S * S)\n", + "\n", + " f /= np.sqrt(2 * np.pi)\n", + " \n", + " f *= 1 / ( sigma_corr * delta )\n", + " \n", + " f *= np.sqrt(1 + S * S)\n", + " \n", + " f /= np.sqrt(1 + z * z)\n", + " \n", + " return f\n", + " \n", + "\n", + "x = np.arange(start = 0, stop = 15, step = 0.001)\n", + "\n", + "\n", + "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605dc968", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/7_lineup_simulation-checkpoint.ipynb b/.ipynb_checkpoints/7_lineup_simulation-checkpoint.ipynb new file mode 100644 index 0000000..7f84e58 --- /dev/null +++ b/.ipynb_checkpoints/7_lineup_simulation-checkpoint.ipynb @@ -0,0 +1,851 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "0d54533f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3654c88", + "metadata": {}, + "outputs": [], + "source": [ + "file = 'outputs/pred_matchday_32.xlsx'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f89d6b70", + "metadata": {}, + "outputs": [], + "source": [ + "db = pd.read_excel(file, index_col = 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f61a1fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SportielloPAtalantaTorino01.00756.2418500.4139805.9037490.3572965.9978540.2496470.6233921.5898396.2113040.449493-0.4828761.12652972.505307
MussoPAtalantaTorino00.0056.2442590.4130184.9517200.5012296.0018300.2513220.6171461.5899035.4102360.623711-0.5188071.0106908.606489
Rossi F.PAtalantaTorino00.0016.2443490.4118844.0809160.6770076.0037430.2532240.6100141.5899354.9092960.706557-0.7887860.9690510.049822
ToloiDAtalantaTorino01.00906.0486180.4858076.3107970.7008966.0118220.5642630.0481190.9962585.8228670.9833890.3520091.5998780.000000
ScalviniDAtalantaTorino01.00906.0304350.4867776.2897150.7113065.9943970.5656850.0470120.9973485.7867890.9893340.3598111.5998740.000000
............................................................
GaichAVeronaCremonese00.55555.9286110.4843926.2328410.7733085.8287360.5416760.1357601.0167155.5882400.9523200.4625961.5998560.000000
DjuricAVeronaCremonese00.45606.0438460.3642316.2313500.4825695.9889440.4166820.0974631.1338355.9301000.7133640.3036061.5999140.000000
KallonAVeronaCremonese00.00405.9197050.4197316.1530880.6303385.8395790.4723100.1251811.0845535.6528730.8104790.4273061.5998810.000000
BraafAVeronaCremonese00.00355.8998510.3886505.9743700.4711265.8752010.4570900.0399281.1065655.7448200.7539110.2228621.5999180.000000
LasagnaAVeronaCremonese01.00805.7561070.3972285.8250530.5198555.7041760.4573590.0839961.1166215.4603840.7262970.3558101.5998960.000000
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525 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV \\\n", + "player \n", + "Sportiello P Atalanta Torino 0 1.00 75 6.241850 \n", + "Musso P Atalanta Torino 0 0.00 5 6.244259 \n", + "Rossi F. P Atalanta Torino 0 0.00 1 6.244349 \n", + "Toloi D Atalanta Torino 0 1.00 90 6.048618 \n", + "Scalvini D Atalanta Torino 0 1.00 90 6.030435 \n", + "... ... ... ... ... ... ... ... \n", + "Gaich A Verona Cremonese 0 0.55 55 5.928611 \n", + "Djuric A Verona Cremonese 0 0.45 60 6.043846 \n", + "Kallon A Verona Cremonese 0 0.00 40 5.919705 \n", + "Braaf A Verona Cremonese 0 0.00 35 5.899851 \n", + "Lasagna A Verona Cremonese 0 1.00 80 5.756107 \n", + "\n", + " MV std FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Sportiello 0.413980 5.903749 0.357296 5.997854 0.249647 0.623392 \n", + "Musso 0.413018 4.951720 0.501229 6.001830 0.251322 0.617146 \n", + "Rossi F. 0.411884 4.080916 0.677007 6.003743 0.253224 0.610014 \n", + "Toloi 0.485807 6.310797 0.700896 6.011822 0.564263 0.048119 \n", + "Scalvini 0.486777 6.289715 0.711306 5.994397 0.565685 0.047012 \n", + "... ... ... ... ... ... ... \n", + "Gaich 0.484392 6.232841 0.773308 5.828736 0.541676 0.135760 \n", + "Djuric 0.364231 6.231350 0.482569 5.988944 0.416682 0.097463 \n", + "Kallon 0.419731 6.153088 0.630338 5.839579 0.472310 0.125181 \n", + "Braaf 0.388650 5.974370 0.471126 5.875201 0.457090 0.039928 \n", + "Lasagna 0.397228 5.825053 0.519855 5.704176 0.457359 0.083996 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Sportiello 1.589839 6.211304 0.449493 -0.482876 1.126529 \n", + "Musso 1.589903 5.410236 0.623711 -0.518807 1.010690 \n", + "Rossi F. 1.589935 4.909296 0.706557 -0.788786 0.969051 \n", + "Toloi 0.996258 5.822867 0.983389 0.352009 1.599878 \n", + "Scalvini 0.997348 5.786789 0.989334 0.359811 1.599874 \n", + "... ... ... ... ... ... \n", + "Gaich 1.016715 5.588240 0.952320 0.462596 1.599856 \n", + "Djuric 1.133835 5.930100 0.713364 0.303606 1.599914 \n", + "Kallon 1.084553 5.652873 0.810479 0.427306 1.599881 \n", + "Braaf 1.106565 5.744820 0.753911 0.222862 1.599918 \n", + "Lasagna 1.116621 5.460384 0.726297 0.355810 1.599896 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Sportiello 72.505307 \n", + "Musso 8.606489 \n", + "Rossi F. 0.049822 \n", + "Toloi 0.000000 \n", + "Scalvini 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Braaf 0.000000 \n", + "Lasagna 0.000000 \n", + "\n", + "[525 rows x 19 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aa1c6af5", + "metadata": {}, + "outputs": [], + "source": [ + "def get_player_distributions(player): \n", + " dist_mv = tfp.distributions.SinhArcsinh(\n", + " db.loc[player, 'MV loc'],\n", + " db.loc[player, 'MV scale'],\n", + " db.loc[player, 'MV skewness'],\n", + " db.loc[player, 'MV tailweight']\n", + " )\n", + " \n", + " dist_fv = tfp.distributions.SinhArcsinh(\n", + " db.loc[player, 'FV loc'],\n", + " db.loc[player, 'FV scale'],\n", + " db.loc[player, 'FV skewness'],\n", + " db.loc[player, 'FV tailweight']\n", + " )\n", + " \n", + " dist_cs = tfp.distributions.Bernoulli(probs = db.loc[player, 'Clean Sheet %']/100)\n", + " \n", + " return [dist_mv, dist_fv, dist_cs];" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8456c70", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_lineup(squad, ITERS = 1000, MOD = False, CS = False, plot = True):\n", + " dist = [None] * len(squad)\n", + "\n", + " defenders = list([0]) * len(squad)\n", + "\n", + "\n", + " for i in range(len(squad)):\n", + " dist[i] = get_player_distributions(squad[i]) \n", + "\n", + " if(db['role'][squad[i]] == 'D'):\n", + " defenders[i] = 1\n", + " \n", + " total_points = np.zeros(ITERS)\n", + " clean_sheets = np.zeros(ITERS)\n", + " mod_points = np.zeros(ITERS)\n", + "\n", + " mv_samples = [None] * len(squad)\n", + " fv_samples = [None] * len(squad)\n", + "\n", + " for i in range(len(squad)):\n", + " mv_samples[i] = dist[i][0].sample(ITERS)\n", + " fv_samples[i] = dist[i][1].sample(ITERS)\n", + "\n", + " cs_samples = dist[0][2].sample(ITERS)\n", + "\n", + " for k in range(ITERS):\n", + " d_points = list([0]) * len(squad)\n", + "\n", + " cleansheet = float(cs_samples[k])\n", + " if(not CS):\n", + " cleansheet = 0;\n", + " \n", + " total_points[k] += cleansheet\n", + " clean_sheets[k] += cleansheet\n", + "\n", + " for i in range(len(squad)): \n", + " if(defenders[i] == 1):\n", + " d_points[i] = float(mv_samples[i][k])\n", + "\n", + " total_points[k] += float(fv_samples[i][k])\n", + "\n", + " d_points.sort(reverse = True)\n", + "\n", + " if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n", + " mod_avg = 0\n", + " for j in range(3):\n", + " mod_avg += round(d_points[j] * 2) / 2\n", + " mod_avg /= 3\n", + "\n", + " if(mod_avg >= 7):\n", + " mod_points[k] = 6\n", + " elif(mod_avg >= 6.5):\n", + " mod_points[k] = 3\n", + " elif(mod_avg >= 6):\n", + " mod_points[k] = 1\n", + "\n", + " total_points[k] += mod_points[k]\n", + " \n", + " loc_0 = total_points.mean()\n", + "\n", + " squad_model = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.Dense(3),\n", + " tfp.layers.DistributionLambda(\n", + " lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n", + " skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n", + " )\n", + " ]\n", + " )\n", + "\n", + " def negloglik(y, distr):\n", + " return -distr.log_prob(y)\n", + "\n", + " squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n", + "\n", + " dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n", + " squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)\n", + " \n", + " squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n", + "\n", + " if(plot):\n", + "\n", + " x = np.arange(start = 0, stop = 200, step = 0.001)\n", + " prb = squad_points_dist.prob(x)\n", + "\n", + " mn = total_points.mean()\n", + " pot = mn + 2 * total_points.std()\n", + "\n", + " f, ax = plt.subplots(1, 2)\n", + "\n", + " ax[0].plot(x, prb)\n", + " ax[0].fill_between(x, prb, color = 'lightblue')\n", + " ax[0].vlines(x = mn, color = 'black', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n", + " ax[0].vlines(x = pot, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'potential = ' + \"{:.2f}\".format(pot))\n", + "\n", + " \n", + " ax[0].set_xlim([40, 140])\n", + " ax[0].set_ylim([0, 0.12])\n", + "\n", + " ax[0].legend()\n", + "\n", + " #ax[0].hist(total_points, bins = 20, density = True)\n", + "\n", + "\n", + " ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + " text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n", + " 'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n", + " 'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n", + "\n", + " ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + " ax[1].axis('off')\n", + "\n", + "\n", + "\n", + " plt.subplots_adjust(right=1.5)\n", + "\n", + " plt.show()\n", + " return [squad_model, squad_points_dist, total_points.mean(), mod_points.mean(), clean_sheets.mean()]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "852ef39e", + "metadata": {}, + "outputs": [], + "source": [ + "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", + " mul = 2 / np.sinh( np.arcsinh(2) * delta) \n", + " z = (x - mu) / (sigma*mul) \n", + " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", + " return np.exp(-0.5 * S * S) * np.sqrt(1 + S * S) / ( sigma * mul * delta ) / np.sqrt(1 + z * z) / np.sqrt(2 * np.pi)\n", + "\n", + "def plot_lineup(squad, config):\n", + " fig, axs = plt.subplots(6, 5, figsize=(14, 10))\n", + "\n", + " i = 0;\n", + " j = 1;\n", + "\n", + " for ax in axs.flat:\n", + " ax.axis('off')\n", + "\n", + " if(i < len(config) and config[i] == j):\n", + " player = squad[i];\n", + "\n", + " mu = db.loc[player, 'FV loc'];\n", + " sigma = db.loc[player, 'FV scale'];\n", + " eps = db.loc[player, 'FV skewness'];\n", + " delta = db.loc[player, 'FV tailweight'];\n", + "\n", + " x = np.arange(start = 0, stop = 30, step = 0.001)\n", + "\n", + " pxf = sinh_archsinh_pdf(x, mu, sigma, eps, delta)\n", + "\n", + " mf = np.average(x, weights = pxf);\n", + "\n", + "\n", + " ax.plot(x, pxf, color = 'g', label = player)\n", + " ax.fill_between(x, pxf, color = 'lightgreen')\n", + "\n", + " ax.vlines(x = mf, color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mfv = ' + '{:.2f}'.format(mf)) \n", + "\n", + " ax.legend(fontsize=\"9\", loc =\"upper right\", handletextpad=0, handlelength=0)\n", + "\n", + " ax.axis(xmin = 0, xmax = 15, ymin = 0, ymax = 1)\n", + "\n", + " ax.axis('on')\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " ax.tick_params(axis='both', labelsize=7)\n", + "\n", + " i = i + 1;\n", + "\n", + " j = j + 1;\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8fa1df8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Szczesny',\n", + " 'Di Lorenzo',\n", + " 'Kim',\n", + " 'Juan Jesus',\n", + " 'Carlos Augusto',\n", + " 'Kostic',\n", + " 'Frattesi',\n", + " 'Barella',\n", + " 'Strefezza',\n", + " 'Rafael Leao',\n", + " 'Lauriente\\'']\n", + "\n", + "config_442 = [3, 6, 7, 9, 10, 15, 17, 19, 21, 27, 29];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_442)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a4fd2fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Szczesny',\n", + " 'Di Lorenzo',\n", + " 'Kim',\n", + " 'Juan Jesus',\n", + " 'Carlos Augusto',\n", + " 'Rovella',\n", + " 'Frattesi',\n", + " 'Barella',\n", + " 'Gonzalez N.',\n", + " 'Rafael Leao',\n", + " 'Lauriente\\'']\n", + "\n", + "config_433 = [3, 6, 7, 9, 10, 13, 17, 19, 26, 28, 30];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_433)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3943571c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Carnesecchi',\n", + " 'Danilo',\n", + " 'Ampadu',\n", + " 'Luperto',\n", + " 'Hernandez T.',\n", + " 'Zambo Anguissa',\n", + " 'Milinkovic-Savic',\n", + " 'Vlasic',\n", + " 'Kvaratskhelia',\n", + " 'Caprari',\n", + " 'Lukaku']\n", + "\n", + "config_4231 = [3, 6, 7, 9, 10, 12, 19, 23, 25, 26, 28];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_4231)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "15d5b4ca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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Ai/G0HQAAgLajGAMAAM6qsLJQN6+8WcGPBivu6Ti9sOEFd4cEAADQbnGbEgAAaJLZZNbwhOGqs9Vp6tKp2p63XZJUUVqh36z+jepMdfqv4f/l5igBAADaH66MAQAATQr0DdSPv/xR3SK6aXvedoUGheq26bfp4uEXS5Lu+eweZRVnuTlKAACA9odiDAAAaNZb29/S8j3LZTFbdMsVt6hnck9NHTlVKZ1SVFlTqfvX3e/uEAEAANodijEAAKBJJVUluufzeyRJk8+frJROKZLqb1+aNGKSJGn5tuUqrip2W4wAAADtEcUYAADQpD99/SfllubKbDJrVP9Rjab16dJHUWFRKq8q1+I9i90TIAAAQDtFMQYAAJwmvyxfL2ysf2KSzbDJ4mNpNN1sMmtIzyGSpGW7l7k8PgAAgPaMYgwAADjNX7//qypqK844z+AegyVJGw5tUHE1tyoBAAC0FMUYAADQyPGK43r+x+fPOl9idKKsIVbV1NXog7QPnB8YAABAB0ExBgAANPL8j8+rpLpEnSI7nXE+k8mknik9JUkfH/zYFaEBAAB0CBRjAACAXU1djV7a+JIkafyQ8Wedv1dyL0nS+rT1To0LAACgI6EYAwAA7D7c+6GOlB5RaFCoBnQfcNb5G4oxmccylVaS5uzwAAAAOgSKMQAAwO6lTfVXxVzQ9wL5WnwVFxmnuMg4mWRqcv6QoBB1iqi/nenfh//tsjgBAADaMx93BwAAADzD/oL9+vzQ5zLJpAv6XyA/Xz/99/X/fdbluiZ0Vd6JPH17+Fvd2u9WF0QKAADQvnFlDAAAkCT9Y9M/JEl9uvRRVFhUi5frGt9VkrQle4tT4gIAAOhoKMYAAABV1lbq9a2vS5Iu7H9hq5btEt9FknQo95BKa0sdHRoAAECHQzEGAADoX7v/pYKKAllDrOrbpa8kqbqmWn956y/6y1t/UXVNdbPLxlpjFRQQpJq6Gn2V/ZWrQgYAAGi3KMYAAAD746wv7H+hzOb6wwNDhnKP5yr3eK4MGc0uazKZ1DWu/lalrw9/7fRYAQAA2juKMQAAeLkdeTu0LnOdzCazzu93/jmtI7lTsiRp85HNjgwNAACgQ6IYAwCAl3t508uSpAHdBig8OPyc1pHSKUWStDdvr8PiAgAA6KgoxgAA4MVKq0u1ZNsSSdKFA1o3cO/JkmPrr4zJPZ6rIxVHHBIbAABAR0UxBgAAL/buzndVUl2i6PBo9Ujucc7rCQ0KlTXEKkOGvsn+xoERAgAAdDwUYwAA8FKGYejFjS9KkkYNGCWzqW2HBQ23Kq3LXtfm2AAAADoyijEAAHipjTkbtfnIZvlYfHRen/NOm26SSRGhEYoIjZBJprOur+FWpa1Htjo6VAAAgA7Fx90BAAAA92h4nPXg1MEKCQw5bbqfr58W3rSwxeuzD+Kbu1eGYchkOnsBBwAAwBtxZQwAAF6osLJQ7+x8R1LbBu49WVJskiQpvyhfmeWZDlknAABAR0QxBgAAL/TmtjdVUVuh+Kh4dY3v6pB1BgcEKyosSpIYxBcAAOAMKMYAAOBlDMPQS5vqb1EaNWBUs7cTVddW6+l3n9bT7z6t6trqFq07uVP9uDHrc9Y7JlgAAIAOiGIMAABeZs3hNdqdv1t+vn4a3mt4s/MZhqHMo5nKPJopwzBatO6U2PpxY7bmbHVEqAAAAB0SxRgAALxMw+Osh/UcpgD/AIeuu+GJSg2D+AIAAOB0FGMAAPAiuaW5Wr57uSRp1MBRDl9/UmySTDLpeMlxHSo55PD1AwAAdAQUYwAA8CKvbn5VNbYadYnroqSYJIevP9A/UJ0iO0mSPs/83OHrBwAA6AgoxgAA4CVqbbV6edPLkqTRA0c7rZ3OcZ0lSeuy1jmtDQAAgPaMYgwAAF5i1b5VyirOUnBAsAalDnJaO13iukiStmRvcVobAAAA7ZmPuwMAAACu8cKGFyRJI/uNlK+Pb4uWCQ4IbnU7DVfG7M/dr5q6GvlaWtYWAACAt6AYAwCAF9iTv0f/OfQfmWTSyAEjW7SMv6+//vdX/9vqtuIi4+Tv66+qmiqty12ncYnjWr0OAACAjozblAAA8AJPffeUJGlA9wGKCotyaltms1kpnVIkSV9lfeXUtgAAANojijEAAHRwR0qO6K0db0mSxg8d75I2G8aN+T7ze5e0BwAA0J5wmxIAAB3c33/4u6rrqtU1vqu6xndt8XLVtdV6eWX905d+feWv5efj1+JlG8aN2Z6zvXXBAgAAeAGKMQAAdGDFVcV6ceOLkqQJQye0alnDMHQw+6D9dWt0Tegqk0zKPZGrA0UHlBqe2qrlAQAAOjJuUwIAoAP72/d/U1FVkWIjYtWvWz+XtRscEKzEmERJ0gcHP3BZuwAAAO0BxRgAADqo4xXH9dT6+oF7p5w/RWaTa7v9nsk9JUmfp33u0nYBAAA8HcUYAAA6qKe/e1rFVcWKj4rXoB6DXN5+j+QekqQNGRtafZsTAABAR0YxBgCADiirOEvP/vCsJGnqBVNdflWMJHVL6CaL2aLjJce15dgWl7cPAADgqSjGAADQAd312V0qrylX1/iu6t+tv1ti8Pf1V7eEbpKkpXuWuiUGAAAAT0QxBgCADubr9K+1bNcymWTSjLEzZDKZznldfj5+rXqk9akGdh8oSVq9b/U5rwMAAKCj4dHWAAB0IGXVZbrlo1skSRcOuFBJsUnnvC5/X389cdsTbYpnQLcBWv7Ncv2U/ZMySjLUObRzm9YHAADQEXBlDAAAHcjd/7lbB44fUHhwuC4beZm7w5E11KqUTikyZOiVHa+4OxwAAACPQDEGAIAO4l+7/6UXN74oSZp9yWwFBQS5OaJ6w3oNkyS9teUtnqoEAAAgijEAAHQI23K36YYPbpAkjRsyTr1SerV5nTW1NfrHh//QPz78h2pqa855Pef1Pk++Fl9lHMvQl4e/bHNcAAAA7R3FGAAA2rm9x/Zq8tuTVV5Trl7JvXTFqCscsl6bYdPu9N3anb5bNsN2zusJCgjS4J6DJUl/Wvcnh8QGAADQnlGMAQCgHduWu00TlkxQbmmu4qPidcOUG2QxW9wd1mkmDp0ok0xas3+N1mWvc3c4AAAAbkUxBgCAdsgwDL2z4x1duOhC5ZTkKC4yTr+Z/huPGSfmVHFRcRrWu37smJs/ulk1ded+2xMAAEB7RzEGAIB2Zn/Bfl393tWa/f5s+61Jv535W4UEhbg7tDO64sIrFOQfpH15+3Tbp7cxmC8AAPBaPu4OAAAAnF2trVafH/pcb2x7Q+/tek82wyaz2axLhl+iS0dc6pG3Jp0qPCRc10y4Ros/WaxXN7wqiyz6+6S/y8/i5+7QAAAAXIpiDAAAHsIwDFXWVqqwslBZxVk6eOKg9hXs0/dZ32td5joVVxXb5+3bpa8uv/ByJUQnuDHi1hvcY7Cml03Xim9X6OUNL2v1/tX67Xm/1aXdL1Xv6N4UZgAAgFegGAMAQBs03Goz7uVxMgeYZRiGbIZNNsMmQye9PuV9wzBk08/vl9eUq6iySLW22mbbCgoI0qDUQRrWe5iSopMkSZXFlU7btqraKun/Vl9ZUinDxzG3FZ3f7XyFmEL0wZoPlJmbqbs/ult3626ZTCZZA6yKCIhQoG+gLCaLzCazLOb6/5tN3F3tTrUV9Z9Nbi8DAKDtTAY9KgAA5+zQoUPq3r27u8MAXObgwYPq1q2bu8MAAKBd48oYAADaIDIyUpJ0+PBhhYeHu7Tt4uJiJScnKzMzU2FhYbRN205VVFSklJQU+2ceAACcO4oxAAC0gdlcf+tMeHi4W74gS1JYWBht07bLNHzmAQDAuaM3BQAAAAAAcCGKMQAAAAAAAC5EMQYAgDbw9/fXwoUL5e/vT9u03WHb9oT2AQDoSHiaEgAAAAAAgAtxZQwAAAAAAIALUYwBAAAAAABwIYoxAAAAAAAALkQxBgAAAAAAwIUoxgAA0EKrVq1Sr1691KNHD7366qunTf/xxx/Vr18/paam6uGHH3ZYu5mZmRo3bpz69u2rgQMH6p///Odp83Tp0kUDBw7U4MGDNXXqVIe1LUk+Pj4aPHiwBg8erF/+8penTXfWdu/du9fe7uDBgxUYGKgPPvig0TyO3u7p06crIiJCM2fOtL/Xku07ePCghg8frtTUVC1YsEDn8nyEU9suLy/X1KlT1bt3b/Xv31/PPfdck8uNGzdOvXv3tu+nc9HUdrdk3zpiuwEA8EoGAAA4q5qaGqNHjx5GVlaWUVxcbKSmphoFBQWN5hk+fLixbds2o6amxhg+fLixY8cOh7Sdk5NjbNmyxTAMw8jLyzMSExON0tLSRvN07tzZKCkpcUh7p4qKijrjdGdt98lKSkqMqKgop2/3l19+aXz44YfGjBkz7O+1ZPuuvvpq46OPPjIMwzCuuuoq++u2tF1WVmZ8/fXXhmEYRmlpqdG7d29j//79py03duzYNu/zpra7JfvWEdsNAIA34soYAABaoOHqiMTERIWGhmrq1Kn69NNP7dNzcnJUW1urgQMHysfHR7Nnz9ZHH33kkLbj4+PtVzzExsYqMjJSx48fd8i628qZ232yDz/8UBMnTlRwcLDD132y8ePHKzQ01P57S7bPMAytX79el112mSRp3rx557QPTm07KChIY8eOlSQFBwerR48eOnLkyLlsVqvbbglHbTcAAN6IYgwAAC2Qk5OjxMRE++9JSUnKzs5u8XRH2bhxo2w2m5KTkxu9bzKZdNFFF2nEiBFavny5Q9ssLi7WsGHDNHr0aH3zzTeNprlqu9977z1de+21p73vzO2WWrZ9BQUFioyMlMlkanaetsrMzNT27ds1dOjQJqfPnj1bQ4cO1QsvvOCwNs+2b12x3QAAdFQ+7g4AAID2wGhiLIyGL6Etme4IBQUFmjdvXpPj1axbt04JCQnKysrShAkTNGjQIKWmpjqk3fT0dCUkJGjnzp267LLLtGPHDoWFhUlyzXYXFxdr3bp1evfdd0+b5sztllq2fc7eB5WVlbr22mv11FNPNXll0NKlS5WQkKDjx49r8uTJ6tevn/2KmrY42751xd8eAICOiitjAABogcTExEZn/bOyshQfH9/i6W1VVVWl6dOn67777tOFF1542vSEhARJ9VcnTJw4UVu3bnVY2w3r7t+/v/r27at9+/bZpzl7uyVp5cqVmjRpkgICApqNzRnbLbVs+6Kjo3X8+HF7ccKR+8AwDN1www2aOnVqo8F1T9awDyIjIzVjxgxt2LDBIW2fbd86c7sBAOjoKMYAANACI0aM0M6dO5Wdna2SkhKtXr1akyZNsk9PSEiQxWLR9u3bVVtbq3feeUdXXHGFQ9o2DEM33nijJkyYoLlz5542vaysTCUlJZKkwsJCffvtt+rTp49D2j5x4oSqqqok1X/Z3r17t7p162af7sztbtDcLUrO3O4GLdk+k8mkCy64QB9//LEkacmSJQ7bB/fdd5+CgoL04IMPNjm9trZWx44dk1R/Bc2nn36qfv36tbndluxbZ243AAAdnnvGDQYAoP1ZuXKl0aNHD6N79+7Gyy+/bBiGYUyZMsXIzs42DMMw1q9fb/Tt29fo1q2bsXDhQoe1u2bNGsNkMhmDBg2y/2zfvt3e9sGDB42BAwcaAwcONPr372+89NJLDmt73bp1Rv/+/Y2BAwcagwYNMlasWGEYhmu22zAMo7Cw0IiNjTWqqqrs7zlzuy+99FIjOjraCAwMNBITE40ff/yx2e2bP3++sWHDBsMwDGPfvn3G0KFDjW7duhm33HKLUVdX1+a2v/32W0OS0bdvX/vf/d///nejtktLS42hQ4caAwYMMPr27Wv88Y9/dMh2f//9983uW0dvNwAA3shkGE3c8AsAAAAAAACn4DYlAAAAAAAAF3LI05RsNptycnIUGhrKKPro0AzDUElJiRISEmQ2e0Ytk/yDt/DE/APgXvSB8Bae2geSg/AWzshBhxRjcnJylJyc7IhVAe1CZmamkpKS3B2GJPIP3seT8g+Ae9EHwtt4Wh9IDsLbODIHHVKMCQ0NlVQfWFhYmCNWCXik4uJiJScn2z/znoD8g7fwxPwD4F70gfAWntoHkoPwFs7IQYcUYxouSQsLCyMJHcAwDB0rr39MZXRQNJf8eSBP+puQf45F/nk+/iYAGtAHOgZ9X/vhaX8bcvDckHPtlyP/Vg4pxsCxymvKFftUrCSp9L5SBfsFuzkiwHuQfwAAb0PfB7gWOQeJpykBAAAAAAC4FMUYAAAAAAAAF+I2JTeqq6tTTU3Nae9XVVepc3Dn+teVVbLYLK4Ozav5+vrKYmGfo20Mw1Btba3q6urcHUq7YrFY5OPjw73TANCONXeMizOjD4SjkIPnxtXfAynGuElpaamysrJkGMZp02yGTS+NekmSdCTriMwmLmByJZPJpKSkJIWEhLg7FLRT1dXVOnLkiMrLy90dSrsUFBSk+Ph4+fn5uTsUAEArnekYF2dHH4i2IgfPnau/B1KMcYO6ujplZWUpKChIMTExp1W/62x1qjhWIUnqEt1FFjNXabiKYRjKz89XVlaWevTowRUyaDWbzaa0tDRZLBYlJCTIz8+PM1wtZBiGqqurlZ+fr7S0NPXo0UNmM8VoAGgvznaMi+bRB8IRyMFz547vgRRj3KCmpkaGYSgmJkaBgYGnTa+z1dn/MgEBARRjXCwmJkbp6emqqamhGINWq66uls1mU3JysoKCgtwdTrsTGBgoX19fZWRkqLq6WgEBAe4OCQDQQmc7xsWZ0QeircjBtnH190CKMW7UXKXSZDIpKjDqjPPAedjn3s3H7KMbBt1gf32uOJt17th3AOBajur7GnAsde7oA72Do3PuVOTguXH1fqMY44HMJrO6RnR1dxiAV/L38dfiqxa7OwwAAFyGvg9wLXIOEo+29gpff/21rFaru8MA4ASelt8LFizQvffe6+4wAACQyWTS1q1bm5x2+PBhhYSEqKioyLVBAe3MjTfeqDvuuOOcl1+zZo2SkpIcF1AHQjHGA61Zs0aTJ09WRESErFarBg0apCeeeELV1dXuDq1JS5Yskclk0osvvujuUOy6dOmiDz74wN1hoB0yDENl1WUqqy5zyij048aNk7+/v0JCQhQZGamxY8dq48aNDm/HXV566SU9/vjj7g4DANAKzu77pMb9X2hoqPr166d//vOfTmmrJVJSUlRaWqrw8PBzWj49PV0mk0mFhYX29zztBAk8lyty7mRTpkzR7bffftr7xcXFCgoK0ldffeW0tseMGaOsrCynrb89oxjjYVatWqWpU6eq78i++ueaf6rgeIGWLVum3bt368iRI61eX21trROibOy1115TZGSkXnvtNae3BThbeU25Qh4LUchjISqvcc6jqR9//HGVlpYqNzdX559/vq6++uom53NF/gIA4Iq+T/q5/ysuLtYTTzyhOXPmKCMjw2nttQV9MJzJVTnX4Je//KWWLl2qqqqqRu+/8847io+P17hx45zSLnl0ZhRjPIhhGPrtb3+ru++5W7NvmS1rpFWS1Lt3by1evFidO3eWJF1//fVKSEhQWFiYhg0b1qiSuXjxYg0ePFgLFy5UXFycrr322tPaKSkp0a9+9SvFx8crPj5eCxYsUFlZmSSpqqpKN998s6KjoxUeHq7+/ftrw4YNzcZ84MABffvtt1q0aJE2b96sbdu22ac1dXbgqquu0h//+Ef77//617+Umpqq8PBw3XLLLbr88svt0xu25WSDBw/W4sWLJUlpaWm6+OKLFR4ersjISI0aNUrl5eX6xS9+ocOHD+u6665TSEiIFixYYI910qRJioyMVPfu3fXss882u12AK/j5+emGG25QZmam8vPz7WfZXn/9daWmpioxMVGSdM8996hz584KDQ1V3759mzyT+Oqrryo5OVlRUVG655577O+f/G9CdHS04uLitGzZMq1bt079+/dXeHi45s+fL5vNJkkqLS3VlVdeqdjYWIWHh+uiiy5qlNd//OMfdcUVV+j222+X1WpVSkqKli1bZp/e1ktZAQAdn8lk0mWXXSar1aq9e/eete/ZvHmzLrjgAoWFhSk6OlpXXHGFfVpubq792Nhqteqiiy5SRUWFffr333+v/v37KywsTNOmTbPflnTqlS033nij5s+fr2uuuUZhYWF68cUXz3jMPGLECElSUlKSQkJC9Pbbb2vKlCkqKipSSEiIQkJCtGbNGknSW2+9pT59+shqtWr06NHasmWLU/cvcKpp06bJx8fntDsHXn/9dc2bN0+XXnqpYmJiFBERocsuu0zp6enNruvgwYO64oorFBMTo86dO+uRRx6xH0c29V2UK8aaRzHGg+zfv19paWmaNWvWGeebOHGi9uzZo4KCAs2aNUszZ85USUmJffrOnTvl4+Ojw4cP68033zxt+d/97nc6cOCAdu7cqR07duinn37S73//e0nSG2+8oW3btunAgQMqLCzU+++/r7i4uGZjee211zRkyBBdeeWVGjNmTKuujtm3b5/mzp2r559/XgUFBRoxYoQ+/fTTFi//wAMPKDU1VceOHVNeXp6efPJJ+fj46J///KdSUlL0zjvvqLS0VC+99JJqa2t1+eWXa9CgQcrJydGKFSv0xBNPaOnSpS1uD3C0iooKvfbaa4qOjlZERIT9/Q8//FAbN25UWlqaJGnQoEHasGGDCgsL9dBDD2nu3Ln2aVJ9gXXHjh3av3+/1q5dq//3//6fvv76a/v0Xbt2yWq1Kjc3V3/+85/1q1/9Ss8884y++eYb7d69W6tWrbJ3zjabTbNnz1ZaWpry8vI0ZMgQXXPNNY0uof300081atQoFRQU6JFHHtEvf/nLRv8GAQBwJjabTStXrlRlZaWGDBly1r7n9ttv1xVXXKHCwkJlZ2fr7rvvtq+n4Uvmrl27dOzYMT366KONnki0bNkyffHFFzp8+LCysrL017/+tdm43nnnHc2fP1+FhYWaP3/+GY+Zf/zxR0lSVlaWSktLNWfOHH3yyScKDw9XaWmpSktLNWbMGK1Zs0a33nqrXn75ZeXn52vmzJmaNGkSY9XApXx9fTV37lwtWrTI/t7u3bu1ceNGXXzxxfrDH/6gzMxMZWRkKCgoSLfcckuT66moqNDEiRM1YcIEZWdna82aNXr33Xf1+uuv2+c523dR/IxijAfJz8+XJPvZ8ObcdNNNCg8Pl6+vr+6++27ZbDZt377dPj08PFwPPPCA/Pz8FBQU1GhZm82mpUuX6rHHHlNUVJSio6P16KOPasmSJbLZbPL19VVJSYn27NkjwzDUs2dPJScnNxlHXV2d3njjDd1wQ/1j2ebNm6e33377tMvfmrNs2TJNnDhRkydPlo+Pj2655Rb17NmzRctK9f+oHDlyROnp6fL19dWFF14oPz+/Juf94YcfdOTIET3yyCMKCAjQwIEDdfvtt9uvsgFc6b777pPValVwcLDeeecdrVixQj4+Pz/cbuHChbJarfb8nTNnjmJjY2WxWDRr1iz17t1b3333nX1+wzD02GOPKSAgQH369NGFF16oTZs22adHR0fr97//vXx8fDRnzhwVFxfrlltuUVRUlBITEzV27Fht3rxZkhQWFqZrr71WwcHBCggI0J/+9Cft27dPOTk59vUNHTpU1113nSwWi+bOnavq6mrt27fP2bsNANDOndz/XX311XrwwQcVExNz1r7H19dXGRkZysnJkb+/vy666CJJ0oYNG7R79269+OKLioiIkI+Pj0aPHi1/f397m/fee686deokq9WqGTNmNOofT3XppZdq0qRJMpvNCggIOOMxc0stWbJE119/vS666CL5+vrqjjvuUEREhD7++ONz3IvAuZk/f74+//xzZWZmSpIWLVqkSZMmadSoUZoyZYoCAgIUFhamBx54QN9++22Tn/NVq1YpIiJCv//97+Xn56eUlBT97ne/a3SC+0zfRdEYxRgPEh0dLUnKzs5udh6bzaYHHnhAPXr0UFhYmKxWq4qKinTs2DH7PImJiY3OCJwsPz9fVVVV6tKli/29bt26qaqqSseOHdPcuXN14403asGCBYqOjtaNN97YaN0nW716tY4dO6bZs2dLkn7xi1+ooqJCK1asaNH25uTknFboSUlJadGykvTkk08qMTFRF198sbp06aI//vGPzXaOWVlZSkhIaFSs6datG4NJwS0ee+wxFRYWKjMzUwkJCY0uxZZOz4O//vWv6tevn8LDw2W1WrVz585GeRkWFtaoswsODm50pUqnTp3srxvmO/mKt6CgIJWWlkqqP+Nx2223qUuXLgoLC7P/W3FyeycvazKZFBgYyJUxAICzauj/KioqtHfvXr3++ut6+eWXz9r3LFq0SJWVlRo2bJh69+6t559/XpKUkZGhxMREBQYGNtvmyX3Wqf3jqU7uf892zNxSWVlZjdYhSV27duUYFC7Xt29fjRgxQm+88YZqa2v11ltvaf78+crPz9fs2bOVnJyssLAwXXTRRaqurm4yV9LT07Vz505ZrVb7z5133qnc3Fz7PGf6LorG2EsepGfPnurSpUuj8RdOtXTpUi1dulQff/yxioqKVFhYqPDw8Ea3EJzpwx8TEyM/P79G9wGmpaXJ399f0dHR8vHx0f33369t27Zpz549Onz4sP70pz81ua7XXntNNptNAwYMUFxcnHr27Kmamhr7rUohISGqqKhoFNvJgxAnJCTYK7MNDh8+bH8dEhKi8vLGA1qdnOixsbF64YUXlJGRoVWrVumll16yF4JO3QdJSUnKyclRTU1No+3mMWtwp8TERL3yyiu69957G115cvLnd+3atfrjH/+oJUuW6MSJEyosLFT//v2dNvL+008/rU2bNmnt2rUqLi62/1vhipH+AQDeIzU1VZdddplWrVp11r6ne/fuWrJkiXJzc/Xqq6/qrrvu0qZNm9S5c2dlZ2c3GiOmLU7uf892zNzU8XZT7yUlJZ02/kZ6ejrHoHCL+fPna/HixVq1apVsNpuuuOIK3XfffSovL9fmzZtVXFysb7/9VlLTx37JyckaNmyYCgsL7T/FxcXatWuXfR4KMS3HnvIgJpNJzz33nJ54/AktW7RMhccLJdWPrTJ//nxlZGSouLhYfn5+io6OVnV1tR5++GEVFxe3uA2z2azZs2frgQce0PHjx1VQUKAHHnhAc+fOldls1pdffqmtW7eqtrbWfqnoybdPNMjLy9PHH3+sJUuWaOvWrfafjz76SF988YXS09PVs2dP+fr6aunSpaqrq9O7777baMCya665Rl988YU+++wz1dbWatGiRY1udRg8eLAOHTqkNWvWqLa2Vk888YQKCgrs09977z0dPnxYhmEoPDxcFovFHmunTp108OBB+7wjRoxQp06d9NBDD6mqqko7d+7U888/b7/FCnCXoUOHaty4cXr00UebnF5cXCwfHx/FxMTIZrNp0aJF2rlzp9PiKS4uVkBAgCIiIlRaWqr777/faW0BALxXRkaGVq9erQEDBpy171myZIny8vJkMpkUEREhs9ksHx8fnXfeeerVq5d+85vfqLCwULW1tVq7dm2Lb5k/k7MdM8fExMhsNjc63uzUqZNKSkrsQw9I9Q/eePvtt7Vu3TrV1tbqueeeU0FBgaZOndrmGNF+7M7frYxC9z85bNasWcrNzdXvf/97zZs3T76+vvbHW1utVhUUFDR7Il6SLr/8cuXl5emFF15QZWWl6urqtHfv3kZjFaLlKMZ4mMsvv1wfr/5YP3z1g64edbWiIqM0c+ZM9e7dW/Hx8brhhhvUr18/de7cWd26dVNgYGCzY7o0529/+5u6dOmivn37ql+/fkpNTdUzzzwjqb7Ict1118lqtapr164KDw/XwoULT1vHG2+8oZSUFM2aNUtxcXH2n8mTJ2vYsGFatGiRwsLC9Morr+i///u/FRUVpbVr12rSpEn2dfTq1UuLFy/WrbfeqqioKK1fv14TJkyw3+ebmpqqJ554QjNnzlR8fLyqqqrUr18/+/KbNm3ShRdeqJCQEI0cOVLz58/XtGnTJEn333+/nn/+eUVEROi2226Tr6+vVq1apU2bNikuLk7Tpk3TH/7wB/stVkADi9mimX1nambfmbKYLS5p84EHHtCrr7562pVikjR58mTNmDFDAwYMUEJCgnbt2qVRo0Y5LZY//OEPslgs6tSpk/r376+RI0c6rS0AgGdwVd9377332p80NGrUKF188cV66KGHztr3fP755xo0aJBCQkI0bdo0Pfnkkxo0aJDMZrM++ugjlZeXq1evXoqOjtaDDz7YqjFdzuRMx8yBgYFauHChpkyZIqvVqqVLl6pXr16aP3++/clJa9eu1dixY/Xcc89p/vz5ioqK0rvvvqtPPvmEp8t4kTUZazTwxYHq92I/ZRXX357mjuNNqf7Og2uuuUbp6emaP3++JOlPf/qTDhw4oIiICPv4MWda/vPPP9cXX3yhLl26KCoqSrNnz2509wJazmQ44Nrz4uJihYeHq6ioSGFhYY6Iq0OrrKxUWlqaunbtqoCAAHeH41F69eql//mf/9H111/vthjO9PfxxM+6J8bkzcjvtmtuH/JZB3Aq/l3wLPSBbdfe+kBPjcuTzF0xV29tf0uSdOeEO/XUmKec1hY52Dau/h7IlTFwq48++kglJSWqqqrS008/rZycHE2ePNndYQEAAABAm23I3mB/vebwGjdGAk9DMQZu9emnn6pz586Kjo7WO++8o5UrV9qfKgUAAAAA7VVpdan2Ffw8JubBowd5KALsTh+ZFW5XZ6vTltz6gW6HxA1x6X2Ervb888/bH08IeIKy6jKFPBYiSSq9r1TBfsFujggAAOei7wOcI+1Emgz9XHw5XnxceVV5CjWHknPgyhgAAAAAABwtryxPkhQXGaeggCAZMrT52GY3RwVPQTEGAAAAAAAHyyutL8aEBYWpU0QnSdKO/B3uDAkehGIMAAAAAAAOdrTsqCQpJChEkWGRkqRDhYfcGRI8CGPGAAAAAADgYA23KYUEhcjfx1+SlFmU6c6Q4EEoxgAAAAAA4GANxZjQwFAFB9YP0ptTnOPOkOBBuE2pnausrNT06dNltVo1YsQId4cDwIHIbwCAt6IPREfQMGZMaFCoIkIjJEn5xfnuDKnFyEHnoxjjgUwmk8L9wxXuHy6TyXTGeZcvX669e/cqLy9PP/74o4sirFdVVaW77rpL8fHxCgkJ0YABA5Sent7s/CtXrtTAgQMVGhqqLl266Kmnnmpyvs8++0wmk0l33HGHcwIHzsBitmhqj6ma2mOq2x8r31HyOysrSxdeeKGioqIUHh6uwYMHa8WKFS7YCgBAS3hS39egPfWBZ5r/+++/16RJkxQdHa3IyEhNmjRJu3fvds2GwO3sV8YEhSoytH7MmILiAtWoxuNy7lTtJQfPdpz59ttvKyQkpNGPyWTSM88846KtaR63KXkgs8msHlE9WjRvWlqaevbsKX9/fydHdbqbbrpJFRUV2rRpk+Lj47V3715ZrdYm583Ly9M111yjRYsWafbs2dq+fbvGjh2r/v37a/Lkyfb5ysrK9Nvf/lYXXHCBi7YCaCzAJ0Afz/7Y3WFI6jj5HRERocWLFys1NVVms1nfffedLrnkEu3cuVNdu3Z17YYBAE7jSX1fg/bSB55t/hMnTuimm27SsmXLFBQUpD//+c+aPHmy0tLSZLF45pdwOE5uaa6kxlfGVNVU6Vj1MY/LuVO1lxw823HmnDlzNGfOHPv8mzZt0ogRI/SLX/zCRVvTPK6M8TBdunTRY489pvPOO0/BwcGaMmWKjh8/rttuu01Wq1U9evTQd999J0m688479fDDD2vVqlUKCQnRwoULNWjQIC1ZsqTROqdMmaK//OUvDo1z165dWrlypRYtWqSEhASZTCb17t272STJzs6WYRiaM2eOTCaTBg0apPPOO087d+5sNN+DDz6oWbNmqVevXg6NF/AE3prfwcHB6tmzp8xmswzDkNlsVl1d3RnPMgIAOpaO2geebf4pU6Zo1qxZslqt8vPz0913363MzExlZGQ4NG54HsMwdKz8mKT6Yoyfr59CAkMkSfsK97k8no6ag609znzttdd06aWXKjk52aFxnwuKMR7onXfe0fLly5Wdna3Dhw9rxIgRmjBhggoKCjRr1iwtWLBAkvT000/r/vvv1+WXX67S0lL96U9/0ty5c/Xmm2/a15WXl6cvvviiUTXwZA3J19zP2rVrm1zum2++Ubdu3fT4448rNjZWPXv2bPa2I0kaPHiwxo0bpzfeeEN1dXXavHmztm3bposvvtg+z4YNG/Tpp5/qvvvuO5fdBrQL3prfkjRw4ED5+/tr5MiRGjVqlMaMGdPa3QcAaMc6Yh94LvNbrValpKS0ZJehHSuqKlJ1XbUk2YswDVfHHCg84JaYOmIONmjJcWZFRYWWLl2qX/7yly3ZXU5HMcYDLViwQMd8j+lQxSFNmTpF0dHRmjlzpiwWi6677jrt3LlT1dXVTS47Z84cffPNN8rOzpYkLV26VGPGjGm28vfCCy+osLCw2Z/Ro0c3udzx48e1c+dOGYahw4cPa8WKFfrrX/+qt99+u8n5zWaz5s2bp9///vfy9/fX8OHDdffdd2vw4MGSpJqaGt1yyy164YUX3HIpHNCgrLpMwY8GK/jRYJVVlzl8/bfddptSUlJktVp12WWXeUV+N9i+fbtKS0v10UcfacqUKVyeDQAewtl9X4OO2Ae2Zv6MjAz9+te/1tNPPy0fH0aL6OgaBu8N8AuQr4+vJNnHjdlfsN8lOXeqjpiDDVpynPmvf/1Lfn5+mjZt2hnX5SoUYzxQXFycbIZNNsOmoKAgxcXF2acFBQXJMAyVl5c3uWx8fLwmTJhg/7AuWbJE8+bNc3iMISEhslgsevjhhxUQEKB+/frp5ptv1sqVK5uc/8svv9Stt96q999/X9XV1dq/f7/eeustvfzyy5KkJ598UkOGDNG4ceMcHivQWuU15SqvaTrH2urUfPaG/D6Zn5+fLr/8cn311Vdn7VQBAK7jzL6vQUfsA1s6f1ZWliZOnKjbb79dN998s8Pjhuc5efDeBtZQqyQpqyjLJTl3qo6Ygyc723Hma6+9pnnz5snX19fhcZ8LijEdUMMlZDt37tS+ffs0Y8aMZuddsGDBaaNLn/yzZs2aJpcbNGiQJJ31aU8NNm/erPPPP1/jxo2T2WxW9+7dNXPmTH300UeS6p+gtHLlSsXFxSkuLk7Lli3TK6+8opEjR7Zy64GOrT3md1Nqamq0f//+Fq0fAADJM/vAlsyfnZ2t8ePHa+7cubr//vtbtF60f0fLjkqSQgN/LsZEhtVfGZNTkuOWmNrKE3OwKU0dZx44cEDffvut5s+ff87rdTSKMR3Q9OnTlZGRobvuukvTp09XSEhIs/O+9NJLKi0tbfanuTEdLrroIvXo0UN/+tOfVFNTo71792rx4sW68sorm5x/5MiR2rBhg9atWyfDMJSRkaHly5dryJAhkqT3339fu3fv1tatW7V161ZNmzZNc+bMOeOXOcAbtcf8/uabb7R+/XpVV1erurpaixcv1ldffaVLLrmk7TsEAOA1PLEPPNv8OTk5GjdunK699lotXLiw7TsB7UbDbUohQT9/ThvGjDlafNQtMbWVJ+ZgS48zX3vtNY0cOVJ9+vQ59x3gYBRjOqCgoCDNmDFDn376qVMuHZMki8WiDz/8UOvXr5fVatXkyZP1u9/9rtEATidXPEeNGqVnnnlGv/zlLxUWFqYLL7xQo0aN0gMPPCBJioyMtF8VExcXp8DAQAUFBSk6Otop8QPtVXvM77KyMv36179WVFSUOnXqpBdffFHvvvtus/cKAwDQFE/sA882/yuvvKIDBw7o2WefbdFVAeg4mrpNqaEYU1BS4JaY2soTc7Alx5l1dXV64403PGbg3gYmwzCMtq6kuLhY4eHhKioqUlhYmCPi6tAqKyuVlpamrl27KiAg4LTpdbY6bcndIkkaEjdEFjODXLrSmf4+nvhZ98SY2rOy6jKFPFZf5S+9r1TBfsGtWv5s+Y2za24f8lkHcCr+XXCMtvZ9DegD26699YGeGpcnWLBqgV7e9LImjZikKRdMkSSVVpTqwVcebDRfW3LuVORg27j6eyBXxgAAAAAA4EBNXRkTHBAsPx8/d4UED8Mz1TyQSSaF+oXaXwNwHbPJrLGdx9pfAwDQ0dH3AY7XMGbMycUYk8kka6hVR08cVe/Y3uoU2Imc82IUYzyQ2WxWr+he7g4D8EqBvoH6+sav3R0GAAAuQ98HOF5TV8ZIUmRopI6eOKpZw2Zp4QgGdfZmlOEAAAAAAHCg5ooxDYP4Hi467PKY4FkoxriRA8ZOhhPwd4Ej8Dk6d+w7AGjf+Hf83LHvOobymnKVVZdJkkIDTynGhNUXYzKLMp3WPp+jc+Pq/UYxxg0slvqnI1VXVzc5vc5Wp625W7U1d6vqbHWuDA36+e/S8HeCdymrLlPMkzGKeTLG3om2hq+vrySpvLzc0aF5jYZ917AvAQDO1da+r8HZjnFxdvSBHcPRsqOSJF+Lr/z9/BtNiwipL8Z8uefLNufcqcjBtnH190DGjHEDHx8fBQUFKT8/X76+vjKbG9fE6mx1qq2ulVT/eC0ebe06NptN+fn5CgoKko8P6eGtjpUfO+dlLRaLrFarjh6t74SDgoJkMjEQd0sYhqHy8nIdPXpUVquVgigAuFBb+r4GZzvGRfPoAzuWhsF7Q4JCTjsOjAyLlFT/nc8ReXcycvDcueN7IN823cBkMik+Pl5paWnKyMg4bbrNsOlYUX1ippemM8K2i5nNZqWkpPAFGucsLi5OkuwFGbSO1Wq170MAQPtxtmNcnB19YMfQ3HgxkmQNtTqtXXKwbVz9PZBijJv4+fmpR48eTV5CVl5drstWXyZJ2vyrzQryC3J1eF7Nz8+PKjLapKEjjI2NVU1NjbvDaVd8fX05GwgA7diZjnFxZvSBHYf9sdaBTRRjgq1ObZscPHeu/h5IMcaNzGazAgICTnu/zlynjLL6SqZ/gL8C/E6fB4Dns1gsHFQBALxOc8e4gLdoGDMmJCjktGkWi0XhweEqKityWvvkYPvA6X8AAACgA6qz1enuz+7WyNdGavnu5e4OB/AaZ7pNSZKsIVYXRgNPRTHGRTIKM7S/YL+7wwAAAICXePb7Z/XU+qf0fdb3unb5tdqYu9HdIQFe4WzFmIbHW8O7UYxxgXd3vqtuf++mns/31BPrnzjr/GaTWcMThmt4wnAG7wVcjPwDAHQEtbZaPfP9M/bf62x1uvvru5ucl74PcCz7bUqBp9+mJEkx1hhJUlhgGDnnxRgzxsnKa8r1m9W/kc2wSZLu//x+Tes1Tb0jeze7TKBvoDbcssFVIQI4CfkHAOgIPj/0uXJKchQSGKJfTfuVnln2jL7d960ySzKVHJrcaF76PsCx7AP4NnNlTFxknP3/gb6BLosLnoUynJO9t+s9Ha84rsiwSKUmpqrOVqeH1z/s7rAAr1VQXmAvjgIA0FH9+8C/JUkDug1QSqcUJccmy2bY9MquV9wcGdDxne02pdiIWEnSkRNHZBiGy+KCZ6EY42Qr966UJJ3f53xNGDZBkvThzg9VVVvlzrAAr1NdV61p70xT9JPR6vdiP2UXZ7s7JAAAnObzQ59Lknql9JIkDe4xWJL0we4P3BQR4B1q6mp0vOK4pKYfbS39fJtSSXmJcipyXBYbPAvFGCeqtdXqy7QvJUl9uvRRr5ReCg4IVlllmZYfan5E+/KacnV5tou6PNtF5TXlrgoX6NAeXfOoPtr3kSTpp/yfdNXyq5o8E0H+AQDau9zSXO3K3yWTTOqR1EOSNLD7QEnSruxdOlZ5rNH89H2A4+SX50uqH4spKDCoyXnMJrNMJpMkaf2R9S6LDZ6FYowTbcjeoOKqYgUFBCkpJkkWs8XeES7dtbTZ5QzDUEZRhjKKMrhsDXCA0upS/e2Hv0mSplwwRT4WH208vFHvH3j/tHnJPwBAe7chu378l06RnRQcGCyp/kx8VFiUbDab/nXwX43mp+8DHKdhvJiQwJBmB+c1ZNhzbVfBLpfFBs9CMcaJfsz+UZLULb6bzOb6XT0wtb4Ys+7QOtlsjFsBuMKKPStUWFmo6PBoXTL8Eo3sP1KS9OT3T7o5MgAAHG9L7hZJUnJs44F6e3euf4DE6gOrXR4T4C3ONl7MqfYV7HNmOPBgFGOcaFveNklSQkyC/b3UxFT5+viqsLRQa3LXuCs0wKus+GmFJGlYr2Eym80aPWC0JOnHtB+1/8R+d4YGAIDDNRRjEmMSG73fp3MfSdL3ad9zBQzgJLmluZJaXow5cPyAM8OBB6MY40QNxZjE6J87Ql8fX6UmpkqSlu9rftwYAI5RXlP+8xMlug+QVH/ZdreEbjIMQy/veNmd4QEA4HBbjjRdjElNSpXZbFZ+Ub42H9vsjtCADq+hGBMWHNai+dOPpTsxGngyijFOUmur1a6j9ff/nVyMkeoH85WkLw996fK4AG/zXeZ3qqitkDXE2igXh/QYIkn6YM8HbooMAADHO15xXBlFGZJOL8YE+AWoW3w3SdK/9v/rtGUBtF1rr4w5WnhUx6uOOzMkeCiKMU6y99heVdVVyd/XX5HhkY2mNVwi+lP2T6eNZg/AsdYdXidJ6p7Y3T5qvVT/iE+TyaSDuQe1o2CHu8IDAMChtuZulSRFhUUpyP/0J7k0jBvz+YHPXRkW4DVaO2aMJK3P5YlK3ohijJM0dIQJ0QmnjaIdY41RdHi06mx1+teB089KmEwm9Y3pq74xfRt9eQTQeusy64sxXeO7Nno/NCjUfsvgqztetb9P/gEA2rOGW5SSYpKanN5wUnB75nb7Y6zp+wDHacltSiaZFBcZJz8fP0nSj7k/uiQ2eBaKMU7S1HgxJ2voCFftX3XatCDfIO26bZd23bZLQb5NP5sewNnV2er0fdb3kk4vxkj1V8dI0qo9P+ch+QcAaM+aG7y3QUJ0gsKCwlRdW60P0z+URN8HOJL9NqXA5q+M8fP1039f/98aNWCUpJ+/O8K7UIxxkqaepHSyvl36SpLWHeQR14Cz7Di6QyXVJQrwC1B8VPxp0wd1HySTyaRDRw9pZ8FON0QIAIBjna0YYzKZ7Lcqfbj/Q5fFBXgLezEm+Oy3KSVE139X/OnoT06NCZ6JYoyTbMv9v2JMdNPFmNSkVPn5+qmwrFBf5XzlytAAr9EwXkyXuC4ym0//5y4kKEQ9knpIkl7b+ZpLYwMAwNHKa8r107H6L3XN3aYk/XyF9ppDa1wSF+AtqmqrVFhZKEkKCzr705SSYuvzNO1omqrqqpwZGjwQxRgnyC3NVV5ZnkwyNXk2Xqp/xHXvlPqzEst+WtZoWnlNufq90E/9Xuhnv5cXQOs1jBfTJb5Ls/M03Kr00Z6PJJF/AID2a0feDtkMm0ICQ844XkXPlJ4ymUzKKsjSvhP76PsAB2kYvNditijQP7DZ+aprqvWXt/6ixZ8slr+vv6prqrX2yFpXhQkPQTHGCRquiomJiJG/r3+z8/Xr2k+S9Pn+xqPZG4ah3fm7tTt/twzDcF6gQAfXUIzpltCt2XkGdhsos8msg3kHtfvYbvIPANBuNdyilBSTdMaBeIMDgtW5U2dJ0rL9y+j7AAc5efDeM+WgIUO5x3OVdzxPybHJkqSvMrlbwttQjHGCsw3e26Bvl74yyaS0o2naX7jfFaEBXiOrOEuHiw7LbDLbDzibEhIUotSk+qcqvbLzFVeFBwCAw53tSUonaxg3ZvW+1U6NCfAmR0qOSGrdY61T4lIkyf7QCXgPijFOYB+8t5nxYhqEBoWqc1z9l8Q397zp9LgAb9IwXkxiTKL8/Zq/Qk2ShvQYIkn6cA8DGQIA2q+zDd57sgHdBkiSNqZv1InKE06NC/AWmcWZkqSIkIgWL9Nw0nBnDg+T8DYUY5xga+5WSS3sCLvXd4Tv737fmSEBXqfhFqWmHml9qgHdB8hsMuvQ0UPaXbDb2aEBAOBwtbZa7Ti6Q9LPg4KeSUJ0gmKsMaqtq9XSfUudHR7gFTKL6osx4SHhLV4mpVP9lTF5hXlKK0lzSlzwTBRjHKyytlJ7j+2VdPYrYyRpaM+hkqRdWbt04MQBp8YGeJPWFGNCAn9+qtKiXYucGhcAAM6wJ3+PKmsr5e/rr6jwqLPObzKZ7IPYr/hphZOjA7xDw5Ux1lBri5cJCgiyf298/wAn6L0JxRgH23V0l+qMOgUHBCs8+OwV0YjQCHVP6C5JenH7i84OD/AKJVUl9ivUuiacvRgjSUN61t+q9P5OOkEAQPtz8i1KZlPLDvEHpw6WJG1M2+issACvklWcJUmyhlhbtVyvlF6SpP8c+o+jQ4IHoxjjYCePF3OmEbRPNqz3MEnS8p3LJdWfqegc3lmdwzu3eB0AfvZD9g+yGTZFhEa0uDMc0mOI/H39dbToqGKDY8k/AEC7svnIZkmyP5mlJey3KtlqZQ200vcBbWQfMyb0zGPGmGRSRGiEIkIjZJJJvZLrizE/pv/IE828CMUYB2vNeDENBqUOksVsUcaxDH2V+ZWCfIOUfke60u9IV5BvkJMiBTquhsF7z/RI61P5+/lrWK/6wujAlIHkHwCgXWnN4L0NTCaTzu97viQpzhpH3we0gc2wKbs4W9LZr4zx8/XTwpsWauFNC+Xn66duid3kY/HRidIT2nB0gwuihSegGONgDR1hSx4p2CA4INj+NJcnf3zSKXEB3mRt5lpJrSvGSNLI/iMlSV/v+1pHSo84PC4AAJzBZtha9Vjrk43oM0Jms1k/HflJG3L5Egicq7zSPNXYamQymRQWHNaqZf18/OzHre/ufdcZ4cEDUYxxIJth07bc+tuUWnNWQpJGDxwtSfrPnv8oryzP4bEB3qLWVqv1mesltb4YkxybrOTYZNXW1eqRHx5xRngAADjcoROHVFJdIh+LjzpFdGrVsmHBYerftb8k6ZmNzzgjPMArNNyiFBYUJovZ0urlGx43v3rvaofGBc9FMcaB0k6k2TvC2IjYVi3bOa6zkmKSVFtXqz+v/7POe+U8nffKeaqoqXBStEDHtC13m8pqyhToH6hOka07IJWkMYPGSJJeWveS8kvzHR0eAAAO1zBeTEJ0giyW1n8JPK/3eZKkZZuX2W+zANA66YXpks4+XowkVddW6+l3n9bT7z6t6tpqSdKA7vXFmL05e5VWxCOuvQHFGAdquEUpPiq+1dVQk8mkcUPGSZIWb1ysjTkbtTFno2yGzcFRAh3b2sP1tyh1je/a4qdJnKx/t/qzgzbDpme2cIYQAOD5NubUPw0pOablg/eerEdyD0mSYRh6fMPjDosL8CYHjx+UJMVYY846r2EYyjyaqcyjmfYBe60hVnWJ6yJJWrRrkdPihOegGONADYP3tvZe3QZDeg5RjDVGZVVlDowK8C7nOl5Mg5MLqS//8LLKq8sdEhcAAM6yPqv+9tyUuJRzWv7kJyi9sekNrswGzsGB4wckSVHhUee8jkGpgyRJy3cvd0hM8GwUYxzoXEaxP5nFbNEl513iyJAAr2IzbPo241tJ9VfGtNWJshP64/o/tnk9AAA4S3Vdtf3KmC7xXdq8vuKKYsZNA87BgRP1xZjo8OhzXseQnkNkMpm0J3uPtuVvc1Ro8FAUYxzEMAz7/brnWoyRpGG9hinW2rrxZgDU23l0p46WHZWfj586x3V2yDqf++455ZUyqDYAwDNty92mytpKBQUEOewY8tm1z6qgvMAh6wK8RcNtStHWcy/GWEOs6tO5jyTpr5v+6pC44LkoxjhIRlGGcktzZTab21SMsZgtmjZ6mv337Ue3OyI8wCv85+B/JEndE7vLx+LT5vUlRCeosrpSt/7n1javCwAAZ/gu8ztJUpe4Lo1uNzpXnSI7qbyqXHd9c1eb1wV4i/KacmWX1A9+HRN+9jFjzuSCfhdIkj7Y8YF9cF90TBRjHKThUbpJMUny8/Fr07oaBlGTpPmr5qvWVtum9QHe4j+H6osxvVJ6OWR9V46+UpK0YvsKrT7IYwYBAJ6nYbwYR9yeK0mXXXCZJGnJhiXacGSDQ9YJdHSHThySJAX6ByooIKhN6+rXpZ9Cg0JVVF6kl3e87Ijw4KEoxjjI91nfS5K6xjmmIwzyr0/iPXl79D/f/o9D1gl0ZJW1lfbxYtpajAkOCFZwQLC6xHfR6AGjJUnzP5qv0urSNscJAICjGIahbzK+kST7U1jOVUPf16tzLw1KHSSbYdP1K6/npCDQArvzd0uSYq2xLb5CrSHnTmWxWHTRoIskSc+sf8b+tCV0PBRjHKSto9ifzN/XX4/++lHNuWSOJOnJNU/q28Pftnm9QEe29vBaVdRWKCwoTHGRcee8Hn9ff/3vr/5X//ur/5W/r78uv/ByWUOsyi3K1eyVs+kQAQAeY3f+buWW5srXx7dNg/ee2vfNGDtDQf5B2pe3Tw98+4DjAgY6qB15OyTV3+LeEqfm3KlGDRglP18/peen61/7/+XQWOE5KMY4QEVNhf1JSo4Yxb7B8N7DNbjHYNXZ6nTVsqt0uPCww9YNdDQf/PSBJKlvl74OuWe+QYB/gOZNniezyayPdn+kv234m8PWDQBAW3x+6HNJUreEbvL18XXYesOCwzT9oumSpCe/fVL/SfuPw9YNdEQ783dKkuKizv2E4MmCAoI0st9ISdL/fPU/nAzsoCjGOMC6zHWqtdUqPDhckaGRDluvyWTSdROvU3xUvE6Un9CkdyepuKrYYesHOgqbYbMXYwZ0H+Dw9XdL6KbLLqy/h/6uT+/SZ4c+c3gbAAC01udp9cWYXsmOGSvtZOf1OU8j+o6QYRi6dvm1yirKcngbQEfRcGVMfFS8w9Z58fCL5efrp725e7V452KHrReeg2KMAzScleiV0sshZ+Sra6v13PLn9Nzy52Qym/TLy3+p4IBg/ZT3kya+PVFl1WVtbgPoSDbmbFR2Sbb8ff3VM7lnm9Z1cv6dPIL9+KHjNTi1/kq16cuma3suTzoDALhPVW2Vvk7/WpKc1vfNHDuz/qRg2QmNf3u8iiqL2tQO0BGVVZfZB/BNiGrZbUrN5dzJQoNCNWHoBEnS/V/er6raKscEDI9BMcYBGooxbe0IGxiGoYPZB3Uw+6AMw1BUeJQWXLVAAX4B2pi5UZcsvUQlVSUOaQvoCJbvXi5J6tO5T5sv0z41/xqYTWbNuXSOuid0V3l1uca9Oc5+FgQAAFf7Mu1LlVaXKiw4TAkxLfsC2Jzm+j4/Xz/98opfKiwoTAfyD2jyu5NVUVPR1tCBDmVr7lYZMhQWHKaQoJAWLdNczp1q/JDxCgsKU25hru795l5HhQwPQTGmjQrKC7T5yGZJjR9J7WjJscn69ZW/lp+vn9ZnrNcFr1+gIyVHnNYe0F7U2er01o63JElDeg5xalu+Pr6af/l8JUQn6ET5CY1+Y7Q252x2apsAADTl/T3vS5IGdBsgs8l5h/RRYVH61ZW/kr+vv77P+F4T357I0wWBkzQ8yKWtTzRrir+fv338pue/e15b87Y6vA24D8WYNvrkwCcyZCg+Kl7hweFObatrfFfdfvXtCgkM0e683Rr66lD9kPWDU9sEPN1/Dv1HOSU5Cg4IVr+u/ZzeXlBAkH5z9W+U0ilFxRXFGr14tH28GgAAXKHOVqeVe1dKkgZ2H+j09pJikvTrK38tf19/rc9Yr7FLxupY+TGntwu0B99nfS/JOcUYSRrcY7D6d+2vOludrnn/GlXWVjqlHbgexZg2Wr6n/vYIV3SEkpTSKUV3/OIOxVhjlFucq9Gvj9Yz3/P8eXivRVsWSZKG9RomH4uPS9oMDgjWrVfdqh5JPVRRU6Grl12tP6/5s2yGzSXtAwC82xdpXyi/PF9B/kFKTUx1SZvdErrp1um3KtA/UJuzN2vwPwZr59GdLmkb8FSGYdiLMZ3jOjulDZPJpF+M/4WCA4K1/+h+zf94vlPagetRjGmDsuoy/fvAvyW5rhgjSdHWaP3h2j9oUOog1dpqdeend2rskrE6cPyAy2IAPMHhosP2y7RH9B3h0rYD/QO14MoFGjVglAwZeujLhzRuyThlFfO0CQCAc7225TVJ9SciLBaLy9rtEtdFv535W0WFRSm7KFsjXh2hJduWcFIQXuvA8QPKLsmWxWxRcmyy09oJDwnX3ElzZZJJS7cu1XMbn3NaW3AdijFt8OHeD1VZW6mosCglRLdt4LTWCvQP1I1TbtSMsTPk6+OrNelr1P/F/vrzt39WeU25S2MB3OXvP/xddUadeiT1UFJMksvbt1gs+sX4X+jaCdfa87Dfi/20aMsirpIBADhFQXmB/fZYV5+IkOof3fuHa/9gvzr0hg9u0Mx/zdTxiuMujwVwt08Pfiqp/soxP18/p7bVu3NvTTp/kiTpd6t/p+U/LXdqe3A+ijFt8MrmVyRJ5/U5zyGPtD6Zn4+f/HzOnNAmk0ljBo3RvXPuVc/knqqqrdJDXz2kbn/vptc2v6ZaW61DYwI8ybHyY/rHpn9Iqn/stCO1JP9ONrL/SN113V1Kjk1WcWWx5n84X+e/dr59cG8AABzl5U0vq7quWkkxSQ49E9+avi84MFgLrlqgKRdMkdlk1vu731eP53votc2vcTICXqXhLok+nfu0etnWHm9K0qQRkzSi7wgZhqHZ/5qtj/d/3Op24TlMhgOuKywuLlZ4eLiKiooUFhbmiLg83v6C/er5fE+ZTCY9dONDigiNcGs8hmFo877N+vi7j3W8pP7MRGdrZ91z4T26cfCNCvINcmt8HYUnftY9MSZX+N0nv9Pff/y7kmKSdOesOx1eED0XdXV1+nbbt/r3D/9WVU2VJGl6n+n687g/q1+s8wcX7ui89bMOoHne9u9CRU2FOj/bWfnl+br+0us1vPdwd4ekw3mH9fZnbyvvRJ4kaVjiMD058UmN7+rYEyXezlM/654alyuUVJUo9qlYVdZW6u7r7lZiTKJL2q2z1en1j1/XzrSd8jH76I3pb2h2/9kuadubOeOzzpUx5+ip756SVF8FdXchRqq/SmZYr2G6b+59unL0lQoOCFZGYYZ+s/o3Sv5rsu79z73aX7Df3WECDvHTsZ/04sYXJUnTRk/ziEKMVH/b0vih43Xf3Ps0rNcwmWTSij0rNODFAZrx3gytO7yO++oBAOfshQ0vKL88X5FhkRrSc4i7w5FU/3CJu2ffrStHXyl/X39tyt6kCUsmaMziMfoq7Sv6PXRYK35aocraSsVYY1w6ZIXFbNGNU2/UkJ5DVGur1Zzlc3Tfl/epzlbnshjgGBRjzsHhosN6fevrkqSLh1/s5mga8/Xx1fih47XwpoWaMXaGosKidLziuJ747gn1fL6nRr8+Wou2LFJBeYG7QwXOSZ2tTjetvEk1thr17dJXPZN7ujuk01hDrJo7aa7unn23BnUfJEOG3t/zvka/PlqD/zFYr25+VUWVRe4OEwDQjuSV5unhbx+WJE06b5IsZtcN3Hs2PhYfjR86XvfPvV+jB4yWxWzR2oy1mrBkgga+PFCvbn6VMQ3R4SzZtkSSNLzXcJefGPSx+GjupXN10aCLJEl/WfMXTXxrojKLMl0aB9qG25TOwax/zdKyXcuUmpiq22fc7vD119TW6PXV9cWem6beJF8f33NeV52tTjsP7dQPu3/Qnow99rMTFpNFY7uM1cw+M3V5z8uVHO680b87Ek/8rHtiTM608KuFevjbhxXgF6B759zr8CvTHJl/DXKO5ejbbd9q00+bVFNXI0nys/jpip5X6PqB12tS90kK9A1sczsdnbd91gGcnbf8u2AYhmYtn6X3dr2n5Nhk/f7a38tsctw5VUf3fSdKTujzjZ/rxz0/qqa2vt8LCwjTzD4zNWfAHI3tPNajikntgad+1j01Lmfbnrddg14aJJPJpAfnPaio8KhWLe/InNv400Yt+3KZamprFOwXrMcnPq5fD/+1fMw+57xOnM4Zn3X+Qq30yf5PtGzXMplMJl015iqntGEzbNqdvtv+ui0sZosGpQ7SoNRBKiwp1IafNmjr/q3KPpatL9O+1JdpX+q21bcpNTJVl3a7VBd3u1hju4xVZGCkIzYFcKhlO5fZzwrOHDvTKbcIOjL/GiREJ2jWxFm64sIr9P3u77VhzwblHs/V8j3LtXzPcgX4BGhC1wm6oucVmtpjqlLCUxzSLgCgY3hty2t6b9d7MpvN+sX4Xzi0ECM5vu+LCI3QL8b/QlNHTtWPu3/U2u1rVVBcoEVbFmnRlkXqFNJJV/W6Spf1uEwTuk5QsF9wm9sEXOnRNY9KkganDm51IUZybM4N7z1cSbFJWvbFMqUdSdPtn9yuv234mx6f8Liu6n2Vx9zOj9NRjGmFw0WHNXfFXEnSRYMuUlKs6x+l2xbWUKsuOe8SXXLeJcovzNeOgzu0/eB2ZeRl6MDxAzpw/IBe2PiCJCk1MlUjk0bq/MTzdX7S+eof218BPgFu3gJ4s2U7l+n6FddLksYNHqfhfdw/aGFrBQcGa+KwiZowdIKyj2Vr00+btGX/FhWWFmr1/tVavX+1pPrBt8d2HquLUi7SmM5j1COyBx0pAHipfx/4t279+FZJ0tQLpiqlU/sp2AcHBGv80PEaO2SsDmUf0qa9m7TtwDbllebp5U0v6+VNL8vP4qeLOl+k8V3Ga1TyKJ2XeB4PnoBH+zbj2/qT8zJp4vCJ7g5HkhQXGaf/mvlfWrd9nT754RPtP7ZfV793tXpG99QdI+7Q3EFzFeIX4u4wcQqKMS2UU5Kji5dcrIKKAiXHJuvyCy93d0htEmON0YRhEzRh2ASVV5XrYNZB7cvcp72Ze3X0xFF7cebN7W9Kkswms1IjUzUgdoD6x/ZX/9j+6h3dW12tXTmbAaeqtdVq4VcL9eja+jMQw3sN17TR09wcVduYTCYlxSQpKSZJ00ZP05GCI9qVtku703crPTddGYUZWlK4xH4vcph/mIbEDdHguMH2//eM6smtTQDQwb29/W3d/OHNqrXValivYZowbIK7QzonZpNZqUmpSk1K1YyxM7Q3c6/2pO/R7ozdOl58XJ8f+lyfH/pckuRj9tHQ+KE6L+E8Dew0UIM6DdKATgMo0MAjFJQX2E/Oj+w/UkkxnnNy3mwya8ygMRree7i+2vyVvtn6jfYd26fbVt+mu/5zl6b1mqZZ/WZpUuokTrJ7CIoxLfBV2le6fsX1yinJUURohG6+7GaHjCPhKYL8gzSg+wAN6D5AklRWUaaMvAxl5GbocN5hZeRlqLyyXPsK9mlfwT4t37O80fKdQjqpu7W7ukV2U/eI7uoc3lnxofFKCE1QQmiCogKjOKuPVjMMQ58e/FR3fXaXduXvklR/RdpVF13l8Muz3clkMikhOkEJ0Qm65LxLVFlVqbTcNB3KOaRD2YeUkZeh4qpifZPxjb7J+Obn5WRSUniSekX1Us/InuoZ1VNdI7oqKSxJyWHJig6KJu8AoJ06WnZUd//nbntRfmD3gbru4us6RP/n4+Ojfl37qV/XfjIMQ3kn8rT38F6l5aTp0JFDKi4r1o/ZP+rH7B/ty5hkUmpkqnpF91JqRKpSI1PVI6qHUiNTlRyWLF9Lxzkuh+c6Vn5MU96eosNFhxUdHq3LR3nmyflA/0BNHTlVE4ZO0A97ftDa7WuVX5ivd3e+q3d3vit/H3+NTh6tS7pdotEpozU4bjAn192EYkwzDMPQusx1+uv3f9X7e96XVH/516+m/cojHmXtTMGBwerbpa/6dukrqX5fFJcV60jBkfqf40eUeyxXRwuPqrK6UnmlecorzdN3Wd81uT4/i5/iQuKUGJqo2OBYRQZGKiowSlFBUae9jgiIUIhfiEL9Qxl0ygsZhqF9Bfu0at8qLdq6SLvz6++lDQoI0i/G/cJjHuPpTAH+AerTuY/6dO4jSaqrq1PeiTxl5WcpKz9L2UezlVOQo4qqCmUWZSqzKNN+NvFk/hZ/JYUlKSksSQmhCYoOilZMUEz9/4Nj7L9HBUUp3D9cAT4BFG8AwI1shk0/ZP2gt3e8rUVbFqmitsJ+G8TUkVM7RCHmVCaTSXGRcYqLjNPYwWNlGIaOFx9X2pE0ZR/LVk5+jrKPZau0olT7j+/X/uP7T1+HTIoOjlZSaJISwxKVEJKgxLBExYXEKSrw/441g6Lsr7mqFK1VZ6vT8j3L9ftPf6+ckhwFBwRr/uXzFeTv2VdrBfgHaOzgsbpo0EU6nHdYm/dt1rYD21RYWqgv0r7QF2lfSKq/oqZXdC8Nix+mnpE97cXO7hHdZQ2wcnzoRF7/bbfWVqviqmIdKz+mg8cP6sDxA9qQs0Ffp3+tzOL6R4OZTCaN7DdSV465Uv6+/m6O2PVMJpPCQ8IVHhKu3p17N5pWVlmmgqKC+p/iAh0rOqai0iIVlRWpqLRIZZVlqq6r1uGiwzpcdLhV7Qb4BNQXZvxCFeofan8d4heiYL9g+Vv8FeAT0Ojn1Pf8ffzt7/uYfeRr8ZWP2afRj6/59PeamrcjHgS5Wp2tTmU1ZSqrLtOx8mPKLslWdnG20gvTtTVvq7Yc2aLskmz7/H6+fhrZb6QuHXGpggO8s2JvsVjsV86M6DNCUn3RqqyyTPmF+Tp64qjyC/OVfyJfJ0pPqLCkUCXlJaqqq9LBEwd18MTBFrXjY/ZRmH+Y/SfUL9T+OsQvRIE+gQr0DVSAT4ACfQLtOdbUew2505BbZ/u94YfOHkBHV1NXo5LqEhVVFimrOEsZRRk6dOKQNh3ZpO+zvtfRsqP2eZNjkzVj7Ax1ie/ivoBdzGQyKSo8SlHhURqun8eGazgpeKzomI4VHtOxomPKL8xXQVGBaupqlF+Wr/yyfG3J3XLWNgJ9AhURGKFw/3CF+IWc9tNwrBniF2I/lvS3+Lfo/z5mH1lMFlnMlmZfczzp2WyGTUWVRcosztSuo7v0fdb3Wrl3pTKKMiRJsRGxunnqzYqLinNzpC1nMpnUOa6zOsd11lVjrlLeiTztz9yvfZn7dDjvsIrKirQnf4/25O85bdlAn0DFhcQpLiRO8aHx6hTcSdYAq8L9wxUeEK5w/3CF+YcpPCBcQb5Bjb6Lnfo9jOO80zmkGNPwuOTxL4+XJdAiQ4ZOfmJ2w2tDhv33lrxuWKYlr09exj79DOutrqtWcVWxKmoqmt0uX19fDew+UGMGjlFcZJyMCkOVFZXnsIdap6q2Svq/ZipLKmX4tPnp405jkUWxgbGKDYyVmvg3qbauViXlJSouL1ZxWbHKKstUXlmu8qpyVVRW2P9fWlWqisoKVVRVyGarH1G88v/+O6ZjLt6q5pmq6/8RccAT4R2mIZYJ/5ggc0B9B99UDjaVP2fKw7PNe2q+NTevzbCpvKZc5TXl9Z/tszCbzeqW0E39uvTT4J6DFegXKFVLldXOzz2p/eSfj3wUHxyv+OB46ZTblWtttSouK1ZRWZEKSwtVWl6qssoylVXU519ZZZlKK0tVXlGu8sry+mVUq+Plx3Vcx92wNfXMJrP9QNVsMsti+vm12WyW/u/j40n5B8C9Tj4GNQeaG73X8Lo1fd25vLb/foZ2ymvKVVpVquq66jNuj5+vn/p07qPhvYcrNTFVJpNJlcUce/rJT52tndXZ2lnq/PP7NsOm8spyFZUXqaSsxN73FZcVq7SitNExZnlluQzDUIUqVFFaoRzluG17LGaLLKb/K9L83+uT+7+GH5PJZH/d8PfxtD6wqe+BJ79/8uumvuc1/H6u0xp+P9v3wpa0X1ZdphMVJ5rczgD/AI0aMEpjB4+Vn49fm/PSnTln9bHqvK7n6byu50mSisqLlJOfoyMFR1RQXKDjRcd1rOiYSspLVKEKpZWmKU1pbWrTZDLJz+JnP7Zr+Lw3fP5Pfn3y8Z/FbJFJJvs6JJ32e1PvtWSZlq6jQW1FrSTH5qDJcMDaDh06pO7duzsiHqBdOHjwoLp16+buMCSRf/A+npR/ANyLPhDextP6QHIQ3saROeiQK2MiIyM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vmpoa/fKXv9TSpUu1YsUKx7aKi4v10UcfacmSJR221foGt7PHZ5991uF6mzZtUnp6un77298qLi5Ow4cP1+9///tO+2S322UYRptphmFo9+7dbaaNGzdO/v7+mjp1qi688EJNnz69S/sM3sFVY+50A3EMnqq+vl4rV67U7bff3ukymzZtUkREhFJTU7u0TXgfcqBzc+C2bdu0YcMGPfTQQ13eVwAGPooxANCJOlud6mx1Tt3m3XffrdTUVEVEROiKK65QTEyMrr32WlksFl1//fXau3evrFZrh+suWbJEmzZtUmFhoSRp5cqVmj59uuPb79M988wzqqio6PQxbdq0DtcrLy/X3r17ZRiG8vLytHr1av3xj3/Uq6++2uHy8+fP1zvvvKMtW7bIZrPpz3/+s/Ly8lRVVdVmud27d6umpkZr167V3LlzZbH0jYu0ou9wxZg73UAcg6f6+9//Lj8/Py1YsKDD+bm5ubrzzjv1hz/8QT4+PmfdHrwXOdA5OdBms+mOO+7QM88845FTrgD0XRRjAMCN4uPjHc+DgoLa/W4YhurqOn7zm5CQoFmzZjneEL788statmyZ02MMCQmRxWLRY489poCAAI0ePVq33nqr1qxZ0+HyM2fO1FNPPaU77rhD8fHx2rZtmy655BJFR0e3W9bPz0/z58/Xxx9/3KUPloCzDcQxeKoXXnhBy5Ytk6+vb7t5BQUFmj17tu655x7deuutTo8bOJuBOP7OlgN/97vf6ZxzztHMmTOdHiuA/o1iDAD0I62Hae/du1cHDx7UokWLOl32rrvuaneHlVMfn376aYfrjR8/XpJkMpm6HNett96qrKwslZWV6fnnn1dWVpZmzJjR6fI2m02HDh3q8vaBvqKvjkFJOnz4sDZv3qzbbrut3bzCwkJdfPHFWrp0qR5++OFubRfoK/rq+DtTDvzggw+0Zs0axcfHKz4+Xm+88Yaef/55TZ06tRs9BzAQUYwBgH5k4cKFys3N1U9/+lMtXLhQISEhnS777LPPtrnDyumPzq7ZctFFFykjI0O//OUvZbPZdODAAS1fvlxXXXVVh8vbbDbt3LlTdrtdZWVluueeezRkyBBdfvnlklrOv9+6dausVqusVquWL1+ujz/+WJdeemnvdwjgZn1xDLZ64YUXNHXqVI0aNarN9KKiIs2cOVPXXXedHnnkke53Gugj+uL4O1sOfPvtt5WVlaWdO3dq586dWrBggZYsWaK1a9f2focA6NcoxgBAPxIUFKRFixZpw4YNLjk8W5IsFoveffddbd26VREREbr88sv1ox/9qM1FEk/9VtFms+mWW25RWFiYhg8frqamJq1du1Zmc0uKqa2t1Z133qno6GgNGjRIf/nLX/T66693er4+0Jf1xTEoSc3NzXrppZc6vHDv888/r8OHD+vJJ5/s0pEBQF/VF8ff2XJgVFSU46iY+Ph4BQYGKigoSDExMS6JH0D/YTJOv/w3gH6lqqpK4eHhqqysVFhYmKfDGTBqrbUKebzlG7eah2oU7BfcpfUaGhqUk5OjIUOGKCAgwJUhDlid7cO++lrvq3H1Nz0dc6djDPbOmfZfX3yt98WYBgJyoOf0txwIoOe4jD4AdMBsMmvG4BmO5wBcizEH9B2MRwBwPYoxANCBQN9AfXLzJ54OA/AajDmg72A8AoDrUeoGAAAAAABwI4oxAOACXI6r59h3cAZeRz3DfoMz8DrqOfYd4D0oxgBAB2qttYr9XaxifxerWmttl9fz9fWVJNXV1bkqtAGvdd+17kt4h56OudNZLBZJktVqdVZoXqV1v7XuR3gncqDnkAMB78E1YwCgEyfqTnR7HYvFooiICJWUlEhquQ2nyWRydmgDkmEYqqurU0lJiSIiIvgw6IV6MuZO5+Pjo6CgIJWWlsrX19dxe1mcnd1uV2lpqYKCguTjw1vEgeTZ7c9qY85GPXjhg5qUOKlL65AD3YscCHgfMi0AOFl8fLwkOd6MonsiIiIc+xDoLpPJpISEBOXk5Cg3N9fT4fQ7ZrNZqampfIAeQD7I/kA/fO+HkqSPcj/S0XuPKtQ/1GXtkQN7hxwIeA+KMQDgZK0fBuPi4mSz2TwdTr/i6+vLt4HoNT8/P2VkZHCqUg/4+flxNNEA8+dtf3Y8L68t15M7n9R/nvefLmuPHNhz5EDAu1CMAQAXsVgsvKkCPMRsNisgIMDTYQAeVW+r14dHPpQkTRoxSV8f+Fqv7XnNpcWYVuRAADgzvvoAAAAABqAvCr5Qna1O4cHhumLqFZKkA0UHdKzumIcjAwBQjAEwoH2e/7nSnkxTxp8ytPv4bk+HAwCA2+w4tkOSlBafpqiwKMVFxslu2PXW4bc8HBkAgGIMgAGryd6kZauXKbcyV4fLD+vqVVfLbti7tK7ZZNbkxMmanDhZZhN/KgFXY8wBzrfjeEsxJjkuWZI0cvBISdKG7A1nXI/xCACuxzVjAAxYHx35SNknsx2/55zI0cpvV+rGUTeedd1A30Btu2ObK8MDcArGHOB8rUfGJMe2FGOGJQ3T5p2btbNg5xnXYzwCgOtR6gYwYL2V1XIY9oVjL9SMCTMkSf+38/88GRIw4BXXFOv+9+/Xf278T9Vaaz0dDuC16mx1OnDigKTvjoxJT0iXJBWVFym3llu/A4AnUYwBMGBtzNkoSRozZIwmj5wsSfryyJeqtFZ6MixgwGq2N+uq16/SU18+pV99+itdu/paT4cEeK3s8mwZMhToH6iQwBBJUkhQiOIi4yRJ/zj6D0+GBwBej2IMgAHpWPUx5VTkyCST0hLSlBybrKiwKFmbrFp5YOVZ16+z1SntyTSlPZmmOludGyIG+r91B9fpy8IvHb+//+37ej/n/S6ty5gDnOtQ+SFJUmxErEwmk2P6kIQhkqRP8z/tdF3GIwC4HsUYAAPS5/mfS5ISYhIU6B8ok8mkMUPGSJLey37vrOsbhqHcylzlVubKMAyXxgoMFMt3LZckzZo4S1NHT5Uk/Wrrr7q0LmMOcK5DZd8VY06VnthyqtL2/O2drst4BADXoxgDYED6ouALSdKQ+CGOaSNSR7TMy/lCdnvX7qoEoGsamxq14XDLHVomjZik6eOnS5K+yP5ChbWFngwN8EqtR8bEhMe0md5ajDlSfETVtmq3xwUAaEExBsCAtLd0ryQpMTbRMW1Y8jBZzBaVVZVpe1nn3wgC6L4vC79UfVO9QoNClRiTqMSYRCXHJqvZ3qxndz/r6fAAr3PqaUqnigmPUUhgiJqam/RB/geeCA0AIIoxAAaovSUtxZiE6ATHNH9ff8c3gqsOrvJIXMBA1XrB7IzkDMf1KSYOnyhJWntgrcfiArxVZ6cpmUwmDU0cKkn68OiHbo8LANCCYgyAAaeioUIFVQWSpPjo+DbzWk9V2nhko9vjAgayzbmbJbUcgdZq7NCxkqQ9+XtUXFfskbgAb1RjrdGxmmOS2hdjJGlocksxZmveVrfGBQD4DsUYAANOVmmWJCkiJEJB/kFt5o0cPFKStDt/N3eIAJzEMAztOLZDkjR40GDH9NiIWMVHxctut2vFtys8FR7gdQ6XH5YkBQcEKyggqN38YUktRdP9Rftlbba6NTYAQAuKMQAGnI5OUWqVFJOksKAwWW1WrT3a+akTJpNJmbGZyozNbHNLUADt5VTkqLKxUhazRfFRbY9GG5vecnTMOwfeOeM2GHOA87SeohQTEdPh/PjoeAX5B8lqs+qjgo/azWc8AoDrUYwBMOC0FmNO/1AotbzBbD06Zs2hNZ1uI8g3SPvu3qd9d+9TkG/7bxUBfOebY99IkhJjEmWxWNrMaz1VaVvONtVYazrdBmMOcJ7Wi/fGRcR1ON9sMjuuobbh6IZ28xmPAOB6FGMADDj7SvdJan+9mFatxZhPj3zqtpiAgaz1FKXk2OR281LiUhQREiGrzao3D7/p7tAAr9R6mlJnR8ZI0tCkluvGbMnb4paYAABtUYwBMOCc6TQlSRqRMkImk0kFZQU6VHHInaEBA9KO4/8qxsS1L8aYTCbHqUpv7X/LrXEB3spRjAk/ezFmX8E+2ZptbokLAPAdijEABpQTdSdUUlsiSRoUNajDZYIDg5U6KFWS9PrB1ztcps5Wp9HPjNboZ0ZzoV/gDE69eG9SbFKHy4wbOk6S9Nnhzzr90MeYA5yntRjT0Z2UWiXFJinQP1D11nptyG97qhLjEQBcj2IMgAHl2xPfSpIiQyPl7+vf6XKjBo+SJG043P5ceanlA2ZWaZaySrNkGIbzAwUGiGM1x1RSWyKzyazEmMQOl0lPSldwQLBqGmq0Lmddh8sw5gDnqLXWOm5rHR0e3elyFrNFI1NbTtt9++DbbeYxHgHA9SjGABhQ9pfulyQNiuz4qJhWrdeN+Tr3a9Xb6l0eFzBQ7Tq+S1LLN/B+Pn4dLmMxWzR6yGhJ0sr9K90WG+CNjpw8IkkK8g9ScEDwGZcdldbyxcTH2R+7PC4AQFsUYwAMKPtP/KsY08kpSq1SB6UqLDhMDdYGLioK9MLu4t2SOj9FqVXrqUobD27km3bAhbJPZks681ExrUYNHiWTTDpaclTZldmuDg0AcAqKMQAGlNbTlM5WjDGbzBo/bLwkaeU+vqkHemp3SUsxprMLZrcanjpcfr5+Kq8p1z/z/+mO0ACv5LheTHjn14tpFRoUqpRBKZKklQfIhQDgThRjAAwojiNjznKakiRNGDZBkvTpoU/VYGtwZVjAgNV6ZExn14tp5efjpzFDxkiSntv5nMvjArxVdnnLES5nuq31qcakt4zLt7PePsuSAABnohgDYMCos9UptyJX0tmPjJGkIYlDFBYcpnprvV4/1PFdlQB0rrGp0XE02tmKMZI0ZdQUSdL7We9TAAVc5PDJliNjunKakiSdk3GOJGlX3i4V1BS4LC4AQFsUYwAMGAdOHJAhQ8EBwQoJDDnr8maTWRMyJkiSXtz1Ypt5JpNJg8MHa3D4YJlMJleEC/R73574Vk32JgX6ByoiJOKsyw9PGa7w4HDVNdbppf0vtZnHmAOcw3FkTHjXjoyJjYhV6qBUGYah5/a0HLXGeAQA16MYA2DA6Or1Yk51fub5kqQth7eosLrQMT3IN0hH7z+qo/cfVZBvkHMDBQYIxylK0Yld+sBmNpt17qhzJUkv7HyhzTzGHNB71marcitbjhDtajFGkiYOnyhJemvPW5IYjwDgDhRjAAwYrdeLiY+M7/I6iTGJGjxosOx2u57Y8YSrQgMGJMedlGLOfCelU52XeZ4kaVvONu0p3eOSuABvlVuRK7thl6+Pr8KCw7q83jnDz5HZbNaBYwf05bEvXRghAKAVxRgAA0ZrMSYuKq5b600dM1WS9Mo3r6jZ3uz0uICBynEnpZgz30npVLERsRqdNlqS9F9b/8slcQHe6lD5IUktR8V05/Si8OBwjUtvuf38/3z1Py6JDQDQFsUYAAPG/tJ/FWMiu1eMOWf4OQoKCFJJZYle2Nty6kS9rV7nPn+uzn3+XNXb6p0eKzAQtB4Z051ijCTNOGeGJGnNnjUqrSuVxJgDnKE1D3bljoKnu3DchZKk9/a9p6KqIsYjALgYxRgAA0JjU6MOlB2QJCVEd++Dob+vv6aPmy5J+u1nv5VhGLIbdm0v2q7tRdtlN+xOjxfo70pqS3S85rhMMikhqntjLiM5Q4kxibI2WfWLLb+QJMYc4AStR4h259pprYYlDVN8VLwabY367fbfMh4BwMUoxgAYELp7V5fTXTT+Ivn5+ulI6RG9eeBN5wcIDDA7j++U1HL7XH8//26tazKZNGfKHEnS37b9Tcdrjjs7PMAr9aYYYzKZNHvSbEnSC1+9cJalAQC9RTEGwICwq3iXpJYL8vbkNpzBgcGaNnaaJOnBDx9Uk73JqfEBA832ou2SpNRBqT1af9zQcUqJS1GjrVEPbHrAmaEBXskwjF6dpiRJE0dMVEx4jGoba50ZGgCgAxRjAAwIu463FGO6c1eX010y+RIFBQQptyxXT+942lmhAQNSazEmJS6lR+ubTCbNv2C+JGnljpXaVbLLabEB3qiktkQnG07KJJNiI2N7tA2L2aLLplzm5MgAAB2hGANgQGi9q0tiTGKPtxEUEKTLz7tckvSbT3/jlLiAgaq3xRhJGpE6QmPTx8put+uO9+5wVmiAV2o9RSkqLEp+Pn493s7kEZN7lUsBAF1DMQZAv2cYhuPImN6+gbxwzIVKiE5QTUONM0IDBqTimmLlV+XLJJOS45J7ta1rZlwjf19/ZR3PclJ0gHfKKm0ZQz25XsypzGazrpp2leP3TXmberU9AEDHKMYA6PeO1RxTaV2pTCaT4qPie7Uti8WiGy69QSa1XHcmxC/EGSECA8rXx76W1HIb+QC/gF5tKzI00nG6kiRFBET0anuAt/rm2DeSpOTY3hVIJSktIU0+Fh9J0q3rblWNlS8oAMDZKMYA6Pe+KvxKkhQfFS8/354fmt0qJS5Fl065VJLUZDTpaMXRXm8TGEhax1xvTlE61bRx0zR6yGhJUmhgqGx2m1O2C3iTHcd3SJKSYnt+7bRW/r7+euz2xxQREqHiymLd+t6tvd4mAKAtijEA+r0vCr6QJA2JH+K0bc6ZMkfDkoapwdageW/MU0VDhdO2DfR3n+Z9KkkakuicMWcymXTDJTcoIiRC+SfzNff1ubI2W52ybcAbWJut2lO8R5JzjoyRpCD/IC25bIlMMumt3W/pj9v+6JTtAgBaUIwB0O+1FmNS43t2i92OWMwW3TT3JkWERCivPE+zX52tOlud07YP9FfWZqu25m+VJA1NHOq07QYHBuv2K2+Xv6+/vsj9Qte/c72a7c1O2z4wkO0r2Seb3aZA/0BFhUU5bbsZyRmaN3WeJOmB9x/QB0c+cNq2AcDbUYwB0K812Zu0rWibJCktPs1p27U2WbX8H8sVGhQqf19/7SjYoXmvz1NDU4PT2gD6o6+LvlZ9U72CA4J7faHQU1mbrFq9ebWiwqJkkklv731bi1cvVpO9yWltAAPVjmMtpyglxybLZDL1envWJqv+tOpP+tOqP+miCRdp4vCJarY366rXr9Ln+Z/3evsAAIoxAPq53cW7VWerU4BfgOKi4py2XcMwlF2YrfySfN16xa3y9fHVpiObNHPFTFU2VDqtHaC/2Zy7WZI0NGmoUz70tWodc8fKjumGS2+Q2WTW3/f+XVe9eRVHpQFn0VogSR3knCNEW8djdmG2JGnxJYuVkZyhBluDLnv1Mn1V8JVT2gEAb0YxBkC/tjFnoyQpPTFdZpNr/qSlJaTpBwt+IH9ff32Z96XO/9v5XNQXXuvDnA8ltYw5Vxk3bJxunnezLGaL1h9Yr/NfPF+FVYUuaw/o71qv4+Sqcenn46fbr7xd6Ynpqm2s1UUvXaS1B9a6pC0A8BYUYwD0ax8eaflgOCJlhEvbyUjO0L2L7lVIYIi+LflW458br/cPve/SNoG+prqxWpuObpIkjUob5dK2xg0dp7sX3q3ggGDtOb5H454bp3UH1rm0TaA/Ol5zXIfKD8kkk4YkOO9C9qfz9/XXDxb8QCNSRqixqVFXv3G1nvjiCRmG4bI2AWAgoxgDoN9qbGp0nDIxPHW4y9tLjkvWv133b0qJS1FVQ5XmrZyne9+/l1Mo4DU+PPKhbHabYsJjFBfhvNMCOzM0aaj+7bp/U2JMosrrynXl61fqjnV3qKqxyuVtA/3FZ3mfSZLio+MVFBDk0rYC/AL0gwU/0JRRU2Q37PrJhp9owZsLuOMgAPQAxRgA/dZneZ+pvqleoUGhio+Kd0ubUWFR+tG1P9IFYy+QIUNPf/m0Mv+SqY+OfOSW9gFPWnew5ciUzLRMp14v5kyiw6P14+//WDMmzJAk/fXrvyrj6Qy9uvtVvpEHJH2Q3XKHo4ykDLe0Z7FYdP0l12vhRQtlMVu07tt1yvxLJkeuAUA3UYwB0G+t2r9KkjRmyBi3fTCUJB8fH33/4u/rzgV3KiIkQrknc3XJiks0b+U8HThxwG1xAO7U2NSot799W5I0Jn2MW9v29fHVwosW6u6FdysmPEYlNSW6cfWNOv/F8/XRkY8oysBrGYbhKJKOGuLaUwdPZTKZNGPCDP3oez9SdFi0jlUd05WvX6lFby1SfmW+2+IAgP6MYgyAfqnZ3qy397d8MBw/bLxL2vDz8ZOfj1+n80eljdKDSx7U9PHTZTaZ9Y9D/9DoZ0Zr2TvLtL90v0tiAjxl/aH1qmioUHhwuIYlDXNJG2cbc8NThuvBJQ9q3tR58vXx1VcFX+mSFZdo+vLp2nB4g+yG3SVxAX3VN8e/0bGaY/Lz9XP6uDzbeJRa7t7070v+XRdPvFgmk0lvZ72tjKcz9MA/H9DJ+pNOjQcABhqTwddJQL9WVVWl8PBwVVZWKiwszNPhuM2HRz7UpSsuVZB/kP7r9v+SxWLxaDzFJ4v17mfval/OPkmSSSYtGLFA959/v2YMnuHWI3cGqr76Wu+rcTnb1a9frTUH1mjWxFlaMG2Bp8NRZU2lPvz6Q32+53M125slScOih+lHU36km8bfpFD/UA9HOPD0xdd6X4zJnR768CH9ZstvNDZ9rG6bf5tHY8kvydfqzat1pOiIJCnUP1Q/nPRD3XfefUoKS/JobAOBt7/WgYGIYgzQz3lrcr72zWu1av8qTRs7TddefK2nw3HIK87Th9s/1O7s3Y5pQ6OG6q5Jd2npuKUaFDLIg9H1b331td5X43Km7PJsZfwpQ4YM/WzJzxQf7Z5rNHVFRXWFNu7YqC+zvlSjrVGSFOgbqKtHXK2l45bq0qGXysfs4+EoB4a++FrvizG5S7O9WalPpqqouki3zLvFZUeJdodhGMo6mqV1n6/TsbJjkiQfs48Wj1msuybdpQtSLuDLiR7y5tc6MFBRjAH6OW9MzgVVBUp7Mk3NRrMeXPKgEqITPB1SO8fLjmvzrs36+sDXjg+IZpNZ01KnafHoxbpm1DUUZrqpr77W+2pcznTfP+7Tn776k0YOHqm7rrrL0+F0qMHaoG37t+mz3Z+p+GSxY3p0ULSuGnGVFgxfoEvSL1GwX7AHo+zf+uJrvS/G5C7rD63XFSuvUFBAkB679TH5+PSdoqPdsCsrJ0sbd2x0HCkjScOihum2c27T4jGLlRaR5rkA+yFvfq0DAxXFGKCf88bk/MN1P9SzXz+rYUnDdM+ie1zShq3Jpr+t/5sk6ZZ5t8jXx7dH22m0NmrHwR36Yt8Xyi3OdUw3yaRJiZM0d9hczRk6R+cln8e392fRV1/rfTUuZzly8ohG/XmUrM1W3b3wbg1Pcc1t5J015gzDUH5JvrZ/u107Du5QTX2NY56fxU8Xp12sS9Mv1Yy0GZoQP4Fx1w198bXeF2NylxnLZ2hz7mbNPGemrp5+tVO37azxKElHjx/V53s/185DO2W1WR3Tx8eP16KRi7Rw1EKNjh3NETNn4c2vdWCg4h0IgH7l2xPf6q/f/FWSNPf8uS5rx27YlXU0y/G8p/z9/DV1zFRNHTNVZVVl2nV4l3Ye2qm84jxtL9qu7UXb9V+b/0th/mGaljpNF6ZcqAtSLtCUpCkK8g1yVneAHjEMQz/54CeyNls1InWEMpJdd+tcZ405k8mk1EGpSh2UqqumXaXDhYe1L2ef9uXsU1lVmTZkb9CG7A2SpBC/EE1Pna7pqdM1OXGyJiVOUlRglFP6A7jSR0c+0ubczbKYLZp5zkynb99Z41GS0uLTlBafpmsuukY7D+3U9m+3K7soW7uO79Ku47v0i09+ofjQeF2Sdolmp8/WrCGzlBqe6oxuAECfRjEGQL/RZG/SrWtuVZO9SZlpmRqaNNTTIXVLdFi0Zk2cpVkTZ6miukLf5n2rA3kHdCD/gKoaqrT+0HqtP7ReUss59uMGjdM58edo/KDxGh8/XuMGjVNEQIRnOwGv8redf9M7374js9msq6Zd1e++ubZYLBqROkIjUkdo4UULVVxerKzcLGUXZOtI0RHVWGv0j8P/0D8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uu+02LV++XOvWrZPdbteVV17Zbpm8vDzddNNN+p//+R+VlpaqoqJC8+bN6/JdLc6WVxMTEzVjxgytXLlSL7/8sq655hoFBwe3205sbKyCgoJ0+PDh7ncUAIBuOD13Wa1WFRUVdekzYXBwcLtroXVVYmKicnNz20zLy8vr8vr9GcWYAcxkMumpp57Sf//3f+t///d/VVJSIqnlm7Z9+/Y5lps3b55KSkr0zDPPqKmpSZ9++qlWrlzZ4SHTp6uqqtL8+fN17733Ok57OnWen5+fYmJiZLVa9dhjj6mqqqrD7cTHx+vqq6/WPffc4/jm8fjx445z6l944QW9/vrrWrt2rYKCgrq1Hx599FG9/fbbXjOoAQCdW7x4sY4fP64f//jHWrZsmXx9fdstU1NTI8MwFBcXJ7PZrPXr1+uDDz7ochvXXXedPvnkE61Zs0bNzc16++239emnn2rx4sWOZZYtW6aXXnrpjPnWZDLpjjvu0E9+8hMdPnxYhmHowIED7d60AgDQWzfeeKOefvppZWVlqbGxUT//+c+VlJSkKVOmnHXdiRMnasWKFWpqatLOnTvbnIp7NldccYUKCwv1/PPPq6mpSe+99542btzYm670GxRj+gGL2aJrM6/VtZnXymK2dGvdq666Su+9957Wr1+v4cOHKywsTNOnT1dcXJzjokmRkZH6xz/+oVdeeUXR0dH6wQ9+oL/85S+aNm3aWbe/Y8cO7du3T48//nibux19+umnuummmzR69GgNHjxY6enpCgwMVEpKSqfbWr58ueP0pNY4W88lXLFihY4fP65hw4Y52pg7d26X9kFycrL+3//7fzp58mSb6a1xAgD6tt7kwdO1XjPt6NGjnZ6ilJmZqf/4j//QrFmzFB0drTfeeEMLFizochvDhg3T22+/rUceeUSRkZF67LHHtHr1aqWnpzuWueaaa5STkyOz2XzGc+N/+9vfavbs2brkkksUFham733veyovL+96hwEA/Yozc153LFu2TPfee6/mz5+v+Ph47dq1S2vXrpWPj89Z1/3Tn/6krVu3KiIiQg8++KBuuummLrcbFRWlNWvW6KmnnlJERIT++te/asmSJb3pSr9hMrp6zO0ZVFVVKTw8XJWVlY4Lt8J9GhoalJOToyFDhiggIMDT4fR7Z9qfvNYBnI6/C30LObF3yIEAuoO/C32LK3NgSkqKnnvuOc2bN8+p2+1L3J0DOTIGAAAAAAB0qLi4WCUlJW2O8ETvUYwBAAAAAADtfPjhhxo5cqTuvvtujRw50tPhDChnPwEMHldrrVXI4yGSpJqHahTs1/6OCwAADFTkQQCAt+hrOe+SSy5pd+1NOAdHxgAAAAAAALgRxRgAAAAAAAA3ohgDAAAAAADgRhRjAAAAAAAA3IhiDAAAAAAAgBtRjEEbDQ0NWrhwoSIiIjRlyhRPhwMAgMeQEwEA3ooc6HoUY/oBi9mieRnzNC9jnixmi0vbWrVqlQ4cOKDi4mJ99dVXLm3rVK+++qpCQkLaPEwmk5544okOl7darbr22muVlpYmk8mkd955p9Ntf/DBBzKZTLr//vtdEzwAwKXcmQdP1V9y4hdffKE5c+YoJiZGUVFRmjNnjrKyshzzCwoKdMEFFyg6Olrh4eGaMGGCVq9e7a7uAAC6wVM573SeyoGS1NjYqJ/+9KdKSEhQSEiIxo4dq6NHj3a6/GuvvaZRo0YpJCRE5557rrZt2+aYd7Yc6UkUY/qBAJ8AvXfDe3rvhvcU4BPg0rZycnI0fPhw+fv7u7Sd0y1ZskQ1NTWOx6ZNm2Q2m/W9732v03WmTZumFStWKDk5udNlamtrdd999+n88893RdgAADdwZx48VX/JiSdPntQtt9yiw4cP6/jx45oyZYouv/xyNTc3S5IiIyO1fPlylZaWqrKyUs8884xuvPFG5eTkuLNbAIAu8FTOO52ncqAk3XLLLcrOztbXX3+t6upqvfXWW4qIiOhw2S1btuiuu+7S8uXLVVlZqdtvv13z5s1TZWWlpLPnSE+iGDPApaWl6fHHH9e5556r4OBgzZ07V+Xl5br77rsVERGhjIwMff7555Kkn/zkJ3rssce0bt06hYSE6JFHHtH48eP18ssvt9nm3Llz9Zvf/Malcb/wwgu67LLLlJKS0uF8Pz8/3X///Zo+fbosls4rxj//+c+1ePFijRgxwlWhAgD6iYGaE+fOnavFixcrIiJCfn5+euCBB5Sfn6/c3FxJUnBwsIYPHy6z2SzDMGQ2m9Xc3HzGbxkBAANLf8mB+/bt05o1a/Tiiy8qMTFRJpNJI0eO7LQYs2bNGl111VU677zzZLFYdOeddyokJMRxBOjZcqQnUYzxAq+99ppWrVqlwsJC5eXlacqUKZo1a5bKysq0ePFi3XXXXZKkP/zhD3r44Yc1f/581dTU6Je//KWWLl2qFStWOLZVXFysjz76SEuWLOmwrdbB3Nnjs88+O2u89fX1WrlypW6//fZe9Xvbtm3asGGDHnrooV5tBwAwcHhDTty0aZMiIiKUmpraZvq4cePk7++vqVOn6sILL9T06dO7vE0AQP/XH3Lgpk2blJ6ert/+9reKi4vT8OHD9fvf/77TPtntdhmG0WaaYRjavXt3p9vvKEd6AsWYfqDWWqvgXwcr+NfBqrXWdnv9u+++W6mpqYqIiNAVV1yhmJgYXXvttbJYLLr++uu1d+9eWa3WDtddsmSJNm3apMLCQknSypUrNX369E6/nXvmmWdUUVHR6WPatGlnjffvf/+7/Pz8tGDBgm73tZXNZtMdd9yhZ555xiOH1gEAnKe3efBUAz0n5ubm6s4779Qf/vAH+fj4tJm3e/du1dTUaO3atZo7d+4ZjywFAHiGM3Pe6fpDDiwvL9fevXtlGIby8vK0evVq/fGPf9Srr77a4fLz58/XO++8oy1btshms+nPf/6z8vLyVFVV1W7ZM+VIT6AY00/U2epUZ6vr0brx8fGO50FBQe1+NwxDdXUdbzshIUGzZs1yvPhffvllLVu2rEdxdNULL7ygZcuWydfXt8fb+N3vfqdzzjlHM2fOdF5gAACP6U0ePNVAzokFBQWaPXu27rnnHt16660dLuPn56f58+fr448/7vSNLQDAs5yV807XH3JgSEiILBaLHnvsMQUEBGj06NG69dZbtWbNmg6Xnzlzpp566indcccdio+P17Zt23TJJZcoOjq6zXJdyZHuRjEGZ9V6SNrevXt18OBBLVq0qNNl77rrrnZ3gDj18emnn56xrcOHD2vz5s267bbbehXzBx98oDVr1ig+Pl7x8fF644039Pzzz2vq1Km92i4AwLv11ZxYWFioiy++WEuXLtXDDz981uVtNpsOHTp01uUAAGjljhw4fvx4SZLJZOpyXLfeequysrJUVlam559/XllZWZoxY4ZjfndzpLtQjMFZLVy4ULm5ufrpT3+qhQsXKiQkpNNln3322TZ3gDj9cbbz01944QVNnTpVo0aNOmtcjY2NamhokGEYstlsamhocFwV++2331ZWVpZ27typnTt3asGCBVqyZInWrl3bvc4DAHCKvpgTi4qKNHPmTF133XV65JFH2s3ftGmTtm7dKqvVKqvVquXLl+vjjz/WpZde2rVOAwAg9+TAiy66SBkZGfrlL38pm82mAwcOaPny5brqqqs6XN5ms2nnzp2y2+0qKyvTPffcoyFDhujyyy+XdPYc6UkUY3BWQUFBWrRokTZs2ODSw7Gbm5v10ksvdXqRwtMrqCNGjFBgYKDy8vL0/e9/X4GBgY6LSkVFRTmOiomPj1dgYKCCgoIUExPjsvgBAANfX8yJzz//vA4fPqwnn3yyw28da2trdeeddyo6OlqDBg3SX/7yF73++utdumYNAACt3JEDLRaL3n33XW3dulURERG6/PLL9aMf/ajNhYJPzXE2m0233HKLwsLCNHz4cDU1NWnt2rUym1tKHWfLkZ5kMk6/9HAPVFVVKTw8XJWVlQoLC3NGXDhFrbVWIY+3VB1rHqpRsF9wm/kNDQ3KycnRkCFDFBDguXvRDxRn2p+81gGcjr8Lrne2PHgqcmLvkAMBdAd/F5yvOznvdOTA3nF3DuTIGAAAAAAAADfy/P2ccFZmk1kzBs9wPAcAwJuQBwEA3oKc5z0oxvQDgb6B+uTmTzwdBgAAHkEeBAB4C3Ke96DUBgAAAAAA4EYUYwYQJ1yLGWI/AsBAwN/ynmG/AUD/x9/ynnH3fqMY0w/UWmsV+7tYxf4uVrXW2nbzLRaLJMlqtbo7tAGpdT+27lcAgGedLQ+eipzYO+RAAPCs7uS805EDe8fdOZBrxvQTJ+pOdDrPx8dHQUFBKi0tla+vr+Oe6ug+u92u0tJSBQUFyceH4QEAfcWZ8uCpyIk9Rw4EgL6hqznvdOTAnvNEDiTTDgAmk0kJCQnKyclRbm6up8Pp98xms1JTU2UymTwdCgCgm8iJvUMOBID+ixzYO+7OgRRjBgg/Pz9lZGRwSJoT+Pn5UUUGgH6MnNhz5EAA6N/IgT3n7hxIMWYAMZvNCggI8HQYAAB4HDkRAOCtyIH9A199AAAAAAAAuBHFGAAAAAAAADfiNKV+wGwya3LiZMdzAAC8CXkQAOAtyHneg2JMPxDoG6htd2zzdBgAAHgEeRAA0NftK9mnJW8vkUzSymtWKjM2s0fbIed5D0ptblJWV6bGpkZPhwEAAAAAcCLDMHTLmlu0q3iXdh3fpflvzpfdsHs6LPRxFGPc4D83/qdifhej1KdStad4j6fDAQAAAAA4ybaibdpW9N3RLDkncrR833LPBYR+gWKMi31V+JV+9emvJEklNSVauGpht6ukdbY6pT2ZprQn01Rnq3NFmAAA9FnkQQBAX7bu4DpJ0oRhEzR70mxJ0vM7n+/Rtsh53oNijIv9edufJUkjU0cqwC9A2aXZWrF/Rbe2YRiGcitzlVuZK8MwXBEmAAB9FnkQANCXbcjeIEnKHJKpySNbLr67LWebjtUd6/a2yHneg2KMC9mabVq9f7Ukac55c3TB2AskSU9vf9qTYQEAAAAAnMBu2B2XokiLT1NCdIISohPUbG/WK9++4uHo0JdRjHGhrwq/UrW1WsEBwRocP1jnZ54vSdpxdIdyq3I9HB0AAM5Vb6vXwx89rCVvL9Hu4t2eDgcAAJfLq8xTfVO9LGaLosOjJUmZaS13Ulp/aL0nQ0MfRzHGhf555J+SpOEpw2U2mRUXGafUQamyG3Y9t/c5D0cHAIBz3fuPe/X4Z49r5Z6VmrZ8mnIr+OIBADCw7S/dL0mKjYiVxWyRJI0eMlqStO3oNtmabR6LDX0bxRgX2lqwVZI0LHmYY9o5GedIktYdWOeRmAAAcIWckzl68ZsXJUlBAUGqbqjWnR/c6eGoAABwrf0nWoox8VHxjmmD4wcryD9ItQ21Wp/H0THoGMUYF9p1fJckKTk22TFtbPpYSdK+/H06Vtv9CzoBANAXvfDNCzJkaGTqSP3w6h9Kkj749gN9W/6thyMDAMB1Wo+MiYuKc0yzmC0aMXiEJOntg297JC70fRRjXKS4pljFtcUyyaSE6ATH9JiIGCVEJ8hu2PXKga5d0MlkMikzNlOZsZkymUyuChkAgB5rva3n5JGTlRKXohEpI2QYhn71xa96vW3yIACgr8o6kSVJGhQ5qM300Wktpyp9cviTbm2PnOc9fDwdwEC1q7jlqJiYiBj5+fq1mTcmfYyOlR3TmgNr9MDEB866rSDfIO27e59L4gQAoLeKa4odeW9Eass3gdPHT9eB/AN6d++7sl5mlZ+P35k2cUbkQQBAX2QYhuM0pUFRbYsxIwePlEkm5Z3I06GKQ8qIyOjSNsl53oMjY1yk9S4SSTFJ7ea1nqq0LWebam21bo0LAABn+yjnI0ktp+WGBoVKkkaljVJoUKiq66v16sFXPRkeAAAuUVpXqpP1J2WSSXGRcW3mhQSGaHD8YEnSq9+SB9EexRgXaf2GMDE2sd28lLgUhQeHy2qz6o1Db7g7NAAAnGpb4TZJ0tCkoY5pFrNFE4dPlCQt37XcE2EBAOBSrdeLiQqL6vAI0Na7Kr1/6H23xoX+gWKMi7RevDcxun0xxmQyadywcZKkN7PePOu26mx1Gv3MaI1+ZrTqbHXODRQAgF7aWbxTkpQU2/Zo0MkjJ0uStmZv1Yn6Ez3ePnkQANAXdXaKUqvMtExJ0jd536jW2rUzIsh53oNijAtYm62OgXn6G9NWE4ZNkCR9evhTNTY1nnF7hmEoqzRLWaVZMgzDqbECANAbhmFo5/Gdktqfmpscm6xBkYNka7bp//b+X6/aIA8CAPqa1iNjTr94b6vEmERFhETI2mTV6iOru7RNcp73oBjjAvtL96vJ3qRA/0BFhER0uMyQhCEKDQpVXWOd3s7mdmcAgP4przJPFQ0Vspgt7b4ZNJlMjqNjVu5a6YnwAABwmbMdGWMymTQqbZQkafXBrhVj4D0oxrhA6/VikmKSOr0dmdls1rihLacqrcziDSoAoH9qPSomPipePpb2N2k8d+S5MsmkfYX7lFWW5eboAABwHcdtrTspxkjf3eJ60+FNHOmCNijGuIDjejEx7a8Xc6rxw8ZLkj459IlszTaXxwUAgLM5TlHq5LTciNAIDU8ZLkn632/+111hAQDgUtWN1SqsKpTU+WlKkjQ8Zbj8fP1UVl2mD/I+cFd46AcoxrjA7pKW21qfrRgzNGmoggOCVVNfozcPn/1CvgAA9DWdXbz3VFMyp0iS3t7ztuyG3R1hAQDgUt+e+FaSFBoUqqCAoE6X8/P109j0sZKkF/e86JbY0D9QjHEywzC6fGSMxWzRxBEtt/386zd/dXlsAAA4W2cX7z3V2PSxCvALUGlVqdYdWeemyAAAcB3H9WLOcFRMq4nDWz7zvb//fTU1N7k0LvQfFGOc7HjNcZXWlcpkMik+Ov6sy5+XeZ4k6bNDn6m4trjDZUwmkwaHD9bg8MGdXoMGAAB3q2io0NGKo5KkxNjOv4Dw8/XTORnnSJKe+eaZbrdDHgQA9DWOOymd4XoxrUamjlRwQLCq6qrOevMWcp73oBjjZLuLW05Rio2IlZ+P31mXT45NVnJssprsTfrTzj91uEyQb5CO3n9UR+8/qiDfzg+BAwDAnVqPBI0MjVSQ/5nz03mjW758+Ojbj1RUU9StdsiDAIC+5mx3UjqVxWLRhIwJkqS/7PjLGZcl53kPijFO1nonpeSY5C6v03p0zPIdyzmXHgDQb7SeopQce/aclxafpsGDBqupuUm//vLXLo4MAADX6s5pSpJ0wZgLJEmbD21WXlWey+JC/0ExxslaizEJMQldXufckecqwC9AheWFev3A664KDQAAp2q9eO/ZrpHWasY5MyRJr+x4RQ22BleFBQCAS1mbrcouz5bUtSNjpJYL3Q9JGCK73a7/2fY/rgwP/QTFGCfr6sV7TxXgH+ColP52y2/bza+31evc58/Vuc+fq3pbvXMCBQCgl852W+vTjR86XhEhEaqsq9ST3zzZ5XbIgwCAvuRQ2SE1G80K8AtQeHB4l9ebNm6apJYvJeqsdR0uQ87zHhRjnKjeVu+4xVlXDtk+1UXjL5LZbNbugt36KO+jNvPshl3bi7Zre9F2TmMCAPQJ1mar9pXsk9T1YozFYtHFEy+WJP3+09+roalrR8eQBwEAfcmppyh15yK744eNV2RopCrrKvU/2zs+Ooac5z0oxjjRvtJ9ajaaFRIYorDgsG6tGxEaoXNHnitJ+rd//psMw3BFiAAAOMX+0v2y2W0K8AtQVGhUl9e7YMwFigiJUFlNmX7z5W9cGCEAAK7Reiel+Kiz3z33VD4WH10y+RJJ0v9+/r8c+eLlKMY4Uevh2okxiT26Ddnl510uH4uPdhfs1qqDq5wcHQAAzvPN8W8ktRwV052c5+vjq8umXCZJemLLEyqrK3NJfAAAuMq+0pYjQ7t6vZhTnTfqPEWEROhk7Uk9tvUxZ4eGfoRijBO1Xi+mu6cotYoMjdRF4y+SJP34gx+rsanRabEBAOBM3xxrKcb0JOedN+o8xUfFq7q+Wnf/825nhwYAgEu1FmPio7t3ZIwk+fj4aO75cyVJT372pAqqCpwaG/oPijFO1N27SnTkknMvUWhQqArKC/TgpgedFBkAAM516pEx3WWxWPS9i78nSXpz55v6OPdjp8YGAICr2JptOnDigKTun6bU6txR52pw/GA12Bp0x/t3ODM89CMUY5zEbtgdR8b05I1pqyD/IC2asUiS9PTnT2v7se1OiQ8AAGexG3bHqbk9PRp0aNJQTRk1RZJ0w+obVNlQ6azwAABwmcPlh2Wz2+Tv66/I0MgebcNsMuvamdfKZDLp/f3v66U9Lzk5SvQHFGOc5FDZIVVbq+Vr8VVcZFyvtjV+2HiNTR+rZnuzFr61UNWN1YoJilFMUIyTogUAoOeOnDyiamu1fCw+GhTZ/fPlW1190dWKCovS8crjWvLukjNevJ48CADoC069XkxPrhPaKiUuRZdOvlSSdM979yi/Mt8xj5znHSjGOMnWgq2SpJRBKbKYLb3alslk0nWzr1NESIQKThbotn/cppKflqj0gVIF+wU7I1wAAHqs9XoxCdEJslh6nvOC/IO0dM5SmU1mvbf/Pf3ys192uFywX7BKHyglDwIAPG5fSUsxJiE6odfbmjNljlLiUlTTWKM5r89Rna2OnOdFKMY4yRcFX0iS0uLTnLK9kMAQ3TT3JpnNZq3fv14/+egnTtkuAAC91Xq9mJS4lF5va0jCEC28aKEk6Zcbf6kVe1b0epsAALjK3tK9knp+vZhTWSwW3TT3JgUHBGv/8f36/urvy27Ye71d9A8UY5yk9ciYwfGDnbbNIQlD9P2Lvy9J+uOWP+p3X/zOadsGAKCnvir8SlLvrpF2qunjp2vGhBmSpFveuUWv7XvNKdsFAMDZvi76WpLzcmBMeIxuveJWWcwWvbf/Pd387s0UZLwExRgnqG6s1t6Slgqps46MaXX+6PM1Z8ocSdK/b/h3PbaZe9EDADynyd6kLwu/lNTypYGzXDXtKk0eMVnN9mYtWbVEL+x8wTGv3lavmctnaubymaq31TutTQAAuqOsrkw5FTmSpOS4nl3AviNDk4bqhktvkMlk0oqdK5T4RKJmLJ9BzhvgKMY4wRcFX8hu2BUZGqnwkHCnb//iiRc7nj/y8SP6yT9/QrUUAOARe0v2qsZaowC/AKccot3KbDbrhktv0JTMKTIMQ7evuV3/9sG/qdneLLth16bcTdqUu4n8BwDwmK+PtRwVExMeoyD/IKdue9KISVpy6RJJUnFNsTbnblZVY5VT20DfQjHGCT488qEkKSM5wyXbP/0q3U98/oTmvDpHFQ0VLmkPAIDOfJ7/uaSW03LNZue+jTCbzVo8e7Hj7hJ/3PpHzXplloqqi5zaDgAAPbG9aLskKXVQqku2P3nkZEdBRpIuevkifXviW5e0Bc+jGOMEH+a0FGNGpI5weVuLZy2Wr8VXH2Z/qLHPjdXm3M0ubxMAgFZb8rdIcu4pSqcym8y64oIrtHTOUvn6+GpzzmZNen6SS9oCAKA7Wq+Z5sxTlE43btg4x/ODpQd1zv+do+e+fk6GYbisTXgGxZheOlF3wnGLz+Epw13e3jkjztF937tPkaGRKqgo0MzlM/Wj93+kGmuNy9sGAHg3wzC0MWejJCk9Md2lbU0aMUk/XfxTpQ5KVXVjtWP67uLdLm0XAICO2A2744vw9ATX5sBWw5KGqcHWoLvW3aWLXrpI+0v3u6VduAfFmF56//D7MmQoITpBoUGhbmkzJS5FD97woM7PPF+GDP3vl/+roX8aqpd2vsS59AAAl9ldvFvHa47Lz8fPLW9EB0UN0o+u/ZEum3KZY9qFL16oxasWK6s0y+XtAwDQanfxbp1sOCl/X3+lxKW4pc3brrxNC6YtkK/FV5/lfqZxz47T/e/fr5LaEre0D9eiGNNLf8/6uyRp3NBxZ1nSuQL8A7T4ksW648o7FB0WrZKaEt285mZNeG6CVu9fTVEGAOB0G7I3SJKGJQ+Tj4+PW9q0WCyaPWm243dDht7Y+4bGPDNGi95cpM/yPuPQbQCAy31y9BNJ0pDEIbJYLG5p02wya9bEWfrZjT/T6LTRarI36akvn1LaU2l6+KOHVVpb6pY44BoUY3qhurFa7x9+X5I0fth4l7bl5+MnPx+/dtNHDxmtn934M82/YL78ff21p3iPrnnzGo3+y2i9vOtlNTQ1uDQuAID3WH9ovSRp5OCRbm+7NQ/ed+19Gps+VoYMvb3/bU3/23SN/sto/fmrP6usrsztcQEAvMMH2R9IkjKSXHPTllOd/tkvOjxadyy4Q3dedadS4lJUb6vX4589ruQ/JuvmNTdr5/GdLo8JzmcynPB1UlVVlcLDw1VZWamwsDBnxNUvvLzrZd30zk2KjYjVw0sfbnfXI3erqa/Rpp2b9OmuT9VgbSnCRARG6Nbxt+rOyXdqeLTrr2kz0Hnrax1A57zl70JRdZGSn0iWIUO/uPkXigqL8mw8J4q0eedmfX3wa9mabJIkH7OPLkm/RNePuV5XjbhK4QHhHo1xoPGW1zqArvOWvwtVjVWK/V2srM1W/WzJzxQfHe+xWAzD0N4je/XP7f9UXnGeY/rExIm6edzNWjxmsWKDYz0W30Dlitc6xZhemPrCVH1R8IXmT52vS869xNPhONQ11mnL7i36fO/nOll90jH9nIRzdMOYG/T90d9Xarhrbsc20Hnrax1A57zl78JTXzyl+zfcryEJQ/Sj7/3I0+E41DXWadv+bfoq6ysVnih0TPcx++jClAs1L2Oe5g6bqzFxYzz+pUl/5y2vdQBd5y1/F17f+7quX3W94iLj9NCND/WJfGIYho4eP6rNOzdr1+FdjstU+Jh9dGn6pVo4cqHmD5+vhNAED0c6MFCM6UO+Lvpak5+fLIvZokdvfdRtF+/tDrvdrv25+7VlzxZ9m/ttm+vITEyYqCsyrtC8jHk6N/FcWczuOe+xv/PG1zqAM/OGvwuGYWjCcxO0u3i3Fs1YpOnjp3s6pA4Vlxfrm0Pf6JuD36j4ZHGbefGh8ZqZOlPTUqdpWuo0jYkbQ+7rJm94rQPoHm/5u3DFyiu0/tB6XTL5Es2/YL6nw2mnuq5aOw7u0PZvtyu/JL/NvEmJk3RlxpWaNWSWpiRNkb+Pv4ei7N8oxvQhC15boLUH12rSiElaOmepS9uyNdn0t/V/kyTdMu8W+fr4dnsbNXU12pW9S98c/EbZhdky9N1/e1RglGYPma3pqdM1ffB0jY0byxvUTnjjax3AmXnD34WPcz7WrJdnyc/HT4/e+qiCAoLc2n5P8mBpRan25+7X/qP7dbjgsGzNtjbzQ/1DdV7SeZqUMEnnxJ+jiQkTNTRqqMwmLqfXGW94rQPoHm/4u5BbkashTw2RIUP/sew/FBvh2lOAevvZ73j5ce3O3q19R/Yptzi3zbwAnwBdkHKBZqXN0tSUqZqUMIlTervIFa9199wKYYDZnLtZaw+ulclk0pwpc1zent2wK+toluN5T4QEhejCsRfqwrEXqrK2Ut/mfqv9R/frQN4BldeX662st/RW1lsty/qF6IKUC3Re0nmON6ip4al94nA8AIB7GYahX336K0nSlFFT3F6IkXqWB2MjYhUbEauLxl8ka5NVucdzdaToiHKKcpRzLEfVjdX68MiH+vDIh451QvxCNH7QeI2JG6ORMSMdj9TwVIo0AOCl/vjFH2XI0PCU4S4vxEi9/+wXHxWv+Kh4XXbuZaqsrVRWTpYO5B/Q4YLDqqmv0cacjdqYs9Gx/PDo4ZqSNEWTEybrnIRzNDp2tKKDop3WH3SOYkw31dnqdPu7t0uSpo6eqrjIOA9H1H3hweE6L/M8nZd5nprtzco9nqvDhYd1pPCIco7lqMZaow+yP3BcMVySIgIiNDF+osbHj9eI6BGON6hxwXEUaQBgAFt3cJ025myUxWzRxRMv9nQ4PeLn46eM5AxlJLfcAaPZ3qyiE0XKL8lXQUmBCksLVXSiSDXWGm3J36It+VvarB/oE6iM6AwNjx6utPA0pUV89xgcMVghfiGe6BYAwMWOVhzVX7b/RZI0e9JsD0fTfeHB4Zo6ZqqmjpkqwzBUXF6sQwWHlF2YrbziPJVXl+tg2UEdLDuoV3a/4lgvLjhOY2LHaHTcaGXGZmpE9AilR6YrOSyZMyiciGJMN9gNu5atXqZD5YcUHhyu+Rf2vfMFu8titig9MV3pienSuS3XmSk6UaScYznKL81XYUmhjpUfU0VDhTYe3aiNRze2WT/MP0yjYkYpIzpDg8MHt7wxDR+swRGDlRqeqgCfAA/1DADQWyW1JfrBuh9IkmaeM1PR4QPjmzKL2aKUuBSlxKU4pjXbm1VyskSFpYUqOVmi4vJiFZ8sVmlFqeqb6rW7eLd2F+/ucHvRQdFKC09TQmiCEkMSlRCaoISQBCWGfvd8UMgg+Zh52wUA/YXdsOv2d2+XtdmqjOQMDU/p33emNZlMio+OV3x0vOPab9V11covyVdecZ7yi/N1rOyYyqvLVVJboo217T/7+Zp9NThisIZGDlV6ZLqGRAxRSniKkkKTlBSWpMTQRD7/dQPvCrqozlanm9+5Wav2r5KP2UfLLl+mIH/3H6rtamazWclxyUqOS3ZMa2pq0rHyYyosLdSxsmMqOVmikpMlKq8qV1Vjlb4s/FJfFn7Z4fbiguOUGp6q+JB4xQfHt/wMidegkEGO53HBcQr1C+UIGwDoQ8rryzX31bk6XnNc8VHxmnOe60/L9SSL2aKE6AQlRLe960SzvVnlVeUqOVmiE5UnVF5VrpPVJ1VeVa7yqnLVNdaprK5MZXVl0rHOt2+SSZGBkYoNilV0ULSiA6MVExTz3c9TpkUERCjMP0zhAeEK9QvlW0gAcDPDMPRvG/5NH+V8JF8fX31/1vcH5GeV0KBQZaZlKjMt0zGtwdqg4vJiHS8/3vIoO66yyjKVVZXJZrfpcPlhHS4/3Ok2IwMjlRya3FKcCUlUXHCcYoJiOnyE+YcNyP3aVRRjzqLZ3qzV367Wzz78mbJPZstitujGOTdqaNJQT4fmNj4+Pu2+QZRaLi51ovKEisuLdaLyhE5Wn3S8QT1ZfVKNtkaV1JaopLbkrG1YTBZFBkYqKjBKkQEtP099HhkYqTD/MIX4hTgeoX6h3z33D1WQbxDn9ANALxmGoQ+yP9Cd6+5UbmWuQgJDdMsVt8jPx8/ToXmExWxxXH+mI3WNdTpZ1ZL/quqqVFX73aOytlJVtVWqrquW3bCrvL5c5fXlUln3YgjxC1GYf1hLgcY/3FGoCfNrmRbsF6wg3yAF+7b8PPXROu/0R6BPoFe/AQaAzhRVF+m+f9ynVftXSZIWz17slmvF9BUBfgEaHD9Yg+MHt5lut9tVWVupE5UnHMWZssoyVdZUqrK2UpU1lbI123Sy/qRO1p/UnpI9Z23L1+yrqKCWz3zh/uEtue1fua719zZ5zz+sw1wX5BskP4tfv8trFGNO0djUqJMNJ5VzMkf7T+zX1vytWn94vYqqiyRJESERWnLZEsc5597O18e3w28RpZY383UNdSqvLldlTaWq66pVXVetqroqVdee8ryuWo22RjUbzTpRd0In6k70KqZg32CF+IcoyCdIAT4BCvQNVIBPQPuHpYNpPgHys/jJ1+IrX7Nvh89tdbazBwEA/UhDU4NKakuUVZqlrwq/0tv739au4l2SpOiwaN1+5e0aFDnIw1H2XUH+QQqKDVJSbFKny9jtdtU21Kqmvka19bWqbfjXo75WdQ11juet0+sb69VgbVBTc5MkqcZaoxprjeP9iLP4W/zl7+Pf5meAT0C7aa0/zVa+8AAwsBiGoarGKuVW5uqbY9/o/ez39c6376ihqUEWs0Xfu/h7mjRikqfD7BPMZrMiQyMVGRrZ4edhwzBU11inqtoqVdRUqLKmUlV1Vd/lvn/luZr6GtU21Mpqs8pmt6m4pljFNcW9j89k/u6LiFMKNoG+gfKz+Mnf4t/y06flp5/5u+et806df+o0V30OdEoxpvXu2Bc/d7EsgRbHbZNPvWt263NDRpvnrfNOX6ez5bqzjTbzz7CczW5TRX2FGpoaOuxfUECQzh99vi4af5EC/ALUUNXxcq7S2NQo/avJhuoGGT69vhu5W1hkUWxArGIDzlxJtjZbVd9Qr/rGlkddQ53qrd/9bJ3XaGuU1WZVo61RjU2Nslr/9dzWqNY7ddc21Kq2utZ1nWps+eGEO8IDGCA6yoGn/43oSp7rSW7sLE+2mdbJOnbDroqGCtVa2//NtFgsmjp6qmZPnq1A30C3573T9dc8eCpf+SrSN1KRvpFSF++I2dTcpAZrgxqtjaq31avR2qhGa6MarA2OR6OtUdYmq6w2q5qammRtssrWZJOtySarzSprs1U2279+b7KqubnZsf3Gf/3rMnIggNM4Kwc6czlHuzLOuI612aqyujI12Zva9Wtw/GDNv3C+UmJT+OzXDRZZWnJdZKQUeeZlrc1W1dW3fCHRYG1QQ2NLTmuwNqje2vLZr6GxwZH3Gq2NqrfWy9Z8So5rssqw/+t9jeyqqa9RjWpc0zkX5ECT4YStHTlyREOHes9pO0B2drbS09M9HQaAPoAcCG9DDgTQihwIb+PMHOiUI2OioqIkSXl5eQoPD3fGJrusqqpKKSkpys/PV1hYF79qou1+27an26+srFRqaqrjNQ8A5EDa9oa2JXIggPbIgbTtDW1LrsmBTinGmM0t5xCHh4d7ZMdIUlhYGG17Uduebr/1NQ8A5EDa9qa2JXIggO+QA2nbm9qWnJsDyaYAAAAAAABuRDEGAAAAAADAjZxSjPH399cjjzwif39/Z2yOtmm7z7bv6b4D6Hu89W8SbXtX232hfQB9j7f+TaRt72rbVe075W5KAAAAAAAA6BpOUwIAAAAAAHAjijEAAAAAAABuRDEGAAAAAADAjSj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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Di Gregorio',\n", + " 'Carlos Augusto',\n", + " 'Rrahmani',\n", + " 'Baschirotto',\n", + " 'Valeri',\n", + " 'Frattesi',\n", + " 'Pereyra',\n", + " 'Lazovic',\n", + " 'Gonzalez N.',\n", + " 'Vlahovic',\n", + " 'Hojlund']\n", + "\n", + "config_433_classic = [3, 6, 7, 9, 10, 17, 18, 19, 26, 28, 30];\n", + "\n", + "s = simulate_lineup(squad, MOD = True)\n", + "\n", + "plot_lineup(squad, config_433_classic)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/1_scraping_fbref.ipynb b/1_scraping_fbref.ipynb index 6940af4..48331af 100644 --- a/1_scraping_fbref.ipynb +++ b/1_scraping_fbref.ipynb @@ -75,7 +75,9 @@ "misc2 = [\"cards_yellow\",\"cards_red\",\"cards_yellow_red\",\"fouls\",\"fouled\",\"offsides\",\"crosses\",\"interceptions\",\"tackles_won\",\"pens_won\",\"pens_conceded\",\"own_goals\",\"ball_recoveries\",\"aerials_won\",\"aerials_lost\",\"aerials_won_pct\"]\n", "\n", "def get_tables(url,text):\n", - " res = requests.get(url)\n", + " headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}\n", + "\n", + " res = requests.get(url, headers = headers)\n", " ## The next two lines get around the issue with comments breaking the parsing.\n", " comm = re.compile(\"\")\n", " soup = BeautifulSoup(comm.sub(\"\",res.text),'lxml')\n", @@ -281,123 +283,123 @@ " \n", " \n", " 0\n", - " Oliver Abildgaard\n", - " dk DEN\n", - " MF\n", - " Hellas Verona\n", - " 26-245\n", - " 1996\n", + " James Abankwah\n", + " ie IRL\n", + " DF\n", + " Udinese\n", + " 19-099\n", + " 2004\n", " 1.0\n", " 0.0\n", - " 11.0\n", + " 5.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " 1.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", + " Oliver Abildgaard\n", + " dk DEN\n", + " MF\n", + " Hellas Verona\n", + " 26-319\n", + " 1996\n", + " 6.0\n", + " 2.0\n", + " 197.0\n", + " 0.0\n", + " ...\n", + " 3.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 15.0\n", + " 15.0\n", + " 5.0\n", + " 75.0\n", + " \n", + " \n", + " 2\n", " Tammy Abraham\n", " eng ENG\n", " FW\n", " Roma\n", - " 25-131\n", + " 25-205\n", " 1997\n", - " 21.0\n", - " 17.0\n", - " 1500.0\n", - " 6.0\n", - " ...\n", - " 21.0\n", - " 32.0\n", - " 9.0\n", - " 1.0\n", - " 0.0\n", - " 0.0\n", " 31.0\n", - " 39.0\n", - " 39.0\n", - " 50.0\n", - " \n", - " \n", - " 2\n", - " Francesco Acerbi\n", - " it ITA\n", - " DF\n", - " Inter\n", - " 35-000\n", - " 1988\n", - " 14.0\n", - " 12.0\n", - " 1110.0\n", - " 0.0\n", + " 22.0\n", + " 1905.0\n", + " 7.0\n", " ...\n", - " 9.0\n", - " 9.0\n", + " 30.0\n", + " 44.0\n", + " 10.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 61.0\n", - " 34.0\n", - " 20.0\n", - " 63.0\n", + " 41.0\n", + " 66.0\n", + " 57.0\n", + " 53.7\n", " \n", " \n", " 3\n", - " Yacine Adli\n", - " fr FRA\n", - " MF,FW\n", - " Milan\n", - " 22-196\n", - " 2000\n", - " 4.0\n", + " Christian Acella\n", + " it ITA\n", + " MF\n", + " Cremonese\n", + " 20-292\n", + " 2002\n", " 1.0\n", - " 116.0\n", + " 0.0\n", + " 15.0\n", " 0.0\n", " ...\n", - " 3.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 5.0\n", - " 0.0\n", - " 3.0\n", - " 0.0\n", " \n", " \n", " 4\n", - " Michel Aebischer\n", - " ch SUI\n", - " FW,MF\n", - " Bologna\n", - " 26-035\n", - " 1997\n", - " 18.0\n", - " 8.0\n", - " 775.0\n", - " 1.0\n", + " Francesco Acerbi\n", + " it ITA\n", + " DF\n", + " Inter\n", + " 35-074\n", + " 1988\n", + " 24.0\n", + " 20.0\n", + " 1915.0\n", + " 0.0\n", " ...\n", + " 12.0\n", " 10.0\n", - " 14.0\n", - " 5.0\n", - " 1.0\n", " 1.0\n", " 0.0\n", - " 43.0\n", - " 5.0\n", - " 9.0\n", - " 35.7\n", + " 0.0\n", + " 0.0\n", + " 122.0\n", + " 69.0\n", + " 32.0\n", + " 68.3\n", " \n", " \n", " ...\n", @@ -424,12 +426,12 @@ " ...\n", " \n", " \n", - " 536\n", + " 575\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 20-322\n", + " 21-031\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -448,12 +450,12 @@ " 0.0\n", " \n", " \n", - " 537\n", + " 576\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Fiorentina\n", - " 25-138\n", + " 25-212\n", " 1997\n", " 2.0\n", " 0.0\n", @@ -472,136 +474,136 @@ " 50.0\n", " \n", " \n", - " 538\n", + " 577\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Spezia\n", - " 25-138\n", + " 25-212\n", " 1997\n", - " 1.0\n", - " 0.0\n", - " 8.0\n", + " 6.0\n", + " 2.0\n", + " 240.0\n", " 0.0\n", " ...\n", + " 8.0\n", + " 5.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", + " 18.0\n", " 3.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2.0\n", + " 60.0\n", " \n", " \n", - " 539\n", + " 578\n", " Milan Đurić\n", " ba BIH\n", " FW\n", " Hellas Verona\n", - " 32-264\n", + " 32-338\n", " 1990\n", - " 16.0\n", - " 7.0\n", - " 703.0\n", + " 21.0\n", + " 8.0\n", + " 863.0\n", " 1.0\n", " ...\n", - " 15.0\n", - " 14.0\n", - " 3.0\n", + " 21.0\n", + " 20.0\n", + " 4.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 16.0\n", - " 113.0\n", - " 28.0\n", - " 80.1\n", + " 19.0\n", + " 132.0\n", + " 35.0\n", + " 79.0\n", " \n", " \n", - " 540\n", + " 579\n", " Filip Đuričić\n", " rs SRB\n", " MF,FW\n", " Sampdoria\n", - " 31-011\n", + " 31-085\n", " 1992\n", - " 20.0\n", - " 17.0\n", - " 1326.0\n", - " 2.0\n", + " 28.0\n", + " 24.0\n", + " 1871.0\n", + " 3.0\n", " ...\n", - " 22.0\n", - " 33.0\n", + " 31.0\n", + " 41.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 73.0\n", - " 8.0\n", - " 13.0\n", - " 38.1\n", + " 98.0\n", + " 11.0\n", + " 21.0\n", + " 34.4\n", " \n", " \n", "\n", - "

541 rows × 115 columns

\n", + "

580 rows × 115 columns

\n", "" ], "text/plain": [ " player nationality position team age birth_year \\\n", - "0 Oliver Abildgaard dk DEN MF Hellas Verona 26-245 1996 \n", - "1 Tammy Abraham eng ENG FW Roma 25-131 1997 \n", - "2 Francesco Acerbi it ITA DF Inter 35-000 1988 \n", - "3 Yacine Adli fr FRA MF,FW Milan 22-196 2000 \n", - "4 Michel Aebischer ch SUI FW,MF Bologna 26-035 1997 \n", + "0 James Abankwah ie IRL DF Udinese 19-099 2004 \n", + "1 Oliver Abildgaard dk DEN MF Hellas Verona 26-319 1996 \n", + "2 Tammy Abraham eng ENG FW Roma 25-205 1997 \n", + "3 Christian Acella it ITA MF Cremonese 20-292 2002 \n", + "4 Francesco Acerbi it ITA DF Inter 35-074 1988 \n", ".. ... ... ... ... ... ... \n", - "536 Petar Zovko ba BIH GK Spezia 20-322 2002 \n", - "537 Szymon Żurkowski pl POL MF Fiorentina 25-138 1997 \n", - "538 Szymon Żurkowski pl POL MF Spezia 25-138 1997 \n", - "539 Milan Đurić ba BIH FW Hellas Verona 32-264 1990 \n", - "540 Filip Đuričić rs SRB MF,FW Sampdoria 31-011 1992 \n", + "575 Petar Zovko ba BIH GK Spezia 21-031 2002 \n", + "576 Szymon Żurkowski pl POL MF Fiorentina 25-212 1997 \n", + "577 Szymon Żurkowski pl POL MF Spezia 25-212 1997 \n", + "578 Milan Đurić ba BIH FW Hellas Verona 32-338 1990 \n", + "579 Filip Đuričić rs SRB MF,FW Sampdoria 31-085 1992 \n", "\n", " games games_starts minutes goals ... fouls fouled offsides \\\n", - "0 1.0 0.0 11.0 0.0 ... 0.0 0.0 0.0 \n", - "1 21.0 17.0 1500.0 6.0 ... 21.0 32.0 9.0 \n", - "2 14.0 12.0 1110.0 0.0 ... 9.0 9.0 1.0 \n", - "3 4.0 1.0 116.0 0.0 ... 3.0 1.0 0.0 \n", - "4 18.0 8.0 775.0 1.0 ... 10.0 14.0 5.0 \n", + "0 1.0 0.0 5.0 0.0 ... 1.0 0.0 0.0 \n", + "1 6.0 2.0 197.0 0.0 ... 3.0 1.0 0.0 \n", + "2 31.0 22.0 1905.0 7.0 ... 30.0 44.0 10.0 \n", + "3 1.0 0.0 15.0 0.0 ... 0.0 0.0 0.0 \n", + "4 24.0 20.0 1915.0 0.0 ... 12.0 10.0 1.0 \n", ".. ... ... ... ... ... ... ... ... \n", - "536 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", - "537 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", - "538 1.0 0.0 8.0 0.0 ... 0.0 0.0 0.0 \n", - "539 16.0 7.0 703.0 1.0 ... 15.0 14.0 3.0 \n", - "540 20.0 17.0 1326.0 2.0 ... 22.0 33.0 0.0 \n", + "575 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "576 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "577 6.0 2.0 240.0 0.0 ... 8.0 5.0 0.0 \n", + "578 21.0 8.0 863.0 1.0 ... 21.0 20.0 4.0 \n", + "579 28.0 24.0 1871.0 3.0 ... 31.0 41.0 0.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 0.0 0.0 0.0 1.0 0.0 \n", - "1 1.0 0.0 0.0 31.0 39.0 \n", - "2 0.0 0.0 0.0 61.0 34.0 \n", - "3 0.0 0.0 0.0 5.0 0.0 \n", - "4 1.0 1.0 0.0 43.0 5.0 \n", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 15.0 15.0 \n", + "2 1.0 0.0 0.0 41.0 66.0 \n", + "3 0.0 0.0 0.0 1.0 0.0 \n", + "4 0.0 0.0 0.0 122.0 69.0 \n", ".. ... ... ... ... ... \n", - "536 0.0 0.0 0.0 2.0 0.0 \n", - "537 0.0 0.0 0.0 2.0 1.0 \n", - "538 0.0 0.0 0.0 3.0 0.0 \n", - "539 0.0 0.0 0.0 16.0 113.0 \n", - "540 0.0 0.0 0.0 73.0 8.0 \n", + "575 0.0 0.0 0.0 2.0 0.0 \n", + "576 0.0 0.0 0.0 2.0 1.0 \n", + "577 0.0 0.0 0.0 18.0 3.0 \n", + "578 0.0 0.0 0.0 19.0 132.0 \n", + "579 0.0 0.0 0.0 98.0 11.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 1.0 0.0 \n", - "1 39.0 50.0 \n", - "2 20.0 63.0 \n", - "3 3.0 0.0 \n", - "4 9.0 35.7 \n", + "0 0.0 0.0 \n", + "1 5.0 75.0 \n", + "2 57.0 53.7 \n", + "3 0.0 0.0 \n", + "4 32.0 68.3 \n", ".. ... ... \n", - "536 0.0 0.0 \n", - "537 1.0 50.0 \n", - "538 0.0 0.0 \n", - "539 28.0 80.1 \n", - "540 13.0 38.1 \n", + "575 0.0 0.0 \n", + "576 1.0 50.0 \n", + "577 2.0 60.0 \n", + "578 35.0 79.0 \n", + "579 21.0 34.4 \n", "\n", - "[541 rows x 115 columns]" + "[580 rows x 115 columns]" ] }, "execution_count": 3, @@ -674,151 +676,199 @@ " it ITA\n", " GK\n", " Sampdoria\n", - " 26-023\n", + " 26-097\n", " 1997\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 36.0\n", + " 25.0\n", + " 25.0\n", + " 2250.0\n", + " 39.0\n", " ...\n", - " 39.4\n", - " 161.0\n", - " 58.4\n", - " 44.8\n", - " 283.0\n", - " 20.0\n", - " 7.1\n", - " 20.0\n", - " 0.95\n", - " 14.7\n", + " 39.1\n", + " 200.0\n", + " 57.0\n", + " 44.2\n", + " 356.0\n", + " 24.0\n", + " 6.7\n", + " 23.0\n", + " 0.92\n", + " 13.9\n", " \n", " \n", " 1\n", + " Francesco Bardi\n", + " it ITA\n", + " GK\n", + " Bologna\n", + " 31-097\n", + " 1992\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 35.6\n", + " 9.0\n", + " 44.4\n", + " 33.4\n", + " 10.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 2\n", " Marco Carnesecchi\n", " it ITA\n", " GK\n", " Cremonese\n", - " 22-224\n", + " 22-298\n", " 2000\n", - " 12.0\n", - " 12.0\n", - " 1080.0\n", - " 18.0\n", + " 22.0\n", + " 22.0\n", + " 1980.0\n", + " 38.0\n", " ...\n", - " 38.9\n", - " 86.0\n", - " 53.5\n", - " 46.1\n", - " 158.0\n", - " 13.0\n", - " 8.2\n", - " 8.0\n", - " 0.67\n", - " 12.9\n", + " 40.9\n", + " 159.0\n", + " 61.6\n", + " 50.7\n", + " 305.0\n", + " 23.0\n", + " 7.5\n", + " 21.0\n", + " 0.95\n", + " 14.0\n", " \n", " \n", - " 2\n", + " 3\n", " Andrea Consigli\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 36-014\n", + " 36-088\n", " 1987\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 28.0\n", + " 29.0\n", + " 29.0\n", + " 2610.0\n", + " 43.0\n", " ...\n", - " 34.3\n", - " 172.0\n", - " 32.6\n", - " 33.6\n", - " 255.0\n", - " 16.0\n", - " 6.3\n", - " 23.0\n", - " 1.21\n", - " 15.2\n", + " 34.2\n", + " 249.0\n", + " 32.5\n", + " 33.4\n", + " 409.0\n", + " 24.0\n", + " 5.9\n", + " 26.0\n", + " 0.90\n", + " 14.1\n", " \n", " \n", - " 3\n", + " 4\n", + " Alessio Cragno\n", + " it ITA\n", + " GK\n", + " Monza\n", + " 28-301\n", + " 1994\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 3.0\n", + " ...\n", + " 22.6\n", + " 6.0\n", + " 16.7\n", + " 33.3\n", + " 7.0\n", + " 0.0\n", + " 0.0\n", + " 2.0\n", + " 2.00\n", + " 25.7\n", + " \n", + " \n", + " 5\n", " Michele Di Gregorio\n", " it ITA\n", " GK\n", " Monza\n", - " 25-198\n", + " 25-272\n", " 1997\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", " 30.0\n", + " 30.0\n", + " 2700.0\n", + " 40.0\n", " ...\n", - " 32.9\n", - " 124.0\n", - " 31.5\n", - " 31.4\n", - " 286.0\n", - " 10.0\n", - " 3.5\n", - " 16.0\n", - " 0.76\n", - " 13.3\n", + " 32.6\n", + " 196.0\n", + " 35.7\n", + " 33.7\n", + " 430.0\n", + " 13.0\n", + " 3.0\n", + " 23.0\n", + " 0.77\n", + " 12.7\n", " \n", " \n", - " 4\n", + " 6\n", " Bartłomiej Drągowski\n", " pl POL\n", " GK\n", " Spezia\n", - " 25-175\n", + " 25-249\n", " 1997\n", - " 19.0\n", - " 19.0\n", - " 1662.0\n", - " 32.0\n", + " 28.0\n", + " 28.0\n", + " 2406.0\n", + " 43.0\n", " ...\n", - " 34.6\n", - " 109.0\n", - " 64.2\n", + " 34.5\n", + " 169.0\n", + " 62.7\n", " 48.0\n", - " 251.0\n", - " 9.0\n", - " 3.6\n", - " 24.0\n", - " 1.30\n", - " 15.3\n", + " 401.0\n", + " 11.0\n", + " 2.7\n", + " 29.0\n", + " 1.08\n", + " 14.2\n", " \n", " \n", - " 5\n", + " 7\n", " Wladimiro Falcone\n", " it ITA\n", " GK\n", " Lecce\n", - " 27-304\n", + " 28-013\n", " 1995\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 24.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 38.0\n", " ...\n", - " 41.3\n", - " 163.0\n", - " 76.1\n", - " 51.7\n", - " 316.0\n", - " 15.0\n", - " 4.7\n", - " 20.0\n", - " 0.95\n", - " 12.5\n", + " 41.9\n", + " 225.0\n", + " 77.3\n", + " 52.4\n", + " 441.0\n", + " 22.0\n", + " 5.0\n", + " 35.0\n", + " 1.13\n", + " 13.7\n", " \n", " \n", - " 6\n", + " 8\n", " Pierluigi Gollini\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 27-329\n", + " 28-038\n", " 1995\n", " 3.0\n", " 3.0\n", @@ -837,60 +887,108 @@ " 9.3\n", " \n", " \n", - " 7\n", + " 9\n", + " Pierluigi Gollini\n", + " it ITA\n", + " GK\n", + " Napoli\n", + " 28-038\n", + " 1995\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 31.7\n", + " 5.0\n", + " 0.0\n", + " 15.4\n", + " 12.0\n", + " 1.0\n", + " 8.3\n", + " 0.0\n", + " 0.00\n", + " 2.0\n", + " \n", + " \n", + " 10\n", " Samir Handanović\n", " si SVN\n", " GK\n", " Inter\n", - " 38-211\n", + " 38-285\n", " 1984\n", - " 8.0\n", - " 8.0\n", - " 720.0\n", - " 13.0\n", + " 11.0\n", + " 11.0\n", + " 990.0\n", + " 16.0\n", " ...\n", - " 26.1\n", - " 45.0\n", - " 4.4\n", - " 24.0\n", - " 79.0\n", + " 25.7\n", + " 59.0\n", + " 8.5\n", + " 25.2\n", + " 112.0\n", " 2.0\n", - " 2.5\n", - " 5.0\n", - " 0.63\n", - " 14.6\n", + " 1.8\n", + " 10.0\n", + " 0.91\n", + " 16.3\n", " \n", " \n", - " 8\n", + " 11\n", " Mike Maignan\n", " fr FRA\n", " GK\n", " Milan\n", - " 27-222\n", + " 27-296\n", " 1995\n", - " 7.0\n", - " 7.0\n", - " 630.0\n", - " 8.0\n", + " 15.0\n", + " 15.0\n", + " 1350.0\n", + " 15.0\n", " ...\n", - " 33.5\n", - " 18.0\n", - " 38.9\n", - " 40.4\n", - " 65.0\n", - " 5.0\n", - " 7.7\n", - " 6.0\n", - " 0.86\n", - " 12.9\n", + " 31.3\n", + " 54.0\n", + " 33.3\n", + " 35.3\n", + " 153.0\n", + " 11.0\n", + " 7.2\n", + " 25.0\n", + " 1.67\n", + " 18.1\n", " \n", " \n", - " 9\n", + " 12\n", + " Federico Marchetti\n", + " it ITA\n", + " GK\n", + " Spezia\n", + " 40-077\n", + " 1983\n", + " 1.0\n", + " 0.0\n", + " 66.0\n", + " 2.0\n", + " ...\n", + " 24.3\n", + " 8.0\n", + " 37.5\n", + " 32.4\n", + " 8.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 1.36\n", + " 22.0\n", + " \n", + " \n", + " 13\n", " Luís Maximiano\n", " pt POR\n", " GK\n", " Lazio\n", - " 24-036\n", + " 24-110\n", " 1999\n", " 1.0\n", " 1.0\n", @@ -909,180 +1007,180 @@ " 19.0\n", " \n", " \n", - " 10\n", + " 14\n", " Alex Meret\n", " it ITA\n", " GK\n", " Napoli\n", - " 25-325\n", + " 26-034\n", " 1997\n", + " 30.0\n", + " 30.0\n", + " 2700.0\n", " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 15.0\n", " ...\n", - " 26.7\n", - " 128.0\n", - " 19.5\n", - " 27.1\n", - " 221.0\n", - " 6.0\n", - " 2.7\n", - " 24.0\n", - " 1.14\n", - " 17.2\n", + " 26.2\n", + " 185.0\n", + " 20.0\n", + " 26.8\n", + " 308.0\n", + " 10.0\n", + " 3.2\n", + " 32.0\n", + " 1.07\n", + " 17.0\n", " \n", " \n", - " 11\n", + " 15\n", " Vanja Milinković-Savić\n", " rs SRB\n", " GK\n", " Torino\n", - " 25-355\n", + " 26-064\n", " 1997\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 22.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 36.0\n", " ...\n", - " 40.9\n", - " 155.0\n", - " 94.2\n", - " 71.7\n", - " 263.0\n", - " 18.0\n", - " 6.8\n", - " 35.0\n", - " 1.67\n", - " 16.3\n", + " 40.0\n", + " 236.0\n", + " 91.5\n", + " 69.8\n", + " 387.0\n", + " 25.0\n", + " 6.5\n", + " 62.0\n", + " 2.00\n", + " 17.1\n", " \n", " \n", - " 12\n", + " 16\n", " Lorenzo Montipò\n", " it ITA\n", " GK\n", " Hellas Verona\n", - " 26-355\n", + " 27-064\n", " 1996\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 33.0\n", + " 30.0\n", + " 30.0\n", + " 2700.0\n", + " 44.0\n", " ...\n", - " 44.1\n", - " 168.0\n", - " 61.9\n", - " 46.7\n", - " 294.0\n", - " 15.0\n", - " 5.1\n", - " 46.0\n", - " 2.19\n", - " 16.8\n", + " 43.9\n", + " 227.0\n", + " 67.8\n", + " 49.6\n", + " 401.0\n", + " 22.0\n", + " 5.5\n", + " 51.0\n", + " 1.70\n", + " 15.8\n", " \n", " \n", - " 13\n", + " 17\n", " Juan Musso\n", " ar ARG\n", " GK\n", " Atalanta\n", - " 28-280\n", + " 28-354\n", " 1994\n", - " 15.0\n", - " 15.0\n", - " 1267.0\n", - " 15.0\n", + " 23.0\n", + " 23.0\n", + " 1987.0\n", + " 25.0\n", " ...\n", - " 32.5\n", - " 98.0\n", - " 61.2\n", - " 47.1\n", - " 174.0\n", - " 10.0\n", + " 32.0\n", + " 149.0\n", + " 62.4\n", + " 48.5\n", + " 247.0\n", + " 14.0\n", " 5.7\n", - " 12.0\n", - " 0.85\n", - " 14.6\n", + " 24.0\n", + " 1.09\n", + " 15.6\n", " \n", " \n", - " 14\n", + " 18\n", " Guillermo Ochoa\n", " mx MEX\n", " GK\n", " Salernitana\n", - " 37-212\n", + " 37-286\n", " 1985\n", - " 6.0\n", - " 6.0\n", - " 540.0\n", - " 17.0\n", + " 14.0\n", + " 14.0\n", + " 1260.0\n", + " 23.0\n", " ...\n", - " 41.9\n", - " 60.0\n", - " 71.7\n", - " 52.2\n", - " 86.0\n", - " 3.0\n", - " 3.5\n", - " 4.0\n", - " 0.67\n", - " 15.7\n", + " 41.5\n", + " 134.0\n", + " 70.1\n", + " 50.0\n", + " 213.0\n", + " 6.0\n", + " 2.8\n", + " 7.0\n", + " 0.50\n", + " 13.1\n", " \n", " \n", - " 15\n", + " 19\n", " André Onana\n", " cm CMR\n", " GK\n", " Inter\n", - " 26-314\n", + " 27-023\n", " 1996\n", - " 13.0\n", - " 13.0\n", - " 1170.0\n", - " 13.0\n", + " 20.0\n", + " 20.0\n", + " 1800.0\n", + " 18.0\n", " ...\n", - " 30.7\n", - " 92.0\n", - " 33.7\n", - " 36.9\n", - " 165.0\n", - " 7.0\n", - " 4.2\n", - " 4.0\n", - " 0.31\n", - " 13.2\n", + " 30.2\n", + " 130.0\n", + " 32.3\n", + " 35.8\n", + " 233.0\n", + " 14.0\n", + " 6.0\n", + " 9.0\n", + " 0.45\n", + " 12.5\n", " \n", " \n", - " 16\n", + " 20\n", " Rui Patrício\n", " pt POR\n", " GK\n", " Roma\n", - " 34-360\n", + " 35-069\n", " 1988\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 18.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 29.0\n", " ...\n", - " 31.8\n", - " 159.0\n", - " 30.8\n", - " 32.8\n", - " 248.0\n", - " 6.0\n", - " 2.4\n", - " 15.0\n", - " 0.71\n", - " 14.0\n", + " 33.6\n", + " 217.0\n", + " 34.1\n", + " 33.6\n", + " 356.0\n", + " 12.0\n", + " 3.4\n", + " 23.0\n", + " 0.74\n", + " 14.1\n", " \n", " \n", - " 17\n", + " 21\n", " Gianluca Pegolo\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 41-322\n", + " 42-031\n", " 1981\n", " 2.0\n", " 2.0\n", @@ -1101,60 +1199,108 @@ " 9.0\n", " \n", " \n", - " 18\n", + " 22\n", + " Simone Perilli\n", + " it ITA\n", + " GK\n", + " Hellas Verona\n", + " 28-108\n", + " 1995\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 57.3\n", + " 13.0\n", + " 100.0\n", + " 69.8\n", + " 19.0\n", + " 1.0\n", + " 5.3\n", + " 1.0\n", + " 1.00\n", + " 12.0\n", + " \n", + " \n", + " 23\n", " Mattia Perin\n", " it ITA\n", " GK\n", " Juventus\n", - " 30-092\n", + " 30-166\n", " 1992\n", - " 7.0\n", - " 6.0\n", - " 588.0\n", - " 4.0\n", - " ...\n", - " 30.3\n", - " 41.0\n", - " 39.0\n", - " 35.3\n", - " 99.0\n", - " 2.0\n", - " 2.0\n", + " 10.0\n", " 9.0\n", - " 1.38\n", - " 17.8\n", + " 858.0\n", + " 7.0\n", + " ...\n", + " 31.1\n", + " 60.0\n", + " 41.7\n", + " 36.5\n", + " 148.0\n", + " 4.0\n", + " 2.7\n", + " 11.0\n", + " 1.15\n", + " 15.7\n", " \n", " \n", - " 19\n", + " 24\n", + " Samuele Perisan\n", + " it ITA\n", + " GK\n", + " Empoli\n", + " 25-247\n", + " 1997\n", + " 7.0\n", + " 7.0\n", + " 630.0\n", + " 9.0\n", + " ...\n", + " 32.7\n", + " 75.0\n", + " 34.7\n", + " 34.3\n", + " 134.0\n", + " 5.0\n", + " 3.7\n", + " 4.0\n", + " 0.57\n", + " 11.6\n", + " \n", + " \n", + " 25\n", " Ivan Provedel\n", " it ITA\n", " GK\n", " Lazio\n", - " 28-330\n", + " 29-039\n", " 1994\n", + " 31.0\n", + " 30.0\n", + " 2783.0\n", " 21.0\n", - " 20.0\n", - " 1883.0\n", - " 17.0\n", " ...\n", " 33.0\n", - " 120.0\n", - " 34.2\n", - " 34.5\n", - " 279.0\n", - " 7.0\n", - " 2.5\n", - " 39.0\n", - " 1.86\n", - " 18.2\n", + " 161.0\n", + " 36.0\n", + " 34.7\n", + " 402.0\n", + " 17.0\n", + " 4.2\n", + " 46.0\n", + " 1.49\n", + " 16.4\n", " \n", " \n", - " 20\n", + " 26\n", " Ionuț Radu\n", " ro ROU\n", " GK\n", " Cremonese\n", - " 25-258\n", + " 25-332\n", " 1997\n", " 9.0\n", " 9.0\n", @@ -1173,228 +1319,300 @@ " 14.5\n", " \n", " \n", - " 21\n", + " 27\n", + " Nicola Ravaglia\n", + " it ITA\n", + " GK\n", + " Sampdoria\n", + " 34-134\n", + " 1988\n", + " 4.0\n", + " 4.0\n", + " 360.0\n", + " 8.0\n", + " ...\n", + " 35.5\n", + " 53.0\n", + " 45.3\n", + " 35.4\n", + " 72.0\n", + " 4.0\n", + " 5.6\n", + " 1.0\n", + " 0.25\n", + " 10.0\n", + " \n", + " \n", + " 28\n", " Luigi Sepe\n", " it ITA\n", " GK\n", " Salernitana\n", - " 31-278\n", + " 31-352\n", " 1991\n", - " 15.0\n", - " 15.0\n", - " 1350.0\n", - " 24.0\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 27.0\n", " ...\n", - " 36.1\n", - " 134.0\n", - " 46.3\n", - " 40.6\n", - " 199.0\n", - " 15.0\n", - " 7.5\n", - " 11.0\n", - " 0.73\n", + " 35.9\n", + " 154.0\n", + " 48.7\n", + " 41.1\n", + " 222.0\n", + " 16.0\n", + " 7.2\n", + " 13.0\n", + " 0.76\n", " 14.2\n", " \n", " \n", - " 22\n", + " 29\n", " Marco Silvestri\n", " it ITA\n", " GK\n", " Udinese\n", - " 31-345\n", + " 32-054\n", " 1991\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 23.0\n", - " ...\n", - " 33.6\n", - " 164.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", " 39.0\n", - " 36.1\n", - " 275.0\n", - " 5.0\n", - " 1.8\n", - " 10.0\n", - " 0.48\n", - " 13.7\n", + " ...\n", + " 32.7\n", + " 237.0\n", + " 37.1\n", + " 35.5\n", + " 441.0\n", + " 11.0\n", + " 2.5\n", + " 16.0\n", + " 0.52\n", + " 12.1\n", " \n", " \n", - " 23\n", + " 30\n", + " Salvatore Sirigu\n", + " it ITA\n", + " GK\n", + " Fiorentina\n", + " 36-103\n", + " 1987\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 37.7\n", + " 8.0\n", + " 50.0\n", + " 41.8\n", + " 12.0\n", + " 0.0\n", + " 0.0\n", + " 2.0\n", + " 2.00\n", + " 26.3\n", + " \n", + " \n", + " 31\n", " Łukasz Skorupski\n", " pl POL\n", " GK\n", " Bologna\n", - " 31-281\n", + " 31-355\n", " 1991\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 31.0\n", + " 30.0\n", + " 30.0\n", + " 2700.0\n", + " 39.0\n", " ...\n", - " 32.7\n", - " 153.0\n", - " 38.6\n", - " 34.5\n", - " 299.0\n", - " 18.0\n", + " 32.5\n", + " 219.0\n", + " 38.8\n", + " 35.4\n", + " 402.0\n", + " 24.0\n", " 6.0\n", - " 17.0\n", - " 0.81\n", - " 13.3\n", + " 22.0\n", + " 0.73\n", + " 13.4\n", " \n", " \n", - " 24\n", + " 32\n", " Marco Sportiello\n", " it ITA\n", " GK\n", " Atalanta\n", - " 30-276\n", + " 30-350\n", " 1992\n", - " 7.0\n", - " 6.0\n", - " 623.0\n", " 9.0\n", + " 8.0\n", + " 803.0\n", + " 11.0\n", " ...\n", - " 28.5\n", - " 61.0\n", - " 68.9\n", - " 48.9\n", - " 86.0\n", - " 7.0\n", - " 8.1\n", + " 29.4\n", + " 82.0\n", + " 74.4\n", + " 52.9\n", + " 126.0\n", + " 13.0\n", + " 10.3\n", " 14.0\n", - " 2.02\n", - " 19.9\n", + " 1.57\n", + " 16.8\n", " \n", " \n", - " 25\n", + " 33\n", " Wojciech Szczęsny\n", " pl POL\n", " GK\n", " Juventus\n", - " 32-298\n", + " 33-007\n", " 1990\n", - " 15.0\n", - " 15.0\n", - " 1302.0\n", - " 13.0\n", + " 22.0\n", + " 22.0\n", + " 1932.0\n", + " 19.0\n", " ...\n", - " 35.0\n", - " 72.0\n", - " 41.7\n", - " 38.7\n", - " 178.0\n", - " 4.0\n", - " 2.2\n", - " 12.0\n", - " 0.83\n", - " 15.8\n", + " 34.3\n", + " 114.0\n", + " 49.1\n", + " 41.5\n", + " 301.0\n", + " 9.0\n", + " 3.0\n", + " 19.0\n", + " 0.89\n", + " 15.4\n", " \n", " \n", - " 26\n", + " 34\n", " Ciprian Tătărușanu\n", " ro ROU\n", " GK\n", " Milan\n", - " 37-001\n", + " 37-075\n", " 1986\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", " 22.0\n", " ...\n", - " 32.6\n", - " 65.0\n", - " 47.7\n", - " 39.9\n", - " 158.0\n", + " 32.9\n", + " 83.0\n", + " 51.8\n", + " 41.8\n", + " 195.0\n", " 9.0\n", - " 5.7\n", - " 10.0\n", - " 0.71\n", - " 13.8\n", + " 4.6\n", + " 13.0\n", + " 0.81\n", + " 14.4\n", " \n", " \n", - " 27\n", + " 35\n", " Pietro Terracciano\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 32-339\n", + " 33-048\n", " 1990\n", - " 18.0\n", - " 18.0\n", - " 1620.0\n", - " 26.0\n", - " ...\n", - " 32.9\n", - " 116.0\n", - " 43.1\n", - " 39.4\n", - " 163.0\n", - " 9.0\n", - " 5.5\n", + " 27.0\n", + " 27.0\n", + " 2430.0\n", " 34.0\n", - " 1.89\n", - " 18.9\n", + " ...\n", + " 33.3\n", + " 184.0\n", + " 44.6\n", + " 39.9\n", + " 264.0\n", + " 12.0\n", + " 4.5\n", + " 52.0\n", + " 1.93\n", + " 18.6\n", " \n", " \n", - " 28\n", + " 36\n", + " Martin Turk\n", + " si SVN\n", + " GK\n", + " Sampdoria\n", + " 19-247\n", + " 2003\n", + " 2.0\n", + " 2.0\n", + " 180.0\n", + " 5.0\n", + " ...\n", + " 37.3\n", + " 22.0\n", + " 90.9\n", + " 57.1\n", + " 37.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 11.5\n", + " \n", + " \n", + " 37\n", " Guglielmo Vicario\n", " it ITA\n", " GK\n", " Empoli\n", - " 26-126\n", + " 26-200\n", " 1996\n", - " 21.0\n", - " 21.0\n", - " 1890.0\n", - " 26.0\n", + " 24.0\n", + " 24.0\n", + " 2160.0\n", + " 31.0\n", " ...\n", - " 34.0\n", - " 108.0\n", - " 47.2\n", - " 42.4\n", - " 426.0\n", - " 25.0\n", - " 5.9\n", - " 12.0\n", - " 0.57\n", - " 10.9\n", + " 33.3\n", + " 139.0\n", + " 48.9\n", + " 42.6\n", + " 489.0\n", + " 28.0\n", + " 5.7\n", + " 15.0\n", + " 0.63\n", + " 10.7\n", " \n", " \n", - " 29\n", + " 38\n", " Jeroen Zoet\n", " nl NED\n", " GK\n", " Spezia\n", - " 32-035\n", + " 32-109\n", " 1991\n", + " 4.0\n", " 3.0\n", + " 244.0\n", " 2.0\n", - " 154.0\n", - " 1.0\n", " ...\n", - " 41.7\n", - " 8.0\n", - " 87.5\n", - " 56.6\n", - " 24.0\n", - " 1.0\n", - " 4.2\n", - " 3.0\n", - " 1.74\n", - " 15.7\n", + " 37.1\n", + " 12.0\n", + " 66.7\n", + " 46.3\n", + " 40.0\n", + " 2.0\n", + " 5.0\n", + " 6.0\n", + " 2.20\n", + " 17.2\n", " \n", " \n", - " 30\n", + " 39\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 20-322\n", + " 21-031\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -1414,209 +1632,263 @@ " \n", " \n", "\n", - "

31 rows × 47 columns

\n", + "

40 rows × 47 columns

\n", "" ], "text/plain": [ " player nationality position team age \\\n", - "0 Emil Audero it ITA GK Sampdoria 26-023 \n", - "1 Marco Carnesecchi it ITA GK Cremonese 22-224 \n", - "2 Andrea Consigli it ITA GK Sassuolo 36-014 \n", - "3 Michele Di Gregorio it ITA GK Monza 25-198 \n", - "4 Bartłomiej Drągowski pl POL GK Spezia 25-175 \n", - "5 Wladimiro Falcone it ITA GK Lecce 27-304 \n", - "6 Pierluigi Gollini it ITA GK Fiorentina 27-329 \n", - "7 Samir Handanović si SVN GK Inter 38-211 \n", - "8 Mike Maignan fr FRA GK Milan 27-222 \n", - "9 Luís Maximiano pt POR GK Lazio 24-036 \n", - "10 Alex Meret it ITA GK Napoli 25-325 \n", - "11 Vanja Milinković-Savić rs SRB GK Torino 25-355 \n", - "12 Lorenzo Montipò it ITA GK Hellas Verona 26-355 \n", - "13 Juan Musso ar ARG GK Atalanta 28-280 \n", - "14 Guillermo Ochoa mx MEX GK Salernitana 37-212 \n", - "15 André Onana cm CMR GK Inter 26-314 \n", - "16 Rui Patrício pt POR GK Roma 34-360 \n", - "17 Gianluca Pegolo it ITA GK Sassuolo 41-322 \n", - "18 Mattia Perin it ITA GK Juventus 30-092 \n", - "19 Ivan Provedel it ITA GK Lazio 28-330 \n", - "20 Ionuț Radu ro ROU GK Cremonese 25-258 \n", - "21 Luigi Sepe it ITA GK Salernitana 31-278 \n", - "22 Marco Silvestri it ITA GK Udinese 31-345 \n", - "23 Łukasz Skorupski pl POL GK Bologna 31-281 \n", - "24 Marco Sportiello it ITA GK Atalanta 30-276 \n", - "25 Wojciech Szczęsny pl POL GK Juventus 32-298 \n", - "26 Ciprian Tătărușanu ro ROU GK Milan 37-001 \n", - "27 Pietro Terracciano it ITA GK Fiorentina 32-339 \n", - "28 Guglielmo Vicario it ITA GK Empoli 26-126 \n", - "29 Jeroen Zoet nl NED GK Spezia 32-035 \n", - "30 Petar Zovko ba BIH GK Spezia 20-322 \n", + "0 Emil Audero it ITA GK Sampdoria 26-097 \n", + "1 Francesco Bardi it ITA GK Bologna 31-097 \n", + "2 Marco Carnesecchi it ITA GK Cremonese 22-298 \n", + "3 Andrea Consigli it ITA GK Sassuolo 36-088 \n", + "4 Alessio Cragno it ITA GK Monza 28-301 \n", + "5 Michele Di Gregorio it ITA GK Monza 25-272 \n", + "6 Bartłomiej Drągowski pl POL GK Spezia 25-249 \n", + "7 Wladimiro Falcone it ITA GK Lecce 28-013 \n", + "8 Pierluigi Gollini it ITA GK Fiorentina 28-038 \n", + "9 Pierluigi Gollini it ITA GK Napoli 28-038 \n", + "10 Samir Handanović si SVN GK Inter 38-285 \n", + "11 Mike Maignan fr FRA GK Milan 27-296 \n", + "12 Federico Marchetti it ITA GK Spezia 40-077 \n", + "13 Luís Maximiano pt POR GK Lazio 24-110 \n", + "14 Alex Meret it ITA GK Napoli 26-034 \n", + "15 Vanja Milinković-Savić rs SRB GK Torino 26-064 \n", + "16 Lorenzo Montipò it ITA GK Hellas Verona 27-064 \n", + "17 Juan Musso ar ARG GK Atalanta 28-354 \n", + "18 Guillermo Ochoa mx MEX GK Salernitana 37-286 \n", + "19 André Onana cm CMR GK Inter 27-023 \n", + "20 Rui Patrício pt POR GK Roma 35-069 \n", + "21 Gianluca Pegolo it ITA GK Sassuolo 42-031 \n", + "22 Simone Perilli it ITA GK Hellas Verona 28-108 \n", + "23 Mattia Perin it ITA GK Juventus 30-166 \n", + "24 Samuele Perisan it ITA GK Empoli 25-247 \n", + "25 Ivan Provedel it ITA GK Lazio 29-039 \n", + "26 Ionuț Radu ro ROU GK Cremonese 25-332 \n", + "27 Nicola Ravaglia it ITA GK Sampdoria 34-134 \n", + "28 Luigi Sepe it ITA GK Salernitana 31-352 \n", + "29 Marco Silvestri it ITA GK Udinese 32-054 \n", + "30 Salvatore Sirigu it ITA GK Fiorentina 36-103 \n", + "31 Łukasz Skorupski pl POL GK Bologna 31-355 \n", + "32 Marco Sportiello it ITA GK Atalanta 30-350 \n", + "33 Wojciech Szczęsny pl POL GK Juventus 33-007 \n", + "34 Ciprian Tătărușanu ro ROU GK Milan 37-075 \n", + "35 Pietro Terracciano it ITA GK Fiorentina 33-048 \n", + "36 Martin Turk si SVN GK Sampdoria 19-247 \n", + "37 Guglielmo Vicario it ITA GK Empoli 26-200 \n", + "38 Jeroen Zoet nl NED GK Spezia 32-109 \n", + "39 Petar Zovko ba BIH GK Spezia 21-031 \n", "\n", " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\n", - "0 1997 21.0 21.0 1890.0 36.0 ... \n", - "1 2000 12.0 12.0 1080.0 18.0 ... \n", - "2 1987 19.0 19.0 1710.0 28.0 ... \n", - "3 1997 21.0 21.0 1890.0 30.0 ... \n", - "4 1997 19.0 19.0 1662.0 32.0 ... \n", - "5 1995 21.0 21.0 1890.0 24.0 ... \n", - "6 1995 3.0 3.0 270.0 2.0 ... \n", - "7 1984 8.0 8.0 720.0 13.0 ... \n", - "8 1995 7.0 7.0 630.0 8.0 ... \n", - "9 1999 1.0 1.0 5.0 0.0 ... \n", - "10 1997 21.0 21.0 1890.0 15.0 ... \n", - "11 1997 21.0 21.0 1890.0 22.0 ... \n", - "12 1996 21.0 21.0 1890.0 33.0 ... \n", - "13 1994 15.0 15.0 1267.0 15.0 ... \n", - "14 1985 6.0 6.0 540.0 17.0 ... \n", - "15 1996 13.0 13.0 1170.0 13.0 ... \n", - "16 1988 21.0 21.0 1890.0 18.0 ... \n", - "17 1981 2.0 2.0 180.0 3.0 ... \n", - "18 1992 7.0 6.0 588.0 4.0 ... \n", - "19 1994 21.0 20.0 1883.0 17.0 ... \n", - "20 1997 9.0 9.0 810.0 19.0 ... \n", - "21 1991 15.0 15.0 1350.0 24.0 ... \n", - "22 1991 21.0 21.0 1890.0 23.0 ... \n", - "23 1991 21.0 21.0 1890.0 31.0 ... \n", - "24 1992 7.0 6.0 623.0 9.0 ... \n", - "25 1990 15.0 15.0 1302.0 13.0 ... \n", - "26 1986 14.0 14.0 1260.0 22.0 ... \n", - "27 1990 18.0 18.0 1620.0 26.0 ... \n", - "28 1996 21.0 21.0 1890.0 26.0 ... \n", - "29 1991 3.0 2.0 154.0 1.0 ... \n", - "30 2002 1.0 0.0 74.0 2.0 ... \n", + "0 1997 25.0 25.0 2250.0 39.0 ... \n", + "1 1992 1.0 1.0 90.0 0.0 ... \n", + "2 2000 22.0 22.0 1980.0 38.0 ... \n", + "3 1987 29.0 29.0 2610.0 43.0 ... \n", + "4 1994 1.0 1.0 90.0 3.0 ... \n", + "5 1997 30.0 30.0 2700.0 40.0 ... \n", + "6 1997 28.0 28.0 2406.0 43.0 ... \n", + "7 1995 31.0 31.0 2790.0 38.0 ... \n", + "8 1995 3.0 3.0 270.0 2.0 ... \n", + "9 1995 1.0 1.0 90.0 0.0 ... \n", + "10 1984 11.0 11.0 990.0 16.0 ... \n", + "11 1995 15.0 15.0 1350.0 15.0 ... \n", + "12 1983 1.0 0.0 66.0 2.0 ... \n", + "13 1999 1.0 1.0 5.0 0.0 ... \n", + "14 1997 30.0 30.0 2700.0 21.0 ... \n", + "15 1997 31.0 31.0 2790.0 36.0 ... \n", + "16 1996 30.0 30.0 2700.0 44.0 ... \n", + "17 1994 23.0 23.0 1987.0 25.0 ... \n", + "18 1985 14.0 14.0 1260.0 23.0 ... \n", + "19 1996 20.0 20.0 1800.0 18.0 ... \n", + "20 1988 31.0 31.0 2790.0 29.0 ... \n", + "21 1981 2.0 2.0 180.0 3.0 ... \n", + "22 1995 1.0 1.0 90.0 0.0 ... \n", + "23 1992 10.0 9.0 858.0 7.0 ... \n", + "24 1997 7.0 7.0 630.0 9.0 ... \n", + "25 1994 31.0 30.0 2783.0 21.0 ... \n", + "26 1997 9.0 9.0 810.0 19.0 ... \n", + "27 1988 4.0 4.0 360.0 8.0 ... \n", + "28 1991 17.0 17.0 1530.0 27.0 ... \n", + "29 1991 31.0 31.0 2790.0 39.0 ... \n", + "30 1987 1.0 1.0 90.0 0.0 ... \n", + "31 1991 30.0 30.0 2700.0 39.0 ... \n", + "32 1992 9.0 8.0 803.0 11.0 ... \n", + "33 1990 22.0 22.0 1932.0 19.0 ... \n", + "34 1986 16.0 16.0 1440.0 22.0 ... \n", + "35 1990 27.0 27.0 2430.0 34.0 ... \n", + "36 2003 2.0 2.0 180.0 5.0 ... \n", + "37 1996 24.0 24.0 2160.0 31.0 ... \n", + "38 1991 4.0 3.0 244.0 2.0 ... \n", + "39 2002 1.0 0.0 74.0 2.0 ... \n", "\n", " gk_passes_length_avg gk_goal_kicks gk_pct_goal_kicks_launched \\\n", - "0 39.4 161.0 58.4 \n", - "1 38.9 86.0 53.5 \n", - "2 34.3 172.0 32.6 \n", - "3 32.9 124.0 31.5 \n", - "4 34.6 109.0 64.2 \n", - "5 41.3 163.0 76.1 \n", - "6 32.4 11.0 63.6 \n", - "7 26.1 45.0 4.4 \n", - "8 33.5 18.0 38.9 \n", - "9 0.0 0.0 0.0 \n", - "10 26.7 128.0 19.5 \n", - "11 40.9 155.0 94.2 \n", - "12 44.1 168.0 61.9 \n", - "13 32.5 98.0 61.2 \n", - "14 41.9 60.0 71.7 \n", - "15 30.7 92.0 33.7 \n", - "16 31.8 159.0 30.8 \n", - "17 29.7 16.0 31.3 \n", - "18 30.3 41.0 39.0 \n", - "19 33.0 120.0 34.2 \n", - "20 37.3 67.0 50.7 \n", - "21 36.1 134.0 46.3 \n", - "22 33.6 164.0 39.0 \n", - "23 32.7 153.0 38.6 \n", - "24 28.5 61.0 68.9 \n", - "25 35.0 72.0 41.7 \n", - "26 32.6 65.0 47.7 \n", - "27 32.9 116.0 43.1 \n", - "28 34.0 108.0 47.2 \n", - "29 41.7 8.0 87.5 \n", - "30 35.1 13.0 76.9 \n", + "0 39.1 200.0 57.0 \n", + "1 35.6 9.0 44.4 \n", + "2 40.9 159.0 61.6 \n", + "3 34.2 249.0 32.5 \n", + "4 22.6 6.0 16.7 \n", + "5 32.6 196.0 35.7 \n", + "6 34.5 169.0 62.7 \n", + "7 41.9 225.0 77.3 \n", + "8 32.4 11.0 63.6 \n", + "9 31.7 5.0 0.0 \n", + "10 25.7 59.0 8.5 \n", + "11 31.3 54.0 33.3 \n", + "12 24.3 8.0 37.5 \n", + "13 0.0 0.0 0.0 \n", + "14 26.2 185.0 20.0 \n", + "15 40.0 236.0 91.5 \n", + "16 43.9 227.0 67.8 \n", + "17 32.0 149.0 62.4 \n", + "18 41.5 134.0 70.1 \n", + "19 30.2 130.0 32.3 \n", + "20 33.6 217.0 34.1 \n", + "21 29.7 16.0 31.3 \n", + "22 57.3 13.0 100.0 \n", + "23 31.1 60.0 41.7 \n", + "24 32.7 75.0 34.7 \n", + "25 33.0 161.0 36.0 \n", + "26 37.3 67.0 50.7 \n", + "27 35.5 53.0 45.3 \n", + "28 35.9 154.0 48.7 \n", + "29 32.7 237.0 37.1 \n", + "30 37.7 8.0 50.0 \n", + "31 32.5 219.0 38.8 \n", + "32 29.4 82.0 74.4 \n", + "33 34.3 114.0 49.1 \n", + "34 32.9 83.0 51.8 \n", + "35 33.3 184.0 44.6 \n", + "36 37.3 22.0 90.9 \n", + "37 33.3 139.0 48.9 \n", + "38 37.1 12.0 66.7 \n", + "39 35.1 13.0 76.9 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 44.8 283.0 20.0 \n", - "1 46.1 158.0 13.0 \n", - "2 33.6 255.0 16.0 \n", - "3 31.4 286.0 10.0 \n", - "4 48.0 251.0 9.0 \n", - "5 51.7 316.0 15.0 \n", - "6 49.3 24.0 1.0 \n", - "7 24.0 79.0 2.0 \n", - "8 40.4 65.0 5.0 \n", - "9 0.0 0.0 0.0 \n", - "10 27.1 221.0 6.0 \n", - "11 71.7 263.0 18.0 \n", - "12 46.7 294.0 15.0 \n", - "13 47.1 174.0 10.0 \n", - "14 52.2 86.0 3.0 \n", - "15 36.9 165.0 7.0 \n", - "16 32.8 248.0 6.0 \n", - "17 28.3 28.0 2.0 \n", - "18 35.3 99.0 2.0 \n", - "19 34.5 279.0 7.0 \n", - "20 46.3 107.0 6.0 \n", - "21 40.6 199.0 15.0 \n", - "22 36.1 275.0 5.0 \n", - "23 34.5 299.0 18.0 \n", - "24 48.9 86.0 7.0 \n", - "25 38.7 178.0 4.0 \n", - "26 39.9 158.0 9.0 \n", - "27 39.4 163.0 9.0 \n", - "28 42.4 426.0 25.0 \n", - "29 56.6 24.0 1.0 \n", - "30 51.1 24.0 1.0 \n", + "0 44.2 356.0 24.0 \n", + "1 33.4 10.0 0.0 \n", + "2 50.7 305.0 23.0 \n", + "3 33.4 409.0 24.0 \n", + "4 33.3 7.0 0.0 \n", + "5 33.7 430.0 13.0 \n", + "6 48.0 401.0 11.0 \n", + "7 52.4 441.0 22.0 \n", + "8 49.3 24.0 1.0 \n", + "9 15.4 12.0 1.0 \n", + "10 25.2 112.0 2.0 \n", + "11 35.3 153.0 11.0 \n", + "12 32.4 8.0 0.0 \n", + "13 0.0 0.0 0.0 \n", + "14 26.8 308.0 10.0 \n", + "15 69.8 387.0 25.0 \n", + "16 49.6 401.0 22.0 \n", + "17 48.5 247.0 14.0 \n", + "18 50.0 213.0 6.0 \n", + "19 35.8 233.0 14.0 \n", + "20 33.6 356.0 12.0 \n", + "21 28.3 28.0 2.0 \n", + "22 69.8 19.0 1.0 \n", + "23 36.5 148.0 4.0 \n", + "24 34.3 134.0 5.0 \n", + "25 34.7 402.0 17.0 \n", + "26 46.3 107.0 6.0 \n", + "27 35.4 72.0 4.0 \n", + "28 41.1 222.0 16.0 \n", + "29 35.5 441.0 11.0 \n", + "30 41.8 12.0 0.0 \n", + "31 35.4 402.0 24.0 \n", + "32 52.9 126.0 13.0 \n", + "33 41.5 301.0 9.0 \n", + "34 41.8 195.0 9.0 \n", + "35 39.9 264.0 12.0 \n", + "36 57.1 37.0 0.0 \n", + "37 42.6 489.0 28.0 \n", + "38 46.3 40.0 2.0 \n", + "39 51.1 24.0 1.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 7.1 20.0 \n", - "1 8.2 8.0 \n", - "2 6.3 23.0 \n", - "3 3.5 16.0 \n", - "4 3.6 24.0 \n", - "5 4.7 20.0 \n", - "6 4.2 0.0 \n", - "7 2.5 5.0 \n", - "8 7.7 6.0 \n", - "9 0.0 0.0 \n", - "10 2.7 24.0 \n", - "11 6.8 35.0 \n", - "12 5.1 46.0 \n", - "13 5.7 12.0 \n", - "14 3.5 4.0 \n", - "15 4.2 4.0 \n", - "16 2.4 15.0 \n", - "17 7.1 0.0 \n", - "18 2.0 9.0 \n", - "19 2.5 39.0 \n", - "20 5.6 9.0 \n", - "21 7.5 11.0 \n", - "22 1.8 10.0 \n", - "23 6.0 17.0 \n", - "24 8.1 14.0 \n", - "25 2.2 12.0 \n", - "26 5.7 10.0 \n", - "27 5.5 34.0 \n", - "28 5.9 12.0 \n", - "29 4.2 3.0 \n", - "30 4.2 2.0 \n", + "0 6.7 23.0 \n", + "1 0.0 0.0 \n", + "2 7.5 21.0 \n", + "3 5.9 26.0 \n", + "4 0.0 2.0 \n", + "5 3.0 23.0 \n", + "6 2.7 29.0 \n", + "7 5.0 35.0 \n", + "8 4.2 0.0 \n", + "9 8.3 0.0 \n", + "10 1.8 10.0 \n", + "11 7.2 25.0 \n", + "12 0.0 1.0 \n", + "13 0.0 0.0 \n", + "14 3.2 32.0 \n", + "15 6.5 62.0 \n", + "16 5.5 51.0 \n", + "17 5.7 24.0 \n", + "18 2.8 7.0 \n", + "19 6.0 9.0 \n", + "20 3.4 23.0 \n", + "21 7.1 0.0 \n", + "22 5.3 1.0 \n", + "23 2.7 11.0 \n", + "24 3.7 4.0 \n", + "25 4.2 46.0 \n", + "26 5.6 9.0 \n", + "27 5.6 1.0 \n", + "28 7.2 13.0 \n", + "29 2.5 16.0 \n", + "30 0.0 2.0 \n", + "31 6.0 22.0 \n", + "32 10.3 14.0 \n", + "33 3.0 19.0 \n", + "34 4.6 13.0 \n", + "35 4.5 52.0 \n", + "36 0.0 0.0 \n", + "37 5.7 15.0 \n", + "38 5.0 6.0 \n", + "39 4.2 2.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \n", - "0 0.95 14.7 \n", - "1 0.67 12.9 \n", - "2 1.21 15.2 \n", - "3 0.76 13.3 \n", - "4 1.30 15.3 \n", - "5 0.95 12.5 \n", - "6 0.00 9.3 \n", - "7 0.63 14.6 \n", - "8 0.86 12.9 \n", - "9 0.00 19.0 \n", - "10 1.14 17.2 \n", - "11 1.67 16.3 \n", - "12 2.19 16.8 \n", - "13 0.85 14.6 \n", - "14 0.67 15.7 \n", - "15 0.31 13.2 \n", - "16 0.71 14.0 \n", - "17 0.00 9.0 \n", - "18 1.38 17.8 \n", - "19 1.86 18.2 \n", - "20 1.00 14.5 \n", - "21 0.73 14.2 \n", - "22 0.48 13.7 \n", - "23 0.81 13.3 \n", - "24 2.02 19.9 \n", - "25 0.83 15.8 \n", - "26 0.71 13.8 \n", - "27 1.89 18.9 \n", - "28 0.57 10.9 \n", - "29 1.74 15.7 \n", - "30 2.47 17.0 \n", + "0 0.92 13.9 \n", + "1 0.00 0.0 \n", + "2 0.95 14.0 \n", + "3 0.90 14.1 \n", + "4 2.00 25.7 \n", + "5 0.77 12.7 \n", + "6 1.08 14.2 \n", + "7 1.13 13.7 \n", + "8 0.00 9.3 \n", + "9 0.00 2.0 \n", + "10 0.91 16.3 \n", + "11 1.67 18.1 \n", + "12 1.36 22.0 \n", + "13 0.00 19.0 \n", + "14 1.07 17.0 \n", + "15 2.00 17.1 \n", + "16 1.70 15.8 \n", + "17 1.09 15.6 \n", + "18 0.50 13.1 \n", + "19 0.45 12.5 \n", + "20 0.74 14.1 \n", + "21 0.00 9.0 \n", + "22 1.00 12.0 \n", + "23 1.15 15.7 \n", + "24 0.57 11.6 \n", + "25 1.49 16.4 \n", + "26 1.00 14.5 \n", + "27 0.25 10.0 \n", + "28 0.76 14.2 \n", + "29 0.52 12.1 \n", + "30 2.00 26.3 \n", + "31 0.73 13.4 \n", + "32 1.57 16.8 \n", + "33 0.89 15.4 \n", + "34 0.81 14.4 \n", + "35 1.93 18.6 \n", + "36 0.00 11.5 \n", + "37 0.63 10.7 \n", + "38 2.20 17.2 \n", + "39 2.47 17.0 \n", "\n", - "[31 rows x 47 columns]" + "[40 rows x 47 columns]" ] }, "execution_count": 4, @@ -1686,482 +1958,482 @@ " \n", " 0\n", " Atalanta\n", - " 24.0\n", - " 49.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 38.0\n", - " 27.0\n", + " 25.0\n", + " 50.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 50.0\n", + " 32.0\n", " 6.0\n", " 8.0\n", " ...\n", - " 261.0\n", - " 217.0\n", - " 30.0\n", + " 381.0\n", + " 320.0\n", + " 44.0\n", " 6.0\n", - " 1.0\n", - " 1.0\n", - " 1220.0\n", - " 317.0\n", - " 260.0\n", - " 54.9\n", + " 3.0\n", + " 2.0\n", + " 1840.0\n", + " 472.0\n", + " 389.0\n", + " 54.8\n", " \n", " \n", " 1\n", " Bologna\n", - " 25.0\n", - " 51.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", " 27.0\n", - " 20.0\n", + " 53.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 39.0\n", + " 32.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 267.0\n", - " 247.0\n", - " 42.0\n", + " 384.0\n", + " 355.0\n", + " 62.0\n", " 3.0\n", - " 5.0\n", + " 8.0\n", " 2.0\n", - " 1160.0\n", - " 193.0\n", - 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" 24.0\n", - " 61.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 26.0\n", + " 61.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 65.0\n", " 51.0\n", - " 40.0\n", - " 5.0\n", " 6.0\n", + " 7.0\n", " ...\n", - " 205.0\n", - " 272.0\n", - " 37.0\n", - " 4.0\n", + " 311.0\n", + " 381.0\n", + " 53.0\n", + " 5.0\n", " 2.0\n", " 0.0\n", - " 1122.0\n", - " 259.0\n", - " 214.0\n", - " 54.8\n", + " 1657.0\n", + " 389.0\n", + " 322.0\n", + " 54.7\n", " \n", " \n", " 13\n", " Roma\n", - " 26.0\n", - " 48.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 28.0\n", - " 19.0\n", - " 3.0\n", - " 5.0\n", + " 27.0\n", + " 49.2\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 43.0\n", + " 30.0\n", + " 6.0\n", + " 9.0\n", " ...\n", - " 253.0\n", - " 275.0\n", - " 35.0\n", - " 4.0\n", - " 0.0\n", - " 0.0\n", - " 1105.0\n", - " 245.0\n", - " 205.0\n", - " 54.4\n", + " 368.0\n", + " 420.0\n", + " 52.0\n", + " 5.0\n", + " 3.0\n", + " 1.0\n", + " 1631.0\n", + " 426.0\n", + " 331.0\n", + " 56.3\n", " \n", " \n", " 14\n", " Salernitana\n", " 28.0\n", - " 45.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 24.0\n", - " 16.0\n", + " 44.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 251.0\n", - " 218.0\n", - " 42.0\n", + " 379.0\n", + " 341.0\n", + " 57.0\n", " 0.0\n", - " 8.0\n", + " 10.0\n", " 0.0\n", - " 1091.0\n", - " 247.0\n", - " 268.0\n", - " 48.0\n", + " 1614.0\n", + " 427.0\n", + " 448.0\n", + " 48.8\n", " \n", " \n", " 15\n", " Sampdoria\n", + " 37.0\n", + " 47.4\n", " 31.0\n", - " 47.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 10.0\n", - " 8.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 301.0\n", - " 319.0\n", - " 40.0\n", - " 0.0\n", - " 6.0\n", + " 341.0\n", + " 2790.0\n", + " 20.0\n", + " 16.0\n", + " 1.0\n", " 2.0\n", - " 1069.0\n", - " 339.0\n", - " 340.0\n", - " 49.9\n", + " ...\n", + " 426.0\n", + " 432.0\n", + " 47.0\n", + " 1.0\n", + " 8.0\n", + " 2.0\n", + " 1585.0\n", + " 517.0\n", + " 535.0\n", + " 49.1\n", " \n", " \n", " 16\n", " Sassuolo\n", " 29.0\n", - " 48.5\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 48.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", " 24.0\n", - " 18.0\n", - " 4.0\n", - " 5.0\n", + " 6.0\n", + " 7.0\n", " ...\n", - " 219.0\n", - " 259.0\n", - " 19.0\n", - " 4.0\n", + " 336.0\n", + " 355.0\n", + " 26.0\n", + " 5.0\n", " 4.0\n", " 0.0\n", - " 956.0\n", - " 195.0\n", - " 243.0\n", - " 44.5\n", + " 1456.0\n", + " 329.0\n", + " 385.0\n", + " 46.1\n", " \n", " \n", " 17\n", " Spezia\n", - " 33.0\n", - " 45.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 34.0\n", + " 47.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", " 15.0\n", - " 10.0\n", - " 2.0\n", - " 2.0\n", + " 4.0\n", + " 4.0\n", " ...\n", - " 300.0\n", - " 211.0\n", - " 38.0\n", - " 1.0\n", + " 431.0\n", + " 298.0\n", + " 45.0\n", " 2.0\n", - " 0.0\n", - " 1190.0\n", - " 285.0\n", - " 337.0\n", - " 45.8\n", + " 6.0\n", + " 2.0\n", + " 1693.0\n", + " 424.0\n", + " 470.0\n", + " 47.4\n", " \n", " \n", " 18\n", " Torino\n", - " 25.0\n", + " 28.0\n", " 53.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 22.0\n", - " 17.0\n", - " 1.0\n", - " 1.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 32.0\n", + " 26.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 299.0\n", - " 214.0\n", - " 50.0\n", - " 1.0\n", - " 4.0\n", + " 427.0\n", + " 318.0\n", + " 70.0\n", + " 2.0\n", + " 6.0\n", " 0.0\n", - " 1118.0\n", - " 319.0\n", - " 347.0\n", - " 47.9\n", + " 1653.0\n", + " 474.0\n", + " 508.0\n", + " 48.3\n", " \n", " \n", " 19\n", " Udinese\n", - " 25.0\n", - " 50.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", " 27.0\n", - " 23.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 251.0\n", - " 261.0\n", - " 34.0\n", - " 0.0\n", - " 2.0\n", + " 48.1\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 41.0\n", + " 36.0\n", " 1.0\n", - " 1097.0\n", - " 264.0\n", - " 222.0\n", - " 54.3\n", + " 2.0\n", + " ...\n", + " 375.0\n", + " 389.0\n", + " 45.0\n", + " 1.0\n", + " 5.0\n", + " 2.0\n", + " 1594.0\n", + " 390.0\n", + " 326.0\n", + " 54.5\n", " \n", " \n", "\n", @@ -2170,92 +2442,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 Atalanta 24.0 49.0 21.0 231.0 1890.0 \n", - "1 Bologna 25.0 51.9 21.0 231.0 1890.0 \n", - "2 Cremonese 30.0 44.2 21.0 231.0 1890.0 \n", - "3 Empoli 27.0 46.8 21.0 231.0 1890.0 \n", - "4 Fiorentina 28.0 57.3 21.0 231.0 1890.0 \n", - "5 Hellas Verona 34.0 43.1 21.0 231.0 1890.0 \n", - "6 Inter 23.0 54.0 21.0 231.0 1890.0 \n", - "7 Juventus 26.0 49.2 21.0 231.0 1890.0 \n", - "8 Lazio 21.0 51.4 21.0 231.0 1890.0 \n", - "9 Lecce 26.0 42.4 21.0 231.0 1890.0 \n", - "10 Milan 27.0 53.9 21.0 231.0 1890.0 \n", - "11 Monza 29.0 55.9 21.0 231.0 1890.0 \n", - "12 Napoli 24.0 61.4 21.0 231.0 1890.0 \n", - "13 Roma 26.0 48.9 21.0 231.0 1890.0 \n", - "14 Salernitana 28.0 45.1 21.0 231.0 1890.0 \n", - "15 Sampdoria 31.0 47.9 21.0 231.0 1890.0 \n", - "16 Sassuolo 29.0 48.5 21.0 231.0 1890.0 \n", - "17 Spezia 33.0 45.9 21.0 231.0 1890.0 \n", - "18 Torino 25.0 53.0 21.0 231.0 1890.0 \n", - "19 Udinese 25.0 50.0 21.0 231.0 1890.0 \n", + "0 Atalanta 25.0 50.0 31.0 341.0 2790.0 \n", + "1 Bologna 27.0 53.4 31.0 341.0 2790.0 \n", + "2 Cremonese 32.0 43.2 31.0 341.0 2790.0 \n", + "3 Empoli 31.0 47.4 31.0 341.0 2790.0 \n", + "4 Fiorentina 29.0 56.6 31.0 341.0 2790.0 \n", + "5 Hellas Verona 36.0 42.0 31.0 341.0 2790.0 \n", + "6 Inter 24.0 56.2 31.0 341.0 2790.0 \n", + "7 Juventus 29.0 48.6 31.0 341.0 2790.0 \n", + "8 Lazio 22.0 51.9 31.0 341.0 2790.0 \n", + "9 Lecce 29.0 41.7 31.0 341.0 2790.0 \n", + "10 Milan 29.0 54.0 31.0 341.0 2790.0 \n", + "11 Monza 31.0 55.0 31.0 341.0 2790.0 \n", + "12 Napoli 26.0 61.8 31.0 341.0 2790.0 \n", + "13 Roma 27.0 49.2 31.0 341.0 2790.0 \n", + "14 Salernitana 28.0 44.6 31.0 341.0 2790.0 \n", + "15 Sampdoria 37.0 47.4 31.0 341.0 2790.0 \n", + "16 Sassuolo 29.0 48.9 31.0 341.0 2790.0 \n", + "17 Spezia 34.0 47.0 31.0 341.0 2790.0 \n", + "18 Torino 28.0 53.0 31.0 341.0 2790.0 \n", + "19 Udinese 27.0 48.1 31.0 341.0 2790.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 38.0 27.0 6.0 8.0 ... 261.0 217.0 30.0 \n", - "1 27.0 20.0 4.0 4.0 ... 267.0 247.0 42.0 \n", - "2 15.0 7.0 2.0 4.0 ... 276.0 218.0 46.0 \n", - "3 19.0 11.0 0.0 0.0 ... 252.0 256.0 27.0 \n", - "4 23.0 18.0 2.0 4.0 ... 271.0 285.0 30.0 \n", - "5 17.0 14.0 0.0 0.0 ... 312.0 203.0 45.0 \n", - "6 40.0 27.0 2.0 2.0 ... 258.0 250.0 37.0 \n", - "7 33.0 25.0 3.0 4.0 ... 251.0 217.0 33.0 \n", - "8 36.0 26.0 3.0 4.0 ... 218.0 282.0 29.0 \n", - "9 20.0 14.0 1.0 2.0 ... 298.0 264.0 37.0 \n", - "10 35.0 30.0 2.0 2.0 ... 263.0 244.0 36.0 \n", - "11 26.0 16.0 4.0 4.0 ... 270.0 284.0 40.0 \n", - "12 51.0 40.0 5.0 6.0 ... 205.0 272.0 37.0 \n", - "13 28.0 19.0 3.0 5.0 ... 253.0 275.0 35.0 \n", - "14 24.0 16.0 1.0 1.0 ... 251.0 218.0 42.0 \n", - "15 10.0 8.0 0.0 0.0 ... 301.0 319.0 40.0 \n", - "16 24.0 18.0 4.0 5.0 ... 219.0 259.0 19.0 \n", - "17 15.0 10.0 2.0 2.0 ... 300.0 211.0 38.0 \n", - "18 22.0 17.0 1.0 1.0 ... 299.0 214.0 50.0 \n", - "19 27.0 23.0 0.0 0.0 ... 251.0 261.0 34.0 \n", + "0 50.0 32.0 6.0 8.0 ... 381.0 320.0 44.0 \n", + "1 39.0 32.0 4.0 4.0 ... 384.0 355.0 62.0 \n", + "2 27.0 13.0 4.0 6.0 ... 393.0 324.0 58.0 \n", + "3 25.0 13.0 1.0 1.0 ... 354.0 365.0 39.0 \n", + "4 35.0 25.0 4.0 6.0 ... 392.0 437.0 40.0 \n", + "5 24.0 18.0 1.0 1.0 ... 456.0 318.0 62.0 \n", + "6 50.0 35.0 4.0 5.0 ... 366.0 343.0 58.0 \n", + "7 47.0 37.0 3.0 5.0 ... 363.0 334.0 52.0 \n", + "8 48.0 29.0 5.0 7.0 ... 320.0 395.0 42.0 \n", + "9 24.0 17.0 1.0 2.0 ... 452.0 362.0 54.0 \n", + "10 48.0 39.0 3.0 3.0 ... 386.0 359.0 50.0 \n", + "11 36.0 23.0 5.0 5.0 ... 404.0 409.0 59.0 \n", + "12 65.0 51.0 6.0 7.0 ... 311.0 381.0 53.0 \n", + "13 43.0 30.0 6.0 9.0 ... 368.0 420.0 52.0 \n", + "14 35.0 25.0 1.0 1.0 ... 379.0 341.0 57.0 \n", + "15 20.0 16.0 1.0 2.0 ... 426.0 432.0 47.0 \n", + "16 37.0 24.0 6.0 7.0 ... 336.0 355.0 26.0 \n", + "17 24.0 15.0 4.0 4.0 ... 431.0 298.0 45.0 \n", + "18 32.0 26.0 2.0 2.0 ... 427.0 318.0 70.0 \n", + "19 41.0 36.0 1.0 2.0 ... 375.0 389.0 45.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 6.0 1.0 1.0 1220.0 317.0 \n", - "1 3.0 5.0 2.0 1160.0 193.0 \n", - "2 3.0 4.0 1.0 1156.0 296.0 \n", - "3 0.0 3.0 0.0 1001.0 211.0 \n", - "4 2.0 3.0 1.0 1130.0 317.0 \n", - "5 0.0 1.0 1.0 1274.0 422.0 \n", - "6 1.0 2.0 2.0 1068.0 266.0 \n", - "7 4.0 2.0 0.0 1089.0 257.0 \n", - "8 2.0 1.0 0.0 1112.0 216.0 \n", - "9 2.0 3.0 1.0 1159.0 316.0 \n", - "10 1.0 4.0 1.0 1118.0 305.0 \n", - "11 4.0 0.0 1.0 1017.0 241.0 \n", - "12 4.0 2.0 0.0 1122.0 259.0 \n", - "13 4.0 0.0 0.0 1105.0 245.0 \n", - "14 0.0 8.0 0.0 1091.0 247.0 \n", - "15 0.0 6.0 2.0 1069.0 339.0 \n", - "16 4.0 4.0 0.0 956.0 195.0 \n", - "17 1.0 2.0 0.0 1190.0 285.0 \n", - "18 1.0 4.0 0.0 1118.0 319.0 \n", - "19 0.0 2.0 1.0 1097.0 264.0 \n", + "0 6.0 3.0 2.0 1840.0 472.0 \n", + "1 3.0 8.0 2.0 1732.0 306.0 \n", + "2 4.0 5.0 1.0 1708.0 462.0 \n", + "3 1.0 4.0 1.0 1471.0 330.0 \n", + "4 3.0 4.0 2.0 1684.0 504.0 \n", + "5 1.0 1.0 1.0 1861.0 615.0 \n", + "6 4.0 3.0 2.0 1609.0 443.0 \n", + "7 5.0 2.0 0.0 1594.0 390.0 \n", + "8 5.0 1.0 0.0 1631.0 319.0 \n", + "9 2.0 5.0 3.0 1742.0 466.0 \n", + "10 1.0 5.0 1.0 1667.0 456.0 \n", + "11 5.0 1.0 1.0 1508.0 357.0 \n", + "12 5.0 2.0 0.0 1657.0 389.0 \n", + "13 5.0 3.0 1.0 1631.0 426.0 \n", + "14 0.0 10.0 0.0 1614.0 427.0 \n", + "15 1.0 8.0 2.0 1585.0 517.0 \n", + "16 5.0 4.0 0.0 1456.0 329.0 \n", + "17 2.0 6.0 2.0 1693.0 424.0 \n", + "18 2.0 6.0 0.0 1653.0 474.0 \n", + "19 1.0 5.0 2.0 1594.0 390.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 260.0 54.9 \n", - "1 239.0 44.7 \n", - "2 388.0 43.3 \n", - "3 242.0 46.6 \n", - "4 274.0 53.6 \n", - "5 385.0 52.3 \n", - "6 221.0 54.6 \n", - "7 247.0 51.0 \n", - "8 207.0 51.1 \n", - "9 396.0 44.4 \n", - "10 249.0 55.1 \n", - "11 226.0 51.6 \n", - "12 214.0 54.8 \n", - "13 205.0 54.4 \n", - "14 268.0 48.0 \n", - "15 340.0 49.9 \n", - "16 243.0 44.5 \n", - "17 337.0 45.8 \n", - "18 347.0 47.9 \n", - "19 222.0 54.3 \n", + "0 389.0 54.8 \n", + "1 379.0 44.7 \n", + "2 594.0 43.8 \n", + "3 412.0 44.5 \n", + "4 425.0 54.3 \n", + "5 619.0 49.8 \n", + "6 344.0 56.3 \n", + "7 381.0 50.6 \n", + "8 319.0 50.0 \n", + "9 582.0 44.5 \n", + "10 387.0 54.1 \n", + "11 340.0 51.2 \n", + "12 322.0 54.7 \n", + "13 331.0 56.3 \n", + "14 448.0 48.8 \n", + "15 535.0 49.1 \n", + "16 385.0 46.1 \n", + "17 470.0 47.4 \n", + "18 508.0 48.3 \n", + "19 326.0 54.5 \n", "\n", "[20 rows x 152 columns]" ] @@ -2327,482 +2599,482 @@ " \n", " 0\n", " vs Atalanta\n", - " 24.0\n", - " 51.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 23.0\n", - " 17.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 232.0\n", - " 244.0\n", + " 25.0\n", + " 50.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 34.0\n", " 26.0\n", + " 3.0\n", + " 3.0\n", + " ...\n", + " 338.0\n", + " 356.0\n", + " 40.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1261.0\n", - " 260.0\n", - " 317.0\n", - " 45.1\n", + " 1865.0\n", + " 389.0\n", + " 472.0\n", + " 45.2\n", " \n", " \n", " 1\n", " vs Bologna\n", - " 25.0\n", - " 48.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 29.0\n", - " 17.0\n", - " 5.0\n", - " 5.0\n", + " 27.0\n", + " 46.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", + " 22.0\n", + " 7.0\n", + " 8.0\n", " ...\n", - " 262.0\n", - " 254.0\n", - " 36.0\n", - " 3.0\n", + " 376.0\n", + " 365.0\n", + " 52.0\n", + " 5.0\n", " 4.0\n", " 1.0\n", - " 1145.0\n", - " 239.0\n", - " 193.0\n", + " 1687.0\n", + " 379.0\n", + " 306.0\n", " 55.3\n", " \n", " \n", " 2\n", " vs Cremonese\n", - " 30.0\n", - " 55.8\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 36.0\n", - " 23.0\n", - " 4.0\n", - " 4.0\n", + " 32.0\n", + " 56.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 56.0\n", + " 38.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 232.0\n", - " 264.0\n", - " 33.0\n", - " 3.0\n", + " 345.0\n", + " 372.0\n", + " 52.0\n", " 4.0\n", + " 6.0\n", " 0.0\n", - " 1168.0\n", - " 388.0\n", - " 296.0\n", - " 56.7\n", + " 1745.0\n", + " 594.0\n", + " 462.0\n", + " 56.3\n", " \n", " \n", " 3\n", " vs Empoli\n", + " 31.0\n", + " 52.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 39.0\n", " 27.0\n", - " 53.2\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 26.0\n", - " 20.0\n", - " 1.0\n", - " 3.0\n", - " ...\n", - " 270.0\n", - " 244.0\n", - " 35.0\n", " 2.0\n", + " 4.0\n", + " ...\n", + " 384.0\n", + " 341.0\n", + " 60.0\n", + " 2.0\n", + " 1.0\n", " 0.0\n", - " 0.0\n", - " 1104.0\n", - " 242.0\n", - " 211.0\n", - " 53.4\n", + " 1607.0\n", + " 412.0\n", + " 330.0\n", + " 55.5\n", " \n", " \n", " 4\n", " vs Fiorentina\n", - " 28.0\n", - " 42.7\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 27.0\n", - " 19.0\n", - " 3.0\n", - " 3.0\n", - " ...\n", - " 296.0\n", - " 252.0\n", - " 54.0\n", - " 1.0\n", + " 29.0\n", + " 43.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 34.0\n", + " 24.0\n", " 4.0\n", - " 0.0\n", - " 1061.0\n", - " 274.0\n", - " 317.0\n", - " 46.4\n", + " 4.0\n", + " ...\n", + " 460.0\n", + " 371.0\n", + " 78.0\n", + " 2.0\n", + " 6.0\n", + " 2.0\n", + " 1625.0\n", + " 425.0\n", + " 504.0\n", + " 45.7\n", " \n", " \n", " 5\n", " vs Hellas Verona\n", - " 34.0\n", - " 56.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 32.0\n", - " 30.0\n", + " 36.0\n", + " 58.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 43.0\n", + " 39.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 213.0\n", - " 302.0\n", - " 24.0\n", + " 337.0\n", + " 438.0\n", + " 32.0\n", + " 1.0\n", " 1.0\n", - " 0.0\n", " 2.0\n", - " 1173.0\n", - " 385.0\n", - " 422.0\n", - " 47.7\n", + " 1728.0\n", + " 619.0\n", + " 615.0\n", + " 50.2\n", " \n", " \n", " 6\n", " vs Inter\n", - " 23.0\n", - " 46.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", " 24.0\n", - " 21.0\n", - " 2.0\n", - " 2.0\n", + " 43.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 32.0\n", + " 27.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 263.0\n", - " 237.0\n", - " 19.0\n", - " 2.0\n", - " 2.0\n", + " 368.0\n", + " 336.0\n", + " 29.0\n", + " 3.0\n", + " 5.0\n", " 1.0\n", - " 960.0\n", - " 221.0\n", - " 266.0\n", - " 45.4\n", + " 1443.0\n", + " 344.0\n", + " 443.0\n", + " 43.7\n", " \n", " \n", " 7\n", " vs Juventus\n", + " 29.0\n", + " 51.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", " 26.0\n", - " 50.8\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 17.0\n", - " 14.0\n", + " 22.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 229.0\n", - " 232.0\n", - " 27.0\n", + " 348.0\n", + " 328.0\n", + " 39.0\n", " 0.0\n", - " 4.0\n", + " 5.0\n", " 0.0\n", - " 1064.0\n", - " 247.0\n", - " 257.0\n", - " 49.0\n", + " 1556.0\n", + " 381.0\n", + " 390.0\n", + " 49.4\n", " \n", " \n", " 8\n", " vs Lazio\n", + " 22.0\n", + " 48.1\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", " 21.0\n", - " 48.6\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 17.0\n", - " 12.0\n", + " 14.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 294.0\n", - " 206.0\n", - " 44.0\n", + " 421.0\n", + " 304.0\n", + " 55.0\n", " 1.0\n", - " 4.0\n", + " 7.0\n", " 1.0\n", - " 1157.0\n", - " 207.0\n", - " 216.0\n", - " 48.9\n", + " 1677.0\n", + " 319.0\n", + " 319.0\n", + " 50.0\n", " \n", " \n", " 9\n", " vs Lecce\n", - " 26.0\n", - " 57.6\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 23.0\n", - " 16.0\n", - " 3.0\n", - " 3.0\n", + " 29.0\n", + " 58.3\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 275.0\n", - " 279.0\n", - " 45.0\n", - " 3.0\n", + " 378.0\n", + " 425.0\n", + " 57.0\n", + " 4.0\n", " 2.0\n", - " 1.0\n", - " 1141.0\n", - " 396.0\n", - " 316.0\n", - " 55.6\n", + " 2.0\n", + " 1694.0\n", + " 582.0\n", + " 466.0\n", + " 55.5\n", " \n", " \n", " 10\n", " vs Milan\n", - " 27.0\n", - " 46.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", " 29.0\n", - " 22.0\n", - " 3.0\n", + " 46.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 36.0\n", + " 28.0\n", " 4.0\n", + " 5.0\n", " ...\n", - " 255.0\n", - " 251.0\n", - " 25.0\n", - " 4.0\n", - " 2.0\n", - " 2.0\n", - " 1059.0\n", - " 249.0\n", - " 305.0\n", - " 44.9\n", + " 378.0\n", + " 369.0\n", + " 31.0\n", + " 5.0\n", + " 3.0\n", + " 3.0\n", + " 1617.0\n", + " 387.0\n", + " 456.0\n", + " 45.9\n", " \n", " \n", " 11\n", " vs Monza\n", - " 29.0\n", - " 44.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 29.0\n", - " 22.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 303.0\n", - " 263.0\n", - " 37.0\n", - " 0.0\n", - " 4.0\n", + " 31.0\n", + " 45.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 42.0\n", + " 27.0\n", " 1.0\n", - " 1075.0\n", - " 226.0\n", - " 241.0\n", - " 48.4\n", + " 1.0\n", + " ...\n", + " 437.0\n", + " 385.0\n", + " 48.0\n", + " 1.0\n", + " 5.0\n", + " 2.0\n", + " 1608.0\n", + " 340.0\n", + " 357.0\n", + " 48.8\n", " \n", " \n", " 12\n", " vs Napoli\n", - " 24.0\n", - " 38.6\n", + " 26.0\n", + " 38.2\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 15.0\n", - " 10.0\n", + " 13.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 291.0\n", - " 192.0\n", - " 28.0\n", + " 405.0\n", + " 287.0\n", + " 39.0\n", " 1.0\n", - " 5.0\n", - " 0.0\n", - " 1049.0\n", - " 214.0\n", - " 259.0\n", - " 45.2\n", + " 6.0\n", + " 2.0\n", + " 1562.0\n", + " 322.0\n", + " 389.0\n", + " 45.3\n", " \n", " \n", " 13\n", " vs Roma\n", - " 26.0\n", - " 51.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 27.0\n", + " 50.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 28.0\n", " 18.0\n", - " 14.0\n", - " 0.0\n", - " 0.0\n", + " 2.0\n", + " 3.0\n", " ...\n", - " 292.0\n", - " 238.0\n", - " 10.0\n", + " 447.0\n", + " 344.0\n", + " 21.0\n", + " 2.0\n", + " 9.0\n", " 0.0\n", - " 5.0\n", - " 0.0\n", - " 1095.0\n", - " 205.0\n", - " 245.0\n", - " 45.6\n", + " 1621.0\n", + " 331.0\n", + " 426.0\n", + " 43.7\n", " \n", " \n", " 14\n", " vs Salernitana\n", " 28.0\n", - " 54.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 41.0\n", - " 25.0\n", - " 5.0\n", - " 8.0\n", + " 55.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 50.0\n", + " 32.0\n", + " 6.0\n", + " 10.0\n", " ...\n", - " 238.0\n", - " 242.0\n", - " 52.0\n", - " 8.0\n", + " 375.0\n", + " 357.0\n", + " 64.0\n", + " 10.0\n", " 1.0\n", - " 1.0\n", - " 1143.0\n", - " 268.0\n", - " 247.0\n", - " 52.0\n", + " 2.0\n", + " 1709.0\n", + " 448.0\n", + " 427.0\n", + " 51.2\n", " \n", " \n", " 15\n", " vs Sampdoria\n", + " 37.0\n", + " 52.6\n", " 31.0\n", - " 52.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 34.0\n", - " 24.0\n", - " 5.0\n", - " 7.0\n", + " 341.0\n", + " 2790.0\n", + " 50.0\n", + " 36.0\n", + " 6.0\n", + " 9.0\n", " ...\n", - " 328.0\n", - " 288.0\n", - " 56.0\n", - " 4.0\n", + " 449.0\n", + " 408.0\n", + " 75.0\n", + " 6.0\n", + " 2.0\n", " 0.0\n", - " 0.0\n", - " 1126.0\n", - " 340.0\n", - " 339.0\n", - " 50.1\n", + " 1693.0\n", + " 535.0\n", + " 517.0\n", + " 50.9\n", " \n", " \n", " 16\n", " vs Sassuolo\n", " 29.0\n", - " 51.5\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 51.1\n", " 31.0\n", - " 22.0\n", + " 341.0\n", + " 2790.0\n", + " 46.0\n", + " 35.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 275.0\n", - " 200.0\n", - " 69.0\n", + " 384.0\n", + " 307.0\n", + " 94.0\n", " 2.0\n", - " 5.0\n", - " 0.0\n", - " 1088.0\n", - " 243.0\n", - " 195.0\n", - " 55.5\n", + " 7.0\n", + " 1.0\n", + " 1611.0\n", + " 385.0\n", + " 329.0\n", + " 53.9\n", " \n", " \n", " 17\n", " vs Spezia\n", + " 34.0\n", + " 53.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 47.0\n", " 33.0\n", - " 54.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 35.0\n", - " 28.0\n", - " 1.0\n", - " 2.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 225.0\n", - " 279.0\n", - " 50.0\n", - " 1.0\n", + " 321.0\n", + " 396.0\n", + " 67.0\n", + " 4.0\n", + " 4.0\n", " 2.0\n", - " 2.0\n", - " 1225.0\n", - " 337.0\n", - " 285.0\n", - " 54.2\n", + " 1708.0\n", + " 470.0\n", + " 424.0\n", + " 52.6\n", " \n", " \n", " 18\n", " vs Torino\n", - " 25.0\n", + " 28.0\n", " 47.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 22.0\n", - " 14.0\n", - " 3.0\n", - " 4.0\n", - " ...\n", - " 229.0\n", - " 287.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 36.0\n", " 23.0\n", - " 3.0\n", - " 1.0\n", + " 5.0\n", + " 6.0\n", + " ...\n", + " 341.0\n", + " 409.0\n", + " 32.0\n", + " 4.0\n", + " 2.0\n", " 0.0\n", - " 1095.0\n", - " 347.0\n", - " 319.0\n", - " 52.1\n", + " 1597.0\n", + " 508.0\n", + " 474.0\n", + " 51.7\n", " \n", " \n", " 19\n", " vs Udinese\n", - " 25.0\n", - " 50.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 22.0\n", - " 16.0\n", - " 2.0\n", - " 2.0\n", + " 27.0\n", + " 51.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", + " 27.0\n", + " 4.0\n", + " 5.0\n", " ...\n", - " 274.0\n", - " 242.0\n", - " 34.0\n", + " 412.0\n", + " 357.0\n", + " 50.0\n", + " 3.0\n", " 2.0\n", - " 0.0\n", " 1.0\n", - " 1063.0\n", - " 222.0\n", - " 264.0\n", - " 45.7\n", + " 1577.0\n", + " 326.0\n", + " 390.0\n", + " 45.5\n", " \n", " \n", "\n", @@ -2811,92 +3083,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 vs Atalanta 24.0 51.0 21.0 231.0 1890.0 \n", - "1 vs Bologna 25.0 48.1 21.0 231.0 1890.0 \n", - "2 vs Cremonese 30.0 55.8 21.0 231.0 1890.0 \n", - "3 vs Empoli 27.0 53.2 21.0 231.0 1890.0 \n", - "4 vs Fiorentina 28.0 42.7 21.0 231.0 1890.0 \n", - "5 vs Hellas Verona 34.0 56.9 21.0 231.0 1890.0 \n", - "6 vs Inter 23.0 46.0 21.0 231.0 1890.0 \n", - "7 vs Juventus 26.0 50.8 21.0 231.0 1890.0 \n", - "8 vs Lazio 21.0 48.6 21.0 231.0 1890.0 \n", - "9 vs Lecce 26.0 57.6 21.0 231.0 1890.0 \n", - "10 vs Milan 27.0 46.1 21.0 231.0 1890.0 \n", - "11 vs Monza 29.0 44.1 21.0 231.0 1890.0 \n", - "12 vs Napoli 24.0 38.6 21.0 231.0 1890.0 \n", - "13 vs Roma 26.0 51.1 21.0 231.0 1890.0 \n", - "14 vs Salernitana 28.0 54.9 21.0 231.0 1890.0 \n", - "15 vs Sampdoria 31.0 52.1 21.0 231.0 1890.0 \n", - "16 vs Sassuolo 29.0 51.5 21.0 231.0 1890.0 \n", - "17 vs Spezia 33.0 54.1 21.0 231.0 1890.0 \n", - "18 vs Torino 25.0 47.0 21.0 231.0 1890.0 \n", - "19 vs Udinese 25.0 50.0 21.0 231.0 1890.0 \n", + "0 vs Atalanta 25.0 50.0 31.0 341.0 2790.0 \n", + "1 vs Bologna 27.0 46.6 31.0 341.0 2790.0 \n", + "2 vs Cremonese 32.0 56.8 31.0 341.0 2790.0 \n", + "3 vs Empoli 31.0 52.6 31.0 341.0 2790.0 \n", + "4 vs Fiorentina 29.0 43.4 31.0 341.0 2790.0 \n", + "5 vs Hellas Verona 36.0 58.0 31.0 341.0 2790.0 \n", + "6 vs Inter 24.0 43.8 31.0 341.0 2790.0 \n", + "7 vs Juventus 29.0 51.4 31.0 341.0 2790.0 \n", + "8 vs Lazio 22.0 48.1 31.0 341.0 2790.0 \n", + "9 vs Lecce 29.0 58.3 31.0 341.0 2790.0 \n", + "10 vs Milan 29.0 46.0 31.0 341.0 2790.0 \n", + "11 vs Monza 31.0 45.0 31.0 341.0 2790.0 \n", + "12 vs Napoli 26.0 38.2 31.0 341.0 2790.0 \n", + "13 vs Roma 27.0 50.8 31.0 341.0 2790.0 \n", + "14 vs Salernitana 28.0 55.4 31.0 341.0 2790.0 \n", + "15 vs Sampdoria 37.0 52.6 31.0 341.0 2790.0 \n", + "16 vs Sassuolo 29.0 51.1 31.0 341.0 2790.0 \n", + "17 vs Spezia 34.0 53.0 31.0 341.0 2790.0 \n", + "18 vs Torino 28.0 47.0 31.0 341.0 2790.0 \n", + "19 vs Udinese 27.0 51.9 31.0 341.0 2790.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 23.0 17.0 1.0 1.0 ... 232.0 244.0 26.0 \n", - "1 29.0 17.0 5.0 5.0 ... 262.0 254.0 36.0 \n", - "2 36.0 23.0 4.0 4.0 ... 232.0 264.0 33.0 \n", - "3 26.0 20.0 1.0 3.0 ... 270.0 244.0 35.0 \n", - "4 27.0 19.0 3.0 3.0 ... 296.0 252.0 54.0 \n", - "5 32.0 30.0 0.0 1.0 ... 213.0 302.0 24.0 \n", - "6 24.0 21.0 2.0 2.0 ... 263.0 237.0 19.0 \n", - "7 17.0 14.0 1.0 2.0 ... 229.0 232.0 27.0 \n", - "8 17.0 12.0 1.0 1.0 ... 294.0 206.0 44.0 \n", - "9 23.0 16.0 3.0 3.0 ... 275.0 279.0 45.0 \n", - "10 29.0 22.0 3.0 4.0 ... 255.0 251.0 25.0 \n", - "11 29.0 22.0 0.0 0.0 ... 303.0 263.0 37.0 \n", - "12 15.0 10.0 1.0 2.0 ... 291.0 192.0 28.0 \n", - "13 18.0 14.0 0.0 0.0 ... 292.0 238.0 10.0 \n", - "14 41.0 25.0 5.0 8.0 ... 238.0 242.0 52.0 \n", - "15 34.0 24.0 5.0 7.0 ... 328.0 288.0 56.0 \n", - "16 31.0 22.0 4.0 4.0 ... 275.0 200.0 69.0 \n", - "17 35.0 28.0 1.0 2.0 ... 225.0 279.0 50.0 \n", - "18 22.0 14.0 3.0 4.0 ... 229.0 287.0 23.0 \n", - "19 22.0 16.0 2.0 2.0 ... 274.0 242.0 34.0 \n", + "0 34.0 26.0 3.0 3.0 ... 338.0 356.0 40.0 \n", + "1 37.0 22.0 7.0 8.0 ... 376.0 365.0 52.0 \n", + "2 56.0 38.0 5.0 5.0 ... 345.0 372.0 52.0 \n", + "3 39.0 27.0 2.0 4.0 ... 384.0 341.0 60.0 \n", + "4 34.0 24.0 4.0 4.0 ... 460.0 371.0 78.0 \n", + "5 43.0 39.0 0.0 1.0 ... 337.0 438.0 32.0 \n", + "6 32.0 27.0 3.0 3.0 ... 368.0 336.0 29.0 \n", + "7 26.0 22.0 1.0 2.0 ... 348.0 328.0 39.0 \n", + "8 21.0 14.0 1.0 1.0 ... 421.0 304.0 55.0 \n", + "9 35.0 25.0 5.0 5.0 ... 378.0 425.0 57.0 \n", + "10 36.0 28.0 4.0 5.0 ... 378.0 369.0 31.0 \n", + "11 42.0 27.0 1.0 1.0 ... 437.0 385.0 48.0 \n", + "12 21.0 13.0 1.0 2.0 ... 405.0 287.0 39.0 \n", + "13 28.0 18.0 2.0 3.0 ... 447.0 344.0 21.0 \n", + "14 50.0 32.0 6.0 10.0 ... 375.0 357.0 64.0 \n", + "15 50.0 36.0 6.0 9.0 ... 449.0 408.0 75.0 \n", + "16 46.0 35.0 4.0 4.0 ... 384.0 307.0 94.0 \n", + "17 47.0 33.0 4.0 6.0 ... 321.0 396.0 67.0 \n", + "18 36.0 23.0 5.0 6.0 ... 341.0 409.0 32.0 \n", + "19 37.0 27.0 4.0 5.0 ... 412.0 357.0 50.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 1.0 8.0 1.0 1261.0 260.0 \n", - "1 3.0 4.0 1.0 1145.0 239.0 \n", - "2 3.0 4.0 0.0 1168.0 388.0 \n", - "3 2.0 0.0 0.0 1104.0 242.0 \n", - "4 1.0 4.0 0.0 1061.0 274.0 \n", - "5 1.0 0.0 2.0 1173.0 385.0 \n", - "6 2.0 2.0 1.0 960.0 221.0 \n", - "7 0.0 4.0 0.0 1064.0 247.0 \n", - "8 1.0 4.0 1.0 1157.0 207.0 \n", - "9 3.0 2.0 1.0 1141.0 396.0 \n", - "10 4.0 2.0 2.0 1059.0 249.0 \n", - "11 0.0 4.0 1.0 1075.0 226.0 \n", - "12 1.0 5.0 0.0 1049.0 214.0 \n", - "13 0.0 5.0 0.0 1095.0 205.0 \n", - "14 8.0 1.0 1.0 1143.0 268.0 \n", - "15 4.0 0.0 0.0 1126.0 340.0 \n", - "16 2.0 5.0 0.0 1088.0 243.0 \n", - "17 1.0 2.0 2.0 1225.0 337.0 \n", - "18 3.0 1.0 0.0 1095.0 347.0 \n", - "19 2.0 0.0 1.0 1063.0 222.0 \n", + "0 1.0 8.0 1.0 1865.0 389.0 \n", + "1 5.0 4.0 1.0 1687.0 379.0 \n", + "2 4.0 6.0 0.0 1745.0 594.0 \n", + "3 2.0 1.0 0.0 1607.0 412.0 \n", + "4 2.0 6.0 2.0 1625.0 425.0 \n", + "5 1.0 1.0 2.0 1728.0 619.0 \n", + "6 3.0 5.0 1.0 1443.0 344.0 \n", + "7 0.0 5.0 0.0 1556.0 381.0 \n", + "8 1.0 7.0 1.0 1677.0 319.0 \n", + "9 4.0 2.0 2.0 1694.0 582.0 \n", + "10 5.0 3.0 3.0 1617.0 387.0 \n", + "11 1.0 5.0 2.0 1608.0 340.0 \n", + "12 1.0 6.0 2.0 1562.0 322.0 \n", + "13 2.0 9.0 0.0 1621.0 331.0 \n", + "14 10.0 1.0 2.0 1709.0 448.0 \n", + "15 6.0 2.0 0.0 1693.0 535.0 \n", + "16 2.0 7.0 1.0 1611.0 385.0 \n", + "17 4.0 4.0 2.0 1708.0 470.0 \n", + "18 4.0 2.0 0.0 1597.0 508.0 \n", + "19 3.0 2.0 1.0 1577.0 326.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 317.0 45.1 \n", - "1 193.0 55.3 \n", - "2 296.0 56.7 \n", - "3 211.0 53.4 \n", - "4 317.0 46.4 \n", - "5 422.0 47.7 \n", - "6 266.0 45.4 \n", - "7 257.0 49.0 \n", - "8 216.0 48.9 \n", - "9 316.0 55.6 \n", - "10 305.0 44.9 \n", - "11 241.0 48.4 \n", - "12 259.0 45.2 \n", - "13 245.0 45.6 \n", - "14 247.0 52.0 \n", - "15 339.0 50.1 \n", - "16 195.0 55.5 \n", - "17 285.0 54.2 \n", - "18 319.0 52.1 \n", - "19 264.0 45.7 \n", + "0 472.0 45.2 \n", + "1 306.0 55.3 \n", + "2 462.0 56.3 \n", + "3 330.0 55.5 \n", + "4 504.0 45.7 \n", + "5 615.0 50.2 \n", + "6 443.0 43.7 \n", + "7 390.0 49.4 \n", + "8 319.0 50.0 \n", + "9 466.0 55.5 \n", + "10 456.0 45.9 \n", + "11 357.0 48.8 \n", + "12 389.0 45.3 \n", + "13 426.0 43.7 \n", + "14 427.0 51.2 \n", + "15 517.0 50.9 \n", + "16 329.0 53.9 \n", + "17 424.0 52.6 \n", + "18 474.0 51.7 \n", + "19 390.0 45.5 \n", "\n", "[20 rows x 152 columns]" ] @@ -3085,385 +3357,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "2e1370cd", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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playernationalitypositionteamagebirth_yeargamesgames_startsminutesgoals...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Tammy Abrahameng ENGFWRoma23199737.036.03084.017.0...38.049.017.01.01.00.068.078.080.049.4
1Francesco Acerbiit ITADFLazio33198830.029.02536.04.0...18.016.04.01.00.00.0162.069.043.061.6
2Michel Aebischerch SUIMFBologna24199712.04.0443.00.0...13.04.00.00.00.00.027.05.04.055.6
3Felix Afena-Gyangh GHAFW,MFRoma18200317.06.0668.02.0...15.024.04.00.00.00.031.011.015.042.3
4Kevin Agudeloco COLMF,FWSpezia22199823.012.01237.03.0...36.024.00.00.00.00.067.015.014.051.7
..................................................................
627Nadir Zorteait ITADF,MFSalernitana22199929.013.01410.01.0...14.06.00.00.00.00.072.08.017.032.0
628Petar Zovkoba BIHGKSpezia1920021.00.029.00.0...0.01.00.00.00.00.00.00.00.00.0
629Szymon Żurkowskipl POLMFEmpoli23199735.029.02307.06.0...41.067.00.00.01.00.0162.024.032.042.9
630Milan Đurićba BIHFWSalernitana31199033.023.02165.05.0...24.045.06.01.00.00.040.0242.083.074.5
631Filip Đuričićrs SRBMF,FWSassuolo29199212.09.0671.02.0...10.014.02.00.00.00.034.03.05.037.5
\n", - "

632 rows × 115 columns

\n", - "
" - ], - "text/plain": [ - " player nationality position team age birth_year games \\\n", - "0 Tammy Abraham eng ENG FW Roma 23 1997 37.0 \n", - "1 Francesco Acerbi it ITA DF Lazio 33 1988 30.0 \n", - "2 Michel Aebischer ch SUI MF Bologna 24 1997 12.0 \n", - "3 Felix Afena-Gyan gh GHA FW,MF Roma 18 2003 17.0 \n", - "4 Kevin Agudelo co COL MF,FW Spezia 22 1998 23.0 \n", - ".. ... ... ... ... .. ... ... \n", - "627 Nadir Zortea it ITA DF,MF Salernitana 22 1999 29.0 \n", - "628 Petar Zovko ba BIH GK Spezia 19 2002 1.0 \n", - "629 Szymon Żurkowski pl POL MF Empoli 23 1997 35.0 \n", - "630 Milan Đurić ba BIH FW Salernitana 31 1990 33.0 \n", - "631 Filip Đuričić rs SRB MF,FW Sassuolo 29 1992 12.0 \n", - "\n", - " games_starts minutes goals ... fouls fouled offsides pens_won \\\n", - "0 36.0 3084.0 17.0 ... 38.0 49.0 17.0 1.0 \n", - "1 29.0 2536.0 4.0 ... 18.0 16.0 4.0 1.0 \n", - "2 4.0 443.0 0.0 ... 13.0 4.0 0.0 0.0 \n", - "3 6.0 668.0 2.0 ... 15.0 24.0 4.0 0.0 \n", - "4 12.0 1237.0 3.0 ... 36.0 24.0 0.0 0.0 \n", - ".. ... ... ... ... ... ... ... ... \n", - "627 13.0 1410.0 1.0 ... 14.0 6.0 0.0 0.0 \n", - "628 0.0 29.0 0.0 ... 0.0 1.0 0.0 0.0 \n", - "629 29.0 2307.0 6.0 ... 41.0 67.0 0.0 0.0 \n", - "630 23.0 2165.0 5.0 ... 24.0 45.0 6.0 1.0 \n", - "631 9.0 671.0 2.0 ... 10.0 14.0 2.0 0.0 \n", - "\n", - " pens_conceded own_goals ball_recoveries aerials_won aerials_lost \\\n", - "0 1.0 0.0 68.0 78.0 80.0 \n", - "1 0.0 0.0 162.0 69.0 43.0 \n", - "2 0.0 0.0 27.0 5.0 4.0 \n", - "3 0.0 0.0 31.0 11.0 15.0 \n", - "4 0.0 0.0 67.0 15.0 14.0 \n", - ".. ... ... ... ... ... \n", - "627 0.0 0.0 72.0 8.0 17.0 \n", - "628 0.0 0.0 0.0 0.0 0.0 \n", - "629 1.0 0.0 162.0 24.0 32.0 \n", - "630 0.0 0.0 40.0 242.0 83.0 \n", - "631 0.0 0.0 34.0 3.0 5.0 \n", - "\n", - " aerials_won_pct \n", - "0 49.4 \n", - "1 61.6 \n", - "2 55.6 \n", - "3 42.3 \n", - "4 51.7 \n", - ".. ... \n", - "627 32.0 \n", - "628 0.0 \n", - "629 42.9 \n", - "630 74.5 \n", - "631 37.5 \n", - "\n", - "[632 rows x 115 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_outfield = get_outfield_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats')\n", "\n", diff --git a/2_votes_dataset_creation.ipynb b/2_votes_dataset_creation.ipynb index ccb530c..a68acd2 100644 --- a/2_votes_dataset_creation.ipynb +++ b/2_votes_dataset_creation.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0d4a330d", "metadata": {}, "outputs": [ @@ -189,27 +189,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loaded votes for matchday 1\n", - "loaded votes for matchday 2\n", - "loaded votes for matchday 3\n", - "loaded votes for matchday 4\n", - "loaded votes for matchday 5\n", - "loaded votes for matchday 6\n", - "loaded votes for matchday 7\n", - "loaded votes for matchday 8\n", - "loaded votes for matchday 9\n", - "loaded votes for matchday 10\n", - "loaded votes for matchday 11\n", - "loaded votes for matchday 12\n", - "loaded votes for matchday 13\n", - "loaded votes for matchday 14\n", - "loaded votes for matchday 15\n", - "loaded votes for matchday 16\n", - "loaded votes for matchday 17\n", - "loaded votes for matchday 18\n", - "loaded votes for matchday 19\n", - "loaded votes for matchday 20\n", - "loaded votes for matchday 21\n" + "loaded votes for matchday 1\n" ] } ], @@ -391,25 +371,51 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", + " 7663\n", + " 27\n", + " Lasagna\n", " Verona\n", - " Lazio\n", - " 1\n", + " Sampdoria\n", + " 0\n", " 6.0\n", " 0\n", " 0\n", - " 0.5\n", + " 0.0\n", + " 6.0\n", + " \n", + " \n", + " 7664\n", + " 27\n", + " Gaich\n", + " Verona\n", + " Sampdoria\n", + " 0\n", + " 6.5\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.5\n", + " \n", + " \n", + " 7665\n", + " 27\n", + " Braaf\n", + " Verona\n", + " Sampdoria\n", + " 0\n", + " 5.5\n", + " 0\n", + " 0\n", + " 0.0\n", " 5.5\n", " \n", " \n", - " 5988\n", - " 21\n", - " Tameze\n", + " 7666\n", + " 27\n", + " Kallon\n", " Verona\n", - " Lazio\n", - " 1\n", + " Sampdoria\n", + " 0\n", " 6.0\n", " 0\n", " 0\n", @@ -417,47 +423,21 @@ " 6.0\n", " \n", " \n", - " 5989\n", - " 21\n", - " Lasagna\n", + " 7667\n", + " 27\n", + " Djuric\n", " Verona\n", - " Lazio\n", - " 1\n", + " Sampdoria\n", + " 0\n", " 6.0\n", " 0\n", " 0\n", " 0.0\n", " 6.0\n", " \n", - " \n", - " 5990\n", - " 21\n", - " Gaich\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 6.0\n", - " \n", - " \n", - " 5991\n", - " 21\n", - " Ngonge\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 7.0\n", - " 1\n", - " 0\n", - " 0.0\n", - " 10.0\n", - " \n", " \n", "\n", - "

5992 rows × 10 columns

\n", + "

7668 rows × 10 columns

\n", "" ], "text/plain": [ @@ -468,11 +448,11 @@ "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + "7663 27 Lasagna Verona Sampdoria 0 6.0 0 0 \n", + "7664 27 Gaich Verona Sampdoria 0 6.5 0 0 \n", + "7665 27 Braaf Verona Sampdoria 0 5.5 0 0 \n", + "7666 27 Kallon Verona Sampdoria 0 6.0 0 0 \n", + "7667 27 Djuric Verona Sampdoria 0 6.0 0 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -481,13 +461,13 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \n", - "5987 0.5 5.5 \n", - "5988 0.0 6.0 \n", - "5989 0.0 6.0 \n", - "5990 0.0 6.0 \n", - "5991 0.0 10.0 \n", + "7663 0.0 6.0 \n", + "7664 0.0 6.5 \n", + "7665 0.0 5.5 \n", + "7666 0.0 6.0 \n", + "7667 0.0 6.0 \n", "\n", - "[5992 rows x 10 columns]" + "[7668 rows x 10 columns]" ] }, "execution_count": 5, diff --git a/3_players_dataset_creation.ipynb b/3_players_dataset_creation.ipynb index bca356e..21ac487 100644 --- a/3_players_dataset_creation.ipynb +++ b/3_players_dataset_creation.ipynb @@ -80,28 +80,28 @@ " \n", " \n", " 0\n", + " James Abankwah\n", + " Udinese\n", + " \n", + " \n", + " 1\n", " Oliver Abildgaard\n", " Hellas Verona\n", " \n", " \n", - " 1\n", + " 2\n", " Tammy Abraham\n", " Roma\n", " \n", " \n", - " 2\n", - " Francesco Acerbi\n", - " Inter\n", - " \n", - " \n", " 3\n", - " Yacine Adli\n", - " Milan\n", + " Christian Acella\n", + " Cremonese\n", " \n", " \n", " 4\n", - " Michel Aebischer\n", - " Bologna\n", + " Francesco Acerbi\n", + " Inter\n", " \n", " \n", " ...\n", @@ -109,50 +109,50 @@ " ...\n", " \n", " \n", - " 567\n", - " Ciprian Tătărușanu\n", - " Milan\n", - " \n", - " \n", - " 568\n", + " 615\n", " Pietro Terracciano\n", " Fiorentina\n", " \n", " \n", - " 569\n", + " 616\n", + " Martin Turk\n", + " Sampdoria\n", + " \n", + " \n", + " 617\n", " Guglielmo Vicario\n", " Empoli\n", " \n", " \n", - " 570\n", + " 618\n", " Jeroen Zoet\n", " Spezia\n", " \n", " \n", - " 571\n", + " 619\n", " Petar Zovko\n", " Spezia\n", " \n", " \n", "\n", - "

572 rows × 2 columns

\n", + "

