From c4a50cecd212bf78614bbb0ab4cace77977736a9 Mon Sep 17 00:00:00 2001 From: Giuseppe Musicco Date: Sat, 28 Jan 2023 11:06:11 +0100 Subject: [PATCH] Update Jan before matchday 20 --- .../1_scraping_fbref-checkpoint.ipynb | 4195 +++++++ .../2_votes_dataset_creation-checkpoint.ipynb | 952 ++ ..._players_dataset_creation-checkpoint.ipynb | 3521 ++++++ ...er_match_dataset_creation-checkpoint.ipynb | 4583 +++++++ ...ng_match_probable_players-checkpoint.ipynb | 762 ++ ...6_neural_network_training-checkpoint.ipynb | 10029 ++++++++++++++++ 1_scraping_fbref.ipynb | 2994 ++--- 2_votes_dataset_creation.ipynb | 148 +- 3_players_dataset_creation.ipynb | 3132 ++--- 4_player_match_dataset_creation.ipynb | 3211 +++-- 5_scraping_match_probable_players.ipynb | 750 +- 6_neural_network_training.ipynb | 8393 ++++++++----- fantacalcio/Quotazioni_Fantacalcio.xlsx | Bin 71347 -> 72362 bytes fantacalcio/Quotazioni_Fantacalcio_old.xlsx | Bin 0 -> 71347 bytes fantacalcio/seriea_calendar.xlsx | Bin 15014 -> 19432 bytes ...tacalcio_Stagione_2022_23_Giornata_15.xlsx | Bin 0 -> 58369 bytes ...tacalcio_Stagione_2022_23_Giornata_16.xlsx | Bin 0 -> 58022 bytes ...tacalcio_Stagione_2022_23_Giornata_17.xlsx | Bin 0 -> 58448 bytes ...tacalcio_Stagione_2022_23_Giornata_18.xlsx | Bin 0 -> 58670 bytes ...tacalcio_Stagione_2022_23_Giornata_19.xlsx | Bin 0 -> 57586 bytes fbref_data/keepers_players.csv | 59 +- fbref_data/outfield_players.csv | 1011 +- fbref_data/season2122/outfield_players.csv | 802 +- fbref_data/season2122/teams.csv | 40 +- fbref_data/teams.csv | 40 +- fbref_data/teams_vs.csv | 40 +- mid_outputs/database_entries.xlsx | Bin 2749814 -> 3786651 bytes mid_outputs/database_entries_gk.xlsx | Bin 153871 -> 205437 bytes mid_outputs/match_probable_players.xlsx | Bin 14883 -> 14740 bytes mid_outputs/players_stats.xlsx | Bin 332049 -> 333861 bytes mid_outputs/players_stats_rwk.xlsx | Bin 497817 -> 474114 bytes mid_outputs/players_votes.xlsx | Bin 172625 -> 230662 bytes mid_outputs/team_data.xlsx | Bin 37299 -> 37773 bytes mid_outputs/~$players_stats.xlsx | Bin 165 -> 0 bytes outputs/pred_avg_seriea.xlsx | Bin 706930 -> 1401552 bytes outputs/pred_matchday_20.xlsx | Bin 0 -> 1402401 bytes outputs/pred_matchday_base.xlsx | Bin 1444353 -> 1444150 bytes outputs/~$pred_matchday_13.xlsx | Bin 165 -> 0 bytes saves/modelb.data-00000-of-00001 | Bin 36549 -> 36597 bytes saves/modelb.index | Bin 2799 -> 2799 bytes saves/modelb_gk.data-00000-of-00001 | Bin 32718 -> 32742 bytes saves/modelb_gk.index | Bin 3179 -> 3179 bytes saves/scaler.pkl | Bin 3362 -> 3362 bytes saves/scaler_gk.pkl | Bin 2762 -> 2762 bytes tmp/playermatchdata.xlsx | Bin 16569 -> 16637 bytes 45 files changed, 36171 insertions(+), 8491 deletions(-) create mode 100644 .ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/2_votes_dataset_creation-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/3_players_dataset_creation-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/5_scraping_match_probable_players-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb create mode 100644 fantacalcio/Quotazioni_Fantacalcio_old.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_15.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_16.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_17.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_18.xlsx create mode 100644 fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_19.xlsx delete mode 100644 mid_outputs/~$players_stats.xlsx create mode 100644 outputs/pred_matchday_20.xlsx delete mode 100644 outputs/~$pred_matchday_13.xlsx diff --git a/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb new file mode 100644 index 0000000..ac8e6a3 --- /dev/null +++ b/.ipynb_checkpoints/1_scraping_fbref-checkpoint.ipynb @@ -0,0 +1,4195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d065632b", + "metadata": {}, + "source": [ + "Scraping from http://fbref.com\n", + "\n", + "Data:\n", + "- outfield players stats\n", + "- goalkeepers stats\n", + "- team stats (of and against)\n", + "\n", + "Original code by parth1902:\n", + "https://github.com/parth1902/Scrape-FBref-data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3b8d2e7f", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import sys, getopt\n", + "import csv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ab920c85", + "metadata": {}, + "outputs": [], + "source": [ + "#standard(stats)\n", + "stats = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"games\",\"games_starts\",\"minutes\",\"goals\",\"assists\",\"pens_made\",\"pens_att\",\"cards_yellow\",\"cards_red\",\"goals_per90\",\"assists_per90\",\"goals_assists_per90\",\"goals_pens_per90\",\"goals_assists_pens_per90\",\"xg\",\"npxg\",\"xa\",\"xg_per90\",\"xa_per90\",\"xg_xa_per90\",\"npxg_per90\",\"npxg_xa_per90\"]\n", + "stats3 = [\"players_used\",\"possession\",\"games\",\"games_starts\",\"minutes\",\"goals\",\"assists\",\"pens_made\",\"pens_att\",\"cards_yellow\",\"cards_red\",\"goals_per90\",\"assists_per90\",\"goals_assists_per90\",\"goals_pens_per90\",\"goals_assists_pens_per90\",\"xg\",\"npxg\",\"xa\",\"xg_per90\",\"xa_per90\",\"xg_xa_per90\",\"npxg_per90\",\"npxg_xa_per90\"] \n", + "#goalkeeping(keepers)\n", + "keepers = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"gk_games\",\"gk_games_starts\",\"gk_minutes\",\"gk_goals_against\",\"gk_goals_against_per90\",\"gk_shots_on_target_against\",\"gk_saves\",\"gk_save_pct\",\"gk_wins\",\"gk_ties\",\"gk_losses\",\"gk_clean_sheets\",\"gk_clean_sheets_pct\",\"gk_pens_att\",\"gk_pens_allowed\",\"gk_pens_saved\",\"gk_pens_missed\"]\n", + "keepers3 = [\"players_used\",\"gk_games\",\"gk_games_starts\",\"gk_minutes\",\"gk_goals_against\",\"gk_goals_against_per90\",\"gk_shots_on_target_against\",\"gk_saves\",\"gk_save_pct\",\"gk_wins\",\"gk_ties\",\"gk_losses\",\"gk_clean_sheets\",\"gk_clean_sheets_pct\",\"gk_pens_att\",\"gk_pens_allowed\",\"gk_pens_saved\",\"gk_pens_missed\"]\n", + "#advance goalkeeping(keepersadv)\n", + "keepersadv = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"gk_goals_against\",\"gk_pens_allowed\",\"gk_free_kick_goals_against\",\"gk_corner_kick_goals_against\",\"gk_own_goals_against\",\"gk_psxg\",\"gk_psnpxg_per_shot_on_target_against\",\"gk_psxg_net\",\"gk_psxg_net_per90\",\"gk_passes_completed_launched\",\"gk_passes_launched\",\"gk_passes_pct_launched\",\"gk_passes\",\"gk_passes_throws\",\"gk_pct_passes_launched\",\"gk_passes_length_avg\",\"gk_goal_kicks\",\"gk_pct_goal_kicks_launched\",\"gk_goal_kick_length_avg\",\"gk_crosses\",\"gk_crosses_stopped\",\"gk_crosses_stopped_pct\",\"gk_def_actions_outside_pen_area\",\"gk_def_actions_outside_pen_area_per90\",\"gk_avg_distance_def_actions\"]\n", + "keepersadv2 = [\"minutes_90s\",\"gk_goals_against\",\"gk_pens_allowed\",\"gk_free_kick_goals_against\",\"gk_corner_kick_goals_against\",\"gk_own_goals_against\",\"gk_psxg\",\"gk_psnpxg_per_shot_on_target_against\",\"gk_psxg_net\",\"gk_psxg_net_per90\",\"gk_passes_completed_launched\",\"gk_passes_launched\",\"gk_passes_pct_launched\",\"gk_passes\",\"gk_passes_throws\",\"gk_pct_passes_launched\",\"gk_passes_length_avg\",\"gk_goal_kicks\",\"gk_pct_goal_kicks_launched\",\"gk_goal_kick_length_avg\",\"gk_crosses\",\"gk_crosses_stopped\",\"gk_crosses_stopped_pct\",\"gk_def_actions_outside_pen_area\",\"gk_def_actions_outside_pen_area_per90\",\"gk_avg_distance_def_actions\"]\n", + "#shooting(shooting)\n", + "shooting = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"goals\",\"pens_made\",\"pens_att\",\"shots_total\",\"shots_on_target\",\"shots_free_kicks\",\"shots_on_target_pct\",\"shots_total_per90\",\"shots_on_target_per90\",\"goals_per_shot\",\"goals_per_shot_on_target\",\"xg\",\"npxg\",\"npxg_per_shot\",\"xg_net\",\"npxg_net\"]\n", + "shooting2 = [\"minutes_90s\",\"goals\",\"pens_made\",\"pens_att\",\"shots_total\",\"shots_on_target\",\"shots_free_kicks\",\"shots_on_target_pct\",\"shots_total_per90\",\"shots_on_target_per90\",\"goals_per_shot\",\"goals_per_shot_on_target\",\"xg\",\"npxg\",\"npxg_per_shot\",\"xg_net\",\"npxg_net\"]\n", + "shooting3 = [\"goals\",\"pens_made\",\"pens_att\",\"shots_total\",\"shots_on_target\",\"shots_free_kicks\",\"shots_on_target_pct\",\"shots_total_per90\",\"shots_on_target_per90\",\"goals_per_shot\",\"goals_per_shot_on_target\",\"xg\",\"npxg\",\"npxg_per_shot\",\"xg_net\",\"npxg_net\"]\n", + "#passing(passing)\n", + "passing = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"passes_completed\",\"passes\",\"passes_pct\",\"passes_total_distance\",\"passes_progressive_distance\",\"passes_completed_short\",\"passes_short\",\"passes_pct_short\",\"passes_completed_medium\",\"passes_medium\",\"passes_pct_medium\",\"passes_completed_long\",\"passes_long\",\"passes_pct_long\",\"assists\",\"xa\",\"xa_net\",\"assisted_shots\",\"passes_into_final_third\",\"passes_into_penalty_area\",\"crosses_into_penalty_area\",\"progressive_passes\"]\n", + "passing2 = [\"passes_completed\",\"passes\",\"passes_pct\",\"passes_total_distance\",\"passes_progressive_distance\",\"passes_completed_short\",\"passes_short\",\"passes_pct_short\",\"passes_completed_medium\",\"passes_medium\",\"passes_pct_medium\",\"passes_completed_long\",\"passes_long\",\"passes_pct_long\",\"assists\",\"xa\",\"xa_net\",\"assisted_shots\",\"passes_into_final_third\",\"passes_into_penalty_area\",\"crosses_into_penalty_area\",\"progressive_passes\"]\n", + "#passtypes(passing_types)\n", + "passing_types = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"passes\",\"passes_live\",\"passes_dead\",\"passes_free_kicks\",\"through_balls\",\"passes_pressure\",\"passes_switches\",\"crosses\",\"corner_kicks\",\"corner_kicks_in\",\"corner_kicks_out\",\"corner_kicks_straight\",\"passes_ground\",\"passes_low\",\"passes_high\",\"passes_left_foot\",\"passes_right_foot\",\"passes_head\",\"throw_ins\",\"passes_other_body\",\"passes_completed\",\"passes_offsides\",\"passes_oob\",\"passes_intercepted\",\"passes_blocked\"]\n", + "passing_types2 = [\"passes\",\"passes_live\",\"passes_dead\",\"passes_free_kicks\",\"through_balls\",\"passes_pressure\",\"passes_switches\",\"crosses\",\"corner_kicks\",\"corner_kicks_in\",\"corner_kicks_out\",\"corner_kicks_straight\",\"passes_ground\",\"passes_low\",\"passes_high\",\"passes_left_foot\",\"passes_right_foot\",\"passes_head\",\"throw_ins\",\"passes_other_body\",\"passes_completed\",\"passes_offsides\",\"passes_oob\",\"passes_intercepted\",\"passes_blocked\"]\n", + "#goal and shot creation(gca)\n", + "gca = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"sca\",\"sca_per90\",\"sca_passes_live\",\"sca_passes_dead\",\"sca_dribbles\",\"sca_shots\",\"sca_fouled\",\"gca\",\"gca_per90\",\"gca_passes_live\",\"gca_passes_dead\",\"gca_dribbles\",\"gca_shots\",\"gca_fouled\",\"gca_defense\"]\n", + "gca2 = [\"sca\",\"sca_per90\",\"sca_passes_live\",\"sca_passes_dead\",\"sca_dribbles\",\"sca_shots\",\"sca_fouled\",\"gca\",\"gca_per90\",\"gca_passes_live\",\"gca_passes_dead\",\"gca_dribbles\",\"gca_shots\",\"gca_fouled\",\"gca_defense\"]\n", + "#defensive actions(defense)\n", + "defense = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"tackles\",\"tackles_won\",\"tackles_def_3rd\",\"tackles_mid_3rd\",\"tackles_att_3rd\",\"dribble_tackles\",\"dribbles_vs\",\"dribble_tackles_pct\",\"dribbled_past\",\"pressures\",\"pressure_regains\",\"pressure_regain_pct\",\"pressures_def_3rd\",\"pressures_mid_3rd\",\"pressures_att_3rd\",\"blocks\",\"blocked_shots\",\"blocked_shots_saves\",\"blocked_passes\",\"interceptions\",\"clearances\",\"errors\"]\n", + "defense2 = [\"tackles\",\"tackles_won\",\"tackles_def_3rd\",\"tackles_mid_3rd\",\"tackles_att_3rd\",\"dribble_tackles\",\"dribbles_vs\",\"dribble_tackles_pct\",\"dribbled_past\",\"pressures\",\"pressure_regains\",\"pressure_regain_pct\",\"pressures_def_3rd\",\"pressures_mid_3rd\",\"pressures_att_3rd\",\"blocks\",\"blocked_shots\",\"blocked_shots_saves\",\"blocked_passes\",\"interceptions\",\"clearances\",\"errors\"]\n", + "#possession(possession)\n", + "possession = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"touches\",\"touches_def_pen_area\",\"touches_def_3rd\",\"touches_mid_3rd\",\"touches_att_3rd\",\"touches_att_pen_area\",\"touches_live_ball\",\"dribbles_completed\",\"dribbles\",\"dribbles_completed_pct\",\"players_dribbled_past\",\"nutmegs\",\"carries\",\"carry_distance\",\"carry_progressive_distance\",\"progressive_carries\",\"carries_into_final_third\",\"carries_into_penalty_area\",\"pass_targets\",\"passes_received\",\"passes_received_pct\",\"miscontrols\",\"dispossessed\"]\n", + "possession2 = [\"touches\",\"touches_def_pen_area\",\"touches_def_3rd\",\"touches_mid_3rd\",\"touches_att_3rd\",\"touches_att_pen_area\",\"touches_live_ball\",\"dribbles_completed\",\"dribbles\",\"dribbles_completed_pct\",\"players_dribbled_past\",\"nutmegs\",\"carries\",\"carry_distance\",\"carry_progressive_distance\",\"progressive_carries\",\"carries_into_final_third\",\"carries_into_penalty_area\",\"pass_targets\",\"passes_received\",\"passes_received_pct\",\"miscontrols\",\"dispossessed\"]\n", + "#playingtime(playingtime)\n", + "playingtime = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"games\",\"minutes\",\"minutes_per_game\",\"minutes_pct\",\"games_starts\",\"minutes_per_start\",\"games_subs\",\"minutes_per_sub\",\"unused_subs\",\"points_per_match\",\"on_goals_for\",\"on_goals_against\",\"plus_minus\",\"plus_minus_per90\",\"plus_minus_wowy\",\"on_xg_for\",\"on_xg_against\",\"xg_plus_minus\",\"xg_plus_minus_per90\",\"xg_plus_minus_wowy\"]\n", + "playingtime2 = [\"games\",\"minutes\",\"minutes_per_game\",\"minutes_pct\",\"games_starts\",\"minutes_per_start\",\"games_subs\",\"minutes_per_sub\",\"unused_subs\",\"points_per_match\",\"on_goals_for\",\"on_goals_against\",\"plus_minus\",\"plus_minus_per90\",\"plus_minus_wowy\",\"on_xg_for\",\"on_xg_against\",\"xg_plus_minus\",\"xg_plus_minus_per90\",\"xg_plus_minus_wowy\"]\n", + "#miscallaneous(misc)\n", + "misc = [\"player\",\"nationality\",\"position\",\"team\",\"age\",\"birth_year\",\"minutes_90s\",\"cards_yellow\",\"cards_red\",\"cards_yellow_red\",\"fouls\",\"fouled\",\"offsides\",\"crosses\",\"interceptions\",\"tackles_won\",\"pens_won\",\"pens_conceded\",\"own_goals\",\"ball_recoveries\",\"aerials_won\",\"aerials_lost\",\"aerials_won_pct\"]\n", + "misc2 = [\"cards_yellow\",\"cards_red\",\"cards_yellow_red\",\"fouls\",\"fouled\",\"offsides\",\"crosses\",\"interceptions\",\"tackles_won\",\"pens_won\",\"pens_conceded\",\"own_goals\",\"ball_recoveries\",\"aerials_won\",\"aerials_lost\",\"aerials_won_pct\"]\n", + "\n", + "def get_tables(url,text):\n", + " res = requests.get(url)\n", + " ## The next two lines get around the issue with comments breaking the parsing.\n", + " comm = re.compile(\"\")\n", + " soup = BeautifulSoup(comm.sub(\"\",res.text),'lxml')\n", + " all_tables = soup.findAll(\"tbody\")\n", + " \n", + " team_table = all_tables[0]\n", + " team_vs_table = all_tables[1]\n", + " player_table = all_tables[2]\n", + " if text == 'for':\n", + " return player_table, team_table\n", + " if text == 'vs':\n", + " return player_table, team_vs_table\n", + "\n", + "def get_frame(features, player_table):\n", + " pre_df_player = dict()\n", + " features_wanted_player = features\n", + " rows_player = player_table.find_all('tr')\n", + " for row in rows_player:\n", + " if(row.find('th',{\"scope\":\"row\"}) != None):\n", + " \n", + " for f in features_wanted_player:\n", + " cell = row.find(\"td\",{\"data-stat\": f})\n", + " if(cell == None):\n", + " continue\n", + " a = cell.text.strip().encode()\n", + " text=a.decode(\"utf-8\")\n", + " if(text == ''):\n", + " text = '0'\n", + " if((f!='player')&(f!='nationality')&(f!='position')&(f!='squad')&(f!='age')&(f!='birth_year')&(f!='team')):\n", + " text = float(text.replace(',','')) \n", + " if f in pre_df_player:\n", + " pre_df_player[f].append(text)\n", + " else:\n", + " pre_df_player[f] = [text]\n", + " df_player = pd.DataFrame.from_dict(pre_df_player)\n", + " return df_player\n", + "\n", + "def get_frame_team(features, team_table):\n", + " pre_df_squad = dict()\n", + " #Note: features does not contain squad name, it requires special treatment\n", + " features_wanted_squad = features\n", + " rows_squad = team_table.find_all('tr')\n", + " for row in rows_squad:\n", + " if(row.find('th',{\"scope\":\"row\"}) != None):\n", + " name = row.find('th',{\"data-stat\":\"team\"}).text.strip().encode().decode(\"utf-8\")\n", + " if 'team' in pre_df_squad:\n", + " pre_df_squad['team'].append(name)\n", + " else:\n", + " pre_df_squad['team'] = [name]\n", + " for f in features_wanted_squad:\n", + " cell = row.find(\"td\",{\"data-stat\": f})\n", + " if(cell == None):\n", + " continue\n", + " a = cell.text.strip().encode()\n", + " text=a.decode(\"utf-8\")\n", + " if(text == ''):\n", + " text = '0'\n", + " if((f!='player')&(f!='nationality')&(f!='position')&(f!='squad')&(f!='age')&(f!='birth_year')&(f!='team')):\n", + " text = float(text.replace(',',''))\n", + " if f in pre_df_squad:\n", + " pre_df_squad[f].append(text)\n", + " else:\n", + " pre_df_squad[f] = [text]\n", + " df_squad = pd.DataFrame.from_dict(pre_df_squad)\n", + " return df_squad\n", + "\n", + "def get_frame_team_2(features, team_table):\n", + " pre_df_squad = dict()\n", + " #Note: features does not contain squad name, it requires special treatment\n", + " features_wanted_squad = features\n", + " rows_squad = team_table.find_all('tr')\n", + " for row in rows_squad:\n", + " if(row.find('th',{\"scope\":\"row\"}) != None):\n", + " name = row.find('th',{\"data-stat\":\"squad\"}).text.strip().encode().decode(\"utf-8\")\n", + " if 'squad' in pre_df_squad:\n", + " pre_df_squad['squad'].append(name)\n", + " else:\n", + " pre_df_squad['squad'] = [name]\n", + " for f in features_wanted_squad:\n", + " cell = row.find(\"td\",{\"data-stat\": f})\n", + " a = cell.text.strip().encode()\n", + " text=a.decode(\"utf-8\")\n", + " if(text == ''):\n", + " text = '0'\n", + " if((f!='player')&(f!='nationality')&(f!='position')&(f!='squad')&(f!='age')&(f!='birth_year')&(f!='team')):\n", + " text = float(text.replace(',',''))\n", + " print(f)\n", + " if f in pre_df_squad:\n", + " pre_df_squad[f].append(text)\n", + " else:\n", + " pre_df_squad[f] = [text]\n", + " df_squad = pd.DataFrame.from_dict(pre_df_squad)\n", + " return df_squad\n", + "\n", + "def frame_for_category(category,top,end,features):\n", + " url = (top + category + end)\n", + " player_table, team_table = get_tables(url,'for')\n", + " df_player = get_frame(features, player_table)\n", + " return df_player\n", + "\n", + "def frame_for_category_team(category,top,end,features,text):\n", + " url = (top + category + end)\n", + " player_table, team_table = get_tables(url,text)\n", + " df_team = get_frame_team(features, team_table)\n", + " return df_team\n", + "\n", + "\n", + "\n", + "\n", + "#Function to get the player data for outfield player, includes all categories - standard stats, shooting\n", + "#passing, passing types, goal and shot creation, defensive actions, possession, and miscallaneous\n", + "def get_outfield_data(top, end):\n", + " df1 = frame_for_category('stats',top,end,stats)\n", + " df2 = frame_for_category('shooting',top,end,shooting2)\n", + " df3 = frame_for_category('passing',top,end,passing2)\n", + " df4 = frame_for_category('passing_types',top,end,passing_types2)\n", + " df5 = frame_for_category('gca',top,end,gca2)\n", + " df6 = frame_for_category('defense',top,end,defense2)\n", + " df7 = frame_for_category('possession',top,end,possession2)\n", + " df8 = frame_for_category('misc',top,end,misc2)\n", + " df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8], axis=1)\n", + " df = df.loc[:,~df.columns.duplicated()]\n", + " return df\n", + "\n", + "\n", + "#Function to get keeping and advance goalkeeping data\n", + "def get_keeper_data(top,end):\n", + " df1 = frame_for_category('keepers',top,end,keepers)\n", + " df2 = frame_for_category('keepersadv',top,end,keepersadv2)\n", + " df = pd.concat([df1, df2], axis=1)\n", + " df = df.loc[:,~df.columns.duplicated()]\n", + " return df\n", + "\n", + "\n", + "\n", + "#Function to get team-wise data accross all categories as mentioned above\n", + "def get_team_data(top,end,text):\n", + " df1 = frame_for_category_team('stats',top,end,stats3,text)\n", + " df2 = frame_for_category_team('keepers',top,end,keepers3,text)\n", + " df3 = frame_for_category_team('keepersadv',top,end,keepersadv2,text)\n", + " df4 = frame_for_category_team('shooting',top,end,shooting3,text)\n", + " df5 = frame_for_category_team('passing',top,end,passing2,text)\n", + " df6 = frame_for_category_team('passing_types',top,end,passing_types2,text)\n", + " df7 = frame_for_category_team('gca',top,end,gca2,text)\n", + " df8 = frame_for_category_team('defense',top,end,defense2,text)\n", + " df9 = frame_for_category_team('possession',top,end,possession2,text)\n", + " df10 = frame_for_category_team('misc',top,end,misc2,text)\n", + " df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9, df10], axis=1)\n", + " df = df.loc[:,~df.columns.duplicated()]\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9141a9ec", + "metadata": {}, + "outputs": [ + { + "data": { + 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playernationalitypositionteamagebirth_yeargamesgames_startsminutesgoals...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Tammy Abrahameng ENGFWRoma25-118199719.015.01338.05.0...19.029.08.01.00.00.028.035.032.052.2
1Francesco Acerbiit ITADFInter34-352198812.010.0930.00.0...7.07.01.00.00.00.052.025.018.058.1
2Yacine Adlifr FRAMF,FWMilan22-18320004.01.0116.00.0...3.01.00.00.00.00.05.00.03.00.0
3Michel Aebischerch SUIFW,MFBologna26-022199717.08.0767.01.0...10.014.05.01.01.00.042.05.08.038.5
4Felix Afena-Gyangh GHAFW,MFCremonese20-009200310.02.0296.00.0...7.03.04.00.00.00.014.08.014.036.4
..................................................................
514Petar Zovkoba BIHGKSpezia20-30920021.00.074.00.0...0.00.00.00.00.00.02.00.00.00.0
515Szymon Żurkowskipl POLMFFiorentina25-12519972.00.032.00.0...1.00.00.00.00.00.02.01.01.050.0
516Szymon Żurkowskipl POLMFSpezia25-12519971.00.08.00.0...0.00.00.00.00.00.03.00.00.00.0
517Milan Đurićba BIHFWHellas Verona32-251199015.06.0618.01.0...15.013.02.00.00.00.014.0103.027.079.2
518Filip Đuričićrs SRBMF,FWSampdoria30-363199218.015.01201.02.0...19.032.00.00.00.00.067.07.011.038.9
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519 rows × 120 columns

