From 99fb244c2671fd3080f466b3cbb0ec906569b335 Mon Sep 17 00:00:00 2001 From: Giuseppe Musicco Date: Sat, 6 May 2023 09:12:45 +0200 Subject: [PATCH] Matchday 34 --- .../1_scraping_fbref-checkpoint.ipynb | 6561 ++++++---------- ...k_training_and_prediction-checkpoint.ipynb | 6226 ++++++++------- 1_scraping_fbref.ipynb | 5768 ++++---------- 2_votes_dataset_creation.ipynb | 115 +- 3_players_dataset_creation.ipynb | 2458 ++---- 4_player_match_dataset_creation.ipynb | 2551 +++--- ...ural_network_training_and_prediction.ipynb | 6922 ++++++++--------- 7_lineup_simulation.ipynb | 557 +- fantacalcio/seriea_calendar.xlsx | Bin 19432 -> 19454 bytes ...tacalcio_Stagione_2022_23_Giornata_32.xlsx | Bin 0 -> 58979 bytes ...tacalcio_Stagione_2022_23_Giornata_33.xlsx | Bin 0 -> 58570 bytes fbref_data/keepers_players.csv | 81 +- fbref_data/outfield_players.csv | 1161 +-- fbref_data/season2122/outfield_players.csv | 294 +- fbref_data/teams.csv | 40 +- fbref_data/teams_vs.csv | 40 +- mid_outputs/database_entries.xlsx | Bin 3945341 -> 5833044 bytes mid_outputs/database_entries_gk.xlsx | Bin 222856 -> 317399 bytes mid_outputs/match_probable_players.xlsx | Bin 15415 -> 15043 bytes mid_outputs/players_stats.xlsx | Bin 332830 -> 334094 bytes mid_outputs/players_stats_rwk.xlsx | Bin 448961 -> 439734 bytes mid_outputs/players_votes.xlsx | Bin 369118 -> 392341 bytes mid_outputs/team_data.xlsx | Bin 37038 -> 37624 bytes outputs/pred_avg_seriea.xlsx | Bin 761165 -> 760953 bytes outputs/pred_matchday_32.xlsx | Bin 760721 -> 760483 bytes outputs/pred_matchday_33.xlsx | Bin 760951 -> 760932 bytes outputs/pred_matchday_34.xlsx | Bin 0 -> 760844 bytes saves/checkpoint | 4 +- saves/modelb.data-00000-of-00001 | Bin 33961 -> 34713 bytes saves/modelb.index | Bin 2482 -> 2799 bytes saves/modelb_gk.data-00000-of-00001 | Bin 34060 -> 34060 bytes saves/modelb_gk.index | Bin 3569 -> 3569 bytes tmp/playermatchdata.xlsx | Bin 16339 -> 16386 bytes 33 files changed, 13772 insertions(+), 19006 deletions(-) create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_32.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_33.xlsx create mode 100644 outputs/pred_matchday_34.xlsx diff --git a/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb index daa6417..ce2beea 100644 --- a/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb +++ b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb @@ -283,59 +283,83 @@ " \n", " \n", " 0\n", + " James Abankwah\n", + " ie IRL\n", + " DF\n", + " Udinese\n", + " 19-109\n", + " 2004\n", + " 1.0\n", + " 0.0\n", + " 5.0\n", + " 0.0\n", + " ...\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", + " 0.0\n", + " \n", + " \n", + " 1\n", " Oliver Abildgaard\n", " dk DEN\n", " MF\n", " Hellas Verona\n", - " 26-250\n", + " 26-329\n", " 1996\n", - " 2.0\n", - " 0.0\n", - " 37.0\n", + " 8.0\n", + " 4.0\n", + " 377.0\n", " 0.0\n", " ...\n", - " 1.0\n", - " 1.0\n", + " 9.0\n", + " 2.0\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", + " 24.0\n", + " 31.0\n", + " 11.0\n", + " 73.8\n", " \n", " \n", - " 1\n", + " 2\n", " Tammy Abraham\n", " eng ENG\n", " FW\n", " Roma\n", - " 25-136\n", + " 25-215\n", " 1997\n", - " 22.0\n", - " 18.0\n", - " 1582.0\n", - " 6.0\n", + " 33.0\n", + " 24.0\n", + " 2084.0\n", + " 8.0\n", " ...\n", - " 23.0\n", - " 36.0\n", - " 9.0\n", + " 32.0\n", + " 45.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 33.0\n", - " 45.0\n", - " 42.0\n", - " 51.7\n", + " 43.0\n", + " 78.0\n", + " 71.0\n", + " 52.3\n", " \n", " \n", - " 2\n", + " 3\n", " Christian Acella\n", " it ITA\n", " MF\n", " Cremonese\n", - " 20-223\n", + " 20-302\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -354,52 +378,28 @@ " 0.0\n", " \n", " \n", - " 3\n", + " 4\n", " Francesco Acerbi\n", " it ITA\n", " DF\n", " Inter\n", - " 35-005\n", + " 35-084\n", " 1988\n", - " 15.0\n", - " 13.0\n", - " 1200.0\n", + " 26.0\n", + " 22.0\n", + " 2095.0\n", " 0.0\n", " ...\n", - " 9.0\n", - " 9.0\n", + " 14.0\n", + " 10.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 66.0\n", - " 38.0\n", - " 21.0\n", - " 64.4\n", - " \n", - " \n", - " 4\n", - " Yacine Adli\n", - " fr FRA\n", - " MF,FW\n", - " Milan\n", - " 22-201\n", - " 2000\n", - " 4.0\n", - " 1.0\n", - " 116.0\n", - " 0.0\n", - " ...\n", - " 3.0\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", + " 131.0\n", + " 71.0\n", + " 35.0\n", + " 67.0\n", " \n", " \n", " ...\n", @@ -426,12 +426,12 @@ " ...\n", " \n", " \n", - " 540\n", + " 576\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 20-327\n", + " 21-041\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -450,12 +450,12 @@ " 0.0\n", " \n", " \n", - " 541\n", + " 577\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Fiorentina\n", - " 25-143\n", + " 25-222\n", " 1997\n", " 2.0\n", " 0.0\n", @@ -474,136 +474,136 @@ " 50.0\n", " \n", " \n", - " 542\n", + " 578\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Spezia\n", - " 25-143\n", + " 25-222\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", - " 543\n", + " 579\n", " Milan Đurić\n", " ba BIH\n", " FW\n", " Hellas Verona\n", - " 32-269\n", + " 32-348\n", " 1990\n", - " 16.0\n", - " 7.0\n", - " 703.0\n", + " 23.0\n", + " 9.0\n", + " 974.0\n", " 1.0\n", " ...\n", - " 15.0\n", - " 14.0\n", - " 3.0\n", + " 22.0\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", + " 23.0\n", + " 146.0\n", + " 38.0\n", + " 79.3\n", " \n", " \n", - " 544\n", + " 580\n", " Filip Đuričić\n", " rs SRB\n", " MF,FW\n", " Sampdoria\n", - " 31-016\n", + " 31-095\n", " 1992\n", - " 21.0\n", - " 18.0\n", - " 1397.0\n", - " 2.0\n", + " 29.0\n", + " 24.0\n", + " 1911.0\n", + " 3.0\n", " ...\n", - " 23.0\n", - " 35.0\n", + " 31.0\n", + " 43.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 78.0\n", - " 8.0\n", - " 13.0\n", - " 38.1\n", + " 99.0\n", + " 12.0\n", + " 22.0\n", + " 35.3\n", " \n", " \n", "\n", - "

545 rows × 115 columns

\n", + "

581 rows × 115 columns

\n", "" ], "text/plain": [ " 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", + "0 James Abankwah ie IRL DF Udinese 19-109 2004 \n", + "1 Oliver Abildgaard dk DEN MF Hellas Verona 26-329 1996 \n", + "2 Tammy Abraham eng ENG FW Roma 25-215 1997 \n", + "3 Christian Acella it ITA MF Cremonese 20-302 2002 \n", + "4 Francesco Acerbi it ITA DF Inter 35-084 1988 \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", + "576 Petar Zovko ba BIH GK Spezia 21-041 2002 \n", + "577 Szymon Żurkowski pl POL MF Fiorentina 25-222 1997 \n", + "578 Szymon Żurkowski pl POL MF Spezia 25-222 1997 \n", + "579 Milan Đurić ba BIH FW Hellas Verona 32-348 1990 \n", + "580 Filip Đuričić rs SRB MF,FW Sampdoria 31-095 1992 \n", "\n", " games games_starts minutes goals ... fouls fouled offsides \\\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", + "0 1.0 0.0 5.0 0.0 ... 1.0 0.0 0.0 \n", + "1 8.0 4.0 377.0 0.0 ... 9.0 2.0 0.0 \n", + "2 33.0 24.0 2084.0 8.0 ... 32.0 45.0 11.0 \n", + "3 1.0 0.0 15.0 0.0 ... 0.0 0.0 0.0 \n", + "4 26.0 22.0 2095.0 0.0 ... 14.0 10.0 1.0 \n", ".. ... ... ... ... ... ... ... ... \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", + "576 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "577 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "578 6.0 2.0 240.0 0.0 ... 8.0 5.0 0.0 \n", + "579 23.0 9.0 974.0 1.0 ... 22.0 20.0 4.0 \n", + "580 29.0 24.0 1911.0 3.0 ... 31.0 43.0 0.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 24.0 31.0 \n", + "2 1.0 0.0 0.0 43.0 78.0 \n", + "3 0.0 0.0 0.0 1.0 0.0 \n", + "4 0.0 0.0 0.0 131.0 71.0 \n", ".. ... ... ... ... ... \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", + "576 0.0 0.0 0.0 2.0 0.0 \n", + "577 0.0 0.0 0.0 2.0 1.0 \n", + "578 0.0 0.0 0.0 18.0 3.0 \n", + "579 0.0 0.0 0.0 23.0 146.0 \n", + "580 0.0 0.0 0.0 99.0 12.0 \n", "\n", " aerials_lost aerials_won_pct \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", + "0 0.0 0.0 \n", + "1 11.0 73.8 \n", + "2 71.0 52.3 \n", + "3 0.0 0.0 \n", + "4 35.0 67.0 \n", ".. ... ... \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", + "576 0.0 0.0 \n", + "577 1.0 50.0 \n", + "578 2.0 60.0 \n", + "579 38.0 79.3 \n", + "580 22.0 35.3 \n", "\n", - "[545 rows x 115 columns]" + "[581 rows x 115 columns]" ] }, "execution_count": 3, @@ -676,151 +676,223 @@ " it ITA\n", " GK\n", " Sampdoria\n", - " 26-028\n", + " 26-107\n", " 1997\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 36.0\n", + " 25.0\n", + " 25.0\n", + " 2250.0\n", + " 39.0\n", " ...\n", - " 39.4\n", - " 178.0\n", - " 57.3\n", - " 44.4\n", - " 315.0\n", - " 21.0\n", + " 39.1\n", + " 200.0\n", + " 57.0\n", + " 44.2\n", + " 356.0\n", + " 24.0\n", " 6.7\n", - " 20.0\n", - " 0.91\n", - " 14.1\n", + " 23.0\n", + " 0.92\n", + " 13.9\n", " \n", " \n", " 1\n", + " Francesco Bardi\n", + " it ITA\n", + " GK\n", + " Bologna\n", + " 31-107\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", - " 22-229\n", + " 22-308\n", " 2000\n", - " 13.0\n", - " 13.0\n", - " 1170.0\n", - " 21.0\n", + " 24.0\n", + " 24.0\n", + " 2160.0\n", + " 40.0\n", " ...\n", - " 39.3\n", - " 94.0\n", - " 56.4\n", - " 47.3\n", - " 174.0\n", - " 13.0\n", - " 7.5\n", + " 41.4\n", + " 179.0\n", + " 65.9\n", + " 52.8\n", + " 351.0\n", + " 28.0\n", " 8.0\n", - " 0.62\n", - " 12.9\n", + " 24.0\n", + " 1.00\n", + " 13.9\n", " \n", " \n", - " 2\n", + " 3\n", + " Michele Cerofolini\n", + " it ITA\n", + " GK\n", + " Fiorentina\n", + " 24-121\n", + " 1999\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 31.1\n", + " 3.0\n", + " 100.0\n", + " 56.3\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 5.0\n", + " \n", + " \n", + " 4\n", " Andrea Consigli\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 36-019\n", + " 36-098\n", " 1987\n", - " 20.0\n", - " 20.0\n", - " 1800.0\n", - " 30.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 46.0\n", " ...\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", + " 33.8\n", + " 265.0\n", + " 31.3\n", + " 32.8\n", + " 424.0\n", + " 26.0\n", + " 6.1\n", + " 26.0\n", + " 0.84\n", + " 13.9\n", " \n", " \n", - " 3\n", + " 5\n", + " Alessio Cragno\n", + " it ITA\n", + " GK\n", + " Monza\n", + " 28-311\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", + " 6\n", " Michele Di Gregorio\n", " it ITA\n", " GK\n", " Monza\n", - " 25-203\n", + " 25-282\n", " 1997\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 30.0\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 41.0\n", " ...\n", - " 33.0\n", - " 138.0\n", - " 36.2\n", - " 33.6\n", - " 313.0\n", - " 10.0\n", - " 3.2\n", - " 16.0\n", - " 0.73\n", + " 32.6\n", + " 209.0\n", + " 37.3\n", + " 34.7\n", + " 469.0\n", " 13.0\n", + " 2.8\n", + " 27.0\n", + " 0.84\n", + " 13.3\n", " \n", " \n", - " 4\n", + " 7\n", " Bartłomiej Drągowski\n", " pl POL\n", " GK\n", " Spezia\n", - " 25-180\n", + " 25-259\n", " 1997\n", - " 20.0\n", - " 20.0\n", - " 1752.0\n", - " 34.0\n", + " 30.0\n", + " 30.0\n", + " 2586.0\n", + " 48.0\n", " ...\n", - " 34.9\n", - " 122.0\n", - " 62.3\n", - " 47.1\n", - " 270.0\n", - " 9.0\n", - " 3.3\n", - " 24.0\n", - " 1.23\n", - " 15.3\n", + " 34.1\n", + " 179.0\n", + " 60.9\n", + " 47.3\n", + " 428.0\n", + " 13.0\n", + " 3.0\n", + " 32.0\n", + " 1.11\n", + " 14.2\n", " \n", " \n", - " 5\n", + " 8\n", " Wladimiro Falcone\n", " it ITA\n", " GK\n", " Lecce\n", - " 27-309\n", + " 28-023\n", " 1995\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 25.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 40.0\n", " ...\n", - " 41.5\n", - " 169.0\n", - " 76.3\n", - " 51.9\n", - " 327.0\n", - " 15.0\n", - " 4.6\n", + " 41.7\n", + " 239.0\n", + " 76.6\n", + " 52.3\n", + " 470.0\n", " 22.0\n", - " 1.00\n", - " 12.8\n", + " 4.7\n", + " 37.0\n", + " 1.12\n", + " 14.1\n", " \n", " \n", - " 6\n", + " 9\n", " Pierluigi Gollini\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 27-334\n", + " 28-048\n", " 1995\n", " 3.0\n", " 3.0\n", @@ -839,60 +911,108 @@ " 9.3\n", " \n", " \n", - " 7\n", + " 10\n", + " Pierluigi Gollini\n", + " it ITA\n", + " GK\n", + " Napoli\n", + " 28-048\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", + " 11\n", " Samir Handanović\n", " si SVN\n", " GK\n", " Inter\n", - " 38-216\n", + " 38-295\n", " 1984\n", - " 8.0\n", - " 8.0\n", - " 720.0\n", - " 13.0\n", + " 12.0\n", + " 12.0\n", + " 1080.0\n", + " 16.0\n", " ...\n", - " 26.1\n", - " 45.0\n", - " 4.4\n", - " 24.0\n", - " 79.0\n", + " 24.7\n", + " 64.0\n", + " 7.8\n", + " 24.4\n", + " 121.0\n", " 2.0\n", - " 2.5\n", - " 5.0\n", - " 0.63\n", - " 14.6\n", + " 1.7\n", + " 10.0\n", + " 0.83\n", + " 16.2\n", " \n", " \n", - " 8\n", + " 12\n", " Mike Maignan\n", " fr FRA\n", " GK\n", " Milan\n", - " 27-227\n", + " 27-306\n", " 1995\n", - " 7.0\n", - " 7.0\n", - " 630.0\n", - " 8.0\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 17.0\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", + " 30.8\n", + " 60.0\n", + " 33.3\n", + " 35.1\n", + " 164.0\n", + " 12.0\n", + " 7.3\n", + " 28.0\n", + " 1.65\n", + " 18.3\n", " \n", " \n", - " 9\n", + " 13\n", + " Federico Marchetti\n", + " it ITA\n", + " GK\n", + " Spezia\n", + " 40-087\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", + " 14\n", " Luís Maximiano\n", " pt POR\n", " GK\n", " Lazio\n", - " 24-041\n", + " 24-120\n", " 1999\n", " 1.0\n", " 1.0\n", @@ -911,180 +1031,180 @@ " 19.0\n", " \n", " \n", - " 10\n", + " 15\n", " Alex Meret\n", " it ITA\n", " GK\n", " Napoli\n", - " 25-330\n", + " 26-044\n", " 1997\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 15.0\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 23.0\n", " ...\n", - " 26.5\n", - " 134.0\n", - " 18.7\n", - " 26.6\n", - " 232.0\n", - " 6.0\n", - " 2.6\n", - " 24.0\n", + " 26.3\n", + " 194.0\n", + " 20.1\n", + " 26.8\n", + " 320.0\n", + " 11.0\n", + " 3.4\n", + " 35.0\n", " 1.09\n", - " 17.2\n", + " 16.9\n", " \n", " \n", - " 11\n", + " 16\n", " Vanja Milinković-Savić\n", " rs SRB\n", " GK\n", " Torino\n", - " 25-360\n", + " 26-074\n", " 1997\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 23.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 38.0\n", " ...\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", + " 39.6\n", + " 251.0\n", + " 91.2\n", + " 69.7\n", + " 407.0\n", + " 27.0\n", + " 6.6\n", + " 66.0\n", + " 2.00\n", + " 17.2\n", " \n", " \n", - " 12\n", + " 17\n", " Lorenzo Montipò\n", " it ITA\n", " GK\n", " Hellas Verona\n", - " 26-360\n", + " 27-074\n", " 1996\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 33.0\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 51.0\n", " ...\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", + " 44.3\n", + " 243.0\n", + " 69.1\n", + " 50.3\n", + " 430.0\n", + " 25.0\n", + " 5.8\n", + " 51.0\n", + " 1.59\n", + " 15.5\n", " \n", " \n", - " 13\n", + " 18\n", " Juan Musso\n", " ar ARG\n", " GK\n", " Atalanta\n", - " 28-285\n", + " 28-364\n", " 1994\n", - " 16.0\n", - " 16.0\n", - " 1357.0\n", - " 15.0\n", + " 24.0\n", + " 24.0\n", + " 2077.0\n", + " 27.0\n", " ...\n", - " 32.8\n", - " 101.0\n", + " 32.1\n", + " 157.0\n", " 62.4\n", - " 47.9\n", - " 186.0\n", - " 10.0\n", - " 5.4\n", + " 48.6\n", + " 253.0\n", " 14.0\n", - " 0.93\n", - " 15.1\n", + " 5.5\n", + " 24.0\n", + " 1.04\n", + " 15.6\n", " \n", " \n", - " 14\n", + " 19\n", " Guillermo Ochoa\n", " mx MEX\n", " GK\n", " Salernitana\n", - " 37-217\n", + " 37-296\n", " 1985\n", - " 6.0\n", - " 6.0\n", - " 540.0\n", - " 17.0\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", + " 27.0\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", + " 164.0\n", + " 68.9\n", + " 49.0\n", + " 260.0\n", + " 8.0\n", + " 3.1\n", + " 7.0\n", + " 0.44\n", + " 11.6\n", " \n", " \n", - " 15\n", + " 20\n", " André Onana\n", " cm CMR\n", " GK\n", " Inter\n", - " 26-319\n", + " 27-033\n", " 1996\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 13.0\n", + " 21.0\n", + " 21.0\n", + " 1890.0\n", + " 19.0\n", " ...\n", - " 30.2\n", - " 95.0\n", + " 29.9\n", + " 132.0\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", + " 35.9\n", + " 240.0\n", + " 14.0\n", + " 5.8\n", + " 9.0\n", + " 0.43\n", + " 12.5\n", " \n", " \n", - " 16\n", + " 21\n", " Rui Patrício\n", " pt POR\n", " GK\n", " Roma\n", - " 35-000\n", + " 35-079\n", " 1988\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 19.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 31.0\n", " ...\n", - " 31.9\n", - " 166.0\n", - " 30.1\n", - " 32.4\n", - " 263.0\n", - " 7.0\n", - " 2.7\n", + " 33.5\n", + " 230.0\n", + " 37.0\n", + " 35.1\n", + " 387.0\n", " 15.0\n", - " 0.68\n", - " 13.7\n", + " 3.9\n", + " 25.0\n", + " 0.76\n", + " 13.9\n", " \n", " \n", - " 17\n", + " 22\n", " Gianluca Pegolo\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 41-327\n", + " 42-041\n", " 1981\n", " 2.0\n", " 2.0\n", @@ -1103,60 +1223,108 @@ " 9.0\n", " \n", " \n", - " 18\n", + " 23\n", + " Simone Perilli\n", + " it ITA\n", + " GK\n", + " Hellas Verona\n", + " 28-118\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", + " 24\n", " Mattia Perin\n", " it ITA\n", " GK\n", " Juventus\n", - " 30-097\n", + " 30-176\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", + " 25\n", + " Samuele Perisan\n", + " it ITA\n", + " GK\n", + " Empoli\n", + " 25-257\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", + " 26\n", " Ivan Provedel\n", " it ITA\n", " GK\n", " Lazio\n", - " 28-335\n", + " 29-049\n", " 1994\n", - " 22.0\n", - " 21.0\n", - " 1973.0\n", - " 19.0\n", - " ...\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", + " 32.0\n", + " 2963.0\n", + " 24.0\n", + " ...\n", + " 33.1\n", + " 167.0\n", + " 36.5\n", + " 34.9\n", + " 433.0\n", + " 18.0\n", + " 4.2\n", + " 46.0\n", + " 1.40\n", + " 16.0\n", " \n", " \n", - " 20\n", + " 27\n", " Ionuț Radu\n", " ro ROU\n", " GK\n", " Cremonese\n", - " 25-263\n", + " 25-342\n", " 1997\n", " 9.0\n", " 9.0\n", @@ -1175,228 +1343,300 @@ " 14.5\n", " \n", " \n", - " 21\n", + " 28\n", + " Nicola Ravaglia\n", + " it ITA\n", + " GK\n", + " Sampdoria\n", + " 34-144\n", + " 1988\n", + " 6.0\n", + " 6.0\n", + " 540.0\n", + " 15.0\n", + " ...\n", + " 37.6\n", + " 74.0\n", + " 51.4\n", + " 37.7\n", + " 112.0\n", + " 6.0\n", + " 5.4\n", + " 1.0\n", + " 0.17\n", + " 11.0\n", + " \n", + " \n", + " 29\n", " Luigi Sepe\n", " it ITA\n", " GK\n", " Salernitana\n", - " 31-283\n", + " 31-362\n", " 1991\n", - " 16.0\n", - " 16.0\n", - " 1440.0\n", - " 25.0\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 27.0\n", " ...\n", - " 36.5\n", - " 142.0\n", - " 47.9\n", - " 40.9\n", - " 207.0\n", - " 15.0\n", + " 35.9\n", + " 154.0\n", + " 48.7\n", + " 41.1\n", + " 222.0\n", + " 16.0\n", " 7.2\n", - " 12.0\n", - " 0.75\n", - " 14.4\n", + " 13.0\n", + " 0.76\n", + " 14.2\n", " \n", " \n", - " 22\n", + " 30\n", " Marco Silvestri\n", " it ITA\n", " GK\n", " Udinese\n", - " 31-350\n", + " 32-064\n", " 1991\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 25.0\n", - " ...\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", + " 33.0\n", + " 2970.0\n", + " 41.0\n", + " ...\n", + " 32.7\n", + " 256.0\n", + " 36.7\n", + " 35.2\n", + " 470.0\n", + " 12.0\n", + " 2.6\n", + " 19.0\n", + " 0.58\n", + " 12.3\n", " \n", " \n", - " 23\n", + " 31\n", + " Salvatore Sirigu\n", + " it ITA\n", + " GK\n", + " Fiorentina\n", + " 36-113\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", + " 32\n", " Łukasz Skorupski\n", " pl POL\n", " GK\n", " Bologna\n", - " 31-286\n", + " 32-000\n", " 1991\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 43.0\n", " ...\n", - " 32.6\n", - " 159.0\n", - " 37.1\n", - " 33.8\n", - " 304.0\n", - " 18.0\n", - " 5.9\n", - " 18.0\n", - " 0.82\n", - " 13.5\n", + " 32.5\n", + " 235.0\n", + " 37.0\n", + " 34.7\n", + " 422.0\n", + " 24.0\n", + " 5.7\n", + " 23.0\n", + " 0.72\n", + " 13.2\n", " \n", " \n", - " 24\n", + " 33\n", " Marco Sportiello\n", " it ITA\n", " GK\n", " Atalanta\n", - " 30-281\n", + " 30-360\n", " 1992\n", - " 7.0\n", - " 6.0\n", - " 623.0\n", + " 10.0\n", " 9.0\n", + " 893.0\n", + " 12.0\n", " ...\n", - " 28.5\n", - " 61.0\n", - " 68.9\n", - " 48.9\n", - " 86.0\n", - " 7.0\n", - " 8.1\n", + " 29.7\n", + " 87.0\n", + " 73.6\n", + " 52.4\n", + " 142.0\n", " 14.0\n", - " 2.02\n", - " 19.9\n", + " 9.9\n", + " 15.0\n", + " 1.51\n", + " 16.0\n", " \n", " \n", - " 25\n", + " 34\n", " Wojciech Szczęsny\n", " pl POL\n", " GK\n", " Juventus\n", - " 32-303\n", + " 33-017\n", " 1990\n", - " 16.0\n", - " 16.0\n", - " 1392.0\n", - " 13.0\n", + " 24.0\n", + " 24.0\n", + " 2112.0\n", + " 21.0\n", " ...\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", + " 34.0\n", + " 129.0\n", + " 48.1\n", + " 40.6\n", + " 338.0\n", + " 9.0\n", + " 2.7\n", + " 19.0\n", + " 0.81\n", + " 15.2\n", " \n", " \n", - " 26\n", + " 35\n", " Ciprian Tătărușanu\n", " ro ROU\n", " GK\n", " Milan\n", - " 37-006\n", + " 37-085\n", " 1986\n", - " 15.0\n", - " 15.0\n", - " 1350.0\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", " 22.0\n", " ...\n", - " 32.6\n", - " 74.0\n", - " 51.4\n", - " 41.1\n", - " 174.0\n", + " 32.9\n", + " 83.0\n", + " 51.8\n", + " 41.8\n", + " 195.0\n", " 9.0\n", - " 5.2\n", - " 10.0\n", - " 0.67\n", - " 13.7\n", + " 4.6\n", + " 13.0\n", + " 0.81\n", + " 14.4\n", " \n", " \n", - " 27\n", + " 36\n", " Pietro Terracciano\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 32-344\n", + " 33-058\n", " 1990\n", - " 19.0\n", - " 19.0\n", - " 1710.0\n", - " 27.0\n", + " 28.0\n", + " 28.0\n", + " 2520.0\n", + " 37.0\n", " ...\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", + " 33.5\n", + " 186.0\n", + " 45.2\n", + " 40.2\n", + " 268.0\n", + " 12.0\n", + " 4.5\n", + " 52.0\n", + " 1.86\n", + " 18.5\n", " \n", " \n", - " 28\n", + " 37\n", + " Martin Turk\n", + " si SVN\n", + " GK\n", + " Sampdoria\n", + " 19-257\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", + " 38\n", " Guglielmo Vicario\n", " it ITA\n", " GK\n", " Empoli\n", - " 26-131\n", + " 26-210\n", " 1996\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 28.0\n", + " 26.0\n", + " 26.0\n", + " 2340.0\n", + " 34.0\n", " ...\n", - " 33.6\n", - " 114.0\n", - " 44.7\n", - " 41.5\n", - " 431.0\n", - " 25.0\n", - " 5.8\n", - " 12.0\n", - " 0.55\n", - " 10.9\n", + " 33.8\n", + " 147.0\n", + " 50.3\n", + " 43.0\n", + " 521.0\n", + " 28.0\n", + " 5.4\n", + " 16.0\n", + " 0.62\n", + " 10.7\n", " \n", " \n", - " 29\n", + " 39\n", " Jeroen Zoet\n", " nl NED\n", " GK\n", " Spezia\n", - " 32-040\n", + " 32-119\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", + " 40\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 20-327\n", + " 21-041\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -1416,209 +1656,269 @@ " \n", " \n", "\n", - "

31 rows × 47 columns

\n", + "

41 rows × 47 columns

\n", "" ], "text/plain": [ " player nationality position team age \\\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", + "0 Emil Audero it ITA GK Sampdoria 26-107 \n", + "1 Francesco Bardi it ITA GK Bologna 31-107 \n", + "2 Marco Carnesecchi it ITA GK Cremonese 22-308 \n", + "3 Michele Cerofolini it ITA GK Fiorentina 24-121 \n", + "4 Andrea Consigli it ITA GK Sassuolo 36-098 \n", + "5 Alessio Cragno it ITA GK Monza 28-311 \n", + "6 Michele Di Gregorio it ITA GK Monza 25-282 \n", + "7 Bartłomiej Drągowski pl POL GK Spezia 25-259 \n", + "8 Wladimiro Falcone it ITA GK Lecce 28-023 \n", + "9 Pierluigi Gollini it ITA GK Fiorentina 28-048 \n", + "10 Pierluigi Gollini it ITA GK Napoli 28-048 \n", + "11 Samir Handanović si SVN GK Inter 38-295 \n", + "12 Mike Maignan fr FRA GK Milan 27-306 \n", + "13 Federico Marchetti it ITA GK Spezia 40-087 \n", + "14 Luís Maximiano pt POR GK Lazio 24-120 \n", + "15 Alex Meret it ITA GK Napoli 26-044 \n", + "16 Vanja Milinković-Savić rs SRB GK Torino 26-074 \n", + "17 Lorenzo Montipò it ITA GK Hellas Verona 27-074 \n", + "18 Juan Musso ar ARG GK Atalanta 28-364 \n", + "19 Guillermo Ochoa mx MEX GK Salernitana 37-296 \n", + "20 André Onana cm CMR GK Inter 27-033 \n", + "21 Rui Patrício pt POR GK Roma 35-079 \n", + "22 Gianluca Pegolo it ITA GK Sassuolo 42-041 \n", + "23 Simone Perilli it ITA GK Hellas Verona 28-118 \n", + "24 Mattia Perin it ITA GK Juventus 30-176 \n", + "25 Samuele Perisan it ITA GK Empoli 25-257 \n", + "26 Ivan Provedel it ITA GK Lazio 29-049 \n", + "27 Ionuț Radu ro ROU GK Cremonese 25-342 \n", + "28 Nicola Ravaglia it ITA GK Sampdoria 34-144 \n", + "29 Luigi Sepe it ITA GK Salernitana 31-362 \n", + "30 Marco Silvestri it ITA GK Udinese 32-064 \n", + "31 Salvatore Sirigu it ITA GK Fiorentina 36-113 \n", + "32 Łukasz Skorupski pl POL GK Bologna 32-000 \n", + "33 Marco Sportiello it ITA GK Atalanta 30-360 \n", + "34 Wojciech Szczęsny pl POL GK Juventus 33-017 \n", + "35 Ciprian Tătărușanu ro ROU GK Milan 37-085 \n", + "36 Pietro Terracciano it ITA GK Fiorentina 33-058 \n", + "37 Martin Turk si SVN GK Sampdoria 19-257 \n", + "38 Guglielmo Vicario it ITA GK Empoli 26-210 \n", + "39 Jeroen Zoet nl NED GK Spezia 32-119 \n", + "40 Petar Zovko ba BIH GK Spezia 21-041 \n", "\n", " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\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 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 22.0 21.0 1973.0 19.0 ... \n", - "20 1997 9.0 9.0 810.0 19.0 ... \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 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", + "0 1997 25.0 25.0 2250.0 39.0 ... \n", + "1 1992 1.0 1.0 90.0 0.0 ... \n", + "2 2000 24.0 24.0 2160.0 40.0 ... \n", + "3 1999 1.0 1.0 90.0 0.0 ... \n", + "4 1987 31.0 31.0 2790.0 46.0 ... \n", + "5 1994 1.0 1.0 90.0 3.0 ... \n", + "6 1997 32.0 32.0 2880.0 41.0 ... \n", + "7 1997 30.0 30.0 2586.0 48.0 ... \n", + "8 1995 33.0 33.0 2970.0 40.0 ... \n", + "9 1995 3.0 3.0 270.0 2.0 ... \n", + "10 1995 1.0 1.0 90.0 0.0 ... \n", + "11 1984 12.0 12.0 1080.0 16.0 ... \n", + "12 1995 17.0 17.0 1530.0 17.0 ... \n", + "13 1983 1.0 0.0 66.0 2.0 ... \n", + "14 1999 1.0 1.0 5.0 0.0 ... \n", + "15 1997 32.0 32.0 2880.0 23.0 ... \n", + "16 1997 33.0 33.0 2970.0 38.0 ... \n", + "17 1996 32.0 32.0 2880.0 51.0 ... \n", + "18 1994 24.0 24.0 2077.0 27.0 ... \n", + "19 1985 16.0 16.0 1440.0 27.0 ... \n", + "20 1996 21.0 21.0 1890.0 19.0 ... \n", + "21 1988 33.0 33.0 2970.0 31.0 ... \n", + "22 1981 2.0 2.0 180.0 3.0 ... \n", + "23 1995 1.0 1.0 90.0 0.0 ... \n", + "24 1992 10.0 9.0 858.0 7.0 ... \n", + "25 1997 7.0 7.0 630.0 9.0 ... \n", + "26 1994 33.0 32.0 2963.0 24.0 ... \n", + "27 1997 9.0 9.0 810.0 19.0 ... \n", + "28 1988 6.0 6.0 540.0 15.0 ... \n", + "29 1991 17.0 17.0 1530.0 27.0 ... \n", + "30 1991 33.0 33.0 2970.0 41.0 ... \n", + "31 1987 1.0 1.0 90.0 0.0 ... \n", + "32 1991 32.0 32.0 2880.0 43.0 ... \n", + "33 1992 10.0 9.0 893.0 12.0 ... \n", + "34 1990 24.0 24.0 2112.0 21.0 ... \n", + "35 1986 16.0 16.0 1440.0 22.0 ... \n", + "36 1990 28.0 28.0 2520.0 37.0 ... \n", + "37 2003 2.0 2.0 180.0 5.0 ... \n", + "38 1996 26.0 26.0 2340.0 34.0 ... \n", + "39 1991 4.0 3.0 244.0 2.0 ... \n", + "40 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 39.4 178.0 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 26.5 134.0 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 33.0 123.0 35.0 \n", - "20 37.3 67.0 50.7 \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 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", + "0 39.1 200.0 57.0 \n", + "1 35.6 9.0 44.4 \n", + "2 41.4 179.0 65.9 \n", + "3 31.1 3.0 100.0 \n", + "4 33.8 265.0 31.3 \n", + "5 22.6 6.0 16.7 \n", + "6 32.6 209.0 37.3 \n", + "7 34.1 179.0 60.9 \n", + "8 41.7 239.0 76.6 \n", + "9 32.4 11.0 63.6 \n", + "10 31.7 5.0 0.0 \n", + "11 24.7 64.0 7.8 \n", + "12 30.8 60.0 33.3 \n", + "13 24.3 8.0 37.5 \n", + "14 0.0 0.0 0.0 \n", + "15 26.3 194.0 20.1 \n", + "16 39.6 251.0 91.2 \n", + "17 44.3 243.0 69.1 \n", + "18 32.1 157.0 62.4 \n", + "19 41.9 164.0 68.9 \n", + "20 29.9 132.0 32.6 \n", + "21 33.5 230.0 37.0 \n", + "22 29.7 16.0 31.3 \n", + "23 57.3 13.0 100.0 \n", + "24 31.1 60.0 41.7 \n", + "25 32.7 75.0 34.7 \n", + "26 33.1 167.0 36.5 \n", + "27 37.3 67.0 50.7 \n", + "28 37.6 74.0 51.4 \n", + "29 35.9 154.0 48.7 \n", + "30 32.7 256.0 36.7 \n", + "31 37.7 8.0 50.0 \n", + "32 32.5 235.0 37.0 \n", + "33 29.7 87.0 73.6 \n", + "34 34.0 129.0 48.1 \n", + "35 32.9 83.0 51.8 \n", + "36 33.5 186.0 45.2 \n", + "37 37.3 22.0 90.9 \n", + "38 33.8 147.0 50.3 \n", + "39 37.1 12.0 66.7 \n", + "40 35.1 13.0 76.9 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\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 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 35.0 290.0 8.0 \n", - "20 46.3 107.0 6.0 \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 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", + "0 44.2 356.0 24.0 \n", + "1 33.4 10.0 0.0 \n", + "2 52.8 351.0 28.0 \n", + "3 56.3 2.0 0.0 \n", + "4 32.8 424.0 26.0 \n", + "5 33.3 7.0 0.0 \n", + "6 34.7 469.0 13.0 \n", + "7 47.3 428.0 13.0 \n", + "8 52.3 470.0 22.0 \n", + "9 49.3 24.0 1.0 \n", + "10 15.4 12.0 1.0 \n", + "11 24.4 121.0 2.0 \n", + "12 35.1 164.0 12.0 \n", + "13 32.4 8.0 0.0 \n", + "14 0.0 0.0 0.0 \n", + "15 26.8 320.0 11.0 \n", + "16 69.7 407.0 27.0 \n", + "17 50.3 430.0 25.0 \n", + "18 48.6 253.0 14.0 \n", + "19 49.0 260.0 8.0 \n", + "20 35.9 240.0 14.0 \n", + "21 35.1 387.0 15.0 \n", + "22 28.3 28.0 2.0 \n", + "23 69.8 19.0 1.0 \n", + "24 36.5 148.0 4.0 \n", + "25 34.3 134.0 5.0 \n", + "26 34.9 433.0 18.0 \n", + "27 46.3 107.0 6.0 \n", + "28 37.7 112.0 6.0 \n", + "29 41.1 222.0 16.0 \n", + "30 35.2 470.0 12.0 \n", + "31 41.8 12.0 0.0 \n", + "32 34.7 422.0 24.0 \n", + "33 52.4 142.0 14.0 \n", + "34 40.6 338.0 9.0 \n", + "35 41.8 195.0 9.0 \n", + "36 40.2 268.0 12.0 \n", + "37 57.1 37.0 0.0 \n", + "38 43.0 521.0 28.0 \n", + "39 46.3 40.0 2.0 \n", + "40 51.1 24.0 1.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\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 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 2.8 39.0 \n", - "20 5.6 9.0 \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 2.5 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", + "0 6.7 23.0 \n", + "1 0.0 0.0 \n", + "2 8.0 24.0 \n", + "3 0.0 0.0 \n", + "4 6.1 26.0 \n", + "5 0.0 2.0 \n", + "6 2.8 27.0 \n", + "7 3.0 32.0 \n", + "8 4.7 37.0 \n", + "9 4.2 0.0 \n", + "10 8.3 0.0 \n", + "11 1.7 10.0 \n", + "12 7.3 28.0 \n", + "13 0.0 1.0 \n", + "14 0.0 0.0 \n", + "15 3.4 35.0 \n", + "16 6.6 66.0 \n", + "17 5.8 51.0 \n", + "18 5.5 24.0 \n", + "19 3.1 7.0 \n", + "20 5.8 9.0 \n", + "21 3.9 25.0 \n", + "22 7.1 0.0 \n", + "23 5.3 1.0 \n", + "24 2.7 11.0 \n", + "25 3.7 4.0 \n", + "26 4.2 46.0 \n", + "27 5.6 9.0 \n", + "28 5.4 1.0 \n", + "29 7.2 13.0 \n", + "30 2.6 19.0 \n", + "31 0.0 2.0 \n", + "32 5.7 23.0 \n", + "33 9.9 15.0 \n", + "34 2.7 19.0 \n", + 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14.1 \n", + "9 0.00 9.3 \n", + "10 0.00 2.0 \n", + "11 0.83 16.2 \n", + "12 1.65 18.3 \n", + "13 1.36 22.0 \n", + "14 0.00 19.0 \n", + "15 1.09 16.9 \n", + "16 2.00 17.2 \n", + "17 1.59 15.5 \n", + "18 1.04 15.6 \n", + "19 0.44 11.6 \n", + "20 0.43 12.5 \n", + "21 0.76 13.9 \n", + "22 0.00 9.0 \n", + "23 1.00 12.0 \n", + "24 1.15 15.7 \n", + "25 0.57 11.6 \n", + "26 1.40 16.0 \n", + "27 1.00 14.5 \n", + "28 0.17 11.0 \n", + "29 0.76 14.2 \n", + "30 0.58 12.3 \n", + "31 2.00 26.3 \n", + "32 0.72 13.2 \n", + "33 1.51 16.0 \n", + "34 0.81 15.2 \n", + "35 0.81 14.4 \n", + "36 1.86 18.5 \n", + "37 0.00 11.5 \n", + "38 0.62 10.7 \n", + "39 2.20 17.2 \n", + "40 2.47 17.0 \n", "\n", - "[31 rows x 47 columns]" + "[41 rows x 47 columns]" ] }, "execution_count": 4, @@ -1688,481 +1988,481 @@ " \n", " 0\n", " Atalanta\n", - " 24.0\n", - " 48.6\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 40.0\n", - " 28.0\n", + " 25.0\n", + " 49.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 55.0\n", + " 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54.6\n", + " 1749.0\n", + " 467.0\n", + " 371.0\n", + " 55.7\n", " \n", " \n", " 14\n", " Salernitana\n", " 28.0\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", + " 44.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 39.0\n", + " 28.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 272.0\n", - " 231.0\n", - " 43.0\n", + " 408.0\n", + " 369.0\n", + " 60.0\n", + " 1.0\n", + " 10.0\n", " 0.0\n", - " 8.0\n", - " 0.0\n", - " 1149.0\n", - " 285.0\n", - " 291.0\n", - " 49.5\n", + " 1706.0\n", + " 462.0\n", + " 495.0\n", + " 48.3\n", " \n", " \n", " 15\n", " Sampdoria\n", - " 31.0\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", - " 314.0\n", - " 330.0\n", - " 40.0\n", - " 0.0\n", - " 6.0\n", + " 37.0\n", + " 46.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 20.0\n", + " 16.0\n", + " 1.0\n", " 2.0\n", - " 1116.0\n", - " 349.0\n", - " 362.0\n", - " 49.1\n", + " ...\n", + " 448.0\n", + " 458.0\n", + " 48.0\n", + " 1.0\n", + " 8.0\n", + " 2.0\n", + " 1671.0\n", + " 554.0\n", + " 569.0\n", + " 49.3\n", " \n", " \n", " 16\n", " Sassuolo\n", " 29.0\n", - " 48.7\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 49.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 39.0\n", " 25.0\n", - " 18.0\n", - " 4.0\n", - " 5.0\n", + " 7.0\n", + " 8.0\n", " ...\n", - " 227.0\n", - " 269.0\n", - " 19.0\n", - " 4.0\n", + " 361.0\n", + " 377.0\n", + " 31.0\n", + " 6.0\n", " 4.0\n", " 0.0\n", - " 994.0\n", - " 206.0\n", - " 256.0\n", - " 44.6\n", + " 1547.0\n", + " 339.0\n", + " 398.0\n", + " 46.0\n", " \n", " \n", " 17\n", " Spezia\n", + " 34.0\n", + " 46.9\n", " 33.0\n", - " 45.5\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 363.0\n", + " 2970.0\n", + " 26.0\n", " 17.0\n", - " 10.0\n", - " 3.0\n", - " 3.0\n", + " 4.0\n", + " 4.0\n", " ...\n", - " 313.0\n", - " 220.0\n", - " 39.0\n", - " 1.0\n", + " 451.0\n", + " 321.0\n", + " 46.0\n", " 2.0\n", - " 0.0\n", - " 1234.0\n", - " 306.0\n", - " 343.0\n", - " 47.1\n", + " 6.0\n", + " 2.0\n", + " 1803.0\n", + " 448.0\n", + " 498.0\n", + " 47.4\n", " \n", " \n", " 18\n", " Torino\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", + " 28.0\n", + " 53.4\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 35.0\n", + " 28.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 319.0\n", - " 224.0\n", - " 50.0\n", - " 1.0\n", - " 4.0\n", + " 445.0\n", + " 339.0\n", + " 73.0\n", + " 2.0\n", + " 6.0\n", " 0.0\n", - " 1184.0\n", - " 333.0\n", - " 367.0\n", - " 47.6\n", + " 1757.0\n", + " 508.0\n", + " 548.0\n", + " 48.1\n", " \n", " \n", " 19\n", " Udinese\n", - " 25.0\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", - " 263.0\n", - " 267.0\n", + " 27.0\n", + " 48.1\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 42.0\n", " 37.0\n", - " 0.0\n", + " 1.0\n", " 2.0\n", + " ...\n", + " 402.0\n", + " 422.0\n", + " 47.0\n", + " 1.0\n", + " 6.0\n", " 2.0\n", - " 1140.0\n", - " 277.0\n", - " 233.0\n", + " 1680.0\n", + " 421.0\n", + " 354.0\n", " 54.3\n", " \n", " \n", @@ -2172,92 +2472,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\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", + "0 Atalanta 25.0 49.8 33.0 363.0 2970.0 \n", + "1 Bologna 27.0 54.0 33.0 363.0 2970.0 \n", + "2 Cremonese 32.0 43.0 33.0 363.0 2970.0 \n", + "3 Empoli 31.0 46.7 33.0 363.0 2970.0 \n", + "4 Fiorentina 30.0 56.8 33.0 363.0 2970.0 \n", + "5 Hellas Verona 36.0 41.7 33.0 363.0 2970.0 \n", + "6 Inter 24.0 56.9 33.0 363.0 2970.0 \n", + "7 Juventus 29.0 48.7 33.0 363.0 2970.0 \n", + "8 Lazio 22.0 51.3 33.0 363.0 2970.0 \n", + "9 Lecce 29.0 41.7 33.0 363.0 2970.0 \n", + "10 Milan 29.0 54.8 33.0 363.0 2970.0 \n", + "11 Monza 31.0 55.0 33.0 363.0 2970.0 \n", + "12 Napoli 26.0 62.2 33.0 363.0 2970.0 \n", + "13 Roma 27.0 48.6 33.0 363.0 2970.0 \n", + "14 Salernitana 28.0 44.2 33.0 363.0 2970.0 \n", + "15 Sampdoria 37.0 46.8 33.0 363.0 2970.0 \n", + "16 Sassuolo 29.0 49.2 33.0 363.0 2970.0 \n", + "17 Spezia 34.0 46.9 33.0 363.0 2970.0 \n", + "18 Torino 28.0 53.4 33.0 363.0 2970.0 \n", + "19 Udinese 27.0 48.1 33.0 363.0 2970.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\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", + "0 55.0 35.0 6.0 8.0 ... 399.0 335.0 49.0 \n", + "1 41.0 32.0 6.0 6.0 ... 412.0 378.0 69.0 \n", + "2 29.0 14.0 4.0 6.0 ... 415.0 349.0 64.0 \n", + "3 28.0 15.0 1.0 1.0 ... 379.0 390.0 43.0 \n", + "4 43.0 32.0 4.0 6.0 ... 421.0 461.0 45.0 \n", + "5 25.0 19.0 1.0 1.0 ... 476.0 337.0 65.0 \n", + "6 58.0 41.0 4.0 5.0 ... 390.0 360.0 61.0 \n", + "7 50.0 37.0 3.0 6.0 ... 383.0 354.0 57.0 \n", + "8 51.0 32.0 5.0 7.0 ... 347.0 421.0 46.0 \n", + "9 26.0 17.0 3.0 4.0 ... 483.0 383.0 63.0 \n", + "10 50.0 40.0 3.0 3.0 ... 404.0 378.0 52.0 \n", + "11 39.0 26.0 5.0 5.0 ... 433.0 433.0 59.0 \n", + "12 67.0 52.0 6.0 7.0 ... 337.0 406.0 55.0 \n", + "13 45.0 31.0 6.0 9.0 ... 392.0 439.0 53.0 \n", + "14 39.0 28.0 2.0 2.0 ... 408.0 369.0 60.0 \n", + "15 20.0 16.0 1.0 2.0 ... 448.0 458.0 48.0 \n", + "16 39.0 25.0 7.0 8.0 ... 361.0 377.0 31.0 \n", + "17 26.0 17.0 4.0 4.0 ... 451.0 321.0 46.0 \n", + "18 35.0 28.0 2.0 2.0 ... 445.0 339.0 73.0 \n", + "19 42.0 37.0 1.0 2.0 ... 402.0 422.0 47.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "0 6.0 3.0 2.0 1968.0 508.0 \n", + "1 5.0 9.0 3.0 1836.0 325.0 \n", + "2 4.0 5.0 1.0 1817.0 500.0 \n", + "3 1.0 6.0 1.0 1557.0 343.0 \n", + "4 3.0 5.0 2.0 1798.0 539.0 \n", + "5 1.0 1.0 2.0 1954.0 666.0 \n", + "6 4.0 3.0 2.0 1723.0 465.0 \n", + "7 6.0 4.0 0.0 1685.0 425.0 \n", + "8 5.0 1.0 0.0 1734.0 334.0 \n", + "9 3.0 5.0 3.0 1843.0 499.0 \n", + "10 1.0 5.0 1.0 1800.0 508.0 \n", + "11 5.0 1.0 1.0 1612.0 388.0 \n", + "12 5.0 2.0 0.0 1760.0 425.0 \n", + "13 5.0 3.0 1.0 1749.0 467.0 \n", + "14 1.0 10.0 0.0 1706.0 462.0 \n", + "15 1.0 8.0 2.0 1671.0 554.0 \n", + "16 6.0 4.0 0.0 1547.0 339.0 \n", + "17 2.0 6.0 2.0 1803.0 448.0 \n", + "18 2.0 6.0 0.0 1757.0 508.0 \n", + "19 1.0 6.0 2.0 1680.0 421.0 \n", "\n", " aerials_lost aerials_won_pct \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", + "0 418.0 54.9 \n", + "1 397.0 45.0 \n", + "2 660.0 43.1 \n", + "3 424.0 44.7 \n", + "4 458.0 54.1 \n", + "5 650.0 50.6 \n", + "6 369.0 55.8 \n", + "7 408.0 51.0 \n", + "8 333.0 50.1 \n", + "9 624.0 44.4 \n", + "10 419.0 54.8 \n", + "11 378.0 50.7 \n", + "12 353.0 54.6 \n", + "13 371.0 55.7 \n", + "14 495.0 48.3 \n", + "15 569.0 49.3 \n", + "16 398.0 46.0 \n", + "17 498.0 47.4 \n", + "18 548.0 48.1 \n", + "19 354.0 54.3 \n", "\n", "[20 rows x 152 columns]" ] @@ -2329,481 +2629,481 @@ " \n", " 0\n", " vs Atalanta\n", - " 24.0\n", - " 51.4\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 23.0\n", - " 17.0\n", - " 1.0\n", - " 1.0\n", + " 25.0\n", + " 50.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", + " 28.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 244.0\n", - " 256.0\n", - " 26.0\n", + " 354.0\n", + " 372.0\n", + " 42.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1335.0\n", - " 273.0\n", - " 328.0\n", - " 45.4\n", + " 1976.0\n", + " 418.0\n", + " 508.0\n", + " 45.1\n", " \n", " \n", " 1\n", " vs Bologna\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", + " 27.0\n", + " 46.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 40.0\n", + " 24.0\n", + " 7.0\n", + " 9.0\n", " ...\n", - " 280.0\n", - " 268.0\n", - " 38.0\n", - " 3.0\n", - " 4.0\n", + " 400.0\n", + " 388.0\n", + " 54.0\n", + " 6.0\n", + " 6.0\n", " 1.0\n", - " 1204.0\n", - " 250.0\n", - " 210.0\n", - " 54.3\n", + " 1775.0\n", + " 397.0\n", + " 325.0\n", + " 55.0\n", " \n", " \n", " 2\n", " vs Cremonese\n", - " 31.0\n", - " 56.2\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 32.0\n", + " 57.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 58.0\n", " 39.0\n", - " 25.0\n", - " 4.0\n", - " 4.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 239.0\n", - " 271.0\n", - " 36.0\n", - " 3.0\n", + " 373.0\n", + " 393.0\n", + " 57.0\n", " 4.0\n", + " 6.0\n", " 0.0\n", - " 1229.0\n", - " 409.0\n", - " 314.0\n", - " 56.6\n", + " 1867.0\n", + " 660.0\n", + " 500.0\n", + " 56.9\n", " \n", " \n", " 3\n", " vs Empoli\n", + " 31.0\n", + " 53.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 42.0\n", " 28.0\n", - " 52.5\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 28.0\n", - " 20.0\n", - " 2.0\n", " 4.0\n", + " 6.0\n", " ...\n", - " 283.0\n", - " 253.0\n", - " 36.0\n", - " 2.0\n", - " 0.0\n", - " 0.0\n", - " 1148.0\n", - " 263.0\n", - " 217.0\n", - " 54.8\n", + " 412.0\n", + " 365.0\n", + " 67.0\n", + " 4.0\n", + " 1.0\n", + " 1.0\n", + " 1693.0\n", + " 424.0\n", + " 343.0\n", + " 55.3\n", " \n", " \n", " 4\n", " vs Fiorentina\n", - " 28.0\n", - " 42.8\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 28.0\n", - " 20.0\n", - " 3.0\n", - " 3.0\n", + " 30.0\n", + " 43.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", + " 26.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 307.0\n", - " 269.0\n", - " 59.0\n", - " 1.0\n", - " 4.0\n", - " 0.0\n", - " 1130.0\n", - " 289.0\n", - " 328.0\n", - " 46.8\n", + " 487.0\n", + " 400.0\n", + " 80.0\n", + " 3.0\n", + " 6.0\n", + " 2.0\n", + " 1708.0\n", + " 458.0\n", + " 539.0\n", + " 45.9\n", " \n", " \n", " 5\n", " vs Hellas Verona\n", - " 34.0\n", - " 57.1\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 32.0\n", - " 30.0\n", + " 36.0\n", + " 58.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 49.0\n", + " 43.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 234.0\n", - " 315.0\n", - " 25.0\n", + " 358.0\n", + " 455.0\n", + " 35.0\n", + " 1.0\n", " 1.0\n", - " 0.0\n", " 2.0\n", - " 1231.0\n", - " 423.0\n", - " 445.0\n", - " 48.7\n", + " 1833.0\n", + " 650.0\n", + " 666.0\n", + " 49.4\n", " \n", " \n", " 6\n", " vs Inter\n", - " 23.0\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", + " 43.1\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 33.0\n", + " 28.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 276.0\n", - " 248.0\n", - " 19.0\n", + " 386.0\n", + " 359.0\n", + " 31.0\n", + " 3.0\n", + " 5.0\n", " 2.0\n", - " 2.0\n", - " 1.0\n", - " 1007.0\n", - " 231.0\n", - " 288.0\n", - " 44.5\n", + " 1532.0\n", + " 369.0\n", + " 465.0\n", + " 44.2\n", " \n", " \n", " 7\n", " vs Juventus\n", - " 26.0\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", - " 246.0\n", - " 242.0\n", " 29.0\n", - " 0.0\n", + " 51.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 28.0\n", + " 22.0\n", + " 3.0\n", " 4.0\n", + " ...\n", + " 370.0\n", + " 345.0\n", + " 47.0\n", + " 1.0\n", + " 6.0\n", " 0.0\n", - " 1134.0\n", - " 258.0\n", - " 272.0\n", - " 48.7\n", + " 1662.0\n", + " 408.0\n", + " 425.0\n", + " 49.0\n", " \n", " \n", " 8\n", " vs Lazio\n", - " 21.0\n", - " 48.2\n", " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 19.0\n", - " 13.0\n", + " 48.7\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 24.0\n", + " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 308.0\n", - " 218.0\n", - " 44.0\n", + " 447.0\n", + " 330.0\n", + " 58.0\n", " 1.0\n", - " 4.0\n", + " 7.0\n", " 1.0\n", - " 1233.0\n", - " 218.0\n", - " 229.0\n", - " 48.8\n", + " 1788.0\n", + " 333.0\n", + " 334.0\n", + " 49.9\n", " \n", " \n", " 9\n", " vs Lecce\n", - " 26.0\n", - " 57.6\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 24.0\n", - " 16.0\n", - " 4.0\n", - " 4.0\n", + " 29.0\n", + " 58.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", + " 25.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 283.0\n", - " 300.0\n", - " 45.0\n", - " 3.0\n", + " 401.0\n", + " 455.0\n", + " 61.0\n", + " 4.0\n", + " 4.0\n", " 2.0\n", - " 2.0\n", - " 1200.0\n", - " 417.0\n", - " 332.0\n", - " 55.7\n", + " 1782.0\n", + " 624.0\n", + " 499.0\n", + " 55.6\n", " \n", " \n", " 10\n", " vs Milan\n", - " 27.0\n", - " 46.5\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", " 29.0\n", - " 22.0\n", - " 3.0\n", + " 45.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 38.0\n", + " 30.0\n", " 4.0\n", + " 5.0\n", " ...\n", - " 275.0\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", + " 399.0\n", + " 387.0\n", + " 35.0\n", + " 5.0\n", + " 3.0\n", + " 3.0\n", + " 1732.0\n", + " 419.0\n", + " 508.0\n", + " 45.2\n", " \n", " \n", " 11\n", " vs Monza\n", - " 29.0\n", + " 31.0\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", - " 318.0\n", - " 281.0\n", - " 37.0\n", - " 0.0\n", - " 4.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 43.0\n", + " 27.0\n", " 1.0\n", - " 1145.0\n", - " 242.0\n", - " 253.0\n", - " 48.9\n", + " 1.0\n", + " ...\n", + " 462.0\n", + " 414.0\n", + " 50.0\n", + " 1.0\n", + " 5.0\n", + " 2.0\n", + " 1719.0\n", + " 378.0\n", + " 388.0\n", + " 49.3\n", " \n", " \n", " 12\n", " vs Napoli\n", - " 24.0\n", - " 38.4\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 26.0\n", + " 37.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 23.0\n", " 15.0\n", - " 10.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 298.0\n", - " 199.0\n", - " 28.0\n", + " 431.0\n", + " 313.0\n", + " 41.0\n", " 1.0\n", - " 5.0\n", - " 0.0\n", - " 1100.0\n", - " 232.0\n", - " 280.0\n", - " 45.3\n", + " 6.0\n", + " 2.0\n", + " 1651.0\n", + " 353.0\n", + " 425.0\n", + " 45.4\n", " \n", " \n", " 13\n", " vs Roma\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", + " 27.0\n", + " 51.4\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 30.0\n", + " 20.0\n", + " 2.0\n", + " 3.0\n", " ...\n", - " 316.0\n", - " 246.0\n", - " 12.0\n", + " 466.0\n", + " 366.0\n", + " 21.0\n", + " 2.0\n", + " 9.0\n", " 0.0\n", - " 6.0\n", - " 0.0\n", - " 1156.0\n", - " 221.0\n", - " 266.0\n", - " 45.4\n", + " 1741.0\n", + " 371.0\n", + " 467.0\n", + " 44.3\n", " \n", " \n", " 14\n", " vs Salernitana\n", " 28.0\n", + " 55.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\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", + " 35.0\n", + " 6.0\n", + " 10.0\n", " ...\n", - " 252.0\n", - " 259.0\n", - " 57.0\n", - " 8.0\n", - " 1.0\n", - " 1.0\n", - " 1213.0\n", - " 291.0\n", - " 285.0\n", - " 50.5\n", + " 403.0\n", + " 384.0\n", + " 65.0\n", + " 10.0\n", + " 2.0\n", + " 2.0\n", + " 1817.0\n", + " 495.0\n", + " 462.0\n", + " 51.7\n", " \n", " \n", " 15\n", " vs Sampdoria\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", + " 37.0\n", + " 53.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 57.0\n", + " 43.0\n", + " 6.0\n", + " 9.0\n", " ...\n", - " 339.0\n", - " 300.0\n", - " 59.0\n", - " 4.0\n", + " 475.0\n", + " 427.0\n", + " 81.0\n", + " 6.0\n", + " 2.0\n", " 0.0\n", - " 0.0\n", - " 1189.0\n", - " 362.0\n", - " 349.0\n", - " 50.9\n", + " 1802.0\n", + " 569.0\n", + " 554.0\n", + " 50.7\n", " \n", " \n", " 16\n", " vs Sassuolo\n", " 29.0\n", - " 51.3\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 50.8\n", " 33.0\n", - " 24.0\n", + " 363.0\n", + " 2970.0\n", + " 49.0\n", + " 37.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 287.0\n", - " 206.0\n", - " 72.0\n", + " 407.0\n", + " 332.0\n", + " 100.0\n", " 2.0\n", - " 5.0\n", + " 8.0\n", " 1.0\n", - " 1131.0\n", - " 256.0\n", - " 206.0\n", - " 55.4\n", + " 1710.0\n", + " 398.0\n", + " 339.0\n", + " 54.0\n", " \n", " \n", " 17\n", " vs Spezia\n", + " 34.0\n", + " 53.1\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", + " 363.0\n", + " 2970.0\n", + " 52.0\n", + " 36.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 235.0\n", - " 291.0\n", - " 53.0\n", - " 1.0\n", - " 3.0\n", + " 345.0\n", + " 414.0\n", + " 70.0\n", + " 4.0\n", + " 4.0\n", " 2.0\n", - " 1275.0\n", - " 343.0\n", - " 306.0\n", - " 52.9\n", + " 1820.0\n", + " 498.0\n", + " 448.0\n", + " 52.6\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", - " 15.0\n", - " 3.0\n", - " 4.0\n", + " 28.0\n", + " 46.6\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 38.0\n", + " 25.0\n", + " 5.0\n", + " 6.0\n", " ...\n", - " 239.0\n", - " 306.0\n", - " 24.0\n", - " 3.0\n", - " 1.0\n", + " 364.0\n", + " 427.0\n", + " 35.0\n", + " 4.0\n", + " 2.0\n", " 0.0\n", - " 1155.0\n", - " 367.0\n", - " 333.0\n", - " 52.4\n", + " 1709.0\n", + " 548.0\n", + " 508.0\n", + " 51.9\n", " \n", " \n", " 19\n", " vs Udinese\n", - " 25.0\n", - " 50.1\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 23.0\n", - " 16.0\n", - " 2.0\n", - " 2.0\n", + " 27.0\n", + " 51.9\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 39.0\n", + " 27.0\n", + " 5.0\n", + " 6.0\n", " ...\n", - " 282.0\n", - " 252.0\n", - " 34.0\n", + " 446.0\n", + " 384.0\n", + " 56.0\n", + " 4.0\n", " 2.0\n", - " 0.0\n", " 1.0\n", - " 1101.0\n", - " 233.0\n", - " 277.0\n", + " 1683.0\n", + " 354.0\n", + " 421.0\n", " 45.7\n", " \n", " \n", @@ -2813,92 +3113,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\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", + "0 vs Atalanta 25.0 50.2 33.0 363.0 2970.0 \n", + "1 vs Bologna 27.0 46.0 33.0 363.0 2970.0 \n", + "2 vs Cremonese 32.0 57.0 33.0 363.0 2970.0 \n", + "3 vs Empoli 31.0 53.3 33.0 363.0 2970.0 \n", + "4 vs Fiorentina 30.0 43.2 33.0 363.0 2970.0 \n", + "5 vs Hellas Verona 36.0 58.3 33.0 363.0 2970.0 \n", + "6 vs Inter 24.0 43.1 33.0 363.0 2970.0 \n", + "7 vs Juventus 29.0 51.3 33.0 363.0 2970.0 \n", + "8 vs Lazio 22.0 48.7 33.0 363.0 2970.0 \n", + "9 vs Lecce 29.0 58.3 33.0 363.0 2970.0 \n", + "10 vs Milan 29.0 45.2 33.0 363.0 2970.0 \n", + "11 vs Monza 31.0 45.0 33.0 363.0 2970.0 \n", + "12 vs Napoli 26.0 37.8 33.0 363.0 2970.0 \n", + "13 vs Roma 27.0 51.4 33.0 363.0 2970.0 \n", + "14 vs Salernitana 28.0 55.8 33.0 363.0 2970.0 \n", + "15 vs Sampdoria 37.0 53.2 33.0 363.0 2970.0 \n", + "16 vs Sassuolo 29.0 50.8 33.0 363.0 2970.0 \n", + "17 vs Spezia 34.0 53.1 33.0 363.0 2970.0 \n", + "18 vs Torino 28.0 46.6 33.0 363.0 2970.0 \n", + "19 vs Udinese 27.0 51.9 33.0 363.0 2970.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\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", + "0 37.0 28.0 3.0 3.0 ... 354.0 372.0 42.0 \n", + "1 40.0 24.0 7.0 9.0 ... 400.0 388.0 54.0 \n", + "2 58.0 39.0 5.0 5.0 ... 373.0 393.0 57.0 \n", + "3 42.0 28.0 4.0 6.0 ... 412.0 365.0 67.0 \n", + "4 37.0 26.0 5.0 5.0 ... 487.0 400.0 80.0 \n", + "5 49.0 43.0 0.0 1.0 ... 358.0 455.0 35.0 \n", + "6 33.0 28.0 3.0 3.0 ... 386.0 359.0 31.0 \n", + "7 28.0 22.0 3.0 4.0 ... 370.0 345.0 47.0 \n", + "8 24.0 16.0 1.0 1.0 ... 447.0 330.0 58.0 \n", + "9 37.0 25.0 5.0 5.0 ... 401.0 455.0 61.0 \n", + "10 38.0 30.0 4.0 5.0 ... 399.0 387.0 35.0 \n", + "11 43.0 27.0 1.0 1.0 ... 462.0 414.0 50.0 \n", + "12 23.0 15.0 1.0 2.0 ... 431.0 313.0 41.0 \n", + "13 30.0 20.0 2.0 3.0 ... 466.0 366.0 21.0 \n", + "14 54.0 35.0 6.0 10.0 ... 403.0 384.0 65.0 \n", + "15 57.0 43.0 6.0 9.0 ... 475.0 427.0 81.0 \n", + "16 49.0 37.0 4.0 4.0 ... 407.0 332.0 100.0 \n", + "17 52.0 36.0 4.0 6.0 ... 345.0 414.0 70.0 \n", + "18 38.0 25.0 5.0 6.0 ... 364.0 427.0 35.0 \n", + "19 39.0 27.0 5.0 6.0 ... 446.0 384.0 56.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "0 1.0 8.0 1.0 1976.0 418.0 \n", + "1 6.0 6.0 1.0 1775.0 397.0 \n", + "2 4.0 6.0 0.0 1867.0 660.0 \n", + "3 4.0 1.0 1.0 1693.0 424.0 \n", + "4 3.0 6.0 2.0 1708.0 458.0 \n", + "5 1.0 1.0 2.0 1833.0 650.0 \n", + "6 3.0 5.0 2.0 1532.0 369.0 \n", + "7 1.0 6.0 0.0 1662.0 408.0 \n", + "8 1.0 7.0 1.0 1788.0 333.0 \n", + "9 4.0 4.0 2.0 1782.0 624.0 \n", + "10 5.0 3.0 3.0 1732.0 419.0 \n", + "11 1.0 5.0 2.0 1719.0 378.0 \n", + "12 1.0 6.0 2.0 1651.0 353.0 \n", + "13 2.0 9.0 0.0 1741.0 371.0 \n", + "14 10.0 2.0 2.0 1817.0 495.0 \n", + "15 6.0 2.0 0.0 1802.0 569.0 \n", + "16 2.0 8.0 1.0 1710.0 398.0 \n", + "17 4.0 4.0 2.0 1820.0 498.0 \n", + "18 4.0 2.0 0.0 1709.0 548.0 \n", + "19 4.0 2.0 1.0 1683.0 354.0 \n", "\n", " aerials_lost aerials_won_pct \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", + "0 508.0 45.1 \n", + "1 325.0 55.0 \n", + "2 500.0 56.9 \n", + "3 343.0 55.3 \n", + "4 539.0 45.9 \n", + "5 666.0 49.4 \n", + "6 465.0 44.2 \n", + "7 425.0 49.0 \n", + "8 334.0 49.9 \n", + "9 499.0 55.6 \n", + "10 508.0 45.2 \n", + "11 388.0 49.3 \n", + "12 425.0 45.4 \n", + "13 467.0 44.3 \n", + "14 462.0 51.7 \n", + "15 554.0 50.7 \n", + "16 339.0 54.0 \n", + "17 448.0 52.6 \n", + "18 508.0 51.9 \n", + "19 421.0 45.7 \n", "\n", "[20 rows x 152 columns]" ] @@ -2918,10 +3218,149 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "11e51337", "metadata": {}, - "outputs": [], + "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_shots'\n", + " 'sca_fouled' 'gca' 'gca_per90' 'gca_passes_live' 'gca_passes_dead'\n", + " 'gca_shots' 'gca_fouled' 'gca_defense' 'tackles' 'tackles_won'\n", + " 'tackles_def_3rd' 'tackles_mid_3rd' 'tackles_att_3rd' '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' 'carries'\n", + " 'progressive_carries' 'carries_into_final_third'\n", + " 'carries_into_penalty_area' 'passes_received' 'miscontrols'\n", + " 'dispossessed' 'cards_yellow_red' 'fouls' 'fouled' 'offsides' 'pens_won'\n", + " 'pens_conceded' 'own_goals' 'ball_recoveries' 'aerials_won'\n", + " 'aerials_lost' '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_shots' 'sca_fouled' 'gca' 'gca_per90'\n", + " 'gca_passes_live' 'gca_passes_dead' 'gca_shots' 'gca_fouled'\n", + " 'gca_defense' 'tackles' 'tackles_won' 'tackles_def_3rd' 'tackles_mid_3rd'\n", + " 'tackles_att_3rd' 'blocks' 'blocked_shots' 'blocked_passes'\n", + " 'interceptions' 'clearances' 'errors' 'touches' 'touches_def_pen_area'\n", + " 'touches_def_3rd' 'touches_mid_3rd' 'touches_att_3rd'\n", + " 'touches_att_pen_area' 'touches_live_ball' 'carries'\n", + " 'progressive_carries' 'carries_into_final_third'\n", + " 'carries_into_penalty_area' 'passes_received' 'miscontrols'\n", + " 'dispossessed' 'cards_yellow_red' 'fouls' 'fouled' 'offsides' 'pens_won'\n", + " 'pens_conceded' 'own_goals' 'ball_recoveries' 'aerials_won'\n", + " '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_shots' 'sca_fouled' 'gca' 'gca_per90'\n", + " 'gca_passes_live' 'gca_passes_dead' 'gca_shots' 'gca_fouled'\n", + " 'gca_defense' 'tackles' 'tackles_won' 'tackles_def_3rd' 'tackles_mid_3rd'\n", + " 'tackles_att_3rd' 'blocks' 'blocked_shots' 'blocked_passes'\n", + " 'interceptions' 'clearances' 'errors' 'touches' 'touches_def_pen_area'\n", + " 'touches_def_3rd' 'touches_mid_3rd' 'touches_att_3rd'\n", + " 'touches_att_pen_area' 'touches_live_ball' 'carries'\n", + " 'progressive_carries' 'carries_into_final_third'\n", + " 'carries_into_penalty_area' 'passes_received' 'miscontrols'\n", + " 'dispossessed' 'cards_yellow_red' 'fouls' 'fouled' 'offsides' 'pens_won'\n", + " 'pens_conceded' 'own_goals' 'ball_recoveries' 'aerials_won'\n", + " 'aerials_lost' 'aerials_won_pct']\n" + ] + } + ], "source": [ "print('Outfield player columns')\n", "print(df_outfield.columns.values)\n", @@ -2948,23 +3387,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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": { @@ -2993,1178 +3418,194 @@ " team\n", " age\n", " birth_year\n", - " gk_games\n", - " gk_games_starts\n", - " gk_minutes\n", - " gk_goals_against\n", + " games\n", + " games_starts\n", + " minutes\n", + " goals\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", + " fouls\n", + " fouled\n", + " offsides\n", + " pens_won\n", + " pens_conceded\n", + " own_goals\n", + " ball_recoveries\n", + " aerials_won\n", + " aerials_lost\n", + " aerials_won_pct\n", " \n", " \n", " \n", " \n", " 0\n", - " Emil Audero\n", - " it ITA\n", - " GK\n", - " Sampdoria\n", - " 24\n", + " Tammy Abraham\n", + " eng ENG\n", + " FW\n", + " Roma\n", + " 23\n", " 1997\n", - " 29.0\n", - " 29.0\n", - " 2560.0\n", - " 48.0\n", + " 37.0\n", + " 36.0\n", + " 3084.0\n", + " 17.0\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", + " 38.0\n", + " 49.0\n", + " 17.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 68.0\n", + " 78.0\n", + " 80.0\n", + " 49.4\n", " \n", " \n", " 1\n", - " Nicola Bagnolini\n", + " Francesco Acerbi\n", " it ITA\n", - " GK\n", - " Bologna\n", - " 17\n", - " 2004\n", - " 1.0\n", - " 0.0\n", - " 3.0\n", - " 0.0\n", + " DF\n", + " Lazio\n", + " 33\n", + " 1988\n", + " 30.0\n", + " 29.0\n", + " 2536.0\n", + " 4.0\n", " ...\n", - " 61.0\n", + " 18.0\n", + " 16.0\n", + " 4.0\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", + " 162.0\n", + " 69.0\n", + " 43.0\n", + " 61.6\n", " \n", " \n", " 2\n", - " Francesco Bardi\n", - " it ITA\n", - " GK\n", + " Michel Aebischer\n", + " ch SUI\n", + " MF\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", + " 24\n", + " 1997\n", + " 12.0\n", + " 4.0\n", + " 443.0\n", " 0.0\n", - " 0.00\n", - " 5.5\n", + " ...\n", + " 13.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 27.0\n", + " 5.0\n", + " 4.0\n", + " 55.6\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", + " Felix Afena-Gyan\n", + " gh GHA\n", + " FW,MF\n", + " Roma\n", + " 18\n", + " 2003\n", + " 17.0\n", + " 6.0\n", + " 668.0\n", + " 2.0\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", + " 15.0\n", + " 24.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 31.0\n", + " 11.0\n", + " 15.0\n", + " 42.3\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", 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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", + " Kevin Agudelo\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", + " MF,FW\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", + " 22\n", + " 1998\n", + " 23.0\n", + " 12.0\n", + " 1237.0\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", + " 36.0\n", + " 24.0\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", + " 67.0\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", + " 51.7\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", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\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", + " 627\n", + " Nadir Zortea\n", " it ITA\n", - " GK\n", - " Sassuolo\n", + " DF,MF\n", + " Salernitana\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", + " 29.0\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", + " 1410.0\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", + " 14.0\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", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 72.0\n", + " 8.0\n", + " 17.0\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", + " 628\n", " Petar Zovko\n", " ba BIH\n", " GK\n", @@ -4176,337 +3617,169 @@ " 29.0\n", " 0.0\n", " ...\n", - " 35.9\n", - " 2.0\n", - " 50.0\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", + " 629\n", + " Szymon Żurkowski\n", + " pl POL\n", + " MF\n", + " Empoli\n", + " 23\n", + " 1997\n", " 35.0\n", - " 4.0\n", + " 29.0\n", + " 2307.0\n", + " 6.0\n", + " ...\n", + " 41.0\n", + " 67.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", + " 0.0\n", + " 162.0\n", + " 24.0\n", + " 32.0\n", + " 42.9\n", + " \n", + " \n", + " 630\n", + " Milan Đurić\n", + " ba BIH\n", + " FW\n", + " Salernitana\n", + " 31\n", + " 1990\n", + " 33.0\n", + " 23.0\n", + " 2165.0\n", + " 5.0\n", + " ...\n", + " 24.0\n", + " 45.0\n", + " 6.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 40.0\n", + " 242.0\n", + " 83.0\n", + " 74.5\n", + " \n", + " \n", + " 631\n", + " Filip Đuričić\n", + " rs SRB\n", + " MF,FW\n", + " Sassuolo\n", + " 29\n", + " 1992\n", + " 12.0\n", + " 9.0\n", + " 671.0\n", " 2.0\n", - " 6.21\n", - " 28.5\n", + " ...\n", + " 10.0\n", + " 14.0\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 34.0\n", + " 3.0\n", + " 5.0\n", + " 37.5\n", " \n", " \n", "\n", - "

49 rows × 47 columns

\n", + "

632 rows × 115 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", + " 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", - " 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", + " 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", - " 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", + " 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", - " 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", + " 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", - " 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]" + "[632 rows x 115 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "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", @@ -4517,637 +3790,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "9172edae", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Atalanta32.055.038.0418.03420.062.048.05.06.0...513.0454.068.03.06.03.02292.0685.0518.056.9
1Bologna36.050.638.0418.03420.043.034.04.05.0...443.0497.056.03.09.02.01972.0492.0520.048.6
2Cagliari33.044.538.0418.03420.034.026.03.04.0...549.0490.070.03.010.01.01995.0696.0742.048.4
3Empoli28.047.438.0418.03420.047.027.07.07.0...506.0463.083.06.010.06.01998.0427.0549.043.8
4Fiorentina28.057.738.0418.03420.059.033.09.012.0...464.0565.057.08.05.02.01885.0469.0456.050.7
5Genoa40.043.938.0418.03420.026.019.06.07.0...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...500.0474.060.05.06.02.01980.0524.0441.054.3
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
16Spezia30.042.738.0418.03420.038.025.03.05.0...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
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20 rows × 152 columns

\n", - "
" - ], - "text/plain": [ - " 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", - " 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_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_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", - "[20 rows x 152 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_team = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'for')\n", "\n", @@ -5158,637 +3804,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fa6258db", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0vs Atalanta32.044.938.0418.03420.045.029.05.06.0...491.0488.069.04.06.03.02240.0518.0685.043.1
1vs Bologna36.049.338.0418.03420.053.032.08.09.0...527.0416.088.08.05.01.02032.0520.0492.051.4
2vs Cagliari33.055.638.0418.03420.067.041.07.010.0...526.0524.076.06.04.00.02112.0742.0696.051.6
3vs Empoli28.052.938.0418.03420.064.044.05.010.0...488.0482.066.07.07.03.02051.0549.0427.056.3
4vs Fiorentina28.041.838.0418.03420.049.033.03.05.0...585.0441.0119.04.012.00.01825.0456.0469.049.3
5vs Genoa40.056.138.0418.03420.058.035.04.05.0...523.0530.059.04.07.01.02257.0743.0635.053.9
6vs Hellas Verona31.049.438.0418.03420.057.038.03.04.0...463.0534.032.03.08.02.02206.0733.0689.051.5
7vs Inter27.043.438.0418.03420.031.019.04.04.0...440.0441.034.03.011.01.01767.0476.0549.046.4
8vs Juventus32.048.338.0418.03420.036.021.04.07.0...525.0471.035.05.06.01.01894.0440.0550.044.4
9vs Lazio27.044.438.0418.03420.057.040.05.06.0...474.0418.073.06.09.03.01956.0427.0411.051.0
10vs Milan28.045.838.0418.03420.029.019.04.05.0...533.0440.063.03.08.03.02106.0517.0521.049.8
11vs Napoli27.041.538.0418.03420.030.019.01.01.0...517.0415.060.00.014.00.01963.0403.0393.050.6
12vs Roma29.048.638.0418.03420.041.023.05.06.0...497.0477.081.05.09.01.01970.0441.0524.045.7
13vs Salernitana42.059.038.0418.03420.076.058.06.08.0...546.0463.063.06.05.01.02043.0506.0657.043.5
14vs Sampdoria34.054.138.0418.03420.061.041.03.04.0...538.0471.064.01.04.04.02174.0506.0609.045.4
15vs Sassuolo29.044.838.0418.03420.065.048.08.09.0...519.0434.0142.06.07.04.02041.0404.0343.054.1
16vs Spezia30.057.338.0418.03420.068.044.010.013.0...474.0477.062.08.05.03.02064.0589.0530.052.6
17vs Torino31.046.438.0418.03420.041.025.09.011.0...485.0611.060.010.06.03.01989.0891.0781.053.3
18vs Udinese29.057.738.0418.03420.057.039.05.07.0...494.0537.057.06.04.03.02010.0534.0453.054.1
19vs Venezia39.057.938.0418.03420.067.039.011.012.0...476.0492.048.05.05.00.02059.0583.0564.050.8
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20 rows × 152 columns

\n", - "
" - ], - "text/plain": [ - " team players_used possession games games_starts minutes \\\n", - "0 vs Atalanta 32.0 44.9 38.0 418.0 3420.0 \n", - "1 vs Bologna 36.0 49.3 38.0 418.0 3420.0 \n", - "2 vs Cagliari 33.0 55.6 38.0 418.0 3420.0 \n", - "3 vs Empoli 28.0 52.9 38.0 418.0 3420.0 \n", - "4 vs Fiorentina 28.0 41.8 38.0 418.0 3420.0 \n", - "5 vs Genoa 40.0 56.1 38.0 418.0 3420.0 \n", - "6 vs Hellas Verona 31.0 49.4 38.0 418.0 3420.0 \n", - "7 vs Inter 27.0 43.4 38.0 418.0 3420.0 \n", - "8 vs Juventus 32.0 48.3 38.0 418.0 3420.0 \n", - "9 vs Lazio 27.0 44.4 38.0 418.0 3420.0 \n", - "10 vs Milan 28.0 45.8 38.0 418.0 3420.0 \n", - "11 vs Napoli 27.0 41.5 38.0 418.0 3420.0 \n", - "12 vs Roma 29.0 48.6 38.0 418.0 3420.0 \n", - "13 vs Salernitana 42.0 59.0 38.0 418.0 3420.0 \n", - "14 vs Sampdoria 34.0 54.1 38.0 418.0 3420.0 \n", - "15 vs Sassuolo 29.0 44.8 38.0 418.0 3420.0 \n", - "16 vs Spezia 30.0 57.3 38.0 418.0 3420.0 \n", - "17 vs Torino 31.0 46.4 38.0 418.0 3420.0 \n", - "18 vs Udinese 29.0 57.7 38.0 418.0 3420.0 \n", - "19 vs Venezia 39.0 57.9 38.0 418.0 3420.0 \n", - "\n", - " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 45.0 29.0 5.0 6.0 ... 491.0 488.0 69.0 \n", - "1 53.0 32.0 8.0 9.0 ... 527.0 416.0 88.0 \n", - "2 67.0 41.0 7.0 10.0 ... 526.0 524.0 76.0 \n", - "3 64.0 44.0 5.0 10.0 ... 488.0 482.0 66.0 \n", - "4 49.0 33.0 3.0 5.0 ... 585.0 441.0 119.0 \n", - "5 58.0 35.0 4.0 5.0 ... 523.0 530.0 59.0 \n", - "6 57.0 38.0 3.0 4.0 ... 463.0 534.0 32.0 \n", - "7 31.0 19.0 4.0 4.0 ... 440.0 441.0 34.0 \n", - "8 36.0 21.0 4.0 7.0 ... 525.0 471.0 35.0 \n", - "9 57.0 40.0 5.0 6.0 ... 474.0 418.0 73.0 \n", - "10 29.0 19.0 4.0 5.0 ... 533.0 440.0 63.0 \n", - "11 30.0 19.0 1.0 1.0 ... 517.0 415.0 60.0 \n", - "12 41.0 23.0 5.0 6.0 ... 497.0 477.0 81.0 \n", - "13 76.0 58.0 6.0 8.0 ... 546.0 463.0 63.0 \n", - "14 61.0 41.0 3.0 4.0 ... 538.0 471.0 64.0 \n", - "15 65.0 48.0 8.0 9.0 ... 519.0 434.0 142.0 \n", - "16 68.0 44.0 10.0 13.0 ... 474.0 477.0 62.0 \n", - "17 41.0 25.0 9.0 11.0 ... 485.0 611.0 60.0 \n", - "18 57.0 39.0 5.0 7.0 ... 494.0 537.0 57.0 \n", - "19 67.0 39.0 11.0 12.0 ... 476.0 492.0 48.0 \n", - "\n", - " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 4.0 6.0 3.0 2240.0 518.0 \n", - "1 8.0 5.0 1.0 2032.0 520.0 \n", - "2 6.0 4.0 0.0 2112.0 742.0 \n", - "3 7.0 7.0 3.0 2051.0 549.0 \n", - "4 4.0 12.0 0.0 1825.0 456.0 \n", - "5 4.0 7.0 1.0 2257.0 743.0 \n", - "6 3.0 8.0 2.0 2206.0 733.0 \n", - "7 3.0 11.0 1.0 1767.0 476.0 \n", - "8 5.0 6.0 1.0 1894.0 440.0 \n", - "9 6.0 9.0 3.0 1956.0 427.0 \n", - "10 3.0 8.0 3.0 2106.0 517.0 \n", - "11 0.0 14.0 0.0 1963.0 403.0 \n", - "12 5.0 9.0 1.0 1970.0 441.0 \n", - "13 6.0 5.0 1.0 2043.0 506.0 \n", - "14 1.0 4.0 4.0 2174.0 506.0 \n", - "15 6.0 7.0 4.0 2041.0 404.0 \n", - "16 8.0 5.0 3.0 2064.0 589.0 \n", - "17 10.0 6.0 3.0 1989.0 891.0 \n", - "18 6.0 4.0 3.0 2010.0 534.0 \n", - "19 5.0 5.0 0.0 2059.0 583.0 \n", - "\n", - " aerials_lost aerials_won_pct \n", - "0 685.0 43.1 \n", - "1 492.0 51.4 \n", - "2 696.0 51.6 \n", - "3 427.0 56.3 \n", - "4 469.0 49.3 \n", - "5 635.0 53.9 \n", - "6 689.0 51.5 \n", - "7 549.0 46.4 \n", - "8 550.0 44.4 \n", - "9 411.0 51.0 \n", - "10 521.0 49.8 \n", - "11 393.0 50.6 \n", - "12 524.0 45.7 \n", - "13 657.0 43.5 \n", - "14 609.0 45.4 \n", - "15 343.0 54.1 \n", - "16 530.0 52.6 \n", - "17 781.0 53.3 \n", - "18 453.0 54.1 \n", - "19 564.0 50.8 \n", - "\n", - "[20 rows x 152 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_vsteam = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'vs')\n", "\n", @@ -5799,7 +3818,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "22ad9be5", "metadata": {}, "outputs": [], diff --git a/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb b/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb index ba55a86..e09cd5b 100644 --- a/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb +++ b/.ipynb_checkpoints/6_neural_network_training_and_prediction-checkpoint.ipynb @@ -134,15 +134,15 @@ " 0.0\n", " 10.0\n", " ...\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", + " 0.006961\n", + " 0.001392\n", + " 0.011137\n", + " 0.006961\n", + " 0.015313\n", + " 0.010209\n", + " 0.386543\n", + " 0.007889\n", + " 0.015777\n", " 0.000000\n", " \n", " \n", @@ -158,16 +158,16 @@ " 0.0\n", " 6.0\n", " ...\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", + " 0.006734\n", + " 0.004209\n", + " 0.008418\n", + " 0.004209\n", + " 0.028620\n", + " 0.022727\n", + " 0.414141\n", + " 0.005051\n", + " 0.005051\n", + " 0.000842\n", " \n", " \n", " 2\n", @@ -188,9 +188,9 @@ " 0.002147\n", " 0.017180\n", " 0.010021\n", - " 0.282749\n", + " 0.284896\n", " 0.011453\n", - " 0.009306\n", + " 0.010021\n", " 0.000716\n", " \n", " \n", @@ -230,16 +230,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.011111\n", - " 0.005556\n", - " 0.016667\n", - " 0.022222\n", - " 0.016667\n", - " 0.027778\n", - " 0.561111\n", - " 0.072222\n", - " 0.055556\n", - " 0.000000\n", + " 0.017279\n", + " 0.006479\n", + " 0.019438\n", + " 0.010799\n", + " 0.006479\n", + " 0.017279\n", + " 0.431965\n", + " 0.034557\n", + " 0.025918\n", + " 0.002160\n", " \n", " \n", " ...\n", @@ -266,7 +266,7 @@ " ...\n", " \n", " \n", - " 24806\n", + " 27154\n", " 38\n", " Tameze\n", " Verona\n", @@ -290,7 +290,7 @@ " 0.003885\n", " \n", " \n", - " 24807\n", + " 27155\n", " 38\n", " Hongla\n", " Verona\n", @@ -314,7 +314,7 @@ " 0.003072\n", " \n", " \n", - " 24808\n", + " 27156\n", " 38\n", " Lasagna\n", " Verona\n", @@ -338,7 +338,7 @@ " 0.005912\n", " \n", " \n", - " 24809\n", + " 27157\n", " 38\n", " Caprari\n", " Verona\n", @@ -362,7 +362,7 @@ " 0.018620\n", " \n", " \n", - " 24810\n", + " 27158\n", " 38\n", " Simeone\n", " Verona\n", @@ -387,7 +387,7 @@ " \n", " \n", "\n", - "

24811 rows × 122 columns

\n", + "

27159 rows × 122 columns

\n", "" ], "text/plain": [ @@ -398,52 +398,52 @@ "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", + "27154 38 Tameze Verona Lazio 0 5.5 0 0 \n", + "27155 38 Hongla Verona Lazio 0 7.0 1 0 \n", + "27156 38 Lasagna Verona Lazio 0 7.0 1 0 \n", + "27157 38 Caprari Verona Lazio 0 6.0 0 0 \n", + "27158 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", + "0 0.0 10.0 ... 0.006961 0.001392 0.011137 \n", + "1 0.0 6.0 ... 0.006734 0.004209 0.008418 \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", + "4 0.5 5.5 ... 0.017279 0.006479 0.019438 \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", + "27154 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", + "27155 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", + "27156 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", + "27157 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", + "27158 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", + "0 0.006961 0.015313 0.010209 0.386543 0.007889 \n", + "1 0.004209 0.028620 0.022727 0.414141 0.005051 \n", + "2 0.002147 0.017180 0.010021 0.284896 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", + "4 0.010799 0.006479 0.017279 0.431965 0.034557 \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", + "27154 0.013209 0.023699 0.021368 0.337218 0.021368 \n", + "27155 0.007680 0.023041 0.026114 0.341014 0.009217 \n", + "27156 0.008446 0.026182 0.041385 0.190878 0.021959 \n", + "27157 0.027017 0.002921 0.009858 0.391384 0.042716 \n", + "27158 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", + "0 0.015777 0.000000 \n", + "1 0.005051 0.000842 \n", + "2 0.010021 0.000716 \n", "3 0.004734 0.000000 \n", - "4 0.055556 0.000000 \n", + "4 0.025918 0.002160 \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", + "27154 0.012821 0.003885 \n", + "27155 0.015361 0.003072 \n", + "27156 0.010980 0.005912 \n", + "27157 0.027747 0.018620 \n", + "27158 0.012816 0.006031 \n", "\n", - "[24811 rows x 122 columns]" + "[27159 rows x 122 columns]" ] }, "execution_count": 4, @@ -521,16 +521,16 @@ " 0.5\n", " 5.5\n", " ...\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", + " 20.000000\n", + " 0.220000\n", + " -3.000000\n", + " 108.0\n", + " 266.0\n", + " 532.000000\n", + " 148.000000\n", + " 149.000000\n", + " 247.000000\n", + " 14.0\n", " \n", " \n", " 1\n", @@ -545,16 +545,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 32.50\n", - " 0.29\n", - " 2.50\n", - " 101.0\n", - " 242.0\n", - " 576.0\n", - " 117.0\n", - " 159.0\n", - " 304.0\n", - " 18.0\n", + " 40.200000\n", + " 0.250000\n", + " 3.200000\n", + " 129.0\n", + " 329.0\n", + " 773.000000\n", + " 169.000000\n", + " 219.000000\n", + " 402.000000\n", + " 24.0\n", " \n", " \n", " 2\n", @@ -569,16 +569,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 27.80\n", - " 0.26\n", - " -0.20\n", - " 102.0\n", - " 313.0\n", - " 806.0\n", + " 32.300000\n", + " 0.280000\n", + " 2.300000\n", " 117.0\n", - " 114.0\n", - " 431.0\n", - " 25.0\n", + " 346.0\n", + " 869.000000\n", + " 123.000000\n", + " 139.000000\n", + " 489.000000\n", + " 28.0\n", " \n", " \n", " 3\n", @@ -593,16 +593,16 @@ " 0.0\n", " 3.0\n", " ...\n", - " 9.45\n", - " 0.24\n", - " 0.95\n", - " 24.0\n", - " 68.0\n", - " 299.5\n", - " 48.5\n", - " 72.5\n", - " 128.0\n", - " 3.5\n", + " 18.616667\n", + " 0.220000\n", + " 1.116667\n", + " 45.5\n", + " 114.0\n", + " 556.166667\n", + " 92.833333\n", + " 155.000000\n", + " 258.666667\n", + " 8.5\n", " \n", " \n", " 4\n", @@ -617,15 +617,15 @@ " 0.0\n", " 5.5\n", " ...\n", - " 10.60\n", - " 0.30\n", - " -1.40\n", - " 27.0\n", - " 52.0\n", - " 266.0\n", - " 46.0\n", - " 45.0\n", - " 79.0\n", + " 12.500000\n", + " 0.270000\n", + " -2.500000\n", + " 31.0\n", + " 62.0\n", + " 345.000000\n", + " 54.000000\n", + " 59.000000\n", + " 112.000000\n", " 2.0\n", " \n", " \n", @@ -653,59 +653,35 @@ " ...\n", " \n", " \n", - " 1300\n", - " 22\n", + " 1843\n", + " 31\n", " Consigli\n", " Sassuolo\n", - " Udinese\n", + " Salernitana\n", " 0\n", - " 7.0\n", - " -2\n", - " 0\n", - " 0.0\n", " 5.0\n", - " ...\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", - " 1301\n", - " 22\n", - " Dragowski\n", - " Spezia\n", - " Empoli\n", - " 0\n", - " 6.5\n", - " -2\n", + " -3\n", " 0\n", " 0.0\n", - " 4.5\n", + " 2.0\n", " ...\n", - " 29.50\n", - " 0.27\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", + " 31.000000\n", + " 0.300000\n", + " -12.000000\n", + " 153.0\n", + " 380.0\n", + " 946.000000\n", + " 154.000000\n", + " 249.000000\n", + " 409.000000\n", + " 24.0\n", " \n", " \n", - " 1302\n", - " 22\n", - " Milinkovic-Savic V.\n", - " Torino\n", - " Milan\n", + " 1844\n", + " 31\n", + " Zoet\n", + " Spezia\n", + " Sampdoria\n", " 0\n", " 6.5\n", " -1\n", @@ -713,68 +689,92 @@ " 0.0\n", " 5.5\n", " ...\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", - " 1303\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", - " 1.00\n", - " 91.0\n", - " 227.0\n", - " 470.0\n", - " 80.0\n", - " 175.0\n", - " 293.0\n", + " 14.566667\n", + " 0.213333\n", + " -0.433333\n", + " 56.0\n", + " 171.0\n", + " 316.333333\n", + " 49.666667\n", + " 64.333333\n", + " 160.333333\n", " 5.0\n", " \n", " \n", - " 1304\n", - " 22\n", - " Montipo'\n", - " Verona\n", - " Salernitana\n", - " 1\n", - " 6.5\n", + " 1845\n", + " 31\n", + " Milinkovic-Savic V.\n", + " Torino\n", + " Lazio\n", + " 0\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\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", + " 31.500000\n", + " 0.240000\n", + " -4.500000\n", + " 224.0\n", + " 762.0\n", + " 1196.000000\n", + " 142.000000\n", + " 236.000000\n", + " 387.000000\n", + " 25.0\n", + " \n", + " \n", + " 1846\n", + " 31\n", + " Silvestri\n", + " Udinese\n", + " Cremonese\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 37.500000\n", + " 0.300000\n", + " 0.500000\n", + " 121.0\n", + " 316.0\n", + " 687.000000\n", + " 120.000000\n", + " 237.000000\n", + " 441.000000\n", + " 11.0\n", + " \n", + " \n", + " 1847\n", + " 31\n", + " Montipo'\n", + " Verona\n", + " Bologna\n", + " 1\n", + " 6.5\n", + " -1\n", + " 0\n", + " 0.0\n", + " 5.5\n", + " ...\n", + " 38.700000\n", + " 0.260000\n", + " -4.300000\n", + " 280.0\n", + " 616.0\n", + " 745.000000\n", + " 99.000000\n", + " 227.000000\n", + " 401.000000\n", + " 22.0\n", " \n", " \n", "\n", - "

1305 rows × 102 columns

\n", + "