620 rows × 2 columns

\n", "" ], "text/plain": [ " player team\n", - "0 Oliver Abildgaard Hellas Verona\n", - "1 Tammy Abraham Roma\n", - "2 Francesco Acerbi Inter\n", - "3 Yacine Adli Milan\n", - "4 Michel Aebischer Bologna\n", + "0 James Abankwah Udinese\n", + "1 Oliver Abildgaard Hellas Verona\n", + "2 Tammy Abraham Roma\n", + "3 Christian Acella Cremonese\n", + "4 Francesco Acerbi Inter\n", ".. ... ...\n", - "567 Ciprian Tătărușanu Milan\n", - "568 Pietro Terracciano Fiorentina\n", - "569 Guglielmo Vicario Empoli\n", - "570 Jeroen Zoet Spezia\n", - "571 Petar Zovko Spezia\n", + "615 Pietro Terracciano Fiorentina\n", + "616 Martin Turk Sampdoria\n", + "617 Guglielmo Vicario Empoli\n", + "618 Jeroen Zoet Spezia\n", + "619 Petar Zovko Spezia\n", "\n", - "[572 rows x 2 columns]" + "[620 rows x 2 columns]" ] }, "execution_count": 3, @@ -212,578 +212,626 @@ "output_type": "stream", "text": [ " player surname\n", - "0 Oliver Abildgaard Abildgaard\n", - "1 Tammy Abraham Abraham\n", - "2 Francesco Acerbi Acerbi\n", - "3 Yacine Adli Adli\n", - "4 Michel Aebischer Aebischer\n", - "5 Felix Afena-Gyan Afena-Gyan\n", - "6 Kevin Agudelo Agudelo\n", - "7 Ola Aina Aina\n", - "8 Emanuel Aiwum Aiwum\n", - "9 Jean-Daniel Akpa-Akpro Akpa-Akpro\n", - "10 Luis Alberto Alberto\n", - "11 Agustín Álvarez Martínez Martinez\n", - "12 Kelvin Amian Amian\n", - "13 Bruno Amione Amione\n", - "14 Bruno Amione Amione\n", - "15 Ethan Ampadu Ampadu\n", - "16 Sofyan Amrabat Amrabat\n", - "17 Felipe Anderson Anderson\n", - "18 Janis Antiste Antiste\n", - "19 Marcos Antônio Antonio\n", - "20 Valentin Antov Antov\n", - "21 Marko Arnautović Arnautovic\n", - "22 Tolgay Arslan Arslan\n", - "23 Arthur Arthur\n", - "24 Santiago Ascacíbar Ascacibar\n", - "25 Kristoffer Askildsen Askildsen\n", - "26 Kristjan Asllani Asllani\n", - "27 Emil Audero Audero\n", - "28 Tommaso Augello Augello\n", - "29 Kaan Ayhan Ayhan\n", - "30 Jaime Báez Baez\n", - "31 Nedim Bajrami Bajrami\n", - "32 Nedim Bajrami Bajrami\n", - "33 Tommaso Baldanzi Baldanzi\n", - "34 Fodé Ballo-Touré Ballo-Toure\n", - "35 Lameck Banda Banda\n", - "36 Filippo Bandinelli Bandinelli\n", - "37 Antonín Barák Barak\n", - "38 Antonín Barák Barak\n", - "39 Andrea Barberis Barberis\n", - "40 Nicolò Barella Barella\n", - "41 Musa Barrow Barrow\n", - "42 Federico Baschirotto Baschirotto\n", - "43 Toma Bašić Basic\n", - "44 Alessandro Bastoni Bastoni\n", - "45 Simone Bastoni Bastoni\n", - "46 Brian Bayeye Bayeye\n", - "47 Rodrigo Becão Becao\n", - "48 Julius Beck Beck\n", - "49 Raoul Bellanova Bellanova\n", - "50 Andrea Belotti Belotti\n", - "51 Marco Benassi Benassi\n", - "52 Marco Benassi Benassi\n", - "53 Ismaël Bennacer Bennacer\n", - "54 Domenico Berardi Berardi\n", - "55 Bartosz Bereszyński Bereszynski\n", - "56 Beto Beto\n", - "57 Matteo Bianchetti Bianchetti\n", - "58 Alessandro Bianco Bianco\n", - "59 Jaka Bijol Bijol\n", - "60 Cristiano Biraghi Biraghi\n", - "61 Samuele Birindelli Birindelli\n", - "62 Kristijan Bistrović Bistrovic\n", - "63 Alexis Blin Blin\n", - "64 Jeremie Boga Boga\n", - "65 Emil Bohinen Bohinen\n", - "66 Giacomo Bonaventura Bonaventura\n", - "67 Federico Bonazzoli Bonazzoli\n", - "68 Warren Bondo Bondo\n", - "69 Kevin Bonifazi Bonifazi\n", - "70 Leonardo Bonucci Bonucci\n", - "71 Erik Botheim Botheim\n", - "72 Mehdi Bourabia Bourabia\n", - "73 Edoardo Bove Bove\n", - "74 Jayden Braaf Braaf\n", - "75 Domagoj Bradarić Bradaric\n", - "76 Josip Brekalo Brekalo\n", - "77 Gleison Bremer Bremer\n", - "78 Dylan Bronn Bronn\n", - "79 Marcelo Brozović Brozovic\n", - "80 Cristian Buonaiuto Buonaiuto\n", - "81 Alessandro Buongiorno Buongiorno\n", - "82 Juan Cabal Cabal\n", - "83 Liberato Cacace Cacace\n", - "84 Davide Calabria Calabria\n", - "85 Mattia Caldara Caldara\n", - "86 Luca Caldirola Caldirola\n", - "87 Hakan Çalhanoğlu Calhanoglu\n", - "88 Mohamed Camara Camara\n", - "89 Nicolò Cambiaghi Cambiaghi\n", - "90 Andrea Cambiaso Cambiaso\n", - "91 Matteo Cancellieri Cancellieri\n", - "92 Antonio Candreva Candreva\n", - "93 Gianluca Caprari Caprari\n", - "94 Francesco Caputo Caputo\n", - "95 Francesco Caputo Caputo\n", - "96 Andrea Carboni Carboni\n", - "97 Valentin Carboni Carboni\n", - "98 Carlos Carlos\n", - "99 Marco Carnesecchi Carnesecchi\n", - "100 Nicolò Casale Casale\n", - "101 Michele Castagnetti Castagnetti\n", - "102 Gaetano Castrovilli Castrovilli\n", - "103 Danilo Cataldi Cataldi\n", - "104 Federico Ceccherini Ceccherini\n", - "105 Assan Ceesay Ceesay\n", - "106 Emil Ceide Ceide\n", - "107 Zeki Çelik Celik\n", - "108 Federico Chiesa Chiesa\n", - "109 Vlad Chiricheș Chiriches\n", - "110 Daniel Ciofani Ciofani\n", - "111 Tio Cipot Cipot\n", - "112 Patrick Ciurria Ciurria\n", - "113 Omar Colley Colley\n", - "114 Lorenzo Colombo Colombo\n", - "115 Andrea Colpani Colpani\n", - "116 Andrea Consigli Consigli\n", - "117 Andrea Conti Conti\n", - "118 Diego Coppola Coppola\n", - "119 Joaquín Correa Correa\n", - "120 Alessandro Cortinovis Cortinovis\n", - "121 Lassana Coulibaly Coulibaly\n", - "122 Bryan Cristante Cristante\n", - "123 Domen Črnigoj Crnigoj\n", - "124 Juan Cuadrado Cuadrado\n", - "125 Mickaël Cuisance Cuisance\n", - "126 Marco D'Alessandro DAlessandro\n", - "127 Danilo D'Ambrosio DAmbrosio\n", - "128 Luca D'Andrea DAndrea\n", - "129 Flavius Daniliuc Daniliuc\n", - "130 Danilo Danilo\n", - "131 Matteo Darmian Darmian\n", - "132 Paweł Dawidowicz Dawidowicz\n", - "133 Charles De Ketelaere Ketelaere\n", - "134 Manuel De Luca Luca\n", - "135 Mattia De Sciglio Sciglio\n", - "136 Lorenzo De Silvestri Silvestri\n", - "137 Koni De Winter Winter\n", - "138 Grégoire Defrel Defrel\n", - "139 Duccio Degli Innocenti Innocenti\n", - "140 Merih Demiral Demiral\n", - "141 Diego Demme Demme\n", - "142 Fabio Depaoli Depaoli\n", - "143 Fabio Depaoli Depaoli\n", - "144 Kastriot Dermaku Dermaku\n", - "145 Cyriel Dessers Dessers\n", - "146 Sergiño Dest Dest\n", - "147 Mattia Destro Destro\n", - "148 Gerard Deulofeu Deulofeu\n", - "149 Samuel Di Carmine Carmine\n", - "150 Federico Di Francesco Francesco\n", - "151 Michele Di Gregorio Gregorio\n", - "152 Giovanni Di Lorenzo Lorenzo\n", - "153 Ángel Di María Maria\n", - "154 Boulaye Dia Dia\n", - "155 Brahim Díaz Diaz\n", - "156 Federico Dimarco Dimarco\n", - "157 Koffi Djidji Djidji\n", - "158 Berat Djimsiti Djimsiti\n", - "159 Dodô Dodo\n", - "160 Josh Doig Doig\n", - "161 Nicolás Domínguez Dominguez\n", - "162 Giulio Donati Donati\n", - "163 Bartłomiej Drągowski Dragowski\n", - "164 Ondrej Duda Duda\n", - "165 Denzel Dumfries Dumfries\n", - "166 Alfred Duncan Duncan\n", - "167 Paulo Dybala Dybala\n", - "168 Edin Džeko Dzeko\n", - "169 Festy Ebosele Ebosele\n", - "170 Enzo Ebosse Ebosse\n", - "171 Tyronne Ebuehi Ebuehi\n", - "172 Éderson Ederson\n", - "173 Kingsley Ehizibue Ehizibue\n", - "174 Albin Ekdal Ekdal\n", - "175 Emmanuel Ekong Ekong\n", - "176 Mikael Ellertsson Ellertsson\n", - "177 Elif Elmas Elmas\n", - "178 Martin Erlic Erlic\n", - "179 Gonzalo Escalante Escalante\n", - "180 Salvatore Esposito Esposito\n", - "181 Nicolò Fagioli Fagioli\n", - "182 Wladimiro Falcone Falcone\n", - "183 Davide Faraoni Faraoni\n", - "184 Federico Fazio Fazio\n", - "185 Jacopo Fazzini Fazzini\n", - "186 Lewis Ferguson Ferguson\n", - "187 Alex Ferrari Ferrari\n", - "188 Alex Ferrari Ferrari\n", - "189 Salvador Ferrer Ferrer\n", - "190 Alessandro Florenzi Florenzi\n", - "191 Davide Frattesi Frattesi\n", - "192 Matteo Gabbia Gabbia\n", - "193 Manolo Gabbiadini Gabbiadini\n", - "194 Gianluca Gaetano Gaetano\n", - "195 Roberto Gagliardini Gagliardini\n", - "196 Adolfo Gaich Gaich\n", - "197 Pablo Galdames Millán Millan\n", - "198 Antonino Gallo Gallo\n", - "199 Federico Gatti Gatti\n", - "200 Valentin Gendrey Gendrey\n", - "201 Paolo Ghiglione Ghiglione\n", - "202 Mario Gila Gila\n", - "203 Olivier Giroud Giroud\n", - "204 Pierluigi Gollini Gollini\n", - "205 Joan Gonzàlez Gonzalez\n", - "206 Nicolás González Gonzalez\n", - "207 Robin Gosens Gosens\n", - "208 Alberto Grassi Grassi\n", - "209 Koray Günter Gunter\n", - "210 Emmanuel Gyasi Gyasi\n", - "211 Norbert Gyömbér Gyomber\n", - "212 Christian Gytkjær Gytkjr\n", - "213 Nicolas Haas Haas\n", - "214 Samir Handanović Handanovic\n", - "215 Abdou Harroui Harroui\n", - "216 Hans Hateboer Hateboer\n", - "217 Liam Henderson Henderson\n", - "218 Jack Hendry Hendry\n", - "219 Matheus Henrique Henrique\n", - "220 Thomas Henry Henry\n", - "221 Theo Hernández Hernandez\n", - "222 Isak Hien Hien\n", - "223 Morten Hjulmand Hjulmand\n", - "224 Emil Holm Holm\n", - "225 Martin Hongla Hongla\n", - "226 Petko Hristov Hristov\n", - "227 Ajdin Hrustic Hrustic\n", - "228 Elseid Hysaj Hysaj\n", - "229 Rasmus Højlund Hjlund\n", - "230 Roger Ibanez Ibanez\n", - "231 Igor Igor\n", - "232 Jonathan Ikone Ikone\n", - "233 Ivan Ilić Ilic\n", - "234 Samuel Iling-Junior Iling-Junior\n", - "235 Emirhan İlkhan Ilkhan\n", - "236 Ciro Immobile Immobile\n", - "237 Ardian Ismajli Ismajli\n", - "238 Armando Izzo Izzo\n", - "239 Mato Jajalo Jajalo\n", - "240 Juan Jesus Jesus\n", - "241 Þórir Jóhann Helgason Helgason\n", - "242 Luka Jović Jovic\n", - "243 Hamed Junior Traorè Traore\n", - "244 Yayah Kallon Kallon\n", - "245 Pierre Kalulu Kalulu\n", - "246 Yann Karamoh Karamoh\n", - "247 Rick Karsdorp Karsdorp\n", - "248 Denso Kasius Kasius\n", - "249 Grigoris Kastanos Kastanos\n", - "250 Moise Kean Kean\n", - "251 Jakub Kiwior Kiwior\n", - "252 Simon Kjær Kjr\n", - "253 Teun Koopmeiners Koopmeiners\n", - "254 Filip Kostić Kostic\n", - "255 Christian Kouamé Kouame\n", - "256 Viktor Kovalenko Kovalenko\n", - "257 Julian Kristoffersen Kristoffersen\n", - "258 Raimonds Krollis Krollis\n", - "259 Rade Krunić Krunic\n", - "260 Marash Kumbulla Kumbulla\n", - "261 Khvicha Kvaratskhelia Kvaratskhelia\n", - "262 Giorgos Kyriakopoulos Kyriakopoulos\n", - "263 Giorgos Kyriakopoulos Kyriakopoulos\n", - "264 Sam Lammers Lammers\n", - "265 Sam Lammers Lammers\n", - "266 Kevin Lasagna Lasagna\n", - "267 Armand Lauriente Lauriente\n", - "268 Valentino Lazaro Lazaro\n", - "269 Marko Lazetić Lazetic\n", - "270 Darko Lazović Lazovic\n", - "271 Manuel Lazzari Lazzari\n", - "272 Rafael Leão Leao\n", - "273 Mehdi Léris Leris\n", - "274 Karol Linetty Linetty\n", - "275 Marcin Listkowski Listkowski\n", - "276 Diego Llorente Llorente\n", - "277 Stanislav Lobotka Lobotka\n", - "278 Manuel Locatelli Locatelli\n", - "279 Luka Lochoshvili Lochoshvili\n", - "280 Ademola Lookman Lookman\n", - "281 Maxime Lopez Lopez\n", - "282 Matteo Lovato Lovato\n", - "283 Sandi Lovrić Lovric\n", - "284 Hirving Lozano Lozano\n", - "285 Jhon Lucumí Lucumi\n", - "286 José Luis Palomino Palomino\n", - "287 Romelu Lukaku Lukaku\n", - "288 Saša Lukić Lukic\n", - "289 Sebastiano Luperto Luperto\n", - "290 Charalambos Lykogiannis Lykogiannis\n", - "291 Giulio Maggiore Maggiore\n", - "292 Giangiacomo Magnani Magnani\n", - "293 Mike Maignan Maignan\n", - "294 Jean-Victor Makengo Makengo\n", - "295 Lorenzo Malagrida Malagrida\n", - "296 Daniel Maldini Maldini\n", - "297 Youssef Maleh Maleh\n", - "298 Youssef Maleh Maleh\n", - "299 Ruslan Malinovskyi Malinovskyi\n", - "300 Gianluca Mancini Mancini\n", - "301 Rolando Mandragora Mandragora\n", - "302 Riccardo Marchizza Marchizza\n", - "303 Gian Marco Ferrari Ferrari\n", - "304 Pablo Marí Mari\n", - "305 Răzvan Marin Marin\n", - "306 Marlon Marlon\n", - "307 Luca Marrone Marrone\n", - "308 Lautaro Martínez Martinez\n", - "309 Lucas Martínez Quarta Quarta\n", - "310 Adam Marušić Marusic\n", - "311 Adam Masina Masina\n", - "312 Nemanja Matić Matic\n", - "313 Luís Maximiano Maximiano\n", - "314 Pasquale Mazzocchi Mazzocchi\n", - "315 Weston McKennie McKennie\n", - "316 Gary Medel Medel\n", - "317 Soualiho Meïté Meite\n", - "318 Alex Meret Meret\n", - "319 Yıldırım Mert Çetin Cetin\n", - "320 Junior Messias Messias\n", - "321 Tommaso Milanese Milanese\n", - "322 Nikola Milenković Milenkovic\n", - "323 Arkadiusz Milik Milik\n", - "324 Sergej Milinković-Savić Milinkovic-Savic\n", - "325 Vanja Milinković-Savić Milinkovic-Savic\n", - "326 Kim Min-jae Min-jae\n", - "327 Aleksei Miranchuk Miranchuk\n", - "328 Fabio Miretti Miretti\n", - "329 Henrikh Mkhitaryan Mkhitaryan\n", - "330 Salvatore Molina Molina\n", - "331 Daniele Montevago Montevago\n", - "332 Lorenzo Montipò Montipo\n", - "333 Nikola Moro Moro\n", - "334 Dany Mota Mota\n", - "335 João Moutinho Moutinho\n", - "336 Mert Müldür Muldur\n", - "337 Luis Muriel Muriel\n", - "338 Jeison Murillo Murillo\n", - "339 Nicola Murru Murru\n", - "340 Juan Musso Musso\n", - "341 Joakim Mæhle Mhle\n", - "342 Michel Ndary Adopo Adopo\n", - "343 Tanguy Ndombele Ndombele\n", - "344 Ilija Nestorovski Nestorovski\n", - "345 Cyril Ngonge Ngonge\n", - "346 Hans Nicolussi Caviglia Caviglia\n", - "347 Dimitris Nikolaou Nikolaou\n", - "348 Bram Nuytinck Nuytinck\n", - "349 Bram Nuytinck Nuytinck\n", - "350 M'Bala Nzola Nzola\n", - "351 Pedro Obiang Obiang\n", - "352 Guillermo Ochoa Ochoa\n", - "353 David Okereke Okereke\n", - "354 Caleb Okoli Okoli\n", - "355 Mathías Olivera Olivera\n", - "356 André Onana Onana\n", - "357 Divock Origi Origi\n", - "358 Riccardo Orsolini Orsolini\n", - "359 Victor Osimhen Osimhen\n", - "360 Remi Oudin Oudin\n", - "361 Adam Ounas Ounas\n", - "362 Simone Pafundi Pafundi\n", - "363 Flavio Paoletti Paoletti\n", - "364 Leandro Paredes Paredes\n", - "365 Fabiano Parisi Parisi\n", - "366 Mario Pašalić Pasalic\n", - "367 Patric Patric\n", - "368 Rui Patrício Patricio\n", - "369 Pedro Pedro\n", - "370 Gianluca Pegolo Pegolo\n", - "371 Pietro Pellegri Pellegri\n", - "372 Lorenzo Pellegrini Pellegrini\n", - "373 Pepín Pepin\n", - "374 Roberto Pereyra Pereyra\n", - "375 Nehuén Pérez Perez\n", - "376 Mattia Perin Perin\n", - "377 Matteo Pessina Pessina\n", - "378 Andrea Petagna Petagna\n", - "379 Giuseppe Pezzella Pezzella\n", - "380 Krzysztof Piątek Piatek\n", - "381 Roberto Piccoli Piccoli\n", - "382 Roberto Piccoli Piccoli\n", - "383 Charles Pickel Pickel\n", - "384 Andrea Pinamonti Pinamonti\n", - "385 Lorenzo Pirola Pirola\n", - "386 Marko Pjaca Pjaca\n", - "387 Tommaso Pobega Pobega\n", - "388 Matteo Politano Politano\n", - "389 Marin Pongračić Pongracic\n", - "390 Stefan Posch Posch\n", - "391 Ivan Provedel Provedel\n", - "392 Ignacio Pussetto Pussetto\n", - "393 Niklas Pyyhtiä Pyyhtia\n", - "394 Fabio Quagliarella Quagliarella\n", - "395 Giacomo Quagliata Quagliata\n", - "396 Adrien Rabiot Rabiot\n", - "397 Nemanja Radonjić Radonjic\n", - "398 Ivan Radovanović Radovanovic\n", - "399 Ionuț Radu Radu\n", - "400 Luca Ranieri Ranieri\n", - "401 Andrea Ranocchia Ranocchia\n", - "402 Filippo Ranocchia Ranocchia\n", - "403 Giacomo Raspadori Raspadori\n", - "404 Giacomo Raspadori Raspadori\n", - "405 Ante Rebić Rebic\n", - "406 Arkadiusz Reca Reca\n", - "407 Panagiotis Retsos Retsos\n", - "408 Franck Ribéry Ribery\n", - "409 Samuele Ricci Ricci\n", - "410 Tomás Rincón Rincon\n", - "411 Pablo Rodríguez Rodriguez\n", - "412 Ricardo Rodríguez Rodriguez\n", - "413 Rogério Rogerio\n", - "414 Alessio Romagnoli Romagnoli\n", - "415 Luka Romero Romero\n", - "416 Marten de Roon Roon\n", - "417 Nicolò Rovella Rovella\n", - "418 Nicolò Rovella Rovella\n", - "419 Amir Rrahmani Rrahmani\n", - "420 Ruan Ruan\n", - "421 Daniele Rugani Rugani\n", - "422 Matteo Ruggeri Ruggeri\n", - "423 Mário Rui Rui\n", - "424 Abdelhamid Sabiri Sabiri\n", - "425 Alexis Saelemaekers Saelemaekers\n", - "426 Jacopo Sala Sala\n", - "427 Lazar Samardzic Samardzic\n", - "428 Junior Sambia Sambia\n", - "429 Antonio Sanabria Sanabria\n", - "430 Leandro Sanca Sanca\n", - "431 Alex Sandro Sandro\n", - "432 Nicola Sansone Sansone\n", - "433 Riccardo Saponara Saponara\n", - "434 Martin Satriano Satriano\n", - "435 Giorgio Scalvini Scalvini\n", - "436 Jerdy Schouten Schouten\n", - "437 Perr Schuurs Schuurs\n", - "438 Demba Seck Seck\n", - "439 Jacopo Segre Segre\n", - "440 Vivaldo Semedo Semedo\n", - "441 Stefano Sensi Sensi\n", - "442 Luigi Sepe Sepe\n", - "443 Leonardo Sernicola Sernicola\n", - "444 Stephan El Shaarawy Shaarawy\n", - "445 Eldor Shomurodov Shomurodov\n", - "446 Eldor Shomurodov Shomurodov\n", - "447 Marco Silvestri Silvestri\n", - "448 Giovanni Simeone Simeone\n", - "449 Wilfried Singo Singo\n", - "450 Leo Skiri Østigård stigard\n", - "451 Łukasz Skorupski Skorupski\n", - "452 Milan Škriniar Skriniar\n", - "453 Chris Smalling Smalling\n", - "454 Ola Solbakken Solbakken\n", - "455 Brandon Soppy Soppy\n", - "456 Brandon Soppy Soppy\n", - "457 Roberto Soriano Soriano\n", - "458 Joaquin Sosa Sosa\n", - "459 Riccardo Sottil Sottil\n", - "460 Matìas Soulé Soule\n", - "461 Adama Soumaoro Soumaoro\n", - "462 Leonardo Spinazzola Spinazzola\n", - "463 Marco Sportiello Sportiello\n", - "464 Petar Stojanović Stojanovic\n", - "465 Gabriel Strefezza Strefezza\n", - "466 Dávid Strelec Strelec\n", - "467 Isaac Success Success\n", - "468 Ibrahim Sulemana Sulemana\n", - "469 Wojciech Szczęsny Szczesny\n", - "470 Benjamin Tahirovic Tahirovic\n", - "471 Adrien Tameze Tameze\n", - "472 Ciprian Tătărușanu Tatarusanu\n", - "473 Filippo Terracciano Terracciano\n", - "474 Pietro Terracciano Terracciano\n", - "475 Aleksa Terzić Terzic\n", - "476 Florian Thauvin Thauvin\n", - "477 Malick Thiaw Thiaw\n", - "478 Kristian Thorstvedt Thorstvedt\n", - "479 Jeremy Toljan Toljan\n", - "480 Rafael Tolói Toloi\n", - "481 Fikayo Tomori Tomori\n", - "482 Sandro Tonali Tonali\n", - "483 William Troost-Ekong Troost-Ekong\n", - "484 Frank Tsadjout Tsadjout\n", - "485 Alessandro Tuia Tuia\n", - "486 Iyenoma Udogie Udogie\n", - "487 Samuel Umtiti Umtiti\n", - "488 Diego Valencia Valencia\n", - "489 Emanuele Valeri Valeri\n", - "490 Mattia Valoti Valoti\n", - "491 Johan Vásquez Vasquez\n", - "492 Matías Vecino Vecino\n", - "493 Miguel Veloso Veloso\n", - "494 Lorenzo Venuti Venuti\n", - "495 Daniele Verde Verde\n", - "496 Simone Verdi Verdi\n", - "497 Valerio Verre Verre\n", - "498 Guglielmo Vicario Vicario\n", - "499 Ronaldo Vieira Vieira\n", - "500 Ronaldo Vieira Vieira\n", - "501 Emanuel Vignato Vignato\n", - "502 Samuele Vignato Vignato\n", - "503 Tonny Vilhena Vilhena\n", - "504 Gonzalo Villar Villar\n", - "505 Matías Viña Vina\n", - "506 Dušan Vlahović Vlahovic\n", - "507 Nikola Vlašić Vlasic\n", - "508 Joel Voelkerling Persson Persson\n", - "509 Mërgim Vojvoda Vojvoda\n", - "510 Cristian Volpato Volpato\n", - "511 Aster Vranckx Vranckx\n", - "512 Stefan de Vrij Vrij\n", - "513 Walace Walace\n", - "514 Sebastian Walukiewicz Walukiewicz\n", - "515 Georginio Wijnaldum Wijnaldum\n", - "516 Harry Winks Winks\n", - "517 Przemysław Wiśniewski Wisniewski\n", - "518 Gerard Yepes Yepes\n", - "519 Mattia Zaccagni Zaccagni\n", - "520 Denis Zakaria Zakaria\n", - "521 Nicola Zalewski Zalewski\n", - "522 Andre-Frank Zambo Anguissa Anguissa\n", - "523 Luca Zanimacchia Zanimacchia\n", - "524 Nicolò Zaniolo Zaniolo\n", - "525 Alessandro Zanoli Zanoli\n", - "526 Alessandro Zanoli Zanoli\n", - "527 Duván Zapata Zapata\n", - "528 Davide Zappacosta Zappacosta\n", - "529 Alessio Zerbin Zerbin\n", - "530 Piotr Zieliński Zielinski\n", - "531 David Zima Zima\n", - "532 Joshua Zirkzee Zirkzee\n", - "533 Jeroen Zoet Zoet\n", - "534 Nadir Zortea Zortea\n", - "535 Nadir Zortea Zortea\n", - "536 Petar Zovko Zovko\n", - "537 Szymon Żurkowski Zurkowski\n", - "538 Szymon Żurkowski Zurkowski\n", - "539 Milan Đurić uric\n", - "540 Filip Đuričić uricic\n", - "541 Emil Audero Audero\n", - "542 Marco Carnesecchi Carnesecchi\n", - "543 Andrea Consigli Consigli\n", - "544 Michele Di Gregorio Gregorio\n", - "545 Bartłomiej Drągowski Dragowski\n", - "546 Wladimiro Falcone Falcone\n", - "547 Pierluigi Gollini Gollini\n", - "548 Samir Handanović Handanovic\n", - "549 Mike Maignan Maignan\n", - "550 Luís Maximiano Maximiano\n", - "551 Alex Meret Meret\n", - "552 Vanja Milinković-Savić Milinkovic-Savic\n", - "553 Lorenzo Montipò Montipo\n", - "554 Juan Musso Musso\n", - "555 Guillermo Ochoa Ochoa\n", - "556 André Onana Onana\n", - "557 Rui Patrício Patricio\n", - "558 Gianluca Pegolo Pegolo\n", - "559 Mattia Perin Perin\n", - "560 Ivan Provedel Provedel\n", - "561 Ionuț Radu Radu\n", - "562 Luigi Sepe Sepe\n", - "563 Marco Silvestri Silvestri\n", - "564 Łukasz Skorupski Skorupski\n", - "565 Marco Sportiello Sportiello\n", - "566 Wojciech Szczęsny Szczesny\n", - "567 Ciprian Tătărușanu Tatarusanu\n", - "568 Pietro Terracciano Terracciano\n", - "569 Guglielmo Vicario Vicario\n", - "570 Jeroen Zoet Zoet\n", - "571 Petar Zovko Zovko\n" + "0 James Abankwah Abankwah\n", + "1 Oliver Abildgaard Abildgaard\n", + "2 Tammy Abraham Abraham\n", + "3 Christian Acella Acella\n", + "4 Francesco Acerbi Acerbi\n", + "5 Yacine Adli Adli\n", + "6 Michel Aebischer Aebischer\n", + "7 Felix Afena-Gyan Afena-Gyan\n", + "8 Kevin Agudelo Agudelo\n", + "9 Ola Aina Aina\n", + "10 Emanuel Aiwum Aiwum\n", + "11 Jean-Daniel Akpa-Akpro Akpa-Akpro\n", + "12 Luis Alberto Alberto\n", + "13 Agustín Álvarez Martínez Martinez\n", + "14 Kelvin Amian Amian\n", + "15 Bruno Amione Amione\n", + "16 Bruno Amione Amione\n", + "17 Ethan Ampadu Ampadu\n", + "18 Sofyan Amrabat Amrabat\n", + "19 Felipe Anderson Anderson\n", + "20 Janis Antiste Antiste\n", + "21 Marcos Antônio Antonio\n", + "22 Valentin Antov Antov\n", + "23 Marko Arnautović Arnautovic\n", + "24 Tolgay Arslan Arslan\n", + "25 Arthur Arthur\n", + "26 Santiago Ascacíbar Ascacibar\n", + "27 Kristoffer Askildsen Askildsen\n", + "28 Kristjan Asllani Asllani\n", + "29 Emil Audero Audero\n", + "30 Tommaso Augello Augello\n", + "31 Kaan Ayhan Ayhan\n", + "32 Jaime Báez Baez\n", + "33 Nedim Bajrami Bajrami\n", + "34 Nedim Bajrami Bajrami\n", + "35 Tiemoué Bakayoko Bakayoko\n", + "36 Tommaso Baldanzi Baldanzi\n", + "37 Fodé Ballo-Touré Ballo-Toure\n", + "38 Lameck Banda Banda\n", + "39 Filippo Bandinelli Bandinelli\n", + "40 Antonín Barák Barak\n", + "41 Antonín Barák Barak\n", + "42 Andrea Barberis Barberis\n", + "43 Tommaso Barbieri Barbieri\n", + "44 Francesco Bardi Bardi\n", + "45 Nicolò Barella Barella\n", + "46 Enzo Barrenechea Barrenechea\n", + "47 Musa Barrow Barrow\n", + "48 Federico Baschirotto Baschirotto\n", + "49 Toma Bašić Basic\n", + "50 Alberto Basso Basso\n", + "51 Alessandro Bastoni Bastoni\n", + "52 Simone Bastoni Bastoni\n", + "53 Brian Bayeye Bayeye\n", + "54 Rodrigo Becão Becao\n", + "55 Julius Beck Beck\n", + "56 Raoul Bellanova Bellanova\n", + "57 Andrea Belotti Belotti\n", + "58 Marco Benassi Benassi\n", + "59 Marco Benassi Benassi\n", + "60 Ismaël Bennacer Bennacer\n", + "61 Domenico Berardi Berardi\n", + "62 Bartosz Bereszyński Bereszynski\n", + "63 Beto Beto\n", + "64 Matteo Bianchetti Bianchetti\n", + "65 Alessandro Bianco Bianco\n", + "66 Jaka Bijol Bijol\n", + "67 Cristiano Biraghi Biraghi\n", + "68 Samuele Birindelli Birindelli\n", + "69 Kristijan Bistrović Bistrovic\n", + "70 Alexis Blin Blin\n", + "71 Jeremie Boga Boga\n", + "72 Emil Bohinen Bohinen\n", + "73 Giacomo Bonaventura Bonaventura\n", + "74 Federico Bonazzoli Bonazzoli\n", + "75 Warren Bondo Bondo\n", + "76 Kevin Bonifazi Bonifazi\n", + "77 Leonardo Bonucci Bonucci\n", + "78 Erik Botheim Botheim\n", + "79 Mehdi Bourabia Bourabia\n", + "80 Edoardo Bove Bove\n", + "81 Jayden Braaf Braaf\n", + "82 Domagoj Bradarić Bradaric\n", + "83 Josip Brekalo Brekalo\n", + "84 Gleison Bremer Bremer\n", + "85 Dylan Bronn Bronn\n", + "86 Marcelo Brozović Brozovic\n", + "87 Cristian Buonaiuto Buonaiuto\n", + "88 Alessandro Buongiorno Buongiorno\n", + "89 Juan Cabal Cabal\n", + "90 Liberato Cacace Cacace\n", + "91 Davide Calabria Calabria\n", + "92 Mattia Caldara Caldara\n", + "93 Luca Caldirola Caldirola\n", + "94 Hakan Çalhanoğlu Calhanoglu\n", + "95 Mohamed Camara Camara\n", + "96 Nicolò Cambiaghi Cambiaghi\n", + "97 Andrea Cambiaso Cambiaso\n", + "98 Matteo Cancellieri Cancellieri\n", + "99 Antonio Candreva Candreva\n", + "100 Gianluca Caprari Caprari\n", + "101 Francesco Caputo Caputo\n", + "102 Francesco Caputo Caputo\n", + "103 Andrea Carboni Carboni\n", + "104 Franco Carboni Carboni\n", + "105 Valentin Carboni Carboni\n", + "106 Carlos Carlos\n", + "107 Marco Carnesecchi Carnesecchi\n", + "108 Nicolò Casale Casale\n", + "109 Tommaso Cassandro Cassandro\n", + "110 Michele Castagnetti Castagnetti\n", + "111 Gaetano Castrovilli Castrovilli\n", + "112 Danilo Cataldi Cataldi\n", + "113 Pietro Ceccaroni Ceccaroni\n", + "114 Federico Ceccherini Ceccherini\n", + "115 Assan Ceesay Ceesay\n", + "116 Emil Ceide Ceide\n", + "117 Zeki Çelik Celik\n", + "118 Federico Chiesa Chiesa\n", + "119 Vlad Chiricheș Chiriches\n", + "120 Daniel Ciofani Ciofani\n", + "121 Tio Cipot Cipot\n", + "122 Patrick Ciurria Ciurria\n", + "123 Omar Colley Colley\n", + "124 Lorenzo Colombo Colombo\n", + "125 Andrea Colpani Colpani\n", + "126 Andrea Consigli Consigli\n", + "127 Andrea Conti Conti\n", + "128 Diego Coppola Coppola\n", + "129 Joaquín Correa Correa\n", + "130 Alessandro Cortinovis Cortinovis\n", + "131 Lassana Coulibaly Coulibaly\n", + "132 Alessio Cragno Cragno\n", + "133 Bryan Cristante Cristante\n", + "134 Domen Črnigoj Crnigoj\n", + "135 Juan Cuadrado Cuadrado\n", + "136 Mickaël Cuisance Cuisance\n", + "137 Marco D'Alessandro DAlessandro\n", + "138 Danilo D'Ambrosio DAmbrosio\n", + "139 Luca D'Andrea DAndrea\n", + "140 Flavius Daniliuc Daniliuc\n", + "141 Danilo Danilo\n", + "142 Matteo Darmian Darmian\n", + "143 Paweł Dawidowicz Dawidowicz\n", + "144 Charles De Ketelaere Ketelaere\n", + "145 Manuel De Luca Luca\n", + "146 Mattia De Sciglio Sciglio\n", + "147 Lorenzo De Silvestri Silvestri\n", + "148 Koni De Winter Winter\n", + "149 Grégoire Defrel Defrel\n", + "150 Duccio Degl'Innocenti DeglInnocenti\n", + "151 Merih Demiral Demiral\n", + "152 Diego Demme Demme\n", + "153 Fabio Depaoli Depaoli\n", + "154 Fabio Depaoli Depaoli\n", + "155 Kastriot Dermaku Dermaku\n", + "156 Cyriel Dessers Dessers\n", + "157 Sergiño Dest Dest\n", + "158 Mattia Destro Destro\n", + "159 Gerard Deulofeu Deulofeu\n", + "160 Samuel Di Carmine Carmine\n", + "161 Federico Di Francesco Francesco\n", + "162 Michele Di Gregorio Gregorio\n", + "163 Giovanni Di Lorenzo Lorenzo\n", + "164 Ángel Di María Maria\n", + "165 Boulaye Dia Dia\n", + "166 Brahim Díaz Diaz\n", + "167 Federico Dimarco Dimarco\n", + "168 Koffi Djidji Djidji\n", + "169 Berat Djimsiti Djimsiti\n", + "170 Dodô Dodo\n", + "171 Josh Doig Doig\n", + "172 Nicolás Domínguez Dominguez\n", + "173 Giulio Donati Donati\n", + "174 Bartłomiej Drągowski Dragowski\n", + "175 Ondrej Duda Duda\n", + "176 Denzel Dumfries Dumfries\n", + "177 Alfred Duncan Duncan\n", + "178 Paulo Dybala Dybala\n", + "179 Edin Džeko Dzeko\n", + "180 Festy Ebosele Ebosele\n", + "181 Enzo Ebosse Ebosse\n", + "182 Tyronne Ebuehi Ebuehi\n", + "183 Éderson Ederson\n", + "184 Kingsley Ehizibue Ehizibue\n", + "185 Albin Ekdal Ekdal\n", + "186 Emmanuel Ekong Ekong\n", + "187 Mikael Ellertsson Ellertsson\n", + "188 Elif Elmas Elmas\n", + "189 Martin Erlic Erlic\n", + "190 Gonzalo Escalante Escalante\n", + "191 Salvatore Esposito Esposito\n", + "192 Nicolò Fagioli Fagioli\n", + "193 Wladimiro Falcone Falcone\n", + "194 Davide Faraoni Faraoni\n", + "195 Federico Fazio Fazio\n", + "196 Jacopo Fazzini Fazzini\n", + "197 Lewis Ferguson Ferguson\n", + "198 Alex Ferrari Ferrari\n", + "199 Alex Ferrari Ferrari\n", + "200 Salvador Ferrer Ferrer\n", + "201 Alessandro Florenzi Florenzi\n", + "202 Davide Frattesi Frattesi\n", + "203 Matteo Gabbia Gabbia\n", + "204 Manolo Gabbiadini Gabbiadini\n", + "205 Gianluca Gaetano Gaetano\n", + "206 Roberto Gagliardini Gagliardini\n", + "207 Adolfo Gaich Gaich\n", + "208 Pablo Galdames Millán Millan\n", + "209 Antonino Gallo Gallo\n", + "210 Federico Gatti Gatti\n", + "211 Valentin Gendrey Gendrey\n", + "212 Paolo Ghiglione Ghiglione\n", + "213 Mario Gila Gila\n", + "214 Gvidas Gineitis Gineitis\n", + "215 Olivier Giroud Giroud\n", + "216 Pierluigi Gollini Gollini\n", + "217 Pierluigi Gollini Gollini\n", + "218 Joan Gonzàlez Gonzalez\n", + "219 Nicolás González Gonzalez\n", + "220 Robin Gosens Gosens\n", + "221 Alberto Grassi Grassi\n", + "222 Andrew Gravillon Gravillon\n", + "223 Koray Günter Gunter\n", + "224 Koray Günter Gunter\n", + "225 Emmanuel Gyasi Gyasi\n", + "226 Norbert Gyömbér Gyomber\n", + "227 Christian Gytkjær Gytkjr\n", + "228 Nicolas Haas Haas\n", + "229 Samir Handanović Handanovic\n", + "230 Abdou Harroui Harroui\n", + "231 Hans Hateboer Hateboer\n", + "232 Liam Henderson Henderson\n", + "233 Jack Hendry Hendry\n", + "234 Matheus Henrique Henrique\n", + "235 Thomas Henry Henry\n", + "236 Theo Hernández Hernandez\n", + "237 Isak Hien Hien\n", + "238 Morten Hjulmand Hjulmand\n", + "239 Emil Holm Holm\n", + "240 Martin Hongla Hongla\n", + "241 Petko Hristov Hristov\n", + "242 Ajdin Hrustic Hrustic\n", + "243 Elseid Hysaj Hysaj\n", + "244 Rasmus Højlund Hjlund\n", + "245 Roger Ibanez Ibanez\n", + "246 Zlatan Ibrahimović Ibrahimovic\n", + "247 Igor Igor\n", + "248 Jonathan Ikone Ikone\n", + "249 Ivan Ilić Ilic\n", + "250 Ivan Ilić Ilic\n", + "251 Samuel Iling-Junior Iling-Junior\n", + "252 Emirhan İlkhan Ilkhan\n", + "253 Emirhan İlkhan Ilkhan\n", + "254 Ciro Immobile Immobile\n", + "255 Ardian Ismajli Ismajli\n", + "256 Armando Izzo Izzo\n", + "257 Mato Jajalo Jajalo\n", + "258 Jesé Jese\n", + "259 Juan Jesus Jesus\n", + "260 Þórir Jóhann Helgason Helgason\n", + "261 Luka Jović Jovic\n", + "262 Hamed Junior Traorè Traore\n", + "263 Yayah Kallon Kallon\n", + "264 Pierre Kalulu Kalulu\n", + "265 Yann Karamoh Karamoh\n", + "266 Rick Karsdorp Karsdorp\n", + "267 Denso Kasius Kasius\n", + "268 Grigoris Kastanos Kastanos\n", + "269 Moise Kean Kean\n", + "270 Jakub Kiwior Kiwior\n", + "271 Simon Kjær Kjr\n", + "272 Teun Koopmeiners Koopmeiners\n", + "273 Filip Kostić Kostic\n", + "274 Christian Kouamé Kouame\n", + "275 Viktor Kovalenko Kovalenko\n", + "276 Julian Kristoffersen Kristoffersen\n", + "277 Raimonds Krollis Krollis\n", + "278 Rade Krunić Krunic\n", + "279 Marash Kumbulla Kumbulla\n", + "280 Khvicha Kvaratskhelia Kvaratskhelia\n", + "281 Giorgos Kyriakopoulos Kyriakopoulos\n", + "282 Giorgos Kyriakopoulos Kyriakopoulos\n", + "283 Sam Lammers Lammers\n", + "284 Sam Lammers Lammers\n", + "285 Kevin Lasagna Lasagna\n", + "286 Armand Lauriente Lauriente\n", + "287 Valentino Lazaro Lazaro\n", + "288 Marko Lazetić Lazetic\n", + "289 Darko Lazović Lazovic\n", + "290 Manuel Lazzari Lazzari\n", + "291 Rafael Leão Leao\n", + "292 Mehdi Léris Leris\n", + "293 Karol Linetty Linetty\n", + "294 Marcin Listkowski Listkowski\n", + "295 Diego Llorente Llorente\n", + "296 Stanislav Lobotka Lobotka\n", + "297 Manuel Locatelli Locatelli\n", + "298 Luka Lochoshvili Lochoshvili\n", + "299 Ademola Lookman Lookman\n", + "300 Maxime Lopez Lopez\n", + "301 Matteo Lovato Lovato\n", + "302 Sandi Lovrić Lovric\n", + "303 Hirving Lozano Lozano\n", + "304 Jhon Lucumí Lucumi\n", + "305 José Luis Palomino Palomino\n", + "306 Romelu Lukaku Lukaku\n", + "307 Saša Lukić Lukic\n", + "308 Sebastiano Luperto Luperto\n", + "309 Charalambos Lykogiannis Lykogiannis\n", + "310 Giulio Maggiore Maggiore\n", + "311 Giangiacomo Magnani Magnani\n", + "312 Mike Maignan Maignan\n", + "313 Jordan Majchrzak Majchrzak\n", + "314 Jean-Victor Makengo Makengo\n", + "315 Lorenzo Malagrida Malagrida\n", + "316 Daniel Maldini Maldini\n", + "317 Youssef Maleh Maleh\n", + "318 Youssef Maleh Maleh\n", + "319 Ruslan Malinovskyi Malinovskyi\n", + "320 Gianluca Mancini Mancini\n", + "321 Rolando Mandragora Mandragora\n", + "322 Federico Marchetti Marchetti\n", + "323 Riccardo Marchizza Marchizza\n", + "324 Gian Marco Ferrari Ferrari\n", + "325 Pablo Marí Mari\n", + "326 Răzvan Marin Marin\n", + "327 Marlon Marlon\n", + "328 Luca Marrone Marrone\n", + "329 Lautaro Martínez Martinez\n", + "330 Lucas Martínez Quarta Quarta\n", + "331 Adam Marušić Marusic\n", + "332 Adam Masina Masina\n", + "333 Nemanja Matić Matic\n", + "334 Luís Maximiano Maximiano\n", + "335 Pasquale Mazzocchi Mazzocchi\n", + "336 Weston McKennie McKennie\n", + "337 Gary Medel Medel\n", + "338 Soualiho Meïté Meite\n", + "339 Alex Meret Meret\n", + "340 Yıldırım Mert Çetin Cetin\n", + "341 Junior Messias Messias\n", + "342 Tommaso Milanese Milanese\n", + "343 Nikola Milenković Milenkovic\n", + "344 Arkadiusz Milik Milik\n", + "345 Sergej Milinković-Savić Milinkovic-Savic\n", + "346 Vanja Milinković-Savić Milinkovic-Savic\n", + "347 Kim Min-jae Min-jae\n", + "348 Aleksei Miranchuk Miranchuk\n", + "349 Fabio Miretti Miretti\n", + "350 Henrikh Mkhitaryan Mkhitaryan\n", + "351 Salvatore Molina Molina\n", + "352 Daniele Montevago Montevago\n", + "353 Lorenzo Montipò Montipo\n", + "354 Nikola Moro Moro\n", + "355 Dany Mota Mota\n", + "356 João Moutinho Moutinho\n", + "357 Mert Müldür Muldur\n", + "358 Luis Muriel Muriel\n", + "359 Jeison Murillo Murillo\n", + "360 Nicola Murru Murru\n", + "361 Juan Musso Musso\n", + "362 Joakim Mæhle Mhle\n", + "363 Herculano Nabian Nabian\n", + "364 Michel Ndary Adopo Adopo\n", + "365 Tanguy Ndombele Ndombele\n", + "366 Ilija Nestorovski Nestorovski\n", + "367 Cyril Ngonge Ngonge\n", + "368 Hans Nicolussi Caviglia Caviglia\n", + "369 Dimitris Nikolaou Nikolaou\n", + "370 Bram Nuytinck Nuytinck\n", + "371 Bram Nuytinck Nuytinck\n", + "372 M'Bala Nzola Nzola\n", + "373 Pedro Obiang Obiang\n", + "374 Guillermo Ochoa Ochoa\n", + "375 Marios Oikonomou Oikonomou\n", + "376 David Okereke Okereke\n", + "377 Caleb Okoli Okoli\n", + "378 Mathías Olivera Olivera\n", + "379 André Onana Onana\n", + "380 Divock Origi Origi\n", + "381 Riccardo Orsolini Orsolini\n", + "382 Victor Osimhen Osimhen\n", + "383 Remi Oudin Oudin\n", + "384 Adam Ounas Ounas\n", + "385 Simone Pafundi Pafundi\n", + "386 Flavio Paoletti Paoletti\n", + "387 Leandro Paredes Paredes\n", + "388 Fabiano Parisi Parisi\n", + "389 Mario Pašalić Pasalic\n", + "390 Patric Patric\n", + "391 Rui Patrício Patricio\n", + "392 Pedro Pedro\n", + "393 Gianluca Pegolo Pegolo\n", + "394 Pietro Pellegri Pellegri\n", + "395 Lorenzo Pellegrini Pellegrini\n", + "396 Luca Pellegrini Pellegrini\n", + "397 Pepín Pepin\n", + "398 Roberto Pereyra Pereyra\n", + "399 Nehuén Pérez Perez\n", + "400 Simone Perilli Perilli\n", + "401 Mattia Perin Perin\n", + "402 Samuele Perisan Perisan\n", + "403 Matteo Pessina Pessina\n", + "404 Andrea Petagna Petagna\n", + "405 Giuseppe Pezzella Pezzella\n", + "406 Krzysztof Piątek Piatek\n", + "407 Roberto Piccoli Piccoli\n", + "408 Roberto Piccoli Piccoli\n", + "409 Charles Pickel Pickel\n", + "410 Andrea Pinamonti Pinamonti\n", + "411 Lorenzo Pirola Pirola\n", + "412 Marko Pjaca Pjaca\n", + "413 Tommaso Pobega Pobega\n", + "414 Paul Pogba Pogba\n", + "415 Matteo Politano Politano\n", + "416 Marin Pongračić Pongracic\n", + "417 Stefan Posch Posch\n", + "418 Ivan Provedel Provedel\n", + "419 Ignacio Pussetto Pussetto\n", + "420 Niklas Pyyhtiä Pyyhtia\n", + "421 Fabio Quagliarella Quagliarella\n", + "422 Giacomo Quagliata Quagliata\n", + "423 Adrien Rabiot Rabiot\n", + "424 Nemanja Radonjić Radonjic\n", + "425 Ivan Radovanović Radovanovic\n", + "426 Ionuț Radu Radu\n", + "427 Antonio Raimondo Raimondo\n", + "428 Luca Ranieri Ranieri\n", + "429 Andrea Ranocchia Ranocchia\n", + "430 Filippo Ranocchia Ranocchia\n", + "431 Giacomo Raspadori Raspadori\n", + "432 Giacomo Raspadori Raspadori\n", + "433 Nicola Ravaglia Ravaglia\n", + "434 Ante Rebić Rebic\n", + "435 Arkadiusz Reca Reca\n", + "436 Panagiotis Retsos Retsos\n", + "437 Franck Ribéry Ribery\n", + "438 Samuele Ricci Ricci\n", + "439 Tomás Rincón Rincon\n", + "440 Pablo Rodríguez Rodriguez\n", + "441 Ricardo Rodríguez Rodriguez\n", + "442 Rogério Rogerio\n", + "443 Alessio Romagnoli Romagnoli\n", + "444 Simone Romagnoli Romagnoli\n", + "445 Luka Romero Romero\n", + "446 Marten de Roon Roon\n", + "447 Nicolò Rovella Rovella\n", + "448 Nicolò Rovella Rovella\n", + "449 Amir Rrahmani Rrahmani\n", + "450 Ruan Ruan\n", + "451 Daniele Rugani Rugani\n", + "452 Matteo Ruggeri Ruggeri\n", + "453 Mário Rui Rui\n", + "454 Abdelhamid Sabiri Sabiri\n", + "455 Alexis Saelemaekers Saelemaekers\n", + "456 Jacopo Sala Sala\n", + "457 Lazar Samardzic Samardzic\n", + "458 Junior Sambia Sambia\n", + "459 Antonio Sanabria Sanabria\n", + "460 Leandro Sanca Sanca\n", + "461 Alex Sandro Sandro\n", + "462 Nicola Sansone Sansone\n", + "463 Riccardo Saponara Saponara\n", + "464 Martin Satriano Satriano\n", + "465 Giorgio Scalvini Scalvini\n", + "466 Jerdy Schouten Schouten\n", + "467 Perr Schuurs Schuurs\n", + "468 Demba Seck Seck\n", + "469 Jacopo Segre Segre\n", + "470 Vivaldo Semedo Semedo\n", + "471 Stefano Sensi Sensi\n", + "472 Luigi Sepe Sepe\n", + "473 Leonardo Sernicola Sernicola\n", + "474 Stephan El Shaarawy Shaarawy\n", + "475 Eldor Shomurodov Shomurodov\n", + "476 Eldor Shomurodov Shomurodov\n", + "477 Marco Silvestri Silvestri\n", + "478 Giovanni Simeone Simeone\n", + "479 Wilfried Singo Singo\n", + "480 Salvatore Sirigu Sirigu\n", + "481 Leo Skiri Østigård stigard\n", + "482 Łukasz Skorupski Skorupski\n", + "483 Milan Škriniar Skriniar\n", + "484 Chris Smalling Smalling\n", + "485 Ola Solbakken Solbakken\n", + "486 Brandon Soppy Soppy\n", + "487 Brandon Soppy Soppy\n", + "488 Roberto Soriano Soriano\n", + "489 Joaquin Sosa Sosa\n", + "490 Riccardo Sottil Sottil\n", + "491 Matìas Soulé Soule\n", + "492 Adama Soumaoro Soumaoro\n", + "493 Leonardo Spinazzola Spinazzola\n", + "494 Marco Sportiello Sportiello\n", + "495 Petar Stojanović Stojanovic\n", + "496 Gabriel Strefezza Strefezza\n", + "497 Dávid Strelec Strelec\n", + "498 Isaac Success Success\n", + "499 Ibrahim Sulemana Sulemana\n", + "500 Wojciech Szczęsny Szczesny\n", + "501 Benjamin Tahirovic Tahirovic\n", + "502 Adrien Tameze Tameze\n", + "503 Ciprian Tătărușanu Tatarusanu\n", + "504 Filippo Terracciano Terracciano\n", + "505 Pietro Terracciano Terracciano\n", + "506 Aleksa Terzić Terzic\n", + "507 Florian Thauvin Thauvin\n", + "508 Malick Thiaw Thiaw\n", + "509 Kristian Thorstvedt Thorstvedt\n", + "510 Jeremy Toljan Toljan\n", + "511 Rafael Tolói Toloi\n", + "512 Fikayo Tomori Tomori\n", + "513 Sandro Tonali Tonali\n", + "514 Lorenzo Tonelli Tonelli\n", + "515 William Troost-Ekong Troost-Ekong\n", + "516 Frank Tsadjout Tsadjout\n", + "517 Alessandro Tuia Tuia\n", + "518 Martin Turk Turk\n", + "519 Iyenoma Udogie Udogie\n", + "520 Samuel Umtiti Umtiti\n", + "521 Diego Valencia Valencia\n", + "522 Emanuele Valeri Valeri\n", + "523 Mattia Valoti Valoti\n", + "524 Johan Vásquez Vasquez\n", + "525 Matías Vecino Vecino\n", + "526 Miguel Veloso Veloso\n", + "527 Lorenzo Venuti Venuti\n", + "528 Daniele Verde Verde\n", + "529 Simone Verdi Verdi\n", + "530 Valerio Verre Verre\n", + "531 Guglielmo Vicario Vicario\n", + "532 Ronaldo Vieira Vieira\n", + "533 Ronaldo Vieira Vieira\n", + "534 Emanuel Vignato Vignato\n", + "535 Emanuel Vignato Vignato\n", + "536 Samuele Vignato Vignato\n", + "537 Tonny Vilhena Vilhena\n", + "538 Gonzalo Villar Villar\n", + "539 Matías Viña Vina\n", + "540 Dušan Vlahović Vlahovic\n", + "541 Nikola Vlašić Vlasic\n", + "542 Joel Voelkerling Persson Persson\n", + "543 Mërgim Vojvoda Vojvoda\n", + "544 Cristian Volpato Volpato\n", + "545 Lukáš Vorlický Vorlicky\n", + "546 Aster Vranckx Vranckx\n", + "547 Stefan de Vrij Vrij\n", + "548 Walace Walace\n", + "549 Sebastian Walukiewicz Walukiewicz\n", + "550 Georginio Wijnaldum Wijnaldum\n", + "551 Harry Winks Winks\n", + "552 Przemysław Wiśniewski Wisniewski\n", + "553 Gerard Yepes Yepes\n", + "554 Mattia Zaccagni Zaccagni\n", + "555 Denis Zakaria Zakaria\n", + "556 Nicola Zalewski Zalewski\n", + "557 Andre-Frank Zambo Anguissa Anguissa\n", + "558 Luca Zanimacchia Zanimacchia\n", + "559 Nicolò Zaniolo Zaniolo\n", + "560 Alessandro Zanoli Zanoli\n", + "561 Alessandro Zanoli Zanoli\n", + "562 Mattia Zanotti Zanotti\n", + "563 Duván Zapata Zapata\n", + "564 Davide Zappacosta Zappacosta\n", + "565 Karim Zedadka Zedadka\n", + "566 Deyovaisio Zeefuik Zeefuik\n", + "567 Marvin Zeegelaar Zeegelaar\n", + "568 Alessio Zerbin Zerbin\n", + "569 Piotr Zieliński Zielinski\n", + "570 David Zima Zima\n", + "571 Joshua Zirkzee Zirkzee\n", + "572 Jeroen Zoet Zoet\n", + "573 Nadir Zortea Zortea\n", + "574 Nadir Zortea Zortea\n", + "575 Petar Zovko Zovko\n", + "576 Szymon Żurkowski Zurkowski\n", + "577 Szymon Żurkowski Zurkowski\n", + "578 Milan Đurić uric\n", + "579 Filip Đuričić uricic\n", + "580 Emil Audero Audero\n", + "581 Francesco Bardi Bardi\n", + "582 Marco Carnesecchi Carnesecchi\n", + "583 Andrea Consigli Consigli\n", + "584 Alessio Cragno Cragno\n", + "585 Michele Di Gregorio Gregorio\n", + "586 Bartłomiej Drągowski Dragowski\n", + "587 Wladimiro Falcone Falcone\n", + "588 Pierluigi Gollini Gollini\n", + "589 Pierluigi Gollini Gollini\n", + "590 Samir Handanović Handanovic\n", + "591 Mike Maignan Maignan\n", + "592 Federico Marchetti Marchetti\n", + "593 Luís Maximiano Maximiano\n", + "594 Alex Meret Meret\n", + "595 Vanja Milinković-Savić Milinkovic-Savic\n", + "596 Lorenzo Montipò Montipo\n", + "597 Juan Musso Musso\n", + "598 Guillermo Ochoa Ochoa\n", + "599 André Onana Onana\n", + "600 Rui Patrício Patricio\n", + "601 Gianluca Pegolo Pegolo\n", + "602 Simone Perilli Perilli\n", + "603 Mattia Perin Perin\n", + "604 Samuele Perisan Perisan\n", + "605 Ivan Provedel Provedel\n", + "606 Ionuț Radu Radu\n", + "607 Nicola Ravaglia Ravaglia\n", + "608 Luigi Sepe Sepe\n", + "609 Marco Silvestri Silvestri\n", + "610 Salvatore Sirigu Sirigu\n", + "611 Łukasz Skorupski Skorupski\n", + "612 Marco Sportiello Sportiello\n", + "613 Wojciech Szczęsny Szczesny\n", + "614 Ciprian Tătărușanu Tatarusanu\n", + "615 Pietro Terracciano Terracciano\n", + "616 Martin Turk Turk\n", + "617 Guglielmo Vicario Vicario\n", + "618 Jeroen Zoet Zoet\n", + "619 Petar Zovko Zovko\n" ] } ], @@ -973,22 +1021,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2263921821.py:17: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2263921821.py:17: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " fc_players['surname'][i] = spl[-1]\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2263921821.py:18: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2263921821.py:18: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " fc_players['initial'][i] = ''\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2263921821.py:14: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2263921821.py:14: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " fc_players['surname'][i] = spl[-2]\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2263921821.py:15: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2263921821.py:15: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -1193,12 +1241,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2200947104.py:4: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2200947104.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " fc_players['fb_ID'][i] = -1\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2200947104.py:11: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2200947104.py:11: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -1245,46 +1293,27 @@ "id": "18f6c5f2", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\362391242.py:10: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " fc_players['fb_ID'][i] = j\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Gollini from previous team stats\n", "Mirante not found\n", "Sarr M. not found\n", "Lamanna not found\n", "Ujkani not found\n", "Berisha not found\n", - "Marchetti not found\n", - "Perilli not found\n", "Padelli not found\n", - "Perisan not found\n", - "Bardi not found\n", "Cordaz not found\n", "Pinsoglio not found\n", "Fiorillo not found\n", - "Cragno not found\n", - "Sirigu not found\n", "Cerofolini not found\n", "Rossi F. not found\n", - "Ravaglia F. not found\n", + "Ravaglia F. from previous team stats\n", "Brancolini not found\n", "Bleve not found\n", "Berardi A. not found\n", "Russo A. not found\n", "Gemello not found\n", - "Ravaglia not found\n", "Boer not found\n", "Adamonis not found\n", "Marfella not found\n", @@ -1295,45 +1324,44 @@ "Ciezkowski not found\n", "Saro not found\n", "Vasquez D. not found\n", - "Turk not found\n", - "Pellegrini Lu. not found\n", - "Gravillon not found\n", - "Gunter from previous team stats\n", - "Ceccaroni not found\n", "Bereszynski from previous team stats\n", "Aiwu not found\n", - "Zeefuik not found\n", - "Romagnoli S. not found\n", - "Tonelli not found\n", "Radu from previous team stats\n", "Paletta not found\n", "Fares not found\n", "Romagna not found\n", - "Cassandro not found\n", "Amey not found\n", - "Zanotti not found\n", "Buta not found\n", - "Abankwah not found\n", "Guessand A. not found\n", "Guarino not found\n", - "Pogba not found\n", - "Ilic from previous team stats\n", "Machin not found\n", "Akpa Akpro not found\n", - "Galdames not found\n", - "Vignato from previous team stats\n", - "Bakayoko not found\n", + "Galdames not found\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\362391242.py:10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " fc_players['fb_ID'][i] = j\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Darboe not found\n", "Urbanski not found\n", "Bertini not found\n", "Trimboli not found\n", "Samek not found\n", - "Ilkhan from previous team stats\n", - "Acella not found\n", + "Degli Innocenti not found\n", "Faticanti not found\n", - "Ibrahimovic not found\n", "Oddei not found\n", - "Raimondo not found\n", "Kaio Jorge not found\n", "Vivaldo not found\n" ] @@ -1402,7 +1430,7 @@ " Napoli\n", " Meret\n", " \n", - " 551\n", + " 594\n", " \n", " \n", " 1\n", @@ -1412,7 +1440,7 @@ " Lazio\n", " Provedel\n", " \n", - " 560\n", + " 605\n", " \n", " \n", " 2\n", @@ -1422,7 +1450,7 @@ " Empoli\n", " Vicario\n", " \n", - " 569\n", + " 617\n", " \n", " \n", " 3\n", @@ -1432,7 +1460,7 @@ " Juventus\n", " Szczesny\n", " \n", - " 566\n", + " 613\n", " \n", " \n", " 4\n", @@ -1442,7 +1470,7 @@ " Lecce\n", " Falcone\n", " \n", - " 546\n", + " 587\n", " \n", " \n", " ...\n", @@ -1462,7 +1490,7 @@ " Sampdoria\n", " Luca\n", " \n", - " 134\n", + " 145\n", " \n", " \n", " 539\n", @@ -1472,7 +1500,7 @@ " Lecce\n", " Persson\n", " \n", - " 508\n", + " 542\n", " \n", " \n", " 540\n", @@ -1482,7 +1510,7 @@ " Sampdoria\n", " Montevago\n", " \n", - " 331\n", + " 352\n", " \n", " \n", " 541\n", @@ -1492,7 +1520,7 @@ " Spezia\n", " Krollis\n", " \n", - " 258\n", + " 277\n", " \n", " \n", " 542\n", @@ -1511,16 +1539,16 @@ ], "text/plain": [ " id r name team surname initial fb_ID\n", - "0 572 P Meret Napoli Meret 551\n", - "1 2814 P Provedel Lazio Provedel 560\n", - "2 4964 P Vicario Empoli Vicario 569\n", - "3 453 P Szczesny Juventus Szczesny 566\n", - "4 2134 P Falcone Lecce Falcone 546\n", + "0 572 P Meret Napoli Meret 594\n", + "1 2814 P Provedel Lazio Provedel 605\n", + "2 4964 P Vicario Empoli Vicario 617\n", + "3 453 P Szczesny Juventus Szczesny 613\n", + "4 2134 P Falcone Lecce Falcone 587\n", ".. ... .. ... ... ... ... ...\n", - "538 5512 A De Luca Sampdoria Luca 134\n", - "539 5837 A Voelkerling Persson Lecce Persson 508\n", - "540 6113 A Montevago Sampdoria Montevago 331\n", - "541 6143 A Krollis Spezia Krollis 258\n", + "538 5512 A De Luca Sampdoria Luca 145\n", + "539 5837 A Voelkerling Persson Lecce Persson 542\n", + "540 6113 A Montevago Sampdoria Montevago 352\n", + "541 6143 A Krollis Spezia Krollis 277\n", "542 6160 A Vivaldo Udinese Vivaldo -1\n", "\n", "[543 rows x 7 columns]" @@ -1553,39 +1581,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy] = 0\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\2261782218.py:9: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2261782218.py:9: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -1652,87 +1680,87 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16864\\3664433845.py:11: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\3664433845.py:11: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -1813,21 +1841,21 @@ " Napoli\n", " Meret\n", " \n", - " 551\n", - " 25-325\n", + " 594\n", + " 26-034\n", " 1997\n", " 0\n", " ...\n", - " 26.7\n", - " 128\n", - " 19.5\n", - " 27.1\n", - " 221\n", - " 6\n", - " 2.7\n", - " 24\n", - " 1.14\n", - " 17.2\n", + " 26.2\n", + " 185\n", + " 20.0\n", + " 26.8\n", + " 308\n", + " 10\n", + " 3.2\n", + " 32\n", + " 1.07\n", + " 17.0\n", " \n", " \n", " 1\n", @@ -1837,21 +1865,21 @@ " Lazio\n", " Provedel\n", " \n", - " 560\n", - " 28-330\n", + " 605\n", + " 29-039\n", " 1994\n", " 0\n", " ...\n", " 33.0\n", - " 120\n", - " 34.2\n", - " 34.5\n", - " 279\n", - " 7\n", - " 2.5\n", - " 39\n", - " 1.86\n", - " 18.2\n", + " 161\n", + " 36.0\n", + " 34.7\n", + " 402\n", + " 17\n", + " 4.2\n", + " 46\n", + " 1.49\n", + " 16.4\n", " \n", " \n", " 2\n", @@ -1861,21 +1889,21 @@ " Empoli\n", " Vicario\n", " \n", - " 569\n", - " 26-126\n", + " 617\n", + " 26-200\n", " 1996\n", " 0\n", " ...\n", - " 34.0\n", - " 108\n", - " 47.2\n", - " 42.4\n", - " 426\n", - " 25\n", - " 5.9\n", - " 12\n", - " 0.57\n", - " 10.9\n", + " 33.3\n", + " 139\n", + " 48.9\n", + " 42.6\n", + " 489\n", + " 28\n", + " 5.7\n", + " 15\n", + " 0.63\n", + " 10.7\n", " \n", " \n", " 3\n", @@ -1885,21 +1913,21 @@ " Juventus\n", " Szczesny\n", " \n", - " 566\n", - " 32-298\n", + " 613\n", + " 33-007\n", " 1990\n", " 0\n", " ...\n", - " 35.0\n", - " 72\n", - " 41.7\n", - " 38.7\n", - " 178\n", - " 4\n", - " 2.2\n", - " 12\n", - " 0.83\n", - " 15.8\n", + " 34.3\n", + " 114\n", + " 49.1\n", + " 41.5\n", + " 301\n", + " 9\n", + " 3.0\n", + " 19\n", + " 0.89\n", + " 15.4\n", " \n", " \n", " 4\n", @@ -1909,21 +1937,21 @@ " Lecce\n", " Falcone\n", " \n", - " 546\n", - " 27-304\n", + " 587\n", + " 28-013\n", " 1995\n", " 0\n", " ...\n", - " 41.3\n", - " 163\n", - " 76.1\n", - " 51.7\n", - " 316\n", - " 15\n", - " 4.7\n", - " 20\n", - " 0.95\n", - " 12.5\n", + " 41.9\n", + " 225\n", + " 77.3\n", + " 52.4\n", + " 441\n", + " 22\n", + " 5.0\n", + " 35\n", + " 1.13\n", + " 13.7\n", " \n", " \n", " ...\n", @@ -1957,10 +1985,10 @@ " Sampdoria\n", " Luca\n", " \n", - " 134\n", - " 24-208\n", + " 145\n", + " 24-282\n", " 1998\n", - " 1\n", + " 2\n", " ...\n", " 0.0\n", " 0\n", @@ -1981,10 +2009,10 @@ " Lecce\n", " Persson\n", " \n", - " 508\n", - " 20-026\n", + " 542\n", + " 20-100\n", " 2003\n", - " 3\n", + " 7\n", " ...\n", " 0.0\n", " 0\n", @@ -2005,8 +2033,8 @@ " Sampdoria\n", " Montevago\n", " \n", - " 331\n", - " 19-329\n", + " 352\n", + " 20-038\n", " 2003\n", " 6\n", " ...\n", @@ -2029,8 +2057,8 @@ " Spezia\n", " Krollis\n", " \n", - " 258\n", - " 21-105\n", + " 277\n", + " 21-179\n", " 2001\n", " 1\n", " ...\n", @@ -2076,37 +2104,37 @@ ], "text/plain": [ " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 551 \n", - "1 2814 P Provedel Lazio Provedel 560 \n", - "2 4964 P Vicario Empoli Vicario 569 \n", - "3 453 P Szczesny Juventus Szczesny 566 \n", - "4 2134 P Falcone Lecce Falcone 546 \n", + "0 572 P Meret Napoli Meret 594 \n", + "1 2814 P Provedel Lazio Provedel 605 \n", + "2 4964 P Vicario Empoli Vicario 617 \n", + "3 453 P Szczesny Juventus Szczesny 613 \n", + "4 2134 P Falcone Lecce Falcone 587 \n", ".. ... .. ... ... ... ... ... \n", - "538 5512 A De Luca Sampdoria Luca 134 \n", - "539 5837 A Voelkerling Persson Lecce Persson 508 \n", - "540 6113 A Montevago Sampdoria Montevago 331 \n", - "541 6143 A Krollis Spezia Krollis 258 \n", + "538 5512 A De Luca Sampdoria Luca 145 \n", + "539 5837 A Voelkerling Persson Lecce Persson 542 \n", + "540 6113 A Montevago Sampdoria Montevago 352 \n", + "541 6143 A Krollis Spezia Krollis 277 \n", "542 6160 A Vivaldo Udinese Vivaldo -1 \n", "\n", " age birth_year games ... gk_passes_length_avg gk_goal_kicks \\\n", - "0 25-325 1997 0 ... 26.7 128 \n", - "1 28-330 1994 0 ... 33.0 120 \n", - "2 26-126 1996 0 ... 34.0 108 \n", - "3 32-298 1990 0 ... 35.0 72 \n", - "4 27-304 1995 0 ... 41.3 163 \n", + "0 26-034 1997 0 ... 26.2 185 \n", + "1 29-039 1994 0 ... 33.0 161 \n", + "2 26-200 1996 0 ... 33.3 139 \n", + "3 33-007 1990 0 ... 34.3 114 \n", + "4 28-013 1995 0 ... 41.9 225 \n", ".. ... ... ... ... ... ... \n", - "538 24-208 1998 1 ... 0.0 0 \n", - "539 20-026 2003 3 ... 0.0 0 \n", - "540 19-329 2003 6 ... 0.0 0 \n", - "541 21-105 2001 1 ... 0.0 0 \n", + "538 24-282 1998 2 ... 0.0 0 \n", + "539 20-100 2003 7 ... 0.0 0 \n", + "540 20-038 2003 6 ... 0.0 0 \n", + "541 21-179 2001 1 ... 0.0 0 \n", "542 0 0 0 ... 0.0 0 \n", "\n", " gk_pct_goal_kicks_launched gk_goal_kick_length_avg gk_crosses \\\n", - "0 19.5 27.1 221 \n", - "1 34.2 34.5 279 \n", - "2 47.2 42.4 426 \n", - "3 41.7 38.7 178 \n", - "4 76.1 51.7 316 \n", + "0 20.0 26.8 308 \n", + "1 36.0 34.7 402 \n", + "2 48.9 42.6 489 \n", + "3 49.1 41.5 301 \n", + "4 77.3 52.4 441 \n", ".. ... ... ... \n", "538 0.0 0.0 0 \n", "539 0.0 0.0 0 \n", @@ -2115,11 +2143,11 @@ "542 0.0 0.0 0 \n", "\n", " gk_crosses_stopped gk_crosses_stopped_pct \\\n", - "0 6 2.7 \n", - "1 7 2.5 \n", - "2 25 5.9 \n", - "3 4 2.2 \n", - "4 15 4.7 \n", + "0 10 3.2 \n", + "1 17 4.2 \n", + "2 28 5.7 \n", + "3 9 3.0 \n", + "4 22 5.0 \n", ".. ... ... \n", "538 0 0.0 \n", "539 0 0.0 \n", @@ -2128,11 +2156,11 @@ "542 0 0.0 \n", "\n", " gk_def_actions_outside_pen_area gk_def_actions_outside_pen_area_per90 \\\n", - "0 24 1.14 \n", - "1 39 1.86 \n", - "2 12 0.57 \n", - "3 12 0.83 \n", - "4 20 0.95 \n", + "0 32 1.07 \n", + "1 46 1.49 \n", + "2 15 0.63 \n", + "3 19 0.89 \n", + "4 35 1.13 \n", ".. ... ... \n", "538 0 0.00 \n", "539 0 0.00 \n", @@ -2141,11 +2169,11 @@ "542 0 0.00 \n", "\n", " gk_avg_distance_def_actions \n", - "0 17.2 \n", - "1 18.2 \n", - "2 10.9 \n", - "3 15.8 \n", - "4 12.5 \n", + "0 17.0 \n", + "1 16.4 \n", + "2 10.7 \n", + "3 15.4 \n", + "4 13.7 \n", ".. ... \n", "538 0.0 \n", "539 0.0 \n", @@ -2203,8 +2231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "vote_avg 6.211716\n", - "vote_std 0.441335\n", + "vote_avg 6.214595\n", + "vote_std 0.457012\n", "dtype: float64\n" ] }, @@ -2236,128 +2264,133 @@ " \n", " \n", " 0\n", - " 6.261905\n", - " 0.478566\n", + " 6.200000\n", + " 0.420317\n", " \n", " \n", " 1\n", - " 6.261905\n", - " 0.365769\n", + " 6.274194\n", + " 0.418112\n", " \n", " \n", " 2\n", - " 6.476190\n", - " 0.392677\n", + " 6.458333\n", + " 0.379601\n", " \n", " \n", " 3\n", - " 6.033333\n", - " 0.339935\n", + " 6.090909\n", + " 0.324610\n", " \n", " \n", " 4\n", - " 6.309524\n", - " 0.361089\n", + " 6.225806\n", + " 0.521145\n", " \n", " \n", " 5\n", - " 6.285714\n", - " 0.424585\n", + " 6.274194\n", + " 0.418112\n", " \n", " \n", " 6\n", " 6.000000\n", - " 0.436436\n", + " 0.595683\n", " \n", " \n", " 7\n", - " 6.115385\n", - " 0.287820\n", + " 6.100000\n", + " 0.300000\n", " \n", " \n", " 8\n", - " 6.366667\n", - " 0.426875\n", + " 6.294118\n", + " 0.455645\n", " \n", " \n", " 9\n", - " 6.142857\n", - " 0.412393\n", + " 6.064516\n", + " 0.396394\n", " \n", " \n", " 10\n", - " 6.178571\n", - " 0.305143\n", + " 6.136364\n", + " 0.431220\n", " \n", " \n", " 11\n", - " 6.357143\n", - " 0.440315\n", + " 6.233333\n", + " 0.359011\n", " \n", " \n", " 12\n", - " 6.375000\n", - " 0.616610\n", + " 6.363636\n", + " 0.504115\n", " \n", " \n", " 13\n", - " 6.261905\n", - " 0.569441\n", + " 6.316667\n", + " 0.524140\n", " \n", " \n", " 14\n", - " 6.285714\n", - " 0.451754\n", + " 6.300000\n", + " 0.447214\n", " \n", " \n", " 15\n", - " 6.261905\n", - " 0.502826\n", + " 6.233333\n", + " 0.460676\n", " \n", " \n", " 16\n", - " 6.119048\n", - " 0.509546\n", + " 6.183333\n", + " 0.539804\n", " \n", " \n", " 17\n", - " 6.105263\n", - " 0.306892\n", + " 6.051724\n", + " 0.546854\n", " \n", " \n", " 18\n", - " 6.131579\n", - " 0.482376\n", + " 6.250000\n", + " 0.508850\n", " \n", " \n", " 19\n", - " 5.972222\n", - " 0.389880\n", + " 6.000000\n", + " 0.451335\n", " \n", " \n", " 20\n", - " 5.928571\n", - " 0.371154\n", + " 6.000000\n", + " 0.395285\n", " \n", " \n", " 21\n", - " 6.062500\n", - " 0.526634\n", + " 6.090909\n", + " 0.467983\n", " \n", " \n", " 22\n", - " 6.071429\n", - " 0.494872\n", + " 6.166667\n", + " 0.527046\n", " \n", " \n", " 23\n", - " 6.428571\n", - " 0.562429\n", + " 6.450000\n", + " 0.522015\n", " \n", " \n", " 24\n", - " 6.500000\n", - " 0.577350\n", + " 6.607143\n", + " 0.602927\n", + " \n", + " \n", + " 25\n", + " 6.214286\n", + " 0.364216\n", " \n", " \n", "\n", @@ -2365,31 +2398,32 @@ ], "text/plain": [ " vote_avg vote_std\n", - "0 6.261905 0.478566\n", - "1 6.261905 0.365769\n", - "2 6.476190 0.392677\n", - "3 6.033333 0.339935\n", - "4 6.309524 0.361089\n", - "5 6.285714 0.424585\n", - "6 6.000000 0.436436\n", - "7 6.115385 0.287820\n", - "8 6.366667 0.426875\n", - "9 6.142857 0.412393\n", - "10 6.178571 0.305143\n", - "11 6.357143 0.440315\n", - "12 6.375000 0.616610\n", - "13 6.261905 0.569441\n", - "14 6.285714 0.451754\n", - "15 6.261905 0.502826\n", - "16 6.119048 0.509546\n", - "17 6.105263 0.306892\n", - "18 6.131579 0.482376\n", - "19 5.972222 0.389880\n", - "20 5.928571 0.371154\n", - "21 6.062500 0.526634\n", - "22 6.071429 0.494872\n", - "23 6.428571 0.562429\n", - "24 6.500000 0.577350" + "0 6.200000 0.420317\n", + "1 6.274194 0.418112\n", + "2 6.458333 0.379601\n", + "3 6.090909 0.324610\n", + "4 6.225806 0.521145\n", + "5 6.274194 0.418112\n", + "6 6.000000 0.595683\n", + "7 6.100000 0.300000\n", + "8 6.294118 0.455645\n", + "9 6.064516 0.396394\n", + "10 6.136364 0.431220\n", + "11 6.233333 0.359011\n", + "12 6.363636 0.504115\n", + "13 6.316667 0.524140\n", + "14 6.300000 0.447214\n", + "15 6.233333 0.460676\n", + "16 6.183333 0.539804\n", + "17 6.051724 0.546854\n", + "18 6.250000 0.508850\n", + "19 6.000000 0.451335\n", + "20 6.000000 0.395285\n", + "21 6.090909 0.467983\n", + "22 6.166667 0.527046\n", + "23 6.450000 0.522015\n", + "24 6.607143 0.602927\n", + "25 6.214286 0.364216" ] }, "execution_count": 17, @@ -2463,28 +2497,28 @@ " \n", " \n", " 0\n", - " 6.261905\n", - " 0.478566\n", + " 6.200000\n", + " 0.420317\n", " \n", " \n", " 1\n", - " 6.261905\n", - " 0.365769\n", + " 6.274194\n", + " 0.418112\n", " \n", " \n", " 2\n", - " 6.476190\n", - " 0.392677\n", + " 6.458333\n", + " 0.379601\n", " \n", " \n", " 3\n", - " 6.033333\n", - " 0.339935\n", + " 6.090909\n", + " 0.324610\n", " \n", " \n", " 4\n", - " 6.309524\n", - " 0.361089\n", + " 6.225806\n", + " 0.521145\n", " \n", " \n", " ...\n", @@ -2493,28 +2527,28 @@ " \n", " \n", " 538\n", - " 5.947397\n", - " 0.490100\n", + " 5.958536\n", + " 0.291436\n", " \n", " \n", " 539\n", - " 5.694209\n", - " 0.383547\n", + " 6.068166\n", + " 0.351902\n", " \n", " \n", " 540\n", - " 5.623932\n", - " 0.280948\n", + " 5.618477\n", + " 0.288497\n", " \n", " \n", " 541\n", - " 6.124287\n", - " 0.483769\n", + " 6.290321\n", + " 0.466766\n", " \n", " \n", " 542\n", - " 6.215679\n", - " 0.378965\n", + " 5.790668\n", + " 0.631282\n", " \n", " \n", "\n", @@ -2523,17 +2557,17 @@ ], "text/plain": [ " vote_avg vote_std\n", - "0 6.261905 0.478566\n", - "1 6.261905 0.365769\n", - "2 6.476190 0.392677\n", - "3 6.033333 0.339935\n", - "4 6.309524 0.361089\n", + "0 6.200000 0.420317\n", + "1 6.274194 0.418112\n", + "2 6.458333 0.379601\n", + "3 6.090909 0.324610\n", + "4 6.225806 0.521145\n", ".. ... ...\n", - "538 5.947397 0.490100\n", - "539 5.694209 0.383547\n", - "540 5.623932 0.280948\n", - "541 6.124287 0.483769\n", - "542 6.215679 0.378965\n", + "538 5.958536 0.291436\n", + "539 6.068166 0.351902\n", + "540 5.618477 0.288497\n", + "541 6.290321 0.466766\n", + "542 5.790668 0.631282\n", "\n", "[543 rows x 2 columns]" ] @@ -2640,21 +2674,21 @@ " Napoli\n", " Meret\n", " \n", - " 551\n", - " 25-325\n", + " 594\n", + " 26-034\n", " 1997\n", " 0\n", " ...\n", - " 19.5\n", - " 27.1\n", - " 221\n", - " 6\n", - " 2.7\n", - " 24\n", - " 1.14\n", - " 17.2\n", - " 6.261905\n", - " 0.478566\n", + " 20.0\n", + " 26.8\n", + " 308\n", + " 10\n", + " 3.2\n", + " 32\n", + " 1.07\n", + " 17.0\n", + " 6.200000\n", + " 0.420317\n", " \n", " \n", " 1\n", @@ -2664,21 +2698,21 @@ " Lazio\n", " Provedel\n", " \n", - " 560\n", - " 28-330\n", + " 605\n", + " 29-039\n", " 1994\n", " 0\n", " ...\n", - " 34.2\n", - " 34.5\n", - " 279\n", - " 7\n", - " 2.5\n", - " 39\n", - " 1.86\n", - " 18.2\n", - " 6.261905\n", - " 0.365769\n", + " 36.0\n", + " 34.7\n", + " 402\n", + " 17\n", + " 4.2\n", + " 46\n", + " 1.49\n", + " 16.4\n", + " 6.274194\n", + " 0.418112\n", " \n", " \n", " 2\n", @@ -2688,21 +2722,21 @@ " Empoli\n", " Vicario\n", " \n", - " 569\n", - " 26-126\n", + " 617\n", + " 26-200\n", " 1996\n", " 0\n", " ...\n", - " 47.2\n", - " 42.4\n", - " 426\n", - " 25\n", - " 5.9\n", - " 12\n", - " 0.57\n", - " 10.9\n", - " 6.476190\n", - " 0.392677\n", + " 48.9\n", + " 42.6\n", + " 489\n", + " 28\n", + " 5.7\n", + " 15\n", + " 0.63\n", + " 10.7\n", + " 6.458333\n", + " 0.379601\n", " \n", " \n", " 3\n", @@ -2712,21 +2746,21 @@ " Juventus\n", " Szczesny\n", " \n", - " 566\n", - " 32-298\n", + " 613\n", + " 33-007\n", " 1990\n", " 0\n", " ...\n", - " 41.7\n", - " 38.7\n", - " 178\n", - " 4\n", - " 2.2\n", - " 12\n", - " 0.83\n", - " 15.8\n", - " 6.033333\n", - " 0.339935\n", + " 49.1\n", + " 41.5\n", + " 301\n", + " 9\n", + " 3.0\n", + " 19\n", + " 0.89\n", + " 15.4\n", + " 6.090909\n", + " 0.324610\n", " \n", " \n", " 4\n", @@ -2736,21 +2770,21 @@ " Lecce\n", " Falcone\n", " \n", - " 546\n", - " 27-304\n", + " 587\n", + " 28-013\n", " 1995\n", " 0\n", " ...\n", - " 76.1\n", - " 51.7\n", - " 316\n", - " 15\n", - " 4.7\n", - " 20\n", - " 0.95\n", - " 12.5\n", - " 6.309524\n", - " 0.361089\n", + " 77.3\n", + " 52.4\n", + " 441\n", + " 22\n", + " 5.0\n", + " 35\n", + " 1.13\n", + " 13.7\n", + " 6.225806\n", + " 0.521145\n", " \n", " \n", " ...\n", @@ -2784,10 +2818,10 @@ " Sampdoria\n", " Luca\n", " \n", - " 134\n", - " 24-208\n", + " 145\n", + " 24-282\n", " 1998\n", - " 1\n", + " 2\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2797,8 +2831,8 @@ " 0\n", " 0.00\n", " 0.0\n", - " 5.947397\n", - " 0.490100\n", + " 5.958536\n", + " 0.291436\n", " \n", " \n", " 539\n", @@ -2808,10 +2842,10 @@ " Lecce\n", " Persson\n", " \n", - " 508\n", - " 20-026\n", + " 542\n", + " 20-100\n", " 2003\n", - " 3\n", + " 7\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2821,8 +2855,8 @@ " 0\n", " 0.00\n", " 0.0\n", - " 5.694209\n", - " 0.383547\n", + " 6.068166\n", + " 0.351902\n", " \n", " \n", " 540\n", @@ -2832,8 +2866,8 @@ " Sampdoria\n", " Montevago\n", " \n", - " 331\n", - " 19-329\n", + " 352\n", + " 20-038\n", " 2003\n", " 6\n", " ...\n", @@ -2845,8 +2879,8 @@ " 0\n", " 0.00\n", " 0.0\n", - " 5.623932\n", - " 0.280948\n", + " 5.618477\n", + " 0.288497\n", " \n", " \n", " 541\n", @@ -2856,8 +2890,8 @@ " Spezia\n", " Krollis\n", " \n", - " 258\n", - " 21-105\n", + " 277\n", + " 21-179\n", " 2001\n", " 1\n", " ...\n", @@ -2869,8 +2903,8 @@ " 0\n", " 0.00\n", " 0.0\n", - " 6.124287\n", - " 0.483769\n", + " 6.290321\n", + " 0.466766\n", " \n", " \n", " 542\n", @@ -2893,8 +2927,8 @@ " 0\n", " 0.00\n", " 0.0\n", - " 6.215679\n", - " 0.378965\n", + " 5.790668\n", + " 0.631282\n", " \n", " \n", "\n", @@ -2903,37 +2937,37 @@ ], "text/plain": [ " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 551 \n", - "1 2814 P Provedel Lazio Provedel 560 \n", - "2 4964 P Vicario Empoli Vicario 569 \n", - "3 453 P Szczesny Juventus Szczesny 566 \n", - "4 2134 P Falcone Lecce Falcone 546 \n", + "0 572 P Meret Napoli Meret 594 \n", + "1 2814 P Provedel Lazio Provedel 605 \n", + "2 4964 P Vicario Empoli Vicario 617 \n", + "3 453 P Szczesny Juventus Szczesny 613 \n", + "4 2134 P Falcone Lecce Falcone 587 \n", ".. ... .. ... ... ... ... ... \n", - "538 5512 A De Luca Sampdoria Luca 134 \n", - "539 5837 A Voelkerling Persson Lecce Persson 508 \n", - "540 6113 A Montevago Sampdoria Montevago 331 \n", - "541 6143 A Krollis Spezia Krollis 258 \n", + "538 5512 A De Luca Sampdoria Luca 145 \n", + "539 5837 A Voelkerling Persson Lecce Persson 542 \n", + "540 6113 A Montevago Sampdoria Montevago 352 \n", + "541 6143 A Krollis Spezia Krollis 277 \n", "542 6160 A Vivaldo Udinese Vivaldo -1 \n", "\n", " age birth_year games ... gk_pct_goal_kicks_launched \\\n", - "0 25-325 1997 0 ... 19.5 \n", - "1 28-330 1994 0 ... 34.2 \n", - "2 26-126 1996 0 ... 47.2 \n", - "3 32-298 1990 0 ... 41.7 \n", - "4 27-304 1995 0 ... 76.1 \n", + "0 26-034 1997 0 ... 20.0 \n", + "1 29-039 1994 0 ... 36.0 \n", + "2 26-200 1996 0 ... 48.9 \n", + "3 33-007 1990 0 ... 49.1 \n", + "4 28-013 1995 0 ... 77.3 \n", ".. ... ... ... ... ... \n", - "538 24-208 1998 1 ... 0.0 \n", - "539 20-026 2003 3 ... 0.0 \n", - "540 19-329 2003 6 ... 0.0 \n", - "541 21-105 2001 1 ... 0.0 \n", + "538 24-282 1998 2 ... 0.0 \n", + "539 20-100 2003 7 ... 0.0 \n", + "540 20-038 2003 6 ... 0.0 \n", + "541 21-179 2001 1 ... 0.0 \n", "542 0 0 0 ... 0.0 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 27.1 221 6 \n", - "1 34.5 279 7 \n", - "2 42.4 426 25 \n", - "3 38.7 178 4 \n", - "4 51.7 316 15 \n", + "0 26.8 308 10 \n", + "1 34.7 402 17 \n", + "2 42.6 489 28 \n", + "3 41.5 301 9 \n", + "4 52.4 441 22 \n", ".. ... ... ... \n", "538 0.0 0 0 \n", "539 0.0 0 0 \n", @@ -2942,11 +2976,11 @@ "542 0.0 0 0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 2.7 24 \n", - "1 2.5 39 \n", - "2 5.9 12 \n", - "3 2.2 12 \n", - "4 4.7 20 \n", + "0 3.2 32 \n", + "1 4.2 46 \n", + "2 5.7 15 \n", + "3 3.0 19 \n", + "4 5.0 35 \n", ".. ... ... \n", "538 0.0 0 \n", "539 0.0 0 \n", @@ -2955,11 +2989,11 @@ "542 0.0 0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", - "0 1.14 17.2 \n", - "1 1.86 18.2 \n", - "2 0.57 10.9 \n", - "3 0.83 15.8 \n", - "4 0.95 12.5 \n", + "0 1.07 17.0 \n", + "1 1.49 16.4 \n", + "2 0.63 10.7 \n", + "3 0.89 15.4 \n", + "4 1.13 13.7 \n", ".. ... ... \n", "538 0.00 0.0 \n", "539 0.00 0.0 \n", @@ -2968,17 +3002,17 @@ "542 0.00 0.0 \n", "\n", " vote_avg vote_std \n", - "0 6.261905 0.478566 \n", - "1 6.261905 0.365769 \n", - "2 6.476190 0.392677 \n", - "3 6.033333 0.339935 \n", - "4 6.309524 0.361089 \n", + "0 6.200000 0.420317 \n", + "1 6.274194 0.418112 \n", + "2 6.458333 0.379601 \n", + "3 6.090909 0.324610 \n", + "4 6.225806 0.521145 \n", ".. ... ... \n", - "538 5.947397 0.490100 \n", - "539 5.694209 0.383547 \n", - "540 5.623932 0.280948 \n", - "541 6.124287 0.483769 \n", - "542 6.215679 0.378965 \n", + "538 5.958536 0.291436 \n", + "539 6.068166 0.351902 \n", + "540 5.618477 0.288497 \n", + "541 6.290321 0.466766 \n", + "542 5.790668 0.631282 \n", "\n", "[543 rows x 160 columns]" ] @@ -3033,33 +3067,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Zoet, 0.5\n", + "Zoet, 0.6666666666666667\n", "Pegolo, 0.33333333333333337\n", - "Gollini, 0.5\n", + "Gollini, 0.16666666666666663\n", "Mirante, 0.0\n", "Sarr M., 0.0\n", "Lamanna, 0.0\n", "Ujkani, 0.0\n", "Berisha, 0.0\n", - "Marchetti, 0.0\n", - "Perilli, 0.0\n", + "Marchetti, 0.16666666666666663\n", + "Perilli, 0.16666666666666663\n", "Padelli, 0.0\n", - "Perisan, 0.0\n", - "Bardi, 0.0\n", + "Bardi, 0.16666666666666663\n", "Cordaz, 0.0\n", "Pinsoglio, 0.0\n", "Fiorillo, 0.0\n", - "Cragno, 0.0\n", - "Sirigu, 0.0\n", + "Cragno, 0.16666666666666663\n", + "Sirigu, 0.16666666666666663\n", "Cerofolini, 0.0\n", "Rossi F., 0.0\n", - "Ravaglia F., 0.0\n", + "Ravaglia F., 0.6666666666666667\n", "Brancolini, 0.0\n", "Bleve, 0.0\n", "Berardi A., 0.0\n", "Russo A., 0.0\n", "Gemello, 0.0\n", - "Ravaglia, 0.0\n", + "Ravaglia, 0.6666666666666667\n", "Boer, 0.0\n", "Adamonis, 0.0\n", "Marfella, 0.0\n", @@ -3072,7 +3105,7 @@ "Ciezkowski, 0.0\n", "Saro, 0.0\n", "Vasquez D., 0.0\n", - "Turk, 0.0\n" + "Turk, 0.33333333333333337\n" ] } ], @@ -3158,21 +3191,21 @@ " Napoli\n", " Meret\n", " \n", - " 551\n", - " 25-325\n", + " 594\n", + " 26-034\n", " 1997\n", " 0\n", " ...\n", - " 19.5\n", - " 27.1\n", - " 221.0\n", - " 6.0\n", - " 2.7\n", - " 24.0\n", - " 1.14\n", - " 17.2\n", - " 6.261905\n", - " 0.478566\n", + " 20.0\n", + " 26.8\n", + " 308.0\n", + " 10.0\n", + " 3.2\n", + " 32.0\n", + " 1.07\n", + " 17.0\n", + " 6.200000\n", + " 0.420317\n", " \n", " \n", " 1\n", @@ -3182,21 +3215,21 @@ " Lazio\n", " Provedel\n", " \n", - " 560\n", - " 28-330\n", + " 605\n", + " 29-039\n", " 1994\n", " 0\n", " ...\n", - " 34.2\n", - " 34.5\n", - " 279.0\n", - " 7.0\n", - " 2.5\n", - " 39.0\n", - " 1.86\n", - " 18.2\n", - " 6.261905\n", - " 0.365769\n", + " 36.0\n", + " 34.7\n", + " 402.0\n", + " 17.0\n", + " 4.2\n", + " 46.0\n", + " 1.49\n", + " 16.4\n", + " 6.274194\n", + " 0.418112\n", " \n", " \n", " 2\n", @@ -3206,21 +3239,21 @@ " Empoli\n", " Vicario\n", " \n", - " 569\n", - " 26-126\n", + " 617\n", + " 26-200\n", " 1996\n", " 0\n", " ...\n", - " 47.2\n", - " 42.4\n", - " 426.0\n", - " 25.0\n", - " 5.9\n", - " 12.0\n", - " 0.57\n", - " 10.9\n", - " 6.476190\n", - " 0.392677\n", + " 48.9\n", + " 42.6\n", + " 489.0\n", + " 28.0\n", + " 5.7\n", + " 15.0\n", + " 0.63\n", + " 10.7\n", + " 6.458333\n", + " 0.379601\n", " \n", " \n", " 3\n", @@ -3230,21 +3263,21 @@ " Juventus\n", " Szczesny\n", " \n", - " 566\n", - " 32-298\n", + " 613\n", + " 33-007\n", " 1990\n", " 0\n", " ...\n", - " 41.7\n", - " 38.7\n", - " 178.0\n", - " 4.0\n", - " 2.2\n", - " 12.0\n", - " 0.83\n", - " 15.8\n", - " 6.033333\n", - " 0.339935\n", + " 49.1\n", + " 41.5\n", + " 301.0\n", + " 9.0\n", + " 3.0\n", + " 19.0\n", + " 0.89\n", + " 15.4\n", + " 6.090909\n", + " 0.324610\n", " \n", " \n", " 4\n", @@ -3254,21 +3287,21 @@ " Lecce\n", " Falcone\n", " \n", - " 546\n", - " 27-304\n", + " 587\n", + " 28-013\n", " 1995\n", " 0\n", " ...\n", - " 76.1\n", - " 51.7\n", - " 316.0\n", - " 15.0\n", - " 4.7\n", - " 20.0\n", - " 0.95\n", - " 12.5\n", - " 6.309524\n", - " 0.361089\n", + " 77.3\n", + " 52.4\n", + " 441.0\n", + " 22.0\n", + " 5.0\n", + " 35.0\n", + " 1.13\n", + " 13.7\n", + " 6.225806\n", + " 0.521145\n", " \n", " \n", " ...\n", @@ -3302,10 +3335,10 @@ " Sampdoria\n", " Luca\n", " \n", - " 134\n", - " 24-208\n", + " 145\n", + " 24-282\n", " 1998\n", - " 1\n", + " 2\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3315,8 +3348,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.947397\n", - " 0.490100\n", + " 5.958536\n", + " 0.291436\n", " \n", " \n", " 539\n", @@ -3326,10 +3359,10 @@ " Lecce\n", " Persson\n", " \n", - " 508\n", - " 20-026\n", + " 542\n", + " 20-100\n", " 2003\n", - " 3\n", + " 7\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3339,8 +3372,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.694209\n", - " 0.383547\n", + " 6.068166\n", + " 0.351902\n", " \n", " \n", " 540\n", @@ -3350,8 +3383,8 @@ " Sampdoria\n", " Montevago\n", " \n", - " 331\n", - " 19-329\n", + " 352\n", + " 20-038\n", " 2003\n", " 6\n", " ...\n", @@ -3363,8 +3396,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.623932\n", - " 0.280948\n", + " 5.618477\n", + " 0.288497\n", " \n", " \n", " 541\n", @@ -3374,8 +3407,8 @@ " Spezia\n", " Krollis\n", " \n", - " 258\n", - " 21-105\n", + " 277\n", + " 21-179\n", " 2001\n", " 1\n", " ...\n", @@ -3387,8 +3420,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.124287\n", - " 0.483769\n", + " 6.290321\n", + " 0.466766\n", " \n", " \n", " 542\n", @@ -3411,8 +3444,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.215679\n", - " 0.378965\n", + " 5.790668\n", + " 0.631282\n", " \n", " \n", "\n", @@ -3421,37 +3454,37 @@ ], "text/plain": [ " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 551 \n", - "1 2814 P Provedel Lazio Provedel 560 \n", - "2 4964 P Vicario Empoli Vicario 569 \n", - "3 453 P Szczesny Juventus Szczesny 566 \n", - "4 2134 P Falcone Lecce Falcone 546 \n", + "0 572 P Meret Napoli Meret 594 \n", + "1 2814 P Provedel Lazio Provedel 605 \n", + "2 4964 P Vicario Empoli Vicario 617 \n", + "3 453 P Szczesny Juventus Szczesny 613 \n", + "4 2134 P Falcone Lecce Falcone 587 \n", ".. ... .. ... ... ... ... ... \n", - "538 5512 A De Luca Sampdoria Luca 134 \n", - "539 5837 A Voelkerling Persson Lecce Persson 508 \n", - "540 6113 A Montevago Sampdoria Montevago 331 \n", - "541 6143 A Krollis Spezia Krollis 258 \n", + "538 5512 A De Luca Sampdoria Luca 145 \n", + "539 5837 A Voelkerling Persson Lecce Persson 542 \n", + "540 6113 A Montevago Sampdoria Montevago 352 \n", + "541 6143 A Krollis Spezia Krollis 277 \n", "542 6160 A Vivaldo Udinese Vivaldo -1 \n", "\n", " age birth_year games ... gk_pct_goal_kicks_launched \\\n", - "0 25-325 1997 0 ... 19.5 \n", - "1 28-330 1994 0 ... 34.2 \n", - "2 26-126 1996 0 ... 47.2 \n", - "3 32-298 1990 0 ... 41.7 \n", - "4 27-304 1995 0 ... 76.1 \n", + "0 26-034 1997 0 ... 20.0 \n", + "1 29-039 1994 0 ... 36.0 \n", + "2 26-200 1996 0 ... 48.9 \n", + "3 33-007 1990 0 ... 49.1 \n", + "4 28-013 1995 0 ... 77.3 \n", ".. ... ... ... ... ... \n", - "538 24-208 1998 1 ... 0.0 \n", - "539 20-026 2003 3 ... 0.0 \n", - "540 19-329 2003 6 ... 0.0 \n", - "541 21-105 2001 1 ... 0.0 \n", + "538 24-282 1998 2 ... 0.0 \n", + "539 20-100 2003 7 ... 0.0 \n", + "540 20-038 2003 6 ... 0.0 \n", + "541 21-179 2001 1 ... 0.0 \n", "542 0 0 0 ... 0.0 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 27.1 221.0 6.0 \n", - "1 34.5 279.0 7.0 \n", - "2 42.4 426.0 25.0 \n", - "3 38.7 178.0 4.0 \n", - "4 51.7 316.0 15.0 \n", + "0 26.8 308.0 10.0 \n", + "1 34.7 402.0 17.0 \n", + "2 42.6 489.0 28.0 \n", + "3 41.5 301.0 9.0 \n", + "4 52.4 441.0 22.0 \n", ".. ... ... ... \n", "538 0.0 0.0 0.0 \n", "539 0.0 0.0 0.0 \n", @@ -3460,11 +3493,11 @@ "542 0.0 0.0 0.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 2.7 24.0 \n", - "1 2.5 39.0 \n", - "2 5.9 12.0 \n", - "3 2.2 12.0 \n", - "4 4.7 20.0 \n", + "0 3.2 32.0 \n", + "1 4.2 46.0 \n", + "2 5.7 15.0 \n", + "3 3.0 19.0 \n", + "4 5.0 35.0 \n", ".. ... ... \n", "538 0.0 0.0 \n", "539 0.0 0.0 \n", @@ -3473,11 +3506,11 @@ "542 0.0 0.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", - "0 1.14 17.2 \n", - "1 1.86 18.2 \n", - "2 0.57 10.9 \n", - "3 0.83 15.8 \n", - "4 0.95 12.5 \n", + "0 1.07 17.0 \n", + "1 1.49 16.4 \n", + "2 0.63 10.7 \n", + "3 0.89 15.4 \n", + "4 1.13 13.7 \n", ".. ... ... \n", "538 0.00 0.0 \n", "539 0.00 0.0 \n", @@ -3486,17 +3519,17 @@ "542 0.00 0.0 \n", "\n", " vote_avg vote_std \n", - "0 6.261905 0.478566 \n", - "1 6.261905 0.365769 \n", - "2 6.476190 0.392677 \n", - "3 6.033333 0.339935 \n", - "4 6.309524 0.361089 \n", + "0 6.200000 0.420317 \n", + "1 6.274194 0.418112 \n", + "2 6.458333 0.379601 \n", + "3 6.090909 0.324610 \n", + "4 6.225806 0.521145 \n", ".. ... ... \n", - "538 5.947397 0.490100 \n", - "539 5.694209 0.383547 \n", - "540 5.623932 0.280948 \n", - "541 6.124287 0.483769 \n", - "542 6.215679 0.378965 \n", + "538 5.958536 0.291436 \n", + "539 6.068166 0.351902 \n", + "540 5.618477 0.288497 \n", + "541 6.290321 0.466766 \n", + "542 5.790668 0.631282 \n", "\n", "[543 rows x 160 columns]" ] diff --git a/4_player_match_dataset_creation.ipynb b/4_player_match_dataset_creation.ipynb index ef992e4..4f198f5 100644 --- a/4_player_match_dataset_creation.ipynb +++ b/4_player_match_dataset_creation.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "3ecf3676", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "id": "8a6867a5", "metadata": {}, "outputs": [ @@ -40,7 +40,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_5304\\552672520.py:19: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3244\\552672520.py:19: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -120,480 +120,480 @@ " Atalanta\n", " Atalanta\n", " 24.0\n", - " 49.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 38.0\n", - " 27.0\n", + " 48.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 40.0\n", + " 28.0\n", " 6.0\n", " 8.0\n", " ...\n", - " 232.0\n", " 244.0\n", + " 256.0\n", " 26.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1261.0\n", - " 260.0\n", - " 317.0\n", - " 45.1\n", + " 1335.0\n", + " 273.0\n", + " 328.0\n", + " 45.4\n", " \n", " \n", " Bologna\n", " Bologna\n", " 25.0\n", - " 51.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 52.4\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 27.0\n", " 20.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 262.0\n", - " 254.0\n", - " 36.0\n", + " 280.0\n", + " 268.0\n", + " 38.0\n", " 3.0\n", " 4.0\n", " 1.0\n", - " 1145.0\n", - " 239.0\n", - " 193.0\n", - " 55.3\n", + " 1204.0\n", + " 250.0\n", + " 210.0\n", + " 54.3\n", " \n", " \n", " Cremonese\n", " Cremonese\n", - " 30.0\n", - " 44.2\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 31.0\n", + " 43.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 15.0\n", " 7.0\n", " 2.0\n", " 4.0\n", " ...\n", - " 232.0\n", - " 264.0\n", - " 33.0\n", + " 239.0\n", + " 271.0\n", + " 36.0\n", " 3.0\n", " 4.0\n", " 0.0\n", - " 1168.0\n", - " 388.0\n", - " 296.0\n", - " 56.7\n", + " 1229.0\n", + " 409.0\n", + " 314.0\n", + " 56.6\n", " \n", " \n", " Empoli\n", " Empoli\n", - " 27.0\n", - " 46.8\n", + " 28.0\n", + " 47.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 21.0\n", - " 231.0\n", - 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" 302.0\n", - " 24.0\n", + " 234.0\n", + " 315.0\n", + " 25.0\n", " 1.0\n", " 0.0\n", " 2.0\n", - " 1173.0\n", - " 385.0\n", - " 422.0\n", - " 47.7\n", + " 1231.0\n", + " 423.0\n", + " 445.0\n", + " 48.7\n", " \n", " \n", " Inter\n", " Inter\n", " 23.0\n", - " 54.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 54.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 40.0\n", " 27.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 263.0\n", - " 237.0\n", + " 276.0\n", + " 248.0\n", " 19.0\n", " 2.0\n", " 2.0\n", " 1.0\n", - " 960.0\n", - " 221.0\n", - " 266.0\n", - " 45.4\n", + " 1007.0\n", + " 231.0\n", + " 288.0\n", + " 44.5\n", " \n", " \n", " Juventus\n", " Juventus\n", " 26.0\n", - " 49.2\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 33.0\n", - " 25.0\n", + " 49.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 34.0\n", + " 26.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 229.0\n", - " 232.0\n", - " 27.0\n", + " 246.0\n", + " 242.0\n", + " 29.0\n", " 0.0\n", " 4.0\n", " 0.0\n", - " 1064.0\n", - " 247.0\n", - " 257.0\n", - " 49.0\n", + " 1134.0\n", + " 258.0\n", + " 272.0\n", + " 48.7\n", " \n", " \n", " Lazio\n", " Lazio\n", " 21.0\n", - " 51.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 51.8\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 36.0\n", " 26.0\n", " 3.0\n", " 4.0\n", " ...\n", - " 294.0\n", - " 206.0\n", + " 308.0\n", + " 218.0\n", " 44.0\n", " 1.0\n", " 4.0\n", " 1.0\n", - " 1157.0\n", - " 207.0\n", - " 216.0\n", - " 48.9\n", + " 1233.0\n", + " 218.0\n", + " 229.0\n", + " 48.8\n", " \n", " \n", " Lecce\n", " Lecce\n", " 26.0\n", " 42.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 20.0\n", " 14.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 275.0\n", - " 279.0\n", + " 283.0\n", + " 300.0\n", " 45.0\n", " 3.0\n", " 2.0\n", - " 1.0\n", - " 1141.0\n", - " 396.0\n", - " 316.0\n", - " 55.6\n", + " 2.0\n", + " 1200.0\n", + " 417.0\n", + " 332.0\n", + " 55.7\n", " \n", " \n", " Milan\n", " Milan\n", " 27.0\n", - " 53.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 35.0\n", - " 30.0\n", + " 53.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 36.0\n", + " 31.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 255.0\n", - " 251.0\n", + " 275.0\n", + " 261.0\n", " 25.0\n", " 4.0\n", " 2.0\n", " 2.0\n", - " 1059.0\n", - " 249.0\n", - " 305.0\n", - " 44.9\n", + " 1125.0\n", + " 263.0\n", + " 325.0\n", + " 44.7\n", " \n", " \n", " Monza\n", " Monza\n", " 29.0\n", - " 55.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 26.0\n", - " 16.0\n", + " 55.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 27.0\n", + " 17.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 303.0\n", - " 263.0\n", + " 318.0\n", + " 281.0\n", " 37.0\n", " 0.0\n", " 4.0\n", " 1.0\n", - " 1075.0\n", - " 226.0\n", - " 241.0\n", - " 48.4\n", + " 1145.0\n", + " 242.0\n", + " 253.0\n", + " 48.9\n", " \n", " \n", " Napoli\n", " Napoli\n", " 24.0\n", - " 61.4\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 51.0\n", - " 40.0\n", + " 61.6\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 54.0\n", + " 42.0\n", " 5.0\n", " 6.0\n", " ...\n", - " 291.0\n", - " 192.0\n", + " 298.0\n", + " 199.0\n", " 28.0\n", " 1.0\n", " 5.0\n", " 0.0\n", - " 1049.0\n", - " 214.0\n", - " 259.0\n", - " 45.2\n", + " 1100.0\n", + " 232.0\n", + " 280.0\n", + " 45.3\n", " \n", " \n", " Roma\n", " Roma\n", " 26.0\n", - " 48.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 28.0\n", + " 49.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", " 19.0\n", - " 3.0\n", - " 5.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 292.0\n", - " 238.0\n", - " 10.0\n", + " 316.0\n", + " 246.0\n", + " 12.0\n", " 0.0\n", - " 5.0\n", + " 6.0\n", " 0.0\n", - " 1095.0\n", - " 205.0\n", - " 245.0\n", - " 45.6\n", + " 1156.0\n", + " 221.0\n", + " 266.0\n", + " 45.4\n", " \n", " \n", " Salernitana\n", " Salernitana\n", " 28.0\n", - " 45.1\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 46.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 24.0\n", " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 238.0\n", - " 242.0\n", - " 52.0\n", + " 252.0\n", + " 259.0\n", + " 57.0\n", " 8.0\n", " 1.0\n", " 1.0\n", - " 1143.0\n", - " 268.0\n", - " 247.0\n", - " 52.0\n", + " 1213.0\n", + " 291.0\n", + " 285.0\n", + " 50.5\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", " 31.0\n", - " 47.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 47.3\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 10.0\n", " 8.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 328.0\n", - " 288.0\n", - " 56.0\n", + " 339.0\n", + " 300.0\n", + " 59.0\n", " 4.0\n", " 0.0\n", " 0.0\n", - " 1126.0\n", - " 340.0\n", - " 339.0\n", - " 50.1\n", + " 1189.0\n", + " 362.0\n", + " 349.0\n", + " 50.9\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", " 29.0\n", - " 48.5\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 24.0\n", + " 48.7\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 25.0\n", " 18.0\n", " 4.0\n", " 5.0\n", " ...\n", - " 275.0\n", - " 200.0\n", - " 69.0\n", + " 287.0\n", + " 206.0\n", + " 72.0\n", " 2.0\n", " 5.0\n", - " 0.0\n", - " 1088.0\n", - " 243.0\n", - " 195.0\n", - " 55.5\n", + " 1.0\n", + " 1131.0\n", + " 256.0\n", + " 206.0\n", + " 55.4\n", " \n", " \n", " Spezia\n", " Spezia\n", " 33.0\n", - " 45.9\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 15.0\n", + " 45.5\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 17.0\n", " 10.0\n", - " 2.0\n", - " 2.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 225.0\n", - " 279.0\n", - " 50.0\n", + " 235.0\n", + " 291.0\n", + " 53.0\n", " 1.0\n", + " 3.0\n", " 2.0\n", - " 2.0\n", - " 1225.0\n", - " 337.0\n", - " 285.0\n", - " 54.2\n", + " 1275.0\n", + " 343.0\n", + " 306.0\n", + " 52.9\n", " \n", " \n", " Torino\n", " Torino\n", - " 25.0\n", + " 27.0\n", " 53.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", " 22.0\n", " 17.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 229.0\n", - " 287.0\n", - " 23.0\n", + " 239.0\n", + " 306.0\n", + " 24.0\n", " 3.0\n", " 1.0\n", " 0.0\n", - " 1095.0\n", - " 347.0\n", - " 319.0\n", - " 52.1\n", + " 1155.0\n", + " 367.0\n", + " 333.0\n", + " 52.4\n", " \n", " \n", " Udinese\n", " Udinese\n", " 25.0\n", - " 50.0\n", - " 21.0\n", - " 231.0\n", - " 1890.0\n", - " 27.0\n", - " 23.0\n", + " 49.9\n", + " 22.0\n", + " 242.0\n", + " 1980.0\n", + " 29.0\n", + " 25.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 274.0\n", - " 242.0\n", + " 282.0\n", + " 252.0\n", " 34.0\n", " 2.0\n", " 0.0\n", " 1.0\n", - " 1063.0\n", - " 222.0\n", - " 264.0\n", + " 1101.0\n", + " 233.0\n", + " 277.0\n", " 45.7\n", " \n", " \n", @@ -604,95 +604,95 @@ "text/plain": [ " team team_players_used team_possession team_games \\\n", "team_idx \n", - "Atalanta Atalanta 24.0 49.0 21.0 \n", - "Bologna Bologna 25.0 51.9 21.0 \n", - "Cremonese Cremonese 30.0 44.2 21.0 \n", - "Empoli Empoli 27.0 46.8 21.0 \n", - "Fiorentina Fiorentina 28.0 57.3 21.0 \n", - "Verona Hellas Verona 34.0 43.1 21.0 \n", - "Inter Inter 23.0 54.0 21.0 \n", - "Juventus Juventus 26.0 49.2 21.0 \n", - "Lazio Lazio 21.0 51.4 21.0 \n", - "Lecce Lecce 26.0 42.4 21.0 \n", - "Milan Milan 27.0 53.9 21.0 \n", - "Monza Monza 29.0 55.9 21.0 \n", - "Napoli Napoli 24.0 61.4 21.0 \n", - "Roma Roma 26.0 48.9 21.0 \n", - "Salernitana Salernitana 28.0 45.1 21.0 \n", - "Sampdoria Sampdoria 31.0 47.9 21.0 \n", - "Sassuolo Sassuolo 29.0 48.5 21.0 \n", - "Spezia Spezia 33.0 45.9 21.0 \n", - "Torino Torino 25.0 53.0 21.0 \n", - "Udinese Udinese 25.0 50.0 21.0 \n", + "Atalanta Atalanta 24.0 48.6 22.0 \n", + "Bologna Bologna 25.0 52.4 22.0 \n", + "Cremonese Cremonese 31.0 43.8 22.0 \n", + "Empoli Empoli 28.0 47.5 22.0 \n", + "Fiorentina Fiorentina 28.0 57.2 22.0 \n", + "Verona Hellas Verona 34.0 42.9 22.0 \n", + "Inter Inter 23.0 54.5 22.0 \n", + "Juventus Juventus 26.0 49.0 22.0 \n", + "Lazio Lazio 21.0 51.8 22.0 \n", + "Lecce Lecce 26.0 42.4 22.0 \n", + "Milan Milan 27.0 53.5 22.0 \n", + "Monza Monza 29.0 55.0 22.0 \n", + "Napoli Napoli 24.0 61.6 22.0 \n", + "Roma Roma 26.0 49.3 22.0 \n", + "Salernitana Salernitana 28.0 46.0 22.0 \n", + "Sampdoria Sampdoria 31.0 47.3 22.0 \n", + "Sassuolo Sassuolo 29.0 48.7 22.0 \n", + "Spezia Spezia 33.0 45.5 22.0 \n", + "Torino Torino 27.0 53.0 22.0 \n", + "Udinese Udinese 25.0 49.9 22.0 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", "team_idx \n", - "Atalanta 231.0 1890.0 38.0 27.0 \n", - "Bologna 231.0 1890.0 27.0 20.0 \n", - "Cremonese 231.0 1890.0 15.0 7.0 \n", - "Empoli 231.0 1890.0 19.0 11.0 \n", - "Fiorentina 231.0 1890.0 23.0 18.0 \n", - "Verona 231.0 1890.0 17.0 14.0 \n", - "Inter 231.0 1890.0 40.0 27.0 \n", - "Juventus 231.0 1890.0 33.0 25.0 \n", - "Lazio 231.0 1890.0 36.0 26.0 \n", - "Lecce 231.0 1890.0 20.0 14.0 \n", - "Milan 231.0 1890.0 35.0 30.0 \n", - "Monza 231.0 1890.0 26.0 16.0 \n", - "Napoli 231.0 1890.0 51.0 40.0 \n", - "Roma 231.0 1890.0 28.0 19.0 \n", - "Salernitana 231.0 1890.0 24.0 16.0 \n", - "Sampdoria 231.0 1890.0 10.0 8.0 \n", - "Sassuolo 231.0 1890.0 24.0 18.0 \n", - "Spezia 231.0 1890.0 15.0 10.0 \n", - "Torino 231.0 1890.0 22.0 17.0 \n", - "Udinese 231.0 1890.0 27.0 23.0 \n", + "Atalanta 242.0 1980.0 40.0 28.0 \n", + "Bologna 242.0 1980.0 27.0 20.0 \n", + "Cremonese 242.0 1980.0 15.0 7.0 \n", + "Empoli 242.0 1980.0 21.0 11.0 \n", + "Fiorentina 242.0 1980.0 23.0 18.0 \n", + "Verona 242.0 1980.0 18.0 15.0 \n", + "Inter 242.0 1980.0 40.0 27.0 \n", + "Juventus 242.0 1980.0 34.0 26.0 \n", + "Lazio 242.0 1980.0 36.0 26.0 \n", + "Lecce 242.0 1980.0 20.0 14.0 \n", + "Milan 242.0 1980.0 36.0 31.0 \n", + "Monza 242.0 1980.0 27.0 17.0 \n", + "Napoli 242.0 1980.0 54.0 42.0 \n", + "Roma 242.0 1980.0 29.0 19.0 \n", + "Salernitana 242.0 1980.0 24.0 16.0 \n", + "Sampdoria 242.0 1980.0 10.0 8.0 \n", + "Sassuolo 242.0 1980.0 25.0 18.0 \n", + "Spezia 242.0 1980.0 17.0 10.0 \n", + "Torino 242.0 1980.0 22.0 17.0 \n", + "Udinese 242.0 1980.0 29.0 25.0 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", "team_idx ... \n", - "Atalanta 6.0 8.0 ... 232.0 \n", - "Bologna 4.0 4.0 ... 262.0 \n", - "Cremonese 2.0 4.0 ... 232.0 \n", - "Empoli 0.0 0.0 ... 270.0 \n", - "Fiorentina 2.0 4.0 ... 296.0 \n", - "Verona 0.0 0.0 ... 213.0 \n", - "Inter 2.0 2.0 ... 263.0 \n", - "Juventus 3.0 4.0 ... 229.0 \n", - "Lazio 3.0 4.0 ... 294.0 \n", - "Lecce 1.0 2.0 ... 275.0 \n", - "Milan 2.0 2.0 ... 255.0 \n", - "Monza 4.0 4.0 ... 303.0 \n", - "Napoli 5.0 6.0 ... 291.0 \n", - "Roma 3.0 5.0 ... 292.0 \n", - "Salernitana 1.0 1.0 ... 238.0 \n", - "Sampdoria 0.0 0.0 ... 328.0 \n", - "Sassuolo 4.0 5.0 ... 275.0 \n", - "Spezia 2.0 2.0 ... 225.0 \n", - "Torino 1.0 1.0 ... 229.0 \n", - "Udinese 0.0 0.0 ... 274.0 \n", + "Atalanta 6.0 8.0 ... 244.0 \n", + "Bologna 4.0 4.0 ... 280.0 \n", + "Cremonese 2.0 4.0 ... 239.0 \n", + "Empoli 0.0 0.0 ... 283.0 \n", + "Fiorentina 2.0 4.0 ... 307.0 \n", + "Verona 0.0 0.0 ... 234.0 \n", + "Inter 2.0 2.0 ... 276.0 \n", + "Juventus 3.0 4.0 ... 246.0 \n", + "Lazio 3.0 4.0 ... 308.0 \n", + "Lecce 1.0 2.0 ... 283.0 \n", + "Milan 2.0 2.0 ... 275.0 \n", + "Monza 4.0 4.0 ... 318.0 \n", + "Napoli 5.0 6.0 ... 298.0 \n", + "Roma 4.0 6.0 ... 316.0 \n", + "Salernitana 1.0 1.0 ... 252.0 \n", + "Sampdoria 0.0 0.0 ... 339.0 \n", + "Sassuolo 4.0 5.0 ... 287.0 \n", + "Spezia 3.0 3.0 ... 235.0 \n", + "Torino 1.0 1.0 ... 239.0 \n", + "Udinese 0.0 0.0 ... 282.0 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", "team_idx \n", - "Atalanta 244.0 26.0 1.0 \n", - "Bologna 254.0 36.0 3.0 \n", - "Cremonese 264.0 33.0 3.0 \n", - "Empoli 244.0 35.0 2.0 \n", - "Fiorentina 252.0 54.0 1.0 \n", - "Verona 302.0 24.0 1.0 \n", - "Inter 237.0 19.0 2.0 \n", - "Juventus 232.0 27.0 0.0 \n", - "Lazio 206.0 44.0 1.0 \n", - "Lecce 279.0 45.0 3.0 \n", - "Milan 251.0 25.0 4.0 \n", - "Monza 263.0 37.0 0.0 \n", - "Napoli 192.0 28.0 1.0 \n", - "Roma 238.0 10.0 0.0 \n", - "Salernitana 242.0 52.0 8.0 \n", - "Sampdoria 288.0 56.0 4.0 \n", - "Sassuolo 200.0 69.0 2.0 \n", - "Spezia 279.0 50.0 1.0 \n", - "Torino 287.0 23.0 3.0 \n", - "Udinese 242.0 34.0 2.0 \n", + "Atalanta 256.0 26.0 1.0 \n", + "Bologna 268.0 38.0 3.0 \n", + "Cremonese 271.0 36.0 3.0 \n", + "Empoli 253.0 36.0 2.0 \n", + "Fiorentina 269.0 59.0 1.0 \n", + "Verona 315.0 25.0 1.0 \n", + "Inter 248.0 19.0 2.0 \n", + "Juventus 242.0 29.0 0.0 \n", + "Lazio 218.0 44.0 1.0 \n", + "Lecce 300.0 45.0 3.0 \n", + "Milan 261.0 25.0 4.0 \n", + "Monza 281.0 37.0 0.0 \n", + "Napoli 199.0 28.0 1.0 \n", + "Roma 246.0 12.0 0.0 \n", + "Salernitana 259.0 57.0 8.0 \n", + "Sampdoria 300.0 59.0 4.0 \n", + "Sassuolo 206.0 72.0 2.0 \n", + "Spezia 291.0 53.0 1.0 \n", + "Torino 306.0 24.0 3.0 \n", + "Udinese 252.0 34.0 2.0 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", "team_idx \n", @@ -705,68 +705,68 @@ "Inter 2.0 1.0 \n", "Juventus 4.0 0.0 \n", "Lazio 4.0 1.0 \n", - "Lecce 2.0 1.0 \n", + "Lecce 2.0 2.0 \n", "Milan 2.0 2.0 \n", "Monza 4.0 1.0 \n", "Napoli 5.0 0.0 \n", - "Roma 5.0 0.0 \n", + "Roma 6.0 0.0 \n", "Salernitana 1.0 1.0 \n", "Sampdoria 0.0 0.0 \n", - "Sassuolo 5.0 0.0 \n", - "Spezia 2.0 2.0 \n", + "Sassuolo 5.0 1.0 \n", + "Spezia 3.0 2.0 \n", "Torino 1.0 0.0 \n", "Udinese 0.0 1.0 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", "team_idx \n", - "Atalanta 1261.0 260.0 \n", - "Bologna 1145.0 239.0 \n", - "Cremonese 1168.0 388.0 \n", - "Empoli 1104.0 242.0 \n", - "Fiorentina 1061.0 274.0 \n", - "Verona 1173.0 385.0 \n", - "Inter 960.0 221.0 \n", - "Juventus 1064.0 247.0 \n", - "Lazio 1157.0 207.0 \n", - "Lecce 1141.0 396.0 \n", - "Milan 1059.0 249.0 \n", - "Monza 1075.0 226.0 \n", - "Napoli 1049.0 214.0 \n", - "Roma 1095.0 205.0 \n", - "Salernitana 1143.0 268.0 \n", - "Sampdoria 1126.0 340.0 \n", - "Sassuolo 1088.0 243.0 \n", - "Spezia 1225.0 337.0 \n", - "Torino 1095.0 347.0 \n", - "Udinese 1063.0 222.0 \n", + "Atalanta 1335.0 273.0 \n", + "Bologna 1204.0 250.0 \n", + "Cremonese 1229.0 409.0 \n", + "Empoli 1148.0 263.0 \n", + "Fiorentina 1130.0 289.0 \n", + "Verona 1231.0 423.0 \n", + "Inter 1007.0 231.0 \n", + "Juventus 1134.0 258.0 \n", + "Lazio 1233.0 218.0 \n", + "Lecce 1200.0 417.0 \n", + "Milan 1125.0 263.0 \n", + "Monza 1145.0 242.0 \n", + "Napoli 1100.0 232.0 \n", + "Roma 1156.0 221.0 \n", + "Salernitana 1213.0 291.0 \n", + "Sampdoria 1189.0 362.0 \n", + "Sassuolo 1131.0 256.0 \n", + "Spezia 1275.0 343.0 \n", + "Torino 1155.0 367.0 \n", + "Udinese 1101.0 233.0 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", "team_idx \n", - "Atalanta 317.0 45.1 \n", - "Bologna 193.0 55.3 \n", - "Cremonese 296.0 56.7 \n", - "Empoli 211.0 53.4 \n", - "Fiorentina 317.0 46.4 \n", - "Verona 422.0 47.7 \n", - "Inter 266.0 45.4 \n", - "Juventus 257.0 49.0 \n", - "Lazio 216.0 48.9 \n", - "Lecce 316.0 55.6 \n", - "Milan 305.0 44.9 \n", - "Monza 241.0 48.4 \n", - "Napoli 259.0 45.2 \n", - "Roma 245.0 45.6 \n", - "Salernitana 247.0 52.0 \n", - "Sampdoria 339.0 50.1 \n", - "Sassuolo 195.0 55.5 \n", - "Spezia 285.0 54.2 \n", - "Torino 319.0 52.1 \n", - "Udinese 264.0 45.7 \n", + "Atalanta 328.0 45.4 \n", + "Bologna 210.0 54.3 \n", + "Cremonese 314.0 56.6 \n", + "Empoli 217.0 54.8 \n", + "Fiorentina 328.0 46.8 \n", + "Verona 445.0 48.7 \n", + "Inter 288.0 44.5 \n", + "Juventus 272.0 48.7 \n", + "Lazio 229.0 48.8 \n", + "Lecce 332.0 55.7 \n", + "Milan 325.0 44.7 \n", + "Monza 253.0 48.9 \n", + "Napoli 280.0 45.3 \n", + "Roma 266.0 45.4 \n", + "Salernitana 285.0 50.5 \n", + "Sampdoria 349.0 50.9 \n", + "Sassuolo 206.0 55.4 \n", + "Spezia 306.0 52.9 \n", + "Torino 333.0 52.4 \n", + "Udinese 277.0 45.7 \n", "\n", "[20 rows x 303 columns]" ] }, - "execution_count": 22, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "id": "b6a3db98", "metadata": {}, "outputs": [], @@ -818,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "id": "c34eb398", "metadata": {}, "outputs": [ @@ -899,21 +899,21 @@ " Napoli\n", " Meret\n", " NaN\n", - " 551\n", - " 25-325\n", + " 555\n", + " 25-329\n", " 1997\n", " 0\n", " ...\n", - " 19.5\n", - " 27.1\n", - " 221.0\n", + " 18.7\n", + " 26.6\n", + " 232.0\n", " 6.0\n", - " 2.7\n", + " 2.6\n", " 24.0\n", - " 1.14\n", + " 1.09\n", " 17.2\n", - " 6.261905\n", - " 0.478566\n", + " 6.250000\n", + " 0.470734\n", " \n", " \n", " Provedel\n", @@ -923,21 +923,21 @@ " Lazio\n", " Provedel\n", " NaN\n", - " 560\n", - " 28-330\n", + " 564\n", + " 28-334\n", " 1994\n", " 0\n", " ...\n", - " 34.2\n", - " 34.5\n", - " 279.0\n", - " 7.0\n", - " 2.5\n", + " 35.0\n", + " 35.0\n", + " 290.0\n", + " 8.0\n", + " 2.8\n", " 39.0\n", - " 1.86\n", - " 18.2\n", - " 6.261905\n", - " 0.365769\n", + " 1.78\n", + " 18.1\n", + " 6.272727\n", + " 0.360784\n", " \n", " \n", " Vicario\n", @@ -947,21 +947,21 @@ " Empoli\n", " Vicario\n", " NaN\n", - " 569\n", - " 26-126\n", + " 573\n", + " 26-130\n", " 1996\n", " 0\n", " ...\n", - " 47.2\n", - " 42.4\n", - " 426.0\n", + " 44.7\n", + " 41.5\n", + " 431.0\n", " 25.0\n", - " 5.9\n", + " 5.8\n", " 12.0\n", - " 0.57\n", + " 0.55\n", " 10.9\n", - " 6.476190\n", - " 0.392677\n", + " 6.454545\n", + " 0.396264\n", " \n", " \n", " Szczesny\n", @@ -971,21 +971,21 @@ " Juventus\n", " Szczesny\n", " NaN\n", - " 566\n", - " 32-298\n", + " 570\n", + " 32-302\n", " 1990\n", " 0\n", " ...\n", - " 41.7\n", - " 38.7\n", - " 178.0\n", - " 4.0\n", - " 2.2\n", + " 47.6\n", + " 41.8\n", + " 198.0\n", + " 5.0\n", + " 2.5\n", " 12.0\n", - " 0.83\n", - " 15.8\n", - " 6.033333\n", - " 0.339935\n", + " 0.78\n", + " 15.4\n", + " 6.031250\n", + " 0.329239\n", " \n", " \n", " Falcone\n", @@ -995,21 +995,21 @@ " Lecce\n", " Falcone\n", " NaN\n", - " 546\n", - " 27-304\n", + " 550\n", + " 27-308\n", " 1995\n", " 0\n", " ...\n", - " 76.1\n", - " 51.7\n", - " 316.0\n", + " 76.3\n", + " 51.9\n", + " 327.0\n", " 15.0\n", - " 4.7\n", - " 20.0\n", - " 0.95\n", - " 12.5\n", - " 6.309524\n", - " 0.361089\n", + " 4.6\n", + " 22.0\n", + " 1.00\n", + " 12.8\n", + " 6.363636\n", + " 0.431220\n", " \n", " \n", " ...\n", @@ -1043,8 +1043,8 @@ " Sampdoria\n", " Luca\n", " NaN\n", - " 134\n", - " 24-208\n", + " 135\n", + " 24-212\n", " 1998\n", " 1\n", " ...\n", @@ -1056,8 +1056,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.947397\n", - " 0.490100\n", + " 6.152892\n", + " 0.608256\n", " \n", " \n", " Voelkerling Persson\n", @@ -1067,10 +1067,10 @@ " Lecce\n", " Persson\n", " NaN\n", - " 508\n", - " 20-026\n", + " 512\n", + " 20-030\n", " 2003\n", - " 3\n", + " 4\n", " ...\n", " 0.0\n", " 0.0\n", @@ -1080,8 +1080,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.694209\n", - " 0.383547\n", + " 6.117574\n", + " 0.756644\n", " \n", " \n", " Montevago\n", @@ -1091,8 +1091,8 @@ " Sampdoria\n", " Montevago\n", " NaN\n", - " 331\n", - " 19-329\n", + " 334\n", + " 19-333\n", " 2003\n", " 6\n", " ...\n", @@ -1104,8 +1104,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.623932\n", - " 0.280948\n", + " 5.803886\n", + " 0.322430\n", " \n", " \n", " Krollis\n", @@ -1115,8 +1115,8 @@ " Spezia\n", " Krollis\n", " NaN\n", - " 258\n", - " 21-105\n", + " 261\n", + " 21-109\n", " 2001\n", " 1\n", " ...\n", @@ -1128,8 +1128,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.124287\n", - " 0.483769\n", + " 5.689979\n", + " 0.321766\n", " \n", " \n", " Vivaldo\n", @@ -1152,8 +1152,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.215679\n", - " 0.378965\n", + " 5.835742\n", + " 0.342133\n", " \n", " \n", "\n", @@ -1163,39 +1163,39 @@ "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID \\\n", "name \n", - "Meret 0 572 P Napoli Meret NaN 551 \n", - "Provedel 1 2814 P Lazio Provedel NaN 560 \n", - "Vicario 2 4964 P Empoli Vicario NaN 569 \n", - "Szczesny 3 453 P Juventus Szczesny NaN 566 \n", - "Falcone 4 2134 P Lecce Falcone NaN 546 \n", + "Meret 0 572 P Napoli Meret NaN 555 \n", + "Provedel 1 2814 P Lazio Provedel NaN 564 \n", + "Vicario 2 4964 P Empoli Vicario NaN 573 \n", + "Szczesny 3 453 P Juventus Szczesny NaN 570 \n", + "Falcone 4 2134 P Lecce Falcone NaN 550 \n", "... ... ... .. ... ... ... ... \n", - "De Luca 538 5512 A Sampdoria Luca NaN 134 \n", - "Voelkerling Persson 539 5837 A Lecce Persson NaN 508 \n", - "Montevago 540 6113 A Sampdoria Montevago NaN 331 \n", - "Krollis 541 6143 A Spezia Krollis NaN 258 \n", + "De Luca 538 5512 A Sampdoria Luca NaN 135 \n", + "Voelkerling Persson 539 5837 A Lecce Persson NaN 512 \n", + "Montevago 540 6113 A Sampdoria Montevago NaN 334 \n", + "Krollis 541 6143 A Spezia Krollis NaN 261 \n", "Vivaldo 542 6160 A Udinese Vivaldo NaN -1 \n", "\n", " age birth_year games ... \\\n", "name ... \n", - "Meret 25-325 1997 0 ... \n", - "Provedel 28-330 1994 0 ... \n", - "Vicario 26-126 1996 0 ... \n", - "Szczesny 32-298 1990 0 ... \n", - "Falcone 27-304 1995 0 ... \n", + "Meret 25-329 1997 0 ... \n", + "Provedel 28-334 1994 0 ... \n", + "Vicario 26-130 1996 0 ... \n", + "Szczesny 32-302 1990 0 ... \n", + "Falcone 27-308 1995 0 ... \n", "... ... ... ... ... \n", - "De Luca 24-208 1998 1 ... \n", - "Voelkerling Persson 20-026 2003 3 ... \n", - "Montevago 19-329 2003 6 ... \n", - "Krollis 21-105 2001 1 ... \n", + "De Luca 24-212 1998 1 ... \n", + "Voelkerling Persson 20-030 2003 4 ... \n", + "Montevago 19-333 2003 6 ... \n", + "Krollis 21-109 2001 1 ... \n", "Vivaldo 0 0 0 ... \n", "\n", " gk_pct_goal_kicks_launched gk_goal_kick_length_avg \\\n", "name \n", - "Meret 19.5 27.1 \n", - "Provedel 34.2 34.5 \n", - "Vicario 47.2 42.4 \n", - "Szczesny 41.7 38.7 \n", - "Falcone 76.1 51.7 \n", + "Meret 18.7 26.6 \n", + "Provedel 35.0 35.0 \n", + "Vicario 44.7 41.5 \n", + "Szczesny 47.6 41.8 \n", + "Falcone 76.3 51.9 \n", "... ... ... \n", "De Luca 0.0 0.0 \n", "Voelkerling Persson 0.0 0.0 \n", @@ -1205,11 +1205,11 @@ "\n", " gk_crosses gk_crosses_stopped gk_crosses_stopped_pct \\\n", "name \n", - "Meret 221.0 6.0 2.7 \n", - "Provedel 279.0 7.0 2.5 \n", - "Vicario 426.0 25.0 5.9 \n", - "Szczesny 178.0 4.0 2.2 \n", - "Falcone 316.0 15.0 4.7 \n", + "Meret 232.0 6.0 2.6 \n", + "Provedel 290.0 8.0 2.8 \n", + "Vicario 431.0 25.0 5.8 \n", + "Szczesny 198.0 5.0 2.5 \n", + "Falcone 327.0 15.0 4.6 \n", "... ... ... ... \n", "De Luca 0.0 0.0 0.0 \n", "Voelkerling Persson 0.0 0.0 0.0 \n", @@ -1223,7 +1223,7 @@ "Provedel 39.0 \n", "Vicario 12.0 \n", "Szczesny 12.0 \n", - "Falcone 20.0 \n", + "Falcone 22.0 \n", "... ... \n", "De Luca 0.0 \n", "Voelkerling Persson 0.0 \n", @@ -1233,11 +1233,11 @@ "\n", " gk_def_actions_outside_pen_area_per90 \\\n", "name \n", - "Meret 1.14 \n", - "Provedel 1.86 \n", - "Vicario 0.57 \n", - "Szczesny 0.83 \n", - "Falcone 0.95 \n", + "Meret 1.09 \n", + "Provedel 1.78 \n", + "Vicario 0.55 \n", + "Szczesny 0.78 \n", + "Falcone 1.00 \n", "... ... \n", "De Luca 0.00 \n", "Voelkerling Persson 0.00 \n", @@ -1247,22 +1247,22 @@ "\n", " gk_avg_distance_def_actions vote_avg vote_std \n", "name \n", - "Meret 17.2 6.261905 0.478566 \n", - "Provedel 18.2 6.261905 0.365769 \n", - "Vicario 10.9 6.476190 0.392677 \n", - "Szczesny 15.8 6.033333 0.339935 \n", - "Falcone 12.5 6.309524 0.361089 \n", + "Meret 17.2 6.250000 0.470734 \n", + "Provedel 18.1 6.272727 0.360784 \n", + "Vicario 10.9 6.454545 0.396264 \n", + "Szczesny 15.4 6.031250 0.329239 \n", + "Falcone 12.8 6.363636 0.431220 \n", "... ... ... ... \n", - "De Luca 0.0 5.947397 0.490100 \n", - "Voelkerling Persson 0.0 5.694209 0.383547 \n", - "Montevago 0.0 5.623932 0.280948 \n", - "Krollis 0.0 6.124287 0.483769 \n", - "Vivaldo 0.0 6.215679 0.378965 \n", + "De Luca 0.0 6.152892 0.608256 \n", + "Voelkerling Persson 0.0 6.117574 0.756644 \n", + "Montevago 0.0 5.803886 0.322430 \n", + "Krollis 0.0 5.689979 0.321766 \n", + "Vivaldo 0.0 5.835742 0.342133 \n", "\n", "[543 rows x 160 columns]" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1284,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "id": "71c8804e", "metadata": {}, "outputs": [], @@ -1319,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "id": "7eb4e666", "metadata": {}, "outputs": [ @@ -1376,21 +1376,21 @@ " Sassuolo\n", " Frattesi\n", " NaN\n", - " 191\n", - " 23-111\n", + " 192\n", + " 23-115\n", " 1999\n", - " 21\n", + " 22\n", " ...\n", - " 232.0\n", " 244.0\n", + " 256.0\n", " 26.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1261.0\n", - " 260.0\n", - " 317.0\n", - " 45.1\n", + " 1335.0\n", + " 273.0\n", + " 328.0\n", + " 45.4\n", " \n", " \n", "\n", @@ -1399,10 +1399,10 @@ ], "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID age \\\n", - "Frattesi 274 2848 C Sassuolo Frattesi NaN 191 23-111 \n", + "Frattesi 274 2848 C Sassuolo Frattesi NaN 192 23-115 \n", "\n", " birth_year games ... opp_vs_team_fouls opp_vs_team_fouled \\\n", - "Frattesi 1999 21 ... 232.0 244.0 \n", + "Frattesi 1999 22 ... 244.0 256.0 \n", "\n", " opp_vs_team_offsides opp_vs_team_pens_won \\\n", "Frattesi 26.0 1.0 \n", @@ -1411,15 +1411,15 @@ "Frattesi 8.0 1.0 \n", "\n", " opp_vs_team_ball_recoveries opp_vs_team_aerials_won \\\n", - "Frattesi 1261.0 260.0 \n", + "Frattesi 1335.0 273.0 \n", "\n", " opp_vs_team_aerials_lost opp_vs_team_aerials_won_pct \n", - "Frattesi 317.0 45.1 \n", + "Frattesi 328.0 45.4 \n", "\n", "[1 rows x 766 columns]" ] }, - "execution_count": 26, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1433,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "id": "6b4c1b00", "metadata": {}, "outputs": [ @@ -1443,7 +1443,7 @@ "'Sassuolo'" ] }, - "execution_count": 27, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1456,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "id": "6ea6df1f", "metadata": {}, "outputs": [ @@ -1556,7 +1556,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1588,7 +1588,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "id": "47bc651f", "metadata": {}, "outputs": [], @@ -1742,7 +1742,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "id": "3f43d509", "metadata": {}, "outputs": [], @@ -1773,7 +1773,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "id": "930d00c7", "metadata": {}, "outputs": [ @@ -1825,26 +1825,26 @@ " \n", " Frattesi\n", " C\n", - " 21\n", - " 21\n", - " 1726\n", - " 47.4\n", + " 22\n", + " 22\n", + " 1816\n", + " 45.0\n", " 0.13\n", " 0.28\n", - " 78.6\n", - " 57.1\n", - " 48.5\n", + " 78.5\n", + " 58.6\n", + " 48.7\n", " ...\n", - " 0.01854\n", - " 0.016222\n", - " 0.015643\n", - " 0.021437\n", - " 0.00927\n", - " 0.006952\n", - " 0.275782\n", - " 0.026072\n", - " 0.020278\n", - " 0.008111\n", + " 0.018722\n", + " 0.015419\n", + " 0.015419\n", + " 0.020925\n", + " 0.009361\n", + " 0.006608\n", + " 0.278084\n", + " 0.025881\n", + " 0.020374\n", + " 0.007709\n", " \n", " \n", "\n", @@ -1853,27 +1853,27 @@ ], "text/plain": [ " r games games_starts minutes shots_on_target_pct \\\n", - "Frattesi C 21 21 1726 47.4 \n", + "Frattesi C 22 22 1816 45.0 \n", "\n", " goals_per_shot goals_per_shot_on_target passes_pct \\\n", - "Frattesi 0.13 0.28 78.6 \n", + "Frattesi 0.13 0.28 78.5 \n", "\n", " aerials_won_pct team_possession ... miscontrols dispossessed \\\n", - "Frattesi 57.1 48.5 ... 0.01854 0.016222 \n", + "Frattesi 58.6 48.7 ... 0.018722 0.015419 \n", "\n", " fouls fouled aerials_won aerials_lost carries \\\n", - "Frattesi 0.015643 0.021437 0.00927 0.006952 0.275782 \n", + "Frattesi 0.015419 0.020925 0.009361 0.006608 0.278084 \n", "\n", " progressive_carries carries_into_final_third \\\n", - "Frattesi 0.026072 0.020278 \n", + "Frattesi 0.025881 0.020374 \n", "\n", " carries_into_penalty_area \n", - "Frattesi 0.008111 \n", + "Frattesi 0.007709 \n", "\n", "[1 rows x 112 columns]" ] }, - "execution_count": 31, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1896,7 +1896,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "id": "3ae718d2", "metadata": {}, "outputs": [ @@ -2013,24 +2013,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2039,11 +2026,24 @@ " 6.0\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2052,24 +2052,24 @@ " 6.0\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2079,22 +2079,22 @@ " \n", " \n", "\n", - "

5992 rows × 10 columns

\n", + "

6268 rows × 10 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -2103,16 +2103,16 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \n", - "5987 0.5 5.5 \n", - "5988 0.0 6.0 \n", - "5989 0.0 6.0 \n", - "5990 0.0 6.0 \n", - "5991 0.0 10.0 \n", + "6263 0.0 6.0 \n", + "6264 0.0 6.0 \n", + "6265 0.0 6.0 \n", + "6266 0.0 6.5 \n", + "6267 0.0 10.0 \n", "\n", - "[5992 rows x 10 columns]" + "[6268 rows x 10 columns]" ] }, - "execution_count": 32, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2132,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "id": "ec0bc56b", "metadata": {}, "outputs": [ @@ -2196,7 +2196,10 @@ "5600\n", "5700\n", "5800\n", - "5900\n" + "5900\n", + "6000\n", + "6100\n", + "6200\n" ] } ], @@ -2224,7 +2227,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "b292f9f9", "metadata": {}, "outputs": [ @@ -2310,15 +2313,15 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.007364\n", - " 0.001473\n", - " 0.006627\n", - " 0.005891\n", - " 0.013991\n", - " 0.011046\n", - " 0.365243\n", - " 0.008100\n", - " 0.016200\n", + " 0.006906\n", + " 0.001381\n", + " 0.007597\n", + " 0.006906\n", + " 0.013812\n", + " 0.010359\n", + " 0.356354\n", + " 0.008287\n", + " 0.015884\n", " 0.000000\n", " \n", " \n", @@ -2334,15 +2337,15 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.001876\n", - " 0.007505\n", - " 0.005629\n", - " 0.005629\n", - " 0.022514\n", - " 0.015009\n", - " 0.487805\n", - " 0.003752\n", - " 0.003752\n", + " 0.003210\n", + " 0.006421\n", + " 0.004815\n", + " 0.004815\n", + " 0.022472\n", + " 0.014446\n", + " 0.462279\n", + " 0.004815\n", + " 0.004815\n", " 0.000000\n", " \n", " \n", @@ -2358,16 +2361,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.005993\n", - " 0.005243\n", - " 0.013483\n", - " 0.001498\n", - " 0.016479\n", - " 0.010487\n", - " 0.283895\n", - " 0.011985\n", - " 0.009738\n", - " 0.000749\n", + " 0.005727\n", + " 0.005011\n", + " 0.013601\n", + " 0.002147\n", + " 0.017180\n", + " 0.010021\n", + " 0.282749\n", + " 0.011453\n", + " 0.009306\n", + " 0.000716\n", " \n", " \n", " 4\n", @@ -2418,35 +2421,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.008130\n", - " 0.252033\n", - " 0.008130\n", - " 0.008130\n", - " 0.000000\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2454,23 +2433,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.012804\n", - " 0.012164\n", - " 0.011524\n", - " 0.010243\n", - " 0.010883\n", - " 0.011524\n", - " 0.213828\n", - " 0.008323\n", - " 0.014725\n", - " 0.001921\n", + " 0.013317\n", + " 0.012107\n", + " 0.011501\n", + " 0.009685\n", + " 0.011501\n", + " 0.010896\n", + " 0.213680\n", + " 0.007869\n", + " 0.013923\n", + " 0.001816\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", + " 0.027027\n", + " 0.027027\n", + " 0.081081\n", + " 0.027027\n", + " 0.108108\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2478,47 +2481,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.035842\n", - " 0.025986\n", - " 0.018817\n", - " 0.014337\n", - " 0.008961\n", - " 0.032258\n", - " 0.170251\n", - " 0.022401\n", - " 0.013441\n", - " 0.009857\n", + " 0.035398\n", + " 0.025664\n", + " 0.018584\n", + " 0.015044\n", + " 0.008850\n", + " 0.037168\n", + " 0.170796\n", + " 0.022124\n", + " 0.013274\n", + " 0.009735\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", - " 0.130435\n", - " 0.043478\n", - " 0.043478\n", + " 0.057471\n", + " 0.045977\n", + " 0.022989\n", + " 0.034483\n", + " 0.034483\n", + " 0.126437\n", + " 0.298851\n", + " 0.011494\n", " 0.000000\n", - " 0.000000\n", - " 0.130435\n", - " 0.260870\n", - " 0.000000\n", - " 0.000000\n", - " 0.043478\n", + " 0.011494\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2526,79 +2529,79 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.011765\n", - " 0.011765\n", - " 0.000000\n", - " 0.023529\n", - " 0.058824\n", - " 0.058824\n", - " 0.247059\n", - " 0.035294\n", - " 0.011765\n", - " 0.023529\n", + " 0.055901\n", + " 0.012422\n", + " 0.006211\n", + " 0.031056\n", + " 0.031056\n", + " 0.086957\n", + " 0.298137\n", + " 0.037267\n", + " 0.012422\n", + " 0.012422\n", " \n", " \n", "\n", - "

5992 rows × 122 columns

\n", + "

6268 rows × 122 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", "0 0.5 5.5 ... 0.000000 0.000000 0.000000 \n", - "1 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "2 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "3 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", + "1 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "2 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "3 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", "4 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", "... ... ... ... ... ... ... \n", - "5987 0.5 5.5 ... 0.016260 0.024390 0.016260 \n", - "5988 0.0 6.0 ... 0.012804 0.012164 0.011524 \n", - "5989 0.0 6.0 ... 0.035842 0.025986 0.018817 \n", - "5990 0.0 6.0 ... 0.130435 0.043478 0.043478 \n", - "5991 0.0 10.0 ... 0.011765 0.011765 0.000000 \n", + "6263 0.0 6.0 ... 0.013317 0.012107 0.011501 \n", + "6264 0.0 6.0 ... 0.000000 0.000000 0.027027 \n", + "6265 0.0 6.0 ... 0.035398 0.025664 0.018584 \n", + "6266 0.0 6.5 ... 0.057471 0.045977 0.022989 \n", + "6267 0.0 10.0 ... 0.055901 0.012422 0.006211 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", "0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "2 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "3 0.001498 0.016479 0.010487 0.283895 0.011985 \n", + "1 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "2 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "3 0.002147 0.017180 0.010021 0.282749 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", "... ... ... ... ... ... \n", - "5987 0.024390 0.016260 0.008130 0.252033 0.008130 \n", - "5988 0.010243 0.010883 0.011524 0.213828 0.008323 \n", - "5989 0.014337 0.008961 0.032258 0.170251 0.022401 \n", - "5990 0.000000 0.000000 0.130435 0.260870 0.000000 \n", - "5991 0.023529 0.058824 0.058824 0.247059 0.035294 \n", + "6263 0.009685 0.011501 0.010896 0.213680 0.007869 \n", + "6264 0.027027 0.081081 0.027027 0.108108 0.000000 \n", + "6265 0.015044 0.008850 0.037168 0.170796 0.022124 \n", + "6266 0.034483 0.034483 0.126437 0.298851 0.011494 \n", + "6267 0.031056 0.031056 0.086957 0.298137 0.037267 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", "0 0.000000 0.000000 \n", - "1 0.016200 0.000000 \n", - "2 0.003752 0.000000 \n", - "3 0.009738 0.000749 \n", + "1 0.015884 0.000000 \n", + "2 0.004815 0.000000 \n", + "3 0.009306 0.000716 \n", "4 0.004734 0.000000 \n", "... ... ... \n", - "5987 0.008130 0.000000 \n", - "5988 0.014725 0.001921 \n", - "5989 0.013441 0.009857 \n", - "5990 0.000000 0.043478 \n", - "5991 0.011765 0.023529 \n", + "6263 0.013923 0.001816 \n", + "6264 0.000000 0.000000 \n", + "6265 0.013274 0.009735 \n", + "6266 0.000000 0.011494 \n", + "6267 0.012422 0.012422 \n", "\n", - "[5992 rows x 122 columns]" + "[6268 rows x 122 columns]" ] }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2617,7 +2620,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "1db9f77f", "metadata": {}, "outputs": [], @@ -2629,7 +2632,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "id": "f56df3ca", "metadata": {}, "outputs": [ @@ -2691,15 +2694,15 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.007364\n", - " 0.001473\n", - " 0.006627\n", - " 0.005891\n", - " 0.013991\n", - " 0.011046\n", - " 0.365243\n", - " 0.008100\n", - " 0.016200\n", + " 0.006906\n", + " 0.001381\n", + " 0.007597\n", + " 0.006906\n", + " 0.013812\n", + " 0.010359\n", + " 0.356354\n", + " 0.008287\n", + " 0.015884\n", " 0.000000\n", " \n", " \n", @@ -2715,15 +2718,15 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.001876\n", - " 0.007505\n", - " 0.005629\n", - " 0.005629\n", - " 0.022514\n", - " 0.015009\n", - " 0.487805\n", - " 0.003752\n", - " 0.003752\n", + " 0.003210\n", + " 0.006421\n", + " 0.004815\n", + " 0.004815\n", + " 0.022472\n", + " 0.014446\n", + " 0.462279\n", + " 0.004815\n", + " 0.004815\n", " 0.000000\n", " \n", " \n", @@ -2739,16 +2742,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.005993\n", - " 0.005243\n", - " 0.013483\n", - " 0.001498\n", - " 0.016479\n", - " 0.010487\n", - " 0.283895\n", - " 0.011985\n", - " 0.009738\n", - " 0.000749\n", + " 0.005727\n", + " 0.005011\n", + " 0.013601\n", + " 0.002147\n", + " 0.017180\n", + " 0.010021\n", + " 0.282749\n", + " 0.011453\n", + " 0.009306\n", + " 0.000716\n", " \n", " \n", " 4\n", @@ -2787,15 +2790,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.000000\n", - " 0.000000\n", - " 0.022222\n", - " 0.000000\n", " 0.011111\n", + " 0.005556\n", + " 0.016667\n", " 0.022222\n", - " 0.533333\n", - " 0.066667\n", - " 0.066667\n", + " 0.016667\n", + " 0.027778\n", + " 0.561111\n", + " 0.072222\n", + " 0.055556\n", " 0.000000\n", " \n", " \n", @@ -2823,35 +2826,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.024390\n", - " 0.016260\n", - " 0.008130\n", - " 0.252033\n", - " 0.008130\n", - " 0.008130\n", - " 0.000000\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2859,23 +2838,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.012804\n", - " 0.012164\n", - " 0.011524\n", - " 0.010243\n", - " 0.010883\n", - " 0.011524\n", - " 0.213828\n", - " 0.008323\n", - " 0.014725\n", - " 0.001921\n", + " 0.013317\n", + " 0.012107\n", + " 0.011501\n", + " 0.009685\n", + " 0.011501\n", + " 0.010896\n", + " 0.213680\n", + " 0.007869\n", + " 0.013923\n", + " 0.001816\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", + " 0.027027\n", + " 0.027027\n", + " 0.081081\n", + " 0.027027\n", + " 0.108108\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -2883,47 +2886,47 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.035842\n", - " 0.025986\n", - " 0.018817\n", - " 0.014337\n", - " 0.008961\n", - " 0.032258\n", - " 0.170251\n", - " 0.022401\n", - " 0.013441\n", - " 0.009857\n", + " 0.035398\n", + " 0.025664\n", + " 0.018584\n", + " 0.015044\n", + " 0.008850\n", + " 0.037168\n", + " 0.170796\n", + " 0.022124\n", + " 0.013274\n", + " 0.009735\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", - " 0.130435\n", - " 0.043478\n", - " 0.043478\n", + " 0.057471\n", + " 0.045977\n", + " 0.022989\n", + " 0.034483\n", + " 0.034483\n", + " 0.126437\n", + " 0.298851\n", + " 0.011494\n", " 0.000000\n", - " 0.000000\n", - " 0.130435\n", - " 0.260870\n", - " 0.000000\n", - " 0.000000\n", - " 0.043478\n", + " 0.011494\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -2931,79 +2934,79 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.011765\n", - " 0.011765\n", - " 0.000000\n", - " 0.023529\n", - " 0.058824\n", - " 0.058824\n", - " 0.247059\n", - " 0.035294\n", - " 0.011765\n", - " 0.023529\n", + " 0.055901\n", + " 0.012422\n", + " 0.006211\n", + " 0.031056\n", + " 0.031056\n", + " 0.086957\n", + " 0.298137\n", + " 0.037267\n", + " 0.012422\n", + " 0.012422\n", " \n", " \n", "\n", - "

5326 rows × 122 columns

\n", + "

5581 rows × 122 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "5 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "5 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", - "1 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "2 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "3 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", + "1 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "2 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "3 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", "4 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", - "5 0.5 5.5 ... 0.000000 0.000000 0.022222 \n", + "5 0.5 5.5 ... 0.011111 0.005556 0.016667 \n", "... ... ... ... ... ... ... \n", - "5987 0.5 5.5 ... 0.016260 0.024390 0.016260 \n", - "5988 0.0 6.0 ... 0.012804 0.012164 0.011524 \n", - "5989 0.0 6.0 ... 0.035842 0.025986 0.018817 \n", - "5990 0.0 6.0 ... 0.130435 0.043478 0.043478 \n", - "5991 0.0 10.0 ... 0.011765 0.011765 0.000000 \n", + "6263 0.0 6.0 ... 0.013317 0.012107 0.011501 \n", + "6264 0.0 6.0 ... 0.000000 0.000000 0.027027 \n", + "6265 0.0 6.0 ... 0.035398 0.025664 0.018584 \n", + "6266 0.0 6.5 ... 0.057471 0.045977 0.022989 \n", + "6267 0.0 10.0 ... 0.055901 0.012422 0.006211 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", - "1 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "2 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "3 0.001498 0.016479 0.010487 0.283895 0.011985 \n", + "1 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "2 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "3 0.002147 0.017180 0.010021 0.282749 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", - "5 0.000000 0.011111 0.022222 0.533333 0.066667 \n", + "5 0.022222 0.016667 0.027778 0.561111 0.072222 \n", "... ... ... ... ... ... \n", - "5987 0.024390 0.016260 0.008130 0.252033 0.008130 \n", - "5988 0.010243 0.010883 0.011524 0.213828 0.008323 \n", - "5989 0.014337 0.008961 0.032258 0.170251 0.022401 \n", - "5990 0.000000 0.000000 0.130435 0.260870 0.000000 \n", - "5991 0.023529 0.058824 0.058824 0.247059 0.035294 \n", + "6263 0.009685 0.011501 0.010896 0.213680 0.007869 \n", + "6264 0.027027 0.081081 0.027027 0.108108 0.000000 \n", + "6265 0.015044 0.008850 0.037168 0.170796 0.022124 \n", + "6266 0.034483 0.034483 0.126437 0.298851 0.011494 \n", + "6267 0.031056 0.031056 0.086957 0.298137 0.037267 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", - "1 0.016200 0.000000 \n", - "2 0.003752 0.000000 \n", - "3 0.009738 0.000749 \n", + "1 0.015884 0.000000 \n", + "2 0.004815 0.000000 \n", + "3 0.009306 0.000716 \n", "4 0.004734 0.000000 \n", - "5 0.066667 0.000000 \n", + "5 0.055556 0.000000 \n", "... ... ... \n", - "5987 0.008130 0.000000 \n", - "5988 0.014725 0.001921 \n", - "5989 0.013441 0.009857 \n", - "5990 0.000000 0.043478 \n", - "5991 0.011765 0.023529 \n", + "6263 0.013923 0.001816 \n", + "6264 0.000000 0.000000 \n", + "6265 0.013274 0.009735 \n", + "6266 0.000000 0.011494 \n", + "6267 0.012422 0.012422 \n", "\n", - "[5326 rows x 122 columns]" + "[5581 rows x 122 columns]" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3022,7 +3025,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "id": "df26c8e0", "metadata": {}, "outputs": [], @@ -3040,7 +3043,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "id": "4d06576a", "metadata": {}, "outputs": [ @@ -3066,119 +3069,110 @@ "15 - goals_per_shot\n", "16 - goals_per_shot_on_target\n", "17 - passes_pct\n", - "18 - dribble_tackles_pct\n", - "19 - dribbles_completed_pct\n", - "20 - aerials_won_pct\n", - "21 - team_possession\n", - "22 - team_goals_assists_per90\n", - "23 - team_goals_pens_per90\n", - "24 - team_goals_assists_pens_per90\n", - "25 - team_xg_per90\n", - "26 - team_gk_goals_against_per90\n", - "27 - team_gk_save_pct\n", - "28 - team_gk_clean_sheets_pct\n", - "29 - team_passes_pct\n", - "30 - team_passes_pct_medium\n", - "31 - team_passes_pct_long\n", - "32 - team_sca_per90\n", - "33 - team_gca_per90\n", - "34 - team_dribble_tackles_pct\n", - "35 - team_aerials_won_pct\n", - "36 - vs_team_possession\n", - "37 - vs_team_goals_per90\n", - "38 - vs_team_assists_per90\n", - "39 - vs_team_xg_per90\n", - "40 - vs_team_gk_save_pct\n", - "41 - vs_team_gk_clean_sheets_pct\n", - "42 - vs_team_gk_pct_passes_launched\n", - "43 - vs_team_gk_crosses_stopped_pct\n", - "44 - vs_team_shots_on_target_per90\n", - "45 - vs_team_passes_pct\n", - "46 - vs_team_passes_pct_short\n", - "47 - vs_team_passes_pct_medium\n", - "48 - vs_team_passes_pct_long\n", - "49 - vs_team_sca_per90\n", - "50 - vs_team_gca_per90\n", - "51 - vs_team_dribble_tackles_pct\n", - "52 - vs_team_dribbles_completed_pct\n", - "53 - vs_team_aerials_won_pct\n", - "54 - opp_team_possession\n", - "55 - opp_team_goals_assists_per90\n", - "56 - opp_team_goals_pens_per90\n", - "57 - opp_team_goals_assists_pens_per90\n", - "58 - opp_team_xg_per90\n", - "59 - opp_team_gk_goals_against_per90\n", - "60 - opp_team_gk_save_pct\n", - "61 - opp_team_gk_clean_sheets_pct\n", - "62 - opp_team_passes_pct\n", - "63 - opp_team_passes_pct_medium\n", - "64 - opp_team_passes_pct_long\n", - "65 - opp_team_sca_per90\n", - "66 - opp_team_gca_per90\n", - "67 - opp_team_dribble_tackles_pct\n", - "68 - opp_team_aerials_won_pct\n", - "69 - opp_vs_team_possession\n", - "70 - opp_vs_team_goals_per90\n", - "71 - opp_vs_team_assists_per90\n", - "72 - opp_vs_team_xg_per90\n", - "73 - opp_vs_team_gk_save_pct\n", - "74 - opp_vs_team_gk_clean_sheets_pct\n", - "75 - opp_vs_team_gk_pct_passes_launched\n", - "76 - opp_vs_team_gk_crosses_stopped_pct\n", - "77 - opp_vs_team_shots_on_target_per90\n", - "78 - opp_vs_team_passes_pct\n", - "79 - opp_vs_team_passes_pct_short\n", - "80 - opp_vs_team_passes_pct_medium\n", - "81 - opp_vs_team_passes_pct_long\n", - "82 - opp_vs_team_sca_per90\n", - "83 - opp_vs_team_gca_per90\n", - "84 - opp_vs_team_dribble_tackles_pct\n", - "85 - opp_vs_team_dribbles_completed_pct\n", - "86 - opp_vs_team_aerials_won_pct\n", - "87 - vote_avg\n", - "88 - vote_std\n", - "89 - goals\n", - "90 - assists\n", - "91 - cards_yellow\n", - "92 - cards_red\n", - "93 - xg\n", - "94 - npxg\n", - "95 - shots_on_target\n", - "96 - passes_completed\n", - "97 - passes_into_final_third\n", - "98 - passes_into_penalty_area\n", - "99 - progressive_passes\n", - "100 - passes_live\n", - "101 - passes_dead\n", - "102 - through_balls\n", - "103 - passes_switches\n", - "104 - crosses\n", - "105 - corner_kicks\n", - "106 - dribble_tackles\n", - "107 - dribbles_vs\n", - "108 - dribbled_past\n", - "109 - blocks\n", - "110 - blocked_shots\n", - "111 - blocked_passes\n", - "112 - interceptions\n", - "113 - clearances\n", - "114 - errors\n", - "115 - touches\n", - "116 - touches_def_pen_area\n", - "117 - touches_def_3rd\n", - "118 - touches_mid_3rd\n", - "119 - touches_att_3rd\n", - "120 - touches_att_pen_area\n", - "121 - touches_live_ball\n", - "122 - dribbles_completed\n", - "123 - dribbles\n", - "124 - passes_received\n", - "125 - miscontrols\n", - "126 - dispossessed\n", - "127 - fouls\n", - "128 - fouled\n", - "129 - aerials_won\n", - "130 - aerials_lost\n" + "18 - aerials_won_pct\n", + "19 - team_possession\n", + "20 - team_goals_assists_per90\n", + "21 - team_goals_pens_per90\n", + "22 - team_goals_assists_pens_per90\n", + "23 - team_xg_per90\n", + "24 - team_gk_goals_against_per90\n", + "25 - team_gk_save_pct\n", + "26 - team_gk_clean_sheets_pct\n", + "27 - team_passes_pct\n", + "28 - team_passes_pct_medium\n", + "29 - team_passes_pct_long\n", + "30 - team_sca_per90\n", + "31 - team_gca_per90\n", + "32 - team_aerials_won_pct\n", + "33 - vs_team_possession\n", + "34 - vs_team_goals_per90\n", + "35 - vs_team_assists_per90\n", + "36 - vs_team_xg_per90\n", + "37 - vs_team_gk_save_pct\n", + "38 - vs_team_gk_clean_sheets_pct\n", + "39 - vs_team_gk_pct_passes_launched\n", + "40 - vs_team_gk_crosses_stopped_pct\n", + "41 - vs_team_shots_on_target_per90\n", + "42 - vs_team_passes_pct\n", + "43 - vs_team_passes_pct_short\n", + "44 - vs_team_passes_pct_medium\n", + "45 - vs_team_passes_pct_long\n", + "46 - vs_team_sca_per90\n", + "47 - vs_team_gca_per90\n", + "48 - vs_team_aerials_won_pct\n", + "49 - opp_team_possession\n", + "50 - opp_team_goals_assists_per90\n", + "51 - opp_team_goals_pens_per90\n", + "52 - opp_team_goals_assists_pens_per90\n", + "53 - opp_team_xg_per90\n", + "54 - opp_team_gk_goals_against_per90\n", + "55 - opp_team_gk_save_pct\n", + "56 - opp_team_gk_clean_sheets_pct\n", + "57 - opp_team_passes_pct\n", + "58 - opp_team_passes_pct_medium\n", + "59 - opp_team_passes_pct_long\n", + "60 - opp_team_sca_per90\n", + "61 - opp_team_gca_per90\n", + "62 - opp_team_aerials_won_pct\n", + "63 - opp_vs_team_possession\n", + "64 - opp_vs_team_goals_per90\n", + "65 - opp_vs_team_assists_per90\n", + "66 - opp_vs_team_xg_per90\n", + "67 - opp_vs_team_gk_save_pct\n", + "68 - opp_vs_team_gk_clean_sheets_pct\n", + "69 - opp_vs_team_gk_pct_passes_launched\n", + "70 - opp_vs_team_gk_crosses_stopped_pct\n", + "71 - opp_vs_team_shots_on_target_per90\n", + "72 - opp_vs_team_passes_pct\n", + "73 - opp_vs_team_passes_pct_short\n", + "74 - opp_vs_team_passes_pct_medium\n", + "75 - opp_vs_team_passes_pct_long\n", + "76 - opp_vs_team_sca_per90\n", + "77 - opp_vs_team_gca_per90\n", + "78 - opp_vs_team_aerials_won_pct\n", + "79 - vote_avg\n", + "80 - vote_std\n", + "81 - goals\n", + "82 - assists\n", + "83 - cards_yellow\n", + "84 - cards_red\n", + "85 - xg\n", + "86 - npxg\n", + "87 - shots_on_target\n", + "88 - passes_completed\n", + "89 - passes_into_final_third\n", + "90 - passes_into_penalty_area\n", + "91 - progressive_passes\n", + "92 - passes_live\n", + "93 - passes_dead\n", + "94 - through_balls\n", + "95 - passes_switches\n", + "96 - crosses\n", + "97 - corner_kicks\n", + "98 - blocks\n", + "99 - blocked_shots\n", + "100 - blocked_passes\n", + "101 - interceptions\n", + "102 - clearances\n", + "103 - errors\n", + "104 - touches\n", + "105 - touches_def_pen_area\n", + "106 - touches_def_3rd\n", + "107 - touches_mid_3rd\n", + "108 - touches_att_3rd\n", + "109 - touches_att_pen_area\n", + "110 - touches_live_ball\n", + "111 - passes_received\n", + "112 - miscontrols\n", + "113 - dispossessed\n", + "114 - fouls\n", + "115 - fouled\n", + "116 - aerials_won\n", + "117 - aerials_lost\n", + "118 - carries\n", + "119 - progressive_carries\n", + "120 - carries_into_final_third\n", + "121 - carries_into_penalty_area\n" ] } ], @@ -3189,7 +3183,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "id": "01bb7413", "metadata": {}, "outputs": [ @@ -3264,7 +3258,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 34, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -3283,7 +3277,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "id": "02f961d8", "metadata": {}, "outputs": [], @@ -3396,7 +3390,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "id": "5e3694a0", "metadata": {}, "outputs": [], @@ -3420,7 +3414,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 22, "id": "9b671646", "metadata": {}, "outputs": [ @@ -3471,27 +3465,27 @@ " \n", " \n", " Consigli\n", - " 19\n", - " 19\n", - " 1710\n", - " 1.47\n", - " 58.3\n", - " 31.6\n", - " -0.25\n", - " 41.6\n", - " 31.0\n", - " 34.3\n", + " 20\n", + " 20\n", + " 1800\n", + " 1.5\n", + " 59.7\n", + " 30.0\n", + " -0.28\n", + " 40.8\n", + " 30.5\n", + " 34.1\n", " ...\n", - " 23.3\n", - " 0.35\n", - " -4.7\n", - " 112.0\n", - " 269.0\n", - " 686.0\n", - " 99.0\n", - " 172.0\n", - " 255.0\n", - " 16.0\n", + " 24.4\n", + " 0.33\n", + " -5.6\n", + " 113.0\n", + " 277.0\n", + " 711.0\n", + " 108.0\n", + " 179.0\n", + " 276.0\n", + " 17.0\n", " \n", " \n", "\n", @@ -3500,30 +3494,30 @@ ], "text/plain": [ " gk_games gk_games_starts gk_minutes gk_goals_against_per90 \\\n", - "Consigli 19 19 1710 1.47 \n", + "Consigli 20 20 1800 1.5 \n", "\n", " gk_save_pct gk_clean_sheets_pct gk_psxg_net_per90 \\\n", - "Consigli 58.3 31.6 -0.25 \n", + "Consigli 59.7 30.0 -0.28 \n", "\n", " gk_passes_pct_launched gk_pct_passes_launched \\\n", - "Consigli 41.6 31.0 \n", + "Consigli 40.8 30.5 \n", "\n", " gk_passes_length_avg ... gk_psxg \\\n", - "Consigli 34.3 ... 23.3 \n", + "Consigli 34.1 ... 24.4 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "Consigli 0.35 -4.7 \n", + "Consigli 0.33 -5.6 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "Consigli 112.0 269.0 686.0 \n", + "Consigli 113.0 277.0 711.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "Consigli 99.0 172.0 255.0 16.0 \n", + "Consigli 108.0 179.0 276.0 17.0 \n", "\n", "[1 rows x 92 columns]" ] }, - "execution_count": 37, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3538,7 +3532,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 23, "id": "33f805a9", "metadata": {}, "outputs": [ @@ -3605,7 +3599,10 @@ "5600\n", "5700\n", "5800\n", - "5900\n" + "5900\n", + "6000\n", + "6100\n", + "6200\n" ] } ], @@ -3635,7 +3632,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 24, "id": "4d484846", "metadata": {}, "outputs": [ @@ -3697,15 +3694,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 12.7\n", - " 0.21\n", - " -1.3\n", - " 81.0\n", - " 185.0\n", - " 372.0\n", - " 107.0\n", - " 98.0\n", - " 174.0\n", + " 13.0\n", + " 0.2\n", + " -1.0\n", + " 88.0\n", + " 201.0\n", + " 397.0\n", + " 112.0\n", + " 101.0\n", + " 186.0\n", " 10.0\n", " \n", " \n", @@ -3829,35 +3826,11 @@ " ...\n", " \n", " \n", - " 5987\n", - " 21\n", - " Duda\n", - " Verona\n", - " Lazio\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.5\n", - " 5.5\n", - " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 5988\n", - " 21\n", + " 6263\n", + " 22\n", " Tameze\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -3877,11 +3850,35 @@ " NaN\n", " \n", " \n", - " 5989\n", - " 21\n", + " 6264\n", + " 22\n", + " Abildgaard\n", + " Verona\n", + " Salernitana\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 6265\n", + " 22\n", " Lasagna\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 6.0\n", " 0\n", @@ -3901,17 +3898,17 @@ " NaN\n", " \n", " \n", - " 5990\n", - " 21\n", + " 6266\n", + " 22\n", " Gaich\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\n", " NaN\n", " NaN\n", @@ -3925,11 +3922,11 @@ " NaN\n", " \n", " \n", - " 5991\n", - " 21\n", + " 6267\n", + " 22\n", " Ngonge\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", " 7.0\n", " 1\n", @@ -3950,79 +3947,79 @@ " \n", " \n", "\n", - "

5992 rows × 102 columns

\n", + "

6268 rows × 102 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "5987 21 Duda Verona Lazio 1 6.0 0 0 \n", - "5988 21 Tameze Verona Lazio 1 6.0 0 0 \n", - "5989 21 Lasagna Verona Lazio 1 6.0 0 0 \n", - "5990 21 Gaich Verona Lazio 1 6.0 0 0 \n", - "5991 21 Ngonge Verona Lazio 1 7.0 1 0 \n", + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 0 0 \n", + "1 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6263 22 Tameze Verona Salernitana 1 6.0 0 0 \n", + "6264 22 Abildgaard Verona Salernitana 1 6.0 0 0 \n", + "6265 22 Lasagna Verona Salernitana 1 6.0 0 0 \n", + "6266 22 Gaich Verona Salernitana 1 6.5 0 0 \n", + "6267 22 Ngonge Verona Salernitana 1 7.0 1 0 \n", "\n", " cards_malus fantavote ... gk_psxg \\\n", - "0 0.5 5.5 ... 12.7 \n", + "0 0.5 5.5 ... 13.0 \n", "1 0.0 10.0 ... NaN \n", "2 0.0 6.0 ... NaN \n", "3 0.5 5.5 ... NaN \n", "4 0.5 5.0 ... NaN \n", "... ... ... ... ... \n", - "5987 0.5 5.5 ... NaN \n", - "5988 0.0 6.0 ... NaN \n", - "5989 0.0 6.0 ... NaN \n", - "5990 0.0 6.0 ... NaN \n", - "5991 0.0 10.0 ... NaN \n", + "6263 0.0 6.0 ... NaN \n", + "6264 0.0 6.0 ... NaN \n", + "6265 0.0 6.0 ... NaN \n", + "6266 0.0 6.5 ... NaN \n", + "6267 0.0 10.0 ... NaN \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.3 \n", + "0 0.2 -1.0 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "5987 NaN NaN \n", - "5988 NaN NaN \n", - "5989 NaN NaN \n", - "5990 NaN NaN \n", - "5991 NaN NaN \n", + "6263 NaN NaN \n", + "6264 NaN NaN \n", + "6265 NaN NaN \n", + "6266 NaN NaN \n", + "6267 NaN NaN \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", + "0 88.0 201.0 397.0 \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "... ... ... ... \n", - "5987 NaN NaN NaN \n", - "5988 NaN NaN NaN \n", - "5989 NaN NaN NaN \n", - "5990 NaN NaN NaN \n", - "5991 NaN NaN NaN \n", + "6263 NaN NaN NaN \n", + "6264 NaN NaN NaN \n", + "6265 NaN NaN NaN \n", + "6266 NaN NaN NaN \n", + "6267 NaN NaN NaN \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", + "0 112.0 101.0 186.0 10.0 \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "... ... ... ... ... \n", - "5987 NaN NaN NaN NaN \n", - "5988 NaN NaN NaN NaN \n", - "5989 NaN NaN NaN NaN \n", - "5990 NaN NaN NaN NaN \n", - "5991 NaN NaN NaN NaN \n", + "6263 NaN NaN NaN NaN \n", + "6264 NaN NaN NaN NaN \n", + "6265 NaN NaN NaN NaN \n", + "6266 NaN NaN NaN NaN \n", + "6267 NaN NaN NaN NaN \n", "\n", - "[5992 rows x 102 columns]" + "[6268 rows x 102 columns]" ] }, - "execution_count": 39, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -4033,7 +4030,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 25, "id": "adab3577", "metadata": {}, "outputs": [], @@ -4043,7 +4040,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 26, "id": "e7ae792d", "metadata": {}, "outputs": [ @@ -4105,15 +4102,15 @@ " 0.5\n", " 5.5\n", " ...\n", - " 12.70\n", - " 0.21\n", - " -1.30\n", - " 81.0\n", - " 185.0\n", - " 372.0\n", - " 107.0\n", - " 98.0\n", - " 174.0\n", + " 13.00\n", + " 0.20\n", + " -1.00\n", + " 88.0\n", + " 201.0\n", + " 397.0\n", + " 112.0\n", + " 101.0\n", + " 186.0\n", " 10.0\n", " \n", " \n", @@ -4129,15 +4126,15 @@ " 0.0\n", " 4.5\n", " ...\n", - " 30.80\n", - " 0.28\n", - " 1.80\n", - " 99.0\n", - " 240.0\n", - " 563.0\n", + " 32.50\n", + " 0.29\n", + " 2.50\n", + " 101.0\n", + " 242.0\n", + " 576.0\n", " 117.0\n", - " 153.0\n", - " 299.0\n", + " 159.0\n", + " 304.0\n", " 18.0\n", " \n", " \n", @@ -4153,15 +4150,15 @@ " 0.0\n", " 4.5\n", " ...\n", - " 26.40\n", - " 0.27\n", - " 0.40\n", + " 27.80\n", + " 0.26\n", + " -0.20\n", " 102.0\n", - " 310.0\n", - " 766.0\n", - " 113.0\n", - " 108.0\n", - " 426.0\n", + " 313.0\n", + " 806.0\n", + " 117.0\n", + " 114.0\n", + " 431.0\n", " 25.0\n", " \n", " \n", @@ -4177,15 +4174,15 @@ " 0.0\n", " 3.0\n", " ...\n", - " 9.35\n", + " 9.45\n", " 0.24\n", - " 0.85\n", + " 0.95\n", " 24.0\n", " 68.0\n", - " 291.5\n", - " 47.0\n", - " 69.5\n", - " 122.5\n", + " 299.5\n", + " 48.5\n", + " 72.5\n", + " 128.0\n", " 3.5\n", " \n", " \n", @@ -4237,83 +4234,59 @@ " ...\n", " \n", " \n", - " 5929\n", - " 21\n", + " 6200\n", + " 22\n", " Consigli\n", " Sassuolo\n", - " Atalanta\n", - " 1\n", - " 6.0\n", + " Udinese\n", " 0\n", + " 7.0\n", + " -2\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.0\n", " ...\n", - " 23.30\n", - " 0.35\n", - " -4.70\n", - " 112.0\n", - " 269.0\n", - " 686.0\n", - " 99.0\n", - " 172.0\n", - " 255.0\n", - " 16.0\n", + " 24.40\n", + " 0.33\n", + " -5.60\n", + " 113.0\n", + " 277.0\n", + " 711.0\n", + " 108.0\n", + " 179.0\n", + " 276.0\n", + " 17.0\n", " \n", " \n", - " 5942\n", - " 21\n", + " 6214\n", + " 22\n", " Dragowski\n", " Spezia\n", - " Napoli\n", - " 1\n", - " 5.0\n", - " -3\n", + " Empoli\n", + " 0\n", + " 6.5\n", + " -2\n", " 0\n", " 0.0\n", - " 2.0\n", + " 4.5\n", " ...\n", - " 28.10\n", + " 29.50\n", " 0.27\n", - " -3.90\n", - " 85.0\n", - " 268.0\n", - " 503.0\n", - " 92.0\n", - " 109.0\n", - " 251.0\n", + " -4.50\n", + " 93.0\n", + " 285.0\n", + " 528.0\n", + " 97.0\n", + " 122.0\n", + " 270.0\n", " 9.0\n", " \n", " \n", - " 5953\n", - " 21\n", + " 6227\n", + " 22\n", " Milinkovic-Savic V.\n", " Torino\n", - " Udinese\n", - " 1\n", - " 6.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 6.5\n", - " ...\n", - " 21.90\n", - " 0.23\n", - " -0.10\n", - " 148.0\n", - " 524.0\n", - " 788.0\n", - " 95.0\n", - " 155.0\n", - " 263.0\n", - " 18.0\n", - " \n", - " \n", - " 5966\n", - " 21\n", - " Silvestri\n", - " Udinese\n", - " Torino\n", + " Milan\n", " 0\n", " 6.5\n", " -1\n", @@ -4321,116 +4294,140 @@ " 0.0\n", " 5.5\n", " ...\n", - " 23.90\n", + " 23.10\n", + " 0.24\n", + " 0.10\n", + " 150.0\n", + " 540.0\n", + " 817.0\n", + " 97.0\n", + " 160.0\n", + " 271.0\n", + " 19.0\n", + " \n", + " \n", + " 6241\n", + " 22\n", + " Silvestri\n", + " Udinese\n", + " Sassuolo\n", + " 1\n", + " 6.0\n", + " -2\n", + " 0\n", + " 0.0\n", + " 4.0\n", + " ...\n", + " 24.00\n", " 0.31\n", - " 1.90\n", - " 90.0\n", - " 225.0\n", - " 450.0\n", - " 76.0\n", - " 164.0\n", - " 275.0\n", + " 1.00\n", + " 91.0\n", + " 227.0\n", + " 470.0\n", + " 80.0\n", + " 175.0\n", + " 293.0\n", " 5.0\n", " \n", " \n", - " 5980\n", - " 21\n", + " 6254\n", + " 22\n", " Montipo'\n", " Verona\n", - " Lazio\n", + " Salernitana\n", " 1\n", - " 6.0\n", - " -1\n", + " 6.5\n", + " 0\n", " 0\n", " 0.0\n", - " 5.0\n", + " 6.5\n", " ...\n", - " 27.60\n", - " 0.25\n", - " -4.40\n", - " 206.0\n", - " 413.0\n", - " 497.0\n", - " 65.0\n", - " 168.0\n", - " 294.0\n", + " 28.70\n", + " 0.26\n", + " -3.30\n", + " 208.0\n", + " 433.0\n", + " 513.0\n", + " 67.0\n", + " 175.0\n", + " 305.0\n", " 15.0\n", " \n", " \n", "\n", - "

415 rows × 102 columns

\n", + "

435 rows × 102 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 \n", - "15 1 Skorupski Bologna Lazio 0 6.5 -2 \n", - "44 1 Vicario Empoli Spezia 0 5.5 -1 \n", - "57 1 Gollini Fiorentina Cremonese 1 5.0 -2 \n", - "71 1 Handanovic Inter Lecce 0 6.5 -1 \n", - "... ... ... ... ... ... ... ... \n", - "5929 21 Consigli Sassuolo Atalanta 1 6.0 0 \n", - "5942 21 Dragowski Spezia Napoli 1 5.0 -3 \n", - "5953 21 Milinkovic-Savic V. Torino Udinese 1 6.5 0 \n", - "5966 21 Silvestri Udinese Torino 0 6.5 -1 \n", - "5980 21 Montipo' Verona Lazio 1 6.0 -1 \n", + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "15 1 Skorupski Bologna Lazio 0 6.5 \n", + "44 1 Vicario Empoli Spezia 0 5.5 \n", + "57 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "71 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "6200 22 Consigli Sassuolo Udinese 0 7.0 \n", + "6214 22 Dragowski Spezia Empoli 0 6.5 \n", + "6227 22 Milinkovic-Savic V. Torino Milan 0 6.5 \n", + "6241 22 Silvestri Udinese Sassuolo 1 6.0 \n", + "6254 22 Montipo' Verona Salernitana 1 6.5 \n", "\n", - " assists cards_malus fantavote ... gk_psxg \\\n", - "0 0 0.5 5.5 ... 12.70 \n", - "15 0 0.0 4.5 ... 30.80 \n", - "44 0 0.0 4.5 ... 26.40 \n", - "57 0 0.0 3.0 ... 9.35 \n", - "71 0 0.0 5.5 ... 10.60 \n", - "... ... ... ... ... ... \n", - "5929 0 0.0 6.0 ... 23.30 \n", - "5942 0 0.0 2.0 ... 28.10 \n", - "5953 0 0.0 6.5 ... 21.90 \n", - "5966 0 0.0 5.5 ... 23.90 \n", - "5980 0 0.0 5.0 ... 27.60 \n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 13.00 \n", + "15 -2 0 0.0 4.5 ... 32.50 \n", + "44 -1 0 0.0 4.5 ... 27.80 \n", + "57 -2 0 0.0 3.0 ... 9.45 \n", + "71 -1 0 0.0 5.5 ... 10.60 \n", + "... ... ... ... ... ... ... \n", + "6200 -2 0 0.0 5.0 ... 24.40 \n", + "6214 -2 0 0.0 4.5 ... 29.50 \n", + "6227 -1 0 0.0 5.5 ... 23.10 \n", + "6241 -2 0 0.0 4.0 ... 24.00 \n", + "6254 0 0 0.0 6.5 ... 28.70 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.30 \n", - "15 0.28 1.80 \n", - "44 0.27 0.40 \n", - "57 0.24 0.85 \n", + "0 0.20 -1.00 \n", + "15 0.29 2.50 \n", + "44 0.26 -0.20 \n", + "57 0.24 0.95 \n", "71 0.30 -1.40 \n", "... ... ... \n", - "5929 0.35 -4.70 \n", - "5942 0.27 -3.90 \n", - "5953 0.23 -0.10 \n", - "5966 0.31 1.90 \n", - "5980 0.25 -4.40 \n", + "6200 0.33 -5.60 \n", + "6214 0.27 -4.50 \n", + "6227 0.24 0.10 \n", + "6241 0.31 1.00 \n", + "6254 0.26 -3.30 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", - "15 99.0 240.0 563.0 \n", - "44 102.0 310.0 766.0 \n", - "57 24.0 68.0 291.5 \n", + "0 88.0 201.0 397.0 \n", + "15 101.0 242.0 576.0 \n", + "44 102.0 313.0 806.0 \n", + "57 24.0 68.0 299.5 \n", "71 27.0 52.0 266.0 \n", "... ... ... ... \n", - "5929 112.0 269.0 686.0 \n", - "5942 85.0 268.0 503.0 \n", - "5953 148.0 524.0 788.0 \n", - "5966 90.0 225.0 450.0 \n", - "5980 206.0 413.0 497.0 \n", + "6200 113.0 277.0 711.0 \n", + "6214 93.0 285.0 528.0 \n", + "6227 150.0 540.0 817.0 \n", + "6241 91.0 227.0 470.0 \n", + "6254 208.0 433.0 513.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", - "15 117.0 153.0 299.0 18.0 \n", - "44 113.0 108.0 426.0 25.0 \n", - "57 47.0 69.5 122.5 3.5 \n", + "0 112.0 101.0 186.0 10.0 \n", + "15 117.0 159.0 304.0 18.0 \n", + "44 117.0 114.0 431.0 25.0 \n", + "57 48.5 72.5 128.0 3.5 \n", "71 46.0 45.0 79.0 2.0 \n", "... ... ... ... ... \n", - "5929 99.0 172.0 255.0 16.0 \n", - "5942 92.0 109.0 251.0 9.0 \n", - "5953 95.0 155.0 263.0 18.0 \n", - "5966 76.0 164.0 275.0 5.0 \n", - "5980 65.0 168.0 294.0 15.0 \n", + "6200 108.0 179.0 276.0 17.0 \n", + "6214 97.0 122.0 270.0 9.0 \n", + "6227 97.0 160.0 271.0 19.0 \n", + "6241 80.0 175.0 293.0 5.0 \n", + "6254 67.0 175.0 305.0 15.0 \n", "\n", - "[415 rows x 102 columns]" + "[435 rows x 102 columns]" ] }, - "execution_count": 41, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4449,7 +4446,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 27, "id": "d8b8b6da", "metadata": {}, "outputs": [], @@ -4459,7 +4456,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "54519b24", "metadata": {}, "outputs": [ @@ -4505,76 +4502,70 @@ "35 - team_passes_pct_long\n", "36 - team_sca_per90\n", "37 - team_gca_per90\n", - "38 - team_dribble_tackles_pct\n", - "39 - team_aerials_won_pct\n", - "40 - vs_team_possession\n", - "41 - vs_team_goals_per90\n", - "42 - vs_team_assists_per90\n", - "43 - vs_team_xg_per90\n", - "44 - vs_team_gk_save_pct\n", - "45 - vs_team_gk_clean_sheets_pct\n", - "46 - vs_team_gk_pct_passes_launched\n", - "47 - vs_team_gk_crosses_stopped_pct\n", - "48 - vs_team_shots_on_target_per90\n", - "49 - vs_team_passes_pct\n", - "50 - vs_team_passes_pct_short\n", - "51 - vs_team_passes_pct_medium\n", - "52 - vs_team_passes_pct_long\n", - "53 - vs_team_sca_per90\n", - "54 - vs_team_gca_per90\n", - "55 - vs_team_dribble_tackles_pct\n", - "56 - vs_team_dribbles_completed_pct\n", - "57 - vs_team_aerials_won_pct\n", - "58 - opp_team_possession\n", - "59 - opp_team_goals_assists_per90\n", - "60 - opp_team_goals_pens_per90\n", - "61 - opp_team_goals_assists_pens_per90\n", - "62 - opp_team_xg_per90\n", - "63 - opp_team_gk_goals_against_per90\n", - "64 - opp_team_gk_save_pct\n", - "65 - opp_team_gk_clean_sheets_pct\n", - "66 - opp_team_passes_pct\n", - "67 - opp_team_passes_pct_medium\n", - "68 - opp_team_passes_pct_long\n", - "69 - opp_team_sca_per90\n", - "70 - opp_team_gca_per90\n", - "71 - opp_team_dribble_tackles_pct\n", - "72 - opp_team_aerials_won_pct\n", - "73 - opp_vs_team_possession\n", - "74 - opp_vs_team_goals_per90\n", - "75 - opp_vs_team_assists_per90\n", - "76 - opp_vs_team_xg_per90\n", - "77 - opp_vs_team_gk_save_pct\n", - "78 - opp_vs_team_gk_clean_sheets_pct\n", - "79 - opp_vs_team_gk_pct_passes_launched\n", - "80 - opp_vs_team_gk_crosses_stopped_pct\n", - "81 - opp_vs_team_shots_on_target_per90\n", - "82 - opp_vs_team_passes_pct\n", - "83 - opp_vs_team_passes_pct_short\n", - "84 - opp_vs_team_passes_pct_medium\n", - "85 - opp_vs_team_passes_pct_long\n", - "86 - opp_vs_team_sca_per90\n", - "87 - opp_vs_team_gca_per90\n", - "88 - opp_vs_team_dribble_tackles_pct\n", - "89 - opp_vs_team_dribbles_completed_pct\n", - "90 - opp_vs_team_aerials_won_pct\n", - "91 - vote_avg\n", - "92 - vote_std\n", - "93 - gk_shots_on_target_against\n", - "94 - gk_saves\n", - "95 - gk_free_kick_goals_against\n", - "96 - gk_corner_kick_goals_against\n", - "97 - gk_own_goals_against\n", - "98 - gk_psxg\n", - "99 - gk_psnpxg_per_shot_on_target_against\n", - "100 - gk_psxg_net\n", - "101 - gk_passes_completed_launched\n", - "102 - gk_passes_launched\n", - "103 - gk_passes\n", - "104 - gk_passes_throws\n", - "105 - gk_goal_kicks\n", - "106 - gk_crosses\n", - "107 - gk_crosses_stopped\n" + "38 - team_aerials_won_pct\n", + "39 - vs_team_possession\n", + "40 - vs_team_goals_per90\n", + "41 - vs_team_assists_per90\n", + "42 - vs_team_xg_per90\n", + "43 - vs_team_gk_save_pct\n", + "44 - vs_team_gk_clean_sheets_pct\n", + "45 - vs_team_gk_pct_passes_launched\n", + "46 - vs_team_gk_crosses_stopped_pct\n", + "47 - vs_team_shots_on_target_per90\n", + "48 - vs_team_passes_pct\n", + "49 - vs_team_passes_pct_short\n", + "50 - vs_team_passes_pct_medium\n", + "51 - vs_team_passes_pct_long\n", + "52 - vs_team_sca_per90\n", + "53 - vs_team_gca_per90\n", + "54 - vs_team_aerials_won_pct\n", + "55 - opp_team_possession\n", + "56 - opp_team_goals_assists_per90\n", + "57 - opp_team_goals_pens_per90\n", + "58 - opp_team_goals_assists_pens_per90\n", + "59 - opp_team_xg_per90\n", + "60 - opp_team_gk_goals_against_per90\n", + "61 - opp_team_gk_save_pct\n", + "62 - opp_team_gk_clean_sheets_pct\n", + "63 - opp_team_passes_pct\n", + "64 - opp_team_passes_pct_medium\n", + "65 - opp_team_passes_pct_long\n", + "66 - opp_team_sca_per90\n", + "67 - opp_team_gca_per90\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_aerials_won_pct\n", + "85 - vote_avg\n", + "86 - vote_std\n", + "87 - gk_shots_on_target_against\n", + "88 - gk_saves\n", + "89 - gk_free_kick_goals_against\n", + "90 - gk_corner_kick_goals_against\n", + "91 - gk_own_goals_against\n", + "92 - gk_psxg\n", + "93 - gk_psnpxg_per_shot_on_target_against\n", + "94 - gk_psxg_net\n", + "95 - gk_passes_completed_launched\n", + "96 - gk_passes_launched\n", + "97 - gk_passes\n", + "98 - gk_passes_throws\n", + "99 - gk_goal_kicks\n", + "100 - gk_crosses\n", + "101 - gk_crosses_stopped\n" ] } ], diff --git a/5_scraping_match_probable_players.ipynb b/5_scraping_match_probable_players.ipynb index aabf3d9..e9fbf73 100644 --- a/5_scraping_match_probable_players.ipynb +++ b/5_scraping_match_probable_players.ipynb @@ -71,33 +71,33 @@ " \n", " \n", " 0\n", - " Tatarusanu\n", + " Falcone\n", " 1\n", " 90.0\n", " \n", " \n", " 1\n", - " Thiaw\n", + " Gendrey\n", " 1\n", - " 65.0\n", + " 90.0\n", " \n", " \n", " 2\n", - " Kjaer\n", + " Baschirotto\n", " 1\n", - " 85.0\n", + " 90.0\n", " \n", " \n", " 3\n", - " Kalulu\n", + " Umtiti\n", " 1\n", " 90.0\n", " \n", " \n", " 4\n", - " Saelemaekers\n", + " Gallo\n", " 1\n", - " 60.0\n", + " 65.0\n", " \n", " \n", " ...\n", @@ -106,55 +106,55 @@ " ...\n", " \n", " \n", - " 433\n", - " Dumfries\n", + " 469\n", + " Barrenechea\n", " 0\n", - " 60.0\n", + " 20.0\n", " \n", " \n", - " 434\n", - " Mkhitaryan\n", - " 0\n", - " 55.0\n", - " \n", - " \n", - " 435\n", - " Asllani\n", - " 0\n", - " 30.0\n", - " \n", - " \n", - " 436\n", - " Gagliardini\n", + " 470\n", + " Pogba\n", " 0\n", " 50.0\n", " \n", " \n", - " 437\n", - " Lukaku\n", + " 471\n", + " Chiesa\n", + " 0\n", + " 55.0\n", + " \n", + " \n", + " 472\n", + " Soule'\n", + " 0\n", + " 20.0\n", + " \n", + " \n", + " 473\n", + " Milik\n", " 0\n", " 60.0\n", " \n", " \n", "\n", - "

438 rows × 3 columns

\n", + "

474 rows × 3 columns

\n", "" ], "text/plain": [ - " player starter percentage\n", - "0 Tatarusanu 1 90.0\n", - "1 Thiaw 1 65.0\n", - "2 Kjaer 1 85.0\n", - "3 Kalulu 1 90.0\n", - "4 Saelemaekers 1 60.0\n", - ".. ... ... ...\n", - "433 Dumfries 0 60.0\n", - "434 Mkhitaryan 0 55.0\n", - "435 Asllani 0 30.0\n", - "436 Gagliardini 0 50.0\n", - "437 Lukaku 0 60.0\n", + " player starter percentage\n", + "0 Falcone 1 90.0\n", + "1 Gendrey 1 90.0\n", + "2 Baschirotto 1 90.0\n", + "3 Umtiti 1 90.0\n", + "4 Gallo 1 65.0\n", + ".. ... ... ...\n", + "469 Barrenechea 0 20.0\n", + "470 Pogba 0 50.0\n", + "471 Chiesa 0 55.0\n", + "472 Soule' 0 20.0\n", + "473 Milik 0 60.0\n", "\n", - "[438 rows x 3 columns]" + "[474 rows x 3 columns]" ] }, "execution_count": 3, @@ -235,381 +235,381 @@ " \n", " \n", " 0\n", - " Saelemaekers\n", - " Calabria\n", - " 60.0\n", + " Gallo\n", + " Pezzella Giu.\n", + " 65.0\n", " \n", " \n", " 1\n", - " Diaz B.\n", - " De Ketelaere\n", + " Ceesay\n", + " Colombo\n", " 60.0\n", " \n", " \n", " 2\n", - " Thiaw\n", - " Messias\n", - " 65.0\n", + " Gonzalez J.\n", + " Oudin\n", + " 55.0\n", " \n", " \n", " 3\n", - " Singo\n", - " Aina\n", - " 55.0\n", - " \n", - " \n", - " 4\n", - " Rodriguez R.\n", - " Buongiorno\n", - " 55.0\n", - " \n", - " \n", - " 5\n", - " Akpa Akpro\n", - " Haas\n", - " 60.0\n", - " \n", - " \n", - " 6\n", - " Satriano\n", - " Piccoli\n", - " 60.0\n", - " \n", - " \n", - " 7\n", - " Ebuehi\n", - " Stojanovic\n", - " 60.0\n", - " \n", - " \n", - " 8\n", - " Holm\n", - " Ferrer\n", - " 55.0\n", - " \n", - " \n", - " 9\n", - " Nzola\n", - " Shomurodov\n", - " 55.0\n", - " \n", - " \n", - " 10\n", - " Agudelo\n", - " Cipot\n", - " 60.0\n", - " \n", - " \n", - " 11\n", - " Di Francesco F.\n", - " Banda\n", - " 65.0\n", - " \n", - " \n", - " 12\n", - " Gallo\n", - " Pezzella Giu.\n", - " 60.0\n", - " \n", - " \n", - " 13\n", - " El Shaarawy\n", - " Celik\n", - " 60.0\n", - " \n", - " \n", - " 14\n", - " Matic\n", - " Bove\n", - " 60.0\n", - " \n", - " \n", - " 15\n", - " Cataldi\n", - " Vecino\n", - " 65.0\n", - " \n", - " \n", - " 16\n", - " Lazzari\n", - " Hysaj\n", - " 55.0\n", - " \n", - " \n", - " 17\n", - " Boga\n", - " Ederson D.s.\n", - " 55.0\n", - " \n", - " \n", - " 18\n", - " Hojlund\n", - " Zapata D.\n", - " 65.0\n", - " \n", - " \n", - " 19\n", - " Djimsiti\n", - " Demiral\n", - " 60.0\n", - " \n", - " \n", - " 20\n", - " Thauvin\n", + " Samardzic\n", " Success\n", " 55.0\n", " \n", " \n", - " 21\n", - " Arslan\n", - " Lovric\n", + " 4\n", + " Esposito Sa.\n", + " Agudelo\n", + " 55.0\n", + " \n", + " \n", + " 5\n", + " Bastoni S.\n", + " Reca\n", " 60.0\n", " \n", " \n", - " 22\n", - " Bajrami\n", - " Thorstvedt\n", - " 55.0\n", - " \n", - " \n", - " 23\n", - " Ruan\n", - " Ferrari G.\n", + " 6\n", + " Verde\n", + " Shomurodov\n", " 60.0\n", " \n", " \n", - " 24\n", - " Lopez M.\n", - " Obiang\n", - " 55.0\n", - " \n", - " \n", - " 25\n", - " Zirkzee\n", - " Barrow\n", - " 65.0\n", - " \n", - " \n", - " 26\n", - " Cambiaso\n", - " Lykogiannis\n", - " 55.0\n", - " \n", - " \n", - " 27\n", - " Soriano\n", - " Aebischer\n", - " 65.0\n", - " \n", - " \n", - " 28\n", - " Marlon\n", + " 7\n", " Caldirola\n", - " 55.0\n", - " \n", - " \n", - " 29\n", - " Petagna\n", - " Mota\n", + " Marlon\n", " 60.0\n", " \n", " \n", - " 30\n", - " Sensi\n", + " 8\n", + " Colpani\n", " Machin\n", " 55.0\n", " \n", " \n", + " 9\n", + " Petagna\n", + " Mota\n", + " 55.0\n", + " \n", + " \n", + " 10\n", + " Lozano\n", + " Politano\n", + " 65.0\n", + " \n", + " \n", + " 11\n", + " Zielinski\n", + " Elmas\n", + " 60.0\n", + " \n", + " \n", + " 12\n", + " Piatek\n", + " Botheim\n", + " 55.0\n", + " \n", + " \n", + " 13\n", + " Kastanos\n", + " Mazzocchi\n", + " 55.0\n", + " \n", + " \n", + " 14\n", + " Lovato\n", + " Troost-Ekong\n", + " 55.0\n", + " \n", + " \n", + " 15\n", + " El Shaarawy\n", + " Dybala\n", + " 65.0\n", + " \n", + " \n", + " 16\n", + " Belotti\n", + " Abraham\n", + " 55.0\n", + " \n", + " \n", + " 17\n", + " Kalulu\n", + " Kjaer\n", + " 60.0\n", + " \n", + " \n", + " 18\n", + " Diaz B.\n", + " Saelemaekers\n", + " 55.0\n", + " \n", + " \n", + " 19\n", + " Giroud\n", + " Origi\n", + " 60.0\n", + " \n", + " \n", + " 20\n", + " Miranchuk\n", + " Vlasic\n", + " 60.0\n", + " \n", + " \n", + " 21\n", + " Gravillon\n", + " Djidji\n", + " 60.0\n", + " \n", + " \n", + " 22\n", + " Ricci S.\n", + " Linetty\n", + " 60.0\n", + " \n", + " \n", + " 23\n", + " Palomino\n", + " Djimsiti\n", + " 60.0\n", + " \n", + " \n", + " 24\n", + " Pasalic\n", + " Ederson D.s.\n", + " 60.0\n", + " \n", + " \n", + " 25\n", + " Hojlund\n", + " Boga\n", + " 60.0\n", + " \n", + " \n", + " 26\n", + " Lukaku\n", + " Dzeko\n", + " 60.0\n", + " \n", + " \n", + " 27\n", + " Dimarco\n", + " Gosens\n", + " 60.0\n", + " \n", + " \n", + " 28\n", + " Immobile\n", + " Pedro\n", + " 60.0\n", + " \n", + " \n", + " 29\n", + " Hysaj\n", + " Lazzari\n", + " 65.0\n", + " \n", + " \n", + " 30\n", + " Castagnetti\n", + " Galdames\n", + " 60.0\n", + " \n", + " \n", " 31\n", - " De Sciglio\n", - " Chiesa\n", + " Vasquez\n", + " Lochoshvili\n", " 60.0\n", " \n", " \n", " 32\n", - " Brekalo\n", - " Saponara\n", + " Buonaiuto\n", + " Ghiglione\n", " 55.0\n", " \n", " \n", " 33\n", - " Brekalo\n", - " Ikone'\n", + " Duda\n", + " Abildgaard\n", " 55.0\n", " \n", " \n", " 34\n", - " Jovic\n", - " Cabral\n", - " 60.0\n", + " Gaich\n", + " Djuric\n", + " 55.0\n", " \n", " \n", " 35\n", - " Barak\n", - " Duncan\n", + " Lazovic\n", + " Doig\n", " 60.0\n", " \n", " \n", " 36\n", - " Lozano\n", - " Elmas\n", - " 55.0\n", + " Erlic\n", + " Ferrari G.\n", + " 60.0\n", " \n", " \n", " 37\n", - " Lozano\n", - " Politano\n", - " 55.0\n", + " Bajrami\n", + " Defrel\n", + " 60.0\n", " \n", " \n", " 38\n", - " Mario Rui\n", - " Olivera\n", + " Matheus Henrique\n", + " Harroui\n", " 60.0\n", " \n", " \n", " 39\n", - " Buonaiuto\n", - " Afena-Gyan\n", - " 60.0\n", + " Vicario\n", + " Perisan\n", + " 55.0\n", " \n", " \n", " 40\n", - " Benassi\n", - " Castagnetti\n", - " 60.0\n", + " Marin\n", + " Grassi\n", + " 65.0\n", " \n", " \n", " 41\n", - " Ceccherini\n", - " Coppola D.\n", - " 60.0\n", + " Cambiaghi\n", + " Satriano\n", + " 55.0\n", " \n", " \n", " 42\n", - " Magnani\n", - " Dawidowicz\n", + " Gonzalez N.\n", + " Ikone'\n", " 55.0\n", " \n", " \n", " 43\n", - " Ngonge\n", - " Gaich\n", + " Saponara\n", + " Sottil\n", " 60.0\n", " \n", " \n", " 44\n", - " Bronn\n", - " Daniliuc\n", + " Martinez Quarta\n", + " Igor\n", " 60.0\n", " \n", " \n", " 45\n", - " Lovato\n", - " Sambia\n", + " Djuricic\n", + " Cuisance\n", " 55.0\n", " \n", " \n", " 46\n", - " Bohinen\n", - " Nicolussi Caviglia\n", - " 55.0\n", - " \n", - " \n", - " 47\n", - " Djuricic\n", - " Sabiri\n", - " 65.0\n", - " \n", - " \n", - " 48\n", - " Murru\n", - " Murillo\n", + " Lammers\n", + " Jese'\n", " 60.0\n", " \n", " \n", + " 47\n", + " Aebischer\n", + " Orsolini\n", + " 60.0\n", + " \n", + " \n", + " 48\n", + " Zirkzee\n", + " Sansone\n", + " 55.0\n", + " \n", + " \n", " 49\n", - " Rincon\n", - " Cuisance\n", + " Dominguez\n", + " Moro N.\n", " 60.0\n", " \n", " \n", " 50\n", - " Brozovic\n", - " Mkhitaryan\n", - " 55.0\n", + " Di Maria\n", + " Chiesa\n", + " 60.0\n", " \n", " \n", " 51\n", - " Dzeko\n", - " Lukaku\n", + " Cuadrado\n", + " De Sciglio\n", " 60.0\n", " \n", " \n", " 52\n", - " Darmian\n", - " Dumfries\n", - " 55.0\n", + " Vlahovic\n", + " Milik\n", + " 60.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " player1 player2 percentage\n", - "0 Saelemaekers Calabria 60.0\n", - "1 Diaz B. De Ketelaere 60.0\n", - "2 Thiaw Messias 65.0\n", - "3 Singo Aina 55.0\n", - "4 Rodriguez R. Buongiorno 55.0\n", - "5 Akpa Akpro Haas 60.0\n", - "6 Satriano Piccoli 60.0\n", - "7 Ebuehi Stojanovic 60.0\n", - "8 Holm Ferrer 55.0\n", - "9 Nzola Shomurodov 55.0\n", - "10 Agudelo Cipot 60.0\n", - "11 Di Francesco F. Banda 65.0\n", - "12 Gallo Pezzella Giu. 60.0\n", - "13 El Shaarawy Celik 60.0\n", - "14 Matic Bove 60.0\n", - "15 Cataldi Vecino 65.0\n", - "16 Lazzari Hysaj 55.0\n", - "17 Boga Ederson D.s. 55.0\n", - "18 Hojlund Zapata D. 65.0\n", - "19 Djimsiti Demiral 60.0\n", - "20 Thauvin Success 55.0\n", - "21 Arslan Lovric 60.0\n", - "22 Bajrami Thorstvedt 55.0\n", - "23 Ruan Ferrari G. 60.0\n", - "24 Lopez M. Obiang 55.0\n", - "25 Zirkzee Barrow 65.0\n", - "26 Cambiaso Lykogiannis 55.0\n", - "27 Soriano Aebischer 65.0\n", - "28 Marlon Caldirola 55.0\n", - "29 Petagna Mota 60.0\n", - "30 Sensi Machin 55.0\n", - "31 De Sciglio Chiesa 60.0\n", - "32 Brekalo Saponara 55.0\n", - "33 Brekalo Ikone' 55.0\n", - "34 Jovic Cabral 60.0\n", - "35 Barak Duncan 60.0\n", - "36 Lozano Elmas 55.0\n", - "37 Lozano Politano 55.0\n", - "38 Mario Rui Olivera 60.0\n", - "39 Buonaiuto Afena-Gyan 60.0\n", - "40 Benassi Castagnetti 60.0\n", - "41 Ceccherini Coppola D. 60.0\n", - "42 Magnani Dawidowicz 55.0\n", - "43 Ngonge Gaich 60.0\n", - "44 Bronn Daniliuc 60.0\n", - "45 Lovato Sambia 55.0\n", - "46 Bohinen Nicolussi Caviglia 55.0\n", - "47 Djuricic Sabiri 65.0\n", - "48 Murru Murillo 60.0\n", - "49 Rincon Cuisance 60.0\n", - "50 Brozovic Mkhitaryan 55.0\n", - "51 Dzeko Lukaku 60.0\n", - "52 Darmian Dumfries 55.0" + " player1 player2 percentage\n", + "0 Gallo Pezzella Giu. 65.0\n", + "1 Ceesay Colombo 60.0\n", + "2 Gonzalez J. Oudin 55.0\n", + "3 Samardzic Success 55.0\n", + "4 Esposito Sa. Agudelo 55.0\n", + "5 Bastoni S. Reca 60.0\n", + "6 Verde Shomurodov 60.0\n", + "7 Caldirola Marlon 60.0\n", + "8 Colpani Machin 55.0\n", + "9 Petagna Mota 55.0\n", + "10 Lozano Politano 65.0\n", + "11 Zielinski Elmas 60.0\n", + "12 Piatek Botheim 55.0\n", + "13 Kastanos Mazzocchi 55.0\n", + "14 Lovato Troost-Ekong 55.0\n", + "15 El Shaarawy Dybala 65.0\n", + "16 Belotti Abraham 55.0\n", + "17 Kalulu Kjaer 60.0\n", + "18 Diaz B. Saelemaekers 55.0\n", + "19 Giroud Origi 60.0\n", + "20 Miranchuk Vlasic 60.0\n", + "21 Gravillon Djidji 60.0\n", + "22 Ricci S. Linetty 60.0\n", + "23 Palomino Djimsiti 60.0\n", + "24 Pasalic Ederson D.s. 60.0\n", + "25 Hojlund Boga 60.0\n", + "26 Lukaku Dzeko 60.0\n", + "27 Dimarco Gosens 60.0\n", + "28 Immobile Pedro 60.0\n", + "29 Hysaj Lazzari 65.0\n", + "30 Castagnetti Galdames 60.0\n", + "31 Vasquez Lochoshvili 60.0\n", + "32 Buonaiuto Ghiglione 55.0\n", + "33 Duda Abildgaard 55.0\n", + "34 Gaich Djuric 55.0\n", + "35 Lazovic Doig 60.0\n", + "36 Erlic Ferrari G. 60.0\n", + "37 Bajrami Defrel 60.0\n", + "38 Matheus Henrique Harroui 60.0\n", + "39 Vicario Perisan 55.0\n", + "40 Marin Grassi 65.0\n", + "41 Cambiaghi Satriano 55.0\n", + "42 Gonzalez N. Ikone' 55.0\n", + "43 Saponara Sottil 60.0\n", + "44 Martinez Quarta Igor 60.0\n", + "45 Djuricic Cuisance 55.0\n", + "46 Lammers Jese' 60.0\n", + "47 Aebischer Orsolini 60.0\n", + "48 Zirkzee Sansone 55.0\n", + "49 Dominguez Moro N. 60.0\n", + "50 Di Maria Chiesa 60.0\n", + "51 Cuadrado De Sciglio 60.0\n", + "52 Vlahovic Milik 60.0" ] }, "execution_count": 5, @@ -674,81 +674,81 @@ " \n", " \n", " \n", - " Tatarusanu\n", + " Falcone\n", " 1\n", " 90.0\n", " \n", " \n", - " Thiaw\n", + " Gendrey\n", + " 1\n", + " 90.0\n", + " \n", + " \n", + " Baschirotto\n", + " 1\n", + " 90.0\n", + " \n", + " \n", + " Umtiti\n", + " 1\n", + " 90.0\n", + " \n", + " \n", + " Gallo\n", " 0.65\n", " 65.0\n", " \n", " \n", - " Kjaer\n", - " 1\n", - " 85.0\n", - " \n", - " \n", - " Kalulu\n", - " 1\n", - " 90.0\n", - " \n", - " \n", - " Saelemaekers\n", - " 0.6\n", - " 60.0\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", " \n", " \n", - " Dumfries\n", - " 0.45\n", - " 60.0\n", - " \n", - " \n", - " Mkhitaryan\n", - " 0.45\n", - " 55.0\n", - " \n", - " \n", - " Asllani\n", + " Barrenechea\n", " 0\n", - " 30.0\n", + " 20.0\n", " \n", " \n", - " Gagliardini\n", + " Pogba\n", " 0\n", " 50.0\n", " \n", " \n", - " Lukaku\n", + " Chiesa\n", + " 0.4\n", + " 55.0\n", + " \n", + " \n", + " Soule'\n", + " 0\n", + " 20.0\n", + " \n", + " \n", + " Milik\n", " 0.4\n", " 60.0\n", " \n", " \n", "\n", - "

438 rows × 2 columns

\n", + "

474 rows × 2 columns

\n", "" ], "text/plain": [ - " starter percentage\n", - "player \n", - "Tatarusanu 1 90.0\n", - "Thiaw 0.65 65.0\n", - "Kjaer 1 85.0\n", - "Kalulu 1 90.0\n", - "Saelemaekers 0.6 60.0\n", - "... ... ...\n", - "Dumfries 0.45 60.0\n", - "Mkhitaryan 0.45 55.0\n", - "Asllani 0 30.0\n", - "Gagliardini 0 50.0\n", - "Lukaku 0.4 60.0\n", + " starter percentage\n", + "player \n", + "Falcone 1 90.0\n", + "Gendrey 1 90.0\n", + "Baschirotto 1 90.0\n", + "Umtiti 1 90.0\n", + "Gallo 0.65 65.0\n", + "... ... ...\n", + "Barrenechea 0 20.0\n", + "Pogba 0 50.0\n", + "Chiesa 0.4 55.0\n", + "Soule' 0 20.0\n", + "Milik 0.4 60.0\n", "\n", - "[438 rows x 2 columns]" + "[474 rows x 2 columns]" ] }, "execution_count": 6, @@ -778,14 +778,6 @@ "source": [ "probables.to_excel('mid_outputs/match_probable_players.xlsx') " ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62164549", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/6_neural_network_training.ipynb b/6_neural_network_training.ipynb deleted file mode 100644 index 469b3ed..0000000 --- a/6_neural_network_training.ipynb +++ /dev/null @@ -1,9740 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4a867836", - "metadata": {}, - "source": [ - "Bayesian Neural Network model traning and prediction data generation." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "210da263", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.neural_network import MLPRegressor\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import r2_score\n", - "\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "edbf3b27", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", - "import tensorflow_datasets as tfds\n", - "import tensorflow_probability as tfp\n", - "\n", - "tfk = tf.keras\n", - "tf.keras.backend.set_floatx(\"float32\")\n", - "import tensorflow_probability as tfp\n", - "tfd = tfp.distributions\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.ensemble import IsolationForest\n", - "\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "markdown", - "id": "9dadf6ec", - "metadata": {}, - "source": [ - "Load the training databases, generated in player_match_database_creation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fa098fa4", - "metadata": {}, - "outputs": [], - "source": [ - "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", - "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", - "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f71fa9a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...miscontrolsdispossessedfoulsfouledaerials_wonaerials_lostcarriesprogressive_carriescarries_into_final_thirdcarries_into_penalty_area
01ToloiAtalantaSampdoria07.0100.010.0...0.0073640.0014730.0066270.0058910.0139910.0110460.3652430.0081000.0162000.000000
11DjimsitiAtalantaSampdoria06.0000.06.0...0.0018760.0075050.0056290.0056290.0225140.0150090.4878050.0037520.0037520.000000
21HateboerAtalantaSampdoria06.0000.55.5...0.0059930.0052430.0134830.0014980.0164790.0104870.2838950.0119850.0097380.000749
31OkoliAtalantaSampdoria05.5000.55.0...0.0130180.0035500.0153850.0082840.0473370.0272190.2698220.0023670.0047340.000000
41ZorteaAtalantaSampdoria06.0000.55.5...0.0000000.0000000.0222220.0000000.0111110.0222220.5333330.0666670.0666670.000000
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2455138TamezeVeronaLazio05.5000.05.5...0.0198140.0101010.0128210.0132090.0236990.0213680.3372180.0213680.0128210.003885
2455238HonglaVeronaLazio07.0100.59.5...0.0184330.0122890.0230410.0076800.0230410.0261140.3410140.0092170.0153610.003072
2455338LasagnaVeronaLazio07.0100.59.5...0.0464530.0228040.0160470.0084460.0261820.0413850.1908780.0219590.0109800.005912
2455438CaprariVeronaLazio06.0000.06.0...0.0339540.0197150.0153340.0270170.0029210.0098580.3913840.0427160.0277470.018620
2455538SimeoneVeronaLazio07.0100.010.0...0.0512630.0301550.0211080.0218620.0226160.0407090.2461360.0150770.0128160.006031
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24556 rows × 122 columns

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" - ], - "text/plain": [ - " matchday player team oppteam home vote goals assists \\\n", - "0 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", - "1 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", - "2 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", - "3 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", - "4 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", - "... ... ... ... ... ... ... ... ... \n", - "24551 38 Tameze Verona Lazio 0 5.5 0 0 \n", - "24552 38 Hongla Verona Lazio 0 7.0 1 0 \n", - "24553 38 Lasagna Verona Lazio 0 7.0 1 0 \n", - "24554 38 Caprari Verona Lazio 0 6.0 0 0 \n", - "24555 38 Simeone Verona Lazio 0 7.0 1 0 \n", - "\n", - " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", - "0 0.0 10.0 ... 0.007364 0.001473 0.006627 \n", - "1 0.0 6.0 ... 0.001876 0.007505 0.005629 \n", - "2 0.5 5.5 ... 0.005993 0.005243 0.013483 \n", - "3 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", - "4 0.5 5.5 ... 0.000000 0.000000 0.022222 \n", - "... ... ... ... ... ... ... \n", - "24551 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", - "24552 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", - "24553 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", - "24554 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", - "24555 0.0 10.0 ... 0.051263 0.030155 0.021108 \n", - "\n", - " fouled aerials_won aerials_lost carries progressive_carries \\\n", - "0 0.005891 0.013991 0.011046 0.365243 0.008100 \n", - "1 0.005629 0.022514 0.015009 0.487805 0.003752 \n", - "2 0.001498 0.016479 0.010487 0.283895 0.011985 \n", - "3 0.008284 0.047337 0.027219 0.269822 0.002367 \n", - "4 0.000000 0.011111 0.022222 0.533333 0.066667 \n", - "... ... ... ... ... ... \n", - "24551 0.013209 0.023699 0.021368 0.337218 0.021368 \n", - "24552 0.007680 0.023041 0.026114 0.341014 0.009217 \n", - "24553 0.008446 0.026182 0.041385 0.190878 0.021959 \n", - "24554 0.027017 0.002921 0.009858 0.391384 0.042716 \n", - "24555 0.021862 0.022616 0.040709 0.246136 0.015077 \n", - "\n", - " carries_into_final_third carries_into_penalty_area \n", - "0 0.016200 0.000000 \n", - "1 0.003752 0.000000 \n", - "2 0.009738 0.000749 \n", - "3 0.004734 0.000000 \n", - "4 0.066667 0.000000 \n", - "... ... ... \n", - "24551 0.012821 0.003885 \n", - "24552 0.015361 0.003072 \n", - "24553 0.010980 0.005912 \n", - "24554 0.027747 0.018620 \n", - "24555 0.012816 0.006031 \n", - "\n", - "[24556 rows x 122 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db = pd.concat([db1, db2, db3], ignore_index = True) \n", - "\n", - "db" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d024554", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...12.700.21-1.3081.0185.0372.0107.098.0174.010.0
11SkorupskiBolognaLazio06.5-200.04.5...30.800.281.8099.0240.0563.0117.0153.0299.018.0
21VicarioEmpoliSpezia05.5-100.04.5...26.400.270.40102.0310.0766.0113.0108.0426.025.0
31GolliniFiorentinaCremonese15.0-200.03.0...9.350.240.8524.068.0291.547.069.5122.53.5
41HandanovicInterLecce06.5-100.05.5...10.600.30-1.4027.052.0266.046.045.079.02.0
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124021ConsigliSassuoloAtalanta16.0000.06.0...23.300.35-4.70112.0269.0686.099.0172.0255.016.0
124121DragowskiSpeziaNapoli15.0-300.02.0...28.100.27-3.9085.0268.0503.092.0109.0251.09.0
124221Milinkovic-Savic V.TorinoUdinese16.5000.06.5...21.900.23-0.10148.0524.0788.095.0155.0263.018.0
124321SilvestriUdineseTorino06.5-100.05.5...23.900.311.9090.0225.0450.076.0164.0275.05.0
124421Montipo'VeronaLazio16.0-100.05.0...27.600.25-4.40206.0413.0497.065.0168.0294.015.0
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1245 rows × 102 columns

\n", - "
" - ], - "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 \n", - "1 1 Skorupski Bologna Lazio 0 6.5 -2 \n", - "2 1 Vicario Empoli Spezia 0 5.5 -1 \n", - "3 1 Gollini Fiorentina Cremonese 1 5.0 -2 \n", - "4 1 Handanovic Inter Lecce 0 6.5 -1 \n", - "... ... ... ... ... ... ... ... \n", - "1240 21 Consigli Sassuolo Atalanta 1 6.0 0 \n", - "1241 21 Dragowski Spezia Napoli 1 5.0 -3 \n", - "1242 21 Milinkovic-Savic V. Torino Udinese 1 6.5 0 \n", - "1243 21 Silvestri Udinese Torino 0 6.5 -1 \n", - "1244 21 Montipo' Verona Lazio 1 6.0 -1 \n", - "\n", - " assists cards_malus fantavote ... gk_psxg \\\n", - "0 0 0.5 5.5 ... 12.70 \n", - "1 0 0.0 4.5 ... 30.80 \n", - "2 0 0.0 4.5 ... 26.40 \n", - "3 0 0.0 3.0 ... 9.35 \n", - "4 0 0.0 5.5 ... 10.60 \n", - "... ... ... ... ... ... \n", - "1240 0 0.0 6.0 ... 23.30 \n", - "1241 0 0.0 2.0 ... 28.10 \n", - "1242 0 0.0 6.5 ... 21.90 \n", - "1243 0 0.0 5.5 ... 23.90 \n", - "1244 0 0.0 5.0 ... 27.60 \n", - "\n", - " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.21 -1.30 \n", - "1 0.28 1.80 \n", - "2 0.27 0.40 \n", - "3 0.24 0.85 \n", - "4 0.30 -1.40 \n", - "... ... ... \n", - "1240 0.35 -4.70 \n", - "1241 0.27 -3.90 \n", - "1242 0.23 -0.10 \n", - "1243 0.31 1.90 \n", - "1244 0.25 -4.40 \n", - "\n", - " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 81.0 185.0 372.0 \n", - "1 99.0 240.0 563.0 \n", - "2 102.0 310.0 766.0 \n", - "3 24.0 68.0 291.5 \n", - "4 27.0 52.0 266.0 \n", - "... ... ... ... \n", - "1240 112.0 269.0 686.0 \n", - "1241 85.0 268.0 503.0 \n", - "1242 148.0 524.0 788.0 \n", - "1243 90.0 225.0 450.0 \n", - "1244 206.0 413.0 497.0 \n", - "\n", - " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 107.0 98.0 174.0 10.0 \n", - "1 117.0 153.0 299.0 18.0 \n", - "2 113.0 108.0 426.0 25.0 \n", - "3 47.0 69.5 122.5 3.5 \n", - "4 46.0 45.0 79.0 2.0 \n", - "... ... ... ... ... \n", - "1240 99.0 172.0 255.0 16.0 \n", - "1241 92.0 109.0 251.0 9.0 \n", - "1242 95.0 155.0 263.0 18.0 \n", - "1243 76.0 164.0 275.0 5.0 \n", - "1244 65.0 168.0 294.0 15.0 \n", - "\n", - "[1245 rows x 102 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", - "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", - "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", - "\n", - "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", - "\n", - "db_gk" - ] - }, - { - "cell_type": "markdown", - "id": "04df0936", - "metadata": {}, - "source": [ - "Load player stats from current season and past seasons" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bc9dae87", - "metadata": {}, - "outputs": [], - "source": [ - "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", - "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", - "\n", - "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", - "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", - "\n", - "players = players_orig" - ] - }, - { - "cell_type": "markdown", - "id": "397babf2", - "metadata": {}, - "source": [ - "Load team data from current season and add an average Serie A team row" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "493b0495", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_11840\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", - " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" - ] - }, - { - "data": { - "text/html": [ - "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0049.0021.0231.01890.038.027.06.008.0...232.0244.026.001.08.001.01261.0260.0317.045.10
BolognaBologna25.0051.9021.0231.01890.027.020.04.004.0...262.0254.036.003.04.001.01145.0239.0193.055.30
CremoneseCremonese30.0044.2021.0231.01890.015.07.02.004.0...232.0264.033.003.04.000.01168.0388.0296.056.70
EmpoliEmpoli27.0046.8021.0231.01890.019.011.00.000.0...270.0244.035.002.00.000.01104.0242.0211.053.40
FiorentinaFiorentina28.0057.3021.0231.01890.023.018.02.004.0...296.0252.054.001.04.000.01061.0274.0317.046.40
VeronaHellas Verona34.0043.1021.0231.01890.017.014.00.000.0...213.0302.024.001.00.002.01173.0385.0422.047.70
InterInter23.0054.0021.0231.01890.040.027.02.002.0...263.0237.019.002.02.001.0960.0221.0266.045.40
JuventusJuventus26.0049.2021.0231.01890.033.025.03.004.0...229.0232.027.000.04.000.01064.0247.0257.049.00
LazioLazio21.0051.4021.0231.01890.036.026.03.004.0...294.0206.044.001.04.001.01157.0207.0216.048.90
LecceLecce26.0042.4021.0231.01890.020.014.01.002.0...275.0279.045.003.02.001.01141.0396.0316.055.60
MilanMilan27.0053.9021.0231.01890.035.030.02.002.0...255.0251.025.004.02.002.01059.0249.0305.044.90
MonzaMonza29.0055.9021.0231.01890.026.016.04.004.0...303.0263.037.000.04.001.01075.0226.0241.048.40
NapoliNapoli24.0061.4021.0231.01890.051.040.05.006.0...291.0192.028.001.05.000.01049.0214.0259.045.20
RomaRoma26.0048.9021.0231.01890.028.019.03.005.0...292.0238.010.000.05.000.01095.0205.0245.045.60
SalernitanaSalernitana28.0045.1021.0231.01890.024.016.01.001.0...238.0242.052.008.01.001.01143.0268.0247.052.00
SampdoriaSampdoria31.0047.9021.0231.01890.010.08.00.000.0...328.0288.056.004.00.000.01126.0340.0339.050.10
SassuoloSassuolo29.0048.5021.0231.01890.024.018.04.005.0...275.0200.069.002.05.000.01088.0243.0195.055.50
SpeziaSpezia33.0045.9021.0231.01890.015.010.02.002.0...225.0279.050.001.02.002.01225.0337.0285.054.20
TorinoTorino25.0053.0021.0231.01890.022.017.01.001.0...229.0287.023.003.01.000.01095.0347.0319.052.10
UdineseUdinese25.0050.0021.0231.01890.027.023.00.000.0...274.0242.034.002.00.001.01063.0222.0264.045.70
AvgAvg27.0549.9921.0231.01890.026.519.32.252.9...263.8249.836.352.12.850.71112.6275.5275.549.86
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21 rows × 303 columns