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" + ], + "text/plain": [ + " player nationality position team age birth_year \\\n", + "0 Tammy Abraham eng ENG FW Roma 25-118 1997 \n", + "1 Francesco Acerbi it ITA DF Inter 34-352 1988 \n", + "2 Yacine Adli fr FRA MF,FW Milan 22-183 2000 \n", + "3 Michel Aebischer ch SUI FW,MF Bologna 26-022 1997 \n", + "4 Felix Afena-Gyan gh GHA FW,MF Cremonese 20-009 2003 \n", + ".. ... ... ... ... ... ... \n", + "514 Petar Zovko ba BIH GK Spezia 20-309 2002 \n", + "515 Szymon Żurkowski pl POL MF Fiorentina 25-125 1997 \n", + "516 Szymon Żurkowski pl POL MF Spezia 25-125 1997 \n", + "517 Milan Đurić ba BIH FW Hellas Verona 32-251 1990 \n", + "518 Filip Đuričić rs SRB MF,FW Sampdoria 30-363 1992 \n", + "\n", + " games games_starts minutes goals ... fouls fouled offsides \\\n", + "0 19.0 15.0 1338.0 5.0 ... 19.0 29.0 8.0 \n", + "1 12.0 10.0 930.0 0.0 ... 7.0 7.0 1.0 \n", + "2 4.0 1.0 116.0 0.0 ... 3.0 1.0 0.0 \n", + "3 17.0 8.0 767.0 1.0 ... 10.0 14.0 5.0 \n", + "4 10.0 2.0 296.0 0.0 ... 7.0 3.0 4.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "514 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "515 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "516 1.0 0.0 8.0 0.0 ... 0.0 0.0 0.0 \n", + "517 15.0 6.0 618.0 1.0 ... 15.0 13.0 2.0 \n", + "518 18.0 15.0 1201.0 2.0 ... 19.0 32.0 0.0 \n", + "\n", + " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", + "0 1.0 0.0 0.0 28.0 35.0 \n", + "1 0.0 0.0 0.0 52.0 25.0 \n", + "2 0.0 0.0 0.0 5.0 0.0 \n", + "3 1.0 1.0 0.0 42.0 5.0 \n", + "4 0.0 0.0 0.0 14.0 8.0 \n", + ".. ... ... ... ... ... \n", + "514 0.0 0.0 0.0 2.0 0.0 \n", + "515 0.0 0.0 0.0 2.0 1.0 \n", + "516 0.0 0.0 0.0 3.0 0.0 \n", + "517 0.0 0.0 0.0 14.0 103.0 \n", + "518 0.0 0.0 0.0 67.0 7.0 \n", + "\n", + " aerials_lost aerials_won_pct \n", + "0 32.0 52.2 \n", + "1 18.0 58.1 \n", + "2 3.0 0.0 \n", + "3 8.0 38.5 \n", + "4 14.0 36.4 \n", + ".. ... ... \n", + "514 0.0 0.0 \n", + "515 1.0 50.0 \n", + "516 0.0 0.0 \n", + "517 27.0 79.2 \n", + "518 11.0 38.9 \n", + "\n", + "[519 rows x 120 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_outfield = get_outfield_data('https://fbref.com/en/comps/11/','/Serie-A-Stats')\n", + "\n", + "df_outfield.to_csv('fbref_data/outfield_players.csv', index=False)\n", + "\n", + "df_outfield" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a30e27c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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playernationalitypositionteamagebirth_yeargk_gamesgk_games_startsgk_minutesgk_goals_against...gk_passes_length_avggk_goal_kicksgk_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_actions
0Emil Auderoit ITAGKSampdoria26-010199719.019.01710.032.0...38.8142.057.744.5243.015.06.220.01.0515.4
1Marco Carnesecchiit ITAGKCremonese22-211200010.010.0900.014.0...40.066.059.149.3108.010.09.37.00.7013.0
2Andrea Consigliit ITAGKSassuolo36-001198717.017.01530.026.0...34.4157.031.833.3220.013.05.915.00.8814.5
3Michele Di Gregorioit ITAGKMonza25-185199719.019.01710.028.0...33.0115.031.331.3246.010.04.112.00.6313.4
4Bartłomiej Drągowskipl POLGKSpezia25-162199718.018.01572.029.0...34.4106.063.247.9235.09.03.824.01.3715.6
5Wladimiro Falconeit ITAGKLecce27-291199520.020.01800.024.0...40.9156.075.051.4304.013.04.320.01.0012.5
6Pierluigi Golliniit ITAGKFiorentina27-31619953.03.0270.02.0...32.411.063.649.324.01.04.20.00.009.3
7Samir Handanovićsi SVNGKInter38-19819848.08.0720.013.0...26.145.04.424.079.02.02.55.00.6314.6
8Mike Maignanfr FRAGKMilan27-20919957.07.0630.08.0...33.518.038.940.465.05.07.76.00.8612.9
9Luís Maximianopt PORGKLazio24-02319991.01.05.00.0...0.00.00.00.00.00.00.00.00.0019.0
10Alex Meretit ITAGKNapoli25-312199719.019.01710.014.0...26.6114.020.227.1197.06.03.023.01.2117.2
11Vanja Milinković-Savićrs SRBGKTorino25-342199719.019.01710.020.0...40.8142.094.472.1232.018.07.833.01.7416.5
12Lorenzo Montipòit ITAGKHellas Verona26-342199619.019.01710.031.0...43.5153.058.244.8261.010.03.846.02.4217.8
13Juan Mussoar ARGGKAtalanta28-267199413.013.01087.014.0...33.086.065.149.4147.08.05.410.00.8314.6
14Guillermo Ochoamx MEXGKSalernitana37-19919855.05.0450.014.0...43.352.078.854.778.02.02.64.00.8016.5
15André Onanacm CMRGKInter26-301199611.011.0990.012.0...31.079.035.437.9150.06.04.03.00.2713.0
16Rui Patríciopt PORGKRoma34-347198819.019.01710.016.0...31.4139.030.232.8221.06.02.713.00.6813.8
17Gianluca Pegoloit ITAGKSassuolo41-30919812.02.0180.03.0...29.716.031.328.328.02.07.10.00.009.0
18Mattia Perinit ITAGKJuventus30-07919927.06.0588.04.0...30.341.039.035.399.02.02.09.01.3817.8
19Ivan Provedelit ITAGKLazio28-317199419.018.01703.015.0...32.5115.034.834.8256.07.02.737.01.9618.1
20Ionuț Raduro ROUGKCremonese25-24519979.09.0810.019.0...37.367.050.746.3107.06.05.69.01.0014.5
21Luigi Sepeit ITAGKSalernitana31-265199115.015.01350.024.0...36.1134.046.340.6199.015.07.511.00.7314.2
22Marco Silvestriit ITAGKUdinese31-332199119.019.01710.021.0...33.5153.039.936.8253.04.01.69.00.4713.5
23Łukasz Skorupskipl POLGKBologna31-268199120.020.01800.030.0...32.1141.037.633.9280.018.06.417.00.8514.2
24Marco Sportielloit ITAGKAtalanta30-26319927.06.0623.09.0...28.561.068.948.986.07.08.114.02.0219.9
25Wojciech Szczęsnypl POLGKJuventus32-285199013.013.01122.011.0...36.161.041.039.2155.04.02.67.00.5614.2
26Ciprian Tătărușanuro ROUGKMilan36-353198612.012.01080.016.0...32.751.035.334.9125.08.06.410.00.8315.1
27Pietro Terraccianoit ITAGKFiorentina32-326199016.016.01440.023.0...33.3101.043.639.7149.07.04.730.01.8818.7
28Guglielmo Vicarioit ITAGKEmpoli26-113199619.019.01710.022.0...33.9103.045.641.8399.025.06.38.00.4210.5
29Jeroen Zoetnl NEDGKSpezia32-02219913.02.0154.01.0...41.78.087.556.624.01.04.23.01.7415.7
30Petar Zovkoba BIHGKSpezia20-30920021.00.074.02.0...35.113.076.951.124.01.04.22.02.4717.0
\n", + "