1848 rows × 102 columns

\n", "" ], "text/plain": [ @@ -785,65 +785,65 @@ "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", + "1843 31 Consigli Sassuolo Salernitana 0 5.0 \n", + "1844 31 Zoet Spezia Sampdoria 0 6.5 \n", + "1845 31 Milinkovic-Savic V. Torino Lazio 0 6.0 \n", + "1846 31 Silvestri Udinese Cremonese 1 6.0 \n", + "1847 31 Montipo' Verona Bologna 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", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 20.000000 \n", + "1 -2 0 0.0 4.5 ... 40.200000 \n", + "2 -1 0 0.0 4.5 ... 32.300000 \n", + "3 -2 0 0.0 3.0 ... 18.616667 \n", + "4 -1 0 0.0 5.5 ... 12.500000 \n", + "... ... ... ... ... ... ... \n", + "1843 -3 0 0.0 2.0 ... 31.000000 \n", + "1844 -1 0 0.0 5.5 ... 14.566667 \n", + "1845 0 0 0.0 6.0 ... 31.500000 \n", + "1846 0 0 0.0 6.0 ... 37.500000 \n", + "1847 -1 0 0.0 5.5 ... 38.700000 \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", + "0 0.220000 -3.000000 \n", + "1 0.250000 3.200000 \n", + "2 0.280000 2.300000 \n", + "3 0.220000 1.116667 \n", + "4 0.270000 -2.500000 \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", + "1843 0.300000 -12.000000 \n", + "1844 0.213333 -0.433333 \n", + "1845 0.240000 -4.500000 \n", + "1846 0.300000 0.500000 \n", + "1847 0.260000 -4.300000 \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", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 108.0 266.0 532.000000 \n", + "1 129.0 329.0 773.000000 \n", + "2 117.0 346.0 869.000000 \n", + "3 45.5 114.0 556.166667 \n", + "4 31.0 62.0 345.000000 \n", + "... ... ... ... \n", + "1843 153.0 380.0 946.000000 \n", + "1844 56.0 171.0 316.333333 \n", + "1845 224.0 762.0 1196.000000 \n", + "1846 121.0 316.0 687.000000 \n", + "1847 280.0 616.0 745.000000 \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", + "0 148.000000 149.000000 247.000000 14.0 \n", + "1 169.000000 219.000000 402.000000 24.0 \n", + "2 123.000000 139.000000 489.000000 28.0 \n", + "3 92.833333 155.000000 258.666667 8.5 \n", + "4 54.000000 59.000000 112.000000 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", + "1843 154.000000 249.000000 409.000000 24.0 \n", + "1844 49.666667 64.333333 160.333333 5.0 \n", + "1845 142.000000 236.000000 387.000000 25.0 \n", + "1846 120.000000 237.000000 441.000000 11.0 \n", + "1847 99.000000 227.000000 401.000000 22.0 \n", "\n", - "[1305 rows x 102 columns]" + "[1848 rows x 102 columns]" ] }, "execution_count": 5, @@ -903,7 +903,7 @@ "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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_5820\\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" ] }, @@ -955,506 +955,506 @@ " \n", " Atalanta\n", " Atalanta\n", - " 24.00\n", - " 48.600\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 40.00\n", - " 28.00\n", - " 6.00\n", - " 8.0\n", - " ...\n", - " 244.00\n", - " 256.00\n", - " 26.0\n", - " 1.0\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", " 8.00\n", - " 1.0\n", - " 1335.00\n", - " 273.00\n", - " 328.00\n", - " 45.40\n", + " ...\n", + " 338.0\n", + " 356.00\n", + " 40.00\n", + " 1.00\n", + " 8.0\n", + " 1.00\n", + " 1865.0\n", + " 389.0\n", + " 472.0\n", + " 45.200\n", " \n", " \n", " Bologna\n", " Bologna\n", - " 25.00\n", - " 52.400\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 27.00\n", - " 20.00\n", - " 4.00\n", + " 27.0\n", + " 53.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 39.0\n", + " 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2.00\n", + " 9.0\n", + " 0.00\n", + " 1621.0\n", + " 331.0\n", + " 426.0\n", + " 43.700\n", " \n", " \n", " Salernitana\n", " Salernitana\n", - " 28.00\n", - " 46.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 24.00\n", - " 16.00\n", - " 1.00\n", + " 28.0\n", + " 44.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", " 1.0\n", + " 1.00\n", " ...\n", - " 252.00\n", - " 259.00\n", - " 57.0\n", - " 8.0\n", - " 1.00\n", + " 375.0\n", + " 357.00\n", + " 64.00\n", + " 10.00\n", " 1.0\n", - " 1213.00\n", - " 291.00\n", - " 285.00\n", - " 50.50\n", + " 2.00\n", + " 1709.0\n", + " 448.0\n", + " 427.0\n", + " 51.200\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", - " 31.00\n", - " 47.300\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 10.00\n", - " 8.00\n", - " 0.00\n", - " 0.0\n", + " 37.0\n", + " 47.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 20.0\n", + " 16.0\n", + " 1.0\n", + " 2.00\n", " ...\n", - " 339.00\n", - " 300.00\n", - " 59.0\n", - " 4.0\n", + " 449.0\n", + " 408.00\n", + " 75.00\n", + " 6.00\n", + " 2.0\n", " 0.00\n", - " 0.0\n", - " 1189.00\n", - " 362.00\n", - " 349.00\n", - " 50.90\n", + " 1693.0\n", + " 535.0\n", + " 517.0\n", + " 50.900\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", - " 29.00\n", - " 48.700\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 25.00\n", - " 18.00\n", - " 4.00\n", - " 5.0\n", + " 29.0\n", + " 48.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", + " 24.0\n", + " 6.0\n", + " 7.00\n", " ...\n", - " 287.00\n", - " 206.00\n", - " 72.0\n", - " 2.0\n", - " 5.00\n", - " 1.0\n", - " 1131.00\n", - " 256.00\n", - " 206.00\n", - " 55.40\n", + " 384.0\n", + " 307.00\n", + " 94.00\n", + " 2.00\n", + " 7.0\n", + " 1.00\n", + " 1611.0\n", + " 385.0\n", + " 329.0\n", + " 53.900\n", " \n", " \n", " Spezia\n", " Spezia\n", - " 33.00\n", - " 45.500\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 17.00\n", - " 10.00\n", - " 3.00\n", - " 3.0\n", + " 34.0\n", + " 47.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 15.0\n", + " 4.0\n", + " 4.00\n", " ...\n", - " 235.00\n", - " 291.00\n", - " 53.0\n", - " 1.0\n", - " 3.00\n", - " 2.0\n", - " 1275.00\n", - " 343.00\n", - " 306.00\n", - " 52.90\n", + " 321.0\n", + " 396.00\n", + " 67.00\n", + " 4.00\n", + " 4.0\n", + " 2.00\n", + " 1708.0\n", + " 470.0\n", + " 424.0\n", + " 52.600\n", " \n", " \n", " Torino\n", " Torino\n", - " 27.00\n", - " 53.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 22.00\n", - " 17.00\n", - " 1.00\n", - " 1.0\n", + " 28.0\n", + " 53.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 32.0\n", + " 26.0\n", + " 2.0\n", + " 2.00\n", " ...\n", - " 239.00\n", - " 306.00\n", - " 24.0\n", - " 3.0\n", - " 1.00\n", - " 0.0\n", - " 1155.00\n", - " 367.00\n", - " 333.00\n", - " 52.40\n", + " 341.0\n", + " 409.00\n", + " 32.00\n", + " 4.00\n", + " 2.0\n", + " 0.00\n", + " 1597.0\n", + " 508.0\n", + " 474.0\n", + " 51.700\n", " \n", " \n", " Udinese\n", " Udinese\n", - " 25.00\n", - " 49.900\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 29.00\n", - " 25.00\n", - " 0.00\n", - " 0.0\n", - " ...\n", - " 282.00\n", - " 252.00\n", - " 34.0\n", - " 2.0\n", - " 0.00\n", + " 27.0\n", + " 48.1\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 41.0\n", + " 36.0\n", " 1.0\n", - " 1101.00\n", - " 233.00\n", - " 277.00\n", - " 45.70\n", + " 2.00\n", + " ...\n", + " 412.0\n", + " 357.00\n", + " 50.00\n", + " 3.00\n", + " 2.0\n", + " 1.00\n", + " 1577.0\n", + " 326.0\n", + " 390.0\n", + " 45.500\n", " \n", " \n", " Avg\n", " Avg\n", - " 27.25\n", - " 49.995\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 27.35\n", - " 19.75\n", - " 2.35\n", - " 3.0\n", + " 29.0\n", + " 50.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.5\n", + " 26.8\n", + " 3.4\n", + " 4.35\n", " ...\n", - " 277.05\n", - " 262.05\n", - " 37.9\n", - " 2.1\n", - " 2.95\n", - " 0.8\n", - " 1172.05\n", - " 292.05\n", - " 292.15\n", - " 49.82\n", + " 385.2\n", + " 362.75\n", + " 50.75\n", + " 3.05\n", + " 4.3\n", + " 1.15\n", + " 1646.5\n", + " 424.8\n", + " 424.8\n", + " 49.815\n", " \n", " \n", "\n", @@ -1463,165 +1463,165 @@ ], "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", + "Atalanta Atalanta 25.0 50.0 31.0 \n", + "Bologna Bologna 27.0 53.4 31.0 \n", + "Cremonese Cremonese 32.0 43.2 31.0 \n", + "Empoli Empoli 31.0 47.4 31.0 \n", + "Fiorentina Fiorentina 29.0 56.6 31.0 \n", + "Verona Hellas Verona 36.0 42.0 31.0 \n", + "Inter Inter 24.0 56.2 31.0 \n", + "Juventus Juventus 29.0 48.6 31.0 \n", + "Lazio Lazio 22.0 51.9 31.0 \n", + "Lecce Lecce 29.0 41.7 31.0 \n", + "Milan Milan 29.0 54.0 31.0 \n", + "Monza Monza 31.0 55.0 31.0 \n", + "Napoli Napoli 26.0 61.8 31.0 \n", + "Roma Roma 27.0 49.2 31.0 \n", + "Salernitana Salernitana 28.0 44.6 31.0 \n", + "Sampdoria Sampdoria 37.0 47.4 31.0 \n", + "Sassuolo Sassuolo 29.0 48.9 31.0 \n", + "Spezia Spezia 34.0 47.0 31.0 \n", + "Torino Torino 28.0 53.0 31.0 \n", + "Udinese Udinese 27.0 48.1 31.0 \n", + "Avg Avg 29.0 50.0 31.0 \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", + "Atalanta 341.0 2790.0 50.0 32.0 \n", + "Bologna 341.0 2790.0 39.0 32.0 \n", + "Cremonese 341.0 2790.0 27.0 13.0 \n", + "Empoli 341.0 2790.0 25.0 13.0 \n", + "Fiorentina 341.0 2790.0 35.0 25.0 \n", + "Verona 341.0 2790.0 24.0 18.0 \n", + "Inter 341.0 2790.0 50.0 35.0 \n", + "Juventus 341.0 2790.0 47.0 37.0 \n", + "Lazio 341.0 2790.0 48.0 29.0 \n", + "Lecce 341.0 2790.0 24.0 17.0 \n", + "Milan 341.0 2790.0 48.0 39.0 \n", + "Monza 341.0 2790.0 36.0 23.0 \n", + "Napoli 341.0 2790.0 65.0 51.0 \n", + "Roma 341.0 2790.0 43.0 30.0 \n", + "Salernitana 341.0 2790.0 35.0 25.0 \n", + "Sampdoria 341.0 2790.0 20.0 16.0 \n", + "Sassuolo 341.0 2790.0 37.0 24.0 \n", + "Spezia 341.0 2790.0 24.0 15.0 \n", + "Torino 341.0 2790.0 32.0 26.0 \n", + "Udinese 341.0 2790.0 41.0 36.0 \n", + "Avg 341.0 2790.0 37.5 26.8 \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", + "Atalanta 6.0 8.00 ... 338.0 \n", + "Bologna 4.0 4.00 ... 376.0 \n", + "Cremonese 4.0 6.00 ... 345.0 \n", + "Empoli 1.0 1.00 ... 384.0 \n", + "Fiorentina 4.0 6.00 ... 460.0 \n", + "Verona 1.0 1.00 ... 337.0 \n", + "Inter 4.0 5.00 ... 368.0 \n", + "Juventus 3.0 5.00 ... 348.0 \n", + "Lazio 5.0 7.00 ... 421.0 \n", + "Lecce 1.0 2.00 ... 378.0 \n", + "Milan 3.0 3.00 ... 378.0 \n", + "Monza 5.0 5.00 ... 437.0 \n", + "Napoli 6.0 7.00 ... 405.0 \n", + "Roma 6.0 9.00 ... 447.0 \n", + "Salernitana 1.0 1.00 ... 375.0 \n", + "Sampdoria 1.0 2.00 ... 449.0 \n", + "Sassuolo 6.0 7.00 ... 384.0 \n", + "Spezia 4.0 4.00 ... 321.0 \n", + "Torino 2.0 2.00 ... 341.0 \n", + "Udinese 1.0 2.00 ... 412.0 \n", + "Avg 3.4 4.35 ... 385.2 \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", + "Atalanta 356.00 40.00 1.00 \n", + "Bologna 365.00 52.00 5.00 \n", + "Cremonese 372.00 52.00 4.00 \n", + "Empoli 341.00 60.00 2.00 \n", + "Fiorentina 371.00 78.00 2.00 \n", + "Verona 438.00 32.00 1.00 \n", + "Inter 336.00 29.00 3.00 \n", + "Juventus 328.00 39.00 0.00 \n", + "Lazio 304.00 55.00 1.00 \n", + "Lecce 425.00 57.00 4.00 \n", + "Milan 369.00 31.00 5.00 \n", + "Monza 385.00 48.00 1.00 \n", + "Napoli 287.00 39.00 1.00 \n", + "Roma 344.00 21.00 2.00 \n", + "Salernitana 357.00 64.00 10.00 \n", + "Sampdoria 408.00 75.00 6.00 \n", + "Sassuolo 307.00 94.00 2.00 \n", + "Spezia 396.00 67.00 4.00 \n", + "Torino 409.00 32.00 4.00 \n", + "Udinese 357.00 50.00 3.00 \n", + "Avg 362.75 50.75 3.05 \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", + "Atalanta 8.0 1.00 \n", + "Bologna 4.0 1.00 \n", + "Cremonese 6.0 0.00 \n", + "Empoli 1.0 0.00 \n", + "Fiorentina 6.0 2.00 \n", + "Verona 1.0 2.00 \n", + "Inter 5.0 1.00 \n", + "Juventus 5.0 0.00 \n", + "Lazio 7.0 1.00 \n", + "Lecce 2.0 2.00 \n", + "Milan 3.0 3.00 \n", + "Monza 5.0 2.00 \n", + "Napoli 6.0 2.00 \n", + "Roma 9.0 0.00 \n", + "Salernitana 1.0 2.00 \n", + "Sampdoria 2.0 0.00 \n", + "Sassuolo 7.0 1.00 \n", + "Spezia 4.0 2.00 \n", + "Torino 2.0 0.00 \n", + "Udinese 2.0 1.00 \n", + "Avg 4.3 1.15 \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", + "Atalanta 1865.0 389.0 \n", + "Bologna 1687.0 379.0 \n", + "Cremonese 1745.0 594.0 \n", + "Empoli 1607.0 412.0 \n", + "Fiorentina 1625.0 425.0 \n", + "Verona 1728.0 619.0 \n", + "Inter 1443.0 344.0 \n", + "Juventus 1556.0 381.0 \n", + "Lazio 1677.0 319.0 \n", + "Lecce 1694.0 582.0 \n", + "Milan 1617.0 387.0 \n", + "Monza 1608.0 340.0 \n", + "Napoli 1562.0 322.0 \n", + "Roma 1621.0 331.0 \n", + "Salernitana 1709.0 448.0 \n", + "Sampdoria 1693.0 535.0 \n", + "Sassuolo 1611.0 385.0 \n", + "Spezia 1708.0 470.0 \n", + "Torino 1597.0 508.0 \n", + "Udinese 1577.0 326.0 \n", + "Avg 1646.5 424.8 \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", + "Atalanta 472.0 45.200 \n", + "Bologna 306.0 55.300 \n", + "Cremonese 462.0 56.300 \n", + "Empoli 330.0 55.500 \n", + "Fiorentina 504.0 45.700 \n", + "Verona 615.0 50.200 \n", + "Inter 443.0 43.700 \n", + "Juventus 390.0 49.400 \n", + "Lazio 319.0 50.000 \n", + "Lecce 466.0 55.500 \n", + "Milan 456.0 45.900 \n", + "Monza 357.0 48.800 \n", + "Napoli 389.0 45.300 \n", + "Roma 426.0 43.700 \n", + "Salernitana 427.0 51.200 \n", + "Sampdoria 517.0 50.900 \n", + "Sassuolo 329.0 53.900 \n", + "Spezia 424.0 52.600 \n", + "Torino 474.0 51.700 \n", + "Udinese 390.0 45.500 \n", + "Avg 424.8 49.815 \n", "\n", "[21 rows x 303 columns]" ] @@ -3031,10 +3031,39 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "id": "8aad9652", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "85/85 [==============================] - 4s 9ms/step - loss: 2.1506 - distribution_lambda_8_loss: 0.8422 - distribution_lambda_9_loss: 1.3084 - val_loss: 2.1163 - val_distribution_lambda_8_loss: 0.8236 - val_distribution_lambda_9_loss: 1.2926\n", + "Epoch 2/1000\n", + "85/85 [==============================] - 0s 2ms/step - loss: 2.1461 - distribution_lambda_8_loss: 0.8394 - distribution_lambda_9_loss: 1.3067 - val_loss: 2.1203 - val_distribution_lambda_8_loss: 0.8253 - val_distribution_lambda_9_loss: 1.2950\n", + "Epoch 3/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1514 - distribution_lambda_8_loss: 0.8435 - distribution_lambda_9_loss: 1.3079 - val_loss: 2.1184 - val_distribution_lambda_8_loss: 0.8249 - val_distribution_lambda_9_loss: 1.2935\n", + "Epoch 4/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1450 - distribution_lambda_8_loss: 0.8390 - distribution_lambda_9_loss: 1.3060 - val_loss: 2.1215 - val_distribution_lambda_8_loss: 0.8261 - val_distribution_lambda_9_loss: 1.2954\n", + "Epoch 5/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1462 - distribution_lambda_8_loss: 0.8403 - distribution_lambda_9_loss: 1.3059 - val_loss: 2.1222 - val_distribution_lambda_8_loss: 0.8268 - val_distribution_lambda_9_loss: 1.2954\n", + "Epoch 6/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1486 - distribution_lambda_8_loss: 0.8415 - distribution_lambda_9_loss: 1.3071 - val_loss: 2.1232 - val_distribution_lambda_8_loss: 0.8268 - val_distribution_lambda_9_loss: 1.2963\n", + "Epoch 7/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1468 - distribution_lambda_8_loss: 0.8398 - distribution_lambda_9_loss: 1.3070 - val_loss: 2.1224 - val_distribution_lambda_8_loss: 0.8261 - val_distribution_lambda_9_loss: 1.2964\n", + "Epoch 8/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1443 - distribution_lambda_8_loss: 0.8388 - distribution_lambda_9_loss: 1.3055 - val_loss: 2.1274 - val_distribution_lambda_8_loss: 0.8291 - val_distribution_lambda_9_loss: 1.2984\n", + "Epoch 9/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1464 - distribution_lambda_8_loss: 0.8401 - distribution_lambda_9_loss: 1.3063 - val_loss: 2.1231 - val_distribution_lambda_8_loss: 0.8262 - val_distribution_lambda_9_loss: 1.2969\n", + "Epoch 10/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1435 - distribution_lambda_8_loss: 0.8387 - distribution_lambda_9_loss: 1.3048 - val_loss: 2.1272 - val_distribution_lambda_8_loss: 0.8301 - val_distribution_lambda_9_loss: 1.2971\n", + "Epoch 11/1000\n", + "85/85 [==============================] - 0s 3ms/step - loss: 2.1467 - distribution_lambda_8_loss: 0.8405 - distribution_lambda_9_loss: 1.3061 - val_loss: 2.1255 - val_distribution_lambda_8_loss: 0.8280 - val_distribution_lambda_9_loss: 1.2975\n" + ] + } + ], "source": [ "load_model_of = True# load scaler and model weights for outfield player predictor\n", "refit_model_of = False\n", @@ -3105,7 +3134,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "id": "4e2bf9dc", "metadata": {}, "outputs": [], @@ -3125,7 +3154,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 38, "id": "c2674211", "metadata": {}, "outputs": [ @@ -3133,13 +3162,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.14539483185579238\n", - "0.16229047232752447\n" + "0.13385223635677934\n", + "0.1536831746216616\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -3151,13 +3180,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.13160209075894191\n", - "0.16563346611529572\n" + "0.127508905428527\n", + "0.147998716039207\n" ] }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -3204,7 +3233,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 30, "id": "41e7e1ee", "metadata": {}, "outputs": [ @@ -3212,1860 +3241,1549 @@ "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n", + "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.momentum_cache\n", "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", + "12/12 [==============================] - 4s 80ms/step - loss: 14.7604 - distribution_lambda_5_loss: 7.3535 - distribution_lambda_6_loss: 6.7090 - distribution_lambda_7_loss: 0.6979 - val_loss: 12.1101 - val_distribution_lambda_5_loss: 6.1507 - val_distribution_lambda_6_loss: 5.3170 - val_distribution_lambda_7_loss: 0.6425\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", + "12/12 [==============================] - 0s 5ms/step - loss: 11.1729 - distribution_lambda_5_loss: 5.3710 - 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val_distribution_lambda_22_loss: 1.6136 - val_distribution_lambda_23_loss: 0.3760\n" + "12/12 [==============================] - 0s 5ms/step - loss: 1.9011 - distribution_lambda_5_loss: 0.2214 - distribution_lambda_6_loss: 1.2549 - distribution_lambda_7_loss: 0.4247 - val_loss: 2.2815 - val_distribution_lambda_5_loss: 0.4756 - val_distribution_lambda_6_loss: 1.3958 - val_distribution_lambda_7_loss: 0.4101\n" ] } ], "source": [ - "load_model_gk = True# load scaler and model weights for goalkeeper player predictor\n", - "refit_model_gk = False\n", + "load_model_gk = False# 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", @@ -5137,7 +4855,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 23, "id": "39a9bdc6", "metadata": {}, "outputs": [], @@ -5157,7 +4875,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 31, "id": "c41cf448", "metadata": {}, "outputs": [ @@ -5165,13 +4883,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.29228994199557035\n", - "0.5457850368866328\n" + "0.32776719099564455\n", + "0.5589799999938707\n" ] }, { "data": { - "image/png": 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EaoyvNh/h2YU7yLUYtIkI4qNx8USH1XJ3s6QUFFhERKTay7NYeWXJbmb8dAiAIe0jePPWTtT218dgVaG/lIiIVGvp53N48Itt/LD/NACP9o/l4X6xeHmpGFxVosAiIiLV1r4Tmdw1M4HDqecJ9PXm7VGdGNIh0t3NknJQYBERkWpp9e4TPPJlImez82gUGshH4+JpGxXs7mZJOSmwiIhItWIYBh+u/403lu/BMKB7TBjTbu9KvTr+7m6aXAYFFhERqTayci08Ne8XFiUeA2BMjyZMHtYOPx8Vg6vqFFhERKRaOJ5xgXs+38Ivv2fg7WVi8h/aMfbqpu5ullQQBRYREanytiaf4Z7Pt3AqM5vQWr58cHtXerWo7+5mSQVSYBERkSrt6y2/88z8HeRYrLQOtxWDa1JPxeCqGwUWERGpkvIsVv6xbA8f/3AQgAFtw3lndGfqqBhctaS/qoiIVDkZF3J5aM42NiSdAuChfi15rH8rFYOrxhRYRESkSjlw6iwTP0vgt9PnCPD14s1bO3FTxyh3N0sqmQKLiIhUGWv3nuThOdvIzMojKiSA6ePiad8oxN3NkitAgUVERDyeYRh8/P1BpizbjdWAuKZ1+fCOOBoEqRhcTaHAIiIiHi0r18IzC3Ywf+tRAEbHR/PyiHb4+3i7uWVyJSmwiIiIxzphzuKez7eQeCQdby8Tz994FeN7NcNk0uDamqbMtYo3bNjAsGHDiIqKwmQysXDhQqf9EyZMwGQyOX1dffXVl7zuvHnzaNu2Lf7+/rRt25YFCxaUtWkiIlKNbD+Szh+m/kDikXRCAn357M/dmXBNjMJKDVXmwHLu3Dk6derE1KlTXR4zePBgjh8/7vhaunRpidfcuHEjo0ePZuzYsWzfvp2xY8cyatQoNm3aVNbmiYhINbBw21Fu/c9GTpizadmwDoseuIZrY1W5tiYzGYZhlPtkk4kFCxYwYsQIx7YJEyaQnp5epOelJKNHj8ZsNrNs2TLHtsGDB1O3bl3mzJlTqmuYzWZCQkLIyMggOFjLh4uIVEUWq8Eby/fwn/W/AdCvTUPe/VNnggJ83dwyqSyl/fyulOUr161bR8OGDWnVqhUTJ07k5MmTJR6/ceNGBg4c6LRt0KBB/PTTTy7Pyc7Oxmw2O32JiEjVZc7K5a7PNjvCyn19W/DRuHiFFQEqIbAMGTKE2bNns2bNGt566y02b95Mv379yM7OdnlOSkoK4eHhTtvCw8NJSUlxec6UKVMICQlxfEVHR1fYaxARkSvr4Olz3Pz+j6zdewp/Hy/e/VNnnhrcBm9VrpV8FT5LaPTo0Y6f27dvT3x8PE2bNmXJkiXccsstLs+7eBCVYRglDqx6+umnmTRpkuOx2WxWaBERqYK+33eKB2ZvxZyVR0RwANPHxdGxcai7myUeptKnNUdGRtK0aVP27dvn8piIiIgivSknT54s0utSmL+/P/7+KhgkIlJVGYbBpz8e4pUlu7Aa0Dk6lOlj42gYHODupokHqpQxLIWlpqZy5MgRIiMjXR7Ts2dPVq5c6bRtxYoV9OrVq7KbJyIibpCdZ+Gpeb/w8re2sHJL10Z8effVCiviUpl7WM6ePcv+/fsdjw8ePEhiYiJhYWGEhYUxefJkRo4cSWRkJIcOHeKZZ56hfv363HzzzY5zxo0bR6NGjZgyZQoAjzzyCL179+b1119n+PDhLFq0iFWrVvHDDz9UwEsUERFPciozm3tnbWHL4TN4meCZoVdx57WqryIlK3NgSUhI4Prrr3c8to8jGT9+PNOmTWPHjh3MnDmT9PR0IiMjuf7665k7dy5BQUGOc5KTk/HyKujc6dWrF19++SXPPfcczz//PC1atGDu3Ln06NHjcl6biIh4mF+PZjBxZgLHM7IICvDh37d1oW/rhu5ullQBl1WHxZOoDouIiGf7Zvsxnvh6O1m5VprXr81H4+Np0aCOu5slblbaz2+tJSQiIpXKajV4e2USU9fahhP0adWA927rQkig6qtI6SmwiIhIpTmbncdjcxNZuesEAHf3bq76KlIuCiwiIlIpklPPc9fMzSSdOIuftxdTbunAyLjG7m6WVFEKLCIiUuF+OnCa+2dvJf18Lg2C/PnP2Di6Nqnr7mZJFabAIiIiFcYwDD7/+TAvfbMLi9WgY+MQpo+NJyJE9VXk8iiwiIhIhcjJs/Li4p3M+V8yAMM7R/H6yI4E+Hq7uWVSHSiwiIjIZUs9m819s7byv0NpmEzw1OA23NO7uYrBSYVRYBERkcuy65iZiTMTOJp+gTr+Prx3W2f6tXG9FpxIeSiwiIhIuS3bcZxJX23nQq6FZvVq8fH4eFo2DLr0iSJlpMAiIiJlZrUavLt6H++u3gfAtS3rM3VMF0Jr+bm5ZVJdKbCIiEiZnMvO469fbee7nSkA/OWaGJ4Z2gYfb69LnClSfgosIiJSakfSzjNxZgJ7UjLx9Tbx6ogOjOoW7e5mSQ2gwCIiIqWy6bdU7pu9lbRzOdSv48eHd8QR3yzM3c2SGkKBRURELumLTcm8sOhX8qwG7RsFM31sPFGhge5ultQgCiwiIuJSrsXKy9/s4vOfDwNwY8dI3vxjJwL9VAxOriwFFhERKVbauRzun72Fn39LA+CJQa25v28LFYMTt1BgERGRIvamZHLXzM0cSbtAbT9v3hndmYHtItzdLKnBFFhERMTJip0pPDY3kXM5FqLDAvl4XDdaR6gYnLiXAouIiAC2lZanrtnPWyuTAOjZvB4f3N6VurVVDE7cT4FFRES4kGPh8a+3s+SX4wCM79mU525qi6+KwYmHUGAREanhjqZf4O6ZCew8ZsbHy8TLw9szpkcTdzdLxIkCi4hIDZZwKI17Z23h9Nkcwmr7Me32rvRoXs/dzRIpQoFFRKSG+mrzEZ5duINci0GbiCA+GhdPdFgtdzdLpFgKLCIiNUyexcorS3Yz46dDAAxpH8Gbt3aitr8+EsRz6b9OEZEaJP18Dg9+sY0f9p8G4NH+sTzcLxYvLxWDE8+mwCIiUkPsO5HJXTMTOJx6nkBfb94e1YkhHSLd3SyRUlFgERGpAVbvPsEjXyZyNjuPRqGBfDQunrZRwe5ulkipKbCIiFRjhmHw4frfeGP5HgwDuseEMe32rtSr4+/upomUiQKLiEg1lZVr4al5v7Ao8RgAY3o0YfKwdvj5qBicVD0KLCIi1dDxjAvc8/kWfvk9A28vE5P/0I6xVzd1d7NEyk2BRUSkmtmafIZ7Pt/CqcxsQmv58sHtXenVor67myVyWcrcL7hhwwaGDRtGVFQUJpOJhQsXOvbl5uby1FNP0aFDB2rXrk1UVBTjxo3j2LFjJV5zxowZmEymIl9ZWVllfkEiIjXZ11t+50//+ZlTmdm0Dg9i8QPXKqxItVDmwHLu3Dk6derE1KlTi+w7f/48W7du5fnnn2fr1q3Mnz+fpKQk/vCHP1zyusHBwRw/ftzpKyAgoKzNExGpkfIsVl75dheP/992cixWBrQNZ979vWhST5VrpXoo8y2hIUOGMGTIkGL3hYSEsHLlSqdt//73v+nevTvJyck0aeJ6MS2TyURERERZmyMiUuNlXMjloTnb2JB0CoCH+rXksf6tVAxOqpVKH8OSkZGByWQiNDS0xOPOnj1L06ZNsVgsdO7cmb///e906dLF5fHZ2dlkZ2c7HpvN5opqsohIlXHg1FkmfpbAb6fPEeDrxZu3duKmjlHubpZIhavUuW1ZWVn87W9/Y8yYMQQHuy5Q1KZNG2bMmMHixYuZM2cOAQEBXHPNNezbt8/lOVOmTCEkJMTxFR0dXRkvQUTEY63be5IR7//Ib6fPERUSwNf39lJYkWrLZBiGUe6TTSYWLFjAiBEjiuzLzc3l1ltvJTk5mXXr1pUYWC5mtVrp2rUrvXv35r333iv2mOJ6WKKjo8nIyCjTc4mIVDWGYfDx9weZsmw3VgPimtblwzviaBCkYnBS9ZjNZkJCQi75+V0pt4Ryc3MZNWoUBw8eZM2aNWUOEF5eXnTr1q3EHhZ/f3/8/fXmFJGaJSvXwjMLdjB/61EARsdH8/KIdvj7eLu5ZSKVq8IDiz2s7Nu3j7Vr11KvXr0yX8MwDBITE+nQoUNFN09EpMo6ac7i7s+3kHgkHW8vE8/feBXjezXDZNLgWqn+yhxYzp49y/79+x2PDx48SGJiImFhYURFRfHHP/6RrVu38u2332KxWEhJSQEgLCwMPz8/AMaNG0ejRo2YMmUKAC+99BJXX301sbGxmM1m3nvvPRITE3n//fcr4jWKiFR524+kc/fnCZwwZxMS6Mv7Y7pybazqq0jNUebAkpCQwPXXX+94PGnSJADGjx/P5MmTWbx4MQCdO3d2Om/t2rX07dsXgOTkZLy8Csb7pqenc/fdd5OSkkJISAhdunRhw4YNdO/evazNExGpdhZuO8qT834hJ89Ky4Z1+HhcPM3q13Z3s0SuqMsadOtJSjtoR0SkqrBYDf65fC8frj8AQL82DXn3T50JCvB1c8tEKo5bB92KiMjlMWfl8uiXiazZcxKA+/q24PGBrfFWMTipoRRYREQ8zMHT57jrs80cOHUOfx8v3vhjR4Z3buTuZom4lQKLiIgH+X7fKR6YvRVzVh4RwQFMHxdHx8ah7m6WiNspsIiIeADDMPj0x0O8smQXVgM6R4cyfWwcDYO1CKwIKLCIiLhddp6F5xf+ylcJvwMwsmtjXr25PQG+KgYnYqfAIiLiRqcys7l31ha2HD6DlwmeGXoVd14bo2JwIhdRYBERcZNfj2YwcWYCxzOyCArw4d+3daFv64bubpaIR1JgERFxg2+2H+OJr7eTlWulef3afDQ+nhYN6ri7WSIeS4FFROQKsloN3l6ZxNS1tiVO+rRqwHu3dSEkUMXgREqiwCIicoWczc7jsbmJrNx1AoC7ezfnqcFtVAxOpBQUWEREroDk1PPcNXMzSSfO4uftxZRbOjAyrrG7myVSZSiwiIhUsp8OnOb+2VtJP59LgyB//jM2jq5N6rq7WSJVigKLiEglMQyDWT8fZvI3u7BYDTo2DmH62HgiQlQMTqSsFFhERCpBTp6VFxfvZM7/kgEY3jmK10d2VDE4kXJSYBERqWCpZ7O5b9ZW/ncoDZMJnhrchnt6N1cxOJHLoMAiIlKBdh0zM3FmAkfTL1DH34f3butMvzbh7m6WSJWnwCIiUkGW7TjOpK+2cyHXQrN6tfh4fDwtGwa5u1ki1YICi4jIZbJaDd5dvY93V+8D4NqW9Zk6pguhtfzc3DKR6kOBRUTkMpzLzuOvX23nu50pAPzlmhieGdoGH28vN7dMpHpRYBERKacjaeeZODOBPSmZ+HqbeHVEB0Z1i3Z3s0SqJQUWEZFy2PRbKvfN3krauRzq1/HjwzviiG8W5u5miVRbCiwiImX0xaZkXlj0K3lWg/aNgpk+Np6o0EB3N0ukWlNgEREppVyLlZe/2cXnPx8G4MaOkbz5x04E+qkYnEhlU2ARESmFtHM53D97Cz//lgbAE4Nac3/fFioGJ3KFKLCIiFzC3pRM7pq5mSNpF6jt5807ozszsF2Eu5slUqMosIiIlGDFzhQem5vIuRwL0WGBfDyuG60jVAxO5EpTYBERKYZhGExds5+3ViYB0LN5PT64vSt1a6sYnIg7KLCIiFzkQo6Fx7/ezpJfjgMwvmdTnrupLb4qBifiNgosIiKFHE2/wN0zE9h5zIyPl4mXh7dnTI8m7m6WSI2nwCIiki/hUBr3ztrC6bM5hNX2Y9rtXenRvJ67myUiKLCIiADw1eYjPLtwB7kWgzYRQXw8Pp7GdWu5u1kikq/MN2Q3bNjAsGHDiIqKwmQysXDhQqf9hmEwefJkoqKiCAwMpG/fvuzcufOS1503bx5t27bF39+ftm3bsmDBgrI2TUSkzPIsViYv3smT834h12IwpH0E8+7rpbAi4mHKHFjOnTtHp06dmDp1arH733jjDd5++22mTp3K5s2biYiIYMCAAWRmZrq85saNGxk9ejRjx45l+/btjB07llGjRrFp06ayNk9EpNTSz+cw4dPNzPjpEACP9o/l/TFdqe2vzmcRT2MyDMMo98kmEwsWLGDEiBGArXclKiqKRx99lKeeegqA7OxswsPDef3117nnnnuKvc7o0aMxm80sW7bMsW3w4MHUrVuXOXPmlKotZrOZkJAQMjIyCA4OLu9LEpEaYt+JTO6amcDh1PME+nrz9qhODOkQ6e5midQ4pf38rtA5egcPHiQlJYWBAwc6tvn7+9OnTx9++uknl+dt3LjR6RyAQYMGlXhOdnY2ZrPZ6UtEpDRW7z7BzR/8xOHU8zQKDWTefb0UVkQ8XIUGlpSUFADCw8OdtoeHhzv2uTqvrOdMmTKFkJAQx1d0dPRltFxEagLDMJi27gB3zUzgbHYe3WPCWPzgNbSNUq+siKerlCpIFy8GZhjGJRcIK+s5Tz/9NBkZGY6vI0eOlL/BIlLtZeVaeHRuIq9/twfDgDE9mjDrzh7Uq+Pv7qaJSClU6MiyiAjbYmApKSlERhZ0r548ebJID8rF513cm3Kpc/z9/fH31/9oROTSUjKyuPvzBH75PQNvLxOT/9COsVc3dXezRKQMKrSHJSYmhoiICFauXOnYlpOTw/r16+nVq5fL83r27Ol0DsCKFStKPEdEpDS2Jp9h2NQf+OX3DEJr+fL5nd0VVkSqoDL3sJw9e5b9+/c7Hh88eJDExETCwsJo0qQJjz76KK+99hqxsbHExsby2muvUatWLcaMGeM4Z9y4cTRq1IgpU6YA8Mgjj9C7d29ef/11hg8fzqJFi1i1ahU//PBDBbxEEampvt7yO8/M30GOxUrr8CA+GhdPk3qqryJSFZU5sCQkJHD99dc7Hk+aNAmA8ePHM2PGDJ588kkuXLjA/fffz5kzZ+jRowcrVqwgKKhgOfbk5GS8vAo6d3r16sWXX37Jc889x/PPP0+LFi2YO3cuPXr0uJzXJiI1lMVq8I9lu/no+4MADGgbzjujO1NH9VVEqqzLqsPiSVSHRUQAMi7k8vCcbaxPOgXAQ/1a8lj/Vnh5lTzwX0Tco7Sf3/rnhohUGwdOnWXiZwn8dvocAb5evHlrJ27qGOXuZolIBVBgEZFqYd3ekzw0ZxuZWXlEhQQwfVw87RuFuLtZIlJBFFhEpEozDIOPvz/IlGW7sRoQ17QuH94RR4MglT0QqU4UWESkysrKtfDMgh3M33oUgNHx0bw8oh3+Pt5ubpmIVDQFFhGpkk6as7j78y0kHknH28vE8zdexfhezS5ZVVtEqiYFFhGpcrYfSefuzxM4Yc4mJNCX98d05drY+u5ulohUIgUWEalSFm47ypPzfiEnz0rLhnX4eFw8zerXdnezRKSSKbCISJVgsRr8c/lePlx/AIB+bRry7p86ExTg6+aWiciVoMAiIh7PnJXLo18msmbPSQDu69uCxwe2xlvF4ERqDAUWEfFoB0+f467PNnPg1Dn8fbx4448dGd65kbubJSJXmAKLiHis7/ed4oHZWzFn5RERHMD0cXF0bBzq7maJiBsosIiIxzEMg09/PMQrS3ZhNaBzdCjTx8bRMDjA3U0TETdRYBERj5KdZ+H5hb/yVcLvAIzs2phXb25PgK+KwYnUZAosIuIxTmVmc++sLWw5fAYvEzwz9CruvDZGxeBERIFFRDzDr0czmDgzgeMZWQQF+PDv27rQt3VDdzdLRDyEAouIuN0324/xxNfbycq10rx+bT4aH0+LBnXc3SwR8SAKLCLiNlarwdsrk5i6dj8AfVo14L3buhASqGJwIuJMgUVE3OJsdh6PzU1k5a4TANzduzlPDW6jYnAiUiwFFhG54pJTz3PXzM0knTiLn7cXU27pwMi4xu5uloh4MAUWEbmifjpwmvtnbyX9fC4Ngvz5z9g4ujap6+5miYiHU2ARkSvCMAxm/XyYyd/swmI16Ng4hOlj44kIUTE4Ebk0BRYRqXQ5eVZeXLyTOf9LBmB45yheH9lRxeBEpNQUWESkUqWezea+WVv536E0TCZ4anAb7undXMXgRKRMFFhEpNLsOmZm4swEjqZfoI6/D+/d1pl+bcLd3SwRqYIUWESkUizbcZxJX23nQq6FZvVq8fH4eFo2DHJ3s0SkilJgEZEKZbUavLt6H++u3gfAtS3rM3VMF0Jr+bm5ZSJSlSmwiEiFOZedx1+/2s53O1MA+Ms1MTwztA0+3l5ubpmIVHUKLCJSIY6knWfizAT2pGTi623i1REdGNUt2t3NEpFqQoFFRC7bpt9SuW/2VtLO5VC/jh8f3hFHfLMwdzdLRKoRBRYRuSxfbErmhUW/kmc1aN8omOlj44kKDXR3s0SkmlFgEZFyybVYefmbXXz+82EAbuwYyZt/7ESgn4rBiUjFq/CRcM2aNcNkMhX5euCBB4o9ft26dcUev2fPnopumohUkLRzOYz9ZJMjrDwxqDVTb+uisCIilabCe1g2b96MxWJxPP71118ZMGAAt956a4nn7d27l+DgYMfjBg0aVHTTRKQC7E3J5K6ZmzmSdoHaft68M7ozA9tFuLtZIlLNVXhguTho/OMf/6BFixb06dOnxPMaNmxIaGhoRTdHRCrQip0pPDY3kXM5FqLDAvl4XDdaR6gYnIhUvkotjpCTk8OsWbP4y1/+csl1Q7p06UJkZCQ33HADa9euveS1s7OzMZvNTl8iUjkMw2Dqmn3c/fkWzuVY6Nm8HosfuFZhRUSumEoNLAsXLiQ9PZ0JEya4PCYyMpLp06czb9485s+fT+vWrbnhhhvYsGFDideeMmUKISEhjq/oaNV7EKkMF3IsPDhnG2+uSAJgfM+mzLyzO3Vrq3KtiFw5JsMwjMq6+KBBg/Dz8+Obb74p03nDhg3DZDKxePFil8dkZ2eTnZ3teGw2m4mOjiYjI8NpLIyIlN+x9AtMnJnAzmNmfLxMvDy8PWN6NHF3s0SkGjGbzYSEhFzy87vSpjUfPnyYVatWMX/+/DKfe/XVVzNr1qwSj/H398ff37+8zRORS0g4lMa9s7Zw+mwOYbX9mHZ7V3o0r+fuZolIDVVpgeXTTz+lYcOG3HjjjWU+d9u2bURGRlZCq0SkNL7afIRnF+4g12LQJiKIj8fH07huLXc3S0RqsEoJLFarlU8//ZTx48fj4+P8FE8//TRHjx5l5syZAPzrX/+iWbNmtGvXzjFId968ecybN68ymiYiJcizWHl16W4+/fEQAEPaR/DmrZ2o7a8akyLiXpXyf6FVq1aRnJzMX/7ylyL7jh8/TnJysuNxTk4Ojz/+OEePHiUwMJB27dqxZMkShg4dWhlNExEX0s/n8OAX2/hh/2kAHu0fy8P9YvHyKnmGn4jIlVCpg26vpNIO2hGRovadyOSumQkcTj1PoK83b4/qxJAOui0rIpXP7YNuRaRqWL37BI98mcjZ7DwahQby0bh42kYp9IuIZ1FgEamhDMPgw/W/8cbyPRgGdI8JY9rtXalXR7PvRMTzKLCI1EBZuRaemvcLixKPATCmRxMmD2uHn0+l1pIUESk3BRaRGiYlI4u7P0/gl98z8PYyMfkP7Rh7dVN3N0tEpEQKLCI1yNbkM9zz+RZOZWYTWsuXD27vSq8W9d3dLBGRS1JgEakhvt7yO8/M30GOxUrr8CA+GhdPk3oqBiciVYMCi0g1Z7Ea/GPZbj76/iAAA9qG887oztRRMTgRqUI0wk6kCnn11Vfp168f//jHP0o8bsKECcTExDDurnv5y4zNjrDyUL+W/OeOOKewYj/2zjvvrNS2i4hcDhWOE/EASUlJHDhwgJYtWxIbG1tk/5o1axg8sD+5loK3q6+3iVVr1tG7d2/Hts8//5w/TxiHxQo+YY1oeMvz+NZrjK/J4J3bunJTx6hij7Xz9oLZX3zJ6NGji7ShS5cu7N69m3bt2rFly5YKeuUiUtOV9vNbgUXEjdLS0hh7xxiWLlvu2DZ0yCBmzZ5D3bp1HdtMJhPeXhQJFxarrZ5K4eN8vMCnaVca/OFJvALqkGc+ycl5r5Bz4oDTc/t4m6jjD8/dDA2D4ZQZ/r4AzmZDXqFg9Le//Y03//l6ked+9rkXeOmllyrwtyEiNVFpP791S0jEjfr0vpbVK5c7bVu9cjl9el/nePzqq6+WeA377aEJEybgZYKwq28m4tYX8QqoQxPLLs7NnYTl1G9Ot3wmTJiAxQoxDeGJL2D8h/D4F7bHFitOx775z9ep4w//HAOf3QtvjoE6/vDqKy9XxK9ARKRU1MMiUolKutWTlJTEVW1aE1IL/j0eereBDXvgoc8g4zzs2ZtEbGwsJpMJE7ZejbxCvRw+9h4WbL0sJh8/6g16kDodbgBgdN3lvNxoGv/3Ux5jp9nOsb/dTSYTXiYIDoSpEwqe+8EZYL4AVsN2bJcuXUhMTKRzU0g8XPDc9sddu3bV7SERuSzqYRFxo7S0NAYPHUzr1q0ZOnQorVq1YvDQwZw5c8ZxzIQJE7AatrDSrTn8egS6t4D3xtkCw4QJExzHGkDeRf+0yDNs2wFOmrOIGDOFOh1uwBsLLX/7kLlv/pv7P86jz1XFt9Fq2MLK7ddAdD3b93+Pt223S0xMxMsEyadh1v2Q/F7+99PgZYKtW7dWxK9LROSS1MMiUglu6H8Da39cizHUgKbAYTAtNXH9tdezeuVqAEJDQ8nIyKBPW1i/q+Bc++PQ0FDOnDmDyWSy7fAGLIWeJP+xX0QsnR/6gBPmbCwXMjm9+B9kHdpecJwJR7Ip3MMCtgByIQcOnICWERDgC00eLjjWftys+22Bxm7WDxTptRERKQ+t1iziJklJSaxZvQZuATrmb+xo+2Bfs2AN+/btIzY2lsjISDIyMli/x/l8++PIyMhLPletq/pQb8jDnDBnk3M6mVPz/07e+eMwAKgNnAfWAzkUdMcUcuObsCO54HGHJs77Q0NDSU9Pp3cb+GQdrNsFN7SDG9rb9hceGCwiUpkUWEQq2Pr1620/NAVOA2eAMKBZwf7Y2Fhat27Nnj17ir0GQOvWrQsemABfoB/5QcSLUMtYQuJvBaBfm4bMuOdWjJwLEAGsLHShcOBEMU9ggh0ngKuATCAYdhzAqUfm8ccf57nnniPmMbDk9+7M+hG8vW0/P/nkk5f+hYiIVAAFFpEKduJEfjqYA6QU2hHuvH/v3r0FQeRGHLeOWALk5O+3M4BQYCWY/AKpP+wJarXsDkDGxv/jo9c+5dM/X7BdLwPnHpbvcQohYOsZOXPmDOQCuwu10ct2nL3n5Nlnn+W555/D4gMML2ijZQlgtU15FhG5EjToVqSMPvnkE8aOHcuMGTOK3R8enp9MTl6045Tz/qSkJFuIuBHbraOQ/O9DAeOiwJIfRHxGRxHx6FvUatkda142p775J+kbPsPby2Qbb2LkX2clsBBYAQTbrucYCwNMmjSpICzdAjyW/93X9lx//etfHa+1pDa6+h2IiFQ0BRaRUtqyZQt+AX7cddddzJo1iz//+c/4BfiRmJjodNyuXbtKDAP220BWa/4c5aYXPVEznPcDGBAwsjMRzd7Gz9SEPE5z4uhTnN+13nGIl5dXQQ9L4ec1257Xy6vg7b5ly5YSg8i2bdsAWLduXYltXL16dQm/MRGRiqPAIpLvUj0nV/e6mlxyncJALrl069HN6bivv/66xDDw1VdfOV/4sPNDDhV97qC4YTSMeglv6pBt2kNKwGPkRO53OsZisdied8hFzzvY9rwWS8EUo++//972g4sgYg8qbdq0KbGN7dq1K9pYEZFKoMAiNd6WLVvwv6jnxP+inpNPPvmEvJy8YkNIXk6eU8g5eTL/XpB90O0+IBVHGHCMcQFbj8gy4AcgEfgR+C5/O5CdZ6HekIcJ638PJpM3Z71XkeL/NBbTmWKDjeN5C2tW9BBHeHERROy9O127drW1ZQmwHVvvzXZgqa2NnTp1ctEIEZGKpUG3UuPFd4svsi0nJ5cuXbtg5FdRmz17tm2HizAwa9YsR6G3nJwc20YXg24d+yG/TC2wqtBx/rZtXrVCGfPRJup0HIhhtXDm+0/JjFxoe85DOEJDkenKhymYTg3FBpvc3NyCsGRQcM38sJSbmwtAixYt8qvWAQsKXcDbdl7Lli2LXlxEpBIosEiN9sknn9h+8AN6U6R2yYwZM2xr9NjHf7gIA4UHtNo2YJvOfHE9lIsDhv3xLTjNEvKLaEGDm59jy+EzWLPOcmrxG2Qlby1aOO7isGLCFmQKh5BlRZ/XarUWDNAtHETCgQvOt48A2wyhRkAatinav190nohIJVNgkRpt8uTJtg/uuhRbu+SFF15gwoQJtG/fntVrVrsMA+3bt3ecGhgYyIULF1xeMzAwsGBb4bEu2L7Xqn0d9Ro8gpdvAM0b1Ob71+4hL+2oLdRcKjQYQORF22OAg86HNW3alD1790A6BaHqHI4p0M2aNQPgwIH8FZ6bYgs39fIvkP9/jv379xdZI0lEpDIosEiNlpGR4Tyzxt7LkR9E0tPTgfzBpQa2uiXF3BopPPi0Xr16/H70d5fXrF+/vnMj7LeZDBOhebcTEvUnAC78toUFLz5N6ONHC47LLXReMxcvqgtwE87B5qLA0q1bN9tspUicQ1V+uOnWzTaQuEWLFrbtLnqWdEtIRK4UBRap0Xr06MGqVasKZtaQ/90AFkDPnj2BQlOCL74Fk//Yx6fgrVSnTh3n2ToXXbNOnTpAQdl7DoOpQyD1cyZRy2p7voxN8zD9soiQwBcKnsvFmBgn9gGyQylxrEurVq1sP7gIN/b9rVq1YtCQQaxavgqLYXFc03uFN/2H9FfviohcMZolJNVeUlISy5YtY9++fUX29enTx/aDi8G0vXv3BmD79vzFBC9+x+Q/ttctAVsQKema9v2hoaFgAp8fwok48ya1rD0xrLmcXv426es/JTQkpOBc+5iYwvVV0nHMJnIw8rctAN6hoDfooqA1atSognDzO9Aw/3t+uBk1apTj2Dmz59C/d3+na/bv3Z85s+cgInKlqIdFqq20tDTG3DGG5cuWO7YNGjKIObPnOErPO6rJurjlkZSUBEDDhg1tH/DewB9wLqNvyd+fLysrq8Rr2ve3bNmS49YgGox4Gu/AYPLOpnFqwavkpOwtOgPHPtYlClsF3UbYelEK3Z4KDQ0lPSPddmwvCsLLFtv3uqEFCxUePHiwYOzOxYNuT8ChQ4ccvSd169blu6XfsW/fPvbv30/Lli3VsyIiV5wCi1RZy5cvZ9OmTfTs2ZMBAwYU2T/mjjGs2rDKaabOqg2ruO322/hu6XcAZGZmlji99+zZswAFZe9dhAZv+2qAwPHjxwt6LwpfM7/34vjx4xiGQYNeIwnvEoXJy5vsrCRO+b2K5epUx1pCjt4fu23A/EKPY5x3+/v7F4SUnwrvsLXD39/fsWnTpk22HwqN/wWglu3bxo0bi/xOY2NjFVRExG0UWKTKOXDgAD169iD1VKpjW70G9di8aTMxMbZP8aSkJFvPykUrF1vCLSxftpx9+/YRGxtrK+Jmr4VSuKch/0P++PHjAHz77be27S5Cw6JFixwLAZ4/f774Abr5Cwuez8rmmQW/8lN2NCYvOGdeR2rD9zBMOU5jXZxuYZmA4zgP4r1obEpgYKDtsQnnHpZttu+FZyc1atSoxGs2adLExW9fRMQ9NIZFqpzuV3cn1Zxq6zkZAQyEVHMq8d0LCsAdOHDA9oGcjvO4jwzAZJuOC1CrVn6XQuHZN2ArlAbUrl0bKFS9Nvmi4/IfO/aDbUqzC16BwdS68W/M+V8yGAZn1n7K6YA3bWHFrpnt25kzZwq2Gdh6cwpX2R2C09iUESNGFNRW+QnYmP89f/HDW265xXHs8ePHS7zm0aNHXb4GERF3qPDAMnnyZEwmk9NXREREieesX7+euLg4AgICaN68OR9++GFFN0uqieXLl5N2Oq2gxslCbCsSh0La6TRWrrR1pxw7dqzED2R7efzg4GBbsPHBKQDhA5jy9wO+vr4FxxUOQPnHFb7dkpeXV+zih76RMUSOfwf/xu2o4+/DoFoHMf9vnsvy+PYZSg6XKLn/zjvvOBesG5H/Pd3WxjfffNNxrNPyAcVcs3AAExHxBJXSw9KuXTuOHz/u+NqxY4fLYw8ePMjQoUO57rrr2LZtG8888wwPP/ww8+bNq4ymSRW3ZMmSElcktt+6cczqcfGBbJ/Vk5GR4Vw4biGOAIRRUIclLCysxAUNHTOD7C46tla7XkSM+Sc+IeHkph1j4QO9OPD9ohLX6Vm7dq3zNUuxSOK333xr6y2yv5aVQG7+9kJuvPHGEq950003Fb24iIgbVcoYFh8fn0v2qth9+OGHNGnShH/9618AXHXVVSQkJPDmm28ycuTIymieVGFeXl4l1jix10txzNpxMVPHvt8xQNZFkbeUFFvhE8dtHhcByDEzqLCmgGEiJO82QvPGgBdcOLSN04tep+X0ibbFFUtYp2fLli1AfuXcrAsuB/EGBhSMTbnxxhsxLAaPP/44K1euZMCAAU49K3aDBg0irH4YaUvSilwzrH5YsYOYk5KSOHDggGYJiYhbVEpg2bdvH1FRUfj7+9OjRw9ee+01mjdvXuyxGzduZODAgU7bBg0axCeffEJubq6tK74Y2dnZZGdnOx6bzeaKewHisRwVZV0EB/v+UaNG8fwLz7sspW+vM3Ly5MkSA5D91tHvv/9u2+ciAB05cqRIW03JAdRr9Ri1rdcAYD6zkDNf/RcM20rIjvV6XKzTY18xOTc3t8QpyHl5eUWee8CAAQQHBxe9rVRIwv8S6NajG6kLig5eLqw008NFRCpbhQeWHj16MHPmTFq1asWJEyd45ZVX6NWrFzt37qRevXpFjk9JSSE83LlkZ3h4OHl5eZw+fZrIyMhin2fKlCm89NJLFd188XCOqb4ugoN9f6tWrfD29caSaynSe+Ht6+3oIfDz87NtdxGA7IHZy8urxKnKhac1A3iHNKRhyPP4WWMwjFxST3zAuS9XOh3jCCwu1umxB5E6derYbk3dhq03xh5sfIB3CirnQulmUNnFxMRw+uRpVq5cycaNGy89PbxQD9Sq5c7Tw0VEKluFj2EZMmQII0eOpEOHDvTv39825gD47LPPXJ5z8Uq3hmEUu72wp59+moyMDMdXcf/Clern+++/L1iRuPC4j/yekx9//BGwDc615FigwUUXqA+WHItjcK6jl87FWA77/qioKOdeDnsV2VDAyJ8mnM+/cTsix72DX4MYLOfOcGL2M5z7bKUtbBSa1eMYqOviue377QsRchhbqInN/55/nGM/2HpM0grCCkBqWipx3eJwZcCAAbzwwgsubwMtX7YcyyCL09gdy8CC6eEiIldCpU9rrl27Nh06dHD5P7aIiAjHOAG7kydP4uPjU2yPjJ2/vz/BwcFOX1L9LVq0yHlFYntwiAQMWLDA1p1SmsJoUKhwnIuBr5mZmUD+4NwSrmefgvzFpmTC//Qq3rVCyE7Zz/HPHiP76O5iX4tjNpGL57b3wNStW7fEkGa/LbN8+XLOpJ0pMjsJXziTdsYR0srCabXmwprZvtmnh4uIVLZKLxyXnZ3N7t27ue6664rd37NnT7755hunbStWrCA+Pt7l+BXxHFd6IKZjzEQX4Bps4z2igbPAwYL9jsJox3CqdMt6nAqjWa3WEou82ceRnD9/vuB6hQfnLrFd70JWDi8s+pWZGw9j8vbh3O4NpC5/F6PQOKuLmc3mEp/bHpJuuOEG24whFytF23tGlixZ4jyVG5zG43z77bfF9qKURKs1i4inqPDA8vjjjzNs2DCaNGnCyZMneeWVVzCbzYwfPx6w3co5evQoM2fOBODee+9l6tSpTJo0iYkTJ7Jx40Y++eQT5szRwmqezF0DMR3XXgRYCu3wdt6/Y8cO5+nKdvkDVe3Tnq1Wqy2I+AG9sfWYnAc2ANkFgeXMmTPOU5XBEQa8vguG6x9k5kbbvZ0z6z/D/PP/OdrkitNzxwFWbGFli/Nzd+3atcTrdOrUCSg0M8pFb0jh9Y5KS6s1i4inqPBbQr///ju33XYbrVu35pZbbsHPz4+ff/6Zpk1t/xc9fvw4yckF5UJjYmJYunQp69ato3Pnzvz973/nvffe05RmD+c0EDP/1oN9nZ7KdPDgwRL3Hzp0CICff/65xHotjltGUNAr0QvonP/9oiqyOTn5lWgvCgO+MU2JGPc2/tEdqO3nzfSxcbawUkzhOHwpfnXlUOBHbJVpf8Q2TqTQczt6OYoZjwMFvRyOFZZdjIkpvAJzWXww9QNCa4U63YILrRXKtPenlet6IiLlUeE9LF9++WWJ+2fMmFFkW58+fdi6dWtFN0UqiWOdnltw6m2wGBaWLyhYp6c8LrWg4alTpwrCQA9sH+z2XglrwTTkU6dOlThduUgl10tUkTWZTLbB4IVujQRaelDf+694hdYiLz2F+ZNH0ToiyLazhFszdl5eXlgNq8saMF4m278nDh48WLDMgP321jnge9tx9pWVW7VqRe8+vdmwZEORmUy9+/Qu99/krrvvIvXMRQN5z6Ry1913sXrl6nJdU0SkrLT4oZRZaQZilvXDsbTTcR23ZqDYFYntlWntqyy7aqNjv52LMRp2MTEx/HbwN0ddl+Dmo6jrMw68IOvwdmpv/5LWEXc6n3SJEOTl5YU1z+q6CJ63LbBs2rSpYKBx4dtbMcBB55WVFy5YyG2338byBUVv1ZVHUlISa9estf1+/4DTIolrVq+5rHAqIlIWCixSZpUxEDOuW1zBTJx8qampdInrQnpaumObv79/wQrFxQymtU8FbtGiha0XxUUbnQoZ2mfgFFNgzh6OOnbsyG+//YbJ8Kee5RFq+/QGwLz1G86s/pibh/+h6Iu6RAjy9va2zRRyEWzstV169Ohh2+BioHHh4nB169blu6XfsW/fPvbv33/Zg6HXr19fYm/R+vXrFVhE5IpQYJEyq+iBmMuXLycjPcM2+PRGnGbgZKRnOErMQ35dlBIG09rrprz44osMHjLYZaE3p6KD9nEkxVSRtdu9ezfeQfVpcNtz+NdtiWHJI23lNM6al4MVdu3a5TjW29sbi9Xiusicly2I1KlTx9ZeF8EmKMh2e8nRw+RioHHhOix2sbGxFRskLtFbJCJS2RRYpFzmzJ5T5NZD/yH9y3XrYdasWS5n4LDAtt8eWNLS0kpc+yctLQ2wLe+ACcjBOYiYbF9O42Ps1ys8PuQHnHpYcoIbEzngObxr18ViZHAq9TWye++09XYcdC6PX79+fdtYGhdrBDVoYBs927lzZ1avWe2yd6dz585A/i04E7Z363Ccp1Rby3cLrrRKW1lYRKSyKbBIuaSlpZGQkOC0LSEhgfT09DJPa7YXZ3P1r/jC60SFhYXZbvW4GPdhLzaYlJRkmybsjXOvhJftsX3shZeXl236cAhFe2wu2MaZfLX5CNY+D+Ht7UPOqYOczHwZS+NTTr0m9urM9t8N4HKNoNRU2zidJ554gtWrV7usr/Lkk0/ammxf8NFFoPPxqby3catWrejXvx9rl661vcZmwCEwLTNxff/rdTtIRK4YBRYplx49e5BqTnXq5Uhdkkq3Ht04ffJ0ma4VEhJi+8HFv+Id+4HIyEj27NnjMtzY155av359wWyiwr0SSwFrwdiLqKgo28KGpy663mnA5EXjYY/y5LxfwNuHc3t/JPW7dzAKr8ycHy4KLyORm5tr+8HFGkH2/YMGDcLLx8tRb8XBAC8fL0cvkGO/i9dc3OKHFenrr74u0ps2cMjAcg/kFREpj0ovzS/Vz/Lly22zeW4EooCT2HoShkLqqdQyl4B3FFBzUaK+cO9FdHS07QcXtUYaN24MwM6dO50Hi+avgWOvr7Jz507A1oPguN1SqGaKV+06NBz1EqY2/QDw2b2c0wv/gXFfli0AdcD2/WHb8xauyuwILy7aaN+flJRkmyXkd9FxvmDNszqWs3Aa5FzM9Sq72qx9IG9SUhJLly4lKSmJ75Z+p5WaReSKUmCRMnMUXdsGTAVmA/8GEm2b7ev0lFbr1q1t4cI+5sO+PlD+YoGtWrVyHHvttdeWGG6uvfZaAH799VfbCU2x9ZbsA1Jx9Ers2LEDgA4dOjjfbgkB3/bRRNz5NoHNuuBtWPjwjq508Dpia8xhbLN1RuZ/P2S73lVXXeVoY0hISIlr/4SGhgKFpoffB4wF+uZ/v9+22b5Oj32Qs/dyb6frea/wZtCQQVfstkxsbCxDhgzRbSARcQvdEpIyc6zTcxznga/5ocG+Tk9pdevWzfaDizEf3bt3dxzrGM9Rl2Jn9djHcxw9etS2fQ6QctFxwLFjx4CCRQvtt1sCLd2on/MEXn61yMs4Qbe8Xxjc/g8kxMWxcNFClzN/4uIKVkOOi4srcWyK/dgi08PzH2JbNcCp56QiBzmLiFRFCixSrJIWNXQsGOiiNkdZx1Q4lmpwMebj8OGCeyGOIHIbth4Ye7jxAd7BadkHTMAZil2s0H6b6X//+1/+k0DwVSMJzRuPCS+yzu/g1GdT+LVpVMH1DFwuVFiYfToyFw1NsT+2ryxelunh9tsyK1as4Oeff3ZZCVhEpLpSYBEnZVrUsIJqc9jL6bsadOvYT37vTuFj7eEmv1fC3ruTk5NT4swa+8BXq9WKydePepaHqZ3XF4DM9GWkzfwPZOVhG6QD4eHhBYN4+1CwSOJ6ICd/fz6LJX9aUjPgYKHXk/+4cKArbc+JuxabFBHxFAos4sRpUcP8XolVy22LGn639Dug4mtzOMLAMpxvt3wHmJzDQFRUVMEYlmJuzdiPdVTNdRGq7PtH3nEnn+73xT+yFYbVQtrKDzmbuMzRc2JfZbxJkyZFAxDY6rYswLG4J8Dw4cP55ptvXFamvfnmmx3HlrYybWn+LiIi1ZkCiziUdlHD8tTmKGlRwz59+tjCQAhFx6VccA5AjjEspouOzV9LyD6GJcs+9dhFqLpw4QLbks/wnbUj/pEWLBfMnFo4hexk22Bc+6rK9lXDnW5bncZ2qykMRwAqfNvquuuus/3gojLtNddcU+T3U1Jl2spcbFJEpKrQLCFxKM2ihnZff/U1A68f6DSrZ+D1A/n6q6+LXLN+w/oMHjyYF198kYEDB1K/YX3bCsT57AHIlG6yVZsdAQwAU4aJfv37OX0YO2qS3Af0y2/bDThm1thvt3h5eZU4Uyfgqj6Mnv4zaRcs5Jw6RMqXj5Edv8MxrRlf23FfffUVAEuXLrU9wRycZ0Z9gfN+ClWm9cVpqrT9moV/j6VRlr+LiEh1pR4WcSjLooalvZXR/erupGWmFSkwF9893mllZkdxsmUlFyfz8srP2NMAe/22Q9hK6VPQw9KiRQsSExOLDpL18SK07wTqdL+FnDwr9S78zrZZT2DcdKHYsS5JSUlA/qyiEgbx2mcdOdpYwqDkslamrYzFJkVEqhr1sIhDeep9lFSbY/ny5aSdTnOqcUJHYCiknU5zKjBX2uJkmzdvLljjp3DvBYCpYNZP+/bti7TH5F+bhre8QEh32wkP9WvJtabdGDkXXPZe2Ev99+rVq0i9FvtrwSio/wIVX5nWU+qwiIi4kwKLOJkzew79e/d3utXTv3f56n0sWbLE9oOL4m3ffvttkXMuVZxs8+bNJQYH+/pG9evXt52QX4DWJ6wRkWPfIjAmHmtuFj0sv/LXga1p366d7QAXVWTbtm0LwMCBAwteS2H5r+WGG25wbKqMyrQV+XcREamKdEtInNh7Ov773/+ydu1abrjhBiZMmFCuazVs2ND2g4vibY79ZeAoe+8iONjrqzh6ZnIhIKYrDf7wJF4Bdcgzn+TkvFdo94Bt9k+fPn1KnHVkH/DruBXl4rZMkds89vEzha+ZP36mPEp7C05EpLpSYBEnBw4csC1smD++ZNasWTz+5ONs3rSZmJiYIseXNPsnMjKyxHEfjpoqZdCsWTPbDy6Cg72NPXr0ABMEdb+ZutdNwGTyJuvCLk59+RrW9HR69uwJ5A/4vaEfa9atuWisC/S7oWDAr2O9IxdTrwvf5jlw4IDtmEicrxkDHLQNki1v2ChpNpGISHWmwCJOSjtI9uJgA1CvQT2nYLNu3boSi7etXbu2fL03pei9aBTdlHpDHqNOB9utmkzv5aTVnQZ982BBoeBD8QN+Bw0Y5HS7pUWLFrbny2+7Q/506sK3eRy3hLoAN+G81MBBDZIVESkPBRZxcAySvajeBwakLbANkrX3ovTo2YNUc2qRYNOtRzdOnzwNQGZmpu0aLm7fmM3mMrcxLS2t+BL5+ev0pKWlcdKcxYMLDlCnww0YhoUzfh+R6f2tLdDkP3fhXo7S3G5p1aoV9RrUs73mgThVuq3XoJ7T8U4l9weWXHJfRERKR4NuLyEpKYlly5axb98+dzel0jkNki2sme2bfZDs8uXLbT0rN2KrXH8S26KFQyH1VKpj9o/T7ZvCg24P4by/DBwhqMFFO/LH2J7IDWDY1B84lAmWC5mcPPoCmT7fFvS+5D93cb0cJQ34TUpKKnjNvYDO+d/zX/PF/31okKyISMVSD4sLNXHtFscgWBfjQ+z7N23aZNuwDZhf6Lj8IS4bN25kwIABBTN1XFR8dewvA0fJ/WIWP6z1XR/2Nh6KYc6mZcM6WDfM5PiqX209Is24rF6O0hRvK26xQg2SFRGpGAosLtw66lbW/rjW6ZbHiqUr+OOoP7J65Wp3N69SjBo1iudfeN7l+JBRo0YBcObMGVuPxXGcB9Pmz6yx3+pxrBHkAwzHedCt1XmNoNKKjo62/VB48UPDi9BjYwn5w60YQL82DXn3T53J+3NHbrs945ILC5ZGeYu3aZCsiEjFUGApRlJSEmtWrykylsMwDNYsWFNt125p1aoV1/W+ju9/+L7I+JDrel/neM3bt28vsZLrtm3bABcLBhY6rvCCgaUVGxvrNA3Z1CyQ+jlPUCusOwCd/E7w0biheHuZIKDiejmcxqUYGpciInKlaQxLMdavX2/7wUX3v2N/NbRowSIGDRzktG3QwEEsWrDI8Tg4ONj2g4vfT1BQEFDxFV8hv1fGAPLAZ10UEea3qFWnO9bcbE4t/ifDmlhtYaWQSxWjK60Ppn5AaK1Qp3EpobVCmfb+tMu6roiIXJoCS0lcVCqtzkpTIn/YsGG2H1z8fkaMGAFUTsXXJk2aABBwW2ciJr6NX/0m5FlPc+LYU5zfvb5cvTZ2lxpgff+D95N+Pt02JmYEMBDSz6dz3wP3lfs5RUSkdHRLqBiO6qcuxnLYq59WZyWNvYiKiiqxiJp9bEpl3EaxWCwExQ2jbtRdmEzeZJv2cKrWq1gizwDl67UpzQDrpKQk2/7CtwkBS20Lyxcsr7a3CUVEPIV6WIphr35KHk7d/+Q5Vz+tqRxF1EJw/v0EU6SIWkVO783Os7DoeBBh/e/BZPLmrPcqUvyfxmI6c1m9NmPuGMOqDaucFlNctWEVt91+m+OY0swSEhGRyqPA4sLXX33NoAEXjeUYMIivv/raTS3yHI7VgzO9YQC22yMDwPts0dWDS7sK86WcysxmzEebWL7PDIaV9B8+IXX3v8Cce1krF9t7TiyDLE6LKVoGWli+bLnj9lBl3N4SEZHS0y0hF6pSHY2kpCQOHDhwRds4Z/acIuXsS5oyfDnTe389msHEmQkcz8giKMCHKcNimbrjPMsLzWQq73Tl0tZX0SwhERH3Mhn25W0ryJQpU5g/fz579uwhMDCQXr168frrr9O6dWuX56xbt47rr7++yPbdu3fTpk2bUj2v2WwmJCSEjIyMglks1ZwnFLer7ED3zfZjPPH1drJyrTRvUJuPxsXTokGdCnvupKQk23+bF41NYTuwwLbffu0zZ84UXXOomhcTFBGpbKX9/K7wwDJ48GD+9Kc/0a1bN/Ly8nj22WfZsWMHu3btonbt2sWeYw8se/fudWpsgwYN8Pb2LtXz1sTAMnjoYFZtWGW7nZFflM17uTf9e/fnu6Xfubt5l8VqNXh7ZRJT19rGhvRp1YD3butCSKBvhT+X4/d48bo/Ln6PVaHXTUSkqnBbYLnYqVOnaNiwIevXr6d3797FHmMPLGfOnCE0NLRcz1PTAktZegaqmrPZeTw2N5GVu04AcHfv5jw1uE2R+ioVRT0nIiLuU9rP70ofw2Jf+yUsLOySx3bp0oWsrCzatm3Lc889V+xtIrvs7Gyys7Mdj8uz8m9VVta1bcrKHeNiAJJTz3PXzM0knTiLn7cXU27pwMi4xpX6nFVpvJKISE1VqYHFMAwmTZrEtddeS/v27V0eFxkZyfTp04mLiyM7O5vPP/+cG264gXXr1rnslZkyZQovvfRSZTXd45V3bZtLKeu4mIoMNj8dOM39s7eSfj6XBkH+/GdsHF2bXLkeDq37IyLiuSr1ltADDzzAkiVL+OGHH2jcuGz/Sh42bBgmk4nFixcXu7+4Hpbo6OiadUuoTWvwB4bgXNwuG5L2lu+W0A39b2Dtj2sxhhqOcTGmpSauv/Z6p0UfK3LAr2EYzPr5MJO/2YXFatCxcQjTx8YTERJQ5vZfDnf1KomI1GSlvSVUaXVYHnroIRYvXszatWvLHFYArr76apcl0gH8/f0JDg52+qpJDhw4YCveFolz8bZIwChfITP7oo/GUMOpJokxxGDNqjVOf4/SFFsrjZw8K88u/JXnF+3EYjUY3jmKr+7peUXDSlpaGoOHDqZ169YMHTqUVq1aMXjoYNuq1CIi4hEqPLAYhsGDDz7I/PnzWbNmDTExMeW6zrZt24iMjKzg1lUfjltCXYCHgNvzv3e2bS7PLaHSLvroVGwtCjgJNCpabO1SUs9mc8fHm/hiUzImE/xtSBv+NbozAb6lmxlWUSoqfImISOWp8DEsDzzwAF988QWLFi0iKCiIlJQUAEJCQggMDATg6aef5ujRo8ycOROAf/3rXzRr1ox27dqRk5PDrFmzmDdvHvPmzavo5lUbToXMelqgDnASvH8qfyGzEydss3JcjYux73cM+N0GzC90XH42Lc2A313HzEycmcDR9AvU8ffhvds6069NeJnbfLmKXSOoI1gMrREkIuJJKjywTJs2DYC+ffs6bf/000+ZMGECAMePHyc5OdmxLycnh8cff5yjR48SGBhIu3btWLJkCUOHDq3o5lUrH0z9gO5Xdyd1VapjW2iDUKa9P61c1wsPDy9x0Uf7ooYtWrSwHXcc2wd9/lgXltqOu1TvzrIdx5n01XYu5FpoVq8WH4+Pp2XDoHK1+XJV9mwrERGpGBUeWEozhnfGjBlOj5988kmefPLJim5KtXf/g/eTfj7dKTSkL0/nvgfuK1fhuD59+tiCSi628TB23oBx0SrVBjAUp14JjIvOu4jVavDu6n28u9p2y+jalvWZOqYLobX8ytzWilJZs61ERKRiafHDKqq0i/aVRatWrejXv5+t96QwE/TrX7BKdXlWLj6Xncf9s7c6wspfrolhxp+7uTWsQKGFHJd724ruZXBZiymKiEjlUGCposoTGkqjNKtUl3Xl4iNp5xk57Se+25mCr7eJN0Z25IVhbfHx9oz//ObMnkP/3v2dZlv1712+xRRFRKRyaLXmKqqybmWUpuprWVYu3vRbKvfN3krauRzq1/HjwzviiG926arHV5Iq3YqIeL5KX0voSqlpawlB2Rftq0ilWX/ni03JvLDoV/KsBu0bBTN9bDxRoYGV2i4REalaPGbxwyulJgYWT1i0r7heiVyLlb9/u4uZG233jG7sGMmbf+xEoN+Vra8iIiKeT4GlBvGkWxlp53J4YPZWNv5mm2r9xKDW3N+3BSZT5ay0LCIiVZvHrNYslc9TFu3bm5LJXTM3cyTtArX9vHlndGcGtotwd7NERKQaUGCRCrFiZwqPzU3kXI6F6LBAPh7XjdYR7ikGJyIi1Y8Ci1wWwzB4f+1+3lyRBEDP5vX44Pau1K3t3voqIiJSvSiwSLldyLHw+NfbWfLLcQDG92zKcze1xddD6quIiEj1ocAi5XIs/QITZyaw85gZHy8TLw9vz5geTdzdLBERqaYUWKTMEg6lce+sLZw+m0NYbT+m3d6VHs3rubtZIiJSjSmwSJl8tfkIzy7cQa7FoE1EEB+Pj6dx3VrubpaIiFRzCixSKnkWK68u3c2nPx4CYEj7CN68tRO1/fWfkIiIVD592sglpZ/P4cEvtvHD/tMAPNo/lof7xeLlpWJwIiJyZSiwSIn2n8zkrs8SOJR6nkBfb94Z3YnB7SPd3SwREalhFFjEpdW7T/DIl4mczc6jUWggH42Lp21UzVr2QEREPIMCixRhGAYfrv+NN5bvwTCge0wY027vSr06/u5umoiI1FAKLOIkK9fCU/N+YVHiMQDG9GjC5GHt8PNRMTgREXEfBRZxSMnI4u7PE/jl9wy8vUxM/kM7xl7d1N3NEhERUWARm23JZ7j78y2cyswmtJYvH9zelV4t6ru7WSIiIoACiwDztvzO0wt2kJNnpXV4EB+Ni6dJPRWDExERz6HAUoNZrAb/WLabj74/CMCAtuG8M7ozdVQMTkREPIw+mWqojAu5PDxnG+uTTgHwUL+WPNa/lYrBiYiIR1JgqYEOnDrLxM8S+O30OQJ8vXjz1k7c1DHK3c0SERFxSYGlhlm39yQPzdlGZlYeUSEBTB8XT/tGIe5uloiISIkUWGoIwzD4+PuDTFm2G6sBcU3r8uEdcTQIUjE4ERHxfAosNUBWroVnFuxg/tajAIyOj+blEe3w9/F2c8tERERKR4GlmjtpzuLuz7eQeCQdby8Tz994FeN7NcNk0uBaERGpOhRYqrHtR9K5+/METpizCQn05f0xXbk2VsXgRESk6lFgqaYWJR7lya9/ITvPSsuGdfh4XDzN6td2d7NERETKpdJWtPvggw+IiYkhICCAuLg4vv/++xKPX79+PXFxcQQEBNC8eXM+/PDDympatWYrBreHR75MJDvPSr82DVlwfy+FFRERqdIqJbDMnTuXRx99lGeffZZt27Zx3XXXMWTIEJKTk4s9/uDBgwwdOpTrrruObdu28cwzz/Dwww8zb968ymhetZWZlcvEmQl8uP4AAPf1bcFH4+IJCvB1c8tEREQuj8kwDKOiL9qjRw+6du3KtGnTHNuuuuoqRowYwZQpU4oc/9RTT7F48WJ2797t2Hbvvfeyfft2Nm7cWKrnNJvNhISEkJGRQXBw8OW/iCrm0Olz3DUzgf0nz+Lv48Ubf+zI8M6N3N0sERGREpX287vCe1hycnLYsmULAwcOdNo+cOBAfvrpp2LP2bhxY5HjBw0aREJCArm5ucWek52djdlsdvqqqX7Yd5rh7//I/pNniQgO4P/u7amwIiIi1UqFB5bTp09jsVgIDw932h4eHk5KSkqx56SkpBR7fF5eHqdPny72nClTphASEuL4io6OrpgXUMWcy87j4S+3kXEhl87RoSx+8Bo6Ng51d7NEREQqVKUNur24zodhGCXW/iju+OK22z399NNkZGQ4vo4cOXKZLa6aavv78O6fOnNrXGO+vPtqGgYHuLtJIiIiFa7CpzXXr18fb2/vIr0pJ0+eLNKLYhcREVHs8T4+PtSrV6/Yc/z9/fH3V1l5gOtiG3BdbAN3N0NERKTSVHgPi5+fH3FxcaxcudJp+8qVK+nVq1ex5/Ts2bPI8StWrCA+Ph5fX81wERERqekq5ZbQpEmT+Pjjj/nvf//L7t27eeyxx0hOTubee+8FbLdzxo0b5zj+3nvv5fDhw0yaNIndu3fz3//+l08++YTHH3+8MponIiIiVUylVLodPXo0qampvPzyyxw/fpz27duzdOlSmjZtCsDx48edarLExMSwdOlSHnvsMd5//32ioqJ47733GDlyZGU0T0RERKqYSqnD4g41vQ6LiIhIVeS2OiwiIiIiFU2BRURERDyeAouIiIh4PAUWERER8XgKLCIiIuLxFFhERETE4ymwiIiIiMdTYBERERGPp8AiIiIiHq9SSvO7g71gr9lsdnNLREREpLTsn9uXKrxfbQJLZmYmANHR0W5uiYiIiJRVZmYmISEhLvdXm7WErFYrx44dIygoCJPJ5O7mXFFms5no6GiOHDmidZQ8jP42nkl/F8+lv41nqsy/i2EYZGZmEhUVhZeX65Eq1aaHxcvLi8aNG7u7GW4VHBysN7iH0t/GM+nv4rn0t/FMlfV3KalnxU6DbkVERMTjKbCIiIiIx1NgqQb8/f158cUX8ff3d3dT5CL623gm/V08l/42nskT/i7VZtCtiIiIVF/qYRERERGPp8AiIiIiHk+BRURERDyeAouIiIh4PAWWauCDDz4gJiaGgIAA4uLi+P77793dpBpt8uTJmEwmp6+IiAh3N6tG2rBhA8OGDSMqKgqTycTChQud9huGweTJk4mKiiIwMJC+ffuyc+dO9zS2BrnU32XChAlF3kNXX321expbg0yZMoVu3boRFBREw4YNGTFiBHv37nU6xp3vGQWWKm7u3Lk8+uijPPvss2zbto3rrruOIUOGkJyc7O6m1Wjt2rXj+PHjjq8dO3a4u0k10rlz5+jUqRNTp04tdv8bb7zB22+/zdSpU9m8eTMREREMGDDAsTaZVI5L/V0ABg8e7PQeWrp06RVsYc20fv16HnjgAX7++WdWrlxJXl4eAwcO5Ny5c45j3PqeMaRK6969u3Hvvfc6bWvTpo3xt7/9zU0tkhdffNHo1KmTu5shFwGMBQsWOB5brVYjIiLC+Mc//uHYlpWVZYSEhBgffvihG1pYM138dzEMwxg/frwxfPhwt7RHCpw8edIAjPXr1xuG4f73jHpYqrCcnBy2bNnCwIEDnbYPHDiQn376yU2tEoB9+/YRFRVFTEwMf/rTn/jtt9/c3SS5yMGDB0lJSXF6//j7+9OnTx+9fzzAunXraNiwIa1atWLixImcPHnS3U2qcTIyMgAICwsD3P+eUWCpwk6fPo3FYiE8PNxpe3h4OCkpKW5qlfTo0YOZM2eyfPlyPvroI1JSUujVqxepqanubpoUYn+P6P3jeYYMGcLs2bNZs2YNb731Fps3b6Zfv35kZ2e7u2k1hmEYTJo0iWuvvZb27dsD7n/PVJvVmmsyk8nk9NgwjCLb5MoZMmSI4+cOHTrQs2dPWrRowWeffcakSZPc2DIpjt4/nmf06NGOn9u3b098fDxNmzZlyZIl3HLLLW5sWc3x4IMP8ssvv/DDDz8U2eeu94x6WKqw+vXr4+3tXSTZnjx5skgCFvepXbs2HTp0YN++fe5uihRin7ml94/ni4yMpGnTpnoPXSEPPfQQixcvZu3atTRu3Nix3d3vGQWWKszPz4+4uDhWrlzptH3lypX06tXLTa2Si2VnZ7N7924iIyPd3RQpJCYmhoiICKf3T05ODuvXr9f7x8OkpqZy5MgRvYcqmWEYPPjgg8yfP581a9YQExPjtN/d7xndEqriJk2axNixY4mPj6dnz55Mnz6d5ORk7r33Xnc3rcZ6/PHHGTZsGE2aNOHkyZO88sormM1mxo8f7+6m1Thnz55l//79jscHDx4kMTGRsLAwmjRpwqOPPsprr71GbGwssbGxvPbaa9SqVYsxY8a4sdXVX0l/l7CwMCZPnszIkSOJjIzk0KFDPPPMM9SvX5+bb77Zja2u/h544AG++OILFi1aRFBQkKMnJSQkhMDAQEwmk3vfM5U+D0kq3fvvv280bdrU8PPzM7p27eqYgibuMXr0aCMyMtLw9fU1oqKijFtuucXYuXOnu5tVI61du9YAinyNHz/eMAzbNM0XX3zRiIiIMPz9/Y3evXsbO3bscG+ja4CS/i7nz583Bg4caDRo0MDw9fU1mjRpYowfP95ITk52d7OrveL+JoDx6aefOo5x53vGlN9IEREREY+lMSwiIiLi8RRYRERExOMpsIiIiIjHU2ARERERj6fAIiIiIh5PgUVEREQ8ngKLiIiIeDwFFhEREfF4CiwiIiLi8RRYRERExOMpsIiIiIjHU2ARERERj/f/TaBGlDEWOgsAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -5183,13 +4901,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.188949616879163\n", - "0.3638259422034872\n" + "0.24975080840924535\n", + "0.4193440300864497\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -5234,13 +4952,13 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 39, "id": "cecf5392", "metadata": {}, "outputs": [], "source": [ - "save_model_of = True\n", - "save_model_gk = True\n", + "save_model_of = False\n", + "save_model_gk = False\n", "\n", "if(save_model_of):\n", " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", @@ -5266,7 +4984,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "ddf433f9", "metadata": {}, "outputs": [], @@ -5393,7 +5111,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "d31bad38", "metadata": {}, "outputs": [], @@ -5419,7 +5137,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "62b9f588", "metadata": {}, "outputs": [ @@ -5538,7 +5256,7 @@ "[380 rows x 3 columns]" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -5557,7 +5275,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "c58ba41d", "metadata": {}, "outputs": [], @@ -5595,7 +5313,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "id": "f79792b6", "metadata": {}, "outputs": [ @@ -5708,7 +5426,7 @@ "[474 rows x 2 columns]" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -5731,7 +5449,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "id": "5e63c2b7", "metadata": { "scrolled": true @@ -5741,561 +5459,561 @@ "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", + "Meret: MV 6.26 ± 0.89; FV 5.91 + 0.93 (67.6% cs)\n", + "Provedel: MV 6.26 ± 0.89; FV 5.88 + 0.98 (65.8% cs)\n", + "Vicario: MV 6.26 ± 0.89; FV 6.02 + 0.89 (44.7% cs)\n", + "Szczesny: MV 6.26 ± 0.89; FV 6.02 + 0.95 (67.6% cs)\n", + "Falcone: MV 6.26 ± 0.89; FV 5.06 + 1.13 (10.0% cs)\n", + "Silvestri: MV 6.26 ± 0.89; FV 5.85 + 0.95 (43.4% cs)\n", + "Rui Patricio: MV 6.26 ± 0.89; FV 5.74 + 1.11 (47.3% cs)\n", + "Onana: MV 6.26 ± 0.89; FV 6.07 + 0.95 (71.6% cs)\n", + "Sepe: MV 6.25 ± 0.91; FV 3.07 + 2.17 (1.4% cs)\n", + "Milinkovic-Savic V.: MV 6.15 ± 0.74; FV 4.91 + 1.52 (34.4% cs)\n", + "Musso: MV 6.26 ± 0.88; FV 5.11 + 0.77 (6.7% cs)\n", + "Maignan: MV 6.26 ± 0.89; FV 5.85 + 0.97 (39.0% cs)\n", + "Carnesecchi: MV 6.26 ± 0.89; FV 5.28 + 0.59 (8.4% cs)\n", + "Di Gregorio: MV 6.26 ± 0.89; FV 5.29 + 1.39 (31.4% cs)\n", + "Audero: MV 6.26 ± 0.89; FV 4.64 + 1.09 (1.2% cs)\n", + "Montipo': MV 6.04 ± 0.70; FV 5.09 + 1.34 (18.1% cs)\n", + "Skorupski: MV 6.26 ± 0.89; FV 6.03 + 0.99 (69.8% cs)\n", + "Consigli: MV 6.26 ± 0.89; FV 4.88 + 0.97 (10.5% cs)\n", + "Dragowski: MV 6.26 ± 0.89; FV 4.80 + 1.22 (3.8% cs)\n", + "Terracciano: MV 6.26 ± 0.89; FV 5.96 + 0.92 (68.3% cs)\n", + "Tatarusanu: MV 6.24 ± 0.89; FV 3.47 + 2.77 (1.2% cs)\n", + "Handanovic: MV 6.26 ± 0.89; FV 5.52 + 1.20 (33.3% cs)\n", + "Sportiello: MV 6.26 ± 0.89; FV 5.04 + 0.65 (12.9% cs)\n", + "Perin: MV 6.26 ± 0.89; FV 5.94 + 0.94 (61.3% cs)\n", + "Zoet: MV 6.26 ± 0.89; FV 5.10 + 1.55 (36.4% cs)\n", + "Ochoa: MV 6.26 ± 0.89; FV 2.62 + 2.40 (0.6% cs)\n", + "Pegolo: MV 6.26 ± 0.89; FV 4.89 + 1.01 (9.1% cs)\n", + "Gollini: MV 6.26 ± 0.89; FV 5.94 + 0.90 (74.0% 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 5.94 + 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", + "Berisha: MV 6.26 ± 0.89; FV 5.66 + 1.07 (21.0% cs)\n", + "Marchetti: MV 6.26 ± 0.89; FV 4.70 + 1.12 (2.3% cs)\n", + "Perilli: MV 6.25 ± 0.87; FV 5.82 + 0.96 (44.5% cs)\n", + "Padelli: MV 6.19 ± 0.77; FV 4.54 + 1.63 (1.8% cs)\n", + "Perisan: MV 6.26 ± 0.89; FV 6.04 + 0.98 (40.3% cs)\n", + "Bardi: MV 6.26 ± 0.89; FV 5.95 + 0.91 (74.1% 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", + "Pinsoglio: MV 5.21 ± 1.13; FV 3.01 + 1.33 (1.6% cs)\n", + "Fiorillo: MV 6.26 ± 0.89; FV 3.64 + 1.17 (0.7% cs)\n", + "Cragno: MV 6.26 ± 0.89; FV 5.07 + 1.66 (32.1% cs)\n", + "Sirigu: MV 6.26 ± 0.89; FV 5.29 + 1.41 (40.6% 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", + "Rossi F.: MV 6.12 ± 0.77; FV 4.51 + 1.02 (1.7% cs)\n", + "Ravaglia F.: MV 6.26 ± 0.89; FV 4.58 + 1.03 (5.2% 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", + "Berardi A.: MV 6.26 ± 0.89; FV 5.20 + 0.96 (16.3% 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", + "Gemello: MV 6.26 ± 0.89; FV 5.95 + 0.90 (73.1% cs)\n", + "Ravaglia: MV 6.26 ± 0.89; FV 4.87 + 1.22 (2.5% 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", + "Marfella: MV 6.26 ± 0.89; FV 5.94 + 0.90 (71.6% cs)\n", + "Zovko: MV 6.26 ± 0.89; FV 4.61 + 1.08 (1.5% 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", + "Luis Maximiano: MV 6.26 ± 0.89; FV 5.94 + 0.91 (74.1% 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: 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" + "Turk: MV 6.26 ± 0.89; FV 4.81 + 1.21 (1.8% cs)\n", + "Dimarco: MV 6.04 ± 0.82; FV 6.32 + 1.20\n", + "Smalling: MV 6.20 ± 0.95; FV 6.56 + 1.57\n", + "Doig: MV 6.15 ± 1.13; FV 6.71 + 2.14\n", + "Carlos Augusto: MV 6.15 ± 1.03; FV 6.68 + 1.99\n", + "Kim: MV 6.29 ± 0.99; FV 6.70 + 1.75\n", + "Posch: MV 6.11 ± 1.06; FV 6.48 + 1.76\n", + "Di Lorenzo: MV 6.24 ± 0.92; FV 6.58 + 1.56\n", + "Danilo: MV 6.20 ± 0.93; FV 6.57 + 1.52\n", + "Hernandez T.: MV 6.03 ± 1.08; FV 6.34 + 1.72\n", + "Udogie: MV 6.01 ± 1.03; FV 6.42 + 1.82\n", + "Parisi: MV 6.10 ± 0.93; FV 6.48 + 1.62\n", + "Mario Rui: MV 6.18 ± 0.92; FV 6.30 + 1.21\n", + "Romagnoli: MV 6.11 ± 0.93; FV 6.28 + 1.22\n", + "Bastoni S.: MV 6.12 ± 0.92; FV 6.55 + 1.72\n", + "Mazzocchi: MV 5.88 ± 0.92; FV 6.04 + 1.24\n", + "Valeri: MV 6.11 ± 0.68; FV 6.36 + 1.08\n", + "Tomori: MV 6.05 ± 0.92; FV 6.23 + 1.20\n", + "Scalvini: MV 5.96 ± 0.98; FV 6.14 + 1.35\n", + "Toloi: MV 5.97 ± 0.96; FV 6.14 + 1.26\n", + "Demiral: MV 5.99 ± 0.91; FV 6.21 + 1.27\n", + "Maehle: MV 5.92 ± 0.97; FV 6.11 + 1.50\n", + "Dumfries: MV 5.84 ± 0.81; FV 5.92 + 1.01\n", + "Baschirotto: MV 6.16 ± 0.99; FV 6.53 + 1.63\n", + "Bijol: MV 5.94 ± 0.97; FV 6.10 + 1.38\n", + "Schuurs: MV 6.12 ± 0.80; FV 6.19 + 0.94\n", + "Juan Jesus: MV 6.18 ± 0.73; FV 6.45 + 1.23\n", + "Depaoli: MV 6.07 ± 0.89; FV 6.40 + 1.43\n", + "Mancini: MV 6.10 ± 0.83; FV 6.27 + 1.09\n", + "Ibanez: MV 5.78 ± 1.13; FV 5.86 + 1.43\n", + "Rodrigo Becao: MV 6.03 ± 0.95; FV 6.28 + 1.37\n", + "Ebuehi: MV 6.06 ± 0.85; FV 6.36 + 1.37\n", + "Gosens: MV 5.83 ± 0.66; FV 5.89 + 0.64\n", + "Darmian: MV 5.98 ± 0.71; FV 6.11 + 0.85\n", + "Reca: MV 5.99 ± 0.91; FV 6.18 + 1.35\n", + "Bremer: MV 6.00 ± 1.07; FV 6.32 + 1.65\n", + "Sernicola: MV 6.00 ± 0.95; FV 6.27 + 1.54\n", + "Rrahmani: MV 6.28 ± 1.00; FV 6.71 + 1.78\n", + "Vojvoda: MV 5.89 ± 0.83; FV 5.95 + 1.03\n", + "Holm: MV 6.00 ± 0.84; FV 6.22 + 1.28\n", + "Bastoni: MV 6.01 ± 0.79; FV 6.01 + 0.77\n", + "Milenkovic: MV 6.11 ± 0.78; FV 6.31 + 1.11\n", + "Kalulu: MV 5.75 ± 1.10; FV 5.79 + 1.31\n", + "Martinez Quarta: MV 6.12 ± 0.81; FV 6.22 + 1.00\n", + "Casale: MV 5.97 ± 0.89; FV 6.08 + 1.17\n", + "Perez N.: MV 6.03 ± 0.87; FV 6.22 + 1.13\n", + "Olivera: MV 6.14 ± 0.67; FV 6.44 + 1.18\n", + "Izzo: MV 6.10 ± 0.82; FV 6.33 + 1.15\n", + "Luperto: MV 5.80 ± 1.02; FV 5.79 + 0.96\n", + "Skriniar: MV 5.83 ± 0.84; FV 5.82 + 0.78\n", + "Rodriguez R.: MV 6.03 ± 0.72; FV 6.01 + 0.68\n", + "Marusic: MV 6.00 ± 0.84; FV 6.07 + 0.93\n", + "Lazzari: MV 6.00 ± 0.85; FV 6.11 + 1.08\n", + "Kyriakopoulos: MV 5.96 ± 0.80; FV 5.96 + 0.82\n", + "Ampadu: MV 5.85 ± 0.87; FV 5.84 + 0.88\n", + "Ismajli: MV 5.84 ± 0.87; FV 5.80 + 0.79\n", + "Llorente D.: MV 5.86 ± 1.01; FV 5.92 + 1.27\n", + "Cambiaso: MV 5.98 ± 0.71; FV 5.96 + 0.66\n", + "Hysaj: MV 5.94 ± 0.66; FV 5.95 + 0.60\n", + "Biraghi: MV 6.15 ± 0.73; FV 6.37 + 1.11\n", + "Medel: MV 6.00 ± 0.65; FV 5.93 + 0.54\n", + "Bonucci: MV 6.00 ± 0.91; FV 6.28 + 1.38\n", + "Calabria: MV 5.88 ± 1.03; FV 6.03 + 1.47\n", + "Acerbi: MV 5.98 ± 0.66; FV 5.93 + 0.54\n", + "Spinazzola: MV 6.12 ± 0.85; FV 6.41 + 1.33\n", + "Lykogiannis: MV 5.97 ± 0.71; FV 6.05 + 0.76\n", + "Pellegrini Lu.: MV 5.96 ± 0.79; FV 6.04 + 0.98\n", + "Djidji: MV 5.85 ± 0.84; FV 5.86 + 1.01\n", + "Lazaro: MV 6.02 ± 0.88; FV 6.15 + 1.11\n", + "Augello: MV 5.91 ± 0.90; FV 6.16 + 1.45\n", + "Gallo: MV 5.87 ± 0.78; FV 5.85 + 0.78\n", + "Singo: MV 5.94 ± 0.87; FV 6.09 + 1.23\n", + "Mari': MV 5.96 ± 0.90; FV 6.09 + 1.08\n", + "Caldirola: MV 5.98 ± 0.88; FV 6.16 + 1.19\n", + "Dodo': MV 5.99 ± 0.79; FV 6.00 + 0.78\n", + "De Vrij: MV 5.82 ± 0.77; FV 5.85 + 0.69\n", + "Patric: MV 5.97 ± 0.92; FV 6.01 + 0.98\n", + "Faraoni: MV 6.08 ± 0.91; FV 6.42 + 1.51\n", + "Ceccherini: MV 5.98 ± 0.94; FV 6.17 + 1.32\n", + "Hateboer: MV 5.86 ± 0.96; FV 5.97 + 1.35\n", + "Rogerio: MV 5.89 ± 0.70; FV 5.87 + 0.61\n", + "Umtiti: MV 5.89 ± 0.91; FV 5.91 + 0.99\n", + "Aina: MV 6.01 ± 0.91; FV 6.19 + 1.31\n", + "Birindelli: MV 5.92 ± 0.69; FV 5.94 + 0.73\n", + "Lucumi': MV 5.94 ± 0.77; FV 5.90 + 0.71\n", + "Ehizibue: MV 5.86 ± 0.89; FV 6.01 + 1.32\n", + "Bianchetti: MV 5.85 ± 0.82; FV 5.83 + 0.89\n", + "Ferrari G.: MV 6.01 ± 0.91; FV 6.17 + 1.15\n", + "Fazio: MV 5.45 ± 1.36; FV 5.50 + 1.29\n", + "Gravillon: MV 6.00 ± 0.83; FV 6.01 + 0.86\n", + "Buongiorno: MV 6.02 ± 0.79; FV 6.03 + 0.81\n", + "Gunter: MV 5.74 ± 1.06; FV 5.71 + 1.08\n", + "Troost-Ekong: MV 5.62 ± 1.09; FV 5.57 + 1.10\n", + "Soumaoro: MV 5.87 ± 0.88; FV 5.82 + 0.81\n", + "Ceccaroni: MV 5.89 ± 1.00; FV 5.99 + 1.23\n", + "Pongracic: MV 5.97 ± 0.76; FV 5.95 + 0.73\n", + "Soppy: MV 5.82 ± 0.76; FV 5.83 + 0.77\n", + "Gendrey: MV 5.89 ± 0.65; FV 5.88 + 0.56\n", + "Hien: MV 5.92 ± 0.77; FV 5.92 + 0.71\n", + "Ferrari A.: MV 5.90 ± 0.82; FV 5.90 + 0.82\n", + "Masina: MV 5.97 ± 0.75; FV 6.38 + 1.45\n", + "Zappacosta: MV 6.00 ± 0.88; FV 6.23 + 1.31\n", + "Gyomber: MV 5.70 ± 1.05; FV 5.65 + 1.05\n", + "Alex Sandro: MV 5.77 ± 0.98; FV 5.71 + 0.89\n", + "Pezzella Giu.: MV 5.88 ± 0.67; FV 5.88 + 0.57\n", + "Bereszynski: MV 5.98 ± 0.67; FV 5.94 + 0.61\n", + "Venuti: MV 5.98 ± 0.67; FV 5.97 + 0.60\n", + "Palomino: MV 5.99 ± 0.92; FV 6.12 + 1.07\n", + "Nuytinck: MV 5.79 ± 1.01; FV 5.80 + 1.09\n", + "Marlon: MV 5.86 ± 0.68; FV 5.85 + 0.60\n", + "Magnani: MV 5.91 ± 0.76; FV 5.89 + 0.69\n", + "Colley: MV 5.73 ± 1.12; FV 5.78 + 1.30\n", + "Nikolaou: MV 5.73 ± 0.75; FV 5.65 + 0.70\n", + "Terzic: MV 6.07 ± 0.54; FV 6.00 + 0.47\n", + "Igor: MV 5.99 ± 0.76; FV 5.94 + 0.70\n", + "Toljan: MV 5.83 ± 0.72; FV 5.80 + 0.63\n", + "Zortea: MV 5.93 ± 0.76; FV 5.97 + 0.81\n", + "Dawidowicz: MV 5.93 ± 0.86; FV 6.00 + 1.04\n", + "Celik: MV 5.78 ± 0.77; FV 5.74 + 0.79\n", + "Bellanova: MV 5.87 ± 0.75; FV 5.90 + 0.77\n", + "Erlic: MV 5.90 ± 0.77; FV 5.83 + 0.68\n", + "Ballo-Toure': MV 6.01 ± 0.75; FV 6.21 + 1.00\n", + "Dest: MV 5.70 ± 0.82; FV 5.71 + 0.86\n", + "Stojanovic: MV 5.72 ± 0.80; FV 5.66 + 0.79\n", + "Amian: MV 5.81 ± 0.75; FV 5.75 + 0.73\n", + "Bradaric: MV 5.60 ± 1.02; FV 5.59 + 1.08\n", + "Daniliuc: MV 5.60 ± 1.14; FV 5.58 + 1.18\n", + "Zima: MV 5.94 ± 0.77; FV 5.95 + 0.78\n", + "De Winter: MV 5.73 ± 0.88; FV 5.68 + 0.82\n", + "Quagliata: MV 6.01 ± 0.58; FV 5.98 + 0.48\n", + "Ebosse: MV 5.81 ± 0.64; FV 5.80 + 0.53\n", + "Aiwu: MV 5.98 ± 0.78; FV 6.06 + 0.86\n", + "Lochoshvili: MV 5.95 ± 0.81; FV 6.04 + 1.02\n", + "Bronn: MV 5.57 ± 0.98; FV 5.46 + 0.93\n", + "Thiaw: MV 5.81 ± 1.06; FV 5.84 + 1.17\n", + "Zeefuik: MV 6.02 ± 0.79; FV 6.12 + 0.90\n", + "Romagnoli S.: MV 5.96 ± 1.12; FV 6.25 + 1.76\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" + "Ghiglione: MV 5.97 ± 0.85; FV 6.19 + 1.31\n", + "Rugani: MV 5.99 ± 0.57; FV 5.94 + 0.43\n", + "De Sciglio: MV 5.81 ± 0.61; FV 5.85 + 0.46\n", + "Djimsiti: MV 5.90 ± 0.74; FV 5.91 + 0.66\n", + "Caldara: MV 5.72 ± 0.98; FV 5.65 + 0.98\n", + "Karsdorp: MV 5.86 ± 0.78; FV 5.87 + 0.78\n", + "Marchizza: MV 6.01 ± 0.64; FV 5.99 + 0.56\n", + "Kjaer: MV 5.90 ± 0.75; FV 5.87 + 0.69\n", + "Okoli: MV 5.73 ± 0.91; FV 5.72 + 0.84\n", + "Amione: MV 5.79 ± 0.95; FV 5.86 + 1.26\n", + "Ruggeri: MV 5.89 ± 0.70; FV 5.90 + 0.61\n", + "Zanoli: MV 6.01 ± 0.88; FV 6.28 + 1.41\n", + "Wisniewski: MV 5.82 ± 0.68; FV 5.77 + 0.60\n", + "Radovanovic: MV 5.49 ± 0.91; FV 5.38 + 0.86\n", + "Dermaku: MV 6.02 ± 0.85; FV 6.15 + 1.04\n", + "D'ambrosio: MV 5.99 ± 0.71; FV 5.95 + 0.65\n", + "De Silvestri: MV 5.88 ± 0.88; FV 5.99 + 1.19\n", + "Chiriches: MV 5.86 ± 0.78; FV 5.81 + 0.69\n", + "Murru: MV 5.68 ± 0.78; FV 5.61 + 0.81\n", + "Bonifazi: MV 5.79 ± 0.80; FV 5.70 + 0.73\n", + "Donati: MV 5.92 ± 1.08; FV 6.18 + 1.72\n", + "Walukiewicz: MV 6.07 ± 0.70; FV 6.03 + 0.64\n", + "Ranieri L.: MV 6.01 ± 0.69; FV 6.09 + 0.77\n", + "Gabbia: MV 5.65 ± 0.90; FV 5.56 + 0.89\n", + "Kumbulla: MV 5.74 ± 1.02; FV 5.69 + 1.04\n", + "Adopo: MV 6.01 ± 0.67; FV 5.99 + 0.61\n", + "Pirola: MV 5.60 ± 1.24; FV 5.66 + 1.43\n", + "Lovato: MV 5.50 ± 1.17; FV 5.43 + 1.01\n", + "Tuia: MV 6.04 ± 0.58; FV 5.98 + 0.48\n", + "Ferrer: MV 5.89 ± 0.84; FV 5.88 + 0.86\n", + "Antov: MV 5.72 ± 0.93; FV 5.67 + 0.90\n", + "Vasquez: MV 5.88 ± 0.69; FV 5.85 + 0.59\n", + "Ruan: MV 5.90 ± 0.79; FV 5.81 + 0.70\n", + "Ostigard: MV 6.11 ± 0.69; FV 6.03 + 0.67\n", + "Coppola D.: MV 5.91 ± 0.69; FV 5.87 + 0.61\n", + "Cacace: MV 5.82 ± 0.64; FV 5.78 + 0.53\n", + "Gatti: MV 5.99 ± 0.81; FV 5.99 + 0.77\n", + "Gila: MV 5.90 ± 1.02; FV 5.99 + 1.19\n", + "Bayeye: MV 5.94 ± 0.88; FV 6.02 + 1.07\n", + "Sambia: MV 5.66 ± 0.88; FV 5.66 + 0.89\n", + "Moutinho J.: MV 5.90 ± 0.73; FV 5.89 + 0.71\n", + "Conti: MV 5.85 ± 0.85; FV 5.98 + 1.24\n", + "Marrone: MV 5.73 ± 0.78; FV 5.66 + 0.80\n", + "Tonelli: MV 5.69 ± 0.89; FV 5.61 + 0.82\n", + "Murillo: MV 5.64 ± 0.80; FV 5.54 + 0.79\n", + "Radu: MV 5.95 ± 0.86; FV 5.89 + 0.78\n", + "Paletta: MV 5.98 ± 0.83; FV 6.10 + 1.00\n", + "Florenzi: MV 6.01 ± 0.75; FV 6.14 + 0.86\n", + "Sala: MV 5.98 ± 0.76; FV 6.07 + 0.91\n", + "Fares: MV 5.76 ± 0.80; FV 5.79 + 0.98\n", + "Romagna: MV 6.00 ± 0.79; FV 6.05 + 0.81\n", + "Cassandro: MV 6.01 ± 0.82; FV 6.10 + 0.96\n", + "Muldur: MV 5.82 ± 0.71; FV 5.79 + 0.62\n", + "Amey: MV 6.02 ± 0.78; FV 6.05 + 0.82\n", + "Zanotti: MV 5.85 ± 0.83; FV 5.86 + 0.84\n", + "Ebosele: MV 5.88 ± 0.63; FV 5.92 + 0.54\n", + "Buta: MV 5.91 ± 0.83; FV 5.98 + 0.87\n", + "Abankwah: MV 5.89 ± 0.78; FV 5.94 + 0.79\n", + "Guessand A.: MV 5.91 ± 0.83; FV 5.98 + 0.87\n", + "Cabal: MV 5.98 ± 0.61; FV 5.93 + 0.49\n", + "Sosa: MV 5.60 ± 0.88; FV 5.49 + 0.83\n", + "Guarino: MV 5.87 ± 0.92; FV 5.92 + 1.04\n", + "Carboni F.: MV 6.00 ± 0.79; FV 6.09 + 0.88\n", + "Zaccagni: MV 6.35 ± 1.16; FV 7.13 + 2.61\n", + "Kvaratskhelia: MV 6.56 ± 1.38; FV 7.51 + 3.23\n", + "Milinkovic-Savic: MV 6.13 ± 1.13; FV 6.68 + 2.21\n", + "Barella: MV 6.12 ± 0.97; FV 6.61 + 1.82\n", + "Zielinski: MV 6.31 ± 1.01; FV 6.92 + 2.05\n", + "Luis Alberto: MV 6.22 ± 1.03; FV 6.78 + 2.02\n", + "Strefezza: MV 6.27 ± 1.04; FV 6.99 + 2.30\n", + "Felipe Anderson: MV 6.11 ± 1.07; FV 6.72 + 2.18\n", + "Koopmeiners: MV 6.14 ± 1.10; FV 6.76 + 2.31\n", + "Calhanoglu: MV 6.14 ± 0.78; FV 6.35 + 1.10\n", + "Frattesi: MV 6.25 ± 1.08; FV 6.88 + 2.21\n", + "Diaz B.: MV 6.06 ± 1.14; FV 6.61 + 2.18\n", + "Vlasic: MV 6.14 ± 1.04; FV 6.68 + 1.94\n", + "Zambo Anguissa: MV 6.27 ± 0.98; FV 6.76 + 1.82\n", + "Elmas: MV 6.28 ± 1.03; FV 6.94 + 2.14\n", + "Miranchuk: MV 6.21 ± 1.07; FV 6.80 + 2.07\n", + "Samardzic: MV 6.06 ± 1.05; FV 6.63 + 2.04\n", + "Pereyra: MV 6.05 ± 1.17; FV 6.68 + 2.42\n", + "Politano: MV 6.23 ± 0.81; FV 6.77 + 1.75\n", + "Rabiot: MV 6.16 ± 1.15; FV 6.91 + 2.63\n", + "Ciurria: MV 6.09 ± 1.11; FV 6.71 + 2.32\n", + "Lazovic: MV 6.24 ± 1.12; FV 6.86 + 2.15\n", + "Lobotka: MV 6.21 ± 0.78; FV 6.51 + 1.34\n", + "Radonjic: MV 6.17 ± 1.00; FV 6.68 + 1.85\n", + "Ferguson: MV 6.08 ± 0.82; FV 6.34 + 1.24\n", + "Bonaventura: MV 6.21 ± 0.85; FV 6.74 + 1.72\n", + "Pessina: MV 6.11 ± 0.94; FV 6.51 + 1.59\n", + "Tonali: MV 6.03 ± 1.07; FV 6.33 + 1.68\n", + "Kostic: MV 6.05 ± 0.96; FV 6.48 + 1.77\n", + "Baldanzi: MV 6.14 ± 0.98; FV 6.66 + 1.93\n", + "Lovric: MV 6.04 ± 0.85; FV 6.44 + 1.46\n", + "Pellegrini Lo.: MV 6.06 ± 1.16; FV 6.59 + 2.24\n", + "El Shaarawy: MV 6.17 ± 0.87; FV 6.66 + 1.69\n", + "Orsolini: MV 6.14 ± 1.23; FV 6.82 + 2.59\n", + "Ikone': MV 6.08 ± 0.96; FV 6.50 + 1.73\n", + "Candreva: MV 5.85 ± 1.04; FV 5.96 + 1.46\n", + "Bennacer: MV 6.08 ± 0.84; FV 6.31 + 1.18\n", + "Pasalic: MV 5.96 ± 1.05; FV 6.42 + 1.96\n", + "Mkhitaryan: MV 5.94 ± 0.83; FV 6.08 + 1.12\n", + "Colpani: MV 6.04 ± 0.80; FV 6.41 + 1.46\n", + "Pogba: MV 6.03 ± 0.83; FV 6.12 + 0.91\n", + "Chiesa: MV 5.99 ± 0.87; FV 6.19 + 1.26\n", + "Bandinelli: MV 5.94 ± 0.82; FV 6.08 + 1.20\n", + "Matic: MV 6.11 ± 0.80; FV 6.35 + 1.16\n", + "Fagioli: MV 6.00 ± 0.95; FV 6.28 + 1.52\n", + "Messias: MV 5.93 ± 0.99; FV 6.30 + 1.71\n", + "Arslan: MV 5.87 ± 0.67; FV 5.95 + 0.65\n", + "Ricci S.: MV 6.09 ± 0.86; FV 6.30 + 1.21\n", + "Ranocchia F.: MV 6.09 ± 0.79; FV 6.44 + 1.35\n", + "Verdi: MV 6.21 ± 1.25; FV 7.05 + 2.94\n", + "Sensi: MV 6.08 ± 1.02; FV 6.58 + 1.96\n", + "Barak: MV 6.00 ± 0.84; FV 6.27 + 1.30\n", + "Soriano: MV 6.02 ± 0.74; FV 6.14 + 0.95\n", + "Dominguez: MV 6.12 ± 0.86; FV 6.37 + 1.30\n", + "Vilhena: MV 5.77 ± 1.01; FV 6.03 + 1.51\n", + "Brozovic: MV 6.02 ± 0.84; FV 6.17 + 1.08\n", + "Cristante: MV 5.96 ± 0.83; FV 6.07 + 1.09\n", + "Thorstvedt: MV 6.02 ± 0.84; FV 6.23 + 1.17\n", + "De Ketelaere: MV 5.80 ± 0.71; FV 5.84 + 0.83\n", + "Saponara: MV 6.18 ± 0.99; FV 6.73 + 1.93\n", + "Vecino: MV 5.94 ± 0.90; FV 6.10 + 1.31\n", + "Locatelli: MV 6.03 ± 0.84; FV 6.14 + 0.95\n", + "Zaniolo: MV 5.88 ± 0.99; FV 6.10 + 1.56\n", + "Duda: MV 5.91 ± 0.66; FV 5.93 + 0.65\n", + "Maldini: MV 6.00 ± 0.84; FV 6.37 + 1.53\n", + "Marin: MV 5.92 ± 0.95; FV 6.07 + 1.35\n", + "Zalewski: MV 6.01 ± 0.77; FV 6.14 + 1.00\n", + "Bajrami: MV 6.18 ± 1.05; FV 6.75 + 2.10\n", + "Coulibaly L.: MV 5.78 ± 1.10; FV 5.91 + 1.52\n", + "Gonzalez J.: MV 5.97 ± 0.82; FV 6.13 + 1.18\n", + "De Roon: MV 6.01 ± 0.93; FV 6.23 + 1.30\n", + "Mandragora: MV 6.11 ± 0.84; FV 6.46 + 1.39\n", + "Wijnaldum: MV 6.12 ± 1.09; FV 6.71 + 2.19\n", + "Bourabia: MV 5.92 ± 0.71; FV 5.93 + 0.72\n", + "Sottil: MV 6.15 ± 0.95; FV 6.57 + 1.69\n", + "Aebischer: MV 5.90 ± 0.73; FV 5.96 + 0.89\n", + "Ederson D.s.: MV 5.87 ± 0.90; FV 5.95 + 1.14\n", + "Miretti: MV 5.89 ± 0.74; FV 5.99 + 0.80\n", + "Blin: MV 5.95 ± 0.75; FV 6.02 + 0.93\n", + "Hjulmand: MV 5.95 ± 0.83; FV 5.97 + 0.88\n", + "Cataldi: MV 5.96 ± 0.76; FV 6.00 + 0.84\n", + "Djuricic: MV 5.78 ± 0.96; FV 5.98 + 1.40\n", + "Linetty: MV 5.94 ± 0.85; FV 5.99 + 1.10\n", + "Haas: MV 5.90 ± 0.78; FV 5.99 + 1.05\n", + "Walace: MV 5.88 ± 0.75; FV 5.91 + 0.70\n", + "Agudelo: MV 5.93 ± 0.69; FV 5.96 + 0.78\n", + "Pobega: MV 5.97 ± 0.88; FV 6.19 + 1.33\n", + "Camara Ma.: MV 6.06 ± 0.85; FV 6.16 + 0.99\n", + "Paredes: MV 5.80 ± 0.67; FV 5.80 + 0.55\n", + "Ndombele': MV 6.07 ± 0.73; FV 6.23 + 0.96\n", + "Nicolussi Caviglia: MV 5.65 ± 1.08; FV 5.75 + 1.37\n", + "Rovella: MV 6.04 ± 0.87; FV 6.21 + 1.10\n", + "Amrabat: MV 6.03 ± 0.76; FV 6.07 + 0.79\n", + "Tameze: MV 5.92 ± 0.72; FV 5.96 + 0.67\n", + "Gyasi: MV 5.90 ± 0.89; FV 5.98 + 1.23\n", + "Ilic: MV 6.04 ± 0.86; FV 6.20 + 1.19\n", + "Matheus Henrique: MV 5.95 ± 0.88; FV 6.11 + 1.21\n", + "Harroui: MV 6.07 ± 0.85; FV 6.40 + 1.40\n", + "Volpato: MV 6.06 ± 1.17; FV 6.60 + 2.24\n", + "Pickel: MV 5.83 ± 0.73; FV 5.79 + 0.76\n", + "Moro N.: MV 6.05 ± 0.76; FV 6.17 + 0.96\n", + "Duncan: MV 5.99 ± 0.71; FV 6.13 + 0.87\n", + "Machin: MV 5.96 ± 0.83; FV 6.08 + 1.04\n", + "Cuadrado: MV 5.89 ± 0.94; FV 5.97 + 1.13\n", + "Ekdal: MV 5.88 ± 0.74; FV 5.86 + 0.76\n", + "Meite': MV 5.89 ± 0.74; FV 5.89 + 0.72\n", + "Schouten: MV 5.97 ± 0.75; FV 5.94 + 0.73\n", + "Obiang: MV 5.97 ± 0.59; FV 5.93 + 0.47\n", + "Kovalenko: MV 5.95 ± 0.74; FV 6.00 + 0.86\n", + "Crnigoj: MV 5.82 ± 0.78; FV 5.91 + 0.97\n", + "Basic: MV 5.92 ± 0.65; FV 5.98 + 0.71\n", + "Asllani: MV 5.95 ± 0.61; FV 5.94 + 0.51\n", + "Sabiri: MV 5.87 ± 0.91; FV 6.05 + 1.34\n", + "Terracciano F.: MV 6.04 ± 0.64; FV 6.08 + 0.65\n", + "Castagnetti: MV 5.94 ± 0.62; FV 5.94 + 0.51\n", + "Oudin: MV 5.91 ± 0.62; FV 5.96 + 0.57\n", + "Grassi: MV 5.88 ± 0.66; FV 5.86 + 0.55\n", + "Krunic: MV 5.90 ± 0.72; FV 5.92 + 0.83\n", + "Rincon: MV 5.79 ± 0.75; FV 5.77 + 0.87\n", + "Miguel Veloso: MV 5.99 ± 0.67; FV 6.02 + 0.65\n", + "Leris: MV 5.87 ± 0.88; FV 5.97 + 1.23\n", + "Esposito Sa.: MV 5.79 ± 0.81; FV 5.70 + 0.78\n", + "Henderson L.: MV 5.88 ± 0.76; FV 5.91 + 0.91\n", + "Lopez M.: MV 5.98 ± 0.72; FV 5.97 + 0.67\n", + "Cuisance: MV 5.81 ± 0.62; FV 5.76 + 0.63\n", + "Saelemaekers: MV 5.84 ± 0.75; FV 5.97 + 1.06\n", + "Maggiore: MV 5.83 ± 0.78; FV 5.87 + 0.92\n", + "Akpa Akpro: MV 5.85 ± 0.92; FV 5.89 + 1.07\n", + "Maleh: MV 5.91 ± 0.68; FV 5.98 + 0.84\n", + "Romero L.: MV 5.95 ± 0.92; FV 6.42 + 1.75\n", + "Ceide: MV 5.91 ± 0.65; FV 5.91 + 0.55\n", + "D'alessandro: MV 6.09 ± 0.63; FV 6.22 + 0.84\n", + "Benassi: MV 5.89 ± 0.68; FV 5.93 + 0.66\n", + "Gagliardini: MV 5.78 ± 0.56; FV 5.77 + 0.44\n", + "Vieira: MV 5.86 ± 0.68; FV 5.82 + 0.79\n", + "Bianco: MV 6.11 ± 0.66; FV 6.17 + 0.80\n", + "Vranckx: MV 5.82 ± 0.66; FV 5.77 + 0.70\n", + "Galdames: MV 5.98 ± 0.79; FV 6.07 + 0.91\n", + "Marcos Antonio: MV 5.93 ± 0.81; FV 6.22 + 1.33\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" + "Fazzini: MV 5.81 ± 0.63; FV 5.76 + 0.56\n", + "Sulemana I.: MV 5.94 ± 0.61; FV 5.94 + 0.52\n", + "Tahirovic: MV 6.02 ± 0.64; FV 6.03 + 0.64\n", + "Abildgaard: MV 5.97 ± 0.58; FV 5.96 + 0.47\n", + "Barberis: MV 5.81 ± 0.75; FV 5.78 + 0.76\n", + "Kastanos: MV 5.84 ± 0.84; FV 5.89 + 1.00\n", + "Vignato: MV 5.97 ± 0.75; FV 6.06 + 0.90\n", + "Valoti: MV 5.80 ± 0.69; FV 5.81 + 0.67\n", + "Winks: MV 5.85 ± 0.83; FV 5.85 + 0.86\n", + "Askildsen: MV 5.79 ± 0.62; FV 5.74 + 0.56\n", + "Bove: MV 5.91 ± 0.88; FV 6.04 + 1.26\n", + "Bohinen: MV 5.74 ± 0.62; FV 5.70 + 0.52\n", + "D'andrea: MV 6.05 ± 0.66; FV 6.09 + 0.69\n", + "Iling-Junior: MV 5.91 ± 0.66; FV 5.93 + 0.62\n", + "Cipot: MV 6.00 ± 0.70; FV 6.06 + 0.76\n", + "Bakayoko: MV 5.73 ± 0.77; FV 5.70 + 0.74\n", + "Gaetano: MV 6.15 ± 0.66; FV 6.27 + 0.95\n", + "Zurkowski: MV 6.06 ± 0.92; FV 6.47 + 1.72\n", + "Castrovilli: MV 6.06 ± 0.69; FV 6.27 + 1.00\n", + "Demme: MV 6.01 ± 0.77; FV 6.18 + 1.05\n", + "Darboe: MV 5.90 ± 0.93; FV 5.97 + 1.03\n", + "Urbanski: MV 5.98 ± 0.79; FV 6.00 + 0.85\n", + "Bertini: MV 5.92 ± 0.93; FV 6.04 + 1.29\n", + "Yepes: MV 5.75 ± 0.90; FV 5.72 + 1.03\n", + "Pyyhtia: MV 5.84 ± 0.78; FV 5.81 + 0.77\n", + "Trimboli: MV 5.88 ± 0.94; FV 5.96 + 1.29\n", + "Pafundi: MV 6.03 ± 0.72; FV 6.05 + 0.69\n", + "Helgason: MV 5.88 ± 0.61; FV 5.88 + 0.51\n", + "Adli: MV 5.85 ± 0.70; FV 5.87 + 0.84\n", + "Vignato S.: MV 5.99 ± 0.80; FV 6.08 + 0.89\n", + "Hrustic: MV 5.80 ± 0.62; FV 5.82 + 0.52\n", + "Samek: MV 5.98 ± 0.86; FV 6.09 + 1.11\n", + "Zerbin: MV 5.94 ± 0.67; FV 5.99 + 0.74\n", + "Ilkhan: MV 5.87 ± 0.80; FV 5.89 + 0.93\n", + "Degli Innocenti: MV 5.85 ± 0.92; FV 5.89 + 1.07\n", + "Acella: MV 6.02 ± 0.77; FV 6.13 + 0.90\n", + "Carboni V.: MV 5.97 ± 0.72; FV 5.96 + 0.68\n", + "Paoletti: MV 5.87 ± 0.58; FV 5.86 + 0.44\n", + "Malagrida: MV 5.91 ± 1.02; FV 6.09 + 1.47\n", + "Faticanti: MV 5.91 ± 0.91; FV 6.03 + 1.20\n", + "Osimhen: MV 6.54 ± 1.47; FV 7.84 + 4.27\n", + "Martinez L.: MV 6.12 ± 1.32; FV 7.31 + 3.56\n", + "Dybala: MV 6.42 ± 1.33; FV 7.47 + 3.45\n", + "Rafael Leao: MV 6.20 ± 1.34; FV 7.20 + 3.31\n", + "Lookman: MV 6.22 ± 1.33; FV 7.38 + 3.64\n", + "Immobile: MV 6.08 ± 1.33; FV 7.26 + 3.52\n", + "Vlahovic: MV 6.03 ± 1.26; FV 7.01 + 3.09\n", + "Arnautovic: MV 6.15 ± 1.22; FV 7.02 + 2.88\n", + "Dia: MV 5.86 ± 1.27; FV 6.61 + 2.56\n", + "Dzeko: MV 6.01 ± 1.12; FV 6.69 + 2.36\n", + "Milik: MV 6.04 ± 1.10; FV 6.69 + 2.35\n", + "Nzola: MV 6.20 ± 1.34; FV 7.35 + 3.62\n", + "Beto: MV 5.99 ± 1.16; FV 6.83 + 2.73\n", + "Giroud: MV 6.03 ± 1.16; FV 6.68 + 2.38\n", + "Abraham: MV 6.08 ± 1.17; FV 6.83 + 2.60\n", + "Deulofeu: MV 6.22 ± 1.23; FV 7.16 + 3.06\n", + "Lauriente': MV 6.38 ± 1.32; FV 7.30 + 3.20\n", + "Simeone: MV 6.25 ± 1.31; FV 7.25 + 3.24\n", + "Lozano: MV 6.18 ± 1.08; FV 6.77 + 2.12\n", + "Correa: MV 5.91 ± 0.87; FV 6.25 + 1.48\n", + "Berardi: MV 6.33 ± 1.37; FV 7.58 + 3.96\n", + "Pedro: MV 6.05 ± 0.96; FV 6.41 + 1.63\n", + "Lukaku: MV 5.93 ± 1.13; FV 6.52 + 2.22\n", + "Sanabria: MV 6.08 ± 1.23; FV 6.87 + 2.69\n", + "Thauvin: MV 6.12 ± 1.13; FV 6.95 + 2.73\n", + "Cabral: MV 6.26 ± 1.19; FV 7.13 + 2.85\n", + "Hojlund: MV 6.00 ± 1.18; FV 6.74 + 2.58\n", + "Caprari: MV 6.00 ± 0.98; FV 6.46 + 1.88\n", + "Di Maria: MV 6.12 ± 1.20; FV 6.86 + 2.71\n", + "Piatek: MV 5.70 ± 0.96; FV 6.13 + 1.63\n", + "Rebic: MV 5.91 ± 1.07; FV 6.36 + 1.91\n", + "Bonazzoli: MV 5.83 ± 0.99; FV 6.15 + 1.55\n", + "Zapata D.: MV 5.85 ± 0.93; FV 6.07 + 1.38\n", + "Kouame': MV 6.20 ± 1.18; FV 6.96 + 2.65\n", + "Gonzalez N.: MV 6.30 ± 1.18; FV 7.10 + 2.71\n", + "Brekalo: MV 6.24 ± 1.10; FV 6.93 + 2.32\n", + "Mota: MV 6.02 ± 1.18; FV 6.79 + 2.64\n", + "Kean: MV 5.97 ± 1.22; FV 6.66 + 2.51\n", + "Okereke: MV 5.97 ± 1.06; FV 6.41 + 1.91\n", + "Ceesay: MV 5.97 ± 1.00; FV 6.46 + 1.88\n", + "Colombo: MV 5.97 ± 1.09; FV 6.48 + 2.05\n", + "Dessers: MV 6.08 ± 1.20; FV 6.94 + 2.85\n", + "Muriel: MV 5.83 ± 0.94; FV 5.84 + 1.11\n", + "Pinamonti: MV 5.88 ± 0.98; FV 6.18 + 1.56\n", + "Di Francesco F.: MV 6.01 ± 0.99; FV 6.39 + 1.72\n", + "Jovic: MV 6.03 ± 1.09; FV 6.57 + 2.10\n", + "Origi: MV 5.82 ± 0.93; FV 6.12 + 1.49\n", + "Caputo: MV 5.98 ± 1.12; FV 6.56 + 2.23\n", + "Boga: MV 6.16 ± 1.12; FV 6.83 + 2.39\n", + "Cambiaghi: MV 6.11 ± 1.01; FV 6.68 + 2.09\n", + "Alvarez A.: MV 6.07 ± 1.09; FV 6.57 + 2.11\n", + "Banda: MV 5.92 ± 0.77; FV 6.06 + 0.98\n", + "Ciofani D.: MV 6.08 ± 1.17; FV 6.87 + 2.68\n", + "Petagna: MV 5.97 ± 1.09; FV 6.52 + 2.13\n", + "Barrow: MV 5.98 ± 1.08; FV 6.34 + 1.84\n", + "Djuric: MV 6.01 ± 0.73; FV 6.22 + 0.95\n", + "Henry: MV 6.03 ± 1.19; FV 6.74 + 2.56\n", + "Success: MV 5.89 ± 0.93; FV 6.23 + 1.58\n", + "Gabbiadini: MV 5.89 ± 1.12; FV 6.35 + 1.98\n", + "Zirkzee: MV 5.95 ± 0.92; FV 6.28 + 1.50\n", + "Lammers: MV 5.81 ± 0.87; FV 6.01 + 1.28\n", + "Satriano: MV 5.89 ± 0.93; FV 6.08 + 1.36\n", + "Kallon: MV 5.88 ± 0.84; FV 6.11 + 1.27\n", + "Nestorovski: MV 6.08 ± 0.80; FV 6.67 + 1.84\n", + "Raspadori: MV 6.13 ± 1.09; FV 6.73 + 2.20\n", + "Botheim: MV 5.83 ± 0.97; FV 6.20 + 1.61\n", + "Gytkjaer: MV 5.82 ± 0.83; FV 5.98 + 1.19\n", + "Solbakken: MV 5.90 ± 0.97; FV 6.20 + 1.55\n", + "Lasagna: MV 5.78 ± 0.79; FV 5.85 + 0.99\n", + "Belotti: MV 5.84 ± 0.84; FV 6.00 + 1.19\n", + "Pellegri: MV 5.92 ± 0.90; FV 6.17 + 1.42\n", + "Buonaiuto: MV 5.97 ± 0.69; FV 6.07 + 0.76\n", + "Verde: MV 6.06 ± 1.11; FV 6.75 + 2.46\n", + "Destro: MV 5.98 ± 1.20; FV 6.52 + 2.27\n", + "Seck: MV 6.07 ± 0.69; FV 6.25 + 0.95\n", + "Sansone: MV 6.04 ± 0.98; FV 6.41 + 1.68\n", + "Quagliarella: MV 5.84 ± 0.78; FV 5.96 + 1.06\n", + "Defrel: MV 5.85 ± 0.87; FV 5.99 + 1.19\n", + "Pjaca: MV 5.85 ± 0.78; FV 5.92 + 0.88\n", + "Gaich: MV 5.91 ± 0.95; FV 6.19 + 1.43\n", + "Soule': MV 6.08 ± 0.76; FV 6.42 + 1.25\n", + "Tsadjout: MV 5.90 ± 0.92; FV 6.17 + 1.42\n", + "Piccoli: MV 5.79 ± 0.70; FV 5.82 + 0.69\n", + "Shomurodov: MV 5.98 ± 1.01; FV 6.48 + 1.93\n", + "Afena-Gyan: MV 5.76 ± 0.67; FV 5.71 + 0.62\n", + "Ngonge: MV 6.14 ± 1.29; FV 7.02 + 3.01\n", + "Karamoh: MV 6.03 ± 1.01; FV 6.53 + 1.91\n", + "Ibrahimovic: MV 6.09 ± 1.13; FV 6.84 + 2.54\n", + "Pussetto: MV 5.90 ± 1.01; FV 6.30 + 1.76\n", + "Cancellieri: MV 5.78 ± 0.64; FV 5.78 + 0.67\n", + "Valencia D.: MV 5.72 ± 0.59; FV 5.67 + 0.65\n", + "Oddei: MV 6.10 ± 0.75; FV 6.19 + 0.88\n", + "Braaf: MV 5.89 ± 0.78; FV 5.94 + 0.90\n", + "Raimondo: MV 5.99 ± 0.82; FV 6.05 + 0.90\n", + "Kaio Jorge: MV 5.82 ± 0.59; FV 5.87 + 0.49\n", + "De Luca: MV 5.89 ± 0.86; FV 5.99 + 1.15\n", + "Voelkerling Persson: MV 5.91 ± 0.63; FV 5.99 + 0.64\n", + "Montevago: MV 5.72 ± 0.64; FV 5.69 + 0.70\n", + "Krollis: MV 5.99 ± 0.88; FV 6.16 + 1.23\n", + "Vivaldo: MV 5.90 ± 0.84; FV 6.00 + 0.95\n" ] }, { @@ -6364,113 +6082,113 @@ " \n", " \n", " \n", - " Sportiello\n", - " P\n", - " Atalanta\n", - " Spezia\n", - " 1\n", - " 1.00\n", - " 75\n", - " 6.178402\n", - " 0.428025\n", - " 5.716108\n", - " 0.546802\n", - " 5.928400\n", - " 0.263585\n", - " 0.609268\n", - " 1.588811\n", - " 6.283780\n", - " 0.623820\n", - " -0.625340\n", - " 1.072692\n", - " 40.422094\n", - " \n", - " \n", " Musso\n", " P\n", " Atalanta\n", - " Spezia\n", - " 1\n", + " Torino\n", + " 0\n", " 0.00\n", " 5\n", - " 6.246836\n", - " 0.412463\n", - " 5.418529\n", - " 0.418830\n", - " 6.005536\n", - " 0.252778\n", - " 0.612203\n", - " 1.589951\n", - " 5.813268\n", - " 0.512082\n", - " -0.541122\n", - " 1.031041\n", - " 24.998909\n", + " 6.258319\n", + " 0.440228\n", + " 5.107213\n", + " 0.387096\n", + " 5.984461\n", + " 0.173902\n", + " 0.849841\n", + " 1.699202\n", + " 5.197373\n", + " 0.573992\n", + " -0.115377\n", + " 0.896385\n", + " 6.705408\n", + " \n", + " \n", + " Sportiello\n", + " P\n", + " Atalanta\n", + " Torino\n", + " 0\n", + " 1.00\n", + " 75\n", + " 6.263943\n", + " 0.445447\n", + " 5.036192\n", + " 0.323843\n", + " 5.984998\n", + " 0.168402\n", + " 0.876580\n", + " 1.699209\n", + " 5.082021\n", + " 0.486595\n", + " -0.069278\n", + " 0.905693\n", + " 12.926556\n", " \n", " \n", " Rossi F.\n", " P\n", " Atalanta\n", - " Spezia\n", - " 1\n", + " Torino\n", + " 0\n", " 0.00\n", " 1\n", - " 6.241959\n", - " 0.413916\n", - " 4.533127\n", - " 0.526911\n", - " 5.998054\n", - " 0.249728\n", - " 0.623062\n", - " 1.589842\n", - " 5.066249\n", - " 0.630503\n", - " -0.590708\n", - " 0.972040\n", - " 0.773813\n", + " 6.115940\n", + " 0.386145\n", + " 4.505535\n", + " 0.510853\n", + " 5.921984\n", + " 0.278113\n", + " 0.469945\n", + " 1.699008\n", + " 4.621354\n", + " 0.759368\n", + " -0.112068\n", + " 0.904705\n", + " 1.720399\n", " \n", " \n", - " Scalvini\n", + " Zappacosta\n", " D\n", " Atalanta\n", - " Spezia\n", - " 1\n", + " Torino\n", + " 0\n", " 1.00\n", " 90\n", - " 6.273323\n", - " 0.537045\n", - " 6.786601\n", - " 0.980843\n", - " 6.157186\n", - " 0.598484\n", - " 0.142554\n", - " 0.937184\n", - " 5.967968\n", - " 1.206433\n", - " 0.463573\n", - " 1.599848\n", + " 6.004314\n", + " 0.439741\n", + " 6.225555\n", + " 0.653844\n", + " 5.952194\n", + " 0.507922\n", + " 0.075895\n", + " 1.099135\n", + " 5.750352\n", + " 0.903563\n", + " 0.368658\n", + " 1.699910\n", " 0.000000\n", " \n", " \n", - " Toloi\n", + " Demiral\n", " D\n", " Atalanta\n", - " Spezia\n", - " 1\n", - " 1.00\n", - " 90\n", - " 6.254010\n", - " 0.510611\n", - " 6.719704\n", - " 0.905750\n", - " 6.162670\n", - " 0.575446\n", - " 0.116788\n", - " 0.958888\n", - " 5.978364\n", - " 1.134393\n", - " 0.448929\n", - " 1.599858\n", + " Torino\n", + " 0\n", + " 0.00\n", + " 35\n", + " 5.991059\n", + " 0.454479\n", + " 6.205947\n", + " 0.634370\n", + " 5.955022\n", + " 0.530763\n", + " 0.050222\n", + " 1.079387\n", + " 5.782825\n", + " 0.923467\n", + " 0.325719\n", + " 1.699914\n", " 0.000000\n", " \n", " \n", @@ -6496,113 +6214,113 @@ " ...\n", " \n", " \n", - " Gaich\n", - " A\n", - " Verona\n", - " Inter\n", - " 1\n", - " 0.55\n", - " 55\n", - " 5.867453\n", - " 0.457420\n", - " 6.084643\n", - " 0.692782\n", - " 5.792738\n", - " 0.519226\n", - " 0.106163\n", - " 1.045169\n", - " 5.522809\n", - " 0.874729\n", - " 0.442301\n", - " 1.599854\n", - " 0.000000\n", - " \n", - " \n", " Djuric\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Cremonese\n", + " 0\n", " 0.45\n", " 60\n", - " 5.927261\n", - " 0.352975\n", - " 6.058505\n", - " 0.437122\n", - " 5.883422\n", - " 0.408226\n", - " 0.079531\n", - " 1.154408\n", - " 5.803053\n", - " 0.662872\n", - " 0.278751\n", - " 1.599918\n", + " 6.005210\n", + " 0.367403\n", + " 6.216372\n", + " 0.475022\n", + " 5.944870\n", + " 0.419086\n", + " 0.106610\n", + " 1.196480\n", + " 5.905115\n", + " 0.697984\n", + " 0.317776\n", + " 1.699933\n", + " 0.000000\n", + " \n", + " \n", + " Gaich\n", + " A\n", + " Verona\n", + " Cremonese\n", + " 0\n", + " 0.55\n", + " 55\n", + " 5.909527\n", + " 0.475470\n", + " 6.191696\n", + " 0.717266\n", + " 5.802748\n", + " 0.528131\n", + " 0.148988\n", + " 1.081866\n", + " 5.593996\n", + " 0.881533\n", + " 0.458995\n", + " 1.699893\n", " 0.000000\n", " \n", " \n", " Kallon\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Cremonese\n", + " 0\n", " 0.00\n", " 40\n", - " 5.874599\n", - " 0.410965\n", - " 6.049230\n", - " 0.592300\n", - " 5.806045\n", - " 0.466689\n", - " 0.108503\n", - " 1.095199\n", - " 5.593708\n", - " 0.780142\n", - " 0.407094\n", - " 1.599884\n", + " 5.884597\n", + " 0.417837\n", + " 6.106965\n", + " 0.635358\n", + " 5.805358\n", + " 0.470929\n", + " 0.124338\n", + " 1.146635\n", + " 5.588847\n", + " 0.798543\n", + " 0.442306\n", + " 1.699905\n", " 0.000000\n", " \n", " \n", " Braaf\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Cremonese\n", + " 0\n", " 0.00\n", " 35\n", - " 5.796274\n", - " 0.440012\n", - " 5.869830\n", - " 0.585489\n", - " 5.757944\n", - " 0.511931\n", - " 0.055347\n", - " 1.060328\n", - " 5.505967\n", - " 0.867116\n", - " 0.301824\n", - " 1.599889\n", + " 5.891928\n", + " 0.387555\n", + " 5.942429\n", + " 0.450916\n", + " 5.871307\n", + " 0.459707\n", + " 0.033268\n", + " 1.164931\n", + " 5.734391\n", + " 0.748957\n", + " 0.203617\n", + " 1.699938\n", " 0.000000\n", " \n", " \n", " Lasagna\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Cremonese\n", + " 0\n", " 1.00\n", " 80\n", - " 5.777834\n", - " 0.389363\n", - " 5.836037\n", - " 0.489194\n", - " 5.727798\n", - " 0.448826\n", - " 0.082488\n", - " 1.123643\n", - " 5.506660\n", - " 0.698520\n", - " 0.336068\n", - " 1.599901\n", + " 5.782344\n", + " 0.396176\n", + " 5.854143\n", + " 0.496508\n", + " 5.722391\n", + " 0.453968\n", + " 0.097786\n", + " 1.177111\n", + " 5.504045\n", + " 0.699853\n", + " 0.352576\n", + " 1.699922\n", " 0.000000\n", " \n", " \n", @@ -6611,58 +6329,58 @@ "" ], "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", + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Torino 0 0.00 5 6.258319 0.440228 \n", + "Sportiello P Atalanta Torino 0 1.00 75 6.263943 0.445447 \n", + "Rossi F. P Atalanta Torino 0 0.00 1 6.115940 0.386145 \n", + "Zappacosta D Atalanta Torino 0 1.00 90 6.004314 0.439741 \n", + "Demiral D Atalanta Torino 0 0.00 35 5.991059 0.454479 \n", + "... ... ... ... ... ... ... ... ... \n", + "Djuric A Verona Cremonese 0 0.45 60 6.005210 0.367403 \n", + "Gaich A Verona Cremonese 0 0.55 55 5.909527 0.475470 \n", + "Kallon A Verona Cremonese 0 0.00 40 5.884597 0.417837 \n", + "Braaf A Verona Cremonese 0 0.00 35 5.891928 0.387555 \n", + "Lasagna A Verona Cremonese 0 1.00 80 5.782344 0.396176 \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", + "Musso 5.107213 0.387096 5.984461 0.173902 0.849841 \n", + "Sportiello 5.036192 0.323843 5.984998 0.168402 0.876580 \n", + "Rossi F. 4.505535 0.510853 5.921984 0.278113 0.469945 \n", + "Zappacosta 6.225555 0.653844 5.952194 0.507922 0.075895 \n", + "Demiral 6.205947 0.634370 5.955022 0.530763 0.050222 \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", + "Djuric 6.216372 0.475022 5.944870 0.419086 0.106610 \n", + "Gaich 6.191696 0.717266 5.802748 0.528131 0.148988 \n", + "Kallon 6.106965 0.635358 5.805358 0.470929 0.124338 \n", + "Braaf 5.942429 0.450916 5.871307 0.459707 0.033268 \n", + "Lasagna 5.854143 0.496508 5.722391 0.453968 0.097786 \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", + "Musso 1.699202 5.197373 0.573992 -0.115377 0.896385 \n", + "Sportiello 1.699209 5.082021 0.486595 -0.069278 0.905693 \n", + "Rossi F. 1.699008 4.621354 0.759368 -0.112068 0.904705 \n", + "Zappacosta 1.099135 5.750352 0.903563 0.368658 1.699910 \n", + "Demiral 1.079387 5.782825 0.923467 0.325719 1.699914 \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", + "Djuric 1.196480 5.905115 0.697984 0.317776 1.699933 \n", + "Gaich 1.081866 5.593996 0.881533 0.458995 1.699893 \n", + "Kallon 1.146635 5.588847 0.798543 0.442306 1.699905 \n", + "Braaf 1.164931 5.734391 0.748957 0.203617 1.699938 \n", + "Lasagna 1.177111 5.504045 0.699853 0.352576 1.699922 \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", + "Musso 6.705408 \n", + "Sportiello 12.926556 \n", + "Rossi F. 1.720399 \n", + "Zappacosta 0.000000 \n", + "Demiral 0.000000 \n", "... ... \n", - "Gaich 0.000000 \n", "Djuric 0.000000 \n", + "Gaich 0.000000 \n", "Kallon 0.000000 \n", "Braaf 0.000000 \n", "Lasagna 0.000000 \n", @@ -6670,7 +6388,7 @@ "[525 rows x 19 columns]" ] }, - "execution_count": 28, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -6726,7 +6444,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 46, "id": "6befd611", "metadata": {}, "outputs": [], @@ -6752,7 +6470,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "2b637a15", "metadata": {}, "outputs": [], @@ -6764,10 +6482,948 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "60d73507", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret (6.26, 0.45); (5.81, 0.51)\n", + "Provedel (6.26, 0.44); (5.84, 0.48)\n", + "Vicario (6.26, 0.45); (5.20, 0.58)\n", + "Szczesny (6.26, 0.45); (5.99, 0.47)\n", + "Falcone (6.19, 0.40); (4.59, 0.47)\n", + "Silvestri (6.26, 0.45); (5.53, 0.59)\n", + "Rui Patricio (6.20, 0.43); (5.48, 0.61)\n", + "Onana (6.15, 0.40); (5.14, 0.58)\n", + "Sepe (6.22, 0.42); (4.99, 0.54)\n", + "Milinkovic-Savic V. (6.09, 0.36); (5.02, 0.71)\n", + "Musso (6.26, 0.44); (5.58, 0.50)\n", + "Maignan (6.26, 0.44); (5.02, 0.47)\n", + "Carnesecchi (6.26, 0.44); (4.84, 0.58)\n", + "Di Gregorio (6.26, 0.45); (5.26, 0.64)\n", + "Audero (6.26, 0.45); (4.96, 0.53)\n", + "Montipo' (6.25, 0.43); (4.78, 0.40)\n", + "Skorupski (6.25, 0.43); (5.51, 0.58)\n", + "Consigli (6.26, 0.44); (5.48, 0.62)\n", + "Dragowski (6.26, 0.45); (5.46, 0.62)\n", + "Terracciano (6.13, 0.40); (4.75, 0.68)\n", + "Tatarusanu (6.26, 0.45); (4.93, 0.80)\n", + "Handanovic (6.26, 0.45); (5.96, 0.53)\n", + "Sportiello (6.23, 0.42); (4.51, 0.40)\n", + "Perin (6.26, 0.45); (5.97, 0.45)\n", + "Zoet (6.26, 0.45); (6.07, 0.50)\n", + "Ochoa (6.26, 0.45); (5.96, 0.52)\n", + "Pegolo (6.26, 0.45); (4.87, 0.56)\n", + "Gollini (6.26, 0.45); (5.91, 0.46)\n", + "Mirante no data\n", + "Sarr M. no data\n", + "Lamanna no data\n", + "Ujkani no data\n", + "Berisha (6.26, 0.45); (5.85, 0.50)\n", + "Marchetti (6.08, 0.47); (3.75, 0.71)\n", + "Perilli (6.26, 0.45); (5.78, 0.52)\n", + "Padelli (6.15, 0.41); (4.19, 0.63)\n", + "Perisan (6.26, 0.45); (5.26, 0.64)\n", + "Bardi (6.26, 0.45); (5.92, 0.46)\n", + "Cordaz no data\n", + "Pinsoglio (6.10, 0.43); (4.30, 0.68)\n", + "Fiorillo (6.23, 0.42); (3.50, 0.56)\n", + "Cragno (6.26, 0.45); (5.36, 0.81)\n", + "Sirigu (6.26, 0.45); (5.69, 0.59)\n", + "Cerofolini no data\n", + "Rossi F. (5.91, 0.39); (4.05, 0.56)\n", + "Ravaglia F. (6.26, 0.45); (4.86, 0.60)\n", + "Brancolini no data\n", + "Bleve no data\n", + "Berardi A. (6.26, 0.45); (4.88, 0.53)\n", + "Russo A. no data\n", + "Gemello (6.26, 0.45); (5.95, 0.45)\n", + "Ravaglia (6.26, 0.45); (4.59, 0.59)\n", + "Boer no data\n", + "Adamonis no data\n", + "Marfella (6.26, 0.45); (5.79, 0.47)\n", + "Zovko (6.13, 0.46); (3.89, 0.67)\n", + "Piana no data\n", + "Bagnolini no data\n", + "Luis Maximiano (5.81, 0.46); (5.58, 0.55)\n", + "Svilar no data\n", + "Sorrentino A. no data\n", + "Ciezkowski no data\n", + "Saro no data\n", + "Vasquez D. no data\n", + "Turk (6.26, 0.45); (4.65, 0.59)\n", + "Dimarco (6.14, 0.45); (6.55, 0.78)\n", + "Smalling (6.21, 0.47); (6.57, 0.77)\n", + "Doig (6.05, 0.52); (6.42, 0.89)\n", + "Carlos Augusto (6.06, 0.52); (6.48, 0.93)\n", + "Kim (6.26, 0.49); (6.59, 0.79)\n", + "Posch (6.14, 0.55); (6.68, 1.07)\n", + "Di Lorenzo (6.21, 0.47); (6.50, 0.72)\n", + "Danilo (6.24, 0.47); (6.64, 0.81)\n", + "Hernandez T. (6.12, 0.55); (6.54, 0.96)\n", + "Udogie (6.06, 0.53); (6.45, 0.92)\n", + "Parisi (6.12, 0.48); (6.47, 0.78)\n", + "Mario Rui (6.13, 0.48); (6.23, 0.57)\n", + "Romagnoli (6.19, 0.45); (6.46, 0.68)\n", + "Bastoni S. (6.07, 0.46); (6.41, 0.77)\n", + "Mazzocchi (6.13, 0.45); (6.51, 0.75)\n", + "Valeri (6.08, 0.37); (6.34, 0.57)\n", + "Tomori (6.10, 0.44); (6.27, 0.57)\n", + "Scalvini (6.03, 0.51); (6.27, 0.73)\n", + "Toloi (6.03, 0.49); (6.23, 0.66)\n", + "Demiral (6.02, 0.44); (6.21, 0.59)\n", + "Maehle (5.93, 0.50); (6.12, 0.73)\n", + "Dumfries (5.92, 0.46); (6.08, 0.66)\n", + "Baschirotto (6.15, 0.50); (6.50, 0.80)\n", + "Bijol (5.93, 0.53); (6.14, 0.78)\n", + "Schuurs (6.11, 0.40); (6.17, 0.45)\n", + "Juan Jesus (6.16, 0.36); (6.40, 0.57)\n", + "Depaoli (5.98, 0.44); (6.18, 0.64)\n", + "Mancini (6.11, 0.41); (6.31, 0.55)\n", + "Ibanez (5.84, 0.54); (5.97, 0.71)\n", + "Rodrigo Becao (6.06, 0.49); (6.34, 0.74)\n", + "Ebuehi (6.05, 0.41); (6.33, 0.62)\n", + "Gosens (5.96, 0.36); (6.14, 0.49)\n", + "Darmian (6.05, 0.38); (6.24, 0.53)\n", + "Reca (5.95, 0.48); (6.09, 0.65)\n", + "Bremer (6.07, 0.51); (6.39, 0.80)\n", + "Sernicola (5.93, 0.49); (6.13, 0.75)\n", + "Rrahmani (6.24, 0.50); (6.58, 0.80)\n", + "Vojvoda (5.89, 0.41); (5.93, 0.47)\n", + "Holm (5.98, 0.43); (6.14, 0.59)\n", + "Bastoni (6.07, 0.41); (6.15, 0.46)\n", + "Milenkovic (5.97, 0.46); (6.13, 0.64)\n", + "Kalulu (5.83, 0.51); (5.87, 0.59)\n", + "Martinez Quarta (5.99, 0.45); (6.09, 0.53)\n", + "Casale (6.05, 0.41); (6.19, 0.51)\n", + "Perez N. (6.06, 0.48); (6.30, 0.69)\n", + "Olivera (6.12, 0.35); (6.39, 0.56)\n", + "Izzo (6.04, 0.44); (6.21, 0.57)\n", + "Luperto (5.86, 0.48); (5.87, 0.47)\n", + "Skriniar (5.91, 0.40); (5.92, 0.39)\n", + "Rodriguez R. (6.02, 0.35); (5.99, 0.33)\n", + "Marusic (6.05, 0.38); (6.06, 0.38)\n", + "Lazzari (6.05, 0.38); (6.11, 0.42)\n", + "Kyriakopoulos (5.98, 0.43); (6.06, 0.49)\n", + "Ampadu (5.85, 0.45); (5.83, 0.47)\n", + "Ismajli (5.92, 0.41); (5.89, 0.38)\n", + "Llorente D. (5.95, 0.48); (6.10, 0.64)\n", + "Cambiaso (5.99, 0.37); (6.01, 0.37)\n", + "Hysaj (6.00, 0.31); (5.98, 0.26)\n", + "Biraghi (6.07, 0.40); (6.22, 0.49)\n", + "Medel (6.01, 0.34); (5.96, 0.30)\n", + "Bonucci (6.12, 0.47); (6.45, 0.76)\n", + "Calabria (5.95, 0.51); (6.16, 0.76)\n", + "Acerbi (6.02, 0.35); (6.01, 0.32)\n", + "Spinazzola (6.13, 0.42); (6.46, 0.68)\n", + "Lykogiannis (6.00, 0.37); (6.12, 0.45)\n", + "Pellegrini Lu. (6.02, 0.36); (6.07, 0.38)\n", + "Djidji (5.89, 0.40); (5.94, 0.47)\n", + "Lazaro (6.01, 0.44); (6.13, 0.53)\n", + "Augello (5.94, 0.44); (6.14, 0.67)\n", + "Gallo (5.85, 0.39); (5.83, 0.38)\n", + "Singo (5.94, 0.44); (6.09, 0.62)\n", + "Mari' (5.86, 0.50); (5.94, 0.58)\n", + "Caldirola (5.90, 0.48); (6.00, 0.61)\n", + "Dodo' (5.86, 0.46); (5.88, 0.51)\n", + "De Vrij (5.96, 0.40); (6.00, 0.41)\n", + "Patric (6.07, 0.39); (6.06, 0.39)\n", + "Faraoni (6.00, 0.44); (6.20, 0.65)\n", + "Ceccherini (5.89, 0.51); (6.03, 0.71)\n", + "Hateboer (5.88, 0.47); (5.98, 0.66)\n", + "Rogerio (5.82, 0.40); (5.79, 0.40)\n", + "Umtiti (5.87, 0.46); (5.87, 0.47)\n", + "Aina (6.00, 0.47); (6.20, 0.69)\n", + "Birindelli (5.84, 0.35); (5.82, 0.36)\n", + "Lucumi' (5.98, 0.39); (5.97, 0.38)\n", + "Ehizibue (5.88, 0.46); (5.99, 0.66)\n", + "Bianchetti (5.76, 0.47); (5.75, 0.56)\n", + "Ferrari G. (5.84, 0.53); (5.92, 0.65)\n", + "Fazio (5.66, 0.60); (5.71, 0.66)\n", + "Gravillon (5.98, 0.40); (5.95, 0.38)\n", + "Buongiorno (6.01, 0.39); (6.00, 0.38)\n", + "Gunter (5.79, 0.49); (5.74, 0.48)\n", + "Troost-Ekong (5.83, 0.44); (5.82, 0.43)\n", + "Soumaoro (5.93, 0.45); (5.93, 0.44)\n", + "Ceccaroni (5.86, 0.52); (5.95, 0.61)\n", + "Pongracic (5.94, 0.38); (5.91, 0.35)\n", + "Soppy (5.86, 0.37); (5.88, 0.38)\n", + "Gendrey (5.88, 0.33); (5.87, 0.28)\n", + "Hien (5.87, 0.41); (5.83, 0.40)\n", + "Ferrari A. (5.76, 0.49); (5.73, 0.54)\n", + "Masina (5.99, 0.38); (6.37, 0.71)\n", + "Zappacosta (6.04, 0.43); (6.28, 0.66)\n", + "Gyomber (5.91, 0.40); (5.88, 0.37)\n", + "Alex Sandro (5.84, 0.47); (5.78, 0.44)\n", + "Pezzella Giu. (5.89, 0.34); (5.89, 0.29)\n", + "Bereszynski (5.87, 0.34); (5.85, 0.31)\n", + "Venuti (5.82, 0.36); (5.82, 0.35)\n", + "Palomino (6.02, 0.45); (6.14, 0.52)\n", + "Nuytinck (5.84, 0.46); (5.84, 0.47)\n", + "Marlon (5.80, 0.36); (5.77, 0.33)\n", + "Magnani (5.85, 0.42); (5.82, 0.40)\n", + "Colley (5.79, 0.52); (5.81, 0.57)\n", + "Nikolaou (5.74, 0.38); (5.66, 0.36)\n", + "Terzic (5.99, 0.28); (5.94, 0.21)\n", + "Igor (5.84, 0.45); (5.79, 0.45)\n", + "Toljan (5.75, 0.41); (5.70, 0.40)\n", + "Zortea (5.86, 0.41); (5.91, 0.51)\n", + "Dawidowicz (5.84, 0.46); (5.87, 0.57)\n", + "Celik (5.84, 0.36); (5.82, 0.34)\n", + "Bellanova (5.93, 0.42); (6.01, 0.51)\n", + "Erlic (5.83, 0.46); (5.79, 0.43)\n", + "Ballo-Toure' (6.09, 0.38); (6.37, 0.57)\n", + "Dest (5.77, 0.41); (5.77, 0.41)\n", + "Stojanovic (5.78, 0.39); (5.74, 0.40)\n", + "Amian (5.81, 0.38); (5.76, 0.38)\n", + "Bradaric (5.82, 0.43); (5.79, 0.46)\n", + "Daniliuc (5.80, 0.49); (5.79, 0.53)\n", + "Zima (5.92, 0.38); (5.93, 0.36)\n", + "De Winter (5.79, 0.42); (5.73, 0.38)\n", + "Quagliata (5.95, 0.30); (5.96, 0.25)\n", + "Ebosse (5.79, 0.33); (5.74, 0.29)\n", + "Aiwu (5.91, 0.45); (5.96, 0.54)\n", + "Lochoshvili (5.87, 0.45); (5.91, 0.59)\n", + "Bronn (5.76, 0.38); (5.72, 0.34)\n", + "Thiaw (5.88, 0.48); (5.91, 0.49)\n", + "Zeefuik (5.94, 0.41); (5.98, 0.44)\n", + "Romagnoli S. (5.93, 0.56); (6.21, 0.85)\n", + "Ghiglione (5.91, 0.43); (6.08, 0.65)\n", + "Rugani (6.04, 0.28); (5.96, 0.22)\n", + "De Sciglio (5.88, 0.31); (5.88, 0.24)\n", + "Djimsiti (5.94, 0.37); (5.94, 0.33)\n", + "Caldara (5.73, 0.48); (5.67, 0.49)\n", + "Karsdorp (5.92, 0.37); (5.93, 0.35)\n", + "Marchizza (5.93, 0.34); (5.92, 0.30)\n", + "Kjaer (5.96, 0.36); (5.92, 0.31)\n", + "Okoli (5.77, 0.44); (5.75, 0.41)\n", + "Amione (5.81, 0.46); (5.82, 0.56)\n", + "Ruggeri (5.91, 0.34); (5.91, 0.30)\n", + "Zanoli (6.04, 0.43); (6.29, 0.65)\n", + "Wisniewski (5.82, 0.34); (5.77, 0.30)\n", + "Radovanovic (5.69, 0.37); (5.64, 0.35)\n", + "Dermaku (6.01, 0.43); (6.12, 0.51)\n", + "D'ambrosio (6.03, 0.37); (6.04, 0.37)\n", + "De Silvestri (5.93, 0.47); (6.08, 0.67)\n", + "Chiriches (5.77, 0.47); (5.73, 0.45)\n", + "Murru (5.72, 0.37); (5.65, 0.36)\n", + "Bonifazi (5.84, 0.39); (5.78, 0.37)\n", + "Donati (5.76, 0.59); (5.93, 0.83)\n", + "Walukiewicz (6.09, 0.35); (6.07, 0.34)\n", + "Ranieri L. (5.88, 0.38); (5.90, 0.46)\n", + "Gabbia (5.72, 0.44); (5.65, 0.42)\n", + "Kumbulla (5.81, 0.48); (5.78, 0.49)\n", + "Adopo (6.01, 0.33); (5.99, 0.30)\n", + "Pirola (5.81, 0.56); (5.93, 0.74)\n", + "Lovato (5.72, 0.49); (5.67, 0.44)\n", + "Tuia (6.03, 0.29); (5.97, 0.24)\n", + "Ferrer (5.88, 0.44); (5.88, 0.46)\n", + "Antov (5.66, 0.52); (5.58, 0.49)\n", + "Vasquez (5.82, 0.39); (5.76, 0.36)\n", + "Ruan (5.83, 0.47); (5.76, 0.44)\n", + "Ostigard (6.08, 0.34); (6.04, 0.33)\n", + "Coppola D. (5.86, 0.35); (5.81, 0.31)\n", + "Cacace (5.84, 0.31); (5.81, 0.25)\n", + "Gatti (6.06, 0.39); (6.05, 0.39)\n", + "Gila (6.01, 0.44); (6.05, 0.47)\n", + "Bayeye (5.96, 0.43); (6.04, 0.51)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sambia (5.86, 0.39); (5.87, 0.36)\n", + "Moutinho J. (5.87, 0.37); (5.85, 0.36)\n", + "Conti (5.87, 0.41); (5.95, 0.54)\n", + "Marrone (5.63, 0.43); (5.56, 0.43)\n", + "Tonelli (5.79, 0.42); (5.73, 0.39)\n", + "Murillo (5.71, 0.38); (5.62, 0.36)\n", + "Radu (6.06, 0.38); (5.93, 0.34)\n", + "Paletta (5.92, 0.44); (6.00, 0.54)\n", + "Florenzi (6.09, 0.38); (6.25, 0.49)\n", + "Sala (5.95, 0.39); (6.03, 0.44)\n", + "Fares (5.84, 0.37); (5.84, 0.39)\n", + "Romagna (5.91, 0.44); (5.94, 0.50)\n", + "Cassandro (6.00, 0.42); (6.07, 0.46)\n", + "Muldur (5.77, 0.40); (5.71, 0.40)\n", + "Amey (6.05, 0.42); (6.16, 0.50)\n", + "Zanotti (5.94, 0.42); (6.01, 0.48)\n", + "Ebosele (5.92, 0.32); (5.92, 0.29)\n", + "Buta (5.94, 0.43); (6.01, 0.50)\n", + "Abankwah (5.90, 0.41); (5.93, 0.44)\n", + "Guessand A. (5.94, 0.43); (6.01, 0.50)\n", + "Cabal (5.94, 0.31); (5.90, 0.24)\n", + "Sosa (5.61, 0.44); (5.54, 0.44)\n", + "Guarino (5.92, 0.44); (5.97, 0.50)\n", + "Carboni F. (5.92, 0.42); (5.97, 0.46)\n", + "Zaccagni (6.39, 0.59); (7.26, 1.40)\n", + "Kvaratskhelia (6.51, 0.68); (7.46, 1.60)\n", + "Milinkovic-Savic (6.18, 0.58); (6.92, 1.31)\n", + "Barella (6.19, 0.52); (6.78, 1.06)\n", + "Zielinski (6.25, 0.49); (6.77, 0.92)\n", + "Luis Alberto (6.28, 0.51); (6.93, 1.06)\n", + "Strefezza (6.25, 0.51); (6.94, 1.12)\n", + "Felipe Anderson (6.20, 0.55); (6.92, 1.23)\n", + "Koopmeiners (6.20, 0.57); (6.88, 1.21)\n", + "Calhanoglu (6.20, 0.43); (6.61, 0.76)\n", + "Frattesi (6.18, 0.55); (6.78, 1.11)\n", + "Diaz B. (6.15, 0.61); (6.82, 1.24)\n", + "Vlasic (6.13, 0.53); (6.67, 1.00)\n", + "Zambo Anguissa (6.22, 0.49); (6.63, 0.83)\n", + "Elmas (6.20, 0.49); (6.78, 0.98)\n", + "Miranchuk (6.20, 0.53); (6.80, 1.05)\n", + "Samardzic (6.12, 0.53); (6.67, 1.04)\n", + "Pereyra (6.09, 0.58); (6.68, 1.16)\n", + "Politano (6.20, 0.40); (6.70, 0.82)\n", + "Rabiot (6.26, 0.60); (7.05, 1.37)\n", + "Ciurria (6.03, 0.54); (6.50, 1.00)\n", + "Lazovic (6.16, 0.51); (6.68, 0.95)\n", + "Lobotka (6.19, 0.40); (6.46, 0.63)\n", + "Radonjic (6.14, 0.49); (6.64, 0.90)\n", + "Ferguson (6.14, 0.45); (6.55, 0.80)\n", + "Bonaventura (6.13, 0.46); (6.54, 0.80)\n", + "Pessina (6.09, 0.50); (6.46, 0.85)\n", + "Tonali (6.10, 0.54); (6.51, 0.93)\n", + "Kostic (6.14, 0.49); (6.63, 0.93)\n", + "Baldanzi (6.14, 0.49); (6.64, 0.93)\n", + "Lovric (6.11, 0.46); (6.56, 0.83)\n", + "Pellegrini Lo. (6.07, 0.58); (6.63, 1.15)\n", + "El Shaarawy (6.17, 0.43); (6.69, 0.86)\n", + "Orsolini (6.21, 0.64); (7.05, 1.48)\n", + "Ikone' (5.98, 0.51); (6.33, 0.87)\n", + "Candreva (6.07, 0.51); (6.45, 0.86)\n", + "Bennacer (6.12, 0.41); (6.38, 0.61)\n", + "Pasalic (6.03, 0.56); (6.56, 1.08)\n", + "Mkhitaryan (6.03, 0.46); (6.40, 0.79)\n", + "Colpani (6.00, 0.40); (6.37, 0.74)\n", + "Pogba (6.09, 0.41); (6.20, 0.48)\n", + "Chiesa (6.07, 0.41); (6.32, 0.63)\n", + "Bandinelli (5.94, 0.40); (6.07, 0.56)\n", + "Matic (6.12, 0.39); (6.39, 0.60)\n", + "Fagioli (6.06, 0.48); (6.41, 0.81)\n", + "Messias (5.98, 0.53); (6.42, 0.96)\n", + "Arslan (5.92, 0.35); (5.98, 0.37)\n", + "Ricci S. (6.09, 0.43); (6.32, 0.61)\n", + "Ranocchia F. (6.05, 0.40); (6.35, 0.65)\n", + "Verdi (6.13, 0.57); (6.77, 1.20)\n", + "Sensi (6.04, 0.52); (6.43, 0.93)\n", + "Barak (5.91, 0.46); (6.12, 0.71)\n", + "Soriano (6.04, 0.40); (6.30, 0.62)\n", + "Dominguez (6.17, 0.49); (6.61, 0.88)\n", + "Vilhena (5.91, 0.49); (6.10, 0.73)\n", + "Brozovic (6.08, 0.43); (6.36, 0.65)\n", + "Cristante (5.98, 0.41); (6.10, 0.51)\n", + "Thorstvedt (5.97, 0.46); (6.24, 0.72)\n", + "De Ketelaere (5.81, 0.36); (5.83, 0.40)\n", + "Saponara (6.04, 0.51); (6.48, 0.91)\n", + "Vecino (6.01, 0.45); (6.21, 0.64)\n", + "Locatelli (6.09, 0.40); (6.21, 0.49)\n", + "Zaniolo (5.91, 0.50); (6.12, 0.77)\n", + "Duda (5.88, 0.33); (5.87, 0.33)\n", + "Maldini (5.97, 0.44); (6.31, 0.75)\n", + "Marin (5.93, 0.47); (6.05, 0.66)\n", + "Zalewski (6.02, 0.37); (6.16, 0.47)\n", + "Bajrami (6.09, 0.53); (6.63, 1.04)\n", + "Coulibaly L. (5.86, 0.55); (6.08, 0.78)\n", + "Gonzalez J. 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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
MussoPAtalantaAvg111006.2577700.4395965.5812510.5034855.9845720.1746250.8465551.6992015.8172010.714061-0.2454710.89703832.650129
SportielloPAtalantaAvg1006.2307670.4166184.5108410.3954335.9839570.2043800.7198761.6991674.4425640.6059520.0665080.9101694.898746
Rossi F.PAtalantaAvg1005.9117650.3878684.0496070.5602375.7470250.3297880.3573711.6987204.1635190.842991-0.1011380.9484582.865818
ZappacostaDAtalantaAvg10456.0386450.4348126.2845630.6568255.9852190.5015800.0788221.1024195.8016890.9004340.3751641.6999110.000000
ScalviniDAtalantaAvg11806.0261120.5053726.2744830.7263926.0045540.5935500.0270201.0273745.7789561.0438460.3358841.6998990.000000
............................................................
GaichAVeronaAvg10355.8807700.4566676.1174210.6828085.7979060.5158570.1185751.0996875.5600610.8570370.4429351.6998910.000000
DjuricAVeronaAvg10675.9520050.3549486.1106910.4382205.9002950.4083800.0938541.2152575.8401180.6622850.2930501.6999360.000000
KallonAVeronaAvg10675.8773790.4061006.0585970.5754735.8060830.4605030.1144711.1596905.6137870.7589940.4050531.6999120.000000
LasagnaAVeronaAvg11835.8035220.3860735.8812030.4790495.7467760.4433770.0947441.1874905.5504260.6833930.3415981.6999250.000000
BraafAVeronaAvg10125.8476330.3986975.8602360.4649335.8273980.4727670.0317501.1547095.6383020.7641480.2117991.6999330.000000
\n", + "

525 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Avg 1 1 100 6.257770 0.439596 \n", + "Sportiello P Atalanta Avg 1 0 0 6.230767 0.416618 \n", + "Rossi F. P Atalanta Avg 1 0 0 5.911765 0.387868 \n", + "Zappacosta D Atalanta Avg 1 0 45 6.038645 0.434812 \n", + "Scalvini D Atalanta Avg 1 1 80 6.026112 0.505372 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Avg 1 0 35 5.880770 0.456667 \n", + "Djuric A Verona Avg 1 0 67 5.952005 0.354948 \n", + "Kallon A Verona Avg 1 0 67 5.877379 0.406100 \n", + "Lasagna A Verona Avg 1 1 83 5.803522 0.386073 \n", + "Braaf A Verona Avg 1 0 12 5.847633 0.398697 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Musso 5.581251 0.503485 5.984572 0.174625 0.846555 \n", + "Sportiello 4.510841 0.395433 5.983957 0.204380 0.719876 \n", + "Rossi F. 4.049607 0.560237 5.747025 0.329788 0.357371 \n", + "Zappacosta 6.284563 0.656825 5.985219 0.501580 0.078822 \n", + "Scalvini 6.274483 0.726392 6.004554 0.593550 0.027020 \n", + "... ... ... ... ... ... \n", + "Gaich 6.117421 0.682808 5.797906 0.515857 0.118575 \n", + "Djuric 6.110691 0.438220 5.900295 0.408380 0.093854 \n", + "Kallon 6.058597 0.575473 5.806083 0.460503 0.114471 \n", + "Lasagna 5.881203 0.479049 5.746776 0.443377 0.094744 \n", + "Braaf 5.860236 0.464933 5.827398 0.472767 0.031750 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Musso 1.699201 5.817201 0.714061 -0.245471 0.897038 \n", + "Sportiello 1.699167 4.442564 0.605952 0.066508 0.910169 \n", + "Rossi F. 1.698720 4.163519 0.842991 -0.101138 0.948458 \n", + "Zappacosta 1.102419 5.801689 0.900434 0.375164 1.699911 \n", + "Scalvini 1.027374 5.778956 1.043846 0.335884 1.699899 \n", + "... ... ... ... ... ... \n", + "Gaich 1.099687 5.560061 0.857037 0.442935 1.699891 \n", + "Djuric 1.215257 5.840118 0.662285 0.293050 1.699936 \n", + "Kallon 1.159690 5.613787 0.758994 0.405053 1.699912 \n", + "Lasagna 1.187490 5.550426 0.683393 0.341598 1.699925 \n", + "Braaf 1.154709 5.638302 0.764148 0.211799 1.699933 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Musso 32.650129 \n", + "Sportiello 4.898746 \n", + "Rossi F. 2.865818 \n", + "Zappacosta 0.000000 \n", + "Scalvini 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Lasagna 0.000000 \n", + "Braaf 0.000000 \n", + "\n", + "[525 rows x 19 columns]" + ] + }, + "execution_count": 43, + "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", @@ -6854,7 +7510,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "b47cbd63", "metadata": {}, "outputs": [], diff --git a/1_scraping_fbref.ipynb b/1_scraping_fbref.ipynb index 48331af..ce2beea 100644 --- a/1_scraping_fbref.ipynb +++ b/1_scraping_fbref.ipynb @@ -287,7 +287,7 @@ " ie IRL\n", " DF\n", " Udinese\n", - " 19-099\n", + " 19-109\n", " 2004\n", " 1.0\n", " 0.0\n", @@ -311,23 +311,23 @@ " dk DEN\n", " MF\n", " Hellas Verona\n", - " 26-319\n", + " 26-329\n", " 1996\n", - " 6.0\n", - " 2.0\n", - " 197.0\n", + " 8.0\n", + " 4.0\n", + " 377.0\n", " 0.0\n", " ...\n", - " 3.0\n", - " 1.0\n", + " 9.0\n", + " 2.0\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", + " 24.0\n", + " 31.0\n", + " 11.0\n", + " 73.8\n", " \n", " \n", " 2\n", @@ -335,23 +335,23 @@ " eng ENG\n", " FW\n", " Roma\n", - " 25-205\n", + " 25-215\n", " 1997\n", - " 31.0\n", - " 22.0\n", - " 1905.0\n", - " 7.0\n", + " 33.0\n", + " 24.0\n", + " 2084.0\n", + " 8.0\n", " ...\n", - " 30.0\n", - " 44.0\n", - " 10.0\n", + " 32.0\n", + " 45.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 41.0\n", - " 66.0\n", - " 57.0\n", - " 53.7\n", + " 43.0\n", + " 78.0\n", + " 71.0\n", + " 52.3\n", " \n", " \n", " 3\n", @@ -359,7 +359,7 @@ " it ITA\n", " MF\n", " Cremonese\n", - " 20-292\n", + " 20-302\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -383,23 +383,23 @@ " it ITA\n", " DF\n", " Inter\n", - " 35-074\n", + " 35-084\n", " 1988\n", - " 24.0\n", - " 20.0\n", - " 1915.0\n", + " 26.0\n", + " 22.0\n", + " 2095.0\n", " 0.0\n", " ...\n", - " 12.0\n", + " 14.0\n", " 10.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 122.0\n", - " 69.0\n", - " 32.0\n", - " 68.3\n", + " 131.0\n", + " 71.0\n", + " 35.0\n", + " 67.0\n", " \n", " \n", " ...\n", @@ -426,12 +426,12 @@ " ...\n", " \n", " \n", - " 575\n", + " 576\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 21-031\n", + " 21-041\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -450,12 +450,12 @@ " 0.0\n", " \n", " \n", - " 576\n", + " 577\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Fiorentina\n", - " 25-212\n", + " 25-222\n", " 1997\n", " 2.0\n", " 0.0\n", @@ -474,12 +474,12 @@ " 50.0\n", " \n", " \n", - " 577\n", + " 578\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Spezia\n", - " 25-212\n", + " 25-222\n", " 1997\n", " 6.0\n", " 2.0\n", @@ -498,112 +498,112 @@ " 60.0\n", " \n", " \n", - " 578\n", + " 579\n", " Milan Đurić\n", " ba BIH\n", " FW\n", " Hellas Verona\n", - " 32-338\n", + " 32-348\n", " 1990\n", - " 21.0\n", - " 8.0\n", - " 863.0\n", + " 23.0\n", + " 9.0\n", + " 974.0\n", " 1.0\n", " ...\n", - " 21.0\n", + " 22.0\n", " 20.0\n", " 4.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 19.0\n", - " 132.0\n", - " 35.0\n", - " 79.0\n", + " 23.0\n", + " 146.0\n", + " 38.0\n", + " 79.3\n", " \n", " \n", - " 579\n", + " 580\n", " Filip Đuričić\n", " rs SRB\n", " MF,FW\n", " Sampdoria\n", - " 31-085\n", + " 31-095\n", " 1992\n", - " 28.0\n", + " 29.0\n", " 24.0\n", - " 1871.0\n", + " 1911.0\n", " 3.0\n", " ...\n", " 31.0\n", - " 41.0\n", + " 43.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 98.0\n", - " 11.0\n", - " 21.0\n", - " 34.4\n", + " 99.0\n", + " 12.0\n", + " 22.0\n", + " 35.3\n", " \n", " \n", "\n", - "

580 rows × 115 columns

\n", + "

581 rows × 115 columns

\n", "" ], "text/plain": [ " player nationality position team age birth_year \\\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", + "0 James Abankwah ie IRL DF Udinese 19-109 2004 \n", + "1 Oliver Abildgaard dk DEN MF Hellas Verona 26-329 1996 \n", + "2 Tammy Abraham eng ENG FW Roma 25-215 1997 \n", + "3 Christian Acella it ITA MF Cremonese 20-302 2002 \n", + "4 Francesco Acerbi it ITA DF Inter 35-084 1988 \n", ".. ... ... ... ... ... ... \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", + "576 Petar Zovko ba BIH GK Spezia 21-041 2002 \n", + "577 Szymon Żurkowski pl POL MF Fiorentina 25-222 1997 \n", + "578 Szymon Żurkowski pl POL MF Spezia 25-222 1997 \n", + "579 Milan Đurić ba BIH FW Hellas Verona 32-348 1990 \n", + "580 Filip Đuričić rs SRB MF,FW Sampdoria 31-095 1992 \n", "\n", " games games_starts minutes goals ... fouls fouled offsides \\\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", + "1 8.0 4.0 377.0 0.0 ... 9.0 2.0 0.0 \n", + "2 33.0 24.0 2084.0 8.0 ... 32.0 45.0 11.0 \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", + "4 26.0 22.0 2095.0 0.0 ... 14.0 10.0 1.0 \n", ".. ... ... ... ... ... ... ... ... \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", + "576 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "577 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "578 6.0 2.0 240.0 0.0 ... 8.0 5.0 0.0 \n", + "579 23.0 9.0 974.0 1.0 ... 22.0 20.0 4.0 \n", + "580 29.0 24.0 1911.0 3.0 ... 31.0 43.0 0.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "1 0.0 0.0 0.0 24.0 31.0 \n", + "2 1.0 0.0 0.0 43.0 78.0 \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", + "4 0.0 0.0 0.0 131.0 71.0 \n", ".. ... ... ... ... ... \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", + "576 0.0 0.0 0.0 2.0 0.0 \n", + "577 0.0 0.0 0.0 2.0 1.0 \n", + "578 0.0 0.0 0.0 18.0 3.0 \n", + "579 0.0 0.0 0.0 23.0 146.0 \n", + "580 0.0 0.0 0.0 99.0 12.0 \n", "\n", " aerials_lost aerials_won_pct \n", "0 0.0 0.0 \n", - "1 5.0 75.0 \n", - "2 57.0 53.7 \n", + "1 11.0 73.8 \n", + "2 71.0 52.3 \n", "3 0.0 0.0 \n", - "4 32.0 68.3 \n", + "4 35.0 67.0 \n", ".. ... ... \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", + "576 0.0 0.0 \n", + "577 1.0 50.0 \n", + "578 2.0 60.0 \n", + "579 38.0 79.3 \n", + "580 22.0 35.3 \n", "\n", - "[580 rows x 115 columns]" + "[581 rows x 115 columns]" ] }, "execution_count": 3, @@ -676,7 +676,7 @@ " it ITA\n", " GK\n", " Sampdoria\n", - " 26-097\n", + " 26-107\n", " 1997\n", " 25.0\n", " 25.0\n", @@ -700,7 +700,7 @@ " it ITA\n", " GK\n", " Bologna\n", - " 31-097\n", + " 31-107\n", " 1992\n", " 1.0\n", " 1.0\n", @@ -724,55 +724,79 @@ " it ITA\n", " GK\n", " Cremonese\n", - " 22-298\n", + " 22-308\n", " 2000\n", - " 22.0\n", - " 22.0\n", - " 1980.0\n", - " 38.0\n", + " 24.0\n", + " 24.0\n", + " 2160.0\n", + " 40.0\n", " ...\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", + " 41.4\n", + " 179.0\n", + " 65.9\n", + " 52.8\n", + " 351.0\n", + " 28.0\n", + " 8.0\n", + " 24.0\n", + " 1.00\n", + " 13.9\n", " \n", " \n", " 3\n", + " Michele Cerofolini\n", + " it ITA\n", + " GK\n", + " Fiorentina\n", + " 24-121\n", + " 1999\n", + " 1.0\n", + " 1.0\n", + " 90.0\n", + " 0.0\n", + " ...\n", + " 31.1\n", + " 3.0\n", + " 100.0\n", + " 56.3\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 5.0\n", + " \n", + " \n", + " 4\n", " Andrea Consigli\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 36-088\n", + " 36-098\n", " 1987\n", - " 29.0\n", - " 29.0\n", - " 2610.0\n", - " 43.0\n", + " 31.0\n", + " 31.0\n", + " 2790.0\n", + " 46.0\n", " ...\n", - " 34.2\n", - " 249.0\n", - " 32.5\n", - " 33.4\n", - " 409.0\n", - " 24.0\n", - " 5.9\n", + " 33.8\n", + " 265.0\n", + " 31.3\n", + " 32.8\n", + " 424.0\n", " 26.0\n", - " 0.90\n", - " 14.1\n", + " 6.1\n", + " 26.0\n", + " 0.84\n", + " 13.9\n", " \n", " \n", - " 4\n", + " 5\n", " Alessio Cragno\n", " it ITA\n", " GK\n", " Monza\n", - " 28-301\n", + " 28-311\n", " 1994\n", " 1.0\n", " 1.0\n", @@ -791,84 +815,84 @@ " 25.7\n", " \n", " \n", - " 5\n", + " 6\n", " Michele Di Gregorio\n", " it ITA\n", " GK\n", " Monza\n", - " 25-272\n", + " 25-282\n", " 1997\n", - " 30.0\n", - " 30.0\n", - " 2700.0\n", - " 40.0\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 41.0\n", " ...\n", " 32.6\n", - " 196.0\n", - " 35.7\n", - " 33.7\n", - " 430.0\n", + " 209.0\n", + " 37.3\n", + " 34.7\n", + " 469.0\n", " 13.0\n", - " 3.0\n", - " 23.0\n", - " 0.77\n", - " 12.7\n", + " 2.8\n", + " 27.0\n", + " 0.84\n", + " 13.3\n", " \n", " \n", - " 6\n", + " 7\n", " Bartłomiej Drągowski\n", " pl POL\n", " GK\n", " Spezia\n", - " 25-249\n", + " 25-259\n", " 1997\n", - " 28.0\n", - " 28.0\n", - " 2406.0\n", - " 43.0\n", - " ...\n", - " 34.5\n", - " 169.0\n", - " 62.7\n", + " 30.0\n", + " 30.0\n", + " 2586.0\n", " 48.0\n", - " 401.0\n", - " 11.0\n", - " 2.7\n", - " 29.0\n", - " 1.08\n", + " ...\n", + " 34.1\n", + " 179.0\n", + " 60.9\n", + " 47.3\n", + " 428.0\n", + " 13.0\n", + " 3.0\n", + " 32.0\n", + " 1.11\n", " 14.2\n", " \n", " \n", - " 7\n", + " 8\n", " Wladimiro Falcone\n", " it ITA\n", " GK\n", " Lecce\n", - " 28-013\n", + " 28-023\n", " 1995\n", - " 31.0\n", - " 31.0\n", - " 2790.0\n", - " 38.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 40.0\n", " ...\n", - " 41.9\n", - " 225.0\n", - " 77.3\n", - " 52.4\n", - " 441.0\n", + " 41.7\n", + " 239.0\n", + " 76.6\n", + " 52.3\n", + " 470.0\n", " 22.0\n", - " 5.0\n", - " 35.0\n", - " 1.13\n", - " 13.7\n", + " 4.7\n", + " 37.0\n", + " 1.12\n", + " 14.1\n", " \n", " \n", - " 8\n", + " 9\n", " Pierluigi Gollini\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 28-038\n", + " 28-048\n", " 1995\n", " 3.0\n", " 3.0\n", @@ -887,12 +911,12 @@ " 9.3\n", " \n", " \n", - " 9\n", + " 10\n", " Pierluigi Gollini\n", " it ITA\n", " GK\n", " Napoli\n", - " 28-038\n", + " 28-048\n", " 1995\n", " 1.0\n", " 1.0\n", @@ -911,60 +935,60 @@ " 2.0\n", " \n", " \n", - " 10\n", + " 11\n", " Samir Handanović\n", " si SVN\n", " GK\n", " Inter\n", - " 38-285\n", + " 38-295\n", " 1984\n", - " 11.0\n", - " 11.0\n", - " 990.0\n", + " 12.0\n", + " 12.0\n", + " 1080.0\n", " 16.0\n", " ...\n", - " 25.7\n", - " 59.0\n", - " 8.5\n", - " 25.2\n", - " 112.0\n", + " 24.7\n", + " 64.0\n", + " 7.8\n", + " 24.4\n", + " 121.0\n", " 2.0\n", - " 1.8\n", + " 1.7\n", " 10.0\n", - " 0.91\n", - " 16.3\n", + " 0.83\n", + " 16.2\n", " \n", " \n", - " 11\n", + " 12\n", " Mike Maignan\n", " fr FRA\n", " GK\n", " Milan\n", - " 27-296\n", + " 27-306\n", " 1995\n", - " 15.0\n", - " 15.0\n", - " 1350.0\n", - " 15.0\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 17.0\n", " ...\n", - " 31.3\n", - " 54.0\n", + " 30.8\n", + " 60.0\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", + " 35.1\n", + " 164.0\n", + " 12.0\n", + " 7.3\n", + " 28.0\n", + " 1.65\n", + " 18.3\n", " \n", " \n", - " 12\n", + " 13\n", " Federico Marchetti\n", " it ITA\n", " GK\n", " Spezia\n", - " 40-077\n", + " 40-087\n", " 1983\n", " 1.0\n", " 0.0\n", @@ -983,12 +1007,12 @@ " 22.0\n", " \n", " \n", - " 13\n", + " 14\n", " Luís Maximiano\n", " pt POR\n", " GK\n", " Lazio\n", - " 24-110\n", + " 24-120\n", " 1999\n", " 1.0\n", " 1.0\n", @@ -1007,180 +1031,180 @@ " 19.0\n", " \n", " \n", - " 14\n", + " 15\n", " Alex Meret\n", " it ITA\n", " GK\n", " Napoli\n", - " 26-034\n", + " 26-044\n", " 1997\n", - " 30.0\n", - " 30.0\n", - " 2700.0\n", - " 21.0\n", - " ...\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", + " 32.0\n", + " 2880.0\n", + " 23.0\n", + " ...\n", + " 26.3\n", + " 194.0\n", + " 20.1\n", + " 26.8\n", + " 320.0\n", + " 11.0\n", + " 3.4\n", + " 35.0\n", + " 1.09\n", + " 16.9\n", " \n", " \n", - " 15\n", + " 16\n", " Vanja Milinković-Savić\n", " rs SRB\n", " GK\n", " Torino\n", - " 26-064\n", + " 26-074\n", " 1997\n", - " 31.0\n", - " 31.0\n", - " 2790.0\n", - " 36.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 38.0\n", " ...\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", + " 39.6\n", + " 251.0\n", + " 91.2\n", + " 69.7\n", + " 407.0\n", + " 27.0\n", + " 6.6\n", + " 66.0\n", " 2.00\n", - " 17.1\n", + " 17.2\n", " \n", " \n", - " 16\n", + " 17\n", " Lorenzo Montipò\n", " it ITA\n", " GK\n", " Hellas Verona\n", - " 27-064\n", + " 27-074\n", " 1996\n", - " 30.0\n", - " 30.0\n", - " 2700.0\n", - " 44.0\n", - " ...\n", - " 43.9\n", - " 227.0\n", - " 67.8\n", - " 49.6\n", - " 401.0\n", - " 22.0\n", - " 5.5\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", " 51.0\n", - " 1.70\n", - " 15.8\n", + " ...\n", + " 44.3\n", + " 243.0\n", + " 69.1\n", + " 50.3\n", + " 430.0\n", + " 25.0\n", + " 5.8\n", + " 51.0\n", + " 1.59\n", + " 15.5\n", " \n", " \n", - " 17\n", + " 18\n", " Juan Musso\n", " ar ARG\n", " GK\n", " Atalanta\n", - " 28-354\n", + " 28-364\n", " 1994\n", - " 23.0\n", - " 23.0\n", - " 1987.0\n", - " 25.0\n", - " ...\n", - " 32.0\n", - " 149.0\n", - " 62.4\n", - " 48.5\n", - " 247.0\n", - " 14.0\n", - " 5.7\n", " 24.0\n", - " 1.09\n", + " 24.0\n", + " 2077.0\n", + " 27.0\n", + " ...\n", + " 32.1\n", + " 157.0\n", + " 62.4\n", + " 48.6\n", + " 253.0\n", + " 14.0\n", + " 5.5\n", + " 24.0\n", + " 1.04\n", " 15.6\n", " \n", " \n", - " 18\n", + " 19\n", " Guillermo Ochoa\n", " mx MEX\n", " GK\n", " Salernitana\n", - " 37-286\n", + " 37-296\n", " 1985\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 23.0\n", + " 16.0\n", + " 16.0\n", + " 1440.0\n", + " 27.0\n", " ...\n", - " 41.5\n", - " 134.0\n", - " 70.1\n", - " 50.0\n", - " 213.0\n", - " 6.0\n", - " 2.8\n", + " 41.9\n", + " 164.0\n", + " 68.9\n", + " 49.0\n", + " 260.0\n", + " 8.0\n", + " 3.1\n", " 7.0\n", - " 0.50\n", - " 13.1\n", + " 0.44\n", + " 11.6\n", " \n", " \n", - " 19\n", + " 20\n", " André Onana\n", " cm CMR\n", " GK\n", " Inter\n", - " 27-023\n", + " 27-033\n", " 1996\n", - " 20.0\n", - " 20.0\n", - " 1800.0\n", - " 18.0\n", + " 21.0\n", + " 21.0\n", + " 1890.0\n", + " 19.0\n", " ...\n", - " 30.2\n", - " 130.0\n", - " 32.3\n", - " 35.8\n", - " 233.0\n", + " 29.9\n", + " 132.0\n", + " 32.6\n", + " 35.9\n", + " 240.0\n", " 14.0\n", - " 6.0\n", + " 5.8\n", " 9.0\n", - " 0.45\n", + " 0.43\n", " 12.5\n", " \n", " \n", - " 20\n", + " 21\n", " Rui Patrício\n", " pt POR\n", " GK\n", " Roma\n", - " 35-069\n", + " 35-079\n", " 1988\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", " 31.0\n", - " 31.0\n", - " 2790.0\n", - " 29.0\n", " ...\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", + " 33.5\n", + " 230.0\n", + " 37.0\n", + " 35.1\n", + " 387.0\n", + " 15.0\n", + " 3.9\n", + " 25.0\n", + " 0.76\n", + " 13.9\n", " \n", " \n", - " 21\n", + " 22\n", " Gianluca Pegolo\n", " it ITA\n", " GK\n", " Sassuolo\n", - " 42-031\n", + " 42-041\n", " 1981\n", " 2.0\n", " 2.0\n", @@ -1199,12 +1223,12 @@ " 9.0\n", " \n", " \n", - " 22\n", + " 23\n", " Simone Perilli\n", " it ITA\n", " GK\n", " Hellas Verona\n", - " 28-108\n", + " 28-118\n", " 1995\n", " 1.0\n", " 1.0\n", @@ -1223,12 +1247,12 @@ " 12.0\n", " \n", " \n", - " 23\n", + " 24\n", " Mattia Perin\n", " it ITA\n", " GK\n", " Juventus\n", - " 30-166\n", + " 30-176\n", " 1992\n", " 10.0\n", " 9.0\n", @@ -1247,12 +1271,12 @@ " 15.7\n", " \n", " \n", - " 24\n", + " 25\n", " Samuele Perisan\n", " it ITA\n", " GK\n", " Empoli\n", - " 25-247\n", + " 25-257\n", " 1997\n", " 7.0\n", " 7.0\n", @@ -1271,36 +1295,36 @@ " 11.6\n", " \n", " \n", - " 25\n", + " 26\n", " Ivan Provedel\n", " it ITA\n", " GK\n", " Lazio\n", - " 29-039\n", + " 29-049\n", " 1994\n", - " 31.0\n", - " 30.0\n", - " 2783.0\n", - " 21.0\n", - " ...\n", " 33.0\n", - " 161.0\n", - " 36.0\n", - " 34.7\n", - " 402.0\n", - " 17.0\n", + " 32.0\n", + " 2963.0\n", + " 24.0\n", + " ...\n", + " 33.1\n", + " 167.0\n", + " 36.5\n", + " 34.9\n", + " 433.0\n", + " 18.0\n", " 4.2\n", " 46.0\n", - " 1.49\n", - " 16.4\n", + " 1.40\n", + " 16.0\n", " \n", " \n", - " 26\n", + " 27\n", " Ionuț Radu\n", " ro ROU\n", " GK\n", " Cremonese\n", - " 25-332\n", + " 25-342\n", " 1997\n", " 9.0\n", " 9.0\n", @@ -1319,36 +1343,36 @@ " 14.5\n", " \n", " \n", - " 27\n", + " 28\n", " Nicola Ravaglia\n", " it ITA\n", " GK\n", " Sampdoria\n", - " 34-134\n", + " 34-144\n", " 1988\n", - " 4.0\n", - " 4.0\n", - " 360.0\n", - " 8.0\n", + " 6.0\n", + " 6.0\n", + " 540.0\n", + " 15.0\n", " ...\n", - " 35.5\n", - " 53.0\n", - " 45.3\n", - " 35.4\n", - " 72.0\n", - " 4.0\n", - " 5.6\n", + " 37.6\n", + " 74.0\n", + " 51.4\n", + " 37.7\n", + " 112.0\n", + " 6.0\n", + " 5.4\n", " 1.0\n", - " 0.25\n", - " 10.0\n", + " 0.17\n", + " 11.0\n", " \n", " \n", - " 28\n", + " 29\n", " Luigi Sepe\n", " it ITA\n", " GK\n", " Salernitana\n", - " 31-352\n", + " 31-362\n", " 1991\n", " 17.0\n", " 17.0\n", @@ -1367,36 +1391,36 @@ " 14.2\n", " \n", " \n", - " 29\n", + " 30\n", " Marco Silvestri\n", " it ITA\n", " GK\n", " Udinese\n", - " 32-054\n", + " 32-064\n", " 1991\n", - " 31.0\n", - " 31.0\n", - " 2790.0\n", - " 39.0\n", + " 33.0\n", + " 33.0\n", + " 2970.0\n", + " 41.0\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", + " 256.0\n", + " 36.7\n", + " 35.2\n", + " 470.0\n", + " 12.0\n", + " 2.6\n", + " 19.0\n", + " 0.58\n", + " 12.3\n", " \n", " \n", - " 30\n", + " 31\n", " Salvatore Sirigu\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 36-103\n", + " 36-113\n", " 1987\n", " 1.0\n", " 1.0\n", @@ -1415,84 +1439,84 @@ " 26.3\n", " \n", " \n", - " 31\n", + " 32\n", " Łukasz Skorupski\n", " pl POL\n", " GK\n", " Bologna\n", - " 31-355\n", + " 32-000\n", " 1991\n", - " 30.0\n", - " 30.0\n", - " 2700.0\n", - " 39.0\n", + " 32.0\n", + " 32.0\n", + " 2880.0\n", + " 43.0\n", " ...\n", " 32.5\n", - " 219.0\n", - " 38.8\n", - " 35.4\n", - " 402.0\n", + " 235.0\n", + " 37.0\n", + " 34.7\n", + " 422.0\n", " 24.0\n", - " 6.0\n", - " 22.0\n", - " 0.73\n", - " 13.4\n", + " 5.7\n", + " 23.0\n", + " 0.72\n", + " 13.2\n", " \n", " \n", - " 32\n", + " 33\n", " Marco Sportiello\n", " it ITA\n", " GK\n", " Atalanta\n", - " 30-350\n", + " 30-360\n", " 1992\n", + " 10.0\n", " 9.0\n", - " 8.0\n", - " 803.0\n", - " 11.0\n", + " 893.0\n", + " 12.0\n", " ...\n", - " 29.4\n", - " 82.0\n", - " 74.4\n", - " 52.9\n", - " 126.0\n", - " 13.0\n", - " 10.3\n", + " 29.7\n", + " 87.0\n", + " 73.6\n", + " 52.4\n", + " 142.0\n", " 14.0\n", - " 1.57\n", - " 16.8\n", + " 9.9\n", + " 15.0\n", + " 1.51\n", + " 16.0\n", " \n", " \n", - " 33\n", + " 34\n", " Wojciech Szczęsny\n", " pl POL\n", " GK\n", " Juventus\n", - " 33-007\n", + " 33-017\n", " 1990\n", - " 22.0\n", - " 22.0\n", - " 1932.0\n", - " 19.0\n", + " 24.0\n", + " 24.0\n", + " 2112.0\n", + " 21.0\n", " ...\n", - " 34.3\n", - " 114.0\n", - " 49.1\n", - " 41.5\n", - " 301.0\n", + " 34.0\n", + " 129.0\n", + " 48.1\n", + " 40.6\n", + " 338.0\n", " 9.0\n", - " 3.0\n", + " 2.7\n", " 19.0\n", - " 0.89\n", - " 15.4\n", + " 0.81\n", + " 15.2\n", " \n", " \n", - " 34\n", + " 35\n", " Ciprian Tătărușanu\n", " ro ROU\n", " GK\n", " Milan\n", - " 37-075\n", + " 37-085\n", " 1986\n", " 16.0\n", " 16.0\n", @@ -1511,36 +1535,36 @@ " 14.4\n", " \n", " \n", - " 35\n", + " 36\n", " Pietro Terracciano\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 33-048\n", + " 33-058\n", " 1990\n", - " 27.0\n", - " 27.0\n", - " 2430.0\n", - " 34.0\n", + " 28.0\n", + " 28.0\n", + " 2520.0\n", + " 37.0\n", " ...\n", - " 33.3\n", - " 184.0\n", - " 44.6\n", - " 39.9\n", - " 264.0\n", + " 33.5\n", + " 186.0\n", + " 45.2\n", + " 40.2\n", + " 268.0\n", " 12.0\n", " 4.5\n", " 52.0\n", - " 1.93\n", - " 18.6\n", + " 1.86\n", + " 18.5\n", " \n", " \n", - " 36\n", + " 37\n", " Martin Turk\n", " si SVN\n", " GK\n", " Sampdoria\n", - " 19-247\n", + " 19-257\n", " 2003\n", " 2.0\n", " 2.0\n", @@ -1559,36 +1583,36 @@ " 11.5\n", " \n", " \n", - " 37\n", + " 38\n", " Guglielmo Vicario\n", " it ITA\n", " GK\n", " Empoli\n", - " 26-200\n", + " 26-210\n", " 1996\n", - " 24.0\n", - " 24.0\n", - " 2160.0\n", - " 31.0\n", + " 26.0\n", + " 26.0\n", + " 2340.0\n", + " 34.0\n", " ...\n", - " 33.3\n", - " 139.0\n", - " 48.9\n", - " 42.6\n", - " 489.0\n", + " 33.8\n", + " 147.0\n", + " 50.3\n", + " 43.0\n", + " 521.0\n", " 28.0\n", - " 5.7\n", - " 15.0\n", - " 0.63\n", + " 5.4\n", + " 16.0\n", + " 0.62\n", " 10.7\n", " \n", " \n", - " 38\n", + " 39\n", " Jeroen Zoet\n", " nl NED\n", " GK\n", " Spezia\n", - " 32-109\n", + " 32-119\n", " 1991\n", " 4.0\n", " 3.0\n", @@ -1607,12 +1631,12 @@ " 17.2\n", " \n", " \n", - " 39\n", + " 40\n", " Petar Zovko\n", " ba BIH\n", " GK\n", " Spezia\n", - " 21-031\n", + " 21-041\n", " 2002\n", " 1.0\n", " 0.0\n", @@ -1632,263 +1656,269 @@ " \n", " \n", "\n", - "

40 rows × 47 columns

\n", + "