\n", - "
" - ], - "text/plain": [ - " team team_players_used team_possession team_games \\\n", - "Atalanta Atalanta 24.00 49.00 21.0 \n", - "Bologna Bologna 25.00 51.90 21.0 \n", - "Cremonese Cremonese 30.00 44.20 21.0 \n", - "Empoli Empoli 27.00 46.80 21.0 \n", - "Fiorentina Fiorentina 28.00 57.30 21.0 \n", - "Verona Hellas Verona 34.00 43.10 21.0 \n", - "Inter Inter 23.00 54.00 21.0 \n", - "Juventus Juventus 26.00 49.20 21.0 \n", - "Lazio Lazio 21.00 51.40 21.0 \n", - "Lecce Lecce 26.00 42.40 21.0 \n", - "Milan Milan 27.00 53.90 21.0 \n", - "Monza Monza 29.00 55.90 21.0 \n", - "Napoli Napoli 24.00 61.40 21.0 \n", - "Roma Roma 26.00 48.90 21.0 \n", - "Salernitana Salernitana 28.00 45.10 21.0 \n", - "Sampdoria Sampdoria 31.00 47.90 21.0 \n", - "Sassuolo Sassuolo 29.00 48.50 21.0 \n", - "Spezia Spezia 33.00 45.90 21.0 \n", - "Torino Torino 25.00 53.00 21.0 \n", - "Udinese Udinese 25.00 50.00 21.0 \n", - "Avg Avg 27.05 49.99 21.0 \n", - "\n", - " team_games_starts team_minutes team_goals team_assists \\\n", - "Atalanta 231.0 1890.0 38.0 27.0 \n", - "Bologna 231.0 1890.0 27.0 20.0 \n", - "Cremonese 231.0 1890.0 15.0 7.0 \n", - "Empoli 231.0 1890.0 19.0 11.0 \n", - "Fiorentina 231.0 1890.0 23.0 18.0 \n", - "Verona 231.0 1890.0 17.0 14.0 \n", - "Inter 231.0 1890.0 40.0 27.0 \n", - "Juventus 231.0 1890.0 33.0 25.0 \n", - "Lazio 231.0 1890.0 36.0 26.0 \n", - "Lecce 231.0 1890.0 20.0 14.0 \n", - "Milan 231.0 1890.0 35.0 30.0 \n", - "Monza 231.0 1890.0 26.0 16.0 \n", - "Napoli 231.0 1890.0 51.0 40.0 \n", - "Roma 231.0 1890.0 28.0 19.0 \n", - "Salernitana 231.0 1890.0 24.0 16.0 \n", - "Sampdoria 231.0 1890.0 10.0 8.0 \n", - "Sassuolo 231.0 1890.0 24.0 18.0 \n", - "Spezia 231.0 1890.0 15.0 10.0 \n", - "Torino 231.0 1890.0 22.0 17.0 \n", - "Udinese 231.0 1890.0 27.0 23.0 \n", - "Avg 231.0 1890.0 26.5 19.3 \n", - "\n", - " team_pens_made team_pens_att ... vs_team_fouls \\\n", - "Atalanta 6.00 8.0 ... 232.0 \n", - "Bologna 4.00 4.0 ... 262.0 \n", - "Cremonese 2.00 4.0 ... 232.0 \n", - "Empoli 0.00 0.0 ... 270.0 \n", - "Fiorentina 2.00 4.0 ... 296.0 \n", - "Verona 0.00 0.0 ... 213.0 \n", - "Inter 2.00 2.0 ... 263.0 \n", - "Juventus 3.00 4.0 ... 229.0 \n", - "Lazio 3.00 4.0 ... 294.0 \n", - "Lecce 1.00 2.0 ... 275.0 \n", - "Milan 2.00 2.0 ... 255.0 \n", - "Monza 4.00 4.0 ... 303.0 \n", - "Napoli 5.00 6.0 ... 291.0 \n", - "Roma 3.00 5.0 ... 292.0 \n", - "Salernitana 1.00 1.0 ... 238.0 \n", - "Sampdoria 0.00 0.0 ... 328.0 \n", - "Sassuolo 4.00 5.0 ... 275.0 \n", - "Spezia 2.00 2.0 ... 225.0 \n", - "Torino 1.00 1.0 ... 229.0 \n", - "Udinese 0.00 0.0 ... 274.0 \n", - "Avg 2.25 2.9 ... 263.8 \n", - "\n", - " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", - "Atalanta 244.0 26.00 1.0 \n", - "Bologna 254.0 36.00 3.0 \n", - "Cremonese 264.0 33.00 3.0 \n", - "Empoli 244.0 35.00 2.0 \n", - "Fiorentina 252.0 54.00 1.0 \n", - "Verona 302.0 24.00 1.0 \n", - "Inter 237.0 19.00 2.0 \n", - "Juventus 232.0 27.00 0.0 \n", - "Lazio 206.0 44.00 1.0 \n", - "Lecce 279.0 45.00 3.0 \n", - "Milan 251.0 25.00 4.0 \n", - "Monza 263.0 37.00 0.0 \n", - "Napoli 192.0 28.00 1.0 \n", - "Roma 238.0 10.00 0.0 \n", - "Salernitana 242.0 52.00 8.0 \n", - "Sampdoria 288.0 56.00 4.0 \n", - "Sassuolo 200.0 69.00 2.0 \n", - "Spezia 279.0 50.00 1.0 \n", - "Torino 287.0 23.00 3.0 \n", - "Udinese 242.0 34.00 2.0 \n", - "Avg 249.8 36.35 2.1 \n", - "\n", - " vs_team_pens_conceded vs_team_own_goals \\\n", - "Atalanta 8.00 1.0 \n", - "Bologna 4.00 1.0 \n", - "Cremonese 4.00 0.0 \n", - "Empoli 0.00 0.0 \n", - "Fiorentina 4.00 0.0 \n", - "Verona 0.00 2.0 \n", - "Inter 2.00 1.0 \n", - "Juventus 4.00 0.0 \n", - "Lazio 4.00 1.0 \n", - "Lecce 2.00 1.0 \n", - "Milan 2.00 2.0 \n", - "Monza 4.00 1.0 \n", - "Napoli 5.00 0.0 \n", - "Roma 5.00 0.0 \n", - "Salernitana 1.00 1.0 \n", - "Sampdoria 0.00 0.0 \n", - "Sassuolo 5.00 0.0 \n", - "Spezia 2.00 2.0 \n", - "Torino 1.00 0.0 \n", - "Udinese 0.00 1.0 \n", - "Avg 2.85 0.7 \n", - "\n", - " vs_team_ball_recoveries vs_team_aerials_won \\\n", - "Atalanta 1261.0 260.0 \n", - "Bologna 1145.0 239.0 \n", - "Cremonese 1168.0 388.0 \n", - "Empoli 1104.0 242.0 \n", - "Fiorentina 1061.0 274.0 \n", - "Verona 1173.0 385.0 \n", - "Inter 960.0 221.0 \n", - "Juventus 1064.0 247.0 \n", - "Lazio 1157.0 207.0 \n", - "Lecce 1141.0 396.0 \n", - "Milan 1059.0 249.0 \n", - "Monza 1075.0 226.0 \n", - "Napoli 1049.0 214.0 \n", - "Roma 1095.0 205.0 \n", - "Salernitana 1143.0 268.0 \n", - "Sampdoria 1126.0 340.0 \n", - "Sassuolo 1088.0 243.0 \n", - "Spezia 1225.0 337.0 \n", - "Torino 1095.0 347.0 \n", - "Udinese 1063.0 222.0 \n", - "Avg 1112.6 275.5 \n", - "\n", - " vs_team_aerials_lost vs_team_aerials_won_pct \n", - "Atalanta 317.0 45.10 \n", - "Bologna 193.0 55.30 \n", - "Cremonese 296.0 56.70 \n", - "Empoli 211.0 53.40 \n", - "Fiorentina 317.0 46.40 \n", - "Verona 422.0 47.70 \n", - "Inter 266.0 45.40 \n", - "Juventus 257.0 49.00 \n", - "Lazio 216.0 48.90 \n", - "Lecce 316.0 55.60 \n", - "Milan 305.0 44.90 \n", - "Monza 241.0 48.40 \n", - "Napoli 259.0 45.20 \n", - "Roma 245.0 45.60 \n", - "Salernitana 247.0 52.00 \n", - "Sampdoria 339.0 50.10 \n", - "Sassuolo 195.0 55.50 \n", - "Spezia 285.0 54.20 \n", - "Torino 319.0 52.10 \n", - "Udinese 264.0 45.70 \n", - "Avg 275.5 49.86 \n", - "\n", - "[21 rows x 303 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", - "\n", - "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", - "avg_row['team']['Avg'] = 'Avg'\n", - "\n", - "team_data = pd.concat([team_data, avg_row])\n", - "\n", - "team_data" - ] - }, - { - "cell_type": "markdown", - "id": "cc1cd13d", - "metadata": {}, - "source": [ - "Data processing functions copied from player_match_dataset_creation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "32f56138", - "metadata": {}, - "outputs": [], - "source": [ - "features_abs = ['r',\n", - " 'games',\n", - " 'games_starts', \n", - " 'minutes',\n", - " 'shots_on_target_pct',\n", - " 'goals_per_shot',\n", - " 'goals_per_shot_on_target',\n", - " 'passes_pct',\n", - " #'dribble_tackles_pct',\n", - " #'dribbles_completed_pct',\n", - " 'aerials_won_pct',\n", - " 'team_possession',\n", - " 'team_goals_assists_per90',\n", - " 'team_goals_pens_per90',\n", - " 'team_goals_assists_pens_per90',\n", - " 'team_xg_per90',\n", - " 'team_gk_goals_against_per90',\n", - " 'team_gk_save_pct',\n", - " 'team_gk_clean_sheets_pct',\n", - " 'team_passes_pct',\n", - " 'team_passes_pct_medium',\n", - " 'team_passes_pct_long',\n", - " 'team_sca_per90',\n", - " 'team_gca_per90',\n", - " #'team_dribble_tackles_pct',\n", - " 'team_aerials_won_pct',\n", - " 'vs_team_possession',\n", - " 'vs_team_goals_per90',\n", - " 'vs_team_assists_per90',\n", - " 'vs_team_xg_per90',\n", - " 'vs_team_gk_save_pct',\n", - " 'vs_team_gk_clean_sheets_pct',\n", - " 'vs_team_gk_pct_passes_launched',\n", - " 'vs_team_gk_crosses_stopped_pct',\n", - " 'vs_team_shots_on_target_per90',\n", - " 'vs_team_passes_pct',\n", - " 'vs_team_passes_pct_short',\n", - " 'vs_team_passes_pct_medium',\n", - " 'vs_team_passes_pct_long',\n", - " 'vs_team_sca_per90',\n", - " 'vs_team_gca_per90',\n", - " #'vs_team_dribble_tackles_pct',\n", - " #'vs_team_dribbles_completed_pct',\n", - " 'vs_team_aerials_won_pct',\n", - " 'opp_team_possession',\n", - " 'opp_team_goals_assists_per90',\n", - " 'opp_team_goals_pens_per90',\n", - " 'opp_team_goals_assists_pens_per90',\n", - " 'opp_team_xg_per90',\n", - " 'opp_team_gk_goals_against_per90',\n", - " 'opp_team_gk_save_pct',\n", - " 'opp_team_gk_clean_sheets_pct',\n", - " 'opp_team_passes_pct',\n", - " 'opp_team_passes_pct_medium',\n", - " 'opp_team_passes_pct_long',\n", - " 'opp_team_sca_per90',\n", - " 'opp_team_gca_per90',\n", - " #'opp_team_dribble_tackles_pct',\n", - " 'opp_team_aerials_won_pct',\n", - " 'opp_vs_team_possession',\n", - " 'opp_vs_team_goals_per90',\n", - " 'opp_vs_team_assists_per90',\n", - " 'opp_vs_team_xg_per90',\n", - " 'opp_vs_team_gk_save_pct',\n", - " 'opp_vs_team_gk_clean_sheets_pct',\n", - " 'opp_vs_team_gk_pct_passes_launched',\n", - " 'opp_vs_team_gk_crosses_stopped_pct',\n", - " 'opp_vs_team_shots_on_target_per90',\n", - " 'opp_vs_team_passes_pct',\n", - " 'opp_vs_team_passes_pct_short',\n", - " 'opp_vs_team_passes_pct_medium',\n", - " 'opp_vs_team_passes_pct_long',\n", - " 'opp_vs_team_sca_per90',\n", - " 'opp_vs_team_gca_per90',\n", - " #'opp_vs_team_dribble_tackles_pct',\n", - " #'opp_vs_team_dribbles_completed_pct',\n", - " 'opp_vs_team_aerials_won_pct',\n", - " \n", - " 'vote_avg',\n", - " 'vote_std']\n", - "\n", - "features_rel = [\n", - " 'goals',\n", - " 'assists',\n", - " 'cards_yellow',\n", - " 'cards_red',\n", - " 'xg',\n", - " 'npxg',\n", - " 'shots_on_target',\n", - " 'passes_completed',\n", - " 'passes_into_final_third',\n", - " 'passes_into_penalty_area',\n", - " 'progressive_passes',\n", - " 'passes_live',\n", - " 'passes_dead',\n", - " 'through_balls',\n", - " 'passes_switches',\n", - " 'crosses',\n", - " 'corner_kicks',\n", - " #'dribble_tackles',\n", - " #'dribbles_vs',\n", - " #'dribbled_past',\n", - " 'blocks',\n", - " 'blocked_shots',\n", - " 'blocked_passes',\n", - " 'interceptions',\n", - " 'clearances',\n", - " 'errors',\n", - " 'touches',\n", - " 'touches_def_pen_area',\n", - " 'touches_def_3rd',\n", - " 'touches_mid_3rd',\n", - " 'touches_att_3rd',\n", - " 'touches_att_pen_area',\n", - " 'touches_live_ball',\n", - " #'dribbles_completed',\n", - " #'dribbles',\n", - " 'passes_received',\n", - " 'miscontrols',\n", - " 'dispossessed',\n", - " 'fouls',\n", - " 'fouled',\n", - " 'aerials_won',\n", - " 'aerials_lost',\n", - " 'carries',\n", - " 'progressive_carries',\n", - " 'carries_into_final_third',\n", - " 'carries_into_penalty_area']\n", - "\n", - "features_rel_gamecorr = [\n", - " 'goals',\n", - " 'assists',\n", - " 'xg',\n", - " 'npxg',\n", - " 'cards_yellow',\n", - " 'cards_red'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4001f3f8", - "metadata": {}, - "outputs": [], - "source": [ - "features_abs_gk = [\n", - " 'gk_games',\n", - " 'gk_games_starts',\n", - " 'gk_minutes',\n", - " 'gk_goals_against_per90', \n", - " 'gk_save_pct',\n", - " 'gk_clean_sheets_pct',\n", - " 'gk_psxg_net_per90',\n", - " 'gk_passes_pct_launched',\n", - " 'gk_pct_passes_launched',\n", - " 'gk_passes_length_avg',\n", - " 'gk_pct_goal_kicks_launched',\n", - " 'gk_goal_kick_length_avg',\n", - " 'gk_crosses_stopped_pct',\n", - " 'gk_def_actions_outside_pen_area_per90',\n", - " 'gk_avg_distance_def_actions',\n", - " \n", - " 'team_possession',\n", - " 'team_goals_assists_per90',\n", - " 'team_goals_pens_per90',\n", - " 'team_goals_assists_pens_per90',\n", - " 'team_xg_per90',\n", - " 'team_gk_goals_against_per90',\n", - " 'team_gk_save_pct',\n", - " 'team_gk_clean_sheets_pct',\n", - " 'team_passes_pct',\n", - " 'team_passes_pct_medium',\n", - " 'team_passes_pct_long',\n", - " 'team_sca_per90',\n", - " 'team_gca_per90',\n", - " #'team_dribble_tackles_pct',\n", - " 'team_aerials_won_pct',\n", - " 'vs_team_possession',\n", - " 'vs_team_goals_per90',\n", - " 'vs_team_assists_per90',\n", - " 'vs_team_xg_per90',\n", - " 'vs_team_gk_save_pct',\n", - " 'vs_team_gk_clean_sheets_pct',\n", - " 'vs_team_gk_pct_passes_launched',\n", - " 'vs_team_gk_crosses_stopped_pct',\n", - " 'vs_team_shots_on_target_per90',\n", - " 'vs_team_passes_pct',\n", - " 'vs_team_passes_pct_short',\n", - " 'vs_team_passes_pct_medium',\n", - " 'vs_team_passes_pct_long',\n", - " 'vs_team_sca_per90',\n", - " 'vs_team_gca_per90',\n", - " #'vs_team_dribble_tackles_pct',\n", - " #'vs_team_dribbles_completed_pct',\n", - " 'vs_team_aerials_won_pct',\n", - " 'opp_team_possession',\n", - " 'opp_team_goals_assists_per90',\n", - " 'opp_team_goals_pens_per90',\n", - " 'opp_team_goals_assists_pens_per90',\n", - " 'opp_team_xg_per90',\n", - " 'opp_team_gk_goals_against_per90',\n", - " 'opp_team_gk_save_pct',\n", - " 'opp_team_gk_clean_sheets_pct',\n", - " 'opp_team_passes_pct',\n", - " 'opp_team_passes_pct_medium',\n", - " 'opp_team_passes_pct_long',\n", - " 'opp_team_sca_per90',\n", - " 'opp_team_gca_per90',\n", - " #'opp_team_dribble_tackles_pct',\n", - " 'opp_team_aerials_won_pct',\n", - " 'opp_vs_team_possession',\n", - " 'opp_vs_team_goals_per90',\n", - " 'opp_vs_team_assists_per90',\n", - " 'opp_vs_team_xg_per90',\n", - " 'opp_vs_team_gk_save_pct',\n", - " 'opp_vs_team_gk_clean_sheets_pct',\n", - " 'opp_vs_team_gk_pct_passes_launched',\n", - " 'opp_vs_team_gk_crosses_stopped_pct',\n", - " 'opp_vs_team_shots_on_target_per90',\n", - " 'opp_vs_team_passes_pct',\n", - " 'opp_vs_team_passes_pct_short',\n", - " 'opp_vs_team_passes_pct_medium',\n", - " 'opp_vs_team_passes_pct_long',\n", - " 'opp_vs_team_sca_per90',\n", - " 'opp_vs_team_gca_per90',\n", - " #'opp_vs_team_dribble_tackles_pct',\n", - " #'opp_vs_team_dribbles_completed_pct',\n", - " 'opp_vs_team_aerials_won_pct',\n", - " \n", - " 'vote_avg',\n", - " 'vote_std']\n", - "\n", - "features_rel_gk = [\n", - " 'gk_shots_on_target_against',\n", - " 'gk_saves',\n", - " 'gk_free_kick_goals_against',\n", - " 'gk_corner_kick_goals_against',\n", - " 'gk_own_goals_against',\n", - " 'gk_psxg',\n", - " 'gk_psnpxg_per_shot_on_target_against',\n", - " 'gk_psxg_net',\n", - " 'gk_passes_completed_launched',\n", - " 'gk_passes_launched',\n", - " 'gk_passes',\n", - " 'gk_passes_throws',\n", - " 'gk_goal_kicks',\n", - " 'gk_crosses',\n", - " 'gk_crosses_stopped',\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f4017b2f", - "metadata": {}, - "outputs": [], - "source": [ - "DEL_G = False\n", - "\n", - "features_to_del = [\n", - " 'goals',\n", - " 'assists',\n", - " 'xg',\n", - " 'npxg'\n", - "]\n", - "\n", - "def player_match_data(player, pteam, oppteam, oldseason = False):\n", - " if(not(player in players.index)):\n", - " return None\n", - " \n", - " if(oldseason):\n", - " pdata = players_old.loc[[player]]\n", - " else:\n", - " pdata = players.loc[[player]]\n", - " \n", - " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", - " \n", - " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", - " \n", - " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", - " \n", - " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", - " \n", - " return(out)\n", - "\n", - "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", - " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", - " \n", - " if(not isinstance(pdata, pd.DataFrame)):\n", - " return None\n", - " \n", - " assert pdata['games'][0] > 0\n", - " \n", - " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", - " \n", - " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", - " \n", - " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", - " \n", - " if(DEL_G):\n", - " out[features_to_del] = 0\n", - " \n", - " return out\n", - "\n", - "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", - " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", - " \n", - " if(not isinstance(pdata, pd.DataFrame)):\n", - " return None\n", - " \n", - " if(pdata['gk_games'][0] <= 0):\n", - " return None\n", - " \n", - " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", - " \n", - " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", - "\n", - " return out\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "21fef3ae", - "metadata": {}, - "source": [ - "Players stats rework:\n", - "the current season stats are averaged (according to a calculated weight) with the past season data.\n", - "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", - "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", - "\n", - "These modified stats are used only for prediction, not for model traning.\n", - "\n", - "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", - "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6f8707b8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "Averaging players stats with past seasons:\n", - "Meret 1\n", - "Provedel 1\n", - "Vicario 1\n", - "Szczesny 1\n", - "Falcone 1\n", - "Silvestri 1\n", - "Rui Patricio 1\n", - "Sepe 1\n", - "Milinkovic-Savic V. 1\n", - "Musso 1\n", - "Maignan 0.7094551282051282\n", - "Audero 1\n", - "Montipo' 1\n", - "Skorupski 1\n", - "Consigli 1\n", - "Dragowski 1\n", - "Terracciano 1\n", - "Tatarusanu 1\n", - "Handanovic 0.7012375012375013\n", - "Sportiello 1\n", - "Perin 1\n", - "Zoet 1\n", - "Pegolo 1\n", - "Mirante 0.0\n", - "Ujkani 0.0\n", - "Berisha 0.0\n", - "Marchetti 0.0\n", - "Padelli 0.0\n", - "Bardi 0.0\n", - "Cordaz 0.0\n", - "Pinsoglio 0.0\n", - "Fiorillo 0.0\n", - "Cragno 0.0\n", - "Sirigu 0.0\n", - "Rossi F. 0.0\n", - "Berardi A. 0.0\n", - "Gemello 0.0\n", - "Ravaglia 0.0\n", - "Boer 0.0\n", - "Adamonis 0.0\n", - "Marfella 0.0\n", - "Zovko 1\n", - "Piana 0.0\n", - "Dimarco 1\n", - "Smalling 1\n", - "Di Lorenzo 1\n", - "Danilo 1\n", - "Hernandez T. 0.9121565934065934\n", - "Udogie 0.7876399790685507\n", - "Parisi 1\n", - "Mario Rui 0.8108058608058609\n", - "Romagnoli 1\n", - "Bastoni S. 0.6800307219662058\n", - "Mazzocchi 1\n", - "Tomori 0.9938910551813778\n", - "Scalvini 1\n", - "Toloi 1\n", - "Demiral 0.9845499738356883\n", - "Maehle 1\n", - "Dumfries 0.8845154845154845\n", - "Juan Jesus 0.5405372405372405\n", - "Depaoli 1\n", - "Mancini 1\n", - "Ibanez 0.953889248006895\n", - "Rodrigo Becao 0.6949764521193094\n", - "Ebuehi 1\n", - "Gosens 1\n", - "Darmian 1\n", - "Reca 1\n", - "Bremer 0.9015984015984014\n", - "Rrahmani 0.6879564879564879\n", - "Vojvoda 0.8387646835922699\n", - "Bastoni 0.8892709441096539\n", - "Milenkovic 0.7631113984055161\n", - "Kalulu 1\n", - "Martinez Quarta 1\n", - "Casale 0.6428062678062678\n", - "Perez N. 1\n", - "Izzo 1\n", - "Luperto 1\n", - "Skriniar 0.9266352694924124\n", - "Rodriguez R. 0.8585003232062055\n", - "Marusic 1\n", - "Lazzari 0.8892709441096539\n", - "Kyriakopoulos 0.056997600101048373\n", - "Ampadu 0.911961601616774\n", - "Ismajli 1\n", - "Llorente D. affine to Ibanez\n", - "Llorente D. 0.04769446240034476\n", - "Cambiaso 1\n", - "Hysaj 0.9505999747379058\n", - "Biraghi 0.8765468765468765\n", - "Medel 0.8353757353757354\n", - "Bonucci 0.6081043956043956\n", - "Calabria 0.8108058608058608\n", - "Acerbi 0.7713675213675213\n", - "Spinazzola 1\n", - "Lykogiannis 0.9445316588173731\n", - "Pellegrini Lu. 0.0\n", - "Djidji 1\n", - "Augello 0.9008954008954009\n", - "Singo 0.6949764521193094\n", - "Mari' 1\n", - "De Vrij 0.7026984126984127\n", - "Patric 0.6756715506715507\n", - "Faraoni 0.40540293040293046\n", - "Ceccherini 0.7154169360051713\n", - "Hateboer 1\n", - "Rogerio 1\n", - "Aina 0.9266352694924122\n", - "Ferrari G. 0.855850630850631\n", - "Fazio 1\n", - "Buongiorno 1\n", - "Gunter 0.7162698412698413\n", - "Troost-Ekong affine to Fazio\n", - "Troost-Ekong 0.20270146520146523\n", - "Soumaoro 0.8687205651491368\n", - "Ceccaroni 0.0\n", - "Soppy 0.5903322867608581\n", - "Ferrari A. 0.359332696289218\n", - "Zappacosta 0.23847231200172375\n", - "Gyomber 0.7323407775020678\n", - "Alex Sandro 0.9845499738356883\n", - "Pezzella Giu. 0.7083987441130296\n", - "Bereszynski 0.7083987441130298\n", - "Venuti 0.6345437171524128\n", - "Palomino 0.23847231200172375\n", - "Nuytinck 0.36731786731786725\n", - "Magnani 1\n", - "Colley 0.8108058608058609\n", - "Nikolaou 0.855850630850631\n", - "Terzic 1\n", - "Igor 0.8648595848595849\n", - "Toljan 1\n", - "Zortea 0.056997600101048373\n", - "Dawidowicz 1\n", - "Bellanova 0.5332033557840009\n", - "Erlic 0.7713675213675213\n", - "Ballo-Toure' affine to Calabria\n", - "Ballo-Toure' 0.1871090448013525\n", - "Stojanovic 0.8353757353757354\n", - "Amian 0.7094551282051282\n", - "Zima 0.7297252747252747\n", - "De Winter 1\n", - "Romagnoli S. 0.0\n", - "Ghiglione 0.7628909551986474\n", - "Rugani 0.4054029304029304\n", - "De Sciglio 0.8108058608058608\n", - "Djimsiti 0.47079049982275784\n", - "Caldara 0.7464846980976013\n", - "Karsdorp 0.4054029304029304\n", - "Marchizza 0.34798534798534797\n", - "Kjaer 1\n", - "Ruggeri 1\n", - "Zanoli 0.6887210012210012\n", - "Radovanovic 1\n", - "D'ambrosio 0.32432234432234436\n", - "De Silvestri 0.47079049982275784\n", - "Chiriches 0.455980800808387\n", - "Murru 0.6633866133866133\n", - "Bonifazi 0.5159673659673659\n", - "Walukiewicz 0.9445316588173731\n", - "Walukiewicz 0.36246197549180287\n", - "Ranieri L. 0.12243928910595576\n", - "Gabbia 1\n", - "Kumbulla 0.38155569920275806\n", - "Lovato 0.7231570512820512\n", - "Tuia 0.18365893365893363\n", - "Ferrer 0.12011938678605345\n", - "Antov 1\n", - "Vasquez 0.5903322867608581\n", - "Ruan 1\n", - "Ostigard 1\n", - "Coppola D. 1\n", - "Cacace 0.6486446886446887\n", - "Conti 0.2316588173731031\n", - "Conti 0.843001582405036\n", - "Marrone 0.15742194313622884\n", - "Tonelli 0.0\n", - "Radu 1\n", - "Florenzi 0.13513431013431015\n", - "Sala 0.5405372405372405\n", - "Fares 0.0\n", - "Fares 0.0\n", - "Romagna 0.0\n", - "Romagna 0.0\n", - "Muldur 0.05231005553586199\n", - "Amey 0.0\n", - "Zaccagni 1\n", - "Milinkovic-Savic 0.8765468765468765\n", - "Barella 0.9008954008954009\n", - "Zielinski 0.972967032967033\n", - "Luis Alberto 0.8585003232062055\n", - "Felipe Anderson 0.8961538461538462\n", - "Koopmeiners 1\n", - "Calhanoglu 0.953889248006895\n", - "Frattesi 0.945940170940171\n", - "Diaz B. 0.9415809996455157\n", - "Zambo Anguissa 1\n", - "Elmas 0.8765468765468765\n", - "Miranchuk 1\n", - "Samardzic 1\n", - "Pereyra 1\n", - "Politano 0.9336552336552336\n", - "Rabiot 0.8614812271062272\n", - "Lazovic 0.8108058608058609\n", - "Lobotka 1\n", - "Bonaventura 0.9415809996455157\n", - "Pessina 1\n", - "Tonali 0.8108058608058608\n", - "Pellegrini Lo. 1\n", - "El Shaarawy 0.900895400895401\n", - "Orsolini 1\n", - "Ikone' 1\n", - "Candreva 0.9182946682946682\n", - "Bennacer 0.9938910551813778\n", - "Pasalic 0.8327195327195327\n", - "Mkhitaryan 0.9597660404112016\n", - "Chiesa 0.8108058608058608\n", - "Bandinelli 0.972967032967033\n", - "Fagioli affine to Henderson L.\n", - "Fagioli 0.512087912087912\n", - "Messias 0.9355452240067625\n", - "Arslan 1\n", - "Ricci S. 1\n", - "Verdi 1\n", - "Sensi 1\n", - "Barak 0.9689592017178223\n", - "Soriano 0.9266352694924124\n", - "Dominguez 1\n", - "Brozovic 0.5096493982208269\n", - "Cristante 1\n", - "Saponara 0.9505999747379058\n", - "Vecino 1\n", - "Locatelli 0.9415809996455157\n", - "Zaniolo 0.7528911564625851\n", - "Maldini 1\n", - "Marin 0.9182946682946682\n", - "Zalewski 1\n", - "Bajrami 0.04722658294086866\n", - "Coulibaly L. 1\n", - "De Roon 1\n", - "Mandragora 1\n", - "Bourabia 1\n", - "Sottil 0.4729700854700855\n", - "Aebischer 1\n", - "Ederson D.s. 1\n", - "Miretti 1\n", - "Cataldi 0.9628319597069598\n", - "Djuricic 1\n", - "Linetty 1\n", - "Haas 1\n", - "Walace 0.945940170940171\n", - "Agudelo 1\n", - "Pobega 0.6010656010656009\n", - "Nicolussi Caviglia 1\n", - "Rovella 0.9445316588173729\n", - "Amrabat 1\n", - "Tameze 0.8534798534798534\n", - "Gyasi 0.8108058608058608\n", - "Ilic 0.5681948260073261\n", - "Matheus Henrique 0.8432380952380952\n", - "Harroui 0.9582251082251082\n", - "Volpato 1\n", - "Duncan 0.6388167388167388\n", - "Cuadrado 0.7862359862359862\n", - "Ekdal 0.8900394477317554\n", - "Schouten 1\n", - "Obiang 1\n", - "Kovalenko 0.6236968160045083\n", - "Crnigoj 0.09723120017237664\n", - "Basic 0.7828470380194518\n", - "Asllani 0.8623984710941232\n", - "Sabiri 1\n", - "Grassi 0.6060744810744811\n", - "Krunic 0.5791470434327577\n", - "Rincon 1\n", - "Miguel Veloso 1\n", - "Henderson L. 0.6401098901098902\n", - "Lopez M. 0.6486446886446886\n", - "Cuisance 0.12714849253310792\n", - "Saelemaekers 0.5855820105820106\n", - "Maggiore 0.4722658294086865\n", - "Akpa Akpro 0.0\n", - "Akpa Akpro 0.0\n", - "Maleh 0.29516614338042907\n", - "Romero L. 1\n", - "Ceide 1\n", - "Benassi 0.4132326007326007\n", - "Gagliardini 0.9008954008954009\n", - "Vieira 0.11806645735217164\n", - "Bianco 1\n", - "Galdames 0.0\n", - "Kastanos 0.7207163207163206\n", - "Vignato 0.20661630036630035\n", - "Askildsen 1\n", - "Bove 1\n", - "Bohinen 1\n", - "Bakayoko 0.0\n", - "Zurkowski 0.04722658294086866\n", - "Castrovilli 0.2115145723841376\n", - "Demme 0.256043956043956\n", - "Darboe 0.0\n", - "Darboe 0.32432234432234436\n", - "Urbanski 0.0\n", - "Yepes 1\n", - "Osimhen 1\n", - "Martinez L. 0.972967032967033\n", - "Dybala 0.8549640015157256\n", - "Rafael Leao 0.953889248006895\n", - "Immobile 0.8369608885737918\n", - "Vlahovic 1\n", - "Arnautovic 0.6879564879564879\n", - "Dzeko 0.945940170940171\n", - "Nzola 1\n", - "Beto 1\n", - "Giroud 1\n", - "Abraham 0.9203742203742203\n", - "Deulofeu 0.7631113984055161\n", - "Simeone 0.5667189952904238\n", - "Lozano 1\n", - "Correa 1\n", - "Berardi 0.6388167388167388\n", - "Pedro 0.9628319597069598\n", - "Sanabria 0.8946823291650877\n", - "Thauvin affine to Deulofeu\n", - "Thauvin 0.04769446240034476\n", - "Cabral 1\n", - "Caprari 0.9917582417582418\n", - "Piatek 1\n", - "Rebic 0.878373015873016\n", - "Bonazzoli 0.9628319597069598\n", - "Zapata D. 0.945940170940171\n", - "Gonzalez N. 0.5405372405372406\n", - "Brekalo 0.051654075091575095\n", - "Kean 0.9628319597069598\n", - "Okereke 1\n", - "Muriel 0.7807760141093475\n", - "Pinamonti 0.7805504680504681\n", - "Di Francesco F. 1\n", - "Caputo 0.27548840048840045\n", - "Boga 1\n", - "Alvarez A. affine to Raspadori\n", - "Alvarez A. 0.7207163207163207\n", - "Petagna 1\n", - "Barrow 0.7154169360051713\n", - "Djuric 1\n", - "Henry 0.8014208014208013\n", - "Success 1\n", - "Gabbiadini 1\n", - "Kallon 1\n", - "Nestorovski 1\n", - "Raspadori 0.6428062678062678\n", - "Lasagna 0.9845499738356883\n", - "Belotti 1\n", - "Pellegri 1\n", - "Verde 0.5405372405372406\n", - "Destro 0.6121964455297788\n", - "Seck 1\n", - "Sansone 0.6005969339302673\n", - "Quagliarella 0.7370962370962372\n", - "Defrel 0.4267399267399267\n", - "Pjaca 0.6887210012210012\n", - "Gaich 0.11019536019536019\n", - "Piccoli 0.3305860805860806\n", - "Piccoli 0.30394544405533425\n", - "Shomurodov 0.05903322867608582\n", - "Afena-Gyan 1\n", - "Ibrahimovic 0.0\n", - "Pussetto 0.29516614338042907\n", - "Cancellieri 1\n", - "Oddei 0.0\n", - "Oddei 0.6486446886446887\n", - "Braaf 0.41323260073260076\n", - "Raimondo 0.0\n", - "Kaio Jorge 0.0\n", - "Players with low quantity of games:\n", - "Gravillon 0.0\n", - "Masina 0.6666666666666667\n", - "Aiwu 0.0\n", - "Thiaw 0.8333333333333334\n", - "Zeefuik 0.0\n", - "Wisniewski 0.16666666666666663\n", - "Dermaku 0.16666666666666663\n", - "Donati 0.6666666666666667\n", - "Antov 0.6666666666666667\n", - "Ostigard 0.6666666666666667\n", - "Gila 0.6666666666666667\n", - "Bayeye 0.16666666666666663\n", - "Moutinho J. 0.6666666666666667\n", - "Paletta 0.0\n", - "Romagna 0.0\n", - "Cassandro 0.0\n", - "Amey 0.16666666666666663\n", - "Zanotti 0.0\n", - "Ebosele 0.8333333333333334\n", - "Buta 0.0\n", - "Abankwah 0.0\n", - "Guessand A. 0.0\n", - "Cabal 0.5\n", - "Sosa 0.8333333333333334\n", - "Guarino 0.0\n", - "Carboni F. 0.6666666666666667\n", - "Pogba 0.0\n", - "Duda 0.33333333333333337\n", - "Wijnaldum 0.16666666666666663\n", - "Nicolussi Caviglia 0.8333333333333334\n", - "Volpato 0.8333333333333334\n", - "Machin 0.0\n", - "Esposito Sa. 0.5\n", - "Akpa Akpro 0.0\n", - "Romero L. 0.8333333333333334\n", - "Bianco 0.5\n", - "Galdames 0.0\n", - "Tahirovic 0.8333333333333334\n", - "Abildgaard 0.16666666666666663\n", - "Winks 0.6666666666666667\n", - "D'andrea 0.8333333333333334\n", - "Cipot 0.33333333333333337\n", - "Gaetano 0.5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Darboe 0.6171184371184371\n", - "Urbanski 0.16666666666666663\n", - "Bertini 0.0\n", - "Yepes 0.8333333333333334\n", - "Pyyhtia 0.5\n", - "Trimboli 0.0\n", - "Pafundi 0.16666666666666663\n", - "Adli 0.6666666666666667\n", - "Vignato S. 0.5\n", - "Samek 0.0\n", - "Zerbin 0.6666666666666667\n", - "Ilkhan 0.6666666666666667\n", - "Degli Innocenti 0.16666666666666663\n", - "Acella 0.0\n", - "Carboni V. 0.33333333333333337\n", - "Paoletti 0.6666666666666667\n", - "Malagrida 0.16666666666666663\n", - "Faticanti 0.0\n", - "Solbakken 0.33333333333333337\n", - "Ngonge 0.33333333333333337\n", - "Oddei 0.5090109890109888\n", - "Braaf 0.4600503663003662\n", - "Raimondo 0.16666666666666663\n", - "De Luca 0.16666666666666663\n", - "Voelkerling Persson 0.5\n", - "Krollis 0.16666666666666663\n", - "Vivaldo 0.0\n" - ] - } - ], - "source": [ - "#for i in range(players.columns.shape[0]):\n", - "# print(str(i) + ' - ' + players.columns[i])\n", - "\n", - "cols_toadapt = players.columns[9:]\n", - "\n", - "players = players_orig.copy()\n", - "\n", - "min_games = 6\n", - "\n", - "current_season_games = max(players_orig['games'])\n", - "\n", - "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", - "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", - "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", - "WEIGHT_mul_gk = 2\n", - "\n", - "rcsv = pd.read_csv('config/affine_players.txt') \n", - "affine_players = pd.DataFrame(rcsv)\n", - "affine_players = affine_players.set_index('player')\n", - "\n", - "\n", - "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", - " if(same_team):\n", - " weight_0 = WEIGHT_0_same_team\n", - " else:\n", - " weight_0 = WEIGHT_0_different_team\n", - "\n", - " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", - " weight = min(weight, 1)\n", - "\n", - " return abs(weight)\n", - "\n", - "print(' ')\n", - "print('Averaging players stats with past seasons:')\n", - "\n", - "for i in range(players.shape[0]):\n", - " p = players.index[i]\n", - " \n", - "\n", - " if(p in players_old.index or p in affine_players.index):\n", - " p_ = p\n", - " affine = 0\n", - " \n", - " if(p in affine_players.index):\n", - " affine = 1\n", - " p_ = affine_players.loc[p]['alike']\n", - " \n", - " print(p + ' affine to ' + p_)\n", - " \n", - " if(players.loc[p]['r'] == 'P'):\n", - " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", - " weight *= WEIGHT_mul_gk\n", - " weight = min(weight, 1)\n", - " else:\n", - " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", - "\n", - " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", - " \n", - " print(p + ' ' + str(weight)) \n", - " \n", - " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", - " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", - " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", - " \n", - " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", - " \n", - " print(p + ' ' + str(weight))\n", - " \n", - " \n", - "# handle players with low quantitites of games\n", - "\n", - "print('Players with low quantity of games:')\n", - "\n", - "def calc_weight_low(current_games, min_games = min_games):\n", - " weight = 1 - (min_games - current_games)/min_games\n", - " \n", - " weight = min(weight, 1)\n", - "\n", - " return abs(weight)\n", - "\n", - "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", - "\n", - "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", - "count = 0\n", - "\n", - "for i in range(players_orig.shape[0]):\n", - " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", - " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", - " count = count + 1\n", - " \n", - "mean_players_stats /= count\n", - "\n", - "for i in range(players.shape[0]):\n", - " p = players.index[i]\n", - " \n", - " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", - " weight = calc_weight_low(players.loc[p]['games'])\n", - " \n", - " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", - " \n", - " print(p + ' ' + str(weight))\n", - " \n", - " \n", - "players_out = players.copy()\n", - "players_out = players_out.set_index(players_out.columns[0])\n", - "players_out.insert(2, 'name', players_out.index)\n", - "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "49c28b07", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", - " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", - " ...\n", - " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", - " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", - " 'gk_def_actions_outside_pen_area',\n", - " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", - " 'vote_avg', 'vote_std'],\n", - " dtype='object', length=151)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "players.columns[9:]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d29102e5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 - matchday\n", - "1 - player\n", - "2 - team\n", - "3 - oppteam\n", - "4 - home\n", - "5 - vote\n", - "6 - goals\n", - "7 - assists\n", - "8 - cards_malus\n", - "9 - fantavote\n", - "10 - r\n", - "11 - games\n", - "12 - games_starts\n", - "13 - minutes\n", - "14 - shots_on_target_pct\n", - "15 - goals_per_shot\n", - "16 - goals_per_shot_on_target\n", - "17 - passes_pct\n", - "18 - aerials_won_pct\n", - "19 - team_possession\n", - "20 - team_goals_assists_per90\n", - "21 - team_goals_pens_per90\n", - "22 - team_goals_assists_pens_per90\n", - "23 - team_xg_per90\n", - "24 - team_gk_goals_against_per90\n", - "25 - team_gk_save_pct\n", - "26 - team_gk_clean_sheets_pct\n", - "27 - team_passes_pct\n", - "28 - team_passes_pct_medium\n", - "29 - team_passes_pct_long\n", - "30 - team_sca_per90\n", - "31 - team_gca_per90\n", - "32 - team_aerials_won_pct\n", - "33 - vs_team_possession\n", - "34 - vs_team_goals_per90\n", - "35 - vs_team_assists_per90\n", - "36 - vs_team_xg_per90\n", - "37 - vs_team_gk_save_pct\n", - "38 - vs_team_gk_clean_sheets_pct\n", - "39 - vs_team_gk_pct_passes_launched\n", - "40 - vs_team_gk_crosses_stopped_pct\n", - "41 - vs_team_shots_on_target_per90\n", - "42 - vs_team_passes_pct\n", - "43 - vs_team_passes_pct_short\n", - "44 - vs_team_passes_pct_medium\n", - "45 - vs_team_passes_pct_long\n", - "46 - vs_team_sca_per90\n", - "47 - vs_team_gca_per90\n", - "48 - vs_team_aerials_won_pct\n", - "49 - opp_team_possession\n", - "50 - opp_team_goals_assists_per90\n", - "51 - opp_team_goals_pens_per90\n", - "52 - opp_team_goals_assists_pens_per90\n", - "53 - opp_team_xg_per90\n", - "54 - opp_team_gk_goals_against_per90\n", - "55 - opp_team_gk_save_pct\n", - "56 - opp_team_gk_clean_sheets_pct\n", - "57 - opp_team_passes_pct\n", - "58 - opp_team_passes_pct_medium\n", - "59 - opp_team_passes_pct_long\n", - "60 - opp_team_sca_per90\n", - "61 - opp_team_gca_per90\n", - "62 - opp_team_aerials_won_pct\n", - "63 - opp_vs_team_possession\n", - "64 - opp_vs_team_goals_per90\n", - "65 - opp_vs_team_assists_per90\n", - "66 - opp_vs_team_xg_per90\n", - "67 - opp_vs_team_gk_save_pct\n", - "68 - opp_vs_team_gk_clean_sheets_pct\n", - "69 - opp_vs_team_gk_pct_passes_launched\n", - "70 - opp_vs_team_gk_crosses_stopped_pct\n", - "71 - opp_vs_team_shots_on_target_per90\n", - "72 - opp_vs_team_passes_pct\n", - "73 - opp_vs_team_passes_pct_short\n", - "74 - opp_vs_team_passes_pct_medium\n", - "75 - opp_vs_team_passes_pct_long\n", - "76 - opp_vs_team_sca_per90\n", - "77 - opp_vs_team_gca_per90\n", - "78 - opp_vs_team_aerials_won_pct\n", - "79 - vote_avg\n", - "80 - vote_std\n", - "81 - goals.1\n", - "82 - assists.1\n", - "83 - cards_yellow\n", - "84 - cards_red\n", - "85 - xg\n", - "86 - npxg\n", - "87 - shots_on_target\n", - "88 - passes_completed\n", - "89 - passes_into_final_third\n", - "90 - passes_into_penalty_area\n", - "91 - progressive_passes\n", - "92 - passes_live\n", - "93 - passes_dead\n", - "94 - through_balls\n", - "95 - passes_switches\n", - "96 - crosses\n", - "97 - corner_kicks\n", - "98 - blocks\n", - "99 - blocked_shots\n", - "100 - blocked_passes\n", - "101 - interceptions\n", - "102 - clearances\n", - "103 - errors\n", - "104 - touches\n", - "105 - touches_def_pen_area\n", - "106 - touches_def_3rd\n", - "107 - touches_mid_3rd\n", - "108 - touches_att_3rd\n", - "109 - touches_att_pen_area\n", - "110 - touches_live_ball\n", - "111 - passes_received\n", - "112 - miscontrols\n", - "113 - dispossessed\n", - "114 - fouls\n", - "115 - fouled\n", - "116 - aerials_won\n", - "117 - aerials_lost\n", - "118 - carries\n", - "119 - progressive_carries\n", - "120 - carries_into_final_third\n", - "121 - carries_into_penalty_area\n" - ] - } - ], - "source": [ - "for i in range(db.columns.shape[0]):\n", - " print(str(i) + \" - \" + str(db.columns[i]))" - ] - }, - { - "cell_type": "markdown", - "id": "089690d6", - "metadata": {}, - "source": [ - "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", - "\n", - "For outfield players: X -> y = [vote, fantavote]\n", - "\n", - "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f19304f6", - "metadata": {}, - "outputs": [], - "source": [ - "npdb = np.array(db)\n", - "\n", - "y = npdb[:, [5,9]] # vote, fantavote\n", - "\n", - "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", - "\n", - "f_start = 14\n", - "\n", - "X = npdb[:, f_start:]\n", - "\n", - "if(DEL_G): \n", - " del_g_idx = [\n", - " list(db.columns).index('goals.1') - f_start,\n", - " list(db.columns).index('assists.1') - f_start,\n", - " list(db.columns).index('xg') - f_start,\n", - " list(db.columns).index('npxg') - f_start,\n", - " list(db.columns).index('shots_on_target') - f_start]\n", - " \n", - " X[:, del_g_idx] = 0\n", - "\n", - "\n", - "# add role and home factor\n", - "toadd = np.zeros((X.shape[0], 4))\n", - "toadd[:, 0] = npdb[:, 4] # home\n", - "\n", - "toadd[:, 1] = npdb[:, 10] == 'D'\n", - "toadd[:, 2] = npdb[:, 10] == 'C'\n", - "toadd[:, 3] = npdb[:, 10] == 'A'\n", - "\n", - "X = np.concatenate((X, toadd), axis = 1)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "370d41d2", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "scaler.fit(X)\n", - "\n", - "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", - "\n", - "X_train = scaler.transform(X_train_)\n", - "X_test = scaler.transform(X_test_)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a7b1fb52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 - matchday\n", - "1 - player\n", - "2 - team\n", - "3 - oppteam\n", - "4 - home\n", - "5 - vote\n", - "6 - goals\n", - "7 - assists\n", - "8 - cards_malus\n", - "9 - fantavote\n", - "10 - gk_games\n", - "11 - gk_games_starts\n", - "12 - gk_minutes\n", - "13 - gk_goals_against_per90\n", - "14 - gk_save_pct\n", - "15 - gk_clean_sheets_pct\n", - "16 - gk_psxg_net_per90\n", - "17 - gk_passes_pct_launched\n", - "18 - gk_pct_passes_launched\n", - "19 - gk_passes_length_avg\n", - "20 - gk_pct_goal_kicks_launched\n", - "21 - gk_goal_kick_length_avg\n", - "22 - gk_crosses_stopped_pct\n", - "23 - gk_def_actions_outside_pen_area_per90\n", - "24 - gk_avg_distance_def_actions\n", - "25 - team_possession\n", - "26 - team_goals_assists_per90\n", - "27 - team_goals_pens_per90\n", - "28 - team_goals_assists_pens_per90\n", - "29 - team_xg_per90\n", - "30 - team_gk_goals_against_per90\n", - "31 - team_gk_save_pct\n", - "32 - team_gk_clean_sheets_pct\n", - "33 - team_passes_pct\n", - "34 - team_passes_pct_medium\n", - "35 - team_passes_pct_long\n", - "36 - team_sca_per90\n", - "37 - team_gca_per90\n", - "38 - team_aerials_won_pct\n", - "39 - vs_team_possession\n", - "40 - vs_team_goals_per90\n", - "41 - vs_team_assists_per90\n", - "42 - vs_team_xg_per90\n", - "43 - vs_team_gk_save_pct\n", - "44 - vs_team_gk_clean_sheets_pct\n", - "45 - vs_team_gk_pct_passes_launched\n", - "46 - vs_team_gk_crosses_stopped_pct\n", - "47 - vs_team_shots_on_target_per90\n", - "48 - vs_team_passes_pct\n", - "49 - vs_team_passes_pct_short\n", - "50 - vs_team_passes_pct_medium\n", - "51 - vs_team_passes_pct_long\n", - "52 - vs_team_sca_per90\n", - "53 - vs_team_gca_per90\n", - "54 - vs_team_aerials_won_pct\n", - "55 - opp_team_possession\n", - "56 - opp_team_goals_assists_per90\n", - "57 - opp_team_goals_pens_per90\n", - "58 - opp_team_goals_assists_pens_per90\n", - "59 - opp_team_xg_per90\n", - "60 - opp_team_gk_goals_against_per90\n", - "61 - opp_team_gk_save_pct\n", - "62 - opp_team_gk_clean_sheets_pct\n", - "63 - opp_team_passes_pct\n", - "64 - opp_team_passes_pct_medium\n", - "65 - opp_team_passes_pct_long\n", - "66 - opp_team_sca_per90\n", - "67 - opp_team_gca_per90\n", - "68 - opp_team_aerials_won_pct\n", - "69 - opp_vs_team_possession\n", - "70 - opp_vs_team_goals_per90\n", - "71 - opp_vs_team_assists_per90\n", - "72 - opp_vs_team_xg_per90\n", - "73 - opp_vs_team_gk_save_pct\n", - "74 - opp_vs_team_gk_clean_sheets_pct\n", - "75 - opp_vs_team_gk_pct_passes_launched\n", - "76 - opp_vs_team_gk_crosses_stopped_pct\n", - "77 - opp_vs_team_shots_on_target_per90\n", - "78 - opp_vs_team_passes_pct\n", - "79 - opp_vs_team_passes_pct_short\n", - "80 - opp_vs_team_passes_pct_medium\n", - "81 - opp_vs_team_passes_pct_long\n", - "82 - opp_vs_team_sca_per90\n", - "83 - opp_vs_team_gca_per90\n", - "84 - opp_vs_team_aerials_won_pct\n", - "85 - vote_avg\n", - "86 - vote_std\n", - "87 - gk_shots_on_target_against\n", - "88 - gk_saves\n", - "89 - gk_free_kick_goals_against\n", - "90 - gk_corner_kick_goals_against\n", - "91 - gk_own_goals_against\n", - "92 - gk_psxg\n", - "93 - gk_psnpxg_per_shot_on_target_against\n", - "94 - gk_psxg_net\n", - "95 - gk_passes_completed_launched\n", - "96 - gk_passes_launched\n", - "97 - gk_passes\n", - "98 - gk_passes_throws\n", - "99 - gk_goal_kicks\n", - "100 - gk_crosses\n", - "101 - gk_crosses_stopped\n" - ] - } - ], - "source": [ - "for i in range(db_gk.columns.shape[0]):\n", - " print(str(i) + \" - \" + str(db_gk.columns[i]))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5a7cf079", - "metadata": {}, - "outputs": [], - "source": [ - "npdb_gk= np.array(db_gk)\n", - "\n", - "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", - "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", - "\n", - "f_start_gk = 13\n", - "\n", - "X_gk = npdb_gk[:, f_start_gk:]\n", - "\n", - "# add home factor\n", - "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", - "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", - "\n", - "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0bc0568b", - "metadata": {}, - "outputs": [], - "source": [ - "scaler_gk = StandardScaler()\n", - "scaler_gk.fit(X_gk)\n", - "\n", - "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", - "\n", - "X_gk_train = scaler_gk.transform(X_gk_train_)\n", - "X_gk_test = scaler_gk.transform(X_gk_test_)" - ] - }, - { - "cell_type": "markdown", - "id": "ebd27493", - "metadata": {}, - "source": [ - "MLP Regressor , to see performance of a simple neural network" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "04564bee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1569906306642661\n", - "0.19152381866355805\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", - "\n", - "regr.fit(X_train, y_train)\n", - "\n", - "\n", - "y_train_predict = regr.predict(X_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", - "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", - "\n", - "\n", - "plt.show()\n", - "\n", - "y_test_predict = regr.predict(X_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", - "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f9513b25", - "metadata": {}, - "source": [ - "Train neural network for outfield players.\n", - "\n", - "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8aad9652", - "metadata": {}, - "outputs": [], - "source": [ - "load_model_of = True# load scaler and model weights for outfield player predictor\n", - "refit_model_of = True\n", - "\n", - "if(load_model_of):\n", - " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", - " \n", - " X_train = scaler.transform(X_train_)\n", - " X_test = scaler.transform(X_test_)\n", - "\n", - "\n", - "n_epochs = 1000\n", - "\n", - "n_samples = X_train.shape[0]\n", - "\n", - "batch_size = 256\n", - "\n", - "X_len = X_train.shape[1]\n", - "y_len = y_train.shape[1]\n", - "\n", - "\n", - "#tailweight_param = 1.1\n", - "\n", - "tailweight_min = 0.5\n", - "tailweight_range = 1.2\n", - "\n", - "\n", - "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", - "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", - "\n", - "\n", - "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", - "x = tfk.layers.Dropout(0.2)(inputs)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "x = tfk.layers.Dropout(0.2)(x)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "\n", - "\n", - "prob_dist_params = 4\n", - "\n", - "def prob_dist(t): \n", - " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", - " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", - " allow_nan_stats = False)\n", - "\n", - "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", - "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", - "\n", - "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", - "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", - "\n", - "\n", - "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", - "\n", - "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", - " loss=neg_log_likelihood)\n", - "\n", - "if(load_model_of):\n", - " modelb.load_weights('saves/modelb')\n", - " \n", - "if( (not load_model_of) or refit_model_of):\n", - " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", - " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", - " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "4e2bf9dc", - "metadata": {}, - "outputs": [], - "source": [ - "def sample_predict(X, iterations = 100):\n", - " y = np.zeros((2, X.shape[0]))\n", - " \n", - " dist = modelb(X)\n", - " \n", - " for i in range(iterations):\n", - " y[0, :] += dist[0].sample()\n", - " y[1, :] += dist[1].sample()\n", - " \n", - " return y.transpose() / iterations\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c2674211", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.14539483185579238\n", - "0.16229047232752447\n" - ] - }, - { - "data": { - "image/png": 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", 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AxvQIw/yHuqGJJ5utxoqvPBERuZQLNwsx/bsUnMsuhLtahTce7IzH+0VApeJ8lcaMCQsREbmMTb9n4YUfjqFQV4EgXw0WT4lDfESgs8MiF8CEhYiInK5Cb8CHW9KwZNcFAEDvyEAsnBSLIF8vJ0dGroIJCxEROVVuoQ6zlh3Bvgu5AIA/DYjEy4md4OGmdnJk5EqYsBARkdMcvXIbTyelIDO/FE083fDe+O4YHRPm7LDIBTFhISKiu04QBCz77QreWncSZXoD2rXwwZKp8YgO9nV2aOSimLAQEdFdVVquxxtrfscPKVcBAAldgvHhIzHw9fJwcmTkypiwEBHRXXMlrxjTk1Jw8roWahXwYkInTB/cjkuWqUZMWIiI6K7YeTYbs5cfRX5JOQJ9PLHgsVjc26GFs8OieoIJCxEROZTBIGDhjvP497Y0CAIQ09ofi6bEo1Uzb2eHRvUIExYiInKY/JJyzFlxFL+cyQYATOrTBm+O7gyNu5uTI6P6hgkLERE5xOlMLaYnpeBybjE83dV4d2xXTOgZ7uywqJ5iwkJERHUu+chVzP3xBErLDWgd4I0lU+LRtZW/s8OieowJCxER1ZmyCgPe3XAK3+6/DAAYFN0Sn0zsgQAfTydHRvUdExYiIqoTWfmlmLE0BakZtwEAzw7tgNn3R8NNzSXLdOeYsBAR0R07cDEXz3yfipzCMvh6uePjiT0w7J5gZ4dFDQgTFiIiqjVBEPDV3nTM//kM9AYBnUJ8sWRKPNq28HF2aNTAMGEhIqJaKdRV4OVVx7HhRCYAYFxsK8wb1w3enlyyTHWPCQsREdntfHYhpiel4Hx2IdzVKvxtdGdM7RvBEvvkMGp7b7B7926MHj0aYWFhUKlUWLNmjeL6J554AiqVSvHVt2/fGs+7evVqdO7cGRqNBp07d0ZycrK9oRER0V2w6fdMjP3sV5zPLkSwnwYrnuqLx/u1ZbJCDmV3wlJUVISYmBgsXLjQ4jEjR45EZmam9LVx40ar59y/fz8mTpyIqVOn4tixY5g6dSomTJiAgwcP2hseERE5SIXegPk/n8b0pFQU6irQOzIQP80agPiIQGeHRo2AShAEodY3VqmQnJyMsWPHSpc98cQTuH37tknPizUTJ06EVqvFzz//LF02cuRIBAQEYNmyZTadQ6vVwt/fH/n5+fDz87P5vomIqGY5hTo8u+wI9l3IBQD8aUAkXk7sBA83uz/3EinY2n475C9t586dCAoKQnR0NP785z8jOzvb6vH79+/HiBEjFJclJCRg3759Fm+j0+mg1WoVX0REVPeOZNzC6AV7se9CLpp4umHhpFi8/mBnJit0V9X5X1tiYiKWLl2K7du346OPPsKhQ4cwdOhQ6HQ6i7fJyspCcLByvX5wcDCysrIs3mb+/Pnw9/eXvsLDuT8FEVFdEgQBSQcuY+LnB5CZX4p2LX2wdua9eLB7mLNDo0aozlcJTZw4Ufq5a9eu6NmzJyIiIrBhwwY89NBDFm9XfbKWIAhWJ3DNnTsXc+bMkX7XarVMWoiI6khpuR6vJf+O1alXAQAju4Tgg0e6w9fLw8mRUWPl8GXNoaGhiIiIwLlz5yweExISYtKbkp2dbdLrIqfRaKDRaOosTiIiEl3JK8ZT36XgVKYWahXw0shOeGpQO64CIqdy+ABkbm4urly5gtDQUIvH9OvXD1u3blVctmXLFvTv39/R4RERkcyOs9l4cMFenMrUItDHE0l/7IPpg9szWSGns7uHpbCwEOfPn5d+T09Px9GjRxEYGIjAwEC89dZbGD9+PEJDQ3Hp0iW8+uqraNGiBcaNGyfd5vHHH0erVq0wf/58AMDs2bMxaNAgvPfeexgzZgzWrl2Lbdu2Ye/evXXwEImIqCYGg4AF28/j41/SIAhATHgzLJ4ch7Bm3s4OjQhALRKWw4cPY8iQIdLvxnkk06ZNw+LFi3HixAl8++23uH37NkJDQzFkyBCsWLECvr6+0m0yMjKgVld17vTv3x/Lly/H66+/jjfeeAPt27fHihUr0KdPnzt5bEREZIP84nI8v/Iotp8RV3RO7tMGfxvdGRp3ltgn13FHdVhcCeuwEBHZ7+T1fDydlIqMvGJo3NV4d2xXPNKTCxjo7rG1/eZeQkREjdSPqVcx98cT0FUY0DrAG0umxKNrK39nh0VkFhMWIqJGpqzCgHfWn8J3By4DAAZHt8Qnj/ZAsyaeTo6MyDImLEREjUhWfimeXpqCIxm3AQCzh0Xh2WFRcFNzFRC5NiYsRESNxP4LuZi1LBU5hWXw83LHx4/2wNBOlutdEbkSJixERA2cIAj4cs9FvLfpLPQGAfeE+mHJlDhENPdxdmhENmPCQkTUgBXqKvDSqmPYeEKsJv5QbCv8Y1w3eHtyyTLVL0xYiIgaqPPZhXjqu8O4cLMIHm4q/O3BzpjSN4JVa6leYsJCRNQA/XwiEy/8cAxFZXoE+2mwaHI84iMCnB0WUa0xYSEiakAq9AZ8sPksPt99EQDQJzIQCyfFoaUvN4ul+o0JCxFRA5FTqMOs749g/8VcAMBfBrXDSwkd4e7m8H1uiRyOCQsRUQOQmnELM5JSkaUthY+nGz54JAajuoU6OyyiOsOEhYioHhMEAUkHM/D3n06iXC+gXUsffDE1Hh2CfGu+MVE9woSFiKieKi3X47Xk37E69SoAILFrCN5/uDt8vTycHBlR3WPCQkRUD2XkFmN6UgpOZWqhVgEvj+yEvwxqxyXL1GAxYSEiqmd2nMnGcyuOIr+kHM19PLFgUiz6t2/h7LCIHIoJCxFRPWEwCPjkl3P4dPs5CALQI7wZFk+JQ6i/t7NDI3I4JixERPXA7eIyPL/iKHacvQkAmNK3Dd54sDM07iyxT40DExYiIhd38no+piel4EpeCTTuavxjXDc8HN/a2WER3VVMWIiIXNjqlKt4NfkEdBUGhAd6Y8mUeHQJ83d2WER3HRMWIiIXpKvQ4531p5B0IAMAMKRjS3w8MRb+TbhkmRonJixERC4mM78ETyel4uiV21CpgNnDovDs0Cio1VyyTI0XExYiIhey70IOZn1/BLlFZfDzcscnj8ZiSKcgZ4dF5HRMWIiIXIAgCPhi90W8t+kMDAJwT6gfPp8SjzbNmzg7NCKXwISFiMjJCnUVePGHY/j59ywAwENxrfCPsd3g7ckly0RGTFiIiJzofHYBnvouBRduFsHDTYW/je6CKX3asMQ+UTVMWIiInGTD8Uy8tOoYisr0CPHzwqIpcYhrE+DssIhcEhMWIqK7rEJvwPubz+KL3RcBAH3bBWLhpDi0aKpxcmRErosJCxHRXXSzQIdZy1Jx4GIeAOCpQe3wYkJHuLupnRwZkWtjwkJEdJekZtzCjKRUZGlL4ePphg8eicGobqHODouoXmDCQkTkYIIgIOnAZfx9/SmU6wW0b+mDz6fGo0OQr7NDI6o3mLAQETlQSZker605gR9TrwEARnULwfsPx6Cphm+/RPawe9B09+7dGD16NMLCwqBSqbBmzRrpuvLycrz88svo1q0bfHx8EBYWhscffxzXr1+3es5vvvkGKpXK5Ku0tNTuB0RE5Cou5xbhocX78GPqNahVwKujOuGzSXFMVohqwe6EpaioCDExMVi4cKHJdcXFxUhNTcUbb7yB1NRU/Pjjj0hLS8Mf/vCHGs/r5+eHzMxMxZeXl5e94RERuYTtZ25g9IK9OJ2pRXMfTyT9qQ/+Mqg966sQ1ZLdaX5iYiISExPNXufv74+tW7cqLluwYAF69+6NjIwMtGnTxuJ5VSoVQkJC7A2HiMilGAwCPvnlHD755RwAILZNMyyaHIdQf28nR0ZUvzm8XzI/Px8qlQrNmjWzelxhYSEiIiKg1+vRo0cPvPPOO4iNjbV4vE6ng06nk37XarV1FTIRUa3cLi7DcyuOYufZmwCAqX0j8PqD90DjzhL7RHfKoQv/S0tL8corr2DSpEnw8/OzeFynTp3wzTffYN26dVi2bBm8vLxw77334ty5cxZvM3/+fPj7+0tf4eHhjngIREQ2+f1aPkYv3IudZ29C467GR4/E4J2xXZmsENURlSAIQq1vrFIhOTkZY8eONbmuvLwcjzzyCDIyMrBz506rCUt1BoMBcXFxGDRoED799FOzx5jrYQkPD0d+fr5d90VEdKdWpVzFa8knoKswoE1gEyyeEocuYf7ODouoXtBqtfD396+x/XbIkFB5eTkmTJiA9PR0bN++3e4EQq1Wo1evXlZ7WDQaDTQalrEmIufRVejx959OYenBDADAkI4t8fHEWPg38XByZEQNT50nLMZk5dy5c9ixYweaN29u9zkEQcDRo0fRrVu3ug6PiKhOZOaX4OmkVBy9chsqFTB7WBSeHRoFtZqrgIgcwe6EpbCwEOfPn5d+T09Px9GjRxEYGIiwsDA8/PDDSE1Nxfr166HX65GVlQUACAwMhKenJwDg8ccfR6tWrTB//nwAwNtvv42+ffsiKioKWq0Wn376KY4ePYrPPvusLh4jEVGd2nc+B7OWHUFuURn8vT3w8aM9MKRjkLPDImrQ7E5YDh8+jCFDhki/z5kzBwAwbdo0vPXWW1i3bh0AoEePHorb7dixA/fddx8AICMjA2p11Xzf27dv4y9/+QuysrLg7++P2NhY7N69G71797Y3PCIihxEEAZ/vvoj3N52BQQA6h/phyZR4tGnexNmhETV4dzTp1pXYOmmHiKg2CkrL8eIPx7HppNhrPD6uNf4xriu8PLgKiOhOOHXSLRFRQ3LuRgGeSkrBxZtF8HBT4c3RXTC5TxtWrSW6i5iwEBFZseF4Jl5cdQzFZXqE+Hlh8ZQ4xLYJcHZYRI0OExYiIjMq9Aa8t+kMvtyTDgDo1645FkyKRYumLKdA5AxMWIiIqrlZoMMz36fiYHoeAOCpwe3w4oiOcHdzaHFwIrKCCQsRkUzK5VuYsTQFN7Q6+Hi64cNHYpDYLdTZYRE1ekxYiIggLln+7sBlvLP+FMr1AjoENcWSKfHoENTU2aEREZiwEBGhpEyPV5NPIPnINQDAA91C8d7D3dFUw7dIIlfB/0YiatQu5xbhqe9ScCarAG5qFV4Z2Ql/GhjJJctELoYJCxE1Wr+cvoHnVhxFQWkFWjT1xILH4tCvvf37nxGR4zFhIaJGR28Q8Mkv5/DpL+KO8HFtmmHR5HiE+Hs5OTIisoQJCxE1KreLyzB7+VHsSrsJAHi8XwRef6AzPN25ZJnIlTFhIaJG4/dr+ZielIKrt0rg5aHGvHHd8FBca2eHRUQ2YMJCRI3CD4ev4PU1v0NXYUCbwCZYMiUencO4USpRfcGEhYgaNF2FHm//dArfH8wAAAztFIR/T+gB/yYeTo6MiOzBhIWIGqzrt0vw9NJUHLtyGyoV8Pz90XhmSAeo1VyyTFTfMGEhogbp1/M5mLXsCPKKyuDv7YFPHu2B+zoGOTssIqolJixE1KAIgoAluy7ig81nYBCALmF+WDIlHuGBTZwdGhHdASYsRNRgFJSW44UfjmHzyRsAgIfjW+PdsV3h5eHm5MiI6E4xYSGiBuHcjQI89V0KLuYUwdNNjTf/0BmTerdhiX2iBoIJCxHVe+uPX8dLq46juEyPUH8vLJ4Sjx7hzZwdFhHVISYsRFRvlesNeO/nM/jP3nQAQP/2zbHgsVg0b6pxcmREVNeYsBBRvZRdUIpnvj+C39LzAADTB7fHCyOi4e7GEvtEDRETFiKqd1Iu52HG0lTc0OrQVOOODx/pjpFdQ50dFhE5EBMWIqo3BEHAt/sv4531p1BhENAhqCmWTIlHh6Cmzg6NiByMCQsR1QslZXq8mnwCyUeuAQAe6B6K98d3h4+Gb2NEjQH/04nI5V3KKcL0pBScySqAm1qFuYmd8McBkVyyTNSIMGEhIpe27dQNPL/yKApKK9CiqScWTopD33bNnR0WEd1lTFiIyCXpDQI+3paGBdvPAwDi2jTDosnxCPH3cnJkROQMTFiIyOXcKirD7BVHsTvtJgBgWr8IvPZAZ3i6c8kyUWPFhIWIXMrv1/IxPSkFV2+VwMtDjfkPdcO42NbODouInIwJCxG5jJWHr+D1Nb+jrMKAiOZNsGRKPO4J9XN2WETkAuzuX929ezdGjx6NsLAwqFQqrFmzRnG9IAh46623EBYWBm9vb9x33304efJkjeddvXo1OnfuDI1Gg86dOyM5Odne0IiontJV6DH3xxN4adVxlFUYMKxTENY9M4DJChFJ7E5YioqKEBMTg4ULF5q9/v3338e//vUvLFy4EIcOHUJISAiGDx+OgoICi+fcv38/Jk6ciKlTp+LYsWOYOnUqJkyYgIMHD9obHhHVM9dul2DCkv1Y9lsGVCrgr8Oj8eXjPeHv7eHs0IjIhagEQRBqfWOVCsnJyRg7diwAsXclLCwMzz33HF5++WUAgE6nQ3BwMN577z089dRTZs8zceJEaLVa/Pzzz9JlI0eOREBAAJYtW2ZTLFqtFv7+/sjPz4efHz+VEdUHv57PwaxlR5BXVAZ/bw988mgP3NcxyNlhEdFdZGv7XadT7tPT05GVlYURI0ZIl2k0GgwePBj79u2zeLv9+/crbgMACQkJVm+j0+mg1WoVX0RUPwiCgEU7z2PqVweRV1SGrq38sH7WACYrRGRRnSYsWVlZAIDg4GDF5cHBwdJ1lm5n723mz58Pf39/6Ss8PPwOIieiu0VbWo7pSSl4f9NZGATgkfjWWDW9P8IDmzg7NCJyYQ4palC9XLYgCDWW0Lb3NnPnzkV+fr70deXKldoHTER3RdqNAoxd+Cs2n7wBTzdxyfL7D3eHl4ebs0MjIhdXp8uaQ0JCAIg9JqGhVVu9Z2dnm/SgVL9d9d6Umm6j0Wig0WjuMGIiult+OnYdL606jpJyPcL8vbBoSjx6hDdzdlhEVE/UaQ9LZGQkQkJCsHXrVumysrIy7Nq1C/3797d4u379+iluAwBbtmyxehsiqh/K9Qa8s/4UZi07gpJyPe7t0Bw/zRrAZIWI7GJ3D0thYSHOnz8v/Z6eno6jR48iMDAQbdq0wXPPPYd58+YhKioKUVFRmDdvHpo0aYJJkyZJt3n88cfRqlUrzJ8/HwAwe/ZsDBo0CO+99x7GjBmDtWvXYtu2bdi7d28dPEQicpbsglI8s/QIfruUBwB4+r72+OvwaLi7scQ+EdnH7oTl8OHDGDJkiPT7nDlzAADTpk3DN998g5deegklJSWYMWMGbt26hT59+mDLli3w9fWVbpORkQG1uuoNq3///li+fDlef/11vPHGG2jfvj1WrFiBPn363MljIyInOnwpDzOWpiK7QIemGnd8+EgMRnYNcXZYRFRP3VEdFlfCOixErkEQBPxv3yW8u+E0KgwCooKa4vOp8WjXsqmzQyMiF2Rr+829hIiozhSXVWDujyew9uh1AMCD3UPx3vju8NHwrYaI7gzfRYioTlzKKcL0pBScySqAm1qFV0fdgyfvbVtjSQMiIlswYSGiO7bt1A08v/IoCkor0KKpBp9NikWfds2dHRYRNSBMWIio1vQGAR9vS8OC7eLKwfiIACyaHIdgPy8nR0ZEDQ0TFiKqlVtFZXh2+RHsOZcDAHiif1u8OuoeeLpzyTIR1T0mLERktxNX8zE9KQXXbpfAy0ONfz7UHWNjWzk7LCJqwJiwEJFdVhzKwBtrT6KswoCI5k2wZEo87gllKQEiciwmLERkk9JyPd7+6SSW/SZuNHr/PUH4aEIP+Ht7ODmyhi8tLQ0XLlxAhw4dEBUV5exwiJyCCQsR1eja7RI8nZSC41fzoVIBfx0ejRn3dYBazSXLjpSXl4dJUyZh88+bpcsSEhOwbOkyBAQEODEyoruPCQsRWbX3XA5mLUvFreJyNGvigU8fjcWg6JbODqtRmDRlErbt3gY8BCACwGVg2+ZteGzyY9i0cZOzwyO6q5iwEJFZBoOAxbsu4KMtZ2EQgG6t/LFochzCA5s4O7RGIS0tTexZeQhA98oLuwN6QY/NyZtx7tw5Dg9Ro8KEhYhMaEvL8deVx7D11A0AwISerfH3MV3h5eHm5MgajwsXLog/RFS7oq347fz580xYqFFhwkJECmezCjA9KQXpOUXwdFPj7TFd8FjvNnV+P5xIal379u3FHy6jqocFAC6J3zp06HCXIyJyLlZ4IiLJumPXMfazX5GeU4Qwfy/8ML1fnScreXl5GDlqJDp27IhRo0YhOjoaI0eNxK1bt+r0fuq76OhoJCQmwG2zG3AMQD6AY4DbFjckJCYwybMgLS0NP//8M86dO+fsUKiOqQRBEJwdRF2wdXtqIjJVrjdg/sYz+O+v6QCAAR1a4NPHYhHo41nn9zVy1Ehs270N+gS9NJHUbbMb7h90PyeSVnPr1i08NvkxrhKyAVdU1V+2tt9MWIgauWxtKWZ+n4pDl8Qejhn3tcdfR3SEm2zJcl0N36SlpaFjx47iRNIwALcABAK4CiBZvJ49B6bOnTuH8+fPc/jMCibC9Zet7TfnsJDLc8RcB86fEB26lIcZS1Nxs0AHX407PpwQg4QuIdL19n5qrel5lSaSHgHwo+yKSPEbJ5KaFxUVxefFCq6oahw4h4VcliPmOtTmnA1xTFwQBHz9azoe++IAbhboEB3cFGufuVeRrADAIxMewZYdW8SG4HkADwFbdmzBwxMeVhxn6/Pavn17QAXgOhTnxHUAKk4kpdqxZUUV1X9MWMhlKYpmVTZs23aLRbPuxjkb6uTQ4rIKPLfiKN7+6RQqDAIe7B6K5Bn3ol3Lporj0tLSsP2X7RBGCeKnVn8A3QEhUcD2bdsVCZxdr5UA4AGIQ0LZAFoBGFV5OVEtqNWVTdnlaldcEr+5u3MwoSFgwkIuydjFq0/QKxpL/Qg9Nv+8uVa9Hfae0xEJU/V4bOm5qcsenvScIoz7bB/WHr0Od7UKf3uwMxY8Fgsfjekb+q5du8QfLHxqNV5vz/O6cuVK8YcjABYCWApgAYCj1a5vBBpiz52zGAwGsefuZyhWVGETABVQUVHhzPCojjBhIZfkiC5ee86paIRlPQE1JUy2NEK29tzUdQ/PlpNZ+MOCvTh7owAtfTX4/s998eSASKhUNewHZOFTq5E9z2t2drbYsGRCOSSUCUBVeX0DVx977lw9uWrfvr3YQ+cPIBnAvyu/+wEQONTYUDBhIZekKJold0n8Vps3IHvOqZgcaqYnoHrCZE8jZGvPTV318OgNAj7YfAZ/+S4FBboK9IwIwIZZA9A7MtDq7QYPHiwmFxuh/NT6MwBV5fWw73mNiYkRG5ZRUPTGIBGAAMTGxtr12OojR/fc1aX6klxJNWsK3IDhAMYCGA64FVqvWePqiRhVIzQQ+fn5AgAhPz/f2aFQHUlITBDcfNwEjIOA5yFgHAQ3HzchITHB4m3Onj0rbNy4UUhLSzN7HVQQ4AUB90PAWAgYXvm7CorbKI59qPL+HzJ/rCJW2bHmYj179qwAVJ7rGQiYDAGzxMcGVJ1Xcdxbsq9qx9Ukt1AnTPnPASHi5fVCxMvrhTfX/i6UVehtuq0gCMLQ+4cKcBfvU/pyhzD0/qHmH38Nr9XGjRvFczxf7XE9L55748aNNsd2p6z9rTjyPmvzujojVkGw/e/aFeTl5QkJiQmKv9WExAQhLy/P5Njc3FybjyXHs7X9Zg8LuaxlS5fh/kH3K7p47x90P5YtXWZyrC2fBHft2iW+NakBbAOwBsDWyt8F2ZwNI2NPgDfEnhYfSD0BcvbM4bC156YuhsSOX72N0Qv2Ys+5HHh7uOGTR3vgrT90gYeb7f/2q1auQsLwBMVlCcMTsGrlKsVlixYuQrMmzRSvVbMmzbD4s8WK465fvy7+YKE35saNGzbHZk5dDsk5gr2vqzNjdcQ8MkcKCAjApo2bkJaWho0bNyItLQ2bNm4yu/y+PvVyURUmLOSyHPIGpAJggHL+hKHychmpYdkEManYCeA7AJXlSOQNiz2NkDR8klHt2MrfjcMndzoktuJQBh5esh/XbpegbfMmSJ7ZH2N6tLJ6G3MEG+tK/ukvf0LurVzFZbm3cvGnv/xJcdlPP/1UNTlyL8RE7VdIkyPXrl1rd4yAY4bkHMHe19WZsdbXpcJRUVFITEy0OgxUnxIxqsKEhVxeXb0BqdVqq/Mn5EsfpXoheiiTGz1M6oUoGqEcAOcA5MJycqEC4FHtvB5QJE213UemtFyPV1Yfx8urT6CswoD77wnGulkD0CmkdtWfbWkw09LSsGP7DrEMpfwxuQPbf1Eufz5z5kxVJ7y8l8sgXnb27FmHxWmM1ZmNlT2vq7NjdcQ8MldQXxMxYsJCTlKXk91sfQO6du1a1XHnIPaaXKg6LiOjqtsjPT3dar2QS5cumQayFsphnnWmh0jDUqOqnbcyaZIPS9kzJAYAV28V45El+7H80BWoVMCLCR3xxdR4+Hl5mD2+JrY2mIrHZCYRlD8mlUolJmYCqiZHjjBeaXuPTm3iBFyjsbL1dXV2rA1188WGmog5mitMUGY1HbqrHLFBmeINqLvsikviN+MbUKtWlUMiiwGUyo7TiN/atKnalXjDhg3iDxZKyK9fvx7Dhw8HUNmwqCD+Nw2FONelGMAuAAYL5eYtnFfOOCRmyz4ye87dxLPLjuBWcTk0qMDTMV6YOeTO3ngVDWYOqvb9aStebHxcJ0+erDpOrvK4U6dOKS8XAARA7FkxCgZwAzUvsa4pTjP3L3/+FQXGzPyt3I0CY7a+rrb+XTvSsqXLxM0Xk6v+X+9PtJw0O1JdbadhTMS2bd4GvVYPNAVQBLjtc8P9iffX20TMUVxpU0kmLHRXKbruKzco27ZZ7Lq/ow3KVAA2QGwM20J8U98IxTDLiRMnxB/Kq922sqbUsWPHpIuCgoKU9UIqYzWeMygoSDpWGmqy0AjLG0FpqbCF8xqXCstZ63UwGAQs3nUBH24+CwGALvMcrq6Zh+e1N/Hus81x6OAhREaayYZsIDXuywBkVXtcqHpcBoNBvMBCw6rX66WLCgoKxMd/G2Yff2Fhod1x2tOwKwqMyf9WnFBgzKb9gWz4u66uLvfJqk2PV11zRIO5aOEi9O7bG7nbquZdNWtpOkmcHPieXQscEqI6U1OXoaPG5KUhiQooi0ZVQDEksWnTJqvzRzZvrnpDzM3NtTrMIZ/MKQ0l3awWWI747fLlan3PVs4rV9NEUm1pOZ5KSsEHlclKwfHNyFr6EvRaMZDcvFzE94q36Tk059ChQ+LzdQvK5+s2ABXw22+/AaisrWJsWOX1WiobVnltlaKiIquPvzYJS3R0NJq3bG72/pu3bK5otOtTgbELFy5Y/bu+GyuKXGE1jSNi+PNf/oy8gjzFOfMK8kwmiTd2zp5HVR0TFrpjtr5ROntMvqYk5ObNqoxjzZo1VmNNTk6WLrpx40ZVItQfQD8A90JKhORLdW0tdw/I3qhlcz2Mb9RnsrT4w4K92HrqBtzVQO6mT5G3YwEwplyRiN3Ku4WtW7eiNs6ePVs1j0f+fFXO40lLSwNQ2Wth7GGSN6zNxOPkvRY6nc7q4y8tLYW90tLSkHsz1+z9597MVbypSsnNLSgKjOG2aXJTW3U11p+amir+0LLaFS3Eb/IeQcD2jSpt5QqNlaO26LB1j6zGztnv2dXVecLStm1bqFQqk6+ZM2eaPX7nzp1mjz9z5kxdh0YOYusnoNpOdtu8eTP+/ve/W2x4pXLvHlBO5KxMGHJyxK6OmhrLsrIy6aKSkhKrsRYXF0sX5eTkVPWO7AOwH+JS3crVMMb7B2TJSw11SKQ3al+9OMy0BsAWQN9Ujz2XSjB24V5cyi1Gq2be6F+8H4XHtlhMxNavX4/a6Nixo/iDhecrOjpaebl3teOamJ6zpk3qpOursZYESG+qjwGYBWBy5fdJ4sXVt1yQkhvj87oVZpMbW+/fqK57OM6cOSP+XedD+XetBaBC1dwhOKYRdoXGyhEx2POhobFztQnKdT6H5dChQ4ox699//x3Dhw/HI488YvV2Z8+ehZ9f1dLLli2rf6wgV2RsWPEQquYPdAf0gh6bk8VPQMZPrYrJboJeGpN322J+stuFCxfQp18fsYGp1Lyl6byMX375RUwOmsHsHJKtW7filVdeqUpYLMx1kK4HEBISghvZN8RhDS2kiXnYA0AFhIaGSsdKczgszI2RrgcQHBxsdQ5FcHCw9NgVjZUPgGI3BKj+CL8ef0BphYCBUS3wyaOxWPTvfeLJLbwBy+fb2KNXr17iD5chrmgyTrq9Kl7cu3dvAPbNy/H29kZRcZF4nfzxV5b79/ZWZj22zF8wmcPSvPLAyg4Is1suWEiuqk+Qtmf+RF2P9Xfq1Klq+Kr633UJ0KVLF+kiWxphe3uPXGHSr0NjsHBOqmLve7aj1XnCUj3R+Oc//4n27dubnUwoFxQUhGbNmtV1OORg9qzQAOxbddC7b29xnFlqsIHcXbno2bunIokpLS2tatzlDWZlI2jsLREEoWpvHDONpTyx0Ol04jE6iPVCjCqX5MqHLqQkxAPAGCgbbIMyuRk8eLByDoV0EgAlVY379evXFY2Vm08AWox9BV6txUZqYPMifPN/o+CmVmHChAl44403LL4BT5gwweS5BWqenCk9H2sh1p8xchO/GYd6pCXgxh4eVH4XxMd46dIl6fyCIIiXl1d7/G4wmcMDiMMcO37doXhdt2wUhzl+2foLAPveVG0t3Gdy/7K/QeMwi/H+AfsSd6PNmzfj4MGD6Nevn7TiTC4uLs7q33VMTIzpE1aHjbArNFaOiEGxR5aZ94Ga2qrGxpVWijl0lVBZWRmSkpIwZ86cGpcrxsbGorS0FJ07d8brr7+OIUOGWD1ep9MpPhFrtdo6iZnsY+8nIFuXdG7evBl5OXlACEw+XebdyMPWrVulN/lbt26JbzyJMNtg3r59G4BsNU8olI1lJIB05XCENFFWDWVjXfm7vA7LqlWrrDbYK1euxCuvvFJ1DuMqGWMjKOu5MZI+Md8ENK27oMWYl+HeNBAGXRFy1v8L6v4d4aaeoDynjatJbO01UKvVVcu15YnYBgCGqlVC0hJwC0mrfAl4TXNU5NcbhzmqJwGCIGB78nZFEuCIN1Xp/qv9DQrBVcMsxvu3J3G3tedQ+nu18HdtdvWZhb+B2jbCrtBY1XUM0dHRGDpsKLbv3K58H3AHhg4bymXN1dhTXsHRHJqwrFmzBrdv38YTTzxh8ZjQ0FB88cUXiI+Ph06nw3fffYdhw4Zh586dGDRokMXbzZ8/H2+//bYDoiZ71PYTUE1LOjds2GBmSARSwy5vBAsKCsQbWWgsjMms1GMQC3FS7FUA4QAKAaQrl3CWlJRYra0izXEBquYHWLh/k8JlxpUf8kTMHdLKj6ioKPE2KsC31x8QMOhJqFTuKNNdws3l81Bx4zrOB7lJNzVZJWVU2WtRfTjA1qGLjIwM5aRbQNFgGpO6mmqbyBNBaZ5Q9d6Uyt/lH0IUwxxm6sDIH5ety2937dpV1Rsmf113AzAozykda6GHQ36sPYl7n359kKvNVZwzd0MuevXphZzsqvlO0uozC39X8tVnUi+XceKxUeWwqLyXyx6u0Fg5IoZVK1eJSZA8aR+e4JReg/rCpmX4DubQhOWrr75CYmIiwsLCLB7TsWPHqsl9APr164crV67gww8/tJqwzJ07F3PmzJF+12q1CA8Pr5vAyS6O+BSWn59vdfw+Pz9fuuiee+4Rq9hehjg34RqqEhFUjfVLjZqFIQ75kJB4A1isrSLn6ekpLte10Fh5eFRVmpV6LdwAdBMfC5oAOCnGZPzUfKugGC0efBE+ncVPxkVuO5HrvwBCPx2QrFxWLU3kHQOxam4equaaJCtXKdVm6MJSg2nUokWLqnk58vk+ewGoKq+vVFFRUZUwPABlr02ZhTooFurAyD084WHs2LVDcdnmrZtNhm5Onjxpdb6TfCLrjRs3qno45FWJR8LkebU1cd+8ebPYs1Lt+YcA5CbnKnoOFRO0zfxdye9f6uV6DOLyemMy3gLAv5UJfm24QmNVlzG4QiJG9nNYwnL58mVs27YNP/74Y80HV9O3b18kJSVZPUaj0UCj0dQ2PKpDjvjn9/f3t9rD4u/vLx07ePBgbNu2TVzxIc85Kj/YGxNfqbG0MMQhnyyuUqnEBMdCbRX5EKenp6fV7nj53+mhQ4fEY1QQq90aacTb/vbbb2jfox90g56Fj18IBKECtzy/QoFb5aaBba08aREQEzzjpFMz/922Vq8FZJV/LTSYERGyTEZA1f5A1R6TnJQ0Wui1EWQ3aNOmjbIOjPz1UlXdf1paGnb8skNKPOUxVR+6ycvLs9prkpeXJ93cOAHaUlVi6fpKtiTuBw8eFH+wkATu379fSixsnaANyCZWW0juajvx2qgui9G5EldIxMh2DktYvv76awQFBeGBBx6w+7ZHjhxRTFSk+qEu//ljYmKs9rDIi5EZh0/gAWAQlMM3ZcohGWtDHPLGsqYhBvn10rJmC0My8mXN0hJsAWYb4WM5ApYv/BWCXwgqCvOQc+uf0EXLSttfEr/JeyUlNky4lIYuLDRs8qGLjIwMq5MTjUMSKSkpyv2B5M+/qvL6SlIiaKHBlieCtg5J1bQMVT50U1BQYHVeiDS8CPurEhsT9y1btuDAgQNmJ9P26dNH/MHCa9WvXz/l/Rv/B6oP85Qo73/ChAl4429vWEzuLE28rokrlWUnckjCYjAY8PXXX2PatGkme3PMnTsX165dw7fffgsA+Pjjj9G2bVt06dJFmqS7evVqrF692hGhUT2RmZlp9ZOwtJEhgHXr1lkdvlm3rtouhDUMcUisrPyR9xxIPTMWhmTkwxzBwcEWGmE1muVOwW+aWEBXgXCvMuz/bDb05bfEybxtoei1GTNmjPKcVnp45J/EjYXTcm/lmjRsZgunCbA4Sdno8OHDVp//w4cPSxe5u7ujvLzcpuEzW/coUhTuM/NayYdOsrKyrJ4zMzNTebkAi5Opq7OlcU9ISBCf/w25Jq9V85bNFQlOdHQ0ht4/FDv27oDQX5B65lSpKgy5f4j518pCcldbrlSWncghCcu2bduQkZGBJ5980uS6zMxMxa64ZWVleOGFF3Dt2jV4e3ujS5cu2LBhA0aNGuWI0MhF1NTFLFVZtfBJ2FhlFaicVGsluZHPdwFg+9JPGxsrtVotzn+xMCTj5lY1ViElN7IGUy34oUWHF+EdLfYaPXlvJHD0R+wtvCX20pjptZEnbNIncQs9PPJP4lLhtIdgsgt1bnKuYvhEGhKKBfAglIlYerUhIePKJzM9EXLu7u4oryi32Gsjf65ycytX0VyG2Tow0vWAza+VNDRi4W8gJCREusjeJfu2Nu5bN29Fn359UJ5cVbjHQ+OBbVvk42miLz//UtzzZl/VYw1sGYj/fPEfxXH2xmqLWs13InIgh5TmHzFiBARBMK2ECeCbb77Bzp07pd9feuklnD9/HiUlJcjLy8OePXuYrDRgtlYDtafKqoeHhzK58a/8PhKAoPzULvVE7AVwFGJFWmubydnQG+Pl5SX+cLnaFZfEb/I5LNKO0ZcB5ACel6IQWvwxvNWxMJSVYnyYFn8b3RlffvF55YOrds7KJOjzzz+XLtqzZ4/4g4US7r/++qt0kdSwHQGwEMBSAAsgPhdQVg5VbBR4FUBQ5fdqGwVKBc4sVNq95557pHOWlJSIx5ZBWUa/TDxWvvpK+htYWy3Wyg4z49+A1INk4bWS9zDNmDHD6r5HM2bMkI61p8qnPSXk5742FwZ3g7hSrXIbB4O7Aa+8Klv6XulPf/mT2Bsmk3sr12TPG0dUJDWZ73QOQC6cVpadiHsJ0V1laxl/aczdwhuwfExeGna00GApEpbKbnVsQ1VZduPl5li4fzm9Xm+1EZRP5pWGHNYCTfckIKT5+3BXB6E87xqyvvsrAgsuAqgcujAmUfKNB1Xil3zoYu3atcraLmMhlnDPF4+V73vUvn175bwM43kzxWPlDZutGwUWFRWJN7Dw/MvnhUiqJ4hmEsZevXpVTZKWx+ouHq+otAtYfK3kPUwJCQlQu6mrCtcZH1M5oHZTmwzJJCQmwG2zm+J1ddvihoTEBEXvgq0l5BWJzXAACQCGm09s0tLSsGP7DrOPf/svynL76enpVfON5H+DlT1X8rpBtlLMd5InjN+LF7vSRpHUODBhobvGnk+h0dHRGDR4kNkkYNDgQea7oit7LaRPgpfEixUTaCsbfEUDoIb5HhYrSYicVBU3AGY3/5PXFjl8+DDg7oHAkbPQfOQsqNw9UFy4H5krnkd57mVxFREqh0as9FrIh06kY5tBse8Q/E2PNalKW+288oYtOjoaQ4YNkVZGSXKBofdXFdi6eFFMsiwlDOnp6crLjfNNzOyYLWcy6dYYa+Xmi8ZJt9HR0Rg4eKBNfytpaWkw6A3i/Y2AYt8pg95gsufOsqXLcP+g+xWv6/2DTJfs29rDYc/eOFJ9HQuvlXyy8cGDB5XzjYx/g6Hisfv374e9FBtFVtutu642iiSyh0PrsBDJ2TvOviZ5jckyUeMkRrmaSsgrEhYr82JMWJkXYtZjlccb53q4Q2w4ZHTuPgiZ9D40oVEQoMdt9yRoW6wChglActUqmYCAALGXxcJzJV+hIW05YGEOj7y+jD1VaYHKYZ/qj1eAOHG2UllZmdVJv/KEzXh7jIJyDk0iTF4DabKshVjlk2k9PDzE115+DnfA3UP5FiclAQ+gal5MOMSVTcmmRfZsWfkD2F6HpVZ749gwLCmtPrIw30i++shWivlOZmrGcA4L3W1MWOiuqW0Z///+97/YsWMHhg0bZrZqstRgWlglYtJg2tAASMbA7Mofs4yTQ6s9LqPdaTeRFvkQNG5e0Av5yNF8gFK3o4oYjFVhIyMjxYTFwnMlL+GenZ1tNRG7ebOqmExNk07l9TrS0tKwZ/cewBMmdWv27N4jNVienp5Wkzuz9ZKOwGxtEzlp7omFWI3Xp6WlYfu2yhL+1V6r6iX8pZVHFu7fuPLIyJ5lvbbUYVEkNlq9VGTPbZ9pZWh76uAkJCTAQ+OB8g3lJqvKPDQetSoa54iJvER3ggkL3TV3ultzUlISXnjpBZM9V6Q5IhZWicjnkACwuOrErAjUWIxNYqGHB1Bh4fZz+GhrGgQ3L+gy03CzdD70nWVV6S6J34xDQjUtVZbXKZJ6kCw0LPIeFmleiIXzGueFANV6I8w8r8beiJCQELG3w0JyZ1JTSQXgOszWC5H35kg1ayysKDLWt1E0rGZeK3nDKk0ktlBbpfrfij3Lem0toLho4SJx5c+2qsm0zVo2w+LPFiuOk+rgWCgcJy/Nn5aWhnJdubjvkTxpDAbKb5TXqjfEFXZrJpJjwkJ3lT1l/GPjY1GgVU7YzM3JRY/YHsi/nW96clt7TiwmFmZYeLO2lUrjgxYPzMGHW8Rl2MUntuLmlkWAR7k48bMtFI1QdnY2AIgr6eTzYowqa5vs2LFDuqhp06ZWY/Xz85MuMhgM4nmrL5duAkBQ1oyxtQ5K27ZtcezYMYsJg9RTYGQlCZI7c+YMrO3sbIzPnobVZG5QtfuXT9Cu7bLemgooznhmBm4X31YkQbc338bTM582rW0iwGLhODkpabMwLFmb3hBX2K2ZSI4JC91Vts4J2Lx5MwryC8TGqdpuydp8rWLPFYktyYVx1Um1IY7qxeCkYzdA/DRu/D0FJj0Blni0iEDLP7wGj8AweLqr8c6YLpj6r3FARbkYg7wRqixjb+wNkXYft9AAyWvLeHt7W+01kZZdQ9a4j4Q4b+MKqvZdSlY27oo6KGaeV+P10jYJFnquzFZEtSG57NSpk/hDSyir8rYAcKNqjyh7Glap6rAN82KcXdtE2prgNoD+qFrhlgrF1gSAmaTNmDQeE7/VtjfEFXZrJjJiwkJ1ZvPmzTh48KDFJASwfU7AokWLrO6qu2jRIuV9WGmwFcmFjZ/upWPLAMgXWFhKVqrNoWmSNxjNm8+C2sMLFfnZWPfaOHRv3Qx/9vISC6cJEFenNIGijL23t7fyvBYaILnAwMCqxsxMEiTffDA6OhpDhw3Fjo07ICQK4iTNS4DqZ9Pqqc2bN7c6JNG8uRiUlLBY6LmS9/CYPC6jS6aHxMXFVe0lZCz5XwTxb0BVuX1DJVsb1vvuu0/cp8zC/Q8bNky6yBFDIvYkQVJvWBmAfbJj1TDpDXNUbwg3CSRXwoSF7lj1uSaAuOyx+lwTwPY5AdJE0mYwW+5d/kkYgNXhExP2TLpVw6SHB3ozxxmHGbq5I6D8SfiF/AEAUJKeipyfPkT3xf8HQNYIWVilYpxDodFoUFJaYtOGitKGfVULd0SV7Zl8LyMAcFMBbhUCKmTPlZu7YDIy1qJFC6tDEsaERao0bKHnqrCwUHliK/NS5MmgWq2uSsTkfwOViZh824+a9n4y+uMf/4inZz4tTk6t9rx6aDwUk7odkQTYkwRJNXM8YHZn6+oJkyN7Q7hJILkCJix0x/r064NcrXJvmtwNuejVpxdysqsaS0V3uGxJq36EaXd4dHQ0Dhw8YHGprtnN/2xYVgzA9nkpVlYemetlcYsIRIuil+HlJg5V3NYvR/4P3wNC1aTX4uJi8QcLq1SM10vLiS0kYfJP1/I6K+bIr09LS8PWbduRNAPo3R44nwV0CAEOngemLlauqJESHQs7VhuHhHbv3m2150pe2RqA1XkpctKkUxWUQyJHYDLp1J7JsVs3b8WQYUMgJFfdocpNhW3bTUvj13USYHcSZEePIHtDqKFjwkJ3ZPPmzVZrNcjnmijKwptprOXd4QEBAVaX6jZr1sw0GBuGT2weOgLs2vhO07oLWupfgZtnAAy6IuSs/wglBb+ZnFOqmWJhlYqxp0BKSCwkYfLVLAUFBVZ7OKT5MKh6DQZ1AracAHaeAoZ1AYZ1Fa+Xvwa5ublWz2tMWIwThS31XEnX14axh0c+JFJt0qm9k2PfePMNCO4CMAzSkJywS8Abf3sDu3buUty9I5IAW5Og2s6hYW8INVRMWOiOHDx4UPzBwpvq/v37pYTFpCx8tcZa3sUtLcW1cF75Ul0ANg8z2F0MrobhI0EQ4NtzDAKGPAmV2g1luku4qZqHiu7XzS7VlWKoIRGShjgsJGHyx//7779b/SQurfZB1ZBEuzmAMSdK+hUwjq7IX4PmzZtbPW9gYCCAyjo48liNLonfTOrg2Gjw4MHKLQeMc1j2AFBVldy3p2FPS0vDnl17lMkNxHPvTt5d65U/9rB1+Or69eviDxaeV5NhUaIGjgkL3RGpwqaFN1WTCps29lrUVDTMpLaHjcMMAOwvBmdh+KhIV4GXVx9H4LA/i79rdyI3aAEElc76ZF7Atnk0NvYGSRtHWjinNMdFdt4KNwB/gJQ0VmyAydwc4yocS+c1Xu/t7S1OJLYQq3wisUajERMYC6+BfG6OcYLw9p3blXNY3IGhw6q2BrBnXsjKlSutPqaVK1fitddegyPZOnx17do1q5Oe5bveEzUG3EuI7khCQoK434iZfVyat2yuWMljzz4qO3bssLqXzy+//FL7oCMg9lhEVX5va+E4K/fvHhCGcYt+xfrjmRD0FcjbugQ5Xh+KyUq1x2XW5Wq/XzJzjHHORjKq9oYxXi4Ps7Kcv6VzStejcvWVvNfEH4r9eRYtWiQda+umgmFhYcqeK2OsFeI5pR2q5Sy8BvJYAWDVylVIGJ6guCxheAJWrVwl/R4dHQ0PjYfZ18pD46HoGZGGpyw8pjsavrKBPftp9enTRznp2fi8+gEQaldun6g+Yw8L1aim5cpbN29Fn359UJ5ctUzFQ+OBbVuUkxiNZectfRKWr/r47bffrE46/e2330wDtbXnxNZJtxaGj7w79EWLB+Yg7UYhgnw1OL74ReiunRYbXVsn89oyfGWccCpf/rzH9DhPT0/oynQWzynvtZAmwFpIGk0myFqLtZI0NGFu6AuVO09XkoaHLLwGpaWlilPYModk8+bNYpVXL5gs6y7XlSvmUT3wwANYsHCBxcf04IMPwpHsGb4yfhjIvZVrsqy7+ocBosaACQtZZOty5bmvzYXB3QB0hjgZsglgOGvAK6++oujilsqiW+jilq98KSmpnFX5GMRVKlchLv9tAeDfstU2chFQLu1ta+ZB2TPpFlAWLVOp0WzEVPh3fwQA0LttIBZOjkXw66ftn8yrhtlKsybHJcLs8mc5aam0hSExk60JAIsJg3x+hVSa37gDsFEkgPSq0vzSpF8Ly28LCpTVim1O2GSszSE5ePCg1eROPo8qISEBAYEBuHX7lslzFRAY4PAkwN7aLocOHkKvPr2Qu9X0f5CosWHCQhbZslxZWqERAnH1TyV9cFUXt2KugbyL26hy1Yf8zVpqOJdBWeU0uNr1chaOVbBn0q0KUtEytZ8fWgS/BG/fHgAA7aE1WPqPz+Hhpq46r611YFQQl0bLG9fKYmgmcRxBjZsEBgQEoKioSDynXOXvxsmxgFg47djxYxYThiFDhkjHSpN1q5WBR7Hy+qZNm+L27dsWJ+dKWwcY2TPfqFJaWhouXLhgtoelVatWVpO76lsDpBxKEZMAeSIeeHeSAHuXNUdGRiInOwdbt27F/v37rRZlJGromLCQWbYuV75w4YLY0FqolyLv4v7hhx+srvpYvXo1XnnlFQCVvQLGhMHMJnkmvQay5EKqirsL5pMAW4eOKpMQz9+j0XLsK3D3DYKhvBS5P3+K4tO74eH2pfJ4W+vAVJ94DJjtObG2okr+mFq0aIGrV69aLHInr3Q7Y8YMfPLJJxYThhkzZkgXST1iFl4D40qle+65B/v3769xci4g1oTR6/UWX4PqNWVsqYwcFla5RbaF5E6awG282MlJQG1quwwfPpyJCjV6TFjILFuXK0vVSI2fbiuLwWEkgGTlvJSVK1dWDTHIV31UDjEsX75cSlgA2F9GPwBmq+KaiIBtOzCrgKatExA4ZDpUag+Ul13DzZX/QPl1C6szbKkDI49Brq2ZY6onNhYef35+vtUid/J9h6Kjo3Fv/77Yd+CAcrqMAPTv31fxCT8mJsbqaxAbGwsAaNmypXidhWEOY0VcQJxvU1JSYvE18PT0VDw2W1bU2LNcXs5ZSQALvBHVDhMWMsvW5crS0koLn27l1Uileh2ZULpe7Xo5Wxp2wGovj0kPiw2TblXunggcPh1Nu48AABSr9yPH798QehebT5iszM0xO9Rh68RfGx5/eXm51eSmvFxZs/+n9RsxZfJj2CjrtUhMSEBStU/4Us+FhRiMPRcBAQFW56XIe3gkl2F2o0T5KiG7CsLZmNy5EhZ4I7IPExYyKyEhAR4aD4t7rphsPGhp6EJGSkgs7HljtsCYPSt6LFTFVbBhcuyVvGIET34fmpAOEAQ9bnt8C637avGYtlbu38LcHBP2TNC14fFLCYmFxKJ6IhgQEIANNnzCt3WCqF6vtzovRX7/Hh4eYg/LWpjdKFHeI2frihpH7KpMRK6HCQuZlZaWJi4VDYFJI1x+o1z6dCsNCVn4dCtvgMrKyqwOXVTvCZA+tWsBNIVivovZXgtbh1nKqj0m2fl2pd3E7OVHoAnpAH1xPnLy3kdplGxs55KZcxrPIZ9DI9tV2Ozqn+qNu1p5nI+PD4qKiywmNj5NfKRjQ0NDxaXFFhILqaekehi2VFy1llxVMtuDIiOfQxIZGSlO+rXwNyBffWZrwuSIXZWJyPWwcByZJX1q9a52RRPxm7HI27FjlY25hWThyJEj0kWFhYXK5Ma/8nsiAMHMrr7G5GIbgDUQ56eUwfJqEgvFwEyMg9hYdqv8PhYAVPDvNxFPfP0bbheXQ3c9DZn/ew6lq46ZLRxnQj6HxhhrMwuxGpO2EZX3PaLyd9l5o6KirBZji46Olo4dP3681SJ348ePV9x9Xl4eRo4aiY4dO2LUqFGIjo7GyFEjqyrmVpKWNRtXPxljqHxcu3btAiCbAD0GwCwAkyu/ixtWKxLRHj16WP0biI+Pl441rqhx2+ymeFxuW9yQkJig2CjTluOIqH5jDwuZdf36datDPcZiYTUVg5Ouh2wTvgiIO/4a5y+0FS+WTw4FYLW2h0kiYGUOhcmxxgmfsZU3zfdBi/F/RZMOvSEIwGO92+CfE8YB+nJxqMLMMIdZFpI7E9UnsgImq4SkYm8WVtPIi8H16tXL6rLq3r17K+7enp2NAdS4+kl6jS1MpJWv/JG2CbCQ4Eo7RFeydUVNXe+qTESuhwkLmXXs2LGqT8Ly1T+JAJLFnpMnnnhCPNiGYQNAtmGfhXopJhsaWlmhYkKAxQJnJmTJlYehLVriVXh0CINQUYYPJvbEhF7h+Ke+sldAXjgOEAvXWaqtYsMSZEkNw1cdO3YUV2pZSAI6deokHSs9bxYSC3lBPnsmsipK85tZ/WS8Xlq2bCFp7dy5s3RRu3btrB4rXV/J1hU1XHlD1PAxYSGzgoKCxB8srP4xXn/p0iWrn+4vXbqkPLGVuh61npdiFAvgQSh7I6onLLLkyqfdfQhUPwO1pxcq8m/gZvI8TPjwvPLYfCjnpey1EKuNq1R8fHzEIm8WGmwfH3FuyoABA/Dtt99aPO7ee++VLjLp5aqWWNRmIisgDrUEtghE3oY8k2Q0sEWgMrGxkrRKiQ8q68B8+onF3jB5HRg5W1fUcOUNUcPFhIXMmjBhAt742xsWew0mTJigvIGFT/cmEzvt6TUBbF8lZBwSSoT1ISEBQHN3BBT8EX7uowEAJddSkLPqQxhKq5WQN678qV7bxdzKH8Cm5Kp79+7Yf2C/xca9R48eAGRJgIWGXZ4E2LPlgT0TVNPS0pCXk2d24nXejTypNyY6OhoDBg7A3r17TSYSDxw0UJFAREdHo1+/fth/cL/JUFu/fv2YbBCRRUxYyDIbeg169uyJtWvXWvx036tXL+nYkJAQcSM8Cw17SEiI8nJ75qXYOCTk5huIFsNegVeYOExxO3MZ8lctA3TV69rD5pU/7u7uYlJgIQmQ93BYXAJcuUrImFxER0dj6LCh2L5zu/I4d2DosKGKht2eLQ/sKQ0v9cZYSEblvTGenp5QeaogDBKk7QZUu1Xw8PAweVo3rN8gzjeRV68dkcD5JkRkFVcJkVm2DB0AlT0txsRCvkKlMrGQ98SMGCEWYbO0michIUG6yMfHR9mwG1eolAMQzOxPA4hDQvJVKj2UV2vCuyL08U/gFdYZhtJCZK/6O/K/XQqUGcwPRwkQJ9nKV/6YmXTboUOHqh4O+XNQ2cMhTxikuSfKgq7i5GIo56asWrkKCcMTFIclDE/AqpWrFJdJq2QK3MTkaiyA4YBboflVMsuWLsP9g+5XPK/3DzKdoKrojWkOIKry+yXZ44bYE7N923YIowSgP8TnvT8gJArYvm07zp07pzivcb5JWloaNm7ciLS0NGzauEkqtU9EZA4TFjJL0VjJXRK/GRur6OhoDBw00GxiUX04YMCAAVYb9gEDBkjHPvzww+IP1T+gV3ZWyBMhHx+fqqTpKoCgyu+VSZOPjw/+s+ciQh79B9yaBqAsJx2Zvz6PEt1vwD0QkweVuCeOkbd35ZKfkQCmAriv8ntCtetRuQpH3sNhfA78AAiyqsEAXnvttaoemv4A+lV+hxjDa6+9Jh1rT8MuJSGy5MpcEmLPeW1dLmxrcltdVFQUEhMTOQxERLYRGoj8/HwBgJCfn+/sUBqMhMQEwc3HTcA4CHgeAsZBcPNxExISExTH5eXlCQmJCQLEZlgAICQkJgh5eXmK4zZt2iReHwLFsQgWv2/ZskVxPFQQoIGA/hDQr/K7BgJUyj/bYcOGieeJrHbeSAgqDy+hy5//JUS8vF6IeHm90PzBvwqqDhqT4wAIkZGR0jnfeeedqvuXPX7j/b/77rvSsV988YV4rDcEDIeAsZXfvcVjv/zyS0W8PXv3FI+Xx6CC0LN3zzt6vQRBENLS0oSNGzcKaWlpd3wuQbDttT179qx43UMQ8Jbsa5x4fF3FQkQNk63td50nLG+++abyjRgQgoODrd5m586dQlxcnKDRaITIyEhh8eLFdt8vE5a6Z2siYlRTY7lx40axofaq1rB7iQ32xo0bFccvX77cbMO+fPlyxXH/+c9/qhrMWRAwWfzuPqGVEPrHz4SIl9cL7eduEO594lWzxxkb1nHjxknnlBphdbX7V5s2wtKxFhKx6s+Hvc+rK6jptbU1uSUiqs6pCUuXLl2EzMxM6Ss7O9vi8RcvXhSaNGkizJ49Wzh16pTw5ZdfCh4eHsKqVavsul8mLI5TV5/a7W3YjZ588kmhbdu2wpNPPmn2eikRkvWGeE/tJ4Q/t1KIeHm90P2N9cLhS7liD4+VXpPqPTwDBw8U4FYtVjcIAwcPNIlh6P1DBZWXSpGIqbxUwtD7h1p8Puq6N8SZ6mMSRkSuwakJS0xMjM3Hv/TSS0KnTp0Ulz311FNC37597bpfJiz1g/RJXNaw3+kncSkR8oIAlVpoNniaNAQU/Nh84cDRU4IgVCY2lb001XttANMeHnsaYTbYooaUhBHR3WFr++2QZc3nzp1DWFgYNBoN+vTpg3nz5plUsDTav39/1eqRSgkJCfjqq69QXl5udlkkIO7sK9/dVyr7Ti5NKqH+c92VUDdODt3+228ImPYCvJvFAAAKjqxBT7dr6BPzCgDZROJgKKvXBgG4YbpJnj3VU1lpVcTCbUTkKHWesPTp0wfffvstoqOjcePGDbz77rvo378/Tp48iebNm5scn5WVpdjNFRB3d62oqEBOTg5CQ0PN3s/8+fPx9ttv13X45GCOathf++ALnP9qHyo8fWEoK0Huz59gYKSPIhGSapDs3gb9cL1UW8Vtn2kNEjl7GmE22EREjqESBFv2mK+9oqIitG/fHi+99BLmzJljcn10dDT+7//+D3PnzpUu+/XXXzFgwABkZmaaFhOrZK6HJTw8HPn5+fDz86v7B0IuSRAEfP9bBt5edwplegNa+3vgyagKDIwxnzjcunXLtGhZoli0jHVAiIjuPq1WC39//xrbb4dXuvXx8UG3bt1MikcZSdVPZbKzs+Hu7m62R8ZIo9Eodqylxqe0XI/X1/yOVSlXAQAJXYLx4SMx8PUyP4wIcOiGiKi+cnjCotPpcPr0aQwcONDs9f369cNPP/2kuGzLli3o2bOnxfkrRFfyijE9KQUnr2uhVgEvJnTC9MHtoFKpar4xOHRDRFTf1Hml2xdeeAG7du1Ceno6Dh48iIcffhharRbTpk0DAMydOxePP/64dPz06dNx+fJlzJkzB6dPn8Z///tffPXVV3jhhRfqOjRqIHaezcaDC/bi5HUtAn088d0f++Dp+9rbnKwQEVH9U+c9LFevXsVjjz2GnJwctGzZEn379sWBAwcQESHW7c7MzERGRoZ0fGRkJDZu3Ijnn38en332GcLCwvDpp59i/PjxdR0a1XMGg4CFO87j39vSIAhATHgzLJ4ch7Bm3jXfmIiI6jWHT7q9W2ydtEP1U35xOZ5feRTbz2QDACb1aYM3R3eGxt3NyZEREdGdcJlJt0R36tR1LaYnpSAjrxie7mq8O7YrJvQMd3ZYRER0FzFhIZeWfOQq5v54AqXlBrQO8MaSKfHo2srf2WEREdFdxoSFXFJZhQHvbjiFb/dfBgAMim6JTyb2QICPp5MjIyIiZ2DCQi4nK78UM5amIDXjNgDg2aEdMPv+aLipuQqIiKixYsJCLuXAxVw8830qcgrL4Ovljo8n9sCwe4JrviERETVoTFjIJQiCgP/sScc/N52B3iCgU4gvPp8aj4jmPs4OjYiIXAATFnK6Ql0FXl51HBtOZAIAxsW2wrxx3eDtySXLREQkYsJCTnU+uxDTk1JwPrsQ7moV/ja6M6b2jWDVWiIiUmDCQk6z6fdMvPDDcRTqKhDsp8GiyXGIjwh0dlhEROSCmLDQXVehN+CDLWfx+a6LAIA+kYFYMCkWQb5eTo6MiIhcFRMWuqtyCnWY9f0R7L+YCwD488BIvDyyE9zd6nwfTiIiakCYsNBdcyTjFmYsTUVmfimaeLrhg4dj8ED3UGeHRURE9QATFnI4QRCw9GAG3v7pJMr1Atq19MHnU+IRFezr7NCIiKieYMJCDlVarsdryb9jdepVAMDILiH44JHu8PXycHJkRERUnzBhIYe5kleMp75LwalMLdQq4OWRnfCXQe24ZJmIiOzGhIUcYsfZbDy3/CjyS8rR3McTCx6LRf8OLZwdFhER1VNMWKhOGQwCPt1+Dp/8cg6CAMSEN8PiyXEIa+bt7NCIiKgeY8JCdSa/uBzPrTiCHWdvAgAm92mDv43uDI07S+wTEdGdYcJCdeLk9Xw8nZSKjLxiaNzVeHdsVzzSM9zZYRERUQPBhIXu2I+pVzH3xxPQVRgQHuiNxZPj0bWVv7PDIiKiBoQJC9VaWYUB76w/he8OXAYA3NexJT6e2APNmng6OTIiImpomLBQrWTml2DG0lQcybgNAJg9LAqzh0VBreaSZSIiqntMWMhu+y/kYtayVOQUlsHPyx0fP9oDQzsFOzssIiJqwJiwkM0EQcCXey7ivU1noTcIuCfUD0umxCGiuY+zQyMiogaOCQvZpFBXgZdWHcPGE1kAgIdiW+Ef47rB25NLlomIyPGYsFCNzmcX4qnvDuPCzSJ4uKnwtwc7Y0rfCJbYJyKiu4YJC1n184lMvPDDMRSV6RHsp8GiyfGIjwhwdlhERNTIMGEhsyr0Bnyw+Sw+330RANC3XSAWPBaHlr4aJ0dGRESNERMWMpFTqMMz36fiwMU8AMBfBrXDSwkd4e6mdnJkRETUWDFhIYXUjFuYkZSKLG0pfDzd8MEjMRjVLdTZYRERUSPHhIUAiEuWkw5m4O8/nUS5XkD7lj74fGo8OgT5Ojs0IiIi1Hkf//z589GrVy/4+voiKCgIY8eOxdmzZ63eZufOnVCpVCZfZ86cqevwyIySMj3++sMxvLHmd5TrBSR2DcHaZwYwWSEiIpdR5z0su3btwsyZM9GrVy9UVFTgtddew4gRI3Dq1Cn4+FgvMHb27Fn4+flJv7ds2bKuw6NqMnKL8VRSCk5naqFWAa8kdsKfB7bjkmUiInIpdZ6wbNq0SfH7119/jaCgIKSkpGDQoEFWbxsUFIRmzZrVdUhkwY4z2Zi9/Ai0pRVo7uOJBZNi0b99C2eHRUREZMLhyz7y8/MBAIGBgTUeGxsbi9DQUAwbNgw7duyweqxOp4NWq1V8kW0MBgH/3pqGJ/93CNrSCvQIb4b1zw5gskJERC7LoQmLIAiYM2cOBgwYgK5du1o8LjQ0FF988QVWr16NH3/8ER07dsSwYcOwe/dui7eZP38+/P39pa/w8HBHPIQG53ZxGf74v0P45JdzEARgat8IrHiqL0L9vZ0dGhERkUUqQRAER5185syZ2LBhA/bu3YvWrVvbddvRo0dDpVJh3bp1Zq/X6XTQ6XTS71qtFuHh4cjPz1fMg6Eqv1/Lx9NLU3AlrwQadzXmjeuG8fH2vS5ERER1SavVwt/fv8b222HLmmfNmoV169Zh9+7ddicrANC3b18kJSVZvF6j0UCjYdVVW61KuYrXkk9AV2FAeKA3lkyJR5cwf2eHRUREZJM6T1gEQcCsWbOQnJyMnTt3IjIyslbnOXLkCEJDWbDsTukq9Hhn/SkkHcgAAAzp2BIfT4yFfxMPJ0dGRERkuzpPWGbOnInvv/8ea9euha+vL7KysgAA/v7+8PYW50nMnTsX165dw7fffgsA+Pjjj9G2bVt06dIFZWVlSEpKwurVq7F69eq6Dq9RycwvwdNJqTh65TZUKmD2sCg8OzQKajWXLBMRUf1S5wnL4sWLAQD33Xef4vKvv/4aTzzxBAAgMzMTGRkZ0nVlZWV44YUXcO3aNXh7e6NLly7YsGEDRo0aVdfhNRr7LuRg1vdHkFtUBj8vd3zyaCyGdApydlhERES14tBJt3eTrZN2GjpBEPDF7ot4b9MZGATgnlA/fD4lHm2aN3F2aERERCacPumW7r5CXQVe/OEYfv5dHIZ7KK4V/jG2G7w93ZwcGRER0Z1hwtJAnM8uwFPfpeDCzSJ4uKnwt9FdMKVPG5bYJyKiBoEJSwOw4XgmXlp1DEVleoT4eWHRlDjEtQlwdlhERER1hglLPVahN+D9zWfxxe6LAIC+7QKxcFIcWjRlfRoiImpYmLDUUzcLdJi1LBUHLuYBAJ4a1A4vJnSEu5vDt4ciIiK665iw1EMpl29h5tJUZGlL4ePphg8fiUFiNxbZIyKihosJSz0iCAKSDlzG39efQrleQPuWPvh8ak90CGrq7NCIiIgciglLPVFSpsdrySfw45FrAIBR3ULw/sMxaKrhS0hERA0fW7t64HJuEaYnpeJ0phZqFfBKYif8eWA7LlkmIqJGgwmLi9t+5gaeW34U2tIKtGjqiU8fi0X/9i2cHRYREdFdxYTFRekNAj755Rw+/eUcACC2TTMsmhyHUH9vJ0dGRER09zFhcUG3i8swe/lR7Eq7CQB4vF8EXn+gMzzduWSZiIgaJyYsLub3a/mYnpSCq7dKoHFXY964bhgf39rZYRERETkVExYXsirlKl5LPgFdhQFtAptg8ZQ4dAnzd3ZYRERETseExQXoKvT4+0+nsPRgBgBgaKcg/HtCD/g38XByZERERK6BCYuTZeaX4OmkVBy9chsqFfDcsGjMGtoBajWXLBMRERkxYXGifedzMGvZEeQWlcHf2wMfP9oDQzoGOTssIiIil8OExQkEQcDnuy/i/U1nYBCAzqF+WDIlHm2aN3F2aERERC6JCctdVlBajhd/OI5NJ7MAAOPjWuMf47rCy8PNyZERERG5LiYsd9G5GwV4KikFF28WwcNNhTdHd8HkPm1YYp+IiKgGTFjukg3HM/HiqmMoLtMj1N8LiybHIbZNgLPDIiIiqheYsDhYhd6A9zadwZd70gEA/do1x4JJsWjRVOPkyIiIiOoPJiwOdLNAh2e+T8XB9DwAwFOD2+HFER3h7sYS+0RERPZgwuIgKZfzMGNpKm5odfDxdMOHj8QgsVuos8MiIiKql5iw1DFBEPDdgct4Z/0plOsFdAhqiiVT4tEhqKmzQyMiIqq3mLDUoZIyPV5NPoHkI9cAAA90C8V7D3dHUw2fZiIiojvBlrSOXM4twlPfpeBMVgHc1CrMTeyEPw6I5JJlIiKiOsCEpQ78cvoGnltxFAWlFWjR1BMLJ8Whb7vmzg6LiIiowWDCcgf0BgGfbEvDp9vPAwDi2jTDosnxCPH3cnJkREREDQsTllq6XVyG2cuPYlfaTQDA4/0i8PoDneHpziXLREREdY0JSy38fi0f05NScPVWCbw81Jg3rhseimvt7LCIiIgaLId1ByxatAiRkZHw8vJCfHw89uzZY/X4Xbt2IT4+Hl5eXmjXrh2WLFniqNDuyMrDVzB+8T5cvVWCNoFN8OPT9zJZISIicjCHJCwrVqzAc889h9deew1HjhzBwIEDkZiYiIyMDLPHp6enY9SoURg4cCCOHDmCV199Fc8++yxWr17tiPBqRVehx9wfT+ClVcehqzBgWKcg/PTMAHQO83N2aERERA2eShAEoa5P2qdPH8TFxWHx4sXSZffccw/Gjh2L+fPnmxz/8ssvY926dTh9+rR02fTp03Hs2DHs37/fpvvUarXw9/dHfn4+/PzqNom4frsETyel4NjVfKhUwPP3R+OZIR2gVnPJMhER0Z2wtf2u8x6WsrIypKSkYMSIEYrLR4wYgX379pm9zf79+02OT0hIwOHDh1FeXm72NjqdDlqtVvHlCL+ez8GDC/bi2NV8+Ht74OsneuHZYVFMVoiIiO6iOk9YcnJyoNfrERwcrLg8ODgYWVlZZm+TlZVl9viKigrk5OSYvc38+fPh7+8vfYWHh9fNA5ApLqvA7OVHkFdUhi5hflg/awDu6xhU5/dDRERE1jls0m31Cq+CIFit+mrueHOXG82dOxf5+fnS15UrV+4wYlNNPN3x74k9MKFna6x+uj/CA5vU+X0QERFRzep8WXOLFi3g5uZm0puSnZ1t0otiFBISYvZ4d3d3NG9uvmKsRqOBRqOpm6CtGBjVEgOjWjr8foiIiMiyOu9h8fT0RHx8PLZu3aq4fOvWrejfv7/Z2/Tr18/k+C1btqBnz57w8PCo6xCJiIionnHIkNCcOXPwn//8B//9739x+vRpPP/888jIyMD06dMBiMM5jz/+uHT89OnTcfnyZcyZMwenT5/Gf//7X3z11Vd44YUXHBEeERER1TMOqXQ7ceJE5Obm4u9//zsyMzPRtWtXbNy4EREREQCAzMxMRU2WyMhIbNy4Ec8//zw+++wzhIWF4dNPP8X48eMdER4RERHVMw6pw+IMjqzDQkRERI7htDosRERERHWNCQsRERG5PCYsRERE5PKYsBAREZHLY8JCRERELo8JCxEREbk8JixERETk8piwEBERkctjwkJEREQuzyGl+Z3BWLBXq9U6ORIiIiKylbHdrqnwfoNJWAoKCgAA4eHhTo6EiIiI7FVQUAB/f3+L1zeYvYQMBgOuX78OX19fqFSqOjuvVqtFeHg4rly5wj2K6gG+XvUHX6v6g69V/VLfXi9BEFBQUICwsDCo1ZZnqjSYHha1Wo3WrVs77Px+fn714oUnEV+v+oOvVf3B16p+qU+vl7WeFSNOuiUiIiKXx4SFiIiIXB4TlhpoNBq8+eab0Gg0zg6FbMDXq/7ga1V/8LWqXxrq69VgJt0SERFRw8UeFiIiInJ5TFiIiIjI5TFhISIiIpfHhIWIiIhcHhOWGixatAiRkZHw8vJCfHw89uzZ4+yQqJq33noLKpVK8RUSEuLssKjS7t27MXr0aISFhUGlUmHNmjWK6wVBwFtvvYWwsDB4e3vjvvvuw8mTJ50TbCNX02v1xBNPmPyv9e3b1znBNnLz589Hr1694Ovri6CgIIwdOxZnz55VHNPQ/reYsFixYsUKPPfcc3jttddw5MgRDBw4EImJicjIyHB2aFRNly5dkJmZKX2dOHHC2SFRpaKiIsTExGDhwoVmr3///ffxr3/9CwsXLsShQ4cQEhKC4cOHS/uD0d1T02sFACNHjlT8r23cuPEuRkhGu3btwsyZM3HgwAFs3boVFRUVGDFiBIqKiqRjGtz/lkAW9e7dW5g+fbrisk6dOgmvvPKKkyIic958800hJibG2WGQDQAIycnJ0u8Gg0EICQkR/vnPf0qXlZaWCv7+/sKSJUucECEZVX+tBEEQpk2bJowZM8Yp8ZB12dnZAgBh165dgiA0zP8t9rBYUFZWhpSUFIwYMUJx+YgRI7Bv3z4nRUWWnDt3DmFhYYiMjMSjjz6KixcvOjskskF6ejqysrIU/2cajQaDBw/m/5mL2rlzJ4KCghAdHY0///nPyM7OdnZIBCA/Px8AEBgYCKBh/m8xYbEgJycHer0ewcHBisuDg4ORlZXlpKjInD59+uDbb7/F5s2b8eWXXyIrKwv9+/dHbm6us0OjGhj/l/h/Vj8kJiZi6dKl2L59Oz766CMcOnQIQ4cOhU6nc3ZojZogCJgzZw4GDBiArl27AmiY/1sNZrdmR1GpVIrfBUEwuYycKzExUfq5W7du6NevH9q3b4///e9/mDNnjhMjI1vx/6x+mDhxovRz165d0bNnT0RERGDDhg146KGHnBhZ4/bMM8/g+PHj2Lt3r8l1Del/iz0sFrRo0QJubm4mmWh2drZJxkquxcfHB926dcO5c+ecHQrVwLiai/9n9VNoaCgiIiL4v+ZEs2bNwrp167Bjxw60bt1aurwh/m8xYbHA09MT8fHx2Lp1q+LyrVu3on///k6Kimyh0+lw+vRphIaGOjsUqkFkZCRCQkIU/2dlZWXYtWsX/8/qgdzcXFy5coX/a04gCAKeeeYZ/Pjjj9i+fTsiIyMV1zfE/y0OCVkxZ84cTJ06FT179kS/fv3wxRdfICMjA9OnT3d2aCTzwgsvYPTo0WjTpg2ys7Px7rvvQqvVYtq0ac4OjQAUFhbi/Pnz0u/p6ek4evQoAgMD0aZNGzz33HOYN28eoqKiEBUVhXnz5qFJkyaYNGmSE6NunKy9VoGBgXjrrbcwfvx4hIaG4tKlS3j11VfRokULjBs3zolRN04zZ87E999/j7Vr18LX11fqSfH394e3tzdUKlXD+99y6hqleuCzzz4TIiIiBE9PTyEuLk5aMkauY+LEiUJoaKjg4eEhhIWFCQ899JBw8uRJZ4dFlXbs2CEAMPmaNm2aIAji8ss333xTCAkJETQajTBo0CDhxIkTzg26kbL2WhUXFwsjRowQWrZsKXh4eAht2rQRpk2bJmRkZDg77EbJ3OsEQPj666+lYxra/5ZKEATh7qdJRERERLbjHBYiIiJyeUxYiIiIyOUxYSEiIiKXx4SFiIiIXB4TFiIiInJ5TFiIiIjI5TFhISIiIpfHhIWIiIhcHhMWIiIicnlMWIiIiMjlMWEhIiIil8eEhYiIiFze/wNCdwMAKQ441wAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_train_predict = sample_predict(X_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", - "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", - "\n", - "plt.show()\n", - "\n", - "y_test_predict = sample_predict(X_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", - "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f574ffdb", - "metadata": {}, - "source": [ - "Train neural network for goalkeepers.\n", - "\n", - "For clean sheet probability prediction, a Bernoulli distribution is used." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "41e7e1ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/2500\n", - "8/8 [==============================] - 4s 92ms/step - loss: 363.3525 - distribution_lambda_26_loss: 106.5460 - distribution_lambda_27_loss: 255.9949 - distribution_lambda_28_loss: 0.8116 - val_loss: 255.7005 - val_distribution_lambda_26_loss: 95.0135 - val_distribution_lambda_27_loss: 159.8931 - val_distribution_lambda_28_loss: 0.7939\n", - "Epoch 2/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 242.5359 - distribution_lambda_26_loss: 90.2925 - distribution_lambda_27_loss: 151.4580 - distribution_lambda_28_loss: 0.7854 - val_loss: 181.1897 - val_distribution_lambda_26_loss: 80.4324 - val_distribution_lambda_27_loss: 99.9920 - val_distribution_lambda_28_loss: 0.7654\n", - "Epoch 3/2500\n", - "8/8 [==============================] - 0s 5ms/step - loss: 172.7781 - distribution_lambda_26_loss: 75.9389 - distribution_lambda_27_loss: 96.0833 - distribution_lambda_28_loss: 0.7559 - val_loss: 137.6391 - val_distribution_lambda_26_loss: 68.3221 - val_distribution_lambda_27_loss: 68.5772 - val_distribution_lambda_28_loss: 0.7398\n", - "Epoch 4/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 134.3410 - distribution_lambda_26_loss: 64.5182 - distribution_lambda_27_loss: 69.0890 - distribution_lambda_28_loss: 0.7337 - val_loss: 109.9033 - val_distribution_lambda_26_loss: 58.6612 - val_distribution_lambda_27_loss: 50.5242 - val_distribution_lambda_28_loss: 0.7179\n", - "Epoch 5/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 107.2862 - distribution_lambda_26_loss: 55.5948 - distribution_lambda_27_loss: 50.9793 - distribution_lambda_28_loss: 0.7121 - val_loss: 90.9552 - val_distribution_lambda_26_loss: 50.7684 - val_distribution_lambda_27_loss: 39.4882 - val_distribution_lambda_28_loss: 0.6985\n", - "Epoch 6/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 89.6161 - distribution_lambda_26_loss: 48.2157 - distribution_lambda_27_loss: 40.7019 - distribution_lambda_28_loss: 0.6985 - val_loss: 77.0989 - val_distribution_lambda_26_loss: 44.2830 - val_distribution_lambda_27_loss: 32.1339 - val_distribution_lambda_28_loss: 0.6820\n", - "Epoch 7/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 76.6649 - distribution_lambda_26_loss: 42.5049 - distribution_lambda_27_loss: 33.4795 - distribution_lambda_28_loss: 0.6805 - val_loss: 66.2563 - val_distribution_lambda_26_loss: 38.8386 - val_distribution_lambda_27_loss: 26.7498 - val_distribution_lambda_28_loss: 0.6679\n", - "Epoch 8/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 65.1514 - distribution_lambda_26_loss: 36.9772 - distribution_lambda_27_loss: 27.5046 - distribution_lambda_28_loss: 0.6695 - val_loss: 57.5291 - val_distribution_lambda_26_loss: 34.2010 - val_distribution_lambda_27_loss: 22.6720 - val_distribution_lambda_28_loss: 0.6561\n", - "Epoch 9/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 57.1919 - distribution_lambda_26_loss: 33.0281 - distribution_lambda_27_loss: 23.5040 - distribution_lambda_28_loss: 0.6597 - val_loss: 50.3063 - val_distribution_lambda_26_loss: 30.2402 - val_distribution_lambda_27_loss: 19.4200 - val_distribution_lambda_28_loss: 0.6460\n", - "Epoch 10/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 49.8005 - distribution_lambda_26_loss: 28.9802 - distribution_lambda_27_loss: 20.1705 - distribution_lambda_28_loss: 0.6498 - val_loss: 44.2238 - val_distribution_lambda_26_loss: 26.8281 - val_distribution_lambda_27_loss: 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val_distribution_lambda_28_loss: 0.2815\n", - "Epoch 1405/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6810 - distribution_lambda_26_loss: 0.3005 - distribution_lambda_27_loss: 1.1295 - distribution_lambda_28_loss: 0.2510 - val_loss: 1.8142 - val_distribution_lambda_26_loss: 0.4112 - val_distribution_lambda_27_loss: 1.1213 - val_distribution_lambda_28_loss: 0.2817\n", - "Epoch 1406/2500\n", - "8/8 [==============================] - 0s 7ms/step - loss: 1.6321 - distribution_lambda_26_loss: 0.2767 - distribution_lambda_27_loss: 1.1065 - distribution_lambda_28_loss: 0.2489 - val_loss: 1.8091 - val_distribution_lambda_26_loss: 0.4108 - val_distribution_lambda_27_loss: 1.1177 - val_distribution_lambda_28_loss: 0.2807\n", - "Epoch 1407/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6636 - distribution_lambda_26_loss: 0.2850 - distribution_lambda_27_loss: 1.1221 - distribution_lambda_28_loss: 0.2565 - val_loss: 1.8142 - val_distribution_lambda_26_loss: 0.4112 - val_distribution_lambda_27_loss: 1.1224 - val_distribution_lambda_28_loss: 0.2805\n", - "Epoch 1408/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6377 - distribution_lambda_26_loss: 0.2683 - distribution_lambda_27_loss: 1.1275 - distribution_lambda_28_loss: 0.2419 - val_loss: 1.8402 - val_distribution_lambda_26_loss: 0.4245 - val_distribution_lambda_27_loss: 1.1346 - val_distribution_lambda_28_loss: 0.2811\n", - "Epoch 1409/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6658 - distribution_lambda_26_loss: 0.2691 - distribution_lambda_27_loss: 1.1491 - distribution_lambda_28_loss: 0.2476 - val_loss: 1.8537 - val_distribution_lambda_26_loss: 0.4351 - val_distribution_lambda_27_loss: 1.1382 - val_distribution_lambda_28_loss: 0.2804\n", - "Epoch 1410/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6474 - distribution_lambda_26_loss: 0.2918 - distribution_lambda_27_loss: 1.1075 - distribution_lambda_28_loss: 0.2481 - val_loss: 1.8183 - val_distribution_lambda_26_loss: 0.4050 - val_distribution_lambda_27_loss: 1.1338 - val_distribution_lambda_28_loss: 0.2795\n", - "Epoch 1411/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6426 - distribution_lambda_26_loss: 0.2748 - distribution_lambda_27_loss: 1.1227 - distribution_lambda_28_loss: 0.2452 - val_loss: 1.8153 - val_distribution_lambda_26_loss: 0.4023 - val_distribution_lambda_27_loss: 1.1315 - val_distribution_lambda_28_loss: 0.2814\n", - "Epoch 1412/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6965 - distribution_lambda_26_loss: 0.2804 - distribution_lambda_27_loss: 1.1505 - distribution_lambda_28_loss: 0.2657 - val_loss: 1.8345 - val_distribution_lambda_26_loss: 0.4169 - val_distribution_lambda_27_loss: 1.1306 - val_distribution_lambda_28_loss: 0.2870\n", - "Epoch 1413/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6683 - distribution_lambda_26_loss: 0.2938 - distribution_lambda_27_loss: 1.1290 - distribution_lambda_28_loss: 0.2455 - val_loss: 1.8302 - val_distribution_lambda_26_loss: 0.4211 - val_distribution_lambda_27_loss: 1.1267 - val_distribution_lambda_28_loss: 0.2824\n", - "Epoch 1414/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6318 - distribution_lambda_26_loss: 0.2861 - distribution_lambda_27_loss: 1.1085 - distribution_lambda_28_loss: 0.2372 - val_loss: 1.8295 - val_distribution_lambda_26_loss: 0.4248 - val_distribution_lambda_27_loss: 1.1256 - val_distribution_lambda_28_loss: 0.2791\n", - "Epoch 1415/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6503 - distribution_lambda_26_loss: 0.2964 - distribution_lambda_27_loss: 1.1161 - distribution_lambda_28_loss: 0.2378 - val_loss: 1.8168 - val_distribution_lambda_26_loss: 0.4143 - val_distribution_lambda_27_loss: 1.1216 - val_distribution_lambda_28_loss: 0.2808\n", - "Epoch 1416/2500\n", - "8/8 [==============================] - 0s 7ms/step - loss: 1.6750 - distribution_lambda_26_loss: 0.2846 - distribution_lambda_27_loss: 1.1224 - distribution_lambda_28_loss: 0.2680 - val_loss: 1.8054 - val_distribution_lambda_26_loss: 0.4101 - val_distribution_lambda_27_loss: 1.1189 - val_distribution_lambda_28_loss: 0.2763\n", - "Epoch 1417/2500\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8/8 [==============================] - 0s 6ms/step - loss: 1.6381 - distribution_lambda_26_loss: 0.2740 - distribution_lambda_27_loss: 1.1117 - distribution_lambda_28_loss: 0.2524 - val_loss: 1.8139 - val_distribution_lambda_26_loss: 0.4185 - val_distribution_lambda_27_loss: 1.1185 - val_distribution_lambda_28_loss: 0.2769\n", - "Epoch 1418/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.5959 - distribution_lambda_26_loss: 0.2659 - distribution_lambda_27_loss: 1.1019 - distribution_lambda_28_loss: 0.2281 - val_loss: 1.8195 - val_distribution_lambda_26_loss: 0.4156 - val_distribution_lambda_27_loss: 1.1225 - val_distribution_lambda_28_loss: 0.2813\n", - "Epoch 1419/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.6285 - distribution_lambda_26_loss: 0.2839 - distribution_lambda_27_loss: 1.1172 - distribution_lambda_28_loss: 0.2274 - val_loss: 1.8219 - val_distribution_lambda_26_loss: 0.4192 - val_distribution_lambda_27_loss: 1.1207 - val_distribution_lambda_28_loss: 0.2820\n", - "Epoch 1420/2500\n", - "8/8 [==============================] - 0s 6ms/step - loss: 1.5751 - distribution_lambda_26_loss: 0.2813 - distribution_lambda_27_loss: 1.0689 - distribution_lambda_28_loss: 0.2249 - val_loss: 1.8140 - val_distribution_lambda_26_loss: 0.4139 - val_distribution_lambda_27_loss: 1.1180 - val_distribution_lambda_28_loss: 0.2821\n" - ] - } - ], - "source": [ - "load_model_gk = True# load scaler and model weights for goalkeeper player predictor\n", - "refit_model_gk = True\n", - "\n", - "if(load_model_gk):\n", - " scaler_gk = pickle.load(open('saves/scaler_gk.pkl', 'rb'))\n", - " \n", - " X_gk_train = scaler_gk.transform(X_gk_train_)\n", - " X_gk_test = scaler_gk.transform(X_gk_test_)\n", - " \n", - " \n", - "n_epochs = 2500\n", - "\n", - "n_samples = X_gk_train.shape[0]\n", - "\n", - "batch_size = 128\n", - "\n", - "X_gk_len = X_gk_train.shape[1]\n", - "y_gk_len = y_gk_train.shape[1]\n", - "\n", - "\n", - "#tailweight_param = 1.1\n", - "\n", - "tailweight_min = 0.5\n", - "tailweight_range = 1.1\n", - "\n", - "\n", - "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 30)\n", - "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", - "\n", - "\n", - "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (inputs)\n", - "x = tfk.layers.Dropout(0.2)(x)\n", - "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", - "\n", - "\n", - "prob_dist_params = 4\n", - "\n", - "def prob_dist(t): \n", - " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", - " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", - " allow_nan_stats = False)\n", - "\n", - "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", - "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", - "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", - "\n", - "x2 = tfk.layers.Dense(16, activation=\"sigmoid\")(x)\n", - "\n", - "x22 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", - "out_2 = tfp.layers.DistributionLambda(prob_dist)(x22)\n", - "\n", - "x23 = tfk.layers.Dense(1, activation=\"sigmoid\")(x2)\n", - "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x23)\n", - "\n", - "\n", - "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", - "\n", - "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", - " loss=neg_log_likelihood)\n", - "\n", - "if(load_model_gk):\n", - " modelb_gk.load_weights('saves/modelb_gk')\n", - "\n", - "if( (not load_model_gk) or refit_model_gk): \n", - " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", - " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", - " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "39a9bdc6", - "metadata": {}, - "outputs": [], - "source": [ - "def sample_predict_gk(X, iterations = 100):\n", - " y = np.zeros((3, X.shape[0]))\n", - " \n", - " dist = modelb_gk(X)\n", - " \n", - " for i in range(iterations):\n", - " y[0, :] += dist[0].sample()\n", - " y[1, :] += dist[1].sample()\n", - " y[2, :] += dist[2].sample()\n", - " \n", - " return y.transpose() / iterations\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c41cf448", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.26941154847581295\n", - "0.6648856762830053\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", - "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", - "\n", - "plt.show()\n", - "\n", - "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", - "\n", - "plt.plot([0, 20], [0, 20])\n", - "\n", - "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", - "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", - "\n", - "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", - "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "91869883", - "metadata": {}, - "source": [ - "Use the following codes to save the scalers and the model weights" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "cecf5392", - "metadata": {}, - "outputs": [], - "source": [ - "save_model_of = True\n", - "save_model_gk = True\n", - "\n", - "if(save_model_of):\n", - " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", - " modelb.save_weights('saves/modelb')\n", - " \n", - "if(save_model_gk):\n", - " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", - " modelb_gk.save_weights('saves/modelb_gk')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "32635a0e", - "metadata": {}, - "source": [ - "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", - "\n", - "Estimate prediction mean and sigma (using a custom definitions).\n", - "\n", - "Generate a plot.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ddf433f9", - "metadata": {}, - "outputs": [], - "source": [ - "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", - " if(players['r'][player] == 'P'):\n", - " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", - "\n", - " x_ptest = np.array(ptest)[:, 3:]\n", - " r = np.array(ptest)[0, 0]\n", - "\n", - " # add home and role\n", - " xadd = np.zeros((1, 1))\n", - " xadd[0, 0] = home\n", - "\n", - " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", - "\n", - " x_scaled = scaler_gk.transform(x_ptest)\n", - "\n", - " dist = modelb_gk(x_scaled)\n", - " \n", - " clean_shoot_prob = dist[2].probs.numpy()[0]\n", - " else:\n", - " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", - "\n", - " x_ptest = np.array(ptest)[:, 4:]\n", - " r = np.array(ptest)[0, 0]\n", - "\n", - " # add home and role\n", - " xadd = np.zeros((1, 4))\n", - " xadd[0, 0] = home\n", - " xadd[0, 1] = r == 'D'\n", - " xadd[0, 2] = r == 'C'\n", - " xadd[0, 3] = r == 'A'\n", - "\n", - " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", - "\n", - " x_scaled = scaler.transform(x_ptest)\n", - "\n", - " dist = modelb(x_scaled)\n", - " \n", - " \n", - " x = np.arange(0, 40, 0.002)\n", - "\n", - " px1 = dist[0].prob(x);\n", - " px2 = dist[1].prob(x);\n", - "\n", - " \n", - " #sample1 = dist[0].sample(10000)\n", - " #sample2 = dist[1].sample(10000)\n", - " \n", - " m1 = np.average(x, weights = px1)\n", - " m2 = np.average(x, weights = px2)\n", - " \n", - " #m1 = np.mean(sample1)\n", - " #m2 = np.mean(sample2)\n", - " \n", - " #s1 = np.std(sample1)\n", - " #s2 = np.std(sample2)\n", - " \n", - " # not standard deviation, but expected range extimated by quantile \n", - " \n", - " if(players['r'][player] == 'P'):\n", - " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", - " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", - " else:\n", - " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", - " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", - " \n", - "\n", - " \n", - " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", - " y_pred_m = np.array([m1, m2]).flatten()\n", - " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", - " y_pred_s = np.array([s1, s2]).flatten()\n", - " \n", - " clean_sheet_text = ''\n", - " if(players['r'][player] == 'P'):\n", - " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", - " \n", - " if(plot):\n", - " ax = plt.gca()\n", - " \n", - " plt.plot(x, px1, \n", - " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", - " color = 'b')\n", - " plt.plot(x, px2, \n", - " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", - " color = 'g')\n", - " \n", - " plt.fill_between(x, px1, color = 'lightblue')\n", - " plt.fill_between(x, px2, color = 'lightgreen')\n", - " \n", - " plt.legend()\n", - " \n", - " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", - " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", - " \n", - " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", - " \n", - " plt.xlim([0, 15])\n", - " \n", - " if(players['r'][player] == 'P'): \n", - " plt.ylim([0, 2.5])\n", - " else:\n", - " plt.ylim([0, 1.5])\n", - " \n", - " plt.show()\n", - " \n", - " if(log):\n", - " print(player + ': ' + \n", - " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", - " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", - " return [y_pred_m, y_pred_s, dist]\n" - ] - }, - { - "cell_type": "markdown", - "id": "98817aa0", - "metadata": {}, - "source": [ - "Load Serie A calendar. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d31bad38", - "metadata": {}, - "outputs": [], - "source": [ - "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", - "\n", - "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", - "\n", - "matchday = 0\n", - "\n", - "for i in range(cal.shape[0]):\n", - " if(cal[i, 0][0].isnumeric()):\n", - " matchday = matchday + 1\n", - " continue\n", - " \n", - " teams = cal[i, 0].split('-')\n", - " \n", - " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", - "\n", - " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "62b9f588", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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380 rows × 3 columns

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" - ], - "text/plain": [ - " matchday team1 team2\n", - "0 1 Fiorentina Cremonese\n", - "1 1 Verona Napoli\n", - "2 1 Juventus Sassuolo\n", - "3 1 Lazio Bologna\n", - "4 1 Lecce Inter\n", - ".. ... ... ...\n", - "375 38 Lecce Bologna\n", - "376 38 Sassuolo Fiorentina\n", - "377 38 Milan Verona\n", - "378 38 Torino Inter\n", - "379 38 Udinese Juventus\n", - "\n", - "[380 rows x 3 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cal_df" - ] - }, - { - "cell_type": "markdown", - "id": "62872819", - "metadata": {}, - "source": [ - "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c58ba41d", - "metadata": {}, - "outputs": [], - "source": [ - "def PlayerMatch(player, match = 0):\n", - " team = players.loc[player]['team']\n", - " \n", - " if(match == 0):\n", - " oppteam = 'Avg'\n", - " home = 1\n", - " else:\n", - " for i in range (cal_df.shape[0]):\n", - " if(cal_df['matchday'][i] == match):\n", - " if(cal_df['team1'][i] == team):\n", - " home = 1\n", - " oppteam = cal_df['team2'][i]\n", - " elif(cal_df['team2'][i] == team):\n", - " home = 0\n", - " oppteam = cal_df['team1'][i]\n", - " \n", - " return [player, team, oppteam, home]\n", - "\n", - "def predict_player(player, match = 0, plot = 0, log = 0, oldseason = False):\n", - " [player, team, oppteam, home] = PlayerMatch(player, match)\n", - " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" - ] - }, - { - "cell_type": "markdown", - "id": "e8b63a98", - "metadata": {}, - "source": [ - "Load current matchday playing probabilities for Serie A players." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f79792b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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starterpercentage
player
Tatarusanu1.0090
Thiaw0.6565
Kjaer1.0085
Kalulu1.0090
Saelemaekers0.6060
.........
Dumfries0.4560
Mkhitaryan0.4555
Asllani0.0030
Gagliardini0.0050
Lukaku0.4060
\n", - "

438 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " starter percentage\n", - "player \n", - "Tatarusanu 1.00 90\n", - "Thiaw 0.65 65\n", - "Kjaer 1.00 85\n", - "Kalulu 1.00 90\n", - "Saelemaekers 0.60 60\n", - "... ... ...\n", - "Dumfries 0.45 60\n", - "Mkhitaryan 0.45 55\n", - "Asllani 0.00 30\n", - "Gagliardini 0.00 50\n", - "Lukaku 0.40 60\n", - "\n", - "[438 rows x 2 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", - "\n", - "probables" - ] - }, - { - "cell_type": "markdown", - "id": "e4751014", - "metadata": {}, - "source": [ - "Generate prediction data for each Serie A player for the current matchday.\n", - "\n", - "Output to excel file, using a template made for data elaboration." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "5e63c2b7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret: MV 6.24 ± 0.83; FV 5.81 + 0.85 (56.5% cs)\n", - "Provedel: MV 6.24 ± 0.83; FV 4.69 + 1.04 (0.9% cs)\n", - "Vicario: MV 6.24 ± 0.83; FV 5.24 + 0.81 (22.3% cs)\n", - "Szczesny: MV 6.24 ± 0.83; FV 5.41 + 1.14 (20.8% cs)\n", - "Falcone: MV 6.24 ± 0.83; FV 5.10 + 0.81 (1.3% cs)\n", - "Silvestri: MV 6.24 ± 0.83; FV 6.32 + 0.70 (90.3% cs)\n", - "Rui Patricio: MV 6.24 ± 0.83; FV 5.21 + 1.01 (36.2% cs)\n", - "Onana: MV 6.24 ± 0.83; FV 5.92 + 0.68 (75.7% cs)\n", - "Sepe: MV 6.24 ± 0.83; FV 5.12 + 0.83 (15.6% cs)\n", - "Milinkovic-Savic V.: MV 6.24 ± 0.83; FV 5.00 + 0.95 (11.2% cs)\n", - "Musso: MV 6.24 ± 0.83; FV 3.81 + 1.49 (0.6% cs)\n", - "Maignan: MV 6.24 ± 0.83; FV 6.00 + 0.73 (73.6% cs)\n", - "Carnesecchi: MV 6.24 ± 0.83; FV 4.57 + 1.04 (0.2% cs)\n", - "Di Gregorio: MV 6.25 ± 0.82; FV 4.11 + 1.48 (1.1% cs)\n", - "Audero: MV 6.24 ± 0.83; FV 4.55 + 1.06 (0.6% cs)\n", - "Montipo': MV 6.24 ± 0.83; FV 4.98 + 0.94 (6.1% cs)\n", - "Skorupski: MV 6.24 ± 0.83; FV 4.59 + 0.97 (0.3% cs)\n", - "Consigli: MV 6.24 ± 0.83; FV 4.17 + 1.27 (0.2% cs)\n", - "Dragowski: MV 6.24 ± 0.83; FV 5.35 + 0.75 (19.2% cs)\n", - "Terracciano: MV 6.24 ± 0.83; FV 4.16 + 1.30 (0.1% cs)\n", - "Tatarusanu: MV 6.24 ± 0.83; FV 4.59 + 1.26 (1.1% cs)\n", - "Handanovic: MV 6.24 ± 0.83; FV 5.88 + 0.68 (73.4% cs)\n", - "Sportiello: MV 6.23 ± 0.83; FV 3.84 + 1.56 (0.6% cs)\n", - "Perin: MV 6.25 ± 0.83; FV 5.67 + 1.10 (36.8% cs)\n", - "Zoet: MV 6.24 ± 0.83; FV 4.94 + 0.82 (6.0% cs)\n", - "Ochoa: MV 6.24 ± 0.83; FV 4.69 + 0.90 (2.1% cs)\n", - "Pegolo: MV 6.19 ± 0.84; FV 3.81 + 1.55 (0.2% cs)\n", - "Gollini: MV 6.24 ± 0.83; FV 5.18 + 0.91 (12.0% cs)\n", - "Mirante no data\n", - "Sarr M. no data\n", - "Lamanna no data\n", - "Ujkani no data\n", - "Berisha: MV 6.24 ± 0.83; FV 5.34 + 0.97 (24.2% cs)\n", - "Marchetti no data\n", - "Perilli no data\n", - "Padelli: MV 6.24 ± 0.83; FV 4.85 + 0.92 (0.8% cs)\n", - "Perisan no data\n", - "Bardi: MV 6.24 ± 0.83; FV 4.52 + 1.11 (1.2% cs)\n", - "Cordaz no data\n", - "Pinsoglio: MV 6.24 ± 0.83; FV 4.93 + 0.88 (5.9% cs)\n", - "Fiorillo: MV 6.24 ± 0.83; FV 4.54 + 1.12 (0.1% cs)\n", - "Cragno: MV 6.24 ± 0.83; FV 4.31 + 1.32 (0.0% cs)\n", - "Sirigu: MV 6.24 ± 0.83; FV 4.39 + 1.28 (0.0% cs)\n", - "Cerofolini no data\n", - "Rossi F.: MV 6.24 ± 0.83; FV 4.15 + 1.31 (0.1% cs)\n", - "Ravaglia F. no data\n", - "Brancolini no data\n", - "Bleve no data\n", - "Berardi A.: MV 6.24 ± 0.83; FV 4.39 + 1.22 (0.1% cs)\n", - "Russo A. no data\n", - "Gemello: MV 6.24 ± 0.83; FV 6.00 + 0.89 (75.0% cs)\n", - "Ravaglia: MV 6.24 ± 0.83; FV 4.33 + 1.32 (0.0% cs)\n", - "Boer no data\n", - "Adamonis no data\n", - "Marfella: MV 6.24 ± 0.83; FV 5.68 + 0.90 (40.5% cs)\n", - "Zovko: MV 6.24 ± 0.83; FV 4.84 + 0.84 (1.3% cs)\n", - "Piana no data\n", - "Bagnolini no data\n", - "Luis Maximiano: MV 6.10 ± 0.86; FV 4.82 + 1.71 (56.1% cs)\n", - "Svilar no data\n", - "Sorrentino A. no data\n", - "Ciezkowski no data\n", - "Saro no data\n", - "Vasquez D. no data\n", - "Turk no data\n", - "Dimarco: MV 6.29 ± 0.95; FV 6.93 + 2.01\n", - "Smalling: MV 6.22 ± 1.00; FV 6.64 + 1.69\n", - "Doig: MV 6.32 ± 1.21; FV 7.07 + 2.57\n", - "Carlos Augusto: MV 6.04 ± 1.05; FV 6.52 + 2.02\n", - "Kim: MV 6.32 ± 0.94; FV 6.85 + 1.93\n", - "Posch: MV 6.17 ± 1.20; FV 6.96 + 2.72\n", - "Di Lorenzo: MV 6.24 ± 0.80; FV 6.70 + 1.63\n", - "Danilo: MV 6.27 ± 0.94; FV 6.74 + 1.77\n", - "Hernandez T.: MV 6.04 ± 1.15; FV 6.44 + 1.95\n", - "Udogie: MV 6.15 ± 1.10; FV 6.75 + 2.24\n", - "Parisi: MV 6.28 ± 0.90; FV 6.93 + 1.99\n", - "Mario Rui: MV 6.23 ± 0.79; FV 6.57 + 1.45\n", - "Romagnoli: MV 6.13 ± 0.99; FV 6.30 + 1.31\n", - "Bastoni S.: MV 6.15 ± 0.95; FV 6.54 + 1.64\n", - "Mazzocchi: MV 6.18 ± 1.05; FV 6.69 + 1.95\n", - "Valeri: MV 5.93 ± 0.81; FV 6.08 + 0.99\n", - "Tomori: MV 6.13 ± 0.91; FV 6.40 + 1.32\n", - "Scalvini: MV 5.82 ± 1.11; FV 5.97 + 1.53\n", - "Toloi: MV 5.99 ± 0.97; FV 6.18 + 1.29\n", - "Demiral: MV 5.83 ± 0.90; FV 5.92 + 1.15\n", - "Maehle: MV 5.83 ± 1.00; FV 5.94 + 1.35\n", - "Dumfries: MV 6.22 ± 1.09; FV 6.79 + 2.06\n", - "Baschirotto: MV 6.17 ± 1.06; FV 6.57 + 1.81\n", - "Bijol: MV 6.00 ± 1.01; FV 6.29 + 1.61\n", - "Schuurs: MV 6.16 ± 0.81; FV 6.36 + 1.17\n", - "Juan Jesus: MV 6.19 ± 0.70; FV 6.51 + 1.31\n", - "Depaoli: MV 6.07 ± 1.04; FV 6.54 + 1.96\n", - "Mancini: MV 6.10 ± 0.72; FV 6.14 + 0.80\n", - "Ibanez: MV 6.09 ± 1.00; FV 6.44 + 1.57\n", - "Rodrigo Becao: MV 6.16 ± 0.88; FV 6.41 + 1.29\n", - "Ebuehi: MV 6.17 ± 0.80; FV 6.55 + 1.41\n", - "Gosens: MV 6.09 ± 0.66; FV 6.41 + 1.16\n", - "Darmian: MV 6.18 ± 0.80; FV 6.68 + 1.61\n", - "Reca: MV 6.03 ± 1.02; FV 6.31 + 1.56\n", - "Bremer: MV 6.08 ± 1.02; FV 6.32 + 1.43\n", - "Sernicola: MV 5.78 ± 0.84; FV 5.86 + 1.03\n", - "Rrahmani: MV 6.30 ± 0.91; FV 6.83 + 1.87\n", - "Vojvoda: MV 6.02 ± 0.99; FV 6.26 + 1.48\n", - "Holm: MV 6.06 ± 0.87; FV 6.28 + 1.28\n", - "Bastoni: MV 6.17 ± 0.83; FV 6.30 + 1.08\n", - "Milenkovic: MV 5.84 ± 1.08; FV 5.92 + 1.41\n", - "Kalulu: MV 5.82 ± 1.04; FV 5.84 + 1.14\n", - "Martinez Quarta: MV 5.90 ± 1.03; FV 5.98 + 1.21\n", - "Casale: MV 5.94 ± 0.94; FV 6.01 + 1.20\n", - "Perez N.: MV 6.09 ± 0.87; FV 6.13 + 0.97\n", - "Olivera: MV 6.17 ± 0.70; FV 6.58 + 1.42\n", - "Izzo: MV 5.93 ± 0.79; FV 5.93 + 0.75\n", - "Luperto: MV 6.13 ± 0.93; FV 6.32 + 1.23\n", - "Skriniar: MV 6.02 ± 0.74; FV 5.97 + 0.69\n", - "Rodriguez R.: MV 5.96 ± 0.87; FV 5.98 + 0.97\n", - "Marusic: MV 5.99 ± 0.81; FV 6.02 + 0.88\n", - "Lazzari: MV 6.03 ± 0.85; FV 6.07 + 0.95\n", - "Kyriakopoulos: MV 5.99 ± 0.94; FV 6.08 + 1.15\n", - "Ampadu: MV 5.88 ± 0.88; FV 5.88 + 1.02\n", - "Ismajli: MV 6.15 ± 0.74; FV 6.16 + 0.85\n", - "Llorente D.: MV 5.98 ± 0.98; FV 6.19 + 1.35\n", - "Cambiaso: MV 6.00 ± 0.81; FV 6.02 + 0.87\n", - "Hysaj: MV 5.96 ± 0.71; FV 6.00 + 0.73\n", - "Biraghi: MV 5.96 ± 0.75; FV 5.97 + 0.72\n", - "Medel: MV 5.99 ± 0.78; FV 5.92 + 0.73\n", - "Bonucci: MV 6.20 ± 0.97; FV 6.66 + 1.78\n", - "Calabria: MV 5.95 ± 1.00; FV 6.17 + 1.48\n", - "Acerbi: MV 6.15 ± 0.74; FV 6.24 + 0.94\n", - "Spinazzola: MV 5.95 ± 0.66; FV 6.03 + 0.65\n", - "Lykogiannis: MV 6.01 ± 0.71; FV 6.08 + 0.79\n", - "Pellegrini Lu.: MV 6.02 ± 0.77; FV 6.06 + 0.86\n", - "Djidji: MV 5.87 ± 1.01; FV 5.99 + 1.42\n", - "Lazaro: MV 5.91 ± 1.06; FV 6.07 + 1.40\n", - "Augello: MV 5.68 ± 0.90; FV 5.72 + 1.07\n", - "Gallo: MV 5.96 ± 0.75; FV 5.93 + 0.72\n", - "Singo: MV 6.03 ± 0.91; FV 6.24 + 1.33\n", - "Mari': MV 5.80 ± 1.11; FV 5.90 + 1.49\n", - "Caldirola: MV 5.77 ± 0.96; FV 5.72 + 0.97\n", - "Dodo': MV 5.77 ± 0.95; FV 5.65 + 0.95\n", - "De Vrij: MV 6.05 ± 0.78; FV 6.14 + 0.89\n", - "Patric: MV 6.00 ± 0.93; FV 6.01 + 1.00\n", - "Faraoni: MV 6.04 ± 0.83; FV 6.32 + 1.33\n", - "Ceccherini: MV 5.96 ± 0.96; FV 6.12 + 1.35\n", - "Hateboer: MV 5.73 ± 0.86; FV 5.75 + 1.02\n", - "Rogerio: MV 5.77 ± 0.93; FV 5.74 + 1.09\n", - "Umtiti: MV 5.92 ± 1.03; FV 6.00 + 1.18\n", - "Aina: MV 6.00 ± 1.03; FV 6.25 + 1.51\n", - "Birindelli: MV 5.80 ± 0.74; FV 5.80 + 0.80\n", - "Lucumi': MV 5.94 ± 0.84; FV 5.91 + 0.89\n", - "Ehizibue: MV 5.93 ± 0.92; FV 6.20 + 1.55\n", - "Bianchetti: MV 5.54 ± 1.02; FV 5.60 + 1.15\n", - "Ferrari G.: MV 5.71 ± 1.10; FV 5.78 + 1.40\n", - "Fazio: MV 5.80 ± 1.31; FV 6.05 + 1.86\n", - "Gravillon: MV 5.92 ± 1.00; FV 6.06 + 1.41\n", - "Buongiorno: MV 5.88 ± 0.89; FV 5.87 + 0.97\n", - "Gunter: MV 5.66 ± 1.00; FV 5.62 + 1.15\n", - "Troost-Ekong: MV 5.86 ± 1.14; FV 5.93 + 1.39\n", - "Soumaoro: MV 5.88 ± 0.98; FV 5.86 + 1.01\n", - "Ceccaroni: MV 5.93 ± 1.03; FV 6.05 + 1.34\n", - "Pongracic: MV 5.96 ± 0.78; FV 5.93 + 0.76\n", - "Soppy: MV 5.77 ± 0.76; FV 5.81 + 0.84\n", - "Gendrey: MV 5.82 ± 0.70; FV 5.80 + 0.63\n", - "Hien: MV 5.91 ± 0.81; FV 5.87 + 0.83\n", - "Ferrari A.: MV 5.57 ± 1.18; FV 5.61 + 1.33\n", - "Masina: MV 6.10 ± 1.06; FV 6.62 + 2.09\n", - "Zappacosta: MV 5.93 ± 0.79; FV 6.08 + 1.03\n", - "Gyomber: MV 5.83 ± 0.85; FV 5.78 + 0.82\n", - "Alex Sandro: MV 5.82 ± 0.91; FV 5.76 + 0.84\n", - "Pezzella Giu.: MV 5.90 ± 0.68; FV 5.91 + 0.60\n", - "Bereszynski: MV 6.00 ± 0.65; FV 5.95 + 0.56\n", - "Venuti: MV 5.75 ± 0.83; FV 5.73 + 0.85\n", - "Palomino: MV 5.86 ± 1.01; FV 5.92 + 1.19\n", - "Nuytinck: MV 5.71 ± 1.10; FV 5.68 + 1.25\n", - "Marlon: MV 5.78 ± 0.76; FV 5.75 + 0.72\n", - "Magnani: MV 5.95 ± 0.84; FV 5.89 + 0.84\n", - "Colley: MV 5.67 ± 1.14; FV 5.68 + 1.36\n", - "Nikolaou: MV 5.74 ± 0.79; FV 5.64 + 0.77\n", - "Terzic: MV 6.00 ± 0.56; FV 5.93 + 0.40\n", - "Igor: MV 5.75 ± 0.98; FV 5.60 + 0.94\n", - "Toljan: MV 5.65 ± 0.86; FV 5.60 + 0.91\n", - "Zortea: MV 5.80 ± 0.84; FV 5.88 + 1.10\n", - "Dawidowicz: MV 5.87 ± 0.97; FV 5.90 + 1.23\n", - "Celik: MV 5.81 ± 0.74; FV 5.81 + 0.68\n", - "Bellanova: MV 6.05 ± 0.78; FV 6.18 + 0.93\n", - "Erlic: MV 5.75 ± 1.06; FV 5.71 + 1.18\n", - "Ballo-Toure': MV 6.14 ± 0.79; FV 6.51 + 1.35\n", - "Dest: MV 5.79 ± 0.83; FV 5.78 + 0.80\n", - "Stojanovic: MV 5.90 ± 0.76; FV 5.91 + 0.76\n", - "Amian: MV 5.80 ± 0.81; FV 5.74 + 0.85\n", - "Bradaric: MV 5.75 ± 0.83; FV 5.73 + 0.98\n", - "Daniliuc: MV 5.81 ± 1.08; FV 5.86 + 1.33\n", - "Zima: MV 5.87 ± 0.94; FV 5.90 + 1.12\n", - "De Winter: MV 5.94 ± 0.76; FV 5.87 + 0.67\n", - "Quagliata: MV 5.73 ± 0.71; FV 5.73 + 0.68\n", - "Ebosse: MV 5.75 ± 0.72; FV 5.69 + 0.64\n", - "Aiwu: MV 5.70 ± 1.06; FV 5.75 + 1.30\n", - "Lochoshvili: MV 5.58 ± 0.90; FV 5.54 + 0.96\n", - "Bronn: MV 5.80 ± 0.70; FV 5.78 + 0.61\n", - "Thiaw: MV 5.99 ± 0.83; FV 5.97 + 0.80\n", - "Zeefuik: MV 5.98 ± 0.86; FV 6.06 + 1.03\n", - "Romagnoli S.: MV 5.98 ± 1.15; FV 6.30 + 1.81\n", - "Ghiglione: MV 5.67 ± 0.97; FV 5.69 + 1.15\n", - "Rugani: MV 6.04 ± 0.62; FV 5.98 + 0.52\n", - "De Sciglio: MV 5.94 ± 0.66; FV 5.94 + 0.57\n", - "Djimsiti: MV 5.80 ± 0.79; FV 5.81 + 0.80\n", - "Caldara: MV 5.79 ± 0.96; FV 5.73 + 1.01\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Karsdorp: MV 5.88 ± 0.79; FV 5.90 + 0.74\n", - "Marchizza: MV 5.75 ± 0.76; FV 5.71 + 0.79\n", - "Kjaer: MV 5.87 ± 0.72; FV 5.83 + 0.63\n", - "Okoli: MV 5.59 ± 0.92; FV 5.53 + 0.89\n", - "Amione: MV 5.52 ± 0.85; FV 5.45 + 0.88\n", - "Ruggeri: MV 5.71 ± 0.65; FV 5.73 + 0.54\n", - "Zanoli: MV 5.70 ± 0.81; FV 5.63 + 0.83\n", - "Wisniewski: MV 5.95 ± 0.87; FV 6.00 + 1.03\n", - "Radovanovic: MV 5.68 ± 0.75; FV 5.60 + 0.71\n", - "Dermaku: MV 5.93 ± 0.88; FV 5.98 + 1.05\n", - "D'ambrosio: MV 6.16 ± 0.68; FV 6.39 + 1.11\n", - "De Silvestri: MV 5.95 ± 0.99; FV 6.14 + 1.46\n", - "Chiriches: MV 5.58 ± 1.22; FV 5.54 + 1.24\n", - "Murru: MV 5.50 ± 0.82; FV 5.44 + 0.84\n", - "Bonifazi: MV 5.83 ± 0.83; FV 5.74 + 0.82\n", - "Donati: MV 5.75 ± 0.89; FV 5.71 + 0.94\n", - "Walukiewicz: MV 5.91 ± 0.70; FV 5.91 + 0.62\n", - "Ranieri L.: MV 5.78 ± 0.85; FV 5.77 + 1.01\n", - "Gabbia: MV 5.65 ± 0.90; FV 5.57 + 0.87\n", - "Kumbulla: MV 5.87 ± 0.65; FV 5.90 + 0.56\n", - "Adopo: MV 5.75 ± 0.66; FV 5.66 + 0.67\n", - "Pirola: MV 5.63 ± 0.90; FV 5.54 + 0.92\n", - "Lovato: MV 5.73 ± 1.03; FV 5.63 + 1.05\n", - "Tuia: MV 5.80 ± 0.90; FV 5.82 + 1.11\n", - "Ferrer: MV 5.90 ± 0.89; FV 5.90 + 0.98\n", - "Antov: MV 5.69 ± 1.06; FV 5.66 + 1.13\n", - "Vasquez: MV 5.62 ± 1.00; FV 5.60 + 1.12\n", - "Ruan: MV 5.70 ± 1.07; FV 5.55 + 1.00\n", - "Ostigard: MV 6.13 ± 0.64; FV 6.09 + 0.68\n", - "Coppola D.: MV 5.80 ± 0.74; FV 5.71 + 0.73\n", - "Cacace: MV 5.94 ± 0.72; FV 5.90 + 0.63\n", - "Gatti: MV 5.95 ± 0.77; FV 5.93 + 0.72\n", - "Gila: MV 5.90 ± 0.88; FV 5.89 + 0.95\n", - "Bayeye: MV 5.91 ± 0.99; FV 6.03 + 1.38\n", - "Sambia: MV 5.83 ± 0.80; FV 5.83 + 0.79\n", - "Moutinho J.: MV 5.79 ± 0.81; FV 5.72 + 0.84\n", - "Conti: MV 5.79 ± 0.90; FV 5.84 + 1.12\n", - "Marrone: MV 5.55 ± 0.92; FV 5.47 + 0.95\n", - "Tonelli: MV 5.88 ± 0.76; FV 5.83 + 0.67\n", - "Murillo: MV 5.54 ± 0.73; FV 5.44 + 0.72\n", - "Radu: MV 6.05 ± 0.74; FV 5.91 + 0.63\n", - "Paletta: MV 5.84 ± 0.94; FV 5.88 + 1.14\n", - "Florenzi: MV 6.15 ± 0.83; FV 6.39 + 1.19\n", - "Sala: MV 5.93 ± 0.73; FV 5.95 + 0.78\n", - "Fares: MV 5.79 ± 0.80; FV 5.78 + 0.93\n", - "Romagna: MV 5.82 ± 0.98; FV 5.84 + 1.22\n", - "Cassandro: MV 5.92 ± 0.87; FV 5.99 + 1.06\n", - "Muldur: MV 5.69 ± 0.86; FV 5.65 + 0.94\n", - "Amey: MV 6.04 ± 0.87; FV 6.12 + 1.04\n", - "Zanotti: MV 6.10 ± 0.81; FV 6.27 + 1.07\n", - "Ebosele: MV 6.03 ± 0.66; FV 5.99 + 0.59\n", - "Buta: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Abankwah: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Guessand A.: MV 5.95 ± 0.86; FV 5.97 + 0.90\n", - "Cabal: MV 6.00 ± 0.89; FV 6.09 + 1.07\n", - "Sosa: MV 5.76 ± 0.96; FV 5.75 + 1.10\n", - "Guarino: MV 6.06 ± 0.81; FV 6.19 + 1.00\n", - "Carboni F.: MV 5.90 ± 0.92; FV 5.97 + 1.06\n", - "Zaccagni: MV 6.40 ± 1.33; FV 7.40 + 3.24\n", - "Kvaratskhelia: MV 6.64 ± 1.45; FV 7.60 + 3.27\n", - "Milinkovic-Savic: MV 6.28 ± 1.28; FV 7.06 + 2.80\n", - "Barella: MV 6.38 ± 1.15; FV 7.20 + 2.56\n", - "Zielinski: MV 6.41 ± 1.10; FV 7.17 + 2.38\n", - "Luis Alberto: MV 6.31 ± 1.08; FV 7.02 + 2.29\n", - "Strefezza: MV 6.29 ± 0.94; FV 7.09 + 2.28\n", - "Felipe Anderson: MV 6.19 ± 1.25; FV 7.02 + 2.81\n", - "Koopmeiners: MV 6.12 ± 1.16; FV 6.75 + 2.37\n", - "Calhanoglu: MV 6.33 ± 0.99; FV 7.01 + 2.14\n", - "Frattesi: MV 6.10 ± 1.20; FV 6.77 + 2.47\n", - "Diaz B.: MV 6.14 ± 1.19; FV 6.89 + 2.56\n", - "Vlasic: MV 6.13 ± 1.16; FV 6.76 + 2.27\n", - "Zambo Anguissa: MV 6.40 ± 1.11; FV 7.13 + 2.35\n", - "Elmas: MV 6.36 ± 1.08; FV 7.09 + 2.28\n", - "Miranchuk: MV 6.21 ± 1.21; FV 6.93 + 2.46\n", - "Samardzic: MV 6.22 ± 1.05; FV 6.86 + 2.14\n", - "Pereyra: MV 6.25 ± 1.09; FV 6.88 + 2.22\n", - "Politano: MV 6.29 ± 0.89; FV 6.98 + 2.05\n", - "Rabiot: MV 6.22 ± 1.12; FV 6.85 + 2.23\n", - "Ciurria: MV 6.04 ± 1.16; FV 6.67 + 2.38\n", - "Lazovic: MV 6.27 ± 1.15; FV 6.95 + 2.31\n", - "Lobotka: MV 6.18 ± 0.69; FV 6.65 + 1.51\n", - "Radonjic: MV 6.13 ± 1.00; FV 6.64 + 1.82\n", - "Ferguson: MV 6.23 ± 0.94; FV 6.81 + 1.94\n", - "Bonaventura: MV 6.09 ± 0.96; FV 6.40 + 1.49\n", - "Pessina: MV 6.11 ± 1.09; FV 6.64 + 2.05\n", - "Tonali: MV 6.10 ± 1.09; FV 6.54 + 1.91\n", - "Kostic: MV 6.20 ± 0.99; FV 6.77 + 1.94\n", - "Baldanzi: MV 6.40 ± 1.07; FV 7.27 + 2.53\n", - "Lovric: MV 6.13 ± 0.76; FV 6.48 + 1.30\n", - "Pellegrini Lo.: MV 6.06 ± 1.17; FV 6.66 + 2.28\n", - "El Shaarawy: MV 6.19 ± 0.97; FV 6.84 + 2.00\n", - "Orsolini: MV 6.29 ± 1.36; FV 7.26 + 3.30\n", - "Ikone': MV 6.04 ± 1.10; FV 6.46 + 1.92\n", - "Candreva: MV 6.05 ± 1.14; FV 6.45 + 1.98\n", - "Bennacer: MV 6.14 ± 0.83; FV 6.43 + 1.25\n", - "Pasalic: MV 5.91 ± 1.01; FV 6.31 + 1.73\n", - "Mkhitaryan: MV 6.16 ± 0.90; FV 6.68 + 1.69\n", - "Colpani: MV 5.96 ± 0.87; FV 6.43 + 1.75\n", - "Pogba: MV 6.04 ± 0.83; FV 6.19 + 1.04\n", - "Chiesa: MV 6.19 ± 0.98; FV 6.69 + 1.81\n", - "Bandinelli: MV 6.06 ± 0.80; FV 6.39 + 1.28\n", - "Matic: MV 6.12 ± 0.78; FV 6.38 + 1.15\n", - "Fagioli: MV 6.19 ± 0.96; FV 6.75 + 1.86\n", - "Messias: MV 5.95 ± 1.07; FV 6.32 + 1.84\n", - "Arslan: MV 6.01 ± 0.70; FV 6.10 + 0.79\n", - "Ricci S.: MV 6.17 ± 0.91; FV 6.52 + 1.49\n", - "Ranocchia F.: MV 6.01 ± 0.92; FV 6.43 + 1.65\n", - "Verdi: MV 6.17 ± 0.84; FV 6.66 + 1.63\n", - "Sensi: MV 6.00 ± 1.10; FV 6.41 + 1.95\n", - "Barak: MV 5.83 ± 0.84; FV 5.97 + 1.20\n", - "Soriano: MV 6.04 ± 0.74; FV 6.20 + 1.02\n", - "Dominguez: MV 6.23 ± 1.09; FV 6.81 + 2.17\n", - "Vilhena: MV 5.86 ± 1.00; FV 6.11 + 1.54\n", - "Brozovic: MV 6.22 ± 0.82; FV 6.67 + 1.59\n", - "Cristante: MV 5.95 ± 0.85; FV 6.09 + 1.06\n", - "Thorstvedt: MV 5.87 ± 0.89; FV 6.06 + 1.30\n", - "De Ketelaere: MV 5.82 ± 0.79; FV 5.88 + 0.90\n", - "Saponara: MV 6.09 ± 1.12; FV 6.49 + 1.89\n", - "Vecino: MV 5.90 ± 0.89; FV 6.01 + 1.22\n", - "Locatelli: MV 6.09 ± 0.70; FV 6.20 + 0.87\n", - "Zaniolo: MV 5.91 ± 1.00; FV 6.18 + 1.59\n", - "Duda: MV 6.08 ± 0.87; FV 6.26 + 1.17\n", - "Maldini: MV 5.96 ± 0.88; FV 6.24 + 1.39\n", - "Marin: MV 6.08 ± 0.99; FV 6.44 + 1.63\n", - "Zalewski: MV 6.01 ± 0.68; FV 6.11 + 0.74\n", - "Bajrami: MV 6.11 ± 1.12; FV 6.70 + 2.19\n", - "Coulibaly L.: MV 5.94 ± 1.15; FV 6.22 + 1.79\n", - "Gonzalez J.: MV 6.04 ± 0.84; FV 6.27 + 1.26\n", - "De Roon: MV 5.91 ± 0.75; FV 5.93 + 0.77\n", - "Mandragora: MV 5.91 ± 0.88; FV 5.98 + 1.16\n", - "Wijnaldum: MV 5.93 ± 0.91; FV 6.01 + 1.05\n", - "Bourabia: MV 5.96 ± 0.78; FV 5.98 + 0.84\n", - "Sottil: MV 6.07 ± 1.11; FV 6.45 + 1.84\n", - "Aebischer: MV 6.02 ± 0.85; FV 6.30 + 1.39\n", - "Ederson D.s.: MV 5.82 ± 0.79; FV 5.90 + 0.99\n", - "Miretti: MV 6.03 ± 0.72; FV 6.18 + 0.90\n", - "Blin: MV 6.01 ± 0.64; FV 5.99 + 0.59\n", - "Hjulmand: MV 5.98 ± 1.07; FV 6.04 + 1.21\n", - "Cataldi: MV 5.99 ± 0.79; FV 6.00 + 0.83\n", - "Djuricic: MV 5.72 ± 0.90; FV 5.81 + 1.17\n", - "Linetty: MV 5.96 ± 0.88; FV 6.12 + 1.28\n", - "Haas: MV 6.02 ± 0.75; FV 6.27 + 1.11\n", - "Walace: MV 5.92 ± 0.74; FV 5.88 + 0.68\n", - "Agudelo: MV 5.89 ± 0.70; FV 5.91 + 0.72\n", - "Pobega: MV 6.00 ± 0.88; FV 6.28 + 1.30\n", - "Camara Ma.: MV 6.12 ± 0.70; FV 6.17 + 0.81\n", - "Paredes: MV 5.87 ± 0.68; FV 5.86 + 0.57\n", - "Ndombele': MV 6.05 ± 0.65; FV 5.99 + 0.58\n", - "Nicolussi Caviglia: MV 5.87 ± 1.19; FV 6.24 + 1.99\n", - "Rovella: MV 5.91 ± 0.95; FV 5.92 + 0.95\n", - "Amrabat: MV 5.87 ± 0.89; FV 5.84 + 0.96\n", - "Tameze: MV 5.96 ± 0.79; FV 6.03 + 0.94\n", - "Gyasi: MV 5.89 ± 1.01; FV 6.04 + 1.49\n", - "Ilic: MV 6.00 ± 0.99; FV 6.23 + 1.48\n", - "Matheus Henrique: MV 5.89 ± 0.84; FV 6.03 + 1.16\n", - "Harroui: MV 5.84 ± 0.80; FV 6.00 + 1.16\n", - "Volpato: MV 6.02 ± 1.11; FV 6.60 + 2.21\n", - "Pickel: MV 5.67 ± 0.80; FV 5.70 + 0.89\n", - "Moro N.: MV 6.05 ± 0.66; FV 6.12 + 0.78\n", - "Duncan: MV 5.88 ± 0.76; FV 5.92 + 0.90\n", - "Machin: MV 5.86 ± 0.94; FV 5.94 + 1.23\n", - "Cuadrado: MV 6.04 ± 0.88; FV 6.19 + 1.10\n", - "Ekdal: MV 5.82 ± 0.71; FV 5.77 + 0.71\n", - "Meite': MV 5.73 ± 1.03; FV 5.76 + 1.28\n", - "Schouten: MV 6.01 ± 0.78; FV 6.02 + 0.84\n", - "Obiang: MV 5.91 ± 0.65; FV 5.93 + 0.64\n", - "Kovalenko: MV 5.97 ± 0.77; FV 6.07 + 0.95\n", - "Crnigoj: MV 5.93 ± 0.89; FV 6.10 + 1.23\n", - "Basic: MV 5.95 ± 0.67; FV 6.02 + 0.73\n", - "Asllani: MV 6.07 ± 0.63; FV 6.08 + 0.65\n", - "Sabiri: MV 5.76 ± 0.84; FV 5.80 + 1.04\n", - "Terracciano F.: MV 6.09 ± 0.63; FV 6.13 + 0.72\n", - "Castagnetti: MV 5.72 ± 0.70; FV 5.70 + 0.67\n", - "Oudin: MV 5.82 ± 0.65; FV 5.86 + 0.60\n", - "Grassi: MV 6.04 ± 0.67; FV 6.00 + 0.62\n", - "Krunic: MV 5.89 ± 0.76; FV 5.95 + 0.78\n", - "Rincon: MV 5.68 ± 0.79; FV 5.60 + 0.85\n", - "Miguel Veloso: MV 6.01 ± 0.72; FV 6.05 + 0.79\n", - "Leris: MV 5.70 ± 0.76; FV 5.66 + 0.87\n", - "Esposito Sa.: MV 5.92 ± 0.95; FV 5.97 + 1.10\n", - "Henderson L.: MV 6.01 ± 0.71; FV 6.16 + 0.90\n", - "Lopez M.: MV 5.85 ± 0.90; FV 5.84 + 1.09\n", - "Cuisance: MV 5.70 ± 0.72; FV 5.64 + 0.74\n", - "Saelemaekers: MV 5.82 ± 0.78; FV 5.89 + 0.82\n", - "Maggiore: MV 5.93 ± 0.81; FV 6.08 + 1.06\n", - "Akpa Akpro: MV 6.05 ± 0.82; FV 6.20 + 1.05\n", - "Maleh: MV 5.95 ± 0.74; FV 6.10 + 0.99\n", - "Romero L.: MV 6.14 ± 1.06; FV 6.71 + 2.08\n", - "Ceide: MV 5.80 ± 0.71; FV 5.83 + 0.73\n", - "D'alessandro: MV 6.02 ± 0.67; FV 6.10 + 0.74\n", - "Benassi: MV 5.71 ± 0.73; FV 5.75 + 0.81\n", - "Gagliardini: MV 5.97 ± 0.63; FV 6.02 + 0.62\n", - "Vieira: MV 5.87 ± 0.73; FV 5.91 + 0.98\n", - "Bianco: MV 6.00 ± 0.85; FV 6.07 + 1.01\n", - "Vranckx: MV 6.07 ± 0.74; FV 6.21 + 0.88\n", - "Galdames: MV 5.76 ± 1.04; FV 5.84 + 1.35\n", - "Marcos Antonio: MV 5.94 ± 0.61; FV 5.92 + 0.51\n", - "Fazzini: MV 5.87 ± 0.63; FV 5.86 + 0.54\n", - "Sulemana I.: MV 5.93 ± 0.64; FV 5.95 + 0.62\n", - "Tahirovic: MV 5.96 ± 0.60; FV 5.97 + 0.50\n", - "Abildgaard: MV 5.97 ± 0.85; FV 6.05 + 1.06\n", - "Barberis: MV 5.87 ± 0.98; FV 5.92 + 1.14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Kastanos: MV 5.82 ± 0.69; FV 5.83 + 0.69\n", - "Vignato: MV 6.05 ± 0.68; FV 6.09 + 0.71\n", - "Valoti: MV 5.72 ± 0.67; FV 5.66 + 0.65\n", - "Winks: MV 5.79 ± 1.00; FV 5.78 + 1.19\n", - "Askildsen: MV 5.72 ± 0.64; FV 5.69 + 0.58\n", - "Bove: MV 5.73 ± 0.74; FV 5.74 + 0.82\n", - "Bohinen: MV 5.75 ± 0.62; FV 5.73 + 0.52\n", - "D'andrea: MV 5.89 ± 0.78; FV 6.05 + 1.09\n", - "Iling-Junior: MV 6.09 ± 0.69; FV 6.25 + 0.94\n", - "Cipot: MV 5.97 ± 0.96; FV 6.08 + 1.23\n", - "Bakayoko: MV 5.82 ± 0.76; FV 5.79 + 0.68\n", - "Gaetano: MV 6.05 ± 0.74; FV 6.09 + 0.81\n", - "Zurkowski: MV 6.18 ± 1.08; FV 6.69 + 2.04\n", - "Castrovilli: MV 6.01 ± 0.90; FV 6.23 + 1.34\n", - "Demme: MV 6.07 ± 0.65; FV 6.24 + 0.90\n", - "Darboe: MV 5.95 ± 0.90; FV 5.99 + 0.94\n", - "Urbanski: MV 6.00 ± 0.88; FV 6.09 + 1.10\n", - "Bertini: MV 5.93 ± 0.93; FV 6.01 + 1.20\n", - "Yepes: MV 5.71 ± 1.15; FV 5.69 + 1.29\n", - "Pyyhtia: MV 6.02 ± 0.76; FV 6.06 + 0.87\n", - "Trimboli: MV 5.78 ± 0.94; FV 5.77 + 1.15\n", - "Pafundi: MV 5.92 ± 0.85; FV 5.93 + 0.88\n", - "Helgason: MV 5.80 ± 0.62; FV 5.80 + 0.54\n", - "Adli: MV 5.87 ± 0.77; FV 5.93 + 0.83\n", - "Vignato S.: MV 5.95 ± 0.91; FV 6.06 + 1.08\n", - "Hrustic: MV 5.80 ± 0.74; FV 5.78 + 0.72\n", - "Samek: MV 5.94 ± 0.86; FV 6.01 + 1.06\n", - "Zerbin: MV 6.10 ± 0.74; FV 6.18 + 0.90\n", - "Ilkhan: MV 5.85 ± 1.06; FV 5.92 + 1.34\n", - "Degli Innocenti: MV 6.04 ± 0.81; FV 6.17 + 0.99\n", - "Acella: MV 5.76 ± 1.04; FV 5.84 + 1.35\n", - "Carboni V.: MV 6.01 ± 0.84; FV 6.13 + 1.00\n", - "Paoletti: MV 5.97 ± 0.87; FV 6.05 + 1.08\n", - "Malagrida: MV 5.80 ± 0.95; FV 5.80 + 1.17\n", - "Faticanti: MV 5.95 ± 0.89; FV 6.06 + 1.04\n", - "Osimhen: MV 6.59 ± 1.51; FV 7.92 + 4.46\n", - "Martinez L.: MV 6.47 ± 1.44; FV 7.71 + 3.99\n", - "Dybala: MV 6.43 ± 1.34; FV 7.49 + 3.41\n", - "Rafael Leao: MV 6.29 ± 1.39; FV 7.41 + 3.60\n", - "Lookman: MV 6.25 ± 1.40; FV 7.54 + 3.85\n", - "Immobile: MV 6.29 ± 1.43; FV 7.63 + 4.01\n", - "Vlahovic: MV 6.39 ± 1.45; FV 7.75 + 4.21\n", - "Arnautovic: MV 6.28 ± 1.35; FV 7.45 + 3.62\n", - "Dia: MV 6.18 ± 1.41; FV 7.42 + 3.75\n", - "Dzeko: MV 6.30 ± 1.36; FV 7.47 + 3.65\n", - "Milik: MV 6.36 ± 1.24; FV 7.27 + 2.98\n", - "Nzola: MV 6.14 ± 1.34; FV 7.26 + 3.45\n", - "Beto: MV 6.15 ± 1.23; FV 7.09 + 2.99\n", - "Giroud: MV 6.13 ± 1.26; FV 6.97 + 2.88\n", - "Abraham: MV 6.12 ± 1.30; FV 7.28 + 3.46\n", - "Deulofeu: MV 6.38 ± 1.28; FV 7.28 + 2.98\n", - "Lauriente': MV 6.20 ± 1.35; FV 7.06 + 3.08\n", - "Simeone: MV 6.38 ± 1.43; FV 7.68 + 4.03\n", - "Lozano: MV 6.35 ± 1.22; FV 7.12 + 2.50\n", - "Correa: MV 6.26 ± 1.04; FV 7.08 + 2.46\n", - "Berardi: MV 6.24 ± 1.42; FV 7.46 + 3.80\n", - "Pedro: MV 6.15 ± 1.16; FV 6.78 + 2.36\n", - "Lukaku: MV 6.08 ± 1.19; FV 6.87 + 2.65\n", - "Sanabria: MV 6.00 ± 1.16; FV 6.52 + 2.14\n", - "Thauvin: MV 6.44 ± 1.30; FV 7.38 + 3.08\n", - "Cabral: MV 5.96 ± 1.10; FV 6.42 + 1.98\n", - "Hojlund: MV 5.98 ± 1.11; FV 6.52 + 2.13\n", - "Caprari: MV 5.95 ± 1.03; FV 6.48 + 2.03\n", - "Di Maria: MV 6.33 ± 1.32; FV 7.28 + 3.16\n", - "Piatek: MV 5.92 ± 1.16; FV 6.46 + 2.17\n", - "Rebic: MV 6.18 ± 1.33; FV 6.97 + 2.88\n", - "Bonazzoli: MV 6.05 ± 1.09; FV 6.56 + 2.03\n", - "Zapata D.: MV 5.82 ± 0.93; FV 6.17 + 1.53\n", - "Kouame': MV 5.97 ± 1.09; FV 6.37 + 1.88\n", - "Gonzalez N.: MV 6.12 ± 1.18; FV 6.71 + 2.27\n", - "Brekalo: MV 5.99 ± 1.06; FV 6.37 + 1.81\n", - "Mota: MV 6.03 ± 1.21; FV 6.83 + 2.71\n", - "Kean: MV 6.11 ± 1.22; FV 6.86 + 2.68\n", - "Okereke: MV 5.76 ± 1.10; FV 6.24 + 1.89\n", - "Ceesay: MV 5.95 ± 0.89; FV 6.32 + 1.48\n", - "Colombo: MV 5.99 ± 1.10; FV 6.48 + 2.01\n", - "Dessers: MV 5.79 ± 1.04; FV 6.25 + 1.81\n", - "Muriel: MV 6.01 ± 1.19; FV 6.41 + 2.09\n", - "Pinamonti: MV 5.88 ± 1.07; FV 6.35 + 1.94\n", - "Di Francesco F.: MV 6.04 ± 0.95; FV 6.39 + 1.56\n", - "Jovic: MV 5.90 ± 1.07; FV 6.31 + 1.88\n", - "Origi: MV 5.92 ± 1.07; FV 6.26 + 1.78\n", - "Caputo: MV 6.18 ± 1.27; FV 7.17 + 3.17\n", - "Boga: MV 6.28 ± 1.22; FV 7.01 + 2.50\n", - "Cambiaghi: MV 6.24 ± 0.95; FV 6.81 + 1.87\n", - "Alvarez A.: MV 5.98 ± 1.08; FV 6.36 + 1.87\n", - "Banda: MV 5.97 ± 0.73; FV 6.09 + 0.88\n", - "Ciofani D.: MV 5.85 ± 1.05; FV 6.29 + 1.82\n", - "Petagna: MV 5.95 ± 1.11; FV 6.48 + 2.09\n", - "Barrow: MV 6.16 ± 1.24; FV 6.74 + 2.39\n", - "Djuric: MV 6.03 ± 0.72; FV 6.23 + 0.99\n", - "Henry: MV 6.10 ± 1.23; FV 6.66 + 2.33\n", - "Success: MV 5.98 ± 0.70; FV 6.09 + 0.84\n", - "Gabbiadini: MV 5.86 ± 1.16; FV 6.39 + 2.11\n", - "Zirkzee: MV 6.13 ± 1.01; FV 6.63 + 1.86\n", - "Lammers: MV 5.82 ± 0.80; FV 5.91 + 1.07\n", - "Satriano: MV 5.92 ± 0.90; FV 6.13 + 1.35\n", - "Kallon: MV 5.89 ± 0.84; FV 6.14 + 1.29\n", - "Nestorovski: MV 6.28 ± 0.99; FV 7.11 + 2.37\n", - "Raspadori: MV 6.17 ± 1.05; FV 6.79 + 2.11\n", - "Botheim: MV 5.90 ± 1.02; FV 6.27 + 1.73\n", - "Gytkjaer: MV 5.84 ± 0.90; FV 6.11 + 1.45\n", - "Solbakken: MV 6.09 ± 0.99; FV 6.36 + 1.40\n", - "Lasagna: MV 5.81 ± 0.90; FV 5.94 + 1.23\n", - "Belotti: MV 5.70 ± 0.65; FV 5.71 + 0.63\n", - "Pellegri: MV 5.94 ± 0.98; FV 6.24 + 1.62\n", - "Buonaiuto: MV 5.90 ± 0.89; FV 6.21 + 1.42\n", - "Verde: MV 6.08 ± 0.95; FV 6.46 + 1.66\n", - "Destro: MV 6.09 ± 1.30; FV 7.00 + 3.01\n", - "Seck: MV 6.10 ± 0.75; FV 6.37 + 1.16\n", - "Sansone: MV 6.01 ± 1.03; FV 6.37 + 1.77\n", - "Quagliarella: MV 5.83 ± 0.76; FV 5.93 + 1.04\n", - "Defrel: MV 5.84 ± 0.92; FV 6.09 + 1.45\n", - "Pjaca: MV 5.98 ± 0.81; FV 6.15 + 1.00\n", - "Gaich: MV 6.01 ± 1.18; FV 6.50 + 2.17\n", - "Soule': MV 6.18 ± 0.76; FV 6.57 + 1.40\n", - "Tsadjout: MV 5.83 ± 0.83; FV 6.00 + 1.20\n", - "Piccoli: MV 6.08 ± 1.23; FV 7.02 + 2.97\n", - "Shomurodov: MV 6.03 ± 1.09; FV 6.56 + 2.07\n", - "Afena-Gyan: MV 5.61 ± 0.76; FV 5.68 + 0.90\n", - "Ngonge: MV 6.09 ± 0.88; FV 6.35 + 1.33\n", - "Karamoh: MV 5.90 ± 0.98; FV 6.26 + 1.66\n", - "Ibrahimovic: MV 6.21 ± 1.25; FV 7.21 + 3.12\n", - "Pussetto: MV 5.85 ± 0.95; FV 6.13 + 1.53\n", - "Cancellieri: MV 5.80 ± 0.59; FV 5.80 + 0.54\n", - "Valencia D.: MV 5.76 ± 0.61; FV 5.64 + 0.62\n", - "Oddei: MV 6.01 ± 0.91; FV 6.22 + 1.32\n", - "Braaf: MV 5.97 ± 0.95; FV 6.16 + 1.41\n", - "Raimondo: MV 6.05 ± 0.95; FV 6.23 + 1.31\n", - "Kaio Jorge: MV 5.96 ± 0.60; FV 5.99 + 0.54\n", - "De Luca: MV 5.78 ± 0.95; FV 5.83 + 1.22\n", - "Voelkerling Persson: MV 5.84 ± 0.77; FV 5.87 + 0.82\n", - "Montevago: MV 5.63 ± 0.63; FV 5.58 + 0.63\n", - "Krollis: MV 5.95 ± 0.90; FV 6.04 + 1.14\n", - "Vivaldo: MV 5.93 ± 0.89; FV 5.98 + 0.98\n" - ] - }, - { - "data": { - "text/html": [ - "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
Rossi F.PAtalantaLazio00.016.2400650.4138364.1523730.6566035.9964780.2503040.6213451.5898294.9945010.649063-0.8560580.9787610.057899
SportielloPAtalantaLazio00.056.2299340.4136063.8428240.7812135.9874150.2522970.6155001.5897754.6774170.889610-0.6578250.9622570.630536
MussoPAtalantaLazio01.0906.2413050.4136883.8050550.7454405.9976820.2499270.6221321.5898484.5882810.860939-0.6380860.9650510.630550
ToloiDAtalantaLazio01.0905.9941160.4856076.1789810.6445575.9571240.5640840.0483940.9989745.7783100.9544990.3019211.5998860.000000
ZappacostaDAtalantaLazio00.0505.9273810.3962256.0800740.5160575.8848010.4593640.0685731.1002375.7558980.7608670.3061171.5999050.000000
............................................................
NgongeAVeronaSalernitana10.6606.0899630.4408746.3521810.6660916.0474860.5106300.0614381.0348305.9045080.9519720.3352371.5998920.000000
DjuricAVeronaSalernitana10.006.0332070.3616166.2300600.4966365.9835210.4157870.0884291.1362815.9244020.7384490.2980061.5999130.000000
BraafAVeronaSalernitana10.0555.9723260.4767096.1579600.7055975.9506810.5584650.0286161.0069615.6948271.0201010.3246051.5998760.000000
KallonAVeronaSalernitana10.0355.8940610.4205456.1435550.6454485.8155070.4739570.1223171.0853365.6341390.8335410.4236691.5998790.000000
LasagnaAVeronaSalernitana11.0705.8089270.4520285.9439230.6160455.7213140.5079060.1271941.0593285.4754300.8179980.4002291.5998800.000000
\n", - "

520 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " role team oppteam home starter vote% MV \\\n", - "player \n", - "Rossi F. P Atalanta Lazio 0 0.0 1 6.240065 \n", - "Sportiello P Atalanta Lazio 0 0.0 5 6.229934 \n", - "Musso P Atalanta Lazio 0 1.0 90 6.241305 \n", - "Toloi D Atalanta Lazio 0 1.0 90 5.994116 \n", - "Zappacosta D Atalanta Lazio 0 0.0 50 5.927381 \n", - "... ... ... ... ... ... ... ... \n", - "Ngonge A Verona Salernitana 1 0.6 60 6.089963 \n", - "Djuric A Verona Salernitana 1 0.0 0 6.033207 \n", - "Braaf A Verona Salernitana 1 0.0 55 5.972326 \n", - "Kallon A Verona Salernitana 1 0.0 35 5.894061 \n", - "Lasagna A Verona Salernitana 1 1.0 70 5.808927 \n", - "\n", - " MV std FV FV std MV loc MV scale MV skewness \\\n", - "player \n", - "Rossi F. 0.413836 4.152373 0.656603 5.996478 0.250304 0.621345 \n", - "Sportiello 0.413606 3.842824 0.781213 5.987415 0.252297 0.615500 \n", - "Musso 0.413688 3.805055 0.745440 5.997682 0.249927 0.622132 \n", - "Toloi 0.485607 6.178981 0.644557 5.957124 0.564084 0.048394 \n", - "Zappacosta 0.396225 6.080074 0.516057 5.884801 0.459364 0.068573 \n", - "... ... ... ... ... ... ... \n", - "Ngonge 0.440874 6.352181 0.666091 6.047486 0.510630 0.061438 \n", - "Djuric 0.361616 6.230060 0.496636 5.983521 0.415787 0.088429 \n", - "Braaf 0.476709 6.157960 0.705597 5.950681 0.558465 0.028616 \n", - "Kallon 0.420545 6.143555 0.645448 5.815507 0.473957 0.122317 \n", - "Lasagna 0.452028 5.943923 0.616045 5.721314 0.507906 0.127194 \n", - "\n", - " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", - "player \n", - "Rossi F. 1.589829 4.994501 0.649063 -0.856058 0.978761 \n", - "Sportiello 1.589775 4.677417 0.889610 -0.657825 0.962257 \n", - "Musso 1.589848 4.588281 0.860939 -0.638086 0.965051 \n", - "Toloi 0.998974 5.778310 0.954499 0.301921 1.599886 \n", - "Zappacosta 1.100237 5.755898 0.760867 0.306117 1.599905 \n", - "... ... ... ... ... ... \n", - "Ngonge 1.034830 5.904508 0.951972 0.335237 1.599892 \n", - "Djuric 1.136281 5.924402 0.738449 0.298006 1.599913 \n", - "Braaf 1.006961 5.694827 1.020101 0.324605 1.599876 \n", - "Kallon 1.085336 5.634139 0.833541 0.423669 1.599879 \n", - "Lasagna 1.059328 5.475430 0.817998 0.400229 1.599880 \n", - "\n", - " Clean Sheet % \n", - "player \n", - "Rossi F. 0.057899 \n", - "Sportiello 0.630536 \n", - "Musso 0.630550 \n", - "Toloi 0.000000 \n", - "Zappacosta 0.000000 \n", - "... ... \n", - "Ngonge 0.000000 \n", - "Djuric 0.000000 \n", - "Braaf 0.000000 \n", - "Kallon 0.000000 \n", - "Lasagna 0.000000 \n", - "\n", - "[520 rows x 19 columns]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "matchday_out = 22\n", - "\n", - "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", - "\n", - "for i in range(players.shape[0]):\n", - " try:\n", - " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", - " \n", - " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", - " \n", - " role = players['r'][player] \n", - " \n", - " starter = 0\n", - " voteperc = 0\n", - " \n", - " cs = 0\n", - " if(role == 'P'):\n", - " cs = dist[2].probs.numpy()[0] * 100\n", - " \n", - " if(player in probables.index):\n", - " starter = probables['starter'][player]\n", - " voteperc = probables['percentage'][player]\n", - " \n", - " row = [player, role, team, oppteam, home, \n", - " starter, voteperc, \n", - " mean[0], std[0], \n", - " mean[1], std[1], \n", - " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", - " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", - " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", - " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", - " cs]\n", - " \n", - " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", - " \n", - " output = pd.concat([output, row_df])\n", - " \n", - " except:\n", - " print(players.index[i] + ' no data')\n", - "\n", - "output = output.set_index('player')\n", - "\n", - "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", - "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", - "\n", - "output" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "6befd611", - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "template_file = 'outputs/pred_matchday_base.xlsx'\n", - "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", - "\n", - "shutil.copyfile(template_file, dest_file)\n", - "\n", - "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", - " output.to_excel(writer, sheet_name='data')" - ] - }, - { - "cell_type": "markdown", - "id": "eed7a7ac", - "metadata": {}, - "source": [ - "Predict average Serie A performance for each player" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "2b637a15", - "metadata": {}, - "outputs": [], - "source": [ - "gk_starters = ['Maignan', 'Ochoa', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", - " 'Terracciano', 'Onana', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Carnesecchi', 'Rui Patricio',\n", - " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "60d73507", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret (6.24, 0.41); (5.58, 0.42)\n", - "Provedel (6.24, 0.41); (5.95, 0.36)\n", - "Vicario (6.24, 0.41); (5.36, 0.42)\n", - "Szczesny (6.24, 0.41); (6.01, 0.36)\n", - "Falcone (6.24, 0.41); (4.85, 0.45)\n", - "Silvestri (6.24, 0.41); (5.11, 0.44)\n", - "Rui Patricio (6.24, 0.41); (5.50, 0.38)\n", - "Onana (6.19, 0.42); (4.75, 0.59)\n", - "Sepe (6.24, 0.41); (5.10, 0.46)\n", - "Milinkovic-Savic V. (6.24, 0.41); (4.90, 0.41)\n", - "Musso (6.24, 0.41); (4.99, 0.63)\n", - "Maignan (6.24, 0.41); (5.81, 0.41)\n", - "Carnesecchi (6.24, 0.41); (5.22, 0.48)\n", - "Di Gregorio (6.25, 0.41); (5.01, 0.61)\n", - "Audero (6.24, 0.41); (4.75, 0.48)\n", - "Montipo' (6.24, 0.41); (4.72, 0.52)\n", - "Skorupski (6.24, 0.41); (4.76, 0.59)\n", - "Consigli (6.24, 0.41); (4.91, 0.50)\n", - "Dragowski (6.24, 0.41); (4.97, 0.54)\n", - "Terracciano (6.18, 0.42); (4.50, 0.65)\n", - "Tatarusanu (6.18, 0.43); (3.89, 0.76)\n", - "Handanovic (5.87, 0.50); (4.47, 0.71)\n", - "Sportiello (6.23, 0.42); (5.75, 0.47)\n", - "Perin (6.24, 0.41); (5.57, 0.41)\n", - "Zoet (6.24, 0.41); (5.55, 0.43)\n", - "Ochoa (6.23, 0.42); (4.02, 0.95)\n", - "Pegolo (6.24, 0.41); (5.55, 0.40)\n", - "Gollini (6.24, 0.41); (5.16, 0.43)\n", - "Mirante no data\n", - "Sarr M. no data\n", - "Lamanna no data\n", - "Ujkani no data\n", - "Berisha (6.24, 0.41); (5.29, 0.44)\n", - "Marchetti no data\n", - "Perilli no data\n", - "Padelli (6.24, 0.41); (4.83, 0.48)\n", - "Perisan no data\n", - "Bardi (6.24, 0.41); (5.27, 0.54)\n", - "Cordaz no data\n", - "Pinsoglio (6.24, 0.41); (5.06, 0.42)\n", - "Fiorillo (6.17, 0.43); (3.83, 0.97)\n", - "Cragno (6.24, 0.41); (4.42, 0.63)\n", - "Sirigu (6.24, 0.41); (4.39, 0.64)\n", - "Cerofolini no data\n", - "Rossi F. (6.00, 0.46); (3.42, 1.07)\n", - "Ravaglia F. no data\n", - "Brancolini no data\n", - "Bleve no data\n", - "Berardi A. (6.24, 0.41); (4.34, 0.60)\n", - "Russo A. no data\n", - "Gemello (6.24, 0.41); (5.79, 0.40)\n", - "Ravaglia (6.24, 0.41); (4.34, 0.66)\n", - "Boer no data\n", - "Adamonis no data\n", - "Marfella (6.24, 0.41); (5.13, 0.46)\n", - "Zovko (5.74, 0.52); (2.94, 1.36)\n", - "Piana no data\n", - "Bagnolini no data\n", - "Luis Maximiano (6.30, 0.40); (6.35, 0.47)\n", - "Svilar no data\n", - "Sorrentino A. no data\n", - "Ciezkowski no data\n", - "Saro no data\n", - "Vasquez D. no data\n", - "Turk no data\n", - "Dimarco (6.22, 0.50); (6.73, 0.92)\n", - "Smalling (6.24, 0.51); (6.68, 0.89)\n", - "Doig (6.21, 0.58); (6.85, 1.17)\n", - "Carlos Augusto (6.10, 0.55); (6.59, 1.02)\n", - "Kim (6.26, 0.48); (6.60, 0.80)\n", - "Posch (6.12, 0.59); (6.78, 1.23)\n", - "Di Lorenzo (6.23, 0.43); (6.55, 0.72)\n", - "Danilo (6.27, 0.51); (6.68, 0.87)\n", - "Hernandez T. (6.03, 0.60); (6.44, 1.02)\n", - "Udogie (6.09, 0.55); (6.55, 1.01)\n", - "Parisi (6.21, 0.44); (6.63, 0.79)\n", - "Mario Rui (6.22, 0.44); (6.47, 0.67)\n", - "Romagnoli (6.19, 0.47); (6.42, 0.68)\n", - "Bastoni S. (6.14, 0.50); (6.58, 0.92)\n", - "Mazzocchi (6.12, 0.50); (6.55, 0.89)\n", - "Valeri (6.08, 0.37); (6.31, 0.55)\n", - "Tomori (6.12, 0.48); (6.37, 0.69)\n", - "Scalvini (6.11, 0.54); (6.48, 0.89)\n", - "Toloi (6.18, 0.50); (6.51, 0.77)\n", - "Demiral (6.08, 0.46); (6.33, 0.68)\n", - "Maehle (5.98, 0.50); (6.22, 0.76)\n", - "Dumfries (6.04, 0.55); (6.44, 0.97)\n", - "Baschirotto (6.19, 0.54); (6.64, 0.96)\n", - "Bijol (5.92, 0.53); (6.12, 0.77)\n", - "Schuurs (6.15, 0.37); (6.23, 0.47)\n", - "Juan Jesus (6.19, 0.38); (6.45, 0.62)\n", - "Depaoli (5.96, 0.48); (6.19, 0.75)\n", - "Mancini (6.11, 0.36); (6.14, 0.40)\n", - "Ibanez (6.11, 0.52); (6.47, 0.85)\n", - "Rodrigo Becao (6.14, 0.47); (6.42, 0.71)\n", - "Ebuehi (6.09, 0.43); (6.38, 0.65)\n", - "Gosens (5.98, 0.34); (6.21, 0.50)\n", - "Darmian (6.08, 0.41); (6.46, 0.71)\n", - "Reca (5.89, 0.55); (6.13, 0.80)\n", - "Bremer (5.97, 0.54); (6.12, 0.68)\n", - "Sernicola (5.94, 0.40); (6.07, 0.55)\n", - "Rrahmani (6.26, 0.49); (6.56, 0.78)\n", - "Vojvoda (6.03, 0.46); (6.18, 0.59)\n", - "Holm (6.00, 0.44); (6.22, 0.66)\n", - "Bastoni (6.10, 0.46); (6.21, 0.56)\n", - "Milenkovic (5.91, 0.53); (6.07, 0.73)\n", - "Kalulu (5.78, 0.56); (5.83, 0.68)\n", - "Martinez Quarta (6.04, 0.52); (6.26, 0.73)\n", - "Casale (6.01, 0.45); (6.12, 0.56)\n", - "Perez N. (6.07, 0.46); (6.20, 0.57)\n", - "Olivera (6.15, 0.38); (6.48, 0.65)\n", - "Izzo (6.01, 0.39); (6.00, 0.39)\n", - "Luperto (5.82, 0.53); (5.82, 0.56)\n", - "Skriniar (5.84, 0.42); (5.81, 0.41)\n", - "Rodriguez R. (6.00, 0.39); (5.98, 0.39)\n", - "Marusic (6.03, 0.39); (6.05, 0.41)\n", - "Lazzari (6.05, 0.41); (6.08, 0.44)\n", - "Kyriakopoulos (5.91, 0.50); (6.00, 0.59)\n", - "Ampadu (5.84, 0.47); (5.86, 0.57)\n", - "Ismajli (6.06, 0.40); (6.03, 0.40)\n", - "Llorente D. (5.93, 0.52); (6.10, 0.72)\n", - "Cambiaso (5.96, 0.41); (5.99, 0.45)\n", - "Hysaj (6.00, 0.35); (6.05, 0.36)\n", - "Biraghi (6.00, 0.39); (6.05, 0.41)\n", - "Medel (5.95, 0.39); (5.91, 0.38)\n", - "Bonucci (6.21, 0.54); (6.66, 0.94)\n", - "Calabria (5.87, 0.53); (6.06, 0.77)\n", - "Acerbi (6.06, 0.41); (6.12, 0.46)\n", - "Spinazzola (6.00, 0.34); (6.05, 0.35)\n", - "Lykogiannis (5.97, 0.37); (6.04, 0.40)\n", - "Pellegrini Lu. (6.04, 0.37); (6.09, 0.41)\n", - "Djidji (5.92, 0.45); (5.99, 0.57)\n", - "Lazaro (6.02, 0.48); (6.16, 0.60)\n", - "Augello (5.83, 0.46); (5.93, 0.61)\n", - "Gallo (5.96, 0.38); (5.93, 0.36)\n", - "Singo (6.04, 0.42); (6.18, 0.53)\n", - "Mari' (5.83, 0.57); (5.94, 0.75)\n", - "Caldirola (5.81, 0.48); (5.77, 0.49)\n", - "Dodo' (5.78, 0.48); (5.70, 0.51)\n", - "De Vrij (5.87, 0.43); (5.90, 0.48)\n", - "Patric (6.04, 0.43); (6.05, 0.46)\n", - "Faraoni (5.98, 0.43); (6.16, 0.62)\n", - "Ceccherini (5.87, 0.52); (5.98, 0.71)\n", - "Hateboer (5.87, 0.48); (5.99, 0.69)\n", - "Rogerio (5.77, 0.45); (5.71, 0.49)\n", - "Umtiti (5.91, 0.50); (5.98, 0.56)\n", - "Aina (6.07, 0.53); (6.38, 0.80)\n", - "Birindelli (5.83, 0.37); (5.84, 0.40)\n", - "Lucumi' (5.91, 0.43); (5.89, 0.46)\n", - "Ehizibue (5.85, 0.45); (5.98, 0.65)\n", - "Bianchetti (5.76, 0.48); (5.78, 0.61)\n", - "Ferrari G. (5.77, 0.56); (5.83, 0.72)\n", - "Fazio (5.66, 0.63); (5.72, 0.71)\n", - "Gravillon (5.97, 0.45); (6.04, 0.54)\n", - "Buongiorno (5.95, 0.41); (5.90, 0.39)\n", - "Gunter (5.79, 0.50); (5.78, 0.62)\n", - "Troost-Ekong (5.77, 0.57); (5.78, 0.63)\n", - "Soumaoro (5.86, 0.50); (5.83, 0.52)\n", - "Ceccaroni (5.89, 0.53); (5.99, 0.66)\n", - "Pongracic (5.95, 0.40); (5.91, 0.38)\n", - "Soppy (5.85, 0.40); (5.87, 0.42)\n", - "Gendrey (5.87, 0.36); (5.83, 0.33)\n", - "Hien (5.81, 0.42); (5.74, 0.44)\n", - "Ferrari A. (5.77, 0.54); (5.80, 0.67)\n", - "Masina (6.05, 0.53); (6.51, 0.98)\n", - "Zappacosta (6.08, 0.41); (6.31, 0.59)\n", - "Gyomber (5.79, 0.46); (5.72, 0.46)\n", - "Alex Sandro (5.83, 0.49); (5.74, 0.45)\n", - "Pezzella Giu. (5.93, 0.35); (5.93, 0.32)\n", - "Bereszynski (5.89, 0.34); (5.86, 0.31)\n", - "Venuti (5.84, 0.41); (5.83, 0.43)\n", - "Palomino (6.08, 0.50); (6.25, 0.63)\n", - "Nuytinck (5.85, 0.51); (5.85, 0.56)\n", - "Marlon (5.81, 0.38); (5.77, 0.36)\n", - "Magnani (5.82, 0.47); (5.74, 0.47)\n", - "Colley (5.77, 0.57); (5.84, 0.72)\n", - "Nikolaou (5.70, 0.43); (5.60, 0.43)\n", - "Terzic (6.04, 0.28); (5.97, 0.22)\n", - "Igor (5.79, 0.48); (5.68, 0.48)\n", - "Toljan (5.71, 0.43); (5.62, 0.44)\n", - "Zortea (5.84, 0.44); (5.89, 0.56)\n", - "Dawidowicz (5.82, 0.52); (5.87, 0.68)\n", - "Celik (5.79, 0.39); (5.75, 0.39)\n", - "Bellanova (5.90, 0.42); (5.96, 0.50)\n", - "Erlic (5.82, 0.50); (5.76, 0.52)\n", - "Ballo-Toure' (6.13, 0.41); (6.50, 0.69)\n", - "Dest (5.78, 0.44); (5.77, 0.48)\n", - "Stojanovic (5.76, 0.43); (5.71, 0.47)\n", - "Amian (5.78, 0.43); (5.72, 0.47)\n", - "Bradaric (5.73, 0.44); (5.71, 0.50)\n", - "Daniliuc (5.75, 0.57); (5.77, 0.65)\n", - "Zima (5.94, 0.41); (5.94, 0.42)\n", - "De Winter (5.75, 0.43); (5.67, 0.40)\n", - "Quagliata (5.94, 0.32); (5.96, 0.29)\n", - "Ebosse (5.77, 0.36); (5.69, 0.34)\n", - "Aiwu (5.89, 0.48); (5.96, 0.64)\n", - "Lochoshvili (5.77, 0.40); (5.70, 0.43)\n", - "Bronn (5.77, 0.37); (5.72, 0.34)\n", - "Thiaw (6.00, 0.43); (6.02, 0.44)\n", - "Zeefuik (5.90, 0.45); (5.93, 0.55)\n", - "Romagnoli S. (5.98, 0.59); (6.34, 0.96)\n", - "Ghiglione (5.82, 0.45); (5.83, 0.57)\n", - "Rugani (6.00, 0.32); (5.93, 0.27)\n", - "De Sciglio (5.87, 0.34); (5.87, 0.29)\n", - "Djimsiti (5.97, 0.39); (5.99, 0.40)\n", - "Caldara (5.73, 0.52); (5.66, 0.56)\n", - "Karsdorp (5.90, 0.40); (5.90, 0.40)\n", - "Marchizza (5.84, 0.38); (5.80, 0.37)\n", - "Kjaer (5.90, 0.38); (5.84, 0.35)\n", - "Okoli (5.75, 0.46); (5.71, 0.45)\n", - "Amione (5.67, 0.42); (5.60, 0.45)\n", - "Ruggeri (5.87, 0.33); (5.85, 0.28)\n", - "Zanoli (5.81, 0.37); (5.78, 0.34)\n", - "Wisniewski (5.88, 0.47); (5.91, 0.57)\n", - "Radovanovic (5.64, 0.40); (5.53, 0.39)\n", - "Dermaku (5.94, 0.45); (5.99, 0.53)\n", - "D'ambrosio (6.13, 0.37); (6.31, 0.52)\n", - "De Silvestri (5.90, 0.51); (6.04, 0.72)\n", - "Chiriches (5.76, 0.52); (5.70, 0.54)\n", - "Murru (5.64, 0.40); (5.58, 0.44)\n", - "Bonifazi (5.80, 0.42); (5.72, 0.41)\n", - "Donati (5.79, 0.44); (5.75, 0.47)\n", - "Walukiewicz (5.78, 0.39); (5.73, 0.38)\n", - "Ranieri L. (5.83, 0.43); (5.85, 0.55)\n", - "Gabbia (5.67, 0.47); (5.56, 0.47)\n", - "Kumbulla (5.92, 0.33); (5.90, 0.29)\n", - "Adopo (5.82, 0.31); (5.78, 0.27)\n", - "Pirola (5.60, 0.49); (5.49, 0.49)\n", - "Lovato (5.67, 0.54); (5.55, 0.52)\n", - "Tuia (5.83, 0.48); (5.89, 0.63)\n", - "Ferrer (5.84, 0.48); (5.85, 0.55)\n", - "Antov (5.75, 0.53); (5.71, 0.56)\n", - "Vasquez (5.79, 0.43); (5.72, 0.47)\n", - "Ruan (5.78, 0.51); (5.63, 0.47)\n", - "Ostigard (6.10, 0.34); (6.07, 0.35)\n", - "Coppola D. (5.74, 0.38); (5.64, 0.39)\n", - "Cacace (5.78, 0.40); (5.71, 0.37)\n", - "Gatti (5.89, 0.40); (5.85, 0.38)\n", - "Gila (5.90, 0.41); (5.88, 0.41)\n", - "Bayeye (5.95, 0.44); (6.00, 0.51)\n", - "Sambia (5.75, 0.43); (5.73, 0.44)\n", - "Moutinho J. (5.76, 0.44); (5.69, 0.47)\n", - "Conti (5.96, 0.45); (6.15, 0.68)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Marrone (5.60, 0.44); (5.50, 0.45)\n", - "Tonelli (5.75, 0.45); (5.66, 0.43)\n", - "Murillo (5.63, 0.35); (5.53, 0.33)\n", - "Radu (6.08, 0.36); (5.94, 0.30)\n", - "Paletta (5.89, 0.47); (5.96, 0.59)\n", - "Florenzi (6.15, 0.44); (6.40, 0.63)\n", - "Sala (5.85, 0.37); (5.86, 0.39)\n", - "Fares (5.81, 0.39); (5.80, 0.42)\n", - "Romagna (5.88, 0.48); (5.88, 0.55)\n", - "Cassandro (5.94, 0.45); (6.00, 0.54)\n", - "Muldur (5.75, 0.43); (5.67, 0.44)\n", - "Amey (6.02, 0.45); (6.13, 0.55)\n", - "Zanotti (5.93, 0.45); (6.00, 0.54)\n", - "Ebosele (5.99, 0.34); (6.02, 0.34)\n", - "Buta (5.94, 0.45); (6.01, 0.56)\n", - "Abankwah (5.94, 0.45); (6.01, 0.56)\n", - "Guessand A. (5.94, 0.45); (6.01, 0.56)\n", - "Cabal (5.88, 0.48); (5.91, 0.57)\n", - "Sosa (5.77, 0.48); (5.76, 0.56)\n", - "Guarino (5.89, 0.46); (5.92, 0.53)\n", - "Carboni F. (5.93, 0.46); (6.01, 0.54)\n", - "Zaccagni (6.44, 0.66); (7.42, 1.59)\n", - "Kvaratskhelia (6.57, 0.72); (7.58, 1.69)\n", - "Milinkovic-Savic (6.28, 0.61); (7.10, 1.39)\n", - "Barella (6.29, 0.58); (7.02, 1.24)\n", - "Zielinski (6.34, 0.55); (7.00, 1.10)\n", - "Luis Alberto (6.33, 0.53); (7.07, 1.15)\n", - "Strefezza (6.33, 0.50); (7.15, 1.19)\n", - "Felipe Anderson (6.20, 0.61); (7.07, 1.43)\n", - "Koopmeiners (6.26, 0.60); (7.06, 1.35)\n", - "Calhanoglu (6.27, 0.50); (6.81, 0.95)\n", - "Frattesi (6.18, 0.62); (6.92, 1.32)\n", - "Diaz B. (6.21, 0.63); (6.95, 1.31)\n", - "Vlasic (6.18, 0.57); (6.83, 1.16)\n", - "Zambo Anguissa (6.32, 0.55); (6.90, 1.04)\n", - "Elmas (6.25, 0.53); (6.89, 1.06)\n", - "Miranchuk (6.27, 0.59); (7.02, 1.26)\n", - "Samardzic (6.19, 0.57); (6.84, 1.15)\n", - "Pereyra (6.18, 0.55); (6.74, 1.06)\n", - "Politano (6.24, 0.45); (6.80, 0.91)\n", - "Rabiot (6.16, 0.57); (6.71, 1.08)\n", - "Ciurria (6.07, 0.59); (6.68, 1.18)\n", - "Lazovic (6.16, 0.55); (6.71, 1.03)\n", - "Lobotka (6.20, 0.38); (6.56, 0.70)\n", - "Radonjic (6.16, 0.48); (6.65, 0.88)\n", - "Ferguson (6.20, 0.48); (6.73, 0.93)\n", - "Bonaventura (6.14, 0.50); (6.57, 0.86)\n", - "Pessina (6.17, 0.56); (6.72, 1.05)\n", - "Tonali (6.10, 0.58); (6.57, 1.05)\n", - "Kostic (6.17, 0.53); (6.69, 0.96)\n", - "Baldanzi (6.31, 0.53); (7.11, 1.20)\n", - "Lovric (6.12, 0.41); (6.50, 0.71)\n", - "Pellegrini Lo. (6.10, 0.59); (6.68, 1.17)\n", - "El Shaarawy (6.24, 0.49); (6.90, 1.04)\n", - "Orsolini (6.23, 0.67); (7.12, 1.56)\n", - "Ikone' (6.03, 0.57); (6.49, 1.02)\n", - "Candreva (5.88, 0.57); (6.14, 0.84)\n", - "Bennacer (6.12, 0.42); (6.37, 0.60)\n", - "Pasalic (6.08, 0.59); (6.68, 1.17)\n", - "Mkhitaryan (6.02, 0.46); (6.39, 0.78)\n", - "Colpani (5.99, 0.43); (6.43, 0.82)\n", - "Pogba (5.97, 0.44); (6.05, 0.52)\n", - "Chiesa (6.17, 0.53); (6.62, 0.92)\n", - "Bandinelli (5.98, 0.44); (6.19, 0.65)\n", - "Matic (6.16, 0.40); (6.48, 0.66)\n", - "Fagioli (6.18, 0.52); (6.69, 0.95)\n", - "Messias (5.97, 0.54); (6.41, 0.98)\n", - "Arslan (5.97, 0.38); (6.11, 0.48)\n", - "Ricci S. (6.17, 0.42); (6.45, 0.66)\n", - "Ranocchia F. (6.07, 0.45); (6.48, 0.79)\n", - "Verdi (6.10, 0.41); (6.45, 0.68)\n", - "Sensi (5.99, 0.58); (6.36, 0.97)\n", - "Barak (5.86, 0.45); (6.03, 0.67)\n", - "Soriano (6.02, 0.38); (6.17, 0.51)\n", - "Dominguez (6.18, 0.53); (6.69, 1.00)\n", - "Vilhena (5.87, 0.50); (6.08, 0.76)\n", - "Brozovic (6.18, 0.46); (6.56, 0.77)\n", - "Cristante (5.98, 0.43); (6.12, 0.57)\n", - "Thorstvedt (5.92, 0.46); (6.11, 0.68)\n", - "De Ketelaere (5.87, 0.40); (6.00, 0.54)\n", - "Saponara (6.07, 0.57); (6.58, 1.05)\n", - "Vecino (5.95, 0.44); (6.09, 0.59)\n", - "Locatelli (6.07, 0.37); (6.14, 0.42)\n", - "Zaniolo (5.91, 0.50); (6.16, 0.79)\n", - "Duda (6.02, 0.46); (6.15, 0.59)\n", - "Maldini (5.94, 0.45); (6.25, 0.72)\n", - "Marin (5.90, 0.53); (6.08, 0.76)\n", - "Zalewski (6.06, 0.35); (6.13, 0.40)\n", - "Bajrami (6.21, 0.58); (6.85, 1.16)\n", - "Coulibaly L. (5.83, 0.56); (6.03, 0.77)\n", - "Gonzalez J. (6.07, 0.46); (6.40, 0.76)\n", - "De Roon (6.03, 0.37); (6.08, 0.41)\n", - "Mandragora (5.93, 0.46); (6.08, 0.68)\n", - "Wijnaldum (5.91, 0.47); (5.98, 0.57)\n", - "Bourabia (5.91, 0.41); (5.94, 0.46)\n", - "Sottil (6.09, 0.58); (6.56, 1.03)\n", - "Aebischer (5.99, 0.45); (6.25, 0.70)\n", - "Ederson D.s. (5.88, 0.44); (6.04, 0.64)\n", - "Miretti (5.98, 0.38); (6.10, 0.44)\n", - "Blin (6.02, 0.33); (6.00, 0.31)\n", - "Hjulmand (5.98, 0.54); (6.09, 0.63)\n", - "Cataldi (6.02, 0.38); (6.03, 0.39)\n", - "Djuricic (5.82, 0.46); (5.94, 0.65)\n", - "Linetty (5.97, 0.43); (6.14, 0.62)\n", - "Haas (5.92, 0.41); (6.08, 0.58)\n", - "Walace (5.93, 0.38); (5.93, 0.40)\n", - "Agudelo (5.86, 0.36); (5.91, 0.42)\n", - "Pobega (5.99, 0.44); (6.28, 0.70)\n", - "Camara Ma. (6.13, 0.35); (6.18, 0.42)\n", - "Paredes (5.85, 0.35); (5.83, 0.31)\n", - "Ndombele' (5.96, 0.34); (5.94, 0.30)\n", - "Nicolussi Caviglia (5.76, 0.56); (5.92, 0.79)\n", - "Rovella (5.92, 0.48); (5.94, 0.48)\n", - "Amrabat (5.89, 0.44); (5.88, 0.48)\n", - "Tameze (5.91, 0.41); (5.94, 0.48)\n", - "Gyasi (5.88, 0.51); (6.07, 0.77)\n", - "Ilic (6.01, 0.45); (6.13, 0.56)\n", - "Matheus Henrique (5.94, 0.44); (6.07, 0.59)\n", - "Harroui (5.88, 0.42); (6.03, 0.59)\n", - "Volpato (6.05, 0.56); (6.58, 1.09)\n", - "Pickel (5.79, 0.38); (5.81, 0.47)\n", - "Moro N. (6.03, 0.34); (6.11, 0.40)\n", - "Duncan (5.92, 0.41); (6.05, 0.58)\n", - "Machin (5.91, 0.47); (6.02, 0.63)\n", - "Cuadrado (5.95, 0.48); (6.05, 0.57)\n", - "Ekdal (5.84, 0.37); (5.81, 0.38)\n", - "Meite' (5.85, 0.46); (5.88, 0.59)\n", - "Schouten (5.99, 0.40); (6.02, 0.44)\n", - "Obiang (5.98, 0.33); (5.98, 0.31)\n", - "Kovalenko (5.92, 0.39); (6.01, 0.50)\n", - "Crnigoj (5.93, 0.44); (6.13, 0.64)\n", - "Basic (5.98, 0.33); (6.05, 0.35)\n", - "Asllani (5.96, 0.33); (5.99, 0.32)\n", - "Sabiri (5.85, 0.46); (5.99, 0.66)\n", - "Terracciano F. (6.06, 0.32); (6.09, 0.34)\n", - "Castagnetti (5.87, 0.32); (5.87, 0.27)\n", - "Oudin (5.87, 0.34); (5.92, 0.33)\n", - "Grassi (5.92, 0.35); (5.91, 0.32)\n", - "Krunic (5.93, 0.40); (6.02, 0.49)\n", - "Rincon (5.80, 0.40); (5.76, 0.45)\n", - "Miguel Veloso (5.95, 0.37); (5.97, 0.38)\n", - "Leris (5.78, 0.40); (5.79, 0.51)\n", - "Esposito Sa. (5.83, 0.52); (5.90, 0.61)\n", - "Henderson L. (5.92, 0.39); (6.03, 0.49)\n", - "Lopez M. (5.90, 0.44); (5.89, 0.49)\n", - "Cuisance (5.79, 0.37); (5.75, 0.39)\n", - "Saelemaekers (5.85, 0.40); (5.97, 0.52)\n", - "Maggiore (5.93, 0.40); (6.05, 0.51)\n", - "Akpa Akpro (5.91, 0.46); (5.95, 0.56)\n", - "Maleh (5.97, 0.39); (6.17, 0.56)\n", - "Romero L. (6.20, 0.52); (6.80, 1.04)\n", - "Ceide (5.87, 0.36); (5.88, 0.36)\n", - "D'alessandro (6.06, 0.33); (6.15, 0.39)\n", - "Benassi (5.80, 0.34); (5.81, 0.35)\n", - "Gagliardini (5.84, 0.32); (5.86, 0.32)\n", - "Vieira (5.86, 0.35); (5.85, 0.42)\n", - "Bianco (6.03, 0.42); (6.15, 0.53)\n", - "Vranckx (6.07, 0.39); (6.25, 0.50)\n", - "Galdames (5.90, 0.47); (5.98, 0.64)\n", - "Marcos Antonio (5.94, 0.30); (5.91, 0.24)\n", - "Fazzini (5.79, 0.33); (5.73, 0.30)\n", - "Sulemana I. (5.87, 0.32); (5.86, 0.32)\n", - "Tahirovic (6.00, 0.30); (5.99, 0.27)\n", - "Abildgaard (5.90, 0.44); (5.93, 0.55)\n", - "Barberis (5.87, 0.49); (5.91, 0.57)\n", - "Kastanos (5.80, 0.33); (5.80, 0.32)\n", - "Vignato (5.95, 0.36); (5.98, 0.35)\n", - "Valoti (5.77, 0.33); (5.71, 0.32)\n", - "Winks (5.88, 0.45); (5.89, 0.51)\n", - "Askildsen (5.74, 0.33); (5.69, 0.32)\n", - "Bove (5.75, 0.37); (5.73, 0.41)\n", - "Bohinen (5.75, 0.31); (5.71, 0.26)\n", - "D'andrea (5.97, 0.39); (6.10, 0.50)\n", - "Iling-Junior (6.06, 0.36); (6.19, 0.45)\n", - "Cipot (5.87, 0.52); (6.00, 0.70)\n", - "Bakayoko (5.81, 0.39); (5.78, 0.36)\n", - "Gaetano (6.00, 0.42); (6.08, 0.51)\n", - "Zurkowski (6.12, 0.55); (6.66, 1.08)\n", - "Castrovilli (6.00, 0.46); (6.28, 0.72)\n", - "Demme (6.03, 0.37); (6.20, 0.48)\n", - "Darboe (5.95, 0.46); (5.98, 0.49)\n", - "Urbanski (5.98, 0.46); (6.09, 0.59)\n", - "Bertini (5.97, 0.45); (6.08, 0.56)\n", - "Yepes (5.79, 0.55); (5.79, 0.61)\n", - "Pyyhtia (6.00, 0.39); (6.07, 0.46)\n", - "Trimboli (5.88, 0.46); (5.92, 0.58)\n", - "Pafundi (5.95, 0.45); (6.04, 0.57)\n", - "Helgason (5.82, 0.32); (5.82, 0.28)\n", - "Adli (5.92, 0.40); (6.02, 0.52)\n", - "Vignato S. (6.04, 0.45); (6.18, 0.57)\n", - "Hrustic (5.75, 0.37); (5.72, 0.37)\n", - "Samek (5.96, 0.45); (6.05, 0.57)\n", - "Zerbin (6.05, 0.42); (6.19, 0.53)\n", - "Ilkhan (5.92, 0.49); (5.99, 0.57)\n", - "Degli Innocenti (5.90, 0.45); (5.94, 0.53)\n", - "Acella (5.90, 0.47); (5.98, 0.64)\n", - "Carboni V. (5.89, 0.47); (5.97, 0.59)\n", - "Paoletti (6.03, 0.40); (6.10, 0.45)\n", - "Malagrida (5.90, 0.46); (5.95, 0.58)\n", - "Faticanti (5.96, 0.45); (6.05, 0.56)\n", - "Osimhen (6.49, 0.75); (7.89, 2.22)\n", - "Martinez L. (6.32, 0.71); (7.68, 2.04)\n", - "Dybala (6.49, 0.67); (7.48, 1.61)\n", - "Rafael Leao (6.32, 0.71); (7.44, 1.82)\n", - "Lookman (6.40, 0.72); (7.68, 2.00)\n", - "Immobile (6.30, 0.71); (7.65, 2.02)\n", - "Vlahovic (6.29, 0.72); (7.73, 2.12)\n", - "Arnautovic (6.21, 0.67); (7.33, 1.73)\n", - "Dia (6.12, 0.68); (7.25, 1.73)\n", - "Dzeko (6.20, 0.67); (7.31, 1.71)\n", - "Milik (6.29, 0.62); (7.19, 1.48)\n", - "Nzola (6.11, 0.66); (7.22, 1.69)\n", - "Beto (6.06, 0.61); (6.94, 1.42)\n", - "Giroud (6.13, 0.65); (6.97, 1.45)\n", - "Abraham (6.19, 0.65); (7.28, 1.69)\n", - "Deulofeu (6.30, 0.64); (7.19, 1.48)\n", - "Lauriente' (6.28, 0.68); (7.19, 1.59)\n", - "Simeone (6.25, 0.70); (7.47, 1.86)\n", - "Lozano (6.15, 0.59); (6.89, 1.27)\n", - "Correa (6.11, 0.50); (6.81, 1.10)\n", - "Berardi (6.28, 0.71); (7.53, 1.94)\n", - "Pedro (6.20, 0.57); (6.86, 1.19)\n", - "Lukaku (5.96, 0.56); (6.48, 1.04)\n", - "Sanabria (6.01, 0.58); (6.58, 1.11)\n", - "Thauvin (6.38, 0.65); (7.30, 1.52)\n", - "Cabral (6.01, 0.57); (6.57, 1.10)\n", - "Hojlund (6.07, 0.62); (6.88, 1.38)\n", - "Caprari (5.99, 0.54); (6.51, 1.03)\n", - "Di Maria (6.26, 0.67); (7.21, 1.61)\n", - "Piatek (5.89, 0.54); (6.34, 0.96)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rebic (6.19, 0.67); (6.98, 1.46)\n", - "Bonazzoli (6.01, 0.50); (6.43, 0.88)\n", - "Zapata D. (5.89, 0.50); (6.25, 0.84)\n", - "Kouame' (6.01, 0.58); (6.51, 1.07)\n", - "Gonzalez N. (6.23, 0.62); (7.02, 1.36)\n", - "Brekalo (6.05, 0.58); (6.64, 1.14)\n", - "Mota (6.07, 0.63); (6.87, 1.38)\n", - "Kean (6.06, 0.62); (6.74, 1.27)\n", - "Okereke (5.95, 0.55); (6.41, 1.00)\n", - "Ceesay (5.99, 0.49); (6.46, 0.90)\n", - "Colombo (6.06, 0.60); (6.68, 1.20)\n", - "Dessers (5.95, 0.50); (6.38, 0.91)\n", - "Muriel (6.12, 0.63); (6.76, 1.28)\n", - "Pinamonti (5.90, 0.55); (6.37, 0.99)\n", - "Di Francesco F. (6.05, 0.52); (6.45, 0.91)\n", - "Jovic (5.96, 0.56); (6.37, 0.98)\n", - "Origi (5.96, 0.56); (6.39, 0.99)\n", - "Caputo (6.01, 0.59); (6.72, 1.26)\n", - "Boga (6.42, 0.65); (7.26, 1.40)\n", - "Cambiaghi (6.10, 0.48); (6.53, 0.84)\n", - "Alvarez A. (6.03, 0.56); (6.50, 1.03)\n", - "Banda (5.99, 0.39); (6.18, 0.53)\n", - "Ciofani D. (5.98, 0.52); (6.44, 0.96)\n", - "Petagna (5.97, 0.56); (6.46, 1.03)\n", - "Barrow (6.05, 0.60); (6.58, 1.12)\n", - "Djuric (5.98, 0.38); (6.17, 0.51)\n", - "Henry (5.89, 0.52); (6.25, 0.87)\n", - "Success (5.94, 0.37); (6.10, 0.50)\n", - "Gabbiadini (5.96, 0.58); (6.45, 1.06)\n", - "Zirkzee (6.04, 0.51); (6.50, 0.89)\n", - "Lammers (5.83, 0.41); (5.94, 0.55)\n", - "Satriano (5.86, 0.45); (6.00, 0.62)\n", - "Kallon (5.87, 0.41); (6.02, 0.58)\n", - "Nestorovski (6.23, 0.50); (7.01, 1.13)\n", - "Raspadori (6.02, 0.52); (6.50, 0.96)\n", - "Botheim (5.90, 0.49); (6.28, 0.84)\n", - "Gytkjaer (5.85, 0.44); (6.08, 0.67)\n", - "Solbakken (6.12, 0.51); (6.38, 0.73)\n", - "Lasagna (5.78, 0.43); (5.89, 0.56)\n", - "Belotti (5.69, 0.33); (5.71, 0.33)\n", - "Pellegri (5.94, 0.47); (6.20, 0.75)\n", - "Buonaiuto (5.92, 0.37); (6.05, 0.48)\n", - "Verde (6.01, 0.48); (6.45, 0.89)\n", - "Destro (5.95, 0.59); (6.47, 1.09)\n", - "Seck (6.10, 0.35); (6.23, 0.45)\n", - "Sansone (5.96, 0.50); (6.23, 0.80)\n", - "Quagliarella (5.83, 0.39); (5.91, 0.50)\n", - "Defrel (5.87, 0.48); (6.10, 0.73)\n", - "Pjaca (5.87, 0.43); (6.01, 0.56)\n", - "Gaich (5.86, 0.52); (6.23, 0.88)\n", - "Soule' (6.18, 0.40); (6.49, 0.65)\n", - "Tsadjout (5.83, 0.39); (5.91, 0.51)\n", - "Piccoli (5.96, 0.56); (6.53, 1.09)\n", - "Shomurodov (6.01, 0.53); (6.52, 1.01)\n", - "Afena-Gyan (5.68, 0.33); (5.65, 0.33)\n", - "Ngonge (6.01, 0.46); (6.19, 0.64)\n", - "Karamoh (5.92, 0.49); (6.24, 0.78)\n", - "Ibrahimovic (6.23, 0.64); (7.23, 1.56)\n", - "Pussetto (5.90, 0.51); (6.26, 0.87)\n", - "Cancellieri (5.77, 0.29); (5.79, 0.24)\n", - "Valencia D. (5.73, 0.29); (5.68, 0.30)\n", - "Oddei (6.08, 0.45); (6.28, 0.62)\n", - "Braaf (5.89, 0.50); (5.99, 0.69)\n", - "Raimondo (6.00, 0.48); (6.18, 0.66)\n", - "Kaio Jorge (5.87, 0.30); (5.93, 0.26)\n", - "De Luca (5.90, 0.47); (5.99, 0.63)\n", - "Voelkerling Persson (5.86, 0.41); (5.88, 0.45)\n", - "Montevago (5.66, 0.32); (5.61, 0.32)\n", - "Krollis (5.91, 0.49); (6.02, 0.67)\n", - "Vivaldo (5.94, 0.47); (6.07, 0.63)\n" - ] - }, - { - "data": { - "text/html": [ - "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaAvg1006.2316510.4153215.7522000.4663985.9918270.2614920.5936561.5897646.1556900.590086-0.4689421.05647451.863781
MussoPAtalantaAvg111006.2360960.4067234.9877620.6286556.0060060.2658510.5673401.5901645.4374330.847580-0.3824341.02644725.157676
Rossi F.PAtalantaAvg1006.0001380.4557283.4212531.0693725.7434560.3022200.5592171.5849534.5156150.947310-0.8582680.9713220.053009
ToloiDAtalantaAvg11766.1824710.4970416.5101970.7675266.1182150.5683530.0832370.9741075.9491161.0457150.3767681.5998780.000000
ScalviniDAtalantaAvg11806.1063350.5422636.4845680.8881506.0352030.6188260.0843940.9432025.8001931.1675020.4074051.5998530.000000
............................................................
NgongeAVeronaAvg1096.0123850.4609146.1927690.6422345.9485070.5269570.0894401.0271535.7537530.9098690.3430631.5998840.000000
DjuricAVeronaAvg10765.9832040.3825596.1697580.5139845.9213250.4355830.1049661.1182635.8214420.7313870.3390151.5999050.000000
KallonAVeronaAvg10765.8726360.4126386.0247730.5804595.8015790.4676570.1121881.0942015.5841900.7714890.3988691.5998870.000000
BraafAVeronaAvg1045.8904890.4995805.9897700.6871505.8540360.5808560.0462501.0004025.5307680.9848640.3323061.5998530.000000
LasagnaAVeronaAvg11805.7785220.4289155.8883830.5595435.7078190.4872410.1070391.0832485.4726440.7542830.3861471.5998870.000000
\n", - "

520 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " role team oppteam home starter vote% MV MV std \\\n", - "player \n", - "Sportiello P Atalanta Avg 1 0 0 6.231651 0.415321 \n", - "Musso P Atalanta Avg 1 1 100 6.236096 0.406723 \n", - "Rossi F. P Atalanta Avg 1 0 0 6.000138 0.455728 \n", - "Toloi D Atalanta Avg 1 1 76 6.182471 0.497041 \n", - "Scalvini D Atalanta Avg 1 1 80 6.106335 0.542263 \n", - "... ... ... ... ... ... ... ... ... \n", - "Ngonge A Verona Avg 1 0 9 6.012385 0.460914 \n", - "Djuric A Verona Avg 1 0 76 5.983204 0.382559 \n", - "Kallon A Verona Avg 1 0 76 5.872636 0.412638 \n", - "Braaf A Verona Avg 1 0 4 5.890489 0.499580 \n", - "Lasagna A Verona Avg 1 1 80 5.778522 0.428915 \n", - "\n", - " FV FV std MV loc MV scale MV skewness \\\n", - "player \n", - "Sportiello 5.752200 0.466398 5.991827 0.261492 0.593656 \n", - "Musso 4.987762 0.628655 6.006006 0.265851 0.567340 \n", - "Rossi F. 3.421253 1.069372 5.743456 0.302220 0.559217 \n", - "Toloi 6.510197 0.767526 6.118215 0.568353 0.083237 \n", - "Scalvini 6.484568 0.888150 6.035203 0.618826 0.084394 \n", - "... ... ... ... ... ... \n", - "Ngonge 6.192769 0.642234 5.948507 0.526957 0.089440 \n", - "Djuric 6.169758 0.513984 5.921325 0.435583 0.104966 \n", - "Kallon 6.024773 0.580459 5.801579 0.467657 0.112188 \n", - "Braaf 5.989770 0.687150 5.854036 0.580856 0.046250 \n", - "Lasagna 5.888383 0.559543 5.707819 0.487241 0.107039 \n", - "\n", - " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", - "player \n", - "Sportiello 1.589764 6.155690 0.590086 -0.468942 1.056474 \n", - "Musso 1.590164 5.437433 0.847580 -0.382434 1.026447 \n", - "Rossi F. 1.584953 4.515615 0.947310 -0.858268 0.971322 \n", - "Toloi 0.974107 5.949116 1.045715 0.376768 1.599878 \n", - "Scalvini 0.943202 5.800193 1.167502 0.407405 1.599853 \n", - "... ... ... ... ... ... \n", - "Ngonge 1.027153 5.753753 0.909869 0.343063 1.599884 \n", - "Djuric 1.118263 5.821442 0.731387 0.339015 1.599905 \n", - "Kallon 1.094201 5.584190 0.771489 0.398869 1.599887 \n", - "Braaf 1.000402 5.530768 0.984864 0.332306 1.599853 \n", - "Lasagna 1.083248 5.472644 0.754283 0.386147 1.599887 \n", - "\n", - " Clean Sheet % \n", - "player \n", - "Sportiello 51.863781 \n", - "Musso 25.157676 \n", - "Rossi F. 0.053009 \n", - "Toloi 0.000000 \n", - "Scalvini 0.000000 \n", - "... ... \n", - "Ngonge 0.000000 \n", - "Djuric 0.000000 \n", - "Kallon 0.000000 \n", - "Braaf 0.000000 \n", - "Lasagna 0.000000 \n", - "\n", - "[520 rows x 19 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", - "\n", - "tot_matches = 2 # home and not home\n", - "\n", - "current_season_games = max(players_orig['games'])\n", - "\n", - "for i in range(players.shape[0]):\n", - " try:\n", - " home = 0\n", - "\n", - " for k in range(tot_matches):\n", - " #matchday_out = k + 1\n", - " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", - "\n", - " player = players.index[i]\n", - " team = players['team'][i]\n", - " oppteam = 'Avg'\n", - " home = not home\n", - "\n", - " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", - "\n", - " role = players['r'][player] \n", - "\n", - " starter = 0\n", - " voteperc = 0\n", - "\n", - " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", - " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", - "\n", - " cs = 0\n", - " if(role == 'P'):\n", - " cs = dist[2].probs.numpy()[0] * 100\n", - "\n", - " starter = int( player in gk_starters )\n", - " if(starter):\n", - " voteperc = 100\n", - " else:\n", - " voteperc = 0\n", - " else:\n", - " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", - " voteperc = int( min( 1, games / current_season_games ) * 100) \n", - "\n", - " if(k == 0):\n", - " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", - "\n", - " numrow_ = [mean[0], std[0], \n", - " mean[1], std[1], \n", - " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", - " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", - " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", - " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", - " cs] \n", - "\n", - " if(k == 0):\n", - " numrow = numrow_\n", - " else:\n", - " for j in range(len(numrow)):\n", - " numrow[j] += numrow_[j]\n", - "\n", - " for j in range(len(numrow)):\n", - " numrow[j] /= tot_matches\n", - "\n", - " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", - " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", - "\n", - " row += numrow # list concat\n", - "\n", - " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", - "\n", - " output = pd.concat([output, row_df])\n", - " except:\n", - " print(players.index[i] + ' no data')\n", - " \n", - " \n", - "\n", - "output = output.set_index('player')\n", - "\n", - "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", - "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", - "\n", - "output" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "b47cbd63", - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "\n", - "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", - "\n", - "template_file = 'outputs/pred_matchday_base.xlsx'\n", - "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", - "\n", - "shutil.copyfile(template_file, dest_file)\n", - "\n", - "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", - " output.to_excel(writer, sheet_name='data')" - ] - }, - { - "cell_type": "markdown", - "id": "cf9df3bb", - "metadata": {}, - "source": [ - "Various predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "7300f3c2", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meret: MV 6.28 ± 0.85; FV 5.50 + 1.44 (51.2% cs)\n", - "Szczesny: MV 6.24 ± 0.82; FV 5.66 + 1.20 (64.1% cs)\n", - "Provedel: MV 6.25 ± 0.83; FV 5.65 + 1.27 (62.9% cs)\n", - "Maignan: MV 6.51 ± 0.90; FV 5.93 + 1.10 (45.0% cs)\n", - "Rui Patricio: MV 6.05 ± 0.77; FV 5.38 + 1.25 (53.8% cs)\n", - "Onana: MV 6.37 ± 0.78; FV 5.16 + 1.17 (18.4% cs)\n", - "Milinkovic-Savic V.: MV 6.20 ± 0.77; FV 5.35 + 0.89 (26.9% cs)\n", - "Musso: MV 6.36 ± 0.86; FV 5.64 + 1.24 (43.8% cs)\n", - "Vicario: MV 6.46 ± 0.87; FV 5.88 + 1.21 (60.8% cs)\n", - "Silvestri: MV 6.39 ± 0.83; FV 5.71 + 1.16 (46.2% cs)\n", - "Terracciano: MV 6.19 ± 0.77; FV 4.58 + 1.10 (4.1% cs)\n", - "Skorupski: MV 6.17 ± 0.71; FV 4.56 + 0.94 (5.2% cs)\n", - "Falcone: MV 6.37 ± 0.84; FV 5.13 + 1.02 (8.7% cs)\n", - "Di Gregorio: MV 6.42 ± 0.87; FV 5.69 + 0.99 (36.9% cs)\n", - "Consigli: MV 6.39 ± 0.78; FV 4.83 + 1.19 (7.7% cs)\n", - "Carnesecchi: MV 6.33 ± 0.82; FV 5.49 + 1.28 (37.1% cs)\n", - "Montipo': MV 6.30 ± 0.89; FV 4.39 + 1.24 (2.4% cs)\n", - "Audero: MV 6.26 ± 0.81; FV 4.60 + 1.03 (4.2% cs)\n", - "Dragowski: MV 6.37 ± 0.81; FV 4.71 + 1.13 (3.1% cs)\n", - "Ochoa: MV 6.44 ± 0.82; FV 5.12 + 1.19 (6.2% cs)\n", - "Tatarusanu: MV 5.86 ± 0.65; FV 3.31 + 2.21 (0.9% cs)\n", - "Handanovic: MV 6.09 ± 0.70; FV 4.79 + 1.18 (11.7% cs)\n", - "Sportiello: MV 6.22 ± 0.78; FV 5.37 + 1.22 (27.8% cs)\n", - "Sepe: MV 6.52 ± 0.80; FV 5.22 + 1.24 (12.7% cs)\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.51791594, 5.21583604]),\n", - " array([0.40012765, 0.61914635], dtype=float32),\n", - " [,\n", - " ,\n", - " ]]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Meret', log = 1, plot = 0)\n", - "predict_player('Szczesny', log = 1, plot = 0)\n", - "predict_player('Provedel', log = 1, plot = 0)\n", - "predict_player('Maignan', log = 1, plot = 0)\n", - "predict_player('Rui Patricio', log = 1, plot = 0)\n", - "predict_player('Onana', log = 1, plot = 0)\n", - "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", - "predict_player('Musso', log = 1, plot = 0)\n", - "predict_player('Vicario', log = 1, plot = 0)\n", - "predict_player('Silvestri', log = 1, plot = 0)\n", - "predict_player('Terracciano', log = 1, plot = 0)\n", - "predict_player('Skorupski', log = 1, plot = 0)\n", - "predict_player('Falcone', log = 1, plot = 0)\n", - "predict_player('Di Gregorio', log = 1, plot = 0)\n", - "predict_player('Consigli', log = 1, plot = 0)\n", - "predict_player('Carnesecchi', log = 1, plot = 0)\n", - "predict_player('Montipo\\'', log = 1, plot = 0)\n", - "predict_player('Audero', log = 1, plot = 0)\n", - "predict_player('Dragowski', log = 1, plot = 0)\n", - "predict_player('Ochoa', log = 1, plot = 0)\n", - "predict_player('Tatarusanu', log = 1, plot = 0)\n", - "predict_player('Handanovic', log = 1, plot = 0)\n", - "predict_player('Sportiello', log = 1, plot = 0)\n", - "predict_player('Sepe', log = 1, plot = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "bd126870", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", - "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", - "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", - "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", - "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", - "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", - "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", - "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", - "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", - "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", - "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", - "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", - "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.41176047, 8.29456946]),\n", - " array([0.70926785, 2.7289915 ], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Muriel', plot = 0, log = 1)\n", - "predict_player('Tonali', plot = 0, log = 1)\n", - "predict_player('Lobotka', plot = 0, log = 1)\n", - "predict_player('Politano', plot = 0, log = 1)\n", - "predict_player('Zapata D.', plot = 0, log = 1)\n", - "predict_player('Frattesi', plot = 0, log = 1)\n", - "predict_player('Gonzalez N.', plot = 0, log = 1)\n", - "predict_player('Abraham', plot = 0, log = 1)\n", - "predict_player('Pobega', plot = 0, log = 1)\n", - "predict_player('Mario Rui', plot = 0, log = 1)\n", - "predict_player('Cuadrado', plot = 0, log = 1)\n", - "predict_player('Skriniar', plot = 0, log = 1)\n", - "predict_player('Lukaku', plot = 0, log = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "4b9f5a7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Skriniar: MV 6.14 ± 0.82; FV 6.38 + 1.13\n", - "Cuadrado: MV 6.29 ± 0.96; FV 6.83 + 1.94\n", - "Bastoni: MV 6.13 ± 0.73; FV 6.32 + 0.98\n", - "Barak: MV 6.31 ± 1.41; FV 7.50 + 3.97\n", - "Politano: MV 6.18 ± 0.82; FV 6.57 + 1.55\n", - "Smalling: MV 6.17 ± 0.86; FV 6.56 + 1.43\n", - "Gosens: MV 6.05 ± 0.54; FV 6.11 + 0.66\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.04807256, 6.10812885]),\n", - " array([0.27137518, 0.32888246], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Skriniar', log = 1, oldseason= True)\n", - "predict_player('Cuadrado', log = 1, oldseason= True)\n", - "predict_player('Bastoni', log = 1, oldseason= True)\n", - "predict_player('Barak', log = 1, oldseason= True)\n", - "predict_player('Politano', log = 1, oldseason= True)\n", - "predict_player('Smalling', log = 1, oldseason= True)\n", - "predict_player('Gosens', log = 1, oldseason= True)\n", - "\n", - "predict_player('Skriniar', log = 1, oldseason= False)\n", - "predict_player('Cuadrado', log = 1, oldseason= False)\n", - "predict_player('Bastoni', log = 1, oldseason= False)\n", - "predict_player('Barak', log = 1, oldseason= False)\n", - "predict_player('Politano', log = 1, oldseason= False)\n", - "predict_player('Smalling', log = 1, oldseason= False)\n", - "predict_player('Gosens', log = 1, oldseason= False)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "10c7ad3e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0XLx40dyH5caNGzhy5AgcHR3x0UcfVeq+fv31V3NScbdhw4bh22+/Rd++fdG7d2+MGzcOAQEByMjIQEJCAo4fP47169cDMH3h/Pe//8WMGTPQtWtXnDlzBrNmzUJoaCj0er35nGPGjMH8+fMxduxYXLp0Cc2bN8eBAwcwe/Zs9OvXDz169Cgz1qK+IGfPnrUY1VSSyMhIREZGVuIdKW7AgAFYsWIFwsLC0KJFC8TGxmLu3LlVMp+Fm5sbBg8ejBUrVuDWrVt48803LfprnDp1Cq+88gqefvppNGjQAEqlErt27cKpU6fw7rvvWjUWmUyGOXPmYPTo0RgwYABefPFFaDQazJ07F7du3cInn3xy33N06tQJ//nPfzB+/HgcO3YMXbp0gaOjI5KTk3HgwAE0b968wgljvXr1YG9vjx9++AGNGzeGk5MT/P394e/vX9lbBVD+z3GRP//8E56enpVKqKiGkrb/LNUmRaNpjh49WmxfQUGBGBQUJDZo0EDU6/WiKIriJ598IoaEhIgqlUps3LixuGTJkhJ79Jc2miY6Olps1KiRqFKpxLp164pRUVHismXLRABiYmKiRdlNmzaJ3bp1E11cXESVSiUGBweLw4YNE//44w9zmYqOpiltKXLy5Elx+PDhore3t2hnZyf6+vqKTzzxhLho0SJzGY1GI7755ptiQECAqFarxTZt2oibNm0Sx44dW2z0Snp6ujhp0iTRz89PVCgUYnBwsDht2jSxsLDwvjFnZWWJTk5O4pw5cyy23z2apiyVHU2TmZkpTpw4UfT29hYdHBzExx9/XNy/f7/YtWtXsWvXrveNozwjdO62Y8cO87/D2bNnLfbduHFDHDdunBgWFiY6OjqKTk5OYosWLcT//e9/5s9kae43gure0TRFNm3aJLZv315Uq9Wio6Oj2L17d/HgwYMVOnd0dLTYvn170dHRUbS3txfr1asnjhkzRjx27Ji5TGk/IyV9jtasWSOGhYWJdnZ2IgBxxowZFnHcLTg4WOzfv3+x897771eRz7HRaBSDg4OLjXAj2yaIYgkzQBFRrfPqq69i586dOH36dJU/g4SoNDt37kSvXr1w+vRphIWFSR0OVRMmI0QEwDQ6o2HDhli2bNkDj5Ihqqxu3bqhfv36WLJkidShUDVinxEiAmCahvuHH34oNqSUqLpkZmaia9eu5iHuVHuwZoSIiIgkxaG9REREJCkmI0RERCQpJiNEREQkqRrRgdVoNOL69etwdnbmkEMiIqIaQhRF5OTkwN/f32KSwXvViGTk+vXrCAwMlDoMIiIiqoQrV66UObNyjUhGiqbUvnLlClxcXCSOhqjmycsDimb0vn4dcHSUNh5ryNPmwf9z001df+M6HJU2cFNENiY7OxuBgYElPhrjbjUiGSlqmnFxcWEyQlQJdz0vEC4utpGMyLVy4PYDmV1cXJiMED3E7tfFgh1YiYiISFI1omaEiB6MQgGMHXtn3RYoZAqMbTnWvE5ENVeNmIE1Ozsbrq6uyMrKYjMNERFRDVHe72/+OUFEZGMMBgN0Op3UYVAtIJfLoVAoHnjaDSYjRLWAKAL5+aZ1BwfAFqbrEUUR+TrTTTnYOXAOottyc3Nx9epV1IBKb7IRDg4O8PPzg1KprPQ5mIwQ1QL5+YCTk2k9N9c2RtPk6/LhFGW6qdxpuRxNA1ONyNWrV+Hg4IA6deowQaMqJYoitFotbt68icTERDRo0KDMic3KwmSEiMhG6HQ6iKKIOnXqwN7eXupwqBawt7eHnZ0dLl++DK1WC7VaXanzcGgvEZGNYY0IVafK1oZYnMMKcRARERFVGpMRIiIikhSTESIiIpIUkxEiIpLUuHHjIAgCJk2aVGzf5MmTIQgCxo0bBwAYOHAgevToUeJ5Dh8+DEEQcPz48VKvlZCQgCeffBKurq5wdnbGY489hqSkpFLLb9y4EeHh4XBzc4OjoyNatWqF77//vtTyUVFREAQBU6ZMKbWMNRQWFmLcuHFo3rw5FAoFBg0aVKHjNRoNWrVqBUEQEBcXZ96enp6OPn36wN/fHyqVCoGBgXjllVeQnZ1t3Ru4B5MRolpALgeGDTMtdz80ryaTy+QY1mQYhjUZBrnMRm6qFgsMDMSPP/6IgoIC87bCwkKsWbMGQUFB5m0TJ07Erl27cPny5WLniI6ORqtWrdCmTZsSr3HhwgU8/vjjCAsLw549e3Dy5El88MEHZY4A8fDwwPTp03H48GGcOnUK48ePx/jx47F9+/ZiZY8ePYrFixejRYsWFbl1AMDMmTPNCVd5GAwG2Nvb47XXXis1OSvL22+/Df+iR3nfRSaT4amnnsIvv/yCs2fPYsWKFfjjjz9KTBStiUN7iWoBtRpYv17qKKxLrVBj/dM2dlNWdvdkd9WtopPrtWnTBhcvXsTGjRsxevRoAKZaicDAQNStW9dcbsCAAfD29saKFSswY8YM8/b8/HysXbsWs2fPLvUa06dPR79+/TBnzhzztrvPXZKIiAiL16+//jpWrlyJAwcOoHfv3ubtubm5GD16NJYsWYKPP/64XPf8IBwdHbFw4UIAwMGDB3Hr1q1yH/v7779jx44d+Omnn/D7779b7HN3d8dLL71kfh0cHIzJkydj7ty5Vom7NKwZISKyUUWT3UmxVCYJGj9+PJYvX25+HR0djQkTJliUUSgUGDNmDFasWGExy+z69euh1WrNicy9jEYjtmzZgoYNG6J3797w9vZG+/btsWnTpnLHJ4oidu7ciTNnzqBLly4W+15++WX079+/UrUU1enGjRt44YUX8P3338PBweG+5a9fv46NGzeia9euVRoXkxEiInooPPfcczhw4AAuXbqEy5cv4+DBg3j22WeLlZswYQIuXbqEPXv2mLdFR0djyJAhcHd3L/HcqampyM3NxSeffII+ffpgx44dGDx4MIYMGYK9e/eWGVdWVhacnJygVCrRv39/fP311+jZs6d5/48//ojY2FhERUWV+173798PJycn8zJ79mz88MMPxbZZkyiKGDduHCZNmoTw8PAyy44cORIODg4ICAiAi4sLli5datVY7sVmGqJaIC/P9qaDz9PmcTr4+3BwMP17S3XtivLy8kL//v2xcuVKiKKI/v37w8vLq1i5sLAwdOzYEdHR0ejWrRsuXLiA/fv3Y8eOHaWe22g0AgCeeuopTJ06FQDQqlUrHDp0CIsWLSrzL39nZ2fExcUhNzcXO3fuRGRkJOrWrYuIiAhcuXIFr7/+Onbs2FGh2UfDw8MtOo5+9dVXuHbtGj799FPzNg8Pj3Kfrzy+/vprZGdnY9q0afct+7///Q8zZszAmTNn8N577yEyMhILFiywajx3q3Aysm/fPsydOxexsbFITk7Gzz//XO5evAcPHkTXrl3RrFkzi38EIiKyPkGoeYnnhAkT8MorrwAA5s+fX2q5iRMn4pVXXsH8+fOxfPlyBAcHo3v37qWW9/LygkKhQJMmTSy2N27cGAcOHCgzJplMhvr16wMwJTAJCQmIiopCREQEYmNjkZqairZt25rLGwwG7Nu3D9988w00Gg3kJfQat7e3N58TMCUe2dnZFtusbdeuXfjzzz+hUqkstoeHh2P06NFYuXKleZuvry98fX0RFhYGT09PdO7cGR988AH8/PyqJLYKN9Pk5eWhZcuW+Oabbyp0XFZWFsaMGVPmh4WIiGq3Pn36QKvVQqvVWnQQvdfw4cMhl8uxevVqrFy5EuPHjy9zGnylUol27drhzJkzFtvPnj2L4ODgCsUoiiI0Gg0AoHv37vj7778RFxdnXoq+3OPi4kpMRKTy1Vdf4eTJk+Y4t27dCgBYu3Yt/u///q/U44r65hTdc1WocM1I37590bdv3wpf6MUXX8SoUaMgl8sr1GGIiIhqD7lcjoSEBPN6aZycnDBixAi89957yMrKKtew2LfeegsjRoxAly5d0K1bN2zbtg2//vqrRd+TMWPGICAgwNz/IyoqCuHh4ahXrx60Wi22bt2K7777zjySxdnZGc2aNbO4jqOjIzw9PYttv5tWq0VGRob5ddHQ2ZSUFIt7dCpqXy1BfHy8+Tw5OTnmFodWrVoBAI4cOYIxY8Zg586dCAgIsBgiXXR+AKhXrx4eeeQRAMDWrVtx48YNtGvXDk5OToiPj8fbb7+NTp06ISQkpNRYHlS19BlZvnw5Lly4gFWrVpVryJNGo7HIwKp6shUiInp4uLi4lKvcxIkTsWzZMvTq1avYF21JBg8ejEWLFiEqKgqvvfYaGjVqhJ9++gmPP/64uUxSUpLFg9/y8vIwefJkXL16Ffb29ggLC8OqVaswYsSIit/YXQ4dOoRu3bqVWWbGjBmYOXNmqfv79etnMd9K69atAdypycjPz8eZM2eg0+nKHZe9vT2WLFmCqVOnQqPRIDAwEEOGDMG7775b7nNUhiDePTaqogcLwn37jJw7dw6PP/449u/fj4YNG2LmzJnYtGlTmX1GZs6ciY8++qjY9qysrHJ/SInoDnZgrR0KCwuRmJiI0NDQSj/KnaiiyvrcZWdnw9XV9b7f31U6tNdgMGDUqFH46KOP0LBhw3IfN23aNGRlZZmXK1euVGGUREREJKUqbabJycnBsWPHcOLECXPvaKPRCFEUoVAosGPHDjzxxBPFjlOpVMV6+xJR5cnlQL9+d9ZtgVwmR78G/czrRFRzVWky4uLigr///tti24IFC7Br1y5s2LABoaGhVXl5IrpNrQa2bJE6CutSK9TYMsrGboqolqpwMpKbm4vz58+bXycmJiIuLg4eHh4ICgrCtGnTcO3aNXz33XeQyWTFehN7e3tDrVaX2cuYiIiIao8KJyPHjh2z6AEcGRkJABg7dixWrFiB5OTkMh/HTERERHS3BxpNU13K2xuXiEqWlwd4e5vWU1NtZzSN92emm0p9M5WjacDRNCQNa4ym4bNpiGoJqR4lX5XydTZ4U0S1EJ/aS0RERJJiMkJERESSYjJCREREkmIyQkREkho3bhwEQSi2nD9/HgMHDkSPHj1KPO7w4cMQBAHHjx8vcX9J5xQEAXPnzi01Fp1Oh1mzZqFevXpQq9Vo2bIltm3bZlFm5syZxc7p6+tb+Teggs6fPw9nZ2e4ubmVWW7Pnj2lvgdHjx41l9u5cyc6duwIZ2dn+Pn54Z133oFer6/iu7DEZISIiCTXp08fJCcnWyyhoaGYOHEidu3aZfFAuCLR0dFo1aoV2rRpU+I57z1fdHQ0BEHA0KFDS43j/fffx7fffouvv/4a8fHxmDRpEgYPHowTJ05YlGvatKnFue+d4PN+Zs6cWa4nDd9Lp9Nh5MiR6Ny5833LduzYsdh78PzzzyMkJATh4eEAgFOnTqFfv37o06cPTpw4gR9//BG//PJLlT8Y714cTUNUC8hkQNeud9ZtgUyQoWtwV/M6FSeKomQjjhzsHCAIQrnLq1SqEmsXBgwYAG9vb6xYsQIzZswwb8/Pz8fatWsxe/bsUs957/k2b96Mbt26oW7duqUe8/3332P69Onod/v5CS+99BK2b9+Ozz//HKtWrTKXUygU1VobUuT9999HWFgYunfvjkOHDpVZVqlUWsSo0+nwyy+/4JVXXjH/2/z4449o0aIFPvzwQwBA/fr1ERUVhZEjR2LGjBlwdnauupu5C5MRolrA3h7Ys0fqKKzL3s4ee8btkTqMh1q+Lt/8ZOPqZq0nKSsUCowZMwYrVqzAhx9+aP4SXb9+PbRaLUaPHl2u89y4cQNbtmzBypUryyyn0WiKzZVhb2+PAwcOWGw7d+4c/P39oVKp0L59e8yePbvMJMcadu3ahfXr1yMuLg4bN26s8PG//PIL0tLSLGpkSrvfwsJCxMbGIiIi4gGjLh/+OUFERJL77bff4OTkZF6efvpp874JEybg0qVL2HNXRh0dHY0hQ4bA3d29XOdfuXIlnJ2dMWTIkDLL9e7dG/PmzcO5c+dgNBoRExODzZs3Izk52Vymffv2+O6777B9+3YsWbIEKSkp6NixI9LT0yt20xWQnp6OcePGYcWKFZWe/HPZsmXo3bs3AgMDzdt69+6NQ4cOYc2aNTAYDLh27Ro+/vhjALC456rGmhEiIhvlYOeA3Gm5kl27Irp164aFCxeaXzveNU1wWFgYOnbsiOjoaHTr1g0XLlzA/v37sWPHjnKfPzo6GqNHj77vzLRffvklXnjhBYSFhUEQBNSrVw/jx4/H8uXLzWX69u1rXm/evDk6dOiAevXqYeXKleZHpNxr//79FsdptVqIoogNGzaYt7333nt47733Sjz+hRdewKhRo9ClS5dy3e+9rl69iu3bt2PdunUW23v16oW5c+di0qRJeO6556BSqfDBBx/gwIEDkFfnI77FGiArK0sEIGZlZUkdClGNlJsril5epiU3V+porCNXkyt6zfESveZ4ibkaG7mpB1RQUCDGx8eLBQUFUodSIWPHjhWfeuqpMsssW7ZMtLe3F7OyssTp06eLISEhotFoLNf59+3bJwIQ4+Liyh1TQUGBePXqVdFoNIpvv/222KRJkzLL9+jRQ5w0aVKp+/Pz88Vz586Zl1dffVUcMmSIxbb09PRSj3d1dRXlcrl5kclkIgBRLpeLy5Ytu+/9zJo1S6xTp46o1WpL3G80GsVr166J+fn5Ynx8vAhAPHLkyH3PK4plf+7K+/3NmhGiWiItTeoIrC8t3wZviko0fPhwvP7661i9ejVWrlyJF154odwdZJctW4a2bduiZcuW5b6eWq1GQEAAdDodfvrpJwwfPrzUshqNBgkJCWWOcLG3t0f9+vXNrz08PJCdnW2xrSyHDx+GwWAwv968eTM+/fRTHDp0CAEBAWUeK4oili9fjjFjxsDOzq7EMoIgwN/fHwCwZs0aBAYGljpKqSowGSEiooeek5MTRowYgffeew9ZWVnlHhabnZ2N9evX4/PPPy9x/5gxYxAQEICoqCgAwF9//YVr166hVatWuHbtGmbOnAmj0Yi3337bfMybb76JgQMHIigoCKmpqfj444+RnZ2NsWPHPvB9lqZx48YWr48dOwaZTIZmzZqZtx05cgRjxozBzp07LRKUXbt2ITExERMnTizx3HPnzkWfPn0gk8mwceNGfPLJJ1i3bl21NtOwAysREdUIEydORGZmJnr06IGgoKByHfPjjz9CFEWMHDmyxP1JSUkWHTULCwvx/vvvo0mTJhg8eDACAgJw4MABiwnGrl69ipEjR6JRo0YYMmQIlEol/vzzTwQHBz/Q/T2o/Px8nDlzBjqdzmL7smXL0LFjx2IJTZHff/8dnTt3Rnh4OLZs2YLNmzdj0KBB1RDxHYIoimK1XrESyvsIYiIqWV4e4HR7hGduLuD44CMuJZenzTMPW7XWMNKarqxHuRNVlbI+d+X9/mbNCBEREUmKyQgRERFJih1YiWoBmQy4/SgKm5oOPtw/3LxORDUXkxGiWsDeHrjrIZ02wd7OHkdfsLGbIqql+OcEEZGNqQHjEsiGWOPzxmSEiMhGFM0LodVqJY6EapP8fNOToUubUK082ExDVAvk5wNNmpjW4+MBh4o9NuShlK/LR5P5ppuKfzm+ws9CsUUKhQIODg64efMm7OzsILOVDkL0UBJFEfn5+UhNTYWbm9sDTZLGZISoFhBF4PLlO+u2QBRFXM66bF4n05Tefn5+SExMxOWif3CiKubm5gZfX98HOgeTESIiG6JUKtGgQQM21VC1sLOzs8q08UxGiIhsjEwm4wysVKOwQZGIiIgkxWSEiIiIJMVkhIiIiCTFPiNEtYAg3BnaKwjSxmItgiCgSZ0m5nUiqrmYjBDVAg4OwOnTUkdhXQ52Djg92cZuiqiWYjMNERERSYrJCBEREUmKyQhRLZCfDzRtalpuP0aixsvX5aPpgqZouqAp8nU2clNEtRT7jBDVAqJoeiZN0botEEUR8TfjzetEVHOxZoSIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJFXhZGTfvn0YOHAg/P39IQgCNm3aVGb5jRs3omfPnqhTpw5cXFzQoUMHbN++vbLxElElCAIQHGxabGXmdEEQEOwajGDXYE4HT1TDVTgZycvLQ8uWLfHNN9+Uq/y+ffvQs2dPbN26FbGxsejWrRsGDhyIEydOVDhYIqocBwfg0iXT4uAgdTTW4WDngEtTLuHSlEtwsLORmyKqpQTxAQboC4KAn3/+GYMGDarQcU2bNsWIESPw4Ycflqt8dnY2XF1dkZWVBRcXl0pESkRERNWtvN/f1T7pmdFoRE5ODjw8PEoto9FooNFozK+zs7OrIzQiIiKSQLV3YP3888+Rl5eH4cOHl1omKioKrq6u5iUwMLAaIySyPQUFQLt2pqWgQOporKNAV4B2S9qh3ZJ2KNDZyE0R1VLVWjOyZs0azJw5E5s3b4a3t3ep5aZNm4bIyEjz6+zsbCYkRA/AaASOHbuzbguMohHHrh8zrxNRzVVtycjatWsxceJErF+/Hj169CizrEqlgkqlqqbIiIiISErV0kyzZs0ajBs3DqtXr0b//v2r45JERERUQ1S4ZiQ3Nxfnz583v05MTERcXBw8PDwQFBSEadOm4dq1a/juu+8AmBKRMWPG4Msvv8Rjjz2GlJQUAIC9vT1cXV2tdBtERERUU1W4ZuTYsWNo3bo1WrduDQCIjIxE69atzcN0k5OTkZSUZC7/7bffQq/X4+WXX4afn595ef311610C0RERFSTVbhmJCIiAmVNTbJixQqL13v27KnoJYiIiKgWqfZ5RohIGl5eUkdgfV4ONnhTRLUQkxGiWsDREbh5U+oorMtR6Yibb9nYTRHVUnxqLxEREUmKyQgRERFJiskIUS1QUABERJgWW5oOPmJFBCJWRHA6eKIajn1GiGoBoxHYu/fOui0wikbsvbzXvE5ENRdrRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUhxNQ1RLODhIHYH1OdjZ4E0R1UJMRohqAUdHIC9P6iisy1HpiLz3bOymiGopNtMQERGRpJiMEBERkaSYjBDVAoWFQP/+pqWwUOporKNQX4j+q/uj/+r+KNTbyE0R1VLsM0JUCxgMwNatd9ZtgcFowNZzW83rRFRzsWaEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxaG9RLWAoyMgilJHYV2OSkeIM2zspohqKdaMEBERkaSYjBAREZGkmIwQ1QKFhcDTT5sWW5oO/un1T+Pp9U9zOniiGo7JCFEtYDAAGzaYFluaDn5D/AZsiN/A6eCJajgmI0RERCQpJiNEREQkKSYjREREJCkmI0RERCQpJiNEREQkKSYjREREJClOB09UCzg4ALm5d9ZtgYOdA3Kn5ZrXiajmYjJCVAsIgun5NLZEEAQ4Km3spohqKTbTEBERkaSYjBDVAhoNMG6cadFopI7GOjR6DcZtGodxm8ZBo7eRmyKqpSqcjOzbtw8DBw6Ev78/BEHApk2b7nvM3r170bZtW6jVatStWxeLFi2qTKxEVEl6PbBypWnR66WOxjr0Rj1WnlyJlSdXQm+0kZsiqqUqnIzk5eWhZcuW+Oabb8pVPjExEf369UPnzp1x4sQJvPfee3jttdfw008/VThYIiIisj0V7sDat29f9O3bt9zlFy1ahKCgIHzxxRcAgMaNG+PYsWP47LPPMHTo0IpenoiIiGxMlfcZOXz4MHr16mWxrXfv3jh27Bh0Ol2Jx2g0GmRnZ1ssREREZJuqPBlJSUmBj4+PxTYfHx/o9XqkpaWVeExUVBRcXV3NS2BgYFWHSURERBKpltE0giBYvBZFscTtRaZNm4asrCzzcuXKlSqPkYiIiKRR5ZOe+fr6IiUlxWJbamoqFAoFPD09SzxGpVJBpVJVdWhERET0EKjyZKRDhw749ddfLbbt2LED4eHhsLOzq+rLExFMU8Cnpt5ZtwUOdg5IfTPVvE5ENVeFm2lyc3MRFxeHuLg4AKahu3FxcUhKSgJgamIZM2aMufykSZNw+fJlREZGIiEhAdHR0Vi2bBnefPNN69wBEd2XIAB16piWUlpHaxxBEFDHsQ7qONYptcmXiGqGCteMHDt2DN26dTO/joyMBACMHTsWK1asQHJysjkxAYDQ0FBs3boVU6dOxfz58+Hv74+vvvqKw3qJiIgIACCIRb1JH2LZ2dlwdXVFVlYWXFxcpA6HqMbRaIDbfzdg3jzAFrpkafQaRG433dS83vOgUtjATRHZmPJ+fzMZIaoF8vIAJyfTem6ubTzBN0+bB6co003lTsvlE3yJHkLl/f7mg/KIiIhIUkxGiIiISFJMRoiIiEhSTEaIiIhIUkxGiIiISFJMRoiIiEhSVT4dPBFJz94eSEy8s24L7O3skfh6onmdiGouJiNEtYBMBoSESB2FdckEGULcQqQOg4isgM00REREJCkmI0S1gFYLvPWWadFqpY7GOrQGLd7a8Rbe2vEWtAYbuSmiWorTwRPVApwOnoikwOngiYiIqEZgMkJERESSYjJCREREkmIyQkRERJJiMkJERESSYjJCREREkuIMrES1gL098M8/d9Ztgb2dPf556R/zOhHVXExGiGoBmQxo2lTqKKxLJsjQ1NvGboqolmIzDREREUmKNSNEtYBWC8yebVp/7z1AqZQ2HmvQGrSYvd90U+91fg9KuQ3cFFEtxengiWoBTgdPRFLgdPBERERUIzAZISIiIkkxGSEiIiJJMRkhIiIiSTEZISIiIkkxGSEiIiJJcZ4RolpArQaOHLmzbgvUCjWOPH/EvE5ENReTEaJaQC4H2rWTOgrrksvkaBdgYzdFVEuxmYaIiIgkxZoRolpAqwW+/NK0/vrrtjMd/Jd/mm7q9cde53TwRDUYp4MnqgU4HTwRSYHTwRMREVGNwGSEiIiIJMVkhIiIiCTFZISIiIgkValkZMGCBQgNDYVarUbbtm2xf//+Msv/8MMPaNmyJRwcHODn54fx48cjPT29UgETERGRbalwMrJ27VpMmTIF06dPx4kTJ9C5c2f07dsXSUlJJZY/cOAAxowZg4kTJ+L06dNYv349jh49iueff/6BgyciIqKar8JDe9u3b482bdpg4cKF5m2NGzfGoEGDEBUVVaz8Z599hoULF+LChQvmbV9//TXmzJmDK1eulOuaHNpL9GAMBqCoArNzZ9OMrDWdwWjA/iTTTXUO6gy5zAZuisjGVMnQXq1Wi9jYWPTq1ctie69evXDo0KESj+nYsSOuXr2KrVu3QhRF3LhxAxs2bED//v1LvY5Go0F2drbFQkSVJ5cDERGmxRYSEcA0HXxESAQiQiKYiBDVcBVKRtLS0mAwGODj42Ox3cfHBykpKSUe07FjR/zwww8YMWIElEolfH194ebmhq+//rrU60RFRcHV1dW8BAYGViRMIiIiqkEq1YFVEASL16IoFttWJD4+Hq+99ho+/PBDxMbGYtu2bUhMTMSkSZNKPf+0adOQlZVlXsrbnENEJdPpgPnzTYtOJ3U01qEz6DD/yHzMPzIfOoON3BRRLVWhZ9N4eXlBLpcXqwVJTU0tVltSJCoqCp06dcJbb70FAGjRogUcHR3RuXNnfPzxx/Dz8yt2jEqlgkqlqkhoRFQGrRZ45RXT+rhxgJ2dpOFYhdagxSu/m25qXKtxsJPbwE0R1VIVqhlRKpVo27YtYmJiLLbHxMSgY8eOJR6Tn58PmczyMvLbjdY14LE4REREVMUq3EwTGRmJpUuXIjo6GgkJCZg6dSqSkpLMzS7Tpk3DmDFjzOUHDhyIjRs3YuHChbh48SIOHjyI1157DY8++ij8/f2tdydERERUI1WomQYARowYgfT0dMyaNQvJyclo1qwZtm7diuDgYABAcnKyxZwj48aNQ05ODr755hu88cYbcHNzwxNPPIFPP/3UendBRERENVaF5xmRAucZIXoweXmAk5NpPTcXcHSUNh5ryNPmwSnKdFO503LhqLSBmyKyMVUyzwgRERGRtTEZISIiIklVuM8IEdU8KhXw22931m2BSqHCbyN/M68TUc3FPiNERERUJdhnhIiIiGoENtMQ1QI6HfDDD6b10aNtYwZWnUGHH/423dTo5qM5AytRDcZmGqJagEN7iUgKbKYhIiKiGoHJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJivOMENUCKhWwbt2ddVugUqiwbtg68zoR1VycZ4SIiIiqBOcZISIiohqBzTREtYBeD/z8s2l98GBAYQM/+XqjHj8nmG5qcOPBUMhs4KaIain+9BLVAhoNMHy4aT031zaSEY1eg+EbTDeVOy0XCqUN3BRRLcVmGiIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpIUkxEiIiKSFJMRIiIikhTHwhHVAkolsHz5nXVboJQrsfyp5eZ1Iqq5OB08ERERVQlOB09EREQ1AptpiGoBvR7Yvt203ru3bczAqjfqsf286aZ61+/N6eCJajD+9BLVAhoNMGCAad2WpoMfsMZ0U5wOnqhmYzMNERERSYrJCBEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJimPhiGoBpRL45ps767ZAKVfim77fmNeJqObidPBERERUJTgdPBEREdUIbKYhqgUMBmD/ftN6586AXC5tPNZgMBqwP8l0U52DOkMus4GbIqqlKlUzsmDBAoSGhkKtVqNt27bYX/RbrhQajQbTp09HcHAwVCoV6tWrh+jo6EoFTEQVV1gIdOtmWgoLpY7GOgr1hei2shu6reyGQr2N3BRRLVXhmpG1a9diypQpWLBgATp16oRvv/0Wffv2RXx8PIKCgko8Zvjw4bhx4waWLVuG+vXrIzU1FXq9/oGDJyIiopqvwh1Y27dvjzZt2mDhwoXmbY0bN8agQYMQFRVVrPy2bdvwzDPP4OLFi/Dw8CjXNTQaDTQajfl1dnY2AgMD2YGVqJLy8gAnJ9N6bi7g6ChtPNaQp82DU5TppnKn5cJRaQM3RWRjqqQDq1arRWxsLHr16mWxvVevXjh06FCJx/zyyy8IDw/HnDlzEBAQgIYNG+LNN99EQUFBqdeJioqCq6ureQkMDKxImERERFSDVKiZJi0tDQaDAT4+PhbbfXx8kJKSUuIxFy9exIEDB6BWq/Hzzz8jLS0NkydPRkZGRqn9RqZNm4bIyEjz66KaESIiIrI9lRpNIwiCxWtRFIttK2I0GiEIAn744Qe4uroCAObNm4dhw4Zh/vz5sLe3L3aMSqWCSqWqTGhERERUw1SomcbLywtyubxYLUhqamqx2pIifn5+CAgIMCcigKmPiSiKuHr1aiVCJiIiIltSoZoRpVKJtm3bIiYmBoMHDzZvj4mJwVNPPVXiMZ06dcL69euRm5sLp9s96M6ePQuZTIZHHnnkAUInovKyswPmzLmzbgvs5HaY02OOeZ2Iaq4Kj6ZZu3YtnnvuOSxatAgdOnTA4sWLsWTJEpw+fRrBwcGYNm0arl27hu+++w4AkJubi8aNG+Oxxx7DRx99hLS0NDz//PPo2rUrlixZUq5rcjp4IiKimqe8398V7jMyYsQIpKenY9asWUhOTkazZs2wdetWBAcHAwCSk5ORlJRkLu/k5ISYmBi8+uqrCA8Ph6enJ4YPH46PP/64ErdFREREtoYPyiOqBQwG4Phx03qbNrYzHfzxZNNNtfFrw+ngiR5CVVYzQkQ1T2Eh8OijpnVbmfSsUF+IR5eaboqTnhHVbHxqLxEREUmKyQgRERFJiskIERERSYrJCBEREUmKyQgRERFJiskIERERSYpDe4lqATs7YMaMO+u2wE5uhxldZ5jXiajm4qRnRDZCFIHft4lYusKIs2cAVzegd0/g1clyuLtLHR0R1Uac9IyoFsnOBkaMNmDbb3IAd2YiPbQXmPe5EdHRwJBBbJUloocTkxGiGi4nB+jU1YB/4uRQ2Ino9Uw+WnTQIP2GHL//4ICr5+0wdDAw/QMjRo6QoXFjQGYDeYlRNCLhZgIAoHGdxpAJNnBTRLUUm2mIajCjEejRx4DdMXK4uBswfXEm6jfXmffrtED0bBfs+PHOVOm2Mh18njYPTlFOADgdPNHDqrzf3/xTgqgGm/O5Ebtj5FDZG4slIgBgpwT+MyMbPUfkmbf9/fdD//cHEdUyTEaIaqgzZ0R8+L4AABj3Tk6xRKSIIADPvZFjfj1mghFabbWESERULkxGiGqo198wQqcV0LKTBj1H5JdZVn5X77BzCXK8P9NYxdEREZUfkxEiCRy6cgitl7ZGk2+bYNOZTRU+fucuEdu3yCGTi5jwXjYEoWLHz5sr4MwZNtcQ0cOByQhRNUvKSkLPVT0Rdy0OCSkJGLp2KHYm7qzQOaa9b6rZ6DUiH4/U01fo2FaPF8KgF/DqVNaOENHDgckIUTWL3BmJfG0+Ar0D0bxucxhFIyb8OgE6Q8l9Pu61/4CIo4dNw3iHvphb4euPisyGXCEi5nc5tu9g7QgRSY/JCFE1Ss1Lxeb4zQCAoRFDMbrnaDjZOyEpMwnLTy4v1zlm/Z+pRiNiUAE8fMpXuyFXiHhyQi6enJCLoPoG9Bll6mPy1rtGPPyD+0tmJ7fDmx3exJsd3uR08EQ1HJMRomr0wz8/QG/QI8gnCCG+IVCr1OjWphsA4H9H/of7Tfvz77/AH9vkEAQRT00of62InRIY+3YOxr6dAzslMHRSLlT2Rvx9Qo7fttTMbEQpV2Jur7mY22sulHKl1OEQ0QNgMkJUjX468xMAoFX9VuZt7Zu0h0KuwL83/sWRa0fKPP6rBQYAQNsIDfxDDZWOw9XDaK4deX9Gza0dISLbwGSEqJrkaHLwV9JfAICmoU3N253sndCyfksAwNJTS0s9vrAQWL3KNGym132G8t7LaARSr8qRelUO4+2Wnacm5EFlb8Sp4zWzdsQoGnHp1iVcunUJRpGdcYlqMiYjRNXkYNJB6A16eLh4wNvd22Jf6watAQCbEjaV+sW6dr0RWZkyePnr0aqzpkLX1hYKeKmHN17q4Q1toSmhcfU0ovczpqTm/z6peV/mBboChH4ZitAvQ1GgK5A6HCJ6AExGiKrJzium4bt1/etCuGdikLCgMKiVaqTlpuFg0sESj1+wyFR70WNYAeTyEotU2ICxeZArRPx1UI5jx2pe7QgR2QYmI0TV5MCVAwCAUL/QYvsUCgWa120OAFgVv6rY/sRE4MghU8fVJ4ZWrImmLJ6+RnTqWwgAiPqs5tWOEJFtYDJCVA0MRgNOXjsJoORkBACa1W0GANh2bluxfd/9YOqs2uwxLTzLOZy3vAaMNY3K2fyTDFevWvXURETlwmSEqBrE34xHga4AKjsVfD18SyzTKLARZDIZkjKTcD7jvMW+1WtM/3+8v/X7RtRrpkfTdhoY9AI++6LyI3SIiCqLyQhRNThx4wQAIKBOAGSykn/s1Co16vnXAwBsOrvJvP3vv0WcjTfNuPpYz8IqiW/AuDwAwIpoAYVVcwkiolIxGSGqBkdSTPOH+Hv6l1muSUgTAJbJyMpVpmaZ1l00cHKtmk6mbSM08PLXIytThjVr2XeEiKoXkxGiahB3Iw4A4OflV2a5xiGNAQBHko4gT5sHUQTWrjONvOn8AE00coWIPqPy0GeUafRMsf1yoOdw0/m/WVAzRtUoZApMDp+MyeGToZAppA6HiB4Af4KJqsGZ1DMAAH+vsmtGfNx94OHigYzsDOxK3AX/3AG4ekkGlb0R4d0q335ipwRe+DC7zDLdh+Zj3TdOOH5EjpMnRbRsKZRZXmoqhQrz+8+XOgwisgLWjBBVsfT8dKTlpgEA/DzLrhkRBAFhwWEAgM3nN2PdT7ebaDproLKv2jjd6xjxaA9TwvPlfDbVEFH1YTJCVMX+Tv0bAODp4gm1Un3f8o2DTE01Oy7swCbTA37xaI+Kzbh6L1EEsjJkyMqQlfkcmqIZWdeuliEn54EuWeVEUcTNvJu4mXfzvg8YJKKHG5MRoip2KvUUAMDXs+Qhvfdq8EgDyGQyXMm8grPJlyCTi2jb9cGGuGgKBEzo6IMJHX2gKSi9+aVZey38Q/TIzxPw3aqHu3YkX5cP78+84f2ZN/J11psIjoiqH5MRoioWnxEPAPBy9SpXebVKfWditPrb0fRRbZWNormXIAA9bz+Eb/FS1jYQUfVgMkJUxc5mnAVQ/mQEABoHm5pqUG87Hn2ieif+6PpUAeQKEaeOy3HqFBMSIqp6TEaIqtjlzMsAAC+38icjgc4tTSt1d6J1t7JHwVibq8edkTuLlj7cTTVEZBuYjBBVIVEUcfWW6YEvFakZyTjSGcj1AZR5SLP7q6rCK9UTQ0xzjqz5QYBWW+2XJ6JaplLJyIIFCxAaGgq1Wo22bdti//795Tru4MGDUCgUaNWqVWUuS1TjJOcmQ6vXQibI4OHsUe7jTm+vD5zvDQA4kfJHVYVXqtadNXCrY8CtDBl+/ZVNNURUtSqcjKxduxZTpkzB9OnTceLECXTu3Bl9+/ZFUlJSmcdlZWVhzJgx6N69e6WDJappzqWfAwC4O7tDLpeX6xi9VoZz+wKBC0XJyK4qi680cgUQMchUO7KQTTVEVMUqnIzMmzcPEydOxPPPP4/GjRvjiy++QGBgIBYuXFjmcS+++CJGjRqFDh06VDpYoprmn/R/AFSsiebin/7Q5CrhmNURAgQkZZ5DRkHKA8UhV4iIGJSPiEH5JU4HX5InBptG1ezeIcP16w90+SqhkCkwtuVYjG05ltPBE9VwFUpGtFotYmNj0atXL4vtvXr1wqFDh0o9bvny5bhw4QJmzJhRrutoNBpkZ2dbLEQ10b8Z/wKoWOfVf3cGAwCadMxHoE8gACAuec8DxWGnBF79JAuvfpIFO2X5jgmoa0Cj1loYjQKWrTA80PWrgkqhwopBK7Bi0AqoFCqpwyGiB1ChZCQtLQ0GgwE+Pj4W2318fJCSUvJfbufOncO7776LH374AQpF+f56iYqKgqurq3kJDAysSJhED43zmecBVKxmJOGPEABA4+6XERZkmhr+xI3q7zcCAE8MNdWOLF+OMmduJSJ6EJXqwCoIljM4iqJYbBsAGAwGjBo1Ch999BEaNmxY7vNPmzYNWVlZ5uXKlSuVCZNIcomZiQAAT1fPcpXPvOqE5AQvCDIjGnVLMj+n5mTKARiMla+dEEWgMF9AYb5QoaSiU59CqOyNSDwvx8GDD1c2Iooi8rR5t59u/HDFRkQVU6FkxMvLC3K5vFgtSGpqarHaEgDIycnBsWPH8Morr0ChUEChUGDWrFk4efIkFAoFdu0quWOeSqWCi4uLxUJUE13LvAYAqONWp1zl/91laqIJanMDjh6FCPYNhlqpRp4mGxcyT1Y6Dk2BgNFtfDG6jW+Z08Hfy95JRIfepjlHvl32cHVkzdflwynKCU5RTpwOnqiGq1AyolQq0bZtW8TExFhsj4mJQceOHYuVd3Fxwd9//424uDjzMmnSJDRq1AhxcXFo3779g0VP9BDLKMhAriYXgOkheeVhbqLpcQkAIJfJ0SiwEQAgLnm31WMsj6I5R37eIENeniQhEJGNq3AzTWRkJJYuXYro6GgkJCRg6tSpSEpKwqRJkwCYmljGjBljOrlMhmbNmlks3t7eUKvVaNasGRwdHa17N0QPkfPppv4iLo4uUJaj16hBJ8PZvab+UY27XzZvL2qqOX4jpsTjqlrjcC18AvXIyxWwfgObQ4jI+iqcjIwYMQJffPEFZs2ahVatWmHfvn3YunUrgoNN1cvJycn3nXOEqDY4nXEaQPk7r1466ovCHBUcPfMR2PqGeXtRMnI+7R/kaDKtH+h9yGR35hxZ/JA11RCRbahUB9bJkyfj0qVL0Gg0iI2NRZcuXcz7VqxYgT179pR67MyZMxEXF1eZyxLVKPHpFXtab/ztJpqwJ5Igu+sn093ZHb4evhBFI07dKN9sx9YWMagAgiDi8H45Ll2SJAQismF8Ng1RFTEP6y3nHCN3hvReKravqHZEiqnhAcA7wIBmj5keUrM0mrUjRGRdTEaIqkjRsN7y1IxkJTvi+j91IAgiwp4o3sxpnm8kZY9kw1ifGGIasbJiJWBkPkJEVsQ5lImqyJVM0/w45UlGiob0Bra+ASevgmL76wXUg53CDrfy05CU9S+C3RpXKBaZXESH3gXm9cpo36MQDk5GXEuSYe9eEd26lX+IcFWQy+QY1mSYeZ2Iai4mI0RVIE+bh4y8DADlS0YS/jAlI2FPXC5xv53CDvUD6iPhcgJOpOyscDKiVAFvfnmrQsfcS2UPdOxbiD/WO+DbZUZ06yZtAqBWqLH+6fWSxkBE1sFmGqIqcDHzIgDAQeUAB7VDmWUNegFn9gQBuDO/SEnMQ3xTpBniC9xpqtm8UYacHMnCICIbw2SEqAoUPSCvPNPAXz7mi4IsNRzcChHc9kap5YqSkTOpJ1Cgk2b2sYatdPAP1aOwQMDatZxzhIisg8kIURVIyEgAUL6RNAk7QwAAjbpdLrM/h7ebNzxcPKA36nHqxr4KxVOYL2BomB+GhvmhML/yfT0EAeg22FQ7skTiUTV52jwIHwkQPhKQp+XUsEQ1GZMRoipwJv0MgIr1F2nco+T+IkUEQUCz0GYAgEPXf37ACCsv4qkCyGQijhyW49w5ycIgIhvCZISoClzIvADg/slITqoDrp40PWSytM6rd2tZvyUAIPbKLugMmgeMsnI8fIxo+bjp2suWc4wvET04JiNEVSAp0zRXyP2SkYRdpo6rj7RIhYvP/Z88G+ofChdHFxTo8hGXsvfBA62kboNNw4RXrgQMBsnCICIbwWSEyMp0Bh1uZJs6ot43GbnnKb33IxNkaFnPVDty6NrGSsf4oNo9UQgnVyNSrsuwc6dkYRCRjWAyQmRll7MuwygaYaewg4ujS6nljAYBZ3abakbCut+/iaZIqwatAADHru6EzqB9oFgrS6kCHu9vqh35dimbaojowTAZIbKyc+mmXp1erl4QhNJHriQd90F+pj3ULhqEtEsu9/lD/ULh4uCCfG0eTt2Qvqnmt18E3LolWRhEZAOYjBBZWdGw3vvNMZKw0zSKplHXJMgV5Z+zQyaToUX9FgCAPUk/lu8YuYg2XQvRpmthpaeDv1e9ZjoENdBBqxGwek31zzkil8nRr0E/9GvQj9PBE9VwTEaIrKxowrM6rnXKLHenv0j5m2iKhIeFAwCOXtmJfN39p0JVqoDp32Zi+reZUKoqfLkSCQLQbYipdmTJsupPRtQKNbaM2oIto7ZArVBX+/WJyHqYjBBZ2fmM8wDKrhnJTVfjyonbQ3q7X6rwNYJ9guHt7g2dQYuDSZsrFac1dBlYAJlcRFysDPHxkoVBRDUckxEiK7uUeQlA2SNpzuwOhigK8GuSBjf/is8eKggCHm38KABg9+XVlYrTGty8jGjb1TTnyFKJZ2QlopqLyQiRFRlFI67eugqg7Kngi/qLlHdIb0nCG4VDgIAzqXFIyS27qacwX8Co1j4Y1drngaaDL0m32w/PW/U9oNdb9dRlytPmwXG2IxxnO3I6eKIajskIkRVdz7kOnUEHmUwGd2f3EssYjcC/RclIBYb03svN2Q0NAxsCAHZdWnXf8poCGTQF1v+Rb9tVAxcPA26myrBtW/X2HcnX5SNfd//J4ojo4cZkhMiKLmSYpoH3cPYodYTHtVPeyE1zgMpJi9D21x/oeu2btgcA7Lz4I/RG3QOdq7IUdqa+IwCwWIKOrERU8zEZIbKiMxmmB+SV1Xm16MF4DbpcgUL5YP0sWtRrAWcHZ9zKT8eRa78/0LkeRNGomt+3CEhLkywMIqqhmIwQWdHp9NMAgDpupQ/rTdgZAuDB+osUUcgV6NC0AwBg64XFD3y+ygpppEfdJjrodQJW/cCOrERUMUxGiKzoXMad2VdLkn9LhUtHfQE8WH+Ru3Vo1gGCICAh5TiSss5Y5ZyV0W2wqe/G0mjJQiCiGorJCJEVXcy4CKD0mpGzewIhGmXwaZgOj8D7T1ZWHu7O7mhetzkAYMuFRVY5Z2V0HlAAhZ2I06dkOHlSsjCIqAZiMkJkJaIoIikzCUDpw3rjH2DW1bJ0btEZALD3ws/IKkwvtl+QiWjaToOm7TQQZFXTydTZXUS7JwoBAAsXG6rkGneTCTJ0De6KrsFdIRP4q4yoJuNPMJGVpOalokBXAEEQ4OlcvAOraUhvCADrNdEUqf9IfQT5BEFn0OK3cwuL7VepgVnfZ2DW9xlQVeHM6d2HmTqyrv5BQH4Vj7i1t7PHnnF7sGfcHtjb2VftxYioSjEZIbKSov4i7s7uUCgUxfZfjfNB9g1HqJy0qNfxmlWvLQgCurftDgDYdm5luZ5XUxVadtLA+xE9crJkWL2GHVmJqHyYjBBZydn0swBK77z6z7ZQAEBYt8tQqKzfjNG8XnN4u3sjX5uH7ReXW/385SGTAb1GmKpEvl7AOUeIqHyYjBBZSXy66UlxpXVePb3dlIw07ZNYJdeXCTJz7cgv/36LQv2ddpLCfAHjO3hjfAdvq08Hf68nhpo6sp46LkdsbNVdJ0+bhzpz66DO3DqcDp6ohmMyQmQlRROelVQzknnVCdf+9oYgiGjS81KVxRDeKByeLp7ILsjEr/f0HcnOlCM7s+RZYa3J1cOIDr1NHVm/+KZqO7Km5achLZ+zrBHVdExGiKzkQqZpKviSakZO7zDVioS0S4aTV0GVxSCXy9GvQz8AwOaERcjRZFbZtcrSe6SppmL9jzLcuiVJCERUgzAZIbICURSRlHF7WG8JNSOnt9UFUHVNNHdr3bA1/L38UaDNw09n/lfl1ytJWBsdghrooCkUEL2CHVmJqGxMRoisIC0/DXnaPAgQij2XRpOnwLn9jwAAmva+WOWxyAQZ+nfoDwDYdmYlUnOvVPk17yUIQO+Rpj4rCxYCIvuyElEZmIwQWcH5jPMAADdnN9gp7Cz2ndkTBL1GAc/gLPiGZVRLPE1CmqDBIw2gM+iw7OS71XLNe3V5sgBqByMunJVh925mI0RUOiYjRFYQn2YaSVNSE80/v99uoul9EULVDmQxEwQBQ7oOgUwmw7EruxGXsrt6LnwXBycRXZ409Y+ZM49NNURUOiYjRFZw8qbpYSw+Hj4W240GAfExVTuktzR+nn7o0rILAGDlqQ9Rt6kG9Zppq2w6+JL0f87UVLNjqwznz1v33DJBhnD/cIT7h3M6eKIajj/BRFZQVDPi426ZjFz80x+5Nx3g4FaI+p2sO+tqefR5tA9cHFyQWngeLWdOxZwN6VU6Hfy9HqmnR+vOhRBFAZ/9z7rDfO3t7HH0haM4+sJRTgdPVMMxGSGygrNpptlX760ZOflrfQBAs74XIber/qYKtUqNoRFDAQCb47/FufQT1R7DwHGmYb7freAwXyIqWaWSkQULFiA0NBRqtRpt27bF/v37Sy27ceNG9OzZE3Xq1IGLiws6dOiA7du3VzpgooeNRq/BtVumWo+7a0aMRuDU7WSk5ZNWbqOogJb1W6JNwzYwikZ8dfRlaA2F1Xr9Fh21CGqgQ0G+gEWL2XeEiIqrcDKydu1aTJkyBdOnT8eJEyfQuXNn9O3bF0lJSSWW37dvH3r27ImtW7ciNjYW3bp1w8CBA3HiRPX/hUZUFc5lnINRNEKtVMPF0cW8/fIxX2QlO0HlpEGjiJJ/PqrLwEdHQPjyEq7P2I0Vx6Kq9dqCAPQfa6od+eprQK+3znnzdfkI+SIEIV+EIF9XxY8IJqIqVeFkZN68eZg4cSKef/55NG7cGF988QUCAwOxcGHxx5YDwBdffIG3334b7dq1Q4MGDTB79mw0aNAAv/766wMHT/QwSLiZAMBUKyLcNVymqFakae9LVfJgvIpwUDlCzAwGskKw/cwP+Ovqlmq9fpeBBXDxMCD5qgwbNlinA60oiricdRmXsy5D5EQmRDVahZIRrVaL2NhY9OrVy2J7r169cOjQoXKdw2g0IicnBx4eHqWW0Wg0yM7OtliIHlZFI2m8PbzN20TxTn+RlgOla6IpzddHpiAl93K1XU+pAno/Y6q9+OQzIydBIyILFUpG0tLSYDAY4ONj2UnPx8cHKSkp5TrH559/jry8PAwfPrzUMlFRUXB1dTUvgYGBFQmTqFr9ffNvAJb9Ra6eqoOMJFcoHXRo3OOSRJGVLMgnGAXaPMw5PBYaffU1b/QZlQ+lSsTJWDl27mQ2QkR3VKoDq3DPzE2iKBbbVpI1a9Zg5syZWLt2Lby9vUstN23aNGRlZZmXK1eqfzprovI6k2Z6Wq+vh695W9zmBgCAxt0vQelgpU4SVjK612g4qh1xOf0s/vfXizCK1dOp1M3LiO7DTMnPzI/ZkZWI7qhQMuLl5QW5XF6sFiQ1NbVYbcm91q5di4kTJ2LdunXo0aNHmWVVKhVcXFwsFqKHkd6oR2KGaTKzomG9RiNwfEMjAEDrwWcli600bk5umDhgIuQyOY5e2YlVf39Ubdd+amIu5AoRB/fKcfgwa0eIyKRCyYhSqUTbtm0RExNjsT0mJgYdO3Ys9bg1a9Zg3LhxWL16Nfr371+5SIkeQufSz0Gr10JppzQ/IC/xL39kXnWBykmDJr2rd9bV8qrrXxcje4wEAGyOX4Lfzy+tluvW8Tei6+0p4lk7QkRFKtxMExkZiaVLlyI6OhoJCQmYOnUqkpKSMGnSJACmJpYxY8aYy69ZswZjxozB559/jsceewwpKSlISUlBVlaW9e6CSCJxN+IAmKZeL5qSPHZ9GACg5cALUNpLO4qmiCAAvo3S4dso3fx8nPCwcPR+tDcAYOmxD7H70o/VEsug53MhCCJ2bJXj5MnK144IgoAmdZqgSZ0m5WomJqKHl6KiB4wYMQLp6emYNWsWkpOT0axZM2zduhXBwcEAgOTkZIs5R7799lvo9Xq8/PLLePnll83bx44dixUrVjz4HRBJ6GjyUQCAv5c/AECvlSFus2kUTdthZySL615KBz3ePbyq2PY+7fugQFOAfSf3Yf5fb0ApV6NT4KAqjSWgrgGP9S7E4W32mPmxET+vl1fqPA52Djg9+bSVoyMiKQhiDRign52dDVdXV2RlZbH/CD1UIlZFYO+FvRgWMQyPt3gc/2wLxdJRT8LFJw8z/1kGmfyh//GCUTRi3c51+DP+T8gEGV55bB66Bpc+2s0aEhMUeHNwHQiCiJMngebNWbNBZIvK+/3NZ9MQPYCEVNOEZ0U1I7Hr73RcrQmJCGB6+u3wJ4YjPCzcNGX84SnYcm5xlV4ztLEe7XsWQBQFvDudfUeIajsmI0SVlFGQgdScVACAv6c/8rOU+Of3egCAtk//K2VoxWjzFfikw7P4pMOz0OYXb52VyWQY1XMUOrfsDACIjp2J1f/MrtKZTUe+buo7svVXOY4erfh18nX5aLqgKZouaMrp4IlqOCYjRJV06sYpAICniyfUKjWObwiDrlABv8ZpCGyVKnF0lkQRSDnjiZQznqXOfioTZBjSZQj6tO8DAPjpn2/w2Z8TqmxitMD6enS5PbLm7fcqXjsiiiLib8Yj/mY8p4MnquGYjBBV0uFrhwEAAXUCAAB/ft8UAPDYc6dRUwd3CIKAPu37YMQTIyCXyfHn5e14b3d/pOVdq5LrDX/FNO/Inj/k2LuXCQVRbcVkhKiSDl47CAAI8gnClZN1cPWUN+RKPcKHP1xNNJXRoVkHTB48GY5qR1xKP4M3Yrrj2PUdVr+Ob6DBPCtr5Nt8Zg1RbcVkhKiSTlw/AcCUjPz5fTMAQIsBF+DoUShlWFZTL6AeIp+JxCN1HkFuYTai9o3D0rhp0Bk0Vr3O0y/lQmVvxPEjcqz5kdkIUW3EZISoEm7m3cT1rOsAAB/nUPMomg7P2da8F54unpjy9BR0adUFAPD7vyvx9h89cSHzb6tdw8PHiMEv5AEA3nxbREGB1U5NRDUEkxGiSjhy7QgAwNvdGwm/tkJhjgqewVmo39n2HuqoUCgwpMsQPD/geTiqHZGUeR7v7uiL707NhNZgnVqgJ8fnwsvPgOSrMnz6GYf6EtU2TEaIKmH/1f0AgCCfYOz9thUAoNOEU5A9pD9RggC4B2bDPTC70p1rm9VthneffRetGrSCUTRic/xiTN3eBceTdz5wfCp74Nk3sgEAcz4RcP36/Y8RBAHBrsEIdg3mdPBENRxnYCWqhC7fd8H+i/vROWAi9r+wFEoHHWb+swwObtbtT/GwOnXhFDbs3oDsfFMC0SrgcUxoORsBLvUrfU5RBN57xhNnTyoxdLgBG9ZWbpp4Inp4cAZWoiqiN+oRezUWAHB925MAgEdHxteaRAQAWtRrgXefexcRrSMgk8kQd+0ApmzrhiUn3kFmQeXmWBEEYOL72ZDJRPy0To7tOx76v5OIyEqYjBBVUFxKHPK1+VAp7HHh534AgM7/iZM2KAk4qBwwqPMgvDP6HTQJaQKj0YBtZ77HS789iuUn30dWYXqFz1m/uQ59RpuG+r7wooh8TqxKVCswGSGqoJ2XTH0kHHLaAUYFwrpfgk+DW9IGdR/aAjk+7/4MPu/+DLQF1m3+8HH3wX+e/A9eGvQSgn2CoTNo8VtCNF76rR2Wn3y/whOmjXo9B56+Bly5JMOHM0vvzFqgK0C7Je3Qbkk7FOg4BIeoJmMyQlRBMZdiAABZh/oDALq/fkzKcMpFNAq4csIHV074QDRWTWfPRkGNMGX4FPznyf8g0DsQGn2hKSnZ8hjm/fVCuYcD2zuJeP6DLADAF/8TcOJEyc01RtGIY9eP4dj1YzCKHIFDVJMVf2IWEZXKKBpx5IppWK/xYgRC219H/U5VM1V6TSQIApqENEHj4MaIvxSP3cd34/y18ziYuAUHE7cgzKc1eoeOx2OBA6CUq0s9z6PdNWjfswB/xdjjmdFGnDwuQF16cSKq4VgzQlQBx64fQ05hDqBxAZLboNcbR2rsc2iqkiAIaBraFK8MfQVvPPMG2jZqC5kgw783TuDLP1/D87+0xNK4abiSdbbUc7w4MxuungacTZDhrXdZ80Fky5iMEFXAprObTCvneyGwRTrCul+WNJ6aINA7EM/1fg4fjvsQfdr3gZuTG/I0Ofj935WY8nsE3vzjCWw+swAZBSkWx7l6GjH5Y1NzzTdfyvDHTo6uIbJVTEaIKmDj6d9MK+f7ovfbf7FWpALcnN3Qp30ffDjuQ/znyf+ged3mkAkyJKb9i+9OfIz/bG6L9/cMwPYLK5GtMY3ECe+mQc/hpqninx0j4uZNKe+AiKoK+4wQlVNqXirOpP0DCECQa0s07X1A6pBqJJlMhiYhTdAkpAly8nMQdy4Ox88eR2JyIhJSjiMh5TiWHHsPDeo0x6P+/dDz5f44feQxXL+kwLARBuyKkUPO+dCIbAqTEaJyWrjrV0AQgeTWGDLtfI2rFXH0fPgm7XB2cEbnlp3RuWVnZGRn4PjZ44g7F4erN6/ibOopnE09BeATeE4KgnzfMOz7tw/e/qAjPp/tCADwcvCS9gaIyCo4HTxROYgi4P56b2R57oDP9Vcx7ZN6Uodk0zJzMvHPxX/wT+I/OH/1PAxGw52dehUauoRjVPue6FevH9r4tYFcxqoSoodReb+/mYwQlcPC79Iw+bwfINfjpW6z0ai5g9Qh1RqFmkL8m/QvTl86jZN/X4ZWYTndvLPaGeGPhKNbUDd0C+6GcP9wqBUcB0z0MGAyQmQlt24Bjzy1GHlPvAhnY338d8orUodUa+m1AuZPaIbEmwlQNNkGeYOd0OgtZ1+1k9uhqW9TPB70OLoHdcejAY/C39lfooiJajcmI0RWMm6iESuNTwAhe9H30YHo/Vh3qUOqMG2BHN8OHwQAeHHdJijtDWUf8BArzFbiy35PI/msI+z+0x1eja6ibVhLJKUm4eL1i8gtyC12jKejJ1r6tUR7//bo6N8R4QHh8HXylSB6otqlvN/f7MBKVIZNm0Ws/OUs8MpeCBDQvmlbqUOqFNEo4MLBR8zrNZnaRYsX127GvH79ke19GMmZQNvgN9CjHSCKItKy0pCYnIiL1y/iUvIl3Mi8gfS8dOw6vwu7zu8yn6eOUx00822Glt4tEe4bjhbeLdDIqxGUcqWEd0dUOzEZISpFaiow8XkRaPstAKBpaFO4ObtJGxQBANwCcvHCml/x+R+m10ufHYiX12+Hg6sWddzqoI5bHTza+FEAgEanwbWb13Al9QqupF7B1dSruJFxAzdzb2L3+d3YfX63+bxymRwhHiFo4t0Erbxboa1PWzSu0xihbqGwk9tJcatEtQKTEaIS6PXA0yMNyMgugNBmBUQAHZt3lDosuot3g0zgdjJy7aQPFjw1FC+u2wxnb8shzCo7Fer610Vd/7rmbRqtBtfSruHazWtITk82L4XaQlxIu4ALaRfwa/yv5vJymRwBrgGo51kPYZ5haObVDE28mqChZ0P4OflBqGnjvIkeMkxGiErw9rtG7Nslh7zrQhhUt1DHrQ7CgsKkDotK4eiZj6unvPFFn6cxacMm1KmbVWZ5lbJ4giKKIm7l3kJyejKup11HSnoKktOTkXorFTq9DkmZSUjKTLKoSQEAezt7BLoHItgtGPXc66GReyM0cG+AUPdQhLiFwMGOI6+I7ofJCNE9Vn5vxP8+lwGKAii7f4oCI9AjvAdkMj494WE1+eefEf3MCKRfcsOXfYdj/IotqNfheoXOIQgC3J3d4e7sjiYhTczbjaIR2bnZSL2VitTMVNy8dRM3b91EamYqMrIzUKArwNnUszibehYxiCl2Xg8HDzzi9giC3YJR370+6rvVR4hrCB5xeQSPuDwCd7U7a1ao1mMyQnSXLVtFTJxg+mJo/OqnSDCmw8PFA+GNwiWOjMriVfcWXt+2DotHPIWrp7wx/6kheOq/+9HlPycfeKZcmSCDm7Mb3Jzd0DCwocU+vUGP9Kx0pGWlIT07HelZ6cjIzkBGdgbSs9NRqC1ERn4GMvIzcOr6qRLPr1Ko4O3sDX9nfwS4BCDYJRghriHmhMXf2R91HOpwYjeyaUxGiG7bu0/E0KGAQS+g3aALOOX5KaAH+rbvC7kNPAxF6aCTOgSrUyrujHxx8cnHq1vWY+2UHjj+UyP8PC0Cl474Ydhnu+HorqmS6yvkCvh4+MDHw6fE/fmF+UjPTjcnJxlZGcjMzURWbhZu5d5CbkEuNHoNrmRewZXMK6VeRybI4GbvBk9HT3g7ecPXyRf+Tv4IcArAI06PwM/ZDz6OPvB18oWHvQdrWqjG4TwjRAB+2yJi2DBAUyigVecC2I19CkevxiDENwSvP/06f7nXIKII7Pu2FTZ/0BlGgwyufrl45ss/0LjHZalDK0an1yErL8ucnBQt5tc5t5CTnwMR5f81rZAp4O7gDg9HD3jae8LLwQt1HOrAx8EHvo6+8HH0gZeDl3nxtPeESqGqwruk2oyTnhGVU/QKI/7zggCDXkDrLoVo9+YCLD7+BmQyGaYOn4pA70CpQ6RKuBzrgx8m90LqOQ8AQPjwBAyccRCufnkSR1YxBqMBeQV5yM7PRk5eDnIKcpCdl42c/Bxk52cjNz/X/DpfU7mHITooHeBm72ZKYuw94OngCU+1JzzsPeCl9oKXvSlpcVO7wd3eHe5qd7jbu8PRzpGJOpWJyQjRfWi1wKtTjFi80NQx9fH+Bej3zn58tO8paPSF6N+hP3q26ylxlPQgtPkK/Pbfjti/uBVEUYDKSYuebxxBlxdOQumglzo8q9Pr9cgpyEFOfg5yC3KRV5CHvMI85BXkIbfw9uvb23ILcpFfmA+jaKz09eQyOZxUTnBRu8BF7WJKVtTucFO7mZIatacpcVG5w1XlCheVC5xVznBRuZgXlVzFhMaGMRkhKsOpUyKeG2fEqROmviDDX85BxLh/MX13P2Tmp6FhYENMemqSzYyg0RXKsXxsfwDA+JVbYKeuudPBF9HpdVi+dTkAYHy/8bBTlD4pWdIJb/z0TgQuH/MDADh75+GJV2PRafzfNpmUlJdRNKJQU2hOTu5OXgo0BcjX5CNfk48CTQEKCgvM6/mafBiNlU9i7qaQKeCocoSj0rQ4q5wtEhZXlStcla6mREflDmelM5yUTnBSOsHRznTM3etMbh4unA6eqAQ5OcD/fWLE53MF6HVyOLoY8dqnt+DX9jTe3zMUmflp8PXwxbh+42wmEQEAo0FAfEyoed0WGEUj4i/Fm9fLEtQ6Fa9vW4fY9WH4PeoxZCS5YvMHXfDHF+F47Nl4dBz3NzyDs6sj7IeKTJDBQe0AB7UD6rjVKfdxoihCq9daJChFSUqBpgD5hXe2aXQaFGoLzYtGa3qt0Zk6FeuNemQVZCGroOy5YSpyT/Z29lDbqeGgdICDnYP5/0VJS1Ey42xnSmxcVa5wtnOGo50j7O3sYa+wN//fwc6h2DaObLI+JiNUKxQUAIuXGjFrFpCRZkoy2j1RiBdnZuGs9je888drKNDmw9vdG5OemgQHFSeqsjUyGdBuxL9oM+Qsjv7YGDHz2iH9sit2fhmOXV+1RVj3y2gz9Aya9b0Iexet1OE+1ARBgMpOBZWdqtKPSDCKRmi1WstE5Z7E5e7kpWjR6rTQ6rXQaDXQ6rXQ6rTQ6DTQG/Tm8+Zp85CnzUN6XroV7/oOhUwBlUIFlUIFtZ0aaoX6zv8VaovkxcHOAQ4KB/P/He0c4WjnaContzcfo1KooJKrzP+/d5taoYZKrrLZRIjJCNm0xERgwbdGLF0C3MowJSG+wXo8OzUHDbtcwvJTH+DgpS0AgGDfYDw/4Hk4OzhLGTJVMbmdEY89dxrtRsbj9PZQHIxugTO7g5HwRwgS/giBXKlH2BNJaNLzEhpFJMEr1Dp/sZMlmSCDWqWGWqW2yvkMRgN0Oh00OlOSotFpzInK3f83r+uLb9PpdZaL4c56UbIDmGpz9Fo98rTV3xlaJsigVCihlCthJ7czr6sUKijld9bVCjWUcqU5iSnappabtqvkKvOiVqihlCnvrMuVsJPZmc9XtNjJLbeVVKayyVKlkpEFCxZg7ty5SE5ORtOmTfHFF1+gc+fOpZbfu3cvIiMjcfr0afj7++Ptt9/GpEmTKhUwUVlEETh9GtjyuxFr1oo4GSsHYEpCvAP0eGpiHsJ6x2HrpQX4cssm6AxaCBAQ0ToCAzoOsIn5RKh85AoRLfpfRIv+F3HzghtiNzTCiZ8b4MZZT5zeVhent5mmivcMzkK9jtcQ1OYGglrfgH/TNChUNb/Pja2Ry+SQq+RWS27uZTQaLZKTkhIXrU5bLIm5d9HqtdAb9OalKNEpddHrLYZ2G0UjCnWFKNQVVsl9PigBAhRyBezkdlDIFZBry/c7tcLJyNq1azFlyhQsWLAAnTp1wrfffou+ffsiPj4eQUFBxconJiaiX79+eOGFF7Bq1SocPHgQkydPRp06dTB06NCKXp7ITBSBlBTg5Eng2AkjjhwTcXCfcLsZxpSAyGQiGndKQ+NB+2AI3IWt17diScwl8zlCfEMwNGIoh+/WcnXq3UKfd/5C77f/QkqCJ/7eWhdn9gTh0lE/pF92RfplVxxZY5oiXq7Uw7v+LXjXz4RPgwx4N8iEV2gWXP1y4eKbB7nioR8TQJUgk8mgkpmapqqTKIowGo2WCYxBVyxhKTOhuSfpMRqN0Bv1MBgMMBgNMBgMZb42H2PQm18bjKZ993ZkFiGakjHD7UkWy5kzVXg0Tfv27dGmTRssXLjQvK1x48YYNGgQoqKiipV/55138MsvvyAhIcG8bdKkSTh58iQOHz5crmtyNI3tMxgAjca0FBaa+nhkZgIZGaYlPV3EzXQjkq4Cly8DSVd0uJ6iRZ6mELDLBxzSbi83IXdNhVuD81AFJUDrcB7pBddx98dcLpOjcUhjRLSKQL2AerWi570mT4F3Al8GAHx6ZT5UjjV/BIlGp8E7C98BAHz60qdV8iWhybXDhcMBuHTUF0nHfZB0wgf5mfallhdkRjh758PNPxfOdfLh4F4Ie1cNHNwL4eBm+r/aWQulvR529jrY2euhvL3Y3d6mUBohyMQHnsaeqDoYRSOMhnuSm6KExWBAXnYevm77tXVH02i1WsTGxuLdd9+12N6rVy8cOnSoxGMOHz6MXr16WWzr3bs3li1bBp1OBzu74sPxNBoNNJo70zdnZZnabL2mdIGgvF3lI9yTQ4n3bBMB0eK1UPJxJc5sWPI2sRxl7uuu64v3xlyhc5d8LYutgli8WLmvV3oZsdznKfu8onhXkbuLlnRuwQAoCgHnQqBVIdC69NETBgDpgCkjv52Vu7u4I9gnGA0CG6BxcGNzB1VNTtVME/6w0eTLAZhGixTmFEA01PxmBo1eY/73LcwphFglNRKFqNv+X9Rt/y8A02c286ozUs+7I/W8G25edEPaBTdkXnVG1g1HiAY5slOA7BQHAA/WCVqQGyBXiJApjJDJTYtcIZrWFUYICtGUtNz+4REE01L082NaBwRBNP+/aL/FPpRQhshKjIZcAF/jfvUeFUpG0tLSYDAY4ONj+RwGHx8fpKSklHhMSkpKieX1ej3S0tLg5+dX7JioqCh89NFHxbbrlp+sSLhEZpm3/4tDnNShSOgtAMCMJvcpVgPN+GSG1CFYnWgA9AYAtSNfJhuXk5MDV1fXUvdXqgPrvdXaoiiWWdVdUvmStheZNm0aIiMjza9v3bqF4OBgJCUllXkzti47OxuBgYG4cuVKrW6u4vtgwvfBhO/DHXwvTPg+mDwM74MoisjJyYG/v3+Z5SqUjHh5eUEulxerBUlNTS1W+1HE19e3xPIKhQKenp4lHqNSqaBSFW//dXV1rdUfrCIuLi58H8D3oQjfBxO+D3fwvTDh+2Ai9ftQnkqECk0xqVQq0bZtW8TExFhsj4mJQceOHUs8pkOHDsXK79ixA+Hh4SX2FyEiIqLapcLzXUdGRmLp0qWIjo5GQkICpk6diqSkJPO8IdOmTcOYMWPM5SdNmoTLly8jMjISCQkJiI6OxrJly/Dmm29a7y6IiIioxqpwn5ERI0YgPT0ds2bNQnJyMpo1a4atW7ciODgYAJCcnIykpCRz+dDQUGzduhVTp07F/Pnz4e/vj6+++qpCc4yoVCrMmDGjxKab2oTvgwnfBxO+DyZ8H+7ge2HC98GkJr0PNeKpvURERGS7bOexpERERFQjMRkhIiIiSTEZISIiIkkxGSEiIiJJMRkhIiIiST30yciCBQsQGhoKtVqNtm3bYv/+/VKHVO2ioqLQrl07ODs7w9vbG4MGDcKZM2ekDktSUVFREAQBU6ZMkToUSVy7dg3PPvssPD094eDggFatWiE2NlbqsKqVXq/H+++/j9DQUNjb26Nu3bqYNWtWsUea25p9+/Zh4MCB8Pf3hyAI2LRpk8V+URQxc+ZM+Pv7w97eHhERETh9+rQ0wVahst4HnU6Hd955B82bN4ejoyP8/f0xZswYXL9+XbqAq8j9Pg93e/HFFyEIAr744otqi6+8HupkZO3atZgyZQqmT5+OEydOoHPnzujbt6/FPCa1wd69e/Hyyy/jzz//RExMDPR6PXr16oW8vDypQ5PE0aNHsXjxYrRo0ULqUCSRmZmJTp06wc7ODr///jvi4+Px+eefw83NTerQqtWnn36KRYsW4ZtvvkFCQgLmzJmDuXPn4uuvv5Y6tCqVl5eHli1b4ptvvilx/5w5czBv3jx88803OHr0KHx9fdGzZ0/k5ORUc6RVq6z3IT8/H8ePH8cHH3yA48ePY+PGjTh79iyefPJJCSKtWvf7PBTZtGkT/vrrr/s+I0Yy4kPs0UcfFSdNmmSxLSwsTHz33XcliujhkJqaKgIQ9+7dK3Uo1S4nJ0ds0KCBGBMTI3bt2lV8/fXXpQ6p2r3zzjvi448/LnUYkuvfv784YcIEi21DhgwRn332WYkiqn4AxJ9//tn82mg0ir6+vuInn3xi3lZYWCi6urqKixYtkiDC6nHv+1CSI0eOiADEy5cvV09QEijtfbh69aoYEBAg/vPPP2JwcLD4v//9r9pju5+HtmZEq9UiNjYWvXr1stjeq1cvHDp0SKKoHg5ZWVkAAA8PD4kjqX4vv/wy+vfvjx49ekgdimR++eUXhIeH4+mnn4a3tzdat26NJUuWSB1WtXv88cexc+dOnD17FgBw8uRJHDhwAP369ZM4MukkJiYiJSXF4vemSqVC165d+XszKwuCINS6GkSj0YjnnnsOb731Fpo2bSp1OKWq8HTw1SUtLQ0Gg6HY04B9fHyKPQW4NhFFEZGRkXj88cfRrFkzqcOpVj/++CNiY2Nx7NgxqUOR1MWLF7Fw4UJERkbivffew5EjR/Daa69BpVJZPBfK1r3zzjvIyspCWFgY5HI5DAYD/u///g8jR46UOjTJFP1uLOn35uXLl6UI6aFQWFiId999F6NGjap1T/H99NNPoVAo8Nprr0kdSpke2mSkiCAIFq9FUSy2rTZ55ZVXcOrUKRw4cEDqUKrVlStX8Prrr2PHjh1Qq9VShyMpo9GI8PBwzJ49GwDQunVrnD59GgsXLqxVycjatWuxatUqrF69Gk2bNkVcXBymTJkCf39/jB07VurwJMXfm3fodDo888wzMBqNWLBggdThVKvY2Fh8+eWXOH78+EP/7//QNtN4eXlBLpcXqwVJTU0tlvXXFq+++ip++eUX7N69G4888ojU4VSr2NhYpKamom3btlAoFFAoFNi7dy+++uorKBQKGAwGqUOsNn5+fmjSpInFtsaNG9e6jt1vvfUW3n33XTzzzDNo3rw5nnvuOUydOhVRUVFShyYZX19fAODvzdt0Oh2GDx+OxMRExMTE1Lpakf379yM1NRVBQUHm35uXL1/GG2+8gZCQEKnDs/DQJiNKpRJt27ZFTEyMxfaYmBh07NhRoqikIYoiXnnlFWzcuBG7du1CaGio1CFVu+7du+Pvv/9GXFyceQkPD8fo0aMRFxcHuVwudYjVplOnTsWGdp89e9b85OzaIj8/HzKZ5a8wuVxu80N7yxIaGgpfX1+L35tarRZ79+6tdb83ixKRc+fO4Y8//oCnp6fUIVW75557DqdOnbL4venv74+33noL27dvlzo8Cw91M01kZCSee+45hIeHo0OHDli8eDGSkpIwadIkqUOrVi+//DJWr16NzZs3w9nZ2fxXj6urK+zt7SWOrno4OzsX6yPj6OgIT0/PWtd3ZurUqejYsSNmz56N4cOH48iRI1i8eDEWL14sdWjVauDAgfi///s/BAUFoWnTpjhx4gTmzZuHCRMmSB1alcrNzcX58+fNrxMTExEXFwcPDw8EBQVhypQpmD17Nho0aIAGDRpg9uzZcHBwwKhRoySM2vrKeh/8/f0xbNgwHD9+HL/99hsMBoP596aHhweUSqVUYVvd/T4P9yZhdnZ28PX1RaNGjao71LJJO5jn/ubPny8GBweLSqVSbNOmTa0czgqgxGX58uVShyap2jq0VxRF8ddffxWbNWsmqlQqMSwsTFy8eLHUIVW77Oxs8fXXXxeDgoJEtVot1q1bV5w+fbqo0WikDq1K7d69u8TfB2PHjhVF0TS8d8aMGaKvr6+oUqnELl26iH///be0QVeBst6HxMTEUn9v7t69W+rQrep+n4d7PaxDewVRFMVqynuIiIiIinlo+4wQERFR7cBkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCT1//0ceJg8nLR+AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" - ] - }, - { - "data": { - "text/plain": [ - "[array([6.3442238 , 7.94607029]),\n", - " array([0.71331024, 2.395806 ], dtype=float32),\n", - " [,\n", - " ]]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predict_player('Rafael Leao', plot = 1, log = 1)" - ] - }, - { - "cell_type": "markdown", - "id": "0a5de8a1", - "metadata": {}, - "source": [ - "Code for predicting the probability distribution of total team points" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "65f9a572", - "metadata": {}, - "outputs": [], - "source": [ - "squad = ['Szczesny',\n", - " 'Demiral',\n", - " 'Kim',\n", - " 'Di Lorenzo',\n", - " 'Valeri',\n", - " 'Kostic',\n", - " 'Frattesi',\n", - " 'Barella',\n", - " 'Strefezza',\n", - " 'Rafael Leao',\n", - " 'Hojlund']\n", - "\n", - "\n", - "dist = [None] * len(squad)\n", - "\n", - "defenders = list([0]) * len(squad)\n", - "\n", - "\n", - "for i in range(len(squad)):\n", - " [X, y, dist[i]] = predict_player(squad[i], plot = 0, log = 0) \n", - " \n", - " if(players['r'][squad[i]] == 'D'):\n", - " defenders[i] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "2e78f674", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Avg Total Points = 76.45314080810547\n", - "Avg Mod Points = 1.548\n", - "Avg Clean Sheets = 0.34\n" - ] - } - ], - "source": [ - "ITERS = 500\n", - "\n", - "MOD = True\n", - "\n", - "total_points = np.zeros(ITERS)\n", - "clean_sheets = np.zeros(ITERS)\n", - "mod_points = np.zeros(ITERS)\n", - "\n", - "mv_samples = [None] * len(squad)\n", - "fv_samples = [None] * len(squad)\n", - "\n", - "for i in range(len(squad)):\n", - " mv_samples[i] = dist[i][0].sample(ITERS)\n", - " fv_samples[i] = dist[i][1].sample(ITERS)\n", - " \n", - "cs_samples = dist[0][2].sample(ITERS)\n", - "\n", - "for k in range(ITERS):\n", - " d_points = list([0]) * len(squad)\n", - " \n", - " cleansheet = float(cs_samples[k])\n", - " total_points[k] += cleansheet\n", - " clean_sheets[k] += cleansheet\n", - " \n", - " for i in range(len(squad)): \n", - " if(defenders[i] == 1):\n", - " d_points[i] = float(mv_samples[i][k])\n", - "\n", - " total_points[k] += float(fv_samples[i][k])\n", - " \n", - " d_points.sort(reverse = True)\n", - "\n", - " if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n", - " mod_avg = 0\n", - " for j in range(3):\n", - " mod_avg += round(d_points[j] * 2) / 2\n", - " mod_avg /= 3\n", - "\n", - " if(mod_avg >= 7):\n", - " mod_points[k] = 6\n", - " elif(mod_avg >= 6.5):\n", - " mod_points[k] = 3\n", - " elif(mod_avg >= 6):\n", - " mod_points[k] = 1\n", - "\n", - " total_points[k] += mod_points[k]\n", - " \n", - " \n", - "print('Avg Total Points = ' + str(total_points.mean()))\n", - "print('Avg Mod Points = ' + str(mod_points.mean()))\n", - "print('Avg Clean Sheets = ' + str(clean_sheets.mean()))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "c5d752d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loc_0 = total_points.mean()\n", - "\n", - "squad_model = tf.keras.Sequential(\n", - " [\n", - " tf.keras.layers.Dense(3),\n", - " tfp.layers.DistributionLambda(\n", - " lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n", - " skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n", - " )\n", - " ]\n", - ")\n", - "\n", - "def negloglik(y, distr):\n", - " return -distr.log_prob(y)\n", - "\n", - "squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n", - "\n", - "dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n", - "squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "9754ec4c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n", - "\n", - "\n", - "x = np.arange(start = 0, stop = 200, step = 0.001)\n", - "prb = squad_points_dist.prob(x)\n", - "\n", - "mn = total_points.mean()\n", - "\n", - "f, ax = plt.subplots(1, 2)\n", - "\n", - "ax[0].plot(x, prb)\n", - "ax[0].fill_between(x, prb, color = 'lightblue')\n", - "ax[0].vlines(x = mn, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n", - "\n", - "ax[0].set_xlim([40, 120])\n", - "ax[0].set_ylim([0, 0.12])\n", - "\n", - "ax[0].legend()\n", - "\n", - "#ax[0].hist(total_points, bins = 20, density = True)\n", - "\n", - "\n", - "ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", - "\n", - "text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n", - " 'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n", - " 'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n", - "\n", - "ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", - "\n", - "ax[1].axis('off')\n", - "\n", - "\n", - "\n", - "plt.subplots_adjust(right=1.5)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "30744d7a", - "metadata": {}, - "source": [ - "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", - "\n", - "Here the code to generate the probability density function is reproduced." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "3ec6c3dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# custom franction to calculate the probability density function\n", - "\n", - "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", - "\n", - " mul = np.sinh( np.arcsinh(2) * delta)\n", - " \n", - " mul = 2 / mul\n", - " \n", - " sigma_corr = sigma * mul\n", - " \n", - " z = (x - mu) / sigma_corr\n", - " \n", - " \n", - " \n", - " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", - " \n", - " f = np.exp(-0.5 * S * S)\n", - "\n", - " f /= np.sqrt(2 * np.pi)\n", - " \n", - " f *= 1 / ( sigma_corr * delta )\n", - " \n", - " f *= np.sqrt(1 + S * S)\n", - " \n", - " f /= np.sqrt(1 + z * z)\n", - " \n", - " return f\n", - " \n", - "\n", - "x = np.arange(start = 0, stop = 15, step = 0.001)\n", - "\n", - "\n", - "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "605dc968", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/6_neural_network_training_and_prediction.ipynb b/6_neural_network_training_and_prediction.ipynb new file mode 100644 index 0000000..ba55a86 --- /dev/null +++ b/6_neural_network_training_and_prediction.ipynb @@ -0,0 +1,7215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a867836", + "metadata": {}, + "source": [ + "Bayesian Neural Network model traning and prediction data generation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "210da263", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPRegressor\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import r2_score\n", + "\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "edbf3b27", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_probability as tfp\n", + "\n", + "tfk = tf.keras\n", + "tf.keras.backend.set_floatx(\"float32\")\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import IsolationForest\n", + "\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "markdown", + "id": "9dadf6ec", + "metadata": {}, + "source": [ + "Load the training databases, generated in player_match_database_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fa098fa4", + "metadata": {}, + "outputs": [], + "source": [ + "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", + "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", + "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f71fa9a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...miscontrolsdispossessedfoulsfouledaerials_wonaerials_lostcarriesprogressive_carriescarries_into_final_thirdcarries_into_penalty_area
01ToloiAtalantaSampdoria07.0100.010.0...0.0069060.0013810.0075970.0069060.0138120.0103590.3563540.0082870.0158840.000000
11DjimsitiAtalantaSampdoria06.0000.06.0...0.0032100.0064210.0048150.0048150.0224720.0144460.4622790.0048150.0048150.000000
21HateboerAtalantaSampdoria06.0000.55.5...0.0057270.0050110.0136010.0021470.0171800.0100210.2827490.0114530.0093060.000716
31OkoliAtalantaSampdoria05.5000.55.0...0.0130180.0035500.0153850.0082840.0473370.0272190.2698220.0023670.0047340.000000
41ZorteaAtalantaSampdoria06.0000.55.5...0.0111110.0055560.0166670.0222220.0166670.0277780.5611110.0722220.0555560.000000
..................................................................
2480638TamezeVeronaLazio05.5000.05.5...0.0198140.0101010.0128210.0132090.0236990.0213680.3372180.0213680.0128210.003885
2480738HonglaVeronaLazio07.0100.59.5...0.0184330.0122890.0230410.0076800.0230410.0261140.3410140.0092170.0153610.003072
2480838LasagnaVeronaLazio07.0100.59.5...0.0464530.0228040.0160470.0084460.0261820.0413850.1908780.0219590.0109800.005912
2480938CaprariVeronaLazio06.0000.06.0...0.0339540.0197150.0153340.0270170.0029210.0098580.3913840.0427160.0277470.018620
2481038SimeoneVeronaLazio07.0100.010.0...0.0512630.0301550.0211080.0218620.0226160.0407090.2461360.0150770.0128160.006031
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24811 rows × 122 columns