31 rows × 47 columns

\n", + "
" + ], + "text/plain": [ + " player nationality position team age \\\n", + "0 Emil Audero it ITA GK Sampdoria 26-010 \n", + "1 Marco Carnesecchi it ITA GK Cremonese 22-211 \n", + "2 Andrea Consigli it ITA GK Sassuolo 36-001 \n", + "3 Michele Di Gregorio it ITA GK Monza 25-185 \n", + "4 Bartłomiej Drągowski pl POL GK Spezia 25-162 \n", + "5 Wladimiro Falcone it ITA GK Lecce 27-291 \n", + "6 Pierluigi Gollini it ITA GK Fiorentina 27-316 \n", + "7 Samir Handanović si SVN GK Inter 38-198 \n", + "8 Mike Maignan fr FRA GK Milan 27-209 \n", + "9 Luís Maximiano pt POR GK Lazio 24-023 \n", + "10 Alex Meret it ITA GK Napoli 25-312 \n", + "11 Vanja Milinković-Savić rs SRB GK Torino 25-342 \n", + "12 Lorenzo Montipò it ITA GK Hellas Verona 26-342 \n", + "13 Juan Musso ar ARG GK Atalanta 28-267 \n", + "14 Guillermo Ochoa mx MEX GK Salernitana 37-199 \n", + "15 André Onana cm CMR GK Inter 26-301 \n", + "16 Rui Patrício pt POR GK Roma 34-347 \n", + "17 Gianluca Pegolo it ITA GK Sassuolo 41-309 \n", + "18 Mattia Perin it ITA GK Juventus 30-079 \n", + "19 Ivan Provedel it ITA GK Lazio 28-317 \n", + "20 Ionuț Radu ro ROU GK Cremonese 25-245 \n", + "21 Luigi Sepe it ITA GK Salernitana 31-265 \n", + "22 Marco Silvestri it ITA GK Udinese 31-332 \n", + "23 Łukasz Skorupski pl POL GK Bologna 31-268 \n", + "24 Marco Sportiello it ITA GK Atalanta 30-263 \n", + "25 Wojciech Szczęsny pl POL GK Juventus 32-285 \n", + "26 Ciprian Tătărușanu ro ROU GK Milan 36-353 \n", + "27 Pietro Terracciano it ITA GK Fiorentina 32-326 \n", + "28 Guglielmo Vicario it ITA GK Empoli 26-113 \n", + "29 Jeroen Zoet nl NED GK Spezia 32-022 \n", + "30 Petar Zovko ba BIH GK Spezia 20-309 \n", + "\n", + " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\n", + "0 1997 19.0 19.0 1710.0 32.0 ... \n", + "1 2000 10.0 10.0 900.0 14.0 ... \n", + "2 1987 17.0 17.0 1530.0 26.0 ... \n", + "3 1997 19.0 19.0 1710.0 28.0 ... \n", + "4 1997 18.0 18.0 1572.0 29.0 ... \n", + "5 1995 20.0 20.0 1800.0 24.0 ... \n", + "6 1995 3.0 3.0 270.0 2.0 ... \n", + "7 1984 8.0 8.0 720.0 13.0 ... \n", + "8 1995 7.0 7.0 630.0 8.0 ... \n", + "9 1999 1.0 1.0 5.0 0.0 ... \n", + "10 1997 19.0 19.0 1710.0 14.0 ... \n", + "11 1997 19.0 19.0 1710.0 20.0 ... \n", + "12 1996 19.0 19.0 1710.0 31.0 ... \n", + "13 1994 13.0 13.0 1087.0 14.0 ... \n", + "14 1985 5.0 5.0 450.0 14.0 ... \n", + "15 1996 11.0 11.0 990.0 12.0 ... \n", + "16 1988 19.0 19.0 1710.0 16.0 ... \n", + "17 1981 2.0 2.0 180.0 3.0 ... \n", + "18 1992 7.0 6.0 588.0 4.0 ... \n", + "19 1994 19.0 18.0 1703.0 15.0 ... \n", + "20 1997 9.0 9.0 810.0 19.0 ... \n", + "21 1991 15.0 15.0 1350.0 24.0 ... \n", + "22 1991 19.0 19.0 1710.0 21.0 ... \n", + "23 1991 20.0 20.0 1800.0 30.0 ... \n", + "24 1992 7.0 6.0 623.0 9.0 ... \n", + "25 1990 13.0 13.0 1122.0 11.0 ... \n", + "26 1986 12.0 12.0 1080.0 16.0 ... \n", + "27 1990 16.0 16.0 1440.0 23.0 ... \n", + "28 1996 19.0 19.0 1710.0 22.0 ... \n", + "29 1991 3.0 2.0 154.0 1.0 ... \n", + "30 2002 1.0 0.0 74.0 2.0 ... \n", + "\n", + " gk_passes_length_avg gk_goal_kicks gk_pct_goal_kicks_launched \\\n", + "0 38.8 142.0 57.7 \n", + "1 40.0 66.0 59.1 \n", + "2 34.4 157.0 31.8 \n", + "3 33.0 115.0 31.3 \n", + "4 34.4 106.0 63.2 \n", + "5 40.9 156.0 75.0 \n", + "6 32.4 11.0 63.6 \n", + "7 26.1 45.0 4.4 \n", + "8 33.5 18.0 38.9 \n", + "9 0.0 0.0 0.0 \n", + "10 26.6 114.0 20.2 \n", + "11 40.8 142.0 94.4 \n", + "12 43.5 153.0 58.2 \n", + "13 33.0 86.0 65.1 \n", + "14 43.3 52.0 78.8 \n", + "15 31.0 79.0 35.4 \n", + "16 31.4 139.0 30.2 \n", + "17 29.7 16.0 31.3 \n", + "18 30.3 41.0 39.0 \n", + "19 32.5 115.0 34.8 \n", + "20 37.3 67.0 50.7 \n", + "21 36.1 134.0 46.3 \n", + "22 33.5 153.0 39.9 \n", + "23 32.1 141.0 37.6 \n", + "24 28.5 61.0 68.9 \n", + "25 36.1 61.0 41.0 \n", + "26 32.7 51.0 35.3 \n", + "27 33.3 101.0 43.6 \n", + "28 33.9 103.0 45.6 \n", + "29 41.7 8.0 87.5 \n", + "30 35.1 13.0 76.9 \n", + "\n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", + "0 44.5 243.0 15.0 \n", + "1 49.3 108.0 10.0 \n", + "2 33.3 220.0 13.0 \n", + "3 31.3 246.0 10.0 \n", + "4 47.9 235.0 9.0 \n", + "5 51.4 304.0 13.0 \n", + "6 49.3 24.0 1.0 \n", + "7 24.0 79.0 2.0 \n", + "8 40.4 65.0 5.0 \n", + "9 0.0 0.0 0.0 \n", + "10 27.1 197.0 6.0 \n", + "11 72.1 232.0 18.0 \n", + "12 44.8 261.0 10.0 \n", + "13 49.4 147.0 8.0 \n", + "14 54.7 78.0 2.0 \n", + "15 37.9 150.0 6.0 \n", + "16 32.8 221.0 6.0 \n", + "17 28.3 28.0 2.0 \n", + "18 35.3 99.0 2.0 \n", + "19 34.8 256.0 7.0 \n", + "20 46.3 107.0 6.0 \n", + "21 40.6 199.0 15.0 \n", + "22 36.8 253.0 4.0 \n", + "23 33.9 280.0 18.0 \n", + "24 48.9 86.0 7.0 \n", + "25 39.2 155.0 4.0 \n", + "26 34.9 125.0 8.0 \n", + "27 39.7 149.0 7.0 \n", + "28 41.8 399.0 25.0 \n", + "29 56.6 24.0 1.0 \n", + "30 51.1 24.0 1.0 \n", + "\n", + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "0 6.2 20.0 \n", + "1 9.3 7.0 \n", + "2 5.9 15.0 \n", + "3 4.1 12.0 \n", + "4 3.8 24.0 \n", + "5 4.3 20.0 \n", + "6 4.2 0.0 \n", + "7 2.5 5.0 \n", + "8 7.7 6.0 \n", + "9 0.0 0.0 \n", + "10 3.0 23.0 \n", + "11 7.8 33.0 \n", + "12 3.8 46.0 \n", + "13 5.4 10.0 \n", + "14 2.6 4.0 \n", + "15 4.0 3.0 \n", + "16 2.7 13.0 \n", + "17 7.1 0.0 \n", + "18 2.0 9.0 \n", + "19 2.7 37.0 \n", + "20 5.6 9.0 \n", + "21 7.5 11.0 \n", + "22 1.6 9.0 \n", + "23 6.4 17.0 \n", + "24 8.1 14.0 \n", + "25 2.6 7.0 \n", + "26 6.4 10.0 \n", + "27 4.7 30.0 \n", + "28 6.3 8.0 \n", + "29 4.2 3.0 \n", + "30 4.2 2.0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \n", + "0 1.05 15.4 \n", + "1 0.70 13.0 \n", + "2 0.88 14.5 \n", + "3 0.63 13.4 \n", + "4 1.37 15.6 \n", + "5 1.00 12.5 \n", + "6 0.00 9.3 \n", + "7 0.63 14.6 \n", + "8 0.86 12.9 \n", + "9 0.00 19.0 \n", + "10 1.21 17.2 \n", + "11 1.74 16.5 \n", + "12 2.42 17.8 \n", + "13 0.83 14.6 \n", + "14 0.80 16.5 \n", + "15 0.27 13.0 \n", + "16 0.68 13.8 \n", + "17 0.00 9.0 \n", + "18 1.38 17.8 \n", + "19 1.96 18.1 \n", + "20 1.00 14.5 \n", + "21 0.73 14.2 \n", + "22 0.47 13.5 \n", + "23 0.85 14.2 \n", + "24 2.02 19.9 \n", + "25 0.56 14.2 \n", + "26 0.83 15.1 \n", + "27 1.88 18.7 \n", + "28 0.42 10.5 \n", + "29 1.74 15.7 \n", + "30 2.47 17.0 \n", + "\n", + "[31 rows x 47 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_keeper = get_keeper_data('https://fbref.com/en/comps/11/','/Serie-A-Stats')\n", + "\n", + "df_keeper.to_csv('fbref_data/keepers_players.csv', index=False)\n", + "\n", + "df_keeper" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b9924851", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Atalanta24.048.419.0209.01710.036.025.06.08.0...231.0196.027.06.01.01.01117.0279.0239.053.9
1Bologna24.052.620.0220.01800.025.019.03.03.0...242.0238.041.03.05.02.01104.0180.0224.044.6
2Cremonese29.044.019.0209.01710.014.06.02.04.0...257.0194.044.03.04.01.01042.0267.0348.043.4
3Empoli26.046.319.0209.01710.017.09.00.00.0...222.0237.026.00.03.00.0907.0189.0217.046.6
4Fiorentina27.057.219.0209.01710.021.017.02.04.0...244.0249.025.02.02.01.01002.0289.0249.053.7
5Hellas Verona29.043.419.0209.01710.016.013.00.00.0...285.0185.043.00.01.01.01142.0388.0345.052.9
6Inter23.052.919.0209.01710.037.025.02.02.0...233.0229.032.01.02.02.0960.0232.0198.054.0
7Juventus26.049.519.0209.01710.030.023.02.03.0...227.0199.027.03.02.00.0965.0230.0230.050.0
8Lazio21.051.219.0209.01710.034.025.03.04.0...194.0252.026.02.01.00.0972.0181.0183.049.7
9Lecce26.042.520.0220.01800.018.012.01.02.0...283.0254.036.02.03.01.01097.0294.0373.044.1
10Milan27.054.519.0209.01710.033.028.02.02.0...236.0223.030.01.03.01.01015.0275.0224.055.1
11Monza29.055.119.0209.01710.022.013.03.03.0...248.0262.038.03.00.01.0906.0215.0197.052.2
12Napoli24.061.319.0209.01710.046.037.04.05.0...179.0240.033.04.02.00.01012.0231.0201.053.5
13Roma25.049.419.0209.01710.025.016.03.05.0...229.0250.032.04.00.00.01001.0225.0184.055.0
14Salernitana28.044.820.0220.01800.024.016.01.01.0...242.0207.042.00.07.00.01034.0240.0257.048.3
15Sampdoria29.049.419.0209.01710.08.07.00.00.0...278.0293.039.00.05.02.0968.0318.0300.051.5
16Sassuolo27.048.719.0209.01710.018.013.03.04.0...199.0236.016.03.04.00.0868.0173.0216.044.5
17Spezia30.046.720.0220.01800.015.010.02.02.0...281.0193.037.01.01.00.01136.0281.0322.046.6
18Torino23.052.819.0209.01710.019.014.01.01.0...277.0195.046.01.04.00.01002.0295.0323.047.7
19Udinese22.050.119.0209.01710.026.022.00.00.0...234.0238.033.00.02.00.0989.0237.0189.055.6
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20 rows × 157 columns

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" + ], + "text/plain": [ + " team players_used possession games games_starts minutes \\\n", + "0 Atalanta 24.0 48.4 19.0 209.0 1710.0 \n", + "1 Bologna 24.0 52.6 20.0 220.0 1800.0 \n", + "2 Cremonese 29.0 44.0 19.0 209.0 1710.0 \n", + "3 Empoli 26.0 46.3 19.0 209.0 1710.0 \n", + "4 Fiorentina 27.0 57.2 19.0 209.0 1710.0 \n", + "5 Hellas Verona 29.0 43.4 19.0 209.0 1710.0 \n", + "6 Inter 23.0 52.9 19.0 209.0 1710.0 \n", + "7 Juventus 26.0 49.5 19.0 209.0 1710.0 \n", + "8 Lazio 21.0 51.2 19.0 209.0 1710.0 \n", + "9 Lecce 26.0 42.5 20.0 220.0 1800.0 \n", + "10 Milan 27.0 54.5 19.0 209.0 1710.0 \n", + "11 Monza 29.0 55.1 19.0 209.0 1710.0 \n", + "12 Napoli 24.0 61.3 19.0 209.0 1710.0 \n", + "13 Roma 25.0 49.4 19.0 209.0 1710.0 \n", + "14 Salernitana 28.0 44.8 20.0 220.0 1800.0 \n", + "15 Sampdoria 29.0 49.4 19.0 209.0 1710.0 \n", + "16 Sassuolo 27.0 48.7 19.0 209.0 1710.0 \n", + "17 Spezia 30.0 46.7 20.0 220.0 1800.0 \n", + "18 Torino 23.0 52.8 19.0 209.0 1710.0 \n", + "19 Udinese 22.0 50.1 19.0 209.0 1710.0 \n", + "\n", + " goals assists pens_made pens_att ... fouls fouled offsides \\\n", + "0 36.0 25.0 6.0 8.0 ... 231.0 196.0 27.0 \n", + "1 25.0 19.0 3.0 3.0 ... 242.0 238.0 41.0 \n", + "2 14.0 6.0 2.0 4.0 ... 257.0 194.0 44.0 \n", + "3 17.0 9.0 0.0 0.0 ... 222.0 237.0 26.0 \n", + "4 21.0 17.0 2.0 4.0 ... 244.0 249.0 25.0 \n", + "5 16.0 13.0 0.0 0.0 ... 285.0 185.0 43.0 \n", + "6 37.0 25.0 2.0 2.0 ... 233.0 229.0 32.0 \n", + "7 30.0 23.0 2.0 3.0 ... 227.0 199.0 27.0 \n", + "8 34.0 25.0 3.0 4.0 ... 194.0 252.0 26.0 \n", + "9 18.0 12.0 1.0 2.0 ... 283.0 254.0 36.0 \n", + "10 33.0 28.0 2.0 2.0 ... 236.0 223.0 30.0 \n", + "11 22.0 13.0 3.0 3.0 ... 248.0 262.0 38.0 \n", + "12 46.0 37.0 4.0 5.0 ... 179.0 240.0 33.0 \n", + "13 25.0 16.0 3.0 5.0 ... 229.0 250.0 32.0 \n", + "14 24.0 16.0 1.0 1.0 ... 242.0 207.0 42.0 \n", + "15 8.0 7.0 0.0 0.0 ... 278.0 293.0 39.0 \n", + "16 18.0 13.0 3.0 4.0 ... 199.0 236.0 16.0 \n", + "17 15.0 10.0 2.0 2.0 ... 281.0 193.0 37.0 \n", + "18 19.0 14.0 1.0 1.0 ... 277.0 195.0 46.0 \n", + "19 26.0 22.0 0.0 0.0 ... 234.0 238.0 33.0 \n", + "\n", + " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", + "0 6.0 1.0 1.0 1117.0 279.0 \n", + "1 3.0 5.0 2.0 1104.0 180.0 \n", + "2 3.0 4.0 1.0 1042.0 267.0 \n", + "3 0.0 3.0 0.0 907.0 189.0 \n", + "4 2.0 2.0 1.0 1002.0 289.0 \n", + "5 0.0 1.0 1.0 1142.0 388.0 \n", + "6 1.0 2.0 2.0 960.0 232.0 \n", + "7 3.0 2.0 0.0 965.0 230.0 \n", + "8 2.0 1.0 0.0 972.0 181.0 \n", + "9 2.0 3.0 1.0 1097.0 294.0 \n", + "10 1.0 3.0 1.0 1015.0 275.0 \n", + "11 3.0 0.0 1.0 906.0 215.0 \n", + "12 4.0 2.0 0.0 1012.0 231.0 \n", + "13 4.0 0.0 0.0 1001.0 225.0 \n", + "14 0.0 7.0 0.0 1034.0 240.0 \n", + "15 0.0 5.0 2.0 968.0 318.0 \n", + "16 3.0 4.0 0.0 868.0 173.0 \n", + "17 1.0 1.0 0.0 1136.0 281.0 \n", + "18 1.0 4.0 0.0 1002.0 295.0 \n", + "19 0.0 2.0 0.0 989.0 237.0 \n", + "\n", + " aerials_lost aerials_won_pct \n", + "0 239.0 53.9 \n", + "1 224.0 44.6 \n", + "2 348.0 43.4 \n", + "3 217.0 46.6 \n", + "4 249.0 53.7 \n", + "5 345.0 52.9 \n", + "6 198.0 54.0 \n", + "7 230.0 50.0 \n", + "8 183.0 49.7 \n", + "9 373.0 44.1 \n", + "10 224.0 55.1 \n", + "11 197.0 52.2 \n", + "12 201.0 53.5 \n", + "13 184.0 55.0 \n", + "14 257.0 48.3 \n", + "15 300.0 51.5 \n", + "16 216.0 44.5 \n", + "17 322.0 46.6 \n", + "18 323.0 47.7 \n", + "19 189.0 55.6 \n", + "\n", + "[20 rows x 157 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_team = get_team_data('https://fbref.com/en/comps/11/','/Serie-A-Stats', 'for')\n", + "\n", + "df_team.to_csv('fbref_data/teams.csv', index=False)\n", + "\n", + "df_team" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5868ec4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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teamplayers_usedpossessiongamesgames_startsminutesgoalsassistspens_madepens_att...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0vs Atalanta24.051.619.0209.01710.022.016.01.01.0...209.0218.025.01.08.01.01165.0239.0279.046.1
1vs Bologna24.047.420.0220.01800.028.016.05.05.0...252.0229.033.03.03.01.01081.0224.0180.055.4
2vs Cremonese29.056.019.0209.01710.032.020.04.04.0...205.0247.029.03.04.00.01052.0348.0267.056.6
3vs Empoli26.053.719.0209.01710.022.016.01.03.0...251.0214.031.02.00.00.0999.0217.0189.053.4
4vs Fiorentina27.042.819.0209.01710.024.018.02.02.0...259.0229.052.01.04.00.0942.0249.0289.046.3
5vs Hellas Verona29.056.619.0209.01710.030.028.00.01.0...192.0276.022.01.00.01.01038.0345.0388.047.1
6vs Inter23.047.119.0209.01710.023.020.02.02.0...242.0214.018.02.02.01.0869.0198.0232.046.0
7vs Juventus26.050.519.0209.01710.015.012.01.02.0...210.0210.025.00.03.00.0947.0230.0230.050.0
8vs Lazio21.048.819.0209.01710.015.011.01.01.0...261.0185.042.01.04.01.01021.0183.0181.050.3
9vs Lecce26.057.620.0220.01800.023.016.03.03.0...263.0265.043.03.02.01.01072.0373.0294.055.9
10vs Milan27.045.519.0209.01710.023.017.02.03.0...234.0226.020.03.02.02.0964.0224.0275.044.9
11vs Monza29.044.919.0209.01710.027.021.00.00.0...279.0242.034.00.03.01.0958.0197.0215.047.8
12vs Napoli24.038.719.0209.01710.014.09.01.02.0...258.0167.027.01.04.00.0933.0201.0231.046.5
13vs Roma25.050.619.0209.01710.016.012.00.00.0...266.0214.010.00.05.00.0981.0184.0225.045.0
14vs Salernitana28.055.320.0220.01800.038.023.04.07.0...226.0233.049.07.01.01.01084.0257.0240.051.7
15vs Sampdoria29.050.619.0209.01710.030.021.04.06.0...299.0266.055.03.00.00.01017.0300.0318.048.5
16vs Sassuolo27.051.319.0209.01710.029.020.04.04.0...249.0182.062.02.04.00.0986.0216.0173.055.5
17vs Spezia30.053.420.0220.01800.032.027.00.01.0...207.0261.046.01.02.02.01182.0322.0281.053.4
18vs Torino23.047.219.0209.01710.020.012.03.04.0...209.0265.021.03.01.00.0998.0323.0295.052.3
19vs Udinese22.049.919.0209.01710.021.015.02.02.0...250.0227.029.02.00.01.0950.0189.0237.044.4
\n", + "