41 rows × 47 columns

\n", "" ], "text/plain": [ " player nationality position team age \\\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", + "0 Emil Audero it ITA GK Sampdoria 26-107 \n", + "1 Francesco Bardi it ITA GK Bologna 31-107 \n", + "2 Marco Carnesecchi it ITA GK Cremonese 22-308 \n", + "3 Michele Cerofolini it ITA GK Fiorentina 24-121 \n", + "4 Andrea Consigli it ITA GK Sassuolo 36-098 \n", + "5 Alessio Cragno it ITA GK Monza 28-311 \n", + "6 Michele Di Gregorio it ITA GK Monza 25-282 \n", + "7 Bartłomiej Drągowski pl POL GK Spezia 25-259 \n", + "8 Wladimiro Falcone it ITA GK Lecce 28-023 \n", + "9 Pierluigi Gollini it ITA GK Fiorentina 28-048 \n", + "10 Pierluigi Gollini it ITA GK Napoli 28-048 \n", + "11 Samir Handanović si SVN GK Inter 38-295 \n", + "12 Mike Maignan fr FRA GK Milan 27-306 \n", + "13 Federico Marchetti it ITA GK Spezia 40-087 \n", + "14 Luís Maximiano pt POR GK Lazio 24-120 \n", + "15 Alex Meret it ITA GK Napoli 26-044 \n", + "16 Vanja Milinković-Savić rs SRB GK Torino 26-074 \n", + "17 Lorenzo Montipò it ITA GK Hellas Verona 27-074 \n", + "18 Juan Musso ar ARG GK Atalanta 28-364 \n", + "19 Guillermo Ochoa mx MEX GK Salernitana 37-296 \n", + "20 André Onana cm CMR GK Inter 27-033 \n", + "21 Rui Patrício pt POR GK Roma 35-079 \n", + "22 Gianluca Pegolo it ITA GK Sassuolo 42-041 \n", + "23 Simone Perilli it ITA GK Hellas Verona 28-118 \n", + "24 Mattia Perin it ITA GK Juventus 30-176 \n", + "25 Samuele Perisan it ITA GK Empoli 25-257 \n", + "26 Ivan Provedel it ITA GK Lazio 29-049 \n", + "27 Ionuț Radu ro ROU GK Cremonese 25-342 \n", + "28 Nicola Ravaglia it ITA GK Sampdoria 34-144 \n", + "29 Luigi Sepe it ITA GK Salernitana 31-362 \n", + "30 Marco Silvestri it ITA GK Udinese 32-064 \n", + "31 Salvatore Sirigu it ITA GK Fiorentina 36-113 \n", + "32 Łukasz Skorupski pl POL GK Bologna 32-000 \n", + "33 Marco Sportiello it ITA GK Atalanta 30-360 \n", + "34 Wojciech Szczęsny pl POL GK Juventus 33-017 \n", + "35 Ciprian Tătărușanu ro ROU GK Milan 37-085 \n", + "36 Pietro Terracciano it ITA GK Fiorentina 33-058 \n", + "37 Martin Turk si SVN GK Sampdoria 19-257 \n", + "38 Guglielmo Vicario it ITA GK Empoli 26-210 \n", + "39 Jeroen Zoet nl NED GK Spezia 32-119 \n", + "40 Petar Zovko ba BIH GK Spezia 21-041 \n", "\n", " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\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", + "2 2000 24.0 24.0 2160.0 40.0 ... \n", + "3 1999 1.0 1.0 90.0 0.0 ... \n", + "4 1987 31.0 31.0 2790.0 46.0 ... \n", + "5 1994 1.0 1.0 90.0 3.0 ... \n", + "6 1997 32.0 32.0 2880.0 41.0 ... \n", + "7 1997 30.0 30.0 2586.0 48.0 ... \n", + "8 1995 33.0 33.0 2970.0 40.0 ... \n", + "9 1995 3.0 3.0 270.0 2.0 ... \n", + "10 1995 1.0 1.0 90.0 0.0 ... \n", + "11 1984 12.0 12.0 1080.0 16.0 ... \n", + "12 1995 17.0 17.0 1530.0 17.0 ... \n", + "13 1983 1.0 0.0 66.0 2.0 ... \n", + "14 1999 1.0 1.0 5.0 0.0 ... \n", + "15 1997 32.0 32.0 2880.0 23.0 ... \n", + "16 1997 33.0 33.0 2970.0 38.0 ... \n", + "17 1996 32.0 32.0 2880.0 51.0 ... \n", + "18 1994 24.0 24.0 2077.0 27.0 ... \n", + "19 1985 16.0 16.0 1440.0 27.0 ... \n", + "20 1996 21.0 21.0 1890.0 19.0 ... \n", + "21 1988 33.0 33.0 2970.0 31.0 ... \n", + "22 1981 2.0 2.0 180.0 3.0 ... \n", + "23 1995 1.0 1.0 90.0 0.0 ... \n", + "24 1992 10.0 9.0 858.0 7.0 ... \n", + "25 1997 7.0 7.0 630.0 9.0 ... \n", + "26 1994 33.0 32.0 2963.0 24.0 ... \n", + "27 1997 9.0 9.0 810.0 19.0 ... \n", + "28 1988 6.0 6.0 540.0 15.0 ... \n", + "29 1991 17.0 17.0 1530.0 27.0 ... \n", + "30 1991 33.0 33.0 2970.0 41.0 ... \n", + "31 1987 1.0 1.0 90.0 0.0 ... \n", + "32 1991 32.0 32.0 2880.0 43.0 ... \n", + "33 1992 10.0 9.0 893.0 12.0 ... \n", + "34 1990 24.0 24.0 2112.0 21.0 ... \n", + "35 1986 16.0 16.0 1440.0 22.0 ... \n", + "36 1990 28.0 28.0 2520.0 37.0 ... \n", + "37 2003 2.0 2.0 180.0 5.0 ... \n", + "38 1996 26.0 26.0 2340.0 34.0 ... \n", + "39 1991 4.0 3.0 244.0 2.0 ... \n", + "40 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 39.1 200.0 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", + "2 41.4 179.0 65.9 \n", + "3 31.1 3.0 100.0 \n", + "4 33.8 265.0 31.3 \n", + "5 22.6 6.0 16.7 \n", + "6 32.6 209.0 37.3 \n", + "7 34.1 179.0 60.9 \n", + "8 41.7 239.0 76.6 \n", + "9 32.4 11.0 63.6 \n", + "10 31.7 5.0 0.0 \n", + "11 24.7 64.0 7.8 \n", + "12 30.8 60.0 33.3 \n", + "13 24.3 8.0 37.5 \n", + "14 0.0 0.0 0.0 \n", + "15 26.3 194.0 20.1 \n", + "16 39.6 251.0 91.2 \n", + "17 44.3 243.0 69.1 \n", + "18 32.1 157.0 62.4 \n", + "19 41.9 164.0 68.9 \n", + "20 29.9 132.0 32.6 \n", + "21 33.5 230.0 37.0 \n", + "22 29.7 16.0 31.3 \n", + "23 57.3 13.0 100.0 \n", + "24 31.1 60.0 41.7 \n", + "25 32.7 75.0 34.7 \n", + "26 33.1 167.0 36.5 \n", + "27 37.3 67.0 50.7 \n", + "28 37.6 74.0 51.4 \n", + "29 35.9 154.0 48.7 \n", + "30 32.7 256.0 36.7 \n", + "31 37.7 8.0 50.0 \n", + "32 32.5 235.0 37.0 \n", + "33 29.7 87.0 73.6 \n", + "34 34.0 129.0 48.1 \n", + "35 32.9 83.0 51.8 \n", + "36 33.5 186.0 45.2 \n", + "37 37.3 22.0 90.9 \n", + "38 33.8 147.0 50.3 \n", + "39 37.1 12.0 66.7 \n", + "40 35.1 13.0 76.9 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\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", + "2 52.8 351.0 28.0 \n", + "3 56.3 2.0 0.0 \n", + "4 32.8 424.0 26.0 \n", + "5 33.3 7.0 0.0 \n", + "6 34.7 469.0 13.0 \n", + "7 47.3 428.0 13.0 \n", + "8 52.3 470.0 22.0 \n", + "9 49.3 24.0 1.0 \n", + "10 15.4 12.0 1.0 \n", + "11 24.4 121.0 2.0 \n", + "12 35.1 164.0 12.0 \n", + "13 32.4 8.0 0.0 \n", + "14 0.0 0.0 0.0 \n", + "15 26.8 320.0 11.0 \n", + "16 69.7 407.0 27.0 \n", + "17 50.3 430.0 25.0 \n", + "18 48.6 253.0 14.0 \n", + "19 49.0 260.0 8.0 \n", + "20 35.9 240.0 14.0 \n", + "21 35.1 387.0 15.0 \n", + "22 28.3 28.0 2.0 \n", + "23 69.8 19.0 1.0 \n", + "24 36.5 148.0 4.0 \n", + "25 34.3 134.0 5.0 \n", + "26 34.9 433.0 18.0 \n", + "27 46.3 107.0 6.0 \n", + "28 37.7 112.0 6.0 \n", + "29 41.1 222.0 16.0 \n", + "30 35.2 470.0 12.0 \n", + "31 41.8 12.0 0.0 \n", + "32 34.7 422.0 24.0 \n", + "33 52.4 142.0 14.0 \n", + "34 40.6 338.0 9.0 \n", + "35 41.8 195.0 9.0 \n", + "36 40.2 268.0 12.0 \n", + "37 57.1 37.0 0.0 \n", + "38 43.0 521.0 28.0 \n", + "39 46.3 40.0 2.0 \n", + "40 51.1 24.0 1.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\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", + "2 8.0 24.0 \n", + "3 0.0 0.0 \n", + "4 6.1 26.0 \n", + "5 0.0 2.0 \n", + "6 2.8 27.0 \n", + "7 3.0 32.0 \n", + "8 4.7 37.0 \n", + "9 4.2 0.0 \n", + "10 8.3 0.0 \n", + "11 1.7 10.0 \n", + "12 7.3 28.0 \n", + "13 0.0 1.0 \n", + "14 0.0 0.0 \n", + "15 3.4 35.0 \n", + "16 6.6 66.0 \n", + "17 5.8 51.0 \n", + "18 5.5 24.0 \n", + "19 3.1 7.0 \n", + "20 5.8 9.0 \n", + "21 3.9 25.0 \n", + "22 7.1 0.0 \n", + "23 5.3 1.0 \n", + "24 2.7 11.0 \n", + "25 3.7 4.0 \n", + "26 4.2 46.0 \n", + "27 5.6 9.0 \n", + "28 5.4 1.0 \n", + "29 7.2 13.0 \n", + "30 2.6 19.0 \n", + "31 0.0 2.0 \n", + "32 5.7 23.0 \n", + "33 9.9 15.0 \n", + "34 2.7 19.0 \n", + "35 4.6 13.0 \n", + "36 4.5 52.0 \n", + "37 0.0 0.0 \n", + "38 5.4 16.0 \n", + "39 5.0 6.0 \n", + "40 4.2 2.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \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", + "2 1.00 13.9 \n", + "3 0.00 5.0 \n", + "4 0.84 13.9 \n", + "5 2.00 25.7 \n", + "6 0.84 13.3 \n", + "7 1.11 14.2 \n", + "8 1.12 14.1 \n", + "9 0.00 9.3 \n", + "10 0.00 2.0 \n", + "11 0.83 16.2 \n", + "12 1.65 18.3 \n", + "13 1.36 22.0 \n", + "14 0.00 19.0 \n", + "15 1.09 16.9 \n", + "16 2.00 17.2 \n", + "17 1.59 15.5 \n", + "18 1.04 15.6 \n", + "19 0.44 11.6 \n", + "20 0.43 12.5 \n", + "21 0.76 13.9 \n", + "22 0.00 9.0 \n", + "23 1.00 12.0 \n", + "24 1.15 15.7 \n", + "25 0.57 11.6 \n", + "26 1.40 16.0 \n", + "27 1.00 14.5 \n", + "28 0.17 11.0 \n", + "29 0.76 14.2 \n", + "30 0.58 12.3 \n", + "31 2.00 26.3 \n", + "32 0.72 13.2 \n", + "33 1.51 16.0 \n", + "34 0.81 15.2 \n", + "35 0.81 14.4 \n", + "36 1.86 18.5 \n", + "37 0.00 11.5 \n", + "38 0.62 10.7 \n", + "39 2.20 17.2 \n", + "40 2.47 17.0 \n", "\n", - 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" 426.0\n", - " 432.0\n", - " 47.0\n", + " 448.0\n", + " 458.0\n", + " 48.0\n", " 1.0\n", " 8.0\n", " 2.0\n", - " 1585.0\n", - " 517.0\n", - " 535.0\n", - " 49.1\n", + " 1671.0\n", + " 554.0\n", + " 569.0\n", + " 49.3\n", " \n", " \n", " 16\n", " Sassuolo\n", " 29.0\n", - " 48.9\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 37.0\n", - " 24.0\n", - " 6.0\n", + " 49.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 39.0\n", + " 25.0\n", " 7.0\n", + " 8.0\n", " ...\n", - " 336.0\n", - " 355.0\n", - " 26.0\n", - " 5.0\n", + " 361.0\n", + " 377.0\n", + " 31.0\n", + " 6.0\n", " 4.0\n", " 0.0\n", - " 1456.0\n", - " 329.0\n", - " 385.0\n", - " 46.1\n", + " 1547.0\n", + " 339.0\n", + " 398.0\n", + " 46.0\n", " \n", " \n", " 17\n", " Spezia\n", " 34.0\n", - " 47.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 24.0\n", - " 15.0\n", + " 46.9\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 26.0\n", + " 17.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 431.0\n", - " 298.0\n", - " 45.0\n", + " 451.0\n", + " 321.0\n", + " 46.0\n", " 2.0\n", " 6.0\n", " 2.0\n", - " 1693.0\n", - " 424.0\n", - " 470.0\n", + " 1803.0\n", + " 448.0\n", + " 498.0\n", " 47.4\n", " \n", " \n", " 18\n", " Torino\n", " 28.0\n", - " 53.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 32.0\n", - " 26.0\n", + " 53.4\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 35.0\n", + " 28.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 427.0\n", - " 318.0\n", - " 70.0\n", + " 445.0\n", + " 339.0\n", + " 73.0\n", " 2.0\n", " 6.0\n", " 0.0\n", - " 1653.0\n", - " 474.0\n", + " 1757.0\n", " 508.0\n", - " 48.3\n", + " 548.0\n", + " 48.1\n", " \n", " \n", " 19\n", " Udinese\n", " 27.0\n", " 48.1\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 41.0\n", - " 36.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 42.0\n", + " 37.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 375.0\n", - " 389.0\n", - " 45.0\n", + " 402.0\n", + " 422.0\n", + " 47.0\n", " 1.0\n", - " 5.0\n", + " 6.0\n", " 2.0\n", - " 1594.0\n", - " 390.0\n", - " 326.0\n", - " 54.5\n", + " 1680.0\n", + " 421.0\n", + " 354.0\n", + " 54.3\n", " \n", " \n", "\n", @@ -2442,92 +2472,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\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", + "0 Atalanta 25.0 49.8 33.0 363.0 2970.0 \n", + "1 Bologna 27.0 54.0 33.0 363.0 2970.0 \n", + "2 Cremonese 32.0 43.0 33.0 363.0 2970.0 \n", + "3 Empoli 31.0 46.7 33.0 363.0 2970.0 \n", + "4 Fiorentina 30.0 56.8 33.0 363.0 2970.0 \n", + "5 Hellas Verona 36.0 41.7 33.0 363.0 2970.0 \n", + "6 Inter 24.0 56.9 33.0 363.0 2970.0 \n", + "7 Juventus 29.0 48.7 33.0 363.0 2970.0 \n", + "8 Lazio 22.0 51.3 33.0 363.0 2970.0 \n", + "9 Lecce 29.0 41.7 33.0 363.0 2970.0 \n", + "10 Milan 29.0 54.8 33.0 363.0 2970.0 \n", + "11 Monza 31.0 55.0 33.0 363.0 2970.0 \n", + "12 Napoli 26.0 62.2 33.0 363.0 2970.0 \n", + "13 Roma 27.0 48.6 33.0 363.0 2970.0 \n", + "14 Salernitana 28.0 44.2 33.0 363.0 2970.0 \n", + "15 Sampdoria 37.0 46.8 33.0 363.0 2970.0 \n", + "16 Sassuolo 29.0 49.2 33.0 363.0 2970.0 \n", + "17 Spezia 34.0 46.9 33.0 363.0 2970.0 \n", + "18 Torino 28.0 53.4 33.0 363.0 2970.0 \n", + "19 Udinese 27.0 48.1 33.0 363.0 2970.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\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", + "0 55.0 35.0 6.0 8.0 ... 399.0 335.0 49.0 \n", + "1 41.0 32.0 6.0 6.0 ... 412.0 378.0 69.0 \n", + "2 29.0 14.0 4.0 6.0 ... 415.0 349.0 64.0 \n", + "3 28.0 15.0 1.0 1.0 ... 379.0 390.0 43.0 \n", + "4 43.0 32.0 4.0 6.0 ... 421.0 461.0 45.0 \n", + "5 25.0 19.0 1.0 1.0 ... 476.0 337.0 65.0 \n", + "6 58.0 41.0 4.0 5.0 ... 390.0 360.0 61.0 \n", + "7 50.0 37.0 3.0 6.0 ... 383.0 354.0 57.0 \n", + "8 51.0 32.0 5.0 7.0 ... 347.0 421.0 46.0 \n", + "9 26.0 17.0 3.0 4.0 ... 483.0 383.0 63.0 \n", + "10 50.0 40.0 3.0 3.0 ... 404.0 378.0 52.0 \n", + "11 39.0 26.0 5.0 5.0 ... 433.0 433.0 59.0 \n", + "12 67.0 52.0 6.0 7.0 ... 337.0 406.0 55.0 \n", + "13 45.0 31.0 6.0 9.0 ... 392.0 439.0 53.0 \n", + "14 39.0 28.0 2.0 2.0 ... 408.0 369.0 60.0 \n", + "15 20.0 16.0 1.0 2.0 ... 448.0 458.0 48.0 \n", + "16 39.0 25.0 7.0 8.0 ... 361.0 377.0 31.0 \n", + "17 26.0 17.0 4.0 4.0 ... 451.0 321.0 46.0 \n", + "18 35.0 28.0 2.0 2.0 ... 445.0 339.0 73.0 \n", + "19 42.0 37.0 1.0 2.0 ... 402.0 422.0 47.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "0 6.0 3.0 2.0 1968.0 508.0 \n", + "1 5.0 9.0 3.0 1836.0 325.0 \n", + "2 4.0 5.0 1.0 1817.0 500.0 \n", + "3 1.0 6.0 1.0 1557.0 343.0 \n", + "4 3.0 5.0 2.0 1798.0 539.0 \n", + "5 1.0 1.0 2.0 1954.0 666.0 \n", + "6 4.0 3.0 2.0 1723.0 465.0 \n", + "7 6.0 4.0 0.0 1685.0 425.0 \n", + "8 5.0 1.0 0.0 1734.0 334.0 \n", + "9 3.0 5.0 3.0 1843.0 499.0 \n", + "10 1.0 5.0 1.0 1800.0 508.0 \n", + "11 5.0 1.0 1.0 1612.0 388.0 \n", + "12 5.0 2.0 0.0 1760.0 425.0 \n", + "13 5.0 3.0 1.0 1749.0 467.0 \n", + "14 1.0 10.0 0.0 1706.0 462.0 \n", + "15 1.0 8.0 2.0 1671.0 554.0 \n", + "16 6.0 4.0 0.0 1547.0 339.0 \n", + "17 2.0 6.0 2.0 1803.0 448.0 \n", + "18 2.0 6.0 0.0 1757.0 508.0 \n", + "19 1.0 6.0 2.0 1680.0 421.0 \n", "\n", " aerials_lost aerials_won_pct \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", + "0 418.0 54.9 \n", + "1 397.0 45.0 \n", + "2 660.0 43.1 \n", + "3 424.0 44.7 \n", + "4 458.0 54.1 \n", + "5 650.0 50.6 \n", + "6 369.0 55.8 \n", + "7 408.0 51.0 \n", + "8 333.0 50.1 \n", + "9 624.0 44.4 \n", + "10 419.0 54.8 \n", + "11 378.0 50.7 \n", + "12 353.0 54.6 \n", + "13 371.0 55.7 \n", + "14 495.0 48.3 \n", + "15 569.0 49.3 \n", + "16 398.0 46.0 \n", + "17 498.0 47.4 \n", + "18 548.0 48.1 \n", + "19 354.0 54.3 \n", "\n", "[20 rows x 152 columns]" ] @@ -2600,481 +2630,481 @@ " 0\n", " vs Atalanta\n", " 25.0\n", - " 50.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 34.0\n", - " 26.0\n", + " 50.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", + " 28.0\n", " 3.0\n", " 3.0\n", " ...\n", - " 338.0\n", - " 356.0\n", - " 40.0\n", + " 354.0\n", + " 372.0\n", + " 42.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1865.0\n", - " 389.0\n", - " 472.0\n", - " 45.2\n", + " 1976.0\n", + " 418.0\n", + " 508.0\n", + " 45.1\n", " \n", " \n", " 1\n", " vs Bologna\n", " 27.0\n", - " 46.6\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 37.0\n", - " 22.0\n", + " 46.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 40.0\n", + " 24.0\n", " 7.0\n", - " 8.0\n", + " 9.0\n", " ...\n", - " 376.0\n", - " 365.0\n", - " 52.0\n", - " 5.0\n", - " 4.0\n", + " 400.0\n", + " 388.0\n", + " 54.0\n", + " 6.0\n", + " 6.0\n", " 1.0\n", - " 1687.0\n", - " 379.0\n", - " 306.0\n", - " 55.3\n", + " 1775.0\n", + " 397.0\n", + " 325.0\n", + " 55.0\n", " \n", " \n", " 2\n", " vs Cremonese\n", " 32.0\n", - " 56.8\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 56.0\n", - " 38.0\n", + " 57.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 58.0\n", + " 39.0\n", " 5.0\n", " 5.0\n", " ...\n", - " 345.0\n", - " 372.0\n", - " 52.0\n", + " 373.0\n", + " 393.0\n", + " 57.0\n", " 4.0\n", " 6.0\n", " 0.0\n", - " 1745.0\n", - " 594.0\n", - " 462.0\n", - " 56.3\n", + " 1867.0\n", + " 660.0\n", + " 500.0\n", + " 56.9\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", - " 2.0\n", + " 53.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 42.0\n", + " 28.0\n", " 4.0\n", + " 6.0\n", " ...\n", - " 384.0\n", - " 341.0\n", - " 60.0\n", - " 2.0\n", - " 1.0\n", - " 0.0\n", - " 1607.0\n", " 412.0\n", - " 330.0\n", - " 55.5\n", + " 365.0\n", + " 67.0\n", + " 4.0\n", + " 1.0\n", + " 1.0\n", + " 1693.0\n", + " 424.0\n", + " 343.0\n", + " 55.3\n", " \n", " \n", " 4\n", " vs Fiorentina\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", - " 4.0\n", + " 30.0\n", + " 43.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", + " 26.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 460.0\n", - " 371.0\n", - " 78.0\n", - " 2.0\n", + " 487.0\n", + " 400.0\n", + " 80.0\n", + " 3.0\n", " 6.0\n", " 2.0\n", - " 1625.0\n", - " 425.0\n", - " 504.0\n", - " 45.7\n", + " 1708.0\n", + " 458.0\n", + " 539.0\n", + " 45.9\n", " \n", " \n", " 5\n", " vs Hellas Verona\n", " 36.0\n", - " 58.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", + " 58.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 49.0\n", " 43.0\n", - " 39.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 337.0\n", - " 438.0\n", - " 32.0\n", + " 358.0\n", + " 455.0\n", + " 35.0\n", " 1.0\n", " 1.0\n", " 2.0\n", - " 1728.0\n", - " 619.0\n", - " 615.0\n", - " 50.2\n", + " 1833.0\n", + " 650.0\n", + " 666.0\n", + " 49.4\n", " \n", " \n", " 6\n", " vs Inter\n", " 24.0\n", - " 43.8\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 32.0\n", - " 27.0\n", + " 43.1\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 33.0\n", + " 28.0\n", " 3.0\n", " 3.0\n", " ...\n", - " 368.0\n", - " 336.0\n", - " 29.0\n", + " 386.0\n", + " 359.0\n", + " 31.0\n", " 3.0\n", " 5.0\n", - " 1.0\n", - " 1443.0\n", - " 344.0\n", - " 443.0\n", - " 43.7\n", + " 2.0\n", + " 1532.0\n", + " 369.0\n", + " 465.0\n", + " 44.2\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", + " 51.3\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 28.0\n", " 22.0\n", - " 1.0\n", - " 2.0\n", + " 3.0\n", + " 4.0\n", " ...\n", - " 348.0\n", - " 328.0\n", - " 39.0\n", + " 370.0\n", + " 345.0\n", + " 47.0\n", + " 1.0\n", + " 6.0\n", " 0.0\n", - " 5.0\n", - " 0.0\n", - " 1556.0\n", - " 381.0\n", - " 390.0\n", - " 49.4\n", + " 1662.0\n", + " 408.0\n", + " 425.0\n", + " 49.0\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", - " 14.0\n", + " 48.7\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 24.0\n", + " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 421.0\n", - " 304.0\n", - " 55.0\n", + " 447.0\n", + " 330.0\n", + " 58.0\n", " 1.0\n", " 7.0\n", " 1.0\n", - " 1677.0\n", - " 319.0\n", - " 319.0\n", - " 50.0\n", + " 1788.0\n", + " 333.0\n", + " 334.0\n", + " 49.9\n", " \n", " \n", " 9\n", " vs Lecce\n", " 29.0\n", " 58.3\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 35.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 37.0\n", " 25.0\n", " 5.0\n", " 5.0\n", " ...\n", - " 378.0\n", - " 425.0\n", - " 57.0\n", + " 401.0\n", + " 455.0\n", + " 61.0\n", + " 4.0\n", " 4.0\n", " 2.0\n", - " 2.0\n", - " 1694.0\n", - " 582.0\n", - " 466.0\n", - " 55.5\n", + " 1782.0\n", + " 624.0\n", + " 499.0\n", + " 55.6\n", " \n", " \n", " 10\n", " vs Milan\n", " 29.0\n", - " 46.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 36.0\n", - " 28.0\n", + " 45.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 38.0\n", + " 30.0\n", " 4.0\n", " 5.0\n", " ...\n", - " 378.0\n", - " 369.0\n", - " 31.0\n", + " 399.0\n", + " 387.0\n", + " 35.0\n", " 5.0\n", " 3.0\n", " 3.0\n", - " 1617.0\n", - " 387.0\n", - " 456.0\n", - " 45.9\n", + " 1732.0\n", + " 419.0\n", + " 508.0\n", + " 45.2\n", " \n", " \n", " 11\n", " vs Monza\n", " 31.0\n", " 45.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 42.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 43.0\n", " 27.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 437.0\n", - " 385.0\n", - " 48.0\n", + " 462.0\n", + " 414.0\n", + " 50.0\n", " 1.0\n", " 5.0\n", " 2.0\n", - " 1608.0\n", - " 340.0\n", - " 357.0\n", - " 48.8\n", + " 1719.0\n", + " 378.0\n", + " 388.0\n", + " 49.3\n", " \n", " \n", " 12\n", " vs Napoli\n", " 26.0\n", - " 38.2\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 21.0\n", - " 13.0\n", + " 37.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 23.0\n", + " 15.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 405.0\n", - " 287.0\n", - " 39.0\n", + " 431.0\n", + " 313.0\n", + " 41.0\n", " 1.0\n", " 6.0\n", " 2.0\n", - " 1562.0\n", - " 322.0\n", - " 389.0\n", - " 45.3\n", + " 1651.0\n", + " 353.0\n", + " 425.0\n", + " 45.4\n", " \n", " \n", " 13\n", " vs Roma\n", " 27.0\n", - " 50.8\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 28.0\n", - " 18.0\n", + " 51.4\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 30.0\n", + " 20.0\n", " 2.0\n", " 3.0\n", " ...\n", - " 447.0\n", - " 344.0\n", + " 466.0\n", + " 366.0\n", " 21.0\n", " 2.0\n", " 9.0\n", " 0.0\n", - " 1621.0\n", - " 331.0\n", - " 426.0\n", - " 43.7\n", + " 1741.0\n", + " 371.0\n", + " 467.0\n", + " 44.3\n", " \n", " \n", " 14\n", " vs Salernitana\n", " 28.0\n", - " 55.4\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 50.0\n", - " 32.0\n", + " 55.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 54.0\n", + " 35.0\n", " 6.0\n", " 10.0\n", " ...\n", - " 375.0\n", - " 357.0\n", - " 64.0\n", + " 403.0\n", + " 384.0\n", + " 65.0\n", " 10.0\n", - " 1.0\n", " 2.0\n", - " 1709.0\n", - " 448.0\n", - " 427.0\n", - " 51.2\n", + " 2.0\n", + " 1817.0\n", + " 495.0\n", + " 462.0\n", + " 51.7\n", " \n", " \n", " 15\n", " vs Sampdoria\n", " 37.0\n", - " 52.6\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 50.0\n", - " 36.0\n", + " 53.2\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 57.0\n", + " 43.0\n", " 6.0\n", " 9.0\n", " ...\n", - " 449.0\n", - " 408.0\n", - " 75.0\n", + " 475.0\n", + " 427.0\n", + " 81.0\n", " 6.0\n", " 2.0\n", " 0.0\n", - " 1693.0\n", - " 535.0\n", - " 517.0\n", - " 50.9\n", + " 1802.0\n", + " 569.0\n", + " 554.0\n", + " 50.7\n", " \n", " \n", " 16\n", " vs Sassuolo\n", " 29.0\n", - " 51.1\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 46.0\n", - " 35.0\n", + " 50.8\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 49.0\n", + " 37.0\n", " 4.0\n", " 4.0\n", " ...\n", - " 384.0\n", - " 307.0\n", - " 94.0\n", + " 407.0\n", + " 332.0\n", + " 100.0\n", " 2.0\n", - " 7.0\n", + " 8.0\n", " 1.0\n", - " 1611.0\n", - " 385.0\n", - " 329.0\n", - " 53.9\n", + " 1710.0\n", + " 398.0\n", + " 339.0\n", + " 54.0\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", + " 53.1\n", " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 52.0\n", + " 36.0\n", " 4.0\n", " 6.0\n", " ...\n", - " 321.0\n", - " 396.0\n", - " 67.0\n", + " 345.0\n", + " 414.0\n", + " 70.0\n", " 4.0\n", " 4.0\n", " 2.0\n", - " 1708.0\n", - " 470.0\n", - " 424.0\n", + " 1820.0\n", + " 498.0\n", + " 448.0\n", " 52.6\n", " \n", " \n", " 18\n", " vs Torino\n", " 28.0\n", - " 47.0\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 36.0\n", - " 23.0\n", + " 46.6\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 38.0\n", + " 25.0\n", " 5.0\n", " 6.0\n", " ...\n", - " 341.0\n", - " 409.0\n", - " 32.0\n", + " 364.0\n", + " 427.0\n", + " 35.0\n", " 4.0\n", " 2.0\n", " 0.0\n", - " 1597.0\n", + " 1709.0\n", + " 548.0\n", " 508.0\n", - " 474.0\n", - " 51.7\n", + " 51.9\n", " \n", " \n", " 19\n", " vs Udinese\n", " 27.0\n", " 51.9\n", - " 31.0\n", - " 341.0\n", - " 2790.0\n", - " 37.0\n", + " 33.0\n", + " 363.0\n", + " 2970.0\n", + " 39.0\n", " 27.0\n", - " 4.0\n", " 5.0\n", + " 6.0\n", " ...\n", - " 412.0\n", - " 357.0\n", - " 50.0\n", - " 3.0\n", + " 446.0\n", + " 384.0\n", + " 56.0\n", + " 4.0\n", " 2.0\n", " 1.0\n", - " 1577.0\n", - " 326.0\n", - " 390.0\n", - " 45.5\n", + " 1683.0\n", + " 354.0\n", + " 421.0\n", + " 45.7\n", " \n", " \n", "\n", @@ -3083,92 +3113,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\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", + "0 vs Atalanta 25.0 50.2 33.0 363.0 2970.0 \n", + "1 vs Bologna 27.0 46.0 33.0 363.0 2970.0 \n", + "2 vs Cremonese 32.0 57.0 33.0 363.0 2970.0 \n", + "3 vs Empoli 31.0 53.3 33.0 363.0 2970.0 \n", + "4 vs Fiorentina 30.0 43.2 33.0 363.0 2970.0 \n", + "5 vs Hellas Verona 36.0 58.3 33.0 363.0 2970.0 \n", + "6 vs Inter 24.0 43.1 33.0 363.0 2970.0 \n", + "7 vs Juventus 29.0 51.3 33.0 363.0 2970.0 \n", + "8 vs Lazio 22.0 48.7 33.0 363.0 2970.0 \n", + "9 vs Lecce 29.0 58.3 33.0 363.0 2970.0 \n", + "10 vs Milan 29.0 45.2 33.0 363.0 2970.0 \n", + "11 vs Monza 31.0 45.0 33.0 363.0 2970.0 \n", + "12 vs Napoli 26.0 37.8 33.0 363.0 2970.0 \n", + "13 vs Roma 27.0 51.4 33.0 363.0 2970.0 \n", + "14 vs Salernitana 28.0 55.8 33.0 363.0 2970.0 \n", + "15 vs Sampdoria 37.0 53.2 33.0 363.0 2970.0 \n", + "16 vs Sassuolo 29.0 50.8 33.0 363.0 2970.0 \n", + "17 vs Spezia 34.0 53.1 33.0 363.0 2970.0 \n", + "18 vs Torino 28.0 46.6 33.0 363.0 2970.0 \n", + "19 vs Udinese 27.0 51.9 33.0 363.0 2970.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\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", + "0 37.0 28.0 3.0 3.0 ... 354.0 372.0 42.0 \n", + "1 40.0 24.0 7.0 9.0 ... 400.0 388.0 54.0 \n", + "2 58.0 39.0 5.0 5.0 ... 373.0 393.0 57.0 \n", + "3 42.0 28.0 4.0 6.0 ... 412.0 365.0 67.0 \n", + "4 37.0 26.0 5.0 5.0 ... 487.0 400.0 80.0 \n", + "5 49.0 43.0 0.0 1.0 ... 358.0 455.0 35.0 \n", + "6 33.0 28.0 3.0 3.0 ... 386.0 359.0 31.0 \n", + "7 28.0 22.0 3.0 4.0 ... 370.0 345.0 47.0 \n", + "8 24.0 16.0 1.0 1.0 ... 447.0 330.0 58.0 \n", + "9 37.0 25.0 5.0 5.0 ... 401.0 455.0 61.0 \n", + "10 38.0 30.0 4.0 5.0 ... 399.0 387.0 35.0 \n", + "11 43.0 27.0 1.0 1.0 ... 462.0 414.0 50.0 \n", + "12 23.0 15.0 1.0 2.0 ... 431.0 313.0 41.0 \n", + "13 30.0 20.0 2.0 3.0 ... 466.0 366.0 21.0 \n", + "14 54.0 35.0 6.0 10.0 ... 403.0 384.0 65.0 \n", + "15 57.0 43.0 6.0 9.0 ... 475.0 427.0 81.0 \n", + "16 49.0 37.0 4.0 4.0 ... 407.0 332.0 100.0 \n", + "17 52.0 36.0 4.0 6.0 ... 345.0 414.0 70.0 \n", + "18 38.0 25.0 5.0 6.0 ... 364.0 427.0 35.0 \n", + "19 39.0 27.0 5.0 6.0 ... 446.0 384.0 56.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\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", + "0 1.0 8.0 1.0 1976.0 418.0 \n", + "1 6.0 6.0 1.0 1775.0 397.0 \n", + "2 4.0 6.0 0.0 1867.0 660.0 \n", + "3 4.0 1.0 1.0 1693.0 424.0 \n", + "4 3.0 6.0 2.0 1708.0 458.0 \n", + "5 1.0 1.0 2.0 1833.0 650.0 \n", + "6 3.0 5.0 2.0 1532.0 369.0 \n", + "7 1.0 6.0 0.0 1662.0 408.0 \n", + "8 1.0 7.0 1.0 1788.0 333.0 \n", + "9 4.0 4.0 2.0 1782.0 624.0 \n", + "10 5.0 3.0 3.0 1732.0 419.0 \n", + "11 1.0 5.0 2.0 1719.0 378.0 \n", + "12 1.0 6.0 2.0 1651.0 353.0 \n", + "13 2.0 9.0 0.0 1741.0 371.0 \n", + "14 10.0 2.0 2.0 1817.0 495.0 \n", + "15 6.0 2.0 0.0 1802.0 569.0 \n", + "16 2.0 8.0 1.0 1710.0 398.0 \n", + "17 4.0 4.0 2.0 1820.0 498.0 \n", + "18 4.0 2.0 0.0 1709.0 548.0 \n", + "19 4.0 2.0 1.0 1683.0 354.0 \n", "\n", " aerials_lost aerials_won_pct \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", + "0 508.0 45.1 \n", + "1 325.0 55.0 \n", + "2 500.0 56.9 \n", + "3 343.0 55.3 \n", + "4 539.0 45.9 \n", + "5 666.0 49.4 \n", + "6 465.0 44.2 \n", + "7 425.0 49.0 \n", + "8 334.0 49.9 \n", + "9 499.0 55.6 \n", + "10 508.0 45.2 \n", + "11 388.0 49.3 \n", + "12 425.0 45.4 \n", + "13 467.0 44.3 \n", + "14 462.0 51.7 \n", + "15 554.0 50.7 \n", + "16 339.0 54.0 \n", + "17 448.0 52.6 \n", + "18 508.0 51.9 \n", + "19 421.0 45.7 \n", "\n", "[20 rows x 152 columns]" ] @@ -3357,23 +3387,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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": { @@ -3402,1178 +3418,194 @@ " team\n", " age\n", " birth_year\n", - " gk_games\n", - " gk_games_starts\n", - " gk_minutes\n", - " gk_goals_against\n", + " games\n", + " games_starts\n", + " minutes\n", + " goals\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", + " fouls\n", + " fouled\n", + " offsides\n", + " pens_won\n", + " pens_conceded\n", + " own_goals\n", + " ball_recoveries\n", + " aerials_won\n", + " aerials_lost\n", + " aerials_won_pct\n", " \n", " \n", " \n", " \n", " 0\n", - " Emil Audero\n", - " it ITA\n", - " GK\n", - " Sampdoria\n", - " 24\n", + " Tammy Abraham\n", + " eng ENG\n", + " FW\n", + " Roma\n", + " 23\n", " 1997\n", - " 29.0\n", - " 29.0\n", - " 2560.0\n", - " 48.0\n", + " 37.0\n", + " 36.0\n", + " 3084.0\n", + " 17.0\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", + " 38.0\n", + " 49.0\n", + " 17.0\n", + " 1.0\n", + " 1.0\n", + " 0.0\n", + " 68.0\n", + " 78.0\n", + " 80.0\n", + " 49.4\n", " \n", " \n", " 1\n", - " Nicola Bagnolini\n", + " Francesco Acerbi\n", " it ITA\n", - " GK\n", - " Bologna\n", - " 17\n", - " 2004\n", - " 1.0\n", - " 0.0\n", - " 3.0\n", - " 0.0\n", + " DF\n", + " Lazio\n", + " 33\n", + " 1988\n", + " 30.0\n", + " 29.0\n", + " 2536.0\n", + " 4.0\n", " ...\n", - " 61.0\n", + " 18.0\n", + " 16.0\n", + " 4.0\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", + " 162.0\n", + " 69.0\n", + " 43.0\n", + " 61.6\n", " \n", " \n", " 2\n", - " Francesco Bardi\n", - " it ITA\n", - " GK\n", + " Michel Aebischer\n", + " ch SUI\n", + " MF\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", + " 24\n", + " 1997\n", + " 12.0\n", + " 4.0\n", + " 443.0\n", " 0.0\n", - " 0.00\n", - " 5.5\n", + " ...\n", + " 13.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 27.0\n", + " 5.0\n", + " 4.0\n", + " 55.6\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", + " Felix Afena-Gyan\n", + " gh GHA\n", + " FW,MF\n", + " Roma\n", + " 18\n", + " 2003\n", + " 17.0\n", + " 6.0\n", + " 668.0\n", + " 2.0\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", + " 15.0\n", + " 24.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 31.0\n", + " 11.0\n", + " 15.0\n", + " 42.3\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", + " Kevin Agudelo\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", + " MF,FW\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", + " 22\n", + " 1998\n", + " 23.0\n", + " 12.0\n", + " 1237.0\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", + " 36.0\n", + " 24.0\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", + " 67.0\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", + " 51.7\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", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\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", + " 627\n", + " Nadir Zortea\n", " it ITA\n", - " GK\n", - " Sassuolo\n", + " DF,MF\n", + " Salernitana\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", + " 29.0\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", + " 1410.0\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", + " 14.0\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", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 72.0\n", + " 8.0\n", + " 17.0\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", + " 628\n", " Petar Zovko\n", " ba BIH\n", " GK\n", @@ -4585,337 +3617,169 @@ " 29.0\n", " 0.0\n", " ...\n", - " 35.9\n", - " 2.0\n", - " 50.0\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", + " 629\n", + " Szymon Żurkowski\n", + " pl POL\n", + " MF\n", + " Empoli\n", + " 23\n", + " 1997\n", " 35.0\n", - " 4.0\n", + " 29.0\n", + " 2307.0\n", + " 6.0\n", + " ...\n", + " 41.0\n", + " 67.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", + " 0.0\n", + " 162.0\n", + " 24.0\n", + " 32.0\n", + " 42.9\n", + " \n", + " \n", + " 630\n", + " Milan Đurić\n", + " ba BIH\n", + " FW\n", + " Salernitana\n", + " 31\n", + " 1990\n", + " 33.0\n", + " 23.0\n", + " 2165.0\n", + " 5.0\n", + " ...\n", + " 24.0\n", + " 45.0\n", + " 6.0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 40.0\n", + " 242.0\n", + " 83.0\n", + " 74.5\n", + " \n", + " \n", + " 631\n", + " Filip Đuričić\n", + " rs SRB\n", + " MF,FW\n", + " Sassuolo\n", + " 29\n", + " 1992\n", + " 12.0\n", + " 9.0\n", + " 671.0\n", " 2.0\n", - " 6.21\n", - " 28.5\n", + " ...\n", + " 10.0\n", + " 14.0\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 34.0\n", + " 3.0\n", + " 5.0\n", + " 37.5\n", " \n", " \n", "\n", - "

49 rows × 47 columns

\n", + "

632 rows × 115 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", + " 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", - " 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", + " 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", - " 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", + " 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", - " 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", + " 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", - " 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]" + "[632 rows x 115 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "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", @@ -4926,637 +3790,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "9172edae", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Atalanta32.055.038.0418.03420.062.048.05.06.0...513.0454.068.03.06.03.02292.0685.0518.056.9
1Bologna36.050.638.0418.03420.043.034.04.05.0...443.0497.056.03.09.02.01972.0492.0520.048.6
2Cagliari33.044.538.0418.03420.034.026.03.04.0...549.0490.070.03.010.01.01995.0696.0742.048.4
3Empoli28.047.438.0418.03420.047.027.07.07.0...506.0463.083.06.010.06.01998.0427.0549.043.8
4Fiorentina28.057.738.0418.03420.059.033.09.012.0...464.0565.057.08.05.02.01885.0469.0456.050.7
5Genoa40.043.938.0418.03420.026.019.06.07.0...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...500.0474.060.05.06.02.01980.0524.0441.054.3
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
16Spezia30.042.738.0418.03420.038.025.03.05.0...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
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20 rows × 152 columns

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" - ], - "text/plain": [ - " 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", - " 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_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_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", - "[20 rows x 152 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_team = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'for')\n", "\n", @@ -5567,637 +3804,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fa6258db", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0vs Atalanta32.044.938.0418.03420.045.029.05.06.0...491.0488.069.04.06.03.02240.0518.0685.043.1
1vs Bologna36.049.338.0418.03420.053.032.08.09.0...527.0416.088.08.05.01.02032.0520.0492.051.4
2vs Cagliari33.055.638.0418.03420.067.041.07.010.0...526.0524.076.06.04.00.02112.0742.0696.051.6
3vs Empoli28.052.938.0418.03420.064.044.05.010.0...488.0482.066.07.07.03.02051.0549.0427.056.3
4vs Fiorentina28.041.838.0418.03420.049.033.03.05.0...585.0441.0119.04.012.00.01825.0456.0469.049.3
5vs Genoa40.056.138.0418.03420.058.035.04.05.0...523.0530.059.04.07.01.02257.0743.0635.053.9
6vs Hellas Verona31.049.438.0418.03420.057.038.03.04.0...463.0534.032.03.08.02.02206.0733.0689.051.5
7vs Inter27.043.438.0418.03420.031.019.04.04.0...440.0441.034.03.011.01.01767.0476.0549.046.4
8vs Juventus32.048.338.0418.03420.036.021.04.07.0...525.0471.035.05.06.01.01894.0440.0550.044.4
9vs Lazio27.044.438.0418.03420.057.040.05.06.0...474.0418.073.06.09.03.01956.0427.0411.051.0
10vs Milan28.045.838.0418.03420.029.019.04.05.0...533.0440.063.03.08.03.02106.0517.0521.049.8
11vs Napoli27.041.538.0418.03420.030.019.01.01.0...517.0415.060.00.014.00.01963.0403.0393.050.6
12vs Roma29.048.638.0418.03420.041.023.05.06.0...497.0477.081.05.09.01.01970.0441.0524.045.7
13vs Salernitana42.059.038.0418.03420.076.058.06.08.0...546.0463.063.06.05.01.02043.0506.0657.043.5
14vs Sampdoria34.054.138.0418.03420.061.041.03.04.0...538.0471.064.01.04.04.02174.0506.0609.045.4
15vs Sassuolo29.044.838.0418.03420.065.048.08.09.0...519.0434.0142.06.07.04.02041.0404.0343.054.1
16vs Spezia30.057.338.0418.03420.068.044.010.013.0...474.0477.062.08.05.03.02064.0589.0530.052.6
17vs Torino31.046.438.0418.03420.041.025.09.011.0...485.0611.060.010.06.03.01989.0891.0781.053.3
18vs Udinese29.057.738.0418.03420.057.039.05.07.0...494.0537.057.06.04.03.02010.0534.0453.054.1
19vs Venezia39.057.938.0418.03420.067.039.011.012.0...476.0492.048.05.05.00.02059.0583.0564.050.8
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20 rows × 152 columns

\n", - "
" - ], - "text/plain": [ - " team players_used possession games games_starts minutes \\\n", - "0 vs Atalanta 32.0 44.9 38.0 418.0 3420.0 \n", - "1 vs Bologna 36.0 49.3 38.0 418.0 3420.0 \n", - "2 vs Cagliari 33.0 55.6 38.0 418.0 3420.0 \n", - "3 vs Empoli 28.0 52.9 38.0 418.0 3420.0 \n", - "4 vs Fiorentina 28.0 41.8 38.0 418.0 3420.0 \n", - "5 vs Genoa 40.0 56.1 38.0 418.0 3420.0 \n", - "6 vs Hellas Verona 31.0 49.4 38.0 418.0 3420.0 \n", - "7 vs Inter 27.0 43.4 38.0 418.0 3420.0 \n", - "8 vs Juventus 32.0 48.3 38.0 418.0 3420.0 \n", - "9 vs Lazio 27.0 44.4 38.0 418.0 3420.0 \n", - "10 vs Milan 28.0 45.8 38.0 418.0 3420.0 \n", - "11 vs Napoli 27.0 41.5 38.0 418.0 3420.0 \n", - "12 vs Roma 29.0 48.6 38.0 418.0 3420.0 \n", - "13 vs Salernitana 42.0 59.0 38.0 418.0 3420.0 \n", - "14 vs Sampdoria 34.0 54.1 38.0 418.0 3420.0 \n", - "15 vs Sassuolo 29.0 44.8 38.0 418.0 3420.0 \n", - "16 vs Spezia 30.0 57.3 38.0 418.0 3420.0 \n", - "17 vs Torino 31.0 46.4 38.0 418.0 3420.0 \n", - "18 vs Udinese 29.0 57.7 38.0 418.0 3420.0 \n", - "19 vs Venezia 39.0 57.9 38.0 418.0 3420.0 \n", - "\n", - " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 45.0 29.0 5.0 6.0 ... 491.0 488.0 69.0 \n", - "1 53.0 32.0 8.0 9.0 ... 527.0 416.0 88.0 \n", - "2 67.0 41.0 7.0 10.0 ... 526.0 524.0 76.0 \n", - "3 64.0 44.0 5.0 10.0 ... 488.0 482.0 66.0 \n", - "4 49.0 33.0 3.0 5.0 ... 585.0 441.0 119.0 \n", - "5 58.0 35.0 4.0 5.0 ... 523.0 530.0 59.0 \n", - "6 57.0 38.0 3.0 4.0 ... 463.0 534.0 32.0 \n", - "7 31.0 19.0 4.0 4.0 ... 440.0 441.0 34.0 \n", - "8 36.0 21.0 4.0 7.0 ... 525.0 471.0 35.0 \n", - "9 57.0 40.0 5.0 6.0 ... 474.0 418.0 73.0 \n", - "10 29.0 19.0 4.0 5.0 ... 533.0 440.0 63.0 \n", - "11 30.0 19.0 1.0 1.0 ... 517.0 415.0 60.0 \n", - "12 41.0 23.0 5.0 6.0 ... 497.0 477.0 81.0 \n", - "13 76.0 58.0 6.0 8.0 ... 546.0 463.0 63.0 \n", - "14 61.0 41.0 3.0 4.0 ... 538.0 471.0 64.0 \n", - "15 65.0 48.0 8.0 9.0 ... 519.0 434.0 142.0 \n", - "16 68.0 44.0 10.0 13.0 ... 474.0 477.0 62.0 \n", - "17 41.0 25.0 9.0 11.0 ... 485.0 611.0 60.0 \n", - "18 57.0 39.0 5.0 7.0 ... 494.0 537.0 57.0 \n", - "19 67.0 39.0 11.0 12.0 ... 476.0 492.0 48.0 \n", - "\n", - " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 4.0 6.0 3.0 2240.0 518.0 \n", - "1 8.0 5.0 1.0 2032.0 520.0 \n", - "2 6.0 4.0 0.0 2112.0 742.0 \n", - "3 7.0 7.0 3.0 2051.0 549.0 \n", - "4 4.0 12.0 0.0 1825.0 456.0 \n", - "5 4.0 7.0 1.0 2257.0 743.0 \n", - "6 3.0 8.0 2.0 2206.0 733.0 \n", - "7 3.0 11.0 1.0 1767.0 476.0 \n", - "8 5.0 6.0 1.0 1894.0 440.0 \n", - "9 6.0 9.0 3.0 1956.0 427.0 \n", - "10 3.0 8.0 3.0 2106.0 517.0 \n", - "11 0.0 14.0 0.0 1963.0 403.0 \n", - "12 5.0 9.0 1.0 1970.0 441.0 \n", - "13 6.0 5.0 1.0 2043.0 506.0 \n", - "14 1.0 4.0 4.0 2174.0 506.0 \n", - "15 6.0 7.0 4.0 2041.0 404.0 \n", - "16 8.0 5.0 3.0 2064.0 589.0 \n", - "17 10.0 6.0 3.0 1989.0 891.0 \n", - "18 6.0 4.0 3.0 2010.0 534.0 \n", - "19 5.0 5.0 0.0 2059.0 583.0 \n", - "\n", - " aerials_lost aerials_won_pct \n", - "0 685.0 43.1 \n", - "1 492.0 51.4 \n", - "2 696.0 51.6 \n", - "3 427.0 56.3 \n", - "4 469.0 49.3 \n", - "5 635.0 53.9 \n", - "6 689.0 51.5 \n", - "7 549.0 46.4 \n", - "8 550.0 44.4 \n", - "9 411.0 51.0 \n", - "10 521.0 49.8 \n", - "11 393.0 50.6 \n", - "12 524.0 45.7 \n", - "13 657.0 43.5 \n", - "14 609.0 45.4 \n", - "15 343.0 54.1 \n", - "16 530.0 52.6 \n", - "17 781.0 53.3 \n", - "18 453.0 54.1 \n", - "19 564.0 50.8 \n", - "\n", - "[20 rows x 152 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_vsteam = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'vs')\n", "\n", @@ -6208,7 +3818,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "22ad9be5", "metadata": {}, "outputs": [], diff --git a/2_votes_dataset_creation.ipynb b/2_votes_dataset_creation.ipynb index a68acd2..ef8ca48 100644 --- a/2_votes_dataset_creation.ipynb +++ b/2_votes_dataset_creation.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "0d4a330d", "metadata": {}, "outputs": [ @@ -189,7 +189,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "loaded votes for matchday 1\n" + "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 22\n", + "loaded votes for matchday 23\n", + "loaded votes for matchday 24\n", + "loaded votes for matchday 25\n", + "loaded votes for matchday 26\n", + "loaded votes for matchday 27\n", + "loaded votes for matchday 28\n", + "loaded votes for matchday 29\n", + "loaded votes for matchday 30\n", + "loaded votes for matchday 31\n", + "loaded votes for matchday 32\n" ] } ], @@ -371,11 +402,11 @@ " ...\n", " \n", " \n", - " 7663\n", - " 27\n", - " Lasagna\n", + " 9097\n", + " 32\n", + " Terracciano F.\n", " Verona\n", - " Sampdoria\n", + " Cremonese\n", " 0\n", " 6.0\n", " 0\n", @@ -384,24 +415,24 @@ " 6.0\n", " \n", " \n", - " 7664\n", - " 27\n", - " Gaich\n", + " 9098\n", + " 32\n", + " Abildgaard\n", " Verona\n", - " Sampdoria\n", + " Cremonese\n", " 0\n", - " 6.5\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 5.5\n", " \n", " \n", - " 7665\n", - " 27\n", + " 9099\n", + " 32\n", " Braaf\n", " Verona\n", - " Sampdoria\n", + " Cremonese\n", " 0\n", " 5.5\n", " 0\n", @@ -410,24 +441,24 @@ " 5.5\n", " \n", " \n", - " 7666\n", - " 27\n", + " 9100\n", + " 32\n", " Kallon\n", " Verona\n", - " Sampdoria\n", + " Cremonese\n", " 0\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " \n", " \n", - " 7667\n", - " 27\n", + " 9101\n", + " 32\n", " Djuric\n", " Verona\n", - " Sampdoria\n", + " Cremonese\n", " 0\n", " 6.0\n", " 0\n", @@ -437,22 +468,22 @@ " \n", " \n", "\n", - "

7668 rows × 10 columns

\n", + "

9102 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", - "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", + " 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", + "9097 32 Terracciano F. Verona Cremonese 0 6.0 0 0 \n", + "9098 32 Abildgaard Verona Cremonese 0 5.5 0 0 \n", + "9099 32 Braaf Verona Cremonese 0 5.5 0 0 \n", + "9100 32 Kallon Verona Cremonese 0 5.5 0 0 \n", + "9101 32 Djuric Verona Cremonese 0 6.0 0 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -461,13 +492,13 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \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", + "9097 0.0 6.0 \n", + "9098 0.0 5.5 \n", + "9099 0.0 5.5 \n", + "9100 0.0 5.5 \n", + "9101 0.0 6.0 \n", "\n", - "[7668 rows x 10 columns]" + "[9102 rows x 10 columns]" ] }, "execution_count": 5, diff --git a/3_players_dataset_creation.ipynb b/3_players_dataset_creation.ipynb index 21ac487..8173e07 100644 --- a/3_players_dataset_creation.ipynb +++ b/3_players_dataset_creation.ipynb @@ -109,33 +109,33 @@ " ...\n", " \n", " \n", - " 615\n", + " 617\n", " Pietro Terracciano\n", " Fiorentina\n", " \n", " \n", - " 616\n", + " 618\n", " Martin Turk\n", " Sampdoria\n", " \n", " \n", - " 617\n", + " 619\n", " Guglielmo Vicario\n", " Empoli\n", " \n", " \n", - " 618\n", + " 620\n", " Jeroen Zoet\n", " Spezia\n", " \n", " \n", - " 619\n", + " 621\n", " Petar Zovko\n", " Spezia\n", " \n", " \n", "\n", - "

620 rows × 2 columns

\n", + "

622 rows × 2 columns

\n", "" ], "text/plain": [ @@ -146,13 +146,13 @@ "3 Christian Acella Cremonese\n", "4 Francesco Acerbi Inter\n", ".. ... ...\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", + "617 Pietro Terracciano Fiorentina\n", + "618 Martin Turk Sampdoria\n", + "619 Guglielmo Vicario Empoli\n", + "620 Jeroen Zoet Spezia\n", + "621 Petar Zovko Spezia\n", "\n", - "[620 rows x 2 columns]" + "[622 rows x 2 columns]" ] }, "execution_count": 3, @@ -330,508 +330,510 @@ "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", + "118 Michele Cerofolini Cerofolini\n", + "119 Federico Chiesa Chiesa\n", + "120 Vlad Chiricheș Chiriches\n", + "121 Daniel Ciofani Ciofani\n", + "122 Tio Cipot Cipot\n", + "123 Patrick Ciurria Ciurria\n", + "124 Omar Colley Colley\n", + "125 Lorenzo Colombo Colombo\n", + "126 Andrea Colpani Colpani\n", + "127 Andrea Consigli Consigli\n", + "128 Andrea Conti Conti\n", + "129 Diego Coppola Coppola\n", + "130 Joaquín Correa Correa\n", + "131 Alessandro Cortinovis Cortinovis\n", + "132 Lassana Coulibaly Coulibaly\n", + "133 Alessio Cragno Cragno\n", + "134 Bryan Cristante Cristante\n", + "135 Domen Črnigoj Crnigoj\n", + "136 Juan Cuadrado Cuadrado\n", + "137 Mickaël Cuisance Cuisance\n", + "138 Marco D'Alessandro DAlessandro\n", + "139 Danilo D'Ambrosio DAmbrosio\n", + "140 Luca D'Andrea DAndrea\n", + "141 Flavius Daniliuc Daniliuc\n", + "142 Danilo Danilo\n", + "143 Matteo Darmian Darmian\n", + "144 Paweł Dawidowicz Dawidowicz\n", + "145 Charles De Ketelaere Ketelaere\n", + "146 Manuel De Luca Luca\n", + "147 Mattia De Sciglio Sciglio\n", + "148 Lorenzo De Silvestri Silvestri\n", + "149 Koni De Winter Winter\n", + "150 Grégoire Defrel Defrel\n", + "151 Duccio Degl'Innocenti DeglInnocenti\n", + "152 Merih Demiral Demiral\n", + "153 Diego Demme Demme\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", + "155 Fabio Depaoli Depaoli\n", + "156 Kastriot Dermaku Dermaku\n", + "157 Cyriel Dessers Dessers\n", + "158 Sergiño Dest Dest\n", + "159 Mattia Destro Destro\n", + "160 Gerard Deulofeu Deulofeu\n", + "161 Samuel Di Carmine Carmine\n", + "162 Federico Di Francesco Francesco\n", + "163 Michele Di Gregorio Gregorio\n", + "164 Giovanni Di Lorenzo Lorenzo\n", + "165 Ángel Di María Maria\n", + "166 Boulaye Dia Dia\n", + "167 Brahim Díaz Diaz\n", + "168 Federico Dimarco Dimarco\n", + "169 Koffi Djidji Djidji\n", + "170 Berat Djimsiti Djimsiti\n", + "171 Dodô Dodo\n", + "172 Josh Doig Doig\n", + "173 Nicolás Domínguez Dominguez\n", + "174 Giulio Donati Donati\n", + "175 Bartłomiej Drągowski Dragowski\n", + "176 Ondrej Duda Duda\n", + "177 Denzel Dumfries Dumfries\n", + "178 Alfred Duncan Duncan\n", + "179 Paulo Dybala Dybala\n", + "180 Edin Džeko Dzeko\n", + "181 Festy Ebosele Ebosele\n", + "182 Enzo Ebosse Ebosse\n", + "183 Tyronne Ebuehi Ebuehi\n", + "184 Éderson Ederson\n", + "185 Kingsley Ehizibue Ehizibue\n", + "186 Albin Ekdal Ekdal\n", + "187 Emmanuel Ekong Ekong\n", + "188 Mikael Ellertsson Ellertsson\n", + "189 Elif Elmas Elmas\n", + "190 Martin Erlic Erlic\n", + "191 Gonzalo Escalante Escalante\n", + "192 Salvatore Esposito Esposito\n", + "193 Nicolò Fagioli Fagioli\n", + "194 Wladimiro Falcone Falcone\n", + "195 Davide Faraoni Faraoni\n", + "196 Federico Fazio Fazio\n", + "197 Jacopo Fazzini Fazzini\n", + "198 Lewis Ferguson Ferguson\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", + "200 Alex Ferrari Ferrari\n", + "201 Salvador Ferrer Ferrer\n", + "202 Alessandro Florenzi Florenzi\n", + "203 Davide Frattesi Frattesi\n", + "204 Matteo Gabbia Gabbia\n", + "205 Manolo Gabbiadini Gabbiadini\n", + "206 Gianluca Gaetano Gaetano\n", + "207 Roberto Gagliardini Gagliardini\n", + "208 Adolfo Gaich Gaich\n", + "209 Pablo Galdames Millán Millan\n", + "210 Antonino Gallo Gallo\n", + "211 Federico Gatti Gatti\n", + "212 Valentin Gendrey Gendrey\n", + "213 Paolo Ghiglione Ghiglione\n", + "214 Mario Gila Gila\n", + "215 Gvidas Gineitis Gineitis\n", + "216 Olivier Giroud Giroud\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", + "218 Pierluigi Gollini Gollini\n", + "219 Joan Gonzàlez Gonzalez\n", + "220 Nicolás González Gonzalez\n", + "221 Robin Gosens Gosens\n", + "222 Alberto Grassi Grassi\n", + "223 Andrew Gravillon Gravillon\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", + "225 Koray Günter Gunter\n", + "226 Emmanuel Gyasi Gyasi\n", + "227 Norbert Gyömbér Gyomber\n", + "228 Christian Gytkjær Gytkjr\n", + "229 Nicolas Haas Haas\n", + "230 Samir Handanović Handanovic\n", + "231 Abdou Harroui Harroui\n", + "232 Hans Hateboer Hateboer\n", + "233 Liam Henderson Henderson\n", + "234 Jack Hendry Hendry\n", + "235 Matheus Henrique Henrique\n", + "236 Thomas Henry Henry\n", + "237 Theo Hernández Hernandez\n", + "238 Isak Hien Hien\n", + "239 Morten Hjulmand Hjulmand\n", + "240 Emil Holm Holm\n", + "241 Martin Hongla Hongla\n", + "242 Petko Hristov Hristov\n", + "243 Ajdin Hrustic Hrustic\n", + "244 Elseid Hysaj Hysaj\n", + "245 Rasmus Højlund Hjlund\n", + "246 Roger Ibanez Ibanez\n", + "247 Zlatan Ibrahimović Ibrahimovic\n", + "248 Igor Igor\n", + "249 Jonathan Ikone Ikone\n", "250 Ivan Ilić Ilic\n", - "251 Samuel Iling-Junior Iling-Junior\n", - "252 Emirhan İlkhan Ilkhan\n", + "251 Ivan Ilić Ilic\n", + "252 Samuel Iling-Junior Iling-Junior\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", + "254 Emirhan İlkhan Ilkhan\n", + "255 Ciro Immobile Immobile\n", + "256 Ardian Ismajli Ismajli\n", + "257 Armando Izzo Izzo\n", + "258 Mato Jajalo Jajalo\n", + "259 Jesé Jese\n", + "260 Juan Jesus Jesus\n", + "261 Þórir Jóhann Helgason Helgason\n", + "262 Luka Jović Jovic\n", + "263 Hamed Junior Traorè Traore\n", + "264 Yayah Kallon Kallon\n", + "265 Pierre Kalulu Kalulu\n", + "266 Yann Karamoh Karamoh\n", + "267 Rick Karsdorp Karsdorp\n", + "268 Denso Kasius Kasius\n", + "269 Grigoris Kastanos Kastanos\n", + "270 Moise Kean Kean\n", + "271 Jakub Kiwior Kiwior\n", + "272 Simon Kjær Kjr\n", + "273 Teun Koopmeiners Koopmeiners\n", + "274 Filip Kostić Kostic\n", + "275 Christian Kouamé Kouame\n", + "276 Viktor Kovalenko Kovalenko\n", + "277 Julian Kristoffersen Kristoffersen\n", + "278 Raimonds Krollis Krollis\n", + "279 Rade Krunić Krunic\n", + "280 Marash Kumbulla Kumbulla\n", + "281 Khvicha Kvaratskhelia Kvaratskhelia\n", "282 Giorgos Kyriakopoulos Kyriakopoulos\n", - "283 Sam Lammers Lammers\n", + "283 Giorgos Kyriakopoulos Kyriakopoulos\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", + "285 Sam Lammers Lammers\n", + "286 Kevin Lasagna Lasagna\n", + "287 Armand Lauriente Lauriente\n", + "288 Valentino Lazaro Lazaro\n", + "289 Marko Lazetić Lazetic\n", + "290 Darko Lazović Lazovic\n", + "291 Manuel Lazzari Lazzari\n", + "292 Rafael Leão Leao\n", + "293 Mehdi Léris Leris\n", + "294 Karol Linetty Linetty\n", + "295 Marcin Listkowski Listkowski\n", + "296 Diego Llorente Llorente\n", + "297 Stanislav Lobotka Lobotka\n", + "298 Manuel Locatelli Locatelli\n", + "299 Luka Lochoshvili Lochoshvili\n", + "300 Ademola Lookman Lookman\n", + "301 Maxime Lopez Lopez\n", + "302 Matteo Lovato Lovato\n", + "303 Sandi Lovrić Lovric\n", + "304 Hirving Lozano Lozano\n", + "305 Jhon Lucumí Lucumi\n", + "306 José Luis Palomino Palomino\n", + "307 Romelu Lukaku Lukaku\n", + "308 Saša Lukić Lukic\n", + "309 Sebastiano Luperto Luperto\n", + "310 Charalambos Lykogiannis Lykogiannis\n", + "311 Giulio Maggiore Maggiore\n", + "312 Giangiacomo Magnani Magnani\n", + "313 Mike Maignan Maignan\n", + "314 Jordan Majchrzak Majchrzak\n", + "315 Jean-Victor Makengo Makengo\n", + "316 Lorenzo Malagrida Malagrida\n", + "317 Daniel Maldini Maldini\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", + "319 Youssef Maleh Maleh\n", + "320 Ruslan Malinovskyi Malinovskyi\n", + "321 Gianluca Mancini Mancini\n", + "322 Rolando Mandragora Mandragora\n", + "323 Federico Marchetti Marchetti\n", + "324 Riccardo Marchizza Marchizza\n", + "325 Gian Marco Ferrari Ferrari\n", + "326 Pablo Marí Mari\n", + "327 Răzvan Marin Marin\n", + "328 Marlon Marlon\n", + "329 Luca Marrone Marrone\n", + "330 Lautaro Martínez Martinez\n", + "331 Lucas Martínez Quarta Quarta\n", + "332 Adam Marušić Marusic\n", + "333 Adam Masina Masina\n", + "334 Nemanja Matić Matic\n", + "335 Luís Maximiano Maximiano\n", + "336 Pasquale Mazzocchi Mazzocchi\n", + "337 Weston McKennie McKennie\n", + "338 Gary Medel Medel\n", + "339 Soualiho Meïté Meite\n", + "340 Alex Meret Meret\n", + "341 Yıldırım Mert Çetin Cetin\n", + "342 Junior Messias Messias\n", + "343 Tommaso Milanese Milanese\n", + "344 Nikola Milenković Milenkovic\n", + "345 Arkadiusz Milik Milik\n", + "346 Sergej Milinković-Savić Milinkovic-Savic\n", + "347 Vanja Milinković-Savić Milinkovic-Savic\n", + "348 Kim Min-jae Min-jae\n", + "349 Aleksei Miranchuk Miranchuk\n", + "350 Fabio Miretti Miretti\n", + "351 Henrikh Mkhitaryan Mkhitaryan\n", + "352 Salvatore Molina Molina\n", + "353 Daniele Montevago Montevago\n", + "354 Lorenzo Montipò Montipo\n", + "355 Nikola Moro Moro\n", + "356 Dany Mota Mota\n", + "357 João Moutinho Moutinho\n", + "358 Mert Müldür Muldur\n", + "359 Luis Muriel Muriel\n", + "360 Jeison Murillo Murillo\n", + "361 Nicola Murru Murru\n", + "362 Juan Musso Musso\n", + "363 Joakim Mæhle Mhle\n", + "364 Herculano Nabian Nabian\n", + "365 Michel Ndary Adopo Adopo\n", + "366 Tanguy Ndombele Ndombele\n", + "367 Ilija Nestorovski Nestorovski\n", + "368 Cyril Ngonge Ngonge\n", + "369 Hans Nicolussi Caviglia Caviglia\n", + "370 Dimitris Nikolaou Nikolaou\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", + "372 Bram Nuytinck Nuytinck\n", + "373 M'Bala Nzola Nzola\n", + "374 Pedro Obiang Obiang\n", + "375 Guillermo Ochoa Ochoa\n", + "376 Marios Oikonomou Oikonomou\n", + "377 David Okereke Okereke\n", + "378 Caleb Okoli Okoli\n", + "379 Mathías Olivera Olivera\n", + "380 André Onana Onana\n", + "381 Divock Origi Origi\n", + "382 Riccardo Orsolini Orsolini\n", + "383 Victor Osimhen Osimhen\n", + "384 Remi Oudin Oudin\n", + "385 Adam Ounas Ounas\n", + "386 Simone Pafundi Pafundi\n", + "387 Flavio Paoletti Paoletti\n", + "388 Leandro Paredes Paredes\n", + "389 Fabiano Parisi Parisi\n", + "390 Mario Pašalić Pasalic\n", + "391 Patric Patric\n", + "392 Rui Patrício Patricio\n", + "393 Pedro Pedro\n", + "394 Gianluca Pegolo Pegolo\n", + "395 Pietro Pellegri Pellegri\n", + "396 Lorenzo Pellegrini Pellegrini\n", + "397 Luca Pellegrini Pellegrini\n", + "398 Pepín Pepin\n", + "399 Roberto Pereyra Pereyra\n", + "400 Nehuén Pérez Perez\n", + "401 Simone Perilli Perilli\n", + "402 Mattia Perin Perin\n", + "403 Samuele Perisan Perisan\n", + "404 Matteo Pessina Pessina\n", + "405 Andrea Petagna Petagna\n", + "406 Giuseppe Pezzella Pezzella\n", + "407 Krzysztof Piątek Piatek\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", + "409 Roberto Piccoli Piccoli\n", + "410 Charles Pickel Pickel\n", + "411 Andrea Pinamonti Pinamonti\n", + "412 Lorenzo Pirola Pirola\n", + "413 Marko Pjaca Pjaca\n", + "414 Tommaso Pobega Pobega\n", + "415 Paul Pogba Pogba\n", + "416 Matteo Politano Politano\n", + "417 Marin Pongračić Pongracic\n", + "418 Stefan Posch Posch\n", + "419 Ivan Provedel Provedel\n", + "420 Ignacio Pussetto Pussetto\n", + "421 Niklas Pyyhtiä Pyyhtia\n", + "422 Fabio Quagliarella Quagliarella\n", + "423 Giacomo Quagliata Quagliata\n", + "424 Adrien Rabiot Rabiot\n", + "425 Nemanja Radonjić Radonjic\n", + "426 Ivan Radovanović Radovanovic\n", + "427 Ionuț Radu Radu\n", + "428 Antonio Raimondo Raimondo\n", + "429 Luca Ranieri Ranieri\n", + "430 Andrea Ranocchia Ranocchia\n", + "431 Filippo Ranocchia Ranocchia\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", + "433 Giacomo Raspadori Raspadori\n", + "434 Nicola Ravaglia Ravaglia\n", + "435 Ante Rebić Rebic\n", + "436 Arkadiusz Reca Reca\n", + "437 Panagiotis Retsos Retsos\n", + "438 Franck Ribéry Ribery\n", + "439 Samuele Ricci Ricci\n", + "440 Tomás Rincón Rincon\n", + "441 Pablo Rodríguez Rodriguez\n", + "442 Ricardo Rodríguez Rodriguez\n", + "443 Rogério Rogerio\n", + "444 Alessio Romagnoli Romagnoli\n", + "445 Simone Romagnoli Romagnoli\n", + "446 Luka Romero Romero\n", + "447 Marten de Roon Roon\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", + "449 Nicolò Rovella Rovella\n", + "450 Amir Rrahmani Rrahmani\n", + "451 Ruan Ruan\n", + "452 Daniele Rugani Rugani\n", + "453 Matteo Ruggeri Ruggeri\n", + "454 Mário Rui Rui\n", + "455 Abdelhamid Sabiri Sabiri\n", + "456 Alexis Saelemaekers Saelemaekers\n", + "457 Jacopo Sala Sala\n", + "458 Lazar Samardzic Samardzic\n", + "459 Junior Sambia Sambia\n", + "460 Antonio Sanabria Sanabria\n", + "461 Leandro Sanca Sanca\n", + "462 Alex Sandro Sandro\n", + "463 Nicola Sansone Sansone\n", + "464 Riccardo Saponara Saponara\n", + "465 Martin Satriano Satriano\n", + "466 Giorgio Scalvini Scalvini\n", + "467 Jerdy Schouten Schouten\n", + "468 Perr Schuurs Schuurs\n", + "469 Demba Seck Seck\n", + "470 Jacopo Segre Segre\n", + "471 Vivaldo Semedo Semedo\n", + "472 Stefano Sensi Sensi\n", + "473 Luigi Sepe Sepe\n", + "474 Leonardo Sernicola Sernicola\n", + "475 Stephan El Shaarawy Shaarawy\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", + "477 Eldor Shomurodov Shomurodov\n", + "478 Marco Silvestri Silvestri\n", + "479 Giovanni Simeone Simeone\n", + "480 Wilfried Singo Singo\n", + "481 Salvatore Sirigu Sirigu\n", + "482 Leo Skiri Østigård stigard\n", + "483 Łukasz Skorupski Skorupski\n", + "484 Milan Škriniar Skriniar\n", + "485 Chris Smalling Smalling\n", + "486 Ola Solbakken Solbakken\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", + "488 Brandon Soppy Soppy\n", + "489 Roberto Soriano Soriano\n", + "490 Joaquin Sosa Sosa\n", + "491 Riccardo Sottil Sottil\n", + "492 Matìas Soulé Soule\n", + "493 Adama Soumaoro Soumaoro\n", + "494 Leonardo Spinazzola Spinazzola\n", + "495 Marco Sportiello Sportiello\n", + "496 Petar Stojanović Stojanovic\n", + "497 Gabriel Strefezza Strefezza\n", + "498 Dávid Strelec Strelec\n", + "499 Isaac Success Success\n", + "500 Ibrahim Sulemana Sulemana\n", + "501 Wojciech Szczęsny Szczesny\n", + "502 Benjamin Tahirovic Tahirovic\n", + "503 Adrien Tameze Tameze\n", + "504 Ciprian Tătărușanu Tatarusanu\n", + "505 Filippo Terracciano Terracciano\n", + "506 Pietro Terracciano Terracciano\n", + "507 Aleksa Terzić Terzic\n", + "508 Florian Thauvin Thauvin\n", + "509 Malick Thiaw Thiaw\n", + "510 Kristian Thorstvedt Thorstvedt\n", + "511 Jeremy Toljan Toljan\n", + "512 Rafael Tolói Toloi\n", + "513 Fikayo Tomori Tomori\n", + "514 Sandro Tonali Tonali\n", + "515 Lorenzo Tonelli Tonelli\n", + "516 William Troost-Ekong Troost-Ekong\n", + "517 Frank Tsadjout Tsadjout\n", + "518 Alessandro Tuia Tuia\n", + "519 Martin Turk Turk\n", + "520 Iyenoma Udogie Udogie\n", + "521 Samuel Umtiti Umtiti\n", + "522 Diego Valencia Valencia\n", + "523 Emanuele Valeri Valeri\n", + "524 Mattia Valoti Valoti\n", + "525 Johan Vásquez Vasquez\n", + "526 Matías Vecino Vecino\n", + "527 Miguel Veloso Veloso\n", + "528 Lorenzo Venuti Venuti\n", + "529 Daniele Verde Verde\n", + "530 Simone Verdi Verdi\n", + "531 Valerio Verre Verre\n", + "532 Guglielmo Vicario Vicario\n", "533 Ronaldo Vieira Vieira\n", - "534 Emanuel Vignato Vignato\n", + "534 Ronaldo Vieira Vieira\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", + "536 Emanuel Vignato Vignato\n", + "537 Samuele Vignato Vignato\n", + "538 Tonny Vilhena Vilhena\n", + "539 Gonzalo Villar Villar\n", + "540 Matías Viña Vina\n", + "541 Dušan Vlahović Vlahovic\n", + "542 Nikola Vlašić Vlasic\n", + "543 Joel Voelkerling Persson Persson\n", + "544 Mërgim Vojvoda Vojvoda\n", + "545 Cristian Volpato Volpato\n", + "546 Lukáš Vorlický Vorlicky\n", + "547 Aster Vranckx Vranckx\n", + "548 Stefan de Vrij Vrij\n", + "549 Walace Walace\n", + "550 Sebastian Walukiewicz Walukiewicz\n", + "551 Georginio Wijnaldum Wijnaldum\n", + "552 Harry Winks Winks\n", + "553 Przemysław Wiśniewski Wisniewski\n", + "554 Gerard Yepes Yepes\n", + "555 Mattia Zaccagni Zaccagni\n", + "556 Denis Zakaria Zakaria\n", + "557 Nicola Zalewski Zalewski\n", + "558 Andre-Frank Zambo Anguissa Anguissa\n", + "559 Luca Zanimacchia Zanimacchia\n", + "560 Nicolò Zaniolo Zaniolo\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", + "562 Alessandro Zanoli Zanoli\n", + "563 Mattia Zanotti Zanotti\n", + "564 Duván Zapata Zapata\n", + "565 Davide Zappacosta Zappacosta\n", + "566 Karim Zedadka Zedadka\n", + "567 Deyovaisio Zeefuik Zeefuik\n", + "568 Marvin Zeegelaar Zeegelaar\n", + "569 Alessio Zerbin Zerbin\n", + "570 Piotr Zieliński Zielinski\n", + "571 David Zima Zima\n", + "572 Joshua Zirkzee Zirkzee\n", + "573 Jeroen Zoet Zoet\n", "574 Nadir Zortea Zortea\n", - "575 Petar Zovko Zovko\n", - "576 Szymon Żurkowski Zurkowski\n", + "575 Nadir Zortea Zortea\n", + "576 Petar Zovko Zovko\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" + "578 Szymon Żurkowski Zurkowski\n", + "579 Milan Đurić uric\n", + "580 Filip Đuričić uricic\n", + "581 Emil Audero Audero\n", + "582 Francesco Bardi Bardi\n", + "583 Marco Carnesecchi Carnesecchi\n", + "584 Michele Cerofolini Cerofolini\n", + "585 Andrea Consigli Consigli\n", + "586 Alessio Cragno Cragno\n", + "587 Michele Di Gregorio Gregorio\n", + "588 Bartłomiej Drągowski Dragowski\n", + "589 Wladimiro Falcone Falcone\n", + "590 Pierluigi Gollini Gollini\n", + "591 Pierluigi Gollini Gollini\n", + "592 Samir Handanović Handanovic\n", + "593 Mike Maignan Maignan\n", + "594 Federico Marchetti Marchetti\n", + "595 Luís Maximiano Maximiano\n", + "596 Alex Meret Meret\n", + "597 Vanja Milinković-Savić Milinkovic-Savic\n", + "598 Lorenzo Montipò Montipo\n", + "599 Juan Musso Musso\n", + "600 Guillermo Ochoa Ochoa\n", + "601 André Onana Onana\n", + "602 Rui Patrício Patricio\n", + "603 Gianluca Pegolo Pegolo\n", + "604 Simone Perilli Perilli\n", + "605 Mattia Perin Perin\n", + "606 Samuele Perisan Perisan\n", + "607 Ivan Provedel Provedel\n", + "608 Ionuț Radu Radu\n", + "609 Nicola Ravaglia Ravaglia\n", + "610 Luigi Sepe Sepe\n", + "611 Marco Silvestri Silvestri\n", + "612 Salvatore Sirigu Sirigu\n", + "613 Łukasz Skorupski Skorupski\n", + "614 Marco Sportiello Sportiello\n", + "615 Wojciech Szczęsny Szczesny\n", + "616 Ciprian Tătărușanu Tatarusanu\n", + "617 Pietro Terracciano Terracciano\n", + "618 Martin Turk Turk\n", + "619 Guglielmo Vicario Vicario\n", + "620 Jeroen Zoet Zoet\n", + "621 Petar Zovko Zovko\n" ] } ], @@ -1021,22 +1023,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2263921821.py:17: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\2263921821.py:18: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\2263921821.py:14: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\2263921821.py:15: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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", @@ -1241,12 +1243,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\2200947104.py:4: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\2200947104.py:11: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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", @@ -1306,7 +1308,6 @@ "Cordaz not found\n", "Pinsoglio not found\n", "Fiorillo not found\n", - "Cerofolini not found\n", "Rossi F. not found\n", "Ravaglia F. from previous team stats\n", "Brancolini not found\n", @@ -1333,17 +1334,14 @@ "Amey not found\n", "Buta not found\n", "Guessand A. not found\n", - "Guarino not found\n", - "Machin not found\n", - "Akpa Akpro not found\n", - "Galdames not found\n" + "Guarino not found\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_12904\\362391242.py:10: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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", @@ -1354,6 +1352,9 @@ "name": "stdout", "output_type": "stream", "text": [ + "Machin not found\n", + "Akpa Akpro not found\n", + "Galdames not found\n", "Darboe not found\n", "Urbanski not found\n", "Bertini not found\n", @@ -1430,7 +1431,7 @@ " Napoli\n", " Meret\n", " \n", - " 594\n", + " 596\n", " \n", " \n", " 1\n", @@ -1440,7 +1441,7 @@ " Lazio\n", " Provedel\n", " \n", - " 605\n", + " 607\n", " \n", " \n", " 2\n", @@ -1450,7 +1451,7 @@ " Empoli\n", " Vicario\n", " \n", - " 617\n", + " 619\n", " \n", " \n", " 3\n", @@ -1460,7 +1461,7 @@ " Juventus\n", " Szczesny\n", " \n", - " 613\n", + " 615\n", " \n", " \n", " 4\n", @@ -1470,7 +1471,7 @@ " Lecce\n", " Falcone\n", " \n", - " 587\n", + " 589\n", " \n", " \n", " ...\n", @@ -1490,7 +1491,7 @@ " Sampdoria\n", " Luca\n", " \n", - " 145\n", + " 146\n", " \n", " \n", " 539\n", @@ -1500,7 +1501,7 @@ " Lecce\n", " Persson\n", " \n", - " 542\n", + " 543\n", " \n", " \n", " 540\n", @@ -1510,7 +1511,7 @@ " Sampdoria\n", " Montevago\n", " \n", - " 352\n", + " 353\n", " \n", " \n", " 541\n", @@ -1520,7 +1521,7 @@ " Spezia\n", " Krollis\n", " \n", - " 277\n", + " 278\n", " \n", " \n", " 542\n", @@ -1539,16 +1540,16 @@ ], "text/plain": [ " id r name team surname initial fb_ID\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", + "0 572 P Meret Napoli Meret 596\n", + "1 2814 P Provedel Lazio Provedel 607\n", + "2 4964 P Vicario Empoli Vicario 619\n", + "3 453 P Szczesny Juventus Szczesny 615\n", + "4 2134 P Falcone Lecce Falcone 589\n", ".. ... .. ... ... ... ... ...\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", + "538 5512 A De Luca Sampdoria Luca 146\n", + "539 5837 A Voelkerling Persson Lecce Persson 543\n", + "540 6113 A Montevago Sampdoria Montevago 353\n", + "541 6143 A Krollis Spezia Krollis 278\n", "542 6160 A Vivaldo Udinese Vivaldo -1\n", "\n", "[543 rows x 7 columns]" @@ -1581,39 +1582,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\2261782218.py:9: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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", @@ -1680,87 +1681,87 @@ "name": "stderr", "output_type": "stream", "text": [ - "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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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_12904\\3664433845.py:11: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_3044\\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", @@ -1841,21 +1842,21 @@ " Napoli\n", " Meret\n", " \n", - " 594\n", - " 26-034\n", + " 596\n", + " 26-040\n", " 1997\n", " 0\n", " ...\n", - " 26.2\n", - " 185\n", - " 20.0\n", - " 26.8\n", - " 308\n", + " 26.3\n", + " 189\n", + " 20.1\n", + " 26.9\n", + " 315\n", " 10\n", " 3.2\n", - " 32\n", - " 1.07\n", - " 17.0\n", + " 35\n", + " 1.13\n", + " 17.1\n", " \n", " \n", " 1\n", @@ -1865,21 +1866,21 @@ " Lazio\n", " Provedel\n", " \n", - " 605\n", - " 29-039\n", + " 607\n", + " 29-045\n", " 1994\n", " 0\n", " ...\n", - " 33.0\n", - " 161\n", - " 36.0\n", + " 33.3\n", + " 166\n", + " 36.1\n", " 34.7\n", - " 402\n", - " 17\n", - " 4.2\n", + " 422\n", + " 18\n", + " 4.3\n", " 46\n", - " 1.49\n", - " 16.4\n", + " 1.44\n", + " 16.0\n", " \n", " \n", " 2\n", @@ -1889,20 +1890,20 @@ " Empoli\n", " Vicario\n", " \n", - " 617\n", - " 26-200\n", + " 619\n", + " 26-206\n", " 1996\n", " 0\n", " ...\n", - " 33.3\n", - " 139\n", - " 48.9\n", + " 33.4\n", + " 142\n", + " 49.3\n", " 42.6\n", - " 489\n", + " 504\n", " 28\n", - " 5.7\n", + " 5.6\n", " 15\n", - " 0.63\n", + " 0.60\n", " 10.7\n", " \n", " \n", @@ -1913,21 +1914,21 @@ " Juventus\n", " Szczesny\n", " \n", - " 613\n", - " 33-007\n", + " 615\n", + " 33-013\n", " 1990\n", " 0\n", " ...\n", - " 34.3\n", - " 114\n", - " 49.1\n", - " 41.5\n", - " 301\n", + " 34.4\n", + " 119\n", + " 49.6\n", + " 41.4\n", + " 312\n", " 9\n", - " 3.0\n", + " 2.9\n", " 19\n", - " 0.89\n", - " 15.4\n", + " 0.85\n", + " 15.1\n", " \n", " \n", " 4\n", @@ -1937,21 +1938,21 @@ " Lecce\n", " Falcone\n", " \n", - " 587\n", - " 28-013\n", + " 589\n", + " 28-019\n", " 1995\n", " 0\n", " ...\n", - " 41.9\n", - " 225\n", - " 77.3\n", - " 52.4\n", - " 441\n", + " 41.8\n", + " 228\n", + " 77.6\n", + " 52.6\n", + " 452\n", " 22\n", - " 5.0\n", - " 35\n", + " 4.9\n", + " 36\n", " 1.13\n", - " 13.7\n", + " 13.9\n", " \n", " \n", " ...\n", @@ -1985,8 +1986,8 @@ " Sampdoria\n", " Luca\n", " \n", - " 145\n", - " 24-282\n", + " 146\n", + " 24-288\n", " 1998\n", " 2\n", " ...\n", @@ -2009,8 +2010,8 @@ " Lecce\n", " Persson\n", " \n", - " 542\n", - " 20-100\n", + " 543\n", + " 20-106\n", " 2003\n", " 7\n", " ...\n", @@ -2033,8 +2034,8 @@ " Sampdoria\n", " Montevago\n", " \n", - " 352\n", - " 20-038\n", + " 353\n", + " 20-044\n", " 2003\n", " 6\n", " ...\n", @@ -2057,10 +2058,10 @@ " Spezia\n", " Krollis\n", " \n", - " 277\n", - " 21-179\n", + " 278\n", + " 21-185\n", " 2001\n", - " 1\n", + " 2\n", " ...\n", " 0.0\n", " 0\n", @@ -2104,37 +2105,37 @@ ], "text/plain": [ " id r name team surname initial fb_ID \\\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", + "0 572 P Meret Napoli Meret 596 \n", + "1 2814 P Provedel Lazio Provedel 607 \n", + "2 4964 P Vicario Empoli Vicario 619 \n", + "3 453 P Szczesny Juventus Szczesny 615 \n", + "4 2134 P Falcone Lecce Falcone 589 \n", ".. ... .. ... ... ... ... ... \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", + "538 5512 A De Luca Sampdoria Luca 146 \n", + "539 5837 A Voelkerling Persson Lecce Persson 543 \n", + "540 6113 A Montevago Sampdoria Montevago 353 \n", + "541 6143 A Krollis Spezia Krollis 278 \n", "542 6160 A Vivaldo Udinese Vivaldo -1 \n", "\n", " age birth_year games ... gk_passes_length_avg gk_goal_kicks \\\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", + "0 26-040 1997 0 ... 26.3 189 \n", + "1 29-045 1994 0 ... 33.3 166 \n", + "2 26-206 1996 0 ... 33.4 142 \n", + "3 33-013 1990 0 ... 34.4 119 \n", + "4 28-019 1995 0 ... 41.8 228 \n", ".. ... ... ... ... ... ... \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", + "538 24-288 1998 2 ... 0.0 0 \n", + "539 20-106 2003 7 ... 0.0 0 \n", + "540 20-044 2003 6 ... 0.0 0 \n", + "541 21-185 2001 2 ... 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 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", + "0 20.1 26.9 315 \n", + "1 36.1 34.7 422 \n", + "2 49.3 42.6 504 \n", + "3 49.6 41.4 312 \n", + "4 77.6 52.6 452 \n", ".. ... ... ... \n", "538 0.0 0.0 0 \n", "539 0.0 0.0 0 \n", @@ -2144,10 +2145,10 @@ "\n", " gk_crosses_stopped gk_crosses_stopped_pct \\\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", + "1 18 4.3 \n", + "2 28 5.6 \n", + "3 9 2.9 \n", + "4 22 4.9 \n", ".. ... ... \n", "538 0 0.0 \n", "539 0 0.0 \n", @@ -2156,11 +2157,11 @@ "542 0 0.0 \n", "\n", " gk_def_actions_outside_pen_area gk_def_actions_outside_pen_area_per90 \\\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", + "0 35 1.13 \n", + "1 46 1.44 \n", + "2 15 0.60 \n", + "3 19 0.85 \n", + "4 36 1.13 \n", ".. ... ... \n", "538 0 0.00 \n", "539 0 0.00 \n", @@ -2169,11 +2170,11 @@ "542 0 0.00 \n", "\n", " gk_avg_distance_def_actions \n", - "0 17.0 \n", - "1 16.4 \n", + "0 17.1 \n", + "1 16.0 \n", "2 10.7 \n", - "3 15.4 \n", - "4 13.7 \n", + "3 15.1 \n", + "4 13.9 \n", ".. ... \n", "538 0.0 \n", "539 0.0 \n", @@ -2231,8 +2232,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "vote_avg 6.214595\n", - "vote_std 0.457012\n", + "vote_avg 6.219162\n", + "vote_std 0.463073\n", "dtype: float64\n" ] }, @@ -2264,43 +2265,43 @@ " \n", " \n", " 0\n", - " 6.200000\n", - " 0.420317\n", + " 6.193548\n", + " 0.414990\n", " \n", " \n", " 1\n", - " 6.274194\n", - " 0.418112\n", + " 6.296875\n", + " 0.430468\n", " \n", " \n", " 2\n", - " 6.458333\n", - " 0.379601\n", + " 6.440000\n", + " 0.382623\n", " \n", " \n", " 3\n", - " 6.090909\n", - " 0.324610\n", + " 6.108696\n", + " 0.328254\n", " \n", " \n", " 4\n", - " 6.225806\n", - " 0.521145\n", + " 6.250000\n", + " 0.530330\n", " \n", " \n", " 5\n", - " 6.274194\n", - " 0.418112\n", + " 6.265625\n", + " 0.414284\n", " \n", " \n", " 6\n", " 6.000000\n", - " 0.595683\n", + " 0.586302\n", " \n", " \n", " 7\n", - " 6.100000\n", - " 0.300000\n", + " 6.142857\n", + " 0.349927\n", " \n", " \n", " 8\n", @@ -2309,8 +2310,8 @@ " \n", " \n", " 9\n", - " 6.064516\n", - " 0.396394\n", + " 6.031250\n", + " 0.431884\n", " \n", " \n", " 10\n", @@ -2319,18 +2320,18 @@ " \n", " \n", " 11\n", - " 6.233333\n", - " 0.359011\n", + " 6.218750\n", + " 0.352170\n", " \n", " \n", " 12\n", - " 6.363636\n", - " 0.504115\n", + " 6.369565\n", + " 0.493818\n", " \n", " \n", " 13\n", - " 6.316667\n", - " 0.524140\n", + " 6.322581\n", + " 0.516633\n", " \n", " \n", " 14\n", @@ -2339,23 +2340,23 @@ " \n", " \n", " 15\n", - " 6.233333\n", - " 0.460676\n", + " 6.258065\n", + " 0.472996\n", " \n", " \n", " 16\n", - " 6.183333\n", - " 0.539804\n", + " 6.241935\n", + " 0.620496\n", " \n", " \n", " 17\n", - " 6.051724\n", - " 0.546854\n", + " 6.050000\n", + " 0.537742\n", " \n", " \n", " 18\n", - " 6.250000\n", - " 0.508850\n", + " 6.258621\n", + " 0.502077\n", " \n", " \n", " 19\n", @@ -2374,8 +2375,8 @@ " \n", " \n", " 22\n", - " 6.166667\n", - " 0.527046\n", + " 6.150000\n", + " 0.502494\n", " \n", " \n", " 23\n", @@ -2384,46 +2385,52 @@ " \n", " \n", " 24\n", - " 6.607143\n", - " 0.602927\n", + " 6.633333\n", + " 0.590668\n", " \n", " \n", " 25\n", " 6.214286\n", " 0.364216\n", " \n", + " \n", + " 26\n", + " 6.200000\n", + " 0.509902\n", + " \n", " \n", "\n", "" ], "text/plain": [ " vote_avg vote_std\n", - "0 6.200000 0.420317\n", - "1 6.274194 0.418112\n", - "2 6.458333 0.379601\n", - 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idrnameteamsurnameinitialfb_IDagebirth_yeargames...gk_pct_goal_kicks_launchedgk_goal_kick_length_avggk_crossesgk_crosses_stoppedgk_crosses_stopped_pctgk_def_actions_outside_pen_areagk_def_actions_outside_pen_area_per90gk_avg_distance_def_actionsvote_avgvote_std
0572PMeretNapoliMeret59426-03419970...20.026.8308103.2321.0717.06.2000000.420317
12814PProvedelLazioProvedel60529-03919940...36.034.7402174.2461.4916.46.2741940.418112
24964PVicarioEmpoliVicario61726-20019960...48.942.6489285.7150.6310.76.4583330.379601
3453PSzczesnyJuventusSzczesny61333-00719900...49.141.530193.0190.8915.46.0909090.324610
42134PFalconeLecceFalcone58728-01319950...77.352.4441225.0351.1313.76.2258060.521145
..................................................................
5385512ADe LucaSampdoriaLuca14524-28219982...0.00.0000.000.000.05.9585360.291436
5395837AVoelkerling PerssonLeccePersson54220-10020037...0.00.0000.000.000.06.0681660.351902
5406113AMontevagoSampdoriaMontevago35220-03820036...0.00.0000.000.000.05.6184770.288497
5416143AKrollisSpeziaKrollis27721-17920011...0.00.0000.000.000.06.2903210.466766
5426160AVivaldoUdineseVivaldo-1000...0.00.0000.000.000.05.7906680.631282
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idrnameteamsurnameinitialfb_IDagebirth_yeargames...gk_pct_goal_kicks_launchedgk_goal_kick_length_avggk_crossesgk_crosses_stoppedgk_crosses_stopped_pctgk_def_actions_outside_pen_areagk_def_actions_outside_pen_area_per90gk_avg_distance_def_actionsvote_avgvote_std
0572PMeretNapoliMeret59426-03419970...20.026.8308.010.03.232.01.0717.06.2000000.420317
12814PProvedelLazioProvedel60529-03919940...36.034.7402.017.04.246.01.4916.46.2741940.418112
24964PVicarioEmpoliVicario61726-20019960...48.942.6489.028.05.715.00.6310.76.4583330.379601
3453PSzczesnyJuventusSzczesny61333-00719900...49.141.5301.09.03.019.00.8915.46.0909090.324610
42134PFalconeLecceFalcone58728-01319950...77.352.4441.022.05.035.01.1313.76.2258060.521145
..................................................................
5385512ADe LucaSampdoriaLuca14524-28219982...0.00.00.00.00.00.00.000.05.9585360.291436
5395837AVoelkerling PerssonLeccePersson54220-10020037...0.00.00.00.00.00.00.000.06.0681660.351902
5406113AMontevagoSampdoriaMontevago35220-03820036...0.00.00.00.00.00.00.000.05.6184770.288497
5416143AKrollisSpeziaKrollis27721-17920011...0.00.00.00.00.00.00.000.06.2903210.466766
5426160AVivaldoUdineseVivaldo-1000...0.00.00.00.00.00.00.000.05.7906680.631282
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543 rows × 160 columns

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" - ], - "text/plain": [ - " id r name team surname initial fb_ID \\\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 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 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 24-282 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 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", - "540 0.0 0.0 0.0 \n", - "541 0.0 0.0 0.0 \n", - "542 0.0 0.0 0.0 \n", - "\n", - " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\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", - "540 0.0 0.0 \n", - "541 0.0 0.0 \n", - "542 0.0 0.0 \n", - "\n", - " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\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", - "540 0.00 0.0 \n", - "541 0.00 0.0 \n", - "542 0.00 0.0 \n", - "\n", - " vote_avg vote_std \n", - "0 6.200000 0.420317 \n", - 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" 57.2\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 23.0\n", - " 18.0\n", - " 2.0\n", + " 29.0\n", + " 56.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", " 4.0\n", + " 6.0\n", " ...\n", - " 307.0\n", - " 269.0\n", - " 59.0\n", - " 1.0\n", - " 4.0\n", - " 0.0\n", - " 1130.0\n", - " 289.0\n", - " 328.0\n", - " 46.8\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", " Verona\n", " Hellas Verona\n", - " 34.0\n", - " 42.9\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", + " 36.0\n", + " 42.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", " 18.0\n", - " 15.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 234.0\n", - " 315.0\n", - " 25.0\n", " 1.0\n", - " 0.0\n", + " 1.0\n", + " ...\n", + " 337.0\n", + " 438.0\n", + " 32.0\n", + " 1.0\n", + " 1.0\n", " 2.0\n", - " 1231.0\n", - " 423.0\n", - " 445.0\n", - " 48.7\n", + " 1728.0\n", + " 619.0\n", + " 615.0\n", + " 50.2\n", " \n", " \n", " Inter\n", " Inter\n", - " 23.0\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", + " 24.0\n", + " 56.2\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 50.0\n", + " 35.0\n", + " 4.0\n", + " 5.0\n", " ...\n", - " 276.0\n", - " 248.0\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", - " 1007.0\n", - " 231.0\n", - " 288.0\n", - " 44.5\n", + " 1443.0\n", + " 344.0\n", + " 443.0\n", + " 43.7\n", " \n", " \n", " Juventus\n", " Juventus\n", - " 26.0\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", - " 246.0\n", - " 242.0\n", " 29.0\n", + " 48.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 47.0\n", + " 37.0\n", + " 3.0\n", + " 5.0\n", + " ...\n", + " 348.0\n", + " 328.0\n", + " 39.0\n", " 0.0\n", - " 4.0\n", + " 5.0\n", " 0.0\n", - " 1134.0\n", - " 258.0\n", - " 272.0\n", - " 48.7\n", + " 1556.0\n", + " 381.0\n", + " 390.0\n", + " 49.4\n", " \n", " \n", " Lazio\n", " Lazio\n", - " 21.0\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", + " 51.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 48.0\n", + " 29.0\n", + " 5.0\n", + " 7.0\n", " ...\n", - " 308.0\n", - " 218.0\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", - " 1233.0\n", - " 218.0\n", - " 229.0\n", - " 48.8\n", + " 1677.0\n", + " 319.0\n", + " 319.0\n", + " 50.0\n", " \n", " \n", " Lecce\n", " Lecce\n", - " 26.0\n", - " 42.4\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 20.0\n", - " 14.0\n", + " 29.0\n", + " 41.7\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 17.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 283.0\n", - " 300.0\n", - " 45.0\n", - " 3.0\n", + " 378.0\n", + " 425.0\n", + " 57.0\n", + " 4.0\n", " 2.0\n", " 2.0\n", - " 1200.0\n", - " 417.0\n", - " 332.0\n", - " 55.7\n", + " 1694.0\n", + " 582.0\n", + " 466.0\n", + " 55.5\n", " \n", " \n", " Milan\n", " Milan\n", - " 27.0\n", - " 53.5\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 36.0\n", + " 29.0\n", + " 54.0\n", " 31.0\n", - " 2.0\n", - " 2.0\n", + " 341.0\n", + " 2790.0\n", + " 48.0\n", + " 39.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 275.0\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", + " 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", " Monza\n", " Monza\n", - " 29.0\n", + " 31.0\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", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 36.0\n", + " 23.0\n", + " 5.0\n", + " 5.0\n", " ...\n", - " 318.0\n", - " 281.0\n", - " 37.0\n", - " 0.0\n", - " 4.0\n", + " 437.0\n", + " 385.0\n", + " 48.0\n", " 1.0\n", - " 1145.0\n", - " 242.0\n", - " 253.0\n", - " 48.9\n", + " 5.0\n", + " 2.0\n", + " 1608.0\n", + " 340.0\n", + " 357.0\n", + " 48.8\n", " \n", " \n", " Napoli\n", " Napoli\n", - " 24.0\n", - " 61.6\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 54.0\n", - " 42.0\n", - " 5.0\n", + " 26.0\n", + " 61.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 65.0\n", + " 51.0\n", " 6.0\n", + " 7.0\n", " ...\n", - " 298.0\n", - " 199.0\n", - " 28.0\n", + " 405.0\n", + " 287.0\n", + " 39.0\n", " 1.0\n", - " 5.0\n", - " 0.0\n", - " 1100.0\n", - " 232.0\n", - " 280.0\n", + " 6.0\n", + " 2.0\n", + " 1562.0\n", + " 322.0\n", + " 389.0\n", " 45.3\n", " \n", " \n", " Roma\n", " Roma\n", - " 26.0\n", - " 49.3\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 29.0\n", - " 19.0\n", - " 4.0\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", - " 316.0\n", - " 246.0\n", - " 12.0\n", + " 447.0\n", + " 344.0\n", + " 21.0\n", + " 2.0\n", + " 9.0\n", " 0.0\n", - " 6.0\n", - " 0.0\n", - " 1156.0\n", - " 221.0\n", - " 266.0\n", - " 45.4\n", + " 1621.0\n", + " 331.0\n", + " 426.0\n", + " 43.7\n", " \n", " \n", " Salernitana\n", " Salernitana\n", " 28.0\n", - " 46.0\n", - " 22.0\n", - " 242.0\n", - " 1980.0\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", - " 252.0\n", - " 259.0\n", - " 57.0\n", - " 8.0\n", + " 375.0\n", + " 357.0\n", + " 64.0\n", + " 10.0\n", " 1.0\n", - " 1.0\n", - " 1213.0\n", - " 291.0\n", - " 285.0\n", - " 50.5\n", + " 2.0\n", + " 1709.0\n", + " 448.0\n", + " 427.0\n", + " 51.2\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", + " 37.0\n", + " 47.4\n", " 31.0\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", + " 341.0\n", + " 2790.0\n", + " 20.0\n", + " 16.0\n", + " 1.0\n", + " 2.0\n", " ...\n", - " 339.0\n", - " 300.0\n", - " 59.0\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", - " 1189.0\n", - " 362.0\n", - " 349.0\n", + " 1693.0\n", + " 535.0\n", + " 517.0\n", " 50.9\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", " 29.0\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", + " 48.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", + " 24.0\n", + " 6.0\n", + " 7.0\n", " ...\n", - " 287.0\n", - " 206.0\n", - " 72.0\n", + " 384.0\n", + " 307.0\n", + " 94.0\n", " 2.0\n", - " 5.0\n", + " 7.0\n", " 1.0\n", - " 1131.0\n", - " 256.0\n", - " 206.0\n", - " 55.4\n", + " 1611.0\n", + " 385.0\n", + " 329.0\n", + " 53.9\n", " \n", " \n", " Spezia\n", " Spezia\n", - " 33.0\n", - " 45.5\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 17.0\n", - " 10.0\n", - " 3.0\n", - " 3.0\n", + " 34.0\n", + " 47.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 15.0\n", + " 4.0\n", + " 4.0\n", " ...\n", - " 235.0\n", - " 291.0\n", - " 53.0\n", - " 1.0\n", - " 3.0\n", + " 321.0\n", + " 396.0\n", + " 67.0\n", + " 4.0\n", + " 4.0\n", " 2.0\n", - " 1275.0\n", - " 343.0\n", - " 306.0\n", - " 52.9\n", + " 1708.0\n", + " 470.0\n", + " 424.0\n", + " 52.6\n", " \n", " \n", " Torino\n", " Torino\n", - " 27.0\n", + " 28.0\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", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 32.0\n", + " 26.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 239.0\n", - " 306.0\n", - " 24.0\n", - " 3.0\n", - " 1.0\n", + " 341.0\n", + " 409.0\n", + " 32.0\n", + " 4.0\n", + " 2.0\n", " 0.0\n", - " 1155.0\n", - " 367.0\n", - " 333.0\n", - " 52.4\n", + " 1597.0\n", + " 508.0\n", + " 474.0\n", + " 51.7\n", " \n", " \n", " Udinese\n", " Udinese\n", - " 25.0\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", - " 282.0\n", - " 252.0\n", - " 34.0\n", - " 2.0\n", - " 0.0\n", + " 27.0\n", + " 48.1\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 41.0\n", + " 36.0\n", " 1.0\n", - " 1101.0\n", - " 233.0\n", - " 277.0\n", - " 45.7\n", + " 2.0\n", + " ...\n", + " 412.0\n", + " 357.0\n", + " 50.0\n", + " 3.0\n", + " 2.0\n", + " 1.0\n", + " 1577.0\n", + " 326.0\n", + " 390.0\n", + " 45.5\n", " \n", " \n", "\n", @@ -604,164 +604,164 @@ "text/plain": [ " team team_players_used team_possession team_games \\\n", "team_idx \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", + "Atalanta Atalanta 25.0 50.0 31.0 \n", + "Bologna Bologna 27.0 53.4 31.0 \n", + "Cremonese Cremonese 32.0 43.2 31.0 \n", + "Empoli Empoli 31.0 47.4 31.0 \n", + "Fiorentina Fiorentina 29.0 56.6 31.0 \n", + "Verona Hellas Verona 36.0 42.0 31.0 \n", + "Inter Inter 24.0 56.2 31.0 \n", + "Juventus Juventus 29.0 48.6 31.0 \n", + "Lazio Lazio 22.0 51.9 31.0 \n", + "Lecce Lecce 29.0 41.7 31.0 \n", + "Milan Milan 29.0 54.0 31.0 \n", + "Monza Monza 31.0 55.0 31.0 \n", + "Napoli Napoli 26.0 61.8 31.0 \n", + "Roma Roma 27.0 49.2 31.0 \n", + "Salernitana Salernitana 28.0 44.6 31.0 \n", + "Sampdoria Sampdoria 37.0 47.4 31.0 \n", + "Sassuolo Sassuolo 29.0 48.9 31.0 \n", + "Spezia Spezia 34.0 47.0 31.0 \n", + "Torino Torino 28.0 53.0 31.0 \n", + "Udinese Udinese 27.0 48.1 31.0 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", "team_idx \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", + "Atalanta 341.0 2790.0 50.0 32.0 \n", + "Bologna 341.0 2790.0 39.0 32.0 \n", + "Cremonese 341.0 2790.0 27.0 13.0 \n", + "Empoli 341.0 2790.0 25.0 13.0 \n", + "Fiorentina 341.0 2790.0 35.0 25.0 \n", + "Verona 341.0 2790.0 24.0 18.0 \n", + "Inter 341.0 2790.0 50.0 35.0 \n", + "Juventus 341.0 2790.0 47.0 37.0 \n", + "Lazio 341.0 2790.0 48.0 29.0 \n", + "Lecce 341.0 2790.0 24.0 17.0 \n", + "Milan 341.0 2790.0 48.0 39.0 \n", + "Monza 341.0 2790.0 36.0 23.0 \n", + "Napoli 341.0 2790.0 65.0 51.0 \n", + "Roma 341.0 2790.0 43.0 30.0 \n", + "Salernitana 341.0 2790.0 35.0 25.0 \n", + "Sampdoria 341.0 2790.0 20.0 16.0 \n", + "Sassuolo 341.0 2790.0 37.0 24.0 \n", + "Spezia 341.0 2790.0 24.0 15.0 \n", + "Torino 341.0 2790.0 32.0 26.0 \n", + "Udinese 341.0 2790.0 41.0 36.0 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", "team_idx ... \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", + "Atalanta 6.0 8.0 ... 338.0 \n", + "Bologna 4.0 4.0 ... 376.0 \n", + "Cremonese 4.0 6.0 ... 345.0 \n", + "Empoli 1.0 1.0 ... 384.0 \n", + "Fiorentina 4.0 6.0 ... 460.0 \n", + "Verona 1.0 1.0 ... 337.0 \n", + "Inter 4.0 5.0 ... 368.0 \n", + "Juventus 3.0 5.0 ... 348.0 \n", + "Lazio 5.0 7.0 ... 421.0 \n", + "Lecce 1.0 2.0 ... 378.0 \n", + "Milan 3.0 3.0 ... 378.0 \n", + "Monza 5.0 5.0 ... 437.0 \n", + "Napoli 6.0 7.0 ... 405.0 \n", + "Roma 6.0 9.0 ... 447.0 \n", + "Salernitana 1.0 1.0 ... 375.0 \n", + "Sampdoria 1.0 2.0 ... 449.0 \n", + "Sassuolo 6.0 7.0 ... 384.0 \n", + "Spezia 4.0 4.0 ... 321.0 \n", + "Torino 2.0 2.0 ... 341.0 \n", + "Udinese 1.0 2.0 ... 412.0 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", "team_idx \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", + "Atalanta 356.0 40.0 1.0 \n", + "Bologna 365.0 52.0 5.0 \n", + "Cremonese 372.0 52.0 4.0 \n", + "Empoli 341.0 60.0 2.0 \n", + "Fiorentina 371.0 78.0 2.0 \n", + "Verona 438.0 32.0 1.0 \n", + "Inter 336.0 29.0 3.0 \n", + "Juventus 328.0 39.0 0.0 \n", + "Lazio 304.0 55.0 1.0 \n", + "Lecce 425.0 57.0 4.0 \n", + "Milan 369.0 31.0 5.0 \n", + "Monza 385.0 48.0 1.0 \n", + "Napoli 287.0 39.0 1.0 \n", + "Roma 344.0 21.0 2.0 \n", + "Salernitana 357.0 64.0 10.0 \n", + "Sampdoria 408.0 75.0 6.0 \n", + "Sassuolo 307.0 94.0 2.0 \n", + "Spezia 396.0 67.0 4.0 \n", + "Torino 409.0 32.0 4.0 \n", + "Udinese 357.0 50.0 3.0 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", "team_idx \n", "Atalanta 8.0 1.0 \n", "Bologna 4.0 1.0 \n", - "Cremonese 4.0 0.0 \n", - "Empoli 0.0 0.0 \n", - "Fiorentina 4.0 0.0 \n", - "Verona 0.0 2.0 \n", - "Inter 2.0 1.0 \n", - "Juventus 4.0 0.0 \n", - "Lazio 4.0 1.0 \n", + "Cremonese 6.0 0.0 \n", + "Empoli 1.0 0.0 \n", + "Fiorentina 6.0 2.0 \n", + "Verona 1.0 2.0 \n", + "Inter 5.0 1.0 \n", + "Juventus 5.0 0.0 \n", + "Lazio 7.0 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 6.0 0.0 \n", - "Salernitana 1.0 1.0 \n", - "Sampdoria 0.0 0.0 \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", + "Milan 3.0 3.0 \n", + "Monza 5.0 2.0 \n", + "Napoli 6.0 2.0 \n", + "Roma 9.0 0.0 \n", + "Salernitana 1.0 2.0 \n", + "Sampdoria 2.0 0.0 \n", + "Sassuolo 7.0 1.0 \n", + "Spezia 4.0 2.0 \n", + "Torino 2.0 0.0 \n", + "Udinese 2.0 1.0 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", "team_idx \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", + "Atalanta 1865.0 389.0 \n", + "Bologna 1687.0 379.0 \n", + "Cremonese 1745.0 594.0 \n", + "Empoli 1607.0 412.0 \n", + "Fiorentina 1625.0 425.0 \n", + "Verona 1728.0 619.0 \n", + "Inter 1443.0 344.0 \n", + "Juventus 1556.0 381.0 \n", + "Lazio 1677.0 319.0 \n", + "Lecce 1694.0 582.0 \n", + "Milan 1617.0 387.0 \n", + "Monza 1608.0 340.0 \n", + "Napoli 1562.0 322.0 \n", + "Roma 1621.0 331.0 \n", + "Salernitana 1709.0 448.0 \n", + "Sampdoria 1693.0 535.0 \n", + "Sassuolo 1611.0 385.0 \n", + "Spezia 1708.0 470.0 \n", + "Torino 1597.0 508.0 \n", + "Udinese 1577.0 326.0 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", "team_idx \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", + "Atalanta 472.0 45.2 \n", + "Bologna 306.0 55.3 \n", + "Cremonese 462.0 56.3 \n", + "Empoli 330.0 55.5 \n", + "Fiorentina 504.0 45.7 \n", + "Verona 615.0 50.2 \n", + "Inter 443.0 43.7 \n", + "Juventus 390.0 49.4 \n", + "Lazio 319.0 50.0 \n", + "Lecce 466.0 55.5 \n", + "Milan 456.0 45.9 \n", + "Monza 357.0 48.8 \n", + "Napoli 389.0 45.3 \n", + "Roma 426.0 43.7 \n", + "Salernitana 427.0 51.2 \n", + "Sampdoria 517.0 50.9 \n", + "Sassuolo 329.0 53.9 \n", + "Spezia 424.0 52.6 \n", + "Torino 474.0 51.7 \n", + "Udinese 390.0 45.5 \n", "\n", "[20 rows x 303 columns]" ] @@ -899,21 +899,21 @@ " Napoli\n", " Meret\n", " NaN\n", - " 555\n", - " 25-329\n", + " 594\n", + " 26-034\n", " 1997\n", " 0\n", " ...\n", - " 18.7\n", - " 26.6\n", - " 232.0\n", - " 6.0\n", - " 2.6\n", - " 24.0\n", - " 1.09\n", - " 17.2\n", - " 6.250000\n", - " 0.470734\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", " Provedel\n", @@ -923,21 +923,21 @@ " Lazio\n", " Provedel\n", " NaN\n", - " 564\n", - " 28-334\n", + " 605\n", + " 29-039\n", " 1994\n", " 0\n", " ...\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", - " 6.272727\n", - " 0.360784\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", " Vicario\n", @@ -947,21 +947,21 @@ " Empoli\n", " Vicario\n", " NaN\n", - " 573\n", - " 26-130\n", + " 617\n", + " 26-200\n", " 1996\n", " 0\n", " ...\n", - " 44.7\n", - " 41.5\n", - " 431.0\n", - " 25.0\n", - " 5.8\n", - " 12.0\n", - " 0.55\n", - " 10.9\n", - " 6.454545\n", - " 0.396264\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", " Szczesny\n", @@ -971,21 +971,21 @@ " Juventus\n", " Szczesny\n", " NaN\n", - " 570\n", - " 32-302\n", + " 613\n", + " 33-007\n", " 1990\n", " 0\n", " ...\n", - " 47.6\n", - " 41.8\n", - " 198.0\n", - " 5.0\n", - " 2.5\n", - " 12.0\n", - " 0.78\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", - " 6.031250\n", - " 0.329239\n", + " 6.090909\n", + " 0.324610\n", " \n", " \n", " Falcone\n", @@ -995,21 +995,21 @@ " Lecce\n", " Falcone\n", " NaN\n", - " 550\n", - " 27-308\n", + " 587\n", + " 28-013\n", " 1995\n", " 0\n", " ...\n", - " 76.3\n", - " 51.9\n", - " 327.0\n", - " 15.0\n", - " 4.6\n", + " 77.3\n", + " 52.4\n", + " 441.0\n", " 22.0\n", - " 1.00\n", - " 12.8\n", - " 6.363636\n", - " 0.431220\n", + " 5.0\n", + " 35.0\n", + " 1.13\n", + " 13.7\n", + " 6.225806\n", + " 0.521145\n", " \n", " \n", " ...\n", @@ -1043,10 +1043,10 @@ " Sampdoria\n", " Luca\n", " NaN\n", - " 135\n", - " 24-212\n", + " 145\n", + " 24-282\n", " 1998\n", - " 1\n", + " 2\n", " ...\n", " 0.0\n", " 0.0\n", @@ -1056,8 +1056,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.152892\n", - " 0.608256\n", + " 5.958536\n", + " 0.291436\n", " \n", " \n", " Voelkerling Persson\n", @@ -1067,10 +1067,10 @@ " Lecce\n", " Persson\n", " NaN\n", - " 512\n", - " 20-030\n", + " 542\n", + " 20-100\n", " 2003\n", - " 4\n", + " 7\n", " ...\n", " 0.0\n", " 0.0\n", @@ -1080,8 +1080,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.117574\n", - " 0.756644\n", + " 6.068166\n", + " 0.351902\n", " \n", " \n", " Montevago\n", @@ -1091,8 +1091,8 @@ " Sampdoria\n", " Montevago\n", " NaN\n", - " 334\n", - " 19-333\n", + " 352\n", + " 20-038\n", " 2003\n", " 6\n", " ...\n", @@ -1104,8 +1104,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.803886\n", - " 0.322430\n", + " 5.618477\n", + " 0.288497\n", " \n", " \n", " Krollis\n", @@ -1115,8 +1115,8 @@ " Spezia\n", " Krollis\n", " NaN\n", - " 261\n", - " 21-109\n", + " 277\n", + " 21-179\n", " 2001\n", " 1\n", " ...\n", @@ -1128,8 +1128,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.689979\n", - " 0.321766\n", + " 6.290321\n", + " 0.466766\n", " \n", " \n", " Vivaldo\n", @@ -1152,8 +1152,8 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.835742\n", - " 0.342133\n", + " 5.790668\n", + " 0.631282\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 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", + "Meret 0 572 P Napoli Meret NaN 594 \n", + "Provedel 1 2814 P Lazio Provedel NaN 605 \n", + "Vicario 2 4964 P Empoli Vicario NaN 617 \n", + "Szczesny 3 453 P Juventus Szczesny NaN 613 \n", + "Falcone 4 2134 P Lecce Falcone NaN 587 \n", "... ... ... .. ... ... ... ... \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", + "De Luca 538 5512 A Sampdoria Luca NaN 145 \n", + "Voelkerling Persson 539 5837 A Lecce Persson NaN 542 \n", + "Montevago 540 6113 A Sampdoria Montevago NaN 352 \n", + "Krollis 541 6143 A Spezia Krollis NaN 277 \n", "Vivaldo 542 6160 A Udinese Vivaldo NaN -1 \n", "\n", " age birth_year games ... \\\n", "name ... \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", + "Meret 26-034 1997 0 ... \n", + "Provedel 29-039 1994 0 ... \n", + "Vicario 26-200 1996 0 ... \n", + "Szczesny 33-007 1990 0 ... \n", + "Falcone 28-013 1995 0 ... \n", "... ... ... ... ... \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", + "De Luca 24-282 1998 2 ... \n", + "Voelkerling Persson 20-100 2003 7 ... \n", + "Montevago 20-038 2003 6 ... \n", + "Krollis 21-179 2001 1 ... \n", "Vivaldo 0 0 0 ... \n", "\n", " gk_pct_goal_kicks_launched gk_goal_kick_length_avg \\\n", "name \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", + "Meret 20.0 26.8 \n", + "Provedel 36.0 34.7 \n", + "Vicario 48.9 42.6 \n", + "Szczesny 49.1 41.5 \n", + "Falcone 77.3 52.4 \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 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", + "Meret 308.0 10.0 3.2 \n", + "Provedel 402.0 17.0 4.2 \n", + "Vicario 489.0 28.0 5.7 \n", + "Szczesny 301.0 9.0 3.0 \n", + "Falcone 441.0 22.0 5.0 \n", "... ... ... ... \n", "De Luca 0.0 0.0 0.0 \n", "Voelkerling Persson 0.0 0.0 0.0 \n", @@ -1219,11 +1219,11 @@ "\n", " gk_def_actions_outside_pen_area \\\n", "name \n", - "Meret 24.0 \n", - "Provedel 39.0 \n", - "Vicario 12.0 \n", - "Szczesny 12.0 \n", - "Falcone 22.0 \n", + "Meret 32.0 \n", + "Provedel 46.0 \n", + "Vicario 15.0 \n", + "Szczesny 19.0 \n", + "Falcone 35.0 \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 1.09 \n", - "Provedel 1.78 \n", - "Vicario 0.55 \n", - "Szczesny 0.78 \n", - "Falcone 1.00 \n", + "Meret 1.07 \n", + "Provedel 1.49 \n", + "Vicario 0.63 \n", + "Szczesny 0.89 \n", + "Falcone 1.13 \n", "... ... \n", "De Luca 0.00 \n", "Voelkerling Persson 0.00 \n", @@ -1247,17 +1247,17 @@ "\n", " gk_avg_distance_def_actions vote_avg vote_std \n", "name \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", + "Meret 17.0 6.200000 0.420317 \n", + "Provedel 16.4 6.274194 0.418112 \n", + "Vicario 10.7 6.458333 0.379601 \n", + "Szczesny 15.4 6.090909 0.324610 \n", + "Falcone 13.7 6.225806 0.521145 \n", "... ... ... ... \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", + "De Luca 0.0 5.958536 0.291436 \n", + "Voelkerling Persson 0.0 6.068166 0.351902 \n", + "Montevago 0.0 5.618477 0.288497 \n", + "Krollis 0.0 6.290321 0.466766 \n", + "Vivaldo 0.0 5.790668 0.631282 \n", "\n", "[543 rows x 160 columns]" ] @@ -1376,21 +1376,21 @@ " Sassuolo\n", " Frattesi\n", " NaN\n", - " 192\n", - " 23-115\n", + " 202\n", + " 23-185\n", " 1999\n", - " 22\n", + " 31\n", " ...\n", - " 244.0\n", - " 256.0\n", - " 26.0\n", + " 338.0\n", + " 356.0\n", + " 40.0\n", " 1.0\n", " 8.0\n", " 1.0\n", - " 1335.0\n", - " 273.0\n", - " 328.0\n", - " 45.4\n", + " 1865.0\n", + " 389.0\n", + " 472.0\n", + " 45.2\n", " \n", " \n", "\n", @@ -1399,22 +1399,22 @@ ], "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID age \\\n", - "Frattesi 274 2848 C Sassuolo Frattesi NaN 192 23-115 \n", + "Frattesi 274 2848 C Sassuolo Frattesi NaN 202 23-185 \n", "\n", " birth_year games ... opp_vs_team_fouls opp_vs_team_fouled \\\n", - "Frattesi 1999 22 ... 244.0 256.0 \n", + "Frattesi 1999 31 ... 338.0 356.0 \n", "\n", " opp_vs_team_offsides opp_vs_team_pens_won \\\n", - "Frattesi 26.0 1.0 \n", + "Frattesi 40.0 1.0 \n", "\n", " opp_vs_team_pens_conceded opp_vs_team_own_goals \\\n", "Frattesi 8.0 1.0 \n", "\n", " opp_vs_team_ball_recoveries opp_vs_team_aerials_won \\\n", - "Frattesi 1335.0 273.0 \n", + "Frattesi 1865.0 389.0 \n", "\n", " opp_vs_team_aerials_lost opp_vs_team_aerials_won_pct \n", - "Frattesi 328.0 45.4 \n", + "Frattesi 472.0 45.2 \n", "\n", "[1 rows x 766 columns]" ] @@ -1825,26 +1825,26 @@ " \n", " Frattesi\n", " C\n", - " 22\n", - " 22\n", - " 1816\n", - " 45.0\n", - " 0.13\n", - " 0.28\n", - " 78.5\n", - " 58.6\n", - " 48.7\n", + " 31\n", + " 30\n", + " 2454\n", + " 39.6\n", + " 0.11\n", + " 0.29\n", + " 77.5\n", + " 50.0\n", + " 48.9\n", " ...\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", + " 0.01956\n", + " 0.014262\n", + " 0.01467\n", + " 0.01956\n", + " 0.008965\n", + " 0.008965\n", + " 0.277914\n", + " 0.026487\n", + " 0.019967\n", + " 0.007742\n", " \n", " \n", "\n", @@ -1853,22 +1853,22 @@ ], "text/plain": [ " r games games_starts minutes shots_on_target_pct \\\n", - "Frattesi C 22 22 1816 45.0 \n", + "Frattesi C 31 30 2454 39.6 \n", "\n", " goals_per_shot goals_per_shot_on_target passes_pct \\\n", - "Frattesi 0.13 0.28 78.5 \n", + "Frattesi 0.11 0.29 77.5 \n", "\n", " aerials_won_pct team_possession ... miscontrols dispossessed \\\n", - "Frattesi 58.6 48.7 ... 0.018722 0.015419 \n", + "Frattesi 50.0 48.9 ... 0.01956 0.014262 \n", "\n", - " fouls fouled aerials_won aerials_lost carries \\\n", - "Frattesi 0.015419 0.020925 0.009361 0.006608 0.278084 \n", + " fouls fouled aerials_won aerials_lost carries \\\n", + "Frattesi 0.01467 0.01956 0.008965 0.008965 0.277914 \n", "\n", " progressive_carries carries_into_final_third \\\n", - "Frattesi 0.025881 0.020374 \n", + "Frattesi 0.026487 0.019967 \n", "\n", " carries_into_penalty_area \n", - "Frattesi 0.007709 \n", + "Frattesi 0.007742 \n", "\n", "[1 rows x 112 columns]" ] @@ -2013,24 +2013,24 @@ " ...\n", " \n", " \n", - " 6263\n", - " 22\n", + " 8809\n", + " 31\n", " Tameze\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " \n", " \n", - " 6264\n", - " 22\n", + " 8810\n", + " 31\n", " Abildgaard\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", " 6.0\n", " 0\n", @@ -2039,62 +2039,62 @@ " 6.0\n", " \n", " \n", - " 6265\n", - " 22\n", + " 8811\n", + " 31\n", " Lasagna\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " \n", " \n", - " 6266\n", - " 22\n", + " 8812\n", + " 31\n", " Gaich\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " \n", " \n", - " 6267\n", - " 22\n", - " Ngonge\n", + " 8813\n", + " 31\n", + " Djuric\n", " Verona\n", - " Salernitana\n", - " 1\n", - " 7.0\n", + " Bologna\n", " 1\n", + " 6.0\n", + " 0\n", " 0\n", " 0.0\n", - " 10.0\n", + " 6.0\n", " \n", " \n", "\n", - "

6268 rows × 10 columns

\n", + "

8814 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", - "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", + " 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", + "8809 31 Tameze Verona Bologna 1 6.5 0 0 \n", + "8810 31 Abildgaard Verona Bologna 1 6.0 0 0 \n", + "8811 31 Lasagna Verona Bologna 1 5.5 0 0 \n", + "8812 31 Gaich Verona Bologna 1 6.0 0 0 \n", + "8813 31 Djuric Verona Bologna 1 6.0 0 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -2103,13 +2103,13 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \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", + "8809 0.0 6.5 \n", + "8810 0.0 6.0 \n", + "8811 0.0 5.5 \n", + "8812 0.0 6.0 \n", + "8813 0.0 6.0 \n", "\n", - "[6268 rows x 10 columns]" + "[8814 rows x 10 columns]" ] }, "execution_count": 12, @@ -2199,7 +2199,32 @@ "5900\n", "6000\n", "6100\n", - "6200\n" + "6200\n", + "6300\n", + "6400\n", + "6500\n", + "6600\n", + "6700\n", + "6800\n", + "6900\n", + "7000\n", + "7100\n", + "7300\n", + "7400\n", + "7500\n", + "7600\n", + "7700\n", + "7800\n", + "7900\n", + "8000\n", + "8100\n", + "8200\n", + "8300\n", + "8400\n", + "8500\n", + "8600\n", + "8700\n", + "8800\n" ] } ], @@ -2313,15 +2338,15 @@ " 0.0\n", " 10.0\n", " ...\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", + " 0.006961\n", + " 0.001392\n", + " 0.011137\n", + " 0.006961\n", + " 0.015313\n", + " 0.010209\n", + " 0.386543\n", + " 0.007889\n", + " 0.015777\n", " 0.000000\n", " \n", " \n", @@ -2337,16 +2362,16 @@ " 0.0\n", " 6.0\n", " ...\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", + " 0.006734\n", + " 0.004209\n", + " 0.008418\n", + " 0.004209\n", + " 0.028620\n", + " 0.022727\n", + " 0.414141\n", + " 0.005051\n", + " 0.005051\n", + " 0.000842\n", " \n", " \n", " 3\n", @@ -2367,9 +2392,9 @@ " 0.002147\n", " 0.017180\n", " 0.010021\n", - " 0.282749\n", + " 0.284896\n", " 0.011453\n", - " 0.009306\n", + " 0.010021\n", " 0.000716\n", " \n", " \n", @@ -2421,35 +2446,35 @@ " ...\n", " \n", " \n", - " 6263\n", - " 22\n", + " 8809\n", + " 31\n", " Tameze\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\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", + " 0.012597\n", + " 0.010565\n", + " 0.010565\n", + " 0.010971\n", + " 0.009752\n", + " 0.010565\n", + " 0.236489\n", + " 0.010158\n", + " 0.012190\n", + " 0.001625\n", " \n", " \n", - " 6264\n", - " 22\n", + " 8810\n", + " 31\n", " Abildgaard\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", " 6.0\n", " 0\n", @@ -2457,148 +2482,148 @@ " 0.0\n", " 6.0\n", " ...\n", + " 0.010152\n", " 0.000000\n", - " 0.000000\n", - " 0.027027\n", - " 0.027027\n", - " 0.081081\n", - " 0.027027\n", - " 0.108108\n", + " 0.015228\n", + " 0.005076\n", + " 0.076142\n", + " 0.025381\n", + " 0.126904\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " \n", " \n", - " 6265\n", - " 22\n", + " 8811\n", + " 31\n", " Lasagna\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " ...\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", + " 0.033694\n", + " 0.021615\n", + " 0.015893\n", + " 0.013986\n", + " 0.012715\n", + " 0.043229\n", + " 0.169739\n", + " 0.022886\n", + " 0.014622\n", + " 0.008900\n", " \n", " \n", - " 6266\n", - " 22\n", + " 8812\n", + " 31\n", " Gaich\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\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", - " 0.011494\n", + " 0.031034\n", + " 0.013793\n", + " 0.027586\n", + " 0.022414\n", + " 0.036207\n", + " 0.075862\n", + " 0.220690\n", + " 0.008621\n", + " 0.005172\n", + " 0.006897\n", " \n", " \n", - " 6267\n", - " 22\n", - " Ngonge\n", + " 8813\n", + " 31\n", + " Djuric\n", " Verona\n", - " Salernitana\n", - " 1\n", - " 7.0\n", + " Bologna\n", " 1\n", + " 6.0\n", + " 0\n", " 0\n", " 0.0\n", - " 10.0\n", + " 6.0\n", " ...\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", + " 0.024334\n", + " 0.008111\n", + " 0.024334\n", + " 0.023175\n", + " 0.152955\n", + " 0.040556\n", + " 0.203940\n", + " 0.001159\n", + " 0.004635\n", + " 0.002317\n", " \n", " \n", "\n", - "

6268 rows × 122 columns

\n", + "

8814 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", - "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", + " 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", + "8809 31 Tameze Verona Bologna 1 6.5 0 0 \n", + "8810 31 Abildgaard Verona Bologna 1 6.0 0 0 \n", + "8811 31 Lasagna Verona Bologna 1 5.5 0 0 \n", + "8812 31 Gaich Verona Bologna 1 6.0 0 0 \n", + "8813 31 Djuric Verona Bologna 1 6.0 0 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 ... 0.006906 0.001381 0.007597 \n", - "2 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", + "1 0.0 10.0 ... 0.006961 0.001392 0.011137 \n", + "2 0.0 6.0 ... 0.006734 0.004209 0.008418 \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", - "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", + "8809 0.0 6.5 ... 0.012597 0.010565 0.010565 \n", + "8810 0.0 6.0 ... 0.010152 0.000000 0.015228 \n", + "8811 0.0 5.5 ... 0.033694 0.021615 0.015893 \n", + "8812 0.0 6.0 ... 0.031034 0.013793 0.027586 \n", + "8813 0.0 6.0 ... 0.024334 0.008111 0.024334 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", "0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.006906 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", + "1 0.006961 0.015313 0.010209 0.386543 0.007889 \n", + "2 0.004209 0.028620 0.022727 0.414141 0.005051 \n", + "3 0.002147 0.017180 0.010021 0.284896 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", "... ... ... ... ... ... \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", + "8809 0.010971 0.009752 0.010565 0.236489 0.010158 \n", + "8810 0.005076 0.076142 0.025381 0.126904 0.000000 \n", + "8811 0.013986 0.012715 0.043229 0.169739 0.022886 \n", + "8812 0.022414 0.036207 0.075862 0.220690 0.008621 \n", + "8813 0.023175 0.152955 0.040556 0.203940 0.001159 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", "0 0.000000 0.000000 \n", - "1 0.015884 0.000000 \n", - "2 0.004815 0.000000 \n", - "3 0.009306 0.000716 \n", + "1 0.015777 0.000000 \n", + "2 0.005051 0.000842 \n", + "3 0.010021 0.000716 \n", "4 0.004734 0.000000 \n", "... ... ... \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", + "8809 0.012190 0.001625 \n", + "8810 0.000000 0.000000 \n", + "8811 0.014622 0.008900 \n", + "8812 0.005172 0.006897 \n", + "8813 0.004635 0.002317 \n", "\n", - "[6268 rows x 122 columns]" + "[8814 rows x 122 columns]" ] }, "execution_count": 14, @@ -2694,15 +2719,15 @@ " 0.0\n", " 10.0\n", " ...\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", + " 0.006961\n", + " 0.001392\n", + " 0.011137\n", + " 0.006961\n", + " 0.015313\n", + " 0.010209\n", + " 0.386543\n", + " 0.007889\n", + " 0.015777\n", " 0.000000\n", " \n", " \n", @@ -2718,16 +2743,16 @@ " 0.0\n", " 6.0\n", " ...\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", + " 0.006734\n", + " 0.004209\n", + " 0.008418\n", + " 0.004209\n", + " 0.028620\n", + " 0.022727\n", + " 0.414141\n", + " 0.005051\n", + " 0.005051\n", + " 0.000842\n", " \n", " \n", " 3\n", @@ -2748,9 +2773,9 @@ " 0.002147\n", " 0.017180\n", " 0.010021\n", - " 0.282749\n", + " 0.284896\n", " 0.011453\n", - " 0.009306\n", + " 0.010021\n", " 0.000716\n", " \n", " \n", @@ -2790,16 +2815,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.011111\n", - " 0.005556\n", - " 0.016667\n", - " 0.022222\n", - " 0.016667\n", - " 0.027778\n", - " 0.561111\n", - " 0.072222\n", - " 0.055556\n", - " 0.000000\n", + " 0.017279\n", + " 0.006479\n", + " 0.019438\n", + " 0.010799\n", + " 0.006479\n", + " 0.017279\n", + " 0.431965\n", + " 0.034557\n", + " 0.025918\n", + " 0.002160\n", " \n", " \n", " ...\n", @@ -2826,35 +2851,35 @@ " ...\n", " \n", " \n", - " 6263\n", - " 22\n", + " 8809\n", + " 31\n", " Tameze\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 6.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 6.5\n", " ...\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", + " 0.012597\n", + " 0.010565\n", + " 0.010565\n", + " 0.010971\n", + " 0.009752\n", + " 0.010565\n", + " 0.236489\n", + " 0.010158\n", + " 0.012190\n", + " 0.001625\n", " \n", " \n", - " 6264\n", - " 22\n", + " 8810\n", + " 31\n", " Abildgaard\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", " 6.0\n", " 0\n", @@ -2862,148 +2887,148 @@ " 0.0\n", " 6.0\n", " ...\n", + " 0.010152\n", " 0.000000\n", - " 0.000000\n", - " 0.027027\n", - " 0.027027\n", - " 0.081081\n", - " 0.027027\n", - " 0.108108\n", + " 0.015228\n", + " 0.005076\n", + " 0.076142\n", + " 0.025381\n", + " 0.126904\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " \n", " \n", - " 6265\n", - " 22\n", + " 8811\n", + " 31\n", " Lasagna\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " ...\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", + " 0.033694\n", + " 0.021615\n", + " 0.015893\n", + " 0.013986\n", + " 0.012715\n", + " 0.043229\n", + " 0.169739\n", + " 0.022886\n", + " 0.014622\n", + " 0.008900\n", " \n", " \n", - " 6266\n", - " 22\n", + " 8812\n", + " 31\n", " Gaich\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\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", - " 0.011494\n", + " 0.031034\n", + " 0.013793\n", + " 0.027586\n", + " 0.022414\n", + " 0.036207\n", + " 0.075862\n", + " 0.220690\n", + " 0.008621\n", + " 0.005172\n", + " 0.006897\n", " \n", " \n", - " 6267\n", - " 22\n", - " Ngonge\n", + " 8813\n", + " 31\n", + " Djuric\n", " Verona\n", - " Salernitana\n", - " 1\n", - " 7.0\n", + " Bologna\n", " 1\n", + " 6.0\n", + " 0\n", " 0\n", " 0.0\n", - " 10.0\n", + " 6.0\n", " ...\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", + " 0.024334\n", + " 0.008111\n", + " 0.024334\n", + " 0.023175\n", + " 0.152955\n", + " 0.040556\n", + " 0.203940\n", + " 0.001159\n", + " 0.004635\n", + " 0.002317\n", " \n", " \n", "\n", - "

5581 rows × 122 columns

\n", + "

7929 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", - "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", + " 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", + "8809 31 Tameze Verona Bologna 1 6.5 0 0 \n", + "8810 31 Abildgaard Verona Bologna 1 6.0 0 0 \n", + "8811 31 Lasagna Verona Bologna 1 5.5 0 0 \n", + "8812 31 Gaich Verona Bologna 1 6.0 0 0 \n", + "8813 31 Djuric Verona Bologna 1 6.0 0 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\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", + "1 0.0 10.0 ... 0.006961 0.001392 0.011137 \n", + "2 0.0 6.0 ... 0.006734 0.004209 0.008418 \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 ... 0.011111 0.005556 0.016667 \n", + "5 0.5 5.5 ... 0.017279 0.006479 0.019438 \n", "... ... ... ... ... ... ... \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", + "8809 0.0 6.5 ... 0.012597 0.010565 0.010565 \n", + "8810 0.0 6.0 ... 0.010152 0.000000 0.015228 \n", + "8811 0.0 5.5 ... 0.033694 0.021615 0.015893 \n", + "8812 0.0 6.0 ... 0.031034 0.013793 0.027586 \n", + "8813 0.0 6.0 ... 0.024334 0.008111 0.024334 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\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", + "1 0.006961 0.015313 0.010209 0.386543 0.007889 \n", + "2 0.004209 0.028620 0.022727 0.414141 0.005051 \n", + "3 0.002147 0.017180 0.010021 0.284896 0.011453 \n", "4 0.008284 0.047337 0.027219 0.269822 0.002367 \n", - "5 0.022222 0.016667 0.027778 0.561111 0.072222 \n", + "5 0.010799 0.006479 0.017279 0.431965 0.034557 \n", "... ... ... ... ... ... \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", + "8809 0.010971 0.009752 0.010565 0.236489 0.010158 \n", + "8810 0.005076 0.076142 0.025381 0.126904 0.000000 \n", + "8811 0.013986 0.012715 0.043229 0.169739 0.022886 \n", + "8812 0.022414 0.036207 0.075862 0.220690 0.008621 \n", + "8813 0.023175 0.152955 0.040556 0.203940 0.001159 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", - "1 0.015884 0.000000 \n", - "2 0.004815 0.000000 \n", - "3 0.009306 0.000716 \n", + "1 0.015777 0.000000 \n", + "2 0.005051 0.000842 \n", + "3 0.010021 0.000716 \n", "4 0.004734 0.000000 \n", - "5 0.055556 0.000000 \n", + "5 0.025918 0.002160 \n", "... ... ... \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", + "8809 0.012190 0.001625 \n", + "8810 0.000000 0.000000 \n", + "8811 0.014622 0.008900 \n", + "8812 0.005172 0.006897 \n", + "8813 0.004635 0.002317 \n", "\n", - "[5581 rows x 122 columns]" + "[7929 rows x 122 columns]" ] }, "execution_count": 16, @@ -3465,27 +3490,27 @@ " \n", " \n", " Consigli\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", + " 29\n", + " 29\n", + " 2610\n", + " 1.48\n", + " 58.3\n", + " 31.0\n", + " -0.41\n", + " 40.3\n", + " 31.6\n", + " 34.2\n", " ...\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", + " 31.0\n", + " 0.3\n", + " -12.0\n", + " 153.0\n", + " 380.0\n", + " 946.0\n", + " 154.0\n", + " 249.0\n", + " 409.0\n", + " 24.0\n", " \n", " \n", "\n", @@ -3494,25 +3519,25 @@ ], "text/plain": [ " gk_games gk_games_starts gk_minutes gk_goals_against_per90 \\\n", - "Consigli 20 20 1800 1.5 \n", + "Consigli 29 29 2610 1.48 \n", "\n", " gk_save_pct gk_clean_sheets_pct gk_psxg_net_per90 \\\n", - "Consigli 59.7 30.0 -0.28 \n", + "Consigli 58.3 31.0 -0.41 \n", "\n", " gk_passes_pct_launched gk_pct_passes_launched \\\n", - "Consigli 40.8 30.5 \n", + "Consigli 40.3 31.6 \n", "\n", " gk_passes_length_avg ... gk_psxg \\\n", - "Consigli 34.1 ... 24.4 \n", + "Consigli 34.2 ... 31.0 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "Consigli 0.33 -5.6 \n", + "Consigli 0.3 -12.0 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "Consigli 113.0 277.0 711.0 \n", + "Consigli 153.0 380.0 946.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "Consigli 108.0 179.0 276.0 17.0 \n", + "Consigli 154.0 249.0 409.0 24.0 \n", "\n", "[1 rows x 92 columns]" ] @@ -3602,7 +3627,33 @@ "5900\n", "6000\n", "6100\n", - "6200\n" + "6200\n", + "6300\n", + "6400\n", + "6500\n", + "6600\n", + "6700\n", + "6800\n", + "6900\n", + "7000\n", + "7100\n", + "7200\n", + "7300\n", + "7400\n", + "7500\n", + "7600\n", + "7700\n", + "7800\n", + "7900\n", + "8000\n", + "8100\n", + "8200\n", + "8300\n", + "8400\n", + "8500\n", + "8600\n", + "8700\n", + "8800\n" ] } ], @@ -3694,16 +3745,16 @@ " 0.5\n", " 5.5\n", " ...\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", + " 20.0\n", + " 0.22\n", + " -3.0\n", + " 108.0\n", + " 266.0\n", + " 532.0\n", + " 148.0\n", + " 149.0\n", + " 247.0\n", + " 14.0\n", " \n", " \n", " 1\n", @@ -3826,17 +3877,17 @@ " ...\n", " \n", " \n", - " 6263\n", - " 22\n", + " 8809\n", + " 31\n", " Tameze\n", " Verona\n", - " Salernitana\n", + " Bologna\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", @@ -3850,11 +3901,11 @@ " NaN\n", " \n", " \n", - " 6264\n", - " 22\n", + " 8810\n", + " 31\n", " Abildgaard\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", " 6.0\n", " 0\n", @@ -3874,17 +3925,17 @@ " NaN\n", " \n", " \n", - " 6265\n", - " 22\n", + " 8811\n", + " 31\n", " Lasagna\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " ...\n", " NaN\n", " NaN\n", @@ -3898,17 +3949,17 @@ " NaN\n", " \n", " \n", - " 6266\n", - " 22\n", + " 8812\n", + " 31\n", " Gaich\n", " Verona\n", - " Salernitana\n", + " Bologna\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\n", " NaN\n", " NaN\n", @@ -3922,17 +3973,17 @@ " NaN\n", " \n", " \n", - " 6267\n", - " 22\n", - " Ngonge\n", + " 8813\n", + " 31\n", + " Djuric\n", " Verona\n", - " Salernitana\n", - " 1\n", - " 7.0\n", + " Bologna\n", " 1\n", + " 6.0\n", + " 0\n", " 0\n", " 0.0\n", - " 10.0\n", + " 6.0\n", " ...\n", " NaN\n", " NaN\n", @@ -3947,76 +3998,76 @@ " \n", " \n", "\n", - "

6268 rows × 102 columns

\n", + "

8814 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", - "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", + " 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", + "8809 31 Tameze Verona Bologna 1 6.5 0 0 \n", + "8810 31 Abildgaard Verona Bologna 1 6.0 0 0 \n", + "8811 31 Lasagna Verona Bologna 1 5.5 0 0 \n", + "8812 31 Gaich Verona Bologna 1 6.0 0 0 \n", + "8813 31 Djuric Verona Bologna 1 6.0 0 0 \n", "\n", " cards_malus fantavote ... gk_psxg \\\n", - "0 0.5 5.5 ... 13.0 \n", + "0 0.5 5.5 ... 20.0 \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", - "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", + "8809 0.0 6.5 ... NaN \n", + "8810 0.0 6.0 ... NaN \n", + "8811 0.0 5.5 ... NaN \n", + "8812 0.0 6.0 ... NaN \n", + "8813 0.0 6.0 ... NaN \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.2 -1.0 \n", + "0 0.22 -3.0 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "6263 NaN NaN \n", - "6264 NaN NaN \n", - "6265 NaN NaN \n", - "6266 NaN NaN \n", - "6267 NaN NaN \n", + "8809 NaN NaN \n", + "8810 NaN NaN \n", + "8811 NaN NaN \n", + "8812 NaN NaN \n", + "8813 NaN NaN \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 88.0 201.0 397.0 \n", + "0 108.0 266.0 532.0 \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "... ... ... ... \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", + "8809 NaN NaN NaN \n", + "8810 NaN NaN NaN \n", + "8811 NaN NaN NaN \n", + "8812 NaN NaN NaN \n", + "8813 NaN NaN NaN \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 112.0 101.0 186.0 10.0 \n", + "0 148.0 149.0 247.0 14.0 \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", - "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", + "8809 NaN NaN NaN NaN \n", + "8810 NaN NaN NaN NaN \n", + "8811 NaN NaN NaN NaN \n", + "8812 NaN NaN NaN NaN \n", + "8813 NaN NaN NaN NaN \n", "\n", - "[6268 rows x 102 columns]" + "[8814 rows x 102 columns]" ] }, "execution_count": 24, @@ -4102,16 +4153,16 @@ " 0.5\n", " 5.5\n", " ...\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", + " 20.000000\n", + " 0.220000\n", + " -3.000000\n", + " 108.0\n", + " 266.0\n", + " 532.000000\n", + " 148.000000\n", + " 149.000000\n", + " 247.000000\n", + " 14.0\n", " \n", " \n", " 15\n", @@ -4126,16 +4177,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 32.50\n", - " 0.29\n", - " 2.50\n", - " 101.0\n", - " 242.0\n", - " 576.0\n", - " 117.0\n", - " 159.0\n", - " 304.0\n", - " 18.0\n", + " 40.200000\n", + " 0.250000\n", + " 3.200000\n", + " 129.0\n", + " 329.0\n", + " 773.000000\n", + " 169.000000\n", + " 219.000000\n", + " 402.000000\n", + " 24.0\n", " \n", " \n", " 44\n", @@ -4150,16 +4201,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 27.80\n", - " 0.26\n", - " -0.20\n", - " 102.0\n", - " 313.0\n", - " 806.0\n", + " 32.300000\n", + " 0.280000\n", + " 2.300000\n", " 117.0\n", - " 114.0\n", - " 431.0\n", - " 25.0\n", + " 346.0\n", + " 869.000000\n", + " 123.000000\n", + " 139.000000\n", + " 489.000000\n", + " 28.0\n", " \n", " \n", " 57\n", @@ -4174,16 +4225,16 @@ " 0.0\n", " 3.0\n", " ...\n", - " 9.45\n", - " 0.24\n", - " 0.95\n", - " 24.0\n", - " 68.0\n", - " 299.5\n", - " 48.5\n", - " 72.5\n", - " 128.0\n", - " 3.5\n", + " 18.616667\n", + " 0.220000\n", + " 1.116667\n", + " 45.5\n", + " 114.0\n", + " 556.166667\n", + " 92.833333\n", + " 155.000000\n", + " 258.666667\n", + " 8.5\n", " \n", " \n", " 71\n", @@ -4198,15 +4249,15 @@ " 0.0\n", " 5.5\n", " ...\n", - " 10.60\n", - " 0.30\n", - " -1.40\n", - " 27.0\n", - " 52.0\n", - " 266.0\n", - " 46.0\n", - " 45.0\n", - " 79.0\n", + " 12.500000\n", + " 0.270000\n", + " -2.500000\n", + " 31.0\n", + " 62.0\n", + " 345.000000\n", + " 54.000000\n", + " 59.000000\n", + " 112.000000\n", " 2.0\n", " \n", " \n", @@ -4234,59 +4285,35 @@ " ...\n", " \n", " \n", - " 6200\n", - " 22\n", + " 8745\n", + " 31\n", " Consigli\n", " Sassuolo\n", - " Udinese\n", + " Salernitana\n", " 0\n", - " 7.0\n", - " -2\n", - " 0\n", - " 0.0\n", " 5.0\n", - " ...\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", - " 6214\n", - " 22\n", - " Dragowski\n", - " Spezia\n", - " Empoli\n", - " 0\n", - " 6.5\n", - " -2\n", + " -3\n", " 0\n", " 0.0\n", - " 4.5\n", + " 2.0\n", " ...\n", - " 29.50\n", - " 0.27\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", + " 31.000000\n", + " 0.300000\n", + " -12.000000\n", + " 153.0\n", + " 380.0\n", + " 946.000000\n", + " 154.000000\n", + " 249.000000\n", + " 409.000000\n", + " 24.0\n", " \n", " \n", - " 6227\n", - " 22\n", - " Milinkovic-Savic V.\n", - " Torino\n", - " Milan\n", + " 8760\n", + " 31\n", + " Zoet\n", + " Spezia\n", + " Sampdoria\n", " 0\n", " 6.5\n", " -1\n", @@ -4294,68 +4321,92 @@ " 0.0\n", " 5.5\n", " ...\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", - " 1.00\n", - " 91.0\n", - " 227.0\n", - " 470.0\n", - " 80.0\n", - " 175.0\n", - " 293.0\n", + " 14.566667\n", + " 0.213333\n", + " -0.433333\n", + " 56.0\n", + " 171.0\n", + " 316.333333\n", + " 49.666667\n", + " 64.333333\n", + " 160.333333\n", " 5.0\n", " \n", " \n", - " 6254\n", - " 22\n", - " Montipo'\n", - " Verona\n", - " Salernitana\n", - " 1\n", - " 6.5\n", + " 8774\n", + " 31\n", + " Milinkovic-Savic V.\n", + " Torino\n", + " Lazio\n", + " 0\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\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", + " 31.500000\n", + " 0.240000\n", + " -4.500000\n", + " 224.0\n", + " 762.0\n", + " 1196.000000\n", + " 142.000000\n", + " 236.000000\n", + " 387.000000\n", + " 25.0\n", + " \n", + " \n", + " 8787\n", + " 31\n", + " Silvestri\n", + " Udinese\n", + " Cremonese\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 37.500000\n", + " 0.300000\n", + " 0.500000\n", + " 121.0\n", + " 316.0\n", + " 687.000000\n", + " 120.000000\n", + " 237.000000\n", + " 441.000000\n", + " 11.0\n", + " \n", + " \n", + " 8800\n", + " 31\n", + " Montipo'\n", + " Verona\n", + " Bologna\n", + " 1\n", + " 6.5\n", + " -1\n", + " 0\n", + " 0.0\n", + " 5.5\n", + " ...\n", + " 38.700000\n", + " 0.260000\n", + " -4.300000\n", + " 280.0\n", + " 616.0\n", + " 745.000000\n", + " 99.000000\n", + " 227.000000\n", + " 401.000000\n", + " 22.0\n", " \n", " \n", "\n", - "

435 rows × 102 columns

\n", + "