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" + ], + "text/plain": [ + " matchday player team oppteam home vote goals assists \\\n", + "0 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", + "1 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", + "2 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", + "3 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", + "4 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "24806 38 Tameze Verona Lazio 0 5.5 0 0 \n", + "24807 38 Hongla Verona Lazio 0 7.0 1 0 \n", + "24808 38 Lasagna Verona Lazio 0 7.0 1 0 \n", + "24809 38 Caprari Verona Lazio 0 6.0 0 0 \n", + "24810 38 Simeone Verona Lazio 0 7.0 1 0 \n", + "\n", + " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", + "0 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", + "1 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "2 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", + "3 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", + "4 0.5 5.5 ... 0.011111 0.005556 0.016667 \n", + "... ... ... ... ... ... ... \n", + "24806 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", + "24807 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", + "24808 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", + "24809 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", + "24810 0.0 10.0 ... 0.051263 0.030155 0.021108 \n", + "\n", + " fouled aerials_won aerials_lost carries progressive_carries \\\n", + "0 0.006906 0.013812 0.010359 0.356354 0.008287 \n", + "1 0.004815 0.022472 0.014446 0.462279 0.004815 \n", + "2 0.002147 0.017180 0.010021 0.282749 0.011453 \n", + "3 0.008284 0.047337 0.027219 0.269822 0.002367 \n", + "4 0.022222 0.016667 0.027778 0.561111 0.072222 \n", + "... ... ... ... ... ... \n", + "24806 0.013209 0.023699 0.021368 0.337218 0.021368 \n", + "24807 0.007680 0.023041 0.026114 0.341014 0.009217 \n", + "24808 0.008446 0.026182 0.041385 0.190878 0.021959 \n", + "24809 0.027017 0.002921 0.009858 0.391384 0.042716 \n", + "24810 0.021862 0.022616 0.040709 0.246136 0.015077 \n", + "\n", + " carries_into_final_third carries_into_penalty_area \n", + "0 0.015884 0.000000 \n", + "1 0.004815 0.000000 \n", + "2 0.009306 0.000716 \n", + "3 0.004734 0.000000 \n", + "4 0.055556 0.000000 \n", + "... ... ... \n", + "24806 0.012821 0.003885 \n", + "24807 0.015361 0.003072 \n", + "24808 0.010980 0.005912 \n", + "24809 0.027747 0.018620 \n", + "24810 0.012816 0.006031 \n", + "\n", + "[24811 rows x 122 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db = pd.concat([db1, db2, db3], ignore_index = True) \n", + "\n", + "db" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1d024554", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...13.000.20-1.0088.0201.0397.0112.0101.0186.010.0
11SkorupskiBolognaLazio06.5-200.04.5...32.500.292.50101.0242.0576.0117.0159.0304.018.0
21VicarioEmpoliSpezia05.5-100.04.5...27.800.26-0.20102.0313.0806.0117.0114.0431.025.0
31GolliniFiorentinaCremonese15.0-200.03.0...9.450.240.9524.068.0299.548.572.5128.03.5
41HandanovicInterLecce06.5-100.05.5...10.600.30-1.4027.052.0266.046.045.079.02.0
..................................................................
130022ConsigliSassuoloUdinese07.0-200.05.0...24.400.33-5.60113.0277.0711.0108.0179.0276.017.0
130122DragowskiSpeziaEmpoli06.5-200.04.5...29.500.27-4.5093.0285.0528.097.0122.0270.09.0
130222Milinkovic-Savic V.TorinoMilan06.5-100.05.5...23.100.240.10150.0540.0817.097.0160.0271.019.0
130322SilvestriUdineseSassuolo16.0-200.04.0...24.000.311.0091.0227.0470.080.0175.0293.05.0
130422Montipo'VeronaSalernitana16.5000.06.5...28.700.26-3.30208.0433.0513.067.0175.0305.015.0
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1305 rows × 102 columns

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" + ], + "text/plain": [ + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "1 1 Skorupski Bologna Lazio 0 6.5 \n", + "2 1 Vicario Empoli Spezia 0 5.5 \n", + "3 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "4 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "1300 22 Consigli Sassuolo Udinese 0 7.0 \n", + "1301 22 Dragowski Spezia Empoli 0 6.5 \n", + "1302 22 Milinkovic-Savic V. Torino Milan 0 6.5 \n", + "1303 22 Silvestri Udinese Sassuolo 1 6.0 \n", + "1304 22 Montipo' Verona Salernitana 1 6.5 \n", + "\n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 13.00 \n", + "1 -2 0 0.0 4.5 ... 32.50 \n", + "2 -1 0 0.0 4.5 ... 27.80 \n", + "3 -2 0 0.0 3.0 ... 9.45 \n", + "4 -1 0 0.0 5.5 ... 10.60 \n", + "... ... ... ... ... ... ... \n", + "1300 -2 0 0.0 5.0 ... 24.40 \n", + "1301 -2 0 0.0 4.5 ... 29.50 \n", + "1302 -1 0 0.0 5.5 ... 23.10 \n", + "1303 -2 0 0.0 4.0 ... 24.00 \n", + "1304 0 0 0.0 6.5 ... 28.70 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.20 -1.00 \n", + "1 0.29 2.50 \n", + "2 0.26 -0.20 \n", + "3 0.24 0.95 \n", + "4 0.30 -1.40 \n", + "... ... ... \n", + "1300 0.33 -5.60 \n", + "1301 0.27 -4.50 \n", + "1302 0.24 0.10 \n", + "1303 0.31 1.00 \n", + "1304 0.26 -3.30 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 88.0 201.0 397.0 \n", + "1 101.0 242.0 576.0 \n", + "2 102.0 313.0 806.0 \n", + "3 24.0 68.0 299.5 \n", + "4 27.0 52.0 266.0 \n", + "... ... ... ... \n", + "1300 113.0 277.0 711.0 \n", + "1301 93.0 285.0 528.0 \n", + "1302 150.0 540.0 817.0 \n", + "1303 91.0 227.0 470.0 \n", + "1304 208.0 433.0 513.0 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 112.0 101.0 186.0 10.0 \n", + "1 117.0 159.0 304.0 18.0 \n", + "2 117.0 114.0 431.0 25.0 \n", + "3 48.5 72.5 128.0 3.5 \n", + "4 46.0 45.0 79.0 2.0 \n", + "... ... ... ... ... \n", + "1300 108.0 179.0 276.0 17.0 \n", + "1301 97.0 122.0 270.0 9.0 \n", + "1302 97.0 160.0 271.0 19.0 \n", + "1303 80.0 175.0 293.0 5.0 \n", + "1304 67.0 175.0 305.0 15.0 \n", + "\n", + "[1305 rows x 102 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", + "\n", + "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", + "\n", + "db_gk" + ] + }, + { + "cell_type": "markdown", + "id": "04df0936", + "metadata": {}, + "source": [ + "Load player stats from current season and past seasons" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bc9dae87", + "metadata": {}, + "outputs": [], + "source": [ + "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", + "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", + "\n", + "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", + "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", + "\n", + "players = players_orig" + ] + }, + { + "cell_type": "markdown", + "id": "397babf2", + "metadata": {}, + "source": [ + "Load team data from current season and add an average Serie A team row" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "493b0495", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_1568\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" + ] + }, + { + "data": { + "text/html": [ + "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0048.60022.0242.01980.040.0028.006.008.0...244.00256.0026.01.08.001.01335.00273.00328.0045.40
BolognaBologna25.0052.40022.0242.01980.027.0020.004.004.0...280.00268.0038.03.04.001.01204.00250.00210.0054.30
CremoneseCremonese31.0043.80022.0242.01980.015.007.002.004.0...239.00271.0036.03.04.000.01229.00409.00314.0056.60
EmpoliEmpoli28.0047.50022.0242.01980.021.0011.000.000.0...283.00253.0036.02.00.000.01148.00263.00217.0054.80
FiorentinaFiorentina28.0057.20022.0242.01980.023.0018.002.004.0...307.00269.0059.01.04.000.01130.00289.00328.0046.80
VeronaHellas Verona34.0042.90022.0242.01980.018.0015.000.000.0...234.00315.0025.01.00.002.01231.00423.00445.0048.70
InterInter23.0054.50022.0242.01980.040.0027.002.002.0...276.00248.0019.02.02.001.01007.00231.00288.0044.50
JuventusJuventus26.0049.00022.0242.01980.034.0026.003.004.0...246.00242.0029.00.04.000.01134.00258.00272.0048.70
LazioLazio21.0051.80022.0242.01980.036.0026.003.004.0...308.00218.0044.01.04.001.01233.00218.00229.0048.80
LecceLecce26.0042.40022.0242.01980.020.0014.001.002.0...283.00300.0045.03.02.002.01200.00417.00332.0055.70
MilanMilan27.0053.50022.0242.01980.036.0031.002.002.0...275.00261.0025.04.02.002.01125.00263.00325.0044.70
MonzaMonza29.0055.00022.0242.01980.027.0017.004.004.0...318.00281.0037.00.04.001.01145.00242.00253.0048.90
NapoliNapoli24.0061.60022.0242.01980.054.0042.005.006.0...298.00199.0028.01.05.000.01100.00232.00280.0045.30
RomaRoma26.0049.30022.0242.01980.029.0019.004.006.0...316.00246.0012.00.06.000.01156.00221.00266.0045.40
SalernitanaSalernitana28.0046.00022.0242.01980.024.0016.001.001.0...252.00259.0057.08.01.001.01213.00291.00285.0050.50
SampdoriaSampdoria31.0047.30022.0242.01980.010.008.000.000.0...339.00300.0059.04.00.000.01189.00362.00349.0050.90
SassuoloSassuolo29.0048.70022.0242.01980.025.0018.004.005.0...287.00206.0072.02.05.001.01131.00256.00206.0055.40
SpeziaSpezia33.0045.50022.0242.01980.017.0010.003.003.0...235.00291.0053.01.03.002.01275.00343.00306.0052.90
TorinoTorino27.0053.00022.0242.01980.022.0017.001.001.0...239.00306.0024.03.01.000.01155.00367.00333.0052.40
UdineseUdinese25.0049.90022.0242.01980.029.0025.000.000.0...282.00252.0034.02.00.001.01101.00233.00277.0045.70
AvgAvg27.2549.99522.0242.01980.027.3519.752.353.0...277.05262.0537.92.12.950.81172.05292.05292.1549.82
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21 rows × 303 columns