20 rows × 157 columns

\n", + "
" + ], + "text/plain": [ + " team players_used possession games games_starts minutes \\\n", + "0 vs Atalanta 24.0 51.6 19.0 209.0 1710.0 \n", + "1 vs Bologna 24.0 47.4 20.0 220.0 1800.0 \n", + "2 vs Cremonese 29.0 56.0 19.0 209.0 1710.0 \n", + "3 vs Empoli 26.0 53.7 19.0 209.0 1710.0 \n", + "4 vs Fiorentina 27.0 42.8 19.0 209.0 1710.0 \n", + "5 vs Hellas Verona 29.0 56.6 19.0 209.0 1710.0 \n", + "6 vs Inter 23.0 47.1 19.0 209.0 1710.0 \n", + "7 vs Juventus 26.0 50.5 19.0 209.0 1710.0 \n", + "8 vs Lazio 21.0 48.8 19.0 209.0 1710.0 \n", + "9 vs Lecce 26.0 57.6 20.0 220.0 1800.0 \n", + "10 vs Milan 27.0 45.5 19.0 209.0 1710.0 \n", + "11 vs Monza 29.0 44.9 19.0 209.0 1710.0 \n", + "12 vs Napoli 24.0 38.7 19.0 209.0 1710.0 \n", + "13 vs Roma 25.0 50.6 19.0 209.0 1710.0 \n", + "14 vs Salernitana 28.0 55.3 20.0 220.0 1800.0 \n", + "15 vs Sampdoria 29.0 50.6 19.0 209.0 1710.0 \n", + "16 vs Sassuolo 27.0 51.3 19.0 209.0 1710.0 \n", + "17 vs Spezia 30.0 53.4 20.0 220.0 1800.0 \n", + "18 vs Torino 23.0 47.2 19.0 209.0 1710.0 \n", + "19 vs Udinese 22.0 49.9 19.0 209.0 1710.0 \n", + "\n", + " goals assists pens_made pens_att ... fouls fouled offsides \\\n", + "0 22.0 16.0 1.0 1.0 ... 209.0 218.0 25.0 \n", + "1 28.0 16.0 5.0 5.0 ... 252.0 229.0 33.0 \n", + "2 32.0 20.0 4.0 4.0 ... 205.0 247.0 29.0 \n", + "3 22.0 16.0 1.0 3.0 ... 251.0 214.0 31.0 \n", + "4 24.0 18.0 2.0 2.0 ... 259.0 229.0 52.0 \n", + "5 30.0 28.0 0.0 1.0 ... 192.0 276.0 22.0 \n", + "6 23.0 20.0 2.0 2.0 ... 242.0 214.0 18.0 \n", + "7 15.0 12.0 1.0 2.0 ... 210.0 210.0 25.0 \n", + "8 15.0 11.0 1.0 1.0 ... 261.0 185.0 42.0 \n", + "9 23.0 16.0 3.0 3.0 ... 263.0 265.0 43.0 \n", + "10 23.0 17.0 2.0 3.0 ... 234.0 226.0 20.0 \n", + "11 27.0 21.0 0.0 0.0 ... 279.0 242.0 34.0 \n", + "12 14.0 9.0 1.0 2.0 ... 258.0 167.0 27.0 \n", + "13 16.0 12.0 0.0 0.0 ... 266.0 214.0 10.0 \n", + "14 38.0 23.0 4.0 7.0 ... 226.0 233.0 49.0 \n", + "15 30.0 21.0 4.0 6.0 ... 299.0 266.0 55.0 \n", + "16 29.0 20.0 4.0 4.0 ... 249.0 182.0 62.0 \n", + "17 32.0 27.0 0.0 1.0 ... 207.0 261.0 46.0 \n", + "18 20.0 12.0 3.0 4.0 ... 209.0 265.0 21.0 \n", + "19 21.0 15.0 2.0 2.0 ... 250.0 227.0 29.0 \n", + "\n", + " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", + "0 1.0 8.0 1.0 1165.0 239.0 \n", + "1 3.0 3.0 1.0 1081.0 224.0 \n", + "2 3.0 4.0 0.0 1052.0 348.0 \n", + "3 2.0 0.0 0.0 999.0 217.0 \n", + "4 1.0 4.0 0.0 942.0 249.0 \n", + "5 1.0 0.0 1.0 1038.0 345.0 \n", + "6 2.0 2.0 1.0 869.0 198.0 \n", + "7 0.0 3.0 0.0 947.0 230.0 \n", + "8 1.0 4.0 1.0 1021.0 183.0 \n", + "9 3.0 2.0 1.0 1072.0 373.0 \n", + "10 3.0 2.0 2.0 964.0 224.0 \n", + "11 0.0 3.0 1.0 958.0 197.0 \n", + "12 1.0 4.0 0.0 933.0 201.0 \n", + "13 0.0 5.0 0.0 981.0 184.0 \n", + "14 7.0 1.0 1.0 1084.0 257.0 \n", + "15 3.0 0.0 0.0 1017.0 300.0 \n", + "16 2.0 4.0 0.0 986.0 216.0 \n", + "17 1.0 2.0 2.0 1182.0 322.0 \n", + "18 3.0 1.0 0.0 998.0 323.0 \n", + "19 2.0 0.0 1.0 950.0 189.0 \n", + "\n", + " aerials_lost aerials_won_pct \n", + "0 279.0 46.1 \n", + "1 180.0 55.4 \n", + "2 267.0 56.6 \n", + "3 189.0 53.4 \n", + "4 289.0 46.3 \n", + "5 388.0 47.1 \n", + "6 232.0 46.0 \n", + "7 230.0 50.0 \n", + "8 181.0 50.3 \n", + "9 294.0 55.9 \n", + "10 275.0 44.9 \n", + "11 215.0 47.8 \n", + "12 231.0 46.5 \n", + "13 225.0 45.0 \n", + "14 240.0 51.7 \n", + "15 318.0 48.5 \n", + "16 173.0 55.5 \n", + "17 281.0 53.4 \n", + "18 295.0 52.3 \n", + "19 237.0 44.4 \n", + "\n", + "[20 rows x 157 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_vsteam = get_team_data('https://fbref.com/en/comps/11/','/Serie-A-Stats', 'vs')\n", + "\n", + "df_vsteam.to_csv('fbref_data/teams_vs.csv', index=False)\n", + "\n", + "df_vsteam" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "11e51337", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outfield player columns\n", + "['player' 'nationality' 'position' 'team' 'age' 'birth_year' 'games'\n", + " 'games_starts' 'minutes' 'goals' 'assists' 'pens_made' 'pens_att'\n", + " 'cards_yellow' 'cards_red' 'goals_per90' 'assists_per90'\n", + " 'goals_assists_per90' 'goals_pens_per90' 'goals_assists_pens_per90' 'xg'\n", + " 'npxg' 'xg_per90' 'npxg_per90' 'minutes_90s' 'shots_on_target'\n", + " 'shots_free_kicks' 'shots_on_target_pct' 'shots_on_target_per90'\n", + " 'goals_per_shot' 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net'\n", + " 'npxg_net' 'passes_completed' 'passes' 'passes_pct'\n", + " 'passes_total_distance' 'passes_progressive_distance'\n", + " 'passes_completed_short' 'passes_short' 'passes_pct_short'\n", + " 'passes_completed_medium' 'passes_medium' 'passes_pct_medium'\n", + " 'passes_completed_long' 'passes_long' 'passes_pct_long' 'assisted_shots'\n", + " 'passes_into_final_third' 'passes_into_penalty_area'\n", + " 'crosses_into_penalty_area' 'progressive_passes' 'passes_live'\n", + " 'passes_dead' 'passes_free_kicks' 'through_balls' 'passes_switches'\n", + " 'crosses' 'corner_kicks' 'corner_kicks_in' 'corner_kicks_out'\n", + " 'corner_kicks_straight' 'throw_ins' 'passes_offsides' 'passes_blocked'\n", + " 'sca' 'sca_per90' 'sca_passes_live' 'sca_passes_dead' 'sca_dribbles'\n", + " 'sca_shots' 'sca_fouled' 'gca' 'gca_per90' 'gca_passes_live'\n", + " 'gca_passes_dead' 'gca_dribbles' 'gca_shots' 'gca_fouled' 'gca_defense'\n", + " 'tackles' 'tackles_won' 'tackles_def_3rd' 'tackles_mid_3rd'\n", + " 'tackles_att_3rd' 'dribble_tackles' 'dribbles_vs' 'dribble_tackles_pct'\n", + " 'dribbled_past' 'blocks' 'blocked_shots' 'blocked_passes' 'interceptions'\n", + " 'clearances' 'errors' 'touches' 'touches_def_pen_area' 'touches_def_3rd'\n", + " 'touches_mid_3rd' 'touches_att_3rd' 'touches_att_pen_area'\n", + " 'touches_live_ball' 'dribbles_completed' 'dribbles'\n", + " 'dribbles_completed_pct' 'passes_received' 'miscontrols' 'dispossessed'\n", + " 'cards_yellow_red' 'fouls' 'fouled' 'offsides' 'pens_won' 'pens_conceded'\n", + " 'own_goals' 'ball_recoveries' 'aerials_won' 'aerials_lost'\n", + " 'aerials_won_pct']\n", + "Keeper player columns\n", + "['player' 'nationality' 'position' 'team' 'age' 'birth_year' 'gk_games'\n", + " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", + " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", + " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", + " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", + " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", + " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", + " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", + " 'gk_passes_completed_launched' 'gk_passes_launched'\n", + " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", + " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", + " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", + " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", + " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", + " 'gk_avg_distance_def_actions']\n", + "Team columns\n", + "['team' 'players_used' 'possession' 'games' 'games_starts' 'minutes'\n", + " 'goals' 'assists' 'pens_made' 'pens_att' 'cards_yellow' 'cards_red'\n", + " 'goals_per90' 'assists_per90' 'goals_assists_per90' 'goals_pens_per90'\n", + " 'goals_assists_pens_per90' 'xg' 'npxg' 'xg_per90' 'npxg_per90' 'gk_games'\n", + " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", + " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", + " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", + " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", + " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", + " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", + " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", + " 'gk_passes_completed_launched' 'gk_passes_launched'\n", + " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", + " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", + " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", + " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", + " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", + " 'gk_avg_distance_def_actions' 'shots_on_target' 'shots_free_kicks'\n", + " 'shots_on_target_pct' 'shots_on_target_per90' 'goals_per_shot'\n", + " 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net' 'npxg_net'\n", + " 'passes_completed' 'passes' 'passes_pct' 'passes_total_distance'\n", + " 'passes_progressive_distance' 'passes_completed_short' 'passes_short'\n", + " 'passes_pct_short' 'passes_completed_medium' 'passes_medium'\n", + " 'passes_pct_medium' 'passes_completed_long' 'passes_long'\n", + " 'passes_pct_long' 'assisted_shots' 'passes_into_final_third'\n", + " 'passes_into_penalty_area' 'crosses_into_penalty_area'\n", + " 'progressive_passes' 'passes_live' 'passes_dead' 'passes_free_kicks'\n", + " 'through_balls' 'passes_switches' 'crosses' 'corner_kicks'\n", + " 'corner_kicks_in' 'corner_kicks_out' 'corner_kicks_straight' 'throw_ins'\n", + " 'passes_offsides' 'passes_blocked' 'sca' 'sca_per90' 'sca_passes_live'\n", + " 'sca_passes_dead' 'sca_dribbles' 'sca_shots' 'sca_fouled' 'gca'\n", + " 'gca_per90' 'gca_passes_live' 'gca_passes_dead' 'gca_dribbles'\n", + " 'gca_shots' 'gca_fouled' 'gca_defense' 'tackles' 'tackles_won'\n", + " 'tackles_def_3rd' 'tackles_mid_3rd' 'tackles_att_3rd' 'dribble_tackles'\n", + " 'dribbles_vs' 'dribble_tackles_pct' 'dribbled_past' 'blocks'\n", + " 'blocked_shots' 'blocked_passes' 'interceptions' 'clearances' 'errors'\n", + " 'touches' 'touches_def_pen_area' 'touches_def_3rd' 'touches_mid_3rd'\n", + " 'touches_att_3rd' 'touches_att_pen_area' 'touches_live_ball'\n", + " 'dribbles_completed' 'dribbles' 'dribbles_completed_pct'\n", + " 'passes_received' 'miscontrols' 'dispossessed' 'cards_yellow_red' 'fouls'\n", + " 'fouled' 'offsides' 'pens_won' 'pens_conceded' 'own_goals'\n", + " 'ball_recoveries' 'aerials_won' 'aerials_lost' 'aerials_won_pct']\n", + "Vs Team columns\n", + "['team' 'players_used' 'possession' 'games' 'games_starts' 'minutes'\n", + " 'goals' 'assists' 'pens_made' 'pens_att' 'cards_yellow' 'cards_red'\n", + " 'goals_per90' 'assists_per90' 'goals_assists_per90' 'goals_pens_per90'\n", + " 'goals_assists_pens_per90' 'xg' 'npxg' 'xg_per90' 'npxg_per90' 'gk_games'\n", + " 'gk_games_starts' 'gk_minutes' 'gk_goals_against'\n", + " 'gk_goals_against_per90' 'gk_shots_on_target_against' 'gk_saves'\n", + " 'gk_save_pct' 'gk_wins' 'gk_ties' 'gk_losses' 'gk_clean_sheets'\n", + " 'gk_clean_sheets_pct' 'gk_pens_att' 'gk_pens_allowed' 'gk_pens_saved'\n", + " 'gk_pens_missed' 'minutes_90s' 'gk_free_kick_goals_against'\n", + " 'gk_corner_kick_goals_against' 'gk_own_goals_against' 'gk_psxg'\n", + " 'gk_psnpxg_per_shot_on_target_against' 'gk_psxg_net' 'gk_psxg_net_per90'\n", + " 'gk_passes_completed_launched' 'gk_passes_launched'\n", + " 'gk_passes_pct_launched' 'gk_passes' 'gk_passes_throws'\n", + " 'gk_pct_passes_launched' 'gk_passes_length_avg' 'gk_goal_kicks'\n", + " 'gk_pct_goal_kicks_launched' 'gk_goal_kick_length_avg' 'gk_crosses'\n", + " 'gk_crosses_stopped' 'gk_crosses_stopped_pct'\n", + " 'gk_def_actions_outside_pen_area' 'gk_def_actions_outside_pen_area_per90'\n", + " 'gk_avg_distance_def_actions' 'shots_on_target' 'shots_free_kicks'\n", + " 'shots_on_target_pct' 'shots_on_target_per90' 'goals_per_shot'\n", + " 'goals_per_shot_on_target' 'npxg_per_shot' 'xg_net' 'npxg_net'\n", + " 'passes_completed' 'passes' 'passes_pct' 'passes_total_distance'\n", + " 'passes_progressive_distance' 'passes_completed_short' 'passes_short'\n", + " 'passes_pct_short' 'passes_completed_medium' 'passes_medium'\n", + " 'passes_pct_medium' 'passes_completed_long' 'passes_long'\n", + " 'passes_pct_long' 'assisted_shots' 'passes_into_final_third'\n", + " 'passes_into_penalty_area' 'crosses_into_penalty_area'\n", + " 'progressive_passes' 'passes_live' 'passes_dead' 'passes_free_kicks'\n", + " 'through_balls' 'passes_switches' 'crosses' 'corner_kicks'\n", + " 'corner_kicks_in' 'corner_kicks_out' 'corner_kicks_straight' 'throw_ins'\n", + " 'passes_offsides' 'passes_blocked' 'sca' 'sca_per90' 'sca_passes_live'\n", + " 'sca_passes_dead' 'sca_dribbles' 'sca_shots' 'sca_fouled' 'gca'\n", + " 'gca_per90' 'gca_passes_live' 'gca_passes_dead' 'gca_dribbles'\n", + " 'gca_shots' 'gca_fouled' 'gca_defense' 'tackles' 'tackles_won'\n", + " 'tackles_def_3rd' 'tackles_mid_3rd' 'tackles_att_3rd' 'dribble_tackles'\n", + " 'dribbles_vs' 'dribble_tackles_pct' 'dribbled_past' 'blocks'\n", + " 'blocked_shots' 'blocked_passes' 'interceptions' 'clearances' 'errors'\n", + " 'touches' 'touches_def_pen_area' 'touches_def_3rd' 'touches_mid_3rd'\n", + " 'touches_att_3rd' 'touches_att_pen_area' 'touches_live_ball'\n", + " 'dribbles_completed' 'dribbles' 'dribbles_completed_pct'\n", + " 'passes_received' 'miscontrols' 'dispossessed' 'cards_yellow_red' 'fouls'\n", + " 'fouled' 'offsides' 'pens_won' 'pens_conceded' 'own_goals'\n", + " 'ball_recoveries' 'aerials_won' 'aerials_lost' 'aerials_won_pct']\n" + ] + } + ], + "source": [ + "print('Outfield player columns')\n", + "print(df_outfield.columns.values)\n", + "\n", + "print('Keeper player columns')\n", + "print(df_keeper.columns.values)\n", + "\n", + "print('Team columns')\n", + "print(df_team.columns.values)\n", + "\n", + "print('Vs Team columns')\n", + "print(df_team.columns.values)" + ] + }, + { + "cell_type": "markdown", + "id": "e4f13ae2", + "metadata": {}, + "source": [ + "Scrape data for past seasons. \n", + "\n", + "Do not repeat if data is already present." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2e1370cd", + "metadata": {}, + "outputs": [ + { + "data": { + 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playernationalitypositionteamagebirth_yeargamesgames_startsminutesgoals...foulsfouledoffsidespens_wonpens_concededown_goalsball_recoveriesaerials_wonaerials_lostaerials_won_pct
0Tammy Abrahameng ENGFWRoma23199737.036.03084.017.0...38.049.017.01.01.00.068.078.080.049.4
1Francesco Acerbiit ITADFLazio33198830.029.02536.04.0...18.016.04.01.00.00.0162.069.043.061.6
2Michel Aebischerch SUIMFBologna24199712.04.0443.00.0...13.04.00.00.00.00.027.05.04.055.6
3Felix Afena-Gyangh GHAFW,MFRoma18200317.06.0668.02.0...15.024.04.00.00.00.031.011.015.042.3
4Kevin Agudeloco COLMF,FWSpezia22199823.012.01237.03.0...36.024.00.00.00.00.067.015.014.051.7
..................................................................
627Nadir Zorteait ITADF,MFSalernitana22199929.013.01410.01.0...14.06.00.00.00.00.072.08.017.032.0
628Petar Zovkoba BIHGKSpezia1920021.00.029.00.0...0.01.00.00.00.00.00.00.00.00.0
629Szymon Żurkowskipl POLMFEmpoli23199735.029.02307.06.0...41.067.00.00.01.00.0162.024.032.042.9
630Milan Đurićba BIHFWSalernitana31199033.023.02165.05.0...24.045.06.01.00.00.040.0242.083.074.5
631Filip Đuričićrs SRBMF,FWSassuolo29199212.09.0671.02.0...10.014.02.00.00.00.034.03.05.037.5
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632 rows × 120 columns