616 rows × 102 columns

\n", "" ], "text/plain": [ @@ -4366,65 +4417,65 @@ "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", + "8745 31 Consigli Sassuolo Salernitana 0 5.0 \n", + "8760 31 Zoet Spezia Sampdoria 0 6.5 \n", + "8774 31 Milinkovic-Savic V. Torino Lazio 0 6.0 \n", + "8787 31 Silvestri Udinese Cremonese 1 6.0 \n", + "8800 31 Montipo' Verona Bologna 1 6.5 \n", "\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", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 20.000000 \n", + "15 -2 0 0.0 4.5 ... 40.200000 \n", + "44 -1 0 0.0 4.5 ... 32.300000 \n", + "57 -2 0 0.0 3.0 ... 18.616667 \n", + "71 -1 0 0.0 5.5 ... 12.500000 \n", + "... ... ... ... ... ... ... \n", + "8745 -3 0 0.0 2.0 ... 31.000000 \n", + "8760 -1 0 0.0 5.5 ... 14.566667 \n", + "8774 0 0 0.0 6.0 ... 31.500000 \n", + "8787 0 0 0.0 6.0 ... 37.500000 \n", + "8800 -1 0 0.0 5.5 ... 38.700000 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\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", + "0 0.220000 -3.000000 \n", + "15 0.250000 3.200000 \n", + "44 0.280000 2.300000 \n", + "57 0.220000 1.116667 \n", + "71 0.270000 -2.500000 \n", "... ... ... \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", + "8745 0.300000 -12.000000 \n", + "8760 0.213333 -0.433333 \n", + "8774 0.240000 -4.500000 \n", + "8787 0.300000 0.500000 \n", + "8800 0.260000 -4.300000 \n", "\n", - " gk_passes_completed_launched gk_passes_launched gk_passes \\\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", - "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", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 108.0 266.0 532.000000 \n", + "15 129.0 329.0 773.000000 \n", + "44 117.0 346.0 869.000000 \n", + "57 45.5 114.0 556.166667 \n", + "71 31.0 62.0 345.000000 \n", + "... ... ... ... \n", + "8745 153.0 380.0 946.000000 \n", + "8760 56.0 171.0 316.333333 \n", + "8774 224.0 762.0 1196.000000 \n", + "8787 121.0 316.0 687.000000 \n", + "8800 280.0 616.0 745.000000 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \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", + "0 148.000000 149.000000 247.000000 14.0 \n", + "15 169.000000 219.000000 402.000000 24.0 \n", + "44 123.000000 139.000000 489.000000 28.0 \n", + "57 92.833333 155.000000 258.666667 8.5 \n", + "71 54.000000 59.000000 112.000000 2.0 \n", "... ... ... ... ... \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", + "8745 154.000000 249.000000 409.000000 24.0 \n", + "8760 49.666667 64.333333 160.333333 5.0 \n", + "8774 142.000000 236.000000 387.000000 25.0 \n", + "8787 120.000000 237.000000 441.000000 11.0 \n", + "8800 99.000000 227.000000 401.000000 22.0 \n", "\n", - "[435 rows x 102 columns]" + "[616 rows x 102 columns]" ] }, "execution_count": 26, @@ -4573,14 +4624,6 @@ "for i in range(db_gk.columns.shape[0]):\n", " print(str(i) + \" - \" + str(db_gk.columns[i]))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0209868", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/6_neural_network_training_and_prediction.ipynb b/6_neural_network_training_and_prediction.ipynb index ba55a86..f5688b5 100644 --- a/6_neural_network_training_and_prediction.ipynb +++ b/6_neural_network_training_and_prediction.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "fa098fa4", "metadata": {}, "outputs": [], @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "f71fa9a4", "metadata": {}, "outputs": [ @@ -134,15 +134,15 @@ " 0.0\n", " 10.0\n", " ...\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", + " 0.006961\n", + " 0.001392\n", + " 0.011137\n", + " 0.006961\n", + " 0.015313\n", + " 0.010209\n", + " 0.386543\n", + " 0.007889\n", + " 0.015777\n", " 0.000000\n", " \n", " \n", @@ -158,16 +158,16 @@ " 0.0\n", " 6.0\n", " ...\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", + " 0.006734\n", + " 0.004209\n", + " 0.008418\n", + " 0.004209\n", + " 0.028620\n", + " 0.022727\n", + " 0.414141\n", + " 0.005051\n", + " 0.005051\n", + " 0.000842\n", " \n", " \n", " 2\n", @@ -188,9 +188,9 @@ " 0.002147\n", " 0.017180\n", " 0.010021\n", - " 0.282749\n", + " 0.284896\n", " 0.011453\n", - " 0.009306\n", + " 0.010021\n", " 0.000716\n", " \n", " \n", @@ -230,16 +230,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.011111\n", - " 0.005556\n", - " 0.016667\n", - " 0.022222\n", - " 0.016667\n", - " 0.027778\n", - " 0.561111\n", - " 0.072222\n", - " 0.055556\n", - " 0.000000\n", + " 0.017279\n", + " 0.006479\n", + " 0.019438\n", + " 0.010799\n", + " 0.006479\n", + " 0.017279\n", + " 0.431965\n", + " 0.034557\n", + " 0.025918\n", + " 0.002160\n", " \n", " \n", " ...\n", @@ -266,7 +266,7 @@ " ...\n", " \n", " \n", - " 24806\n", + " 27154\n", " 38\n", " Tameze\n", " Verona\n", @@ -290,7 +290,7 @@ " 0.003885\n", " \n", " \n", - " 24807\n", + " 27155\n", " 38\n", " Hongla\n", " Verona\n", @@ -314,7 +314,7 @@ " 0.003072\n", " \n", " \n", - " 24808\n", + " 27156\n", " 38\n", " Lasagna\n", " Verona\n", @@ -338,7 +338,7 @@ " 0.005912\n", " \n", " \n", - " 24809\n", + " 27157\n", " 38\n", " Caprari\n", " Verona\n", @@ -362,7 +362,7 @@ " 0.018620\n", " \n", " \n", - " 24810\n", + " 27158\n", " 38\n", " Simeone\n", " Verona\n", @@ -387,7 +387,7 @@ " \n", " \n", "\n", - "

24811 rows × 122 columns

\n", + "

27159 rows × 122 columns

\n", "" ], "text/plain": [ @@ -398,55 +398,55 @@ "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", + "27154 38 Tameze Verona Lazio 0 5.5 0 0 \n", + "27155 38 Hongla Verona Lazio 0 7.0 1 0 \n", + "27156 38 Lasagna Verona Lazio 0 7.0 1 0 \n", + "27157 38 Caprari Verona Lazio 0 6.0 0 0 \n", + "27158 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", + "0 0.0 10.0 ... 0.006961 0.001392 0.011137 \n", + "1 0.0 6.0 ... 0.006734 0.004209 0.008418 \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", + "4 0.5 5.5 ... 0.017279 0.006479 0.019438 \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", + "27154 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", + "27155 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", + "27156 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", + "27157 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", + "27158 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", + "0 0.006961 0.015313 0.010209 0.386543 0.007889 \n", + "1 0.004209 0.028620 0.022727 0.414141 0.005051 \n", + "2 0.002147 0.017180 0.010021 0.284896 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", + "4 0.010799 0.006479 0.017279 0.431965 0.034557 \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", + "27154 0.013209 0.023699 0.021368 0.337218 0.021368 \n", + "27155 0.007680 0.023041 0.026114 0.341014 0.009217 \n", + "27156 0.008446 0.026182 0.041385 0.190878 0.021959 \n", + "27157 0.027017 0.002921 0.009858 0.391384 0.042716 \n", + "27158 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", + "0 0.015777 0.000000 \n", + "1 0.005051 0.000842 \n", + "2 0.010021 0.000716 \n", "3 0.004734 0.000000 \n", - "4 0.055556 0.000000 \n", + "4 0.025918 0.002160 \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", + "27154 0.012821 0.003885 \n", + "27155 0.015361 0.003072 \n", + "27156 0.010980 0.005912 \n", + "27157 0.027747 0.018620 \n", + "27158 0.012816 0.006031 \n", "\n", - "[24811 rows x 122 columns]" + "[27159 rows x 122 columns]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1d024554", "metadata": {}, "outputs": [ @@ -521,16 +521,16 @@ " 0.5\n", " 5.5\n", " ...\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", + " 20.000000\n", + " 0.220000\n", + " -3.000000\n", + " 108.0\n", + " 266.0\n", + " 532.000000\n", + " 148.000000\n", + " 149.000000\n", + " 247.000000\n", + " 14.0\n", " \n", " \n", " 1\n", @@ -545,16 +545,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 32.50\n", - " 0.29\n", - " 2.50\n", - " 101.0\n", - " 242.0\n", - " 576.0\n", - " 117.0\n", - " 159.0\n", - " 304.0\n", - " 18.0\n", + " 40.200000\n", + " 0.250000\n", + " 3.200000\n", + " 129.0\n", + " 329.0\n", + " 773.000000\n", + " 169.000000\n", + " 219.000000\n", + " 402.000000\n", + " 24.0\n", " \n", " \n", " 2\n", @@ -569,16 +569,16 @@ " 0.0\n", " 4.5\n", " ...\n", - " 27.80\n", - " 0.26\n", - " -0.20\n", - " 102.0\n", - " 313.0\n", - " 806.0\n", + " 32.300000\n", + " 0.280000\n", + " 2.300000\n", " 117.0\n", - " 114.0\n", - " 431.0\n", - " 25.0\n", + " 346.0\n", + " 869.000000\n", + " 123.000000\n", + " 139.000000\n", + " 489.000000\n", + " 28.0\n", " \n", " \n", " 3\n", @@ -593,16 +593,16 @@ " 0.0\n", " 3.0\n", " ...\n", - " 9.45\n", - " 0.24\n", - " 0.95\n", - " 24.0\n", - " 68.0\n", - " 299.5\n", - " 48.5\n", - " 72.5\n", - " 128.0\n", - " 3.5\n", + " 18.616667\n", + " 0.220000\n", + " 1.116667\n", + " 45.5\n", + " 114.0\n", + " 556.166667\n", + " 92.833333\n", + " 155.000000\n", + " 258.666667\n", + " 8.5\n", " \n", " \n", " 4\n", @@ -617,15 +617,15 @@ " 0.0\n", " 5.5\n", " ...\n", - " 10.60\n", - " 0.30\n", - " -1.40\n", - " 27.0\n", - " 52.0\n", - " 266.0\n", - " 46.0\n", - " 45.0\n", - " 79.0\n", + " 12.500000\n", + " 0.270000\n", + " -2.500000\n", + " 31.0\n", + " 62.0\n", + " 345.000000\n", + " 54.000000\n", + " 59.000000\n", + " 112.000000\n", " 2.0\n", " \n", " \n", @@ -653,59 +653,35 @@ " ...\n", " \n", " \n", - " 1300\n", - " 22\n", + " 1843\n", + " 31\n", " Consigli\n", " Sassuolo\n", - " Udinese\n", + " Salernitana\n", " 0\n", - " 7.0\n", - " -2\n", - " 0\n", - " 0.0\n", " 5.0\n", - " ...\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", - " 1301\n", - " 22\n", - " Dragowski\n", - " Spezia\n", - " Empoli\n", - " 0\n", - " 6.5\n", - " -2\n", + " -3\n", " 0\n", " 0.0\n", - " 4.5\n", + " 2.0\n", " ...\n", - " 29.50\n", - " 0.27\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", + " 31.000000\n", + " 0.300000\n", + " -12.000000\n", + " 153.0\n", + " 380.0\n", + " 946.000000\n", + " 154.000000\n", + " 249.000000\n", + " 409.000000\n", + " 24.0\n", " \n", " \n", - " 1302\n", - " 22\n", - " Milinkovic-Savic V.\n", - " Torino\n", - " Milan\n", + " 1844\n", + " 31\n", + " Zoet\n", + " Spezia\n", + " Sampdoria\n", " 0\n", " 6.5\n", " -1\n", @@ -713,68 +689,92 @@ " 0.0\n", " 5.5\n", " ...\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", - " 1303\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", - " 1.00\n", - " 91.0\n", - " 227.0\n", - " 470.0\n", - " 80.0\n", - " 175.0\n", - " 293.0\n", + " 14.566667\n", + " 0.213333\n", + " -0.433333\n", + " 56.0\n", + " 171.0\n", + " 316.333333\n", + " 49.666667\n", + " 64.333333\n", + " 160.333333\n", " 5.0\n", " \n", " \n", - " 1304\n", - " 22\n", - " Montipo'\n", - " Verona\n", - " Salernitana\n", - " 1\n", - " 6.5\n", + " 1845\n", + " 31\n", + " Milinkovic-Savic V.\n", + " Torino\n", + " Lazio\n", + " 0\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\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", + " 31.500000\n", + " 0.240000\n", + " -4.500000\n", + " 224.0\n", + " 762.0\n", + " 1196.000000\n", + " 142.000000\n", + " 236.000000\n", + " 387.000000\n", + " 25.0\n", + " \n", + " \n", + " 1846\n", + " 31\n", + " Silvestri\n", + " Udinese\n", + " Cremonese\n", + " 1\n", + " 6.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 6.0\n", + " ...\n", + " 37.500000\n", + " 0.300000\n", + " 0.500000\n", + " 121.0\n", + " 316.0\n", + " 687.000000\n", + " 120.000000\n", + " 237.000000\n", + " 441.000000\n", + " 11.0\n", + " \n", + " \n", + " 1847\n", + " 31\n", + " Montipo'\n", + " Verona\n", + " Bologna\n", + " 1\n", + " 6.5\n", + " -1\n", + " 0\n", + " 0.0\n", + " 5.5\n", + " ...\n", + " 38.700000\n", + " 0.260000\n", + " -4.300000\n", + " 280.0\n", + " 616.0\n", + " 745.000000\n", + " 99.000000\n", + " 227.000000\n", + " 401.000000\n", + " 22.0\n", " \n", " \n", "\n", - "

1305 rows × 102 columns

\n", + "

1848 rows × 102 columns

\n", "" ], "text/plain": [ @@ -785,68 +785,68 @@ "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", + "1843 31 Consigli Sassuolo Salernitana 0 5.0 \n", + "1844 31 Zoet Spezia Sampdoria 0 6.5 \n", + "1845 31 Milinkovic-Savic V. Torino Lazio 0 6.0 \n", + "1846 31 Silvestri Udinese Cremonese 1 6.0 \n", + "1847 31 Montipo' Verona Bologna 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", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 20.000000 \n", + "1 -2 0 0.0 4.5 ... 40.200000 \n", + "2 -1 0 0.0 4.5 ... 32.300000 \n", + "3 -2 0 0.0 3.0 ... 18.616667 \n", + "4 -1 0 0.0 5.5 ... 12.500000 \n", + "... ... ... ... ... ... ... \n", + "1843 -3 0 0.0 2.0 ... 31.000000 \n", + "1844 -1 0 0.0 5.5 ... 14.566667 \n", + "1845 0 0 0.0 6.0 ... 31.500000 \n", + "1846 0 0 0.0 6.0 ... 37.500000 \n", + "1847 -1 0 0.0 5.5 ... 38.700000 \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", + "0 0.220000 -3.000000 \n", + "1 0.250000 3.200000 \n", + "2 0.280000 2.300000 \n", + "3 0.220000 1.116667 \n", + "4 0.270000 -2.500000 \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", + "1843 0.300000 -12.000000 \n", + "1844 0.213333 -0.433333 \n", + "1845 0.240000 -4.500000 \n", + "1846 0.300000 0.500000 \n", + "1847 0.260000 -4.300000 \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", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 108.0 266.0 532.000000 \n", + "1 129.0 329.0 773.000000 \n", + "2 117.0 346.0 869.000000 \n", + "3 45.5 114.0 556.166667 \n", + "4 31.0 62.0 345.000000 \n", + "... ... ... ... \n", + "1843 153.0 380.0 946.000000 \n", + "1844 56.0 171.0 316.333333 \n", + "1845 224.0 762.0 1196.000000 \n", + "1846 121.0 316.0 687.000000 \n", + "1847 280.0 616.0 745.000000 \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", + "0 148.000000 149.000000 247.000000 14.0 \n", + "1 169.000000 219.000000 402.000000 24.0 \n", + "2 123.000000 139.000000 489.000000 28.0 \n", + "3 92.833333 155.000000 258.666667 8.5 \n", + "4 54.000000 59.000000 112.000000 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", + "1843 154.000000 249.000000 409.000000 24.0 \n", + "1844 49.666667 64.333333 160.333333 5.0 \n", + "1845 142.000000 236.000000 387.000000 25.0 \n", + "1846 120.000000 237.000000 441.000000 11.0 \n", + "1847 99.000000 227.000000 401.000000 22.0 \n", "\n", - "[1305 rows x 102 columns]" + "[1848 rows x 102 columns]" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -871,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "bc9dae87", "metadata": {}, "outputs": [], @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "493b0495", "metadata": {}, "outputs": [ @@ -903,7 +903,7 @@ "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", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_6676\\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" ] }, @@ -955,506 +955,506 @@ " \n", " Atalanta\n", " Atalanta\n", - " 24.00\n", - " 48.600\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 40.00\n", - " 28.00\n", - " 6.00\n", - " 8.0\n", - " ...\n", - " 244.00\n", - " 256.00\n", - " 26.0\n", - " 1.0\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", " 8.00\n", - " 1.0\n", - " 1335.00\n", - " 273.00\n", - " 328.00\n", - " 45.40\n", + " ...\n", + " 338.0\n", + " 356.00\n", + " 40.00\n", + " 1.00\n", + " 8.0\n", + " 1.00\n", + " 1865.0\n", + " 389.0\n", + " 472.0\n", + " 45.200\n", " \n", " \n", " Bologna\n", " Bologna\n", - " 25.00\n", - " 52.400\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 27.00\n", - " 20.00\n", - " 4.00\n", + " 27.0\n", + " 53.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 39.0\n", + " 32.0\n", " 4.0\n", - " ...\n", - " 280.00\n", - " 268.00\n", - " 38.0\n", - " 3.0\n", " 4.00\n", - " 1.0\n", - " 1204.00\n", - " 250.00\n", - " 210.00\n", - " 54.30\n", + " ...\n", + " 376.0\n", + " 365.00\n", + " 52.00\n", + " 5.00\n", + " 4.0\n", + " 1.00\n", + " 1687.0\n", + " 379.0\n", + " 306.0\n", + " 55.300\n", " \n", " \n", " Cremonese\n", " Cremonese\n", - " 31.00\n", - " 43.800\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 15.00\n", - " 7.00\n", - " 2.00\n", + " 32.0\n", + " 43.2\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 27.0\n", + " 13.0\n", " 4.0\n", + " 6.00\n", " ...\n", - " 239.00\n", - " 271.00\n", - " 36.0\n", - " 3.0\n", + " 345.0\n", + " 372.00\n", + " 52.00\n", " 4.00\n", - " 0.0\n", - " 1229.00\n", - " 409.00\n", - " 314.00\n", - " 56.60\n", + " 6.0\n", + " 0.00\n", + " 1745.0\n", + " 594.0\n", + " 462.0\n", + " 56.300\n", " \n", " \n", " Empoli\n", " Empoli\n", - " 28.00\n", - " 47.500\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 21.00\n", - " 11.00\n", - " 0.00\n", - " 0.0\n", + " 31.0\n", + " 47.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 25.0\n", + " 13.0\n", + " 1.0\n", + " 1.00\n", " ...\n", - " 283.00\n", - " 253.00\n", - " 36.0\n", - " 2.0\n", + " 384.0\n", + " 341.00\n", + " 60.00\n", + " 2.00\n", + " 1.0\n", " 0.00\n", - " 0.0\n", - " 1148.00\n", - " 263.00\n", - " 217.00\n", - " 54.80\n", + " 1607.0\n", + " 412.0\n", + " 330.0\n", + " 55.500\n", " \n", " \n", " Fiorentina\n", " Fiorentina\n", - " 28.00\n", - " 57.200\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 23.00\n", - " 18.00\n", - " 2.00\n", + " 29.0\n", + " 56.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", " 4.0\n", + " 6.00\n", " ...\n", - " 307.00\n", - " 269.00\n", - " 59.0\n", - " 1.0\n", - " 4.00\n", - " 0.0\n", - " 1130.00\n", - " 289.00\n", - " 328.00\n", - " 46.80\n", + " 460.0\n", + " 371.00\n", + " 78.00\n", + " 2.00\n", + " 6.0\n", + " 2.00\n", + " 1625.0\n", + " 425.0\n", + " 504.0\n", + " 45.700\n", " \n", " \n", " Verona\n", " Hellas Verona\n", - " 34.00\n", - " 42.900\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 18.00\n", - " 15.00\n", - " 0.00\n", - " 0.0\n", - " ...\n", - " 234.00\n", - " 315.00\n", - " 25.0\n", + " 36.0\n", + " 42.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 18.0\n", " 1.0\n", - " 0.00\n", - " 2.0\n", - " 1231.00\n", - " 423.00\n", - " 445.00\n", - " 48.70\n", + " 1.00\n", + " ...\n", + " 337.0\n", + " 438.00\n", + " 32.00\n", + " 1.00\n", + " 1.0\n", + " 2.00\n", + " 1728.0\n", + " 619.0\n", + " 615.0\n", + " 50.200\n", " \n", " \n", " Inter\n", " Inter\n", - " 23.00\n", - " 54.500\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 40.00\n", - " 27.00\n", - " 2.00\n", - " 2.0\n", + " 24.0\n", + " 56.2\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 50.0\n", + " 35.0\n", + " 4.0\n", + " 5.00\n", " ...\n", - " 276.00\n", - " 248.00\n", - " 19.0\n", - " 2.0\n", - " 2.00\n", - " 1.0\n", - " 1007.00\n", - " 231.00\n", - " 288.00\n", - " 44.50\n", + " 368.0\n", + " 336.00\n", + " 29.00\n", + " 3.00\n", + " 5.0\n", + " 1.00\n", + " 1443.0\n", + " 344.0\n", + " 443.0\n", + " 43.700\n", " \n", " \n", " Juventus\n", " Juventus\n", - " 26.00\n", - " 49.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 34.00\n", - " 26.00\n", - " 3.00\n", - " 4.0\n", - " ...\n", - " 246.00\n", - " 242.00\n", " 29.0\n", - " 0.0\n", - " 4.00\n", - " 0.0\n", - " 1134.00\n", - " 258.00\n", - " 272.00\n", - " 48.70\n", + " 48.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 47.0\n", + " 37.0\n", + " 3.0\n", + " 5.00\n", + " ...\n", + " 348.0\n", + " 328.00\n", + " 39.00\n", + " 0.00\n", + " 5.0\n", + " 0.00\n", + " 1556.0\n", + " 381.0\n", + " 390.0\n", + " 49.400\n", " \n", " \n", " Lazio\n", " Lazio\n", - " 21.00\n", - " 51.800\n", " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 36.00\n", - " 26.00\n", - " 3.00\n", - " 4.0\n", + " 51.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 48.0\n", + " 29.0\n", + " 5.0\n", + " 7.00\n", " ...\n", - " 308.00\n", - " 218.00\n", - " 44.0\n", - " 1.0\n", - " 4.00\n", - " 1.0\n", - " 1233.00\n", - " 218.00\n", - " 229.00\n", - " 48.80\n", + " 421.0\n", + " 304.00\n", + " 55.00\n", + " 1.00\n", + " 7.0\n", + " 1.00\n", + " 1677.0\n", + " 319.0\n", + " 319.0\n", + " 50.000\n", " \n", " \n", " Lecce\n", " Lecce\n", - " 26.00\n", - " 42.400\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 20.00\n", - " 14.00\n", - " 1.00\n", - " 2.0\n", - " ...\n", - " 283.00\n", - " 300.00\n", - " 45.0\n", - " 3.0\n", + " 29.0\n", + " 41.7\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 17.0\n", + " 1.0\n", " 2.00\n", + " ...\n", + " 378.0\n", + " 425.00\n", + " 57.00\n", + " 4.00\n", " 2.0\n", - " 1200.00\n", - " 417.00\n", - " 332.00\n", - " 55.70\n", + " 2.00\n", + " 1694.0\n", + " 582.0\n", + " 466.0\n", + " 55.500\n", " \n", " \n", " Milan\n", " Milan\n", - " 27.00\n", - " 53.500\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 36.00\n", - " 31.00\n", - " 2.00\n", - " 2.0\n", + " 29.0\n", + " 54.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 48.0\n", + " 39.0\n", + " 3.0\n", + " 3.00\n", " ...\n", - " 275.00\n", - " 261.00\n", - " 25.0\n", - " 4.0\n", - " 2.00\n", - " 2.0\n", - " 1125.00\n", - " 263.00\n", - " 325.00\n", - " 44.70\n", + " 378.0\n", + " 369.00\n", + " 31.00\n", + " 5.00\n", + " 3.0\n", + " 3.00\n", + " 1617.0\n", + " 387.0\n", + " 456.0\n", + " 45.900\n", " \n", " \n", " Monza\n", " Monza\n", - " 29.00\n", - " 55.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 27.00\n", - " 17.00\n", - " 4.00\n", - " 4.0\n", + " 31.0\n", + " 55.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 36.0\n", + " 23.0\n", + " 5.0\n", + " 5.00\n", " ...\n", - " 318.00\n", - " 281.00\n", - " 37.0\n", - " 0.0\n", - " 4.00\n", - " 1.0\n", - " 1145.00\n", - " 242.00\n", - " 253.00\n", - " 48.90\n", + " 437.0\n", + " 385.00\n", + " 48.00\n", + " 1.00\n", + " 5.0\n", + " 2.00\n", + " 1608.0\n", + " 340.0\n", + " 357.0\n", + " 48.800\n", " \n", " \n", " Napoli\n", " Napoli\n", - " 24.00\n", - " 61.600\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 54.00\n", - " 42.00\n", - " 5.00\n", + " 26.0\n", + " 61.8\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 65.0\n", + " 51.0\n", " 6.0\n", + " 7.00\n", " ...\n", - " 298.00\n", - " 199.00\n", - " 28.0\n", - " 1.0\n", - " 5.00\n", - " 0.0\n", - " 1100.00\n", - " 232.00\n", - " 280.00\n", - " 45.30\n", + " 405.0\n", + " 287.00\n", + " 39.00\n", + " 1.00\n", + " 6.0\n", + " 2.00\n", + " 1562.0\n", + " 322.0\n", + " 389.0\n", + " 45.300\n", " \n", " \n", " Roma\n", " Roma\n", - " 26.00\n", - " 49.300\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 29.00\n", - " 19.00\n", - " 4.00\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", + " 9.00\n", " ...\n", - " 316.00\n", - " 246.00\n", - " 12.0\n", - " 0.0\n", - " 6.00\n", - " 0.0\n", - " 1156.00\n", - " 221.00\n", - " 266.00\n", - " 45.40\n", + " 447.0\n", + " 344.00\n", + " 21.00\n", + " 2.00\n", + " 9.0\n", + " 0.00\n", + " 1621.0\n", + " 331.0\n", + " 426.0\n", + " 43.700\n", " \n", " \n", " Salernitana\n", " Salernitana\n", - " 28.00\n", - " 46.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 24.00\n", - " 16.00\n", - " 1.00\n", + " 28.0\n", + " 44.6\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 35.0\n", + " 25.0\n", " 1.0\n", + " 1.00\n", " ...\n", - " 252.00\n", - " 259.00\n", - " 57.0\n", - " 8.0\n", - " 1.00\n", + " 375.0\n", + " 357.00\n", + " 64.00\n", + " 10.00\n", " 1.0\n", - " 1213.00\n", - " 291.00\n", - " 285.00\n", - " 50.50\n", + " 2.00\n", + " 1709.0\n", + " 448.0\n", + " 427.0\n", + " 51.200\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", - " 31.00\n", - " 47.300\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 10.00\n", - " 8.00\n", - " 0.00\n", - " 0.0\n", + " 37.0\n", + " 47.4\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 20.0\n", + " 16.0\n", + " 1.0\n", + " 2.00\n", " ...\n", - " 339.00\n", - " 300.00\n", - " 59.0\n", - " 4.0\n", + " 449.0\n", + " 408.00\n", + " 75.00\n", + " 6.00\n", + " 2.0\n", " 0.00\n", - " 0.0\n", - " 1189.00\n", - " 362.00\n", - " 349.00\n", - " 50.90\n", + " 1693.0\n", + " 535.0\n", + " 517.0\n", + " 50.900\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", - " 29.00\n", - " 48.700\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 25.00\n", - " 18.00\n", - " 4.00\n", - " 5.0\n", + " 29.0\n", + " 48.9\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.0\n", + " 24.0\n", + " 6.0\n", + " 7.00\n", " ...\n", - " 287.00\n", - " 206.00\n", - " 72.0\n", - " 2.0\n", - " 5.00\n", - " 1.0\n", - " 1131.00\n", - " 256.00\n", - " 206.00\n", - " 55.40\n", + " 384.0\n", + " 307.00\n", + " 94.00\n", + " 2.00\n", + " 7.0\n", + " 1.00\n", + " 1611.0\n", + " 385.0\n", + " 329.0\n", + " 53.900\n", " \n", " \n", " Spezia\n", " Spezia\n", - " 33.00\n", - " 45.500\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 17.00\n", - " 10.00\n", - " 3.00\n", - " 3.0\n", + " 34.0\n", + " 47.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 24.0\n", + " 15.0\n", + " 4.0\n", + " 4.00\n", " ...\n", - " 235.00\n", - " 291.00\n", - " 53.0\n", - " 1.0\n", - " 3.00\n", - " 2.0\n", - " 1275.00\n", - " 343.00\n", - " 306.00\n", - " 52.90\n", + " 321.0\n", + " 396.00\n", + " 67.00\n", + " 4.00\n", + " 4.0\n", + " 2.00\n", + " 1708.0\n", + " 470.0\n", + " 424.0\n", + " 52.600\n", " \n", " \n", " Torino\n", " Torino\n", - " 27.00\n", - " 53.000\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 22.00\n", - " 17.00\n", - " 1.00\n", - " 1.0\n", + " 28.0\n", + " 53.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 32.0\n", + " 26.0\n", + " 2.0\n", + " 2.00\n", " ...\n", - " 239.00\n", - " 306.00\n", - " 24.0\n", - " 3.0\n", - " 1.00\n", - " 0.0\n", - " 1155.00\n", - " 367.00\n", - " 333.00\n", - " 52.40\n", + " 341.0\n", + " 409.00\n", + " 32.00\n", + " 4.00\n", + " 2.0\n", + " 0.00\n", + " 1597.0\n", + " 508.0\n", + " 474.0\n", + " 51.700\n", " \n", " \n", " Udinese\n", " Udinese\n", - " 25.00\n", - " 49.900\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 29.00\n", - " 25.00\n", - " 0.00\n", - " 0.0\n", - " ...\n", - " 282.00\n", - " 252.00\n", - " 34.0\n", - " 2.0\n", - " 0.00\n", + " 27.0\n", + " 48.1\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 41.0\n", + " 36.0\n", " 1.0\n", - " 1101.00\n", - " 233.00\n", - " 277.00\n", - " 45.70\n", + " 2.00\n", + " ...\n", + " 412.0\n", + " 357.00\n", + " 50.00\n", + " 3.00\n", + " 2.0\n", + " 1.00\n", + " 1577.0\n", + " 326.0\n", + " 390.0\n", + " 45.500\n", " \n", " \n", " Avg\n", " Avg\n", - " 27.25\n", - " 49.995\n", - " 22.0\n", - " 242.0\n", - " 1980.0\n", - " 27.35\n", - " 19.75\n", - " 2.35\n", - " 3.0\n", + " 29.0\n", + " 50.0\n", + " 31.0\n", + " 341.0\n", + " 2790.0\n", + " 37.5\n", + " 26.8\n", + " 3.4\n", + " 4.35\n", " ...\n", - " 277.05\n", - " 262.05\n", - " 37.9\n", - " 2.1\n", - " 2.95\n", - " 0.8\n", - " 1172.05\n", - " 292.05\n", - " 292.15\n", - " 49.82\n", + " 385.2\n", + " 362.75\n", + " 50.75\n", + " 3.05\n", + " 4.3\n", + " 1.15\n", + " 1646.5\n", + " 424.8\n", + " 424.8\n", + " 49.815\n", " \n", " \n", "\n", @@ -1463,170 +1463,170 @@ ], "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", + "Atalanta Atalanta 25.0 50.0 31.0 \n", + "Bologna Bologna 27.0 53.4 31.0 \n", + "Cremonese Cremonese 32.0 43.2 31.0 \n", + "Empoli Empoli 31.0 47.4 31.0 \n", + "Fiorentina Fiorentina 29.0 56.6 31.0 \n", + "Verona Hellas Verona 36.0 42.0 31.0 \n", + "Inter Inter 24.0 56.2 31.0 \n", + "Juventus Juventus 29.0 48.6 31.0 \n", + "Lazio Lazio 22.0 51.9 31.0 \n", + "Lecce Lecce 29.0 41.7 31.0 \n", + "Milan Milan 29.0 54.0 31.0 \n", + "Monza Monza 31.0 55.0 31.0 \n", + "Napoli Napoli 26.0 61.8 31.0 \n", + "Roma Roma 27.0 49.2 31.0 \n", + "Salernitana Salernitana 28.0 44.6 31.0 \n", + "Sampdoria Sampdoria 37.0 47.4 31.0 \n", + "Sassuolo Sassuolo 29.0 48.9 31.0 \n", + "Spezia Spezia 34.0 47.0 31.0 \n", + "Torino Torino 28.0 53.0 31.0 \n", + "Udinese Udinese 27.0 48.1 31.0 \n", + "Avg Avg 29.0 50.0 31.0 \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", + "Atalanta 341.0 2790.0 50.0 32.0 \n", + "Bologna 341.0 2790.0 39.0 32.0 \n", + "Cremonese 341.0 2790.0 27.0 13.0 \n", + "Empoli 341.0 2790.0 25.0 13.0 \n", + "Fiorentina 341.0 2790.0 35.0 25.0 \n", + "Verona 341.0 2790.0 24.0 18.0 \n", + "Inter 341.0 2790.0 50.0 35.0 \n", + "Juventus 341.0 2790.0 47.0 37.0 \n", + "Lazio 341.0 2790.0 48.0 29.0 \n", + "Lecce 341.0 2790.0 24.0 17.0 \n", + "Milan 341.0 2790.0 48.0 39.0 \n", + "Monza 341.0 2790.0 36.0 23.0 \n", + "Napoli 341.0 2790.0 65.0 51.0 \n", + "Roma 341.0 2790.0 43.0 30.0 \n", + "Salernitana 341.0 2790.0 35.0 25.0 \n", + "Sampdoria 341.0 2790.0 20.0 16.0 \n", + "Sassuolo 341.0 2790.0 37.0 24.0 \n", + "Spezia 341.0 2790.0 24.0 15.0 \n", + "Torino 341.0 2790.0 32.0 26.0 \n", + "Udinese 341.0 2790.0 41.0 36.0 \n", + "Avg 341.0 2790.0 37.5 26.8 \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", + "Atalanta 6.0 8.00 ... 338.0 \n", + "Bologna 4.0 4.00 ... 376.0 \n", + "Cremonese 4.0 6.00 ... 345.0 \n", + "Empoli 1.0 1.00 ... 384.0 \n", + "Fiorentina 4.0 6.00 ... 460.0 \n", + "Verona 1.0 1.00 ... 337.0 \n", + "Inter 4.0 5.00 ... 368.0 \n", + "Juventus 3.0 5.00 ... 348.0 \n", + "Lazio 5.0 7.00 ... 421.0 \n", + "Lecce 1.0 2.00 ... 378.0 \n", + "Milan 3.0 3.00 ... 378.0 \n", + "Monza 5.0 5.00 ... 437.0 \n", + "Napoli 6.0 7.00 ... 405.0 \n", + "Roma 6.0 9.00 ... 447.0 \n", + "Salernitana 1.0 1.00 ... 375.0 \n", + "Sampdoria 1.0 2.00 ... 449.0 \n", + "Sassuolo 6.0 7.00 ... 384.0 \n", + "Spezia 4.0 4.00 ... 321.0 \n", + "Torino 2.0 2.00 ... 341.0 \n", + "Udinese 1.0 2.00 ... 412.0 \n", + "Avg 3.4 4.35 ... 385.2 \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", + "Atalanta 356.00 40.00 1.00 \n", + "Bologna 365.00 52.00 5.00 \n", + "Cremonese 372.00 52.00 4.00 \n", + "Empoli 341.00 60.00 2.00 \n", + "Fiorentina 371.00 78.00 2.00 \n", + "Verona 438.00 32.00 1.00 \n", + "Inter 336.00 29.00 3.00 \n", + "Juventus 328.00 39.00 0.00 \n", + "Lazio 304.00 55.00 1.00 \n", + "Lecce 425.00 57.00 4.00 \n", + "Milan 369.00 31.00 5.00 \n", + "Monza 385.00 48.00 1.00 \n", + "Napoli 287.00 39.00 1.00 \n", + "Roma 344.00 21.00 2.00 \n", + "Salernitana 357.00 64.00 10.00 \n", + "Sampdoria 408.00 75.00 6.00 \n", + "Sassuolo 307.00 94.00 2.00 \n", + "Spezia 396.00 67.00 4.00 \n", + "Torino 409.00 32.00 4.00 \n", + "Udinese 357.00 50.00 3.00 \n", + "Avg 362.75 50.75 3.05 \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", + "Atalanta 8.0 1.00 \n", + "Bologna 4.0 1.00 \n", + "Cremonese 6.0 0.00 \n", + "Empoli 1.0 0.00 \n", + "Fiorentina 6.0 2.00 \n", + "Verona 1.0 2.00 \n", + "Inter 5.0 1.00 \n", + "Juventus 5.0 0.00 \n", + "Lazio 7.0 1.00 \n", + "Lecce 2.0 2.00 \n", + "Milan 3.0 3.00 \n", + "Monza 5.0 2.00 \n", + "Napoli 6.0 2.00 \n", + "Roma 9.0 0.00 \n", + "Salernitana 1.0 2.00 \n", + "Sampdoria 2.0 0.00 \n", + "Sassuolo 7.0 1.00 \n", + "Spezia 4.0 2.00 \n", + "Torino 2.0 0.00 \n", + "Udinese 2.0 1.00 \n", + "Avg 4.3 1.15 \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", + "Atalanta 1865.0 389.0 \n", + "Bologna 1687.0 379.0 \n", + "Cremonese 1745.0 594.0 \n", + "Empoli 1607.0 412.0 \n", + "Fiorentina 1625.0 425.0 \n", + "Verona 1728.0 619.0 \n", + "Inter 1443.0 344.0 \n", + "Juventus 1556.0 381.0 \n", + "Lazio 1677.0 319.0 \n", + "Lecce 1694.0 582.0 \n", + "Milan 1617.0 387.0 \n", + "Monza 1608.0 340.0 \n", + "Napoli 1562.0 322.0 \n", + "Roma 1621.0 331.0 \n", + "Salernitana 1709.0 448.0 \n", + "Sampdoria 1693.0 535.0 \n", + "Sassuolo 1611.0 385.0 \n", + "Spezia 1708.0 470.0 \n", + "Torino 1597.0 508.0 \n", + "Udinese 1577.0 326.0 \n", + "Avg 1646.5 424.8 \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", + "Atalanta 472.0 45.200 \n", + "Bologna 306.0 55.300 \n", + "Cremonese 462.0 56.300 \n", + "Empoli 330.0 55.500 \n", + "Fiorentina 504.0 45.700 \n", + "Verona 615.0 50.200 \n", + "Inter 443.0 43.700 \n", + "Juventus 390.0 49.400 \n", + "Lazio 319.0 50.000 \n", + "Lecce 466.0 55.500 \n", + "Milan 456.0 45.900 \n", + "Monza 357.0 48.800 \n", + "Napoli 389.0 45.300 \n", + "Roma 426.0 43.700 \n", + "Salernitana 427.0 51.200 \n", + "Sampdoria 517.0 50.900 \n", + "Sassuolo 329.0 53.900 \n", + "Spezia 424.0 52.600 \n", + "Torino 474.0 51.700 \n", + "Udinese 390.0 45.500 \n", + "Avg 424.8 49.815 \n", "\n", "[21 rows x 303 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1652,7 +1652,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "32f56138", "metadata": {}, "outputs": [], @@ -1798,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "4001f3f8", "metadata": {}, "outputs": [], @@ -1911,7 +1911,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "f4017b2f", "metadata": {}, "outputs": [], @@ -1998,7 +1998,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "6f8707b8", "metadata": {}, "outputs": [ @@ -2026,7 +2026,7 @@ "Dragowski 1\n", "Terracciano 1\n", "Tatarusanu 1\n", - "Handanovic 0.7372677888806921\n", + "Handanovic 0.7727839727839729\n", "Sportiello 1\n", "Perin 1\n", "Zoet 1\n", @@ -2040,8 +2040,8 @@ "Cordaz 0.0\n", "Pinsoglio 0.0\n", "Fiorillo 0.0\n", - "Cragno 0.07071960297766748\n", - "Sirigu 0.0668969217356314\n", + "Cragno 0.0676989676989677\n", + "Sirigu 0.06403956403956404\n", "Rossi F. 0.0\n", "Berardi A. 0.0\n", "Gemello 0.0\n", @@ -2056,11 +2056,11 @@ "Di Lorenzo 1\n", "Danilo 1\n", "Hernandez T. 1\n", - "Udogie 0.9565331442750797\n", + "Udogie 0.9871395271395272\n", "Parisi 1\n", - "Mario Rui 0.7658517004816814\n", + "Mario Rui 0.7358494446729742\n", "Romagnoli 1\n", - "Bastoni S. 0.6399743856559673\n", + "Bastoni S. 0.6917662982179112\n", "Mazzocchi 1\n", "Tomori 1\n", "Scalvini 1\n", @@ -2068,133 +2068,133 @@ "Demiral 1\n", "Maehle 1\n", "Dumfries 1\n", - "Juan Jesus 0.649497814013943\n", + "Juan Jesus 0.6807858807858809\n", "Depaoli 1\n", "Mancini 1\n", - "Ibanez 0.9846664720478762\n", - "Rodrigo Becao 0.8502516838000708\n", + "Ibanez 1\n", + "Rodrigo Becao 0.885021645021645\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", + "Bremer 0.9693261284170375\n", + "Rrahmani 0.9025570389206753\n", + "Vojvoda 1\n", + "Bastoni 0.9223550642905484\n", + "Milenkovic 0.8409707939119705\n", "Kalulu 1\n", "Martinez Quarta 1\n", - "Casale 0.756306865177833\n", + "Casale 0.7898212898212899\n", "Perez N. 1\n", "Izzo 1\n", "Luperto 1\n", - "Skriniar 0.743970223325062\n", + "Skriniar 0.7148251748251749\n", "Rodriguez R. 1\n", "Marusic 1\n", - "Lazzari 0.8799647802769551\n", - "Kyriakopoulos 0.4694318473517583\n", + "Lazzari 0.9223550642905484\n", + "Kyriakopoulos 0.49023390402700745\n", "Ampadu 1\n", "Ismajli 1\n", "Llorente D. affine to Ibanez\n", - "Llorente D. 0.2188147715661947\n", + "Llorente D. 0.21024269847799262\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", + "Biraghi 0.9981792981792983\n", + "Medel 0.9025570389206753\n", + "Bonucci 0.6949689199689202\n", + "Calabria 0.9164425318271473\n", + "Acerbi 1\n", "Spinazzola 1\n", - "Lykogiannis 0.8397952853598015\n", - "Pellegrini Lu. 0.13751033912324234\n", + "Lykogiannis 0.8462370962370962\n", + "Pellegrini Lu. 0.19745532245532246\n", "Djidji 1\n", "Augello 1\n", - "Singo 0.8856788372917405\n", + "Singo 0.9190609390609392\n", "Mari' 1\n", - "De Vrij 0.826633581472291\n", - "Patric 0.8266335814722912\n", - "Faraoni 0.658723635235732\n", - "Ceccherini 0.6564443146985842\n", - "Hateboer 1\n", + "De Vrij 0.8736752136752136\n", + "Patric 0.8438908313908315\n", + "Faraoni 0.6701486013986016\n", + "Ceccherini 0.7008089949266421\n", + "Hateboer 0.9644466644466645\n", "Rogerio 1\n", - "Aina 0.9447240931111898\n", - "Ferrari G. 0.8955197132616488\n", + "Aina 0.9077145077145077\n", + "Ferrari G. 0.9266252266252268\n", "Fazio 1\n", "Buongiorno 1\n", - "Gunter 0.1650124069478908\n", + "Gunter 0.23694638694638692\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", + "Troost-Ekong 0.44676573426573435\n", + "Soumaoro 0.9786297036297038\n", + "Ceccaroni 0.0676989676989677\n", + "Soppy 0.5500541125541125\n", + "Ferrari A. 0.6696310935441369\n", + "Zappacosta 0.5606471959413136\n", + "Gyomber 0.9607865253026544\n", + "Alex Sandro 0.9786297036297038\n", + "Pezzella Giu. 0.7334054834054832\n", + "Bereszynski 0.5077422577422577\n", + "Venuti 0.6215871085436304\n", + "Palomino 0.5256067461949815\n", + "Nuytinck 0.6581844081844082\n", "Magnani 1\n", - "Colley 0.6199751861042183\n", - "Nikolaou 0.9988489109456851\n", + "Colley 0.5956876456876458\n", + "Nikolaou 1\n", "Terzic 1\n", - "Igor 0.9092969396195203\n", + "Igor 0.9133877233877236\n", "Toljan 1\n", - "Zortea 0.2560537349191409\n", + "Zortea 0.28596977734908763\n", "Dawidowicz 1\n", - "Bellanova 0.518990634755463\n", - "Erlic 0.9075682382133995\n", + "Bellanova 0.5350402285886157\n", + "Erlic 0.9477855477855477\n", "Ballo-Toure' affine to Calabria\n", - "Ballo-Toure' 0.23845199465546857\n", - "Stojanovic 0.8266335814722912\n", - "Amian 0.9299627791563275\n", - "Zima 0.5579776674937966\n", + "Ballo-Toure' 0.2749327595481442\n", + "Stojanovic 0.8303524758070213\n", + "Amian 0.9679924242424244\n", + "Zima 0.5361188811188812\n", "De Winter 1\n", - "Romagnoli S. 0.195409429280397\n", + "Romagnoli S. 0.31177156177156173\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", + "Rugani 0.5956876456876459\n", + "De Sciglio 1\n", + "Djimsiti 0.7301977592300174\n", + "Caldara 0.7643431836980223\n", + "Karsdorp 0.4302188552188553\n", + "Marchizza 0.5611888111888111\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", + "Radovanovic 0.8509823509823511\n", + "D'ambrosio 0.7148251748251749\n", + "De Silvestri 0.4227460711331679\n", + "Chiriches 0.49023390402700745\n", + "Murru 0.8664547573638484\n", + "Bonifazi 0.4332273786819242\n", "Walukiewicz 1\n", - "Ranieri L. 0.22918389853873725\n", + "Ranieri L. 0.30715272381939046\n", "Gabbia 1\n", - "Kumbulla 0.4376295431323894\n", - "Lovato 0.9281947890818858\n", - "Ferrer 0.13777226357871517\n", - "Vasquez 0.7955955334987593\n", + "Kumbulla 0.49056629644864946\n", + "Lovato 1\n", + "Ferrer 0.17650004316670986\n", + "Vasquez 0.8462370962370962\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", + "Conti 0.17019647019647022\n", + "Conti 0.6439363984182801\n", + "Marrone 0.11283161283161282\n", + "Tonelli 0.08509823509823511\n", "Radu 1\n", - "Florenzi 0.258322994210091\n", - "Sala 0.4133167907361455\n", + "Florenzi 0.24820318570318575\n", + "Sala 0.3971250971250972\n", "Fares 0.0\n", "Fares 0.0\n", "Romagna 0.0\n", "Romagna 0.0\n", - "Muldur 0.03999839910349796\n", + "Muldur 0.03843146101210618\n", "Amey 0.0\n", "Zaccagni 1\n", - "Milinkovic-Savic 0.9718529944336394\n", + "Milinkovic-Savic 0.9981792981792983\n", "Barella 1\n", "Zielinski 1\n", "Luis Alberto 1\n", @@ -2208,48 +2208,48 @@ "Miranchuk 1\n", "Samardzic 1\n", "Pereyra 1\n", - "Politano 0.9393563425821488\n", + "Politano 0.9025570389206753\n", "Rabiot 1\n", - "Lazovic 0.8752590862647788\n", + "Lazovic 0.9110516934046347\n", "Lobotka 1\n", "Bonaventura 1\n", "Pessina 1\n", - "Tonali 0.9299627791563276\n", + "Tonali 0.9597189847189849\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", + "Bennacer 1\n", + "Pasalic 0.8693819693819695\n", "Mkhitaryan 1\n", "Chiesa 1\n", "Bandinelli 1\n", "Fagioli affine to Henderson L.\n", - "Fagioli 0.7178660049627792\n", - "Messias 0.9538079786218743\n", + "Fagioli 0.7524475524475526\n", + "Messias 0.9622646584185047\n", "Arslan 1\n", "Ricci S. 1\n", "Verdi 1\n", "Sensi 1\n", "Barak 1\n", - "Soriano 0.9565331442750797\n", + "Soriano 0.9190609390609392\n", "Dominguez 1\n", - "Brozovic 0.743970223325062\n", + "Brozovic 0.7829037629037631\n", "Cristante 1\n", "Saponara 1\n", "Vecino 1\n", "Locatelli 1\n", - "Zaniolo 0.5756912442396314\n", + "Zaniolo 0.5531385281385282\n", "Maldini 1\n", - "Marin 0.996949958643507\n", + "Marin 1\n", "Zalewski 1\n", - "Bajrami 0.3889578163771712\n", + "Bajrami 0.44004329004328996\n", "Coulibaly L. 1\n", "De Roon 1\n", "Mandragora 1\n", "Bourabia 1\n", - "Sottil 0.6716397849462366\n", + "Sottil 0.7446095571095572\n", "Aebischer 1\n", "Ederson D.s. 1\n", "Miretti 1\n", @@ -2259,122 +2259,122 @@ "Haas 1\n", "Walace 1\n", "Agudelo 1\n", - "Pobega 0.5625422964132641\n", + "Pobega 0.538514515787243\n", "Rovella 1\n", "Amrabat 1\n", - "Tameze 0.9789081885856079\n", - "Gyasi 0.9644058450510063\n", - "Ilic 0.27072348014888337\n", + "Tameze 1\n", + "Gyasi 0.9928127428127429\n", + "Ilic 0.3332058566433566\n", "Matheus Henrique 1\n", "Harroui 1\n", "Volpato 1\n", - "Duncan 0.6763365666591473\n", - "Cuadrado 0.9393563425821488\n", + "Duncan 0.7220456311365403\n", + "Cuadrado 0.9747616020343295\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", + "Kovalenko 0.7331540254617178\n", + "Crnigoj 0.243915398327163\n", + "Basic 0.8627200385821077\n", + "Asllani 0.875671430019256\n", "Sabiri 1\n", - "Grassi 0.7425558312655087\n", - "Krunic 0.7528270116979794\n", + "Grassi 0.7898212898212897\n", + "Krunic 0.8084332334332336\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", + "Henderson L. 0.6270396270396271\n", + "Lopez M. 0.850982350982351\n", + "Cuisance 1\n", + "Saelemaekers 0.8273439523439524\n", + "Maggiore 0.47389277389277384\n", "Akpa Akpro 0.0\n", "Akpa Akpro 0.0\n", - "Maleh 0.5745967741935485\n", - "Romero L. 0.9299627791563275\n", + "Maleh 0.6346778221778221\n", + "Romero L. 0.8935314685314687\n", "Ceide 1\n", "Benassi 1\n", - "Gagliardini 0.9644058450510063\n", - "Vieira 0.08839950372208435\n", + "Gagliardini 0.9928127428127429\n", + "Vieira 0.08462370962370963\n", "Bianco 1\n", "Galdames 0.0\n", - "Kastanos 0.9644058450510062\n", - "Vignato 0.2062655086848635\n", + "Kastanos 1\n", + "Vignato 0.19745532245532246\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", + "Bakayoko 0.2552947052947053\n", + "Zurkowski 0.2030969030969031\n", + "Castrovilli 0.6215871085436304\n", + "Demme 0.31351981351981356\n", "Darboe 0.0\n", - "Darboe 0.24799007444168736\n", + "Darboe 0.23827505827505832\n", "Urbanski 0.0\n", "Yepes 1\n", "Osimhen 1\n", "Martinez L. 1\n", - "Dybala 0.9815393171900401\n", + "Dybala 0.9396149827184309\n", "Rafael Leao 1\n", - "Immobile 0.9599615784839509\n", + "Immobile 0.9992179863147606\n", "Vlahovic 1\n", - "Arnautovic 0.6011880592525753\n", + "Arnautovic 0.5776365049092322\n", "Dzeko 1\n", "Nzola 1\n", "Beto 1\n", "Giroud 1\n", "Abraham 1\n", - "Deulofeu 0.5835060575098525\n", - "Simeone 0.6718362282878412\n", + "Deulofeu 0.5606471959413136\n", + "Simeone 0.6769896769896769\n", "Lozano 1\n", "Correa 1\n", - "Berardi 0.7139108203624331\n", + "Berardi 0.7581479126933673\n", "Pedro 1\n", "Sanabria 1\n", "Thauvin affine to Deulofeu\n", - "Thauvin 0.36469128594365785\n", + "Thauvin 0.42048539695598525\n", "Cabral 1\n", "Caprari 1\n", "Piatek 1\n", "Rebic 1\n", - "Bonazzoli 0.9299627791563275\n", + "Bonazzoli 0.8935314685314687\n", "Zapata D. 1\n", - "Gonzalez N. 0.7139108203624331\n", - "Brekalo 0.15469913151364761\n", - "Kean 0.9299627791563275\n", + "Gonzalez N. 0.7581479126933673\n", + "Brekalo 0.14809149184149184\n", + "Kean 0.8935314685314687\n", "Okereke 1\n", "Muriel 1\n", - "Pinamonti 0.9281947890818859\n", + "Pinamonti 0.9214581714581714\n", "Di Francesco F. 1\n", - "Caputo 0.5500413564929694\n", + "Caputo 0.5923659673659674\n", "Boga 1\n", "Alvarez A. affine to Raspadori\n", - "Alvarez A. 0.688861317893576\n", + "Alvarez A. 0.6949689199689201\n", "Petagna 1\n", - "Barrow 0.9117282148591446\n", + "Barrow 0.9460921431509668\n", "Djuric 1\n", - "Henry 0.6000451161741484\n", + "Henry 0.5744154835063926\n", "Success 1\n", "Gabbiadini 1\n", "Kallon 1\n", "Nestorovski 1\n", - "Raspadori 0.6531741108354012\n", + "Raspadori 0.6581844081844082\n", "Lasagna 1\n", "Belotti 1\n", "Pellegri 1\n", - "Verde 0.7514850740657192\n", - "Destro 0.5500413564929694\n", + "Verde 0.7942501942501942\n", + "Destro 0.5704264870931537\n", "Seck 1\n", - "Sansone 0.688861317893576\n", - "Quagliarella 0.6763365666591473\n", - "Defrel 0.6526054590570719\n", - "Pjaca 0.6703629032258065\n", + "Sansone 0.6618751618751619\n", + "Quagliarella 0.6498410680228862\n", + "Defrel 0.6897435897435897\n", + "Pjaca 0.6910936285936286\n", "Piccoli 1\n", - "Shomurodov 0.44199751861042186\n", + "Shomurodov 0.5077422577422578\n", "Afena-Gyan 1\n", - "Ibrahimovic 0.2156435429927716\n", - "Pussetto 0.22099875930521093\n", + "Ibrahimovic 0.20719570284787678\n", + "Pussetto 0.21155927405927405\n", "Cancellieri 1\n", "Oddei 0.0\n", - "Oddei 0.4959801488833747\n", + "Oddei 0.47655011655011664\n", "Braaf 1\n", "Raimondo 1\n", "Kaio Jorge 0.0\n", @@ -2390,40 +2390,39 @@ "Romagna 0.0\n", "Cassandro 0.16666666666666663\n", "Amey 0.16666666666666663\n", - "Zanotti 0.16666666666666663\n", + "Zanotti 0.33333333333333337\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", + "Pogba 0.6666666666666667\n", "Machin 0.0\n", "Akpa Akpro 0.0\n", - "Bianco 0.6666666666666667\n", + "Bianco 0.8333333333333334\n", "Galdames 0.0\n", "D'andrea 0.8333333333333334\n", - "Cipot 0.8333333333333334\n", "Gaetano 0.8333333333333334\n", - "Darboe 0.668006617038875\n", + "Darboe 0.6744832944832944\n", "Urbanski 0.16666666666666663\n", "Bertini 0.0\n", "Yepes 0.8333333333333334\n", - "Pyyhtia 0.6666666666666667\n", + "Pyyhtia 0.8333333333333334\n", "Trimboli 0.0\n", "Adli 0.8333333333333334\n", "Vignato S. 0.5\n", "Samek 0.0\n", - "Ilkhan 0.5\n", + "Ilkhan 0.6666666666666667\n", "Degli Innocenti 0.0\n", "Acella 0.16666666666666663\n", "Carboni V. 0.8333333333333334\n", - "Malagrida 0.6666666666666667\n", + "Malagrida 0.8333333333333334\n", "Faticanti 0.0\n", - "Oddei 0.5853432588916461\n", - "Braaf 0.6666666666666667\n", + "Oddei 0.5950582750582749\n", + "Braaf 0.8333333333333334\n", "Raimondo 0.33333333333333337\n", "De Luca 0.33333333333333337\n", - "Krollis 0.16666666666666663\n", + "Krollis 0.5\n", "Vivaldo 0.0\n" ] } @@ -2540,7 +2539,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "49c28b07", "metadata": {}, "outputs": [ @@ -2558,7 +2557,7 @@ " dtype='object', length=151)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2569,7 +2568,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "d29102e5", "metadata": {}, "outputs": [ @@ -2721,7 +2720,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "f19304f6", "metadata": {}, "outputs": [], @@ -2761,7 +2760,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "370d41d2", "metadata": {}, "outputs": [], @@ -2777,7 +2776,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "a7b1fb52", "metadata": {}, "outputs": [ @@ -2897,7 +2896,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "5a7cf079", "metadata": {}, "outputs": [], @@ -2921,7 +2920,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "0bc0568b", "metadata": {}, "outputs": [], @@ -3031,7 +3030,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "8aad9652", "metadata": {}, "outputs": [], @@ -3105,7 +3104,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "4e2bf9dc", "metadata": {}, "outputs": [], @@ -3125,7 +3124,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 38, "id": "c2674211", "metadata": {}, "outputs": [ @@ -3133,13 +3132,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.14539483185579238\n", - "0.16229047232752447\n" + "0.13385223635677934\n", + "0.1536831746216616\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -3151,13 +3150,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.13160209075894191\n", - "0.16563346611529572\n" + "0.127508905428527\n", + "0.147998716039207\n" ] }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -3204,1865 +3203,10 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 22, "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 - 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\n", 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0bR3u7rDkCpSwiIhIvbFo2wmeXrqPQotBh6hg3p+UQGxYA3eHJZWghEVEROq8IouVF1YcYM53xwAY2TmKV+7uRpC/boO1hT4pERGp07IuFvDwp7vYdDgdgEeHtOP3g9vh5aVicLWJEhYREamzDp25wH1zt3M84yKBvt68OrYbI7tEuzssuQpKWEREpE76+sAZ/vDZbnLyi2gWGsj7kxLoGNPI3WHJVVLCIiIidYphGLyb/BMvJ/2IYUCfuDDeuacn4Q393R2a/AJKWEREpM7IK7TwxOK9LNt9CoAJfVsw87ZO+PmoGFxtp4RFRETqhNPZl3jgkx3s/Tkbby8TM3/ViYnXt3R3WFJNlLCIiEittzP1PA98soNzF/IJbeDL2/f0pF+bJu4OS6qREhYREanVPt/xM099sY8Ci5X2kbZicC3CVQyurlHCIiIitVKRxcpfV/3IB5uOAjC0YySvjetOQxWDq5P0qYqISK2TfamQRxbsYkPKOQAeGdyWx4bEqxhcHaaERUREapUj53KY+vF2fkrPJcDXi1fu7satXWPcHZbUMCUsIiJSa6w7eJbfL9jFhbwiYkICeG9SAp2bhbg7LLkGlLCIiIjHMwyDDzYeZdaqA1gN6NWyMe/e24umwSoGV18oYREREY+WV2jhqSX7+GLnSQDGJcTy/JhO+Pt4uzkyuZaUsIiIiMc6Y87jgU92sPtEFt5eJp695Tom92uFyaTBtfVNlWsVb9iwgdtuu42YmBhMJhNLly512j9lyhRMJpPT4/rrr7/idRcvXkzHjh3x9/enY8eOLFmypKqhiYhIHbLnRBa/mr2J3SeyCAn05ePf9GHKDXFKVuqpKicsubm5dOvWjdmzZ7s8ZsSIEZw+fdrxWLlyZYXX3Lx5M+PGjWPixIns2bOHiRMnMnbsWLZu3VrV8EREpA5Yuuskd/9zM2fM+bSNaMiy6TfQv50q19ZnJsMwjKs+2WRiyZIljBkzxrFtypQpZGVllWl5qci4ceMwm82sWrXKsW3EiBE0btyYBQsWVOoaZrOZkJAQsrOzadRIy4eLiNRGFqvBy0k/8s/knwAY3CGC13/dneAAXzdHJjWlsvfvGlm+cv369URERBAfH8/UqVM5e/Zshcdv3ryZYcOGOW0bPnw43333nctz8vPzMZvNTg8REam9zHmF3PfxNkey8tCgNrw/KUHJigA1kLCMHDmS+fPn88033/D3v/+dbdu2MXjwYPLz812ek5aWRmRkpNO2yMhI0tLSXJ4za9YsQkJCHI/Y2Nhqew8iInJtHU3P5fa3vmXdwXP4+3jx+q+788SIDnircq0Uq/ZZQuPGjXP83rlzZxISEmjZsiUrVqzgjjvucHne5YOoDMOocGDVk08+yYwZMxzPzWazkhYRkVpo46FzTJ+/E3NeEVGNAnhvUi+6Ng91d1jiYWp8WnN0dDQtW7bk0KFDLo+Jiooq05py9uzZMq0upfn7++Pvr4JBIiK1lWEYfPTtMV5Y8QNWA7rHhvLexF5ENApwd2jigWpkDEtpGRkZnDhxgujoaJfHJCYmsmbNGqdtq1evpl+/fjUdnoiIuEF+kYUnFu/l+f/YkpU7ejbjs/uvV7IiLlW5hSUnJ4fDhw87nh89epTdu3cTFhZGWFgYM2fO5M477yQ6Oppjx47x1FNP0aRJE26//XbHOZMmTaJZs2bMmjULgD/84Q8MGDCAl156idGjR7Ns2TLWrl3Lpk2bquEtioiIJzl3IZ8H5+1gx/HzeJngqVHX8bv+qq8iFatywrJ9+3Zuuukmx3P7OJLJkyfzzjvvsG/fPubOnUtWVhbR0dHcdNNNLFy4kODgYMc5qampeHmVNO7069ePzz77jGeeeYZnn32WNm3asHDhQvr27ftL3puIiHiY/57MZurc7ZzOziM4wIc3x/dgUPsId4cltcAvqsPiSVSHRUTEs3255xR//HwPeYVWWjcJ4v3JCbRp2tDdYYmbVfb+rbWERESkRlmtBq+uSWH2OttwgoHxTXljfA9CAlVfRSpPCYuIiNSYnPwiHlu4mzU/nAHg/gGtVV9FrooSFhERqRGpGRe5b+42Us7k4Oftxaw7unBnr+buDktqKSUsIiJS7b47ks60+TvJulhI02B//jmxFz1bNHZ3WFKLKWEREZFqYxgGn2w5zp+//AGL1aBr8xDem5hAVIjqq8gvo4RFRESqRUGRleeW72fB96kAjO4ew0t3diXA19vNkUldoIRFRER+sYycfB6at5Pvj2ViMsETIzrwwIDWKgYn1UYJi4iI/CI/nDIzde52TmZdoqG/D2+M787gDq7XghO5GkpYRETkqq3ad5oZi/ZwqdBCq/AGfDA5gbYRwVc+UaSKlLCIiEiVWa0Gr399iNe/PgRA/7ZNmD2hB6EN/NwcmdRVSlhERKRKcvOL+J9Fe/hqfxoAv70hjqdGdcDH2+sKZ4pcPSUsIiJSaScyLzJ17nZ+TLuAr7eJv4zpwtjese4OS+oBJSwiIlIpW3/K4KH5O8nMLaBJQz/evbcXCa3C3B2W1BNKWERE5Io+3ZrK/1v2X4qsBp2bNeK9iQnEhAa6OyypR5SwiIiIS4UWK89/+QOfbDkOwC1do3nlrm4E+qkYnFxbSlhERKRcmbkFTJu/gy0/ZQLwx+HtmTaojYrBiVsoYRERkTIOpl3gvrnbOJF5iSA/b14b151hnaLcHZbUY0pYRETEyer9aTy2cDe5BRZiwwL5YFJv2kepGJy4lxIWEREBbCstz/7mMH9fkwJAYutw3r6nJ42DVAxO3E8Ji4iIcKnAwuOf72HF3tMATE5syTO3dsRXxeDEQyhhERGp505mXeL+udvZf8qMj5eJ50d3ZkLfFu4OS8SJEhYRkXps+7FMHpy3g/ScAsKC/Hjnnp70bR3u7rBEylDCIiJSTy3adoKnl+6j0GLQISqY9yclEBvWwN1hiZRLCYuISD1TZLHywooDzPnuGAAjO0fxyt3dCPLXLUE8l/51iojUI1kXC3j4011sOpwOwKND2vH7we3w8lIxOPFsSlhEROqJQ2cucN/c7RzPuEigrzevju3GyC7R7g5LpFKUsIiI1ANfHzjDHz7bTU5+Ec1CA3l/UgIdYxq5OyyRSlPCIiJShxmGwbvJP/Fy0o8YBvSJC+Ode3oS3tDf3aGJVIkSFhGROiqv0MITi/eybPcpACb0bcHM2zrh56NicFL7KGEREamDTmdf4oFPdrD352y8vUzM/FUnJl7f0t1hiVw1JSwiInXMztTzPPDJDs5dyCe0gS9v39OTfm2auDsskV+kyu2CGzZs4LbbbiMmJgaTycTSpUsd+woLC3niiSfo0qULQUFBxMTEMGnSJE6dOlXhNefMmYPJZCrzyMvLq/IbEhGpzz7f8TO//ucWzl3Ip31kMMun91eyInVClROW3NxcunXrxuzZs8vsu3jxIjt37uTZZ59l586dfPHFF6SkpPCrX/3qitdt1KgRp0+fdnoEBARUNTwRkXqpyGLlhf/8wOP/3kOBxcrQjpEsntaPFuGqXCt1Q5W7hEaOHMnIkSPL3RcSEsKaNWuctr355pv06dOH1NRUWrRwvZiWyWQiKiqqquGIiNR72ZcKeWTBLjaknAPgkcFteWxIvIrBSZ1S42NYsrOzMZlMhIaGVnhcTk4OLVu2xGKx0L17d/7v//6PHj16uDw+Pz+f/Px8x3Oz2VxdIYuI1BpHzuUw9ePt/JSeS4CvF6/c3Y1bu8a4OyyRalejc9vy8vL43//9XyZMmECjRq4LFHXo0IE5c+awfPlyFixYQEBAADfccAOHDh1yec6sWbMICQlxPGJjY2viLYiIeKz1B88y5q1v+Sk9l5iQAD5/sJ+SFamzTIZhGFd9ssnEkiVLGDNmTJl9hYWF3H333aSmprJ+/foKE5bLWa1WevbsyYABA3jjjTfKPaa8FpbY2Fiys7Or9FoiIrWNYRh8sPEos1YdwGpAr5aNeffeXjQNVjE4qX3MZjMhISFXvH/XSJdQYWEhY8eO5ejRo3zzzTdVTiC8vLzo3bt3hS0s/v7++Pvryyki9UteoYWnluzji50nARiXEMvzYzrh7+Pt5shEala1Jyz2ZOXQoUOsW7eO8PDwKl/DMAx2795Nly5dqjs8EZFa66w5j/s/2cHuE1l4e5l49pbrmNyvFSaTBtdK3VflhCUnJ4fDhw87nh89epTdu3cTFhZGTEwMd911Fzt37uQ///kPFouFtLQ0AMLCwvDz8wNg0qRJNGvWjFmzZgHw5z//meuvv5527dphNpt544032L17N2+99VZ1vEcRkVpvz4ks7v9kO2fM+YQE+vLWhJ70b6f6KlJ/VDlh2b59OzfddJPj+YwZMwCYPHkyM2fOZPny5QB0797d6bx169YxaNAgAFJTU/HyKhnvm5WVxf33309aWhohISH06NGDDRs20KdPn6qGJyJS5yzddZI/Ld5LQZGVthEN+WBSAq2aBLk7LJFr6hcNuvUklR20IyJSW1isBn9LOsi7yUcAGNwhgtd/3Z3gAF83RyZSfdw66FZERH4Zc14hj362m29+PAvAQ4Pa8Piw9nirGJzUU0pYREQ8zNH0XO77eBtHzuXi7+PFy3d1ZXT3Zu4OS8StlLCIiHiQjYfOMX3+Tsx5RUQ1CuC9Sb3o2jzU3WGJuJ0SFhERD2AYBh99e4wXVvyA1YDusaG8N7EXEY20CKwIKGEREXG7/CILzy79L4u2/wzAnT2b85fbOxPgq2JwInZKWERE3OjchXwenLeDHcfP42WCp0Zdx+/6x6kYnMhllLCIiLjJf09mM3Xudk5n5xEc4MOb43swqH2Eu8MS8UhKWERE3ODLPaf44+d7yCu00rpJEO9PTqBN04buDkvEYylhERG5hqxWg1fXpDB7nW2Jk4HxTXljfA9CAlUMTqQiSlhERK6RnPwiHlu4mzU/nAHg/gGteWJEBxWDE6kEJSwiItdAasZF7pu7jZQzOfh5ezHrji7c2au5u8MSqTWUsIiI1LDvjqQzbf5Osi4W0jTYn39O7EXPFo3dHZZIraKERUSkhhiGwbwtx5n55Q9YrAZdm4fw3sQEokJUDE6kqpSwiIjUgIIiK88t38+C71MBGN09hpfu7KpicCJXSQmLiEg1y8jJ56F5O/n+WCYmEzwxogMPDGitYnAiv4ASFhGRavTDKTNT527nZNYlGvr78Mb47gzuEOnusERqPSUsIiLVZNW+08xYtIdLhRZahTfgg8kJtI0IdndYInWCEhYRkV/IajV4/etDvP71IQD6t23C7Ak9CG3g5+bIROoOJSwiIr9Abn4R/7NoD1/tTwPgtzfE8dSoDvh4e7k5MpG6RQmLiMhVOpF5kalzt/Nj2gV8vU38ZUwXxvaOdXdYInWSEhYRkauw9acMHpq/k8zcApo09OPde3uR0CrM3WGJ1FlKWEREqujTran8v2X/pchq0LlZI96bmEBMaKC7wxKp05SwiIhUUqHFyvNf/sAnW44DcEvXaF65qxuBfioGJ1LTlLCIiFRCZm4B0+bvYMtPmQD8cXh7pg1qo2JwIteIEhYRkSs4mHaB++Zu40TmJYL8vHltXHeGdYpyd1gi9YoSFhGRCqzen8ZjC3eTW2AhNiyQDyb1pn2UisGJXGtKWEREymEYBrO/Oczf16QAkNg6nLfv6UnjIBWDE3EHJSwiIpe5VGDh8c/3sGLvaQAmJ7bkmVs74qticCJuo4RFRKSUk1mXuH/udvafMuPjZeL50Z2Z0LeFu8MSqfeUsIiIFNt+LJMH5+0gPaeAsCA/3rmnJ31bh7s7LBFBCYuICACLtp3g6aX7KLQYdIgK5oPJCTRv3MDdYYlIsSp3yG7YsIHbbruNmJgYTCYTS5cuddpvGAYzZ84kJiaGwMBABg0axP79+6943cWLF9OxY0f8/f3p2LEjS5YsqWpoIiJVVmSxMnP5fv60eC+FFoORnaNY/FA/JSsiHqbKCUtubi7dunVj9uzZ5e5/+eWXefXVV5k9ezbbtm0jKiqKoUOHcuHCBZfX3Lx5M+PGjWPixIns2bOHiRMnMnbsWLZu3VrV8EREKi3rYgFTPtrGnO+OAfDokHa8NaEnQf5qfBbxNCbDMIyrPtlkYsmSJYwZMwawta7ExMTw6KOP8sQTTwCQn59PZGQkL730Eg888EC51xk3bhxms5lVq1Y5to0YMYLGjRuzYMGCSsViNpsJCQkhOzubRo0aXe1bEpF64tCZC9w3dzvHMy4S6OvNq2O7MbJLtLvDEql3Knv/rtY5ekePHiUtLY1hw4Y5tvn7+zNw4EC+++47l+dt3rzZ6RyA4cOHV3hOfn4+ZrPZ6SEiUhlfHzjD7W9/x/GMizQLDWTxQ/2UrIh4uGpNWNLS0gCIjIx02h4ZGenY5+q8qp4za9YsQkJCHI/Y2NhfELmI1AeGYfDO+iPcN3c7OflF9IkLY/nDN9AxRq2yIp6uRqogXb4YmGEYV1wgrKrnPPnkk2RnZzseJ06cuPqARaTOyyu08OjC3bz01Y8YBkzo24J5v+tLeEN/d4cmIpVQrSPLoqJsi4GlpaURHV3SvHr27NkyLSiXn3d5a8qVzvH398ffX/+hEZErS8vO4/5PtrP352y8vUzM/FUnJl7f0t1hiUgVVGsLS1xcHFFRUaxZs8axraCggOTkZPr16+fyvMTERKdzAFavXl3hOSIilbEz9Ty3zd7E3p+zCW3gyye/66NkRaQWqnILS05ODocPH3Y8P3r0KLt37yYsLIwWLVrw6KOP8uKLL9KuXTvatWvHiy++SIMGDZgwYYLjnEmTJtGsWTNmzZoFwB/+8AcGDBjASy+9xOjRo1m2bBlr165l06ZN1fAWRaS++nzHzzz1xT4KLFbaRwbz/qQEWoSrvopIbVTlhGX79u3cdNNNjuczZswAYPLkycyZM4c//elPXLp0iWnTpnH+/Hn69u3L6tWrCQ4uWY49NTUVL6+Sxp1+/frx2Wef8cwzz/Dss8/Spk0bFi5cSN++fX/JexORespiNfjrqgO8v/EoAEM7RvLauO40VH0VkVrrF9Vh8SSqwyIiANmXCvn9gl0kp5wD4JHBbXlsSDxeXhUP/BcR96js/Vv/uyEidcaRczlM/Xg7P6XnEuDrxSt3d+PWrjHuDktEqoESFhGpE9YfPMsjC3ZxIa+ImJAA3puUQOdmIe4OS0SqiRIWEanVDMPgg41HmbXqAFYDerVszLv39qJpsMoeiNQlSlhEpNbKK7Tw1JJ9fLHzJADjEmJ5fkwn/H283RyZiFQ3JSwiUiudNedx/yc72H0iC28vE8/ech2T+7W6YlVtEamdlLCISK2z50QW93+ynTPmfEICfXlrQk/6t2vi7rBEpAYpYRGRWmXprpP8afFeCoqstI1oyAeTEmjVJMjdYYlIDVPCIiK1gsVq8Lekg7ybfASAwR0ieP3X3QkO8HVzZCJyLShhERGPZ84r5NHPdvPNj2cBeGhQGx4f1h5vFYMTqTeUsIiIRzuanst9H2/jyLlc/H28ePmurozu3szdYYnINaaERUQ81sZD55g+fyfmvCKiGgXw3qRedG0e6u6wRMQNlLCIiMcxDIOPvj3GCyt+wGpA99hQ3pvYi4hGAe4OTUTcRAmLiHiU/CILzy79L4u2/wzAnT2b85fbOxPgq2JwIvWZEhYR8RjnLuTz4Lwd7Dh+Hi8TPDXqOn7XP07F4ERECYuIeIb/nsxm6tztnM7OIzjAhzfH92BQ+wh3hyUiHkIJi4i43Zd7TvHHz/eQV2ildZMg3p+cQJumDd0dloh4ECUsIuI2VqvBq2tSmL3uMAAD45vyxvgehASqGJyIOFPCIiJukZNfxGMLd7PmhzMA3D+gNU+M6KBicCJSLiUsInLNpWZc5L6520g5k4Oftxez7ujCnb2auzssEfFgSlhE5Jr67kg60+bvJOtiIU2D/fnnxF70bNHY3WGJiIdTwiIi14RhGMzbcpyZX/6AxWrQtXkI701MICpExeBE5MqUsIhIjSsosvLc8v0s+D4VgNHdY3jpzq4qBicilaaERURqVEZOPg/N28n3xzIxmeCJER14YEBrFYMTkSpRwiIiNeaHU2amzt3OyaxLNPT34Y3x3RncIdLdYYlILaSERURqxKp9p5mxaA+XCi20Cm/AB5MTaBsR7O6wRKSWUsIiItXKajV4/etDvP71IQD6t23C7Ak9CG3g5+bIRKQ2U8IiItUmN7+I/1m0h6/2pwHw2xvieGpUB3y8vdwcmYjUdkpYRKRanMi8yNS52/kx7QK+3ib+MqYLY3vHujssEakjlLCIyC+29acMHpq/k8zcApo09OPde3uR0CrM3WGJSB2ihEVEfpFPt6by/5b9lyKrQedmjXhvYgIxoYHuDktE6hglLCJyVQotVp7/8gc+2XIcgFu6RvPKXd0I9FMxOBGpftU+Eq5Vq1aYTKYyj+nTp5d7/Pr168s9/scff6zu0ESkmmTmFjDxw62OZOWPw9sze3wPJSsiUmOqvYVl27ZtWCwWx/P//ve/DB06lLvvvrvC8w4ePEijRo0cz5s2bVrdoYlINTiYdoH75m7jROYlgvy8eW1cd4Z1inJ3WCJSx1V7wnJ5ovHXv/6VNm3aMHDgwArPi4iIIDQ0tLrDEZFqtHp/Go8t3E1ugYXYsEA+mNSb9lEqBiciNa9GiyMUFBQwb948fvvb315x3ZAePXoQHR3NzTffzLp166547fz8fMxms9NDRGqGYRjM/uYQ93+yg9wCC4mtw1k+vb+SFRG5Zmo0YVm6dClZWVlMmTLF5THR0dG89957LF68mC+++IL27dtz8803s2HDhgqvPWvWLEJCQhyP2FjVexCpCZcKLDy8YBevrE4BYHJiS+b+rg+Ng1S5VkSuHZNhGEZNXXz48OH4+fnx5ZdfVum82267DZPJxPLly10ek5+fT35+vuO52WwmNjaW7Oxsp7EwInL1TmVdYurc7ew/ZcbHy8TzozszoW8Ld4clInWI2WwmJCTkivfvGpvWfPz4cdauXcsXX3xR5XOvv/565s2bV+Ex/v7++Pv7X214InIF249l8uC8HaTnFBAW5Mc79/Skb+twd4clIvVUjSUsH330EREREdxyyy1VPnfXrl1ER0fXQFQiUhmLtp3g6aX7KLQYdIgK5oPJCTRv3MDdYYlIPVYjCYvVauWjjz5i8uTJ+Pg4v8STTz7JyZMnmTt3LgD/+Mc/aNWqFZ06dXIM0l28eDGLFy+uidBEpAJFFit/WXmAj749BsDIzlG8cnc3gvxVY1JE3KtG/iu0du1aUlNT+e1vf1tm3+nTp0lNTXU8Lygo4PHHH+fkyZMEBgbSqVMnVqxYwahRo2oiNBFxIetiAQ9/uotNh9MBeHRIO34/uB1eXhXP8BMRuRZqdNDttVTZQTsiUtahMxe4b+52jmdcJNDXm1fHdmNkF3XLikjNc/ugWxGpHb4+cIY/fLabnPwimoUG8v6kBDrGKOkXEc+ihEWknjIMg3eTf+LlpB8xDOgTF8Y79/QkvKFm34mI51HCIlIP5RVaeGLxXpbtPgXAhL4tmHlbJ/x8arSWpIjIVVPCIlLPpGXncf8n29n7czbeXiZm/qoTE69v6e6wREQqpIRFpB7ZmXqeBz7ZwbkL+YQ28OXte3rSr00Td4clInJFSlhE6onPd/zMU1/so8BipX1kMO9PSqBFuIrBiUjtoIRFpI6zWA3+uuoA7288CsDQjpG8Nq47DVUMTkRqEf0XS6QOSklJ4ciRI0TFxvGP780kp5wD4JHBbXlsSLyKwYlIraOERcQD2BOMtm3b0q5dO5fHffjhh6xfv56bb76ZKVOmlNmfmZnJhHsnkLQqCZ+wZkTc8Sy+4c0J8PHilbHduLVrTA2+CxGRmqNKtyJulJmZycR7J7ByVZJj26iRw5k3fwGNGzd2bNuxYwc33pDIpfxCx7ZAf1++2/I93bt3d2wbMWoEazesxXdEN5rG/Qkv74YUXThH29NrWf9F+SugN2zYkNzcXIKCgsjJyXEZ65WSJRGRq1HZ+7cSFhE3umXUCLZsWssdvSxk5ECThrB4hzfX9x/CipVfOY4zmUx4e4HFWnKu/bn9K5ySkkL79u0JHnw7jXtNweTlTd7PP3Bu04tYj2eRkpLi1HozaNAgkjckQ+n/Aphg2NBhJCWVJFCVTZbsKttaJCIClb9/q0qUiJukpKSwclUSuZcsfLAelmyH99dD7iULK1clcejQIcDWsgFw+agT+/M5c+YA8OOhw4SPeoyw3r/D5OXNhT1JnPnsKaw/ZwFw+PBhp/OTk5PL/hfAC1avXu20KfH63lgthU7brJZC+vTu6bQtMzOTW0aNoH379owaNYr4+HhuGTWC8+fPV/IvIiLimhIWETc5cuQIXiawXNbGaTHAy1SSYNx33314mSDIH/42AT5+EF6ZYHvuZYLf/OY3nDXn8cDCAzTscjOG1ULm2XfJvO5NGF0EvoAJpxXQGzZsaMt4fIF+QCJwA45jGzZsCNiSpcIigyKrc4xFVigsMhzJEsCdd4zh6zVJTsd9vSaJO24fXQ1/LRGp79QlJFJFVenyqGjcx9ChQ1n79Vq8AsDaAbgENACvA2DNK+maMZlsbSldWsC+1JLz7c/9otrR/ZG3OWPOx3LpAukZfyWPPXASiAVygCW2c+xfd/s1CQDySgXlD+SXHBsVFcW5s2fw8oIiS8lhPt5gtUJEZBSnT58mJSWF6zq0J6QBvDkZBnSADT/CIx9D9kX48WCKuodEpFwawyJSRVWZgWM3fORwFlw2QBZs4z4Sb0iksNS4D19/X74vNe4jODjYNsjVGyiVDNifBwcHYzabS5KLco5rED+Q8JG/x8vXn4L0VM4t/j+KCk7DxVLHNcDx3ClhMQF+wC1AS+A4sAIoAAzbsRW9tv25YRi88MILPPvss8ybBvfcUHLYvE0w8R144YUXePrpp8v8TUVEKnv/1rRmqfcuTy7mzZvH/Q/e75RcAEy4dwJrN6yFoUAQcBHWbljL+HvG81WpAbIAfRP7YvGyQCC2lpNAKLQWktAngaKCIsDW7ZKTm2P7Fo7GOWmwQlBQUMkFTdiO64ttkKyXF6H+EwnpfTcAgztEMOeBuzEKLtmSidLyXbxxA1uy0rX4edfibUsq93cr7ezZs4CtZaW0gdc57xcRuVoawyL1XuINiRRSCHcAjwF3QCGF9Lm+j+OYlJQUklYlYQm2wBpgKbAaLA0tJJUaIAu2lhpLocXWUnGpeOMloAAshRbHuI8GDRo4Jw0hxT9HAcZlCYuBLWn5Dkw7Amka9YwjWcne/G/en5RgS1aq6vI1D1s5P/X39y8Z61Lq72Mf6+Lv7w/ALbfcAti6gUpLPmD7eeutt1Y9NhGRUpSwSL324Ycf2lpWykkaCvMLHcnFkSNHbDfu8zjfuLMAk/MMnDfffLPC1/zHP/4BwE8//WTb4CJpOHLkSMk2E2CAz7gYoh79Ow3a9sFalM+5L/9G1oaP8bZXrq0guSjXceAQsB44Ahxz3v3ggw/akqVROP99RtrieeihhwAYPnw4EU3CmP6RrRvoRIbt58NzIKJJGEOHDq3wbyIiciXqEpJ6bf369bZfXCQNX3/9NVOmTOHUqVMVdqGcOXPGceqePXsqHB+yZ88e59c6XuqaUCZpANvrBNzdnSbN/hdvGlJEOudOvkDBD4fLHMcoIAY4CzTDlly46uZZCpSeAXTZ/8J06tTJ9ouLv49jP7Dl++0kXt+bie9kOLZFRoSzecs2Fy8uIlJ5SlikXmvSpIntFxdJQ2RkJAALFiywbXBx4543b57zQN3Kjg8xAauK97Uqft2vcLSo2AX3uo3GMfdhwpt804+c8/8LlmgX9U12AV+Ueh5X/mGO1hgXg24BBg4caPvFxd/HsR+Ii4sj7Uw6a9asYfPmzSQmJqplRUSqjRIWqde2bt1qu3GvxDlpWAWYYPPmzQDs27fPdoKLG/f+/fvLXvwK40Og+DVDcE5kInGMfckvshA+8vc07DoMgBzvtWT4vgWmwvJbYkzAaWxdQfYkZCVlEiDHa18hqYqPjyesSRiZKzKd/z4rIaxJWLlTlYcOHapERUSqnRIWqddSU1NtN+JonJOGOOBo8X6goKDAdtNfQZkbNybIzy9nKs5xbF0z54Ew4OdyAjBhGwdjn3mUC2y0bfcKDGXC+1tp2HUYhtXC+Y0fcaHhUmhYfNwGyiYipcebwJVn/lwhqUpJSSEzPdM286j0NbwhMz2TQ4cOqb6KiFwTSlikXuvduzcnT560tUqUdqpkPxTXLTGwdZeUvnGX13Jht4yytUsuZwCF2GYelTrOL6INTe94hh3Hz2PNy+Hc8pfJO7azzLo/5b52JVp2AgMDuXTpkssWowYNGgClBhv7ADdTUtMlGbDaBhsrYRGRa0GzhKReu/XWW0tu/KVn1gCY4Fe/+hUAfn5+jm1Oip/bp/c6bfe57Jo+5ZwP0NT5aYOEG4m85yV8GjWlddMgTn/yP7Zkxe+y6/m5uN7xy54fK3tI+/btS7rC9gDZxT+Lu8Lat28P4DzYuB/Qvfhn8dTr0oONRURqkhIWuSZSUlJYtWqVU70ST3jt5cuXV1gLZdmyZQBER0fbTnCRsDj221VwzTLnZ1G8no+J0PH30nTQE3j5BnDpp+0smXYDRZknq3a9VTgnIfZBvKXceeedJa07S4DXin8W2q555513Athan8Blq429y0xEpKYpYZEalZmZyYjLVvAdUU0r+F4pCarMax8/Xtwc4eKGfPTo0ZJtVa1xUtlBtwVg2h5I06inCGnxawCyty7m7OfPExLoW+nrObqt7IN47UlII9vrOMrsU9LV5UqfPraieX379rVtcNFqk5iYWOF1RESqixIWqVGOcvalbvL2cvZXq7JJ0N1j72b1utVOr7163WruGnuX4xhHwuLihmxvQfj5558rLKB24sSJsoFeoWvGx8cHTODTJJKoaa/QID4Rw1pIetKrZCV/hI/3ZV/PK1yvWbNmJcXthgJjin9mASZo3ry549hVq1bZfrmsO4riWd4rV64EbAXhfP19bYONS7farLStjaTZQCJyrWjQrdQYezl77sBp1orFsJC0JOmqZ5iMvn00mzZtctqWlJTE6NtHs2H9Bsdrf/P1N2Ve2zAMvlnyjeO1L1y4UOHsH7PZDEBRkW39H1oC6ZTM/Gll22yxlB5dS4VTpe3dOCaTCf/YLjQd+yTe3o0oIpNzgX+hIO4g7C5pETGZTBgYLmM0FTfxtGvXzpZYFVFmEC+GbYqynWOa9nhsx2cWvx8f4LWS/SkpKbZKwAE4Dzb2t1UC1iwhEblWlLBIjXGUlnfRlVHeDJOUlBSOHDlC27Zty70RpqSksGnDprIzbkywMXmj4waanJxc4WsnJyfTrl072+rFFcz+sa9u7BhUuwBIK3Wcra5c2UG3pceH2BUnDmC7bmDX4YQOnorJy5v8ghTO/fcvWKIyHDFarVbHsY6YLksaMLAlM0C3bt1Yt26drdWkdIxNgDM4LeToaG2xzxIKL96xx3m/4zN8CFuidgKILb7ma5olJCLXjrqEpMa0adPG9ouLroy2bds6NlW2m2fRokUVzsBZtGgRUGr2iovXtu83DKOkjP4wbN0ow3DMwLEnCzk5ORWuJXThwoWyf4DyCrUBePnw1JL/0njIg5i8vMn9cT1n3vxfLGsy4BPgbUpio7ilxWR7Hdvg3OKfXrZt9pYYRyvPeOBeYBAwEZhg21xYWOgIpX///iUtS5d19WAq3s9ln2Gb4mu2odzPUESkJqmFRWpMfHw8w0cOZ23SWiyGxdGV4b3amyEjhzj9n7nTWJfiCq1rk2xjXb5a+ZXjuIMHD1ZYoTUlJQUoLqlfQbeMveS+t7e3rbun9PXAVsRtCXj72JpyLl68WOHrXrx4sewfYAy27pajQGvAC7y+akTT259iwfepGIaVrPUfY96x2LleS3Hvkz0RadiwoS0hCgG+K3VccUXc4EbBAERERNi2u2gFcuwHvLy8bLE3pmyV3TPF42uo2mcoIlKTqr2FZebMmZhMJqdHVFRUheckJyfTq1cvAgICaN26Ne+++251hyVusmD+AoYMGOI0a2XIgCEsmL/AcYx9rItluMVpQKtlmIWkVUlOs4C8vIr/ybro6rHvb9GiRYXTdlu2bFml6zm6ZVwc59hf2i5gObAPWAa+R+OInvwaAbGdaejvQ8aSFzBvW3zFmUd+fn6259k4D6Y1247z9bXNJBo7dmyFrUBjx451XNPRZTYeeAS4p/hncWvMunXrHMdW5jMUEalpNdLC0qlTJ9auXet47u1dXolPm6NHjzJq1CimTp3KvHnz+Pbbb5k2bRpNmzZ11IKQ2qtx48Z8tfIrDh06xOHDh8sdm1KVsS4hISG2jS4qtDZq1AgoGf9BC2wtHHbFz+2DaAsKCkquV04Zfft+X19fW5eKi9d1FJazM2GrllvcYtQgox/hTWbg5RtAYeYplr7wa9o9/73tWBel9O1dPFartWS6cunBtMUtLPb3unHjxgpbgb799lvH39GRYLkYw1JaZT5DEZGaViMJi4+PzxVbVezeffddWrRowT/+8Q8ArrvuOrZv384rr7yihKUOadeuncubnKOVw0UyYO+eANvA0oq6enr06AEUV2gFxyKCDsU9N2UqtF6hjH5QUBBZ2VkuV1YOCgpyPsGeOHQxEVI0nlA/W9PFpWO7SF/2Em3fm1py7BXqq5RpYbGvObTJ9tr2ZMle5M7V9ZYsWeJYUfree+9l3vx5Lmce3XvvvVyuos9QRKSm1UjCcujQIWJiYvD396dv3768+OKLtG7dutxjN2/ezLBhw5y2DR8+nA8//JDCwkJHc/fl8vPznRacs08/Fc9U0eyf1NTUkgqt5SQDjlopFE+3rWAGzp49tiaCPXv2OHePFI+LYYXtmrt27WLKlCl4e3uXnZJc+rLFrYPe3t4VrqxcXiuiqWUA4QWPEWS9AQCzZSnnF/0LDKvzgS4SNbsuXbpwZu0Zly0sXbvaTm7cuHHJ9cppLXLsx/YdCwkNITs72/n9eEFIaIjqq4iIx6n2MSx9+/Zl7ty5JCUl8f7775OWlka/fv3IyMgo9/i0tDTHAEi7yMhIioqKSE9Pd/k6s2bNIiQkxPGIjY2t1vch1aPSlW4rqNBa2tatW22/XJ7H+jjvz87OLmnliAHOAs1wlLO3J7ihoaEVVrC13+QdqzWfx3mmTpbtOEfXUjHvRhFEGX8jyHoDBoWk+77O+SMflE1WKpipYzdw4EDnVZ3HFP/Mth03cOBA52suA2YD84E3sY2jwbnSLcCuHbsIDw932hYeHs6uHbsQEfE01d7CMnLkSMfvXbp0ITExkTZt2vDxxx8zY8aMcs+5/D+kTtM5XXjyySedrmc2m5W0eCDH7B97V8bFkkq39tk/jhuyvUKrvctjA2VuyI7ZPyagB7YunwbAAds2+0wYxxiNXcAXpQKKs/2wj/swm80lFWxLJzYjgSXFiQ+lZgmZcJ6pU1wLJTc3t2RT8040vf0pvP1DsBSd55zxIvkpB0oSkdJJmIGtdah0K0cD52MiIyNtz0Mp28JypmTGU1ZWVsmU79E4typZbcljaXFxcaSfTWfNmjVs3ryZxMREtayIiMeq8WnNQUFBdOnSxeV6L1FRUaSlpTltO3v2LD4+PmX+7680f3//ssW6xKM4Kt1G4XSjtUSWzP5p164d8fHxNA5rzPms82UqtDYOa+zUhRQXF2e7eRvYkhG74sTBXhckLCzMdvM+jXOXUHHSEBYWBpSqYOsisbHvd9RrMV12veKxM/YE6dOtqUT++i+YvH3IzzjMuYUvYLlQ3FJYnGCUMQJbkmYvypaDUwJT2YSuT58+tsUcXQy6dbXuz9ChQ5WoiIjHq/GEJT8/nwMHDnDjjTeWuz8xMZEvv/zSadvq1atJSEhwOX5FagfH7J9zl+0ovn/bZ/+kpKRwPuO8LRFoRkmZ+J/h/JLzTuXfjxw5UtJKUc7YFHtifP78eee1f8Dp5p2VlQWUSkRcJDb2RMTPz4+8vDxby0s51/Nv0ID/t+y/zN18HJO3D7kHNpDR5nWMKfllyt7beXl5YTWstqRnBLYWo2M4xu54mWw9tvHx8YQ2DrUN+r0soQttHOr42zjqrLgYdNukSRNERGqrah/D8vjjj5OcnMzRo0fZunUrd911F2azmcmTJwO2rpxJkyY5jn/wwQc5fvw4M2bM4MCBA/zrX//iww8/5PHHH6/u0OQa8/LyqnB8iH32j9O05nCgXfHPVrbNhw8fdlzTMfjV3opQXLPFPjbFPvj14MGDJdcsrfiajv1Q4aKGdo5ZQOVczyuwEeF3/D/mbrYNDj6f/DHpy1/GOJ7v/H6OOZ/qNF25nLE79mQpJSWFrMwsWzdP6Zopv4KszKyyrZcuqvuKiNRm1Z6w/Pzzz4wfP5727dtzxx134Ofnx5YtWxyFuk6fPu1YARdsTfwrV65k/fr1dO/enf/7v//jjTfe0JTmOiA1NbXCZMA++6cqJfz37t1r+6UltpaaQ0AGjkTEPkuoYcOGFV6zzDTkK0wtdswkuux6vqdaEjXpVbxjOhLk5817E3th3vLvkqnXpQfT2hc/LK10V88YnFZXticsTusilZPQ2fc7uo5cvG6ZwbkiIrVItXcJffbZZxXunzNnTpltAwcOZOfOndUdiniKKyQD8fHxhDcNJ2NFRpmaIOFNw53GsDjGO7koP2+vrzJo0CC+/uZrl1OlBw0a5BzEFaYW5+fnl1nVOfBcX5rE/A9efg2wZJ/hi+fupn2UrUz+lRY/hFLF6Apx7urxsh1Xpkv0CjHGx8cz+ObBfLP+G+fX9YHBNw9WDRURqdW0+KHUGMf/0bto5bDvT0lJIeNchm0NndJdI0WQcS7DqcvDMebEXl/lsvLz9tk/GRkZFXa3OE2rrsTUYkf3TWPbdRptGUtEs2fx8mtA3vE9ZHz2REmyYtf0svd92RAS+8DfcltdwDHovCotJ58v+pzhQ4c7XW740OF8vuhzRERqMy1+KDUmPj6ewUMGs27lOlui0Qo4BqZVJm4acpPzQNoKlC7NX1RUVGH5efusnn//+98VVodduHAhr7zyiu18g5Jkye6y1hA/Pz/y8/Mxjfcn3OsPBPkMAMBs+ZLziz4gOKhByan2YnTji6972aBb+zibqKgoW4vQZbVm7M/t05Wr0nKiMvoiUlcpYZEa9f4/36fP9X3IWFJSODCsaRgfvPeB4/nOnTtLBueWrh+yErDaxqXY6/sEBQXZZvi46Gayj13JysqqcP2dMoXrRlNmhlLp5KBBgwZcxJ+m1mfw92uLQRGZvu+Qk5IEVucxMT4+PraExcU6PfbBxnFxcezZu8f2vm/BecZTAU7VoT9f9Dnj7xlvmyZebPjQ4S4XIFQZfRGpa9QlJDVq2sPTyLqYBcOwDSodBlkXs3ho+kOOY77++usKB+euWVOScVxpMK19f3BwcIUrHAcHX9Z942JAq50poi3Rk1/DP6AtlqJszhQ+Tc7+JMeYmNIc1XNddOHYq+c2bNiwwhlPpWO0t5ykpKSwcuVKUlJS+GrlV07l9kVE6jK1sEiNcRSOu4OStW1iwRJkIWlJSeE4+wrLrlpNSt+4c3Jyygx+Lb1oX05ODgDDhg1j7ty5LltYhg93HudR0YDWRdtO4D/iT+DlTUHmUc4ufB6L+ZzT9Uq3hkRHR9u6elwMurUvDOoojOjifZdXOFEtJyJSX6mFRWqMY2zKLpzXttlt22yvr3LbbbfZNrhoNRkzZoxjk6NVopwBuhglLSxPP/207YTLK8uexWm/r6+v60G3Xl6ED32APy3eC17e5B78lrQFf8TS91yZKchTp5asvhwQEFDh3yUwMBCATp06Vfi+O3bsWOF1RETqE7WwSI1x1FdJvWxH8XN7fZWYmJgKV2suvThmeHg4P//8s8sxJ/ZWifj4eFs67gsMwLGOEclAIY5WioEDB7J27VrH7B87r9iGNOn3BIGtegBwS0sTb7/0VzAZzi02xVV3S8cYHx/Pli1bbLOESk+9bgKcKY6NUrN/XLQWqW6KiEgJJSxSsypYjM+uTZs2zlOQ7Yq7W0oXjnOUl29ZfLy916T4X7I9Yfnwww9tr9GYchcMnDNnDlOmTGHs2LG2hKXUjB7f8FiaBjyLrykGX5OVN+9JwHRyL29j2JIgS6nrFT+3D6QF6N+/v607ajy2ZQl+xrZGUBPgNdt+UN0UEZGqUJeQ1Jjk5OQKB5XaK7TGx8czfORwvC94Qz8gEegH3jneDB853OnG7Sj45qIb5aabbgLgzTfftCVLWZQMuh2GrcvHBK+//jpQvHyA/XoGBAb0Jirg7/iaYijKPsPvWmYxonM0q1atqnCZgZUrVzpCcbSMLADmAeuBT4BPL9uP6qaIiFSWWlikxtirzroaVOrYD7w9+23b9OfvSqY/hzYN5Z233nE6tXfv3hV2o/Tp0wcoVTgulHJbWNLTbSsw2kv5swwaJdxJ6MDJmExe5J3Yx7klszg9dQowka1bt1a4mOLWrVsdLxEfH09YkzAyz2eWWaAxrEmY6qaIiFwFJSxSrpSUFI4cOfKLbqCOcR0uZuCUHvfhmP5c6gaflWSb/vzVyq8cx61atcqp4mzJiwFnbC0dQ4cOtQ2+vbxw3EVgI2DCMTPJy8sLk68f4aN+T1CHQQBcyF5F5pJ/Ql6RowXGEauL5MuxUjK2v11menGycllyk7kk02n1aTvN/hERqZi6hMRJZmYmI0aNoH379owaNYr4+HhGjBpRttBasZSUFFatWlV2xWCgRYsWFdYjsS+IaZ/+bBluceo6sgyzkLQqyenaP/30k+2X8TivXDzhsv3gXDhuKbCaMishHzp5jsjxfyWowyAMLGT4vkVm1FswwlZR175Q50MPFdeNcdEVNX36dMcmp9WnS2tl+1F69WkREakcJSzi5K6xd5G0JslpW9KaJO4ae5fTtsokNo71d+z1SOxTkAsBo6SMflVu8KNHj7b9chznQm/HbJtvv/12oNSaQ9k4jzkpLhwHsCv1PD80uw3/6Hgshpkzfs+Q47PK6bXtsQ0fPpyQxiHlTn8OaRzC0KFDHTFWZfVpERGpHCUs4pCSksK6b9bZOgpL3+R94Juvv3Fq6Rh9+2iSki5LbJKSGH37aMdzx4BWF2vl2GfWVPkGbx/DsglbTZdvKbNYYfPmzW2vMxLnAb8jbK8f3GUI497bghEQTMG5Y6Qde4x8731lXtsRG7Brxy7CG4U7JV/hjcLZtWOXU3iOQcRJ3k7JjffqsoOIRUSkcpSwiINjVo+LEvn2WT0pKSls2rip3BkzGzdsdCQ2qamptiTC77Lj/AATHD9uy1CqcoNfv369LUZvYC22rp41OKrIfv3110Cp2URlWm28CL3pt5yJG05BkZWW3lmkzf8jRcvPlNtt1bNnT8epcXFxpJ9N51//+hf33nsvH330Eeln04mLiyvzt1wwfwFDBgxxSm6GDBjicu0fERGpmAbdSlkuumbsFi1aVOGKyYsWLeLpp58uWYnYxcya0rOEFsxfYFvcb0lJq82QkWVv8B06dLAlQVacZ+AUt7DYq8f27t3bdkKpAb8mI4imhX8ksE8CAI8MbkvwsQ1syL8EcTgP4o0DjjoPDM7MzGTCvRMcCxDOmzePzxZ9xoL5C8qs6aPZPyIi1UsJizg41SQpZ1aPvQvn7Nni+vYuEhvHfrsrJEBQ+Ru8YRgVJkEWi62qm6N1p7h6rk+rZkTwLL5BzbEW5nFX80v8z7D2JCUVv7kewK04V849WjIwGGDCvRNYu2GtU6K0Nmkt4+8Z7zSTqTTN/hERqR7qEhKHkydPltzkS3ePFJfIt8+YueWWW2wnuBhzcuuttwKXTWsu57jSrRd27dq1Y+TIkS5v8lVKlopnCQXs7km09e/4+jWnKOcsafP+RMfgPKB4YLB9TMzPQETxz+IWG/vA4KrMZBIRkeqnFhZxaNasWYUl8lu0aAHYZsyEhoWStSKrTPG2xmGNHTNmamKtnFtuucVWxdZFK5A9WbK/dnDc7TS+cQomkzd5OT9wbsGLWM9nOV7bsSyAfTFFu+IxMfYBv04zmXYUv17r4ge2mUxqSRERqTlKWMTB0dqQRUmxtVwcxdbsrQ0AXbp0YePGjc43eS/o3KWz80UrKPJWnqSkJLZu3UpiYqLTVGG7uLi4ktoupZOg4kGyrVq1AqBFXBs6/e4lcsJtY1ou7Ekic807YCoqf52e0UADStb9yXGO2TFb6A1K1hLahy2xQVOVRURqmhIWcWYA0TiXsy8egGqXkpLCxuSNtrEcFuAnbC0NXrBxyUZHJVdHq0SphQUJw/av7jXnVokjR47QN7EvGedKSvOHNw1n29ZtTrNwjhw5UhJjOYNkDx8+TEhkLPd/soOc8E4YVgvnv36fCzv/A4DJ28Rzzz7nfD2AXc7vkeKXtMcYHx+PyduE4WOUWcjRhEmtKyIiNUwJizg4Bt26GIBqH3Rb7k1+H2Vu8k71VbpSsrJy8fI9pVsl+ib2JcOc4VRGPyM5g959e5N+Nt1xnOOaLmK0hDTnttmbOGPOx5p3gXMr/kpe1z0w0BaHscLg5qE3U5hfWHI9E3CKMuv+YCqJ8cMPP8SwFCcrlw32NZYYjtWfRUSkZmjQrTg4DbotPQD1skG3jpv8aZzrq5zG6SbvVF+lVJG3y+urJCUl2VpWGuNcRj8UMs5lsGZN6eYeSrqESse4Chp0HMiMFSc4Y86niV8Rpz+eYUtWYoCzQDNgFBQVFDFnzhwAjh49WuGK0seOHQOK67+Ay8G+9vovIiJSM5SwiEPfvn2dB93aS+kXr7+TmJhYcrB9anHpZKC4wFxpb89+m9AGoU5F3kIbOK/CvHXr1grL6G/evNlxbJkuodeApV6EDptM09v+SIHFYHCHCII3v0tR1mlbK9BsYD7wJrakCVsNFYAVK1bYNrhIRP7zH1tXUocOHWwbXMx4std/ERGRmqGERRyGDx9O4/DGcO6yHenQOLxk9o9Tl1A5yUDptX8m/2YyGRkZlJaRkcHk30x2PHfMTnJRRt8+OwlKdVtdsv0w+QXS9I5nCLnubgBGtvTi/UkJNGrgV2ErUHBwMFBqlWUXiYh9f8+ePUtmPF22lhAm6NatGyIiUnOUsIiTLp27lLuWUJfOXRzHeHl5VZgM2Me6VLaEf0xMjO3CLlo5StdrsVqttl/OgU/jGKIm/p0GbftgLczn3PK/8atW4O1lslWerWCZgbCwMADGjh1b4YrSY8eOBcqZ/mxvfSrCafqziIjUDCUsbpCSksKqVas8rthYSkoKG5I3lDueY0PyBke8q1atqrBLaOXKlUA5JfwvGx+yaNEioGqLH9qTpYA23Yn63av4NWlBUWE6Zz5/gos/JjuSJft4G1dJ0NGjttHC8fHx3DjgxnJXlL5xwI2OcTbx8fH0H9C/3IUcbxx4o2YJiYjUMCUs11BmZiYjRo2gffv2jBo1ivj4eEaMGsH58+fdHRpwWXG00lrZfti7ehwDUF10Cdn3p6SkVHg9x36osLultO+/30Zwz9uIGPNnvL0bkm/6kbTgxyjodRgM+P7774FSXTQukqDSixouW7KM4cOGOx02fNhwli1Z5rTthx9+KLe1aP/+/fxSnprEioh4CiUs15DTWjTFN7y1G2xr0XiCyrZ0GIZRYZeQnb3bxdX17Psdq0S76G6xrxKdX2RhTXZTwoY8gMnkTY73WtL8n8RiOl+mNP+wYcMqTIJuvvlmRzj2dYxSUlJYuXIlKSkpfLXyK6cFDZOSkshMzyy3tSgzPbPsTKZK8vQkVkTEU6gOyzViX4uGO3Cq42ExLCQtSXIUW3Mn+zTktUlrsRgWRxVZ79XeDBk5xBFfUFBQhQsQNmjQAIAmTZpUWJq/SZMmQPGqzSZs/xpvxlZx9iKQDFht+89dyOfBeTtI9WluKwaX8REXYpeWJEjHbD/spfkdY05MOBeY88flmJOKFircunWr7RcXrUWbN28utzLvlVzNgooiIvWRWliukcp2t7jbgvkLGDJgiFNLx5ABQ1gwf4HjGPuKyK7ei31/enq6LWnwxrnlpHidHvvsIcdxtwD9gO7FP4vHuhzOyOdXszex4/h5ggN8yFj6AhfmL4W9OLWc+Pj5OJIGp7EppRVd3ZiTZs2a2X5x0VpUeiZTZWlBRRGRyqv2hGXWrFn07t2b4OBgIiIiGDNmDAcPHqzwnPXr12Mymco8fvzxx+oOz22qMrDUnSrTPdKvXz/bLy7eS//+/YFSM3pGABOBQcU/i4eL2BMbx3HlJEANOtzIBt8ETmfn0bppEP+4pRm5h7aV231UVFDkdJPf/8P+cmc8Xc2Yk5iYmAq7mMpbefpKaksSKyLiCao9YUlOTmb69Ols2bKFNWvWUFRUxLBhw8jNzb3iuQcPHuT06dOOh7u7SKpTZau+eop27doxcuTIcuOq7PiQW265peS4HGzl9HNKjrN335Q7QNYwEXrhXpqOfgKLyZuB8U1ZMu0GCtJP2K7pB9wAJBb/9LNd036Tr+4xJ2W6mOyJElz1tObaksSKiHiCah/D8tVXzv3uH330EREREezYsYMBAwZUeG5ERAShoaHVHZLHeHv22/S5vg8Za0sKqYU2da76Wht4eXlVOD7EPrU4Li7OeTCtXXGXkH1lZafWCwNMrQJpUjCDBuG2yrpDmsM/p/TG28tU8tr2InN2EbbXsL92dY85cYzv2bAWSz+L4/177/FmyOAhV5VwVnbMkIiIXIMxLNnZ2UCpGSMV6NGjB9HR0dx8882sW7euwmPz8/Mxm81OD0837eFpZF3McuqiyLqYxUPTH3JzZFXj6MJ5COeunmm2zUVFRUBJnRVGA48A9xT//BVO+0u3Xvh8E0lU9is0aJiIUVRI+n9e5Ynh8Xh7mZxf20UiYn/tvn372ja4aL1wWmagkhzje74DNgPflR3fc9XXrGDMkIiI1PAsIcMwmDFjBv3796dz584uj4uOjua9996jV69e5Ofn88knn3DzzTezfv16l60ys2bN4s9//nNNhV7tasMsocoqswpz8dPLV2G2TzGmJbYuGftqzcX/6uz77S0NG4+cIezWP+Ht24iigkwyl81iQPsWTn+XMq9td8z5tYcPH05403AyVmSUmaEU3jT8qmb02Mf3HDp0iMOHD9O2bdtf/JnVxDVFROqiGk1YHn74Yfbu3cumTZsqPK59+/a0b9/e8TwxMZETJ07wyiuvuExYnnzySWbMmOF4bjabiY2NrZ7Aa0BlBlhe7Y0qJSWFI0eOXLObXWW7MpzGppSTXPTo0QOwJba3/8+r/Lj6CJi8yD+dwrkv/sKQGxPKtDQ4vbbZAg2BXPD+rmw3yrat2+jdtzcZS0q64MKbhrNt67Zf9P4rmv7sSdcUEalLaixheeSRR1i+fDkbNmygefPmVT7/+uuvd6yoWx5/f3/8/f1/SYjXVGVbBqoiMzOTCfdOsLXcFBs+cjgL5i9wmtVTExbMX8D4e8aTtKTktYeMdO7KcIxNWYlzK0fxOj2RkZEUFFmZ+eV+Pt2aCiYvBrcJZnT/9nR8ZrPLG3hlxwLFxcWRfjadNWvWsHnzZhITE6+qZUVERNyv2hMWwzB45JFHWLJkCevXr7cNvLwKu3btIjo6upqjc5+aGGDpzqJjlenKcAyQDcV50G0kcAYuWb2594OtfH8sE5MJnhjRgQcGtMZkuqwe/2WcxgIVv++sJNtYoPLe99ChQ5WoiIjUctWesEyfPp1PP/2UZcuWERwcTFpaGgAhISEEBgYCtu6ckydPMnfuXAD+8Y9/0KpVKzp16kRBQQHz5s1j8eLFLF68uLrDc6vKtEpUlqeMiamoK8NqtdpaWLKBoUAQkAtsAt+IOF74vpDM/Ewa+vvwxvjuDO5w5VomnvK+RUTk2qr2hOWdd2zN8oMGDXLa/tFHHzFlyhQATp8+XbKaLlBQUMDjjz/OyZMnCQwMpFOnTqxYsYJRo0ZVd3huVZ0DLJ3GxKQD54EwqmVMTHVxzP4JAUqVPWnQpx/hN8wgMx9ahTfgg8kJtI0IrtQ1a3IskIiIeC6TYRiGu4OoDmazmZCQELKzs2nUqJG7w6lxKSkptoHKUUBaqR3F3S0pKSkeceMeMWpESe2SIBMhjcYTGj0BgP5tmzB7Qg9CG/hV+nqO9126hQVsM5SWeM77FhGRyqns/VtrCdVmJmwtK6VLz2fhtGKyu9nrjJjWB9CE/3UkK/ckRDPnN72rlKzAZRWDS1XZ9dSKwSIiUj20WnMtlZycXLJgYDkrJicnJ3vEzbtx48a8P+8LJn/wLT9lFuDjBS/e3pWxva9+Cnp1jgUSEZHaQQlLbediLIen2PpTBg/N30lmbgFNGvrx7r29SGh15arHFVGxNRGR+kcJSy01cOBA2y8u6ro49rvRp1tT+X/L/kuR1aBzs0a8NzGBmNDAaru+iq2JiNQfSlhqqfj4eAYPGcy6leswDMNR18W0ysRNQ25y64280GLl//7zA3M32xbyuaVrNK/c1Y1AP2+3xSQiIrWbEpZa7PNFn5cZyzFs5DC3juXIzC1g+vydbP7JVoX2j8PbM21QmysWgxMREamIEpZazNPGchxMu8B9c7dxIvMSQX7evDauO8M6RbktHhERqTuUsNQBnjCWY/X+NB5buJvcAguxYYF8MKk37aMqVwxORETkSpSwXMG1Xgm5tjEMg7fWHeaV1SkAJLYO5+17etI4qGr1VURERCqihMUFd66EXFtcKrDw+Od7WLH3NACTE1vyzK0d8fVWPUIREaleSlhccOdKyLXBqaxLTJ27nf2nzPh4mXh+dGcm9G3h7rBERKSOUsJSDq0IXLHtxzJ5cN4O0nMKCAvy4517etK3dbi7wxIRkTpMbfflqMyKwPXVom0nGP/+FtJzCugQFczyh29QsiIiIjVOLSzlaNOmje0XF1Vk27Zte40jcr8ii5W/rDzAR98eA2Bk5yheubsbQf76JyQiIjVPd5ty2FcEXpu0FothcVSR9V7tzZCRQ+pdd1DWxQIe/nQXmw6nA/DokHb8fnA7vLxUDE5ERK4NJSwuvD37bfpc34eMJRmObaFNQ3nnrXfcGNW1d/jsBe77eDvHMi4S6OvNa+O6MaJztLvDEhGRekZjWFyY9vA0si5mwTBgDDAMsi5m8dD0h9wb2DX09YEzjHnrO45lXKRZaCCLH+qnZEVERNxCLSzlKHeWEGAJqh+zhAzD4N3kn3g56UcMA/rEhfHOPT0Jb+jv7tBERKSeUgtLOerzLKG8QguPLtzNS1/ZkpUJfVsw73d9layIiIhbqYWlHPV1llBadh73f7KdvT9n4+1lYuavOjHx+suzNhERkWtPCUs56uMsoV2p57n/kx2cu5BPaANf3r6nJ/3aNHF3WCIiIoASFpcWzF/A+HvGk7SkZC2hISOHsGD+AjdGVTMW7/iZJ5fso6DISvvIYN6flECL8AbuDktERMRBCYsLjRs35quVX3Ho0CEOHz5cJ1drtlgN/rrqAO9vPArA0I6RvDauOw1VDE5ERDyM7kxX0K5duzqXqABkXyrk9wt2kZxyDoBHBrflsSHxKgYnIiIeSQlLPXTkXA5TP97OT+m5BPh68crd3bi1a4y7wxIREXFJCUs9s/7gWR5ZsIsLeUXEhATw3qQEOjcLcXdYIiIiFVLCUk8YhsEHG48ya9UBrAb0atmYd+/tRdNg1VcRERHPp4SlHsgrtPDUkn18sfMkAOMSYnl+TCf8fbzdHJmIiEjlKGGp486a87j/kx3sPpGFt5eJZ2+5jsn9WmEyaXCtiIjUHkpY6rA9J7K4/5PtnDHnExLoy1sTetK/nYrBiYhI7aOEpY5atvskf/p8L/lFVtpGNOSDSQm0ahLk7rBERESuSo0tfvj2228TFxdHQEAAvXr1YuPGjRUen5ycTK9evQgICKB169a8++67NRVanWYrBvcjf/hsN/lFVgZ3iGDJtH5KVkREpFarkYRl4cKFPProozz99NPs2rWLG2+8kZEjR5Kamlru8UePHmXUqFHceOON7Nq1i6eeeorf//73LF68uCbCq7Mu5BUyde523k22rTb90KA2vD8pgeAAXzdHJiIi8suYDMMwqvuiffv2pWfPnrzzzjuObddddx1jxoxh1qxZZY5/4oknWL58OQcOHHBse/DBB9mzZw+bN2+u1GuazWZCQkLIzs6mUaNGv/xN1DLH0nO5b+52Dp/Nwd/Hi5fv6sro7s3cHZaIiEiFKnv/rvYWloKCAnbs2MGwYcOctg8bNozvvvuu3HM2b95c5vjhw4ezfft2CgsLyz0nPz8fs9ns9KivNh1KZ/Rb33L4bA5RjQL494OJSlZERKROqfaEJT09HYvFQmRkpNP2yMhI0tLSyj0nLS2t3OOLiopIT08v95xZs2YREhLieMTGxlbPG6hlcvOL+P1nu8i+VEj32FCWP3wDXZuHujssERGRalVjg24vr/NhGEaFtT/KO7687XZPPvkk2dnZjseJEyd+YcS1U5C/D6//ujt392rOZ/dfT0SjAHeHJCIiUu2qfVpzkyZN8Pb2LtOacvbs2TKtKHZRUVHlHu/j40N4eHi55/j7++Pvr7LyADe2a8qN7Zq6OwwREZEaU+0tLH5+fvTq1Ys1a9Y4bV+zZg39+vUr95zExMQyx69evZqEhAR8fTXDRUREpL6rkS6hGTNm8MEHH/Cvf/2LAwcO8Nhjj5GamsqDDz4I2LpzJk2a5Dj+wQcf5Pjx48yYMYMDBw7wr3/9iw8//JDHH3+8JsITERGRWqZGKt2OGzeOjIwMnn/+eU6fPk3nzp1ZuXIlLVu2BOD06dNONVni4uJYuXIljz32GG+99RYxMTG88cYb3HnnnTURnoiIiNQyNVKHxR3qex0WERGR2shtdVhEREREqpsSFhEREfF4SlhERETE4ylhEREREY+nhEVEREQ8nhIWERER8XhKWERERMTjKWERERERj6eERURERDxejZTmdwd7wV6z2ezmSERERKSy7PftKxXerzMJy4ULFwCIjY11cyQiIiJSVRcuXCAkJMTl/jqzlpDVauXUqVMEBwdjMpncHc41ZTabiY2N5cSJE1pHycPos/FM+lw8lz4bz1STn4thGFy4cIGYmBi8vFyPVKkzLSxeXl40b97c3WG4VaNGjfQF91D6bDyTPhfPpc/GM9XU51JRy4qdBt2KiIiIx1PCIiIiIh5PCUsd4O/vz3PPPYe/v7+7Q5HL6LPxTPpcPJc+G8/kCZ9LnRl0KyIiInWXWlhERETE4ylhEREREY+nhEVEREQ8nhIWERER8XhKWOqAt99+m7i4OAICAujVqxcbN250d0j12syZMzGZTE6PqKgod4dVL23YsIHbbruNmJgYTCYTS5cuddpvGAYzZ84kJiaGwMBABg0axP79+90TbD1ypc9lypQpZb5D119/vXuCrUdmzZpF7969CQ4OJiIigjFjxnDw4EGnY9z5nVHCUsstXLiQRx99lKeffppdu3Zx4403MnLkSFJTU90dWr3WqVMnTp8+7Xjs27fP3SHVS7m5uXTr1o3Zs2eXu//ll1/m1VdfZfbs2Wzbto2oqCiGDh3qWJtMasaVPheAESNGOH2HVq5ceQ0jrJ+Sk5OZPn06W7ZsYc2aNRQVFTFs2DByc3Mdx7j1O2NIrdanTx/jwQcfdNrWoUMH43//93/dFJE899xzRrdu3dwdhlwGMJYsWeJ4brVajaioKOOvf/2rY1teXp4REhJivPvuu26IsH66/HMxDMOYPHmyMXr0aLfEIyXOnj1rAEZycrJhGO7/zqiFpRYrKChgx44dDBs2zGn7sGHD+O6779wUlQAcOnSImJgY4uLi+PWvf81PP/3k7pDkMkePHiUtLc3p++Pv78/AgQP1/fEA69evJyIigvj4eKZOncrZs2fdHVK9k52dDUBYWBjg/u+MEpZaLD09HYvFQmRkpNP2yMhI0tLS3BSV9O3bl7lz55KUlMT7779PWloa/fr1IyMjw92hSSn274i+P55n5MiRzJ8/n2+++Ya///3vbNu2jcGDB5Ofn+/u0OoNwzCYMWMG/fv3p3PnzoD7vzN1ZrXm+sxkMjk9NwyjzDa5dkaOHOn4vUuXLiQmJtKmTRs+/vhjZsyY4cbIpDz6/niecePGOX7v3LkzCQkJtGzZkhUrVnDHHXe4MbL64+GHH2bv3r1s2rSpzD53fWfUwlKLNWnSBG9v7zKZ7dmzZ8tkwOI+QUFBdOnShUOHDrk7FCnFPnNL3x/PFx0dTcuWLfUdukYeeeQRli9fzrp162jevLlju7u/M0pYajE/Pz969erFmjVrnLavWbOGfv36uSkquVx+fj4HDhwgOjra3aFIKXFxcURFRTl9fwoKCkhOTtb3x8NkZGRw4sQJfYdqmGEYPPzww3zxxRd88803xMXFOe1393dGXUK13IwZM5g4cSIJCQkkJiby3nvvkZqayoMPPuju0Oqtxx9/nNtuu40WLVpw9uxZXnjhBcxmM5MnT3Z3aPVOTk4Ohw8fdjw/evQou3fvJiwsjBYtWvDoo4/y4osv0q5dO9q1a8eLL75IgwYNmDBhghujrvsq+lzCwsKYOXMmd955J9HR0Rw7doynnnqKJk2acPvtt7sx6rpv+vTpfPrppyxbtozg4GBHS0pISAiBgYGYTCb3fmdqfB6S1Li33nrLaNmypeHn52f07NnTMQVN3GPcuHFGdHS04evra8TExBh33HGHsX//fneHVS+tW7fOAMo8Jk+ebBiGbZrmc889Z0RFRRn+/v7GgAEDjH379rk36Hqgos/l4sWLxrBhw4ymTZsavr6+RosWLYzJkycbqamp7g67zivvMwGMjz76yHGMO78zpuIgRURERDyWxrCIiIiIx1PCIiIiIh5PCYuIiIh4PCUsIiIi4vGUsIiIiIjHU8IiIiIiHk8Ji4iIiHg8JSwiIiLi8ZSwiIiIiMdTwiIiIiIeTwmLiIiIeDwlLCIiIuLx/j9l5LzWSvkPHwAAAABJRU5ErkJggg==\n", 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" ] @@ -5183,13 +3327,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.188949616879163\n", - "0.3638259422034872\n" + "0.262934291751185\n", + "0.42164057262465204\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -5234,13 +3378,13 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 24, "id": "cecf5392", "metadata": {}, "outputs": [], "source": [ - "save_model_of = True\n", - "save_model_gk = True\n", + "save_model_of = False\n", + "save_model_gk = False\n", "\n", "if(save_model_of):\n", " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", @@ -5266,7 +3410,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "ddf433f9", "metadata": {}, "outputs": [], @@ -5393,7 +3537,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 44, "id": "d31bad38", "metadata": {}, "outputs": [], @@ -5419,132 +3563,400 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 45, "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" + "name": "stdout", + "output_type": "stream", + "text": [ + " 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", + "5 1 Milan Udinese\n", + "6 1 Monza Torino\n", + "7 1 Salernitana Roma\n", + "8 1 Sampdoria Atalanta\n", + "9 1 Spezia Empoli\n", + "10 2 Atalanta Milan\n", + "11 2 Bologna Verona\n", + "12 2 Empoli Fiorentina\n", + "13 2 Inter Spezia\n", + "14 2 Napoli Monza\n", + "15 2 Roma Cremonese\n", + "16 2 Sampdoria Juventus\n", + "17 2 Sassuolo Lecce\n", + "18 2 Torino Lazio\n", + "19 2 Udinese Salernitana\n", + "20 3 Cremonese Torino\n", + "21 3 Fiorentina Napoli\n", + "22 3 Verona Atalanta\n", + "23 3 Juventus Roma\n", + "24 3 Lecce Empoli\n", + "25 3 Lazio Inter\n", + "26 3 Milan Bologna\n", + "27 3 Monza Udinese\n", + "28 3 Salernitana Sampdoria\n", + "29 3 Spezia Sassuolo\n", + "30 4 Atalanta Torino\n", + "31 4 Bologna Salernitana\n", + "32 4 Empoli Verona\n", + "33 4 Inter Cremonese\n", + "34 4 Juventus Spezia\n", + "35 4 Napoli Lecce\n", + "36 4 Roma Monza\n", + "37 4 Sampdoria Lazio\n", + "38 4 Sassuolo Milan\n", + "39 4 Udinese Fiorentina\n", + "40 5 Cremonese Sassuolo\n", + "41 5 Fiorentina Juventus\n", + "42 5 Verona Sampdoria\n", + "43 5 Lazio Napoli\n", + "44 5 Milan Inter\n", + "45 5 Monza Atalanta\n", + "46 5 Salernitana Empoli\n", + "47 5 Spezia Bologna\n", + "48 5 Torino Lecce\n", + "49 5 Udinese Roma\n", + "50 6 Atalanta Cremonese\n", + "51 6 Bologna Fiorentina\n", + "52 6 Empoli Roma\n", + "53 6 Inter Torino\n", + "54 6 Juventus Salernitana\n", + "55 6 Lazio Verona\n", + "56 6 Lecce Monza\n", + "57 6 Napoli Spezia\n", + "58 6 Sampdoria Milan\n", + "59 6 Sassuolo Udinese\n", + "60 7 Bologna Empoli\n", + "61 7 Cremonese Lazio\n", + "62 7 Fiorentina Verona\n", + "63 7 Milan Napoli\n", + "64 7 Monza Juventus\n", + "65 7 Roma Atalanta\n", + "66 7 Salernitana Lecce\n", + "67 7 Spezia Sampdoria\n", + "68 7 Torino Sassuolo\n", + "69 7 Udinese Inter\n", + "70 8 Atalanta Fiorentina\n", + "71 8 Empoli Milan\n", + "72 8 Verona Udinese\n", + "73 8 Inter Roma\n", + "74 8 Lazio Spezia\n", + "75 8 Napoli Torino\n", + "76 8 Juventus Bologna\n", + "77 8 Lecce Cremonese\n", + "78 8 Sampdoria Monza\n", + "79 8 Sassuolo Salernitana\n", + "80 9 Bologna Sampdoria\n", + "81 9 Cremonese Napoli\n", + "82 9 Fiorentina Lazio\n", + "83 9 Monza Spezia\n", + "84 9 Udinese Atalanta\n", + "85 9 Torino Empoli\n", + "86 9 Salernitana Verona\n", + "87 9 Sassuolo Inter\n", + "88 9 Milan Juventus\n", + "89 9 Roma Lecce\n", + "90 10 Atalanta Sassuolo\n", + "91 10 Empoli Monza\n", + "92 10 Verona Milan\n", + "93 10 Inter Salernitana\n", + "94 10 Lazio Udinese\n", + "95 10 Lecce Fiorentina\n", + "96 10 Napoli Bologna\n", + "97 10 Sampdoria Roma\n", + "98 10 Spezia Cremonese\n", + "99 10 Torino Juventus\n", + "100 11 Atalanta Lazio\n", + "101 11 Bologna Lecce\n", + "102 11 Cremonese Sampdoria\n", + "103 11 Fiorentina Inter\n", + "104 11 Juventus Empoli\n", + "105 11 Milan Monza\n", + "106 11 Roma Napoli\n", + "107 11 Salernitana Spezia\n", + "108 11 Sassuolo Verona\n", + "109 11 Udinese Torino\n", + "110 12 Cremonese Udinese\n", + "111 12 Verona Roma\n", + "112 12 Inter Sampdoria\n", + "113 12 Lazio Salernitana\n", + "114 12 Napoli Sassuolo\n", + "115 12 Empoli Atalanta\n", + "116 12 Monza Bologna\n", + "117 12 Spezia Fiorentina\n", + "118 12 Lecce Juventus\n", + "119 12 Torino Milan\n", + "120 13 Atalanta Napoli\n", + "121 13 Bologna Torino\n", + "122 13 Empoli Sassuolo\n", + "123 13 Juventus Inter\n", + "124 13 Milan Spezia\n", + "125 13 Monza Verona\n", + "126 13 Roma Lazio\n", + "127 13 Salernitana Cremonese\n", + "128 13 Sampdoria Fiorentina\n", + "129 13 Udinese Lecce\n", + "130 14 Cremonese Milan\n", + "131 14 Fiorentina Salernitana\n", + "132 14 Verona Juventus\n", + "133 14 Lazio Monza\n", + "134 14 Spezia Udinese\n", + "135 14 Lecce Atalanta\n", + "136 14 Inter Bologna\n", + "137 14 Napoli Empoli\n", + "138 14 Sassuolo Roma\n", + "139 14 Torino Sampdoria\n", + "140 15 Atalanta Inter\n", + "141 15 Bologna Sassuolo\n", + "142 15 Verona Spezia\n", + "143 15 Juventus Lazio\n", + "144 15 Monza Salernitana\n", + "145 15 Napoli Udinese\n", + "146 15 Roma Torino\n", + "147 15 Empoli Cremonese\n", + "148 15 Milan Fiorentina\n", + "149 15 Sampdoria Lecce\n", + "150 16 Cremonese Juventus\n", + "151 16 Fiorentina Monza\n", + "152 16 Inter Napoli\n", + "153 16 Spezia Atalanta\n", + "154 16 Roma Bologna\n", + "155 16 Udinese Empoli\n", + "156 16 Torino Verona\n", + "157 16 Lecce Lazio\n", + "158 16 Salernitana Milan\n", + "159 16 Sassuolo Sampdoria\n", + "160 17 Bologna Atalanta\n", + "161 17 Fiorentina Sassuolo\n", + "162 17 Verona Cremonese\n", + "163 17 Juventus Udinese\n", + "164 17 Lazio Empoli\n", + "165 17 Milan Roma\n", + "166 17 Monza Inter\n", + "167 17 Salernitana Torino\n", + "168 17 Sampdoria Napoli\n", + "169 17 Spezia Lecce\n", + "170 18 Atalanta Salernitana\n", + "171 18 Cremonese Monza\n", + "172 18 Empoli Sampdoria\n", + "173 18 Lecce Milan\n", + "174 18 Udinese Bologna\n", + "175 18 Roma Fiorentina\n", + "176 18 Inter Verona\n", + "177 18 Napoli Juventus\n", + "178 18 Sassuolo Lazio\n", + "179 18 Torino Spezia\n", + "180 19 Bologna Cremonese\n", + "181 19 Fiorentina Torino\n", + "182 19 Verona Lecce\n", + "183 19 Inter Empoli\n", + "184 19 Juventus Atalanta\n", + "185 19 Lazio Milan\n", + "186 19 Monza Sassuolo\n", + "187 19 Salernitana Napoli\n", + "188 19 Sampdoria Udinese\n", + "189 19 Spezia Roma\n", + "190 20 Atalanta Sampdoria\n", + "191 20 Bologna Spezia\n", + "192 20 Cremonese Inter\n", + "193 20 Empoli Torino\n", + "194 20 Juventus Monza\n", + "195 20 Lazio Fiorentina\n", + "196 20 Lecce Salernitana\n", + "197 20 Milan Sassuolo\n", + "198 20 Napoli Roma\n", + "199 20 Udinese Verona\n", + "200 21 Cremonese Lecce\n", + "201 21 Verona Lazio\n", + "202 21 Inter Milan\n", + "203 21 Monza Sampdoria\n", + "204 21 Torino Udinese\n", + "205 21 Sassuolo Atalanta\n", + "206 21 Fiorentina Bologna\n", + "207 21 Roma Empoli\n", + "208 21 Salernitana Juventus\n", + "209 21 Spezia Napoli\n", + "210 22 Bologna Monza\n", + "211 22 Empoli Spezia\n", + "212 22 Verona Salernitana\n", + "213 22 Lecce Roma\n", + "214 22 Milan Torino\n", + "215 22 Lazio Atalanta\n", + "216 22 Napoli Cremonese\n", + "217 22 Juventus Fiorentina\n", + "218 22 Sampdoria Inter\n", + "219 22 Udinese Sassuolo\n", + "220 23 Atalanta Lecce\n", + "221 23 Fiorentina Empoli\n", + "222 23 Inter Udinese\n", + "223 23 Monza Milan\n", + "224 23 Roma Verona\n", + "225 23 Salernitana Lazio\n", + "226 23 Sampdoria Bologna\n", + "227 23 Sassuolo Napoli\n", + "228 23 Spezia Juventus\n", + "229 23 Torino Cremonese\n", + "230 24 Bologna Inter\n", + "231 24 Cremonese Roma\n", + "232 24 Empoli Napoli\n", + "233 24 Juventus Torino\n", + "234 24 Lazio Sampdoria\n", + "235 24 Lecce Sassuolo\n", + "236 24 Milan Atalanta\n", + "237 24 Verona Fiorentina\n", + "238 24 Salernitana Monza\n", + "239 24 Udinese Spezia\n", + "240 25 Atalanta Udinese\n", + "241 25 Fiorentina Milan\n", + "242 25 Inter Lecce\n", + "243 25 Torino Bologna\n", + "244 25 Sassuolo Cremonese\n", + "245 25 Monza Empoli\n", + "246 25 Spezia Verona\n", + "247 25 Roma Juventus\n", + "248 25 Napoli Lazio\n", + "249 25 Sampdoria Salernitana\n", + "250 26 Bologna Lazio\n", + "251 26 Cremonese Fiorentina\n", + "252 26 Empoli Udinese\n", + "253 26 Verona Monza\n", + "254 26 Juventus Sampdoria\n", + "255 26 Lecce Torino\n", + "256 26 Milan Salernitana\n", + "257 26 Napoli Atalanta\n", + "258 26 Roma Sassuolo\n", + "259 26 Spezia Inter\n", + "260 27 Atalanta Empoli\n", + "261 27 Fiorentina Lecce\n", + "262 27 Inter Juventus\n", + "263 27 Lazio Roma\n", + "264 27 Sassuolo Spezia\n", + "265 27 Salernitana Bologna\n", + "266 27 Monza Cremonese\n", + "267 27 Sampdoria Verona\n", + "268 27 Udinese Milan\n", + "269 27 Torino Napoli\n", + "270 28 Bologna Udinese\n", + "271 28 Cremonese Atalanta\n", + "272 28 Empoli Lecce\n", + "273 28 Inter Fiorentina\n", + "274 28 Juventus Verona\n", + "275 28 Monza Lazio\n", + "276 28 Napoli Milan\n", + "277 28 Roma Sampdoria\n", + "278 28 Sassuolo Torino\n", + "279 28 Spezia Salernitana\n", + "280 29 Atalanta Bologna\n", + "281 29 Fiorentina Spezia\n", + "282 29 Verona Sassuolo\n", + "283 29 Lecce Napoli\n", + "284 29 Sampdoria Cremonese\n", + "285 29 Milan Empoli\n", + "286 29 Salernitana Inter\n", + "287 29 Lazio Juventus\n", + "288 29 Udinese Monza\n", + "289 29 Torino Roma\n", + "290 30 Bologna Milan\n", + "291 30 Cremonese Empoli\n", + "292 30 Fiorentina Atalanta\n", + "293 30 Inter Monza\n", + "294 30 Lecce Sampdoria\n", + "295 30 Napoli Verona\n", + "296 30 Roma Udinese\n", + "297 30 Sassuolo Juventus\n", + "298 30 Spezia Lazio\n", + "299 30 Torino Salernitana\n", + "300 31 Atalanta Roma\n", + "301 31 Empoli Inter\n", + "302 31 Verona Bologna\n", + "303 31 Juventus Napoli\n", + "304 31 Lazio Torino\n", + "305 31 Milan Lecce\n", + "306 31 Monza Fiorentina\n", + "307 31 Salernitana Sassuolo\n", + "308 31 Sampdoria Spezia\n", + "309 31 Udinese Cremonese\n", + "310 32 Bologna Juventus\n", + "311 32 Cremonese Verona\n", + "312 32 Fiorentina Sampdoria\n", + "313 32 Inter Lazio\n", + "314 32 Lecce Udinese\n", + "315 32 Napoli Salernitana\n", + "316 32 Torino Atalanta\n", + "317 32 Sassuolo Empoli\n", + "318 32 Roma Milan\n", + "319 32 Spezia Monza\n", + "320 33 Atalanta Spezia\n", + "321 33 Verona Inter\n", + "322 33 Juventus Lecce\n", + "323 33 Lazio Sassuolo\n", + "324 33 Monza Roma\n", + "325 33 Sampdoria Torino\n", + "326 33 Empoli Bologna\n", + "327 33 Milan Cremonese\n", + "328 33 Salernitana Fiorentina\n", + "329 33 Udinese Napoli\n", + "330 34 Atalanta Juventus\n", + "331 34 Cremonese Spezia\n", + "332 34 Empoli Salernitana\n", + "333 34 Lecce Verona\n", + "334 34 Milan Lazio\n", + "335 34 Napoli Fiorentina\n", + "336 34 Roma Inter\n", + "337 34 Sassuolo Bologna\n", + "338 34 Torino Monza\n", + "339 34 Udinese Sampdoria\n", + "340 35 Bologna Roma\n", + "341 35 Fiorentina Udinese\n", + "342 35 Verona Torino\n", + "343 35 Inter Sassuolo\n", + "344 35 Lazio Lecce\n", + "345 35 Monza Napoli\n", + "346 35 Salernitana Atalanta\n", + "347 35 Juventus Cremonese\n", + "348 35 Sampdoria Empoli\n", + "349 35 Spezia Milan\n", + "350 36 Atalanta Verona\n", + "351 36 Empoli Juventus\n", + "352 36 Lecce Spezia\n", + "353 36 Milan Sampdoria\n", + "354 36 Roma Salernitana\n", + "355 36 Cremonese Bologna\n", + "356 36 Torino Fiorentina\n", + "357 36 Napoli Inter\n", + "358 36 Udinese Lazio\n", + "359 36 Sassuolo Monza\n", + "360 37 Bologna Napoli\n", + "361 37 Fiorentina Roma\n", + "362 37 Juventus Milan\n", + "363 37 Salernitana Udinese\n", + "364 37 Sampdoria Sassuolo\n", + "365 37 Spezia Torino\n", + "366 37 Inter Atalanta\n", + "367 37 Lazio Cremonese\n", + "368 37 Verona Empoli\n", + "369 37 Monza Lecce\n", + "370 38 Atalanta Monza\n", + "371 38 Cremonese Salernitana\n", + "372 38 Empoli Lazio\n", + "373 38 Napoli Sampdoria\n", + "374 38 Roma Spezia\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" + ] } ], "source": [ - "cal_df" + "print(cal_df.to_string())" ] }, { @@ -5631,29 +4043,29 @@ " \n", " \n", " \n", - " Falcone\n", + " Maignan\n", " 1.00\n", " 90\n", " \n", " \n", - " Gendrey\n", - " 1.00\n", - " 90\n", + " Calabria\n", + " 0.60\n", + " 60\n", " \n", " \n", - " Baschirotto\n", - " 1.00\n", - " 90\n", + " Kjaer\n", + " 0.55\n", + " 55\n", " \n", " \n", - " Umtiti\n", + " Tomori\n", " 1.00\n", - " 90\n", + " 70\n", " \n", " \n", - " Gallo\n", - " 0.65\n", - " 65\n", + " Hernandez T.\n", + " 1.00\n", + " 90\n", " \n", " \n", " ...\n", @@ -5661,51 +4073,51 @@ " ...\n", " \n", " \n", - " Barrenechea\n", - " 0.00\n", - " 20\n", + " Bonifazi\n", + " 0.40\n", + " 30\n", " \n", " \n", - " Pogba\n", + " Pyyhtia\n", + " 0.00\n", + " 30\n", + " \n", + " \n", + " Aebischer\n", + " 0.45\n", + " 60\n", + " \n", + " \n", + " Arnautovic\n", " 0.00\n", " 50\n", " \n", " \n", - " Chiesa\n", - " 0.40\n", + " Zirkzee\n", + " 0.45\n", " 55\n", " \n", - " \n", - " Soule'\n", - " 0.00\n", - " 20\n", - " \n", - " \n", - " Milik\n", - " 0.40\n", - " 60\n", - " \n", " \n", "\n", - "

474 rows × 2 columns

\n", + "