\n", + "
" + ], + "text/plain": [ + " team team_players_used team_possession team_games \\\n", + "Atalanta Atalanta 24.00 48.600 22.0 \n", + "Bologna Bologna 25.00 52.400 22.0 \n", + "Cremonese Cremonese 31.00 43.800 22.0 \n", + "Empoli Empoli 28.00 47.500 22.0 \n", + "Fiorentina Fiorentina 28.00 57.200 22.0 \n", + "Verona Hellas Verona 34.00 42.900 22.0 \n", + "Inter Inter 23.00 54.500 22.0 \n", + "Juventus Juventus 26.00 49.000 22.0 \n", + "Lazio Lazio 21.00 51.800 22.0 \n", + "Lecce Lecce 26.00 42.400 22.0 \n", + "Milan Milan 27.00 53.500 22.0 \n", + "Monza Monza 29.00 55.000 22.0 \n", + "Napoli Napoli 24.00 61.600 22.0 \n", + "Roma Roma 26.00 49.300 22.0 \n", + "Salernitana Salernitana 28.00 46.000 22.0 \n", + "Sampdoria Sampdoria 31.00 47.300 22.0 \n", + "Sassuolo Sassuolo 29.00 48.700 22.0 \n", + "Spezia Spezia 33.00 45.500 22.0 \n", + "Torino Torino 27.00 53.000 22.0 \n", + "Udinese Udinese 25.00 49.900 22.0 \n", + "Avg Avg 27.25 49.995 22.0 \n", + "\n", + " team_games_starts team_minutes team_goals team_assists \\\n", + "Atalanta 242.0 1980.0 40.00 28.00 \n", + "Bologna 242.0 1980.0 27.00 20.00 \n", + "Cremonese 242.0 1980.0 15.00 7.00 \n", + "Empoli 242.0 1980.0 21.00 11.00 \n", + "Fiorentina 242.0 1980.0 23.00 18.00 \n", + "Verona 242.0 1980.0 18.00 15.00 \n", + "Inter 242.0 1980.0 40.00 27.00 \n", + "Juventus 242.0 1980.0 34.00 26.00 \n", + "Lazio 242.0 1980.0 36.00 26.00 \n", + "Lecce 242.0 1980.0 20.00 14.00 \n", + "Milan 242.0 1980.0 36.00 31.00 \n", + "Monza 242.0 1980.0 27.00 17.00 \n", + "Napoli 242.0 1980.0 54.00 42.00 \n", + "Roma 242.0 1980.0 29.00 19.00 \n", + "Salernitana 242.0 1980.0 24.00 16.00 \n", + "Sampdoria 242.0 1980.0 10.00 8.00 \n", + "Sassuolo 242.0 1980.0 25.00 18.00 \n", + "Spezia 242.0 1980.0 17.00 10.00 \n", + "Torino 242.0 1980.0 22.00 17.00 \n", + "Udinese 242.0 1980.0 29.00 25.00 \n", + "Avg 242.0 1980.0 27.35 19.75 \n", + "\n", + " team_pens_made team_pens_att ... vs_team_fouls \\\n", + "Atalanta 6.00 8.0 ... 244.00 \n", + "Bologna 4.00 4.0 ... 280.00 \n", + "Cremonese 2.00 4.0 ... 239.00 \n", + "Empoli 0.00 0.0 ... 283.00 \n", + "Fiorentina 2.00 4.0 ... 307.00 \n", + "Verona 0.00 0.0 ... 234.00 \n", + "Inter 2.00 2.0 ... 276.00 \n", + "Juventus 3.00 4.0 ... 246.00 \n", + "Lazio 3.00 4.0 ... 308.00 \n", + "Lecce 1.00 2.0 ... 283.00 \n", + "Milan 2.00 2.0 ... 275.00 \n", + "Monza 4.00 4.0 ... 318.00 \n", + "Napoli 5.00 6.0 ... 298.00 \n", + "Roma 4.00 6.0 ... 316.00 \n", + "Salernitana 1.00 1.0 ... 252.00 \n", + "Sampdoria 0.00 0.0 ... 339.00 \n", + "Sassuolo 4.00 5.0 ... 287.00 \n", + "Spezia 3.00 3.0 ... 235.00 \n", + "Torino 1.00 1.0 ... 239.00 \n", + "Udinese 0.00 0.0 ... 282.00 \n", + "Avg 2.35 3.0 ... 277.05 \n", + "\n", + " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", + "Atalanta 256.00 26.0 1.0 \n", + "Bologna 268.00 38.0 3.0 \n", + "Cremonese 271.00 36.0 3.0 \n", + "Empoli 253.00 36.0 2.0 \n", + "Fiorentina 269.00 59.0 1.0 \n", + "Verona 315.00 25.0 1.0 \n", + "Inter 248.00 19.0 2.0 \n", + "Juventus 242.00 29.0 0.0 \n", + "Lazio 218.00 44.0 1.0 \n", + "Lecce 300.00 45.0 3.0 \n", + "Milan 261.00 25.0 4.0 \n", + "Monza 281.00 37.0 0.0 \n", + "Napoli 199.00 28.0 1.0 \n", + "Roma 246.00 12.0 0.0 \n", + "Salernitana 259.00 57.0 8.0 \n", + "Sampdoria 300.00 59.0 4.0 \n", + "Sassuolo 206.00 72.0 2.0 \n", + "Spezia 291.00 53.0 1.0 \n", + "Torino 306.00 24.0 3.0 \n", + "Udinese 252.00 34.0 2.0 \n", + "Avg 262.05 37.9 2.1 \n", + "\n", + " vs_team_pens_conceded vs_team_own_goals \\\n", + "Atalanta 8.00 1.0 \n", + "Bologna 4.00 1.0 \n", + "Cremonese 4.00 0.0 \n", + "Empoli 0.00 0.0 \n", + "Fiorentina 4.00 0.0 \n", + "Verona 0.00 2.0 \n", + "Inter 2.00 1.0 \n", + "Juventus 4.00 0.0 \n", + "Lazio 4.00 1.0 \n", + "Lecce 2.00 2.0 \n", + "Milan 2.00 2.0 \n", + "Monza 4.00 1.0 \n", + "Napoli 5.00 0.0 \n", + "Roma 6.00 0.0 \n", + "Salernitana 1.00 1.0 \n", + "Sampdoria 0.00 0.0 \n", + "Sassuolo 5.00 1.0 \n", + "Spezia 3.00 2.0 \n", + "Torino 1.00 0.0 \n", + "Udinese 0.00 1.0 \n", + "Avg 2.95 0.8 \n", + "\n", + " vs_team_ball_recoveries vs_team_aerials_won \\\n", + "Atalanta 1335.00 273.00 \n", + "Bologna 1204.00 250.00 \n", + "Cremonese 1229.00 409.00 \n", + "Empoli 1148.00 263.00 \n", + "Fiorentina 1130.00 289.00 \n", + "Verona 1231.00 423.00 \n", + "Inter 1007.00 231.00 \n", + "Juventus 1134.00 258.00 \n", + "Lazio 1233.00 218.00 \n", + "Lecce 1200.00 417.00 \n", + "Milan 1125.00 263.00 \n", + "Monza 1145.00 242.00 \n", + "Napoli 1100.00 232.00 \n", + "Roma 1156.00 221.00 \n", + "Salernitana 1213.00 291.00 \n", + "Sampdoria 1189.00 362.00 \n", + "Sassuolo 1131.00 256.00 \n", + "Spezia 1275.00 343.00 \n", + "Torino 1155.00 367.00 \n", + "Udinese 1101.00 233.00 \n", + "Avg 1172.05 292.05 \n", + "\n", + " vs_team_aerials_lost vs_team_aerials_won_pct \n", + "Atalanta 328.00 45.40 \n", + "Bologna 210.00 54.30 \n", + "Cremonese 314.00 56.60 \n", + "Empoli 217.00 54.80 \n", + "Fiorentina 328.00 46.80 \n", + "Verona 445.00 48.70 \n", + "Inter 288.00 44.50 \n", + "Juventus 272.00 48.70 \n", + "Lazio 229.00 48.80 \n", + "Lecce 332.00 55.70 \n", + "Milan 325.00 44.70 \n", + "Monza 253.00 48.90 \n", + "Napoli 280.00 45.30 \n", + "Roma 266.00 45.40 \n", + "Salernitana 285.00 50.50 \n", + "Sampdoria 349.00 50.90 \n", + "Sassuolo 206.00 55.40 \n", + "Spezia 306.00 52.90 \n", + "Torino 333.00 52.40 \n", + "Udinese 277.00 45.70 \n", + "Avg 292.15 49.82 \n", + "\n", + "[21 rows x 303 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", + "\n", + "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", + "avg_row['team']['Avg'] = 'Avg'\n", + "\n", + "team_data = pd.concat([team_data, avg_row])\n", + "\n", + "team_data" + ] + }, + { + "cell_type": "markdown", + "id": "cc1cd13d", + "metadata": {}, + "source": [ + "Data processing functions copied from player_match_dataset_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "32f56138", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs = ['r',\n", + " 'games',\n", + " 'games_starts', \n", + " 'minutes',\n", + " 'shots_on_target_pct',\n", + " 'goals_per_shot',\n", + " 'goals_per_shot_on_target',\n", + " 'passes_pct',\n", + " #'dribble_tackles_pct',\n", + " #'dribbles_completed_pct',\n", + " 'aerials_won_pct',\n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " #'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " #'vs_team_dribble_tackles_pct',\n", + " #'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " #'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " #'opp_vs_team_dribble_tackles_pct',\n", + " #'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel = [\n", + " 'goals',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red',\n", + " 'xg',\n", + " 'npxg',\n", + " 'shots_on_target',\n", + " 'passes_completed',\n", + " 'passes_into_final_third',\n", + " 'passes_into_penalty_area',\n", + " 'progressive_passes',\n", + " 'passes_live',\n", + " 'passes_dead',\n", + " 'through_balls',\n", + " 'passes_switches',\n", + " 'crosses',\n", + " 'corner_kicks',\n", + " #'dribble_tackles',\n", + " #'dribbles_vs',\n", + " #'dribbled_past',\n", + " 'blocks',\n", + " 'blocked_shots',\n", + " 'blocked_passes',\n", + " 'interceptions',\n", + " 'clearances',\n", + " 'errors',\n", + " 'touches',\n", + " 'touches_def_pen_area',\n", + " 'touches_def_3rd',\n", + " 'touches_mid_3rd',\n", + " 'touches_att_3rd',\n", + " 'touches_att_pen_area',\n", + " 'touches_live_ball',\n", + " #'dribbles_completed',\n", + " #'dribbles',\n", + " 'passes_received',\n", + " 'miscontrols',\n", + " 'dispossessed',\n", + " 'fouls',\n", + " 'fouled',\n", + " 'aerials_won',\n", + " 'aerials_lost',\n", + " 'carries',\n", + " 'progressive_carries',\n", + " 'carries_into_final_third',\n", + " 'carries_into_penalty_area']\n", + "\n", + "features_rel_gamecorr = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg',\n", + " 'cards_yellow',\n", + " 'cards_red'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4001f3f8", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs_gk = [\n", + " 'gk_games',\n", + " 'gk_games_starts',\n", + " 'gk_minutes',\n", + " 'gk_goals_against_per90', \n", + " 'gk_save_pct',\n", + " 'gk_clean_sheets_pct',\n", + " 'gk_psxg_net_per90',\n", + " 'gk_passes_pct_launched',\n", + " 'gk_pct_passes_launched',\n", + " 'gk_passes_length_avg',\n", + " 'gk_pct_goal_kicks_launched',\n", + " 'gk_goal_kick_length_avg',\n", + " 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area_per90',\n", + " 'gk_avg_distance_def_actions',\n", + " \n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " #'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " #'vs_team_dribble_tackles_pct',\n", + " #'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " #'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " #'opp_vs_team_dribble_tackles_pct',\n", + " #'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel_gk = [\n", + " 'gk_shots_on_target_against',\n", + " 'gk_saves',\n", + " 'gk_free_kick_goals_against',\n", + " 'gk_corner_kick_goals_against',\n", + " 'gk_own_goals_against',\n", + " 'gk_psxg',\n", + " 'gk_psnpxg_per_shot_on_target_against',\n", + " 'gk_psxg_net',\n", + " 'gk_passes_completed_launched',\n", + " 'gk_passes_launched',\n", + " 'gk_passes',\n", + " 'gk_passes_throws',\n", + " 'gk_goal_kicks',\n", + " 'gk_crosses',\n", + " 'gk_crosses_stopped',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f4017b2f", + "metadata": {}, + "outputs": [], + "source": [ + "DEL_G = False\n", + "\n", + "features_to_del = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg'\n", + "]\n", + "\n", + "def player_match_data(player, pteam, oppteam, oldseason = False):\n", + " if(not(player in players.index)):\n", + " return None\n", + " \n", + " if(oldseason):\n", + " pdata = players_old.loc[[player]]\n", + " else:\n", + " pdata = players.loc[[player]]\n", + " \n", + " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", + " \n", + " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", + " \n", + " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", + " \n", + " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", + " \n", + " return(out)\n", + "\n", + "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " assert pdata['games'][0] > 0\n", + " \n", + " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", + " \n", + " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", + " \n", + " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", + " \n", + " if(DEL_G):\n", + " out[features_to_del] = 0\n", + " \n", + " return out\n", + "\n", + "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " if(pdata['gk_games'][0] <= 0):\n", + " return None\n", + " \n", + " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", + " \n", + " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", + "\n", + " return out\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "21fef3ae", + "metadata": {}, + "source": [ + "Players stats rework:\n", + "the current season stats are averaged (according to a calculated weight) with the past season data.\n", + "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", + "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", + "\n", + "These modified stats are used only for prediction, not for model traning.\n", + "\n", + "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", + "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f8707b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Averaging players stats with past seasons:\n", + "Meret 1\n", + "Provedel 1\n", + "Vicario 1\n", + "Szczesny 1\n", + "Falcone 1\n", + "Silvestri 1\n", + "Rui Patricio 1\n", + "Sepe 1\n", + "Milinkovic-Savic V. 1\n", + "Musso 1\n", + "Maignan 1\n", + "Audero 1\n", + "Montipo' 1\n", + "Skorupski 1\n", + "Consigli 1\n", + "Dragowski 1\n", + "Terracciano 1\n", + "Tatarusanu 1\n", + "Handanovic 0.7372677888806921\n", + "Sportiello 1\n", + "Perin 1\n", + "Zoet 1\n", + "Pegolo 1\n", + "Mirante 0.0\n", + "Ujkani 0.0\n", + "Berisha 0.0\n", + "Marchetti 1\n", + "Padelli 0.0\n", + "Bardi 1\n", + "Cordaz 0.0\n", + "Pinsoglio 0.0\n", + "Fiorillo 0.0\n", + "Cragno 0.07071960297766748\n", + "Sirigu 0.0668969217356314\n", + "Rossi F. 0.0\n", + "Berardi A. 0.0\n", + "Gemello 0.0\n", + "Ravaglia 1\n", + "Boer 0.0\n", + "Adamonis 0.0\n", + "Marfella 0.0\n", + "Zovko 1\n", + "Piana 0.0\n", + "Dimarco 1\n", + "Smalling 1\n", + "Di Lorenzo 1\n", + "Danilo 1\n", + "Hernandez T. 1\n", + "Udogie 0.9565331442750797\n", + "Parisi 1\n", + "Mario Rui 0.7658517004816814\n", + "Romagnoli 1\n", + "Bastoni S. 0.6399743856559673\n", + "Mazzocchi 1\n", + "Tomori 1\n", + "Scalvini 1\n", + "Toloi 1\n", + "Demiral 1\n", + "Maehle 1\n", + "Dumfries 1\n", + "Juan Jesus 0.649497814013943\n", + "Depaoli 1\n", + "Mancini 1\n", + "Ibanez 0.9846664720478762\n", + "Rodrigo Becao 0.8502516838000708\n", + "Ebuehi 1\n", + "Gosens 1\n", + "Darmian 1\n", + "Reca 1\n", + "Bremer 0.9750733137829912\n", + "Rrahmani 0.8642078351755771\n", + "Vojvoda 0.9834089158894498\n", + "Bastoni 0.9199631793804531\n", + "Milenkovic 0.8387899576704131\n", + "Kalulu 1\n", + "Martinez Quarta 1\n", + "Casale 0.756306865177833\n", + "Perez N. 1\n", + "Izzo 1\n", + "Luperto 1\n", + "Skriniar 0.743970223325062\n", + "Rodriguez R. 1\n", + "Marusic 1\n", + "Lazzari 0.8799647802769551\n", + "Kyriakopoulos 0.4694318473517583\n", + "Ampadu 1\n", + "Ismajli 1\n", + "Llorente D. affine to Ibanez\n", + "Llorente D. 0.2188147715661947\n", + "Cambiaso 1\n", + "Hysaj 1\n", + "Biraghi 0.9718529944336394\n", + "Medel 0.9017820888788629\n", + "Bonucci 0.6716397849462366\n", + "Calabria 0.858427180759687\n", + "Acerbi 0.9900744416873448\n", + "Spinazzola 1\n", + "Lykogiannis 0.8397952853598015\n", + "Pellegrini Lu. 0.13751033912324234\n", + "Djidji 1\n", + "Augello 1\n", + "Singo 0.8856788372917405\n", + "Mari' 1\n", + "De Vrij 0.826633581472291\n", + "Patric 0.8266335814722912\n", + "Faraoni 0.658723635235732\n", + "Ceccherini 0.6564443146985842\n", + "Hateboer 1\n", + "Rogerio 1\n", + "Aina 0.9447240931111898\n", + "Ferrari G. 0.8955197132616488\n", + "Fazio 1\n", + "Buongiorno 1\n", + "Gunter 0.1650124069478908\n", + "Troost-Ekong affine to Fazio\n", + "Troost-Ekong 0.46498138957816376\n", + "Soumaoro 0.9299627791563275\n", + "Ceccaroni 0.03535980148883374\n", + "Soppy 0.5303970223325062\n", + "Ferrari A. 0.5918923292696083\n", + "Zappacosta 0.510567800321121\n", + "Gyomber 0.9199631793804531\n", + "Alex Sandro 0.9742467210209147\n", + "Pezzella Giu. 0.7071960297766748\n", + "Bereszynski 0.5303970223325062\n", + "Venuti 0.593019743230122\n", + "Palomino 0.47409867172675524\n", + "Nuytinck 0.6417149159084642\n", + "Magnani 1\n", + "Colley 0.6199751861042183\n", + "Nikolaou 0.9988489109456851\n", + "Terzic 1\n", + "Igor 0.9092969396195203\n", + "Toljan 1\n", + "Zortea 0.2560537349191409\n", + "Dawidowicz 1\n", + "Bellanova 0.518990634755463\n", + "Erlic 0.9075682382133995\n", + "Ballo-Toure' affine to Calabria\n", + "Ballo-Toure' 0.23845199465546857\n", + "Stojanovic 0.8266335814722912\n", + "Amian 0.9299627791563275\n", + "Zima 0.5579776674937966\n", + "De Winter 1\n", + "Romagnoli S. 0.195409429280397\n", + "Ghiglione 1\n", + "Rugani 0.6199751861042183\n", + "De Sciglio 0.9919602977667494\n", + "Djimsiti 0.6799727847594653\n", + "Caldara 0.7984471303930201\n", + "Karsdorp 0.4477598566308244\n", + "Marchizza 0.521091811414392\n", + "Kjaer 1\n", + "Ruggeri 1\n", + "Zanoli 1\n", + "Radovanovic 0.8856788372917405\n", + "D'ambrosio 0.6199751861042183\n", + "De Silvestri 0.39998399103497956\n", + "Chiriches 0.4694318473517583\n", + "Murru 0.901782088878863\n", + "Bonifazi 0.39452966388450256\n", + "Walukiewicz 1\n", + "Ranieri L. 0.22918389853873725\n", + "Gabbia 1\n", + "Kumbulla 0.4376295431323894\n", + "Lovato 0.9281947890818858\n", + "Ferrer 0.13777226357871517\n", + "Vasquez 0.7955955334987593\n", + "Ruan 1\n", + "Ostigard 1\n", + "Coppola D. 1\n", + "Cacace 1\n", + "Conti 0.1771357674583481\n", + "Conti 0.6679835812950357\n", + "Marrone 0.11786600496277913\n", + "Tonelli 0.08856788372917405\n", + "Radu 1\n", + "Florenzi 0.258322994210091\n", + "Sala 0.4133167907361455\n", + "Fares 0.0\n", + "Fares 0.0\n", + "Romagna 0.0\n", + "Romagna 0.0\n", + "Muldur 0.03999839910349796\n", + "Amey 0.0\n", + "Zaccagni 1\n", + "Milinkovic-Savic 0.9718529944336394\n", + "Barella 1\n", + "Zielinski 1\n", + "Luis Alberto 1\n", + "Felipe Anderson 1\n", + "Koopmeiners 1\n", + "Calhanoglu 1\n", + "Frattesi 1\n", + "Diaz B. 1\n", + "Zambo Anguissa 1\n", + "Elmas 1\n", + "Miranchuk 1\n", + "Samardzic 1\n", + "Pereyra 1\n", + "Politano 0.9393563425821488\n", + "Rabiot 1\n", + "Lazovic 0.8752590862647788\n", + "Lobotka 1\n", + "Bonaventura 1\n", + "Pessina 1\n", + "Tonali 0.9299627791563276\n", + "Pellegrini Lo. 1\n", + "El Shaarawy 1\n", + "Orsolini 1\n", + "Ikone' 1\n", + "Candreva 1\n", + "Bennacer 0.9999599775874489\n", + "Pasalic 0.8378043055462409\n", + "Mkhitaryan 1\n", + "Chiesa 1\n", + "Bandinelli 1\n", + "Fagioli affine to Henderson L.\n", + "Fagioli 0.7178660049627792\n", + "Messias 0.9538079786218743\n", + "Arslan 1\n", + "Ricci S. 1\n", + "Verdi 1\n", + "Sensi 1\n", + "Barak 1\n", + "Soriano 0.9565331442750797\n", + "Dominguez 1\n", + "Brozovic 0.743970223325062\n", + "Cristante 1\n", + "Saponara 1\n", + "Vecino 1\n", + "Locatelli 1\n", + "Zaniolo 0.5756912442396314\n", + "Maldini 1\n", + "Marin 0.996949958643507\n", + "Zalewski 1\n", + "Bajrami 0.3889578163771712\n", + "Coulibaly L. 1\n", + "De Roon 1\n", + "Mandragora 1\n", + "Bourabia 1\n", + "Sottil 0.6716397849462366\n", + "Aebischer 1\n", + "Ederson D.s. 1\n", + "Miretti 1\n", + "Cataldi 1\n", + "Djuricic 1\n", + "Linetty 1\n", + "Haas 1\n", + "Walace 1\n", + "Agudelo 1\n", + "Pobega 0.5625422964132641\n", + "Rovella 1\n", + "Amrabat 1\n", + "Tameze 0.9789081885856079\n", + "Gyasi 0.9644058450510063\n", + "Ilic 0.27072348014888337\n", + "Matheus Henrique 1\n", + "Harroui 1\n", + "Volpato 1\n", + "Duncan 0.6763365666591473\n", + "Cuadrado 0.9393563425821488\n", + "Ekdal 1\n", + "Schouten 1\n", + "Obiang 1\n", + "Kovalenko 0.7153559839664058\n", + "Crnigoj 0.2547985695518902\n", + "Basic 0.8551381877299562\n", + "Asllani 0.8609342971194303\n", + "Sabiri 1\n", + "Grassi 0.7425558312655087\n", + "Krunic 0.7528270116979794\n", + "Rincon 1\n", + "Miguel Veloso 1\n", + "Henderson L. 0.6199751861042183\n", + "Lopez M. 0.8502516838000708\n", + "Cuisance 0.8567951899217408\n", + "Saelemaekers 0.7921905155776124\n", + "Maggiore 0.45967741935483875\n", + "Akpa Akpro 0.0\n", + "Akpa Akpro 0.0\n", + "Maleh 0.5745967741935485\n", + "Romero L. 0.9299627791563275\n", + "Ceide 1\n", + "Benassi 1\n", + "Gagliardini 0.9644058450510063\n", + "Vieira 0.08839950372208435\n", + "Bianco 1\n", + "Galdames 0.0\n", + "Kastanos 0.9644058450510062\n", + "Vignato 0.2062655086848635\n", + "Askildsen 1\n", + "Bove 1\n", + "Bohinen 1\n", + "Bakayoko 0.26570365118752215\n", + "Zurkowski 0.21215880893300246\n", + "Castrovilli 0.5391088574819289\n", + "Demme 0.32630272952853595\n", + "Darboe 0.0\n", + "Darboe 0.24799007444168736\n", + "Urbanski 0.0\n", + "Yepes 1\n", + "Osimhen 1\n", + "Martinez L. 1\n", + "Dybala 0.9815393171900401\n", + "Rafael Leao 1\n", + "Immobile 0.9599615784839509\n", + "Vlahovic 1\n", + "Arnautovic 0.6011880592525753\n", + "Dzeko 1\n", + "Nzola 1\n", + "Beto 1\n", + "Giroud 1\n", + "Abraham 1\n", + "Deulofeu 0.5835060575098525\n", + "Simeone 0.6718362282878412\n", + "Lozano 1\n", + "Correa 1\n", + "Berardi 0.7139108203624331\n", + "Pedro 1\n", + "Sanabria 1\n", + "Thauvin affine to Deulofeu\n", + "Thauvin 0.36469128594365785\n", + "Cabral 1\n", + "Caprari 1\n", + "Piatek 1\n", + "Rebic 1\n", + "Bonazzoli 0.9299627791563275\n", + "Zapata D. 1\n", + "Gonzalez N. 0.7139108203624331\n", + "Brekalo 0.15469913151364761\n", + "Kean 0.9299627791563275\n", + "Okereke 1\n", + "Muriel 1\n", + "Pinamonti 0.9281947890818859\n", + "Di Francesco F. 1\n", + "Caputo 0.5500413564929694\n", + "Boga 1\n", + "Alvarez A. affine to Raspadori\n", + "Alvarez A. 0.688861317893576\n", + "Petagna 1\n", + "Barrow 0.9117282148591446\n", + "Djuric 1\n", + "Henry 0.6000451161741484\n", + "Success 1\n", + "Gabbiadini 1\n", + "Kallon 1\n", + "Nestorovski 1\n", + "Raspadori 0.6531741108354012\n", + "Lasagna 1\n", + "Belotti 1\n", + "Pellegri 1\n", + "Verde 0.7514850740657192\n", + "Destro 0.5500413564929694\n", + "Seck 1\n", + "Sansone 0.688861317893576\n", + "Quagliarella 0.6763365666591473\n", + "Defrel 0.6526054590570719\n", + "Pjaca 0.6703629032258065\n", + "Piccoli 1\n", + "Shomurodov 0.44199751861042186\n", + "Afena-Gyan 1\n", + "Ibrahimovic 0.2156435429927716\n", + "Pussetto 0.22099875930521093\n", + "Cancellieri 1\n", + "Oddei 0.0\n", + "Oddei 0.4959801488833747\n", + "Braaf 1\n", + "Raimondo 1\n", + "Kaio Jorge 0.0\n", + "Players with low quantity of games:\n", + "Aiwu 0.0\n", + "Zeefuik 0.16666666666666663\n", + "Dermaku 0.16666666666666663\n", + "Ostigard 0.8333333333333334\n", + "Gila 0.6666666666666667\n", + "Bayeye 0.16666666666666663\n", + "Moutinho J. 0.6666666666666667\n", + "Paletta 0.0\n", + "Romagna 0.0\n", + "Cassandro 0.16666666666666663\n", + "Amey 0.16666666666666663\n", + "Zanotti 0.16666666666666663\n", + "Buta 0.0\n", + "Abankwah 0.16666666666666663\n", + "Guessand A. 0.0\n", + "Guarino 0.0\n", + "Carboni F. 0.33333333333333337\n", + "Pogba 0.5\n", + "Machin 0.0\n", + "Akpa Akpro 0.0\n", + "Bianco 0.6666666666666667\n", + "Galdames 0.0\n", + "D'andrea 0.8333333333333334\n", + "Cipot 0.8333333333333334\n", + "Gaetano 0.8333333333333334\n", + "Darboe 0.668006617038875\n", + "Urbanski 0.16666666666666663\n", + "Bertini 0.0\n", + "Yepes 0.8333333333333334\n", + "Pyyhtia 0.6666666666666667\n", + "Trimboli 0.0\n", + "Adli 0.8333333333333334\n", + "Vignato S. 0.5\n", + "Samek 0.0\n", + "Ilkhan 0.5\n", + "Degli Innocenti 0.0\n", + "Acella 0.16666666666666663\n", + "Carboni V. 0.8333333333333334\n", + "Malagrida 0.6666666666666667\n", + "Faticanti 0.0\n", + "Oddei 0.5853432588916461\n", + "Braaf 0.6666666666666667\n", + "Raimondo 0.33333333333333337\n", + "De Luca 0.33333333333333337\n", + "Krollis 0.16666666666666663\n", + "Vivaldo 0.0\n" + ] + } + ], + "source": [ + "#for i in range(players.columns.shape[0]):\n", + "# print(str(i) + ' - ' + players.columns[i])\n", + "\n", + "cols_toadapt = players.columns[9:]\n", + "\n", + "players = players_orig.copy()\n", + "\n", + "min_games = 6\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", + "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", + "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", + "WEIGHT_mul_gk = 2\n", + "\n", + "rcsv = pd.read_csv('config/affine_players.txt') \n", + "affine_players = pd.DataFrame(rcsv)\n", + "affine_players = affine_players.set_index('player')\n", + "\n", + "\n", + "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", + " if(same_team):\n", + " weight_0 = WEIGHT_0_same_team\n", + " else:\n", + " weight_0 = WEIGHT_0_different_team\n", + "\n", + " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "print(' ')\n", + "print('Averaging players stats with past seasons:')\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + "\n", + " if(p in players_old.index or p in affine_players.index):\n", + " p_ = p\n", + " affine = 0\n", + " \n", + " if(p in affine_players.index):\n", + " affine = 1\n", + " p_ = affine_players.loc[p]['alike']\n", + " \n", + " print(p + ' affine to ' + p_)\n", + " \n", + " if(players.loc[p]['r'] == 'P'):\n", + " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + " weight *= WEIGHT_mul_gk\n", + " weight = min(weight, 1)\n", + " else:\n", + " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + "\n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight)) \n", + " \n", + " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", + " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", + " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", + " \n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "# handle players with low quantitites of games\n", + "\n", + "print('Players with low quantity of games:')\n", + "\n", + "def calc_weight_low(current_games, min_games = min_games):\n", + " weight = 1 - (min_games - current_games)/min_games\n", + " \n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", + "\n", + "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", + "count = 0\n", + "\n", + "for i in range(players_orig.shape[0]):\n", + " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", + " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", + " count = count + 1\n", + " \n", + "mean_players_stats /= count\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", + " weight = calc_weight_low(players.loc[p]['games'])\n", + " \n", + " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "players_out = players.copy()\n", + "players_out = players_out.set_index(players_out.columns[0])\n", + "players_out.insert(2, 'name', players_out.index)\n", + "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "49c28b07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", + " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", + " ...\n", + " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", + " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area',\n", + " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", + " 'vote_avg', 'vote_std'],\n", + " dtype='object', length=151)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "players.columns[9:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d29102e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - r\n", + "11 - games\n", + "12 - games_starts\n", + "13 - minutes\n", + "14 - shots_on_target_pct\n", + "15 - goals_per_shot\n", + "16 - goals_per_shot_on_target\n", + "17 - passes_pct\n", + "18 - aerials_won_pct\n", + "19 - team_possession\n", + "20 - team_goals_assists_per90\n", + "21 - team_goals_pens_per90\n", + "22 - team_goals_assists_pens_per90\n", + "23 - team_xg_per90\n", + "24 - team_gk_goals_against_per90\n", + "25 - team_gk_save_pct\n", + "26 - team_gk_clean_sheets_pct\n", + "27 - team_passes_pct\n", + "28 - team_passes_pct_medium\n", + "29 - team_passes_pct_long\n", + "30 - team_sca_per90\n", + "31 - team_gca_per90\n", + "32 - team_aerials_won_pct\n", + "33 - vs_team_possession\n", + "34 - vs_team_goals_per90\n", + "35 - vs_team_assists_per90\n", + "36 - vs_team_xg_per90\n", + "37 - vs_team_gk_save_pct\n", + "38 - vs_team_gk_clean_sheets_pct\n", + "39 - vs_team_gk_pct_passes_launched\n", + "40 - vs_team_gk_crosses_stopped_pct\n", + "41 - vs_team_shots_on_target_per90\n", + "42 - vs_team_passes_pct\n", + "43 - vs_team_passes_pct_short\n", + "44 - vs_team_passes_pct_medium\n", + "45 - vs_team_passes_pct_long\n", + "46 - vs_team_sca_per90\n", + "47 - vs_team_gca_per90\n", + "48 - vs_team_aerials_won_pct\n", + "49 - opp_team_possession\n", + "50 - opp_team_goals_assists_per90\n", + "51 - opp_team_goals_pens_per90\n", + "52 - opp_team_goals_assists_pens_per90\n", + "53 - opp_team_xg_per90\n", + "54 - opp_team_gk_goals_against_per90\n", + "55 - opp_team_gk_save_pct\n", + "56 - opp_team_gk_clean_sheets_pct\n", + "57 - opp_team_passes_pct\n", + "58 - opp_team_passes_pct_medium\n", + "59 - opp_team_passes_pct_long\n", + "60 - opp_team_sca_per90\n", + "61 - opp_team_gca_per90\n", + "62 - opp_team_aerials_won_pct\n", + "63 - opp_vs_team_possession\n", + "64 - opp_vs_team_goals_per90\n", + "65 - opp_vs_team_assists_per90\n", + "66 - opp_vs_team_xg_per90\n", + "67 - opp_vs_team_gk_save_pct\n", + "68 - opp_vs_team_gk_clean_sheets_pct\n", + "69 - opp_vs_team_gk_pct_passes_launched\n", + "70 - opp_vs_team_gk_crosses_stopped_pct\n", + "71 - opp_vs_team_shots_on_target_per90\n", + "72 - opp_vs_team_passes_pct\n", + "73 - opp_vs_team_passes_pct_short\n", + "74 - opp_vs_team_passes_pct_medium\n", + "75 - opp_vs_team_passes_pct_long\n", + "76 - opp_vs_team_sca_per90\n", + "77 - opp_vs_team_gca_per90\n", + "78 - opp_vs_team_aerials_won_pct\n", + "79 - vote_avg\n", + "80 - vote_std\n", + "81 - goals.1\n", + "82 - assists.1\n", + "83 - cards_yellow\n", + "84 - cards_red\n", + "85 - xg\n", + "86 - npxg\n", + "87 - shots_on_target\n", + "88 - passes_completed\n", + "89 - passes_into_final_third\n", + "90 - passes_into_penalty_area\n", + "91 - progressive_passes\n", + "92 - passes_live\n", + "93 - passes_dead\n", + "94 - through_balls\n", + "95 - passes_switches\n", + "96 - crosses\n", + "97 - corner_kicks\n", + "98 - blocks\n", + "99 - blocked_shots\n", + "100 - blocked_passes\n", + "101 - interceptions\n", + "102 - clearances\n", + "103 - errors\n", + "104 - touches\n", + "105 - touches_def_pen_area\n", + "106 - touches_def_3rd\n", + "107 - touches_mid_3rd\n", + "108 - touches_att_3rd\n", + "109 - touches_att_pen_area\n", + "110 - touches_live_ball\n", + "111 - passes_received\n", + "112 - miscontrols\n", + "113 - dispossessed\n", + "114 - fouls\n", + "115 - fouled\n", + "116 - aerials_won\n", + "117 - aerials_lost\n", + "118 - carries\n", + "119 - progressive_carries\n", + "120 - carries_into_final_third\n", + "121 - carries_into_penalty_area\n" + ] + } + ], + "source": [ + "for i in range(db.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db.columns[i]))" + ] + }, + { + "cell_type": "markdown", + "id": "089690d6", + "metadata": {}, + "source": [ + "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", + "\n", + "For outfield players: X -> y = [vote, fantavote]\n", + "\n", + "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f19304f6", + "metadata": {}, + "outputs": [], + "source": [ + "npdb = np.array(db)\n", + "\n", + "y = npdb[:, [5,9]] # vote, fantavote\n", + "\n", + "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", + "\n", + "f_start = 14\n", + "\n", + "X = npdb[:, f_start:]\n", + "\n", + "if(DEL_G): \n", + " del_g_idx = [\n", + " list(db.columns).index('goals.1') - f_start,\n", + " list(db.columns).index('assists.1') - f_start,\n", + " list(db.columns).index('xg') - f_start,\n", + " list(db.columns).index('npxg') - f_start,\n", + " list(db.columns).index('shots_on_target') - f_start]\n", + " \n", + " X[:, del_g_idx] = 0\n", + "\n", + "\n", + "# add role and home factor\n", + "toadd = np.zeros((X.shape[0], 4))\n", + "toadd[:, 0] = npdb[:, 4] # home\n", + "\n", + "toadd[:, 1] = npdb[:, 10] == 'D'\n", + "toadd[:, 2] = npdb[:, 10] == 'C'\n", + "toadd[:, 3] = npdb[:, 10] == 'A'\n", + "\n", + "X = np.concatenate((X, toadd), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "370d41d2", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "scaler.fit(X)\n", + "\n", + "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", + "\n", + "X_train = scaler.transform(X_train_)\n", + "X_test = scaler.transform(X_test_)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a7b1fb52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - gk_games\n", + "11 - gk_games_starts\n", + "12 - gk_minutes\n", + "13 - gk_goals_against_per90\n", + "14 - gk_save_pct\n", + "15 - gk_clean_sheets_pct\n", + "16 - gk_psxg_net_per90\n", + "17 - gk_passes_pct_launched\n", + "18 - gk_pct_passes_launched\n", + "19 - gk_passes_length_avg\n", + "20 - gk_pct_goal_kicks_launched\n", + "21 - gk_goal_kick_length_avg\n", + "22 - gk_crosses_stopped_pct\n", + "23 - gk_def_actions_outside_pen_area_per90\n", + "24 - gk_avg_distance_def_actions\n", + "25 - team_possession\n", + "26 - team_goals_assists_per90\n", + "27 - team_goals_pens_per90\n", + "28 - team_goals_assists_pens_per90\n", + "29 - team_xg_per90\n", + "30 - team_gk_goals_against_per90\n", + "31 - team_gk_save_pct\n", + "32 - team_gk_clean_sheets_pct\n", + "33 - team_passes_pct\n", + "34 - team_passes_pct_medium\n", + "35 - team_passes_pct_long\n", + "36 - team_sca_per90\n", + "37 - team_gca_per90\n", + "38 - team_aerials_won_pct\n", + "39 - vs_team_possession\n", + "40 - vs_team_goals_per90\n", + "41 - vs_team_assists_per90\n", + "42 - vs_team_xg_per90\n", + "43 - vs_team_gk_save_pct\n", + "44 - vs_team_gk_clean_sheets_pct\n", + "45 - vs_team_gk_pct_passes_launched\n", + "46 - vs_team_gk_crosses_stopped_pct\n", + "47 - vs_team_shots_on_target_per90\n", + "48 - vs_team_passes_pct\n", + "49 - vs_team_passes_pct_short\n", + "50 - vs_team_passes_pct_medium\n", + "51 - vs_team_passes_pct_long\n", + "52 - vs_team_sca_per90\n", + "53 - vs_team_gca_per90\n", + "54 - vs_team_aerials_won_pct\n", + "55 - opp_team_possession\n", + "56 - opp_team_goals_assists_per90\n", + "57 - opp_team_goals_pens_per90\n", + "58 - opp_team_goals_assists_pens_per90\n", + "59 - opp_team_xg_per90\n", + "60 - opp_team_gk_goals_against_per90\n", + "61 - opp_team_gk_save_pct\n", + "62 - opp_team_gk_clean_sheets_pct\n", + "63 - opp_team_passes_pct\n", + "64 - opp_team_passes_pct_medium\n", + "65 - opp_team_passes_pct_long\n", + "66 - opp_team_sca_per90\n", + "67 - opp_team_gca_per90\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_aerials_won_pct\n", + "85 - vote_avg\n", + "86 - vote_std\n", + "87 - gk_shots_on_target_against\n", + "88 - gk_saves\n", + "89 - gk_free_kick_goals_against\n", + "90 - gk_corner_kick_goals_against\n", + "91 - gk_own_goals_against\n", + "92 - gk_psxg\n", + "93 - gk_psnpxg_per_shot_on_target_against\n", + "94 - gk_psxg_net\n", + "95 - gk_passes_completed_launched\n", + "96 - gk_passes_launched\n", + "97 - gk_passes\n", + "98 - gk_passes_throws\n", + "99 - gk_goal_kicks\n", + "100 - gk_crosses\n", + "101 - gk_crosses_stopped\n" + ] + } + ], + "source": [ + "for i in range(db_gk.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db_gk.columns[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5a7cf079", + "metadata": {}, + "outputs": [], + "source": [ + "npdb_gk= np.array(db_gk)\n", + "\n", + "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", + "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", + "\n", + "f_start_gk = 13\n", + "\n", + "X_gk = npdb_gk[:, f_start_gk:]\n", + "\n", + "# add home factor\n", + "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", + "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", + "\n", + "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0bc0568b", + "metadata": {}, + "outputs": [], + "source": [ + "scaler_gk = StandardScaler()\n", + "scaler_gk.fit(X_gk)\n", + "\n", + "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", + "\n", + "X_gk_train = scaler_gk.transform(X_gk_train_)\n", + "X_gk_test = scaler_gk.transform(X_gk_test_)" + ] + }, + { + "cell_type": "markdown", + "id": "ebd27493", + "metadata": {}, + "source": [ + "MLP Regressor , to see performance of a simple neural network" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "04564bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1569906306642661\n", + "0.19152381866355805\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", + "\n", + "regr.fit(X_train, y_train)\n", + "\n", + "\n", + "y_train_predict = regr.predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = regr.predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f9513b25", + "metadata": {}, + "source": [ + "Train neural network for outfield players.\n", + "\n", + "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8aad9652", + "metadata": {}, + "outputs": [], + "source": [ + "load_model_of = True# load scaler and model weights for outfield player predictor\n", + "refit_model_of = False\n", + "\n", + "if(load_model_of):\n", + " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", + " \n", + " X_train = scaler.transform(X_train_)\n", + " X_test = scaler.transform(X_test_)\n", + "\n", + "\n", + "n_epochs = 1000\n", + "\n", + "n_samples = X_train.shape[0]\n", + "\n", + "batch_size = 256\n", + "\n", + "X_len = X_train.shape[1]\n", + "y_len = y_train.shape[1]\n", + "\n", + "\n", + "#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.2\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", + "x = tfk.layers.Dropout(0.2)(inputs)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "x = tfk.layers.Dropout(0.2)(x)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", + "\n", + "\n", + "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", + "\n", + "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_of):\n", + " modelb.load_weights('saves/modelb')\n", + " \n", + "if( (not load_model_of) or refit_model_of):\n", + " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", + " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4e2bf9dc", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict(X, iterations = 100):\n", + " y = np.zeros((2, X.shape[0]))\n", + " \n", + " dist = modelb(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " \n", + " return y.transpose() / iterations\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "c2674211", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.14539483185579238\n", + "0.16229047232752447\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train_predict = sample_predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = sample_predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f574ffdb", + "metadata": {}, + "source": [ + "Train neural network for goalkeepers.\n", + "\n", + "For clean sheet probability prediction, a Bernoulli distribution is used." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "41e7e1ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2500\n", + "9/9 [==============================] - 4s 92ms/step - loss: 105.9979 - distribution_lambda_21_loss: 99.1596 - distribution_lambda_22_loss: 5.7268 - distribution_lambda_23_loss: 1.1114 - val_loss: 85.6839 - val_distribution_lambda_21_loss: 79.7444 - val_distribution_lambda_22_loss: 4.8681 - val_distribution_lambda_23_loss: 1.0713\n", + "Epoch 2/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 78.7734 - distribution_lambda_21_loss: 72.8187 - distribution_lambda_22_loss: 4.8669 - distribution_lambda_23_loss: 1.0879 - val_loss: 63.5727 - val_distribution_lambda_21_loss: 58.3870 - val_distribution_lambda_22_loss: 4.1357 - val_distribution_lambda_23_loss: 1.0500\n", + "Epoch 3/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 59.3165 - distribution_lambda_21_loss: 54.1085 - distribution_lambda_22_loss: 4.1424 - distribution_lambda_23_loss: 1.0655 - val_loss: 48.0409 - val_distribution_lambda_21_loss: 43.4830 - val_distribution_lambda_22_loss: 3.5318 - val_distribution_lambda_23_loss: 1.0260\n", + "Epoch 4/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 45.1089 - distribution_lambda_21_loss: 40.5643 - distribution_lambda_22_loss: 3.4981 - distribution_lambda_23_loss: 1.0466 - val_loss: 37.6652 - val_distribution_lambda_21_loss: 33.5340 - val_distribution_lambda_22_loss: 3.1345 - val_distribution_lambda_23_loss: 0.9967\n", + "Epoch 5/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 36.3207 - distribution_lambda_21_loss: 32.1626 - distribution_lambda_22_loss: 3.1592 - distribution_lambda_23_loss: 0.9990 - val_loss: 30.6755 - val_distribution_lambda_21_loss: 26.8368 - val_distribution_lambda_22_loss: 2.8735 - val_distribution_lambda_23_loss: 0.9652\n", + "Epoch 6/2500\n", + "9/9 [==============================] - 0s 7ms/step - loss: 30.0433 - distribution_lambda_21_loss: 26.1435 - distribution_lambda_22_loss: 2.9074 - distribution_lambda_23_loss: 0.9924 - val_loss: 25.8243 - val_distribution_lambda_21_loss: 22.1859 - val_distribution_lambda_22_loss: 2.7075 - val_distribution_lambda_23_loss: 0.9309\n", + "Epoch 7/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 25.5853 - distribution_lambda_21_loss: 21.8961 - distribution_lambda_22_loss: 2.7424 - distribution_lambda_23_loss: 0.9468 - val_loss: 22.3435 - val_distribution_lambda_21_loss: 18.8484 - val_distribution_lambda_22_loss: 2.5991 - val_distribution_lambda_23_loss: 0.8960\n", + "Epoch 8/2500\n", + "9/9 [==============================] - 0s 5ms/step - loss: 22.2391 - distribution_lambda_21_loss: 18.7085 - distribution_lambda_22_loss: 2.6146 - distribution_lambda_23_loss: 0.9160 - val_loss: 19.7279 - val_distribution_lambda_21_loss: 16.3430 - val_distribution_lambda_22_loss: 2.5234 - val_distribution_lambda_23_loss: 0.8615\n", + "Epoch 9/2500\n", + "9/9 [==============================] - 0s 5ms/step - loss: 19.7891 - distribution_lambda_21_loss: 16.3677 - distribution_lambda_22_loss: 2.5346 - distribution_lambda_23_loss: 0.8867 - val_loss: 17.6639 - val_distribution_lambda_21_loss: 14.3678 - val_distribution_lambda_22_loss: 2.4684 - val_distribution_lambda_23_loss: 0.8277\n", + "Epoch 10/2500\n", + "9/9 [==============================] - 0s 6ms/step - loss: 17.8097 - distribution_lambda_21_loss: 14.4868 - distribution_lambda_22_loss: 2.4627 - distribution_lambda_23_loss: 0.8602 - val_loss: 16.0307 - val_distribution_lambda_21_loss: 12.8070 - val_distribution_lambda_22_loss: 2.4279 - val_distribution_lambda_23_loss: 0.7958\n", + "Epoch 11/2500\n", + 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"#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.1\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 50)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (inputs)\n", + "x = tfk.layers.Dropout(0.2)(x)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "\n", + "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", + "\n", + "x3 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x3 = tfk.layers.Dropout(0.5)(x3)\n", + "x3 = tfk.layers.Dense(1, activation=\"sigmoid\")(x3)\n", + "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x3)\n", + "\n", + "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", + "\n", + "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_gk):\n", + " modelb_gk.load_weights('saves/modelb_gk')\n", + "\n", + "if( (not load_model_gk) or refit_model_gk): \n", + " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", + " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "39a9bdc6", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict_gk(X, iterations = 100):\n", + " y = np.zeros((3, X.shape[0]))\n", + " \n", + " dist = modelb_gk(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " y[2, :] += dist[2].sample()\n", + " \n", + " return y.transpose() / iterations\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c41cf448", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.29228994199557035\n", + "0.5457850368866328\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", + "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", + "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "91869883", + "metadata": {}, + "source": [ + "Use the following codes to save the scalers and the model weights" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "cecf5392", + "metadata": {}, + "outputs": [], + "source": [ + "save_model_of = True\n", + "save_model_gk = True\n", + "\n", + "if(save_model_of):\n", + " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", + " modelb.save_weights('saves/modelb')\n", + " \n", + "if(save_model_gk):\n", + " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", + " modelb_gk.save_weights('saves/modelb_gk')\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "32635a0e", + "metadata": {}, + "source": [ + "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", + "\n", + "Estimate prediction mean and sigma (using a custom definitions).\n", + "\n", + "Generate a plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ddf433f9", + "metadata": {}, + "outputs": [], + "source": [ + "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", + " if(players['r'][player] == 'P'):\n", + " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 3:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 1))\n", + " xadd[0, 0] = home\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler_gk.transform(x_ptest)\n", + "\n", + " dist = modelb_gk(x_scaled)\n", + " \n", + " clean_shoot_prob = dist[2].probs.numpy()[0]\n", + " else:\n", + " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 4:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 4))\n", + " xadd[0, 0] = home\n", + " xadd[0, 1] = r == 'D'\n", + " xadd[0, 2] = r == 'C'\n", + " xadd[0, 3] = r == 'A'\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler.transform(x_ptest)\n", + "\n", + " dist = modelb(x_scaled)\n", + " \n", + " \n", + " x = np.arange(0, 40, 0.002)\n", + "\n", + " px1 = dist[0].prob(x);\n", + " px2 = dist[1].prob(x);\n", + "\n", + " \n", + " #sample1 = dist[0].sample(10000)\n", + " #sample2 = dist[1].sample(10000)\n", + " \n", + " m1 = np.average(x, weights = px1)\n", + " m2 = np.average(x, weights = px2)\n", + " \n", + " #m1 = np.mean(sample1)\n", + " #m2 = np.mean(sample2)\n", + " \n", + " #s1 = np.std(sample1)\n", + " #s2 = np.std(sample2)\n", + " \n", + " # not standard deviation, but expected range extimated by quantile \n", + " \n", + " if(players['r'][player] == 'P'):\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", + " else:\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", + " \n", + "\n", + " \n", + " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", + " y_pred_m = np.array([m1, m2]).flatten()\n", + " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", + " y_pred_s = np.array([s1, s2]).flatten()\n", + " \n", + " clean_sheet_text = ''\n", + " if(players['r'][player] == 'P'):\n", + " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", + " \n", + " if(plot):\n", + " ax = plt.gca()\n", + " \n", + " plt.plot(x, px1, \n", + " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", + " color = 'b')\n", + " plt.plot(x, px2, \n", + " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", + " color = 'g')\n", + " \n", + " plt.fill_between(x, px1, color = 'lightblue')\n", + " plt.fill_between(x, px2, color = 'lightgreen')\n", + " \n", + " plt.legend()\n", + " \n", + " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " \n", + " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", + " \n", + " plt.xlim([0, 15])\n", + " \n", + " if(players['r'][player] == 'P'): \n", + " plt.ylim([0, 2.5])\n", + " else:\n", + " plt.ylim([0, 1.5])\n", + " \n", + " plt.show()\n", + " \n", + " if(log):\n", + " print(player + ': ' + \n", + " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", + " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", + " return [y_pred_m, y_pred_s, dist]\n" + ] + }, + { + "cell_type": "markdown", + "id": "98817aa0", + "metadata": {}, + "source": [ + "Load Serie A calendar. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d31bad38", + "metadata": {}, + "outputs": [], + "source": [ + "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", + "\n", + "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", + "\n", + "matchday = 0\n", + "\n", + "for i in range(cal.shape[0]):\n", + " if(cal[i, 0][0].isnumeric()):\n", + " matchday = matchday + 1\n", + " continue\n", + " \n", + " teams = cal[i, 0].split('-')\n", + " \n", + " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", + "\n", + " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "62b9f588", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " matchday team1 team2\n", + "0 1 Fiorentina Cremonese\n", + "1 1 Verona Napoli\n", + "2 1 Juventus Sassuolo\n", + "3 1 Lazio Bologna\n", + "4 1 Lecce Inter\n", + ".. ... ... ...\n", + "375 38 Lecce Bologna\n", + "376 38 Sassuolo Fiorentina\n", + "377 38 Milan Verona\n", + "378 38 Torino Inter\n", + "379 38 Udinese Juventus\n", + "\n", + "[380 rows x 3 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df" + ] + }, + { + "cell_type": "markdown", + "id": "62872819", + "metadata": {}, + "source": [ + "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c58ba41d", + "metadata": {}, + "outputs": [], + "source": [ + "def PlayerMatch(player, match = 0):\n", + " team = players.loc[player]['team']\n", + " \n", + " if(match == 0):\n", + " oppteam = 'Avg'\n", + " home = 1\n", + " else:\n", + " for i in range (cal_df.shape[0]):\n", + " if(cal_df['matchday'][i] == match):\n", + " if(cal_df['team1'][i] == team):\n", + " home = 1\n", + " oppteam = cal_df['team2'][i]\n", + " elif(cal_df['team2'][i] == team):\n", + " home = 0\n", + " oppteam = cal_df['team1'][i]\n", + " \n", + " return [player, team, oppteam, home]\n", + "\n", + "def predict_player(player, match = 0, plot = 0, log = 0, oldseason = False):\n", + " [player, team, oppteam, home] = PlayerMatch(player, match)\n", + " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" + ] + }, + { + "cell_type": "markdown", + "id": "e8b63a98", + "metadata": {}, + "source": [ + "Load current matchday playing probabilities for Serie A players." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f79792b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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starterpercentage
player
Falcone1.0090
Gendrey1.0090
Baschirotto1.0090
Umtiti1.0090
Gallo0.6565
.........
Barrenechea0.0020
Pogba0.0050
Chiesa0.4055
Soule'0.0020
Milik0.4060
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474 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " starter percentage\n", + "player \n", + "Falcone 1.00 90\n", + "Gendrey 1.00 90\n", + "Baschirotto 1.00 90\n", + "Umtiti 1.00 90\n", + "Gallo 0.65 65\n", + "... ... ...\n", + "Barrenechea 0.00 20\n", + "Pogba 0.00 50\n", + "Chiesa 0.40 55\n", + "Soule' 0.00 20\n", + "Milik 0.40 60\n", + "\n", + "[474 rows x 2 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", + "\n", + "probables" + ] + }, + { + "cell_type": "markdown", + "id": "e4751014", + "metadata": {}, + "source": [ + "Generate prediction data for each Serie A player for the current matchday.\n", + "\n", + "Output to excel file, using a template made for data elaboration." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5e63c2b7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret: MV 6.24 ± 0.83; FV 5.13 + 1.01 (14.5% cs)\n", + "Provedel: MV 6.24 ± 0.83; FV 6.62 + 0.71 (96.3% cs)\n", + "Vicario: MV 6.24 ± 0.83; FV 5.09 + 0.90 (17.2% cs)\n", + "Szczesny: MV 6.24 ± 0.83; FV 6.39 + 0.69 (92.6% cs)\n", + "Falcone: MV 6.24 ± 0.83; FV 4.83 + 0.90 (1.1% cs)\n", + "Silvestri: MV 6.24 ± 0.83; FV 4.39 + 1.17 (0.9% cs)\n", + "Rui Patricio: MV 6.24 ± 0.83; FV 4.75 + 0.91 (3.2% cs)\n", + "Onana: MV 6.24 ± 0.83; FV 4.67 + 0.91 (3.5% cs)\n", + "Sepe: MV 6.24 ± 0.83; FV 5.77 + 0.83 (58.3% cs)\n", + "Milinkovic-Savic V.: MV 6.24 ± 0.83; FV 4.84 + 0.87 (6.9% cs)\n", + "Musso: MV 6.25 ± 0.82; FV 5.42 + 0.84 (25.0% cs)\n", + "Maignan: MV 6.24 ± 0.83; FV 5.36 + 0.82 (14.1% cs)\n", + "Carnesecchi: MV 6.24 ± 0.83; FV 4.39 + 1.15 (0.1% cs)\n", + "Di Gregorio: MV 6.24 ± 0.83; FV 4.55 + 1.07 (1.9% cs)\n", + "Audero: MV 6.24 ± 0.83; FV 5.17 + 0.83 (18.3% cs)\n", + "Montipo': MV 6.24 ± 0.83; FV 4.62 + 1.04 (0.1% cs)\n", + "Skorupski: MV 6.24 ± 0.83; FV 4.27 + 1.07 (1.9% cs)\n", + "Consigli: MV 6.24 ± 0.83; FV 4.27 + 1.30 (0.1% cs)\n", + "Dragowski: MV 6.24 ± 0.83; FV 4.49 + 1.03 (0.4% cs)\n", + "Terracciano: MV 6.24 ± 0.83; FV 5.35 + 0.85 (30.5% cs)\n", + "Tatarusanu: MV 6.24 ± 0.83; FV 5.44 + 0.97 (19.7% cs)\n", + "Handanovic: MV 6.24 ± 0.83; FV 4.94 + 0.89 (4.0% cs)\n", + "Sportiello: MV 6.18 ± 0.86; FV 5.72 + 1.09 (40.4% cs)\n", + "Perin: MV 6.24 ± 0.83; FV 5.31 + 0.79 (24.2% cs)\n", + "Zoet: MV 6.24 ± 0.83; FV 5.52 + 0.75 (18.9% cs)\n", + "Ochoa: MV 6.24 ± 0.83; FV 5.22 + 0.82 (15.7% cs)\n", + "Pegolo: MV 6.24 ± 0.83; FV 4.16 + 1.34 (0.1% cs)\n", + "Gollini: MV 6.25 ± 0.83; FV 4.93 + 1.34 (43.4% cs)\n", + "Mirante no data\n", + "Sarr M. no data\n", + "Lamanna no data\n", + "Ujkani no data\n", + "Berisha: MV 6.24 ± 0.83; FV 5.94 + 0.88 (65.1% cs)\n", + "Marchetti: MV 6.24 ± 0.83; FV 4.18 + 1.24 (0.1% cs)\n", + "Perilli: MV 6.24 ± 0.83; FV 4.29 + 1.29 (1.1% cs)\n", + "Padelli: MV 6.23 ± 0.83; FV 4.02 + 1.38 (0.3% cs)\n", + "Perisan: MV 6.26 ± 0.81; FV 5.53 + 0.94 (32.5% cs)\n", + "Bardi: MV 6.24 ± 0.83; FV 6.04 + 0.78 (78.7% cs)\n", + "Cordaz no data\n", + "Pinsoglio: MV 6.24 ± 0.83; FV 6.19 + 0.70 (86.7% cs)\n", + "Fiorillo: MV 6.24 ± 0.83; FV 4.96 + 0.82 (0.3% cs)\n", + "Cragno: MV 6.24 ± 0.83; FV 4.65 + 1.12 (0.1% cs)\n", + "Sirigu: MV 6.24 ± 0.83; FV 4.35 + 1.30 (0.0% cs)\n", + "Cerofolini no data\n", + "Rossi F.: MV 6.24 ± 0.83; FV 4.53 + 1.05 (0.8% cs)\n", + "Ravaglia F.: MV 6.24 ± 0.83; FV 4.70 + 0.92 (1.4% cs)\n", + "Brancolini no data\n", + "Bleve no data\n", + "Berardi A.: MV 6.23 ± 0.83; FV 3.86 + 1.53 (0.1% cs)\n", + "Russo A. no data\n", + "Gemello: MV 6.24 ± 0.83; FV 6.15 + 0.84 (78.0% cs)\n", + "Ravaglia: MV 6.24 ± 0.83; FV 5.89 + 0.71 (59.6% cs)\n", + "Boer no data\n", + "Adamonis no data\n", + "Marfella: MV 6.24 ± 0.83; FV 5.17 + 0.97 (11.7% cs)\n", + "Zovko: MV 6.24 ± 0.83; FV 4.26 + 1.19 (0.2% cs)\n", + "Piana no data\n", + "Bagnolini no data\n", + "Luis Maximiano: MV 6.24 ± 0.83; FV 6.75 + 0.74 (97.3% cs)\n", + "Svilar no data\n", + "Sorrentino A. no data\n", + "Ciezkowski no data\n", + "Saro no data\n", + "Vasquez D. no data\n", + "Turk: MV 6.24 ± 0.83; FV 5.24 + 0.78 (5.8% cs)\n", + "Dimarco: MV 6.24 ± 1.00; FV 6.79 + 1.87\n", + "Smalling: MV 6.25 ± 1.01; FV 6.66 + 1.74\n", + "Doig: MV 5.99 ± 1.03; FV 6.26 + 1.61\n", + "Carlos Augusto: MV 6.08 ± 1.06; FV 6.49 + 1.85\n", + "Kim: MV 6.23 ± 1.02; FV 6.49 + 1.50\n", + "Posch: MV 6.16 ± 1.11; FV 6.62 + 2.00\n", + "Di Lorenzo: MV 6.21 ± 0.98; FV 6.51 + 1.50\n", + "Danilo: MV 6.30 ± 0.99; FV 6.73 + 1.74\n", + "Hernandez T.: MV 6.32 ± 1.13; FV 6.96 + 2.22\n", + "Udogie: MV 5.90 ± 1.07; FV 6.07 + 1.53\n", + "Parisi: MV 6.17 ± 0.98; FV 6.54 + 1.59\n", + "Mario Rui: MV 6.14 ± 1.00; FV 6.33 + 1.32\n", + "Romagnoli: MV 6.20 ± 0.87; FV 6.37 + 1.23\n", + "Bastoni S.: MV 5.98 ± 0.94; FV 6.16 + 1.37\n", + "Mazzocchi: MV 6.13 ± 0.86; FV 6.52 + 1.49\n", + "Valeri: MV 6.08 ± 0.84; FV 6.36 + 1.28\n", + "Tomori: MV 6.17 ± 0.80; FV 6.37 + 1.17\n", + "Scalvini: MV 6.27 ± 1.07; FV 6.79 + 1.96\n", + "Toloi: MV 6.25 ± 1.02; FV 6.72 + 1.81\n", + "Demiral: MV 6.13 ± 0.80; FV 6.37 + 1.21\n", + "Maehle: MV 6.19 ± 0.99; FV 6.69 + 1.81\n", + "Dumfries: MV 6.00 ± 1.03; FV 6.30 + 1.64\n", + "Baschirotto: MV 6.06 ± 0.99; FV 6.32 + 1.46\n", + "Bijol: MV 5.76 ± 1.19; FV 5.85 + 1.55\n", + "Schuurs: MV 6.20 ± 0.82; FV 6.30 + 1.09\n", + "Juan Jesus: MV 6.15 ± 0.74; FV 6.33 + 1.06\n", + "Depaoli: MV 5.92 ± 0.87; FV 6.05 + 1.20\n", + "Mancini: MV 6.14 ± 0.86; FV 6.37 + 1.25\n", + "Ibanez: MV 5.79 ± 1.19; FV 5.92 + 1.57\n", + "Rodrigo Becao: MV 5.80 ± 1.09; FV 5.80 + 1.31\n", + "Ebuehi: MV 6.08 ± 0.82; FV 6.35 + 1.20\n", + "Gosens: MV 6.04 ± 0.75; FV 6.31 + 1.12\n", + "Darmian: MV 6.10 ± 0.76; FV 6.37 + 1.17\n", + "Reca: MV 5.88 ± 1.02; FV 6.01 + 1.44\n", + "Bremer: MV 6.20 ± 1.02; FV 6.60 + 1.66\n", + "Sernicola: MV 5.87 ± 1.07; FV 6.03 + 1.55\n", + "Rrahmani: MV 6.20 ± 1.02; FV 6.47 + 1.50\n", + "Vojvoda: MV 6.02 ± 0.79; FV 6.09 + 0.86\n", + "Holm: MV 5.89 ± 0.85; FV 6.01 + 1.17\n", + "Bastoni: MV 6.14 ± 0.84; FV 6.27 + 1.07\n", + "Milenkovic: MV 6.04 ± 0.93; FV 6.22 + 1.32\n", + "Kalulu: MV 6.06 ± 0.92; FV 6.24 + 1.17\n", + "Martinez Quarta: MV 6.06 ± 0.94; FV 6.24 + 1.29\n", + "Casale: MV 6.09 ± 0.82; FV 6.18 + 0.98\n", + "Perez N.: MV 5.79 ± 1.17; FV 5.90 + 1.55\n", + "Olivera: MV 6.10 ± 0.71; FV 6.30 + 1.00\n", + "Izzo: MV 6.04 ± 0.92; FV 6.18 + 1.19\n", + "Luperto: MV 5.90 ± 0.96; FV 5.88 + 0.92\n", + "Skriniar: MV 5.99 ± 0.79; FV 6.00 + 0.79\n", + "Rodriguez R.: MV 6.12 ± 0.69; FV 6.09 + 0.72\n", + "Marusic: MV 6.08 ± 0.79; FV 6.00 + 0.76\n", + "Lazzari: MV 6.06 ± 0.75; FV 6.02 + 0.73\n", + "Kyriakopoulos: MV 5.99 ± 0.91; FV 6.04 + 1.01\n", + "Ampadu: MV 5.77 ± 0.99; FV 5.73 + 1.14\n", + "Ismajli: MV 5.95 ± 0.85; FV 5.88 + 0.78\n", + "Llorente D.: MV 5.90 ± 1.07; FV 6.08 + 1.55\n", + "Cambiaso: MV 5.97 ± 0.77; FV 5.96 + 0.74\n", + "Hysaj: MV 6.04 ± 0.65; FV 5.95 + 0.55\n", + "Biraghi: MV 6.16 ± 0.87; FV 6.42 + 1.28\n", + "Medel: MV 5.97 ± 0.72; FV 5.91 + 0.64\n", + "Bonucci: MV 6.24 ± 1.04; FV 6.68 + 1.79\n", + "Calabria: MV 6.15 ± 0.96; FV 6.58 + 1.65\n", + "Acerbi: MV 6.06 ± 0.72; FV 6.05 + 0.71\n", + "Spinazzola: MV 6.16 ± 0.87; FV 6.53 + 1.50\n", + "Lykogiannis: MV 6.03 ± 0.81; FV 6.14 + 0.97\n", + "Pellegrini Lu.: MV 6.04 ± 0.72; FV 6.01 + 0.69\n", + "Djidji: MV 6.00 ± 0.79; FV 6.08 + 0.89\n", + "Lazaro: MV 6.16 ± 0.86; FV 6.34 + 1.16\n", + "Augello: MV 6.01 ± 0.91; FV 6.33 + 1.49\n", + "Gallo: MV 5.77 ± 0.79; FV 5.74 + 0.75\n", + "Singo: MV 6.12 ± 0.85; FV 6.39 + 1.25\n", + "Mari': MV 5.86 ± 1.07; FV 5.91 + 1.22\n", + "Caldirola: MV 5.89 ± 1.02; FV 5.99 + 1.30\n", + "Dodo': MV 5.93 ± 0.95; FV 5.98 + 1.13\n", + "De Vrij: MV 6.01 ± 0.82; FV 6.07 + 0.88\n", + "Patric: MV 6.07 ± 0.80; FV 5.96 + 0.75\n", + "Faraoni: MV 5.94 ± 0.88; FV 6.08 + 1.21\n", + "Ceccherini: MV 5.85 ± 1.07; FV 5.95 + 1.46\n", + "Hateboer: MV 6.06 ± 0.89; FV 6.34 + 1.41\n", + "Rogerio: MV 5.66 ± 0.84; FV 5.63 + 0.86\n", + "Umtiti: MV 5.80 ± 0.97; FV 5.75 + 1.00\n", + "Aina: MV 6.15 ± 0.87; FV 6.46 + 1.36\n", + "Birindelli: MV 5.80 ± 0.71; FV 5.78 + 0.74\n", + "Lucumi': MV 5.94 ± 0.81; FV 5.90 + 0.79\n", + "Ehizibue: MV 5.73 ± 1.01; FV 5.83 + 1.32\n", + "Bianchetti: MV 5.69 ± 1.03; FV 5.72 + 1.28\n", + "Ferrari G.: MV 5.65 ± 1.13; FV 5.66 + 1.29\n", + "Fazio: MV 5.65 ± 1.26; FV 5.69 + 1.40\n", + "Gravillon: MV 6.08 ± 0.79; FV 6.02 + 0.78\n", + "Buongiorno: MV 6.11 ± 0.75; FV 6.06 + 0.76\n", + "Gunter: MV 5.80 ± 0.95; FV 5.72 + 0.88\n", + "Troost-Ekong: MV 5.79 ± 0.93; FV 5.77 + 0.94\n", + "Soumaoro: MV 5.91 ± 0.92; FV 5.88 + 0.88\n", + "Ceccaroni: MV 5.81 ± 1.05; FV 5.83 + 1.21\n", + "Pongracic: MV 5.89 ± 0.79; FV 5.83 + 0.74\n", + "Soppy: MV 6.03 ± 0.73; FV 6.10 + 0.81\n", + "Gendrey: MV 5.84 ± 0.65; FV 5.83 + 0.55\n", + "Hien: MV 5.81 ± 0.94; FV 5.76 + 1.06\n", + "Ferrari A.: MV 5.69 ± 1.13; FV 5.70 + 1.35\n", + "Masina: MV 5.79 ± 0.92; FV 6.10 + 1.37\n", + "Zappacosta: MV 6.16 ± 0.81; FV 6.53 + 1.42\n", + "Gyomber: MV 5.90 ± 0.85; FV 5.88 + 0.81\n", + "Alex Sandro: MV 5.90 ± 0.87; FV 5.81 + 0.78\n", + "Pezzella Giu.: MV 5.85 ± 0.68; FV 5.84 + 0.57\n", + "Bereszynski: MV 5.82 ± 0.72; FV 5.79 + 0.74\n", + "Venuti: MV 5.90 ± 0.79; FV 5.92 + 0.85\n", + "Palomino: MV 6.17 ± 0.85; FV 6.40 + 1.26\n", + "Nuytinck: MV 5.90 ± 0.91; FV 5.88 + 0.89\n", + "Marlon: MV 5.74 ± 0.79; FV 5.67 + 0.77\n", + "Magnani: MV 5.77 ± 0.96; FV 5.71 + 1.04\n", + "Colley: MV 5.87 ± 1.02; FV 5.91 + 1.11\n", + "Nikolaou: MV 5.66 ± 0.88; FV 5.58 + 0.96\n", + "Terzic: MV 6.04 ± 0.61; FV 6.00 + 0.53\n", + "Igor: MV 5.87 ± 0.93; FV 5.84 + 1.00\n", + "Toljan: MV 5.60 ± 0.90; FV 5.55 + 0.90\n", + "Zortea: MV 5.74 ± 0.83; FV 5.77 + 0.96\n", + "Dawidowicz: MV 5.76 ± 1.00; FV 5.76 + 1.24\n", + "Celik: MV 5.77 ± 0.75; FV 5.72 + 0.74\n", + "Bellanova: MV 5.99 ± 0.87; FV 6.17 + 1.15\n", + "Erlic: MV 5.68 ± 1.06; FV 5.60 + 1.07\n", + "Ballo-Toure': MV 6.17 ± 0.74; FV 6.56 + 1.39\n", + "Dest: MV 5.93 ± 0.77; FV 5.95 + 0.74\n", + "Stojanovic: MV 5.75 ± 0.78; FV 5.70 + 0.76\n", + "Amian: MV 5.68 ± 0.82; FV 5.65 + 0.91\n", + "Bradaric: MV 5.74 ± 0.93; FV 5.73 + 1.05\n", + "Daniliuc: MV 5.75 ± 1.02; FV 5.74 + 1.15\n", + "Zima: MV 6.03 ± 0.75; FV 5.99 + 0.73\n", + "De Winter: MV 5.77 ± 0.82; FV 5.71 + 0.74\n", + "Quagliata: MV 5.88 ± 0.69; FV 5.90 + 0.70\n", + "Ebosse: MV 5.62 ± 0.82; FV 5.52 + 0.83\n", + "Aiwu: MV 5.83 ± 1.09; FV 5.93 + 1.48\n", + "Lochoshvili: MV 5.79 ± 0.99; FV 5.84 + 1.30\n", + "Bronn: MV 5.69 ± 0.80; FV 5.65 + 0.74\n", + "Thiaw: MV 6.03 ± 0.89; FV 6.05 + 0.91\n", + "Zeefuik: MV 5.86 ± 0.94; FV 5.91 + 1.23\n", + "Romagnoli S.: MV 5.84 ± 1.10; FV 5.98 + 1.54\n", + "Ghiglione: MV 5.81 ± 0.93; FV 5.92 + 1.28\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rugani: MV 6.07 ± 0.59; FV 5.97 + 0.48\n", + "De Sciglio: MV 5.91 ± 0.62; FV 5.91 + 0.48\n", + "Djimsiti: MV 6.08 ± 0.72; FV 6.07 + 0.75\n", + "Caldara: MV 5.63 ± 1.02; FV 5.56 + 1.09\n", + "Karsdorp: MV 5.88 ± 0.80; FV 5.86 + 0.79\n", + "Marchizza: MV 5.79 ± 0.70; FV 5.79 + 0.62\n", + "Kjaer: MV 6.06 ± 0.70; FV 5.98 + 0.63\n", + "Okoli: MV 5.93 ± 0.79; FV 5.89 + 0.73\n", + "Amione: MV 5.85 ± 0.96; FV 5.91 + 1.25\n", + "Ruggeri: MV 6.07 ± 0.68; FV 6.07 + 0.70\n", + "Zanoli: MV 6.08 ± 0.85; FV 6.34 + 1.26\n", + "Wisniewski: MV 5.68 ± 0.77; FV 5.59 + 0.78\n", + "Radovanovic: MV 5.63 ± 0.80; FV 5.56 + 0.77\n", + "Dermaku: MV 5.95 ± 0.86; FV 5.97 + 0.94\n", + "D'ambrosio: MV 6.09 ± 0.75; FV 6.14 + 0.85\n", + "De Silvestri: MV 5.98 ± 1.01; FV 6.16 + 1.42\n", + "Chiriches: MV 5.68 ± 1.14; FV 5.64 + 1.22\n", + "Murru: MV 5.70 ± 0.76; FV 5.63 + 0.71\n", + "Bonifazi: MV 5.78 ± 0.83; FV 5.69 + 0.80\n", + "Donati: MV 5.78 ± 1.22; FV 5.99 + 1.71\n", + "Walukiewicz: MV 6.11 ± 0.70; FV 6.08 + 0.71\n", + "Ranieri L.: MV 5.92 ± 0.82; FV 5.97 + 1.04\n", + "Gabbia: MV 5.84 ± 0.77; FV 5.80 + 0.70\n", + "Kumbulla: MV 5.74 ± 1.09; FV 5.67 + 1.12\n", + "Adopo: MV 6.10 ± 0.66; FV 6.08 + 0.67\n", + "Pirola: MV 5.89 ± 1.10; FV 6.15 + 1.72\n", + "Lovato: MV 5.65 ± 1.02; FV 5.59 + 0.96\n", + "Tuia: MV 5.99 ± 0.60; FV 5.95 + 0.49\n", + "Ferrer: MV 5.80 ± 0.95; FV 5.80 + 1.16\n", + "Antov: MV 5.68 ± 1.06; FV 5.54 + 1.00\n", + "Vasquez: MV 5.74 ± 0.96; FV 5.69 + 1.09\n", + "Ruan: MV 5.69 ± 1.07; FV 5.55 + 1.02\n", + "Ostigard: MV 6.08 ± 0.75; FV 6.10 + 0.82\n", + "Coppola D.: MV 5.74 ± 0.78; FV 5.64 + 0.82\n", + "Cacace: MV 5.82 ± 0.64; FV 5.82 + 0.51\n", + "Gatti: MV 6.10 ± 0.80; FV 6.06 + 0.82\n", + "Gila: MV 6.02 ± 0.89; FV 5.96 + 0.87\n", + "Bayeye: MV 6.08 ± 0.82; FV 6.17 + 0.97\n", + "Sambia: MV 5.83 ± 0.81; FV 5.84 + 0.78\n", + "Moutinho J.: MV 5.79 ± 0.87; FV 5.76 + 1.03\n", + "Conti: MV 5.93 ± 0.83; FV 6.07 + 1.13\n", + "Marrone: MV 5.64 ± 0.88; FV 5.53 + 0.89\n", + "Tonelli: MV 5.77 ± 0.83; FV 5.71 + 0.75\n", + "Murillo: MV 5.68 ± 0.79; FV 5.60 + 0.72\n", + "Radu: MV 6.06 ± 0.78; FV 5.83 + 0.69\n", + "Paletta: MV 5.91 ± 0.96; FV 6.00 + 1.23\n", + "Florenzi: MV 6.16 ± 0.73; FV 6.42 + 1.19\n", + "Sala: MV 5.85 ± 0.80; FV 5.90 + 0.97\n", + "Fares: MV 5.80 ± 0.76; FV 5.76 + 0.72\n", + "Romagna: MV 5.73 ± 0.96; FV 5.72 + 1.08\n", + "Cassandro: MV 5.93 ± 0.82; FV 5.94 + 0.87\n", + "Muldur: MV 5.60 ± 0.86; FV 5.56 + 0.87\n", + "Amey: MV 6.03 ± 0.89; FV 6.09 + 1.00\n", + "Zanotti: MV 6.00 ± 0.84; FV 6.11 + 1.01\n", + "Ebosele: MV 5.86 ± 0.70; FV 5.87 + 0.72\n", + "Buta: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", + "Abankwah: MV 5.74 ± 1.01; FV 5.70 + 1.16\n", + "Guessand A.: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", + "Cabal: MV 5.84 ± 0.67; FV 5.76 + 0.62\n", + "Sosa: MV 5.67 ± 0.86; FV 5.59 + 0.89\n", + "Guarino: MV 5.97 ± 0.88; FV 6.01 + 0.94\n", + "Carboni F.: MV 5.92 ± 0.90; FV 5.97 + 1.05\n", + "Zaccagni: MV 6.47 ± 1.27; FV 7.42 + 3.02\n", + "Kvaratskhelia: MV 6.49 ± 1.41; FV 7.50 + 3.35\n", + "Milinkovic-Savic: MV 6.23 ± 1.22; FV 7.11 + 2.90\n", + "Barella: MV 6.29 ± 1.16; FV 7.02 + 2.42\n", + "Zielinski: MV 6.24 ± 1.04; FV 6.74 + 1.84\n", + "Luis Alberto: MV 6.31 ± 1.01; FV 7.00 + 2.17\n", + "Strefezza: MV 6.20 ± 1.01; FV 6.77 + 1.92\n", + "Felipe Anderson: MV 6.29 ± 1.15; FV 7.10 + 2.62\n", + "Koopmeiners: MV 6.38 ± 1.16; FV 7.19 + 2.55\n", + "Calhanoglu: MV 6.31 ± 0.98; FV 6.91 + 1.98\n", + "Frattesi: MV 6.04 ± 1.06; FV 6.48 + 1.89\n", + "Diaz B.: MV 6.31 ± 1.31; FV 7.21 + 2.92\n", + "Vlasic: MV 6.30 ± 1.07; FV 6.93 + 2.11\n", + "Zambo Anguissa: MV 6.21 ± 0.98; FV 6.61 + 1.63\n", + "Elmas: MV 6.16 ± 1.00; FV 6.73 + 1.91\n", + "Miranchuk: MV 6.39 ± 1.16; FV 7.14 + 2.40\n", + "Samardzic: MV 6.04 ± 1.08; FV 6.46 + 1.90\n", + "Pereyra: MV 5.95 ± 1.20; FV 6.35 + 2.07\n", + "Politano: MV 6.18 ± 0.82; FV 6.61 + 1.51\n", + "Rabiot: MV 6.32 ± 1.23; FV 7.16 + 2.71\n", + "Ciurria: MV 6.06 ± 1.08; FV 6.51 + 1.94\n", + "Lazovic: MV 6.14 ± 1.01; FV 6.63 + 1.83\n", + "Lobotka: MV 6.18 ± 0.81; FV 6.45 + 1.26\n", + "Radonjic: MV 6.26 ± 0.96; FV 6.80 + 1.81\n", + "Ferguson: MV 6.13 ± 0.94; FV 6.46 + 1.47\n", + "Bonaventura: MV 6.20 ± 1.00; FV 6.71 + 1.83\n", + "Pessina: MV 6.09 ± 1.05; FV 6.44 + 1.70\n", + "Tonali: MV 6.29 ± 1.14; FV 6.93 + 2.19\n", + "Kostic: MV 6.24 ± 1.06; FV 6.84 + 2.01\n", + "Baldanzi: MV 6.20 ± 1.03; FV 6.80 + 2.01\n", + "Lovric: MV 6.06 ± 0.94; FV 6.40 + 1.53\n", + "Pellegrini Lo.: MV 6.09 ± 1.17; FV 6.68 + 2.34\n", + "El Shaarawy: MV 6.20 ± 0.88; FV 6.78 + 1.84\n", + "Orsolini: MV 6.15 ± 1.32; FV 7.02 + 3.02\n", + "Ikone': MV 6.02 ± 1.07; FV 6.48 + 1.91\n", + "Candreva: MV 6.15 ± 1.07; FV 6.67 + 2.01\n", + "Bennacer: MV 6.16 ± 0.75; FV 6.48 + 1.28\n", + "Pasalic: MV 6.25 ± 1.22; FV 7.05 + 2.69\n", + "Mkhitaryan: MV 6.12 ± 1.00; FV 6.63 + 1.80\n", + "Colpani: MV 6.01 ± 0.81; FV 6.37 + 1.41\n", + "Pogba: MV 6.14 ± 0.82; FV 6.23 + 1.00\n", + "Chiesa: MV 6.10 ± 0.82; FV 6.34 + 1.15\n", + "Bandinelli: MV 5.99 ± 0.80; FV 6.15 + 1.05\n", + "Matic: MV 6.16 ± 0.82; FV 6.48 + 1.35\n", + "Fagioli: MV 6.21 ± 1.05; FV 6.71 + 1.83\n", + "Messias: MV 6.13 ± 1.14; FV 6.84 + 2.39\n", + "Arslan: MV 5.86 ± 0.77; FV 5.94 + 0.94\n", + "Ricci S.: MV 6.19 ± 0.82; FV 6.46 + 1.29\n", + "Ranocchia F.: MV 6.07 ± 0.83; FV 6.39 + 1.34\n", + "Verdi: MV 6.18 ± 1.21; FV 6.73 + 2.29\n", + "Sensi: MV 6.03 ± 1.08; FV 6.39 + 1.83\n", + "Barak: MV 5.94 ± 0.96; FV 6.26 + 1.59\n", + "Soriano: MV 6.06 ± 0.88; FV 6.36 + 1.40\n", + "Dominguez: MV 6.14 ± 0.98; FV 6.50 + 1.59\n", + "Vilhena: MV 5.93 ± 0.97; FV 6.22 + 1.58\n", + "Brozovic: MV 6.13 ± 0.86; FV 6.44 + 1.34\n", + "Cristante: MV 5.99 ± 0.87; FV 6.11 + 1.16\n", + "Thorstvedt: MV 5.85 ± 0.84; FV 6.01 + 1.13\n", + "De Ketelaere: MV 5.84 ± 0.73; FV 5.96 + 0.79\n", + "Saponara: MV 6.12 ± 1.13; FV 6.67 + 2.09\n", + "Vecino: MV 6.06 ± 0.87; FV 6.26 + 1.21\n", + "Locatelli: MV 6.11 ± 0.81; FV 6.21 + 0.97\n", + "Zaniolo: MV 5.94 ± 1.02; FV 6.20 + 1.64\n", + "Duda: MV 5.81 ± 0.69; FV 5.80 + 0.78\n", + "Maldini: MV 5.95 ± 0.91; FV 6.22 + 1.46\n", + "Marin: MV 6.01 ± 0.94; FV 6.21 + 1.30\n", + "Zalewski: MV 6.04 ± 0.77; FV 6.21 + 1.04\n", + "Bajrami: MV 5.95 ± 1.00; FV 6.30 + 1.63\n", + "Coulibaly L.: MV 5.97 ± 1.04; FV 6.29 + 1.73\n", + "Gonzalez J.: MV 5.93 ± 0.80; FV 6.04 + 1.00\n", + "De Roon: MV 6.19 ± 0.87; FV 6.59 + 1.55\n", + "Mandragora: MV 6.04 ± 0.98; FV 6.35 + 1.59\n", + "Wijnaldum: MV 6.19 ± 1.10; FV 6.85 + 2.29\n", + "Bourabia: MV 5.85 ± 0.79; FV 5.87 + 0.95\n", + "Sottil: MV 6.11 ± 1.10; FV 6.57 + 1.92\n", + "Aebischer: MV 5.95 ± 0.83; FV 6.13 + 1.19\n", + "Ederson D.s.: MV 6.02 ± 0.79; FV 6.23 + 1.10\n", + "Miretti: MV 6.02 ± 0.71; FV 6.15 + 0.80\n", + "Blin: MV 5.90 ± 0.74; FV 5.96 + 0.83\n", + "Hjulmand: MV 5.87 ± 0.90; FV 5.83 + 0.90\n", + "Cataldi: MV 6.03 ± 0.74; FV 5.97 + 0.68\n", + "Djuricic: MV 5.85 ± 0.94; FV 6.01 + 1.35\n", + "Linetty: MV 6.06 ± 0.82; FV 6.25 + 1.10\n", + "Haas: MV 5.96 ± 0.77; FV 6.11 + 0.99\n", + "Walace: MV 5.85 ± 0.92; FV 5.85 + 1.07\n", + "Agudelo: MV 5.84 ± 0.75; FV 5.92 + 0.96\n", + "Pobega: MV 6.11 ± 0.88; FV 6.54 + 1.51\n", + "Camara Ma.: MV 6.09 ± 0.87; FV 6.18 + 1.02\n", + "Paredes: MV 5.92 ± 0.69; FV 5.85 + 0.58\n", + "Ndombele': MV 5.98 ± 0.76; FV 6.14 + 0.97\n", + "Nicolussi Caviglia: MV 5.90 ± 1.05; FV 6.16 + 1.68\n", + "Rovella: MV 5.97 ± 1.08; FV 6.14 + 1.36\n", + "Amrabat: MV 5.98 ± 0.90; FV 6.06 + 1.11\n", + "Tameze: MV 5.86 ± 0.78; FV 5.83 + 0.87\n", + "Gyasi: MV 5.78 ± 0.91; FV 5.89 + 1.24\n", + "Ilic: MV 6.18 ± 0.86; FV 6.51 + 1.39\n", + "Matheus Henrique: MV 5.81 ± 0.91; FV 5.94 + 1.22\n", + "Harroui: MV 5.89 ± 0.87; FV 6.10 + 1.24\n", + "Volpato: MV 6.02 ± 1.14; FV 6.66 + 2.34\n", + "Pickel: MV 5.77 ± 0.82; FV 5.78 + 0.99\n", + "Moro N.: MV 6.11 ± 0.93; FV 6.43 + 1.43\n", + "Duncan: MV 5.94 ± 0.82; FV 6.12 + 1.23\n", + "Machin: MV 5.94 ± 0.95; FV 6.05 + 1.27\n", + "Cuadrado: MV 6.06 ± 0.92; FV 6.19 + 1.09\n", + "Ekdal: MV 5.82 ± 0.78; FV 5.82 + 0.91\n", + "Meite': MV 5.85 ± 0.94; FV 5.87 + 1.21\n", + "Schouten: MV 5.98 ± 0.85; FV 5.99 + 0.89\n", + "Obiang: MV 5.78 ± 0.64; FV 5.75 + 0.57\n", + "Kovalenko: MV 5.85 ± 0.79; FV 5.92 + 1.01\n", + "Crnigoj: MV 5.95 ± 0.79; FV 6.11 + 1.04\n", + "Basic: MV 6.04 ± 0.63; FV 6.03 + 0.61\n", + "Asllani: MV 6.04 ± 0.66; FV 6.08 + 0.68\n", + "Sabiri: MV 5.91 ± 0.92; FV 6.08 + 1.35\n", + "Terracciano F.: MV 5.97 ± 0.70; FV 6.04 + 0.79\n", + "Castagnetti: MV 5.88 ± 0.79; FV 5.89 + 0.90\n", + "Oudin: MV 5.87 ± 0.62; FV 5.90 + 0.52\n", + "Grassi: MV 5.93 ± 0.66; FV 5.91 + 0.55\n", + "Krunic: MV 6.00 ± 0.70; FV 6.06 + 0.75\n", + "Rincon: MV 5.79 ± 0.76; FV 5.73 + 0.77\n", + "Miguel Veloso: MV 5.87 ± 0.73; FV 5.85 + 0.78\n", + "Leris: MV 5.87 ± 0.88; FV 5.98 + 1.24\n", + "Esposito Sa.: MV 5.74 ± 0.87; FV 5.63 + 0.91\n", + "Henderson L.: MV 5.94 ± 0.76; FV 6.04 + 0.89\n", + "Lopez M.: MV 5.80 ± 0.84; FV 5.75 + 0.89\n", + "Cuisance: MV 5.82 ± 0.63; FV 5.82 + 0.57\n", + "Saelemaekers: MV 5.98 ± 0.76; FV 6.24 + 1.05\n", + "Maggiore: MV 5.95 ± 0.77; FV 6.08 + 0.96\n", + "Akpa Akpro: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", + "Maleh: MV 5.87 ± 0.67; FV 5.94 + 0.74\n", + "Romero L.: MV 6.12 ± 0.98; FV 6.80 + 2.21\n", + "Ceide: MV 5.78 ± 0.66; FV 5.81 + 0.61\n", + "D'alessandro: MV 6.05 ± 0.65; FV 6.12 + 0.73\n", + "Benassi: MV 5.83 ± 0.80; FV 5.94 + 1.05\n", + "Gagliardini: MV 5.86 ± 0.64; FV 5.89 + 0.62\n", + "Vieira: MV 5.92 ± 0.66; FV 5.96 + 0.70\n", + "Bianco: MV 6.10 ± 0.80; FV 6.26 + 1.07\n", + "Vranckx: MV 5.85 ± 0.67; FV 5.90 + 0.63\n", + "Galdames: MV 5.87 ± 1.06; FV 5.98 + 1.47\n", + "Marcos Antonio: MV 6.11 ± 0.84; FV 6.73 + 1.90\n", + "Fazzini: MV 5.82 ± 0.64; FV 5.80 + 0.55\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sulemana I.: MV 5.83 ± 0.65; FV 5.79 + 0.65\n", + "Tahirovic: MV 6.03 ± 0.65; FV 6.02 + 0.65\n", + "Abildgaard: MV 5.89 ± 0.61; FV 5.88 + 0.55\n", + "Barberis: MV 5.76 ± 0.84; FV 5.74 + 0.90\n", + "Kastanos: MV 5.97 ± 0.86; FV 6.16 + 1.18\n", + "Vignato: MV 6.01 ± 0.76; FV 6.13 + 0.86\n", + "Valoti: MV 5.84 ± 0.68; FV 5.83 + 0.70\n", + "Winks: MV 5.92 ± 0.78; FV 5.91 + 0.71\n", + "Askildsen: MV 5.73 ± 0.62; FV 5.71 + 0.53\n", + "Bove: MV 5.98 ± 0.86; FV 6.25 + 1.38\n", + "Bohinen: MV 5.82 ± 0.61; FV 5.84 + 0.48\n", + "D'andrea: MV 5.89 ± 0.72; FV 5.98 + 0.82\n", + "Iling-Junior: MV 6.01 ± 0.64; FV 6.04 + 0.63\n", + "Cipot: MV 5.91 ± 0.80; FV 5.98 + 1.00\n", + "Bakayoko: MV 5.86 ± 0.72; FV 5.83 + 0.63\n", + "Gaetano: MV 6.15 ± 0.74; FV 6.35 + 1.10\n", + "Zurkowski: MV 5.97 ± 0.96; FV 6.25 + 1.55\n", + "Castrovilli: MV 6.00 ± 0.80; FV 6.25 + 1.25\n", + "Demme: MV 5.90 ± 0.80; FV 5.99 + 1.00\n", + "Darboe: MV 5.91 ± 0.93; FV 5.93 + 1.02\n", + "Urbanski: MV 5.99 ± 0.90; FV 6.05 + 1.04\n", + "Bertini: MV 5.99 ± 0.86; FV 6.02 + 0.91\n", + "Yepes: MV 5.75 ± 0.89; FV 5.67 + 0.92\n", + "Pyyhtia: MV 5.87 ± 0.82; FV 5.83 + 0.84\n", + "Trimboli: MV 5.97 ± 0.88; FV 6.05 + 1.04\n", + "Pafundi: MV 5.96 ± 0.74; FV 5.88 + 0.65\n", + "Helgason: MV 5.85 ± 0.62; FV 5.85 + 0.51\n", + "Adli: MV 5.94 ± 0.69; FV 6.03 + 0.75\n", + "Vignato S.: MV 5.96 ± 0.91; FV 6.03 + 1.10\n", + "Hrustic: MV 5.67 ± 0.69; FV 5.64 + 0.71\n", + "Samek: MV 5.92 ± 0.85; FV 5.96 + 0.95\n", + "Zerbin: MV 5.82 ± 0.67; FV 5.84 + 0.74\n", + "Ilkhan: MV 5.93 ± 0.78; FV 5.95 + 0.78\n", + "Degli Innocenti: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", + "Acella: MV 5.90 ± 1.05; FV 6.03 + 1.47\n", + "Carboni V.: MV 6.05 ± 0.77; FV 6.14 + 0.87\n", + "Paoletti: MV 5.92 ± 0.60; FV 5.91 + 0.45\n", + "Malagrida: MV 6.04 ± 0.96; FV 6.26 + 1.32\n", + "Faticanti: MV 5.94 ± 0.92; FV 6.02 + 1.15\n", + "Osimhen: MV 6.38 ± 1.48; FV 7.89 + 4.50\n", + "Martinez L.: MV 6.23 ± 1.44; FV 7.69 + 4.20\n", + "Dybala: MV 6.47 ± 1.37; FV 7.51 + 3.43\n", + "Rafael Leao: MV 6.44 ± 1.45; FV 7.65 + 3.92\n", + "Lookman: MV 6.46 ± 1.42; FV 7.65 + 3.83\n", + "Immobile: MV 6.30 ± 1.40; FV 7.57 + 3.90\n", + "Vlahovic: MV 6.16 ± 1.34; FV 7.32 + 3.51\n", + "Arnautovic: MV 6.16 ± 1.33; FV 7.23 + 3.36\n", + "Dia: MV 6.19 ± 1.33; FV 7.29 + 3.44\n", + "Dzeko: MV 6.16 ± 1.34; FV 7.25 + 3.36\n", + "Milik: MV 6.25 ± 1.20; FV 7.07 + 2.67\n", + "Nzola: MV 6.04 ± 1.35; FV 7.05 + 3.21\n", + "Beto: MV 5.86 ± 1.09; FV 6.38 + 2.02\n", + "Giroud: MV 6.27 ± 1.35; FV 7.38 + 3.54\n", + "Abraham: MV 6.14 ± 1.22; FV 7.03 + 2.90\n", + "Deulofeu: MV 6.15 ± 1.21; FV 6.84 + 2.47\n", + "Lauriente': MV 6.17 ± 1.34; FV 7.04 + 3.07\n", + "Simeone: MV 6.12 ± 1.32; FV 7.11 + 3.12\n", + "Lozano: MV 6.03 ± 1.04; FV 6.59 + 2.04\n", + "Correa: MV 6.05 ± 1.08; FV 6.68 + 2.23\n", + "Berardi: MV 6.17 ± 1.38; FV 7.30 + 3.51\n", + "Pedro: MV 6.20 ± 1.05; FV 6.86 + 2.24\n", + "Lukaku: MV 6.12 ± 1.34; FV 7.15 + 3.26\n", + "Sanabria: MV 6.24 ± 1.28; FV 7.22 + 3.15\n", + "Thauvin: MV 6.07 ± 1.10; FV 6.54 + 1.95\n", + "Cabral: MV 6.15 ± 1.26; FV 7.08 + 2.93\n", + "Hojlund: MV 6.25 ± 1.36; FV 7.39 + 3.56\n", + "Caprari: MV 5.97 ± 0.95; FV 6.29 + 1.53\n", + "Di Maria: MV 6.30 ± 1.26; FV 7.09 + 2.67\n", + "Piatek: MV 5.85 ± 0.99; FV 6.21 + 1.66\n", + "Rebic: MV 6.22 ± 1.36; FV 7.14 + 3.19\n", + "Bonazzoli: MV 6.01 ± 0.96; FV 6.41 + 1.71\n", + "Zapata D.: MV 6.09 ± 0.95; FV 6.49 + 1.60\n", + "Kouame': MV 6.09 ± 1.24; FV 6.84 + 2.60\n", + "Gonzalez N.: MV 6.21 ± 1.27; FV 7.05 + 2.81\n", + "Brekalo: MV 6.14 ± 1.18; FV 6.82 + 2.39\n", + "Mota: MV 5.99 ± 1.16; FV 6.55 + 2.23\n", + "Kean: MV 6.13 ± 1.30; FV 7.05 + 3.06\n", + "Okereke: MV 5.85 ± 1.05; FV 6.24 + 1.79\n", + "Ceesay: MV 5.89 ± 0.95; FV 6.21 + 1.52\n", + "Colombo: MV 5.87 ± 0.99; FV 6.24 + 1.68\n", + "Dessers: MV 5.95 ± 1.16; FV 6.53 + 2.25\n", + "Muriel: MV 6.13 ± 1.05; FV 6.51 + 1.72\n", + "Pinamonti: MV 5.77 ± 0.96; FV 6.11 + 1.58\n", + "Di Francesco F.: MV 5.92 ± 0.91; FV 6.13 + 1.31\n", + "Jovic: MV 5.97 ± 1.14; FV 6.47 + 2.12\n", + "Origi: MV 5.97 ± 1.06; FV 6.40 + 1.91\n", + "Caputo: MV 5.99 ± 1.13; FV 6.62 + 2.31\n", + "Boga: MV 6.42 ± 1.23; FV 7.25 + 2.65\n", + "Cambiaghi: MV 6.14 ± 1.04; FV 6.73 + 2.07\n", + "Alvarez A.: MV 5.88 ± 1.01; FV 6.22 + 1.64\n", + "Banda: MV 5.91 ± 0.74; FV 6.02 + 0.86\n", + "Ciofani D.: MV 6.01 ± 1.12; FV 6.48 + 2.05\n", + "Petagna: MV 5.96 ± 1.01; FV 6.32 + 1.68\n", + "Barrow: MV 5.98 ± 1.17; FV 6.42 + 2.08\n", + "Djuric: MV 5.93 ± 0.71; FV 6.06 + 0.87\n", + "Henry: MV 5.89 ± 1.06; FV 6.30 + 1.84\n", + "Success: MV 5.89 ± 0.92; FV 6.11 + 1.36\n", + "Gabbiadini: MV 5.94 ± 1.09; FV 6.43 + 2.01\n", + "Zirkzee: MV 6.01 ± 1.08; FV 6.37 + 1.77\n", + "Lammers: MV 5.82 ± 0.87; FV 6.01 + 1.26\n", + "Satriano: MV 5.89 ± 0.92; FV 6.09 + 1.33\n", + "Kallon: MV 5.87 ± 0.82; FV 6.05 + 1.18\n", + "Nestorovski: MV 6.06 ± 0.72; FV 6.52 + 1.43\n", + "Raspadori: MV 6.01 ± 1.05; FV 6.54 + 2.00\n", + "Botheim: MV 5.93 ± 0.95; FV 6.32 + 1.68\n", + "Gytkjaer: MV 5.84 ± 0.84; FV 6.05 + 1.27\n", + "Solbakken: MV 5.92 ± 1.00; FV 6.36 + 1.83\n", + "Lasagna: MV 5.78 ± 0.78; FV 5.84 + 0.98\n", + "Belotti: MV 5.78 ± 0.78; FV 5.90 + 1.01\n", + "Pellegri: MV 5.99 ± 0.89; FV 6.30 + 1.44\n", + "Buonaiuto: MV 5.91 ± 0.84; FV 6.10 + 1.17\n", + "Verde: MV 5.92 ± 1.09; FV 6.29 + 1.85\n", + "Destro: MV 6.00 ± 1.20; FV 6.61 + 2.37\n", + "Seck: MV 6.13 ± 0.66; FV 6.25 + 0.89\n", + "Sansone: MV 6.11 ± 1.16; FV 6.63 + 2.15\n", + "Quagliarella: MV 5.86 ± 0.78; FV 6.01 + 1.08\n", + "Defrel: MV 5.77 ± 0.87; FV 5.93 + 1.21\n", + "Pjaca: MV 5.92 ± 0.81; FV 6.04 + 0.93\n", + "Gaich: MV 5.87 ± 0.91; FV 6.08 + 1.39\n", + "Soule': MV 6.18 ± 0.80; FV 6.64 + 1.52\n", + "Tsadjout: MV 5.88 ± 0.98; FV 6.26 + 1.67\n", + "Piccoli: MV 5.81 ± 0.74; FV 5.86 + 0.75\n", + "Shomurodov: MV 5.88 ± 0.97; FV 6.23 + 1.63\n", + "Afena-Gyan: MV 5.69 ± 0.74; FV 5.72 + 0.87\n", + "Ngonge: MV 6.10 ± 1.16; FV 6.59 + 2.12\n", + "Karamoh: MV 6.22 ± 1.06; FV 6.92 + 2.30\n", + "Ibrahimovic: MV 6.37 ± 1.33; FV 7.38 + 3.18\n", + "Pussetto: MV 5.94 ± 1.06; FV 6.42 + 1.99\n", + "Cancellieri: MV 5.83 ± 0.62; FV 5.83 + 0.50\n", + "Valencia D.: MV 5.70 ± 0.60; FV 5.65 + 0.63\n", + "Oddei: MV 5.94 ± 0.86; FV 6.08 + 1.11\n", + "Braaf: MV 5.80 ± 0.88; FV 5.87 + 1.17\n", + "Raimondo: MV 6.00 ± 0.93; FV 6.08 + 1.07\n", + "Kaio Jorge: MV 5.95 ± 0.59; FV 5.98 + 0.50\n", + "De Luca: MV 5.97 ± 0.82; FV 6.06 + 0.93\n", + "Voelkerling Persson: MV 5.89 ± 0.65; FV 5.96 + 0.64\n", + "Montevago: MV 5.65 ± 0.64; FV 5.63 + 0.62\n", + "Krollis: MV 5.86 ± 0.95; FV 5.94 + 1.30\n", + "Vivaldo: MV 5.82 ± 1.04; FV 5.90 + 1.39\n" + ] + }, + { + "data": { + "text/html": [ + "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaSpezia11.00756.1784020.4280255.7161080.5468025.9284000.2635850.6092681.5888116.2837800.623820-0.6253401.07269240.422094
MussoPAtalantaSpezia10.0056.2468360.4124635.4185290.4188306.0055360.2527780.6122031.5899515.8132680.512082-0.5411221.03104124.998909
Rossi F.PAtalantaSpezia10.0016.2419590.4139164.5331270.5269115.9980540.2497280.6230621.5898425.0662490.630503-0.5907080.9720400.773813
ScalviniDAtalantaSpezia11.00906.2733230.5370456.7866010.9808436.1571860.5984840.1425540.9371845.9679681.2064330.4635731.5998480.000000
ToloiDAtalantaSpezia11.00906.2540100.5106116.7197040.9057506.1626700.5754460.1167880.9588885.9783641.1343930.4489291.5998580.000000
............................................................
GaichAVeronaInter10.55555.8674530.4574206.0846430.6927825.7927380.5192260.1061631.0451695.5228090.8747290.4423011.5998540.000000
DjuricAVeronaInter10.45605.9272610.3529756.0585050.4371225.8834220.4082260.0795311.1544085.8030530.6628720.2787511.5999180.000000
KallonAVeronaInter10.00405.8745990.4109656.0492300.5923005.8060450.4666890.1085031.0951995.5937080.7801420.4070941.5998840.000000
BraafAVeronaInter10.00355.7962740.4400125.8698300.5854895.7579440.5119310.0553471.0603285.5059670.8671160.3018241.5998890.000000
LasagnaAVeronaInter11.00805.7778340.3893635.8360370.4891945.7277980.4488260.0824881.1236435.5066600.6985200.3360681.5999010.000000
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525 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Sportiello P Atalanta Spezia 1 1.00 75 6.178402 0.428025 \n", + "Musso P Atalanta Spezia 1 0.00 5 6.246836 0.412463 \n", + "Rossi F. P Atalanta Spezia 1 0.00 1 6.241959 0.413916 \n", + "Scalvini D Atalanta Spezia 1 1.00 90 6.273323 0.537045 \n", + "Toloi D Atalanta Spezia 1 1.00 90 6.254010 0.510611 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Inter 1 0.55 55 5.867453 0.457420 \n", + "Djuric A Verona Inter 1 0.45 60 5.927261 0.352975 \n", + "Kallon A Verona Inter 1 0.00 40 5.874599 0.410965 \n", + "Braaf A Verona Inter 1 0.00 35 5.796274 0.440012 \n", + "Lasagna A Verona Inter 1 1.00 80 5.777834 0.389363 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Sportiello 5.716108 0.546802 5.928400 0.263585 0.609268 \n", + "Musso 5.418529 0.418830 6.005536 0.252778 0.612203 \n", + "Rossi F. 4.533127 0.526911 5.998054 0.249728 0.623062 \n", + "Scalvini 6.786601 0.980843 6.157186 0.598484 0.142554 \n", + "Toloi 6.719704 0.905750 6.162670 0.575446 0.116788 \n", + "... ... ... ... ... ... \n", + "Gaich 6.084643 0.692782 5.792738 0.519226 0.106163 \n", + "Djuric 6.058505 0.437122 5.883422 0.408226 0.079531 \n", + "Kallon 6.049230 0.592300 5.806045 0.466689 0.108503 \n", + "Braaf 5.869830 0.585489 5.757944 0.511931 0.055347 \n", + "Lasagna 5.836037 0.489194 5.727798 0.448826 0.082488 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Sportiello 1.588811 6.283780 0.623820 -0.625340 1.072692 \n", + "Musso 1.589951 5.813268 0.512082 -0.541122 1.031041 \n", + "Rossi F. 1.589842 5.066249 0.630503 -0.590708 0.972040 \n", + "Scalvini 0.937184 5.967968 1.206433 0.463573 1.599848 \n", + "Toloi 0.958888 5.978364 1.134393 0.448929 1.599858 \n", + "... ... ... ... ... ... \n", + "Gaich 1.045169 5.522809 0.874729 0.442301 1.599854 \n", + "Djuric 1.154408 5.803053 0.662872 0.278751 1.599918 \n", + "Kallon 1.095199 5.593708 0.780142 0.407094 1.599884 \n", + "Braaf 1.060328 5.505967 0.867116 0.301824 1.599889 \n", + "Lasagna 1.123643 5.506660 0.698520 0.336068 1.599901 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Sportiello 40.422094 \n", + "Musso 24.998909 \n", + "Rossi F. 0.773813 \n", + "Scalvini 0.000000 \n", + "Toloi 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Braaf 0.000000 \n", + "Lasagna 0.000000 \n", + "\n", + "[525 rows x 19 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matchday_out = 33\n", + "\n", + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " try:\n", + " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + " \n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", + " \n", + " role = players['r'][player] \n", + " \n", + " starter = 0\n", + " voteperc = 0\n", + " \n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + " \n", + " if(player in probables.index):\n", + " starter = probables['starter'][player]\n", + " voteperc = probables['percentage'][player]\n", + " \n", + " row = [player, role, team, oppteam, home, \n", + " starter, voteperc, \n", + " mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs]\n", + " \n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + " \n", + " output = pd.concat([output, row_df])\n", + " \n", + " except:\n", + " print(players.index[i] + ' no data')\n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6befd611", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "eed7a7ac", + "metadata": {}, + "source": [ + "Predict average Serie A performance for each player" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b637a15", + "metadata": {}, + "outputs": [], + "source": [ + "gk_starters = ['Maignan', 'Ochoa', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", + " 'Terracciano', 'Onana', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Carnesecchi', 'Rui Patricio',\n", + " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60d73507", + "metadata": {}, + "outputs": [], + "source": [ + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "tot_matches = 2 # home and not home\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " try:\n", + " home = 0\n", + "\n", + " for k in range(tot_matches):\n", + " #matchday_out = k + 1\n", + " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + "\n", + " player = players.index[i]\n", + " team = players['team'][i]\n", + " oppteam = 'Avg'\n", + " home = not home\n", + "\n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", + "\n", + " role = players['r'][player] \n", + "\n", + " starter = 0\n", + " voteperc = 0\n", + "\n", + " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", + " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", + "\n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + "\n", + " starter = int( player in gk_starters )\n", + " if(starter):\n", + " voteperc = 100\n", + " else:\n", + " voteperc = 0\n", + " else:\n", + " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", + " voteperc = int( min( 1, games / current_season_games ) * 100) \n", + "\n", + " if(k == 0):\n", + " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", + "\n", + " numrow_ = [mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs] \n", + "\n", + " if(k == 0):\n", + " numrow = numrow_\n", + " else:\n", + " for j in range(len(numrow)):\n", + " numrow[j] += numrow_[j]\n", + "\n", + " for j in range(len(numrow)):\n", + " numrow[j] /= tot_matches\n", + "\n", + " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", + " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", + "\n", + " row += numrow # list concat\n", + "\n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + "\n", + " output = pd.concat([output, row_df])\n", + " except:\n", + " print(players.index[i] + ' no data')\n", + " \n", + " \n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b47cbd63", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "cf9df3bb", + "metadata": {}, + "source": [ + "Various predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7300f3c2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret: MV 6.08 ± 0.61; FV 5.48 + 1.00 (64.3% cs)\n", + "Szczesny: MV 6.12 ± 0.63; FV 5.79 + 0.88 (87.3% cs)\n", + "Provedel: MV 6.18 ± 0.65; FV 5.49 + 1.00 (55.3% cs)\n", + "Maignan: MV 6.50 ± 0.78; FV 5.78 + 0.88 (47.3% cs)\n", + "Rui Patricio: MV 6.36 ± 0.72; FV 6.14 + 0.74 (82.2% cs)\n", + "Onana: MV 6.11 ± 0.62; FV 5.88 + 0.85 (82.9% cs)\n", + "Milinkovic-Savic V.: MV 6.09 ± 0.62; FV 4.87 + 1.25 (6.9% cs)\n", + "Musso: MV 6.56 ± 0.81; FV 6.04 + 0.79 (72.9% cs)\n", + "Vicario: MV 6.68 ± 0.87; FV 5.75 + 0.88 (37.2% cs)\n", + "Silvestri: MV 6.11 ± 1.17; FV 4.85 + 0.64 (13.9% cs)\n", + "Terracciano: MV 6.12 ± 0.63; FV 5.38 + 1.01 (29.3% cs)\n", + "Skorupski: MV 6.27 ± 0.68; FV 5.46 + 1.00 (47.3% cs)\n", + "Falcone: MV 6.42 ± 0.75; FV 5.29 + 1.02 (2.8% cs)\n", + "Di Gregorio: MV 6.39 ± 0.75; FV 5.73 + 0.89 (61.6% cs)\n", + "Consigli: MV 6.16 ± 0.64; FV 5.22 + 1.11 (45.3% cs)\n", + "Carnesecchi: MV 6.27 ± 0.77; FV 5.12 + 0.93 (7.8% cs)\n", + "Montipo': MV 6.47 ± 0.77; FV 5.20 + 1.05 (6.4% cs)\n", + "Audero: MV 6.48 ± 0.77; FV 5.26 + 0.95 (7.2% cs)\n", + "Dragowski: MV 6.52 ± 0.80; FV 6.02 + 0.79 (76.4% cs)\n", + "Ochoa: MV 6.46 ± 0.78; FV 5.69 + 0.79 (23.2% cs)\n", + "Tatarusanu: MV 6.04 ± 0.69; FV 4.11 + 1.16 (3.0% cs)\n", + "Handanovic: MV 6.41 ± 0.74; FV 6.19 + 0.74 (92.1% cs)\n", + "Sportiello: MV 6.39 ± 0.75; FV 5.18 + 0.93 (15.2% cs)\n", + "Sepe: MV 6.21 ± 0.71; FV 4.87 + 1.05 (4.9% cs)\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.21465971, 4.86660706]),\n", + " array([0.35387683, 0.5225544 ], dtype=float32),\n", + " [,\n", + " ,\n", + " ]]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Meret', log = 1, plot = 0)\n", + "predict_player('Szczesny', log = 1, plot = 0)\n", + "predict_player('Provedel', log = 1, plot = 0)\n", + "predict_player('Maignan', log = 1, plot = 0)\n", + "predict_player('Rui Patricio', log = 1, plot = 0)\n", + "predict_player('Onana', log = 1, plot = 0)\n", + "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", + "predict_player('Musso', log = 1, plot = 0)\n", + "predict_player('Vicario', log = 1, plot = 0)\n", + "predict_player('Silvestri', log = 1, plot = 0)\n", + "predict_player('Terracciano', log = 1, plot = 0)\n", + "predict_player('Skorupski', log = 1, plot = 0)\n", + "predict_player('Falcone', log = 1, plot = 0)\n", + "predict_player('Di Gregorio', log = 1, plot = 0)\n", + "predict_player('Consigli', log = 1, plot = 0)\n", + "predict_player('Carnesecchi', log = 1, plot = 0)\n", + "predict_player('Montipo\\'', log = 1, plot = 0)\n", + "predict_player('Audero', log = 1, plot = 0)\n", + "predict_player('Dragowski', log = 1, plot = 0)\n", + "predict_player('Ochoa', log = 1, plot = 0)\n", + "predict_player('Tatarusanu', log = 1, plot = 0)\n", + "predict_player('Handanovic', log = 1, plot = 0)\n", + "predict_player('Sportiello', log = 1, plot = 0)\n", + "predict_player('Sepe', log = 1, plot = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd126870", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", + "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", + "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", + "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", + "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", + "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", + "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", + "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", + "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", + "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", + "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", + "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", + "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.41176047, 8.29456946]),\n", + " array([0.70926785, 2.7289915 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Muriel', plot = 0, log = 1)\n", + "predict_player('Tonali', plot = 0, log = 1)\n", + "predict_player('Lobotka', plot = 0, log = 1)\n", + "predict_player('Politano', plot = 0, log = 1)\n", + "predict_player('Zapata D.', plot = 0, log = 1)\n", + "predict_player('Frattesi', plot = 0, log = 1)\n", + "predict_player('Gonzalez N.', plot = 0, log = 1)\n", + "predict_player('Abraham', plot = 0, log = 1)\n", + "predict_player('Pobega', plot = 0, log = 1)\n", + "predict_player('Mario Rui', plot = 0, log = 1)\n", + "predict_player('Cuadrado', plot = 0, log = 1)\n", + "predict_player('Skriniar', plot = 0, log = 1)\n", + "predict_player('Lukaku', plot = 0, log = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4b9f5a7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skriniar: MV 6.14 ± 0.82; FV 6.38 + 1.13\n", + "Cuadrado: MV 6.29 ± 0.96; FV 6.83 + 1.94\n", + "Bastoni: MV 6.13 ± 0.73; FV 6.32 + 0.98\n", + "Barak: MV 6.31 ± 1.41; FV 7.50 + 3.97\n", + "Politano: MV 6.18 ± 0.82; FV 6.57 + 1.55\n", + "Smalling: MV 6.17 ± 0.86; FV 6.56 + 1.43\n", + "Gosens: MV 6.05 ± 0.54; FV 6.11 + 0.66\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.04807256, 6.10812885]),\n", + " array([0.27137518, 0.32888246], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Skriniar', log = 1, oldseason= True)\n", + "predict_player('Cuadrado', log = 1, oldseason= True)\n", + "predict_player('Bastoni', log = 1, oldseason= True)\n", + "predict_player('Barak', log = 1, oldseason= True)\n", + "predict_player('Politano', log = 1, oldseason= True)\n", + "predict_player('Smalling', log = 1, oldseason= True)\n", + "predict_player('Gosens', log = 1, oldseason= True)\n", + "\n", + "predict_player('Skriniar', log = 1, oldseason= False)\n", + "predict_player('Cuadrado', log = 1, oldseason= False)\n", + "predict_player('Bastoni', log = 1, oldseason= False)\n", + "predict_player('Barak', log = 1, oldseason= False)\n", + "predict_player('Politano', log = 1, oldseason= False)\n", + "predict_player('Smalling', log = 1, oldseason= False)\n", + "predict_player('Gosens', log = 1, oldseason= False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "10c7ad3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.3442238 , 7.94607029]),\n", + " array([0.71331024, 2.395806 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Rafael Leao', plot = 1, log = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "30744d7a", + "metadata": {}, + "source": [ + "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", + "\n", + "Here the code to generate the probability density function is reproduced." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "3ec6c3dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# custom franction to calculate the probability density function\n", + "\n", + "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", + "\n", + " mul = np.sinh( np.arcsinh(2) * delta)\n", + " \n", + " mul = 2 / mul\n", + " \n", + " sigma_corr = sigma * mul\n", + " \n", + " z = (x - mu) / sigma_corr\n", + " \n", + " \n", + " \n", + " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", + " \n", + " f = np.exp(-0.5 * S * S)\n", + "\n", + " f /= np.sqrt(2 * np.pi)\n", + " \n", + " f *= 1 / ( sigma_corr * delta )\n", + " \n", + " f *= np.sqrt(1 + S * S)\n", + " \n", + " f /= np.sqrt(1 + z * z)\n", + " \n", + " return f\n", + " \n", + "\n", + "x = np.arange(start = 0, stop = 15, step = 0.001)\n", + "\n", + "\n", + "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605dc968", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/7_lineup_simulation.ipynb b/7_lineup_simulation.ipynb new file mode 100644 index 0000000..7f84e58 --- /dev/null +++ b/7_lineup_simulation.ipynb @@ -0,0 +1,851 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "0d54533f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3654c88", + "metadata": {}, + "outputs": [], + "source": [ + "file = 'outputs/pred_matchday_32.xlsx'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f89d6b70", + "metadata": {}, + "outputs": [], + "source": [ + "db = pd.read_excel(file, index_col = 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f61a1fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaTorino01.00756.2418500.4139805.9037490.3572965.9978540.2496470.6233921.5898396.2113040.449493-0.4828761.12652972.505307
MussoPAtalantaTorino00.0056.2442590.4130184.9517200.5012296.0018300.2513220.6171461.5899035.4102360.623711-0.5188071.0106908.606489
Rossi F.PAtalantaTorino00.0016.2443490.4118844.0809160.6770076.0037430.2532240.6100141.5899354.9092960.706557-0.7887860.9690510.049822
ToloiDAtalantaTorino01.00906.0486180.4858076.3107970.7008966.0118220.5642630.0481190.9962585.8228670.9833890.3520091.5998780.000000
ScalviniDAtalantaTorino01.00906.0304350.4867776.2897150.7113065.9943970.5656850.0470120.9973485.7867890.9893340.3598111.5998740.000000
............................................................
GaichAVeronaCremonese00.55555.9286110.4843926.2328410.7733085.8287360.5416760.1357601.0167155.5882400.9523200.4625961.5998560.000000
DjuricAVeronaCremonese00.45606.0438460.3642316.2313500.4825695.9889440.4166820.0974631.1338355.9301000.7133640.3036061.5999140.000000
KallonAVeronaCremonese00.00405.9197050.4197316.1530880.6303385.8395790.4723100.1251811.0845535.6528730.8104790.4273061.5998810.000000
BraafAVeronaCremonese00.00355.8998510.3886505.9743700.4711265.8752010.4570900.0399281.1065655.7448200.7539110.2228621.5999180.000000
LasagnaAVeronaCremonese01.00805.7561070.3972285.8250530.5198555.7041760.4573590.0839961.1166215.4603840.7262970.3558101.5998960.000000
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525 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV \\\n", + "player \n", + "Sportiello P Atalanta Torino 0 1.00 75 6.241850 \n", + "Musso P Atalanta Torino 0 0.00 5 6.244259 \n", + "Rossi F. P Atalanta Torino 0 0.00 1 6.244349 \n", + "Toloi D Atalanta Torino 0 1.00 90 6.048618 \n", + "Scalvini D Atalanta Torino 0 1.00 90 6.030435 \n", + "... ... ... ... ... ... ... ... \n", + "Gaich A Verona Cremonese 0 0.55 55 5.928611 \n", + "Djuric A Verona Cremonese 0 0.45 60 6.043846 \n", + "Kallon A Verona Cremonese 0 0.00 40 5.919705 \n", + "Braaf A Verona Cremonese 0 0.00 35 5.899851 \n", + "Lasagna A Verona Cremonese 0 1.00 80 5.756107 \n", + "\n", + " MV std FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Sportiello 0.413980 5.903749 0.357296 5.997854 0.249647 0.623392 \n", + "Musso 0.413018 4.951720 0.501229 6.001830 0.251322 0.617146 \n", + "Rossi F. 0.411884 4.080916 0.677007 6.003743 0.253224 0.610014 \n", + "Toloi 0.485807 6.310797 0.700896 6.011822 0.564263 0.048119 \n", + "Scalvini 0.486777 6.289715 0.711306 5.994397 0.565685 0.047012 \n", + "... ... ... ... ... ... ... \n", + "Gaich 0.484392 6.232841 0.773308 5.828736 0.541676 0.135760 \n", + "Djuric 0.364231 6.231350 0.482569 5.988944 0.416682 0.097463 \n", + "Kallon 0.419731 6.153088 0.630338 5.839579 0.472310 0.125181 \n", + "Braaf 0.388650 5.974370 0.471126 5.875201 0.457090 0.039928 \n", + "Lasagna 0.397228 5.825053 0.519855 5.704176 0.457359 0.083996 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Sportiello 1.589839 6.211304 0.449493 -0.482876 1.126529 \n", + "Musso 1.589903 5.410236 0.623711 -0.518807 1.010690 \n", + "Rossi F. 1.589935 4.909296 0.706557 -0.788786 0.969051 \n", + "Toloi 0.996258 5.822867 0.983389 0.352009 1.599878 \n", + "Scalvini 0.997348 5.786789 0.989334 0.359811 1.599874 \n", + "... ... ... ... ... ... \n", + "Gaich 1.016715 5.588240 0.952320 0.462596 1.599856 \n", + "Djuric 1.133835 5.930100 0.713364 0.303606 1.599914 \n", + "Kallon 1.084553 5.652873 0.810479 0.427306 1.599881 \n", + "Braaf 1.106565 5.744820 0.753911 0.222862 1.599918 \n", + "Lasagna 1.116621 5.460384 0.726297 0.355810 1.599896 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Sportiello 72.505307 \n", + "Musso 8.606489 \n", + "Rossi F. 0.049822 \n", + "Toloi 0.000000 \n", + "Scalvini 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Braaf 0.000000 \n", + "Lasagna 0.000000 \n", + "\n", + "[525 rows x 19 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aa1c6af5", + "metadata": {}, + "outputs": [], + "source": [ + "def get_player_distributions(player): \n", + " dist_mv = tfp.distributions.SinhArcsinh(\n", + " db.loc[player, 'MV loc'],\n", + " db.loc[player, 'MV scale'],\n", + " db.loc[player, 'MV skewness'],\n", + " db.loc[player, 'MV tailweight']\n", + " )\n", + " \n", + " dist_fv = tfp.distributions.SinhArcsinh(\n", + " db.loc[player, 'FV loc'],\n", + " db.loc[player, 'FV scale'],\n", + " db.loc[player, 'FV skewness'],\n", + " db.loc[player, 'FV tailweight']\n", + " )\n", + " \n", + " dist_cs = tfp.distributions.Bernoulli(probs = db.loc[player, 'Clean Sheet %']/100)\n", + " \n", + " return [dist_mv, dist_fv, dist_cs];" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8456c70", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_lineup(squad, ITERS = 1000, MOD = False, CS = False, plot = True):\n", + " dist = [None] * len(squad)\n", + "\n", + " defenders = list([0]) * len(squad)\n", + "\n", + "\n", + " for i in range(len(squad)):\n", + " dist[i] = get_player_distributions(squad[i]) \n", + "\n", + " if(db['role'][squad[i]] == 'D'):\n", + " defenders[i] = 1\n", + " \n", + " total_points = np.zeros(ITERS)\n", + " clean_sheets = np.zeros(ITERS)\n", + " mod_points = np.zeros(ITERS)\n", + "\n", + " mv_samples = [None] * len(squad)\n", + " fv_samples = [None] * len(squad)\n", + "\n", + " for i in range(len(squad)):\n", + " mv_samples[i] = dist[i][0].sample(ITERS)\n", + " fv_samples[i] = dist[i][1].sample(ITERS)\n", + "\n", + " cs_samples = dist[0][2].sample(ITERS)\n", + "\n", + " for k in range(ITERS):\n", + " d_points = list([0]) * len(squad)\n", + "\n", + " cleansheet = float(cs_samples[k])\n", + " if(not CS):\n", + " cleansheet = 0;\n", + " \n", + " total_points[k] += cleansheet\n", + " clean_sheets[k] += cleansheet\n", + "\n", + " for i in range(len(squad)): \n", + " if(defenders[i] == 1):\n", + " d_points[i] = float(mv_samples[i][k])\n", + "\n", + " total_points[k] += float(fv_samples[i][k])\n", + "\n", + " d_points.sort(reverse = True)\n", + "\n", + " if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n", + " mod_avg = 0\n", + " for j in range(3):\n", + " mod_avg += round(d_points[j] * 2) / 2\n", + " mod_avg /= 3\n", + "\n", + " if(mod_avg >= 7):\n", + " mod_points[k] = 6\n", + " elif(mod_avg >= 6.5):\n", + " mod_points[k] = 3\n", + " elif(mod_avg >= 6):\n", + " mod_points[k] = 1\n", + "\n", + " total_points[k] += mod_points[k]\n", + " \n", + " loc_0 = total_points.mean()\n", + "\n", + " squad_model = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.Dense(3),\n", + " tfp.layers.DistributionLambda(\n", + " lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n", + " skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n", + " )\n", + " ]\n", + " )\n", + "\n", + " def negloglik(y, distr):\n", + " return -distr.log_prob(y)\n", + "\n", + " squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n", + "\n", + " dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n", + " squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)\n", + " \n", + " squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n", + "\n", + " if(plot):\n", + "\n", + " x = np.arange(start = 0, stop = 200, step = 0.001)\n", + " prb = squad_points_dist.prob(x)\n", + "\n", + " mn = total_points.mean()\n", + " pot = mn + 2 * total_points.std()\n", + "\n", + " f, ax = plt.subplots(1, 2)\n", + "\n", + " ax[0].plot(x, prb)\n", + " ax[0].fill_between(x, prb, color = 'lightblue')\n", + " ax[0].vlines(x = mn, color = 'black', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n", + " ax[0].vlines(x = pot, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'potential = ' + \"{:.2f}\".format(pot))\n", + "\n", + " \n", + " ax[0].set_xlim([40, 140])\n", + " ax[0].set_ylim([0, 0.12])\n", + "\n", + " ax[0].legend()\n", + "\n", + " #ax[0].hist(total_points, bins = 20, density = True)\n", + "\n", + "\n", + " ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + " text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n", + " 'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n", + " 'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n", + "\n", + " ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + " ax[1].axis('off')\n", + "\n", + "\n", + "\n", + " plt.subplots_adjust(right=1.5)\n", + "\n", + " plt.show()\n", + " return [squad_model, squad_points_dist, total_points.mean(), mod_points.mean(), clean_sheets.mean()]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "852ef39e", + "metadata": {}, + "outputs": [], + "source": [ + "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", + " mul = 2 / np.sinh( np.arcsinh(2) * delta) \n", + " z = (x - mu) / (sigma*mul) \n", + " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", + " return np.exp(-0.5 * S * S) * np.sqrt(1 + S * S) / ( sigma * mul * delta ) / np.sqrt(1 + z * z) / np.sqrt(2 * np.pi)\n", + "\n", + "def plot_lineup(squad, config):\n", + " fig, axs = plt.subplots(6, 5, figsize=(14, 10))\n", + "\n", + " i = 0;\n", + " j = 1;\n", + "\n", + " for ax in axs.flat:\n", + " ax.axis('off')\n", + "\n", + " if(i < len(config) and config[i] == j):\n", + " player = squad[i];\n", + "\n", + " mu = db.loc[player, 'FV loc'];\n", + " sigma = db.loc[player, 'FV scale'];\n", + " eps = db.loc[player, 'FV skewness'];\n", + " delta = db.loc[player, 'FV tailweight'];\n", + "\n", + " x = np.arange(start = 0, stop = 30, step = 0.001)\n", + "\n", + " pxf = sinh_archsinh_pdf(x, mu, sigma, eps, delta)\n", + "\n", + " mf = np.average(x, weights = pxf);\n", + "\n", + "\n", + " ax.plot(x, pxf, color = 'g', label = player)\n", + " ax.fill_between(x, pxf, color = 'lightgreen')\n", + "\n", + " ax.vlines(x = mf, color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mfv = ' + '{:.2f}'.format(mf)) \n", + "\n", + " ax.legend(fontsize=\"9\", loc =\"upper right\", handletextpad=0, handlelength=0)\n", + "\n", + " ax.axis(xmin = 0, xmax = 15, ymin = 0, ymax = 1)\n", + "\n", + " ax.axis('on')\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " ax.tick_params(axis='both', labelsize=7)\n", + "\n", + " i = i + 1;\n", + "\n", + " j = j + 1;\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8fa1df8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Szczesny',\n", + " 'Di Lorenzo',\n", + " 'Kim',\n", + " 'Juan Jesus',\n", + " 'Carlos Augusto',\n", + " 'Kostic',\n", + " 'Frattesi',\n", + " 'Barella',\n", + " 'Strefezza',\n", + " 'Rafael Leao',\n", + " 'Lauriente\\'']\n", + "\n", + "config_442 = [3, 6, 7, 9, 10, 15, 17, 19, 21, 27, 29];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_442)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a4fd2fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Szczesny',\n", + " 'Di Lorenzo',\n", + " 'Kim',\n", + " 'Juan Jesus',\n", + " 'Carlos Augusto',\n", + " 'Rovella',\n", + " 'Frattesi',\n", + " 'Barella',\n", + " 'Gonzalez N.',\n", + " 'Rafael Leao',\n", + " 'Lauriente\\'']\n", + "\n", + "config_433 = [3, 6, 7, 9, 10, 13, 17, 19, 26, 28, 30];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_433)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3943571c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Carnesecchi',\n", + " 'Danilo',\n", + " 'Ampadu',\n", + " 'Luperto',\n", + " 'Hernandez T.',\n", + " 'Zambo Anguissa',\n", + " 'Milinkovic-Savic',\n", + " 'Vlasic',\n", + " 'Kvaratskhelia',\n", + " 'Caprari',\n", + " 'Lukaku']\n", + "\n", + "config_4231 = [3, 6, 7, 9, 10, 12, 19, 23, 25, 26, 28];\n", + "\n", + "s = simulate_lineup(squad)\n", + "\n", + "plot_lineup(squad, config_4231)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "15d5b4ca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad = ['Di Gregorio',\n", + " 'Carlos Augusto',\n", + " 'Rrahmani',\n", + " 'Baschirotto',\n", + " 'Valeri',\n", + " 'Frattesi',\n", + " 'Pereyra',\n", + " 'Lazovic',\n", + " 'Gonzalez N.',\n", + " 'Vlahovic',\n", + " 'Hojlund']\n", + "\n", + "config_433_classic = [3, 6, 7, 9, 10, 17, 18, 19, 26, 28, 30];\n", + "\n", + "s = simulate_lineup(squad, MOD = True)\n", + "\n", + "plot_lineup(squad, config_433_classic)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git 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b/fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_31.xlsx differ diff --git a/fbref_data/keepers_players.csv b/fbref_data/keepers_players.csv index e765dcb..3447246 100644 --- a/fbref_data/keepers_players.csv +++ b/fbref_data/keepers_players.csv @@ -1,32 +1,41 @@ player,nationality,position,team,age,birth_year,gk_games,gk_games_starts,gk_minutes,gk_goals_against,gk_goals_against_per90,gk_shots_on_target_against,gk_saves,gk_save_pct,gk_wins,gk_ties,gk_losses,gk_clean_sheets,gk_clean_sheets_pct,gk_pens_att,gk_pens_allowed,gk_pens_saved,gk_pens_missed,minutes_90s,gk_free_kick_goals_against,gk_corner_kick_goals_against,gk_own_goals_against,gk_psxg,gk_psnpxg_per_shot_on_target_against,gk_psxg_net,gk_psxg_net_per90,gk_passes_completed_launched,gk_passes_launched,gk_passes_pct_launched,gk_passes,gk_passes_throws,gk_pct_passes_launched,gk_passes_length_avg,gk_goal_kicks,gk_pct_goal_kicks_launched,gk_goal_kick_length_avg,gk_crosses,gk_crosses_stopped,gk_crosses_stopped_pct,gk_def_actions_outside_pen_area,gk_def_actions_outside_pen_area_per90,gk_avg_distance_def_actions -Emil Audero,it ITA,GK,Sampdoria,26-023,1997,21.0,21.0,1890.0,36.0,1.71,103.0,67.0,69.9,2.0,4.0,15.0,2.0,9.5,7.0,5.0,2.0,0.0,21.0,0.0,3.0,2.0,28.9,0.22,-5.1,-0.24,124.0,366.0,33.9,596.0,86.0,45.6,39.4,161.0,58.4,44.8,283.0,20.0,7.1,20.0,0.95,14.7 -Marco Carnesecchi,it ITA,GK,Cremonese,22-224,2000,12.0,12.0,1080.0,18.0,1.5,63.0,46.0,73.0,0.0,5.0,7.0,2.0,16.7,1.0,1.0,0.0,0.0,12.0,1.0,3.0,1.0,18.7,0.29,1.7,0.14,51.0,180.0,28.3,297.0,65.0,45.1,38.9,86.0,53.5,46.1,158.0,13.0,8.2,8.0,0.67,12.9 -Andrea Consigli,it ITA,GK,Sassuolo,36-014,1987,19.0,19.0,1710.0,28.0,1.47,60.0,32.0,58.3,6.0,4.0,9.0,6.0,31.6,3.0,3.0,0.0,0.0,19.0,0.0,3.0,0.0,23.3,0.35,-4.7,-0.25,112.0,269.0,41.6,686.0,99.0,31.0,34.3,172.0,32.6,33.6,255.0,16.0,6.3,23.0,1.21,15.2 -Michele Di Gregorio,it ITA,GK,Monza,25-198,1997,21.0,21.0,1890.0,30.0,1.43,90.0,61.0,66.7,7.0,5.0,9.0,6.0,28.6,0.0,0.0,0.0,0.0,21.0,0.0,3.0,1.0,29.2,0.33,0.2,0.01,90.0,261.0,34.5,707.0,133.0,31.4,32.9,124.0,31.5,31.4,286.0,10.0,3.5,16.0,0.76,13.3 -Bartłomiej Drągowski,pl POL,GK,Spezia,25-175,1997,19.0,19.0,1662.0,32.0,1.73,100.0,68.0,69.0,4.0,4.0,11.0,3.0,15.8,2.0,1.0,0.0,1.0,18.5,1.0,4.0,0.0,28.1,0.27,-3.9,-0.21,85.0,268.0,31.7,503.0,92.0,39.4,34.6,109.0,64.2,48.0,251.0,9.0,3.6,24.0,1.3,15.3 -Wladimiro Falcone,it ITA,GK,Lecce,27-304,1995,21.0,21.0,1890.0,24.0,1.14,89.0,66.0,76.4,5.0,8.0,8.0,3.0,14.3,3.0,3.0,0.0,0.0,21.0,1.0,4.0,1.0,24.0,0.24,1.0,0.05,106.0,400.0,26.5,506.0,83.0,54.5,41.3,163.0,76.1,51.7,316.0,15.0,4.7,20.0,0.95,12.5 -Pierluigi Gollini,it ITA,GK,Fiorentina,27-329,1995,3.0,3.0,270.0,2.0,0.67,8.0,6.0,75.0,1.0,2.0,0.0,2.0,66.7,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,1.7,0.21,-0.3,-0.1,13.0,38.0,34.2,115.0,17.0,27.0,32.4,11.0,63.6,49.3,24.0,1.0,4.2,0.0,0.0,9.3 -Samir Handanović,si SVN,GK,Inter,38-211,1984,8.0,8.0,720.0,13.0,1.62,36.0,24.0,63.9,4.0,0.0,4.0,2.0,25.0,0.0,0.0,0.0,0.0,8.0,0.0,1.0,1.0,10.6,0.3,-1.4,-0.17,27.0,52.0,51.9,266.0,46.0,18.8,26.1,45.0,4.4,24.0,79.0,2.0,2.5,5.0,0.63,14.6 -Mike Maignan,fr FRA,GK,Milan,27-222,1995,7.0,7.0,630.0,8.0,1.14,22.0,13.0,68.2,4.0,2.0,1.0,2.0,28.6,2.0,1.0,1.0,0.0,7.0,0.0,1.0,0.0,8.2,0.29,0.2,0.03,29.0,74.0,39.2,220.0,32.0,30.5,33.5,18.0,38.9,40.4,65.0,5.0,7.7,6.0,0.86,12.9 -Luís Maximiano,pt POR,GK,Lazio,24-036,1999,1.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,66.7,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0 -Alex Meret,it ITA,GK,Napoli,25-325,1997,21.0,21.0,1890.0,15.0,0.71,56.0,40.0,75.0,18.0,2.0,1.0,9.0,42.9,2.0,1.0,1.0,0.0,21.0,0.0,1.0,0.0,17.0,0.27,2.0,0.09,35.0,98.0,35.7,468.0,77.0,15.6,26.7,128.0,19.5,27.1,221.0,6.0,2.7,24.0,1.14,17.2 -Vanja Milinković-Savić,rs SRB,GK,Torino,25-355,1997,21.0,21.0,1890.0,22.0,1.05,82.0,60.0,76.8,8.0,6.0,7.0,5.0,23.8,4.0,3.0,0.0,1.0,21.0,0.0,3.0,0.0,21.9,0.23,-0.1,-0.01,148.0,524.0,28.2,788.0,95.0,48.0,40.9,155.0,94.2,71.7,263.0,18.0,6.8,35.0,1.67,16.3 -Lorenzo Montipò,it ITA,GK,Hellas Verona,26-355,1996,21.0,21.0,1890.0,33.0,1.57,107.0,74.0,69.2,3.0,5.0,13.0,2.0,9.5,1.0,0.0,1.0,0.0,21.0,0.0,2.0,1.0,27.6,0.25,-4.4,-0.21,206.0,413.0,49.9,497.0,65.0,62.2,44.1,168.0,61.9,46.7,294.0,15.0,5.1,46.0,2.19,16.8 -Juan Musso,ar ARG,GK,Atalanta,28-280,1994,15.0,15.0,1267.0,15.0,1.07,56.0,42.0,75.0,9.0,3.0,3.0,5.0,33.3,1.0,1.0,0.0,0.0,14.1,0.0,1.0,1.0,12.7,0.21,-1.3,-0.09,81.0,185.0,43.8,372.0,107.0,33.6,32.5,98.0,61.2,47.1,174.0,10.0,5.7,12.0,0.85,14.6 -Guillermo Ochoa,mx MEX,GK,Salernitana,37-212,1985,6.0,6.0,540.0,17.0,2.83,44.0,26.0,65.9,1.0,1.0,4.0,0.0,0.0,3.0,2.0,1.0,0.0,6.0,0.0,1.0,0.0,16.9,0.32,-0.1,-0.01,43.0,134.0,32.1,155.0,16.0,58.7,41.9,60.0,71.7,52.2,86.0,3.0,3.5,4.0,0.67,15.7 -André Onana,cm CMR,GK,Inter,26-314,1996,13.0,13.0,1170.0,13.0,1.0,44.0,32.0,75.0,10.0,1.0,2.0,5.0,38.5,2.0,2.0,0.0,0.0,13.0,0.0,2.0,1.0,10.6,0.2,-1.4,-0.11,59.0,156.0,37.8,432.0,61.0,28.9,30.7,92.0,33.7,36.9,165.0,7.0,4.2,4.0,0.31,13.2 -Rui Patrício,pt POR,GK,Roma,34-360,1988,21.0,21.0,1890.0,18.0,0.86,60.0,42.0,70.0,12.0,4.0,5.0,8.0,38.1,0.0,0.0,0.0,0.0,21.0,1.0,3.0,0.0,12.4,0.21,-5.6,-0.27,68.0,173.0,39.3,380.0,75.0,32.6,31.8,159.0,30.8,32.8,248.0,6.0,2.4,15.0,0.71,14.0 -Gianluca Pegolo,it ITA,GK,Sassuolo,41-322,1981,2.0,2.0,180.0,3.0,1.5,6.0,3.0,66.7,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,3.2,0.38,0.2,0.12,9.0,18.0,50.0,61.0,10.0,21.3,29.7,16.0,31.3,28.3,28.0,2.0,7.1,0.0,0.0,9.0 -Mattia Perin,it ITA,GK,Juventus,30-092,1992,7.0,6.0,588.0,4.0,0.61,24.0,19.0,87.5,2.0,3.0,1.0,4.0,66.7,2.0,1.0,1.0,0.0,6.5,0.0,0.0,0.0,7.9,0.25,3.9,0.6,24.0,71.0,33.8,198.0,36.0,27.8,30.3,41.0,39.0,35.3,99.0,2.0,2.0,9.0,1.38,17.8 -Ivan Provedel,it ITA,GK,Lazio,28-330,1994,21.0,20.0,1883.0,17.0,0.81,69.0,52.0,76.8,10.0,6.0,4.0,11.0,55.0,1.0,1.0,0.0,0.0,20.9,0.0,4.0,0.0,16.2,0.22,-0.8,-0.04,99.0,232.0,42.7,650.0,95.0,29.4,33.0,120.0,34.2,34.5,279.0,7.0,2.5,39.0,1.86,18.2 -Ionuț Radu,ro ROU,GK,Cremonese,25-258,1997,9.0,9.0,810.0,19.0,2.11,64.0,45.0,75.0,0.0,3.0,6.0,1.0,11.1,3.0,3.0,0.0,0.0,9.0,0.0,3.0,0.0,21.0,0.29,2.0,0.22,38.0,143.0,26.6,265.0,34.0,41.1,37.3,67.0,50.7,46.3,107.0,6.0,5.6,9.0,1.0,14.5 -Luigi Sepe,it ITA,GK,Salernitana,31-278,1991,15.0,15.0,1350.0,24.0,1.6,76.0,50.0,72.4,4.0,5.0,6.0,3.0,20.0,5.0,3.0,2.0,0.0,15.0,0.0,0.0,0.0,26.0,0.28,2.0,0.14,57.0,227.0,25.1,414.0,72.0,39.9,36.1,134.0,46.3,40.6,199.0,15.0,7.5,11.0,0.73,14.2 -Marco Silvestri,it ITA,GK,Udinese,31-345,1991,21.0,21.0,1890.0,23.0,1.1,70.0,48.0,70.0,7.0,8.0,6.0,6.0,28.6,2.0,2.0,0.0,0.0,21.0,1.0,2.0,1.0,23.9,0.31,1.9,0.09,90.0,225.0,40.0,450.0,76.0,35.8,33.6,164.0,39.0,36.1,275.0,5.0,1.8,10.0,0.48,13.7 -Łukasz Skorupski,pl POL,GK,Bologna,31-281,1991,21.0,21.0,1890.0,31.0,1.48,94.0,65.0,72.3,8.0,5.0,8.0,3.0,14.3,5.0,5.0,0.0,0.0,21.0,1.0,4.0,2.0,30.8,0.28,1.8,0.08,99.0,240.0,41.3,563.0,117.0,32.1,32.7,153.0,38.6,34.5,299.0,18.0,6.0,17.0,0.81,13.3 -Marco Sportiello,it ITA,GK,Atalanta,30-276,1992,7.0,6.0,623.0,9.0,1.3,26.0,17.0,65.4,2.0,2.0,2.0,2.0,33.3,0.0,0.0,0.0,0.0,6.9,1.0,2.0,0.0,7.8,0.3,-1.2,-0.17,31.0,77.0,40.3,181.0,45.0,19.3,28.5,61.0,68.9,48.9,86.0,7.0,8.1,14.0,2.02,19.9 -Wojciech Szczęsny,pl POL,GK,Juventus,32-298,1990,15.0,15.0,1302.0,13.0,0.9,45.0,32.0,71.1,10.0,2.0,3.0,9.0,60.0,0.0,0.0,0.0,0.0,14.5,0.0,3.0,0.0,12.7,0.28,-0.3,-0.02,62.0,147.0,42.2,340.0,60.0,34.4,35.0,72.0,41.7,38.7,178.0,4.0,2.2,12.0,0.83,15.8 -Ciprian Tătărușanu,ro ROU,GK,Milan,37-001,1986,14.0,14.0,1260.0,22.0,1.57,54.0,33.0,63.0,7.0,3.0,4.0,2.0,14.3,2.0,2.0,0.0,0.0,14.0,2.0,4.0,1.0,15.9,0.26,-5.1,-0.36,77.0,162.0,47.5,420.0,69.0,31.2,32.6,65.0,47.7,39.9,158.0,9.0,5.7,10.0,0.71,13.8 -Pietro Terracciano,it ITA,GK,Fiorentina,32-339,1990,18.0,18.0,1620.0,26.0,1.44,61.0,36.0,62.3,5.0,4.0,9.0,2.0,11.1,3.0,3.0,0.0,0.0,18.0,0.0,3.0,1.0,20.6,0.29,-4.4,-0.25,88.0,224.0,39.3,536.0,68.0,32.5,32.9,116.0,43.1,39.4,163.0,9.0,5.5,34.0,1.89,18.9 -Guglielmo Vicario,it ITA,GK,Empoli,26-126,1996,21.0,21.0,1890.0,26.0,1.24,92.0,65.0,72.8,6.0,8.0,7.0,7.0,33.3,3.0,1.0,1.0,1.0,21.0,0.0,6.0,0.0,26.4,0.27,0.4,0.02,102.0,310.0,32.9,766.0,113.0,33.8,34.0,108.0,47.2,42.4,426.0,25.0,5.9,12.0,0.57,10.9 -Jeroen Zoet,nl NED,GK,Spezia,32-035,1991,3.0,2.0,154.0,1.0,0.58,6.0,5.0,83.3,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.7,0.0,0.0,0.0,0.7,0.11,-0.3,-0.19,15.0,43.0,34.9,57.0,5.0,63.2,41.7,8.0,87.5,56.6,24.0,1.0,4.2,3.0,1.74,15.7 -Petar Zovko,ba BIH,GK,Spezia,20-322,2002,1.0,0.0,74.0,2.0,2.43,3.0,1.0,33.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,1.7,0.55,-0.3,-0.42,7.0,21.0,33.3,23.0,3.0,47.8,35.1,13.0,76.9,51.1,24.0,1.0,4.2,2.0,2.47,17.0 +Emil Audero,it ITA,GK,Sampdoria,26-097,1997,25.0,25.0,2250.0,39.0,1.56,121.0,82.0,71.9,2.0,6.0,17.0,4.0,16.0,7.0,5.0,2.0,0.0,25.0,0.0,4.0,2.0,32.3,0.21,-4.7,-0.19,159.0,441.0,36.1,714.0,104.0,45.8,39.1,200.0,57.0,44.2,356.0,24.0,6.7,23.0,0.92,13.9 +Francesco Bardi,it ITA,GK,Bologna,31-097,1992,1.0,1.0,90.0,0.0,0.0,4.0,4.0,100.0,1.0,0.0,0.0,1.0,100.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.6,0.16,0.6,0.56,5.0,9.0,55.6,13.0,5.0,38.5,35.6,9.0,44.4,33.4,10.0,0.0,0.0,0.0,0.0,0.0 +Marco Carnesecchi,it ITA,GK,Cremonese,22-298,2000,22.0,22.0,1980.0,38.0,1.73,119.0,82.0,69.7,3.0,7.0,12.0,3.0,13.6,2.0,2.0,0.0,0.0,22.0,2.0,4.0,1.0,35.7,0.29,-1.3,-0.06,126.0,406.0,31.0,600.0,116.0,51.3,40.9,159.0,61.6,50.7,305.0,23.0,7.5,21.0,0.95,14.0 +Andrea Consigli,it ITA,GK,Sassuolo,36-088,1987,29.0,29.0,2610.0,43.0,1.48,96.0,53.0,58.3,11.0,6.0,12.0,9.0,31.0,3.0,3.0,0.0,0.0,29.0,0.0,5.0,0.0,31.0,0.3,-12.0,-0.41,153.0,380.0,40.3,946.0,154.0,31.6,34.2,249.0,32.5,33.4,409.0,24.0,5.9,26.0,0.9,14.1 +Alessio Cragno,it ITA,GK,Monza,28-301,1994,1.0,1.0,90.0,3.0,3.0,5.0,2.0,40.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.7,0.35,-1.3,-1.33,2.0,4.0,50.0,25.0,4.0,12.0,22.6,6.0,16.7,33.3,7.0,0.0,0.0,2.0,2.0,25.7 +Michele Di Gregorio,it ITA,GK,Monza,25-272,1997,30.0,30.0,2700.0,40.0,1.33,129.0,90.0,69.8,11.0,8.0,11.0,8.0,26.7,1.0,1.0,0.0,0.0,30.0,1.0,4.0,1.0,39.8,0.31,0.8,0.03,126.0,376.0,33.5,984.0,187.0,31.1,32.6,196.0,35.7,33.7,430.0,13.0,3.0,23.0,0.77,12.7 +Bartłomiej Drągowski,pl POL,GK,Spezia,25-249,1997,28.0,28.0,2406.0,43.0,1.61,142.0,100.0,72.5,5.0,9.0,14.0,4.0,14.3,6.0,4.0,1.0,1.0,26.7,1.0,4.0,2.0,40.7,0.26,-0.3,-0.01,130.0,403.0,32.3,775.0,133.0,38.3,34.5,169.0,62.7,48.0,401.0,11.0,2.7,29.0,1.08,14.2 +Wladimiro Falcone,it ITA,GK,Lecce,28-013,1995,31.0,31.0,2790.0,38.0,1.23,132.0,97.0,75.0,6.0,10.0,15.0,3.0,9.7,5.0,5.0,0.0,0.0,31.0,1.0,6.0,3.0,36.9,0.25,1.9,0.06,154.0,592.0,26.0,745.0,124.0,56.1,41.9,225.0,77.3,52.4,441.0,22.0,5.0,35.0,1.13,13.7 +Pierluigi Gollini,it ITA,GK,Fiorentina,28-038,1995,3.0,3.0,270.0,2.0,0.67,8.0,6.0,75.0,1.0,2.0,0.0,2.0,66.7,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,1.7,0.21,-0.3,-0.1,13.0,38.0,34.2,115.0,17.0,27.0,32.4,11.0,63.6,49.3,24.0,1.0,4.2,0.0,0.0,9.3 +Pierluigi Gollini,it ITA,GK,Napoli,28-038,1995,1.0,1.0,90.0,0.0,0.0,3.0,3.0,100.0,1.0,0.0,0.0,1.0,100.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.2,0.07,0.2,0.21,3.0,9.0,33.3,32.0,7.0,28.1,31.7,5.0,0.0,15.4,12.0,1.0,8.3,0.0,0.0,2.0 +Samir Handanović,si SVN,GK,Inter,38-285,1984,11.0,11.0,990.0,16.0,1.45,42.0,27.0,64.3,6.0,0.0,5.0,3.0,27.3,1.0,1.0,0.0,0.0,11.0,0.0,1.0,1.0,12.5,0.27,-2.5,-0.23,31.0,62.0,50.0,345.0,54.0,16.5,25.7,59.0,8.5,25.2,112.0,2.0,1.8,10.0,0.91,16.3 +Mike Maignan,fr FRA,GK,Milan,27-296,1995,15.0,15.0,1350.0,15.0,1.0,48.0,32.0,72.9,7.0,5.0,3.0,6.0,40.0,3.0,2.0,1.0,0.0,15.0,0.0,1.0,0.0,14.8,0.25,-0.2,-0.01,62.0,149.0,41.6,490.0,83.0,26.7,31.3,54.0,33.3,35.3,153.0,11.0,7.2,25.0,1.67,18.1 +Federico Marchetti,it ITA,GK,Spezia,40-077,1983,1.0,0.0,66.0,2.0,2.73,3.0,1.0,33.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.0,0.0,0.0,1.5,0.48,-0.5,-0.74,2.0,4.0,50.0,10.0,2.0,10.0,24.3,8.0,37.5,32.4,8.0,0.0,0.0,1.0,1.36,22.0 +Luís Maximiano,pt POR,GK,Lazio,24-110,1999,1.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,66.7,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0 +Alex Meret,it ITA,GK,Napoli,26-034,1997,30.0,30.0,2700.0,21.0,0.7,81.0,59.0,75.3,24.0,3.0,3.0,15.0,50.0,2.0,1.0,1.0,0.0,30.0,0.0,1.0,0.0,22.3,0.25,1.3,0.04,54.0,135.0,40.0,661.0,110.0,14.8,26.2,185.0,20.0,26.8,308.0,10.0,3.2,32.0,1.07,17.0 +Vanja Milinković-Savić,rs SRB,GK,Torino,26-064,1997,31.0,31.0,2790.0,36.0,1.16,113.0,77.0,72.6,11.0,9.0,11.0,8.0,25.8,6.0,5.0,0.0,1.0,31.0,0.0,6.0,0.0,31.5,0.24,-4.5,-0.15,224.0,762.0,29.4,1196.0,142.0,45.7,40.0,236.0,91.5,69.8,387.0,25.0,6.5,62.0,2.0,17.1 +Lorenzo Montipò,it ITA,GK,Hellas Verona,27-064,1996,30.0,30.0,2700.0,44.0,1.47,144.0,100.0,69.4,6.0,7.0,17.0,4.0,13.3,1.0,0.0,1.0,0.0,30.0,1.0,4.0,1.0,38.7,0.26,-4.3,-0.14,280.0,616.0,45.5,745.0,99.0,62.0,43.9,227.0,67.8,49.6,401.0,22.0,5.5,51.0,1.7,15.8 +Juan Musso,ar ARG,GK,Atalanta,28-354,1994,23.0,23.0,1987.0,25.0,1.13,85.0,62.0,72.9,12.0,4.0,7.0,7.0,30.4,2.0,2.0,0.0,0.0,22.1,0.0,3.0,2.0,20.0,0.22,-3.0,-0.14,108.0,266.0,40.6,532.0,148.0,32.5,32.0,149.0,62.4,48.5,247.0,14.0,5.7,24.0,1.09,15.6 +Guillermo Ochoa,mx MEX,GK,Salernitana,37-286,1985,14.0,14.0,1260.0,23.0,1.64,78.0,54.0,73.1,3.0,7.0,4.0,3.0,21.4,3.0,2.0,1.0,0.0,14.0,0.0,3.0,0.0,26.3,0.3,3.3,0.24,97.0,295.0,32.9,371.0,31.0,54.2,41.5,134.0,70.1,50.0,213.0,6.0,2.8,7.0,0.5,13.1 +André Onana,cm CMR,GK,Inter,27-023,1996,20.0,20.0,1800.0,18.0,0.9,63.0,46.0,74.6,11.0,3.0,6.0,7.0,35.0,2.0,2.0,0.0,0.0,20.0,0.0,4.0,1.0,14.3,0.2,-2.7,-0.14,89.0,229.0,38.9,701.0,101.0,26.7,30.2,130.0,32.3,35.8,233.0,14.0,6.0,9.0,0.45,12.5 +Rui Patrício,pt POR,GK,Roma,35-069,1988,31.0,31.0,2790.0,29.0,0.94,96.0,67.0,71.9,17.0,5.0,9.0,13.0,41.9,3.0,2.0,1.0,0.0,31.0,1.0,4.0,1.0,23.2,0.21,-4.8,-0.15,122.0,301.0,40.5,599.0,109.0,37.9,33.6,217.0,34.1,33.6,356.0,12.0,3.4,23.0,0.74,14.1 +Gianluca Pegolo,it ITA,GK,Sassuolo,42-031,1981,2.0,2.0,180.0,3.0,1.5,6.0,3.0,66.7,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,3.2,0.38,0.2,0.12,9.0,18.0,50.0,61.0,10.0,21.3,29.7,16.0,31.3,28.3,28.0,2.0,7.1,0.0,0.0,9.0 +Simone Perilli,it ITA,GK,Hellas Verona,28-108,1995,1.0,1.0,90.0,0.0,0.0,4.0,4.0,100.0,0.0,1.0,0.0,1.0,100.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.8,0.19,0.8,0.75,6.0,30.0,20.0,21.0,0.0,81.0,57.3,13.0,100.0,69.8,19.0,1.0,5.3,1.0,1.0,12.0 +Mattia Perin,it ITA,GK,Juventus,30-166,1992,10.0,9.0,858.0,7.0,0.73,38.0,30.0,84.2,4.0,3.0,2.0,5.0,55.6,2.0,1.0,1.0,0.0,9.5,0.0,1.0,0.0,11.0,0.24,4.0,0.42,31.0,106.0,29.2,271.0,49.0,29.9,31.1,60.0,41.7,36.5,148.0,4.0,2.7,11.0,1.15,15.7 +Samuele Perisan,it ITA,GK,Empoli,25-247,1997,7.0,7.0,630.0,9.0,1.29,24.0,15.0,62.5,1.0,1.0,5.0,2.0,28.6,0.0,0.0,0.0,0.0,7.0,0.0,2.0,0.0,7.5,0.31,-1.5,-0.22,25.0,75.0,33.3,153.0,39.0,32.0,32.7,75.0,34.7,34.3,134.0,5.0,3.7,4.0,0.57,11.6 +Ivan Provedel,it ITA,GK,Lazio,29-039,1994,31.0,30.0,2783.0,21.0,0.68,102.0,81.0,80.4,17.0,7.0,6.0,18.0,60.0,1.0,1.0,0.0,0.0,30.9,0.0,5.0,0.0,24.4,0.24,3.4,0.11,135.0,320.0,42.2,913.0,136.0,28.7,33.0,161.0,36.0,34.7,402.0,17.0,4.2,46.0,1.49,16.4 +Ionuț Radu,ro ROU,GK,Cremonese,25-332,1997,9.0,9.0,810.0,19.0,2.11,64.0,45.0,75.0,0.0,3.0,6.0,1.0,11.1,3.0,3.0,0.0,0.0,9.0,0.0,3.0,0.0,21.0,0.29,2.0,0.22,38.0,143.0,26.6,265.0,34.0,41.1,37.3,67.0,50.7,46.3,107.0,6.0,5.6,9.0,1.0,14.5 +Nicola Ravaglia,it ITA,GK,Sampdoria,34-134,1988,4.0,4.0,360.0,8.0,2.0,31.0,23.0,77.4,0.0,2.0,2.0,0.0,0.0,1.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,7.8,0.23,-0.2,-0.05,28.0,67.0,41.8,100.0,21.0,43.0,35.5,53.0,45.3,35.4,72.0,4.0,5.6,1.0,0.25,10.0 +Luigi Sepe,it ITA,GK,Salernitana,31-352,1991,17.0,17.0,1530.0,27.0,1.59,87.0,57.0,73.6,4.0,5.0,8.0,3.0,17.6,7.0,4.0,3.0,0.0,17.0,0.0,0.0,0.0,30.3,0.27,3.3,0.2,76.0,274.0,27.7,490.0,84.0,40.6,35.9,154.0,48.7,41.1,222.0,16.0,7.2,13.0,0.76,14.2 +Marco Silvestri,it ITA,GK,Udinese,32-054,1991,31.0,31.0,2790.0,39.0,1.26,112.0,75.0,68.8,10.0,12.0,9.0,9.0,29.0,5.0,4.0,0.0,1.0,31.0,1.0,2.0,2.0,37.5,0.3,0.5,0.01,121.0,316.0,38.3,687.0,120.0,33.2,32.7,237.0,37.1,35.5,441.0,11.0,2.5,16.0,0.52,12.1 +Salvatore Sirigu,it ITA,GK,Fiorentina,36-103,1987,1.0,1.0,90.0,0.0,0.0,5.0,5.0,100.0,1.0,0.0,0.0,1.0,100.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.19,1.0,0.96,6.0,16.0,37.5,30.0,9.0,40.0,37.7,8.0,50.0,41.8,12.0,0.0,0.0,2.0,2.0,26.3 +Łukasz Skorupski,pl POL,GK,Bologna,31-355,1991,30.0,30.0,2700.0,39.0,1.3,132.0,94.0,75.8,11.0,8.0,11.0,6.0,20.0,8.0,7.0,1.0,0.0,30.0,1.0,6.0,2.0,40.2,0.25,3.2,0.11,129.0,329.0,39.2,773.0,169.0,31.6,32.5,219.0,38.8,35.4,402.0,24.0,6.0,22.0,0.73,13.4 +Marco Sportiello,it ITA,GK,Atalanta,30-350,1992,9.0,8.0,803.0,11.0,1.23,34.0,23.0,70.6,3.0,3.0,2.0,2.0,25.0,1.0,1.0,0.0,0.0,8.9,1.0,2.0,0.0,10.3,0.28,-0.7,-0.07,39.0,107.0,36.4,212.0,55.0,21.7,29.4,82.0,74.4,52.9,126.0,13.0,10.3,14.0,1.57,16.8 +Wojciech Szczęsny,pl POL,GK,Juventus,33-007,1990,22.0,22.0,1932.0,19.0,0.89,68.0,49.0,72.1,14.0,2.0,6.0,12.0,54.5,0.0,0.0,0.0,0.0,21.5,0.0,4.0,0.0,17.8,0.26,-1.2,-0.05,102.0,227.0,44.9,505.0,99.0,33.9,34.3,114.0,49.1,41.5,301.0,9.0,3.0,19.0,0.89,15.4 +Ciprian Tătărușanu,ro ROU,GK,Milan,37-075,1986,16.0,16.0,1440.0,22.0,1.37,61.0,40.0,67.2,9.0,3.0,4.0,4.0,25.0,2.0,2.0,0.0,0.0,16.0,2.0,4.0,1.0,16.8,0.25,-4.2,-0.26,94.0,195.0,48.2,473.0,75.0,32.1,32.9,83.0,51.8,41.8,195.0,9.0,4.6,13.0,0.81,14.4 +Pietro Terracciano,it ITA,GK,Fiorentina,33-048,1990,27.0,27.0,2430.0,34.0,1.26,90.0,58.0,66.7,9.0,7.0,11.0,5.0,18.5,4.0,4.0,0.0,0.0,27.0,0.0,3.0,2.0,29.6,0.29,-2.4,-0.09,141.0,339.0,41.6,790.0,104.0,32.5,33.3,184.0,44.6,39.9,264.0,12.0,4.5,52.0,1.93,18.6 +Martin Turk,si SVN,GK,Sampdoria,19-247,2003,2.0,2.0,180.0,5.0,2.5,13.0,8.0,61.5,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,2.0,0.0,3.0,0.0,3.5,0.32,-1.5,-0.75,15.0,42.0,35.7,46.0,4.0,47.8,37.3,22.0,90.9,57.1,37.0,0.0,0.0,0.0,0.0,11.5 +Guglielmo Vicario,it ITA,GK,Empoli,26-200,1996,24.0,24.0,2160.0,31.0,1.29,104.0,73.0,72.1,6.0,10.0,8.0,7.0,29.2,4.0,2.0,1.0,1.0,24.0,0.0,7.0,1.0,32.3,0.28,2.3,0.1,117.0,346.0,33.8,869.0,123.0,32.0,33.3,139.0,48.9,42.6,489.0,28.0,5.7,15.0,0.63,10.7 +Jeroen Zoet,nl NED,GK,Spezia,32-109,1991,4.0,3.0,244.0,2.0,0.74,8.0,6.0,75.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.7,0.0,1.0,0.0,1.5,0.19,-0.5,-0.18,19.0,55.0,34.5,87.0,8.0,54.0,37.1,12.0,66.7,46.3,40.0,2.0,5.0,6.0,2.2,17.2 +Petar Zovko,ba BIH,GK,Spezia,21-031,2002,1.0,0.0,74.0,2.0,2.43,3.0,1.0,33.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,1.7,0.55,-0.3,-0.42,7.0,21.0,33.3,23.0,3.0,47.8,35.1,13.0,76.9,51.1,24.0,1.0,4.2,2.0,2.47,17.0 diff --git a/fbref_data/outfield_players.csv b/fbref_data/outfield_players.csv index 4671651..b841d33 100644 --- a/fbref_data/outfield_players.csv +++ b/fbref_data/outfield_players.csv @@ -1,542 +1,581 @@ player,nationality,position,team,age,birth_year,games,games_starts,minutes,goals,assists,pens_made,pens_att,cards_yellow,cards_red,goals_per90,assists_per90,goals_assists_per90,goals_pens_per90,goals_assists_pens_per90,xg,npxg,xg_per90,npxg_per90,minutes_90s,shots_on_target,shots_free_kicks,shots_on_target_pct,shots_on_target_per90,goals_per_shot,goals_per_shot_on_target,npxg_per_shot,xg_net,npxg_net,passes_completed,passes,passes_pct,passes_total_distance,passes_progressive_distance,passes_completed_short,passes_short,passes_pct_short,passes_completed_medium,passes_medium,passes_pct_medium,passes_completed_long,passes_long,passes_pct_long,assisted_shots,passes_into_final_third,passes_into_penalty_area,crosses_into_penalty_area,progressive_passes,passes_live,passes_dead,passes_free_kicks,through_balls,passes_switches,crosses,corner_kicks,corner_kicks_in,corner_kicks_out,corner_kicks_straight,throw_ins,passes_offsides,passes_blocked,sca,sca_per90,sca_passes_live,sca_passes_dead,sca_shots,sca_fouled,gca,gca_per90,gca_passes_live,gca_passes_dead,gca_shots,gca_fouled,gca_defense,tackles,tackles_won,tackles_def_3rd,tackles_mid_3rd,tackles_att_3rd,blocks,blocked_shots,blocked_passes,interceptions,clearances,errors,touches,touches_def_pen_area,touches_def_3rd,touches_mid_3rd,touches_att_3rd,touches_att_pen_area,touches_live_ball,carries,progressive_carries,carries_into_final_third,carries_into_penalty_area,passes_received,miscontrols,dispossessed,cards_yellow_red,fouls,fouled,offsides,pens_won,pens_conceded,own_goals,ball_recoveries,aerials_won,aerials_lost,aerials_won_pct -Oliver Abildgaard,dk DEN,MF,Hellas Verona,26-245,1996,1.0,0.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,66.7,31.0,9.0,1.0,1.0,100.0,1.0,1.0,100.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,2.0,2.0,1.0,0.0,5.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 -Tammy Abraham,eng ENG,FW,Roma,25-131,1997,21.0,17.0,1500.0,6.0,3.0,0.0,0.0,1.0,0.0,0.36,0.18,0.54,0.36,0.54,8.7,8.7,0.52,0.52,16.7,19.0,0.0,43.2,1.14,0.14,0.32,0.2,-2.7,-2.7,224.0,329.0,68.1,3174.0,780.0,133.0,168.0,79.2,64.0,105.0,61.0,15.0,22.0,68.2,24.0,10.0,9.0,1.0,32.0,301.0,25.0,1.0,1.0,1.0,5.0,0.0,0.0,0.0,0.0,1.0,3.0,5.0,51.0,3.05,34.0,0.0,10.0,4.0,9.0,0.54,4.0,0.0,3.0,1.0,0.0,13.0,7.0,3.0,6.0,4.0,8.0,4.0,4.0,2.0,6.0,0.0,510.0,11.0,32.0,265.0,222.0,81.0,510.0,274.0,24.0,17.0,10.0,396.0,36.0,21.0,0.0,21.0,32.0,9.0,1.0,0.0,0.0,31.0,39.0,39.0,50.0 -Francesco Acerbi,it ITA,DF,Inter,35-000,1988,14.0,12.0,1110.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.08,0.08,0.0,0.08,0.3,0.3,0.02,0.02,12.3,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.3,-0.3,636.0,725.0,87.7,12004.0,3837.0,232.0,252.0,92.1,317.0,352.0,90.1,74.0,100.0,74.0,6.0,38.0,3.0,0.0,44.0,685.0,39.0,26.0,0.0,9.0,3.0,0.0,0.0,0.0,0.0,7.0,1.0,2.0,12.0,0.97,10.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,7.0,14.0,2.0,0.0,7.0,4.0,3.0,12.0,41.0,0.0,827.0,101.0,374.0,423.0,35.0,6.0,827.0,451.0,13.0,11.0,1.0,544.0,7.0,3.0,0.0,9.0,9.0,1.0,0.0,0.0,0.0,61.0,34.0,20.0,63.0 -"Yacine Adli,fr FRA,""MF,FW"",Milan,22-196,2000,4.0,1.0,116.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.3,2.0,0.0,100.0,1.55,0.0,0.0,0.04,-0.1,-0.1,38.0,60.0,63.3,666.0,216.0,19.0,24.0,79.2,12.0,18.0,66.7,5.0,11.0,45.5,1.0,4.0,2.0,0.0,8.0,58.0,2.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,1.0,0.78,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,2.0,4.0,0.0,2.0,0.0,2.0,1.0,1.0,0.0,78.0,1.0,11.0,38.0,32.0,1.0,78.0,44.0,5.0,4.0,0.0,58.0,2.0,2.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,5.0,0.0,3.0,0.0" -"Michel Aebischer,ch SUI,""FW,MF"",Bologna,26-035,1997,18.0,8.0,775.0,1.0,0.0,0.0,0.0,3.0,0.0,0.12,0.0,0.12,0.12,0.12,1.1,1.1,0.13,0.13,8.6,2.0,1.0,20.0,0.23,0.1,0.5,0.11,-0.1,-0.1,280.0,342.0,81.9,4277.0,1290.0,155.0,173.0,89.6,98.0,114.0,86.0,20.0,33.0,60.6,8.0,19.0,1.0,0.0,22.0,326.0,13.0,5.0,0.0,2.0,9.0,2.0,0.0,2.0,0.0,5.0,3.0,9.0,19.0,2.21,13.0,2.0,2.0,1.0,2.0,0.23,0.0,0.0,1.0,1.0,0.0,4.0,1.0,2.0,0.0,2.0,11.0,2.0,9.0,3.0,6.0,0.0,406.0,9.0,82.0,195.0,130.0,16.0,406.0,238.0,7.0,6.0,2.0,308.0,15.0,10.0,0.0,10.0,14.0,5.0,1.0,1.0,0.0,43.0,5.0,9.0,35.7" -"Felix Afena-Gyan,gh GHA,""FW,MF"",Cremonese,20-022,2003,12.0,2.0,359.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,1.2,0.29,0.29,4.0,1.0,0.0,10.0,0.25,0.0,0.0,0.12,-1.2,-1.2,71.0,90.0,78.9,937.0,106.0,49.0,55.0,89.1,16.0,20.0,80.0,3.0,5.0,60.0,1.0,3.0,0.0,0.0,6.0,87.0,3.0,0.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,7.0,1.75,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,3.0,1.0,0.0,1.0,0.0,1.0,0.0,3.0,0.0,137.0,5.0,19.0,44.0,76.0,17.0,137.0,89.0,14.0,5.0,2.0,99.0,16.0,8.0,0.0,8.0,4.0,4.0,0.0,0.0,0.0,17.0,8.0,16.0,33.3" -"Kevin Agudelo,co COL,""MF,FW"",Spezia,24-088,1998,20.0,15.0,1365.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.07,0.07,15.2,5.0,0.0,27.8,0.33,0.0,0.0,0.06,-1.0,-1.0,299.0,424.0,70.5,4480.0,1593.0,173.0,218.0,79.4,93.0,122.0,76.2,18.0,33.0,54.5,14.0,37.0,14.0,1.0,63.0,405.0,14.0,2.0,2.0,1.0,21.0,5.0,3.0,2.0,0.0,4.0,5.0,17.0,29.0,1.91,20.0,1.0,3.0,2.0,2.0,0.13,1.0,0.0,0.0,1.0,0.0,47.0,22.0,14.0,20.0,13.0,28.0,4.0,24.0,7.0,2.0,0.0,645.0,7.0,96.0,305.0,263.0,30.0,645.0,367.0,45.0,36.0,11.0,389.0,41.0,38.0,0.0,26.0,27.0,2.0,0.0,0.0,0.0,88.0,9.0,20.0,31.0" -Ola Aina,ng NGA,DF,Torino,26-125,1996,12.0,8.0,676.0,1.0,1.0,0.0,0.0,4.0,0.0,0.13,0.13,0.27,0.13,0.27,0.7,0.7,0.1,0.1,7.5,3.0,0.0,42.9,0.4,0.14,0.33,0.11,0.3,0.3,311.0,418.0,74.4,5198.0,2091.0,163.0,176.0,92.6,99.0,133.0,74.4,39.0,73.0,53.4,9.0,23.0,12.0,8.0,35.0,334.0,80.0,6.0,0.0,1.0,26.0,1.0,0.0,0.0,0.0,73.0,4.0,14.0,22.0,2.92,17.0,1.0,0.0,1.0,3.0,0.4,2.0,0.0,0.0,0.0,0.0,13.0,10.0,3.0,7.0,3.0,9.0,2.0,7.0,8.0,20.0,0.0,514.0,18.0,111.0,234.0,177.0,11.0,514.0,275.0,29.0,21.0,4.0,301.0,11.0,5.0,0.0,12.0,12.0,1.0,0.0,1.0,0.0,47.0,9.0,6.0,60.0 -Emanuel Aiwum,at AUT,DF,Cremonese,22-047,2000,15.0,11.0,1029.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.01,0.01,11.4,1.0,0.0,33.3,0.09,0.0,0.0,0.05,-0.2,-0.2,415.0,539.0,77.0,7092.0,2637.0,191.0,211.0,90.5,186.0,229.0,81.2,30.0,71.0,42.3,4.0,36.0,7.0,1.0,39.0,460.0,77.0,17.0,1.0,4.0,4.0,0.0,0.0,0.0,0.0,55.0,2.0,10.0,10.0,0.87,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,43.0,26.0,25.0,16.0,2.0,15.0,9.0,6.0,19.0,37.0,1.0,669.0,70.0,336.0,298.0,45.0,5.0,669.0,331.0,17.0,10.0,0.0,336.0,6.0,3.0,0.0,13.0,8.0,0.0,0.0,0.0,0.0,80.0,19.0,7.0,73.1 -Jean-Daniel Akpa-Akpro,ci CIV,MF,Empoli,30-122,1992,11.0,6.0,547.0,0.0,0.0,0.0,0.0,6.0,2.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,6.1,1.0,0.0,20.0,0.16,0.0,0.0,0.05,-0.2,-0.2,146.0,192.0,76.0,2109.0,823.0,75.0,91.0,82.4,51.0,61.0,83.6,7.0,11.0,63.6,1.0,11.0,2.0,1.0,18.0,190.0,2.0,2.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,9.0,1.48,5.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,16.0,9.0,8.0,4.0,21.0,4.0,17.0,6.0,12.0,0.0,289.0,13.0,80.0,154.0,60.0,9.0,289.0,148.0,8.0,11.0,4.0,169.0,11.0,8.0,1.0,14.0,7.0,0.0,0.0,0.0,0.0,31.0,6.0,4.0,60.0 -Luis Alberto,es ESP,MF,Lazio,30-135,1992,18.0,10.0,963.0,4.0,3.0,1.0,1.0,1.0,0.0,0.37,0.28,0.65,0.28,0.56,1.9,1.1,0.18,0.1,10.7,6.0,2.0,37.5,0.56,0.19,0.5,0.07,2.1,1.9,563.0,705.0,79.9,8895.0,3080.0,314.0,350.0,89.7,180.0,213.0,84.5,49.0,102.0,48.0,24.0,56.0,20.0,0.0,93.0,612.0,90.0,30.0,7.0,3.0,61.0,45.0,22.0,19.0,0.0,2.0,3.0,10.0,36.0,3.37,24.0,12.0,0.0,0.0,6.0,0.56,5.0,1.0,0.0,0.0,0.0,19.0,10.0,7.0,9.0,3.0,12.0,1.0,11.0,4.0,1.0,0.0,796.0,12.0,100.0,439.0,262.0,21.0,795.0,417.0,32.0,29.0,5.0,576.0,15.0,12.0,0.0,6.0,3.0,2.0,0.0,0.0,0.0,64.0,3.0,6.0,33.3 -"Agustín Álvarez Martínez,uy URU,""FW,MF"",Sassuolo,21-267,2001,16.0,1.0,296.0,1.0,1.0,0.0,0.0,1.0,0.0,0.3,0.3,0.61,0.3,0.61,1.3,1.3,0.39,0.39,3.3,3.0,0.0,27.3,0.91,0.09,0.33,0.12,-0.3,-0.3,52.0,65.0,80.0,700.0,123.0,35.0,42.0,83.3,9.0,13.0,69.2,4.0,5.0,80.0,1.0,3.0,2.0,0.0,5.0,61.0,4.0,0.0,0.0,1.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,6.0,1.82,2.0,0.0,2.0,1.0,2.0,0.61,1.0,0.0,1.0,0.0,0.0,3.0,2.0,0.0,2.0,1.0,3.0,1.0,2.0,3.0,3.0,0.0,119.0,4.0,11.0,55.0,55.0,11.0,119.0,59.0,2.0,0.0,1.0,75.0,14.0,7.0,0.0,7.0,3.0,2.0,0.0,0.0,0.0,15.0,8.0,9.0,47.1" -Kelvin Amian,fr FRA,DF,Spezia,25-002,1998,14.0,10.0,959.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.09,0.09,0.0,0.09,0.5,0.5,0.04,0.04,10.7,1.0,1.0,12.5,0.09,0.0,0.0,0.06,-0.5,-0.5,325.0,439.0,74.0,5378.0,2284.0,164.0,194.0,84.5,133.0,179.0,74.3,22.0,46.0,47.8,6.0,26.0,5.0,3.0,35.0,347.0,91.0,9.0,1.0,2.0,7.0,0.0,0.0,0.0,0.0,82.0,1.0,10.0,11.0,1.03,9.0,0.0,1.0,0.0,1.0,0.09,0.0,0.0,1.0,0.0,0.0,22.0,12.0,14.0,3.0,5.0,11.0,5.0,6.0,11.0,33.0,1.0,547.0,45.0,211.0,206.0,133.0,16.0,547.0,233.0,18.0,9.0,1.0,265.0,10.0,5.0,0.0,18.0,2.0,1.0,0.0,0.0,0.0,43.0,17.0,19.0,47.2 -Bruno Amione,ar ARG,DF,Hellas Verona,21-038,2002,1.0,1.0,58.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,16.0,68.8,167.0,40.0,6.0,6.0,100.0,5.0,7.0,71.4,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,4.0,0.0,20.0,9.0,17.0,4.0,0.0,0.0,20.0,7.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 -Bruno Amione,ar ARG,DF,Sampdoria,21-038,2002,10.0,10.0,790.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.11,0.11,0.0,0.11,0.2,0.2,0.03,0.03,8.8,1.0,0.0,20.0,0.11,0.0,0.0,0.05,-0.2,-0.2,323.0,419.0,77.1,5650.0,2159.0,135.0,157.0,86.0,163.0,198.0,82.3,24.0,48.0,50.0,2.0,8.0,3.0,1.0,22.0,362.0,55.0,18.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,37.0,2.0,10.0,6.0,0.68,5.0,0.0,1.0,0.0,1.0,0.11,0.0,0.0,1.0,0.0,0.0,23.0,14.0,17.0,6.0,0.0,10.0,4.0,6.0,11.0,21.0,1.0,510.0,42.0,284.0,190.0,41.0,12.0,510.0,238.0,2.0,4.0,0.0,247.0,8.0,0.0,0.0,10.0,6.0,0.0,0.0,1.0,0.0,47.0,29.0,16.0,64.4 -"Ethan Ampadu,wls WAL,""DF,MF"",Spezia,22-149,2000,16.0,16.0,1360.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.07,0.07,0.0,0.07,0.9,0.9,0.06,0.06,15.1,4.0,0.0,33.3,0.26,0.0,0.0,0.08,-0.9,-0.9,550.0,736.0,74.7,10402.0,4298.0,208.0,252.0,82.5,274.0,323.0,84.8,60.0,129.0,46.5,8.0,64.0,9.0,4.0,80.0,666.0,65.0,31.0,1.0,12.0,10.0,0.0,0.0,0.0,0.0,34.0,5.0,11.0,27.0,1.79,23.0,0.0,2.0,1.0,4.0,0.26,4.0,0.0,0.0,0.0,0.0,44.0,25.0,18.0,22.0,4.0,28.0,11.0,17.0,18.0,31.0,0.0,903.0,71.0,342.0,472.0,100.0,13.0,903.0,449.0,9.0,9.0,0.0,499.0,10.0,4.0,0.0,24.0,12.0,1.0,0.0,1.0,0.0,107.0,35.0,20.0,63.6" -Sofyan Amrabat,ma MAR,MF,Fiorentina,26-173,1996,18.0,14.0,1165.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.02,0.02,12.9,2.0,0.0,28.6,0.15,0.0,0.0,0.03,-0.2,-0.2,803.0,904.0,88.8,16162.0,4290.0,265.0,297.0,89.2,403.0,430.0,93.7,125.0,152.0,82.2,7.0,107.0,13.0,5.0,120.0,845.0,57.0,52.0,0.0,12.0,12.0,0.0,0.0,0.0,0.0,4.0,2.0,11.0,22.0,1.7,21.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,15.0,10.0,7.0,5.0,14.0,4.0,10.0,15.0,8.0,1.0,996.0,24.0,152.0,685.0,173.0,3.0,996.0,678.0,21.0,19.0,0.0,727.0,17.0,13.0,0.0,28.0,20.0,0.0,0.0,0.0,0.0,95.0,7.0,7.0,50.0 -Felipe Anderson,br BRA,FW,Lazio,29-301,1993,21.0,19.0,1630.0,6.0,2.0,0.0,0.0,1.0,0.0,0.33,0.11,0.44,0.33,0.44,4.1,4.1,0.22,0.22,18.1,12.0,0.0,54.5,0.66,0.27,0.5,0.18,1.9,1.9,548.0,717.0,76.4,7839.0,1717.0,347.0,408.0,85.0,158.0,200.0,79.0,29.0,51.0,56.9,29.0,33.0,22.0,3.0,62.0,695.0,20.0,2.0,1.0,5.0,22.0,3.0,1.0,2.0,0.0,7.0,2.0,20.0,57.0,3.15,47.0,1.0,2.0,1.0,9.0,0.5,5.0,0.0,1.0,0.0,0.0,39.0,24.0,11.0,24.0,4.0,31.0,2.0,29.0,10.0,15.0,0.0,941.0,13.0,109.0,426.0,421.0,76.0,941.0,502.0,48.0,35.0,17.0,702.0,49.0,30.0,0.0,22.0,20.0,4.0,0.0,0.0,0.0,72.0,6.0,15.0,28.6 -Janis Antiste,fr FRA,FW,Sassuolo,20-176,2002,2.0,0.0,52.0,1.0,0.0,0.0,0.0,0.0,0.0,1.73,0.0,1.73,1.73,1.73,0.2,0.2,0.31,0.31,0.6,1.0,0.0,100.0,1.73,1.0,1.0,0.18,0.8,0.8,13.0,18.0,72.2,159.0,43.0,8.0,11.0,72.7,4.0,5.0,80.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,0.0,7.0,9.0,7.0,1.0,23.0,14.0,0.0,0.0,0.0,16.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,66.7 -Marcos Antônio,br BRA,MF,Lazio,22-242,2000,11.0,3.0,311.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,184.0,206.0,89.3,2643.0,697.0,111.0,119.0,93.3,52.0,60.0,86.7,12.0,16.0,75.0,0.0,19.0,1.0,0.0,14.0,204.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.58,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,3.0,2.0,0.0,2.0,0.0,2.0,3.0,11.0,0.0,245.0,14.0,61.0,162.0,23.0,0.0,245.0,143.0,0.0,2.0,0.0,180.0,5.0,3.0,0.0,6.0,11.0,0.0,0.0,0.0,0.0,22.0,0.0,1.0,0.0 -Valentin Antov,bg BUL,DF,Monza,22-093,2000,4.0,1.0,172.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,92.0,111.0,82.9,1648.0,862.0,42.0,49.0,85.7,39.0,44.0,88.6,10.0,16.0,62.5,1.0,9.0,2.0,0.0,9.0,101.0,10.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,8.0,0.0,1.0,2.0,1.05,2.0,0.0,0.0,0.0,1.0,0.52,1.0,0.0,0.0,0.0,0.0,5.0,4.0,3.0,2.0,0.0,4.0,3.0,1.0,0.0,6.0,1.0,131.0,18.0,65.0,54.0,12.0,0.0,131.0,58.0,0.0,2.0,0.0,76.0,2.0,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,14.0,3.0,7.0,30.0 -Marko Arnautović,at AUT,FW,Bologna,33-297,1989,14.0,14.0,1189.0,8.0,0.0,3.0,3.0,4.0,0.0,0.61,0.0,0.61,0.38,0.38,5.7,3.3,0.43,0.25,13.2,10.0,0.0,47.6,0.76,0.24,0.5,0.16,2.3,1.7,257.0,364.0,70.6,3648.0,648.0,145.0,188.0,77.1,82.0,105.0,78.1,11.0,20.0,55.0,19.0,13.0,12.0,1.0,28.0,327.0,31.0,0.0,2.0,0.0,8.0,0.0,0.0,0.0,0.0,3.0,6.0,12.0,34.0,2.57,28.0,0.0,2.0,2.0,3.0,0.23,2.0,0.0,0.0,0.0,0.0,8.0,5.0,2.0,4.0,2.0,7.0,1.0,6.0,2.0,8.0,0.0,479.0,12.0,31.0,251.0,202.0,45.0,476.0,270.0,11.0,10.0,6.0,380.0,30.0,16.0,0.0,18.0,13.0,10.0,0.0,0.0,0.0,27.0,7.0,10.0,41.2 -Tolgay Arslan,de GER,MF,Udinese,32-178,1990,20.0,10.0,815.0,1.0,0.0,0.0,0.0,1.0,0.0,0.11,0.0,0.11,0.11,0.11,1.4,1.4,0.16,0.16,9.1,5.0,1.0,26.3,0.55,0.05,0.2,0.07,-0.4,-0.4,306.0,384.0,79.7,5597.0,1706.0,137.0,160.0,85.6,110.0,131.0,84.0,45.0,62.0,72.6,10.0,30.0,15.0,2.0,51.0,355.0,29.0,16.0,0.0,10.0,20.0,8.0,3.0,1.0,0.0,5.0,0.0,10.0,25.0,2.76,17.0,3.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,9.0,8.0,7.0,1.0,11.0,0.0,11.0,7.0,9.0,0.0,499.0,10.0,81.0,253.0,167.0,21.0,499.0,303.0,31.0,20.0,10.0,332.0,20.0,12.0,0.0,11.0,21.0,0.0,0.0,0.0,0.0,44.0,5.0,2.0,71.4 -Arthur,br BRA,FW,Fiorentina,24-291,1998,16.0,6.0,663.0,3.0,1.0,1.0,1.0,1.0,0.0,0.41,0.14,0.54,0.27,0.41,3.1,2.3,0.42,0.31,7.4,9.0,0.0,37.5,1.22,0.08,0.22,0.1,-0.1,-0.3,99.0,138.0,71.7,1273.0,212.0,65.0,82.0,79.3,20.0,24.0,83.3,5.0,7.0,71.4,7.0,2.0,5.0,0.0,11.0,127.0,11.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,7.0,17.0,2.3,13.0,0.0,2.0,1.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,6.0,2.0,1.0,1.0,4.0,6.0,0.0,6.0,0.0,3.0,0.0,240.0,3.0,13.0,79.0,150.0,51.0,239.0,130.0,5.0,2.0,4.0,177.0,34.0,8.0,0.0,9.0,7.0,1.0,0.0,0.0,0.0,14.0,22.0,24.0,47.8 -Santiago Ascacíbar,ar ARG,MF,Cremonese,25-350,1997,13.0,7.0,727.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,8.1,3.0,0.0,21.4,0.37,0.0,0.0,0.03,-0.4,-0.4,358.0,435.0,82.3,6100.0,2189.0,182.0,207.0,87.9,135.0,145.0,93.1,34.0,56.0,60.7,8.0,50.0,7.0,2.0,41.0,412.0,21.0,11.0,0.0,4.0,5.0,5.0,0.0,3.0,0.0,5.0,2.0,9.0,25.0,3.09,17.0,1.0,7.0,0.0,3.0,0.37,1.0,0.0,2.0,0.0,0.0,30.0,14.0,18.0,10.0,2.0,14.0,2.0,12.0,13.0,11.0,0.0,554.0,26.0,178.0,280.0,100.0,4.0,554.0,254.0,1.0,3.0,0.0,314.0,8.0,4.0,0.0,11.0,9.0,0.0,0.0,0.0,0.0,62.0,10.0,20.0,33.3 -Kristoffer Askildsen,no NOR,MF,Lecce,22-032,2001,15.0,6.0,568.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.07,0.07,6.3,3.0,1.0,33.3,0.48,0.0,0.0,0.05,-0.4,-0.4,133.0,210.0,63.3,2435.0,847.0,63.0,85.0,74.1,43.0,65.0,66.2,20.0,40.0,50.0,5.0,17.0,2.0,0.0,19.0,178.0,31.0,11.0,0.0,7.0,19.0,15.0,7.0,6.0,0.0,3.0,1.0,7.0,13.0,2.06,7.0,4.0,0.0,2.0,1.0,0.16,1.0,0.0,0.0,0.0,0.0,7.0,3.0,2.0,3.0,2.0,11.0,3.0,8.0,10.0,6.0,0.0,279.0,14.0,58.0,143.0,81.0,6.0,279.0,108.0,9.0,2.0,1.0,133.0,10.0,3.0,0.0,13.0,6.0,1.0,0.0,1.0,0.0,35.0,16.0,14.0,53.3 -Kristjan Asllani,al ALB,MF,Inter,20-338,2002,12.0,2.0,295.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.08,0.08,3.3,0.0,1.0,0.0,0.0,0.0,0.0,0.04,-0.2,-0.2,196.0,226.0,86.7,3610.0,816.0,94.0,97.0,96.9,68.0,73.0,93.2,30.0,47.0,63.8,2.0,12.0,1.0,0.0,10.0,215.0,10.0,5.0,1.0,2.0,8.0,5.0,2.0,2.0,0.0,0.0,1.0,3.0,7.0,2.14,3.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,4.0,0.0,5.0,2.0,2.0,0.0,2.0,1.0,3.0,0.0,254.0,5.0,59.0,147.0,49.0,1.0,254.0,164.0,2.0,8.0,1.0,191.0,3.0,2.0,0.0,5.0,4.0,0.0,0.0,0.0,0.0,24.0,0.0,2.0,0.0 -Emil Audero,it ITA,GK,Sampdoria,26-023,1997,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,501.0,762.0,65.7,15621.0,11520.0,74.0,74.0,100.0,202.0,204.0,99.0,224.0,476.0,47.1,0.0,12.0,0.0,0.0,0.0,523.0,234.0,73.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,4.0,0.19,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,818.0,667.0,806.0,14.0,0.0,0.0,818.0,448.0,0.0,0.0,0.0,344.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,32.0,4.0,0.0,100.0 -Tommaso Augello,it ITA,DF,Sampdoria,28-164,1994,20.0,18.0,1626.0,1.0,2.0,0.0,0.0,1.0,0.0,0.06,0.11,0.17,0.06,0.17,0.3,0.3,0.01,0.01,18.1,2.0,0.0,33.3,0.11,0.17,0.5,0.04,0.7,0.7,680.0,945.0,72.0,11958.0,5157.0,308.0,344.0,89.5,295.0,405.0,72.8,65.0,127.0,51.2,21.0,44.0,29.0,19.0,58.0,748.0,196.0,26.0,0.0,4.0,81.0,2.0,0.0,2.0,0.0,168.0,1.0,38.0,39.0,2.16,32.0,4.0,1.0,0.0,4.0,0.22,4.0,0.0,0.0,0.0,0.0,9.0,7.0,7.0,1.0,1.0,13.0,6.0,7.0,7.0,31.0,1.0,1056.0,48.0,317.0,459.0,296.0,8.0,1056.0,559.0,45.0,26.0,3.0,606.0,11.0,9.0,0.0,8.0,9.0,1.0,0.0,0.0,0.0,84.0,17.0,15.0,53.1 -Kaan Ayhan,tr TUR,DF,Sassuolo,28-092,1994,10.0,5.0,545.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.1,1.0,0.0,100.0,0.17,0.0,0.0,0.01,0.0,0.0,288.0,332.0,86.7,6000.0,1702.0,84.0,87.0,96.6,154.0,167.0,92.2,48.0,72.0,66.7,0.0,20.0,0.0,0.0,15.0,311.0,20.0,15.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,5.0,0.83,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,4.0,6.0,4.0,0.0,5.0,4.0,1.0,5.0,26.0,1.0,385.0,54.0,198.0,182.0,6.0,0.0,385.0,235.0,8.0,5.0,0.0,264.0,0.0,1.0,0.0,4.0,8.0,0.0,0.0,0.0,0.0,28.0,2.0,7.0,22.2 -Jaime Báez,uy URU,DF,Cremonese,27-291,1995,2.0,1.0,96.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.12,0.12,1.1,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,32.0,48.0,66.7,640.0,379.0,8.0,10.0,80.0,13.0,17.0,76.5,7.0,14.0,50.0,2.0,7.0,2.0,2.0,4.0,44.0,4.0,2.0,0.0,0.0,9.0,2.0,0.0,1.0,0.0,0.0,0.0,2.0,5.0,4.69,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,62.0,3.0,10.0,37.0,16.0,2.0,62.0,34.0,5.0,3.0,0.0,34.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 -"Nedim Bajrami,al ALB,""MF,FW"",Empoli,23-347,1999,19.0,8.0,941.0,1.0,1.0,0.0,0.0,1.0,0.0,0.1,0.1,0.19,0.1,0.19,1.2,1.2,0.12,0.12,10.5,7.0,6.0,33.3,0.67,0.05,0.14,0.06,-0.2,-0.2,260.0,348.0,74.7,3968.0,1323.0,149.0,182.0,81.9,79.0,96.0,82.3,20.0,43.0,46.5,15.0,19.0,6.0,0.0,29.0,305.0,43.0,9.0,0.0,4.0,35.0,18.0,10.0,5.0,0.0,8.0,0.0,8.0,39.0,3.74,27.0,8.0,1.0,1.0,4.0,0.38,2.0,0.0,0.0,1.0,0.0,8.0,4.0,3.0,3.0,2.0,5.0,0.0,5.0,7.0,4.0,0.0,457.0,1.0,66.0,185.0,219.0,27.0,457.0,267.0,21.0,21.0,10.0,304.0,24.0,17.0,0.0,6.0,6.0,3.0,0.0,0.0,0.0,49.0,2.0,7.0,22.2" -Nedim Bajrami,al ALB,MF,Sassuolo,23-347,1999,1.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,2.06,2.06,0.3,1.0,0.0,50.0,3.75,0.0,0.0,0.27,-0.5,-0.5,5.0,7.0,71.4,93.0,4.0,1.0,1.0,100.0,4.0,6.0,66.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,1.0,4.0,5.0,2.0,9.0,9.0,1.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Tommaso Baldanzi,it ITA,MF,Empoli,19-324,2003,12.0,10.0,744.0,4.0,0.0,0.0,0.0,0.0,0.0,0.48,0.0,0.48,0.48,0.48,1.0,1.0,0.12,0.12,8.3,6.0,0.0,54.5,0.73,0.36,0.67,0.09,3.0,3.0,253.0,305.0,83.0,3656.0,890.0,133.0,148.0,89.9,91.0,101.0,90.1,11.0,21.0,52.4,14.0,19.0,11.0,0.0,38.0,286.0,18.0,2.0,2.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,30.0,3.62,24.0,1.0,3.0,2.0,2.0,0.24,2.0,0.0,0.0,0.0,0.0,15.0,7.0,5.0,9.0,1.0,7.0,0.0,7.0,1.0,2.0,0.0,381.0,3.0,59.0,189.0,138.0,16.0,381.0,249.0,28.0,27.0,6.0,271.0,16.0,6.0,0.0,7.0,13.0,0.0,0.0,0.0,0.0,26.0,2.0,3.0,40.0 -Fodé Ballo-Touré,sn SEN,DF,Milan,26-038,1997,3.0,2.0,196.0,1.0,0.0,0.0,0.0,0.0,0.0,0.46,0.0,0.46,0.46,0.46,0.1,0.1,0.07,0.07,2.2,1.0,0.0,100.0,0.46,1.0,1.0,0.14,0.9,0.9,103.0,118.0,87.3,1741.0,566.0,46.0,48.0,95.8,47.0,53.0,88.7,9.0,13.0,69.2,1.0,8.0,5.0,3.0,11.0,90.0,28.0,2.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,26.0,0.0,2.0,3.0,1.38,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,1.0,4.0,0.0,3.0,2.0,1.0,1.0,1.0,0.0,146.0,3.0,26.0,69.0,52.0,4.0,146.0,58.0,5.0,2.0,0.0,76.0,2.0,3.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,16.0,3.0,1.0,75.0 -"Lameck Banda,zm ZAM,""FW,MF"",Lecce,22-012,2001,21.0,11.0,1034.0,1.0,1.0,0.0,0.0,3.0,0.0,0.09,0.09,0.17,0.09,0.17,1.5,1.5,0.13,0.13,11.5,7.0,0.0,36.8,0.61,0.05,0.14,0.08,-0.5,-0.5,155.0,250.0,62.0,2106.0,571.0,92.0,124.0,74.2,46.0,74.0,62.2,10.0,21.0,47.6,8.0,12.0,6.0,1.0,18.0,239.0,10.0,1.0,0.0,0.0,29.0,6.0,3.0,0.0,0.0,3.0,1.0,14.0,22.0,1.91,10.0,1.0,1.0,3.0,3.0,0.26,3.0,0.0,0.0,0.0,0.0,19.0,11.0,8.0,9.0,2.0,18.0,2.0,16.0,3.0,2.0,0.0,433.0,6.0,51.0,165.0,231.0,43.0,433.0,298.0,49.0,36.0,21.0,296.0,37.0,22.0,0.0,18.0,26.0,9.0,0.0,0.0,0.0,50.0,5.0,16.0,23.8" -Filippo Bandinelli,it ITA,MF,Empoli,27-318,1995,21.0,17.0,1422.0,2.0,0.0,0.0,0.0,4.0,0.0,0.13,0.0,0.13,0.13,0.13,1.4,1.4,0.09,0.09,15.8,5.0,0.0,29.4,0.32,0.12,0.4,0.08,0.6,0.6,474.0,649.0,73.0,7886.0,2416.0,224.0,263.0,85.2,191.0,235.0,81.3,39.0,85.0,45.9,23.0,39.0,26.0,16.0,64.0,598.0,48.0,7.0,1.0,4.0,64.0,9.0,3.0,5.0,0.0,31.0,3.0,29.0,44.0,2.78,38.0,1.0,2.0,2.0,2.0,0.13,2.0,0.0,0.0,0.0,0.0,33.0,20.0,11.0,14.0,8.0,12.0,3.0,9.0,9.0,26.0,0.0,834.0,36.0,186.0,401.0,259.0,19.0,834.0,468.0,26.0,26.0,5.0,540.0,25.0,21.0,0.0,33.0,33.0,0.0,0.0,0.0,0.0,86.0,6.0,14.0,30.0 -Antonín Barák,cz CZE,MF,Hellas Verona,28-069,1994,1.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,14.0,78.6,140.0,24.0,7.0,10.0,70.0,3.0,3.0,100.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,2.0,13.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.0,10.0,6.0,0.0,16.0,12.0,0.0,0.0,0.0,11.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -Antonín Barák,cz CZE,MF,Fiorentina,28-069,1994,17.0,11.0,999.0,1.0,0.0,0.0,0.0,2.0,0.0,0.09,0.0,0.09,0.09,0.09,1.1,1.1,0.1,0.1,11.1,3.0,0.0,18.8,0.27,0.06,0.33,0.07,-0.1,-0.1,325.0,403.0,80.6,4994.0,1111.0,179.0,196.0,91.3,113.0,141.0,80.1,23.0,35.0,65.7,12.0,38.0,8.0,1.0,42.0,391.0,11.0,4.0,2.0,2.0,7.0,0.0,0.0,0.0,0.0,2.0,1.0,13.0,21.0,1.9,17.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,7.0,3.0,4.0,5.0,8.0,1.0,7.0,3.0,8.0,0.0,504.0,9.0,50.0,262.0,198.0,35.0,504.0,312.0,23.0,20.0,3.0,368.0,22.0,14.0,0.0,7.0,9.0,1.0,0.0,1.0,0.0,40.0,9.0,18.0,33.3 -Andrea Barberis,it ITA,MF,Monza,29-061,1993,7.0,3.0,316.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.28,0.28,0.0,0.28,0.1,0.1,0.03,0.03,3.5,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,133.0,158.0,84.2,2154.0,599.0,64.0,72.0,88.9,60.0,70.0,85.7,7.0,10.0,70.0,3.0,14.0,1.0,1.0,13.0,150.0,8.0,5.0,0.0,0.0,4.0,2.0,0.0,2.0,0.0,1.0,0.0,2.0,9.0,2.57,8.0,1.0,0.0,0.0,2.0,0.57,1.0,1.0,0.0,0.0,0.0,6.0,2.0,4.0,2.0,0.0,0.0,0.0,0.0,8.0,1.0,0.0,182.0,6.0,38.0,114.0,31.0,2.0,182.0,95.0,3.0,7.0,1.0,131.0,2.0,3.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,14.0,2.0,1.0,66.7 -Nicolò Barella,it ITA,MF,Inter,26-003,1997,20.0,19.0,1609.0,5.0,5.0,0.0,0.0,4.0,0.0,0.28,0.28,0.56,0.28,0.56,2.1,2.1,0.12,0.12,17.9,8.0,1.0,47.1,0.45,0.29,0.63,0.12,2.9,2.9,755.0,936.0,80.7,11722.0,3595.0,414.0,464.0,89.2,276.0,326.0,84.7,43.0,89.0,48.3,27.0,77.0,30.0,7.0,103.0,907.0,23.0,11.0,4.0,11.0,48.0,3.0,0.0,1.0,0.0,7.0,6.0,15.0,67.0,3.75,57.0,2.0,0.0,5.0,9.0,0.5,8.0,0.0,0.0,0.0,0.0,24.0,13.0,11.0,11.0,2.0,18.0,4.0,14.0,7.0,6.0,0.0,1100.0,22.0,181.0,562.0,376.0,40.0,1100.0,738.0,46.0,45.0,7.0,831.0,30.0,25.0,0.0,21.0,32.0,5.0,0.0,0.0,0.0,106.0,14.0,7.0,66.7 -"Musa Barrow,gm GAM,""FW,MF"",Bologna,24-088,1998,15.0,10.0,763.0,2.0,1.0,0.0,0.0,1.0,0.0,0.24,0.12,0.35,0.24,0.35,1.3,1.3,0.15,0.15,8.5,6.0,0.0,21.4,0.71,0.07,0.33,0.05,0.7,0.7,199.0,293.0,67.9,3698.0,1015.0,95.0,110.0,86.4,58.0,83.0,69.9,36.0,73.0,49.3,16.0,15.0,8.0,6.0,15.0,248.0,43.0,8.0,1.0,9.0,50.0,28.0,10.0,16.0,0.0,5.0,2.0,9.0,29.0,3.42,15.0,9.0,1.0,2.0,2.0,0.24,1.0,1.0,0.0,0.0,0.0,7.0,2.0,4.0,2.0,1.0,13.0,0.0,13.0,0.0,2.0,0.0,397.0,2.0,44.0,171.0,187.0,12.0,397.0,223.0,22.0,16.0,5.0,269.0,16.0,10.0,0.0,10.0,10.0,1.0,0.0,0.0,0.0,40.0,2.0,13.0,13.3" -Federico Baschirotto,it ITA,DF,Lecce,26-143,1996,21.0,21.0,1890.0,3.0,0.0,0.0,0.0,4.0,0.0,0.14,0.0,0.14,0.14,0.14,1.4,1.4,0.07,0.07,21.0,3.0,0.0,27.3,0.14,0.27,1.0,0.13,1.6,1.6,619.0,843.0,73.4,13501.0,4961.0,157.0,183.0,85.8,341.0,411.0,83.0,112.0,219.0,51.1,2.0,53.0,3.0,1.0,58.0,769.0,69.0,45.0,1.0,16.0,8.0,0.0,0.0,0.0,0.0,18.0,5.0,7.0,7.0,0.33,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,16.0,17.0,3.0,0.0,24.0,16.0,8.0,26.0,106.0,0.0,1060.0,152.0,580.0,439.0,48.0,18.0,1060.0,505.0,8.0,2.0,0.0,546.0,6.0,1.0,0.0,19.0,8.0,0.0,0.0,0.0,0.0,115.0,51.0,42.0,54.8 -Toma Bašić,hr CRO,MF,Lazio,26-077,1996,14.0,4.0,373.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.11,0.11,4.1,3.0,0.0,50.0,0.72,0.0,0.0,0.07,-0.4,-0.4,155.0,213.0,72.8,2229.0,430.0,74.0,90.0,82.2,66.0,76.0,86.8,4.0,22.0,18.2,5.0,9.0,4.0,2.0,12.0,196.0,17.0,5.0,2.0,0.0,17.0,5.0,2.0,3.0,0.0,0.0,0.0,10.0,7.0,1.69,4.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,3.0,1.0,1.0,7.0,0.0,7.0,5.0,6.0,0.0,262.0,8.0,54.0,133.0,77.0,8.0,262.0,159.0,11.0,5.0,1.0,178.0,9.0,5.0,0.0,8.0,9.0,0.0,0.0,0.0,0.0,20.0,8.0,6.0,57.1 -Alessandro Bastoni,it ITA,DF,Inter,23-303,1999,17.0,15.0,1267.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.14,0.14,0.0,0.14,0.8,0.8,0.06,0.06,14.1,3.0,0.0,30.0,0.21,0.0,0.0,0.08,-0.8,-0.8,822.0,985.0,83.5,16378.0,5905.0,283.0,313.0,90.4,413.0,459.0,90.0,115.0,181.0,63.5,9.0,73.0,16.0,4.0,92.0,897.0,85.0,36.0,0.0,13.0,20.0,0.0,0.0,0.0,0.0,42.0,3.0,12.0,34.0,2.41,27.0,2.0,2.0,2.0,4.0,0.28,2.0,1.0,0.0,1.0,0.0,25.0,12.0,11.0,9.0,5.0,14.0,5.0,9.0,17.0,28.0,0.0,1095.0,104.0,459.0,483.0,158.0,20.0,1095.0,667.0,26.0,23.0,2.0,768.0,9.0,4.0,0.0,12.0,12.0,1.0,0.0,0.0,0.0,68.0,28.0,11.0,71.8 -"Simone Bastoni,it ITA,""MF,DF"",Spezia,26-097,1996,13.0,12.0,946.0,2.0,2.0,0.0,0.0,4.0,0.0,0.19,0.19,0.38,0.19,0.38,1.1,1.1,0.1,0.1,10.5,6.0,1.0,30.0,0.57,0.1,0.33,0.05,0.9,0.9,281.0,469.0,59.9,4709.0,1739.0,142.0,168.0,84.5,100.0,165.0,60.6,32.0,90.0,35.6,15.0,24.0,9.0,6.0,24.0,374.0,94.0,21.0,2.0,4.0,86.0,42.0,24.0,14.0,0.0,31.0,1.0,29.0,26.0,2.47,15.0,8.0,2.0,0.0,2.0,0.19,2.0,0.0,0.0,0.0,0.0,37.0,18.0,16.0,16.0,5.0,16.0,4.0,12.0,7.0,9.0,0.0,591.0,24.0,146.0,238.0,213.0,20.0,591.0,270.0,12.0,12.0,2.0,312.0,13.0,11.0,0.0,23.0,11.0,2.0,0.0,0.0,0.0,71.0,11.0,8.0,57.9" -Brian Bayeye,fr FRA,DF,Torino,22-225,2000,1.0,1.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,46.0,63.0,472.0,262.0,14.0,16.0,87.5,11.0,16.0,68.8,3.0,4.0,75.0,0.0,2.0,0.0,0.0,3.0,35.0,11.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,11.0,0.0,5.0,2.0,3.33,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,1.0,1.0,0.0,1.0,2.0,0.0,59.0,6.0,28.0,21.0,10.0,3.0,59.0,21.0,3.0,2.0,2.0,26.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,9.0,0.0,1.0,0.0 -Rodrigo Becão,br BRA,DF,Udinese,27-022,1996,15.0,15.0,1322.0,1.0,1.0,0.0,0.0,6.0,0.0,0.07,0.07,0.14,0.07,0.14,0.9,0.9,0.06,0.06,14.7,3.0,0.0,33.3,0.2,0.11,0.33,0.1,0.1,0.1,597.0,762.0,78.3,11248.0,4767.0,235.0,267.0,88.0,285.0,333.0,85.6,69.0,124.0,55.6,7.0,49.0,2.0,1.0,56.0,645.0,116.0,32.0,0.0,4.0,7.0,0.0,0.0,0.0,0.0,84.0,1.0,21.0,18.0,1.23,10.0,4.0,1.0,2.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,46.0,21.0,33.0,11.0,2.0,23.0,14.0,9.0,10.0,31.0,0.0,900.0,69.0,419.0,376.0,109.0,24.0,900.0,462.0,6.0,7.0,1.0,510.0,6.0,2.0,0.0,19.0,17.0,4.0,0.0,0.0,1.0,85.0,32.0,17.0,65.3 -Julius Beck,dk DEN,MF,Spezia,17-289,2005,1.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.0,100.0,116.0,29.0,0.0,0.0,0.0,4.0,4.0,100.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,2.0,5.0,1.0,0.0,8.0,5.0,0.0,0.0,0.0,6.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 -"Raoul Bellanova,it ITA,""DF,MF"",Inter,22-269,2000,10.0,0.0,159.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.17,0.17,1.8,0.0,0.0,0.0,0.0,0.0,0.0,0.15,-0.3,-0.3,86.0,115.0,74.8,1406.0,312.0,47.0,54.0,87.0,28.0,40.0,70.0,8.0,14.0,57.1,1.0,2.0,1.0,1.0,4.0,102.0,13.0,0.0,0.0,2.0,14.0,0.0,0.0,0.0,0.0,13.0,0.0,4.0,4.0,2.24,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,4.0,1.0,0.0,2.0,1.0,1.0,3.0,5.0,0.0,143.0,8.0,39.0,53.0,51.0,4.0,143.0,85.0,8.0,6.0,0.0,89.0,2.0,2.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,7.0,2.0,0.0,100.0" -Andrea Belotti,it ITA,FW,Roma,29-052,1993,17.0,2.0,398.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.2,0.44,0.26,4.4,4.0,0.0,50.0,0.9,0.0,0.0,0.15,-2.0,-1.2,64.0,95.0,67.4,834.0,161.0,34.0,44.0,77.3,16.0,25.0,64.0,4.0,5.0,80.0,8.0,3.0,1.0,0.0,4.0,86.0,7.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,2.0,2.0,3.0,10.0,2.27,8.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,1.0,1.0,3.0,3.0,1.0,2.0,2.0,2.0,0.0,148.0,3.0,13.0,56.0,80.0,19.0,147.0,83.0,12.0,3.0,5.0,111.0,13.0,5.0,0.0,9.0,10.0,4.0,0.0,0.0,0.0,9.0,7.0,17.0,29.2 -Marco Benassi,it ITA,DF,Fiorentina,28-155,1994,2.0,1.0,93.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,91.0,105.0,86.7,2107.0,426.0,32.0,32.0,100.0,33.0,41.0,80.5,26.0,31.0,83.9,1.0,11.0,1.0,0.0,9.0,94.0,11.0,5.0,0.0,4.0,7.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,1.0,0.97,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,112.0,1.0,25.0,64.0,24.0,3.0,112.0,81.0,0.0,4.0,0.0,85.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,10.0,3.0,2.0,60.0 -Marco Benassi,it ITA,MF,Cremonese,28-155,1994,3.0,2.0,185.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.16,0.16,2.1,2.0,0.0,50.0,0.97,0.0,0.0,0.08,-0.3,-0.3,51.0,65.0,78.5,831.0,201.0,26.0,30.0,86.7,21.0,25.0,84.0,3.0,7.0,42.9,4.0,3.0,2.0,2.0,2.0,57.0,8.0,1.0,0.0,0.0,9.0,3.0,1.0,2.0,0.0,1.0,0.0,2.0,6.0,2.92,4.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,2.0,0.0,2.0,1.0,4.0,0.0,85.0,3.0,18.0,40.0,27.0,4.0,85.0,42.0,4.0,3.0,0.0,48.0,2.0,4.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,11.0,1.0,1.0,50.0 -Ismaël Bennacer,dz ALG,MF,Milan,25-071,1997,19.0,16.0,1413.0,1.0,1.0,0.0,0.0,5.0,0.0,0.06,0.06,0.13,0.06,0.13,0.3,0.3,0.02,0.02,15.7,2.0,2.0,28.6,0.13,0.14,0.5,0.04,0.7,0.7,867.0,1006.0,86.2,15616.0,4453.0,373.0,402.0,92.8,340.0,372.0,91.4,120.0,178.0,67.4,36.0,91.0,19.0,3.0,99.0,922.0,82.0,48.0,3.0,7.0,46.0,28.0,9.0,17.0,0.0,4.0,2.0,15.0,69.0,4.39,48.0,15.0,1.0,3.0,2.0,0.13,1.0,0.0,1.0,0.0,0.0,48.0,24.0,23.0,24.0,1.0,12.0,6.0,6.0,18.0,10.0,1.0,1172.0,22.0,226.0,751.0,215.0,6.0,1172.0,670.0,19.0,21.0,3.0,738.0,26.0,17.0,0.0,28.0,21.0,1.0,0.0,0.0,0.0,150.0,17.0,16.0,51.5 -Domenico Berardi,it ITA,FW,Sassuolo,28-193,1994,13.0,10.0,914.0,4.0,3.0,2.0,3.0,4.0,0.0,0.39,0.3,0.69,0.2,0.49,4.7,2.4,0.47,0.23,10.2,8.0,4.0,18.2,0.79,0.05,0.25,0.05,-0.7,-0.4,301.0,443.0,67.9,5767.0,1545.0,135.0,169.0,79.9,112.0,156.0,71.8,45.0,79.0,57.0,21.0,18.0,27.0,4.0,59.0,409.0,33.0,9.0,5.0,19.0,39.0,21.0,13.0,5.0,0.0,3.0,1.0,17.0,46.0,4.53,35.0,6.0,2.0,1.0,4.0,0.39,4.0,0.0,0.0,0.0,0.0,13.0,10.0,7.0,3.0,3.0,3.0,0.0,3.0,9.0,9.0,0.0,572.0,7.0,58.0,220.0,303.0,44.0,569.0,371.0,23.0,14.0,9.0,445.0,27.0,16.0,0.0,16.0,20.0,5.0,0.0,0.0,0.0,45.0,5.0,11.0,31.3 -Bartosz Bereszyński,pl POL,DF,Sampdoria,30-213,1992,15.0,15.0,1198.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,13.3,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,471.0,644.0,73.1,6989.0,3219.0,286.0,316.0,90.5,143.0,210.0,68.1,28.0,73.0,38.4,3.0,32.0,10.0,3.0,44.0,480.0,161.0,28.0,0.0,1.0,31.0,0.0,0.0,0.0,0.0,133.0,3.0,19.0,8.0,0.6,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28.0,18.0,15.0,11.0,2.0,18.0,4.0,14.0,21.0,25.0,0.0,759.0,48.0,255.0,317.0,198.0,13.0,759.0,329.0,20.0,16.0,1.0,354.0,12.0,5.0,0.0,13.0,10.0,0.0,0.0,0.0,0.0,68.0,18.0,11.0,62.1 -Beto,gw GNB,FW,Udinese,25-010,1998,21.0,13.0,1203.0,7.0,1.0,0.0,0.0,1.0,0.0,0.52,0.07,0.6,0.52,0.6,6.0,6.0,0.45,0.45,13.4,12.0,0.0,38.7,0.9,0.23,0.58,0.19,1.0,1.0,114.0,184.0,62.0,1449.0,167.0,77.0,110.0,70.0,24.0,40.0,60.0,5.0,10.0,50.0,12.0,6.0,2.0,0.0,6.0,177.0,6.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,25.0,1.87,18.0,0.0,2.0,2.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,13.0,9.0,0.0,8.0,5.0,11.0,1.0,10.0,2.0,15.0,0.0,382.0,18.0,22.0,154.0,210.0,81.0,382.0,220.0,13.0,8.0,10.0,285.0,67.0,27.0,0.0,22.0,16.0,4.0,0.0,0.0,0.0,17.0,45.0,39.0,53.6 -Matteo Bianchetti,it ITA,DF,Cremonese,29-330,1993,15.0,13.0,1243.0,1.0,0.0,0.0,0.0,1.0,0.0,0.07,0.0,0.07,0.07,0.07,0.7,0.7,0.05,0.05,13.8,4.0,0.0,66.7,0.29,0.17,0.25,0.12,0.3,0.3,412.0,537.0,76.7,7922.0,3360.0,148.0,171.0,86.5,216.0,253.0,85.4,42.0,97.0,43.3,4.0,26.0,5.0,1.0,28.0,490.0,44.0,28.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,1.0,3.0,5.0,10.0,0.72,6.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,10.0,12.0,5.0,1.0,22.0,14.0,8.0,10.0,69.0,0.0,683.0,122.0,406.0,251.0,28.0,10.0,683.0,309.0,4.0,1.0,0.0,341.0,3.0,2.0,0.0,12.0,3.0,0.0,0.0,1.0,0.0,70.0,31.0,22.0,58.5 -Alessandro Bianco,it ITA,MF,Fiorentina,20-132,2002,3.0,2.0,112.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,1.2,1.0,0.0,50.0,0.8,0.0,0.0,0.03,-0.1,-0.1,46.0,54.0,85.2,826.0,125.0,20.0,24.0,83.3,20.0,24.0,83.3,5.0,5.0,100.0,1.0,9.0,1.0,0.0,6.0,52.0,2.0,2.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.41,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,2.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,64.0,0.0,10.0,42.0,12.0,1.0,64.0,34.0,3.0,0.0,0.0,47.0,2.0,4.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,5.0,1.0,1.0,50.0 -Jaka Bijol,si SVN,DF,Udinese,24-005,1999,17.0,17.0,1383.0,2.0,0.0,0.0,0.0,4.0,0.0,0.13,0.0,0.13,0.13,0.13,1.3,1.3,0.08,0.08,15.4,3.0,0.0,30.0,0.2,0.2,0.67,0.13,0.7,0.7,536.0,638.0,84.0,11364.0,4105.0,130.0,150.0,86.7,329.0,356.0,92.4,70.0,111.0,63.1,2.0,21.0,0.0,0.0,31.0,620.0,15.0,14.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,7.0,0.46,6.0,0.0,1.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,16.0,10.0,13.0,3.0,0.0,23.0,12.0,11.0,15.0,97.0,0.0,825.0,142.0,478.0,325.0,24.0,17.0,825.0,424.0,2.0,4.0,1.0,441.0,4.0,2.0,0.0,10.0,2.0,0.0,0.0,0.0,0.0,72.0,45.0,29.0,60.8 -Cristiano Biraghi,it ITA,DF,Fiorentina,30-162,1992,20.0,19.0,1622.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.11,0.11,0.0,0.11,1.4,0.6,0.08,0.03,18.0,1.0,6.0,7.1,0.06,0.0,0.0,0.04,-1.4,-0.6,934.0,1297.0,72.0,17466.0,6664.0,398.0,448.0,88.8,409.0,513.0,79.7,117.0,283.0,41.3,57.0,80.0,26.0,17.0,109.0,946.0,346.0,55.0,0.0,14.0,203.0,111.0,47.0,46.0,0.0,180.0,5.0,29.0,88.0,4.89,39.0,47.0,1.0,0.0,4.0,0.22,2.0,2.0,0.0,0.0,0.0,39.0,23.0,17.0,20.0,2.0,16.0,5.0,11.0,5.0,26.0,1.0,1429.0,47.0,338.0,571.0,531.0,17.0,1428.0,660.0,44.0,35.0,4.0,796.0,13.0,5.0,0.0,16.0,23.0,1.0,0.0,0.0,0.0,86.0,11.0,16.0,40.7 -Samuele Birindelli,it ITA,DF,Monza,23-206,1999,18.0,10.0,880.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.1,0.1,0.0,0.1,1.0,1.0,0.1,0.1,9.8,3.0,0.0,25.0,0.31,0.0,0.0,0.08,-1.0,-1.0,364.0,500.0,72.8,6286.0,1800.0,173.0,202.0,85.6,154.0,206.0,74.8,31.0,69.0,44.9,14.0,19.0,12.0,9.0,25.0,418.0,82.0,4.0,0.0,3.0,55.0,0.0,0.0,0.0,0.0,78.0,0.0,11.0,26.0,2.66,22.0,0.0,2.0,1.0,4.0,0.41,4.0,0.0,0.0,0.0,0.0,20.0,10.0,12.0,6.0,2.0,17.0,5.0,12.0,12.0,16.0,0.0,614.0,28.0,150.0,247.0,226.0,23.0,614.0,312.0,39.0,23.0,4.0,379.0,17.0,7.0,0.0,8.0,9.0,2.0,0.0,0.0,0.0,48.0,2.0,5.0,28.6 -Kristijan Bistrović,hr CRO,MF,Lecce,24-307,1998,11.0,5.0,461.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.07,0.07,5.1,1.0,4.0,12.5,0.2,0.0,0.0,0.04,-0.3,-0.3,101.0,140.0,72.1,1867.0,682.0,45.0,52.0,86.5,35.0,48.0,72.9,16.0,25.0,64.0,6.0,10.0,2.0,1.0,16.0,119.0,21.0,11.0,0.0,3.0,21.0,7.0,3.0,3.0,0.0,0.0,0.0,6.0,10.0,1.95,3.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,3.0,0.0,3.0,0.0,4.0,0.0,169.0,5.0,29.0,85.0,55.0,3.0,169.0,83.0,4.0,1.0,0.0,99.0,3.0,5.0,0.0,2.0,4.0,1.0,0.0,0.0,0.0,15.0,2.0,5.0,28.6 -"Alexis Blin,fr FRA,""MF,DF"",Lecce,26-147,1996,19.0,12.0,1163.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.05,0.05,12.9,1.0,0.0,20.0,0.08,0.0,0.0,0.12,-0.6,-0.6,428.0,534.0,80.1,7071.0,2077.0,204.0,235.0,86.8,168.0,204.0,82.4,35.0,56.0,62.5,7.0,46.0,8.0,2.0,57.0,505.0,28.0,10.0,0.0,6.0,16.0,1.0,1.0,0.0,0.0,8.0,1.0,6.0,17.0,1.32,15.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,13.0,9.0,13.0,7.0,26.0,11.0,15.0,19.0,49.0,0.0,703.0,56.0,217.0,359.0,128.0,10.0,703.0,299.0,12.0,10.0,1.0,375.0,13.0,6.0,0.0,17.0,16.0,0.0,0.0,0.0,0.0,67.0,22.0,38.0,36.7" -"Jeremie Boga,ci CIV,""MF,FW"",Atalanta,26-038,1997,11.0,3.0,392.0,1.0,5.0,0.0,0.0,0.0,0.0,0.23,1.15,1.38,0.23,1.38,0.7,0.7,0.16,0.16,4.4,2.0,0.0,25.0,0.46,0.13,0.5,0.09,0.3,0.3,170.0,213.0,79.8,2529.0,722.0,98.0,118.0,83.1,45.0,53.0,84.9,15.0,23.0,65.2,21.0,7.0,13.0,4.0,24.0,190.0,22.0,4.0,2.0,0.0,21.0,13.0,10.0,1.0,0.0,5.0,1.0,3.0,31.0,7.14,18.0,8.0,0.0,1.0,6.0,1.38,5.0,0.0,0.0,0.0,1.0,3.0,2.0,2.0,0.0,1.0,3.0,0.0,3.0,0.0,0.0,0.0,277.0,0.0,20.0,101.0,160.0,19.0,277.0,190.0,31.0,28.0,11.0,226.0,19.0,3.0,0.0,3.0,10.0,0.0,0.0,0.0,0.0,13.0,3.0,3.0,50.0" -Emil Bohinen,no NOR,MF,Salernitana,23-335,1999,10.0,4.0,338.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,156.0,179.0,87.2,2201.0,546.0,92.0,99.0,92.9,51.0,59.0,86.4,7.0,11.0,63.6,5.0,12.0,4.0,1.0,14.0,170.0,9.0,9.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,7.0,1.88,6.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,8.0,9.0,2.0,3.0,8.0,3.0,5.0,8.0,5.0,0.0,229.0,13.0,68.0,118.0,47.0,2.0,229.0,119.0,3.0,7.0,0.0,134.0,4.0,4.0,0.0,5.0,6.0,0.0,0.0,0.0,0.0,33.0,1.0,3.0,25.0 -Giacomo Bonaventura,it ITA,MF,Fiorentina,33-172,1989,18.0,16.0,1335.0,3.0,0.0,0.0,0.0,3.0,0.0,0.2,0.0,0.2,0.2,0.2,2.7,2.7,0.18,0.18,14.8,9.0,1.0,25.7,0.61,0.09,0.33,0.08,0.3,0.3,538.0,648.0,83.0,8541.0,1916.0,277.0,309.0,89.6,198.0,228.0,86.8,39.0,62.0,62.9,18.0,38.0,21.0,4.0,71.0,621.0,27.0,14.0,3.0,2.0,27.0,3.0,0.0,3.0,0.0,4.0,0.0,16.0,47.0,3.17,31.0,3.0,5.0,5.0,4.0,0.27,3.0,0.0,0.0,1.0,0.0,27.0,19.0,11.0,12.0,4.0,9.0,0.0,9.0,14.0,4.0,1.0,807.0,10.0,105.0,412.0,308.0,44.0,807.0,516.0,40.0,44.0,6.0,580.0,16.0,19.0,0.0,14.0,32.0,2.0,1.0,0.0,0.0,102.0,9.0,12.0,42.9 -Federico Bonazzoli,it ITA,FW,Salernitana,25-265,1997,19.0,10.0,904.0,2.0,1.0,0.0,0.0,1.0,0.0,0.2,0.1,0.3,0.2,0.3,2.2,2.2,0.22,0.22,10.0,6.0,1.0,21.4,0.6,0.07,0.33,0.08,-0.2,-0.2,207.0,276.0,75.0,3783.0,756.0,96.0,115.0,83.5,75.0,94.0,79.8,30.0,41.0,73.2,8.0,26.0,6.0,0.0,37.0,253.0,22.0,1.0,2.0,12.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,10.0,22.0,2.19,18.0,0.0,2.0,1.0,3.0,0.3,3.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,1.0,2.0,6.0,1.0,5.0,3.0,0.0,0.0,390.0,0.0,24.0,209.0,161.0,28.0,390.0,240.0,9.0,9.0,3.0,297.0,37.0,12.0,0.0,12.0,22.0,6.0,0.0,0.0,0.0,30.0,4.0,11.0,26.7 -Warren Bondo,fr FRA,MF,Monza,19-148,2003,4.0,0.0,60.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.09,0.09,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,45.0,51.0,88.2,720.0,106.0,24.0,26.0,92.3,20.0,22.0,90.9,1.0,1.0,100.0,0.0,2.0,0.0,0.0,5.0,48.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,3.0,4.5,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,60.0,0.0,8.0,40.0,13.0,0.0,60.0,45.0,3.0,3.0,0.0,51.0,1.0,1.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,100.0 -Kevin Bonifazi,it ITA,DF,Bologna,26-267,1996,7.0,4.0,416.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,4.6,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.2,-0.2,215.0,251.0,85.7,4622.0,1533.0,64.0,71.0,90.1,109.0,120.0,90.8,41.0,57.0,71.9,1.0,14.0,0.0,0.0,12.0,242.0,9.0,9.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,0.65,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,4.0,4.0,2.0,0.0,6.0,4.0,2.0,6.0,21.0,0.0,309.0,48.0,171.0,126.0,15.0,4.0,309.0,175.0,3.0,3.0,0.0,208.0,3.0,4.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,28.0,1.0,10.0,9.1 -Leonardo Bonucci,it ITA,DF,Juventus,35-285,1987,9.0,6.0,600.0,1.0,0.0,0.0,1.0,1.0,0.0,0.15,0.0,0.15,0.15,0.15,0.9,0.1,0.14,0.01,6.7,1.0,0.0,50.0,0.15,0.5,1.0,0.05,0.1,0.9,299.0,351.0,85.2,6502.0,2460.0,82.0,89.0,92.1,167.0,184.0,90.8,49.0,75.0,65.3,2.0,23.0,3.0,0.0,16.0,322.0,28.0,10.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.9,4.0,0.0,1.0,0.0,1.0,0.15,0.0,0.0,1.0,0.0,0.0,3.0,3.0,1.0,2.0,0.0,8.0,6.0,2.0,8.0,22.0,0.0,403.0,67.0,217.0,184.0,6.0,5.0,402.0,219.0,1.0,2.0,0.0,269.0,2.0,0.0,0.0,8.0,4.0,1.0,0.0,0.0,0.0,36.0,11.0,13.0,45.8 -Erik Botheim,no NOR,FW,Salernitana,23-031,2000,15.0,3.0,335.0,1.0,1.0,0.0,0.0,1.0,0.0,0.27,0.27,0.54,0.27,0.54,0.4,0.4,0.1,0.1,3.7,1.0,0.0,25.0,0.27,0.25,1.0,0.09,0.6,0.6,54.0,85.0,63.5,707.0,146.0,28.0,41.0,68.3,17.0,25.0,68.0,2.0,3.0,66.7,6.0,4.0,2.0,0.0,11.0,82.0,2.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,9.0,2.43,6.0,0.0,1.0,1.0,1.0,0.27,1.0,0.0,0.0,0.0,0.0,4.0,3.0,0.0,2.0,2.0,1.0,0.0,1.0,0.0,1.0,0.0,115.0,1.0,10.0,43.0,63.0,11.0,115.0,59.0,7.0,2.0,0.0,87.0,8.0,5.0,0.0,7.0,6.0,0.0,0.0,0.0,0.0,11.0,11.0,13.0,45.8 -Mehdi Bourabia,ma MAR,MF,Spezia,31-187,1991,21.0,20.0,1576.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.11,0.11,0.0,0.11,0.5,0.5,0.03,0.03,17.5,1.0,1.0,7.7,0.06,0.0,0.0,0.04,-0.5,-0.5,603.0,791.0,76.2,9661.0,3293.0,298.0,360.0,82.8,244.0,293.0,83.3,45.0,91.0,49.5,23.0,66.0,21.0,7.0,72.0,740.0,46.0,23.0,4.0,3.0,36.0,19.0,14.0,3.0,0.0,4.0,5.0,19.0,46.0,2.63,35.0,5.0,1.0,1.0,6.0,0.34,6.0,0.0,0.0,0.0,0.0,31.0,13.0,12.0,15.0,4.0,22.0,10.0,12.0,23.0,28.0,0.0,999.0,58.0,279.0,537.0,200.0,8.0,999.0,482.0,20.0,18.0,3.0,550.0,32.0,17.0,0.0,25.0,14.0,1.0,0.0,0.0,0.0,136.0,10.0,14.0,41.7 -Edoardo Bove,it ITA,MF,Roma,20-270,2002,10.0,1.0,179.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.2,0.2,2.0,1.0,0.0,20.0,0.5,0.0,0.0,0.08,-0.4,-0.4,46.0,58.0,79.3,653.0,127.0,29.0,35.0,82.9,14.0,17.0,82.4,2.0,3.0,66.7,1.0,0.0,1.0,0.0,4.0,54.0,4.0,1.0,0.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,1.51,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,4.0,3.0,2.0,3.0,4.0,1.0,3.0,1.0,4.0,0.0,94.0,1.0,25.0,42.0,28.0,6.0,94.0,49.0,1.0,2.0,1.0,52.0,9.0,6.0,0.0,5.0,3.0,0.0,0.0,0.0,0.0,18.0,1.0,3.0,25.0 -Jayden Braaf,nl NED,MF,Hellas Verona,20-163,2002,1.0,1.0,72.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.2,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.16,-0.2,-0.2,9.0,12.0,75.0,122.0,38.0,8.0,8.0,100.0,1.0,2.0,50.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,28.0,0.0,9.0,8.0,12.0,2.0,28.0,15.0,1.0,0.0,0.0,15.0,3.0,5.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,5.0,0.0,2.0,0.0 -Domagoj Bradarić,hr CRO,DF,Salernitana,23-062,1999,16.0,10.0,995.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.09,0.09,0.0,0.09,0.1,0.1,0.01,0.01,11.1,0.0,1.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,372.0,496.0,75.0,6018.0,1830.0,190.0,220.0,86.4,149.0,185.0,80.5,24.0,64.0,37.5,7.0,15.0,10.0,6.0,21.0,388.0,106.0,20.0,0.0,5.0,38.0,3.0,1.0,1.0,0.0,83.0,2.0,14.0,20.0,1.81,15.0,1.0,2.0,1.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,10.0,7.0,3.0,6.0,1.0,10.0,2.0,8.0,6.0,18.0,0.0,591.0,23.0,150.0,260.0,189.0,10.0,591.0,309.0,28.0,22.0,4.0,337.0,14.0,6.0,0.0,11.0,8.0,2.0,0.0,0.0,0.0,57.0,4.0,9.0,30.8 -Josip Brekalo,hr CRO,FW,Fiorentina,24-232,1998,1.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,14.0,19.0,73.7,227.0,114.0,6.0,8.0,75.0,8.0,10.0,80.0,0.0,0.0,0.0,3.0,1.0,2.0,1.0,3.0,19.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,14.21,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,0.0,3.0,8.0,12.0,1.0,23.0,14.0,1.0,1.0,0.0,15.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.0 -Gleison Bremer,br BRA,DF,Juventus,25-329,1997,18.0,18.0,1574.0,1.0,0.0,0.0,0.0,4.0,0.0,0.06,0.0,0.06,0.06,0.06,2.6,2.6,0.15,0.15,17.5,2.0,0.0,11.8,0.11,0.06,0.5,0.16,-1.6,-1.6,907.0,1013.0,89.5,17623.0,5367.0,303.0,321.0,94.4,493.0,529.0,93.2,104.0,148.0,70.3,5.0,18.0,1.0,0.0,21.0,976.0,37.0,12.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,5.0,15.0,0.86,13.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,12.0,13.0,3.0,1.0,31.0,19.0,12.0,28.0,74.0,2.0,1222.0,196.0,714.0,464.0,48.0,28.0,1222.0,672.0,5.0,5.0,0.0,795.0,11.0,4.0,0.0,19.0,7.0,1.0,0.0,1.0,0.0,104.0,40.0,29.0,58.0 -Dylan Bronn,tn TUN,DF,Salernitana,27-236,1995,18.0,14.0,1248.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.04,0.04,13.9,1.0,0.0,14.3,0.07,0.0,0.0,0.08,-0.6,-0.6,638.0,754.0,84.6,11659.0,4597.0,262.0,286.0,91.6,305.0,334.0,91.3,61.0,104.0,58.7,5.0,46.0,4.0,1.0,57.0,708.0,42.0,25.0,1.0,12.0,5.0,0.0,0.0,0.0,0.0,17.0,4.0,5.0,12.0,0.87,10.0,0.0,2.0,0.0,2.0,0.14,2.0,0.0,0.0,0.0,0.0,25.0,12.0,13.0,12.0,0.0,25.0,16.0,9.0,18.0,42.0,0.0,900.0,79.0,406.0,456.0,40.0,8.0,900.0,494.0,9.0,6.0,1.0,568.0,5.0,1.0,0.0,19.0,6.0,0.0,0.0,0.0,0.0,80.0,25.0,18.0,58.1 -Marcelo Brozović,hr CRO,MF,Inter,30-086,1992,11.0,7.0,641.0,2.0,0.0,0.0,0.0,5.0,0.0,0.28,0.0,0.28,0.28,0.28,1.6,1.6,0.23,0.23,7.1,4.0,0.0,36.4,0.56,0.18,0.5,0.15,0.4,0.4,425.0,491.0,86.6,7549.0,1967.0,183.0,202.0,90.6,195.0,217.0,89.9,37.0,54.0,68.5,4.0,29.0,3.0,0.0,31.0,463.0,27.0,21.0,1.0,2.0,7.0,6.0,1.0,2.0,0.0,0.0,1.0,4.0,14.0,1.97,8.0,2.0,4.0,0.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,10.0,5.0,3.0,7.0,0.0,10.0,3.0,7.0,12.0,10.0,0.0,565.0,28.0,148.0,344.0,78.0,5.0,565.0,354.0,10.0,9.0,2.0,405.0,8.0,1.0,0.0,9.0,2.0,0.0,0.0,0.0,0.0,44.0,1.0,4.0,20.0 -"Cristian Buonaiuto,it ITA,""MF,FW"",Cremonese,30-043,1992,16.0,3.0,524.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5,1.5,0.26,0.26,5.8,7.0,5.0,29.2,1.2,0.0,0.0,0.06,-1.5,-1.5,168.0,267.0,62.9,2550.0,898.0,88.0,111.0,79.3,60.0,91.0,65.9,11.0,45.0,24.4,10.0,15.0,10.0,2.0,22.0,215.0,51.0,8.0,0.0,2.0,42.0,26.0,15.0,7.0,0.0,10.0,1.0,5.0,34.0,5.82,14.0,5.0,5.0,3.0,1.0,0.17,0.0,0.0,1.0,0.0,0.0,13.0,6.0,3.0,5.0,5.0,8.0,0.0,8.0,9.0,3.0,0.0,367.0,5.0,35.0,140.0,197.0,29.0,367.0,204.0,16.0,16.0,4.0,224.0,20.0,12.0,0.0,9.0,10.0,0.0,0.0,0.0,0.0,43.0,3.0,12.0,20.0" -Alessandro Buongiorno,it ITA,DF,Torino,23-249,1999,17.0,15.0,1356.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,1.1,1.1,0.07,0.07,15.1,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-1.1,-1.1,567.0,688.0,82.4,9441.0,3117.0,240.0,274.0,87.6,295.0,337.0,87.5,20.0,46.0,43.5,2.0,35.0,2.0,1.0,45.0,668.0,17.0,12.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,3.0,3.0,11.0,10.0,0.66,8.0,0.0,0.0,1.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,19.0,10.0,11.0,8.0,0.0,18.0,10.0,8.0,33.0,49.0,2.0,880.0,78.0,371.0,460.0,53.0,17.0,880.0,433.0,6.0,10.0,1.0,495.0,22.0,5.0,0.0,35.0,19.0,0.0,0.0,0.0,0.0,86.0,51.0,19.0,72.9 -Juan Cabal,co COL,DF,Hellas Verona,22-033,2001,3.0,0.0,57.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,27.0,66.7,286.0,115.0,10.0,12.0,83.3,7.0,9.0,77.8,1.0,5.0,20.0,1.0,3.0,1.0,1.0,3.0,24.0,3.0,1.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,3.0,4.82,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,35.0,7.0,15.0,18.0,4.0,0.0,35.0,17.0,0.0,1.0,0.0,16.0,2.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,9.0,0.0,4.0,0.0 -Liberato Cacace,nz NZL,DF,Empoli,22-136,2000,4.0,2.0,209.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,2.3,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,102.0,134.0,76.1,1565.0,589.0,52.0,58.0,89.7,44.0,60.0,73.3,3.0,9.0,33.3,2.0,8.0,0.0,0.0,10.0,105.0,29.0,3.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,26.0,0.0,2.0,7.0,3.03,5.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,5.0,7.0,4.0,2.0,7.0,3.0,4.0,2.0,3.0,0.0,179.0,11.0,56.0,79.0,47.0,5.0,179.0,77.0,12.0,9.0,2.0,88.0,2.0,2.0,0.0,4.0,2.0,0.0,0.0,1.0,0.0,12.0,2.0,9.0,18.2 -Davide Calabria,it ITA,DF,Milan,26-066,1996,13.0,13.0,998.0,1.0,2.0,0.0,0.0,4.0,0.0,0.09,0.18,0.27,0.09,0.27,0.9,0.9,0.08,0.08,11.1,1.0,0.0,14.3,0.09,0.14,1.0,0.13,0.1,0.1,583.0,727.0,80.2,10284.0,3737.0,248.0,266.0,93.2,264.0,302.0,87.4,58.0,127.0,45.7,9.0,36.0,11.0,8.0,54.0,606.0,117.0,10.0,0.0,2.0,29.0,5.0,0.0,5.0,0.0,102.0,4.0,13.0,18.0,1.62,16.0,1.0,0.0,1.0,3.0,0.27,2.0,0.0,0.0,1.0,0.0,50.0,26.0,23.0,21.0,6.0,13.0,4.0,9.0,17.0,20.0,0.0,849.0,42.0,270.0,421.0,169.0,9.0,849.0,427.0,15.0,15.0,1.0,511.0,6.0,3.0,0.0,10.0,9.0,1.0,1.0,1.0,0.0,67.0,5.0,7.0,41.7 -Mattia Caldara,it ITA,DF,Spezia,28-281,1994,14.0,10.0,969.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.04,0.04,10.8,2.0,0.0,25.0,0.19,0.0,0.0,0.06,-0.5,-0.5,353.0,458.0,77.1,6280.0,2385.0,143.0,170.0,84.1,170.0,209.0,81.3,33.0,65.0,50.8,1.0,15.0,1.0,0.0,25.0,412.0,44.0,22.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,8.0,2.0,4.0,5.0,0.46,5.0,0.0,0.0,0.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,19.0,11.0,9.0,8.0,2.0,15.0,9.0,6.0,14.0,64.0,2.0,600.0,100.0,335.0,237.0,37.0,13.0,600.0,289.0,5.0,2.0,0.0,274.0,5.0,4.0,0.0,14.0,9.0,0.0,0.0,0.0,0.0,71.0,25.0,19.0,56.8 -Luca Caldirola,it ITA,DF,Monza,32-009,1991,17.0,15.0,1312.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.04,0.04,14.6,1.0,0.0,20.0,0.07,0.0,0.0,0.13,-0.6,-0.6,732.0,833.0,87.9,13220.0,5285.0,300.0,315.0,95.2,355.0,391.0,90.8,66.0,101.0,65.3,3.0,35.0,3.0,0.0,45.0,796.0,36.0,25.0,1.0,6.0,3.0,0.0,0.0,0.0,0.0,4.0,1.0,5.0,8.0,0.55,8.0,0.0,0.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,14.0,7.0,8.0,3.0,3.0,15.0,7.0,8.0,14.0,33.0,0.0,939.0,110.0,463.0,428.0,52.0,7.0,939.0,554.0,9.0,7.0,0.0,677.0,2.0,1.0,0.0,18.0,15.0,0.0,0.0,0.0,0.0,56.0,26.0,17.0,60.5 -Hakan Çalhanoğlu,tr TUR,MF,Inter,29-002,1994,20.0,19.0,1527.0,2.0,5.0,1.0,1.0,2.0,0.0,0.12,0.29,0.41,0.06,0.35,1.9,1.1,0.11,0.06,17.0,7.0,6.0,25.0,0.41,0.04,0.14,0.04,0.1,-0.1,824.0,988.0,83.4,14979.0,4469.0,366.0,401.0,91.3,335.0,380.0,88.2,98.0,157.0,62.4,44.0,88.0,22.0,7.0,98.0,880.0,105.0,44.0,4.0,14.0,88.0,57.0,17.0,36.0,0.0,4.0,3.0,6.0,74.0,4.36,43.0,24.0,3.0,3.0,8.0,0.47,5.0,3.0,0.0,0.0,0.0,35.0,19.0,12.0,19.0,4.0,22.0,4.0,18.0,19.0,8.0,1.0,1160.0,25.0,237.0,617.0,318.0,15.0,1159.0,662.0,24.0,21.0,2.0,754.0,21.0,7.0,0.0,31.0,19.0,1.0,0.0,0.0,0.0,127.0,11.0,12.0,47.8 -Mohamed Camara,gn GUI,MF,Roma,25-347,1997,9.0,5.0,377.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,4.2,0.0,1.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,145.0,186.0,78.0,2404.0,466.0,70.0,79.0,88.6,63.0,75.0,84.0,10.0,18.0,55.6,2.0,16.0,2.0,1.0,16.0,183.0,2.0,2.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,7.0,1.67,3.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,9.0,3.0,6.0,1.0,1.0,0.0,1.0,5.0,4.0,0.0,219.0,7.0,39.0,149.0,39.0,1.0,219.0,138.0,10.0,5.0,1.0,138.0,3.0,5.0,0.0,9.0,5.0,0.0,0.0,0.0,0.0,35.0,2.0,4.0,33.3 -"Nicolò Cambiaghi,it ITA,""FW,MF"",Empoli,22-044,2000,15.0,2.0,430.0,1.0,1.0,0.0,0.0,1.0,0.0,0.21,0.21,0.42,0.21,0.42,1.6,1.6,0.33,0.33,4.8,4.0,0.0,36.4,0.84,0.09,0.25,0.14,-0.6,-0.6,123.0,163.0,75.5,1735.0,453.0,75.0,86.0,87.2,31.0,43.0,72.1,8.0,11.0,72.7,4.0,10.0,5.0,0.0,18.0,158.0,5.0,1.0,0.0,0.0,14.0,1.0,0.0,0.0,0.0,2.0,0.0,10.0,19.0,3.99,9.0,1.0,3.0,3.0,4.0,0.84,1.0,1.0,1.0,0.0,0.0,12.0,9.0,6.0,3.0,3.0,6.0,1.0,5.0,3.0,5.0,0.0,235.0,8.0,42.0,85.0,115.0,20.0,235.0,167.0,23.0,10.0,8.0,169.0,14.0,9.0,0.0,7.0,19.0,0.0,0.0,0.0,0.0,24.0,2.0,4.0,33.3" -Andrea Cambiaso,it ITA,DF,Bologna,22-355,2000,19.0,13.0,1150.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.16,0.16,0.0,0.16,0.5,0.5,0.04,0.04,12.8,1.0,0.0,14.3,0.08,0.0,0.0,0.08,-0.5,-0.5,549.0,697.0,78.8,8182.0,3028.0,311.0,347.0,89.6,192.0,232.0,82.8,30.0,73.0,41.1,15.0,26.0,13.0,6.0,41.0,563.0,132.0,11.0,0.0,2.0,35.0,1.0,1.0,0.0,0.0,120.0,2.0,22.0,28.0,2.19,22.0,3.0,0.0,1.0,5.0,0.39,5.0,0.0,0.0,0.0,0.0,33.0,18.0,20.0,12.0,1.0,14.0,2.0,12.0,18.0,19.0,1.0,859.0,41.0,281.0,383.0,208.0,19.0,859.0,468.0,33.0,30.0,8.0,503.0,25.0,11.0,0.0,17.0,19.0,4.0,0.0,0.0,0.0,87.0,3.0,11.0,21.4 -Matteo Cancellieri,it ITA,FW,Lazio,20-363,2002,14.0,1.0,208.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.14,0.14,2.3,1.0,0.0,25.0,0.43,0.0,0.0,0.08,-0.3,-0.3,55.0,75.0,73.3,690.0,97.0,39.0,44.0,88.6,14.0,21.0,66.7,0.0,5.0,0.0,3.0,1.0,1.0,1.0,2.0,74.0,1.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,5.0,2.17,4.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,3.0,4.0,1.0,2.0,3.0,0.0,3.0,1.0,2.0,0.0,109.0,2.0,18.0,42.0,51.0,13.0,109.0,68.0,12.0,6.0,7.0,76.0,9.0,2.0,0.0,11.0,5.0,4.0,0.0,0.0,0.0,13.0,4.0,1.0,80.0 -"Antonio Candreva,it ITA,""DF,MF"",Salernitana,35-347,1987,20.0,18.0,1613.0,2.0,1.0,0.0,0.0,3.0,1.0,0.11,0.06,0.17,0.11,0.17,1.5,1.5,0.08,0.08,17.9,9.0,2.0,39.1,0.5,0.09,0.22,0.06,0.5,0.5,724.0,1004.0,72.1,12768.0,4698.0,358.0,388.0,92.3,271.0,370.0,73.2,78.0,183.0,42.6,30.0,72.0,28.0,15.0,91.0,829.0,172.0,28.0,4.0,19.0,134.0,36.0,3.0,18.0,0.0,106.0,3.0,31.0,51.0,2.84,35.0,10.0,4.0,2.0,5.0,0.28,3.0,1.0,1.0,0.0,0.0,8.0,2.0,8.0,0.0,0.0,5.0,2.0,3.0,16.0,25.0,0.0,1125.0,38.0,222.0,489.0,426.0,22.0,1125.0,640.0,44.0,26.0,4.0,786.0,16.0,6.0,1.0,3.0,13.0,4.0,0.0,1.0,0.0,81.0,0.0,0.0,0.0" -"Gianluca Caprari,it ITA,""MF,FW"",Monza,29-195,1993,21.0,18.0,1327.0,4.0,1.0,1.0,1.0,4.0,0.0,0.27,0.07,0.34,0.2,0.27,4.2,3.4,0.28,0.23,14.7,8.0,0.0,24.2,0.54,0.09,0.38,0.1,-0.2,-0.4,477.0,597.0,79.9,6960.0,1211.0,297.0,334.0,88.9,132.0,173.0,76.3,31.0,46.0,67.4,15.0,29.0,13.0,3.0,44.0,568.0,26.0,4.0,2.0,7.0,37.0,14.0,1.0,11.0,0.0,3.0,3.0,16.0,40.0,2.71,30.0,5.0,3.0,1.0,8.0,0.54,6.0,1.0,0.0,1.0,0.0,10.0,4.0,3.0,6.0,1.0,11.0,1.0,10.0,2.0,1.0,0.0,764.0,4.0,59.0,364.0,346.0,69.0,763.0,509.0,56.0,23.0,22.0,621.0,51.0,18.0,0.0,24.0,26.0,6.0,0.0,0.0,0.0,55.0,4.0,11.0,26.7" -Francesco Caputo,it ITA,FW,Sampdoria,35-188,1987,15.0,13.0,1094.0,1.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.08,0.08,0.08,1.9,1.9,0.15,0.15,12.2,5.0,0.0,26.3,0.41,0.05,0.2,0.1,-0.9,-0.9,168.0,237.0,70.9,2130.0,315.0,96.0,130.0,73.8,43.0,60.0,71.7,8.0,10.0,80.0,9.0,11.0,4.0,0.0,11.0,208.0,28.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,2.0,1.0,9.0,23.0,1.89,17.0,0.0,3.0,1.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,3.0,0.0,309.0,4.0,11.0,126.0,172.0,49.0,309.0,181.0,6.0,4.0,7.0,243.0,21.0,14.0,0.0,5.0,24.0,11.0,0.0,0.0,0.0,18.0,6.0,21.0,22.2 -"Francesco Caputo,it ITA,""FW,MF"",Empoli,35-188,1987,6.0,6.0,503.0,1.0,2.0,0.0,0.0,1.0,0.0,0.18,0.36,0.54,0.18,0.54,1.4,1.4,0.26,0.26,5.6,4.0,0.0,30.8,0.72,0.08,0.25,0.11,-0.4,-0.4,88.0,119.0,73.9,1018.0,244.0,65.0,79.0,82.3,18.0,23.0,78.3,0.0,1.0,0.0,8.0,2.0,2.0,0.0,8.0,118.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,10.0,1.8,8.0,0.0,1.0,1.0,2.0,0.36,2.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,2.0,2.0,0.0,2.0,0.0,169.0,3.0,15.0,76.0,78.0,32.0,169.0,102.0,4.0,2.0,2.0,144.0,13.0,8.0,0.0,8.0,10.0,6.0,0.0,0.0,0.0,11.0,5.0,5.0,50.0" -Andrea Carboni,it ITA,DF,Monza,22-006,2001,4.0,1.0,167.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,1.9,1.0,0.0,100.0,0.54,0.0,0.0,0.05,-0.1,-0.1,69.0,87.0,79.3,1170.0,558.0,33.0,38.0,86.8,29.0,31.0,93.5,7.0,14.0,50.0,1.0,3.0,2.0,2.0,9.0,74.0,13.0,1.0,0.0,1.0,5.0,1.0,0.0,1.0,0.0,11.0,0.0,3.0,3.0,1.62,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,3.0,2.0,1.0,3.0,2.0,1.0,104.0,4.0,41.0,43.0,20.0,4.0,104.0,50.0,4.0,2.0,0.0,66.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0 -"Valentin Carboni,it ITA,""MF,FW"",Inter,17-342,2005,2.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,8.0,62.5,96.0,9.0,2.0,2.0,100.0,2.0,4.0,50.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,9.0,0.0,1.0,5.0,3.0,0.0,9.0,7.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0" -Carlos,br BRA,DF,Monza,24-034,1999,20.0,20.0,1759.0,4.0,3.0,0.0,0.0,2.0,0.0,0.2,0.15,0.36,0.2,0.36,2.1,2.1,0.11,0.11,19.5,9.0,0.0,36.0,0.46,0.16,0.44,0.09,1.9,1.9,837.0,1019.0,82.1,13099.0,4143.0,432.0,472.0,91.5,336.0,398.0,84.4,43.0,77.0,55.8,11.0,44.0,16.0,2.0,56.0,826.0,188.0,12.0,2.0,3.0,34.0,0.0,0.0,0.0,0.0,171.0,5.0,29.0,31.0,1.59,24.0,2.0,2.0,1.0,5.0,0.26,4.0,0.0,1.0,0.0,0.0,32.0,17.0,18.0,9.0,5.0,13.0,4.0,9.0,11.0,46.0,1.0,1208.0,69.0,391.0,519.0,310.0,38.0,1208.0,597.0,50.0,45.0,7.0,739.0,23.0,15.0,0.0,29.0,12.0,2.0,0.0,0.0,0.0,93.0,33.0,19.0,63.5 -Marco Carnesecchi,it ITA,GK,Cremonese,22-224,2000,12.0,12.0,1080.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,246.0,385.0,63.9,7332.0,5539.0,40.0,40.0,100.0,115.0,118.0,97.5,85.0,219.0,38.8,0.0,11.0,0.0,0.0,0.0,257.0,126.0,40.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,4.0,0.33,2.0,2.0,0.0,0.0,1.0,0.08,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,418.0,373.0,418.0,0.0,0.0,0.0,418.0,220.0,0.0,0.0,0.0,155.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,12.0,1.0,0.0,100.0 -Nicolò Casale,it ITA,DF,Lazio,24-361,1998,14.0,14.0,1225.0,1.0,1.0,0.0,0.0,4.0,0.0,0.07,0.07,0.15,0.07,0.15,0.6,0.6,0.04,0.04,13.6,2.0,0.0,28.6,0.15,0.14,0.5,0.08,0.4,0.4,693.0,755.0,91.8,11950.0,4351.0,287.0,308.0,93.2,319.0,339.0,94.1,59.0,73.0,80.8,3.0,20.0,1.0,0.0,33.0,702.0,53.0,50.0,2.0,6.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,6.0,0.44,4.0,1.0,1.0,0.0,2.0,0.15,1.0,0.0,1.0,0.0,0.0,29.0,13.0,17.0,12.0,0.0,9.0,6.0,3.0,9.0,40.0,0.0,864.0,122.0,427.0,425.0,19.0,12.0,864.0,494.0,8.0,1.0,0.0,553.0,4.0,1.0,0.0,11.0,4.0,0.0,0.0,0.0,0.0,75.0,23.0,18.0,56.1 -Michele Castagnetti,it ITA,MF,Cremonese,33-045,1989,14.0,7.0,683.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,7.6,1.0,0.0,16.7,0.13,0.0,0.0,0.04,-0.3,-0.3,287.0,394.0,72.8,5098.0,2145.0,120.0,149.0,80.5,133.0,176.0,75.6,28.0,55.0,50.9,17.0,45.0,12.0,2.0,62.0,352.0,41.0,23.0,1.0,5.0,24.0,11.0,7.0,4.0,0.0,6.0,1.0,7.0,30.0,3.95,27.0,3.0,0.0,0.0,1.0,0.13,1.0,0.0,0.0,0.0,0.0,22.0,11.0,9.0,12.0,1.0,6.0,2.0,4.0,6.0,12.0,0.0,458.0,17.0,104.0,258.0,103.0,6.0,458.0,207.0,9.0,5.0,0.0,253.0,9.0,0.0,0.0,10.0,7.0,0.0,0.0,0.0,0.0,60.0,6.0,7.0,46.2 -Gaetano Castrovilli,it ITA,MF,Fiorentina,25-358,1997,3.0,0.0,56.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.24,0.24,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.1,-0.1,23.0,24.0,95.8,314.0,75.0,16.0,17.0,94.1,5.0,5.0,100.0,2.0,2.0,100.0,0.0,0.0,1.0,0.0,2.0,23.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,2.0,3.21,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,33.0,0.0,6.0,12.0,15.0,2.0,33.0,19.0,2.0,2.0,0.0,18.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,2.0,33.3 -Danilo Cataldi,it ITA,MF,Lazio,28-188,1994,19.0,18.0,1340.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.07,0.07,0.0,0.07,0.2,0.2,0.01,0.01,14.9,1.0,1.0,16.7,0.07,0.0,0.0,0.03,-0.2,-0.2,780.0,919.0,84.9,12059.0,4352.0,446.0,485.0,92.0,235.0,278.0,84.5,69.0,108.0,63.9,14.0,64.0,5.0,0.0,61.0,850.0,65.0,31.0,0.0,3.0,43.0,32.0,6.0,19.0,0.0,2.0,4.0,10.0,30.0,2.01,16.0,13.0,1.0,0.0,2.0,0.13,1.0,0.0,1.0,0.0,0.0,20.0,12.0,5.0,13.0,2.0,14.0,8.0,6.0,18.0,22.0,0.0,1039.0,59.0,278.0,642.0,126.0,0.0,1039.0,550.0,6.0,7.0,0.0,710.0,4.0,5.0,0.0,10.0,16.0,0.0,0.0,0.0,0.0,116.0,1.0,4.0,20.0 -Federico Ceccherini,it ITA,DF,Hellas Verona,30-275,1992,15.0,14.0,1033.0,1.0,0.0,0.0,0.0,7.0,1.0,0.09,0.0,0.09,0.09,0.09,0.7,0.7,0.06,0.06,11.5,1.0,0.0,20.0,0.09,0.2,1.0,0.14,0.3,0.3,359.0,475.0,75.6,6432.0,2602.0,152.0,172.0,88.4,164.0,199.0,82.4,37.0,76.0,48.7,4.0,38.0,4.0,0.0,43.0,431.0,44.0,13.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,28.0,0.0,14.0,11.0,0.95,8.0,1.0,0.0,0.0,1.0,0.09,0.0,0.0,0.0,0.0,1.0,18.0,14.0,10.0,7.0,1.0,11.0,4.0,7.0,10.0,37.0,0.0,585.0,62.0,216.0,298.0,76.0,10.0,585.0,269.0,20.0,12.0,2.0,303.0,11.0,5.0,0.0,26.0,14.0,0.0,0.0,0.0,0.0,74.0,21.0,12.0,63.6 -Assan Ceesay,gm GAM,FW,Lecce,28-330,1994,18.0,10.0,929.0,3.0,0.0,0.0,0.0,1.0,0.0,0.29,0.0,0.29,0.29,0.29,2.7,2.7,0.27,0.27,10.3,4.0,0.0,16.7,0.39,0.13,0.75,0.11,0.3,0.3,118.0,196.0,60.2,1761.0,262.0,58.0,97.0,59.8,42.0,60.0,70.0,9.0,12.0,75.0,3.0,5.0,2.0,0.0,12.0,181.0,14.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,4.0,1.0,5.0,18.0,1.74,12.0,0.0,1.0,3.0,2.0,0.19,1.0,0.0,0.0,0.0,0.0,16.0,13.0,6.0,5.0,5.0,9.0,1.0,8.0,1.0,18.0,0.0,336.0,22.0,51.0,142.0,146.0,44.0,336.0,151.0,11.0,7.0,6.0,207.0,34.0,12.0,0.0,17.0,19.0,10.0,0.0,0.0,0.0,24.0,35.0,30.0,53.8 -Emil Ceide,no NOR,FW,Sassuolo,21-160,2001,12.0,3.0,312.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.05,0.05,3.5,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.2,-0.2,67.0,96.0,69.8,945.0,291.0,40.0,49.0,81.6,20.0,28.0,71.4,3.0,5.0,60.0,6.0,3.0,7.0,1.0,11.0,95.0,1.0,0.0,1.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,13.0,3.77,11.0,0.0,0.0,1.0,2.0,0.58,0.0,0.0,0.0,1.0,0.0,4.0,1.0,4.0,0.0,0.0,2.0,0.0,2.0,1.0,0.0,0.0,132.0,0.0,16.0,37.0,79.0,24.0,132.0,91.0,22.0,6.0,14.0,99.0,6.0,5.0,0.0,3.0,5.0,0.0,1.0,0.0,0.0,11.0,1.0,3.0,25.0 -Zeki Çelik,tr TUR,DF,Roma,25-358,1997,15.0,10.0,992.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,11.0,1.0,0.0,25.0,0.09,0.0,0.0,0.08,-0.3,-0.3,454.0,597.0,76.0,7132.0,3018.0,239.0,271.0,88.2,164.0,205.0,80.0,34.0,72.0,47.2,13.0,32.0,14.0,3.0,41.0,475.0,120.0,10.0,0.0,2.0,24.0,0.0,0.0,0.0,0.0,110.0,2.0,10.0,29.0,2.63,23.0,3.0,1.0,1.0,2.0,0.18,2.0,0.0,0.0,0.0,0.0,34.0,16.0,17.0,15.0,2.0,22.0,5.0,17.0,12.0,28.0,1.0,721.0,30.0,241.0,306.0,184.0,15.0,721.0,333.0,23.0,18.0,5.0,389.0,12.0,11.0,0.0,17.0,5.0,1.0,0.0,0.0,0.0,64.0,8.0,7.0,53.3 -"Federico Chiesa,it ITA,""DF,FW"",Juventus,25-108,1997,7.0,1.0,241.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.75,0.75,0.0,0.75,0.4,0.4,0.16,0.16,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.4,-0.4,83.0,112.0,74.1,1209.0,349.0,45.0,53.0,84.9,31.0,43.0,72.1,2.0,4.0,50.0,5.0,8.0,3.0,1.0,7.0,93.0,19.0,1.0,1.0,0.0,11.0,3.0,0.0,1.0,0.0,15.0,0.0,5.0,9.0,3.36,6.0,1.0,0.0,0.0,2.0,0.75,2.0,0.0,0.0,0.0,0.0,8.0,5.0,4.0,2.0,2.0,2.0,1.0,1.0,0.0,1.0,0.0,144.0,4.0,23.0,57.0,68.0,9.0,144.0,77.0,12.0,8.0,5.0,89.0,6.0,4.0,0.0,4.0,2.0,1.0,0.0,0.0,0.0,17.0,0.0,0.0,0.0" -Vlad Chiricheș,ro ROU,DF,Cremonese,33-088,1989,8.0,8.0,595.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,216.0,252.0,85.7,4192.0,1938.0,78.0,85.0,91.8,106.0,118.0,89.8,27.0,40.0,67.5,1.0,8.0,0.0,0.0,5.0,240.0,12.0,11.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,0.61,4.0,0.0,0.0,0.0,1.0,0.15,1.0,0.0,0.0,0.0,0.0,24.0,14.0,18.0,5.0,1.0,19.0,11.0,8.0,16.0,22.0,0.0,337.0,67.0,218.0,117.0,5.0,2.0,337.0,153.0,2.0,2.0,0.0,175.0,1.0,3.0,0.0,12.0,4.0,0.0,0.0,0.0,1.0,43.0,10.0,17.0,37.0 -Daniel Ciofani,it ITA,FW,Cremonese,37-194,1985,18.0,5.0,464.0,3.0,0.0,1.0,2.0,0.0,0.0,0.58,0.0,0.58,0.39,0.39,2.6,0.9,0.5,0.17,5.2,6.0,0.0,42.9,1.16,0.14,0.33,0.06,0.4,1.1,50.0,87.0,57.5,725.0,264.0,28.0,39.0,71.8,13.0,25.0,52.0,5.0,8.0,62.5,5.0,6.0,3.0,0.0,5.0,84.0,2.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,12.0,2.33,8.0,0.0,2.0,1.0,2.0,0.39,1.0,0.0,1.0,0.0,0.0,4.0,2.0,1.0,1.0,2.0,3.0,0.0,3.0,0.0,2.0,0.0,140.0,3.0,10.0,52.0,78.0,32.0,138.0,62.0,3.0,5.0,0.0,100.0,14.0,5.0,0.0,3.0,7.0,3.0,0.0,0.0,0.0,13.0,21.0,29.0,42.0 -"Tio Cipot,si SVN,""DF,FW"",Spezia,19-296,2003,2.0,0.0,39.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.04,0.4,1.0,0.0,100.0,2.31,0.0,0.0,0.02,0.0,0.0,13.0,18.0,72.2,188.0,46.0,5.0,7.0,71.4,7.0,8.0,87.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,15.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,25.0,1.0,3.0,13.0,10.0,2.0,25.0,15.0,1.0,0.0,0.0,19.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0" -"Patrick Ciurria,it ITA,""DF,MF"",Monza,28-001,1995,19.0,14.0,1245.0,3.0,4.0,0.0,0.0,2.0,0.0,0.22,0.29,0.51,0.22,0.51,2.4,2.4,0.17,0.17,13.8,8.0,0.0,34.8,0.58,0.13,0.38,0.1,0.6,0.6,491.0,611.0,80.4,7772.0,2421.0,260.0,290.0,89.7,176.0,227.0,77.5,38.0,61.0,62.3,21.0,30.0,20.0,10.0,40.0,513.0,96.0,6.0,1.0,1.0,41.0,11.0,0.0,7.0,0.0,79.0,2.0,6.0,37.0,2.67,27.0,4.0,3.0,2.0,3.0,0.22,3.0,0.0,0.0,0.0,0.0,20.0,9.0,12.0,8.0,0.0,13.0,1.0,12.0,2.0,13.0,0.0,752.0,16.0,167.0,315.0,276.0,27.0,752.0,377.0,31.0,23.0,5.0,484.0,28.0,10.0,0.0,13.0,19.0,3.0,0.0,0.0,0.0,60.0,5.0,6.0,45.5" -Omar Colley,gm GAM,DF,Sampdoria,30-109,1992,16.0,15.0,1384.0,1.0,0.0,0.0,0.0,5.0,0.0,0.07,0.0,0.07,0.07,0.07,1.2,1.2,0.08,0.08,15.4,4.0,0.0,66.7,0.26,0.17,0.25,0.2,-0.2,-0.2,615.0,714.0,86.1,12155.0,4061.0,165.0,186.0,88.7,380.0,412.0,92.2,58.0,96.0,60.4,0.0,38.0,1.0,0.0,33.0,681.0,32.0,22.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,5.0,0.33,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,10.0,10.0,8.0,0.0,9.0,5.0,4.0,22.0,69.0,1.0,861.0,107.0,451.0,391.0,20.0,9.0,861.0,512.0,3.0,1.0,0.0,517.0,4.0,2.0,0.0,15.0,18.0,1.0,0.0,0.0,0.0,74.0,27.0,30.0,47.4 -Lorenzo Colombo,it ITA,FW,Lecce,20-339,2002,19.0,11.0,921.0,4.0,2.0,0.0,1.0,3.0,0.0,0.39,0.2,0.59,0.39,0.59,4.2,3.4,0.41,0.33,10.2,8.0,0.0,29.6,0.78,0.15,0.5,0.13,-0.2,0.6,142.0,220.0,64.5,1643.0,335.0,98.0,124.0,79.0,23.0,37.0,62.2,5.0,17.0,29.4,11.0,12.0,3.0,0.0,16.0,211.0,7.0,0.0,0.0,5.0,6.0,0.0,0.0,0.0,0.0,0.0,2.0,7.0,21.0,2.05,16.0,0.0,4.0,0.0,2.0,0.2,1.0,0.0,1.0,0.0,0.0,12.0,4.0,0.0,6.0,6.0,12.0,0.0,12.0,2.0,4.0,0.0,384.0,4.0,12.0,177.0,197.0,49.0,383.0,208.0,7.0,8.0,1.0,279.0,53.0,29.0,0.0,27.0,8.0,6.0,0.0,0.0,0.0,24.0,21.0,73.0,22.3 -Andrea Colpani,it ITA,MF,Monza,23-275,1999,15.0,4.0,481.0,2.0,0.0,0.0,0.0,1.0,0.0,0.37,0.0,0.37,0.37,0.37,1.0,1.0,0.19,0.19,5.3,3.0,0.0,25.0,0.56,0.17,0.67,0.08,1.0,1.0,172.0,217.0,79.3,2825.0,609.0,91.0,103.0,88.3,59.0,71.0,83.1,16.0,28.0,57.1,8.0,17.0,9.0,2.0,27.0,198.0,15.0,3.0,1.0,5.0,15.0,11.0,3.0,8.0,0.0,1.0,4.0,2.0,16.0,2.98,12.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,4.0,4.0,1.0,1.0,10.0,1.0,9.0,3.0,2.0,0.0,285.0,2.0,28.0,129.0,135.0,15.0,285.0,191.0,12.0,14.0,1.0,208.0,12.0,8.0,0.0,12.0,13.0,0.0,0.0,0.0,0.0,34.0,1.0,9.0,10.0 -Andrea Consigli,it ITA,GK,Sassuolo,36-014,1987,19.0,19.0,1710.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,676.0,859.0,78.7,19486.0,12248.0,107.0,107.0,100.0,274.0,277.0,98.9,293.0,472.0,62.1,0.0,11.0,0.0,0.0,1.0,622.0,236.0,64.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.32,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,894.0,696.0,884.0,9.0,1.0,0.0,894.0,510.0,0.0,0.0,0.0,458.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,22.0,6.0,0.0,100.0 -Andrea Conti,it ITA,DF,Sampdoria,28-345,1994,1.0,0.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,11.0,54.5,76.0,25.0,5.0,7.0,71.4,1.0,3.0,33.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,7.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,8.0,3.0,0.0,11.0,4.0,0.0,0.0,0.0,6.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Diego Coppola,it ITA,DF,Hellas Verona,19-044,2003,8.0,7.0,619.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.05,0.05,6.9,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.3,-0.3,228.0,315.0,72.4,4260.0,1960.0,85.0,103.0,82.5,121.0,152.0,79.6,19.0,47.0,40.4,0.0,20.0,3.0,1.0,25.0,270.0,42.0,3.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,39.0,3.0,3.0,7.0,1.02,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,8.0,6.0,8.0,0.0,13.0,4.0,9.0,7.0,21.0,0.0,388.0,36.0,174.0,175.0,43.0,4.0,388.0,174.0,6.0,7.0,1.0,183.0,3.0,1.0,0.0,7.0,0.0,0.0,0.0,1.0,0.0,41.0,15.0,18.0,45.5 -Joaquín Correa,ar ARG,FW,Inter,28-181,1994,17.0,4.0,436.0,3.0,1.0,0.0,0.0,1.0,0.0,0.62,0.21,0.83,0.62,0.83,2.1,2.1,0.44,0.44,4.8,5.0,0.0,55.6,1.03,0.33,0.6,0.24,0.9,0.9,90.0,122.0,73.8,1388.0,270.0,46.0,57.0,80.7,31.0,37.0,83.8,7.0,7.0,100.0,5.0,8.0,1.0,0.0,8.0,118.0,2.0,1.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,8.0,14.0,2.9,9.0,0.0,3.0,2.0,3.0,0.62,3.0,0.0,0.0,0.0,0.0,7.0,4.0,1.0,4.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,181.0,1.0,10.0,89.0,86.0,18.0,181.0,117.0,5.0,8.0,6.0,143.0,22.0,12.0,0.0,6.0,10.0,1.0,0.0,0.0,0.0,16.0,4.0,9.0,30.8 -Alessandro Cortinovis,it ITA,MF,Hellas Verona,22-016,2001,1.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,6.0,83.3,77.0,35.0,3.0,3.0,100.0,2.0,2.0,100.0,0.0,1.0,0.0,0.0,0.0,2.0,1.0,2.0,6.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0,5.0,0.0,8.0,5.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0 -Lassana Coulibaly,ml MLI,MF,Salernitana,26-306,1996,19.0,19.0,1572.0,1.0,2.0,0.0,0.0,6.0,0.0,0.06,0.11,0.17,0.06,0.17,0.4,0.4,0.02,0.02,17.5,3.0,0.0,37.5,0.17,0.13,0.33,0.05,0.6,0.6,507.0,607.0,83.5,7695.0,2236.0,257.0,291.0,88.3,208.0,234.0,88.9,19.0,39.0,48.7,11.0,51.0,6.0,1.0,60.0,597.0,9.0,8.0,2.0,2.0,8.0,0.0,0.0,0.0,0.0,1.0,1.0,16.0,34.0,1.95,29.0,1.0,0.0,3.0,5.0,0.29,5.0,0.0,0.0,0.0,0.0,53.0,28.0,26.0,22.0,5.0,19.0,2.0,17.0,9.0,15.0,2.0,808.0,40.0,198.0,479.0,151.0,11.0,808.0,469.0,28.0,20.0,5.0,502.0,35.0,16.0,0.0,29.0,22.0,1.0,0.0,0.0,0.0,111.0,11.0,17.0,39.3 -Bryan Cristante,it ITA,MF,Roma,27-344,1995,21.0,21.0,1749.0,1.0,0.0,0.0,0.0,4.0,0.0,0.05,0.0,0.05,0.05,0.05,1.1,1.1,0.06,0.06,19.4,3.0,0.0,17.6,0.15,0.06,0.33,0.06,-0.1,-0.1,843.0,1034.0,81.5,15634.0,4759.0,363.0,405.0,89.6,346.0,398.0,86.9,112.0,186.0,60.2,9.0,108.0,12.0,3.0,99.0,991.0,39.0,37.0,6.0,22.0,8.0,0.0,0.0,0.0,0.0,2.0,4.0,12.0,28.0,1.44,27.0,0.0,1.0,0.0,2.0,0.1,2.0,0.0,0.0,0.0,0.0,71.0,35.0,33.0,31.0,7.0,25.0,6.0,19.0,8.0,27.0,0.0,1216.0,48.0,331.0,777.0,118.0,18.0,1216.0,635.0,12.0,10.0,3.0,801.0,19.0,8.0,0.0,29.0,14.0,2.0,0.0,0.0,0.0,134.0,34.0,22.0,60.7 -"Domen Črnigoj,si SVN,""MF,FW"",Salernitana,27-084,1995,2.0,0.0,49.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,24.0,66.7,211.0,36.0,12.0,16.0,75.0,4.0,4.0,100.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.84,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,0.0,5.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,33.0,0.0,2.0,19.0,14.0,3.0,33.0,15.0,2.0,1.0,1.0,20.0,4.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0" -"Juan Cuadrado,co COL,""DF,FW"",Juventus,34-260,1988,16.0,13.0,1059.0,0.0,3.0,0.0,0.0,3.0,1.0,0.0,0.25,0.25,0.0,0.25,0.9,0.9,0.07,0.07,11.8,5.0,2.0,27.8,0.42,0.0,0.0,0.05,-0.9,-0.9,535.0,640.0,83.6,10475.0,2664.0,222.0,247.0,89.9,217.0,248.0,87.5,90.0,125.0,72.0,27.0,24.0,15.0,7.0,44.0,563.0,76.0,13.0,0.0,11.0,46.0,20.0,7.0,12.0,0.0,43.0,1.0,8.0,44.0,3.74,23.0,12.0,3.0,4.0,6.0,0.51,2.0,2.0,0.0,2.0,0.0,30.0,18.0,22.0,8.0,0.0,9.0,1.0,8.0,13.0,17.0,0.0,787.0,20.0,208.0,370.0,214.0,29.0,787.0,427.0,19.0,15.0,11.0,539.0,21.0,5.0,0.0,15.0,18.0,0.0,0.0,0.0,0.0,50.0,6.0,4.0,60.0" -Mickaël Cuisance,fr FRA,MF,Sampdoria,23-178,1999,1.0,1.0,45.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,16.0,56.3,180.0,38.0,3.0,3.0,100.0,5.0,7.0,71.4,1.0,4.0,25.0,0.0,0.0,1.0,0.0,1.0,15.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,3.0,2.0,0.0,2.0,0.0,2.0,0.0,0.0,0.0,26.0,3.0,7.0,12.0,7.0,0.0,26.0,12.0,0.0,1.0,0.0,10.0,2.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.0 -"Marco D'Alessandro,it ITA,""DF,FW"",Monza,31-358,1991,8.0,3.0,290.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.1,0.1,3.2,1.0,0.0,25.0,0.31,0.0,0.0,0.08,-0.3,-0.3,79.0,115.0,68.7,1182.0,302.0,50.0,57.0,87.7,23.0,36.0,63.9,4.0,9.0,44.4,3.0,4.0,0.0,0.0,3.0,100.0,14.0,2.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,12.0,1.0,3.0,8.0,2.49,7.0,0.0,0.0,1.0,1.0,0.31,1.0,0.0,0.0,0.0,0.0,4.0,4.0,3.0,1.0,0.0,2.0,0.0,2.0,1.0,3.0,0.0,141.0,1.0,37.0,55.0,52.0,10.0,141.0,80.0,14.0,4.0,5.0,89.0,4.0,5.0,0.0,6.0,9.0,1.0,0.0,0.0,0.0,19.0,2.0,2.0,50.0" -Danilo D'Ambrosio,it ITA,DF,Inter,34-154,1988,4.0,1.0,107.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,53.0,61.0,86.9,1004.0,337.0,17.0,18.0,94.4,32.0,34.0,94.1,4.0,9.0,44.4,0.0,5.0,1.0,0.0,7.0,60.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,2.0,1.68,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,3.0,1.0,0.0,4.0,2.0,2.0,2.0,6.0,0.0,79.0,12.0,44.0,29.0,6.0,1.0,79.0,46.0,2.0,1.0,1.0,47.0,0.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,50.0 -Luca D'Andrea,it ITA,FW,Sassuolo,18-157,2004,5.0,5.0,316.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.28,0.28,0.0,0.28,0.3,0.3,0.08,0.08,3.5,3.0,0.0,42.9,0.85,0.0,0.0,0.04,-0.3,-0.3,61.0,84.0,72.6,896.0,334.0,32.0,42.0,76.2,24.0,31.0,77.4,3.0,6.0,50.0,10.0,6.0,3.0,2.0,9.0,80.0,4.0,0.0,1.0,0.0,7.0,3.0,0.0,0.0,0.0,1.0,0.0,1.0,15.0,4.27,12.0,1.0,1.0,0.0,1.0,0.28,1.0,0.0,0.0,0.0,0.0,4.0,0.0,2.0,1.0,1.0,2.0,0.0,2.0,1.0,1.0,0.0,129.0,0.0,17.0,61.0,53.0,12.0,129.0,81.0,11.0,4.0,5.0,91.0,11.0,7.0,0.0,4.0,9.0,1.0,0.0,0.0,0.0,13.0,2.0,3.0,40.0 -Flavius Daniliuc,at AUT,DF,Salernitana,21-289,2001,13.0,11.0,1031.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.09,0.09,0.0,0.09,0.4,0.4,0.03,0.03,11.5,3.0,1.0,60.0,0.26,0.0,0.0,0.08,-0.4,-0.4,504.0,581.0,86.7,10115.0,3754.0,164.0,177.0,92.7,266.0,291.0,91.4,66.0,93.0,71.0,2.0,33.0,0.0,0.0,37.0,555.0,24.0,18.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,7.0,10.0,0.87,8.