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" + ], + "text/plain": [ + " player nationality position team age birth_year games \\\n", + "0 Tammy Abraham eng ENG FW Roma 23 1997 37.0 \n", + "1 Francesco Acerbi it ITA DF Lazio 33 1988 30.0 \n", + "2 Michel Aebischer ch SUI MF Bologna 24 1997 12.0 \n", + "3 Felix Afena-Gyan gh GHA FW,MF Roma 18 2003 17.0 \n", + "4 Kevin Agudelo co COL MF,FW Spezia 22 1998 23.0 \n", + ".. ... ... ... ... .. ... ... \n", + "627 Nadir Zortea it ITA DF,MF Salernitana 22 1999 29.0 \n", + "628 Petar Zovko ba BIH GK Spezia 19 2002 1.0 \n", + "629 Szymon Żurkowski pl POL MF Empoli 23 1997 35.0 \n", + "630 Milan Đurić ba BIH FW Salernitana 31 1990 33.0 \n", + "631 Filip Đuričić rs SRB MF,FW Sassuolo 29 1992 12.0 \n", + "\n", + " games_starts minutes goals ... fouls fouled offsides pens_won \\\n", + "0 36.0 3084.0 17.0 ... 38.0 49.0 17.0 1.0 \n", + "1 29.0 2536.0 4.0 ... 18.0 16.0 4.0 1.0 \n", + "2 4.0 443.0 0.0 ... 13.0 4.0 0.0 0.0 \n", + "3 6.0 668.0 2.0 ... 15.0 24.0 4.0 0.0 \n", + "4 12.0 1237.0 3.0 ... 36.0 24.0 0.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "627 13.0 1410.0 1.0 ... 14.0 6.0 0.0 0.0 \n", + "628 0.0 29.0 0.0 ... 0.0 1.0 0.0 0.0 \n", + "629 29.0 2307.0 6.0 ... 41.0 67.0 0.0 0.0 \n", + "630 23.0 2165.0 5.0 ... 24.0 45.0 6.0 1.0 \n", + "631 9.0 671.0 2.0 ... 10.0 14.0 2.0 0.0 \n", + "\n", + " pens_conceded own_goals ball_recoveries aerials_won aerials_lost \\\n", + "0 1.0 0.0 68.0 78.0 80.0 \n", + "1 0.0 0.0 162.0 69.0 43.0 \n", + "2 0.0 0.0 27.0 5.0 4.0 \n", + "3 0.0 0.0 31.0 11.0 15.0 \n", + "4 0.0 0.0 67.0 15.0 14.0 \n", + ".. ... ... ... ... ... \n", + "627 0.0 0.0 72.0 8.0 17.0 \n", + "628 0.0 0.0 0.0 0.0 0.0 \n", + "629 1.0 0.0 162.0 24.0 32.0 \n", + "630 0.0 0.0 40.0 242.0 83.0 \n", + "631 0.0 0.0 34.0 3.0 5.0 \n", + "\n", + " aerials_won_pct \n", + "0 49.4 \n", + "1 61.6 \n", + "2 55.6 \n", + "3 42.3 \n", + "4 51.7 \n", + ".. ... \n", + "627 32.0 \n", + "628 0.0 \n", + "629 42.9 \n", + "630 74.5 \n", + "631 37.5 \n", + "\n", + "[632 rows x 120 columns]" + ] + }, + "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", + "df_keeper.to_csv('fbref_data/season2122/keepers_players.csv', index=False)\n", + "\n", + "df_keeper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9172edae", + "metadata": {}, + "outputs": [], + "source": [ + "df_team = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'for')\n", + "\n", + "df_team.to_csv('fbref_data/season2122/teams.csv', index=False)\n", + "\n", + "df_team" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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
\n", + "

20 rows × 157 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 157 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_vsteam = get_team_data('https://fbref.com/en/comps/11/2021-2022/','/2021-2022-Serie-A-Stats', 'vs')\n", + "\n", + "df_vsteam.to_csv('fbref_data/season2122/teams_vs.csv', index=False)\n", + "\n", + "df_vsteam" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "22ad9be5", + "metadata": {}, + "outputs": [], + "source": [ + "df_outfield = get_outfield_data('https://fbref.com/en/comps/11/2020-2021/','/2020-2021-Serie-A-Stats')\n", + "df_outfield.to_csv('fbref_data/season2021/outfield_players.csv', index=False)\n", + "\n", + "df_keeper = get_keeper_data('https://fbref.com/en/comps/11/2020-2021/','/2020-2021-Serie-A-Stats')\n", + "df_keeper.to_csv('fbref_data/season2021/keepers_players.csv', index=False)\n", + "\n", + "df_team = get_team_data('https://fbref.com/en/comps/11/2020-2021/','/2020-2021-Serie-A-Stats', 'for')\n", + "df_team.to_csv('fbref_data/season2021/teams.csv', index=False)\n", + "\n", + "df_vsteam = get_team_data('https://fbref.com/en/comps/11/2020-2021/','/2020-2021-Serie-A-Stats', 'vs')\n", + "df_vsteam.to_csv('fbref_data/season2021/teams_vs.csv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "125223ae", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/2_votes_dataset_creation-checkpoint.ipynb b/.ipynb_checkpoints/2_votes_dataset_creation-checkpoint.ipynb new file mode 100644 index 0000000..9f1882f --- /dev/null +++ b/.ipynb_checkpoints/2_votes_dataset_creation-checkpoint.ipynb @@ -0,0 +1,952 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5c9991b1", + "metadata": {}, + "source": [ + "Generate player votes database, containing votes for Serie A matchday and player.\n", + "\n", + "Votes data is manually downloaded from https://www.fantacalcio.it/voti-fantacalcio-serie-a\n", + "\n", + "Serie A calendar is loaded from another file, to add information not containing in votes files: home/away, team opponent." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b63beb69", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a3c39376", + "metadata": {}, + "outputs": [], + "source": [ + "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", + "\n", + "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", + "\n", + "matchday = 0\n", + "\n", + "for i in range(cal.shape[0]):\n", + " if(cal[i, 0][0].isnumeric()):\n", + " matchday = matchday + 1\n", + " continue\n", + " \n", + " teams = cal[i, 0].split('-')\n", + " \n", + " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", + "\n", + " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "92d3c6fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayteam1team2
01FiorentinaCremonese
11VeronaNapoli
21JuventusSassuolo
31LazioBologna
41LecceInter
............
37538LecceBologna
37638SassuoloFiorentina
37738MilanVerona
37838TorinoInter
37938UdineseJuventus
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380 rows × 3 columns

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" + ], + "text/plain": [ + " matchday team1 team2\n", + "0 1 Fiorentina Cremonese\n", + "1 1 Verona Napoli\n", + "2 1 Juventus Sassuolo\n", + "3 1 Lazio Bologna\n", + "4 1 Lecce Inter\n", + ".. ... ... ...\n", + "375 38 Lecce Bologna\n", + "376 38 Sassuolo Fiorentina\n", + "377 38 Milan Verona\n", + "378 38 Torino Inter\n", + "379 38 Udinese Juventus\n", + "\n", + "[380 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0d4a330d", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(columns = ['matchday', 'player', 'team', 'oppteam', 'home', 'vote', 'goals', 'assists', 'cards_malus', 'fantavote'])\n", + "\n", + "LAST_MATCH = 19\n", + "\n", + "for matchday in range(1, LAST_MATCH + 1):\n", + " \n", + " votes_file = 'fantacalcio/voti/Voti_Fantacalcio_Stagione_2022_23_Giornata_' + str(matchday) + '.xlsx'\n", + "\n", + "\n", + "\n", + "\n", + " rx = np.array(pd.read_excel(votes_file, header = None))\n", + "\n", + " read = 0\n", + " for i in range(rx.shape[0]):\n", + " if(rx[i, 0] == \"Cod.\"):\n", + " read = 1\n", + " team = rx[i-1, 0];\n", + " continue\n", + "\n", + " if(read):\n", + " if(isinstance(rx[i, 0], int)):\n", + " if((isinstance(rx[i, 3], float) or isinstance(rx[i, 3], int)) and rx[i, 1] != \"ALL\") :\n", + " player = rx[i, 2];\n", + " vote = float(rx[i, 3])\n", + " goals = rx[i, 4] + rx[i, 8] - rx[i, 5]\n", + " assists = rx[i, 12]\n", + " cards_malus = rx[i, 10] * 0.5 + rx[i, 11]\n", + " \n", + " oppteam = ''\n", + " home = 0\n", + " for j in range(cal_df.shape[0]):\n", + " if(cal_df['matchday'][j] == matchday):\n", + " if(cal_df['team1'][j] == team):\n", + " oppteam = cal_df['team2'][j] \n", + " home = 1\n", + " elif(cal_df['team2'][j] == team):\n", + " oppteam = cal_df['team1'][j] \n", + " home = 0\n", + "\n", + " goals_gen = goals * 3;\n", + " if(goals < 0):\n", + " goals_gen = goals\n", + " \n", + " fantavote = vote + goals_gen + assists - cards_malus \n", + " \n", + " frame = pd.DataFrame([[matchday, player, team, oppteam, home, vote, goals, assists, cards_malus, fantavote]], columns = df.columns)\n", + "\n", + " df = pd.concat([df, frame], axis = 0, ignore_index = True)\n", + " else:\n", + " read = 0\n", + " continue\n", + "\n", + " df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6ade957d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote
01MussoAtalantaSampdoria06.0000.55.5
11ToloiAtalantaSampdoria07.0100.010.0
21DjimsitiAtalantaSampdoria06.0000.06.0
31HateboerAtalantaSampdoria06.0000.55.5
41OkoliAtalantaSampdoria05.5000.55.0
.................................
542319IlicVeronaLecce17.0010.08.0
542419Sulemana I.VeronaLecce16.0000.06.0
542519LasagnaVeronaLecce15.5000.05.5
542619KallonVeronaLecce16.0000.06.0
542719DjuricVeronaLecce16.0000.06.0
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5428 rows × 10 columns

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" + ], + "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", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", + "\n", + " cards_malus fantavote \n", + "0 0.5 5.5 \n", + "1 0.0 10.0 \n", + "2 0.0 6.0 \n", + "3 0.5 5.5 \n", + "4 0.5 5.0 \n", + "... ... ... \n", + "5423 0.0 8.0 \n", + "5424 0.0 6.0 \n", + "5425 0.0 5.5 \n", + "5426 0.0 6.0 \n", + "5427 0.0 6.0 \n", + "\n", + "[5428 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "230213fe", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_excel('mid_outputs/players_votes.xlsx')" + ] + }, + { + "cell_type": "markdown", + "id": "ef91ece3", + "metadata": {}, + "source": [ + "Elaborate data for past seasons.\n", + "\n", + "Do not repeat if data is already present." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "da35bdf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayteam1team2
01VeronaSassuolo
11InterGenoa
21EmpoliLazio
31TorinoAtalanta
41BolognaSalernitana
............
37538SpeziaNapoli
37638SassuoloMilan
37738InterSampdoria
37838SalernitanaUdinese
37938VeneziaCagliari
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380 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " matchday team1 team2\n", + "0 1 Verona Sassuolo\n", + "1 1 Inter Genoa\n", + "2 1 Empoli Lazio\n", + "3 1 Torino Atalanta\n", + "4 1 Bologna Salernitana\n", + ".. ... ... ...\n", + "375 38 Spezia Napoli\n", + "376 38 Sassuolo Milan\n", + "377 38 Inter Sampdoria\n", + "378 38 Salernitana Udinese\n", + "379 38 Venezia Cagliari\n", + "\n", + "[380 rows x 3 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df = pd.read_excel('fantacalcio/season2122/seriea_calendar.xlsx')\n", + "\n", + "to_drop = list()\n", + "\n", + "for i in range(cal_df.shape[0]):\n", + " if(not isinstance(cal_df['matchday'][i], int)):\n", + " to_drop.append(i)\n", + " \n", + "cal_df = cal_df.drop(to_drop)\n", + "\n", + "cal_df = cal_df.reset_index(drop = True)\n", + "\n", + "cal_df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0313a0f4", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(columns = ['matchday', 'player', 'team', 'oppteam', 'home', 'vote', 'goals', 'assists', 'cards_malus', 'fantavote'])\n", + "\n", + "LAST_MATCH = 38\n", + "\n", + "for matchday in range(1, LAST_MATCH + 1):\n", + " \n", + " votes_file = 'fantacalcio/season2122/voti/Voti_Fantacalcio_Stagione_2021_22_Giornata_' + str(matchday) + '.xlsx'\n", + "\n", + "\n", + "\n", + "\n", + " rx = np.array(pd.read_excel(votes_file, header = None))\n", + "\n", + " read = 0\n", + " for i in range(rx.shape[0]):\n", + " if(rx[i, 0] == \"Cod.\"):\n", + " read = 1\n", + " team = rx[i-1, 0];\n", + " continue\n", + "\n", + " if(read):\n", + " if(isinstance(rx[i, 0], int)):\n", + " if((isinstance(rx[i, 3], float) or isinstance(rx[i, 3], int)) and rx[i, 1] != \"ALL\") :\n", + " player = rx[i, 2];\n", + " vote = float(rx[i, 3])\n", + " goals = rx[i, 4] + rx[i, 8] - rx[i, 5]\n", + " assists = rx[i, 12]\n", + " cards_malus = rx[i, 10] * 0.5 + rx[i, 11]\n", + " \n", + " oppteam = ''\n", + " home = 0\n", + " for j in range(cal_df.shape[0]):\n", + " if(cal_df['matchday'][j] == matchday):\n", + " if(cal_df['team1'][j] == team):\n", + " oppteam = cal_df['team2'][j] \n", + " home = 1\n", + " elif(cal_df['team2'][j] == team):\n", + " oppteam = cal_df['team1'][j] \n", + " home = 0\n", + "\n", + " goals_gen = goals * 3;\n", + " if(goals < 0):\n", + " goals_gen = goals\n", + " \n", + " fantavote = vote + goals_gen + assists - cards_malus \n", + " \n", + " frame = pd.DataFrame([[matchday, player, team, oppteam, home, vote, goals, assists, cards_malus, fantavote]], columns = df.columns)\n", + "\n", + " df = pd.concat([df, frame], axis = 0, ignore_index = True)\n", + " else:\n", + " read = 0\n", + " continue\n", + "\n", + " df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "82b1cfc8", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_excel('mid_outputs/season2122/players_votes.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d1fca3ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayteam1team2
01FiorentinaTorino
11VeronaRoma
21ParmaNapoli
31GenoaCrotone
41SassuoloCagliari
............
37538SassuoloLazio
37638AtalantaMilan
37738TorinoBenevento
37838BolognaJuventus
37938NapoliVerona
\n", + "

380 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " matchday team1 team2\n", + "0 1 Fiorentina Torino\n", + "1 1 Verona Roma\n", + "2 1 Parma Napoli\n", + "3 1 Genoa Crotone\n", + "4 1 Sassuolo Cagliari\n", + ".. ... ... ...\n", + "375 38 Sassuolo Lazio\n", + "376 38 Atalanta Milan\n", + "377 38 Torino Benevento\n", + "378 38 Bologna Juventus\n", + "379 38 Napoli Verona\n", + "\n", + "[380 rows x 3 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df = pd.read_excel('fantacalcio/season2021/seriea_calendar.xlsx')\n", + "\n", + "to_drop = list()\n", + "\n", + "for i in range(cal_df.shape[0]):\n", + " if(not isinstance(cal_df['matchday'][i], int)):\n", + " to_drop.append(i)\n", + " \n", + "cal_df = cal_df.drop(to_drop)\n", + "\n", + "cal_df = cal_df.reset_index(drop = True)\n", + "\n", + "cal_df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6d5d4101", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(columns = ['matchday', 'player', 'team', 'oppteam', 'home', 'vote', 'goals', 'assists', 'cards_malus', 'fantavote'])\n", + "\n", + "LAST_MATCH = 38\n", + "\n", + "for matchday in range(1, LAST_MATCH + 1):\n", + " \n", + " votes_file = 'fantacalcio/season2021/voti/Voti_Fantacalcio_Stagione_2020_21_Giornata_' + str(matchday) + '.xlsx'\n", + "\n", + "\n", + "\n", + "\n", + " rx = np.array(pd.read_excel(votes_file, header = None))\n", + "\n", + " read = 0\n", + " for i in range(rx.shape[0]):\n", + " if(rx[i, 0] == \"Cod.\"):\n", + " read = 1\n", + " team = rx[i-1, 0];\n", + " continue\n", + "\n", + " if(read):\n", + " if(isinstance(rx[i, 0], int)):\n", + " if((isinstance(rx[i, 3], float) or isinstance(rx[i, 3], int)) and rx[i, 1] != \"ALL\") :\n", + " player = rx[i, 2];\n", + " vote = float(rx[i, 3])\n", + " goals = rx[i, 4] + rx[i, 8] - rx[i, 5]\n", + " assists = rx[i, 12]\n", + " cards_malus = rx[i, 10] * 0.5 + rx[i, 11]\n", + " \n", + " oppteam = ''\n", + " home = 0\n", + " for j in range(cal_df.shape[0]):\n", + " if(cal_df['matchday'][j] == matchday):\n", + " if(cal_df['team1'][j] == team):\n", + " oppteam = cal_df['team2'][j] \n", + " home = 1\n", + " elif(cal_df['team2'][j] == team):\n", + " oppteam = cal_df['team1'][j] \n", + " home = 0\n", + "\n", + " goals_gen = goals * 3;\n", + " if(goals < 0):\n", + " goals_gen = goals\n", + " \n", + " fantavote = vote + goals_gen + assists - cards_malus \n", + " \n", + " frame = pd.DataFrame([[matchday, player, team, oppteam, home, vote, goals, assists, cards_malus, fantavote]], columns = df.columns)\n", + "\n", + " df = pd.concat([df, frame], axis = 0, ignore_index = True)\n", + " else:\n", + " read = 0\n", + " continue\n", + "\n", + " df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0a4bbffd", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_excel('mid_outputs/season2021/players_votes.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48a341c2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/3_players_dataset_creation-checkpoint.ipynb b/.ipynb_checkpoints/3_players_dataset_creation-checkpoint.ipynb new file mode 100644 index 0000000..50c1cfa --- /dev/null +++ b/.ipynb_checkpoints/3_players_dataset_creation-checkpoint.ipynb @@ -0,0 +1,3521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2cd2177", + "metadata": {}, + "source": [ + "Players dataset creation\n", + "\n", + "The Fantacalcio players list is manually downloaded from https://www.fantacalcio.it/quotazioni-fantacalcio\n", + "\n", + "Here, the players database is generated, by merging the Fantacalcio list to stats downloaded from http://fbref.com" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7c65df92", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "5ab35d0c", + "metadata": {}, + "source": [ + "Load fbref data for outfield players and goalkeepers.\n", + "\n", + "Generate fbref player list, adding player surname (with special characters replaced to normal ones)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b2d7073e", + "metadata": {}, + "outputs": [], + "source": [ + "rcsv = pd.read_csv('fbref_data/outfield_players.csv') \n", + "outfield_players = pd.DataFrame(rcsv)\n", + "\n", + "rcsv = pd.read_csv('fbref_data/keepers_players.csv') \n", + "keeper_players = pd.DataFrame(rcsv)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "667970f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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playerteam
0Tammy AbrahamRoma
1Francesco AcerbiInter
2Yacine AdliMilan
3Michel AebischerBologna
4Felix Afena-GyanCremonese
.........
545Ciprian TătărușanuMilan
546Pietro TerraccianoFiorentina
547Guglielmo VicarioEmpoli
548Jeroen ZoetSpezia
549Petar ZovkoSpezia
\n", + "