456 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", + " starter percentage\n", + "player \n", + "Maignan 1.00 90\n", + "Calabria 0.60 60\n", + "Kjaer 0.55 55\n", + "Tomori 1.00 70\n", + "Hernandez T. 1.00 90\n", + "... ... ...\n", + "Bonifazi 0.40 30\n", + "Pyyhtia 0.00 30\n", + "Aebischer 0.45 60\n", + "Arnautovic 0.00 50\n", + "Zirkzee 0.45 55\n", "\n", - "[474 rows x 2 columns]" + "[456 rows x 2 columns]" ] }, "execution_count": 27, @@ -5731,7 +4143,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 46, "id": "5e63c2b7", "metadata": { "scrolled": true @@ -5741,561 +4153,561 @@ "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", + "Meret: MV 6.25 ± 0.82; FV 5.94 + 0.90 (73.6% cs)\n", + "Provedel: MV 6.25 ± 0.82; FV 6.05 + 1.08 (71.1% cs)\n", + "Vicario: MV 6.25 ± 0.82; FV 5.79 + 0.85 (45.4% cs)\n", + "Szczesny: MV 6.25 ± 0.82; FV 4.92 + 1.54 (3.6% cs)\n", + "Falcone: MV 6.25 ± 0.82; FV 5.76 + 0.80 (36.1% cs)\n", + "Silvestri: MV 6.25 ± 0.82; FV 5.73 + 1.07 (51.7% cs)\n", + "Rui Patricio: MV 6.25 ± 0.82; FV 5.41 + 1.33 (40.3% cs)\n", + "Onana: MV 6.25 ± 0.82; FV 5.41 + 1.26 (25.4% cs)\n", + "Sepe: MV 6.25 ± 0.82; FV 5.34 + 0.83 (10.1% cs)\n", + "Milinkovic-Savic V.: MV 6.25 ± 0.82; FV 5.88 + 1.06 (43.5% cs)\n", + "Musso: MV 6.25 ± 0.82; FV 5.04 + 0.69 (8.0% cs)\n", + "Maignan: MV 6.25 ± 0.82; FV 5.85 + 0.93 (43.2% cs)\n", + "Carnesecchi: MV 6.25 ± 0.82; FV 5.11 + 1.47 (36.7% cs)\n", + "Di Gregorio: MV 6.25 ± 0.82; FV 5.33 + 0.99 (10.5% cs)\n", + "Audero: MV 6.25 ± 0.82; FV 4.88 + 1.26 (14.6% cs)\n", + "Montipo': MV 6.07 ± 0.70; FV 5.07 + 1.32 (29.0% cs)\n", + "Skorupski: MV 6.06 ± 0.71; FV 5.09 + 1.46 (20.9% cs)\n", + "Consigli: MV 6.25 ± 0.82; FV 5.02 + 1.23 (8.9% cs)\n", + "Dragowski: MV 6.25 ± 0.82; FV 5.76 + 1.10 (40.5% cs)\n", + "Terracciano: MV 6.24 ± 0.83; FV 4.12 + 1.65 (4.4% cs)\n", + "Tatarusanu: MV 6.25 ± 0.82; FV 4.89 + 1.56 (30.9% cs)\n", + "Handanovic: MV 6.25 ± 0.82; FV 3.71 + 2.44 (2.3% cs)\n", + "Sportiello: MV 6.25 ± 0.82; FV 4.21 + 0.65 (1.2% cs)\n", + "Perin: MV 6.25 ± 0.82; FV 5.98 + 0.88 (47.4% cs)\n", + "Zoet: MV 6.25 ± 0.82; FV 5.97 + 0.90 (73.7% cs)\n", + "Ochoa: MV 6.25 ± 0.82; FV 5.70 + 0.83 (31.2% cs)\n", + "Pegolo: MV 6.25 ± 0.82; FV 4.61 + 1.08 (1.6% cs)\n", + "Gollini: MV 6.25 ± 0.82; FV 5.94 + 0.89 (74.1% 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", + "Berisha: MV 6.25 ± 0.82; FV 5.74 + 1.09 (59.5% cs)\n", + "Marchetti: MV 6.09 ± 0.75; FV 4.84 + 1.44 (17.4% cs)\n", + "Perilli: MV 6.25 ± 0.82; FV 5.94 + 0.89 (43.1% cs)\n", + "Padelli: MV 6.25 ± 0.82; FV 5.82 + 1.06 (55.1% cs)\n", + "Perisan: MV 6.25 ± 0.82; FV 5.59 + 1.01 (40.8% cs)\n", + "Bardi: MV 6.25 ± 0.82; FV 5.85 + 0.95 (47.5% 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", + "Pinsoglio: MV 6.23 ± 0.80; FV 3.55 + 1.51 (0.9% cs)\n", + "Fiorillo: MV 6.24 ± 0.81; FV 2.54 + 1.67 (0.5% cs)\n", + "Cragno: MV 6.25 ± 0.82; FV 5.34 + 1.54 (38.8% cs)\n", + "Sirigu: MV 6.25 ± 0.82; FV 4.00 + 2.06 (2.0% cs)\n", + "Cerofolini: MV 6.25 ± 0.82; FV 5.85 + 1.18 (59.8% cs)\n", + "Rossi F.: MV 6.23 ± 0.81; FV 4.29 + 0.84 (2.0% cs)\n", + "Ravaglia F.: MV 6.25 ± 0.82; FV 4.22 + 1.33 (0.7% 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", + "Berardi A.: MV 6.25 ± 0.82; FV 5.43 + 0.69 (8.2% 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", + "Gemello: MV 6.25 ± 0.82; FV 5.95 + 0.89 (73.9% cs)\n", + "Ravaglia: MV 6.25 ± 0.82; FV 5.21 + 1.23 (10.7% 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", + "Marfella: MV 6.25 ± 0.82; FV 5.94 + 0.89 (73.7% cs)\n", + "Zovko: MV 6.25 ± 0.82; FV 5.47 + 1.22 (22.0% 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", + "Luis Maximiano: MV 5.70 ± 0.87; FV 5.19 + 1.36 (66.0% 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" + "Turk: MV 6.25 ± 0.82; FV 4.73 + 1.47 (11.2% cs)\n", + "Dimarco: MV 6.06 ± 0.87; FV 6.35 + 1.35\n", + "Smalling: MV 6.20 ± 0.97; FV 6.56 + 1.59\n", + "Doig: MV 6.00 ± 1.02; FV 6.35 + 1.72\n", + "Carlos Augusto: MV 6.03 ± 1.01; FV 6.42 + 1.81\n", + "Kim: MV 6.25 ± 0.95; FV 6.60 + 1.58\n", + "Posch: MV 6.10 ± 1.12; FV 6.59 + 2.11\n", + "Di Lorenzo: MV 6.21 ± 0.89; FV 6.55 + 1.49\n", + "Danilo: MV 6.15 ± 0.96; FV 6.44 + 1.44\n", + "Hernandez T.: MV 5.97 ± 1.03; FV 6.18 + 1.46\n", + "Udogie: MV 6.23 ± 1.04; FV 6.82 + 2.04\n", + "Parisi: MV 6.15 ± 0.95; FV 6.52 + 1.55\n", + "Mario Rui: MV 6.13 ± 0.91; FV 6.27 + 1.16\n", + "Romagnoli: MV 6.10 ± 0.92; FV 6.27 + 1.20\n", + "Bastoni S.: MV 6.12 ± 0.95; FV 6.53 + 1.67\n", + "Mazzocchi: MV 6.16 ± 0.85; FV 6.55 + 1.46\n", + "Valeri: MV 6.12 ± 0.70; FV 6.42 + 1.19\n", + "Tomori: MV 6.01 ± 0.85; FV 6.06 + 0.92\n", + "Scalvini: MV 5.99 ± 0.99; FV 6.16 + 1.31\n", + "Toloi: MV 5.98 ± 0.91; FV 6.07 + 1.06\n", + "Demiral: MV 5.98 ± 0.85; FV 6.07 + 1.00\n", + "Maehle: MV 5.94 ± 0.93; FV 6.04 + 1.28\n", + "Dumfries: MV 5.86 ± 0.85; FV 5.99 + 1.22\n", + "Baschirotto: MV 6.21 ± 0.91; FV 6.59 + 1.55\n", + "Bijol: MV 6.18 ± 0.98; FV 6.56 + 1.63\n", + "Schuurs: MV 6.12 ± 0.78; FV 6.15 + 0.90\n", + "Juan Jesus: MV 6.15 ± 0.69; FV 6.42 + 1.17\n", + "Depaoli: MV 5.95 ± 0.85; FV 6.17 + 1.20\n", + "Mancini: MV 6.08 ± 0.81; FV 6.20 + 1.01\n", + "Ibanez: MV 5.79 ± 1.12; FV 5.89 + 1.45\n", + "Rodrigo Becao: MV 6.20 ± 0.90; FV 6.61 + 1.58\n", + "Ebuehi: MV 6.10 ± 0.78; FV 6.37 + 1.20\n", + "Gosens: MV 5.87 ± 0.77; FV 6.01 + 1.04\n", + "Darmian: MV 5.95 ± 0.74; FV 6.07 + 0.94\n", + "Reca: MV 6.05 ± 0.85; FV 6.27 + 1.22\n", + "Bremer: MV 5.93 ± 1.10; FV 6.16 + 1.65\n", + "Sernicola: MV 6.04 ± 0.93; FV 6.37 + 1.57\n", + "Rrahmani: MV 6.22 ± 0.93; FV 6.55 + 1.51\n", + "Vojvoda: MV 5.95 ± 0.82; FV 6.03 + 1.01\n", + "Holm: MV 6.03 ± 0.86; FV 6.28 + 1.26\n", + "Bastoni: MV 6.03 ± 0.86; FV 6.13 + 1.01\n", + "Milenkovic: MV 5.75 ± 1.07; FV 5.82 + 1.39\n", + "Kalulu: MV 5.74 ± 1.05; FV 5.71 + 1.11\n", + "Martinez Quarta: MV 5.75 ± 1.12; FV 5.84 + 1.44\n", + "Casale: MV 6.00 ± 0.89; FV 6.13 + 1.15\n", + "Perez N.: MV 6.18 ± 0.83; FV 6.44 + 1.29\n", + "Olivera: MV 6.14 ± 0.71; FV 6.48 + 1.27\n", + "Izzo: MV 5.98 ± 0.87; FV 6.12 + 1.11\n", + "Luperto: MV 5.99 ± 0.85; FV 6.00 + 0.87\n", + "Skriniar: MV 5.83 ± 0.89; FV 5.86 + 0.98\n", + "Rodriguez R.: MV 6.05 ± 0.70; FV 6.01 + 0.68\n", + "Marusic: MV 5.97 ± 0.85; FV 6.03 + 0.93\n", + "Lazzari: MV 5.98 ± 0.81; FV 6.04 + 0.93\n", + "Kyriakopoulos: MV 5.92 ± 0.86; FV 5.95 + 0.95\n", + "Ampadu: MV 5.91 ± 0.80; FV 5.91 + 0.80\n", + "Ismajli: MV 6.00 ± 0.78; FV 5.96 + 0.74\n", + "Llorente D.: MV 5.87 ± 1.03; FV 5.97 + 1.39\n", + "Cambiaso: MV 5.92 ± 0.75; FV 5.91 + 0.73\n", + "Hysaj: MV 5.92 ± 0.66; FV 5.92 + 0.58\n", + "Biraghi: MV 5.94 ± 0.90; FV 6.08 + 1.20\n", + "Medel: MV 5.98 ± 0.69; FV 5.91 + 0.61\n", + "Bonucci: MV 6.05 ± 1.06; FV 6.35 + 1.65\n", + "Calabria: MV 5.87 ± 1.00; FV 5.95 + 1.31\n", + "Acerbi: MV 5.98 ± 0.81; FV 6.02 + 0.84\n", + "Spinazzola: MV 6.09 ± 0.83; FV 6.34 + 1.23\n", + "Lykogiannis: MV 5.98 ± 0.77; FV 6.08 + 0.91\n", + "Pellegrini Lu.: MV 6.00 ± 0.80; FV 6.08 + 0.92\n", + "Djidji: MV 5.94 ± 0.79; FV 6.00 + 0.91\n", + "Lazaro: MV 6.04 ± 0.84; FV 6.16 + 1.02\n", + "Augello: MV 5.82 ± 0.88; FV 5.89 + 1.16\n", + "Gallo: MV 5.94 ± 0.72; FV 5.92 + 0.65\n", + "Singo: MV 6.01 ± 0.86; FV 6.25 + 1.30\n", + "Mari': MV 5.84 ± 0.99; FV 5.92 + 1.17\n", + "Caldirola: MV 5.90 ± 1.00; FV 6.07 + 1.49\n", + "Dodo': MV 5.71 ± 1.15; FV 5.76 + 1.42\n", + "De Vrij: MV 5.87 ± 0.84; FV 5.93 + 0.92\n", + "Patric: MV 5.98 ± 0.91; FV 6.02 + 0.96\n", + "Faraoni: MV 5.97 ± 0.89; FV 6.19 + 1.31\n", + "Ceccherini: MV 5.84 ± 1.11; FV 6.03 + 1.52\n", + "Hateboer: MV 5.83 ± 0.90; FV 5.87 + 1.19\n", + "Rogerio: MV 5.81 ± 0.77; FV 5.79 + 0.70\n", + "Umtiti: MV 6.00 ± 0.82; FV 6.00 + 0.82\n", + "Aina: MV 6.06 ± 0.91; FV 6.34 + 1.45\n", + "Birindelli: MV 5.81 ± 0.71; FV 5.77 + 0.75\n", + "Lucumi': MV 5.86 ± 0.83; FV 5.83 + 0.81\n", + "Ehizibue: MV 6.03 ± 0.82; FV 6.29 + 1.28\n", + "Bianchetti: MV 5.91 ± 0.82; FV 5.93 + 0.99\n", + "Ferrari G.: MV 5.83 ± 1.04; FV 5.90 + 1.22\n", + "Fazio: MV 5.69 ± 1.13; FV 5.67 + 1.16\n", + "Gravillon: MV 6.00 ± 0.74; FV 5.94 + 0.70\n", + "Buongiorno: MV 6.13 ± 0.89; FV 6.40 + 1.34\n", + "Gunter: MV 5.73 ± 1.02; FV 5.66 + 1.07\n", + "Troost-Ekong: MV 5.86 ± 0.81; FV 5.83 + 0.75\n", + "Soumaoro: MV 5.85 ± 0.92; FV 5.84 + 0.87\n", + "Ceccaroni: MV 6.04 ± 0.84; FV 6.15 + 0.98\n", + "Pongracic: MV 6.03 ± 0.70; FV 5.96 + 0.62\n", + "Soppy: MV 5.85 ± 0.72; FV 5.85 + 0.70\n", + "Gendrey: MV 6.00 ± 0.64; FV 5.95 + 0.54\n", + "Hien: MV 5.89 ± 0.82; FV 5.87 + 0.80\n", + "Ferrari A.: MV 5.95 ± 0.80; FV 5.98 + 0.84\n", + "Masina: MV 6.12 ± 0.71; FV 6.60 + 1.49\n", + "Zappacosta: MV 6.06 ± 0.93; FV 6.30 + 1.40\n", + "Gyomber: MV 5.93 ± 0.77; FV 5.87 + 0.70\n", + "Alex Sandro: MV 5.75 ± 0.99; FV 5.67 + 1.00\n", + "Pezzella Giu.: MV 5.97 ± 0.64; FV 5.94 + 0.54\n", + "Bereszynski: MV 5.89 ± 0.69; FV 5.88 + 0.64\n", + "Venuti: MV 5.61 ± 0.84; FV 5.61 + 0.89\n", + "Palomino: MV 5.97 ± 0.82; FV 6.01 + 0.87\n", + "Nuytinck: MV 5.79 ± 0.98; FV 5.77 + 1.11\n", + "Marlon: MV 5.77 ± 0.74; FV 5.74 + 0.70\n", + "Magnani: MV 5.88 ± 0.90; FV 5.88 + 0.91\n", + "Colley: MV 5.73 ± 1.07; FV 5.75 + 1.27\n", + "Nikolaou: MV 5.85 ± 0.69; FV 5.82 + 0.62\n", + "Terzic: MV 5.91 ± 0.68; FV 5.96 + 0.75\n", + "Igor: MV 5.61 ± 1.15; FV 5.52 + 1.11\n", + "Toljan: MV 5.75 ± 0.82; FV 5.71 + 0.76\n", + "Zortea: MV 5.84 ± 0.81; FV 5.85 + 0.88\n", + "Dawidowicz: MV 5.91 ± 0.86; FV 5.97 + 1.02\n", + "Celik: MV 5.79 ± 0.91; FV 5.75 + 1.01\n", + "Bellanova: MV 5.87 ± 0.80; FV 5.95 + 1.04\n", + "Erlic: MV 5.78 ± 0.92; FV 5.74 + 0.83\n", + "Ballo-Toure': MV 6.00 ± 0.70; FV 6.12 + 0.77\n", + "Dest: MV 5.70 ± 0.78; FV 5.70 + 0.73\n", + "Stojanovic: MV 5.83 ± 0.74; FV 5.79 + 0.73\n", + "Amian: MV 5.88 ± 0.72; FV 5.88 + 0.71\n", + "Bradaric: MV 5.83 ± 0.83; FV 5.81 + 0.85\n", + "Daniliuc: MV 5.81 ± 0.87; FV 5.78 + 0.90\n", + "Zima: MV 5.98 ± 0.75; FV 5.97 + 0.75\n", + "De Winter: MV 5.88 ± 0.77; FV 5.81 + 0.71\n", + "Quagliata: MV 5.97 ± 0.75; FV 5.97 + 0.72\n", + "Ebosse: MV 5.95 ± 0.62; FV 5.90 + 0.50\n", + "Aiwu: MV 6.01 ± 0.80; FV 6.15 + 0.99\n", + "Lochoshvili: MV 5.99 ± 0.82; FV 6.15 + 1.11\n", + "Bronn: MV 5.81 ± 0.69; FV 5.78 + 0.60\n", + "Thiaw: MV 5.81 ± 1.02; FV 5.79 + 0.97\n", + "Zeefuik: MV 5.92 ± 0.83; FV 5.98 + 0.88\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" + "Romagnoli S.: MV 6.12 ± 0.99; FV 6.47 + 1.57\n", + "Ghiglione: MV 5.99 ± 0.83; FV 6.20 + 1.28\n", + "Rugani: MV 5.98 ± 0.62; FV 5.95 + 0.51\n", + "De Sciglio: MV 5.84 ± 0.64; FV 5.84 + 0.53\n", + "Djimsiti: MV 5.96 ± 0.73; FV 5.94 + 0.66\n", + "Caldara: MV 5.83 ± 0.85; FV 5.81 + 0.87\n", + "Karsdorp: MV 5.89 ± 0.80; FV 5.90 + 0.85\n", + "Marchizza: MV 5.81 ± 0.81; FV 5.77 + 0.76\n", + "Kjaer: MV 5.93 ± 0.74; FV 5.86 + 0.66\n", + "Okoli: MV 5.76 ± 0.88; FV 5.69 + 0.81\n", + "Amione: MV 5.72 ± 0.98; FV 5.66 + 1.15\n", + "Ruggeri: MV 5.91 ± 0.66; FV 5.89 + 0.56\n", + "Zanoli: MV 5.93 ± 0.90; FV 6.07 + 1.28\n", + "Wisniewski: MV 5.84 ± 0.64; FV 5.81 + 0.55\n", + "Radovanovic: MV 5.75 ± 0.70; FV 5.71 + 0.62\n", + "Dermaku: MV 6.02 ± 0.77; FV 6.08 + 0.83\n", + "D'ambrosio: MV 6.02 ± 0.79; FV 6.08 + 0.89\n", + "De Silvestri: MV 5.88 ± 1.01; FV 6.00 + 1.34\n", + "Chiriches: MV 5.94 ± 0.76; FV 5.91 + 0.70\n", + "Murru: MV 5.66 ± 0.75; FV 5.59 + 0.78\n", + "Bonifazi: MV 5.69 ± 0.85; FV 5.63 + 0.82\n", + "Donati: MV 5.72 ± 1.15; FV 5.80 + 1.55\n", + "Walukiewicz: MV 6.06 ± 0.65; FV 5.97 + 0.57\n", + "Ranieri L.: MV 5.70 ± 0.81; FV 5.69 + 0.94\n", + "Gabbia: MV 5.68 ± 0.90; FV 5.57 + 0.84\n", + "Kumbulla: MV 5.69 ± 1.00; FV 5.61 + 1.04\n", + "Adopo: MV 5.81 ± 0.69; FV 5.77 + 0.63\n", + "Pirola: MV 5.96 ± 1.05; FV 6.21 + 1.60\n", + "Lovato: MV 5.74 ± 0.87; FV 5.69 + 0.79\n", + "Tuia: MV 6.07 ± 0.56; FV 5.97 + 0.47\n", + "Ferrer: MV 5.95 ± 0.82; FV 6.01 + 0.88\n", + "Antov: MV 5.62 ± 1.06; FV 5.57 + 1.05\n", + "Vasquez: MV 5.93 ± 0.70; FV 5.90 + 0.62\n", + "Ruan: MV 5.79 ± 0.96; FV 5.72 + 0.88\n", + "Ostigard: MV 6.06 ± 0.62; FV 6.02 + 0.57\n", + "Coppola D.: MV 5.85 ± 0.68; FV 5.84 + 0.59\n", + "Cacace: MV 5.81 ± 0.72; FV 5.74 + 0.64\n", + "Gatti: MV 6.04 ± 0.86; FV 6.09 + 0.93\n", + "Gila: MV 5.94 ± 0.93; FV 6.01 + 1.05\n", + "Bayeye: MV 6.01 ± 0.85; FV 6.10 + 1.01\n", + "Sambia: MV 5.90 ± 0.74; FV 5.88 + 0.67\n", + "Moutinho J.: MV 5.91 ± 0.73; FV 5.92 + 0.73\n", + "Conti: MV 5.81 ± 0.82; FV 5.85 + 1.05\n", + "Marrone: MV 5.60 ± 0.90; FV 5.54 + 0.94\n", + "Tonelli: MV 5.85 ± 0.79; FV 5.80 + 0.73\n", + "Murillo: MV 5.64 ± 0.85; FV 5.52 + 0.86\n", + "Radu: MV 5.69 ± 1.08; FV 5.68 + 0.95\n", + "Paletta: MV 5.87 ± 0.92; FV 5.93 + 1.14\n", + "Florenzi: MV 5.99 ± 0.67; FV 6.03 + 0.63\n", + "Sala: MV 5.93 ± 0.71; FV 6.01 + 0.82\n", + "Fares: MV 5.78 ± 0.82; FV 5.82 + 1.04\n", + "Romagna: MV 5.89 ± 0.88; FV 5.90 + 0.90\n", + "Cassandro: MV 6.02 ± 0.77; FV 6.07 + 0.82\n", + "Muldur: MV 5.77 ± 0.77; FV 5.73 + 0.72\n", + "Amey: MV 5.99 ± 0.85; FV 6.04 + 0.94\n", + "Zanotti: MV 5.84 ± 0.96; FV 5.88 + 1.20\n", + "Ebosele: MV 6.05 ± 0.59; FV 6.05 + 0.59\n", + "Buta: MV 6.08 ± 0.75; FV 6.18 + 0.91\n", + "Abankwah: MV 6.07 ± 0.74; FV 6.14 + 0.85\n", + "Guessand A.: MV 6.08 ± 0.75; FV 6.18 + 0.91\n", + "Cabal: MV 5.94 ± 0.67; FV 5.91 + 0.57\n", + "Sosa: MV 5.50 ± 0.91; FV 5.43 + 0.90\n", + "Guarino: MV 5.97 ± 0.84; FV 6.04 + 0.95\n", + "Carboni F.: MV 5.96 ± 0.85; FV 6.05 + 0.96\n", + "Zaccagni: MV 6.36 ± 1.17; FV 7.12 + 2.57\n", + "Kvaratskhelia: MV 6.49 ± 1.31; FV 7.41 + 3.07\n", + "Milinkovic-Savic: MV 6.12 ± 1.15; FV 6.66 + 2.23\n", + "Barella: MV 6.11 ± 0.98; FV 6.55 + 1.72\n", + "Zielinski: MV 6.22 ± 0.92; FV 6.72 + 1.76\n", + "Luis Alberto: MV 6.21 ± 1.05; FV 6.77 + 2.05\n", + "Strefezza: MV 6.33 ± 1.04; FV 7.07 + 2.32\n", + "Felipe Anderson: MV 6.16 ± 1.14; FV 6.85 + 2.42\n", + "Koopmeiners: MV 6.18 ± 1.07; FV 6.70 + 2.02\n", + "Calhanoglu: MV 6.18 ± 0.88; FV 6.54 + 1.47\n", + "Frattesi: MV 6.13 ± 1.07; FV 6.70 + 2.15\n", + "Diaz B.: MV 6.05 ± 1.11; FV 6.52 + 2.03\n", + "Vlasic: MV 6.17 ± 0.99; FV 6.72 + 1.93\n", + "Zambo Anguissa: MV 6.20 ± 0.89; FV 6.60 + 1.56\n", + "Elmas: MV 6.20 ± 0.93; FV 6.78 + 1.91\n", + "Miranchuk: MV 6.23 ± 1.02; FV 6.87 + 2.11\n", + "Samardzic: MV 6.24 ± 1.01; FV 6.85 + 2.01\n", + "Pereyra: MV 6.27 ± 1.14; FV 6.97 + 2.39\n", + "Politano: MV 6.19 ± 0.78; FV 6.71 + 1.64\n", + "Rabiot: MV 6.19 ± 1.20; FV 6.84 + 2.44\n", + "Ciurria: MV 6.01 ± 1.05; FV 6.44 + 1.93\n", + "Lazovic: MV 6.10 ± 0.99; FV 6.61 + 1.78\n", + "Lobotka: MV 6.17 ± 0.75; FV 6.44 + 1.22\n", + "Radonjic: MV 6.19 ± 0.95; FV 6.71 + 1.85\n", + "Ferguson: MV 6.12 ± 0.88; FV 6.51 + 1.55\n", + "Bonaventura: MV 6.02 ± 0.88; FV 6.29 + 1.32\n", + "Pessina: MV 6.01 ± 0.95; FV 6.28 + 1.51\n", + "Tonali: MV 6.04 ± 1.02; FV 6.32 + 1.58\n", + "Kostic: MV 6.08 ± 1.01; FV 6.44 + 1.68\n", + "Baldanzi: MV 6.17 ± 0.95; FV 6.64 + 1.73\n", + "Lovric: MV 6.21 ± 0.85; FV 6.79 + 1.79\n", + "Pellegrini Lo.: MV 6.04 ± 1.12; FV 6.46 + 2.01\n", + "El Shaarawy: MV 6.17 ± 0.86; FV 6.67 + 1.68\n", + "Orsolini: MV 6.24 ± 1.30; FV 7.16 + 3.16\n", + "Ikone': MV 5.93 ± 1.01; FV 6.21 + 1.60\n", + "Candreva: MV 6.11 ± 1.00; FV 6.51 + 1.68\n", + "Bennacer: MV 6.06 ± 0.77; FV 6.18 + 0.93\n", + "Pasalic: MV 5.92 ± 0.94; FV 6.19 + 1.47\n", + "Mkhitaryan: MV 5.96 ± 0.87; FV 6.19 + 1.32\n", + "Colpani: MV 5.99 ± 0.84; FV 6.34 + 1.49\n", + "Pogba: MV 5.96 ± 0.68; FV 5.99 + 0.66\n", + "Chiesa: MV 6.01 ± 0.87; FV 6.22 + 1.27\n", + "Bandinelli: MV 5.93 ± 0.82; FV 6.01 + 1.09\n", + "Matic: MV 6.09 ± 0.78; FV 6.27 + 1.07\n", + "Fagioli: MV 6.00 ± 1.01; FV 6.25 + 1.56\n", + "Messias: MV 5.99 ± 1.02; FV 6.42 + 1.81\n", + "Arslan: MV 6.04 ± 0.63; FV 6.11 + 0.73\n", + "Ricci S.: MV 6.11 ± 0.80; FV 6.34 + 1.17\n", + "Ranocchia F.: MV 6.02 ± 0.83; FV 6.29 + 1.31\n", + "Verdi: MV 6.11 ± 1.14; FV 6.82 + 2.48\n", + "Sensi: MV 5.98 ± 0.98; FV 6.27 + 1.64\n", + "Barak: MV 5.81 ± 0.88; FV 5.96 + 1.22\n", + "Soriano: MV 6.03 ± 0.80; FV 6.27 + 1.24\n", + "Dominguez: MV 6.10 ± 0.89; FV 6.42 + 1.45\n", + "Vilhena: MV 5.93 ± 0.92; FV 6.11 + 1.37\n", + "Brozovic: MV 6.05 ± 0.96; FV 6.30 + 1.44\n", + "Cristante: MV 5.95 ± 0.82; FV 6.02 + 1.07\n", + "Thorstvedt: MV 5.98 ± 0.88; FV 6.20 + 1.28\n", + "De Ketelaere: MV 5.80 ± 0.66; FV 5.80 + 0.66\n", + "Saponara: MV 5.92 ± 0.97; FV 6.22 + 1.54\n", + "Vecino: MV 5.92 ± 0.97; FV 6.08 + 1.42\n", + "Locatelli: MV 6.07 ± 0.86; FV 6.23 + 1.13\n", + "Zaniolo: MV 5.89 ± 0.98; FV 6.04 + 1.46\n", + "Duda: MV 5.85 ± 0.64; FV 5.86 + 0.62\n", + "Maldini: MV 5.99 ± 0.91; FV 6.38 + 1.61\n", + "Marin: MV 5.97 ± 0.94; FV 6.13 + 1.36\n", + "Zalewski: MV 6.02 ± 0.77; FV 6.18 + 1.08\n", + "Bajrami: MV 6.09 ± 1.02; FV 6.61 + 1.97\n", + "Coulibaly L.: MV 5.98 ± 1.08; FV 6.28 + 1.70\n", + "Gonzalez J.: MV 6.03 ± 0.80; FV 6.22 + 1.11\n", + "De Roon: MV 6.05 ± 0.87; FV 6.23 + 1.20\n", + "Mandragora: MV 5.92 ± 0.90; FV 6.03 + 1.26\n", + "Wijnaldum: MV 6.11 ± 1.05; FV 6.66 + 2.02\n", + "Bourabia: MV 5.96 ± 0.80; FV 6.13 + 1.04\n", + "Sottil: MV 5.92 ± 0.94; FV 6.17 + 1.43\n", + "Aebischer: MV 5.89 ± 0.80; FV 6.00 + 1.12\n", + "Ederson D.s.: MV 5.92 ± 0.82; FV 5.96 + 0.94\n", + "Miretti: MV 5.93 ± 0.79; FV 6.08 + 1.07\n", + "Blin: MV 6.02 ± 0.70; FV 6.09 + 0.78\n", + "Hjulmand: MV 6.05 ± 0.80; FV 6.09 + 0.87\n", + "Cataldi: MV 5.95 ± 0.76; FV 5.99 + 0.81\n", + "Djuricic: MV 5.77 ± 0.90; FV 5.87 + 1.20\n", + "Linetty: MV 5.98 ± 0.84; FV 6.14 + 1.22\n", + "Haas: MV 5.93 ± 0.76; FV 6.02 + 0.96\n", + "Walace: MV 6.05 ± 0.67; FV 6.05 + 0.68\n", + "Agudelo: MV 5.93 ± 0.72; FV 6.01 + 0.86\n", + "Pobega: MV 5.97 ± 0.84; FV 6.14 + 1.22\n", + "Camara Ma.: MV 6.06 ± 0.82; FV 6.14 + 0.96\n", + "Paredes: MV 5.82 ± 0.88; FV 5.85 + 1.03\n", + "Ndombele': MV 6.00 ± 0.76; FV 6.16 + 0.97\n", + "Nicolussi Caviglia: MV 5.88 ± 1.03; FV 6.00 + 1.43\n", + "Rovella: MV 5.89 ± 0.97; FV 5.95 + 1.17\n", + "Amrabat: MV 5.78 ± 1.07; FV 5.80 + 1.29\n", + "Tameze: MV 5.88 ± 0.76; FV 5.92 + 0.74\n", + "Gyasi: MV 5.90 ± 0.96; FV 6.09 + 1.46\n", + "Ilic: MV 6.08 ± 0.87; FV 6.34 + 1.33\n", + "Matheus Henrique: MV 5.94 ± 0.96; FV 6.08 + 1.38\n", + "Harroui: MV 5.98 ± 0.87; FV 6.28 + 1.41\n", + "Volpato: MV 6.05 ± 1.02; FV 6.53 + 1.90\n", + "Pickel: MV 5.86 ± 0.74; FV 5.83 + 0.81\n", + "Moro N.: MV 6.06 ± 0.83; FV 6.27 + 1.18\n", + "Duncan: MV 5.80 ± 0.83; FV 5.89 + 1.09\n", + "Machin: MV 5.86 ± 0.93; FV 5.93 + 1.21\n", + "Cuadrado: MV 5.87 ± 0.97; FV 5.96 + 1.31\n", + "Ekdal: MV 5.88 ± 0.72; FV 5.89 + 0.72\n", + "Meite': MV 5.91 ± 0.76; FV 5.93 + 0.81\n", + "Schouten: MV 5.92 ± 0.83; FV 5.92 + 0.84\n", + "Obiang: MV 5.89 ± 0.62; FV 5.88 + 0.52\n", + "Kovalenko: MV 5.90 ± 0.80; FV 6.03 + 0.98\n", + "Crnigoj: MV 5.95 ± 0.73; FV 6.02 + 0.78\n", + "Basic: MV 5.96 ± 0.77; FV 6.14 + 1.09\n", + "Asllani: MV 5.94 ± 0.65; FV 5.97 + 0.62\n", + "Sabiri: MV 5.85 ± 0.87; FV 5.99 + 1.25\n", + "Terracciano F.: MV 5.97 ± 0.62; FV 6.00 + 0.54\n", + "Castagnetti: MV 6.00 ± 0.65; FV 6.01 + 0.61\n", + "Oudin: MV 5.99 ± 0.59; FV 5.98 + 0.51\n", + "Grassi: MV 6.00 ± 0.65; FV 5.95 + 0.58\n", + "Krunic: MV 5.87 ± 0.68; FV 5.86 + 0.66\n", + "Rincon: MV 5.78 ± 0.71; FV 5.73 + 0.79\n", + "Miguel Veloso: MV 5.92 ± 0.66; FV 5.95 + 0.59\n", + "Leris: MV 5.85 ± 0.80; FV 5.89 + 1.04\n", + "Esposito Sa.: MV 5.86 ± 0.72; FV 5.82 + 0.67\n", + "Henderson L.: MV 5.92 ± 0.75; FV 5.97 + 0.90\n", + "Lopez M.: MV 5.87 ± 0.77; FV 5.85 + 0.73\n", + "Cuisance: MV 5.79 ± 0.62; FV 5.75 + 0.66\n", + "Saelemaekers: MV 5.87 ± 0.75; FV 5.97 + 0.94\n", + "Maggiore: MV 5.92 ± 0.72; FV 5.95 + 0.74\n", + "Akpa Akpro: MV 5.97 ± 0.84; FV 6.05 + 1.01\n", + "Maleh: MV 5.98 ± 0.64; FV 6.03 + 0.69\n", + "Romero L.: MV 5.98 ± 0.94; FV 6.44 + 1.77\n", + "Ceide: MV 5.84 ± 0.67; FV 5.85 + 0.61\n", + "D'alessandro: MV 6.01 ± 0.65; FV 6.08 + 0.71\n", + "Benassi: MV 5.92 ± 0.69; FV 5.97 + 0.73\n", + "Gagliardini: MV 5.77 ± 0.59; FV 5.71 + 0.55\n", + "Vieira: MV 5.90 ± 0.67; FV 5.88 + 0.80\n", + "Bianco: MV 5.88 ± 0.94; FV 5.95 + 1.21\n", + "Vranckx: MV 5.85 ± 0.61; FV 5.84 + 0.52\n", + "Galdames: MV 6.00 ± 0.81; FV 6.13 + 1.02\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" + "Marcos Antonio: MV 6.04 ± 0.89; FV 6.32 + 1.43\n", + "Fazzini: MV 5.86 ± 0.63; FV 5.81 + 0.57\n", + "Sulemana I.: MV 5.88 ± 0.60; FV 5.90 + 0.51\n", + "Tahirovic: MV 5.84 ± 0.77; FV 5.78 + 0.85\n", + "Abildgaard: MV 5.77 ± 0.63; FV 5.82 + 0.58\n", + "Barberis: MV 5.75 ± 0.81; FV 5.72 + 0.87\n", + "Kastanos: MV 5.98 ± 0.81; FV 6.10 + 0.98\n", + "Vignato: MV 6.01 ± 0.73; FV 6.10 + 0.84\n", + "Valoti: MV 5.74 ± 0.65; FV 5.70 + 0.61\n", + "Winks: MV 5.83 ± 0.86; FV 5.81 + 0.98\n", + "Askildsen: MV 5.85 ± 0.61; FV 5.84 + 0.50\n", + "Bove: MV 5.99 ± 0.82; FV 6.20 + 1.20\n", + "Bohinen: MV 5.89 ± 0.58; FV 5.86 + 0.45\n", + "D'andrea: MV 6.04 ± 0.77; FV 6.18 + 0.95\n", + "Iling-Junior: MV 6.03 ± 0.74; FV 6.18 + 1.01\n", + "Cipot: MV 6.08 ± 0.63; FV 6.26 + 0.92\n", + "Bakayoko: MV 5.80 ± 0.74; FV 5.79 + 0.65\n", + "Gaetano: MV 5.93 ± 0.75; FV 5.98 + 0.82\n", + "Zurkowski: MV 6.05 ± 0.97; FV 6.47 + 1.76\n", + "Castrovilli: MV 5.90 ± 0.85; FV 6.07 + 1.25\n", + "Demme: MV 6.02 ± 0.73; FV 6.22 + 1.03\n", + "Darboe: MV 5.91 ± 0.94; FV 5.97 + 1.11\n", + "Urbanski: MV 5.93 ± 0.86; FV 5.98 + 0.98\n", + "Bertini: MV 5.88 ± 0.96; FV 5.98 + 1.31\n", + "Yepes: MV 5.70 ± 0.75; FV 5.64 + 0.80\n", + "Pyyhtia: MV 5.95 ± 0.65; FV 5.93 + 0.58\n", + "Trimboli: MV 5.86 ± 0.91; FV 5.89 + 1.18\n", + "Pafundi: MV 6.20 ± 0.75; FV 6.48 + 1.29\n", + "Helgason: MV 5.96 ± 0.60; FV 5.93 + 0.49\n", + "Adli: MV 5.90 ± 0.69; FV 5.93 + 0.74\n", + "Vignato S.: MV 5.80 ± 0.81; FV 5.78 + 0.91\n", + "Hrustic: MV 5.72 ± 0.55; FV 5.77 + 0.42\n", + "Samek: MV 6.03 ± 0.78; FV 6.11 + 0.89\n", + "Zerbin: MV 5.86 ± 0.67; FV 5.86 + 0.76\n", + "Ilkhan: MV 5.86 ± 0.74; FV 5.84 + 0.82\n", + "Degli Innocenti: MV 5.97 ± 0.84; FV 6.05 + 1.01\n", + "Acella: MV 6.03 ± 0.79; FV 6.16 + 0.97\n", + "Carboni V.: MV 5.82 ± 1.03; FV 5.88 + 1.18\n", + "Paoletti: MV 5.90 ± 0.57; FV 5.87 + 0.45\n", + "Malagrida: MV 5.83 ± 0.73; FV 5.84 + 0.89\n", + "Faticanti: MV 5.92 ± 0.92; FV 6.04 + 1.29\n", + "Osimhen: MV 6.48 ± 1.45; FV 7.80 + 4.24\n", + "Martinez L.: MV 6.14 ± 1.37; FV 7.42 + 3.73\n", + "Dybala: MV 6.41 ± 1.30; FV 7.37 + 3.19\n", + "Rafael Leao: MV 6.21 ± 1.30; FV 7.09 + 3.03\n", + "Lookman: MV 6.27 ± 1.31; FV 7.26 + 3.23\n", + "Immobile: MV 6.08 ± 1.30; FV 7.11 + 3.20\n", + "Vlahovic: MV 6.04 ± 1.30; FV 6.86 + 2.84\n", + "Arnautovic: MV 6.19 ± 1.27; FV 7.20 + 3.21\n", + "Dia: MV 6.16 ± 1.34; FV 7.30 + 3.52\n", + "Dzeko: MV 6.03 ± 1.20; FV 6.77 + 2.55\n", + "Milik: MV 6.04 ± 1.15; FV 6.65 + 2.29\n", + "Nzola: MV 6.15 ± 1.37; FV 7.40 + 3.72\n", + "Beto: MV 6.18 ± 1.21; FV 7.14 + 3.04\n", + "Giroud: MV 6.00 ± 1.09; FV 6.54 + 2.07\n", + "Abraham: MV 6.07 ± 1.13; FV 6.75 + 2.37\n", + "Deulofeu: MV 6.43 ± 1.24; FV 7.30 + 2.82\n", + "Lauriente': MV 6.22 ± 1.29; FV 7.11 + 3.08\n", + "Simeone: MV 6.21 ± 1.25; FV 7.09 + 2.95\n", + "Lozano: MV 6.13 ± 1.01; FV 6.70 + 2.04\n", + "Correa: MV 5.87 ± 0.90; FV 6.19 + 1.48\n", + "Berardi: MV 6.31 ± 1.36; FV 7.49 + 3.74\n", + "Pedro: MV 6.02 ± 0.97; FV 6.36 + 1.64\n", + "Lukaku: MV 5.97 ± 1.16; FV 6.56 + 2.25\n", + "Sanabria: MV 6.18 ± 1.20; FV 7.04 + 2.83\n", + "Thauvin: MV 6.32 ± 1.11; FV 7.10 + 2.51\n", + "Cabral: MV 5.95 ± 1.02; FV 6.38 + 1.82\n", + "Hojlund: MV 5.97 ± 1.14; FV 6.49 + 2.13\n", + "Caprari: MV 5.89 ± 0.92; FV 6.15 + 1.47\n", + "Di Maria: MV 6.15 ± 1.22; FV 6.77 + 2.45\n", + "Piatek: MV 5.85 ± 0.96; FV 6.13 + 1.47\n", + "Rebic: MV 5.88 ± 1.04; FV 6.32 + 1.83\n", + "Bonazzoli: MV 5.98 ± 0.94; FV 6.25 + 1.49\n", + "Zapata D.: MV 5.93 ± 0.92; FV 6.20 + 1.38\n", + "Kouame': MV 5.95 ± 1.04; FV 6.35 + 1.79\n", + "Gonzalez N.: MV 5.96 ± 1.00; FV 6.33 + 1.70\n", + "Brekalo: MV 5.92 ± 1.07; FV 6.34 + 1.85\n", + "Mota: MV 5.95 ± 1.07; FV 6.41 + 1.95\n", + "Kean: MV 5.97 ± 1.23; FV 6.52 + 2.31\n", + "Okereke: MV 6.06 ± 1.15; FV 6.77 + 2.49\n", + "Ceesay: MV 6.07 ± 1.08; FV 6.69 + 2.22\n", + "Colombo: MV 6.01 ± 1.11; FV 6.47 + 1.97\n", + "Dessers: MV 6.09 ± 1.18; FV 6.90 + 2.73\n", + "Muriel: MV 5.96 ± 0.96; FV 6.07 + 1.26\n", + "Pinamonti: MV 5.81 ± 0.95; FV 6.16 + 1.57\n", + "Di Francesco F.: MV 6.06 ± 1.02; FV 6.46 + 1.78\n", + "Jovic: MV 5.82 ± 1.12; FV 6.33 + 1.99\n", + "Origi: MV 5.84 ± 0.89; FV 6.09 + 1.37\n", + "Caputo: MV 6.00 ± 1.15; FV 6.66 + 2.35\n", + "Boga: MV 6.19 ± 1.07; FV 6.74 + 2.02\n", + "Cambiaghi: MV 6.23 ± 1.09; FV 6.96 + 2.42\n", + "Alvarez A.: MV 5.97 ± 0.99; FV 6.33 + 1.70\n", + "Banda: MV 6.02 ± 0.76; FV 6.19 + 1.01\n", + "Ciofani D.: MV 6.08 ± 1.15; FV 6.87 + 2.63\n", + "Petagna: MV 5.90 ± 0.99; FV 6.20 + 1.60\n", + "Barrow: MV 6.01 ± 1.13; FV 6.46 + 2.06\n", + "Djuric: MV 5.88 ± 0.68; FV 5.98 + 0.70\n", + "Henry: MV 5.95 ± 1.08; FV 6.38 + 1.91\n", + "Success: MV 6.08 ± 0.92; FV 6.52 + 1.71\n", + "Gabbiadini: MV 5.89 ± 1.11; FV 6.34 + 1.94\n", + "Zirkzee: MV 5.98 ± 0.98; FV 6.36 + 1.68\n", + "Lammers: MV 5.81 ± 0.81; FV 5.97 + 1.17\n", + "Satriano: MV 5.86 ± 0.88; FV 6.03 + 1.24\n", + "Kallon: MV 5.82 ± 0.80; FV 5.97 + 1.07\n", + "Nestorovski: MV 6.13 ± 0.87; FV 6.69 + 1.76\n", + "Raspadori: MV 6.06 ± 1.02; FV 6.58 + 2.02\n", + "Botheim: MV 5.96 ± 0.96; FV 6.29 + 1.59\n", + "Gytkjaer: MV 5.80 ± 0.87; FV 6.01 + 1.27\n", + "Solbakken: MV 5.89 ± 0.95; FV 6.17 + 1.50\n", + "Lasagna: MV 5.78 ± 0.77; FV 5.87 + 0.97\n", + "Belotti: MV 5.86 ± 0.81; FV 6.02 + 1.19\n", + "Pellegri: MV 5.99 ± 0.99; FV 6.47 + 1.83\n", + "Buonaiuto: MV 5.99 ± 0.69; FV 6.11 + 0.83\n", + "Verde: MV 6.10 ± 1.16; FV 6.77 + 2.45\n", + "Destro: MV 6.03 ± 1.22; FV 6.64 + 2.40\n", + "Seck: MV 6.04 ± 0.66; FV 6.14 + 0.79\n", + "Sansone: MV 6.08 ± 1.07; FV 6.58 + 2.05\n", + "Quagliarella: MV 5.85 ± 0.74; FV 5.94 + 1.00\n", + "Defrel: MV 5.82 ± 0.89; FV 6.02 + 1.30\n", + "Pjaca: MV 5.89 ± 0.77; FV 5.99 + 0.90\n", + "Gaich: MV 5.83 ± 0.92; FV 6.07 + 1.32\n", + "Soule': MV 6.01 ± 1.04; FV 6.43 + 1.77\n", + "Tsadjout: MV 5.94 ± 0.94; FV 6.29 + 1.60\n", + "Piccoli: MV 5.80 ± 0.69; FV 5.82 + 0.69\n", + "Shomurodov: MV 5.95 ± 1.08; FV 6.49 + 2.07\n", + "Afena-Gyan: MV 5.75 ± 0.67; FV 5.70 + 0.65\n", + "Ngonge: MV 6.01 ± 1.12; FV 6.61 + 2.25\n", + "Karamoh: MV 6.08 ± 1.00; FV 6.71 + 2.13\n", + "Ibrahimovic: MV 6.10 ± 1.06; FV 6.83 + 2.36\n", + "Pussetto: MV 5.92 ± 1.05; FV 6.34 + 1.86\n", + "Cancellieri: MV 5.76 ± 0.64; FV 5.75 + 0.62\n", + "Valencia D.: MV 5.72 ± 0.59; FV 5.67 + 0.54\n", + "Oddei: MV 6.07 ± 0.81; FV 6.24 + 1.04\n", + "Braaf: MV 5.83 ± 0.78; FV 5.89 + 0.92\n", + "Raimondo: MV 5.88 ± 0.93; FV 5.93 + 1.07\n", + "Kaio Jorge: MV 5.81 ± 0.63; FV 5.83 + 0.65\n", + "De Luca: MV 5.87 ± 0.98; FV 5.96 + 1.36\n", + "Voelkerling Persson: MV 6.00 ± 0.65; FV 6.07 + 0.64\n", + "Montevago: MV 5.83 ± 0.81; FV 5.97 + 1.12\n", + "Krollis: MV 6.02 ± 0.98; FV 6.25 + 1.38\n", + "Vivaldo: MV 6.08 ± 0.78; FV 6.24 + 1.01\n" ] }, { @@ -6364,113 +4776,113 @@ " \n", " \n", " \n", - " Sportiello\n", - " P\n", - " Atalanta\n", - " Spezia\n", - " 1\n", - " 1.00\n", - " 75\n", - " 6.178402\n", - " 0.428025\n", - " 5.716108\n", - " 0.546802\n", - " 5.928400\n", - " 0.263585\n", - " 0.609268\n", - " 1.588811\n", - " 6.283780\n", - " 0.623820\n", - " -0.625340\n", - " 1.072692\n", - " 40.422094\n", - " \n", - " \n", " Musso\n", " P\n", " Atalanta\n", - " Spezia\n", + " Juventus\n", " 1\n", - " 0.00\n", - " 5\n", - " 6.246836\n", - " 0.412463\n", - " 5.418529\n", - " 0.418830\n", - " 6.005536\n", - " 0.252778\n", - " 0.612203\n", - " 1.589951\n", - " 5.813268\n", - " 0.512082\n", - " -0.541122\n", - " 1.031041\n", - " 24.998909\n", + " 0.55\n", + " 55\n", + " 6.254405\n", + " 0.411209\n", + " 5.035751\n", + " 0.342500\n", + " 5.984879\n", + " 0.167509\n", + " 0.880999\n", + " 1.599276\n", + " 5.014495\n", + " 0.525508\n", + " 0.029776\n", + " 0.913046\n", + " 8.038829\n", " \n", " \n", " Rossi F.\n", " P\n", " Atalanta\n", - " Spezia\n", + " Juventus\n", " 1\n", " 0.00\n", " 1\n", - " 6.241959\n", - " 0.413916\n", - " 4.533127\n", - " 0.526911\n", - " 5.998054\n", - " 0.249728\n", - " 0.623062\n", - " 1.589842\n", - " 5.066249\n", - " 0.630503\n", - " -0.590708\n", - " 0.972040\n", - " 0.773813\n", + " 6.231914\n", + " 0.405208\n", + " 4.289062\n", + " 0.420042\n", + " 5.969362\n", + " 0.175638\n", + " 0.840497\n", + " 1.599256\n", + " 4.279346\n", + " 0.646178\n", + " 0.011079\n", + " 0.935691\n", + " 1.963351\n", + " \n", + " \n", + " Sportiello\n", + " P\n", + " Atalanta\n", + " Juventus\n", + " 1\n", + " 0.45\n", + " 5\n", + " 6.254370\n", + " 0.411102\n", + " 4.214338\n", + " 0.326933\n", + " 5.984973\n", + " 0.167680\n", + " 0.880164\n", + " 1.599276\n", + " 4.193475\n", + " 0.500380\n", + " 0.030683\n", + " 0.902728\n", + " 1.160084\n", + " \n", + " \n", + " Zappacosta\n", + " D\n", + " Atalanta\n", + " Juventus\n", + " 1\n", + " 1.00\n", + " 80\n", + " 6.056584\n", + " 0.466738\n", + " 6.304006\n", + " 0.701212\n", + " 5.997294\n", + " 0.535322\n", + " 0.081726\n", + " 1.021092\n", + " 5.791334\n", + " 0.955869\n", + " 0.377559\n", + " 1.599911\n", + " 0.000000\n", " \n", " \n", " Scalvini\n", " D\n", " Atalanta\n", - " Spezia\n", + " Juventus\n", " 1\n", " 1.00\n", " 90\n", - " 6.273323\n", - " 0.537045\n", - " 6.786601\n", - " 0.980843\n", - " 6.157186\n", - " 0.598484\n", - " 0.142554\n", - " 0.937184\n", - " 5.967968\n", - " 1.206433\n", - " 0.463573\n", - " 1.599848\n", - " 0.000000\n", - " \n", - " \n", - " Toloi\n", - " D\n", - " Atalanta\n", - " Spezia\n", - " 1\n", - " 1.00\n", - " 90\n", - " 6.254010\n", - " 0.510611\n", - " 6.719704\n", - " 0.905750\n", - " 6.162670\n", - " 0.575446\n", - " 0.116788\n", - " 0.958888\n", - " 5.978364\n", - " 1.134393\n", - " 0.448929\n", - " 1.599858\n", + " 5.989745\n", + " 0.493339\n", + " 6.162027\n", + " 0.656128\n", + " 6.000084\n", + " 0.586076\n", + " -0.013022\n", + " 0.993997\n", + " 5.786531\n", + " 1.002288\n", + " 0.271447\n", + " 1.599920\n", " 0.000000\n", " \n", " \n", @@ -6499,167 +4911,167 @@ " Gaich\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", - " 0.55\n", - " 55\n", - " 5.867453\n", - " 0.457420\n", - " 6.084643\n", - " 0.692782\n", - " 5.792738\n", - " 0.519226\n", - " 0.106163\n", - " 1.045169\n", - " 5.522809\n", - " 0.874729\n", - " 0.442301\n", - " 1.599854\n", + " Lecce\n", + " 0\n", + " 0.00\n", + " 60\n", + " 5.825243\n", + " 0.460409\n", + " 6.066322\n", + " 0.662139\n", + " 5.738146\n", + " 0.518114\n", + " 0.123950\n", + " 1.051589\n", + " 5.519838\n", + " 0.823035\n", + " 0.455078\n", + " 1.599903\n", " 0.000000\n", " \n", " \n", " Djuric\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", - " 0.45\n", - " 60\n", - " 5.927261\n", - " 0.352975\n", - " 6.058505\n", - " 0.437122\n", - " 5.883422\n", - " 0.408226\n", - " 0.079531\n", - " 1.154408\n", - " 5.803053\n", - " 0.662872\n", - " 0.278751\n", - " 1.599918\n", + " Lecce\n", + " 0\n", + " 1.00\n", + " 80\n", + " 5.880368\n", + " 0.341485\n", + " 5.978157\n", + " 0.351269\n", + " 5.841517\n", + " 0.396984\n", + " 0.072532\n", + " 1.175740\n", + " 5.826691\n", + " 0.577211\n", + " 0.193079\n", + " 1.599953\n", " 0.000000\n", " \n", " \n", " Kallon\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Lecce\n", + " 0\n", " 0.00\n", " 40\n", - " 5.874599\n", - " 0.410965\n", - " 6.049230\n", - " 0.592300\n", - " 5.806045\n", - " 0.466689\n", - " 0.108503\n", - " 1.095199\n", - " 5.593708\n", - " 0.780142\n", - " 0.407094\n", - " 1.599884\n", + " 5.824849\n", + " 0.398117\n", + " 5.971637\n", + " 0.533920\n", + " 5.761679\n", + " 0.453794\n", + " 0.102902\n", + " 1.116911\n", + " 5.576400\n", + " 0.722054\n", + " 0.384482\n", + " 1.599924\n", " 0.000000\n", " \n", " \n", " Braaf\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", + " Lecce\n", + " 0\n", " 0.00\n", " 35\n", - " 5.796274\n", - " 0.440012\n", - " 5.869830\n", - " 0.585489\n", - " 5.757944\n", - " 0.511931\n", - " 0.055347\n", - " 1.060328\n", - " 5.505967\n", - " 0.867116\n", - " 0.301824\n", - " 1.599889\n", + " 5.834104\n", + " 0.389789\n", + " 5.887941\n", + " 0.461835\n", + " 5.792829\n", + " 0.452717\n", + " 0.067486\n", + " 1.119556\n", + " 5.624584\n", + " 0.706353\n", + " 0.270221\n", + " 1.599937\n", " 0.000000\n", " \n", " \n", " Lasagna\n", " A\n", " Verona\n", - " Inter\n", - " 1\n", - " 1.00\n", - " 80\n", - " 5.777834\n", - " 0.389363\n", - " 5.836037\n", - " 0.489194\n", - " 5.727798\n", - " 0.448826\n", - " 0.082488\n", - " 1.123643\n", - " 5.506660\n", - " 0.698520\n", - " 0.336068\n", - " 1.599901\n", + " Lecce\n", + " 0\n", + " 0.00\n", + " 0\n", + " 5.781860\n", + " 0.386175\n", + " 5.867917\n", + " 0.485962\n", + " 5.726385\n", + " 0.442926\n", + " 0.092660\n", + " 1.132638\n", + " 5.528002\n", + " 0.680042\n", + " 0.354367\n", + " 1.599930\n", " 0.000000\n", " \n", " \n", "\n", - "

525 rows × 19 columns

\n", + "

526 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", + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Juventus 1 0.55 55 6.254405 0.411209 \n", + "Rossi F. P Atalanta Juventus 1 0.00 1 6.231914 0.405208 \n", + "Sportiello P Atalanta Juventus 1 0.45 5 6.254370 0.411102 \n", + "Zappacosta D Atalanta Juventus 1 1.00 80 6.056584 0.466738 \n", + "Scalvini D Atalanta Juventus 1 1.00 90 5.989745 0.493339 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Lecce 0 0.00 60 5.825243 0.460409 \n", + "Djuric A Verona Lecce 0 1.00 80 5.880368 0.341485 \n", + "Kallon A Verona Lecce 0 0.00 40 5.824849 0.398117 \n", + "Braaf A Verona Lecce 0 0.00 35 5.834104 0.389789 \n", + "Lasagna A Verona Lecce 0 0.00 0 5.781860 0.386175 \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", + "Musso 5.035751 0.342500 5.984879 0.167509 0.880999 \n", + "Rossi F. 4.289062 0.420042 5.969362 0.175638 0.840497 \n", + "Sportiello 4.214338 0.326933 5.984973 0.167680 0.880164 \n", + "Zappacosta 6.304006 0.701212 5.997294 0.535322 0.081726 \n", + "Scalvini 6.162027 0.656128 6.000084 0.586076 -0.013022 \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", + "Gaich 6.066322 0.662139 5.738146 0.518114 0.123950 \n", + "Djuric 5.978157 0.351269 5.841517 0.396984 0.072532 \n", + "Kallon 5.971637 0.533920 5.761679 0.453794 0.102902 \n", + "Braaf 5.887941 0.461835 5.792829 0.452717 0.067486 \n", + "Lasagna 5.867917 0.485962 5.726385 0.442926 0.092660 \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", + "Musso 1.599276 5.014495 0.525508 0.029776 0.913046 \n", + "Rossi F. 1.599256 4.279346 0.646178 0.011079 0.935691 \n", + "Sportiello 1.599276 4.193475 0.500380 0.030683 0.902728 \n", + "Zappacosta 1.021092 5.791334 0.955869 0.377559 1.599911 \n", + "Scalvini 0.993997 5.786531 1.002288 0.271447 1.599920 \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", + "Gaich 1.051589 5.519838 0.823035 0.455078 1.599903 \n", + "Djuric 1.175740 5.826691 0.577211 0.193079 1.599953 \n", + "Kallon 1.116911 5.576400 0.722054 0.384482 1.599924 \n", + "Braaf 1.119556 5.624584 0.706353 0.270221 1.599937 \n", + "Lasagna 1.132638 5.528002 0.680042 0.354367 1.599930 \n", "\n", " Clean Sheet % \n", "player \n", - "Sportiello 40.422094 \n", - "Musso 24.998909 \n", - "Rossi F. 0.773813 \n", + "Musso 8.038829 \n", + "Rossi F. 1.963351 \n", + "Sportiello 1.160084 \n", + "Zappacosta 0.000000 \n", "Scalvini 0.000000 \n", - "Toloi 0.000000 \n", "... ... \n", "Gaich 0.000000 \n", "Djuric 0.000000 \n", @@ -6667,16 +5079,16 @@ "Braaf 0.000000 \n", "Lasagna 0.000000 \n", "\n", - "[525 rows x 19 columns]" + "[526 rows x 19 columns]" ] }, - "execution_count": 28, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "matchday_out = 33\n", + "matchday_out = 34\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", @@ -6726,7 +5138,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 47, "id": "6befd611", "metadata": {}, "outputs": [], @@ -6752,7 +5164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "2b637a15", "metadata": {}, "outputs": [], @@ -6764,10 +5176,948 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "60d73507", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret (6.25, 0.41); (5.72, 0.53)\n", + "Provedel (6.19, 0.38); (5.54, 0.56)\n", + "Vicario (6.25, 0.41); (5.39, 0.57)\n", + "Szczesny (6.25, 0.41); (6.02, 0.47)\n", + "Falcone (6.17, 0.38); (4.61, 0.47)\n", + "Silvestri (6.25, 0.41); (5.65, 0.60)\n", + "Rui Patricio (6.19, 0.40); (5.37, 0.64)\n", + "Onana (6.17, 0.38); (5.20, 0.58)\n", + "Sepe (6.21, 0.40); (4.99, 0.54)\n", + "Milinkovic-Savic V. (5.98, 0.36); (4.91, 0.74)\n", + "Musso (6.25, 0.41); (5.55, 0.52)\n", + "Maignan (6.25, 0.41); (4.89, 0.50)\n", + "Carnesecchi (6.25, 0.41); (5.31, 0.59)\n", + "Di Gregorio (6.18, 0.39); (5.11, 0.72)\n", + "Audero (6.25, 0.41); (4.96, 0.53)\n", + "Montipo' (6.19, 0.38); (4.69, 0.39)\n", + "Skorupski (6.25, 0.41); (5.51, 0.55)\n", + "Consigli (6.24, 0.40); (5.42, 0.63)\n", + "Dragowski (6.25, 0.41); (5.46, 0.63)\n", + "Terracciano (6.06, 0.39); (4.65, 0.75)\n", + "Tatarusanu (6.25, 0.41); (4.93, 0.79)\n", + "Handanovic (6.25, 0.41); (5.99, 0.51)\n", + "Sportiello (6.22, 0.39); (4.55, 0.40)\n", + "Perin (6.25, 0.41); (5.97, 0.45)\n", + "Zoet (6.25, 0.41); (6.03, 0.51)\n", + "Ochoa (6.25, 0.41); (5.71, 0.56)\n", + "Pegolo (6.25, 0.41); (4.75, 0.58)\n", + "Gollini (6.25, 0.41); (5.89, 0.47)\n", + "Mirante no data\n", + "Sarr M. no data\n", + "Lamanna no data\n", + "Ujkani no data\n", + "Berisha (6.25, 0.41); (5.85, 0.49)\n", + "Marchetti (6.12, 0.42); (3.84, 0.71)\n", + "Perilli (6.25, 0.41); (5.93, 0.47)\n", + "Padelli (6.15, 0.39); (4.19, 0.64)\n", + "Perisan (6.25, 0.41); (5.26, 0.64)\n", + "Bardi (6.25, 0.41); (5.91, 0.46)\n", + "Cordaz no data\n", + "Pinsoglio (6.09, 0.40); (4.30, 0.68)\n", + "Fiorillo (6.22, 0.39); (3.50, 0.57)\n", + "Cragno (6.25, 0.41); (5.37, 0.80)\n", + "Sirigu (6.25, 0.41); (5.69, 0.59)\n", + "Cerofolini (6.25, 0.41); (5.84, 0.48)\n", + "Rossi F. (5.91, 0.38); (4.05, 0.56)\n", + "Ravaglia F. (6.25, 0.41); (5.74, 0.46)\n", + "Brancolini no data\n", + "Bleve no data\n", + "Berardi A. (6.25, 0.41); (4.88, 0.53)\n", + "Russo A. no data\n", + "Gemello (6.25, 0.41); (5.95, 0.45)\n", + "Ravaglia (6.25, 0.41); (4.65, 0.53)\n", + "Boer no data\n", + "Adamonis no data\n", + "Marfella (6.25, 0.41); (5.79, 0.47)\n", + "Zovko (6.19, 0.41); (4.05, 0.74)\n", + "Piana no data\n", + "Bagnolini no data\n", + "Luis Maximiano (5.59, 0.47); (5.18, 0.65)\n", + "Svilar no data\n", + "Sorrentino A. no data\n", + "Ciezkowski no data\n", + "Saro no data\n", + "Vasquez D. no data\n", + "Turk (6.25, 0.41); (4.68, 0.60)\n", + "Dimarco (6.16, 0.45); (6.56, 0.78)\n", + "Smalling (6.21, 0.48); (6.56, 0.78)\n", + "Doig (5.99, 0.53); (6.29, 0.87)\n", + "Carlos Augusto (6.08, 0.52); (6.53, 0.96)\n", + "Kim (6.26, 0.49); (6.58, 0.79)\n", + "Posch (6.11, 0.55); (6.60, 1.04)\n", + "Di Lorenzo (6.21, 0.47); (6.48, 0.71)\n", + "Danilo (6.22, 0.48); (6.58, 0.78)\n", + "Hernandez T. (6.10, 0.54); (6.48, 0.91)\n", + "Udogie (6.04, 0.53); (6.41, 0.91)\n", + "Parisi (6.09, 0.48); (6.41, 0.76)\n", + "Mario Rui (6.13, 0.48); (6.23, 0.59)\n", + "Romagnoli (6.18, 0.45); (6.42, 0.66)\n", + "Bastoni S. (6.06, 0.46); (6.36, 0.76)\n", + "Mazzocchi (6.14, 0.44); (6.51, 0.73)\n", + "Valeri (6.07, 0.37); (6.32, 0.57)\n", + "Tomori (6.09, 0.44); (6.25, 0.56)\n", + "Scalvini (6.04, 0.51); (6.29, 0.74)\n", + "Toloi (6.03, 0.49); (6.24, 0.65)\n", + "Demiral (6.02, 0.44); (6.21, 0.60)\n", + "Maehle (5.94, 0.49); (6.14, 0.72)\n", + "Dumfries (5.91, 0.45); (6.07, 0.64)\n", + "Baschirotto (6.15, 0.49); (6.47, 0.77)\n", + "Bijol (5.94, 0.53); (6.16, 0.78)\n", + "Schuurs (6.11, 0.40); (6.16, 0.46)\n", + "Juan Jesus (6.16, 0.36); (6.39, 0.57)\n", + "Depaoli (5.95, 0.45); (6.09, 0.63)\n", + "Mancini (6.12, 0.40); (6.30, 0.54)\n", + "Ibanez (5.84, 0.54); (5.95, 0.68)\n", + "Rodrigo Becao (6.04, 0.49); (6.28, 0.72)\n", + "Ebuehi (6.08, 0.41); (6.38, 0.64)\n", + "Gosens (5.99, 0.39); (6.26, 0.61)\n", + "Darmian (6.05, 0.37); (6.24, 0.52)\n", + "Reca (5.98, 0.46); (6.12, 0.62)\n", + "Bremer (6.08, 0.52); (6.40, 0.81)\n", + "Sernicola (5.93, 0.50); (6.14, 0.76)\n", + "Rrahmani (6.23, 0.49); (6.52, 0.75)\n", + "Vojvoda (5.90, 0.42); (5.95, 0.50)\n", + "Holm (5.98, 0.43); (6.13, 0.60)\n", + "Bastoni (6.07, 0.41); (6.15, 0.47)\n", + "Milenkovic (5.99, 0.46); (6.15, 0.65)\n", + "Kalulu (5.81, 0.52); (5.84, 0.60)\n", + "Martinez Quarta (5.95, 0.46); (6.04, 0.56)\n", + "Casale (6.07, 0.41); (6.22, 0.52)\n", + "Perez N. (6.07, 0.47); (6.30, 0.67)\n", + "Olivera (6.15, 0.37); (6.46, 0.63)\n", + "Izzo (6.03, 0.43); (6.18, 0.56)\n", + "Luperto (5.88, 0.48); (5.89, 0.48)\n", + "Skriniar (5.92, 0.41); (5.94, 0.41)\n", + "Rodriguez R. (6.02, 0.36); (5.99, 0.34)\n", + "Marusic (6.03, 0.39); (6.04, 0.40)\n", + "Lazzari (6.04, 0.37); (6.10, 0.41)\n", + "Kyriakopoulos (5.99, 0.43); (6.07, 0.51)\n", + "Ampadu (5.84, 0.45); (5.82, 0.47)\n", + "Ismajli (5.92, 0.42); (5.89, 0.40)\n", + "Llorente D. (5.96, 0.48); (6.11, 0.65)\n", + "Cambiaso (5.98, 0.37); (6.00, 0.38)\n", + "Hysaj (6.00, 0.31); (5.97, 0.27)\n", + "Biraghi (6.12, 0.43); (6.41, 0.65)\n", + "Medel (6.02, 0.34); (5.96, 0.31)\n", + "Bonucci (6.17, 0.51); (6.56, 0.85)\n", + "Calabria (5.96, 0.50); (6.18, 0.74)\n", + "Acerbi (6.02, 0.39); (6.02, 0.39)\n", + "Spinazzola (6.12, 0.42); (6.42, 0.65)\n", + "Lykogiannis (6.00, 0.38); (6.12, 0.46)\n", + "Pellegrini Lu. (6.07, 0.37); (6.16, 0.43)\n", + "Djidji (5.90, 0.40); (5.95, 0.47)\n", + "Lazaro (6.00, 0.43); (6.10, 0.51)\n", + "Augello (5.91, 0.44); (6.09, 0.66)\n", + "Gallo (5.86, 0.40); (5.84, 0.40)\n", + "Singo (5.93, 0.44); (6.08, 0.61)\n", + "Mari' (5.89, 0.50); (5.98, 0.58)\n", + "Caldirola (5.95, 0.51); (6.14, 0.74)\n", + "Dodo' (5.89, 0.51); (6.01, 0.67)\n", + "De Vrij (5.97, 0.41); (6.02, 0.43)\n", + "Patric (6.07, 0.39); (6.05, 0.39)\n", + "Faraoni (5.96, 0.46); (6.10, 0.66)\n", + "Ceccherini (5.82, 0.55); (5.95, 0.75)\n", + "Hateboer (5.88, 0.47); (5.98, 0.67)\n", + "Rogerio (5.82, 0.39); (5.79, 0.39)\n", + "Umtiti (5.89, 0.46); (5.91, 0.49)\n", + "Aina (6.00, 0.47); (6.20, 0.69)\n", + "Birindelli (5.85, 0.35); (5.83, 0.36)\n", + "Lucumi' (5.95, 0.39); (5.94, 0.39)\n", + "Ehizibue (5.88, 0.45); (6.00, 0.66)\n", + "Bianchetti (5.76, 0.47); (5.75, 0.58)\n", + "Ferrari G. (5.84, 0.53); (5.91, 0.66)\n", + "Fazio (5.66, 0.61); (5.71, 0.68)\n", + "Gravillon (5.95, 0.38); (5.91, 0.37)\n", + "Buongiorno (6.08, 0.45); (6.29, 0.62)\n", + "Gunter (5.78, 0.49); (5.73, 0.49)\n", + "Troost-Ekong (5.83, 0.46); (5.82, 0.46)\n", + "Soumaoro (5.95, 0.44); (5.96, 0.44)\n", + "Ceccaroni (5.86, 0.52); (5.94, 0.62)\n", + "Pongracic (5.94, 0.39); (5.91, 0.36)\n", + "Soppy (5.86, 0.38); (5.88, 0.39)\n", + "Gendrey (5.90, 0.34); (5.88, 0.29)\n", + "Hien (5.84, 0.44); (5.80, 0.44)\n", + "Ferrari A. (5.78, 0.48); (5.74, 0.53)\n", + "Masina (6.01, 0.37); (6.37, 0.68)\n", + "Zappacosta (6.09, 0.48); (6.42, 0.78)\n", + "Gyomber (5.92, 0.40); (5.89, 0.38)\n", + "Alex Sandro (5.83, 0.47); (5.78, 0.45)\n", + "Pezzella Giu. (5.88, 0.34); (5.87, 0.30)\n", + "Bereszynski (5.87, 0.35); (5.85, 0.33)\n", + "Venuti (5.83, 0.37); (5.83, 0.36)\n", + "Palomino (6.01, 0.44); (6.11, 0.51)\n", + "Nuytinck (5.84, 0.46); (5.83, 0.48)\n", + "Marlon (5.81, 0.36); (5.77, 0.34)\n", + "Magnani (5.83, 0.49); (5.81, 0.51)\n", + "Colley (5.79, 0.52); (5.81, 0.58)\n", + "Nikolaou (5.75, 0.38); (5.67, 0.37)\n", + "Terzic (6.04, 0.33); (6.17, 0.42)\n", + "Igor (5.82, 0.47); (5.76, 0.48)\n", + "Toljan (5.76, 0.42); (5.70, 0.42)\n", + "Zortea (5.86, 0.42); (5.89, 0.51)\n", + "Dawidowicz (5.84, 0.46); (5.87, 0.57)\n", + "Celik (5.85, 0.42); (5.85, 0.44)\n", + "Bellanova (5.93, 0.41); (6.01, 0.49)\n", + "Erlic (5.81, 0.46); (5.77, 0.44)\n", + "Ballo-Toure' (6.09, 0.38); (6.36, 0.57)\n", + "Dest (5.77, 0.41); (5.77, 0.42)\n", + "Stojanovic (5.78, 0.39); (5.74, 0.41)\n", + "Amian (5.81, 0.37); (5.77, 0.38)\n", + "Bradaric (5.83, 0.45); (5.82, 0.50)\n", + "Daniliuc (5.79, 0.48); (5.78, 0.54)\n", + "Zima (5.92, 0.38); (5.93, 0.37)\n", + "De Winter (5.79, 0.42); (5.73, 0.39)\n", + "Quagliata (5.86, 0.41); (5.83, 0.41)\n", + "Ebosse (5.79, 0.33); (5.74, 0.30)\n", + "Aiwu (5.90, 0.46); (5.95, 0.56)\n", + "Lochoshvili (5.89, 0.45); (5.93, 0.59)\n", + "Bronn (5.76, 0.38); (5.72, 0.35)\n", + "Thiaw (5.87, 0.49); (5.90, 0.50)\n", + "Zeefuik (5.90, 0.43); (5.92, 0.48)\n", + "Romagnoli S. (5.89, 0.55); (6.09, 0.79)\n", + "Ghiglione (5.90, 0.43); (6.02, 0.63)\n", + "Rugani (6.01, 0.30); (5.94, 0.24)\n", + "De Sciglio (5.88, 0.31); (5.89, 0.24)\n", + "Djimsiti (5.98, 0.38); (5.98, 0.36)\n", + "Caldara (5.74, 0.49); (5.67, 0.51)\n", + "Karsdorp (5.92, 0.38); (5.94, 0.37)\n", + "Marchizza (5.81, 0.41); (5.78, 0.42)\n", + "Kjaer (5.99, 0.37); (5.94, 0.33)\n", + "Okoli (5.77, 0.44); (5.75, 0.42)\n", + "Amione (5.77, 0.48); (5.73, 0.57)\n", + "Ruggeri (5.91, 0.34); (5.91, 0.31)\n", + "Zanoli (5.99, 0.45); (6.19, 0.66)\n", + "Wisniewski (5.75, 0.34); (5.69, 0.31)\n", + "Radovanovic (5.70, 0.38); (5.65, 0.37)\n", + "Dermaku (5.93, 0.44); (5.99, 0.51)\n", + "D'ambrosio (6.05, 0.38); (6.08, 0.41)\n", + "De Silvestri (5.93, 0.47); (6.07, 0.67)\n", + "Chiriches (5.79, 0.47); (5.75, 0.46)\n", + "Murru (5.72, 0.38); (5.65, 0.38)\n", + "Bonifazi (5.82, 0.40); (5.75, 0.39)\n", + "Donati (5.76, 0.59); (5.93, 0.84)\n", + "Walukiewicz (6.00, 0.32); (5.95, 0.28)\n", + "Ranieri L. (5.90, 0.37); (5.92, 0.43)\n", + "Gabbia (5.72, 0.44); (5.65, 0.44)\n", + "Kumbulla (5.78, 0.48); (5.73, 0.50)\n", + "Adopo (5.76, 0.35); (5.71, 0.34)\n", + "Pirola (5.82, 0.56); (5.94, 0.75)\n", + "Lovato (5.71, 0.48); (5.66, 0.45)\n", + "Tuia (6.03, 0.29); (5.97, 0.24)\n", + "Ferrer (5.88, 0.45); (5.90, 0.48)\n", + "Antov (5.66, 0.52); (5.58, 0.51)\n", + "Vasquez (5.81, 0.38); (5.75, 0.36)\n", + "Ruan (5.81, 0.48); (5.74, 0.46)\n", + "Ostigard (6.07, 0.32); (6.01, 0.29)\n", + "Coppola D. (5.85, 0.35); (5.80, 0.32)\n", + "Cacace (5.73, 0.38); (5.67, 0.35)\n", + "Gatti (6.06, 0.39); (6.03, 0.39)\n", + "Gila (6.03, 0.41); (6.06, 0.43)\n", + "Bayeye (5.96, 0.45); (6.04, 0.53)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sambia (5.87, 0.37); (5.89, 0.35)\n", + "Moutinho J. (5.82, 0.39); (5.78, 0.40)\n", + "Conti (5.88, 0.42); (5.96, 0.56)\n", + "Marrone (5.63, 0.43); (5.56, 0.45)\n", + "Tonelli (5.80, 0.43); (5.75, 0.41)\n", + "Murillo (5.69, 0.41); (5.57, 0.40)\n", + "Radu (5.80, 0.47); (5.75, 0.42)\n", + "Paletta (5.91, 0.45); (6.00, 0.56)\n", + "Florenzi (6.08, 0.37); (6.22, 0.46)\n", + "Sala (5.85, 0.35); (5.86, 0.37)\n", + "Fares (5.84, 0.37); (5.84, 0.40)\n", + "Romagna (5.90, 0.45); (5.93, 0.52)\n", + "Cassandro (5.93, 0.44); (5.98, 0.50)\n", + "Muldur (5.78, 0.39); (5.73, 0.40)\n", + "Amey (6.04, 0.42); (6.15, 0.51)\n", + "Zanotti (5.93, 0.44); (6.00, 0.52)\n", + "Ebosele (5.93, 0.32); (5.93, 0.28)\n", + "Buta (5.93, 0.44); (6.00, 0.51)\n", + "Abankwah (5.91, 0.43); (5.95, 0.49)\n", + "Guessand A. (5.93, 0.44); (6.00, 0.51)\n", + "Cabal (5.95, 0.33); (5.91, 0.28)\n", + "Sosa (5.61, 0.44); (5.54, 0.46)\n", + "Guarino (5.91, 0.45); (5.96, 0.52)\n", + "Carboni F. (6.02, 0.42); (6.11, 0.49)\n", + "Zaccagni (6.39, 0.58); (7.21, 1.33)\n", + "Kvaratskhelia (6.49, 0.66); (7.39, 1.52)\n", + "Milinkovic-Savic (6.15, 0.57); (6.84, 1.23)\n", + "Barella (6.19, 0.52); (6.76, 1.03)\n", + "Zielinski (6.23, 0.49); (6.71, 0.89)\n", + "Luis Alberto (6.27, 0.51); (6.89, 1.04)\n", + "Strefezza (6.26, 0.52); (6.95, 1.12)\n", + "Felipe Anderson (6.24, 0.57); (6.99, 1.27)\n", + "Koopmeiners (6.20, 0.55); (6.82, 1.12)\n", + "Calhanoglu (6.22, 0.45); (6.69, 0.83)\n", + "Frattesi (6.15, 0.56); (6.72, 1.09)\n", + "Diaz B. (6.11, 0.61); (6.72, 1.20)\n", + "Vlasic (6.12, 0.52); (6.63, 0.95)\n", + "Zambo Anguissa (6.22, 0.47); (6.58, 0.78)\n", + "Elmas (6.19, 0.48); (6.74, 0.94)\n", + "Miranchuk (6.18, 0.52); (6.74, 0.98)\n", + "Samardzic (6.09, 0.53); (6.60, 1.01)\n", + "Pereyra (6.08, 0.57); (6.61, 1.10)\n", + "Politano (6.20, 0.40); (6.69, 0.81)\n", + "Rabiot (6.25, 0.59); (6.99, 1.31)\n", + "Ciurria (6.05, 0.54); (6.54, 1.02)\n", + "Lazovic (6.15, 0.50); (6.65, 0.92)\n", + "Lobotka (6.18, 0.39); (6.43, 0.61)\n", + "Radonjic (6.14, 0.49); (6.62, 0.89)\n", + "Ferguson (6.12, 0.43); (6.48, 0.73)\n", + "Bonaventura (6.14, 0.46); (6.56, 0.80)\n", + "Pessina (6.07, 0.48); (6.39, 0.78)\n", + "Tonali (6.10, 0.53); (6.50, 0.91)\n", + "Kostic (6.14, 0.50); (6.62, 0.91)\n", + "Baldanzi (6.13, 0.47); (6.58, 0.86)\n", + "Lovric (6.13, 0.47); (6.61, 0.87)\n", + "Pellegrini Lo. (6.06, 0.57); (6.58, 1.10)\n", + "El Shaarawy (6.19, 0.44); (6.75, 0.90)\n", + "Orsolini (6.23, 0.64); (7.11, 1.52)\n", + "Ikone' (6.00, 0.54); (6.42, 0.95)\n", + "Candreva (6.07, 0.50); (6.43, 0.84)\n", + "Bennacer (6.11, 0.41); (6.36, 0.59)\n", + "Pasalic (6.00, 0.54); (6.43, 0.97)\n", + "Mkhitaryan (6.05, 0.46); (6.41, 0.77)\n", + "Colpani (6.01, 0.41); (6.38, 0.74)\n", + "Pogba (6.00, 0.32); (5.99, 0.29)\n", + "Chiesa (6.03, 0.41); (6.24, 0.59)\n", + "Bandinelli (5.93, 0.41); (6.04, 0.58)\n", + "Matic (6.12, 0.39); (6.39, 0.60)\n", + "Fagioli (6.07, 0.49); (6.41, 0.83)\n", + "Messias (6.01, 0.55); (6.52, 1.02)\n", + "Arslan (5.91, 0.35); (5.96, 0.37)\n", + "Ricci S. (6.09, 0.42); (6.30, 0.58)\n", + "Ranocchia F. (6.05, 0.40); (6.35, 0.65)\n", + "Verdi (6.15, 0.57); (6.82, 1.21)\n", + "Sensi (6.03, 0.52); (6.39, 0.91)\n", + "Barak (5.90, 0.45); (6.10, 0.69)\n", + "Soriano (6.03, 0.40); (6.28, 0.61)\n", + "Dominguez (6.15, 0.48); (6.54, 0.84)\n", + "Vilhena (5.91, 0.48); (6.09, 0.71)\n", + "Brozovic (6.13, 0.48); (6.46, 0.76)\n", + "Cristante (5.98, 0.41); (6.10, 0.52)\n", + "Thorstvedt (5.97, 0.45); (6.23, 0.71)\n", + "De Ketelaere (5.79, 0.37); (5.80, 0.40)\n", + "Saponara (6.04, 0.51); (6.47, 0.90)\n", + "Vecino (6.00, 0.45); (6.18, 0.63)\n", + "Locatelli (6.08, 0.40); (6.19, 0.48)\n", + "Zaniolo (5.92, 0.50); (6.11, 0.78)\n", + "Duda (5.87, 0.33); (5.85, 0.33)\n", + "Maldini (5.97, 0.44); (6.30, 0.74)\n", + "Marin (5.91, 0.51); (6.07, 0.71)\n", + "Zalewski (6.02, 0.37); (6.14, 0.46)\n", + "Bajrami (6.08, 0.52); (6.58, 0.99)\n", + "Coulibaly L. (5.88, 0.55); (6.10, 0.79)\n", + "Gonzalez J. (5.98, 0.44); (6.15, 0.65)\n", + "De Roon (6.07, 0.45); (6.32, 0.67)\n", + "Mandragora (5.96, 0.46); (6.12, 0.67)\n", + "Wijnaldum (6.13, 0.54); (6.72, 1.06)\n", + "Bourabia (5.94, 0.41); (6.03, 0.52)\n", + "Sottil (6.03, 0.47); (6.32, 0.74)\n", + "Aebischer (5.92, 0.39); (6.05, 0.54)\n", + "Ederson D.s. (5.93, 0.43); (6.04, 0.56)\n", + "Miretti (5.95, 0.36); (6.05, 0.42)\n", + "Blin (5.95, 0.38); (6.01, 0.45)\n", + "Hjulmand (5.96, 0.43); (6.00, 0.47)\n", + "Cataldi (6.01, 0.35); (6.02, 0.35)\n", + "Djuricic (5.83, 0.46); (5.97, 0.65)\n", + "Linetty (5.95, 0.43); (6.06, 0.61)\n", + "Haas (5.91, 0.38); (6.02, 0.52)\n", + "Walace (5.94, 0.38); (5.95, 0.38)\n", + "Agudelo (5.91, 0.35); (5.96, 0.41)\n", + "Pobega (6.04, 0.46); (6.36, 0.76)\n", + "Camara Ma. 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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
MussoPAtalantaAvg111006.2466620.4055045.5518110.5204795.9840980.1763510.8383411.5992655.8109780.729997-0.2628060.86071929.652037
SportielloPAtalantaAvg1006.2155030.3867854.5519340.3985195.9836780.2149510.6825811.5992264.4835880.6069080.0682080.8790325.408993
Rossi F.PAtalantaAvg1005.9105750.3791124.0494850.5645285.7470250.3297880.3573711.5988274.1635190.842991-0.1011380.9110872.865818
ZappacostaDAtalantaAvg10486.0927790.4768836.4211090.7774296.0434120.5501270.0661961.0045755.8137731.0118950.4167261.5999060.000000
ScalviniDAtalantaAvg11816.0364150.5090056.2943440.7421646.0101080.5940760.0328010.9822195.7846751.0483510.3447491.5999070.000000
............................................................
GaichAVeronaAvg10395.8578980.4625086.1315000.7179015.7715400.5208820.1221801.0465895.5250540.8722280.4728621.5998930.000000
DjuricAVeronaAvg10695.9360110.3560346.0889060.4464045.8848340.4088010.0926811.1565915.8134590.6629870.2988951.5999410.000000
KallonAVeronaAvg10665.8714540.4034446.0384250.5665145.8023060.4575650.1116121.1084585.6078730.7522840.3996251.5999210.000000
LasagnaAVeronaAvg11785.8035990.3870595.8791930.4820695.7467760.4433770.0947441.1301995.5504260.6833930.3415981.5999310.000000
BraafAVeronaAvg10155.8541730.4037475.8766570.4861475.8217500.4720460.0508281.0991335.6251810.7642870.2392461.5999360.000000
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526 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Avg 1 1 100 6.246662 0.405504 \n", + "Sportiello P Atalanta Avg 1 0 0 6.215503 0.386785 \n", + "Rossi F. P Atalanta Avg 1 0 0 5.910575 0.379112 \n", + "Zappacosta D Atalanta Avg 1 0 48 6.092779 0.476883 \n", + "Scalvini D Atalanta Avg 1 1 81 6.036415 0.509005 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Avg 1 0 39 5.857898 0.462508 \n", + "Djuric A Verona Avg 1 0 69 5.936011 0.356034 \n", + "Kallon A Verona Avg 1 0 66 5.871454 0.403444 \n", + "Lasagna A Verona Avg 1 1 78 5.803599 0.387059 \n", + "Braaf A Verona Avg 1 0 15 5.854173 0.403747 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Musso 5.551811 0.520479 5.984098 0.176351 0.838341 \n", + "Sportiello 4.551934 0.398519 5.983678 0.214951 0.682581 \n", + "Rossi F. 4.049485 0.564528 5.747025 0.329788 0.357371 \n", + "Zappacosta 6.421109 0.777429 6.043412 0.550127 0.066196 \n", + "Scalvini 6.294344 0.742164 6.010108 0.594076 0.032801 \n", + "... ... ... ... ... ... \n", + "Gaich 6.131500 0.717901 5.771540 0.520882 0.122180 \n", + "Djuric 6.088906 0.446404 5.884834 0.408801 0.092681 \n", + "Kallon 6.038425 0.566514 5.802306 0.457565 0.111612 \n", + "Lasagna 5.879193 0.482069 5.746776 0.443377 0.094744 \n", + "Braaf 5.876657 0.486147 5.821750 0.472046 0.050828 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Musso 1.599265 5.810978 0.729997 -0.262806 0.860719 \n", + "Sportiello 1.599226 4.483588 0.606908 0.068208 0.879032 \n", + "Rossi F. 1.598827 4.163519 0.842991 -0.101138 0.911087 \n", + "Zappacosta 1.004575 5.813773 1.011895 0.416726 1.599906 \n", + "Scalvini 0.982219 5.784675 1.048351 0.344749 1.599907 \n", + "... ... ... ... ... ... \n", + "Gaich 1.046589 5.525054 0.872228 0.472862 1.599893 \n", + "Djuric 1.156591 5.813459 0.662987 0.298895 1.599941 \n", + "Kallon 1.108458 5.607873 0.752284 0.399625 1.599921 \n", + "Lasagna 1.130199 5.550426 0.683393 0.341598 1.599931 \n", + "Braaf 1.099133 5.625181 0.764287 0.239246 1.599936 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Musso 29.652037 \n", + "Sportiello 5.408993 \n", + "Rossi F. 2.865818 \n", + "Zappacosta 0.000000 \n", + "Scalvini 0.000000 \n", + "... ... \n", + "Gaich 0.000000 \n", + "Djuric 0.000000 \n", + "Kallon 0.000000 \n", + "Lasagna 0.000000 \n", + "Braaf 0.000000 \n", + "\n", + "[526 rows x 19 columns]" + ] + }, + "execution_count": 31, + "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", @@ -6854,7 +6204,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "b47cbd63", "metadata": {}, "outputs": [], @@ -6882,7 +6232,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "id": "7300f3c2", "metadata": { "scrolled": true @@ -6892,43 +6242,43 @@ "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" + "Meret: MV 6.25 ± 0.82; FV 5.94 + 0.91 (72.7% cs)\n", + "Szczesny: MV 6.25 ± 0.82; FV 5.99 + 0.94 (72.5% cs)\n", + "Provedel: MV 6.12 ± 0.71; FV 5.15 + 1.31 (49.0% cs)\n", + "Maignan: MV 6.25 ± 0.82; FV 5.23 + 0.95 (18.0% cs)\n", + "Rui Patricio: MV 6.25 ± 0.82; FV 5.98 + 0.92 (69.0% cs)\n", + "Onana: MV 6.25 ± 0.82; FV 5.94 + 0.96 (67.5% cs)\n", + "Milinkovic-Savic V.: MV 6.07 ± 0.70; FV 5.14 + 1.36 (40.4% cs)\n", + "Musso: MV 6.25 ± 0.81; FV 5.66 + 1.06 (33.3% cs)\n", + "Vicario: MV 6.25 ± 0.82; FV 5.44 + 1.31 (41.5% cs)\n", + "Silvestri: MV 6.25 ± 0.82; FV 5.67 + 1.21 (45.3% cs)\n", + "Terracciano: MV 6.12 ± 0.73; FV 5.00 + 1.47 (21.3% cs)\n", + "Skorupski: MV 6.25 ± 0.82; FV 5.39 + 1.23 (30.1% cs)\n", + "Falcone: MV 6.25 ± 0.82; FV 4.75 + 0.86 (4.2% cs)\n", + "Di Gregorio: MV 6.25 ± 0.82; FV 5.22 + 1.38 (19.6% cs)\n", + "Consigli: MV 6.25 ± 0.82; FV 5.48 + 1.35 (41.0% cs)\n", + "Carnesecchi: MV 6.25 ± 0.82; FV 5.58 + 1.23 (40.8% cs)\n", + "Montipo': MV 6.25 ± 0.82; FV 4.99 + 0.64 (1.3% cs)\n", + "Audero: MV 6.25 ± 0.82; FV 4.76 + 1.14 (3.5% cs)\n", + "Dragowski: MV 6.25 ± 0.82; FV 5.30 + 1.33 (18.8% cs)\n", + "Ochoa: MV 6.25 ± 0.82; FV 6.02 + 1.06 (42.9% cs)\n", + "Tatarusanu: MV 6.25 ± 0.82; FV 5.66 + 1.17 (44.0% cs)\n", + "Handanovic: MV 6.25 ± 0.82; FV 5.98 + 0.99 (69.2% cs)\n", + "Sportiello: MV 6.25 ± 0.81; FV 4.80 + 0.69 (8.2% cs)\n", + "Sepe: MV 6.17 ± 0.77; FV 5.04 + 1.09 (4.0% cs)\n" ] }, { "data": { "text/plain": [ - "[array([6.21465971, 4.86660706]),\n", - " array([0.35387683, 0.5225544 ], dtype=float32),\n", + "[array([6.17110676, 5.03714256]),\n", + " array([0.3830576 , 0.54427254], dtype=float32),\n", " [,\n", " ,\n", " ]]" ] }, - "execution_count": 36, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } diff --git a/7_lineup_simulation.ipynb b/7_lineup_simulation.ipynb index 7f84e58..9db0c48 100644 --- a/7_lineup_simulation.ipynb +++ b/7_lineup_simulation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "0d54533f", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ "metadata": {}, "outputs": [], "source": [ - "file = 'outputs/pred_matchday_32.xlsx'" + "file = 'outputs/pred_matchday_33.xlsx'" ] }, { @@ -110,113 +110,113 @@ " \n", " \n", " \n", - " Sportiello\n", - " P\n", - " Atalanta\n", - " Torino\n", - " 0\n", - " 1.00\n", - " 75\n", - " 6.241850\n", - " 0.413980\n", - " 5.903749\n", - " 0.357296\n", - " 5.997854\n", - " 0.249647\n", - " 0.623392\n", - " 1.589839\n", - " 6.211304\n", - " 0.449493\n", - " -0.482876\n", - " 1.126529\n", - " 72.505307\n", - " \n", - " \n", " Musso\n", " P\n", " Atalanta\n", - " Torino\n", - " 0\n", + " Spezia\n", + " 1\n", " 0.00\n", " 5\n", - " 6.244259\n", - " 0.413018\n", - " 4.951720\n", - " 0.501229\n", - " 6.001830\n", - " 0.251322\n", - " 0.617146\n", - " 1.589903\n", - " 5.410236\n", - " 0.623711\n", - " -0.518807\n", - " 1.010690\n", - " 8.606489\n", + " 6.254639\n", + " 0.411284\n", + " 5.946916\n", + " 0.450058\n", + " 5.985031\n", + " 0.167425\n", + " 0.881443\n", + " 1.599276\n", + " 6.369741\n", + " 0.568063\n", + " -0.528498\n", + " 0.866981\n", + " 65.361601\n", + " \n", + " \n", + " Sportiello\n", + " P\n", + " Atalanta\n", + " Spezia\n", + " 1\n", + " 1.00\n", + " 80\n", + " 6.251123\n", + " 0.408416\n", + " 5.167640\n", + " 0.400064\n", + " 5.984908\n", + " 0.171609\n", + " 0.860805\n", + " 1.599271\n", + " 5.377477\n", + " 0.562605\n", + " -0.271637\n", + " 0.856012\n", + " 29.907155\n", " \n", " \n", " Rossi F.\n", " P\n", " Atalanta\n", - " Torino\n", - " 0\n", + " Spezia\n", + " 1\n", " 0.00\n", " 1\n", - " 6.244349\n", - " 0.411884\n", - " 4.080916\n", - " 0.677007\n", - " 6.003743\n", - " 0.253224\n", - " 0.610014\n", - " 1.589935\n", - " 4.909296\n", - " 0.706557\n", - " -0.788786\n", - " 0.969051\n", - " 0.049822\n", - " \n", - " \n", - " Toloi\n", - " D\n", - " Atalanta\n", - " Torino\n", - " 0\n", - " 1.00\n", - " 90\n", - " 6.048618\n", - " 0.485807\n", - " 6.310797\n", - " 0.700896\n", - " 6.011822\n", - " 0.564263\n", - " 0.048119\n", - " 0.996258\n", - " 5.822867\n", - " 0.983389\n", - " 0.352009\n", - " 1.599878\n", - " 0.000000\n", + " 6.061734\n", + " 0.351279\n", + " 5.129675\n", + " 0.639138\n", + " 5.971167\n", + " 0.378423\n", + " 0.176554\n", + " 1.599066\n", + " 5.736819\n", + " 0.811367\n", + " -0.532390\n", + " 0.811936\n", + " 19.623594\n", " \n", " \n", " Scalvini\n", " D\n", " Atalanta\n", - " Torino\n", - " 0\n", + " Spezia\n", + " 1\n", " 1.00\n", " 90\n", - " 6.030435\n", - " 0.486777\n", - " 6.289715\n", - " 0.711306\n", - " 5.994397\n", - " 0.565685\n", - " 0.047012\n", - " 0.997348\n", - " 5.786789\n", - " 0.989334\n", - " 0.359811\n", - " 1.599874\n", + " 6.217599\n", + " 0.504713\n", + " 6.653731\n", + " 0.868059\n", + " 6.140544\n", + " 0.573256\n", + " 0.098986\n", + " 0.968883\n", + " 5.961493\n", + " 1.111705\n", + " 0.430606\n", + " 1.599904\n", + " 0.000000\n", + " \n", + " \n", + " Zappacosta\n", + " D\n", + " Atalanta\n", + " Spezia\n", + " 1\n", + " 1.00\n", + " 90\n", + " 6.164116\n", + " 0.437797\n", + " 6.573402\n", + " 0.771070\n", + " 6.108955\n", + " 0.502768\n", + " 0.081000\n", + " 1.039319\n", + " 5.945317\n", + " 0.969838\n", + " 0.445450\n", + " 1.599910\n", " 0.000000\n", " \n", " \n", @@ -245,175 +245,175 @@ " Gaich\n", " A\n", " Verona\n", - " Cremonese\n", - " 0\n", + " Inter\n", + " 1\n", " 0.55\n", " 55\n", - " 5.928611\n", - " 0.484392\n", - " 6.232841\n", - " 0.773308\n", - " 5.828736\n", - " 0.541676\n", - " 0.135760\n", - " 1.016715\n", - " 5.588240\n", - " 0.952320\n", - " 0.462596\n", - " 1.599856\n", - " 0.000000\n", - " \n", - " \n", - " Djuric\n", - " A\n", - " Verona\n", - " Cremonese\n", - " 0\n", - " 0.45\n", - " 60\n", - " 6.043846\n", - " 0.364231\n", - " 6.231350\n", - " 0.482569\n", - " 5.988944\n", - " 0.416682\n", - " 0.097463\n", - " 1.133835\n", - " 5.930100\n", - " 0.713364\n", - " 0.303606\n", - " 1.599914\n", + " 5.889443\n", + " 0.456912\n", + " 6.155655\n", + " 0.718308\n", + " 5.801504\n", + " 0.513575\n", + " 0.126238\n", + " 1.050550\n", + " 5.545915\n", + " 0.868884\n", + " 0.476943\n", + " 1.599893\n", " 0.000000\n", " \n", " \n", " Kallon\n", " A\n", " Verona\n", - " Cremonese\n", - " 0\n", + " Inter\n", + " 1\n", " 0.00\n", " 40\n", - " 5.919705\n", - " 0.419731\n", - " 6.153088\n", - " 0.630338\n", - " 5.839579\n", - " 0.472310\n", - " 0.125181\n", - " 1.084553\n", - " 5.652873\n", - " 0.810479\n", - " 0.427306\n", - " 1.599881\n", + " 5.890604\n", + " 0.410215\n", + " 6.084521\n", + " 0.610053\n", + " 5.816953\n", + " 0.463717\n", + " 0.117285\n", + " 1.100079\n", + " 5.598563\n", + " 0.781981\n", + " 0.429863\n", + " 1.599914\n", " 0.000000\n", " \n", " \n", - " Braaf\n", + " Djuric\n", " A\n", " Verona\n", - " Cremonese\n", - " 0\n", - " 0.00\n", - " 35\n", - " 5.899851\n", - " 0.388650\n", - " 5.974370\n", - " 0.471126\n", - " 5.875201\n", - " 0.457090\n", - " 0.039928\n", - " 1.106565\n", - " 5.744820\n", - " 0.753911\n", - " 0.222862\n", - " 1.599918\n", + " Inter\n", + " 1\n", + " 0.45\n", + " 60\n", + " 5.929804\n", + " 0.343351\n", + " 6.060709\n", + " 0.435960\n", + " 5.884566\n", + " 0.396357\n", + " 0.084547\n", + " 1.171547\n", + " 5.795485\n", + " 0.651227\n", + " 0.293543\n", + " 1.599941\n", " 0.000000\n", " \n", " \n", " Lasagna\n", " A\n", " Verona\n", - " Cremonese\n", + " Inter\n", + " 1\n", + " 0.00\n", " 0\n", - " 1.00\n", - " 80\n", - " 5.756107\n", - " 0.397228\n", - " 5.825053\n", - " 0.519855\n", - " 5.704176\n", - " 0.457359\n", - " 0.083996\n", - " 1.116621\n", - " 5.460384\n", - " 0.726297\n", - " 0.355810\n", - " 1.599896\n", + " 5.816626\n", + " 0.392675\n", + " 5.906178\n", + " 0.511994\n", + " 5.756489\n", + " 0.448620\n", + " 0.099122\n", + " 1.123268\n", + " 5.539504\n", + " 0.706812\n", + " 0.366451\n", + " 1.599927\n", + " 0.000000\n", + " \n", + " \n", + " Braaf\n", + " A\n", + " Verona\n", + " Inter\n", + " 1\n", + " 0.00\n", + " 35\n", + " 5.788721\n", + " 0.370759\n", + " 5.802216\n", + " 0.441945\n", + " 5.755557\n", + " 0.433772\n", + " 0.056644\n", + " 1.144138\n", + " 5.557937\n", + " 0.682864\n", + " 0.259868\n", + " 1.599939\n", " 0.000000\n", " \n", " \n", "\n", - "

525 rows × 19 columns

\n", + "