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,26.0,13.0,20.0,6.0,0.0,15.0,7.0,8.0,12.0,50.0,0.0,703.0,114.0,403.0,288.0,18.0,6.0,703.0,374.0,9.0,3.0,0.0,432.0,4.0,1.0,0.0,16.0,1.0,0.0,0.0,0.0,0.0,63.0,23.0,13.0,63.9 -Danilo,br BRA,DF,Juventus,31-210,1991,21.0,20.0,1813.0,2.0,2.0,0.0,0.0,3.0,0.0,0.1,0.1,0.2,0.1,0.2,1.6,1.6,0.08,0.08,20.1,2.0,0.0,13.3,0.1,0.13,1.0,0.11,0.4,0.4,1011.0,1208.0,83.7,19787.0,7295.0,351.0,402.0,87.3,521.0,569.0,91.6,128.0,201.0,63.7,12.0,99.0,8.0,3.0,84.0,1100.0,102.0,25.0,3.0,10.0,16.0,0.0,0.0,0.0,0.0,67.0,6.0,11.0,31.0,1.54,28.0,1.0,1.0,1.0,2.0,0.1,1.0,0.0,1.0,0.0,0.0,47.0,29.0,30.0,14.0,3.0,20.0,11.0,9.0,28.0,67.0,1.0,1418.0,141.0,561.0,723.0,142.0,28.0,1418.0,727.0,20.0,15.0,0.0,913.0,12.0,5.0,0.0,20.0,10.0,0.0,0.0,0.0,0.0,102.0,29.0,20.0,59.2 -Matteo Darmian,it ITA,DF,Inter,33-070,1989,16.0,12.0,993.0,1.0,2.0,0.0,0.0,2.0,0.0,0.09,0.18,0.27,0.09,0.27,0.8,0.8,0.07,0.07,11.0,1.0,0.0,33.3,0.09,0.33,1.0,0.28,0.2,0.2,450.0,543.0,82.9,7555.0,2652.0,212.0,233.0,91.0,193.0,229.0,84.3,33.0,53.0,62.3,13.0,23.0,12.0,7.0,39.0,458.0,84.0,3.0,1.0,1.0,21.0,0.0,0.0,0.0,0.0,81.0,1.0,9.0,28.0,2.54,22.0,4.0,0.0,1.0,4.0,0.36,3.0,0.0,0.0,1.0,0.0,31.0,16.0,15.0,7.0,9.0,17.0,3.0,14.0,9.0,17.0,0.0,646.0,32.0,186.0,301.0,162.0,17.0,646.0,321.0,7.0,10.0,2.0,400.0,8.0,5.0,0.0,12.0,12.0,3.0,0.0,0.0,0.0,49.0,5.0,9.0,35.7 -Paweł Dawidowicz,pl POL,DF,Hellas Verona,27-266,1995,11.0,10.0,745.0,1.0,0.0,0.0,0.0,5.0,1.0,0.12,0.0,0.12,0.12,0.12,0.9,0.9,0.11,0.11,8.3,2.0,0.0,28.6,0.24,0.14,0.5,0.13,0.1,0.1,245.0,339.0,72.3,4386.0,1815.0,109.0,127.0,85.8,105.0,137.0,76.6,25.0,62.0,40.3,1.0,22.0,1.0,0.0,22.0,308.0,31.0,9.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,22.0,0.0,3.0,5.0,0.6,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,15.0,14.0,7.0,1.0,7.0,5.0,2.0,8.0,31.0,0.0,423.0,43.0,203.0,182.0,43.0,9.0,423.0,170.0,12.0,7.0,1.0,201.0,4.0,1.0,0.0,21.0,6.0,0.0,0.0,0.0,0.0,53.0,17.0,10.0,63.0 -"Charles De Ketelaere,be BEL,""MF,FW"",Milan,21-337,2001,17.0,6.0,690.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.13,0.13,0.0,0.13,1.1,1.1,0.14,0.14,7.7,3.0,0.0,25.0,0.39,0.0,0.0,0.09,-1.1,-1.1,174.0,230.0,75.7,2506.0,848.0,100.0,129.0,77.5,58.0,70.0,82.9,9.0,13.0,69.2,16.0,18.0,13.0,0.0,34.0,219.0,11.0,1.0,2.0,2.0,7.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,32.0,4.17,29.0,0.0,0.0,1.0,2.0,0.26,2.0,0.0,0.0,0.0,0.0,10.0,4.0,3.0,6.0,1.0,7.0,1.0,6.0,3.0,1.0,0.0,317.0,3.0,28.0,141.0,154.0,40.0,317.0,178.0,28.0,17.0,7.0,206.0,16.0,15.0,0.0,8.0,8.0,1.0,0.0,0.0,0.0,39.0,13.0,16.0,44.8" -Manuel De Luca,it ITA,FW,Sampdoria,24-208,1998,1.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,7.0,71.4,66.0,1.0,3.0,4.0,75.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.73,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,0.0,0.0,6.0,4.0,2.0,10.0,10.0,0.0,0.0,0.0,11.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -Mattia De Sciglio,it ITA,DF,Juventus,30-113,1992,10.0,6.0,603.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,6.7,0.0,0.0,0.0,0.0,0.0,0.0,0.02,-0.1,-0.1,300.0,368.0,81.5,5388.0,1996.0,129.0,149.0,86.6,134.0,157.0,85.4,34.0,53.0,64.2,5.0,33.0,6.0,4.0,39.0,312.0,56.0,4.0,0.0,4.0,15.0,0.0,0.0,0.0,0.0,52.0,0.0,0.0,11.0,1.64,10.0,0.0,0.0,1.0,1.0,0.15,1.0,0.0,0.0,0.0,0.0,8.0,6.0,2.0,3.0,3.0,5.0,2.0,3.0,3.0,11.0,0.0,421.0,18.0,113.0,238.0,72.0,2.0,421.0,227.0,6.0,4.0,0.0,273.0,5.0,5.0,0.0,2.0,6.0,0.0,0.0,0.0,0.0,24.0,8.0,8.0,50.0 -Lorenzo De Silvestri,it ITA,DF,Bologna,34-263,1988,9.0,5.0,518.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.07,0.07,5.8,2.0,0.0,50.0,0.35,0.0,0.0,0.1,-0.4,-0.4,184.0,260.0,70.8,2890.0,1461.0,101.0,114.0,88.6,73.0,104.0,70.2,7.0,31.0,22.6,4.0,18.0,4.0,1.0,20.0,197.0,63.0,5.0,1.0,1.0,6.0,0.0,0.0,0.0,0.0,58.0,0.0,3.0,8.0,1.39,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,1.0,5.0,5.0,0.0,2.0,16.0,0.0,304.0,20.0,105.0,138.0,65.0,6.0,304.0,140.0,8.0,8.0,0.0,153.0,4.0,4.0,0.0,4.0,5.0,2.0,0.0,0.0,1.0,28.0,9.0,8.0,52.9 -Koni De Winter,be BEL,DF,Empoli,20-243,2002,11.0,9.0,805.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,356.0,407.0,87.5,6728.0,2357.0,130.0,140.0,92.9,199.0,214.0,93.0,27.0,49.0,55.1,2.0,14.0,1.0,0.0,19.0,389.0,18.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,4.0,0.45,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,6.0,9.0,3.0,0.0,10.0,10.0,0.0,6.0,42.0,0.0,494.0,88.0,318.0,175.0,3.0,0.0,494.0,289.0,1.0,1.0,0.0,314.0,5.0,2.0,0.0,10.0,2.0,0.0,0.0,0.0,0.0,37.0,14.0,7.0,66.7 -Grégoire Defrel,mq MTQ,FW,Sassuolo,31-238,1991,10.0,4.0,403.0,1.0,0.0,0.0,0.0,2.0,0.0,0.22,0.0,0.22,0.22,0.22,1.8,1.8,0.39,0.39,4.5,5.0,0.0,71.4,1.12,0.14,0.2,0.25,-0.8,-0.8,60.0,75.0,80.0,805.0,128.0,33.0,39.0,84.6,20.0,23.0,87.0,2.0,4.0,50.0,6.0,2.0,0.0,0.0,3.0,67.0,8.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,8.0,1.79,6.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,1.0,0.0,1.0,1.0,2.0,0.0,117.0,3.0,17.0,55.0,47.0,15.0,117.0,76.0,4.0,2.0,2.0,80.0,11.0,3.0,0.0,8.0,6.0,2.0,0.0,0.0,0.0,15.0,1.0,7.0,12.5 -Duccio Degli Innocenti,it ITA,MF,Empoli,19-288,2003,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Merih Demiral,tr TUR,DF,Atalanta,24-342,1998,17.0,12.0,1200.0,1.0,0.0,0.0,0.0,4.0,0.0,0.07,0.0,0.07,0.07,0.07,0.7,0.7,0.05,0.05,13.3,1.0,0.0,20.0,0.07,0.2,1.0,0.14,0.3,0.3,401.0,475.0,84.4,7453.0,2149.0,152.0,170.0,89.4,199.0,228.0,87.3,43.0,67.0,64.2,3.0,12.0,0.0,0.0,21.0,423.0,52.0,28.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,3.0,0.23,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,20.0,23.0,8.0,0.0,18.0,5.0,13.0,20.0,49.0,1.0,611.0,124.0,378.0,221.0,17.0,9.0,611.0,246.0,1.0,3.0,1.0,254.0,4.0,2.0,0.0,12.0,9.0,1.0,0.0,0.0,0.0,94.0,38.0,22.0,63.3 -Diego Demme,de GER,MF,Napoli,31-081,1991,3.0,0.0,22.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,47.0,47.0,100.0,747.0,174.0,23.0,23.0,100.0,15.0,15.0,100.0,7.0,7.0,100.0,0.0,5.0,1.0,0.0,7.0,47.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,49.0,0.0,0.0,40.0,9.0,0.0,49.0,30.0,0.0,1.0,0.0,46.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 -Fabio Depaoli,it ITA,DF,Hellas Verona,25-292,1997,16.0,13.0,1170.0,2.0,2.0,0.0,0.0,3.0,0.0,0.15,0.15,0.31,0.15,0.31,1.3,1.3,0.1,0.1,13.0,4.0,0.0,33.3,0.31,0.17,0.5,0.1,0.7,0.7,280.0,420.0,66.7,5215.0,3039.0,119.0,149.0,79.9,113.0,163.0,69.3,42.0,78.0,53.8,12.0,30.0,14.0,10.0,44.0,311.0,106.0,2.0,0.0,1.0,33.0,2.0,1.0,1.0,0.0,102.0,3.0,11.0,29.0,2.23,21.0,4.0,0.0,3.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,26.0,14.0,14.0,8.0,4.0,15.0,4.0,11.0,7.0,20.0,0.0,574.0,34.0,141.0,249.0,191.0,22.0,574.0,238.0,15.0,7.0,3.0,257.0,23.0,12.0,0.0,17.0,16.0,1.0,0.0,0.0,0.0,73.0,13.0,26.0,33.3 -"Fabio Depaoli,it ITA,""DF,MF"",Sampdoria,25-292,1997,3.0,1.0,136.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.12,0.12,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.2,-0.2,74.0,95.0,77.9,1373.0,584.0,31.0,36.0,86.1,31.0,36.0,86.1,11.0,21.0,52.4,4.0,6.0,4.0,4.0,10.0,75.0,20.0,1.0,1.0,0.0,8.0,0.0,0.0,0.0,0.0,19.0,0.0,1.0,6.0,3.97,5.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,2.0,3.0,0.0,1.0,0.0,1.0,1.0,4.0,0.0,117.0,6.0,28.0,56.0,34.0,3.0,117.0,73.0,2.0,3.0,0.0,70.0,2.0,1.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,6.0,0.0,2.0,0.0" -Kastriot Dermaku,al ALB,DF,Lecce,31-026,1992,1.0,0.0,34.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,50.0,27.0,0.0,0.0,0.0,0.0,1.0,2.0,50.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Cyriel Dessers,ng NGA,FW,Cremonese,28-064,1994,18.0,15.0,1204.0,3.0,0.0,0.0,1.0,2.0,0.0,0.22,0.0,0.22,0.22,0.22,5.3,4.5,0.4,0.34,13.4,8.0,0.0,21.6,0.6,0.08,0.38,0.12,-2.3,-1.5,136.0,213.0,63.8,1950.0,317.0,76.0,112.0,67.9,47.0,66.0,71.2,6.0,11.0,54.5,17.0,9.0,6.0,0.0,22.0,189.0,24.0,0.0,1.0,1.0,7.0,0.0,0.0,0.0,0.0,3.0,0.0,6.0,29.0,2.16,18.0,2.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,3.0,3.0,1.0,11.0,2.0,9.0,3.0,3.0,0.0,356.0,6.0,12.0,126.0,220.0,91.0,355.0,203.0,13.0,7.0,17.0,256.0,44.0,17.0,0.0,26.0,11.0,19.0,0.0,0.0,0.0,20.0,8.0,35.0,18.6 -Sergiño Dest,us USA,DF,Milan,22-099,2000,8.0,2.0,329.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.08,0.08,3.7,1.0,0.0,50.0,0.27,0.0,0.0,0.15,-0.3,-0.3,169.0,198.0,85.4,2535.0,793.0,95.0,102.0,93.1,65.0,73.0,89.0,5.0,12.0,41.7,4.0,15.0,3.0,1.0,14.0,154.0,44.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,43.0,0.0,5.0,11.0,3.0,8.0,2.0,0.0,0.0,1.0,0.27,1.0,0.0,0.0,0.0,0.0,12.0,6.0,3.0,5.0,4.0,1.0,0.0,1.0,4.0,2.0,0.0,246.0,7.0,75.0,116.0,61.0,3.0,246.0,133.0,11.0,3.0,0.0,137.0,3.0,5.0,0.0,6.0,4.0,0.0,0.0,1.0,0.0,29.0,3.0,1.0,75.0 -Mattia Destro,it ITA,FW,Empoli,31-327,1991,10.0,6.0,456.0,1.0,0.0,0.0,0.0,2.0,0.0,0.2,0.0,0.2,0.2,0.2,1.2,1.2,0.24,0.24,5.1,6.0,0.0,33.3,1.18,0.06,0.17,0.07,-0.2,-0.2,33.0,59.0,55.9,387.0,85.0,23.0,32.0,71.9,5.0,15.0,33.3,2.0,4.0,50.0,4.0,1.0,1.0,0.0,2.0,57.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,8.0,1.58,4.0,0.0,1.0,1.0,1.0,0.2,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,2.0,0.0,2.0,1.0,4.0,0.0,113.0,5.0,8.0,49.0,56.0,25.0,113.0,66.0,4.0,1.0,3.0,82.0,12.0,5.0,0.0,9.0,3.0,7.0,0.0,1.0,0.0,6.0,6.0,16.0,27.3 -Gerard Deulofeu,es ESP,FW,Udinese,28-334,1994,16.0,15.0,1211.0,2.0,6.0,0.0,0.0,1.0,0.0,0.15,0.45,0.59,0.15,0.59,5.1,5.1,0.38,0.38,13.5,12.0,10.0,27.3,0.89,0.05,0.17,0.11,-3.1,-3.1,443.0,588.0,75.3,6574.0,2331.0,260.0,294.0,88.4,139.0,181.0,76.8,27.0,64.0,42.2,48.0,40.0,42.0,2.0,82.0,475.0,110.0,27.0,1.0,0.0,59.0,43.0,20.0,14.0,0.0,14.0,3.0,14.0,83.0,6.16,55.0,14.0,5.0,3.0,9.0,0.67,7.0,2.0,0.0,0.0,0.0,8.0,5.0,5.0,1.0,2.0,8.0,1.0,7.0,2.0,1.0,0.0,725.0,5.0,44.0,295.0,400.0,82.0,725.0,455.0,72.0,39.0,33.0,506.0,22.0,18.0,0.0,8.0,11.0,8.0,0.0,0.0,0.0,44.0,1.0,4.0,20.0 -"Samuel Di Carmine,it ITA,""FW,MF"",Cremonese,34-134,1988,2.0,0.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.28,0.28,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.1,-0.1,6.0,8.0,75.0,79.0,30.0,4.0,5.0,80.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,1.0,0.0,0.0,1.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,4.0,5.0,2.0,9.0,6.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0" -"Federico Di Francesco,it ITA,""FW,MF"",Lecce,28-241,1994,21.0,12.0,1042.0,1.0,2.0,0.0,0.0,3.0,0.0,0.09,0.17,0.26,0.09,0.26,1.8,1.8,0.15,0.15,11.6,10.0,1.0,45.5,0.86,0.05,0.1,0.08,-0.8,-0.8,203.0,296.0,68.6,2805.0,829.0,121.0,141.0,85.8,60.0,91.0,65.9,9.0,30.0,30.0,15.0,10.0,11.0,1.0,28.0,264.0,30.0,1.0,4.0,2.0,22.0,22.0,0.0,2.0,0.0,7.0,2.0,10.0,27.0,2.33,16.0,4.0,1.0,3.0,5.0,0.43,2.0,1.0,1.0,0.0,0.0,15.0,10.0,3.0,6.0,6.0,11.0,0.0,11.0,6.0,9.0,0.0,425.0,11.0,52.0,163.0,216.0,44.0,425.0,263.0,30.0,14.0,13.0,290.0,28.0,17.0,0.0,23.0,16.0,4.0,1.0,0.0,0.0,45.0,4.0,8.0,33.3" -Michele Di Gregorio,it ITA,GK,Monza,25-198,1997,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,631.0,831.0,75.9,16298.0,10836.0,156.0,156.0,100.0,270.0,275.0,98.2,202.0,397.0,50.9,0.0,10.0,0.0,0.0,0.0,648.0,183.0,59.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,8.0,1.0,878.0,713.0,874.0,4.0,0.0,0.0,878.0,529.0,0.0,0.0,0.0,463.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,2.0,1.0,66.7 -Giovanni Di Lorenzo,it ITA,DF,Napoli,29-190,1993,21.0,21.0,1878.0,1.0,3.0,0.0,0.0,2.0,0.0,0.05,0.14,0.19,0.05,0.19,1.3,1.3,0.06,0.06,20.9,4.0,0.0,25.0,0.19,0.06,0.25,0.08,-0.3,-0.3,1318.0,1532.0,86.0,21485.0,8027.0,676.0,726.0,93.1,518.0,589.0,87.9,97.0,143.0,67.8,29.0,103.0,37.0,8.0,155.0,1299.0,229.0,45.0,1.0,13.0,54.0,1.0,0.0,0.0,0.0,182.0,4.0,23.0,72.0,3.45,63.0,4.0,2.0,2.0,12.0,0.58,12.0,0.0,0.0,0.0,0.0,36.0,18.0,19.0,13.0,4.0,19.0,4.0,15.0,16.0,35.0,0.0,1683.0,59.0,433.0,833.0,433.0,56.0,1683.0,1044.0,39.0,34.0,2.0,1186.0,12.0,13.0,0.0,16.0,20.0,4.0,0.0,0.0,0.0,102.0,24.0,21.0,53.3 -Ángel Di María,ar ARG,FW,Juventus,34-361,1988,12.0,8.0,608.0,3.0,2.0,1.0,1.0,0.0,1.0,0.44,0.3,0.74,0.3,0.59,1.8,1.0,0.27,0.15,6.8,6.0,0.0,35.3,0.89,0.12,0.33,0.06,1.2,1.0,213.0,284.0,75.0,4001.0,1508.0,100.0,112.0,89.3,71.0,91.0,78.0,36.0,59.0,61.0,21.0,25.0,17.0,6.0,37.0,237.0,45.0,14.0,5.0,5.0,43.0,20.0,7.0,11.0,0.0,8.0,2.0,8.0,39.0,5.77,21.0,11.0,3.0,0.0,4.0,0.59,3.0,1.0,0.0,0.0,0.0,7.0,4.0,2.0,4.0,1.0,7.0,0.0,7.0,2.0,0.0,0.0,367.0,1.0,29.0,156.0,188.0,17.0,366.0,223.0,16.0,15.0,4.0,272.0,18.0,18.0,0.0,4.0,7.0,3.0,0.0,0.0,0.0,25.0,1.0,0.0,100.0 -Boulaye Dia,sn SEN,FW,Salernitana,26-086,1996,20.0,16.0,1465.0,8.0,4.0,0.0,0.0,1.0,0.0,0.49,0.25,0.74,0.49,0.74,5.0,5.0,0.31,0.31,16.3,13.0,0.0,52.0,0.8,0.32,0.62,0.2,3.0,3.0,262.0,345.0,75.9,3897.0,800.0,136.0,167.0,81.4,89.0,107.0,83.2,16.0,24.0,66.7,22.0,14.0,13.0,2.0,31.0,339.0,4.0,0.0,2.0,3.0,6.0,0.0,0.0,0.0,0.0,1.0,2.0,18.0,40.0,2.46,33.0,0.0,4.0,1.0,5.0,0.31,5.0,0.0,0.0,0.0,0.0,8.0,3.0,2.0,4.0,2.0,3.0,0.0,3.0,4.0,12.0,1.0,497.0,12.0,64.0,216.0,225.0,46.0,497.0,358.0,24.0,20.0,8.0,391.0,42.0,37.0,0.0,14.0,22.0,8.0,0.0,0.0,0.0,56.0,11.0,26.0,29.7 -Brahim Díaz,es ESP,MF,Milan,23-191,1999,18.0,14.0,958.0,4.0,3.0,0.0,0.0,2.0,0.0,0.38,0.28,0.66,0.38,0.66,2.6,2.6,0.25,0.25,10.6,8.0,1.0,38.1,0.75,0.19,0.5,0.13,1.4,1.4,284.0,365.0,77.8,4221.0,1320.0,147.0,171.0,86.0,106.0,127.0,83.5,15.0,30.0,50.0,21.0,45.0,21.0,1.0,66.0,341.0,23.0,4.0,6.0,0.0,12.0,5.0,0.0,4.0,0.0,5.0,1.0,12.0,44.0,4.13,31.0,1.0,4.0,2.0,8.0,0.75,3.0,0.0,2.0,0.0,1.0,15.0,8.0,2.0,10.0,3.0,9.0,0.0,9.0,5.0,4.0,0.0,494.0,3.0,33.0,277.0,198.0,33.0,494.0,326.0,45.0,27.0,9.0,359.0,30.0,26.0,0.0,16.0,19.0,2.0,0.0,0.0,0.0,56.0,2.0,9.0,18.2 -Federico Dimarco,it ITA,DF,Inter,25-092,1997,21.0,19.0,1467.0,3.0,1.0,0.0,0.0,0.0,0.0,0.18,0.06,0.25,0.18,0.25,2.0,2.0,0.13,0.13,16.3,9.0,5.0,29.0,0.55,0.1,0.33,0.07,1.0,1.0,647.0,896.0,72.2,12549.0,4360.0,275.0,321.0,85.7,255.0,323.0,78.9,100.0,205.0,48.8,37.0,41.0,24.0,17.0,57.0,736.0,158.0,31.0,0.0,20.0,146.0,44.0,10.0,28.0,0.0,78.0,2.0,12.0,66.0,4.04,40.0,22.0,1.0,1.0,4.0,0.25,3.0,0.0,0.0,0.0,0.0,21.0,16.0,5.0,11.0,5.0,13.0,4.0,9.0,13.0,19.0,0.0,1037.0,35.0,232.0,400.0,413.0,45.0,1037.0,556.0,32.0,20.0,3.0,671.0,15.0,8.0,0.0,10.0,20.0,3.0,0.0,1.0,0.0,83.0,3.0,9.0,25.0 -Koffi Djidji,ci CIV,DF,Torino,30-072,1992,18.0,16.0,1442.0,1.0,0.0,0.0,0.0,2.0,0.0,0.06,0.0,0.06,0.06,0.06,0.9,0.9,0.06,0.06,16.0,4.0,0.0,50.0,0.25,0.13,0.25,0.12,0.1,0.1,719.0,839.0,85.7,13056.0,3697.0,288.0,310.0,92.9,354.0,391.0,90.5,66.0,107.0,61.7,2.0,57.0,3.0,2.0,55.0,816.0,20.0,8.0,0.0,9.0,10.0,0.0,0.0,0.0,0.0,11.0,3.0,12.0,9.0,0.56,7.0,0.0,0.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,28.0,13.0,19.0,8.0,1.0,17.0,9.0,8.0,25.0,44.0,0.0,989.0,78.0,436.0,489.0,70.0,7.0,989.0,520.0,6.0,7.0,0.0,639.0,4.0,4.0,0.0,19.0,2.0,1.0,0.0,2.0,0.0,80.0,27.0,27.0,50.0 -Berat Djimsiti,al ALB,DF,Atalanta,29-356,1993,9.0,5.0,533.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,5.9,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.1,-0.1,319.0,356.0,89.6,5322.0,1675.0,154.0,168.0,91.7,144.0,150.0,96.0,17.0,31.0,54.8,1.0,17.0,0.0,0.0,14.0,344.0,11.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,2.0,0.34,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,7.0,10.0,4.0,0.0,6.0,3.0,3.0,9.0,20.0,0.0,415.0,43.0,211.0,181.0,24.0,4.0,415.0,260.0,2.0,2.0,0.0,281.0,1.0,4.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,28.0,12.0,8.0,60.0 -Dodô,br BRA,DF,Fiorentina,24-085,1998,17.0,15.0,1191.0,0.0,1.0,0.0,0.0,5.0,1.0,0.0,0.08,0.08,0.0,0.08,0.2,0.2,0.02,0.02,13.2,1.0,0.0,25.0,0.08,0.0,0.0,0.05,-0.2,-0.2,643.0,769.0,83.6,11302.0,3186.0,288.0,314.0,91.7,288.0,341.0,84.5,60.0,90.0,66.7,10.0,39.0,12.0,2.0,75.0,666.0,102.0,13.0,0.0,1.0,22.0,0.0,0.0,0.0,0.0,89.0,1.0,12.0,30.0,2.27,27.0,1.0,0.0,1.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,18.0,8.0,8.0,7.0,3.0,11.0,0.0,11.0,11.0,13.0,0.0,857.0,23.0,214.0,404.0,243.0,10.0,857.0,530.0,26.0,29.0,3.0,573.0,17.0,7.0,1.0,22.0,16.0,0.0,0.0,1.0,0.0,88.0,10.0,5.0,66.7 -Josh Doig,sct SCO,DF,Hellas Verona,20-268,2002,14.0,10.0,937.0,2.0,3.0,0.0,0.0,0.0,0.0,0.19,0.29,0.48,0.19,0.48,1.7,1.7,0.17,0.17,10.4,6.0,0.0,40.0,0.58,0.13,0.33,0.12,0.3,0.3,234.0,355.0,65.9,4027.0,2001.0,120.0,148.0,81.1,83.0,123.0,67.5,26.0,55.0,47.3,11.0,22.0,11.0,7.0,17.0,277.0,75.0,0.0,0.0,0.0,41.0,1.0,1.0,0.0,0.0,74.0,3.0,16.0,27.0,2.59,19.0,2.0,1.0,2.0,6.0,0.58,4.0,0.0,0.0,1.0,0.0,15.0,4.0,3.0,5.0,7.0,11.0,3.0,8.0,9.0,6.0,0.0,469.0,12.0,116.0,162.0,202.0,23.0,469.0,219.0,36.0,20.0,4.0,244.0,22.0,12.0,0.0,8.0,6.0,2.0,0.0,0.0,0.0,70.0,11.0,11.0,50.0 -Nicolás Domínguez,ar ARG,MF,Bologna,24-227,1998,18.0,15.0,1336.0,1.0,2.0,0.0,0.0,6.0,0.0,0.07,0.13,0.2,0.07,0.2,1.1,1.1,0.07,0.07,14.8,6.0,0.0,28.6,0.4,0.05,0.17,0.05,-0.1,-0.1,563.0,721.0,78.1,8832.0,2597.0,299.0,350.0,85.4,184.0,228.0,80.7,49.0,80.0,61.3,22.0,60.0,14.0,0.0,80.0,678.0,38.0,22.0,13.0,9.0,12.0,0.0,0.0,0.0,0.0,13.0,5.0,18.0,42.0,2.83,35.0,2.0,0.0,2.0,4.0,0.27,4.0,0.0,0.0,0.0,0.0,51.0,31.0,25.0,19.0,7.0,17.0,2.0,15.0,9.0,10.0,1.0,886.0,21.0,171.0,488.0,236.0,20.0,886.0,440.0,23.0,20.0,3.0,574.0,36.0,12.0,0.0,32.0,20.0,0.0,0.0,1.0,0.0,91.0,18.0,18.0,50.0 -Giulio Donati,it ITA,DF,Monza,33-005,1990,4.0,2.0,212.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.4,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,137.0,161.0,85.1,2784.0,1026.0,40.0,45.0,88.9,80.0,86.0,93.0,16.0,26.0,61.5,0.0,10.0,0.0,0.0,14.0,143.0,17.0,4.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,13.0,1.0,2.0,1.0,0.42,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,2.0,0.0,2.0,2.0,1.0,1.0,4.0,2.0,0.0,178.0,15.0,91.0,65.0,25.0,0.0,178.0,111.0,2.0,4.0,0.0,126.0,1.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,13.0,2.0,1.0,66.7 -Bartłomiej Drągowski,pl POL,GK,Spezia,25-175,1997,19.0,19.0,1662.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,408.0,613.0,66.6,11086.0,7727.0,106.0,107.0,99.1,169.0,171.0,98.8,128.0,328.0,39.0,0.0,9.0,1.0,0.0,0.0,455.0,157.0,48.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,3.0,0.16,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,2.0,671.0,548.0,668.0,3.0,0.0,0.0,671.0,394.0,0.0,0.0,0.0,313.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,32.0,6.0,1.0,85.7 -Ondrej Duda,sk SVK,MF,Hellas Verona,28-067,1994,2.0,1.0,123.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.03,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,35.0,55.0,63.6,713.0,139.0,10.0,15.0,66.7,16.0,21.0,76.2,7.0,12.0,58.3,1.0,6.0,1.0,0.0,7.0,52.0,3.0,2.0,1.0,1.0,3.0,1.0,0.0,1.0,0.0,0.0,0.0,4.0,3.0,2.2,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,2.0,1.0,1.0,0.0,1.0,4.0,2.0,0.0,72.0,3.0,11.0,40.0,22.0,1.0,72.0,31.0,1.0,1.0,0.0,29.0,2.0,3.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,14.0,2.0,1.0,66.7 -Denzel Dumfries,nl NED,DF,Inter,26-298,1996,18.0,14.0,1195.0,1.0,2.0,0.0,0.0,3.0,0.0,0.08,0.15,0.23,0.08,0.23,2.5,2.5,0.19,0.19,13.3,9.0,0.0,45.0,0.68,0.05,0.11,0.12,-1.5,-1.5,358.0,490.0,73.1,5925.0,1988.0,171.0,199.0,85.9,149.0,203.0,73.4,29.0,59.0,49.2,19.0,9.0,16.0,7.0,33.0,405.0,84.0,2.0,0.0,1.0,43.0,0.0,0.0,0.0,0.0,82.0,1.0,11.0,45.0,3.39,28.0,3.0,7.0,5.0,6.0,0.45,4.0,0.0,1.0,1.0,0.0,12.0,6.0,4.0,6.0,2.0,17.0,4.0,13.0,10.0,18.0,0.0,632.0,25.0,132.0,264.0,248.0,54.0,632.0,332.0,33.0,21.0,12.0,387.0,21.0,10.0,0.0,13.0,22.0,2.0,0.0,0.0,1.0,50.0,23.0,16.0,59.0 -Alfred Duncan,gh GHA,MF,Fiorentina,29-337,1993,13.0,6.0,537.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.2,-0.2,217.0,284.0,76.4,3924.0,1371.0,87.0,103.0,84.5,97.0,115.0,84.3,24.0,46.0,52.2,3.0,31.0,8.0,4.0,39.0,274.0,9.0,7.0,2.0,1.0,11.0,0.0,0.0,0.0,0.0,2.0,1.0,6.0,17.0,2.83,16.0,0.0,1.0,0.0,2.0,0.33,2.0,0.0,0.0,0.0,0.0,12.0,7.0,0.0,12.0,0.0,4.0,0.0,4.0,3.0,4.0,0.0,335.0,5.0,48.0,206.0,84.0,3.0,335.0,194.0,8.0,10.0,1.0,223.0,7.0,10.0,0.0,11.0,3.0,0.0,0.0,0.0,0.0,34.0,8.0,3.0,72.7 -"Paulo Dybala,ar ARG,""MF,FW"",Roma,29-087,1993,15.0,14.0,1089.0,7.0,6.0,1.0,1.0,3.0,0.0,0.58,0.5,1.07,0.5,0.99,5.0,4.2,0.41,0.34,12.1,13.0,2.0,37.1,1.07,0.17,0.46,0.12,2.0,1.8,398.0,504.0,79.0,7132.0,1853.0,207.0,231.0,89.6,115.0,144.0,79.9,61.0,90.0,67.8,36.0,30.0,14.0,0.0,44.0,455.0,47.0,13.0,1.0,16.0,44.0,29.0,6.0,22.0,0.0,4.0,2.0,14.0,63.0,5.21,33.0,18.0,6.0,5.0,7.0,0.58,3.0,2.0,1.0,1.0,0.0,8.0,1.0,1.0,4.0,3.0,9.0,0.0,9.0,6.0,3.0,0.0,655.0,1.0,60.0,328.0,275.0,47.0,654.0,417.0,34.0,28.0,11.0,480.0,38.0,19.0,0.0,7.0,30.0,2.0,2.0,0.0,0.0,52.0,2.0,8.0,20.0" -Edin Džeko,ba BIH,FW,Inter,36-330,1986,21.0,15.0,1298.0,7.0,3.0,0.0,0.0,3.0,0.0,0.49,0.21,0.69,0.49,0.69,7.5,7.5,0.52,0.52,14.4,18.0,0.0,34.6,1.25,0.13,0.39,0.14,-0.5,-0.5,261.0,366.0,71.3,4141.0,1068.0,134.0,175.0,76.6,93.0,123.0,75.6,21.0,38.0,55.3,16.0,29.0,16.0,1.0,50.0,350.0,14.0,0.0,4.0,5.0,14.0,0.0,0.0,0.0,0.0,1.0,2.0,10.0,47.0,3.25,38.0,0.0,9.0,0.0,10.0,0.69,7.0,0.0,3.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,12.0,0.0,12.0,2.0,20.0,0.0,538.0,23.0,56.0,253.0,236.0,76.0,538.0,279.0,21.0,8.0,8.0,415.0,39.0,12.0,0.0,21.0,19.0,8.0,0.0,0.0,0.0,47.0,30.0,25.0,54.5 -Festy Ebosele,ie IRL,DF,Udinese,20-192,2002,5.0,0.0,45.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.22,0.22,0.5,1.0,0.0,50.0,2.0,0.0,0.0,0.05,-0.1,-0.1,21.0,28.0,75.0,373.0,237.0,9.0,11.0,81.8,11.0,14.0,78.6,1.0,2.0,50.0,0.0,4.0,4.0,1.0,8.0,25.0,3.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,2.0,4.09,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,0.0,1.0,0.0,1.0,0.0,3.0,0.0,44.0,2.0,9.0,18.0,18.0,3.0,44.0,25.0,6.0,4.0,0.0,25.0,4.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,0.0,100.0 -Enzo Ebosse,cm CMR,DF,Udinese,23-336,1999,18.0,8.0,908.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,10.1,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,443.0,545.0,81.3,8612.0,3132.0,175.0,197.0,88.8,198.0,234.0,84.6,64.0,100.0,64.0,1.0,42.0,4.0,1.0,51.0,467.0,77.0,17.0,0.0,11.0,15.0,1.0,0.0,0.0,0.0,59.0,1.0,3.0,8.0,0.8,7.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,8.0,11.0,3.0,0.0,25.0,5.0,20.0,10.0,29.0,0.0,649.0,49.0,284.0,296.0,72.0,0.0,649.0,328.0,11.0,7.0,1.0,372.0,7.0,3.0,0.0,9.0,4.0,1.0,0.0,0.0,0.0,48.0,15.0,14.0,51.7 -Tyronne Ebuehi,ng NGA,DF,Empoli,27-056,1995,12.0,8.0,764.0,1.0,1.0,0.0,0.0,0.0,0.0,0.12,0.12,0.24,0.12,0.24,1.5,1.5,0.17,0.17,8.5,2.0,0.0,25.0,0.24,0.13,0.5,0.18,-0.5,-0.5,288.0,371.0,77.6,4758.0,1709.0,135.0,156.0,86.5,122.0,152.0,80.3,22.0,40.0,55.0,3.0,13.0,4.0,3.0,12.0,291.0,78.0,4.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,74.0,2.0,13.0,10.0,1.18,9.0,1.0,0.0,0.0,1.0,0.12,1.0,0.0,0.0,0.0,0.0,10.0,5.0,2.0,5.0,3.0,11.0,7.0,4.0,4.0,30.0,1.0,447.0,46.0,180.0,193.0,80.0,10.0,447.0,221.0,10.0,9.0,2.0,247.0,5.0,5.0,0.0,4.0,9.0,1.0,0.0,0.0,0.0,38.0,4.0,13.0,23.5 -"Éderson,br BRA,""MF,FW"",Atalanta,23-218,1999,18.0,11.0,990.0,1.0,0.0,0.0,0.0,2.0,0.0,0.09,0.0,0.09,0.09,0.09,1.2,1.2,0.11,0.11,11.0,4.0,0.0,22.2,0.36,0.06,0.25,0.07,-0.2,-0.2,358.0,450.0,79.6,6131.0,1710.0,169.0,196.0,86.2,136.0,162.0,84.0,38.0,53.0,71.7,11.0,25.0,10.0,3.0,34.0,433.0,16.0,9.0,0.0,6.0,11.0,3.0,1.0,0.0,0.0,1.0,1.0,12.0,25.0,2.27,17.0,1.0,5.0,1.0,2.0,0.18,1.0,0.0,0.0,1.0,0.0,37.0,20.0,7.0,24.0,6.0,20.0,1.0,19.0,14.0,8.0,0.0,623.0,22.0,143.0,310.0,178.0,32.0,623.0,353.0,14.0,14.0,8.0,408.0,29.0,14.0,0.0,14.0,21.0,0.0,0.0,1.0,0.0,72.0,6.0,13.0,31.6" -Kingsley Ehizibue,nl NED,DF,Udinese,27-261,1995,16.0,5.0,622.0,1.0,0.0,0.0,0.0,3.0,0.0,0.14,0.0,0.14,0.14,0.14,0.6,0.6,0.09,0.09,6.9,1.0,0.0,20.0,0.14,0.2,1.0,0.13,0.4,0.4,201.0,284.0,70.8,3420.0,1419.0,90.0,116.0,77.6,87.0,115.0,75.7,20.0,36.0,55.6,4.0,19.0,7.0,4.0,23.0,246.0,38.0,1.0,0.0,1.0,18.0,0.0,0.0,0.0,0.0,37.0,0.0,14.0,14.0,2.03,9.0,2.0,2.0,1.0,2.0,0.29,1.0,0.0,0.0,1.0,0.0,16.0,12.0,8.0,7.0,1.0,5.0,1.0,4.0,3.0,19.0,0.0,356.0,22.0,124.0,121.0,115.0,12.0,356.0,176.0,14.0,7.0,1.0,179.0,6.0,5.0,0.0,16.0,8.0,1.0,0.0,0.0,0.0,38.0,9.0,6.0,60.0 -Albin Ekdal,se SWE,MF,Spezia,33-197,1989,14.0,5.0,547.0,0.0,1.0,0.0,0.0,3.0,1.0,0.0,0.16,0.16,0.0,0.16,0.6,0.6,0.11,0.11,6.1,1.0,0.0,16.7,0.16,0.0,0.0,0.11,-0.6,-0.6,160.0,226.0,70.8,2374.0,935.0,90.0,115.0,78.3,55.0,75.0,73.3,7.0,16.0,43.8,3.0,17.0,6.0,0.0,27.0,219.0,5.0,4.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,6.0,18.0,2.97,15.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,8.0,5.0,6.0,0.0,6.0,0.0,6.0,5.0,10.0,0.0,295.0,12.0,68.0,170.0,61.0,9.0,295.0,157.0,4.0,6.0,0.0,157.0,15.0,5.0,1.0,12.0,12.0,0.0,0.0,0.0,0.0,43.0,7.0,7.0,50.0 -"Emmanuel Ekong,se SWE,""FW,MF"",Empoli,20-230,2002,2.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,3.0,4.0,75.0,54.0,0.0,2.0,3.0,66.7,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,4.0,2.0,0.0,6.0,3.0,1.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0" -"Mikael Ellertsson,is ISL,""MF,DF"",Spezia,20-336,2002,11.0,1.0,171.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.1,-0.1,24.0,40.0,60.0,322.0,82.0,16.0,18.0,88.9,7.0,14.0,50.0,0.0,5.0,0.0,1.0,3.0,0.0,0.0,3.0,39.0,1.0,0.0,0.0,0.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,5.0,2.65,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,3.0,0.0,2.0,1.0,1.0,2.0,0.0,0.0,60.0,3.0,11.0,31.0,19.0,0.0,60.0,21.0,3.0,2.0,0.0,25.0,2.0,2.0,0.0,6.0,3.0,0.0,0.0,0.0,0.0,7.0,2.0,2.0,50.0" -"Elif Elmas,mk MKD,""FW,MF"",Napoli,23-139,1999,20.0,6.0,824.0,5.0,1.0,1.0,1.0,1.0,0.0,0.55,0.11,0.66,0.44,0.55,3.1,2.3,0.34,0.25,9.2,7.0,0.0,43.8,0.76,0.25,0.57,0.15,1.9,1.7,354.0,428.0,82.7,4644.0,1086.0,232.0,258.0,89.9,92.0,117.0,78.6,12.0,20.0,60.0,10.0,21.0,10.0,2.0,27.0,409.0,16.0,4.0,1.0,0.0,7.0,7.0,2.0,1.0,0.0,3.0,3.0,3.0,26.0,2.84,20.0,0.0,2.0,1.0,5.0,0.55,3.0,0.0,1.0,0.0,0.0,11.0,5.0,5.0,3.0,3.0,5.0,0.0,5.0,9.0,3.0,0.0,522.0,5.0,55.0,238.0,234.0,31.0,521.0,331.0,17.0,16.0,8.0,402.0,17.0,12.0,0.0,11.0,6.0,2.0,0.0,0.0,0.0,35.0,1.0,8.0,11.1" -Martin Erlic,hr CRO,DF,Sassuolo,25-017,1998,14.0,14.0,1221.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,13.6,0.0,0.0,0.0,0.0,0.0,0.0,0.14,-0.1,-0.1,558.0,640.0,87.2,12100.0,3873.0,149.0,162.0,92.0,304.0,328.0,92.7,101.0,140.0,72.1,0.0,25.0,0.0,0.0,31.0,611.0,28.0,26.0,0.0,11.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,5.0,7.0,0.52,5.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,8.0,8.0,4.0,1.0,23.0,13.0,10.0,17.0,59.0,0.0,776.0,96.0,433.0,341.0,5.0,1.0,776.0,453.0,3.0,1.0,1.0,484.0,6.0,1.0,0.0,12.0,8.0,0.0,0.0,0.0,0.0,63.0,19.0,18.0,51.4 -Gonzalo Escalante,ar ARG,MF,Cremonese,29-320,1993,9.0,7.0,555.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.16,0.16,0.0,0.16,0.2,0.2,0.03,0.03,6.2,1.0,0.0,16.7,0.16,0.0,0.0,0.03,-0.2,-0.2,222.0,290.0,76.6,3755.0,1430.0,99.0,117.0,84.6,103.0,122.0,84.4,16.0,36.0,44.4,4.0,30.0,2.0,0.0,30.0,279.0,8.0,4.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,8.0,10.0,1.62,8.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,8.0,8.0,8.0,0.0,7.0,1.0,6.0,10.0,4.0,0.0,352.0,16.0,103.0,199.0,56.0,7.0,352.0,174.0,3.0,4.0,0.0,226.0,7.0,7.0,0.0,6.0,5.0,0.0,0.0,0.0,0.0,39.0,9.0,9.0,50.0 -Salvatore Esposito,it ITA,MF,Spezia,22-126,2000,3.0,1.0,142.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,1.6,0.0,1.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,89.0,116.0,76.7,1351.0,437.0,54.0,62.0,87.1,31.0,39.0,79.5,2.0,10.0,20.0,0.0,9.0,0.0,0.0,9.0,106.0,10.0,7.0,0.0,1.0,5.0,3.0,0.0,3.0,0.0,0.0,0.0,1.0,3.0,1.9,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,2.0,0.0,2.0,0.0,3.0,0.0,127.0,3.0,24.0,78.0,26.0,0.0,127.0,65.0,0.0,0.0,0.0,85.0,3.0,1.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,13.0,2.0,0.0,100.0 -Nicolò Fagioli,it ITA,MF,Juventus,21-363,2001,12.0,6.0,567.0,2.0,2.0,0.0,0.0,0.0,0.0,0.32,0.32,0.63,0.32,0.63,0.4,0.4,0.06,0.06,6.3,4.0,0.0,66.7,0.63,0.33,0.5,0.06,1.6,1.6,216.0,259.0,83.4,3778.0,773.0,104.0,116.0,89.7,82.0,91.0,90.1,25.0,42.0,59.5,11.0,17.0,8.0,2.0,25.0,243.0,16.0,6.0,0.0,7.0,13.0,6.0,1.0,3.0,0.0,4.0,0.0,4.0,18.0,2.86,12.0,2.0,2.0,2.0,3.0,0.48,2.0,0.0,0.0,1.0,0.0,10.0,4.0,3.0,5.0,2.0,6.0,2.0,4.0,6.0,2.0,0.0,320.0,8.0,50.0,188.0,86.0,7.0,320.0,190.0,11.0,6.0,1.0,221.0,14.0,11.0,0.0,6.0,11.0,1.0,1.0,0.0,0.0,46.0,0.0,2.0,0.0 -Wladimiro Falcone,it ITA,GK,Lecce,27-304,1995,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,357.0,673.0,53.0,11512.0,8844.0,53.0,53.0,100.0,142.0,148.0,95.9,159.0,465.0,34.2,1.0,7.0,1.0,0.0,0.0,439.0,230.0,67.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,2.0,0.1,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,14.0,1.0,740.0,619.0,738.0,2.0,0.0,0.0,740.0,367.0,0.0,0.0,0.0,253.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,22.0,10.0,0.0,100.0 -Davide Faraoni,it ITA,DF,Hellas Verona,31-108,1991,8.0,7.0,478.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.38,0.38,0.0,0.38,1.2,1.2,0.23,0.23,5.3,3.0,0.0,30.0,0.56,0.0,0.0,0.12,-1.2,-1.2,130.0,199.0,65.3,2383.0,1103.0,55.0,71.0,77.5,55.0,75.0,73.3,16.0,36.0,44.4,3.0,11.0,5.0,2.0,21.0,157.0,39.0,0.0,1.0,6.0,14.0,0.0,0.0,0.0,0.0,39.0,3.0,3.0,9.0,1.69,4.0,0.0,3.0,0.0,3.0,0.56,1.0,0.0,1.0,0.0,0.0,8.0,5.0,2.0,5.0,1.0,9.0,4.0,5.0,6.0,7.0,0.0,249.0,16.0,61.0,106.0,83.0,12.0,249.0,101.0,3.0,3.0,1.0,118.0,3.0,2.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,23.0,3.0,3.0,50.0 -Federico Fazio,ar ARG,DF,Salernitana,35-330,1987,12.0,12.0,1059.0,1.0,0.0,0.0,0.0,2.0,1.0,0.08,0.0,0.08,0.08,0.08,0.7,0.7,0.06,0.06,11.8,2.0,0.0,18.2,0.17,0.09,0.5,0.06,0.3,0.3,408.0,528.0,77.3,7706.0,3184.0,139.0,168.0,82.7,212.0,246.0,86.2,47.0,92.0,51.1,3.0,24.0,3.0,0.0,40.0,495.0,33.0,18.0,0.0,8.0,4.0,0.0,0.0,0.0,0.0,15.0,0.0,6.0,12.0,1.02,7.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,12.0,8.0,7.0,0.0,9.0,4.0,5.0,28.0,61.0,0.0,669.0,82.0,312.0,307.0,56.0,15.0,669.0,345.0,13.0,11.0,1.0,356.0,11.0,5.0,0.0,11.0,6.0,0.0,0.0,3.0,0.0,73.0,36.0,13.0,73.5 -Jacopo Fazzini,it ITA,MF,Empoli,19-331,2003,11.0,2.0,245.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,76.0,92.0,82.6,1004.0,250.0,50.0,56.0,89.3,21.0,26.0,80.8,2.0,6.0,33.3,2.0,3.0,1.0,1.0,4.0,88.0,4.0,2.0,0.0,0.0,3.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,5.0,1.84,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,8.0,13.0,2.0,0.0,6.0,2.0,4.0,4.0,8.0,0.0,155.0,9.0,65.0,65.0,27.0,3.0,155.0,79.0,1.0,3.0,0.0,82.0,6.0,10.0,0.0,10.0,4.0,0.0,0.0,0.0,0.0,16.0,5.0,11.0,31.3 -Lewis Ferguson,sct SCO,MF,Bologna,23-170,1999,15.0,12.0,1007.0,3.0,0.0,0.0,0.0,3.0,0.0,0.27,0.0,0.27,0.27,0.27,2.4,2.4,0.21,0.21,11.2,9.0,0.0,47.4,0.8,0.16,0.33,0.13,0.6,0.6,371.0,454.0,81.7,5210.0,991.0,221.0,246.0,89.8,114.0,136.0,83.8,16.0,29.0,55.2,7.0,25.0,1.0,0.0,26.0,443.0,9.0,4.0,2.0,0.0,6.0,0.0,0.0,0.0,0.0,5.0,2.0,11.0,26.0,2.32,16.0,1.0,6.0,2.0,3.0,0.27,2.0,0.0,1.0,0.0,0.0,22.0,15.0,9.0,10.0,3.0,10.0,4.0,6.0,11.0,13.0,0.0,580.0,21.0,113.0,309.0,162.0,31.0,580.0,290.0,11.0,14.0,4.0,419.0,21.0,7.0,0.0,21.0,22.0,1.0,0.0,0.0,0.0,57.0,7.0,16.0,30.4 -Alex Ferrari,it ITA,DF,Sampdoria,28-224,1994,12.0,10.0,868.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.02,0.02,9.6,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.2,-0.2,384.0,456.0,84.2,7605.0,2387.0,114.0,126.0,90.5,220.0,242.0,90.9,41.0,67.0,61.2,3.0,17.0,1.0,0.0,25.0,433.0,23.0,21.0,0.0,3.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,8.0,4.0,0.41,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,5.0,7.0,2.0,0.0,21.0,10.0,11.0,19.0,25.0,0.0,560.0,51.0,276.0,265.0,25.0,5.0,560.0,304.0,7.0,1.0,0.0,308.0,10.0,3.0,0.0,12.0,7.0,0.0,0.0,1.0,0.0,62.0,21.0,15.0,58.3 -Alex Ferrari,it ITA,DF,Cremonese,28-224,1994,5.0,5.0,373.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,156.0,202.0,77.2,3141.0,1042.0,55.0,66.0,83.3,79.0,91.0,86.8,21.0,40.0,52.5,2.0,14.0,3.0,0.0,11.0,191.0,11.0,7.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,2.0,4.0,0.97,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,9.0,16.0,0.0,0.0,3.0,1.0,2.0,12.0,17.0,1.0,255.0,22.0,132.0,121.0,5.0,0.0,255.0,134.0,1.0,1.0,0.0,130.0,1.0,0.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,30.0,4.0,2.0,66.7 -Salvador Ferrer,es ESP,DF,Spezia,25-020,1998,2.0,0.0,92.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,53.0,65.0,81.5,994.0,301.0,21.0,22.0,95.5,23.0,30.0,76.7,8.0,10.0,80.0,1.0,1.0,0.0,0.0,1.0,58.0,7.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,1.0,0.98,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,2.0,0.0,1.0,1.0,0.0,1.0,2.0,0.0,72.0,8.0,27.0,36.0,9.0,0.0,72.0,42.0,0.0,0.0,0.0,53.0,1.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,3.0,2.0,3.0,40.0 -Alessandro Florenzi,it ITA,DF,Milan,31-336,1991,2.0,1.0,97.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,62.0,79.0,78.5,1115.0,324.0,26.0,28.0,92.9,32.0,36.0,88.9,4.0,14.0,28.6,1.0,4.0,1.0,0.0,7.0,58.0,20.0,3.0,0.0,0.0,5.0,3.0,0.0,3.0,0.0,14.0,1.0,0.0,2.0,1.87,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,85.0,2.0,21.0,41.0,23.0,1.0,85.0,51.0,0.0,1.0,0.0,47.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0 -Davide Frattesi,it ITA,MF,Sassuolo,23-111,1999,21.0,21.0,1726.0,5.0,0.0,0.0,0.0,3.0,0.0,0.26,0.0,0.26,0.26,0.26,4.3,4.3,0.22,0.22,19.2,18.0,0.0,47.4,0.94,0.13,0.28,0.11,0.7,0.7,484.0,616.0,78.6,8330.0,2285.0,230.0,277.0,83.0,177.0,214.0,82.7,58.0,87.0,66.7,20.0,46.0,17.0,1.0,84.0,600.0,16.0,12.0,4.0,14.0,9.0,0.0,0.0,0.0,0.0,4.0,0.0,13.0,61.0,3.18,49.0,0.0,4.0,3.0,4.0,0.21,4.0,0.0,0.0,0.0,0.0,42.0,19.0,14.0,22.0,6.0,16.0,2.0,14.0,6.0,24.0,0.0,852.0,38.0,172.0,429.0,265.0,57.0,852.0,476.0,45.0,35.0,14.0,573.0,32.0,28.0,0.0,27.0,37.0,0.0,0.0,0.0,0.0,100.0,16.0,12.0,57.1 -Matteo Gabbia,it ITA,DF,Milan,23-112,1999,10.0,6.0,583.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,6.5,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,276.0,344.0,80.2,6174.0,1982.0,72.0,86.0,83.7,141.0,156.0,90.4,58.0,89.0,65.2,0.0,12.0,0.0,0.0,14.0,332.0,12.0,6.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,4.0,0.62,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,10.0,12.0,5.0,0.0,8.0,3.0,5.0,4.0,28.0,0.0,409.0,52.0,249.0,154.0,7.0,1.0,409.0,179.0,1.0,0.0,1.0,239.0,1.0,0.0,0.0,6.0,1.0,0.0,0.0,0.0,0.0,35.0,15.0,4.0,78.9 -"Manolo Gabbiadini,it ITA,""FW,MF"",Sampdoria,31-076,1991,19.0,11.0,1058.0,4.0,1.0,0.0,0.0,4.0,0.0,0.34,0.09,0.43,0.34,0.43,4.4,4.4,0.37,0.37,11.8,17.0,2.0,41.5,1.45,0.1,0.24,0.11,-0.4,-0.4,199.0,309.0,64.4,2705.0,700.0,125.0,161.0,77.6,47.0,76.0,61.8,12.0,32.0,37.5,9.0,15.0,12.0,1.0,23.0,281.0,25.0,4.0,2.0,1.0,21.0,12.0,2.0,8.0,0.0,3.0,3.0,9.0,26.0,2.21,15.0,1.0,8.0,2.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,7.0,4.0,3.0,2.0,2.0,6.0,0.0,6.0,2.0,15.0,0.0,448.0,16.0,50.0,177.0,223.0,45.0,448.0,257.0,15.0,9.0,5.0,341.0,30.0,16.0,0.0,21.0,38.0,9.0,0.0,0.0,0.0,32.0,22.0,36.0,37.9" -Gianluca Gaetano,it ITA,MF,Napoli,22-281,2000,3.0,0.0,33.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.73,2.73,0.0,2.73,0.1,0.1,0.31,0.31,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,18.0,25.0,72.0,242.0,75.0,10.0,11.0,90.9,7.0,9.0,77.8,0.0,1.0,0.0,1.0,1.0,0.0,0.0,3.0,21.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,2.73,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,0.0,1.0,19.0,11.0,2.0,31.0,23.0,1.0,0.0,1.0,23.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0 -Roberto Gagliardini,it ITA,MF,Inter,28-309,1994,10.0,3.0,394.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.12,0.12,4.4,2.0,0.0,20.0,0.46,0.0,0.0,0.05,-0.5,-0.5,139.0,177.0,78.5,2119.0,593.0,82.0,99.0,82.8,46.0,53.0,86.8,8.0,15.0,53.3,3.0,15.0,8.0,3.0,22.0,173.0,3.0,2.0,1.0,1.0,6.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,10.0,2.28,8.0,0.0,1.0,1.0,2.0,0.46,1.0,0.0,1.0,0.0,0.0,12.0,5.0,6.0,5.0,1.0,7.0,2.0,5.0,1.0,6.0,0.0,231.0,6.0,51.0,106.0,76.0,20.0,231.0,115.0,12.0,3.0,1.0,158.0,2.0,2.0,0.0,7.0,8.0,0.0,0.0,0.0,0.0,21.0,11.0,6.0,64.7 -Adolfo Gaich,ar ARG,FW,Hellas Verona,23-349,1999,1.0,0.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,8.0,37.5,28.0,6.0,2.0,6.0,33.3,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,2.0,10.0,1.0,12.0,6.0,0.0,0.0,1.0,10.0,3.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,3.0,0.0 -Pablo Galdames Millán,cl CHI,DF,Cremonese,26-042,1996,1.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,26.0,69.2,256.0,69.0,13.0,17.0,76.5,4.0,5.0,80.0,1.0,3.0,33.3,0.0,1.0,0.0,0.0,3.0,25.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,2.81,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,32.0,1.0,5.0,22.0,5.0,0.0,32.0,22.0,0.0,0.0,0.0,22.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 -Antonino Gallo,it ITA,DF,Lecce,23-036,2000,17.0,14.0,1259.0,0.0,1.0,0.0,0.0,3.0,1.0,0.0,0.07,0.07,0.0,0.07,0.0,0.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,453.0,673.0,67.3,8466.0,3883.0,194.0,230.0,84.3,198.0,278.0,71.2,56.0,133.0,42.1,19.0,62.0,19.0,13.0,82.0,521.0,147.0,17.0,0.0,7.0,48.0,0.0,0.0,0.0,0.0,130.0,5.0,12.0,32.0,2.29,27.0,1.0,0.0,1.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,29.0,16.0,18.0,6.0,5.0,19.0,3.0,16.0,10.0,33.0,0.0,799.0,42.0,266.0,334.0,212.0,7.0,799.0,371.0,36.0,23.0,3.0,382.0,13.0,6.0,1.0,10.0,10.0,2.0,0.0,0.0,0.0,104.0,17.0,15.0,53.1 -Federico Gatti,it ITA,DF,Juventus,24-231,1998,6.0,6.0,540.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.04,0.04,6.0,1.0,0.0,16.7,0.17,0.0,0.0,0.04,-0.3,-0.3,308.0,372.0,82.8,6500.0,2102.0,101.0,110.0,91.8,149.0,165.0,90.3,55.0,85.0,64.7,1.0,20.0,0.0,0.0,22.0,352.0,18.0,13.0,0.0,11.0,1.0,0.0,0.0,0.0,0.0,3.0,2.0,4.0,9.0,1.5,8.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,4.0,0.0,1.0,7.0,5.0,2.0,9.0,26.0,1.0,440.0,40.0,217.0,200.0,26.0,10.0,440.0,266.0,5.0,3.0,0.0,303.0,2.0,2.0,0.0,6.0,8.0,0.0,0.0,0.0,0.0,39.0,14.0,12.0,53.8 -Valentin Gendrey,fr FRA,DF,Lecce,22-234,2000,21.0,19.0,1724.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.02,0.02,19.2,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.5,-0.5,606.0,862.0,70.3,9821.0,4899.0,313.0,373.0,83.9,242.0,333.0,72.7,38.0,113.0,33.6,12.0,40.0,10.0,5.0,70.0,641.0,218.0,12.0,1.0,4.0,40.0,0.0,0.0,0.0,0.0,206.0,3.0,20.0,23.0,1.2,17.0,2.0,0.0,3.0,1.0,0.05,0.0,0.0,0.0,1.0,0.0,52.0,33.0,34.0,13.0,5.0,31.0,3.0,28.0,28.0,53.0,0.0,1088.0,69.0,400.0,445.0,256.0,10.0,1088.0,472.0,29.0,21.0,3.0,471.0,30.0,9.0,0.0,13.0,21.0,1.0,1.0,1.0,0.0,119.0,30.0,29.0,50.8 -Paolo Ghiglione,it ITA,DF,Cremonese,26-008,1997,6.0,6.0,467.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.19,0.19,0.0,0.19,0.1,0.1,0.02,0.02,5.2,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,102.0,195.0,52.3,2052.0,1106.0,33.0,52.0,63.5,54.0,92.0,58.7,15.0,39.0,38.5,7.0,14.0,10.0,8.0,15.0,149.0,46.0,1.0,0.0,1.0,29.0,2.0,0.0,2.0,0.0,43.0,0.0,9.0,10.0,1.93,8.0,2.0,0.0,0.0,2.0,0.39,2.0,0.0,0.0,0.0,0.0,10.0,6.0,7.0,3.0,0.0,9.0,4.0,5.0,3.0,8.0,0.0,245.0,12.0,65.0,95.0,86.0,5.0,245.0,96.0,3.0,3.0,1.0,113.0,4.0,4.0,0.0,7.0,5.0,1.0,0.0,1.0,0.0,26.0,2.0,5.0,28.6 -Mario Gila,es ESP,DF,Lazio,22-165,2000,4.0,0.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,56.0,61.0,91.8,953.0,330.0,25.0,27.0,92.6,24.0,26.0,92.3,6.0,7.0,85.7,0.0,1.0,0.0,0.0,6.0,56.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,0.0,71.0,6.0,36.0,33.0,2.0,0.0,71.0,42.0,1.0,1.0,0.0,46.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,6.0,0.0,3.0,0.0 -Olivier Giroud,fr FRA,FW,Milan,36-133,1986,19.0,15.0,1264.0,6.0,4.0,1.0,1.0,6.0,1.0,0.43,0.28,0.71,0.36,0.64,7.0,6.4,0.5,0.45,14.0,15.0,3.0,29.4,1.07,0.1,0.33,0.12,-1.0,-1.4,199.0,315.0,63.2,2432.0,536.0,126.0,189.0,66.7,49.0,73.0,67.1,6.0,11.0,54.5,16.0,9.0,5.0,0.0,28.0,292.0,22.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,5.0,32.0,2.28,24.0,0.0,6.0,2.0,5.0,0.36,2.0,0.0,3.0,0.0,0.0,4.0,3.0,2.0,1.0,1.0,8.0,2.0,6.0,2.0,11.0,0.0,464.0,17.0,38.0,192.0,242.0,98.0,463.0,199.0,8.0,8.0,6.0,354.0,39.0,9.0,1.0,21.0,14.0,11.0,0.0,0.0,0.0,29.0,66.0,39.0,62.9 -Pierluigi Gollini,it ITA,GK,Fiorentina,27-329,1995,3.0,3.0,270.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,96.0,126.0,76.2,2622.0,1774.0,18.0,18.0,100.0,42.0,42.0,100.0,36.0,66.0,54.5,0.0,1.0,0.0,0.0,0.0,111.0,15.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,131.0,116.0,131.0,0.0,0.0,0.0,131.0,104.0,0.0,0.0,0.0,91.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -Joan Gonzàlez,es ESP,MF,Lecce,21-009,2002,21.0,18.0,1532.0,1.0,3.0,0.0,0.0,4.0,0.0,0.06,0.18,0.23,0.06,0.23,1.2,1.2,0.07,0.07,17.0,3.0,0.0,21.4,0.18,0.07,0.33,0.09,-0.2,-0.2,394.0,516.0,76.4,6270.0,1717.0,194.0,242.0,80.2,151.0,175.0,86.3,30.0,58.0,51.7,13.0,49.0,7.0,3.0,47.0,489.0,26.0,6.0,2.0,5.0,19.0,0.0,0.0,0.0,0.0,7.0,1.0,10.0,30.0,1.76,21.0,0.0,3.0,4.0,3.0,0.18,2.0,0.0,0.0,0.0,1.0,32.0,16.0,15.0,11.0,6.0,28.0,5.0,23.0,12.0,21.0,0.0,727.0,34.0,171.0,368.0,198.0,25.0,727.0,367.0,12.0,8.0,5.0,426.0,32.0,19.0,0.0,27.0,36.0,1.0,0.0,0.0,1.0,94.0,22.0,40.0,35.5 -"Nicolás González,ar ARG,""FW,MF"",Fiorentina,24-310,1998,11.0,4.0,450.0,3.0,1.0,1.0,1.0,2.0,0.0,0.6,0.2,0.8,0.4,0.6,2.7,2.2,0.54,0.44,5.0,7.0,0.0,36.8,1.4,0.11,0.29,0.12,0.3,-0.2,119.0,178.0,66.9,2059.0,374.0,53.0,73.0,72.6,51.0,62.0,82.3,11.0,21.0,52.4,9.0,8.0,8.0,1.0,18.0,169.0,7.0,5.0,0.0,3.0,13.0,0.0,0.0,0.0,0.0,2.0,2.0,8.0,22.0,4.4,16.0,0.0,3.0,2.0,3.0,0.6,2.0,0.0,1.0,0.0,0.0,4.0,2.0,1.0,3.0,0.0,1.0,0.0,1.0,1.0,2.0,0.0,264.0,2.0,14.0,120.0,133.0,29.0,263.0,183.0,13.0,7.0,4.0,206.0,18.0,6.0,0.0,6.0,18.0,2.0,0.0,0.0,0.0,20.0,14.0,9.0,60.9" -Robin Gosens,de GER,DF,Inter,28-220,1994,18.0,1.0,363.0,1.0,0.0,0.0,0.0,1.0,0.0,0.25,0.0,0.25,0.25,0.25,0.8,0.8,0.2,0.2,4.0,1.0,0.0,16.7,0.25,0.17,1.0,0.13,0.2,0.2,172.0,233.0,73.8,2690.0,1115.0,93.0,100.0,93.0,68.0,95.0,71.6,7.0,21.0,33.3,4.0,15.0,7.0,4.0,14.0,196.0,34.0,0.0,0.0,0.0,19.0,0.0,0.0,0.0,0.0,34.0,3.0,6.0,10.0,2.51,9.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,9.0,6.0,4.0,2.0,5.0,0.0,5.0,4.0,3.0,0.0,281.0,6.0,60.0,97.0,127.0,16.0,281.0,145.0,13.0,9.0,3.0,178.0,10.0,0.0,0.0,8.0,0.0,1.0,0.0,0.0,0.0,22.0,6.0,5.0,54.5 -Alberto Grassi,it ITA,MF,Empoli,27-340,1995,11.0,6.0,469.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,5.2,1.0,0.0,25.0,0.19,0.0,0.0,0.04,-0.2,-0.2,181.0,221.0,81.9,2644.0,866.0,101.0,115.0,87.8,57.0,69.0,82.6,11.0,23.0,47.8,3.0,12.0,2.0,0.0,22.0,214.0,7.0,7.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,1.15,5.0,0.0,0.0,1.0,1.0,0.19,1.0,0.0,0.0,0.0,0.0,16.0,13.0,9.0,7.0,0.0,9.0,6.0,3.0,14.0,16.0,0.0,298.0,29.0,111.0,156.0,33.0,3.0,298.0,117.0,2.0,3.0,0.0,159.0,6.0,5.0,0.0,11.0,6.0,0.0,0.0,0.0,0.0,37.0,11.0,5.0,68.8 -Koray Günter,de GER,DF,Hellas Verona,28-178,1994,13.0,11.0,1003.0,1.0,0.0,0.0,0.0,3.0,0.0,0.09,0.0,0.09,0.09,0.09,0.9,0.9,0.08,0.08,11.1,1.0,0.0,11.1,0.09,0.11,1.0,0.1,0.1,0.1,323.0,422.0,76.5,6871.0,3172.0,102.0,126.0,81.0,160.0,183.0,87.4,58.0,101.0,57.4,8.0,31.0,1.0,1.0,36.0,400.0,21.0,20.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,1.0,1.0,5.0,13.0,1.17,10.0,1.0,2.0,0.0,1.0,0.09,0.0,0.0,1.0,0.0,0.0,10.0,6.0,1.0,8.0,1.0,24.0,15.0,9.0,30.0,46.0,0.0,557.0,82.0,283.0,252.0,29.0,10.0,557.0,221.0,7.0,6.0,0.0,230.0,3.0,1.0,0.0,17.0,10.0,0.0,0.0,0.0,0.0,81.0,22.0,21.0,51.2 -"Emmanuel Gyasi,gh GHA,""FW,DF"",Spezia,29-030,1994,18.0,18.0,1476.0,1.0,0.0,0.0,0.0,5.0,0.0,0.06,0.0,0.06,0.06,0.06,2.5,2.5,0.15,0.15,16.4,8.0,0.0,36.4,0.49,0.05,0.13,0.11,-1.5,-1.5,314.0,390.0,80.5,3964.0,705.0,214.0,244.0,87.7,83.0,106.0,78.3,5.0,9.0,55.6,12.0,7.0,5.0,0.0,23.0,337.0,53.0,3.0,1.0,1.0,10.0,2.0,0.0,0.0,0.0,25.0,0.0,12.0,23.0,1.4,20.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,5.0,4.0,6.0,0.0,15.0,3.0,12.0,3.0,13.0,0.0,550.0,16.0,86.0,236.0,233.0,55.0,550.0,293.0,18.0,18.0,9.0,366.0,40.0,22.0,0.0,22.0,16.0,16.0,0.0,0.0,0.0,60.0,9.0,32.0,22.0" -Norbert Gyömbér,sk SVK,DF,Salernitana,30-222,1992,14.0,11.0,803.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.04,0.04,8.9,0.0,0.0,0.0,0.0,0.0,0.0,0.17,-0.3,-0.3,332.0,385.0,86.2,6112.0,2374.0,123.0,136.0,90.4,164.0,178.0,92.1,39.0,61.0,63.9,1.0,26.0,0.0,0.0,29.0,368.0,17.0,12.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,2.0,7.0,0.78,5.0,1.0,0.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,16.0,13.0,11.0,5.0,0.0,17.0,11.0,6.0,15.0,37.0,0.0,475.0,58.0,238.0,235.0,7.0,1.0,475.0,235.0,5.0,0.0,0.0,274.0,1.0,1.0,0.0,11.0,3.0,0.0,0.0,1.0,0.0,56.0,12.0,8.0,60.0 -Christian Gytkjær,dk DEN,FW,Monza,32-280,1990,14.0,2.0,413.0,1.0,0.0,0.0,0.0,0.0,0.0,0.22,0.0,0.22,0.22,0.22,2.7,2.7,0.6,0.6,4.6,5.0,1.0,45.5,1.09,0.09,0.2,0.25,-1.7,-1.7,50.0,73.0,68.5,642.0,126.0,27.0,38.0,71.1,13.0,20.0,65.0,3.0,3.0,100.0,2.0,2.0,2.0,0.0,5.0,65.0,7.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,7.0,1.54,5.0,0.0,2.0,0.0,2.0,0.44,1.0,0.0,1.0,0.0,0.0,6.0,2.0,1.0,4.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,110.0,0.0,2.0,58.0,50.0,17.0,110.0,58.0,1.0,2.0,1.0,81.0,9.0,5.0,0.0,9.0,4.0,10.0,0.0,0.0,0.0,7.0,6.0,14.0,30.0 -Nicolas Haas,ch SUI,MF,Empoli,27-018,1996,15.0,9.0,841.0,1.0,0.0,0.0,0.0,2.0,0.0,0.11,0.0,0.11,0.11,0.11,1.1,1.1,0.12,0.12,9.3,3.0,0.0,50.0,0.32,0.17,0.33,0.19,-0.1,-0.1,206.0,279.0,73.8,3394.0,975.0,99.0,121.0,81.8,88.0,112.0,78.6,15.0,26.0,57.7,6.0,22.0,5.0,0.0,27.0,271.0,8.0,3.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,2.0,0.0,12.0,16.0,1.71,14.0,1.0,0.0,0.0,1.0,0.11,0.0,1.0,0.0,0.0,0.0,22.0,10.0,11.0,6.0,5.0,12.0,4.0,8.0,5.0,19.0,0.0,390.0,34.0,133.0,171.0,88.0,14.0,390.0,187.0,9.0,10.0,2.0,235.0,15.0,7.0,0.0,16.0,8.0,1.0,0.0,0.0,0.0,44.0,3.0,12.0,20.0 -Samir Handanović,si SVN,GK,Inter,38-211,1984,8.0,8.0,720.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,280.0,314.0,89.2,6390.0,4422.0,88.0,89.0,98.9,130.0,131.0,99.2,61.0,90.0,67.8,0.0,6.0,0.0,0.0,0.0,253.0,58.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,0.25,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,331.0,266.0,328.0,2.0,1.0,0.0,331.0,191.0,0.0,0.0,0.0,199.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0 -Abdou Harroui,nl NED,MF,Sassuolo,25-028,1998,13.0,2.0,295.0,1.0,0.0,0.0,0.0,1.0,0.0,0.31,0.0,0.31,0.31,0.31,0.6,0.6,0.17,0.17,3.3,3.0,0.0,33.3,0.92,0.11,0.33,0.06,0.4,0.4,106.0,135.0,78.5,1693.0,410.0,49.0,54.0,90.7,49.0,56.0,87.5,6.0,11.0,54.5,1.0,16.0,3.0,1.0,11.0,132.0,3.0,0.0,1.0,1.0,8.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,4.0,1.23,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,3.0,2.0,2.0,3.0,1.0,2.0,0.0,2.0,0.0,171.0,7.0,20.0,93.0,62.0,4.0,171.0,96.0,7.0,7.0,1.0,122.0,4.0,7.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,19.0,4.0,3.0,57.1 -Hans Hateboer,nl NED,DF,Atalanta,29-032,1994,16.0,16.0,1335.0,1.0,1.0,0.0,0.0,7.0,0.0,0.07,0.07,0.13,0.07,0.13,1.1,1.1,0.08,0.08,14.8,2.0,0.0,22.2,0.13,0.11,0.5,0.12,-0.1,-0.1,538.0,675.0,79.7,7780.0,2776.0,326.0,362.0,90.1,165.0,214.0,77.1,31.0,60.0,51.7,11.0,27.0,16.0,7.0,53.0,547.0,126.0,3.0,2.0,1.0,23.0,0.0,0.0,0.0,0.0,123.0,2.0,12.0,20.0,1.35,18.0,1.0,0.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,20.0,12.0,11.0,7.0,2.0,20.0,4.0,16.0,12.0,24.0,0.0,786.0,35.0,210.0,327.0,252.0,24.0,786.0,379.0,16.0,13.0,1.0,483.0,8.0,7.0,0.0,18.0,2.0,1.0,0.0,0.0,0.0,47.0,22.0,14.0,61.1 -Liam Henderson,sct SCO,MF,Empoli,26-291,1996,15.0,8.0,687.0,0.0,1.0,0.0,0.0,6.0,0.0,0.0,0.13,0.13,0.0,0.13,0.3,0.3,0.04,0.04,7.6,4.0,0.0,57.1,0.52,0.0,0.0,0.04,-0.3,-0.3,285.0,350.0,81.4,4731.0,1344.0,115.0,135.0,85.2,139.0,153.0,90.8,20.0,40.0,50.0,12.0,18.0,6.0,6.0,27.0,331.0,15.0,7.0,3.0,2.0,17.0,2.0,1.0,0.0,0.0,2.0,4.0,6.0,26.0,3.41,23.0,1.0,1.0,0.0,1.0,0.13,1.0,0.0,0.0,0.0,0.0,8.0,7.0,4.0,3.0,1.0,6.0,1.0,5.0,7.0,6.0,0.0,406.0,12.0,87.0,238.0,86.0,12.0,406.0,243.0,9.0,7.0,0.0,285.0,11.0,3.0,0.0,26.0,6.0,2.0,0.0,0.0,0.0,40.0,4.0,6.0,40.0 -Jack Hendry,sct SCO,DF,Cremonese,27-279,1995,4.0,2.0,220.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,118.0,137.0,86.1,2402.0,1087.0,40.0,44.0,90.9,58.0,67.0,86.6,17.0,22.0,77.3,1.0,12.0,1.0,0.0,9.0,122.0,15.0,7.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,5.0,0.0,1.0,4.0,1.64,4.0,0.0,0.0,0.0,1.0,0.41,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,7.0,0.0,148.0,13.0,71.0,73.0,7.0,0.0,148.0,94.0,1.0,2.0,0.0,90.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,14.0,8.0,3.0,72.7 -Matheus Henrique,br BRA,MF,Sassuolo,25-053,1997,13.0,6.0,687.0,1.0,0.0,0.0,0.0,2.0,0.0,0.13,0.0,0.13,0.13,0.13,0.7,0.7,0.09,0.09,7.6,6.0,0.0,66.7,0.79,0.11,0.17,0.08,0.3,0.3,305.0,357.0,85.4,5240.0,1351.0,131.0,149.0,87.9,130.0,141.0,92.2,32.0,41.0,78.0,11.0,35.0,4.0,0.0,39.0,344.0,13.0,8.0,2.0,2.0,0.0,1.0,0.0,0.0,0.0,4.0,0.0,9.0,27.0,3.54,21.0,1.0,1.0,2.0,1.0,0.13,1.0,0.0,0.0,0.0,0.0,10.0,6.0,4.0,5.0,1.0,4.0,0.0,4.0,8.0,6.0,0.0,420.0,10.0,74.0,228.0,122.0,9.0,420.0,267.0,11.0,19.0,1.0,298.0,10.0,4.0,0.0,4.0,11.0,0.0,0.0,0.0,0.0,36.0,4.0,5.0,44.4 -Thomas Henry,fr FRA,FW,Hellas Verona,28-143,1994,16.0,13.0,1029.0,2.0,0.0,0.0,0.0,4.0,0.0,0.17,0.0,0.17,0.17,0.17,1.7,1.7,0.15,0.15,11.4,5.0,0.0,27.8,0.44,0.11,0.4,0.1,0.3,0.3,115.0,226.0,50.9,1405.0,437.0,71.0,124.0,57.3,19.0,46.0,41.3,7.0,12.0,58.3,13.0,14.0,6.0,0.0,18.0,213.0,9.0,0.0,1.0,2.0,5.0,0.0,0.0,0.0,0.0,1.0,4.0,9.0,22.0,1.92,16.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,1.0,1.0,3.0,6.0,2.0,4.0,0.0,14.0,0.0,374.0,15.0,25.0,160.0,192.0,48.0,374.0,193.0,3.0,7.0,1.0,303.0,52.0,25.0,0.0,27.0,16.0,9.0,0.0,0.0,0.0,29.0,46.0,39.0,54.1 -Theo Hernández,fr FRA,DF,Milan,25-127,1997,18.0,18.0,1559.0,2.0,2.0,1.0,1.0,5.0,0.0,0.12,0.12,0.23,0.06,0.17,2.3,1.5,0.13,0.09,17.3,6.0,4.0,18.8,0.35,0.03,0.17,0.05,-0.3,-0.5,685.0,881.0,77.8,12034.0,3985.0,311.0,350.0,88.9,289.0,353.0,81.9,70.0,123.0,56.9,26.0,50.0,16.0,6.0,63.0,688.0,192.0,27.0,1.0,17.0,47.0,12.0,0.0,10.0,0.0,153.0,1.0,20.0,49.0,2.83,26.0,12.0,4.0,5.0,6.0,0.35,4.0,2.0,0.0,0.0,0.0,33.0,19.0,20.0,12.0,1.0,24.0,6.0,18.0,10.0,17.0,1.0,1079.0,41.0,268.0,539.0,289.0,39.0,1078.0,577.0,68.0,45.0,12.0,621.0,28.0,11.0,0.0,14.0,39.0,0.0,0.0,0.0,1.0,109.0,21.0,8.0,72.4 -Isak Hien,se SWE,DF,Hellas Verona,24-028,1999,18.0,14.0,1421.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.8,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.0,468.0,629.0,74.4,9766.0,3962.0,129.0,170.0,75.9,258.0,314.0,82.2,74.0,114.0,64.9,2.0,31.0,2.0,1.0,39.0,588.0,38.0,16.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,22.0,3.0,14.0,13.0,0.82,10.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,43.0,20.0,32.0,11.0,0.0,16.0,6.0,10.0,19.0,44.0,0.0,800.0,75.0,425.0,343.0,43.0,10.0,800.0,341.0,12.0,8.0,0.0,342.0,16.0,7.0,0.0,35.0,7.0,0.0,0.0,0.0,0.0,127.0,48.0,25.0,65.8 -Morten Hjulmand,dk DEN,MF,Lecce,23-230,1999,19.0,19.0,1624.0,0.0,4.0,0.0,0.0,5.0,1.0,0.0,0.22,0.22,0.0,0.22,0.3,0.3,0.01,0.01,18.0,1.0,0.0,12.5,0.06,0.0,0.0,0.03,-0.3,-0.3,547.0,700.0,78.1,10019.0,3117.0,232.0,280.0,82.9,240.0,278.0,86.3,66.0,111.0,59.5,18.0,79.0,9.0,2.0,85.0,662.0,36.0,35.0,0.0,8.0,6.0,0.0,0.0,0.0,0.0,0.0,2.0,12.0,34.0,1.89,30.0,1.0,1.0,2.0,5.0,0.28,5.0,0.0,0.0,0.0,0.0,50.0,30.0,27.0,23.0,0.0,23.0,7.0,16.0,41.0,37.0,0.0,898.0,64.0,271.0,514.0,119.0,5.0,898.0,408.0,3.0,6.0,0.0,410.0,17.0,13.0,0.0,30.0,21.0,0.0,0.0,0.0,0.0,138.0,25.0,14.0,64.1 -Emil Holm,se SWE,DF,Spezia,22-273,2000,19.0,17.0,1366.0,1.0,1.0,0.0,0.0,4.0,0.0,0.07,0.07,0.13,0.07,0.13,1.2,1.2,0.08,0.08,15.2,5.0,0.0,33.3,0.33,0.07,0.2,0.08,-0.2,-0.2,334.0,559.0,59.7,5064.0,1645.0,171.0,237.0,72.2,127.0,203.0,62.6,16.0,61.0,26.2,13.0,28.0,12.0,5.0,39.0,450.0,107.0,1.0,1.0,3.0,49.0,0.0,0.0,0.0,0.0,106.0,2.0,19.0,35.0,2.31,23.0,5.0,2.0,3.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,25.0,15.0,13.0,7.0,5.0,20.0,6.0,14.0,9.0,40.0,0.0,782.0,41.0,174.0,310.0,309.0,40.0,782.0,333.0,40.0,24.0,10.0,418.0,45.0,21.0,0.0,20.0,15.0,2.0,0.0,0.0,0.0,79.0,61.0,61.0,50.0 -Martin Hongla,cm CMR,MF,Hellas Verona,24-331,1998,9.0,6.0,523.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,5.8,2.0,0.0,40.0,0.34,0.0,0.0,0.03,-0.2,-0.2,166.0,212.0,78.3,3239.0,992.0,62.0,74.0,83.8,75.0,89.0,84.3,24.0,41.0,58.5,3.0,18.0,2.0,1.0,24.0,204.0,8.0,5.0,0.0,3.0,8.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,11.0,1.89,9.0,0.0,1.0,0.0,1.0,0.17,1.0,0.0,0.0,0.0,0.0,8.0,5.0,4.0,4.0,0.0,7.0,1.0,6.0,6.0,9.0,0.0,258.0,12.0,66.0,142.0,54.0,3.0,258.0,131.0,6.0,4.0,1.0,141.0,4.0,3.0,0.0,11.0,4.0,0.0,0.0,0.0,0.0,48.0,5.0,8.0,38.5 -Petko Hristov,bg BUL,DF,Spezia,23-346,1999,9.0,4.0,365.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,4.1,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.1,-0.1,160.0,207.0,77.3,2653.0,997.0,82.0,90.0,91.1,64.0,79.0,81.0,11.0,29.0,37.9,0.0,11.0,2.0,1.0,8.0,168.0,38.0,9.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,29.0,1.0,1.0,3.0,0.74,1.0,0.0,1.0,1.0,1.0,0.25,0.0,0.0,0.0,1.0,0.0,6.0,4.0,2.0,4.0,0.0,4.0,2.0,2.0,4.0,12.0,0.0,251.0,19.0,101.0,122.0,29.0,3.0,251.0,112.0,2.0,2.0,0.0,133.0,3.0,0.0,0.0,1.0,4.0,0.0,1.0,0.0,0.0,21.0,10.0,5.0,66.7 -Ajdin Hrustic,au AUS,MF,Hellas Verona,26-220,1996,6.0,3.0,226.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.13,0.13,2.5,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.3,-0.3,75.0,94.0,79.8,1069.0,341.0,40.0,42.0,95.2,25.0,33.0,75.8,4.0,7.0,57.1,3.0,1.0,5.0,1.0,8.0,82.0,12.0,6.0,0.0,1.0,7.0,4.0,2.0,0.0,0.0,0.0,0.0,4.0,7.0,2.79,6.0,1.0,0.0,0.0,1.0,0.4,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,2.0,0.0,2.0,0.0,2.0,1.0,121.0,4.0,17.0,59.0,47.0,6.0,121.0,63.0,7.0,6.0,2.0,77.0,8.0,3.0,0.0,7.0,2.0,0.0,0.0,0.0,0.0,11.0,1.0,3.0,25.0 -Elseid Hysaj,al ALB,DF,Lazio,28-355,1994,17.0,7.0,746.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.12,0.12,0.0,0.12,0.0,0.0,0.0,0.0,8.3,1.0,0.0,100.0,0.12,0.0,0.0,0.03,0.0,0.0,414.0,510.0,81.2,6104.0,2761.0,240.0,263.0,91.3,137.0,164.0,83.5,25.0,56.0,44.6,4.0,30.0,6.0,2.0,29.0,431.0,78.0,9.0,2.0,2.0,8.0,0.0,0.0,0.0,0.0,69.0,1.0,6.0,15.0,1.81,12.0,0.0,0.0,1.0,1.0,0.12,1.0,0.0,0.0,0.0,0.0,21.0,10.0,11.0,6.0,4.0,9.0,4.0,5.0,9.0,18.0,0.0,598.0,34.0,236.0,272.0,95.0,4.0,598.0,300.0,15.0,13.0,2.0,344.0,13.0,6.0,0.0,7.0,6.0,1.0,0.0,0.0,0.0,62.0,12.0,9.0,57.1 -Rasmus Højlund,dk DEN,FW,Atalanta,20-006,2003,17.0,9.0,866.0,4.0,1.0,0.0,0.0,1.0,0.0,0.42,0.1,0.52,0.42,0.52,3.2,3.2,0.34,0.34,9.6,12.0,0.0,46.2,1.25,0.15,0.33,0.12,0.8,0.8,158.0,206.0,76.7,1912.0,292.0,102.0,126.0,81.0,34.0,45.0,75.6,6.0,6.0,100.0,14.0,10.0,7.0,0.0,22.0,196.0,9.0,0.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,25.0,2.6,16.0,0.0,3.0,4.0,3.0,0.31,1.0,0.0,0.0,2.0,0.0,3.0,2.0,0.0,1.0,2.0,5.0,0.0,5.0,0.0,3.0,0.0,330.0,3.0,12.0,120.0,202.0,67.0,330.0,225.0,15.0,13.0,15.0,270.0,42.0,29.0,0.0,11.0,18.0,3.0,2.0,0.0,0.0,33.0,16.0,31.0,34.0 -Roger Ibanez,br BRA,DF,Roma,24-079,1998,20.0,20.0,1800.0,3.0,0.0,0.0,0.0,5.0,0.0,0.15,0.0,0.15,0.15,0.15,1.8,1.8,0.09,0.09,20.0,6.0,0.0,35.3,0.3,0.18,0.5,0.1,1.2,1.2,963.0,1086.0,88.7,17969.0,6482.0,369.0,405.0,91.1,475.0,507.0,93.7,107.0,145.0,73.8,3.0,45.0,3.0,0.0,50.0,1049.0,36.0,27.0,2.0,8.0,1.0,0.0,0.0,0.0,0.0,8.0,1.0,7.0,19.0,0.95,10.0,1.0,5.0,2.0,2.0,0.1,1.0,0.0,1.0,0.0,0.0,47.0,22.0,25.0,20.0,2.0,17.0,7.0,10.0,43.0,64.0,1.0,1305.0,148.0,660.0,617.0,47.0,17.0,1305.0,792.0,18.0,12.0,2.0,848.0,10.0,15.0,0.0,27.0,33.0,0.0,1.0,0.0,0.0,131.0,33.0,11.0,75.0 -Igor,br BRA,DF,Fiorentina,25-003,1998,16.0,13.0,1131.0,0.0,0.0,0.0,0.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.03,0.03,12.6,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.4,-0.4,724.0,809.0,89.5,15090.0,4780.0,205.0,215.0,95.3,419.0,444.0,94.4,96.0,136.0,70.6,3.0,53.0,2.0,0.0,52.0,777.0,31.0,25.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,4.0,13.0,1.03,11.0,0.0,0.0,0.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,21.0,11.0,9.0,8.0,4.0,23.0,11.0,12.0,17.0,26.0,1.0,917.0,68.0,366.0,522.0,36.0,7.0,917.0,583.0,14.0,10.0,0.0,616.0,5.0,7.0,1.0,19.0,6.0,0.0,0.0,0.0,0.0,91.0,24.0,9.0,72.7 -Jonathan Ikone,fr FRA,FW,Fiorentina,24-284,1998,19.0,13.0,1168.0,2.0,2.0,0.0,0.0,2.0,0.0,0.15,0.15,0.31,0.15,0.31,1.7,1.7,0.13,0.13,13.0,6.0,0.0,20.0,0.46,0.07,0.33,0.06,0.3,0.3,315.0,379.0,83.1,5345.0,936.0,164.0,179.0,91.6,108.0,131.0,82.4,33.0,39.0,84.6,17.0,12.0,16.0,1.0,35.0,361.0,17.0,3.0,1.0,6.0,13.0,0.0,0.0,0.0,0.0,14.0,1.0,9.0,32.0,2.47,26.0,0.0,0.0,0.0,4.0,0.31,4.0,0.0,0.0,0.0,0.0,8.0,1.0,2.0,3.0,3.0,13.0,1.0,12.0,3.0,3.0,0.0,528.0,5.0,46.0,210.0,281.0,63.0,528.0,371.0,45.0,25.0,31.0,412.0,34.0,31.0,0.0,16.0,14.0,3.0,0.0,0.0,0.0,49.0,7.0,15.0,31.8 -Ivan Ilić,rs SRB,MF,Hellas Verona,21-330,2001,11.0,10.0,851.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.11,0.11,0.0,0.11,0.4,0.4,0.04,0.04,9.5,2.0,0.0,22.2,0.21,0.0,0.0,0.04,-0.4,-0.4,279.0,401.0,69.6,5107.0,1432.0,113.0,138.0,81.9,128.0,168.0,76.2,29.0,66.0,43.9,16.0,19.0,10.0,1.0,39.0,332.0,68.0,17.0,1.0,3.0,47.0,39.0,14.0,19.0,0.0,2.0,1.0,13.0,27.0,2.86,14.0,12.0,0.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,16.0,10.0,9.0,7.0,0.0,10.0,1.0,9.0,8.0,14.0,0.0,490.0,26.0,98.0,250.0,149.0,11.0,490.0,219.0,18.0,13.0,4.0,238.0,17.0,5.0,0.0,9.0,9.0,0.0,0.0,0.0,0.0,72.0,10.0,13.0,43.5 -"Samuel Iling-Junior,eng ENG,""DF,MF"",Juventus,19-129,2003,6.0,0.0,105.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.86,0.86,0.0,0.86,0.0,0.0,0.01,0.01,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,28.0,34.0,82.4,463.0,164.0,14.0,17.0,82.4,12.0,14.0,85.7,1.0,2.0,50.0,2.0,2.0,2.0,1.0,4.0,32.0,2.0,1.0,0.0,0.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,4.0,3.43,4.0,0.0,0.0,0.0,1.0,0.86,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,3.0,0.0,55.0,1.0,13.0,17.0,27.0,0.0,55.0,28.0,5.0,7.0,0.0,30.0,5.0,2.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,8.0,1.0,1.0,50.0" -Emirhan İlkhan,tr TUR,MF,Torino,18-254,2004,4.0,1.0,87.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,36.0,43.0,83.7,671.0,140.0,16.0,19.0,84.2,12.0,13.0,92.3,7.0,9.0,77.8,1.0,6.0,2.0,0.0,8.0,41.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.17,5.0,0.0,0.0,0.0,1.0,1.03,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,3.0,1.0,2.0,2.0,2.0,0.0,58.0,4.0,12.0,34.0,12.0,0.0,58.0,39.0,2.0,4.0,0.0,38.0,2.0,2.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,2.0,33.3 -Ciro Immobile,it ITA,FW,Lazio,32-355,1990,16.0,14.0,1097.0,7.0,3.0,1.0,2.0,3.0,0.0,0.57,0.25,0.82,0.49,0.74,7.2,5.7,0.59,0.46,12.2,15.0,0.0,40.5,1.23,0.16,0.4,0.15,-0.2,0.3,234.0,319.0,73.4,3012.0,481.0,151.0,181.0,83.4,56.0,75.0,74.7,10.0,15.0,66.7,13.0,9.0,6.0,0.0,21.0,300.0,16.0,0.0,4.0,2.0,5.0,0.0,0.0,0.0,0.0,6.0,3.0,12.0,37.0,3.03,26.0,0.0,6.0,3.0,9.0,0.74,7.0,0.0,2.0,0.0,0.0,6.0,3.0,2.0,2.0,2.0,9.0,1.0,8.0,1.0,8.0,0.0,443.0,11.0,27.0,189.0,231.0,89.0,441.0,251.0,22.0,10.0,14.0,345.0,28.0,12.0,0.0,10.0,12.0,9.0,1.0,0.0,0.0,29.0,8.0,19.0,29.6 -Ardian Ismajli,al ALB,DF,Empoli,26-133,1996,14.0,13.0,1205.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.04,0.04,13.4,2.0,0.0,28.6,0.15,0.0,0.0,0.08,-0.5,-0.5,447.0,536.0,83.4,8783.0,2769.0,137.0,149.0,91.9,268.0,297.0,90.2,39.0,70.0,55.7,1.0,11.0,0.0,0.0,17.0,503.0,32.0,18.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,11.0,4.0,0.3,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,19.0,16.0,8.0,0.0,34.0,22.0,12.0,16.0,76.0,0.0,709.0,156.0,447.0,247.0,17.0,14.0,709.0,351.0,6.0,0.0,0.0,384.0,2.0,1.0,0.0,13.0,9.0,0.0,0.0,0.0,0.0,54.0,28.0,17.0,62.2 -Armando Izzo,it ITA,DF,Monza,30-345,1992,14.0,13.0,1106.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.05,0.05,12.3,1.0,0.0,16.7,0.08,0.0,0.0,0.11,-0.7,-0.7,614.0,722.0,85.0,11155.0,3652.0,253.0,281.0,90.0,285.0,313.0,91.1,62.0,102.0,60.8,8.0,44.0,5.0,1.0,52.0,671.0,46.0,39.0,1.0,10.0,4.0,0.0,0.0,0.0,0.0,7.0,5.0,4.0,14.0,1.14,12.0,0.0,0.0,1.0,1.0,0.08,0.0,0.0,0.0,1.0,0.0,44.0,29.0,28.0,14.0,2.0,13.0,5.0,8.0,21.0,37.0,0.0,862.0,70.0,429.0,378.0,64.0,10.0,862.0,488.0,15.0,6.0,1.0,572.0,7.0,4.0,0.0,18.0,26.0,1.0,1.0,0.0,0.0,68.0,22.0,17.0,56.4 -Mato Jajalo,ba BIH,MF,Udinese,34-261,1988,3.0,0.0,63.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,23.0,29.0,79.3,401.0,166.0,10.0,13.0,76.9,9.0,9.0,100.0,3.0,4.0,75.0,1.0,1.0,1.0,0.0,4.0,29.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,4.29,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,36.0,4.0,7.0,14.0,16.0,1.0,36.0,17.0,0.0,2.0,0.0,22.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,0.0,100.0 -Juan Jesus,br BRA,DF,Napoli,31-245,1991,7.0,7.0,599.0,1.0,0.0,0.0,0.0,2.0,0.0,0.15,0.0,0.15,0.15,0.15,0.2,0.2,0.03,0.03,6.7,1.0,0.0,25.0,0.15,0.25,1.0,0.05,0.8,0.8,404.0,449.0,90.0,7398.0,2359.0,147.0,154.0,95.5,220.0,237.0,92.8,31.0,43.0,72.1,1.0,21.0,1.0,0.0,28.0,430.0,18.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,8.0,1.2,5.0,0.0,2.0,0.0,1.0,0.15,1.0,0.0,0.0,0.0,0.0,9.0,5.0,5.0,3.0,1.0,14.0,7.0,7.0,6.0,22.0,0.0,513.0,43.0,209.0,298.0,9.0,6.0,513.0,321.0,3.0,1.0,0.0,349.0,3.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,36.0,19.0,4.0,82.6 -Þórir Jóhann Helgason,is ISL,MF,Lecce,22-135,2000,7.0,2.0,154.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,1.7,0.0,1.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,35.0,48.0,72.9,648.0,280.0,14.0,19.0,73.7,14.0,16.0,87.5,6.0,11.0,54.5,3.0,1.0,0.0,0.0,2.0,40.0,8.0,1.0,0.0,0.0,9.0,6.0,5.0,1.0,0.0,0.0,0.0,0.0,4.0,2.34,2.0,2.0,0.0,0.0,1.0,0.58,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,2.0,2.0,0.0,61.0,4.0,17.0,23.0,23.0,0.0,61.0,26.0,1.0,2.0,0.0,28.0,3.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0 -"Luka Jović,rs SRB,""FW,MF"",Fiorentina,25-049,1997,19.0,12.0,1010.0,3.0,0.0,0.0,1.0,2.0,0.0,0.27,0.0,0.27,0.27,0.27,4.5,3.7,0.4,0.33,11.2,11.0,0.0,33.3,0.98,0.09,0.27,0.11,-1.5,-0.7,144.0,190.0,75.8,2312.0,366.0,73.0,89.0,82.0,56.0,71.0,78.9,11.0,16.0,68.8,14.0,9.0,3.0,0.0,16.0,166.0,23.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,28.0,2.5,19.0,0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,1.0,4.0,1.0,4.0,2.0,2.0,2.0,8.0,0.0,287.0,9.0,20.0,133.0,136.0,51.0,286.0,138.0,4.0,4.0,4.0,214.0,18.0,12.0,0.0,16.0,7.0,7.0,0.0,0.0,0.0,11.0,18.0,35.0,34.0" -"Hamed Junior Traorè,ci CIV,""MF,FW"",Sassuolo,22-359,2000,11.0,6.0,591.0,0.0,3.0,0.0,0.0,1.0,0.0,0.0,0.46,0.46,0.0,0.46,1.0,1.0,0.15,0.15,6.6,4.0,2.0,50.0,0.61,0.0,0.0,0.12,-1.0,-1.0,184.0,245.0,75.1,3077.0,823.0,87.0,101.0,86.1,68.0,83.0,81.9,20.0,34.0,58.8,17.0,17.0,7.0,3.0,26.0,219.0,22.0,3.0,1.0,4.0,26.0,16.0,6.0,4.0,0.0,2.0,4.0,14.0,26.0,3.96,21.0,5.0,0.0,0.0,6.0,0.91,4.0,2.0,0.0,0.0,0.0,11.0,6.0,4.0,6.0,1.0,4.0,0.0,4.0,2.0,0.0,1.0,308.0,3.0,43.0,142.0,127.0,24.0,308.0,194.0,14.0,7.0,7.0,226.0,16.0,23.0,0.0,9.0,2.0,1.0,0.0,0.0,0.0,35.0,3.0,5.0,37.5" -"Yayah Kallon,sl SLE,""MF,FW"",Hellas Verona,21-225,2001,16.0,6.0,623.0,1.0,1.0,0.0,0.0,1.0,0.0,0.14,0.14,0.29,0.14,0.29,0.8,0.8,0.12,0.12,6.9,3.0,0.0,30.0,0.43,0.1,0.33,0.08,0.2,0.2,114.0,177.0,64.4,1605.0,415.0,66.0,85.0,77.6,34.0,60.0,56.7,6.0,11.0,54.5,12.0,6.0,10.0,4.0,12.0,165.0,11.0,2.0,0.0,0.0,25.0,0.0,0.0,0.0,0.0,4.0,1.0,6.0,27.0,3.9,23.0,0.0,0.0,2.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,9.0,6.0,3.0,1.0,5.0,8.0,0.0,8.0,8.0,2.0,0.0,258.0,2.0,19.0,92.0,153.0,29.0,258.0,172.0,26.0,16.0,10.0,184.0,21.0,15.0,0.0,11.0,17.0,4.0,0.0,0.0,0.0,24.0,7.0,16.0,30.4" -Pierre Kalulu,fr FRA,DF,Milan,22-250,2000,21.0,16.0,1580.0,1.0,0.0,0.0,0.0,2.0,0.0,0.06,0.0,0.06,0.06,0.06,1.4,1.4,0.08,0.08,17.6,2.0,0.0,20.0,0.11,0.1,0.5,0.14,-0.4,-0.4,947.0,1088.0,87.0,17963.0,5892.0,327.0,359.0,91.1,536.0,580.0,92.4,77.0,128.0,60.2,3.0,59.0,1.0,1.0,73.0,994.0,93.0,33.0,1.0,0.0,11.0,0.0,0.0,0.0,0.0,55.0,1.0,7.0,12.0,0.68,8.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32.0,19.0,16.0,14.0,2.0,29.0,13.0,16.0,25.0,32.0,1.0,1254.0,87.0,582.0,606.0,81.0,13.0,1254.0,741.0,26.0,12.0,0.0,827.0,12.0,2.0,0.0,15.0,13.0,0.0,0.0,1.0,0.0,97.0,18.0,17.0,51.4 -"Yann Karamoh,fr FRA,""FW,MF"",Torino,24-217,1998,8.0,1.0,207.0,1.0,0.0,0.0,0.0,1.0,0.0,0.43,0.0,0.43,0.43,0.43,1.5,1.5,0.64,0.64,2.3,4.0,0.0,50.0,1.74,0.13,0.25,0.18,-0.5,-0.5,41.0,56.0,73.2,528.0,114.0,25.0,28.0,89.3,14.0,18.0,77.8,0.0,2.0,0.0,4.0,1.0,3.0,1.0,4.0,52.0,4.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,5.0,2.18,4.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,102.0,0.0,9.0,27.0,66.0,11.0,102.0,70.0,8.0,1.0,4.0,82.0,10.0,7.0,0.0,7.0,5.0,7.0,0.0,0.0,0.0,5.0,2.0,9.0,18.2" -Rick Karsdorp,nl NED,DF,Roma,27-364,1995,9.0,7.0,547.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.04,0.04,6.1,0.0,0.0,0.0,0.0,0.0,0.0,0.25,-0.3,-0.3,219.0,281.0,77.9,3937.0,1156.0,93.0,104.0,89.4,103.0,121.0,85.1,22.0,41.0,53.7,5.0,9.0,7.0,1.0,16.0,226.0,54.0,1.0,2.0,1.0,12.0,0.0,0.0,0.0,0.0,53.0,1.0,6.0,13.0,2.14,10.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,11.0,10.0,6.0,2.0,0.0,0.0,0.0,6.0,13.0,1.0,334.0,25.0,122.0,137.0,79.0,6.0,334.0,170.0,11.0,5.0,2.0,190.0,10.0,3.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,35.0,4.0,1.0,80.0 -Denso Kasius,nl NED,DF,Bologna,20-127,2002,7.0,4.0,342.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.53,0.53,0.0,0.53,0.1,0.1,0.03,0.03,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,143.0,192.0,74.5,2576.0,864.0,65.0,72.0,90.3,53.0,74.0,71.6,20.0,36.0,55.6,6.0,6.0,8.0,6.0,9.0,156.0,35.0,1.0,0.0,3.0,12.0,0.0,0.0,0.0,0.0,34.0,1.0,3.0,15.0,3.94,13.0,0.0,0.0,0.0,2.0,0.52,2.0,0.0,0.0,0.0,0.0,12.0,6.0,7.0,4.0,1.0,6.0,3.0,3.0,3.0,6.0,0.0,243.0,6.0,75.0,105.0,65.0,3.0,243.0,127.0,9.0,10.0,1.0,144.0,11.0,7.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,18.0,1.0,4.0,20.0 -"Grigoris Kastanos,cy CYP,""MF,DF"",Salernitana,25-011,1998,12.0,2.0,306.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.13,0.13,3.4,3.0,0.0,33.3,0.88,0.0,0.0,0.05,-0.4,-0.4,99.0,140.0,70.7,1570.0,452.0,50.0,56.0,89.3,38.0,55.0,69.1,6.0,12.0,50.0,5.0,12.0,2.0,0.0,14.0,132.0,5.0,4.0,0.0,4.0,5.0,1.0,0.0,0.0,0.0,0.0,3.0,6.0,9.0,2.66,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,7.0,2.0,8.0,2.0,5.0,2.0,3.0,2.0,1.0,0.0,184.0,2.0,24.0,93.0,67.0,7.0,184.0,113.0,10.0,7.0,2.0,132.0,6.0,3.0,0.0,9.0,10.0,0.0,0.0,0.0,0.0,12.0,6.0,2.0,75.0" -Moise Kean,it ITA,FW,Juventus,22-347,2000,19.0,8.0,725.0,4.0,0.0,0.0,0.0,3.0,0.0,0.5,0.0,0.5,0.5,0.5,4.7,4.7,0.58,0.58,8.1,10.0,0.0,33.3,1.24,0.13,0.4,0.16,-0.7,-0.7,97.0,134.0,72.4,1216.0,278.0,62.0,75.0,82.7,23.0,32.0,71.9,2.0,3.0,66.7,8.0,5.0,5.0,1.0,10.0,126.0,6.0,0.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,2.0,7.0,27.0,3.36,18.0,0.0,5.0,0.0,1.0,0.12,1.0,0.0,0.0,0.0,0.0,8.0,6.0,2.0,5.0,1.0,1.0,0.0,1.0,4.0,2.0,0.0,241.0,3.0,24.0,108.0,115.0,40.0,241.0,150.0,23.0,9.0,13.0,178.0,20.0,15.0,0.0,9.0,12.0,5.0,0.0,0.0,0.0,27.0,4.0,9.0,30.8 -Jakub Kiwior,pl POL,DF,Spezia,22-360,2000,17.0,17.0,1443.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.04,0.04,16.0,2.0,1.0,20.0,0.12,0.0,0.0,0.06,-0.6,-0.6,653.0,775.0,84.3,13165.0,4165.0,209.0,238.0,87.8,343.0,371.0,92.5,93.0,147.0,63.3,4.0,25.0,5.0,0.0,37.0,683.0,89.0,34.0,1.0,3.0,14.0,9.0,6.0,3.0,0.0,12.0,3.0,4.0,15.0,0.94,9.0,3.0,1.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,22.0,17.0,15.0,7.0,0.0,26.0,18.0,8.0,24.0,71.0,1.0,953.0,177.0,594.0,307.0,58.0,15.0,953.0,464.0,13.0,3.0,1.0,498.0,8.0,5.0,0.0,16.0,5.0,0.0,0.0,0.0,0.0,93.0,22.0,12.0,64.7 -Simon Kjær,dk DEN,DF,Milan,33-321,1989,10.0,7.0,598.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,342.0,393.0,87.0,7403.0,2630.0,99.0,112.0,88.4,176.0,186.0,94.6,64.0,90.0,71.1,0.0,31.0,1.0,0.0,30.0,388.0,5.0,4.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,4.0,0.6,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,4.0,1.0,0.0,6.0,4.0,2.0,7.0,14.0,0.0,430.0,41.0,219.0,205.0,8.0,1.0,430.0,259.0,0.0,1.0,0.0,309.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,37.0,9.0,5.0,64.3 -Teun Koopmeiners,nl NED,MF,Atalanta,24-347,1998,20.0,19.0,1745.0,6.0,3.0,2.0,4.0,5.0,0.0,0.31,0.15,0.46,0.21,0.36,5.6,2.2,0.29,0.11,19.4,14.0,5.0,43.8,0.72,0.13,0.29,0.07,0.4,1.8,798.0,1071.0,74.5,14886.0,5967.0,322.0,371.0,86.8,347.0,435.0,79.8,101.0,209.0,48.3,32.0,100.0,31.0,7.0,117.0,977.0,91.0,36.0,4.0,15.0,85.0,35.0,14.0,16.0,0.0,17.0,3.0,19.0,71.0,3.66,49.0,14.0,6.0,2.0,12.0,0.62,7.0,3.0,2.0,0.0,0.0,28.0,16.0,8.0,16.0,4.0,25.0,4.0,21.0,16.0,28.0,1.0,1281.0,70.0,332.0,571.0,391.0,38.0,1277.0,710.0,34.0,35.0,5.0,872.0,29.0,17.0,0.0,25.0,12.0,1.0,0.0,0.0,0.0,115.0,20.0,12.0,62.5 -"Filip Kostić,rs SRB,""DF,FW"",Juventus,30-101,1992,21.0,19.0,1484.0,2.0,5.0,0.0,0.0,1.0,0.0,0.12,0.3,0.42,0.12,0.42,1.6,1.6,0.1,0.1,16.5,7.0,0.0,31.8,0.42,0.09,0.29,0.07,0.4,0.4,493.0,708.0,69.6,8305.0,2711.0,237.0,283.0,83.7,191.0,257.0,74.3,50.0,109.0,45.9,37.0,32.0,30.0,20.0,49.0,619.0,87.0,7.0,2.0,3.0,132.0,27.0,2.0,24.0,0.0,53.0,2.0,34.0,54.0,3.27,41.0,8.0,3.0,0.0,7.0,0.42,5.0,0.0,2.0,0.0,0.0,29.0,20.0,12.0,13.0,4.0,17.0,1.0,16.0,6.0,13.0,0.0,868.0,17.0,124.0,359.0,393.0,31.0,868.0,459.0,53.0,26.0,12.0,557.0,35.0,8.0,0.0,24.0,6.0,0.0,0.0,0.0,0.0,85.0,6.0,7.0,46.2" -Christian Kouamé,ci CIV,FW,Fiorentina,25-066,1997,19.0,16.0,1347.0,2.0,2.0,0.0,0.0,3.0,0.0,0.13,0.13,0.27,0.13,0.27,3.8,3.8,0.25,0.25,15.0,11.0,1.0,33.3,0.73,0.06,0.18,0.12,-1.8,-1.8,363.0,510.0,71.2,5428.0,1246.0,213.0,265.0,80.4,102.0,150.0,68.0,25.0,43.0,58.1,28.0,23.0,24.0,6.0,43.0,486.0,23.0,2.0,1.0,5.0,36.0,2.0,0.0,0.0,0.0,15.0,1.0,12.0,62.0,4.14,48.0,0.0,4.0,6.0,4.0,0.27,3.0,0.0,0.0,0.0,0.0,16.0,12.0,6.0,4.0,6.0,13.0,3.0,10.0,12.0,3.0,0.0,721.0,4.0,58.0,240.0,430.0,86.0,721.0,442.0,43.0,23.0,19.0,537.0,58.0,31.0,0.0,25.0,45.0,3.0,1.0,0.0,0.0,73.0,44.0,39.0,53.0 -Viktor Kovalenko,ua UKR,MF,Spezia,26-361,1996,10.0,5.0,390.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.08,0.08,4.3,0.0,1.0,0.0,0.0,0.0,0.0,0.07,-0.4,-0.4,134.0,167.0,80.2,2031.0,446.0,75.0,81.0,92.6,48.0,60.0,80.0,9.0,15.0,60.0,5.0,3.0,4.0,1.0,17.0,160.0,7.0,2.0,0.0,3.0,6.0,2.0,1.0,0.0,0.0,3.0,0.0,6.0,7.0,1.61,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,3.0,0.0,5.0,2.0,10.0,2.0,8.0,1.0,5.0,0.0,211.0,7.0,36.0,110.0,66.0,11.0,211.0,109.0,1.0,2.0,1.0,136.0,7.0,0.0,0.0,7.0,7.0,1.0,0.0,0.0,0.0,17.0,2.0,9.0,18.2 -Julian Kristoffersen,no NOR,FW,Salernitana,25-276,1997,1.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,4.0,0.0,0.0,2.0,2.0,0.0,4.0,1.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,25.0 -Raimonds Krollis,lv LVA,FW,Spezia,21-105,2001,1.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.31,0.31,0.2,1.0,0.0,100.0,6.0,0.0,0.0,0.05,-0.1,-0.1,1.0,2.0,50.0,23.0,5.0,0.0,1.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,1.0,4.0,0.0,5.0,3.0,1.0,0.0,0.0,3.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -"Rade Krunić,ba BIH,""MF,FW"",Milan,29-126,1993,10.0,7.0,631.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.14,0.14,0.0,0.14,0.5,0.5,0.08,0.08,7.0,1.0,0.0,16.7,0.14,0.0,0.0,0.09,-0.5,-0.5,152.0,198.0,76.8,2416.0,788.0,70.0,87.0,80.5,65.0,75.0,86.7,10.0,16.0,62.5,8.0,18.0,6.0,0.0,26.0,191.0,7.0,3.0,1.0,1.0,3.0,2.0,1.0,1.0,0.0,2.0,0.0,10.0,16.0,2.29,12.0,1.0,2.0,0.0,2.0,0.29,2.0,0.0,0.0,0.0,0.0,15.0,9.0,7.0,7.0,1.0,10.0,5.0,5.0,8.0,11.0,0.0,269.0,21.0,76.0,146.0,50.0,14.0,269.0,129.0,10.0,11.0,2.0,133.0,6.0,5.0,0.0,6.0,6.0,0.0,0.0,0.0,0.0,35.0,8.0,10.0,44.4" -Marash Kumbulla,al ALB,DF,Roma,23-002,2000,4.0,2.0,134.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,76.0,81.0,93.8,1383.0,419.0,28.0,28.0,100.0,41.0,44.0,93.2,5.0,6.0,83.3,1.0,2.0,0.0,0.0,3.0,80.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.67,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,4.0,0.0,91.0,10.0,49.0,39.0,4.0,2.0,91.0,56.0,1.0,1.0,0.0,74.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,7.0,3.0,2.0,60.0 -Khvicha Kvaratskhelia,ge GEO,FW,Napoli,21-363,2001,17.0,16.0,1232.0,8.0,9.0,1.0,1.0,0.0,0.0,0.58,0.66,1.24,0.51,1.17,5.4,4.6,0.39,0.33,13.7,19.0,1.0,40.4,1.39,0.15,0.37,0.1,2.6,2.4,346.0,446.0,77.6,4795.0,873.0,217.0,244.0,88.9,89.0,115.0,77.4,20.0,39.0,51.3,28.0,16.0,18.0,4.0,33.0,428.0,15.0,2.0,6.0,5.0,26.0,4.0,1.0,1.0,0.0,9.0,3.0,14.0,70.0,5.11,44.0,1.0,9.0,4.0,16.0,1.17,8.0,1.0,4.0,2.0,0.0,24.0,10.0,5.0,6.0,13.0,11.0,0.0,11.0,1.0,4.0,1.0,675.0,5.0,56.0,211.0,417.0,102.0,674.0,437.0,65.0,24.0,47.0,514.0,39.0,31.0,0.0,9.0,24.0,4.0,2.0,0.0,0.0,58.0,2.0,5.0,28.6 -Giorgos Kyriakopoulos,gr GRE,FW,Bologna,27-005,1996,1.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"Giorgos Kyriakopoulos,gr GRE,""FW,DF"",Sassuolo,27-005,1996,12.0,9.0,766.0,1.0,1.0,0.0,0.0,3.0,0.0,0.12,0.12,0.23,0.12,0.23,1.1,1.1,0.13,0.13,8.5,4.0,0.0,30.8,0.47,0.08,0.25,0.08,-0.1,-0.1,300.0,391.0,76.7,5430.0,2158.0,148.0,165.0,89.7,103.0,125.0,82.4,44.0,81.0,54.3,14.0,23.0,14.0,9.0,32.0,332.0,56.0,7.0,0.0,6.0,39.0,10.0,2.0,5.0,0.0,39.0,3.0,9.0,26.0,3.05,21.0,0.0,1.0,1.0,3.0,0.35,2.0,0.0,1.0,0.0,0.0,21.0,8.0,7.0,9.0,5.0,7.0,1.0,6.0,11.0,12.0,1.0,505.0,14.0,140.0,187.0,186.0,16.0,505.0,312.0,17.0,15.0,5.0,333.0,14.0,18.0,0.0,9.0,13.0,0.0,1.0,0.0,0.0,37.0,7.0,13.0,35.0" -Sam Lammers,nl NED,FW,Sampdoria,25-286,1997,6.0,6.0,481.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.19,0.19,0.0,0.19,0.9,0.9,0.16,0.16,5.3,1.0,0.0,10.0,0.19,0.0,0.0,0.09,-0.9,-0.9,76.0,105.0,72.4,972.0,217.0,56.0,68.0,82.4,14.0,21.0,66.7,3.0,3.0,100.0,7.0,5.0,3.0,0.0,5.0,101.0,4.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,13.0,2.43,11.0,0.0,1.0,0.0,1.0,0.19,1.0,0.0,0.0,0.0,0.0,5.0,2.0,0.0,3.0,2.0,5.0,0.0,5.0,3.0,4.0,0.0,179.0,4.0,13.0,96.0,71.0,16.0,179.0,120.0,6.0,4.0,2.0,154.0,23.0,10.0,0.0,7.0,11.0,0.0,0.0,0.0,0.0,10.0,13.0,26.0,33.3 -Sam Lammers,nl NED,FW,Empoli,25-286,1997,14.0,10.0,883.0,1.0,1.0,0.0,0.0,0.0,0.0,0.1,0.1,0.2,0.1,0.2,2.3,2.3,0.23,0.23,9.8,7.0,0.0,25.0,0.71,0.04,0.14,0.08,-1.3,-1.3,143.0,230.0,62.2,1814.0,451.0,94.0,129.0,72.9,41.0,60.0,68.3,1.0,5.0,20.0,10.0,13.0,4.0,0.0,19.0,226.0,3.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,8.0,25.0,2.55,18.0,1.0,1.0,2.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,20.0,10.0,5.0,9.0,6.0,4.0,1.0,3.0,3.0,8.0,0.0,358.0,12.0,35.0,151.0,179.0,49.0,358.0,193.0,9.0,12.0,6.0,268.0,37.0,14.0,0.0,6.0,8.0,1.0,0.0,0.0,0.0,27.0,18.0,16.0,52.9 -"Kevin Lasagna,it ITA,""FW,MF"",Hellas Verona,30-184,1992,17.0,12.0,1116.0,1.0,2.0,0.0,0.0,1.0,0.0,0.08,0.16,0.24,0.08,0.24,4.8,4.8,0.38,0.38,12.4,8.0,0.0,20.5,0.65,0.03,0.13,0.12,-3.8,-3.8,109.0,183.0,59.6,1662.0,407.0,53.0,80.0,66.3,40.0,61.0,65.6,5.0,10.0,50.0,10.0,8.0,5.0,0.0,12.0,169.0,10.0,1.0,0.0,1.0,9.0,0.0,0.0,0.0,0.0,1.0,4.0,12.0,27.0,2.18,17.0,0.0,4.0,2.0,3.0,0.24,1.0,0.0,1.0,0.0,0.0,9.0,3.0,7.0,1.0,1.0,11.0,1.0,10.0,3.0,9.0,0.0,348.0,9.0,43.0,130.0,182.0,53.0,348.0,190.0,25.0,15.0,11.0,228.0,40.0,29.0,0.0,21.0,16.0,12.0,0.0,0.0,0.0,50.0,10.0,36.0,21.7" -Armand Lauriente,fr FRA,FW,Sassuolo,24-068,1998,15.0,15.0,1224.0,4.0,3.0,1.0,1.0,5.0,1.0,0.29,0.22,0.51,0.22,0.44,2.6,1.8,0.19,0.13,13.6,16.0,6.0,44.4,1.18,0.08,0.19,0.05,1.4,1.2,331.0,456.0,72.6,5104.0,1591.0,187.0,212.0,88.2,105.0,144.0,72.9,25.0,57.0,43.9,42.0,23.0,25.0,6.0,46.0,406.0,50.0,11.0,1.0,2.0,72.0,31.0,20.0,7.0,0.0,7.0,0.0,15.0,62.0,4.56,46.0,5.0,1.0,3.0,6.0,0.44,4.0,0.0,0.0,1.0,0.0,10.0,6.0,1.0,6.0,3.0,10.0,0.0,10.0,4.0,6.0,0.0,623.0,4.0,41.0,235.0,359.0,53.0,622.0,422.0,73.0,36.0,29.0,466.0,31.0,37.0,1.0,11.0,27.0,3.0,1.0,0.0,0.0,47.0,1.0,4.0,20.0 -Valentino Lazaro,at AUT,DF,Torino,26-323,1996,15.0,13.0,1065.0,0.0,2.0,0.0,0.0,4.0,0.0,0.0,0.17,0.17,0.0,0.17,0.4,0.4,0.03,0.03,11.8,2.0,0.0,28.6,0.17,0.0,0.0,0.06,-0.4,-0.4,524.0,695.0,75.4,8590.0,3209.0,263.0,297.0,88.6,194.0,255.0,76.1,48.0,99.0,48.5,22.0,37.0,16.0,8.0,58.0,525.0,168.0,20.0,0.0,1.0,65.0,33.0,9.0,15.0,1.0,115.0,2.0,16.0,42.0,3.55,25.0,13.0,2.0,0.0,3.0,0.25,2.0,1.0,0.0,0.0,0.0,10.0,5.0,7.0,0.0,3.0,11.0,2.0,9.0,2.0,18.0,0.0,785.0,29.0,149.0,320.0,325.0,22.0,785.0,381.0,38.0,21.0,8.0,454.0,15.0,4.0,0.0,11.0,3.0,1.0,0.0,1.0,0.0,73.0,10.0,10.0,50.0 -Marko Lazetić,rs SRB,FW,Milan,19-019,2004,1.0,0.0,9.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.63,0.63,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,1.0,1.0,100.0,18.0,0.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,2.0,2.0,1.0,4.0,2.0,0.0,0.0,0.0,3.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 -"Darko Lazović,rs SRB,""DF,MF"",Hellas Verona,32-148,1990,17.0,16.0,1323.0,3.0,3.0,0.0,0.0,0.0,0.0,0.2,0.2,0.41,0.2,0.41,2.4,2.4,0.17,0.17,14.7,8.0,1.0,32.0,0.54,0.12,0.38,0.1,0.6,0.6,310.0,491.0,63.1,5213.0,2201.0,147.0,188.0,78.2,117.0,166.0,70.5,34.0,87.0,39.1,20.0,28.0,21.0,9.0,53.0,424.0,61.0,4.0,2.0,1.0,64.0,17.0,8.0,5.0,0.0,40.0,6.0,25.0,33.0,2.24,22.0,8.0,0.0,0.0,3.0,0.2,1.0,2.0,0.0,0.0,0.0,17.0,9.0,6.0,8.0,3.0,8.0,2.0,6.0,10.0,13.0,0.0,626.0,19.0,110.0,237.0,290.0,38.0,626.0,309.0,44.0,23.0,14.0,374.0,22.0,8.0,0.0,5.0,2.0,6.0,0.0,0.0,0.0,81.0,5.0,13.0,27.8" -Manuel Lazzari,it ITA,DF,Lazio,29-073,1993,17.0,15.0,1184.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,13.2,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,624.0,708.0,88.1,8743.0,3137.0,370.0,397.0,93.2,209.0,237.0,88.2,22.0,30.0,73.3,6.0,23.0,8.0,4.0,32.0,587.0,120.0,12.0,0.0,1.0,19.0,0.0,0.0,0.0,0.0,108.0,1.0,12.0,18.0,1.37,13.0,1.0,0.0,2.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,26.0,16.0,10.0,12.0,4.0,14.0,3.0,11.0,12.0,21.0,1.0,832.0,30.0,292.0,395.0,155.0,13.0,832.0,444.0,47.0,34.0,9.0,536.0,15.0,3.0,0.0,9.0,27.0,0.0,0.0,0.0,0.0,54.0,5.0,10.0,33.3 -"Rafael Leão,pt POR,""FW,MF"",Milan,23-245,1999,20.0,16.0,1434.0,8.0,5.0,0.0,0.0,6.0,1.0,0.5,0.31,0.82,0.5,0.82,6.3,6.3,0.39,0.39,15.9,15.0,0.0,25.9,0.94,0.14,0.53,0.11,1.7,1.7,332.0,450.0,73.8,6007.0,1907.0,149.0,192.0,77.6,132.0,162.0,81.5,42.0,63.0,66.7,30.0,41.0,35.0,9.0,74.0,428.0,20.0,2.0,2.0,14.0,35.0,3.0,0.0,0.0,0.0,15.0,2.0,15.0,67.0,4.2,47.0,1.0,3.0,5.0,8.0,0.5,7.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,2.0,0.0,2.0,1.0,1.0,8.0,4.0,0.0,639.0,6.0,33.0,263.0,357.0,92.0,639.0,433.0,75.0,52.0,43.0,492.0,38.0,21.0,1.0,13.0,16.0,7.0,0.0,0.0,0.0,50.0,15.0,15.0,50.0" -"Mehdi Léris,dz ALG,""MF,DF"",Sampdoria,24-263,1998,19.0,16.0,1355.0,0.0,1.0,0.0,0.0,10.0,0.0,0.0,0.07,0.07,0.0,0.07,1.9,1.9,0.13,0.13,15.1,6.0,0.0,40.0,0.4,0.0,0.0,0.13,-1.9,-1.9,350.0,527.0,66.4,5259.0,1901.0,204.0,243.0,84.0,104.0,165.0,63.0,27.0,58.0,46.6,11.0,24.0,10.0,3.0,32.0,497.0,26.0,1.0,3.0,5.0,35.0,0.0,0.0,0.0,0.0,25.0,4.0,26.0,24.0,1.6,18.0,0.0,3.0,2.0,2.0,0.13,0.0,0.0,2.0,0.0,0.0,36.0,28.0,20.0,11.0,5.0,23.0,3.0,20.0,17.0,13.0,0.0,725.0,22.0,166.0,305.0,266.0,36.0,725.0,414.0,26.0,14.0,5.0,485.0,46.0,30.0,0.0,32.0,25.0,4.0,0.0,0.0,0.0,88.0,37.0,21.0,63.8" -Karol Linetty,pl POL,MF,Torino,28-008,1995,20.0,14.0,1195.0,1.0,0.0,0.0,0.0,6.0,0.0,0.08,0.0,0.08,0.08,0.08,0.8,0.8,0.06,0.06,13.3,4.0,0.0,40.0,0.3,0.1,0.25,0.08,0.2,0.2,491.0,580.0,84.7,8113.0,1859.0,238.0,273.0,87.2,206.0,225.0,91.6,35.0,47.0,74.5,14.0,49.0,4.0,1.0,48.0,549.0,27.0,17.0,2.0,9.0,10.0,9.0,2.0,5.0,0.0,1.0,4.0,7.0,27.0,2.04,20.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,13.0,5.0,18.0,2.0,16.0,5.0,11.0,14.0,16.0,0.0,716.0,23.0,109.0,426.0,183.0,17.0,716.0,362.0,14.0,16.0,3.0,453.0,16.0,9.0,0.0,29.0,9.0,3.0,0.0,0.0,0.0,78.0,14.0,14.0,50.0 -"Marcin Listkowski,pl POL,""MF,FW"",Lecce,25-000,1998,5.0,0.0,77.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.06,0.06,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,16.0,24.0,66.7,204.0,71.0,14.0,16.0,87.5,0.0,3.0,0.0,1.0,4.0,25.0,0.0,0.0,0.0,0.0,1.0,23.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.15,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,0.0,34.0,2.0,10.0,11.0,13.0,1.0,34.0,17.0,6.0,1.0,0.0,20.0,1.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0" -Diego Llorente,es ESP,DF,Roma,29-178,1993,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Stanislav Lobotka,sk SVK,MF,Napoli,28-077,1994,21.0,19.0,1745.0,1.0,1.0,0.0,0.0,1.0,0.0,0.05,0.05,0.1,0.05,0.1,0.3,0.3,0.01,0.01,19.4,2.0,0.0,40.0,0.1,0.2,0.5,0.05,0.7,0.7,1225.0,1300.0,94.2,20117.0,5070.0,614.0,640.0,95.9,479.0,502.0,95.4,99.0,115.0,86.1,9.0,122.0,6.0,0.0,95.0,1279.0,21.0,17.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,43.0,2.22,40.0,0.0,1.0,2.0,5.0,0.26,4.0,0.0,1.0,0.0,0.0,49.0,24.0,22.0,19.0,8.0,17.0,5.0,12.0,8.0,10.0,0.0,1427.0,44.0,307.0,928.0,201.0,7.0,1427.0,929.0,24.0,35.0,4.0,1113.0,14.0,8.0,0.0,14.0,37.0,0.0,0.0,0.0,0.0,138.0,8.0,11.0,42.1 -Manuel Locatelli,it ITA,MF,Juventus,25-033,1998,18.0,17.0,1400.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9,0.9,0.06,0.06,15.6,7.0,0.0,43.8,0.45,0.0,0.0,0.06,-0.9,-0.9,653.0,803.0,81.3,12154.0,3999.0,290.0,331.0,87.6,258.0,298.0,86.6,89.0,133.0,66.9,10.0,84.0,8.0,0.0,78.0,764.0,39.0,30.0,0.0,16.0,8.0,4.0,0.0,3.0,0.0,4.0,0.0,17.0,32.0,2.05,25.0,3.0,2.0,1.0,5.0,0.32,3.0,0.0,0.0,1.0,0.0,34.0,19.0,15.0,15.0,4.0,26.0,10.0,16.0,18.0,27.0,0.0,975.0,41.0,261.0,550.0,174.0,11.0,975.0,463.0,18.0,10.0,3.0,649.0,19.0,13.0,0.0,25.0,20.0,0.0,0.0,0.0,0.0,100.0,24.0,14.0,63.2 -Luka Lochoshvili,ge GEO,DF,Cremonese,24-257,1998,14.0,14.0,1136.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.08,0.08,0.0,0.08,0.3,0.3,0.02,0.02,12.6,1.0,0.0,25.0,0.08,0.0,0.0,0.07,-0.3,-0.3,394.0,503.0,78.3,7165.0,2683.0,151.0,168.0,89.9,217.0,246.0,88.2,24.0,75.0,32.0,4.0,32.0,3.0,1.0,52.0,480.0,22.0,17.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,5.0,1.0,4.0,12.0,0.95,8.0,0.0,2.0,0.0,2.0,0.16,1.0,0.0,1.0,0.0,0.0,31.0,10.0,21.0,7.0,3.0,22.0,4.0,18.0,14.0,44.0,0.0,645.0,65.0,329.0,281.0,45.0,5.0,645.0,342.0,12.0,13.0,0.0,368.0,8.0,4.0,0.0,23.0,5.0,1.0,0.0,1.0,0.0,57.0,20.0,11.0,64.5 -"Ademola Lookman,ng NGA,""FW,MF"",Atalanta,25-113,1997,20.0,15.0,1210.0,12.0,3.0,3.0,3.0,2.0,0.0,0.89,0.22,1.12,0.67,0.89,7.5,5.1,0.55,0.38,13.4,13.0,1.0,36.1,0.97,0.25,0.69,0.14,4.5,3.9,342.0,443.0,77.2,4863.0,1460.0,185.0,217.0,85.3,108.0,139.0,77.7,20.0,34.0,58.8,24.0,23.0,23.0,3.0,57.0,407.0,36.0,5.0,5.0,2.0,32.0,16.0,2.0,7.0,0.0,7.0,0.0,12.0,58.0,4.31,40.0,6.0,4.0,5.0,9.0,0.67,5.0,0.0,2.0,1.0,0.0,8.0,3.0,2.0,4.0,2.0,13.0,0.0,13.0,3.0,0.0,0.0,588.0,1.0,37.0,243.0,315.0,85.0,585.0,390.0,50.0,17.0,22.0,464.0,31.0,34.0,0.0,12.0,27.0,9.0,1.0,0.0,0.0,60.0,2.0,7.0,22.2" -Maxime Lopez,fr FRA,MF,Sassuolo,25-068,1997,14.0,12.0,1120.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.04,0.04,12.4,2.0,0.0,33.3,0.16,0.0,0.0,0.09,-0.5,-0.5,665.0,762.0,87.3,11512.0,4137.0,308.0,334.0,92.2,267.0,306.0,87.3,71.0,93.0,76.3,17.0,91.0,6.0,2.0,104.0,739.0,21.0,14.0,2.0,6.0,12.0,5.0,1.0,1.0,0.0,2.0,2.0,6.0,33.0,2.66,31.0,2.0,0.0,0.0,2.0,0.16,2.0,0.0,0.0,0.0,0.0,24.0,9.0,15.0,7.0,2.0,11.0,1.0,10.0,11.0,11.0,0.0,865.0,33.0,215.0,509.0,149.0,6.0,865.0,543.0,27.0,26.0,1.0,649.0,9.0,8.0,0.0,11.0,8.0,0.0,0.0,0.0,0.0,87.0,3.0,2.0,60.0 -Matteo Lovato,it ITA,DF,Salernitana,22-361,2000,7.0,4.0,377.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.2,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,153.0,180.0,85.0,2855.0,1066.0,56.0,61.0,91.8,80.0,92.0,87.0,15.0,22.0,68.2,0.0,7.0,0.0,0.0,7.0,172.0,8.0,8.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,7.0,6.0,2.0,0.0,5.0,2.0,3.0,6.0,16.0,2.0,222.0,21.0,118.0,98.0,9.0,0.0,222.0,129.0,1.0,0.0,0.0,142.0,3.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,20.0,9.0,6.0,60.0 -Sandi Lovrić,si SVN,MF,Udinese,24-319,1998,20.0,12.0,1048.0,2.0,1.0,0.0,0.0,2.0,0.0,0.17,0.09,0.26,0.17,0.26,1.8,1.8,0.16,0.16,11.6,5.0,2.0,13.9,0.43,0.06,0.4,0.05,0.2,0.2,399.0,500.0,79.8,6682.0,1833.0,191.0,217.0,88.0,136.0,163.0,83.4,44.0,79.0,55.7,21.0,55.0,13.0,4.0,59.0,468.0,29.0,13.0,2.0,10.0,38.0,13.0,3.0,6.0,0.0,3.0,3.0,7.0,41.0,3.52,28.0,5.0,3.0,1.0,4.0,0.34,3.0,0.0,0.0,0.0,0.0,24.0,10.0,7.0,11.0,6.0,8.0,0.0,8.0,8.0,13.0,0.0,656.0,18.0,90.0,332.0,246.0,28.0,656.0,400.0,19.0,20.0,5.0,443.0,28.0,10.0,0.0,16.0,12.0,1.0,0.0,0.0,0.0,70.0,2.0,5.0,28.6 -Hirving Lozano,mx MEX,FW,Napoli,27-195,1995,20.0,11.0,981.0,3.0,3.0,1.0,1.0,2.0,0.0,0.28,0.28,0.55,0.18,0.46,4.6,3.8,0.42,0.35,10.9,10.0,0.0,33.3,0.92,0.07,0.2,0.13,-1.6,-1.8,252.0,339.0,74.3,3672.0,1015.0,139.0,158.0,88.0,87.0,109.0,79.8,16.0,30.0,53.3,19.0,15.0,25.0,14.0,35.0,333.0,4.0,0.0,0.0,1.0,40.0,1.0,0.0,0.0,0.0,3.0,2.0,15.0,37.0,3.4,30.0,0.0,2.0,2.0,7.0,0.64,7.0,0.0,0.0,0.0,0.0,11.0,6.0,5.0,4.0,2.0,18.0,0.0,18.0,4.0,5.0,0.0,472.0,4.0,47.0,159.0,274.0,53.0,471.0,335.0,51.0,14.0,11.0,361.0,29.0,8.0,0.0,19.0,21.0,2.0,0.0,0.0,0.0,36.0,4.0,6.0,40.0 -Jhon Lucumí,co COL,DF,Bologna,24-229,1998,18.0,18.0,1550.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.06,0.06,0.0,0.06,0.4,0.4,0.02,0.02,17.2,2.0,0.0,33.3,0.12,0.0,0.0,0.07,-0.4,-0.4,802.0,931.0,86.1,15063.0,5187.0,327.0,345.0,94.8,362.0,398.0,91.0,101.0,149.0,67.8,4.0,51.0,5.0,1.0,44.0,881.0,49.0,38.0,1.0,12.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,12.0,0.7,10.0,0.0,1.0,0.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,35.0,24.0,23.0,11.0,1.0,21.0,14.0,7.0,23.0,64.0,0.0,1112.0,149.0,603.0,490.0,26.0,10.0,1112.0,607.0,15.0,7.0,2.0,680.0,8.0,5.0,0.0,15.0,9.0,0.0,0.0,2.0,0.0,111.0,27.0,15.0,64.3 -José Luis Palomino,ar ARG,DF,Atalanta,33-036,1990,5.0,5.0,360.0,1.0,0.0,0.0,0.0,0.0,0.0,0.25,0.0,0.25,0.25,0.25,0.1,0.1,0.03,0.03,4.0,2.0,0.0,66.7,0.5,0.33,0.5,0.04,0.9,0.9,197.0,241.0,81.7,3713.0,1407.0,65.0,76.0,85.5,112.0,124.0,90.3,19.0,32.0,59.4,1.0,10.0,2.0,1.0,13.0,227.0,14.0,13.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,1.0,0.25,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,3.0,8.0,1.0,0.0,3.0,2.0,1.0,15.0,12.0,0.0,288.0,28.0,124.0,154.0,12.0,4.0,288.0,143.0,1.0,0.0,0.0,161.0,2.0,0.0,0.0,4.0,4.0,0.0,0.0,0.0,1.0,35.0,13.0,9.0,59.1 -Romelu Lukaku,be BEL,FW,Inter,29-273,1993,9.0,4.0,395.0,1.0,1.0,0.0,0.0,0.0,0.0,0.23,0.23,0.46,0.23,0.46,1.3,1.3,0.3,0.3,4.4,4.0,0.0,44.4,0.91,0.11,0.25,0.15,-0.3,-0.3,53.0,85.0,62.4,604.0,105.0,32.0,45.0,71.1,13.0,18.0,72.2,1.0,4.0,25.0,5.0,1.0,5.0,1.0,7.0,83.0,1.0,0.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,10.0,2.28,8.0,0.0,1.0,0.0,3.0,0.68,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,123.0,2.0,5.0,35.0,83.0,30.0,123.0,70.0,2.0,4.0,1.0,106.0,16.0,8.0,0.0,4.0,1.0,3.0,0.0,0.0,0.0,6.0,12.0,14.0,46.2 -Saša Lukić,rs SRB,MF,Torino,26-181,1996,16.0,13.0,1195.0,2.0,0.0,1.0,1.0,4.0,0.0,0.15,0.0,0.15,0.08,0.08,1.7,0.9,0.13,0.07,13.3,3.0,1.0,20.0,0.23,0.07,0.33,0.06,0.3,0.1,627.0,732.0,85.7,11788.0,2410.0,232.0,265.0,87.5,307.0,339.0,90.6,78.0,102.0,76.5,16.0,54.0,8.0,3.0,44.0,692.0,38.0,29.0,1.0,6.0,25.0,4.0,0.0,4.0,0.0,5.0,2.0,7.0,44.0,3.31,35.0,3.0,2.0,3.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,21.0,14.0,10.0,10.0,1.0,17.0,7.0,10.0,13.0,10.0,0.0,850.0,29.0,129.0,537.0,190.0,23.0,849.0,448.0,26.0,18.0,4.0,556.0,17.0,8.0,0.0,26.0,10.0,0.0,0.0,0.0,0.0,81.0,22.0,22.0,50.0 -Sebastiano Luperto,it ITA,DF,Empoli,26-157,1996,19.0,19.0,1624.0,1.0,0.0,0.0,0.0,3.0,2.0,0.06,0.0,0.06,0.06,0.06,0.4,0.4,0.02,0.02,18.0,3.0,0.0,50.0,0.17,0.17,0.33,0.07,0.6,0.6,725.0,836.0,86.7,13915.0,4900.0,246.0,259.0,95.0,396.0,431.0,91.9,75.0,131.0,57.3,2.0,33.0,2.0,1.0,41.0,720.0,115.0,47.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,4.0,9.0,0.5,7.0,1.0,1.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,18.0,8.0,11.0,6.0,1.0,26.0,19.0,7.0,19.0,84.0,0.0,1011.0,218.0,605.0,389.0,23.0,7.0,1011.0,534.0,6.0,4.0,1.0,552.0,4.0,2.0,1.0,11.0,13.0,0.0,0.0,0.0,0.0,87.0,29.0,28.0,50.9 -Charalambos Lykogiannis,gr GRE,DF,Bologna,29-111,1993,16.0,10.0,894.0,1.0,0.0,0.0,0.0,3.0,0.0,0.1,0.0,0.1,0.1,0.1,0.5,0.5,0.05,0.05,9.9,5.0,4.0,31.3,0.5,0.06,0.2,0.03,0.5,0.5,384.0,552.0,69.6,6490.0,2653.0,184.0,208.0,88.5,161.0,218.0,73.9,31.0,85.0,36.5,14.0,35.0,12.0,9.0,35.0,458.0,91.0,7.0,1.0,7.0,53.0,11.0,6.0,3.0,0.0,73.0,3.0,23.0,23.0,2.32,15.0,4.0,2.0,1.0,1.0,0.1,0.0,1.0,0.0,0.0,0.0,10.0,5.0,3.0,4.0,3.0,6.0,3.0,3.0,15.0,27.0,0.0,655.0,27.0,198.0,297.0,168.0,3.0,655.0,347.0,23.0,16.0,0.0,411.0,7.0,0.0,0.0,11.0,9.0,1.0,0.0,0.0,0.0,47.0,14.0,13.0,51.9 -Giulio Maggiore,it ITA,MF,Salernitana,24-335,1998,10.0,9.0,653.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,7.3,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.2,-0.2,273.0,358.0,76.3,4772.0,1288.0,121.0,153.0,79.1,117.0,132.0,88.6,28.0,50.0,56.0,3.0,30.0,11.0,0.0,46.0,347.0,7.0,5.0,3.0,3.0,2.0,0.0,0.0,0.0,0.0,1.0,4.0,6.0,14.0,1.93,11.0,0.0,2.0,1.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,14.0,6.0,6.0,5.0,3.0,17.0,4.0,13.0,8.0,4.0,0.0,436.0,11.0,98.0,272.0,67.0,5.0,436.0,223.0,5.0,2.0,0.0,266.0,13.0,3.0,0.0,11.0,9.0,0.0,0.0,1.0,0.0,59.0,6.0,9.0,40.0 -Giangiacomo Magnani,it ITA,DF,Hellas Verona,27-129,1995,9.0,4.0,426.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.06,0.06,4.7,1.0,0.0,25.0,0.21,0.0,0.0,0.08,-0.3,-0.3,82.0,119.0,68.9,1844.0,771.0,23.0,30.0,76.7,43.0,54.0,79.6,15.0,30.0,50.0,1.0,7.0,1.0,0.0,8.0,116.0,3.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,4.0,0.85,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,7.0,7.0,5.0,0.0,10.0,8.0,2.0,10.0,25.0,0.0,190.0,39.0,97.0,86.0,8.0,3.0,190.0,68.0,0.0,2.0,0.0,65.0,2.0,0.0,0.0,9.0,3.0,0.0,0.0,0.0,0.0,24.0,13.0,10.0,56.5 -Mike Maignan,fr FRA,GK,Milan,27-222,1995,7.0,7.0,630.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,186.0,241.0,77.2,5066.0,3245.0,32.0,32.0,100.0,91.0,92.0,98.9,62.0,113.0,54.9,0.0,7.0,0.0,0.0,0.0,213.0,25.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,256.0,200.0,256.0,0.0,0.0,0.0,256.0,188.0,0.0,0.0,0.0,161.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,13.0,1.0,0.0,100.0 -Jean-Victor Makengo,fr FRA,MF,Udinese,24-243,1998,16.0,11.0,805.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.11,0.11,0.0,0.11,0.4,0.4,0.04,0.04,8.9,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.4,-0.4,264.0,316.0,83.5,3718.0,884.0,167.0,180.0,92.8,79.0,96.0,82.3,9.0,13.0,69.2,5.0,19.0,6.0,1.0,36.0,315.0,1.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,13.0,1.45,11.0,0.0,0.0,1.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,16.0,13.0,7.0,7.0,2.0,8.0,2.0,6.0,9.0,3.0,0.0,393.0,11.0,64.0,223.0,112.0,6.0,393.0,258.0,18.0,17.0,0.0,290.0,15.0,11.0,0.0,9.0,13.0,0.0,0.0,0.0,0.0,39.0,5.0,1.0,83.3 -Lorenzo Malagrida,it ITA,MF,Sampdoria,19-109,2003,1.0,0.0,10.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.74,0.74,0.1,1.0,0.0,100.0,9.0,0.0,0.0,0.08,-0.1,-0.1,2.0,2.0,100.0,19.0,16.0,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,0.0,1.0,1.0,2.0,1.0,4.0,2.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"Daniel Maldini,it ITA,""FW,MF"",Spezia,21-122,2001,9.0,1.0,322.0,1.0,0.0,0.0,0.0,0.0,0.0,0.28,0.0,0.28,0.28,0.28,0.7,0.7,0.21,0.21,3.6,4.0,0.0,57.1,1.12,0.14,0.25,0.11,0.3,0.3,76.0,105.0,72.4,1018.0,191.0,42.0,52.0,80.8,22.0,28.0,78.6,4.0,10.0,40.0,1.0,6.0,3.0,1.0,4.0,95.0,10.0,1.0,0.0,0.0,6.0,4.0,4.0,0.0,0.0,0.0,0.0,3.0,10.0,2.8,6.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,2.0,0.0,3.0,4.0,4.0,0.0,4.0,1.0,6.0,0.0,156.0,6.0,20.0,71.0,66.0,10.0,156.0,80.0,7.0,6.0,2.0,96.0,12.0,2.0,0.0,5.0,8.0,0.0,0.0,0.0,0.0,16.0,5.0,10.0,33.3" -Youssef Maleh,it ITA,MF,Fiorentina,24-172,1998,7.0,4.0,297.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.17,0.17,3.3,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.6,-0.6,95.0,116.0,81.9,1462.0,376.0,55.0,63.0,87.3,31.0,35.0,88.6,7.0,12.0,58.3,6.0,8.0,5.0,2.0,11.0,111.0,4.0,3.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,7.0,2.12,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,3.0,0.0,5.0,0.0,5.0,1.0,3.0,0.0,150.0,4.0,18.0,79.0,53.0,11.0,150.0,93.0,3.0,4.0,1.0,96.0,6.0,6.0,0.0,8.0,3.0,0.0,0.0,0.0,0.0,20.0,3.0,4.0,42.9 -Youssef Maleh,it ITA,MF,Lecce,24-172,1998,5.0,2.0,177.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.09,0.09,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.2,-0.2,35.0,50.0,70.0,488.0,114.0,23.0,28.0,82.1,11.0,16.0,68.8,1.0,3.0,33.3,1.0,4.0,1.0,0.0,5.0,48.0,2.0,0.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,1.0,0.0,2.0,1.0,0.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,4.0,2.0,4.0,2.0,4.0,0.0,4.0,0.0,4.0,0.0,80.0,4.0,25.0,30.0,25.0,3.0,80.0,33.0,2.0,1.0,1.0,37.0,2.0,3.0,0.0,8.0,1.0,1.0,0.0,0.0,0.0,13.0,5.0,2.0,71.4 -"Ruslan Malinovskyi,ua UKR,""MF,FW"",Atalanta,29-282,1993,15.0,5.0,553.0,1.0,2.0,0.0,0.0,2.0,0.0,0.16,0.33,0.49,0.16,0.49,0.6,0.6,0.1,0.1,6.1,6.0,0.0,31.6,0.98,0.05,0.17,0.03,0.4,0.4,248.0,332.0,74.7,4851.0,1645.0,106.0,123.0,86.2,97.0,121.0,80.2,39.0,64.0,60.9,13.0,36.0,15.0,0.0,50.0,304.0,23.0,8.0,2.0,11.0,23.0,13.0,6.0,6.0,0.0,2.0,5.0,6.0,26.0,4.25,16.0,7.0,1.0,1.0,2.0,0.33,2.0,0.0,0.0,0.0,0.0,14.0,8.0,5.0,6.0,3.0,8.0,1.0,7.0,5.0,2.0,0.0,425.0,2.0,61.0,220.0,152.0,8.0,425.0,278.0,19.0,17.0,3.0,303.0,21.0,9.0,0.0,10.0,11.0,0.0,0.0,0.0,0.0,57.0,5.0,7.0,41.7" -Gianluca Mancini,it ITA,DF,Roma,26-299,1996,21.0,21.0,1731.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.05,0.05,0.0,0.05,1.1,1.1,0.06,0.06,19.2,2.0,0.0,16.7,0.1,0.0,0.0,0.09,-1.1,-1.1,812.0,987.0,82.3,15657.0,5566.0,300.0,332.0,90.4,396.0,439.0,90.2,105.0,187.0,56.1,8.0,60.0,9.0,3.0,45.0,938.0,43.0,37.0,1.0,14.0,15.0,0.0,0.0,0.0,0.0,4.0,6.0,8.0,23.0,1.2,19.0,0.0,3.0,1.0,2.0,0.1,1.0,0.0,0.0,1.0,0.0,29.0,14.0,22.0,6.0,1.0,19.0,8.0,11.0,33.0,40.0,0.0,1137.0,132.0,567.0,520.0,60.0,15.0,1137.0,697.0,9.0,8.0,0.0,822.0,8.0,5.0,0.0,24.0,16.0,1.0,0.0,0.0,0.0,66.0,36.0,18.0,66.7 -Rolando Mandragora,it ITA,MF,Fiorentina,25-226,1997,15.0,10.0,916.0,1.0,1.0,0.0,0.0,5.0,0.0,0.1,0.1,0.2,0.1,0.2,0.8,0.8,0.08,0.08,10.2,4.0,0.0,18.2,0.39,0.05,0.25,0.04,0.2,0.2,405.0,492.0,82.3,7883.0,2377.0,152.0,178.0,85.4,190.0,211.0,90.0,50.0,72.0,69.4,14.0,58.0,8.0,3.0,80.0,455.0,35.0,21.0,1.0,11.0,26.0,12.0,6.0,3.0,0.0,2.0,2.0,8.0,41.0,4.03,31.0,6.0,3.0,0.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,22.0,12.0,6.0,13.0,3.0,10.0,1.0,9.0,9.0,15.0,0.0,614.0,19.0,93.0,348.0,179.0,22.0,614.0,328.0,13.0,11.0,3.0,384.0,10.0,4.0,0.0,19.0,9.0,1.0,0.0,0.0,0.0,54.0,15.0,14.0,51.7 -Riccardo Marchizza,it ITA,DF,Sassuolo,24-321,1998,4.0,1.0,107.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.84,0.84,0.0,0.84,0.0,0.0,0.0,0.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,37.0,52.0,71.2,633.0,175.0,18.0,21.0,85.7,15.0,21.0,71.4,4.0,10.0,40.0,1.0,4.0,0.0,0.0,3.0,38.0,14.0,2.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,2.0,1.68,2.0,0.0,0.0,0.0,1.0,0.84,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,60.0,4.0,15.0,30.0,15.0,0.0,60.0,30.0,1.0,3.0,0.0,31.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,5.0,2.0,0.0,100.0 -Gian Marco Ferrari,it ITA,DF,Sassuolo,30-360,1992,19.0,17.0,1529.0,1.0,0.0,0.0,0.0,5.0,0.0,0.06,0.0,0.06,0.06,0.06,0.9,0.9,0.05,0.05,17.0,1.0,0.0,20.0,0.06,0.2,1.0,0.18,0.1,0.1,704.0,828.0,85.0,14552.0,5164.0,205.0,225.0,91.1,385.0,425.0,90.6,105.0,157.0,66.9,1.0,42.0,2.0,0.0,55.0,732.0,94.0,64.0,0.0,13.0,1.0,0.0,0.0,0.0,0.0,4.0,2.0,4.0,12.0,0.71,10.0,1.0,0.0,1.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,19.0,13.0,9.0,9.0,1.0,17.0,14.0,3.0,15.0,62.0,3.0,959.0,144.0,513.0,419.0,33.0,9.0,959.0,526.0,8.0,6.0,0.0,586.0,5.0,1.0,0.0,17.0,24.0,0.0,0.0,2.0,0.0,65.0,24.0,12.0,66.7 -Pablo Marí,es ESP,DF,Monza,29-163,1993,14.0,12.0,1138.0,1.0,0.0,0.0,0.0,2.0,0.0,0.08,0.0,0.08,0.08,0.08,0.4,0.4,0.03,0.03,12.6,2.0,0.0,40.0,0.16,0.2,0.5,0.07,0.6,0.6,563.0,652.0,86.3,11363.0,4396.0,165.0,187.0,88.2,318.0,341.0,93.3,74.0,108.0,68.5,1.0,39.0,0.0,0.0,25.0,610.0,39.0,21.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,5.0,4.0,0.32,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,9.0,15.0,3.0,0.0,4.0,4.0,0.0,14.0,45.0,2.0,760.0,124.0,428.0,320.0,14.0,8.0,760.0,400.0,1.0,2.0,0.0,474.0,3.0,2.0,0.0,10.0,6.0,0.0,0.0,0.0,0.0,63.0,22.0,10.0,68.8 -Răzvan Marin,ro ROU,MF,Empoli,26-263,1996,20.0,15.0,1413.0,2.0,2.0,0.0,0.0,5.0,0.0,0.13,0.13,0.25,0.13,0.25,0.7,0.7,0.05,0.05,15.7,6.0,4.0,35.3,0.38,0.12,0.33,0.04,1.3,1.3,668.0,830.0,80.5,11954.0,4304.0,296.0,310.0,95.5,267.0,320.0,83.4,81.0,141.0,57.4,24.0,51.0,13.0,4.0,56.0,729.0,97.0,55.0,2.0,6.0,53.0,40.0,16.0,14.0,0.0,2.0,4.0,18.0,45.0,2.87,29.0,15.0,0.0,1.0,2.0,0.13,0.0,2.0,0.0,0.0,0.0,30.0,13.0,20.0,10.0,0.0,20.0,10.0,10.0,12.0,18.0,0.0,955.0,51.0,275.0,491.0,201.0,4.0,955.0,561.0,14.0,22.0,1.0,597.0,13.0,14.0,0.0,17.0,11.0,0.0,0.0,1.0,0.0,92.0,8.0,6.0,57.1 -Marlon,br BRA,DF,Monza,27-156,1995,17.0,13.0,1066.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,11.8,1.0,0.0,33.3,0.08,0.0,0.0,0.04,-0.1,-0.1,642.0,697.0,92.1,11341.0,4020.0,260.0,279.0,93.2,336.0,354.0,94.9,40.0,52.0,76.9,1.0,55.0,5.0,2.0,44.0,659.0,38.0,25.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,4.0,0.34,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,12.0,10.0,10.0,0.0,7.0,2.0,5.0,15.0,34.0,0.0,799.0,71.0,355.0,415.0,31.0,5.0,799.0,452.0,13.0,7.0,0.0,564.0,2.0,1.0,0.0,13.0,4.0,0.0,0.0,0.0,1.0,47.0,26.0,12.0,68.4 -Luca Marrone,it ITA,DF,Monza,32-319,1990,2.0,2.0,135.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.1,-0.1,78.0,84.0,92.9,1371.0,634.0,34.0,35.0,97.1,39.0,41.0,95.1,5.0,8.0,62.5,0.0,0.0,0.0,0.0,4.0,78.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,4.0,1.0,93.0,12.0,66.0,25.0,2.0,1.0,93.0,59.0,0.0,0.0,0.0,64.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,8.0,4.0,5.0,44.4 -Lautaro Martínez,ar ARG,FW,Inter,25-172,1997,21.0,19.0,1675.0,12.0,3.0,1.0,1.0,1.0,0.0,0.64,0.16,0.81,0.59,0.75,9.4,8.6,0.51,0.46,18.6,28.0,0.0,38.4,1.5,0.15,0.39,0.12,2.6,2.4,309.0,427.0,72.4,4816.0,1113.0,157.0,210.0,74.8,106.0,133.0,79.7,23.0,30.0,76.7,29.0,23.0,14.0,0.0,52.0,392.0,34.0,1.0,4.0,6.0,7.0,0.0,0.0,0.0,0.0,2.0,1.0,13.0,58.0,3.11,42.0,1.0,5.0,6.0,9.0,0.48,4.0,0.0,1.0,2.0,1.0,13.0,6.0,1.0,8.0,4.0,20.0,1.0,19.0,9.0,12.0,1.0,666.0,16.0,55.0,288.0,331.0,105.0,665.0,370.0,32.0,20.0,14.0,491.0,40.0,29.0,0.0,31.0,41.0,7.0,1.0,0.0,0.0,57.0,24.0,28.0,46.2 -Lucas Martínez Quarta,ar ARG,DF,Fiorentina,26-276,1996,15.0,13.0,1181.0,1.0,1.0,0.0,0.0,2.0,0.0,0.08,0.08,0.15,0.08,0.15,1.7,1.7,0.13,0.13,13.1,8.0,0.0,50.0,0.61,0.06,0.13,0.11,-0.7,-0.7,722.0,872.0,82.8,15775.0,6255.0,207.0,233.0,88.8,385.0,424.0,90.8,124.0,189.0,65.6,5.0,65.0,4.0,0.0,69.0,843.0,27.0,25.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,6.0,13.0,0.99,9.0,0.0,3.0,0.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,49.0,26.0,28.0,19.0,2.0,25.0,13.0,12.0,27.0,45.0,1.0,1053.0,78.0,485.0,515.0,67.0,23.0,1053.0,623.0,16.0,20.0,1.0,649.0,6.0,2.0,0.0,9.0,19.0,0.0,0.0,0.0,0.0,103.0,46.0,18.0,71.9 -Adam Marušić,me MNE,DF,Lazio,30-116,1992,21.0,20.0,1828.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.05,0.05,0.0,0.05,0.4,0.4,0.02,0.02,20.3,6.0,0.0,54.5,0.3,0.0,0.0,0.04,-0.4,-0.4,1029.0,1192.0,86.3,15603.0,6242.0,586.0,636.0,92.1,342.0,395.0,86.6,71.0,110.0,64.5,5.0,64.0,5.0,3.0,74.0,1031.0,159.0,22.0,0.0,7.0,9.0,0.0,0.0,0.0,0.0,137.0,2.0,9.0,24.0,1.18,18.0,3.0,1.0,0.0,2.0,0.1,1.0,0.0,1.0,0.0,0.0,24.0,18.0,11.0,9.0,4.0,12.0,3.0,9.0,23.0,44.0,0.0,1329.0,58.0,519.0,653.0,166.0,11.0,1329.0,691.0,31.0,23.0,3.0,866.0,11.0,8.0,0.0,15.0,10.0,0.0,0.0,0.0,0.0,113.0,23.0,16.0,59.0 -Adam Masina,ma MAR,DF,Udinese,29-039,1994,4.0,4.0,304.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.3,0.3,0.3,0.0,0.0,0.01,0.01,3.4,1.0,0.0,100.0,0.3,1.0,1.0,0.03,1.0,1.0,112.0,148.0,75.7,2453.0,1159.0,35.0,42.0,83.3,54.0,68.0,79.4,21.0,33.0,63.6,1.0,8.0,2.0,2.0,8.0,128.0,18.0,5.0,0.0,3.0,5.0,0.0,0.0,0.0,0.0,13.0,2.0,0.0,1.0,0.3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,2.0,2.0,0.0,2.0,2.0,0.0,7.0,12.0,1.0,175.0,18.0,89.0,73.0,16.0,3.0,175.0,108.0,2.0,1.0,1.0,101.0,1.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,12.0,13.0,5.0,72.2 -Nemanja Matić,rs SRB,MF,Roma,34-193,1988,21.0,10.0,1141.0,1.0,1.0,0.0,0.0,2.0,0.0,0.08,0.08,0.16,0.08,0.16,0.7,0.7,0.06,0.06,12.7,3.0,0.0,37.5,0.24,0.13,0.33,0.09,0.3,0.3,676.0,796.0,84.9,11796.0,3343.0,307.0,327.0,93.9,269.0,314.0,85.7,73.0,111.0,65.8,12.0,82.0,8.0,1.0,73.0,753.0,39.0,36.0,1.0,9.0,4.0,1.0,0.0,0.0,0.0,2.0,4.0,10.0,27.0,2.13,25.0,0.0,1.0,0.0,4.0,0.32,2.0,0.0,1.0,0.0,0.0,34.0,17.0,18.0,12.0,4.0,15.0,3.0,12.0,16.0,16.0,0.0,917.0,34.0,221.0,575.0,137.0,15.0,917.0,546.0,27.0,25.0,3.0,619.0,16.0,10.0,0.0,14.0,7.0,1.0,0.0,0.0,0.0,115.0,10.0,10.0,50.0 -Luís Maximiano,pt POR,GK,Lazio,24-036,1999,1.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,75.0,98.0,90.0,1.0,1.0,100.0,0.0,0.0,0.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,4.0,0.0,0.0,0.0,4.0,3.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Pasquale Mazzocchi,it ITA,DF,Salernitana,27-198,1995,15.0,15.0,1199.0,2.0,2.0,0.0,0.0,2.0,0.0,0.15,0.15,0.3,0.15,0.3,0.9,0.9,0.07,0.07,13.3,3.0,0.0,27.3,0.23,0.18,0.67,0.08,1.1,1.1,435.0,583.0,74.6,7209.0,2374.0,220.0,244.0,90.2,163.0,214.0,76.2,39.0,79.0,49.4,26.0,27.0,16.0,9.0,38.0,452.0,129.0,8.0,1.0,3.0,59.0,15.0,0.0,14.0,0.0,106.0,2.0,20.0,49.0,3.67,36.0,8.0,0.0,1.0,7.0,0.52,5.0,0.0,0.0,0.0,0.0,20.0,9.0,14.0,3.0,3.0,5.0,2.0,3.0,6.0,21.0,1.0,721.0,34.0,210.0,269.0,252.0,24.0,721.0,384.0,44.0,34.0,8.0,406.0,30.0,19.0,0.0,15.0,8.0,5.0,0.0,0.0,0.0,82.0,7.0,5.0,58.3 -"Weston McKennie,us USA,""MF,DF"",Juventus,24-166,1998,15.0,13.0,1052.0,1.0,1.0,0.0,0.0,0.0,0.0,0.09,0.09,0.17,0.09,0.17,0.9,0.9,0.08,0.08,11.7,4.0,0.0,50.0,0.34,0.13,0.25,0.11,0.1,0.1,336.0,418.0,80.4,5385.0,1516.0,175.0,207.0,84.5,125.0,147.0,85.0,28.0,38.0,73.7,8.0,23.0,6.0,4.0,39.0,389.0,27.0,3.0,1.0,2.0,15.0,0.0,0.0,0.0,0.0,24.0,2.0,7.0,17.0,1.45,15.0,0.0,2.0,0.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,14.0,5.0,3.0,9.0,2.0,15.0,4.0,11.0,11.0,15.0,1.0,527.0,25.0,115.0,250.0,164.0,22.0,527.0,231.0,19.0,11.0,2.0,349.0,20.0,12.0,0.0,13.0,7.0,3.0,0.0,0.0,0.0,43.0,12.0,16.0,42.9" -"Gary Medel,cl CHI,""MF,DF"",Bologna,35-191,1987,17.0,16.0,1267.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.07,0.07,0.0,0.07,0.1,0.1,0.0,0.0,14.1,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,800.0,903.0,88.6,14755.0,5092.0,313.0,338.0,92.6,374.0,397.0,94.2,91.0,135.0,67.4,4.0,54.0,4.0,0.0,46.0,858.0,45.0,42.0,1.0,7.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,19.0,1.35,16.0,0.0,0.0,1.0,2.0,0.14,2.0,0.0,0.0,0.0,0.0,21.0,14.0,12.0,7.0,2.0,12.0,8.0,4.0,23.0,32.0,0.0,1007.0,112.0,435.0,525.0,53.0,0.0,1007.0,524.0,1.0,3.0,0.0,641.0,6.0,4.0,0.0,15.0,16.0,0.0,0.0,0.0,0.0,121.0,13.0,13.0,50.0" -Soualiho Meïté,fr FRA,MF,Cremonese,28-330,1994,15.0,14.0,1139.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.08,0.08,0.0,0.08,0.3,0.3,0.02,0.02,12.7,1.0,0.0,10.0,0.08,0.0,0.0,0.03,-0.3,-0.3,389.0,478.0,81.4,6439.0,1777.0,200.0,226.0,88.5,148.0,168.0,88.1,32.0,56.0,57.1,7.0,38.0,14.0,3.0,48.0,462.0,12.0,9.0,1.0,5.0,8.0,0.0,0.0,0.0,0.0,2.0,4.0,8.0,23.0,1.82,21.0,1.0,0.0,0.0,2.0,0.16,2.0,0.0,0.0,0.0,0.0,25.0,16.0,15.0,7.0,3.0,11.0,2.0,9.0,11.0,15.0,0.0,605.0,24.0,166.0,343.0,104.0,8.0,605.0,341.0,12.0,8.0,3.0,376.0,17.0,16.0,0.0,15.0,20.0,0.0,0.0,0.0,0.0,86.0,15.0,9.0,62.5 -Alex Meret,it ITA,GK,Napoli,25-325,1997,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,516.0,596.0,86.6,11970.0,7265.0,121.0,121.0,100.0,265.0,268.0,98.9,127.0,204.0,62.3,0.0,1.0,0.0,0.0,0.0,438.0,158.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,627.0,545.0,626.0,1.0,0.0,0.0,627.0,341.0,0.0,0.0,0.0,305.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,40.0,2.0,0.0,100.0 -Yıldırım Mert Çetin,tr TUR,DF,Lecce,26-040,1997,1.0,1.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,66.7,38.0,8.0,1.0,1.0,100.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,8.0,2.0,5.0,3.0,0.0,0.0,8.0,3.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0 -"Junior Messias,br BRA,""FW,DF"",Milan,31-273,1991,15.0,11.0,879.0,2.0,2.0,0.0,0.0,1.0,0.0,0.2,0.2,0.41,0.2,0.41,1.1,1.1,0.11,0.11,9.8,7.0,0.0,41.2,0.72,0.12,0.29,0.07,0.9,0.9,224.0,308.0,72.7,3545.0,751.0,121.0,148.0,81.8,84.0,107.0,78.5,15.0,32.0,46.9,8.0,19.0,3.0,1.0,31.0,299.0,8.0,3.0,2.0,1.0,14.0,0.0,0.0,0.0,0.0,5.0,1.0,7.0,32.0,3.27,20.0,0.0,4.0,4.0,5.0,0.51,4.0,0.0,0.0,0.0,0.0,12.0,9.0,3.0,5.0,4.0,12.0,3.0,9.0,4.0,12.0,0.0,416.0,16.0,56.0,175.0,191.0,31.0,416.0,251.0,24.0,21.0,8.0,290.0,17.0,9.0,0.0,15.0,16.0,1.0,0.0,0.0,0.0,35.0,19.0,25.0,43.2" -Tommaso Milanese,it ITA,MF,Cremonese,20-194,2002,2.0,0.0,27.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,14.0,71.4,182.0,41.0,4.0,5.0,80.0,3.0,5.0,60.0,2.0,3.0,66.7,3.0,3.0,0.0,0.0,2.0,11.0,3.0,1.0,0.0,0.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,6.43,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,19.0,0.0,1.0,7.0,12.0,2.0,19.0,13.0,2.0,1.0,0.0,14.0,3.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -Nikola Milenković,rs SRB,DF,Fiorentina,25-121,1997,16.0,14.0,1288.0,2.0,1.0,0.0,0.0,2.0,0.0,0.14,0.07,0.21,0.14,0.21,1.6,1.6,0.11,0.11,14.3,3.0,0.0,25.0,0.21,0.17,0.67,0.13,0.4,0.4,710.0,827.0,85.9,13882.0,4094.0,242.0,268.0,90.3,377.0,420.0,89.8,85.0,121.0,70.2,6.0,42.0,1.0,0.0,46.0,798.0,26.0,22.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,4.0,19.0,1.33,16.0,0.0,3.0,0.0,2.0,0.14,2.0,0.0,0.0,0.0,0.0,21.0,14.0,10.0,8.0,3.0,16.0,6.0,10.0,17.0,38.0,0.0,966.0,90.0,436.0,482.0,53.0,26.0,966.0,548.0,3.0,7.0,0.0,587.0,5.0,0.0,0.0,24.0,13.0,0.0,0.0,0.0,1.0,75.0,39.0,14.0,73.6 -Arkadiusz Milik,pl POL,FW,Juventus,28-347,1994,17.0,10.0,986.0,6.0,1.0,0.0,0.0,3.0,1.0,0.55,0.09,0.64,0.55,0.64,4.2,4.2,0.39,0.39,11.0,16.0,1.0,55.2,1.46,0.21,0.38,0.15,1.8,1.8,218.0,279.0,78.1,3030.0,498.0,130.0,154.0,84.4,68.0,82.0,82.9,7.0,13.0,53.8,8.0,10.0,6.0,0.0,20.0,260.0,15.0,2.0,2.0,2.0,4.0,0.0,0.0,0.0,0.0,0.0,4.0,6.0,20.0,1.84,13.0,0.0,4.0,1.0,3.0,0.28,2.0,0.0,0.0,0.0,1.0,7.0,5.0,1.0,4.0,2.0,7.0,0.0,7.0,0.0,9.0,0.0,381.0,10.0,29.0,186.0,171.0,46.0,381.0,210.0,8.0,11.0,1.0,299.0,31.0,9.0,1.0,12.0,17.0,3.0,0.0,0.0,0.0,26.0,22.0,39.0,36.1 -Sergej Milinković-Savić,rs SRB,MF,Lazio,27-348,1995,20.0,19.0,1656.0,4.0,8.0,0.0,0.0,7.0,0.0,0.22,0.43,0.65,0.22,0.65,2.6,2.6,0.14,0.14,18.4,8.0,2.0,25.8,0.43,0.13,0.5,0.08,1.4,1.4,827.0,1095.0,75.5,13106.0,3786.0,463.0,556.0,83.3,261.0,328.0,79.6,76.0,134.0,56.7,27.0,89.0,31.0,3.0,104.0,1070.0,21.0,14.0,11.0,13.0,18.0,0.0,0.0,0.0,0.0,7.0,4.0,26.0,48.0,2.61,42.0,1.0,3.0,1.0,11.0,0.6,10.0,0.0,1.0,0.0,0.0,38.0,21.0,17.0,15.0,6.0,25.0,7.0,18.0,20.0,29.0,0.0,1380.0,47.0,258.0,789.0,346.0,38.0,1380.0,826.0,29.0,20.0,7.0,1099.0,65.0,38.0,0.0,22.0,45.0,1.0,0.0,0.0,0.0,106.0,51.0,30.0,63.0 -Vanja Milinković-Savić,rs SRB,GK,Torino,25-355,1997,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,551.0,952.0,57.9,18065.0,13131.0,63.0,64.0,98.4,274.0,279.0,98.2,213.0,599.0,35.6,0.0,52.0,2.0,0.0,0.0,730.0,213.0,57.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,0.0,3.0,0.14,3.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,12.0,1.0,1001.0,706.0,988.0,14.0,1.0,1.0,1001.0,639.0,0.0,0.0,0.0,510.0,1.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,27.0,5.0,0.0,100.0 -Kim Min-jae,kr KOR,DF,Napoli,26-087,1996,20.0,20.0,1755.0,2.0,0.0,0.0,0.0,4.0,0.0,0.1,0.0,0.1,0.1,0.1,0.5,0.5,0.03,0.03,19.5,2.0,0.0,40.0,0.1,0.4,1.0,0.1,1.5,1.5,1383.0,1534.0,90.2,25387.0,9543.0,549.0,589.0,93.2,683.0,723.0,94.5,127.0,175.0,72.6,8.0,109.0,5.0,1.0,89.0,1500.0,31.0,29.0,1.0,23.0,3.0,0.0,0.0,0.0,0.0,1.0,3.0,10.0,25.0,1.28,23.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,35.0,22.0,25.0,10.0,0.0,26.0,21.0,5.0,20.0,80.0,1.0,1735.0,197.0,754.0,948.0,42.0,12.0,1735.0,1059.0,21.0,8.0,1.0,1252.0,10.0,7.0,0.0,17.0,3.0,0.0,0.0,0.0,0.0,131.0,50.0,28.0,64.1 -Aleksei Miranchuk,ru RUS,MF,Torino,27-116,1995,15.0,13.0,1072.0,4.0,2.0,0.0,0.0,0.0,0.0,0.34,0.17,0.5,0.34,0.5,1.8,1.8,0.15,0.15,11.9,10.0,1.0,45.5,0.84,0.18,0.4,0.08,2.2,2.2,337.0,414.0,81.4,5183.0,1234.0,177.0,200.0,88.5,112.0,130.0,86.2,26.0,49.0,53.1,23.0,29.0,15.0,3.0,48.0,395.0,17.0,3.0,3.0,1.0,11.0,10.0,5.0,0.0,0.0,4.0,2.0,8.0,42.0,3.52,34.0,2.0,2.0,3.0,5.0,0.42,4.0,0.0,0.0,1.0,0.0,5.0,2.0,1.0,3.0,1.0,5.0,0.0,5.0,1.0,3.0,0.0,518.0,4.0,32.0,255.0,235.0,29.0,518.0,333.0,25.0,26.0,5.0,411.0,24.0,13.0,0.0,4.0,17.0,0.0,1.0,0.0,0.0,44.0,4.0,13.0,23.5 -"Fabio Miretti,it ITA,""MF,FW"",Juventus,19-191,2003,18.0,9.0,829.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.11,0.11,0.0,0.11,1.4,1.4,0.15,0.15,9.2,5.0,0.0,29.4,0.54,0.0,0.0,0.08,-1.4,-1.4,261.0,369.0,70.7,4095.0,1074.0,141.0,173.0,81.5,93.0,122.0,76.2,18.0,45.0,40.0,12.0,25.0,8.0,1.0,35.0,348.0,19.0,4.0,1.0,3.0,17.0,8.0,3.0,4.0,0.0,5.0,2.0,12.0,34.0,3.7,21.0,4.0,5.0,1.0,5.0,0.54,4.0,0.0,0.0,1.0,0.0,17.0,7.0,10.0,5.0,2.0,14.0,1.0,13.0,2.0,8.0,0.0,501.0,11.0,76.0,252.0,180.0,36.0,501.0,285.0,21.0,13.0,4.0,343.0,29.0,19.0,0.0,16.0,15.0,1.0,1.0,0.0,0.0,59.0,4.0,12.0,25.0" -Henrikh Mkhitaryan,am ARM,MF,Inter,34-020,1989,18.0,13.0,1149.0,1.0,1.0,0.0,0.0,3.0,0.0,0.08,0.08,0.16,0.08,0.16,1.1,1.1,0.09,0.09,12.8,1.0,0.0,7.7,0.08,0.08,1.0,0.09,-0.1,-0.1,499.0,576.0,86.6,8073.0,1977.0,259.0,278.0,93.2,182.0,212.0,85.8,39.0,52.0,75.0,11.0,50.0,15.0,6.0,67.0,563.0,11.0,4.0,2.0,2.0,13.0,2.0,1.0,1.0,0.0,5.0,2.0,6.0,41.0,3.22,33.0,0.0,3.0,4.0,6.0,0.47,5.0,0.0,0.0,0.0,1.0,25.0,16.0,7.0,16.0,2.0,17.0,2.0,15.0,10.0,10.0,0.0,694.0,15.0,106.0,400.0,200.0,22.0,694.0,391.0,28.0,25.0,7.0,480.0,25.0,11.0,0.0,16.0,15.0,0.0,0.0,0.0,0.0,93.0,3.0,8.0,27.3 -Salvatore Molina,it ITA,DF,Monza,31-040,1992,5.0,1.0,229.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,2.5,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,86.0,114.0,75.4,1260.0,424.0,49.0,55.0,89.1,36.0,45.0,80.0,1.0,8.0,12.5,2.0,4.0,5.0,4.0,7.0,96.0,17.0,2.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,15.0,1.0,3.0,5.0,1.97,5.0,0.0,0.0,0.0,1.0,0.39,1.0,0.0,0.0,0.0,0.0,6.0,5.0,2.0,4.0,0.0,4.0,2.0,2.0,1.0,2.0,0.0,139.0,6.0,29.0,47.0,65.0,1.0,139.0,79.0,9.0,5.0,0.0,85.0,4.0,2.0,0.0,4.0,3.0,0.0,0.0,0.0,0.0,10.0,0.0,1.0,0.0 -Daniele Montevago,it ITA,FW,Sampdoria,19-329,2003,6.0,3.0,231.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.19,0.19,2.6,1.0,0.0,14.3,0.39,0.0,0.0,0.07,-0.5,-0.5,23.0,40.0,57.5,228.0,22.0,14.0,20.0,70.0,4.0,8.0,50.0,0.0,3.0,0.0,4.0,1.0,0.0,0.0,0.0,37.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,2.75,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,2.0,1.0,1.0,0.0,1.0,0.0,79.0,2.0,4.0,31.0,44.0,18.0,79.0,42.0,1.0,1.0,1.0,61.0,9.0,4.0,0.0,8.0,4.0,4.0,0.0,0.0,0.0,5.0,14.0,23.0,37.8 -Lorenzo Montipò,it ITA,GK,Hellas Verona,26-355,1996,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,435.0,667.0,65.2,16626.0,13914.0,49.0,50.0,98.0,145.0,149.0,97.3,238.0,463.0,51.4,0.0,36.0,1.0,0.0,0.0,436.0,229.0,61.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,0.33,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,23.0,2.0,744.0,595.0,737.0,9.0,0.0,0.0,744.0,324.0,0.0,1.0,0.0,258.0,4.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,39.0,2.0,2.0,50.0 -Nikola Moro,hr CRO,MF,Bologna,24-335,1998,9.0,4.0,418.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.22,0.22,0.0,0.22,0.1,0.1,0.03,0.03,4.6,1.0,0.0,20.0,0.22,0.0,0.0,0.02,-0.1,-0.1,183.0,242.0,75.6,3553.0,1191.0,67.0,88.0,76.1,82.0,97.0,84.5,30.0,44.0,68.2,5.0,27.0,3.0,0.0,31.0,233.0,7.0,5.0,1.0,5.0,3.0,0.0,0.0,0.0,0.0,2.0,2.0,4.0,12.0,2.58,12.0,0.0,0.0,0.0,2.0,0.43,2.0,0.0,0.0,0.0,0.0,8.0,4.0,3.0,5.0,0.0,6.0,0.0,6.0,3.0,6.0,0.0,280.0,11.0,68.0,175.0,41.0,2.0,280.0,150.0,1.0,3.0,0.0,178.0,2.0,0.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,36.0,6.0,5.0,54.5 -"Dany Mota,pt POR,""FW,MF"",Monza,24-284,1998,15.0,11.0,996.0,3.0,0.0,0.0,0.0,1.0,0.0,0.27,0.0,0.27,0.27,0.27,3.6,3.6,0.32,0.32,11.1,4.0,0.0,26.7,0.36,0.2,0.75,0.24,-0.6,-0.6,211.0,312.0,67.6,2995.0,379.0,128.0,165.0,77.6,64.0,94.0,68.1,10.0,17.0,58.8,11.0,8.0,7.0,4.0,14.0,296.0,16.0,1.0,1.0,2.0,28.0,0.0,0.0,0.0,0.0,3.0,0.0,19.0,30.0,2.71,22.0,0.0,3.0,3.0,2.0,0.18,0.0,0.0,0.0,1.0,0.0,5.0,4.0,0.0,2.0,3.0,5.0,1.0,4.0,2.0,14.0,0.0,443.0,15.0,33.0,200.0,212.0,49.0,443.0,275.0,37.0,20.0,15.0,337.0,47.0,23.0,0.0,14.0,19.0,10.0,1.0,0.0,0.0,31.0,20.0,29.0,40.8" -"João Moutinho,pt POR,""DF,FW"",Spezia,25-029,1998,4.0,0.0,58.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,42.0,71.4,405.0,162.0,18.0,20.0,90.0,9.0,12.0,75.0,1.0,6.0,16.7,1.0,4.0,0.0,0.0,1.0,34.0,8.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,8.0,0.0,2.0,1.0,1.55,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,5.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,51.0,3.0,11.0,20.0,20.0,0.0,51.0,18.0,0.0,1.0,0.0,24.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,0.0" -Mert Müldür,tr TUR,DF,Sassuolo,23-313,1999,1.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,100.0,11.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Luis Muriel,co COL,FW,Atalanta,31-300,1991,13.0,6.0,579.0,1.0,2.0,1.0,1.0,2.0,2.0,0.16,0.31,0.47,0.0,0.31,1.3,0.6,0.21,0.09,6.4,3.0,2.0,27.3,0.47,0.0,0.0,0.05,-0.3,-0.6,153.0,240.0,63.8,2567.0,758.0,79.0,106.0,74.5,49.0,70.0,70.0,17.0,42.0,40.5,15.0,12.0,18.0,2.0,37.0,212.0,27.0,4.0,3.0,8.0,28.0,10.0,6.0,2.0,0.0,5.0,1.0,5.0,29.0,4.5,24.0,2.0,0.0,1.0,4.0,0.62,3.0,0.0,0.0,1.0,0.0,5.0,3.0,1.0,2.0,2.0,5.0,0.0,5.0,0.0,2.0,0.0,328.0,3.0,16.0,129.0,185.0,39.0,327.0,192.0,31.0,19.0,18.0,258.0,26.0,15.0,1.0,9.0,7.0,2.0,1.0,0.0,0.0,21.0,6.0,14.0,30.0 -Jeison Murillo,co COL,DF,Sampdoria,30-259,1992,11.0,8.0,685.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.6,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,299.0,367.0,81.5,6228.0,2354.0,90.0,99.0,90.9,165.0,186.0,88.7,43.0,73.0,58.9,1.0,26.0,1.0,0.0,30.0,330.0,36.0,23.0,1.0,5.0,1.0,0.0,0.0,0.0,0.0,13.0,1.0,4.0,5.0,0.66,4.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,9.0,11.0,5.0,0.0,13.0,8.0,5.0,5.0,24.0,0.0,440.0,53.0,209.0,214.0,22.0,2.0,440.0,209.0,8.0,4.0,0.0,248.0,5.0,1.0,0.0,9.0,13.0,0.0,0.0,0.0,1.0,44.0,25.0,16.0,61.0 -Nicola Murru,it ITA,DF,Sampdoria,28-056,1994,9.0,3.0,381.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,4.2,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,111.0,151.0,73.5,2008.0,1012.0,45.0,50.0,90.0,50.0,64.0,78.1,12.0,28.0,42.9,2.0,12.0,3.0,2.0,15.0,133.0,17.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,16.0,1.0,1.0,3.0,0.71,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,10.0,10.0,6.0,1.0,10.0,3.0,7.0,2.0,13.0,0.0,199.0,20.0,86.0,84.0,30.0,1.0,199.0,87.0,2.0,1.0,0.0,103.0,4.0,1.0,0.0,5.0,2.0,1.0,0.0,2.0,0.0,16.0,5.0,4.0,55.6 -Juan Musso,ar ARG,GK,Atalanta,28-280,1994,15.0,15.0,1267.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,352.0,471.0,74.7,10121.0,7082.0,61.0,61.0,100.0,160.0,162.0,98.8,129.0,244.0,52.9,1.0,0.0,0.0,0.0,0.0,340.0,130.0,32.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,491.0,434.0,488.0,3.0,0.0,0.0,491.0,305.0,0.0,1.0,1.0,214.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,0.0 -"Joakim Mæhle,dk DEN,""DF,MF"",Atalanta,25-266,1997,19.0,12.0,1050.0,2.0,1.0,0.0,0.0,2.0,1.0,0.17,0.09,0.26,0.17,0.26,2.3,2.3,0.2,0.2,11.7,4.0,0.0,30.8,0.34,0.15,0.5,0.18,-0.3,-0.3,578.0,716.0,80.7,8099.0,2920.0,367.0,409.0,89.7,170.0,209.0,81.3,23.0,44.0,52.3,20.0,38.0,24.0,5.0,72.0,589.0,125.0,3.0,1.0,2.0,20.0,0.0,0.0,0.0,0.0,122.0,2.0,22.0,45.0,3.86,38.0,3.0,1.0,1.0,3.0,0.26,2.0,1.0,0.0,0.0,0.0,19.0,11.0,6.0,4.0,9.0,12.0,2.0,10.0,10.0,9.0,0.0,848.0,17.0,175.0,330.0,354.0,34.0,848.0,482.0,42.0,31.0,6.0,553.0,25.0,11.0,0.0,12.0,8.0,2.0,0.0,0.0,0.0,71.0,3.0,6.0,33.3" -"Michel Ndary Adopo,fr FRA,""MF,DF"",Torino,22-206,2000,6.0,2.0,177.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,66.0,81.0,81.5,1040.0,264.0,30.0,36.0,83.3,31.0,36.0,86.1,4.0,5.0,80.0,0.0,4.0,0.0,0.0,6.0,79.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,1.0,1.0,2.0,2.0,0.0,0.0,3.0,0.0,100.0,6.0,22.0,63.0,15.0,1.0,100.0,51.0,1.0,0.0,0.0,61.0,2.0,3.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,12.0,6.0,5.0,54.5" -Tanguy Ndombele,fr FRA,MF,Napoli,26-044,1996,19.0,6.0,545.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.09,0.09,6.1,1.0,0.0,7.7,0.17,0.0,0.0,0.04,-0.5,-0.5,362.0,404.0,89.6,4946.0,1252.0,232.0,251.0,92.4,103.0,106.0,97.2,12.0,20.0,60.0,8.0,29.0,8.0,0.0,42.0,395.0,9.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,8.0,16.0,2.66,14.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,3.0,2.0,5.0,1.0,3.0,0.0,3.0,5.0,5.0,1.0,471.0,8.0,56.0,291.0,127.0,21.0,471.0,323.0,14.0,14.0,3.0,402.0,18.0,13.0,0.0,9.0,16.0,0.0,0.0,1.0,0.0,31.0,4.0,2.0,66.7 -"Ilija Nestorovski,mk MKD,""FW,MF"",Udinese,32-335,1990,10.0,0.0,115.0,1.0,1.0,0.0,0.0,2.0,0.0,0.78,0.78,1.57,0.78,1.57,0.3,0.3,0.21,0.21,1.3,1.0,0.0,33.3,0.78,0.33,1.0,0.09,0.7,0.7,24.0,33.0,72.7,291.0,73.0,19.0,22.0,86.4,5.0,8.0,62.5,0.0,1.0,0.0,1.0,2.0,0.0,0.0,5.0,33.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.79,1.0,0.0,0.0,0.0,1.0,0.79,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,2.0,0.0,2.0,0.0,45.0,2.0,3.0,22.0,21.0,7.0,45.0,17.0,0.0,1.0,0.0,28.0,3.0,0.0,0.0,4.0,1.0,2.0,0.0,0.0,0.0,4.0,1.0,6.0,14.3" -"Cyril Ngonge,be BEL,""FW,MF"",Hellas Verona,22-260,2000,2.0,1.0,85.0,1.0,0.0,0.0,0.0,0.0,0.0,1.06,0.0,1.06,1.06,1.06,0.5,0.5,0.58,0.58,0.9,3.0,0.0,50.0,3.18,0.17,0.33,0.09,0.5,0.5,13.0,21.0,61.9,288.0,115.0,2.0,4.0,50.0,9.0,10.0,90.0,2.0,4.0,50.0,2.0,2.0,3.0,2.0,3.0,19.0,2.0,0.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,8.0,8.47,3.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,39.0,0.0,6.0,14.0,20.0,4.0,39.0,21.0,3.0,1.0,2.0,22.0,1.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,3.0,5.0,5.0,50.0" -Hans Nicolussi Caviglia,it ITA,MF,Salernitana,22-237,2000,5.0,4.0,385.0,1.0,0.0,0.0,0.0,1.0,0.0,0.23,0.0,0.23,0.23,0.23,0.1,0.1,0.02,0.02,4.3,1.0,0.0,50.0,0.23,0.5,1.0,0.05,0.9,0.9,123.0,157.0,78.3,2193.0,668.0,47.0,54.0,87.0,59.0,71.0,83.1,10.0,18.0,55.6,2.0,12.0,0.0,0.0,15.0,148.0,8.0,6.0,0.0,1.0,2.0,2.0,0.0,0.0,0.0,0.0,1.0,2.0,6.0,1.4,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,4.0,5.0,4.0,1.0,5.0,0.0,5.0,4.0,7.0,1.0,200.0,11.0,57.0,114.0,30.0,3.0,200.0,89.0,1.0,2.0,0.0,109.0,6.0,3.0,0.0,9.0,5.0,0.0,0.0,1.0,0.0,28.0,1.0,3.0,25.0 -Dimitris Nikolaou,gr GRE,DF,Spezia,24-181,1998,19.0,18.0,1599.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,17.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,718.0,903.0,79.5,13016.0,5557.0,312.0,340.0,91.8,313.0,359.0,87.2,77.0,171.0,45.0,5.0,62.0,2.0,1.0,58.0,843.0,55.0,25.0,0.0,15.0,4.0,0.0,0.0,0.0,0.0,20.0,5.0,6.0,14.0,0.79,13.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,18.0,18.0,6.0,0.0,25.0,18.0,7.0,16.0,37.0,0.0,1041.0,123.0,541.0,458.0,52.0,9.0,1041.0,645.0,13.0,12.0,0.0,693.0,10.0,7.0,0.0,11.0,11.0,0.0,0.0,0.0,0.0,123.0,10.0,14.0,41.7 -Bram Nuytinck,nl NED,DF,Udinese,32-282,1990,6.0,3.0,385.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,4.3,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,113.0,139.0,81.3,2516.0,1038.0,24.0,27.0,88.9,67.0,75.0,89.3,20.0,32.0,62.5,0.0,4.0,1.0,0.0,4.0,124.0,14.0,4.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,9.0,1.0,2.0,2.0,0.47,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,4.0,3.0,4.0,0.0,6.0,6.0,0.0,5.0,22.0,0.0,185.0,37.0,117.0,65.0,4.0,2.0,185.0,90.0,1.0,0.0,0.0,88.0,1.0,0.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,19.0,7.0,5.0,58.3 -Bram Nuytinck,nl NED,DF,Sampdoria,32-282,1990,6.0,6.0,485.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,5.4,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.2,-0.2,172.0,204.0,84.3,3353.0,1169.0,47.0,55.0,85.5,107.0,115.0,93.0,16.0,28.0,57.1,1.0,6.0,0.0,0.0,9.0,186.0,16.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,1.0,0.19,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,15.0,17.0,5.0,0.0,13.0,7.0,6.0,6.0,23.0,0.0,271.0,49.0,168.0,99.0,5.0,5.0,271.0,131.0,1.0,0.0,0.0,141.0,0.0,0.0,0.0,8.0,1.0,0.0,0.0,1.0,0.0,27.0,6.0,6.0,50.0 -M'Bala Nzola,ao ANG,FW,Spezia,26-176,1996,18.0,18.0,1596.0,9.0,1.0,2.0,2.0,4.0,0.0,0.51,0.06,0.56,0.39,0.45,6.8,5.2,0.38,0.3,17.7,15.0,0.0,37.5,0.85,0.18,0.47,0.13,2.2,1.8,270.0,342.0,78.9,3354.0,543.0,186.0,219.0,84.9,62.0,79.0,78.5,5.0,6.0,83.3,16.0,19.0,11.0,2.0,34.0,327.0,13.0,1.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,2.0,2.0,8.0,31.0,1.75,26.0,0.0,3.0,1.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,6.0,3.0,0.0,3.0,3.0,13.0,4.0,9.0,5.0,17.0,0.0,580.0,25.0,46.0,217.0,323.0,84.0,578.0,364.0,27.0,13.0,13.0,465.0,73.0,53.0,0.0,38.0,21.0,7.0,0.0,0.0,0.0,42.0,27.0,57.0,32.1 -Pedro Obiang,gq EQG,MF,Sassuolo,30-320,1992,10.0,7.0,560.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.16,0.16,0.0,0.16,0.3,0.3,0.05,0.05,6.2,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.3,-0.3,202.0,238.0,84.9,3686.0,1264.0,101.0,116.0,87.1,72.0,79.0,91.1,26.0,34.0,76.5,2.0,13.0,0.0,0.0,20.0,217.0,20.0,20.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,8.0,1.28,6.0,0.0,1.0,0.0,1.0,0.16,1.0,0.0,0.0,0.0,0.0,21.0,13.0,10.0,10.0,1.0,8.0,1.0,7.0,7.0,12.0,0.0,298.0,20.0,81.0,188.0,30.0,2.0,298.0,134.0,0.0,3.0,0.0,169.0,3.0,1.0,0.0,10.0,5.0,0.0,0.0,0.0,0.0,29.0,8.0,14.0,36.4 -Guillermo Ochoa,mx MEX,GK,Salernitana,37-212,1985,6.0,6.0,540.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,117.0,217.0,53.9,4279.0,3439.0,12.0,12.0,100.0,41.0,43.0,95.3,64.0,160.0,40.0,0.0,4.0,0.0,0.0,0.0,135.0,80.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.33,2.0,0.0,0.0,0.0,2.0,0.33,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,243.0,206.0,242.0,1.0,0.0,0.0,243.0,97.0,0.0,0.0,0.0,87.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0 -"David Okereke,ng NGA,""FW,MF"",Cremonese,25-165,1997,20.0,17.0,1444.0,5.0,0.0,1.0,1.0,4.0,0.0,0.31,0.0,0.31,0.25,0.25,3.2,2.4,0.2,0.15,16.0,11.0,0.0,30.6,0.69,0.11,0.36,0.07,1.8,1.6,220.0,327.0,67.3,3814.0,946.0,104.0,145.0,71.7,83.0,104.0,79.8,27.0,37.0,73.0,15.0,20.0,19.0,5.0,53.0,313.0,10.0,1.0,2.0,1.0,19.0,0.0,0.0,0.0,0.0,6.0,4.0,19.0,52.0,3.24,31.0,1.0,6.0,7.0,1.0,0.06,0.0,0.0,0.0,1.0,0.0,7.0,5.0,3.0,2.0,2.0,19.0,3.0,16.0,4.0,9.0,0.0,506.0,15.0,38.0,197.0,276.0,57.0,505.0,280.0,35.0,30.0,8.0,375.0,40.0,27.0,0.0,13.0,24.0,7.0,2.0,1.0,0.0,31.0,30.0,55.0,35.3" -Caleb Okoli,it ITA,DF,Atalanta,21-212,2001,13.0,9.0,845.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,9.4,2.0,0.0,40.0,0.21,0.0,0.0,0.07,-0.3,-0.3,297.0,387.0,76.7,5230.0,2068.0,127.0,155.0,81.9,134.0,164.0,81.7,27.0,43.0,62.8,0.0,9.0,1.0,0.0,13.0,363.0,24.0,17.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,5.0,0.0,6.0,1.0,0.11,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,17.0,14.0,6.0,1.0,9.0,5.0,4.0,26.0,41.0,3.0,514.0,56.0,302.0,188.0,30.0,8.0,514.0,228.0,2.0,4.0,0.0,239.0,11.0,3.0,0.0,13.0,7.0,0.0,0.0,0.0,0.0,59.0,40.0,23.0,63.5 -Mathías Olivera,uy URU,DF,Napoli,25-102,1997,15.0,5.0,531.0,1.0,0.0,0.0,0.0,1.0,0.0,0.17,0.0,0.17,0.17,0.17,0.4,0.4,0.07,0.07,5.9,1.0,0.0,50.0,0.17,0.5,1.0,0.2,0.6,0.6,405.0,469.0,86.4,5436.0,1824.0,273.0,297.0,91.9,96.0,109.0,88.1,20.0,31.0,64.5,10.0,34.0,7.0,2.0,31.0,387.0,81.0,10.0,0.0,2.0,7.0,0.0,0.0,0.0,0.0,71.0,1.0,10.0,16.0,2.71,14.0,1.0,0.0,0.0,2.0,0.34,2.0,0.0,0.0,0.0,0.0,16.0,9.0,5.0,7.0,4.0,6.0,2.0,4.0,6.0,14.0,0.0,544.0,18.0,131.0,269.0,149.0,10.0,544.0,334.0,28.0,21.0,1.0,345.0,9.0,14.0,0.0,6.0,10.0,0.0,0.0,0.0,0.0,38.0,4.0,5.0,44.4 -André Onana,cm CMR,GK,Inter,26-314,1996,13.0,13.0,1170.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,414.0,525.0,78.9,10395.0,7082.0,108.0,108.0,100.0,186.0,192.0,96.9,117.0,221.0,52.9,1.0,6.0,0.0,0.0,2.0,420.0,104.0,12.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.23,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,546.0,469.0,536.0,10.0,0.0,0.0,546.0,359.0,0.0,0.0,0.0,317.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0 -Divock Origi,be BEL,FW,Milan,27-298,1995,14.0,5.0,564.0,2.0,1.0,0.0,0.0,1.0,0.0,0.32,0.16,0.48,0.32,0.48,1.2,1.2,0.2,0.2,6.3,8.0,0.0,47.1,1.28,0.12,0.25,0.07,0.8,0.8,72.0,108.0,66.7,937.0,222.0,42.0,54.0,77.8,19.0,32.0,59.4,3.0,7.0,42.9,7.0,4.0,3.0,0.0,9.0,102.0,6.0,0.0,0.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,17.0,2.72,10.0,0.0,1.0,3.0,1.0,0.16,1.0,0.0,0.0,0.0,0.0,3.0,1.0,2.0,1.0,0.0,2.0,1.0,1.0,0.0,11.0,0.0,195.0,11.0,21.0,79.0,99.0,24.0,195.0,106.0,11.0,5.0,9.0,142.0,26.0,13.0,0.0,7.0,12.0,4.0,0.0,0.0,0.0,13.0,9.0,17.0,34.6 -Riccardo Orsolini,it ITA,FW,Bologna,26-017,1997,18.0,12.0,1151.0,5.0,3.0,1.0,1.0,3.0,1.0,0.39,0.23,0.63,0.31,0.55,5.4,4.6,0.42,0.36,12.8,14.0,2.0,30.4,1.09,0.09,0.29,0.1,-0.4,-0.6,287.0,465.0,61.7,5463.0,1524.0,138.0,190.0,72.6,80.0,130.0,61.5,51.0,93.0,54.8,20.0,29.0,5.0,3.0,38.0,407.0,57.0,16.0,0.0,22.0,57.0,36.0,19.0,11.0,0.0,3.0,1.0,26.0,48.0,3.77,25.0,14.0,6.0,2.0,3.0,0.24,0.0,2.0,1.0,0.0,0.0,17.0,11.0,9.0,6.0,2.0,19.0,2.0,17.0,6.0,12.0,0.0,655.0,16.0,109.0,255.0,295.0,55.0,654.0,378.0,26.0,22.0,17.0,465.0,34.0,23.0,0.0,21.0,27.0,8.0,0.0,0.0,0.0,64.0,7.0,16.0,30.4 -Victor Osimhen,ng NGA,FW,Napoli,24-043,1998,17.0,16.0,1401.0,16.0,3.0,0.0,0.0,2.0,0.0,1.03,0.19,1.22,1.03,1.22,10.2,10.2,0.66,0.65,15.6,28.0,0.0,40.6,1.8,0.23,0.57,0.15,5.8,5.8,143.0,201.0,71.1,1925.0,289.0,85.0,103.0,82.5,38.0,54.0,70.4,6.0,9.0,66.7,18.0,11.0,5.0,0.0,13.0,193.0,8.0,0.0,1.0,2.0,5.0,0.0,0.0,0.0,0.0,2.0,0.0,13.0,37.0,2.38,27.0,0.0,4.0,4.0,6.0,0.39,4.0,0.0,1.0,1.0,0.0,3.0,1.0,0.0,1.0,2.0,4.0,1.0,3.0,2.0,15.0,0.0,409.0,16.0,23.0,129.0,265.0,121.0,409.0,244.0,23.0,10.0,15.0,311.0,52.0,27.0,0.0,26.0,22.0,16.0,1.0,1.0,0.0,24.0,33.0,34.0,49.3 -"Remi Oudin,fr FRA,""FW,MF"",Lecce,26-084,1996,14.0,2.0,309.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.07,0.07,3.4,4.0,0.0,44.4,1.17,0.0,0.0,0.03,-0.3,-0.3,92.0,161.0,57.1,1773.0,591.0,39.0,54.0,72.2,27.0,48.0,56.3,19.0,40.0,47.5,5.0,14.0,0.0,0.0,16.0,147.0,13.0,3.0,0.0,2.0,20.0,8.0,4.0,2.0,0.0,2.0,1.0,7.0,13.0,3.79,8.0,2.0,3.0,0.0,2.0,0.58,2.0,0.0,0.0,0.0,0.0,10.0,4.0,3.0,5.0,2.0,6.0,0.0,6.0,9.0,7.0,0.0,217.0,8.0,43.0,102.0,77.0,11.0,217.0,124.0,7.0,5.0,1.0,138.0,5.0,2.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,27.0,4.0,6.0,40.0" -Adam Ounas,dz ALG,FW,Napoli,26-091,1996,2.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23,0.23,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,13.0,17.0,76.5,297.0,80.0,6.0,7.0,85.7,2.0,3.0,66.7,5.0,5.0,100.0,0.0,2.0,1.0,0.0,4.0,16.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,12.0,0.0,0.0,1.0,0.0,1.0,6.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,19.0,0.0,4.0,12.0,5.0,1.0,19.0,14.0,3.0,2.0,0.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,100.0 -Simone Pafundi,it ITA,MF,Udinese,16-361,2006,1.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,80.0,44.0,4.0,3.0,3.0,100.0,1.0,1.0,100.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,6.0,0.0,0.0,6.0,5.0,0.0,0.0,0.0,5.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -Flavio Paoletti,it ITA,MF,Sampdoria,20-025,2003,4.0,0.0,82.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,46.0,82.6,733.0,181.0,15.0,18.0,83.3,18.0,18.0,100.0,5.0,5.0,100.0,1.0,1.0,0.0,0.0,4.0,41.0,4.0,2.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,4.0,2.0,2.2,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,4.0,3.0,0.0,55.0,3.0,12.0,31.0,12.0,0.0,55.0,33.0,4.0,3.0,0.0,34.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.0 -Leandro Paredes,ar ARG,MF,Juventus,28-226,1994,13.0,4.0,523.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.06,0.06,5.8,1.0,2.0,11.1,0.17,0.0,0.0,0.04,-0.3,-0.3,343.0,378.0,90.7,6373.0,2434.0,146.0,152.0,96.1,144.0,153.0,94.1,48.0,64.0,75.0,7.0,34.0,7.0,2.0,37.0,332.0,45.0,31.0,1.0,1.0,17.0,11.0,6.0,5.0,0.0,3.0,1.0,2.0,17.0,2.93,11.0,4.0,1.0,0.0,2.0,0.34,2.0,0.0,0.0,0.0,0.0,14.0,9.0,4.0,9.0,1.0,4.0,0.0,4.0,9.0,10.0,0.0,427.0,16.0,111.0,240.0,77.0,4.0,427.0,211.0,6.0,3.0,2.0,295.0,3.0,2.0,0.0,10.0,6.0,0.0,0.0,1.0,0.0,33.0,1.0,1.0,50.0 -Fabiano Parisi,it ITA,DF,Empoli,22-093,2000,19.0,19.0,1670.0,2.0,0.0,0.0,0.0,5.0,0.0,0.11,0.0,0.11,0.11,0.11,1.5,1.5,0.08,0.08,18.6,2.0,0.0,14.3,0.11,0.14,1.0,0.11,0.5,0.5,700.0,925.0,75.7,11318.0,5480.0,370.0,412.0,89.8,262.0,349.0,75.1,53.0,107.0,49.5,15.0,53.0,26.0,9.0,81.0,729.0,191.0,23.0,1.0,4.0,33.0,1.0,1.0,0.0,0.0,167.0,5.0,25.0,37.0,1.99,27.0,2.0,2.0,4.0,4.0,0.22,4.0,0.0,0.0,0.0,0.0,38.0,21.0,23.0,11.0,4.0,27.0,11.0,16.0,22.0,55.0,0.0,1187.0,95.0,434.0,497.0,281.0,13.0,1187.0,612.0,54.0,40.0,5.0,619.0,33.0,23.0,0.0,17.0,43.0,0.0,0.0,0.0,0.0,134.0,12.0,7.0,63.2 -"Mario Pašalić,hr CRO,""MF,FW"",Atalanta,28-001,1995,19.0,12.0,1023.0,2.0,2.0,0.0,0.0,2.0,0.0,0.18,0.18,0.35,0.18,0.35,2.6,2.6,0.23,0.23,11.4,4.0,0.0,30.8,0.35,0.15,0.5,0.2,-0.6,-0.6,313.0,386.0,81.1,4898.0,1330.0,164.0,193.0,85.0,110.0,126.0,87.3,26.0,42.0,61.9,10.0,30.0,12.0,1.0,51.0,373.0,12.0,4.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,31.0,2.72,27.0,0.0,2.0,0.0,5.0,0.44,4.0,0.0,1.0,0.0,0.0,17.0,6.0,4.0,11.0,2.0,9.0,1.0,8.0,13.0,7.0,0.0,494.0,10.0,80.0,276.0,141.0,29.0,494.0,300.0,10.0,6.0,1.0,364.0,19.0,17.0,0.0,17.0,12.0,1.0,0.0,0.0,0.0,52.0,25.0,20.0,55.6" -Patric,es ESP,DF,Lazio,29-299,1993,10.0,9.0,753.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,8.4,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.0,445.0,510.0,87.3,8841.0,2647.0,156.0,168.0,92.9,188.0,203.0,92.6,81.0,117.0,69.2,0.0,11.0,2.0,0.0,10.0,462.0,47.0,23.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.36,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,7.0,7.0,2.0,2.0,11.0,9.0,2.0,5.0,25.0,1.0,577.0,124.0,367.0,209.0,5.0,0.0,577.0,293.0,0.0,0.0,0.0,371.0,2.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,52.0,3.0,11.0,21.4 -Rui Patrício,pt POR,GK,Roma,34-360,1988,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,423.0,540.0,78.3,10952.0,7122.0,94.0,94.0,100.0,204.0,207.0,98.6,124.0,237.0,52.3,0.0,3.0,0.0,0.0,0.0,346.0,193.0,34.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,2.0,564.0,518.0,563.0,1.0,0.0,0.0,564.0,302.0,0.0,0.0,0.0,228.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,26.0,1.0,0.0,100.0 -Pedro,es ESP,FW,Lazio,35-197,1987,19.0,9.0,1086.0,3.0,2.0,0.0,0.0,1.0,0.0,0.25,0.17,0.41,0.25,0.41,1.9,1.9,0.16,0.16,12.1,7.0,0.0,33.3,0.58,0.14,0.43,0.09,1.1,1.1,433.0,543.0,79.7,5968.0,1271.0,277.0,304.0,91.1,105.0,147.0,71.4,24.0,50.0,48.0,10.0,23.0,15.0,1.0,31.0,523.0,17.0,8.0,2.0,8.0,20.0,2.0,2.0,0.0,0.0,7.0,3.0,7.0,28.0,2.32,20.0,1.0,3.0,2.0,6.0,0.5,4.0,0.0,1.0,1.0,0.0,14.0,9.0,9.0,3.0,2.0,10.0,1.0,9.0,4.0,3.0,1.0,664.0,5.0,91.0,337.0,241.0,47.0,664.0,434.0,41.0,29.0,13.0,536.0,27.0,21.0,0.0,9.0,20.0,2.0,1.0,0.0,0.0,52.0,0.0,1.0,0.0 -Gianluca Pegolo,it ITA,GK,Sassuolo,41-322,1981,2.0,2.0,180.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,64.0,77.0,83.1,1652.0,1058.0,13.0,13.0,100.0,31.0,32.0,96.9,19.0,31.0,61.3,0.0,0.0,0.0,0.0,0.0,56.0,21.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,81.0,71.0,81.0,0.0,0.0,0.0,81.0,46.0,0.0,0.0,0.0,43.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Pietro Pellegri,it ITA,FW,Torino,21-330,2001,11.0,4.0,369.0,1.0,0.0,0.0,0.0,2.0,0.0,0.24,0.0,0.24,0.24,0.24,0.4,0.4,0.1,0.1,4.1,3.0,0.0,50.0,0.73,0.17,0.33,0.07,0.6,0.6,42.0,58.0,72.4,526.0,30.0,28.0,37.0,75.7,11.0,12.0,91.7,1.0,3.0,33.3,4.0,2.0,2.0,0.0,5.0,50.0,8.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,1.21,5.0,0.0,0.0,0.0,1.0,0.24,1.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,1.0,2.0,4.0,1.0,3.0,0.0,2.0,0.0,111.0,1.0,6.0,43.0,62.0,12.0,111.0,68.0,2.0,1.0,1.0,86.0,17.0,15.0,0.0,9.0,13.0,3.0,0.0,0.0,0.0,9.0,8.0,26.0,23.5 -Lorenzo Pellegrini,it ITA,MF,Roma,26-236,1996,18.0,18.0,1523.0,2.0,5.0,2.0,3.0,2.0,0.0,0.12,0.3,0.41,0.0,0.3,4.9,2.6,0.29,0.15,16.9,10.0,2.0,29.4,0.59,0.0,0.0,0.08,-2.9,-2.6,513.0,688.0,74.6,8937.0,2664.0,235.0,271.0,86.7,174.0,215.0,80.9,76.0,150.0,50.7,41.0,41.0,13.0,2.0,65.0,580.0,108.0,44.0,6.0,7.0,85.0,56.0,24.0,32.0,0.0,2.0,0.0,16.0,74.0,4.37,39.0,25.0,4.0,2.0,8.0,0.47,2.0,5.0,1.0,0.0,0.0,29.0,16.0,13.0,13.0,3.0,17.0,2.0,15.0,13.0,11.0,0.0,897.0,15.0,118.0,485.0,303.0,34.0,894.0,485.0,39.0,23.0,4.0,581.0,31.0,16.0,0.0,15.0,34.0,4.0,0.0,0.0,0.0,75.0,7.0,15.0,31.8 -Pepín,gq EQG,MF,Monza,26-180,1996,10.0,7.0,570.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.16,0.16,0.0,0.16,0.3,0.3,0.04,0.04,6.3,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.3,-0.3,277.0,339.0,81.7,4567.0,1259.0,137.0,156.0,87.8,117.0,135.0,86.7,15.0,25.0,60.0,6.0,33.0,13.0,2.0,41.0,330.0,9.0,7.0,1.0,1.0,9.0,1.0,0.0,1.0,0.0,1.0,0.0,7.0,14.0,2.21,14.0,0.0,0.0,0.0,1.0,0.16,1.0,0.0,0.0,0.0,0.0,13.0,7.0,3.0,5.0,5.0,7.0,2.0,5.0,7.0,8.0,0.0,405.0,9.0,70.0,230.0,108.0,7.0,405.0,239.0,10.0,15.0,2.0,293.0,7.0,6.0,0.0,14.0,6.0,0.0,0.0,0.0,0.0,31.0,4.0,3.0,57.1 -"Roberto Pereyra,ar ARG,""DF,MF"",Udinese,32-034,1991,18.0,18.0,1549.0,2.0,5.0,0.0,0.0,4.0,0.0,0.12,0.29,0.41,0.12,0.41,1.9,1.9,0.11,0.11,17.2,9.0,1.0,36.0,0.52,0.08,0.22,0.08,0.1,0.1,609.0,764.0,79.7,9282.0,3166.0,333.0,363.0,91.7,209.0,267.0,78.3,40.0,64.0,62.5,25.0,53.0,30.0,5.0,96.0,694.0,68.0,21.0,1.0,6.0,45.0,4.0,2.0,1.0,0.0,42.0,2.0,25.0,64.0,3.71,57.0,0.0,4.0,1.0,8.0,0.46,8.0,0.0,0.0,0.0,0.0,22.0,17.0,12.0,8.0,2.0,13.0,2.0,11.0,12.0,13.0,0.0,940.0,29.0,187.0,381.0,390.0,75.0,940.0,575.0,43.0,28.0,11.0,635.0,19.0,17.0,0.0,15.0,25.0,2.0,0.0,0.0,0.0,101.0,2.0,4.0,33.3" -Nehuén Pérez,ar ARG,DF,Udinese,22-231,2000,19.0,19.0,1573.0,1.0,0.0,0.0,0.0,3.0,1.0,0.06,0.0,0.06,0.06,0.06,0.8,0.8,0.05,0.05,17.5,6.0,0.0,50.0,0.34,0.08,0.17,0.07,0.2,0.2,894.0,1051.0,85.1,17645.0,6614.0,326.0,353.0,92.4,424.0,471.0,90.0,125.0,195.0,64.1,7.0,71.0,6.0,3.0,70.0,938.0,112.0,31.0,0.0,30.0,15.0,0.0,0.0,0.0,0.0,77.0,1.0,9.0,21.0,1.2,17.0,2.0,1.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,40.0,20.0,29.0,10.0,1.0,24.0,12.0,12.0,16.0,69.0,1.0,1246.0,123.0,557.0,569.0,125.0,17.0,1246.0,711.0,16.0,16.0,0.0,801.0,9.0,8.0,0.0,15.0,3.0,0.0,0.0,0.0,0.0,81.0,19.0,9.0,67.9 -Mattia Perin,it ITA,GK,Juventus,30-092,1992,7.0,6.0,588.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,185.0,239.0,77.4,4418.0,2848.0,44.0,45.0,97.8,103.0,103.0,100.0,38.0,91.0,41.8,0.0,6.0,0.0,0.0,1.0,183.0,56.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,246.0,224.0,243.0,3.0,0.0,0.0,246.0,154.0,0.0,0.0,0.0,128.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0 -Matteo Pessina,it ITA,MF,Monza,25-295,1997,19.0,18.0,1534.0,3.0,2.0,2.0,2.0,2.0,0.0,0.18,0.12,0.29,0.06,0.18,3.2,1.6,0.19,0.09,17.0,4.0,0.0,25.0,0.23,0.06,0.25,0.1,-0.2,-0.6,854.0,1006.0,84.9,14112.0,4012.0,417.0,462.0,90.3,350.0,384.0,91.1,63.0,90.0,70.0,14.0,86.0,24.0,2.0,114.0,954.0,47.0,26.0,1.0,4.0,28.0,15.0,0.0,12.0,0.0,5.0,5.0,27.0,41.0,2.41,36.0,1.0,0.0,4.0,6.0,0.35,5.0,0.0,0.0,1.0,0.0,21.0,13.0,7.0,9.0,5.0,30.0,3.0,27.0,20.0,14.0,1.0,1184.0,35.0,236.0,649.0,312.0,34.0,1182.0,721.0,49.0,27.0,5.0,857.0,37.0,25.0,0.0,19.0,28.0,1.0,0.0,0.0,0.0,108.0,17.0,18.0,48.6 -Andrea Petagna,it ITA,FW,Monza,27-225,1995,16.0,10.0,860.0,2.0,3.0,1.0,1.0,1.0,0.0,0.21,0.31,0.52,0.1,0.42,3.2,2.4,0.34,0.25,9.6,6.0,0.0,35.3,0.63,0.06,0.17,0.14,-1.2,-1.4,172.0,241.0,71.4,2593.0,401.0,109.0,135.0,80.7,43.0,70.0,61.4,11.0,15.0,73.3,16.0,14.0,4.0,0.0,18.0,214.0,25.0,0.0,1.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,2.0,6.0,22.0,2.3,18.0,0.0,3.0,1.0,4.0,0.42,2.0,0.0,1.0,1.0,0.0,5.0,4.0,2.0,2.0,1.0,8.0,3.0,5.0,2.0,10.0,0.0,355.0,12.0,29.0,174.0,154.0,55.0,354.0,204.0,11.0,6.0,6.0,270.0,38.0,20.0,0.0,12.0,15.0,4.0,1.0,0.0,0.0,26.0,16.0,14.0,53.3 -Giuseppe Pezzella,it ITA,DF,Lecce,25-073,1997,9.0,7.0,630.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.14,0.14,0.0,0.14,0.1,0.1,0.01,0.01,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,288.0,362.0,79.6,4701.0,1837.0,142.0,157.0,90.4,121.0,152.0,79.6,20.0,37.0,54.1,3.0,28.0,8.0,2.0,48.0,275.0,86.0,9.0,0.0,3.0,20.0,1.0,0.0,1.0,0.0,76.0,1.0,9.0,11.0,1.57,11.0,0.0,0.0,0.0,2.0,0.29,2.0,0.0,0.0,0.0,0.0,17.0,7.0,13.0,2.0,2.0,8.0,2.0,6.0,6.0,21.0,1.0,447.0,26.0,162.0,168.0,120.0,5.0,447.0,213.0,14.0,11.0,1.0,217.0,8.0,8.0,0.0,10.0,6.0,1.0,0.0,0.0,0.0,37.0,5.0,4.0,55.6 -Krzysztof Piątek,pl POL,FW,Salernitana,27-224,1995,17.0,13.0,1149.0,3.0,2.0,1.0,1.0,3.0,0.0,0.23,0.16,0.39,0.16,0.31,4.8,4.1,0.38,0.32,12.8,12.0,0.0,38.7,0.94,0.06,0.17,0.13,-1.8,-2.1,217.0,305.0,71.1,2984.0,419.0,128.0,166.0,77.1,62.0,82.0,75.6,11.0,20.0,55.0,10.0,16.0,6.0,0.0,20.0,269.0,33.0,0.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,1.0,3.0,5.0,23.0,1.8,17.0,0.0,2.0,3.0,4.0,0.31,3.0,0.0,1.0,0.0,0.0,8.0,5.0,0.0,4.0,4.0,6.0,1.0,5.0,0.0,19.0,0.0,452.0,20.0,48.0,223.0,185.0,42.0,451.0,233.0,18.0,6.0,8.0,331.0,43.0,20.0,0.0,20.0,24.0,15.0,0.0,0.0,0.0,30.0,45.0,56.0,44.6 -Roberto Piccoli,it ITA,FW,Hellas Verona,22-014,2001,7.0,2.0,192.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.19,0.19,2.1,2.0,0.0,50.0,0.94,0.0,0.0,0.1,-0.4,-0.4,13.0,25.0,52.0,178.0,55.0,8.0,12.0,66.7,3.0,6.0,50.0,1.0,4.0,25.0,1.0,1.0,0.0,0.0,0.0,23.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.47,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,0.0,2.0,0.0,2.0,0.0,55.0,3.0,8.0,16.0,31.0,11.0,55.0,36.0,2.0,3.0,1.0,41.0,7.0,4.0,0.0,2.0,3.0,4.0,0.0,0.0,0.0,4.0,3.0,13.0,18.8 -Roberto Piccoli,it ITA,FW,Empoli,22-014,2001,1.0,0.0,29.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.05,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,2.0,3.0,66.7,26.0,0.0,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,9.0,3.0,9.0,6.0,0.0,0.0,0.0,6.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0 -Charles Pickel,ch SUI,MF,Cremonese,25-271,1997,19.0,17.0,1395.0,1.0,0.0,0.0,0.0,6.0,0.0,0.06,0.0,0.06,0.06,0.06,1.3,1.3,0.08,0.08,15.5,6.0,0.0,35.3,0.39,0.06,0.17,0.08,-0.3,-0.3,447.0,575.0,77.7,7049.0,2083.0,234.0,286.0,81.8,169.0,195.0,86.7,31.0,53.0,58.5,11.0,56.0,8.0,0.0,74.0,560.0,12.0,5.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,2.0,3.0,14.0,29.0,1.87,28.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,37.0,14.0,17.0,16.0,4.0,20.0,3.0,17.0,11.0,14.0,0.0,739.0,26.0,154.0,413.0,176.0,32.0,739.0,352.0,9.0,16.0,1.0,422.0,19.0,13.0,0.0,18.0,9.0,3.0,0.0,0.0,0.0,97.0,30.0,47.0,39.0 -Andrea Pinamonti,it ITA,FW,Sassuolo,23-267,1999,17.0,16.0,1305.0,3.0,0.0,1.0,1.0,2.0,0.0,0.21,0.0,0.21,0.14,0.14,4.1,3.3,0.28,0.23,14.5,11.0,0.0,28.2,0.76,0.05,0.18,0.08,-1.1,-1.3,187.0,295.0,63.4,2253.0,346.0,131.0,174.0,75.3,37.0,71.0,52.1,5.0,13.0,38.5,11.0,9.0,11.0,2.0,19.0,261.0,34.0,0.0,1.0,1.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,36.0,2.48,24.0,0.0,6.0,5.0,2.0,0.14,0.0,0.0,1.0,1.0,0.0,6.0,4.0,0.0,3.0,3.0,13.0,1.0,12.0,2.0,5.0,0.0,441.0,5.0,12.0,210.0,224.0,73.0,440.0,213.0,10.0,5.0,6.0,330.0,33.0,34.0,0.0,11.0,17.0,4.0,1.0,0.0,0.0,20.0,21.0,53.0,28.4 -Lorenzo Pirola,it ITA,DF,Salernitana,20-355,2002,9.0,6.0,590.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,6.6,0.0,0.0,0.0,0.0,0.0,0.0,0.12,-0.2,-0.2,206.0,264.0,78.0,3876.0,1179.0,78.0,89.0,87.6,105.0,124.0,84.7,21.0,38.0,55.3,0.0,14.0,1.0,0.0,12.0,246.0,16.0,11.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,5.0,2.0,7.0,3.0,0.46,3.0,0.0,0.0,0.0,1.0,0.15,1.0,0.0,0.0,0.0,0.0,16.0,12.0,10.0,6.0,0.0,14.0,11.0,3.0,12.0,30.0,0.0,346.0,48.0,200.0,130.0,16.0,2.0,346.0,143.0,7.0,3.0,0.0,202.0,2.0,0.0,0.0,11.0,2.0,0.0,0.0,0.0,0.0,15.0,10.0,12.0,45.5 -"Marko Pjaca,hr CRO,""MF,FW"",Empoli,27-280,1995,10.0,5.0,407.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.14,0.14,4.5,2.0,0.0,15.4,0.44,0.0,0.0,0.05,-0.6,-0.6,146.0,184.0,79.3,2298.0,553.0,76.0,92.0,82.6,48.0,61.0,78.7,14.0,20.0,70.0,8.0,13.0,6.0,3.0,10.0,163.0,21.0,4.0,0.0,0.0,16.0,12.0,5.0,4.0,0.0,0.0,0.0,3.0,18.0,3.98,12.0,2.0,2.0,1.0,1.0,0.22,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,238.0,1.0,22.0,117.0,99.0,13.0,238.0,130.0,15.0,6.0,7.0,180.0,9.0,14.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,21.0,4.0,6.0,40.0" -Tommaso Pobega,it ITA,MF,Milan,23-210,1999,12.0,6.0,572.0,1.0,0.0,0.0,0.0,2.0,0.0,0.16,0.0,0.16,0.16,0.16,0.8,0.8,0.12,0.12,6.4,3.0,0.0,30.0,0.47,0.1,0.33,0.08,0.2,0.2,169.0,223.0,75.8,2897.0,695.0,78.0,98.0,79.6,71.0,82.0,86.6,17.0,29.0,58.6,4.0,15.0,4.0,1.0,18.0,218.0,5.0,4.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,18.0,2.83,13.0,0.0,2.0,1.0,2.0,0.31,2.0,0.0,0.0,0.0,0.0,14.0,7.0,3.0,9.0,2.0,6.0,2.0,4.0,7.0,8.0,0.0,281.0,9.0,59.0,152.0,70.0,11.0,281.0,111.0,7.0,8.0,0.0,168.0,8.0,3.0,0.0,24.0,4.0,0.0,0.0,0.0,0.0,31.0,9.0,11.0,45.0 -Matteo Politano,it ITA,FW,Napoli,29-191,1993,19.0,10.0,827.0,3.0,3.0,2.0,3.0,1.0,0.0,0.33,0.33,0.65,0.11,0.44,3.7,1.4,0.41,0.15,9.2,3.0,1.0,15.0,0.33,0.05,0.33,0.07,-0.7,-0.4,325.0,449.0,72.4,5025.0,1675.0,189.0,212.0,89.2,96.0,132.0,72.7,26.0,57.0,45.6,27.0,17.0,19.0,3.0,48.0,403.0,42.0,10.0,2.0,6.0,50.0,19.0,13.0,2.0,0.0,13.0,4.0,20.0,55.0,5.98,35.0,7.0,5.0,1.0,4.0,0.43,3.0,0.0,1.0,0.0,0.0,15.0,7.0,8.0,3.0,4.0,3.0,0.0,3.0,6.0,5.0,0.0,561.0,7.0,79.0,183.0,305.0,46.0,558.0,368.0,60.0,28.0,22.0,419.0,22.0,11.0,0.0,10.0,17.0,1.0,0.0,0.0,0.0,48.0,3.0,4.0,42.9 -Marin Pongračić,hr CRO,DF,Lecce,25-152,1997,9.0,9.0,790.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.8,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,296.0,361.0,82.0,6388.0,2314.0,73.0,94.0,77.7,171.0,195.0,87.7,47.0,63.0,74.6,2.0,16.0,2.0,0.0,19.0,324.0,37.0,37.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,0.68,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,14.0,8.0,7.0,1.0,4.0,3.0,1.0,16.0,31.0,0.0,435.0,48.0,232.0,201.0,11.0,4.0,435.0,223.0,3.0,1.0,0.0,210.0,5.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,59.0,9.0,10.0,47.4 -Stefan Posch,at AUT,DF,Bologna,25-272,1997,14.0,14.0,1228.0,4.0,1.0,0.0,0.0,1.0,0.0,0.29,0.07,0.37,0.29,0.37,1.4,1.4,0.1,0.1,13.6,7.0,0.0,58.3,0.51,0.33,0.57,0.12,2.6,2.6,576.0,794.0,72.5,10057.0,4675.0,271.0,316.0,85.8,242.0,297.0,81.5,49.0,130.0,37.7,13.0,51.0,15.0,3.0,71.0,655.0,137.0,10.0,3.0,4.0,27.0,0.0,0.0,0.0,0.0,127.0,2.0,25.0,24.0,1.76,20.0,2.0,0.0,1.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,41.0,25.0,22.0,14.0,5.0,23.0,7.0,16.0,23.0,40.0,0.0,966.0,63.0,383.0,430.0,164.0,17.0,966.0,442.0,19.0,20.0,2.0,546.0,20.0,6.0,0.0,18.0,10.0,1.0,0.0,0.0,0.0,66.0,13.0,12.0,52.0 -Ivan Provedel,it ITA,GK,Lazio,28-330,1994,21.0,20.0,1883.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,611.0,774.0,78.9,16785.0,12130.0,128.0,128.0,100.0,228.0,237.0,96.2,251.0,401.0,62.6,1.0,11.0,0.0,0.0,0.0,593.0,177.0,57.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,0.19,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,0.0,821.0,699.0,818.0,5.0,0.0,0.0,821.0,496.0,0.0,0.0,0.0,431.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,38.0,2.0,0.0,100.0 -"Ignacio Pussetto,ar ARG,""MF,DF"",Sampdoria,27-051,1995,5.0,1.0,152.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.09,0.09,1.7,2.0,0.0,100.0,1.18,0.0,0.0,0.08,-0.2,-0.2,34.0,56.0,60.7,404.0,91.0,25.0,32.0,78.1,7.0,12.0,58.3,0.0,4.0,0.0,2.0,2.0,0.0,0.0,1.0,55.0,1.0,1.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.18,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,2.0,3.0,1.0,4.0,0.0,4.0,0.0,3.0,0.0,81.0,2.0,11.0,23.0,47.0,5.0,81.0,34.0,2.0,1.0,0.0,51.0,7.0,1.0,0.0,7.0,1.0,0.0,0.0,0.0,0.0,4.0,4.0,5.0,44.4" -Niklas Pyyhtiä,fi FIN,MF,Bologna,19-138,2003,3.0,0.0,37.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.22,0.22,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,14.0,20.0,70.0,220.0,6.0,8.0,11.0,72.7,5.0,6.0,83.3,1.0,2.0,50.0,0.0,0.0,0.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,24.0,0.0,7.0,11.0,6.0,2.0,24.0,16.0,1.0,0.0,0.0,20.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,3.0,0.0 -Fabio Quagliarella,it ITA,FW,Sampdoria,40-010,1983,15.0,2.0,360.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.6,1.6,0.39,0.39,4.0,5.0,0.0,27.8,1.25,0.0,0.0,0.09,-1.6,-1.6,72.0,107.0,67.3,1101.0,281.0,34.0,39.0,87.2,23.0,33.0,69.7,6.0,15.0,40.0,2.0,8.0,4.0,0.0,12.0,100.0,7.0,1.0,1.0,3.0,7.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,10.0,2.48,9.0,0.0,1.0,0.0,1.0,0.25,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,0.0,4.0,0.0,3.0,0.0,160.0,3.0,9.0,57.0,95.0,28.0,160.0,104.0,3.0,2.0,1.0,120.0,15.0,2.0,0.0,3.0,8.0,5.0,0.0,0.0,0.0,10.0,5.0,16.0,23.8 -Giacomo Quagliata,it ITA,DF,Cremonese,22-356,2000,13.0,4.0,493.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.09,0.09,5.5,1.0,1.0,11.1,0.18,0.0,0.0,0.05,-0.5,-0.5,153.0,249.0,61.4,2576.0,1123.0,77.0,88.0,87.5,63.0,98.0,64.3,11.0,44.0,25.0,6.0,16.0,8.0,6.0,23.0,191.0,57.0,1.0,0.0,0.0,40.0,4.0,0.0,3.0,0.0,52.0,1.0,13.0,17.0,3.1,10.0,3.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,7.0,6.0,0.0,2.0,5.0,2.0,3.0,8.0,5.0,0.0,313.0,9.0,83.0,104.0,135.0,8.0,313.0,133.0,13.0,13.0,0.0,145.0,9.0,4.0,0.0,10.0,8.0,0.0,0.0,0.0,0.0,43.0,2.0,8.0,20.0 -Adrien Rabiot,fr FRA,MF,Juventus,27-313,1995,17.0,16.0,1398.0,3.0,2.0,0.0,0.0,3.0,0.0,0.19,0.13,0.32,0.19,0.32,2.4,2.4,0.15,0.15,15.5,12.0,0.0,54.5,0.77,0.14,0.25,0.11,0.6,0.6,485.0,587.0,82.6,7982.0,2197.0,242.0,274.0,88.3,184.0,218.0,84.4,43.0,57.0,75.4,18.0,51.0,15.0,1.0,75.0,568.0,17.0,7.0,2.0,3.0,17.0,0.0,0.0,0.0,0.0,10.0,2.0,12.0,48.0,3.09,40.0,0.0,3.0,2.0,4.0,0.26,3.0,0.0,0.0,1.0,0.0,34.0,17.0,14.0,14.0,6.0,25.0,4.0,21.0,16.0,15.0,1.0,779.0,27.0,163.0,398.0,231.0,36.0,779.0,420.0,27.0,31.0,2.0,492.0,26.0,20.0,0.0,18.0,24.0,2.0,0.0,0.0,0.0,102.0,19.0,18.0,51.4 -"Nemanja Radonjić,rs SRB,""MF,FW"",Torino,26-360,1996,18.0,10.0,905.0,2.0,1.0,0.0,0.0,0.0,0.0,0.2,0.1,0.3,0.2,0.3,2.9,2.9,0.29,0.29,10.1,12.0,0.0,34.3,1.19,0.06,0.17,0.08,-0.9,-0.9,180.0,285.0,63.2,2648.0,696.0,113.0,146.0,77.4,41.0,70.0,58.6,17.0,37.0,45.9,11.0,10.0,10.0,4.0,25.0,267.0,17.0,0.0,1.0,4.0,42.0,15.0,7.0,5.0,0.0,2.0,1.0,12.0,28.0,2.79,20.0,3.0,1.0,0.0,4.0,0.4,4.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,2.0,0.0,2.0,2.0,1.0,0.0,443.0,2.0,21.0,142.0,289.0,57.0,443.0,324.0,51.0,33.0,31.0,357.0,39.0,21.0,0.0,14.0,7.0,9.0,0.0,0.0,0.0,38.0,6.0,9.0,40.0" -"Ivan Radovanović,rs SRB,""MF,DF"",Salernitana,34-165,1988,10.0,7.0,671.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,7.5,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.2,-0.2,214.0,265.0,80.8,4318.0,1602.0,75.0,84.0,89.3,105.0,115.0,91.3,32.0,57.0,56.1,1.0,19.0,0.0,0.0,16.0,252.0,12.0,12.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,0.27,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,4.0,8.0,4.0,0.0,9.0,3.0,6.0,7.0,9.0,1.0,323.0,30.0,130.0,182.0,14.0,2.0,323.0,175.0,2.0,1.0,0.0,203.0,6.0,0.0,0.0,4.0,7.0,0.0,0.0,0.0,0.0,47.0,8.0,12.0,40.0" -Ionuț Radu,ro ROU,GK,Cremonese,25-258,1997,9.0,9.0,810.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,221.0,336.0,65.8,6117.0,4391.0,51.0,52.0,98.1,100.0,102.0,98.0,69.0,177.0,39.0,2.0,10.0,2.0,0.0,0.0,248.0,84.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,6.0,0.67,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,366.0,305.0,366.0,0.0,0.0,0.0,366.0,205.0,0.0,0.0,0.0,170.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,11.0,3.0,1.0,75.0 -Luca Ranieri,it ITA,DF,Fiorentina,23-293,1999,2.0,2.0,175.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,77.0,97.0,79.4,1591.0,456.0,17.0,19.0,89.5,50.0,55.0,90.9,10.0,19.0,52.6,1.0,4.0,0.0,0.0,4.0,96.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,5.0,7.0,1.0,117.0,12.0,60.0,55.0,2.0,1.0,117.0,71.0,1.0,0.0,0.0,79.0,2.0,0.0,0.0,2.0,2.0,1.0,0.0,0.0,0.0,8.0,4.0,10.0,28.6 -Andrea Ranocchia,it ITA,DF,Monza,34-359,1988,1.0,1.0,47.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,19.0,84.2,347.0,109.0,2.0,2.0,100.0,11.0,12.0,91.7,2.0,4.0,50.0,0.0,2.0,0.0,0.0,0.0,15.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,2.0,0.0,5.0,5.0,0.0,0.0,7.0,0.0,35.0,13.0,28.0,7.0,0.0,0.0,35.0,11.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,33.3 -Filippo Ranocchia,it ITA,MF,Monza,21-272,2001,11.0,5.0,477.0,1.0,0.0,0.0,0.0,1.0,0.0,0.19,0.0,0.19,0.19,0.19,0.8,0.8,0.15,0.15,5.3,5.0,4.0,38.5,0.94,0.08,0.2,0.06,0.2,0.2,167.0,212.0,78.8,3258.0,983.0,71.0,80.0,88.8,68.0,83.0,81.9,26.0,40.0,65.0,6.0,19.0,5.0,1.0,22.0,200.0,12.0,6.0,3.0,8.0,13.0,4.0,0.0,4.0,0.0,2.0,0.0,4.0,13.0,2.45,5.0,4.0,2.0,1.0,1.0,0.19,1.0,0.0,0.0,0.0,0.0,10.0,6.0,5.0,3.0,2.0,5.0,1.0,4.0,4.0,5.0,0.0,272.0,12.0,63.0,140.0,74.0,2.0,272.0,176.0,8.0,12.0,0.0,196.0,8.0,8.0,0.0,5.0,10.0,0.0,0.0,0.0,0.0,28.0,1.0,6.0,14.3 -"Giacomo Raspadori,it ITA,""FW,MF"",Napoli,22-357,2000,14.0,7.0,572.0,1.0,0.0,0.0,0.0,1.0,0.0,0.16,0.0,0.16,0.16,0.16,2.4,2.4,0.38,0.38,6.4,12.0,3.0,48.0,1.89,0.04,0.08,0.1,-1.4,-1.4,224.0,294.0,76.2,3477.0,553.0,115.0,138.0,83.3,76.0,90.0,84.4,16.0,35.0,45.7,8.0,8.0,5.0,0.0,17.0,273.0,21.0,7.0,0.0,6.0,13.0,8.0,4.0,1.0,0.0,1.0,0.0,10.0,23.0,3.63,16.0,2.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,7.0,0.0,5.0,3.0,6.0,0.0,6.0,2.0,2.0,0.0,377.0,2.0,32.0,168.0,178.0,36.0,377.0,237.0,8.0,6.0,4.0,293.0,21.0,9.0,0.0,5.0,5.0,1.0,0.0,0.0,0.0,20.0,1.0,6.0,14.3" -Giacomo Raspadori,it ITA,FW,Sassuolo,22-357,2000,1.0,0.0,45.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,12.0,83.3,207.0,33.0,2.0,2.0,100.0,6.0,7.0,85.7,1.0,2.0,50.0,0.0,1.0,0.0,0.0,2.0,9.0,3.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,0.0,0.0,7.0,8.0,2.0,15.0,10.0,0.0,1.0,0.0,12.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Ante Rebić,hr CRO,FW,Milan,29-142,1993,13.0,5.0,514.0,3.0,2.0,0.0,0.0,4.0,0.0,0.53,0.35,0.88,0.53,0.88,2.3,2.3,0.4,0.4,5.7,9.0,1.0,50.0,1.58,0.17,0.33,0.13,0.7,0.7,91.0,172.0,52.9,1476.0,452.0,51.0,68.0,75.0,26.0,53.0,49.1,9.0,21.0,42.9,9.0,11.0,5.0,1.0,16.0,163.0,6.0,2.0,4.0,1.0,17.0,0.0,0.0,0.0,0.0,1.0,3.0,11.0,21.0,3.67,14.0,0.0,2.0,3.0,3.0,0.52,3.0,0.0,0.0,0.0,0.0,5.0,3.0,0.0,1.0,4.0,4.0,0.0,4.0,1.0,4.0,0.0,232.0,3.0,11.0,87.0,138.0,27.0,232.0,141.0,10.0,9.0,3.0,188.0,17.0,10.0,0.0,13.0,8.0,5.0,0.0,0.0,0.0,18.0,8.0,5.0,61.5 -Arkadiusz Reca,pl POL,DF,Spezia,27-238,1995,17.0,15.0,1250.0,1.0,1.0,0.0,0.0,3.0,0.0,0.07,0.07,0.14,0.07,0.14,0.6,0.6,0.04,0.04,13.9,4.0,0.0,36.4,0.29,0.09,0.25,0.05,0.4,0.4,420.0,612.0,68.6,7255.0,3149.0,197.0,253.0,77.9,161.0,214.0,75.2,50.0,103.0,48.5,11.0,32.0,16.0,12.0,40.0,488.0,122.0,2.0,0.0,7.0,41.0,0.0,0.0,0.0,0.0,120.0,2.0,20.0,28.0,2.02,23.0,1.0,2.0,1.0,2.0,0.14,1.0,0.0,1.0,0.0,0.0,27.0,15.0,14.0,7.0,6.0,14.0,3.0,11.0,16.0,36.0,0.0,777.0,62.0,249.0,345.0,190.0,15.0,777.0,371.0,35.0,29.0,3.0,421.0,21.0,7.0,0.0,17.0,6.0,1.0,0.0,1.0,0.0,72.0,7.0,8.0,46.7 -Panagiotis Retsos,gr GRE,DF,Hellas Verona,24-185,1998,2.0,1.0,122.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,44.0,56.0,78.6,781.0,321.0,20.0,23.0,87.0,20.0,24.0,83.3,4.0,7.0,57.1,1.0,3.0,0.0,0.0,2.0,43.0,12.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,9.0,1.0,1.0,1.0,0.73,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,2.0,2.0,1.0,4.0,1.0,3.0,2.0,3.0,0.0,76.0,8.0,31.0,38.0,7.0,1.0,76.0,35.0,2.0,0.0,0.0,33.0,0.0,1.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,7.0,3.0,3.0,50.0 -Franck Ribéry,fr FRA,MF,Salernitana,39-309,1983,1.0,0.0,37.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0,22.0,86.4,235.0,19.0,11.0,12.0,91.7,7.0,8.0,87.5,0.0,0.0,0.0,0.0,2.0,0.0,0.0,1.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,2.43,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,28.0,0.0,3.0,16.0,10.0,0.0,28.0,15.0,1.0,1.0,0.0,24.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -Samuele Ricci,it ITA,MF,Torino,21-173,2001,15.0,13.0,1197.0,1.0,1.0,0.0,0.0,4.0,0.0,0.08,0.08,0.15,0.08,0.15,0.7,0.7,0.05,0.05,13.3,4.0,0.0,36.4,0.3,0.09,0.25,0.07,0.3,0.3,621.0,699.0,88.8,10582.0,2523.0,282.0,312.0,90.4,270.0,287.0,94.1,47.0,61.0,77.0,8.0,63.0,8.0,1.0,63.0,668.0,28.0,25.0,2.0,4.0,9.0,2.0,1.0,1.0,0.0,1.0,3.0,7.0,25.0,1.88,20.0,0.0,2.0,2.0,1.0,0.08,0.0,0.0,1.0,0.0,0.0,15.0,7.0,7.0,4.0,4.0,15.0,4.0,11.0,10.0,14.0,0.0,809.0,32.0,145.0,523.0,148.0,5.0,809.0,515.0,25.0,17.0,1.0,577.0,12.0,11.0,0.0,13.0,35.0,0.0,0.0,0.0,0.0,84.0,5.0,5.0,50.0 -Tomás Rincón,ve VEN,MF,Sampdoria,35-028,1988,18.0,16.0,1324.0,0.0,2.0,0.0,0.0,3.0,1.0,0.0,0.14,0.14,0.0,0.14,0.5,0.5,0.03,0.03,14.7,3.0,0.0,33.3,0.2,0.0,0.0,0.06,-0.5,-0.5,507.0,627.0,80.9,8636.0,2481.0,247.0,281.0,87.9,198.0,232.0,85.3,49.0,87.0,56.3,14.0,74.0,15.0,3.0,73.0,602.0,22.0,14.0,1.0,10.0,11.0,1.0,0.0,1.0,0.0,4.0,3.0,4.0,25.0,1.7,23.0,0.0,0.0,0.0,3.0,0.2,2.0,0.0,0.0,0.0,1.0,35.0,17.0,12.0,17.0,6.0,18.0,0.0,18.0,19.0,7.0,0.0,766.0,12.0,118.0,481.0,178.0,13.0,766.0,435.0,16.0,19.0,3.0,505.0,16.0,14.0,0.0,27.0,15.0,0.0,0.0,0.0,0.0,84.0,17.0,8.0,68.0 -"Pablo Rodríguez,es ESP,""FW,MF"",Lecce,21-190,2001,4.0,0.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,21.0,66.7,215.0,66.0,6.0,10.0,60.0,6.0,7.0,85.7,1.0,1.0,100.0,1.0,2.0,0.0,0.0,3.0,18.0,3.0,0.0,0.0,0.0,4.0,2.0,0.0,1.0,0.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,0.0,32.0,0.0,4.0,18.0,11.0,0.0,32.0,21.0,2.0,2.0,0.0,22.0,1.0,2.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0" -Ricardo Rodríguez,ch SUI,DF,Torino,30-169,1992,18.0,13.0,1283.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.02,0.02,14.3,2.0,1.0,33.3,0.14,0.0,0.0,0.04,-0.3,-0.3,692.0,830.0,83.4,12636.0,4167.0,289.0,317.0,91.2,324.0,354.0,91.5,65.0,127.0,51.2,12.0,60.0,10.0,5.0,64.0,783.0,45.0,26.0,0.0,12.0,32.0,9.0,6.0,3.0,0.0,5.0,2.0,11.0,29.0,2.04,25.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,16.0,16.0,9.0,4.0,18.0,10.0,8.0,20.0,34.0,0.0,959.0,68.0,379.0,463.0,122.0,5.0,959.0,552.0,18.0,23.0,3.0,630.0,5.0,3.0,0.0,16.0,6.0,0.0,0.0,0.0,0.0,86.0,18.0,21.0,46.2 -Rogério,br BRA,DF,Sassuolo,25-028,1998,20.0,19.0,1648.0,0.0,2.0,0.0,0.0,5.0,0.0,0.0,0.11,0.11,0.0,0.11,0.3,0.3,0.01,0.01,18.3,1.0,0.0,16.7,0.05,0.0,0.0,0.04,-0.3,-0.3,925.0,1160.0,79.7,16820.0,6315.0,420.0,452.0,92.9,370.0,449.0,82.4,117.0,199.0,58.8,9.0,65.0,27.0,12.0,98.0,946.0,211.0,29.0,0.0,15.0,57.0,0.0,0.0,0.0,0.0,182.0,3.0,27.0,32.0,1.75,28.0,3.0,0.0,1.0,3.0,0.16,3.0,0.0,0.0,0.0,0.0,22.0,14.0,14.0,3.0,5.0,19.0,5.0,14.0,18.0,40.0,0.0,1302.0,51.0,467.0,589.0,260.0,9.0,1302.0,715.0,48.0,34.0,4.0,812.0,13.0,4.0,0.0,13.0,21.0,0.0,0.0,0.0,0.0,89.0,10.0,20.0,33.3 -Alessio Romagnoli,it ITA,DF,Lazio,28-029,1995,20.0,19.0,1734.0,1.0,0.0,0.0,0.0,1.0,0.0,0.05,0.0,0.05,0.05,0.05,0.5,0.5,0.02,0.02,19.3,2.0,0.0,28.6,0.1,0.14,0.5,0.07,0.5,0.5,905.0,1019.0,88.8,15600.0,6110.0,413.0,441.0,93.7,377.0,410.0,92.0,87.0,127.0,68.5,2.0,44.0,3.0,0.0,33.0,926.0,93.0,56.0,1.0,8.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,7.0,0.36,6.0,1.0,0.0,0.0,3.0,0.16,3.0,0.0,0.0,0.0,0.0,26.0,13.0,15.0,11.0,0.0,22.0,17.0,5.0,25.0,68.0,0.0,1186.0,194.0,640.0,532.0,17.0,10.0,1186.0,557.0,3.0,2.0,1.0,741.0,4.0,3.0,0.0,19.0,13.0,1.0,0.0,0.0,0.0,79.0,45.0,25.0,64.3 -Luka Romero,ar ARG,FW,Lazio,18-084,2004,5.0,1.0,138.0,1.0,0.0,0.0,0.0,0.0,0.0,0.65,0.0,0.65,0.65,0.65,0.8,0.8,0.53,0.53,1.5,1.0,0.0,25.0,0.65,0.25,1.0,0.2,0.2,0.2,63.0,72.0,87.5,736.0,147.0,50.0,53.0,94.3,9.0,11.0,81.8,1.0,2.0,50.0,2.0,2.0,0.0,0.0,4.0,70.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,3.0,1.96,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,3.0,1.0,0.0,102.0,1.0,13.0,46.0,43.0,5.0,102.0,79.0,4.0,3.0,2.0,81.0,4.0,4.0,0.0,2.0,6.0,0.0,0.0,0.0,0.0,9.0,2.0,1.0,66.7 -Marten de Roon,nl NED,MF,Atalanta,31-318,1991,19.0,18.0,1524.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.06,0.06,0.0,0.06,0.4,0.4,0.02,0.02,16.9,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.4,-0.4,727.0,891.0,81.6,13773.0,4019.0,277.0,312.0,88.8,356.0,419.0,85.0,80.0,122.0,65.6,6.0,63.0,6.0,0.0,74.0,851.0,38.0,27.0,0.0,16.0,7.0,0.0,0.0,0.0,0.0,7.0,2.0,10.0,23.0,1.36,20.0,0.0,0.0,2.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,32.0,16.0,16.0,16.0,0.0,17.0,2.0,15.0,19.0,33.0,0.0,1030.0,70.0,329.0,566.0,139.0,7.0,1030.0,520.0,6.0,14.0,0.0,605.0,10.0,8.0,0.0,23.0,13.0,0.0,0.0,0.0,0.0,113.0,25.0,16.0,61.0 -Nicolò Rovella,it ITA,MF,Monza,21-068,2001,12.0,10.0,879.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,9.8,1.0,0.0,11.1,0.1,0.0,0.0,0.04,-0.3,-0.3,664.0,753.0,88.2,12592.0,3095.0,270.0,292.0,92.5,297.0,323.0,92.0,89.0,116.0,76.7,14.0,69.0,9.0,2.0,59.0,715.0,35.0,28.0,0.0,14.0,17.0,4.0,0.0,3.0,0.0,3.0,3.0,8.0,31.0,3.17,30.0,1.0,0.0,0.0,3.0,0.31,3.0,0.0,0.0,0.0,0.0,21.0,15.0,12.0,8.0,1.0,13.0,1.0,12.0,20.0,6.0,0.0,847.0,20.0,181.0,521.0,155.0,4.0,847.0,533.0,13.0,25.0,1.0,632.0,10.0,4.0,0.0,13.0,11.0,0.0,0.0,0.0,0.0,70.0,4.0,4.0,50.0 -Nicolò Rovella,it ITA,MF,Juventus,21-068,2001,3.0,0.0,27.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.3,0.3,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,22.0,25.0,88.0,448.0,82.0,7.0,8.0,87.5,11.0,12.0,91.7,3.0,4.0,75.0,0.0,3.0,0.0,0.0,1.0,22.0,3.0,1.0,0.0,2.0,3.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,0.0,4.0,17.0,9.0,0.0,29.0,21.0,1.0,1.0,0.0,24.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Amir Rrahmani,xk KVX,DF,Napoli,28-351,1994,14.0,13.0,1206.0,1.0,0.0,0.0,0.0,1.0,0.0,0.07,0.0,0.07,0.07,0.07,1.2,1.2,0.09,0.09,13.4,1.0,0.0,8.3,0.07,0.08,1.0,0.1,-0.2,-0.2,941.0,1039.0,90.6,18904.0,6573.0,297.0,321.0,92.5,495.0,519.0,95.4,132.0,172.0,76.7,5.0,58.0,1.0,0.0,61.0,999.0,39.0,37.0,0.0,17.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,12.0,0.9,10.0,0.0,1.0,0.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,22.0,14.0,17.0,5.0,0.0,21.0,13.0,8.0,8.0,44.0,1.0,1148.0,132.0,492.0,631.0,30.0,13.0,1148.0,707.0,10.0,4.0,1.0,819.0,3.0,1.0,0.0,8.0,9.0,1.0,0.0,0.0,0.0,77.0,40.0,26.0,60.6 -Ruan,br BRA,DF,Sassuolo,23-248,1999,8.0,6.0,506.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,5.6,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,217.0,256.0,84.8,4347.0,1018.0,60.0,69.0,87.0,132.0,148.0,89.2,22.0,31.0,71.0,0.0,5.0,0.0,0.0,1.0,241.0,15.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,3.0,0.53,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,12.0,12.0,3.0,0.0,7.0,4.0,3.0,7.0,29.0,1.0,321.0,51.0,202.0,118.0,3.0,2.0,321.0,134.0,2.0,1.0,0.0,174.0,1.0,0.0,0.0,10.0,6.0,0.0,0.0,1.0,0.0,32.0,18.0,16.0,52.9 -Daniele Rugani,it ITA,DF,Juventus,28-196,1994,3.0,3.0,258.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.09,0.09,2.9,1.0,0.0,50.0,0.35,0.0,0.0,0.13,-0.3,-0.3,142.0,164.0,86.6,2985.0,854.0,34.0,38.0,89.5,96.0,103.0,93.2,12.0,23.0,52.2,0.0,7.0,0.0,0.0,5.0,163.0,1.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.35,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,3.0,2.0,1.0,2.0,10.0,0.0,186.0,21.0,105.0,75.0,6.0,2.0,186.0,126.0,2.0,0.0,0.0,139.0,1.0,0.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,6.0,7.0,2.0,77.8 -"Matteo Ruggeri,it ITA,""DF,MF"",Atalanta,20-214,2002,10.0,4.0,462.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,5.1,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.2,-0.2,260.0,323.0,80.5,4244.0,1794.0,140.0,159.0,88.1,95.0,117.0,81.2,19.0,31.0,61.3,4.0,19.0,8.0,4.0,28.0,269.0,52.0,4.0,0.0,2.0,20.0,0.0,0.0,0.0,0.0,48.0,2.0,3.0,8.0,1.56,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,7.0,4.0,6.0,2.0,9.0,3.0,6.0,10.0,9.0,0.0,374.0,16.0,119.0,153.0,103.0,6.0,374.0,183.0,11.0,6.0,2.0,221.0,1.0,4.0,0.0,5.0,4.0,0.0,0.0,0.0,0.0,25.0,9.0,2.0,81.8" -Mário Rui,pt POR,DF,Napoli,31-259,1991,17.0,16.0,1359.0,0.0,6.0,0.0,0.0,2.0,0.0,0.0,0.4,0.4,0.0,0.4,0.5,0.5,0.03,0.03,15.1,2.0,1.0,25.0,0.13,0.0,0.0,0.06,-0.5,-0.5,1088.0,1338.0,81.3,19937.0,6728.0,526.0,576.0,91.3,409.0,488.0,83.8,141.0,229.0,61.6,42.0,111.0,39.0,12.0,120.0,1095.0,238.0,56.0,1.0,26.0,81.0,31.0,13.0,15.0,0.0,151.0,5.0,16.0,72.0,4.77,51.0,18.0,0.0,1.0,13.0,0.86,11.0,1.0,0.0,0.0,1.0,25.0,15.0,13.0,10.0,2.0,15.0,3.0,12.0,15.0,20.0,1.0,1445.0,33.0,299.0,735.0,420.0,7.0,1445.0,850.0,44.0,47.0,1.0,956.0,7.0,6.0,0.0,14.0,25.0,0.0,0.0,0.0,0.0,113.0,10.0,17.0,37.0 -"Abdelhamid Sabiri,ma MAR,""MF,FW"",Sampdoria,26-074,1996,13.0,11.0,914.0,1.0,0.0,0.0,0.0,4.0,0.0,0.1,0.0,0.1,0.1,0.1,1.1,1.1,0.1,0.1,10.2,8.0,6.0,32.0,0.79,0.04,0.13,0.04,-0.1,-0.1,271.0,404.0,67.1,4896.0,1483.0,122.0,152.0,80.3,103.0,142.0,72.5,36.0,78.0,46.2,14.0,40.0,12.0,0.0,45.0,342.0,60.0,29.0,3.0,7.0,48.0,28.0,7.0,17.0,0.0,2.0,2.0,11.0,35.0,3.44,19.0,6.0,3.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,7.0,5.0,8.0,4.0,15.0,2.0,13.0,2.0,4.0,0.0,538.0,12.0,53.0,266.0,225.0,26.0,538.0,309.0,5.0,18.0,4.0,367.0,29.0,22.0,0.0,23.0,31.0,0.0,0.0,0.0,0.0,43.0,26.0,18.0,59.1" -"Alexis Saelemaekers,be BEL,""FW,DF"",Milan,23-228,1999,13.0,7.0,599.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.15,0.15,0.0,0.15,0.7,0.7,0.1,0.1,6.7,3.0,0.0,30.0,0.45,0.0,0.0,0.07,-0.7,-0.7,176.0,240.0,73.3,2546.0,713.0,102.0,129.0,79.1,60.0,79.0,75.9,7.0,13.0,53.8,6.0,11.0,13.0,6.0,21.0,233.0,7.0,1.0,1.0,2.0,19.0,0.0,0.0,0.0,0.0,6.0,0.0,6.0,18.0,2.72,15.0,0.0,2.0,1.0,2.0,0.3,2.0,0.0,0.0,0.0,0.0,13.0,9.0,6.0,6.0,1.0,4.0,1.0,3.0,5.0,3.0,0.0,320.0,6.0,41.0,128.0,154.0,21.0,320.0,201.0,22.0,8.0,7.0,239.0,19.0,12.0,0.0,14.0,10.0,0.0,0.0,1.0,0.0,17.0,5.0,1.0,83.3" -Jacopo Sala,it ITA,MF,Spezia,31-067,1991,6.0,1.0,112.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,36.0,46.0,78.3,589.0,199.0,20.0,21.0,95.2,13.0,15.0,86.7,3.0,5.0,60.0,1.0,4.0,2.0,0.0,6.0,40.0,6.0,4.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,2.0,1.62,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,0.0,2.0,1.0,2.0,0.0,57.0,2.0,14.0,28.0,16.0,2.0,57.0,32.0,2.0,0.0,0.0,34.0,1.0,0.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 -Lazar Samardzic,de GER,MF,Udinese,20-351,2002,20.0,7.0,836.0,4.0,1.0,0.0,0.0,0.0,0.0,0.43,0.11,0.54,0.43,0.54,1.8,1.8,0.19,0.19,9.3,12.0,4.0,38.7,1.29,0.13,0.33,0.06,2.2,2.2,341.0,427.0,79.9,6691.0,2117.0,145.0,153.0,94.8,118.0,142.0,83.1,66.0,109.0,60.6,27.0,41.0,12.0,2.0,56.0,368.0,57.0,20.0,1.0,11.0,48.0,35.0,18.0,9.0,0.0,2.0,2.0,6.0,56.0,6.03,30.0,14.0,5.0,1.0,4.0,0.43,2.0,1.0,0.0,0.0,1.0,17.0,8.0,5.0,9.0,3.0,6.0,0.0,6.0,7.0,6.0,0.0,542.0,8.0,72.0,258.0,221.0,27.0,542.0,315.0,20.0,17.0,6.0,342.0,20.0,14.0,0.0,11.0,7.0,0.0,0.0,0.0,0.0,66.0,5.0,14.0,26.3 -"Junior Sambia,fr FRA,""DF,MF"",Salernitana,26-156,1996,10.0,3.0,319.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.06,0.06,3.5,2.0,2.0,40.0,0.56,0.0,0.0,0.04,-0.2,-0.2,159.0,190.0,83.7,2252.0,906.0,93.0,102.0,91.2,51.0,59.0,86.4,6.0,17.0,35.3,1.0,6.0,4.0,0.0,15.0,158.0,32.0,3.0,0.0,1.0,14.0,5.0,3.0,1.0,0.0,24.0,0.0,1.0,8.0,2.26,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,2.0,4.0,2.0,1.0,4.0,2.0,2.0,2.0,9.0,1.0,229.0,14.0,51.0,103.0,75.0,5.0,229.0,130.0,12.0,11.0,2.0,147.0,2.0,3.0,0.0,6.0,7.0,0.0,0.0,0.0,0.0,13.0,2.0,5.0,28.6" -Antonio Sanabria,py PAR,FW,Torino,26-343,1996,16.0,11.0,1003.0,4.0,1.0,0.0,0.0,3.0,0.0,0.36,0.09,0.45,0.36,0.45,2.9,2.9,0.26,0.26,11.1,8.0,0.0,36.4,0.72,0.18,0.5,0.13,1.1,1.1,219.0,294.0,74.5,2919.0,392.0,146.0,175.0,83.4,56.0,79.0,70.9,5.0,8.0,62.5,10.0,14.0,8.0,0.0,21.0,268.0,25.0,2.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,8.0,19.0,1.71,15.0,0.0,1.0,1.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,5.0,4.0,0.0,2.0,3.0,2.0,1.0,1.0,3.0,4.0,0.0,398.0,7.0,27.0,184.0,193.0,42.0,398.0,199.0,5.0,5.0,1.0,294.0,34.0,10.0,0.0,17.0,12.0,7.0,0.0,0.0,0.0,31.0,35.0,51.0,40.7 -"Leandro Sanca,pt POR,""FW,MF"",Spezia,23-037,2000,4.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,12.0,75.0,171.0,27.0,4.0,5.0,80.0,2.0,4.0,50.0,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,11.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,0.0,1.0,6.0,11.0,0.0,17.0,10.0,1.0,1.0,0.0,11.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,2.0,0.0" -Alex Sandro,br BRA,DF,Juventus,32-015,1991,17.0,13.0,1140.0,0.0,1.0,0.0,0.0,3.0,1.0,0.0,0.08,0.08,0.0,0.08,0.1,0.1,0.01,0.01,12.7,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,703.0,819.0,85.8,12825.0,4080.0,291.0,319.0,91.2,326.0,358.0,91.1,74.0,108.0,68.5,7.0,55.0,3.0,1.0,65.0,715.0,101.0,12.0,0.0,6.0,11.0,0.0,0.0,0.0,0.0,79.0,3.0,8.0,20.0,1.58,17.0,0.0,0.0,1.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,17.0,9.0,7.0,9.0,1.0,14.0,7.0,7.0,13.0,31.0,0.0,931.0,67.0,317.0,496.0,125.0,8.0,931.0,483.0,34.0,23.0,2.0,586.0,16.0,9.0,0.0,19.0,16.0,0.0,1.0,0.0,0.0,84.0,25.0,11.0,69.4 -Nicola Sansone,it ITA,FW,Bologna,31-153,1991,10.0,5.0,421.0,1.0,0.0,0.0,0.0,2.0,0.0,0.21,0.0,0.21,0.21,0.21,1.6,1.6,0.34,0.34,4.7,3.0,1.0,21.4,0.64,0.07,0.33,0.12,-0.6,-0.6,83.0,114.0,72.8,1128.0,230.0,45.0,55.0,81.8,24.0,31.0,77.4,4.0,9.0,44.4,1.0,4.0,2.0,0.0,6.0,102.0,10.0,0.0,0.0,1.0,9.0,5.0,4.0,0.0,0.0,0.0,2.0,4.0,14.0,2.99,8.0,0.0,1.0,3.0,2.0,0.43,0.0,0.0,0.0,2.0,0.0,4.0,2.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,173.0,3.0,16.0,74.0,88.0,17.0,173.0,118.0,14.0,11.0,6.0,138.0,16.0,8.0,0.0,7.0,10.0,4.0,2.0,0.0,0.0,14.0,0.0,8.0,0.0 -Riccardo Saponara,it ITA,FW,Fiorentina,31-051,1991,17.0,7.0,728.0,2.0,2.0,0.0,0.0,3.0,0.0,0.25,0.25,0.49,0.25,0.49,1.4,1.4,0.17,0.17,8.1,8.0,0.0,34.8,0.99,0.09,0.25,0.06,0.6,0.6,227.0,304.0,74.7,3284.0,887.0,147.0,170.0,86.5,62.0,84.0,73.8,10.0,23.0,43.5,13.0,19.0,16.0,3.0,34.0,300.0,3.0,0.0,2.0,6.0,18.0,0.0,0.0,0.0,0.0,3.0,1.0,6.0,33.0,4.09,22.0,0.0,5.0,2.0,3.0,0.37,2.0,0.0,1.0,0.0,0.0,5.0,2.0,3.0,1.0,1.0,3.0,1.0,2.0,4.0,5.0,0.0,396.0,9.0,46.0,154.0,200.0,39.0,396.0,274.0,37.0,23.0,10.0,310.0,17.0,13.0,0.0,7.0,13.0,3.0,0.0,0.0,0.0,41.0,4.0,4.0,50.0 -Martin Satriano,uy URU,FW,Empoli,21-355,2001,19.0,16.0,1233.0,1.0,1.0,0.0,0.0,3.0,0.0,0.07,0.07,0.15,0.07,0.15,2.9,2.9,0.21,0.21,13.7,8.0,0.0,27.6,0.58,0.03,0.13,0.1,-1.9,-1.9,193.0,281.0,68.7,2611.0,501.0,107.0,139.0,77.0,61.0,89.0,68.5,8.0,17.0,47.1,10.0,13.0,7.0,1.0,18.0,272.0,8.0,0.0,1.0,1.0,13.0,0.0,0.0,0.0,0.0,6.0,1.0,7.0,23.0,1.68,17.0,0.0,2.0,3.0,3.0,0.22,2.0,0.0,1.0,0.0,0.0,10.0,6.0,3.0,6.0,1.0,5.0,0.0,5.0,2.0,13.0,0.0,439.0,11.0,46.0,197.0,205.0,51.0,439.0,246.0,22.0,19.0,8.0,337.0,38.0,31.0,0.0,14.0,21.0,4.0,0.0,0.0,0.0,40.0,32.0,34.0,48.5 -"Giorgio Scalvini,it ITA,""DF,MF"",Atalanta,19-061,2003,17.0,14.0,1173.0,2.0,0.0,0.0,0.0,4.0,0.0,0.15,0.0,0.15,0.15,0.15,1.0,1.0,0.07,0.07,13.0,4.0,0.0,40.0,0.31,0.2,0.5,0.1,1.0,1.0,590.0,738.0,79.9,9867.0,4373.0,298.0,337.0,88.4,226.0,260.0,86.9,53.0,107.0,49.5,3.0,68.0,9.0,2.0,83.0,719.0,17.0,14.0,1.0,14.0,6.0,0.0,0.0,0.0,0.0,3.0,2.0,10.0,17.0,1.3,15.0,0.0,0.0,0.0,3.0,0.23,3.0,0.0,0.0,0.0,0.0,34.0,19.0,18.0,10.0,6.0,18.0,7.0,11.0,24.0,26.0,1.0,873.0,59.0,315.0,440.0,127.0,13.0,873.0,468.0,12.0,11.0,3.0,560.0,7.0,2.0,0.0,31.0,3.0,3.0,0.0,0.0,0.0,93.0,21.0,12.0,63.6" -Jerdy Schouten,nl NED,MF,Bologna,26-029,1997,17.0,13.0,1158.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.08,0.08,0.0,0.08,0.2,0.2,0.01,0.01,12.9,1.0,0.0,25.0,0.08,0.0,0.0,0.04,-0.2,-0.2,483.0,584.0,82.7,7835.0,2385.0,242.0,276.0,87.7,191.0,229.0,83.4,38.0,51.0,74.5,7.0,49.0,4.0,0.0,62.0,581.0,2.0,2.0,2.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,8.0,23.0,1.79,21.0,0.0,1.0,0.0,2.0,0.16,2.0,0.0,0.0,0.0,0.0,27.0,15.0,12.0,13.0,2.0,26.0,12.0,14.0,22.0,27.0,1.0,738.0,47.0,211.0,458.0,78.0,5.0,738.0,386.0,5.0,6.0,0.0,448.0,24.0,10.0,0.0,14.0,15.0,1.0,0.0,0.0,1.0,105.0,11.0,12.0,47.8 -Perr Schuurs,nl NED,DF,Torino,23-076,1999,15.0,14.0,1110.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.16,0.16,0.0,0.16,0.4,0.4,0.03,0.03,12.3,1.0,1.0,16.7,0.08,0.0,0.0,0.06,-0.4,-0.4,453.0,528.0,85.8,9007.0,2762.0,131.0,148.0,88.5,274.0,297.0,92.3,43.0,66.0,65.2,4.0,13.0,0.0,0.0,23.0,513.0,15.0,13.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,8.0,0.65,6.0,0.0,0.0,1.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,26.0,14.0,14.0,9.0,3.0,9.0,5.0,4.0,17.0,46.0,0.0,663.0,67.0,336.0,306.0,31.0,6.0,663.0,301.0,5.0,6.0,0.0,339.0,11.0,6.0,0.0,20.0,2.0,0.0,0.0,0.0,0.0,78.0,32.0,28.0,53.3 -"Demba Seck,sn SEN,""FW,MF"",Torino,22-000,2001,10.0,5.0,361.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.08,0.08,4.0,2.0,0.0,28.6,0.5,0.0,0.0,0.05,-0.3,-0.3,81.0,110.0,73.6,1119.0,211.0,52.0,57.0,91.2,26.0,35.0,74.3,2.0,5.0,40.0,5.0,6.0,2.0,0.0,10.0,106.0,3.0,0.0,2.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,9.0,2.24,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,3.0,2.0,0.0,3.0,1.0,2.0,1.0,1.0,0.0,164.0,1.0,14.0,75.0,79.0,19.0,164.0,115.0,16.0,12.0,8.0,131.0,20.0,10.0,0.0,4.0,14.0,6.0,0.0,0.0,0.0,17.0,2.0,8.0,20.0" -Jacopo Segre,it ITA,DF,Torino,25-358,1997,1.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,9.0,88.9,103.0,22.0,6.0,7.0,85.7,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,4.0,8.0,0.0,0.0,12.0,8.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 -Vivaldo Semedo,pt POR,FW,Udinese,18-013,2005,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -Stefano Sensi,it ITA,MF,Monza,27-189,1995,14.0,12.0,966.0,2.0,0.0,0.0,0.0,5.0,0.0,0.19,0.0,0.19,0.19,0.19,0.7,0.7,0.06,0.06,10.7,6.0,2.0,46.2,0.56,0.15,0.33,0.05,1.3,1.3,683.0,797.0,85.7,12617.0,4325.0,305.0,334.0,91.3,283.0,310.0,91.3,83.0,120.0,69.2,17.0,84.0,12.0,1.0,93.0,731.0,64.0,37.0,2.0,13.0,35.0,26.0,8.0,13.0,0.0,1.0,2.0,8.0,37.0,3.45,21.0,12.0,1.0,2.0,4.0,0.37,3.0,0.0,0.0,1.0,0.0,37.0,24.0,22.0,12.0,3.0,12.0,2.0,10.0,13.0,3.0,0.0,911.0,16.0,185.0,551.0,182.0,4.0,911.0,567.0,17.0,18.0,1.0,687.0,19.0,10.0,0.0,12.0,32.0,0.0,1.0,0.0,0.0,70.0,7.0,5.0,58.3 -Luigi Sepe,it ITA,GK,Salernitana,31-278,1991,15.0,15.0,1350.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,373.0,552.0,67.6,9975.0,7472.0,66.0,66.0,100.0,188.0,189.0,99.5,117.0,291.0,40.2,0.0,6.0,0.0,0.0,0.0,370.0,178.0,44.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,591.0,522.0,591.0,0.0,0.0,0.0,591.0,289.0,0.0,0.0,0.0,226.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,24.0,3.0,0.0,100.0 -Leonardo Sernicola,it ITA,DF,Cremonese,25-195,1997,17.0,15.0,1355.0,1.0,1.0,0.0,0.0,5.0,0.0,0.07,0.07,0.13,0.07,0.13,1.0,1.0,0.06,0.06,15.1,4.0,0.0,22.2,0.27,0.06,0.25,0.05,0.0,0.0,360.0,559.0,64.4,6132.0,2598.0,177.0,214.0,82.7,131.0,216.0,60.6,40.0,91.0,44.0,15.0,32.0,17.0,12.0,40.0,438.0,117.0,3.0,2.0,5.0,72.0,11.0,0.0,10.0,0.0,103.0,4.0,12.0,40.0,2.66,29.0,5.0,3.0,3.0,2.0,0.13,2.0,0.0,0.0,0.0,0.0,30.0,20.0,16.0,8.0,6.0,12.0,2.0,10.0,24.0,29.0,0.0,721.0,25.0,187.0,281.0,264.0,19.0,721.0,340.0,38.0,23.0,2.0,356.0,17.0,13.0,0.0,27.0,25.0,3.0,0.0,0.0,0.0,81.0,18.0,12.0,60.0 -"Stephan El Shaarawy,it ITA,""DF,MF"",Roma,30-106,1992,15.0,4.0,619.0,3.0,0.0,0.0,0.0,1.0,0.0,0.44,0.0,0.44,0.44,0.44,2.7,2.7,0.4,0.4,6.9,8.0,0.0,53.3,1.16,0.2,0.38,0.18,0.3,0.3,300.0,363.0,82.6,4906.0,1388.0,157.0,169.0,92.9,115.0,140.0,82.1,21.0,32.0,65.6,11.0,15.0,9.0,1.0,27.0,306.0,57.0,6.0,2.0,6.0,9.0,1.0,1.0,0.0,0.0,50.0,0.0,12.0,27.0,3.93,18.0,4.0,1.0,0.0,2.0,0.29,1.0,0.0,0.0,0.0,0.0,7.0,3.0,3.0,4.0,0.0,5.0,1.0,4.0,10.0,7.0,0.0,457.0,10.0,101.0,188.0,175.0,30.0,457.0,264.0,24.0,15.0,10.0,286.0,13.0,2.0,0.0,10.0,6.0,3.0,0.0,0.0,0.0,33.0,4.0,7.0,36.4" -Eldor Shomurodov,uz UZB,FW,Spezia,27-226,1995,1.0,1.0,75.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,10.0,90.0,104.0,38.0,7.0,7.0,100.0,2.0,2.0,100.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,3.0,8.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.4,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,16.0,1.0,2.0,10.0,4.0,0.0,16.0,3.0,2.0,0.0,0.0,8.0,3.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,5.0,0.0 -Eldor Shomurodov,uz UZB,FW,Roma,27-226,1995,6.0,1.0,119.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.45,0.45,1.3,3.0,0.0,50.0,2.27,0.0,0.0,0.1,-0.6,-0.6,25.0,33.0,75.8,371.0,69.0,15.0,19.0,78.9,9.0,11.0,81.8,1.0,1.0,100.0,0.0,0.0,1.0,0.0,4.0,31.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,2.0,1.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,53.0,1.0,4.0,16.0,34.0,11.0,53.0,34.0,3.0,4.0,2.0,43.0,4.0,5.0,0.0,1.0,2.0,2.0,0.0,0.0,0.0,2.0,2.0,2.0,50.0 -Marco Silvestri,it ITA,GK,Udinese,31-345,1991,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,473.0,615.0,76.9,12931.0,9627.0,88.0,88.0,100.0,240.0,242.0,99.2,141.0,280.0,50.4,0.0,7.0,0.0,0.0,0.0,407.0,207.0,43.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.29,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,1.0,663.0,586.0,663.0,0.0,0.0,0.0,663.0,325.0,0.0,0.0,0.0,271.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,22.0,3.0,0.0,100.0 -Giovanni Simeone,ar ARG,FW,Napoli,27-220,1995,12.0,0.0,180.0,3.0,0.0,0.0,0.0,1.0,0.0,1.5,0.0,1.5,1.5,1.5,0.6,0.6,0.28,0.28,2.0,4.0,0.0,80.0,2.0,0.6,0.75,0.11,2.4,2.4,41.0,58.0,70.7,602.0,143.0,20.0,27.0,74.1,13.0,19.0,68.4,3.0,3.0,100.0,3.0,3.0,1.0,0.0,7.0,57.0,1.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,5.0,2.5,5.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,12.0,2.0,4.0,5.0,3.0,0.0,0.0,0.0,1.0,2.0,0.0,104.0,2.0,11.0,40.0,54.0,12.0,104.0,67.0,7.0,6.0,1.0,79.0,12.0,7.0,0.0,4.0,4.0,1.0,0.0,0.0,0.0,7.0,3.0,10.0,23.1 -Wilfried Singo,ci CIV,DF,Torino,22-047,2000,15.0,10.0,981.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.09,0.09,0.0,0.09,0.9,0.9,0.08,0.08,10.9,2.0,0.0,15.4,0.18,0.0,0.0,0.07,-0.9,-0.9,421.0,567.0,74.3,7256.0,2225.0,191.0,228.0,83.8,185.0,250.0,74.0,35.0,61.0,57.4,9.0,24.0,12.0,4.0,38.0,452.0,109.0,12.0,1.0,3.0,23.0,0.0,0.0,0.0,0.0,97.0,6.0,4.0,17.0,1.56,11.0,0.0,5.0,0.0,3.0,0.28,2.0,0.0,1.0,0.0,0.0,13.0,9.0,9.0,2.0,2.0,8.0,0.0,8.0,6.0,21.0,0.0,676.0,25.0,155.0,309.0,218.0,26.0,676.0,341.0,25.0,15.0,5.0,402.0,14.0,10.0,0.0,19.0,15.0,0.0,0.0,0.0,0.0,54.0,22.0,11.0,66.7 -Leo Skiri Østigård,no NOR,DF,Napoli,23-074,1999,4.0,2.0,220.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,2.4,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,164.0,193.0,85.0,3434.0,1036.0,49.0,58.0,84.5,83.0,92.0,90.2,26.0,33.0,78.8,0.0,14.0,0.0,0.0,9.0,184.0,8.0,7.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,3.0,1.23,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,3.0,10.0,0.0,219.0,12.0,93.0,115.0,11.0,1.0,219.0,139.0,3.0,3.0,0.0,146.0,3.0,0.0,0.0,2.0,4.0,0.0,0.0,0.0,0.0,7.0,19.0,2.0,90.5 -Łukasz Skorupski,pl POL,GK,Bologna,31-281,1991,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,554.0,719.0,77.1,14838.0,10386.0,103.0,104.0,99.0,244.0,248.0,98.4,181.0,338.0,53.6,0.0,0.0,0.0,0.0,0.0,503.0,213.0,60.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,0.1,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,1.0,773.0,693.0,769.0,4.0,0.0,0.0,773.0,405.0,0.0,0.0,0.0,327.0,1.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,24.0,4.0,0.0,100.0 -Milan Škriniar,sk SVK,DF,Inter,27-364,1995,20.0,19.0,1679.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.04,0.04,18.7,2.0,0.0,25.0,0.11,0.0,0.0,0.09,-0.7,-0.7,1178.0,1291.0,91.2,21788.0,7046.0,421.0,452.0,93.1,647.0,677.0,95.6,91.0,129.0,70.5,5.0,67.0,3.0,1.0,73.0,1228.0,61.0,41.0,1.0,13.0,4.0,0.0,0.0,0.0,0.0,18.0,2.0,6.0,9.0,0.48,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,18.0,14.0,11.0,4.0,17.0,12.0,5.0,11.0,43.0,3.0,1431.0,139.0,693.0,662.0,82.0,11.0,1431.0,919.0,16.0,10.0,0.0,1053.0,13.0,3.0,1.0,33.0,14.0,1.0,0.0,0.0,1.0,106.0,17.0,17.0,50.0 -Chris Smalling,eng ENG,DF,Roma,33-080,1989,21.0,20.0,1863.0,3.0,0.0,0.0,0.0,4.0,0.0,0.14,0.0,0.14,0.14,0.14,1.2,1.2,0.06,0.06,20.7,3.0,0.0,25.0,0.14,0.25,1.0,0.1,1.8,1.8,718.0,826.0,86.9,13602.0,4558.0,218.0,239.0,91.2,432.0,472.0,91.5,61.0,95.0,64.2,2.0,21.0,3.0,0.0,27.0,805.0,19.0,18.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,4.0,6.0,0.29,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,10.0,8.0,7.0,0.0,27.0,19.0,8.0,33.0,93.0,0.0,1038.0,167.0,629.0,382.0,34.0,20.0,1038.0,525.0,4.0,6.0,0.0,622.0,7.0,2.0,0.0,12.0,12.0,0.0,0.0,0.0,0.0,86.0,35.0,16.0,68.6 -Ola Solbakken,no NOR,FW,Roma,24-156,1998,2.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -Brandon Soppy,fr FRA,DF,Udinese,20-354,2002,1.0,1.0,90.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.0,1.0,0.0,50.0,1.0,0.0,0.0,0.04,-0.1,-0.1,22.0,32.0,68.8,322.0,140.0,13.0,14.0,92.9,7.0,10.0,70.0,1.0,6.0,16.7,2.0,2.0,1.0,0.0,4.0,29.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,4.0,4.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,0.0,4.0,1.0,1.0,0.0,48.0,2.0,8.0,19.0,21.0,3.0,48.0,33.0,4.0,4.0,0.0,27.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,6.0,1.0,0.0,100.0 -Brandon Soppy,fr FRA,DF,Atalanta,20-354,2002,10.0,7.0,523.0,0.0,3.0,0.0,0.0,2.0,0.0,0.0,0.52,0.52,0.0,0.52,0.2,0.2,0.04,0.04,5.8,1.0,0.0,20.0,0.17,0.0,0.0,0.04,-0.2,-0.2,191.0,247.0,77.3,2816.0,1210.0,106.0,118.0,89.8,70.0,95.0,73.7,9.0,18.0,50.0,8.0,19.0,7.0,2.0,27.0,196.0,51.0,0.0,1.0,0.0,12.0,0.0,0.0,0.0,0.0,51.0,0.0,8.0,17.0,2.91,13.0,0.0,0.0,3.0,6.0,1.03,5.0,0.0,0.0,1.0,0.0,6.0,3.0,2.0,4.0,0.0,8.0,0.0,8.0,2.0,7.0,0.0,321.0,8.0,76.0,146.0,104.0,16.0,321.0,178.0,20.0,10.0,5.0,185.0,13.0,7.0,0.0,14.0,15.0,1.0,1.0,0.0,0.0,43.0,2.0,2.0,50.0 -"Roberto Soriano,it ITA,""FW,MF"",Bologna,32-002,1991,20.0,11.0,1025.0,0.0,3.0,0.0,0.0,1.0,0.0,0.0,0.26,0.26,0.0,0.26,0.4,0.4,0.04,0.04,11.4,1.0,0.0,11.1,0.09,0.0,0.0,0.05,-0.4,-0.4,359.0,455.0,78.9,5206.0,1619.0,224.0,262.0,85.5,104.0,128.0,81.3,21.0,32.0,65.6,19.0,30.0,9.0,2.0,49.0,448.0,7.0,2.0,5.0,2.0,10.0,0.0,0.0,0.0,0.0,5.0,0.0,11.0,38.0,3.34,30.0,1.0,1.0,4.0,3.0,0.26,2.0,0.0,1.0,0.0,0.0,26.0,13.0,8.0,12.0,6.0,20.0,2.0,18.0,5.0,7.0,0.0,561.0,17.0,76.0,302.0,194.0,22.0,561.0,365.0,23.0,27.0,0.0,427.0,18.0,12.0,0.0,17.0,22.0,2.0,0.0,0.0,0.0,61.0,10.0,12.0,45.5" -Joaquin Sosa,uy URU,DF,Bologna,21-031,2002,5.0,2.0,250.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,2.8,1.0,0.0,50.0,0.36,0.0,0.0,0.01,0.0,0.0,100.0,130.0,76.9,2255.0,891.0,25.0,32.0,78.1,53.0,59.0,89.8,20.0,34.0,58.8,0.0,6.0,0.0,0.0,3.0,123.0,7.0,7.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.36,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,2.0,1.0,1.0,2.0,14.0,0.0,155.0,16.0,90.0,66.0,0.0,0.0,155.0,93.0,3.0,0.0,0.0,100.0,1.0,0.0,0.0,6.0,5.0,0.0,0.0,1.0,0.0,12.0,5.0,4.0,55.6 -Riccardo Sottil,it ITA,FW,Fiorentina,23-252,1999,7.0,5.0,432.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.42,0.42,0.0,0.42,0.6,0.6,0.12,0.12,4.8,7.0,1.0,58.3,1.46,0.0,0.0,0.05,-0.6,-0.6,127.0,157.0,80.9,2249.0,411.0,56.0,63.0,88.9,57.0,72.0,79.2,13.0,16.0,81.3,10.0,8.0,4.0,1.0,18.0,155.0,2.0,1.0,1.0,1.0,20.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,26.0,5.42,21.0,0.0,2.0,2.0,2.0,0.42,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,206.0,0.0,8.0,43.0,159.0,25.0,206.0,174.0,35.0,23.0,11.0,178.0,13.0,6.0,0.0,3.0,11.0,3.0,0.0,0.0,0.0,21.0,2.0,2.0,50.0 -"Matìas Soulé,ar ARG,""MF,DF"",Juventus,19-301,2003,9.0,2.0,238.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.1,0.1,2.6,3.0,0.0,60.0,1.13,0.0,0.0,0.05,-0.3,-0.3,91.0,111.0,82.0,1524.0,306.0,53.0,58.0,91.4,24.0,31.0,77.4,13.0,18.0,72.2,7.0,5.0,2.0,1.0,8.0,99.0,12.0,1.0,0.0,3.0,6.0,2.0,2.0,0.0,0.0,9.0,0.0,3.0,15.0,5.67,8.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,1.0,1.0,2.0,3.0,0.0,3.0,2.0,1.0,0.0,148.0,1.0,22.0,61.0,66.0,8.0,148.0,94.0,9.0,6.0,3.0,104.0,8.0,4.0,0.0,3.0,5.0,0.0,0.0,0.0,0.0,13.0,2.0,8.0,20.0" -Adama Soumaoro,fr FRA,DF,Bologna,30-237,1992,15.0,14.0,1232.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.02,0.02,13.7,1.0,0.0,50.0,0.07,0.0,0.0,0.16,-0.3,-0.3,579.0,633.0,91.5,10868.0,3763.0,206.0,218.0,94.5,324.0,344.0,94.2,43.0,53.0,81.1,1.0,19.0,1.0,0.0,29.0,595.0,32.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,4.0,6.0,0.44,6.0,0.0,0.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,15.0,9.0,6.0,7.0,2.0,10.0,7.0,3.0,14.0,50.0,1.0,739.0,90.0,396.0,323.0,20.0,7.0,739.0,416.0,6.0,3.0,0.0,479.0,5.0,0.0,1.0,16.0,7.0,0.0,0.0,0.0,0.0,57.0,20.0,10.0,66.7 -Leonardo Spinazzola,it ITA,DF,Roma,29-322,1993,13.0,9.0,807.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.11,0.11,0.0,0.11,0.3,0.3,0.03,0.03,9.0,2.0,0.0,50.0,0.22,0.0,0.0,0.07,-0.3,-0.3,344.0,447.0,77.0,6055.0,2035.0,155.0,175.0,88.6,154.0,193.0,79.8,29.0,57.0,50.9,8.0,14.0,13.0,4.0,28.0,351.0,96.0,7.0,2.0,8.0,22.0,0.0,0.0,0.0,0.0,89.0,0.0,10.0,23.0,2.57,16.0,2.0,0.0,3.0,2.0,0.22,1.0,0.0,0.0,0.0,0.0,5.0,3.0,3.0,1.0,1.0,10.0,3.0,7.0,4.0,13.0,0.0,536.0,21.0,146.0,240.0,156.0,22.0,536.0,302.0,45.0,22.0,11.0,320.0,15.0,5.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,47.0,5.0,6.0,45.5 -Marco Sportiello,it ITA,GK,Atalanta,30-276,1992,7.0,6.0,623.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,194.0,242.0,80.2,5332.0,3655.0,32.0,32.0,100.0,107.0,109.0,98.2,55.0,101.0,54.5,0.0,1.0,0.0,0.0,2.0,170.0,72.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,251.0,224.0,246.0,5.0,0.0,0.0,251.0,145.0,0.0,0.0,0.0,103.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,13.0,1.0,0.0,100.0 -Petar Stojanović,si SVN,DF,Empoli,27-126,1995,17.0,13.0,1144.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.08,0.08,0.0,0.08,0.2,0.2,0.01,0.01,12.7,1.0,1.0,20.0,0.08,0.0,0.0,0.03,-0.2,-0.2,547.0,703.0,77.8,8873.0,3679.0,303.0,346.0,87.6,188.0,237.0,79.3,49.0,89.0,55.1,10.0,36.0,12.0,5.0,59.0,553.0,149.0,13.0,2.0,15.0,20.0,1.0,0.0,1.0,0.0,135.0,1.0,18.0,26.0,2.04,16.0,5.0,1.0,2.0,2.0,0.16,2.0,0.0,0.0,0.0,0.0,33.0,17.0,20.0,9.0,4.0,14.0,3.0,11.0,9.0,51.0,0.0,848.0,51.0,298.0,355.0,201.0,7.0,848.0,436.0,28.0,17.0,2.0,465.0,9.0,12.0,0.0,8.0,13.0,0.0,0.0,0.0,0.0,65.0,2.0,6.0,25.0 -"Gabriel Strefezza,br BRA,""FW,MF"",Lecce,25-298,1997,19.0,17.0,1356.0,7.0,0.0,1.0,1.0,2.0,0.0,0.46,0.0,0.46,0.4,0.4,2.8,2.0,0.19,0.13,15.1,14.0,2.0,42.4,0.93,0.18,0.43,0.06,4.2,4.0,308.0,509.0,60.5,5646.0,1803.0,153.0,198.0,77.3,94.0,149.0,63.1,52.0,116.0,44.8,20.0,34.0,15.0,7.0,50.0,456.0,47.0,15.0,2.0,9.0,74.0,23.0,11.0,8.0,0.0,9.0,6.0,20.0,48.0,3.19,38.0,5.0,0.0,1.0,4.0,0.27,4.0,0.0,0.0,0.0,0.0,33.0,16.0,12.0,16.0,5.0,17.0,2.0,15.0,10.0,12.0,0.0,733.0,8.0,87.0,321.0,343.0,34.0,732.0,475.0,45.0,33.0,13.0,514.0,44.0,32.0,0.0,27.0,40.0,0.0,0.0,0.0,0.0,73.0,11.0,17.0,39.3" -Dávid Strelec,sk SVK,FW,Spezia,21-312,2001,7.0,1.0,169.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.28,0.28,1.9,1.0,0.0,33.3,0.53,0.0,0.0,0.17,-0.5,-0.5,25.0,43.0,58.1,294.0,30.0,19.0,27.0,70.4,5.0,9.0,55.6,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,40.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,2.16,3.0,0.0,0.0,1.0,2.0,1.08,2.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,60.0,0.0,2.0,29.0,29.0,12.0,60.0,32.0,2.0,3.0,0.0,45.0,3.0,4.0,0.0,4.0,1.0,2.0,0.0,0.0,0.0,1.0,5.0,6.0,45.5 -Isaac Success,ng NGA,FW,Udinese,27-034,1996,21.0,14.0,1278.0,0.0,4.0,0.0,0.0,3.0,0.0,0.0,0.28,0.28,0.0,0.28,2.1,2.1,0.15,0.15,14.2,2.0,0.0,8.7,0.14,0.0,0.0,0.09,-2.1,-2.1,276.0,429.0,64.3,3964.0,772.0,158.0,222.0,71.2,66.0,101.0,65.3,26.0,41.0,63.4,17.0,16.0,12.0,1.0,36.0,415.0,7.0,0.0,7.0,7.0,9.0,0.0,0.0,0.0,0.0,0.0,7.0,14.0,52.0,3.67,36.0,0.0,1.0,12.0,6.0,0.42,5.0,0.0,0.0,1.0,0.0,7.0,3.0,2.0,4.0,1.0,8.0,2.0,6.0,3.0,14.0,0.0,609.0,16.0,37.0,253.0,321.0,73.0,609.0,417.0,21.0,15.0,8.0,547.0,63.0,32.0,0.0,26.0,58.0,8.0,0.0,0.0,0.0,32.0,29.0,34.0,46.0 -Ibrahim Sulemana,gh GHA,MF,Hellas Verona,19-264,2003,11.0,3.0,357.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.08,0.08,4.0,3.0,0.0,37.5,0.76,0.0,0.0,0.04,-0.3,-0.3,73.0,103.0,70.9,1357.0,488.0,31.0,36.0,86.1,31.0,39.0,79.5,9.0,16.0,56.3,3.0,14.0,1.0,1.0,11.0,103.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,9.0,2.26,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,7.0,6.0,4.0,1.0,3.0,2.0,1.0,7.0,8.0,0.0,166.0,13.0,50.0,91.0,31.0,6.0,166.0,82.0,3.0,4.0,0.0,72.0,6.0,5.0,0.0,7.0,3.0,1.0,0.0,0.0,0.0,31.0,6.0,8.0,42.9 -Wojciech Szczęsny,pl POL,GK,Juventus,32-298,1990,15.0,15.0,1302.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,317.0,413.0,76.8,9409.0,6585.0,39.0,39.0,100.0,156.0,157.0,99.4,122.0,214.0,57.0,0.0,3.0,0.0,0.0,0.0,314.0,98.0,26.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,434.0,381.0,432.0,2.0,0.0,0.0,434.0,258.0,0.0,0.0,0.0,225.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,2.0,0.0,100.0 -Benjamin Tahirovic,se SWE,MF,Roma,19-344,2003,5.0,1.0,136.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,57.0,65.0,87.7,1078.0,184.0,23.0,25.0,92.0,24.0,27.0,88.9,9.0,10.0,90.0,3.0,3.0,1.0,1.0,1.0,64.0,1.0,1.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,2.67,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,4.0,0.0,4.0,2.0,1.0,0.0,80.0,3.0,19.0,48.0,14.0,1.0,80.0,43.0,0.0,1.0,0.0,50.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,9.0,2.0,1.0,66.7 -Adrien Tameze,cm CMR,MF,Hellas Verona,29-006,1994,20.0,18.0,1562.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.04,0.04,17.4,1.0,0.0,14.3,0.06,0.0,0.0,0.1,-0.7,-0.7,394.0,530.0,74.3,6981.0,2139.0,173.0,221.0,78.3,171.0,209.0,81.8,41.0,69.0,59.4,12.0,44.0,8.0,0.0,61.0,502.0,24.0,17.0,0.0,2.0,7.0,0.0,0.0,0.0,0.0,2.0,4.0,8.0,25.0,1.44,21.0,1.0,0.0,2.0,3.0,0.17,3.0,0.0,0.0,0.0,0.0,26.0,13.0,13.0,10.0,3.0,32.0,11.0,21.0,20.0,33.0,2.0,725.0,56.0,193.0,398.0,144.0,16.0,725.0,334.0,13.0,23.0,3.0,367.0,20.0,19.0,0.0,18.0,16.0,1.0,0.0,0.0,0.0,103.0,17.0,18.0,48.6 -Ciprian Tătărușanu,ro ROU,GK,Milan,37-001,1986,14.0,14.0,1260.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.0,0.07,0.0,0.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,384.0,488.0,78.7,11114.0,7906.0,53.0,53.0,100.0,178.0,183.0,97.3,151.0,247.0,61.1,2.0,8.0,0.0,0.0,0.0,397.0,88.0,23.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,7.0,0.5,5.0,2.0,0.0,0.0,2.0,0.14,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,512.0,441.0,512.0,0.0,0.0,0.0,512.0,327.0,0.0,0.0,0.0,286.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,19.0,1.0,1.0,50.0 -"Filippo Terracciano,it ITA,""DF,MF"",Hellas Verona,20-002,2003,12.0,4.0,521.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,5.8,2.0,0.0,66.7,0.35,0.0,0.0,0.03,-0.1,-0.1,143.0,208.0,68.8,2593.0,922.0,54.0,73.0,74.0,67.0,89.0,75.3,14.0,28.0,50.0,5.0,11.0,6.0,2.0,24.0,181.0,27.0,6.0,0.0,3.0,16.0,2.0,1.0,1.0,0.0,19.0,0.0,7.0,8.0,1.38,5.0,1.0,1.0,0.0,1.0,0.17,1.0,0.0,0.0,0.0,0.0,10.0,7.0,4.0,4.0,2.0,4.0,3.0,1.0,6.0,9.0,0.0,268.0,13.0,62.0,130.0,78.0,7.0,268.0,128.0,11.0,8.0,0.0,145.0,11.0,3.0,0.0,4.0,12.0,0.0,0.0,0.0,0.0,27.0,13.0,15.0,46.4" -Pietro Terracciano,it ITA,GK,Fiorentina,32-339,1990,18.0,18.0,1620.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,497.0,655.0,75.9,13993.0,9502.0,93.0,93.0,100.0,214.0,219.0,97.7,188.0,337.0,55.8,0.0,8.0,0.0,0.0,0.0,479.0,173.0,57.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,692.0,534.0,683.0,9.0,0.0,0.0,692.0,355.0,0.0,0.0,0.0,364.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,31.0,5.0,0.0,100.0 -Aleksa Terzić,rs SRB,DF,Fiorentina,23-177,1999,11.0,2.0,329.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.05,0.05,3.7,1.0,0.0,20.0,0.27,0.0,0.0,0.04,-0.2,-0.2,256.0,308.0,83.1,4801.0,1441.0,97.0,104.0,93.3,123.0,145.0,84.8,28.0,44.0,63.6,11.0,17.0,13.0,11.0,21.0,254.0,53.0,5.0,0.0,0.0,30.0,4.0,0.0,3.0,0.0,44.0,1.0,6.0,19.0,5.18,13.0,4.0,1.0,0.0,2.0,0.55,0.0,1.0,1.0,0.0,0.0,8.0,4.0,5.0,2.0,1.0,7.0,3.0,4.0,4.0,5.0,0.0,351.0,10.0,87.0,152.0,114.0,3.0,351.0,225.0,16.0,13.0,1.0,233.0,9.0,4.0,0.0,0.0,4.0,2.0,0.0,0.0,0.0,17.0,2.0,5.0,28.6 -"Florian Thauvin,fr FRA,""MF,DF"",Udinese,30-015,1993,1.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,11.0,18.2,49.0,35.0,0.0,1.0,0.0,2.0,5.0,40.0,0.0,3.0,0.0,0.0,1.0,1.0,0.0,2.0,8.0,3.0,1.0,0.0,0.0,5.0,2.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,4.74,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,13.0,1.0,1.0,4.0,9.0,2.0,13.0,8.0,1.0,1.0,0.0,8.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0" -Malick Thiaw,de GER,DF,Milan,21-186,2001,5.0,2.0,144.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.04,0.04,1.6,1.0,0.0,100.0,0.62,0.0,0.0,0.07,-0.1,-0.1,97.0,117.0,82.9,2056.0,550.0,24.0,28.0,85.7,63.0,69.0,91.3,10.0,17.0,58.8,1.0,8.0,1.0,0.0,9.0,115.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,1.0,0.62,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,3.0,3.0,0.0,1.0,9.0,1.0,136.0,16.0,74.0,60.0,2.0,1.0,136.0,80.0,0.0,0.0,0.0,87.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,7.0,11.0,3.0,78.6 -Kristian Thorstvedt,no NOR,MF,Sassuolo,23-334,1999,19.0,11.0,879.0,1.0,1.0,0.0,0.0,4.0,0.0,0.1,0.1,0.2,0.1,0.2,1.6,1.6,0.17,0.17,9.8,6.0,0.0,42.9,0.61,0.07,0.17,0.12,-0.6,-0.6,267.0,350.0,76.3,3893.0,1220.0,152.0,185.0,82.2,88.0,110.0,80.0,13.0,23.0,56.5,7.0,35.0,5.0,1.0,40.0,340.0,10.0,5.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,5.0,0.0,12.0,19.0,1.94,13.0,0.0,3.0,1.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,12.0,6.0,4.0,6.0,2.0,11.0,1.0,10.0,10.0,12.0,0.0,447.0,16.0,67.0,260.0,127.0,24.0,447.0,239.0,25.0,11.0,5.0,326.0,14.0,8.0,0.0,15.0,19.0,0.0,0.0,0.0,0.0,40.0,23.0,15.0,60.5 -Jeremy Toljan,de GER,DF,Sassuolo,28-186,1994,19.0,18.0,1679.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.05,0.05,0.0,0.05,0.4,0.4,0.02,0.02,18.7,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.4,-0.4,844.0,1003.0,84.1,13931.0,5087.0,421.0,468.0,90.0,334.0,379.0,88.1,71.0,106.0,67.0,19.0,75.0,18.0,5.0,94.0,807.0,196.0,20.0,2.0,6.0,13.0,0.0,0.0,0.0,0.0,176.0,0.0,22.0,34.0,1.82,33.0,1.0,0.0,0.0,2.0,0.11,2.0,0.0,0.0,0.0,0.0,24.0,17.0,13.0,10.0,1.0,19.0,7.0,12.0,13.0,31.0,1.0,1130.0,42.0,402.0,541.0,195.0,16.0,1130.0,586.0,31.0,21.0,2.0,679.0,17.0,6.0,0.0,5.0,8.0,0.0,0.0,1.0,0.0,90.0,4.0,8.0,33.3 -Rafael Tolói,it ITA,DF,Atalanta,32-123,1990,16.0,16.0,1358.0,1.0,1.0,0.0,0.0,3.0,0.0,0.07,0.07,0.13,0.07,0.13,1.7,1.7,0.11,0.11,15.1,4.0,0.0,33.3,0.27,0.08,0.25,0.14,-0.7,-0.7,603.0,774.0,77.9,11422.0,4314.0,256.0,287.0,89.2,270.0,320.0,84.4,73.0,142.0,51.4,14.0,66.0,7.0,2.0,69.0,743.0,28.0,10.0,2.0,7.0,12.0,0.0,0.0,0.0,0.0,17.0,3.0,12.0,27.0,1.79,23.0,0.0,2.0,0.0,6.0,0.4,5.0,0.0,1.0,0.0,0.0,29.0,20.0,17.0,12.0,0.0,21.0,11.0,10.0,37.0,42.0,0.0,947.0,80.0,410.0,419.0,127.0,20.0,947.0,496.0,11.0,22.0,0.0,574.0,10.0,2.0,0.0,9.0,8.0,1.0,0.0,0.0,0.0,115.0,19.0,15.0,55.9 -Fikayo Tomori,eng ENG,DF,Milan,25-053,1997,19.0,18.0,1577.0,1.0,0.0,0.0,0.0,2.0,0.0,0.06,0.0,0.06,0.06,0.06,1.1,1.1,0.06,0.06,17.5,3.0,0.0,50.0,0.17,0.17,0.33,0.19,-0.1,-0.1,910.0,1038.0,87.7,18958.0,5834.0,287.0,309.0,92.9,467.0,504.0,92.7,150.0,198.0,75.8,3.0,49.0,3.0,0.0,64.0,942.0,91.0,31.0,0.0,12.0,3.0,0.0,0.0,0.0,0.0,10.0,5.0,12.0,14.0,0.8,11.0,1.0,0.0,1.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,45.0,22.0,29.0,16.0,0.0,23.0,12.0,11.0,17.0,45.0,0.0,1202.0,145.0,600.0,573.0,37.0,8.0,1202.0,654.0,17.0,6.0,0.0,735.0,7.0,2.0,0.0,15.0,17.0,0.0,0.0,0.0,0.0,105.0,36.0,21.0,63.2 -Sandro Tonali,it ITA,MF,Milan,22-278,2000,18.0,16.0,1475.0,2.0,4.0,0.0,0.0,6.0,0.0,0.12,0.24,0.37,0.12,0.37,1.8,1.8,0.11,0.11,16.4,10.0,4.0,45.5,0.61,0.09,0.2,0.08,0.2,0.2,493.0,695.0,70.9,9194.0,3146.0,200.0,249.0,80.3,203.0,256.0,79.3,72.0,144.0,50.0,33.0,63.0,18.0,4.0,67.0,599.0,88.0,45.0,5.0,5.0,56.0,30.0,9.0,17.0,0.0,11.0,8.0,13.0,57.0,3.47,32.0,14.0,5.0,2.0,8.0,0.49,4.0,2.0,1.0,0.0,0.0,31.0,20.0,17.0,11.0,3.0,13.0,5.0,8.0,19.0,9.0,1.0,842.0,22.0,176.0,436.0,242.0,17.0,842.0,416.0,35.0,17.0,1.0,498.0,24.0,7.0,0.0,18.0,16.0,3.0,0.0,0.0,0.0,98.0,8.0,11.0,42.1 -William Troost-Ekong,ng NGA,DF,Salernitana,29-162,1993,2.0,2.0,180.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,73.0,86.0,84.9,1519.0,538.0,16.0,17.0,94.1,48.0,53.0,90.6,7.0,13.0,53.8,0.0,1.0,1.0,1.0,4.0,84.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.5,1.0,0.0,0.0,0.0,1.0,0.5,1.0,0.0,0.0,0.0,0.0,6.0,2.0,5.0,1.0,0.0,2.0,1.0,1.0,2.0,3.0,0.0,100.0,12.0,52.0,48.0,1.0,0.0,100.0,67.0,0.0,0.0,0.0,66.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,3.0,50.0 -"Frank Tsadjout,it ITA,""FW,MF"",Cremonese,23-197,1999,7.0,2.0,216.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.42,0.42,0.0,0.42,0.5,0.5,0.21,0.21,2.4,4.0,0.0,66.7,1.67,0.0,0.0,0.08,-0.5,-0.5,37.0,57.0,64.9,423.0,71.0,26.0,41.0,63.4,9.0,12.0,75.0,0.0,0.0,0.0,3.0,3.0,0.0,0.0,2.0,56.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.26,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,1.0,3.0,0.0,2.0,0.0,2.0,3.0,1.0,0.0,91.0,2.0,9.0,39.0,43.0,8.0,91.0,41.0,0.0,1.0,0.0,55.0,7.0,5.0,0.0,7.0,6.0,0.0,0.0,0.0,0.0,14.0,11.0,10.0,52.4" -Alessandro Tuia,it ITA,DF,Lecce,32-247,1990,3.0,2.0,199.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.04,0.04,2.2,1.0,0.0,50.0,0.45,0.0,0.0,0.05,-0.1,-0.1,62.0,87.0,71.3,1388.0,460.0,14.0,18.0,77.8,36.0,42.0,85.7,11.0,26.0,42.3,0.0,6.0,0.0,0.0,8.0,81.0,6.0,5.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.81,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,5.0,6.0,0.0,103.0,10.0,53.0,48.0,3.0,2.0,103.0,47.0,1.0,1.0,0.0,57.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,12.0,1.0,5.0,16.7 -Iyenoma Udogie,it ITA,DF,Udinese,20-074,2002,17.0,17.0,1463.0,2.0,2.0,0.0,0.0,4.0,0.0,0.12,0.12,0.25,0.12,0.25,2.0,2.0,0.12,0.12,16.3,4.0,0.0,23.5,0.25,0.12,0.5,0.12,0.0,0.0,509.0,620.0,82.1,7445.0,2678.0,296.0,339.0,87.3,178.0,212.0,84.0,21.0,26.0,80.8,23.0,37.0,22.0,2.0,63.0,524.0,95.0,11.0,1.0,1.0,15.0,2.0,0.0,0.0,0.0,82.0,1.0,20.0,55.0,3.38,39.0,4.0,2.0,6.0,5.0,0.31,5.0,0.0,0.0,0.0,0.0,44.0,26.0,29.0,11.0,4.0,20.0,5.0,15.0,20.0,10.0,0.0,840.0,29.0,187.0,331.0,340.0,54.0,840.0,497.0,58.0,44.0,20.0,501.0,39.0,19.0,0.0,25.0,31.0,3.0,0.0,1.0,0.0,95.0,8.0,11.0,42.1 -Samuel Umtiti,fr FRA,DF,Lecce,29-088,1993,11.0,10.0,853.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,9.5,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.3,-0.3,296.0,355.0,83.4,5721.0,1968.0,110.0,128.0,85.9,133.0,142.0,93.7,46.0,72.0,63.9,1.0,23.0,3.0,1.0,20.0,333.0,21.0,21.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,4.0,0.42,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,12.0,9.0,13.0,0.0,11.0,7.0,4.0,12.0,32.0,0.0,462.0,46.0,226.0,229.0,11.0,3.0,462.0,223.0,7.0,4.0,0.0,225.0,3.0,3.0,0.0,12.0,15.0,0.0,0.0,0.0,0.0,66.0,15.0,17.0,46.9 -"Diego Valencia,cl CHI,""MF,FW"",Salernitana,23-027,2000,10.0,0.0,175.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.06,0.06,1.9,1.0,0.0,100.0,0.51,0.0,0.0,0.12,-0.1,-0.1,42.0,58.0,72.4,613.0,159.0,29.0,34.0,85.3,7.0,13.0,53.8,4.0,4.0,100.0,2.0,1.0,1.0,1.0,3.0,52.0,6.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,6.0,0.0,2.0,1.0,0.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,4.0,4.0,1.0,1.0,6.0,0.0,6.0,1.0,2.0,0.0,84.0,3.0,21.0,35.0,30.0,3.0,84.0,43.0,1.0,0.0,0.0,47.0,6.0,3.0,0.0,6.0,4.0,0.0,0.0,0.0,0.0,8.0,2.0,3.0,40.0" -"Emanuele Valeri,it ITA,""DF,FW"",Cremonese,24-065,1998,20.0,18.0,1490.0,1.0,1.0,0.0,0.0,2.0,0.0,0.06,0.06,0.12,0.06,0.12,1.2,1.2,0.07,0.07,16.6,3.0,2.0,13.0,0.18,0.04,0.33,0.05,-0.2,-0.2,349.0,615.0,56.7,6455.0,3137.0,150.0,189.0,79.4,141.0,241.0,58.5,46.0,124.0,37.1,24.0,33.0,30.0,16.0,49.0,445.0,162.0,13.0,2.0,9.0,79.0,11.0,0.0,11.0,0.0,138.0,8.0,27.0,45.0,2.72,28.0,10.0,0.0,5.0,2.0,0.12,1.0,1.0,0.0,0.0,0.0,29.0,12.0,16.0,12.0,1.0,6.0,2.0,4.0,12.0,27.0,0.0,798.0,38.0,202.0,292.0,325.0,35.0,798.0,369.0,59.0,32.0,9.0,383.0,30.0,13.0,0.0,17.0,19.0,2.0,0.0,0.0,0.0,100.0,9.0,17.0,34.6" -Mattia Valoti,it ITA,MF,Monza,29-157,1993,7.0,2.0,244.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.04,0.04,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.1,-0.1,72.0,90.0,80.0,1100.0,275.0,36.0,40.0,90.0,25.0,32.0,78.1,6.0,9.0,66.7,2.0,12.0,0.0,0.0,10.0,88.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,4.0,1.48,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,5.0,3.0,5.0,1.0,5.0,1.0,4.0,2.0,3.0,0.0,119.0,2.0,30.0,71.0,20.0,3.0,119.0,58.0,3.0,2.0,0.0,81.0,6.0,1.0,0.0,8.0,6.0,0.0,0.0,0.0,0.0,12.0,8.0,4.0,66.7 -Johan Vásquez,mx MEX,DF,Cremonese,24-111,1998,10.0,6.0,628.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.04,0.04,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.3,-0.3,204.0,278.0,73.4,3454.0,1213.0,89.0,110.0,80.9,102.0,121.0,84.3,10.0,35.0,28.6,1.0,13.0,0.0,0.0,16.0,243.0,34.0,16.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,17.0,1.0,4.0,6.0,0.86,5.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,7.0,10.0,1.0,0.0,14.0,10.0,4.0,17.0,32.0,1.0,368.0,58.0,197.0,144.0,31.0,9.0,368.0,159.0,6.0,4.0,0.0,172.0,5.0,3.0,0.0,12.0,9.0,0.0,0.0,0.0,0.0,40.0,12.0,19.0,38.7 -Matías Vecino,uy URU,MF,Lazio,31-170,1991,19.0,9.0,938.0,1.0,0.0,0.0,0.0,2.0,0.0,0.1,0.0,0.1,0.1,0.1,1.9,1.9,0.18,0.18,10.4,3.0,0.0,20.0,0.29,0.07,0.33,0.13,-0.9,-0.9,431.0,516.0,83.5,6367.0,1781.0,246.0,273.0,90.1,145.0,169.0,85.8,25.0,44.0,56.8,3.0,42.0,6.0,0.0,50.0,514.0,1.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,7.0,16.0,1.53,15.0,0.0,0.0,0.0,2.0,0.19,2.0,0.0,0.0,0.0,0.0,22.0,13.0,6.0,9.0,7.0,14.0,3.0,11.0,8.0,19.0,0.0,618.0,31.0,150.0,363.0,112.0,15.0,618.0,337.0,15.0,9.0,1.0,449.0,15.0,2.0,0.0,16.0,9.0,2.0,0.0,0.0,0.0,61.0,14.0,14.0,50.0 -Miguel Veloso,pt POR,MF,Hellas Verona,36-275,1986,15.0,10.0,845.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,9.4,2.0,2.0,15.4,0.21,0.0,0.0,0.03,-0.4,-0.4,313.0,436.0,71.8,6308.0,2454.0,119.0,138.0,86.2,135.0,162.0,83.3,53.0,112.0,47.3,12.0,34.0,6.0,3.0,63.0,375.0,59.0,33.0,1.0,3.0,45.0,20.0,12.0,6.0,1.0,4.0,2.0,10.0,26.0,2.77,15.0,7.0,4.0,0.0,2.0,0.21,2.0,0.0,0.0,0.0,0.0,20.0,4.0,14.0,5.0,1.0,20.0,5.0,15.0,4.0,9.0,0.0,527.0,26.0,126.0,280.0,130.0,7.0,527.0,217.0,12.0,8.0,1.0,240.0,5.0,3.0,0.0,12.0,7.0,0.0,0.0,0.0,1.0,85.0,7.0,10.0,41.2 -Lorenzo Venuti,it ITA,DF,Fiorentina,27-304,1995,9.0,5.0,556.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.16,0.16,0.0,0.16,0.3,0.3,0.05,0.05,6.2,4.0,0.0,100.0,0.65,0.0,0.0,0.07,-0.3,-0.3,325.0,390.0,83.3,6024.0,1904.0,143.0,150.0,95.3,143.0,163.0,87.7,39.0,67.0,58.2,6.0,27.0,8.0,3.0,33.0,325.0,64.0,9.0,0.0,0.0,21.0,3.0,0.0,2.0,0.0,52.0,1.0,8.0,15.0,2.43,10.0,3.0,1.0,0.0,2.0,0.32,1.0,1.0,0.0,0.0,0.0,13.0,5.0,6.0,6.0,1.0,2.0,2.0,0.0,4.0,13.0,2.0,445.0,20.0,140.0,218.0,90.0,5.0,445.0,248.0,15.0,11.0,0.0,292.0,8.0,0.0,0.0,3.0,6.0,0.0,0.0,0.0,0.0,28.0,8.0,4.0,66.7 -"Daniele Verde,it ITA,""FW,MF"",Spezia,26-235,1996,11.0,4.0,425.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.15,0.15,4.7,3.0,0.0,23.1,0.64,0.0,0.0,0.06,-0.7,-0.7,98.0,162.0,60.5,1595.0,517.0,49.0,69.0,71.0,40.0,58.0,69.0,8.0,28.0,28.6,7.0,4.0,3.0,0.0,12.0,120.0,41.0,9.0,0.0,1.0,27.0,20.0,9.0,10.0,0.0,5.0,1.0,2.0,19.0,4.02,11.0,5.0,0.0,1.0,1.0,0.21,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,4.0,2.0,0.0,202.0,6.0,24.0,84.0,96.0,8.0,202.0,110.0,7.0,3.0,2.0,117.0,6.0,6.0,0.0,2.0,9.0,1.0,0.0,0.0,0.0,24.0,0.0,0.0,0.0" -Simone Verdi,it ITA,MF,Hellas Verona,30-213,1992,10.0,5.0,496.0,1.0,0.0,0.0,0.0,1.0,0.0,0.18,0.0,0.18,0.18,0.18,0.8,0.8,0.15,0.15,5.5,3.0,2.0,25.0,0.54,0.08,0.33,0.07,0.2,0.2,127.0,206.0,61.7,2319.0,871.0,67.0,86.0,77.9,34.0,51.0,66.7,20.0,44.0,45.5,9.0,20.0,5.0,2.0,21.0,166.0,37.0,7.0,3.0,7.0,40.0,19.0,10.0,7.0,0.0,4.0,3.0,15.0,20.0,3.65,12.0,5.