550 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " player team\n", + "0 Tammy Abraham Roma\n", + "1 Francesco Acerbi Inter\n", + "2 Yacine Adli Milan\n", + "3 Michel Aebischer Bologna\n", + "4 Felix Afena-Gyan Cremonese\n", + ".. ... ...\n", + "545 Ciprian Tătărușanu Milan\n", + "546 Pietro Terracciano Fiorentina\n", + "547 Guglielmo Vicario Empoli\n", + "548 Jeroen Zoet Spezia\n", + "549 Petar Zovko Spezia\n", + "\n", + "[550 rows x 2 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "players = pd.concat( [ outfield_players[['player', 'team']], keeper_players[['player', 'team']] ], axis = 0, ignore_index = True)\n", + "\n", + "players" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e1e64596", + "metadata": {}, + "outputs": [], + "source": [ + "import unicodedata\n", + "\n", + "def normalize_name(input_str):\n", + " nfkd_form = unicodedata.normalize('NFKD', input_str)\n", + " only_ascii = nfkd_form.encode('ASCII', 'ignore')\n", + " return only_ascii.decode('utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3078d6f3", + "metadata": {}, + "outputs": [], + "source": [ + "players['surname'] = players['player']\n", + "players['initial'] = players['player']\n", + "\n", + "for i in range(players.shape[0]):\n", + " players['surname'][i] = players['surname'][i].split(' ')[-1]\n", + " players['surname'][i] = normalize_name(players['surname'][i]).replace('\\'', '')\n", + " \n", + " \n", + " players['initial'][i] = players['player'][i][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ffd6091c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " player surname\n", + "0 Tammy Abraham Abraham\n", + "1 Francesco Acerbi Acerbi\n", + "2 Yacine Adli Adli\n", + "3 Michel Aebischer Aebischer\n", + "4 Felix Afena-Gyan Afena-Gyan\n", + "5 Kevin Agudelo Agudelo\n", + "6 Ola Aina Aina\n", + "7 Emanuel Aiwum Aiwum\n", + "8 Jean-Daniel Akpa-Akpro Akpa-Akpro\n", + "9 Luis Alberto Alberto\n", + "10 Agustín Álvarez Martínez Martinez\n", + "11 Kelvin Amian Amian\n", + "12 Bruno Amione Amione\n", + "13 Bruno Amione Amione\n", + "14 Ethan Ampadu Ampadu\n", + "15 Sofyan Amrabat Amrabat\n", + "16 Felipe Anderson Anderson\n", + "17 Janis Antiste Antiste\n", + "18 Marcos Antônio Antonio\n", + "19 Valentin Antov Antov\n", + "20 Marko Arnautović Arnautovic\n", + "21 Tolgay Arslan Arslan\n", + "22 Arthur Arthur\n", + "23 Santiago Ascacíbar Ascacibar\n", + "24 Kristoffer Askildsen Askildsen\n", + "25 Kristjan Asllani Asllani\n", + "26 Emil Audero Audero\n", + "27 Tommaso Augello Augello\n", + "28 Kaan Ayhan Ayhan\n", + "29 Jaime Báez Baez\n", + "30 Nedim Bajrami Bajrami\n", + "31 Tommaso Baldanzi Baldanzi\n", + "32 Fodé Ballo-Touré Ballo-Toure\n", + "33 Lameck Banda Banda\n", + "34 Filippo Bandinelli Bandinelli\n", + "35 Antonín Barák Barak\n", + "36 Antonín Barák Barak\n", + "37 Andrea Barberis Barberis\n", + "38 Nicolò Barella Barella\n", + "39 Musa Barrow Barrow\n", + "40 Federico Baschirotto Baschirotto\n", + "41 Toma Bašić Basic\n", + "42 Alessandro Bastoni Bastoni\n", + "43 Simone Bastoni Bastoni\n", + "44 Rodrigo Becão Becao\n", + "45 Julius Beck Beck\n", + "46 Raoul Bellanova Bellanova\n", + "47 Andrea Belotti Belotti\n", + "48 Marco Benassi Benassi\n", + "49 Marco Benassi Benassi\n", + "50 Ismaël Bennacer Bennacer\n", + "51 Domenico Berardi Berardi\n", + "52 Bartosz Bereszyński Bereszynski\n", + "53 Beto Beto\n", + "54 Matteo Bianchetti Bianchetti\n", + "55 Alessandro Bianco Bianco\n", + "56 Jaka Bijol Bijol\n", + "57 Cristiano Biraghi Biraghi\n", + "58 Samuele Birindelli Birindelli\n", + "59 Kristijan Bistrović Bistrovic\n", + "60 Alexis Blin Blin\n", + "61 Jeremie Boga Boga\n", + "62 Emil Bohinen Bohinen\n", + "63 Giacomo Bonaventura Bonaventura\n", + "64 Federico Bonazzoli Bonazzoli\n", + "65 Warren Bondo Bondo\n", + "66 Kevin Bonifazi Bonifazi\n", + "67 Leonardo Bonucci Bonucci\n", + "68 Erik Botheim Botheim\n", + "69 Mehdi Bourabia Bourabia\n", + "70 Edoardo Bove Bove\n", + "71 Domagoj Bradarić Bradaric\n", + "72 Gleison Bremer Bremer\n", + "73 Dylan Bronn Bronn\n", + "74 Marcelo Brozović Brozovic\n", + "75 Cristian Buonaiuto Buonaiuto\n", + "76 Alessandro Buongiorno Buongiorno\n", + "77 Juan Cabal Cabal\n", + "78 Liberato Cacace Cacace\n", + "79 Davide Calabria Calabria\n", + "80 Mattia Caldara Caldara\n", + "81 Luca Caldirola Caldirola\n", + "82 Hakan Çalhanoğlu Calhanoglu\n", + "83 Mohamed Camara Camara\n", + "84 Nicolò Cambiaghi Cambiaghi\n", + "85 Andrea Cambiaso Cambiaso\n", + "86 Matteo Cancellieri Cancellieri\n", + "87 Antonio Candreva Candreva\n", + "88 Gianluca Caprari Caprari\n", + "89 Francesco Caputo Caputo\n", + "90 Francesco Caputo Caputo\n", + "91 Andrea Carboni Carboni\n", + "92 Valentin Carboni Carboni\n", + "93 Carlos Carlos\n", + "94 Marco Carnesecchi Carnesecchi\n", + "95 Nicolò Casale Casale\n", + "96 Michele Castagnetti Castagnetti\n", + "97 Gaetano Castrovilli Castrovilli\n", + "98 Danilo Cataldi Cataldi\n", + "99 Federico Ceccherini Ceccherini\n", + "100 Assan Ceesay Ceesay\n", + "101 Emil Ceide Ceide\n", + "102 Zeki Çelik Celik\n", + "103 Federico Chiesa Chiesa\n", + "104 Vlad Chiricheș Chiriches\n", + "105 Daniel Ciofani Ciofani\n", + "106 Tio Cipot Cipot\n", + "107 Patrick Ciurria Ciurria\n", + "108 Omar Colley Colley\n", + "109 Lorenzo Colombo Colombo\n", + "110 Andrea Colpani Colpani\n", + "111 Andrea Consigli Consigli\n", + "112 Andrea Conti Conti\n", + "113 Diego Coppola Coppola\n", + "114 Joaquín Correa Correa\n", + "115 Alessandro Cortinovis Cortinovis\n", + "116 Lassana Coulibaly Coulibaly\n", + "117 Bryan Cristante Cristante\n", + "118 Domen Črnigoj Crnigoj\n", + "119 Juan Cuadrado Cuadrado\n", + "120 Marco D'Alessandro DAlessandro\n", + "121 Danilo D'Ambrosio DAmbrosio\n", + "122 Luca D'Andrea DAndrea\n", + "123 Flavius Daniliuc Daniliuc\n", + "124 Danilo Danilo\n", + "125 Matteo Darmian Darmian\n", + "126 Paweł Dawidowicz Dawidowicz\n", + "127 Charles De Ketelaere Ketelaere\n", + "128 Manuel De Luca Luca\n", + "129 Mattia De Sciglio Sciglio\n", + "130 Lorenzo De Silvestri Silvestri\n", + "131 Koni De Winter Winter\n", + "132 Grégoire Defrel Defrel\n", + "133 Duccio Degli Innocenti Innocenti\n", + "134 Merih Demiral Demiral\n", + "135 Diego Demme Demme\n", + "136 Fabio Depaoli Depaoli\n", + "137 Fabio Depaoli Depaoli\n", + "138 Kastriot Dermaku Dermaku\n", + "139 Cyriel Dessers Dessers\n", + "140 Sergiño Dest Dest\n", + "141 Mattia Destro Destro\n", + "142 Gerard Deulofeu Deulofeu\n", + "143 Samuel Di Carmine Carmine\n", + "144 Federico Di Francesco Francesco\n", + "145 Michele Di Gregorio Gregorio\n", + "146 Giovanni Di Lorenzo Lorenzo\n", + "147 Ángel Di María Maria\n", + "148 Boulaye Dia Dia\n", + "149 Brahim Díaz Diaz\n", + "150 Federico Dimarco Dimarco\n", + "151 Koffi Djidji Djidji\n", + "152 Berat Djimsiti Djimsiti\n", + "153 Dodô Dodo\n", + "154 Josh Doig Doig\n", + "155 Nicolás Domínguez Dominguez\n", + "156 Giulio Donati Donati\n", + "157 Bartłomiej Drągowski Dragowski\n", + "158 Denzel Dumfries Dumfries\n", + "159 Alfred Duncan Duncan\n", + "160 Paulo Dybala Dybala\n", + "161 Edin Džeko Dzeko\n", + "162 Festy Ebosele Ebosele\n", + "163 Enzo Ebosse Ebosse\n", + "164 Tyronne Ebuehi Ebuehi\n", + "165 Éderson Ederson\n", + "166 Kingsley Ehizibue Ehizibue\n", + "167 Albin Ekdal Ekdal\n", + "168 Emmanuel Ekong Ekong\n", + "169 Mikael Ellertsson Ellertsson\n", + "170 Elif Elmas Elmas\n", + "171 Martin Erlic Erlic\n", + "172 Gonzalo Escalante Escalante\n", + "173 Salvatore Esposito Esposito\n", + "174 Nicolò Fagioli Fagioli\n", + "175 Wladimiro Falcone Falcone\n", + "176 Davide Faraoni Faraoni\n", + "177 Federico Fazio Fazio\n", + "178 Jacopo Fazzini Fazzini\n", + "179 Lewis Ferguson Ferguson\n", + "180 Alex Ferrari Ferrari\n", + "181 Alex Ferrari Ferrari\n", + "182 Salvador Ferrer Ferrer\n", + "183 Alessandro Florenzi Florenzi\n", + "184 Davide Frattesi Frattesi\n", + "185 Matteo Gabbia Gabbia\n", + "186 Manolo Gabbiadini Gabbiadini\n", + "187 Gianluca Gaetano Gaetano\n", + "188 Roberto Gagliardini Gagliardini\n", + "189 Antonino Gallo Gallo\n", + "190 Federico Gatti Gatti\n", + "191 Valentin Gendrey Gendrey\n", + "192 Paolo Ghiglione Ghiglione\n", + "193 Mario Gila Gila\n", + "194 Olivier Giroud Giroud\n", + "195 Pierluigi Gollini Gollini\n", + "196 Joan Gonzàlez Gonzalez\n", + "197 Nicolás González Gonzalez\n", + "198 Robin Gosens Gosens\n", + "199 Alberto Grassi Grassi\n", + "200 Koray Günter Gunter\n", + "201 Emmanuel Gyasi Gyasi\n", + "202 Norbert Gyömbér Gyomber\n", + "203 Christian Gytkjær Gytkjr\n", + "204 Nicolas Haas Haas\n", + "205 Samir Handanović Handanovic\n", + "206 Abdou Harroui Harroui\n", + "207 Hans Hateboer Hateboer\n", + "208 Liam Henderson Henderson\n", + "209 Jack Hendry Hendry\n", + "210 Matheus Henrique Henrique\n", + "211 Thomas Henry Henry\n", + "212 Theo Hernández Hernandez\n", + "213 Isak Hien Hien\n", + "214 Morten Hjulmand Hjulmand\n", + "215 Emil Holm Holm\n", + "216 Martin Hongla Hongla\n", + "217 Petko Hristov Hristov\n", + "218 Ajdin Hrustic Hrustic\n", + "219 Elseid Hysaj Hysaj\n", + "220 Rasmus Højlund Hjlund\n", + "221 Roger Ibanez Ibanez\n", + "222 Igor Igor\n", + "223 Jonathan Ikone Ikone\n", + "224 Ivan Ilić Ilic\n", + "225 Samuel Iling-Junior Iling-Junior\n", + "226 Emirhan İlkhan Ilkhan\n", + "227 Ciro Immobile Immobile\n", + "228 Ardian Ismajli Ismajli\n", + "229 Armando Izzo Izzo\n", + "230 Mato Jajalo Jajalo\n", + "231 Juan Jesus Jesus\n", + "232 Þórir Jóhann Helgason Helgason\n", + "233 Luka Jović Jovic\n", + "234 Hamed Junior Traorè Traore\n", + "235 Yayah Kallon Kallon\n", + "236 Pierre Kalulu Kalulu\n", + "237 Yann Karamoh Karamoh\n", + "238 Rick Karsdorp Karsdorp\n", + "239 Denso Kasius Kasius\n", + "240 Grigoris Kastanos Kastanos\n", + "241 Moise Kean Kean\n", + "242 Jakub Kiwior Kiwior\n", + "243 Simon Kjær Kjr\n", + "244 Teun Koopmeiners Koopmeiners\n", + "245 Filip Kostić Kostic\n", + "246 Christian Kouamé Kouame\n", + "247 Viktor Kovalenko Kovalenko\n", + "248 Julian Kristoffersen Kristoffersen\n", + "249 Rade Krunić Krunic\n", + "250 Marash Kumbulla Kumbulla\n", + "251 Khvicha Kvaratskhelia Kvaratskhelia\n", + "252 Giorgos Kyriakopoulos Kyriakopoulos\n", + "253 Sam Lammers Lammers\n", + "254 Sam Lammers Lammers\n", + "255 Kevin Lasagna Lasagna\n", + "256 Armand Lauriente Lauriente\n", + "257 Valentino Lazaro Lazaro\n", + "258 Marko Lazetić Lazetic\n", + "259 Darko Lazović Lazovic\n", + "260 Manuel Lazzari Lazzari\n", + "261 Rafael Leão Leao\n", + "262 Mehdi Léris Leris\n", + "263 Karol Linetty Linetty\n", + "264 Marcin Listkowski Listkowski\n", + "265 Stanislav Lobotka Lobotka\n", + "266 Manuel Locatelli Locatelli\n", + "267 Luka Lochoshvili Lochoshvili\n", + "268 Ademola Lookman Lookman\n", + "269 Maxime Lopez Lopez\n", + "270 Matteo Lovato Lovato\n", + "271 Sandi Lovrić Lovric\n", + "272 Hirving Lozano Lozano\n", + "273 Jhon Lucumí Lucumi\n", + "274 José Luis Palomino Palomino\n", + "275 Romelu Lukaku Lukaku\n", + "276 Saša Lukić Lukic\n", + "277 Sebastiano Luperto Luperto\n", + "278 Charalambos Lykogiannis Lykogiannis\n", + "279 Giulio Maggiore Maggiore\n", + "280 Giangiacomo Magnani Magnani\n", + "281 Mike Maignan Maignan\n", + "282 Jean-Victor Makengo Makengo\n", + "283 Daniel Maldini Maldini\n", + "284 Youssef Maleh Maleh\n", + "285 Youssef Maleh Maleh\n", + "286 Ruslan Malinovskyi Malinovskyi\n", + "287 Gianluca Mancini Mancini\n", + "288 Rolando Mandragora Mandragora\n", + "289 Riccardo Marchizza Marchizza\n", + "290 Gian Marco Ferrari Ferrari\n", + "291 Pablo Marí Mari\n", + "292 Răzvan Marin Marin\n", + "293 Marlon Marlon\n", + "294 Luca Marrone Marrone\n", + "295 Lautaro Martínez Martinez\n", + "296 Lucas Martínez Quarta Quarta\n", + "297 Adam Marušić Marusic\n", + "298 Adam Masina Masina\n", + "299 Nemanja Matić Matic\n", + "300 Luís Maximiano Maximiano\n", + "301 Pasquale Mazzocchi Mazzocchi\n", + "302 Weston McKennie McKennie\n", + "303 Gary Medel Medel\n", + "304 Soualiho Meïté Meite\n", + "305 Alex Meret Meret\n", + "306 Yıldırım Mert Çetin Cetin\n", + "307 Junior Messias Messias\n", + "308 Tommaso Milanese Milanese\n", + "309 Nikola Milenković Milenkovic\n", + "310 Arkadiusz Milik Milik\n", + "311 Sergej Milinković-Savić Milinkovic-Savic\n", + "312 Vanja Milinković-Savić Milinkovic-Savic\n", + "313 Kim Min-jae Min-jae\n", + "314 Aleksei Miranchuk Miranchuk\n", + "315 Fabio Miretti Miretti\n", + "316 Henrikh Mkhitaryan Mkhitaryan\n", + "317 Salvatore Molina Molina\n", + "318 Daniele Montevago Montevago\n", + "319 Lorenzo Montipò Montipo\n", + "320 Nikola Moro Moro\n", + "321 Dany Mota Mota\n", + "322 João Moutinho Moutinho\n", + "323 Mert Müldür Muldur\n", + "324 Luis Muriel Muriel\n", + "325 Jeison Murillo Murillo\n", + "326 Nicola Murru Murru\n", + "327 Juan Musso Musso\n", + "328 Joakim Mæhle Mhle\n", + "329 Michel Ndary Adopo Adopo\n", + "330 Tanguy Ndombele Ndombele\n", + "331 Ilija Nestorovski Nestorovski\n", + "332 Hans Nicolussi Caviglia Caviglia\n", + "333 Dimitris Nikolaou Nikolaou\n", + "334 Bram Nuytinck Nuytinck\n", + "335 Bram Nuytinck Nuytinck\n", + "336 M'Bala Nzola Nzola\n", + "337 Pedro Obiang Obiang\n", + "338 Guillermo Ochoa Ochoa\n", + "339 David Okereke Okereke\n", + "340 Caleb Okoli Okoli\n", + "341 Mathías Olivera Olivera\n", + "342 André Onana Onana\n", + "343 Divock Origi Origi\n", + "344 Riccardo Orsolini Orsolini\n", + "345 Victor Osimhen Osimhen\n", + "346 Remi Oudin Oudin\n", + "347 Adam Ounas Ounas\n", + "348 Flavio Paoletti Paoletti\n", + "349 Leandro Paredes Paredes\n", + "350 Fabiano Parisi Parisi\n", + "351 Mario Pašalić Pasalic\n", + "352 Patric Patric\n", + "353 Rui Patrício Patricio\n", + "354 Pedro Pedro\n", + "355 Gianluca Pegolo Pegolo\n", + "356 Pietro Pellegri Pellegri\n", + "357 Lorenzo Pellegrini Pellegrini\n", + "358 Pepín Pepin\n", + "359 Roberto Pereyra Pereyra\n", + "360 Nehuén Pérez Perez\n", + "361 Mattia Perin Perin\n", + "362 Matteo Pessina Pessina\n", + "363 Andrea Petagna Petagna\n", + "364 Giuseppe Pezzella Pezzella\n", + "365 Krzysztof Piątek Piatek\n", + "366 Roberto Piccoli Piccoli\n", + "367 Charles Pickel Pickel\n", + "368 Andrea Pinamonti Pinamonti\n", + "369 Lorenzo Pirola Pirola\n", + "370 Marko Pjaca Pjaca\n", + "371 Tommaso Pobega Pobega\n", + "372 Matteo Politano Politano\n", + "373 Marin Pongračić Pongracic\n", + "374 Stefan Posch Posch\n", + "375 Ivan Provedel Provedel\n", + "376 Ignacio Pussetto Pussetto\n", + "377 Niklas Pyyhtiä Pyyhtia\n", + "378 Fabio Quagliarella Quagliarella\n", + "379 Giacomo Quagliata Quagliata\n", + "380 Adrien Rabiot Rabiot\n", + "381 Nemanja Radonjić Radonjic\n", + "382 Ivan Radovanović Radovanovic\n", + "383 Ionuț Radu Radu\n", + "384 Luca Ranieri Ranieri\n", + "385 Andrea Ranocchia Ranocchia\n", + "386 Filippo Ranocchia Ranocchia\n", + "387 Giacomo Raspadori Raspadori\n", + "388 Giacomo Raspadori Raspadori\n", + "389 Ante Rebić Rebic\n", + "390 Arkadiusz Reca Reca\n", + "391 Panagiotis Retsos Retsos\n", + "392 Franck Ribéry Ribery\n", + "393 Samuele Ricci Ricci\n", + "394 Tomás Rincón Rincon\n", + "395 Pablo Rodríguez Rodriguez\n", + "396 Ricardo Rodríguez Rodriguez\n", + "397 Rogério Rogerio\n", + "398 Alessio Romagnoli Romagnoli\n", + "399 Luka Romero Romero\n", + "400 Marten de Roon Roon\n", + "401 Nicolò Rovella Rovella\n", + "402 Nicolò Rovella Rovella\n", + "403 Amir Rrahmani Rrahmani\n", + "404 Ruan Ruan\n", + "405 Daniele Rugani Rugani\n", + "406 Matteo Ruggeri Ruggeri\n", + "407 Mário Rui Rui\n", + "408 Abdelhamid Sabiri Sabiri\n", + "409 Alexis Saelemaekers Saelemaekers\n", + "410 Jacopo Sala Sala\n", + "411 Lazar Samardzic Samardzic\n", + "412 Junior Sambia Sambia\n", + "413 Antonio Sanabria Sanabria\n", + "414 Leandro Sanca Sanca\n", + "415 Alex Sandro Sandro\n", + "416 Nicola Sansone Sansone\n", + "417 Riccardo Saponara Saponara\n", + "418 Martin Satriano Satriano\n", + "419 Giorgio Scalvini Scalvini\n", + "420 Jerdy Schouten Schouten\n", + "421 Perr Schuurs Schuurs\n", + "422 Demba Seck Seck\n", + "423 Jacopo Segre Segre\n", + "424 Stefano Sensi Sensi\n", + "425 Luigi Sepe Sepe\n", + "426 Leonardo Sernicola Sernicola\n", + "427 Stephan El Shaarawy Shaarawy\n", + "428 Eldor Shomurodov Shomurodov\n", + "429 Marco Silvestri Silvestri\n", + "430 Giovanni Simeone Simeone\n", + "431 Wilfried Singo Singo\n", + "432 Leo Skiri Østigård stigard\n", + "433 Łukasz Skorupski Skorupski\n", + "434 Milan Škriniar Skriniar\n", + "435 Chris Smalling Smalling\n", + "436 Ola Solbakken Solbakken\n", + "437 Brandon Soppy Soppy\n", + "438 Brandon Soppy Soppy\n", + "439 Roberto Soriano Soriano\n", + "440 Joaquin Sosa Sosa\n", + "441 Riccardo Sottil Sottil\n", + "442 Matìas Soulé Soule\n", + "443 Adama Soumaoro Soumaoro\n", + "444 Leonardo Spinazzola Spinazzola\n", + "445 Marco Sportiello Sportiello\n", + "446 Petar Stojanović Stojanovic\n", + "447 Gabriel Strefezza Strefezza\n", + "448 Dávid Strelec Strelec\n", + "449 Isaac Success Success\n", + "450 Ibrahim Sulemana Sulemana\n", + "451 Wojciech Szczęsny Szczesny\n", + "452 Benjamin Tahirovic Tahirovic\n", + "453 Adrien Tameze Tameze\n", + "454 Ciprian Tătărușanu Tatarusanu\n", + "455 Filippo Terracciano Terracciano\n", + "456 Pietro Terracciano Terracciano\n", + "457 Aleksa Terzić Terzic\n", + "458 Malick Thiaw Thiaw\n", + "459 Kristian Thorstvedt Thorstvedt\n", + "460 Jeremy Toljan Toljan\n", + "461 Rafael Tolói Toloi\n", + "462 Fikayo Tomori Tomori\n", + "463 Sandro Tonali Tonali\n", + "464 William Troost-Ekong Troost-Ekong\n", + "465 Frank Tsadjout Tsadjout\n", + "466 Alessandro Tuia Tuia\n", + "467 Iyenoma Udogie Udogie\n", + "468 Samuel Umtiti Umtiti\n", + "469 Diego Valencia Valencia\n", + "470 Emanuele Valeri Valeri\n", + "471 Mattia Valoti Valoti\n", + "472 Johan Vásquez Vasquez\n", + "473 Matías Vecino Vecino\n", + "474 Miguel Veloso Veloso\n", + "475 Lorenzo Venuti Venuti\n", + "476 Daniele Verde Verde\n", + "477 Simone Verdi Verdi\n", + "478 Valerio Verre Verre\n", + "479 Guglielmo Vicario Vicario\n", + "480 Ronaldo Vieira Vieira\n", + "481 Emanuel Vignato Vignato\n", + "482 Samuele Vignato Vignato\n", + "483 Tonny Vilhena Vilhena\n", + "484 Gonzalo Villar Villar\n", + "485 Matías Viña Vina\n", + "486 Dušan Vlahović Vlahovic\n", + "487 Nikola Vlašić Vlasic\n", + "488 Joel Voelkerling Persson Persson\n", + "489 Mërgim Vojvoda Vojvoda\n", + "490 Cristian Volpato Volpato\n", + "491 Aster Vranckx Vranckx\n", + "492 Stefan de Vrij Vrij\n", + "493 Walace Walace\n", + "494 Sebastian Walukiewicz Walukiewicz\n", + "495 Georginio Wijnaldum Wijnaldum\n", + "496 Harry Winks Winks\n", + "497 Gerard Yepes Yepes\n", + "498 Mattia Zaccagni Zaccagni\n", + "499 Denis Zakaria Zakaria\n", + "500 Nicola Zalewski Zalewski\n", + "501 Andre-Frank Zambo Anguissa Anguissa\n", + "502 Luca Zanimacchia Zanimacchia\n", + "503 Nicolò Zaniolo Zaniolo\n", + "504 Alessandro Zanoli Zanoli\n", + "505 Alessandro Zanoli Zanoli\n", + "506 Duván Zapata Zapata\n", + "507 Davide Zappacosta Zappacosta\n", + "508 Alessio Zerbin Zerbin\n", + "509 Piotr Zieliński Zielinski\n", + "510 David Zima Zima\n", + "511 Joshua Zirkzee Zirkzee\n", + "512 Jeroen Zoet Zoet\n", + "513 Nadir Zortea Zortea\n", + "514 Petar Zovko Zovko\n", + "515 Szymon Żurkowski Zurkowski\n", + "516 Szymon Żurkowski Zurkowski\n", + "517 Milan Đurić uric\n", + "518 Filip Đuričić uricic\n", + "519 Emil Audero Audero\n", + "520 Marco Carnesecchi Carnesecchi\n", + "521 Andrea Consigli Consigli\n", + "522 Michele Di Gregorio Gregorio\n", + "523 Bartłomiej Drągowski Dragowski\n", + "524 Wladimiro Falcone Falcone\n", + "525 Pierluigi Gollini Gollini\n", + "526 Samir Handanović Handanovic\n", + "527 Mike Maignan Maignan\n", + "528 Luís Maximiano Maximiano\n", + "529 Alex Meret Meret\n", + "530 Vanja Milinković-Savić Milinkovic-Savic\n", + "531 Lorenzo Montipò Montipo\n", + "532 Juan Musso Musso\n", + "533 Guillermo Ochoa Ochoa\n", + "534 André Onana Onana\n", + "535 Rui Patrício Patricio\n", + "536 Gianluca Pegolo Pegolo\n", + "537 Mattia Perin Perin\n", + "538 Ivan Provedel Provedel\n", + "539 Ionuț Radu Radu\n", + "540 Luigi Sepe Sepe\n", + "541 Marco Silvestri Silvestri\n", + "542 Łukasz Skorupski Skorupski\n", + "543 Marco Sportiello Sportiello\n", + "544 Wojciech Szczęsny Szczesny\n", + "545 Ciprian Tătărușanu Tatarusanu\n", + "546 Pietro Terracciano Terracciano\n", + "547 Guglielmo Vicario Vicario\n", + "548 Jeroen Zoet Zoet\n", + "549 Petar Zovko Zovko\n" + ] + } + ], + "source": [ + "print(players[['player', 'surname']].to_string())" + ] + }, + { + "cell_type": "markdown", + "id": "9ab06a5c", + "metadata": {}, + "source": [ + "Replace the surname for some specific players, according to config/name_fix.txt file.\n", + "\n", + "This is done for players for which the decoded fbref surname doesn't correspond to Fantacalcio list." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "3e759b1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ostigard\n", + "Kim\n", + "Hojlund\n", + "Gytkjaer\n", + "Augusto\n", + "Maehle\n", + "Kjaer\n", + "Djuricic\n", + "Djuric\n", + "Cabral\n", + "Alvarez\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FROM TO TEAM\n", + "0 stigard Ostigard Napoli\n", + "1 Min-jae Kim Napoli\n", + "2 Hjlund Hojlund Atalanta\n", + "3 Gytkjr Gytkjaer Monza\n", + "4 Carlos Augusto Monza\n", + "5 Mhle Maehle Atalanta\n", + "6 Kjr Kjaer Milan\n", + "7 uricic Djuricic Sampdoria\n", + "8 uric Djuric Hellas Verona\n", + "9 Arthur Cabral Fiorentina\n", + "10 Martinez Alvarez Sassuolo" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rcsv = pd.read_csv('config/name_fix.txt') \n", + "name_fix = pd.DataFrame(rcsv)\n", + "\n", + "for i in range(name_fix.shape[0]):\n", + " for j in range(players.shape[0]):\n", + " if(players['surname'][j].lower() == name_fix['FROM'][i].lower() and players['team'][j].lower() == name_fix['TEAM'][i].lower()):\n", + " players['surname'][j] = name_fix['TO'][i]\n", + " print(name_fix['TO'][i])\n", + "\n", + "name_fix\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c686d9f", + "metadata": {}, + "source": [ + "Load players from Fantacalcio list." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f96eaaa1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_14056\\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_14056\\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_14056\\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", + " fc_players['initial'][i] = spl[-1][0]\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " id r name team surname initial\n", + "0 572 P Meret Napoli Meret \n", + "1 2814 P Provedel Lazio Provedel \n", + "2 4964 P Vicario Empoli Vicario \n", + "3 453 P Szczesny Juventus Szczesny \n", + "4 2134 P Falcone Lecce Falcone \n", + ".. ... .. ... ... ... ...\n", + "542 5505 A Kaio Jorge Juventus Jorge \n", + "543 5512 A De Luca Sampdoria Luca \n", + "544 5785 A Lazetic Milan Lazetic \n", + "545 6113 A Montevago Sampdoria Montevago \n", + "546 6143 A Krollis Spezia Krollis \n", + "\n", + "[547 rows x 6 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_data = pd.read_excel('fantacalcio/Quotazioni_Fantacalcio.xlsx', 'Tutti', header = 1)\n", + "\n", + "fc_players = fc_data [['Id', 'R', 'Nome', 'Squadra']]\n", + "\n", + "fc_players = fc_players.rename(columns = {'Id' : 'id', 'R': 'r', 'Nome' : 'name', 'Squadra' : 'team'})\n", + "\n", + "fc_players['surname'] = fc_players['name']\n", + "fc_players['initial'] = fc_players['name']\n", + "\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " spl = normalize_name( fc_players['name'][i].replace('\\'', '') ).split(' ')\n", + " if('.' in spl[-1]):\n", + " fc_players['surname'][i] = spl[-2]\n", + " fc_players['initial'][i] = spl[-1][0]\n", + " else:\n", + " fc_players['surname'][i] = spl[-1]\n", + " fc_players['initial'][i] = ''\n", + " \n", + "fc_players\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d944c720", + "metadata": {}, + "source": [ + "Associate players from Fantacalcio list to ID for FBref data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "9c50e4b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\1456862974.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_14056\\1456862974.py:10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " fc_players['fb_ID'][i] = j\n" + ] + } + ], + "source": [ + "fc_players['fb_ID'] = fc_players['id']\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " fc_players['fb_ID'][i] = -1\n", + " \n", + " for j in range(players.shape[0]):\n", + " if(fc_players['team'][i].lower() in players['team'][j].lower()):\n", + " if(fc_players['surname'][i].lower() == players['surname'][j].lower()):\n", + " # if(fc_players['initial'][i] == '' or fc_players['initial'][i].lower() == players['initial'][j].lower()):\n", + " fc_players['fb_ID'][i] = j\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "4e60fd2e", + "metadata": {}, + "source": [ + "Print players for which the association failed.\n", + "\n", + "Most of them are players who didn't play a single Serie A game this season. \n", + "\n", + "Others are ones for which the FBRef surname doesn't correspond to Fantacalcio one.\n", + "\n", + "For example, Cabral is Arthur for FBref.\n", + "\n", + "Correction is made in the name_fix code above." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5a3e2771", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gollini\n", + "Mirante\n", + "Sarr M.\n", + "Lamanna\n", + "Ujkani\n", + "Berisha\n", + "Marchetti\n", + "Perilli\n", + "Padelli\n", + "Perisan\n", + "Bardi\n", + "Cordaz\n", + "Pinsoglio\n", + "Fiorillo\n", + "Cragno\n", + "Sirigu\n", + "Cerofolini\n", + "Rossi F.\n", + "Ravaglia F.\n", + "Brancolini\n", + "Bleve\n", + "Berardi A.\n", + "Russo A.\n", + "Gemello\n", + "Ravaglia\n", + "Boer\n", + "Adamonis\n", + "Marfella\n", + "Piana\n", + "Bagnolini\n", + "Svilar\n", + "Sorrentino A.\n", + "Ciezkowski\n", + "Chiesa M.\n", + "Saro\n", + "Vasquez D.\n", + "Turk\n", + "Bereszynski\n", + "Aiwu\n", + "Zeefuik\n", + "Wisniewski\n", + "Tonelli\n", + "Radu\n", + "Paletta\n", + "Fares\n", + "Romagna\n", + "Cassandro\n", + "Amey\n", + "Ferrarini\n", + "Zanotti\n", + "Bayeye\n", + "Buta\n", + "Abankwah\n", + "Guessand A.\n", + "Guarino\n", + "Pogba\n", + "Machin\n", + "Akpa Akpro\n", + "Capezzi\n", + "Bakayoko\n", + "Scozzarella\n", + "Darboe\n", + "Urbanski\n", + "Bertini\n", + "Trimboli\n", + "Pafundi\n", + "Samek\n", + "Acella\n", + "Garbett\n", + "Ngonge\n", + "Ibrahimovic\n", + "Edera\n", + "Oddei\n", + "Braaf\n", + "Raimondo\n", + "Kaio Jorge\n", + "Krollis\n" + ] + } + ], + "source": [ + "for i in range(fc_players.shape[0]):\n", + " if(fc_players['fb_ID'][i] == -1):\n", + " print(fc_players['name'][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3b4a36af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idrnameteamsurnameinitialfb_ID
0572PMeretNapoliMeret529
12814PProvedelLazioProvedel538
24964PVicarioEmpoliVicario547
3453PSzczesnyJuventusSzczesny544
42134PFalconeLecceFalcone524
........................
5425505AKaio JorgeJuventusJorge-1
5435512ADe LucaSampdoriaLuca128
5445785ALazeticMilanLazetic258
5456113AMontevagoSampdoriaMontevago318
5466143AKrollisSpeziaKrollis-1
\n", + "