526 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", + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Spezia 1 0.00 5 6.254639 0.411284 \n", + "Sportiello P Atalanta Spezia 1 1.00 80 6.251123 0.408416 \n", + "Rossi F. P Atalanta Spezia 1 0.00 1 6.061734 0.351279 \n", + "Scalvini D Atalanta Spezia 1 1.00 90 6.217599 0.504713 \n", + "Zappacosta D Atalanta Spezia 1 1.00 90 6.164116 0.437797 \n", + "... ... ... ... ... ... ... ... ... \n", + "Gaich A Verona Inter 1 0.55 55 5.889443 0.456912 \n", + "Kallon A Verona Inter 1 0.00 40 5.890604 0.410215 \n", + "Djuric A Verona Inter 1 0.45 60 5.929804 0.343351 \n", + "Lasagna A Verona Inter 1 0.00 0 5.816626 0.392675 \n", + "Braaf A Verona Inter 1 0.00 35 5.788721 0.370759 \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", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Musso 5.946916 0.450058 5.985031 0.167425 0.881443 \n", + "Sportiello 5.167640 0.400064 5.984908 0.171609 0.860805 \n", + "Rossi F. 5.129675 0.639138 5.971167 0.378423 0.176554 \n", + "Scalvini 6.653731 0.868059 6.140544 0.573256 0.098986 \n", + "Zappacosta 6.573402 0.771070 6.108955 0.502768 0.081000 \n", + "... ... ... ... ... ... \n", + "Gaich 6.155655 0.718308 5.801504 0.513575 0.126238 \n", + "Kallon 6.084521 0.610053 5.816953 0.463717 0.117285 \n", + "Djuric 6.060709 0.435960 5.884566 0.396357 0.084547 \n", + "Lasagna 5.906178 0.511994 5.756489 0.448620 0.099122 \n", + "Braaf 5.802216 0.441945 5.755557 0.433772 0.056644 \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", + "Musso 1.599276 6.369741 0.568063 -0.528498 0.866981 \n", + "Sportiello 1.599271 5.377477 0.562605 -0.271637 0.856012 \n", + "Rossi F. 1.599066 5.736819 0.811367 -0.532390 0.811936 \n", + "Scalvini 0.968883 5.961493 1.111705 0.430606 1.599904 \n", + "Zappacosta 1.039319 5.945317 0.969838 0.445450 1.599910 \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", + "Gaich 1.050550 5.545915 0.868884 0.476943 1.599893 \n", + "Kallon 1.100079 5.598563 0.781981 0.429863 1.599914 \n", + "Djuric 1.171547 5.795485 0.651227 0.293543 1.599941 \n", + "Lasagna 1.123268 5.539504 0.706812 0.366451 1.599927 \n", + "Braaf 1.144138 5.557937 0.682864 0.259868 1.599939 \n", "\n", " Clean Sheet % \n", "player \n", - "Sportiello 72.505307 \n", - "Musso 8.606489 \n", - "Rossi F. 0.049822 \n", - "Toloi 0.000000 \n", + "Musso 65.361601 \n", + "Sportiello 29.907155 \n", + "Rossi F. 19.623594 \n", "Scalvini 0.000000 \n", + "Zappacosta 0.000000 \n", "... ... \n", "Gaich 0.000000 \n", - "Djuric 0.000000 \n", "Kallon 0.000000 \n", - "Braaf 0.000000 \n", + "Djuric 0.000000 \n", "Lasagna 0.000000 \n", + "Braaf 0.000000 \n", "\n", - "[525 rows x 19 columns]" + "[526 rows x 19 columns]" ] }, "execution_count": 5, @@ -640,13 +640,28 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "c8fa1df8", + "execution_count": 19, + "id": "dda9acb8", + "metadata": {}, + "outputs": [], + "source": [ + "config_442 = [3, 6, 7, 9, 10, 15, 17, 19, 21, 27, 29];\n", + "config_433 = [3, 6, 7, 9, 10, 13, 17, 19, 26, 28, 30];\n", + "config_343 = [3, 7, 8, 9, 11, 15, 17, 19, 26, 28, 30];\n", + "config_4231 = [3, 6, 7, 9, 10, 12, 19, 23, 25, 26, 28];\n", + "config_433_classic = [3, 6, 7, 9, 10, 17, 18, 19, 26, 28, 30];\n", + "config_343_classic = [3, 7, 8, 9, 16, 17, 19, 20, 26, 28, 30];" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "beea77cf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -669,16 +684,14 @@ "squad = ['Szczesny',\n", " 'Di Lorenzo',\n", " 'Kim',\n", - " 'Juan Jesus',\n", + " 'Bastoni',\n", " 'Carlos Augusto',\n", " 'Kostic',\n", " 'Frattesi',\n", " 'Barella',\n", - " 'Strefezza',\n", + " 'El Shaarawy',\n", " 'Rafael Leao',\n", - " 'Lauriente\\'']\n", - "\n", - "config_442 = [3, 6, 7, 9, 10, 15, 17, 19, 21, 27, 29];\n", + " 'Hojlund']\n", "\n", "s = simulate_lineup(squad)\n", "\n", @@ -687,13 +700,13 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "1a4fd2fe", + "execution_count": 17, + "id": "acb6d8c0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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p3LgxwcHBartPemb/97//UbNmTSwsLPD39ycmJgaAGTNmsG7dOn755Re17eDg4GxDgjMyMrR6Id3d3fn6668L/Fq8jIiICB48eMCsWbNwd3enVq1aTJ8+nbi4OKKiogAIDw9n2bJl/PLLL7z77ru4urri6emJj49Pru36+/uzZs0aWrduTZUqVXj33XcZO3YsO3bsyFa3TJky2Nvbq5upqekru14hhBBC6Ib0sIpSwdTUVCtxKGkMDQ3p3bu3rsPIl7lz5+a6r1q1anz88cfq8wULFuS6XI+zs7PWNX/99dc5Lk00ffr0Ase4bt06+vXrx4kTJzh9+jQDBgzA2dmZ/v37A1nDcq9cucKuXbuwsrLis88+IyAggEuXLuHt7c3ixYuZNm0aERERAFhYWADQp08fIiMj2bJlC46OjuzcuRN/f38uXLhAtWrVgKzllRYsWMCGDRvQ09Oje/fujB07lo0bNzJ27FjCw8NJSEhgzZo1AJQtW5bo6Git+DMzM6lUqRI//vgjtra2hISEMGDAABwcHOjcuXO+XoOoqCg8PDzyrNO9e/dch9m6u7tja2vLqlWrmDRpEhkZGaxatYpatWrh7OwMwK+//kqVKlXYvXs3/v7+KIqCj48P8+fPp2zZsvmKEyA+Pj7H+kOHDuWTTz7B1dWVfv36MWDAgBL5g5QQQgghcicJqxCi1HFycuKrr75Co9Hg7u7OhQsX+Oqrr+jfv7+aqB47dgxvb28ANm7ciJOTEz///DMffvgh1tbWaDQarWG6165dY/Pmzfz99984OjoCMHbsWAIDA1mzZo1632ZaWhrLly+natWqQFbSNWvWLCAr8TU1NSUlJSXPIcCGhobMnDlTfe7q6kpISAg//vhjvhNWR0fH507iZGVlles+S0tLgoOD6dChA7NnzwagevXq/O9//8PAIOuj5fr169y8eZOffvqJ9evXk5GRwahRo+jUqRNBQUH5ivPatWt88803LFy4UKt89uzZtGrVClNTUw4ePMiYMWO4d+8eU6ZMyVe7QgghhCgZJGEVQhSqiRMn5rrv2d6vsWPH5lr32aHPI0aMeLnAnvLWW29pte/l5cXChQvJyMggPDwcAwMDGjdurO4vV64c7u7uhIeH59rmmTNnUBSF6tWra5WnpKRQrlw59bmZmZmarAI4ODgQFxdX4GtYvnw5K1eu5ObNmzx69IjU1FQ8PT3zfbyBgQFubm4FPu8Tjx49om/fvjRp0oTNmzeTkZHBggULCAgI4NSpU5iampKZmUlKSgrr169XX5dVq1bRsGFDIiIicHd3z/Mc0dHR+Pv78+GHH/LJJ59o7Xs6MX1y3bNmzZKEVQghhHjNSMIqSoWkpCRcXFyArFlIzc3NdRtQAaWmpqr3KI4YMQIjIyMdR5S7gsT2quq+jKfvZX22PK/7hzMzM9HX1yc0NBR9fX2tfU+GDAPqZERPaDSaXM+Zmx9//JFRo0axcOFCvLy8sLS05Msvv+TEiRP5buNlhwRv2rSJyMhIjh8/rv4QsWnTJmxsbPjll1/46KOPcHBwwMDAQCuJr1mzpnr+vBLW6OhoWrZsiZeXFz/88MNzr+ett94iISGBO3fuYGdn99z6QgghhCgZJGEVpUZeM5OWBDndvylezB9//JHtebVq1dDX18fDw4P09HROnDihDgm+f/8+ly9fVpMtIyMjMjIytNqoX78+GRkZxMXF0bRp0xeOLae2n3XkyBG8vb0ZPHiwWnbt2rUCnedlhwQnJyejp6enlcQ/eZ6ZmQlAkyZNSE9P59q1a2qv8uXLlwHU+1xzcvv2bVq2bEnDhg1Zs2ZNvu5LPXv2LCYmJq9suSEhhBBC6IbMTiGEKHVu3brF6NGjiYiIYPPmzXzzzTfqkONq1arRoUMH+vfvz9GjRzl37hzdu3enYsWKdOjQAQAXFxcSExM5ePAg9+7dIzk5merVq9OtWzd69uzJjh07uHHjBqdOnWLevHns2bMn37G5uLhw/vx5IiIiuHfvXo6TUrm5uXH69Gn+97//cfnyZaZOncqpU6cK9Bo8GRKc11ahQoVcj/f19eXBgwcMGTKE8PBw/vzzT/r06YOBgQEtW7YEwMfHhwYNGtC3b1/Onj1LaGgon376Kb6+vmqv68mTJ6lRowa3b98GsnpWW7RogZOTEwsWLODu3bvExsYSGxurnvvXX39lxYoVXLx4kWvXrrFy5UomT57MgAEDMDY2LtDrIIQQQojiTRJWIUSp07NnTx49esSbb77JkCFDGDZsGAMGDFD3r1mzhoYNG9KuXTu8vLxQFIU9e/aow3m9vb0ZOHAgXbp0oXz58syfP189rmfPnowZMwZ3d3feffddTpw4gZOTU75j69+/P+7u7jRq1Ijy5ctz7NixbHUGDhzI+++/T5cuXWjcuDH379/X6m0tCjVq1ODXX3/l/PnzeHl50bRpU6KjowkMDMTBwQHI6nH99ddfsbW1pVmzZrRt25aaNWuyZcsWtZ3k5GQiIiLUxHzfvn1cvXqVoKAgKlWqhIODg7o9YWhoyNKlS/Hy8qJu3bp8/fXXzJo1K9vETEIIIYQo+TRKQW+eKqYSEhKwtrYmPj4+z2FsonRKSkpS7yNMTEwskfewPlkuZuLEiTq/h/Xx48fcuHEDV1dXTExMdBpLQbVo0QJPT08WL16s61BEEcrrPSufH0IIIUTxJT2sQgghhBBCCCGKJUlYhRBCCCGEEEIUSzJLsCgV9PT0aNSokfq4pNFoNDg6OqqPxYsLDg7WdQhCCCGEECKfJGEVpYKpqWmBZ1EtTgwNDenfv7+uwxBCCCGEEKJIlbyuJiFEsfGazNkmSgF5rwohhBAlkySsQogCe7K8S3Jyso4jESJ/nrxXn7x3hRBCCFEyyJBgUSokJyfj4eEBwKVLlzAzM9NxRAWTlpbGd999B8CQIUN0/qVbX1+fMmXKEBcXB4CZmZncWyuKJUVRSE5OJi4ujjJlyqCvr6/rkIQQQghRAJKwilJBURRu3rypPi5pFEUhPj5efVwc2NvbA6hJqxDFWZkyZdT3rBBCCCFKDklYhRAvRKPR4ODgQIUKFUhLS9N1OELkytDQUHpWhRBCiBJKElYhxEvR19eXZEAIIYQQQrwSMumSEEIIIYQQQohiSRJWIYQQQgghhBDF0gslrEuXLsXV1RUTExMaNmzIkSNHcq0bExPDxx9/jLu7O3p6eowcOTLHetu3b8fDwwNjY2M8PDzYuXPni4QmhBBCCCGEEOI1UeCEdevWrYwcOZLJkydz9uxZmjZtSps2bYiKisqxfkpKCuXLl2fy5MnUq1cvxzrHjx+nS5cu9OjRg3PnztGjRw86d+7MiRMnChqeEDnSaDR4eHjg4eFRIpdf0Wg0lC9fnvLly5fI+IUQQgghhHgRGqWAa2Q0btyYBg0asGzZMrWsZs2adOzYkblz5+Z5bIsWLfD09GTx4sVa5V26dCEhIYG9e/eqZf7+/tjY2LB58+Yc20pJSSElJUV9npCQgJOTE/Hx8VhZWRXkkoQQQpRiCQkJWFtby+eHEEIIUQwVqIc1NTWV0NBQWrdurVXeunVrQkJCXjiI48ePZ2vTz88vzzbnzp2LtbW1ujk5Ob3w+YUQQgghhBBCFD8FSljv3btHRkYGdnZ2WuV2dnbExsa+cBCxsbEFbnPixInEx8er261bt174/EIIIYQQQgghip8XWof12XvoFEV56fvqCtqmsbExxsbGL3VOUXokJyfzxhtvAHDq1CnMzMx0HFHBpKWlsWLFCgD69++PoaGhjiMSQgghhBDi1StQwmpra4u+vn62ns+4uLhsPaQFYW9vX+htCvE0RVG4dOmS+rikURSFu3fvqo+FEEIIIYQoDQo0JNjIyIiGDRuyf/9+rfL9+/fj7e39wkF4eXlla3Pfvn0v1aYQQgghhBBCiJKtwEOCR48eTY8ePWjUqBFeXl788MMPREVFMXDgQCDr3tLbt2+zfv169ZiwsDAAEhMTuXv3LmFhYRgZGeHh4QHAiBEjaNasGfPmzaNDhw788ssvHDhwgKNHjxbCJQohhBBCCCGEKIkKnLB26dKF+/fvM2vWLGJiYqhduzZ79uzB2dkZgJiYmGxrstavX199HBoayqZNm3B2diYyMhIAb29vtmzZwpQpU5g6dSpVq1Zl69atNG7c+CUuTQghhBBCCCFESVbgdViLK1lHT+QlKSkJCwsLIKun39zcXMcRFUxqaqq6zvHEiRMxMjLScURCvD7k80MIIYQovgp0D6sQQgghhBBCCFFUXmhZGyFKGo1Gow5bf9klmHRBo9FgbW2tPhZCCCGEEKI0kCHBQgghSjX5/BBCCCGKLxkSLIQQQgghhBCiWJKEVQghhBBCCCFEsSQJqygVHj16xBtvvMEbb7zBo0ePdB1OgaWlpbFixQpWrFhBWlqarsMRQgghhMhRixYtGDly5Cs9h0aj4eeff36l5xDFhySsolTIzMzk9OnTnD59mszMTF2HU2CKohAdHU10dDSvyW3nQghR5OLi4vj000+pXLkyxsbG2Nvb4+fnx/Hjx3UdmiglQkJC0NfXx9/f/5Wfy8XFBY1Gk+vWokWL5x6/ePHiQo9r7dq1WnE4ODjQuXNnbty4ke82YmJiaNOmTYHOWaZMmReItvA8e91Pb3FxcdnqX716FUtLywLFnZKSgqenJxqNhrCwMK19UVFRtG/fHnNzc2xtbRk+fDipqakveVVFQ2YJFkIIIUSp8MEHH5CWlsa6deuoUqUKd+7c4eDBg/zzzz+6Dk2UEqtXr2bYsGGsXLmSqKgoKleu/MrOderUKTIyMoCsRPmDDz4gIiJCnVxOl2u6W1lZERERgaIo/PXXX3z66ae8++67hIWFoa+v/9zj7e3tiyDKwtWlS5dsP1T07t2bx48fU6FCBa3ytLQ0unbtStOmTQkJCcn3OcaPH4+joyPnzp3TKs/IyKBt27aUL1+eo0ePcv/+fXr16oWiKHzzzTcvflFFRHpYhRBCCPHae/jwIUePHmXevHm0bNkSZ2dn3nzzTSZOnEjbtm1z7f2YMWOG2sbq1aupVasWxsbGODg4MHToUCD3npOnj12zZg01a9bExMSEGjVqsHTpUnVfamoqQ4cOxcHBARMTE1xcXJg7d666X6PRsHLlSt577z3MzMyoVq0au3btArJG4Li5ubFgwQKt67148SJ6enpcu3btFbya4kUkJSXx448/MmjQINq1a8fatWvVfV5eXkyYMEGr/t27dzE0NOTQoUNAVq9i27ZtMTU1xdXVlU2bNuXZC1q+fHns7e2xt7enbNmyAFSoUEEtO3TokPp+dnFxYeHCheqxLVq04ObNm4waNUp9PwPcv3+frl27UqlSJczMzKhTpw6bN28u8Guh0Wiwt7fHwcGBli1bMn36dC5evMjVq1cBWLZsGVWrVsXIyAh3d3c2bNiQ7fgnQ4IjIyPRaDTs2LGDli1bYmZmRr169dSRE8HBwfTp04f4+Phsf5tLly6lWrVqmJiYYGdnR6dOnQp8Lfllamqqvvb29vbo6+sTFBREv379stWdMmUKNWrUoHPnzvluf+/evezbty/b/wUA+/bt49KlS/z3v/+lfv36+Pj4sHDhQlasWEFCQsJLXVdRkIRVCCGEEK89CwsLLCws+Pnnn0lJScm2v0uXLsTExKjb5s2bMTAwoEmTJkDWF+ghQ4YwYMAALly4wK5du3Bzc8vXsStWrGDy5Ml88cUXhIeHM2fOHKZOncq6desAWLJkCbt27eLHH38kIiKC//73v7i4uGjFN3PmTDp37sz58+cJCAigW7du/PPPP2g0Gvr27cuaNWu06q9evZqmTZtStWrVwn4pxQvaunUr7u7uuLu70717d9asWaPe5tOtWzc2b96sddvP1q1bsbOzo3nz5gD07NmT6OhogoOD2b59Oz/88EOOQ0nzIzQ0lM6dO/PRRx9x4cIFZsyYwdSpU9UkeseOHVSqVIlZs2ap72uAx48f07BhQ3bv3s3FixcZMGAAPXr04MSJEy/xymQlc5DVs7hz505GjBjBmDFjuHjxIp9++il9+vRRE/fcTJ48mbFjxxIWFkb16tXp2rUr6enpeHt7s3jxYqysrNRrGTt2LKdPn2b48OHMmjWLiIgIAgMDadasWa7tR0VFqf+P5LYNHDgw39e8fv16zMzMsiXJQUFB/PTTT3z33Xf5buvOnTv079+fDRs2YGZmlm3/8ePHqV27No6OjmqZn58fKSkphIaG5vs8OqO8JuLj4xVAiY+P13UoohhKTExUAAVQEhMTdR1OgaWkpCgzZsxQZsyYoaSkpOg6HCFeK/L5UXps27ZNsbGxUUxMTBRvb29l4sSJyrlz57LVu3r1qlKuXDll/vz5apmjo6MyefLk554jp2OdnJyUTZs2adWbPXu24uXlpSiKogwbNkx55513lMzMzBzbBJQpU6aozxMTExWNRqPs3btXURRFiY6OVvT19ZUTJ04oiqIoqampSvny5ZW1a9c+N15RdLy9vZXFixcriqIoaWlpiq2trbJ//35FURQlLi5OMTAwUH7//Xe1vpeXlzJu3DhFURQlPDxcAZRTp06p+69cuaIAyldfffXccx86dEgBlAcPHiiKoigff/yx4uvrq1Vn3LhxioeHh/rc2dk5X20HBAQoY8aMUZ83b95cGTFiRK7116xZo1hbW6vPb926pbz11ltKpUqVlJSUFMXb21vp37+/1jEffvihEhAQoD4HlJ07dyqKoig3btxQAGXlypXq/j///FMBlPDw8BzPqSiKsn37dsXKykpJSEh47jUqSta/2ZUrV/Lc7ty5k6+2FEVRPDw8lEGDBmmV3bt3T3FyclIOHz6ca9zPyszMVPz9/ZXZs2crivJ/r8fZs2fVOv3798/2760oimJkZJTt/6biSHpYhRBCCFEqfPDBB0RHR7Nr1y78/PwIDg6mQYMGWkMz4+PjadeuHW3atGHcuHFA1mRN0dHRtGrVKs/2czr27t273Lp1i379+mn1xHz++efqcN3evXsTFhaGu7s7w4cPZ9++fdnarlu3rvrY3NwcS0tLtXfNwcGBtm3bsnr1agB2797N48eP+fDDD1/8xRKFKiIigpMnT/LRRx8BYGBgQJcuXdR/s/Lly+Pr68vGjRsBuHHjBsePH6dbt27q8QYGBjRo0EBt083NDRsbmxeKJzw8XB0B8ESTJk24cuWKet9rTjIyMvjiiy+oW7cu5cqVw8LCgn379hEVFVWg88fHx2NhYYG5uTlOTk6kpqayY8cOjIyMco0tPDw8zzaf/htxcHAAyLMH2tfXF2dnZ6pUqUKPHj3YuHEjycnJudY3MDDAzc0tz+3Ze1Fzc/z4cS5dupRtOHD//v35+OOP8+zpfdY333xDQkICEydOzLPek2HdT1MUJcfy4kYSVlFq2NraYmtrq+swXpiZmVmOwzyEEELkn4mJCb6+vkybNo2QkBB69+7N9OnTgawv4126dMHKyooVK1aoxzwZrpiX3I59MjP9ihUrCAsLU7eLFy/yxx9/ANCgQQNu3LjB7NmzefToEZ07d842TNDQ0FDruUaj0Zr1/pNPPmHLli08evSINWvW0KVLF/nMKEZWrVpFeno6FStWxMDAAAMDA5YtW8aOHTt48OABkDUseNu2baSlpbFp0yZq1apFvXr1AHJdISC38ufJKVHJT1sLFy7kq6++Yvz48QQFBREWFoafn1+BZ5u1tLQkLCyMCxcukJiYSGhoKG+88Ya6P6fYnpdYPf038qRuXitDWFpacubMGTZv3oyDgwPTpk2jXr16PHz4MMf6hTkkeOXKlXh6etKwYUOt8qCgIBYsWKC+R/r160d8fDwGBgbqjxvPCgoK4o8//sDY2FhNqgEaNWpEr169gKxJqmJjY7WOe/DgAWlpadjZ2eUrZl2SWYJFqWBubs7du3d1HcYLMzIyUn+tF0IIUXg8PDzUyVtGjRrFhQsXOHXqFCYmJmodS0tLXFxcOHjwIC1btsyxndyOtbOzo2LFily/fl3tLcuJlZUVXbp0oUuXLnTq1Al/f3/++ecfdbKc5wkICMDc3Jxly5axd+9efv/993wdJ1699PR01q9fz8KFC2ndurXWvg8++ICNGzcydOhQOnbsyKeffkpgYCCbNm2iR48ear0aNWqQnp7O2bNn1STn6tWruSZXz+Ph4cHRo0e1ykJCQqhevbo6S6+RkVG23tYjR47QoUMHunfvDmQlhFeuXKFmzZoFOr+enp6aWD2rZs2aHD16lJ49e2rFVtBzPC2na4GsXlMfHx98fHyYPn06ZcqUISgoiPfffz9bXUdHx2xLxTzryQzMeUlMTOTHH3/UmljtiePHj2vF+csvvzBv3jxCQkKoWLFiju0tWbKEzz//XH0eHR2Nn58fW7dupXHjxkDWpF5ffPEFMTExau/zvn37MDY2zpY0F0eSsAohhBDitXf//n0+/PBD+vbtS926dbG0tOT06dPMnz+fDh06sGbNGpYuXcrOnTvR09NTeyOe9JzMmDGDgQMHUqFCBdq0acO///7LsWPHGDZsWL6OHT58OFZWVrRp04aUlBROnz7NgwcPGD16NF999RUODg54enqip6fHTz/9hL29fYHWX9TX16d3795MnDgRNzc3vLy8XsXLKF7A7t27efDgAf369cPa2lprX6dOnVi1ahVDhw7F3NycDh06MHXqVMLDw/n444/VejVq1MDHx4cBAwawbNkyDA0NGTNmDKampi80pHPMmDG88cYbzJ49my5dunD8+HG+/fZbrdmrXVxc+P333/noo48wNjbG1tYWNzc3tm/fTkhICDY2NixatIjY2NiXSiafNW7cODp37kyDBg1o1aoVv/76Kzt27ODAgQMv3KaLiwuJiYkcPHiQevXqYWZmRlBQENevX6dZs2bY2NiwZ88eMjMzcXd3z7GNp3svX8bWrVtJT0/P8QesZ1/H06dPo6enR+3atdWykydP0rNnTw4ePEjFihWzLY1kYWEBQNWqValUqRIArVu3xsPDgx49evDll1/yzz//MHbsWPr375+vJFvndHkDbWGSSTOEEEK8CPn8KB0eP36sTJgwQWnQoIFibW2tmJmZKe7u7sqUKVOU5ORkpVevXurkfE9v06dPV9tYvny54u7urhgaGioODg7KsGHDFEVR8nXsxo0bFU9PT8XIyEixsbFRmjVrpuzYsUNRFEX54YcfFE9PT8Xc3FyxsrJSWrVqpZw5c0Y9lqcmmHnC2tpaWbNmjVbZtWvXFEBrwiehe+3atdOaMOhpoaGhCqCEhoYqiqIov/32mwIozZo1y1Y3OjpaadOmjWJsbKw4OzsrmzZtUipUqKAsX778uTE8O+mSomRNQubh4aEYGhoqlStXVr788kutY44fP67UrVtXMTY2Vp6kDPfv31c6dOigWFhYKBUqVFCmTJmi9OzZU+nQoYN6XEEnXcrJ0qVLlSpVqiiGhoZK9erVlfXr12vtJ4dJl56eZOjBgwcKoBw6dEgtGzhwoFKuXDn1b/PIkSNK8+bNFRsbG8XU1FSpW7eusnXr1jzjKgxeXl7Kxx9/nK+6Ob1WT/4tb9y4keMxOb0eiqIoN2/eVNq2bauYmpoqZcuWVYYOHao8fvz4Ba6g6GkU5QUHvxczCQkJWFtbEx8fXzJ+KRBF6tGjR7Rp0wbIWqcqP/cjFSdpaWnqRAzdunXLdi+TEOLFyeeHeF0cO3aMFi1a8Pfff5eI+9LEy/n7779xcnLiwIEDz50QTIiSTIYEi1IhMzOTw4cPq49LGkVRuHnzpvpYCCGEeCIlJYVbt24xdepUOnfuLMnqayooKIjExETq1KlDTEwM48ePx8XFpUAzygpREskswUIIIYQQJdjmzZtxd3cnPj6e+fPn6zoc8YqkpaUxadIkatWqxXvvvUf58uUJDg6WUVfitSc9rEIIIYQQJVjv3r3p3bu3rsMQr5ifnx9+fn66DkOIIic9rEIIIYQQQgghiiVJWIUQQgghhBBCFEuSsAohhBBCPEWj0fDzzz/rOgwhSoTIyEg0Gg1hYWGv7Bxr164t0LrE4vUiCasoNczMzDAzM9N1GC/M0NBQJlYQQogX1Lt3bzQaDRqNBkNDQ+zs7PD19WX16tXZZo+PiYlRl0LLra2OHTu+4ojF6ygkJAR9fX38/f2L5HwzZsxAo9HkeL758+ej0Who0aLFK4+jRYsW6t+fsbEx1atXZ86cOWRkZOTr+C5dunD58uUCn3PkyJEvEG3h2r59Ox4eHhgbG+Ph4cHOnTufe8yFCxdo3rw5pqamVKxYkVmzZmVbJeLw4cM0bNgQExMTqlSpwvLly1/VJeicJKyiVDA3NycpKYmkpCTMzc11HU6BGRkZMWnSJCZNmoSRkZGuwxFCiBLJ39+fmJgYIiMj2bt3Ly1btmTEiBG0a9eO9PR0tZ69vT3GxsZFFldqamqRnUvo1urVqxk2bBhHjx4lKiqqSM7p4ODAoUOH+Pvvv7XK16xZQ+XKlYskBoD+/fsTExNDREQEw4cPZ8qUKSxYsCBfx5qamlKhQoVXHGHhO378OF26dKFHjx6cO3eOHj160LlzZ06cOJHrMQkJCfj6+uLo6MipU6f45ptvWLBgAYsWLVLr3Lhxg4CAAJo2bcrZs2eZNGkSw4cPZ/v27UVxWUVOElYhhBBClArGxsbY29tTsWJFGjRowKRJk/jll1/Yu3cva9euVeu97JDgw4cP8+abb2JsbIyDgwMTJkzQSohbtGjB0KFDGT16NLa2tvj6+gJw6dIlAgICsLCwwM7Ojh49enDv3j2t44YPH8748eMpW7Ys9vb2zJgxQ92/du1atRfr6e1JnczMTGbNmkWlSpUwNjbG09OTwMDAF75OUTBJSUn8+OOPDBo0iHbt2mm957y8vJgwYYJW/bt372JoaMihQ4eArJ7/tm3bYmpqiqurK5s2bcLFxYXFixfned4KFSrQunVr1q1bp5aFhIRw79492rZtq1U3P++RkydPUr9+fUxMTGjUqBFnz57N1/WbmZlhb2+Pi4sLQ4cOpVWrVurf2YMHD+jZsyc2NjaYmZnRpk0brly5oh777JDgGTNm4OnpyYYNG3BxccHa2pqPPvqIf//9F8gaBXH48GG+/vpr9e8gMjKSBw8e0K1bN8qXL4+pqSnVqlVjzZo1+Yr/RSxevBhfX18mTpxIjRo1mDhxIq1atcrz32zjxo08fvyYtWvXUrt2bd5//30mTZrEokWL1F7W5cuXU7lyZRYvXkzNmjX55JNP6Nu3b75/AChpJGEVQgghRKn1zjvvUK9ePXbs2FEo7d2+fZuAgADeeOMNzp07x7Jly1i1ahWff/65Vr1169ZhYGDAsWPH+P7774mJiaF58+Z4enpy+vRpAgMDuXPnDp07d852nLm5OSdOnGD+/PnMmjWL/fv3A1nDJmNiYtRt8+bNGBgY0KRJEwC+/vprFi5cyIIFCzh//jx+fn68++67WomBeHW2bt2Ku7s77u7udO/enTVr1qgJSLdu3di8ebPWsM+tW7diZ2dH8+bNAejZsyfR0dEEBwezfft2fvjhB+Li4vJ17r59+2olyKtXr6Zbt27ZRm097z2SlJREu3btcHd3JzQ0lBkzZjB27NgXej1MTU1JS0sDshLM06dPs2vXLo4fP46iKAQEBKj7c3Lt2jV+/vlndu/eze7duzl8+DD/+c9/1Ovw8vJSe3VjYmJwcnJi6tSpXLp0ib179xIeHs6yZcuwtbXN9Rxz5szBwsIiz+3IkSO5Hn/8+HFat26tVebn50dISEiexzRv3lxrlIefnx/R0dFERkbm2e7p06fzfM1KLOU1ER8frwBKfHy8rkMRxdCjR4+UgIAAJSAgQHn06JGuwymwtLQ0ZePGjcrGjRuVtLQ0XYcjxGtFPj9Kh169eikdOnTIcV+XLl2UmjVrqs8BZefOnS/U1qRJkxR3d3clMzNTLfvuu+8UCwsLJSMjQ1EURWnevLni6empddzUqVOV1q1ba5XdunVLAZSIiAj1uLffflurzhtvvKF89tln2eK4evWqUq5cOWX+/PlqmaOjo/LFF19kO37w4MG5XqsoPN7e3srixYsVRcn6XLe1tVX279+vKIqixMXFKQYGBsrvv/+u1vfy8lLGjRunKIqihIeHK4By6tQpdf+VK1cUQPnqq69yPef06dOVevXqKampqUqFChWUw4cPK4mJiYqlpaVy7tw5ZcSIEUrz5s3V+s97j3z//fdK2bJllaSkJHX/smXLFEA5e/ZsrnE0b95cGTFihKIoipKRkaHs3btXMTIyUsaPH69cvnxZAZRjx46p9e/du6eYmpoqP/74o6IoirJmzRrF2tpa67rMzMyUhIQEtWzcuHFK48aNczznE+3bt1f69OmTa5zPun//vnLlypU8t+Tk5FyPNzQ0VDZu3KhVtnHjRsXIyCjXY3x9fZX+/ftrld2+fVsBlJCQEEVRFKVatWrZ/p2OHTumAEp0dHS+r6+kMNBVoixEUcrIyGDPnj3q45ImMzNT/XXz2clBhBBCvBxFUdBoNIXSVnh4OF5eXlrtNWnShMTERP7++2/1nsFGjRppHRcaGsqhQ4ewsLDI1ua1a9eoXr06AHXr1tXa5+DgkK2XLT4+nnbt2tGmTRvGjRsHZN0XFx0drfa2Ph3buXPnXvBqRX5FRERw8uRJtSffwMCALl26sHr1anx8fChfvjy+vr5s3LiRpk2bcuPGDY4fP86yZcvU4w0MDGjQoIHappubGzY2Nvk6v6Ghodqre/36dapXr57tvZSf90h4eDj16tXTmsTSy8srXzEsXbqUlStXqvds9+jRg+nTp3PgwAEMDAxo3LixWrdcuXK4u7sTHh6ea3suLi5YWlqqz3P6W3jWoEGD+OCDDzhz5gytW7emY8eOeHt751q/bNmylC1bNl/Xl5tn/2/Jz/83OR3zbHl+6rwuJGEVQgghRKkWHh6Oq6trobSV05fRnL5IPjsBYGZmJu3bt2fevHnZ2nRwcFAfPztbvEaj0fohMyMjgy5dumBlZcWKFSuytfUiX57Fy1u1ahXp6elUrFhRLVMUBUNDQx48eICNjQ3dunVjxIgRfPPNN2zatIlatWpRr149tW5OcivPSd++fWncuDEXL16kb9++udbL6z1SkPM9q1u3bkyePBljY2McHR3R19fPs83nvTef97eQkzZt2nDz5k1+++03Dhw4QKtWrRgyZEiu937OmTOHOXPm5Nnm3r17adq0aY777O3tiY2N1SqLi4vDzs4u1/ZyOwZQj8utjoGBAeXKlcsz3pJI7mEVQgghRKkVFBTEhQsX+OCDDwqlPQ8PD0JCQrS+hIeEhGBpaamVrDyrQYMG/Pnnn7i4uODm5qa1FWR2+1GjRnHhwgV27tyJiYmJWm5lZYWjoyNHjx7Vqh8SEkLNmjULcIWioNLT01m/fj0LFy4kLCxM3c6dO4ezszMbN24EoGPHjjx+/JjAwEA2bdpE9+7d1TZq1KhBenq61gRHV69e5eHDh/mOo1atWtSqVYuLFy/y8ccfZ9ufn/eIh4cH586d49GjR+r+P/74I1/nt7a2xs3NDScnJzVZfdJmenq61sy59+/f5/Llyy/13jQyMspxVF358uXp3bs3//3vf1m8eDE//PBDrm0MHDhQ698sp+3Z0RJP8/LyUu8xf2Lfvn159up6eXnx+++/a80evm/fPhwdHXFxccmz3UaNGr2WSyBKD6sQQgghSoWUlBRiY2PJyMjgzp07BAYGMnfuXNq1a0fPnj0L1FZ8fDxhYWFaZWXLlmXw4MEsXryYYcOGMXToUCIiIpg+fTqjR49GTy/3foIhQ4awYsUKunbtyrhx47C1teXq1ats2bKFFStWaH3Bz82aNWtYunQpO3fuRE9PT+2BeTI5zLhx45g+fTpVq1bF09OTNWvWEBYWpiZM4tXYvXs3Dx48oF+/flhbW2vt69SpE6tWrWLo0KGYm5vToUMHpk6dSnh4uFZSWaNGDXx8fBgwYADLli3D0NCQMWPGYGpqWqAe8qCgINLS0rRm3H3a894jH3/8MZMnT6Zfv35MmTKFyMjIl56Ztlq1anTo0IH+/fvz/fffY2lpyYQJE6hYsSIdOnR44XZdXFw4ceIEkZGRWFhYULZsWWbMmEHDhg2pVasWKSkp7N69O8+k+GWHBI8YMYJmzZoxb948OnTowC+//MKBAwe0fhT49ttv2blzJwcPHgSyXuOZM2fSu3dvJk2axJUrV5gzZw7Tpk1T/60HDhzIt99+y+jRo+nfvz/Hjx9n1apVbN68+YVjLc6kh1UIIYQQpUJgYCAODg64uLjg7+/PoUOHWLJkCb/88ku+EsKnBQcHU79+fa1t2rRpVKxYkT179nDy5Enq1avHwIED1S/3eXF0dOTYsWNkZGTg5+dH7dq1GTFiBNbW1nkmuk87fPgwGRkZvPvuuzg4OKjbk4Ri+PDhjBkzhjFjxlCnTh0CAwPZtWsX1apVK9C1i4JZtWoVPj4+2ZJVgA8++ICwsDDOnDkDZA2bPXfuHE2bNs22Rur69euxs7OjWbNmvPfee/Tv3x9LS0utnvTnMTc3zzVZhee/RywsLPj111+5dOkS9evXZ/LkyTkOYy+oNWvW0LBhQ9q1a4eXlxeKorBnz56X6i0cO3Ys+vr6eHh4UL58eaKiojAyMmLixInUrVuXZs2aoa+vz5YtW146/tx4e3uzZcsW1qxZQ926dVm7di1bt27Vul/33r17XLt2TX1ubW3N/v37+fvvv2nUqBGDBw9m9OjRjB49Wq3j6urKnj17CA4OxtPTk9mzZ7NkyZJCGylS3GiUlxmMXowkJCRgbW1NfHw8VlZWug5HFDNJSUnqRBaJiYkFGl5VHKSmpjJ37lwAJk6cmG0aeiHEi5PPDyFESfT333/j5OSk3ospxOtKhgQLIYQQQghRzAUFBZGYmEidOnWIiYlh/PjxuLi40KxZM12HJsQrJQmrKBXMzc1famY7XTMyMmL69Om6DkMIIYQQOpKWlsakSZO4fv06lpaWeHt7s3Hjxtdykh0hniZDgoUQQpRq8vkhhBBCFF8y6ZIQQgghhBBCiGJJElZRKjx+/JgPP/yQDz/8kMePH+s6nAJLT0/np59+4qeffiI9PV3X4QghxGund+/edOzYUddhCKETLi4uLF68WNdh5ItGo+Hnn3/WdRiiCEnCKkqFjIwMtm3bxrZt23JcRLq4y8zM5NKlS1y6dInMzExdhyOEECVOTgnptm3bMDExYf78+Xz99desXbtWJ7GJ0iMkJAR9fX38/f2L7JwJCQlMnjyZGjVqYGJigr29PT4+PuzYsaPYze8RFxfHp59+SuXKlTE2Nsbe3h4/Pz+OHz9epHEEBwej0Wh4+PBhkZ73WQ8ePKBHjx5YW1tjbW1Njx49nhuToijMmDEDR0dHTE1NadGiBX/++adWnZSUFIYNG4atrS3m5ua8++67/P3336/wSl6OJKxCCCGEKHVWrlxJt27d+Pbbbxk/fjzW1tZ5rk8pRGFYvXo1w4YN4+jRo0RFRb3y8z18+BBvb2/Wr1/PxIkTOXPmDL///jtdunRh/PjxxMfHv/IYCuKDDz7g3LlzrFu3jsuXL7Nr1y5atGjBP//8o+vQdOLjjz8mLCyMwMBAAgMDCQsLo0ePHnkeM3/+fBYtWsS3337LqVOnsLe3x9fXl3///VetM3LkSHbu3MmWLVs4evQoiYmJtGvXrvh26iivifj4eAVQ4uPjdR2KKIYSExMVQAGUxMREXYdTYCkpKcqMGTOUGTNmKCkpKboOR4jXinx+lA69evVSOnTooCiKosybN08xNjZWtm3bluN+RVGU5s2bK0OHDlVGjBihlClTRqlQoYLy/fffK4mJiUrv3r0VCwsLpUqVKsqePXuK+EpESZWYmKhYWloqf/31l9KlSxdl5syZ6r633npL+eyzz7Tqx8XFKQYGBkpQUJCiKIoSHR2tBAQEKCYmJoqLi4uyceNGxdnZWfnqq69yPeegQYMUc3Nz5fbt29n2/fvvv0paWpqiKEq2dh4+fKj0799fKV++vGJpaam0bNlSCQsLU/dfvXpVeffdd5UKFSoo5ubmSqNGjZT9+/drte/s7Kx88cUXSp8+fRQLCwvFyclJ+f7773ON9cGDBwqgBAcH51pHURQFUFasWKF07NhRMTU1Vdzc3JRffvlFq86ff/6ptGnTRjE3N1cqVKigdO/eXbl79666PzMzU5k3b57i6uqqmJiYKHXr1lV++uknRVEU5caNG+p3xidbr169FEVRlJ9++kmpXbu2YmJiopQtW1Zp1arVK/teeenSJQVQ/vjjD7Xs+PHjCqD89ddfOR6TmZmp2NvbK//5z3/UssePHyvW1tbK8uXLFUXJ+rc1NDRUtmzZota5ffu2oqenpwQGBr6Sa3lZ0sMqhBBCiFJjwoQJzJ49m927d/PBBx/kWXfdunXY2tpy8uRJhg0bxqBBg/jwww/x9vbmzJkz+Pn50aNHD5KTk4soelGSbd26FXd3d9zd3enevTtr1qxRh+R269aNzZs3aw3R3bp1K3Z2djRv3hyAnj17Eh0dTXBwMNu3b+eHH34gLi4u1/NlZmayZcsWunXrhqOjY7b9FhYWGBhkX+FSURTatm1LbGwse/bsITQ0lAYNGtCqVSu1pzMxMZGAgAAOHDjA2bNn8fPzo3379tl6jRcuXEijRo04e/YsgwcPZtCgQfz11185xmthYYGFhQU///wzKSkpeb6WM2fOpHPnzpw/f56AgAC6deumxhYTE0Pz5s3x9PTk9OnTBAYGcufOHTp37qweP2XKFNasWcOyZcv4888/GTVqFN27d+fw4cM4OTmxfft2ACIiIoiJieHrr78mJiaGrl270rdvX8LDwwkODub999/Pc1j1k2vKbWvTpk2uxx4/fhxra2saN26slr311ltYW1sTEhKS4zE3btwgNjaW1q1bq2XGxsY0b95cPSY0NJS0tDStOo6OjtSuXTvXdnVOt/ly4ZFfyEVepIdVCJEb+fwoHXr16qUYGRkpgHLw4MEc9z/bw/r222+rz9PT0xVzc3OlR48eallMTIwCKMePH3+lsYvXg7e3t7J48WJFURQlLS1NsbW1VXsln/Sm/v7772p9Ly8vZdy4cYqiKEp4eLgCKKdOnVL3X7lyRQFy7WG9c+eOAiiLFi16bmxP97AePHhQsbKyUh4/fqxVp2rVqnn2kHp4eCjffPONVpvdu3dXn2dmZioVKlRQli1blmsb27ZtU2xsbBQTExPF29tbmThxonLu3DmtOoAyZcoU9XliYqKi0WiUvXv3KoqiKFOnTlVat26tdcytW7cUQImIiFASExMVExMTJSQkRKtOv379lK5duyqKoiiHDh1SAOXBgwfq/tDQUAVQIiMjc43/WVeuXMlz+/vvv3M99osvvlCqVauWrbxatWrKnDlzcjzm2LFjCpCtR71///7qa7Jx40bFyMgo27G+vr7KgAED8n1tRSn7zypCCCGEEK+hunXrcu/ePaZNm8Ybb7yBpaXlc+s/oa+vT7ly5ahTp45aZmdnB5BnL5cQkNVTd/LkSXbs2AGAgYEBXbp0YfXq1fj4+FC+fHl8fX3ZuHEjTZs25caNGxw/fpxly5apxxsYGNCgQQO1TTc3N2xsbHI9p/L/e/40Gk2BYg0NDSUxMZFy5cpplT969Ihr164BkJSUxMyZM9m9ezfR0dGkp6fz6NGjbD2sT/8NaTQa7O3t8/x7+eCDD2jbti1Hjhzh+PHjBAYGMn/+fFauXEnv3r1zbNfc3BxLS0u13dDQUA4dOoSFhUW29q9du0Z8fDyPHz/G19dXa19qair169fPNbZ69erRqlUr6tSpg5+fH61bt6ZTp055/hu4ubnlui8/cvq3UxTluf+mz+7PzzH5qaMrkrAKIYQQolSoWLEi27dvp2XLlvj7+xMYGJhn0mpoaKj1XKPRaJU9+XIns7eL51m1ahXp6elUrFhRLVMUBUNDQx48eICNjQ3dunVjxIgRfPPNN2zatIlatWpRr149tW5OcisHKF++PDY2NoSHhxco1szMTBwcHAgODs6278nEZOPGjeN///sfCxYswM3NDVNTUzp16kRqaqpW/Zz+hp7392JiYoKvry++vr5MmzaNTz75hOnTp2slrHm1m5mZSfv27Zk3b162th0cHLh48SIAv/32m9a/B2QNn82Nvr4++/fvJyQkhH379vHNN98wefJkTpw4gaura47H5JQ0P61p06bs3bs3x3329vbcuXMnW/ndu3fVH8tyOgYgNjYWBwcHtTwuLk49xt7entTUVPV993Qdb2/vPOPVFUlYRalgZmZGYmKi+rikMTQ0ZOLEiepjIYQQL6Zy5cocPnyYli1b0rp1a/73v/9hZWWl67DEayw9PZ3169ezcOFCrfsGIatHcePGjQwdOpSOHTvy6aefEhgYyKZNm7Rmg61Rowbp6emcPXuWhg0bAnD16tU8lzjR09OjS5cubNiwgenTp2e7jzUpKQljY+Ns97E2aNCA2NhYDAwMcHFxybHtI0eO0Lt3b9577z0g657WyMjIfL4iBePh4VGgdVcbNGjA9u3bcXFxyfEeXQ8PD4yNjYmKilLvD36WkZERQLZZczUaDU2aNKFJkyZMmzYNZ2dndu7cyejRo3NsJywsLM9YTU1Nc93n5eVFfHw8J0+e5M033wTgxIkTxMfH55pYurq6Ym9vz/79+9Xe4tTUVA4fPqwm8A0bNsTQ0JD9+/er9/XGxMRw8eJF5s+fn2e8uiIJqygVNBoN5ubmug7jhWk0GvU/TyGEEC+nUqVKBAcHayWtQrwqu3fv5sGDB/Tr1w9ra2utfZ06dWLVqlUMHToUc3NzOnTowNSpUwkPD+fjjz9W69WoUQMfHx8GDBjAsmXLMDQ0ZMyYMZiamuY5jHPOnDkEBwfTuHFjvvjiCxo1aoShoSFHjhxh7ty5nDp1KttyTj4+Pnh5edGxY0fmzZuHu7s70dHR7Nmzh44dO9KoUSPc3NzYsWMH7du3R6PRMHXq1JceaXD//n0+/PBD+vbtS926dbG0tOT06dPMnz+fDh065LudIUOGsGLFCrp27cq4ceOwtbXl6tWrbNmyhRUrVmBpacnYsWMZNWoUmZmZvP322yQkJBASEoKFhQW9evXC2dkZjUbD7t27CQgIwNTUlD///JODBw/SunVrKlSowIkTJ7h79y41a9bMNZaXGRJcs2ZN/P396d+/P99//z0AAwYMoF27dri7u6v1atSowdy5c3nvvffQaDSMHDmSOXPmUK1aNapVq8acOXMwMzNT30/W1tb069ePMWPGUK5cOcqWLcvYsWOpU6cOPj4+LxzvqySzBAshhBCi1KlYsSKHDx/m4cOH+Pr65tlTJcTLWLVqFT4+PtmSVcjqYQ0LC+PMmTNA1mzB586do2nTplSuXFmr7vr167Gzs6NZs2a899579O/fH0tLS0xMTHI9t42NDX/88Qfdu3fn888/p379+jRt2pTNmzfz5Zdf5hiTRqNhz549NGvWjL59+1K9enU++ugjIiMj1WGlX331FTY2Nnh7e9O+fXv8/Py07q99ERYWFjRu3JivvvqKZs2aUbt2baZOnUr//v359ttv892Oo6Mjx44dIyMjAz8/P2rXrs2IESOwtrZGTy8r9Zk9ezbTpk1j7ty51KxZEz8/P3799Vd1aG/FihWZOXMmEyZMwM7OjqFDh2JlZcXvv/9OQEAA1atXZ8qUKSxcuDDPmX5f1saNG6lTpw6tW7emdevW1K1blw0bNmjViYiI0FpPd/z48YwcOZLBgwfTqFEjbt++zb59+7Ruf/jqq6/o2LEjnTt3pkmTJpiZmfHrr7+ir6//yq7lZWiUvAa/lyAJCQlYW1sTHx8vQ3tENikpKXz66acAfP/993neo1Acpaens3v3bgDatWuX4xAXIcSLkc8PIURJ9Pfff+Pk5MSBAwdo1aqVrsMR4pV5oR7WpUuX4urqiomJCQ0bNuTIkSN51j98+DANGzbExMSEKlWqsHz58mx1Fi9ejLu7O6ampjg5OTFq1CgeP378IuEJkU16ejrr1q1j3bp1pKen6zqcAsvMzOTcuXOcO3dOJvcQQgghSqGgoCB27drFjRs3CAkJ4aOPPsLFxYVmzZrpOjQhXqkCJ6xbt25l5MiRTJ48mbNnz9K0aVPatGmTbRrrJ27cuEFAQABNmzbl7NmzTJo0ieHDh6sL8kJWd/eECROYPn064eHhrFq1iq1bt6qTzAghhBBCCFGapaWlMWnSJGrVqsV7771H+fLlCQ4OlskYxWuvwEOCGzduTIMGDdR1oSDrpuCOHTsyd+7cbPU/++wzdu3apTWl9sCBAzl37hzHjx8HYOjQoYSHh3Pw4EG1zpgxYzh58uRze2+fkCFdIi9JSUnq1OKJiYklbgKm1NRU9e9r4sSJMgGTEIVIPj+EEEKI4qtAPaypqamEhoZmm5K7devWhISE5HjM8ePHs9X38/Pj9OnTpKWlAfD2228TGhrKyZMnAbh+/Tp79uyhbdu2ucaSkpJCQkKC1iaEEEIIIYQQ4vVRoJlb7t27R0ZGRrbFau3s7IiNjc3xmNjY2Bzrp6enc+/ePRwcHPjoo4+4e/cub7/9NoqikJ6ezqBBg5gwYUKuscydO5eZM2cWJHwhhBBCCCGEECXIC0269Ox6T4qi5LkGVE71ny4PDg7miy++YOnSpZw5c4YdO3awe/duZs+enWubEydOJD4+Xt1u3br1IpcihBBCiFKgd+/edOzYUacxREZGotFoCAsL02kcQghRkhSoh9XW1hZ9ff1svalxcXHZelGfsLe3z7G+gYEB5cqVA2Dq1Kn06NGDTz75BIA6deqQlJTEgAEDmDx5srpm0tOMjY1L3NIkQgghhBBCCCHyr0A9rEZGRjRs2JD9+/drle/fvx9vb+8cj/Hy8spWf9++fTRq1Eid1Sw5OTlbUqqvr4+iKLwmy8QKHTMzMyMuLo64uDjMzMx0HU6BGRoaMnbsWMaOHSuzAQohxEtycXFh8eLFWmWenp7MmDFDfb5o0SLq1KmDubk5Tk5ODB48mMTERHX/2rVrKVOmDP/73/+oWbMmFhYW+Pv7ExMTU6BYLl26REBAABYWFtjZ2dGjRw/u3bun7t+2bRt16tTB1NSUcuXK4ePjQ1JSEgAtWrRg5MiRWu117NiR3r17q8+XLl1KtWrVMDExwc7Ojk6dOhUoPiGE0LUCDwkePXo0K1euZPXq1YSHhzNq1CiioqIYOHAgkDVUt2fPnmr9gQMHcvPmTUaPHk14eDirV69m1apVjB07Vq3Tvn17li1bxpYtW7hx4wb79+9n6tSpvPvuu+jr6xfCZYrSTqPRUL58ecqXL5/n8PXiSqPRYG5ujrm5eYmMXwghSho9PT2WLFnCxYsXWbduHUFBQYwfP16rTnJyMgsWLGDDhg38/vvvREVFaX2/eZ6YmBiaN2+Op6cnp0+fJjAwkDt37tC5c2d1f9euXenbty/h4eEEBwfz/vvv5/vH/NOnTzN8+HBmzZpFREQEgYGBsmanEKLEKdCQYIAuXbpw//59Zs2aRUxMDLVr12bPnj04OzsDWf+5Pr0mq6urK3v27GHUqFF89913ODo6smTJEj744AO1zpQpU9BoNEyZMoXbt29Tvnx52rdvzxdffFEIlyiEEEIIUTBP91y6uroye/ZsBg0axNKlS9XytLQ0li9fTtWqVYGsZfpmzZqV73MsW7aMBg0aMGfOHLVs9erVODk5cfnyZRITE0lPT+f9999Xv2fVqVMn3+1HRUVhbm5Ou3btsLS0xNnZmfr16+f7eCGEKA4KnLACDB48mMGDB+e4b+3atdnKmjdvzpkzZ3IPwsCA6dOnM3369BcJR4jnSklJYfTo0UDWMK+Sdv9zeno6//vf/4CsZaEMDF7oT1cIIUQ+HTp0iDlz5nDp0iUSEhJIT0/n8ePHJCUlqWt5m5mZqckqgIODA3Fxcfk+R2hoKIcOHVLXCX/atWvXaN26Na1ataJOnTr4+fnRunVrOnXqhI2NTb7a9/X1xdnZmSpVquDv74+/vz/vvfdeibw1RghRer3QLMFClDTp6eksXbqUpUuXkp6erutwCiwzM5PTp09z+vRpMjMzdR2OEEKUaHp6etmG1T5ZGx7g5s2bBAQEULt2bbZv305oaCjfffddtnrPzimg0WgKNPdGZmYm7du3JywsTGu7cuUKzZo1Q19fn/3797N37148PDz45ptvcHd358aNG/m6DktLS86cOcPmzZtxcHBg2rRp1KtXj4cPH+Y7RiGE0DVJWIUQQghRqpQvX15rcqSEhAQ1CYSsez/T09NZuHAhb731FtWrVyc6OrrQ42jQoAF//vknLi4uuLm5aW1PenE1Gg1NmjRh5syZnD17FiMjI3bu3JnjdWRkZHDx4kWtcxgYGODj48P8+fM5f/48kZGRBAUFFfq1CCHEqyIJqxBCCCFKlXfeeYcNGzZw5MgRLl68SK9evbQmeaxatSrp6el88803XL9+nQ0bNrB8+fJCj2PIkCH8888/dO3alZMnT3L9+nX27dtH3759ycjI4MSJE8yZM4fTp08TFRXFjh07uHv3LjVr1lSv47fffuO3337jr7/+YvDgwVq9p7t372bJkiWEhYVx8+ZN1q9fT2ZmJu7u7oV+LUII8apIwiqEEEKI115mZqZ6///EiRNp1qwZ7dq1IyAggI4dO2rdi+rp6cmiRYuYN28etWvXZuPGjcydO7dQYgDUOBwdHTl27BgZGRn4+flRu3ZtRowYgbW1NXp6elhZWfH7778TEBBA9erVmTJlCgsXLqRNmzYA9O3bl169etGzZ0+aN2+Oq6srLVu2VM9XpkwZduzYwTvvvEPNmjVZvnw5mzdvplatWi99LUIIUVQ0ymuy0GlCQgLW1tbEx8djZWWl63BEMZOUlKROapGYmKgOtSopUlNT1S9LEydOxMjISMcRCfH6kM+P0sHf3x83Nze+/fZbncXwxx9/4OXlxd27d7G1tdVZHEIIUZJID6sQQgghXlsPHjzgt99+Izg4GB8fH53EkJ6eztWrV/nyyy+pV6+eJKtCCFEAsjaGEEIIIV5bffv25dSpU4wZM4YOHTroJIaLFy/i7e2Np6cn69ev10kMQghRUsmQYFEqZGZmEhUVBUDlypXR0ytZgwsURSE+Ph4Aa2trNBqNjiMS4vUhnx9CCCFE8SU9rKJU0NPTw8XFRddhvDCNRkOZMmV0HYYQQgghhBBFqmR1MwkhhBBCFEO9e/emY8eOug5DCCFeO5KwilIhNTWVcePGMW7cOFJTU3UdToFlZGSwb98+9u3bR0ZGhq7DEUKIEik2NpZhw4ZRpUoVjI2NcXJyon379hw8eFDXoeVbSEgI+vr6+Pv76zqUXLVo0YKRI0fqOgwhxGtCElZRKqSlpbFgwQIWLFhAWlqarsMpsIyMDI4fP87x48clYRVCiBcQGRlJw4YNCQoKYv78+Vy4cIHAwEBatmzJkCFDXrjdjIwMdX3VorB69WqGDRvG0aNH1bkZhBDidSYJqxBCCCFee4MHD0aj0XDy5Ek6depE9erVqVWrFqNHj+aPP/5Q6y1atIg6depgbm6Ok5MTgwcPJjExUd2/du1aypQpw+7du/Hw8MDY2JibN29mO19KSgrDhw+nQoUKmJiY8Pbbb3Pq1Cl1/4MHD+jWrRvly5fH1NSUatWqsWbNmjyvISkpiR9//JFBgwbRrl071q5dq7X/SWxP+/nnn7NN1Pf5559ToUIFLC0t+eSTT5gwYQKenp7q/px6SDt27Ejv3r3V50uXLqVatWqYmJhgZ2dHp06dgKyh0YcPH+brr79Go9Gg0WiIjIwE4PDhw7z55psYGxvj4ODAhAkTSE9Pz/OahRBCElYhhBBCvNb++ecfAgMDGTJkCObm5tn2P53k6enpsWTJEi5evMi6desICgpi/PjxWvWTk5OZO3cuK1eu5M8//6RChQrZ2hw/fjzbt29n3bp1nDlzBjc3N/z8/Pjnn38AmDp1KpcuXWLv3r2Eh4ezbNmy567PunXrVtzd3XF3d6d79+6sWbOGgi72sHHjRr744gvmzZtHaGgolStXZtmyZQVq4/Tp0wwfPpxZs2YRERFBYGAgzZo1A+Drr7/Gy8uL/v37ExMTQ0xMDE5OTty+fZuAgADeeOMNzp07x7Jly1i1ahWff/55gc4thCh9ZJZgIYQQQrzWrl69iqIo1KhR47l1n+5ZdHV1Zfbs2QwaNIilS5eq5WlpaSxdupR69erl2EZSUhLLli1j7dq1tGnTBoAVK1awf/9+Vq1axbhx44iKiqJ+/fo0atQIIF8z2a9atYru3bsD4O/vT2JiIgcPHsTHx+e5xz7xzTff0K9fP/r06QPAtGnT2Ldvn1Yv8vNERUVhbm5Ou3btsLS0xNnZmfr16wNZS68ZGRlhZmaGvb29eszSpUtxcnLi22+/RaPRUKNGDaKjo/nss8+YNm1aiVtuTghRdOR/ByGEEEK81p70QuZnDetDhw7h6+tLxYoVsbS0pGfPnty/f5+kpCS1jpGREXXr1s21jWvXrpGWlkaTJk3UMkNDQ958803Cw8MBGDRoEFu2bMHT05Px48cTEhKSZ1wRERGcPHmSjz76CAADAwO6dOnC6tWrn3tNz7bz5ptvapU9+/x5fH19cXZ2pkqVKvTo0YONGzeSnJyc5zHh4eF4eXlp/Rs0adKExMRE/v777wKdXwhRukjCKoQQQojXWrVq1dBoNGqymJubN28SEBBA7dq12b59O6GhoXz33XcAWhP2mZqa5pn85pYgK4qilrVp04abN28ycuRIoqOjadWqFWPHjs21zVWrVpGenk7FihUxMDDAwMCAZcuWsWPHDh48eABkDWd+dohwThMN5hTX057XjqWlJWfOnGHz5s04ODgwbdo06tWrx8OHD3ON/+lrf/a8+fkhQQhReknCKoQQQojXWtmyZfHz8+O7777T6il94kmidfr0adLT01m4cCFvvfUW1atXJzo6usDnc3Nzw8jIiKNHj6plaWlpnD59mpo1a6pl5cuXp3fv3vz3v/9l8eLF/PDDDzm2l56ezvr161m4cCFhYWHqdu7cOZydndm4caPa3r///qt1jWFhYVptubu7c/LkSa2y06dPaz0vX748MTEx6vOMjAwuXryoVcfAwAAfHx/mz5/P+fPniYyMJCgoCMjqgX52RnsPDw9CQkK0EuGQkBAsLS2pWLFijtcthBAg97CKUsLU1FT9sDU1NdVxNAVnaGjIoEGD1MdCCCEKZunSpXh7e/Pmm28ya9Ys6tatS3p6Ovv372fZsmWEh4dTtWpV0tPT+eabb2jfvj3Hjh1j+fLlBT6Xubk5gwYNYty4cZQtW5bKlSszf/58kpOT6devH5B172jDhg2pVasWKSkp7N69WyuZfdru3bt58OAB/fr1w9raWmtfp06dWLVqFUOHDqVx48aYmZkxadIkhg0bxsmTJ7PNJDxs2DD69+9Po0aN8Pb2ZuvWrZw/f54qVaqodd555x1Gjx7Nb7/9RtWqVfnqq6+0ek93797N9evXadasGTY2NuzZs4fMzEzc3d2BrPtxT5w4QWRkJBYWFpQtW5bBgwezePFihg0bxtChQ4mIiGD69OmMHj1a7l8VQuRNeU3Ex8crgBIfH6/rUIQQQpQg8vlRekRHRytDhgxRnJ2dFSMjI6VixYrKu+++qxw6dEits2jRIsXBwUExNTVV/Pz8lPXr1yuA8uDBA0VRFGXNmjWKtbV1trZ79eqldOjQQX3+6NEjZdiwYYqtra1ibGysNGnSRDl58qS6f/bs2UrNmjUVU1NTpWzZskqHDh2U69ev5xh3u3btlICAgBz3hYaGKoASGhqqKIqi7Ny5U3Fzc1NMTEyUdu3aKT/88IPy7Ne9WbNmKba2toqFhYXSt29fZfjw4cpbb72l7k9NTVUGDRqklC1bVqlQoYIyd+5cpUOHDkqvXr0URVGUI0eOKM2bN1dsbGwUU1NTpW7dusrWrVvV4yMiIpS33npLMTU1VQDlxo0biqIoSnBwsPLGG28oRkZGir29vfLZZ58paWlpOV6XEEI8oVGUAs6HXkwlJCRgbW1NfHw8VlZWug5HCCFECSGfH6K08/X1xd7eng0bNug6FCGEyEaGBItSITU1lTlz5gAwadIkjIyMdBxRwWRkZHDkyBEAmjZtir6+vo4jEkIIURIlJyezfPly/Pz80NfXZ/PmzRw4cID9+/frOjQhhMiRJKyiVEhLS2PmzJkAjBs3rkQmrIcPHwbA29tbElYhhBAvRKPRsGfPHj7//HNSUlJwd3dn+/btBVrLVQghipIkrEIIIYQQpYSpqSkHDhzQdRhCCJFvMi2bEEIIIYQQQohiSRJWIYQQQogisHbtWsqUKaM+nzFjBp6enjqLRwghSgJJWIUQQgjx2uvduzcajQaNRoOBgQGVK1dm0KBBPHjwQNehCSGEyIMkrEIIIYQoFfz9/YmJiSEyMpKVK1fy66+/MnjwYF2HJYQQIg+SsIoS5TVZNlgIIYQOGBsbY29vT6VKlWjdujVdunRh3759AGRmZjJr1iwqVaqEsbExnp6eBAYGqsd6eXkxYcIErfbu3r2LoaEhhw4dArKWUBs/fjwVK1bE3Nycxo0bExwcnO/4Tp06ha+vL7a2tlhbW9O8eXPOnDnz8hcuhBAlmCSsothTFIV1IZF4zz1Itcl7+XB5CGejCjaEy8TEhJMnT3Ly5ElMTExeUaSvjoGBAZ988gmffPIJBgYyubcQQrys69evExgYiKGhIQBff/01CxcuZMGCBZw/fx4/Pz/effddrly5AkC3bt3YvHmz1g+nW7duxc7OjubNmwPQp08fjh07xpYtWzh//jwffvgh/v7+ahvP8++//9KrVy+OHDnCH3/8QbVq1QgICODff/8t5KsXQoiSQ6O8Jl1WCQkJWFtbEx8fj5WVla7DEYVo7p5wvv/9ulaZvp6Grz/ypF1dRx1FJYR4XcjnR+nQu3dv/vvf/2JiYkJGRgaPHz8GYNGiRYwaNYqKFSsyZMgQJk2apB7z5ptv8sYbb/Ddd99x9+5dHB0dCQoKomnTpkDWuthvv/028+fP59q1a1SrVo2///4bR8f/+2zy8fHhzTffZM6cOaxdu5aRI0fy8OFDIGvSpZ9//pmwsLAcY87IyMDGxoZNmzbRrl27V/PCCCFEMSddNaJYO/RXnJqsvvtGRWpWsmbPmWgu3HzIyK1h2Fub0Mi5rI6jFEIIURK0bNmSZcuWkZyczMqVK7l8+TLDhg0jISGB6OhomjRpolW/SZMmnDt3DoDy5cvj6+vLxo0badq0KTdu3OD48eMsW7YMgDNnzqAoCtWrV9dqIyUlhXLlyuUrvri4OKZNm0ZQUBB37twhIyOD5ORkoqKiCuHqhRCiZJIhwaLYSs/IZPbuSwA086hAi9p22JUxoVcLV2o5WZOeoTB001mSU9Kf21ZqaipffvklX375Jampqa869EKXkZHBsWPHOHbsGBkZGboORwghSiRzc3Pc3NyoW7cuS5YsISUlhZkzZ6r7NRqNVn1FUbTKunXrxrZt20hLS2PTpk3UqlWLevXqAVn3wOrr6xMaGkpYWJi6hYeH8/XXX+crvt69exMaGsrixYsJCQkhLCyMcuXKlcjPLSGEKCySsIpi69fz0Vy/l4S5sT7+DRzUcj09DR83c6GMmSGx8Y+ZtefSc9tKS0tj/PjxjB8/nrS0tFcZ9iuRkZHBgQMHOHDggCSsQghRSKZPn86CBQtITEzE0dGRo0ePau0PCQmhZs2a6vOOHTvy+PFjAgMD2bRpE927d1f31a9fn4yMDOLi4nBzc9Pa7O3t8xXPkSNHGD58OAEBAdSqVQtjY2Pu3btXOBcrhBAllCSsotj67x9ZQ6CaeVTAxFBfa5+pkT6dvCsD8NOpv7l2L7HI4xNCCFGytWjRglq1ajFnzhzGjRvHvHnz2Lp1KxEREUyYMIGwsDBGjBih1jc3N6dDhw5MnTqV8PBwPv74Y3Vf9erV6datGz179mTHjh3cuHGDU6dOMW/ePPbs2ZOveNzc3NiwYQPh4eGcOHGCbt26YWpqWujXLYQQJYkkrKJYun43kdCbD9DTQOPqtjnW8XCyprqjJRmZCjN/fX4vqxBCCPGs0aNHs2LFCt577z3GjBnDmDFjqFOnDoGBgezatYtq1app1e/WrRvnzp2jadOmVK5cWWvfmjVr6NmzJ2PGjMHd3Z13332XEydO4OTklK9YVq9ezYMHD6hfvz49evRg+PDhVKhQodCuVQghSiKZJVgUS4v2RbAk6Coelaz4xNct13q37yezcNdfAOwZ/jYejtY51ktKSsLCwgKAxMREzM3NCz/oVyg1NZW5c+cCMHHiRIyMjHQckRCvD/n8EEIIIYov6WEVxdL+8DgAPF1t8qxXsZwZtStnJamLDuZvnTshhBBCCCFEySAJqyh2YuIfER6TgAaoUSnnHtOntaqbNZlF0KU73Lyf9IqjE0IIIYQQQhQVSVhFsRP0V1bvqnMFcyxMnr9UsHN5c9zsLchUYMmhq686PCGEEEIIIUQReX42IEQRO3I5awr/mpXyfy9Zi9p2XI1NZO+FGGa1r4W5sfZb28TEhEOHDqmPSxoDAwN69eqlPhZCCCGEEKI0kG++olhRFIVTN/8BwM3eMt/H1ahoRVkLI/5JTGXjqZsMeLuq1n59fX1atGhRmKEWKT09PVxcXHQdhhBCCCGEEEVKhgSLYiXyfjL3E1Mx0NfgZGuW7+P09DR418ha/mbziVuvKjwhhBCiyLVo0YKRI0fqOgwhhNAJSVhFsXLqRlbvqpOtGQb6BXt7vlnNFgN9DTfuJnH8+n2tfWlpaXz33Xd89913pKWlFVq8RSUjI4OTJ09y8uRJMjIydB2OEEKUOL1790aj0WTbrl59sbkP1q5dS5kyZbKVu7i4sHjx4pcL9hk7duxg9uzZhdqmEEKUFJKwimLlVGRWwlrFzqLAx1qYGODpkrUMzspj17X2paamMnToUIYOHUpqaurLB1rEMjIy2Lt3L3v37pWEVQghXpC/vz8xMTFam6urq1ad4vgZUbZsWSwt83+bjBBCvE4kYRXFypmoBwC4Vih4wgrg5Z41LPhIxD0SHpW8nlQhhBCvjrGxMfb29lpbq1atGDp0KKNHj8bW1hZfX18AFi1aRJ06dTA3N8fJyYnBgweTmJgIQHBwMH369CE+Pl7tqZ0xYwYtWrTg5s2bjBo1Si1/IiQkhGbNmmFqaoqTkxPDhw8nKen/lmJbunQp1apVw8TEBDs7Ozp16qTukyHBQojSTBJWUWwkp6Zz/V7Wh3elcvm/f/VpLhXMKW9lTGp6JptPRxVmeEIIIV5T69atw8DAgGPHjvH9998DWZPdLVmyhIsXL7Ju3TqCgoIYP348AN7e3ixevBgrKyu1p3bs2LHs2LGDSpUqMWvWLLUc4MKFC/j5+fH+++9z/vx5tm7dytGjRxk6dCgAp0+fZvjw4cyaNYuIiAgCAwNp1qyZbl4MIYQoZmSWYFFshMf8i6KAlakhVmaGL9SGRqPhjWrl2BMazY6zt/m0adXnHySEEKJU2L17NxYW/zeCp02bNgC4ubkxf/58rbpP92i6uroye/ZsBg0axNKlSzEyMsLa2hqNRoO9vb3Wcfr6+lhaWmqVf/nll3z88cdqm9WqVWPJkiU0b96cZcuWERUVhbm5Oe3atcPS0hJnZ2fq169fyFcvhBAlkySsotj4MzoegIrlTF+qnUZVy7L3TDQR0f/y151/qWEn9/0IIYSAli1bsmzZMvW5ubk5Xbt2pVGjRtnqHjp0iDlz5nDp0iUSEhJIT0/n8ePHJCUlYW5uXqDzhoaGcvXqVTZu3KiWKYpCZmYmN27cwNfXF2dnZ6pUqYK/vz/+/v689957mJm92GgjIYR4nciQYFFs/Hk7AYCKZV/uA7qMuRHVHa0AWPdH5MuGJYQQ4jVhbm6Om5ubujk4OKjlT7t58yYBAQHUrl2b7du3ExoaynfffQfwQjPNZ2Zm8umnnxIWFqZu586d48qVK1StWhVLS0vOnDnD5s2bcXBwYNq0adSrV4+HDx++9DULIURJJwmrKDb+jCmcHlaAN93KAhB4PpaMjMyXbk8IIUTpcfr0adLT01m4cCFvvfUW1atXJzo6WquOkZFRjrO251TeoEED/vzzT61k+clmZGQEgIGBAT4+PsyfP5/z588TGRlJUFDQq7tIIYQoIV4oYV26dCmurq6YmJjQsGFDjhw5kmf9w4cP07BhQ0xMTKhSpQrLly/PVufhw4cMGTIEBwcHTExMqFmzJnv27HmR8EQJlJaRSUTsv8CLT7j0tNqVy2BqpM+DpFT2/XUHY2Njdu/eze7duzE2Nn7p9ouagYEBXbt2pWvXrhgYyEh+IYR4lapWrUp6ejrffPMN169fZ8OGDdm+u7i4uJCYmMjBgwe5d+8eycnJavnvv//O7du3uXfvHgCfffYZx48fZ8iQIYSFhXHlyhV27drFsGHDgKx7a5csWUJYWBg3b95k/fr1ZGZm4u7uXrQXLoQQxVCBE9atW7cycuRIJk+ezNmzZ2natClt2rQhKirnGVlv3LhBQEAATZs25ezZs0yaNInhw4ezfft2tU5qaiq+vr5ERkaybds2IiIiWLFiBRUrVnzxKxMlyo17SaRlKBgb6lHWwuil2zM00MPTNWtN1p9C/8bAwIC2bdvStm3bEpnw6enpUb16dapXr46engyMEEKIV8nT05NFixYxb948ateuzcaNG5k7d65WHW9vbwYOHEiXLl0oX768OmnTrFmziIyMpGrVqpQvXx6AunXrcvjwYa5cuULTpk2pX78+U6dOVYcklylThh07dvDOO+9Qs2ZNli9fzubNm6lVq1bRXrgQQhRDGkVRlIIc0LhxYxo0aKA1aUHNmjXp2LFjtv/MIetXxV27dhEeHq6WDRw4kHPnznH8+HEAli9fzpdffslff/2FoWH+ZodNSUkhJSVFfZ6QkICTkxPx8fFYWVkV5JJEMbDnQgyDN56hsq0ZI9vXKJQ2r8cm8u3eyxgb6nF2qi9mRiUvURVCvHoJCQlYW1vL54cQQghRDBWoqyY1NZXQ0FBat26tVd66dWtCQkJyPOb48ePZ6vv5+XH69Gl14oJdu3bh5eXFkCFDsLOzo3bt2syZMyfHe0OemDt3LtbW1urm5ORUkEsRxcyVO1mLsduXMSm0Nl3szLExNyIlLZMfT0eydu1a1q5d+0ITZuhaRkaGOlFHXn8XQgghhBBCvE4KlLDeu3ePjIwM7OzstMrt7OyIjY3N8ZjY2Ngc66enp6v3dly/fp1t27aRkZHBnj17mDJlCgsXLuSLL77INZaJEycSHx+vbrdu3SrIpYhi5urdrIS1QiEmrHoaDfWrZA0L/iU0ij59+tCnTx9SU1ML7RxFJSMjg19++YVffvlFElYhhBBCCFFqvNAYSY1Go/VcUZRsZc+r/3R5ZmYmFSpU4IcffkBfX5+GDRsSHR3Nl19+ybRp03Js09jYuEROniNydjUuK2G1sy68hBWgYdWyBF24Q9jNh4XarhBCCCGEEOLVK1DCamtri76+frbe1Li4uGy9qE/Y29vnWN/AwIBy5coB4ODggKGhIfr6+mqdmjVrEhsbS2pqqjrlu3g9ZWQqXPv/Pax2ZV5+SZunOdiY4mBjyu07jwu1XSGEEEIIIcSrV6AhwUZGRjRs2JD9+/drle/fvx9vb+8cj/Hy8spWf9++fTRq1EidYKlJkyZcvXqVzMz/Wy/z8uXLODg4SLJaCvz9IJnU9EwM9DWFMkPwsxr8/2HBQgghhC7MmDEDT09P9Xnv3r3p2LGjzuIRQoiSpMDrY4wePZqVK1eyevVqwsPDGTVqFFFRUQwcOBDIure0Z8+eav2BAwdy8+ZNRo8eTXh4OKtXr2bVqlWMHTtWrTNo0CDu37/PiBEjuHz5Mr/99htz5sxhyJAhhXCJorh7Mhy4vJUJenq5Dy1/UQ2qlC30NoUQQpQsvXv3RqPRqFu5cuXw9/fn/Pnzug5NCCFEHgqcsHbp0oXFixcza9YsPD09+f3339mzZw/Ozs4AxMTEaK3J6urqyp49ewgODsbT05PZs2ezZMkSPvjgA7WOk5MT+/bt49SpU9StW5fhw4czYsQIJkyYUAiXKIq7JwlrYc4Q/DQbCyNcK5i/kraFEEKUHP7+/sTExBATE8PBgwcxMDCgXbt2L9xeSZx1XgghSpoCJ6wAgwcPJjIykpSUFEJDQ2nWrJm6b+3atQQHB2vVb968OWfOnCElJYUbN26ovbFP8/Ly4o8//uDx48dcu3aNSZMmad3TKl5fkfeTAbC1enWTaHnKsGAhhCj1jI2Nsbe3x97eHk9PTz777DNu3brF3bt3gay146tXr46ZmRlVqlRh6tSpWknpk6G9q1evpkqVKhgbG6MoCvHx8QwYMIAKFSpgZWXFO++8w7lz5/IdV2BgIG+//TZlypShXLlytGvXjmvXrhX69QshREn0QrMEC1GYov5JAqCc5atLWOu7VaBCxwlkKvDX3Uc0NC9ZPa4GBgZ06tRJfSyEEOLlJCYmsnHjRtzc3NRJIC0tLVm7di2Ojo5cuHCB/v37Y2lpyfjx49Xjrl69yo8//sj27dvVH9bbtm1L2bJl2bNnD9bW1nz//fe0atWKy5cvU7bs829LSUpKYvTo0dSpU4ekpCSmTZvGe++9R1hYGHp6L9S3IIQQrw355it07mYR9LBamZvQyKctf96KZ8e5WBq62L6yc70Kenp61KpVS9dhCCFEibZ7924sLCyArCTRwcGB3bt3q0nhlClT1LouLi6MGTOGrVu3aiWsqampbNiwgfLlywMQFBTEhQsXiIuLU5fbW7BgAT///DPbtm1jwIABz43r6dukAFatWkWFChW4dOkStWvXfrmLFkKIEk5+thM6lZqeSfTDRwDYvsIeVgBP16xhwQfD76hrAQshhCg9WrZsSVhYGGFhYZw4cYLWrVvTpk0bbt68CcC2bdt4++23sbe3x8LCgqlTp2rNywHg7OysJqsAoaGhJCYmUq5cOSwsLNTtxo0b+R7We+3aNT7++GOqVKmClZUVrq6uANnOLYQQpZH0sAqduv3wEZkKGBnoYWn66t6OGenpJIYf4fHlm8S4eXE66gFvOJec2YMzMzMJDw8HstYoliFiQghRcObm5ri5uanPGzZsiLW1NStWrKBdu3Z89NFHzJw5Ez8/P6ytrdmyZQsLFy7M1sbTMjMzcXBwyDZ/B0CZMmXyFVf79u1xcnJixYoVODo6kpmZSe3atUlNTS3wNQohxOtGElahU5H3s+5fLWthhEZT+EvaPJGWmsqSsYMAcBq1jZ/O/F2iEtb09HS2bdsGZC0dJesTCyHEy9NoNOjp6fHo0SOOHTuGs7MzkydPVvc/6XnNS4MGDYiNjcXAwAAXF5cCx3D//n3Cw8P5/vvvadq0KQBHjx4tcDtCCPG6koRV6FRUEdy/mpOgS3FkdsiUnkohhChFUlJSiI2NBeDBgwd8++23JCYm0r59e+Lj44mKimLLli288cYb/Pbbb+zcufO5bfr4+ODl5UXHjh2ZN28e7u7uREdHs2fPHjp27EijRo3yPN7GxoZy5crxww8/4ODgQFRUlCzrJ4QQT5Fv60Kn1AmXXvH9q08zMtDj3r8phNz4p8jOKYQQQvcCAwNxcHDAwcGBxo0bc+rUKX766SdatGhBhw4dGDVqFEOHDsXT05OQkBCmTp363DY1Gg179uyhWbNm9O3bl+rVq/PRRx8RGRmJnZ3dc4/X09Njy5YthIaGUrt2bUaNGsWXX35ZGJcrhBCvBY3ymsw+k5CQgLW1NfHx8VhZWek6HJFPn6w7xYHwOD7wcqJJjfLPP+AFPU5OpluDrPuW2n59gIvRj3mvYUW++tDzlZ2zMKWmpjJ37lxAhgQLUdjk80MIIYQovqSHVeiULnpY67qUAeBwxF0yMzOL7LxCCCGEEEKIgpGEVehMZqZC1D9ZCWu5IkxYqztYYWygxz+JqRy+eq/IziuEEEIIIYQoGElYhc7E/ZtCSnomehqwsSi6Ia5GBnrUqmwNwI6zt4vsvEIIIYQQQoiCkVmChc7cfpjVu2ptboS+3qtb0gbAwNCQIXO+Uh/Xd7XhzPUH/B5xl4yMTPT1i/dvN/r6+nTo0EF9LIQQQgghRGkgCavQmdsPHwNgY/7qe1cNDA155/0u6nP3ilaYGOkTn5xG0OW7+NZ8/kyOuqSvr4+np6euwxBCCFFEWrRogaenJ4sXL9Z1KEIIoVPFu1tJvNZuP3gEFO1w4CcM9PWo82RYcJgMCxZCiNIgNjaWESNG4ObmhomJCXZ2drz99tssX76c5ORkXYdXpGbMmIFGo2HgwIFa5WFhYWg0GiIjI3UTmBBCPEMSVqEz0Q//f8JaBD2sGenphAYfIDT4ABnp6QB4utoAcDTiLmnpGa88hpeRmZnJ5cuXuXz5ssxsLIQQL+D69evUr1+fffv2MWfOHM6ePcuBAwcYNWoUv/76KwcOHNB1iEXOxMSEVatWcfnyZV2HIoQQuZKEVejM7YdF18OalprKnIE9mTOwJ2mpqQBUd7TC3Fiffx+ns/+vuFcew8tIT09n8+bNbN68mfT/n3ALIYTIv8GDB2NgYMDp06fp3LkzNWvWpE6dOnzwwQf89ttvtG/fXq0bFRVFhw4dsLCwwMrKis6dO3Pnzh11/4wZM/D09GTDhg24uLhgbW3NRx99xL///gtAZGQkGo0m29aiRQsA7t+/T9euXalUqRJmZmbUqVOHzZs35xl/amoq48ePp2LFipibm9O4cWOCg4PV/S1atMjxnHn1lLq7u9OyZUumTJlS8BdUCCGKiCSsQmduP8gafqWLIcEA+noa6jiXAWCnDAsWQojX1v3799m3bx9DhgzB3Nw8xzoaTdbkf4qi0LFjR/755x8OHz7M/v37uXbtGl26dNGqf+3aNX7++Wd2797N7t27OXz4MP/5z38AcHJyIiYmRt3Onj1LuXLlaNasGQCPHz+mYcOG7N69m4sXLzJgwAB69OjBiRMncr2GPn36cOzYMbZs2cL58+f58MMP8ff358qVKwDs2LFD65zvv/8+7u7u2NnlPUfDf/7zH7Zv386pU6fy92IKIUQRk4RV6Mz/TbpkqLMYngwLDrlyv9gPCxZCCPFirl69iqIouLu7a5Xb2tpiYWGBhYUFn332GQAHDhzg/PnzbNq0iYYNG9K4cWM2bNjA4cOHtZK6zMxM1q5dS+3atWnatCk9evTg4MGDQNZEefb29tjb21OmTBkGDhyIl5cXM2bMAKBixYqMHTsWT09PqlSpwrBhw/Dz8+Onn37KMf5r166xefNmfvrpJ5o2bUrVqlUZO3Ysb7/9NmvWrAGgbNmy6jk3b95MUFAQu3btwtTUNM/XpkGDBnTu3JkJEya80GsrhBCvmswSLHQi/lEaiSlZQ1vLFME9rLmpam+JubE+SSlZw4IDajvoLBYhhBCv1pNe1CdOnjxJZmYm3bp1IyUlBYDw8HCcnJxwcnJS63l4eFCmTBnCw8N54403AHBxccHS0lKt4+DgQFxc9ttL+vXrx7///sv+/fvR08vqJ8jIyOA///kPW7du5fbt26SkpJCSkpJr7++ZM2dQFIXq1atrlaekpFCuXDmtsr179zJhwgR+/fXXbPVz8/nnn1OzZk327dtHhQoV8nWMEEIUFUlYhU48mXDJ3NgAY0PdrSuqr6ehtnMZTly+z67z0ZKwCiHEa8jNzQ2NRsNff/2lVV6lShUArV5IRVGyJbY5lRsaao8O0mg02SbF+/zzzwkMDOTkyZNaye3ChQv56quvWLx4MXXq1MHc3JyRI0eS+v/nWHhWZmYm+vr6hIaGZluL28LCQn186dIlPvroI/7zn//QunXrHNvKSdWqVenfvz8TJkxg1apV+T5OCCGKggwJFjrxf0va6G448BN1//99rMeu3CM9Q2bgFUKI1025cuXw9fXl22+/JSkpKc+6Hh4eREVFcevWLbXs0qVLxMfHU7NmzXyfc/v27cyaNYsff/yRqlWrau07cuQIHTp0oHv37tSrV48qVaqo96LmpH79+mRkZBAXF4ebm5vWZm9vD2Tdp9u+fXvef/99Ro0ale84n5g2bRqXL19my5YtBT5WCCFeJUlYhU7cLsIlbZ6nmoMlpkb6/PsonaDLd3UdjhBCiFdg6dKlpKen06hRI7Zu3Up4eDgRERH897//5a+//lJ7Ln18fKhbty7dunXjzJkznDx5kp49e9K8eXMaNWqUr3NdvHiRnj178tlnn1GrVi1iY2OJjY3ln3/+AbJ6fPfv309ISAjh4eF8+umnxMbG5tpe9erV6datGz179mTHjh3cuHGDU6dOMW/ePPbs2QPA+++/j6mpKTNmzFDPFxsbS0ZG/uZnsLOzY/To0SxZsiTbvho1arBz5858tSOEEIVNElahE0+GBJcpohmCDQwN+WTqF3wy9QsMnhnGZaCvR+3K1gDsOlc8ZwvW19enTZs2tGnTJttwMCGEEM9XtWpVzp49i4+PDxMnTqRevXo0atSIb775hrFjxzJ79mwga2jvzz//jI2NDc2aNcPHx4cqVaqwdevWfJ/r9OnTJCcn8/nnn+Pg4KBu77//PgBTp06lQYMG+Pn50aJFC+zt7enYsWOeba5Zs4aePXsyZswY3N3deffddzlx4oR6r+3vv//On3/+iYuLi9Y5n+4pfp5x48ZpDTF+IiIigvj4+Hy3I4QQhUmjKIqi6yAKQ0JCAtbW1sTHx2NlZaXrcMRzDNl0ht/Ox9DhzYo0r5X3lPtF4c+oh6w6eJ0yZoaETvZBX19+yxGitJDPDyGEEKL4km/lQieii9GQYIDqjlYYG+rxMDmN36/d03U4QgghhBBCCCRhFTryf5MuFU3CmpGRwcUTIVw8EZLj/TyGBnrUcsoaFvzLuegiiakgMjMziYyMJDIyMtsslEIIIYQQQryuJGEVRS41PZO7iVnr3RVVwpqWksL0Xp2Y3qsTaf9/rb1nPZkt+PeIu8UuKUxPT2fdunWsW7eO9PR0XYcjhBBCCCFEkZCEVRS5OwmPURQw0Ndgblx8lgKuUckaIwM9/klM5XjkP7oORwghhBBCiFJPElZR5GITHgNgbWaY4+LsumJkoEfNSlkTruwMK56zBQshhNC9GTNmYGdnp84oXFhtenp6FkpbQgjxOpGEVRS52PgnCWvxmHDpaU+GBR/+q/gNCxZCCPHievfujUajQaPRYGBgQOXKlRk0aBAPHjwoUDvh4eHMnDmT77//npiYGNq0afOKItYWGRmJRqMhLCysSM4nhBDFhSSsosjdeaqHtbip6WSNgb6GuwkphN56qOtwhBBCFCJ/f39iYmKIjIxk5cqV/PrrrwwePLhAbVy7dg2ADh06YG9vj7Gx8asIVQghxP8nCasock8SVqtimLCaGOpTo2LWsOAdZ2VYsBBCvE6MjY2xt7enUqVKtG7dmi5durBv3z51f0ZGBv369cPV1RVTU1Pc3d35+uuv1f0zZsygffv2AOjp6am3tZw6dQpfX19sbW2xtramefPmnDlzRuvc8fHxDBgwgAoVKmBlZcU777zDuXPnCu3aFEVh/vz5VKlSBVNTU+rVq8e2bdvyfW2QNSP9rFmzqFSpEsbGxnh6ehIYGFhoMQohxIuQhFUUudiErFl6i2MPK0BdlzIABP8Vp9tAhBBCvDLXr18nMDAQQ8P/+yzKzMykUqVK/Pjjj1y6dIlp06YxadIkfvzxRwDGjh3LmjVrAIiJiSEmJgaAf//9l169enHkyBH++OMPqlWrRkBAAP/++y+QlUy2bduW2NhY9uzZQ2hoKA0aNKBVq1b880/hTPI3ZcoU1qxZw7Jly/jzzz8ZNWoU3bt35/Dhw/m6NoCvv/6ahQsXsmDBAs6fP4+fnx/vvvsuV65cKZQYhRDiRRSfKVpFqXEnvuiHBOsbGNBj3BT1cV5qOZVBXy+KmIePOXf7IfUqlimCCPOmr6+Pj4+P+lgIIUTB7d69GwsLCzIyMnj8OOuzaNGiRep+Q0NDZs6cqT53dXUlJCSEH3/8kc6dO2NhYUGZMmUAsLe3V+u98847Wuf5/vvvsbGx4fDhw7Rr145Dhw5x4cIF4uLi1CHECxYs4Oeff2bbtm0MGDDgpa4rKSmJRYsWERQUhJeXFwBVqlTh6NGjfP/99zRv3vy51/Ykps8++4yPPvoIgHnz5nHo0CEWL17Md99991IxCiHEi5KEVRS5/7uHtegmXTI0MqJjv/zdp2RqpE81B0v+up3Az2HRxSZhbdKkia7DEEKIEq1ly5YsW7aM5ORkVq5cyeXLlxk2bJhWneXLl7Ny5Upu3rzJo0ePSE1Nfe7svXFxcUybNo2goCDu3LlDRkYGycnJREVFARAaGkpiYiLlypXTOu7Ro0fqPbEv49KlSzx+/BhfX1+t8tTUVOrXr5+va0tISCA6OjrbZ02TJk0KdeiyEEIUlCSsokgpivJ/y9qYF88hwQB1nMvw1+0EgiPimN7WQ9fhCCGEKATm5ua4ubkBsGTJElq2bMnMmTOZPXs2AD/++COjRo1i4cKFeHl5YWlpyZdffsmJEyfybLd3797cvXuXxYsX4+zsjLGxMV5eXqSmpgJZw3EdHBwIDg7OduyTHtuX8WRW+99++42KFStq7XvSo5vfa3t2uTlFUYrVEnRCiNJHElZRpOIfpZGSnvXBamVadAlrRkYGNy5dAMDVo85zh9XWcrJmG3AjLonIf5JwKWteBFHmLjMzU71XysHBAT09uf1cCCFe1vTp02nTpg2DBg3C0dGRI0eO4O3trTVzcH56QI8cOcLSpUsJCAgA4NatW9y7d0/d36BBA2JjYzEwMMDFxaXQr8PDwwNjY2OioqJo3rx5rjHmdW1WVlY4Ojpy9OhRmjVrppaHhITw5ptvFnrMQgiRX5KwiiL1pHfV3FgfQ4OiS7rSUlL47MOsLxIbz1xF38wsz/pWZoY4VzAnMi6JHWdvM7pV9aIIM1fp6emsXLkSgIkTJ2JkVPzWsBVCiJKmRYsW1KpVizlz5vDtt9/i5ubG+vXr+d///oerqysbNmzg1KlTuLq65tmOm5sbGzZsoFGjRiQkJDBu3DhMTU3V/T4+Pnh5edGxY0fmzZuHu7s70dHR7Nmzh44dO9KoUaN8xxwREZGtzMPDg7FjxzJq1CgyMzN5++23SUhIICQkBAsLC3r16pWvaxs3bhzTp0+natWqeHp6smbNGsLCwti4cWO+4xNCiMImCasoUnf+/wzBxXFJm2fVrlyGyLgkgsLjdJ6wCiGEeDVGjx5Nnz59+Oyzzxg4cCBhYWF06dIFjUZD165dGTx4MHv37s2zjdWrVzNgwADq169P5cqVmTNnDmPHjlX3azQa9uzZw+TJk+nbty93797F3t6eZs2aYWdnV6B4n0yI9LQbN24we/ZsKlSowNy5c7l+/TplypShQYMGTJo0CSBf1zZ8+HASEhIYM2YMcXFxeHh4sGvXLqpVq1agGIUQojBpFEVRdB1EYUhISMDa2pr4+HisrKx0HY7IxY+nbjF++3lqVLRiQGu3Ijvv4+RkujXIOt/GM1cxeU4PK8Dd+MfM3XEJPQ2cnOyDrYXuFodPTU1l7ty5gPSwClHY5PNDCCGEKL7kRjhRpNQJl0pAD2t5axPsypiQqcDP527rOhwhhBBCCCFKHUlYRZEqCTMEP61O5TIA7A+/o9tA/l97dx4eVXn2D/x7Zs86WUkIZEWQQBAkaX1BFFQEQbFYLbiBvPjSUmxlKYpb6/aDCFIKFgOiUFttkbYKKoISKUSWVEjY9y0LkJkshEyWSWYmM+f3R5KBkIVMMpNzJvl+rmsuJs95zjn3eRyv5J5nIyIiIiLqhpiwUqcqqk9YO3OF4I4YFKsHABzMLYPZUitxNERERERE3QsTVupURlNDD6t3zMHsHeoLva8a1loHvjlhlDocIiIiIqJuhQkrdSqp5rAqVSpMem4eJj03D0pV2xfHFgQBSfXDgr89ZvBQdDenVCoxcuRIjBw58qZ7yBIRkXzExcVh+fLlUodBROS1uK0NdRqb3YErVVYAnZ+wqjUaTP7t/JtXbMagWD32nCrGj+dLUWt3QKXs/O95lEolRo0a1en3JSLqKqZNm4aysjJs2rSpU++7f/9++Pn5ufWao0aNwpAhQ1xOhKdNm4a4uDi88cYbbo2HiMiT2MNKnaa4wgJRBJQKAX467/mupE9kAHw0SlTW1GLn2WKpwyEiIi9gtdZ9QRseHg7fNmylRkREzWPCSp3GeN2CSwpB6NR7OxwO5J89jfyzp+FwOFw6V6kQMCC6bvGlr49KMyxYFEUUFRWhqKgIXWTrZCIi2Vi2bBkGDRoEPz8/REdHY9asWaisrHQef+ONNzBkyJBG5yxfvhxxcXHOn6dNm4aJEyciNTUVUVFR6NevH4CmQ4JNJhN++ctfokePHggMDMS9996Lw4cPN7nXJ598gri4OOj1ejz++OOoqKhw3icjIwMrVqyAIAgQBAG5ubkAgBMnTmD8+PHw9/dHREQEpkyZgpKSEvc2FhFRJ2PCSp2m0CTdHqzWmhrMnXAP5k64B9aaGpfPT4qpS1j3nClxOeF1B5vNhlWrVmHVqlWw2Wydfn8ioq5MoVDgvffew7Fjx/DXv/4V//nPf/Diiy+6fJ3t27fj5MmTSE9Px+bNm5scF0URDz74IIxGI7Zs2YLs7GwMHToU9913H0pLS531zp8/j02bNmHz5s3YvHkzMjIy8M477wAAVqxYgWHDhmHGjBkwGAwwGAyIjo6GwWDAyJEjMWTIEGRlZeHbb79FYWEhJk2a1P6GISKSAe8Zl0lez9v2YL1e/16BUCkFlFRYcOBSGVJiQqQOiYiI3GTOnDnO9/Hx8Xj77bfx61//GmlpaS5dx8/PDx999BE0muZXwt+xYweOHj2KoqIiaLVaAMDSpUuxadMm/Pvf/8Yvf/lLAHWjgj7++GMEBAQAAKZMmYLt27dj4cKF0Ov10Gg08PX1RWRkpPPaq1atwtChQ7Fo0SJn2bp16xAdHY0zZ86gX79++Pjjj116HiIiOWhXD2taWhri4+Oh0+mQnJyMXbt2tVo/IyMDycnJ0Ol0SEhIwOrVq1us+9lnn0EQBEycOLE9oZGMGb1sD9bradVK3BoVCAD48nCBxNEQEZE77dixA/fffz969eqFgIAATJ06FVeuXEFVVZVL1xk0aFCLySoAZGdno7KyEqGhofD393e+cnJycP78eWe9uLg4Z7IKAD179kRRUVGr987OzsaOHTsaXbd///4A0OjaRETexuUe1g0bNmDOnDlIS0vDnXfeiQ8++ADjxo3DiRMnEBMT06R+Tk4Oxo8fjxkzZuDTTz/Fnj17MGvWLISHh+PRRx9tVDcvLw/z58/HXXfd1f4nItkqKrcA8M4eVqBuWPDxiyZknC4GJkgdDRERuUNeXh7Gjx+PmTNn4u2330ZISAh2796NZ5991jkFQ6FQNFk/oLnpGTdbDdjhcKBnz57YuXNnk2NBQUHO92p149+TgiDcdDqKw+HAhAkTsHjx4ibHevbs2eq5RERy5nLCumzZMjz77LP4v//7PwB1iw589913WLVqFVJTU5vUX716NWJiYpwLDiQmJiIrKwtLly5tlLDa7XY89dRTePPNN7Fr1y6UlZW174lItozOOawtf/ssZwNj9BD2APklZlwoqURCmL/UIRERUQdlZWWhtrYWf/zjH6FQ1A08++c//9moTnh4OIxGI0RRhFC/aOChQ4dcvtfQoUNhNBqhUqkaLdjkKo1GA7vd3uTan3/+OeLi4qByYb9xIiK5c2lIsNVqRXZ2NsaMGdOofMyYMdi7d2+z52RmZjapP3bsWGRlZTX6dvKtt95CeHg4nn322TbFYrFYUF5e3uhF8uacwyrBokvu4K9TI65H3bfnXx7hsGAiIm9jMplw6NChRq/w8HDU1tbiz3/+My5cuIBPPvmkydSlUaNGobi4GEuWLMH58+fx/vvvY+vWrS7ff/To0Rg2bBgmTpyI7777Drm5udi7dy9ee+01ZGVltfk6cXFx+PHHH5Gbm4uSkrrFAJ977jmUlpbiiSeewL59+3DhwgVs27YN06dPb5LcEhF5E5cS1pKSEtjtdkRERDQqj4iIgNFobPYco9HYbP3a2lrnUut79uzB2rVr8eGHH7Y5ltTUVOj1eucrOjralUehTiaKIgob5rB6acIK1PWyAsB/TrU+l4iIiORn586duP322xu91q1bh2XLlmHx4sVISkrC3//+9yYjxhITE5GWlob3338fgwcPxr59+zB//nyX7y8IArZs2YK7774b06dPR79+/fD4448jNze3yd9KrZk/fz6USiUGDBiA8PBw5OfnIyoqCnv27IHdbsfYsWORlJSE2bNnQ6/XO3uOiYi8kSC6sKljQUEBevXqhb1792LYsGHO8oULF+KTTz7BqVOnmpzTr18//O///i9efvllZ9mePXswYsQIGAwG+Pn54bbbbkNaWhrGjRsHoG6PsbKyMmzatKnFWCwWCywWi/Pn8vJyREdHw2QyITAwsK2PRJ2kosaGQW9sAwCkPj0YWrWyU+9vs1rxj+V1WwI8OeclqFtZFKM1hWU1WLzxBJQKAVm/H41gn84Z3my327F9+3YAwH333QelsnPbj6grKy8vh16v5+8PIiIiGXJpkkNYWBiUSmWT3tSioqIWvxmMjIxstr5KpUJoaCiOHz+O3NxcTJhwbRWbhoUFVCoVTp8+jT59+jS5rlardS4JT/LX0Luq0yg7PVkFALVGg2de/EOHr9NDr0V4oBbF5RZ8c8yAp38S64bobk6pVDYZWk9ERERE1NW5NEZEo9EgOTkZ6enpjcrT09MxfPjwZs8ZNmxYk/rbtm1DSkoK1Go1+vfvj6NHjzaaT/Lwww/jnnvuwaFDhzjUt4swmupXCPbi4cBA3XCuAdF1w4LTTxRKHA0RERERUdfm8jJy8+bNw5QpU5CSkoJhw4ZhzZo1yM/Px8yZMwEAL7/8Mi5fvoy//e1vAICZM2di5cqVmDdvHmbMmIHMzEysXbsW69evBwDodDokJSU1ukfD0u43lpP3knrBJYfDgZKCywCAsKheHZrPMzBGj4zjRci6UAprrR0aled7jEVRhMlkAgDo9XrnKpVERERERF2Zywnr5MmTceXKFbz11lswGAxISkrCli1bEBtbNzTSYDAgPz/fWT8+Ph5btmzB3Llz8f777yMqKgrvvfdekz1YqWsrlDhhtdbU4Nej7wAA/P3AOeh8fdt9rfge/vDRKFFlsSPjbDHuT4x0V5gtstlsWLFiBYC6L4Va25ieiIiIiKiraNdGXbNmzcKsWbOaPfbxxx83KRs5ciQOHDjQ5us3dw3yblInrO6kVNQNC84+X4otx4ydkrASEREREXVHXOecOoXR1JCwdo2ewYH181j3nrsicSRERERERF0XE1bqFF1hD9br9e8VCKVCQKGpBkcKTFKHQ0RERETUJTFhpU5RWN41VgluoNMo0SfSHwDw9ZECiaMhIiIiIuqamLCSx9kdIoor6xNWv66RsALXhgVnnC6WOBIiIiIioq6JCSt5XEmlBXaHCEEA/HVdKGGNqUtYzxorYKyokTgaIiIiIqKup12rBBO5omHBpQAfNZQKafYPVaqUeODJZ5zv3SHEX4uoEB8UlFbj68MFmDEiwS3XbY5CoUBKSorzPRERERFRd8CElTxODlvaqDVazPhDqtuvOzBaj4LSanx/qtCjCatKpcKDDz7osesTEREREckRu2rI4xoS1qAusuDS9ZLqhwUfyi2D2VorcTRERERERF0LE1byOKMMtrQRRRGm0iswlV6BKIpuu26vUF8E+qhhqXUg/VSR2657I1EUUVVVhaqqKrfGT0REREQkZ0xYyeOMpoYtbTSSxWCprsb04YMwffggWKqr3XZdhSBgQP1qwd8eN7rtujey2WxYunQpli5dCpvN5rH7EBERERHJCRNW8riiCunnsHpSw7Dg/567AofDIXE0RERERERdBxNW8riGVYK70h6s17ulZwA0KgWuVlmxP++q1OEQEREREXUZTFjJ4+Qwh9WTNCoF+kUFAAA2HzNIHA0RERERUdfBhJU8ymytRUVN3eq5Us5h9bSkmCAAwA9niqUNhIiIiIioC2HCSh7VMBxYo1JAp+66H7fE3oEQAOQVm5FXWiV1OEREREREXULXzSBIFgrLG1YIVkMQBImj8ZwAHzViw/0AAJsOF0gcDRERERFR16CSOgDq2grL5bFCsFKlxKiJk5zvPWFgjB65xVXYcaoIs+/p69ZrKxQKDB482PmeiIiIiKg7YMJKHtWw4JLeT9r5q2qNFr99Z7lH7zEwRo9vsgtw7JIJpmob9D7uS9JVKhUmTpzotusREREREXkDdtWQRzXMYQ10Y/ImVxF6HUIDtKi1i9hynKsFExERERF1FBNW8ijnkGCJ92AVRRE1ZjNqzGaIouiRewiCgIHRegDAtuOFbr22KIqwWq2wWq0ei5+IiIiISG6YsJJHyWUOq6W6Gk8NvQVPDb0Flupqj91nYExdwro/pxS1dofbrmuz2ZCamorU1FTYbDa3XZeIiIiISM6YsJJHOeewduE9WK+XEOEPH40SlTW12HW+ROpwiIiIiIi8GhNW8hiHQ0TRddvadAdKhYDE3oEAgG+OGiWOhoiIiIjIuzFhJY+5UmVFrUOEACCwmySsAJzzWPecLZY4EiIiIiIi78aElTymYf6qv48KSoUgcTSdp39vPRQCYCirwQmDSepwiIiIiIi8FhNW8hi5LLjU2Xw0StzSMwAAsOlwgcTREBERERF5Lyas5DHdbcGl6yXFBAEAdpwqkjYQIiIiIiIvppI6AOq6Ck3y6WFVKBUYNvYh53tPS4rR44v/XsQ5YyUKTNWI0vt06HoKhQIDBgxwviciIiIi6g6YsJLHGGU0JFij1WH+ijWddr8gPw2iw3xxscSMTYcLMOvuPh26nkqlwi9+8Qs3RUdERERE5B3YVUMeY2zY0sav+w0JBoBB9cOC008UShsIEREREZGXYsJKHlNU38Panba0uV5SbN32Nkfyy2CqtkkcDRERERGR92HCSh5jlNEc1hqzGY/2j8Kj/aNQYzZ3yj0j9DqEB2phd4jYfLRjqwVbrVa8+eabePPNN2G1Wt0UIRERERGRvDFhJY+osdlRVt+rKIeEVQqCICAppq6X9dvjRomjISIiIiLyPkxYySMa9mBVKwX4aJQSRyOdhu1tsnKuwmKzSxsMEREREZGXYcJKHnFtOLAGgiBIHI10Ynv4IcBHhWqrHd+f5p6sRERERESuYMJKHlFYUbdCcHddcKmBQhAwMDoIAPDNUYO0wRAREREReRkmrOQRhfU9rEF+3TthBYBB9asF7zlbArvdIXE0RERERETegwkreYRBRisES61vzwBoVQqYzDb8N7dU6nCIiIiIiLyGSuoAqGsqKKsGAAT5aSSOpI5CqcDQkfc533cmlVKBxN6BOJRbhq+OFODOPmEuX0OhUKBv377O90RERERE3QETVvIIg6kuYQ2WScKq0erw6gefSHb/pNggHMotQ8bp4nadr1Kp8OSTT7o5KiIiIiIieWNXDXlEgXMOqzwSVqkl9tZDqRBgLKvBsYIyqcMhIiIiIvIKTFjJ7Sy1dhTXrxIc5M85rADgo1Hilp4BAICNhwokjoaIiIiIyDswYSW3KzTVJasqpQA/rTxGndeYzXjy9j548vY+qDGbJYlhUEzdasH/Oen6fqxWqxWLFi3CokWLYLVa3R0aEREREZEsMWEltyu4bv6qIAgSR3ONpboalupqye4/KDYIggDkFFfhXFGFy+fbbDbYbDYPREZEREREJE9MWMnt5LZCsFwE+KjRJ8IfAPDPA5ckjoaIiIiISP6YsJLbGZwLLnH+6o2GxAcDANKPF0ocCRERERGR/DFhJbdjD2vLOjosmIiIiIioO2HCSm7HhLVl1w8L/tfByxJHQ0REREQkb0xYye04JLh1g53Dgo0SR0JEREREJG/y2HOEupSGHtZgf/n0sAoKAQN/Msz5Xkq3xQbhi/9exIWiKpwrqsQtPfxveo4gCIiNjXW+JyIiIiLqDpiwkltVWmpRXlMLQF5DgrU6H7z1yedShwHg2rDgc8ZK/OvgJbw8tv9Nz1Gr1Zg2bZrngyMiIiIikpF2DQlOS0tDfHw8dDodkpOTsWvXrlbrZ2RkIDk5GTqdDgkJCVi9enWj4x9++CHuuusuBAcHIzg4GKNHj8a+ffvaExpJzFDfu6rTKKFTKyWORr44LJiIiIiI6OZcTlg3bNiAOXPm4NVXX8XBgwdx1113Ydy4ccjPz2+2fk5ODsaPH4+77roLBw8exCuvvILnn38en39+rbdr586deOKJJ7Bjxw5kZmYiJiYGY8aMweXLXJTG2xTUz18N5vzVVt1Wv1rwhaIqnCvmasFERERERM0RRFEUXTnhjjvuwNChQ7Fq1SpnWWJiIiZOnIjU1NQm9RcsWICvvvoKJ0+edJbNnDkThw8fRmZmZrP3sNvtCA4OxsqVKzF16tQ2xVVeXg69Xg+TyYTAwEBXHoncaP2+fLz8xVEk9g7EjPtvkTocpxqzGb++76cAgFXb90Hn6ytxREDa1jM4Z6zEL0cl4JUHEluta7VasWLFCgDA7NmzodHIZ7g1kbfj7w8iIiL5cqmH1Wq1Ijs7G2PGjGlUPmbMGOzdu7fZczIzM5vUHzt2LLKysmCz2Zo9x2w2w2azISQkpMVYLBYLysvLG71Iepev1i+4JKP5qw3Kr5ai/Gqp1GE4DakfFvzdsbYNCzabzTCbzZ4MiYiIiIhIVlxKWEtKSmC32xEREdGoPCIiAkZj8390G43GZuvX1taipKSk2XNeeukl9OrVC6NHj24xltTUVOj1eucrOjralUchD7l4tS6hCgmQX8IqN4PjgqEQgLwSMw5fKpM6HCIiIiIi2WnXoks3bqshimKrW200V7+5cgBYsmQJ1q9fjy+++AI6na7Fa7788sswmUzO18WLF115BPKQi6X1Cau/VuJI5M9Pp0Jibz0AYH1W83PAiYiIiIi6M5cS1rCwMCiVyia9qUVFRU16URtERkY2W1+lUiE0NLRR+dKlS7Fo0SJs27YNt912W6uxaLVaBAYGNnqR9C7WDwkOZQ9rmyT3qRv2nn6sEA6HS9PJiYiIiIi6PJcSVo1Gg+TkZKSnpzcqT09Px/Dhw5s9Z9iwYU3qb9u2DSkpKVCrr60k++677+Ltt9/Gt99+i5SUFFfCIpmosdlRXGEBwB7WthoQrYdWrcCVSisyzhVLHQ4RERERkay4PCR43rx5+Oijj7Bu3TqcPHkSc+fORX5+PmbOnAmgbqju9Sv7zpw5E3l5eZg3bx5OnjyJdevWYe3atZg/f76zzpIlS/Daa69h3bp1iIuLg9FohNFoRGVlpRsekTrLpfreVa1aAV8t92BtC41KgcFxdYsvbcjisHYiIiIiouupXD1h8uTJuHLlCt566y0YDAYkJSVhy5YtiI2NBQAYDIZGe7LGx8djy5YtmDt3Lt5//31ERUXhvffew6OPPuqsk5aWBqvViscee6zRvV5//XW88cYb7Xw06mzOBZf8Na3OaZaCoBDQJ2mw872cDE0Ixr6zV/DD6WLU2GqhUzf931IQBERFRTnfExERERF1By7vwypX3EdPep9k5uL3Xx5HUrQe00f3kTocr+FwiHjrX8dQbrbh3Um34RdDueI1UWfi7w8iIiL5atcqwUTNaVhwiVvauEahEDA0oW5Y8OcHLkscDRERERGRfDBhJbfhljbtl5xQt1rw/gulKK60SBwNEREREZE8MGElt7kk4x5WS7UZM+/9KWbe+1NYqs1Sh9NEr1Bf9Arxgd0h4pMf85oct9lsWL58OZYvXw6bzSZBhEREREREnY8JK7mNnHtYRREoLriE4oJLkOus7Tv6hQEANmZfxo1Ty0VRhMlkgslkanKMiIiIiKirYsJKblFRY0NZdV3PX6gMe1i9wdCEYKiUAi6WmrEvt1TqcIiIiIiIJMeEldwi70pd76q/TgWtmnuwtoevVoXbYoMAAB9n5koaCxERERGRHDBhJbfIKakCAIQHym84sDdpGBa842QRqiycq0pERERE3RsTVnILZ8Kq10kciXfrE+mP0AANamwOrM++KHU4RERERESSYsJKbpFbn7CGsYe1QxSCgJ/2DQUA/Gv/JYmjISIiIiKSFhNWcosLMh8SLAhA71v6ofct/SAIUkfTup/cEgpBAE4bKnD4chkAQBAEhIeHIzw8HILcH4CIiIiIyE1UUgdAXUOuzBNWrY8vVmzeKXUYbRLkp0FSTBCO5pXhgx8uIO2JoVCr1Zg1a5bUoRERERERdSr2sFKHXa2yOre0CQvkHFZ3GJEYDgD4/nghysxWiaMhIiIiIpIGE1bqsJwrdb2rQb5qaFT8SLnDLZH+iAzSwVrrwDpucUNERERE3RSzC+qwnGL5rxBsqTZj9kOjMPuhUbBUm6UO56YEQXD2sm7Ylw+LxYq0tDSkpaXBZuN2N0RERETUPXAOK3VY7hX5rxAsisClc2ec771Bcp8QbM4uQKHJgu9OGFBcXAwAEL3lAYiIiIiIOog9rNRhF4rlveCSt9KqlbijfoubjzPzJI6GiIiIiKjzMWGlDjtdWAEAiAz2kTiSrmdEYjgEATiSXyZ1KEREREREnY4JK3WIpdbu3NImMki+c1i9VWiAFkPigqUOg4iIiIhIEkxYqUNySqpQ6xCh0yih91VLHU6XdM+gCKlDICIiIiKSBBNW6pDTxvrhwEE6CIIgcTRdU+9QX/SNCpQ6DCIiIiKiTseElTrkbGElAKBnkLznrwoCEB7VG+FRveGNefXIgT1Q4dCgUtSgqMIidThERERERJ2C29pQhzQsuBQRLO/5q1ofX6z+zz6pw2i3vr2CsCXgf5BfYobihxwsfuQ2qUMiIiIiIvI49rBSh5ypT1jl3sPq7QRBwJghPQEAX2RdRmF5jcQRERERERF5HhNWarcqSy3yS80AgEiZ97B2BYm9AxEb7geb3YHF352WOhwiIiIiIo9jwkrtdsJQDlEEAn3UCPCR9wrBlppqvPjYOLz42DhYaqqlDsdljtpanE/fhPtwFEo48NXBy7hY/2UBEREREVFXxYSV2u3oJRMAoHeY/IcDiw4R548dxvljhyE6RKnDaQcR1aXFECtLkRDhh1qHiNTvTkkdFBERERGRRzFhpXY7drkuYY0O9ZU4ku5lbP1c1q1HDM7/BkREREREXRETVmq3o/XJUm8mrJ0qrocfBscFQRSBV788BlH0xh5jIiIiIqKbY8JK7WK21uJ8cd0erL3DmLB2todSekGpEHA4vwzfHDNIHQ4RERERkUcwYaV2OVFQDocIBPiooPfVSB1OtxMaoMXIgT0AAP/vm5Ow1NoljoiIiIiIyP2YsFK7ZOddBQDEhvlJHEn3NXpwJAJ8VDCW1eDdbdzmhoiIiIi6Hias1C77c+sS1vgIf4kjabvA4BAEBodIHUa7KbU6KLXX9rvVqZV45I5oAMBfdufipKFcqtCIiIiIiDxCJXUA5H0cDhFZuaUAgIRI70hYdb6++EvmManDaDeFSo0Bj0xtUj44LggDo/U4ftGEuf88hC2/vQsKhSBBhERERERE7sceVnLZ+eJKlFXboFYK6BUi/z1YuzJBEPDosGho1QqcMlQgLeO81CEREREREbkNE1Zy2b763tXYcD+olPwISS3IT4MJKb0AAMvTz+DoJe7NSkRERERdA7MNctnec1cAeM9wYACw1FTjD1MexR+mPApLTbXU4bjMUVuLC9u/xoXtX8NRW9vk+LBbwzAwWo9ah4hf/yMbZmvTOkRERERE3oYJK7mk1u7ArrPFAID+vQIljqbtRIeI4/szcXx/JkSHKHU47SCiqtiAqmIDgKbxC4KAySNiEeirxqXSarz4+RGIojc+JxERERHRNUxYySWHL5WhvKYWPholYriljaz461R46q44CAA2Hzbgoz05UodERERERNQhTFjJJRmnr/WucjVa+ekbFYAHU6IAAKnfnHT2hhMREREReSMmrOSSbScKAXjXcODu5p6kCCT3CYFDBGZ+mo1T3J+ViIiIiLwUE1Zqs7OFFThlrIBSIWBgjF7qcKgFgiBg0vAYxIX7ocpixxMf/YjckkqpwyIiIiIichkTVmqzr48YAAC39gqAr1YlcTTUGrVKgWfv74OewTpcrbJi8pr/4mKpWeqwiIiIiIhcwoSV2sThEPHlocsAgCHxwRJH0z5aHx9ofXykDqPdBKUKgrLtXxT4aVX41Zi+CAvQorDcgolpe3DayOHBREREROQ9BLGL7H1RXl4OvV4Pk8mEwEDOr3S3naeLMO0v+6HTKPH6pCRo1UqpQ6I2Kquy4oNt51BYVgN/rQp/mZaCn8SHSh0WkWzw9wcREZF8sYeV2uSTzDwAwE9vCWGy6mWC/DT4zfh+iAn3RaWlFo9/+CP+ujeX+7QSERERkewxYaWbOl5gwvZTRRAADO8fLnU41A5+WhV+PbYvhsQFwe4Q8fpXx/Gb9QdhMtukDo2IiIiIqEVMWOmm/pR+FkDd3NUeep3E0bSP1VKDhb+agoW/mgKrpUbqcFzmsNciN2MrcjO2wmGvbdc1tGolpoyKx4SUXhAE4JsjBty7bCfSTxjdHC0RERERkXtwqVdq1X9OFeL7k4UQBGDs7T2lDqfdHHYHDmRsd773OqKICsNF5/v2EgQB9wyKQHyEH9bvykNxuQUz/paNEbeE4fcPDcCtkQFuCpiIiIiIqOPYw0otKq2y4tWNxwAAIwf08NreVWoqroc/fvezRNyT1ANKhYDd50owbsUP+O36AzheYJI6PCIiIiIiAOxhpRbU2OyY9fdsGEw1CAvQ4oGhUVKHRG6mUSkw4Se98T+3huGbrAIcySvD14cN+PqwAXfEh2BSSjQeSIqEH/fcJSIiIiKJ8C9RasJktuG5fxzAfy+UQqtSYPp9CdCo2BnfVYUH6jDt3gRcumLGjmOFOJxzFT/mlOLHnFK8tukYRt0ajnv698CofuHoEchediIiIiLqPO3KQtLS0hAfHw+dTofk5GTs2rWr1foZGRlITk6GTqdDQkICVq9e3aTO559/jgEDBkCr1WLAgAHYuHFje0KjDrA7RHx9uAAPrPgBu8+VQKNSYProPogM9pE6NOoEvUN9MWVkPF55bCAeuL0nwgK0qLbZsfWYES/++wh+umg7Rr67A3M3HMLHe3KwL6cUxRUWbo9DRERERB7jcg/rhg0bMGfOHKSlpeHOO+/EBx98gHHjxuHEiROIiYlpUj8nJwfjx4/HjBkz8Omnn2LPnj2YNWsWwsPD8eijjwIAMjMzMXnyZLz99tt45JFHsHHjRkyaNAm7d+/GHXfc0fGnpCZEUYTZaoexvAanDBXIzruK744bcbmsGgAQHqjF0yPjER3mK3Gk1NlC/LUYM6Qn7h8cifwSM05eMuHkpXJcLDEj70rda+PBy876AToV4kL9EBWkQ3iAFj0CdOgRoEWwnwYBWhX8dSr4X/evVqWEUiFI+IRERERE5C0E0cXukTvuuANDhw7FqlWrnGWJiYmYOHEiUlNTm9RfsGABvvrqK5w8edJZNnPmTBw+fBiZmZkAgMmTJ6O8vBxbt2511nnggQcQHByM9evXNxuHxWKBxWJx/mwymRATE4MHFm6ESnstyWp4uOufstED1x8QmxbVl7d+/FpZ883YUCxed4XmYrn+/MbxNS1rrm5zMTe6/3XH7Q4RZdU2WGubrpar0ygwon847kwMh1atbPaZvFGN2YwZd98OAPjwh4PQ+XpXIu6oteHkpk8BAIkTn4ZCpe70GMw1tbh4xYxLJWZcumJGkakGZVU2tKd/VSEAKqUCaqUAtUKARqWASqmASiFAoRAgAFAIAgQBEFD/r/PnujKFAECo+7fuLZNgT+gOrWqrqcLWVx9BWVkZ9Hq91OEQERHRdVzqYbVarcjOzsZLL73UqHzMmDHYu3dvs+dkZmZizJgxjcrGjh2LtWvXwmazQa1WIzMzE3Pnzm1SZ/ny5S3GkpqaijfffLNJ+bevPtLGp6GWnAXwF6mD8KCGxNVrvfOO1BEQdUlXrlxhwkpERCQzLiWsJSUlsNvtiIiIaFQeEREBo9HY7DlGo7HZ+rW1tSgpKUHPnj1brNPSNQHg5Zdfxrx585w/l5WVITY2Fvn5+fyDox3Ky8sRHR2NixcvIjAwUOpwvA7br2PYfh3D9uuYhhE6ISEhUodCREREN2jXKsE3Dr0TRbHV4XjN1b+x3NVrarVaaLXaJuV6vZ5/sHVAYGAg268D2H4dw/brGLZfxygUXA2diIhIblz67RwWFgalUtmk57OoqKhJD2mDyMjIZuurVCqEhoa2WqelaxIREREREVHX51LCqtFokJycjPT09Ebl6enpGD58eLPnDBs2rEn9bdu2ISUlBWq1utU6LV2TiIiIiIiIuj6XhwTPmzcPU6ZMQUpKCoYNG4Y1a9YgPz8fM2fOBFA3t/Ty5cv429/+BqBuReCVK1di3rx5mDFjBjIzM7F27dpGq//Onj0bd999NxYvXoyf/exn+PLLL/H9999j9+7dbY5Lq9Xi9ddfb3aYMN0c269j2H4dw/brGLZfx7D9iIiI5MvlbW0AIC0tDUuWLIHBYEBSUhL+9Kc/4e677wYATJs2Dbm5udi5c6ezfkZGBubOnYvjx48jKioKCxYscCa4Df7973/jtddew4ULF9CnTx8sXLgQP//5zzv2dEREREREROS12pWwEhEREREREXkal0QkIiIiIiIiWWLCSkRERERERLLEhJWIiIiIiIhkiQkrERERERERyZJXJ6ypqakQBAFz5sxxlomiiDfeeANRUVHw8fHBqFGjcPz4cemClJnLly/j6aefRmhoKHx9fTFkyBBkZ2c7j7P9WlZbW4vXXnsN8fHx8PHxQUJCAt566y04HA5nHbbfNT/88AMmTJiAqKgoCIKATZs2NTrelrayWCz47W9/i7CwMPj5+eHhhx/GpUuXOvEppNNa+9lsNixYsACDBg2Cn58foqKiMHXqVBQUFDS6Btuv5c/f9X71q19BEAQsX768UXl3bj8iIiK58NqEdf/+/VizZg1uu+22RuVLlizBsmXLsHLlSuzfvx+RkZG4//77UVFRIVGk8nH16lXceeedUKvV2Lp1K06cOIE//vGPCAoKctZh+7Vs8eLFWL16NVauXImTJ09iyZIlePfdd/HnP//ZWYftd01VVRUGDx6MlStXNnu8LW01Z84cbNy4EZ999hl2796NyspKPPTQQ7Db7Z31GJJprf3MZjMOHDiA3//+9zhw4AC++OILnDlzBg8//HCjemy/lj9/DTZt2oQff/wRUVFRTY515/YjIiKSDdELVVRUiH379hXT09PFkSNHirNnzxZFURQdDocYGRkpvvPOO866NTU1ol6vF1evXi1RtPKxYMECccSIES0eZ/u17sEHHxSnT5/eqOznP/+5+PTTT4uiyPZrDQBx48aNzp/b0lZlZWWiWq0WP/vsM2edy5cviwqFQvz22287LXY5uLH9mrNv3z4RgJiXlyeKItvvei2136VLl8RevXqJx44dE2NjY8U//elPzmNsPyIiInnwyh7W5557Dg8++CBGjx7dqDwnJwdGoxFjxoxxlmm1WowcORJ79+7t7DBl56uvvkJKSgp+8YtfoEePHrj99tvx4YcfOo+z/Vo3YsQIbN++HWfOnAEAHD58GLt378b48eMBsP1c0Za2ys7Ohs1ma1QnKioKSUlJbM9mmEwmCILgHDHB9mudw+HAlClT8MILL2DgwIFNjrP9iIiI5EEldQCu+uyzz5CdnY2srKwmx4xGIwAgIiKiUXlERATy8vI6JT45u3DhAlatWoV58+bhlVdewb59+/D8889Dq9Vi6tSpbL+bWLBgAUwmE/r37w+lUgm73Y6FCxfiiSeeAMDPnyva0lZGoxEajQbBwcFN6jScT3Vqamrw0ksv4cknn0RgYCAAtt/NLF68GCqVCs8//3yzx9l+RERE8uBVCevFixcxe/ZsbNu2DTqdrsV6giA0+lkUxSZl3ZHD4UBKSgoWLVoEALj99ttx/PhxrFq1ClOnTnXWY/s1b8OGDfj000/xj3/8AwMHDsShQ4cwZ84cREVF4ZlnnnHWY/u1XXvaiu3ZmM1mw+OPPw6Hw4G0tLSb1mf71fWerlixAgcOHHC5Ldh+REREncurhgRnZ2ejqKgIycnJUKlUUKlUyMjIwHvvvQeVSuXsrbnx2++ioqImPTndUc+ePTFgwIBGZYmJicjPzwcAREZGAmD7teSFF17ASy+9hMcffxyDBg3ClClTMHfuXKSmpgJg+7miLW0VGRkJq9WKq1evtlinu7PZbJg0aRJycnKQnp7u7F0F2H6t2bVrF4qKihATE+P8XZKXl4ff/e53iIuLA8D2IyIikguvSljvu+8+HD16FIcOHXK+UlJS8NRTT+HQoUNISEhAZGQk0tPTnedYrVZkZGRg+PDhEkYuD3feeSdOnz7dqOzMmTOIjY0FAMTHx7P9WmE2m6FQNP5fRqlUOre1Yfu1XVvaKjk5GWq1ulEdg8GAY8eOsT1xLVk9e/Ysvv/+e4SGhjY6zvZr2ZQpU3DkyJFGv0uioqLwwgsv4LvvvgPA9iMiIpILrxoSHBAQgKSkpEZlfn5+CA0NdZbPmTMHixYtQt++fdG3b18sWrQIvr6+ePLJJ6UIWVbmzp2L4cOHY9GiRZg0aRL27duHNWvWYM2aNQDg3NOW7de8CRMmYOHChYiJicHAgQNx8OBBLFu2DNOnTwfA9rtRZWUlzp075/w5JycHhw4dQkhICGJiYm7aVnq9Hs8++yx+97vfITQ0FCEhIZg/fz4GDRrUZMG1rqi19ouKisJjjz2GAwcOYPPmzbDb7c7e6pCQEGg0GrbfTT5/Nyb4arUakZGRuPXWWwHw80dERCQbEq5Q7BbXb2sjinXbZbz++utiZGSkqNVqxbvvvls8evSodAHKzNdffy0mJSWJWq1W7N+/v7hmzZpGx9l+LSsvLxdnz54txsTEiDqdTkxISBBfffVV0WKxOOuw/a7ZsWOHCKDJ65lnnhFFsW1tVV1dLf7mN78RQ0JCRB8fH/Ghhx4S8/PzJXiaztda++Xk5DR7DIC4Y8cO5zXYfi1//m5047Y2oti924+IiEguBFEUxU7NkImIiIiIiIjawKvmsBIREREREVH3wYSViIiIiIiIZIkJKxEREREREckSE1YiIiIiIiKSJSasREREREREJEtMWImIiIiIiEiWmLASERERERGRLDFhJSIiIiIiIlliwkpERERERESyxISViIiIiIiIZIkJKxEREREREcnS/wcAqmdYGRa1kgAAAABJRU5ErkJggg==\n", + "image/png": 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QoEE4c+YMwsPDMWTIEFhaWmLw4MEA8ofl3rhxA3v37oWenh6mTJkCT09PXLt2Dc7OzvDz88Ps2bMRHR0NANDR0QEAfPXVV4iNjcXWrVtRq1Yt7N69G126dMHly5fRsGFDAPnLLy1duhS///47lJSU0K9fP0ycOBGBgYGYOHEioqKikJKSgoCAAABAzZo1ER8fLxd/Xl4e6tSpg+3bt8PIyAihoaEYMmQIzM3N0atXr1K9BnFxcbCzsyuxTL9+/bB69eoij9na2sLIyAjr16/H9OnTkZubi/Xr16NJkyawtLQsts7k5GQ0bty4xHYfPHiAwYMHY8+ePdDS0qqwtomIiKjqYcJKRO8dCwsLrFixAjKZDLa2trh8+TJWrFiBwYMHS4nqqVOn4OzsDAAIDAyEhYUF9uzZg88//xz6+vqQyWQwMzOT6rx16xa2bNmCe/fuScNYJ06ciODgYAQEBGDhwoUA8tfiXb16NRo0aAAAGDlyJObPnw8gP/HV1NREZmamXN2vUlVVxbx586Tn9erVQ2hoKLZv317qhLVWrVqvvedTT0+v2GO6urrSPaoLFiwAANjY2OCff/4pdujyjh07cO7cOaxZs6bYeoUQ8PX1xdChQ+Ho6IjY2NgKaZuIiIiqJn6yE1GFmjZtWrHHXh2uPXHixGLLvjpsesyYMW8W2Es+/PBDufqdnJywbNky5ObmIioqCioqKmjTpo103NDQELa2toiKiiq2zvPnz0MIARsbG7n9mZmZMDQ0lJ5raWlJySoAmJubIykpqczXsHr1aqxbtw537tzBixcvkJWVBXt7+1Kfr6KiAmtr6zK3W+DFixcYOHAg2rZtiy1btiA3NxdLly6Fp6cnzp07B01NTbnyISEh8PX1xdq1a9GkSZNi6/3pp5+QkpJS4vuorG0TERFR1cWElaqFtLQ0WFlZAchfAkRbW1uxARUhKytLus9wzJgxUFNTU3BEb0dZruttlX0TL9/L+ur+ku49zsvLg7KyMiIiIqCsrCx3rGDIMJDfO/oymUxWbJvF2b59O8aNG4dly5bByckJurq6+OGHH3DmzJlS1/GmQ4I3b96M2NhYhIWFST9EbN68GQYGBvjzzz/Ru3dvqeyxY8fQvXt3LF++HAMGDCixzSNHjuD06dNQV1eX2+/o6Ii+ffti06ZNZWqbiIiIqjYmrFRtPHr0SNEhvFZR92DSu3f69OlCzxs2bAhlZWXY2dkhJycHZ86ckYYEP378GNevX5fuvVRTU0Nubq5cHS1btkRubi6SkpLQrl27csdWVN2vOnHiBJydnTF8+HBp361bt8rUzpsOCU5PT4eSkpJcEl/wPC8vT9oXEhKCbt26YfHixRgyZMhr4/rxxx/x7bffSs/j4+Ph7u6Obdu2Sb3epW2biIiIqr7KOZ0qEdFbdPfuXYwfPx7R0dHYsmULfvrpJ2nIccOGDeHl5YXBgwfj5MmTuHjxIvr164fatWvDy8sLAGBlZYXU1FQcPnwYjx49Qnp6OmxsbNC3b18MGDAAu3btQkxMDM6dO4fFixdj3759pY7NysoKly5dQnR0NB49elTkpFTW1tYIDw/HP//8g+vXr2PWrFk4d+5cmV6DgiHBJW0mJibFnu/m5oanT59ixIgRiIqKwtWrV/HVV19BRUUFHTp0AJCfrHbt2hWjR49Gjx49kJiYiMTERDx58kSq5+zZs2jUqBHu378PAKhbty6aNm0qbQVDrBs0aIA6deqUum0iIiKqHpiwEtF7Z8CAAXjx4gU++OADjBgxAqNGjZLr/QsICICDgwO6desGJycnCCGwb98+aTivs7Mzhg4dCh8fHxgbG2PJkiXSeQMGDMCECRNga2uLTz75BGfOnIGFhUWpYxs8eDBsbW3h6OgIY2NjnDp1qlCZoUOH4rPPPoOPjw/atGmDx48fy/W2vguNGjXCX3/9hUuXLsHJyQnt2rVDfHw8goODYW5uDiB/GZ/09HQsWrQI5ubm0vbZZ59J9aSnpyM6OrpM66eWpm0iIiKqHmSirDdPVVIpKSnQ19dHcnJyicPYqHpKS0uT7hNMTU2ttPewFiz5Mm3atCp9D2tGRgZiYmJQr149aGhoKDqcMnF1dYW9vT38/PwUHQq9QyW9Z/n5QUREVHmxh5WIiIiIiIgqJSasREREREREVClxlmCqFpSUlODo6Cg9roxkMhlq1aolPSbFCAkJUXQIRERERFRKTFipWtDU1CzzLKnvmqqqKgYPHqzoMIiIiIiIqozK2RVFRFVCNZmzjd4DfK8SERFVTUxYiajMCpZ3SU9PV3AkRKVT8F4teO8SERFR1cAhwVQtpKenw87ODgBw7do1aGlpKTiiwrKzs/HLL78AAEaMGFGlvzgrKyujRo0aSEpKAgBoaWnxvlyqlIQQSE9PR1JSEmrUqAFlZWVFh0RERERlwISVqgUhBO7cuSM9royEEEhOTpYeV3VmZmYAICWtRJVZjRo1pPcsERERVR1MWImoXGQyGczNzWFiYoLs7GxFh0NULFVVVfasEhERVVFMWInojSgrKzMZICIiIqK3gpMuERERERERUaXEhJWIiIiIiIgqpXIlrKtWrUK9evWgoaEBBwcHnDhxotiyCQkJ6NOnD2xtbaGkpISxY8cWWW7nzp2ws7ODuro67OzssHv37vKERkRERERERNVEmRPWbdu2YezYsZgxYwYuXLiAdu3awcPDA3FxcUWWz8zMhLGxMWbMmIEWLVoUWSYsLAw+Pj7o378/Ll68iP79+6NXr144c+ZMWcOj95RMJoOdnR3s7Owq7fIqMpkMxsbGMDY2rrQxEhERERFVJjJRxvU12rRpg1atWsHf31/a17hxY3h7e2PRokUlnuvq6gp7e3v4+fnJ7ffx8UFKSgr2798v7evSpQsMDAywZcuWIuvKzMxEZmam9DwlJQUWFhZITk6Gnp5eWS6JiIjeYykpKdDX1+fnBxERUSVUph7WrKwsREREoHPnznL7O3fujNDQ0HIHERYWVqhOd3f3EutctGgR9PX1pc3CwqLc7RMREREREVHlU6aE9dGjR8jNzYWpqancflNTUyQmJpY7iMTExDLXOW3aNCQnJ0vb3bt3y90+ERERERERVT7lWof11fvvhBBvfE9eWetUV1eHurr6G7VJ1Ud6ejpat24NADh37hy0tLQUHFFh2dnZWLt2LQBg8ODBUFVVVXBERERERESVW5kSViMjIygrKxfq+UxKSirUQ1oWZmZmFV4nvV+EELh27Zr0uDISQuDhw4fSYyIiIiIiKlmZhgSrqanBwcEBBw8elNt/8OBBODs7lzsIJyenQnUeOHDgjeokIiIiIiKiqq3MQ4LHjx+P/v37w9HREU5OTvj1118RFxeHoUOHAsi/t/T+/fv47bffpHMiIyMBAKmpqXj48CEiIyOhpqYGOzs7AMCYMWPQvn17LF68GF5eXvjzzz9x6NAhnDx5sgIukYiIiIiIiKqiMiesPj4+ePz4MebPn4+EhAQ0bdoU+/btg6WlJQAgISGh0JqsLVu2lB5HRERg8+bNsLS0RGxsLADA2dkZW7duxcyZMzFr1iw0aNAA27ZtQ5s2bd7g0oiIiIiIiKgqK/M6rJUV19F7v6WlpUFHRwdAfk++tra2giMqLCsrS1qreNq0aVBTU1NwREQE8PODiIioMivTPaxERERERERE70q5lrUhqmxkMpk0LP1Nl1h6W2QyGfT19aXHRERERERUMg4JJiKi9xo/P4iIiCovDgkmIiIiIiKiSokJKxEREREREVVKTFipWnjx4gVat26N1q1b48WLF4oOp0jZ2dlYu3Yt1q5di+zsbEWHQ0RERFThXF1dMXbs2Lfahkwmw549e95qG1R5MGGlaiEvLw/h4eEIDw9HXl6eosMpkhAC8fHxiI+PRzW5dZyIqEpJSkrCN998g7p160JdXR1mZmZwd3dHWFiYokOj90RoaCiUlZXRpUuXt96WlZUVZDJZsZurq+trz/fz86vwuDZu3CgXh7m5OXr16oWYmJhS15GQkAAPD48ytVmjRo1yRFtxXr3ul7ekpCQAQEhICLy8vGBubg5tbW3Y29sjMDCwxHpjY2MxaNAg1KtXD5qammjQoAHmzJmDrKysMrVdmXGWYCIiInov9OjRA9nZ2di0aRPq16+PBw8e4PDhw3jy5ImiQ6P3xIYNGzBq1CisW7cOcXFxqFu37ltr69y5c8jNzQWQnyj36NED0dHR0uRyilwPXk9PD9HR0RBC4N9//8U333yDTz75BJGRkVBWVn7t+WZmZu8gyorl4+NT6IcKX19fZGRkwMTEBED+v1Pz5s0xZcoUmJqa4u+//8aAAQOgp6eH7t27F1nvv//+i7y8PKxZswbW1ta4cuUKBg8ejLS0NCxdurTUbVdqoppITk4WAERycrKiQyEFSE1NFQAEAJGamqrocIqUmZkp5s6dK+bOnSsyMzMVHQ4R/YefH++Hp0+fCgAiJCSkyOMBAQHS58jL25w5c6Qy69evF3Z2dkJNTU2YmZmJESNGlPrcDRs2iEaNGgl1dXVha2srfvnlF+lYZmamGDFihDAzMxPq6urC0tJSLFy4UDoOQKxdu1Z4e3sLTU1NYW1tLf78808hhBB5eXmiQYMG4ocffpC7nsuXLwuZTCZu3rz5pi8dVZDU1FShq6sr/v33X+Hj4yPmzZsnHfvwww/FlClT5MonJSUJFRUVceTIESGEEPHx8cLT01NoaGgIKysrERgYKCwtLcWKFSte2/bRo0cFAPH06VNp344dO6T3s6WlpVi6dKl0zMXFpdD7WQghHj16JHr37i1q164tNDU1RdOmTcXmzZvl2nJxcRFjxowpNpaAgAChr68vt+9///ufACD+/fdfIYQQq1atEvXr1xeqqqrCxsZG/Pbbb3LlAYjdu3cLIYSIiYkRAMTOnTuFq6ur0NTUFM2bNxehoaFy117U3+Yvv/wirK2thbq6ujAxMRE9evR47WtZUZKSkoSqqmqha3uVp6en+Oqrr8pU95IlS0S9evXeuO3KgkOCiYiIqNrT0dGBjo4O9uzZg8zMzELHfXx8kJCQIG1btmyBiooK2rZtCwDw9/fHiBEjMGTIEFy+fBl79+6FtbV1qc5du3YtZsyYge+++w5RUVFYuHAhZs2ahU2bNgEAfvzxR+zduxfbt29HdHQ0/ve//8HKykouvnnz5qFXr164dOkSPD090bdvXzx58gQymQwDBw5EQECAXPkNGzagXbt2aNCgQUW/lFRO27Ztg62tLWxtbdGvXz8EBARItwj17dsXW7ZskbtlaNu2bTA1NYWLiwsAYMCAAYiPj0dISAh27tyJX3/9tdzDOSMiItCrVy/07t0bly9fxty5czFr1ixs3LgRALBr1y7UqVMH8+fPl97XAJCRkQEHBwcEBQXhypUrGDJkCPr3748zZ868wSsDaGpqAsif72P37t0YM2YMJkyYgCtXruCbb77BV199haNHj5ZYx4wZMzBx4kRERkbCxsYGX3zxBXJycuDs7Aw/Pz/o6elJ1zJx4kSEh4dj9OjRmD9/PqKjoxEcHIz27dsXW39cXJz0/0hx29ChQ0t9zb/99hu0tLTQs2fPEsslJyejZs2apa63NOeUtu1KQ9EZc0XhL+TvN/awElF58fPj/bFjxw5hYGAgNDQ0hLOzs5g2bZq4ePFioXI3b94UhoaGYsmSJdK+WrVqiRkzZry2jaLOtbCwKNQLtWDBAuHk5CSEEGLUqFHi448/Fnl5eUXWCUDMnDlTep6amipkMpnYv3+/ECK/501ZWVmcOXNGCCFEVlaWMDY2Fhs3bnxtvPTuODs7Cz8/PyGEENnZ2cLIyEgcPHhQCPH/vanHjx+Xyjs5OYlJkyYJIYSIiooSAMS5c+ek4zdu3BAAytXD2qdPH+Hm5iZXZtKkScLOzk56XtreW09PTzFhwgTpeVl7WO/evSs+/PBDUadOHZGZmSmcnZ3F4MGD5c75/PPPhaenp/QcRfSwrlu3Tjp+9epVAUBERUUV2aYQQuzcuVPo6emJlJSU116jEPn/Zjdu3Chxe/DgQanqEkIIOzs7MWzYsBLL/PHHH0JNTU1cuXKl1PXevHlT6OnpibVr175R25UJe1iJiIjovdCjRw/Ex8dj7969cHd3R0hICFq1aiX1KgH5PRPdunWDh4cHJk2aBCB/sqb4+Hh07NixxPqLOvfhw4e4e/cuBg0aJNcT8+233+LWrVsA8u8li4yMhK2tLUaPHo0DBw4Uqrt58+bSY21tbejq6kq9a+bm5ujatSs2bNgAAAgKCkJGRgY+//zz8r9YVKGio6Nx9uxZ9O7dGwCgoqICHx8f6d/M2NgYbm5u0gQ7MTExCAsLQ9++faXzVVRU0KpVK6lOa2trGBgYlCueqKgoaQRAgbZt2+LGjRvSfa9Fyc3NxXfffYfmzZvD0NAQOjo6OHDgAOLi4srUfnJyMnR0dKCtrQ0LCwtkZWVh165dUFNTKza2qKioEut8+W/E3NwcAErsgXZzc4OlpSXq16+P/v37IzAwEOnp6cWWV1FRgbW1dYlbae8HDQsLw7Vr1zBo0KBiy4SEhMDX1xdr165FkyZNSlVvfHw8unTpgs8//xxff/11uduubJiwUrVhZGQEIyMjRYdRIi0tLWhpaSk6DCKi95aGhgbc3Nwwe/ZshIaGwtfXF3PmzAGQ/2Xcx8cHenp6WLt2rXROwXDFkhR3bsHM9WvXrkVkZKS0XblyBadPnwYAtGrVCjExMViwYAFevHiBXr16FRqqp6qqKvdcJpPJzYr/9ddfY+vWrXjx4gUCAgLg4+PDz5tKZP369cjJyUHt2rWhoqICFRUV+Pv7Y9euXXj69CmA/GHBO3bsQHZ2NjZv3owmTZqgRYsWAFDs6gLF7X8dIQRkMlmZ61q2bBlWrFiByZMn48iRI4iMjIS7u7vcjLSloauri8jISFy+fBmpqamIiIhA69atpeNFxfbqvle9/DdSULaklSN0dXVx/vx5bNmyBebm5pg9ezZatGiBZ8+eFVm+IocEr1u3Dvb29nBwcCjy+LFjx9C9e3csX74cAwYMKFWd8fHx6NChA5ycnPDrr7+Wu+3KiLMEU7Wgra2Nhw8fKjqMEqmpqUm/uBMRUeVgZ2cnrec4btw4XL58GefOnYOGhoZURldXF1ZWVjh8+DA6dOhQZD3FnWtqaoratWvj9u3bUm9ZUfT09ODj4wMfHx/07NkTXbp0wZMnT0p975qnpye0tbXh7++P/fv34/jx46U6j96+nJwc/Pbbb1i2bBk6d+4sd6xHjx4IDAzEyJEj4e3tjW+++QbBwcHYvHkz+vfvL5Vr1KgRcnJycOHCBSnRuHnzZrHJ1evY2dnh5MmTcvtCQ0NhY2MjzdKrpqZWqLf1xIkT8PLyQr9+/QDkJ4Q3btxA48aNy9S+kpKSdA/4qxo3boyTJ0/KJWqhoaFlbuNlRV0LkN9r2qlTJ3Tq1Alz5sxBjRo1cOTIEXz22WeFytaqVQuRkZEltlMwA3NJUlNTsX37dixatKjI4yEhIejWrRsWL16MIUOGvLY+ALh//z46dOgABwcHBAQEQEmp6D7J17VdWTFhJSIiomrv8ePH+PzzzzFw4EA0b94curq6CA8Px5IlS+Dl5YWAgACsWrUKu3fvhpKSEhITEwH8/2RNc+fOxdChQ2FiYgIPDw88f/4cp06dwqhRo0p17ujRo6GnpwcPDw9kZmYiPDwcT58+xfjx47FixQqYm5vD3t4eSkpK+OOPP2BmZlamdSOVlZXh6+uLadOmwdraGk5OTm/jZaRyCAoKwtOnTzFo0CDo6+vLHevZsyfWr1+PkSNHQltbG15eXpg1axaioqLQp08fqVyjRo3QqVMnDBkyBP7+/lBVVcWECROgqan52p7HokyYMAGtW7fGggUL4OPjg7CwMPz8889YtWqVVMbKygrHjx9H7969oa6uDiMjI1hbW2Pnzp0IDQ2FgYEBli9fjsTExDdKJl81adIk9OrVC61atULHjh3x119/YdeuXTh06FC567SyskJqaioOHz6MFi1aQEtLC0eOHMHt27fRvn17GBgYYN++fcjLy4OtrW2RdRQMCX5T27ZtQ05OTpE/YIWEhKBr164YM2YMevToIf1foqamJv14dfbsWQwYMACHDx9G7dq1ER8fD1dXV9StWxdLly6V68B5dfmfktqu1BR6B20F4qQZRERUHvz8eD9kZGSIqVOnilatWgl9fX2hpaUlbG1txcyZM0V6err48ssvX7s0zerVq4Wtra1QVVUV5ubmYtSoUUIIUapzAwMDhb29vVBTUxMGBgaiffv2YteuXUIIIX799Vdhb28vtLW1hZ6enujYsaM4f/68dC5emmCmgL6+vggICJDbd+vWLQFAbsInUrxu3brJTRj0soiICAFARERECCGE+PvvvwUA0b59+0Jl4+PjhYeHh7T00ebNm4WJiYlYvXr1a2MoaVkbVVVVUbdu3UJLI4WFhYnmzZsLdXV1aVmbx48fCy8vL6GjoyNMTEzEzJkzxYABA4SXl5d0XnmWtXlVeZa1uXDhgnS8YBmro0ePSvuGDh0qDA0Npb/NEydOCBcXF2FgYCAthbNt27YS46oITk5Ook+fPkUeK+7/EhcXF6lMwb9lTEyMEKL4ZbWKSvNKarsykwlRzsHvlUxKSgr09fWRnJxcqu54ql5evHgBDw8PAMD+/ftLdb/Ru5adnS1NptC3b99C9yMRkWLw84Oqi1OnTsHV1RX37t2DqamposOht+zevXuwsLDAoUOHXjshGFFVxiHBVC3k5eXh2LFj0uPKSAiBO3fuSI+JiIgqQmZmJu7evYtZs2ahV69eTFarqSNHjiA1NRXNmjVDQkICJk+eDCsrqxLXDiWqDjhLMBEREVEVtmXLFtja2iI5ORlLlixRdDj0lmRnZ2P69Olo0qQJPv30UxgbGyMkJIQjtqjaYw8rERERURXm6+sLX19fRYdBb5m7uzvc3d0VHQbRO8ceViIiIiIiIqqUmLASERERERFRpcSElYiIiOglMpkMe/bsUXQYRFVCbGwsZDIZIiMj31obGzduLNO6xFS9MGGlakNLSwtaWlqKDqNEqqqqnByBiEgBfH19IZPJIJPJoKqqClNTU7i5uWHDhg2FZpdPSEiQlkorri5vb++3HDFVR6GhoVBWVkaXLl3eSXtz586FTCYrsr0lS5ZAJpPB1dX1rcfh6uoq/f2pq6vDxsYGCxcuRG5ubqnO9/HxwfXr18vc5tixY8sRbcXauXMn7OzsoK6uDjs7O+zevfu151y+fBkuLi7Q1NRE7dq1MX/+/EIrTBw7dgwODg7Q0NBA/fr1sXr16rd1CQrHhJWqBW1tbaSlpSEtLQ3a2tqKDqdIampqmD59OqZPnw41NTVFh0NE9N7p0qULEhISEBsbi/3796NDhw4YM2YMunXrhpycHKmcmZkZ1NXV31lcWVlZ76wtUqwNGzZg1KhROHnyJOLi4t5Jm+bm5jh69Cju3bsntz8gIAB169Z9JzEAwODBg5GQkIDo6GiMHj0aM2fOxNKlS0t1rqamJkxMTN5yhBUvLCwMPj4+6N+/Py5evIj+/fujV69eOHPmTLHnpKSkwM3NDbVq1cK5c+fw008/YenSpVi+fLlUJiYmBp6enmjXrh0uXLiA6dOnY/To0di5c+e7uKx3jgkrERERvRfU1dVhZmaG2rVro1WrVpg+fTr+/PNP7N+/Hxs3bpTKvemQ4GPHjuGDDz6Auro6zM3NMXXqVLmE2NXVFSNHjsT48eNhZGQENzc3AMC1a9fg6ekJHR0dmJqaon///nj06JHceaNHj8bkyZNRs2ZNmJmZYe7cudLxjRs3Sr1YL28FZfLy8jB//nzUqVMH6urqsLe3R3BwcLmvk8omLS0N27dvx7Bhw9CtWze595yTkxOmTp0qV/7hw4dQVVXF0aNHAeT3/Hft2hWampqoV68eNm/eDCsrK/j5+ZXYromJCTp37oxNmzZJ+0JDQ/Ho0SN07dpVrmxp3iNnz55Fy5YtoaGhAUdHR1y4cKFU16+lpQUzMzNYWVlh5MiR6Nixo/R39vTpUwwYMAAGBgbQ0tKCh4cHbty4IZ376pDguXPnwt7eHr///jusrKygr6+P3r174/nz5wDyR0EcO3YMK1eulP4OYmNj8fTpU/Tt2xfGxsbQ1NREw4YNERAQUKr4y8PPzw9ubm6YNm0aGjVqhGnTpqFjx44l/psFBgYiIyMDGzduRNOmTfHZZ59h+vTpWL58udTLunr1atStWxd+fn5o3Lgxvv76awwcOLDUPwBUNUxYiYiI6L318ccfo0WLFti1a1eF1Hf//n14enqidevWuHjxIvz9/bF+/Xp8++23cuU2bdoEFRUVnDp1CmvWrEFCQgJcXFxgb2+P8PBwBAcH48GDB+jVq1eh87S1tXHmzBksWbIE8+fPx8GDBwHkD5tMSEiQti1btkBFRQVt27YFAKxcuRLLli3D0qVLcenSJbi7u+OTTz6RSwzo7dm2bRtsbW1ha2uLfv36ISAgQEpA+vbtiy1btsgN+9y2bRtMTU3h4uICABgwYADi4+MREhKCnTt34tdff0VSUlKp2h44cKBcgrxhwwb07du30Iiv171H0tLS0K1bN9ja2iIiIgJz587FxIkTy/V6aGpqIjs7G0B+ghkeHo69e/ciLCwMQgh4enpKx4ty69Yt7NmzB0FBQQgKCsKxY8fw/fffS9fh5OQk9eomJCTAwsICs2bNwrVr17B//35ERUXB398fRkZGxbaxcOFC6OjolLidOHGi2PPDwsLQuXNnuX3u7u4IDQ0t8RwXFxe5UR7u7u6Ij49HbGxsifWGh4eX+JpVWaKaSE5OFgBEcnKyokMhBXjx4oXw9PQUnp6e4sWLF4oOp0jZ2dkiMDBQBAYGiuzsbEWHQ0T/4efH++HLL78UXl5eRR7z8fERjRs3lp4DELt37y5XXdOnTxe2trYiLy9P2vfLL78IHR0dkZubK4QQwsXFRdjb28udN2vWLNG5c2e5fXfv3hUARHR0tHTeRx99JFemdevWYsqUKYXiuHnzpjA0NBRLliyR9tWqVUt89913hc4fPnx4sddKFcfZ2Vn4+fkJIfK/ExgZGYmDBw8KIYRISkoSKioq4vjx41J5JycnMWnSJCGEEFFRUQKAOHfunHT8xo0bAoBYsWJFsW3OmTNHtGjRQmRlZQkTExNx7NgxkZqaKnR1dcXFixfFmDFjhIuLi1T+de+RNWvWiJo1a4q0tDTpuL+/vwAgLly4UGwcLi4uYsyYMUIIIXJzc8X+/fuFmpqamDx5srh+/boAIE6dOiWVf/TokdDU1BTbt28XQggREBAg9PX15a5LS0tLpKSkSPsmTZok2rRpU2SbBbp37y6++uqrYuN81ePHj8WNGzdK3NLT04s9X1VVVQQGBsrtCwwMFGpqasWe4+bmJgYPHiy37/79+wKACA0NFUII0bBhw0L/TqdOnRIARHx8fKmvr6pQUVSiTFSRcnNzsW/fPulxZZSXlyf9QvnqBB9ERKQ4QgjIZLIKqSsqKgpOTk5y9bVt2xapqam4d++edM+go6Oj3HkRERE4evQodHR0CtV569Yt2NjYAACaN28ud8zc3LxQL1tycjK6desGDw8PTJo0CUD+fXHx8fFSb+vLsV28eLGcV0ulFR0djbNnz0o9+SoqKvDx8cGGDRvQqVMnGBsbw83NDYGBgWjXrh1iYmIQFhYGf39/6XwVFRW0atVKqtPa2hoGBgalal9VVVXq1b19+zZsbGwKvZdK8x6JiopCixYt5Ca5dHJyKlUMq1atwrp166R7tvv37485c+bg0KFDUFFRQZs2baSyhoaGsLW1RVRUVLH1WVlZQVdXV3pe1N/Cq4YNG4YePXrg/Pnz6Ny5M7y9veHs7Fxs+Zo1a6JmzZqlur7ivPp/S2n+vynqnFf3l6ZMdcGElYiIiN5rUVFRqFevXoXUVdSX0aK+SL46QWBeXh66d++OxYsXF6rT3NxcevzqTPMymUzuR9Dc3Fz4+PhAT08Pa9euLVRXeb4805tbv349cnJyULt2bWmfEAKqqqp4+vQpDAwM0LdvX4wZMwY//fQTNm/ejCZNmqBFixZS2aIUt78oAwcORJs2bXDlyhUMHDiw2HIlvUfK0t6r+vbtixkzZkBdXR21atWCsrJyiXW+7r35ur+Fonh4eODOnTv4+++/cejQIXTs2BEjRowo9t7PhQsXYuHChSXWuX//frRr167IY2ZmZkhMTJTbl5SUBFNT02LrK+4cANJ5xZVRUVGBoaFhifFWRbyHlYiIiN5bR44cweXLl9GjR48Kqc/Ozg6hoaFyX8JDQ0Ohq6srl6y8qlWrVrh69SqsrKxgbW0tt5Vl9vtx48bh8uXL2L17NzQ0NKT9enp6qFWrFk6ePClXPjQ0FI0bNy7DFVJZ5eTk4LfffsOyZcsQGRkpbRcvXoSlpSUCAwMBAN7e3sjIyEBwcDA2b96Mfv36SXU0atQIOTk5chMc3bx5E8+ePSt1HE2aNEGTJk1w5coV9OnTp9Dx0rxH7OzscPHiRbx48UI6fvr06VK1r6+vD2tra1hYWEjJakGdOTk5cjPnPn78GNevX3+j96aamlqRo+6MjY3h6+uL//3vf/Dz88Ovv/5abB1Dhw6V+zcrant1tMTLnJycpHvMCxw4cKDEXl0nJyccP35cbvbwAwcOoFatWrCysiqxXkdHx2q5fCJ7WImIiOi9kJmZicTEROTm5uLBgwcIDg7GokWL0K1bNwwYMKBMdSUnJyMyMlJuX82aNTF8+HD4+flh1KhRGDlyJKKjozFnzhyMHz8eSkrF9xOMGDECa9euxRdffIFJkybByMgIN2/exNatW7F27Vq5L/jFCQgIwKpVq7B7924oKSlJPTAFk8NMmjQJc+bMQYMGDWBvb4+AgABERkZKCRO9HUFBQXj69CkGDRoEfX19uWM9e/bE+vXrMXLkSGhra8PLywuzZs1CVFSUXFLZqFEjdOrUCUOGDIG/vz9UVVUxYcIEaGpqlqmH/MiRI8jOzpabcfdlr3uP9OnTBzNmzMCgQYMwc+ZMxMbGvvHMtA0bNoSXlxcGDx6MNWvWQFdXF1OnTkXt2rXh5eVV7nqtrKxw5swZxMbGQkdHBzVr1sTcuXPh4OCAJk2aIDMzE0FBQSUmxW86JHjMmDFo3749Fi9eDC8vL/z55584dOiQ3I8CP//8M3bv3o3Dhw8DyH+N582bB19fX0yfPh03btzAwoULMXv2bOnfeujQofj5558xfvx4DB48GGFhYVi/fj22bNlS7lgrM/awEhER0XshODgY5ubmsLKyQpcuXXD06FH8+OOP+PPPP0uVEL4sJCQELVu2lNtmz56N2rVrY9++fTh79ixatGiBoUOHSl/uS1KrVi2cOnUKubm5cHd3R9OmTTFmzBjo6+uXmOi+7NixY8jNzcUnn3wCc3NzaStIKEaPHo0JEyZgwoQJaNasGYKDg7F37140bNiwTNdOZbN+/Xp06tSpULIKAD169EBkZCTOnz8PIH/Y7MWLF9GuXbtCa6T+9ttvMDU1Rfv27fHpp59i8ODB0NXVletJfx1tbe1ik1Xg9e8RHR0d/PXXX7h27RpatmyJGTNmFDmMvawCAgLg4OCAbt26wcnJCUII7Nu37416CydOnAhlZWXY2dnB2NgYcXFxUFNTw7Rp09C8eXO0b98eysrK2Lp16xvHXxxnZ2ds3boVAQEBaN68OTZu3Iht27bJ3a/76NEj3Lp1S3qur6+PgwcP4t69e3B0dMTw4cMxfvx4jB8/XipTr1497Nu3DyEhIbC3t8eCBQvw448/VthIkcpGJt5kMHolkpKSAn19fSQnJ0NPT0/R4dA7lpaWJk1UkZqaWqbhU+9KVlYWFi1aBACYNm1aoankiUgx+PlBRFXRvXv3YGFhId2LSVRdcUgwEREREVEld+TIEaSmpqJZs2ZISEjA5MmTYWVlhfbt2ys6NKK3igkrVQva2tpvNHPdu6CmpoY5c+YoOgwiIiKqgrKzszF9+nTcvn0burq6cHZ2RmBgYLWcZIfoZRwSTERE7zV+fhAREVVenHSJiIiIiIiIKiUmrFQtZGRk4PPPP8fnn3+OjIwMRYdTpJycHPzxxx/4448/kJOTo+hwiIjoJb6+vvD29lZ0GEQKYWVlBT8/P0WHUSoymQx79uxRdBj0DjFhpWohNzcXO3bswI4dO4pcJLoyyMvLw7Vr13Dt2jXk5eUpOhwiovdKUQnpjh07oKGhgSVLlmDlypXYuHGjQmKj90doaCiUlZXRpUuXd9ZmSkoKZsyYgUaNGkFDQwNmZmbo1KkTdu3aVenm/0hKSsI333yDunXrQl1dHWZmZnB3d0dYWNg7jSMkJAQymQzPnj17p+2+6unTp+jfvz/09fWhr6+P/v37vzYmIQTmzp2LWrVqQVNTE66urrh69apcmczMTIwaNQpGRkbQ1tbGJ598gnv37r3FK3kzTFiJiIjovbNu3Tr07dsXP//8MyZPngx9ff0S16ckqggbNmzAqFGjcPLkScTFxb319p49ewZnZ2f89ttvmDZtGs6fP4/jx4/Dx8cHkydPRnJy8luPoSx69OiBixcvYtOmTbh+/Tr27t0LV1dXPHnyRNGhKUSfPn0QGRmJ4OBgBAcHIzIyEv379y/xnCVLlmD58uX4+eefce7cOZiZmcHNzQ3Pnz+XyowdOxa7d+/G1q1bcfLkSaSmpqJbt26VttMHoppITk4WAERycrKiQyEFSE1NFQAEAJGamqrocIqUmZkp5s6dK+bOnSsyMzMVHQ4R/YefH++HL7/8Unh5eQkhhFi8eLFQV1cXO3bsKPK4EEK4uLiIkSNHijFjxogaNWoIExMTsWbNGpGamip8fX2Fjo6OqF+/vti3b987vhKqqlJTU4Wurq74999/hY+Pj5g3b5507MMPPxRTpkyRK5+UlCRUVFTEkSNHhBBCxMfHC09PT6GhoSGsrKxEYGCgsLS0FCtWrCi2zWHDhgltbW1x//79QseeP38usrOzhRCiUD3Pnj0TgwcPFsbGxkJXV1d06NBBREZGSsdv3rwpPvnkE2FiYiK0tbWFo6OjOHjwoFz9lpaW4rvvvhNfffWV0NHRERYWFmLNmjXFxvr06VMBQISEhBRbRgghAIi1a9cKb29voampKaytrcWff/4pV+bq1avCw8NDaGtrCxMTE9GvXz/x8OFD6XheXp5YvHixqFevntDQ0BDNmzcXf/zxhxBCiJiYGOk7ZcH25ZdfCiGE+OOPP0TTpk2FhoaGqFmzpujYseNb+9557do1AUCcPn1a2hcWFiYAiH///bfIc/Ly8oSZmZn4/vvvpX0ZGRlCX19frF69WgiR/2+rqqoqtm7dKpW5f/++UFJSEsHBwW/lWt4Ue1iJiIjovTF16lQsWLAAQUFB6NGjR4llN23aBCMjI5w9exajRo3CsGHD8Pnnn8PZ2Rnnz5+Hu7s7+vfvj/T09HcUPVVl27Ztg62tLWxtbdGvXz8EBARIQ3L79u2LLVu2yA3R3bZtG0xNTeHi4gIAGDBgAOLj4xESEoKdO3fi119/RVJSUrHt5eXlYevWrejbty9q1apV6LiOjg5UVAqvcCmEQNeuXZGYmIh9+/YhIiICrVq1QseOHaWeztTUVHh6euLQoUO4cOEC3N3d0b1790K9xsuWLYOjoyMuXLiA4cOHY9iwYfj333+LjFdHRwc6OjrYs2cPMjMzS3wt582bh169euHSpUvw9PRE3759pdgSEhLg4uICe3t7hIeHIzg4GA8ePECvXr2k82fOnImAgAD4+/vj6tWrGDduHPr164djx47BwsICO3fuBABER0cjISEBK1euREJCAr744gsMHDgQUVFRCAkJwWeffVbisOqCaypu8/DwKPbcsLAw6Ovro02bNtK+Dz/8EPr6+ggNDS3ynJiYGCQmJqJz587SPnV1dbi4uEjnREREIDs7W65MrVq10LRp02LrVTjF5ssVh7+Qv9/Yw0pE5cXPj/fDl19+KdTU1AQAcfjw4SKPv9rD+tFHH0nPc3JyhLa2tujfv7+0LyEhQQAQYWFhbzV2qh6cnZ2Fn5+fEEKI7OxsYWRkJPVKFvSmHj9+XCrv5OQkJk2aJIQQIioqSgAQ586dk47fuHFDACi2h/XBgwcCgFi+fPlrY3u5h/Xw4cNCT09PZGRkyJVp0KBBiT2kdnZ24qeffpKrs1+/ftLzvLw8YWJiIvz9/YutY8eOHcLAwEBoaGgIZ2dnMW3aNHHx4kW5MgDEzJkzpeepqalCJpOJ/fv3CyGEmDVrlujcubPcOXfv3hUARHR0tEhNTRUaGhoiNDRUrsygQYPEF198IYQQ4ujRowKAePr0qXQ8IiJCABCxsbHFxv+qGzdulLjdu3ev2HO/++470bBhw0L7GzZsKBYuXFjkOadOnRIACvWoDx48WHpNAgMDhZqaWqFz3dzcxJAhQ0p9be9S4Z9ViIiIiKqh5s2b49GjR5g9ezZat24NXV3d15YvoKysDENDQzRr1kzaZ2pqCgAl9nIRAfk9dWfPnsWuXbsAACoqKvDx8cGGDRvQqVMnGBsbw83NDYGBgWjXrh1iYmIQFhYGf39/6XwVFRW0atVKqtPa2hoGBgbFtin+6/mTyWRlijUiIgKpqakwNDSU2//ixQvcunULAJCWloZ58+YhKCgI8fHxyMnJwYsXLwr1sL78NySTyWBmZlbi30uPHj3QtWtXnDhxAmFhYQgODsaSJUuwbt06+Pr6FlmvtrY2dHV1pXojIiJw9OhR6OjoFKr/1q1bSE5ORkZGBtzc3OSOZWVloWXLlsXG1qJFC3Ts2BHNmjWDu7s7OnfujJ49e5b4b2BtbV3ssdIo6t9OCPHaf9NXj5fmnNKUURQmrERERPReqF27Nnbu3IkOHTqgS5cuCA4OLjFpVVVVlXsuk8nk9hV8uePM7/Q669evR05ODmrXri3tE0JAVVUVT58+hYGBAfr27YsxY8bgp59+wubNm9GkSRO0aNFCKluU4vYDgLGxMQwMDBAVFVWmWPPy8mBubo6QkJBCxwomJps0aRL++ecfLF26FNbW1tDU1ETPnj2RlZUlV76ov6HX/b1oaGjAzc0Nbm5umD17Nr7++mvMmTNHLmEtqd68vDx0794dixcvLlS3ubk5rly5AgD4+++/5f49gPzhs8VRVlbGwYMHERoaigMHDuCnn37CjBkzcObMGdSrV6/Ic4pKml/Wrl077N+/v8hjZmZmePDgQaH9Dx8+lH4sK+ocAEhMTIS5ubm0PykpSTrHzMwMWVlZ0vvu5TLOzs4lxqsoTFipWtDS0kJqaqr0uDJSVVXFtGnTpMdERPTu1a1bF8eOHUOHDh3QuXNn/PPPP9DT01N0WFSN5eTk4LfffsOyZcvk7hsE8nsUAwMDMXLkSHh7e+Obb75BcHAwNm/eLDcbbKNGjZCTk4MLFy7AwcEBAHDz5s0SlzhRUlKCj48Pfv/9d8yZM6fQfaxpaWlQV1cvdB9rq1atkJiYCBUVFVhZWRVZ94kTJ+Dr64tPP/0UQP49rbGxsaV8RcrGzs6uTOuutmrVCjt37oSVlVWR9+ja2dlBXV0dcXFx0v3Br1JTUwOAQrPmymQytG3bFm3btsXs2bNhaWmJ3bt3Y/z48UXWExkZWWKsmpqaxR5zcnJCcnIyzp49iw8++AAAcObMGSQnJxebWNarVw9mZmY4ePCg1FuclZWFY8eOSQm8g4MDVFVVcfDgQem+3oSEBFy5cgVLliwpMV5FYcJK1YJMJoO2traiwyiRTCaT/gMkIiLFqVOnDkJCQuSSVqK3JSgoCE+fPsWgQYOgr68vd6xnz55Yv349Ro4cCW1tbXh5eWHWrFmIiopCnz59pHKNGjVCp06dMGTIEPj7+0NVVRUTJkyApqZmicM4Fy5ciJCQELRp0wbfffcdHB0doaqqihMnTmDRokU4d+5coeWcOnXqBCcnJ3h7e2Px4sWwtbVFfHw89u3bB29vbzg6OsLa2hq7du1C9+7dIZPJMGvWrDceafD48WN8/vnnGDhwIJo3bw5dXV2Eh4djyZIl8PLyKnU9I0aMwNq1a/HFF19g0qRJMDIyws2bN7F161asXbsWurq6mDhxIsaNG4e8vDx89NFHSElJQWhoKHR0dPDll1/C0tISMpkMQUFB8PT0hKamJq5evYrDhw+jc+fOMDExwZkzZ/Dw4UM0bty42FjeZEhw48aN0aVLFwwePBhr1qwBAAwZMgTdunWDra2tVK5Ro0ZYtGgRPv30U8hkMowdOxYLFy5Ew4YN0bBhQyxcuBBaWlrS+0lfXx+DBg3ChAkTYGhoiJo1a2LixIlo1qwZOnXqVO543ybOEkxERETvndq1a+PYsWN49uwZ3NzcSuypInoT69evR6dOnQolq0B+D2tkZCTOnz8PIH+24IsXL6Jdu3aoW7euXNnffvsNpqamaN++PT799FMMHjwYurq60NDQKLZtAwMDnD59Gv369cO3336Lli1bol27dtiyZQt++OGHImOSyWTYt28f2rdvj4EDB8LGxga9e/dGbGysNKx0xYoVMDAwgLOzM7p37w53d3e5+2vLQ0dHB23atMGKFSvQvn17NG3aFLNmzcLgwYPx888/l7qeWrVq4dSpU8jNzYW7uzuaNm2KMWPGQF9fH0pK+anPggULMHv2bCxatAiNGzeGu7s7/vrrL2lob+3atTFv3jxMnToVpqamGDlyJPT09HD8+HF4enrCxsYGM2fOxLJly0qc6fdNBQYGolmzZujcuTM6d+6M5s2b4/fff5crEx0dLbee7uTJkzF27FgMHz4cjo6OuH//Pg4cOCB3+8OKFSvg7e2NXr16oW3bttDS0sJff/0FZWXlt3Ytb0ImShr8XoWkpKRAX18fycnJHNrzHsrMzMQ333wDAFizZk2J9yAoSk5ODoKCggAA3bp1K3KYChG9e/z8IKKq6N69e7CwsMChQ4fQsWNHRYdD9NaUq4d11apVqFevHjQ0NODg4IATJ06UWP7YsWNwcHCAhoYG6tevj9WrVxcq4+fnB1tbW2hqasLCwgLjxo1DRkZGecKj91BOTg42bdqETZs2IScnR9HhFCkvLw8XL17ExYsXOUEHERERlcmRI0ewd+9exMTEIDQ0FL1794aVlRXat2+v6NCI3qoyJ6zbtm3D2LFjMWPGDFy4cAHt2rWDh4dHoWmsC8TExMDT0xPt2rXDhQsXMH36dIwePVpakBfI7+6eOnUq5syZg6ioKKxfvx7btm2TJqghIiIiInqfZWdnY/r06WjSpAk+/fRTGBsbIyQkhBM5UrVX5iHBbdq0QatWraR1oYD8m4K9vb2xaNGiQuWnTJmCvXv3yk2pPXToUFy8eBFhYWEAgJEjRyIqKgqHDx+WykyYMAFnz559be9tAQ7per+lpaVJU4enpqZWygmYsrKypL+RadOmcQImokqCnx9ERESVV5l6WLOyshAREVFoSu7OnTsjNDS0yHPCwsIKlXd3d0d4eDiys7MBAB999BEiIiJw9uxZAMDt27exb98+dO3atdhYMjMzkZKSIrcRERERERFR9VGmWV8ePXqE3NzcQovVmpqaIjExschzEhMTiyyfk5ODR48ewdzcHL1798bDhw/x0UcfQQiBnJwcDBs2DFOnTi02lkWLFmHevHllCZ+IiIioyvH19cWzZ8/KtBYlEVF1Ua5Jl15d70kIUeIaUEWVf3l/SEgIvvvuO6xatQrnz5/Hrl27EBQUhAULFhRb57Rp05CcnCxtd+/eLc+lEBER0XvA19cXMplM2gwNDdGlSxdcunSpQurfuHFjofUsK8rKlSuxcePGt1I3EVFlV6aE1cjICMrKyoV6U5OSkgr1ohYwMzMrsryKigoMDQ0BALNmzUL//v3x9ddfo1mzZvj000+xcOFCLFq0qNjZVNXV1aGnpye3ERERERWnS5cuSEhIQEJCAg4fPgwVFRV069ZN0WG9lr6+/ltLhomIKrsyJaxqampwcHDAwYMH5fYfPHgQzs7ORZ7j5ORUqPyBAwfg6OgozWqWnp4uLeRbQFlZGUIIVJNlYukt09LSQlJSEpKSkqClpaXocIqkqqqKiRMnYuLEiZzRj4hIAdTV1WFmZgYzMzPY29tjypQpuHv3Lh4+fAggf6JIGxsbaGlpoX79+pg1a5Y03wYAXLx4ER06dICuri709PTg4OCA8PBwhISE4KuvvkJycrLUgzt37lwAwNOnTzFgwAAYGBhAS0sLHh4euHHjhlRnQc/sP//8g8aNG0NHR0dKrAv4+vrC29v7nbxGRESVTZmHBI8fPx7r1q3Dhg0bEBUVhXHjxiEuLg5Dhw4FkD9Ud8CAAVL5oUOH4s6dOxg/fjyioqKwYcMGrF+/HhMnTpTKdO/eHf7+/ti6dStiYmJw8OBBzJo1C5988gmUlZUr4DKpupPJZDA2NoaxsXGJw9MVSSaTQVtbG9ra2pU2RiKi90VqaioCAwNhbW0tjfjS1dXFxo0bce3aNaxcuRJr167FihUrpHP69u2LOnXq4Ny5c4iIiMDUqVOhqqoKZ2dn+Pn5QU9PT+rBLfie4+vri/DwcOzduxdhYWEQQsDT01MuEU5PT8fSpUvx+++/4/jx44iLi5P7nkRE9D4r06RLAODj44PHjx9j/vz5SEhIQNOmTbFv3z5YWloCABISEuTWZK1Xrx727duHcePG4ZdffkGtWrXw448/okePHlKZmTNnQiaTYebMmbh//z6MjY3RvXt3fPfddxVwiURERERAUFCQtARaWloazM3NERQUJI3ymjlzplTWysoKEyZMwLZt2zB58mQAQFxcHCZNmoRGjRoBABo2bCiV19fXh0wmg5mZmbTvxo0b2Lt3L06dOiWNRAsMDISFhQX27NmDzz//HED++pqrV69GgwYNAOQv9zd//vy39TIQEVUpZU5YAWD48OEYPnx4kceKmhTAxcUF58+fLz4IFRXMmTMHc+bMKU84RMjMzMT48eMBAMuXL4e6urqCIyosJycH//zzD4D8pZ1UVMr150dEROXUoUMHaR35J0+eYNWqVfDw8MDZs2dhaWmJHTt2wM/PDzdv3kRqaipycnLk5sgYP348vv76a/z+++/o1KkTPv/8cynJLEpUVBRUVFTQpk0baZ+hoSFsbW3l1qfX0tKSq8fc3BxJSUkVeelERFVWuWYJJqpscnJysGrVKqxatQo5OTmKDqdIeXl5CA8PR3h4eLGTiRER0dujra0Na2trWFtb44MPPsD69euRlpaGtWvX4vTp0+jduzc8PDwQFBSECxcuYMaMGcjKypLOnzt3Lq5evYquXbviyJEjsLOzw+7du4ttr7h5OF5dXeHVeQ1kMhnn8CAi+g8TViIiInovyWQyKCkp4cWLFzh16hQsLS0xY8YMODo6omHDhrhz506hc2xsbDBu3DgcOHAAn332GQICAgDkT0yZm5srV9bOzg45OTk4c+aMtO/x48e4fv06Gjdu/HYvjoiommDCSkRERO+FzMxMJCYmIjExEVFRURg1ahRSU1PRvXt3WFtbIy4uDlu3bsWtW7fw448/yvWevnjxAiNHjkRISAju3LmDU6dO4dy5c1LiaWVlhdTUVBw+fBiPHj1Ceno6GjZsCC8vLwwePBgnT57ExYsX0a9fP9SuXRteXl6KehmIiKoUJqxERET0XggODoa5uTnMzc3Rpk0bnDt3Dn/88QdcXV3h5eWFcePGYeTIkbC3t0doaChmzZolnausrIzHjx9jwIABsLGxQa9eveDh4YF58+YBAJydnTF06FD4+PjA2NgYS5YsAQAEBATAwcEB3bp1g5OTE4QQ2LdvH5c3IyIqJZmoJjdJpKSkQF9fH8nJyXITJND7IS0tTZr5MTU1Fdra2gqOqLCsrCwsWrQIQP7yT2pqagqOiIgAfn4QERFVZuxhJSIiIiIiokqJCSsRERERERFVSlwIkqoFTU1NxMTESI8rI1VVVYwZM0Z6TEREREREJWPCStWCkpISrKysFB1GiWQyGWrUqKHoMIiIiIiIqgwOCSYiIiJ6Q76+vvD29lZ0GERE1Q4TVqoWsrKyMGnSJEyaNAlZWVmKDqdIubm5OHDgAA4cOFBocXkiInr7EhMTMWrUKNSvXx/q6uqwsLBA9+7dcfjwYUWHVmqhoaFQVlZGly5dFB1KsVxdXTF27FhFh0FE1QQTVqoWsrOzsXTpUixduhTZ2dmKDqdIubm5CAsLQ1hYGBNWIqJ3LDY2Fg4ODjhy5AiWLFmCy5cvIzg4GB06dMCIESPKXW9ubi7y8vIqMNKSbdiwAaNGjcLJkycRFxf3ztolIlIUJqxERERU7Q0fPhwymQxnz55Fz549YWNjgyZNmmD8+PE4ffq0VG758uVo1qwZtLW1YWFhgeHDhyM1NVU6vnHjRtSoUQNBQUGws7ODuro67ty5U6i9zMxMjB49GiYmJtDQ0MBHH32Ec+fOScefPn2Kvn37wtjYGJqammjYsCECAgJKvIa0tDRs374dw4YNQ7du3bBx40a54wWxvWzPnj2QyWRy+7799luYmJhAV1cXX3/9NaZOnQp7e3vpeFE9pN7e3vD19ZWer1q1Cg0bNoSGhgZMTU3Rs2dPAPlDo48dO4aVK1dCJpNBJpMhNjYWAHDs2DF88MEHUFdXh7m5OaZOnYqcnJwSr5mIiAkrERERVWtPnjxBcHAwRowYAW1t7ULHX07ylJSU8OOPP+LKlSvYtGkTjhw5gsmTJ8uVT09Px6JFi7Bu3TpcvXoVJiYmheqcPHkydu7ciU2bNuH8+fOwtraGu7s7njx5AgCYNWsWrl27hv379yMqKgr+/v4wMjIq8Tq2bdsGW1tb2Nraol+/fggICIAQokyvRWBgIL777jssXrwYERERqFu3Lvz9/ctUR3h4OEaPHo358+cjOjoawcHBaN++PQBg5cqVcHJywuDBg5GQkICEhARYWFjg/v378PT0ROvWrXHx4kX4+/tj/fr1+Pbbb8vUNhG9fzhLMBEREVVrN2/ehBACjRo1em3Zl3sW69WrhwULFmDYsGFYtWqVtD87OxurVq1CixYtiqwjLS0N/v7+2LhxIzw8PAAAa9euxcGDB7F+/XpMmjQJcXFxaNmyJRwdHQGgVDPdr1+/Hv369QMAdOnSBampqTh8+DA6der02nML/PTTTxg0aBC++uorAMDs2bNx4MABuV7k14mLi4O2tja6desGXV1dWFpaomXLlgAAfX19qKmpQUtLC2ZmZtI5q1atgoWFBX7++WfIZDI0atQI8fHxmDJlCmbPng0lJfahEFHR+L8DERERVWsFvZCvDo0tytGjR+Hm5obatWtDV1cXAwYMwOPHj5GWliaVUVNTQ/PmzYut49atW8jOzkbbtm2lfaqqqvjggw8QFRUFABg2bBi2bt0Ke3t7TJ48GaGhoSXGFR0djbNnz6J3794AABUVFfj4+GDDhg2vvaZX6/nggw/k9r36/HXc3NxgaWmJ+vXro3///ggMDER6enqJ50RFRcHJyUnu36Bt27ZITU3FvXv3ytQ+Eb1fmLASERFRtdawYUPIZDIpWSzOnTt34OnpiaZNm2Lnzp2IiIjAL7/8AgByE/ppamqWmPwWlyALIaR9Hh4euHPnDsaOHYv4+Hh07NgREydOLLbO9evXIycnB7Vr14aKigpUVFTg7++PXbt24enTpwDyhzO/OkS4qIkIi4rrZa+rR1dXF+fPn8eWLVtgbm6O2bNno0WLFnj27Fmx8b987a+2W5ofEojo/cWElYiIiKq1mjVrwt3dHb/88otcT2mBgkQrPDwcOTk5WLZsGT788EPY2NggPj6+zO1ZW1tDTU0NJ0+elPZlZ2cjPDwcjRs3lvYZGxvD19cX//vf/+Dn54dff/21yPpycnLw22+/YdmyZYiMjJS2ixcvwtLSEoGBgVJ9z58/l7vGyMhIubpsbW1x9uxZuX3h4eFyz42NjZGQkCA9z83NxZUrV+TKqKiooFOnTliyZAkuXbqE2NhYHDlyBEB+D/Srs+Hb2dkhNDRULhEODQ2Frq4uateuXeR1ExEBvIeVqglNTU3pw1RTU1PB0RRNVVUVw4YNkx4TEdG7s2rVKjg7O+ODDz7A/Pnz0bx5c+Tk5ODgwYPw9/dHVFQUGjRogJycHPz000/o3r07Tp06hdWrV5e5LW1tbQwbNgyTJk1CzZo1UbduXSxZsgTp6ekYNGgQgPx7Rx0cHNCkSRNkZmYiKChILpl9WVBQEJ4+fYpBgwZBX19f7ljPnj2xfv16jBw5Em3atIGWlhamT5+OUaNG4ezZs4VmEh41ahQGDx4MR0dHODs7Y9u2bbh06RLq168vlfn4448xfvx4/P3332jQoAFWrFgh13saFBSE27dvo3379jAwMMC+ffuQl5cHW1tbAPn34545cwaxsbHQ0dFBzZo1MXz4cPj5+WHUqFEYOXIkoqOjMWfOHIwfP573rxJRyUQ1kZycLACI5ORkRYdCRERVCD8/3h/x8fFixIgRwtLSUqipqYnatWuLTz75RBw9elQqs3z5cmFubi40NTWFu7u7+O233wQA8fTpUyGEEAEBAUJfX79Q3V9++aXw8vKSnr948UKMGjVKGBkZCXV1ddG2bVtx9uxZ6fiCBQtE48aNhaampqhZs6bw8vISt2/fLjLubt26CU9PzyKPRURECAAiIiJCCCHE7t27hbW1tdDQ0BDdunUTv/76q3j16978+fOFkZGR0NHREQMHDhSjR48WH374oXQ8KytLDBs2TNSsWVOYmJiIRYsWCS8vL/Hll18KIYQ4ceKEcHFxEQYGBkJTU1M0b95cbNu2TTo/OjpafPjhh0JTU1MAEDExMUIIIUJCQkTr1q2FmpqaMDMzE1OmTBHZ2dlFXhcRUQGZEGWcD72SSklJgb6+PpKTk6Gnp6focIiIqIrg5we979zc3GBmZobff/9d0aEQERXCIcFULWRlZWHhwoUAgOnTp0NNTU3BERWWm5uLEydOAADatWsHZWVlBUdERETvm/T0dKxevRru7u5QVlbGli1bcOjQIRw8eFDRoRERFYkJK1UL2dnZmDdvHgBg0qRJlTZhPXbsGADA2dmZCSsREb1zMpkM+/btw7fffovMzEzY2tpi586dZVrLlYjoXWLCSkRERPSe0NTUxKFDhxQdBhFRqXFaNiIiIiIiIqqUmLASERERvQMbN25EjRo1pOdz586Fvb29wuIhIqoKmLASERFRtefr6wuZTAaZTAYVFRXUrVsXw4YNw9OnTxUdGhERlYAJKxEREb0XunTpgoSEBMTGxmLdunX466+/MHz4cEWHRUREJWDCSkRERO8FdXV1mJmZoU6dOujcuTN8fHxw4MABAEBeXh7mz5+POnXqQF1dHfb29ggODpbOdXJywtSpU+Xqe/jwIVRVVXH06FEA+UusTZ48GbVr14a2tjbatGmDkJCQUsd37tw5uLm5wcjICPr6+nBxccH58+ff/MKJiKowJqxULWhoaODs2bM4e/YsNDQ0FB1OkVRUVPD111/j66+/hooKJ+gmIlKk27dvIzg4GKqqqgCAlStXYtmyZVi6dCkuXboEd3d3fPLJJ7hx4wYAoG/fvtiyZQuEEFId27Ztg6mpKVxcXAAAX331FU6dOoWtW7fi0qVL+Pzzz9GlSxepjtd5/vw5vvzyS5w4cQKnT59Gw4YN4enpiefPn1fw1RMRVR381kzVgrKyMlq3bq3oMEqkpKSE2rVrKzoMIqL3VlBQEHR0dJCbm4uMjAwAwPLlywEAS5cuxZQpU9C7d28AwOLFi3H06FH4+fnhl19+gY+PD8aNG4eTJ0+iXbt2AIDNmzejT58+UFJSwq1bt7Blyxbcu3cPtWrVAgBMnDgRwcHBCAgIwMKFC18b38cffyz3fM2aNTAwMMCxY8fQrVu3CnsdiIiqEiasRERE9F7o0KED/P39kZ6ejnXr1uH69esYNWoUUlJSEB8fj7Zt28qVb9u2LS5evAgAMDY2hpubGwIDA9GuXTvExMQgLCwM/v7+AIDz589DCAEbGxu5OjIzM2FoaFiq+JKSkjB79mwcOXIEDx48QG5uLtLT0xEXF1cBV09EVDUxYaVqISsrCytXrgQAjBkzBmpqagqOqLDc3FycPn0aAPDhhx9CWVlZwREREb1ftLW1YW1tDQD48ccf0aFDB8ybNw+TJk0CAMhkMrnyQgi5fX379sWYMWPw008/YfPmzWjSpAlatGgBIP8eWGVlZURERBT6/11HR6dU8fn6+uLhw4fw8/ODpaUl1NXV4eTkhKysrHJfMxFRVcd7WKlayM7OxuTJkzF58mRkZ2crOpwi5ebm4tChQzh06BByc3MVHQ4R0Xtvzpw5WLp0KVJTU1GrVi2cPHlS7nhoaCgaN24sPff29kZGRgaCg4OxefNm9OvXTzrWsmVL5ObmIikpCdbW1nKbmZlZqeI5ceIERo8eDU9PTzRp0gTq6up49OhRxVwsEVEVxR5WIiIiei+5urqiSZMmWLhwISZNmoQ5c+agQYMGsLe3R0BAACIjIxEYGCiV19bWhpeXF2bNmoWoqCj06dNHOmZjY4O+fftiwIABWLZsGVq2bIlHjx7hyJEjaNasGTw9PV8bj7W1NX7//Xc4OjoiJSUFkyZNgqam5lu5diKiqoIJK1V6D1IysDj4X/yb8BwOlgYY26khDHXUFR0WERFVA+PHj8dXX32F69evIyUlBRMmTEBSUhLs7Oywd+9eNGzYUK5837590bVrV7Rv3x5169aVOxYQEIBvv/0WEyZMwP3792FoaAgnJ6dSJasAsGHDBgwZMgQtW7ZE3bp1sXDhQkycOLHCrpWIqCqSiZfnZ6/CUlJSoK+vj+TkZOjp6Sk6HKogj1Mz0f3nk4h/liHtM9fXwO7hzjDT//9fndPS0qR7hFJTU6Gtrf3OY32drKwsLFq0CAAwbdq0SnmfLdH7iJ8fRERElRfvYaVKbc7eq4h/lgEjXXV80c4ShrpqSEjOwMBN4cjNqxa/tRARERERUTGYsFKldTU+GUGXEiADMKBDPbS2NsSQztbQUFXCtfgU/HrytqJDJCIiIiKit4gJK1Va/zudv+5cC6saqGOoBQAw1tOAp0NtAMBPh24g+QWn+iciIiIiqq446RJVShnZufjrYjwAwKmRsdwxZ1sjnPr3IR48y8BPR29ipqcdNDQ0cPToUQCAhobGO4+3NFRUVPDll19Kj4mIiIiIqGTsYaVKKST6IVIzc2Cgo4YGZvILrispyeDWIn9Nuy1n4pCakQ1lZWW4urrC1dW10ILtlYWSkhKsrKxgZWUFJSX+6RERERERvQ6/NVOldOz6QwBAUwt9KMlkhY7bWxnASFcdaZm5+O3MnXcdHhER0Tvj6uqKsWPHKjoMIiKFYMJKlY4QAsf/S1htaxe9xISSkgzOjYwAAFvO3kVWVhZ++eUX/PLLL8jOzn5nsZZFbm4uzp49i7NnzyI3N1fR4RARvVd8fX0hk8kKbTdv3ixXfRs3bkSNGjUK7beysoKfn9+bBfuKXbt2YcGCBRVaJxFRVcEb6ajSuf0oDfefvYCKsqzQcOCXtW5oiH3n43H3cTqORMVj5MiRAPK/lKiqqr6rcEstNzcX+/fvBwDY29tX2qHLRETVVZcuXRAQECC3z9hYfp6ErKysSrdOds2aNRUdAhGRwrCHlSqdiDtPAQB1jbShrlp8UqetroJW9fM/xH/nsGAiInoNdXV1mJmZyW0dO3bEyJEjMX78eBgZGcHNzQ0AsHz5cjRr1gza2tqwsLDA8OHDkZqaCgAICQnBV199heTkZKmndu7cuXB1dcWdO3cwbtw4aX+B0NBQtG/fHpqamrCwsMDo0aORlpYmHV+1ahUaNmwIDQ0NmJqaomfPntIxDgkmovcZE1aqdC7EPQMAWBlrv7Zsm4aGAIAzN5+8zZCIiKga27RpE1RUVHDq1CmsWbMGQP5EeT/++COuXLmCTZs24ciRI5g8eTIAwNnZGX5+ftDT00NCQgISEhIwceJE7Nq1C3Xq1MH8+fOl/QBw+fJluLu747PPPsOlS5ewbds2nDx5UhoZFB4ejtGjR2P+/PmIjo5GcHAw2rdvr5gXg4iokuGQYKp0LsT918Nq8vqE1cpEG4a66nj4OONth0VERFVcUFAQdHT+/1YTDw8PAIC1tTWWLFkiV/blHs169ephwYIFGDZsGFatWgU1NTXo6+tDJpPBzMxM7jxlZWXo6urK7f/hhx/Qp08fqc6GDRvixx9/hIuLC/z9/REXFwdtbW1069YNurq6sLS0RMuWLSv46omIqiYmrFSppGXm4PqD5wAAS2Ot15aXyWRwaGCA4MfJbzs0IiKq4jp06AB/f3/puba2Nr744gs4OjoWKnv06FEsXLgQ165dQ0pKCnJycpCRkYG0tDRoa7/+B9WXRURE4ObNmwgMDJT2CSGQl5eHmJgYuLm5wdLSEvXr10eXLl3QpUsXfPrpp9DSev3nIBFRdcchwVSpXL6fjDwB1NBShb5W6Sa9cGjAySiIiOj1tLW1YW1tLW3m5ubS/pfduXMHnp6eaNq0KXbu3ImIiAj88ssvAFCumejz8vLwzTffIDIyUtouXryIGzduoEGDBtDV1cX58+exZcsWmJubY/bs2WjRogWePXv2xtdMRFTVsYeVKpXoxPze1TqGpf9V2VhPA5bGWrj7toIiIqL3Snh4OHJycrBs2TIoKeX/tr99+3a5MmpqakUuUVbU/latWuHq1auwtrYutk0VFRV06tQJnTp1wpw5c1CjRg0cOXIEn332WQVcERFR1VWuHtZVq1ahXr160NDQgIODA06cOFFi+WPHjsHBwQEaGhqoX78+Vq9eXajMs2fPMGLECJibm0NDQwONGzfGvn37yhMeVWHR/w0HNjPQKNN5DjamMO45B80GLYS6uvrbCO2Nqaio4IsvvsAXX3wBFRX+VkREVFk1aNAAOTk5+Omnn3D79m38/vvvhb67WFlZITU1FYcPH8ajR4+Qnp4u7T9+/Dju37+PR48eAQCmTJmCsLAwjBgxApGRkbhx4wb27t2LUaNGAci/t/bHH39EZGQk7ty5g99++w15eXmwtbV9txdORFQJlTlh3bZtG8aOHYsZM2bgwoULaNeuHTw8PBAXF1dk+ZiYGHh6eqJdu3a4cOECpk+fjtGjR2Pnzp1SmaysLLi5uSE2NhY7duxAdHQ01q5di9q1a5f/yqhKup5YkLBqluk8+/pG0G7QGilGzRH7tHJOwKSkpAQbGxvY2NhIv9gTEVHlY29vj+XLl2Px4sVo2rQpAgMDsWjRIrkyzs7OGDp0KHx8fGBsbCxN2jR//nzExsaiQYMG0hqvzZs3x7Fjx3Djxg20a9cOLVu2xKxZs6QhyTVq1MCuXbvw8ccfo3Hjxli9ejW2bNmCJk2avNsLJyKqhGRCCFGWE9q0aYNWrVrJTVrQuHFjeHt7F/rPHMj/VXHv3r2IioqS9g0dOhQXL15EWFgYAGD16tX44Ycf8O+//0JVVbVUcWRmZiIzM1N6npKSAgsLCyQnJ0NPT68sl0SVhBACzecdwPOMHEz0aoxaNcuWtP6y/zpuJabiG9cGmNal0VuKkoiqm5SUFOjr6/Pzg4iIqBIqUzdPVlYWIiIi0LlzZ7n9nTt3RmhoaJHnhIWFFSrv7u6O8PBwaeKCvXv3wsnJCSNGjICpqSmaNm2KhQsXFnlvSIFFixZBX19f2iwsLMpyKVQJJaZk4HlGDpRkgIl+2Yb15mRnQ+X2caRePoR/Lt97SxG+mdzcXGmyjZLe20RERERElK9MCeujR4+Qm5sLU1NTuf2mpqZITEws8pzExMQiy+fk5Ej3dty+fRs7duxAbm4u9u3bh5kzZ2LZsmX47rvvio1l2rRpSE5Olra7dznlTlVXMOGSsb4GVJTLNmQ2JzsbB36ajcf7/HA7IRk3Hqa+jRDfSG5uLv7880/8+eefTFiJiIiIiEqhXDO/yGQyuedCiEL7Xlf+5f15eXkwMTHBr7/+CmVlZTg4OCA+Ph4//PADZs+eXWSd6urqlXZyHSqfm0n5SaZZjbJNuFSUHRH3OCyYiIiIiKiKK1M3lpGREZSVlQv1piYlJRXqRS1gZmZWZHkVFRUYGhoCAMzNzWFjYwNlZWWpTOPGjZGYmIisrKyyhEhVWOzjNACAsd6b/xBx8FrRPf5ERERERFR1lClhVVNTg4ODAw4ePCi3/+DBg3B2di7yHCcnp0LlDxw4AEdHR2mCpbZt2+LmzZvIy8uTyly/fh3m5uZQU1MrS4hUhd15nL8kgJHem/WwygDcTkrDzUo4LJiIiN4/c+fOhb29vfTc19cX3t7eCouHiKgqKfPaGuPHj8e6deuwYcMGREVFYdy4cYiLi8PQoUMB5N9bOmDAAKn80KFDcefOHYwfPx5RUVHYsGED1q9fj4kTJ0plhg0bhsePH2PMmDG4fv06/v77byxcuBAjRoyogEukqkJKWHXfrIfVykQbAPDH+co5+RIREb17vr6+kMlk0mZoaIguXbrg0qVLig6NiIhKUOaE1cfHB35+fpg/fz7s7e1x/Phx7Nu3D5aWlgCAhIQEuTVZ69Wrh3379iEkJAT29vZYsGABfvzxR/To0UMqY2FhgQMHDuDcuXNo3rw5Ro8ejTFjxmDq1KkVcIlUFWTl5OHe0/yE1VDvzXrVm1nWAAAcuvbgTcMiIqJqpEuXLkhISEBCQgIOHz4MFRUVdOvWrdz1Fax2QEREb0+ZE1YAGD58OGJjY5GZmYmIiAi0b99eOrZx40aEhITIlXdxccH58+eRmZmJmJgYqTf2ZU5OTjh9+jQyMjJw69YtTJ8+Xe6eVqre7j97gTwBqCrLoKdZurV4i9PUUh8yALcepCLmEYcFExFRPnV1dZiZmcHMzAz29vaYMmUK7t69i4cPHwLIXzvexsYGWlpaqF+/PmbNmiWXlBYM7d2wYQPq168PdXV1CCGQnJyMIUOGwMTEBHp6evj4449x8eLFUscVHByMjz76CDVq1IChoSG6deuGW7duVfj1ExFVReWaJZioohVMuGSkp17ijNPFUVVTwwS/NQAAQ30dWJlqI+ZBGnZcuI9JbrYVGmt5qaiooGfPntJjIiJSnNTUVAQGBsLa2lqaBFJXVxcbN25ErVq1cPnyZQwePBi6urqYPHmydN7Nmzexfft27Ny5U/phvWvXrqhZsyb27dsHfX19rFmzBh07dsT169dRs2bN18aSlpaG8ePHo1mzZkhLS8Ps2bPx6aefIjIyEkpK5epbICKqNvitmSqFO4/+S1jLef+qsooKnLt0l563sDRAzIM0HLz2oNIkrEpKSmjSpImiwyAiem8FBQVBR0cHQH6SaG5ujqCgICkpnDlzplTWysoKEyZMwLZt2+QS1qysLPz+++8wNjYGABw5cgSXL19GUlKStNze0qVLsWfPHuzYsQNDhgx5bVwv3yYFAOvXr4eJiQmuXbuGpk2bvtlFExFVcfzZjiqFO08KZgiumLV1m1vVAABcT3iOuP/qJiKi91uHDh0QGRmJyMhInDlzBp07d4aHhwfu3LkDANixYwc++ugjmJmZQUdHB7NmzZKblwMALC0tpWQVACIiIpCamgpDQ0Po6OhIW0xMTKmH9d66dQt9+vRB/fr1oaenh3r16gFAobaJiN5H7GGlSiHuvxmCDcvZw5qbk4Mzh/YDANp08kANbTVYGWsj9mEadl64h3EdbSos1vLKy8tDVFQUgPx1hjnMi4jo3dLW1oa1tbX03MHBAfr6+li7di26deuG3r17Y968eXB3d4e+vj62bt2KZcuWFarjZXl5eTA3Ny80fwcA1KhRo1Rxde/eHRYWFli7di1q1aqFvLw8NG3alGvRExGBCStVEvefvQAA1CxnwpqdlYVlY78BAASevwllFRU0t6qB2IdpOHD1QaVIWHNycrBjxw4A+cs/cY1hIiLFkslkUFJSwosXL3Dq1ClYWlpixowZ0vGCnteStGrVComJiVBRUYGVlVWZY3j8+DGioqKwZs0atGvXDgBw8uTJMtdDRFRdMWGlSiH+v4S1hvabzRD8suZWNbD33H38G5+C+GcvUKuGZoXVTUREVU9mZiYSExMBAE+fPsXPP/+M1NRUdO/eHcnJyYiLi8PWrVvRunVr/P3339i9e/dr6+zUqROcnJzg7e2NxYsXw9bWFvHx8di3bx+8vb3h6OhY4vkGBgYwNDTEr7/+CnNzc8TFxXFZPyKil3BMIinc84xspGTkAABqaFdcr2NNHXXUNdKCALDjwr0Kq5eIiKqm4OBgmJubw9zcHG3atMG5c+fwxx9/wNXVFV5eXhg3bhxGjhwJe3t7hIaGYtasWa+tUyaTYd++fWjfvj0GDhwIGxsb9O7dG7GxsTA1NX3t+UpKSti6dSsiIiLQtGlTjBs3Dj/88ENFXC4RUbUgE0IIRQdREVJSUqCvr4/k5GTo6ekpOhwqg+sPnqPziuPQVFPGd31blKuOjPR09G2Vf19S4Pmb0NDSAgAcufwAQeH30aSOHv4e2a7CYi6PrKwsLFq0CACHBBNVJvz8ICIiqrzYw0oKV3D/qoFOxSdwLf6bLfja/RQ8SHlR4fUTEREREdHbw4SVFK7g/lWDChwOXMBQVx11DDUhBLDjwv0Kr5+IiIiIiN4eJqykcAnPMgBU7IRLL2thZQAACL6S+FbqJyIiIiKit4OzBJPC/f8MweXvYVVRVcWIhSukxy9rblkDf0fE4+q9ZDxMzYCxjkb5g30DysrK8PLykh4TEREREVHJmLCSwlXEPawqqqr4+DOfIo8Z62ugVk1NxD95gZ0X7mNouwblbudNKCsrw97eXiFtExFR1eLq6gp7e3v4+fkpOhQiIoXikGBSuLd5D2uBgsmXOCyYiOj9lZiYiDFjxsDa2hoaGhowNTXFRx99hNWrVyM9PV3R4b1Tc+fOhUwmw9ChQ+X2R0ZGQiaTITY2VjGBERG9ggkrKVRenkBiSsE9rOVPWHNzchARcggRIYeQm5NT6HjBfayX7ybjcVpmudt5E3l5ebh+/TquX7+OvLw8hcRARPS+un37Nlq2bIkDBw5g4cKFuHDhAg4dOoRx48bhr7/+wqFDhxQd4junoaGB9evX4/r164oOhYioWExYSaEepWYiO1dAJgP0tMo/6VJ2VhYWDh2AhUMHIDsrq9BxE30NmBtoIDdP4I/z994k5HLLycnBli1bsGXLFuQUkVQTEdHbM3z4cKioqCA8PBy9evVC48aN0axZM/To0QN///03unfvLpWNi4uDl5cXdHR0oKenh169euHBgwfS8blz58Le3h6///47rKysoK+vj969e+P58+cAgNjYWMhkskKbq6srAODx48f44osvUKdOHWhpaaFZs2bYsmVLifFnZWVh8uTJqF27NrS1tdGmTRuEhIRIx11dXYtss6SeUltbW3To0AEzZ84s+wtKRPSOMGElhSroXdXTVIWykuytttWyfk0AwF8XE95qO0REVLk8fvwYBw4cwIgRI6CtrV1kGZks/zNICAFvb288efIEx44dw8GDB3Hr1i34+MjPk3Dr1i3s2bMHQUFBCAoKwrFjx/D9998DACwsLJCQkCBtFy5cgKGhIdq3bw8AyMjIgIODA4KCgnDlyhUMGTIE/fv3x5kzZ4q9hq+++gqnTp3C1q1bcenSJXz++efo0qULbty4AQDYtWuXXJufffYZbG1tYWpqWuJr8/3332Pnzp04d+5c6V5MIqJ3jAkrKdSDlPzhuW/Su1paLevlDwu+di8Z95++X/cqERG9z27evAkhBGxtbeX2GxkZQUdHBzo6OpgyZQoA4NChQ7h06RI2b94MBwcHtGnTBr///juOHTsml9Tl5eVh48aNaNq0Kdq1a4f+/fvj8OHDAPIn2TMzM4OZmRlq1KiBoUOHwsnJCXPnzgUA1K5dGxMnToS9vT3q16+PUaNGwd3dHX/88UeR8d+6dQtbtmzBH3/8gXbt2qFBgwaYOHEiPvroIwQEBAAAatasKbW5ZcsWHDlyBHv37oWmpmaJr02rVq3Qq1cvTJ06tVyvLRHR28ZZgkmhkp7n97Dqa779hNVQVx1WJtqITUrDlvC7mOhm+/qTiIio2ijoRS1w9uxZ5OXloW/fvsjMzP8BNSoqChYWFrCwsJDK2dnZoUaNGoiKikLr1q0BAFZWVtDV1ZXKmJubIykpqVCbgwYNwvPnz3Hw4EEoKeX3E+Tm5uL777/Htm3bcP/+fWRmZiIzM7PY3t/z589DCAEbGxu5/ZmZmTA0NJTbt3//fkydOhV//fVXofLF+fbbb9G4cWMcOHAAJiYmpTqHiOhdYcJKClXQw6r7DnpYgfxe1tikNOy7nMCElYjoPWFtbQ2ZTIZ///1Xbn/9+vUBQK4XUghRKLEtar/qK2t+y2SyQhPqffvttwgODsbZs2flkttly5ZhxYoV8PPzQ7NmzaCtrY2xY8ciq4g5GID83lxlZWVEREQUWsdbR0dHenzt2jX07t0b33//PTp37lxkXUVp0KABBg8ejKlTp2L9+vWlPo+I6F3gkGBSqIf/9bC+iyHBAGBfzwBKMuB2UhqiH6S8kzaJiEixDA0N4ebmhp9//hlpaWkllrWzs0NcXBzu3r0r7bt27RqSk5PRuHHjUre5c+dOzJ8/H9u3b0eDBvLrf584cQJeXl7o168fWrRogfr160v3ohalZcuWyM3NRVJSEqytreU2MzMzAPn36Xbv3h2fffYZxo0bV+o4C8yePRvXr1/H1q1by3wuEdHbxISVFCqp4B7WdzAkGAB0NVXRsJYeAGDLubuvKU1ERNXFqlWrkJOTA0dHR2zbtg1RUVGIjo7G//73P/z7779Sz2WnTp3QvHlz9O3bF+fPn8fZs2cxYMAAuLi4wNHRsVRtXblyBQMGDMCUKVPQpEkTJCYmIjExEU+ePAGQ3+N78OBBhIaGIioqCt988w0SE4tfJ9zGxgZ9+/bFgAEDsGvXLsTExODcuXNYvHgx9u3bBwD47LPPoKmpiblz50rtJSYmIjc3t1Qxm5qaYvz48fjxxx8LHWvUqBF2795dqnqIiCoaE1ZSqAcV1MOqoqqKr2d9h69nfQcV1ZLralU/f/Kl4CuJEEK8UbtloaysDA8PD3h4eBQa0kVERG9XgwYNcOHCBXTq1AnTpk1DixYt4OjoiJ9++gkTJ07EggULAOQP7d2zZw8MDAzQvn17dOrUCfXr18e2bdtK3VZ4eDjS09Px7bffwtzcXNo+++wzAMCsWbPQqlUruLu7w9XVFWZmZvD29i6xzoCAAAwYMAATJkyAra0tPvnkE5w5c0a61/b48eO4evUqrKys5Np8uaf4dSZNmiQ3xLhAdHQ0kpOTS10PEVFFkol3+Y39LUpJSYG+vj6Sk5Ohp6en6HColD747hCSnmdifPdGqGOk9U7azMjKxeytl5CTK7BjmBMcLWu+k3aJqHLi5wcREVHlxR5WUpjcPIFHqe9uWZsCGmrKaGKhD4DDgomIiIiIKjMmrKQwj1MzkScAmQzQ0XizCatzc3Nx5UworpwJLdX9Oq3q5/eqHrr2ADm5ea8pXTHy8vIQGxuL2NjYQjNJEhERERFRYUxYSWGSnv+3pI2GKpSUCi8hUBbZmZmY82VPzPmyJ7L/W0uvJI3r6EFLXRnJ6dn4J+rBG7VdWjk5Odi0aRM2bdqEnJycd9ImEREREVFVxoSVFOZBSsGES+9+OWAVZSW0rJffy7qNw4KJiIiIiColJqykMAU9rO/y/tWXfdAwP2ENu/kIz14UvVg7ERHRy1xdXTF27FjpuZWVFfz8/N56u3PnzoW9vf1bb4eIqLJhwkoKI/WwvqM1WF9Vx1ALpjU0kJ0rOPkSEVE15+vrW+TSMSEhIZDJZHj27Fmp6tm1a5e0BA4REb19TFhJYRTdwyqTydDaOr+XdfeF+wqJgYiIqpaaNWtCV1dX0WEQEb03mLCSwiSl/JewKqiHFQAcGtSETAZcT3iO6AcpCouDiIgqh507d6JJkyZQV1eHlZUVli1bJnf81SHBL4uNjYVMJkNkZKS079mzZ5DJZAgJCQHw/z26hw8fhqOjI7S0tODs7Izo6Gi5ur7//nuYmppCV1cXgwYNQkZGRkVeJhFRlcGElRQm6XnBpEuKS1j1tdRgW0sPAPDb6TsKi4OIiBQvIiICvXr1Qu/evXH58mXMnTsXs2bNwsaNGyu8rRkzZmDZsmUIDw+HiooKBg4cKB3bvn075syZg++++w7h4eEwNzfHqlWrKjwGIqKq4N1Pz0r0n4rsYVVWUUH/STOlx2XR2rom/r2fgv2XEzGvWxOoKL+d33GUlZXRqVMn6TEREb1bQUFB0NHRkdv38trdy5cvR8eOHTFr1iwAgI2NDa5du4YffvgBvr6+FRrLd999BxcXFwDA1KlT0bVrV2RkZEBDQwN+fn4YOHAgvv76awDAt99+i0OHDrGXlYjeS+xhJYUQQuBx2n/rsFZAwqqqpgbvQcPhPWg4VNXUynRu07o1oKmmjCepWdh/LfGNYymOsrIy2rZti7Zt2zJhJSJSgA4dOiAyMlJuW7dunXQ8KioKbdu2lTunbdu2uHHjhlxiWxGaN28uPTY3NwcAJCUlSXE4OTnJlX/1ORHR+4I9rKQQKRk5yM4VAAAdDcW+DVVVlODYoCZORD1E4Jk4dG9WS6HxEBHR26GtrQ1ra2u5fffu3ZMeCyEgk8nkjgshSl2/kpJSoXOys7OLLKuq+v8/1ha0mZeXV+q2iIjeF+xhJYV4nJrfu6qhqgRVlTd/G+bm5uLm5UjcvBxZrl/BP7Q1AgCcvfUYCclvZ8hVXl4e7t+/j/v37/NLCRFRJWRnZ4eTJ0/K7QsNDYWNjU2pRsYYGxsDABISEqR9L0/AVFqNGzfG6dOn5fa9+pyI6H3BHlZSiMdpWQAAHY2KmXApOzMTUz73BAAEnr8JZS2tMp1vbqAJK2NtxD5MQ0BYDKZ3aVwhcb0sJydHGno2bdo0qJVx6DIREb1dEyZMQOvWrbFgwQL4+PggLCwMP//8c6knPNLU1MSHH36I77//HlZWVnj06BFmzpxZ5jjGjBmDL7/8Eo6Ojvjoo48QGBiIq1evon79+mWui4ioqmMPKylEQQ+rjmbl+c2koJd19/n7yMsr/RAwIiKqHlq1aoXt27dj69ataNq0KWbPno358+eXacKlDRs2IDs7G46OjhgzZgy+/fbbMsfh4+OD2bNnY8qUKXBwcMCdO3cwbNiwMtdDRFQdVJ5sgd4rj1Lze1h1FXz/6staWNXAnjN38TAlE4eiH6BzYzNFh0RERBWkuKVpXF1d5e457dGjB3r06FFsPZmZmXIzDcfGxsodb9y4McLCwuT2vVz/q+0BgL29faF906dPx/Tp0+X2LV68uNi4iIiqK/awkkI8Tq3YIcEVQV1VGQ4NagIANoVxTVYiIvp/mZmZCA8Px9WrV9GkSRNFh0NE9N5gwkoKUbCkTWUaEgwAH9rkDws+ffMxklK43h0REeXbv38/Pv74Y3Tv3h09e/ZUdDhERO+NypUt0Hvj/3tYK9dbsLahFiyMtHD3UToCTsdiSudGig6JiIgqAW9vb6SkpCg6DCKi9w57WEkhHhVMulTJElbg/3tZd0XcL9P6e0REREREVLGYsJJCSMvaaFbMPazKKiroNWI8eo0YD2WVN0uCW9Y3gLqKEh4kZ+Dgvw8qJD4AUFZWhouLC1xcXEq1nh8REVVOc+fOhampKWQyGfbs2VNhddrb21dIXURE1QkTVlKIgh7WipolWFVNDT6jJsJn1ESovuH6phqqynC0zp98af3JmIoID0B+wurq6gpXV1cmrERE75ivry9kMhlkMhlUVFRQt25dDBs2DE+fPi1TPVFRUZg3bx7WrFmDhIQEeHh4vKWI5cXGxkImkyEyMvKdtEdEVFkwYaV3Lic3D8/SswFUvkmXCrRtZAwAOHv7Ce48SVNwNEREVBG6dOmChIQExMbGYt26dfjrr78wfPjwMtVx69YtAICXlxfMzMygrq7+NkIlIqL/MGGld+5Jev5wYJkM0FKrmIQ1Ly8PcTeiEXcjGnl5eW9cn5mBJqzNdCAEsObE7QqIMH8dvqSkJCQlJfHeWCIiBVBXV4eZmRnq1KmDzp07w8fHBwcOHJCO5+bmYtCgQahXrx40NTVha2uLlStXSsfnzp2L7t27AwCUlJQgk8kAAOfOnYObmxuMjIygr68PFxcXnD9/Xq7t5ORkDBkyBCYmJtDT08PHH3+MixcvVti1CSGwZMkS1K9fH5qammjRogV27NhR6msD8j9L58+fjzp16kBdXR329vYIDg6usBiJiMqDCSu9cwUzBGurq0BJSVYhdWZlZGBc9w4Y170DsjIqZjmato3ze1n/uhCPjOycN64vOzsb/v7+8Pf3R3Z29hvXR0RE5Xf79m0EBwdDVfX/51LIy8tDnTp1sH37dly7dg2zZ8/G9OnTsX37dgDAxIkTERAQAABISEhAQkICAOD58+f48ssvceLECZw+fRoNGzaEp6cnnj9/DiA/mezatSsSExOxb98+REREoFWrVujYsSOePHlSIdczc+ZMBAQEwN/fH1evXsW4cePQr18/HDt2rFTXBgArV67EsmXLsHTpUly6dAnu7u745JNPcOPGjQqJkYioPCrneEyq1irrkjavalq3BmpoqeJZeja2RdzDlx9aKTokIiJ6A0FBQdDR0UFubi4y/vtxc/ny5dJxVVVVzJs3T3per149hIaGYvv27ejVqxd0dHRQo0YNAICZmZlU7uOPP5ZrZ82aNTAwMMCxY8fQrVs3HD16FJcvX0ZSUpI0hHjp0qXYs2cPduzYgSFDhrzRdaWlpWH58uU4cuQInJycAAD169fHyZMnsWbNGri4uLz22gpimjJlCnr37g0AWLx4MY4ePQo/Pz/88ssvbxQjEVF5lauHddWqVahXrx40NDTg4OCAEydOlFj+2LFjcHBwgIaGBurXr4/Vq1cXW3br1q2QyWTw9vYuT2hUBTxO+2/CpQqaIfhtUVaSwck2f4mb/52+o+BoiIjoTXXo0AGRkZE4c+YMRo0aBXd3d4waNUquzOrVq+Ho6AhjY2Po6Ohg7dq1iIuLK7HepKQkDB06FDY2NtDX14e+vj5SU1Ol8yIiIpCamgpDQ0Po6OhIW0xMjHRP7Ju4du0aMjIy4ObmJlf/b7/9Jld/SdeWkpKC+Ph4tG3bVq7utm3bIioq6o1jJCIqrzJ3cW3btg1jx47FqlWr0LZtW6xZswYeHh64du0a6tatW6h8TEwMPD09MXjwYPzvf//DqVOnMHz4cBgbG6NHjx5yZe/cuYOJEyeiXbt25b8iqvQeVZEeVgD40NYIBy4m4kZiKs7FPkFrq5qKDomIiMpJW1sb1tbWAIAff/wRHTp0wLx587BgwQIAwPbt2zFu3DgsW7YMTk5O0NXVxQ8//IAzZ86UWK+vry8ePnwIPz8/WFpaQl1dHU5OTsjKyv+8y8vLg7m5OUJCQgqdW9Bj+yYK5m74+++/Ubt2bbljBT26pb22gvtyCwghCu0jInqXypwxLF++HIMGDcLXX38NAPDz88M///wDf39/LFq0qFD51atXo27duvDz8wMANG7cGOHh4Vi6dKlcwpqbm4u+ffti3rx5OHHiBJ49e1a+K6JK7/F/S9pUhYRVV1MVLaxq4Pztp/j1xG0mrERE1cicOXPg4eGBYcOGoVatWjhx4gScnZ3lZg4uTQ/oiRMnsGrV/7V35/FR1ff+x99nZpLJAglZICFA2ARZAgpJRVCBqwgVl+JScENv8edPbnvLpl6wWrX6E9zqVkSEQutyXW6rWO1FBSkiS5QdBdm3sCSEBEgISSYzmfP7I2QwZIGQmTmT5PV8PObh5JzvOecz38f4CJ98vsssjRw5UpJ04MAB5eXl+c73799fOTk5cjgc6tSpk98/R69eveR0OpWVlaUhQ4bUGmNdny0mJkYpKSlasWKFBg8e7Du+atUqXXbZZX6PGQDOV72GBJeVlWndunUaPnx4lePDhw/XqlWrarwmMzOzWvsRI0Zo7dq1VRaeeeqpp9S6dWvdd9995xWLy+VSYWFhlRcaB98c1hDd0uZslVvcfL0t17d/LACg8Rs6dKh69+6t6dOnS5IuuugirV27Vl9++aV27Nih3//+91qzZs0573PRRRfpnXfe0datW/Xdd9/prrvuUmRkpO/8sGHDNHDgQI0aNUpffvml9u3bp1WrVumxxx7T2rVr6xXz9u3btXHjxiovp9Ophx56SJMnT9Zbb72l3bt3a8OGDXr99df11ltvnfdne/jhh/Xcc8/pww8/1Pbt2zVt2jRt3LhREydOrFeMAOBP9UpY8/LyVF5erqSkpCrHk5KSlJOTU+M1OTk5Nbb3eDy+vz6uXLlS8+bN09y5c887lhkzZvjmicTGxqpDhw71+SiwUOUc1hYRoT2HtVKnNtFqFx8pd7mpuSv8s8UNACA0TJkyRXPnztWBAwc0fvx43XLLLRozZowGDBig/Pz889qndf78+Tp+/Lj69eunsWPHasKECWrTpo3vvGEYWrhwoQYPHqxx48ape/fuuv3227Vv375q/0Y6l9tvv139+vWr8jp8+LCefvppPf7445oxY4Z69uypESNG6LPPPlPnzp0l6bw+24QJE/Tggw/qwQcfVJ8+ffTFF1/o008/Vbdu3eoVIwD4k2HWY0PIw4cPq127dlq1apVvFTpJeuaZZ/TOO+9o27Zt1a7p3r27fvWrX+mRRx7xHVu5cqWuvPJKZWdnKzo6Wn379tWsWbN03XXXSaqYC3LixAl98skntcbicrnkcp2pdhUWFqpDhw4qKChQTEzM+X4kWGDU6yu18cAJjbu6i9I6tvLLPd1lZXrvlWclSXdOmqaw8HC/3LfS6p35+mDFfsVFh+u7R65WuMNe73uUl5dryZIlkqRrrrlGdnv97wHA/woLCxUbG8vvDwAAQlC9xmQmJibKbrdXq6bm5ubW+hfC5OTkGts7HA4lJCRoy5Yt2rdvn28jbunM4gEOh0Pbt29X165dq93X6XT6FhJA4+Kbw+rHIcFh4eG6978e99v9ztavc5z+ufaQjp8q00cbDumOn1VfYOxc7HZ7teHxAAAAAGpXryHB4eHhSk9P1+LFi6scX7x4sQYNGlTjNQMHDqzWftGiRcrIyFBYWJh69OihH374ocpcjJtuusm39DxDfZuevFOVqwQ3jiHBkhTmsGlQj4otbuav3GdtMAAAAEAzUe8S15QpUzR27FhlZGRo4MCBmjNnjrKysjR+/HhJ0iOPPKJDhw7p7bffllQxZ2LmzJmaMmWK7r//fmVmZmrevHl6//33JUkRERFKS0ur8ozKJd7PPo7Gr7jMo5Kyckn+rbB6vV7lHT4kSUpMaSeb7YK2GK7ToItba8n3R7Qz56Qy9+RrYJeEel1vmqYKCgokSbGxsWwTAAAAAJxDvTOGMWPGKD8/X0899ZSys7OVlpamhQsXqmPHjpKk7OzsKhtsd+7cWQsXLtTkyZP1+uuvKyUlRa+99lq1PVjRPFSuEOywG3I6/JdUlpWW6j+GDZAk/ff6XYqIivLbvSvFRIWpf5c4rdl1TLO/2V3vhNXtduvVV1+VVPGHnXA/z7MFAAAAmpoLKnH9+te/rnXVvL/+9a/Vjg0ZMkTr168/7/vXdA80DZXbwrSMCGuUFcarerXRml3HtHz7UR04fkod4qKtDgkAAABosvw/bhKoQ2WFtWUj2YP1bO0TotQ1uYW8pvTGMra4AQAAAAKJhBVBdWYP1saZsErS4F4Ve+v9Y8MhFbs8FkcDAAAANF0krAiqvNMV1haRjWeF4LP17hCrhJbhOuUq11+/3Wd1OAAAAECTRcKKoKocEtyYK6w2m6GrelZUWd/J3C+v17Q4IgAAAKBpImFFUDWFIcGSdFm3BDnDbMo+UaqFW7KtDgcAAABokhp31oBGx1dh9fOiS3aHXT+/817f+0CLCLfr8u6JWrYlV7OX7dENfVLOeY3NZlNGRobvPQAAAIC6kbAiqH66rY0/hYU7df/jM/x6z3MZ3KuNlv+Yq80HC7Q265gyUuPrbO9wOHT99dcHKToAAACg8aPMg6DKP9X457BWimsRrks7x0mSZv5rl8XRAAAAAE0PCSuCxus1dexUYIYEm6apgmP5KjiWL9MM3iJIQ3snSZK+2X5UWcdO1dnWNE2dOnVKp06dCmqMAAAAQGNFwoqgKShxq/z0irrRTv8mrK6SEo0b1EfjBvWRq6TEr/euS/vEKHVr21JeU3ptad1VVrfbrRdffFEvvvii3G53kCIEAAAAGi8SVgRN5QrBkeF2OexN56s3NK1ii5vPNh5WQXGZxdEAAAAATUfTyRoQ8vJOrxDc0s/Dga3Wo12MkltFyOX26s0Ve6wOBwAAAGgySFgRNL4tbfy8QrDVDMPQ0LSKuazvfZelMo/X4ogAAACApoGEFUFTOSS4KawQfLb+XeIUExmmE6fcen9tltXhAAAAAE0CCSuCJq+o6WxpczaH3aarerWWJP15+V5WAQYAAAD8gIQVQZNfdLrC2sTmsFYaeHGiwh02Hcgv1qIfj1gdDgAAANDoNc3MASGpcg5rywDMYbU77Bo6arTvvRWinA4N6J6g5T8e1evLdmlE7+Qq5202my655BLfewAAAAB1I2FF0Bw7VZGwRgdgSHBYuFO/ffYVv9+3vob0aqOVW4/q+6wCbThwXP06xPnOORwOjRo1yrrgAAAAgEaGMg+CJu9U0x4SLEnxLZ26pFNFkvra0l0WRwMAAAA0biSsCJozQ4L9n7CapqnS4mKVFhdbvuDR0LQ2kqRlW3OVdeyU77hpmiorK1NZWZnlMQIAAACNAQkrgqLM41VBiVtSYPZhdZWU6K7+F+mu/hfJVVLi9/vXR4fEaHVNbiGvKf3pJ1VWt9utGTNmaMaMGXK73RZGCAAAADQOJKwIiuPFFdVVmyFFOq1ZFCmY/i0tSZL02cbDOlFSZnE0AAAAQONEwoqgyDu9pU10hEM2w7A4msDr0T5GSa0iVOr2avY3e6wOBwAAAGiUSFgRFJXzVwMxHDgU2QxDV5+usr73bZZKyzwWRwQAAAA0PiSsCIrKLW1aBGDBpVDVr0ucWkWHqbDErb98u9/qcAAAAIBGh4QVQVE5JLhlE97S5mwOu01DT1dZ/7JirzzlXosjAgAAABoXElYERX4zrLBK0oBuCYp22pVb6NKCjYesDgcAAABoVJpX9gDL5J+usAZqDqvNbtPAETf43ocKZ5hdV/Zqoy83ZOvPK/bpP3r2lGEYstlCJ0YAAAAgVJGwIih8iy4FaEhwuDNCD706JyD3bqgre7bW0h+OaG9+qVpcd5VGprW1OiQAAACgUaDMg6DIa6ZDgiUp2unQwIsTJUkzl+6yOBoAAACg8SBhRVCcGRLc/BJWSRrSu43sNkM/HirUqj15VocDAAAANAokrAiKM9vaBGYOa2lxsW7tkaJbe6SotLg4IM9oiFbR4croHKNfRa7V4ndeV1lZmdUhAQAAACGPhBUBV1zmUXFZuaTAzWFtDIb2buN7vzW70MJIAAAAgMaBhBUBV7ngksNuyOlovl+51rERvvd/WrrTwkgAAACAxqH5Zg8Imso9WFtGhMkwDIujCQ3fbD+q/fmnrA4DAAAACGkkrAg434JLzXg48Nm8pvTyEqqsAAAAQF1IWBFwvj1Ym+kKwbX5303Zyi0stToMAAAAIGSRsCLg8k417y1tatIuIUrucq9eZV9WAAAAoFYkrAi4Y0GosNrsNvUfco36D7lGNnuIfq0NQy3bdlDLth10dZ9kSdJHaw/o2OmEHgAAAEBVlLwQcPkB3oNVksKdEXr0zXcCdn9/sNkd6jTkOkmS1zTVNu6Iso+X6rWlu/TkDb0tjg4AAAAIPSFaikJTkseiS9XYDEPXXtJWkvTh6gMqKCmzOCIAAAAg9JCwIuBYdKlmfTu1UlJshErKyvX617utDgcAAAAIOSSsCLh836JLgRsSXFpcrDv7ddWd/bqqtLg4YM9pCK/Hrc1/m6/Nf5svr8ctm2Fo2CUVc1n/+9v9OlnqtjhCAAAAILSQsCKgvF7TV2FtGeAhwa6SErlKSgL6jIYyyz0yyz2+ny/tHKfWMU6dcpVr9jd7LIwMAAAACD0krAio48Vl8nhNSVLLyMBVWBsru83QNX0rqqzvZO5TsctzjisAAACA5oOEFQF19PSCS9FOh+w2w+JoQlN613jFtwhXYYlHc1butTocAAAAIGSQsCKgjp6sSFgDPRy4MftplfWvK/aqtIwqKwAAACCRsCLAKhPWmCiGA9flZxfFq1V0mE4Uu/XnVfusDgcAAAAICSSsCKjcyoSV+at1cthturpPRZV13vK9KnVTZQUAAABIWBFQwRoSbNgM9f7ZQPX+2UAZITtX1lB067aKbt1WUvUYB3RLUExkmI6fKtN8qqwAAACAmFiIgDqTsAa2wuqMiNRT73wU0Gc0lM3hUJdrbqz1fJjDpmGXJOvjbw9ozrI9+tXATooM539RAAAANF8XVGGdNWuWOnfurIiICKWnp2v58uV1tl+2bJnS09MVERGhLl26aPbs2VXOz507V1dddZXi4uIUFxenYcOGafXq1RcSGkJMsBLWpuLy7gmKiw7XiWK3Zi9nX1YAAAA0b/VOWD/88ENNmjRJjz76qDZs2KCrrrpK1113nbKysmpsv3fvXo0cOVJXXXWVNmzYoN/97neaMGGCPvroTDXs66+/1h133KGlS5cqMzNTqampGj58uA4dOnThnwwhIfdkqSQWXTpfDrtN1156Zi5rUanb4ogAAAAA6ximaZr1uWDAgAHq37+/3njjDd+xnj17atSoUZoxY0a19lOnTtWnn36qrVu3+o6NHz9emzZtUmZmZo3PKC8vV1xcnGbOnKl77rnnvOIqLCxUbGysCgoKFBMTU5+PhADq++SXKiz1aOrNPZXUKjJgzyktLtZ/XHOZJOmNJasVERUVsGddKK/HrW2fvS9J6nHjHbI5ak7iy72mnvv4R+WddOmBoV31yM97BDNMoNnh9wcAAKGrXhXWsrIyrVu3TsOHD69yfPjw4Vq1alWN12RmZlZrP2LECK1du1Zud83Vo+LiYrndbsXHx9cai8vlUmFhYZUXQkupu1yFpRWr3QZjSHDh8WMqPH4s4M9piHJXqcpdpXW2sdsMjejXVpL0zqp9KiguC0ZoAAAAQMipV8Kal5en8vJyJSUlVTmelJSknJycGq/Jycmpsb3H41FeXl6N10ybNk3t2rXTsGHDao1lxowZio2N9b06dOhQn4+CIMgrqpi/arcZigy3WxxN49Kvc5ySW0WouKxcLy3ZaXU4AAAAgCUuaNElw6i6JYdpmtWOnat9Tccl6fnnn9f777+vjz/+WBEREbXe85FHHlFBQYHvdeDAgfp8BATB0Z/swVrX9wPV2WyGfn66yvrB6izlnay7KgsAAAA0RfVKWBMTE2W326tVU3Nzc6tVUSslJyfX2N7hcCghIaHK8RdffFHTp0/XokWL1Ldv3zpjcTqdiomJqfJCaMmtXCE4iq1ZLkSfjq3ULj5SLrdXL361w+pwAAAAgKCrV8IaHh6u9PR0LV68uMrxxYsXa9CgQTVeM3DgwGrtFy1apIyMDIWFnZnX+MILL+jpp5/WF198oYyMjPqEhRD10wor6s8wDF3XP0WS9NHagzp8osTiiAAAAIDgqveQ4ClTpujPf/6z5s+fr61bt2ry5MnKysrS+PHjJVUM1f3pyr7jx4/X/v37NWXKFG3dulXz58/XvHnz9NBDD/naPP/883rsscc0f/58derUSTk5OcrJyVFRUZEfPiKswh6sDdezfYw6tYmWu9zU9C+2nvsCAAAAoAmp91jNMWPGKD8/X0899ZSys7OVlpamhQsXqmPHjpKk7OzsKnuydu7cWQsXLtTkyZP1+uuvKyUlRa+99ppuvfVWX5tZs2aprKxMt912W5VnPfHEE3ryyScv8KPBakeLKhPWwA8JNmyGuqZd4nsfmgxFxrf2vT+vKwxDN6S308zPd2jhpmxtHVqonskMfwcAAEDzUO99WEMV++iFnv/z1lp9tfWIbhvYQYN6tD73BajV/CW7tTmrQJd3TdAH919udThAk8LvDwAAQtcFrRIMnI+cwoo5l7HR4RZH0vhdn95ONkP6dne+lu88anU4AAAAQFCQsCJgcgoqtmKJjWIOa0MltYrQ5d0TJUlP/fNHNZGBEQAAAECdSFgREGUer/KKyiRJraIDn7C6Soo1/urLNP7qy+QqKQ748y6E1+PRtk/f07ZP35PX46n39cMvbatwh007jxTp7+sPBiBCAAAAILSQsCIgjhRWVFftNkPRzsAvumSa0tHDB3X08EGFbvHRlLu4SO7iIkn1DzImKkz/1qdiv+MXvtwul6fcz/EBAAAAoYWEFQGRczphbRUdJsMI1VV7G5+hvduoZaRDuYUuvf71bqvDAQAAAAKKhBUBcWb+Kgsu+ZMzzK7r09tJkuYs260jBSUWRwQAAAAEDgkrAqIyYQ3G/NXmJuOieHVIjFKp26vHP9tidTgAAABAwJCwIiCyWSE4YGyGoVsu7yBJ+nLzEa3em29xRAAAAEBgkLAiINiDNbA6to7WZd0SJEm/+2SzvN6QXWkKAAAAuGAkrAiIYO/BahhS+4u6q/1F3RW6azwZcsbEyRkTJ6nhQV6fnqKIMJt2HSnS29/tb3h4AAAAQIgJ/H4jaJbOzGENToXVGRmlV//5dVCedaFsDoe6j/yl3+7XMjJMwy9tq0/XHNIfF23XzZeksMgVAAAAmhQqrPC7cq+pIyddkpjDGmhX9mytNrFOnSzx6Il//mh1OAAAAIBfkbDC746edKnca8pmVFQBETgOu023Xp4qSfrH+kP6jgWYAAAA0ISQsMLvDh4vllQxHNhuC86EUldJsSbeMFQTbxgqV0lxUJ5ZX16PRzsW/k07Fv5NXo/Hb/ftltJSP7soXqakh//+vdzlXr/dGwAAALASCSv87uDxihWC41oEbz6laUoHd+3QwV07ZIbsgrmmXIXH5So8Lsm/Qd74s/aKdjqUlV+sV5bs9Ou9AQAAAKuQsMLvKiusCUFMWJu7FhEO/WJAO0nSnGW7tftokcURAQAAAA1Hwgq/O3DsdIW1pdPiSJqX9C7x6p7SUu5yU5P/ZyN7swIAAKDRI2GF3x08UVFhjafCGlSGYeiXg1IV7rDp+wMFeuOb3VaHBAAAADQICSv8rnIOKwlr8CW0dOqmn1UMDX5l8Q7tPHLS4ogAAACAC0fCCr8q95o6fKIyYWVIsBUGXpyoi9vFyF1u6jfvb5CHVYMBAADQSJGwwq+OFJbKXW7KbjMUGxW8PVgNQ2qd0l6tU9rLCM5OOhfAUFhUC4VFtZAUuCANw9CYK1IVGW7XjpyTeumrHQF7FgAAABBIDqsDQNNSORy4VXS4bEHag1WSnJFRmv2v1UF73oWwORzqcdOdQXlWq+hw3Xx5e733zX7N/nqPru2ZpH6pcUF5NgAAAOAvVFjhV/vzT0li/mooSO8Sr0s6tZLXNDX+v9ersNRtdUgAAABAvZCwwq/25lUkrG1imb9qNcMwNHpQquJbhOtIQakmfLBBpslWNwAAAGg8SFjhV3uOViSsrWMigvpcV2mJ/uu26/Rft10nV2lJUJ99vrwej3YtWqBdixbI6/EE5ZmRTofGDu0su83Q19uOat7KvUF5LgAAAOAPJKzwq8oKa+sgV1hNr6ndmzdp9+ZNMr2hWkU0VXLsqEqOHZUUvBg7to7WDRkpkqRnF27TpgMngvZsAAAAoCFIWOE35V5Te/MrhwQHt8KKug3u1Ua9O8TK4zV139trlVfksjokAAAA4JxIWOE3h0+UqMzjld1mKC6aRZdCiWEYuuOqjkps6VTeSZfG/XWNyjzszwoAAIDQRsIKv9lzejhwYowzqFva4PxEOR26b1gXRYTZ9P3BAk39+HsWYQIAAEBII2GF3+w5WiRJahPDCsGhKqlVpO4e0lmGpAXrD2nO8j1WhwQAAADUioQVfrPjyElJUlIr5q+Gsl4dYnV9RjtJFYswfbLxkMURAQAAADUjYYXfbM2uSFhT4qMseX5MXLxi4uItefb5sjsjZHdan9D/W1obXdGjtUxJD/7PJn2z46jVIQEAAADVOKwOAE1DudfU9pyKhLVtXGTQnx8RFaW/ZG4O+nPrw+YIU6+b77E6DEkVizDdPKC9ikrd2rTvhB54Z53+54HL1ad9K6tDAwAAAHyosMIvso4Vq8RdLofdUGvmsDYKNpuhO6/qpK7JLVTiLtcdf/5OWw4VWB0WAAAA4EPCCr/Yml0oqaK6ygrBjUeYw6Zx13RVamKUiko9GjP3W20+dMLqsAAAAABJJKzwk8qENcWC4cCS5Cot0eNjb9XjY2+Vq7TEkhjOxevxaM+Sz7RnyWfyejxWh+MTGW7XAyO6KbV1RdJ6+5zv9MPBE1aHBQAAAJCwwj82ZJ2QJHVItGbBJdNrasuaTG1ZkynTG6p7i5o6dTRbp45mSwqtGCPD7XpgeDd1bB2tIpdHo+d8q292shATAAAArEXCigYr95raeOCEJKlj62hrg8EFq0haL6qY01pWrl/9ZY0+WnfQ6rAAAADQjJGwosF25RapyOVRuMOmZIuGBMM/Ik4nrZd2jlO519SDf9uklxbvkDdkq9YAAABoykhY0WDrs45LklITo2RnwaVGz2G36e4hnTS0dxtJ0mtLdmrcW2tUWOq2ODIAAAA0NySsaLDVe49Jkjq3aWFxJPAXm2Hopsvaa/QVqXLYDX29/aiuf225tuUUWh0aAAAAmhESVjSI12v6FufpltLS4mjgb5d3T9RvR3ZXq+gwHThWohv+tEJvLtvNEGEAAAAEBQkrGmRrTqHyi8rkdNjUqY21Cy45IyPljAztObSG3SHD7rA6jHrpkBitKTf1VK/2MfKUm5rx+TaNfjNTWfnFVocGAACAJs4wTbNJlEoKCwsVGxurgoICxcTEWB1Os/H60l164cvt6t0hVvcN62p1OAgg0zT17Y58/WP1QZV5vAp32PSboV31wJCuigizWx0ecMH4/QEAQOiiwooG+d/vsyVJvTrEWhwJAs0wDA28OFEPj+qpbm1bqszj1ctf7dSwl5bpi83ZaiJ/+wIAAEAIIWHFBduVW6QfswtlM6S+HVtZHQ6CJKGlU+NHXKSxQzopJipMB4+XaPy76zXytRVauj2XxBUAAAB+07gm0yGk/G3dAUlS95QYRUdY+1Uqc5XqhQn3S5Iefm2uwp0RlsZTE2+5R1krFkuSUq+8VrZGNpf1pwzDUL8u8erZIVZLfziib7bkamt2oX71lzXqnRKj/zu4i65La6twB38TAwAAwIVrvP9ihqVOuTx6/7ssSdKgHokWRyN5y71av2yJ731IMk2dzD7ge98URITZdV3/FF3Vq7X+9f0Rrdx2VFsOF2riBxv1/1pu1d0DUnVL//bqEB9ldagAAABohEhYcUHeztyvwlKPElo61as981ebuxYRYbrpsva6um+SVm3L08ptR3X0pEsvf7VTL3+1U5d1jtfN/drpmp5t1KZl6FW/AQAAEJpIWFFvOQWlen3pLknS8EuSZbMZFkeEUNEiIkzDL22rq/skaeO+41qz85h2ZZ/U6r3HtHrvMUlS3/axurZnkq7slqi0drEKszNsGAAAADUjYUW9uDzlmvD+BhW5PEpNjFL6RfFWh4QQ5LDblNE1QRldE3S8qEzr9xzT9/tP6EBesb4/WKDvDxboj4t3KCLMpn6pcbqsU7z6to9Vj7YxSomNkGHwRxAAAABc4CrBs2bNUufOnRUREaH09HQtX768zvbLli1Tenq6IiIi1KVLF82ePbtam48++ki9evWS0+lUr169tGDBggsJDQGUX+TS/3lrrVbvOyZnmE13De4kG4kFziGuRbiu6ZusyTf20BNj0vTLQalKS41VVLhdpW6vMnfn69UlO3XfW2t1xbP/0iV/WKTRb2bq0QU/aPay3Vr4Q7Y2HypQQbGbFYgBAACamXpXWD/88ENNmjRJs2bN0hVXXKE333xT1113nX788UelpqZWa793716NHDlS999/v959912tXLlSv/71r9W6dWvdeuutkqTMzEyNGTNGTz/9tG6++WYtWLBAo0eP1ooVKzRgwICGf0pcsCKXR9uyC7VkW67e/y5LJ0rcCnfYNO7qrmody1xE1E9sVLgGXpyogRcnymuaOnKiVHuOFGlf7ikdPlasIydKVVjqqTKE+KecDpsSWziV2NKp1i3CldjCqfjocLWIcKil06EWEQ5FhztO/xymyHCbwu12OcNsCrfbFO6oeDlsBlVcAACARsAw61myGDBggPr376833njDd6xnz54aNWqUZsyYUa391KlT9emnn2rr1q2+Y+PHj9emTZuUmZkpSRozZowKCwv1+eef+9r8/Oc/V1xcnN5///0a43C5XHK5XL6fCwoKlJqaqp8/s0AO55kVSX/64X76Sat86J+cqL39udtUvWXNJ2q9Zy2xmbXEpnq2P6/nnhXySZdbRaXlVY61iXVq9BWpSgmxVV9Li4t1/+B+kqS532xQRFRoxSdJXo9bWz95V5LUc9TdsjnCLI4o9HjKvcotLFXO8VLlF7p0rKhMx4vKdKzIVe272BCGIYXZbQp3GHIYhmyGIZut4r+GIdkMQ3abIdvp97afvDeMiveGqie8Z+fA1VrUkCSffaSmPLp6m3PfB+fPXXpKnz96s06cOKHYWBaRAwAglNSrwlpWVqZ169Zp2rRpVY4PHz5cq1atqvGazMxMDR8+vMqxESNGaN68eXK73QoLC1NmZqYmT55crc0rr7xSaywzZszQH/7wh2rHv3j05vP8NLgQByStszqIc6hMXEPas89aHQGAs+Tn55OwAgAQYuqVsObl5am8vFxJSUlVjiclJSknJ6fGa3Jycmps7/F4lJeXp7Zt29baprZ7StIjjzyiKVOm+H4+ceKEOnbsqKysLP7BcQEKCwvVoUMHHThwQDExMVaH0+jQfw1D/zUM/dcwlSN04uNZRA4AgFBzQasEnz0czTTNOueD1dT+7OP1vafT6ZTT6ax2PDY2ln+wNUBMTAz91wD0X8PQfw1D/zWMzcYWSwAAhJp6/XZOTEyU3W6vVvnMzc2tViGtlJycXGN7h8OhhISEOtvUdk8AAAAAQNNXr4Q1PDxc6enpWrx4cZXjixcv1qBBg2q8ZuDAgdXaL1q0SBkZGQoLC6uzTW33BAAAAAA0ffUeEjxlyhSNHTtWGRkZGjhwoObMmaOsrCyNHz9eUsXc0kOHDuntt9+WVLEi8MyZMzVlyhTdf//9yszM1Lx586qs/jtx4kQNHjxYzz33nH7xi1/oH//4h7766iutWLHivONyOp164oknahwmjHOj/xqG/msY+q9h6L+Gof8AAAhd9d7WRpJmzZql559/XtnZ2UpLS9PLL7+swYMHS5L+/d//Xfv27dPXX3/ta79s2TJNnjxZW7ZsUUpKiqZOnepLcCv9/e9/12OPPaY9e/aoa9eueuaZZ3TLLbc07NMBAAAAABqtC0pYAQAAAAAINJZEBAAAAACEJBJWAAAAAEBIImEFAAAAAIQkElYAAAAAQEhq1AnrjBkzZBiGJk2a5DtmmqaefPJJpaSkKDIyUkOHDtWWLVusCzLEHDp0SHfffbcSEhIUFRWlSy+9VOvWrfOdp/9q5/F49Nhjj6lz586KjIxUly5d9NRTT8nr9fra0H9nfPPNN7rxxhuVkpIiwzD0ySefVDl/Pn3lcrn029/+VomJiYqOjtZNN92kgwcPBvFTWKeu/nO73Zo6dar69Omj6OhopaSk6J577tHhw4er3IP+q/3791MPPPCADMPQK6+8UuV4c+4/AABCRaNNWNesWaM5c+aob9++VY4///zzeumllzRz5kytWbNGycnJuvbaa3Xy5EmLIg0dx48f1xVXXKGwsDB9/vnn+vHHH/XHP/5RrVq18rWh/2r33HPPafbs2Zo5c6a2bt2q559/Xi+88IL+9Kc/+drQf2ecOnVKl1xyiWbOnFnj+fPpq0mTJmnBggX64IMPtGLFChUVFemGG25QeXl5sD6GZerqv+LiYq1fv16///3vtX79en388cfasWOHbrrppirt6L/av3+VPvnkE3333XdKSUmpdq459x8AACHDbIROnjxpduvWzVy8eLE5ZMgQc+LEiaZpmqbX6zWTk5PNZ5991te2tLTUjI2NNWfPnm1RtKFj6tSp5pVXXlnrefqvbtdff705bty4KsduueUW8+677zZNk/6riyRzwYIFvp/Pp69OnDhhhoWFmR988IGvzaFDh0ybzWZ+8cUXQYs9FJzdfzVZvXq1Kcncv3+/aZr030/V1n8HDx4027VrZ27evNns2LGj+fLLL/vO0X8AAISGRllh/c1vfqPrr79ew4YNq3J87969ysnJ0fDhw33HnE6nhgwZolWrVgU7zJDz6aefKiMjQ7/85S/Vpk0b9evXT3PnzvWdp//qduWVV2rJkiXasWOHJGnTpk1asWKFRo4cKYn+q4/z6at169bJ7XZXaZOSkqK0tDT6swYFBQUyDMM3YoL+q5vX69XYsWP18MMPq3fv3tXO038AAIQGh9UB1NcHH3ygdevWae3atdXO5eTkSJKSkpKqHE9KStL+/fuDEl8o27Nnj9544w1NmTJFv/vd77R69WpNmDBBTqdT99xzD/13DlOnTlVBQYF69Oghu92u8vJyPfPMM7rjjjsk8f2rj/Ppq5ycHIWHhysuLq5am8rrUaG0tFTTpk3TnXfeqZiYGEn037k899xzcjgcmjBhQo3n6T8AAEJDo0pYDxw4oIkTJ2rRokWKiIiotZ1hGFV+Nk2z2rHmyOv1KiMjQ9OnT5ck9evXT1u2bNEbb7yhe+65x9eO/qvZhx9+qHfffVfvvfeeevfurY0bN2rSpElKSUnRvffe62tH/52/C+kr+rMqt9ut22+/XV6vV7NmzTpne/qvonr66quvav369fXuC/oPAIDgalRDgtetW6fc3Fylp6fL4XDI4XBo2bJleu211+RwOHzVmrP/+p2bm1utktMctW3bVr169apyrGfPnsrKypIkJScnS6L/avPwww9r2rRpuv3229WnTx+NHTtWkydP1owZMyTRf/VxPn2VnJyssrIyHT9+vNY2zZ3b7dbo0aO1d+9eLV682Fddlei/uixfvly5ublKTU31/S7Zv3+/HnzwQXXq1EkS/QcAQKhoVAnrNddcox9++EEbN270vTIyMnTXXXdp48aN6tKli5KTk7V48WLfNWVlZVq2bJkGDRpkYeSh4YorrtD27durHNuxY4c6duwoSercuTP9V4fi4mLZbFX/l7Hb7b5tbei/83c+fZWenq6wsLAqbbKzs7V582b6U2eS1Z07d+qrr75SQkJClfP0X+3Gjh2r77//vsrvkpSUFD388MP68ssvJdF/AACEikY1JLhly5ZKS0urciw6OloJCQm+45MmTdL06dPVrVs3devWTdOnT1dUVJTuvPNOK0IOKZMnT9agQYM0ffp0jR49WqtXr9acOXM0Z84cSfLtaUv/1ezGG2/UM888o9TUVPXu3VsbNmzQSy+9pHHjxkmi/85WVFSkXbt2+X7eu3evNm7cqPj4eKWmpp6zr2JjY3XffffpwQcfVEJCguLj4/XQQw+pT58+1RZca4rq6r+UlBTddtttWr9+vf75z3+qvLzcV62Oj49XeHg4/XeO79/ZCX5YWJiSk5N18cUXS+L7BwBAyLBwhWK/+Om2NqZZsV3GE088YSYnJ5tOp9McPHiw+cMPP1gXYIj57LPPzLS0NNPpdJo9evQw58yZU+U8/Ve7wsJCc+LEiWZqaqoZERFhdunSxXz00UdNl8vla0P/nbF06VJTUrXXvffea5rm+fVVSUmJ+Z//+Z9mfHy8GRkZad5www1mVlaWBZ8m+Orqv71799Z4TpK5dOlS3z3ov9q/f2c7e1sb02ze/QcAQKgwTNM0g5ohAwAAAABwHhrVHFYAAAAAQPNBwgoAAAAACEkkrAAAAACAkETCCgAAAAAISSSsAAAAAICQRMIKAAAAAAhJJKwAAAAAgJBEwgoAAAAACEkkrAAAAACAkETCCgAAAAAISSSsAAAAAICQ9P8Bqi51+38JqocAAAAASUVORK5CYII=\n", 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AL6IYAwAAAAAA4EWcptSDNDY2yul0+jqMbs/f318Wi8XXYQAAThJ5sPPIgQDQ/ZEPT423cyDFmB7C4XCosLBQhmH4OpRuz2QyqV+/frJarb4OBQDQQeRB9yAHAkD3Rj48dd7OgRRjeoDGxkYVFhYqJCREsbGxXF27EwzD0KFDh1RYWKj09HT2DgJAN0AedA9yIAB0b+TDU+eLHEgxpgdwOp0yDEOxsbEKDg72dTjdXmxsrHJzc+V0OvkgCgDdAHnQfciBANB9kQ87x9s5kGJMN+Bn9tPV2Ve7nreHyqd7sB0BoOvpSC7k/bvz2IYA0PV09PtgC97LT423txvFmG4g0C9QL170oq/DAADAZ8iFAIDeihzYM3Fra/jMiy++qBEjRvg6DAAAjut//ud/FBUVpb59+3a6rUWLFun222/vfFAAAHRB+fn5slqtqqio8HUoXR7FmG7AMAxV1Vepqr7qlK6K/dlnn2nmzJmKiopSeHi4Bg0apB/96EfKzc11f7BdwP333y+TyaQnn3yy1fQRI0boxRdf9E1QAIBOOdVcOHnyZAUGBspqtSoqKkqTJk3Sxo0bO7z+2rVr9a9//Uv79u3TgQMHTiX0k2IymbRp0yaP9wMA6D46+31Qas6Hf/zjH90bWBuSk5PlcDgUERHhlvZONi+uXLlSkydPdkvfnkYxphuodlbL+rBV1oetqnZWn9S6y5cv14wZM3Tuuedq586dstvtWrVqlVJTU/Xpp596KGLfi46O1q9//Ws5HA5fhwIAcIPO5MJHH31UDodDBw4c0BlnnKGLL764w+vu27dPycnJbvtQCQDAyepMDvSmhoYGX4fQrVCM6cEMw9Ctt96qe+65R7fffrv69OkjSYqPj9ePf/xjXXPNNa5lN27cqAkTJshmsykjI0NLlixxzbv//vt1/vnn65ZbbpHNZlNycrJee+01SdLBgwdltVpbPUwmk1auXClJuuqqq5SQkKDw8HCdfvrpxy0AORwO3XLLLUpOTlZcXJwWLlzoOrzt9ttvb9VHQEDAcSueEydO1ODBg/XEE0+c6uYDAPQwAQEBuvrqq1VQUKBDhw5Jaj6c+pxzzlFsbKwiIyM1a9Ys15Gjf/rTn3T99ddr69atslqtWrRokaQT57aPPvpIY8aMkc1mU2ZmppYtW+aW+I/X7ocffqhRo0YpIiJC8fHxuvnmm1VTU+OaX1JSossuu0yxsbFKTk7Wvffey4dmAICeeOIJpaenKywsTAMHDmx1dsHKlStls9laLX/RRRfp/vvvbzX/6aefVnJyssaNG6fc3FyZTCaVl5dLav5O+qc//UlDhgyRzWbT5MmTtXPnTld7/fv31+9+9zuNHTtWYWFhmjRpkgoKCiRJY8aMkSSNHz9eVqtVDz30kCQpJydH559/vmJjY5WSkqLf/OY3ampq8tAW8hyKMT3Y7t27lZubq3nz5h13ufLycp133nm6/PLLdejQIT399NO64YYbtHbtWtcyH3zwgSZMmKCysjL95je/0fXXX6/KykrFxcXJ4XC4Hj//+c+VmZmpkSNHSpKmTZumnTt3qqysTJdffrkuueQSVVZWthnHtddeq8OHD2vLli3at2+fnE6nbrnlFknSH//4R1cfe/bsUd++fbVgwYLjjuvRRx/V73//e9cHbgBA71ZTU6MXXnhBMTExioyMlCQ1NTXpJz/5iQoKCpSXl6eQkBDdcMMNkqRbb71VzzzzjIYPHy6Hw+E61fV4uW3Lli269NJL9cgjj+jw4cN69tlntWDBAu3atatTsZ+o3eDgYD333HM6fPiw1q5dq08//bTVDon58+fL399f+/bt05o1a/T222/rd7/7XadiAgB0fykpKfrkk09kt9v1/PPP684772z1PfBEKisrtXnzZn377bdatWrVMfOffvppvfDCC1q+fLlKS0t18cUX6/zzz1d9fb1rmZdfflmLFy/WoUOHFBoaql/+8peSpPXr10uSPv/8czkcDt1zzz2qqanRtGnTNHXqVBUVFWnNmjV69dVX9fe//11S8+lYLQcGdHUUY3qw0tJSSVJCQoJr2gMPPCCbzSar1arLLrtMkvTuu+8qNjZWP/rRj+Tv769JkyZp/vz5eumll1zrjRw5UldccYUsFosWLFig+vp67d69u1V/r7/+up588km98847Cg8PlyRdc801ioiIkL+/v+688041NTVpy5Ytx8R66NAhvfnmm3ryySdls9kUGhqqBx98UK+99poaGxtdy1VXV+uCCy7QFVdcoeuuu+644x83bpymTp2q3/72tye55QAAPcndd9/tyi1LlizRW2+9JT+/5htK9u/fXzNmzFBQUJDCw8N17733avXq1cfdw3a83Pbss89q0aJFmjp1qsxmsyZOnKjZs2fr9ddf79QYTtTumWeeqdNOO00Wi0Wpqam68cYbXR9Gi4qK9Mknn+j3v/+9rFarUlJSdO+993IdNQCA5s6dq6SkJJlMJk2ZMkXTp08/qWJGU1OTHnnkEYWEhCgkJOSY+X/5y1/04IMPKj09XX5+frr11ltVU1OjL7/80rXMLbfcotTUVAUFBenKK6/UV1991W5/77zzjiIjI/XjH/9YAQEBSk5O1m233abFixef1Li7AooxPVhMTIwkqbi42DXtvvvuU3l5ue644w5XNbKwsFD9+/dvtW5qaqoKCwtdvx99BwmTyaTg4OBWR7h88cUXuummm/T222+72mpqatK9996r9PR0hYeHy2azqaKiwlUkOlpubq6ampqUmpoqm80mm82m0aNHy2w2uy6YaBiGFixYoOTkZD3yyCMd2gYPPfSQnnvuuR57sWIAwIk9/PDDKi8vV0FBgRISErR582bXvEOHDmn+/PlKSkpSeHi4zjrrLNXX17d7FOeJcltubq6eeeYZVy6z2WxaunRpq1x8Kk7U7oYNG3T22WerT58+Cg8P1z333OOKqbCwUEFBQa1y+Q/zPACgd3rllVc0cuRIRUZGymazacWKFW1+X2tPWFjYMacyHS03N1dXXXVVq/x15MiRdr9rhoaGtpuDW9rbtm1bq/Z++tOfeuUi++5GMaYHGzRokFJSUk64N65fv37HFCv27dunfv36daif3NxcXXTRRXrmmWc0duxY1/TFixdr8eLFevfdd1VRUaHy8nJFRES0eQXwpKQkmc1mFRcXq7y83PWora1VYmKiJOmuu+5Sfn6+/vGPf8hkMnUotqFDh+qKK67Qr371qw4tDwDouRITE/Xcc8/prrvuchUx7r77blVXV+vrr7+W3W7X6tWrJandu1WcKLclJSXptttua5XLHA6Hnn766U7FfqJ2r7jiCk2ZMkXfffed7Ha7HnroIVdM/fr1U21trUpKSlztnUyeBwD0TPn5+br66qv1u9/9TocOHVJ5eblmzpzpyh9Wq1U1NTWtcuL+/ftbtWE2H7+kkJSUpDfeeKNV/qqurtYVV1zRoRh/+L0vKSlJp59+eqv27Ha7tm/f3qH2uhKKMT2YyWTS//3f/+m3v/2t/vSnP+ngwYOSmvcCHv1inTlzpg4ePKinnnpKDQ0NWrNmjRYvXqyFCxeesA+73a7Zs2frRz/6keu0p6PnBQQEKCYmRvX19XrwwQdlt9vbbKdv37666KKLdMstt7gqsQcOHNBbb70lSXrhhRf06quvavny5W0e/nY8999/v/79738rPz//pNYDAPQ8I0eO1OTJk10XAbTb7QoJCZHNZlNZWZkeeOCB465/otx244036u9//7s+/fRTNTY2qq6uTuvWrWt1scITqa+vV21trevhdDpP2K7dbnedirVz585WxZ/ExERNmTJFd9xxh6qqqpSfn6+HHnpIV1999clsOgBAN9fQ0NAqvxw5ckSGYSguLk5ms1krVqzQhx9+6Fp+0KBB8vf31+LFi9XY2KhXX31V33zzzUn1+f/+3//Tr371K9c1zux2u5YuXXrco1+O1qdPH+Xk5Lh+nz17tkpKSvTUU0+ptrZWjY2N2rVrV7e5TszRKMZ0AxazRZdkXKJLMi6RxWw5qXUvvPBCvfvuu1qxYoUGDRqk8PBwnXnmmYqLi9Mf/vAHSVJkZKTee+89/fOf/1R0dLT+53/+R08//bQmTpx4wva//vprbd++XQ8//HCrux2tWbNGV199tTIzM5WSkqLU1FQFBwcrKSmp3bZefPFF1+lJLXG2nC/4j3/8QwcOHFBaWpqrjxkzZnRoG/Tr10//7//9Px05cqTV9JY4AQBdX2dy4Q/de++9ev7551VQUKAHHnhAe/fuVWRkpCZMmHDC3HKi3HbaaadpyZIl+sUvfqHY2FglJibql7/8perq6joc3xlnnKHg4GDX44Ybbjhhu88++6wef/xxWa1W3XTTTbr88stbtbl48WLV1NQoJSVFEyZM0KxZs/Szn/3sJLYaAMBX3JUD77zzzlb55cILL9S9996rqVOnKjo6Wq+99pouuOAC1/Lh4eF67rnn9POf/1zR0dH67LPPNH369JPq85ZbbtGiRYt08cUXKzw8XEOHDj2p67v8+te/1q233qrIyEg98sgjslqt+uijj/Txxx+rf//+io6O1vz587vlaUomo73jcE+C3W5XRESEKioqXBduhffU1tZq3759GjBggIKCgnwdTrd3vO3Jax3AD/G+4HvkQfchBwI4GbwvdC3kw87xdg7kyBgAAAAAAAAvohgDAAAAAADgRRRjuoGq+iqZHjDJ9IBJVfVVvg4HAACvIxcCAHorcmDPRDEGAAAAAADAiyjGAAAAAAAAeBHFGAAAAAAAAC+iGAMAAAAAAOBFFGMAAAAAAAC8iGIMWqmtrdWcOXNks9k0ZswYX4cDAIBXkQcBACAfegPFmG7AYrZoZvpMzUyfKYvZ4tG+3nzzTe3atUslJSVav369R/s62iuvvCKr1drqYTKZ9MQTT7S5fH19vS655BL1799fJpNJb7/9drttf/jhhzKZTLr99ts9EzwAwOO8lQu7Sx4sLCzU+PHjFR0drYiICI0YMUJvvfVWq2V27typCRMmKCQkRIMGDdKyZcu8MRQAgJt58/tgi56UD+vq6nTHHXcoPj5eVqtVw4cPV25urhdGc3wUY7qBIL8gvTv/Xb07/10F+QV5tK99+/Zp0KBBCgwM9Gg/P3TllVfK4XC4HqtWrZLZbNall17a7joTJ07UP/7xD/Xr16/dZaqqqnTrrbdq7NixnggbAOAl3sqF3SUPRkZG6sUXX9ShQ4dUUVGhp556SldddZX27dsnSXI6nTr//PM1bdo0HT58WE888YTmz5+vvXv3enNYAAA38Ob3wRY9JR9K0jXXXKOcnBx99dVXqqys1BtvvCGbzealEbWPYkwP179/fz388MMaPXq0QkNDNWPGDB0+fFg333yzbDab0tPT9fnnn0uSfvrTn+rBBx/UO++8I6vVqvvuu0/Z2dl6+eWXW7U5Y8YMPfLIIx6N+4UXXtC5556rpKSkNucHBATo9ttv15lnnimLpf3q8C9+8QtdfvnlGjx4sKdCBQB0YT01D4aGhmrQoEEym80yDENms1mNjY2uPX2rV69WWVmZfvnLXyooKEizZ8/WpEmT9I9//MOjcQMAuqbemg+3b9+upUuX6m9/+5sSEhJkMpk0ZMgQijHwjiVLlujNN99UUVGR8vPzNWbMGE2dOlVlZWW6/PLLddNNN0mSfv/73+uee+7R7Nmz5XA49MADD2jBggWtPriVlJTo448/1pVXXtlmXy1/zO09PvvssxPGW1NTo8WLF+v666/v1Lg3bNigDz74QHfffXen2gEAdG89OQ9mZWUpMDBQ48aN04QJE3TmmWdKkrZs2aLMzEz5+/u7lh0xYoS2bNlywjYBAD1Tb8yHq1atUmpqqh599FHFxcVp0KBBevzxxzuyuTyOYkw3UFVfpdCHQhX6UKiq6qtOev2bb75ZycnJstlsmjVrlmJiYnTJJZfIYrHoiiuu0LZt21RfX9/muldeeaVWrVqloqIiSdLixYt15plntluZfOqpp1ReXt7uY+LEiSeM91//+pcCAgJ0wQUXnPRYWzidTt1www166qmnvH5oHQDA/TqTC3tyHtyyZYscDoeWL1+uGTNmuI4WdTgcx+z1s9lsqqysPGGbAICupbPfB1v0xnx4+PBhbdu2TYZhKD8/X2+99Zb+8Ic/6JVXXjlhm55GMaabqHZWq9pZfUrr9u3b1/U8JCTkmN8Nw1B1ddttx8fHa+rUqa4X68svv6yFCxeeUhwd9cILL2jhwoWt9uadrMcee0ynnXaaJk+e7L7AAAA+daq5sKfnwYCAAM2ePVuffvqpK06r1aqKiopWy1VUVCgsLMzt8QIAPK8z3wdb9NZ8aLFY9OCDDyooKEiZmZm69tprtXTpUk+G3iEUY3BCLYekbdu2Tbt379bcuXPbXfamm2465urXRz/WrFlz3L727t2r1atX67rrrutUzB9++KGWLl2qvn37qm/fvnrttdf03HPPady4cZ1qFwDQ+3SXPOh0OrVnzx5JzYdrb9++XU6n0zV/06ZNGj58+Em3CwCA1D3zYXZ2tiTJZDKddDueRjEGJzRnzhzl5eXpjjvu0Jw5c2S1Wttd9plnnml19esfPlrO3WvPCy+8oHHjxmno0KEnjKuurk61tbUyDENOp1O1tbVqbGyUJP373//Wjh07tGnTJm3atEkXXHCBrrzySi1fvvzkBg8A6PW6Yh5ctWqV1q1bp/r6etXX1+vFF1/Up59+qnPOOUeSdNZZZykqKkq//e1vVVdXpxUrVmjlypUe34sJAOi5ums+TE9P1wMPPCCn06ldu3bpxRdf1IUXXnjyG8DNKMbghEJCQjR37lx98MEHHv0Q19jYqJdeeqndCzT9sII6ePBgBQcHKz8/X5dddpmCg4NdF5WKiopyHRXTt29fBQcHKyQkRDExMR6LHwDQM3XFPFhVVaUbb7xR0dHR6tOnj55++mm9+uqrrnPw/f39tWzZMv3nP/+RzWbTbbfdpldeeUVpaWkeix8A0LN1x3xosVi0bNkyrVu3TjabTeedd55uu+22di887E0mwzCMzjZit9sVERGhiooKhYeHuyMuHKWqvkrWh5urjo67HQoNCG01v7a2Vvv27dOAAQMUFOSd+873ZMfbnrzWAfwQ7wvecbxcSB50H3IggJPB+4J3nOj7YAvyYed4OwdyZAwAAAAAAIAX+fk6AJyY2WTWpJRJrucAAPQ25EIAQG9FDuyZKMZ0A8H+wVq5aKWvwwAAwGfIhQCA3ooc2DNRVgMAAAAAAPAiijE9iBuuxQyxHQGgu+L9u/PYhgDQ/fFefmq8vd0oxnQDVfVVin0sVrGPxaqqvuqY+RaLRZJUX1/v7dB6pJbt2LJdAQC+d7xcSB50H3IgAHQ9J/o+2IJ82DnezoFcM6abKK0ubXeen5+fQkJCdOjQIfn7+8tspsZ2qpqamnTo0CGFhITIz48/DwDoStrLheRB9yAHAkDXdbzvgy3Ih6fOFzmQTNsDmEwmxcfHa9++fcrLy/N1ON2e2WxWcnKyTCaTr0MBAHQAedB9yIEA0H2RDzvH2zmQYkwPERAQoPT0dA5Jc4OAgACqyADQzZAH3YMcCADdG/nw1Hk7B1KM6UHMZrOCgoJ8HQYAAD5BHgQAgHzYXbDrAwAAAAAAwIsoxgAAAAAAAHgRpyl1A2aTWaMSRrmeAwDQ25ALAQC9FTmwZ6IY0w0E+wdrww0bfB0GAAA+Qy4EAPRW5MCeibKal5RVl6muoc7XYQAAAAAAAB+jGOMFv/zkl4p5LEbJ/5esrSVbfR0OAAAAAADwIYoxHra+aL1+s+Y3kqSDjoOa8+YcNRlNJ9VGtbNa/f/YX/3/2F/VzmpPhAkAQJdGLgQA9FbkwJ6JYoyH/WXDXyRJQ5KHKCggSDmHcvSPnf84qTYMw1BeRZ7yKvJkGIYnwgQAoEsjFwIAeityYM9EMcaDnI1OvbXzLUnS9DOma/zw8ZKkJzc+6cuwAAAAAACAD1GM8aD1RetVWV+p0KBQpfRN0diMsZKkr3O/Vp49z8fRAQAAAAAAX6AY40H/+e4/kqRBSYNkNpkVFxmn5D7JajKa9Oy2Z30cHQAAAAAA8AWKMR60rnCdJCmtX5pr2mnpp0mS3tn1jk9iAgAAAAAAvkUxxoM2H9gsSeoX2881bXjqcEnS9oLt2l+13ydxAQAAAAAA36EY4yEljhKVVJXIJJPio+Nd02NsMYqPjleT0aR/7vpnh9oymUzKiM1QRmyGTCaTp0IGAKDLIhcCALqTPWV7tP3gdrfc/Ygc2DP5+TqAnmpzSfNRMTG2GAX4B7SaNyx1mPaX7dfSXUt158g7T9hWiH+Itt+83SNxAgDgbnnledp0YJMmJE9QTEiMW9okFwIAuou7/nOXfvf57yRJFw69UG/MfUP+Fv9Tbo8c2DNxZIyHbCnZIklKjEk8Zl7LqUob9m1QlbPKq3EBAOBJb3/7tgY/OVgXvXaRBv5poFbmrvR1SAAAeM3b377tKsRI0tKdS3XLf27xYUToqijGeEjLkTEJsQnHzEuKS1JEaITqnfV6bc9r3g4NAACP2F+5X4veXqS6xjoF+AXIXmfXBa9doCJ7ka9DAwDA4wzD0H0r75MkTTt9mq6dda0k6bn1z2nD/g2+DA1dEMUYD2m5eG9C9LHFGJPJpKy0LEnS6zteP2Fb1c5qZT6VqcynMlXtrHZvoAAAuMkfvviDKuoqlBSXpAevf1D9YvupsrZS175/bafbJhcCALq6VXmrtKVkiwL8AzT19KnKGpil7LRsGYahH/3nR6fcLjmwZ6IY4wH1jfXaWbpTkpQYe+xpSpI0Im2EJGnN3jWqa6g7bnuGYWjHoR3acWiHWy4ABQCAu9U4a/TsV89Kks474zwFBQRp3rR5MplM+nDnh1pbtLZT7ZMLAQBd3avbXpUknT7odIUGhUqSZo+fLZPJpC/3fam1xaeWC8mBPRPFGA/YeWinGpoaFBwYLJvV1uYyA+IHKCwkTNV11fp3zr+9GyAAAG72/t73Za+zKzIsUkP7D5XUfFruyEEjJUl3rbzLl+EBAOBRjU2NeuvbtyTJdRaEJMXaYjUifYQk6Zerf+mL0NBFUYzxgJbrxSTGJLZ76zGz2aysgc1/pIt3LPZabAAAeELLabcj0kbIbPr+48W5o8+VSSat3btW64rX+So8AAA86vOCz3Ww6qCCA4OV3i+91bxzRp0jSVq5e6V2lO7wRXjogijGeIDrejExx14v5mjZadmSpJV7VsrZ6PR4XAAAeEJdQ53e2f2OJLn2/rXoE9VHpw06TZJ0z6p7vB0aAABe8dF3H0mShqYMlZ/Fr9W8hJgEDU0Z2nyB38/v80V46IIoxnjAloPNt7U+UTFmYOJAhQaFylHj0Ot7T3whXwAAuqIvCr+Qo96hsJAwJfdJPmb+OaOb9wiu2r1Kmw9u9nZ4AAB43Ke5n0rSMUfFtJh6+lRJ0tItS3XAccBrcaHrohjjZoZhdPjIGIvZopGDm8+lf/6b5z0eGwAAnvDxvo8lNX8Abev03PjoeA1LHSZDhu5dc6+3wwMAwKOqndX6suhLSVJav7Q2l0lLTFNyn2Q5G516cN2D3gwPXRTFGDc74DigQ9WHZDKZ1De67wmXPyPjDEnSZ3s+U0lVSZvLmEwmpUSkKCUipd1r0AAA4CstxZhBSYPaXebc0edKkt7b/p72HN5z0n2QCwEAXdW6gnWqb6xXRGiEYiJi2lzGZDJp2unTJEkvf/WyKusqO9w+ObBnohjjZltKmk9RirXFKsAv4ITL94vtp36x/dTQ1KA/b/pzm8uE+Ico9/Zc5d6eqxD/ELfGCwBAZ1TWVWp90XpJUnpS24dmS1Jyn2QNTh6sJqPplI6OIRcCALqqlbkrJbV/hGiL4anDFWuLVVVdlZ7Y+ESH2ycH9kwUY9ys5U5K/WL6dXidlqNjXvz6RTUZTR6JCwAAT1idt1oNTQ2KDo9WdHj0cZdtOTrmra1vqaCiwBvhAQDgcS3Xi2nvFKUWZrNZU0c2XzvmyS+f5CYuvRzFGDdrKcbEx8R3eJ3RQ0YrKCBIRYeL9OquVz0VGgAAbtdyitLgpMEnXHZg4kClJqSqobFBv1z7S0+HBgCAx1XVV7mOED1RMUaSRg0ZpbCQMJVWluqvW/7q6fDQhVGMcbOOXrz3aEGBQRo/bLwk6dG1jx4zv8ZZo9HPjdbo50arxlnjnkABAHCDlmJMWtKJP4BK399Zack3S1TiaPtaaW0hFwIAuqLPCz6Xs8mpyLDIEx4hKkn+fv6aNGKSpObvfo1NjSdchxzYM1GMcaMaZ42+Lf1WUvO1YE7GWdlnyWw2a0vhFn2c/3GreU1GkzYWb9TG4o2cxgQA6DJKq0td10pr71aePzQkeYiS4pJU31Cvn63+WYf7IhcCALoi1/ViEo9/vZijjR8+XsGBwSooK9Cfvv7TCZcnB/ZMFGPcaPuh7Wo0GmUNtio8NPyk1rWF2TR6yGhJ0k/+8xMZhuGJEAEAcJvVeaslSX2j+iosJKxD65hMJs0cN1OS9M+v/qktB7d4LD4AADxtZd5KSdLAfgM7vE5IYIimj5kuSXpw5YOy19o9ERq6OIoxbrTpwCZJzaconcotx8474zz5Wfy0pXCL3tz9ppujAwDAvT7d13zBwo4eFdNiaMpQDRswTE1NTfqf9/6HHRAAgG6p1fViEjt2um6LiVkTFRMRo/Kqct21+i5PhIcujmKMG7VcL+ZkT1FqERkWqbOyz5Ik/fjDH6uuoc5tsQEA4G4tewM7csHCH7rozItkMVv0Ze6Xem7zc26ODAAAz/u84HM1NDUoMixSUeFRJ7Wun8VPF0y8QJL01y/+qvXF6z0RIrowijFutKlkk6STu3jvD509+myFhYSp8HCh7lpFhRQA0DUdqjqkbQe3SWq+S9LJirHF6Nwxzbe6/sn7P+FW1wCAbmdV3ipJzUfFnMqZEVkDs5Q9MFtNRpOuXHolt7ruZSjGuEmT0eQ6MiYxNvGU2wkJDNHcSXMlSU9+/qQ27t/olvgAAHCnlg+g8dHxsgZbT6mNs0edreQ+yaqqq9Ilb13SoTtKAADQVbRcvPdUdkq0uGTKJQoNCtXeg3v1o//8yE2RoTugGOMme8r2qLK+Uv4Wf8VFxnWqrey0bA1PHa7GpkbNeWOOKusqFRMSo5iQGDdFCwBA55zq9WKOZjFbdOW5VyrAL0Dr89brto9uO+7y5EIAQFdR7aw+5evFHC0sJEyXTb1MkvTsl89qyfYlbS5HDux5KMa4ybrCdZKkpD5JspgtnWrLZDJp3rR5slltKjxSqOveu04H7zioQ3ceUmhAqDvCBQCgUz7NbS7GnMr1Yo7WJ7KPLj/7cknSX9b9Rc9ver7N5UIDQnXozkPkQgBAl/B5wedyNjlls9oUHRHdqbay07I15bQpkqRrl16rr4q/ajWfHNgzUYxxky8Kv5Ak9e/b3y3tWYOtunrG1TKbzVqxc4V++vFP3dIuAACdlV+Rr52lO2UymTq1N7DFyEEjdfaosyVJNy2/Sf/+9t+dbhMAAE/6MOdDSc1HiJ7K9WJ+aPaE2Urvl65aZ63OfuVs7S7d3ek20bVRjHGTliNjUvqmuK3NAfEDdNmU5kPW/rD2D3rsi8fc1jYAAKfqvT3vSWreARESFOKWNmeOm6lRQ0apsalR8/41T8t2LXNLuwAAeMJ7e5tz4ZCUIW5pz2K26LpZ16lfbD+VV5frrH+cpV2lu9zSNromijFuUFlX6bqjhLuOjGkxNnOspo+ZLkn62Qc/04OrH3Rr+wAAnKyWD6AZ/TPc1qbZZNYVZ1+h7IHZamhs0JzX5uivX//VNb/GWaPJL07W5Bcnq8ZZ47Z+AQA4WQUVBdp2cJtMJpOGJLunGCNJQYFBuvHCGxUXGacSe4nG/n2sNhZtJAf2UBRj3OCLwi/UZDQpMixSEdYIt7c/ZeQU1/P7Pr1PP/3PT9VkNLm9HwAATqSuoU4fffeRJPftDWxhMVu04LwFGj1ktJqMJt24/Eb9+MMfy9noVJPRpFV5q7QqbxU5EADgUx/kfCBJSu6TrNBg917DJSwkTLfOvdV1hMzEFyfqla2vkAN7IIoxbtDyobQzd5Q4nh+eg/jE509o+ivTVV5b7pH+AABoz6q8VapyViksJEyJsYlub9/P4qf558zXOaPOkST9cd0fNf7F8SqwF7i9LwAATsW7e96VJGWkuO8I0aNZQ6y65eJbNDh5sOoa6nTD8hs80g98i2KMG3y0r7kYMzh5sMf7unzq5fK3+OujnI80/NnhWp232uN9AgDQ4vXtr0uSsgZmyWzyzMcIk8mkWeNnadGMRQoKCNLGwo0a+exIj/QFAMDJqKyrdF07bVjqMI/1ExQYpBsvuFHnjD6n1fS1+Ws91ie8i2JMJ5VWl+qb/d9IkgYlDfJ4f6cNPk23XnqrIsMiVVheqMkvTtZt798mR73D430DAHo3Z6NT/97ZfKejEWkjPN7fiPQRuuOKO9S/b3/VNHx/jvyavDUe7xsAgLYs27VMdY11iouMU0JMgkf7MpvNmjVulq6dea1r2vRXpuvKf1+pggqOGO3uKMZ00vt735chQ/HR8QoLCfNKn0lxSbpr/l0amzFWhgz96cs/aeCfB+qlTS9xDiEAwGM++u4jHak9orCQMA1MHOiVPmMiYnTrpbfqojMvck2bsXiGzv3nudpYvNErMQAA0OL1Hc1HiJ6WfppbbmndEYNTWp+BsXjrYqU/ma47/3OnDjgOeCUGuB/FmE76145/SWo+XNubggKDdPnZl+uG829QdHi0DjoOatHSRRrx7Ai9tfMtijIAALf7+6a/S2o+KsZs9t5HCLPJrHHDxrl+N5lM+k/OfzT6udGa+PeJWrZrGXkPAOBxBxwHtGLPCknNR2/6wo/m/kgDEwaqrqFOj3/+uFL+mKL/ffd/tffwXp/Eg1NHMaYTKusq9f7e9yVJ2WnZHu0rwC9AAX4Bx0zPHJCpn1/1c80eP1uB/oHaWrJVF79+sTKfztTLm19WbUOtR+MCAPQO+yv3661v35KkVoURb2rJhXdefqdGDRkls9mstflrdeGrFyrl/1J036f3Kedwjk9iAwD0fH/75m9qaGrQgPgBio+O92rfLTmwX1w/3TL3Fl0/+3ql9ElRfWO9ntn4jNL/nK4pL03Rkq1L+A7YTZgMwzA624jdbldERIQqKioUHh7ujri6hZc3v6yr375asbZY3bPgHq8dptYeR41Dqzat0prNa1Rb3/wHaAu26drsa3XjqBs1KNrz17Tp6Xrrax1A+3rL+8KvV/1av1r5Kw2IH6DbLr3N1+FIksod5VqzeY0+3/a5auq+v6bM+KTxujTjUl04+EINiBzgwwh7lt7yWgfQcb3pfaGhqUFpf0pTXkWerjznSo0eOtrXIckwDOUU5eiTrz/RztydMtT81T4iKEIXDb5Il2VeprNTz1aA5did+jg5nnitU4zphHEvjNMXhV9o9rjZOnv02b4Ox6W6rlprt6zV59s+15HKI67pp8WfpvnD5uuyzMuUHJHswwi7r976WgfQvt7wvlBZV6kB/zdAZTVlWjB9gU4ffLqvQ2qlvqFeW3O2av3O9dqdv9v1YVSShvcZrgsHXaizU8/W2H5jFegX6MNIu7fe8FoHcHJ60/vCS5te0qKli2QNtupX1/yqzbMWfOmw/bC+3PGlvtzxpcod5a7p4YHhmpk+U+cNPE/nDDxHCWGevehwT0Uxpgv5qvgrjXpulCxmi+6/9n6vXbz3ZDQ1NWln3k6t3bpW3+Z92+p8+pHxIzUrfZZmps/U6ITRspgtPoy0++iNr3UAx9cb3hce+ewR3f3x3Yq1xernV/28S+eM8spybc7ZrK05W5VTnKOjP+YE+QVpYvJETRswTROSJmhk/EiFBoT6MNrupTe81gGcnN7yvuBsdGroX4Yq50iOLphwgaaePtXXIbWrqalJ3xV/p817N2tzzmbZq+yt5mfGZerc1HM1MXmixvUbp/gw755u1V1RjOlCLlhygZbvXq7TB5+uBdMXeLQvZ4NTf1/RfNHEa2ZeI38//5Nuw1Ht0Oaczfpm9zfKKcpptdcwKjhK0wZM05nJZ+rMlDM1PG54l/6g7Uu98bUO4Ph6+vtCob1QQ/8yVI56h6469yqNGjLKJ3GcSi501Di0I3eHvs37VnsK96iyurLVfIvJosy4TI1NHKsxiWM0Mn6khsYOVZBfkEfG0N319Nc6gJPXW94XHlv7mH720c9kDbbql1f/UoEB3j3K8lS/DzYZTcrdn6udeTu1K3+XCkoKWn0PlKT+tv6akDRBZySeodPiT1NWnyyFB/bc/8tT5YnXup9bWullVuet1vLdy2UymTR9zHSP99dkNGlH7g7X81NhDbFqwvAJmjB8giqqKvRt3rfamdv8R3m45rDe2PGG3tjxRvOyAVaNTxrf/AfZ9zSNjB+p5Ihkn18TBwDgXYZh6OZ3b5aj3qEB8QM0cvBIn8VyKrnQGmzVmKFjNGboGBmGoQOHD2hP4R7tLdyr3AO5slfZtaVki7aUbNFfv/6rpOY7N6VFpSmrT5aGxw3XsLhhGhIzRKmRqRRpAKAX2lO2R/evul+SdMGEC7xeiJFO/fug2WRWakKqUhNSNWvcLDlqHNpdsNuVB/eX7lduea5yy3P1ytZXXOsNiBygEX1GaETfEcqMzdTgmMFKi0ojD7oZxZiTVO2s1vXLrpckjcscp7jIOB9HdPIiQiN0RsYZOiPjDDU2NSrvQJ72Fu3Vd0Xfad/+fXLUO/Rhzof6MOdD1zq2IJtG9h2p7L7ZGhw9WENihmhIzBDFhcZRpAGAHuqxzx/T8t3LZTFbdOmUS2U2dd+bMJpMJsVHxys+Ol5nZZ8lqfmUprySPOWV5Cn/QL6KS4tVXVet3WW7tbtst/6141/fry+TEsMTlR6VrrSoNKVFpWlg5ECl2FKUFJ6k2NDYbr19AADHqqqv0tzX56raWa20xLQ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\n", "text/plain": [ "
" ] @@ -716,16 +729,14 @@ "squad = ['Szczesny',\n", " 'Di Lorenzo',\n", " 'Kim',\n", - " 'Juan Jesus',\n", + " 'Bastoni',\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", + " 'Hojlund',\n", + " 'Rafael Leao']\n", "\n", "s = simulate_lineup(squad)\n", "\n", @@ -734,13 +745,13 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "3943571c", + "execution_count": 18, + "id": "3be44a67", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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FXovJYmket8ZmOzQe6Tjn+XSeXxEREfGMIF8HICIiIl3bkpwlADQ2tT5bkvMxpV2V3hvANygoCKvVSnFxMcHBwZjNur/UWQ6Hg+LiYqxWK0FB+oooIiLiSWppRURE5LDK68tZu2ftEddxJmP2Ve3zQkTNTCYTKSkp5OTkkJfn/cej/JXZbKZXr16YTCZfhyIiIuLXlIwRERGRw1qauxSDI48j4nxMqbSq1AsR/SokJISsrCw9quRGISEh6mUkIiLiBUrGiIiIyGF9ufPLo64THRENQGVtJTbDRogpxNNhuZjNZsLCwrxWnoiIiIg76NaHiIiIHNZXOV8ddZ1oa3Mypt5WT0lDiadDEhEREen2lIwRERGRVu2q3MUvJb8cdb2wkDCCLcEA5FVp/BYRERGRo1EyRkRERFrl7BWTnpRORlIGGUkZrQ7sajKZiLRGApBfne/VGEVERES6I40ZIyIiIq1yjhczMGMg540974jrRluj2V+1n8KqQm+EJiIiItKtqWeMiIiIHMIwDFfPmAG9Bhx1/aiIKAB2V+/2aFwiIiIi/kDJGBERETnEltItFFUVEWQJIjMl86jrOwfx3Vezz8ORiYiIiHR/ekxJREREDuF8RKlPSh8w4KF/PwTArCtnERJ86NTVUdbmnjH7qpWMERERETkaJWNERETkEM5HlI7JOAYDg/1V+wEwMFpdPzqiuWdMSY2mthYRERE5Gj2mJCIiIi00OZpYkrMEgKyMrDZt4+wZU1ZT5rG4RERERPyFkjEiIiLSwk9FP1HRUEFYSBgZSRlt2saZjKmorcAwWu89IyIiIiLNlIwRERGRFpzjxQzIGIDZ3LavCs4BfKtqqqhz1HksNhERERF/oGSMiIiItPBlzq/JmLZyTm1tb7Kzt36vR+ISERER8RdKxoiIiIhLja2GFQUrADim1zFt3i4kKITQ4FAA8qvyPRKbiIiIiL/QbEoiIiLisix/GbYmG3FRcSTEJABgwkRyfLLr9eFER0RTXF5MYXWhV2IVERER6a6UjBERERGXA8eLMZmaEy8hwSHce+W9R902yhpFcXkxu6p2eTRGERERke5OjymJiIiIS0fGi3Fyzqi0u2a3W2MSERER8TdKxoiIiAgAxTXFrNuzDuhYMsY5o9K+6n3uDEtERETE7+gxJREREQHg65yvAUhNSHX1cgGw2W08/c7TANw1/S5CgkNa3d45o5KSMSIiIiJHpmSMiIiIAC3HizmQgcGesj2u14cTFd6cjCmpLfFQhCIiIiL+QY8piYiICIZh8MXOLwA4JqPtU1ofKDqi+TGl/TX73RaXiIiIiD9SMkZERETYsX8HeRV5WMwW+qb17dA+nI82VdRUuDM0EREREb+jZIyIiIjw+fbPAchMySQ0OLRD+3AmYyrrKmlwNLgtNhERERF/o2SMiIiI8Nn2zwAYnDm4w/twjhnjcDgoqilyS1wiIiIi/kjJGBERkQBXZ69jSc4SAAb2Htjh/QQFBWENswKQV53nlthERERE/JGSMSIiIgHum7xvqGusIyYihpQeKYcsN2EiLiqOuKg4TJiOuK9oa/MgvgVVBR6JVURERMQfaGprERGRAPfZtuZHlAZlDsJkOjTZEhIcwuxrZ7dpX1HWKPaU7WFX9S63xigiIiLiT9QzRkREJMA5x4s5NvPYTu/LOYjv7urdnd6XiIiIiL9SMkZERCSAbS/bzraybZjNZgakD+j0/pzJmD3Vezq9LxERERF/pceUREREApjzEaV+qf0ICw1rdR1bo41n33sWgN9N+x0hQSGH3Z8zGVNcU+zmSEVERET8h5IxIiIiAWzh1oUAHNv78I8oGYZBwb4C1+sjiY5oHsC3pKbETRGKiIiI+B89piQiIhKg9tftZ0lu85TWQ/oOccs+nT1j9tfsd8v+RERERPyRkjEiIiIB6pNtn9DoaCQ5PpmkuCS37NOZjKmoqThqLxoRERGRQKVkjIiISID68JcPARjWb5jb9hltbX5Mqbq+mpqmGrftV0RERMSfKBkjIiISgOrsdXy+/XMAhvYb6rb9RoZHYjKZMAyDvJo8t+1XRERExJ8oGSMiIhKA/rvjv9Taa4mLiiM9Md1t+zWbzUSERQCQV6VkjIiIiEhrlIwREREJQPM3zweaH1EymUxHXT8iLMKVZDmamIgYAHIrcjscn4iIiIg/09TWIiIiAabGVsNHv3wEwIisEUddPzQ4lL/99m9t3n9cVBy7SnaRU5HTwQhFRERE/Jt6xoiIiASYBVsWUGOvoUd0DzKTM92+/7joOADyy/Pdvm8RERERf6BkjIiISIB5c+ObAIwaOKpNjyi1V3xUPAC7Kne5fd8iIiIi/kCPKYmIiASQ4ppiFm9fDMDIY0a2aRtbo40XFrwAwI0X3khIUMgR14+Pbk7G7KnY04lIRURERPyXkjEiIiIB5D/r/0OT0URGUgY943q2aRvDMNixa4fr9dHERTU/plRcVYxhGB7pfSMiIiLSnekxJRERkQDhMBy8sLq5h8vJQ072WDnOx5Qqayopt5d7rBwRERGR7krJGBERkQDx1c6v2F62nbCQsDY/otQREeERBAcFA/BL+S8eK0dERESku1IyRkREJEA8/9PzAJww8ARCg0M9Vo7JZHL1jtlcttlj5YiIiIh0V0rGiIiIBIBfSn5hwS8LABg7dKzHy+sZ3zwezabiTR4vS0RERKS7UTJGREQkADy+/HEMDIb0HUJyj2SPl+ccHHhLyRaPlyUiIiLS3Wg2JRERET+XW57LGxveAODsUWd3aB9Hm876YM6eMTtLd3aoPBERERF/pmSMiIiIn5v11SwaHY0ck3EMvZN7t3v70OBQnrjliXZt40zGFJYWanprERERkYPoMSURERE/trJgJW9nv40JE+efcr7Xyk2KS8KEier6arZVbvNauSIiIiLdgZIxIiIifsrWZOOmT24CYPSg0aQnpnut7NDgUFfvmK8Lv/ZauSIiIiLdgZIxIiIifurhbx5mw94NRIRFcN7J53V4P/ZGOy9+/CIvfvwi9kZ7m7fLTM4EYFnBsg6XLSIiIuKPlIwRERHxQ59u+5RHvnsEgEvPuJQoa1SH9+UwHGzO3czm3M04DEebt3OOT7N219oOly0iIiLij5SMERER8TOri1Zz2XuXYWBw8pCTGZ413Cdx9EvrB8CWoi2U1JX4JAYRERGRrkjJGBERET+yLH8ZZ752JlW2Kvql9ePi0y/2WSxJcUkkxSXR5GjijV/e8FkcIiIiIl2NkjEiIiJ+wN5k54nlT3DGf86goqGCvql9ueG8GwiyBPk0ruH9mnvlzF0316dxiIiIiHQlSsaIiIh0Y3X2Ov6z7j8MnzOcP375RxodjYwcMJIbL7iRsNAwX4fHyUNPxmw2sz5/PZ/u/NTX4YiIiIh0Cb69XSYiIiJtYhgG1bZq9tXsY0vpFjYXb+a7/O9YkrOEKlsVAJHhkZx/8vmMHjQak8nk44ibxUXFcdLgk1ixcQUz3p/Buxe/y5l9z8Rs0v0gERERCVxKxoiIiHSCYRgAjHthHOYws+s9AwOH4XC9PvBfh+E45L0W/x70Xq29lvL6cpocTa3GEBsVy4nHnsiJQ07EGmKloarBrcfY0NgA9c2v66vqMYKMdm0/YdgEdu7cyZ7SPZzz8jlEhkaSGplKREgEZpMZk8mECZPrX+maGusagV8/8yIiItJxJkMtqoiISIft3LmTfv36+ToMEa/ZsWMHffv29XUYIiIi3Zp6xoiIiHRCfHw8APn5+cTExHi17MrKSjIyMigoKCA6Olplq2yPqqiooFevXq7PvIiIiHSckjEiIiKdYDY3P5oUExPjkwtkgOjoaJWtsr3G+ZkXERGRjlNrKiIiIiIiIiLiRUrGiIiIiIiIiIh4kZIxIiIinRAaGsrs2bMJDQ1V2Srbb8vuCuWLiIj4E82mJCIiIiIiIiLiReoZIyIiIiIiIiLiRUrGiIiIiIiIiIh4kZIxIiIiIiIiIiJepGSMiIiIiIiIiIgXKRkjIiLSRosWLeKYY44hKyuLl19++ZDlP/74I4MHD6Z///48/PDDbiu3oKCAcePGMWjQIIYNG8b8+fMPWSczM5Nhw4YxYsQIJk+e7LayAYKCghgxYgQjRozg+uuvP2S5p457y5YtrnJHjBhBeHg4H330UYt13H3cU6ZMIS4ujmnTprnea8vx7dixg1GjRtG/f39uuukmOjI/wsFl19bWMnnyZAYOHMiQIUN49tlnW91u3LhxDBw40HWeOqK1427LuXXHcYuIiAQkQ0RERI7KbrcbWVlZRmFhoVFZWWn079/fKC0tbbHOqFGjjPXr1xt2u90YNWqUsXHjRreUXVRUZKxdu9YwDMPYu3evkZaWZlRXV7dYp3fv3kZVVZVbyjtYjx49jrjcU8d9oKqqKqNHjx4eP+6vv/7a+Pjjj42pU6e63mvL8V188cXGwoULDcMwjIsuusj1ujNl19TUGEuXLjUMwzCqq6uNgQMHGtu2bTtku9NPP73T57y1427LuXXHcYuIiAQi9YwRERFpA2fviLS0NKKiopg8eTKLFy92LS8qKqKxsZFhw4YRFBTEFVdcwcKFC91SdkpKiqvHQ1JSEvHx8ZSVlbll353lyeM+0Mcff8yZZ55JRESE2/d9oDPOOIOoqCjX7205PsMwWLlyJeeeey4AV111VYfOwcFlW61WTj/9dAAiIiLIyspi9+7dHTmsdpfdFu46bhERkUCkZIyIiEgbFBUVkZaW5vo9PT2dXbt2tXm5u/z00084HA4yMjJavG8ymTjttNMYPXo077//vlvLrKys5Pjjj+eUU07hm2++abHMW8f97rvvMn369EPe9+RxQ9uOr7S0lPj4eEwm02HX6ayCggI2bNjAyJEjW11+xRVXMHLkSJ5//nm3lXm0c+uN4xYREfFXQb4OQEREpDswWhkLw3kR2pbl7lBaWspVV13V6ng1y5cvJzU1lcLCQsaPH8/w4cPp37+/W8rNzc0lNTWV7Oxszj33XDZu3Eh0dDTgneOurKxk+fLlvP3224cs8+RxQ9uOz9PnoL6+nunTp/Pkk0+22jNo3rx5pKamUlZWxsSJExk8eLCrR01nHO3ceuP/XkRExF+pZ4yIiEgbpKWltbjrX1hYSEpKSpuXd1ZDQwNTpkxh1qxZnHzyyYcsT01NBZp7J5x55pmsW7fObWU79z1kyBAGDRrE1q1bXcs8fdwACxYsYMKECYSFhR02Nk8cN7Tt+BISEigrK3MlJ9x5DgzD4Oqrr2by5MktBtc9kPMcxMfHM3XqVFatWuWWso92bj153CIiIv5OyRgREZE2GD16NNnZ2ezatYuqqio+/fRTJkyY4FqempqKxWJhw4YNNDY28tZbb3H++ee7pWzDMLjmmmsYP348M2fOPGR5TU0NVVVVAJSXl/Ptt99y7LHHuqXs/fv309DQADRfbG/evJm+ffu6lnvyuJ0O94iSJ4/bqS3HZzKZOOmkk/jkk08AeO2119x2DmbNmoXVauWBBx5odXljYyMlJSVAcw+axYsXM3jw4E6X25Zz68njFhER8Xu+GTdYRESk+1mwYIGRlZVl9OvXz3jhhRcMwzCMSZMmGbt27TIMwzBWrlxpDBo0yOjbt68xe/Zst5X73XffGSaTyRg+fLjrZ8OGDa6yd+zYYQwbNswYNmyYMWTIEGPOnDluK3v58uXGkCFDjGHDhhnDhw83PvzwQ8MwvHPchmEY5eXlRlJSktHQ0OB6z5PHfc455xgJCQlGeHi4kZaWZvz444+HPb7rrrvOWLVqlWEYhrF161Zj5MiRRt++fY0bbrjBaGpq6nTZ3377rQEYgwYNcv2/f/755y3Krq6uNkaOHGkMHTrUGDRokPHggw+65bi///77w55bdx+3iIhIIDIZRisP/IqIiIiIiIiIiEfoMSURERERERERES9yy2xKDoeDoqIioqKiNIq++DXDMKiqqiI1NRWzuWvkMlX/JFB0xfonIr6lNlACRVdtA1UHJVB4og66JRlTVFRERkaGO3Yl0i0UFBSQnp7u6zAA1T8JPF2p/omIb6kNlEDT1dpA1UEJNO6sg25JxkRFRQHNgUVHR7tjlyJdUmVlJRkZGa7PfFeg+ieBoivWPxHxLbWBEii6ahuoOiiBwhN10C3JGGeXtOjoaFXCDjIMg5La5qkpE6wJ6ubXxXWl/x/Vv9apTvkv/V+KiJPawMNTO+ifutr/o+pg+6ludm/u/P9ySzJGOq/WXkvSk0kAVM+qJiIkwscRiXRvqlMiIhLI1A6KdE2qm+LUdUZ/EhEREREREREJAErGiIiIiIiIiIh4kR5T8mNNTU3Y7XZfh9HtBAcHY7FYfB2GdHOGYdDY2EhTU5OvQ+lWLBYLQUFBen5aRKQbUxvYMWoD5XBUp7zD29eBSsb4qerqagoLCzEMw9ehdDsmk4n09HQiIyN9HYp0Uzabjd27d1NbW+vrULolq9VKSkoKISEhvg5FRETaSW1g56gNlIOpTnmPt68DlYzxQ01NTRQWFmK1WklMTFR2vR0Mw6C4uJjCwkKysrLUQ0bazeFwkJOTg8ViITU1lZCQENXBNjIMA5vNRnFxMTk5OWRlZWE262laEZHuQm1gx6kNlNaoTnmPL64DlYzxQ3a7HcMwSExMJDw83NfhdDuJiYnk5uZit9uVjJF2s9lsOBwOMjIysFqtvg6n2wkPDyc4OJi8vDxsNhthYWG+DklERNpIbWDnqA2Ug6lOeZe3rwOVjOkigsxBXD38atdrd1DWtGN03vyDJ+pUe+huVsfp3ImIdJ4v20H9He84nTv/15G6qc+Fd3j7OlDJmC4iNCiUuRfN9XUYIn5DdUpERAKZ2kGRrkl1U5yUYhO3uummm/jjH/8IQG5uLiaTifLyct8GJSJHVV5ejslkIjc319ehiIiIiEiAGDFiBHPnzvV1GD6hZEwXYRgGNbYaamw1Hp0Bady4cYSGhhIVFUVMTAxDhgzh97//PcXFxW7Z/5w5c3j88cfdsi+RzvBWneqI3/zmN5hMJn7++WdfhyIiIn6qK7aD48aN45lnnvF1GDz44INcdNFFvg5DApS76ubh6pPJZGLdunUdD7Cb++6774iMjHT9mEwmrFar6/dHHnnE1yG6KBnTRdTaa4l8NJLIRyOptXt22rLHH3+cqqoqysvLeffdd9m1axfHH388e/fu9Wi5It7kzTrVHtXV1bz77rvEx8fzyiuv+DocERHxU121HfS1xsZGX4cgAa4r101/qB+nnnoq1dXVrh+AFStWuH6/7777fBzhr5SMCWAmk4lBgwbxxhtvEBMTw9NPP011dTUXXnghSUlJxMTEcNppp7F+/XrXNg8++CDnn38+t956K7GxsfTq1Yt33nnHtfyaa67hjjvuaLU8u93OrFmz6NWrF4mJiUyfPt1tPXJEuou3336biIgIHn/8cV577TXsdjsAc+fOZcSIEcyePZuEhASSk5N55513WL58OUOGDCEmJobrrrsOh8MBwNKlS4mNjeXZZ58lJSWF5ORkZs+e7brDkp+fz9lnn01iYiJxcXGce+65LR5Bamho4OabbyY+Pp4+ffrw3nvvtYjz4Lst69at0+DWIiLids727EAXXXQRDz74YIvlh2vvAL788ktGjx5NbGwsgwcP5uOPP3Ytu+aaa7juuuu49NJLiY6O5tFHH+WRRx5h0aJFrjvloO+p4r/efvtthg0bRmxsLCeccAIrVqxwLRs3bhx/+MMfOOecc4iIiOCzzz4jMzOTJ554gpNOOomoqChOP/10CgoKXNv84Q9/oHfv3kRFRTFo0CDmz5/vWuasry+//DIZGRn06NGDP/zhDy3iee6551zL7r///kPiPVx93rdvX4seL85eL0uXLnXzGfMeJWOEoKAgLrzwQpYuXYrD4eCKK64gJyeHvXv3ctxxx3HppZe2aPAWL17M2LFjKS0t5a9//SvXX389VVVVRy3n0UcfZdGiRSxbtoycnBxMJhMzZszw5KGJdDmvvPIKM2bM4LLLLqO2tpaFCxe6lm3atInY2Fj27NnDX/7yF37729/y9NNP880337B582YWLVrERx995Fq/qqqKNWvWsGPHDpYuXcqrr77Ka6+9BoDD4eCuu+6ioKCAvLw8rFYrN9xwg2vbv/3tb6xcuZLs7GzWrl3LBx984LVzICIi0h5Hau82bNjAJZdcwmOPPUZZWRkvvPACM2fOZMuWLa7t33rrLa677jrKy8v5/e9/z3333cd5553X4s65vqeKP/r000+5++67mTt3LmVlZcyaNYvzzz+f0tJS1zpz587lr3/9K9XV1Zx11lkAvPbaa8ybN4/i4mIiIiL405/+5Fp/+PDhrFq1ivLycv785z8zc+ZMcnJyXMurqqrYuHEj27ZtY9myZfzzn/90JUy+/vpr7r//ft599112794NQHZ2tmvbI9XnpKSkFj1e7r33XgYPHszIkSM9eQo9SskYASAtLY2ysjKio6OZPn06ERERhIWF8dBDD7F161aKiopc644cOZLLL78ci8XCzJkzsdlsbN269ahlvP766zzwwAP06tWLyMhInn76ab744osW+xbxZ5s3b+b777/n6quvJjIykilTprR4VCkhIYE777yToKAgZsyYQWVlJTfccAM9evQgLS2N008/nTVr1rjWdzgcPP7441itVgYOHMitt97K66+/DkBmZiaTJk0iLCyM6Oho7r//fr799ltXz5o333yT++67j9TUVGJjY5k9e7Z3T4aIiEgbHam9e+GFF7jmmmsYP348ZrOZU045hfPOO493333Xtf0555zDhAkTMJvNWK3WVsvQ91TpjmbNmkVsbGyLnwP985//5J577mHkyJGYzWYuvvhiBg4cyKeffupa54orrmD06NGYTCbCw8MBuPXWW+nbty9hYWHMmDGD1atXu9afMWMGSUlJWCwWLrvsMgYOHNiit41hGDz66KOEhYVx7LHHcvLJJ7u2f/PNN5kxYwZjxowhJCSEBx98kIiICNe2banPAO+++y7PPfccixYtIjo62m3n09uUjBEAdu3aRXx8PHV1ddxyyy1kZmYSHR1NZmYmACUlJa51k5OTXa+dlbYtPWMKCwtd+wNITU0lNDSUwsJCtx2HSFf2yiuvMHz4cIYPHw7A1VdfzeLFi9m1axcAPXv2dK3r/LJ4YH2zWq2uO3gAYWFhJCUluX7v3bu3a1/FxcVcccUVZGRkEB0dzWmnnYbNZnPV1aKiInr37t1iWxERka7oSO1dbm4uc+bMaXExumDBghZJlF69eh21DH1Ple7o0Ucfpby8vMXPgXJzc7nvvvta1I9169a56g+0Xj8O/P4ZERHR4lrvH//4B4MHDyYmJobY2Fiys7NbXCtGR0e3SHoeuP3B3z+Dg4NJSUlpEe/R6vP333/PTTfdxEcffdSiznZHSsYIjY2NLFiwgHHjxvHUU0+xevVqli1bRmVlpWuMCXeMwp+ent5izIo9e/bQ0NBAenp6p/ct0tXZ7XZef/11tm7dSnJyMsnJycyYMYOmpqYOT+dXX1/Pvn37XL/n5+eTlpYGNN8pqa2tZc2aNVRWVvLtt98Cv9bl1NRU8vLyWmx7oMjISGprfx1UztmVVERExJ0iIyOpq6tr8V3z4DbnSO1dRkYGt99+e4uL0erqav71r3+51jebW17yHPw76Huq+KeMjAyeeuqpFvWjpqaGe++917VOa/XhcJYtW8aDDz7Ia6+9xv79+ykvL2fIkCFtvlY8+Pun3W5vUd+PVp9zc3O56KKLmDNnDieddFKb4+6qlIwJcL/88gtXX301FRUV3HXXXVRWVhIWFkZcXJzbR5u+8soreeSRRygoKKC6upq77rqLs846i9TUVLeVIdJVffzxx1RWVrJmzRrWrVvHunXrWL9+PX/605949dVXO5TwNJvNzJo1i7q6OrZs2cI///lP1/PtlZWVWK1WYmNjKS0t5aGHHmqx7eWXX85jjz1GUVER5eXlPPzwwy2Wjxw5kg8++ICKigr27dvHE0880fGDFxER+T+NjY3U19e7frKysggODmbevHk0NTXx9ttvs3bt2hbbHKm9u/HGG/n3v//NkiVLaGpqoqGhgZUrV/Lzzz8fNoaePXuSl5dHU1OT6z19TxV/dOutt/L3v/+d1atXYxgGtbW1fPnllx3u8VVZWUlQUBCJiYk4HA5effXVFmO+HM3ll1/Om2++yQ8//IDNZuPhhx+mpqbGtfxI9bmyspLzzjuP3/3ud1x66aUdir+rUTKmi7CYLUwbNI1pg6ZhMVs8WtYf//hHoqKiiImJ4eKLLyY5OZmffvqJnj17ctddd2GxWOjZsydDhgxhzJgxbit31qxZTJgwgTFjxpCZmYndbueNN95w2/5FDuTNOtUWr7zyCpdffjkDBw509YxJTk7mtttuo6ioqEPJmKioKEaMGEHfvn057bTTuOqqq7j66qsBeOihh9i+fTtxcXGMHTuWSZMmtdj2gQceYNSoUQwZMoQRI0Zw0UUXtVh+5513kpKSQkZGBuPHj2f69OkdPnYREfG+rtYOOt1zzz2Eh4e7foYPH85LL73EvffeS48ePVi2bBkTJkxosc2R2rvjjjuOt956iwceeIDExETS0tL405/+RENDw2FjuOSSS4iOjiYhIcE1xoa+p4q3eLNunnfeeTz22GPccMMNxMXF0adPH/7nf/7HNYZge02cOJGpU6cydOhQUlNT2bRpE2PHjm3z9meddRZ/+ctfmDp1KikpKTgcDoYMGeJafqT6vGbNGjZt2sSjjz7aYkal7777rl3HcNNNN3HTTTe1axtPMRlueP6ksrKSmJgYKioquvUAOv6ivr6enJwc+vTpQ1hYmK/D6XaOdP664me9K8YUyLxV/5YuXcpFF110yLPB/uBw51CfdRE5mP4udC2eaAP9ub1rTXdrA7tqXP5C13Xe5e3rQPWMERERERERERHxIiVjRERERERERES8SMmYLqLGVoPpIROmh0zU2GqOvoGIHJG/16lx48YFTJdtERFpP39pB9Xeib/xl7opnadkjIiIiIiIiIiIFykZIyIiIiIiIiLiRUrGiIiIiIiIiIh4kZIxIiIiIiIiIiJepGSMiIiIiIiIiIgXKRkjR1RfX8+UKVOIjY1l9OjRvg5HJKCo/omISKBSGyjiXqpTXY+SMV2ExWxhctZkJmdNxmK2+Docl/fff58tW7awd+9efvzxR6+W3dDQwN13301KSgqRkZEMHTqU3NzcDq3//fffM2HCBBISEoiPj2fChAls3rzZOwciPtFV61R7+LL+mUwmrFYrkZGRREZGMnz48MOu++abb7rWc/6YTCaefvppQPVPRMQXuns72F3aQIAFCxYwbNgwoqKiyMzM5Mknn2yx/Oeff2bs2LFYrVYGDBjAxx9/7MnwpYvzVd3sLtd1hYWFnHzyyfTo0YOYmBhGjBjBhx9+2Obl3UmQrwOQZmFBYXxyxSe+DuMQOTk5DBgwgNDQUK+Xfe2111JXV8fq1atJSUlhy5YtxMbGdmj9/fv3c+211/LOO+9gtVr5y1/+wsSJE8nJycFi6X5fUOToumqdag9f1j+AFStWMGLEiKOuN2PGDGbMmOH6ffXq1YwePZpLLrkEUP0TEfGF7t4Odpc2cO/evVx66aW8+uqrXHHFFWzYsIHTTz+dIUOGMHHiROx2O+effz5XXHEFX331FV9++SWXXXYZ69ato3///p4/EOlyfFU3u8t1XVxcHHPnzqV///6YzWZWrFjB2WefTXZ2Nn369Dnq8m7FcIOKigoDMCoqKtyxO+mkuro6Y/PmzUZdXd0hy3r37m088sgjxqhRowyr1WpMnDjRKC0tNW6++WYjJibG6N+/v7F8+XLDMAzjrrvuMoKDgw2LxWJEREQYf/7zn41hw4YZ//nPf1rsc+LEicajjz7q1mPIzs42rFarUVZW5pH1nZ/ZHTt2HLLsSOevK37Wu2JMgcwf6p9hGAZgrF27tkPb3nzzzcbEiRMPu/xI9c8wDn8O9VkXkYPp70LXEoht4OrVq43g4OAW75111lnG3//+d8MwDOPLL780YmNjDZvN5lo+efJk489//nOr++tubWBXjctf+EOdau912oEcDoexcuVKIzQ01Pj666/bvby9vH0dqGSMHzpapR06dKiRl5dn7N+/3xg0aJDRr18/Y/78+UZjY6PxwAMPGEOHDnWtP3v2bOPCCy90/f73v//dOOuss1y/79mzxwgODjby8/NbjcX5x+BwP999912r2/3zn/80hgwZYvzxj380EhMTjaysLFej5o71P/74YyM2Ntaw2+2HLFMyRjrDH+qfYTR/EU1OTjYSEhKM8ePHGytXrmzT8dfW1hoxMTHGe++9d9h1jlT/DKP7fREVEd/R34WuJRDbwKamJuPss8825s6dazQ2NhqrV682EhMTXcmcp59+2hg7dmyLbe677z7joosuanV/3a0N7Kpx+Qt/qFPtvU5zGjp0qBEcHGwAxvjx4w/53ni05R3h7etAjRnTRdTYaoh4JIKIRyKosdV4tKxbbrmFXr16ERsby7nnnktCQgLTpk3DYrFw+eWXk52djc1ma3XbGTNm8M0337Br1y4A5s2bx6mnnkpGRkar6z///POUl5cf9ueUU05pdbuysjKys7MxDIP8/Hw+/PBD/vGPf/Dmm292ev28vDxuvPFGnnrqKYKC9KSev6pqqCLkLyEEPRzE5uKuMz5Jd6h/AF9//TW5ubnk5uYyefJkzjnnHPLz8496fO+99x4hISFccMEFrS5X/RMR8Q5vfrdsK39sA81mM1dddRV33nknoaGhjBo1invuucf1iFN1dfUhj2PExsZSVVV1lLMl/sqddbM71Kn2Xtc5bdiwgerqahYuXMikSZMOebT9aMu7AyVjupBaey219lqPl5OcnOx6bbVaD/ndMAxqa1uPIyUlhfHjx7sqz2uvvcZVV13l9hgjIyOxWCw8/PDDhIWFMXjwYH7zm9+wYMGCTq1fWFjImWeeya233spvfvMbt8ctXcfb2W9jd9hpMpqY8fGMo2/gJd2h/gGcccYZhIaGEhERwe9//3sGDhzIp59+etTtXnnlFa666iqCg4MPWab6JyLiXd76btlW/tgGfv3119x888188MEH2Gw2tm3bxhtvvMELL7wANH9HraioaLFNRUUFUVFRHoldugd31c3uUKfae113oJCQEM477zyWLFnSavLmaMu7OiVjpN1mzpzJ66+/TnZ2Nlu3bmXq1KmHXfemm246ZJaVA3++++67VrdzjlpvMpnaFFNb1t+1axdnnHEGM2fO5L777mvTfqX7ennNy67X6wrXsb50vQ+jcR9v1L/WmM1Hby62b9/Ot99+y3XXXXfIMtU/ERHprK7YBq5Zs4YTTzyRcePGYTab6devH9OmTWPhwoUADBs2jE2bNmG3213brFu3jqFDh7a5fBFP6YrXda2x2+1s27atw8u7KiVjpN2mTJlCXl4ed999N1OmTCEyMvKw686ZM4fq6urD/px66qmtbnfaaaeRlZXFQw89hN1uZ8uWLcydO5cLL7ywQ+sXFRUxbtw4pk+fzuzZszt/EqRLq6iv4MeillP2vf7z6z6Kxr28Uf+ys7NZvXo1drud+vp6/vd//5dNmzYxYcKEI8b2yiuvMGbMGI499tgW76v+iYj41siXRrK7arevw+i0rtgGjhkzhlWrVrF8+XIMwyAvL4/333+f4447Dmj+jhofH8/f/vY3Ghoa+PTTT1m6dKnHevWItEdXvK775ptvWLlyJTabDZvNxty5c1myZAlnn312m5Z3J0rGSLtZrVamTp3K4sWLPdaQWCwWPv74Y1auXElsbCwTJ07k9ttvbzF97oEZ2KOt/9JLL7F9+3aeeeaZDt8Vke5jWf6yQ95bkb/CB5G4nzfqX3FxMVdeeSWxsbGkpaXxwQcf8Pnnn7eYLvDg+tPU1MR//vMfrr/++kP2p/onIuJ9hmG4Xm8t2cr/++r/+TAa9+iKbeDYsWN5+umnuf7664mOjubkk09m7Nix3H///QAEBwfz8ccf88UXXxAbG8vtt9/Om2++qWmtpUvoitd1NTU13HjjjfTo0YOePXvyr3/9i7fffts1Js3RlncnJuPAv9QdVFlZSUxMDBUVFURHR7sjLr/03ub3eHXtq/z2+N9y0cCLWiyrsdUQ+WhzJrJ6VjURIREdLqe+vp6cnBz69OlDWFhYZ0IOSEc6f13xs94VY/K1P37xR55Y8USL96Ijotl/1/42PW7TGap/nXe4c6jPuogcTH8XDm9pzlLOeO0M1++hwaGU3lNKRHDHv2MejdrAzutubWBXjasra891n+qUd3n7OlA9Y7wkvyKfGR/M4LPtn3Hpe5dSUFng65BE/NaGfRsOea+yppJfKn/xQTQiIiLet3DrQtfrqPAoGuwNvL/zfR9GJCIiB1Iyxkvezn4bW1PztGL2JjtPrG55195sMnN679M5vffpmE36bxHpDOdU1mkJafRL60dibCIAK3b7x6NKIiIiR7N2z1oAEmMTGdBrAABf5n7py5BEBF33ya+CfB1AoFi0dREA6YnpFBYX8t9t/4Vfe44SHhzO0muW+iY4ET9SbasmvyIfgFum3EJEeARzP5tLcXkxa/augWOPsgMREZFuzjAMsvdlA3DlOVeyq2QXq7esZs2uNT6OTER03SdOSsV5QaOjkdW7VwNw7phzAdi2Zxu7a7v/qPYiXc0vJc2PIkVZo4gIb34GNzUhFYDN+zb7LC4RERFv2VO9h+LaYkwmEyk9UshMzgRg+57tNDQ1+DY4EREBlIzxis3Fm6m11xIaHMoxvY8hISYBwzD4PP9zj5brhrGZA5LOW/fmfEQpOT7Z9V5qj+ZkzI7iHV6LQ5+jjtO5ExHpnHV71gGQFJtESHAIyfHJhASF0GBv4MfiHz1evv6Od5zOnbRGnwvv8PZ5VjLGC34q+gmAXj17YTaZ6Z3cG4DvCn+d1rXGVkPi3xNJ/HsiNbaaTpVnsVgAsNlsndpPoHKeN+d5lO7FmYxJiE3g/hfv5/4X76dHTA8Adpftpqapc/XraIKDgwGora31aDn+zHnunOdSRETaZ/3e9QCUVpZy/4v3Y2+yk9yj+SbFD3t/8Fi5agM7T22g/2vPdZ/qlHd5+zpQY8Z4gfOxiZQeKUBzUmb1ltWsK1rXYr2S2hK3lBcUFITVaqW4uJjg4GCPT+XrTxwOB8XFxVitVoKCVD26I2cypmdcT2rqmxu42KhYQoJDsNltrC5ezWnJp3msfIvFQmxsLPv27QPAarViMpk8Vp4/MQyD2tpa9u3bR2xsrBKiIiId5EzGNDY10tjUCDT3Es3fm8/6PethuGfKVRvYcWoDA0tbr/tUp7zHF9eButr0gh37mx+NcN6dd/aM2bZ7G02OJixm9/6xNZlMpKSkkJOTQ15enlv3HQjMZjO9evXSH7puypmMSYpLcr1nNplJjk8mf28+q/d6NhkDkJzcfPfR2XBK+8TGxrrOoYiItJ/zMaUDpSQ03xT8pfgXj5atNrBz1Ab6hzW71/Cfdf/hvAHncXa/szu1L9Up7/H2daCSMV6wo6w5GZMQkwBAekI6FrOF6vpq1u9fz8geI91eZkhICFlZWXpUqQNCQkLUm6ibqrPXsXP/TqC5Z8yBkns0J2Ocs0t4kjMhmpSUhN1u93h5/iQ4OFh3A0VEOqHOXsfW0q2HvO8cPy23OBfDMDx2saE2sOPUBvqHoqoiTp97OtW2ap5b9RxLrlnCab06fiNQdcp7vH0dqGSMhxmGcUjPmKCgIFITUinYV8C3hd96JBkDzZm9sLAwj+xbpCvaUroFAwNrmNU1k5JTSrx37ggeyGKx6EuViIh4Vfa+bByGA2uoldqGX8eZcPaMKaksYU/DHlLCUjwah9pACVRPrXiKals1AA7DwZ1f38nqa1Z3er+qU/5Ht/89rLi2mGpbNSZM9Iju4Xo/IykDgB+LPD+ivUigOHAmpYPv+DkHLswpzfF6XCIiIt7iHC8mNSG1xfuR4ZFEW6MB+H7P916PSyQQGIbB/M3zAbjo1IswYWJN3hp+2e+9m4HSfSgZ42HOR5RiImMIDvp1VHRnMiZ7t+cfmRAJFK1Na+3k7Bmzd/9eauyenVFJRETEV9bv+b9kTI/UQ5Y5J5NYs3eNV2MSCRRrdq+hoLKAkOAQxg4dS9/UvgC8tPElH0cmXZGSMR7mfETJOV6MU0bP5mTM9r3bcTgcmE1mRqWOYlTqKMwm/beIdMTPJT8D0DO+JyaTiYykDDKSMjCZTMRExhAWEobDcLCmRF9CRUTEPzl7xqQkpLRoB53vAWzat8ln8Yn4s5WFKwHon9af4KBgRmSNAOCzrZ+51tF1nzhpzBgPO3jwXqeU+BSCLEHU2epYX7qe4xKPY9UNq3wRoojfOLBnTEhQCL+/7Pctlqf0SCFndw4/7fmJU1NO9UWIIiIiHmMYhisZ0zu5N2OGjGmx3NkzZmvxoQP8ikjnOWcyS09MB+DY3scCsHX3VkrrS+kR1oPw4HBd9wmgnjEed/DgvU4Wi4W0hDQAvtn1jdfjEvE3tiYb20q3Ac09Y1rjHDdmY/FGr8UlIiLiLbnluVQ2VGIxW0iKSzpkuTMZk1+Sj2EY3g5PxO85k6Fpic3XeT1iehAfHU+To4mPcj7yYWTSFSkZ42GHe0wJfn1USYP4inTettJtNBlNhIWEERMR0+o6zrFkvDmjkoiIiLc4LwST45MJshzaAb5nfE9MmKiqqyKnWgPai7hTo6ORjXubb/g5b7qbTCYGZAwA4PMdn/ssNumalIzxsO1l2wFIiG0lGfN/g/hu2L2BWnstmc9kkvlMJrX22kPWFZEj21Tc/Py7c7wYm93GQ/9+iIf+/RA2uw349Y7gzpKdPotTRETEU5yD96YlprXaDoYGh7p6a2tGJRH32lKyhYamBkKDQ4mPiXe970zGfJ/XXOd03SdOSsZ4UFVDFftq9gGHPqYE0KtnL6B5EN8mRxN5FXnkVeSp26hIBzjHi3HOmmRgsL9qP/ur9mPQXKecyZh95fuotanxExER/3LgtNattYPwa1u4du9an8Qo4q+c48WkJaS1GJh3QHpzMqawpJDcqlwMw9B1nwBKxnjUzv3Nd9+tYVasodZDlifFJRESFEKDvYENJRu8HZ6IX3EmYw43XgxAZHgkEWERGBj8tO8nb4UmIiLiFQcmYw7HmYzRjEoi7uVMxqQmtqx/kdZIV51csHOBt8OSLkzJGA860ngxABazxTW407e7vvVaXCL+yPmYknNcmNaYTCbXIL5KxoiIiD+pbKh03Qh0jlfRGmcyZlvJNq/EJRIoXIP3tlL/stKzAPgq5yuvxiRdm5IxHnS4aa0P5BzE96fdujAU6Sh7k52tpc3TdDqTLYfjfIxp/b71Ho9LRETEW5zjxcRGxhIRHnHY9ZzJmIKSApocTV6JTcTfGYbB2j3Nj/45b7YfyJmM+TFPE7fIr5SM8aDDTWt9oF5JzePGZO/J9kpMIv5oe9l2Gh2NhAaHEhsZe8R1nckazagkIiL+xPmIRHpi+hHXS4xNxGK20GBvYNN+Paok4g67q3dTUlvSohf2gfql9cNkMrG3fC/bytUrTZopGeNBR3tMCX6dUSmnWNMLinTUwTMpHYnzjmBOieqciIj4jyPdlT+QxWJxja/2w54fPB6XSCBwJkN7xvUkJCjkkOXhoeGum/CLdi7yZmjShQX5OgB/1pbHlBLjEgkNDqXB3kBmXCbWIOtRLyZFpKWDZ1ICMGFyjR9j4tc65XyvuLKYyoZKokOjvRipiIiIZxycjDlcOwjNNyaKSopYu28tDPZunCL+yDWT0hGSoVkZWeTtzWNp3lIGJQ4C0HVfgFMyxkPsTXbyK/KBIz+mZDaZSU9KZ8euHdww5gbuO+E+b4Uo4jcO7BnjFBIcwr1X3nvIuhHhEURbo6msreTHvT9yVq+zvBaniIiIJ9iabK7ZkZwXg4drB+HXXqKb9232ToAifu5Ig/c6ZaVn8eVPX7KmcA2ld5UqESN6TMlT8iryaDKaCLYEEx1x5Dvvzi5r3+/63huhifgd592IlISUI6/4f5zP8q7au8pTIYmIiHjNpn2bsDvsWEOtxEfFH3V9ZzJmR8kOT4cmEhBc01ofYVr5Pql9sJgt7K/ez/oSTSQhSsZ4zPay7UBzrxiz6cinOTMlE4DVBas9HZaI36m2VbOttHkgtKMNWujkTMZs2LfBY3GJiIh4y4GPKLXlbrszGbO7bDe1jbUejU3E3x34XfRIjymFBIXQJ6UPAB/u+NArsUnXpmSMhzjHi0mMTTzquv3T+gNQVFZE1rNZ1NrVKIq01fo96zEwiI6IJsoa5XrfZrfx2BuP8dgbj2Gz21ps4xxbRjMqiYiIP1i7+9DBe4/UDsZFxREaHEqTo4nv96lntkhnbNy7sdXvoq3Jymie4vrJJU8y+PnBuu4LcErGeIizZ8yRBu91igiPcHVp2162HcMwPBqbiD9x3g08uFeMgcGesj3sKduDQcs65ewZk1ua65UYRUREPOnHoh+BX2fphCO3gyaTifSk5nbzu8LvvBeoiB9yTSufcPQe2gMyBgBQa6tlc/FmXfcFOCVjPGT7/v9LxsQePRkDkJWW5clwRPyW825gWx9Rgl9nVCqvLqegpsAjcYmIiHhDfWO9qy3MTM5s83a9e/YG4KeinzwRlkjAcA7e25axC3sl9Wp16msJTErGeEh7esYA9Evv53rtcDg8EpOIPzp4Ks+2CA8Np0d08yxnXxd87ZG4REREvGF10WrsDjtR1ijio48+eK9Tr57NE0hs3L3RU6GJBIS2TGvtZLFYGNBrgIcjku5CyRgPaHI0sXP/TqDtyZi+KX1dr/Xsrkjb2JpsZO/LBtqXjIFfB85W92wREenOvi9s/t6YmZzZrqlyeyc394wpKCmg0lbpkdhE/F2To4kNe5snhGjrd9EhfYZ4MiTpRpSM8YBdVbuwNdmwmC3ERsW2aZvg4GDX63d+ecdDkYn4l7W71zZP5RlmdfV0aStnV+7VhZrFTEREuq+VhSuB9j2iBBAbGUuUNQqHw8HSXUvdH5hIANhWto26xjpCgkJIjDn6xC0Ax/Y+1vV6zb41ngpNugElYzzA+YhSfHQ8FrOl3dt/sf0Ld4ck4pdWFKwAoE9yn3bdDYRfe8Zs2b2FJkeTu0MTERHxOMMwfk3G/F+71lYmk8n1qNJ3u9RLVKQjnOM1pSSkYDa37dI6LDTM9fr1n1/3SFzSPSgZ4wHtmdbayYSJ2MhYAPKL8/mlTFPuihyN8wton5Q+hywzYSIuKo64qDhMHJqoSe2RSnBQMHW2On7Y94PHYxUREXG3Hft3UFRVhMVscc2O5HS0dhB+HcT3h0K1gyIdsWZ3c8+WjMSMo6z5KxMmrGFWABb9vEgzKgUwJWM8YGvpVqDt48UAhASH8OBvHqRfavNAvi9lv+SJ0ET8hmEYLC9YDrR+NzAkOITZ185m9rWzCQk+dNR6i8XimgL0i3z1RhMRke7n65zmQeh7J/cmNDi0xbKjtYMAfVObxyzcULhBE0iIdIBzIomDk6FHEhIcwp+u/hPBQcHs3r9bk0kEMCVjPGBzyWYAesb3bPe2I48ZCcD8DfOVJRU5goLKAoqqijCbzGT0bPvdiAM5e9R8l6fu2SIi0v18lfMVAAMyOjY7S+/k3gRZgqioqWB1icZQE2kPwzBcPWPaO5FEeGg4w/oNA+Cfa//p9tike1AyxgM27dsEQHJ8cru3PW7AcQRZgigoLeDbXd+6OzQRv7E8v7lXTGpC6iF3A9vK+eV1dd5q3REUEZFuxWE4XD1jOpqMCQ4Kdg38uyhnkbtCEwkI+RX57K/fj8VsISU+pd3bjz52NACfb/6cGluNu8OTbkDJGDertlWTV5EHtC8ZY2u08dTbT/GvD//F4D6DAXh2zbMeiVHEH7juBqa3/gXUWaeeevspbI22Vtfpk9qHIEsQ5TXlrCpe5bFYRURE3C17XzYltSWEBIe4BuI9UFvaQYD+6f0B+DZXNwFF2sPZKyY5PpmgoKA2b+esm4tWLCIuKo46Wx3/s+5/PBWmdGFKxrjZLyXNA+9GhkcSER7R5u0Mw6BgXwEF+wo4/pjjAfhk0yeU15d7IkyRbs0wDL7Y2TzOS1ZG1mHXcdapwz3yFxIU4npe/qMdH3kkVhEREU/4bNtnAPRL7UeQ5dALwba0gwD905qTMWsL1qqXqEg7uAbvTWrf4/IH1s2xQ8cC8PwPz+MwVP8CjZIxbra5uHm8mJQe7e+q5pSVkUVSXBL1tnqe/OlJd4Um4je2l20nvyIfi9lC37S+ndqXs2v3kp1L3BGaiIiIVyzYsgCAIX2HdGo/vZN7ExwUTEVNBd/t0RhqIm31Y9GPQPsG7z3Y6IGjCQsJY1fZLt7d+q67QpNuQskYN3MmYzoyXoyT2WRm3HHjAHhx1Ys0OhrdEJmI//hy55dA8yxKHR0vxumYXscAsC5/HTV2Pa8rIiJd397qvXxf+D2A6/H2jgoOCnbdmJi/ZX6nYxMJBE2OJlcdbG1Wz7YKDQ1lzOAxADz8zcOawCXAKBnjZhv2bgA6NpPSgUYNHEVEWATFlcX8a/2/3BGaiN/4fMfnAByTcUyn95WWmEZMRAwN9gbmb9eXUBER6foWbV2EgUFGUgaxkbGd3p8zofPFti86vS+RQLC5eDOVDZWEBod26okIgHEjxxEcFMzPu3/m7V/edlOE0h0oGeNGhmGwenfztIDpiR3vrgbNY1mcMfIMAP72zd+wNR1+4DWRQFJjq+G/O/4LdL5rNjT3RBvabygA839WMkZERLq+D375AIChfYe6ZX+DMgcBsG33NgqqC9yyTxF/tqJgBdD8mJ/FbOnUvmIiYjht+GkA3Pf1fRo7JoAoGeNGRVVF7KvZh8lkIjUhtdP7O234aURbo9lbsZe/r/q7GyIU6f4+3/459Y319Iju0ek7EU7D+g0D4Lvt39HYpMcCRUSk69pbvZfF2xcDMDxruFv2GRsZS3piOgYGr//8ulv2KeLPVhQ2J2P6pPRxy/7GHz+esJAwcktyeXrV027Zp3R9Ssa4kbNXTHJ8MiHBIe3ePiIsgoiwX2dgCgkO4ZzR5wDw2DePUVJT4p5ARbqxD3/5EICh/YZiMpmOuO7Bdepw+qX2wxpqpaquio9yPnJHmCIiIh7xzqZ3aDKa6NWzFz3jjvxYfFvbQfj1xsS72RpEVORoluUvA5p7xnTEwXUzIiyCSSdNAuDBrx9kb/XezgcpXV7bJ0SXo+ro9GYAocGh/O23fzvk/TGDx7B843J2l+7mxv/eyPtT3u90nCLdVZ29joVbFwK/fmk8nMPVqdZYLBaG9x/Oyk0reXndy0zrP63TsYqIiHjC6xuae66cMPCEI67XnnYQYOQxI/n0+0/ZULCBvMo8ekd37CJTxN/t3L+Tnft3Yjab6Zva/lk9D1c3Txl2Cqt+XkVhcSHXfnYtn17yqTvClS5MPWPc6Kein4DOTW92MIvFwqXjLwXggw0f8PnOz922b5HuZsGWBVQ2VBIXFdepketbM3rQaACWbFlCRX2FW/ctIiLiDmt3r+Wnop8wm80cl3WcW/edEJNAZnImhmFo8giRI/hiR/NA15nJmYSFhLltvxZz83WfyWTis82f8fL6l922b+malIxxE4fhYHnBcgB693TvnYQ+KX04ecjJAFz54ZWU1pa6df8i3cXcdXMBGH3saMwm9/75ykzOJCkuCVujjX9t1JdQERHpep798VkARvQfQaQ10u37P/6Y4wF4e8PbmmJX5DC+2NmcjHHHrJ4H69WzFxNGTwDgtk9vY1vpNreXIV2HkjFukr0vm/L6ckKCQzrUM8bWaOPZ95/l2fefxdZ46MxJF55yIYmxiZRWl3LJh5eogZSAU1hZ6Gr8jtY1G45epw5mMpkYfWxz75iXfnpJdUxERLqU4ppi5m2cB8Cpw0896vrtbQeh+VGl4KBg8kry+Cz3s07FK+KPGh2NfJ3zNQDH9OpYMuZodfPsE86mb2pf6mx1THp7EpUNlZ2KWbouJWPc5Nu8b4HmXiwdmd7MMAx27NrBjl07Wr0IDA0J5epJV2MxW1iyfQl/XPLHTscs0p089+NzOAwH/dP6kxCbcNT1j1anWnPS4JMIDgpm576dLNq5qLMhi4iIuM2zPz5LQ1MDGUkZZCZnHnX9jrSDEWERrt4xT/7wZGfCFfFL3+V9x/76/VjDrGT0bP84oXD0umkxW7hqwlVER0Szo2QHF7x7AY0Ozfbpj5SMcZNv8r4BoH9af4+VkZ6YziVnXALA37/7Oy+tfcljZYl0JdW2al5Y/QIA444b57FyIsMjOfHYEwH42/K2D3ooIiLiSWV1ZTzz/TMAnHn8mUedTbAzTht+GgDfbP2G7eXbPVaOSHf0/s/Nk6kM7Tu0Qzfg2yo2Kpbrz7ue4KBgvtn5DVd8dAVNjiaPlSe+oWSMG9ib7Hy580sA+qd7LhkDzXfuzzz+TABuXnQz83+e79HyRLqCV9a8Qnl9OQkxCQzqM8ijZZ1+3OmYMPFDzg98W/itR8sSERFpi6dWPEWVrYrUhFSG9T/ybIKdlZqQyoCMATgMB39cqp7YIk4Ow8EHP38AHH1WT3fo1bMXMyfMxGwyM3/jfGYsmKGEjJ9RMsYNlhcsp7y+nIiwCLcP3tuac08+l1HHjKLJ0cTl713OO5vf8XiZIr5Sbavm0WWPAnDGyDPcPnDvwRJjExk1cBQAt//3do0dIyIiPpWzP4env38agEknTvJ4Owgw8cSJAHy08SO2lm31eHki3cF3ed+xu3o3YSFhHhm8tzXD+g3jqolXYTaZeWfDO1w4/0Jq7bVeKVs8T8kYN1i0tXlsiUGZgzCbPX9KzSYzV5x9RYuEzD9++IfHyxXxhWe+f4a9NXtJiEngpEEneaXMyWMmE2QJYl3BOt7d8q5XyhQREWnN7Z/fTn1jPVnpWQzpO8QrZfZN7cvAXgNxOBzc+sWtXilTpKt7aU3zEBHHZR1HUFCQ18odkTWCqyZehcVs4ZNfPuHkuSezp3qP18oXz1EyppMchsP17ODgPoO9Vq7Z3JyQOWnwSRiGwV2f38X1i66nobHBazGIeFp+RT6PL38cgHPHnIvF4rlncw8UFxXnemb+d5/+jqqGKq+UKyIicqB3st9h4daFmM1mpo6b6tGxYg523snnYTKZ+OKXL1i4faHXyhXpisrqynhv83sAjBkyxuvlj8gawS1TbsEaZmV90XqGzBnC4u2LvR6HuJeSMZ30Xd535JbnEhocyqDMzo1lERIUQkhQSJvXN5vNTB8/nQvGXoAJE6+sfoVRL4/il5JfOhWHSFdgGAY3f3Iz1bZq+qT0YXjW8Hbvo7116kATRk8gPjqe4qpiblp8U4f2ISIi0lF55XncuOhGAM46/iyS45PbvY/OtIPpSemcOqx5Cu2bPrmJGltNh/Yj4g9eXP0iDU0NpCakkpHUsVmUDtSRutkvrR93XnonKT1SKK0pZeKbE7l98e2qm92YyXDDgAiVlZXExMRQUVFBdHS0O+LqNn6z4Df8e92/OWnQSVx21mU+i2NTzibmfTGPmvoaQoNC+csZf+GOE+8g2BLss5j8UVf8rHfFmNzhpdUv8dtFvyXIHMTdV9zdoS+hnbW1YCvPf/g8AK9NfY2ZQ2Z6PQb5lb9+1kWk4/z170KtvZZxc8exqmgVvZN7c9vU27zWO/RA9Q31PPrGo1TUVDB16FTeu/g9r8cgzbrqZ72rxuVOtfZaMp/JpLi2mCvPvpJRx47yaTy2RhsLvlvA8o3LAUiNTmXO5Dmcf8z5Po3L33nis66eMZ2wr2Yfb2W/BcDoQaN9GsvgPoP5wxV/YEDGABoaG/jDF39g2AvDWJKzxKdxiXTEmt1r+N1nvwNg0kmTfJKIARiQMYDxI8cD8NuPf8v6Pet9EoeIiASOJkcTV35wJauKVmENszJzwkyfJGIAwkLDmDlhJiaTifc3vs9zq5/zSRwivvTcj89RXFtMj+geHHfMcb4Oh5CgEC454xJuOP8G4qLiKKos4oK3L+D0/5zO94Xf+zo8aQclYzrh2R+epb6xnl49e9EnpY+vwyEmMoabLrqJy868jIiwCH4p/oXxr43njNfOYEXBCl+HJ9ImO/fv5Lx559HQ1MDgzMGccfwZPo3n3JPPJSs9i3p7PePfGM/WEs0qISIintHoaOSqj67iw18+JMgcxPXnXU9CTIJPY+qf3p9JJ04C4PZPbuejLR/5NB4RbyqqKuIv3/4FaH6E3WL2TWK0NYP7DObeK+9l/PHjsZgtfJv7LWNeGcOkeZNYmrtUM4J2A0rGdFBRVRHP/PAMAGcef2anB1SzN9p58eMXefHjF7E32ju8H7PJzEmDT+K+mfdxytBTsJgtLM1ZythXxzL232N5d9O72Js6vn8RT9q5fydnv342u6t3k9IjhRnnzOjwFJ7uqlMWs4VrJl9DakIqZTVlnPKfU1i3e12H9yciItKayoZKpr47lXkb52E2m5k5cSZ9U/t2eH/uagcBzj7hbE449gQchoNL51/qmrxCxJ8dOH5h7+Tebns8yZ11MzQ4lAvGXsD9V93PiYNOxGQy8fm2zznjP2cwdM5QXl7zMhX1FW6JW9xPyZgOuvu/d7sq5tB+Qzu9P4fhYHPuZjbnbsZhODq9v4jwCKadMY37r7qfMYPHYDabWZG/gunvTSfjmQzu+e89rNm9RhlT6TK+L/yek14+iZ37d9Ijugc3XXQT1jBrh/fnzjoVERbBzRfdTM+4nhRXF3Pyv0/WF1EREXGbjXs3cuLLJ/Lxlo8JMgdx3eTrGN6//QPXH8id7aDJZOKy8ZcxtO9Q7E12Lp1/KU+ufFLfI8Wv/c8P/+Oqk5eccUmHbxAezN3XfQDx0fFcftblzLpyFmOHjiUkKIRN+zZxw8Ib6PlUTy6dfykLtyykzl7nlvLEPZSM6YDX1r/GW9lvYTKZmHr6VLdVTE+Ij45n+pnT+fM1f2bC6AlEWaPYW72XJ1c+yfEvHs+A5wZw75f3sjR3KbYmm6/DlQDU6Gjkr9/+lVP/fSrFtcWkJaRx27TbiImI8XVoLURZo7j9ktvJSs+izl7HtHenMfOjmZTVlfk6NBER6aZq7bU88PUDjHxxJL+U/EJMRAy3TruVwX0H+zq0Q1gszT1FTxp0Eg7DwT3/vYfJb01mV+UuX4cm4nbvbnqXuxbfBcAFp1xAemK6jyNqm6S4JC454xJm/2Y2F4y9gJ5xPWlobGD+5vlc8PYFxD8Rz3nzzmPOT3PYuX+nEqo+FuTrALqb/+74LzcsvAGAiaMn0qtnLx9H1DaxkbFMOmkSZ59wNptyNrFm6xo252xme9l2Hl/+OI8vf5yIkAjG9R7HKb1O4aT0kxiVOorIkEhfhy5+yjAMFm5dyL1f3svPJT8DMKL/CC476zLCQsJ8HF3rrGFWbrzwRj5d+SlL1izhjfVvsOCXBdx3yn3ccsItRIf65ywCIiLiXpUNlby0+iWeWPEE+2r2ATCk7xCmj59OlDXKx9EdnsVsYfqZ00lNTOXjZR/z+bbP6f9cf2aNncVtJ95GbFisr0MU6RTDMHjux+e4c/GdGBicPORkTh1+qq/DareIsAjGHz+eM0aeQWFxIT/98hPrt6+nvLqcT7Z9wifbPgEgOTKZU3udyim9TuHEtBMZkjSEiJAIH0cfOJSMaSPDMJjz0xzuWHwHtiYbw/oN4+wTzvZ1WO0WZAlieP/hDO8/nHpbPZtyNvFz3s/8kvcL1XXVLSqn2WRmcNJghiYNZUjiEAYnDWZw4mAyYzO71OBV0r3sq9nHu5ve5Z+r/skvJb8AzQ3GlNOmcPwxx3d6/CVPC7IEccEpFzCk7xDeW/oeRSVFzPpqFn/97q/MHDaTK4ZcwckZJ6uOiIhIC/WN9Xyb9y3zNs5j/ub51NprAegR3YMLT7mQYf2H+TjCtjGZTJw2/DT6p/Vn/pL55OzOYfbS2Ty+/HGuGXENVw69kpPST+ry7bnIwXL25/C7z37nuhY6cdCJTBs3rVt/lk0mExlJGWQkZXDRqRexu3Q3m3I2sTl3M/l789lTvYf5m+czf/P85vUx0T++PyOSRzCs5zCy4rPoH9+ffvH9lGz1ACVjjqLJ0cRn2z/jke8eYWXhSqD57v2VE67EbO66jye1RVhIGMcfczzHH3M8DsNBUXER2wq3kbcnj9w9uZRXl7Nx70Y27t3YYrsgcxAZMRn0ie1DZkwmmbGZpEen0zOyJ0kRSfSMaP43NCjUR0cmXUlFfQU/7vqR7wu/54udX7C8YLnr+djQ4FBOGXYKZ406i/DQcB9H2j59U/ty92V389OWn/jqp6/Yu38vc36aw5yf5pAQkcA5fc5hbK+xjEkfw7GJxxIW1DV7+4iIiGcU1xSzevdqfir6iZWFK1mau9SVgIHmxwnOHHkmowaO8tnU1Z2RmpDKbdNuY+22tXyx6gt2l+7m+VXP8/yq50mLTuPsvmczrvc4Tko/iX7x/Qgy67JDuh57k52vc77m9Q2v83b22zQZTVjMFs47+TzGHTeuWydiDmYymUhNSCU1IZWzTzgbW6ONgr0F7Czayc6inRQWF1JVW8W2sm1sK9vmStA4xYfH0z+uP2nRaaRGpbp+UiJTSIlKIcGaQFxYHOHB3es7vS/pr+L/aXQ0UtlQSUltCTvKdrCtbBsrClawJHeJq/toSFAIk0+azOnHne5XFROae8GkJ6WTnvTr85Dl1eUU7itkd+lu9pTtYU/pHvbu30tjUyM5+3PI2Z9zxH3GhMaQYE0gJiyGmNAYokOjiQmLITrk//4NjSYiOILw4HDCgsIID2r+Nywo7JD3gi3BBJmDXD8Wk8X12mwy+93/R1fW5GiirrGOOnsddY11VNuqKaktoaS2hOKaYopri8krz2P7/u1sK93GrqpDnyVPT0znxEEncsLAEwgL7b5JCrPZzOhjR3PCwBPYXridHzb/wKbcTZTUlDAvex7zsuc1r2cy0zu2N8cmHEuv6F4kRyaTHJlMSlQKsWGxRIVEERUa5fo3PChcn2kRkS7CMAwamhqos9dR31hPXWMdtfZayurKKK0tpbSulNLaUopri8ktzyWnPIed+3dSXl9+yL5iImIYlDmI0YNGk5mc2e3/1ptMJkYOGMlxWcextWArP/78I9k7s9lVuYu56+Yyd91cAEItoQxIGMAxPY4hLSqNtKjmiznn98To0GjXd0VrsFW9S8VtDMOgvrGeKlsVZXVl5Ffkk1+Rz/ay7awqWsWqXauoslW51j8m4ximnDaF5B7JPozaO0KCQuiX1o9+af1c71XVVrGreBdFJUXsLt1NaUUpJRUlVNZWUlZXxo91P0LRkfcbFhRGXFgc8eHxxIfHExceR1RIFBHBEUSERGANtrpeH/iv85ovxBLS6k+wueUyi9mCCVO3/jvqlmSMc+CfM144A0u4BQPjkMGADMPAwGix/oHrHbjMHeu1WPcI29gddirrK1vcqTiYNczKyAEjOXX4qcRExNBQ1dCe09MmDY0NUN/8ur6qHiPI94MphRFG/4T+9E/o73rPYTiorKlkf9V+9lftp6yqjP1V+6msqaSmrobqumqq66pxOBxU1FdQUeGdqdQsZkvLZI3ZgsVkcSVqnBXVbDIf+vr/fj/w3wO3O/C10dDy89MVOGMZ98I4zGHNvbUchsP1eT/wtWH83++tvAZw0Pp29iZ78xdQex2NjsZ2xxgXHUfvnr3p1bMXgzIHERsZ27ygAeob6t1yHg7m7TqVEZNBxpgMppw4hZzdOeTuziV3Ty4F+wposDWQU5dDzu4jJzAPZDFbXA1PkCXI1QAFm4Mxm8yHfLYP9++Bn3mgxbLupqmuCeha9U9EfMv59+D0OadjDjO3aL+cbZzDcBzy3oFt3YHrOddxGA5sTTbqG+ub25MOSohLID0hnbTENPql9SMlPsX199cT3ycP5O12sHdsb3qP6c2Foy9k566d5OzOIacoh6LSIhrqG9hYs5GNeRuPviOa2yrXBdgBF2dBpqDDfp9r7bubc5IN5/ue5sm2tau2gQdeB5rDzS2vxQ669jr4mq4zy13rHGZ5o6ORGlsN1bbqo85YFGmNZHCfwZxw7AmkJzTfmK6v9Mz3U+ia131OwQSTGZdJZlwmZP36foO9gdLKUsoqy6isqaSqtorKmkoqaytdv9c21IIB9dSzu3o3u9ntlZiddd157Wc2mbGYf3198HsHrnfg35Kj/euob/4cubMOmgw37G3nzp3069fv6CuK+IkdO3bQt29fX4cBqP5J4OlK9U9EfEttoASartYGqg5KoHFnHXRLz5j4+HgA8vPziYnx7nS0lZWVZGRkUFBQQHS0d2cyUdneL9vX5VdUVNCrVy/XZ74rUP0LvM9hoJbdFeufiPiW2kC1gYFSdldtA1UHVXYglA2eqYNuScY4B7KNiYnxyYkBiI6OVtkBVLavy+9Kgzer/gXu5zBQy+5K9U9EfEttoNrAQCu7q7WBqoMqO5DKBvfWwa5Vm0VERERERERE/JySMSIiIiIiIiIiXuSWZExoaCizZ88mNDTUHbtT2Sq7y5bv62NvTaCeD1//XwTqsQdq2SLSNQXq3yRf/z0M1GMP1LKPJFDPicoOrLI9Vb5bZlMSEREREREREZG20WNKIiIiIiIiIiJepGSMiIiIiIiIiIgXKRkjIiIiIiIiIuJFSsaIiIiIiIiIiHhRu5MxixYt4phjjiErK4uXX375kOU//vgjgwcPpn///jz88MNuCRKgoKCAcePGMWjQIIYNG8b8+fMPWSczM5Nhw4YxYsQIJk+e7LayAYKCghgxYgQjRozg+uuvP2S5p457y5YtrnJHjBhBeHg4H330UYt13H3cU6ZMIS4ujmnTprnea8vx7dixg1GjRtG/f39uuukmOjI29MFl19bWMnnyZAYOHMiQIUN49tlnW91u3LhxDBw40HWeOqK1427LuXXHcbdVoNY/CJw6GKj1r7XyoevVQRHxnUBtA33V/oHaQLWBLakO+vd3UAjcOuiz+me0g91uN7KysozCwkKjsrLS6N+/v1FaWtpinVGjRhnr16837Ha7MWrUKGPjxo3tKeKwioqKjLVr1xqGYRh79+410tLSjOrq6hbr9O7d26iqqnJLeQfr0aPHEZd76rgPVFVVZfTo0cPjx/31118bH3/8sTF16lTXe205vosvvthYuHChYRiGcdFFF7led6bsmpoaY+nSpYZhGEZ1dbUxcOBAY9u2bYdsd/rpp3f6nLd23G05t+447rYI5PpnGIFTBwO1/rVWvmF0rTooIr4TyG1gV2j/DENtYKC3gaqDh+cv30ENI3DroK/qX7t6xjizYmlpaURFRTF58mQWL17sWl5UVERjYyPDhg0jKCiIK664goULF7YvO3QYKSkprkxXUlIS8fHxlJWVuWXfneXJ4z7Qxx9/zJlnnklERITb932gM844g6ioKNfvbTk+wzBYuXIl5557LgBXXXVVh87BwWVbrVZOP/10ACIiIsjKymL37t0dOax2l90W7jrutlD9Ozx/qoOBWv9aK78tvFkHRcR31Aa2zlvtH6gNDPQ2UHWwdf70HRQCtw76qv61KxlTVFREWlqa6/f09HR27drV5uXu8tNPP+FwOMjIyGjxvslk4rTTTmP06NG8//77bi2zsrKS448/nlNOOYVvvvmmxTJvHfe7777L9OnTD3nfk8cNbTu+0tJS4uPjMZlMh12nswoKCtiwYQMjR45sdfkVV1zByJEjef75591W5tHOrTeO2ymQ6x8Ebh0M5PoHXasOiojvBHIb2BXaP1AbGOhtoOpg4H0HhcCug96of0HtWdlo5RkoZ+FtWe4OpaWlXHXVVa0+p7h8+XJSU1MpLCxk/PjxDB8+nP79+7ul3NzcXFJTU8nOzubcc89l48aNREdHA9457srKSpYvX87bb799yDJPHje07fg8fQ7q6+uZPn06Tz75ZKsZ4Xnz5pGamkpZWRkTJ05k8ODBrkxqZxzt3Hrj/76tZflz/YPArYOBXP+ga9VBEfGdQG4Dfd3+gdpAtYGqg4H4HRQCuw56o/61q2dMWlpai2xPYWEhKSkpbV7eWQ0NDUyZMoVZs2Zx8sknH7I8NTUVaM5KnXnmmaxbt85tZTv3PWTIEAYNGsTWrVtdyzx93AALFixgwoQJhIWFHTY2Txw3tO34EhISKCsrc30o3XkODMPg6quvZvLkyS0GVTqQ8xzEx8czdepUVq1a5Zayj3ZuPXncBwvk+nfg/gOtDgZy/Ttw312hDoqI7wRyG+jr9g/UBqoNVB2EwPsOCoFdB71R/9qVjBk9ejTZ2dns2rWLqqoqPv30UyZMmNAiYIvFwoYNG2hsbOStt97i/PPPb1dAh2MYBtdccw3jx49n5syZhyyvqamhqqoKgPLycr799luOPfZYt5S9f/9+GhoagOaTvHnzZvr27eta7snjdjpc1zRPHrdTW47PZDJx0kkn8cknnwDw2muvue0czJo1C6vVygMPPNDq8sbGRkpKSoDmzOnixYsZPHhwp8tty7n15HEfLFDrHwR2HQzU+gddrw6KiO8EahvYFdo/UBuoNlB1EALvOygEbh30Wv1r13C/hmEsWLDAyMrKMvr162e88MILhmEYxqRJk4xdu3YZhmEYK1euNAYNGmT07dvXmD17dnt3f1jfffedYTKZjOHDh7t+NmzY4Cp7x44dxrBhw4xhw4YZQ4YMMebMmeO2spcvX24MGTLEGDZsmDF8+HDjww8/NAzDO8dtGIZRXl5uJCUlGQ0NDa73PHnc55xzjpGQkGCEh4cbaWlpxo8//njY47vuuuuMVatWGYZhGFu3bjVGjhxp9O3b17jhhhuMpqamTpf97bffGoAxaNAg1//7559/3qLs6upqY+TIkcbQoUONQYMGGQ8++KBbjvv7778/7Ll193G3VSDWP8MIrDoYqPWvtfK7Yh0UEd8JxDbQ1+2fYagNVBv4K9VB//4OahiBWwd9Vf9MhuHByehFRERERERERKSFdj2mJCIiIiIiIiIindOu2ZREpOtxOBwUFRURFRWlWWTErxmGQVVVFampqZjNupcgIiIiIt2XkjEi3VxRUREZGRm+DkPEawoKCkhPT/d1GCIiIiIiHaZkjEg3FxUVBTRfoEZHR/s4GhHPqaysJCMjw/WZFxERERHprpSMEenmnI8mRUdHKxnjJoZhUFLbPE1egjVBj391Mfr/EBEREZHuTskYEZGD1NprSXoyCYDqWdVEhET4OCIREREREfEnGgFRRERERERERMSLlIwREREREREREfEiPaYkIn6nqakJu93e4e0bbA30jujd/Lq+AYvD4q7Q5CgsFgtBQUEaF0ZERERE/JqSMSLiV6qrqyksLMQwjA7vw2E4mDN2DgC7C3djNqkToTdZrVZSUlIICQnxdSgiIiIiIh6hZIyI+I2mpiYKCwuxWq0kJiZ2uHdFk6OJupI6ADITMrGY1TPGGwzDwGazUVxcTE5ODllZWZjNSoSJiIiIiP9RMkZE/IbdbscwDBITEwkPD+/wfpocTa6/jmFhYUrGeFF4eDjBwcHk5eVhs9kICwvzdUgiIiIiIm6nZIyI+J3OjjdiMpnoEd7DLfuS9lNvGBERERHxd0rGiIgcxGwy0yeuj6/DEBERERERP6XbjyIiHvDggw9y0UUX+ToMt5k0aRLPP/+8r8MQEREREfELSsaIiN978803iYyMPOTHYrFw5plnHrK+YRg0OZpocjR1alYmTxg/fjzh4eHs37/fq+V+9tln3HLLLV4tU0RERETEXykZIyJ+b8aMGVRXV7f4+fzzzwkNDeWBBx44ZH2H4WDtnrWs3bMWh+HwQcSt27lzJ0uXLsVqtfLmm2/6OhwREREREekgJWNEJOAUFRUxbdo0HnnkEc444wwA8vPzOfvss0lMTCShRwJ3zLyDooIi1zbXXHMN119/PdOmTSMyMpLBgweTnZ3NnDlzSE9PJzEx8ZDHeBobG7nuuuuIjo4mKyuLDz/80LXMbrcza9YsevXqRWJiItOnT6e4uPiIcb/66quMGDGC3/3ud7zyyistlj344IOcf/753HrrrcTGxtKrVy/eeecd1/KGhgZuuukm4uPj6dOnD6+88gomk4nc3FwAxo0bxzPPPONaf926dS0GLz5weVlZGVOmTCE+Pp7Y2FiOP/548vLygOZeSFlZWURFRZGWlsZf/vIXAKqrq7nwwgtJSkoiJiaG0047jfXr1x/xeEVERERE/JWSMSISUGw2G1OnTuWss87ijjvucL3vcDi46667KCgoYGfOTsLCw/jbPX9rse27777L7bffTnl5OaNGjeKCCy5g27Zt7Ny5k3nz5nHnnXeyd+9e1/qff/45o0ePpqysjKeffprLL7+cHTt2APDoo4+yaNEili1bRk5ODiaTiRkzZhw27qamJubOncs111zDVVddxfr161mzZk2LdRYvXszYsWMpLS3lr3/9K9dffz1VVVUA/PWvf+Wnn35i06ZNrFu3rkViqL2efPJJGhsbKSwspLS0lFdeeYWoqChqamq45ppreOWVV6iqqmLTpk1MnDjRdX6vuOIKcnJy2Lt3L8cddxyXXnppl3sMTERERETEG5SMEZGAcuutt9LQ0MBLL73U4v3MzEwmTZpEWFgY0dHRXHvbtaz9YS0Ox6+PKU2ePJlTTz2VoKAgpk+fTl5eHn/5y18ICQnh7LPPJiYmho0bN7rWHzBgADfeeCNBQUGcf/75nHHGGbz11lsAvP766zzwwAP06tWLyMhInn76ab744guKiopozeLFi9m3bx+XX345ffv2ZezYsYf0jhk5ciSXX345FouFmTNnYrPZ2Lp1KwDz5s3j3nvvJSUlhZiYGGbPnt3hcxgcHExpaSnbtm3DYrEwYsQI4uPjXct+/vlnKisriY2N5YQTTgAgOjqa6dOnExERQVhYGA899BBbt2497PGKiIiIiPgzJWNEJGC8+OKLfPDBB3zwwQeEh4e3WFZcXMwVV1xBRkYGcbFx3Dj1Ruw2u6tnCUBycrLrtdVqJSoqCqvV2uK96upq1++9e/duUUbv3r3ZtWsXAIWFhWRmZrqWpaamEhoaSmFhYauxv/LKK0yePJnExEQArr76aubNm0ddXV2r8ZlMJsLDw13xFxUVkZGR4Vreq1evw5ylo7vnnns49dRTufTSS0lOTub222+nrq6OiIgIFi5cyIIFC8jIyOCUU05hyZIlANTV1XHLLbeQmZlJdHS069hLSko6HIeIiIiISHelZIyIBITvv/+eO++8k3feeadFEsRp1qxZ1NbWsmbNGvaX7+eF918A6NRjNM5xVJzy8/NJS0sDID093TVeC8CePXtoaGggPT39kP0UFxezcOFCvvrqK5KTk0lOTubee++lvLycDz74oE2xpKamUlBQ0CKWA0VGRlJbW+v6fffu3YfdV2RkJI8//jhbtmxh5cqVfPXVV67xcs4880w+/fRTSkpKuOSSS5gyZQoOh4OnnnqK1atXs2zZMiorK13HrseURERERCQQKRkjIn5vz549TJ06lYcffrjVqawBKisrsVqtxMbGUlpaysv/eLnT5W7dupWXXnqJxsZGPvnkE77++mumT58OwJVXXskjjzxCQUEB1dXV3HXXXZx11lmkpqYesp/XXnuN+Ph4fvnlF9atW8e6devIzs52jc/SFpdffjlPPPEEe/bsoaKiwjWwrtPIkSP54IMPqKioYN++fTzxxBOH3deiRYvYunUrDoeD6OhogoODCQoKYu/evXz44YdUVVURFBREdHQ0FosFaD6/YWFhxMXFUV1dzX333dfW0ygiIiIi4neUjBERv/fiiy9SVFTE7NmziYyMPOQH4KGHHmL79u3ExcVx2qmncdY5ZwG0mFGovSZOnMj3339PfHw8t99+O2+88QZZWVlAc0+cCRMmMGbMGDIzM7Hb7bzxxhut7ueVV17h5ptvJi0tzdUzJjk5md///vcsXbrUNSjwkTzwwAMMHz6cQYMGMWLECCZPngxAaGgoAHfeeScpKSlkZGQwfvx4V9KoNdu3b2fixIlERUUxaNAgxowZw80334zD4eB//ud/yMjIICYmhn/+85+89957mM1m7rrrLiwWCz179mTIkCGMGTOmvadTRERERMRvmAz1ERfp1iorK4mJiaGiooLo6Ghfh+NT9fX15OTk0KdPH8LCwnwdTpe2YsUKxo0bR319PWZz18rLH+7/UZ91EREREfEXXesbuIiIeMS+fftYsmQJTU1NFBUVcd999zF16tQul4gREREREQkE+hYuIhIAmpqauPPOO4mJiWH48OGkpKTw7LPP+josEREREZGAFOTrAEREupomRxNr96wF4Ljk47CYLT6OqPNSUlJYt26dr8MQERERERHUM0ZERERERERExKuUjBERERERERER8SIlY0REREREREREvEjJGBERERERERERL1IyRkRERERERETEi5SMERFpRUN9A/dcdw894nswevRoX4cjIiIiIiJ+RMkYEZGDmEwmvv/v9xTsLGD3nt38+OOPXi2/oaGBu+++m5SUFCIjIxk6dCi5ubmHXX/BggUMGzaMqKgoMjMzefLJJ9u1XEREREREvCvI1wGIiHQ1ZpOZupI6hhw7hPCwcK+Xf+2111JXV8fq1atJSUlhy5YtxMbGtrru3r17ufTSS3n11Ve54oor2LBhA6effjpDhgxh4sSJR10uIiIiIiLep54xIhIQMjMzefTRRznhhBOIiIhg0qRJlJWVccsttxAbG0tWVhYrVqwA4Pe//z0PP/wwixYtIjIyktmzZzN8+HBee+21FvucNGkSjz32mFvj3LRpEwsWLODVV18lNTUVk8nEwIEDD5uM2bVrF4ZhMGPGDEwmE8OHD+eEE04gOzu7TctFRERERMT7lIwRkYDx1ltv8f7777Nr1y7y8/MZPXo048ePp7S0lMsuu4ybbroJgKeeeor77ruP8847j+rqah566CFmzpzJ66+/7trX3r17+eqrr5gxY0arZTmTPIf7WbZsWavbffPNN/Tt25fHH3+cpKQkBgwYcMTHikaMGMG4ceP4z3/+Q1NTE2vWrGH9+vWcddZZbVouIiIiIiLep2SMiASMW265hV69ehEbG8u5555LQkIC06ZNw2KxcPnll5OdnY3NZqPJ0cTuqt2U15fT5GgCYMaMGXzzzTfs2rULgHnz5nHqqaeSkZHRalnPP/885eXlh/055ZRTWt2urKyM7OxsDMMgPz+fDz/8kH/84x+8+eabra5vNpu56qqruPPOOwkNDWXUqFHcc889jBgxok3LRURERETE+5SMEZGAkZyc7HpttVoP+d0wDGprawEwMFpsm5KSwvjx411Jkddee42rrrrK7TFGRkZisVh4+OGHCQsLY/DgwfzmN79hwYIFra7/9ddfc/PNN/PBBx9gs9nYtm0bb7zxBi+88EKblouIiIiIiPcpGSMi0kbOR5Wys7PZunUrU6dOPey6N910E5GRkYf9+e6771rdbvjw4UDzjE5tsWbNGk488UTGjRuH2WymX79+TJs2jYULF7ZpuYiIiIiIeJ+SMSIibTRlyhTy8vK4++67mTJlCpGRkYddd86cOVRXVx/259RTT211u9NOO42srCweeugh7HY7W7ZsYe7cuVx44YWtrj9mzBhWrVrF8uXLMQyDvLw83n//fY477rg2LRcREREREe9TMkZEpI2sVitTp05l8eLFHnlECcBisfDxxx+zcuVKYmNjmThxIrfffnuLgYIP7FkzduxYnn76aa6//nqio6M5+eSTGTt2LPfff3+blouIiIiIiPeZDMMwjr6aiHRVlZWVxMTEUFFRQXR0tK/D8an6+npycnLo06cPYWFhHd5Pk6OJtXvWAnBc8nFYzBZ3hShtcLj/R33WRURERMRfqGeMiIiIiIiIiIgXBfk6ABGRrsaEiaiQKNdrERERERERd1IyRkTkIGazmWMSjvF1GCIiIiIi4qf0mJKIiIiIiIiIiBcpGSMifkfjkndv+v8TEREREX+nZIyI+A2LpXnWI5vN1qn9NDmaWLdnHev2rKPJ0eSO0KQdamtrAQgODvZxJCIiIiIinqExY0TEbwQFBWG1WikuLiY4OBizuWP55iZHE422RqB5mmVNbe0dhmFQW1vLvn37iI2NdSXXRERERET8jZIxIuI3TCYTKSkp5OTkkJeX1+H9OAwHJRUlAORW52I2qROhN8XGxpKcnOzrMEREREREPEbJGBHxKyEhIWRlZXXqUaVaWy3nfnouAGt+uwZriNVd4clRBAcHq0eMiIiIiPg9JWNExO+YzWbCwsI6vH2TuYm8muaeNaFhoYSFdHxfIiIiIiIiB1PfexEJCPYmO9Pfm07kI5H8/r+/14w9IiIiIiLiM0rGiEhAePbHZ3l307vU2Gt4euXTvPnzm74OSUREREREApSSMSLi9wzD4PlVzwO4Hjn667K/HnZ9s8nMqNRRjEodpcF7RURERETE7XSVISJ+76ein9ixfwehwaHcffndWMwWtuzewg97fmh1/fDgcFbdsIpVN6wiPDjcy9GKiIiIiIi/UzJGRPzeVzlfATAgYwAJMQkc0+sYAF7d9KovwxIRERERkQClZIyI+L2luUsB6J/eH4Bh/YYB8Pkvn/sqJBERERERCWBKxoiIX2t0NLIsfxkAWelZAAzpMwQTJvJL8tlesf2QbWrttWQ+k0nmM5nU2mu9Gq+IiIiIiPg/JWNExK/9XPwzNfYaQoNDSe6RDECkNZL0pHQA5m+ff8g2hmGQV5FHXkWepsAWERERERG3UzJGRPzauj3rAEhPTG8xM9KAjAEAfLHjC1+EJSIiIiIiAUzJGBHxa85kTFpiWov3B/YaCMDqvNU4HA5vhyUiIiIiIgFMyRgR8Wtr96wFDk3G9EnpQ0hQCJW1lXyz+xtfhCYiIiIiIgFKyRgR8VuGYfzaMyahZTImKCiIfmn9APho+0dejkxERERERAKZkjEi4rfyK/LZX78fi9niGrz3QAN7Nz+qtHTnUi9HJiIiIiIigSzI1wGIiHiKs1dMz/ieBFkO/XPnTMZsKtxERUMFMaExAJhMJgYlDnK9FhERERERcSf1jBERv7V+73oA0hPSW12eFJtEXFQcTY4mPtz5oet9a7CVTbdsYtMtm7AGW70Sq4iIiIiIBA4lY0TEb2XvywYgOeHQR5SgudeLs3fMJ9s/8VpcIiIiIiIS2JSMERG/5UzGpMSnHHadY3sfC8CynGVeiUlERERERETJGBHxSw2NDWwt3QpASsLhkzFZ6VmYTWb27N/DptJNANTaaxn8/GAGPz+YWnutV+IVEREREZHAoWSMiPilX0p+ocloIjw0nJiImMOuFx4aTmZKJgDvbHsHaJ4Se3PxZjYXb8YwDG+EKyIiIiIiAUTJGBHxS65HlHqkHHVGJOejSp9u/dTjcYmIiIiIiCgZIyJ+aeO+jQCk9kg96rrD+g0DYF3eOkrqSjwal4iIiIiIiJIxIuKXXDMp9Wh9JqUD9YzvSXJ8Mk2OJuZunuvhyEREREREJNApGSMifsnZMyalx+EH7z3Q8P7DAXj/5/c9FpOIiIiIiAgoGSMifqiyoZL8inygbT1jAEZkjQDgp9yfKK0r9VRoIiIiIiIiBPk6ABERd9u0r3mK6piIGCLCItq0TXJ8Msnxyewp28OLm16kd0xvgKMO/isiIiIiItJe6hkjIn6nvY8oQXPS5aTBJwHwzoZ3yL0jl9w7crEGWz0So4iIiIiIBC4lY0TE72zc2/5kDMCogaOwmC1s37udFUUrPBGaiIiIiIiIkjEi4n/W7FkDQFpiWru2iwyPZGjfoQD8/ce/uz0uERERERERUDJGRPxMk6OJdXvWAZCelN7u7ccOGwvAgvULGDFnBHX2OneGJyIiIiIiomSMiPiXraVbqbXXEhIcQlJsUru375/Wn/SkdAwM1u9dj8NweCBKEREREREJZErGiIhfWbP7/x5RSkjDbG7/nziTycTpw093/V5RX+G22EREREREREDJGBHxM85kTEZSRof3MaTvENfrB1c82NmQREREREREWlAyRkT8SkcH7z3QgT1q5v40l8LKwk7HJSIiIiIi4qRkjIj4jSZHE6uLVgOd6xlzIHuTnZu/uNkt+xIREREREQElY0TEj2zct5EqWxVhIWEkxye7bb+LshexaPsit+1PREREREQCm5IxIuI3lucvB6B3cu8ODd57oIiwCCLCIjh5yMkA3LDoBmpsNZ2OUUREREREJMjXAYiIuMvyguZkTN+Uvp3aT2hwKH/77d8AqLfVszl3M3sq9nDlwiv54OIPMJlMnY5VREREREQCl3rGiIjfcCZj+qT2cds+w0LCmDlhJiaTiY+yP+Kfa/7ptn2LiIiIiEhgUjJGRPxCbnku+RX5mE1mevfs7dZ990vrx7ljzgXgrs/uYmnuUrfuX0REREREAouSMSLiFxZvXww0jxcTGhLaqX3ZGm08+/6zPPv+s9gabQCMP348w/oNw95k57y3z2PTvk2djllERERERAKTkjEi4hcW72hOxhzb+9hO78swDHbs2sGOXTswDAMAs8nMlROupE9KH2oaahj3+jh+Lv6502WJiIiIiEjgUTJGRLo9e5Odr3K+AmBg74EeKyckKITrz7ue5PhkSqpLOHnuyazbvc5j5YmIiIiIiH9SMkZEur3lBcupbKgkIiyC9MR0j5YVER7BrVNvJT0xnfLack6ZewqLti7yaJkiIiIiIuJflIwRkW7vnex3ABjSdwhms+f/rEWGR3LLxbfQL7UfNbYaLnjrAv627G84DIfHyxYRERERke5PyRgR6dYaHY28//P7AByXdZzXyrWGWrl5ys2MGTIGA4MHvnqA8a+Pp7Cy0GsxiIiIiIhI96RkjIh0a1/t/Iri2mIiwiLISs/yatlBliAuPeNSLh1/KcFBwXyT8w2Dnh/ES6tfUi8ZERERERE5LCVjRKRbm7N6DgAjB4zEYrG4bb8hQSGEBIUcdT2TycTJQ07mnsvvoVfPXlQ1VPHbRb/l+JeO5/vC790Wj4iIiIiI+A+T4Zy3VUS6pcrKSmJiYqioqCA6OtrX4XhVQUUBmf+TicNwcO+V95Icn+zTeJqamvhuw3d8/sPn1NvqAZicNZmHxz3M8anH+zQ2fxDIn3URERER8S/qGSMi3daTK57EYTjISs/yeSIGwGKxMO64cdw38z5GHzsak8nEp9s+ZdRLo5jw5gQ+3/65Hl8SERERERH1jBHp7gK1t0BBRQH9n+2PrcnGLVNuYUDGAF+HdIi9+/fyxY9fsHrrapx/avvG9+X/jfp/XD7kclKiUnwcYfcSqJ91EREREfE/SsaIdHOBeoF66fxLmb95Pv3S+nHrxbdiMpnctm97o51/f/pvAK6dfC3BQcGd2l9xeTHLNizjh80/uB5fMpvMjMscx4yhM7jgmAtIsCZ0Om5/F6ifdRERERHxP0G+DkBEpL0++PkD5m+ej9lk5qJTL3JrIgbA8f/Zu/P4qKr7/+PvmclkmewrZCMhJBCSQNjdEFkUBKxKwQ0EUdQvX7/9/rTVLtp+69JvaxerbW39aq1WsYBLXUBEcQdBZFG2ALKEhJCEJSRk3ybJ/f2RZkoggZDMkuX19DGPzNxz7zmfczNyJp8591yjSXty9zied1VkSKRmTZilGRfP0NZ9W7Vl7xblHsvVpzmf6tOcT2U2mTUudpyuSblGM1JmKLN/pswmriIFAAAAeiuSMQB6lL1Fe7XwnYWSpEmjJik+Kt6zAV0AH28fXTbsMl027DKdLDupbfu3aduBbSo8Waiv8r/SV/lf6Wef/UzBvsG6NO5STUiYoPEDxmt09Gj5Wf08HT4AAAAAJyEZA6DH2FO0R1cuuVIV9RUaFDNI0y+e7umQOi0iOEJXjb1KV429SqcqTmnv4b3ak7NH+/P3q6y2TO8ffF/vH3xfUvMlTUMihmhk/5Ea0W+ERvQfobTINMUExjh9VhAAAAAA1yMZA6DbMwxDS3ct1T3v3aOK+gpFh0fr9hm3y8vSO/4JCw0M1aUZl+rSjEvV2NSogqICHSo8pJzCHB06ekgV1RXaW7RXe4v2atmuZY7jbFabUsJSlBKeosFhgzUobJDig+IVFxSnuKA4BfoEerBXAAAAANrTO/6SAdAr1TbUauW+lfr9xt9rc8FmSVJSTJIWzVwkfz9/D0fnGhazRQP6DdCAfgM0ceREGYah8qpyFZwsUEFR86PwZKFOlp1Utb1aO47v0I7jO9qsK8gnSPFB8YoNilWUf5QibZGKsEUowhbR6nmYX5iCfILk6+XLTBsAAADADUjGAPAowzBUZa9SWW2Z8svzlVOao/3F+/XlkS+14cgGVdZXSpK8vbx15ZgrNWXMFFnMFg9H7T4mk0nBAcEKDghWWmKaY3tjY6NKKkp04tQJFZUWqai0SMVlxSqtLFVpZalq62tVXleu3UW7tbtod4faspgsCvIJcjwCfQIdz/2t/vL18pWfl1/zT6tfq+enl/l6+crL7CWrxdr809z8s61tLa9btplNZhJCAAAA6PVIxgA9XMvd6Sf/dbIsfhYZhiFDRquyludnbjdkdOh5W8e399zRns5db6PRqMq6SlXUV7Q67kzBAcEaNXiULsm4REG2INkr7bLL3okz1XF1DXVS8x2oVVtRK8Or/fg8KdAcqMDwQA0KH3RWWa29VuWV5SqrKlNZVZmqaqpUVVelqpoqVddWt3pdW9fc2UY16lTNKZ3SKXd3pRWzySyzySyL2dL809T801zffIepc71fAAAAgJ7AZPCpFujRDh06pEGDzv5jHOitsrOzlZSU5OkwAAAAgE5jZgzQw4WFhUmS8vLyFBwc7Na2y8vLFR8fryNHjigoKKjPtO3p9vtq22VlZRowYIDjPQ8AAAD0VCRjgB7ObG6+dCM4ONgjSQlJCgoK6pNte7r9vtp2y3seAAAA6Kn4RAsAAAAAAOBGJGMAAAAAAADciGQM0MP5+Pjo4Ycflo+PD233kfZp2zO/cwAAAMBZuJsSAAAAAACAGzEzBgAAAAAAwI1IxgAAAAAAALgRyRgAAAAAAAA3IhkDAAAAAADgRiRjgB5k1apVGjJkiFJSUvS3v/3trPLNmzcrPT1dycnJeuyxx5zW7pEjRzRx4kSlpaVp+PDheuONN87aJzExUcOHD9eIESM0Y8YMp7XdwsvLSyNGjNCIESN05513nlXuqr7v27fP0e6IESPk5+end955p9U+zuz7rFmzFBoaqjlz5ji2daRv2dnZGjNmjJKTk7V48WJ1Zm32M9uurq7WjBkzlJqaqoyMDD399NNtHjdx4kSlpqY6zlFntdX3jpxbZ/QdAAAAcCsDQI9gt9uNlJQUIz8/3ygvLzeSk5ON4uLiVvuMGTPG2LFjh2G3240xY8YYu3btckrbhYWFxrZt2wzDMIzjx48bsbGxRmVlZat9EhISjIqKCqe015bw8PBzlruq76erqKgwwsPDXdr3Tz/91Fi5cqUxe/Zsx7aO9O273/2u8e677xqGYRjXX3+943lX2q6qqjI+//xzwzAMo7Ky0khNTTUOHDhw1nFXXHGFU853W33vyLl1Rt8BAAAAd2JmDNBDtMyOiI2NVWBgoGbMmKE1a9Y4ygsLC9XQ0KDhw4fLy8tLc+fO1bvvvuuUtqOjox0zHqKiohQWFqaSkhKn1O0Mruz76VauXKkpU6bI39/f6XW3mDRpkgIDAx2vO9I3wzC0ceNGzZw5U5K0YMGCTvX/zLZtNpuuuOIKSZK/v79SUlJ09OjRznSrU+13hLP6DgAAALgTyRighygsLFRsbKzjdVxcnAoKCjpc7ixbt25VU1OT4uPjW203mUyaMGGCxo0bpzfffNPp7ZaXl2v06NEaP3681q5d26rMXX1//fXXddNNN5213ZV970jfiouLFRYWJpPJ1O4+XXXkyBHt3LlTo0aNarN87ty5GjVqlJ555hmntnu+c+uOvgMAAADO5uXpAAB0jNHGOhgtf4B2pNwZiouLtWDBgjbXq9mwYYNiYmKUn5+vyZMnKzMzU8nJyU5rOzc3VzExMcrKytLMmTO1a9cuBQUFSXJP38vLy7Vhwwa9+uqrZ5W5su8d6Zur+19bW6ubbrpJTzzxRJuzgpYtW6aYmBiVlJTo6quvVnp6umNGTVed79y643cPAAAAOBszY4AeIjY2ttU3/vn5+YqOju5weVfV1dVp1qxZevDBB3XppZeeVR4TEyOpeWbClClTtH37dqe1fXr9GRkZSktL0/79+x1lru67JK1YsULTpk2Tr69vu7G5ou8d6VtERIRKSkociQln9t8wDN12222aMWNGq4V1T9fS/7CwMM2ePVtbtmxxStun193euXVl3wEAAABXIRkD9BDjxo1TVlaWCgoKVFFRodWrV2vatGmO8piYGFksFu3cuVMNDQ1avny5vvOd7zilbcMwtHDhQk2ePFnz588/q7yqqkoVFRWSpNLSUq1bt05Dhw51StuSdOrUKdXV1Ulq/mN7z549SkpKcpS7su8t2rtEydV970jfTCaTLr74Yr333nuSpCVLljit/w8++KBsNpt+9rOftVne0NCgkydPSmqeQbNmzRqlp6c7pe2OnFtX9h0AAABwGc+sGwygM1asWGGkpKQYgwYNMp577jnDMAxj+vTpRkFBgWEYhrFx40YjLS3NSEpKMh5++GGntfvFF18YJpPJyMzMdDx27tzpaDs7O9sYPny4MXz4cCMjI8N49tlnnda2YRjGhg0bjIyMDGP48OFGZmam8fbbbxuG4Z6+G4ZhlJaWGlFRUUZdXZ1jm6v6PnXqVCMiIsLw8/MzYmNjjc2bN7fbt0WLFhlbtmwxDMMw9u/fb4waNcpISkoy7rrrLqOxsbHLba9bt86QZKSlpTl+7x988EGrtisrK41Ro0YZw4YNM9LS0oxHHnnEaX3/6quv2j23zu47AAAA4E4mw2jjgnsAAAAAAAC4BJcpAQAAAAAAuBF3UwIAAECP1NTUpMLCQgUGBnInNfRqhmGooqJCMTExMpv5Ph3oDUjGAAAAoEcqLCxUfHy8p8MA3ObIkSOKi4vzdBgAnIBkDAAAAHqkwMBASc1/oAYFBXk4GsB1ysvLFR8f73jPA+j5SMYAAACgR2q5NCkoKIhkTBcZhqGT1SclSRG2CC776qb4vQC9B8kYAAAAoI+rtlcr6okoSVLlg5Xy9/b3cEQA0Lux+hMAAAAAAIAbkYwBAAAAAABwIy5TAgAAAHopwzDU0NCgxsbGc+5XV1+nBP+E5ue1dbI0WdwRHk5jsVjk5eXFujBAH0EyBgAAAOiF6uvrdfToUVVXV5933yajSc9e9qwk6Wj+UZlNTKD3BJvNpujoaHl7e3s6FAAuRjIGAAAA6GWampqUk5Mji8WimJgYeXt7n3PGRWNTo2pO1kiSEiMSZTEzM8adDMNQfX29ioqKlJOTo5SUFJnNJMSA3oxkDAAAANDL1NfXq6mpSfHx8bLZbOfdv7Gp0fGXga+vL8kYD/Dz85PVatXhw4dVX18vX19fT4cEwIVIxgAAAAC9VEdnV5hMJoX7hTuewzOYDQP0HSRjAAAAgD7ObDJrYOhAT4cBAH0GqVcAAAAAZ8nLy1NAQIDKysokSQsXLtR9993XZtn5TJw4UX/4wx+cHmNAQIB27drl9Hq7Kj09XatWrfJ0GAC6MZIxAAAAQB8zceJEmUwmffzxx5KaF5BtbGrUb377G5lMJt13330aMGCAKisrFRwcfNbx5ypzp8rKSg0bNqxTx27atEmTJk1SaGioQkJCNHz4cL300ktOiWv37t265pprnFIXgN6JZAwAAADQBw0ZMkR///vfJTXf2nrbsW167oXnlJqa6uHIXK+iokJXX321brrpJp04cUJFRUV64YUXFBUV5enQAPQRJGMAAACAPujmm2/W+++/77jUKOubLMmQxo0bJ0nKzc2VyWRSaWnpWceeWbZw4ULddddduvnmmxUYGKghQ4bo888/b7PdyspKTZs2TfPmzZPdbtfBgwc1bdo0hYWFadCgQY7Lmex2uyIjI7Vu3bpWx6elpenVV1+V1LzY8Pbt2x1ly5cvV2ZmpoKCgpSQkNDuTJd9+/apqqpKd999t6xWq6xWq8aOHasZM2Y49vnRj36khIQEBQYGKi0tTW+88YajLDMzU0uWLGlV5/Tp0/XrX/9akpSYmKh33nnHUfbRRx/poosuUkhIiKKjo/X444+3GReAvoNkDAAAANAHhYSE6Oqrr9by5cslSStfXanv3PSdTtf36quv6u6771Zpaanmz5+vhQsXnrVPUVGRJk2apPT0dP3jH/+QyWTSNddco8zMTBUWFurtt9/Wb3/7Wy1btkxWq1U33XSTXnnlFcfxW7duVUFBga677rqz6n733Xf1ve99T0899ZRKS0u1ZcsWZWZmthnrkCFDFBISoptvvlkrVqzQsWPHztonMzNTW7ZsUWlpqX7+859r/vz5ysnJkSTNnz+/VVzHjx/XJ598onnz5p1Vz7Zt23TdddfpRz/6kYqKivTtt99q0qRJ5z2fAHo3kjEAAABAH3X77bfr73//u2pqavTp6k81ffb0Ttc1c+ZMTZ48WRaLRbfffrsOHz6s4uJiR/mhQ4d02WWX6YYbbtCTTz4pk8mkTZs26ejRo/rf//1f+fr6avjw4fre977nmNGyYMECvfHGG6qtrZUkvfLKK5ozZ478/PzOav+ZZ57Rvffeq8mTJ8tsNisqKkojR45sM9bAwEB9+eWXCgsL0w9+8APFxMTooosu0jfffOPYZ968eYqKipLFYtHNN9+s1NRUffnll46ytWvXqqCgQJK0bNkyXX755YqPjz+rrb/+9a+6+eabNXv2bFmtVgUHB+viiy/u3EkG0GuQjAEAAAD6qClTpujYsWP63//9Xw0bPUwRURGdrqt///6O5/7+/pKa12Zp8frrr8tsNus///M/Hdvy8/MVExMjb29vx7akpCTl5+dLar5kqn///lq5cqUaGhr06quvasGCBW22f/jwYaWkpLRZFhAQ4Hh88cUXkqTk5GQ9++yzys7OVn5+vpKTk3XttdfKMAxJ0lNPPaX09HQFBwcrJCREWVlZOnnypCQpOjpakydP1tKlSyVJS5Ys6VRcAPoukjEAAABAH2U2m7VgwQL99je/7dIlSh3xox/9SJdccommTZum8vJySVJcXJwKCwtlt9sd++Xk5CguLs7xuuWSoA8++EB+fn6aMGFCm/UnJCTo4MGDbZZVVlY6HpdffvlZ5TExMfrJT36igoIClZSUaP369XrkkUe0ZMkSnTp1SqWlpcrIyHAkak6PKysrS/v379fs2bMvOC4AfRfJGAAAAKAP+/73v6/3P3hfE65qO8nhLGazWS+++KLS09N11VVXqaysTOPGjVO/fv3085//XHV1dcrKytKf//xn3XbbbY7j5s+frw8//FBPPfWUbr31VplMpjbr/4//+A/98Y9/1Nq1a9XU1KQTJ05o27Ztbe777bff6je/+Y1yc3PV1NSk0tJS/fnPf9bgwYMVHh6u8vJyeXl5KTIyUk1NTXrxxReVlZXVqo5Zs2bp8OHDeuCBBzRr1iwFBAS02dZdd92l5cuX6+2331ZDQ4PKysr01VdfdfIsAugtSMYAAAAAfVhYWJiuuuoqRQZGKtQ3VGo71+EUJpNJf/3rXzVy5EhdeeWVqqys1KpVq/T111+rf//+uvbaa/WDH/xAc+fOdRwzYMAAXXrppfr00081f/78duu+/vrr9eSTT+q//uu/FBwcrLFjx2rXrl1t7hsYGKht27bp8ssvV1BQkIYMGaKioiK9++67kqSrr75as2fP1rBhwxQTE6Pdu3frsssua1WHzWbT7NmztWbNmnYvUZKkUaNG6c0339Qvf/lLhYWFaejQoVq7du2FnDYAvZDJOH2uHQAAANBDlJeXKzg4WGVlZQoKCvJ0ON1KbW2tcnJyNHDgQPn6+no6HHRQe7833utA78PMGAAAAAAAADciGQMAAAAAAOBGXp4OAAAAAIBnNTY1atux5sVuR/YfKYvZ4uGIAKB3Y2YMAAAAAACAG5GMAQAAAAAAcCOSMQAAAAAAAG5EMgYAAAAAAMCNSMYAAAAAAAC4EckYAAAAAAAANyIZAwAAAPRxJpNJwT7BCvYJlslkalVWW1urWbNmKSQkROPGjfNQhADQu5CMAQAAAPo4s8mslPAUpYSnyGxq/SfCm2++qX379un48ePavHmzW+Oqq6vTAw88oOjoaAUEBGjYsGHKzc1tc9+lS5cqICCg1cNkMunJJ5+UJNXX12vOnDlKTEyUyWTSO++8476OAMAZSMYAAAAAaFdOTo4GDx4sHx8ft7d9++23Kzs7W19//bUqKir0xhtvKCQkpM19582bp8rKSsdj7dq1MpvNuuGGGxz7jB8/Xq+88ori4uLc1AMAaBvJGAAAAKAPSUxM1OOPP66xY8fK399f06dPV0lJie655x6FhIQoJSVFX375pSTp/vvv12OPPaZVq1YpICBADz/8sDIzM7VkyZJWdU6fPl2//vWvnRrn7t27tWLFCr344ouKiYmRyWRSampqu8mYM73wwguaOnWq4uPjJUne3t667777dPnll8tisTg1VgC4UCRjAAAAgD5m+fLlevPNN1VQUKC8vDyNGzdOiSMT9VHWR7rxphu1ePFiSdLvf/97PfTQQ7rmmmtUWVmpRx99VPPnz9crr7ziqOv48eP65JNPNG/evDbbaknytPdYv359m8etXbtWSUlJ+s1vfqOoqCgNHjxYTzzxRIf6V1NTo2XLlunOO++8wDMDAO5BMgYAAADoY+655x4NGDBAISEhmjlzpsIjwjV55mSZzCbdfPPNysrKUn19fZvHzps3T2vXrlVBQYEkadmyZbr88ssdM1DO9Mwzz6i0tLTdx/jx49s8rqSkRFlZWTIMQ3l5eXr77bf11FNPaenSpeft3z//+U95e3vr2muv7eAZAQD3IhkDAAAA9DH9+/d3PLfZbOrfr/VrwzBUXV3d5rHR0dGaPHmyIymyZMkSLViwwOkxBgQEyGKx6LHHHpOvr6/S09N1xx13aMWKFec99oUXXtCCBQtktVqdHhcAOAPJGAAAAAAXpOVSpaysLO3fv1+zZ89ud9/FixefdZej0x9ffPFFm8dlZmZK0lm32j6fgwcPat26dVq0aNEFHQcA7kQyBgAAAMAFmTVrlg4fPqwHHnhAs2bNUkBAQLv7Pvvss63ucnTm4/LLL2/zuAkTJiglJUWPPvqo7Ha79u3bp5deeknXXXfdOWN74YUXdMkll2jo0KFnldXV1am2tlaGYchut6u2tlaNjY0X1nkAcAKSMQAAAAAuiM1m0+zZs7VmzRqXXKIkSRaLRStXrtTGjRsVEhKiq6++Wvfee2+rhYLPnFnT2Niol19+ud2Fe4cMGSI/Pz/l5eXpxhtvlJ+fX6vFiAHAXUyGYRieDgIAAAC4UOXl5QoODlZZWZmCgoI8HU63Ultbq5ycHA0cOFC+vr7n3b+xqVHbjm2TJI3sP1IWM7d+9oT2fm+814Hex8vTAQAAAADwLJNMCvQOdDwHALgWyRgAAACgjzObzRoSMcTTYQBAn8GaMQAAAAAAAG5EMgYAAADopVgesmfh9wX0HSRjAAAAgF7GarVKkqqrqzu0f2NTo7Yf267tx7arsYlbPXtKy++r5fcHoPdizRgAAACgl7FYLAoJCdGJEyckNd+K2mRqf2HexqZGNdQ3SGq+ow93U3IvwzBUXV2tEydOKCQkRBYL5x/o7UjGAAAAAL1Q//79JcmRkDmXJqNJJ8tOSpJyK3NlNjGB3hNCQkIcvzcAvRvJGAAAAKAXMplMio6OVlRUlOx2+zn3ra6v1szVMyVJ39z9jWzeNneEiNNYrVZmxAB9CMkYAAAAoBezWCzn/SO/0dyow1WHJUk+vj7y9fZ1R2gA0Gcx/xAAAAAAAMCNSMYAAAAAAAC4EZcpAQAAAH2c2WTWmJgxjucAANciGQMAAAD0cX5WP225a4unwwCAPoO0NwAAAAAAgBuRjAEAAAB6EcMw9Mev/qi5b87V57mfezocAEAbSMYAAAAAvcgTXz6h+9bcp+VZy3XVK1dpY8HG8x5Tba9W4h8SlfiHRFXbq90QJQD0bSRjAAAAgF6irLZMv/zil5Ikm69NDU0Nuuv9u2QYxjmPMwxDh8sO63DZ4fPuCwDoOpIxAAAAQC/x+u7XVVZXpqjQKP3olh/Jy+Kl3QW79UHuB54ODQBwGpIxAAAAQC/x1rdvSZLGpY5TSGCIRg8ZLUn649d/9GRYAIAzkIwBAAAAeoHyunJ9cugTSdKw5GGSpIvSLpIkfbb/M5XWlXoqNADAGUjGAAAAAL3AV/lfyd5kV3hQuPqF9pMkDYweqIjgCNXb6/XS3pc8GyAAwIFkDAAAANALbMrfJElK7J/o2GYymZSZnClJ+ufef3oiLABAG0jGAAAAAL3A5sLNkqQB/Qe02j4iZYQkacuhLSqvL2/zWJPJpLTINKVFpslkMrk0TgAAyRgAAACgxzMMwzEzJqFfQquyuMg4hQWFqb6hXv/Y9482j7dZbdp9z27tvme3bFaby+MFgL6OZAwAAADQw+WW5qqoukgWs0WxkbGtykwmkzIH/etSpT1cqgQA3QHJGAAAAKCH21TQPCsmJiJGVi/rWeXDk4dLkjZmb1SVvcqtsQEAzkYyBgAAAOjhHJco9U9oszyhf4JCAkJUa6/V0n1Lzyqvtlcr/Zl0pT+Trmp7tUtjBQCQjAEAAAB6vJaZMWeuF9PCbDI7FvJ9dferZ5UbhqE9RXu0p2iPDMNwWZwAgGYkYwAAAIAerL6xXt8c/UZS+zNjJGlkykhJ0oaDG1RRV+GW2AAAbSMZAwAAAPRgO4/vVF1jnWw+NkWGRLa734B+AxQeFK76hnq98u0rbowQAHAmkjEAAABAD7a5YLOk5mSLyWRqdz+TyeS4VGn57uXuCA0A0A6SMQAAAEAP1rJezID+A867b8ulSpsObVJxTbFL4wIAtI9kDAAAANCDOe6k1M7ivaeLjYxVv9B+sjfa9Zcdf3F1aACAdpCMAQAAAHqoUzWntK94n6SOJWNMJpMuybhEkvTSNy857pxkMpmUEJyghOCEc17qBABwDpIxAAAAQA+1pXCLJCk8KFwBtoAOHTM2day8LF7KKcrRuoJ1kiSb1abc+3KVe1+ubFaby+IFADQjGQMAAAD0UC2XKCX2T+zwMf5+/spMzpQk/X7z710RFgDgPEjGAAAAAD1Uy+K98f3iL+i4y4ZdJkl6f8/7OlJ+xOlxAQDOjWQMAAAA0AMZhqGv8r+SJCVGJ17QsUkxSRoYPVANjQ16ZOMjqrHXaOzzYzX2+bGqsde4IFoAwOlIxgAAAAA9UPapbBXXFMtitiguIu6Cj58yeookadk3y3Sy+qS2Fm7V1sKtajKanB0qAOAMJGMAAACAHmjjkY2SpPioeHl5eV3w8WkD0xQdHq3a+lr9fMPPnR0eAOAcSMYAAAAAPZDjEqULWLz3dGaTWTMvmSlJ+sfX/3BWWACADiAZAwAAAPRAXxV0br2Y06UPTNeg2EFqaGpwUlQAgI4gGQMAAAD0MNX2au04tkOSlNA/odP1mEwmXTf+OmeFBQDoIJIxAAAAQA/z5ZEv1Wg0KiQgRCEBIV2qa0C/Abos4zLH64q6ii5GBwA4H5IxAAAAQA/zac6nkqSUuBSZTKYu13f1RVc76vmPNf/R5foAAOdGMgYAAADoYT7L/UySlBKf4pT6Av0Ddc+se2QymbRy90r95eu/OKVeAEDbSMYAAAAAPUh5Xbm2FGyR1DwzxllS4lI04+IZkqTvv/99fZH3hdPqBgC0RjIGAAAA6EG+OPyFGo1GRQRHKDQw1Kl1TxkzRRkDM2RvtGvm8pnac2KPU+sHADQjGQMAAAD0IO8deE+SNGTAEKfVWd9Qr6fffFp/eesvuvnKm5XQL0EVtRWa9I9JOlB8wGntAACakYwBAAAAegjDMLRy30pJUvrAdKfWm12QreyCbFm9rLrr2rsUFRqlExUndMnfL9Gu47uc1hYAgGQMAAAA0GNsO7ZNBRUF8rZ6O3W9mDMF+AXov2f/t6LDo1VcVazLXrpMH2V/5LL2AKCvIRkDAAAA9BDvfPuOJCl1QKqsXlaXthVoC9T3Zn9PCf2bL1m6eunV+s2Xv5FhGC5tFwD6ApIxAAAAQA9gGIaW7loqSRo+aLhb2vT39df3vvs9jR06Vk1Gk37y0U80delUHa046pb2AaC3IhkDAAAA9AAbjmzQoVOH5GP10bBBw9zWrtXLqrlXztXsK2bLYrbo4+yPNfSZoXp5+8tqMprcFgcA9CYkYwAAAIAe4KXtL0mSMpMz5WP1cWvbJpNJl2dergdueUBxkXEqqy3TwhULddELF2lLwRa3xgIAvQHJGAAAAKCbO1l90nGJ0kVpF7mkDW8vb3l7eZ9zn+jwaH3/xu/rmkuvkbfVW1sLtmrc38Zp1muztPP4TpfEBQC9kclgBS4AAAD0QOXl5QoODlZZWZmCgoI8HY5L/WLtL/Tzz3+uuMg43X/z/TKZTJ4OSWWVZXr3y3e19dutjm3XDL5G919yv65IuKJbxNhb9KX3OtBXkIwBAABAj9RX/kAtrS1V8p+SVVxTrPnT5mv0kNGeDqmVo8VH9eGWD7V9/3YZav7TYkjEEP332P/W3GFzFeoX6uEIe76+8l4H+hKSMQAAAOiR+sofqD/+6Mf67Ze/VVRolH4878eymC2eDqlNx0qOad32ddq6b6vq7fWSJKvZqquTr9a8YfP0nSHfkc1q83CUPVNfea8DfQnJGAAAAPRIfeEP1J3Hd2rs82NV31ivu75zl9IHprukHXuDXX9f/XdJ0u0zbpfVy9rpumrqarTl2y3amLVRR4v/fQtsXy9fTR44WTNTZmpGygwlhiR2New+oy+814G+xsvTAQAAAAA4W7W9WvPfnq/6xnqlD0xXWmKay9pqMpq0J3eP43lX+Pn4aULmBE3InKCjxUf1zf5v9M2+b1RcXqzVB1Zr9YHVkqTB4YM1MWGiLk+4XOMHjFdCcALrzADoM0jGAAAAAN1MY1Oj5r89XzuP75S/r79unnJzj0xURIdHa+YlMzXj4hk6WnxUe3L3aG/uXuUczdH+4v3aX7xff/3mr5KkuKA4XRR7kUb2H6mR0SM1sv9IRQdGe7gHAOAaJGMAAACAbqS2oVa3vnWr3tr7lrzMXrpj5h0KtAV6OqwuMZlMiomIUUxEjK4cc6Wqa6uVXZCtQ4WHdKjwkI4UHVF+eb7yy/P15t43HcdF+UdpWNQwDQkfoiERQzQ4fLCGhA/RgOAB3XbtHADoCJIxAAAAQDextXCrbl9xu7JOZMnL7KUFVy/QoNhBng7L6Wy+Ng0bNEzDBg2TJNXZ65R3LE9Hio6ooKhA+SfydaL0hE5UndAnOZ/ok5xPWh3vY/FRfHC8EoITNCB4QKtHXFCc+vn3U4hvSI+cTQSgbyAZAwAAAHiQvdGuT3I+0XNfP6d3vn1HkhTgF6Dbrr5NKfEpng3OTXysPkqJT2nV33p7vQqLC3W85LhOnDqhotIix8+6xjodLDmogyUH263TarYqwj9C/fz7Kco/yvEzzC9MIb4hCvENUbBP8L+f+zY/97f6k8QB4HIkYwAAAAAXa2xqVGV9pcrqynSk7IhyS3N1sOSgNhVs0sb8jSqtLZUkmWTSqCGjNOvyWQqwBXg2aA/ztnorsX+iEvsnttre1NSkUxWn/v2obP5ZWlHa/LOyVLX1tbI32XW04qiOVhxtu4F2WEwWBfoEyma1yd/qL5vV1vzc2//sbVZ/+Xr5ysfLR94W7zYfPpb2yyxmi7zMXrKY/vWzndddXVQZQPdDMgYAAAA9kmEYkqRJz02S2c/calvLc0NGq+ct5ed7fuZxbT1v2f9c9dU11qmirkI19ppz9sXfz1/DkobpkvRL1C+sn9Qg1ZbXdvrcXKi6hjrpX83VVtTK8DLOfYCH+Zv85R/kr7iguDbLGxobVFlTqcraSlVWV6qqtkqV1ZWqrKlUdV216urrVFNfo9q62lY/jSZDjWpUaU2pSlXq3k6dS13zj9Pf3wB6NpPB/9EAAADogQ4dOqRBg3rfeipAe7Kzs5WUlOTpMAA4ATNjAAAA0COFhYVJkvLy8hQcHOzWtsvLyxUfH68jR44oKCioz7Tt6fb7attlZWUaMGCA4z0PoOcjGQMAAIAeyWxuvjQpODjYI0kJSQoKCuqTbXu6/b7adst7HkDPx//NAAAAAAAAbkQyBgAAAAAAwI1IxgAAAKBH8vHx0cMPPywfHx/a7iPt07ZnfucAnI+7KQEAAAAAALgRM2MAAAAAAADciGQMAAAAAACAG5GMAQAAAAAAcCOSMQAAAAAAAG5EMgYAAADd3qpVqzRkyBClpKTob3/721nlmzdvVnp6upKTk/XYY485rd0jR45o4sSJSktL0/Dhw/XGG2+ctU9iYqKGDx+uESNGaMaMGU5rW5K8vLw0YsQIjRgxQnfeeedZ5a7qtyTt27fP0faIESPk5+end955p9U+zuz7rFmzFBoaqjlz5ji2daR/2dnZGjNmjJKTk7V48WJ15v4kZ7ZdXV2tGTNmKDU1VRkZGXr66afbPG7ixIlKTU11nKPOaqvvHTm3zug7AA8xAAAAgG7MbrcbKSkpRn5+vlFeXm4kJycbxcXFrfYZM2aMsWPHDsNutxtjxowxdu3a5ZS2CwsLjW3bthmGYRjHjx83YmNjjcrKylb7JCQkGBUVFU5p70zh4eHnLHdVv89UUVFhhIeHu7Tvn376qbFy5Upj9uzZjm0d6d93v/td49133zUMwzCuv/56x/OutF1VVWV8/vnnhmEYRmVlpZGammocOHDgrOOuuOIKp5zztvrekXPrjL4D8AxmxgAAAKBba5kdERsbq8DAQM2YMUNr1qxxlBcWFqqhoUHDhw+Xl5eX5s6dq3fffdcpbUdHRztmPERFRSksLEwlJSVOqburXNnvM61cuVJTpkyRv7+/S+qXpEmTJikwMNDxuiP9MwxDGzdu1MyZMyVJCxYs6NQ5OLNtm82mK664QpLk7++vlJQUHT16tDPd6lT7HeGsvgPwDJIxAAAA6NYKCwsVGxvreB0XF6eCgoIOlzvL1q1b1dTUpPj4+FbbTSaTJkyYoHHjxunNN990apvl5eUaPXq0xo8fr7Vr17Yqc1e/Jen111/XTTfddNZ2V/a9I/0rLi5WWFiYTCZTu/t01ZEjR7Rz506NGjWqzfK5c+dq1KhReuaZZ5za7vnOrTv6DsB1vDwdAAAAAHAuRhvrYLT8AdqRcmcoLi7WggUL2lyvZsOGDYqJiVF+fr4mT56szMxMJScnO6Xd3NxcxcTEKCsrSzNnztSuXbsUFBQkyT39lpoTQhs2bNCrr756Vpkr+96R/rn6HNTW1uqmm27SE0880easoGXLlikmJkYlJSW6+uqrlZ6e7phR01XnO7fu+v0DcA1mxgAAAKBbi42NbfWNf35+vqKjoztc3lV1dXWaNWuWHnzwQV166aVnlcfExEhqnpkwZcoUbd++3Wltt9SdkZGhtLQ07d+/31Hm6n63WLFihaZNmyZfX99243NF3zvSv4iICJWUlDgSE848B4Zh6LbbbtOMGTNaLax7upb+h4WFafbs2dqyZYtT2j697vbOrSv7DsD1SMYAAACgWxs3bpyysrJUUFCgiooKrV69WtOmTXOUx8TEyGKxaOfOnWpoaNDy5cv1ne98xyltG4ahhQsXavLkyZo/f/5Z5VVVVaqoqJAklZaWat26dRo6dKhT2j516pTq6uokNf+hvWfPHiUlJTnKXdnv07V3iZIr+y51rH8mk0kXX3yx3nvvPUnSkiVLnHYOHnzwQdlsNv3sZz9rs7yhoUEnT56U1DyDZs2aNUpPT3dK2x05t67sOwA38My6wQAAAEDHrVixwkhJSTEGDRpkPPfcc4ZhGMb06dONgoICwzAMY+PGjUZaWpqRlJRkPPzww05r94svvjBMJpORmZnpeOzcudPRdnZ2tjF8+HBj+PDhRkZGhvHss886re0NGzYYGRkZxvDhw43MzEzj7bffNgzDPf1uUVpaakRFRRl1dXWOba7q+9SpU42IiAjDz8/PiI2NNTZv3txu/xYtWmRs2bLFMAzD2L9/vzFq1CgjKSnJuOuuu4zGxsYut71u3TpDkpGWlub4vX/wwQet2q6srDRGjRplDBs2zEhLSzMeeeQRp/X9q6++avfcOrvvADzDZBjcjB4AAAAAAMBduEwJAAAAAADAjbibEgAAXdDU1KTCwkIFBgZyFwv0aoZhqKKiQjExMTKb+T4PAGMg+g5XjIEkYwAA6ILCwkLFx8d7OgzAbY4cOaK4uDhPhwGgG2AMRF/jzDGQZAwAAF0QGBgoqXlwDgoK8nA0gOuUl5crPj7e8Z4HAMZA9BWuGANJxgAA0AUt07KDgoL4INoFhmHoZHXzLWIjbBFMd+/G+N0AaMEY6H6Ml57lzPNNMgYAAHhctb1aUU9ESZIqH6yUv7e/hyMCAKD7YbzsPVh9DQAAAAAAwI1IxgAAAAAAALgRlykBAIBep7GxUXa73dNh9DhWq1UWi8XTYQBAn2cYhhoaGtTY2Nhqe119nRL8E5qf19bJ0sS/2c7i7jGQZAwAAOhVKisrlZ+fL8MwPB1Kj2MymRQXF6eAgABPhwIAfVZ9fb2OHj2q6urqs8qajCY9e9mzkqSj+UdlNnGxi7O4ewwkGQMAAHqNxsZG5efny2azKTIykrtMXADDMFRUVKT8/HylpKQwQwYAPKCpqUk5OTmyWCyKiYmRt7d3q7GssalRNSdrJEmJEYmymPm32hk8MQaSjAEAAL2G3W6XYRiKjIyUn5+fp8PpcSIjI5Wbmyu73U4yBgA8oL6+Xk1NTYqPj5fNZjurvLGp0fFXvK+vL8kYJ3L3GEgyBgAAeJyX2Uu3Zd7meN5VzIjpHM4bAHQPZnPblx+ZTCaF+4U7nsN53H0+ScYAAACP8/Hy0UvXv+TpMAAA6NbMJrMGhg70dBhwAlb7AQAAfd5LL72kESNGdLmexYsX68c//nHXAwIAoBc51zibm5srk8mk0tLSTtX9yCOP6Prrr3e8DggI0K5duzpVlzuRjAEAAB5nGIaq6qtUVV/lkrsgTZ8+Xd/73vfO2l5eXi6bzea0Np999ln95je/cUpdAACcaeLEiXryqSfV2NQowzCUnZ2tpKQk3XvvvW6/i+DEiRP1hz/8wa1tdkRlZaWGDRvm6TDOi2QMAADwuGp7tQIeD1DA4wGqtp99K8+uuvPOO7Vs2TLV1dW12r58+XJFR0c7vT0AAFzBkKH88nxtO7ZN23ds1/jx47VgwQL98Y9/dOqaJw0NDU6rC20jGQMAAHq9a6+9Vl5eXnrnnXdabf/73/+uO+6446wPsE8++aRSUlIUGBioQYMG6c9//rOjrK6uTnfccYciIiIUHBysjIwMbdmyRZK0cOFC3XfffY59Dxw4oGuvvVaRkZEKCwvTd7/7XZf1EQDQd+zYskNXTrlSDz30kB555BG99dZbGjRoUKt9Nm3apJCQENXW1iovL09XXXWVIiMjFRoaqpkzZyo3N9ex78KFC7Vo0SLdeOONCgoK0v/93/9p27ZtGj9+vMLCwhQZGalbbrlFxcXFkqT7779fX3zxhX784x8rICBA06dPl9Q8fg4YMECBgYFKTEzU3/72tzbjf/bZZ5WUlKRvv/3Wse3dd99VcnKyQkJCtHDhQtntdkfZN998o0mTJiksLEzJycl6/vnn2z03JpNJ27dvl6Rz9sHTSMYAAIBez2q1av78+XrxxRcd2/bs2aOtW7dq4cKFZ+2fkJCgTz/9VOXl5frb3/6mH/7wh9qwYYMk6eWXX9aOHTt08OBBlZaW6q233lL//v3PqqOqqkpXXnmlMjIylJubq2PHjum///u/XdZHAEDfsHXDVt07/149+eSTjnHlmmuuUWlpqWOskqRXXnlFN9xwg3x9fdXU1KQf/OAHOnLkiA4fPiybzaa77rqrVb3Lly/XokWLVFpaqkWLFslsNuvXv/61jh8/rqysLBUUFOgnP/mJJOn3v/+9Lr/8cv3mN79RZWWl3n//fe3fv18/+9nP9OGHH6qiokKbNm3SuHHjzor/4Ycf1l/+8hd98cUXSk1NdWx/77339M0332jPnj36+OOPtXTpUknSsWPHdNVVV+k///M/VVRUpHfeeUcPP/ywPvnkk/Oeq3P1wdNIxgAAgD5h0aJF+vjjj3XkyBFJ0osvvqhp06YpNjb2rH1nz56t+Ph4mUwmTZo0SdOmTdPnn38uqTmxU1FRob1798owDA0ePFjx8fFn1bFq1SpZrVb98pe/lL+/v7y9vTVp0iSX9hEA0Pt9s/EbhYWHafqM6Y5t3t7euummm/TKK69Ikux2u1577TUtWLBAkpSYmKjp06fL19dXQUFB+ulPf6p169apqanJUcfUqVM1bdo0mc1m2Ww2ZWZmavz48bJarerXr59+8IMfOMbCtlgsFhmGod27d6umpkb9+vXT8OHDHeWNjY26++679cknn2jdunVnjb+PPPKIgoKCFBMTo+nTp+vrr7+W1JxUmjBhgm688UZZLBZlZGTo9ttv17Jly857ri60D+5EMgYAAPQJaWlpGjdunF5++WU1NDToH//4hxYtWtTmvkuXLtWoUaMUGhqqkJAQrV69WidPnpQkzZ8/XwsXLtTixYsVERGhhQsXOspOd/jwYQ0aNMip1/ADAHDHvXcoITlBV115VavxZ8GCBXr99ddVV1en1atXKzAwUOPHj5ckFRUVae7cuYqPj1dQUJAmTJig+vp6VVRUOI4fMGBAq3YOHjyo6667TjExMQoKCtKtt97a5njXYtCgQXr55Zf15z//Wf369dPUqVMdlwtJ0pEjR7RkyRL9/Oc/V2ho6FnHnz7L1N/f3xFbbm6uVq9erZCQEMfjT3/6k44ePXrec3WhfXAnkjEAAKDPWLRokV566SWtWrVKTU1N+s53vnPWPnl5ebrtttv029/+VkVFRSotLdWMGTMcd6nw8vLSQw89pB07dmjv3r3Ky8vTo48+elY9CQkJys7OdvvdLQAAvZuX1Uu/ff63SkhI0KRJk1RUVCRJuvjiixUREaFVq1bplVde0a233ur4QuDBBx9UdXW1vvnmG5WXl2vdunWS1GqMMptbpwcWL16s2NhY7dmzR+Xl5frHP/5xzv0l6cYbb9Rnn32m48ePKzMzU/Pnz3eUJSYm6u2339bcuXMvaHZKfHy8Zs2apdLSUsejoqJCq1evPu+x5+uDJ5GMAQAAfcbNN9+sY8eO6fvf/74WLFggq9V61j6VlZUyDENRUVEym81avXq1PvzwQ0f5p59+qu3bt6uhoUH+/v7y9fWVl5fXWfXMnDlTdXV1+vnPf66qqirV19frs88+c2n/AAB9g9XbqtffeF0pKSmaNGmSTpw4Ial59uaf//xnvffee45LlCSpvLxcNptNISEhKi4ubvNLhDOVl5crMDBQQUFBOnLkiH73u9+1Ku/Xr5+ys7Mdr/ft26ePPvpINTU18vb2VkBAwFnj4/Tp07Vs2TLNmTOnQ2u+tPTp008/1Ztvvim73S673a7t27c7Fs/vSh88iWQMAADwOIvZojlpczQnbY4sZovL2gkICNCNN96o3Nzcdi9RSktL009/+lNNnjxZ4eHheu2113Tttdc6yo8fP65bbrlFISEhGjhwoIKDg/Xwww+32dbHH3+sr7/+WgMGDFB0dLT+8pe/uKxvAIDezySTbFabQn1D5e3trddee02pqamaOHGijh07pvnz52vt2rUaOXKkkpOTHcc9+uijOnjwoEJDQ3XZZZc57n50Lk8++aRWrVqloKAgXXfddZo9e3ar8vvuu08ff/yxQkJCdM0116i+vl7/8z//o379+ik8PFyffvqpXnrppbPqnTp1ql577TXddNNNrb7saE9sbKzWrFmj5557TtHR0erXr5/+67/+S+Xl5V3ugyeZjO4yRwcAgB6ovLxcwcHBKisrU1BQkKfD6fNqa2uVk5OjgQMHytfX19Ph9DjnOn+81wGciX8XnI9xzHPcPQYyMwYAAAAAAMCNSMYAAAAAAAC4EckYAADgcVX1VTI9apLpUZOq6qs8HQ4AAN1SY1OjthZu1dbCrWpsavR0OOgCkjEAAAAAAABuRDIGAAAAAADAjUjGAAAAAAAAuBHJGAAAAAAAADciGQMAAAAAAOBGJGMAAADaUFtbq1mzZikkJETjxo3zdDgAAFwQxrHujWQMAADwOIvZohkpMzQjZYYsZounw5Ekvfnmm9q3b5+OHz+uzZs3u7Xturo6PfDAA4qOjlZAQICGDRum3NzcNvddunSpAgICWj1MJpOefPJJSVJ9fb3mzJmjxMREmUwmvfPOO+7rCADAqUwmk4J9ghXsEyyTyXTOfT01jp1vXGqLyWSSzWZz7J+Zmdml+noCkjEAAMDjfL189d7c9/Te3Pfk6+Xr6XAkSTk5ORo8eLB8fHzc3vbtt9+u7Oxsff3116qoqNAbb7yhkJCQNvedN2+eKisrHY+1a9fKbDbrhhtucOwzfvx4vfLKK4qLi3NTDwAArmA2mZUSnqKU8BSZTef+c95T41hHxqW2fPnll45jduzY0eX6ujuSMQAAoE9ITEzU448/rrFjx8rf31/Tp09XSUmJ7rnnHoWEhCglJUVffvmlJOn+++/XY489plWrVikgIEAPP/ywMjMztWTJklZ1Tp8+Xb/+9a+dGufu3bu1YsUKvfjii4qJiZHJZFJqamq7yZgzvfDCC5o6dari4+MlSd7e3rrvvvt0+eWXy2LpHrOOAAAXrqeMY2c6c1zqbvV5CskYAADQZyxfvlxvvvmmCgoKlJeXp3Hjxmny5MkqLi7WzTffrMWLF0uSfv/73+uhhx7SNddco8rKSj366KOaP3++XnnlFUddx48f1yeffKJ58+a12VbLh+P2HuvXr2/zuLVr1yopKUm/+c1vFBUVpcGDB+uJJ57oUP9qamq0bNky3XnnnRd4ZgAAPUFPGMdOdyHj0vTp0xUZGakpU6boq6++6nJ93R3JGAAA4HFV9VXy/5W//H/lr6r6Kpe1c88992jAgAEKCQnRzJkzFRERoTlz5shiseiWW25RVlaW6uvr2zx23rx5Wrt2rQoKCiRJy5Yt0+WXX97uN3PPPPOMSktL232MHz++zeNKSkqUlZUlwzCUl5ent99+W0899ZSWLl163v7985//lLe3t6699toOnhEAQE+yePFinbSe1KGaQ5o+Y3q3HMdO19Fx6dNPP1Vubq5yc3M1Y8YMTZ06VXl5eZ2urycgGQMAALqFanu1qu3VLm2jf//+juc2m+2s14ZhqLq67Riio6M1efJkR1JkyZIlWrBggdNjDAgIkMVi0WOPPSZfX1+lp6frjjvu0IoVK8577AsvvKAFCxbIarU6PS4AgOf1799fTUaTmoymbjuOna6j49KkSZPk4+Mjf39/3X///UpNTdXq1as7XV9PQDIGAACgg1qmeGdlZWn//v2aPXt2u/suXrz4rLs/nP744osv2jyu5Q4S57tLxpkOHjyodevWadGiRRd0HACg73DHONaiK+OS2Xx2qqK3jXMkYwAAADpo1qxZOnz4sB544AHNmjVLAQEB7e777LPPtrr7w5mPyy+/vM3jJkyYoJSUFD366KOy2+3at2+fXnrpJV133XXnjO2FF17QJZdcoqFDh55VVldXp9raWhmGIbvdrtraWjU2Nl5Y5wEAPZ47xrEW5xqXTpeVlaWvv/7aMT796U9/0u7duzVt2rRO1ddTkIwBAADoIJvNptmzZ2vNmjUum9ptsVi0cuVKbdy4USEhIbr66qt17733tlpg8cxvJBsbG/Xyyy+3u6DhkCFD5Ofnp7y8PN14443y8/NrtYgjAKBvcMc4Jp1/XDp9HCsqKtKtt96qkJAQxcbG6q233tIHH3yggQMHdri+nshkGIbh6SAAAOipysvLFRwcrLKyMgUFBXk6nB6rqr5KAY83fztX+WCl/L39O1VPbW2tcnJyNHDgQPn6+jozxD7hXOeP9zqAM/HvgvOdbxxrbGrUtmPbJEkj+4+UxWxxd4i9lrvHQGbGAAAAAAAAuJGXpwMAAAAwm8y6IuEKx3MAAHA2k0wK9A50PEfPRTIGAAB4nJ/VT58v/NzTYQAA0K2ZzWYNiRji6TDgBHz1BAAAAAAA4EYkYwAAQK/D/Qk6h/MGAN0D/x67n7vPOckYAADgcVX1VYr8XaQifxepqvqqTtdjsTTfVaK+vt5ZofUpLeet5TwCANzLarVKkqqrq9ssb2xq1PZj27X92HY1NjW6M7Rez91jIGvGAAAAl6ix1+jbk99qeL/hHbr15snqk11u08vLSzabTUVFRbJarTKb+d6po5qamlRUVCSbzSYvLz4iAoAnWCwWhYSE6MSJE5Ikm80mk+nfC/U2NjWqob5BUvOtmLm1tXN4YgxkpAUAAE5Xba/W2OfHak/RHl2RdIU+vfVTt9wlyWQyKTo6Wjk5OTp8+LDL2+ttzGazBgwY0OqDPwDAvfr37y9JjoTM6ZqMJp0sa/7yIrcylzsQOpG7x0CSMQAAwOme2/qc9hTtkSStPbRWL2a9qDuH3emWtr29vZWSksKlSp3g7e3NbCIA8LCWLxaioqJkt9tblVXXV2vm6pmSpG/u/kY2b5snQuyV3D0GkowBAABO9/qe1yVJfj5+qqmr0TPfPOO2ZIzU/O2Wr6+v29oDAMDZLBbLWeuXNJobdbiqeeanj6+PfL0Z63oqvvoAAABOVVRVpK/yv5Ik3XlNcwJm++HtOlp11JNhAQAAdBskYwAAgFNtLtgsSeoX2k+DYgepX2g/GYah1w6+5uHIAAAAugeSMQAAwKm2FG6RJCX0S5AkpSakSpI+OPhBu8eYTWaNiRmjMTFjWIwQAIB2MF72HqwZAwAAnKolGRPfL15SczJm7fa12pq3VYZhtHmXAj+rn7bctcWtcQIA0NMwXvYepNIAAIBTfXP0G0lSfFRzMmZg9ECZTCYVlxdrd+luT4YGAADQLZCMAQAATlNWW6ZjlcckSf3D+0uSfL19FRcZJ0lanbvaY7EBAAB0FyRjAACA0xwoOSBJCvIPanW7zaSYJEnS+sPr2zyu2l6txD8kKvEPiaq2V7s+UAAAeiDGy96DZAwAAHCa/cX7JUlRIVGttrckY7blb2vzOMMwdLjssA6XHZZhGK4NEgCAHorxsvcgGQMAAJymJRkTGRLZantLMqaguED5VflujwsAAKA7IRkDAACcxpGMCW2djAm0BSoyJFKGDNaNAQAAfR7JGAAA4DTtXaYkSYNiBkmSPjv8mVtjAgAA6G5IxgAAAKcwDKPdy5QkaVBsczJmc95mt8YFAADQ3ZCMAQAATnG86rgq6itkMpkUHhx+VnlyXLIkKedEjk7WnnR3eAAAAN0GyRgAAOAULbNiwoPC5WXxOqs8NDBUEcERMgxDq3JWtSozmUxKi0xTWmSaTCaTW+IFAKCnYbzsPc7+pAQAANAJ51ovpkVyXLJOlp3UhzkfauHQhY7tNqtNu+/Z7eoQAQDo0Rgvew9mxgAAAKdo705Kp0uObb5UaVPeJrfEBACAJxmGodUHVuuxtY9p45GNng4H3QjJGAAA4BTnWry3xenrxpyoOeGWuAAA8JT/+ex/NHPZTD38+cO69MVL9duNv/V0SOgmSMYAAACn6EgyJiQgRJEhkTIMQ29lv+XYXm2vVvoz6Up/Jl3V9mqXxwoAgKutO7xOv/zil5KkgdEDJUk/+fAnei/7vU7XyXjZe5CMAQAAXdbY1KiDJQclSVGh7a8ZI0mpA1IlSasO/HsRX8MwtKdoj/YU7ZFhGK4LFAAAN/nJxz+RJF2acan+35z/p4vTL5YhQ3etuku1DbWdqpPxsvcgGQMAALrscNlh2Zvs8rJ4KTgg+Jz7pg9MlyStz16vxqZGd4QHAIBbfXP0G23M3yiL2aKrL7paJpNJ14+/XsH+wTpaelS/+OoXng4RHkYyBgAAdNnplyiZTef+eJEcmywfq4/Kqsq0tnCtO8IDAMCtXvjmBUlSZnKmgvyDJEm+Pr6afvF0SdLTXz6t8rpyj8UHzyMZAwAAuqwj68W08PLy0pABQyRJr377qkvjAgDA3RqaGrQ8a7kk6aK0i1qVjR06VhHBEaqoqdAjGx/xQHToLkjGAACALmtJxkSFnHu9mBYtlyp9sO8Dl8UEAIAnbDyyUadqT8nf118pcSmtylouW5Kkv371V5XVlnkiRHQDJGMAAECXOWbGhJ5/ZowkDUsaJovZoiMnj2jj0Y2uDA0AALdafWC1JCk1IVVm89l/co8aPEpRoVGqqqvSoxsfdXd46CZIxgAAgC67kMuUJMnma1NaYpok6bkdz8lkMikhOEEJwQkymUwuixMAAFd7/+D7kqShCUPbLDebzZo2bpok6a+b/qrSmtIO18142XuQjAEAAF1SY69RXlmepI4nYyRp9JDRkqR397wrXy9f5d6Xq9z7cmWz2lwSJwAArlZQXqAdx3fIJJNSE1Lb3W9kykj1C+2nqroqPfbVYx2u32a1MV72EiRjAABAl2SfypYhQ77evgrwC+jwcekD0+Xr7auSihL988A/XRghAADu8cHB5rXQBvQbcM4x0Ww2a9pFzbNjntv0nE7VnHJLfOg+SMYAAIAucSzeGxp1QVOmrV5WjRs6TpL01KanXBIbAADu5LhEKbHtS5RONyJlhPqH9Vd1XTVrx/RBJGMAAECXXOidlE43fvh4SdJXh77SsP8bprHPj1WNvcap8QEA4A72Rrs+OvSRJDnWRTsXs+nfa8c8v+n5Ds2OqbHXaOzzYxkvewGSMQAAoEv2ntwrqeN3UjpdVGiUUgc0X1OfdSJLWwu3qslocmp8AAC4w5dHvlR5XbkC/AIUFxXXoWMyUzIVHR6t6vpq/Wjdj867f5PRpK2FWxkvewGSMQAAoEt2Hd8lSYoOj+7U8ZNGTXJmOAAAeETLLa2HJgyV2dSxP7XNJrOuufQaSdLfN/9dWUVZLosP3QvJGAAA0GmNTY3aU7RHUueTMYPjByuxf6ITowIAwP1a1os5112U2pI+MF3pA9PV2NSoO967Q4ZhuCI8dDMkYwAAQKdln8pWXWOdrF5WhQeFd6oOk8mkqWOnOl7vLd7rrPAAAHCLI2VHtOvELplMJsfltxdi1oRZ8rJ4acvhLfq/bf/nggjR3ZCMAQAAndZyiVL/sP4ymzv/sWJQ3CDH83vW3MO3ggCAHqVlVkxCvwT5+/lf8PERwRG6+qKrJUkPrHlA2SXZTo0P3Q/JGAAA0GlZJ5qvbY8Jj3FanVsOb9HSPUudVh8AAK723oH3JDVfctRZk0dNVlJMkmrqazT7rdmyN9qdFR66IZIxAACg03ae2ClJ6h/ev8t1+fv6y+pllST913v/pWMVx7pcJwAArlbbUKuPD30sSRqaOLTT9ZjNZs27ap58rD7aUbBDd71/V5v7RdgiFGGL6HQ76B5IxgAAgE7bWrhVkjp8C8/2+Fh99Mu7f6lf3f0rxUTEqLymXDe8cwOXKwEAur11h9ep2l6tYP9gxUbEdqmu8OBwzZ82XyaZ9PLXL+vJzU+2Kvf39lfRD4tU9MMi+Xtf+OVQ6D5IxgAAgE45XnlceWV5Msmk+Kh4p9Rp9bJq/rT58rJ4af2h9Xpo7UNOqRcAAFd5b3/zJUppiWkymUxdri8jKUMzL50pSXrg/Qf08s6Xu1wnuh+SMQAAoFO2FG6RJPUL6ydfb1+n1RsdHq3ZV8yWJP167a+1NIv1YwAA3ZNhGFp1YJWkrl2idKYpo6fosmGXyZChO965g7GwFyIZAwAAOmVzwWZJzXeO6Kr6hno9/ebTevrNp1XfUK9LMi7RFSOukCTd/s7tWpO9psttAADgbNuObdOhU4dk9bJqyIAhTqvXZDJp9sTZGpc2Tk1Gk+a/OV9PbHxCNfYaTXxpoia+NFE19hqntQf38/J0AAAAoGfaVLBJkhTfr+uXKBmGoeyCbMdzSbp2/LUqLitWVk6Wrn31Wr13y3u6MunKLrcFAICzvL77dUlSemK6fKw+Tq3bbDLr5sk3y9virfW71uuHH/5Qu07s0trDayVJTUaTU9uDezEzBgAAXLD6xnptyNsgSUqKSXJJGxazRQunL1RaYprqG+o1c/lMvb7ndZe0BQDAhTIMw5GMGZEywiVtmM1mzZ44W9eOv1aStGT7Epe0A/cjGQMAAC7YloItqrJXKcAvwCm3tW6Pl5eXbp9xu9IHpqu+oV43v3GzHl//OHdZAgB43OaCzcopzZG3l7dT14s5k8lk0uRRk3X3tXfL5mNzbH/qq6fU0NTgsnbhWiRjAADABfs051NJUkpciswm136csHpZdcfMOzR++HgZMvTQJw/pmteuUUlNiUvbBQDgXP72zd8kScMGDXP6JUptSUtM03033Od4/T+f/Y9G/nWk1uaudXnbcD6SMQAA4IJ9dOgjSVJyXLJb2rOYLZp9xWzNvmK2LGaLVu9brYxnM/TBgQ/c0j4AAKerqKvQ8qzlkqRLMy51W7vBgcGO537efso6nqWJL0/UVf+4Sl8Xfu22ONB1JGMAAMAFOV55XOvz1ktq/pbOXUwmky7PvFz33XifIoIjdLT8qKYvm64b/3mjjlUec1scAAAs3bVUVfYqRYVGuWzttPN54JYHNH7YeJnNZn2c/bHGPD9Gk5dM1vsH3mdx3x6AZAwAALggK/etlCFDA/oNUGhgqNPq9fbylreX93n3i4+K1w9v+aGuGHGFTCaT3tj9hgY9PUgPf/awyuvKnRYPAABtaWhq0BNfPiFJumzYZTKZTG5tv2W8DLAFaM6kOXro1oc0eshomU1mfZbzmWYsm6HUv6Tqdxt+p6MVR90aGzrOZLACHgAAnVZeXq7g4GCVlZUpKCjI0+G4xZVLrtQnOZ/omkuu0ZVjPXur6SMnjuiNz95Q3vE8SVKoX6geuOQBLR6zWGF+YR6Nrbfpi+91AOfWV/9dWL5ruea+NVf+vv76+e0/d8t6MR1RXF6sL3Z8oY1ZG1Vnr5PUfHvsqYOmam7GXM0cPJOxsZNc8V4nGQMAQBf0tQ+iB0sOKuXpFJlk0s8W/kzhQeGeDkmGYWjHwR1avXG1TpSekCT5Wf10x4g79L1x31NqRKqHI+wd+tp7HcD59cV/F+oa6pTxfxk6WHJQMy6eoanjpno6pLPU1tVq24Ft2rx3s3KO5ji2W0wWTUiYoFmps3R18tVKDkt2+6yensoV73Uvp9QCAAD6hL9+/VdJ0pCEId0iESM1ryUzImWEhg0apq/3fa3Pt32uwpOF+suWv+gvW/6icbHjdMeIO3RTxk0K8Q3xdLgAgB7sT5v+pIMlBxVkC9KEERM8HU6bfH18dUnGJbok4xKdOHVCW/dt1a7sXTpafFSf5X6mz3I/kyTFBcVpysApmjJwiiYmTlRcUBzJGTdiZgwAAF3Ql74VPFl9UgP/OFCV9ZW685o7lZGU4bS67Q12/X313yVJt8+4XVYva6frMgxDB/IPaO32tdqbu9exiKHVbNXkgZM1K3WWrh1yraIDo50Se1/Rl97rADqmr/27sO/kPo366yhV26s198q5Gpc2zu0xdGW8PFl6UrsO7dLunN3KOZqjxqbGVuX9A/rr4tiLdVHcRboo9iKNiRmjQJ9Ap8bfUzEzBgAAeMyvvviVKusrFRcZp/SB6U6tu8lo0p7cPY7nXWEymTQ4frAGxw9WeVW5vt73tTbt2aRjJce0JnuN1mSv0eL3Fmtk9EhdOfBKTR44WeMHjFeAd4AzugIA6IWq7dWa+9ZcVdurNTh+sMYMHeOROLoyXkaERGjSqEmaNGqS6u31yjmao/1H9mv/kf0qKCrQscpjemffO3pn3zuOYxJDEjW833BlRGZoWL9hGhY1TIPDB8tq6fyXJmhGMgYAAJzX5oLN+uOmP0qSZl46s8dMYw7yD3J88Dxecly7Du3SruxdOnz8sLYd3aZtR7fpd1/+Tl5mL42OHu34JnBs7FgNDh8ss4kbTwJAX9fQ1KBb3rxF3xz9RjZfm+ZeNbfHjw/eVm8NGTBEQwYMkSTV2+uVX5Svw8cONz+OH9apilPKLc1VbmmuVu5b6TjWy+ylhJAEpYSlKDk0Wclh/34khCTI18vXU93qUUjGAACAczpWeUw3vHGDmowmjR4yWkMThno6pE7pF9ZP/cL66coxV6qsskwH8g9of/5+Hcw/qJLyEm0q2KRNBZsc+wf6BCqzX6bSI9OVFpmmtMg0pUemq39A/x6TjAIAdE1FXYVu+udNev/g+/KyeOnOa+5USECIp8NyOm+rt5JikpQUk+TYVllTqaPFR5sfJ486ntfZ65Rdkq3skuw264qwRWhA8ADFBcUpPihe8UHxzc+D4xUdEK0o/ygF+QT1+bGUZAwAAGjXoVOHNGPpDOWV5SkyJFKzr5jt6ZCcIjggWGNSx2hMavM08+KyYuUczVHeiTwdOX5E+SfyVVFXofV567U+b32rYwN9AjUwZKDjkRiS6HhEB0Yr3C9cFrPFE90CADjRJ4c+0V3v3qWc0hxZvay6ffrtrZIVvV2AX4BS4lKUEpfi2NZkNKm8qlxFpUU6WXpSJ8tOOn4WlRWp3l6vk9UndbL6pL45+k27dXtbvBXpH6n+/v0V5R/V6hHuF65Qv1CF+IYo1PdfP/1CFegd2KsSOCRjAADAWSrqKvTc18/p0bWPqrK+UiEBIbr72rtl87V5OjSXCA8OV3hwuCM509jUqGPFx3S0+KiOlxzXsZJjOl5yXEVlRaqoq9DO4zu18/jONuuymCyK9I9UdEC0+gf0V/+A/urn309hfmEK9QtVqG+oQv1Cm1//63lv+4AJAD1VVX2V3jvwnp7Z8ozWHl4rSQoNDNXC6QuV0D/Bw9F5ntlkVkhAiEICQlolaaTmBfSra6t1qvKUSitKVVrZ/CirLNOpilMqrSxVRXWF6ux1qm+sV0F5gQrKCy6s7dMSNCG+IQr0CVSAd4ACrAHNP8/z8Pf2l6+Xr+PhY/Hx2PhLMgYAgD7MMAxV26tVXFOsA8UHtKdojz4//Lk+zP5QlfWVkqSkmCQtuHpBr5yW3R6L2aLYyFjFRsa22m5vsKu4rFglFSUqKT/jUVGiqpoqNRqNOlZ5TMcqj3W8PZNFgT6B8rf6y9/bX/5Wf8eHRsfzf5XZrDZ5W7zlY/GRj5dPh55bzVZ5mb1kMVtkMVnafO5l9pLFZHE87+nrIQBAe+ob61VRV6FTtad0uPSwDpcd1rcnv9Wmgk3aUrBFNQ01kprHgkszLtXMS2bK14d1UM7HZDLJ389f/n7+iouMa3e/enu9KmsqVVlTqYrqCsfPluc1tTWqrqtWdV21aupqVF1brcamRjUZTSqpKVFJTYlT4z49OdPy8PPya/XaUu/8Ga8kYwAA6ALDMCRJk56bJIufRYYMx7bT9zFktNr/9P1OLzvffh05plX5OfaraahRWW2Z7I32NvsWERqhiZkTNWrIKJmbzKotr73Q09NhdQ110r+qr62oleFlnPsADwqxhigkLERJYWdPVW80GlVZXamKmormn//6cFlRU6Ha2lrV1Nc0f7Csq1ZtXa2q66rV2NioRjWqtKZUpSp1f4fOwWwy/ztxY2/+IHrm+xtA33W+MbAjY1lXnp/ehqO8nXHPUPOXDxV1Fe2Oey1Cg0KVmZypi9Mubv4iok6qrXPdGHghetJ4eS422WTzsynKL+q8+xqGIXuTXTW1zWNoTX2Nauuax9R6e33zTBt7veO53W5XfcO/t9fZ65pf1zf/bGhoaFV/7b/+O6e6f8fiLCaDERUAgE47dOiQBg0a5OkwALfJzs5WUlLfWTMBQPsYA9HXOHMMZGYMAABdEBYWJknKy8tTcHCwW9suLy9XfHy8jhw5oqCgINqmbZcqKyvTgAEDHO95AGAMpO2+0LbkmjGQZAwAAF1gNjevqxEcHOyRDweSFBQURNu07TYt73kAYAyk7b7UtuTcMZDRFAAAAAAAwI1IxgAAAAAAALgRyRgAALrAx8dHDz/8sHx8fGibtntt292hfQDdT1/9N5G2+1bbrmqfuykBAAAAAAC4ETNjAAAAAAAA3IhkDAAAAAAAgBuRjAEAAAAAAHAjkjEAAAAAAABuRDIGAIAOWrVqlYYMGaKUlBT97W9/O6t88+bNSk9PV3Jysh577DGntXvkyBFNnDhRaWlpGj58uN54442z9klMTNTw4cM1YsQIzZgxw2ltS5KXl5dGjBihESNG6M477zyr3FX93rdvn6PdESNGyM/PT++8806rfZzd71mzZik0NFRz5sxxbOtI/7KzszVmzBglJydr8eLF6sz9Ec5su7q6WjNmzFBqaqoyMjL09NNPt3ncxIkTlZqa6jhPndFWvztybp3RbwA9A2MgYyBjYGtd7rcBAADOy263GykpKUZ+fr5RXl5uJCcnG8XFxa32GTNmjLFjxw7DbrcbY8aMMXbt2uWUtgsLC41t27YZhmEYx48fN2JjY43KyspW+yQkJBgVFRVOae9M4eHh5yx3Vb9PV1FRYYSHh7u8359++qmxcuVKY/bs2Y5tHenfd7/7XePdd981DMMwrr/+esfzrrRdVVVlfP7554ZhGEZlZaWRmppqHDhw4Kzjrrjiii6f87b63ZFz64x+A+j+GAPbxxjIGGgYnes3M2MAAOiAlm+GYmNjFRgYqBkzZmjNmjWO8sLCQjU0NGj48OHy8vLS3Llz9e677zql7ejoaMe3PVFRUQoLC1NJSYlT6u4qV/b7dCtXrtSUKVPk7+/v9LpPN2nSJAUGBjped6R/hmFo48aNmjlzpiRpwYIFnToHZ7Zts9l0xRVXSJL8/f2VkpKio0ePdqZbF9x2Rzir3wC6P8bAtjEGMgZ2pd8kYwAA6IDCwkLFxsY6XsfFxamgoKDD5c6ydetWNTU1KT4+vtV2k8mkCRMmaNy4cXrzzTed2mZ5eblGjx6t8ePHa+3ata3K3NXv119/XTfddNNZ213Zb6lj/SsuLlZYWJhMJlO7+3TVkSNHtHPnTo0aNarN8rlz52rUqFF65plnnNbm+c6tO/oNoHtgDGQMlBgDT+eMfns5JVIAAHo5o43rgFsG4I6UO0NxcbEWLFjQ5rX6GzZsUExMjPLz8zV58mRlZmYqOTnZKe3m5uYqJiZGWVlZmjlzpnbt2qWgoCBJ7ul3eXm5NmzYoFdfffWsMlf2W+pY/1x9Dmpra3XTTTfpiSeeaPNb0WXLlikmJkYlJSW6+uqrlZ6e7vg2sSvOd27d8bsH0D0wBjIGtmAMbOaMfjMzBgCADoiNjW31jUd+fr6io6M7XN5VdXV1mjVrlh588EFdeumlZ5XHxMRIav5mZsqUKdq+fbvT2m6pOyMjQ2lpadq/f7+jzNX9lqQVK1Zo2rRp8vX1bTc2V/Rb6lj/IiIiVFJS4vhg5sxzYBiGbrvtNs2YMaPVwoKnazkHYWFhmj17trZs2eKUts93bl3ZbwDdC2MgY6DEGHg6Z/SbZAwAAB0wbtw4ZWVlqaCgQBUVFVq9erWmTZvmKI+JiZHFYtHOnTvV0NCg5cuX6zvf+Y5T2jYMQwsXLtTkyZM1f/78s8qrqqpUUVEhSSotLdW6des0dOhQp7R96tQp1dXVSWr+oLFnzx4lJSU5yl3Z7xbtTc92Zb9bdKR/JpNJF198sd577z1J0pIlS5x2Dh588EHZbDb97Gc/a7O8oaFBJ0+elNT87eGaNWuUnp7e5XY7cm5d2W8A3QtjIGMgY6ALxsALWu4XAIA+bMWKFUZKSooxaNAg47nnnjMMwzCmT59uFBQUGIZhGBs3bjTS0tKMpKQk4+GHH3Zau1988YVhMpmMzMxMx2Pnzp2OtrOzs43hw4cbw4cPNzIyMoxnn33WaW1v2LDByMjIMIYPH25kZmYab7/9tmEY7um3YRhGaWmpERUVZdTV1Tm2ubLfU6dONSIiIgw/Pz8jNjbW2Lx5c7v9W7RokbFlyxbDMAxj//79xqhRo4ykpCTjrrvuMhobG7vc9rp16wxJRlpamuP3/sEHH7Rqu7Ky0hg1apQxbNgwIy0tzXjkkUec0u+vvvqq3XPr7H4D6BkYAxkDGQOd22+TYXTiJuAAAAAAAADoFC5TAgAAAAAAcCOn3E2pqalJhYWFCgwMZBV99GqGYaiiokIxMTEym8llAmAMRN/BGAgAgPM4JRlTWFh41r3egd7syJEjiouL83QYALoBxkD0NYyBAAB0nVOSMYGBgZKaB+eWe64DvVF5ebni4+Md73kAYAxEX8EYCACA8zglGdMyLTsoKIgPol1gGIZOVjffmivCFsF0926M3w2AFoyBzsM42DPwewEAoOuckoyBc1TbqxX1RJQkqfLBSvl7+3s4IgAA3IdxEAAA9BWsvgYAAAAAAOBGJGMAAAAAAADciMuUerHGxkbZ7XZPh9HjWK1WWSwWT4cBAOgCxsDOYQwEAMA9SMb0UpWVlcrPz5dhGJ4OpccxmUyKi4tTQECAp0MBAHQCY2DnMQYCAOAeJGN6ocbGRuXn58tmsykyMpK7HlwAwzBUVFSk/Px8paSk8O0gAPQwjIGdxxgIAID7kIzphex2uwzDUGRkpPz8/DwdTo8TGRmp3Nxc2e12PogCQA/DGNg1jIEAALgHyZhuxMvspdsyb3M87yq+DewczhsAeIYzx0H+Le8czhsAAO5BMqYb8fHy0UvXv+TpMAAA8AjGQQAA0Fdwa2t0S7/61a90yy23eDoMAEAf9/nnnyskJKRTxz7yyCO6/vrrnRoPAADoHUjGdCOGYaiqvkpV9VUuvwPE+vXrNX36dIWGhiokJESZmZn67W9/q/r6epe221EPPfSQli9f7ukwAABu5OpxcOLEifrDH/7g9HoBAAAuFMmYbqTaXq2AxwMU8HiAqu3VLmtn1apVmj59uqZNm6YDBw6otLRUr732mvbs2aOjR486ta2Ghga3HAMA6PncNQ4CAAB4GsmYPsYwDP2///f/9OMf/1j33XefIiIiJEmpqal66aWXlJCQoFtvvVUxMTEKCgrS6NGj9dlnnzmOf+mllzRixAg99NBDCg8P14ABA/TMM884yh955BFdc801+s///E+FhYXpxz/+sfLy8nTVVVcpMjJSoaGhmjlzpnJzcx3HLFy4UIsWLdKNN96ooKAg/d///R9TuwEAbtHWZUjXX3+9HnnkkTb3X7FihWJjY7VhwwZVVlbquuuuU1RUlIKDgzVhwgTt2LGj3bZ+9rOfKTMzU0ePHj1rls727dtZPBcAgD6EZEwfc+DAAeXk5JxzPZYpU6Zo7969Ki4u1s0336w5c+aooqLCUZ6VlSWTyaSjR4/qtdde009+8hOtW7fOUf7BBx/ooosu0okTJ/SLX/xCTU1N+sEPfqAjR47o8OHDstlsuuuuu1q1uXz5ci1atEilpaVatGiR8zsOAEAXPf/887r33nv14Ycf6rLLLlNTU5Pmzp2rnJwcHT9+XCNHjtSNN9541iVWDQ0NWrRokdavX69169YpOjraQz0AAADdBcmYPqaoqEiSFBsb2+4+t99+u4KDg2W1WvXDH/5QTU1N2rlzp6Pc399fjzzyiLy9vXXJJZdo3rx5WrJkiaM8IyNDCxculJeXl2w2mxITEzV9+nT5+voqKChIP/3pT7Vu3To1NTU5jpk6daqmTZsms9ksm83mgp4DANB5v/jFL/TUU09p3bp1Sk9PlyQFBQXppptukr+/v3x9ffXoo49q//79KiwsdBxXXV2tWbNmqbS0VGvWrFFwcLCnugAAALoRkjF9TMtlSQUFBW2WNzU16ac//alSUlIUFBSkkJAQlZWV6eTJk459YmJiZLVaHa8TEhJa1TdgwIBWdRYVFWnu3LmKj49XUFCQJkyYoPr6+lazbc48BgCA7qKmpkZPPvmk7rvvvlbjVU1Nje655x4lJiYqKChIiYmJktRqzNy+fbs++ugjPfroo/Lx8XF36AAAoJsiGdPHDB48WImJiXr11VfbLF+2bJmWLVum9957T2VlZSotLVVwcHCrKdeFhYWy2+2O13l5ea1m2pjNrd9WDz74oKqrq/XNN9+ovLzccUnT6XWeeQwAAO4QEBCgmpqaVmPSmYvZ+/n56eOPP9bPfvazVuPn73//e3399ddav369ysvLHeuhnV7XpZdeqr/85S+66qqrtHv37lbtVlf/e5FiZy+gDwAAujf+Au5jTCaTnn76af3617/W008/reLiYknS/v37tWjRImVnZ8vb21sRERGqr6/XY489pvLy8lZ1VFVV6Re/+IXq6+u1adMmLV26VPPmzWu3zfLyctlsNoWEhKi4uFiPPvqoS/sIAEB7GhoaVFtb63ikpKTIarVq2bJlamxs1Kuvvqpt27adddzo0aO1Zs0a3XvvvfrHP/4hqXl88/X1VWhoqCorK/XQQw+12eaiRYv0+OOPa8qUKY7LfkeNGqW33npLZWVlOnHihH7729+6rtMAAKDbIRnTjVjMFs1Jm6M5aXNkMVtc1s4111yj999/X++9954GDRqkkJAQzZkzR6mpqbrvvvuUnp6uhIQEJSUlyc/PT/Hx8a2Oz8jIUENDg6KjozVnzhz98pe/1KRJk9pt79FHH9XBgwcVGhqqyy67TNOnT3dZ3wAAPZc7xsEf/vCH8vPzczwyMzP1/PPP6yc/+YnCw8O1fv16TZs2rc1jR44cqY8++kgPPPCAXnrpJf3gBz+QxWJRv379lJGRoUsuuaTddhcuXKjf/e53uuqqq7Rt2zZ9//vfV3R0tOLj4zV58mTddNNNLukvAADonkzGmUv+d0J5ebmCg4NVVlamoKAgZ8SFLqitrVVOTo4GDhwoX19fp9b90ksv6Q9/+IO2b9/u1Hq7k3OdP97rAM7EvwvdiyvHwL6AMRAAAPdgZgwAAAAAAIAbkYwBAAAAAABwI5Ix3UhVfZVMj5pketSkqvoqT4fTpoULF/bqS5QAAJ7TE8ZBAAAAZyAZAwAAAAAA4EYkYwAAAAAAANyIZAwAAAAAAIAbkYwBAAAAAABwI5IxAAAAAAAAbkQyBudUW1urWbNmKSQkROPGjfN0OAAAuA1jIAAAcBWSMd2IxWzRjJQZmpEyQxazxdPhSJLefPNN7du3T8ePH9fmzZvd2nZdXZ0eeOABRUdHKyAgQMOGDVNubm6n9zeZTLLZbAoICFBAQIAyMzNd3wkAQId1t3Gwp4yBS5cudYxtLQ+TyaQnn3yyQ+UAAMD9vDwdAP7N18tX7819z9NhtJKTk6PBgwfLx8fH7W3ffvvtqqmp0ddff63o6Gjt27dPISEhXdr/yy+/1IgRI1waNwCgc7rbONhTxsB58+Zp3rx5jtdff/21xo0bpxtuuKFD5QAAwP2YGdPHJCYm6vHHH9fYsWPl7++v6dOnq6SkRPfcc49CQkKUkpKiL7/8UpJ0//3367HHHtOqVasUEBCghx9+WJmZmVqyZEmrOqdPn65f//rXTo1z9+7dWrFihV588UXFxMTIZDIpNTW13Q+iF7o/AKDv6a1j4JleeOEFTZ06VfHx8Z0qBwAArkcypg9avny53nzzTRUUFCgvL0/jxo3T5MmTVVxcrJtvvlmLFy+WJP3+97/XQw89pGuuuUaVlZV69NFHNX/+fL3yyiuOuo4fP65PPvmk1Tdup2v5gNveY/369W0et3btWiUlJek3v/mNoqKiNHjwYD3xxBPt9qmj+0+fPl2RkZGaMmWKvvrqqws5bQCAXqA3joGnq6mp0bJly3TnnXd2qhwAALgHyZhupKq+Sv6/8pf/r/xVVV/lsnbuueceDRgwQCEhIZo5c6YiIiI0Z84cWSwW3XLLLcrKylJ9fX2bx86bN09r165VQUGBJGnZsmW6/PLL2/127ZlnnlFpaWm7j/Hjx7d5XElJibKysmQYhvLy8vT222/rqaee0tKlSzu9/6effqrc3Fzl5uZqxowZmjp1qvLy8i7k1AEAXMgd42BvHANP989//lPe3t669tprO1UOAADcg2RMN1Ntr1a1vdqlbfTv39/x3GaznfXaMAxVV7cdQ3R0tCZPnuz4QLhkyRItWLDA6TEGBATIYrHosccek6+vr9LT03XHHXdoxYoVnd5/0qRJ8vHxkb+/v+6//36lpqZq9erVTo8dANB5rh4He+MYeLoXXnhBCxYskNVq7VQ5AABwD5IxuGAt07SzsrK0f/9+zZ49u919Fy9efNYdHE5/fPHFF20e13KnI5PJ1KGYLnR/STKbefsDAC5MdxwDWxw8eFDr1q3TokWLOlUOAADch79GccFmzZqlw4cP64EHHtCsWbMUEBDQ7r7PPvusKisr231cfvnlbR43YcIEpaSk6NFHH5Xdbte+ffv00ksv6brrruvU/llZWfr6669lt9tVW1urP/3pT9q9e7emTZvW9RMCAOgzuuMY2OKFF17QJZdcoqFDh3aqHAAAuA/JGFwwm82m2bNna82aNS6Zni1JFotFK1eu1MaNGxUSEqKrr75a9957b6tFEk//VvF8+xcVFenWW29VSEiIYmNj9dZbb+mDDz7QwIEDXRI/AKB36o5joCQ1Njbq5Zdfbndh3vOVAwAA9zIZhmF0tZLy8nIFBwerrKxMQUFBzoirT6qqr1LA483fsFU+WCl/b/9O1VNbW6ucnBwNHDhQvr6+zgyxTzjX+eO9DuBM/LvgPM4YBxkDu4YxEAAA92BmDAAAAAAAgBt5eToA/JvZZNYVCVc4ngMA0JcwDgIAgL6CZEw34mf10+cLP/d0GAAAeATjIAAA6Cv42gkAAAAAAMCNSMb0Yk5Ym7lP4rwBQM/Hv+Wdw3kDAMA9SMZ0I1X1VYr8XaQifxepqvqqTtdjsVgkSfX19c4KrU9pOW8t5xEA4B7OGAcZA7uGMRAAAPdgzZhu5mT1yS7X4eXlJZvNpqKiIlmtVpnN5Nw6qqmpSUVFRbLZbPLy4n8PAHC3ro6DjIGdxxgIAID7MNL2QiaTSdHR0crJydHhw4c9HU6PYzabNWDAAJlMJk+HAgC4QIyBXcMYCACAe5CMcaF/7PyHXtv9mu4ceaeuS73OrW17e3srJSWFadqd4O3tzTepANCDMQZ2HmMgAADuQTLGRbYWbtX8t+dLkt4/+L62/sdWjYga4dYYzGazfH193domAADdAWMgAADozvjqw0We3vy043ljU6N++sVPPRgNAAAAAADoLkjGuIC90a6V+1ZKkq659BpJ0sfffqzy+nJPhgUAgMv9fdvflfFMhu7/8H41GU2eDgcAAKBb4jIlF9hauFWltaXy9/XX5FGT9WXWlyopL9GrB17V3el3t3uc2WTWmJgxjucAAPQk+07u092r7lZDU4N2F+1WTGiM7h97f4ePZxwEAAB9BZ90XGBTwSZJUmL/RJnNZqUnpkuS3tn/zjmP87P6actdW7Tlri3ys/q5OkwAAJzqhW0vqKGpwfH6dxt+p8amxg4fzzgIAAD6CpIxLtCSjEnonyBJSktMkyR9degrNTUxZRsA0PsYhqE39rwhSZp31Tz5evvqeNlxvXPoHc8GBgAA0A2RjHGBTfmtkzFJsUmymC06VXlK20q2eTI0AABc4kj5EeWW5spsNmt48nANTx4uSVq2d5mHIwMAAOh+SMY4WUVdhXJKcyRJcVFxkiQfq48G9BsgSVp1aFW7x1bbq5X4h0Ql/iFR1fZq1wcLAICTfHnkS0lSbESsfKw+Gp7UnIz5bP9nHZ4VyjgIAAD6CpIxTra/eL8kKcAvQP6+/o7tyXHJkqS1h9e2e6xhGDpcdliHyw7LMAzXBgoAgBNtPLJRUvN6aZI0ZMAQeXt561TlKa07uq5DdTAOAgCAvoJkjJN9e/JbSVK/0H6ttifHNidjtudtZ90YAECvs/XoVkn/vkTX6mVVUmySJGlF9gqPxQUAANAdkYxxMkcyJqx1MmZg9EDHujHfFH/jidAAAHAJwzC0t2ivJCk6PNqxfUj8EEnS5zmfeyIsAACAbotkjJN9W9ycjIkKjWq13dvq7fi28N1D77o9LgAAXOVk9Umdqj0lSYoMiXRsHzKgORmzO3+3Ku2VHokNAACgOyIZ42QtM2POTMZIUkpciiS+IQQA9C4tY19oYKi8rd6O7dHh0QqyBcneYNe7uXwRAQAA0IJkjBM1NjU6FvA9c80YSRocP1iStC1vG+vGAAB6jX3F+ySdPfaZTCbH2Lc6e7Xb4wIAAOiuSMY4UW5pruob62W1WBUaGHpWeUK/BFm9rKqoqdDG4xvPKjeZTEqLTFNaZJpMJpM7QgYAoMscs0LDzp4VOnhAczJmQ86G89bDOAgAAPoKL08H0Ju0fBiNDI2U2Xx2nsvLy0tJMUnal7dPK7NX6rLoy1qV26w27b5nt1tiBQDAWRzJmJCzkzGpA1IlSTkncpRbnqvEoMR262EcBAAAfQUzY5yovdtan65lujbrxgAAeov2LlOSpCD/IMVFxkmSXjvwmlvjAgAA6K5IxjjRuRbvbdGyiO/O/J2qb6x3S1wAALhKXUOdDp06JKnty5QkaWjCUEnSBwc/cFtcAAAA3RnJGCdq77bWp4uLjJOfj59q62v1cf7Hrcqq7dVKfyZd6c+kq9pe7dJYAQBwhoMlB9VkNMnH6qMgW1Cb+wxNbE7GbMndoobGhnbrYhwEAAB9BckYJ+rIZUpms1mD45ovVXpr/1utygzD0J6iPdpTtEeGYbguUAAAnMRxiVJYv3YX3U3onyA/Hz9V1VZpzZE17dbFOAgAAPoKkjFOUlxdrJPVJyU1L+B7LulJ6ZKkjw98fM79AADo7jryRYTFbNGQAUMkSW/uf9MtcQEAAHRnJGOcpOXDaEhAiHysPufcNz0xXSaTSYeLDmtvyV53hAcAgEt0ZL00SUpLTJMkfbT/I5fHBAAA0N2RjHGSlg+j/cP6n3dffz9/JcUkSZJe3vOyS+MCAMCVWi5TOl8yJmNghixmi/KL87Xl+BZ3hAYAANBtkYxxko5+M9hiWNIwSdJ7+95zWUwAALiSYRgdHv9svjbHpUov7HrB5bEBAAB0ZyRjnKSj3wy2GD5ouCRpd/5uHTh1wGVxAQDgKscqj6m8rlwmk0mRwedeL02SRqaMlCSt2ruKBXoBAECfRjLGSS50ZkxYUJgGxQ6SIUN/2fEXSZLJZFJCcIISghPavSMFAADdRcvYFx4ULi8vr/Pun5GUIS+LlwpKCrSuYN1Z5YyDAACgryAZ4wR1DXU6dOqQpHPfTeJMY1PHSpL+ueufMgxDNqtNufflKve+XNmsNpfECgCAs3TkTkqn8/Pxc1ym+4etfzirnHEQAAD0FSRjnCD7VLYajUb5WH0U5B/U4eNGJI+Q1WJVQUmBPsrj7hIAgJ7FkYwJ6/gXEZdmXCpJen/P+yqrLXNJXAAAAN0dyRgnOP3D6IVMq/b18dWIwSMkSb/b9DtXhAYAgMt8W3xhl+hKUnJcsiJDIlVnr9PTO552VWgAAADdGskYJ9h38l+L94Z0/MNoiysyr5AkfbrvU+0t3quxz4/V2OfHqsZe49QYAQBwtr1FeyVdWDLGZDLpsmGXSZKe+eoZNTY1Ospq7DWMgwAAoE8gGeMEe07ukSRFhV14MiYuKk6DYgepqalJv9r0K20t3KqthVvVZDQ5O0wAAJymsr5SR8qPSLqw9dIk6eL0i2Xzselo6VG9mPWiY3uT0cQ4CAAA+gSSMU6w8/hOSVJMeEynjp80cpIk6fVtrzstJgAAXGl/8X5JUoBfgPz9/C/oWF9vX00YMUGS9Kv1vyLxAgAA+hySMV1U31jvmKYdE9G5ZEz6wHTFR8WrvqHemaEBAOAynblE6XSXZ14uH6uPcoty9cy2Z5wZGgAAQLdHMqaLvj35rexNdvl6+yo0MLRTdZhMJs24ZIaTIwMAwHVaFq/vH9a/U8f7+/pr6ripkqSHP31YFXUVTosNAACguyMZ00U7ju2Q1Dwr5kLupHSm1AGpGhQzyPHaMIwuxwYAgKvsLtotqfMzY6TmRezDg8JVUlWiBz5/wFmhAQAAdHskY7qoZb2Y2IjYLtVjMpl03YTrHK9f2/dal+oDAMCVth/bLqlr45+Xl5dmTZglSXp+0/Naf2S9M0IDAADo9kjGdNGO4/+eGdNV/UL7yepllSTd/8H9OlZxrMt1AgDgbGW1ZcopzZEkxUR2bfzLSMrQmNQxMgxDt6+6XeF+4YqwRTgjTAAAgG6LZEwXGIahrYVbJUmxkV2bGSNJPlYf/fLuXyo6PFplNWWa884c7jABAOh2WmaFhgSEyN/3wu6k1JbvTviugv2DdbT0qEYPGK0TD5yQv3fX6wUAAOiuSMZ0wYGSAzpVe0pWi7XLlym18Pby1oKrF8jqZdWGQxv04OcPOqVeAACcpeUSpbjIOKfUZ/O1aeGMhbKYLfpw34f66dqfOqVeAACA7opkTBd8lf+VJCm+X7wsFovT6o0Oj9bsK2ZLkn677rf6+46/O61uAAC6yrFejBNmhbYYGD1QcybOkSQ9vvZx/WHzH5xWNwAAQHdDMqYLWpIxif0TnVJffUO9nn7zaT395tMaNWSUJo+aLEm6+927tWr/Kqe0AQBAV20p3CLJuckYSRqdOlqhAaGSpO+//309981zTq0fAACguyAZ0wUb8zdKkhL6JzilPsMwlF2QreyCbBmGoWsuu0YjkkeoobFBs16fpXf3v+uUdgAA6Kyy2jJlnciS5LwvI1oYhqFTlaccrxe/u1iPrntUhmE4tR0AAABPIxnTScXVxdpxrPlOSonRiS5pw2wya/60+Ro+aLgaGhv03de+qxe3v+iStgAA6IhNBZtkyFB4ULiC/INc1s7EERMlSY989ogWrFigGnuNy9oCAABwN5IxnfRJzicyZCg6PFrB/sEua8disei2q2/TyJSRamhq0KIVi/TDj3+oxqZGl7UJAEB7vjzypaTmNV5cafol0/XdK74rk0z6x45/aPTfRmt/8X6XtgkAAOAuJGM66eNDH0uShsQPcXlbFotF86+er6vGXCVJemLDE7piyRUqKC9wedsAAJzui7wvJLluVujpJmRO0OLrFyvAL0B7T+zV8GeH63df/k4NTQ0ubxsAAMCVSMZ0gmEYWpO9RpI0eMBgt7RpNpk189KZunXqrfL28taGwxuU/n/pWrZrGdfSAwDcorK+Uuvz1kuSBse7Z/wbMmCIfnjLD5USl6K6hjr96KMfafTzo7Uhb4Nb2gcAAHAFkjGdsLVwq/LK8uTt5a3k2GS3tj0mdYweuOUBxUfFq6y2TPPemqdJSyZp38l9bo0DAND3fJbzmeob6xUeFK7IkEi3tRscEKx7Zt2jm6fcLD8fP+08tlPj/z5e31n+He0t2uu2OAAAAJyFZEwnvLHnDUlS+sB0eVu9nVq3t5e3vL3OXWdUaJTuveFeTb94urwsXlqbu1YZ/5ehxe8t5tIlAIDLvH/wfUlSakKqTCaTS9pobxw0mUy6OP1iPXjrg7ok4xKZTWat2r9K6c+k69pXr3WsZQMAANATmAwnXONSXl6u4OBglZWVKSjIdXdW6A4amxqV9Kck5ZXl6fYZtyszOdOj8ZwsPam31r2lPbl7JEk+Xj66e/Td+v5F39fAUNcurtgX9aX3OoCO6Sv/LjQ0NSj2yVidqDqhu6+9W2mJaR6N53jJcb238T3tzN7p2DYubpwWj1qsG9JvUIB3gAej6536ynsdAAB3YGbMBVp9YLXyyvJk87VpaOJQT4ejiJAI3X3t3fred7+npJgk1TXU6elNTyv56WRd9+p1+iznMzUZTZ4OEwDQw31y6BOdqDohf19/tyxefz79wvrpjpl36Ce3/kQXp10si9mizfmbdcfKO9T/9/21aOUifXLoE9kb7Z4OFQAA4CzMjLlA0/4xTR9mf6jJoybr2vHXejqcVgzD0L68ffp82+f6Nu9bx/b44HjdNvw2zc+cr8Hh7llwsbfqS+91AB3TV/5duPmfN+u13a/psmGX6YZJN3g6nLOUV5Vr897N2rRnk4pKixzbQ/1Cdd3g6/Tdod/VpIGTmDHTBX3lvQ4AgDuQjLkAXx75Upe9eJnMJrMemv+QIkIinFq/vcGuv6/+uyTp9hm3y+pl7XRdx4qPad2Odfp639eqs9c5tmdEZej6Idfr2iHXanTMaJlNTI66EH3lvQ6g4/rCvwt5ZXlK+mOSGo1GPXDzA4qLinNJO84YBw3D0KHCQ9ry7Rbtyt6lqtoqR5nVbNXFcRdr6qCpujLpSo2OHi2rpfNjbV/TF97rAAC4i5enA+gpmowm/eijH0mSLkq7yOmJmJY2WtZ+6eqlRf3D++vGyTfq+suvV1ZOlrZ+u1XfHv5WWSeylHUiS//7xf8qKiBKVw28ShMTJ+qKhCuUHJbssgUZAQA916/X/1qNRqNS4lJcloiRnDMOmkwmDYodpEGxg3TDpBt0qPCQdmbv1O6c3SopL9EXeV/oi7wv9D+f/Y98vXw1JmaMLom7RBfHXayL4y5WTGCMM7sEAADQJpIxHfT0pqe14cgGeVu9NXXcVE+H02HeVm+NGjxKowaPUlVNlfbk7lFWTpa+PfytTlSe0NJdS7V011JJUnRgtC6Lu0xjYsZodMxojY4erVC/UA/3AADgSTuP79RzXz8nSZo2bpqHo7kwFrNFKXEpSolL0XcnfFfFZcXad2Sf9uft14H8A6quq9b6vPVan7fecUykf6SGRw3X8H7DNSxqmIb3G66hkUNls9o82BMAANDbkIzpgE9zPtUDHz0gSbrususUGtgzExT+fv4aO3Ssxg4dq4aGBh06ekgHCw4qOz9bucdydbTiqP6595/6595/Oo4ZGDpQw6OGa2jEUKVGpGpoZPPPIB+mJwNAb1dVX6Vb3rxFTUaTRiSPUHJcsqdD6jSTyaSIkAhFhETosmGXqcloUtGpIuUey9XhY4ebx8HioyqqKtInOZ/ok5xPWh0fGxSrlNAUJYclOx6DwgZpQPAAhfqGMrMUAABcEJIx5/H23rc17615amhq0MiUkbp02KWeDskpvLy8NDh+sAbHNy/oW99Qr8NHDyvvRJ6OHD+iIyeOqLi8WDmncpRzKkcr9q1odXy/gH4aGDxQCSEJSgxJVEJwghJCEpQQnKDowGg+mAJAD1dZX6nrXr1Oe4r2KMg/SLMnzvZ0SE5lNpnVL6yf+oX100VpF0mS6ux1Ol5yXIUnC1VYXKhjJ4+p4GSBqmqrVFBeoILyAn1++POz6vLz8lNcUNxZj37+/RTpH6lIW6Qi/SMV5hfGWm0AAEASyZh27Snao/9d979anrVckpSakKq5V83ttQkGby9vpcSnKCU+xbGtqrZKBUUFOlZyTCdKTuj4qeM6fuq4yqvKdbzyuI5XHtdXBV+1WZ/VbFWUf5T6B/RXv4B+6uff/IiwRSjEN0QhviEK9Qv993PfUAX5BMlitrirywCANhiGoU9yPtF/rf4v7S/eLx+rj+6YcYcCbYGeDs3lfKw+GtBvgAb0G+DYZhiGqmurVVRWpJOlJ3Wy7KROlp5UUWmRTpadVFVtlWoaanSg5IAOlBw4Z/1mk1lhfmGK8o9SpC1S4bZwBfsENz98gxXiG+J4fvrPIJ8g2aw22aw2xkkAAHqJPp+MaWhqUGltqQrKC7SveJ92HNuhDw99qK2FWyVJJpl0xcgr9J3LvtPnPgD5+/q3mj3TorquWidLT+pUxSmVlJe0+nmq4pSq66plb7KroKJABRUFF9RmywfOAO8A+Vv95e/tL3+rv2xWm+N5y3Y/Lz95W7zl4+Ujb4t383PLac//tf30bd4Wb3mZvWQxW2QxWVo9t5j/9fpfz1t+8i0mgN7KMAxV2at0vPK4dhft1tbCrXrn23e068QuSVKwf7Bun3G7EqMTPRuoB5lMJvn7+cvfz1+J/RPPKrc32FVWWabSylLHo6yqTKUVpaqsqXQ8aupq1GQ06WT1SZ2sPtnpeHwsPo7ETMvY2Oq11V++Xr5tjodtjZVtlXuZvdp81FTWdOFMAgCA0zklGdNyd+xJz02Sxc8iQ4ZOv2N2y3NDhuN1e89b9uvI8/aOb1XezvGNRqPKastUVf/vW16eKT0pXRNHTlR8ZLzslXbZZb/wk3MB6hrqpNrm57UVtTK8/n979/YS1RrGcfw3JbjNMlIRnOmEB7LRRjFxR0QnoYMRdIIgyIIK+heCoOi6uyAKuuqiooiyE3UTZYihQaURZBiRhwjSbGZ0Zjz07Au3Q6buZnTNZLvvJ8Jc75r1zPMq/YZ3zVoz7U8dT4hZmqWctBzlpOVIOePHh4aHFAwFFQgFFOwfeREa6A8oEAooFA4pPBBWOBJWf6Rf4YGwQpGQBodG5tYf9ssvf5I7+rno4szgyIKcA58ID+B/4mcZ8aXKSgAAB2tJREFUOLpPPLk3lceMq6n/fszA8IB6Qj0aHB6fbbNnz9bf3r9VVVGl9NR0hf3hqU1OnH6XHPzR3FlzNTdjrhZmTP5JU8PfhhUMB9Uf7ldfqE/BcFChcEihSEiRwcjI14HISEb+m42j2wcGB6LHifz754u+JKO1sSIjX8hAAACmz2UOJOq7d++Un5/vxPMBfgttbW3Ky8v71U8DwAxABuJPQwYCADB9jrwzJjMzU5L04cMHzZ8/34lDxszv92vRokVqb29XRkZyP+GH2smv/avrf/36VYsXL47+zgMAGUjtP6G2RAYCAOAkRxZjZs0auafG/Pnzf8mLA0nKyMig9h9U+1fXH/2dBwAykNp/Um2JDAQAwAmkKQAAAAAAQBKxGAMAAAAAAJBEjizGpKam6sSJE0pNTXXicNSm9oyt/6t7BzDz/Kn/J1H7z6o9E+oDAPB/4sinKQEAAAAAACA2XKYEAAAAAACQRCzGAAAAAAAAJBGLMQAAAAAAAEnEYgwAAAAAAEASxb0Yc+fOHS1btkyFhYW6cOHCuPHGxkYVFxeroKBAp06dcuRJSlJ7e7vWr18vr9crn8+na9eujdtn6dKl8vl8KisrU3V1tWO1JSklJUVlZWUqKyvT4cOHx40nqu83b95E65aVlSktLU03b94cs4/Tfe/cuVMLFizQnj17otti6a+trU0VFRUqKCjQ0aNHNZV7Q/9Yu7+/X9XV1SoqKlJJSYnOnDkz4ePWr1+voqKi6DxNxUR9xzK3TvQN4PdABpKBZOBYZCAAAFNkcRgcHLTCwkLr6Ogwv99vBQUF1t3dPWafiooKe/nypQ0ODlpFRYW1tLTEU2JSXV1d9vz5czMz+/Tpk3k8HgsGg2P2WbJkiQUCAUfq/SgrK+s/xxPV9/cCgYBlZWUlvO+HDx/arVu3bPfu3dFtsfS3a9cuu337tpmZ7dixI/rv6dTu6+uzR48emZlZMBi0oqIie/v27bjHrVu3btpzPlHfscytE30DmPnIwMmRgWSgGRkIAEA84npnzOiZIY/Ho3nz5qm6uloPHjyIjnd1dWloaEg+n08pKSnat2+fbt++7ciiUW5ubvRsT05OjjIzM9XT0+PIsacrkX1/79atW6qqqlJ6errjx/7ehg0bNG/evOj3sfRnZmpoaNC2bdskSTU1NVOagx9rz5kzR+vWrZMkpaenq7CwUB8/fpxKW3HXjoVTfQOY+cjAiZGBZCAZCABA/OJajOnq6pLH44l+v3DhQnV2dsY87pRnz57p27dvWrRo0ZjtLpdLa9euVWVlpa5fv+5oTb/fr5UrV2rNmjV6/PjxmLFk9X316lXt3bt33PZE9i3F1l93d7cyMzPlcrkm3We62tvb1dzcrPLy8gnH9+3bp/Lycp09e9axmj+b22T0DWBmIAPJQIkM/B4ZCADA1KXEs7NNcB3waADHMu6E7u5u1dTUTHitfn19vdxutzo6OrRx40aVlpaqoKDAkbrv37+X2+3Wq1evtG3bNrW0tCgjI0NScvr2+/2qr6/XlStXxo0lsm8ptv4SPQfhcFh79+7V6dOnJzwreunSJbndbvX09GjLli0qLi6Onk2cjp/NbTJ+9gBmBjKQDBxFBo4gAwEAmLq43hnj8XjGnPHo6OhQbm5uzOPTFYlEtHPnTh07dkyrV68eN+52uyWNnJmpqqrSixcvHKs9euySkhJ5vV61trZGxxLdtyTV1tZq8+bN+uuvvyZ9bonoW4qtv+zsbPX09ERfmDk5B2amAwcOqLq6esyNBb83OgeZmZnavXu3mpqaHKn9s7lNZN8AZhYykAyUyMDvkYEAAExdXIsxlZWVevXqlTo7OxUIBHTv3j1t3rw5Ou52uzV79mw1NzdraGhIly9f1vbt2x15omamgwcPauPGjdq/f/+48b6+PgUCAUlSb2+v6urqtHz5ckdqf/nyRZFIRNLIC43Xr18rLy8vOp7IvkdN9vbsRPY9Kpb+XC6XVq1apbt370qSLl686NgcHDt2THPmzNHx48cnHB8aGtLnz58ljZw9fPDggYqLi6ddN5a5TWTfAGYWMpAMJAPJQAAAHBPvHX9ra2utsLDQ8vPz7fz582ZmtnXrVuvs7DQzs4aGBvN6vZaXl2cnTpyY0l2FJ/LkyRNzuVxWWloa/dvc3Byt3dbWZj6fz3w+n5WUlNi5c+ccq11fX28lJSXm8/mstLTUbty4YWbJ6dvMrLe313JyciwSiUS3JbLvTZs2WXZ2tqWlpZnH47HGxsZJ+zt06JA1NTWZmVlra6uVl5dbXl6eHTlyxIaHh6ddu66uziSZ1+uN/tzv378/pnYwGLTy8nJbsWKFeb1eO3nypCN9P336dNK5dbpvAL8HMpAMJAPJQAAAnOAym+CCXwAAAAAAACREXJcpAQAAAAAAYHpYjAEAAAAAAEgiFmMAAAAAAACSiMUYAAAAAACAJGIxBgAAAAAAIIlYjAEAAAAAAEgiFmMAAAAAAACSiMUYAAAAAACAJGIxBgAAAAAAIIlYjAEAAAAAAEiifwDrXG+nl76K4wAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -760,34 +771,32 @@ } ], "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", + "squad = ['Szczesny',\n", + " 'Kim',\n", + " 'Buongiorno',\n", + " 'Bastoni',\n", + " 'Di Lorenzo',\n", + " 'Kostic',\n", + " 'Frattesi',\n", + " 'Barella',\n", + " 'Gonzalez N.',\n", + " 'Hojlund',\n", + " 'Rafael Leao']\n", "\n", "s = simulate_lineup(squad)\n", "\n", - "plot_lineup(squad, config_4231)" + "plot_lineup(squad, config_343)" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "15d5b4ca", + "execution_count": 20, + "id": "747b573f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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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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TJNXV1Z2RTY2KilJtbW2XygQAmMvTsc9lIMTAiooK7dq1S4Zh6NixY3rxxRf18MMP65lnnulw+0svvVQvvfSSNmzYILvdrj/84Q86duyYampqJElWq1XXX3+9vv/97yswMFCTJ0/Wj370I40fP76bRw8A4G3ein+Sb8ZAlx07dqiurk6vvPKKFi5cKJut7+bjcSEZM0A02BvUYG/wSFlDhw51Pw8JCTnjd8Mw1NDQcV2JiYmaO3euuzM8/fTTuv766z3Srs489dRTuv7669uNaulMYWGh5s2bp+985zu6+eab3cvDwsLc1w26VFdXKzw83OPtBQB4hidjn8tAiIFhYWGy2Wx64IEHFBQUpDFjxujmm2/WmjVrOtx+zpw5+t3vfqfbbrtNQ4cO1aZNm3TRRRcpNjZWkvTee+/pm9/8pv7973+rpaVFBw8e1D/+8Q898cQTHm87AKD3vBH/JN+MgacLCAjQpZdeqvfff7/T5I6ZSMag21xD1Hbt2qUDBw5o6dKlnW57++23n3FXpNMfH3744VnrOnTokNavX69bbrnlnO0qKirShRdeqOuuu0533313u3V5eXnavXu37Ha7e9m2bds0duzYc5YLAICLGTFw3LhxkiSLxdLldt18883as2ePysvL9eSTT2rPnj2aPXu2JGnLli2aNm2a5syZI6vVqszMTF111VV65ZVXurHnAABfNxBjYEfsdrsOHjzY5fK9hWQMum3JkiUqKCjQnXfeqSVLligsLKzTbR9//PF2d0X68uNc1+o99dRTmj59ukaPHn3W7YqLizVnzhwtW7ZM99577xnrZ82apZiYGD344INqbm7W66+/rg8++MDro3oAAIOLGTFw1qxZysrK0v333y+73a79+/dr5cqVuvzyyzvc3m63a9u2bXI4HCovL9d3vvMdDR8+XJdccokkafr06dq0aZM2bNggwzBUUFCgF154QRMmTOj9AQEA+IyBGAPXrVunjRs3qqWlRS0tLVq5cqXef/99XXzxxb0/IL1EMgbdFhISoqVLl2rt2rVeTWa0tbXpb3/7W6cT956eUX3yySd16NAhPfLIIx1mXP39/fXyyy/r7bffVlRUlO644w4988wzGjFihNfaDwAYfMyIgTabTS+//LI2btyoqKgoXXLJJbrjjjvaTZJ4eoyz2+266aabFBERoezsbLW2tuqVV16R1er8mHf++efroYce0q233qqIiAjNmDFD559/vu655x6vtB8AMDgNxBhYX1+vb3zjG4qNjdWQIUP0xz/+UatXr+7S3KXeZjG+PPVwD9TU1CgyMlLV1dWKiIjwRLtwmvqWeoX90pl1rLurTqEBoWfdvqmpSUePHtXw4cMVFNR396YfqM52/HivA/gy/i54R3djnwsxsHeIgQC6g78LntfT+CcRA3vL7BjIyBgAAAAAAAAT+fV1A3BuVotVs4fNdj8HAGCwI/YBAHwR8c93kIwZAIL9g/XBjR/0dTMAADANsQ8A4IuIf76DVBsAAAAAAICJSMYMYh6Ym9kncdwAYODjb3nPcNwAYODjb3nPmH3cSMYMAPUt9Yr/dbzifx2v+pb6c25vs9kkSS0tLd5u2qDkOm6u4wgAMF93Y58LMbB3iIEA0Ld6Gv8kYmBvmR0DmTNmgChrKOvytn5+fgoJCVFpaan8/f3d91jHuTkcDpWWliokJER+fnQPAOhL3Yl9LsTAniMGAkD/0JP4JxEDe6MvYiCRdhCyWCxKTEzU0aNHVVBQ0NfNGXCsVqvS0tJksVj6uikAgG4iBvYOMRAABi5iYO+YHQNJxgxSAQEBysrKYohaDwQEBJBFBoABjBjYc8RAABjYiIE9Z3YMJBkziFmtVgUFBfV1MwAAMB0xEADgq4iBAwOnPgAAAAAAAExEMgYAAAAAAA+oaKzQ8erjfd0MDABcpjQAWC1WTU6a7H4OAMBgR+wDAAw0G45t0Px/zFeDvUGPLHxEd0y9o9tlEP98B8mYASDYP1ibbtvU180AAMA0xD4AwEDz/bXfV4O9QZJ051t3aunopUoJT+lWGcQ/30GqDQAAeExJXYn+uvWvOlJ5pK+bAgCAaQ6WH9Sm4k2yWq2KjYhVa1urHvz0wb5uFvoxkjEAAMAjqpuqNfnJybr55Zs19vGx2l26u6+bBACAKV7e/7IkaUTyCC08b6Ekac2eNTIMoy+bhX6MZMwA0GBvUPoj6Up/JN097A0AgP7m4U8eVmFNoSSpoaVBt715W4/LIvYBAAaSD499KEkaPWy0cofnyma16UTlCX1c8nG3yiH++Q6SMQOAYRgqqC5QQXUBmVUAQL9kGIae2fmMJOmSaZfIYrFo45GN2lyyucflEfsAAAPFpmLnPC9pQ9IUFBikUWmjJEnP7HmmW+UQ/3wHyRgAANBre0r36FDFIfnb/HXhhAuVk54jSfq/bf/Xxy0DAMC7imuLVVxbLIvFopR454S9o9NHS5LeP/J+XzYN/RjJGAAA0GsfHftIkjQ8abgCAwI1eaTztpyv7n5VDoejL5sGAIBXbS52jgIdEj1EgQGBkuQeGXPgxAGVNpb2WdvQf5GMAQAAvbbh+AZJ0vDE4ZKkMRljFOAfoPLacr1fxFlBAMDgtfuUc8J616gYSYqLilNcZJwcDodeOPJCXzUN/RjJGC/ZUbJD0/48TVmPZmn1rtV93RwAALzKlYxJT0yXJAX4BbjPCq7at6qvmgUAgNftL98vSYqPjm+3fGTaSEnSm4feNL1N6P9IxnhBXUudLnv2Mn1W9JkOVRzS8heW680jdEAAwOBU2VipI5VHJEnDhg5zLx8zfIwk6d2D7/ZJuwAAMIMrGZMQldBuueukxMajG01vE/o/kjFe8MTmJ3Ss+piiw6OVOzxXhgzd+uqtanW09qg8i8WinPgc5cTnyGKxeLi1AAD0zu5S5/DsqLAohQSGuJePSR8jiywqKC3Q3sq93SqT2AcAGAgMw9D+MmcyZkjMkHbrslKyZLVadar6lLaWbu1SecQ/30EyxsMMw9ATnz8hSZo/Zb6+Nv9rCgkKUVFlUY/vKBHiH6Ld39qt3d/arRD/kHO/AAAAE7mulU+MTWy3PCwkzH3Z0j/2/aNbZRL7AAADQVlDmSqbKmWRRXFRce3WBQUGKSMxQ5L0/MHnu1Qe8c93kIzxsK0nt+pgxUEF+AdoYvZEBQUGae7EuZKk333yO+4VDwAYdHad2iXpzGSM9MWlSq/vf93UNgEAYAbXJUpR4VEK8As4Y/2oYc5Lld4+9Lap7UL/RzLGw14/6PywOTJ1pPu2ZtNzp8vf5q/80ny9UfBGXzYPAACP21XqTMYMjR16xrqxmWMlSTuO7+DWngCAQcd9iVL0kA7X56TnSJK2H9+uBnuDae1C/0cyxsNcyRhXp5Ok0KBQTRw5UZL0m09/0+0yG+wNGvPYGI15bAwdGADQ73R2mZLk/HA6JHqIHA6Hntn3TJfLJPYBAAYC9+S90Qkdrk+MTVRkaKTsrXa9eOTFc5ZH/PMdJGM8qLqpWp8UfiJJGj1sdLt1F+RdIElaf2C9iuqKulWuYRjaU7pHe0r3cJkTAKBfOVV/SqUNpbLI0ulZQdfomBf3nftDqAuxDwAwELhvax0V3+F6i8Xi/m645uCac5ZH/PMdJGM86NOiT2XIUGxErKLCo9qtS01IVdqQNLU52vSbzd0fHQMAQH/kmi8mNjJWAf5nXisvSXmZeZKkT49+ylk+AMCg4rpMqbORMZI0Ot2ZjPnwyIemtAkDA8kYD3KNihmeOLzD9eePPV+S9MzWZ9TmaDOtXQAAeMue0j2SpKExZ84X45KSkKLI0Eg125v1r0P/MqtpAAB4VaujVYcrD0s6ezImOzVbNqtNJytP6pOTn5jVPPRzJGM8aGPhRknSsKHDOlw/IXuCggODVVpTqn8d5MMoAGDg21u6V5I0JKbjS5QkyWqxamyG81Kl1XtXm9IuAAC8raCqQK2OVvnb/BUZFtnpdsGBwRqV5ryr0l93/dWs5qGfIxnjIQ7D4R4Zk56Y3uE2AX4Bmjp6qiTp95t+b1bTAADwmn3l+ySd/YygJI3LGidJen//+6pvqfd6uwAA8LaDFQclSXFRcbJazv7VenzWeEnSa3tfYy4YSCIZ4zH7y/arqqlKAX4BSopL6nS7GWNnSJI2HtmoI1VHzGoeAABe0ZWRMZKUmZyp6PBoNbU06a97OCsIABj4DpQfkNT55L2ny83Ilc1qU1FFkT49+am3m4YBgGSMh7guUUobkiab1dbpdkOihygrJUuGYeh/N/1vl8q2WCwaFjlMwyKHyWKxeKS9AAD0VnVTtU7UnZCkTu+k5GK1WDVl1BRJ0t+2/+2cZRP7AAD93cFy58iYriRjggOD3XdVemz7Y51uR/zzHSRjPGTjcWcyJn1o+jm3dU3ku3rbatnb7OfcPsQ/RPnfy1f+9/IV4h/Sq3YCAOAp+8qclyhFhEYoODD4nNtPGe1Mxnxe8LmOVJ99dCixDwDQ37kuU+pKMkaSpuY4p6xYs3ONmlubO9yG+Oc7SMZ4iGtkTGfzxZxubMZYRYREqLqhWk/tfsrLLQMAwDtcyZhzjYpxiY+KV0ZShgzD0P989j/ebBoAAF7X3WTMmPQxigiNUE1DjVbuXenFlmEgIBnjAdVN1e5be3Z2J6XT2Ww2Tc+dLkn61Ue/ksNweLV9AAB4Q3eTMZI0a9wsSdLqravV0NLglXYBAOBtLW0tyq/Kl+ScwLcrbDabzss5T5L0x81/9FbTMECQjPGAz4o+kyFDsRGxCg8J79JrZo2fpUD/QOWX5usfe/5x1m0b7Y2a8uQUTXlyihrtjZ5oMgAAvba3zDl5b0LM2e+kdLqxmWMVHR6t2sZaPbrt0U63I/YBAPqzo5VH5TAcCvQPVERIRJdfNz13uqwWq7Yf264PCz88Yz3xz3eQjPGAc93SuiOhQaHus4P3r7v/rKNjHIZDm4s3a3PxZkbRAAD6jZ6MjLFZbe7498jHj3Q6dxqxDwDQn51+J6XuTLQbHR6tidkTJUk/+/BnZ6wn/vkOkjEe8EnRf5IxXZi893RzJsxRoH+gjpQe0ePbHvd8wwAA8JKWthYdqjgkqXvJGEmakTtDYcFhOll9Uo98/ogXWgcAgHd1d76Y0100+SJJ0roD6/T5yc892i4MHCRjeskwDPfImK7MF3O60OBQd0f86bs/VU1TjcfbBwCANxyuOKw2o02B/oGKDIvs1msDAwJ18ZSLJUn/8+H/qL6l3htNBADAa/aX7ZfUs2TM0NihGpc5TpL0nbe/49F2YeAgGdNLhyoOqaKxQv42fyXFJXX79XMmzFFcZJwq6yv1o3U/8nwDAQDwgp2ndkqShsQM6dbwbJfzc89XdHi0KuoqiH8AgAFnd+luSc442BOXnn+pbFabPjnyiV448IInm4YBgmRML7lGxaQkpMjP5tft1/v7+euKmVdIkp789EmtP7bek80DAMArdpTskCSlxKX06PV+fn5aMmuJJOlPn/xJ20u2e6xtAAB4k2EY2nVqlyQpMTaxR2XER8W751D77pvfVYOdOwz6GpIxveRKxgwfOrzHZeRm5GryyMkyDEPLX1zOcG0AQL/nSp4kxvXsQ6gk5WXmKXd4rtocbbp2zbWdTuYLAEB/UlxbrOrmalktViVEdf2Ogl928dSLFRkaqeLKYi5X8kEkY3ppY+FGSVLa0LRelXPlnCsVGRqpoqoiXfPSNTIMo936uJA4xYV07f71AAB4m2tkTE8u0T3d0jlLFRwYrL0n9uqOd+5ot47YBwDoj1yXKMVFxcnPr/tXR7iEBIbomnnXSJL+uumvev3Q685yiX8+gWRML1Q1VbnPDA5P7PnIGMnZEa+/5HpZrVa9uvdV3f/R/e51oQGhKv1RqUp/VKrQgNBe1QMAQG9VNVXpWPUxSb0bGSM5b/F57UXXSpL++MkftXrPaknEPgBA/7X7lDMZMzRmaK/LGp0+WjNyZ0iSrnnhGpXUlRD/fATJmF74sOBDOQyH4qPiu30niY5kJmfqyllXSpIeeO8B/WXbX3pdJgAAnuYaFRMdHq2QwJBel5eXmee+bv76f1+vDcc39LpMAAC8pbfzxXzZFbOuUGpCqmqbarVg9QJVN1V7pFz0byRjeuH9/PclSdkp2R4r84K8CzRz3EwZMnTry7fq2d3PeqxsAAA8YeuJrZKk5Lhkj5V5+czLlZOeI3ubXYtWLdK2E9s8VjYAAJ609aQzDnoqGRPgF6BbFt+i8JBwHSo9pLnPzGUeUR9AMqYXPsj/QJKUmZLp0XKXzFqiaTnTZBiGVrywQg99+pDmrJyjOSvnqNHe6NG6AADork+LPpUkDRs6zGNl2qw23XDJDUobkqaaphrN/NtMTXpiErEPANCvNLU2aeepnZKktCG9mzf0dFHhUfrG5d9QUECQthRuUeJDiSqpK/FY+eh/SMb0UEVjhbad3CbJeXmRJ1ktVi2bu8ydkPnhmz/UuoJ1WlewTg7D4dG6AADoLtedBD35IVSSAgMCdfsVtyt9aLrqmuu05eQWYh8AoF/ZfnK7Wh2tCgsOU3R4tEfLTolP0S2Lb5Ek1TbXaubTM1VcW+zROtB/kIzpobWH1sqQoaExQxUZ2vv5Yr7MarXqmnnX6JJpl7RbfqLuhMfrAgCgq0rqSnS06qgssng8GSM5J7T/5hXf1IjkEe5l96+/X22ONo/XBQBAd20u3ixJSk1IlcVi8Xj5p9+l92DpQY3/03itz1/v8XrQ90jG9NArB16RJOUOz/VaHRaLRZdMu8R9lwlJmvzkZD29/ekzbn0NAIAZXJcoDYkZouDAYK/UERgQqJsX3+z+/dcbfq0Zf52hwxWHvVIfAABdtal4kyTPjw7tSEJUgkrrSnXh0xfqgfUPqNXR6vU6YR6SMT1gb7PrjUNvSJJyhud4vb7xWePdz6ubqnXDSzfowqcv1N7SvV6vGwCA07nmS8tIzPBqPTabzf08wC9AnxV+ptzHc/XLj36p5tZmr9YNAEBn1hWskySlD033el3/tfS/NHnkZDkMh+59/15N+NMEfV78udfrhTlIxvTAh8c+VFVTlUKDQk3phKdbMHWB/G3+Wpe/Trl/zNWNa27UsepjprYBAOC73j7ytiQpKzXLtDp/sOwHykrJUpO9SXe/e7dGPjZS/977b+aSAQCYKr8qX/lV+bJarcpI8u5JCUkKCAjQivkrtPzi5QoODNaukl2a+uepuu3V23SilukrBjqSMT3wjx3/kCTlZebJajX3EM6dNFc/XvFj5WbkymE49Ldtf9OIR0foxjU3amfJTlPbAgDwLSdqT2jXqV2yyKLs1GzT6o2OiNY3l3xTKy5eoYjQCBVUFmjpc0uV93ient/9PEkZAIAp3j/6viQpLSFNgQGBptRpsVg0dfRU3fW1uzQhe4IchkN//vzPyvh9hn78zo91qv6UKe2A55GM6aYGe4Oe3/O8JGnyqMmm1RvgF6AAvwBJUlxUnG699FZ976vf04jkEbK32fW3bX9T3uN5mvv0XD23+zk1tTaZ1jYAgG9Ye3itJCklIUWhwaFer+/02Ge1WDVl9BTdfd3dmj9lvgL9A7X71G5d/a+rlflopn7z8W9U3lDu9TYBAHzXW0fekiRlpXh3dOjp8c8lIjRCN1xyg/5r6X9p2NBhampt0v9u+F+lPpyqr7/yde0r2+fVNsHzLIYHZoKtqalRZGSkqqurFRER4Yl29VtPb39aN7x0g2IiYvTTG34qq6Xv81lHTxzVuq3rtP3wdvfEvpFBkbpmzDVaMXaFZqTOkM1qO0cp6Apfeq8D6Bpf+ruweNVivX7wdV0y7ZIz7vZntvqmeq3ftl7rt69XY3OjJCnAFqClOUv1tbFf00UZFynAFnCOUtAdvvReB9A1vvR3obm1WfG/jldtS63u+OodGp44vM/aYhiGdh3Zpbc3v61jJV9MWXFB2gW6ZcItuirnKoUFhPVZ+wYjb7zXScZ0g2EYmvinidp2cpsWT1+si6dc3NdNaqe8plyf7P5Em/ZuUlVdlXt5bEisvpL9FS0ZtUTzMuYpxD+k7xo5wPnKex1A1/nK34WKxgoN+c0QtTpa9ZOv/URDY4b2dZMkSc32Zm3Zv0Ubdm5QYWmhe3lUcJSuGn2Vrhx1peakz1Gwv3fu/ORLfOW9DqDrfOnvwhsH39CiVYsUERqh+26+r1+clDcMQ0eKj+j9Le9r99HdMuT8ah/iH6LLR16uK0dfqUtGXEJixgO88V7380gpPuLdo+9q28ltCvAL0IzcGX3dnDPERsRq8fTFWjhtoQ4WHtTmfZu16+gulTeU66/b/qq/bvur/K3+Oi/lPM0bPk9zh8/VtJRpnDkEAJzTszufVaujVYmxif0mESNJgf6Bmp47XeeNOU/HSo7p8/2fa9vBbapqqNKft/xZf97yZwX6BerC9Au1aMQiXZx5sUbGjpTFYunrpgMABpC/7/i7JCkvI69fJGIk53wymcmZykzOVFVtlTbt26RP93yqsuoyPbvrWT2761kF+gXq4oyLtThrseYNn6cRMSOIgf0EI2O6yGE4NOXJKdpyYotmjZulK2dfaVrd9la7/vr6XyVJNy26Sf5+/l1+bVtbmw4XH9bOIzu168guVdZWtlsf6BeoiUMn6ryU8zQteZqmJk9VelQ6HbQTvvBeB9A9vvB3wWE4lPOHHO0v36+ls5dq5riZXq+zN7HP4XDocNFhbT20VXuO7mk3WlSS4kLiNDNtpi5Iu0Az02Zq3NBxnJjoAl94rwPoHl/5u3Cq/pRSHkqR3WHXD6/5oVITUr1WV2/in+QcLZN/Ml87Du3QjsM7VF7Tfj611IhUXZRxkS5Mv1DTUqYpKyaL735dwMiYPvSXrX/RlhNbFBQQZPrlSQ7DoT35e9zPu8Nmsyk7NVvZqdm6ctaVKq8u14HCAzp4/KAOFh5UXWOdNhZu1MbCje7XxIXEKS8hT2OHjFVuQq7GJozVmIQxDG8DAB/10r6XtL98v4ICgjRl9BRT6uxN7LNarcpKzVJWapaMOYZOVpzU3vy92luwV/kn8lXWUKYX972oF/e9KEnyt/prTMIYTRw6URMTnY+xQ8YS9wAAkqRHP31UdoddaUPSvJqIkXoX/yTnaJnhicM1PHG4vnLBV3Si/IR2HdmlA8cP6OiJozpec9x91YQkRQVFaVryNJ2Xcp4mJ03W2ISxSotMI0FjApIxXXCw/KC+9+b3JEmXTL1E4SHhfdugHrJYLIqLilNcVJxm5M6QYRgqrSpVwckCFZQU6FjJMRWVFqmsoUzv5b+n9/Lfa/f6tMg0Zcdka0TMiHaPjOgMrsUHgEGqubVZP3nnJ5KkWeNmKSggqI9b1D0Wi0WJsYlKjE3U3Elz1draquOlx3Wk+IiOFB3R0RNH1dDcoG0nt2nbyW36y7a/uF+bEpGi0XGjNSpulPtndmy2EsMT+80QdQCAd52sO6mHP3lYkjRv0rw+bk33WCwWJcUlKSkuSfOnzleLvUVHio/oQOEBHS0+qsJThapqqtLaw2vdd0yUpPDAcI2NH6uxQ8ZqbMJYjY4frRExI5QSkUL88yCSMedwsu6kFj6zUPX2eo1IHqFZ42f1dZM8xmKxKCE6QQnRCe4znfZWu06Un/jiUeb8WdNQo2PVx3Ss+pjeOfrOGWUNCRuitIg0pUSkKCUiRakRqe7nKREpSgxPVJDfwPoADwCQ7nr3Lh2sOKiw4DDNnTS3r5vTa35+fu4zhvMmzZNhGKqorVDhqUIVlhaqqLRIhacKVdNQo8KaQhXWFOrtI2+3KyPAFqC0yDQNjxqu9Kh0pUela1jkMA2LGqak8CQlhiVykgIABgHDMPSNV7+henu90oakKS8zr6+b1CsB/gEaNWyURg0bJUlqbWtVcVmx++R8UWmRSipLVNtcq48LP9bHhR+3e32gLVDp0enKisnSiOgRyozJVGpEqpIjkpUSkaKE0ASSNd1AMuYstp/criX/XKKjVUcVGxGr6y65Tlbr4H5z+fv5K21ImtKGpLVbXtdYp5KKEpVVl6msukzl1eUqqyrTqapTamppUkldiUrqSrSpeFOnZYcHhCshNKHDR3xIvGJDYhUdFK2ooCj3w9/WvWskAQCe8/tPf+8+G3jtRdcOuFExXWGxWBQbEavYiFiNGzHOvbyusU6nKk/pVOUplVSW6FSF82d5Tbla2lp0qOKQDlUc6rTciMAIJYYlKjE8UYlhiRoaNlSJYYmKC4lTbEisYoJjFBsc6459xDsA6F8Mw9Dd796tl/e/LD+rn66Zd82gu3THz+bn/u43U8754FrbWnWq8pROlJ9QcVmxTpafVGl1qcqry9Xc1qz9Zfu1v2x/h+X5W/2VGJ7oPjE/JHSI87teaLziQ+IVHxrv/u4XFRQ16I5nd5GM6UB+Vb5+98nv9H+b/k+tjlbFRsTq9ituV2RoZF83rc+EBYcpLDlMmcmZ7ZYbhqH6pnpV1laqqrZKVXXOR3Vdtft5VV2VWttaVdtSq9qWWh2uPNzlekP9Q9slZ6KDoxUZGKnwgHCFBYQpNCBUYQFhzuf+oWddFuQXJD8rb3kAOJdT9ad01zt3uS/ZWTB1gcYMH9PHrTJXWHCYwoLDlJGU0W55W1ubquqrVFlTqYraClXUfPGorK1UTUON7K121TTXqKa5RvvLO/7A+mURgRHuBE1McIzCA8MVHhCuiMAIhQeEKzyw8+ch/iEK9gtWsH+wgv2CZbPavHFIAMBnnKw7qe+v/b5W71otSbrqwquUFJfUx60yh5/Nz31p06SRk9zLHQ6HKusqVVblPDlfVlWm8ppy9/e+mvoa2R1299UU56zH6tfuZHxkYGT7n0Htfw8LCFOIf4hCA0IV6h/qfh7sFzxgkzo+/c3U3mZXVVOVimqLdKD8gHaW7NQ7R9/RZ0WfuSdLys3I1bXzrlVocGgft7Z/slgs7g+snU1mZRiGGlsaVddQp7pG56O2odb9vK7B+Xtjc6MamhvU2NyoppYmSVK9vV719noV1RZ5pL1Wi1VBfkHtHoG2wDOX+Z22zBakAFuAAmwBMpp6ffMxAOhXHIZDVU1VOlp5VNtLtuutw2/plQOvqMHeIIssWnjeQtMnru/PbDabeyRNRwzDUFNLk2rqa1TTUOP8WV+j2oZa1dTXqKGpQfVN9apvqldDkzPmGTLcyZv8qvxet9Hf6u9M0PwnOdPZT1ds6+qjtbG1120DgP7GMJx/gwuqC7T1xFatPbxWL+17SY2tjbJarbpy1pU6b8x5fd3MPme1Wt3xb6RGnrG+ra1NNQ017U7Mu77z1TfWt/v+12xvVquj1X11RW9YZFGIf8gZiZpg/2AF2gIV6BeoQFugAmwB7ueu5QG2gHbbnL6tv81f/lZ/+Vn95G/zV0t9S6/a2RGPJGNcd8e+8IkLZQu2yZCh0++Y7Xp++vKzPXe9prvP3fXp7HXYHXZVN1Wr0d7Y6T5lpmRqVt4sjUwbKdmlJntTzw6OBzS3Nkv/qb6ptkmG38BLCFhlVYQtQhFhEVIXbk7hMBxqbGlUU3PTFz+bG52PlkbZ7XY1tzarxd7ift7c0ix7q13N9ma1tLaoxd6iZnuz7Hb7F+XKoYb//OuRZucPD9wRHsAgca4Y6Nrmy/GqK7Gvo+268pp268+yXYO9QRWNFR3+TUuOT9bi6YuVkZSh5trmnhyaXhnIsc8iiyL9IhUZESmd4+6XDsPhPBnR1KCG5gZngqalUc0tzWpqaVJLa4vzZ4szpjW1NKnF7lzWbG92xjx7ixyOL+64YZdd1f/551HEQABf0lEMPH356c97GvfOtl1n69qtP8t2zW3NqmioUKvjzGRzakKqLj3/Ug0bMkxNNeZ9FxzI8S9YwQoODVZiaKI0pPPt7G121TfWq66pTs3Nzc7vey1Nzofr+19Lk5pbmt0n6ptbnd/rXN/z2traJDn/f+ub6lWvepWq1Hs754UYaDE8UNqRI0eUmZl57g2BQeLw4cPKyMg494YABj1iIHwNMRCACzEQvsaTMdAjI2NiYmIkSceOHVNkpLnzqtTU1Cg1NVXHjx9XRMQ5Tj9R94Cvu6/rr66uVlpamvs9DwDEQOr2hbolYiCAMxEDqdsX6pa8EwM9koxx3WEoMjKyTw6MJEVERFC3D9Xd1/UP9rtqAeg6YiB1+1LdEjEQwBeIgdTtS3VLno2BRFMAAAAAAAATkYwBAAAAAAAwkUeSMYGBgbr33nsVGBjoieKom7r7bf19ve8A+h9f/ZtE3b5Vd3+oH0D/46t/E6nbt+r2Vv0euZsSAAAAAAAAuobLlAAAAAAAAExEMgYAAAAAAMBEJGMAAAAAAABMRDIGAAAAAADARN1Oxrz66qsaOXKksrKy9Oc///mM9Z999pnGjBmjESNG6IEHHvBIIyXp+PHjmjNnjnJycpSXl6fnn3/+jG3S09OVl5en8ePHa9GiRR6rW5L8/Pw0fvx4jR8/XrfeeusZ67213/v373fXO378eAUHB+ull15qt42n93vJkiWKjo7WVVdd5V7Wlf07fPiwJk+erBEjRuj2229XT+aG/nLdDQ0NWrRokUaNGqXc3Fw9+uijHb5uzpw5GjVqlPs49URH+92VY+uJ/QYwMBADiYHEwPaIgYDvIAYSA4mB7fV6v41usNvtRlZWllFYWGjU1NQYI0aMMMrLy9ttM3nyZGP79u2G3W43Jk+ebOzcubM7VXSquLjY2Lp1q2EYhlFSUmIkJycbdXV17bYZNmyYUVtb65H6viw2Nvas672136erra01YmNjvb7f7733nvHyyy8bS5cudS/ryv5deeWVxiuvvGIYhmFcccUV7ue9qbu+vt744IMPDMMwjLq6OmPUqFHGwYMHz3jd7Nmze33MO9rvrhxbT+w3gP6PGNg5YiAx0DCIgcBgRgzsHDGQGGgYPdvvbo2McWXFkpOTFR4erkWLFmnt2rXu9cXFxWptbVVeXp78/Py0fPlyvfLKK93LDnUiMTHRnelKSEhQTEyMKioqPFJ2b3lzv0/38ssva968eQoNDfV42ae78MILFR4e7v69K/tnGIY2btyoxYsXS5Kuv/76Hh2DL9cdEhKi2bNnS5JCQ0OVlZWlEydO9GS3ul13V3hqvwH0f8TAjhEDiYHEQGDwIwZ2jBhIDOzNfncrGVNcXKzk5GT37ykpKSoqKuryek/ZvHmzHA6HUlNT2y23WCyaNWuWpk6dqhdeeMGjddbU1GjSpEm64IILtG7dunbrzNrv5557TsuWLTtjuTf3W+ra/pWXlysmJkYWi6XTbXrr+PHj2rFjhyZOnNjh+uXLl2vixIl67LHHPFbnuY6tGfsNoH8gBhIDJWLg6YiBgO8gBhIDJWLg6Tyx337d2djo4BooV+VdWe8J5eXluv766zu8TnHDhg1KSkpSYWGh5s6dq3HjxmnEiBEeqTc/P19JSUnatWuXFi9erJ07dyoiIkKSOftdU1OjDRs2aPXq1Wes8+Z+S13bP28fg6amJi1btky/+c1vOswIr1q1SklJSaqoqNAll1yiMWPGuDOpvXGuY2vG/z2A/oEYSAx0IQY6EQMB30EMJAa6EAOdPLHf3RoZk5yc3C7bU1hYqMTExC6v763m5mYtWbJEd911l2bMmHHG+qSkJEnOrNS8efO0bds2j9XtKjs3N1c5OTk6cOCAe52391uS1qxZowULFigoKKjTtnljv6Wu7V9cXJwqKircb0pPHgPDMHTDDTdo0aJF7SZVOp3rGMTExGjp0qXatGmTR+o+17H15n4D6F+IgcRAiRh4OmIg4DuIgcRAiRh4Ok/sd7eSMVOnTtWuXbtUVFSk2tpavf7661qwYEG7BttsNu3YsUOtra169tlnddlll3WrQZ0xDEM33nij5s6dq+uuu+6M9fX19aqtrZUkVVVVaf369Ro9erRH6q6srFRzc7Mk50Hes2ePMjIy3Ou9ud8unQ1N8+Z+u3Rl/ywWi8477zy99tprkqSnn37aY8fgrrvuUkhIiH760592uL61tVVlZWWSnJnTtWvXasyYMb2utyvH1pv7DaB/IQYSA4mBxEDAVxEDiYHEQC/EwG5N92sYxpo1a4ysrCwjMzPTeOKJJwzDMIyFCxcaRUVFhmEYxsaNG42cnBwjIyPDuPfee7tbfKc+/PBDw2KxGOPGjXM/duzY4a778OHDRl5enpGXl2fk5uYajz/+uMfq3rBhg5Gbm2vk5eUZ48aNM1588UXDMMzZb8MwjKqqKiMhIcFobm52L/Pmfs+fP9+Ii4szgoODjeTkZOOzzz7rdP9uueUWY9OmTYZhGMaBAweMiRMnGhkZGcZtt91mtLW19bru9evXG5KMnJwc9//7m2++2a7uuro6Y+LEicbYsWONnJwc47777vPIfn/yySedHltP7zeAgYEYSAwkBhIDAV9FDCQGEgM9u98Ww+jBTcABAAAAAADQI926TAkAAAAAAAC9QzIGAAAAAADARCRjAAAAAAAATEQyBgAAAAAAwEQkYwAAAAAAAExEMgYAAAAAAMBEJGMAAAAAAABMRDIGAAAAAADARCRjAAAAAAAATEQyBgAAAAAAwET/PyvnhxcCPdf0AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -807,24 +816,30 @@ } ], "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", + "squad = ['Onana',\n", + " 'Di Lorenzo',\n", + " 'Posch',\n", + " 'Bastoni',\n", + " 'Strefezza',\n", + " 'Pasalic',\n", + " 'Miranchuk',\n", + " 'Ciurria',\n", + " 'Giroud',\n", + " 'Lukaku',\n", + " 'Abraham']\n", "\n", - "config_433_classic = [3, 6, 7, 9, 10, 17, 18, 19, 26, 28, 30];\n", + "s = simulate_lineup(squad)\n", "\n", - "s = simulate_lineup(squad, MOD = True)\n", - "\n", - "plot_lineup(squad, config_433_classic)" + "plot_lineup(squad, config_343_classic)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5518f8c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/fantacalcio/seriea_calendar.xlsx b/fantacalcio/seriea_calendar.xlsx index 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