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,6.0,5.0,3.0,2.0,4.0,0.0,4.0,0.0,1.0,0.0,282.0,1.0,28.0,134.0,127.0,8.0,282.0,159.0,20.0,11.0,1.0,171.0,17.0,18.0,0.0,6.0,9.0,1.0,0.0,0.0,0.0,40.0,3.0,5.0,37.5 -Valerio Verre,it ITA,MF,Sampdoria,29-030,1994,18.0,6.0,677.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.08,0.08,7.5,3.0,0.0,21.4,0.4,0.0,0.0,0.04,-0.6,-0.6,282.0,398.0,70.9,5116.0,1682.0,136.0,161.0,84.5,99.0,135.0,73.3,41.0,74.0,55.4,13.0,30.0,14.0,4.0,45.0,361.0,33.0,16.0,3.0,2.0,33.0,9.0,2.0,6.0,0.0,4.0,4.0,15.0,30.0,3.98,22.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,7.0,6.0,5.0,3.0,9.0,1.0,8.0,9.0,3.0,0.0,493.0,8.0,64.0,285.0,153.0,16.0,493.0,284.0,16.0,13.0,6.0,330.0,22.0,20.0,0.0,14.0,13.0,2.0,0.0,0.0,0.0,61.0,12.0,12.0,50.0 -Guglielmo Vicario,it ITA,GK,Empoli,26-126,1996,21.0,21.0,1890.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,644.0,875.0,73.6,17534.0,12258.0,109.0,110.0,99.1,329.0,336.0,97.9,205.0,427.0,48.0,0.0,16.0,0.0,0.0,0.0,693.0,181.0,73.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,0.24,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,2.0,934.0,770.0,933.0,1.0,0.0,0.0,934.0,577.0,0.0,0.0,0.0,509.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,19.0,6.0,1.0,85.7 -Ronaldo Vieira,gw GNB,MF,Torino,24-206,1998,1.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Ronaldo Vieira,gw GNB,MF,Sampdoria,24-206,1998,16.0,8.0,815.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.1,1.0,0.0,16.7,0.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"Emanuel Vignato,it ITA,""MF,FW"",Bologna,22-170,2000,8.0,2.0,242.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.37,0.37,0.0,0.37,0.1,0.1,0.03,0.03,2.7,1.0,0.0,100.0,0.37,0.0,0.0,0.09,-0.1,-0.1,79.0,99.0,79.8,1201.0,338.0,40.0,48.0,83.3,29.0,34.0,85.3,5.0,8.0,62.5,3.0,9.0,2.0,1.0,9.0,97.0,2.0,0.0,0.0,2.0,2.0,1.0,1.0,0.0,0.0,1.0,0.0,2.0,5.0,1.86,5.0,0.0,0.0,0.0,1.0,0.37,1.0,0.0,0.0,0.0,0.0,4.0,4.0,2.0,2.0,0.0,2.0,0.0,2.0,3.0,0.0,0.0,116.0,0.0,23.0,57.0,37.0,2.0,116.0,76.0,3.0,3.0,0.0,89.0,2.0,4.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,10.0,2.0,2.0,50.0" -"Samuele Vignato,it ITA,""FW,MF"",Monza,18-351,2004,3.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,13.0,84.6,163.0,15.0,5.0,5.0,100.0,4.0,5.0,80.0,1.0,1.0,100.0,0.0,0.0,1.0,1.0,0.0,13.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.0,8.0,8.0,3.0,16.0,11.0,1.0,0.0,0.0,14.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0" -Tonny Vilhena,nl NED,MF,Salernitana,28-038,1995,20.0,17.0,1410.0,3.0,1.0,0.0,0.0,4.0,0.0,0.19,0.06,0.26,0.19,0.26,2.2,2.2,0.14,0.14,15.7,7.0,1.0,28.0,0.45,0.12,0.43,0.09,0.8,0.8,406.0,536.0,75.7,6420.0,1960.0,213.0,245.0,86.9,137.0,161.0,85.1,33.0,73.0,45.2,12.0,38.0,8.0,5.0,55.0,484.0,49.0,18.0,0.0,8.0,36.0,23.0,3.0,13.0,0.0,8.0,3.0,14.0,34.0,2.17,22.0,7.0,0.0,3.0,3.0,0.19,3.0,0.0,0.0,0.0,0.0,31.0,17.0,8.0,20.0,3.0,10.0,4.0,6.0,8.0,10.0,0.0,687.0,16.0,111.0,377.0,211.0,18.0,687.0,380.0,21.0,27.0,3.0,438.0,26.0,21.0,0.0,17.0,23.0,1.0,0.0,1.0,0.0,82.0,6.0,18.0,25.0 -Gonzalo Villar,es ESP,MF,Sampdoria,24-324,1998,15.0,8.0,696.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.7,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,317.0,381.0,83.2,5395.0,1357.0,136.0,162.0,84.0,145.0,165.0,87.9,27.0,41.0,65.9,0.0,25.0,1.0,0.0,26.0,345.0,35.0,31.0,0.0,0.0,7.0,3.0,1.0,2.0,0.0,0.0,1.0,1.0,9.0,1.17,7.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,7.0,4.0,8.0,1.0,11.0,3.0,8.0,4.0,9.0,1.0,453.0,19.0,102.0,288.0,65.0,1.0,453.0,246.0,9.0,6.0,0.0,274.0,10.0,4.0,0.0,17.0,18.0,0.0,0.0,1.0,0.0,55.0,9.0,5.0,64.3 -Matías Viña,uy URU,DF,Roma,25-093,1997,3.0,1.0,55.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,30.0,73.3,392.0,68.0,8.0,10.0,80.0,11.0,16.0,68.8,2.0,3.0,66.7,1.0,3.0,0.0,0.0,2.0,20.0,10.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,10.0,0.0,0.0,4.0,6.55,3.0,0.0,0.0,0.0,1.0,1.64,1.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,2.0,1.0,5.0,1.0,4.0,2.0,5.0,0.0,50.0,3.0,17.0,19.0,14.0,1.0,50.0,19.0,2.0,1.0,1.0,14.0,3.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 -Dušan Vlahović,rs SRB,FW,Juventus,23-013,2000,12.0,11.0,953.0,8.0,2.0,2.0,2.0,0.0,0.0,0.76,0.19,0.94,0.57,0.76,5.6,4.0,0.52,0.38,10.6,12.0,3.0,34.3,1.13,0.17,0.5,0.11,2.4,2.0,140.0,197.0,71.1,2347.0,437.0,67.0,86.0,77.9,55.0,69.0,79.7,11.0,17.0,64.7,14.0,15.0,8.0,1.0,26.0,183.0,13.0,1.0,0.0,2.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,10.0,28.0,2.64,17.0,0.0,5.0,3.0,3.0,0.28,1.0,0.0,1.0,1.0,0.0,7.0,4.0,0.0,3.0,4.0,6.0,2.0,4.0,1.0,4.0,1.0,313.0,7.0,14.0,141.0,163.0,52.0,311.0,167.0,16.0,14.0,9.0,244.0,30.0,22.0,0.0,10.0,13.0,11.0,1.0,0.0,0.0,28.0,19.0,21.0,47.5 -"Nikola Vlašić,hr CRO,""MF,FW"",Torino,25-129,1997,21.0,19.0,1765.0,4.0,2.0,0.0,0.0,1.0,0.0,0.2,0.1,0.31,0.2,0.31,3.7,3.7,0.19,0.19,19.6,15.0,1.0,35.7,0.76,0.1,0.27,0.09,0.3,0.3,497.0,631.0,78.8,6985.0,1393.0,287.0,331.0,86.7,154.0,187.0,82.4,24.0,35.0,68.6,39.0,43.0,15.0,4.0,66.0,615.0,13.0,2.0,0.0,6.0,28.0,4.0,1.0,2.0,0.0,1.0,3.0,31.0,76.0,3.88,64.0,2.0,5.0,1.0,4.0,0.2,3.0,0.0,0.0,1.0,0.0,19.0,13.0,4.0,11.0,4.0,15.0,1.0,14.0,3.0,8.0,0.0,834.0,9.0,70.0,339.0,440.0,80.0,834.0,496.0,53.0,36.0,17.0,630.0,67.0,34.0,0.0,19.0,22.0,12.0,0.0,0.0,0.0,79.0,19.0,36.0,34.5" -Joel Voelkerling Persson,se SWE,FW,Lecce,20-026,2003,3.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,13.0,69.2,77.0,17.0,7.0,9.0,77.8,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,13.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,5.63,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,16.0,0.0,0.0,8.0,8.0,0.0,16.0,6.0,0.0,0.0,0.0,11.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,5.0,6.0,45.5 -Mërgim Vojvoda,xk KVX,DF,Torino,28-009,1995,15.0,10.0,958.0,0.0,4.0,0.0,0.0,1.0,0.0,0.0,0.38,0.38,0.0,0.38,0.7,0.7,0.07,0.07,10.6,3.0,0.0,33.3,0.28,0.0,0.0,0.08,-0.7,-0.7,484.0,643.0,75.3,8166.0,3491.0,247.0,286.0,86.4,192.0,246.0,78.0,35.0,76.0,46.1,12.0,53.0,13.0,1.0,77.0,541.0,98.0,6.0,3.0,10.0,27.0,6.0,3.0,0.0,0.0,86.0,4.0,10.0,33.0,3.1,27.0,3.0,0.0,0.0,7.0,0.66,6.0,0.0,0.0,0.0,0.0,15.0,8.0,9.0,5.0,1.0,6.0,1.0,5.0,7.0,17.0,1.0,739.0,22.0,177.0,339.0,229.0,20.0,739.0,405.0,13.0,17.0,3.0,472.0,10.0,13.0,0.0,10.0,4.0,0.0,0.0,0.0,0.0,65.0,5.0,13.0,27.8 -Cristian Volpato,it ITA,MF,Roma,19-087,2003,5.0,2.0,191.0,1.0,1.0,0.0,0.0,0.0,0.0,0.47,0.47,0.94,0.47,0.94,0.4,0.4,0.17,0.17,2.1,1.0,0.0,25.0,0.47,0.25,1.0,0.09,0.6,0.6,67.0,87.0,77.0,1203.0,352.0,30.0,33.0,90.9,25.0,31.0,80.6,11.0,18.0,61.1,4.0,6.0,5.0,2.0,15.0,82.0,5.0,1.0,1.0,0.0,9.0,2.0,1.0,1.0,0.0,1.0,0.0,3.0,6.0,2.83,5.0,1.0,0.0,0.0,1.0,0.47,1.0,0.0,0.0,0.0,0.0,3.0,2.0,1.0,1.0,1.0,2.0,0.0,2.0,2.0,0.0,0.0,114.0,2.0,8.0,55.0,53.0,10.0,114.0,79.0,7.0,7.0,3.0,88.0,8.0,5.0,0.0,6.0,5.0,0.0,0.0,0.0,0.0,16.0,1.0,0.0,100.0 -Aster Vranckx,be BEL,MF,Milan,20-129,2002,7.0,0.0,76.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,36.0,58.3,350.0,136.0,10.0,16.0,62.5,7.0,10.0,70.0,3.0,8.0,37.5,1.0,1.0,2.0,1.0,2.0,36.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.15,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,1.0,1.0,2.0,1.0,1.0,0.0,1.0,0.0,45.0,0.0,11.0,23.0,11.0,1.0,45.0,31.0,2.0,0.0,0.0,32.0,1.0,7.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,3.0,3.0,2.0,60.0 -Stefan de Vrij,nl NED,DF,Inter,31-005,1992,13.0,12.0,1085.0,1.0,0.0,0.0,0.0,1.0,0.0,0.08,0.0,0.08,0.08,0.08,0.7,0.7,0.06,0.06,12.1,2.0,0.0,22.2,0.17,0.11,0.5,0.08,0.3,0.3,551.0,625.0,88.2,9963.0,2901.0,186.0,203.0,91.6,326.0,347.0,93.9,32.0,58.0,55.2,0.0,26.0,0.0,0.0,33.0,613.0,12.0,11.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,4.0,0.33,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,8.0,11.0,4.0,0.0,6.0,4.0,2.0,26.0,41.0,0.0,740.0,70.0,377.0,350.0,14.0,11.0,740.0,413.0,6.0,0.0,0.0,472.0,2.0,3.0,0.0,7.0,6.0,0.0,0.0,1.0,0.0,55.0,37.0,18.0,67.3 -Walace,br BRA,MF,Udinese,27-312,1995,21.0,21.0,1810.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9,0.9,0.04,0.04,20.1,5.0,0.0,22.7,0.25,0.0,0.0,0.04,-0.9,-0.9,854.0,1034.0,82.6,15294.0,4973.0,363.0,425.0,85.4,361.0,408.0,88.5,104.0,148.0,70.3,9.0,97.0,6.0,2.0,107.0,991.0,39.0,30.0,0.0,14.0,16.0,0.0,0.0,0.0,0.0,5.0,4.0,18.0,30.0,1.49,24.0,1.0,3.0,2.0,3.0,0.15,2.0,0.0,0.0,1.0,0.0,52.0,28.0,25.0,21.0,6.0,30.0,8.0,22.0,27.0,28.0,0.0,1275.0,66.0,364.0,751.0,171.0,8.0,1275.0,730.0,30.0,21.0,0.0,749.0,25.0,22.0,0.0,23.0,25.0,0.0,0.0,0.0,0.0,189.0,14.0,16.0,46.7 -Sebastian Walukiewicz,pl POL,DF,Empoli,22-311,2000,4.0,1.0,149.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.15,0.15,1.7,1.0,0.0,100.0,0.6,0.0,0.0,0.25,-0.2,-0.2,68.0,78.0,87.2,1481.0,560.0,16.0,17.0,94.1,41.0,44.0,93.2,10.0,14.0,71.4,0.0,3.0,0.0,0.0,3.0,75.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,0.6,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,3.0,3.0,0.0,0.0,9.0,0.0,94.0,24.0,62.0,29.0,3.0,2.0,94.0,47.0,0.0,0.0,0.0,64.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,0.0 -Georginio Wijnaldum,nl NED,MF,Roma,32-091,1990,1.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.63,0.63,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.1,-0.1,10.0,11.0,90.9,119.0,37.0,7.0,7.0,100.0,3.0,3.0,100.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,7.5,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,7.0,5.0,1.0,12.0,7.0,2.0,0.0,1.0,12.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Harry Winks,eng ENG,MF,Sampdoria,27-008,1996,4.0,3.0,299.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.04,0.04,3.3,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,144.0,183.0,78.7,2522.0,1104.0,58.0,65.0,89.2,64.0,72.0,88.9,16.0,34.0,47.1,3.0,11.0,1.0,0.0,18.0,158.0,25.0,13.0,0.0,0.0,18.0,10.0,6.0,4.0,0.0,0.0,0.0,4.0,8.0,2.41,4.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,6.0,2.0,8.0,1.0,4.0,3.0,1.0,2.0,3.0,0.0,222.0,11.0,52.0,131.0,40.0,1.0,222.0,99.0,3.0,4.0,0.0,115.0,6.0,3.0,0.0,2.0,5.0,1.0,0.0,0.0,0.0,22.0,0.0,2.0,0.0 -Przemysław Wiśniewski,pl POL,DF,Spezia,24-198,1998,1.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,80.0,72.0,39.0,0.0,0.0,0.0,2.0,2.0,100.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,1.0,4.0,2.0,0.0,0.0,6.0,5.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Gerard Yepes,es ESP,MF,Sampdoria,20-169,2002,5.0,2.0,198.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,2.2,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,77.0,105.0,73.3,1205.0,271.0,43.0,55.0,78.2,28.0,34.0,82.4,5.0,10.0,50.0,1.0,5.0,0.0,0.0,7.0,99.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.36,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,2.0,3.0,0.0,3.0,0.0,3.0,4.0,3.0,0.0,128.0,6.0,20.0,90.0,19.0,2.0,128.0,59.0,3.0,3.0,1.0,72.0,3.0,1.0,0.0,7.0,4.0,0.0,0.0,0.0,0.0,13.0,5.0,2.0,71.4 -"Mattia Zaccagni,it ITA,""FW,MF"",Lazio,27-239,1995,19.0,19.0,1517.0,8.0,4.0,1.0,1.0,4.0,0.0,0.47,0.24,0.71,0.42,0.65,5.1,4.3,0.3,0.26,16.9,15.0,1.0,42.9,0.89,0.2,0.47,0.12,2.9,2.7,392.0,497.0,78.9,5228.0,1275.0,245.0,276.0,88.8,108.0,139.0,77.7,11.0,26.0,42.3,20.0,23.0,14.0,0.0,44.0,486.0,11.0,2.0,0.0,5.0,32.0,6.0,4.0,1.0,0.0,3.0,0.0,18.0,50.0,2.97,33.0,1.0,5.0,5.0,7.0,0.42,5.0,1.0,1.0,0.0,0.0,27.0,17.0,12.0,11.0,4.0,18.0,3.0,15.0,14.0,6.0,1.0,695.0,9.0,91.0,298.0,318.0,74.0,694.0,486.0,59.0,26.0,22.0,549.0,30.0,19.0,0.0,31.0,63.0,3.0,0.0,1.0,0.0,69.0,6.0,17.0,26.1" -Denis Zakaria,ch SUI,MF,Juventus,26-082,1996,2.0,1.0,123.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32.0,38.0,84.2,469.0,119.0,18.0,20.0,90.0,11.0,12.0,91.7,2.0,2.0,100.0,0.0,2.0,0.0,0.0,3.0,37.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,1.0,0.73,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,1.0,4.0,1.0,2.0,0.0,2.0,2.0,1.0,0.0,51.0,2.0,14.0,30.0,9.0,1.0,51.0,27.0,2.0,3.0,0.0,36.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0 -Nicola Zalewski,pl POL,DF,Roma,21-018,2002,17.0,12.0,1042.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.09,0.09,0.0,0.09,0.7,0.7,0.06,0.06,11.6,3.0,0.0,25.0,0.26,0.0,0.0,0.06,-0.7,-0.7,440.0,583.0,75.5,7664.0,2375.0,204.0,241.0,84.6,171.0,208.0,82.2,51.0,99.0,51.5,5.0,26.0,14.0,4.0,42.0,474.0,105.0,9.0,2.0,4.0,35.0,4.0,2.0,1.0,0.0,92.0,4.0,11.0,27.0,2.33,18.0,4.0,2.0,0.0,5.0,0.43,4.0,1.0,0.0,0.0,0.0,22.0,10.0,12.0,7.0,3.0,15.0,0.0,15.0,15.0,9.0,0.0,709.0,14.0,192.0,315.0,210.0,13.0,709.0,395.0,41.0,29.0,8.0,449.0,17.0,5.0,0.0,8.0,21.0,1.0,0.0,0.0,0.0,69.0,3.0,7.0,30.0 -Andre-Frank Zambo Anguissa,cm CMR,MF,Napoli,27-086,1995,19.0,19.0,1594.0,2.0,4.0,0.0,0.0,2.0,0.0,0.11,0.23,0.34,0.11,0.34,3.1,3.1,0.17,0.17,17.7,8.0,0.0,30.8,0.45,0.08,0.25,0.12,-1.1,-1.1,969.0,1113.0,87.1,13758.0,3392.0,563.0,609.0,92.4,318.0,358.0,88.8,43.0,69.0,62.3,16.0,90.0,14.0,0.0,99.0,1099.0,12.0,10.0,1.0,0.0,9.0,0.0,0.0,0.0,0.0,2.0,2.0,17.0,42.0,2.37,35.0,0.0,3.0,1.0,6.0,0.34,6.0,0.0,0.0,0.0,0.0,35.0,21.0,11.0,17.0,7.0,27.0,1.0,26.0,21.0,17.0,0.0,1340.0,31.0,243.0,742.0,378.0,51.0,1340.0,864.0,26.0,26.0,5.0,1038.0,36.0,29.0,0.0,17.0,28.0,2.0,1.0,0.0,0.0,117.0,29.0,15.0,65.9 -"Luca Zanimacchia,it ITA,""MF,FW"",Cremonese,24-206,1998,15.0,9.0,746.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,1.2,0.14,0.14,8.3,4.0,2.0,21.1,0.48,0.0,0.0,0.06,-1.2,-1.2,213.0,312.0,68.3,3230.0,932.0,122.0,155.0,78.7,68.0,102.0,66.7,18.0,37.0,48.6,13.0,12.0,12.0,2.0,26.0,271.0,40.0,8.0,0.0,1.0,46.0,21.0,9.0,10.0,0.0,2.0,1.0,9.0,34.0,4.11,19.0,6.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,13.0,11.0,6.0,4.0,9.0,0.0,9.0,9.0,5.0,0.0,416.0,8.0,54.0,156.0,211.0,33.0,416.0,208.0,22.0,20.0,8.0,242.0,20.0,12.0,0.0,9.0,10.0,2.0,1.0,0.0,0.0,48.0,3.0,10.0,23.1" -"Nicolò Zaniolo,it ITA,""MF,FW"",Roma,23-223,1999,13.0,12.0,890.0,1.0,0.0,0.0,0.0,4.0,0.0,0.1,0.0,0.1,0.1,0.1,2.9,2.9,0.29,0.29,9.9,7.0,0.0,25.0,0.71,0.04,0.14,0.1,-1.9,-1.9,125.0,196.0,63.8,2173.0,488.0,63.0,85.0,74.1,46.0,67.0,68.7,15.0,26.0,57.7,8.0,10.0,7.0,1.0,19.0,184.0,11.0,4.0,3.0,6.0,20.0,5.0,4.0,1.0,0.0,1.0,1.0,12.0,27.0,2.73,19.0,0.0,4.0,3.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,8.0,5.0,2.0,5.0,1.0,11.0,0.0,11.0,2.0,0.0,0.0,345.0,1.0,36.0,154.0,165.0,45.0,345.0,245.0,33.0,27.0,9.0,267.0,39.0,35.0,0.0,22.0,32.0,4.0,0.0,0.0,0.0,39.0,5.0,8.0,38.5" -"Alessandro Zanoli,it ITA,""DF,MF"",Sampdoria,22-130,2000,5.0,0.0,122.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,56.0,79.0,70.9,1013.0,333.0,23.0,24.0,95.8,26.0,32.0,81.3,7.0,14.0,50.0,2.0,1.0,3.0,2.0,2.0,68.0,11.0,2.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,9.0,0.0,5.0,4.0,2.95,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,89.0,5.0,31.0,34.0,25.0,2.0,89.0,41.0,3.0,1.0,1.0,60.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,7.0,1.0,3.0,25.0" -Alessandro Zanoli,it ITA,DF,Napoli,22-130,2000,1.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.47,0.47,0.1,1.0,0.0,100.0,7.5,0.0,0.0,0.06,-0.1,-0.1,9.0,11.0,81.8,125.0,20.0,4.0,5.0,80.0,5.0,5.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,7.5,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,14.0,0.0,5.0,6.0,3.0,1.0,14.0,7.0,1.0,1.0,1.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 -Duván Zapata,co COL,FW,Atalanta,31-315,1991,14.0,8.0,668.0,1.0,1.0,0.0,0.0,1.0,0.0,0.13,0.13,0.27,0.13,0.27,2.2,2.2,0.29,0.29,7.4,5.0,0.0,25.0,0.67,0.05,0.2,0.11,-1.2,-1.2,140.0,210.0,66.7,2146.0,445.0,77.0,107.0,72.0,47.0,66.0,71.2,12.0,20.0,60.0,8.0,10.0,8.0,1.0,21.0,199.0,9.0,1.0,0.0,4.0,11.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,20.0,2.69,16.0,0.0,0.0,1.0,3.0,0.4,2.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,9.0,0.0,280.0,9.0,14.0,132.0,139.0,39.0,280.0,186.0,16.0,6.0,11.0,243.0,18.0,21.0,0.0,7.0,17.0,4.0,1.0,0.0,0.0,18.0,24.0,21.0,53.3 -"Davide Zappacosta,it ITA,""DF,MF"",Atalanta,30-244,1992,5.0,3.0,226.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.4,0.4,0.0,0.4,0.3,0.3,0.1,0.1,2.5,1.0,0.0,33.3,0.4,0.0,0.0,0.09,-0.3,-0.3,96.0,126.0,76.2,1356.0,513.0,58.0,66.0,87.9,30.0,43.0,69.8,5.0,7.0,71.4,2.0,5.0,0.0,0.0,8.0,98.0,28.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,27.0,0.0,4.0,4.0,1.59,2.0,0.0,1.0,0.0,2.0,0.8,1.0,0.0,1.0,0.0,0.0,7.0,3.0,3.0,3.0,1.0,3.0,0.0,3.0,2.0,3.0,0.0,167.0,8.0,42.0,68.0,61.0,11.0,167.0,101.0,10.0,7.0,4.0,98.0,7.0,2.0,0.0,5.0,2.0,1.0,0.0,0.0,0.0,18.0,0.0,2.0,0.0" -Alessio Zerbin,it ITA,FW,Napoli,23-344,1999,4.0,0.0,55.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.6,1.0,0.0,100.0,1.64,0.0,0.0,0.04,0.0,0.0,10.0,17.0,58.8,120.0,21.0,6.0,8.0,75.0,2.0,4.0,50.0,1.0,1.0,100.0,0.0,1.0,0.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,29.0,4.0,8.0,15.0,6.0,2.0,29.0,22.0,0.0,0.0,0.0,23.0,2.0,3.0,0.0,4.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -Piotr Zieliński,pl POL,MF,Napoli,28-266,1994,21.0,16.0,1314.0,3.0,6.0,0.0,0.0,1.0,0.0,0.21,0.41,0.62,0.21,0.62,2.4,2.4,0.16,0.16,14.6,14.0,3.0,40.0,0.96,0.09,0.21,0.07,0.6,0.6,689.0,833.0,82.7,9982.0,2801.0,407.0,442.0,92.1,189.0,231.0,81.8,52.0,88.0,59.1,42.0,64.0,22.0,4.0,69.0,755.0,75.0,17.0,0.0,5.0,57.0,41.0,19.0,14.0,0.0,1.0,3.0,17.0,69.0,4.72,42.0,18.0,3.0,4.0,8.0,0.55,5.0,2.0,1.0,0.0,0.0,10.0,6.0,1.0,7.0,2.0,8.0,3.0,5.0,12.0,5.0,0.0,964.0,14.0,122.0,456.0,397.0,35.0,964.0,592.0,36.0,42.0,7.0,746.0,15.0,9.0,0.0,8.0,18.0,2.0,0.0,0.0,0.0,59.0,2.0,8.0,20.0 -David Zima,cz CZE,DF,Torino,22-094,2000,9.0,4.0,406.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.17,0.17,4.5,2.0,0.0,50.0,0.44,0.0,0.0,0.2,-0.8,-0.8,154.0,197.0,78.2,2708.0,743.0,60.0,77.0,77.9,73.0,86.0,84.9,12.0,20.0,60.0,2.0,6.0,3.0,0.0,12.0,194.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,0.89,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,5.0,5.0,4.0,0.0,7.0,2.0,5.0,9.0,11.0,2.0,245.0,20.0,114.0,121.0,12.0,5.0,245.0,111.0,3.0,2.0,0.0,129.0,1.0,2.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,24.0,15.0,9.0,62.5 -Joshua Zirkzee,nl NED,FW,Bologna,21-264,2001,9.0,4.0,464.0,1.0,1.0,0.0,0.0,1.0,0.0,0.19,0.19,0.39,0.19,0.39,1.8,1.8,0.35,0.35,5.2,4.0,0.0,25.0,0.78,0.06,0.25,0.11,-0.8,-0.8,105.0,138.0,76.1,1280.0,239.0,69.0,85.0,81.2,25.0,32.0,78.1,3.0,3.0,100.0,8.0,6.0,3.0,0.0,9.0,127.0,11.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,21.0,4.08,13.0,0.0,4.0,0.0,3.0,0.58,3.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,1.0,2.0,1.0,1.0,0.0,3.0,2.0,0.0,205.0,4.0,13.0,92.0,101.0,21.0,205.0,122.0,5.0,8.0,4.0,163.0,20.0,14.0,0.0,3.0,4.0,1.0,0.0,0.0,0.0,10.0,8.0,15.0,34.8 -Jeroen Zoet,nl NED,GK,Spezia,32-035,1991,3.0,2.0,154.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,34.0,65.0,52.3,1150.0,997.0,8.0,8.0,100.0,11.0,12.0,91.7,15.0,45.0,33.3,0.0,3.0,0.0,0.0,0.0,54.0,11.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.58,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,68.0,59.0,67.0,1.0,0.0,0.0,68.0,47.0,0.0,0.0,0.0,34.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -"Nadir Zortea,it ITA,""DF,MF"",Atalanta,23-236,1999,9.0,1.0,222.0,1.0,0.0,0.0,0.0,1.0,0.0,0.41,0.0,0.41,0.41,0.41,0.1,0.1,0.05,0.05,2.5,3.0,0.0,75.0,1.22,0.25,0.33,0.03,0.9,0.9,60.0,98.0,61.2,937.0,602.0,31.0,38.0,81.6,22.0,35.0,62.9,5.0,13.0,38.5,3.0,3.0,3.0,1.0,9.0,83.0,15.0,0.0,1.0,0.0,10.0,0.0,0.0,0.0,0.0,15.0,0.0,6.0,8.0,3.27,5.0,0.0,1.0,0.0,1.0,0.41,1.0,0.0,0.0,0.0,0.0,10.0,5.0,3.0,7.0,0.0,4.0,0.0,4.0,0.0,4.0,0.0,153.0,7.0,45.0,50.0,60.0,7.0,153.0,78.0,11.0,7.0,2.0,82.0,6.0,2.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,14.0,5.0,1.0,83.3" -Nadir Zortea,it ITA,DF,Sassuolo,23-236,1999,1.0,1.0,90.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.04,1.0,1.0,0.0,50.0,1.0,0.0,0.0,0.02,0.0,0.0,59.0,67.0,88.1,946.0,400.0,28.0,29.0,96.6,27.0,32.0,84.4,3.0,5.0,60.0,3.0,4.0,3.0,1.0,9.0,57.0,10.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,4.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,78.0,5.0,17.0,39.0,24.0,0.0,78.0,48.0,6.0,6.0,0.0,49.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,2.0,33.3 -Petar Zovko,ba BIH,GK,Spezia,20-322,2002,1.0,0.0,74.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,36.0,61.1,683.0,458.0,4.0,4.0,100.0,9.0,9.0,100.0,9.0,23.0,39.1,0.0,1.0,0.0,0.0,0.0,21.0,15.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,29.0,36.0,2.0,0.0,0.0,38.0,18.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 -Szymon Żurkowski,pl POL,MF,Fiorentina,25-138,1997,2.0,0.0,32.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.81,2.81,0.0,2.81,0.1,0.1,0.33,0.33,0.4,1.0,0.0,50.0,2.81,0.0,0.0,0.06,-0.1,-0.1,17.0,19.0,89.5,298.0,21.0,6.0,7.0,85.7,7.0,7.0,100.0,2.0,2.0,100.0,1.0,1.0,1.0,0.0,1.0,19.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,5.63,2.0,0.0,0.0,0.0,1.0,2.81,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,0.0,1.0,8.0,16.0,3.0,25.0,16.0,3.0,1.0,1.0,18.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 -Szymon Żurkowski,pl POL,MF,Spezia,25-138,1997,1.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,50.0,24.0,3.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,8.0,2.0,2.0,3.0,3.0,0.0,8.0,3.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0 -Milan Đurić,ba BIH,FW,Hellas Verona,32-264,1990,16.0,7.0,703.0,1.0,0.0,0.0,0.0,3.0,0.0,0.13,0.0,0.13,0.13,0.13,0.5,0.5,0.07,0.07,7.8,2.0,0.0,25.0,0.26,0.13,0.5,0.07,0.5,0.5,154.0,288.0,53.5,1800.0,425.0,106.0,185.0,57.3,32.0,67.0,47.8,3.0,5.0,60.0,12.0,14.0,3.0,0.0,18.0,287.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,22.0,2.81,18.0,0.0,2.0,2.0,1.0,0.13,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,1.0,5.0,3.0,2.0,0.0,11.0,0.0,354.0,15.0,28.0,172.0,154.0,34.0,354.0,144.0,1.0,2.0,2.0,307.0,19.0,4.0,0.0,15.0,14.0,3.0,0.0,0.0,0.0,16.0,113.0,28.0,80.1 -"Filip Đuričić,rs SRB,""MF,FW"",Sampdoria,31-011,1992,20.0,17.0,1326.0,2.0,0.0,0.0,0.0,6.0,0.0,0.14,0.0,0.14,0.14,0.14,1.4,1.4,0.1,0.1,14.7,7.0,0.0,41.2,0.48,0.12,0.29,0.08,0.6,0.6,345.0,454.0,76.0,5687.0,1392.0,181.0,215.0,84.2,120.0,145.0,82.8,33.0,51.0,64.7,27.0,33.0,17.0,4.0,52.0,424.0,29.0,10.0,1.0,3.0,23.0,8.0,0.0,7.0,0.0,7.0,1.0,22.0,51.0,3.47,34.0,7.0,2.0,5.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,28.0,13.0,12.0,12.0,4.0,11.0,2.0,9.0,5.0,3.0,0.0,617.0,10.0,83.0,288.0,256.0,21.0,617.0,367.0,26.0,30.0,3.0,425.0,40.0,33.0,0.0,22.0,33.0,0.0,0.0,0.0,0.0,73.0,8.0,13.0,38.1" +James Abankwah,ie IRL,DF,Udinese,19-099,2004,1.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.0,100.0,73.0,12.0,2.0,2.0,100.0,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,3.0,3.0,0.0,0.0,6.0,2.0,0.0,0.0,0.0,3.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Oliver Abildgaard,dk DEN,MF,Hellas Verona,26-319,1996,6.0,2.0,197.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28.0,54.0,51.9,407.0,232.0,18.0,28.0,64.3,7.0,16.0,43.8,2.0,4.0,50.0,3.0,4.0,1.0,1.0,3.0,53.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,0.91,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,2.0,1.0,1.0,3.0,0.0,3.0,1.0,7.0,0.0,78.0,6.0,27.0,36.0,15.0,4.0,78.0,25.0,0.0,0.0,0.0,33.0,2.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,15.0,15.0,5.0,75.0 +Tammy Abraham,eng ENG,FW,Roma,25-205,1997,31.0,22.0,1905.0,7.0,3.0,0.0,0.0,2.0,0.0,0.33,0.14,0.47,0.33,0.47,10.1,10.1,0.48,0.48,21.2,24.0,0.0,42.9,1.13,0.13,0.29,0.18,-3.1,-3.1,293.0,439.0,66.7,4279.0,1039.0,171.0,225.0,76.0,88.0,143.0,61.5,21.0,29.0,72.4,28.0,14.0,11.0,1.0,45.0,404.0,32.0,1.0,2.0,3.0,7.0,0.0,0.0,0.0,0.0,1.0,3.0,6.0,64.0,3.02,42.0,0.0,10.0,7.0,9.0,0.42,4.0,0.0,3.0,1.0,0.0,12.0,7.0,3.0,5.0,4.0,10.0,4.0,6.0,3.0,11.0,0.0,677.0,16.0,45.0,346.0,295.0,106.0,677.0,371.0,28.0,17.0,13.0,530.0,54.0,26.0,0.0,30.0,44.0,10.0,1.0,0.0,0.0,41.0,66.0,57.0,53.7 +Christian Acella,it ITA,MF,Cremonese,20-292,2002,1.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,9.0,100.0,129.0,4.0,5.0,5.0,100.0,4.0,4.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,0.0,1.0,9.0,1.0,0.0,11.0,7.0,0.0,0.0,0.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Francesco Acerbi,it ITA,DF,Inter,35-074,1988,24.0,20.0,1915.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.05,0.05,0.0,0.05,0.8,0.8,0.04,0.04,21.3,2.0,0.0,12.5,0.09,0.0,0.0,0.05,-0.8,-0.8,1168.0,1329.0,87.9,21789.0,6461.0,427.0,473.0,90.3,596.0,653.0,91.3,124.0,166.0,74.7,14.0,72.0,11.0,3.0,92.0,1259.0,66.0,37.0,1.0,9.0,20.0,0.0,0.0,0.0,0.0,16.0,4.0,3.0,30.0,1.41,27.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27.0,15.0,17.0,6.0,4.0,15.0,9.0,6.0,31.0,64.0,0.0,1514.0,151.0,621.0,754.0,150.0,26.0,1514.0,843.0,35.0,29.0,2.0,1009.0,8.0,4.0,0.0,12.0,10.0,1.0,0.0,0.0,0.0,122.0,69.0,32.0,68.3 +Yacine Adli,fr FRA,"MF,FW",Milan,22-270,2000,5.0,1.0,124.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.06,0.06,1.4,2.0,0.0,100.0,1.45,0.0,0.0,0.04,-0.1,-0.1,43.0,65.0,66.2,778.0,231.0,20.0,25.0,80.0,14.0,20.0,70.0,7.0,13.0,53.8,1.0,5.0,2.0,0.0,9.0,63.0,2.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,2.0,1.45,2.0,0.0,0.0,0.0,1.0,0.73,1.0,0.0,0.0,0.0,0.0,6.0,3.0,2.0,4.0,0.0,2.0,0.0,2.0,1.0,1.0,0.0,83.0,1.0,11.0,42.0,33.0,1.0,83.0,49.0,5.0,4.0,0.0,62.0,2.0,3.0,0.0,4.0,1.0,1.0,0.0,0.0,0.0,6.0,0.0,3.0,0.0 +Michel Aebischer,ch SUI,"FW,MF",Bologna,26-109,1997,26.0,14.0,1185.0,1.0,0.0,0.0,0.0,3.0,0.0,0.08,0.0,0.08,0.08,0.08,1.2,1.2,0.09,0.09,13.2,2.0,1.0,16.7,0.15,0.08,0.5,0.1,-0.2,-0.2,406.0,507.0,80.1,6056.0,1700.0,220.0,250.0,88.0,143.0,169.0,84.6,25.0,46.0,54.3,13.0,27.0,6.0,1.0,34.0,480.0,24.0,9.0,0.0,3.0,19.0,4.0,1.0,3.0,0.0,10.0,3.0,14.0,28.0,2.13,19.0,3.0,2.0,2.0,2.0,0.15,0.0,0.0,1.0,1.0,0.0,10.0,4.0,3.0,4.0,3.0,13.0,2.0,11.0,6.0,6.0,0.0,605.0,11.0,106.0,294.0,207.0,21.0,605.0,369.0,10.0,10.0,3.0,463.0,25.0,16.0,0.0,16.0,18.0,7.0,1.0,1.0,0.0,58.0,8.0,21.0,27.6 +Felix Afena-Gyan,gh GHA,"FW,MF",Cremonese,20-096,2003,18.0,5.0,662.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,1.8,1.8,0.24,0.24,7.4,1.0,0.0,5.3,0.14,0.0,0.0,0.1,-1.8,-1.8,125.0,162.0,77.2,1604.0,148.0,85.0,99.0,85.9,29.0,38.0,76.3,4.0,6.0,66.7,3.0,3.0,0.0,0.0,7.0,158.0,4.0,0.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,3.0,0.0,4.0,14.0,1.9,9.0,0.0,4.0,0.0,1.0,0.14,0.0,0.0,1.0,0.0,0.0,6.0,3.0,4.0,1.0,1.0,5.0,1.0,4.0,1.0,3.0,0.0,264.0,5.0,28.0,100.0,141.0,25.0,264.0,172.0,20.0,11.0,3.0,188.0,28.0,16.0,0.0,16.0,7.0,4.0,0.0,0.0,0.0,30.0,12.0,29.0,29.3 +Kevin Agudelo,co COL,"MF,FW",Spezia,24-162,1998,29.0,21.0,1867.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.05,0.05,0.0,0.05,1.2,1.2,0.06,0.06,20.7,5.0,0.0,22.7,0.24,0.0,0.0,0.06,-1.2,-1.2,456.0,626.0,72.8,6733.0,2303.0,256.0,304.0,84.2,147.0,191.0,77.0,25.0,45.0,55.6,23.0,53.0,29.0,2.0,99.0,607.0,14.0,2.0,4.0,1.0,27.0,5.0,3.0,2.0,0.0,4.0,5.0,31.0,47.0,2.27,33.0,1.0,3.0,3.0,3.0,0.14,2.0,0.0,0.0,1.0,0.0,66.0,34.0,19.0,28.0,19.0,36.0,5.0,31.0,9.0,6.0,0.0,924.0,11.0,151.0,426.0,380.0,44.0,924.0,557.0,68.0,59.0,17.0,583.0,54.0,47.0,0.0,30.0,32.0,2.0,0.0,0.0,0.0,121.0,14.0,24.0,36.8 +Ola Aina,ng NGA,DF,Torino,26-199,1996,16.0,9.0,807.0,1.0,1.0,0.0,0.0,5.0,0.0,0.11,0.11,0.22,0.11,0.22,0.7,0.7,0.08,0.08,9.0,3.0,0.0,42.9,0.33,0.14,0.33,0.11,0.3,0.3,370.0,498.0,74.3,6183.0,2427.0,194.0,213.0,91.1,120.0,161.0,74.5,45.0,83.0,54.2,11.0,29.0,13.0,9.0,39.0,392.0,102.0,8.0,0.0,1.0,31.0,3.0,0.0,0.0,0.0,91.0,4.0,16.0,26.0,2.9,20.0,2.0,0.0,1.0,3.0,0.33,2.0,0.0,0.0,0.0,0.0,15.0,11.0,5.0,7.0,3.0,11.0,2.0,9.0,9.0,26.0,0.0,620.0,19.0,136.0,268.0,225.0,12.0,620.0,325.0,33.0,25.0,4.0,356.0,14.0,6.0,0.0,15.0,15.0,1.0,0.0,1.0,0.0,53.0,12.0,9.0,57.1 +Emanuel Aiwum,at AUT,DF,Cremonese,22-121,2000,21.0,15.0,1473.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.01,0.01,16.4,1.0,0.0,33.3,0.06,0.0,0.0,0.05,-0.2,-0.2,561.0,713.0,78.7,9315.0,3251.0,264.0,293.0,90.1,251.0,300.0,83.7,33.0,85.0,38.8,4.0,45.0,7.0,1.0,43.0,622.0,89.0,21.0,1.0,5.0,4.0,0.0,0.0,0.0,0.0,59.0,2.0,12.0,10.0,0.61,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,48.0,29.0,29.0,17.0,2.0,25.0,15.0,10.0,24.0,50.0,3.0,879.0,97.0,447.0,396.0,47.0,5.0,879.0,451.0,19.0,11.0,0.0,460.0,6.0,4.0,0.0,19.0,10.0,0.0,0.0,0.0,0.0,103.0,25.0,13.0,65.8 +Jean-Daniel Akpa-Akpro,ci CIV,MF,Empoli,30-196,1992,17.0,12.0,960.0,0.0,0.0,0.0,0.0,8.0,2.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.03,0.03,10.7,3.0,0.0,33.3,0.28,0.0,0.0,0.04,-0.4,-0.4,242.0,317.0,76.3,3396.0,1332.0,132.0,158.0,83.5,80.0,96.0,83.3,11.0,21.0,52.4,5.0,19.0,7.0,2.0,32.0,310.0,7.0,6.0,1.0,1.0,9.0,0.0,0.0,0.0,0.0,1.0,0.0,14.0,19.0,1.78,12.0,0.0,1.0,1.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,37.0,28.0,18.0,14.0,5.0,29.0,4.0,25.0,12.0,23.0,0.0,477.0,20.0,124.0,260.0,105.0,16.0,477.0,251.0,14.0,19.0,6.0,274.0,15.0,9.0,1.0,22.0,13.0,0.0,0.0,0.0,0.0,54.0,11.0,9.0,55.0 +Luis Alberto,es ESP,MF,Lazio,30-209,1992,28.0,20.0,1813.0,5.0,4.0,1.0,2.0,2.0,0.0,0.25,0.2,0.45,0.2,0.4,3.9,2.2,0.2,0.11,20.1,13.0,3.0,36.1,0.65,0.11,0.31,0.06,1.1,1.8,1233.0,1494.0,82.5,18843.0,6448.0,693.0,755.0,91.8,415.0,482.0,86.1,82.0,173.0,47.4,41.0,144.0,34.0,0.0,175.0,1310.0,178.0,60.0,9.0,6.0,110.0,88.0,41.0,39.0,0.0,5.0,6.0,23.0,73.0,3.63,49.0,21.0,3.0,0.0,9.0,0.45,8.0,1.0,0.0,0.0,0.0,33.0,18.0,14.0,13.0,6.0,27.0,5.0,22.0,7.0,3.0,0.0,1666.0,24.0,210.0,949.0,521.0,34.0,1664.0,943.0,56.0,54.0,8.0,1234.0,26.0,21.0,0.0,10.0,11.0,2.0,0.0,0.0,0.0,125.0,6.0,10.0,37.5 +Agustín Álvarez Martínez,uy URU,"FW,MF",Sassuolo,21-341,2001,20.0,1.0,334.0,1.0,1.0,0.0,0.0,1.0,0.0,0.27,0.27,0.54,0.27,0.54,1.5,1.5,0.4,0.4,3.7,4.0,0.0,28.6,1.08,0.07,0.25,0.11,-0.5,-0.5,64.0,79.0,81.0,901.0,179.0,40.0,48.0,83.3,13.0,17.0,76.5,6.0,8.0,75.0,2.0,4.0,2.0,0.0,6.0,72.0,7.0,0.0,0.0,1.0,4.0,3.0,0.0,3.0,0.0,0.0,0.0,1.0,9.0,2.42,3.0,0.0,3.0,1.0,2.0,0.54,1.0,0.0,1.0,0.0,0.0,6.0,3.0,0.0,5.0,1.0,4.0,1.0,3.0,3.0,3.0,0.0,147.0,4.0,12.0,70.0,68.0,14.0,147.0,76.0,2.0,1.0,2.0,93.0,16.0,10.0,0.0,8.0,4.0,2.0,0.0,0.0,0.0,19.0,8.0,11.0,42.1 +Kelvin Amian,fr FRA,DF,Spezia,25-076,1998,24.0,20.0,1859.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.05,0.05,0.0,0.05,1.6,1.6,0.08,0.08,20.7,5.0,1.0,25.0,0.24,0.0,0.0,0.08,-1.6,-1.6,648.0,859.0,75.4,10551.0,4499.0,322.0,374.0,86.1,257.0,330.0,77.9,46.0,97.0,47.4,14.0,62.0,15.0,9.0,80.0,686.0,170.0,11.0,1.0,2.0,26.0,1.0,0.0,1.0,0.0,158.0,3.0,23.0,33.0,1.6,25.0,2.0,2.0,0.0,2.0,0.1,1.0,0.0,1.0,0.0,0.0,45.0,28.0,27.0,10.0,8.0,26.0,11.0,15.0,21.0,66.0,1.0,1084.0,83.0,393.0,419.0,277.0,33.0,1084.0,496.0,39.0,24.0,3.0,562.0,20.0,13.0,0.0,32.0,2.0,1.0,0.0,1.0,0.0,86.0,26.0,31.0,45.6 +Bruno Amione,ar ARG,DF,Hellas Verona,21-112,2002,1.0,1.0,58.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,16.0,68.8,167.0,40.0,6.0,6.0,100.0,5.0,7.0,71.4,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,4.0,0.0,20.0,9.0,17.0,4.0,0.0,0.0,20.0,7.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 +Bruno Amione,ar ARG,DF,Sampdoria,21-112,2002,20.0,20.0,1677.0,1.0,1.0,0.0,0.0,7.0,0.0,0.05,0.05,0.11,0.05,0.11,0.6,0.6,0.03,0.03,18.6,2.0,0.0,22.2,0.11,0.11,0.5,0.07,0.4,0.4,689.0,874.0,78.8,11906.0,4678.0,287.0,328.0,87.5,344.0,402.0,85.6,48.0,98.0,49.0,2.0,29.0,3.0,1.0,52.0,802.0,70.0,30.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,40.0,2.0,22.0,9.0,0.48,6.0,0.0,3.0,0.0,2.0,0.11,1.0,0.0,1.0,0.0,0.0,44.0,28.0,30.0,14.0,0.0,21.0,11.0,10.0,21.0,49.0,1.0,1063.0,111.0,585.0,422.0,70.0,18.0,1063.0,560.0,10.0,14.0,0.0,575.0,15.0,1.0,0.0,19.0,12.0,0.0,0.0,1.0,0.0,105.0,59.0,36.0,62.1 +Ethan Ampadu,wls WAL,"DF,MF",Spezia,22-223,2000,24.0,24.0,2073.0,0.0,1.0,0.0,0.0,8.0,1.0,0.0,0.04,0.04,0.0,0.04,1.0,1.0,0.04,0.04,23.0,5.0,0.0,35.7,0.22,0.0,0.0,0.08,-1.0,-1.0,999.0,1248.0,80.0,19030.0,7648.0,382.0,440.0,86.8,494.0,558.0,88.5,110.0,210.0,52.4,10.0,91.0,11.0,4.0,104.0,1147.0,96.0,39.0,3.0,16.0,10.0,0.0,0.0,0.0,0.0,43.0,5.0,14.0,31.0,1.35,27.0,0.0,2.0,1.0,4.0,0.17,4.0,0.0,0.0,0.0,0.0,54.0,30.0,24.0,25.0,5.0,38.0,17.0,21.0,28.0,76.0,0.0,1514.0,177.0,719.0,694.0,120.0,14.0,1514.0,808.0,20.0,14.0,0.0,881.0,14.0,6.0,1.0,32.0,17.0,1.0,0.0,2.0,0.0,163.0,52.0,33.0,61.2 +Sofyan Amrabat,ma MAR,MF,Fiorentina,26-247,1996,26.0,21.0,1752.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.02,0.02,19.5,3.0,0.0,27.3,0.15,0.0,0.0,0.04,-0.4,-0.4,1124.0,1280.0,87.8,22678.0,6109.0,377.0,427.0,88.3,550.0,589.0,93.4,181.0,220.0,82.3,11.0,141.0,21.0,10.0,155.0,1194.0,84.0,75.0,1.0,18.0,24.0,1.0,0.0,0.0,0.0,7.0,2.0,23.0,34.0,1.75,31.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,19.0,13.0,12.0,6.0,22.0,5.0,17.0,20.0,15.0,2.0,1414.0,39.0,247.0,946.0,237.0,6.0,1414.0,922.0,34.0,25.0,1.0,1010.0,20.0,16.0,0.0,42.0,28.0,0.0,0.0,1.0,0.0,144.0,11.0,10.0,52.4 +Felipe Anderson,br BRA,FW,Lazio,30-010,1993,31.0,29.0,2465.0,7.0,2.0,0.0,0.0,2.0,0.0,0.26,0.07,0.33,0.26,0.33,5.3,5.3,0.19,0.19,27.4,19.0,0.0,57.6,0.69,0.21,0.37,0.16,1.7,1.7,888.0,1153.0,77.0,12478.0,2702.0,537.0,633.0,84.8,278.0,350.0,79.4,39.0,69.0,56.5,41.0,48.0,38.0,4.0,101.0,1119.0,31.0,2.0,4.0,7.0,41.0,3.0,1.0,2.0,0.0,18.0,3.0,34.0,84.0,3.07,67.0,1.0,4.0,3.0,13.0,0.47,6.0,0.0,2.0,1.0,0.0,58.0,38.0,20.0,32.0,6.0,45.0,4.0,41.0,20.0,19.0,0.0,1497.0,22.0,173.0,668.0,679.0,109.0,1497.0,795.0,72.0,58.0,28.0,1135.0,72.0,38.0,0.0,30.0,27.0,7.0,1.0,0.0,0.0,111.0,12.0,20.0,37.5 +Janis Antiste,fr FRA,FW,Sassuolo,20-250,2002,2.0,0.0,52.0,1.0,0.0,0.0,0.0,0.0,0.0,1.73,0.0,1.73,1.73,1.73,0.2,0.2,0.31,0.31,0.6,1.0,0.0,100.0,1.73,1.0,1.0,0.18,0.8,0.8,13.0,18.0,72.2,159.0,43.0,8.0,11.0,72.7,4.0,5.0,80.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,0.0,7.0,9.0,7.0,1.0,23.0,14.0,0.0,0.0,0.0,16.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,66.7 +Marcos Antônio,br BRA,MF,Lazio,22-316,2000,13.0,3.0,359.0,1.0,0.0,0.0,0.0,2.0,0.0,0.25,0.0,0.25,0.25,0.25,0.9,0.9,0.22,0.22,4.0,1.0,0.0,100.0,0.25,1.0,1.0,0.88,0.1,0.1,215.0,239.0,90.0,3219.0,847.0,122.0,131.0,93.1,67.0,75.0,89.3,16.0,20.0,80.0,1.0,23.0,1.0,0.0,15.0,237.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,1.26,3.0,0.0,0.0,1.0,1.0,0.25,0.0,0.0,0.0,0.0,0.0,5.0,4.0,3.0,2.0,0.0,2.0,0.0,2.0,6.0,11.0,0.0,284.0,16.0,68.0,190.0,29.0,1.0,284.0,168.0,1.0,3.0,1.0,207.0,5.0,4.0,0.0,7.0,11.0,0.0,0.0,0.0,0.0,28.0,0.0,2.0,0.0 +Valentin Antov,bg BUL,DF,Monza,22-167,2000,7.0,2.0,289.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,183.0,210.0,87.1,3274.0,1423.0,76.0,84.0,90.5,84.0,92.0,91.3,18.0,28.0,64.3,1.0,12.0,2.0,0.0,14.0,194.0,16.0,4.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,12.0,0.0,1.0,2.0,0.62,2.0,0.0,0.0,0.0,1.0,0.31,1.0,0.0,0.0,0.0,0.0,6.0,5.0,3.0,3.0,0.0,5.0,4.0,1.0,2.0,10.0,1.0,241.0,27.0,111.0,112.0,18.0,0.0,241.0,128.0,2.0,2.0,0.0,158.0,3.0,2.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,22.0,5.0,10.0,33.3 +Marko Arnautović,at AUT,FW,Bologna,34-006,1989,16.0,14.0,1231.0,8.0,0.0,3.0,3.0,5.0,0.0,0.58,0.0,0.58,0.37,0.37,5.7,3.3,0.41,0.24,13.7,10.0,0.0,47.6,0.73,0.24,0.5,0.16,2.3,1.7,265.0,376.0,70.5,3760.0,658.0,149.0,193.0,77.2,85.0,110.0,77.3,11.0,21.0,52.4,19.0,13.0,12.0,1.0,28.0,338.0,32.0,0.0,2.0,0.0,9.0,0.0,0.0,0.0,0.0,3.0,6.0,12.0,35.0,2.55,28.0,0.0,2.0,3.0,4.0,0.29,2.0,0.0,0.0,1.0,0.0,8.0,5.0,2.0,4.0,2.0,9.0,1.0,8.0,2.0,8.0,0.0,497.0,12.0,31.0,262.0,209.0,46.0,494.0,282.0,11.0,10.0,6.0,394.0,32.0,16.0,0.0,20.0,14.0,10.0,0.0,0.0,0.0,30.0,9.0,10.0,47.4 +Tolgay Arslan,de GER,MF,Udinese,32-252,1990,29.0,11.0,982.0,1.0,0.0,0.0,0.0,2.0,0.0,0.09,0.0,0.09,0.09,0.09,1.6,1.6,0.15,0.15,10.9,5.0,1.0,21.7,0.46,0.04,0.2,0.07,-0.6,-0.6,383.0,482.0,79.5,7037.0,2127.0,173.0,201.0,86.1,136.0,161.0,84.5,59.0,85.0,69.4,12.0,38.0,17.0,3.0,64.0,441.0,40.0,24.0,0.0,12.0,24.0,11.0,5.0,1.0,0.0,5.0,1.0,11.0,36.0,3.3,22.0,5.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,14.0,11.0,11.0,1.0,13.0,0.0,13.0,7.0,9.0,1.0,614.0,12.0,113.0,313.0,194.0,23.0,614.0,373.0,37.0,25.0,10.0,402.0,22.0,13.0,0.0,14.0,25.0,0.0,0.0,0.0,0.0,58.0,5.0,3.0,62.5 +Arthur,br BRA,FW,Fiorentina,25-000,1998,26.0,13.0,1277.0,7.0,1.0,2.0,2.0,2.0,0.0,0.49,0.07,0.56,0.35,0.42,7.1,5.6,0.5,0.39,14.2,17.0,0.0,36.2,1.2,0.11,0.29,0.12,-0.1,-0.6,176.0,256.0,68.8,2277.0,412.0,113.0,148.0,76.4,39.0,49.0,79.6,9.0,13.0,69.2,13.0,3.0,7.0,0.0,21.0,238.0,16.0,0.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,3.0,2.0,12.0,34.0,2.39,26.0,0.0,4.0,3.0,2.0,0.14,1.0,0.0,1.0,0.0,0.0,8.0,2.0,1.0,3.0,4.0,10.0,1.0,9.0,1.0,11.0,0.0,453.0,13.0,34.0,158.0,265.0,93.0,451.0,252.0,10.0,12.0,8.0,339.0,55.0,22.0,0.0,24.0,17.0,1.0,0.0,0.0,0.0,35.0,47.0,46.0,50.5 +Santiago Ascacíbar,ar ARG,MF,Cremonese,26-059,1997,13.0,7.0,727.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,8.1,3.0,0.0,21.4,0.37,0.0,0.0,0.03,-0.4,-0.4,358.0,435.0,82.3,6100.0,2189.0,182.0,207.0,87.9,135.0,145.0,93.1,34.0,56.0,60.7,8.0,50.0,7.0,2.0,41.0,412.0,21.0,11.0,0.0,4.0,5.0,5.0,0.0,3.0,0.0,5.0,2.0,9.0,25.0,3.09,17.0,1.0,7.0,0.0,3.0,0.37,1.0,0.0,2.0,0.0,0.0,29.0,14.0,17.0,10.0,2.0,14.0,2.0,12.0,13.0,11.0,0.0,553.0,26.0,177.0,280.0,100.0,4.0,553.0,254.0,1.0,3.0,0.0,314.0,8.0,4.0,0.0,11.0,9.0,0.0,0.0,0.0,0.0,62.0,10.0,20.0,33.3 +Kristoffer Askildsen,no NOR,MF,Lecce,22-106,2001,19.0,6.0,633.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.06,0.06,7.0,3.0,1.0,33.3,0.43,0.0,0.0,0.05,-0.4,-0.4,146.0,235.0,62.1,2678.0,942.0,69.0,98.0,70.4,46.0,72.0,63.9,23.0,44.0,52.3,6.0,19.0,2.0,0.0,21.0,203.0,31.0,11.0,0.0,7.0,19.0,15.0,7.0,6.0,0.0,3.0,1.0,7.0,14.0,1.99,8.0,4.0,0.0,2.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,7.0,3.0,2.0,3.0,2.0,11.0,3.0,8.0,11.0,6.0,0.0,311.0,16.0,64.0,163.0,88.0,6.0,311.0,119.0,9.0,2.0,1.0,150.0,13.0,3.0,0.0,17.0,7.0,1.0,0.0,1.0,0.0,41.0,18.0,17.0,51.4 +Kristjan Asllani,al ALB,MF,Inter,21-047,2002,16.0,4.0,446.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.07,0.07,5.0,0.0,2.0,0.0,0.0,0.0,0.0,0.04,-0.4,-0.4,317.0,363.0,87.3,6172.0,1681.0,138.0,141.0,97.9,121.0,131.0,92.4,54.0,81.0,66.7,10.0,23.0,4.0,0.0,24.0,336.0,26.0,11.0,1.0,4.0,19.0,14.0,5.0,8.0,0.0,1.0,1.0,4.0,20.0,4.04,9.0,9.0,0.0,1.0,1.0,0.2,1.0,0.0,0.0,0.0,0.0,9.0,5.0,1.0,6.0,2.0,2.0,0.0,2.0,5.0,3.0,0.0,400.0,5.0,88.0,245.0,69.0,1.0,400.0,259.0,5.0,8.0,1.0,303.0,3.0,2.0,0.0,5.0,4.0,0.0,0.0,0.0,0.0,30.0,0.0,3.0,0.0 +Emil Audero,it ITA,GK,Sampdoria,26-097,1997,25.0,25.0,2250.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,617.0,919.0,67.1,19319.0,14234.0,91.0,91.0,100.0,246.0,248.0,99.2,279.0,572.0,48.8,0.0,16.0,0.0,0.0,0.0,628.0,286.0,86.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,5.0,0.2,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,982.0,811.0,968.0,17.0,0.0,0.0,982.0,548.0,0.0,0.0,0.0,414.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,41.0,6.0,0.0,100.0 +Tommaso Augello,it ITA,DF,Sampdoria,28-238,1994,30.0,28.0,2431.0,2.0,5.0,0.0,0.0,2.0,0.0,0.07,0.19,0.26,0.07,0.26,0.7,0.7,0.02,0.02,27.0,3.0,0.0,30.0,0.11,0.2,0.67,0.07,1.3,1.3,1045.0,1444.0,72.4,18034.0,7780.0,485.0,558.0,86.9,447.0,601.0,74.4,93.0,185.0,50.3,32.0,64.0,40.0,27.0,94.0,1140.0,303.0,37.0,0.0,5.0,126.0,7.0,0.0,7.0,0.0,259.0,1.0,58.0,56.0,2.07,45.0,6.0,1.0,1.0,7.0,0.26,6.0,1.0,0.0,0.0,0.0,15.0,12.0,11.0,2.0,2.0,20.0,7.0,13.0,10.0,46.0,1.0,1608.0,69.0,498.0,704.0,434.0,17.0,1608.0,861.0,70.0,35.0,5.0,937.0,15.0,14.0,0.0,13.0,20.0,1.0,0.0,1.0,0.0,143.0,33.0,23.0,58.9 +Kaan Ayhan,tr TUR,DF,Sassuolo,28-166,1994,10.0,5.0,545.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.1,1.0,0.0,100.0,0.17,0.0,0.0,0.01,0.0,0.0,288.0,332.0,86.7,6000.0,1702.0,84.0,87.0,96.6,154.0,167.0,92.2,48.0,72.0,66.7,0.0,20.0,0.0,0.0,15.0,311.0,20.0,15.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,5.0,0.83,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,4.0,6.0,4.0,0.0,5.0,4.0,1.0,5.0,26.0,1.0,385.0,54.0,198.0,182.0,6.0,0.0,385.0,235.0,8.0,5.0,0.0,264.0,0.0,1.0,0.0,4.0,8.0,0.0,0.0,0.0,0.0,28.0,2.0,7.0,22.2 +Jaime Báez,uy URU,DF,Cremonese,28-000,1995,2.0,1.0,96.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.12,0.12,1.1,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,32.0,48.0,66.7,640.0,379.0,8.0,10.0,80.0,13.0,17.0,76.5,7.0,14.0,50.0,2.0,7.0,2.0,2.0,4.0,44.0,4.0,2.0,0.0,0.0,9.0,2.0,0.0,1.0,0.0,0.0,0.0,2.0,5.0,4.69,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,62.0,3.0,10.0,37.0,16.0,2.0,62.0,34.0,5.0,3.0,0.0,34.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 +Nedim Bajrami,al ALB,"MF,FW",Empoli,24-056,1999,19.0,8.0,940.0,1.0,1.0,0.0,0.0,1.0,0.0,0.1,0.1,0.19,0.1,0.19,1.2,1.2,0.12,0.12,10.4,7.0,6.0,33.3,0.67,0.05,0.14,0.06,-0.2,-0.2,260.0,348.0,74.7,3968.0,1323.0,149.0,182.0,81.9,79.0,96.0,82.3,20.0,43.0,46.5,15.0,19.0,6.0,0.0,29.0,305.0,43.0,9.0,0.0,4.0,35.0,18.0,10.0,5.0,0.0,8.0,0.0,8.0,39.0,3.74,27.0,8.0,1.0,1.0,4.0,0.38,2.0,0.0,0.0,1.0,0.0,8.0,4.0,3.0,3.0,2.0,5.0,0.0,5.0,7.0,4.0,0.0,457.0,1.0,66.0,185.0,219.0,27.0,457.0,267.0,22.0,21.0,10.0,304.0,24.0,17.0,0.0,6.0,6.0,3.0,0.0,0.0,0.0,49.0,2.0,7.0,22.2 +Nedim Bajrami,al ALB,"FW,MF",Sassuolo,24-056,1999,11.0,4.0,507.0,1.0,0.0,0.0,0.0,0.0,0.0,0.18,0.0,0.18,0.18,0.18,1.6,1.6,0.28,0.28,5.6,6.0,3.0,40.0,1.07,0.07,0.17,0.1,-0.6,-0.6,142.0,220.0,64.5,2074.0,527.0,85.0,103.0,82.5,46.0,67.0,68.7,8.0,31.0,25.8,8.0,9.0,5.0,0.0,25.0,191.0,29.0,5.0,0.0,0.0,38.0,23.0,8.0,11.0,0.0,1.0,0.0,11.0,15.0,2.66,7.0,5.0,1.0,0.0,1.0,0.18,0.0,0.0,1.0,0.0,0.0,5.0,2.0,3.0,2.0,0.0,6.0,1.0,5.0,6.0,5.0,0.0,285.0,7.0,46.0,100.0,143.0,20.0,285.0,175.0,14.0,10.0,9.0,179.0,13.0,6.0,0.0,4.0,4.0,1.0,0.0,0.0,0.0,31.0,2.0,3.0,40.0 +Tiemoué Bakayoko,fr FRA,MF,Milan,28-251,1994,3.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,20.0,85.0,305.0,99.0,4.0,5.0,80.0,12.0,13.0,92.3,1.0,1.0,100.0,0.0,3.0,0.0,0.0,2.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,30.0,3.0,16.0,12.0,2.0,0.0,30.0,16.0,0.0,1.0,0.0,15.0,1.0,1.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,6.0,1.0,1.0,50.0 +Tommaso Baldanzi,it ITA,MF,Empoli,20-033,2003,22.0,20.0,1486.0,4.0,0.0,0.0,0.0,1.0,0.0,0.24,0.0,0.24,0.24,0.24,2.1,2.1,0.13,0.13,16.5,12.0,0.0,44.4,0.73,0.15,0.33,0.08,1.9,1.9,472.0,569.0,83.0,6648.0,1599.0,268.0,292.0,91.8,155.0,178.0,87.1,19.0,39.0,48.7,18.0,35.0,16.0,1.0,63.0,520.0,47.0,6.0,2.0,4.0,18.0,6.0,1.0,0.0,0.0,3.0,2.0,17.0,50.0,3.02,32.0,2.0,4.0,7.0,4.0,0.24,4.0,0.0,0.0,0.0,0.0,19.0,9.0,8.0,10.0,1.0,17.0,1.0,16.0,2.0,4.0,0.0,729.0,11.0,99.0,353.0,285.0,35.0,729.0,471.0,47.0,48.0,9.0,525.0,28.0,13.0,0.0,11.0,29.0,0.0,0.0,0.0,0.0,55.0,3.0,8.0,27.3 +Fodé Ballo-Touré,sn SEN,DF,Milan,26-112,1997,5.0,4.0,376.0,1.0,0.0,0.0,0.0,0.0,0.0,0.24,0.0,0.24,0.24,0.24,0.6,0.6,0.14,0.14,4.2,3.0,0.0,75.0,0.72,0.25,0.33,0.15,0.4,0.4,177.0,212.0,83.5,3014.0,1005.0,82.0,86.0,95.3,82.0,95.0,86.3,12.0,25.0,48.0,3.0,10.0,7.0,5.0,18.0,164.0,48.0,5.0,0.0,1.0,16.0,0.0,0.0,0.0,0.0,43.0,0.0,2.0,6.0,1.44,3.0,1.0,0.0,1.0,1.0,0.24,0.0,0.0,0.0,0.0,0.0,11.0,6.0,6.0,5.0,0.0,10.0,3.0,7.0,1.0,3.0,0.0,268.0,6.0,61.0,124.0,86.0,8.0,268.0,113.0,11.0,6.0,0.0,139.0,5.0,4.0,0.0,5.0,7.0,0.0,0.0,0.0,0.0,26.0,7.0,4.0,63.6 +Lameck Banda,zm ZAM,"FW,MF",Lecce,22-086,2001,30.0,15.0,1307.0,1.0,1.0,0.0,0.0,4.0,0.0,0.07,0.07,0.14,0.07,0.14,2.4,2.4,0.17,0.17,14.5,8.0,0.0,29.6,0.55,0.04,0.13,0.09,-1.4,-1.4,217.0,334.0,65.0,2948.0,735.0,131.0,169.0,77.5,64.0,98.0,65.3,12.0,27.0,44.4,10.0,16.0,7.0,1.0,21.0,319.0,14.0,1.0,0.0,1.0,38.0,10.0,5.0,1.0,0.0,3.0,1.0,18.0,29.0,2.0,14.0,2.0,1.0,3.0,3.0,0.21,3.0,0.0,0.0,0.0,0.0,25.0,15.0,11.0,11.0,3.0,21.0,2.0,19.0,3.0,3.0,0.0,575.0,9.0,61.0,215.0,316.0,53.0,575.0,391.0,61.0,43.0,26.0,402.0,55.0,29.0,0.0,20.0,29.0,9.0,0.0,0.0,0.0,64.0,5.0,27.0,15.6 +Filippo Bandinelli,it ITA,MF,Empoli,28-027,1995,30.0,25.0,2031.0,2.0,0.0,0.0,0.0,7.0,0.0,0.09,0.0,0.09,0.09,0.09,1.6,1.6,0.07,0.07,22.6,6.0,0.0,27.3,0.27,0.09,0.33,0.07,0.4,0.4,696.0,940.0,74.0,11264.0,3572.0,346.0,398.0,86.9,269.0,335.0,80.3,52.0,117.0,44.4,30.0,57.0,36.0,21.0,93.0,872.0,62.0,10.0,3.0,5.0,85.0,9.0,3.0,5.0,0.0,41.0,6.0,39.0,57.0,2.52,48.0,2.0,2.0,3.0,2.0,0.09,2.0,0.0,0.0,0.0,0.0,48.0,29.0,17.0,23.0,8.0,18.0,6.0,12.0,12.0,35.0,0.0,1201.0,56.0,271.0,589.0,358.0,27.0,1201.0,664.0,37.0,38.0,6.0,783.0,40.0,26.0,0.0,45.0,35.0,0.0,0.0,0.0,0.0,118.0,9.0,16.0,36.0 +Antonín Barák,cz CZE,MF,Hellas Verona,28-143,1994,1.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,14.0,78.6,140.0,24.0,7.0,10.0,70.0,3.0,3.0,100.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,2.0,13.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.0,10.0,6.0,0.0,16.0,12.0,0.0,0.0,0.0,11.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Antonín Barák,cz CZE,MF,Fiorentina,28-143,1994,26.0,17.0,1475.0,2.0,0.0,0.0,0.0,4.0,0.0,0.12,0.0,0.12,0.12,0.12,2.1,2.1,0.13,0.13,16.4,7.0,0.0,24.1,0.43,0.07,0.29,0.07,-0.1,-0.1,503.0,615.0,81.8,7635.0,1763.0,277.0,308.0,89.9,171.0,210.0,81.4,34.0,47.0,72.3,20.0,56.0,11.0,2.0,72.0,592.0,21.0,6.0,4.0,5.0,14.0,0.0,0.0,0.0,0.0,6.0,2.0,16.0,38.0,2.32,32.0,0.0,2.0,2.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,20.0,9.0,5.0,6.0,9.0,13.0,1.0,12.0,3.0,10.0,0.0,774.0,12.0,73.0,397.0,314.0,61.0,774.0,484.0,39.0,25.0,8.0,558.0,30.0,25.0,0.0,11.0,13.0,2.0,0.0,1.0,0.0,70.0,15.0,26.0,36.6 +Andrea Barberis,it ITA,MF,Monza,29-135,1993,9.0,3.0,338.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.27,0.27,0.0,0.27,0.1,0.1,0.03,0.03,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,140.0,166.0,84.3,2307.0,639.0,67.0,75.0,89.3,63.0,74.0,85.1,8.0,11.0,72.7,4.0,16.0,1.0,1.0,14.0,157.0,9.0,6.0,0.0,0.0,4.0,2.0,0.0,2.0,0.0,1.0,0.0,2.0,10.0,2.67,9.0,1.0,0.0,0.0,2.0,0.53,1.0,1.0,0.0,0.0,0.0,7.0,2.0,4.0,3.0,0.0,0.0,0.0,0.0,8.0,1.0,0.0,194.0,6.0,38.0,126.0,31.0,2.0,194.0,100.0,3.0,7.0,1.0,137.0,3.0,6.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,16.0,2.0,2.0,50.0 +Tommaso Barbieri,it ITA,DF,Juventus,20-242,2002,1.0,1.0,56.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,31.0,80.6,422.0,122.0,13.0,15.0,86.7,9.0,9.0,100.0,2.0,4.0,50.0,0.0,0.0,0.0,0.0,0.0,26.0,4.0,1.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,2.0,0.0,2.0,0.0,2.0,0.0,42.0,1.0,16.0,15.0,11.0,2.0,42.0,23.0,2.0,1.0,0.0,30.0,4.0,0.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 +Francesco Bardi,it ITA,GK,Bologna,31-097,1992,1.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,23.0,78.3,548.0,399.0,6.0,6.0,100.0,5.0,5.0,100.0,7.0,11.0,63.6,0.0,1.0,0.0,0.0,0.0,12.0,10.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,23.0,25.0,0.0,0.0,0.0,25.0,10.0,0.0,0.0,0.0,7.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Nicolò Barella,it ITA,MF,Inter,26-077,1997,30.0,27.0,2269.0,5.0,6.0,0.0,0.0,6.0,0.0,0.2,0.24,0.44,0.2,0.44,2.8,2.8,0.11,0.11,25.2,11.0,1.0,39.3,0.44,0.18,0.45,0.1,2.2,2.2,1147.0,1417.0,80.9,18718.0,5757.0,601.0,680.0,88.4,426.0,501.0,85.0,90.0,160.0,56.3,47.0,135.0,47.0,14.0,192.0,1385.0,26.0,11.0,6.0,15.0,81.0,5.0,0.0,2.0,0.0,8.0,6.0,25.0,109.0,4.33,93.0,3.0,2.0,5.0,13.0,0.52,11.0,0.0,0.0,0.0,0.0,39.0,20.0,14.0,20.0,5.0,24.0,4.0,20.0,9.0,10.0,0.0,1667.0,32.0,242.0,851.0,600.0,54.0,1667.0,1118.0,69.0,62.0,9.0,1262.0,47.0,33.0,0.0,28.0,39.0,9.0,0.0,0.0,0.0,153.0,19.0,11.0,63.3 +Enzo Barrenechea,ar ARG,MF,Juventus,21-338,2001,3.0,3.0,158.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.04,0.04,1.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,98.0,115.0,85.2,1544.0,361.0,49.0,52.0,94.2,42.0,45.0,93.3,6.0,11.0,54.5,1.0,6.0,0.0,0.0,5.0,111.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,1.0,0.57,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,3.0,3.0,4.0,1.0,3.0,0.0,3.0,2.0,3.0,0.0,138.0,3.0,36.0,90.0,13.0,2.0,138.0,79.0,1.0,1.0,0.0,93.0,1.0,1.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,9.0,1.0,1.0,50.0 +Musa Barrow,gm GAM,"FW,MF",Bologna,24-162,1998,25.0,19.0,1428.0,3.0,5.0,0.0,0.0,3.0,0.0,0.19,0.32,0.5,0.19,0.5,2.4,2.4,0.15,0.15,15.9,12.0,1.0,23.5,0.76,0.06,0.25,0.05,0.6,0.6,404.0,579.0,69.8,6876.0,1662.0,214.0,245.0,87.3,124.0,172.0,72.1,49.0,107.0,45.8,32.0,31.0,17.0,8.0,38.0,503.0,68.0,14.0,2.0,14.0,76.0,37.0,19.0,16.0,0.0,8.0,8.0,16.0,49.0,3.09,29.0,12.0,2.0,3.0,6.0,0.38,5.0,1.0,0.0,0.0,0.0,16.0,3.0,8.0,5.0,3.0,18.0,1.0,17.0,1.0,5.0,0.0,769.0,2.0,81.0,342.0,358.0,34.0,769.0,472.0,41.0,41.0,13.0,573.0,33.0,24.0,0.0,16.0,15.0,3.0,0.0,0.0,0.0,62.0,7.0,25.0,21.9 +Federico Baschirotto,it ITA,DF,Lecce,26-217,1996,30.0,30.0,2700.0,3.0,0.0,0.0,0.0,5.0,0.0,0.1,0.0,0.1,0.1,0.1,1.7,1.7,0.06,0.06,30.0,3.0,0.0,18.8,0.1,0.19,1.0,0.11,1.3,1.3,863.0,1180.0,73.1,18755.0,7171.0,223.0,264.0,84.5,483.0,583.0,82.8,147.0,296.0,49.7,7.0,74.0,3.0,1.0,87.0,1087.0,86.0,59.0,1.0,18.0,9.0,0.0,0.0,0.0,0.0,21.0,7.0,9.0,14.0,0.47,12.0,0.0,1.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,34.0,24.0,30.0,4.0,0.0,38.0,23.0,15.0,34.0,159.0,0.0,1509.0,202.0,823.0,627.0,69.0,29.0,1509.0,701.0,9.0,3.0,0.0,760.0,7.0,2.0,0.0,27.0,10.0,0.0,0.0,0.0,0.0,171.0,76.0,55.0,58.0 +Toma Bašić,hr CRO,MF,Lazio,26-151,1996,20.0,4.0,449.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.1,0.1,5.0,4.0,0.0,57.1,0.8,0.0,0.0,0.07,-0.5,-0.5,191.0,261.0,73.2,2807.0,542.0,89.0,109.0,81.7,83.0,97.0,85.6,6.0,26.0,23.1,5.0,12.0,4.0,2.0,18.0,241.0,20.0,6.0,2.0,0.0,19.0,7.0,3.0,4.0,0.0,0.0,0.0,12.0,7.0,1.4,4.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,3.0,1.0,1.0,9.0,0.0,9.0,7.0,8.0,0.0,325.0,10.0,69.0,163.0,95.0,12.0,325.0,199.0,13.0,7.0,1.0,220.0,11.0,6.0,0.0,9.0,14.0,0.0,0.0,0.0,0.0,27.0,10.0,6.0,62.5 +Alberto Basso,it ITA,MF,Cremonese,18-300,2004,1.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,1.0,0.0,100.0,5.63,0.0,0.0,0.04,0.0,0.0,2.0,2.0,100.0,41.0,5.0,1.0,1.0,100.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,3.0,1.0,3.0,2.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,100.0 +Alessandro Bastoni,it ITA,DF,Inter,24-012,1999,23.0,21.0,1779.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.1,0.1,0.0,0.1,1.0,1.0,0.05,0.05,19.8,3.0,0.0,25.0,0.15,0.0,0.0,0.09,-1.0,-1.0,1147.0,1387.0,82.7,23246.0,8338.0,393.0,436.0,90.1,568.0,631.0,90.0,173.0,270.0,64.1,13.0,114.0,24.0,9.0,129.0,1256.0,123.0,42.0,2.0,20.0,44.0,0.0,0.0,0.0,0.0,70.0,8.0,22.0,46.0,2.32,37.0,4.0,2.0,2.0,5.0,0.25,3.0,1.0,0.0,1.0,0.0,37.0,18.0,18.0,12.0,7.0,21.0,9.0,12.0,20.0,35.0,0.0,1539.0,136.0,614.0,698.0,236.0,28.0,1539.0,942.0,42.0,29.0,3.0,1081.0,11.0,6.0,0.0,16.0,14.0,1.0,0.0,0.0,0.0,95.0,36.0,19.0,65.5 +Simone Bastoni,it ITA,"MF,DF",Spezia,26-171,1996,16.0,13.0,1065.0,2.0,3.0,0.0,0.0,6.0,0.0,0.17,0.25,0.42,0.17,0.42,1.1,1.1,0.09,0.09,11.8,6.0,2.0,28.6,0.51,0.1,0.33,0.05,0.9,0.9,320.0,527.0,60.7,5322.0,1995.0,163.0,190.0,85.8,109.0,182.0,59.9,36.0,99.0,36.4,16.0,29.0,9.0,6.0,28.0,420.0,106.0,22.0,2.0,4.0,96.0,46.0,26.0,15.0,0.0,38.0,1.0,34.0,28.0,2.36,17.0,8.0,2.0,0.0,3.0,0.25,3.0,0.0,0.0,0.0,0.0,42.0,22.0,20.0,17.0,5.0,17.0,5.0,12.0,8.0,12.0,0.0,666.0,29.0,164.0,267.0,243.0,23.0,666.0,303.0,14.0,14.0,2.0,350.0,18.0,12.0,0.0,27.0,11.0,2.0,0.0,0.0,0.0,77.0,11.0,9.0,55.0 +Brian Bayeye,fr FRA,DF,Torino,22-299,2000,1.0,1.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,46.0,63.0,472.0,262.0,14.0,16.0,87.5,11.0,16.0,68.8,3.0,4.0,75.0,0.0,2.0,0.0,0.0,3.0,35.0,11.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,11.0,0.0,5.0,2.0,3.33,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,1.0,1.0,0.0,1.0,2.0,0.0,59.0,6.0,28.0,21.0,10.0,3.0,59.0,21.0,3.0,2.0,2.0,26.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,9.0,0.0,1.0,0.0 +Rodrigo Becão,br BRA,DF,Udinese,27-096,1996,24.0,24.0,2125.0,2.0,1.0,0.0,0.0,10.0,0.0,0.08,0.04,0.13,0.08,0.13,1.3,1.3,0.05,0.05,23.6,4.0,0.0,25.0,0.17,0.13,0.5,0.08,0.7,0.7,921.0,1189.0,77.5,17518.0,7286.0,356.0,402.0,88.6,443.0,531.0,83.4,110.0,204.0,53.9,10.0,70.0,4.0,2.0,85.0,995.0,193.0,61.0,1.0,8.0,16.0,0.0,0.0,0.0,0.0,132.0,1.0,28.0,26.0,1.1,17.0,4.0,1.0,2.0,1.0,0.04,1.0,0.0,0.0,0.0,0.0,65.0,32.0,48.0,14.0,3.0,39.0,21.0,18.0,18.0,58.0,1.0,1423.0,115.0,677.0,600.0,155.0,39.0,1423.0,706.0,14.0,12.0,1.0,785.0,16.0,2.0,0.0,32.0,27.0,6.0,0.0,0.0,1.0,135.0,56.0,26.0,68.3 +Julius Beck,dk DEN,MF,Spezia,17-363,2005,1.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.0,100.0,116.0,29.0,0.0,0.0,0.0,4.0,4.0,100.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,2.0,5.0,1.0,0.0,8.0,5.0,0.0,0.0,0.0,6.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 +Raoul Bellanova,it ITA,DF,Inter,22-343,2000,13.0,1.0,263.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.21,0.21,2.9,1.0,0.0,33.3,0.34,0.0,0.0,0.2,-0.6,-0.6,162.0,208.0,77.9,2548.0,543.0,89.0,97.0,91.8,53.0,73.0,72.6,12.0,23.0,52.2,2.0,5.0,5.0,4.0,8.0,186.0,22.0,0.0,0.0,3.0,27.0,0.0,0.0,0.0,0.0,22.0,0.0,6.0,6.0,2.04,3.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,5.0,6.0,1.0,1.0,4.0,1.0,3.0,3.0,10.0,0.0,251.0,14.0,64.0,82.0,107.0,10.0,251.0,162.0,20.0,7.0,1.0,165.0,2.0,2.0,0.0,0.0,7.0,2.0,0.0,0.0,0.0,14.0,3.0,2.0,60.0 +Andrea Belotti,it ITA,FW,Roma,29-126,1993,25.0,5.0,730.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.25,0.25,0.0,0.25,2.8,2.0,0.35,0.25,8.1,7.0,0.0,53.8,0.86,0.0,0.0,0.15,-2.8,-2.0,113.0,169.0,66.9,1448.0,293.0,68.0,85.0,80.0,26.0,39.0,66.7,5.0,9.0,55.6,12.0,6.0,4.0,0.0,8.0,158.0,8.0,1.0,1.0,1.0,4.0,0.0,0.0,0.0,0.0,2.0,3.0,9.0,16.0,1.98,13.0,0.0,2.0,1.0,2.0,0.25,2.0,0.0,0.0,0.0,0.0,9.0,5.0,3.0,2.0,4.0,8.0,1.0,7.0,3.0,2.0,0.0,274.0,3.0,25.0,112.0,140.0,35.0,273.0,159.0,15.0,9.0,7.0,213.0,25.0,10.0,0.0,16.0,24.0,6.0,0.0,0.0,0.0,21.0,20.0,44.0,31.3 +Marco Benassi,it ITA,DF,Fiorentina,28-229,1994,2.0,1.0,93.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,91.0,105.0,86.7,2107.0,426.0,32.0,32.0,100.0,33.0,41.0,80.5,26.0,31.0,83.9,1.0,11.0,1.0,0.0,9.0,94.0,11.0,5.0,0.0,4.0,7.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,1.0,0.97,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,112.0,1.0,25.0,64.0,24.0,3.0,112.0,81.0,0.0,4.0,0.0,85.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,10.0,3.0,2.0,60.0 +Marco Benassi,it ITA,MF,Cremonese,28-229,1994,12.0,9.0,851.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.1,1.1,0.11,0.11,9.5,8.0,0.0,50.0,0.85,0.0,0.0,0.07,-1.1,-1.1,226.0,311.0,72.7,3921.0,1017.0,107.0,127.0,84.3,96.0,120.0,80.0,19.0,42.0,45.2,7.0,15.0,4.0,2.0,26.0,270.0,39.0,5.0,1.0,2.0,24.0,13.0,3.0,8.0,0.0,4.0,2.0,13.0,16.0,1.69,8.0,6.0,0.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,22.0,12.0,8.0,8.0,6.0,11.0,0.0,11.0,10.0,10.0,0.0,403.0,13.0,82.0,195.0,129.0,15.0,403.0,201.0,10.0,13.0,0.0,203.0,11.0,8.0,0.0,10.0,6.0,0.0,0.0,0.0,0.0,64.0,5.0,11.0,31.3 +Ismaël Bennacer,dz ALG,MF,Milan,25-145,1997,25.0,21.0,1800.0,1.0,2.0,0.0,0.0,5.0,0.0,0.05,0.1,0.15,0.05,0.15,0.7,0.7,0.03,0.03,20.0,2.0,3.0,16.7,0.1,0.08,0.5,0.06,0.3,0.3,1114.0,1294.0,86.1,19738.0,5493.0,500.0,545.0,91.7,424.0,471.0,90.0,146.0,214.0,68.2,42.0,113.0,27.0,4.0,124.0,1188.0,104.0,52.0,3.0,11.0,67.0,45.0,19.0,22.0,0.0,5.0,2.0,15.0,83.0,4.15,56.0,18.0,3.0,4.0,3.0,0.15,1.0,1.0,1.0,0.0,0.0,56.0,30.0,25.0,29.0,2.0,21.0,7.0,14.0,24.0,15.0,1.0,1513.0,29.0,284.0,934.0,319.0,8.0,1513.0,878.0,27.0,26.0,4.0,977.0,36.0,19.0,0.0,34.0,23.0,1.0,0.0,0.0,0.0,176.0,20.0,21.0,48.8 +Domenico Berardi,it ITA,FW,Sassuolo,28-267,1994,19.0,16.0,1317.0,6.0,6.0,4.0,5.0,5.0,0.0,0.41,0.41,0.82,0.14,0.55,7.4,3.5,0.51,0.24,14.6,13.0,4.0,22.8,0.89,0.04,0.15,0.06,-1.4,-1.5,423.0,630.0,67.1,8139.0,2277.0,184.0,237.0,77.6,161.0,228.0,70.6,65.0,112.0,58.0,26.0,24.0,34.0,4.0,81.0,583.0,46.0,10.0,5.0,25.0,52.0,33.0,20.0,5.0,0.0,3.0,1.0,24.0,70.0,4.79,49.0,8.0,5.0,3.0,13.0,0.89,7.0,1.0,3.0,1.0,0.0,18.0,12.0,7.0,6.0,5.0,9.0,0.0,9.0,13.0,14.0,0.0,820.0,13.0,80.0,327.0,426.0,68.0,815.0,526.0,34.0,20.0,17.0,624.0,46.0,19.0,0.0,22.0,29.0,5.0,1.0,0.0,0.0,65.0,9.0,16.0,36.0 +Bartosz Bereszyński,pl POL,DF,Sampdoria,30-287,1992,15.0,15.0,1198.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,13.3,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,471.0,644.0,73.1,6989.0,3219.0,286.0,316.0,90.5,143.0,210.0,68.1,28.0,73.0,38.4,3.0,32.0,10.0,3.0,44.0,480.0,161.0,28.0,0.0,1.0,31.0,0.0,0.0,0.0,0.0,133.0,3.0,19.0,8.0,0.6,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28.0,18.0,15.0,11.0,2.0,18.0,4.0,14.0,21.0,25.0,0.0,759.0,48.0,255.0,317.0,198.0,13.0,759.0,329.0,20.0,16.0,1.0,354.0,12.0,5.0,0.0,13.0,10.0,0.0,0.0,0.0,0.0,68.0,18.0,11.0,62.1 +Beto,gw GNB,FW,Udinese,25-084,1998,30.0,21.0,1938.0,10.0,1.0,1.0,1.0,2.0,0.0,0.46,0.05,0.51,0.42,0.46,9.9,9.2,0.46,0.42,21.5,19.0,0.0,34.5,0.88,0.16,0.47,0.17,0.1,-0.2,174.0,278.0,62.6,2227.0,269.0,117.0,166.0,70.5,38.0,63.0,60.3,6.0,12.0,50.0,20.0,7.0,4.0,0.0,12.0,268.0,9.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,1.0,10.0,45.0,2.09,28.0,0.0,4.0,5.0,4.0,0.19,3.0,0.0,0.0,0.0,0.0,16.0,9.0,0.0,9.0,7.0,16.0,2.0,14.0,4.0,25.0,0.0,603.0,28.0,40.0,250.0,319.0,124.0,602.0,338.0,23.0,13.0,14.0,444.0,107.0,41.0,0.0,33.0,25.0,8.0,0.0,0.0,0.0,27.0,65.0,59.0,52.4 +Matteo Bianchetti,it ITA,DF,Cremonese,30-039,1993,23.0,21.0,1963.0,1.0,0.0,0.0,0.0,5.0,0.0,0.05,0.0,0.05,0.05,0.05,0.8,0.8,0.03,0.03,21.8,4.0,0.0,57.1,0.18,0.14,0.25,0.11,0.2,0.2,660.0,852.0,77.5,12977.0,5501.0,222.0,260.0,85.4,362.0,414.0,87.4,69.0,151.0,45.7,4.0,42.0,6.0,1.0,43.0,789.0,59.0,42.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,1.0,4.0,8.0,13.0,0.6,8.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32.0,19.0,21.0,9.0,2.0,29.0,17.0,12.0,17.0,106.0,0.0,1075.0,178.0,631.0,415.0,35.0,13.0,1075.0,506.0,6.0,2.0,0.0,524.0,6.0,2.0,0.0,18.0,8.0,0.0,0.0,1.0,0.0,127.0,60.0,38.0,61.2 +Alessandro Bianco,it ITA,MF,Fiorentina,20-206,2002,4.0,2.0,113.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,1.3,1.0,0.0,50.0,0.8,0.0,0.0,0.03,-0.1,-0.1,47.0,57.0,82.5,851.0,126.0,20.0,24.0,83.3,21.0,27.0,77.8,5.0,5.0,100.0,1.0,10.0,1.0,0.0,7.0,55.0,2.0,2.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.39,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,2.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,67.0,1.0,11.0,44.0,12.0,1.0,67.0,35.0,3.0,0.0,0.0,49.0,2.0,4.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,6.0,1.0,1.0,50.0 +Jaka Bijol,si SVN,DF,Udinese,24-079,1999,26.0,26.0,2193.0,3.0,2.0,0.0,0.0,7.0,0.0,0.12,0.08,0.21,0.12,0.21,2.0,2.0,0.08,0.08,24.4,5.0,0.0,31.3,0.21,0.19,0.6,0.13,1.0,1.0,901.0,1064.0,84.7,19280.0,7990.0,240.0,276.0,87.0,523.0,562.0,93.1,128.0,200.0,64.0,8.0,43.0,4.0,1.0,49.0,1032.0,27.0,21.0,3.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,19.0,0.78,16.0,0.0,3.0,0.0,3.0,0.12,3.0,0.0,0.0,0.0,0.0,27.0,16.0,22.0,5.0,0.0,35.0,20.0,15.0,25.0,142.0,0.0,1348.0,232.0,818.0,490.0,44.0,31.0,1348.0,738.0,2.0,6.0,2.0,778.0,7.0,2.0,0.0,20.0,4.0,0.0,0.0,1.0,0.0,111.0,71.0,40.0,64.0 +Cristiano Biraghi,it ITA,DF,Fiorentina,30-236,1992,29.0,26.0,2271.0,1.0,3.0,0.0,1.0,0.0,0.0,0.04,0.12,0.16,0.04,0.16,1.6,0.8,0.06,0.03,25.2,3.0,11.0,15.8,0.12,0.05,0.33,0.04,-0.6,0.2,1270.0,1783.0,71.2,24244.0,9379.0,537.0,607.0,88.5,537.0,689.0,77.9,178.0,415.0,42.9,73.0,101.0,31.0,20.0,140.0,1276.0,502.0,84.0,0.0,25.0,272.0,147.0,66.0,58.0,0.0,271.0,5.0,35.0,118.0,4.68,51.0,64.0,2.0,0.0,5.0,0.2,2.0,3.0,0.0,0.0,0.0,58.0,33.0,27.0,27.0,4.0,27.0,7.0,20.0,14.0,40.0,1.0,1975.0,67.0,486.0,787.0,719.0,22.0,1974.0,889.0,58.0,43.0,7.0,1064.0,14.0,6.0,0.0,25.0,35.0,1.0,0.0,0.0,1.0,128.0,17.0,22.0,43.6 +Samuele Birindelli,it ITA,DF,Monza,23-280,1999,24.0,13.0,1143.0,0.0,1.0,0.0,0.0,6.0,0.0,0.0,0.08,0.08,0.0,0.08,1.1,1.1,0.09,0.09,12.7,4.0,0.0,30.8,0.31,0.0,0.0,0.08,-1.1,-1.1,441.0,612.0,72.1,7538.0,2203.0,205.0,245.0,83.7,191.0,252.0,75.8,36.0,85.0,42.4,15.0,24.0,14.0,9.0,33.0,515.0,97.0,4.0,0.0,5.0,60.0,0.0,0.0,0.0,0.0,93.0,0.0,13.0,29.0,2.29,24.0,0.0,2.0,1.0,4.0,0.32,4.0,0.0,0.0,0.0,0.0,25.0,13.0,15.0,8.0,2.0,22.0,7.0,15.0,14.0,24.0,0.0,755.0,40.0,211.0,297.0,258.0,27.0,755.0,390.0,48.0,30.0,7.0,466.0,20.0,10.0,0.0,12.0,10.0,2.0,0.0,0.0,0.0,59.0,5.0,9.0,35.7 +Kristijan Bistrović,hr CRO,MF,Lecce,25-016,1998,11.0,5.0,461.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.07,0.07,5.1,1.0,4.0,12.5,0.2,0.0,0.0,0.04,-0.3,-0.3,101.0,140.0,72.1,1867.0,682.0,45.0,52.0,86.5,35.0,48.0,72.9,16.0,25.0,64.0,6.0,10.0,2.0,1.0,16.0,119.0,21.0,11.0,0.0,3.0,21.0,7.0,3.0,3.0,0.0,0.0,0.0,6.0,10.0,1.95,3.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,3.0,0.0,3.0,0.0,4.0,0.0,168.0,5.0,29.0,84.0,55.0,3.0,168.0,83.0,4.0,1.0,0.0,99.0,3.0,5.0,0.0,2.0,4.0,1.0,0.0,0.0,0.0,15.0,2.0,5.0,28.6 +Alexis Blin,fr FRA,MF,Lecce,26-221,1996,28.0,20.0,1835.0,1.0,0.0,0.0,0.0,5.0,0.0,0.05,0.0,0.05,0.05,0.05,1.8,1.8,0.09,0.09,20.4,3.0,0.0,21.4,0.15,0.07,0.33,0.13,-0.8,-0.8,612.0,764.0,80.1,9805.0,3037.0,308.0,362.0,85.1,225.0,274.0,82.1,48.0,76.0,63.2,9.0,74.0,11.0,2.0,79.0,718.0,45.0,13.0,1.0,9.0,23.0,2.0,1.0,0.0,0.0,12.0,1.0,10.0,27.0,1.32,21.0,1.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,57.0,30.0,22.0,26.0,9.0,45.0,13.0,32.0,28.0,68.0,0.0,1043.0,77.0,306.0,547.0,193.0,22.0,1043.0,428.0,13.0,11.0,1.0,534.0,26.0,13.0,0.0,34.0,22.0,1.0,0.0,0.0,0.0,96.0,37.0,58.0,38.9 +Jeremie Boga,ci CIV,"MF,FW",Atalanta,26-112,1997,20.0,5.0,704.0,2.0,5.0,0.0,0.0,0.0,0.0,0.26,0.64,0.89,0.26,0.89,1.9,1.9,0.24,0.24,7.8,3.0,2.0,23.1,0.38,0.15,0.67,0.14,0.1,0.1,350.0,439.0,79.7,5486.0,1439.0,199.0,231.0,86.1,94.0,111.0,84.7,37.0,61.0,60.7,32.0,22.0,22.0,8.0,48.0,395.0,43.0,8.0,3.0,3.0,52.0,29.0,14.0,10.0,0.0,6.0,1.0,8.0,55.0,7.05,31.0,13.0,1.0,2.0,8.0,1.03,7.0,0.0,0.0,0.0,1.0,8.0,4.0,2.0,2.0,4.0,6.0,0.0,6.0,1.0,1.0,0.0,556.0,1.0,39.0,213.0,317.0,33.0,556.0,381.0,57.0,44.0,18.0,448.0,28.0,9.0,0.0,4.0,14.0,1.0,0.0,0.0,0.0,32.0,5.0,4.0,55.6 +Emil Bohinen,no NOR,MF,Salernitana,24-044,1999,17.0,6.0,577.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,6.4,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,278.0,329.0,84.5,3904.0,1037.0,165.0,185.0,89.2,87.0,103.0,84.5,12.0,17.0,70.6,7.0,16.0,4.0,1.0,22.0,316.0,13.0,13.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,8.0,1.25,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0,10.0,11.0,6.0,2.0,14.0,5.0,9.0,11.0,15.0,0.0,413.0,25.0,122.0,228.0,70.0,4.0,413.0,221.0,7.0,9.0,0.0,245.0,7.0,6.0,0.0,9.0,10.0,1.0,0.0,0.0,0.0,54.0,5.0,11.0,31.3 +Giacomo Bonaventura,it ITA,MF,Fiorentina,33-246,1989,26.0,20.0,1762.0,4.0,0.0,0.0,0.0,4.0,0.0,0.2,0.0,0.2,0.2,0.2,3.9,3.9,0.2,0.2,19.6,13.0,2.0,27.1,0.66,0.08,0.31,0.08,0.1,0.1,689.0,837.0,82.3,10753.0,2457.0,360.0,404.0,89.1,246.0,284.0,86.6,50.0,83.0,60.2,29.0,49.0,28.0,4.0,90.0,795.0,41.0,23.0,4.0,4.0,44.0,5.0,0.0,3.0,0.0,4.0,1.0,22.0,70.0,3.58,46.0,5.0,7.0,8.0,5.0,0.26,4.0,0.0,0.0,1.0,0.0,33.0,21.0,14.0,15.0,4.0,14.0,0.0,14.0,15.0,4.0,1.0,1046.0,13.0,129.0,519.0,424.0,64.0,1046.0,658.0,56.0,56.0,7.0,745.0,22.0,29.0,0.0,23.0,38.0,2.0,1.0,0.0,0.0,131.0,12.0,20.0,37.5 +Federico Bonazzoli,it ITA,"FW,MF",Salernitana,25-339,1997,24.0,12.0,1119.0,2.0,1.0,0.0,0.0,1.0,0.0,0.16,0.08,0.24,0.16,0.24,2.4,2.4,0.19,0.19,12.4,7.0,1.0,21.9,0.56,0.06,0.29,0.07,-0.4,-0.4,264.0,354.0,74.6,4654.0,966.0,135.0,162.0,83.3,84.0,104.0,80.8,37.0,53.0,69.8,8.0,30.0,6.0,0.0,43.0,325.0,28.0,3.0,3.0,13.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,14.0,26.0,2.09,20.0,0.0,3.0,2.0,3.0,0.24,3.0,0.0,0.0,0.0,0.0,5.0,3.0,0.0,3.0,2.0,9.0,1.0,8.0,3.0,0.0,0.0,487.0,0.0,33.0,269.0,191.0,31.0,487.0,304.0,11.0,10.0,3.0,378.0,42.0,12.0,0.0,18.0,32.0,7.0,0.0,0.0,0.0,37.0,6.0,15.0,28.6 +Warren Bondo,fr FRA,MF,Monza,19-222,2003,4.0,0.0,60.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.09,0.09,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,45.0,51.0,88.2,720.0,106.0,24.0,26.0,92.3,20.0,22.0,90.9,1.0,1.0,100.0,0.0,2.0,0.0,0.0,5.0,48.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,3.0,4.5,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,60.0,0.0,8.0,40.0,13.0,0.0,60.0,45.0,3.0,3.0,0.0,51.0,1.0,1.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,100.0 +Kevin Bonifazi,it ITA,DF,Bologna,26-341,1996,7.0,4.0,416.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,4.6,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.2,-0.2,215.0,251.0,85.7,4622.0,1533.0,64.0,71.0,90.1,109.0,120.0,90.8,41.0,57.0,71.9,1.0,14.0,0.0,0.0,12.0,242.0,9.0,9.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,0.65,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,4.0,4.0,2.0,0.0,6.0,4.0,2.0,6.0,21.0,0.0,309.0,48.0,171.0,126.0,15.0,4.0,309.0,175.0,3.0,3.0,0.0,208.0,3.0,4.0,0.0,3.0,0.0,1.0,0.0,0.0,0.0,28.0,1.0,10.0,9.1 +Leonardo Bonucci,it ITA,DF,Juventus,35-359,1987,13.0,7.0,692.0,1.0,0.0,0.0,1.0,2.0,0.0,0.13,0.0,0.13,0.13,0.13,0.9,0.1,0.12,0.01,7.7,1.0,0.0,50.0,0.13,0.5,1.0,0.05,0.1,0.9,380.0,439.0,86.6,8099.0,3068.0,105.0,112.0,93.8,220.0,239.0,92.1,54.0,84.0,64.3,3.0,28.0,3.0,0.0,24.0,403.0,35.0,12.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,8.0,1.04,6.0,0.0,1.0,0.0,1.0,0.13,0.0,0.0,1.0,0.0,0.0,5.0,4.0,1.0,4.0,0.0,10.0,7.0,3.0,9.0,24.0,0.0,499.0,74.0,255.0,241.0,7.0,5.0,498.0,293.0,2.0,2.0,0.0,342.0,2.0,1.0,0.0,10.0,5.0,1.0,0.0,0.0,0.0,41.0,11.0,18.0,37.9 +Erik Botheim,no NOR,"FW,MF",Salernitana,23-105,2000,21.0,4.0,443.0,1.0,2.0,0.0,0.0,1.0,0.0,0.2,0.41,0.61,0.2,0.61,0.4,0.4,0.08,0.08,4.9,1.0,0.0,25.0,0.2,0.25,1.0,0.09,0.6,0.6,74.0,115.0,64.3,964.0,188.0,42.0,59.0,71.2,22.0,34.0,64.7,3.0,4.0,75.0,7.0,4.0,3.0,0.0,13.0,112.0,2.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,11.0,2.24,8.0,0.0,1.0,1.0,2.0,0.41,2.0,0.0,0.0,0.0,0.0,7.0,5.0,1.0,4.0,2.0,1.0,0.0,1.0,0.0,1.0,0.0,152.0,1.0,13.0,63.0,77.0,13.0,152.0,83.0,8.0,3.0,1.0,116.0,9.0,6.0,0.0,7.0,8.0,1.0,0.0,0.0,0.0,17.0,12.0,18.0,40.0 +Mehdi Bourabia,ma MAR,MF,Spezia,31-261,1991,31.0,30.0,2380.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.08,0.08,0.0,0.08,1.5,1.5,0.06,0.06,26.4,3.0,1.0,12.0,0.11,0.0,0.0,0.06,-1.5,-1.5,877.0,1139.0,77.0,13575.0,4405.0,451.0,527.0,85.6,333.0,404.0,82.4,58.0,121.0,47.9,31.0,86.0,30.0,10.0,99.0,1070.0,63.0,29.0,4.0,4.0,60.0,28.0,21.0,3.0,0.0,6.0,6.0,31.0,69.0,2.61,52.0,6.0,2.0,1.0,6.0,0.23,6.0,0.0,0.0,0.0,0.0,37.0,17.0,13.0,19.0,5.0,35.0,12.0,23.0,29.0,45.0,0.0,1439.0,83.0,381.0,736.0,342.0,15.0,1439.0,709.0,33.0,24.0,5.0,830.0,49.0,23.0,0.0,29.0,16.0,1.0,0.0,0.0,0.0,175.0,16.0,26.0,38.1 +Edoardo Bove,it ITA,MF,Roma,20-344,2002,15.0,5.0,485.0,1.0,0.0,0.0,0.0,2.0,0.0,0.19,0.0,0.19,0.19,0.19,0.7,0.4,0.12,0.08,5.4,2.0,0.0,33.3,0.37,0.17,0.5,0.07,0.3,0.6,130.0,172.0,75.6,1840.0,270.0,69.0,85.0,81.2,45.0,58.0,77.6,7.0,10.0,70.0,2.0,3.0,1.0,0.0,8.0,166.0,6.0,1.0,0.0,1.0,5.0,3.0,0.0,1.0,0.0,1.0,0.0,8.0,7.0,1.3,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,8.0,4.0,9.0,4.0,6.0,1.0,5.0,4.0,6.0,0.0,242.0,4.0,45.0,139.0,61.0,11.0,242.0,135.0,5.0,5.0,2.0,137.0,19.0,11.0,0.0,12.0,11.0,0.0,0.0,0.0,0.0,36.0,4.0,5.0,44.4 +Jayden Braaf,nl NED,MF,Hellas Verona,20-237,2002,4.0,2.0,225.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.16,0.16,2.5,2.0,0.0,40.0,0.8,0.0,0.0,0.08,-0.4,-0.4,39.0,52.0,75.0,478.0,115.0,30.0,32.0,93.8,6.0,8.0,75.0,1.0,3.0,33.3,1.0,4.0,0.0,0.0,3.0,51.0,1.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,1.6,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,2.0,3.0,0.0,1.0,0.0,1.0,2.0,0.0,0.0,93.0,0.0,12.0,47.0,37.0,10.0,93.0,61.0,5.0,6.0,2.0,66.0,12.0,10.0,0.0,5.0,1.0,2.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0 +Domagoj Bradarić,hr CRO,DF,Salernitana,23-136,1999,26.0,20.0,1895.0,0.0,2.0,0.0,0.0,4.0,0.0,0.0,0.09,0.09,0.0,0.09,0.2,0.2,0.01,0.01,21.1,0.0,1.0,0.0,0.0,0.0,0.0,0.03,-0.2,-0.2,703.0,948.0,74.2,11151.0,3585.0,376.0,439.0,85.6,265.0,342.0,77.5,46.0,124.0,37.1,16.0,33.0,17.0,9.0,44.0,737.0,208.0,31.0,0.0,6.0,65.0,5.0,1.0,1.0,0.0,172.0,3.0,21.0,37.0,1.76,29.0,2.0,3.0,1.0,3.0,0.14,3.0,0.0,0.0,0.0,0.0,20.0,15.0,11.0,7.0,2.0,17.0,3.0,14.0,18.0,37.0,0.0,1112.0,39.0,319.0,480.0,333.0,21.0,1112.0,588.0,56.0,41.0,8.0,607.0,23.0,9.0,0.0,14.0,14.0,4.0,0.0,0.0,0.0,124.0,9.0,20.0,31.0 +Josip Brekalo,hr CRO,"FW,MF",Fiorentina,24-306,1998,4.0,0.0,113.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.3,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,42.0,58.0,72.4,812.0,209.0,15.0,19.0,78.9,19.0,25.0,76.0,7.0,8.0,87.5,6.0,2.0,7.0,2.0,10.0,58.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,9.0,7.17,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,73.0,0.0,7.0,26.0,41.0,5.0,73.0,52.0,6.0,4.0,1.0,57.0,3.0,3.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,7.0,0.0,5.0,0.0 +Gleison Bremer,br BRA,DF,Juventus,26-038,1997,26.0,26.0,2286.0,3.0,1.0,0.0,0.0,5.0,0.0,0.12,0.04,0.16,0.12,0.16,3.9,3.9,0.16,0.16,25.4,5.0,0.0,20.0,0.2,0.12,0.6,0.16,-0.9,-0.9,1289.0,1425.0,90.5,25002.0,7679.0,435.0,468.0,92.9,708.0,749.0,94.5,138.0,188.0,73.4,7.0,24.0,1.0,0.0,37.0,1371.0,54.0,21.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,21.0,0.83,17.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,16.0,20.0,4.0,1.0,40.0,21.0,19.0,35.0,110.0,2.0,1708.0,260.0,967.0,685.0,63.0,39.0,1708.0,965.0,6.0,6.0,0.0,1115.0,14.0,6.0,0.0,24.0,11.0,1.0,0.0,1.0,0.0,147.0,61.0,43.0,58.7 +Dylan Bronn,tn TUN,DF,Salernitana,27-310,1995,22.0,17.0,1503.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.04,0.04,16.7,1.0,0.0,12.5,0.06,0.0,0.0,0.08,-0.7,-0.7,761.0,902.0,84.4,13644.0,5463.0,327.0,357.0,91.6,353.0,386.0,91.5,70.0,124.0,56.5,5.0,55.0,4.0,1.0,66.0,842.0,54.0,32.0,1.0,12.0,7.0,0.0,0.0,0.0,0.0,22.0,6.0,7.0,14.0,0.84,12.0,0.0,2.0,0.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,31.0,16.0,17.0,13.0,1.0,31.0,20.0,11.0,18.0,53.0,1.0,1076.0,99.0,499.0,534.0,46.0,10.0,1076.0,569.0,9.0,5.0,0.0,681.0,6.0,2.0,0.0,26.0,8.0,0.0,0.0,1.0,0.0,92.0,31.0,21.0,59.6 +Marcelo Brozović,hr CRO,MF,Inter,30-160,1992,21.0,13.0,1211.0,2.0,1.0,0.0,0.0,7.0,0.0,0.15,0.07,0.22,0.15,0.22,2.1,2.1,0.15,0.15,13.5,4.0,1.0,21.1,0.3,0.11,0.5,0.11,-0.1,-0.1,888.0,1028.0,86.4,15943.0,4351.0,380.0,411.0,92.5,404.0,449.0,90.0,86.0,136.0,63.2,14.0,76.0,11.0,3.0,82.0,955.0,72.0,43.0,2.0,11.0,39.0,26.0,11.0,10.0,0.0,3.0,1.0,9.0,31.0,2.3,19.0,7.0,5.0,0.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,20.0,12.0,6.0,12.0,2.0,17.0,7.0,10.0,17.0,21.0,0.0,1160.0,48.0,256.0,694.0,221.0,12.0,1160.0,753.0,20.0,22.0,4.0,822.0,13.0,3.0,0.0,18.0,6.0,0.0,0.0,0.0,0.0,93.0,5.0,8.0,38.5 +Cristian Buonaiuto,it ITA,"MF,FW",Cremonese,30-117,1992,22.0,5.0,766.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.23,0.23,8.5,10.0,6.0,29.4,1.17,0.0,0.0,0.06,-2.0,-2.0,244.0,382.0,63.9,3958.0,1421.0,121.0,149.0,81.2,86.0,129.0,66.7,24.0,73.0,32.9,15.0,24.0,13.0,3.0,33.0,311.0,70.0,15.0,1.0,4.0,61.0,35.0,21.0,10.0,0.0,13.0,1.0,8.0,52.0,6.09,22.0,10.0,7.0,3.0,1.0,0.12,0.0,0.0,1.0,0.0,0.0,15.0,9.0,4.0,6.0,5.0,9.0,0.0,9.0,11.0,6.0,0.0,522.0,7.0,46.0,223.0,262.0,38.0,522.0,320.0,27.0,24.0,7.0,334.0,25.0,20.0,0.0,13.0,19.0,1.0,0.0,0.0,0.0,55.0,5.0,21.0,19.2 +Alessandro Buongiorno,it ITA,DF,Torino,23-323,1999,27.0,23.0,2091.0,0.0,1.0,0.0,0.0,8.0,0.0,0.0,0.04,0.04,0.0,0.04,1.7,1.7,0.07,0.07,23.2,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-1.7,-1.7,1004.0,1195.0,84.0,16870.0,5369.0,434.0,490.0,88.6,503.0,565.0,89.0,47.0,89.0,52.8,4.0,61.0,3.0,2.0,65.0,1163.0,28.0,20.0,1.0,3.0,7.0,0.0,0.0,0.0,0.0,6.0,4.0,17.0,17.0,0.73,12.0,0.0,2.0,1.0,2.0,0.09,1.0,0.0,1.0,0.0,0.0,31.0,20.0,19.0,12.0,0.0,30.0,15.0,15.0,45.0,77.0,2.0,1482.0,124.0,648.0,759.0,82.0,26.0,1482.0,773.0,10.0,14.0,1.0,907.0,29.0,6.0,0.0,53.0,26.0,0.0,0.0,0.0,0.0,131.0,73.0,29.0,71.6 +Juan Cabal,co COL,DF,Hellas Verona,22-107,2001,6.0,0.0,172.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,74.0,99.0,74.7,1449.0,499.0,29.0,34.0,85.3,36.0,39.0,92.3,9.0,20.0,45.0,2.0,7.0,2.0,2.0,11.0,93.0,6.0,3.0,0.0,4.0,3.0,0.0,0.0,0.0,0.0,3.0,0.0,4.0,4.0,2.11,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,3.0,0.0,0.0,5.0,4.0,1.0,3.0,5.0,0.0,123.0,16.0,58.0,61.0,8.0,1.0,123.0,61.0,2.0,1.0,0.0,56.0,3.0,0.0,0.0,3.0,2.0,1.0,0.0,0.0,0.0,23.0,8.0,10.0,44.4 +Liberato Cacace,nz NZL,DF,Empoli,22-210,2000,9.0,3.0,400.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,4.4,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.2,-0.2,222.0,279.0,79.6,3452.0,1090.0,111.0,122.0,91.0,97.0,121.0,80.2,9.0,22.0,40.9,5.0,18.0,1.0,1.0,32.0,226.0,53.0,4.0,0.0,1.0,18.0,0.0,0.0,0.0,0.0,49.0,0.0,6.0,14.0,3.16,11.0,0.0,2.0,0.0,1.0,0.23,0.0,0.0,1.0,0.0,0.0,19.0,7.0,10.0,6.0,3.0,10.0,5.0,5.0,4.0,11.0,0.0,354.0,24.0,106.0,166.0,88.0,7.0,354.0,174.0,20.0,14.0,4.0,192.0,3.0,4.0,0.0,6.0,3.0,0.0,0.0,1.0,0.0,26.0,5.0,17.0,22.7 +Davide Calabria,it ITA,DF,Milan,26-140,1996,18.0,15.0,1200.0,1.0,3.0,0.0,0.0,5.0,0.0,0.07,0.23,0.3,0.07,0.3,1.0,1.0,0.08,0.08,13.3,1.0,0.0,10.0,0.07,0.1,1.0,0.1,0.0,0.0,679.0,856.0,79.3,11833.0,4305.0,296.0,318.0,93.1,301.0,353.0,85.3,68.0,149.0,45.6,14.0,47.0,16.0,9.0,69.0,714.0,138.0,11.0,0.0,4.0,34.0,5.0,0.0,5.0,0.0,122.0,4.0,16.0,26.0,1.95,23.0,1.0,0.0,2.0,4.0,0.3,3.0,0.0,0.0,1.0,0.0,55.0,31.0,25.0,24.0,6.0,15.0,4.0,11.0,20.0,27.0,0.0,1000.0,47.0,302.0,496.0,213.0,11.0,1000.0,498.0,21.0,20.0,1.0,604.0,6.0,3.0,0.0,12.0,12.0,1.0,1.0,1.0,0.0,80.0,7.0,10.0,41.2 +Mattia Caldara,it ITA,DF,Spezia,28-355,1994,20.0,13.0,1253.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.06,0.06,13.9,4.0,0.0,36.4,0.29,0.0,0.0,0.07,-0.8,-0.8,461.0,600.0,76.8,8308.0,3214.0,183.0,220.0,83.2,223.0,270.0,82.6,45.0,93.0,48.4,1.0,23.0,1.0,0.0,35.0,540.0,58.0,31.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,9.0,2.0,4.0,7.0,0.5,7.0,0.0,0.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,23.0,14.0,10.0,11.0,2.0,23.0,16.0,7.0,17.0,88.0,2.0,791.0,145.0,450.0,304.0,46.0,17.0,791.0,380.0,5.0,4.0,0.0,361.0,9.0,4.0,0.0,16.0,10.0,1.0,0.0,1.0,1.0,93.0,35.0,24.0,59.3 +Luca Caldirola,it ITA,DF,Monza,32-083,1991,25.0,21.0,1868.0,1.0,0.0,0.0,0.0,4.0,0.0,0.05,0.0,0.05,0.05,0.05,1.1,1.1,0.05,0.05,20.8,3.0,0.0,33.3,0.14,0.11,0.33,0.12,-0.1,-0.1,993.0,1136.0,87.4,17586.0,6512.0,414.0,431.0,96.1,483.0,533.0,90.6,79.0,136.0,58.1,4.0,43.0,4.0,1.0,50.0,1085.0,49.0,31.0,1.0,7.0,5.0,0.0,0.0,0.0,0.0,5.0,2.0,7.0,10.0,0.48,10.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,22.0,11.0,15.0,4.0,3.0,22.0,12.0,10.0,19.0,49.0,0.0,1294.0,163.0,673.0,563.0,62.0,11.0,1294.0,764.0,11.0,9.0,0.0,932.0,4.0,1.0,0.0,25.0,21.0,0.0,0.0,0.0,0.0,76.0,31.0,29.0,51.7 +Hakan Çalhanoğlu,tr TUR,MF,Inter,29-076,1994,28.0,24.0,2030.0,2.0,6.0,1.0,1.0,2.0,0.0,0.09,0.27,0.35,0.04,0.31,2.8,2.0,0.12,0.09,22.6,12.0,9.0,25.5,0.53,0.02,0.08,0.04,-0.8,-1.0,1176.0,1411.0,83.3,21769.0,6664.0,519.0,564.0,92.0,468.0,536.0,87.3,158.0,248.0,63.7,63.0,135.0,34.0,10.0,155.0,1254.0,153.0,60.0,5.0,19.0,138.0,88.0,27.0,54.0,0.0,5.0,4.0,11.0,113.0,5.01,62.0,41.0,5.0,4.0,10.0,0.44,7.0,3.0,0.0,0.0,0.0,40.0,21.0,15.0,21.0,4.0,26.0,4.0,22.0,22.0,10.0,1.0,1639.0,31.0,300.0,849.0,508.0,24.0,1638.0,980.0,41.0,44.0,6.0,1098.0,28.0,11.0,0.0,37.0,24.0,1.0,0.0,0.0,0.0,169.0,14.0,14.0,50.0 +Mohamed Camara,gn GUI,MF,Roma,26-056,1997,10.0,5.0,412.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.07,0.07,4.6,1.0,1.0,14.3,0.22,0.0,0.0,0.04,-0.3,-0.3,156.0,205.0,76.1,2606.0,529.0,74.0,85.0,87.1,69.0,84.0,82.1,11.0,20.0,55.0,2.0,18.0,2.0,1.0,20.0,202.0,2.0,2.0,1.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,10.0,2.18,4.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,9.0,3.0,6.0,1.0,2.0,0.0,2.0,7.0,4.0,0.0,245.0,7.0,42.0,167.0,45.0,4.0,245.0,152.0,10.0,5.0,1.0,150.0,4.0,5.0,0.0,11.0,5.0,0.0,0.0,0.0,0.0,37.0,6.0,4.0,60.0 +Nicolò Cambiaghi,it ITA,"FW,MF",Empoli,22-118,2000,21.0,5.0,717.0,3.0,1.0,0.0,0.0,2.0,0.0,0.38,0.13,0.5,0.38,0.5,2.4,2.4,0.3,0.3,8.0,9.0,0.0,40.9,1.13,0.14,0.33,0.11,0.6,0.6,203.0,264.0,76.9,2835.0,680.0,126.0,141.0,89.4,53.0,72.0,73.6,11.0,15.0,73.3,7.0,15.0,10.0,1.0,33.0,257.0,6.0,1.0,0.0,0.0,19.0,1.0,0.0,0.0,0.0,3.0,1.0,15.0,30.0,3.77,18.0,1.0,4.0,4.0,4.0,0.5,1.0,1.0,1.0,0.0,0.0,16.0,12.0,8.0,4.0,4.0,9.0,1.0,8.0,6.0,5.0,0.0,377.0,8.0,53.0,130.0,205.0,38.0,377.0,260.0,40.0,18.0,14.0,278.0,21.0,12.0,0.0,10.0,29.0,1.0,0.0,0.0,0.0,33.0,3.0,12.0,20.0 +Andrea Cambiaso,it ITA,DF,Bologna,23-064,2000,25.0,19.0,1620.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.11,0.11,0.0,0.11,0.6,0.6,0.03,0.03,18.0,3.0,0.0,30.0,0.17,0.0,0.0,0.06,-0.6,-0.6,878.0,1084.0,81.0,13392.0,4677.0,480.0,533.0,90.1,324.0,381.0,85.0,50.0,106.0,47.2,19.0,37.0,16.0,6.0,61.0,895.0,187.0,17.0,0.0,5.0,46.0,1.0,1.0,0.0,0.0,169.0,2.0,31.0,40.0,2.22,30.0,3.0,1.0,2.0,6.0,0.33,6.0,0.0,0.0,0.0,0.0,42.0,24.0,23.0,17.0,2.0,20.0,2.0,18.0,21.0,27.0,1.0,1298.0,52.0,441.0,569.0,308.0,26.0,1298.0,730.0,52.0,45.0,13.0,802.0,32.0,14.0,0.0,19.0,26.0,4.0,0.0,0.0,0.0,118.0,3.0,15.0,16.7 +Matteo Cancellieri,it ITA,FW,Lazio,21-072,2002,19.0,1.0,246.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.14,0.14,2.7,1.0,0.0,16.7,0.37,0.0,0.0,0.06,-0.4,-0.4,61.0,86.0,70.9,757.0,98.0,44.0,50.0,88.0,15.0,25.0,60.0,0.0,5.0,0.0,3.0,1.0,1.0,1.0,2.0,85.0,1.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,8.0,2.94,4.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,4.0,5.0,2.0,2.0,4.0,0.0,4.0,1.0,2.0,0.0,130.0,2.0,20.0,48.0,64.0,16.0,130.0,85.0,14.0,8.0,9.0,95.0,12.0,2.0,0.0,14.0,9.0,5.0,1.0,0.0,0.0,13.0,4.0,3.0,57.1 +Antonio Candreva,it ITA,"DF,MF",Salernitana,36-056,1987,30.0,28.0,2413.0,4.0,3.0,0.0,0.0,3.0,1.0,0.15,0.11,0.26,0.15,0.26,3.1,3.1,0.12,0.12,26.8,16.0,2.0,41.0,0.6,0.1,0.25,0.08,0.9,0.9,1024.0,1410.0,72.6,17637.0,6293.0,524.0,566.0,92.6,356.0,487.0,73.1,113.0,258.0,43.8,50.0,92.0,46.0,27.0,123.0,1184.0,222.0,47.0,5.0,23.0,189.0,48.0,7.0,22.0,0.0,123.0,4.0,43.0,84.0,3.13,57.0,18.0,4.0,3.0,9.0,0.34,7.0,1.0,1.0,0.0,0.0,13.0,5.0,11.0,1.0,1.0,6.0,2.0,4.0,16.0,30.0,0.0,1584.0,43.0,304.0,695.0,605.0,41.0,1584.0,951.0,67.0,45.0,8.0,1126.0,20.0,12.0,1.0,7.0,30.0,7.0,0.0,1.0,0.0,118.0,2.0,4.0,33.3 +Gianluca Caprari,it ITA,"MF,FW",Monza,29-269,1993,30.0,26.0,2016.0,4.0,2.0,1.0,1.0,6.0,0.0,0.18,0.09,0.27,0.13,0.22,4.7,3.9,0.21,0.17,22.4,11.0,0.0,25.0,0.49,0.07,0.27,0.09,-0.7,-0.9,695.0,879.0,79.1,10537.0,2035.0,414.0,469.0,88.3,202.0,262.0,77.1,55.0,80.0,68.8,24.0,42.0,19.0,4.0,67.0,826.0,47.0,10.0,3.0,10.0,65.0,27.0,6.0,18.0,0.0,4.0,6.0,29.0,60.0,2.67,44.0,8.0,3.0,1.0,11.0,0.49,8.0,2.0,0.0,1.0,0.0,15.0,6.0,3.0,10.0,2.0,19.0,1.0,18.0,7.0,3.0,0.0,1127.0,7.0,105.0,540.0,490.0,93.0,1126.0,744.0,86.0,42.0,38.0,894.0,71.0,27.0,0.0,33.0,44.0,9.0,0.0,0.0,0.0,88.0,6.0,16.0,27.3 +Francesco Caputo,it ITA,FW,Sampdoria,35-262,1987,15.0,13.0,1094.0,1.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.08,0.08,0.08,1.9,1.9,0.15,0.15,12.2,5.0,0.0,26.3,0.41,0.05,0.2,0.1,-0.9,-0.9,168.0,237.0,70.9,2130.0,315.0,96.0,130.0,73.8,43.0,60.0,71.7,8.0,10.0,80.0,9.0,11.0,4.0,0.0,11.0,208.0,28.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,2.0,1.0,9.0,23.0,1.89,17.0,0.0,3.0,1.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,3.0,0.0,309.0,4.0,11.0,126.0,172.0,49.0,309.0,181.0,6.0,4.0,7.0,243.0,21.0,14.0,0.0,5.0,24.0,11.0,0.0,0.0,0.0,18.0,6.0,21.0,22.2 +Francesco Caputo,it ITA,FW,Empoli,35-262,1987,16.0,15.0,1275.0,2.0,4.0,1.0,1.0,1.0,0.0,0.14,0.28,0.42,0.07,0.35,4.0,3.2,0.28,0.23,14.2,11.0,0.0,39.3,0.78,0.04,0.09,0.11,-2.0,-2.2,192.0,267.0,71.9,2227.0,434.0,134.0,170.0,78.8,40.0,54.0,74.1,1.0,4.0,25.0,15.0,8.0,2.0,0.0,16.0,260.0,7.0,1.0,1.0,0.0,6.0,0.0,0.0,0.0,0.0,1.0,0.0,10.0,25.0,1.77,22.0,0.0,2.0,1.0,3.0,0.21,3.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,1.0,1.0,5.0,2.0,3.0,1.0,5.0,0.0,378.0,7.0,30.0,164.0,185.0,77.0,377.0,225.0,13.0,5.0,5.0,315.0,38.0,21.0,0.0,11.0,19.0,14.0,0.0,0.0,0.0,32.0,11.0,23.0,32.4 +Andrea Carboni,it ITA,DF,Monza,22-080,2001,4.0,1.0,167.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,1.9,1.0,0.0,100.0,0.54,0.0,0.0,0.05,-0.1,-0.1,69.0,87.0,79.3,1170.0,558.0,33.0,38.0,86.8,29.0,31.0,93.5,7.0,14.0,50.0,1.0,3.0,2.0,2.0,9.0,74.0,13.0,1.0,0.0,1.0,5.0,1.0,0.0,1.0,0.0,11.0,0.0,3.0,3.0,1.62,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,3.0,2.0,1.0,3.0,2.0,1.0,104.0,4.0,41.0,43.0,20.0,4.0,104.0,50.0,4.0,2.0,0.0,66.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0 +Franco Carboni,it ITA,DF,Monza,20-021,2003,2.0,0.0,58.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.12,0.12,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.1,-0.1,28.0,42.0,66.7,459.0,132.0,15.0,16.0,93.8,11.0,13.0,84.6,2.0,5.0,40.0,1.0,3.0,2.0,1.0,2.0,35.0,7.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,7.0,0.0,5.0,1.0,1.55,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,2.0,0.0,2.0,1.0,0.0,0.0,49.0,1.0,12.0,22.0,20.0,1.0,49.0,28.0,6.0,2.0,1.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,0.0,100.0 +Valentin Carboni,it ITA,"MF,DF",Inter,18-051,2005,5.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,17.0,70.6,192.0,12.0,5.0,7.0,71.4,4.0,6.0,66.7,2.0,2.0,100.0,1.0,0.0,0.0,0.0,1.0,17.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,7.2,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,25.0,0.0,4.0,12.0,10.0,1.0,25.0,16.0,2.0,0.0,0.0,15.0,2.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,6.0,0.0,4.0,0.0 +Carlos,br BRA,DF,Monza,24-108,1999,28.0,28.0,2453.0,5.0,5.0,0.0,0.0,3.0,0.0,0.18,0.18,0.37,0.18,0.37,2.6,2.6,0.09,0.09,27.3,11.0,0.0,36.7,0.4,0.17,0.45,0.09,2.4,2.4,1149.0,1380.0,83.3,18062.0,5426.0,605.0,659.0,91.8,452.0,531.0,85.1,60.0,103.0,58.3,20.0,60.0,23.0,4.0,82.0,1133.0,242.0,17.0,2.0,4.0,43.0,0.0,0.0,0.0,0.0,220.0,5.0,36.0,45.0,1.65,37.0,2.0,2.0,1.0,8.0,0.29,7.0,0.0,1.0,0.0,0.0,51.0,28.0,29.0,16.0,6.0,19.0,5.0,14.0,18.0,69.0,1.0,1648.0,93.0,533.0,735.0,395.0,49.0,1648.0,815.0,64.0,54.0,10.0,1012.0,33.0,23.0,0.0,36.0,15.0,4.0,0.0,0.0,0.0,130.0,44.0,27.0,62.0 +Marco Carnesecchi,it ITA,GK,Cremonese,22-298,2000,22.0,22.0,1980.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.05,0.05,0.0,0.05,0.0,0.0,0.0,0.0,22.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,465.0,762.0,61.0,14759.0,11474.0,81.0,82.0,98.8,191.0,196.0,97.4,187.0,475.0,39.4,1.0,27.0,1.0,0.0,0.0,528.0,231.0,72.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,7.0,0.32,4.0,3.0,0.0,0.0,2.0,0.09,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,1.0,822.0,720.0,822.0,0.0,0.0,0.0,822.0,454.0,0.0,0.0,0.0,327.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,23.0,3.0,1.0,75.0 +Nicolò Casale,it ITA,DF,Lazio,25-070,1998,22.0,21.0,1877.0,1.0,1.0,0.0,0.0,5.0,0.0,0.05,0.05,0.1,0.05,0.1,0.6,0.6,0.03,0.03,20.9,2.0,0.0,25.0,0.1,0.13,0.5,0.08,0.4,0.4,1096.0,1197.0,91.6,19440.0,6914.0,441.0,473.0,93.2,503.0,535.0,94.0,113.0,134.0,84.3,4.0,41.0,1.0,0.0,57.0,1128.0,68.0,64.0,2.0,9.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,5.0,11.0,0.53,9.0,1.0,1.0,0.0,3.0,0.14,2.0,0.0,1.0,0.0,0.0,35.0,17.0,21.0,14.0,0.0,16.0,9.0,7.0,14.0,53.0,0.0,1338.0,163.0,627.0,695.0,26.0,14.0,1338.0,780.0,9.0,2.0,0.0,889.0,5.0,1.0,0.0,19.0,5.0,0.0,0.0,0.0,0.0,118.0,40.0,23.0,63.5 +Tommaso Cassandro,it ITA,DF,Lecce,23-106,2000,1.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,6.0,100.0,125.0,104.0,3.0,3.0,100.0,2.0,2.0,100.0,1.0,1.0,100.0,0.0,1.0,1.0,0.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,8.0,1.0,4.0,3.0,1.0,0.0,8.0,2.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Michele Castagnetti,it ITA,MF,Cremonese,33-119,1989,20.0,11.0,1061.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.08,0.08,0.0,0.08,0.5,0.5,0.04,0.04,11.8,1.0,1.0,10.0,0.08,0.0,0.0,0.05,-0.5,-0.5,471.0,616.0,76.5,8149.0,3251.0,215.0,247.0,87.0,203.0,258.0,78.7,43.0,87.0,49.4,21.0,75.0,13.0,2.0,95.0,555.0,58.0,34.0,3.0,6.0,32.0,15.0,9.0,5.0,0.0,7.0,3.0,10.0,44.0,3.72,37.0,3.0,3.0,1.0,3.0,0.25,3.0,0.0,0.0,0.0,0.0,32.0,16.0,15.0,16.0,1.0,9.0,2.0,7.0,13.0,17.0,0.0,717.0,25.0,153.0,426.0,148.0,8.0,717.0,342.0,11.0,8.0,0.0,411.0,14.0,3.0,0.0,15.0,11.0,0.0,0.0,0.0,0.0,85.0,8.0,11.0,42.1 +Gaetano Castrovilli,it ITA,MF,Fiorentina,26-067,1997,10.0,3.0,378.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.11,0.11,4.2,3.0,0.0,33.3,0.71,0.0,0.0,0.05,-0.5,-0.5,100.0,128.0,78.1,1503.0,365.0,58.0,65.0,89.2,30.0,36.0,83.3,9.0,22.0,40.9,4.0,3.0,3.0,1.0,7.0,116.0,12.0,8.0,1.0,1.0,8.0,2.0,0.0,2.0,0.0,1.0,0.0,2.0,9.0,2.14,6.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,5.0,0.0,5.0,2.0,4.0,1.0,3.0,3.0,2.0,0.0,181.0,8.0,30.0,83.0,72.0,11.0,181.0,105.0,6.0,7.0,0.0,106.0,8.0,5.0,0.0,9.0,8.0,0.0,0.0,0.0,0.0,21.0,2.0,7.0,22.2 +Danilo Cataldi,it ITA,MF,Lazio,28-262,1994,27.0,25.0,1853.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.05,0.05,0.0,0.05,0.2,0.2,0.01,0.01,20.6,1.0,1.0,14.3,0.05,0.0,0.0,0.03,-0.2,-0.2,1108.0,1283.0,86.4,17001.0,6000.0,632.0,682.0,92.7,344.0,398.0,86.4,92.0,139.0,66.2,15.0,99.0,9.0,0.0,82.0,1204.0,75.0,39.0,3.0,4.0,44.0,33.0,6.0,20.0,0.0,3.0,4.0,14.0,34.0,1.65,20.0,13.0,1.0,0.0,2.0,0.1,1.0,0.0,1.0,0.0,0.0,23.0,14.0,6.0,14.0,3.0,22.0,9.0,13.0,24.0,32.0,0.0,1441.0,77.0,356.0,923.0,173.0,0.0,1441.0,735.0,8.0,9.0,0.0,1011.0,7.0,7.0,0.0,12.0,19.0,0.0,0.0,0.0,0.0,153.0,1.0,5.0,16.7 +Pietro Ceccaroni,it ITA,DF,Lecce,27-125,1995,1.0,0.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,5.0,40.0,57.0,29.0,0.0,0.0,0.0,1.0,2.0,50.0,1.0,3.0,33.3,0.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,3.0,1.0,0.0,10.0,3.0,7.0,3.0,0.0,0.0,10.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0 +Federico Ceccherini,it ITA,DF,Hellas Verona,30-349,1992,18.0,16.0,1207.0,2.0,0.0,0.0,0.0,8.0,1.0,0.15,0.0,0.15,0.15,0.15,0.8,0.8,0.06,0.06,13.4,2.0,0.0,33.3,0.15,0.33,1.0,0.13,1.2,1.2,408.0,535.0,76.3,7452.0,3075.0,167.0,190.0,87.9,187.0,225.0,83.1,47.0,88.0,53.4,4.0,40.0,5.0,1.0,46.0,485.0,50.0,19.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,28.0,0.0,14.0,15.0,1.11,9.0,2.0,0.0,0.0,1.0,0.07,0.0,0.0,0.0,0.0,1.0,20.0,15.0,11.0,8.0,1.0,15.0,8.0,7.0,15.0,47.0,0.0,669.0,76.0,258.0,331.0,87.0,13.0,669.0,312.0,20.0,14.0,2.0,345.0,12.0,7.0,0.0,29.0,16.0,0.0,0.0,0.0,0.0,85.0,25.0,14.0,64.1 +Assan Ceesay,gm GAM,FW,Lecce,29-039,1994,27.0,17.0,1413.0,5.0,0.0,0.0,0.0,1.0,0.0,0.32,0.0,0.32,0.32,0.32,3.7,3.7,0.24,0.24,15.7,8.0,0.0,23.5,0.51,0.15,0.63,0.11,1.3,1.3,188.0,322.0,58.4,2523.0,328.0,108.0,164.0,65.9,54.0,87.0,62.1,11.0,20.0,55.0,6.0,8.0,4.0,0.0,16.0,300.0,19.0,0.0,0.0,1.0,11.0,1.0,0.0,0.0,0.0,5.0,3.0,14.0,30.0,1.91,19.0,0.0,2.0,4.0,6.0,0.38,2.0,0.0,1.0,0.0,0.0,21.0,16.0,7.0,7.0,7.0,12.0,2.0,10.0,3.0,24.0,0.0,531.0,28.0,62.0,216.0,258.0,59.0,531.0,248.0,15.0,13.0,6.0,339.0,57.0,19.0,0.0,25.0,25.0,16.0,0.0,0.0,0.0,40.0,49.0,52.0,48.5 +Emil Ceide,no NOR,FW,Sassuolo,21-234,2001,16.0,3.0,368.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.11,0.11,4.1,1.0,0.0,20.0,0.24,0.0,0.0,0.09,-0.5,-0.5,82.0,118.0,69.5,1114.0,362.0,53.0,64.0,82.8,22.0,34.0,64.7,3.0,5.0,60.0,7.0,4.0,8.0,1.0,13.0,115.0,3.0,0.0,1.0,0.0,5.0,0.0,0.0,0.0,0.0,3.0,0.0,7.0,15.0,3.68,12.0,0.0,0.0,1.0,2.0,0.49,0.0,0.0,0.0,1.0,0.0,4.0,1.0,4.0,0.0,0.0,2.0,0.0,2.0,1.0,0.0,0.0,162.0,0.0,24.0,47.0,91.0,28.0,162.0,107.0,24.0,6.0,16.0,122.0,8.0,6.0,0.0,3.0,5.0,0.0,1.0,0.0,0.0,13.0,1.0,3.0,25.0 +Zeki Çelik,tr TUR,DF,Roma,26-067,1997,19.0,12.0,1181.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,13.1,1.0,0.0,25.0,0.08,0.0,0.0,0.08,-0.3,-0.3,552.0,728.0,75.8,8865.0,3695.0,291.0,327.0,89.0,197.0,251.0,78.5,46.0,91.0,50.5,13.0,40.0,19.0,6.0,50.0,581.0,145.0,10.0,0.0,4.0,31.0,0.0,0.0,0.0,0.0,135.0,2.0,14.0,34.0,2.59,28.0,3.0,1.0,1.0,3.0,0.23,3.0,0.0,0.0,0.0,0.0,33.0,16.0,16.0,15.0,2.0,26.0,5.0,21.0,13.0,30.0,1.0,863.0,32.0,273.0,379.0,222.0,17.0,863.0,406.0,24.0,22.0,5.0,471.0,13.0,11.0,0.0,21.0,5.0,1.0,0.0,0.0,0.0,79.0,10.0,9.0,52.6 +Federico Chiesa,it ITA,"DF,MF",Juventus,25-182,1997,14.0,2.0,477.0,0.0,3.0,0.0,0.0,2.0,0.0,0.0,0.57,0.57,0.0,0.57,0.7,0.7,0.14,0.14,5.3,1.0,1.0,9.1,0.19,0.0,0.0,0.07,-0.7,-0.7,135.0,184.0,73.4,2100.0,654.0,73.0,86.0,84.9,47.0,63.0,74.6,8.0,15.0,53.3,11.0,11.0,7.0,4.0,18.0,161.0,23.0,4.0,1.0,2.0,24.0,3.0,0.0,1.0,0.0,16.0,0.0,9.0,22.0,4.15,13.0,1.0,1.0,1.0,3.0,0.57,3.0,0.0,0.0,0.0,0.0,14.0,5.0,5.0,5.0,4.0,8.0,1.0,7.0,0.0,1.0,0.0,257.0,4.0,40.0,94.0,131.0,21.0,257.0,146.0,23.0,13.0,8.0,168.0,16.0,7.0,0.0,8.0,8.0,2.0,0.0,0.0,0.0,27.0,0.0,3.0,0.0 +Vlad Chiricheș,ro ROU,DF,Cremonese,33-162,1989,11.0,10.0,726.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,266.0,316.0,84.2,5252.0,2451.0,98.0,106.0,92.5,127.0,145.0,87.6,36.0,55.0,65.5,1.0,12.0,0.0,0.0,11.0,299.0,17.0,16.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,5.0,0.62,5.0,0.0,0.0,0.0,1.0,0.12,1.0,0.0,0.0,0.0,0.0,27.0,15.0,20.0,6.0,1.0,21.0,12.0,9.0,16.0,30.0,0.0,414.0,85.0,264.0,148.0,6.0,2.0,414.0,197.0,2.0,2.0,0.0,215.0,1.0,3.0,0.0,13.0,4.0,0.0,0.0,0.0,1.0,53.0,12.0,17.0,41.4 +Daniel Ciofani,it ITA,FW,Cremonese,37-268,1985,26.0,6.0,710.0,6.0,0.0,3.0,4.0,1.0,0.0,0.76,0.0,0.76,0.38,0.38,5.1,1.8,0.64,0.23,7.9,8.0,0.0,34.8,1.01,0.13,0.38,0.08,0.9,1.2,85.0,146.0,58.2,1141.0,309.0,50.0,72.0,69.4,22.0,40.0,55.0,5.0,10.0,50.0,7.0,8.0,3.0,0.0,6.0,142.0,3.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,20.0,2.53,13.0,0.0,5.0,1.0,4.0,0.51,1.0,0.0,3.0,0.0,0.0,5.0,3.0,1.0,2.0,2.0,5.0,1.0,4.0,0.0,3.0,0.0,224.0,5.0,14.0,80.0,130.0,46.0,220.0,111.0,5.0,7.0,1.0,167.0,18.0,9.0,0.0,6.0,10.0,3.0,0.0,0.0,0.0,18.0,26.0,50.0,34.2 +Tio Cipot,si SVN,"FW,DF",Spezia,20-005,2003,5.0,0.0,93.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.04,1.0,1.0,0.0,50.0,0.97,0.0,0.0,0.02,0.0,0.0,27.0,39.0,69.2,349.0,100.0,16.0,18.0,88.9,10.0,15.0,66.7,0.0,1.0,0.0,2.0,2.0,1.0,0.0,4.0,35.0,4.0,1.0,0.0,0.0,3.0,1.0,1.0,0.0,0.0,2.0,0.0,3.0,2.0,1.94,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,6.0,3.0,3.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,54.0,1.0,9.0,26.0,24.0,4.0,54.0,33.0,4.0,2.0,1.0,38.0,3.0,4.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,0.0 +Patrick Ciurria,it ITA,"DF,MF",Monza,28-075,1995,29.0,24.0,2145.0,4.0,5.0,0.0,0.0,3.0,0.0,0.17,0.21,0.38,0.17,0.38,3.9,3.9,0.17,0.17,23.8,16.0,0.0,43.2,0.67,0.11,0.25,0.11,0.1,0.1,825.0,1042.0,79.2,12992.0,4012.0,441.0,498.0,88.6,296.0,376.0,78.7,61.0,111.0,55.0,32.0,48.0,26.0,13.0,64.0,869.0,171.0,15.0,1.0,2.0,77.0,25.0,0.0,19.0,0.0,131.0,2.0,14.0,55.0,2.31,35.0,9.0,5.0,2.0,4.0,0.17,3.0,1.0,0.0,0.0,0.0,27.0,15.0,17.0,9.0,1.0,20.0,2.0,18.0,3.0,26.0,0.0,1262.0,34.0,305.0,540.0,427.0,40.0,1262.0,661.0,49.0,36.0,7.0,818.0,41.0,13.0,0.0,26.0,32.0,7.0,0.0,0.0,0.0,98.0,9.0,11.0,45.0 +Omar Colley,gm GAM,DF,Sampdoria,30-183,1992,16.0,15.0,1384.0,1.0,0.0,0.0,0.0,5.0,0.0,0.07,0.0,0.07,0.07,0.07,1.2,1.2,0.08,0.08,15.4,4.0,0.0,66.7,0.26,0.17,0.25,0.2,-0.2,-0.2,615.0,714.0,86.1,12155.0,4062.0,165.0,186.0,88.7,380.0,412.0,92.2,58.0,96.0,60.4,0.0,38.0,1.0,0.0,33.0,681.0,32.0,22.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,5.0,0.33,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,10.0,10.0,7.0,0.0,9.0,5.0,4.0,22.0,69.0,1.0,860.0,107.0,451.0,390.0,20.0,9.0,860.0,511.0,3.0,1.0,0.0,517.0,4.0,2.0,0.0,15.0,18.0,1.0,0.0,0.0,0.0,73.0,27.0,30.0,47.4 +Lorenzo Colombo,it ITA,FW,Lecce,21-048,2002,26.0,14.0,1276.0,4.0,2.0,0.0,1.0,4.0,0.0,0.28,0.14,0.42,0.28,0.42,4.7,4.0,0.33,0.28,14.2,11.0,0.0,29.7,0.78,0.11,0.36,0.11,-0.7,0.0,187.0,292.0,64.0,2329.0,511.0,120.0,160.0,75.0,38.0,57.0,66.7,9.0,23.0,39.1,16.0,14.0,6.0,2.0,19.0,278.0,12.0,0.0,0.0,6.0,9.0,0.0,0.0,0.0,0.0,2.0,2.0,12.0,36.0,2.54,25.0,0.0,6.0,1.0,2.0,0.14,1.0,0.0,1.0,0.0,0.0,13.0,5.0,1.0,7.0,5.0,19.0,0.0,19.0,2.0,5.0,0.0,506.0,5.0,16.0,232.0,261.0,61.0,505.0,266.0,13.0,11.0,4.0,365.0,68.0,34.0,0.0,34.0,11.0,7.0,0.0,0.0,0.0,40.0,30.0,98.0,23.4 +Andrea Colpani,it ITA,MF,Monza,23-349,1999,23.0,7.0,760.0,3.0,0.0,0.0,0.0,1.0,0.0,0.36,0.0,0.36,0.36,0.36,1.8,1.8,0.21,0.21,8.4,7.0,0.0,31.8,0.83,0.14,0.43,0.08,1.2,1.2,255.0,322.0,79.2,4306.0,1010.0,123.0,140.0,87.9,100.0,118.0,84.7,24.0,42.0,57.1,14.0,23.0,9.0,2.0,43.0,291.0,26.0,4.0,1.0,6.0,27.0,18.0,4.0,14.0,0.0,3.0,5.0,5.0,30.0,3.54,19.0,4.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,9.0,6.0,5.0,3.0,14.0,2.0,12.0,3.0,4.0,0.0,435.0,6.0,47.0,214.0,188.0,27.0,435.0,293.0,24.0,27.0,6.0,323.0,19.0,16.0,0.0,19.0,19.0,0.0,0.0,0.0,0.0,48.0,4.0,9.0,30.8 +Andrea Consigli,it ITA,GK,Sassuolo,36-088,1987,29.0,29.0,2610.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,934.0,1196.0,78.1,26579.0,17401.0,152.0,153.0,99.3,396.0,399.0,99.2,384.0,641.0,59.9,0.0,15.0,0.0,0.0,1.0,860.0,335.0,86.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.21,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,3.0,1243.0,999.0,1233.0,9.0,1.0,0.0,1243.0,714.0,0.0,0.0,0.0,623.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,26.0,9.0,0.0,100.0 +Andrea Conti,it ITA,DF,Sampdoria,29-054,1994,1.0,0.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,11.0,54.5,76.0,25.0,5.0,7.0,71.4,1.0,3.0,33.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,7.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,8.0,3.0,0.0,11.0,4.0,0.0,0.0,0.0,6.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Diego Coppola,it ITA,DF,Hellas Verona,19-118,2003,15.0,11.0,1060.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,11.8,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.3,-0.3,365.0,505.0,72.3,6884.0,3007.0,127.0,153.0,83.0,204.0,250.0,81.6,30.0,76.0,39.5,0.0,27.0,3.0,1.0,37.0,448.0,54.0,11.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,43.0,3.0,9.0,9.0,0.77,6.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,16.0,13.0,9.0,0.0,18.0,6.0,12.0,13.0,34.0,0.0,628.0,57.0,296.0,278.0,60.0,9.0,628.0,288.0,9.0,8.0,0.0,291.0,8.0,2.0,0.0,10.0,0.0,0.0,0.0,1.0,0.0,65.0,39.0,33.0,54.2 +Joaquín Correa,ar ARG,FW,Inter,28-255,1994,22.0,8.0,713.0,3.0,1.0,0.0,0.0,1.0,0.0,0.38,0.13,0.5,0.38,0.5,3.0,3.0,0.37,0.37,7.9,8.0,0.0,44.4,1.01,0.17,0.38,0.17,0.0,0.0,178.0,224.0,79.5,2761.0,529.0,91.0,110.0,82.7,66.0,75.0,88.0,11.0,12.0,91.7,13.0,16.0,7.0,0.0,21.0,216.0,5.0,1.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,3.0,9.0,32.0,4.05,23.0,0.0,5.0,2.0,3.0,0.38,3.0,0.0,0.0,0.0,0.0,11.0,7.0,2.0,6.0,3.0,3.0,0.0,3.0,1.0,0.0,0.0,305.0,1.0,13.0,142.0,156.0,34.0,305.0,204.0,13.0,12.0,8.0,241.0,25.0,13.0,0.0,9.0,14.0,1.0,0.0,0.0,0.0,27.0,4.0,10.0,28.6 +Alessandro Cortinovis,it ITA,MF,Hellas Verona,22-090,2001,1.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,6.0,83.3,77.0,35.0,3.0,3.0,100.0,2.0,2.0,100.0,0.0,1.0,0.0,0.0,0.0,2.0,1.0,2.0,6.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,8.0,0.0,3.0,0.0,5.0,0.0,8.0,5.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0 +Lassana Coulibaly,ml MLI,MF,Salernitana,27-015,1996,28.0,27.0,2301.0,3.0,2.0,0.0,0.0,10.0,0.0,0.12,0.08,0.2,0.12,0.2,0.7,0.7,0.03,0.03,25.6,6.0,0.0,46.2,0.23,0.23,0.5,0.05,2.3,2.3,754.0,911.0,82.8,11940.0,3418.0,372.0,426.0,87.3,316.0,356.0,88.8,40.0,67.0,59.7,16.0,66.0,7.0,1.0,96.0,893.0,16.0,14.0,2.0,4.0,9.0,0.0,0.0,0.0,0.0,2.0,2.0,23.0,47.0,1.84,42.0,1.0,0.0,3.0,7.0,0.27,7.0,0.0,0.0,0.0,0.0,67.0,39.0,33.0,28.0,6.0,26.0,5.0,21.0,16.0,23.0,2.0,1198.0,65.0,303.0,715.0,208.0,16.0,1198.0,704.0,38.0,29.0,6.0,749.0,50.0,26.0,0.0,38.0,36.0,1.0,0.0,0.0,0.0,174.0,25.0,28.0,47.2 +Alessio Cragno,it ITA,GK,Monza,28-301,1994,1.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,31.0,93.5,621.0,431.0,7.0,7.0,100.0,18.0,18.0,100.0,4.0,6.0,66.7,0.0,0.0,0.0,0.0,0.0,24.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32.0,26.0,32.0,0.0,0.0,0.0,32.0,19.0,0.0,0.0,0.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Bryan Cristante,it ITA,MF,Roma,28-053,1995,29.0,29.0,2440.0,1.0,1.0,0.0,1.0,6.0,1.0,0.04,0.04,0.07,0.04,0.07,2.1,1.3,0.08,0.05,27.1,3.0,0.0,15.0,0.11,0.05,0.33,0.07,-1.1,-0.3,1173.0,1431.0,82.0,21636.0,6556.0,506.0,573.0,88.3,485.0,551.0,88.0,150.0,248.0,60.5,13.0,140.0,15.0,3.0,125.0,1370.0,56.0,53.0,6.0,29.0,8.0,0.0,0.0,0.0,0.0,3.0,5.0,15.0,36.0,1.33,33.0,0.0,3.0,0.0,6.0,0.22,5.0,0.0,1.0,0.0,0.0,97.0,54.0,47.0,42.0,8.0,36.0,8.0,28.0,13.0,39.0,0.0,1689.0,69.0,454.0,1082.0,167.0,25.0,1688.0,872.0,14.0,11.0,3.0,1103.0,21.0,9.0,0.0,44.0,16.0,2.0,0.0,0.0,0.0,175.0,63.0,32.0,66.3 +Domen Črnigoj,si SVN,MF,Salernitana,27-158,1995,7.0,4.0,296.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,3.3,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.1,-0.1,79.0,111.0,71.2,1189.0,263.0,42.0,55.0,76.4,31.0,36.0,86.1,4.0,10.0,40.0,2.0,8.0,0.0,0.0,3.0,110.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,6.0,1.83,4.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,3.0,0.0,7.0,1.0,9.0,1.0,8.0,9.0,6.0,0.0,160.0,5.0,36.0,95.0,32.0,6.0,160.0,64.0,2.0,2.0,1.0,89.0,13.0,1.0,0.0,6.0,2.0,0.0,0.0,0.0,0.0,21.0,5.0,7.0,41.7 +Juan Cuadrado,co COL,"DF,FW",Juventus,34-334,1988,25.0,19.0,1578.0,1.0,3.0,0.0,0.0,4.0,1.0,0.06,0.17,0.23,0.06,0.23,1.2,1.2,0.07,0.07,17.5,8.0,4.0,30.8,0.46,0.04,0.13,0.05,-0.2,-0.2,838.0,1005.0,83.4,16169.0,4002.0,355.0,392.0,90.6,346.0,397.0,87.2,127.0,179.0,70.9,34.0,39.0,19.0,9.0,67.0,850.0,151.0,25.0,0.0,19.0,72.0,32.0,11.0,20.0,0.0,94.0,4.0,12.0,60.0,3.42,30.0,19.0,3.0,5.0,6.0,0.34,2.0,2.0,0.0,2.0,0.0,38.0,25.0,26.0,11.0,1.0,12.0,1.0,11.0,17.0,24.0,0.0,1197.0,33.0,311.0,571.0,325.0,42.0,1197.0,677.0,32.0,26.0,17.0,807.0,29.0,9.0,0.0,25.0,31.0,0.0,1.0,0.0,0.0,82.0,9.0,8.0,52.9 +Mickaël Cuisance,fr FRA,MF,Sampdoria,23-252,1999,9.0,8.0,522.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.08,0.08,5.8,2.0,1.0,22.2,0.34,0.0,0.0,0.05,-0.4,-0.4,154.0,195.0,79.0,2526.0,663.0,77.0,83.0,92.8,58.0,69.0,84.1,14.0,31.0,45.2,5.0,10.0,8.0,0.0,20.0,176.0,19.0,8.0,2.0,4.0,16.0,7.0,4.0,3.0,0.0,2.0,0.0,6.0,11.0,1.9,7.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,9.0,6.0,5.0,1.0,8.0,0.0,8.0,4.0,4.0,0.0,255.0,8.0,39.0,124.0,97.0,10.0,255.0,161.0,10.0,12.0,3.0,157.0,13.0,13.0,0.0,8.0,4.0,1.0,0.0,0.0,0.0,34.0,4.0,9.0,30.8 +Marco D'Alessandro,it ITA,"DF,FW",Monza,32-067,1991,8.0,3.0,290.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.1,0.1,3.2,1.0,0.0,25.0,0.31,0.0,0.0,0.08,-0.3,-0.3,79.0,115.0,68.7,1182.0,302.0,50.0,57.0,87.7,23.0,36.0,63.9,4.0,9.0,44.4,3.0,4.0,0.0,0.0,3.0,100.0,14.0,2.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,12.0,1.0,3.0,8.0,2.49,7.0,0.0,0.0,1.0,1.0,0.31,1.0,0.0,0.0,0.0,0.0,4.0,4.0,3.0,1.0,0.0,2.0,0.0,2.0,1.0,3.0,0.0,141.0,1.0,37.0,55.0,52.0,10.0,141.0,80.0,14.0,4.0,5.0,89.0,4.0,5.0,0.0,6.0,9.0,1.0,0.0,0.0,0.0,19.0,2.0,2.0,50.0 +Danilo D'Ambrosio,it ITA,DF,Inter,34-228,1988,10.0,3.0,370.0,0.0,1.0,0.0,0.0,2.0,1.0,0.0,0.24,0.24,0.0,0.24,0.0,0.0,0.0,0.0,4.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,218.0,249.0,87.6,3754.0,1232.0,93.0,96.0,96.9,113.0,126.0,89.7,9.0,21.0,42.9,4.0,15.0,3.0,1.0,18.0,236.0,13.0,5.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,8.0,0.0,2.0,10.0,2.43,8.0,0.0,0.0,1.0,1.0,0.24,1.0,0.0,0.0,0.0,0.0,16.0,9.0,12.0,3.0,1.0,8.0,3.0,5.0,9.0,10.0,0.0,300.0,20.0,120.0,130.0,50.0,8.0,300.0,192.0,4.0,4.0,2.0,199.0,3.0,1.0,0.0,10.0,5.0,0.0,1.0,0.0,0.0,19.0,6.0,6.0,50.0 +Luca D'Andrea,it ITA,FW,Sassuolo,18-231,2004,5.0,5.0,316.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.28,0.28,0.0,0.28,0.3,0.3,0.08,0.08,3.5,3.0,0.0,42.9,0.85,0.0,0.0,0.04,-0.3,-0.3,61.0,84.0,72.6,896.0,334.0,32.0,42.0,76.2,24.0,31.0,77.4,3.0,6.0,50.0,10.0,6.0,3.0,2.0,9.0,80.0,4.0,0.0,1.0,0.0,7.0,3.0,0.0,0.0,0.0,1.0,0.0,1.0,15.0,4.27,12.0,1.0,1.0,0.0,1.0,0.28,1.0,0.0,0.0,0.0,0.0,4.0,0.0,2.0,1.0,1.0,2.0,0.0,2.0,1.0,1.0,0.0,129.0,0.0,17.0,61.0,53.0,12.0,129.0,81.0,11.0,4.0,5.0,91.0,11.0,7.0,0.0,4.0,9.0,1.0,0.0,0.0,0.0,13.0,2.0,3.0,40.0 +Flavius Daniliuc,at AUT,DF,Salernitana,21-363,2001,21.0,19.0,1718.0,0.0,2.0,0.0,0.0,6.0,0.0,0.0,0.1,0.1,0.0,0.1,0.6,0.6,0.03,0.03,19.1,3.0,1.0,37.5,0.16,0.0,0.0,0.07,-0.6,-0.6,772.0,905.0,85.3,14856.0,5640.0,283.0,315.0,89.8,389.0,427.0,91.1,87.0,132.0,65.9,4.0,45.0,0.0,0.0,53.0,862.0,40.0,31.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,9.0,3.0,11.0,13.0,0.68,10.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,45.0,23.0,30.0,13.0,2.0,28.0,12.0,16.0,23.0,69.0,0.0,1109.0,171.0,628.0,453.0,40.0,12.0,1109.0,576.0,10.0,8.0,0.0,669.0,11.0,3.0,0.0,26.0,3.0,0.0,0.0,0.0,0.0,106.0,34.0,18.0,65.4 +Danilo,br BRA,DF,Juventus,31-284,1991,31.0,29.0,2644.0,3.0,3.0,0.0,0.0,4.0,0.0,0.1,0.1,0.2,0.1,0.2,2.3,2.3,0.08,0.08,29.4,5.0,1.0,20.8,0.17,0.13,0.6,0.1,0.7,0.7,1487.0,1782.0,83.4,28712.0,10787.0,546.0,615.0,88.8,740.0,809.0,91.5,186.0,308.0,60.4,16.0,142.0,11.0,3.0,132.0,1617.0,157.0,44.0,3.0,16.0,17.0,0.0,0.0,0.0,0.0,102.0,8.0,18.0,43.0,1.46,38.0,1.0,2.0,2.0,3.0,0.1,2.0,0.0,1.0,0.0,0.0,73.0,47.0,49.0,17.0,7.0,32.0,17.0,15.0,37.0,93.0,1.0,2086.0,220.0,869.0,1032.0,196.0,38.0,2086.0,1093.0,31.0,22.0,0.0,1357.0,18.0,8.0,0.0,29.0,16.0,0.0,0.0,0.0,0.0,150.0,44.0,30.0,59.5 +Matteo Darmian,it ITA,DF,Inter,33-144,1989,25.0,21.0,1701.0,1.0,2.0,0.0,0.0,3.0,0.0,0.05,0.11,0.16,0.05,0.16,1.0,1.0,0.05,0.05,18.9,1.0,0.0,16.7,0.05,0.17,1.0,0.16,0.0,0.0,938.0,1098.0,85.4,17028.0,5937.0,365.0,395.0,92.4,479.0,538.0,89.0,80.0,129.0,62.0,18.0,61.0,15.0,9.0,80.0,983.0,112.0,15.0,1.0,12.0,27.0,0.0,0.0,0.0,0.0,97.0,3.0,14.0,37.0,1.96,31.0,4.0,0.0,1.0,4.0,0.21,3.0,0.0,0.0,1.0,0.0,46.0,25.0,22.0,12.0,12.0,29.0,5.0,24.0,19.0,28.0,0.0,1256.0,63.0,411.0,633.0,220.0,22.0,1256.0,717.0,22.0,18.0,4.0,850.0,13.0,8.0,0.0,17.0,18.0,3.0,0.0,0.0,0.0,99.0,12.0,18.0,40.0 +Paweł Dawidowicz,pl POL,DF,Hellas Verona,27-340,1995,19.0,17.0,1400.0,1.0,1.0,0.0,0.0,6.0,1.0,0.06,0.06,0.13,0.06,0.13,1.1,1.1,0.07,0.07,15.6,2.0,0.0,18.2,0.13,0.09,0.5,0.1,-0.1,-0.1,525.0,701.0,74.9,9533.0,3577.0,216.0,249.0,86.7,245.0,310.0,79.0,55.0,117.0,47.0,5.0,40.0,2.0,0.0,45.0,649.0,51.0,20.0,1.0,1.0,11.0,0.0,0.0,0.0,0.0,31.0,1.0,10.0,16.0,1.03,13.0,1.0,1.0,0.0,1.0,0.06,0.0,0.0,1.0,0.0,0.0,43.0,28.0,26.0,14.0,3.0,12.0,8.0,4.0,18.0,60.0,0.0,869.0,80.0,419.0,374.0,84.0,17.0,869.0,400.0,18.0,11.0,1.0,430.0,7.0,4.0,0.0,34.0,10.0,0.0,0.0,0.0,0.0,107.0,36.0,22.0,62.1 +Charles De Ketelaere,be BEL,"MF,FW",Milan,22-046,2001,27.0,8.0,969.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.09,0.09,0.0,0.09,1.7,1.7,0.16,0.16,10.8,3.0,0.0,20.0,0.28,0.0,0.0,0.11,-1.7,-1.7,248.0,318.0,78.0,3468.0,1092.0,152.0,184.0,82.6,78.0,91.0,85.7,10.0,15.0,66.7,19.0,24.0,17.0,0.0,50.0,301.0,17.0,2.0,2.0,2.0,9.0,0.0,0.0,0.0,0.0,5.0,0.0,12.0,43.0,3.99,36.0,2.0,0.0,1.0,2.0,0.19,2.0,0.0,0.0,0.0,0.0,23.0,10.0,6.0,13.0,4.0,13.0,2.0,11.0,3.0,8.0,0.0,447.0,10.0,45.0,203.0,208.0,49.0,447.0,254.0,40.0,22.0,11.0,287.0,24.0,23.0,0.0,17.0,12.0,2.0,0.0,0.0,0.0,49.0,17.0,26.0,39.5 +Manuel De Luca,it ITA,"FW,MF",Sampdoria,24-282,1998,2.0,0.0,44.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,8.0,62.5,66.0,1.0,3.0,5.0,60.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,0.0,0.0,9.0,5.0,2.0,14.0,14.0,0.0,0.0,0.0,14.0,2.0,2.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0 +Mattia De Sciglio,it ITA,DF,Juventus,30-187,1992,16.0,10.0,1011.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,11.2,0.0,0.0,0.0,0.0,0.0,0.0,0.02,-0.1,-0.1,441.0,555.0,79.5,7888.0,2991.0,186.0,221.0,84.2,207.0,245.0,84.5,44.0,74.0,59.5,8.0,49.0,8.0,6.0,50.0,461.0,93.0,7.0,0.0,6.0,24.0,0.0,0.0,0.0,0.0,86.0,1.0,4.0,15.0,1.34,14.0,0.0,0.0,1.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,12.0,6.0,6.0,3.0,3.0,9.0,3.0,6.0,4.0,18.0,0.0,636.0,29.0,188.0,328.0,123.0,4.0,636.0,331.0,14.0,11.0,0.0,394.0,8.0,7.0,0.0,4.0,11.0,0.0,0.0,0.0,0.0,40.0,10.0,11.0,47.6 +Lorenzo De Silvestri,it ITA,DF,Bologna,34-337,1988,10.0,5.0,539.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.07,0.07,6.0,2.0,0.0,50.0,0.33,0.0,0.0,0.1,-0.4,-0.4,206.0,283.0,72.8,3199.0,1537.0,114.0,128.0,89.1,82.0,113.0,72.6,7.0,31.0,22.6,4.0,21.0,4.0,1.0,23.0,220.0,63.0,5.0,1.0,1.0,6.0,0.0,0.0,0.0,0.0,58.0,0.0,3.0,8.0,1.34,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,1.0,5.0,5.0,0.0,2.0,17.0,0.0,329.0,20.0,110.0,158.0,66.0,6.0,329.0,157.0,11.0,8.0,0.0,172.0,5.0,4.0,0.0,4.0,5.0,2.0,0.0,0.0,1.0,31.0,10.0,8.0,55.6 +Koni De Winter,be BEL,DF,Empoli,20-317,2002,14.0,12.0,1024.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,424.0,494.0,85.8,7908.0,2790.0,166.0,181.0,91.7,226.0,249.0,90.8,31.0,58.0,53.4,2.0,15.0,1.0,0.0,22.0,473.0,21.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,5.0,0.44,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,9.0,11.0,3.0,0.0,14.0,12.0,2.0,6.0,65.0,0.0,614.0,118.0,405.0,207.0,6.0,0.0,614.0,347.0,2.0,1.0,0.0,370.0,8.0,3.0,0.0,13.0,2.0,0.0,0.0,0.0,0.0,58.0,16.0,11.0,59.3 +Grégoire Defrel,mq MTQ,FW,Sassuolo,31-312,1991,20.0,9.0,835.0,2.0,0.0,0.0,0.0,2.0,0.0,0.22,0.0,0.22,0.22,0.22,2.4,2.4,0.26,0.26,9.3,9.0,0.0,75.0,0.97,0.17,0.22,0.2,-0.4,-0.4,144.0,176.0,81.8,1727.0,271.0,93.0,105.0,88.6,34.0,41.0,82.9,3.0,8.0,37.5,8.0,9.0,1.0,0.0,8.0,155.0,21.0,0.0,1.0,1.0,1.0,2.0,1.0,0.0,0.0,1.0,0.0,5.0,12.0,1.29,10.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,7.0,5.0,2.0,2.0,8.0,0.0,8.0,1.0,5.0,0.0,263.0,7.0,31.0,123.0,114.0,24.0,263.0,156.0,8.0,7.0,2.0,178.0,23.0,11.0,0.0,12.0,11.0,3.0,0.0,0.0,0.0,27.0,3.0,20.0,13.0 +Duccio Degl'Innocenti,it ITA,MF,Empoli,19-362,2003,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Merih Demiral,tr TUR,DF,Atalanta,25-051,1998,25.0,14.0,1500.0,1.0,0.0,0.0,0.0,5.0,0.0,0.06,0.0,0.06,0.06,0.06,0.7,0.7,0.04,0.04,16.7,1.0,0.0,16.7,0.06,0.17,1.0,0.12,0.3,0.3,543.0,634.0,85.6,10140.0,2866.0,197.0,217.0,90.8,283.0,319.0,88.7,54.0,85.0,63.5,4.0,14.0,1.0,1.0,26.0,569.0,65.0,40.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,3.0,0.18,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,39.0,27.0,29.0,10.0,0.0,24.0,7.0,17.0,23.0,56.0,1.0,808.0,139.0,482.0,309.0,26.0,14.0,808.0,334.0,1.0,3.0,1.0,361.0,5.0,2.0,0.0,13.0,12.0,1.0,0.0,0.0,0.0,118.0,49.0,29.0,62.8 +Diego Demme,de GER,MF,Napoli,31-155,1991,5.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,151.0,159.0,95.0,2426.0,682.0,75.0,77.0,97.4,61.0,63.0,96.8,12.0,13.0,92.3,1.0,11.0,1.0,0.0,15.0,156.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,0.0,2.0,0.0,1.0,0.0,171.0,3.0,15.0,135.0,21.0,0.0,171.0,116.0,3.0,1.0,0.0,154.0,2.0,0.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0 +Fabio Depaoli,it ITA,DF,Hellas Verona,26-001,1997,24.0,19.0,1738.0,2.0,2.0,0.0,0.0,4.0,0.0,0.1,0.1,0.21,0.1,0.21,1.5,1.5,0.08,0.08,19.3,4.0,0.0,25.0,0.21,0.13,0.5,0.09,0.5,0.5,381.0,579.0,65.8,6901.0,3983.0,164.0,207.0,79.2,164.0,235.0,69.8,45.0,97.0,46.4,13.0,36.0,20.0,13.0,58.0,426.0,150.0,4.0,0.0,1.0,48.0,2.0,1.0,1.0,0.0,144.0,3.0,15.0,37.0,1.92,26.0,6.0,0.0,4.0,3.0,0.16,3.0,0.0,0.0,0.0,0.0,39.0,23.0,24.0,11.0,4.0,25.0,6.0,19.0,12.0,27.0,2.0,791.0,47.0,202.0,347.0,252.0,32.0,791.0,346.0,25.0,13.0,4.0,354.0,29.0,14.0,0.0,31.0,24.0,3.0,0.0,0.0,0.0,99.0,20.0,34.0,37.0 +Fabio Depaoli,it ITA,"DF,MF",Sampdoria,26-001,1997,3.0,1.0,136.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.12,0.12,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.2,-0.2,74.0,95.0,77.9,1373.0,584.0,31.0,36.0,86.1,31.0,36.0,86.1,11.0,21.0,52.4,4.0,6.0,4.0,4.0,10.0,75.0,20.0,1.0,1.0,0.0,8.0,0.0,0.0,0.0,0.0,19.0,0.0,1.0,6.0,3.97,5.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,2.0,3.0,0.0,1.0,0.0,1.0,1.0,4.0,0.0,117.0,6.0,28.0,56.0,34.0,3.0,117.0,73.0,2.0,3.0,0.0,70.0,2.0,1.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,6.0,0.0,2.0,0.0 +Kastriot Dermaku,al ALB,DF,Lecce,31-100,1992,1.0,0.0,34.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,50.0,27.0,0.0,0.0,0.0,0.0,1.0,2.0,50.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Cyriel Dessers,ng NGA,FW,Cremonese,28-138,1994,25.0,19.0,1566.0,6.0,1.0,0.0,1.0,3.0,0.0,0.34,0.06,0.4,0.34,0.4,6.9,6.1,0.4,0.35,17.4,11.0,0.0,22.9,0.63,0.13,0.55,0.13,-0.9,-0.1,177.0,276.0,64.1,2472.0,437.0,105.0,151.0,69.5,57.0,80.0,71.3,7.0,14.0,50.0,23.0,11.0,8.0,1.0,27.0,247.0,28.0,0.0,1.0,1.0,8.0,0.0,0.0,0.0,0.0,3.0,1.0,8.0,44.0,2.53,28.0,2.0,5.0,3.0,2.0,0.11,1.0,0.0,0.0,0.0,0.0,11.0,4.0,4.0,4.0,3.0,14.0,2.0,12.0,3.0,5.0,0.0,458.0,8.0,18.0,159.0,284.0,113.0,457.0,262.0,21.0,10.0,24.0,333.0,59.0,21.0,0.0,33.0,14.0,24.0,0.0,0.0,0.0,29.0,15.0,46.0,24.6 +Sergiño Dest,us USA,DF,Milan,22-173,2000,8.0,2.0,329.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.08,0.08,3.7,1.0,0.0,50.0,0.27,0.0,0.0,0.15,-0.3,-0.3,169.0,198.0,85.4,2535.0,793.0,95.0,102.0,93.1,65.0,73.0,89.0,5.0,12.0,41.7,4.0,15.0,3.0,1.0,14.0,154.0,44.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,43.0,0.0,5.0,11.0,3.0,8.0,2.0,0.0,0.0,1.0,0.27,1.0,0.0,0.0,0.0,0.0,12.0,6.0,3.0,5.0,4.0,1.0,0.0,1.0,4.0,2.0,0.0,246.0,7.0,75.0,116.0,61.0,3.0,246.0,134.0,11.0,3.0,0.0,137.0,3.0,5.0,0.0,6.0,4.0,0.0,0.0,1.0,0.0,29.0,3.0,1.0,75.0 +Mattia Destro,it ITA,FW,Empoli,32-036,1991,12.0,6.0,479.0,1.0,0.0,0.0,0.0,2.0,0.0,0.19,0.0,0.19,0.19,0.19,1.3,1.3,0.25,0.25,5.3,7.0,0.0,36.8,1.32,0.05,0.14,0.07,-0.3,-0.3,37.0,66.0,56.1,456.0,107.0,25.0,36.0,69.4,6.0,16.0,37.5,3.0,5.0,60.0,4.0,1.0,2.0,0.0,4.0,63.0,3.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,9.0,1.69,4.0,0.0,1.0,1.0,1.0,0.19,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,2.0,0.0,2.0,1.0,5.0,0.0,122.0,6.0,10.0,52.0,60.0,25.0,122.0,68.0,4.0,1.0,3.0,88.0,13.0,5.0,0.0,10.0,3.0,7.0,0.0,1.0,0.0,6.0,7.0,17.0,29.2 +Gerard Deulofeu,es ESP,FW,Udinese,29-043,1994,16.0,15.0,1211.0,2.0,6.0,0.0,0.0,1.0,0.0,0.15,0.45,0.59,0.15,0.59,5.1,5.1,0.38,0.38,13.5,12.0,10.0,27.3,0.89,0.05,0.17,0.11,-3.1,-3.1,443.0,588.0,75.3,6574.0,2331.0,260.0,294.0,88.4,139.0,181.0,76.8,27.0,64.0,42.2,48.0,40.0,42.0,2.0,82.0,475.0,110.0,27.0,1.0,0.0,59.0,43.0,20.0,14.0,0.0,14.0,3.0,14.0,82.0,6.09,54.0,14.0,5.0,3.0,9.0,0.67,7.0,2.0,0.0,0.0,0.0,8.0,5.0,5.0,1.0,2.0,8.0,1.0,7.0,2.0,1.0,0.0,725.0,5.0,44.0,295.0,400.0,82.0,725.0,455.0,72.0,39.0,33.0,506.0,22.0,18.0,0.0,8.0,11.0,8.0,0.0,0.0,0.0,44.0,1.0,4.0,20.0 +Samuel Di Carmine,it ITA,"FW,MF",Cremonese,34-208,1988,2.0,0.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.28,0.28,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.1,-0.1,6.0,8.0,75.0,79.0,30.0,4.0,5.0,80.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,1.0,0.0,0.0,1.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,4.0,5.0,2.0,9.0,6.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0 +Federico Di Francesco,it ITA,"FW,MF",Lecce,28-315,1994,30.0,20.0,1699.0,2.0,2.0,0.0,0.0,5.0,0.0,0.11,0.11,0.21,0.11,0.21,2.6,2.6,0.14,0.14,18.9,15.0,1.0,44.1,0.79,0.06,0.13,0.08,-0.6,-0.6,306.0,450.0,68.0,4396.0,1463.0,175.0,207.0,84.5,95.0,138.0,68.8,18.0,53.0,34.0,23.0,26.0,16.0,2.0,54.0,407.0,40.0,2.0,7.0,3.0,36.0,28.0,0.0,2.0,0.0,10.0,3.0,21.0,44.0,2.33,26.0,5.0,2.0,5.0,5.0,0.27,2.0,1.0,1.0,0.0,0.0,25.0,15.0,7.0,11.0,7.0,15.0,0.0,15.0,7.0,12.0,0.0,653.0,12.0,77.0,253.0,334.0,70.0,653.0,388.0,47.0,23.0,18.0,437.0,48.0,25.0,0.0,39.0,24.0,10.0,1.0,0.0,0.0,73.0,6.0,17.0,26.1 +Michele Di Gregorio,it ITA,GK,Monza,25-272,1997,30.0,30.0,2700.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.03,0.03,0.0,0.03,0.0,0.0,0.0,0.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,890.0,1181.0,75.4,22779.0,15217.0,210.0,210.0,100.0,399.0,408.0,97.8,275.0,556.0,49.5,1.0,11.0,1.0,0.0,0.0,903.0,277.0,81.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.07,2.0,0.0,0.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,16.0,2.0,1250.0,1031.0,1245.0,5.0,0.0,0.0,1250.0,747.0,0.0,0.0,0.0,652.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,4.0,1.0,80.0 +Giovanni Di Lorenzo,it ITA,DF,Napoli,29-264,1993,31.0,31.0,2778.0,2.0,4.0,0.0,0.0,2.0,0.0,0.06,0.13,0.19,0.06,0.19,2.3,2.3,0.08,0.08,30.9,9.0,0.0,36.0,0.29,0.08,0.22,0.09,-0.3,-0.3,1938.0,2288.0,84.7,31485.0,11106.0,1014.0,1096.0,92.5,744.0,852.0,87.3,138.0,211.0,65.4,41.0,155.0,48.0,9.0,220.0,1946.0,332.0,54.0,5.0,22.0,70.0,2.0,0.0,0.0,0.0,274.0,10.0,52.0,95.0,3.08,81.0,5.0,4.0,3.0,13.0,0.42,13.0,0.0,0.0,0.0,0.0,52.0,26.0,30.0,16.0,6.0,27.0,7.0,20.0,26.0,54.0,0.0,2521.0,90.0,638.0,1267.0,637.0,77.0,2521.0,1529.0,62.0,50.0,4.0,1775.0,20.0,16.0,0.0,28.0,29.0,4.0,0.0,0.0,0.0,151.0,39.0,30.0,56.5 +Ángel Di María,ar ARG,"FW,MF",Juventus,35-070,1988,20.0,12.0,1061.0,4.0,4.0,1.0,1.0,1.0,1.0,0.34,0.34,0.68,0.25,0.59,2.6,1.8,0.22,0.15,11.8,10.0,1.0,34.5,0.85,0.1,0.3,0.06,1.4,1.2,372.0,519.0,71.7,7075.0,2508.0,163.0,187.0,87.2,137.0,177.0,77.4,61.0,112.0,54.5,32.0,48.0,30.0,7.0,72.0,436.0,79.0,23.0,6.0,12.0,79.0,35.0,13.0,20.0,0.0,15.0,4.0,18.0,61.0,5.17,31.0,19.0,5.0,0.0,7.0,0.59,5.0,2.0,0.0,0.0,0.0,8.0,4.0,2.0,5.0,1.0,12.0,0.0,12.0,5.0,0.0,0.0,660.0,1.0,55.0,288.0,331.0,33.0,659.0,423.0,42.0,35.0,10.0,493.0,26.0,25.0,0.0,5.0,15.0,4.0,0.0,0.0,0.0,43.0,1.0,4.0,20.0 +Boulaye Dia,sn SEN,"FW,MF",Salernitana,26-160,1996,28.0,22.0,2068.0,11.0,6.0,0.0,0.0,3.0,0.0,0.48,0.26,0.74,0.48,0.74,6.7,6.7,0.29,0.29,23.0,17.0,0.0,42.5,0.74,0.28,0.65,0.17,4.3,4.3,368.0,485.0,75.9,5528.0,1109.0,189.0,229.0,82.5,127.0,157.0,80.9,21.0,29.0,72.4,31.0,28.0,15.0,2.0,45.0,470.0,12.0,0.0,2.0,6.0,6.0,0.0,0.0,0.0,0.0,3.0,3.0,26.0,56.0,2.43,46.0,0.0,5.0,3.0,7.0,0.3,7.0,0.0,0.0,0.0,0.0,10.0,4.0,3.0,4.0,3.0,4.0,0.0,4.0,5.0,24.0,1.0,726.0,27.0,95.0,330.0,316.0,64.0,726.0,510.0,39.0,36.0,11.0,555.0,63.0,46.0,0.0,24.0,34.0,12.0,0.0,0.0,0.0,80.0,17.0,48.0,26.2 +Brahim Díaz,es ESP,MF,Milan,23-265,1999,27.0,21.0,1491.0,5.0,5.0,0.0,0.0,2.0,0.0,0.3,0.3,0.6,0.3,0.6,4.3,4.3,0.26,0.26,16.6,15.0,1.0,50.0,0.91,0.17,0.33,0.14,0.7,0.7,454.0,561.0,80.9,6675.0,2051.0,249.0,282.0,88.3,159.0,189.0,84.1,24.0,41.0,58.5,33.0,67.0,34.0,1.0,104.0,529.0,29.0,6.0,7.0,2.0,15.0,5.0,0.0,4.0,0.0,8.0,3.0,15.0,72.0,4.35,51.0,1.0,5.0,4.0,12.0,0.72,5.0,0.0,2.0,0.0,1.0,24.0,11.0,4.0,17.0,3.0,10.0,0.0,10.0,5.0,5.0,0.0,774.0,4.0,52.0,431.0,312.0,48.0,774.0,529.0,70.0,53.0,13.0,588.0,57.0,42.0,0.0,23.0,35.0,2.0,0.0,0.0,0.0,82.0,2.0,16.0,11.1 +Federico Dimarco,it ITA,DF,Inter,25-166,1997,27.0,21.0,1707.0,3.0,2.0,0.0,0.0,0.0,0.0,0.16,0.11,0.26,0.16,0.26,2.4,2.4,0.12,0.12,19.0,9.0,6.0,25.0,0.47,0.08,0.33,0.07,0.6,0.6,777.0,1066.0,72.9,14755.0,5046.0,339.0,389.0,87.1,306.0,385.0,79.5,111.0,236.0,47.0,46.0,54.0,33.0,23.0,72.0,879.0,185.0,36.0,0.0,20.0,177.0,50.0,11.0,33.0,0.0,94.0,2.0,17.0,83.0,4.37,52.0,25.0,2.0,2.0,5.0,0.26,4.0,0.0,0.0,0.0,0.0,25.0,19.0,6.0,14.0,5.0,15.0,4.0,11.0,15.0,20.0,0.0,1230.0,39.0,260.0,471.0,510.0,58.0,1230.0,669.0,37.0,24.0,4.0,806.0,16.0,10.0,0.0,11.0,27.0,4.0,0.0,1.0,0.0,100.0,4.0,9.0,30.8 +Koffi Djidji,ci CIV,DF,Torino,30-146,1992,28.0,22.0,2045.0,1.0,0.0,0.0,0.0,2.0,0.0,0.04,0.0,0.04,0.04,0.04,0.9,0.9,0.04,0.04,22.7,4.0,0.0,50.0,0.18,0.13,0.25,0.12,0.1,0.1,1045.0,1212.0,86.2,18747.0,5513.0,423.0,452.0,93.6,512.0,561.0,91.3,89.0,152.0,58.6,2.0,76.0,3.0,2.0,76.0,1182.0,27.0,12.0,1.0,9.0,13.0,0.0,0.0,0.0,0.0,14.0,3.0,18.0,15.0,0.66,13.0,0.0,0.0,0.0,1.0,0.04,1.0,0.0,0.0,0.0,0.0,41.0,21.0,28.0,12.0,1.0,24.0,11.0,13.0,32.0,65.0,0.0,1419.0,110.0,627.0,715.0,84.0,8.0,1419.0,784.0,7.0,8.0,0.0,943.0,7.0,6.0,0.0,22.0,3.0,1.0,0.0,2.0,0.0,118.0,38.0,41.0,48.1 +Berat Djimsiti,al ALB,DF,Atalanta,30-065,1993,17.0,13.0,1188.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.03,0.03,13.2,0.0,0.0,0.0,0.0,0.0,0.0,0.14,-0.4,-0.4,635.0,741.0,85.7,10474.0,3392.0,317.0,346.0,91.6,276.0,306.0,90.2,34.0,67.0,50.7,1.0,28.0,2.0,0.0,36.0,709.0,31.0,17.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,13.0,1.0,6.0,3.0,0.23,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,15.0,17.0,8.0,0.0,13.0,6.0,7.0,21.0,49.0,1.0,876.0,93.0,443.0,388.0,48.0,8.0,876.0,492.0,6.0,6.0,1.0,541.0,8.0,5.0,0.0,10.0,5.0,0.0,0.0,0.0,0.0,83.0,34.0,27.0,55.7 +Dodô,br BRA,DF,Fiorentina,24-159,1998,27.0,24.0,2010.0,0.0,2.0,0.0,0.0,5.0,1.0,0.0,0.09,0.09,0.0,0.09,0.5,0.5,0.02,0.02,22.3,3.0,0.0,30.0,0.13,0.0,0.0,0.05,-0.5,-0.5,1078.0,1323.0,81.5,18892.0,5989.0,481.0,534.0,90.1,476.0,571.0,83.4,102.0,166.0,61.4,22.0,81.0,24.0,7.0,126.0,1126.0,195.0,19.0,2.0,5.0,49.0,0.0,0.0,0.0,0.0,176.0,2.0,26.0,59.0,2.64,52.0,2.0,2.0,1.0,4.0,0.18,4.0,0.0,0.0,0.0,0.0,33.0,19.0,16.0,12.0,5.0,21.0,2.0,19.0,23.0,35.0,0.0,1510.0,50.0,412.0,703.0,409.0,21.0,1510.0,911.0,57.0,50.0,5.0,955.0,33.0,12.0,1.0,27.0,34.0,0.0,0.0,1.0,0.0,159.0,18.0,9.0,66.7 +Josh Doig,sct SCO,DF,Hellas Verona,20-342,2002,21.0,15.0,1309.0,2.0,3.0,0.0,0.0,1.0,0.0,0.14,0.21,0.34,0.14,0.34,1.8,1.8,0.13,0.13,14.5,6.0,0.0,33.3,0.41,0.11,0.33,0.1,0.2,0.2,345.0,512.0,67.4,5834.0,2749.0,174.0,212.0,82.1,135.0,198.0,68.2,30.0,68.0,44.1,11.0,28.0,11.0,7.0,26.0,394.0,115.0,1.0,0.0,0.0,45.0,2.0,2.0,0.0,0.0,112.0,3.0,19.0,32.0,2.2,22.0,4.0,1.0,2.0,7.0,0.48,5.0,0.0,0.0,1.0,0.0,18.0,7.0,5.0,7.0,6.0,24.0,6.0,18.0,10.0,12.0,1.0,677.0,25.0,180.0,265.0,250.0,27.0,677.0,327.0,41.0,24.0,5.0,346.0,33.0,16.0,0.0,14.0,7.0,2.0,0.0,0.0,0.0,91.0,15.0,22.0,40.5 +Nicolás Domínguez,ar ARG,MF,Bologna,24-301,1998,25.0,20.0,1828.0,2.0,2.0,0.0,0.0,9.0,0.0,0.1,0.1,0.2,0.1,0.2,2.0,2.0,0.1,0.1,20.3,13.0,0.0,40.6,0.64,0.06,0.15,0.06,0.0,0.0,887.0,1093.0,81.2,13492.0,3792.0,485.0,550.0,88.2,293.0,355.0,82.5,62.0,98.0,63.3,26.0,88.0,19.0,0.0,112.0,1030.0,56.0,33.0,14.0,12.0,12.0,0.0,0.0,0.0,0.0,19.0,7.0,21.0,54.0,2.66,46.0,2.0,0.0,3.0,5.0,0.25,5.0,0.0,0.0,0.0,0.0,63.0,40.0,27.0,28.0,8.0,22.0,2.0,20.0,18.0,25.0,1.0,1321.0,37.0,248.0,757.0,329.0,29.0,1321.0,677.0,31.0,32.0,5.0,883.0,45.0,18.0,0.0,37.0,28.0,2.0,0.0,1.0,0.0,146.0,29.0,23.0,55.8 +Giulio Donati,it ITA,DF,Monza,33-079,1990,8.0,4.0,422.0,1.0,0.0,0.0,0.0,4.0,1.0,0.21,0.0,0.21,0.21,0.21,0.4,0.4,0.08,0.08,4.7,1.0,0.0,50.0,0.21,0.5,1.0,0.2,0.6,0.6,273.0,321.0,85.0,5396.0,1868.0,94.0,104.0,90.4,143.0,159.0,89.9,33.0,46.0,71.7,1.0,19.0,0.0,0.0,21.0,285.0,34.0,9.0,0.0,4.0,9.0,1.0,0.0,0.0,0.0,24.0,2.0,4.0,4.0,0.85,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,1.0,2.0,3.0,1.0,2.0,4.0,11.0,0.0,349.0,26.0,151.0,159.0,44.0,1.0,349.0,219.0,4.0,6.0,0.0,259.0,3.0,1.0,1.0,7.0,6.0,0.0,0.0,0.0,0.0,21.0,4.0,3.0,57.1 +Bartłomiej Drągowski,pl POL,GK,Spezia,25-249,1997,28.0,28.0,2406.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.04,0.04,0.0,0.04,0.0,0.0,0.0,0.0,26.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,644.0,945.0,68.1,17462.0,12124.0,169.0,170.0,99.4,272.0,276.0,98.6,196.0,490.0,40.0,1.0,18.0,3.0,0.0,0.0,703.0,241.0,72.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,6.0,0.22,5.0,1.0,0.0,0.0,2.0,0.07,2.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,3.0,1027.0,846.0,1022.0,5.0,0.0,0.0,1027.0,596.0,0.0,0.0,0.0,496.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,36.0,7.0,1.0,87.5 +Ondrej Duda,sk SVK,MF,Hellas Verona,28-141,1994,12.0,10.0,934.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.08,0.08,10.4,4.0,1.0,33.3,0.39,0.0,0.0,0.07,-0.8,-0.8,277.0,373.0,74.3,4883.0,1438.0,127.0,151.0,84.1,111.0,136.0,81.6,29.0,51.0,56.9,5.0,34.0,5.0,1.0,37.0,345.0,25.0,9.0,2.0,6.0,11.0,5.0,2.0,1.0,0.0,2.0,3.0,8.0,18.0,1.73,11.0,1.0,2.0,2.0,2.0,0.19,1.0,0.0,1.0,0.0,0.0,20.0,13.0,7.0,11.0,2.0,10.0,0.0,10.0,12.0,8.0,0.0,502.0,19.0,78.0,312.0,118.0,11.0,502.0,271.0,11.0,11.0,0.0,262.0,13.0,15.0,0.0,16.0,24.0,0.0,0.0,0.0,0.0,88.0,9.0,12.0,42.9 +Denzel Dumfries,nl NED,DF,Inter,27-007,1996,28.0,21.0,1853.0,1.0,3.0,0.0,0.0,4.0,0.0,0.05,0.15,0.19,0.05,0.19,3.2,3.2,0.16,0.16,20.6,13.0,0.0,44.8,0.63,0.03,0.08,0.11,-2.2,-2.2,589.0,809.0,72.8,9809.0,3136.0,281.0,329.0,85.4,252.0,338.0,74.6,44.0,91.0,48.4,28.0,24.0,28.0,15.0,50.0,664.0,142.0,3.0,0.0,1.0,73.0,0.0,0.0,0.0,0.0,139.0,3.0,21.0,63.0,3.06,42.0,4.0,8.0,7.0,10.0,0.49,5.0,0.0,2.0,3.0,0.0,27.0,13.0,12.0,11.0,4.0,28.0,8.0,20.0,12.0,23.0,0.0,1023.0,34.0,195.0,430.0,415.0,90.0,1023.0,536.0,52.0,28.0,22.0,632.0,33.0,16.0,0.0,31.0,33.0,2.0,2.0,1.0,1.0,74.0,35.0,34.0,50.7 +Alfred Duncan,gh GHA,MF,Fiorentina,30-046,1993,18.0,7.0,642.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.04,0.04,7.1,1.0,0.0,25.0,0.14,0.0,0.0,0.07,-0.3,-0.3,254.0,330.0,77.0,4684.0,1560.0,101.0,119.0,84.9,113.0,135.0,83.7,31.0,55.0,56.4,4.0,37.0,8.0,4.0,43.0,318.0,11.0,8.0,2.0,1.0,12.0,0.0,0.0,0.0,0.0,3.0,1.0,7.0,21.0,2.93,19.0,0.0,1.0,1.0,2.0,0.28,2.0,0.0,0.0,0.0,0.0,14.0,8.0,0.0,14.0,0.0,6.0,0.0,6.0,5.0,6.0,0.0,396.0,6.0,55.0,238.0,108.0,5.0,396.0,224.0,11.0,12.0,2.0,260.0,11.0,10.0,0.0,14.0,6.0,0.0,0.0,0.0,0.0,42.0,8.0,3.0,72.7 +Paulo Dybala,ar ARG,"MF,FW",Roma,29-161,1993,23.0,20.0,1629.0,11.0,6.0,4.0,4.0,3.0,0.0,0.61,0.33,0.94,0.39,0.72,8.2,5.1,0.46,0.28,18.1,17.0,3.0,30.9,0.94,0.13,0.41,0.09,2.8,1.9,599.0,770.0,77.8,10723.0,2778.0,308.0,349.0,88.3,176.0,220.0,80.0,94.0,138.0,68.1,51.0,45.0,20.0,1.0,66.0,680.0,88.0,20.0,5.0,26.0,77.0,52.0,14.0,35.0,0.0,9.0,2.0,26.0,97.0,5.36,47.0,28.0,9.0,9.0,8.0,0.44,4.0,2.0,1.0,1.0,0.0,14.0,4.0,1.0,8.0,5.0,11.0,0.0,11.0,8.0,4.0,0.0,1007.0,2.0,81.0,507.0,432.0,71.0,1003.0,631.0,51.0,39.0,19.0,724.0,60.0,30.0,0.0,10.0,49.0,5.0,2.0,0.0,0.0,73.0,4.0,15.0,21.1 +Edin Džeko,ba BIH,FW,Inter,37-039,1986,30.0,17.0,1595.0,7.0,3.0,0.0,0.0,3.0,0.0,0.39,0.17,0.56,0.39,0.56,8.8,8.8,0.49,0.49,17.7,21.0,0.0,32.3,1.18,0.11,0.33,0.14,-1.8,-1.8,325.0,472.0,68.9,5046.0,1212.0,175.0,226.0,77.4,111.0,158.0,70.3,24.0,47.0,51.1,21.0,35.0,18.0,2.0,61.0,450.0,19.0,1.0,6.0,6.0,22.0,0.0,0.0,0.0,0.0,1.0,3.0,14.0,57.0,3.21,44.0,0.0,11.0,1.0,11.0,0.62,7.0,0.0,3.0,0.0,0.0,2.0,2.0,0.0,2.0,0.0,14.0,1.0,13.0,3.0,23.0,0.0,673.0,28.0,62.0,308.0,313.0,101.0,673.0,357.0,25.0,11.0,9.0,523.0,44.0,16.0,0.0,23.0,23.0,10.0,0.0,0.0,0.0,59.0,38.0,41.0,48.1 +Festy Ebosele,ie IRL,DF,Udinese,20-266,2002,13.0,2.0,249.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.07,0.07,2.8,1.0,0.0,25.0,0.36,0.0,0.0,0.05,-0.2,-0.2,64.0,91.0,70.3,1159.0,586.0,28.0,35.0,80.0,30.0,39.0,76.9,6.0,12.0,50.0,3.0,8.0,8.0,5.0,15.0,81.0,10.0,1.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,9.0,0.0,3.0,7.0,2.54,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,8.0,6.0,5.0,0.0,3.0,0.0,3.0,1.0,4.0,0.0,149.0,6.0,36.0,59.0,62.0,8.0,149.0,84.0,20.0,13.0,4.0,83.0,11.0,7.0,0.0,5.0,5.0,0.0,0.0,0.0,0.0,21.0,3.0,2.0,60.0 +Enzo Ebosse,cm CMR,DF,Udinese,24-045,1999,20.0,9.0,930.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,10.3,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,453.0,557.0,81.3,8801.0,3202.0,179.0,201.0,89.1,203.0,240.0,84.6,65.0,102.0,63.7,1.0,43.0,4.0,1.0,51.0,479.0,77.0,17.0,0.0,11.0,15.0,1.0,0.0,0.0,0.0,59.0,1.0,3.0,8.0,0.78,7.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,9.0,11.0,4.0,0.0,27.0,6.0,21.0,11.0,29.0,0.0,667.0,51.0,292.0,306.0,72.0,0.0,667.0,338.0,11.0,7.0,1.0,383.0,8.0,3.0,0.0,9.0,4.0,1.0,0.0,0.0,0.0,48.0,15.0,14.0,51.7 +Tyronne Ebuehi,ng NGA,DF,Empoli,27-130,1995,20.0,16.0,1401.0,2.0,1.0,0.0,0.0,0.0,0.0,0.13,0.06,0.19,0.13,0.19,2.3,2.3,0.15,0.15,15.6,4.0,0.0,28.6,0.26,0.14,0.5,0.16,-0.3,-0.3,518.0,666.0,77.8,8641.0,3158.0,237.0,274.0,86.5,223.0,274.0,81.4,43.0,78.0,55.1,7.0,23.0,10.0,6.0,28.0,516.0,148.0,6.0,0.0,0.0,25.0,0.0,0.0,0.0,0.0,142.0,2.0,24.0,21.0,1.35,18.0,3.0,0.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,16.0,10.0,7.0,6.0,3.0,18.0,11.0,7.0,7.0,55.0,1.0,811.0,77.0,324.0,331.0,163.0,16.0,811.0,405.0,21.0,16.0,3.0,447.0,10.0,9.0,0.0,8.0,14.0,1.0,0.0,0.0,0.0,64.0,10.0,27.0,27.0 +Éderson,br BRA,MF,Atalanta,23-292,1999,28.0,20.0,1805.0,1.0,1.0,0.0,0.0,4.0,0.0,0.05,0.05,0.1,0.05,0.1,1.7,1.7,0.08,0.08,20.1,6.0,0.0,20.7,0.3,0.03,0.17,0.06,-0.7,-0.7,736.0,908.0,81.1,12926.0,3678.0,321.0,366.0,87.7,308.0,357.0,86.3,79.0,110.0,71.8,18.0,73.0,12.0,3.0,80.0,883.0,23.0,14.0,0.0,18.0,13.0,4.0,2.0,0.0,0.0,2.0,2.0,24.0,42.0,2.09,29.0,1.0,6.0,1.0,4.0,0.2,3.0,0.0,0.0,1.0,0.0,60.0,32.0,17.0,37.0,6.0,47.0,6.0,41.0,29.0,22.0,0.0,1204.0,42.0,280.0,641.0,300.0,41.0,1204.0,705.0,29.0,27.0,9.0,798.0,38.0,21.0,0.0,25.0,35.0,0.0,0.0,1.0,0.0,146.0,18.0,18.0,50.0 +Kingsley Ehizibue,nl NED,DF,Udinese,27-335,1995,25.0,14.0,1365.0,2.0,0.0,0.0,0.0,6.0,0.0,0.13,0.0,0.13,0.13,0.13,1.8,1.8,0.12,0.12,15.2,5.0,0.0,50.0,0.33,0.2,0.4,0.18,0.2,0.2,381.0,523.0,72.8,6288.0,2409.0,179.0,216.0,82.9,154.0,197.0,78.2,35.0,64.0,54.7,7.0,31.0,11.0,6.0,40.0,452.0,71.0,1.0,0.0,2.0,34.0,1.0,0.0,0.0,0.0,69.0,0.0,28.0,29.0,1.91,21.0,2.0,4.0,1.0,3.0,0.2,2.0,0.0,0.0,1.0,0.0,44.0,29.0,25.0,13.0,6.0,17.0,3.0,14.0,10.0,37.0,1.0,708.0,44.0,231.0,261.0,221.0,27.0,708.0,361.0,27.0,15.0,7.0,372.0,17.0,10.0,0.0,31.0,18.0,1.0,0.0,0.0,0.0,71.0,13.0,14.0,48.1 +Albin Ekdal,se SWE,MF,Spezia,33-271,1989,24.0,13.0,1291.0,0.0,2.0,0.0,0.0,5.0,1.0,0.0,0.14,0.14,0.0,0.14,2.4,2.4,0.17,0.17,14.3,3.0,0.0,20.0,0.21,0.0,0.0,0.17,-2.4,-2.4,461.0,596.0,77.3,6944.0,2455.0,250.0,304.0,82.2,169.0,204.0,82.8,23.0,46.0,50.0,8.0,49.0,11.0,1.0,61.0,572.0,22.0,18.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,2.0,2.0,11.0,26.0,1.81,19.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,18.0,11.0,17.0,2.0,20.0,1.0,19.0,22.0,34.0,0.0,770.0,43.0,205.0,435.0,140.0,28.0,770.0,380.0,11.0,15.0,2.0,430.0,21.0,10.0,1.0,24.0,21.0,0.0,0.0,0.0,0.0,90.0,25.0,13.0,65.8 +Emmanuel Ekong,se SWE,"FW,MF",Empoli,20-304,2002,2.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,3.0,4.0,75.0,54.0,0.0,2.0,3.0,66.7,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,4.0,2.0,0.0,6.0,3.0,1.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0 +Mikael Ellertsson,is ISL,"MF,DF",Spezia,21-045,2002,11.0,1.0,171.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.9,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.1,-0.1,24.0,40.0,60.0,322.0,82.0,16.0,18.0,88.9,7.0,14.0,50.0,0.0,5.0,0.0,1.0,3.0,0.0,0.0,3.0,39.0,1.0,0.0,0.0,0.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,5.0,2.65,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,3.0,0.0,2.0,1.0,1.0,2.0,0.0,0.0,60.0,3.0,11.0,31.0,19.0,0.0,60.0,21.0,3.0,2.0,0.0,25.0,2.0,2.0,0.0,6.0,3.0,0.0,0.0,0.0,0.0,7.0,2.0,2.0,50.0 +Elif Elmas,mk MKD,"MF,FW",Napoli,23-213,1999,30.0,9.0,1227.0,6.0,3.0,1.0,1.0,3.0,0.0,0.44,0.22,0.66,0.37,0.59,3.7,2.9,0.27,0.21,13.6,9.0,1.0,37.5,0.66,0.21,0.56,0.12,2.3,2.1,648.0,750.0,86.4,8934.0,2035.0,402.0,436.0,92.2,189.0,221.0,85.5,26.0,40.0,65.0,20.0,46.0,21.0,5.0,52.0,718.0,29.0,9.0,3.0,1.0,17.0,11.0,2.0,5.0,0.0,6.0,3.0,8.0,42.0,3.08,30.0,4.0,2.0,2.0,8.0,0.59,5.0,1.0,1.0,0.0,0.0,16.0,9.0,5.0,8.0,3.0,10.0,1.0,9.0,11.0,4.0,0.0,893.0,10.0,101.0,461.0,343.0,38.0,892.0,548.0,32.0,27.0,9.0,703.0,27.0,16.0,0.0,16.0,11.0,3.0,0.0,0.0,0.0,63.0,3.0,11.0,21.4 +Martin Erlic,hr CRO,DF,Sassuolo,25-091,1998,22.0,21.0,1878.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.01,0.01,20.9,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.2,-0.2,882.0,1030.0,85.6,18671.0,6396.0,242.0,270.0,89.6,492.0,537.0,91.6,143.0,209.0,68.4,2.0,37.0,0.0,0.0,47.0,983.0,45.0,41.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,6.0,8.0,0.38,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,10.0,14.0,5.0,1.0,31.0,20.0,11.0,26.0,97.0,1.0,1247.0,161.0,713.0,528.0,11.0,7.0,1247.0,721.0,5.0,1.0,1.0,770.0,12.0,1.0,0.0,17.0,12.0,0.0,0.0,0.0,0.0,96.0,40.0,36.0,52.6 +Gonzalo Escalante,ar ARG,MF,Cremonese,30-029,1993,9.0,7.0,555.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.16,0.16,0.0,0.16,0.2,0.2,0.03,0.03,6.2,1.0,0.0,16.7,0.16,0.0,0.0,0.03,-0.2,-0.2,222.0,290.0,76.6,3755.0,1430.0,99.0,117.0,84.6,103.0,122.0,84.4,16.0,36.0,44.4,4.0,30.0,2.0,0.0,30.0,279.0,8.0,4.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,8.0,10.0,1.62,8.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,8.0,8.0,8.0,0.0,7.0,1.0,6.0,10.0,4.0,0.0,352.0,16.0,103.0,199.0,56.0,7.0,352.0,174.0,3.0,4.0,0.0,226.0,7.0,7.0,0.0,6.0,5.0,0.0,0.0,0.0,0.0,39.0,9.0,9.0,50.0 +Salvatore Esposito,it ITA,MF,Spezia,22-200,2000,8.0,4.0,411.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,4.6,1.0,2.0,50.0,0.22,0.0,0.0,0.04,-0.1,-0.1,251.0,311.0,80.7,4140.0,1133.0,142.0,155.0,91.6,86.0,102.0,84.3,18.0,41.0,43.9,2.0,23.0,2.0,0.0,21.0,284.0,27.0,16.0,0.0,5.0,16.0,11.0,7.0,3.0,0.0,0.0,0.0,2.0,9.0,1.97,4.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,7.0,3.0,3.0,1.0,5.0,1.0,4.0,8.0,11.0,0.0,350.0,11.0,84.0,209.0,58.0,1.0,350.0,186.0,2.0,1.0,1.0,237.0,6.0,3.0,1.0,5.0,13.0,0.0,0.0,0.0,0.0,30.0,4.0,1.0,80.0 +Nicolò Fagioli,it ITA,MF,Juventus,22-072,2001,22.0,13.0,1220.0,2.0,3.0,0.0,0.0,3.0,0.0,0.15,0.22,0.37,0.15,0.37,0.6,0.6,0.05,0.05,13.6,5.0,0.0,50.0,0.37,0.2,0.4,0.06,1.4,1.4,402.0,482.0,83.4,6436.0,1389.0,224.0,246.0,91.1,136.0,153.0,88.9,34.0,62.0,54.8,13.0,35.0,12.0,2.0,41.0,454.0,28.0,8.0,3.0,9.0,20.0,9.0,2.0,4.0,0.0,11.0,0.0,11.0,29.0,2.14,18.0,4.0,3.0,4.0,5.0,0.37,3.0,1.0,0.0,1.0,0.0,23.0,15.0,9.0,12.0,2.0,12.0,2.0,10.0,8.0,6.0,1.0,602.0,15.0,104.0,321.0,182.0,10.0,602.0,376.0,21.0,15.0,3.0,422.0,29.0,20.0,0.0,20.0,20.0,1.0,1.0,0.0,0.0,85.0,0.0,6.0,0.0 +Wladimiro Falcone,it ITA,GK,Lecce,28-013,1995,31.0,31.0,2790.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,501.0,976.0,51.3,16268.0,12785.0,80.0,80.0,100.0,191.0,200.0,95.5,227.0,686.0,33.1,2.0,11.0,2.0,0.0,0.0,656.0,314.0,89.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,3.0,0.1,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,19.0,3.0,1067.0,883.0,1063.0,4.0,0.0,0.0,1067.0,554.0,0.0,0.0,0.0,367.0,1.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,35.0,10.0,1.0,90.9 +Davide Faraoni,it ITA,DF,Hellas Verona,31-182,1991,17.0,15.0,1097.0,1.0,3.0,0.0,0.0,5.0,0.0,0.08,0.25,0.33,0.08,0.33,2.0,2.0,0.17,0.17,12.2,4.0,0.0,26.7,0.33,0.07,0.25,0.13,-1.0,-1.0,297.0,452.0,65.7,5317.0,2424.0,132.0,159.0,83.0,127.0,182.0,69.8,32.0,79.0,40.5,8.0,26.0,12.0,5.0,45.0,356.0,93.0,2.0,1.0,6.0,30.0,0.0,0.0,0.0,0.0,91.0,3.0,13.0,21.0,1.72,13.0,2.0,4.0,0.0,4.0,0.33,2.0,0.0,1.0,0.0,0.0,25.0,16.0,12.0,11.0,2.0,24.0,9.0,15.0,11.0,28.0,0.0,583.0,43.0,164.0,259.0,162.0,22.0,583.0,249.0,8.0,5.0,2.0,264.0,9.0,2.0,0.0,14.0,8.0,2.0,0.0,0.0,0.0,68.0,13.0,18.0,41.9 +Federico Fazio,ar ARG,DF,Salernitana,36-039,1987,14.0,12.0,1074.0,1.0,0.0,0.0,0.0,2.0,1.0,0.08,0.0,0.08,0.08,0.08,0.7,0.7,0.06,0.06,11.9,2.0,0.0,18.2,0.17,0.09,0.5,0.06,0.3,0.3,410.0,536.0,76.5,7740.0,3216.0,139.0,170.0,81.8,214.0,250.0,85.6,47.0,94.0,50.0,3.0,24.0,3.0,0.0,40.0,503.0,33.0,18.0,0.0,8.0,4.0,0.0,0.0,0.0,0.0,15.0,0.0,6.0,12.0,1.0,7.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,12.0,8.0,7.0,0.0,9.0,4.0,5.0,28.0,61.0,0.0,679.0,84.0,318.0,309.0,58.0,17.0,679.0,347.0,13.0,11.0,1.0,358.0,11.0,5.0,0.0,11.0,6.0,0.0,0.0,3.0,0.0,74.0,36.0,13.0,73.5 +Jacopo Fazzini,it ITA,MF,Empoli,20-040,2003,17.0,5.0,586.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.1,0.1,6.5,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.7,-0.7,202.0,251.0,80.5,2649.0,721.0,135.0,151.0,89.4,51.0,59.0,86.4,7.0,18.0,38.9,7.0,11.0,4.0,1.0,16.0,242.0,8.0,5.0,1.0,0.0,5.0,1.0,0.0,1.0,0.0,2.0,1.0,5.0,16.0,2.47,12.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,14.0,16.0,7.0,2.0,13.0,2.0,11.0,7.0,18.0,0.0,368.0,19.0,117.0,160.0,95.0,12.0,368.0,192.0,9.0,13.0,0.0,221.0,18.0,18.0,0.0,18.0,8.0,0.0,0.0,0.0,0.0,45.0,10.0,23.0,30.3 +Lewis Ferguson,sct SCO,MF,Bologna,23-244,1999,25.0,22.0,1867.0,4.0,0.0,0.0,0.0,4.0,0.0,0.19,0.0,0.19,0.19,0.19,3.5,3.5,0.17,0.17,20.7,12.0,0.0,37.5,0.58,0.13,0.33,0.11,0.5,0.5,796.0,946.0,84.1,11174.0,2183.0,484.0,534.0,90.6,250.0,287.0,87.1,30.0,55.0,54.5,15.0,53.0,7.0,0.0,67.0,921.0,22.0,9.0,3.0,0.0,11.0,0.0,0.0,0.0,0.0,11.0,3.0,16.0,54.0,2.6,37.0,1.0,8.0,6.0,5.0,0.24,4.0,0.0,1.0,0.0,0.0,35.0,22.0,14.0,17.0,4.0,17.0,6.0,11.0,16.0,19.0,0.0,1145.0,34.0,214.0,621.0,318.0,58.0,1145.0,623.0,25.0,29.0,6.0,875.0,29.0,16.0,0.0,40.0,35.0,5.0,0.0,0.0,0.0,87.0,21.0,31.0,40.4 +Alex Ferrari,it ITA,DF,Sampdoria,28-298,1994,12.0,10.0,868.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.02,0.02,9.6,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.2,-0.2,384.0,456.0,84.2,7605.0,2387.0,114.0,126.0,90.5,220.0,242.0,90.9,41.0,67.0,61.2,3.0,17.0,1.0,0.0,25.0,433.0,23.0,21.0,0.0,3.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,8.0,4.0,0.41,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,5.0,7.0,2.0,0.0,21.0,10.0,11.0,19.0,25.0,0.0,560.0,51.0,276.0,265.0,25.0,5.0,560.0,304.0,7.0,1.0,0.0,308.0,10.0,3.0,0.0,12.0,7.0,0.0,0.0,1.0,0.0,62.0,21.0,15.0,58.3 +Alex Ferrari,it ITA,DF,Cremonese,28-298,1994,11.0,10.0,749.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,270.0,362.0,74.6,5192.0,1837.0,101.0,124.0,81.5,136.0,164.0,82.9,31.0,62.0,50.0,3.0,24.0,3.0,0.0,22.0,337.0,25.0,15.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,10.0,0.0,6.0,4.0,0.48,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,18.0,26.0,4.0,0.0,13.0,3.0,10.0,19.0,33.0,1.0,462.0,38.0,234.0,212.0,20.0,1.0,462.0,230.0,1.0,3.0,0.0,217.0,3.0,1.0,0.0,7.0,5.0,0.0,0.0,0.0,0.0,58.0,11.0,5.0,68.8 +Salvador Ferrer,es ESP,DF,Spezia,25-094,1998,3.0,0.0,102.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,53.0,65.0,81.5,994.0,301.0,21.0,22.0,95.5,23.0,30.0,76.7,8.0,10.0,80.0,1.0,1.0,0.0,0.0,1.0,58.0,7.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,1.0,0.88,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,2.0,0.0,1.0,1.0,0.0,1.0,2.0,0.0,76.0,9.0,29.0,38.0,9.0,0.0,76.0,42.0,0.0,0.0,0.0,55.0,2.0,1.0,0.0,1.0,3.0,0.0,0.0,1.0,0.0,4.0,3.0,3.0,50.0 +Alessandro Florenzi,it ITA,DF,Milan,32-045,1991,5.0,2.0,196.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.35,0.35,2.2,1.0,1.0,20.0,0.46,0.0,0.0,0.15,-0.8,-0.8,124.0,165.0,75.2,2281.0,618.0,50.0,55.0,90.9,61.0,74.0,82.4,12.0,34.0,35.3,6.0,7.0,6.0,5.0,10.0,131.0,33.0,7.0,0.0,1.0,22.0,6.0,2.0,4.0,0.0,20.0,1.0,0.0,9.0,4.15,4.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,4.0,3.0,2.0,1.0,1.0,0.0,1.0,3.0,2.0,0.0,188.0,3.0,40.0,78.0,70.0,10.0,188.0,101.0,1.0,1.0,0.0,110.0,3.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,13.0,1.0,0.0,100.0 +Davide Frattesi,it ITA,MF,Sassuolo,23-185,1999,31.0,30.0,2454.0,6.0,0.0,0.0,0.0,4.0,0.0,0.22,0.0,0.22,0.22,0.22,5.9,5.9,0.22,0.22,27.3,21.0,0.0,39.6,0.77,0.11,0.29,0.11,0.1,0.1,680.0,877.0,77.5,11542.0,3193.0,331.0,401.0,82.5,252.0,309.0,81.6,71.0,106.0,67.0,25.0,68.0,18.0,1.0,114.0,853.0,23.0,17.0,4.0,17.0,19.0,0.0,0.0,0.0,0.0,6.0,1.0,25.0,72.0,2.64,55.0,0.0,5.0,5.0,4.0,0.15,4.0,0.0,0.0,0.0,0.0,53.0,29.0,19.0,27.0,7.0,19.0,3.0,16.0,12.0,39.0,0.0,1210.0,60.0,249.0,594.0,387.0,90.0,1210.0,682.0,65.0,49.0,19.0,839.0,48.0,35.0,0.0,36.0,48.0,0.0,0.0,0.0,0.0,133.0,22.0,22.0,50.0 +Matteo Gabbia,it ITA,DF,Milan,23-186,1999,12.0,6.0,613.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,6.8,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,289.0,359.0,80.5,6382.0,2047.0,78.0,92.0,84.8,147.0,163.0,90.2,59.0,91.0,64.8,0.0,14.0,0.0,0.0,15.0,346.0,13.0,7.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,4.0,0.59,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,10.0,12.0,5.0,0.0,8.0,3.0,5.0,5.0,29.0,0.0,427.0,54.0,258.0,163.0,7.0,1.0,427.0,187.0,1.0,0.0,1.0,248.0,1.0,0.0,0.0,8.0,1.0,0.0,0.0,0.0,0.0,37.0,15.0,4.0,78.9 +Manolo Gabbiadini,it ITA,"FW,MF",Sampdoria,31-150,1991,28.0,20.0,1772.0,6.0,2.0,0.0,0.0,5.0,0.0,0.3,0.1,0.41,0.3,0.41,6.3,6.3,0.32,0.32,19.7,23.0,4.0,38.3,1.17,0.1,0.26,0.11,-0.3,-0.3,316.0,479.0,66.0,4215.0,1076.0,196.0,250.0,78.4,85.0,126.0,67.5,12.0,36.0,33.3,16.0,24.0,18.0,1.0,40.0,447.0,29.0,4.0,4.0,1.0,23.0,12.0,2.0,8.0,0.0,3.0,3.0,17.0,44.0,2.23,25.0,1.0,8.0,6.0,3.0,0.15,2.0,0.0,0.0,1.0,0.0,12.0,7.0,3.0,5.0,4.0,11.0,1.0,10.0,4.0,21.0,0.0,687.0,25.0,72.0,296.0,323.0,71.0,687.0,413.0,22.0,16.0,8.0,523.0,43.0,22.0,0.0,29.0,53.0,10.0,1.0,0.0,0.0,52.0,38.0,59.0,39.2 +Gianluca Gaetano,it ITA,MF,Napoli,22-355,2000,5.0,0.0,40.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.25,2.25,0.0,2.25,0.3,0.3,0.74,0.74,0.4,1.0,0.0,33.3,2.25,0.0,0.0,0.11,-0.3,-0.3,25.0,33.0,75.8,390.0,108.0,13.0,14.0,92.9,8.0,11.0,72.7,3.0,4.0,75.0,1.0,2.0,0.0,0.0,4.0,29.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,4.5,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,1.0,2.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,40.0,0.0,2.0,24.0,14.0,4.0,40.0,30.0,1.0,0.0,1.0,31.0,0.0,4.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0 +Roberto Gagliardini,it ITA,MF,Inter,29-018,1994,14.0,4.0,529.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.12,0.12,5.9,2.0,0.0,15.4,0.34,0.0,0.0,0.06,-0.7,-0.7,199.0,245.0,81.2,2993.0,889.0,118.0,139.0,84.9,63.0,73.0,86.3,12.0,19.0,63.2,5.0,21.0,10.0,4.0,29.0,241.0,3.0,2.0,1.0,1.0,7.0,0.0,0.0,0.0,0.0,1.0,1.0,3.0,16.0,2.72,14.0,0.0,1.0,1.0,1.0,0.17,0.0,0.0,1.0,0.0,0.0,15.0,5.0,8.0,6.0,1.0,8.0,2.0,6.0,1.0,9.0,0.0,314.0,9.0,65.0,149.0,104.0,22.0,314.0,167.0,17.0,9.0,1.0,216.0,5.0,2.0,0.0,10.0,8.0,0.0,0.0,0.0,0.0,28.0,14.0,7.0,66.7 +Adolfo Gaich,ar ARG,FW,Hellas Verona,24-058,1999,11.0,7.0,580.0,1.0,0.0,0.0,0.0,1.0,0.0,0.16,0.0,0.16,0.16,0.16,1.5,1.5,0.24,0.24,6.4,4.0,0.0,26.7,0.62,0.07,0.25,0.1,-0.5,-0.5,81.0,143.0,56.6,901.0,164.0,54.0,88.0,61.4,18.0,31.0,58.1,0.0,2.0,0.0,5.0,6.0,0.0,0.0,8.0,140.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,17.0,2.64,13.0,0.0,0.0,2.0,1.0,0.16,0.0,0.0,0.0,1.0,0.0,5.0,3.0,0.0,3.0,2.0,6.0,2.0,4.0,2.0,6.0,1.0,220.0,8.0,17.0,114.0,91.0,17.0,220.0,128.0,5.0,3.0,4.0,170.0,18.0,8.0,0.0,16.0,13.0,3.0,1.0,0.0,0.0,11.0,21.0,44.0,32.3 +Pablo Galdames Millán,cl CHI,"MF,DF",Cremonese,26-116,1996,6.0,3.0,274.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.05,0.05,3.0,1.0,0.0,50.0,0.33,0.0,0.0,0.08,-0.2,-0.2,90.0,127.0,70.9,1470.0,425.0,54.0,61.0,88.5,26.0,37.0,70.3,9.0,19.0,47.4,6.0,8.0,2.0,0.0,12.0,115.0,12.0,6.0,0.0,3.0,11.0,3.0,2.0,1.0,0.0,2.0,0.0,6.0,9.0,2.96,7.0,2.0,0.0,0.0,1.0,0.33,1.0,0.0,0.0,0.0,0.0,9.0,6.0,6.0,1.0,2.0,7.0,1.0,6.0,4.0,4.0,0.0,157.0,6.0,36.0,83.0,38.0,5.0,157.0,75.0,0.0,1.0,0.0,96.0,3.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,15.0,1.0,3.0,25.0 +Antonino Gallo,it ITA,DF,Lecce,23-110,2000,26.0,22.0,1968.0,0.0,1.0,0.0,0.0,4.0,1.0,0.0,0.05,0.05,0.0,0.05,0.5,0.5,0.02,0.02,21.9,1.0,0.0,16.7,0.05,0.0,0.0,0.09,-0.5,-0.5,691.0,1024.0,67.5,12647.0,5803.0,297.0,351.0,84.6,301.0,421.0,71.5,80.0,203.0,39.4,32.0,100.0,27.0,17.0,129.0,799.0,219.0,20.0,0.0,12.0,73.0,0.0,0.0,0.0,0.0,199.0,6.0,19.0,52.0,2.38,43.0,5.0,0.0,1.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,49.0,31.0,31.0,10.0,8.0,28.0,3.0,25.0,11.0,53.0,0.0,1230.0,69.0,382.0,528.0,340.0,13.0,1230.0,560.0,58.0,33.0,5.0,578.0,19.0,7.0,1.0,11.0,13.0,2.0,0.0,0.0,2.0,160.0,23.0,17.0,57.5 +Federico Gatti,it ITA,DF,Juventus,24-305,1998,12.0,11.0,994.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,11.0,2.0,0.0,25.0,0.18,0.0,0.0,0.04,-0.3,-0.3,562.0,673.0,83.5,11489.0,3784.0,195.0,207.0,94.2,273.0,299.0,91.3,88.0,144.0,61.1,4.0,33.0,1.0,0.0,36.0,651.0,19.0,14.0,0.0,19.0,1.0,0.0,0.0,0.0,0.0,3.0,3.0,9.0,16.0,1.45,14.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,8.0,8.0,2.0,1.0,14.0,9.0,5.0,15.0,46.0,1.0,791.0,83.0,419.0,339.0,41.0,15.0,791.0,490.0,9.0,5.0,1.0,563.0,5.0,3.0,0.0,12.0,12.0,0.0,0.0,0.0,0.0,72.0,20.0,17.0,54.1 +Valentin Gendrey,fr FRA,DF,Lecce,22-308,2000,31.0,29.0,2598.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.03,0.03,0.0,0.03,0.5,0.5,0.02,0.02,28.9,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.5,-0.5,883.0,1250.0,70.6,14247.0,7058.0,474.0,558.0,84.9,330.0,461.0,71.6,61.0,167.0,36.5,24.0,61.0,18.0,11.0,94.0,923.0,322.0,14.0,1.0,8.0,63.0,0.0,0.0,0.0,0.0,308.0,5.0,32.0,40.0,1.39,31.0,4.0,0.0,3.0,1.0,0.03,0.0,0.0,0.0,1.0,0.0,76.0,48.0,48.0,20.0,8.0,42.0,4.0,38.0,35.0,72.0,0.0,1567.0,91.0,554.0,665.0,363.0,14.0,1567.0,641.0,38.0,27.0,3.0,674.0,44.0,12.0,0.0,20.0,25.0,1.0,1.0,1.0,0.0,169.0,40.0,40.0,50.0 +Paolo Ghiglione,it ITA,DF,Cremonese,26-082,1997,13.0,7.0,612.0,1.0,1.0,0.0,0.0,2.0,0.0,0.15,0.15,0.29,0.15,0.29,0.5,0.5,0.08,0.08,6.8,1.0,0.0,20.0,0.15,0.2,1.0,0.1,0.5,0.5,128.0,239.0,53.6,2538.0,1370.0,45.0,68.0,66.2,64.0,108.0,59.3,19.0,50.0,38.0,11.0,15.0,15.0,12.0,18.0,185.0,54.0,1.0,0.0,1.0,35.0,2.0,0.0,2.0,0.0,51.0,0.0,11.0,16.0,2.35,13.0,3.0,0.0,0.0,2.0,0.29,2.0,0.0,0.0,0.0,0.0,13.0,9.0,9.0,3.0,1.0,9.0,4.0,5.0,4.0,11.0,0.0,303.0,15.0,82.0,115.0,108.0,7.0,303.0,125.0,4.0,5.0,1.0,143.0,5.0,4.0,0.0,14.0,5.0,2.0,0.0,1.0,0.0,34.0,5.0,6.0,45.5 +Mario Gila,es ESP,DF,Lazio,22-239,2000,4.0,0.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,56.0,61.0,91.8,953.0,330.0,25.0,27.0,92.6,24.0,26.0,92.3,6.0,7.0,85.7,0.0,1.0,0.0,0.0,6.0,56.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,0.0,71.0,6.0,36.0,33.0,2.0,0.0,71.0,42.0,1.0,1.0,0.0,46.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,6.0,0.0,3.0,0.0 +Gvidas Gineitis,lt LTU,MF,Torino,19-010,2004,3.0,2.0,136.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,75.0,90.0,83.3,1268.0,262.0,36.0,43.0,83.7,31.0,35.0,88.6,6.0,7.0,85.7,0.0,6.0,0.0,0.0,6.0,87.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,3.0,3.0,1.99,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,102.0,1.0,9.0,74.0,19.0,3.0,102.0,58.0,3.0,3.0,1.0,75.0,1.0,1.0,0.0,6.0,1.0,0.0,0.0,0.0,0.0,11.0,0.0,5.0,0.0 +Olivier Giroud,fr FRA,FW,Milan,36-207,1986,26.0,20.0,1671.0,8.0,4.0,1.0,1.0,8.0,1.0,0.43,0.22,0.65,0.38,0.59,9.3,8.6,0.5,0.46,18.6,22.0,3.0,33.8,1.18,0.11,0.32,0.13,-1.3,-1.6,267.0,434.0,61.5,3208.0,638.0,167.0,256.0,65.2,66.0,99.0,66.7,6.0,12.0,50.0,21.0,12.0,5.0,0.0,31.0,408.0,25.0,2.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,1.0,10.0,40.0,2.16,32.0,0.0,6.0,2.0,5.0,0.27,2.0,0.0,3.0,0.0,0.0,7.0,5.0,2.0,3.0,2.0,10.0,3.0,7.0,2.0,19.0,0.0,637.0,25.0,52.0,281.0,312.0,132.0,636.0,271.0,10.0,8.0,7.0,489.0,53.0,11.0,1.0,34.0,19.0,14.0,0.0,0.0,0.0,36.0,86.0,53.0,61.9 +Pierluigi Gollini,it ITA,GK,Fiorentina,28-038,1995,3.0,3.0,270.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,96.0,126.0,76.2,2622.0,1774.0,18.0,18.0,100.0,42.0,42.0,100.0,36.0,66.0,54.5,0.0,1.0,0.0,0.0,0.0,111.0,15.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,131.0,116.0,131.0,0.0,0.0,0.0,131.0,104.0,0.0,0.0,0.0,91.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Pierluigi Gollini,it ITA,GK,Napoli,28-038,1995,1.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,37.0,83.8,774.0,527.0,4.0,4.0,100.0,16.0,16.0,100.0,11.0,17.0,64.7,0.0,0.0,0.0,0.0,0.0,31.0,6.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,32.0,38.0,0.0,0.0,0.0,38.0,30.0,0.0,0.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Joan Gonzàlez,es ESP,MF,Lecce,21-083,2002,30.0,24.0,2036.0,1.0,3.0,0.0,0.0,6.0,0.0,0.04,0.13,0.18,0.04,0.18,1.5,1.5,0.07,0.07,22.6,4.0,0.0,23.5,0.18,0.06,0.25,0.09,-0.5,-0.5,546.0,710.0,76.9,8424.0,2356.0,285.0,347.0,82.1,196.0,229.0,85.6,39.0,74.0,52.7,14.0,65.0,8.0,3.0,64.0,668.0,40.0,7.0,2.0,6.0,26.0,1.0,0.0,0.0,0.0,13.0,2.0,15.0,39.0,1.72,30.0,0.0,3.0,4.0,3.0,0.13,2.0,0.0,0.0,0.0,1.0,47.0,28.0,20.0,18.0,9.0,37.0,8.0,29.0,15.0,27.0,0.0,991.0,43.0,211.0,513.0,282.0,35.0,991.0,486.0,18.0,19.0,6.0,573.0,44.0,25.0,0.0,32.0,43.0,2.0,0.0,0.0,1.0,124.0,29.0,53.0,35.4 +Nicolás González,ar ARG,"FW,MF",Fiorentina,25-019,1998,19.0,10.0,1026.0,4.0,1.0,2.0,2.0,3.0,0.0,0.35,0.09,0.44,0.18,0.26,5.1,3.8,0.44,0.33,11.4,13.0,0.0,33.3,1.14,0.05,0.15,0.1,-1.1,-1.8,300.0,446.0,67.3,5305.0,1189.0,125.0,182.0,68.7,141.0,167.0,84.4,26.0,53.0,49.1,18.0,28.0,18.0,5.0,50.0,422.0,21.0,8.0,1.0,10.0,28.0,1.0,0.0,0.0,0.0,12.0,3.0,18.0,47.0,4.12,31.0,0.0,5.0,6.0,3.0,0.26,2.0,0.0,1.0,0.0,0.0,10.0,5.0,2.0,5.0,3.0,11.0,0.0,11.0,2.0,5.0,0.0,629.0,4.0,57.0,288.0,294.0,58.0,627.0,404.0,32.0,26.0,7.0,479.0,42.0,11.0,0.0,11.0,41.0,3.0,0.0,0.0,0.0,48.0,45.0,25.0,64.3 +Robin Gosens,de GER,DF,Inter,28-294,1994,27.0,9.0,966.0,2.0,0.0,0.0,0.0,1.0,0.0,0.19,0.0,0.19,0.19,0.19,2.4,2.4,0.23,0.23,10.7,4.0,0.0,17.4,0.37,0.09,0.5,0.11,-0.4,-0.4,432.0,574.0,75.3,6886.0,2559.0,227.0,248.0,91.5,175.0,232.0,75.4,23.0,65.0,35.4,14.0,31.0,14.0,7.0,39.0,490.0,80.0,1.0,1.0,2.0,39.0,0.0,0.0,0.0,0.0,79.0,4.0,14.0,35.0,3.27,29.0,1.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,15.0,12.0,9.0,9.0,13.0,1.0,12.0,21.0,12.0,0.0,711.0,22.0,152.0,269.0,294.0,44.0,711.0,366.0,24.0,18.0,5.0,457.0,17.0,3.0,0.0,15.0,2.0,3.0,0.0,0.0,0.0,45.0,17.0,10.0,63.0 +Alberto Grassi,it ITA,MF,Empoli,28-049,1995,18.0,7.0,631.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,7.0,2.0,0.0,33.3,0.29,0.0,0.0,0.06,-0.4,-0.4,234.0,295.0,79.3,3458.0,1154.0,134.0,156.0,85.9,70.0,89.0,78.7,17.0,32.0,53.1,3.0,16.0,4.0,0.0,29.0,284.0,11.0,11.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,6.0,0.86,5.0,0.0,0.0,1.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,18.0,16.0,9.0,9.0,0.0,13.0,8.0,5.0,19.0,30.0,0.0,405.0,50.0,150.0,212.0,45.0,6.0,405.0,155.0,2.0,3.0,0.0,210.0,10.0,6.0,0.0,13.0,7.0,0.0,0.0,0.0,0.0,45.0,14.0,7.0,66.7 +Andrew Gravillon,gp GLP,DF,Torino,25-076,1998,7.0,4.0,311.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.5,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,131.0,163.0,80.4,2543.0,762.0,46.0,51.0,90.2,75.0,85.0,88.2,10.0,21.0,47.6,0.0,5.0,0.0,0.0,9.0,156.0,6.0,4.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,0.0,3.0,0.0,4.0,2.0,2.0,9.0,16.0,0.0,212.0,25.0,121.0,77.0,14.0,0.0,212.0,112.0,1.0,2.0,0.0,131.0,3.0,1.0,0.0,9.0,1.0,0.0,0.0,0.0,0.0,10.0,6.0,5.0,54.5 +Koray Günter,de GER,DF,Hellas Verona,28-252,1994,13.0,11.0,1003.0,1.0,0.0,0.0,0.0,3.0,0.0,0.09,0.0,0.09,0.09,0.09,0.9,0.9,0.08,0.08,11.1,1.0,0.0,11.1,0.09,0.11,1.0,0.1,0.1,0.1,323.0,422.0,76.5,6871.0,3172.0,102.0,126.0,81.0,160.0,183.0,87.4,58.0,101.0,57.4,8.0,31.0,1.0,1.0,36.0,400.0,21.0,20.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,1.0,1.0,5.0,13.0,1.17,10.0,1.0,2.0,0.0,1.0,0.09,0.0,0.0,1.0,0.0,0.0,10.0,6.0,1.0,8.0,1.0,24.0,15.0,9.0,30.0,46.0,0.0,557.0,82.0,283.0,252.0,29.0,10.0,557.0,221.0,7.0,6.0,0.0,230.0,3.0,1.0,0.0,17.0,10.0,0.0,0.0,0.0,0.0,81.0,22.0,21.0,51.2 +Koray Günter,de GER,"DF,MF",Sampdoria,28-252,1994,4.0,3.0,326.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,3.6,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.1,-0.1,138.0,169.0,81.7,2741.0,810.0,42.0,49.0,85.7,86.0,95.0,90.5,10.0,22.0,45.5,1.0,6.0,1.0,1.0,10.0,160.0,9.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,2.0,0.55,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,3.0,2.0,0.0,6.0,4.0,2.0,4.0,20.0,0.0,210.0,34.0,96.0,103.0,12.0,4.0,210.0,115.0,3.0,1.0,0.0,119.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,19.0,8.0,5.0,61.5 +Emmanuel Gyasi,gh GHA,"FW,DF",Spezia,29-104,1994,28.0,28.0,2247.0,1.0,0.0,0.0,0.0,8.0,0.0,0.04,0.0,0.04,0.04,0.04,3.2,3.2,0.13,0.13,25.0,10.0,0.0,35.7,0.4,0.04,0.1,0.12,-2.2,-2.2,520.0,649.0,80.1,6532.0,1228.0,347.0,397.0,87.4,140.0,175.0,80.0,8.0,19.0,42.1,18.0,13.0,14.0,2.0,41.0,582.0,67.0,3.0,1.0,1.0,18.0,2.0,0.0,0.0,0.0,39.0,0.0,20.0,38.0,1.52,32.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,11.0,7.0,8.0,2.0,21.0,6.0,15.0,8.0,20.0,0.0,885.0,35.0,158.0,358.0,377.0,78.0,885.0,509.0,28.0,27.0,11.0,612.0,57.0,34.0,0.0,37.0,25.0,19.0,0.0,0.0,0.0,96.0,16.0,42.0,27.6 +Norbert Gyömbér,sk SVK,DF,Salernitana,30-296,1992,23.0,19.0,1493.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.06,0.06,0.0,0.06,0.7,0.7,0.04,0.04,16.6,0.0,0.0,0.0,0.0,0.0,0.0,0.09,-0.7,-0.7,545.0,652.0,83.6,9978.0,3945.0,193.0,218.0,88.5,290.0,323.0,89.8,52.0,94.0,55.3,3.0,32.0,2.0,1.0,42.0,622.0,30.0,25.0,0.0,5.0,5.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,11.0,0.66,8.0,1.0,1.0,0.0,3.0,0.18,2.0,0.0,1.0,0.0,0.0,27.0,20.0,21.0,5.0,1.0,31.0,21.0,10.0,31.0,80.0,0.0,839.0,117.0,464.0,358.0,22.0,9.0,839.0,395.0,5.0,1.0,0.0,426.0,2.0,1.0,0.0,17.0,5.0,0.0,0.0,1.0,0.0,95.0,38.0,21.0,64.4 +Christian Gytkjær,dk DEN,"FW,MF",Monza,32-354,1990,19.0,3.0,521.0,1.0,0.0,0.0,0.0,0.0,0.0,0.17,0.0,0.17,0.17,0.17,3.1,3.1,0.54,0.54,5.8,5.0,1.0,41.7,0.86,0.08,0.2,0.26,-2.1,-2.1,65.0,98.0,66.3,851.0,152.0,38.0,54.0,70.4,17.0,25.0,68.0,3.0,3.0,100.0,3.0,3.0,2.0,0.0,6.0,90.0,7.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,9.0,1.57,7.0,0.0,2.0,0.0,2.0,0.35,1.0,0.0,1.0,0.0,0.0,6.0,2.0,1.0,4.0,1.0,3.0,1.0,2.0,0.0,1.0,0.0,149.0,2.0,5.0,76.0,68.0,24.0,149.0,78.0,1.0,2.0,2.0,112.0,16.0,5.0,0.0,9.0,4.0,10.0,0.0,0.0,0.0,9.0,9.0,19.0,32.1 +Nicolas Haas,ch SUI,MF,Empoli,27-092,1996,21.0,11.0,1052.0,1.0,0.0,0.0,0.0,2.0,0.0,0.09,0.0,0.09,0.09,0.09,1.2,1.2,0.1,0.1,11.7,3.0,0.0,37.5,0.26,0.13,0.33,0.15,-0.2,-0.2,256.0,352.0,72.7,4210.0,1151.0,125.0,151.0,82.8,105.0,139.0,75.5,20.0,34.0,58.8,7.0,26.0,5.0,0.0,32.0,343.0,9.0,4.0,0.0,1.0,11.0,0.0,0.0,0.0,0.0,2.0,0.0,15.0,18.0,1.54,16.0,1.0,0.0,0.0,1.0,0.09,0.0,1.0,0.0,0.0,0.0,25.0,11.0,14.0,6.0,5.0,13.0,4.0,9.0,7.0,26.0,0.0,490.0,41.0,170.0,209.0,113.0,16.0,490.0,236.0,9.0,12.0,2.0,295.0,17.0,8.0,0.0,19.0,10.0,1.0,0.0,0.0,0.0,53.0,6.0,16.0,27.3 +Samir Handanović,si SVN,GK,Inter,38-285,1984,11.0,11.0,990.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,366.0,407.0,89.9,8358.0,5621.0,110.0,111.0,99.1,173.0,175.0,98.9,81.0,116.0,69.8,0.0,6.0,0.0,0.0,0.0,331.0,73.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,3.0,0.27,2.0,1.0,0.0,0.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,428.0,348.0,425.0,2.0,1.0,0.0,428.0,249.0,0.0,0.0,0.0,261.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0 +Abdou Harroui,nl NED,MF,Sassuolo,25-102,1998,19.0,3.0,442.0,2.0,0.0,0.0,0.0,1.0,0.0,0.41,0.0,0.41,0.41,0.41,1.4,1.4,0.29,0.29,4.9,4.0,0.0,30.8,0.81,0.15,0.5,0.11,0.6,0.6,172.0,214.0,80.4,2696.0,694.0,88.0,99.0,88.9,68.0,79.0,86.1,11.0,17.0,64.7,5.0,22.0,6.0,2.0,23.0,210.0,4.0,0.0,1.0,1.0,11.0,0.0,0.0,0.0,0.0,2.0,0.0,6.0,11.0,2.24,11.0,0.0,0.0,0.0,1.0,0.2,1.0,0.0,0.0,0.0,0.0,15.0,5.0,5.0,6.0,4.0,6.0,1.0,5.0,1.0,9.0,0.0,282.0,14.0,40.0,153.0,93.0,11.0,282.0,148.0,10.0,12.0,2.0,190.0,7.0,11.0,0.0,11.0,0.0,0.0,0.0,0.0,0.0,27.0,8.0,5.0,61.5 +Hans Hateboer,nl NED,DF,Atalanta,29-106,1994,17.0,17.0,1397.0,1.0,1.0,0.0,0.0,7.0,0.0,0.06,0.06,0.13,0.06,0.13,1.3,1.3,0.09,0.09,15.5,4.0,0.0,33.3,0.26,0.08,0.25,0.11,-0.3,-0.3,560.0,702.0,79.8,8088.0,2850.0,339.0,376.0,90.2,173.0,223.0,77.6,31.0,60.0,51.7,13.0,28.0,16.0,7.0,55.0,571.0,129.0,3.0,2.0,1.0,23.0,0.0,0.0,0.0,0.0,126.0,2.0,13.0,22.0,1.42,19.0,1.0,1.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,21.0,13.0,11.0,8.0,2.0,23.0,4.0,19.0,12.0,25.0,0.0,823.0,37.0,220.0,343.0,263.0,29.0,823.0,398.0,16.0,14.0,1.0,505.0,8.0,7.0,0.0,19.0,3.0,1.0,0.0,0.0,0.0,50.0,24.0,14.0,63.2 +Liam Henderson,sct SCO,MF,Empoli,27-000,1996,19.0,9.0,796.0,0.0,1.0,0.0,0.0,8.0,0.0,0.0,0.11,0.11,0.0,0.11,0.3,0.3,0.04,0.04,8.8,5.0,0.0,55.6,0.57,0.0,0.0,0.04,-0.3,-0.3,337.0,412.0,81.8,5569.0,1632.0,141.0,161.0,87.6,161.0,179.0,89.9,22.0,48.0,45.8,15.0,21.0,10.0,9.0,28.0,389.0,19.0,8.0,3.0,3.0,21.0,2.0,1.0,0.0,0.0,3.0,4.0,6.0,28.0,3.17,25.0,1.0,1.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,9.0,8.0,4.0,4.0,1.0,8.0,2.0,6.0,9.0,8.0,0.0,478.0,15.0,108.0,276.0,99.0,12.0,478.0,290.0,9.0,7.0,0.0,338.0,11.0,3.0,0.0,31.0,7.0,2.0,0.0,0.0,0.0,45.0,4.0,7.0,36.4 +Jack Hendry,sct SCO,DF,Cremonese,27-353,1995,4.0,2.0,220.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,118.0,137.0,86.1,2402.0,1087.0,40.0,44.0,90.9,58.0,67.0,86.6,17.0,22.0,77.3,1.0,12.0,1.0,0.0,9.0,122.0,15.0,7.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,5.0,0.0,1.0,4.0,1.64,4.0,0.0,0.0,0.0,1.0,0.41,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,7.0,0.0,148.0,13.0,71.0,73.0,7.0,0.0,148.0,94.0,1.0,2.0,0.0,90.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,14.0,8.0,3.0,72.7 +Matheus Henrique,br BRA,MF,Sassuolo,25-127,1997,23.0,15.0,1452.0,2.0,0.0,0.0,0.0,3.0,0.0,0.12,0.0,0.12,0.12,0.12,1.5,1.5,0.09,0.09,16.1,9.0,0.0,56.3,0.56,0.13,0.22,0.09,0.5,0.5,574.0,672.0,85.4,9745.0,2689.0,263.0,297.0,88.6,233.0,253.0,92.1,56.0,73.0,76.7,21.0,70.0,11.0,0.0,86.0,648.0,23.0,11.0,3.0,7.0,7.0,3.0,0.0,0.0,0.0,6.0,1.0,19.0,47.0,2.91,40.0,1.0,1.0,2.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,15.0,9.0,7.0,5.0,3.0,13.0,4.0,9.0,10.0,8.0,0.0,787.0,22.0,126.0,436.0,236.0,20.0,787.0,528.0,29.0,32.0,2.0,580.0,18.0,11.0,0.0,14.0,27.0,1.0,0.0,0.0,0.0,74.0,9.0,18.0,33.3 +Thomas Henry,fr FRA,FW,Hellas Verona,28-217,1994,16.0,13.0,1030.0,2.0,0.0,0.0,0.0,4.0,0.0,0.17,0.0,0.17,0.17,0.17,1.7,1.7,0.15,0.15,11.4,5.0,0.0,27.8,0.44,0.11,0.4,0.1,0.3,0.3,115.0,226.0,50.9,1405.0,437.0,71.0,124.0,57.3,19.0,46.0,41.3,7.0,12.0,58.3,13.0,14.0,6.0,0.0,18.0,213.0,9.0,0.0,1.0,2.0,5.0,0.0,0.0,0.0,0.0,1.0,4.0,9.0,22.0,1.92,16.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,1.0,1.0,3.0,6.0,2.0,4.0,0.0,14.0,0.0,374.0,15.0,25.0,160.0,192.0,48.0,374.0,193.0,3.0,7.0,1.0,303.0,52.0,25.0,0.0,27.0,16.0,9.0,0.0,0.0,0.0,29.0,46.0,39.0,54.1 +Theo Hernández,fr FRA,DF,Milan,25-201,1997,26.0,26.0,2279.0,3.0,3.0,1.0,1.0,5.0,0.0,0.12,0.12,0.24,0.08,0.2,3.9,3.1,0.15,0.12,25.3,9.0,4.0,19.6,0.36,0.04,0.22,0.07,-0.9,-1.1,1056.0,1350.0,78.2,17791.0,6044.0,508.0,571.0,89.0,434.0,538.0,80.7,87.0,158.0,55.1,32.0,76.0,27.0,9.0,104.0,1070.0,277.0,38.0,1.0,20.0,72.0,17.0,2.0,13.0,0.0,222.0,3.0,32.0,74.0,2.92,42.0,16.0,6.0,7.0,7.0,0.28,5.0,2.0,0.0,0.0,0.0,39.0,22.0,24.0,14.0,1.0,32.0,7.0,25.0,14.0,27.0,1.0,1627.0,55.0,386.0,800.0,461.0,59.0,1626.0,888.0,88.0,61.0,14.0,971.0,40.0,15.0,0.0,18.0,52.0,1.0,0.0,0.0,1.0,156.0,29.0,14.0,67.4 +Isak Hien,se SWE,DF,Hellas Verona,24-102,1999,25.0,21.0,2006.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.3,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.0,599.0,812.0,73.8,12421.0,4786.0,161.0,216.0,74.5,337.0,406.0,83.0,90.0,149.0,60.4,2.0,35.0,2.0,1.0,45.0,769.0,40.0,18.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,22.0,3.0,17.0,16.0,0.72,13.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60.0,28.0,44.0,16.0,0.0,29.0,11.0,18.0,22.0,71.0,1.0,1064.0,128.0,619.0,415.0,44.0,11.0,1064.0,477.0,13.0,8.0,0.0,454.0,22.0,10.0,0.0,45.0,14.0,0.0,0.0,0.0,0.0,178.0,59.0,42.0,58.4 +Morten Hjulmand,dk DEN,MF,Lecce,23-304,1999,29.0,29.0,2487.0,0.0,4.0,0.0,0.0,7.0,1.0,0.0,0.14,0.14,0.0,0.14,0.4,0.4,0.01,0.01,27.6,2.0,0.0,18.2,0.07,0.0,0.0,0.04,-0.4,-0.4,841.0,1080.0,77.9,15520.0,4834.0,351.0,424.0,82.8,373.0,431.0,86.5,100.0,170.0,58.8,21.0,114.0,13.0,3.0,127.0,1010.0,66.0,64.0,1.0,17.0,11.0,0.0,0.0,0.0,0.0,1.0,4.0,18.0,47.0,1.7,41.0,2.0,1.0,3.0,5.0,0.18,5.0,0.0,0.0,0.0,0.0,76.0,43.0,42.0,34.0,0.0,39.0,10.0,29.0,64.0,50.0,0.0,1375.0,92.0,409.0,799.0,179.0,6.0,1375.0,614.0,8.0,13.0,0.0,639.0,25.0,18.0,0.0,54.0,33.0,0.0,0.0,1.0,0.0,213.0,35.0,21.0,62.5 +Emil Holm,se SWE,DF,Spezia,22-347,2000,20.0,17.0,1393.0,1.0,1.0,0.0,0.0,4.0,0.0,0.06,0.06,0.13,0.06,0.13,1.2,1.2,0.08,0.08,15.5,5.0,0.0,33.3,0.32,0.07,0.2,0.08,-0.2,-0.2,342.0,573.0,59.7,5181.0,1707.0,178.0,246.0,72.4,127.0,204.0,62.3,17.0,63.0,27.0,13.0,30.0,12.0,5.0,40.0,463.0,108.0,1.0,1.0,3.0,50.0,0.0,0.0,0.0,0.0,107.0,2.0,21.0,36.0,2.33,24.0,5.0,2.0,3.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,26.0,15.0,13.0,8.0,5.0,21.0,6.0,15.0,9.0,41.0,0.0,804.0,42.0,179.0,318.0,318.0,40.0,804.0,338.0,41.0,24.0,10.0,430.0,47.0,22.0,0.0,22.0,16.0,2.0,0.0,0.0,0.0,81.0,65.0,62.0,51.2 +Martin Hongla,cm CMR,MF,Hellas Verona,25-040,1998,9.0,6.0,523.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,5.8,2.0,0.0,40.0,0.34,0.0,0.0,0.03,-0.2,-0.2,166.0,212.0,78.3,3239.0,992.0,62.0,74.0,83.8,75.0,89.0,84.3,24.0,41.0,58.5,3.0,18.0,2.0,1.0,24.0,204.0,8.0,5.0,0.0,3.0,8.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,11.0,1.89,9.0,0.0,1.0,0.0,1.0,0.17,1.0,0.0,0.0,0.0,0.0,8.0,5.0,4.0,4.0,0.0,7.0,1.0,6.0,6.0,9.0,0.0,258.0,12.0,66.0,142.0,54.0,3.0,258.0,131.0,6.0,4.0,1.0,141.0,4.0,3.0,0.0,11.0,4.0,0.0,0.0,0.0,0.0,48.0,5.0,8.0,38.5 +Petko Hristov,bg BUL,DF,Spezia,24-055,1999,9.0,4.0,365.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,4.1,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.1,-0.1,160.0,207.0,77.3,2653.0,997.0,82.0,90.0,91.1,64.0,79.0,81.0,11.0,29.0,37.9,0.0,11.0,2.0,1.0,8.0,168.0,38.0,9.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,29.0,1.0,1.0,3.0,0.74,1.0,0.0,1.0,1.0,1.0,0.25,0.0,0.0,0.0,1.0,0.0,6.0,4.0,2.0,4.0,0.0,4.0,2.0,2.0,4.0,12.0,0.0,251.0,19.0,101.0,122.0,29.0,3.0,251.0,112.0,2.0,2.0,0.0,133.0,3.0,0.0,0.0,1.0,4.0,0.0,1.0,0.0,0.0,21.0,10.0,5.0,66.7 +Ajdin Hrustic,au AUS,MF,Hellas Verona,26-294,1996,6.0,3.0,226.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.13,0.13,2.5,0.0,0.0,0.0,0.0,0.0,0.0,0.11,-0.3,-0.3,75.0,94.0,79.8,1069.0,341.0,40.0,42.0,95.2,25.0,33.0,75.8,4.0,7.0,57.1,3.0,1.0,5.0,1.0,8.0,82.0,12.0,6.0,0.0,1.0,7.0,4.0,2.0,0.0,0.0,0.0,0.0,4.0,7.0,2.79,6.0,1.0,0.0,0.0,1.0,0.4,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,2.0,0.0,2.0,0.0,2.0,1.0,121.0,4.0,17.0,59.0,47.0,6.0,121.0,63.0,7.0,6.0,2.0,77.0,8.0,3.0,0.0,7.0,2.0,0.0,0.0,0.0,0.0,11.0,1.0,3.0,25.0 +Elseid Hysaj,al ALB,DF,Lazio,29-064,1994,27.0,16.0,1507.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.06,0.06,0.0,0.06,0.1,0.1,0.01,0.01,16.7,1.0,0.0,33.3,0.06,0.0,0.0,0.03,-0.1,-0.1,891.0,1051.0,84.8,13134.0,5566.0,513.0,548.0,93.6,304.0,352.0,86.4,45.0,90.0,50.0,10.0,79.0,11.0,3.0,75.0,907.0,143.0,16.0,2.0,4.0,13.0,0.0,0.0,0.0,0.0,127.0,1.0,16.0,27.0,1.61,24.0,0.0,0.0,1.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,28.0,14.0,17.0,7.0,4.0,20.0,6.0,14.0,15.0,37.0,0.0,1196.0,50.0,440.0,587.0,181.0,7.0,1196.0,623.0,28.0,23.0,2.0,761.0,17.0,10.0,0.0,16.0,10.0,2.0,0.0,0.0,0.0,106.0,21.0,19.0,52.5 +Rasmus Højlund,dk DEN,FW,Atalanta,20-080,2003,27.0,16.0,1480.0,7.0,2.0,0.0,0.0,1.0,0.0,0.43,0.12,0.55,0.43,0.55,7.7,7.7,0.47,0.47,16.4,21.0,0.0,51.2,1.28,0.17,0.33,0.19,-0.7,-0.7,268.0,370.0,72.4,3345.0,525.0,168.0,214.0,78.5,62.0,82.0,75.6,12.0,14.0,85.7,22.0,21.0,11.0,1.0,34.0,352.0,15.0,0.0,2.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,3.0,16.0,49.0,2.98,33.0,0.0,5.0,4.0,4.0,0.24,2.0,0.0,0.0,2.0,0.0,4.0,3.0,0.0,2.0,2.0,8.0,0.0,8.0,1.0,5.0,0.0,582.0,5.0,14.0,210.0,364.0,112.0,582.0,387.0,31.0,22.0,23.0,477.0,77.0,48.0,0.0,13.0,26.0,4.0,2.0,0.0,0.0,52.0,27.0,48.0,36.0 +Roger Ibanez,br BRA,DF,Roma,24-153,1998,27.0,27.0,2371.0,3.0,0.0,0.0,0.0,10.0,1.0,0.11,0.0,0.11,0.11,0.11,2.5,2.5,0.1,0.1,26.3,6.0,0.0,27.3,0.23,0.14,0.5,0.11,0.5,0.5,1292.0,1470.0,87.9,24269.0,8577.0,471.0,519.0,90.8,670.0,716.0,93.6,137.0,198.0,69.2,5.0,59.0,3.0,0.0,63.0,1414.0,54.0,41.0,2.0,8.0,1.0,0.0,0.0,0.0,0.0,12.0,2.0,10.0,24.0,0.91,13.0,1.0,7.0,2.0,2.0,0.08,1.0,0.0,1.0,0.0,0.0,59.0,29.0,33.0,23.0,3.0,23.0,8.0,15.0,56.0,87.0,1.0,1770.0,195.0,903.0,832.0,65.0,25.0,1770.0,1083.0,23.0,14.0,2.0,1134.0,21.0,16.0,1.0,36.0,44.0,0.0,1.0,0.0,1.0,186.0,49.0,22.0,69.0 +Zlatan Ibrahimović,se SWE,FW,Milan,41-204,1981,4.0,1.0,146.0,1.0,0.0,1.0,1.0,0.0,0.0,0.62,0.0,0.62,0.0,0.0,1.2,0.4,0.72,0.24,1.6,2.0,1.0,40.0,1.23,0.0,0.0,0.08,-0.2,-0.4,36.0,51.0,70.6,517.0,102.0,22.0,31.0,71.0,10.0,13.0,76.9,2.0,2.0,100.0,1.0,3.0,2.0,0.0,4.0,45.0,6.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,1.85,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,2.0,0.0,72.0,3.0,5.0,31.0,36.0,7.0,71.0,38.0,0.0,1.0,1.0,55.0,5.0,2.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,3.0,8.0,4.0,66.7 +Igor,br BRA,DF,Fiorentina,25-077,1998,22.0,18.0,1632.0,0.0,0.0,0.0,0.0,9.0,1.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.03,0.03,18.1,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.5,-0.5,929.0,1062.0,87.5,19309.0,6124.0,262.0,281.0,93.2,533.0,569.0,93.7,124.0,182.0,68.1,3.0,63.0,2.0,0.0,63.0,1004.0,57.0,47.0,0.0,11.0,1.0,0.0,0.0,0.0,0.0,10.0,1.0,12.0,14.0,0.77,11.0,0.0,0.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,32.0,19.0,16.0,12.0,4.0,30.0,13.0,17.0,20.0,47.0,2.0,1228.0,99.0,523.0,670.0,44.0,11.0,1228.0,729.0,17.0,13.0,0.0,779.0,8.0,9.0,1.0,24.0,12.0,0.0,0.0,0.0,0.0,122.0,35.0,25.0,58.3 +Jonathan Ikone,fr FRA,FW,Fiorentina,24-358,1998,28.0,20.0,1692.0,2.0,4.0,0.0,0.0,4.0,0.0,0.11,0.21,0.32,0.11,0.32,2.6,2.6,0.14,0.14,18.8,6.0,0.0,14.3,0.32,0.05,0.33,0.06,-0.6,-0.6,472.0,579.0,81.5,8038.0,1564.0,246.0,271.0,90.8,162.0,200.0,81.0,51.0,64.0,79.7,26.0,20.0,24.0,2.0,58.0,555.0,23.0,4.0,1.0,10.0,20.0,0.0,0.0,0.0,0.0,18.0,1.0,17.0,53.0,2.82,41.0,0.0,1.0,2.0,9.0,0.48,6.0,0.0,0.0,1.0,0.0,14.0,6.0,5.0,5.0,4.0,17.0,1.0,16.0,4.0,3.0,0.0,801.0,7.0,75.0,322.0,422.0,89.0,801.0,559.0,68.0,39.0,41.0,615.0,51.0,38.0,0.0,22.0,21.0,5.0,1.0,0.0,0.0,75.0,14.0,23.0,37.8 +Ivan Ilić,rs SRB,MF,Hellas Verona,22-039,2001,11.0,10.0,851.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.11,0.11,0.0,0.11,0.4,0.4,0.04,0.04,9.5,2.0,0.0,22.2,0.21,0.0,0.0,0.04,-0.4,-0.4,279.0,401.0,69.6,5107.0,1432.0,113.0,138.0,81.9,128.0,168.0,76.2,29.0,66.0,43.9,16.0,19.0,10.0,1.0,39.0,332.0,68.0,17.0,1.0,3.0,47.0,39.0,14.0,19.0,0.0,2.0,1.0,13.0,28.0,2.96,14.0,12.0,0.0,0.0,2.0,0.21,1.0,0.0,0.0,0.0,0.0,16.0,10.0,9.0,7.0,0.0,10.0,1.0,9.0,8.0,14.0,0.0,489.0,26.0,98.0,249.0,149.0,11.0,489.0,219.0,18.0,13.0,4.0,238.0,17.0,5.0,0.0,9.0,9.0,0.0,0.0,0.0,0.0,72.0,10.0,13.0,43.5 +Ivan Ilić,rs SRB,MF,Torino,22-039,2001,7.0,6.0,576.0,1.0,1.0,0.0,0.0,1.0,0.0,0.16,0.16,0.31,0.16,0.31,0.1,0.1,0.01,0.01,6.4,1.0,0.0,25.0,0.16,0.25,1.0,0.02,0.9,0.9,332.0,380.0,87.4,5389.0,946.0,159.0,172.0,92.4,142.0,161.0,88.2,21.0,27.0,77.8,3.0,29.0,5.0,3.0,31.0,363.0,16.0,11.0,0.0,0.0,7.0,3.0,3.0,0.0,0.0,2.0,1.0,6.0,8.0,1.25,6.0,1.0,0.0,1.0,2.0,0.31,1.0,0.0,0.0,1.0,0.0,6.0,3.0,2.0,3.0,1.0,10.0,2.0,8.0,3.0,8.0,0.0,450.0,12.0,73.0,301.0,82.0,2.0,450.0,252.0,10.0,13.0,0.0,304.0,6.0,3.0,0.0,7.0,12.0,1.0,1.0,0.0,0.0,38.0,9.0,9.0,50.0 +Samuel Iling-Junior,eng ENG,"DF,FW",Juventus,19-203,2003,7.0,0.0,115.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.78,0.78,0.0,0.78,0.0,0.0,0.01,0.01,1.3,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,31.0,40.0,77.5,519.0,211.0,16.0,19.0,84.2,12.0,15.0,80.0,2.0,4.0,50.0,2.0,3.0,2.0,1.0,4.0,38.0,2.0,1.0,0.0,0.0,4.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,5.0,3.91,5.0,0.0,0.0,0.0,1.0,0.78,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,3.0,0.0,61.0,1.0,15.0,18.0,30.0,1.0,61.0,33.0,6.0,8.0,1.0,36.0,5.0,2.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,8.0,1.0,1.0,50.0 +Emirhan İlkhan,tr TUR,MF,Torino,18-328,2004,4.0,1.0,87.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,36.0,43.0,83.7,671.0,140.0,16.0,19.0,84.2,12.0,13.0,92.3,7.0,9.0,77.8,1.0,6.0,2.0,0.0,8.0,41.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,5.17,5.0,0.0,0.0,0.0,1.0,1.03,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,3.0,1.0,2.0,2.0,2.0,0.0,58.0,4.0,12.0,34.0,12.0,0.0,58.0,39.0,2.0,4.0,0.0,38.0,2.0,2.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,2.0,33.3 +Emirhan İlkhan,tr TUR,MF,Sampdoria,18-328,2004,3.0,0.0,60.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,29.0,86.2,429.0,148.0,14.0,16.0,87.5,7.0,7.0,100.0,3.0,4.0,75.0,1.0,3.0,1.0,0.0,3.0,26.0,3.0,1.0,0.0,1.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,1.0,2.0,3.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,1.0,3.0,0.0,3.0,0.0,2.0,0.0,39.0,4.0,8.0,25.0,6.0,0.0,39.0,25.0,1.0,1.0,0.0,26.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,0.0 +Ciro Immobile,it ITA,FW,Lazio,33-064,1990,24.0,20.0,1647.0,10.0,3.0,3.0,4.0,3.0,0.0,0.55,0.16,0.71,0.38,0.55,10.9,7.8,0.59,0.42,18.3,20.0,0.0,39.2,1.09,0.14,0.35,0.15,-0.9,-0.8,355.0,485.0,73.2,4590.0,764.0,222.0,271.0,81.9,90.0,119.0,75.6,15.0,22.0,68.2,18.0,14.0,9.0,0.0,34.0,464.0,17.0,0.0,4.0,2.0,5.0,0.0,0.0,0.0,0.0,6.0,4.0,20.0,49.0,2.68,34.0,0.0,7.0,4.0,10.0,0.55,7.0,0.0,2.0,1.0,0.0,8.0,5.0,2.0,4.0,2.0,9.0,1.0,8.0,1.0,10.0,0.0,671.0,13.0,41.0,294.0,344.0,124.0,667.0,387.0,32.0,16.0,20.0,529.0,48.0,17.0,0.0,17.0,19.0,13.0,2.0,0.0,0.0,44.0,12.0,26.0,31.6 +Ardian Ismajli,al ALB,DF,Empoli,26-207,1996,22.0,20.0,1855.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.03,0.03,20.6,2.0,0.0,22.2,0.1,0.0,0.0,0.08,-0.7,-0.7,763.0,896.0,85.2,15088.0,4938.0,231.0,249.0,92.8,450.0,498.0,90.4,77.0,122.0,63.1,5.0,22.0,2.0,0.0,35.0,856.0,39.0,22.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,14.0,10.0,0.49,8.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,30.0,23.0,15.0,0.0,45.0,29.0,16.0,26.0,109.0,0.0,1149.0,225.0,711.0,417.0,24.0,17.0,1149.0,614.0,8.0,2.0,0.0,670.0,2.0,2.0,0.0,18.0,13.0,0.0,0.0,0.0,1.0,84.0,44.0,31.0,58.7 +Armando Izzo,it ITA,DF,Monza,31-054,1992,23.0,22.0,1880.0,1.0,0.0,0.0,0.0,8.0,0.0,0.05,0.0,0.05,0.05,0.05,1.6,1.6,0.08,0.08,20.9,3.0,0.0,23.1,0.14,0.08,0.33,0.13,-0.6,-0.6,1033.0,1208.0,85.5,18729.0,6008.0,416.0,452.0,92.0,499.0,556.0,89.7,98.0,162.0,60.5,12.0,66.0,7.0,2.0,74.0,1130.0,72.0,50.0,1.0,13.0,7.0,0.0,0.0,0.0,0.0,21.0,6.0,8.0,20.0,0.96,17.0,0.0,1.0,1.0,1.0,0.05,0.0,0.0,0.0,1.0,0.0,57.0,39.0,34.0,21.0,2.0,20.0,10.0,10.0,40.0,69.0,0.0,1445.0,136.0,720.0,641.0,95.0,21.0,1445.0,795.0,18.0,7.0,1.0,956.0,9.0,7.0,0.0,32.0,38.0,1.0,1.0,0.0,0.0,114.0,34.0,22.0,60.7 +Mato Jajalo,ba BIH,MF,Udinese,34-335,1988,3.0,0.0,63.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.07,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,23.0,29.0,79.3,401.0,166.0,10.0,13.0,76.9,9.0,9.0,100.0,3.0,4.0,75.0,1.0,1.0,1.0,0.0,4.0,29.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,4.29,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,36.0,4.0,7.0,14.0,16.0,1.0,36.0,17.0,0.0,2.0,0.0,22.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,0.0,100.0 +Jesé,es ESP,FW,Sampdoria,30-058,1993,9.0,1.0,185.0,1.0,1.0,0.0,0.0,0.0,0.0,0.49,0.49,0.97,0.49,0.97,0.3,0.3,0.15,0.15,2.1,2.0,0.0,66.7,0.97,0.33,0.5,0.1,0.7,0.7,41.0,56.0,73.2,628.0,87.0,24.0,27.0,88.9,13.0,17.0,76.5,3.0,4.0,75.0,2.0,0.0,0.0,0.0,1.0,49.0,7.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,4.0,7.0,3.41,5.0,0.0,1.0,1.0,2.0,0.97,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,3.0,1.0,2.0,1.0,2.0,0.0,80.0,3.0,5.0,40.0,36.0,6.0,80.0,45.0,1.0,4.0,0.0,52.0,5.0,3.0,0.0,3.0,3.0,2.0,0.0,0.0,0.0,7.0,2.0,12.0,14.3 +Juan Jesus,br BRA,DF,Napoli,31-319,1991,11.0,9.0,804.0,1.0,0.0,0.0,0.0,2.0,0.0,0.11,0.0,0.11,0.11,0.11,0.2,0.2,0.02,0.02,8.9,1.0,0.0,20.0,0.11,0.2,1.0,0.04,0.8,0.8,612.0,673.0,90.9,11140.0,3941.0,231.0,242.0,95.5,325.0,351.0,92.6,47.0,62.0,75.8,2.0,43.0,1.0,0.0,51.0,645.0,27.0,23.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,11.0,1.23,8.0,0.0,2.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,12.0,8.0,7.0,4.0,1.0,15.0,8.0,7.0,9.0,26.0,0.0,754.0,49.0,271.0,470.0,18.0,7.0,754.0,504.0,12.0,8.0,0.0,540.0,4.0,0.0,0.0,8.0,3.0,0.0,0.0,0.0,0.0,49.0,25.0,7.0,78.1 +Þórir Jóhann Helgason,is ISL,MF,Lecce,22-209,2000,11.0,2.0,213.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,2.4,0.0,1.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,50.0,70.0,71.4,877.0,346.0,23.0,31.0,74.2,18.0,21.0,85.7,7.0,14.0,50.0,3.0,2.0,0.0,0.0,2.0,62.0,8.0,1.0,0.0,0.0,9.0,6.0,5.0,1.0,0.0,0.0,0.0,0.0,5.0,2.11,3.0,2.0,0.0,0.0,1.0,0.42,1.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,0.0,1.0,3.0,1.0,2.0,3.0,3.0,0.0,89.0,8.0,25.0,38.0,28.0,2.0,89.0,38.0,2.0,2.0,1.0,45.0,3.0,2.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,12.0,4.0,6.0,40.0 +Luka Jović,rs SRB,"FW,MF",Fiorentina,25-123,1997,26.0,13.0,1194.0,4.0,0.0,0.0,1.0,2.0,0.0,0.3,0.0,0.3,0.3,0.3,5.4,4.6,0.4,0.34,13.3,13.0,0.0,31.7,0.98,0.1,0.31,0.11,-1.4,-0.6,169.0,229.0,73.8,2680.0,439.0,89.0,110.0,80.9,63.0,82.0,76.8,13.0,19.0,68.4,15.0,11.0,3.0,0.0,19.0,201.0,26.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,6.0,32.0,2.41,22.0,0.0,6.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,1.0,3.0,1.0,7.0,2.0,5.0,2.0,8.0,0.0,341.0,9.0,22.0,155.0,166.0,63.0,340.0,167.0,4.0,5.0,4.0,258.0,22.0,15.0,0.0,18.0,7.0,9.0,0.0,0.0,0.0,15.0,25.0,43.0,36.8 +Hamed Junior Traorè,ci CIV,"MF,FW",Sassuolo,23-068,2000,11.0,6.0,591.0,0.0,3.0,0.0,0.0,1.0,0.0,0.0,0.46,0.46,0.0,0.46,1.0,1.0,0.15,0.15,6.6,4.0,2.0,50.0,0.61,0.0,0.0,0.12,-1.0,-1.0,184.0,245.0,75.1,3077.0,823.0,87.0,101.0,86.1,68.0,83.0,81.9,20.0,34.0,58.8,17.0,17.0,7.0,3.0,26.0,219.0,22.0,3.0,1.0,4.0,26.0,16.0,6.0,4.0,0.0,2.0,4.0,14.0,26.0,3.96,21.0,5.0,0.0,0.0,6.0,0.91,4.0,2.0,0.0,0.0,0.0,11.0,6.0,4.0,6.0,1.0,4.0,0.0,4.0,2.0,0.0,1.0,308.0,3.0,43.0,142.0,127.0,24.0,308.0,194.0,14.0,7.0,7.0,226.0,16.0,23.0,0.0,9.0,2.0,1.0,0.0,0.0,0.0,35.0,3.0,5.0,37.5 +Yayah Kallon,sl SLE,"MF,FW",Hellas Verona,21-299,2001,21.0,7.0,801.0,1.0,1.0,0.0,0.0,1.0,0.0,0.11,0.11,0.22,0.11,0.22,1.6,1.6,0.18,0.18,8.9,6.0,0.0,31.6,0.67,0.05,0.17,0.09,-0.6,-0.6,142.0,225.0,63.1,2061.0,603.0,78.0,102.0,76.5,46.0,77.0,59.7,8.0,17.0,47.1,12.0,9.0,13.0,5.0,21.0,212.0,12.0,2.0,0.0,0.0,31.0,1.0,0.0,0.0,0.0,4.0,1.0,10.0,29.0,3.25,23.0,0.0,1.0,3.0,2.0,0.22,1.0,0.0,1.0,0.0,0.0,14.0,10.0,5.0,4.0,5.0,9.0,0.0,9.0,8.0,5.0,0.0,340.0,5.0,29.0,121.0,199.0,40.0,340.0,225.0,32.0,20.0,14.0,228.0,27.0,15.0,0.0,13.0,21.0,4.0,0.0,0.0,0.0,38.0,8.0,19.0,29.6 +Pierre Kalulu,fr FRA,DF,Milan,22-324,2000,29.0,24.0,2300.0,1.0,0.0,0.0,0.0,4.0,0.0,0.04,0.0,0.04,0.04,0.04,1.5,1.5,0.06,0.06,25.6,2.0,0.0,18.2,0.08,0.09,0.5,0.14,-0.5,-0.5,1379.0,1578.0,87.4,25471.0,8739.0,534.0,580.0,92.1,719.0,781.0,92.1,116.0,182.0,63.7,9.0,101.0,8.0,4.0,122.0,1452.0,123.0,46.0,3.0,2.0,22.0,0.0,0.0,0.0,0.0,72.0,3.0,12.0,28.0,1.1,24.0,1.0,2.0,0.0,2.0,0.08,2.0,0.0,0.0,0.0,0.0,51.0,35.0,29.0,19.0,3.0,39.0,15.0,24.0,38.0,49.0,1.0,1828.0,107.0,804.0,897.0,145.0,16.0,1828.0,1098.0,46.0,21.0,0.0,1210.0,21.0,5.0,0.0,19.0,19.0,1.0,0.0,1.0,0.0,154.0,26.0,25.0,51.0 +Yann Karamoh,fr FRA,"MF,FW",Torino,24-291,1998,15.0,4.0,469.0,3.0,0.0,0.0,0.0,1.0,0.0,0.58,0.0,0.58,0.58,0.58,2.6,2.6,0.49,0.49,5.2,7.0,0.0,50.0,1.34,0.21,0.43,0.18,0.4,0.4,101.0,131.0,77.1,1469.0,189.0,63.0,73.0,86.3,32.0,39.0,82.1,4.0,7.0,57.1,4.0,1.0,3.0,1.0,6.0,124.0,6.0,1.0,0.0,3.0,7.0,0.0,0.0,0.0,0.0,1.0,1.0,8.0,14.0,2.69,9.0,0.0,1.0,1.0,2.0,0.38,0.0,0.0,0.0,0.0,0.0,5.0,2.0,3.0,2.0,0.0,4.0,0.0,4.0,0.0,2.0,0.0,232.0,2.0,13.0,89.0,135.0,23.0,232.0,141.0,15.0,4.0,6.0,167.0,24.0,11.0,0.0,10.0,8.0,9.0,0.0,0.0,0.0,19.0,4.0,16.0,20.0 +Rick Karsdorp,nl NED,DF,Roma,28-073,1995,13.0,8.0,711.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,7.9,0.0,0.0,0.0,0.0,0.0,0.0,0.14,-0.3,-0.3,295.0,380.0,77.6,5109.0,1611.0,134.0,153.0,87.6,131.0,155.0,84.5,27.0,53.0,50.9,6.0,13.0,11.0,1.0,24.0,306.0,73.0,4.0,3.0,1.0,14.0,0.0,0.0,0.0,0.0,69.0,1.0,8.0,16.0,2.03,13.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,13.0,11.0,7.0,3.0,0.0,0.0,0.0,7.0,15.0,1.0,444.0,30.0,156.0,185.0,110.0,9.0,444.0,236.0,16.0,8.0,2.0,260.0,11.0,6.0,0.0,4.0,0.0,1.0,0.0,0.0,0.0,45.0,5.0,2.0,71.4 +Denso Kasius,nl NED,DF,Bologna,20-201,2002,7.0,4.0,342.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.53,0.53,0.0,0.53,0.1,0.1,0.03,0.03,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,143.0,192.0,74.5,2576.0,864.0,65.0,72.0,90.3,53.0,74.0,71.6,20.0,36.0,55.6,6.0,6.0,8.0,6.0,9.0,156.0,35.0,1.0,0.0,3.0,12.0,0.0,0.0,0.0,0.0,34.0,1.0,3.0,15.0,3.94,13.0,0.0,0.0,0.0,2.0,0.52,2.0,0.0,0.0,0.0,0.0,12.0,6.0,7.0,4.0,1.0,6.0,3.0,3.0,3.0,6.0,0.0,243.0,6.0,75.0,105.0,65.0,3.0,243.0,127.0,9.0,10.0,1.0,144.0,11.0,7.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,18.0,1.0,4.0,20.0 +Grigoris Kastanos,cy CYP,"MF,DF",Salernitana,25-085,1998,21.0,9.0,858.0,1.0,0.0,0.0,0.0,3.0,0.0,0.1,0.0,0.1,0.1,0.1,1.3,1.3,0.14,0.14,9.5,5.0,0.0,26.3,0.52,0.05,0.2,0.07,-0.3,-0.3,265.0,367.0,72.2,4005.0,1400.0,142.0,159.0,89.3,81.0,111.0,73.0,22.0,47.0,46.8,8.0,26.0,7.0,2.0,31.0,316.0,46.0,13.0,1.0,5.0,16.0,11.0,2.0,0.0,0.0,22.0,5.0,15.0,21.0,2.21,16.0,4.0,0.0,1.0,2.0,0.21,1.0,1.0,0.0,0.0,0.0,25.0,16.0,10.0,13.0,2.0,13.0,3.0,10.0,7.0,5.0,0.0,471.0,10.0,83.0,226.0,167.0,20.0,471.0,285.0,18.0,12.0,3.0,313.0,13.0,11.0,0.0,17.0,32.0,0.0,0.0,0.0,0.0,38.0,16.0,14.0,53.3 +Moise Kean,it ITA,FW,Juventus,23-056,2000,24.0,10.0,871.0,6.0,0.0,0.0,0.0,5.0,1.0,0.62,0.0,0.62,0.62,0.62,6.0,6.0,0.62,0.62,9.7,13.0,0.0,38.2,1.34,0.18,0.46,0.18,0.0,0.0,113.0,159.0,71.1,1415.0,299.0,71.0,88.0,80.7,26.0,35.0,74.3,3.0,6.0,50.0,8.0,6.0,5.0,1.0,11.0,150.0,7.0,1.0,1.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,2.0,10.0,28.0,2.9,19.0,0.0,5.0,0.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,8.0,6.0,2.0,5.0,1.0,2.0,0.0,2.0,4.0,2.0,0.0,283.0,3.0,28.0,127.0,135.0,46.0,283.0,184.0,24.0,11.0,14.0,218.0,23.0,25.0,0.0,14.0,15.0,8.0,0.0,0.0,0.0,31.0,7.0,10.0,41.2 +Jakub Kiwior,pl POL,DF,Spezia,23-069,2000,17.0,17.0,1443.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.04,0.04,16.0,2.0,1.0,20.0,0.12,0.0,0.0,0.06,-0.6,-0.6,653.0,775.0,84.3,13165.0,4165.0,209.0,238.0,87.8,343.0,371.0,92.5,93.0,147.0,63.3,4.0,25.0,5.0,0.0,37.0,683.0,89.0,34.0,1.0,3.0,14.0,9.0,6.0,3.0,0.0,12.0,3.0,4.0,15.0,0.94,9.0,3.0,1.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,22.0,17.0,15.0,7.0,0.0,26.0,18.0,8.0,24.0,71.0,1.0,953.0,177.0,594.0,307.0,58.0,15.0,953.0,464.0,13.0,3.0,1.0,498.0,8.0,5.0,0.0,16.0,5.0,0.0,0.0,0.0,0.0,93.0,22.0,12.0,64.7 +Simon Kjær,dk DEN,DF,Milan,34-030,1989,13.0,9.0,789.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,438.0,512.0,85.5,9351.0,3273.0,126.0,143.0,88.1,231.0,244.0,94.7,78.0,120.0,65.0,0.0,33.0,1.0,0.0,32.0,499.0,13.0,12.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,5.0,0.57,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,5.0,6.0,5.0,0.0,10.0,8.0,2.0,8.0,22.0,1.0,568.0,71.0,317.0,246.0,8.0,1.0,568.0,329.0,0.0,1.0,0.0,392.0,1.0,0.0,0.0,7.0,1.0,0.0,0.0,0.0,0.0,45.0,14.0,10.0,58.3 +Teun Koopmeiners,nl NED,MF,Atalanta,25-056,1998,26.0,25.0,2228.0,7.0,3.0,2.0,4.0,6.0,0.0,0.28,0.12,0.4,0.2,0.32,5.9,2.6,0.24,0.1,24.8,17.0,6.0,41.5,0.69,0.12,0.29,0.06,1.1,2.4,1013.0,1373.0,73.8,18609.0,7495.0,427.0,495.0,86.3,418.0,526.0,79.5,130.0,274.0,47.4,40.0,128.0,40.0,13.0,153.0,1247.0,122.0,45.0,4.0,17.0,115.0,51.0,21.0,23.0,0.0,23.0,4.0,25.0,93.0,3.76,62.0,19.0,9.0,2.0,13.0,0.53,8.0,3.0,2.0,0.0,0.0,36.0,21.0,12.0,19.0,5.0,33.0,5.0,28.0,17.0,31.0,1.0,1638.0,75.0,404.0,733.0,518.0,43.0,1634.0,910.0,45.0,50.0,7.0,1114.0,41.0,23.0,0.0,37.0,20.0,3.0,0.0,0.0,0.0,150.0,25.0,20.0,55.6 +Filip Kostić,rs SRB,"DF,FW",Juventus,30-175,1992,31.0,28.0,2212.0,3.0,8.0,0.0,0.0,3.0,0.0,0.12,0.33,0.45,0.12,0.45,2.2,2.2,0.09,0.09,24.6,9.0,0.0,27.3,0.37,0.09,0.33,0.07,0.8,0.8,685.0,1005.0,68.2,11703.0,3555.0,329.0,396.0,83.1,262.0,362.0,72.4,73.0,162.0,45.1,57.0,37.0,42.0,27.0,64.0,868.0,134.0,12.0,4.0,7.0,188.0,37.0,2.0,33.0,0.0,85.0,3.0,49.0,81.0,3.3,59.0,13.0,6.0,0.0,12.0,0.49,8.0,2.0,2.0,0.0,0.0,37.0,23.0,14.0,19.0,4.0,23.0,2.0,21.0,12.0,24.0,0.0,1247.0,32.0,200.0,498.0,563.0,54.0,1247.0,662.0,88.0,45.0,19.0,786.0,46.0,18.0,0.0,28.0,10.0,2.0,0.0,0.0,0.0,120.0,11.0,9.0,55.0 +Christian Kouamé,ci CIV,FW,Fiorentina,25-140,1997,22.0,19.0,1596.0,3.0,3.0,0.0,0.0,3.0,0.0,0.17,0.17,0.34,0.17,0.34,4.1,4.1,0.23,0.23,17.7,12.0,1.0,33.3,0.68,0.08,0.25,0.12,-1.1,-1.1,419.0,587.0,71.4,6257.0,1458.0,247.0,306.0,80.7,119.0,173.0,68.8,29.0,49.0,59.2,35.0,27.0,26.0,8.0,46.0,557.0,29.0,2.0,1.0,6.0,44.0,2.0,0.0,0.0,0.0,19.0,1.0,15.0,72.0,4.06,58.0,0.0,4.0,6.0,5.0,0.28,4.0,0.0,0.0,0.0,0.0,23.0,17.0,8.0,8.0,7.0,15.0,3.0,12.0,12.0,4.0,0.0,833.0,5.0,66.0,286.0,494.0,98.0,833.0,510.0,48.0,29.0,24.0,626.0,73.0,39.0,0.0,27.0,51.0,4.0,1.0,0.0,0.0,83.0,49.0,44.0,52.7 +Viktor Kovalenko,ua UKR,MF,Spezia,27-070,1996,15.0,6.0,477.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.13,0.13,5.3,0.0,1.0,0.0,0.0,0.0,0.0,0.08,-0.7,-0.7,155.0,203.0,76.4,2435.0,551.0,85.0,93.0,91.4,56.0,72.0,77.8,12.0,24.0,50.0,5.0,3.0,4.0,1.0,17.0,188.0,15.0,3.0,0.0,3.0,14.0,9.0,5.0,3.0,0.0,3.0,0.0,8.0,9.0,1.69,8.0,0.0,0.0,1.0,1.0,0.19,0.0,0.0,0.0,1.0,0.0,9.0,5.0,1.0,6.0,2.0,11.0,2.0,9.0,3.0,7.0,0.0,262.0,9.0,50.0,130.0,84.0,15.0,262.0,127.0,1.0,2.0,1.0,154.0,8.0,2.0,0.0,8.0,9.0,1.0,1.0,0.0,0.0,26.0,4.0,13.0,23.5 +Julian Kristoffersen,no NOR,FW,Salernitana,25-350,1997,1.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,4.0,0.0,0.0,2.0,2.0,0.0,4.0,1.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,25.0 +Raimonds Krollis,lv LVA,FW,Spezia,21-179,2001,1.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.31,0.31,0.2,1.0,0.0,100.0,6.0,0.0,0.0,0.05,-0.1,-0.1,1.0,2.0,50.0,23.0,5.0,0.0,1.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,1.0,4.0,0.0,5.0,3.0,1.0,0.0,0.0,3.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +Rade Krunić,ba BIH,"MF,FW",Milan,29-200,1993,17.0,13.0,1182.0,0.0,1.0,0.0,0.0,6.0,0.0,0.0,0.08,0.08,0.0,0.08,0.7,0.7,0.05,0.05,13.1,1.0,0.0,12.5,0.08,0.0,0.0,0.09,-0.7,-0.7,477.0,578.0,82.5,7167.0,2232.0,238.0,273.0,87.2,191.0,222.0,86.0,23.0,35.0,65.7,11.0,34.0,7.0,0.0,48.0,558.0,20.0,12.0,1.0,1.0,3.0,3.0,1.0,1.0,0.0,4.0,0.0,16.0,30.0,2.29,25.0,1.0,2.0,1.0,5.0,0.38,5.0,0.0,0.0,0.0,0.0,33.0,23.0,16.0,14.0,3.0,21.0,9.0,12.0,14.0,16.0,0.0,704.0,33.0,182.0,418.0,114.0,17.0,704.0,375.0,22.0,21.0,2.0,417.0,9.0,6.0,0.0,12.0,16.0,0.0,0.0,0.0,0.0,87.0,18.0,17.0,51.4 +Marash Kumbulla,al ALB,DF,Roma,23-076,2000,6.0,4.0,245.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,156.0,178.0,87.6,2825.0,766.0,65.0,67.0,97.0,79.0,83.0,95.2,9.0,22.0,40.9,1.0,3.0,0.0,0.0,8.0,174.0,4.0,4.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.73,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,2.0,2.0,0.0,2.0,1.0,1.0,4.0,6.0,0.0,201.0,15.0,100.0,99.0,5.0,2.0,201.0,135.0,2.0,1.0,0.0,153.0,2.0,0.0,0.0,3.0,5.0,0.0,0.0,1.0,0.0,15.0,7.0,4.0,63.6 +Khvicha Kvaratskhelia,ge GEO,FW,Napoli,22-072,2001,27.0,25.0,2016.0,12.0,10.0,2.0,2.0,1.0,0.0,0.54,0.45,0.98,0.45,0.89,7.7,6.1,0.34,0.27,22.4,25.0,3.0,34.7,1.12,0.14,0.4,0.09,4.3,3.9,652.0,826.0,78.9,9326.0,1791.0,405.0,450.0,90.0,172.0,227.0,75.8,44.0,84.0,52.4,45.0,30.0,31.0,6.0,65.0,784.0,39.0,6.0,11.0,11.0,55.0,16.0,6.0,3.0,0.0,17.0,3.0,17.0,110.0,4.91,71.0,3.0,12.0,6.0,22.0,0.98,10.0,1.0,5.0,3.0,0.0,35.0,19.0,11.0,6.0,18.0,16.0,0.0,16.0,4.0,6.0,1.0,1159.0,7.0,94.0,362.0,720.0,143.0,1157.0,753.0,109.0,44.0,62.0,884.0,54.0,40.0,0.0,15.0,33.0,5.0,3.0,0.0,0.0,94.0,8.0,9.0,47.1 +Giorgos Kyriakopoulos,gr GRE,"DF,FW",Bologna,27-079,1996,11.0,7.0,638.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.28,0.28,0.0,0.28,0.2,0.2,0.03,0.03,7.1,3.0,0.0,42.9,0.42,0.0,0.0,0.03,-0.2,-0.2,283.0,369.0,76.7,4400.0,1978.0,156.0,172.0,90.7,94.0,115.0,81.7,24.0,58.0,41.4,7.0,20.0,9.0,4.0,22.0,315.0,52.0,11.0,0.0,0.0,35.0,9.0,8.0,1.0,0.0,32.0,2.0,8.0,18.0,2.54,12.0,3.0,1.0,1.0,3.0,0.42,2.0,1.0,0.0,0.0,0.0,13.0,6.0,4.0,5.0,4.0,14.0,2.0,12.0,3.0,9.0,0.0,440.0,15.0,122.0,170.0,151.0,8.0,440.0,260.0,17.0,14.0,1.0,296.0,13.0,7.0,0.0,7.0,11.0,0.0,0.0,0.0,0.0,34.0,5.0,10.0,33.3 +Giorgos Kyriakopoulos,gr GRE,"FW,DF",Sassuolo,27-079,1996,12.0,9.0,766.0,1.0,1.0,0.0,0.0,3.0,0.0,0.12,0.12,0.23,0.12,0.23,1.1,1.1,0.13,0.13,8.5,4.0,0.0,30.8,0.47,0.08,0.25,0.08,-0.1,-0.1,300.0,391.0,76.7,5430.0,2158.0,148.0,165.0,89.7,103.0,125.0,82.4,44.0,81.0,54.3,14.0,23.0,14.0,9.0,32.0,332.0,56.0,7.0,0.0,6.0,39.0,10.0,2.0,5.0,0.0,39.0,3.0,9.0,26.0,3.05,21.0,0.0,1.0,1.0,3.0,0.35,2.0,0.0,1.0,0.0,0.0,21.0,8.0,7.0,9.0,5.0,7.0,1.0,6.0,11.0,12.0,1.0,505.0,14.0,140.0,187.0,186.0,16.0,505.0,312.0,17.0,15.0,5.0,333.0,14.0,18.0,0.0,9.0,13.0,0.0,1.0,0.0,0.0,37.0,7.0,13.0,35.0 +Sam Lammers,nl NED,FW,Sampdoria,25-360,1997,13.0,11.0,885.0,1.0,1.0,0.0,0.0,1.0,0.0,0.1,0.1,0.2,0.1,0.2,2.0,2.0,0.21,0.21,9.8,6.0,0.0,33.3,0.61,0.06,0.17,0.11,-1.0,-1.0,137.0,204.0,67.2,1821.0,441.0,90.0,122.0,73.8,37.0,54.0,68.5,4.0,4.0,100.0,13.0,11.0,7.0,1.0,15.0,197.0,6.0,0.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,1.0,7.0,24.0,2.44,19.0,0.0,2.0,0.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,12.0,5.0,2.0,7.0,3.0,10.0,3.0,7.0,4.0,11.0,0.0,338.0,15.0,31.0,178.0,134.0,34.0,338.0,218.0,15.0,10.0,4.0,279.0,44.0,15.0,0.0,13.0,20.0,0.0,0.0,0.0,0.0,19.0,25.0,35.0,41.7 +Sam Lammers,nl NED,FW,Empoli,25-360,1997,14.0,10.0,883.0,1.0,1.0,0.0,0.0,0.0,0.0,0.1,0.1,0.2,0.1,0.2,2.3,2.3,0.23,0.23,9.8,7.0,0.0,25.0,0.71,0.04,0.14,0.08,-1.3,-1.3,143.0,230.0,62.2,1814.0,451.0,94.0,129.0,72.9,41.0,60.0,68.3,1.0,5.0,20.0,10.0,13.0,4.0,0.0,19.0,226.0,3.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,8.0,25.0,2.55,18.0,1.0,1.0,2.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,20.0,10.0,5.0,9.0,6.0,4.0,1.0,3.0,3.0,8.0,0.0,358.0,12.0,35.0,151.0,179.0,49.0,358.0,193.0,9.0,12.0,6.0,268.0,37.0,14.0,0.0,6.0,8.0,1.0,0.0,0.0,0.0,27.0,18.0,16.0,52.9 +Kevin Lasagna,it ITA,"FW,MF",Hellas Verona,30-258,1992,26.0,17.0,1573.0,1.0,2.0,0.0,0.0,2.0,0.0,0.06,0.11,0.17,0.06,0.17,6.0,6.0,0.35,0.35,17.5,11.0,0.0,21.2,0.63,0.02,0.09,0.12,-5.0,-5.0,157.0,258.0,60.9,2338.0,547.0,77.0,112.0,68.8,54.0,81.0,66.7,8.0,18.0,44.4,17.0,9.0,9.0,0.0,19.0,236.0,16.0,1.0,0.0,1.0,13.0,0.0,0.0,0.0,0.0,3.0,6.0,17.0,39.0,2.23,25.0,0.0,4.0,3.0,3.0,0.17,1.0,0.0,1.0,0.0,0.0,12.0,6.0,7.0,3.0,2.0,17.0,1.0,16.0,5.0,13.0,0.0,482.0,15.0,62.0,182.0,249.0,67.0,482.0,267.0,36.0,23.0,14.0,317.0,53.0,34.0,0.0,25.0,22.0,15.0,0.0,0.0,0.0,64.0,20.0,68.0,22.7 +Armand Lauriente,fr FRA,FW,Sassuolo,24-142,1998,24.0,24.0,1959.0,7.0,6.0,1.0,1.0,8.0,1.0,0.32,0.28,0.6,0.28,0.55,4.5,3.7,0.21,0.17,21.8,20.0,10.0,38.5,0.92,0.12,0.3,0.07,2.5,2.3,522.0,729.0,71.6,7920.0,2399.0,303.0,357.0,84.9,161.0,220.0,73.2,35.0,86.0,40.7,59.0,36.0,42.0,9.0,79.0,660.0,69.0,20.0,2.0,5.0,100.0,41.0,24.0,7.0,0.0,7.0,0.0,23.0,105.0,4.83,72.0,10.0,2.0,4.0,12.0,0.55,7.0,0.0,1.0,2.0,0.0,17.0,11.0,3.0,7.0,7.0,20.0,0.0,20.0,11.0,7.0,0.0,998.0,6.0,73.0,368.0,584.0,95.0,997.0,680.0,114.0,62.0,48.0,732.0,50.0,49.0,1.0,17.0,38.0,5.0,1.0,0.0,0.0,98.0,1.0,5.0,16.7 +Valentino Lazaro,at AUT,DF,Torino,27-032,1996,18.0,13.0,1148.0,0.0,3.0,0.0,0.0,4.0,0.0,0.0,0.24,0.24,0.0,0.24,0.4,0.4,0.03,0.03,12.8,2.0,0.0,28.6,0.16,0.0,0.0,0.06,-0.4,-0.4,560.0,754.0,74.3,9270.0,3455.0,277.0,318.0,87.1,213.0,281.0,75.8,51.0,110.0,46.4,25.0,38.0,18.0,10.0,62.0,571.0,180.0,20.0,0.0,1.0,71.0,36.0,11.0,16.0,1.0,124.0,3.0,17.0,45.0,3.52,28.0,13.0,2.0,0.0,4.0,0.31,3.0,1.0,0.0,0.0,0.0,10.0,5.0,6.0,0.0,4.0,15.0,3.0,12.0,2.0,21.0,0.0,853.0,30.0,165.0,353.0,345.0,23.0,853.0,414.0,39.0,24.0,8.0,494.0,15.0,4.0,0.0,11.0,3.0,1.0,0.0,1.0,0.0,77.0,11.0,12.0,47.8 +Marko Lazetić,rs SRB,FW,Milan,19-093,2004,1.0,0.0,9.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.63,0.63,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,1.0,1.0,100.0,18.0,0.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,2.0,2.0,1.0,4.0,2.0,0.0,0.0,0.0,3.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0 +Darko Lazović,rs SRB,"MF,DF",Hellas Verona,32-222,1990,24.0,22.0,1846.0,3.0,4.0,0.0,0.0,0.0,0.0,0.15,0.2,0.34,0.15,0.34,3.1,3.1,0.15,0.15,20.5,12.0,3.0,32.4,0.59,0.08,0.25,0.08,-0.1,-0.1,433.0,690.0,62.8,7132.0,2998.0,221.0,280.0,78.9,156.0,225.0,69.3,42.0,118.0,35.6,33.0,40.0,27.0,12.0,68.0,590.0,91.0,9.0,6.0,1.0,99.0,33.0,12.0,15.0,0.0,48.0,9.0,32.0,52.0,2.54,35.0,10.0,2.0,0.0,4.0,0.2,2.0,2.0,0.0,0.0,0.0,23.0,12.0,9.0,11.0,3.0,12.0,3.0,9.0,12.0,19.0,0.0,882.0,25.0,157.0,341.0,399.0,57.0,882.0,445.0,62.0,32.0,19.0,521.0,32.0,11.0,0.0,7.0,2.0,8.0,0.0,0.0,0.0,118.0,5.0,16.0,23.8 +Manuel Lazzari,it ITA,DF,Lazio,29-147,1993,22.0,18.0,1495.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.01,0.01,16.6,1.0,0.0,25.0,0.06,0.0,0.0,0.04,-0.2,-0.2,796.0,911.0,87.4,10911.0,3966.0,475.0,509.0,93.3,256.0,294.0,87.1,27.0,41.0,65.9,9.0,37.0,11.0,6.0,45.0,759.0,150.0,12.0,0.0,1.0,28.0,0.0,0.0,0.0,0.0,138.0,2.0,16.0,25.0,1.5,20.0,1.0,0.0,2.0,3.0,0.18,3.0,0.0,0.0,0.0,0.0,27.0,17.0,10.0,13.0,4.0,18.0,6.0,12.0,13.0,28.0,1.0,1063.0,38.0,338.0,508.0,230.0,22.0,1063.0,584.0,71.0,47.0,13.0,695.0,19.0,3.0,0.0,10.0,29.0,0.0,0.0,0.0,0.0,67.0,6.0,11.0,35.3 +Rafael Leão,pt POR,"FW,MF",Milan,23-319,1999,29.0,23.0,2033.0,12.0,6.0,0.0,0.0,7.0,1.0,0.53,0.27,0.8,0.53,0.8,8.6,8.6,0.38,0.38,22.6,25.0,0.0,30.9,1.11,0.15,0.48,0.11,3.4,3.4,477.0,675.0,70.7,8334.0,2547.0,230.0,295.0,78.0,179.0,240.0,74.6,57.0,88.0,64.8,40.0,53.0,46.0,11.0,96.0,641.0,31.0,2.0,4.0,16.0,59.0,5.0,0.0,0.0,0.0,23.0,3.0,24.0,102.0,4.51,67.0,2.0,6.0,7.0,11.0,0.49,9.0,0.0,0.0,0.0,0.0,5.0,4.0,1.0,3.0,1.0,5.0,2.0,3.0,13.0,8.0,0.0,960.0,9.0,58.0,395.0,532.0,122.0,960.0,643.0,112.0,82.0,55.0,737.0,57.0,34.0,1.0,14.0,23.0,9.0,0.0,0.0,0.0,70.0,27.0,27.0,50.0 +Mehdi Léris,dz ALG,"MF,DF",Sampdoria,24-337,1998,28.0,25.0,2158.0,1.0,1.0,0.0,0.0,13.0,0.0,0.04,0.04,0.08,0.04,0.08,2.6,2.6,0.11,0.11,24.0,10.0,0.0,40.0,0.42,0.04,0.1,0.11,-1.6,-1.6,545.0,829.0,65.7,7823.0,2634.0,327.0,392.0,83.4,156.0,255.0,61.2,34.0,83.0,41.0,18.0,40.0,16.0,4.0,55.0,781.0,42.0,3.0,4.0,6.0,53.0,0.0,0.0,0.0,0.0,39.0,6.0,39.0,41.0,1.71,31.0,0.0,4.0,3.0,4.0,0.17,1.0,0.0,3.0,0.0,0.0,57.0,42.0,27.0,22.0,8.0,35.0,5.0,30.0,24.0,27.0,0.0,1152.0,43.0,238.0,520.0,416.0,67.0,1152.0,662.0,50.0,26.0,11.0,774.0,71.0,40.0,0.0,46.0,41.0,7.0,0.0,0.0,0.0,132.0,75.0,47.0,61.5 +Karol Linetty,pl POL,MF,Torino,28-082,1995,28.0,21.0,1784.0,1.0,0.0,0.0,0.0,8.0,0.0,0.05,0.0,0.05,0.05,0.05,0.9,0.9,0.05,0.05,19.8,4.0,0.0,26.7,0.2,0.07,0.25,0.06,0.1,0.1,774.0,901.0,85.9,12751.0,2934.0,369.0,416.0,88.7,334.0,361.0,92.5,51.0,79.0,64.6,18.0,67.0,5.0,1.0,68.0,851.0,46.0,31.0,3.0,9.0,17.0,14.0,4.0,7.0,0.0,1.0,4.0,8.0,37.0,1.87,29.0,3.0,1.0,2.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,31.0,16.0,7.0,21.0,3.0,28.0,8.0,20.0,18.0,25.0,0.0,1097.0,37.0,202.0,662.0,239.0,19.0,1097.0,580.0,24.0,23.0,3.0,711.0,25.0,11.0,0.0,45.0,20.0,4.0,0.0,1.0,0.0,119.0,24.0,24.0,50.0 +Marcin Listkowski,pl POL,"MF,FW",Lecce,25-074,1998,5.0,0.0,77.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.06,0.06,0.9,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.1,-0.1,16.0,24.0,66.7,204.0,71.0,14.0,16.0,87.5,0.0,3.0,0.0,1.0,4.0,25.0,0.0,0.0,0.0,0.0,1.0,23.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.15,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,0.0,34.0,2.0,10.0,11.0,13.0,1.0,34.0,17.0,6.0,1.0,0.0,20.0,1.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 +Diego Llorente,es ESP,DF,Roma,29-252,1993,6.0,4.0,393.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.08,0.08,4.4,1.0,0.0,33.3,0.23,0.0,0.0,0.12,-0.4,-0.4,172.0,202.0,85.1,3149.0,978.0,65.0,73.0,89.0,96.0,105.0,91.4,11.0,20.0,55.0,0.0,5.0,0.0,0.0,4.0,198.0,2.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.23,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,4.0,2.0,0.0,0.0,0.0,0.0,6.0,14.0,0.0,236.0,26.0,145.0,82.0,10.0,6.0,236.0,149.0,0.0,0.0,0.0,158.0,1.0,1.0,0.0,8.0,2.0,0.0,0.0,0.0,0.0,24.0,7.0,2.0,77.8 +Stanislav Lobotka,sk SVK,MF,Napoli,28-151,1994,31.0,28.0,2529.0,1.0,1.0,0.0,0.0,2.0,0.0,0.04,0.04,0.07,0.04,0.07,0.5,0.5,0.02,0.02,28.1,4.0,0.0,40.0,0.14,0.1,0.25,0.05,0.5,0.5,1776.0,1886.0,94.2,29268.0,7102.0,871.0,910.0,95.7,710.0,744.0,95.4,142.0,160.0,88.8,16.0,173.0,11.0,0.0,140.0,1848.0,36.0,28.0,0.0,11.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,8.0,61.0,2.17,52.0,1.0,2.0,4.0,7.0,0.25,5.0,1.0,1.0,0.0,0.0,71.0,37.0,32.0,28.0,11.0,26.0,6.0,20.0,16.0,14.0,0.0,2078.0,56.0,447.0,1364.0,284.0,9.0,2078.0,1321.0,34.0,49.0,5.0,1596.0,21.0,12.0,0.0,22.0,49.0,0.0,0.0,0.0,0.0,210.0,11.0,16.0,40.7 +Manuel Locatelli,it ITA,MF,Juventus,25-107,1998,26.0,24.0,2033.0,0.0,1.0,0.0,0.0,8.0,0.0,0.0,0.04,0.04,0.0,0.04,1.3,1.3,0.06,0.06,22.6,11.0,0.0,44.0,0.49,0.0,0.0,0.05,-1.3,-1.3,934.0,1146.0,81.5,17450.0,5925.0,404.0,462.0,87.4,384.0,439.0,87.5,125.0,186.0,67.2,14.0,118.0,11.0,1.0,111.0,1091.0,53.0,42.0,1.0,17.0,10.0,4.0,0.0,3.0,0.0,5.0,2.0,25.0,44.0,1.95,35.0,3.0,3.0,1.0,6.0,0.27,4.0,0.0,0.0,1.0,0.0,49.0,26.0,22.0,21.0,6.0,40.0,19.0,21.0,25.0,49.0,0.0,1411.0,84.0,406.0,785.0,235.0,12.0,1411.0,660.0,22.0,14.0,3.0,906.0,25.0,18.0,0.0,34.0,27.0,0.0,0.0,0.0,0.0,147.0,29.0,26.0,52.7 +Luka Lochoshvili,ge GEO,DF,Cremonese,24-331,1998,19.0,17.0,1404.0,1.0,1.0,0.0,0.0,4.0,0.0,0.06,0.06,0.13,0.06,0.13,0.5,0.5,0.03,0.03,15.6,2.0,0.0,33.3,0.13,0.17,0.5,0.09,0.5,0.5,483.0,616.0,78.4,9008.0,3350.0,179.0,199.0,89.9,264.0,304.0,86.8,38.0,96.0,39.6,4.0,37.0,3.0,1.0,63.0,587.0,28.0,22.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,5.0,1.0,6.0,13.0,0.83,9.0,0.0,2.0,0.0,2.0,0.13,1.0,0.0,1.0,0.0,0.0,39.0,16.0,28.0,8.0,3.0,25.0,7.0,18.0,18.0,62.0,0.0,792.0,85.0,403.0,351.0,48.0,7.0,792.0,416.0,14.0,14.0,0.0,445.0,9.0,4.0,0.0,26.0,9.0,1.0,0.0,1.0,0.0,69.0,28.0,19.0,59.6 +Ademola Lookman,ng NGA,"FW,MF",Atalanta,25-187,1997,28.0,20.0,1632.0,13.0,4.0,3.0,3.0,3.0,0.0,0.72,0.22,0.94,0.55,0.77,8.9,6.5,0.49,0.36,18.1,22.0,1.0,46.8,1.21,0.21,0.45,0.14,4.1,3.5,470.0,619.0,75.9,6416.0,1889.0,273.0,319.0,85.6,138.0,181.0,76.2,21.0,39.0,53.8,36.0,26.0,33.0,4.0,73.0,571.0,46.0,7.0,5.0,2.0,37.0,16.0,2.0,7.0,0.0,11.0,2.0,23.0,82.0,4.52,56.0,6.0,5.0,5.0,10.0,0.55,6.0,0.0,2.0,1.0,0.0,11.0,3.0,2.0,6.0,3.0,18.0,0.0,18.0,4.0,0.0,0.0,821.0,1.0,54.0,334.0,444.0,108.0,818.0,543.0,71.0,25.0,32.0,641.0,43.0,45.0,0.0,20.0,33.0,10.0,1.0,0.0,0.0,84.0,3.0,12.0,20.0 +Maxime Lopez,fr FRA,MF,Sassuolo,25-142,1997,24.0,20.0,1851.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.03,0.03,20.6,3.0,0.0,27.3,0.15,0.0,0.0,0.06,-0.7,-0.7,1113.0,1279.0,87.0,19089.0,6835.0,512.0,561.0,91.3,463.0,520.0,89.0,106.0,144.0,73.6,27.0,138.0,14.0,3.0,169.0,1230.0,46.0,32.0,2.0,8.0,23.0,11.0,1.0,6.0,0.0,3.0,3.0,12.0,56.0,2.73,50.0,6.0,0.0,0.0,3.0,0.15,3.0,0.0,0.0,0.0,0.0,38.0,16.0,23.0,11.0,4.0,22.0,6.0,16.0,20.0,20.0,0.0,1440.0,52.0,377.0,821.0,254.0,10.0,1440.0,911.0,48.0,46.0,2.0,1066.0,14.0,10.0,0.0,28.0,11.0,0.0,0.0,0.0,0.0,144.0,4.0,2.0,66.7 +Matteo Lovato,it ITA,DF,Salernitana,23-070,2000,12.0,5.0,518.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.8,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,198.0,232.0,85.3,3615.0,1397.0,77.0,85.0,90.6,95.0,109.0,87.2,20.0,29.0,69.0,0.0,8.0,0.0,0.0,7.0,220.0,12.0,12.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,9.0,9.0,2.0,0.0,8.0,5.0,3.0,9.0,20.0,2.0,290.0,35.0,162.0,122.0,9.0,0.0,290.0,161.0,1.0,1.0,1.0,176.0,4.0,3.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,25.0,10.0,7.0,58.8 +Sandi Lovrić,si SVN,MF,Udinese,25-028,1998,30.0,21.0,1789.0,4.0,4.0,0.0,0.0,4.0,0.0,0.2,0.2,0.4,0.2,0.4,4.1,4.1,0.21,0.21,19.9,14.0,4.0,23.7,0.7,0.07,0.29,0.07,-0.1,-0.1,622.0,775.0,80.3,10370.0,3096.0,297.0,339.0,87.6,221.0,263.0,84.0,67.0,113.0,59.3,34.0,74.0,21.0,8.0,86.0,716.0,54.0,25.0,4.0,11.0,54.0,26.0,5.0,10.0,0.0,3.0,5.0,10.0,69.0,3.47,41.0,13.0,9.0,1.0,8.0,0.4,5.0,1.0,1.0,0.0,0.0,36.0,15.0,13.0,16.0,7.0,18.0,1.0,17.0,18.0,16.0,0.0,1028.0,27.0,160.0,530.0,357.0,41.0,1028.0,605.0,34.0,29.0,8.0,664.0,41.0,14.0,0.0,27.0,27.0,1.0,0.0,0.0,0.0,121.0,7.0,12.0,36.8 +Hirving Lozano,mx MEX,FW,Napoli,27-269,1995,29.0,18.0,1496.0,3.0,3.0,1.0,1.0,3.0,0.0,0.18,0.18,0.36,0.12,0.3,5.4,4.6,0.32,0.28,16.6,13.0,0.0,31.7,0.78,0.05,0.15,0.11,-2.4,-2.6,405.0,544.0,74.4,5922.0,1444.0,229.0,267.0,85.8,131.0,170.0,77.1,28.0,47.0,59.6,27.0,24.0,35.0,19.0,45.0,536.0,6.0,0.0,0.0,3.0,68.0,1.0,0.0,0.0,0.0,5.0,2.0,23.0,61.0,3.67,45.0,0.0,4.0,2.0,8.0,0.48,7.0,0.0,1.0,0.0,0.0,24.0,13.0,10.0,6.0,8.0,30.0,0.0,30.0,14.0,6.0,0.0,759.0,9.0,71.0,263.0,438.0,74.0,758.0,532.0,83.0,26.0,22.0,563.0,39.0,13.0,0.0,26.0,27.0,4.0,0.0,0.0,0.0,56.0,7.0,12.0,36.8 +Jhon Lucumí,co COL,DF,Bologna,24-303,1998,27.0,27.0,2360.0,0.0,1.0,0.0,0.0,8.0,0.0,0.0,0.04,0.04,0.0,0.04,0.5,0.5,0.02,0.02,26.2,2.0,0.0,22.2,0.08,0.0,0.0,0.06,-0.5,-0.5,1347.0,1528.0,88.2,25329.0,9048.0,534.0,565.0,94.5,641.0,689.0,93.0,158.0,226.0,69.9,6.0,81.0,6.0,1.0,69.0,1452.0,73.0,56.0,2.0,20.0,1.0,0.0,0.0,0.0,0.0,1.0,3.0,13.0,17.0,0.65,15.0,0.0,1.0,0.0,2.0,0.08,2.0,0.0,0.0,0.0,0.0,47.0,31.0,34.0,12.0,1.0,35.0,24.0,11.0,31.0,92.0,1.0,1787.0,251.0,967.0,790.0,40.0,12.0,1787.0,1066.0,33.0,11.0,2.0,1165.0,14.0,5.0,0.0,25.0,16.0,0.0,0.0,3.0,0.0,171.0,36.0,27.0,57.1 +José Luis Palomino,ar ARG,DF,Atalanta,33-110,1990,13.0,8.0,718.0,1.0,0.0,0.0,0.0,3.0,0.0,0.13,0.0,0.13,0.13,0.13,0.5,0.5,0.06,0.06,8.0,3.0,0.0,60.0,0.38,0.2,0.33,0.1,0.5,0.5,411.0,498.0,82.5,7665.0,2805.0,161.0,182.0,88.5,207.0,233.0,88.8,42.0,68.0,61.8,2.0,23.0,4.0,2.0,26.0,468.0,30.0,19.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,11.0,0.0,8.0,9.0,1.13,7.0,0.0,1.0,0.0,2.0,0.25,1.0,0.0,1.0,0.0,0.0,20.0,12.0,14.0,6.0,0.0,10.0,4.0,6.0,28.0,19.0,0.0,583.0,45.0,251.0,302.0,34.0,8.0,583.0,297.0,3.0,1.0,1.0,324.0,3.0,3.0,0.0,7.0,5.0,0.0,0.0,0.0,1.0,73.0,24.0,12.0,66.7 +Romelu Lukaku,be BEL,FW,Inter,29-347,1993,19.0,13.0,1131.0,5.0,3.0,2.0,2.0,1.0,0.0,0.4,0.24,0.64,0.24,0.48,6.6,5.0,0.53,0.4,12.6,12.0,0.0,36.4,0.95,0.09,0.25,0.15,-1.6,-2.0,181.0,279.0,64.9,2299.0,479.0,100.0,134.0,74.6,46.0,69.0,66.7,10.0,21.0,47.6,19.0,12.0,12.0,1.0,30.0,269.0,8.0,0.0,3.0,2.0,19.0,0.0,0.0,0.0,0.0,0.0,2.0,14.0,44.0,3.5,36.0,0.0,4.0,1.0,6.0,0.48,5.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,10.0,0.0,384.0,10.0,15.0,121.0,249.0,76.0,382.0,226.0,17.0,10.0,9.0,326.0,31.0,16.0,0.0,11.0,2.0,10.0,0.0,0.0,0.0,16.0,31.0,25.0,55.4 +Saša Lukić,rs SRB,MF,Torino,26-255,1996,16.0,13.0,1195.0,2.0,0.0,1.0,1.0,4.0,0.0,0.15,0.0,0.15,0.08,0.08,1.7,0.9,0.13,0.07,13.3,3.0,1.0,20.0,0.23,0.07,0.33,0.06,0.3,0.1,627.0,732.0,85.7,11788.0,2410.0,232.0,265.0,87.5,307.0,339.0,90.6,78.0,102.0,76.5,16.0,54.0,8.0,3.0,44.0,692.0,38.0,29.0,1.0,6.0,25.0,4.0,0.0,4.0,0.0,5.0,2.0,7.0,44.0,3.31,35.0,3.0,2.0,3.0,2.0,0.15,2.0,0.0,0.0,0.0,0.0,21.0,14.0,10.0,10.0,1.0,17.0,7.0,10.0,13.0,10.0,0.0,850.0,29.0,129.0,537.0,190.0,23.0,849.0,448.0,26.0,18.0,4.0,556.0,17.0,8.0,0.0,26.0,10.0,0.0,0.0,0.0,0.0,81.0,22.0,22.0,50.0 +Sebastiano Luperto,it ITA,DF,Empoli,26-231,1996,29.0,29.0,2505.0,1.0,0.0,0.0,0.0,4.0,2.0,0.04,0.0,0.04,0.04,0.04,0.8,0.8,0.03,0.03,27.8,3.0,0.0,21.4,0.11,0.07,0.33,0.06,0.2,0.2,1140.0,1320.0,86.4,22332.0,8012.0,368.0,391.0,94.1,614.0,670.0,91.6,137.0,226.0,60.6,3.0,49.0,2.0,1.0,65.0,1187.0,132.0,58.0,1.0,8.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,6.0,11.0,0.4,9.0,1.0,1.0,0.0,2.0,0.07,2.0,0.0,0.0,0.0,0.0,28.0,17.0,21.0,7.0,0.0,44.0,32.0,12.0,32.0,136.0,1.0,1612.0,343.0,972.0,609.0,39.0,16.0,1612.0,872.0,13.0,6.0,1.0,913.0,4.0,3.0,1.0,17.0,19.0,0.0,0.0,0.0,0.0,138.0,54.0,52.0,50.9 +Charalambos Lykogiannis,gr GRE,DF,Bologna,29-185,1993,19.0,11.0,1072.0,2.0,0.0,0.0,0.0,3.0,0.0,0.17,0.0,0.17,0.17,0.17,0.8,0.8,0.06,0.06,11.9,6.0,5.0,31.6,0.5,0.11,0.33,0.04,1.2,1.2,486.0,689.0,70.5,8330.0,3370.0,226.0,257.0,87.9,212.0,279.0,76.0,40.0,108.0,37.0,17.0,47.0,16.0,11.0,54.0,564.0,122.0,11.0,1.0,9.0,64.0,11.0,6.0,3.0,0.0,100.0,3.0,27.0,27.0,2.27,18.0,5.0,2.0,1.0,1.0,0.08,0.0,1.0,0.0,0.0,0.0,15.0,7.0,4.0,6.0,5.0,9.0,4.0,5.0,15.0,30.0,0.0,812.0,31.0,222.0,378.0,220.0,5.0,812.0,438.0,29.0,19.0,0.0,512.0,9.0,1.0,0.0,14.0,10.0,1.0,0.0,0.0,0.0,52.0,18.0,16.0,52.9 +Giulio Maggiore,it ITA,MF,Salernitana,25-044,1998,13.0,9.0,760.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,8.4,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.4,-0.4,307.0,408.0,75.2,5295.0,1451.0,139.0,174.0,79.9,128.0,147.0,87.1,31.0,60.0,51.7,3.0,31.0,11.0,0.0,49.0,397.0,7.0,5.0,3.0,4.0,2.0,0.0,0.0,0.0,0.0,1.0,4.0,7.0,14.0,1.65,11.0,0.0,2.0,1.0,1.0,0.12,1.0,0.0,0.0,0.0,0.0,17.0,7.0,9.0,5.0,3.0,17.0,4.0,13.0,8.0,8.0,0.0,502.0,16.0,122.0,306.0,75.0,6.0,502.0,255.0,6.0,2.0,0.0,308.0,16.0,3.0,0.0,14.0,13.0,0.0,0.0,1.0,0.0,67.0,7.0,10.0,41.2 +Giangiacomo Magnani,it ITA,DF,Hellas Verona,27-203,1995,18.0,13.0,1066.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.04,0.04,11.8,1.0,0.0,20.0,0.08,0.0,0.0,0.08,-0.4,-0.4,262.0,357.0,73.4,5483.0,1881.0,82.0,102.0,80.4,133.0,157.0,84.7,43.0,86.0,50.0,4.0,21.0,3.0,0.0,22.0,348.0,9.0,6.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,10.0,0.85,9.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27.0,18.0,17.0,8.0,2.0,18.0,13.0,5.0,18.0,57.0,0.0,502.0,80.0,256.0,233.0,16.0,5.0,502.0,226.0,1.0,3.0,1.0,218.0,4.0,3.0,0.0,17.0,6.0,0.0,0.0,0.0,0.0,64.0,24.0,18.0,57.1 +Mike Maignan,fr FRA,GK,Milan,27-296,1995,15.0,15.0,1350.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,439.0,548.0,80.1,11449.0,7419.0,84.0,85.0,98.8,222.0,228.0,97.4,132.0,230.0,57.4,0.0,14.0,0.0,0.0,1.0,472.0,72.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.07,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,1.0,580.0,431.0,571.0,9.0,0.0,0.0,580.0,422.0,0.0,0.0,0.0,348.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,25.0,2.0,0.0,100.0 +Jordan Majchrzak,pl POL,FW,Roma,18-199,2004,1.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,75.0,27.0,15.0,3.0,3.0,100.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,2.0,2.0,0.0,4.0,3.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,20.0 +Jean-Victor Makengo,fr FRA,MF,Udinese,24-317,1998,16.0,11.0,805.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.11,0.11,0.0,0.11,0.4,0.4,0.04,0.04,8.9,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.4,-0.4,264.0,316.0,83.5,3718.0,884.0,167.0,180.0,92.8,79.0,96.0,82.3,9.0,13.0,69.2,5.0,19.0,6.0,1.0,36.0,315.0,1.0,1.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,13.0,1.45,11.0,0.0,0.0,1.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,16.0,13.0,7.0,7.0,2.0,8.0,2.0,6.0,9.0,3.0,0.0,393.0,11.0,64.0,223.0,112.0,6.0,393.0,259.0,18.0,17.0,0.0,290.0,15.0,11.0,0.0,9.0,13.0,0.0,0.0,0.0,0.0,39.0,5.0,1.0,83.3 +Lorenzo Malagrida,it ITA,"DF,MF",Sampdoria,19-183,2003,4.0,0.0,51.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.14,0.14,0.6,1.0,0.0,100.0,1.76,0.0,0.0,0.08,-0.1,-0.1,12.0,18.0,66.7,132.0,33.0,10.0,12.0,83.3,2.0,3.0,66.7,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,18.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,25.0,0.0,6.0,12.0,7.0,2.0,25.0,9.0,0.0,0.0,0.0,14.0,0.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,1.0,0.0 +Daniel Maldini,it ITA,"FW,MF",Spezia,21-196,2001,18.0,4.0,627.0,2.0,0.0,0.0,0.0,1.0,0.0,0.29,0.0,0.29,0.29,0.29,1.2,1.2,0.17,0.17,7.0,6.0,1.0,40.0,0.86,0.13,0.33,0.08,0.8,0.8,125.0,187.0,66.8,1665.0,330.0,72.0,89.0,80.9,33.0,47.0,70.2,7.0,25.0,28.0,4.0,9.0,5.0,1.0,10.0,164.0,22.0,4.0,0.0,1.0,19.0,9.0,8.0,1.0,0.0,1.0,1.0,7.0,22.0,3.16,10.0,1.0,2.