547 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " id r name team surname initial fb_ID\n", + "0 572 P Meret Napoli Meret 529\n", + "1 2814 P Provedel Lazio Provedel 538\n", + "2 4964 P Vicario Empoli Vicario 547\n", + "3 453 P Szczesny Juventus Szczesny 544\n", + "4 2134 P Falcone Lecce Falcone 524\n", + ".. ... .. ... ... ... ... ...\n", + "542 5505 A Kaio Jorge Juventus Jorge -1\n", + "543 5512 A De Luca Sampdoria Luca 128\n", + "544 5785 A Lazetic Milan Lazetic 258\n", + "545 6113 A Montevago Sampdoria Montevago 318\n", + "546 6143 A Krollis Spezia Krollis -1\n", + "\n", + "[547 rows x 7 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_players" + ] + }, + { + "cell_type": "markdown", + "id": "f06afbb6", + "metadata": {}, + "source": [ + "Populate players dataset with stats from FBref, for outfield players and goalkeepers" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1d73a312", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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", + " fc_players[columns_to_copy[j]][i] = outfield_players[columns_to_copy[j]][fc_players['fb_ID'][i]]\n" + ] + } + ], + "source": [ + "#Data for Outfield players\n", + "columns_to_copy = outfield_players.columns[4:]\n", + "\n", + "fc_players[columns_to_copy] = 0\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " if(fc_players['fb_ID'][i] != -1 and fc_players['r'][i] != 'P'):\n", + " for j in range(columns_to_copy.shape[0]):\n", + " fc_players[columns_to_copy[j]][i] = outfield_players[columns_to_copy[j]][fc_players['fb_ID'][i]]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7acb93e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['age', 'birth_year', 'gk_games', 'gk_games_starts', 'gk_minutes',\n", + " 'gk_goals_against', 'gk_goals_against_per90',\n", + " 'gk_shots_on_target_against', 'gk_saves', 'gk_save_pct', 'gk_wins',\n", + " 'gk_ties', 'gk_losses', 'gk_clean_sheets', 'gk_clean_sheets_pct',\n", + " 'gk_pens_att', 'gk_pens_allowed', 'gk_pens_saved', 'gk_pens_missed',\n", + " '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',\n", + " 'gk_psxg_net_per90', 'gk_passes_completed_launched',\n", + " 'gk_passes_launched', 'gk_passes_pct_launched', 'gk_passes',\n", + " 'gk_passes_throws', 'gk_pct_passes_launched', 'gk_passes_length_avg',\n", + " 'gk_goal_kicks', 'gk_pct_goal_kicks_launched',\n", + " 'gk_goal_kick_length_avg', 'gk_crosses', 'gk_crosses_stopped',\n", + " 'gk_crosses_stopped_pct', 'gk_def_actions_outside_pen_area',\n", + " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions'],\n", + " dtype='object')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "keeper_players.columns[4:]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "eb9127a9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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_14056\\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", + " fc_players[columns_to_copy[j]][i] = keeper_players[columns_to_copy[j]][fc_players['fb_ID'][i] - delta_k]\n" + ] + } + ], + "source": [ + "#Data for Keepers\n", + "columns_to_copy = keeper_players.columns[4:]\n", + "\n", + "fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n", + "\n", + "delta_k = outfield_players.shape[0]\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " if(fc_players['fb_ID'][i] != -1 and fc_players['r'][i] == 'P'):\n", + " for j in range(columns_to_copy.shape[0]):\n", + " fc_players[columns_to_copy[j]][i] = keeper_players[columns_to_copy[j]][fc_players['fb_ID'][i] - delta_k]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "da977789", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idrnameteamsurnameinitialfb_IDagebirth_yeargames...gk_passes_length_avggk_goal_kicksgk_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_actions
0572PMeretNapoliMeret52925-31219970...26.611420.227.119763.0231.2117.2
12814PProvedelLazioProvedel53828-31719940...32.511534.834.825672.7371.9618.1
24964PVicarioEmpoliVicario54726-11319960...33.910345.641.8399256.380.4210.5
3453PSzczesnyJuventusSzczesny54432-28519900...36.16141.039.215542.670.5614.2
42134PFalconeLecceFalcone52427-29119950...40.915675.051.4304134.3201.0012.5
..................................................................
5425505AKaio JorgeJuventusJorge-1000...0.000.00.0000.000.000.0
5435512ADe LucaSampdoriaLuca12824-19519981...0.000.00.0000.000.000.0
5445785ALazeticMilanLazetic25819-00620041...0.000.00.0000.000.000.0
5456113AMontevagoSampdoriaMontevago31819-31620036...0.000.00.0000.000.000.0
5466143AKrollisSpeziaKrollis-1000...0.000.00.0000.000.000.0
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547 rows × 163 columns