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,5.0,3.0,4.0,5.0,9.0,0.0,9.0,1.0,8.0,0.0,289.0,7.0,40.0,121.0,132.0,15.0,289.0,158.0,13.0,8.0,4.0,183.0,24.0,8.0,0.0,13.0,18.0,0.0,0.0,0.0,0.0,32.0,12.0,17.0,41.4 +Youssef Maleh,it ITA,MF,Fiorentina,24-246,1998,7.0,4.0,297.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.17,0.17,3.3,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.6,-0.6,95.0,116.0,81.9,1462.0,376.0,55.0,63.0,87.3,31.0,35.0,88.6,7.0,12.0,58.3,6.0,8.0,5.0,2.0,11.0,111.0,4.0,3.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,7.0,2.12,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,3.0,0.0,5.0,0.0,5.0,1.0,3.0,0.0,150.0,4.0,18.0,79.0,53.0,11.0,150.0,93.0,3.0,4.0,1.0,96.0,6.0,6.0,0.0,8.0,3.0,0.0,0.0,0.0,0.0,20.0,3.0,4.0,42.9 +Youssef Maleh,it ITA,MF,Lecce,24-246,1998,13.0,6.0,550.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.05,0.05,6.1,1.0,0.0,16.7,0.16,0.0,0.0,0.05,-0.3,-0.3,108.0,157.0,68.8,1648.0,427.0,59.0,75.0,78.7,39.0,59.0,66.1,6.0,13.0,46.2,3.0,13.0,1.0,0.0,11.0,150.0,7.0,2.0,0.0,0.0,7.0,2.0,0.0,0.0,0.0,2.0,0.0,5.0,5.0,0.82,3.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,6.0,3.0,5.0,7.0,16.0,1.0,15.0,4.0,7.0,0.0,230.0,11.0,60.0,103.0,68.0,13.0,230.0,107.0,7.0,5.0,3.0,126.0,5.0,9.0,0.0,19.0,9.0,2.0,0.0,0.0,0.0,43.0,5.0,7.0,41.7 +Ruslan Malinovskyi,ua UKR,"MF,FW",Atalanta,29-356,1993,15.0,5.0,553.0,1.0,2.0,0.0,0.0,2.0,0.0,0.16,0.33,0.49,0.16,0.49,0.6,0.6,0.1,0.1,6.1,6.0,0.0,31.6,0.98,0.05,0.17,0.03,0.4,0.4,248.0,332.0,74.7,4851.0,1645.0,106.0,123.0,86.2,97.0,121.0,80.2,39.0,64.0,60.9,13.0,36.0,15.0,0.0,50.0,304.0,23.0,8.0,2.0,11.0,23.0,13.0,6.0,6.0,0.0,2.0,5.0,6.0,26.0,4.25,16.0,7.0,1.0,1.0,2.0,0.33,2.0,0.0,0.0,0.0,0.0,14.0,8.0,5.0,6.0,3.0,8.0,1.0,7.0,5.0,2.0,0.0,425.0,2.0,61.0,220.0,152.0,8.0,425.0,279.0,19.0,17.0,3.0,303.0,21.0,9.0,0.0,10.0,11.0,0.0,0.0,0.0,0.0,57.0,5.0,7.0,41.7 +Gianluca Mancini,it ITA,DF,Roma,27-008,1996,29.0,29.0,2425.0,1.0,2.0,0.0,0.0,6.0,0.0,0.04,0.07,0.11,0.04,0.11,1.9,1.9,0.07,0.07,26.9,5.0,0.0,29.4,0.19,0.06,0.2,0.11,-0.9,-0.9,1117.0,1387.0,80.5,21337.0,7786.0,424.0,468.0,90.6,543.0,608.0,89.3,137.0,271.0,50.6,15.0,84.0,12.0,5.0,70.0,1313.0,66.0,60.0,2.0,15.0,19.0,0.0,0.0,0.0,0.0,4.0,8.0,11.0,31.0,1.15,26.0,1.0,3.0,1.0,3.0,0.11,2.0,0.0,0.0,1.0,0.0,33.0,16.0,24.0,8.0,1.0,35.0,15.0,20.0,40.0,57.0,0.0,1595.0,172.0,767.0,740.0,101.0,24.0,1595.0,944.0,14.0,13.0,0.0,1129.0,12.0,6.0,0.0,34.0,23.0,2.0,0.0,1.0,0.0,95.0,48.0,21.0,69.6 +Rolando Mandragora,it ITA,MF,Fiorentina,25-300,1997,24.0,19.0,1667.0,2.0,3.0,0.0,0.0,6.0,0.0,0.11,0.16,0.27,0.11,0.27,1.3,1.3,0.07,0.07,18.5,5.0,1.0,15.2,0.27,0.06,0.4,0.04,0.7,0.7,691.0,857.0,80.6,12976.0,4138.0,282.0,321.0,87.9,309.0,349.0,88.5,81.0,135.0,60.0,31.0,99.0,16.0,5.0,121.0,776.0,78.0,43.0,1.0,16.0,62.0,29.0,14.0,10.0,0.0,5.0,3.0,14.0,78.0,4.21,53.0,18.0,5.0,1.0,6.0,0.32,4.0,1.0,0.0,1.0,0.0,37.0,20.0,10.0,21.0,6.0,19.0,3.0,16.0,13.0,21.0,0.0,1042.0,27.0,155.0,585.0,312.0,35.0,1042.0,523.0,18.0,19.0,3.0,633.0,13.0,7.0,0.0,27.0,15.0,1.0,0.0,0.0,0.0,102.0,31.0,23.0,57.4 +Federico Marchetti,it ITA,GK,Spezia,40-077,1983,1.0,0.0,66.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,18.0,77.8,316.0,175.0,6.0,6.0,100.0,4.0,4.0,100.0,4.0,8.0,50.0,0.0,0.0,0.0,0.0,0.0,9.0,9.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,17.0,18.0,0.0,0.0,0.0,18.0,7.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Riccardo Marchizza,it ITA,DF,Sassuolo,25-030,1998,8.0,2.0,190.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.47,0.47,0.0,0.47,0.0,0.0,0.0,0.0,2.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,93.0,120.0,77.5,1604.0,537.0,46.0,49.0,93.9,39.0,47.0,83.0,8.0,23.0,34.8,2.0,9.0,2.0,1.0,8.0,98.0,22.0,3.0,0.0,1.0,8.0,1.0,0.0,1.0,0.0,18.0,0.0,1.0,6.0,2.83,5.0,0.0,0.0,0.0,2.0,0.94,2.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,6.0,0.0,138.0,7.0,35.0,75.0,28.0,0.0,138.0,84.0,1.0,4.0,0.0,90.0,2.0,2.0,0.0,5.0,0.0,1.0,0.0,0.0,0.0,6.0,4.0,2.0,66.7 +Gian Marco Ferrari,it ITA,DF,Sassuolo,31-069,1992,26.0,21.0,1895.0,1.0,0.0,0.0,0.0,5.0,0.0,0.05,0.0,0.05,0.05,0.05,1.2,1.2,0.06,0.06,21.1,1.0,0.0,12.5,0.05,0.13,1.0,0.15,-0.2,-0.2,871.0,1021.0,85.3,17556.0,6527.0,268.0,295.0,90.8,474.0,525.0,90.3,118.0,178.0,66.3,3.0,59.0,5.0,1.0,81.0,911.0,108.0,74.0,0.0,13.0,3.0,0.0,0.0,0.0,0.0,4.0,2.0,4.0,16.0,0.76,14.0,1.0,0.0,1.0,2.0,0.09,2.0,0.0,0.0,0.0,0.0,21.0,15.0,10.0,10.0,1.0,24.0,18.0,6.0,21.0,76.0,3.0,1185.0,176.0,610.0,533.0,51.0,13.0,1185.0,665.0,11.0,8.0,0.0,725.0,9.0,1.0,0.0,19.0,27.0,0.0,0.0,2.0,0.0,87.0,40.0,19.0,67.8 +Pablo Marí,es ESP,DF,Monza,29-237,1993,24.0,22.0,2032.0,1.0,0.0,0.0,0.0,3.0,0.0,0.04,0.0,0.04,0.04,0.04,0.6,0.6,0.03,0.03,22.6,2.0,0.0,20.0,0.09,0.1,0.5,0.06,0.4,0.4,1158.0,1313.0,88.2,24199.0,9543.0,318.0,353.0,90.1,667.0,702.0,95.0,167.0,235.0,71.1,2.0,67.0,2.0,0.0,47.0,1243.0,65.0,36.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,7.0,5.0,7.0,12.0,0.53,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27.0,12.0,23.0,4.0,0.0,17.0,11.0,6.0,21.0,91.0,3.0,1518.0,255.0,891.0,599.0,30.0,15.0,1518.0,858.0,6.0,7.0,1.0,1027.0,8.0,3.0,0.0,21.0,8.0,0.0,0.0,0.0,0.0,107.0,42.0,20.0,67.7 +Răzvan Marin,ro ROU,MF,Empoli,26-337,1996,29.0,24.0,2191.0,2.0,2.0,0.0,0.0,7.0,0.0,0.08,0.08,0.16,0.08,0.16,1.2,1.2,0.05,0.05,24.3,7.0,9.0,23.3,0.29,0.07,0.29,0.04,0.8,0.8,1092.0,1372.0,79.6,20055.0,7013.0,465.0,493.0,94.3,432.0,517.0,83.6,155.0,276.0,56.2,45.0,96.0,22.0,5.0,97.0,1180.0,185.0,105.0,2.0,16.0,115.0,77.0,32.0,30.0,0.0,3.0,7.0,24.0,78.0,3.2,49.0,26.0,1.0,1.0,2.0,0.08,0.0,2.0,0.0,0.0,0.0,42.0,22.0,22.0,19.0,1.0,28.0,10.0,18.0,12.0,31.0,0.0,1564.0,73.0,390.0,824.0,368.0,14.0,1564.0,917.0,32.0,38.0,2.0,1012.0,18.0,20.0,0.0,29.0,17.0,0.0,0.0,1.0,0.0,131.0,12.0,11.0,52.2 +Marlon,br BRA,DF,Monza,27-230,1995,22.0,16.0,1283.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.01,0.01,14.3,1.0,0.0,33.3,0.07,0.0,0.0,0.04,-0.1,-0.1,758.0,822.0,92.2,13530.0,4550.0,300.0,323.0,92.9,401.0,420.0,95.5,50.0,64.0,78.1,1.0,60.0,5.0,2.0,49.0,781.0,41.0,28.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,5.0,0.35,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,16.0,16.0,13.0,0.0,8.0,2.0,6.0,18.0,43.0,0.0,949.0,88.0,438.0,481.0,33.0,5.0,949.0,540.0,14.0,9.0,0.0,670.0,3.0,1.0,0.0,19.0,5.0,0.0,0.0,0.0,1.0,55.0,32.0,14.0,69.6 +Luca Marrone,it ITA,DF,Monza,33-028,1990,2.0,2.0,135.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.1,-0.1,78.0,84.0,92.9,1371.0,634.0,34.0,35.0,97.1,39.0,41.0,95.1,5.0,8.0,62.5,0.0,0.0,0.0,0.0,4.0,78.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,4.0,1.0,93.0,12.0,66.0,25.0,2.0,1.0,93.0,59.0,0.0,0.0,0.0,64.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,8.0,4.0,5.0,44.4 +Lautaro Martínez,ar ARG,FW,Inter,25-246,1997,31.0,24.0,2225.0,15.0,3.0,1.0,2.0,2.0,0.0,0.61,0.12,0.73,0.57,0.69,14.3,12.7,0.58,0.51,24.7,41.0,0.0,38.3,1.66,0.13,0.34,0.12,0.7,1.3,422.0,577.0,73.1,6277.0,1549.0,223.0,292.0,76.4,136.0,171.0,79.5,29.0,39.0,74.4,37.0,36.0,22.0,1.0,71.0,534.0,41.0,1.0,5.0,6.0,12.0,0.0,0.0,0.0,0.0,4.0,2.0,19.0,78.0,3.15,54.0,2.0,10.0,8.0,9.0,0.36,4.0,0.0,1.0,2.0,1.0,22.0,11.0,2.0,13.0,7.0,29.0,1.0,28.0,12.0,13.0,1.0,930.0,19.0,67.0,406.0,468.0,146.0,928.0,523.0,40.0,28.0,17.0,678.0,64.0,40.0,0.0,43.0,55.0,10.0,1.0,0.0,0.0,80.0,38.0,40.0,48.7 +Lucas Martínez Quarta,ar ARG,DF,Fiorentina,26-350,1996,23.0,21.0,1856.0,1.0,1.0,0.0,0.0,4.0,0.0,0.05,0.05,0.1,0.05,0.1,2.4,2.4,0.12,0.12,20.6,10.0,0.0,38.5,0.48,0.04,0.1,0.09,-1.4,-1.4,1039.0,1275.0,81.5,22695.0,9262.0,291.0,334.0,87.1,560.0,622.0,90.0,179.0,283.0,63.3,6.0,95.0,5.0,1.0,101.0,1227.0,46.0,40.0,0.0,32.0,3.0,0.0,0.0,0.0,0.0,6.0,2.0,11.0,23.0,1.12,17.0,1.0,4.0,0.0,2.0,0.1,2.0,0.0,0.0,0.0,0.0,72.0,42.0,40.0,29.0,3.0,34.0,16.0,18.0,40.0,71.0,1.0,1552.0,122.0,719.0,750.0,100.0,33.0,1552.0,894.0,19.0,24.0,1.0,938.0,11.0,5.0,0.0,18.0,24.0,1.0,0.0,0.0,0.0,143.0,70.0,29.0,70.7 +Adam Marušić,me MNE,DF,Lazio,30-190,1992,29.0,28.0,2536.0,0.0,2.0,0.0,0.0,5.0,1.0,0.0,0.07,0.07,0.0,0.07,0.7,0.7,0.02,0.02,28.2,7.0,0.0,46.7,0.25,0.0,0.0,0.04,-0.7,-0.7,1476.0,1720.0,85.8,22515.0,9107.0,825.0,894.0,92.3,514.0,600.0,85.7,95.0,152.0,62.5,9.0,103.0,7.0,4.0,108.0,1481.0,236.0,27.0,0.0,8.0,16.0,0.0,0.0,0.0,0.0,209.0,3.0,17.0,35.0,1.24,28.0,3.0,1.0,0.0,3.0,0.11,2.0,0.0,1.0,0.0,0.0,37.0,28.0,20.0,13.0,4.0,18.0,6.0,12.0,34.0,57.0,1.0,1914.0,89.0,730.0,946.0,249.0,17.0,1914.0,1012.0,36.0,37.0,4.0,1252.0,12.0,11.0,0.0,19.0,14.0,1.0,0.0,0.0,0.0,165.0,27.0,23.0,54.0 +Adam Masina,ma MAR,DF,Udinese,29-113,1994,10.0,5.0,509.0,1.0,0.0,0.0,0.0,1.0,0.0,0.18,0.0,0.18,0.18,0.18,0.0,0.0,0.01,0.01,5.7,1.0,0.0,100.0,0.18,1.0,1.0,0.03,1.0,1.0,208.0,271.0,76.8,4534.0,1900.0,68.0,77.0,88.3,98.0,120.0,81.7,40.0,67.0,59.7,1.0,15.0,4.0,3.0,16.0,240.0,29.0,8.0,0.0,5.0,8.0,0.0,0.0,0.0,0.0,21.0,2.0,2.0,2.0,0.35,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,2.0,3.0,0.0,6.0,3.0,3.0,10.0,19.0,1.0,315.0,29.0,150.0,134.0,35.0,4.0,315.0,183.0,3.0,2.0,1.0,186.0,3.0,1.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,25.0,16.0,6.0,72.7 +Nemanja Matić,rs SRB,MF,Roma,34-267,1988,30.0,14.0,1584.0,1.0,2.0,0.0,0.0,4.0,0.0,0.06,0.11,0.17,0.06,0.17,0.7,0.7,0.04,0.04,17.6,3.0,0.0,37.5,0.17,0.13,0.33,0.09,0.3,0.3,968.0,1124.0,86.1,17064.0,5274.0,440.0,469.0,93.8,388.0,441.0,88.0,107.0,157.0,68.2,19.0,115.0,14.0,3.0,118.0,1061.0,56.0,53.0,2.0,14.0,8.0,1.0,0.0,0.0,0.0,2.0,7.0,13.0,42.0,2.39,39.0,1.0,1.0,0.0,7.0,0.4,5.0,0.0,1.0,0.0,0.0,41.0,22.0,21.0,16.0,4.0,22.0,4.0,18.0,23.0,20.0,1.0,1279.0,46.0,290.0,817.0,191.0,18.0,1279.0,784.0,38.0,33.0,3.0,873.0,20.0,15.0,0.0,16.0,15.0,1.0,0.0,0.0,0.0,150.0,14.0,13.0,51.9 +Luís Maximiano,pt POR,GK,Lazio,24-110,1999,1.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,75.0,98.0,90.0,1.0,1.0,100.0,0.0,0.0,0.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,4.0,0.0,0.0,0.0,4.0,3.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Pasquale Mazzocchi,it ITA,DF,Salernitana,27-272,1995,20.0,17.0,1440.0,2.0,2.0,0.0,0.0,2.0,0.0,0.12,0.12,0.25,0.12,0.25,1.1,1.1,0.07,0.07,16.0,5.0,0.0,35.7,0.31,0.14,0.4,0.08,0.9,0.9,508.0,687.0,73.9,8296.0,2873.0,264.0,295.0,89.5,186.0,250.0,74.4,43.0,91.0,47.3,28.0,30.0,18.0,10.0,47.0,534.0,151.0,9.0,2.0,4.0,70.0,17.0,0.0,16.0,0.0,125.0,2.0,22.0,55.0,3.44,39.0,9.0,0.0,1.0,7.0,0.44,5.0,0.0,0.0,0.0,0.0,24.0,12.0,17.0,3.0,4.0,8.0,2.0,6.0,9.0,25.0,2.0,856.0,41.0,252.0,325.0,292.0,29.0,856.0,467.0,54.0,38.0,11.0,480.0,34.0,24.0,0.0,18.0,9.0,5.0,0.0,0.0,0.0,97.0,7.0,7.0,50.0 +Weston McKennie,us USA,"MF,DF",Juventus,24-240,1998,15.0,13.0,1052.0,1.0,1.0,0.0,0.0,0.0,0.0,0.09,0.09,0.17,0.09,0.17,0.9,0.9,0.08,0.08,11.7,4.0,0.0,50.0,0.34,0.13,0.25,0.11,0.1,0.1,336.0,418.0,80.4,5385.0,1516.0,175.0,207.0,84.5,125.0,147.0,85.0,28.0,38.0,73.7,8.0,23.0,6.0,4.0,39.0,389.0,27.0,3.0,1.0,2.0,15.0,0.0,0.0,0.0,0.0,24.0,2.0,7.0,18.0,1.54,15.0,0.0,2.0,0.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,14.0,5.0,3.0,9.0,2.0,15.0,4.0,11.0,11.0,15.0,1.0,526.0,25.0,115.0,249.0,164.0,22.0,526.0,231.0,19.0,11.0,2.0,349.0,20.0,12.0,0.0,13.0,7.0,3.0,0.0,0.0,0.0,43.0,12.0,16.0,42.9 +Gary Medel,cl CHI,"MF,DF",Bologna,35-265,1987,24.0,16.0,1358.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.07,0.07,0.0,0.07,0.1,0.1,0.0,0.0,15.1,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,883.0,997.0,88.6,16231.0,5515.0,349.0,377.0,92.6,408.0,434.0,94.0,101.0,148.0,68.2,6.0,59.0,4.0,0.0,52.0,952.0,45.0,42.0,1.0,8.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,22.0,1.46,19.0,0.0,0.0,1.0,2.0,0.13,2.0,0.0,0.0,0.0,0.0,24.0,15.0,15.0,7.0,2.0,14.0,8.0,6.0,26.0,33.0,0.0,1112.0,116.0,474.0,591.0,56.0,0.0,1112.0,573.0,1.0,3.0,0.0,712.0,6.0,4.0,0.0,17.0,16.0,0.0,0.0,0.0,0.0,136.0,14.0,14.0,50.0 +Soualiho Meïté,fr FRA,MF,Cremonese,29-039,1994,24.0,21.0,1736.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.05,0.05,0.0,0.05,0.4,0.4,0.02,0.02,19.3,2.0,0.0,14.3,0.1,0.0,0.0,0.03,-0.4,-0.4,618.0,739.0,83.6,10345.0,2718.0,314.0,354.0,88.7,238.0,267.0,89.1,53.0,83.0,63.9,12.0,61.0,15.0,3.0,67.0,716.0,18.0,13.0,3.0,5.0,9.0,0.0,0.0,0.0,0.0,2.0,5.0,9.0,36.0,1.87,31.0,1.0,0.0,0.0,2.0,0.1,2.0,0.0,0.0,0.0,0.0,36.0,24.0,19.0,11.0,6.0,20.0,4.0,16.0,20.0,26.0,0.0,923.0,46.0,245.0,535.0,156.0,11.0,923.0,528.0,23.0,16.0,4.0,573.0,23.0,23.0,0.0,21.0,31.0,0.0,0.0,0.0,0.0,130.0,20.0,11.0,64.5 +Alex Meret,it ITA,GK,Napoli,26-034,1997,30.0,30.0,2700.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,743.0,846.0,87.8,17162.0,10607.0,175.0,175.0,100.0,389.0,392.0,99.2,176.0,276.0,63.8,0.0,1.0,0.0,0.0,0.0,618.0,228.0,43.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,887.0,762.0,885.0,2.0,0.0,0.0,887.0,485.0,0.0,0.0,0.0,430.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,54.0,4.0,0.0,100.0 +Yıldırım Mert Çetin,tr TUR,DF,Lecce,26-114,1997,1.0,1.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,66.7,38.0,8.0,1.0,1.0,100.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,8.0,2.0,5.0,3.0,0.0,0.0,8.0,3.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0 +Junior Messias,br BRA,"FW,DF",Milan,31-347,1991,20.0,15.0,1195.0,4.0,2.0,0.0,0.0,2.0,0.0,0.3,0.15,0.45,0.3,0.45,2.0,2.0,0.15,0.15,13.3,9.0,0.0,37.5,0.68,0.17,0.44,0.08,2.0,2.0,330.0,438.0,75.3,5249.0,1389.0,177.0,211.0,83.9,124.0,155.0,80.0,22.0,43.0,51.2,11.0,26.0,3.0,1.0,39.0,407.0,30.0,4.0,2.0,1.0,18.0,2.0,0.0,1.0,0.0,24.0,1.0,10.0,40.0,3.01,26.0,1.0,4.0,4.0,6.0,0.45,5.0,0.0,0.0,0.0,0.0,20.0,16.0,7.0,8.0,5.0,18.0,4.0,14.0,6.0,22.0,0.0,597.0,26.0,100.0,261.0,248.0,43.0,597.0,351.0,35.0,33.0,13.0,393.0,23.0,11.0,0.0,22.0,20.0,1.0,0.0,0.0,0.0,60.0,21.0,31.0,40.4 +Tommaso Milanese,it ITA,MF,Cremonese,20-268,2002,2.0,0.0,27.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,14.0,71.4,182.0,41.0,4.0,5.0,80.0,3.0,5.0,60.0,2.0,3.0,66.7,3.0,3.0,0.0,0.0,2.0,11.0,3.0,1.0,0.0,0.0,2.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,6.43,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,19.0,0.0,1.0,7.0,12.0,2.0,19.0,13.0,2.0,1.0,0.0,14.0,3.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +Nikola Milenković,rs SRB,DF,Fiorentina,25-195,1997,23.0,20.0,1822.0,2.0,1.0,0.0,0.0,3.0,0.0,0.1,0.05,0.15,0.1,0.15,1.7,1.7,0.09,0.09,20.2,4.0,0.0,28.6,0.2,0.14,0.5,0.12,0.3,0.3,936.0,1100.0,85.1,18283.0,5657.0,333.0,372.0,89.5,485.0,546.0,88.8,111.0,159.0,69.8,6.0,53.0,2.0,0.0,70.0,1057.0,39.0,35.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,5.0,23.0,1.14,20.0,0.0,3.0,0.0,2.0,0.1,2.0,0.0,0.0,0.0,0.0,32.0,20.0,15.0,14.0,3.0,25.0,13.0,12.0,20.0,62.0,0.0,1299.0,125.0,624.0,619.0,63.0,28.0,1299.0,698.0,8.0,10.0,0.0,752.0,6.0,0.0,0.0,34.0,17.0,0.0,0.0,0.0,1.0,107.0,59.0,22.0,72.8 +Arkadiusz Milik,pl POL,FW,Juventus,29-056,1994,21.0,13.0,1218.0,6.0,1.0,0.0,0.0,3.0,1.0,0.44,0.07,0.52,0.44,0.52,4.6,4.6,0.34,0.34,13.5,18.0,1.0,56.3,1.33,0.19,0.33,0.15,1.4,1.4,271.0,348.0,77.9,3911.0,661.0,159.0,193.0,82.4,86.0,102.0,84.3,12.0,18.0,66.7,10.0,15.0,7.0,0.0,27.0,326.0,18.0,3.0,2.0,3.0,4.0,0.0,0.0,0.0,0.0,1.0,4.0,7.0,26.0,1.93,15.0,1.0,4.0,2.0,3.0,0.22,2.0,0.0,0.0,0.0,1.0,8.0,6.0,1.0,5.0,2.0,8.0,0.0,8.0,1.0,13.0,0.0,474.0,14.0,38.0,252.0,189.0,49.0,474.0,264.0,9.0,13.0,1.0,371.0,39.0,11.0,1.0,17.0,25.0,4.0,0.0,0.0,0.0,33.0,28.0,43.0,39.4 +Sergej Milinković-Savić,rs SRB,MF,Lazio,28-057,1995,29.0,28.0,2460.0,6.0,8.0,0.0,0.0,8.0,0.0,0.22,0.29,0.51,0.22,0.51,4.3,4.3,0.16,0.16,27.3,14.0,8.0,28.0,0.51,0.12,0.43,0.09,1.7,1.7,1197.0,1587.0,75.4,18716.0,5168.0,669.0,809.0,82.7,384.0,481.0,79.8,100.0,184.0,54.3,38.0,122.0,35.0,3.0,139.0,1548.0,33.0,21.0,12.0,16.0,25.0,0.0,0.0,0.0,0.0,12.0,6.0,32.0,67.0,2.45,59.0,1.0,3.0,2.0,13.0,0.48,12.0,0.0,1.0,0.0,0.0,46.0,28.0,20.0,18.0,8.0,42.0,9.0,33.0,23.0,35.0,0.0,1984.0,58.0,359.0,1106.0,540.0,62.0,1984.0,1182.0,41.0,26.0,9.0,1588.0,95.0,53.0,0.0,36.0,65.0,1.0,0.0,0.0,0.0,145.0,74.0,61.0,54.8 +Vanja Milinković-Savić,rs SRB,GK,Torino,26-064,1997,31.0,31.0,2790.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,871.0,1445.0,60.3,27886.0,19896.0,114.0,116.0,98.3,432.0,439.0,98.4,323.0,874.0,37.0,0.0,72.0,2.0,0.0,0.0,1117.0,315.0,78.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,1.0,13.0,1.0,5.0,0.16,3.0,2.0,0.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,15.0,1.0,1506.0,1096.0,1490.0,17.0,1.0,1.0,1506.0,959.0,0.0,0.0,0.0,797.0,1.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,47.0,10.0,1.0,90.9 +Kim Min-jae,kr KOR,DF,Napoli,26-161,1996,30.0,30.0,2630.0,2.0,2.0,0.0,0.0,4.0,0.0,0.07,0.07,0.14,0.07,0.14,1.0,1.0,0.03,0.03,29.2,3.0,0.0,37.5,0.1,0.25,0.67,0.13,1.0,1.0,2186.0,2422.0,90.3,39977.0,14833.0,860.0,921.0,93.4,1109.0,1175.0,94.4,185.0,261.0,70.9,10.0,168.0,6.0,2.0,141.0,2377.0,41.0,38.0,2.0,27.0,5.0,0.0,0.0,0.0,0.0,2.0,4.0,13.0,32.0,1.1,29.0,0.0,1.0,0.0,3.0,0.1,2.0,0.0,1.0,0.0,0.0,49.0,26.0,30.0,18.0,1.0,34.0,25.0,9.0,35.0,111.0,1.0,2714.0,270.0,1166.0,1479.0,82.0,26.0,2714.0,1656.0,37.0,17.0,2.0,2012.0,12.0,7.0,0.0,22.0,7.0,0.0,0.0,0.0,0.0,188.0,83.0,45.0,64.8 +Aleksei Miranchuk,ru RUS,MF,Torino,27-190,1995,23.0,20.0,1693.0,4.0,5.0,0.0,0.0,0.0,0.0,0.21,0.27,0.48,0.21,0.48,2.5,2.5,0.13,0.13,18.8,11.0,1.0,35.5,0.58,0.13,0.36,0.08,1.5,1.5,543.0,676.0,80.3,8620.0,2100.0,286.0,327.0,87.5,183.0,218.0,83.9,46.0,84.0,54.8,39.0,58.0,29.0,6.0,84.0,643.0,30.0,4.0,6.0,4.0,28.0,19.0,13.0,0.0,0.0,7.0,3.0,13.0,65.0,3.45,55.0,3.0,2.0,3.0,8.0,0.42,7.0,0.0,0.0,1.0,0.0,8.0,4.0,1.0,4.0,3.0,8.0,0.0,8.0,1.0,3.0,0.0,829.0,8.0,50.0,432.0,351.0,37.0,829.0,554.0,37.0,39.0,6.0,662.0,41.0,23.0,0.0,6.0,20.0,0.0,1.0,0.0,0.0,75.0,8.0,19.0,29.6 +Fabio Miretti,it ITA,"MF,FW",Juventus,19-265,2003,23.0,11.0,1036.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,0.17,0.17,0.0,0.17,1.4,1.4,0.12,0.12,11.5,5.0,0.0,29.4,0.43,0.0,0.0,0.08,-1.4,-1.4,309.0,436.0,70.9,4851.0,1320.0,168.0,207.0,81.2,111.0,144.0,77.1,21.0,52.0,40.4,15.0,29.0,12.0,3.0,44.0,411.0,23.0,4.0,2.0,3.0,22.0,11.0,5.0,5.0,0.0,5.0,2.0,15.0,43.0,3.75,28.0,4.0,5.0,1.0,7.0,0.61,6.0,0.0,0.0,1.0,0.0,20.0,8.0,13.0,5.0,2.0,17.0,1.0,16.0,2.0,10.0,0.0,590.0,12.0,97.0,289.0,213.0,40.0,590.0,341.0,26.0,16.0,5.0,410.0,35.0,21.0,0.0,20.0,21.0,2.0,1.0,0.0,0.0,62.0,9.0,18.0,33.3 +Henrikh Mkhitaryan,am ARM,MF,Inter,34-094,1989,27.0,21.0,1782.0,3.0,1.0,0.0,0.0,4.0,0.0,0.15,0.05,0.2,0.15,0.2,2.5,2.4,0.12,0.12,19.8,5.0,0.0,17.2,0.25,0.1,0.6,0.09,0.5,0.6,829.0,952.0,87.1,13714.0,3354.0,406.0,445.0,91.2,323.0,368.0,87.8,73.0,90.0,81.1,15.0,84.0,27.0,8.0,115.0,933.0,16.0,5.0,3.0,5.0,19.0,2.0,1.0,1.0,0.0,9.0,3.0,10.0,72.0,3.64,53.0,0.0,5.0,8.0,6.0,0.3,5.0,0.0,0.0,0.0,1.0,36.0,24.0,10.0,21.0,5.0,25.0,4.0,21.0,17.0,16.0,0.0,1152.0,30.0,167.0,638.0,364.0,33.0,1152.0,669.0,41.0,40.0,9.0,803.0,39.0,19.0,0.0,23.0,25.0,0.0,0.0,0.0,0.0,152.0,9.0,10.0,47.4 +Salvatore Molina,it ITA,DF,Monza,31-114,1992,5.0,1.0,229.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.02,2.5,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,86.0,114.0,75.4,1260.0,424.0,49.0,55.0,89.1,36.0,45.0,80.0,1.0,8.0,12.5,2.0,4.0,5.0,4.0,7.0,96.0,17.0,2.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,15.0,1.0,3.0,5.0,1.97,5.0,0.0,0.0,0.0,1.0,0.39,1.0,0.0,0.0,0.0,0.0,6.0,5.0,2.0,4.0,0.0,4.0,2.0,2.0,1.0,2.0,0.0,139.0,6.0,29.0,47.0,65.0,1.0,139.0,79.0,9.0,5.0,0.0,85.0,4.0,2.0,0.0,4.0,3.0,0.0,0.0,0.0,0.0,10.0,0.0,1.0,0.0 +Daniele Montevago,it ITA,FW,Sampdoria,20-038,2003,6.0,3.0,231.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.19,0.19,2.6,1.0,0.0,14.3,0.39,0.0,0.0,0.07,-0.5,-0.5,23.0,40.0,57.5,228.0,22.0,14.0,20.0,70.0,4.0,8.0,50.0,0.0,3.0,0.0,4.0,1.0,0.0,0.0,0.0,37.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,7.0,2.75,6.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,2.0,1.0,1.0,0.0,1.0,0.0,79.0,2.0,4.0,31.0,44.0,18.0,79.0,42.0,1.0,1.0,1.0,61.0,9.0,4.0,0.0,8.0,4.0,4.0,0.0,0.0,0.0,5.0,14.0,23.0,37.8 +Lorenzo Montipò,it ITA,GK,Hellas Verona,27-064,1996,30.0,30.0,2700.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,610.0,974.0,62.6,23305.0,19505.0,62.0,63.0,98.4,217.0,222.0,97.7,328.0,684.0,48.0,0.0,47.0,1.0,0.0,0.0,651.0,321.0,94.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,8.0,0.27,2.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,24.0,2.0,1071.0,864.0,1059.0,14.0,0.0,0.0,1071.0,509.0,0.0,1.0,0.0,392.0,4.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,46.0,3.0,2.0,60.0 +Nikola Moro,hr CRO,MF,Bologna,25-044,1998,19.0,9.0,907.0,1.0,3.0,0.0,0.0,1.0,0.0,0.1,0.3,0.4,0.1,0.4,0.5,0.5,0.05,0.05,10.1,3.0,0.0,21.4,0.3,0.07,0.33,0.04,0.5,0.5,444.0,572.0,77.6,8164.0,2687.0,191.0,237.0,80.6,184.0,217.0,84.8,63.0,92.0,68.5,14.0,63.0,10.0,2.0,77.0,557.0,12.0,6.0,3.0,8.0,15.0,0.0,0.0,0.0,0.0,6.0,3.0,12.0,34.0,3.37,30.0,0.0,0.0,1.0,4.0,0.4,3.0,0.0,0.0,0.0,0.0,26.0,17.0,8.0,13.0,5.0,13.0,1.0,12.0,7.0,12.0,0.0,669.0,20.0,140.0,396.0,141.0,14.0,669.0,392.0,18.0,16.0,1.0,451.0,11.0,6.0,0.0,7.0,9.0,0.0,0.0,0.0,0.0,72.0,18.0,10.0,64.3 +Dany Mota,pt POR,"FW,MF",Monza,24-358,1998,22.0,14.0,1374.0,4.0,0.0,0.0,0.0,1.0,0.0,0.26,0.0,0.26,0.26,0.26,4.0,4.0,0.26,0.26,15.3,6.0,0.0,31.6,0.39,0.21,0.67,0.21,0.0,0.0,291.0,426.0,68.3,4117.0,537.0,175.0,221.0,79.2,90.0,134.0,67.2,12.0,21.0,57.1,21.0,11.0,9.0,4.0,21.0,402.0,23.0,1.0,1.0,2.0,35.0,0.0,0.0,0.0,0.0,5.0,1.0,26.0,46.0,3.01,34.0,0.0,3.0,4.0,5.0,0.33,0.0,0.0,0.0,2.0,0.0,5.0,4.0,0.0,2.0,3.0,5.0,1.0,4.0,3.0,18.0,0.0,587.0,19.0,46.0,275.0,271.0,64.0,587.0,374.0,52.0,26.0,20.0,450.0,57.0,31.0,0.0,15.0,24.0,13.0,2.0,0.0,0.0,47.0,22.0,43.0,33.8 +João Moutinho,pt POR,"DF,FW",Spezia,25-103,1998,4.0,0.0,58.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,42.0,71.4,405.0,162.0,18.0,20.0,90.0,9.0,12.0,75.0,1.0,6.0,16.7,1.0,4.0,0.0,0.0,1.0,34.0,8.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,8.0,0.0,2.0,1.0,1.55,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,1.0,5.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,51.0,3.0,11.0,20.0,20.0,0.0,51.0,18.0,0.0,1.0,0.0,24.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,0.0 +Mert Müldür,tr TUR,DF,Sassuolo,24-022,1999,1.0,1.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,100.0,11.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Luis Muriel,co COL,FW,Atalanta,32-009,1991,22.0,8.0,854.0,1.0,2.0,1.0,1.0,2.0,2.0,0.11,0.21,0.32,0.0,0.21,1.6,0.8,0.17,0.09,9.5,6.0,3.0,33.3,0.63,0.0,0.0,0.05,-0.6,-0.8,258.0,412.0,62.6,4473.0,1449.0,132.0,176.0,75.0,82.0,119.0,68.9,33.0,80.0,41.3,26.0,18.0,26.0,4.0,53.0,351.0,59.0,5.0,6.0,11.0,65.0,33.0,15.0,16.0,0.0,11.0,2.0,14.0,47.0,4.95,33.0,8.0,1.0,1.0,5.0,0.53,4.0,0.0,0.0,1.0,0.0,8.0,5.0,2.0,3.0,3.0,8.0,0.0,8.0,2.0,2.0,0.0,549.0,3.0,34.0,196.0,322.0,56.0,548.0,320.0,45.0,28.0,21.0,426.0,41.0,21.0,1.0,13.0,13.0,2.0,1.0,0.0,0.0,30.0,9.0,24.0,27.3 +Jeison Murillo,co COL,DF,Sampdoria,30-333,1992,17.0,12.0,994.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.02,0.02,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.2,-0.2,429.0,527.0,81.4,8619.0,3181.0,136.0,149.0,91.3,239.0,270.0,88.5,52.0,93.0,55.9,2.0,29.0,1.0,0.0,33.0,477.0,47.0,24.0,1.0,7.0,1.0,0.0,0.0,0.0,0.0,23.0,3.0,7.0,8.0,0.72,5.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,11.0,14.0,6.0,0.0,21.0,13.0,8.0,7.0,33.0,0.0,628.0,73.0,295.0,310.0,28.0,4.0,628.0,320.0,8.0,4.0,0.0,366.0,7.0,1.0,1.0,15.0,17.0,0.0,0.0,0.0,1.0,62.0,27.0,20.0,57.4 +Nicola Murru,it ITA,DF,Sampdoria,28-130,1994,16.0,3.0,550.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,6.1,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,169.0,230.0,73.5,3006.0,1528.0,73.0,83.0,88.0,74.0,94.0,78.7,17.0,39.0,43.6,2.0,17.0,4.0,3.0,19.0,199.0,30.0,2.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,28.0,1.0,3.0,5.0,0.82,3.0,2.0,0.0,0.0,1.0,0.16,0.0,1.0,0.0,0.0,0.0,18.0,11.0,11.0,6.0,1.0,13.0,4.0,9.0,6.0,18.0,0.0,295.0,29.0,118.0,129.0,50.0,1.0,295.0,141.0,4.0,4.0,0.0,154.0,6.0,1.0,0.0,11.0,5.0,1.0,0.0,2.0,0.0,27.0,6.0,6.0,50.0 +Juan Musso,ar ARG,GK,Atalanta,28-354,1994,23.0,23.0,1987.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,500.0,682.0,73.3,14179.0,9934.0,90.0,90.0,100.0,235.0,238.0,98.7,172.0,348.0,49.4,1.0,0.0,0.0,0.0,0.0,484.0,197.0,48.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,1.0,722.0,637.0,719.0,3.0,0.0,0.0,722.0,424.0,0.0,1.0,1.0,307.0,2.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,1.0,18.0,1.0,0.0,100.0 +Joakim Mæhle,dk DEN,"DF,MF",Atalanta,25-340,1997,28.0,19.0,1676.0,3.0,1.0,0.0,0.0,4.0,1.0,0.16,0.05,0.21,0.16,0.21,3.0,3.0,0.16,0.16,18.6,6.0,0.0,27.3,0.32,0.14,0.5,0.13,0.0,0.0,922.0,1135.0,81.2,12666.0,4136.0,602.0,670.0,89.9,255.0,313.0,81.5,34.0,66.0,51.5,26.0,55.0,35.0,6.0,110.0,935.0,197.0,5.0,1.0,2.0,28.0,1.0,0.0,1.0,0.0,191.0,3.0,37.0,61.0,3.28,50.0,3.0,1.0,2.0,4.0,0.21,2.0,1.0,0.0,0.0,0.0,23.0,12.0,9.0,5.0,9.0,22.0,5.0,17.0,18.0,22.0,0.0,1342.0,37.0,280.0,550.0,530.0,47.0,1342.0,740.0,59.0,50.0,8.0,862.0,31.0,21.0,0.0,15.0,14.0,4.0,0.0,0.0,0.0,111.0,3.0,10.0,23.1 +Herculano Nabian,pt POR,MF,Empoli,19-090,2004,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Michel Ndary Adopo,fr FRA,"MF,DF",Torino,22-280,2000,8.0,3.0,255.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,2.8,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,117.0,136.0,86.0,1728.0,444.0,64.0,73.0,87.7,46.0,52.0,88.5,5.0,6.0,83.3,1.0,7.0,0.0,0.0,8.0,131.0,5.0,4.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,1.06,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,3.0,2.0,1.0,2.0,2.0,0.0,1.0,4.0,0.0,166.0,7.0,33.0,109.0,25.0,2.0,166.0,84.0,2.0,1.0,0.0,101.0,4.0,3.0,0.0,6.0,2.0,0.0,0.0,0.0,0.0,20.0,8.0,5.0,61.5 +Tanguy Ndombele,fr FRA,MF,Napoli,26-118,1996,28.0,7.0,743.0,1.0,0.0,0.0,0.0,5.0,0.0,0.12,0.0,0.12,0.12,0.12,1.0,1.0,0.12,0.12,8.3,2.0,0.0,12.5,0.24,0.06,0.5,0.06,0.0,0.0,476.0,540.0,88.1,6466.0,1624.0,292.0,318.0,91.8,135.0,145.0,93.1,18.0,28.0,64.3,9.0,40.0,9.0,0.0,52.0,529.0,11.0,7.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,12.0,19.0,2.31,15.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,8.0,4.0,8.0,2.0,8.0,1.0,7.0,6.0,7.0,1.0,645.0,11.0,74.0,395.0,181.0,27.0,645.0,433.0,24.0,18.0,4.0,545.0,23.0,17.0,0.0,13.0,22.0,0.0,0.0,1.0,0.0,37.0,7.0,3.0,70.0 +Ilija Nestorovski,mk MKD,"FW,MF",Udinese,33-044,1990,14.0,0.0,181.0,1.0,1.0,0.0,0.0,2.0,0.0,0.5,0.5,0.99,0.5,0.99,0.3,0.3,0.15,0.15,2.0,1.0,0.0,25.0,0.5,0.25,1.0,0.08,0.7,0.7,39.0,58.0,67.2,533.0,140.0,26.0,36.0,72.2,10.0,13.0,76.9,2.0,4.0,50.0,1.0,4.0,1.0,0.0,9.0,56.0,2.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,1.49,2.0,0.0,0.0,1.0,2.0,0.99,1.0,0.0,0.0,1.0,0.0,3.0,0.0,0.0,1.0,2.0,2.0,0.0,2.0,0.0,3.0,0.0,79.0,3.0,5.0,41.0,34.0,12.0,79.0,37.0,0.0,1.0,0.0,51.0,4.0,0.0,0.0,5.0,3.0,2.0,1.0,0.0,0.0,7.0,2.0,10.0,16.7 +Cyril Ngonge,be BEL,"MF,FW",Hellas Verona,22-334,2000,9.0,5.0,378.0,2.0,1.0,0.0,0.0,1.0,0.0,0.48,0.24,0.71,0.48,0.71,1.2,1.2,0.29,0.29,4.2,5.0,0.0,35.7,1.19,0.14,0.4,0.09,0.8,0.8,60.0,98.0,61.2,981.0,391.0,30.0,42.0,71.4,23.0,29.0,79.3,7.0,18.0,38.9,7.0,6.0,6.0,4.0,11.0,89.0,7.0,1.0,1.0,1.0,11.0,5.0,2.0,2.0,0.0,0.0,2.0,2.0,16.0,3.8,7.0,2.0,4.0,0.0,1.0,0.24,0.0,1.0,0.0,0.0,0.0,5.0,2.0,1.0,2.0,2.0,4.0,0.0,4.0,2.0,2.0,0.0,169.0,2.0,18.0,75.0,80.0,11.0,169.0,105.0,12.0,8.0,6.0,114.0,12.0,6.0,0.0,5.0,7.0,2.0,0.0,0.0,0.0,15.0,11.0,24.0,31.4 +Hans Nicolussi Caviglia,it ITA,MF,Salernitana,22-311,2000,9.0,6.0,538.0,1.0,0.0,0.0,0.0,2.0,0.0,0.17,0.0,0.17,0.17,0.17,0.1,0.1,0.02,0.02,6.0,1.0,0.0,33.3,0.17,0.33,1.0,0.04,0.9,0.9,165.0,221.0,74.7,2853.0,838.0,67.0,80.0,83.8,74.0,93.0,79.6,14.0,26.0,53.8,2.0,17.0,0.0,0.0,17.0,210.0,10.0,8.0,0.0,1.0,3.0,2.0,0.0,0.0,0.0,0.0,1.0,4.0,8.0,1.34,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,6.0,5.0,7.0,2.0,7.0,0.0,7.0,7.0,7.0,1.0,277.0,14.0,78.0,160.0,40.0,3.0,277.0,122.0,3.0,3.0,0.0,144.0,8.0,4.0,0.0,11.0,5.0,0.0,0.0,1.0,0.0,42.0,7.0,9.0,43.8 +Dimitris Nikolaou,gr GRE,DF,Spezia,24-255,1998,29.0,28.0,2454.0,0.0,0.0,0.0,0.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.01,0.01,27.3,1.0,0.0,14.3,0.04,0.0,0.0,0.04,-0.3,-0.3,1187.0,1454.0,81.6,21413.0,8678.0,524.0,565.0,92.7,506.0,578.0,87.5,129.0,257.0,50.2,7.0,91.0,3.0,2.0,86.0,1332.0,117.0,38.0,0.0,23.0,10.0,0.0,0.0,0.0,0.0,58.0,5.0,9.0,20.0,0.73,17.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,37.0,27.0,28.0,8.0,1.0,36.0,23.0,13.0,22.0,66.0,0.0,1677.0,214.0,864.0,728.0,97.0,11.0,1677.0,1015.0,18.0,17.0,0.0,1108.0,15.0,9.0,0.0,20.0,16.0,0.0,0.0,0.0,0.0,173.0,23.0,24.0,48.9 +Bram Nuytinck,nl NED,DF,Udinese,32-356,1990,6.0,3.0,385.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,4.3,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,113.0,139.0,81.3,2516.0,1038.0,24.0,27.0,88.9,67.0,75.0,89.3,20.0,32.0,62.5,0.0,4.0,1.0,0.0,4.0,124.0,14.0,4.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,9.0,1.0,2.0,2.0,0.47,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,4.0,3.0,4.0,0.0,6.0,6.0,0.0,5.0,22.0,0.0,185.0,37.0,117.0,65.0,4.0,2.0,185.0,90.0,1.0,0.0,0.0,88.0,1.0,0.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,19.0,7.0,5.0,58.3 +Bram Nuytinck,nl NED,DF,Sampdoria,32-356,1990,14.0,14.0,1115.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.08,0.08,0.0,0.08,0.2,0.2,0.02,0.02,12.4,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.2,-0.2,405.0,475.0,85.3,8204.0,2672.0,101.0,119.0,84.9,260.0,279.0,93.2,41.0,68.0,60.3,3.0,12.0,0.0,0.0,16.0,451.0,22.0,20.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,3.0,3.0,0.24,3.0,0.0,0.0,0.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,41.0,29.0,27.0,14.0,0.0,32.0,17.0,15.0,19.0,68.0,0.0,650.0,119.0,431.0,209.0,12.0,9.0,650.0,344.0,2.0,0.0,0.0,347.0,1.0,2.0,0.0,19.0,8.0,0.0,0.0,1.0,0.0,57.0,19.0,23.0,45.2 +M'Bala Nzola,ao ANG,FW,Spezia,26-250,1996,26.0,25.0,2255.0,13.0,2.0,3.0,3.0,6.0,0.0,0.52,0.08,0.6,0.4,0.48,10.0,7.7,0.4,0.31,25.1,19.0,0.0,36.5,0.76,0.19,0.53,0.15,3.0,2.3,392.0,497.0,78.9,4675.0,781.0,264.0,312.0,84.6,88.0,112.0,78.6,6.0,8.0,75.0,26.0,20.0,17.0,2.0,45.0,475.0,20.0,2.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,4.0,2.0,15.0,54.0,2.16,42.0,0.0,4.0,3.0,6.0,0.24,3.0,0.0,0.0,0.0,0.0,12.0,5.0,0.0,6.0,6.0,19.0,5.0,14.0,6.0,20.0,0.0,825.0,29.0,60.0,313.0,461.0,120.0,822.0,524.0,40.0,20.0,23.0,657.0,107.0,61.0,0.0,49.0,31.0,8.0,0.0,0.0,0.0,57.0,34.0,73.0,31.8 +Pedro Obiang,gq EQG,MF,Sassuolo,31-029,1992,15.0,9.0,706.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.13,0.13,0.0,0.13,0.3,0.3,0.04,0.04,7.8,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.3,-0.3,282.0,345.0,81.7,5248.0,1798.0,135.0,159.0,84.9,109.0,122.0,89.3,35.0,50.0,70.0,2.0,19.0,1.0,0.0,32.0,322.0,21.0,21.0,0.0,8.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,6.0,8.0,1.02,6.0,0.0,1.0,0.0,1.0,0.13,1.0,0.0,0.0,0.0,0.0,25.0,16.0,11.0,12.0,2.0,9.0,1.0,8.0,7.0,13.0,0.0,418.0,28.0,113.0,268.0,38.0,2.0,418.0,203.0,0.0,3.0,0.0,252.0,5.0,2.0,0.0,14.0,6.0,0.0,0.0,0.0,0.0,42.0,14.0,17.0,45.2 +Guillermo Ochoa,mx MEX,GK,Salernitana,37-286,1985,14.0,14.0,1260.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,293.0,508.0,57.7,10082.0,7731.0,30.0,30.0,100.0,122.0,124.0,98.4,141.0,351.0,40.2,0.0,11.0,0.0,0.0,0.0,318.0,187.0,53.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,5.0,0.36,5.0,0.0,0.0,0.0,2.0,0.14,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,553.0,466.0,550.0,3.0,0.0,0.0,553.0,262.0,0.0,0.0,0.0,212.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.0,0.0 +Marios Oikonomou,gr GRE,DF,Sampdoria,30-201,1992,2.0,0.0,28.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,16.0,56.3,211.0,86.0,3.0,4.0,75.0,4.0,7.0,57.1,2.0,5.0,40.0,0.0,0.0,0.0,0.0,1.0,15.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,3.0,0.0,22.0,4.0,13.0,9.0,0.0,0.0,22.0,10.0,0.0,1.0,0.0,8.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,1.0,50.0 +David Okereke,ng NGA,"FW,MF",Cremonese,25-239,1997,27.0,21.0,1848.0,5.0,0.0,1.0,1.0,4.0,0.0,0.24,0.0,0.24,0.19,0.19,4.4,3.6,0.21,0.18,20.5,16.0,0.0,32.0,0.78,0.08,0.25,0.07,0.6,0.4,296.0,425.0,69.6,4976.0,1231.0,149.0,197.0,75.6,105.0,131.0,80.2,32.0,45.0,71.1,18.0,27.0,23.0,5.0,64.0,411.0,10.0,1.0,3.0,3.0,20.0,0.0,0.0,0.0,0.0,6.0,4.0,22.0,65.0,3.17,38.0,1.0,7.0,9.0,3.0,0.15,0.0,0.0,1.0,2.0,0.0,11.0,7.0,4.0,5.0,2.0,20.0,3.0,17.0,6.0,11.0,0.0,659.0,18.0,52.0,264.0,352.0,74.0,658.0,385.0,46.0,38.0,11.0,486.0,48.0,30.0,0.0,17.0,29.0,7.0,3.0,1.0,0.0,48.0,39.0,67.0,36.8 +Caleb Okoli,it ITA,DF,Atalanta,21-286,2001,13.0,9.0,845.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.03,0.03,9.4,2.0,0.0,40.0,0.21,0.0,0.0,0.07,-0.3,-0.3,297.0,387.0,76.7,5230.0,2068.0,127.0,155.0,81.9,134.0,164.0,81.7,27.0,43.0,62.8,0.0,9.0,1.0,0.0,13.0,363.0,24.0,17.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,5.0,0.0,6.0,1.0,0.11,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,17.0,14.0,6.0,1.0,9.0,5.0,4.0,26.0,41.0,3.0,514.0,56.0,302.0,188.0,30.0,8.0,514.0,228.0,2.0,4.0,0.0,239.0,11.0,3.0,0.0,13.0,7.0,0.0,0.0,0.0,0.0,59.0,40.0,23.0,63.5 +Mathías Olivera,uy URU,DF,Napoli,25-176,1997,24.0,11.0,1112.0,1.0,2.0,0.0,0.0,1.0,0.0,0.08,0.16,0.24,0.08,0.24,0.5,0.5,0.04,0.04,12.4,2.0,0.0,50.0,0.16,0.25,0.5,0.11,0.5,0.5,831.0,962.0,86.4,11366.0,3366.0,531.0,571.0,93.0,231.0,268.0,86.2,37.0,66.0,56.1,21.0,67.0,10.0,3.0,60.0,810.0,149.0,20.0,1.0,3.0,14.0,2.0,1.0,0.0,0.0,127.0,3.0,14.0,35.0,2.83,30.0,3.0,0.0,0.0,3.0,0.24,3.0,0.0,0.0,0.0,0.0,30.0,16.0,11.0,13.0,6.0,19.0,2.0,17.0,18.0,23.0,0.0,1117.0,36.0,256.0,573.0,295.0,26.0,1117.0,640.0,41.0,26.0,8.0,714.0,15.0,19.0,0.0,15.0,16.0,1.0,0.0,0.0,0.0,66.0,19.0,21.0,47.5 +André Onana,cm CMR,GK,Inter,27-023,1996,20.0,20.0,1800.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,670.0,833.0,80.4,16764.0,11307.0,171.0,171.0,100.0,308.0,316.0,97.5,185.0,338.0,54.7,2.0,15.0,1.0,0.0,2.0,677.0,154.0,24.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,6.0,0.3,4.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,862.0,720.0,851.0,11.0,0.0,0.0,862.0,587.0,0.0,0.0,0.0,523.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,20.0,1.0,0.0,100.0 +Divock Origi,be BEL,"FW,MF",Milan,28-007,1995,23.0,8.0,866.0,2.0,1.0,0.0,0.0,1.0,0.0,0.21,0.1,0.31,0.21,0.31,1.9,1.9,0.19,0.19,9.6,10.0,0.0,40.0,1.04,0.08,0.2,0.07,0.1,0.1,109.0,164.0,66.5,1415.0,296.0,63.0,83.0,75.9,29.0,47.0,61.7,5.0,10.0,50.0,11.0,6.0,5.0,1.0,12.0,156.0,8.0,0.0,0.0,1.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,21.0,2.19,13.0,0.0,2.0,3.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,8.0,3.0,4.0,1.0,3.0,4.0,2.0,2.0,0.0,12.0,0.0,292.0,13.0,29.0,111.0,158.0,43.0,292.0,165.0,17.0,10.0,11.0,212.0,37.0,22.0,0.0,13.0,14.0,6.0,0.0,0.0,0.0,22.0,14.0,26.0,35.0 +Riccardo Orsolini,it ITA,FW,Bologna,26-091,1997,26.0,16.0,1656.0,8.0,3.0,1.0,1.0,5.0,1.0,0.43,0.16,0.6,0.38,0.54,7.0,6.2,0.38,0.34,18.4,19.0,6.0,28.4,1.03,0.1,0.37,0.09,1.0,0.8,435.0,684.0,63.6,8195.0,2461.0,212.0,288.0,73.6,131.0,201.0,65.2,71.0,131.0,54.2,32.0,37.0,9.0,4.0,52.0,610.0,73.0,21.0,0.0,26.0,79.0,46.0,27.0,11.0,0.0,4.0,1.0,33.0,69.0,3.76,35.0,20.0,6.0,4.0,4.0,0.22,0.0,3.0,1.0,0.0,0.0,22.0,14.0,9.0,9.0,4.0,26.0,2.0,24.0,8.0,15.0,0.0,960.0,17.0,158.0,381.0,429.0,82.0,959.0,558.0,41.0,30.0,27.0,692.0,54.0,30.0,0.0,24.0,33.0,12.0,0.0,0.0,0.0,92.0,12.0,22.0,35.3 +Victor Osimhen,ng NGA,FW,Napoli,24-117,1998,25.0,23.0,2011.0,21.0,4.0,0.0,0.0,4.0,0.0,0.94,0.18,1.12,0.94,1.12,16.7,16.6,0.75,0.74,22.3,41.0,0.0,38.7,1.83,0.2,0.51,0.16,4.3,4.4,206.0,301.0,68.4,2899.0,413.0,112.0,144.0,77.8,61.0,89.0,68.5,13.0,18.0,72.2,25.0,16.0,6.0,0.0,20.0,289.0,12.0,0.0,1.0,3.0,11.0,0.0,0.0,0.0,0.0,5.0,0.0,20.0,55.0,2.46,37.0,0.0,9.0,4.0,8.0,0.36,6.0,0.0,1.0,1.0,0.0,5.0,3.0,1.0,1.0,3.0,6.0,1.0,5.0,4.0,20.0,0.0,608.0,21.0,32.0,186.0,405.0,176.0,608.0,369.0,33.0,20.0,22.0,460.0,76.0,38.0,0.0,32.0,29.0,27.0,1.0,1.0,0.0,47.0,49.0,42.0,53.8 +Remi Oudin,fr FRA,"FW,MF",Lecce,26-158,1996,24.0,5.0,738.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,1.4,0.17,0.17,8.2,8.0,2.0,33.3,0.98,0.0,0.0,0.06,-1.4,-1.4,213.0,350.0,60.9,4478.0,1566.0,84.0,106.0,79.2,65.0,106.0,61.3,52.0,104.0,50.0,16.0,22.0,4.0,0.0,30.0,296.0,53.0,14.0,2.0,7.0,64.0,30.0,9.0,18.0,0.0,9.0,1.0,16.0,34.0,4.15,13.0,15.0,6.0,0.0,3.0,0.37,2.0,1.0,0.0,0.0,0.0,20.0,9.0,7.0,10.0,3.0,11.0,0.0,11.0,20.0,15.0,0.0,467.0,16.0,84.0,193.0,197.0,26.0,467.0,258.0,11.0,9.0,4.0,276.0,13.0,8.0,0.0,7.0,7.0,1.0,0.0,0.0,0.0,56.0,8.0,8.0,50.0 +Adam Ounas,dz ALG,FW,Napoli,26-165,1996,2.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23,0.23,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,13.0,17.0,76.5,297.0,80.0,6.0,7.0,85.7,2.0,3.0,66.7,5.0,5.0,100.0,0.0,2.0,1.0,0.0,4.0,16.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,12.0,0.0,0.0,1.0,0.0,1.0,6.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,19.0,0.0,4.0,12.0,5.0,1.0,19.0,14.0,3.0,2.0,0.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,100.0 +Simone Pafundi,it ITA,MF,Udinese,17-070,2006,6.0,0.0,70.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.1,0.1,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.08,-0.1,-0.1,46.0,54.0,85.2,700.0,152.0,23.0,28.0,82.1,13.0,14.0,92.9,5.0,7.0,71.4,3.0,6.0,2.0,0.0,5.0,50.0,4.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,3.0,0.0,0.0,4.0,5.07,3.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,4.0,1.0,4.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,73.0,0.0,6.0,44.0,26.0,1.0,73.0,52.0,1.0,1.0,0.0,54.0,1.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,6.0,1.0,0.0,100.0 +Flavio Paoletti,it ITA,MF,Sampdoria,20-099,2003,7.0,0.0,127.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,70.0,86.0,81.4,1388.0,306.0,24.0,30.0,80.0,36.0,38.0,94.7,10.0,12.0,83.3,1.0,3.0,0.0,0.0,5.0,78.0,7.0,4.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,3.0,1.0,4.0,3.0,2.11,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,4.0,6.0,0.0,98.0,6.0,28.0,55.0,15.0,0.0,98.0,60.0,4.0,4.0,0.0,68.0,1.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,3.0,25.0 +Leandro Paredes,ar ARG,MF,Juventus,28-300,1994,19.0,6.0,707.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.05,0.05,7.9,1.0,2.0,10.0,0.13,0.0,0.0,0.04,-0.4,-0.4,459.0,523.0,87.8,8683.0,3251.0,187.0,200.0,93.5,201.0,214.0,93.9,66.0,98.0,67.3,11.0,42.0,9.0,2.0,44.0,455.0,67.0,37.0,2.0,2.0,31.0,26.0,17.0,8.0,0.0,4.0,1.0,3.0,29.0,3.69,18.0,8.0,1.0,0.0,3.0,0.38,3.0,0.0,0.0,0.0,0.0,16.0,10.0,6.0,9.0,1.0,5.0,0.0,5.0,12.0,17.0,0.0,589.0,28.0,159.0,326.0,106.0,4.0,589.0,295.0,7.0,4.0,2.0,397.0,4.0,3.0,0.0,11.0,7.0,0.0,0.0,1.0,0.0,49.0,2.0,1.0,66.7 +Fabiano Parisi,it ITA,DF,Empoli,22-167,2000,28.0,28.0,2387.0,2.0,0.0,0.0,0.0,7.0,1.0,0.08,0.0,0.08,0.08,0.08,1.6,1.6,0.06,0.06,26.5,2.0,0.0,12.5,0.08,0.13,1.0,0.1,0.4,0.4,1094.0,1436.0,76.2,17592.0,8286.0,583.0,657.0,88.7,404.0,523.0,77.2,85.0,170.0,50.0,19.0,92.0,34.0,10.0,141.0,1158.0,272.0,27.0,1.0,6.0,52.0,1.0,1.0,0.0,0.0,244.0,6.0,44.0,57.0,2.15,39.0,2.0,2.0,6.0,8.0,0.3,6.0,0.0,0.0,1.0,0.0,53.0,27.0,31.0,16.0,6.0,44.0,14.0,30.0,32.0,73.0,0.0,1802.0,134.0,628.0,798.0,416.0,18.0,1802.0,972.0,87.0,63.0,10.0,982.0,47.0,33.0,0.0,26.0,63.0,0.0,1.0,1.0,0.0,202.0,18.0,12.0,60.0 +Mario Pašalić,hr CRO,"MF,FW",Atalanta,28-075,1995,25.0,17.0,1355.0,3.0,2.0,0.0,0.0,3.0,0.0,0.2,0.13,0.33,0.2,0.33,4.2,4.2,0.28,0.28,15.1,5.0,0.0,21.7,0.33,0.13,0.6,0.18,-1.2,-1.2,427.0,531.0,80.4,6907.0,1676.0,217.0,257.0,84.4,155.0,178.0,87.1,39.0,57.0,68.4,12.0,38.0,16.0,1.0,64.0,509.0,20.0,9.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,2.0,2.0,11.0,36.0,2.38,32.0,0.0,2.0,0.0,6.0,0.4,5.0,0.0,1.0,0.0,0.0,20.0,9.0,6.0,12.0,2.0,16.0,2.0,14.0,15.0,11.0,0.0,684.0,17.0,111.0,369.0,209.0,45.0,684.0,410.0,15.0,11.0,3.0,490.0,28.0,21.0,0.0,21.0,15.0,1.0,0.0,0.0,0.0,76.0,33.0,26.0,55.9 +Patric,es ESP,DF,Lazio,30-008,1993,16.0,14.0,1233.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.7,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.0,0.0,790.0,888.0,89.0,16372.0,5122.0,257.0,278.0,92.4,348.0,366.0,95.1,157.0,210.0,74.8,1.0,25.0,2.0,0.0,21.0,817.0,70.0,37.0,0.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,4.0,0.29,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,10.0,10.0,3.0,2.0,17.0,12.0,5.0,10.0,47.0,1.0,994.0,192.0,611.0,383.0,5.0,0.0,994.0,523.0,0.0,0.0,0.0,665.0,3.0,0.0,0.0,3.0,4.0,0.0,0.0,0.0,0.0,85.0,6.0,22.0,21.4 +Rui Patrício,pt POR,GK,Roma,35-069,1988,31.0,31.0,2790.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,624.0,819.0,76.2,16857.0,11315.0,126.0,126.0,100.0,284.0,287.0,99.0,212.0,401.0,52.9,0.0,6.0,0.0,0.0,0.0,536.0,280.0,63.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,0.06,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,5.0,3.0,862.0,775.0,860.0,2.0,0.0,0.0,862.0,461.0,0.0,0.0,0.0,354.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,36.0,1.0,0.0,100.0 +Pedro,es ESP,FW,Lazio,35-271,1987,29.0,15.0,1616.0,4.0,2.0,0.0,0.0,1.0,0.0,0.22,0.11,0.33,0.22,0.33,3.0,3.0,0.17,0.17,18.0,11.0,0.0,33.3,0.61,0.12,0.36,0.09,1.0,1.0,650.0,805.0,80.7,8969.0,1834.0,402.0,444.0,90.5,176.0,232.0,75.9,32.0,62.0,51.6,17.0,32.0,20.0,2.0,46.0,778.0,23.0,8.0,2.0,9.0,28.0,3.0,2.0,0.0,0.0,11.0,4.0,15.0,45.0,2.51,30.0,1.0,6.0,2.0,6.0,0.33,4.0,0.0,1.0,1.0,0.0,25.0,18.0,13.0,7.0,5.0,14.0,2.0,12.0,6.0,4.0,1.0,983.0,11.0,128.0,489.0,378.0,68.0,983.0,642.0,55.0,37.0,16.0,793.0,36.0,24.0,0.0,18.0,27.0,4.0,1.0,0.0,0.0,79.0,0.0,4.0,0.0 +Gianluca Pegolo,it ITA,GK,Sassuolo,42-031,1981,2.0,2.0,180.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,64.0,77.0,83.1,1652.0,1058.0,13.0,13.0,100.0,31.0,32.0,96.9,19.0,31.0,61.3,0.0,0.0,0.0,0.0,0.0,56.0,21.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,81.0,71.0,81.0,0.0,0.0,0.0,81.0,46.0,0.0,0.0,0.0,43.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Pietro Pellegri,it ITA,FW,Torino,22-039,2001,13.0,4.0,403.0,1.0,0.0,0.0,0.0,2.0,0.0,0.22,0.0,0.22,0.22,0.22,0.5,0.5,0.1,0.1,4.5,3.0,0.0,42.9,0.67,0.14,0.33,0.06,0.5,0.5,44.0,60.0,73.3,547.0,30.0,30.0,39.0,76.9,11.0,12.0,91.7,1.0,3.0,33.3,4.0,2.0,2.0,0.0,5.0,52.0,8.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,1.11,5.0,0.0,0.0,0.0,1.0,0.22,1.0,0.0,0.0,0.0,0.0,5.0,1.0,1.0,2.0,2.0,4.0,1.0,3.0,0.0,2.0,0.0,121.0,1.0,6.0,47.0,68.0,12.0,121.0,76.0,2.0,2.0,1.0,95.0,20.0,15.0,0.0,10.0,14.0,6.0,0.0,0.0,0.0,10.0,8.0,27.0,22.9 +Lorenzo Pellegrini,it ITA,MF,Roma,26-310,1996,26.0,25.0,2119.0,4.0,5.0,2.0,3.0,2.0,0.0,0.17,0.21,0.38,0.08,0.3,6.4,4.1,0.27,0.17,23.5,14.0,6.0,30.4,0.59,0.04,0.14,0.09,-2.4,-2.1,693.0,926.0,74.8,12265.0,3697.0,321.0,371.0,86.5,240.0,300.0,80.0,102.0,195.0,52.3,56.0,55.0,19.0,3.0,89.0,774.0,151.0,60.0,9.0,9.0,117.0,76.0,32.0,42.0,0.0,4.0,1.0,19.0,105.0,4.46,56.0,34.0,6.0,5.0,9.0,0.38,2.0,6.0,1.0,0.0,0.0,44.0,25.0,21.0,20.0,3.0,27.0,4.0,23.0,22.0,17.0,0.0,1222.0,24.0,156.0,651.0,427.0,41.0,1219.0,668.0,50.0,33.0,4.0,780.0,44.0,31.0,0.0,22.0,48.0,5.0,0.0,0.0,0.0,106.0,8.0,19.0,29.6 +Luca Pellegrini,it ITA,DF,Lazio,24-049,1999,2.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,33.0,42.0,78.6,533.0,189.0,20.0,20.0,100.0,9.0,11.0,81.8,4.0,8.0,50.0,3.0,3.0,2.0,1.0,2.0,35.0,7.0,1.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,6.0,0.0,2.0,3.0,15.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,43.0,0.0,8.0,20.0,15.0,0.0,43.0,31.0,2.0,2.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0 +Pepín,gq EQG,MF,Monza,26-254,1996,20.0,10.0,1006.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.09,0.09,0.0,0.09,0.4,0.4,0.04,0.04,11.2,3.0,1.0,33.3,0.27,0.0,0.0,0.05,-0.4,-0.4,505.0,604.0,83.6,8584.0,2512.0,240.0,269.0,89.2,217.0,245.0,88.6,35.0,57.0,61.4,7.0,62.0,17.0,2.0,69.0,587.0,16.0,11.0,2.0,2.0,10.0,2.0,0.0,1.0,0.0,3.0,1.0,10.0,21.0,1.88,20.0,0.0,0.0,1.0,2.0,0.18,2.0,0.0,0.0,0.0,0.0,23.0,15.0,8.0,9.0,6.0,13.0,3.0,10.0,11.0,15.0,0.0,711.0,21.0,137.0,429.0,153.0,9.0,711.0,431.0,17.0,23.0,3.0,524.0,13.0,11.0,0.0,18.0,12.0,0.0,0.0,0.0,0.0,59.0,6.0,7.0,46.2 +Roberto Pereyra,ar ARG,"DF,MF",Udinese,32-108,1991,27.0,26.0,2197.0,4.0,6.0,0.0,1.0,5.0,0.0,0.16,0.25,0.41,0.16,0.41,4.0,3.2,0.16,0.13,24.4,14.0,1.0,40.0,0.57,0.11,0.29,0.09,0.0,0.8,834.0,1023.0,81.5,12778.0,4244.0,454.0,493.0,92.1,295.0,361.0,81.7,51.0,83.0,61.4,40.0,73.0,41.0,10.0,126.0,938.0,83.0,28.0,1.0,7.0,58.0,4.0,2.0,1.0,0.0,46.0,2.0,33.0,87.0,3.56,77.0,0.0,4.0,1.0,9.0,0.37,9.0,0.0,0.0,0.0,0.0,33.0,19.0,18.0,12.0,3.0,18.0,4.0,14.0,14.0,23.0,0.0,1290.0,43.0,256.0,566.0,491.0,95.0,1289.0,776.0,58.0,37.0,15.0,863.0,36.0,30.0,0.0,23.0,40.0,2.0,0.0,1.0,0.0,136.0,3.0,7.0,30.0 +Nehuén Pérez,ar ARG,DF,Udinese,22-305,2000,27.0,27.0,2281.0,2.0,0.0,0.0,0.0,6.0,1.0,0.08,0.0,0.08,0.08,0.08,0.9,0.9,0.04,0.04,25.3,7.0,0.0,50.0,0.28,0.14,0.29,0.07,1.1,1.1,1238.0,1452.0,85.3,24568.0,8765.0,451.0,486.0,92.8,587.0,653.0,89.9,176.0,272.0,64.7,8.0,93.0,6.0,3.0,93.0,1295.0,156.0,47.0,0.0,45.0,16.0,0.0,0.0,0.0,0.0,103.0,1.0,12.0,24.0,0.95,20.0,2.0,1.0,0.0,1.0,0.04,1.0,0.0,0.0,0.0,0.0,59.0,30.0,42.0,15.0,2.0,39.0,18.0,21.0,30.0,91.0,1.0,1726.0,185.0,800.0,796.0,140.0,20.0,1726.0,992.0,23.0,21.0,1.0,1103.0,15.0,9.0,0.0,22.0,8.0,0.0,0.0,0.0,1.0,119.0,26.0,18.0,59.1 +Simone Perilli,it ITA,GK,Hellas Verona,28-108,1995,1.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,34.0,29.4,455.0,391.0,1.0,1.0,100.0,3.0,3.0,100.0,6.0,30.0,20.0,0.0,3.0,0.0,0.0,0.0,19.0,15.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,35.0,38.0,0.0,0.0,0.0,38.0,20.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Mattia Perin,it ITA,GK,Juventus,30-166,1992,10.0,9.0,858.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,247.0,332.0,74.4,5854.0,3808.0,61.0,62.0,98.4,137.0,137.0,100.0,49.0,132.0,37.1,0.0,6.0,0.0,0.0,1.0,252.0,79.0,19.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,343.0,311.0,340.0,3.0,0.0,0.0,343.0,217.0,0.0,0.0,0.0,175.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,18.0,2.0,0.0,100.0 +Samuele Perisan,it ITA,GK,Empoli,25-247,1997,7.0,7.0,630.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,174.0,230.0,75.7,4436.0,3030.0,53.0,53.0,100.0,68.0,69.0,98.6,53.0,105.0,50.5,0.0,0.0,0.0,0.0,0.0,134.0,94.0,19.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,242.0,218.0,242.0,0.0,0.0,0.0,242.0,103.0,0.0,0.0,0.0,88.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,50.0 +Matteo Pessina,it ITA,MF,Monza,26-004,1997,28.0,27.0,2310.0,4.0,2.0,3.0,3.0,5.0,0.0,0.16,0.08,0.23,0.04,0.12,4.5,2.1,0.17,0.08,25.7,4.0,0.0,20.0,0.16,0.05,0.25,0.1,-0.5,-1.1,1292.0,1525.0,84.7,21604.0,6031.0,619.0,686.0,90.2,540.0,595.0,90.8,102.0,143.0,71.3,20.0,123.0,29.0,2.0,151.0,1445.0,72.0,48.0,1.0,6.0,31.0,15.0,0.0,12.0,0.0,6.0,8.0,40.0,55.0,2.14,46.0,1.0,0.0,5.0,7.0,0.27,6.0,0.0,0.0,1.0,0.0,46.0,28.0,21.0,19.0,6.0,40.0,10.0,30.0,29.0,23.0,1.0,1806.0,61.0,406.0,1025.0,394.0,41.0,1803.0,1098.0,66.0,36.0,5.0,1288.0,53.0,30.0,0.0,32.0,44.0,1.0,0.0,0.0,0.0,165.0,30.0,23.0,56.6 +Andrea Petagna,it ITA,FW,Monza,27-299,1995,25.0,16.0,1360.0,2.0,4.0,1.0,1.0,2.0,0.0,0.13,0.26,0.4,0.07,0.33,4.4,3.7,0.29,0.24,15.1,9.0,0.0,30.0,0.6,0.03,0.11,0.12,-2.4,-2.7,269.0,372.0,72.3,3919.0,624.0,165.0,201.0,82.1,73.0,108.0,67.6,16.0,24.0,66.7,24.0,18.0,9.0,0.0,29.0,331.0,35.0,0.0,4.0,9.0,3.0,0.0,0.0,0.0,0.0,1.0,6.0,8.0,33.0,2.18,26.0,0.0,4.0,1.0,5.0,0.33,2.0,0.0,1.0,1.0,0.0,7.0,5.0,2.0,3.0,2.0,14.0,4.0,10.0,2.0,16.0,0.0,548.0,18.0,44.0,284.0,224.0,79.0,547.0,319.0,20.0,11.0,10.0,424.0,54.0,37.0,0.0,22.0,24.0,8.0,1.0,1.0,0.0,41.0,30.0,28.0,51.7 +Giuseppe Pezzella,it ITA,DF,Lecce,25-147,1997,12.0,9.0,821.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.11,0.11,0.0,0.11,0.1,0.1,0.01,0.01,9.1,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,374.0,478.0,78.2,6158.0,2430.0,179.0,205.0,87.3,162.0,197.0,82.2,25.0,52.0,48.1,5.0,39.0,10.0,4.0,58.0,362.0,115.0,10.0,0.0,3.0,29.0,1.0,0.0,1.0,0.0,104.0,1.0,15.0,14.0,1.53,12.0,1.0,0.0,1.0,2.0,0.22,2.0,0.0,0.0,0.0,0.0,21.0,9.0,16.0,2.0,3.0,11.0,3.0,8.0,6.0,27.0,1.0,582.0,37.0,201.0,227.0,160.0,7.0,582.0,274.0,22.0,15.0,2.0,287.0,11.0,10.0,0.0,15.0,9.0,1.0,0.0,0.0,0.0,45.0,9.0,6.0,60.0 +Krzysztof Piątek,pl POL,FW,Salernitana,27-298,1995,27.0,19.0,1712.0,3.0,3.0,1.0,1.0,4.0,0.0,0.16,0.16,0.32,0.11,0.26,7.8,7.1,0.41,0.37,19.0,18.0,0.0,38.3,0.95,0.04,0.11,0.15,-4.8,-5.1,307.0,452.0,67.9,4171.0,584.0,182.0,241.0,75.5,87.0,126.0,69.0,15.0,27.0,55.6,19.0,20.0,10.0,0.0,29.0,407.0,40.0,0.0,0.0,1.0,12.0,0.0,0.0,0.0,0.0,1.0,5.0,9.0,45.0,2.36,32.0,0.0,4.0,3.0,8.0,0.42,5.0,0.0,2.0,0.0,0.0,14.0,7.0,1.0,7.0,6.0,12.0,3.0,9.0,2.0,31.0,0.0,690.0,35.0,69.0,342.0,287.0,68.0,689.0,355.0,24.0,12.0,10.0,495.0,66.0,29.0,0.0,34.0,30.0,18.0,0.0,0.0,0.0,49.0,72.0,88.0,45.0 +Roberto Piccoli,it ITA,FW,Hellas Verona,22-088,2001,7.0,2.0,192.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.19,0.19,2.1,2.0,0.0,50.0,0.94,0.0,0.0,0.1,-0.4,-0.4,13.0,25.0,52.0,178.0,55.0,8.0,12.0,66.7,3.0,6.0,50.0,1.0,4.0,25.0,1.0,1.0,0.0,0.0,0.0,23.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.47,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,0.0,2.0,0.0,2.0,0.0,55.0,3.0,8.0,16.0,31.0,11.0,55.0,36.0,2.0,3.0,1.0,41.0,7.0,4.0,0.0,2.0,3.0,4.0,0.0,0.0,0.0,4.0,3.0,13.0,18.8 +Roberto Piccoli,it ITA,FW,Empoli,22-088,2001,8.0,4.0,353.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.18,0.18,3.9,2.0,0.0,28.6,0.51,0.0,0.0,0.1,-0.7,-0.7,50.0,75.0,66.7,596.0,136.0,36.0,45.0,80.0,11.0,17.0,64.7,0.0,3.0,0.0,2.0,2.0,0.0,0.0,3.0,74.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,6.0,1.53,4.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,8.0,0.0,132.0,8.0,21.0,55.0,60.0,14.0,132.0,77.0,4.0,2.0,0.0,99.0,14.0,8.0,0.0,4.0,3.0,2.0,0.0,0.0,0.0,7.0,13.0,16.0,44.8 +Charles Pickel,ch SUI,MF,Cremonese,25-345,1997,28.0,25.0,2071.0,1.0,0.0,0.0,0.0,8.0,0.0,0.04,0.0,0.04,0.04,0.04,1.3,1.3,0.06,0.06,23.0,6.0,0.0,35.3,0.26,0.06,0.17,0.08,-0.3,-0.3,651.0,846.0,77.0,10454.0,3181.0,322.0,392.0,82.1,266.0,317.0,83.9,44.0,80.0,55.0,15.0,72.0,12.0,0.0,99.0,811.0,31.0,8.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,13.0,4.0,20.0,45.0,1.96,43.0,1.0,1.0,0.0,2.0,0.09,2.0,0.0,0.0,0.0,0.0,48.0,20.0,22.0,22.0,4.0,34.0,5.0,29.0,21.0,31.0,1.0,1089.0,43.0,234.0,617.0,246.0,35.0,1089.0,504.0,13.0,18.0,1.0,593.0,29.0,16.0,0.0,27.0,14.0,3.0,0.0,0.0,0.0,142.0,40.0,60.0,40.0 +Andrea Pinamonti,it ITA,FW,Sassuolo,23-341,1999,27.0,22.0,1886.0,5.0,0.0,1.0,1.0,3.0,0.0,0.24,0.0,0.24,0.19,0.19,6.1,5.3,0.29,0.25,21.0,19.0,0.0,34.5,0.91,0.07,0.21,0.1,-1.1,-1.3,276.0,433.0,63.7,3399.0,473.0,185.0,247.0,74.9,59.0,105.0,56.2,9.0,20.0,45.0,15.0,13.0,12.0,2.0,23.0,388.0,45.0,0.0,1.0,3.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,49.0,2.34,31.0,0.0,9.0,7.0,3.0,0.14,0.0,0.0,2.0,1.0,0.0,10.0,7.0,0.0,5.0,5.0,17.0,2.0,15.0,2.0,10.0,0.0,651.0,10.0,23.0,315.0,319.0,101.0,650.0,329.0,16.0,9.0,11.0,499.0,56.0,53.0,0.0,18.0,22.0,6.0,1.0,0.0,0.0,28.0,34.0,76.0,30.9 +Lorenzo Pirola,it ITA,DF,Salernitana,21-064,2002,19.0,16.0,1371.0,2.0,0.0,0.0,0.0,3.0,0.0,0.13,0.0,0.13,0.13,0.13,1.4,1.4,0.09,0.09,15.2,2.0,0.0,18.2,0.13,0.18,1.0,0.13,0.6,0.6,522.0,664.0,78.6,9634.0,3519.0,201.0,225.0,89.3,266.0,317.0,83.9,51.0,97.0,52.6,3.0,33.0,2.0,0.0,37.0,642.0,18.0,13.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,5.0,4.0,8.0,11.0,0.72,5.0,1.0,4.0,1.0,1.0,0.07,0.0,0.0,1.0,0.0,0.0,29.0,25.0,17.0,12.0,0.0,37.0,27.0,10.0,25.0,63.0,1.0,851.0,106.0,494.0,326.0,36.0,16.0,851.0,430.0,10.0,10.0,0.0,518.0,6.0,2.0,0.0,23.0,7.0,0.0,0.0,0.0,0.0,51.0,38.0,26.0,59.4 +Marko Pjaca,hr CRO,"MF,FW",Empoli,27-354,1995,13.0,5.0,461.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.12,0.12,5.1,2.0,0.0,14.3,0.39,0.0,0.0,0.05,-0.6,-0.6,181.0,229.0,79.0,3036.0,717.0,92.0,111.0,82.9,60.0,77.0,77.9,21.0,30.0,70.0,11.0,15.0,8.0,3.0,18.0,201.0,28.0,4.0,0.0,0.0,24.0,17.0,8.0,6.0,0.0,2.0,0.0,3.0,26.0,5.08,16.0,4.0,2.0,1.0,1.0,0.2,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,286.0,1.0,25.0,135.0,128.0,14.0,286.0,177.0,19.0,7.0,8.0,223.0,13.0,15.0,0.0,0.0,7.0,1.0,0.0,0.0,0.0,25.0,4.0,6.0,40.0 +Tommaso Pobega,it ITA,MF,Milan,23-284,1999,15.0,8.0,754.0,2.0,0.0,0.0,0.0,4.0,0.0,0.24,0.0,0.24,0.24,0.24,1.2,1.2,0.15,0.15,8.4,4.0,0.0,23.5,0.48,0.12,0.5,0.07,0.8,0.8,267.0,343.0,77.8,4853.0,1334.0,117.0,145.0,80.7,115.0,129.0,89.1,31.0,51.0,60.8,4.0,34.0,7.0,2.0,32.0,334.0,8.0,6.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,1.0,1.0,9.0,21.0,2.51,14.0,0.0,3.0,2.0,2.0,0.24,2.0,0.0,0.0,0.0,0.0,20.0,11.0,5.0,11.0,4.0,9.0,2.0,7.0,8.0,8.0,0.0,423.0,9.0,82.0,246.0,98.0,14.0,423.0,185.0,8.0,11.0,0.0,265.0,9.0,6.0,0.0,27.0,5.0,0.0,0.0,0.0,0.0,43.0,12.0,15.0,44.4 +Paul Pogba,fr FRA,"MF,DF",Juventus,30-041,1993,3.0,0.0,44.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.22,0.22,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,19.0,28.0,67.9,329.0,73.0,11.0,13.0,84.6,5.0,6.0,83.3,2.0,2.0,100.0,0.0,4.0,0.0,0.0,1.0,28.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,8.0,1.0,0.0,1.0,2.0,1.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,33.0,0.0,0.0,20.0,13.0,5.0,33.0,22.0,2.0,0.0,1.0,29.0,2.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,75.0 +Matteo Politano,it ITA,FW,Napoli,29-265,1993,25.0,14.0,1129.0,3.0,3.0,2.0,3.0,1.0,0.0,0.24,0.24,0.48,0.08,0.32,4.0,1.6,0.32,0.13,12.5,5.0,1.0,17.9,0.4,0.04,0.2,0.06,-1.0,-0.6,451.0,613.0,73.6,7196.0,2388.0,249.0,279.0,89.2,144.0,193.0,74.6,40.0,84.0,47.6,32.0,30.0,26.0,7.0,67.0,547.0,60.0,13.0,2.0,8.0,75.0,29.0,16.0,3.0,0.0,18.0,6.0,23.0,68.0,5.42,43.0,8.0,5.0,4.0,4.0,0.32,3.0,0.0,1.0,0.0,0.0,18.0,9.0,9.0,5.0,4.0,6.0,0.0,6.0,7.0,7.0,0.0,765.0,10.0,101.0,258.0,416.0,54.0,762.0,501.0,76.0,37.0,24.0,564.0,30.0,11.0,0.0,11.0,22.0,1.0,0.0,0.0,0.0,64.0,3.0,6.0,33.3 +Marin Pongračić,hr CRO,DF,Lecce,25-226,1997,9.0,9.0,790.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.8,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,296.0,361.0,82.0,6388.0,2314.0,73.0,94.0,77.7,171.0,195.0,87.7,47.0,63.0,74.6,2.0,16.0,2.0,0.0,19.0,324.0,37.0,37.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,0.68,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,14.0,8.0,7.0,1.0,4.0,3.0,1.0,16.0,31.0,0.0,435.0,48.0,232.0,201.0,11.0,4.0,435.0,223.0,3.0,1.0,0.0,210.0,5.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,59.0,9.0,10.0,47.4 +Stefan Posch,at AUT,DF,Bologna,25-346,1997,24.0,24.0,2057.0,5.0,2.0,0.0,0.0,3.0,0.0,0.22,0.09,0.31,0.22,0.31,2.1,2.1,0.09,0.09,22.9,9.0,1.0,42.9,0.39,0.24,0.56,0.1,2.9,2.9,1131.0,1473.0,76.8,20153.0,8940.0,524.0,585.0,89.6,468.0,558.0,83.9,118.0,250.0,47.2,23.0,93.0,26.0,6.0,127.0,1220.0,249.0,20.0,4.0,12.0,44.0,0.0,0.0,0.0,0.0,229.0,4.0,38.0,48.0,2.1,38.0,4.0,0.0,3.0,4.0,0.18,3.0,0.0,0.0,0.0,0.0,72.0,38.0,37.0,25.0,10.0,34.0,11.0,23.0,30.0,59.0,0.0,1753.0,101.0,692.0,786.0,297.0,29.0,1753.0,888.0,38.0,34.0,3.0,1040.0,37.0,9.0,0.0,36.0,20.0,1.0,0.0,0.0,0.0,130.0,21.0,23.0,47.7 +Ivan Provedel,it ITA,GK,Lazio,29-039,1994,31.0,30.0,2783.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,855.0,1078.0,79.3,23573.0,17165.0,183.0,183.0,100.0,316.0,325.0,97.2,351.0,561.0,62.6,1.0,18.0,1.0,0.0,0.0,839.0,235.0,74.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,7.0,0.23,7.0,0.0,0.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,1.0,1148.0,992.0,1143.0,6.0,0.0,0.0,1148.0,697.0,0.0,0.0,0.0,608.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,56.0,3.0,1.0,75.0 +Ignacio Pussetto,ar ARG,"MF,DF",Sampdoria,27-125,1995,5.0,1.0,152.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.09,0.09,1.7,2.0,0.0,100.0,1.18,0.0,0.0,0.08,-0.2,-0.2,34.0,56.0,60.7,404.0,91.0,25.0,32.0,78.1,7.0,12.0,58.3,0.0,4.0,0.0,2.0,2.0,0.0,0.0,1.0,55.0,1.0,1.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,1.18,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,2.0,3.0,1.0,4.0,0.0,4.0,0.0,3.0,0.0,81.0,2.0,11.0,23.0,47.0,5.0,81.0,34.0,2.0,1.0,0.0,51.0,7.0,1.0,0.0,7.0,1.0,0.0,0.0,0.0,0.0,4.0,4.0,5.0,44.4 +Niklas Pyyhtiä,fi FIN,MF,Bologna,19-212,2003,4.0,0.0,66.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.12,0.12,0.7,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,23.0,30.0,76.7,339.0,23.0,14.0,17.0,82.4,8.0,9.0,88.9,1.0,2.0,50.0,0.0,1.0,0.0,0.0,1.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,40.0,2.0,11.0,22.0,7.0,2.0,40.0,23.0,1.0,0.0,0.0,27.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,5.0,0.0 +Fabio Quagliarella,it ITA,FW,Sampdoria,40-084,1983,18.0,2.0,411.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.6,1.6,0.36,0.36,4.6,5.0,0.0,25.0,1.09,0.0,0.0,0.08,-1.6,-1.6,78.0,118.0,66.1,1180.0,306.0,39.0,46.0,84.8,23.0,34.0,67.6,7.0,16.0,43.8,3.0,9.0,4.0,0.0,13.0,111.0,7.0,1.0,1.0,3.0,8.0,0.0,0.0,0.0,0.0,2.0,0.0,9.0,11.0,2.39,10.0,0.0,1.0,0.0,1.0,0.22,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,0.0,4.0,0.0,6.0,0.0,181.0,6.0,13.0,64.0,105.0,32.0,181.0,120.0,3.0,3.0,1.0,135.0,18.0,3.0,0.0,3.0,10.0,5.0,0.0,0.0,0.0,14.0,5.0,23.0,17.9 +Giacomo Quagliata,it ITA,DF,Cremonese,23-065,2000,16.0,5.0,602.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.15,0.15,0.0,0.15,0.5,0.5,0.07,0.07,6.7,1.0,1.0,11.1,0.15,0.0,0.0,0.05,-0.5,-0.5,194.0,304.0,63.8,3366.0,1531.0,92.0,107.0,86.0,82.0,121.0,67.8,17.0,56.0,30.4,11.0,20.0,10.0,8.0,28.0,236.0,67.0,2.0,0.0,0.0,51.0,8.0,3.0,4.0,0.0,57.0,1.0,13.0,24.0,3.59,15.0,5.0,1.0,3.0,1.0,0.15,1.0,0.0,0.0,0.0,0.0,9.0,7.0,7.0,0.0,2.0,6.0,2.0,4.0,10.0,7.0,0.0,379.0,14.0,99.0,128.0,161.0,13.0,379.0,173.0,18.0,16.0,2.0,183.0,9.0,5.0,0.0,10.0,10.0,0.0,0.0,0.0,0.0,52.0,2.0,13.0,13.3 +Adrien Rabiot,fr FRA,MF,Juventus,28-022,1995,26.0,25.0,2208.0,8.0,3.0,0.0,0.0,6.0,0.0,0.33,0.12,0.45,0.33,0.45,5.2,4.9,0.21,0.2,24.5,20.0,0.0,58.8,0.82,0.24,0.4,0.15,2.8,3.1,733.0,914.0,80.2,11755.0,3354.0,379.0,437.0,86.7,265.0,325.0,81.5,61.0,87.0,70.1,25.0,74.0,18.0,1.0,104.0,884.0,24.0,10.0,2.0,5.0,27.0,0.0,0.0,0.0,0.0,13.0,6.0,20.0,67.0,2.73,56.0,0.0,4.0,3.0,7.0,0.29,4.0,0.0,1.0,1.0,0.0,49.0,26.0,23.0,19.0,7.0,32.0,7.0,25.0,22.0,36.0,1.0,1213.0,59.0,271.0,612.0,348.0,62.0,1213.0,669.0,46.0,51.0,2.0,767.0,47.0,27.0,0.0,31.0,36.0,2.0,0.0,0.0,0.0,148.0,44.0,33.0,57.1 +Nemanja Radonjić,rs SRB,"MF,FW",Torino,27-069,1996,28.0,16.0,1461.0,2.0,2.0,0.0,0.0,1.0,0.0,0.12,0.12,0.25,0.12,0.25,5.0,5.0,0.31,0.31,16.2,17.0,0.0,32.7,1.05,0.04,0.12,0.1,-3.0,-3.0,298.0,473.0,63.0,4373.0,1199.0,181.0,231.0,78.4,77.0,124.0,62.1,25.0,62.0,40.3,18.0,20.0,19.0,8.0,42.0,440.0,29.0,1.0,2.0,5.0,76.0,25.0,17.0,5.0,0.0,3.0,4.0,23.0,49.0,3.03,28.0,4.0,2.0,1.0,6.0,0.37,5.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,3.0,0.0,3.0,2.0,4.0,0.0,714.0,2.0,47.0,236.0,444.0,90.0,714.0,520.0,86.0,48.0,52.0,568.0,53.0,34.0,0.0,17.0,17.0,12.0,0.0,0.0,0.0,63.0,7.0,14.0,33.3 +Ivan Radovanović,rs SRB,"MF,DF",Salernitana,34-239,1988,10.0,7.0,671.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,7.5,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.2,-0.2,214.0,265.0,80.8,4318.0,1602.0,75.0,84.0,89.3,105.0,115.0,91.3,32.0,57.0,56.1,1.0,19.0,0.0,0.0,16.0,252.0,12.0,12.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,0.27,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,4.0,8.0,4.0,0.0,9.0,3.0,6.0,7.0,9.0,1.0,323.0,30.0,130.0,182.0,14.0,2.0,323.0,175.0,2.0,1.0,0.0,203.0,6.0,0.0,0.0,4.0,7.0,0.0,0.0,0.0,0.0,47.0,8.0,12.0,40.0 +Ionuț Radu,ro ROU,GK,Cremonese,25-332,1997,9.0,9.0,810.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,221.0,336.0,65.8,6117.0,4391.0,51.0,52.0,98.1,100.0,102.0,98.0,69.0,177.0,39.0,2.0,10.0,2.0,0.0,0.0,248.0,84.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,6.0,0.67,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,366.0,305.0,366.0,0.0,0.0,0.0,366.0,205.0,0.0,0.0,0.0,170.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,11.0,3.0,1.0,75.0 +Antonio Raimondo,it ITA,FW,Bologna,19-038,2004,2.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,75.0,21.0,3.0,2.0,3.0,66.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.0,1.0,2.0,3.0,1.0,6.0,4.0,1.0,1.0,0.0,5.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0 +Luca Ranieri,it ITA,DF,Fiorentina,24-002,1999,5.0,3.0,297.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,125.0,162.0,77.2,2433.0,834.0,41.0,44.0,93.2,70.0,78.0,89.7,14.0,35.0,40.0,1.0,10.0,0.0,0.0,5.0,155.0,7.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,3.0,3.0,0.91,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,4.0,2.0,0.0,2.0,1.0,1.0,6.0,11.0,1.0,192.0,20.0,92.0,92.0,9.0,1.0,192.0,107.0,2.0,1.0,0.0,121.0,3.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,0.0,16.0,7.0,11.0,38.9 +Andrea Ranocchia,it ITA,DF,Monza,35-068,1988,1.0,1.0,47.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,19.0,84.2,347.0,109.0,2.0,2.0,100.0,11.0,12.0,91.7,2.0,4.0,50.0,0.0,2.0,0.0,0.0,0.0,15.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,2.0,0.0,5.0,5.0,0.0,0.0,7.0,0.0,35.0,13.0,28.0,7.0,0.0,0.0,35.0,11.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,2.0,33.3 +Filippo Ranocchia,it ITA,MF,Monza,21-346,2001,14.0,5.0,530.0,1.0,0.0,0.0,0.0,1.0,0.0,0.17,0.0,0.17,0.17,0.17,1.2,1.2,0.21,0.21,5.9,8.0,5.0,42.1,1.36,0.05,0.13,0.07,-0.2,-0.2,201.0,255.0,78.8,3906.0,1183.0,81.0,93.0,87.1,83.0,98.0,84.7,32.0,49.0,65.3,7.0,23.0,7.0,1.0,26.0,242.0,13.0,7.0,3.0,9.0,13.0,4.0,0.0,4.0,0.0,2.0,0.0,6.0,19.0,3.23,8.0,4.0,5.0,1.0,1.0,0.17,1.0,0.0,0.0,0.0,0.0,11.0,6.0,5.0,4.0,2.0,5.0,1.0,4.0,4.0,7.0,0.0,328.0,14.0,70.0,174.0,90.0,4.0,328.0,213.0,13.0,18.0,0.0,236.0,10.0,10.0,0.0,7.0,12.0,0.0,0.0,0.0,0.0,32.0,2.0,6.0,25.0 +Giacomo Raspadori,it ITA,"FW,MF",Napoli,23-066,2000,19.0,9.0,735.0,2.0,0.0,0.0,0.0,1.0,0.0,0.24,0.0,0.24,0.24,0.24,2.9,2.9,0.36,0.36,8.2,13.0,3.0,41.9,1.59,0.06,0.15,0.09,-0.9,-0.9,267.0,354.0,75.4,4151.0,642.0,139.0,166.0,83.7,90.0,108.0,83.3,18.0,44.0,40.9,8.0,10.0,5.0,0.0,18.0,327.0,27.0,8.0,0.0,6.0,19.0,12.0,6.0,2.0,0.0,1.0,0.0,12.0,27.0,3.31,17.0,4.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,7.0,0.0,5.0,4.0,6.0,0.0,6.0,4.0,3.0,0.0,454.0,3.0,39.0,195.0,221.0,42.0,454.0,272.0,8.0,6.0,4.0,353.0,25.0,10.0,0.0,5.0,7.0,1.0,0.0,0.0,0.0,23.0,2.0,6.0,25.0 +Giacomo Raspadori,it ITA,FW,Sassuolo,23-066,2000,1.0,0.0,45.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,12.0,83.3,207.0,33.0,2.0,2.0,100.0,6.0,7.0,85.7,1.0,2.0,50.0,0.0,1.0,0.0,0.0,2.0,9.0,3.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,0.0,0.0,7.0,8.0,2.0,15.0,10.0,0.0,1.0,0.0,12.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Nicola Ravaglia,it ITA,GK,Sampdoria,34-134,1988,4.0,4.0,360.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,110.0,153.0,71.9,3025.0,2264.0,32.0,32.0,100.0,38.0,38.0,100.0,40.0,83.0,48.2,0.0,1.0,0.0,0.0,0.0,81.0,72.0,19.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,171.0,151.0,169.0,2.0,0.0,0.0,171.0,78.0,0.0,0.0,0.0,49.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,3.0,1.0,0.0,100.0 +Ante Rebić,hr CRO,"FW,MF",Milan,29-216,1993,21.0,9.0,887.0,3.0,2.0,0.0,0.0,4.0,0.0,0.3,0.2,0.51,0.3,0.51,3.4,3.4,0.34,0.34,9.9,13.0,2.0,50.0,1.32,0.12,0.23,0.13,-0.4,-0.4,174.0,310.0,56.1,2537.0,784.0,103.0,137.0,75.2,47.0,90.0,52.2,14.0,37.0,37.8,13.0,12.0,11.0,3.0,26.0,293.0,14.0,2.0,4.0,1.0,34.0,4.0,2.0,1.0,0.0,4.0,3.0,14.0,32.0,3.24,22.0,1.0,3.0,4.0,3.0,0.3,3.0,0.0,0.0,0.0,0.0,9.0,7.0,1.0,2.0,6.0,5.0,0.0,5.0,1.0,4.0,0.0,406.0,3.0,18.0,126.0,266.0,53.0,406.0,240.0,14.0,10.0,5.0,319.0,25.0,15.0,0.0,18.0,14.0,6.0,0.0,0.0,0.0,35.0,12.0,12.0,50.0 +Arkadiusz Reca,pl POL,DF,Spezia,27-312,1995,23.0,19.0,1651.0,1.0,1.0,0.0,0.0,5.0,0.0,0.05,0.05,0.11,0.05,0.11,0.7,0.7,0.04,0.04,18.3,6.0,0.0,40.0,0.33,0.07,0.17,0.05,0.3,0.3,617.0,883.0,69.9,10458.0,4253.0,305.0,372.0,82.0,227.0,298.0,76.2,66.0,148.0,44.6,15.0,53.0,22.0,17.0,58.0,706.0,173.0,3.0,0.0,9.0,66.0,0.0,0.0,0.0,0.0,170.0,4.0,31.0,38.0,2.07,30.0,1.0,3.0,1.0,2.0,0.11,1.0,0.0,1.0,0.0,0.0,36.0,21.0,19.0,10.0,7.0,15.0,3.0,12.0,22.0,44.0,0.0,1104.0,75.0,353.0,465.0,299.0,27.0,1104.0,547.0,65.0,42.0,7.0,618.0,29.0,8.0,0.0,27.0,9.0,2.0,0.0,1.0,0.0,94.0,10.0,14.0,41.7 +Panagiotis Retsos,gr GRE,DF,Hellas Verona,24-259,1998,2.0,1.0,122.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,44.0,56.0,78.6,781.0,321.0,20.0,23.0,87.0,20.0,24.0,83.3,4.0,7.0,57.1,1.0,3.0,0.0,0.0,2.0,43.0,12.0,3.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,9.0,1.0,1.0,1.0,0.73,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,4.0,2.0,2.0,1.0,4.0,1.0,3.0,2.0,3.0,0.0,76.0,8.0,31.0,38.0,7.0,1.0,76.0,35.0,2.0,0.0,0.0,33.0,0.0,1.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,7.0,3.0,3.0,50.0 +Franck Ribéry,fr FRA,MF,Salernitana,40-018,1983,1.0,0.0,37.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,19.0,22.0,86.4,235.0,19.0,11.0,12.0,91.7,7.0,8.0,87.5,0.0,0.0,0.0,0.0,2.0,0.0,0.0,1.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,2.43,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,28.0,0.0,3.0,16.0,10.0,0.0,28.0,15.0,1.0,1.0,0.0,24.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Samuele Ricci,it ITA,MF,Torino,21-247,2001,21.0,17.0,1524.0,1.0,1.0,0.0,0.0,5.0,0.0,0.06,0.06,0.12,0.06,0.12,0.9,0.9,0.05,0.05,16.9,5.0,0.0,35.7,0.3,0.07,0.2,0.06,0.1,0.1,796.0,902.0,88.2,13648.0,3246.0,356.0,392.0,90.8,349.0,374.0,93.3,64.0,89.0,71.9,13.0,78.0,10.0,2.0,82.0,846.0,52.0,39.0,2.0,4.0,23.0,12.0,11.0,1.0,0.0,1.0,4.0,8.0,33.0,1.95,24.0,2.0,4.0,2.0,1.0,0.06,0.0,0.0,1.0,0.0,0.0,23.0,14.0,10.0,8.0,5.0,16.0,5.0,11.0,13.0,20.0,1.0,1050.0,41.0,177.0,685.0,197.0,6.0,1050.0,668.0,32.0,21.0,1.0,747.0,15.0,14.0,0.0,16.0,46.0,0.0,0.0,0.0,0.0,107.0,6.0,9.0,40.0 +Tomás Rincón,ve VEN,MF,Sampdoria,35-102,1988,27.0,23.0,1985.0,0.0,2.0,0.0,0.0,6.0,1.0,0.0,0.09,0.09,0.0,0.09,0.6,0.6,0.03,0.03,22.1,3.0,0.0,30.0,0.14,0.0,0.0,0.06,-0.6,-0.6,789.0,970.0,81.3,14366.0,4179.0,346.0,394.0,87.8,324.0,380.0,85.3,99.0,155.0,63.9,19.0,105.0,19.0,5.0,102.0,926.0,41.0,24.0,2.0,20.0,19.0,1.0,0.0,1.0,0.0,5.0,3.0,8.0,32.0,1.45,29.0,0.0,1.0,0.0,3.0,0.14,2.0,0.0,0.0,0.0,1.0,46.0,24.0,16.0,23.0,7.0,27.0,5.0,22.0,25.0,17.0,0.0,1179.0,32.0,225.0,738.0,232.0,19.0,1179.0,685.0,24.0,25.0,6.0,767.0,22.0,22.0,0.0,41.0,24.0,0.0,0.0,0.0,0.0,133.0,29.0,13.0,69.0 +Pablo Rodríguez,es ESP,"FW,MF",Lecce,21-264,2001,4.0,0.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,21.0,66.7,215.0,66.0,6.0,10.0,60.0,6.0,7.0,85.7,1.0,1.0,100.0,1.0,2.0,0.0,0.0,3.0,18.0,3.0,0.0,0.0,0.0,4.0,2.0,0.0,1.0,0.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,0.0,32.0,0.0,4.0,18.0,11.0,0.0,32.0,21.0,2.0,2.0,0.0,22.0,1.0,2.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0 +Ricardo Rodríguez,ch SUI,DF,Torino,30-243,1992,28.0,23.0,2132.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.01,0.01,23.7,2.0,1.0,25.0,0.08,0.0,0.0,0.04,-0.3,-0.3,1167.0,1419.0,82.2,20674.0,7056.0,542.0,592.0,91.6,507.0,581.0,87.3,104.0,197.0,52.8,24.0,99.0,20.0,10.0,115.0,1286.0,128.0,37.0,0.0,15.0,61.0,13.0,10.0,3.0,0.0,73.0,5.0,24.0,50.0,2.11,40.0,8.0,1.0,0.0,2.0,0.08,2.0,0.0,0.0,0.0,0.0,48.0,30.0,28.0,15.0,5.0,25.0,12.0,13.0,31.0,53.0,0.0,1620.0,102.0,570.0,795.0,264.0,10.0,1620.0,908.0,37.0,52.0,4.0,1057.0,11.0,11.0,0.0,24.0,8.0,0.0,0.0,0.0,0.0,135.0,25.0,25.0,50.0 +Rogério,br BRA,DF,Sassuolo,25-102,1998,29.0,28.0,2446.0,0.0,2.0,0.0,0.0,5.0,0.0,0.0,0.07,0.07,0.0,0.07,0.5,0.5,0.02,0.02,27.2,4.0,0.0,36.4,0.15,0.0,0.0,0.04,-0.5,-0.5,1334.0,1678.0,79.5,23387.0,9323.0,656.0,711.0,92.3,501.0,602.0,83.2,151.0,276.0,54.7,20.0,106.0,36.0,19.0,141.0,1343.0,331.0,40.0,0.0,18.0,94.0,0.0,0.0,0.0,0.0,291.0,4.0,44.0,57.0,2.1,46.0,6.0,1.0,3.0,4.0,0.15,3.0,0.0,0.0,1.0,0.0,33.0,19.0,19.0,7.0,7.0,28.0,7.0,21.0,30.0,60.0,0.0,1901.0,81.0,658.0,861.0,404.0,21.0,1901.0,1023.0,85.0,47.0,9.0,1147.0,22.0,5.0,0.0,27.0,28.0,0.0,0.0,0.0,0.0,134.0,21.0,27.0,43.8 +Alessio Romagnoli,it ITA,DF,Lazio,28-103,1995,28.0,27.0,2402.0,1.0,0.0,0.0,0.0,4.0,0.0,0.04,0.0,0.04,0.04,0.04,0.5,0.5,0.02,0.02,26.7,2.0,0.0,28.6,0.07,0.14,0.5,0.07,0.5,0.5,1299.0,1442.0,90.1,22181.0,8895.0,598.0,631.0,94.8,531.0,573.0,92.7,126.0,180.0,70.0,2.0,70.0,3.0,0.0,44.0,1307.0,135.0,76.0,1.0,10.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,11.0,0.41,10.0,1.0,0.0,0.0,3.0,0.11,3.0,0.0,0.0,0.0,0.0,32.0,15.0,19.0,13.0,0.0,28.0,22.0,6.0,27.0,95.0,0.0,1657.0,266.0,883.0,757.0,21.0,10.0,1657.0,815.0,3.0,4.0,1.0,1061.0,6.0,3.0,0.0,29.0,15.0,1.0,0.0,0.0,0.0,110.0,60.0,37.0,61.9 +Simone Romagnoli,it ITA,DF,Lecce,33-075,1990,3.0,0.0,39.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,26.0,76.9,378.0,212.0,6.0,7.0,85.7,13.0,17.0,76.5,1.0,2.0,50.0,0.0,1.0,0.0,0.0,2.0,23.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,32.0,7.0,23.0,10.0,0.0,0.0,32.0,9.0,0.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,7.0,3.0,70.0 +Luka Romero,ar ARG,FW,Lazio,18-158,2004,6.0,1.0,151.0,1.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.6,0.6,0.6,0.9,0.9,0.52,0.52,1.7,1.0,0.0,20.0,0.6,0.2,1.0,0.17,0.1,0.1,70.0,80.0,87.5,800.0,152.0,56.0,60.0,93.3,10.0,12.0,83.3,1.0,2.0,50.0,2.0,2.0,0.0,0.0,4.0,78.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,4.0,2.38,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,3.0,1.0,0.0,112.0,2.0,14.0,50.0,48.0,7.0,112.0,84.0,4.0,3.0,2.0,91.0,5.0,4.0,0.0,3.0,6.0,0.0,0.0,0.0,0.0,10.0,2.0,1.0,66.7 +Marten de Roon,nl NED,MF,Atalanta,32-027,1991,28.0,27.0,2334.0,2.0,1.0,0.0,0.0,6.0,0.0,0.08,0.04,0.12,0.08,0.12,1.0,1.0,0.04,0.04,25.9,4.0,0.0,25.0,0.15,0.13,0.5,0.06,1.0,1.0,1192.0,1448.0,82.3,21953.0,6630.0,484.0,543.0,89.1,556.0,647.0,85.9,127.0,198.0,64.1,14.0,117.0,16.0,0.0,130.0,1381.0,64.0,44.0,0.0,20.0,10.0,0.0,0.0,0.0,0.0,16.0,3.0,20.0,43.0,1.66,38.0,0.0,0.0,3.0,2.0,0.08,2.0,0.0,0.0,0.0,0.0,56.0,25.0,28.0,25.0,3.0,29.0,4.0,25.0,30.0,50.0,0.0,1686.0,98.0,494.0,937.0,262.0,16.0,1686.0,855.0,13.0,26.0,2.0,1014.0,14.0,9.0,0.0,36.0,18.0,0.0,0.0,0.0,0.0,174.0,43.0,23.0,65.2 +Nicolò Rovella,it ITA,MF,Monza,21-142,2001,18.0,16.0,1323.0,1.0,0.0,0.0,0.0,4.0,1.0,0.07,0.0,0.07,0.07,0.07,1.1,1.1,0.08,0.08,14.7,5.0,1.0,27.8,0.34,0.06,0.2,0.06,-0.1,-0.1,919.0,1041.0,88.3,17270.0,4416.0,377.0,408.0,92.4,416.0,450.0,92.4,115.0,151.0,76.2,17.0,84.0,12.0,2.0,78.0,985.0,53.0,43.0,0.0,20.0,21.0,6.0,0.0,5.0,0.0,4.0,3.0,13.0,42.0,2.86,34.0,3.0,1.0,2.0,3.0,0.2,3.0,0.0,0.0,0.0,0.0,35.0,25.0,18.0,16.0,1.0,16.0,1.0,15.0,35.0,16.0,0.0,1193.0,38.0,286.0,731.0,190.0,8.0,1193.0,736.0,15.0,27.0,1.0,860.0,13.0,7.0,0.0,20.0,18.0,0.0,0.0,0.0,0.0,109.0,5.0,7.0,41.7 +Nicolò Rovella,it ITA,MF,Juventus,21-142,2001,3.0,0.0,27.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.3,0.3,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.1,-0.1,22.0,25.0,88.0,448.0,82.0,7.0,8.0,87.5,11.0,12.0,91.7,3.0,4.0,75.0,0.0,3.0,0.0,0.0,1.0,22.0,3.0,1.0,0.0,2.0,3.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,0.0,4.0,17.0,9.0,0.0,29.0,21.0,1.0,1.0,0.0,24.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Amir Rrahmani,xk KVX,DF,Napoli,29-060,1994,23.0,21.0,1908.0,2.0,1.0,0.0,0.0,1.0,0.0,0.09,0.05,0.14,0.09,0.14,1.6,1.6,0.07,0.07,21.2,2.0,0.0,11.1,0.09,0.11,1.0,0.09,0.4,0.4,1510.0,1665.0,90.7,30660.0,10472.0,458.0,494.0,92.7,825.0,861.0,95.8,201.0,268.0,75.0,9.0,87.0,2.0,0.0,92.0,1612.0,51.0,48.0,0.0,27.0,1.0,0.0,0.0,0.0,0.0,3.0,2.0,6.0,20.0,0.94,17.0,0.0,1.0,0.0,4.0,0.19,4.0,0.0,0.0,0.0,0.0,29.0,15.0,19.0,10.0,0.0,31.0,17.0,14.0,13.0,66.0,1.0,1840.0,201.0,801.0,1001.0,45.0,21.0,1840.0,1174.0,12.0,4.0,1.0,1354.0,8.0,1.0,0.0,14.0,10.0,1.0,0.0,0.0,0.0,116.0,53.0,34.0,60.9 +Ruan,br BRA,DF,Sassuolo,23-322,1999,17.0,15.0,1288.0,0.0,0.0,0.0,0.0,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.03,0.03,14.3,1.0,0.0,20.0,0.07,0.0,0.0,0.1,-0.5,-0.5,599.0,679.0,88.2,11749.0,3168.0,173.0,195.0,88.7,361.0,391.0,92.3,55.0,70.0,78.6,0.0,15.0,0.0,0.0,16.0,634.0,44.0,24.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,6.0,4.0,0.28,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28.0,19.0,20.0,7.0,1.0,21.0,13.0,8.0,19.0,69.0,2.0,834.0,143.0,517.0,304.0,16.0,9.0,834.0,412.0,5.0,3.0,0.0,467.0,3.0,4.0,0.0,18.0,9.0,0.0,0.0,1.0,0.0,81.0,47.0,30.0,61.0 +Daniele Rugani,it ITA,DF,Juventus,28-270,1994,6.0,5.0,445.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.09,0.09,4.9,1.0,0.0,25.0,0.2,0.0,0.0,0.12,-0.5,-0.5,198.0,225.0,88.0,4048.0,1202.0,54.0,59.0,91.5,130.0,137.0,94.9,14.0,29.0,48.3,0.0,7.0,0.0,0.0,5.0,219.0,6.0,3.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,5.0,1.0,0.0,5.0,3.0,2.0,5.0,25.0,0.0,275.0,47.0,174.0,92.0,9.0,4.0,275.0,160.0,2.0,0.0,0.0,184.0,1.0,0.0,0.0,4.0,0.0,1.0,0.0,0.0,0.0,13.0,9.0,7.0,56.3 +Matteo Ruggeri,it ITA,"DF,MF",Atalanta,20-288,2002,15.0,8.0,829.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.11,0.11,0.0,0.11,0.6,0.6,0.06,0.06,9.2,1.0,0.0,10.0,0.11,0.0,0.0,0.06,-0.6,-0.6,515.0,631.0,81.6,8203.0,3341.0,278.0,311.0,89.4,187.0,227.0,82.4,36.0,57.0,63.2,10.0,38.0,12.0,6.0,55.0,527.0,102.0,5.0,0.0,2.0,38.0,1.0,1.0,0.0,0.0,96.0,2.0,10.0,17.0,1.85,17.0,0.0,0.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,17.0,12.0,5.0,9.0,3.0,14.0,3.0,11.0,12.0,16.0,0.0,722.0,24.0,198.0,304.0,224.0,17.0,722.0,342.0,22.0,14.0,3.0,444.0,7.0,5.0,0.0,10.0,5.0,0.0,0.0,0.0,0.0,57.0,13.0,6.0,68.4 +Mário Rui,pt POR,DF,Napoli,31-333,1991,21.0,20.0,1674.0,0.0,6.0,0.0,0.0,2.0,1.0,0.0,0.32,0.32,0.0,0.32,0.6,0.6,0.03,0.03,18.6,4.0,1.0,40.0,0.22,0.0,0.0,0.06,-0.6,-0.6,1346.0,1663.0,80.9,24444.0,8424.0,652.0,715.0,91.2,502.0,602.0,83.4,171.0,281.0,60.9,45.0,137.0,50.0,15.0,147.0,1364.0,292.0,68.0,2.0,29.0,105.0,42.0,15.0,19.0,0.0,182.0,7.0,22.0,83.0,4.47,59.0,21.0,0.0,1.0,13.0,0.7,11.0,1.0,0.0,0.0,1.0,31.0,20.0,16.0,11.0,4.0,17.0,4.0,13.0,15.0,24.0,1.0,1787.0,36.0,375.0,908.0,518.0,11.0,1787.0,1057.0,52.0,55.0,2.0,1191.0,9.0,7.0,0.0,18.0,31.0,0.0,0.0,0.0,0.0,146.0,13.0,24.0,35.1 +Abdelhamid Sabiri,ma MAR,MF,Sampdoria,26-148,1996,17.0,12.0,1051.0,2.0,0.0,1.0,2.0,5.0,0.0,0.17,0.0,0.17,0.09,0.09,3.0,1.4,0.26,0.12,11.7,9.0,8.0,29.0,0.77,0.03,0.11,0.04,-1.0,-0.4,305.0,456.0,66.9,5474.0,1574.0,137.0,176.0,77.8,117.0,161.0,72.7,39.0,85.0,45.9,14.0,43.0,12.0,0.0,49.0,387.0,67.0,32.0,3.0,8.0,54.0,32.0,9.0,19.0,0.0,2.0,2.0,11.0,41.0,3.51,22.0,7.0,4.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,7.0,5.0,8.0,4.0,17.0,3.0,14.0,2.0,5.0,0.0,608.0,14.0,60.0,298.0,256.0,31.0,606.0,357.0,7.0,20.0,5.0,416.0,32.0,26.0,0.0,28.0,37.0,0.0,0.0,0.0,0.0,50.0,30.0,28.0,51.7 +Alexis Saelemaekers,be BEL,"FW,DF",Milan,23-302,1999,23.0,12.0,1077.0,1.0,2.0,0.0,0.0,1.0,0.0,0.08,0.17,0.25,0.08,0.25,1.5,1.5,0.13,0.13,12.0,7.0,0.0,31.8,0.58,0.05,0.14,0.07,-0.5,-0.5,368.0,476.0,77.3,5317.0,1454.0,214.0,253.0,84.6,116.0,151.0,76.8,21.0,33.0,63.6,14.0,23.0,24.0,9.0,37.0,439.0,36.0,2.0,2.0,2.0,35.0,1.0,0.0,1.0,0.0,33.0,1.0,10.0,47.0,3.93,33.0,2.0,4.0,2.0,6.0,0.5,5.0,0.0,0.0,0.0,0.0,28.0,18.0,12.0,14.0,2.0,11.0,1.0,10.0,13.0,9.0,0.0,635.0,12.0,88.0,255.0,302.0,38.0,635.0,364.0,40.0,16.0,14.0,426.0,32.0,18.0,0.0,21.0,18.0,1.0,0.0,1.0,0.0,40.0,7.0,4.0,63.6 +Jacopo Sala,it ITA,MF,Spezia,31-141,1991,6.0,1.0,112.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,36.0,46.0,78.3,589.0,199.0,20.0,21.0,95.2,13.0,15.0,86.7,3.0,5.0,60.0,1.0,4.0,2.0,0.0,6.0,40.0,6.0,4.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,2.0,1.62,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,2.0,0.0,2.0,1.0,2.0,0.0,57.0,2.0,14.0,28.0,16.0,2.0,57.0,32.0,2.0,0.0,0.0,34.0,1.0,0.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 +Lazar Samardzic,rs SRB,MF,Udinese,21-060,2002,30.0,13.0,1330.0,5.0,4.0,0.0,0.0,1.0,0.0,0.34,0.27,0.61,0.34,0.61,2.2,2.2,0.15,0.15,14.8,16.0,7.0,38.1,1.08,0.12,0.31,0.05,2.8,2.8,534.0,666.0,80.2,10476.0,3363.0,216.0,236.0,91.5,198.0,232.0,85.3,101.0,162.0,62.3,41.0,66.0,18.0,6.0,77.0,569.0,94.0,34.0,2.0,16.0,79.0,56.0,29.0,16.0,0.0,3.0,3.0,12.0,84.0,5.68,45.0,23.0,6.0,1.0,8.0,0.54,3.0,3.0,0.0,0.0,2.0,29.0,14.0,7.0,18.0,4.0,8.0,0.0,8.0,9.0,10.0,0.0,831.0,12.0,117.0,413.0,313.0,36.0,831.0,458.0,28.0,23.0,7.0,518.0,24.0,17.0,0.0,20.0,10.0,0.0,0.0,0.0,0.0,90.0,8.0,17.0,32.0 +Junior Sambia,fr FRA,DF,Salernitana,26-230,1996,17.0,6.0,586.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.09,0.09,6.5,5.0,4.0,41.7,0.77,0.0,0.0,0.05,-0.6,-0.6,255.0,325.0,78.5,3781.0,1600.0,141.0,159.0,88.7,90.0,114.0,78.9,12.0,30.0,40.0,3.0,12.0,8.0,1.0,26.0,253.0,72.0,4.0,0.0,1.0,25.0,8.0,3.0,4.0,0.0,60.0,0.0,6.0,17.0,2.61,13.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,5.0,6.0,4.0,1.0,9.0,3.0,6.0,4.0,16.0,1.0,417.0,21.0,96.0,183.0,141.0,11.0,417.0,213.0,19.0,18.0,4.0,241.0,9.0,7.0,0.0,10.0,14.0,0.0,0.0,0.0,0.0,30.0,13.0,9.0,59.1 +Antonio Sanabria,py PAR,FW,Torino,27-052,1996,26.0,21.0,1864.0,9.0,2.0,1.0,1.0,4.0,0.0,0.43,0.1,0.53,0.39,0.48,7.5,6.7,0.36,0.32,20.7,18.0,0.0,36.7,0.87,0.16,0.44,0.14,1.5,1.3,398.0,521.0,76.4,5480.0,659.0,255.0,309.0,82.5,102.0,139.0,73.4,17.0,21.0,81.0,20.0,21.0,11.0,0.0,36.0,472.0,48.0,2.0,1.0,4.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,13.0,38.0,1.84,29.0,0.0,2.0,1.0,2.0,0.1,2.0,0.0,0.0,0.0,0.0,8.0,4.0,1.0,3.0,4.0,7.0,2.0,5.0,3.0,7.0,0.0,710.0,11.0,39.0,338.0,342.0,84.0,709.0,375.0,10.0,12.0,5.0,529.0,59.0,17.0,0.0,27.0,22.0,12.0,0.0,0.0,0.0,52.0,59.0,93.0,38.8 +Leandro Sanca,pt POR,"FW,MF",Spezia,23-111,2000,4.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,12.0,75.0,171.0,27.0,4.0,5.0,80.0,2.0,4.0,50.0,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,11.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,0.0,1.0,6.0,11.0,0.0,17.0,10.0,1.0,1.0,0.0,11.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,2.0,0.0 +Alex Sandro,br BRA,DF,Juventus,32-089,1991,22.0,18.0,1545.0,0.0,1.0,0.0,0.0,5.0,1.0,0.0,0.06,0.06,0.0,0.06,0.2,0.2,0.01,0.01,17.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.2,-0.2,940.0,1086.0,86.6,17057.0,5652.0,400.0,434.0,92.2,422.0,465.0,90.8,103.0,148.0,69.6,11.0,75.0,7.0,2.0,87.0,961.0,122.0,18.0,0.0,7.0,14.0,0.0,0.0,0.0,0.0,93.0,3.0,9.0,33.0,1.92,29.0,0.0,0.0,1.0,3.0,0.17,3.0,0.0,0.0,0.0,0.0,25.0,14.0,11.0,12.0,2.0,22.0,9.0,13.0,13.0,57.0,0.0,1254.0,109.0,452.0,647.0,166.0,12.0,1254.0,647.0,42.0,28.0,3.0,779.0,23.0,11.0,0.0,24.0,22.0,1.0,1.0,0.0,0.0,115.0,35.0,18.0,66.0 +Nicola Sansone,it ITA,FW,Bologna,31-227,1991,15.0,8.0,664.0,3.0,1.0,0.0,0.0,2.0,0.0,0.41,0.14,0.54,0.41,0.54,2.6,2.6,0.35,0.35,7.4,6.0,2.0,31.6,0.81,0.16,0.5,0.14,0.4,0.4,143.0,192.0,74.5,1867.0,348.0,90.0,109.0,82.6,35.0,49.0,71.4,5.0,10.0,50.0,4.0,6.0,4.0,1.0,12.0,176.0,14.0,0.0,0.0,2.0,13.0,5.0,4.0,0.0,0.0,0.0,2.0,7.0,23.0,3.11,15.0,0.0,1.0,4.0,5.0,0.68,3.0,0.0,0.0,2.0,0.0,11.0,4.0,5.0,4.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,277.0,4.0,22.0,121.0,139.0,26.0,277.0,189.0,16.0,13.0,7.0,226.0,19.0,13.0,0.0,11.0,14.0,9.0,2.0,0.0,0.0,18.0,0.0,11.0,0.0 +Riccardo Saponara,it ITA,FW,Fiorentina,31-125,1991,24.0,12.0,1038.0,3.0,2.0,0.0,0.0,3.0,0.0,0.26,0.17,0.43,0.26,0.43,2.5,2.5,0.22,0.22,11.5,11.0,0.0,32.4,0.95,0.09,0.27,0.07,0.5,0.5,288.0,398.0,72.4,4109.0,1112.0,185.0,222.0,83.3,76.0,107.0,71.0,13.0,29.0,44.8,18.0,23.0,23.0,4.0,49.0,393.0,3.0,0.0,2.0,6.0,23.0,0.0,0.0,0.0,0.0,3.0,2.0,10.0,46.0,3.99,33.0,0.0,5.0,3.0,4.0,0.35,3.0,0.0,1.0,0.0,0.0,10.0,5.0,3.0,6.0,1.0,6.0,1.0,5.0,4.0,8.0,0.0,532.0,11.0,61.0,203.0,278.0,54.0,532.0,361.0,51.0,35.0,14.0,413.0,25.0,17.0,0.0,11.0,21.0,4.0,0.0,0.0,0.0,57.0,7.0,6.0,53.8 +Martin Satriano,uy URU,FW,Empoli,22-064,2001,28.0,20.0,1656.0,2.0,1.0,0.0,0.0,4.0,0.0,0.11,0.05,0.16,0.11,0.16,3.8,3.8,0.21,0.21,18.4,10.0,0.0,27.0,0.54,0.05,0.2,0.1,-1.8,-1.8,244.0,375.0,65.1,3220.0,582.0,141.0,191.0,73.8,70.0,105.0,66.7,11.0,23.0,47.8,14.0,16.0,9.0,1.0,21.0,366.0,8.0,0.0,2.0,1.0,21.0,0.0,0.0,0.0,0.0,6.0,1.0,15.0,31.0,1.68,19.0,0.0,5.0,4.0,3.0,0.16,2.0,0.0,1.0,0.0,0.0,13.0,8.0,5.0,6.0,2.0,5.0,0.0,5.0,3.0,21.0,0.0,582.0,18.0,63.0,246.0,284.0,75.0,582.0,336.0,28.0,21.0,12.0,453.0,51.0,44.0,0.0,18.0,28.0,5.0,0.0,0.0,0.0,51.0,43.0,55.0,43.9 +Giorgio Scalvini,it ITA,DF,Atalanta,19-135,2003,25.0,22.0,1785.0,2.0,0.0,0.0,0.0,6.0,0.0,0.1,0.0,0.1,0.1,0.1,1.1,1.1,0.06,0.06,19.8,4.0,0.0,33.3,0.2,0.17,0.5,0.09,0.9,0.9,868.0,1091.0,79.6,14241.0,6051.0,451.0,513.0,87.9,330.0,384.0,85.9,68.0,141.0,48.2,4.0,102.0,13.0,2.0,121.0,1057.0,31.0,24.0,1.0,20.0,8.0,0.0,0.0,0.0,0.0,7.0,3.0,14.0,25.0,1.26,22.0,1.0,0.0,0.0,4.0,0.2,4.0,0.0,0.0,0.0,0.0,44.0,23.0,26.0,12.0,6.0,28.0,11.0,17.0,41.0,48.0,1.0,1303.0,100.0,509.0,639.0,166.0,19.0,1303.0,683.0,14.0,13.0,4.0,817.0,12.0,8.0,0.0,43.0,4.0,6.0,0.0,0.0,0.0,133.0,38.0,20.0,65.5 +Jerdy Schouten,nl NED,MF,Bologna,26-103,1997,27.0,23.0,1995.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.09,0.09,0.0,0.09,0.3,0.3,0.01,0.01,22.2,2.0,0.0,33.3,0.09,0.0,0.0,0.05,-0.3,-0.3,952.0,1114.0,85.5,15531.0,4370.0,493.0,551.0,89.5,363.0,415.0,87.5,73.0,97.0,75.3,11.0,84.0,6.0,0.0,101.0,1107.0,5.0,5.0,4.0,11.0,3.0,0.0,0.0,0.0,0.0,0.0,2.0,12.0,41.0,1.85,39.0,0.0,1.0,0.0,4.0,0.18,4.0,0.0,0.0,0.0,0.0,52.0,32.0,23.0,27.0,2.0,37.0,15.0,22.0,37.0,40.0,1.0,1373.0,70.0,380.0,858.0,147.0,8.0,1373.0,760.0,12.0,10.0,0.0,895.0,38.0,20.0,0.0,21.0,21.0,1.0,0.0,0.0,1.0,175.0,23.0,24.0,48.9 +Perr Schuurs,nl NED,DF,Torino,23-150,1999,24.0,23.0,1896.0,0.0,2.0,0.0,0.0,5.0,0.0,0.0,0.09,0.09,0.0,0.09,0.6,0.6,0.03,0.03,21.1,1.0,1.0,10.0,0.05,0.0,0.0,0.06,-0.6,-0.6,864.0,982.0,88.0,17141.0,5089.0,247.0,277.0,89.2,525.0,565.0,92.9,86.0,116.0,74.1,5.0,24.0,2.0,0.0,49.0,954.0,28.0,24.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,7.0,14.0,0.66,11.0,0.0,1.0,1.0,2.0,0.09,2.0,0.0,0.0,0.0,0.0,41.0,20.0,22.0,15.0,4.0,23.0,14.0,9.0,24.0,88.0,0.0,1219.0,131.0,636.0,547.0,51.0,13.0,1219.0,625.0,8.0,12.0,0.0,668.0,19.0,7.0,0.0,28.0,3.0,2.0,0.0,1.0,0.0,143.0,50.0,42.0,54.3 +Demba Seck,sn SEN,"FW,MF",Torino,22-074,2001,15.0,5.0,397.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.09,0.09,4.4,2.0,0.0,25.0,0.45,0.0,0.0,0.05,-0.4,-0.4,92.0,121.0,76.0,1246.0,252.0,60.0,65.0,92.3,28.0,37.0,75.7,2.0,5.0,40.0,5.0,6.0,3.0,0.0,12.0,117.0,3.0,0.0,2.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,9.0,2.03,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,3.0,2.0,0.0,3.0,1.0,2.0,1.0,1.0,0.0,181.0,1.0,17.0,83.0,86.0,20.0,181.0,126.0,18.0,13.0,8.0,144.0,23.0,11.0,0.0,4.0,14.0,6.0,0.0,0.0,0.0,18.0,3.0,10.0,23.1 +Jacopo Segre,it ITA,DF,Torino,26-067,1997,1.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,9.0,88.9,103.0,22.0,6.0,7.0,85.7,2.0,2.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,4.0,8.0,0.0,0.0,12.0,8.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 +Vivaldo Semedo,pt POR,FW,Udinese,18-087,2005,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +Stefano Sensi,it ITA,MF,Monza,27-263,1995,23.0,20.0,1479.0,3.0,1.0,0.0,0.0,6.0,0.0,0.18,0.06,0.24,0.18,0.24,1.4,1.4,0.08,0.08,16.4,7.0,4.0,38.9,0.43,0.17,0.43,0.08,1.6,1.6,996.0,1172.0,85.0,18355.0,5904.0,433.0,483.0,89.6,423.0,463.0,91.4,120.0,176.0,68.2,22.0,117.0,19.0,3.0,134.0,1083.0,87.0,47.0,5.0,18.0,48.0,31.0,9.0,16.0,0.0,4.0,2.0,13.0,60.0,3.65,36.0,17.0,2.0,4.0,7.0,0.43,6.0,0.0,0.0,1.0,0.0,39.0,25.0,24.0,12.0,3.0,12.0,2.0,10.0,16.0,3.0,0.0,1317.0,19.0,247.0,795.0,286.0,11.0,1317.0,824.0,26.0,31.0,2.0,1014.0,30.0,15.0,0.0,16.0,43.0,2.0,1.0,0.0,0.0,111.0,8.0,8.0,50.0 +Luigi Sepe,it ITA,GK,Salernitana,31-352,1991,17.0,17.0,1530.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,441.0,648.0,68.1,11985.0,9017.0,79.0,79.0,100.0,216.0,217.0,99.5,143.0,345.0,41.4,0.0,8.0,0.0,0.0,0.0,435.0,209.0,55.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.06,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,692.0,605.0,692.0,0.0,0.0,0.0,692.0,346.0,0.0,0.0,0.0,273.0,1.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,27.0,4.0,0.0,100.0 +Leonardo Sernicola,it ITA,DF,Cremonese,25-269,1997,26.0,23.0,1940.0,2.0,2.0,0.0,0.0,10.0,0.0,0.09,0.09,0.19,0.09,0.19,1.3,1.3,0.06,0.06,21.6,5.0,0.0,20.0,0.23,0.08,0.4,0.05,0.7,0.7,537.0,821.0,65.4,9292.0,4117.0,259.0,311.0,83.3,202.0,327.0,61.8,59.0,130.0,45.4,20.0,45.0,26.0,19.0,56.0,640.0,176.0,10.0,2.0,6.0,107.0,12.0,0.0,11.0,0.0,154.0,5.0,20.0,48.0,2.23,35.0,5.0,3.0,4.0,3.0,0.14,3.0,0.0,0.0,0.0,0.0,37.0,26.0,19.0,11.0,7.0,18.0,5.0,13.0,30.0,41.0,0.0,1046.0,50.0,283.0,426.0,353.0,22.0,1046.0,511.0,54.0,41.0,4.0,536.0,24.0,19.0,0.0,35.0,38.0,3.0,0.0,1.0,0.0,109.0,23.0,18.0,56.1 +Stephan El Shaarawy,it ITA,"DF,MF",Roma,30-180,1992,24.0,10.0,1185.0,4.0,1.0,0.0,0.0,3.0,0.0,0.3,0.08,0.38,0.3,0.38,3.6,3.6,0.27,0.27,13.2,14.0,0.0,48.3,1.06,0.14,0.29,0.12,0.4,0.4,555.0,678.0,81.9,8759.0,2523.0,302.0,336.0,89.9,203.0,247.0,82.2,36.0,56.0,64.3,16.0,29.0,15.0,2.0,51.0,583.0,95.0,10.0,3.0,6.0,16.0,2.0,2.0,0.0,0.0,82.0,0.0,16.0,45.0,3.42,29.0,6.0,3.0,1.0,4.0,0.3,3.0,0.0,0.0,0.0,0.0,17.0,9.0,8.0,7.0,2.0,10.0,1.0,9.0,14.0,9.0,0.0,843.0,13.0,165.0,374.0,315.0,44.0,843.0,492.0,44.0,31.0,15.0,547.0,26.0,8.0,0.0,14.0,11.0,9.0,0.0,0.0,0.0,74.0,11.0,15.0,42.3 +Eldor Shomurodov,uz UZB,FW,Spezia,27-300,1995,10.0,5.0,475.0,1.0,0.0,0.0,0.0,1.0,0.0,0.19,0.0,0.19,0.19,0.19,2.4,2.4,0.45,0.45,5.3,3.0,0.0,23.1,0.57,0.08,0.33,0.18,-1.4,-1.4,91.0,121.0,75.2,1190.0,284.0,55.0,67.0,82.1,28.0,33.0,84.8,3.0,5.0,60.0,7.0,6.0,1.0,0.0,11.0,111.0,10.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,11.0,2.08,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,2.0,0.0,1.0,3.0,0.0,3.0,1.0,3.0,0.0,196.0,2.0,19.0,86.0,94.0,17.0,196.0,106.0,5.0,3.0,2.0,133.0,23.0,9.0,0.0,6.0,6.0,2.0,0.0,0.0,0.0,23.0,13.0,18.0,41.9 +Eldor Shomurodov,uz UZB,FW,Roma,27-300,1995,6.0,1.0,119.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.45,0.45,1.3,3.0,0.0,50.0,2.27,0.0,0.0,0.1,-0.6,-0.6,25.0,33.0,75.8,371.0,69.0,15.0,19.0,78.9,9.0,11.0,81.8,1.0,1.0,100.0,0.0,0.0,1.0,0.0,4.0,31.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,2.0,1.51,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,53.0,1.0,4.0,16.0,34.0,11.0,53.0,34.0,3.0,4.0,2.0,43.0,4.0,5.0,0.0,1.0,2.0,2.0,0.0,0.0,0.0,2.0,2.0,2.0,50.0 +Marco Silvestri,it ITA,GK,Udinese,32-054,1991,31.0,31.0,2790.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,714.0,927.0,77.0,18903.0,14043.0,138.0,138.0,100.0,373.0,377.0,98.9,199.0,405.0,49.1,0.0,9.0,0.0,0.0,0.0,624.0,300.0,63.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,6.0,0.19,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,7.0,1.0,996.0,898.0,996.0,0.0,0.0,0.0,996.0,510.0,0.0,0.0,0.0,418.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,33.0,7.0,0.0,100.0 +Giovanni Simeone,ar ARG,FW,Napoli,27-294,1995,19.0,1.0,320.0,3.0,0.0,0.0,0.0,1.0,0.0,0.84,0.0,0.84,0.84,0.84,0.9,0.9,0.24,0.24,3.6,5.0,0.0,55.6,1.41,0.33,0.6,0.1,2.1,2.1,56.0,82.0,68.3,785.0,160.0,29.0,40.0,72.5,16.0,25.0,64.0,4.0,5.0,80.0,4.0,3.0,2.0,0.0,8.0,80.0,2.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,7.0,1.96,7.0,0.0,0.0,0.0,2.0,0.56,2.0,0.0,0.0,0.0,0.0,12.0,3.0,3.0,5.0,4.0,2.0,1.0,1.0,1.0,3.0,0.0,153.0,3.0,15.0,68.0,71.0,18.0,153.0,112.0,10.0,8.0,2.0,123.0,22.0,10.0,0.0,6.0,8.0,1.0,0.0,0.0,0.0,10.0,4.0,16.0,20.0 +Wilfried Singo,ci CIV,DF,Torino,22-121,2000,25.0,19.0,1665.0,2.0,1.0,0.0,0.0,4.0,0.0,0.11,0.05,0.16,0.11,0.16,1.6,1.6,0.09,0.09,18.5,7.0,0.0,28.0,0.38,0.08,0.29,0.06,0.4,0.4,703.0,946.0,74.3,11835.0,3402.0,324.0,379.0,85.5,316.0,418.0,75.6,47.0,98.0,48.0,18.0,37.0,18.0,7.0,55.0,758.0,180.0,19.0,1.0,4.0,40.0,0.0,0.0,0.0,0.0,161.0,8.0,13.0,39.0,2.11,23.0,0.0,7.0,2.0,4.0,0.22,2.0,0.0,1.0,0.0,0.0,25.0,16.0,13.0,10.0,2.0,12.0,1.0,11.0,9.0,28.0,1.0,1126.0,36.0,242.0,544.0,352.0,46.0,1126.0,580.0,41.0,26.0,7.0,698.0,24.0,16.0,0.0,35.0,33.0,0.0,0.0,0.0,0.0,91.0,44.0,20.0,68.8 +Salvatore Sirigu,it ITA,GK,Fiorentina,36-103,1987,1.0,1.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28.0,38.0,73.7,930.0,572.0,2.0,2.0,100.0,12.0,12.0,100.0,14.0,24.0,58.3,0.0,0.0,0.0,0.0,0.0,26.0,12.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,40.0,38.0,40.0,0.0,0.0,0.0,40.0,26.0,0.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Leo Skiri Østigård,no NOR,DF,Napoli,23-148,1999,5.0,2.0,239.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.01,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,176.0,206.0,85.4,3589.0,1111.0,56.0,66.0,84.8,88.0,97.0,90.7,26.0,33.0,78.8,0.0,14.0,0.0,0.0,9.0,197.0,8.0,7.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,2.0,0.75,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,4.0,10.0,0.0,235.0,14.0,105.0,119.0,11.0,1.0,235.0,151.0,3.0,3.0,0.0,158.0,4.0,0.0,0.0,2.0,4.0,0.0,0.0,0.0,0.0,7.0,19.0,2.0,90.5 +Łukasz Skorupski,pl POL,GK,Bologna,31-355,1991,30.0,30.0,2700.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,769.0,996.0,77.2,20596.0,13981.0,142.0,143.0,99.3,341.0,345.0,98.8,260.0,478.0,54.4,0.0,3.0,0.0,0.0,0.0,699.0,293.0,74.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,3.0,0.1,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,1.0,1070.0,962.0,1065.0,5.0,0.0,0.0,1070.0,577.0,0.0,0.0,0.0,450.0,1.0,0.0,0.0,1.0,6.0,0.0,0.0,1.0,0.0,44.0,4.0,1.0,80.0 +Milan Škriniar,sk SVK,DF,Inter,28-073,1995,21.0,20.0,1769.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.04,0.04,19.7,2.0,0.0,22.2,0.1,0.0,0.0,0.09,-0.8,-0.8,1275.0,1397.0,91.3,23769.0,7731.0,445.0,479.0,92.9,707.0,739.0,95.7,103.0,144.0,71.5,6.0,79.0,4.0,1.0,81.0,1328.0,67.0,43.0,1.0,13.0,5.0,0.0,0.0,0.0,0.0,22.0,2.0,6.0,11.0,0.56,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.0,18.0,14.0,11.0,4.0,18.0,13.0,5.0,13.0,46.0,3.0,1546.0,151.0,741.0,721.0,90.0,13.0,1546.0,976.0,16.0,11.0,0.0,1137.0,14.0,3.0,1.0,35.0,14.0,1.0,0.0,0.0,1.0,112.0,17.0,18.0,48.6 +Chris Smalling,eng ENG,DF,Roma,33-154,1989,29.0,28.0,2583.0,3.0,1.0,0.0,0.0,7.0,0.0,0.1,0.03,0.14,0.1,0.14,1.5,1.5,0.05,0.05,28.7,4.0,0.0,26.7,0.14,0.2,0.75,0.1,1.5,1.5,1004.0,1164.0,86.3,19228.0,6219.0,300.0,330.0,90.9,606.0,666.0,91.0,89.0,135.0,65.9,5.0,27.0,3.0,0.0,43.0,1138.0,24.0,22.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,2.0,2.0,9.0,13.0,0.45,11.0,1.0,1.0,0.0,1.0,0.03,1.0,0.0,0.0,0.0,0.0,23.0,13.0,13.0,10.0,0.0,45.0,33.0,12.0,38.0,118.0,0.0,1451.0,234.0,886.0,527.0,47.0,29.0,1451.0,739.0,4.0,7.0,0.0,852.0,9.0,2.0,0.0,18.0,14.0,1.0,0.0,0.0,0.0,135.0,63.0,21.0,75.0 +Ola Solbakken,no NOR,"MF,FW",Roma,24-230,1998,9.0,3.0,258.0,1.0,1.0,0.0,0.0,1.0,0.0,0.35,0.35,0.7,0.35,0.7,0.4,0.4,0.13,0.13,2.9,1.0,1.0,20.0,0.35,0.2,1.0,0.07,0.6,0.6,67.0,90.0,74.4,1142.0,224.0,32.0,41.0,78.0,25.0,29.0,86.2,7.0,14.0,50.0,5.0,5.0,3.0,1.0,10.0,86.0,4.0,0.0,1.0,1.0,3.0,2.0,2.0,0.0,0.0,2.0,0.0,1.0,6.0,2.1,5.0,1.0,0.0,0.0,1.0,0.35,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,3.0,0.0,128.0,4.0,12.0,64.0,55.0,11.0,128.0,85.0,4.0,7.0,3.0,97.0,8.0,8.0,0.0,4.0,6.0,0.0,0.0,0.0,0.0,10.0,4.0,8.0,33.3 +Brandon Soppy,fr FRA,DF,Udinese,21-063,2002,1.0,1.0,90.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.07,0.07,1.0,1.0,0.0,50.0,1.0,0.0,0.0,0.04,-0.1,-0.1,22.0,32.0,68.8,322.0,140.0,13.0,14.0,92.9,7.0,10.0,70.0,1.0,6.0,16.7,2.0,2.0,1.0,0.0,4.0,29.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,4.0,4.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,0.0,4.0,1.0,1.0,0.0,48.0,2.0,8.0,19.0,21.0,3.0,48.0,33.0,4.0,4.0,0.0,27.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,6.0,1.0,0.0,100.0 +Brandon Soppy,fr FRA,DF,Atalanta,21-063,2002,12.0,7.0,569.0,0.0,3.0,0.0,0.0,2.0,0.0,0.0,0.47,0.47,0.0,0.47,0.3,0.3,0.05,0.05,6.3,2.0,0.0,33.3,0.32,0.0,0.0,0.05,-0.3,-0.3,213.0,280.0,76.1,3110.0,1287.0,119.0,135.0,88.1,79.0,106.0,74.5,9.0,21.0,42.9,9.0,23.0,7.0,2.0,30.0,222.0,58.0,0.0,1.0,0.0,14.0,0.0,0.0,0.0,0.0,58.0,0.0,10.0,20.0,3.15,15.0,0.0,1.0,3.0,6.0,0.94,5.0,0.0,0.0,1.0,0.0,7.0,4.0,3.0,4.0,0.0,8.0,0.0,8.0,2.0,7.0,0.0,360.0,8.0,79.0,166.0,120.0,19.0,360.0,199.0,23.0,10.0,6.0,212.0,15.0,7.0,0.0,14.0,18.0,2.0,1.0,0.0,0.0,44.0,2.0,2.0,50.0 +Roberto Soriano,it ITA,"FW,MF",Bologna,32-076,1991,27.0,15.0,1326.0,1.0,3.0,0.0,0.0,1.0,0.0,0.07,0.2,0.27,0.07,0.27,0.6,0.6,0.04,0.04,14.7,2.0,0.0,15.4,0.14,0.08,0.5,0.05,0.4,0.4,486.0,605.0,80.3,6944.0,1994.0,306.0,349.0,87.7,139.0,171.0,81.3,26.0,42.0,61.9,20.0,35.0,10.0,2.0,56.0,595.0,10.0,4.0,5.0,3.0,11.0,0.0,0.0,0.0,0.0,6.0,0.0,15.0,45.0,3.06,35.0,1.0,2.0,4.0,3.0,0.2,2.0,0.0,1.0,0.0,0.0,31.0,16.0,9.0,16.0,6.0,23.0,2.0,21.0,6.0,14.0,0.0,739.0,25.0,110.0,393.0,249.0,27.0,739.0,470.0,30.0,34.0,0.0,568.0,22.0,15.0,0.0,20.0,33.0,2.0,0.0,0.0,0.0,77.0,11.0,16.0,40.7 +Joaquin Sosa,uy URU,DF,Bologna,21-105,2002,9.0,6.0,598.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.6,1.0,0.0,50.0,0.15,0.0,0.0,0.01,0.0,0.0,238.0,299.0,79.6,5140.0,1836.0,66.0,80.0,82.5,130.0,146.0,89.0,39.0,66.0,59.1,0.0,14.0,0.0,0.0,9.0,284.0,15.0,15.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,0.3,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,5.0,1.0,0.0,5.0,3.0,2.0,5.0,30.0,0.0,359.0,39.0,209.0,147.0,4.0,3.0,359.0,213.0,5.0,0.0,0.0,223.0,5.0,2.0,0.0,14.0,6.0,0.0,0.0,2.0,0.0,38.0,13.0,10.0,56.5 +Riccardo Sottil,it ITA,FW,Fiorentina,23-326,1999,13.0,6.0,597.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.3,0.3,0.0,0.3,0.9,0.9,0.14,0.14,6.6,8.0,1.0,47.1,1.21,0.0,0.0,0.05,-0.9,-0.9,159.0,202.0,78.7,2708.0,464.0,74.0,85.0,87.1,66.0,84.0,78.6,15.0,21.0,71.4,13.0,10.0,6.0,1.0,23.0,200.0,2.0,1.0,1.0,2.0,24.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,35.0,5.28,27.0,0.0,3.0,3.0,2.0,0.3,2.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,278.0,0.0,12.0,64.0,207.0,40.0,278.0,232.0,48.0,29.0,20.0,238.0,20.0,9.0,0.0,4.0,21.0,3.0,0.0,0.0,0.0,26.0,3.0,5.0,37.5 +Matìas Soulé,ar ARG,"MF,FW",Juventus,20-010,2003,12.0,4.0,386.0,1.0,0.0,0.0,0.0,0.0,0.0,0.23,0.0,0.23,0.23,0.23,1.3,1.3,0.3,0.3,4.3,5.0,0.0,55.6,1.17,0.11,0.2,0.15,-0.3,-0.3,130.0,158.0,82.3,2024.0,380.0,77.0,85.0,90.6,34.0,45.0,75.6,15.0,21.0,71.4,8.0,6.0,3.0,1.0,12.0,146.0,12.0,1.0,0.0,3.0,6.0,2.0,2.0,0.0,0.0,9.0,0.0,3.0,18.0,4.2,10.0,1.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,4.0,3.0,2.0,2.0,5.0,1.0,4.0,3.0,2.0,0.0,218.0,3.0,41.0,88.0,91.0,14.0,218.0,133.0,11.0,10.0,4.0,147.0,9.0,7.0,0.0,3.0,5.0,0.0,0.0,0.0,0.0,29.0,2.0,13.0,13.3 +Adama Soumaoro,fr FRA,DF,Bologna,30-311,1992,21.0,20.0,1740.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.02,0.02,19.3,1.0,0.0,50.0,0.05,0.0,0.0,0.16,-0.3,-0.3,926.0,1003.0,92.3,17248.0,5713.0,334.0,350.0,95.4,511.0,541.0,94.5,73.0,90.0,81.1,1.0,26.0,1.0,0.0,48.0,952.0,45.0,44.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,6.0,5.0,7.0,0.36,7.0,0.0,0.0,0.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,23.0,15.0,10.0,11.0,2.0,16.0,11.0,5.0,15.0,66.0,1.0,1140.0,140.0,596.0,524.0,23.0,7.0,1140.0,680.0,7.0,5.0,0.0,777.0,5.0,1.0,1.0,19.0,10.0,0.0,0.0,0.0,0.0,87.0,30.0,14.0,68.2 +Leonardo Spinazzola,it ITA,DF,Roma,30-031,1993,22.0,16.0,1435.0,1.0,4.0,0.0,0.0,2.0,0.0,0.06,0.25,0.31,0.06,0.31,0.9,0.9,0.06,0.06,15.9,6.0,0.0,66.7,0.38,0.11,0.17,0.1,0.1,0.1,653.0,857.0,76.2,11320.0,3655.0,311.0,358.0,86.9,267.0,340.0,78.5,62.0,116.0,53.4,17.0,28.0,23.0,10.0,51.0,699.0,155.0,13.0,2.0,12.0,50.0,0.0,0.0,0.0,0.0,142.0,3.0,18.0,40.0,2.51,31.0,2.0,1.0,3.0,7.0,0.44,6.0,0.0,0.0,0.0,0.0,15.0,8.0,7.0,6.0,2.0,12.0,3.0,9.0,8.0,25.0,0.0,1012.0,34.0,275.0,483.0,271.0,38.0,1012.0,605.0,92.0,40.0,23.0,642.0,22.0,9.0,0.0,12.0,13.0,1.0,0.0,0.0,0.0,89.0,9.0,8.0,52.9 +Marco Sportiello,it ITA,GK,Atalanta,30-350,1992,9.0,8.0,803.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,224.0,294.0,76.2,6317.0,4421.0,36.0,36.0,100.0,123.0,125.0,98.4,65.0,133.0,48.9,0.0,1.0,0.0,0.0,2.0,199.0,95.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.11,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,307.0,277.0,302.0,5.0,0.0,0.0,307.0,175.0,0.0,0.0,0.0,118.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,14.0,2.0,0.0,100.0 +Petar Stojanović,si SVN,DF,Empoli,27-200,1995,22.0,15.0,1406.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.06,0.06,0.0,0.06,0.2,0.2,0.02,0.02,15.6,1.0,1.0,11.1,0.06,0.0,0.0,0.03,-0.2,-0.2,748.0,942.0,79.4,12922.0,4765.0,382.0,434.0,88.0,275.0,335.0,82.1,81.0,133.0,60.9,16.0,53.0,19.0,6.0,84.0,757.0,184.0,19.0,3.0,26.0,31.0,1.0,0.0,1.0,0.0,164.0,1.0,23.0,43.0,2.75,31.0,6.0,2.0,2.0,3.0,0.19,3.0,0.0,0.0,0.0,0.0,40.0,21.0,26.0,10.0,4.0,15.0,4.0,11.0,11.0,53.0,0.0,1107.0,57.0,354.0,489.0,272.0,13.0,1107.0,606.0,46.0,25.0,4.0,642.0,10.0,14.0,0.0,12.0,15.0,0.0,0.0,0.0,0.0,83.0,3.0,6.0,33.3 +Gabriel Strefezza,br BRA,"FW,MF",Lecce,26-007,1997,29.0,24.0,1957.0,7.0,2.0,1.0,1.0,4.0,0.0,0.32,0.09,0.41,0.28,0.37,4.1,3.3,0.19,0.15,21.7,17.0,3.0,32.1,0.78,0.11,0.35,0.06,2.9,2.7,453.0,740.0,61.2,8359.0,2671.0,224.0,290.0,77.2,140.0,222.0,63.1,77.0,168.0,45.8,38.0,51.0,34.0,14.0,84.0,666.0,66.0,17.0,6.0,19.0,109.0,34.0,15.0,9.0,0.0,15.0,8.0,28.0,78.0,3.59,59.0,6.0,3.0,2.0,6.0,0.28,6.0,0.0,0.0,0.0,0.0,40.0,21.0,16.0,18.0,6.0,29.0,3.0,26.0,14.0,14.0,0.0,1066.0,9.0,114.0,447.0,530.0,50.0,1065.0,693.0,59.0,50.0,13.0,767.0,66.0,49.0,0.0,39.0,59.0,0.0,0.0,1.0,0.0,111.0,15.0,33.0,31.3 +Dávid Strelec,sk SVK,FW,Spezia,22-021,2001,7.0,1.0,169.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.28,0.28,1.9,1.0,0.0,33.3,0.53,0.0,0.0,0.17,-0.5,-0.5,25.0,43.0,58.1,294.0,30.0,19.0,27.0,70.4,5.0,9.0,55.6,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,40.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,4.0,2.16,3.0,0.0,0.0,1.0,2.0,1.08,2.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,60.0,0.0,2.0,29.0,29.0,12.0,60.0,32.0,2.0,3.0,0.0,45.0,3.0,4.0,0.0,4.0,1.0,2.0,0.0,0.0,0.0,1.0,5.0,6.0,45.5 +Isaac Success,ng NGA,FW,Udinese,27-108,1996,30.0,21.0,1816.0,1.0,6.0,0.0,0.0,4.0,0.0,0.05,0.3,0.35,0.05,0.35,3.3,3.3,0.17,0.17,20.2,5.0,0.0,14.3,0.25,0.03,0.2,0.1,-2.3,-2.3,372.0,570.0,65.3,5429.0,1119.0,207.0,288.0,71.9,99.0,144.0,68.8,32.0,51.0,62.7,31.0,22.0,19.0,1.0,53.0,552.0,11.0,0.0,9.0,8.0,13.0,0.0,0.0,0.0,0.0,0.0,7.0,23.0,81.0,4.02,57.0,0.0,3.0,14.0,10.0,0.5,7.0,0.0,0.0,2.0,0.0,8.0,4.0,2.0,5.0,1.0,13.0,2.0,11.0,4.0,18.0,0.0,831.0,20.0,48.0,356.0,439.0,101.0,831.0,569.0,37.0,26.0,13.0,735.0,95.0,44.0,0.0,34.0,77.0,11.0,0.0,0.0,0.0,51.0,38.0,46.0,45.2 +Ibrahim Sulemana,gh GHA,MF,Hellas Verona,19-338,2003,12.0,3.0,367.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.08,0.08,4.1,3.0,0.0,37.5,0.74,0.0,0.0,0.04,-0.3,-0.3,75.0,106.0,70.8,1405.0,497.0,31.0,36.0,86.1,33.0,41.0,80.5,9.0,17.0,52.9,3.0,14.0,1.0,1.0,12.0,106.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,9.0,2.2,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,7.0,6.0,4.0,1.0,4.0,2.0,2.0,9.0,8.0,0.0,171.0,13.0,54.0,91.0,32.0,6.0,171.0,84.0,3.0,4.0,0.0,73.0,7.0,5.0,0.0,7.0,3.0,1.0,0.0,0.0,0.0,33.0,6.0,8.0,42.9 +Wojciech Szczęsny,pl POL,GK,Juventus,33-007,1990,22.0,22.0,1932.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,480.0,621.0,77.3,14246.0,10059.0,70.0,70.0,100.0,224.0,226.0,99.1,186.0,321.0,57.9,0.0,4.0,1.0,0.0,0.0,460.0,159.0,44.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,1.0,1.0,0.05,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,652.0,570.0,649.0,3.0,0.0,0.0,652.0,389.0,0.0,0.0,0.0,319.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,26.0,7.0,0.0,100.0 +Benjamin Tahirovic,ba BIH,MF,Roma,20-053,2003,6.0,1.0,137.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60.0,68.0,88.2,1096.0,184.0,25.0,27.0,92.6,24.0,27.0,88.9,9.0,10.0,90.0,3.0,3.0,1.0,1.0,1.0,67.0,1.0,1.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,2.65,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,0.0,0.0,4.0,0.0,4.0,2.0,1.0,0.0,82.0,3.0,20.0,50.0,13.0,1.0,82.0,43.0,0.0,1.0,0.0,52.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,9.0,2.0,1.0,66.7 +Adrien Tameze,cm CMR,MF,Hellas Verona,29-080,1994,30.0,28.0,2461.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7,0.7,0.03,0.03,27.3,1.0,0.0,12.5,0.04,0.0,0.0,0.09,-0.7,-0.7,678.0,895.0,75.8,11814.0,3740.0,306.0,386.0,79.3,291.0,346.0,84.1,66.0,107.0,61.7,18.0,76.0,11.0,1.0,90.0,841.0,49.0,39.0,0.0,5.0,17.0,0.0,0.0,0.0,0.0,4.0,5.0,21.0,41.0,1.5,35.0,1.0,0.0,3.0,3.0,0.11,3.0,0.0,0.0,0.0,0.0,43.0,25.0,19.0,20.0,4.0,45.0,13.0,32.0,30.0,49.0,2.0,1176.0,86.0,322.0,680.0,194.0,18.0,1176.0,582.0,25.0,30.0,4.0,607.0,31.0,26.0,0.0,26.0,27.0,1.0,0.0,0.0,0.0,169.0,24.0,26.0,48.0 +Ciprian Tătărușanu,ro ROU,GK,Milan,37-075,1986,16.0,16.0,1440.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.06,0.06,0.0,0.06,0.0,0.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,434.0,560.0,77.5,12788.0,9173.0,60.0,60.0,100.0,198.0,204.0,97.1,174.0,290.0,60.0,2.0,9.0,0.0,0.0,0.0,445.0,111.0,28.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,7.0,0.44,5.0,2.0,0.0,0.0,2.0,0.12,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,587.0,509.0,587.0,0.0,0.0,0.0,587.0,359.0,0.0,0.0,0.0,315.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,23.0,1.0,1.0,50.0 +Filippo Terracciano,it ITA,"DF,MF",Hellas Verona,20-076,2003,16.0,5.0,646.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,7.2,3.0,0.0,75.0,0.42,0.0,0.0,0.03,-0.1,-0.1,182.0,261.0,69.7,3289.0,1296.0,70.0,90.0,77.8,86.0,111.0,77.5,17.0,37.0,45.9,5.0,15.0,6.0,2.0,27.0,223.0,36.0,7.0,0.0,4.0,17.0,2.0,1.0,1.0,0.0,27.0,2.0,9.0,9.0,1.25,5.0,1.0,1.0,0.0,1.0,0.14,1.0,0.0,0.0,0.0,0.0,13.0,9.0,7.0,4.0,2.0,4.0,3.0,1.0,8.0,11.0,0.0,333.0,17.0,88.0,163.0,85.0,7.0,333.0,172.0,12.0,8.0,0.0,183.0,15.0,4.0,0.0,6.0,15.0,0.0,0.0,0.0,0.0,36.0,14.0,17.0,45.2 +Pietro Terracciano,it ITA,GK,Fiorentina,33-048,1990,27.0,27.0,2430.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,748.0,977.0,76.6,21704.0,14920.0,121.0,122.0,99.2,319.0,325.0,98.2,304.0,522.0,58.2,0.0,15.0,0.0,0.0,0.0,718.0,256.0,72.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.11,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,13.0,0.0,1035.0,811.0,1025.0,10.0,0.0,0.0,1035.0,536.0,0.0,0.0,0.0,547.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,44.0,6.0,0.0,100.0 +Aleksa Terzić,rs SRB,DF,Fiorentina,23-251,1999,16.0,5.0,585.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.03,0.03,6.5,1.0,0.0,16.7,0.15,0.0,0.0,0.04,-0.2,-0.2,408.0,504.0,81.0,7558.0,2409.0,172.0,187.0,92.0,176.0,212.0,83.0,52.0,81.0,64.2,16.0,27.0,18.0,16.0,31.0,414.0,89.0,5.0,0.0,0.0,54.0,8.0,0.0,5.0,0.0,76.0,1.0,13.0,29.0,4.45,21.0,5.0,1.0,0.0,3.0,0.46,1.0,1.0,1.0,0.0,0.0,12.0,6.0,8.0,3.0,1.0,12.0,4.0,8.0,4.0,11.0,0.0,577.0,18.0,157.0,241.0,183.0,5.0,577.0,351.0,31.0,20.0,2.0,371.0,10.0,5.0,0.0,0.0,9.0,2.0,0.0,0.0,0.0,31.0,6.0,9.0,40.0 +Florian Thauvin,fr FRA,"FW,MF",Udinese,30-089,1993,10.0,2.0,289.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.1,0.1,3.2,1.0,0.0,25.0,0.31,0.0,0.0,0.08,-0.3,-0.3,67.0,110.0,60.9,1169.0,311.0,34.0,41.0,82.9,25.0,36.0,69.4,7.0,26.0,26.9,4.0,4.0,5.0,1.0,13.0,87.0,22.0,4.0,1.0,0.0,26.0,12.0,6.0,2.0,0.0,1.0,1.0,3.0,10.0,3.1,5.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,3.0,0.0,3.0,1.0,1.0,0.0,155.0,1.0,3.0,69.0,85.0,11.0,155.0,100.0,10.0,10.0,2.0,112.0,12.0,4.0,0.0,6.0,5.0,0.0,0.0,0.0,0.0,7.0,4.0,4.0,50.0 +Malick Thiaw,de GER,DF,Milan,21-260,2001,14.0,11.0,913.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.06,0.06,10.1,2.0,0.0,40.0,0.2,0.0,0.0,0.12,-0.6,-0.6,517.0,608.0,85.0,10466.0,3399.0,144.0,171.0,84.2,306.0,333.0,91.9,64.0,90.0,71.1,2.0,33.0,2.0,0.0,39.0,595.0,13.0,6.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,5.0,0.0,5.0,8.0,0.79,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20.0,17.0,16.0,4.0,0.0,10.0,5.0,5.0,10.0,46.0,1.0,708.0,69.0,339.0,339.0,33.0,11.0,708.0,402.0,5.0,7.0,0.0,454.0,4.0,2.0,0.0,8.0,4.0,0.0,0.0,0.0,0.0,55.0,39.0,14.0,73.6 +Kristian Thorstvedt,no NOR,MF,Sassuolo,24-043,1999,26.0,12.0,1035.0,2.0,1.0,0.0,0.0,4.0,0.0,0.17,0.09,0.26,0.17,0.26,2.1,2.1,0.18,0.18,11.5,8.0,0.0,42.1,0.7,0.11,0.25,0.11,-0.1,-0.1,334.0,441.0,75.7,5039.0,1636.0,184.0,224.0,82.1,117.0,149.0,78.5,18.0,30.0,60.0,9.0,43.0,6.0,1.0,55.0,429.0,12.0,6.0,2.0,1.0,8.0,0.0,0.0,0.0,0.0,6.0,0.0,15.0,25.0,2.17,18.0,0.0,3.0,1.0,1.0,0.09,1.0,0.0,0.0,0.0,0.0,15.0,7.0,4.0,8.0,3.0,16.0,4.0,12.0,12.0,13.0,0.0,569.0,19.0,88.0,329.0,161.0,33.0,569.0,299.0,27.0,15.0,5.0,398.0,19.0,11.0,0.0,16.0,22.0,0.0,0.0,0.0,0.0,55.0,26.0,20.0,56.5 +Jeremy Toljan,de GER,DF,Sassuolo,28-260,1994,25.0,24.0,2219.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.04,0.04,0.0,0.04,0.5,0.5,0.02,0.02,24.7,0.0,0.0,0.0,0.0,0.0,0.0,0.1,-0.5,-0.5,1108.0,1315.0,84.3,18041.0,6708.0,577.0,638.0,90.4,419.0,480.0,87.3,90.0,138.0,65.2,21.0,102.0,22.0,7.0,111.0,1053.0,262.0,25.0,2.0,9.0,21.0,0.0,0.0,0.0,0.0,237.0,0.0,27.0,39.0,1.58,38.0,1.0,0.0,0.0,2.0,0.08,2.0,0.0,0.0,0.0,0.0,32.0,21.0,17.0,11.0,4.0,26.0,11.0,15.0,17.0,50.0,1.0,1495.0,62.0,529.0,704.0,270.0,20.0,1495.0,763.0,41.0,27.0,3.0,886.0,22.0,10.0,0.0,8.0,12.0,0.0,0.0,1.0,0.0,125.0,8.0,16.0,33.3 +Rafael Tolói,it ITA,DF,Atalanta,32-197,1990,25.0,25.0,2155.0,2.0,1.0,0.0,0.0,6.0,0.0,0.08,0.04,0.13,0.08,0.13,2.6,2.6,0.11,0.11,23.9,8.0,0.0,33.3,0.33,0.08,0.25,0.11,-0.6,-0.6,1019.0,1295.0,78.7,18674.0,7167.0,444.0,493.0,90.1,456.0,534.0,85.4,109.0,226.0,48.2,14.0,99.0,10.0,2.0,117.0,1252.0,39.0,18.0,2.0,8.0,21.0,0.0,0.0,0.0,0.0,20.0,4.0,17.0,39.0,1.63,33.0,0.0,2.0,0.0,8.0,0.33,7.0,0.0,1.0,0.0,0.0,50.0,34.0,28.0,22.0,0.0,31.0,14.0,17.0,49.0,69.0,1.0,1564.0,129.0,638.0,701.0,243.0,33.0,1564.0,833.0,17.0,34.0,0.0,981.0,15.0,3.0,0.0,24.0,15.0,3.0,0.0,2.0,0.0,180.0,33.0,22.0,60.0 +Fikayo Tomori,eng ENG,DF,Milan,25-127,1997,27.0,26.0,2282.0,1.0,0.0,0.0,0.0,4.0,0.0,0.04,0.0,0.04,0.04,0.04,1.2,1.2,0.05,0.05,25.4,4.0,0.0,50.0,0.16,0.13,0.25,0.15,-0.2,-0.2,1337.0,1531.0,87.3,27445.0,8647.0,435.0,472.0,92.2,694.0,752.0,92.3,201.0,267.0,75.3,8.0,75.0,7.0,0.0,98.0,1420.0,105.0,38.0,4.0,16.0,4.0,0.0,0.0,0.0,0.0,13.0,6.0,16.0,20.0,0.79,16.0,2.0,0.0,1.0,1.0,0.04,1.0,0.0,0.0,0.0,0.0,62.0,31.0,40.0,21.0,1.0,37.0,19.0,18.0,25.0,65.0,1.0,1776.0,195.0,850.0,882.0,56.0,13.0,1776.0,993.0,21.0,6.0,0.0,1124.0,9.0,2.0,0.0,24.0,22.0,1.0,0.0,1.0,0.0,155.0,51.0,28.0,64.6 +Sandro Tonali,it ITA,MF,Milan,22-352,2000,27.0,24.0,2167.0,2.0,6.0,0.0,0.0,7.0,0.0,0.08,0.25,0.33,0.08,0.33,2.1,2.1,0.09,0.09,24.1,13.0,5.0,50.0,0.54,0.08,0.15,0.08,-0.1,-0.1,850.0,1143.0,74.4,15719.0,4814.0,348.0,413.0,84.3,365.0,442.0,82.6,113.0,221.0,51.1,44.0,95.0,23.0,6.0,117.0,990.0,143.0,77.0,5.0,13.0,87.0,45.0,17.0,22.0,0.0,18.0,10.0,21.0,75.0,3.11,47.0,15.0,6.0,2.0,10.0,0.41,6.0,1.0,1.0,0.0,1.0,54.0,36.0,29.0,20.0,5.0,24.0,8.0,16.0,22.0,19.0,1.0,1359.0,36.0,279.0,749.0,346.0,23.0,1359.0,681.0,46.0,30.0,2.0,824.0,34.0,8.0,0.0,29.0,27.0,3.0,0.0,0.0,0.0,146.0,13.0,15.0,46.4 +Lorenzo Tonelli,it ITA,DF,Empoli,33-098,1990,1.0,0.0,19.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,7.0,71.4,81.0,46.0,3.0,3.0,100.0,1.0,2.0,50.0,1.0,2.0,50.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,4.0,4.0,0.0,0.0,8.0,4.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +William Troost-Ekong,ng NGA,DF,Salernitana,29-236,1993,6.0,3.0,339.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,120.0,145.0,82.8,2501.0,914.0,26.0,30.0,86.7,81.0,89.0,91.0,11.0,20.0,55.0,0.0,1.0,1.0,1.0,6.0,143.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.26,1.0,0.0,0.0,0.0,1.0,0.26,1.0,0.0,0.0,0.0,0.0,10.0,3.0,7.0,3.0,0.0,4.0,3.0,1.0,3.0,6.0,0.0,174.0,21.0,100.0,74.0,1.0,0.0,174.0,102.0,0.0,0.0,0.0,107.0,2.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,10.0,13.0,7.0,65.0 +Frank Tsadjout,it ITA,"FW,MF",Cremonese,23-271,1999,17.0,12.0,872.0,2.0,1.0,0.0,0.0,1.0,0.0,0.21,0.1,0.31,0.21,0.31,1.6,1.6,0.17,0.17,9.7,7.0,0.0,33.3,0.72,0.1,0.29,0.08,0.4,0.4,158.0,235.0,67.2,1997.0,347.0,101.0,139.0,72.7,43.0,62.0,69.4,3.0,8.0,37.5,10.0,11.0,4.0,0.0,15.0,231.0,4.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,17.0,1.76,14.0,0.0,2.0,1.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,9.0,7.0,3.0,5.0,1.0,6.0,1.0,5.0,7.0,9.0,0.0,350.0,11.0,33.0,168.0,153.0,43.0,350.0,177.0,14.0,10.0,4.0,256.0,31.0,7.0,0.0,21.0,16.0,4.0,0.0,0.0,0.0,34.0,54.0,54.0,50.0 +Alessandro Tuia,it ITA,DF,Lecce,32-321,1990,7.0,6.0,509.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,5.7,1.0,0.0,50.0,0.18,0.0,0.0,0.05,-0.1,-0.1,142.0,204.0,69.6,3226.0,1193.0,31.0,42.0,73.8,82.0,100.0,82.0,26.0,58.0,44.8,1.0,15.0,1.0,1.0,20.0,193.0,11.0,10.0,0.0,6.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.88,4.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,6.0,6.0,4.0,0.0,3.0,1.0,2.0,15.0,29.0,0.0,267.0,26.0,136.0,127.0,7.0,2.0,267.0,106.0,1.0,1.0,0.0,123.0,1.0,0.0,0.0,9.0,2.0,0.0,0.0,0.0,0.0,34.0,10.0,14.0,41.7 +Martin Turk,si SVN,GK,Sampdoria,19-247,2003,2.0,2.0,180.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,68.0,55.9,1335.0,1114.0,4.0,4.0,100.0,14.0,15.0,93.3,19.0,48.0,39.6,0.0,0.0,0.0,0.0,0.0,40.0,28.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,75.0,70.0,75.0,0.0,0.0,0.0,75.0,35.0,0.0,0.0,0.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,100.0 +Iyenoma Udogie,it ITA,DF,Udinese,20-148,2002,27.0,27.0,2339.0,3.0,3.0,0.0,0.0,4.0,0.0,0.12,0.12,0.23,0.12,0.23,2.8,2.8,0.11,0.11,26.0,6.0,0.0,24.0,0.23,0.12,0.5,0.11,0.2,0.2,784.0,958.0,81.8,11600.0,4127.0,450.0,511.0,88.1,283.0,338.0,83.7,30.0,41.0,73.2,30.0,58.0,28.0,4.0,94.0,820.0,137.0,20.0,1.0,2.0,24.0,4.0,0.0,0.0,0.0,113.0,1.0,34.0,78.0,3.0,53.0,6.0,4.0,6.0,7.0,0.27,6.0,0.0,1.0,0.0,0.0,64.0,39.0,38.0,21.0,5.0,31.0,5.0,26.0,31.0,22.0,0.0,1287.0,58.0,285.0,560.0,471.0,68.0,1287.0,763.0,86.0,57.0,26.0,759.0,51.0,33.0,0.0,30.0,46.0,5.0,0.0,1.0,0.0,156.0,14.0,13.0,51.9 +Samuel Umtiti,fr FRA,DF,Lecce,29-162,1993,18.0,17.0,1484.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.05,0.05,16.5,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.8,-0.8,513.0,626.0,81.9,9960.0,3380.0,181.0,212.0,85.4,250.0,270.0,92.6,72.0,119.0,60.5,1.0,39.0,4.0,1.0,40.0,587.0,36.0,34.0,0.0,5.0,2.0,0.0,0.0,0.0,0.0,2.0,3.0,9.0,8.0,0.49,6.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,39.0,25.0,17.0,22.0,0.0,18.0,10.0,8.0,19.0,60.0,0.0,813.0,87.0,385.0,408.0,27.0,9.0,813.0,374.0,10.0,7.0,0.0,384.0,6.0,4.0,0.0,24.0,22.0,0.0,0.0,0.0,0.0,117.0,30.0,22.0,57.7 +Diego Valencia,cl CHI,"MF,FW",Salernitana,23-101,2000,12.0,0.0,231.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,2.6,1.0,0.0,100.0,0.39,0.0,0.0,0.12,-0.1,-0.1,52.0,75.0,69.3,753.0,195.0,37.0,44.0,84.1,8.0,14.0,57.1,5.0,7.0,71.4,2.0,1.0,2.0,1.0,4.0,69.0,6.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,6.0,0.0,3.0,1.0,0.39,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,5.0,4.0,2.0,2.0,10.0,1.0,9.0,1.0,2.0,0.0,109.0,4.0,24.0,46.0,41.0,4.0,109.0,59.0,1.0,0.0,0.0,66.0,7.0,3.0,0.0,8.0,6.0,0.0,0.0,0.0,0.0,10.0,2.0,4.0,33.3 +Emanuele Valeri,it ITA,DF,Cremonese,24-139,1998,30.0,26.0,2278.0,2.0,2.0,0.0,0.0,3.0,0.0,0.08,0.08,0.16,0.08,0.16,1.7,1.7,0.07,0.07,25.3,5.0,2.0,14.7,0.2,0.06,0.4,0.05,0.3,0.3,594.0,1035.0,57.4,11022.0,5202.0,257.0,320.0,80.3,242.0,405.0,59.8,79.0,225.0,35.1,42.0,52.0,45.0,25.0,81.0,750.0,276.0,22.0,3.0,18.0,122.0,13.0,0.0,12.0,0.0,241.0,9.0,41.0,75.0,2.96,51.0,15.0,0.0,7.0,4.0,0.16,2.0,2.0,0.0,0.0,0.0,45.0,21.0,25.0,17.0,3.0,11.0,2.0,9.0,14.0,40.0,0.0,1321.0,52.0,336.0,511.0,503.0,49.0,1321.0,643.0,89.0,48.0,15.0,661.0,53.0,23.0,0.0,21.0,32.0,3.0,0.0,0.0,0.0,156.0,11.0,19.0,36.7 +Mattia Valoti,it ITA,"MF,FW",Monza,29-231,1993,13.0,3.0,359.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,4.0,1.0,0.0,50.0,0.25,0.0,0.0,0.08,-0.2,-0.2,110.0,143.0,76.9,1723.0,391.0,51.0,61.0,83.6,42.0,56.0,75.0,10.0,14.0,71.4,2.0,15.0,2.0,1.0,14.0,141.0,1.0,1.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,6.0,1.5,3.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,8.0,5.0,8.0,1.0,10.0,3.0,7.0,3.0,4.0,0.0,193.0,6.0,42.0,113.0,41.0,5.0,193.0,95.0,5.0,3.0,0.0,130.0,9.0,3.0,0.0,15.0,8.0,1.0,0.0,0.0,0.0,18.0,14.0,13.0,51.9 +Johan Vásquez,mx MEX,DF,Cremonese,24-185,1998,18.0,13.0,1259.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.4,0.03,0.03,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.4,-0.4,416.0,561.0,74.2,7226.0,2834.0,173.0,207.0,83.6,206.0,244.0,84.4,28.0,82.0,34.1,4.0,29.0,2.0,0.0,38.0,511.0,48.0,24.0,1.0,1.0,3.0,0.0,0.0,0.0,0.0,19.0,2.0,12.0,13.0,0.93,12.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,18.0,26.0,4.0,0.0,21.0,15.0,6.0,29.0,70.0,1.0,741.0,114.0,401.0,292.0,57.0,15.0,741.0,350.0,15.0,7.0,1.0,361.0,9.0,6.0,0.0,17.0,15.0,0.0,0.0,0.0,0.0,93.0,22.0,36.0,37.9 +Matías Vecino,uy URU,MF,Lazio,31-244,1991,27.0,13.0,1356.0,2.0,0.0,0.0,0.0,5.0,0.0,0.13,0.0,0.13,0.13,0.13,2.1,2.1,0.14,0.14,15.1,4.0,0.0,20.0,0.27,0.1,0.5,0.11,-0.1,-0.1,667.0,790.0,84.4,9974.0,2829.0,381.0,421.0,90.5,233.0,269.0,86.6,36.0,60.0,60.0,4.0,70.0,7.0,0.0,74.0,786.0,3.0,3.0,2.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,11.0,21.0,1.39,20.0,0.0,0.0,0.0,3.0,0.2,3.0,0.0,0.0,0.0,0.0,38.0,23.0,10.0,20.0,8.0,27.0,8.0,19.0,13.0,30.0,0.0,946.0,52.0,229.0,579.0,146.0,19.0,946.0,478.0,21.0,10.0,2.0,673.0,17.0,3.0,0.0,23.0,9.0,2.0,0.0,0.0,0.0,85.0,26.0,18.0,59.1 +Miguel Veloso,pt POR,MF,Hellas Verona,36-349,1986,19.0,12.0,1015.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.04,0.04,11.3,2.0,2.0,14.3,0.18,0.0,0.0,0.03,-0.5,-0.5,389.0,536.0,72.6,7743.0,2930.0,147.0,171.0,86.0,173.0,205.0,84.4,62.0,130.0,47.7,18.0,42.0,9.0,4.0,74.0,458.0,76.0,42.0,1.0,3.0,57.0,27.0,16.0,8.0,1.0,5.0,2.0,12.0,34.0,3.01,19.0,11.0,4.0,0.0,2.0,0.18,2.0,0.0,0.0,0.0,0.0,24.0,7.0,16.0,7.0,1.0,25.0,5.0,20.0,5.0,11.0,0.0,644.0,29.0,146.0,361.0,153.0,7.0,644.0,274.0,17.0,12.0,1.0,294.0,6.0,7.0,0.0,16.0,7.0,0.0,0.0,0.0,1.0,106.0,13.0,14.0,48.1 +Lorenzo Venuti,it ITA,DF,Fiorentina,28-013,1995,11.0,6.0,615.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.15,0.15,0.0,0.15,0.3,0.3,0.05,0.05,6.8,4.0,0.0,80.0,0.59,0.0,0.0,0.07,-0.3,-0.3,363.0,437.0,83.1,6649.0,2088.0,161.0,168.0,95.8,162.0,183.0,88.5,40.0,73.0,54.8,6.0,29.0,8.0,3.0,36.0,361.0,75.0,10.0,0.0,1.0,24.0,3.0,0.0,2.0,0.0,62.0,1.0,10.0,17.0,2.49,11.0,4.0,1.0,0.0,2.0,0.29,1.0,1.0,0.0,0.0,0.0,14.0,6.0,6.0,7.0,1.0,3.0,2.0,1.0,4.0,14.0,2.0,499.0,21.0,162.0,240.0,101.0,7.0,499.0,277.0,18.0,13.0,0.0,323.0,9.0,1.0,0.0,4.0,8.0,0.0,0.0,0.0,0.0,36.0,9.0,4.0,69.2 +Daniele Verde,it ITA,"FW,MF",Spezia,26-309,1996,20.0,10.0,872.0,3.0,0.0,1.0,1.0,1.0,0.0,0.31,0.0,0.31,0.21,0.21,2.8,2.0,0.29,0.2,9.7,9.0,0.0,31.0,0.93,0.07,0.22,0.07,0.2,0.0,264.0,376.0,70.2,4537.0,1437.0,121.0,149.0,81.2,109.0,142.0,76.8,28.0,61.0,45.9,15.0,22.0,14.0,5.0,32.0,296.0,78.0,14.0,1.0,5.0,56.0,34.0,20.0,10.0,0.0,15.0,2.0,6.0,38.0,3.92,23.0,11.0,0.0,1.0,1.0,0.1,0.0,1.0,0.0,0.0,0.0,3.0,3.0,2.0,0.0,1.0,6.0,0.0,6.0,8.0,3.0,0.0,464.0,9.0,60.0,193.0,216.0,24.0,463.0,261.0,15.0,8.0,6.0,292.0,14.0,11.0,0.0,2.0,13.0,1.0,0.0,0.0,0.0,47.0,0.0,2.0,0.0 +Simone Verdi,it ITA,MF,Hellas Verona,30-287,1992,18.0,8.0,794.0,4.0,0.0,1.0,1.0,3.0,0.0,0.45,0.0,0.45,0.34,0.34,2.1,1.3,0.23,0.14,8.8,8.0,6.0,38.1,0.91,0.14,0.38,0.06,1.9,1.7,200.0,309.0,64.7,3745.0,1434.0,96.0,116.0,82.8,59.0,84.0,70.2,35.0,73.0,47.9,17.0,31.0,8.0,4.0,35.0,251.0,55.0,11.0,5.0,7.0,58.0,29.0,13.0,12.0,0.0,5.0,3.0,20.0,35.0,3.98,19.0,8.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,9.0,7.0,4.0,3.0,7.0,0.0,7.0,0.0,2.0,0.0,428.0,5.0,42.0,206.0,191.0,15.0,427.0,249.0,27.0,16.0,3.0,258.0,26.0,23.0,0.0,13.0,18.0,1.0,0.0,0.0,0.0,54.0,4.0,10.0,28.6 +Valerio Verre,it ITA,MF,Sampdoria,29-104,1994,18.0,6.0,677.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.6,0.08,0.08,7.5,3.0,0.0,21.4,0.4,0.0,0.0,0.04,-0.6,-0.6,282.0,398.0,70.9,5116.0,1682.0,136.0,161.0,84.5,99.0,135.0,73.3,41.0,74.0,55.4,13.0,30.0,14.0,4.0,45.0,361.0,33.0,16.0,3.0,2.0,33.0,9.0,2.0,6.0,0.0,4.0,4.0,15.0,30.0,3.98,22.0,2.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,7.0,6.0,5.0,3.0,9.0,1.0,8.0,9.0,3.0,0.0,493.0,8.0,64.0,285.0,153.0,16.0,493.0,284.0,16.0,13.0,6.0,330.0,22.0,20.0,0.0,14.0,13.0,2.0,0.0,0.0,0.0,61.0,12.0,12.0,50.0 +Guglielmo Vicario,it ITA,GK,Empoli,26-200,1996,24.0,24.0,2160.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,751.0,1009.0,74.4,20335.0,14141.0,135.0,136.0,99.3,385.0,392.0,98.2,230.0,479.0,48.0,0.0,16.0,0.0,0.0,0.0,786.0,222.0,83.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,6.0,0.25,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,2.0,1078.0,889.0,1077.0,1.0,0.0,0.0,1078.0,656.0,0.0,0.0,0.0,586.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,23.0,6.0,2.0,75.0 +Ronaldo Vieira,gw GNB,MF,Torino,24-280,1998,1.0,0.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Ronaldo Vieira,gw GNB,MF,Sampdoria,24-280,1998,16.0,8.0,815.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.1,1.0,0.0,16.7,0.11,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Emanuel Vignato,it ITA,"MF,FW",Bologna,22-244,2000,8.0,2.0,242.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.37,0.37,0.0,0.37,0.1,0.1,0.03,0.03,2.7,1.0,0.0,100.0,0.37,0.0,0.0,0.09,-0.1,-0.1,79.0,99.0,79.8,1201.0,338.0,40.0,48.0,83.3,29.0,34.0,85.3,5.0,8.0,62.5,3.0,9.0,2.0,1.0,9.0,97.0,2.0,0.0,0.0,2.0,2.0,1.0,1.0,0.0,0.0,1.0,0.0,2.0,5.0,1.86,5.0,0.0,0.0,0.0,1.0,0.37,1.0,0.0,0.0,0.0,0.0,4.0,4.0,2.0,2.0,0.0,2.0,0.0,2.0,3.0,0.0,0.0,116.0,0.0,23.0,57.0,37.0,2.0,116.0,76.0,3.0,3.0,0.0,89.0,2.0,4.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,10.0,2.0,2.0,50.0 +Emanuel Vignato,it ITA,"MF,FW",Empoli,22-244,2000,4.0,0.0,43.0,1.0,0.0,0.0,0.0,0.0,0.0,2.09,0.0,2.09,2.09,2.09,0.2,0.2,0.4,0.4,0.5,1.0,0.0,50.0,2.09,0.5,1.0,0.1,0.8,0.8,26.0,35.0,74.3,431.0,61.0,14.0,17.0,82.4,9.0,11.0,81.8,2.0,4.0,50.0,1.0,2.0,1.0,0.0,4.0,34.0,1.0,0.0,0.0,1.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,4.0,8.37,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,41.0,1.0,7.0,16.0,18.0,3.0,41.0,23.0,1.0,1.0,1.0,28.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 +Samuele Vignato,it ITA,"FW,MF",Monza,19-060,2004,3.0,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,13.0,84.6,163.0,15.0,5.0,5.0,100.0,4.0,5.0,80.0,1.0,1.0,100.0,0.0,0.0,1.0,1.0,0.0,13.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.0,8.0,8.0,3.0,16.0,12.0,1.0,0.0,0.0,14.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Tonny Vilhena,nl NED,MF,Salernitana,28-112,1995,27.0,23.0,1899.0,4.0,1.0,0.0,0.0,4.0,0.0,0.19,0.05,0.24,0.19,0.24,2.3,2.3,0.11,0.11,21.1,8.0,1.0,29.6,0.38,0.15,0.5,0.09,1.7,1.7,544.0,715.0,76.1,8771.0,2705.0,284.0,326.0,87.1,182.0,215.0,84.7,49.0,101.0,48.5,17.0,49.0,12.0,5.0,69.0,654.0,57.0,23.0,1.0,8.0,45.0,26.0,4.0,15.0,0.0,8.0,4.0,20.0,47.0,2.23,33.0,8.0,0.0,3.0,3.0,0.14,3.0,0.0,0.0,0.0,0.0,39.0,19.0,12.0,23.0,4.0,13.0,5.0,8.0,14.0,17.0,0.0,910.0,24.0,168.0,500.0,256.0,18.0,910.0,492.0,28.0,32.0,3.0,567.0,35.0,23.0,0.0,28.0,25.0,1.0,0.0,1.0,0.0,111.0,9.0,28.0,24.3 +Gonzalo Villar,es ESP,MF,Sampdoria,25-033,1998,15.0,8.0,696.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.7,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,317.0,381.0,83.2,5395.0,1357.0,136.0,162.0,84.0,145.0,165.0,87.9,27.0,41.0,65.9,0.0,25.0,1.0,0.0,26.0,345.0,35.0,31.0,0.0,0.0,7.0,3.0,1.0,2.0,0.0,0.0,1.0,1.0,9.0,1.17,7.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,7.0,4.0,8.0,1.0,11.0,3.0,8.0,4.0,9.0,1.0,453.0,19.0,102.0,288.0,65.0,1.0,453.0,247.0,9.0,6.0,0.0,274.0,10.0,4.0,0.0,17.0,18.0,0.0,0.0,1.0,0.0,55.0,9.0,5.0,64.3 +Matías Viña,uy URU,DF,Roma,25-167,1997,3.0,1.0,55.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,30.0,73.3,392.0,68.0,8.0,10.0,80.0,11.0,16.0,68.8,2.0,3.0,66.7,1.0,3.0,0.0,0.0,2.0,20.0,10.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,10.0,0.0,0.0,4.0,6.55,3.0,0.0,0.0,0.0,1.0,1.64,1.0,0.0,0.0,0.0,0.0,3.0,1.0,1.0,1.0,1.0,5.0,1.0,4.0,2.0,5.0,0.0,49.0,3.0,17.0,18.0,14.0,1.0,49.0,19.0,2.0,1.0,1.0,14.0,3.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,100.0 +Dušan Vlahović,rs SRB,FW,Juventus,23-087,2000,22.0,19.0,1645.0,8.0,2.0,2.0,3.0,0.0,0.0,0.44,0.11,0.55,0.33,0.44,8.5,6.1,0.47,0.34,18.3,16.0,4.0,29.1,0.88,0.11,0.38,0.11,-0.5,-0.1,241.0,337.0,71.5,4098.0,719.0,109.0,142.0,76.8,96.0,120.0,80.0,22.0,36.0,61.1,19.0,19.0,12.0,1.0,37.0,310.0,26.0,1.0,1.0,4.0,6.0,0.0,0.0,0.0,0.0,0.0,1.0,14.0,40.0,2.19,24.0,0.0,10.0,3.0,6.0,0.33,2.0,0.0,3.0,1.0,0.0,9.0,5.0,0.0,5.0,4.0,9.0,3.0,6.0,3.0,7.0,1.0,529.0,11.0,30.0,254.0,254.0,83.0,526.0,295.0,18.0,20.0,10.0,400.0,50.0,32.0,0.0,17.0,19.0,20.0,1.0,0.0,0.0,49.0,32.0,43.0,42.7 +Nikola Vlašić,hr CRO,"MF,FW",Torino,25-203,1997,27.0,23.0,2190.0,4.0,3.0,0.0,0.0,1.0,0.0,0.16,0.12,0.29,0.16,0.29,3.8,3.8,0.16,0.16,24.3,16.0,1.0,36.4,0.66,0.09,0.25,0.09,0.2,0.2,631.0,795.0,79.4,8960.0,1840.0,358.0,411.0,87.1,197.0,237.0,83.1,34.0,48.0,70.8,49.0,54.0,19.0,7.0,83.0,774.0,17.0,3.0,1.0,7.0,37.0,7.0,4.0,2.0,0.0,1.0,4.0,37.0,91.0,3.74,76.0,4.0,5.0,1.0,5.0,0.21,4.0,0.0,0.0,1.0,0.0,26.0,18.0,7.0,14.0,5.0,18.0,1.0,17.0,6.0,9.0,0.0,1044.0,12.0,102.0,444.0,520.0,93.0,1044.0,630.0,64.0,50.0,20.0,779.0,83.0,40.0,0.0,24.0,27.0,13.0,0.0,0.0,0.0,110.0,21.0,41.0,33.9 +Joel Voelkerling Persson,se SWE,FW,Lecce,20-100,2003,7.0,0.0,96.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.04,1.1,0.0,0.0,0.0,0.0,0.0,0.0,0.04,0.0,0.0,22.0,35.0,62.9,221.0,55.0,14.0,17.0,82.4,5.0,12.0,41.7,0.0,1.0,0.0,1.0,0.0,1.0,0.0,3.0,34.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.87,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,44.0,0.0,0.0,24.0,21.0,3.0,44.0,19.0,1.0,1.0,0.0,32.0,4.0,3.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,6.0,8.0,10.0,44.4 +Mërgim Vojvoda,xk KVX,DF,Torino,28-083,1995,23.0,13.0,1275.0,0.0,4.0,0.0,0.0,3.0,0.0,0.0,0.28,0.28,0.0,0.28,1.1,1.1,0.08,0.08,14.2,5.0,0.0,31.3,0.35,0.0,0.0,0.07,-1.1,-1.1,650.0,851.0,76.4,10895.0,4597.0,334.0,386.0,86.5,258.0,326.0,79.1,47.0,95.0,49.5,16.0,66.0,19.0,3.0,93.0,712.0,135.0,11.0,3.0,12.0,34.0,8.0,5.0,0.0,0.0,116.0,4.0,14.0,43.0,3.03,34.0,4.0,1.0,0.0,7.0,0.49,6.0,0.0,0.0,0.0,0.0,18.0,10.0,12.0,5.0,1.0,7.0,1.0,6.0,9.0,23.0,1.0,976.0,27.0,230.0,457.0,298.0,27.0,976.0,525.0,18.0,24.0,4.0,633.0,13.0,16.0,0.0,17.0,6.0,2.0,0.0,0.0,0.0,81.0,9.0,16.0,36.0 +Cristian Volpato,it ITA,MF,Roma,19-161,2003,6.0,2.0,205.0,1.0,1.0,0.0,0.0,0.0,0.0,0.44,0.44,0.88,0.44,0.88,0.4,0.4,0.15,0.15,2.3,1.0,0.0,25.0,0.44,0.25,1.0,0.09,0.6,0.6,73.0,94.0,77.7,1296.0,387.0,34.0,38.0,89.5,27.0,33.0,81.8,11.0,18.0,61.1,4.0,8.0,5.0,2.0,17.0,89.0,5.0,1.0,1.0,0.0,9.0,2.0,1.0,1.0,0.0,1.0,0.0,3.0,6.0,2.63,5.0,1.0,0.0,0.0,1.0,0.44,1.0,0.0,0.0,0.0,0.0,3.0,2.0,1.0,1.0,1.0,2.0,0.0,2.0,2.0,0.0,0.0,123.0,2.0,11.0,59.0,55.0,10.0,123.0,88.0,7.0,7.0,3.0,96.0,9.0,5.0,0.0,6.0,6.0,0.0,0.0,0.0,0.0,18.0,1.0,0.0,100.0 +Lukáš Vorlický,cz CZE,"MF,FW",Atalanta,21-097,2002,3.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.18,0.18,0.3,1.0,0.0,100.0,3.75,0.0,0.0,0.05,0.0,0.0,19.0,31.0,61.3,271.0,44.0,13.0,16.0,81.3,3.0,9.0,33.3,2.0,3.0,66.7,0.0,1.0,1.0,0.0,3.0,30.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,3.75,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,36.0,0.0,5.0,14.0,19.0,2.0,36.0,30.0,2.0,4.0,2.0,26.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,2.0,50.0 +Aster Vranckx,be BEL,MF,Milan,20-203,2002,8.0,1.0,166.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,1.8,0.0,0.0,0.0,0.0,0.0,0.0,0.03,-0.1,-0.1,50.0,72.0,69.4,718.0,196.0,27.0,33.0,81.8,18.0,23.0,78.3,3.0,10.0,30.0,1.0,2.0,2.0,1.0,3.0,72.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,1.07,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,1.0,1.0,3.0,1.0,2.0,2.0,1.0,0.0,96.0,0.0,18.0,54.0,24.0,3.0,96.0,60.0,2.0,1.0,1.0,65.0,2.0,7.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,7.0,3.0,4.0,42.9 +Stefan de Vrij,nl NED,DF,Inter,31-079,1992,20.0,18.0,1568.0,1.0,0.0,0.0,0.0,3.0,0.0,0.06,0.0,0.06,0.06,0.06,1.5,1.5,0.09,0.09,17.4,4.0,0.0,23.5,0.23,0.06,0.25,0.09,-0.5,-0.5,872.0,982.0,88.8,16045.0,4789.0,273.0,295.0,92.5,535.0,568.0,94.2,53.0,91.0,58.2,1.0,39.0,0.0,0.0,52.0,963.0,18.0,16.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,3.0,11.0,0.63,8.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,14.0,14.0,9.0,0.0,13.0,8.0,5.0,34.0,64.0,0.0,1158.0,102.0,536.0,594.0,30.0,21.0,1158.0,651.0,11.0,2.0,0.0,747.0,3.0,3.0,0.0,11.0,7.0,0.0,0.0,1.0,0.0,84.0,71.0,23.0,75.5 +Walace,br BRA,MF,Udinese,28-021,1995,30.0,30.0,2619.0,0.0,1.0,0.0,0.0,5.0,0.0,0.0,0.03,0.03,0.0,0.03,1.0,1.0,0.04,0.04,29.1,6.0,0.0,21.4,0.21,0.0,0.0,0.04,-1.0,-1.0,1196.0,1445.0,82.8,22066.0,6980.0,481.0,556.0,86.5,529.0,597.0,88.6,152.0,212.0,71.7,14.0,132.0,10.0,3.0,144.0,1378.0,61.0,43.0,1.0,19.0,19.0,1.0,0.0,0.0,0.0,6.0,6.0,29.0,44.0,1.51,37.0,1.0,4.0,2.0,6.0,0.21,5.0,0.0,0.0,1.0,0.0,72.0,35.0,35.0,29.0,8.0,40.0,11.0,29.0,43.0,42.0,0.0,1781.0,101.0,512.0,1062.0,225.0,12.0,1781.0,996.0,45.0,26.0,0.0,1047.0,33.0,28.0,0.0,32.0,30.0,0.0,0.0,1.0,0.0,256.0,18.0,24.0,42.9 +Sebastian Walukiewicz,pl POL,DF,Empoli,23-020,2000,6.0,1.0,212.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.15,0.15,2.4,1.0,0.0,50.0,0.42,0.0,0.0,0.17,-0.3,-0.3,115.0,131.0,87.8,2286.0,893.0,37.0,38.0,97.4,65.0,69.0,94.2,11.0,19.0,57.9,1.0,5.0,1.0,0.0,7.0,127.0,2.0,2.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,0.42,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,3.0,1.0,0.0,4.0,4.0,0.0,1.0,15.0,0.0,158.0,34.0,99.0,54.0,5.0,3.0,158.0,80.0,4.0,1.0,0.0,104.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,2.0,2.0,50.0 +Georginio Wijnaldum,nl NED,MF,Roma,32-165,1990,10.0,7.0,583.0,2.0,0.0,0.0,0.0,0.0,0.0,0.31,0.0,0.31,0.31,0.31,1.9,1.9,0.3,0.3,6.5,4.0,0.0,25.0,0.62,0.13,0.5,0.12,0.1,0.1,144.0,169.0,85.2,1915.0,390.0,87.0,99.0,87.9,46.0,52.0,88.5,4.0,5.0,80.0,5.0,6.0,4.0,2.0,12.0,160.0,8.0,6.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,15.0,2.33,12.0,0.0,1.0,1.0,3.0,0.47,1.0,0.0,0.0,1.0,0.0,2.0,1.0,0.0,2.0,0.0,4.0,1.0,3.0,2.0,2.0,0.0,218.0,7.0,26.0,128.0,67.0,22.0,218.0,124.0,10.0,8.0,3.0,149.0,7.0,5.0,0.0,3.0,6.0,1.0,1.0,0.0,0.0,22.0,12.0,3.0,80.0 +Harry Winks,eng ENG,MF,Sampdoria,27-082,1996,14.0,13.0,1185.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.3,0.02,0.02,13.2,0.0,0.0,0.0,0.0,0.0,0.0,0.04,-0.3,-0.3,559.0,690.0,81.0,10473.0,3796.0,224.0,251.0,89.2,248.0,281.0,88.3,74.0,125.0,59.2,8.0,53.0,7.0,1.0,84.0,627.0,62.0,36.0,0.0,11.0,38.0,19.0,7.0,7.0,0.0,0.0,1.0,13.0,20.0,1.52,12.0,5.0,1.0,1.0,1.0,0.08,1.0,0.0,0.0,0.0,0.0,18.0,10.0,3.0,13.0,2.0,8.0,6.0,2.0,16.0,17.0,0.0,796.0,34.0,194.0,488.0,127.0,4.0,796.0,496.0,15.0,17.0,0.0,512.0,14.0,10.0,0.0,13.0,9.0,1.0,0.0,0.0,0.0,83.0,4.0,9.0,30.8 +Przemysław Wiśniewski,pl POL,DF,Spezia,24-272,1998,7.0,4.0,373.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.2,0.04,0.04,4.1,0.0,0.0,0.0,0.0,0.0,0.0,0.16,-0.2,-0.2,169.0,208.0,81.3,3386.0,1021.0,43.0,47.0,91.5,104.0,113.0,92.0,19.0,41.0,46.3,1.0,9.0,1.0,0.0,5.0,196.0,12.0,8.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,3.0,1.0,0.24,0.0,0.0,1.0,0.0,1.0,0.24,0.0,0.0,1.0,0.0,0.0,4.0,1.0,3.0,1.0,0.0,8.0,2.0,6.0,3.0,25.0,0.0,257.0,37.0,153.0,97.0,7.0,1.0,257.0,135.0,0.0,0.0,0.0,162.0,3.0,1.0,0.0,8.0,2.0,0.0,0.0,0.0,1.0,18.0,11.0,6.0,64.7 +Gerard Yepes,es ESP,MF,Sampdoria,20-243,2002,5.0,2.0,198.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.03,0.03,2.2,0.0,0.0,0.0,0.0,0.0,0.0,0.06,-0.1,-0.1,77.0,105.0,73.3,1205.0,271.0,43.0,55.0,78.2,28.0,34.0,82.4,5.0,10.0,50.0,1.0,5.0,0.0,0.0,7.0,99.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.36,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,2.0,3.0,0.0,3.0,0.0,3.0,4.0,3.0,0.0,128.0,6.0,20.0,90.0,19.0,2.0,128.0,59.0,3.0,3.0,1.0,72.0,3.0,1.0,0.0,7.0,4.0,0.0,0.0,0.0,0.0,13.0,5.0,2.0,71.4 +Mattia Zaccagni,it ITA,FW,Lazio,27-313,1995,28.0,27.0,2242.0,10.0,5.0,1.0,1.0,7.0,0.0,0.4,0.2,0.6,0.36,0.56,6.2,5.4,0.25,0.22,24.9,22.0,1.0,46.8,0.88,0.19,0.41,0.11,3.8,3.6,670.0,837.0,80.0,8749.0,1806.0,441.0,493.0,89.5,173.0,214.0,80.8,20.0,44.0,45.5,25.0,36.0,21.0,2.0,61.0,818.0,17.0,4.0,1.0,7.0,49.0,6.0,4.0,1.0,0.0,7.0,2.0,28.0,67.0,2.69,46.0,1.0,5.0,8.0,9.0,0.36,6.0,1.0,1.0,1.0,0.0,34.0,21.0,16.0,14.0,4.0,31.0,3.0,28.0,20.0,10.0,1.0,1121.0,12.0,134.0,481.0,526.0,98.0,1120.0,792.0,94.0,50.0,33.0,912.0,44.0,25.0,0.0,43.0,95.0,4.0,0.0,1.0,0.0,97.0,9.0,22.0,29.0 +Denis Zakaria,ch SUI,MF,Juventus,26-156,1996,2.0,1.0,123.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32.0,38.0,84.2,469.0,119.0,18.0,20.0,90.0,11.0,12.0,91.7,2.0,2.0,100.0,0.0,2.0,0.0,0.0,3.0,37.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,1.0,0.73,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,1.0,4.0,1.0,2.0,0.0,2.0,2.0,1.0,0.0,51.0,2.0,14.0,30.0,9.0,1.0,51.0,27.0,2.0,3.0,0.0,36.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0 +Nicola Zalewski,pl POL,DF,Roma,21-092,2002,27.0,20.0,1700.0,1.0,1.0,0.0,0.0,2.0,0.0,0.05,0.05,0.11,0.05,0.11,1.0,1.0,0.05,0.05,18.9,6.0,0.0,28.6,0.32,0.05,0.17,0.05,0.0,0.0,716.0,975.0,73.4,12568.0,3881.0,331.0,384.0,86.2,289.0,369.0,78.3,79.0,168.0,47.0,8.0,40.0,19.0,5.0,62.0,776.0,194.0,14.0,2.0,12.0,54.0,7.0,4.0,2.0,0.0,173.0,5.0,19.0,43.0,2.28,26.0,6.0,5.0,0.0,6.0,0.32,4.0,1.0,1.0,0.0,0.0,35.0,19.0,19.0,13.0,3.0,24.0,1.0,23.0,24.0,16.0,0.0,1177.0,24.0,337.0,536.0,321.0,25.0,1177.0,639.0,63.0,41.0,15.0,727.0,21.0,11.0,0.0,15.0,38.0,1.0,0.0,0.0,0.0,109.0,3.0,12.0,20.0 +Andre-Frank Zambo Anguissa,cm CMR,MF,Napoli,27-160,1995,29.0,29.0,2460.0,2.0,4.0,0.0,0.0,3.0,0.0,0.07,0.15,0.22,0.07,0.22,3.5,3.5,0.13,0.13,27.3,9.0,1.0,26.5,0.33,0.06,0.22,0.1,-1.5,-1.5,1571.0,1790.0,87.8,22389.0,5355.0,897.0,970.0,92.5,526.0,593.0,88.7,68.0,105.0,64.8,28.0,138.0,20.0,0.0,153.0,1767.0,21.0,15.0,2.0,1.0,19.0,0.0,0.0,0.0,0.0,6.0,2.0,20.0,64.0,2.34,55.0,0.0,4.0,1.0,8.0,0.29,8.0,0.0,0.0,0.0,0.0,47.0,28.0,16.0,24.0,7.0,45.0,3.0,42.0,36.0,20.0,0.0,2115.0,39.0,355.0,1215.0,578.0,64.0,2115.0,1390.0,47.0,42.0,7.0,1671.0,65.0,42.0,0.0,34.0,37.0,2.0,1.0,0.0,0.0,187.0,35.0,26.0,57.4 +Luca Zanimacchia,it ITA,"MF,FW",Cremonese,24-280,1998,15.0,9.0,746.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.2,1.2,0.14,0.14,8.3,4.0,2.0,21.1,0.48,0.0,0.0,0.06,-1.2,-1.2,213.0,312.0,68.3,3230.0,932.0,122.0,155.0,78.7,68.0,102.0,66.7,18.0,37.0,48.6,14.0,12.0,12.0,2.0,26.0,271.0,40.0,8.0,0.0,1.0,46.0,21.0,9.0,10.0,0.0,2.0,1.0,9.0,33.0,3.99,19.0,6.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.0,13.0,11.0,6.0,4.0,9.0,0.0,9.0,9.0,5.0,0.0,416.0,8.0,54.0,156.0,211.0,33.0,416.0,208.0,22.0,20.0,8.0,242.0,20.0,12.0,0.0,9.0,10.0,2.0,1.0,0.0,0.0,48.0,3.0,10.0,23.1 +Nicolò Zaniolo,it ITA,"MF,FW",Roma,23-297,1999,13.0,12.0,890.0,1.0,0.0,0.0,0.0,4.0,0.0,0.1,0.0,0.1,0.1,0.1,2.9,2.9,0.29,0.29,9.9,7.0,0.0,25.0,0.71,0.04,0.14,0.1,-1.9,-1.9,125.0,196.0,63.8,2173.0,488.0,63.0,85.0,74.1,46.0,67.0,68.7,15.0,26.0,57.7,8.0,10.0,7.0,1.0,19.0,184.0,11.0,4.0,3.0,6.0,20.0,5.0,4.0,1.0,0.0,1.0,1.0,12.0,27.0,2.73,19.0,0.0,4.0,3.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,7.0,5.0,2.0,4.0,1.0,11.0,0.0,11.0,2.0,0.0,0.0,344.0,1.0,36.0,153.0,165.0,45.0,344.0,246.0,34.0,27.0,10.0,267.0,39.0,35.0,0.0,22.0,32.0,4.0,0.0,0.0,0.0,39.0,5.0,8.0,38.5 +Alessandro Zanoli,it ITA,DF,Sampdoria,22-204,2000,15.0,9.0,909.0,1.0,2.0,0.0,0.0,2.0,0.0,0.1,0.2,0.3,0.1,0.3,1.4,1.4,0.14,0.14,10.1,4.0,0.0,40.0,0.4,0.1,0.25,0.14,-0.4,-0.4,340.0,443.0,76.7,5679.0,1860.0,161.0,180.0,89.4,145.0,181.0,80.1,26.0,52.0,50.0,9.0,13.0,11.0,6.0,23.0,377.0,65.0,7.0,0.0,1.0,25.0,0.0,0.0,0.0,0.0,58.0,1.0,14.0,24.0,2.38,18.0,1.0,0.0,0.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,6.0,3.0,5.0,1.0,0.0,10.0,7.0,3.0,6.0,21.0,0.0,532.0,43.0,195.0,205.0,141.0,13.0,532.0,295.0,32.0,19.0,5.0,327.0,10.0,6.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,46.0,8.0,16.0,33.3 +Alessandro Zanoli,it ITA,DF,Napoli,22-204,2000,1.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.47,0.47,0.1,1.0,0.0,100.0,7.5,0.0,0.0,0.06,-0.1,-0.1,9.0,11.0,81.8,125.0,20.0,4.0,5.0,80.0,5.0,5.0,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,7.5,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,14.0,0.0,5.0,6.0,3.0,1.0,14.0,7.0,1.0,1.0,1.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +Mattia Zanotti,it ITA,DF,Inter,20-104,2003,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,100.0,18.0,15.0,0.0,0.0,0.0,1.0,1.0,100.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,90.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Duván Zapata,co COL,FW,Atalanta,32-024,1991,21.0,13.0,1107.0,1.0,2.0,0.0,0.0,1.0,0.0,0.08,0.16,0.24,0.08,0.24,2.7,2.7,0.22,0.22,12.3,6.0,0.0,19.4,0.49,0.03,0.17,0.09,-1.7,-1.7,231.0,343.0,67.3,3699.0,734.0,125.0,169.0,74.0,79.0,110.0,71.8,21.0,33.0,63.6,15.0,16.0,13.0,4.0,34.0,315.0,24.0,1.0,0.0,5.0,25.0,1.0,0.0,0.0,0.0,5.0,4.0,11.0,36.0,2.92,28.0,0.0,0.0,3.0,5.0,0.41,4.0,0.0,0.0,1.0,0.0,3.0,2.0,0.0,1.0,2.0,5.0,2.0,3.0,3.0,15.0,0.0,473.0,15.0,24.0,211.0,243.0,65.0,473.0,328.0,30.0,15.0,17.0,399.0,37.0,36.0,0.0,16.0,33.0,5.0,1.0,0.0,0.0,31.0,34.0,32.0,51.5 +Davide Zappacosta,it ITA,DF,Atalanta,30-318,1992,14.0,11.0,877.0,1.0,1.0,0.0,0.0,4.0,0.0,0.1,0.1,0.21,0.1,0.21,0.9,0.9,0.1,0.1,9.7,4.0,0.0,30.8,0.41,0.08,0.25,0.07,0.1,0.1,469.0,563.0,83.3,6643.0,2292.0,281.0,301.0,93.4,156.0,194.0,80.4,19.0,32.0,59.4,9.0,28.0,10.0,8.0,38.0,438.0,125.0,5.0,0.0,0.0,36.0,2.0,2.0,0.0,0.0,118.0,0.0,14.0,21.0,2.16,18.0,0.0,1.0,1.0,4.0,0.41,3.0,0.0,1.0,0.0,0.0,21.0,14.0,11.0,7.0,3.0,15.0,1.0,14.0,6.0,9.0,0.0,691.0,19.0,152.0,264.0,284.0,35.0,691.0,396.0,33.0,29.0,9.0,436.0,22.0,5.0,0.0,14.0,8.0,1.0,0.0,0.0,0.0,49.0,0.0,7.0,0.0 +Karim Zedadka,fr FRA,"FW,DF",Napoli,22-320,2000,2.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,8.0,75.0,89.0,0.0,5.0,6.0,83.3,1.0,2.0,50.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,4.0,4.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,0.0,0.0,5.0,4.0,0.0,9.0,5.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Deyovaisio Zeefuik,nl NED,DF,Hellas Verona,25-045,1998,1.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,7.0,71.4,83.0,14.0,2.0,2.0,100.0,3.0,3.0,100.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.0,0.0,2.0,6.0,3.0,2.0,10.0,6.0,1.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +Marvin Zeegelaar,nl NED,DF,Udinese,32-256,1990,2.0,1.0,92.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.14,0.14,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,-0.1,-0.1,56.0,63.0,88.9,990.0,346.0,25.0,29.0,86.2,27.0,27.0,100.0,4.0,7.0,57.1,1.0,0.0,0.0,0.0,3.0,59.0,4.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,1.0,0.98,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,2.0,0.0,76.0,6.0,38.0,30.0,8.0,3.0,76.0,54.0,0.0,0.0,0.0,57.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,0.0,100.0 +Alessio Zerbin,it ITA,"FW,MF",Napoli,24-053,1999,7.0,0.0,68.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.05,0.8,1.0,0.0,100.0,1.32,0.0,0.0,0.04,0.0,0.0,13.0,24.0,54.2,147.0,34.0,9.0,14.0,64.3,2.0,5.0,40.0,1.0,1.0,100.0,0.0,2.0,0.0,0.0,0.0,23.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,3.0,4.0,3.0,1.0,2.0,0.0,2.0,0.0,2.0,0.0,44.0,4.0,13.0,24.0,7.0,2.0,44.0,26.0,0.0,0.0,0.0,27.0,3.0,3.0,0.0,5.0,3.0,1.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0 +Piotr Zieliński,pl POL,MF,Napoli,28-340,1994,30.0,22.0,1832.0,3.0,7.0,0.0,0.0,2.0,0.0,0.15,0.34,0.49,0.15,0.49,2.8,2.8,0.14,0.14,20.4,17.0,4.0,38.6,0.84,0.07,0.18,0.06,0.2,0.2,974.0,1181.0,82.5,13892.0,3956.0,577.0,632.0,91.3,269.0,324.0,83.0,66.0,121.0,54.5,60.0,88.0,32.0,7.0,94.0,1057.0,121.0,27.0,0.0,5.0,86.0,68.0,24.0,26.0,1.0,3.0,3.0,23.0,102.0,5.0,64.0,26.0,3.0,6.0,10.0,0.49,6.0,3.0,1.0,0.0,0.0,14.0,8.0,1.0,9.0,4.0,11.0,4.0,7.0,14.0,6.0,0.0,1349.0,17.0,162.0,637.0,563.0,43.0,1349.0,831.0,48.0,55.0,9.0,1038.0,18.0,14.0,0.0,15.0,29.0,2.0,0.0,0.0,0.0,77.0,4.0,10.0,28.6 +David Zima,cz CZE,DF,Torino,22-168,2000,9.0,4.0,406.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.8,0.17,0.17,4.5,2.0,0.0,50.0,0.44,0.0,0.0,0.2,-0.8,-0.8,154.0,197.0,78.2,2708.0,743.0,60.0,77.0,77.9,73.0,86.0,84.9,12.0,20.0,60.0,2.0,6.0,3.0,0.0,12.0,194.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,0.89,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.0,5.0,5.0,4.0,0.0,7.0,2.0,5.0,9.0,11.0,2.0,245.0,20.0,114.0,121.0,12.0,5.0,245.0,111.0,3.0,2.0,0.0,129.0,1.0,2.0,0.0,4.0,2.0,0.0,0.0,0.0,0.0,24.0,15.0,9.0,62.5 +Joshua Zirkzee,nl NED,FW,Bologna,21-338,2001,14.0,5.0,691.0,1.0,2.0,0.0,0.0,1.0,0.0,0.13,0.26,0.39,0.13,0.39,2.7,2.7,0.35,0.35,7.7,6.0,0.0,26.1,0.78,0.04,0.17,0.12,-1.7,-1.7,175.0,225.0,77.8,2215.0,377.0,118.0,138.0,85.5,41.0,52.0,78.8,6.0,7.0,85.7,13.0,10.0,5.0,0.0,15.0,210.0,14.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,9.0,32.0,4.17,20.0,0.0,4.0,1.0,4.0,0.52,4.0,0.0,0.0,0.0,0.0,4.0,2.0,1.0,1.0,2.0,2.0,1.0,1.0,4.0,3.0,0.0,323.0,6.0,16.0,152.0,157.0,29.0,323.0,198.0,9.0,15.0,5.0,266.0,30.0,19.0,0.0,7.0,7.0,2.0,0.0,0.0,0.0,14.0,10.0,24.0,29.4 +Jeroen Zoet,nl NED,GK,Spezia,32-109,1991,4.0,3.0,244.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.7,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,59.0,99.0,59.6,1678.0,1438.0,18.0,18.0,100.0,21.0,23.0,91.3,19.0,57.0,33.3,0.0,3.0,0.0,0.0,0.0,83.0,16.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.37,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,103.0,88.0,102.0,1.0,0.0,0.0,103.0,68.0,0.0,0.0,0.0,50.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0 +Nadir Zortea,it ITA,"DF,MF",Atalanta,23-310,1999,9.0,1.0,222.0,1.0,0.0,0.0,0.0,1.0,0.0,0.41,0.0,0.41,0.41,0.41,0.1,0.1,0.05,0.05,2.5,3.0,0.0,75.0,1.22,0.25,0.33,0.03,0.9,0.9,60.0,98.0,61.2,937.0,602.0,31.0,38.0,81.6,22.0,35.0,62.9,5.0,13.0,38.5,3.0,3.0,3.0,1.0,9.0,83.0,15.0,0.0,1.0,0.0,10.0,0.0,0.0,0.0,0.0,15.0,0.0,6.0,8.0,3.27,5.0,0.0,1.0,0.0,1.0,0.41,1.0,0.0,0.0,0.0,0.0,10.0,5.0,3.0,7.0,0.0,4.0,0.0,4.0,0.0,4.0,0.0,153.0,7.0,45.0,50.0,60.0,7.0,153.0,78.0,11.0,7.0,2.0,82.0,6.0,2.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,14.0,5.0,1.0,83.3 +Nadir Zortea,it ITA,DF,Sassuolo,23-310,1999,6.0,5.0,463.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.02,0.02,5.1,1.0,0.0,25.0,0.19,0.0,0.0,0.02,-0.1,-0.1,232.0,298.0,77.9,3636.0,1462.0,125.0,137.0,91.2,90.0,111.0,81.1,13.0,39.0,33.3,5.0,16.0,7.0,4.0,26.0,244.0,54.0,6.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,48.0,0.0,7.0,9.0,1.75,7.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,8.0,10.0,2.0,1.0,6.0,3.0,3.0,3.0,19.0,0.0,375.0,22.0,117.0,176.0,84.0,4.0,375.0,200.0,16.0,12.0,1.0,220.0,8.0,3.0,0.0,9.0,5.0,0.0,0.0,0.0,0.0,27.0,3.0,8.0,27.3 +Petar Zovko,ba BIH,GK,Spezia,21-031,2002,1.0,0.0,74.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22.0,36.0,61.1,683.0,458.0,4.0,4.0,100.0,9.0,9.0,100.0,9.0,23.0,39.1,0.0,1.0,0.0,0.0,0.0,21.0,15.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38.0,29.0,36.0,2.0,0.0,0.0,38.0,18.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0 +Szymon Żurkowski,pl POL,MF,Fiorentina,25-212,1997,2.0,0.0,32.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.81,2.81,0.0,2.81,0.1,0.1,0.33,0.33,0.4,1.0,0.0,50.0,2.81,0.0,0.0,0.06,-0.1,-0.1,17.0,19.0,89.5,298.0,21.0,6.0,7.0,85.7,7.0,7.0,100.0,2.0,2.0,100.0,1.0,1.0,1.0,0.0,1.0,19.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,5.63,2.0,0.0,0.0,0.0,1.0,2.81,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0,0.0,1.0,8.0,16.0,3.0,25.0,16.0,3.0,1.0,1.0,18.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,50.0 +Szymon Żurkowski,pl POL,MF,Spezia,25-212,1997,6.0,2.0,240.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1,0.1,0.05,0.05,2.7,1.0,0.0,33.3,0.37,0.0,0.0,0.04,-0.1,-0.1,46.0,66.0,69.7,618.0,157.0,28.0,33.0,84.8,15.0,18.0,83.3,1.0,5.0,20.0,0.0,7.0,0.0,0.0,7.0,66.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,2.0,0.75,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,3.0,1.0,4.0,1.0,6.0,2.0,4.0,4.0,6.0,0.0,109.0,15.0,31.0,49.0,30.0,3.0,109.0,52.0,8.0,1.0,2.0,49.0,5.0,1.0,0.0,8.0,5.0,0.0,0.0,0.0,0.0,18.0,3.0,2.0,60.0 +Milan Đurić,ba BIH,FW,Hellas Verona,32-338,1990,21.0,8.0,863.0,1.0,0.0,0.0,0.0,3.0,0.0,0.1,0.0,0.1,0.1,0.1,0.6,0.6,0.06,0.06,9.6,2.0,0.0,22.2,0.21,0.11,0.5,0.07,0.4,0.4,177.0,339.0,52.2,2077.0,479.0,120.0,220.0,54.5,39.0,79.0,49.4,3.0,5.0,60.0,12.0,18.0,3.0,0.0,20.0,337.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,23.0,2.39,19.0,0.0,2.0,2.0,1.0,0.1,1.0,0.0,0.0,0.0,0.0,2.0,1.0,1.0,0.0,1.0,5.0,3.0,2.0,1.0,17.0,0.0,420.0,22.0,38.0,205.0,178.0,40.0,420.0,176.0,1.0,4.0,2.0,367.0,21.0,7.0,0.0,21.0,20.0,4.0,0.0,0.0,0.0,19.0,132.0,35.0,79.0 +Filip Đuričić,rs SRB,"MF,FW",Sampdoria,31-085,1992,28.0,24.0,1871.0,3.0,0.0,0.0,0.0,8.0,0.0,0.14,0.0,0.14,0.14,0.14,2.4,2.4,0.12,0.12,20.8,11.0,1.0,45.8,0.53,0.13,0.27,0.1,0.6,0.6,504.0,658.0,76.6,8388.0,1961.0,257.0,300.0,85.7,186.0,222.0,83.8,46.0,77.0,59.7,32.0,46.0,18.0,4.0,74.0,609.0,48.0,15.0,1.0,3.0,39.0,22.0,5.0,13.0,0.0,7.0,1.0,32.0,66.0,3.18,43.0,9.0,3.0,5.0,3.0,0.14,3.0,0.0,0.0,0.0,0.0,38.0,20.0,14.0,20.0,4.0,14.0,4.0,10.0,7.0,6.0,0.0,870.0,11.0,116.0,420.0,353.0,30.0,870.0,519.0,35.0,41.0,3.0,596.0,54.0,40.0,0.0,31.0,41.0,0.0,0.0,0.0,0.0,98.0,11.0,21.0,34.4 diff --git a/fbref_data/teams.csv b/fbref_data/teams.csv index 7a59b86..0055275 100644 --- a/fbref_data/teams.csv +++ b/fbref_data/teams.csv @@ -1,21 +1,21 @@ team,players_used,possession,games,games_starts,minutes,goals,assists,pens_made,pens_att,cards_yellow,cards_red,goals_per90,assists_per90,goals_assists_per90,goals_pens_per90,goals_assists_pens_per90,xg,npxg,xg_per90,npxg_per90,gk_games,gk_games_starts,gk_minutes,gk_goals_against,gk_goals_against_per90,gk_shots_on_target_against,gk_saves,gk_save_pct,gk_wins,gk_ties,gk_losses,gk_clean_sheets,gk_clean_sheets_pct,gk_pens_att,gk_pens_allowed,gk_pens_saved,gk_pens_missed,minutes_90s,gk_free_kick_goals_against,gk_corner_kick_goals_against,gk_own_goals_against,gk_psxg,gk_psnpxg_per_shot_on_target_against,gk_psxg_net,gk_psxg_net_per90,gk_passes_completed_launched,gk_passes_launched,gk_passes_pct_launched,gk_passes,gk_passes_throws,gk_pct_passes_launched,gk_passes_length_avg,gk_goal_kicks,gk_pct_goal_kicks_launched,gk_goal_kick_length_avg,gk_crosses,gk_crosses_stopped,gk_crosses_stopped_pct,gk_def_actions_outside_pen_area,gk_def_actions_outside_pen_area_per90,gk_avg_distance_def_actions,shots_on_target,shots_free_kicks,shots_on_target_pct,shots_on_target_per90,goals_per_shot,goals_per_shot_on_target,npxg_per_shot,xg_net,npxg_net,passes_completed,passes,passes_pct,passes_total_distance,passes_progressive_distance,passes_completed_short,passes_short,passes_pct_short,passes_completed_medium,passes_medium,passes_pct_medium,passes_completed_long,passes_long,passes_pct_long,assisted_shots,passes_into_final_third,passes_into_penalty_area,crosses_into_penalty_area,progressive_passes,passes_live,passes_dead,passes_free_kicks,through_balls,passes_switches,crosses,corner_kicks,corner_kicks_in,corner_kicks_out,corner_kicks_straight,throw_ins,passes_offsides,passes_blocked,sca,sca_per90,sca_passes_live,sca_passes_dead,sca_shots,sca_fouled,gca,gca_per90,gca_passes_live,gca_passes_dead,gca_shots,gca_fouled,gca_defense,tackles,tackles_won,tackles_def_3rd,tackles_mid_3rd,tackles_att_3rd,blocks,blocked_shots,blocked_passes,interceptions,clearances,errors,touches,touches_def_pen_area,touches_def_3rd,touches_mid_3rd,touches_att_3rd,touches_att_pen_area,touches_live_ball,carries,progressive_carries,carries_into_final_third,carries_into_penalty_area,passes_received,miscontrols,dispossessed,cards_yellow_red,fouls,fouled,offsides,pens_won,pens_conceded,own_goals,ball_recoveries,aerials_won,aerials_lost,aerials_won_pct -Atalanta,24.0,49.0,21.0,231.0,1890.0,38.0,27.0,6.0,8.0,52.0,3.0,1.81,1.29,3.1,1.52,2.81,33.0,26.6,1.57,1.27,21.0,21.0,1890.0,24.0,1.14,82.0,59.0,72.0,11.0,5.0,5.0,7.0,33.3,1.0,1.0,0.0,0.0,21.0,1.0,3.0,1.0,20.5,0.24,-2.5,-0.12,112.0,262.0,42.7,553.0,152.0,28.9,31.2,159.0,64.2,47.8,260.0,17.0,6.5,26.0,1.24,16.8,87.0,8.0,32.8,4.14,0.12,0.37,0.1,5.0,5.4,8083.0,10311.0,78.4,142248.0,54886.0,3752.0,4307.0,87.1,3233.0,3866.0,83.6,862.0,1555.0,55.4,225.0,610.0,220.0,45.0,899.0,9253.0,1028.0,240.0,25.0,98.0,332.0,90.0,39.0,32.0,0.0,461.0,30.0,170.0,494.0,23.52,372.0,42.0,27.0,23.0,69.0,3.29,49.0,4.0,7.0,7.0,1.0,361.0,204.0,165.0,152.0,44.0,237.0,51.0,186.0,237.0,344.0,7.0,12695.0,1329.0,4185.0,5353.0,3273.0,519.0,12687.0,7036.0,365.0,286.0,119.0,8021.0,340.0,214.0,1.0,261.0,217.0,30.0,6.0,1.0,1.0,1220.0,317.0,260.0,54.9 -Bologna,25.0,51.9,21.0,231.0,1890.0,27.0,20.0,4.0,4.0,56.0,2.0,1.29,0.95,2.24,1.1,2.05,24.6,21.7,1.17,1.04,21.0,21.0,1890.0,31.0,1.48,94.0,65.0,72.3,8.0,5.0,8.0,3.0,14.3,5.0,5.0,0.0,0.0,21.0,1.0,4.0,2.0,30.8,0.28,1.8,0.08,99.0,240.0,41.3,563.0,117.0,32.1,32.7,153.0,38.6,34.5,299.0,18.0,6.0,17.0,0.81,13.3,77.0,8.0,30.4,3.67,0.09,0.3,0.09,2.4,1.3,8149.0,10353.0,78.7,144147.0,53606.0,3718.0,4247.0,87.5,3263.0,3844.0,84.9,899.0,1598.0,56.3,197.0,566.0,126.0,39.0,685.0,9268.0,1043.0,285.0,35.0,102.0,306.0,84.0,41.0,32.0,0.0,457.0,42.0,220.0,452.0,21.52,335.0,40.0,27.0,22.0,45.0,2.14,33.0,4.0,4.0,3.0,0.0,360.0,213.0,181.0,135.0,44.0,237.0,80.0,157.0,197.0,397.0,5.0,12622.0,1428.0,4470.0,5725.0,2541.0,319.0,12618.0,6817.0,272.0,246.0,60.0,8071.0,325.0,168.0,1.0,267.0,247.0,42.0,3.0,5.0,2.0,1160.0,193.0,239.0,44.7 -Cremonese,30.0,44.2,21.0,231.0,1890.0,15.0,7.0,2.0,4.0,48.0,1.0,0.71,0.33,1.05,0.62,0.95,22.3,19.1,1.06,0.91,21.0,21.0,1890.0,37.0,1.76,127.0,91.0,74.0,0.0,8.0,13.0,3.0,14.3,4.0,4.0,0.0,0.0,21.0,1.0,6.0,1.0,39.7,0.29,3.7,0.18,89.0,323.0,27.6,562.0,99.0,43.2,38.1,153.0,52.3,46.2,265.0,19.0,7.2,17.0,0.81,13.6,69.0,10.0,24.9,3.29,0.05,0.19,0.07,-7.3,-6.1,6061.0,8473.0,71.5,110090.0,45732.0,2663.0,3200.0,83.2,2593.0,3270.0,79.3,662.0,1501.0,44.1,191.0,559.0,183.0,65.0,690.0,7378.0,1049.0,251.0,12.0,52.0,417.0,97.0,32.0,54.0,0.0,463.0,46.0,182.0,474.0,22.57,330.0,51.0,37.0,30.0,21.0,1.0,13.0,2.0,5.0,1.0,0.0,417.0,221.0,244.0,132.0,41.0,244.0,76.0,168.0,216.0,392.0,8.0,10894.0,1339.0,3926.0,4414.0,2681.0,432.0,10890.0,5417.0,305.0,231.0,55.0,6000.0,313.0,180.0,0.0,276.0,218.0,46.0,3.0,4.0,1.0,1156.0,296.0,388.0,43.3 -Empoli,27.0,46.8,21.0,231.0,1890.0,19.0,11.0,0.0,0.0,52.0,4.0,0.9,0.52,1.43,0.9,1.43,20.1,20.1,0.96,0.96,21.0,21.0,1890.0,26.0,1.24,92.0,65.0,72.8,6.0,8.0,7.0,7.0,33.3,3.0,1.0,1.0,1.0,21.0,0.0,6.0,0.0,26.4,0.27,0.4,0.02,102.0,310.0,32.9,766.0,113.0,33.8,34.0,108.0,47.2,42.4,426.0,25.0,5.9,12.0,0.57,10.9,75.0,11.0,30.5,3.57,0.08,0.25,0.08,-1.1,-1.1,7157.0,9174.0,78.0,126244.0,49779.0,3184.0,3630.0,87.7,3037.0,3584.0,84.7,723.0,1391.0,52.0,179.0,433.0,142.0,50.0,602.0,8071.0,1076.0,291.0,14.0,46.0,314.0,85.0,36.0,30.0,0.0,459.0,27.0,207.0,426.0,20.29,315.0,40.0,23.0,25.0,33.0,1.57,24.0,4.0,2.0,1.0,0.0,364.0,208.0,189.0,130.0,45.0,252.0,112.0,140.0,154.0,503.0,3.0,11636.0,1687.0,4564.0,4695.0,2509.0,358.0,11636.0,6377.0,313.0,261.0,74.0,7073.0,311.0,219.0,2.0,252.0,256.0,27.0,0.0,3.0,0.0,1001.0,211.0,242.0,46.6 -Fiorentina,28.0,57.3,21.0,231.0,1890.0,23.0,18.0,2.0,4.0,53.0,2.0,1.1,0.86,1.95,1.0,1.86,29.0,26.2,1.38,1.25,21.0,21.0,1890.0,28.0,1.33,69.0,42.0,63.8,6.0,6.0,9.0,4.0,19.0,3.0,3.0,0.0,0.0,21.0,0.0,4.0,1.0,22.3,0.28,-4.7,-0.22,101.0,262.0,38.5,651.0,85.0,31.5,32.8,127.0,44.9,40.3,187.0,10.0,5.3,34.0,1.62,18.0,97.0,9.0,29.5,4.62,0.06,0.22,0.08,-6.0,-5.2,8952.0,11074.0,80.8,171234.0,56690.0,3574.0,4017.0,89.0,4011.0,4607.0,87.1,1194.0,1936.0,61.7,263.0,719.0,211.0,65.0,968.0,9963.0,1081.0,335.0,13.0,108.0,479.0,135.0,53.0,57.0,0.0,435.0,30.0,180.0,585.0,27.86,423.0,64.0,43.0,24.0,40.0,1.9,31.0,4.0,3.0,1.0,0.0,327.0,182.0,135.0,143.0,49.0,192.0,53.0,139.0,158.0,247.0,8.0,13263.0,1102.0,3656.0,6033.0,3699.0,564.0,13259.0,7944.0,434.0,348.0,107.0,8875.0,341.0,206.0,2.0,271.0,285.0,30.0,2.0,3.0,1.0,1130.0,317.0,274.0,53.6 -Hellas Verona,34.0,43.1,21.0,231.0,1890.0,17.0,14.0,0.0,0.0,61.0,3.0,0.81,0.67,1.48,0.81,1.48,21.4,21.4,1.02,1.02,21.0,21.0,1890.0,33.0,1.57,107.0,74.0,69.2,3.0,5.0,13.0,2.0,9.5,1.0,0.0,1.0,0.0,21.0,0.0,2.0,1.0,27.6,0.25,-4.4,-0.21,206.0,413.0,49.9,497.0,65.0,62.2,44.1,168.0,61.9,46.7,294.0,15.0,5.1,46.0,2.19,16.8,64.0,5.0,26.8,3.05,0.07,0.27,0.09,-4.4,-4.4,5320.0,7738.0,68.8,105279.0,48760.0,2122.0,2716.0,78.1,2223.0,2875.0,77.3,814.0,1606.0,50.7,178.0,496.0,138.0,50.0,640.0,6709.0,984.0,230.0,13.0,48.0,392.0,106.0,49.0,40.0,1.0,420.0,45.0,194.0,405.0,19.29,279.0,54.0,26.0,15.0,30.0,1.43,20.0,2.0,3.0,1.0,1.0,330.0,179.0,172.0,117.0,41.0,246.0,85.0,161.0,188.0,406.0,5.0,10142.0,1235.0,3420.0,4289.0,2567.0,389.0,10142.0,4652.0,309.0,219.0,66.0,5235.0,352.0,197.0,0.0,312.0,203.0,45.0,0.0,1.0,1.0,1274.0,422.0,385.0,52.3 -Inter,23.0,54.0,21.0,231.0,1890.0,40.0,27.0,2.0,2.0,41.0,1.0,1.9,1.29,3.19,1.81,3.1,36.0,34.5,1.71,1.64,21.0,21.0,1890.0,26.0,1.24,80.0,56.0,70.0,14.0,1.0,6.0,7.0,33.3,2.0,2.0,0.0,0.0,21.0,0.0,3.0,2.0,21.2,0.24,-2.8,-0.13,86.0,208.0,41.3,698.0,107.0,25.1,28.9,137.0,24.1,32.7,244.0,9.0,3.7,9.0,0.43,13.7,104.0,13.0,32.2,4.95,0.12,0.37,0.11,4.0,3.5,9203.0,11205.0,82.1,167144.0,57939.0,3902.0,4372.0,89.2,4091.0,4658.0,87.8,979.0,1634.0,59.9,241.0,643.0,198.0,66.0,846.0,10205.0,963.0,264.0,26.0,107.0,468.0,117.0,31.0,70.0,0.0,378.0,37.0,138.0,561.0,26.71,405.0,68.0,40.0,31.0,69.0,3.29,48.0,4.0,6.0,5.0,2.0,305.0,169.0,128.0,132.0,45.0,213.0,55.0,158.0,168.0,302.0,5.0,13309.0,1410.0,4369.0,5920.0,3133.0,517.0,13307.0,7752.0,339.0,262.0,73.0,9105.0,294.0,148.0,1.0,258.0,250.0,37.0,1.0,2.0,2.0,1068.0,266.0,221.0,54.6 -Juventus,26.0,49.2,21.0,231.0,1890.0,33.0,25.0,3.0,4.0,36.0,4.0,1.57,1.19,2.76,1.43,2.62,31.2,28.0,1.49,1.33,21.0,21.0,1890.0,17.0,0.81,69.0,51.0,76.8,12.0,5.0,4.0,13.0,61.9,2.0,1.0,1.0,0.0,21.0,0.0,3.0,0.0,20.6,0.27,3.6,0.17,86.0,218.0,39.4,538.0,96.0,32.0,33.3,113.0,40.7,37.5,277.0,6.0,2.2,21.0,1.0,16.6,99.0,8.0,34.0,4.71,0.1,0.3,0.1,1.8,2.0,8418.0,10323.0,81.5,158691.0,54696.0,3427.0,3878.0,88.4,3745.0,4245.0,88.2,1084.0,1733.0,62.6,227.0,629.0,161.0,56.0,751.0,9352.0,938.0,240.0,20.0,108.0,387.0,103.0,29.0,64.0,0.0,382.0,33.0,167.0,501.0,23.86,358.0,47.0,43.0,19.0,52.0,2.48,35.0,3.0,5.0,7.0,1.0,333.0,193.0,156.0,133.0,44.0,233.0,78.0,155.0,184.0,371.0,7.0,12614.0,1349.0,4199.0,5765.0,2760.0,432.0,12610.0,6815.0,339.0,241.0,74.0,8352.0,337.0,190.0,1.0,251.0,217.0,33.0,4.0,2.0,0.0,1089.0,257.0,247.0,51.0 -Lazio,21.0,51.4,21.0,231.0,1890.0,36.0,26.0,3.0,4.0,44.0,1.0,1.71,1.24,2.95,1.57,2.81,27.6,24.7,1.31,1.17,21.0,21.0,1888.0,17.0,0.81,69.0,52.0,76.8,11.0,6.0,4.0,11.0,52.4,1.0,1.0,0.0,0.0,21.0,0.0,4.0,0.0,16.2,0.22,-0.8,-0.04,101.0,235.0,43.0,654.0,96.0,29.7,33.0,120.0,34.2,34.5,279.0,7.0,2.5,39.0,1.86,18.2,83.0,6.0,36.7,3.95,0.15,0.4,0.11,8.4,8.3,9445.0,11410.0,82.8,153449.0,56912.0,4969.0,5508.0,90.2,3213.0,3728.0,86.2,914.0,1499.0,61.0,171.0,574.0,150.0,19.0,715.0,10386.0,995.0,327.0,33.0,82.0,259.0,93.0,37.0,44.0,0.0,355.0,29.0,155.0,396.0,18.86,295.0,35.0,23.0,19.0,62.0,2.95,47.0,2.0,9.0,1.0,0.0,343.0,197.0,154.0,142.0,47.0,226.0,68.0,158.0,177.0,355.0,4.0,13576.0,1483.0,4589.0,6390.0,2710.0,436.0,13572.0,7572.0,384.0,255.0,104.0,9372.0,310.0,175.0,0.0,218.0,282.0,29.0,2.0,1.0,0.0,1112.0,216.0,207.0,51.1 -Lecce,26.0,42.4,21.0,231.0,1890.0,20.0,14.0,1.0,2.0,47.0,2.0,0.95,0.67,1.62,0.9,1.57,18.6,17.1,0.89,0.81,21.0,21.0,1890.0,24.0,1.14,89.0,66.0,76.4,5.0,8.0,8.0,3.0,14.3,3.0,3.0,0.0,0.0,21.0,1.0,4.0,1.0,24.0,0.24,1.0,0.05,106.0,400.0,26.5,506.0,83.0,54.5,41.3,163.0,76.1,51.7,316.0,15.0,4.7,20.0,0.95,12.5,60.0,9.0,28.6,2.86,0.09,0.32,0.08,1.4,1.9,5710.0,8109.0,70.4,106591.0,42553.0,2420.0,2951.0,82.0,2345.0,2975.0,78.8,770.0,1675.0,46.0,157.0,530.0,112.0,39.0,681.0,6981.0,1091.0,307.0,10.0,87.0,375.0,93.0,34.0,24.0,0.0,476.0,37.0,163.0,350.0,16.67,254.0,32.0,16.0,23.0,32.0,1.52,25.0,1.0,2.0,1.0,1.0,399.0,226.0,195.0,146.0,58.0,271.0,65.0,206.0,223.0,474.0,2.0,10671.0,1256.0,3760.0,4500.0,2532.0,327.0,10669.0,5311.0,298.0,199.0,70.0,5648.0,351.0,196.0,1.0,298.0,264.0,37.0,2.0,3.0,1.0,1159.0,316.0,396.0,44.4 -Milan,27.0,53.9,21.0,231.0,1890.0,35.0,30.0,2.0,2.0,59.0,2.0,1.67,1.43,3.1,1.57,3.0,31.7,30.3,1.51,1.44,21.0,21.0,1890.0,30.0,1.43,76.0,46.0,64.5,11.0,5.0,5.0,4.0,19.0,4.0,3.0,1.0,0.0,21.0,2.0,5.0,1.0,24.1,0.27,-4.9,-0.23,106.0,236.0,44.9,640.0,101.0,30.9,32.9,83.0,45.8,40.0,223.0,14.0,6.3,16.0,0.76,13.5,101.0,15.0,32.4,4.81,0.11,0.33,0.1,3.3,2.7,8037.0,10119.0,79.4,151270.0,53627.0,3208.0,3729.0,86.0,3582.0,4104.0,87.3,1051.0,1747.0,60.2,246.0,637.0,191.0,46.0,838.0,9079.0,1004.0,262.0,29.0,74.0,335.0,88.0,19.0,57.0,0.0,460.0,36.0,175.0,551.0,26.24,383.0,53.0,38.0,35.0,61.0,2.9,43.0,4.0,7.0,1.0,1.0,384.0,213.0,182.0,166.0,36.0,203.0,75.0,128.0,170.0,263.0,5.0,12372.0,1217.0,3972.0,5674.0,2884.0,497.0,12370.0,6912.0,444.0,293.0,112.0,7936.0,331.0,184.0,2.0,263.0,244.0,36.0,1.0,4.0,1.0,1118.0,305.0,249.0,55.1 -Monza,29.0,55.9,21.0,231.0,1890.0,26.0,16.0,4.0,4.0,44.0,1.0,1.24,0.76,2.0,1.05,1.81,27.3,24.1,1.3,1.15,21.0,21.0,1890.0,30.0,1.43,90.0,61.0,66.7,7.0,5.0,9.0,6.0,28.6,0.0,0.0,0.0,0.0,21.0,0.0,3.0,1.0,29.2,0.33,0.2,0.01,90.0,261.0,34.5,707.0,133.0,31.4,32.9,124.0,31.5,31.4,286.0,10.0,3.5,16.0,0.76,13.3,69.0,7.0,29.5,3.29,0.09,0.32,0.11,-1.3,-2.1,9419.0,11415.0,82.5,167594.0,57863.0,4230.0,4705.0,89.9,4022.0,4592.0,87.6,963.0,1573.0,61.2,180.0,697.0,170.0,49.0,798.0,10318.0,1057.0,329.0,17.0,97.0,370.0,89.0,12.0,62.0,0.0,432.0,40.0,177.0,411.0,19.57,325.0,31.0,22.0,20.0,50.0,2.38,38.0,2.0,3.0,6.0,0.0,343.0,200.0,183.0,121.0,39.0,212.0,59.0,153.0,184.0,325.0,8.0,13586.0,1403.0,4576.0,6171.0,2949.0,398.0,13582.0,7839.0,411.0,304.0,77.0,9330.0,341.0,181.0,0.0,270.0,284.0,40.0,4.0,0.0,1.0,1017.0,241.0,226.0,51.6 -Napoli,24.0,61.4,21.0,231.0,1890.0,51.0,40.0,5.0,6.0,29.0,0.0,2.43,1.9,4.33,2.19,4.1,40.1,35.3,1.91,1.68,21.0,21.0,1890.0,15.0,0.71,56.0,40.0,75.0,18.0,2.0,1.0,9.0,42.9,2.0,1.0,1.0,0.0,21.0,0.0,1.0,0.0,17.0,0.27,2.0,0.09,35.0,98.0,35.7,468.0,77.0,15.6,26.7,128.0,19.5,27.1,221.0,6.0,2.7,24.0,1.14,17.2,121.0,9.0,35.1,5.76,0.13,0.38,0.1,10.9,10.7,11246.0,13132.0,85.6,188425.0,61874.0,5500.0,5978.0,92.0,4380.0,4894.0,89.5,1019.0,1529.0,66.6,284.0,856.0,225.0,50.0,992.0,12066.0,1029.0,302.0,14.0,121.0,356.0,112.0,52.0,34.0,0.0,449.0,37.0,187.0,636.0,30.29,478.0,52.0,41.0,24.0,91.0,4.33,71.0,4.0,10.0,3.0,1.0,338.0,177.0,150.0,128.0,60.0,204.0,60.0,144.0,146.0,306.0,6.0,15378.0,1181.0,4086.0,7467.0,3958.0,626.0,15372.0,9618.0,484.0,343.0,135.0,11156.0,324.0,211.0,0.0,205.0,272.0,37.0,4.0,2.0,0.0,1122.0,259.0,214.0,54.8 -Roma,26.0,48.9,21.0,231.0,1890.0,28.0,19.0,3.0,5.0,43.0,0.0,1.33,0.9,2.24,1.19,2.1,34.4,30.5,1.64,1.45,21.0,21.0,1890.0,18.0,0.86,60.0,42.0,70.0,12.0,4.0,5.0,8.0,38.1,0.0,0.0,0.0,0.0,21.0,1.0,3.0,0.0,12.4,0.21,-5.6,-0.27,68.0,173.0,39.3,380.0,75.0,32.6,31.8,159.0,30.8,32.8,248.0,6.0,2.4,15.0,0.71,14.0,89.0,5.0,32.7,4.24,0.09,0.28,0.12,-6.4,-5.5,7964.0,9903.0,80.4,145157.0,49470.0,3381.0,3797.0,89.0,3434.0,3984.0,86.2,950.0,1614.0,58.9,206.0,540.0,147.0,28.0,658.0,8844.0,1024.0,290.0,31.0,108.0,307.0,100.0,38.0,57.0,0.0,433.0,35.0,153.0,477.0,22.71,325.0,63.0,39.0,23.0,48.0,2.29,26.0,8.0,7.0,3.0,0.0,375.0,193.0,184.0,149.0,42.0,221.0,62.0,159.0,221.0,354.0,5.0,12203.0,1205.0,4193.0,5721.0,2437.0,430.0,12198.0,6891.0,382.0,269.0,92.0,7881.0,315.0,183.0,0.0,253.0,275.0,35.0,4.0,0.0,0.0,1105.0,245.0,205.0,54.4 -Salernitana,28.0,45.1,21.0,231.0,1890.0,24.0,16.0,1.0,1.0,46.0,3.0,1.14,0.76,1.9,1.1,1.86,20.7,20.1,0.99,0.96,21.0,21.0,1890.0,41.0,1.95,120.0,76.0,70.0,5.0,6.0,10.0,3.0,14.3,8.0,5.0,3.0,0.0,21.0,0.0,1.0,0.0,43.0,0.29,2.0,0.09,100.0,361.0,27.7,569.0,88.0,45.0,37.7,194.0,54.1,44.2,285.0,18.0,6.3,15.0,0.71,14.5,67.0,8.0,30.9,3.19,0.11,0.34,0.09,3.3,2.9,7089.0,9179.0,77.2,127942.0,48498.0,3072.0,3500.0,87.8,3019.0,3538.0,85.3,782.0,1570.0,49.8,162.0,520.0,126.0,42.0,674.0,8132.0,1005.0,280.0,18.0,103.0,335.0,85.0,10.0,47.0,0.0,384.0,42.0,183.0,379.0,18.05,287.0,32.0,24.0,20.0,42.0,2.0,37.0,1.0,2.0,0.0,0.0,338.0,183.0,172.0,132.0,34.0,208.0,80.0,128.0,179.0,397.0,9.0,11385.0,1410.0,4053.0,5082.0,2366.0,274.0,11384.0,6168.0,304.0,221.0,50.0,7012.0,326.0,173.0,1.0,251.0,218.0,42.0,0.0,8.0,0.0,1091.0,247.0,268.0,48.0 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+Sampdoria,37.0,47.4,31.0,341.0,2790.0,20.0,16.0,1.0,2.0,94.0,2.0,0.65,0.52,1.16,0.61,1.13,27.8,26.1,0.9,0.84,31.0,31.0,2790.0,52.0,1.68,165.0,113.0,72.1,3.0,8.0,20.0,4.0,12.9,9.0,6.0,2.0,1.0,31.0,0.0,7.0,2.0,43.6,0.22,-6.4,-0.21,202.0,550.0,36.7,860.0,129.0,45.6,38.6,275.0,57.5,43.5,465.0,28.0,6.0,24.0,0.77,13.5,97.0,14.0,31.0,3.13,0.06,0.2,0.09,-7.8,-7.1,10364.0,13691.0,75.7,190765.0,73231.0,4370.0,5084.0,86.0,4487.0,5410.0,82.9,1235.0,2349.0,52.6,222.0,741.0,207.0,65.0,951.0,12009.0,1635.0,505.0,24.0,87.0,514.0,114.0,30.0,68.0,0.0,653.0,47.0,326.0,522.0,16.84,375.0,38.0,40.0,31.0,31.0,1.0,23.0,2.0,4.0,1.0,1.0,487.0,293.0,230.0,206.0,51.0,358.0,121.0,237.0,259.0,548.0,7.0,16930.0,2002.0,5838.0,7593.0,3689.0,498.0,16928.0,9447.0,391.0,309.0,68.0,10261.0,482.0,284.0,1.0,426.0,432.0,47.0,1.0,8.0,2.0,1585.0,517.0,535.0,49.1 +Sassuolo,29.0,48.9,31.0,341.0,2790.0,37.0,24.0,6.0,7.0,71.0,2.0,1.19,0.77,1.97,1.0,1.77,40.7,35.3,1.31,1.14,31.0,31.0,2790.0,46.0,1.48,102.0,56.0,58.8,11.0,7.0,13.0,9.0,29.0,4.0,4.0,0.0,0.0,31.0,0.0,5.0,0.0,34.3,0.31,-11.7,-0.38,162.0,398.0,40.7,1007.0,164.0,31.0,33.9,265.0,32.5,33.1,437.0,26.0,5.9,26.0,0.84,13.9,137.0,19.0,34.9,4.42,0.08,0.23,0.09,-3.7,-4.3,11782.0,14710.0,80.1,217288.0,81006.0,5070.0,5772.0,87.8,4900.0,5686.0,86.2,1523.0,2491.0,61.1,308.0,885.0,256.0,69.0,1230.0,13008.0,1676.0,451.0,26.0,158.0,502.0,147.0,63.0,42.0,0.0,685.0,26.0,296.0,711.0,22.94,548.0,46.0,35.0,30.0,65.0,2.1,43.0,3.0,9.0,6.0,0.0,427.0,237.0,203.0,159.0,65.0,306.0,100.0,206.0,235.0,575.0,13.0,17897.0,2035.0,6181.0,7729.0,4166.0,645.0,17890.0,10295.0,605.0,406.0,166.0,11666.0,446.0,310.0,1.0,336.0,355.0,26.0,5.0,4.0,0.0,1456.0,329.0,385.0,46.1 +Spezia,34.0,47.0,31.0,341.0,2790.0,24.0,15.0,4.0,4.0,83.0,5.0,0.77,0.48,1.26,0.65,1.13,33.1,30.1,1.07,0.97,31.0,31.0,2790.0,49.0,1.58,156.0,108.0,71.2,5.0,12.0,14.0,4.0,12.9,6.0,4.0,1.0,1.0,31.0,1.0,5.0,2.0,45.3,0.26,-1.7,-0.05,158.0,483.0,32.7,895.0,146.0,39.8,34.7,202.0,62.9,47.5,473.0,14.0,3.0,38.0,1.23,14.7,97.0,9.0,29.0,3.13,0.06,0.21,0.09,-9.1,-10.1,10143.0,13497.0,75.2,175913.0,68371.0,4773.0,5542.0,86.1,4038.0,4899.0,82.4,1004.0,2193.0,45.8,228.0,752.0,214.0,68.0,943.0,11836.0,1616.0,372.0,19.0,93.0,513.0,156.0,98.0,41.0,0.0,719.0,45.0,299.0,561.0,18.1,406.0,45.0,34.0,25.0,37.0,1.19,27.0,1.0,3.0,3.0,0.0,528.0,306.0,237.0,207.0,84.0,391.0,140.0,251.0,261.0,685.0,7.0,17188.0,2250.0,6466.0,6969.0,3941.0,562.0,17184.0,9052.0,454.0,321.0,98.0,10069.0,548.0,302.0,3.0,431.0,298.0,45.0,2.0,6.0,2.0,1693.0,424.0,470.0,47.4 +Torino,28.0,53.0,31.0,341.0,2790.0,32.0,26.0,2.0,2.0,70.0,0.0,1.03,0.84,1.87,0.97,1.81,33.6,32.1,1.08,1.03,31.0,31.0,2790.0,36.0,1.16,113.0,77.0,72.6,11.0,9.0,11.0,8.0,25.8,6.0,5.0,0.0,1.0,31.0,0.0,6.0,0.0,31.5,0.24,-4.5,-0.15,224.0,762.0,29.4,1196.0,142.0,45.7,40.0,236.0,91.5,69.8,387.0,25.0,6.5,62.0,2.0,17.1,113.0,5.0,31.3,3.65,0.08,0.27,0.09,-1.6,-2.1,12426.0,15684.0,79.2,223358.0,75278.0,5482.0,6211.0,88.3,5400.0,6229.0,86.7,1226.0,2409.0,50.9,298.0,924.0,218.0,75.0,1094.0,14168.0,1446.0,365.0,24.0,102.0,492.0,144.0,78.0,38.0,1.0,628.0,70.0,271.0,664.0,21.42,506.0,49.0,33.0,20.0,57.0,1.84,44.0,1.0,3.0,3.0,0.0,396.0,225.0,193.0,157.0,46.0,282.0,94.0,188.0,244.0,524.0,8.0,18961.0,1896.0,5922.0,8757.0,4450.0,588.0,18959.0,10697.0,531.0,442.0,137.0,12326.0,512.0,271.0,0.0,427.0,318.0,70.0,2.0,6.0,0.0,1653.0,474.0,508.0,48.3 +Udinese,27.0,48.1,31.0,341.0,2790.0,41.0,36.0,1.0,2.0,71.0,1.0,1.32,1.16,2.48,1.29,2.45,41.1,39.6,1.32,1.28,31.0,31.0,2790.0,39.0,1.26,112.0,75.0,68.8,10.0,12.0,9.0,9.0,29.0,5.0,4.0,0.0,1.0,31.0,1.0,2.0,2.0,37.5,0.3,0.5,0.01,121.0,316.0,38.3,687.0,120.0,33.2,32.7,237.0,37.1,35.5,441.0,11.0,2.5,16.0,0.52,12.1,123.0,23.0,28.4,3.97,0.09,0.33,0.09,-0.1,0.4,10857.0,13665.0,79.5,200030.0,77026.0,4618.0,5285.0,87.4,4588.0,5341.0,85.9,1335.0,2248.0,59.4,325.0,862.0,258.0,60.0,1165.0,12106.0,1514.0,432.0,29.0,163.0,478.0,160.0,68.0,44.0,0.0,602.0,45.0,275.0,767.0,24.74,535.0,74.0,49.0,47.0,72.0,2.32,54.0,6.0,2.0,5.0,2.0,555.0,307.0,299.0,201.0,55.0,355.0,105.0,250.0,256.0,587.0,6.0,17373.0,1930.0,5826.0,7499.0,4244.0,750.0,17371.0,9890.0,554.0,386.0,145.0,10751.0,545.0,292.0,0.0,375.0,389.0,45.0,1.0,5.0,2.0,1594.0,390.0,326.0,54.5 diff --git a/fbref_data/teams_vs.csv b/fbref_data/teams_vs.csv index 207a2be..8cd4235 100644 --- a/fbref_data/teams_vs.csv +++ b/fbref_data/teams_vs.csv @@ -1,21 +1,21 @@ team,players_used,possession,games,games_starts,minutes,goals,assists,pens_made,pens_att,cards_yellow,cards_red,goals_per90,assists_per90,goals_assists_per90,goals_pens_per90,goals_assists_pens_per90,xg,npxg,xg_per90,npxg_per90,gk_games,gk_games_starts,gk_minutes,gk_goals_against,gk_goals_against_per90,gk_shots_on_target_against,gk_saves,gk_save_pct,gk_wins,gk_ties,gk_losses,gk_clean_sheets,gk_clean_sheets_pct,gk_pens_att,gk_pens_allowed,gk_pens_saved,gk_pens_missed,minutes_90s,gk_free_kick_goals_against,gk_corner_kick_goals_against,gk_own_goals_against,gk_psxg,gk_psnpxg_per_shot_on_target_against,gk_psxg_net,gk_psxg_net_per90,gk_passes_completed_launched,gk_passes_launched,gk_passes_pct_launched,gk_passes,gk_passes_throws,gk_pct_passes_launched,gk_passes_length_avg,gk_goal_kicks,gk_pct_goal_kicks_launched,gk_goal_kick_length_avg,gk_crosses,gk_crosses_stopped,gk_crosses_stopped_pct,gk_def_actions_outside_pen_area,gk_def_actions_outside_pen_area_per90,gk_avg_distance_def_actions,shots_on_target,shots_free_kicks,shots_on_target_pct,shots_on_target_per90,goals_per_shot,goals_per_shot_on_target,npxg_per_shot,xg_net,npxg_net,passes_completed,passes,passes_pct,passes_total_distance,passes_progressive_distance,passes_completed_short,passes_short,passes_pct_short,passes_completed_medium,passes_medium,passes_pct_medium,passes_completed_long,passes_long,passes_pct_long,assisted_shots,passes_into_final_third,passes_into_penalty_area,crosses_into_penalty_area,progressive_passes,passes_live,passes_dead,passes_free_kicks,through_balls,passes_switches,crosses,corner_kicks,corner_kicks_in,corner_kicks_out,corner_kicks_straight,throw_ins,passes_offsides,passes_blocked,sca,sca_per90,sca_passes_live,sca_passes_dead,sca_shots,sca_fouled,gca,gca_per90,gca_passes_live,gca_passes_dead,gca_shots,gca_fouled,gca_defense,tackles,tackles_won,tackles_def_3rd,tackles_mid_3rd,tackles_att_3rd,blocks,blocked_shots,blocked_passes,interceptions,clearances,errors,touches,touches_def_pen_area,touches_def_3rd,touches_mid_3rd,touches_att_3rd,touches_att_pen_area,touches_live_ball,carries,progressive_carries,carries_into_final_third,carries_into_penalty_area,passes_received,miscontrols,dispossessed,cards_yellow_red,fouls,fouled,offsides,pens_won,pens_conceded,own_goals,ball_recoveries,aerials_won,aerials_lost,aerials_won_pct -vs Atalanta,24.0,51.0,21.0,231.0,1890.0,23.0,17.0,1.0,1.0,50.0,0.0,1.1,0.81,1.9,1.05,1.86,22.6,21.8,1.07,1.04,21.0,21.0,1890.0,39.0,1.86,95.0,55.0,65.3,5.0,5.0,11.0,2.0,9.5,8.0,6.0,2.0,0.0,21.0,0.0,2.0,1.0,34.1,0.28,-3.9,-0.19,103.0,320.0,32.2,740.0,100.0,35.5,34.5,146.0,39.0,37.7,258.0,12.0,4.7,34.0,1.62,16.0,81.0,8.0,31.2,3.86,0.08,0.27,0.08,0.4,0.2,8419.0,10710.0,78.6,149166.0,55125.0,3731.0,4229.0,88.2,3471.0,4112.0,84.4,936.0,1702.0,55.0,196.0,597.0,162.0,42.0,730.0,9601.0,1083.0,282.0,16.0,65.0,338.0,89.0,21.0,38.0,0.0,480.0,26.0,201.0,458.0,21.81,354.0,37.0,21.0,22.0,42.0,2.0,32.0,2.0,2.0,3.0,0.0,385.0,232.0,211.0,129.0,45.0,229.0,76.0,153.0,199.0,352.0,4.0,13119.0,1413.0,4508.0,5849.0,2904.0,416.0,13118.0,7375.0,338.0,263.0,83.0,8301.0,365.0,180.0,0.0,232.0,244.0,26.0,1.0,8.0,1.0,1261.0,260.0,317.0,45.1 -vs Bologna,25.0,48.1,21.0,231.0,1890.0,29.0,17.0,5.0,5.0,47.0,2.0,1.38,0.81,2.19,1.14,1.95,28.0,24.3,1.34,1.16,21.0,21.0,1888.0,28.0,1.33,81.0,54.0,70.4,8.0,5.0,8.0,4.0,19.0,4.0,4.0,0.0,0.0,21.0,0.0,4.0,1.0,22.8,0.24,-4.2,-0.2,109.0,308.0,35.4,528.0,96.0,41.5,35.8,155.0,57.4,45.8,238.0,14.0,5.9,29.0,1.38,16.6,89.0,3.0,32.0,4.24,0.09,0.27,0.09,1.0,-0.3,7345.0,9547.0,76.9,130333.0,49358.0,3309.0,3838.0,86.2,2960.0,3541.0,83.6,826.0,1529.0,54.0,210.0,606.0,159.0,47.0,764.0,8414.0,1097.0,297.0,19.0,75.0,396.0,99.0,34.0,55.0,0.0,472.0,36.0,191.0,477.0,22.71,339.0,46.0,41.0,18.0,46.0,2.19,33.0,1.0,6.0,4.0,1.0,291.0,164.0,130.0,120.0,41.0,263.0,57.0,206.0,179.0,320.0,7.0,11798.0,1140.0,3470.0,5326.0,3120.0,454.0,11793.0,6198.0,345.0,281.0,77.0,7286.0,340.0,202.0,1.0,262.0,254.0,36.0,3.0,4.0,1.0,1145.0,239.0,193.0,55.3 -vs Cremonese,30.0,55.8,21.0,231.0,1890.0,36.0,23.0,4.0,4.0,39.0,0.0,1.71,1.1,2.81,1.52,2.62,37.6,34.5,1.79,1.64,21.0,21.0,1890.0,15.0,0.71,73.0,56.0,82.2,13.0,8.0,0.0,10.0,47.6,4.0,2.0,2.0,0.0,21.0,0.0,2.0,0.0,18.7,0.21,3.7,0.18,92.0,242.0,38.0,541.0,103.0,32.9,34.7,177.0,36.2,35.8,321.0,21.0,6.5,19.0,0.9,13.8,123.0,15.0,37.7,5.86,0.1,0.26,0.11,-1.6,-2.5,8496.0,10776.0,78.8,157038.0,59255.0,3715.0,4301.0,86.4,3459.0,4118.0,84.0,1112.0,1808.0,61.5,249.0,648.0,182.0,49.0,882.0,9644.0,1099.0,302.0,18.0,107.0,352.0,99.0,30.0,49.0,0.0,470.0,33.0,188.0,585.0,27.86,398.0,65.0,47.0,34.0,63.0,3.0,39.0,3.0,9.0,5.0,2.0,321.0,190.0,161.0,115.0,45.0,236.0,77.0,159.0,187.0,459.0,8.0,13305.0,1377.0,4340.0,5749.0,3355.0,497.0,13301.0,7462.0,390.0,314.0,103.0,8429.0,346.0,208.0,0.0,232.0,264.0,33.0,3.0,4.0,0.0,1168.0,388.0,296.0,56.7 -vs Empoli,27.0,53.2,21.0,231.0,1890.0,26.0,20.0,1.0,3.0,47.0,2.0,1.24,0.95,2.19,1.19,2.14,32.3,30.0,1.54,1.43,21.0,21.0,1890.0,19.0,0.9,75.0,56.0,74.7,7.0,8.0,6.0,6.0,28.6,0.0,0.0,0.0,0.0,21.0,1.0,3.0,0.0,16.1,0.22,-2.9,-0.14,90.0,211.0,42.7,535.0,96.0,30.3,33.3,135.0,36.3,36.3,240.0,6.0,2.5,15.0,0.71,14.3,90.0,13.0,26.8,4.29,0.07,0.28,0.09,-6.3,-5.0,8307.0,10394.0,79.9,153964.0,54440.0,3659.0,4152.0,88.1,3288.0,3903.0,84.2,1143.0,1781.0,64.2,247.0,650.0,210.0,72.0,952.0,9307.0,1052.0,263.0,15.0,183.0,541.0,131.0,42.0,58.0,1.0,468.0,35.0,185.0,590.0,28.1,437.0,56.0,42.0,29.0,47.0,2.24,34.0,7.0,4.0,1.0,0.0,401.0,213.0,183.0,165.0,53.0,257.0,73.0,184.0,187.0,312.0,6.0,12759.0,1147.0,3760.0,5413.0,3683.0,561.0,12756.0,7173.0,485.0,330.0,112.0,8238.0,324.0,213.0,1.0,270.0,244.0,35.0,2.0,0.0,0.0,1104.0,242.0,211.0,53.4 -vs Fiorentina,28.0,42.7,21.0,231.0,1890.0,27.0,19.0,3.0,3.0,52.0,4.0,1.29,0.9,2.19,1.14,2.05,24.5,22.5,1.17,1.07,21.0,21.0,1890.0,23.0,1.1,101.0,76.0,79.2,9.0,6.0,6.0,7.0,33.3,4.0,2.0,2.0,0.0,21.0,0.0,4.0,0.0,25.4,0.22,2.4,0.11,146.0,429.0,34.0,707.0,104.0,44.8,37.9,173.0,64.7,49.4,367.0,17.0,4.6,14.0,0.67,11.9,66.0,6.0,32.7,3.14,0.12,0.36,0.11,2.5,1.5,6148.0,8208.0,74.9,114173.0,45470.0,2600.0,3044.0,85.4,2634.0,3157.0,83.4,740.0,1488.0,49.7,151.0,394.0,119.0,32.0,534.0,7162.0,992.0,291.0,24.0,72.0,247.0,64.0,20.0,38.0,0.0,390.0,54.0,155.0,344.0,16.38,242.0,34.0,24.0,15.0,44.0,2.1,32.0,3.0,5.0,2.0,1.0,361.0,192.0,192.0,137.0,32.0,242.0,98.0,144.0,195.0,456.0,7.0,10541.0,1681.0,4593.0,4114.0,1978.0,346.0,10538.0,5097.0,259.0,178.0,81.0,6068.0,300.0,162.0,1.0,296.0,252.0,54.0,1.0,4.0,0.0,1061.0,274.0,317.0,46.4 -vs Hellas Verona,34.0,56.9,21.0,231.0,1890.0,32.0,30.0,0.0,1.0,41.0,3.0,1.52,1.43,2.95,1.52,2.95,27.2,26.5,1.3,1.26,21.0,21.0,1890.0,19.0,0.9,64.0,47.0,70.3,13.0,5.0,3.0,6.0,28.6,0.0,0.0,0.0,0.0,21.0,0.0,3.0,2.0,17.6,0.28,0.6,0.03,158.0,436.0,36.2,720.0,75.0,49.3,39.1,126.0,64.3,48.4,289.0,15.0,5.2,25.0,1.19,16.0,106.0,10.0,34.1,5.05,0.1,0.3,0.09,4.8,5.5,7876.0,10308.0,76.4,142457.0,57456.0,3436.0,3994.0,86.0,3314.0,3978.0,83.3,891.0,1756.0,50.7,238.0,652.0,187.0,49.0,780.0,9153.0,1131.0,344.0,21.0,58.0,357.0,119.0,43.0,57.0,0.0,445.0,24.0,181.0,533.0,25.38,385.0,57.0,33.0,29.0,59.0,2.81,49.0,4.0,2.0,1.0,0.0,367.0,194.0,202.0,126.0,39.0,217.0,58.0,159.0,162.0,407.0,1.0,12773.0,1372.0,4220.0,5712.0,2950.0,505.0,12772.0,6571.0,302.0,216.0,96.0,7774.0,379.0,179.0,0.0,213.0,302.0,24.0,1.0,0.0,2.0,1173.0,385.0,422.0,47.7 -vs Inter,23.0,46.0,21.0,231.0,1890.0,24.0,21.0,2.0,2.0,55.0,0.0,1.14,1.0,2.14,1.05,2.05,19.5,17.9,0.93,0.85,21.0,21.0,1890.0,41.0,1.95,106.0,66.0,63.2,6.0,1.0,14.0,2.0,9.5,2.0,2.0,0.0,0.0,21.0,2.0,6.0,1.0,34.6,0.32,-5.4,-0.26,99.0,291.0,34.0,552.0,95.0,33.5,35.1,180.0,58.9,46.7,370.0,17.0,4.6,13.0,0.62,11.7,78.0,15.0,34.4,3.71,0.1,0.28,0.08,4.5,4.1,7667.0,9592.0,79.9,140669.0,49721.0,3274.0,3694.0,88.6,3344.0,3843.0,87.0,884.0,1549.0,57.1,176.0,560.0,151.0,48.0,692.0,8562.0,1011.0,276.0,6.0,84.0,301.0,74.0,28.0,38.0,0.0,402.0,19.0,177.0,408.0,19.43,314.0,31.0,21.0,25.0,43.0,2.05,31.0,6.0,2.0,4.0,0.0,259.0,143.0,120.0,97.0,42.0,178.0,71.0,107.0,189.0,428.0,6.0,11721.0,1417.0,4076.0,5277.0,2474.0,348.0,11719.0,6271.0,374.0,254.0,73.0,7615.0,293.0,169.0,0.0,263.0,237.0,19.0,2.0,2.0,1.0,960.0,221.0,266.0,45.4 -vs Juventus,26.0,50.8,21.0,231.0,1890.0,17.0,14.0,1.0,2.0,46.0,1.0,0.81,0.67,1.48,0.76,1.43,23.0,21.6,1.1,1.03,21.0,21.0,1890.0,33.0,1.57,103.0,69.0,70.9,4.0,5.0,12.0,4.0,19.0,4.0,3.0,1.0,0.0,21.0,3.0,5.0,0.0,31.2,0.27,-1.8,-0.09,92.0,235.0,39.1,503.0,88.0,33.0,33.1,150.0,46.0,40.3,298.0,7.0,2.3,17.0,0.81,15.0,67.0,6.0,26.3,3.19,0.06,0.24,0.09,-6.0,-5.6,8769.0,10733.0,81.7,152409.0,50779.0,4192.0,4646.0,90.2,3492.0,4069.0,85.8,883.0,1491.0,59.2,192.0,706.0,146.0,41.0,780.0,9692.0,1014.0,275.0,14.0,87.0,365.0,96.0,32.0,47.0,0.0,422.0,27.0,178.0,436.0,20.76,355.0,26.0,22.0,12.0,30.0,1.43,20.0,2.0,4.0,0.0,1.0,326.0,182.0,150.0,127.0,49.0,194.0,66.0,128.0,196.0,353.0,8.0,12893.0,1299.0,3820.0,6017.0,3196.0,357.0,12891.0,7537.0,389.0,347.0,72.0,8706.0,322.0,175.0,0.0,229.0,232.0,27.0,0.0,4.0,0.0,1064.0,247.0,257.0,49.0 -vs Lazio,21.0,48.6,21.0,231.0,1890.0,17.0,12.0,1.0,1.0,53.0,2.0,0.81,0.57,1.38,0.76,1.33,23.2,22.4,1.1,1.07,21.0,21.0,1890.0,37.0,1.76,86.0,50.0,60.5,4.0,6.0,11.0,3.0,14.3,4.0,3.0,0.0,1.0,21.0,0.0,3.0,1.0,31.2,0.33,-4.8,-0.23,97.0,242.0,40.1,688.0,96.0,28.1,30.9,129.0,38.0,35.6,205.0,14.0,6.8,31.0,1.48,14.5,68.0,4.0,26.2,3.24,0.06,0.24,0.09,-6.2,-6.4,8770.0,10770.0,81.4,165186.0,53969.0,3496.0,3923.0,89.1,4066.0,4574.0,88.9,1038.0,1743.0,59.6,193.0,620.0,167.0,51.0,741.0,9771.0,955.0,241.0,19.0,116.0,372.0,103.0,44.0,36.0,0.0,415.0,44.0,195.0,442.0,21.05,311.0,49.0,28.0,18.0,31.0,1.48,20.0,3.0,3.0,3.0,1.0,315.0,171.0,121.0,144.0,50.0,186.0,50.0,136.0,186.0,277.0,9.0,12869.0,1458.0,4743.0,5567.0,2693.0,433.0,12868.0,7617.0,333.0,211.0,86.0,8707.0,305.0,185.0,2.0,294.0,206.0,44.0,1.0,4.0,1.0,1157.0,207.0,216.0,48.9 -vs Lecce,26.0,57.6,21.0,231.0,1890.0,23.0,16.0,3.0,3.0,51.0,0.0,1.1,0.76,1.86,0.95,1.71,23.7,21.3,1.13,1.01,21.0,21.0,1890.0,21.0,1.0,62.0,41.0,67.7,8.0,8.0,5.0,6.0,28.6,2.0,1.0,1.0,0.0,21.0,0.0,4.0,1.0,19.0,0.28,-1.0,-0.05,111.0,288.0,38.5,620.0,109.0,35.0,33.8,138.0,51.4,42.1,283.0,20.0,7.1,21.0,1.0,15.7,86.0,7.0,35.0,4.1,0.08,0.23,0.09,-0.7,-1.3,8513.0,11019.0,77.3,153174.0,56512.0,3721.0,4355.0,85.4,3635.0,4276.0,85.0,912.0,1699.0,53.7,188.0,649.0,163.0,48.0,786.0,9732.0,1242.0,322.0,28.0,75.0,404.0,122.0,39.0,67.0,0.0,584.0,45.0,234.0,433.0,20.62,332.0,34.0,25.0,22.0,37.0,1.76,27.0,1.0,4.0,4.0,0.0,339.0,181.0,173.0,128.0,38.0,193.0,57.0,136.0,183.0,379.0,5.0,13354.0,1236.0,4228.0,6180.0,3075.0,444.0,13351.0,7521.0,349.0,246.0,76.0,8425.0,371.0,241.0,0.0,275.0,279.0,45.0,3.0,2.0,1.0,1141.0,396.0,316.0,55.6 -vs Milan,27.0,46.1,21.0,231.0,1890.0,29.0,22.0,3.0,4.0,69.0,0.0,1.38,1.05,2.43,1.24,2.29,24.3,21.1,1.16,1.01,21.0,21.0,1890.0,37.0,1.76,103.0,68.0,66.0,5.0,5.0,11.0,4.0,19.0,2.0,2.0,0.0,0.0,21.0,0.0,2.0,2.0,34.9,0.32,-0.1,-0.01,112.0,309.0,36.2,539.0,120.0,42.7,36.9,162.0,48.8,41.5,266.0,13.0,4.9,29.0,1.38,15.8,72.0,9.0,30.5,3.43,0.11,0.36,0.09,4.7,4.9,6703.0,8708.0,77.0,118568.0,48922.0,3137.0,3626.0,86.5,2565.0,3109.0,82.5,786.0,1466.0,53.6,192.0,503.0,140.0,36.0,650.0,7674.0,1009.0,282.0,13.0,108.0,289.0,90.0,43.0,30.0,0.0,400.0,25.0,150.0,423.0,20.14,316.0,43.0,26.0,28.0,51.0,2.43,36.0,6.0,3.0,6.0,0.0,347.0,200.0,182.0,131.0,34.0,234.0,82.0,152.0,174.0,388.0,9.0,11029.0,1342.0,3776.0,4812.0,2552.0,349.0,11025.0,6050.0,302.0,240.0,64.0,6634.0,364.0,227.0,0.0,255.0,251.0,25.0,4.0,2.0,2.0,1059.0,249.0,305.0,44.9 -vs Monza,29.0,44.1,21.0,231.0,1890.0,29.0,22.0,0.0,0.0,40.0,3.0,1.38,1.05,2.43,1.38,2.43,26.4,26.4,1.26,1.26,21.0,21.0,1890.0,27.0,1.29,73.0,47.0,68.5,9.0,5.0,7.0,5.0,23.8,4.0,4.0,0.0,0.0,21.0,2.0,1.0,1.0,23.2,0.26,-2.8,-0.13,92.0,239.0,38.5,536.0,88.0,33.4,33.9,139.0,43.2,40.3,288.0,16.0,5.6,27.0,1.29,16.2,90.0,9.0,35.3,4.29,0.11,0.32,0.11,2.6,2.6,7054.0,8988.0,78.5,128839.0,49257.0,3108.0,3541.0,87.8,2880.0,3409.0,84.5,886.0,1533.0,57.8,204.0,579.0,163.0,46.0,743.0,7922.0,1029.0,298.0,15.0,109.0,366.0,108.0,45.0,47.0,0.0,411.0,37.0,175.0,454.0,21.62,328.0,48.0,31.0,20.0,51.0,2.43,38.0,4.0,6.0,0.0,1.0,322.0,183.0,157.0,120.0,45.0,217.0,64.0,153.0,186.0,343.0,4.0,11206.0,1231.0,3560.0,5032.0,2723.0,437.0,11206.0,6201.0,391.0,257.0,95.0,6982.0,353.0,192.0,1.0,303.0,263.0,37.0,0.0,4.0,1.0,1075.0,226.0,241.0,48.4 -vs Napoli,24.0,38.6,21.0,231.0,1890.0,15.0,10.0,1.0,2.0,61.0,3.0,0.71,0.48,1.19,0.67,1.14,18.3,16.7,0.87,0.8,21.0,21.0,1890.0,51.0,2.43,127.0,75.0,63.8,1.0,2.0,18.0,2.0,9.5,6.0,5.0,1.0,0.0,21.0,0.0,10.0,0.0,40.0,0.27,-11.0,-0.52,119.0,368.0,32.3,602.0,99.0,45.3,37.2,152.0,62.5,47.0,278.0,6.0,2.2,16.0,0.76,15.0,54.0,4.0,25.7,2.57,0.07,0.26,0.08,-3.3,-2.7,6370.0,8285.0,76.9,113925.0,41904.0,2879.0,3293.0,87.4,2666.0,3142.0,84.9,672.0,1403.0,47.9,159.0,453.0,132.0,35.0,572.0,7320.0,937.0,236.0,13.0,66.0,280.0,89.0,42.0,37.0,0.0,353.0,28.0,167.0,367.0,17.48,270.0,36.0,21.0,14.0,26.0,1.24,20.0,0.0,4.0,0.0,1.0,416.0,241.0,223.0,140.0,53.0,248.0,95.0,153.0,188.0,378.0,4.0,10628.0,1489.0,4136.0,4385.0,2215.0,343.0,10626.0,5498.0,289.0,204.0,79.0,6319.0,314.0,195.0,2.0,291.0,192.0,28.0,1.0,5.0,0.0,1049.0,214.0,259.0,45.2 -vs Roma,26.0,51.1,21.0,231.0,1890.0,18.0,14.0,0.0,0.0,66.0,4.0,0.86,0.67,1.52,0.86,1.52,15.5,15.5,0.74,0.74,21.0,21.0,1890.0,28.0,1.33,92.0,64.0,72.8,5.0,4.0,12.0,4.0,19.0,5.0,3.0,0.0,2.0,21.0,0.0,7.0,0.0,29.5,0.29,1.5,0.07,83.0,231.0,35.9,595.0,97.0,27.7,32.5,128.0,51.6,42.5,239.0,10.0,4.2,32.0,1.52,16.3,60.0,16.0,26.4,2.86,0.08,0.3,0.07,2.5,2.5,8447.0,10442.0,80.9,147660.0,52459.0,3832.0,4319.0,88.7,3529.0,4090.0,86.3,844.0,1480.0,57.0,181.0,634.0,120.0,41.0,772.0,9456.0,976.0,271.0,9.0,74.0,324.0,82.0,34.0,32.0,0.0,418.0,10.0,187.0,404.0,19.24,301.0,44.0,15.0,29.0,29.0,1.38,25.0,1.0,2.0,1.0,0.0,364.0,222.0,156.0,155.0,53.0,215.0,76.0,139.0,160.0,309.0,9.0,12637.0,1275.0,3810.0,6012.0,2923.0,295.0,12637.0,7346.0,329.0,274.0,56.0,8374.0,313.0,205.0,1.0,292.0,238.0,10.0,0.0,5.0,0.0,1095.0,205.0,245.0,45.6 -vs Salernitana,28.0,54.9,21.0,231.0,1890.0,41.0,25.0,5.0,8.0,41.0,4.0,1.95,1.19,3.14,1.71,2.9,39.8,33.1,1.9,1.58,21.0,21.0,1890.0,25.0,1.19,68.0,44.0,64.7,10.0,6.0,5.0,7.0,33.3,1.0,1.0,0.0,0.0,21.0,0.0,2.0,1.0,23.3,0.33,-0.7,-0.04,57.0,185.0,30.8,522.0,101.0,28.2,31.8,123.0,30.9,31.6,259.0,14.0,5.4,19.0,0.9,16.6,112.0,7.0,34.8,5.33,0.11,0.32,0.11,1.2,2.9,9135.0,11180.0,81.7,160217.0,55935.0,4138.0,4609.0,89.8,3801.0,4373.0,86.9,947.0,1614.0,58.7,245.0,720.0,206.0,40.0,905.0,10160.0,968.0,277.0,34.0,87.0,369.0,93.0,44.0,45.0,1.0,404.0,52.0,161.0,570.0,27.14,430.0,38.0,35.0,28.0,70.0,3.33,50.0,1.0,9.0,5.0,1.0,321.0,182.0,120.0,154.0,47.0,206.0,55.0,151.0,165.0,321.0,4.0,13389.0,1173.0,3889.0,6394.0,3247.0,544.0,13381.0,8011.0,451.0,329.0,112.0,9057.0,312.0,194.0,1.0,238.0,242.0,52.0,8.0,1.0,1.0,1143.0,268.0,247.0,52.0 -vs Sampdoria,31.0,52.1,21.0,231.0,1890.0,34.0,24.0,5.0,7.0,47.0,1.0,1.62,1.14,2.76,1.38,2.52,32.7,27.4,1.56,1.3,21.0,21.0,1890.0,10.0,0.48,67.0,57.0,85.1,15.0,4.0,2.0,13.0,61.9,0.0,0.0,0.0,0.0,21.0,0.0,1.0,0.0,13.8,0.21,3.8,0.18,92.0,272.0,33.8,599.0,101.0,36.4,34.7,140.0,38.6,39.1,284.0,10.0,3.5,16.0,0.76,15.0,96.0,9.0,35.3,4.57,0.11,0.3,0.1,1.3,1.6,8049.0,10254.0,78.5,144492.0,52267.0,3493.0,4008.0,87.2,3399.0,3990.0,85.2,898.0,1614.0,55.6,211.0,609.0,194.0,47.0,819.0,9177.0,1021.0,325.0,20.0,102.0,357.0,97.0,33.0,38.0,0.0,415.0,56.0,196.0,490.0,23.33,349.0,48.0,35.0,30.0,55.0,2.62,43.0,4.0,4.0,3.0,0.0,354.0,216.0,169.0,142.0,43.0,239.0,55.0,184.0,179.0,390.0,3.0,12577.0,1292.0,4285.0,5448.0,2968.0,472.0,12570.0,6922.0,367.0,263.0,81.0,7967.0,302.0,194.0,1.0,328.0,288.0,56.0,4.0,0.0,0.0,1126.0,340.0,339.0,50.1 -vs Sassuolo,29.0,51.5,21.0,231.0,1890.0,31.0,22.0,4.0,4.0,47.0,3.0,1.48,1.05,2.52,1.29,2.33,27.8,25.2,1.32,1.2,21.0,21.0,1890.0,24.0,1.14,100.0,75.0,80.0,10.0,5.0,6.0,7.0,33.3,5.0,4.0,1.0,0.0,21.0,0.0,3.0,0.0,27.2,0.22,3.2,0.15,110.0,264.0,41.7,648.0,114.0,31.2,31.0,136.0,45.6,39.3,247.0,9.0,3.6,17.0,0.81,14.9,62.0,12.0,25.3,2.95,0.11,0.44,0.11,3.2,1.8,8498.0,10500.0,80.9,153288.0,55071.0,3632.0,4066.0,89.3,3721.0,4235.0,87.9,918.0,1610.0,57.0,194.0,614.0,199.0,59.0,803.0,9490.0,941.0,220.0,28.0,89.0,373.0,93.0,32.0,40.0,0.0,422.0,69.0,166.0,446.0,21.24,343.0,42.0,20.0,19.0,50.0,2.38,39.0,1.0,5.0,2.0,0.0,368.0,195.0,179.0,146.0,43.0,245.0,77.0,168.0,182.0,326.0,6.0,12635.0,1403.0,4347.0,5474.0,2931.0,462.0,12631.0,7332.0,383.0,255.0,92.0,8420.0,286.0,174.0,1.0,275.0,200.0,69.0,2.0,5.0,0.0,1088.0,243.0,195.0,55.5 -vs Spezia,33.0,54.1,21.0,231.0,1890.0,35.0,28.0,1.0,2.0,40.0,1.0,1.67,1.33,3.0,1.62,2.95,33.9,32.3,1.61,1.54,21.0,21.0,1890.0,17.0,0.81,66.0,51.0,77.3,11.0,6.0,4.0,10.0,47.6,2.0,2.0,0.0,0.0,21.0,0.0,2.0,2.0,16.9,0.23,1.9,0.09,78.0,240.0,32.5,546.0,87.0,33.3,34.9,140.0,41.4,39.5,271.0,17.0,6.3,28.0,1.33,15.6,108.0,8.0,30.9,5.14,0.1,0.31,0.09,1.1,1.7,8348.0,10683.0,78.1,151538.0,54882.0,3560.0,4174.0,85.3,3567.0,4188.0,85.2,979.0,1698.0,57.7,274.0,595.0,197.0,51.0,818.0,9513.0,1120.0,327.0,41.0,90.0,375.0,106.0,37.0,48.0,0.0,496.0,50.0,190.0,612.0,29.14,429.0,56.0,52.0,25.0,63.0,3.0,49.0,3.0,5.0,1.0,0.0,374.0,204.0,181.0,149.0,44.0,226.0,64.0,162.0,221.0,391.0,4.0,13192.0,1233.0,4296.0,5896.0,3117.0,548.0,13190.0,7265.0,385.0,277.0,116.0,8271.0,341.0,233.0,1.0,225.0,279.0,50.0,1.0,2.0,2.0,1225.0,337.0,285.0,54.2 -vs Torino,25.0,47.0,21.0,231.0,1890.0,22.0,14.0,3.0,4.0,39.0,0.0,1.05,0.67,1.71,0.9,1.57,24.8,21.7,1.18,1.03,21.0,21.0,1890.0,22.0,1.05,85.0,63.0,75.3,7.0,6.0,8.0,5.0,23.8,1.0,1.0,0.0,0.0,21.0,0.0,0.0,0.0,24.9,0.28,2.9,0.14,145.0,368.0,39.4,693.0,93.0,42.4,36.7,131.0,56.5,43.4,257.0,11.0,4.3,11.0,0.52,12.7,79.0,8.0,31.5,3.76,0.08,0.24,0.09,-2.8,-2.7,6915.0,9195.0,75.2,127810.0,51556.0,2962.0,3461.0,85.6,2924.0,3570.0,81.9,844.0,1678.0,50.3,193.0,501.0,146.0,46.0,631.0,8125.0,1047.0,331.0,11.0,47.0,340.0,87.0,35.0,30.0,0.0,411.0,23.0,148.0,431.0,20.52,314.0,42.0,32.0,23.0,35.0,1.67,25.0,3.0,4.0,3.0,0.0,353.0,172.0,182.0,139.0,32.0,233.0,73.0,160.0,175.0,360.0,3.0,11472.0,1383.0,4288.0,4901.0,2417.0,390.0,11468.0,5901.0,277.0,217.0,69.0,6807.0,350.0,161.0,0.0,229.0,287.0,23.0,3.0,1.0,0.0,1095.0,347.0,319.0,52.1 -vs Udinese,25.0,50.0,21.0,231.0,1890.0,22.0,16.0,2.0,2.0,51.0,2.0,1.05,0.76,1.81,0.95,1.71,26.6,25.1,1.27,1.2,21.0,21.0,1890.0,28.0,1.33,83.0,56.0,66.3,6.0,8.0,7.0,5.0,23.8,0.0,0.0,0.0,0.0,21.0,1.0,3.0,1.0,22.3,0.27,-4.7,-0.22,92.0,256.0,35.9,502.0,91.0,36.7,35.9,127.0,56.7,46.5,255.0,18.0,7.1,31.0,1.48,14.9,68.0,11.0,27.9,3.24,0.08,0.29,0.11,-4.6,-5.1,7641.0,9691.0,78.8,138480.0,51372.0,3429.0,3915.0,87.6,3084.0,3607.0,85.5,923.0,1602.0,57.6,172.0,589.0,160.0,49.0,787.0,8612.0,1045.0,270.0,28.0,74.0,361.0,107.0,38.0,47.0,0.0,444.0,34.0,199.0,423.0,20.14,303.0,38.0,30.0,27.0,42.0,2.0,33.0,1.0,2.0,3.0,1.0,403.0,215.0,218.0,135.0,50.0,265.0,98.0,167.0,157.0,363.0,9.0,12045.0,1281.0,3878.0,5568.0,2727.0,416.0,12043.0,6644.0,335.0,269.0,64.0,7566.0,343.0,189.0,1.0,274.0,242.0,34.0,2.0,0.0,1.0,1063.0,222.0,264.0,45.7 +vs Atalanta,25.0,50.0,31.0,341.0,2790.0,34.0,26.0,3.0,3.0,70.0,0.0,1.1,0.84,1.94,1.0,1.84,33.7,31.4,1.09,1.01,31.0,31.0,2790.0,51.0,1.65,143.0,91.0,68.5,9.0,7.0,15.0,6.0,19.4,8.0,6.0,2.0,0.0,31.0,0.0,3.0,1.0,48.5,0.29,-1.5,-0.05,170.0,503.0,33.8,1042.0,140.0,38.8,35.7,229.0,43.2,39.5,411.0,15.0,3.6,43.0,1.39,16.0,116.0,15.0,30.9,3.74,0.08,0.27,0.08,0.3,-0.4,12385.0,15785.0,78.5,218820.0,81779.0,5599.0,6353.0,88.1,5015.0,5929.0,84.6,1370.0,2504.0,54.7,283.0,907.0,220.0,64.0,1080.0,14173.0,1572.0,406.0,25.0,95.0,485.0,123.0,32.0,54.0,0.0,698.0,40.0,324.0,660.0,21.29,496.0,53.0,31.0,34.0,60.0,1.94,45.0,4.0,3.0,3.0,0.0,607.0,377.0,335.0,208.0,64.0,377.0,108.0,269.0,299.0,576.0,7.0,19456.0,2106.0,6629.0,8776.0,4256.0,576.0,19453.0,11179.0,502.0,394.0,114.0,12239.0,531.0,266.0,0.0,338.0,356.0,40.0,1.0,8.0,1.0,1865.0,389.0,472.0,45.2 +vs Bologna,27.0,46.6,31.0,341.0,2790.0,37.0,22.0,7.0,8.0,71.0,2.0,1.19,0.71,1.9,0.97,1.68,39.5,33.2,1.27,1.07,31.0,31.0,2788.0,40.0,1.29,119.0,80.0,69.7,11.0,8.0,12.0,7.0,22.6,4.0,4.0,0.0,0.0,31.0,0.0,6.0,1.0,32.8,0.25,-6.2,-0.2,180.0,489.0,36.8,832.0,139.0,42.9,36.5,223.0,59.2,46.4,364.0,20.0,5.5,40.0,1.29,16.8,128.0,9.0,32.7,4.13,0.08,0.23,0.09,-2.5,-3.2,10902.0,14087.0,77.4,192804.0,72954.0,4873.0,5629.0,86.6,4529.0,5350.0,84.7,1144.0,2195.0,52.1,292.0,858.0,229.0,71.0,1065.0,12454.0,1581.0,423.0,28.0,88.0,550.0,141.0,55.0,68.0,0.0,687.0,52.0,287.0,681.0,21.97,472.0,67.0,57.0,28.0,61.0,1.97,39.0,3.0,7.0,6.0,1.0,454.0,273.0,211.0,185.0,58.0,378.0,92.0,286.0,262.0,484.0,7.0,17418.0,1719.0,5266.0,7971.0,4361.0,651.0,17410.0,9466.0,460.0,391.0,109.0,10822.0,486.0,296.0,1.0,376.0,365.0,52.0,5.0,4.0,1.0,1687.0,379.0,306.0,55.3 +vs Cremonese,32.0,56.8,31.0,341.0,2790.0,56.0,38.0,5.0,5.0,56.0,0.0,1.81,1.23,3.03,1.65,2.87,55.3,51.4,1.78,1.66,31.0,31.0,2790.0,27.0,0.87,105.0,76.0,78.1,18.0,10.0,3.0,13.0,41.9,6.0,4.0,2.0,0.0,31.0,0.0,2.0,0.0,27.7,0.21,0.7,0.02,136.0,366.0,37.2,812.0,162.0,32.3,34.2,258.0,40.3,37.5,447.0,28.0,6.3,31.0,1.0,14.5,178.0,18.0,38.7,5.74,0.11,0.29,0.12,0.7,-0.4,13160.0,16560.0,79.5,241080.0,88260.0,5772.0,6637.0,87.0,5451.0,6394.0,85.3,1618.0,2715.0,59.6,353.0,946.0,281.0,77.0,1286.0,14894.0,1614.0,427.0,38.0,159.0,552.0,154.0,52.0,69.0,0.0,695.0,52.0,285.0,830.0,26.77,578.0,83.0,58.0,43.0,100.0,3.23,70.0,4.0,10.0,8.0,2.0,451.0,270.0,218.0,170.0,63.0,340.0,107.0,233.0,251.0,639.0,11.0,20159.0,2019.0,6716.0,8787.0,4865.0,733.0,20154.0,11468.0,605.0,455.0,150.0,13054.0,504.0,283.0,0.0,345.0,372.0,52.0,4.0,6.0,0.0,1745.0,594.0,462.0,56.3 +vs Empoli,31.0,52.6,31.0,341.0,2790.0,39.0,27.0,2.0,4.0,69.0,4.0,1.26,0.87,2.13,1.19,2.06,48.1,45.1,1.55,1.45,31.0,31.0,2790.0,25.0,0.81,108.0,83.0,77.8,13.0,11.0,7.0,11.0,35.5,1.0,1.0,0.0,0.0,31.0,1.0,3.0,0.0,23.2,0.21,-1.8,-0.06,125.0,314.0,39.8,774.0,148.0,30.5,33.7,203.0,38.4,38.0,379.0,11.0,2.9,29.0,0.94,15.3,125.0,19.0,25.1,4.03,0.07,0.3,0.09,-9.1,-8.1,12300.0,15379.0,80.0,228637.0,80884.0,5375.0,6072.0,88.5,4940.0,5853.0,84.4,1665.0,2627.0,63.4,375.0,997.0,331.0,111.0,1408.0,13772.0,1547.0,368.0,27.0,267.0,796.0,199.0,63.0,84.0,2.0,702.0,60.0,278.0,894.0,28.84,662.0,88.0,54.0,40.0,68.0,2.19,50.0,8.0,7.0,1.0,0.0,567.0,312.0,265.0,226.0,76.0,385.0,115.0,270.0,275.0,527.0,7.0,18873.0,1754.0,5597.0,7896.0,5553.0,842.0,18869.0,10602.0,708.0,470.0,167.0,12209.0,471.0,290.0,2.0,384.0,341.0,60.0,2.0,1.0,0.0,1607.0,412.0,330.0,55.5 +vs Fiorentina,29.0,43.4,31.0,341.0,2790.0,34.0,24.0,4.0,4.0,87.0,4.0,1.1,0.77,1.87,0.97,1.74,36.2,33.4,1.17,1.08,31.0,31.0,2790.0,37.0,1.19,145.0,108.0,77.2,11.0,9.0,11.0,8.0,25.8,6.0,4.0,2.0,0.0,31.0,1.0,7.0,2.0,37.4,0.22,2.4,0.08,223.0,635.0,35.1,1036.0,163.0,44.6,37.8,256.0,67.6,50.6,563.0,28.0,5.0,24.0,0.77,12.1,99.0,8.0,33.4,3.19,0.1,0.3,0.12,-2.2,-3.4,8988.0,12116.0,74.2,168766.0,67965.0,3769.0,4428.0,85.1,3839.0,4617.0,83.1,1137.0,2313.0,49.2,228.0,610.0,185.0,59.0,777.0,10538.0,1500.0,416.0,35.0,104.0,391.0,103.0,33.0,60.0,0.0,610.0,78.0,254.0,510.0,16.45,349.0,49.0,34.0,23.0,58.0,1.87,41.0,3.0,5.0,3.0,1.0,538.0,297.0,294.0,195.0,49.0,382.0,144.0,238.0,273.0,715.0,8.0,15611.0,2501.0,6796.0,6107.0,2906.0,509.0,15607.0,7664.0,383.0,251.0,116.0,8888.0,445.0,246.0,1.0,460.0,371.0,78.0,2.0,6.0,2.0,1625.0,425.0,504.0,45.7 +vs Hellas Verona,36.0,58.0,31.0,341.0,2790.0,43.0,39.0,0.0,1.0,63.0,4.0,1.39,1.26,2.65,1.39,2.65,39.3,38.5,1.27,1.24,31.0,31.0,2790.0,26.0,0.84,95.0,71.0,73.7,17.0,8.0,6.0,11.0,35.5,1.0,1.0,0.0,0.0,31.0,0.0,5.0,2.0,24.9,0.25,0.9,0.03,228.0,600.0,38.0,1029.0,122.0,46.6,37.5,188.0,64.4,47.8,405.0,20.0,4.9,36.0,1.16,15.6,147.0,16.0,33.6,4.74,0.1,0.29,0.09,3.7,4.5,12402.0,15964.0,77.7,220791.0,87444.0,5599.0,6446.0,86.9,5180.0,6150.0,84.2,1306.0,2526.0,51.7,325.0,1035.0,275.0,61.0,1209.0,14257.0,1675.0,499.0,35.0,77.0,510.0,162.0,60.0,78.0,0.0,689.0,32.0,285.0,750.0,24.19,544.0,70.0,49.0,38.0,77.0,2.48,62.0,5.0,2.0,2.0,0.0,507.0,282.0,259.0,191.0,57.0,315.0,85.0,230.0,224.0,583.0,2.0,19505.0,1943.0,6136.0,9079.0,4466.0,726.0,19504.0,10427.0,485.0,360.0,130.0,12256.0,560.0,263.0,0.0,337.0,438.0,32.0,1.0,1.0,2.0,1728.0,619.0,615.0,50.2 +vs Inter,24.0,43.8,31.0,341.0,2790.0,32.0,27.0,3.0,3.0,76.0,1.0,1.03,0.87,1.9,0.94,1.81,28.0,25.6,0.9,0.83,31.0,31.0,2790.0,51.0,1.65,160.0,109.0,70.6,11.0,3.0,17.0,7.0,22.6,5.0,4.0,1.0,0.0,31.0,2.0,6.0,1.0,50.1,0.29,0.1,0.0,147.0,464.0,31.7,798.0,145.0,37.1,36.1,287.0,58.5,46.2,614.0,24.0,3.9,15.0,0.48,10.7,102.0,17.0,31.4,3.29,0.09,0.28,0.08,4.0,3.4,10730.0,13569.0,79.1,195481.0,70263.0,4624.0,5223.0,88.5,4604.0,5356.0,86.0,1238.0,2247.0,55.1,259.0,737.0,209.0,71.0,975.0,12054.0,1486.0,401.0,10.0,121.0,430.0,112.0,39.0,54.0,0.0,579.0,29.0,260.0,587.0,18.94,459.0,43.0,26.0,31.0,57.0,1.84,42.0,7.0,3.0,5.0,0.0,375.0,204.0,182.0,148.0,45.0,308.0,129.0,179.0,277.0,714.0,8.0,16859.0,2248.0,6156.0,7386.0,3484.0,488.0,16856.0,9006.0,546.0,385.0,103.0,10652.0,439.0,254.0,0.0,368.0,336.0,29.0,3.0,5.0,1.0,1443.0,344.0,443.0,43.7 +vs Juventus,29.0,51.4,31.0,341.0,2790.0,26.0,22.0,1.0,2.0,63.0,2.0,0.84,0.71,1.55,0.81,1.52,32.8,31.3,1.06,1.01,31.0,31.0,2790.0,47.0,1.52,144.0,96.0,69.4,8.0,5.0,18.0,7.0,22.6,5.0,3.0,1.0,1.0,31.0,3.0,10.0,0.0,41.9,0.28,-5.1,-0.16,152.0,361.0,42.1,758.0,126.0,33.5,33.8,221.0,48.4,41.2,443.0,12.0,2.7,25.0,0.81,14.7,104.0,13.0,28.0,3.35,0.07,0.24,0.09,-6.8,-6.3,13076.0,15957.0,81.9,229244.0,77069.0,6140.0,6805.0,90.2,5278.0,6113.0,86.3,1361.0,2263.0,60.1,281.0,1075.0,224.0,72.0,1151.0,14450.0,1468.0,399.0,21.0,116.0,589.0,154.0,60.0,63.0,0.0,592.0,39.0,273.0,644.0,20.77,511.0,46.0,30.0,19.0,45.0,1.45,32.0,2.0,7.0,0.0,1.0,474.0,266.0,212.0,196.0,66.0,307.0,97.0,210.0,294.0,536.0,8.0,19199.0,1910.0,5617.0,8941.0,4857.0,534.0,19197.0,11214.0,607.0,503.0,107.0,12974.0,490.0,244.0,0.0,348.0,328.0,39.0,0.0,5.0,0.0,1556.0,381.0,390.0,49.4 +vs Lazio,22.0,48.1,31.0,341.0,2790.0,21.0,14.0,1.0,1.0,86.0,6.0,0.68,0.45,1.13,0.65,1.1,33.7,32.9,1.09,1.06,31.0,31.0,2790.0,49.0,1.58,130.0,81.0,66.2,6.0,7.0,18.0,6.0,19.4,7.0,5.0,1.0,1.0,31.0,1.0,3.0,1.0,44.6,0.3,-3.4,-0.11,161.0,366.0,44.0,1009.0,141.0,28.0,30.5,194.0,42.8,38.1,305.0,17.0,5.6,46.0,1.48,15.1,101.0,5.0,27.1,3.26,0.05,0.2,0.09,-12.7,-12.9,13390.0,16296.0,82.2,249763.0,80361.0,5361.0,5992.0,89.5,6209.0,6925.0,89.7,1526.0,2541.0,60.1,282.0,884.0,225.0,71.0,1054.0,14849.0,1392.0,355.0,21.0,171.0,530.0,150.0,64.0,50.0,0.0,602.0,55.0,300.0,647.0,20.87,459.0,64.0,42.0,23.0,38.0,1.23,26.0,3.0,4.0,3.0,1.0,433.0,240.0,175.0,197.0,61.0,307.0,79.0,228.0,272.0,444.0,12.0,19357.0,2139.0,7258.0,8388.0,3927.0,585.0,19356.0,11627.0,485.0,333.0,118.0,13309.0,429.0,247.0,4.0,421.0,304.0,55.0,1.0,7.0,1.0,1677.0,319.0,319.0,50.0 +vs Lecce,29.0,58.3,31.0,341.0,2790.0,35.0,25.0,5.0,5.0,70.0,1.0,1.13,0.81,1.94,0.97,1.77,34.2,30.5,1.1,0.99,31.0,31.0,2790.0,26.0,0.84,88.0,63.0,71.6,15.0,10.0,6.0,12.0,38.7,2.0,1.0,1.0,0.0,31.0,0.0,5.0,2.0,24.6,0.26,0.6,0.02,150.0,401.0,37.4,937.0,170.0,33.1,32.8,218.0,41.7,37.5,430.0,29.0,6.7,30.0,0.97,15.4,127.0,16.0,35.5,4.1,0.08,0.24,0.09,0.8,-0.5,13045.0,16734.0,78.0,231160.0,84831.0,5816.0,6753.0,86.1,5560.0,6521.0,85.3,1307.0,2451.0,53.3,270.0,934.0,227.0,66.0,1152.0,14816.0,1861.0,480.0,41.0,120.0,568.0,169.0,56.0,86.0,0.0,898.0,57.0,359.0,629.0,20.29,459.0,61.0,33.0,40.0,58.0,1.87,40.0,3.0,4.0,6.0,0.0,483.0,269.0,254.0,175.0,54.0,304.0,92.0,212.0,259.0,559.0,7.0,20189.0,1894.0,6569.0,9283.0,4536.0,625.0,20184.0,11209.0,509.0,374.0,108.0,12913.0,559.0,337.0,0.0,378.0,425.0,57.0,4.0,2.0,2.0,1694.0,582.0,466.0,55.5 +vs Milan,29.0,46.0,31.0,341.0,2790.0,36.0,28.0,4.0,5.0,94.0,0.0,1.16,0.9,2.06,1.03,1.94,33.5,29.6,1.08,0.95,31.0,31.0,2790.0,51.0,1.65,154.0,106.0,68.8,7.0,8.0,16.0,5.0,16.1,3.0,3.0,0.0,0.0,31.0,0.0,4.0,3.0,50.0,0.31,2.0,0.06,152.0,450.0,33.8,758.0,155.0,41.7,36.8,247.0,54.3,43.9,424.0,17.0,4.0,38.0,1.23,15.1,104.0,14.0,31.1,3.35,0.1,0.31,0.09,2.5,2.4,10215.0,13209.0,77.3,178177.0,71502.0,4861.0,5564.0,87.4,3880.0,4694.0,82.7,1140.0,2144.0,53.2,274.0,748.0,203.0,55.0,986.0,11667.0,1511.0,412.0,16.0,147.0,450.0,145.0,74.0,42.0,0.0,605.0,31.0,254.0,597.0,19.26,427.0,61.0,34.0,40.0,63.0,2.03,42.0,6.0,4.0,7.0,1.0,521.0,304.0,282.0,189.0,50.0,338.0,115.0,223.0,271.0,633.0,12.0,16693.0,2000.0,5776.0,7335.0,3769.0,498.0,16688.0,9320.0,449.0,369.0,84.0,10126.0,529.0,337.0,0.0,378.0,369.0,31.0,5.0,3.0,3.0,1617.0,387.0,456.0,45.9 +vs Monza,31.0,45.0,31.0,341.0,2790.0,42.0,27.0,1.0,1.0,63.0,3.0,1.35,0.87,2.23,1.32,2.19,39.8,39.0,1.28,1.26,31.0,31.0,2790.0,38.0,1.23,114.0,78.0,71.1,12.0,8.0,11.0,8.0,25.8,5.0,5.0,0.0,0.0,31.0,2.0,3.0,2.0,33.4,0.25,-2.6,-0.08,131.0,325.0,40.3,805.0,132.0,30.8,32.9,194.0,39.7,38.5,388.0,24.0,6.2,41.0,1.32,16.0,133.0,11.0,34.6,4.29,0.11,0.31,0.1,2.2,2.0,10812.0,13653.0,79.2,196683.0,74552.0,4786.0,5464.0,87.6,4394.0,5168.0,85.0,1346.0,2285.0,58.9,296.0,885.0,250.0,67.0,1143.0,12121.0,1484.0,447.0,21.0,165.0,563.0,149.0,54.0,70.0,0.0,595.0,48.0,247.0,687.0,22.16,498.0,72.0,45.0,30.0,72.0,2.32,54.0,5.0,8.0,2.0,1.0,457.0,268.0,201.0,183.0,73.0,315.0,93.0,222.0,269.0,468.0,5.0,16856.0,1750.0,5147.0,7687.0,4209.0,660.0,16855.0,9654.0,602.0,400.0,133.0,10708.0,517.0,272.0,1.0,437.0,385.0,48.0,1.0,5.0,2.0,1608.0,340.0,357.0,48.8 +vs Napoli,26.0,38.2,31.0,341.0,2790.0,21.0,13.0,1.0,2.0,84.0,3.0,0.68,0.42,1.1,0.65,1.06,25.7,24.1,0.83,0.78,31.0,31.0,2790.0,67.0,2.16,174.0,108.0,64.9,3.0,3.0,25.0,5.0,16.1,7.0,6.0,1.0,0.0,31.0,0.0,14.0,2.0,51.6,0.26,-13.4,-0.43,168.0,515.0,32.6,873.0,141.0,43.2,36.7,221.0,62.4,47.3,427.0,13.0,3.0,21.0,0.68,12.9,82.0,9.0,27.7,2.65,0.07,0.24,0.08,-4.7,-4.1,9386.0,12234.0,76.7,165873.0,62646.0,4346.0,4974.0,87.4,3849.0,4573.0,84.2,951.0,1989.0,47.8,226.0,643.0,189.0,49.0,828.0,10812.0,1383.0,353.0,18.0,87.0,407.0,125.0,61.0,51.0,0.0,538.0,39.0,262.0,521.0,16.81,367.0,53.0,31.0,22.0,36.0,1.16,25.0,0.0,5.0,0.0,2.0,578.0,333.0,325.0,192.0,61.0,369.0,140.0,229.0,268.0,593.0,12.0,15664.0,2205.0,6160.0,6462.0,3208.0,477.0,15662.0,8167.0,426.0,309.0,107.0,9319.0,449.0,265.0,2.0,405.0,287.0,39.0,1.0,6.0,2.0,1562.0,322.0,389.0,45.3 +vs Roma,27.0,50.8,31.0,341.0,2790.0,28.0,18.0,2.0,3.0,98.0,7.0,0.9,0.58,1.48,0.84,1.42,24.7,22.4,0.8,0.72,31.0,31.0,2790.0,43.0,1.39,136.0,93.0,72.8,9.0,5.0,17.0,5.0,16.1,9.0,6.0,0.0,3.0,31.0,0.0,7.0,0.0,43.8,0.28,0.8,0.03,132.0,397.0,33.2,877.0,140.0,32.7,34.2,187.0,58.8,46.2,363.0,18.0,5.0,52.0,1.68,16.4,93.0,23.0,29.0,3.0,0.08,0.28,0.07,3.3,3.6,12382.0,15378.0,80.5,218765.0,76184.0,5506.0,6242.0,88.2,5290.0,6118.0,86.5,1255.0,2240.0,56.0,241.0,933.0,159.0,47.0,1131.0,13968.0,1389.0,393.0,13.0,113.0,461.0,117.0,51.0,44.0,0.0,579.0,21.0,269.0,578.0,18.65,418.0,54.0,30.0,39.0,47.0,1.52,35.0,1.0,7.0,3.0,0.0,528.0,325.0,237.0,226.0,65.0,321.0,111.0,210.0,249.0,491.0,10.0,18626.0,1898.0,5675.0,9003.0,4121.0,426.0,18623.0,10944.0,508.0,413.0,98.0,12271.0,472.0,273.0,2.0,447.0,344.0,21.0,2.0,9.0,0.0,1621.0,331.0,426.0,43.7 +vs Salernitana,28.0,55.4,31.0,341.0,2790.0,50.0,32.0,6.0,10.0,58.0,5.0,1.61,1.03,2.65,1.42,2.45,55.7,47.3,1.8,1.53,31.0,31.0,2790.0,37.0,1.19,99.0,64.0,63.6,12.0,12.0,7.0,10.0,32.3,1.0,1.0,0.0,0.0,31.0,0.0,5.0,2.0,31.5,0.31,-3.5,-0.11,95.0,315.0,30.2,791.0,140.0,30.7,32.6,177.0,40.7,37.0,366.0,20.0,5.5,36.0,1.16,16.7,155.0,12.0,32.3,5.0,0.09,0.28,0.1,-5.7,-3.3,13633.0,16740.0,81.4,239387.0,82347.0,6163.0,6918.0,89.1,5763.0,6624.0,87.0,1370.0,2373.0,57.7,361.0,1068.0,297.0,65.0,1349.0,15213.0,1463.0,413.0,40.0,117.0,557.0,151.0,79.0,65.0,1.0,620.0,64.0,249.0,840.0,27.1,621.0,59.0,59.0,38.0,85.0,2.74,60.0,3.0,9.0,7.0,1.0,445.0,256.0,173.0,207.0,65.0,306.0,80.0,226.0,236.0,480.0,7.0,19970.0,1704.0,5684.0,9774.0,4720.0,768.0,19960.0,11873.0,662.0,473.0,164.0,13535.0,448.0,268.0,2.0,375.0,357.0,64.0,10.0,1.0,2.0,1709.0,448.0,427.0,51.2 +vs Sampdoria,37.0,52.6,31.0,341.0,2790.0,50.0,36.0,6.0,9.0,66.0,1.0,1.61,1.16,2.77,1.42,2.58,51.3,44.5,1.66,1.43,31.0,31.0,2790.0,20.0,0.65,99.0,78.0,80.8,20.0,8.0,3.0,17.0,54.8,2.0,1.0,1.0,0.0,31.0,0.0,3.0,0.0,22.2,0.21,2.2,0.07,138.0,386.0,35.8,868.0,143.0,36.2,34.3,188.0,38.3,38.4,408.0,22.0,5.4,25.0,0.81,14.2,157.0,20.0,33.7,5.06,0.09,0.28,0.1,-1.3,-0.5,12187.0,15398.0,79.1,217800.0,79991.0,5340.0,6094.0,87.6,5152.0,6000.0,85.9,1330.0,2394.0,55.6,354.0,949.0,297.0,77.0,1269.0,13830.0,1493.0,445.0,39.0,137.0,590.0,178.0,66.0,74.0,0.0,609.0,75.0,271.0,823.0,26.55,571.0,89.0,66.0,46.0,82.0,2.65,62.0,6.0,8.0,4.0,0.0,491.0,311.0,223.0,203.0,65.0,367.0,85.0,282.0,259.0,565.0,4.0,18852.0,1884.0,6163.0,8318.0,4580.0,776.0,18843.0,10478.0,553.0,413.0,132.0,12061.0,442.0,270.0,1.0,449.0,408.0,75.0,6.0,2.0,0.0,1693.0,535.0,517.0,50.9 +vs Sassuolo,29.0,51.1,31.0,341.0,2790.0,46.0,35.0,4.0,4.0,69.0,4.0,1.48,1.13,2.61,1.35,2.48,40.2,37.6,1.3,1.21,31.0,31.0,2790.0,38.0,1.23,144.0,106.0,77.8,13.0,7.0,11.0,9.0,29.0,7.0,6.0,1.0,0.0,31.0,1.0,5.0,1.0,38.9,0.23,1.9,0.06,164.0,411.0,39.9,928.0,161.0,33.2,32.1,220.0,46.8,39.7,384.0,17.0,4.4,23.0,0.74,13.9,98.0,13.0,25.3,3.16,0.11,0.43,0.1,5.8,4.4,12383.0,15407.0,80.4,224055.0,80728.0,5387.0,6031.0,89.3,5317.0,6109.0,87.0,1358.0,2419.0,56.1,304.0,885.0,276.0,91.0,1128.0,13870.0,1443.0,342.0,41.0,139.0,579.0,151.0,66.0,51.0,0.0,628.0,94.0,261.0,684.0,22.06,503.0,74.0,34.0,25.0,73.0,2.35,54.0,4.0,7.0,2.0,0.0,545.0,309.0,269.0,216.0,60.0,355.0,100.0,255.0,251.0,514.0,9.0,18652.0,2079.0,6432.0,8155.0,4232.0,690.0,18648.0,10730.0,553.0,368.0,140.0,12266.0,454.0,246.0,1.0,384.0,307.0,94.0,2.0,7.0,1.0,1611.0,385.0,329.0,53.9 +vs Spezia,34.0,53.0,31.0,341.0,2790.0,47.0,33.0,4.0,6.0,52.0,2.0,1.52,1.06,2.58,1.39,2.45,52.3,47.6,1.69,1.54,31.0,31.0,2790.0,26.0,0.84,101.0,77.0,78.2,14.0,12.0,5.0,14.0,45.2,4.0,4.0,0.0,0.0,31.0,0.0,2.0,2.0,26.6,0.23,2.6,0.08,117.0,365.0,32.1,794.0,125.0,33.0,34.5,224.0,46.0,40.6,397.0,23.0,5.8,43.0,1.39,15.2,151.0,12.0,29.5,4.87,0.08,0.28,0.1,-5.3,-4.6,12067.0,15438.0,78.2,218730.0,79866.0,5171.0,6016.0,86.0,5176.0,6082.0,85.1,1374.0,2442.0,56.3,393.0,861.0,279.0,81.0,1223.0,13706.0,1665.0,457.0,56.0,123.0,602.0,168.0,58.0,75.0,0.0,741.0,67.0,284.0,904.0,29.16,639.0,85.0,73.0,36.0,83.0,2.68,59.0,4.0,8.0,3.0,0.0,539.0,299.0,255.0,206.0,78.0,347.0,95.0,252.0,301.0,572.0,6.0,19074.0,1836.0,6147.0,8393.0,4704.0,831.0,19068.0,10360.0,569.0,407.0,170.0,11965.0,485.0,320.0,1.0,321.0,396.0,67.0,4.0,4.0,2.0,1708.0,470.0,424.0,52.6 +vs Torino,28.0,47.0,31.0,341.0,2790.0,36.0,23.0,5.0,6.0,52.0,0.0,1.16,0.74,1.9,1.0,1.74,35.1,30.5,1.13,0.98,31.0,31.0,2790.0,32.0,1.03,115.0,83.0,73.9,11.0,9.0,11.0,8.0,25.8,2.0,2.0,0.0,0.0,31.0,0.0,1.0,0.0,35.7,0.29,3.7,0.12,201.0,521.0,38.6,946.0,138.0,42.5,36.8,205.0,58.0,43.7,387.0,19.0,4.9,15.0,0.48,12.4,108.0,13.0,30.3,3.48,0.09,0.29,0.09,0.9,0.5,10583.0,13924.0,76.0,190853.0,75740.0,4703.0,5449.0,86.3,4318.0,5248.0,82.3,1241.0,2458.0,50.5,271.0,756.0,208.0,66.0,918.0,12350.0,1542.0,471.0,18.0,74.0,511.0,125.0,46.0,48.0,0.0,632.0,32.0,233.0,627.0,20.23,462.0,61.0,43.0,33.0,60.0,1.94,39.0,8.0,8.0,5.0,0.0,506.0,268.0,268.0,195.0,43.0,338.0,102.0,236.0,259.0,509.0,5.0,17167.0,1980.0,6250.0,7435.0,3670.0,569.0,17161.0,9225.0,430.0,340.0,108.0,10445.0,507.0,233.0,0.0,341.0,409.0,32.0,4.0,2.0,0.0,1597.0,508.0,474.0,51.7 +vs Udinese,27.0,51.9,31.0,341.0,2790.0,37.0,27.0,4.0,5.0,71.0,2.0,1.19,0.87,2.06,1.06,1.94,41.9,38.3,1.35,1.23,31.0,31.0,2790.0,42.0,1.35,125.0,83.0,67.2,9.0,12.0,10.0,8.0,25.8,2.0,1.0,1.0,0.0,31.0,1.0,4.0,1.0,34.7,0.26,-6.3,-0.2,110.0,322.0,34.2,705.0,142.0,32.6,34.1,200.0,46.0,41.3,371.0,22.0,5.9,36.0,1.16,14.5,108.0,16.0,29.5,3.48,0.09,0.31,0.11,-4.9,-5.3,11950.0,14951.0,79.9,213742.0,75865.0,5431.0,6131.0,88.6,4827.0,5606.0,86.1,1382.0,2361.0,58.5,258.0,870.0,226.0,67.0,1195.0,13372.0,1529.0,397.0,40.0,123.0,574.0,166.0,65.0,74.0,0.0,641.0,50.0,303.0,637.0,20.55,444.0,57.0,46.0,40.0,69.0,2.23,54.0,1.0,4.0,4.0,1.0,591.0,329.0,300.0,218.0,73.0,378.0,132.0,246.0,245.0,506.0,11.0,18395.0,1843.0,5731.0,8647.0,4207.0,609.0,18390.0,10302.0,529.0,425.0,101.0,11838.0,477.0,265.0,1.0,412.0,357.0,50.0,3.0,2.0,1.0,1577.0,326.0,390.0,45.5 diff --git a/mid_outputs/database_entries.xlsx b/mid_outputs/database_entries.xlsx index ecf235b..94dfba0 100644 Binary files a/mid_outputs/database_entries.xlsx 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