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" + ], + "text/plain": [ + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", + "\n", + " birth_year games ... gk_passes_length_avg gk_goal_kicks \\\n", + "0 1997 0 ... 26.6 114 \n", + "1 1994 0 ... 32.5 115 \n", + "2 1996 0 ... 33.9 103 \n", + "3 1990 0 ... 36.1 61 \n", + "4 1995 0 ... 40.9 156 \n", + ".. ... ... ... ... ... \n", + "542 0 0 ... 0.0 0 \n", + "543 1998 1 ... 0.0 0 \n", + "544 2004 1 ... 0.0 0 \n", + "545 2003 6 ... 0.0 0 \n", + "546 0 0 ... 0.0 0 \n", + "\n", + " gk_pct_goal_kicks_launched gk_goal_kick_length_avg gk_crosses \\\n", + "0 20.2 27.1 197 \n", + "1 34.8 34.8 256 \n", + "2 45.6 41.8 399 \n", + "3 41.0 39.2 155 \n", + "4 75.0 51.4 304 \n", + ".. ... ... ... \n", + "542 0.0 0.0 0 \n", + "543 0.0 0.0 0 \n", + "544 0.0 0.0 0 \n", + "545 0.0 0.0 0 \n", + "546 0.0 0.0 0 \n", + "\n", + " gk_crosses_stopped gk_crosses_stopped_pct \\\n", + "0 6 3.0 \n", + "1 7 2.7 \n", + "2 25 6.3 \n", + "3 4 2.6 \n", + "4 13 4.3 \n", + ".. ... ... \n", + "542 0 0.0 \n", + "543 0 0.0 \n", + "544 0 0.0 \n", + "545 0 0.0 \n", + "546 0 0.0 \n", + "\n", + " gk_def_actions_outside_pen_area gk_def_actions_outside_pen_area_per90 \\\n", + "0 23 1.21 \n", + "1 37 1.96 \n", + "2 8 0.42 \n", + "3 7 0.56 \n", + "4 20 1.00 \n", + ".. ... ... \n", + "542 0 0.00 \n", + "543 0 0.00 \n", + "544 0 0.00 \n", + "545 0 0.00 \n", + "546 0 0.00 \n", + "\n", + " gk_avg_distance_def_actions \n", + "0 17.2 \n", + "1 18.1 \n", + "2 10.5 \n", + "3 14.2 \n", + "4 12.5 \n", + ".. ... \n", + "542 0.0 \n", + "543 0.0 \n", + "544 0.0 \n", + "545 0.0 \n", + "546 0.0 \n", + "\n", + "[547 rows x 163 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_players" + ] + }, + { + "cell_type": "markdown", + "id": "81a17f84", + "metadata": {}, + "source": [ + "Load votes database, to add data to players database (mean vote and its standard deviation)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6a0e43cd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "votes = pd.read_excel('mid_outputs/players_votes.xlsx', index_col = 0)" + ] + }, + { + "cell_type": "markdown", + "id": "6c1b733a", + "metadata": {}, + "source": [ + "Compute the average Serie A Goal Keeper mean vote and vote std" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "61fac91a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vote_avg 6.198757\n", + "vote_std 0.428693\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/html": [ + "
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vote_avgvote_std
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" + ], + "text/plain": [ + " vote_avg vote_std\n", + "0 6.263158 0.496525\n", + "1 6.236842 0.339055\n", + "2 6.473684 0.379532\n", + "3 6.038462 0.364878\n", + "4 6.342105 0.364642\n", + "5 6.289474 0.438772\n", + "6 5.973684 0.443482\n", + "7 6.157895 0.399099\n", + "8 6.366667 0.426875\n", + "9 6.357143 0.440315\n", + "10 6.136364 0.308288\n", + "11 6.315789 0.464830\n", + "12 6.125000 0.297560\n", + "13 6.300000 0.640312\n", + "14 6.263158 0.522354\n", + "15 6.210526 0.521026\n", + "16 5.958333 0.246503\n", + "17 6.117647 0.322190\n", + "18 6.176471 0.418020\n", + "19 6.000000 0.395285\n", + "20 6.071429 0.494872\n", + "21 6.105263 0.475144\n", + "22 6.428571 0.562429\n", + "23 6.062500 0.526634" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_votes = 6\n", + "\n", + "perf_df_P = pd.DataFrame(columns = ['vote_avg', 'vote_std'])\n", + "\n", + "for i in range(fc_players.shape[0]): \n", + " if(fc_players.loc[i]['r'] == 'P'):\n", + " v = np.array([])\n", + " for j in range(votes.shape[0]):\n", + " if(fc_players['name'][i] == votes['player'][j]):\n", + " v = np.append(v, votes['vote'][j])\n", + "\n", + " if(v.shape[0] >= min_votes - 1):\n", + " row_df = pd.DataFrame(data = [[np.mean(v), np.std(v)]], columns = perf_df_P.columns)\n", + " perf_df_P = pd.concat([perf_df_P, row_df], ignore_index = True)\n", + "\n", + "\n", + "print(perf_df_P.mean())\n", + "\n", + "perf_df_P\n" + ] + }, + { + "cell_type": "markdown", + "id": "dbb344e2", + "metadata": {}, + "source": [ + "Add to players data their mean vote (and its standard deviation).\n", + "\n", + "For players who don't have a minimum amount of games, more data to reach this value is computed, according to the average Serie A player vote (and std). For goalkeepers, this values are different.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c9312080", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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vote_avgvote_std
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" + ], + "text/plain": [ + " vote_avg vote_std\n", + "0 6.263158 0.496525\n", + "1 6.236842 0.339055\n", + "2 6.473684 0.379532\n", + "3 6.038462 0.364878\n", + "4 6.342105 0.364642\n", + ".. ... ...\n", + "542 6.092280 0.894197\n", + "543 6.284927 0.532298\n", + "544 6.094004 0.625570\n", + "545 5.711934 0.225193\n", + "546 5.748895 0.595450\n", + "\n", + "[547 rows x 2 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_votes = 6\n", + "\n", + "#outfield players\n", + "mean_def = 6\n", + "std_def = 0.58\n", + "\n", + "#goalkeepers\n", + "mean_def_P = 6.22\n", + "std_def_P = 0.43\n", + "\n", + "\n", + "perf_df = pd.DataFrame(columns = ['vote_avg', 'vote_std'])\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " v = np.array([])\n", + " for j in range(votes.shape[0]):\n", + " if(fc_players['name'][i] == votes['player'][j]):\n", + " v = np.append(v, votes['vote'][j])\n", + " \n", + " mean_def_i = mean_def\n", + " std_def_i = std_def\n", + " \n", + " if(fc_players['r'][i] == 'P'):\n", + " mean_def_i = mean_def_P\n", + " std_def_i = std_def_P\n", + " \n", + " if(v.shape[0] < min_votes):\n", + " for k in range(min_votes - v.shape[0]):\n", + " v = np.append( v, np.random.normal(mean_def_i, std_def_i) )\n", + " \n", + " row_df = pd.DataFrame(data = [[np.mean(v), np.std(v)]], columns = perf_df.columns)\n", + " perf_df = pd.concat([perf_df, row_df], ignore_index = True)\n", + " \n", + "perf_df\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b2570ce5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
0572PMeretNapoliMeret52925-31219970...20.227.119763.0231.2117.26.2631580.496525
12814PProvedelLazioProvedel53828-31719940...34.834.825672.7371.9618.16.2368420.339055
24964PVicarioEmpoliVicario54726-11319960...45.641.8399256.380.4210.56.4736840.379532
3453PSzczesnyJuventusSzczesny54432-28519900...41.039.215542.670.5614.26.0384620.364878
42134PFalconeLecceFalcone52427-29119950...75.051.4304134.3201.0012.56.3421050.364642
..................................................................
5425505AKaio JorgeJuventusJorge-1000...0.00.0000.000.000.06.0922800.894197
5435512ADe LucaSampdoriaLuca12824-19519981...0.00.0000.000.000.06.2849270.532298
5445785ALazeticMilanLazetic25819-00620041...0.00.0000.000.000.06.0940040.625570
5456113AMontevagoSampdoriaMontevago31819-31620036...0.00.0000.000.000.05.7119340.225193
5466143AKrollisSpeziaKrollis-1000...0.00.0000.000.000.05.7488950.595450
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547 rows × 165 columns

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" + ], + "text/plain": [ + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", + "\n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "0 1997 0 ... 20.2 \n", + "1 1994 0 ... 34.8 \n", + "2 1996 0 ... 45.6 \n", + "3 1990 0 ... 41.0 \n", + "4 1995 0 ... 75.0 \n", + ".. ... ... ... ... \n", + "542 0 0 ... 0.0 \n", + "543 1998 1 ... 0.0 \n", + "544 2004 1 ... 0.0 \n", + "545 2003 6 ... 0.0 \n", + "546 0 0 ... 0.0 \n", + "\n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", + "0 27.1 197 6 \n", + "1 34.8 256 7 \n", + "2 41.8 399 25 \n", + "3 39.2 155 4 \n", + "4 51.4 304 13 \n", + ".. ... ... ... \n", + "542 0.0 0 0 \n", + "543 0.0 0 0 \n", + "544 0.0 0 0 \n", + "545 0.0 0 0 \n", + "546 0.0 0 0 \n", + "\n", + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "0 3.0 23 \n", + "1 2.7 37 \n", + "2 6.3 8 \n", + "3 2.6 7 \n", + "4 4.3 20 \n", + ".. ... ... \n", + "542 0.0 0 \n", + "543 0.0 0 \n", + "544 0.0 0 \n", + "545 0.0 0 \n", + "546 0.0 0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", + "0 1.21 17.2 \n", + "1 1.96 18.1 \n", + "2 0.42 10.5 \n", + "3 0.56 14.2 \n", + "4 1.00 12.5 \n", + ".. ... ... \n", + "542 0.00 0.0 \n", + "543 0.00 0.0 \n", + "544 0.00 0.0 \n", + "545 0.00 0.0 \n", + "546 0.00 0.0 \n", + "\n", + " vote_avg vote_std \n", + "0 6.263158 0.496525 \n", + "1 6.236842 0.339055 \n", + "2 6.473684 0.379532 \n", + "3 6.038462 0.364878 \n", + "4 6.342105 0.364642 \n", + ".. ... ... \n", + "542 6.092280 0.894197 \n", + "543 6.284927 0.532298 \n", + "544 6.094004 0.625570 \n", + "545 5.711934 0.225193 \n", + "546 5.748895 0.595450 \n", + "\n", + "[547 rows x 165 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_players = pd.concat([fc_players, perf_df], axis = 1)\n", + "\n", + "fc_players" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f7620abe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'gk_games'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_players.columns[123]" + ] + }, + { + "cell_type": "markdown", + "id": "c550c370", + "metadata": {}, + "source": [ + "For goalkeepers who didn't play a miminum amount of games, data is weightly averaged with one of the main goalkeeper of their same team." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "700b7a7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Zoet, 0.5\n", + "Gollini, 0.0\n", + "Ochoa, 0.8333333333333334\n", + "Pegolo, 0.33333333333333337\n", + "Mirante, 0.0\n", + "Sarr M., 0.0\n", + "Lamanna, 0.0\n", + "Ujkani, 0.0\n", + "Berisha, 0.0\n", + "Marchetti, 0.0\n", + "Perilli, 0.0\n", + "Padelli, 0.0\n", + "Perisan, 0.0\n", + "Bardi, 0.0\n", + "Cordaz, 0.0\n", + "Pinsoglio, 0.0\n", + "Fiorillo, 0.0\n", + "Cragno, 0.0\n", + "Sirigu, 0.0\n", + "Cerofolini, 0.0\n", + "Rossi F., 0.0\n", + "Ravaglia F., 0.0\n", + "Brancolini, 0.0\n", + "Bleve, 0.0\n", + "Berardi A., 0.0\n", + "Russo A., 0.0\n", + "Gemello, 0.0\n", + "Ravaglia, 0.0\n", + "Boer, 0.0\n", + "Adamonis, 0.0\n", + "Marfella, 0.0\n", + "Zovko, 0.16666666666666663\n", + "Piana, 0.0\n", + "Bagnolini, 0.0\n", + "Luis Maximiano, 0.16666666666666663\n", + "Svilar, 0.0\n", + "Sorrentino A., 0.0\n", + "Ciezkowski, 0.0\n", + "Chiesa M., 0.0\n", + "Saro, 0.0\n", + "Vasquez D., 0.0\n", + "Turk, 0.0\n" + ] + } + ], + "source": [ + "min_gk_games = 6\n", + "\n", + "fc_players_newgk = fc_players.copy()\n", + "\n", + "columns_to_avg = fc_players.columns[123:] # from gk_games to end\n", + "\n", + "for i in range(fc_players.shape[0]):\n", + " if(fc_players['r'][i] == 'P'):\n", + " if(fc_players['gk_games'][i] < min_gk_games):\n", + " for j in range(fc_players.shape[0]):\n", + " if(fc_players['team'][i] == fc_players['team'][j] and fc_players['gk_games'][j] >= min_gk_games):\n", + " break\n", + " \n", + " weight = 1 - (min_gk_games - fc_players['gk_games'][i]) / min_gk_games\n", + " \n", + " fc_players_newgk.at[i, columns_to_avg] = fc_players.loc[i][columns_to_avg] * weight + (1 - weight) * fc_players.loc[j][columns_to_avg]\n", + " \n", + " print(fc_players['name'][i] + ', ' + str(weight))\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2d9eee99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
0572PMeretNapoliMeret52925-31219970...20.227.1197.06.03.023.01.2117.26.2631580.496525
12814PProvedelLazioProvedel53828-31719940...34.834.8256.07.02.737.01.9618.16.2368420.339055
24964PVicarioEmpoliVicario54726-11319960...45.641.8399.025.06.38.00.4210.56.4736840.379532
3453PSzczesnyJuventusSzczesny54432-28519900...41.039.2155.04.02.67.00.5614.26.0384620.364878
42134PFalconeLecceFalcone52427-29119950...75.051.4304.013.04.320.01.0012.56.3421050.364642
..................................................................
5425505AKaio JorgeJuventusJorge-1000...0.00.00.00.00.00.00.000.06.0922800.894197
5435512ADe LucaSampdoriaLuca12824-19519981...0.00.00.00.00.00.00.000.06.2849270.532298
5445785ALazeticMilanLazetic25819-00620041...0.00.00.00.00.00.00.000.06.0940040.625570
5456113AMontevagoSampdoriaMontevago31819-31620036...0.00.00.00.00.00.00.000.05.7119340.225193
5466143AKrollisSpeziaKrollis-1000...0.00.00.00.00.00.00.000.05.7488950.595450
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547 rows × 165 columns

\n", + "
" + ], + "text/plain": [ + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", + "\n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "0 1997 0 ... 20.2 \n", + "1 1994 0 ... 34.8 \n", + "2 1996 0 ... 45.6 \n", + "3 1990 0 ... 41.0 \n", + "4 1995 0 ... 75.0 \n", + ".. ... ... ... ... \n", + "542 0 0 ... 0.0 \n", + "543 1998 1 ... 0.0 \n", + "544 2004 1 ... 0.0 \n", + "545 2003 6 ... 0.0 \n", + "546 0 0 ... 0.0 \n", + "\n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", + "0 27.1 197.0 6.0 \n", + "1 34.8 256.0 7.0 \n", + "2 41.8 399.0 25.0 \n", + "3 39.2 155.0 4.0 \n", + "4 51.4 304.0 13.0 \n", + ".. ... ... ... \n", + "542 0.0 0.0 0.0 \n", + "543 0.0 0.0 0.0 \n", + "544 0.0 0.0 0.0 \n", + "545 0.0 0.0 0.0 \n", + "546 0.0 0.0 0.0 \n", + "\n", + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "0 3.0 23.0 \n", + "1 2.7 37.0 \n", + "2 6.3 8.0 \n", + "3 2.6 7.0 \n", + "4 4.3 20.0 \n", + ".. ... ... \n", + "542 0.0 0.0 \n", + "543 0.0 0.0 \n", + "544 0.0 0.0 \n", + "545 0.0 0.0 \n", + "546 0.0 0.0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", + "0 1.21 17.2 \n", + "1 1.96 18.1 \n", + "2 0.42 10.5 \n", + "3 0.56 14.2 \n", + "4 1.00 12.5 \n", + ".. ... ... \n", + "542 0.00 0.0 \n", + "543 0.00 0.0 \n", + "544 0.00 0.0 \n", + "545 0.00 0.0 \n", + "546 0.00 0.0 \n", + "\n", + " vote_avg vote_std \n", + "0 6.263158 0.496525 \n", + "1 6.236842 0.339055 \n", + "2 6.473684 0.379532 \n", + "3 6.038462 0.364878 \n", + "4 6.342105 0.364642 \n", + ".. ... ... \n", + "542 6.092280 0.894197 \n", + "543 6.284927 0.532298 \n", + "544 6.094004 0.625570 \n", + "545 5.711934 0.225193 \n", + "546 5.748895 0.595450 \n", + "\n", + "[547 rows x 165 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fc_players = fc_players_newgk\n", + "\n", + "fc_players" + ] + }, + { + "cell_type": "markdown", + "id": "2b13240e", + "metadata": {}, + "source": [ + "Save to file." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "8336c025", + "metadata": {}, + "outputs": [], + "source": [ + "fc_players.to_excel('mid_outputs/players_stats.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3fc9b1b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb b/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb new file mode 100644 index 0000000..fbc323e --- /dev/null +++ b/.ipynb_checkpoints/4_player_match_dataset_creation-checkpoint.ipynb @@ -0,0 +1,4583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8f597771", + "metadata": {}, + "source": [ + "Player/match dataset creation\n", + "\n", + "For each serie A (already happened) match and player, a database entry is generated, with this form:\n", + " \n", + "[y = player votes data, player data, team data, vs team data, opp team data, vs opp team data]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "3ecf3676", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "a7d3a9d6", + "metadata": {}, + "source": [ + "Load team data from FBref" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8a6867a5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_7952\\552672520.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " team_data['team_idx'][i] = team_data['team_idx'][i].split(' ')[-1]\n" + ] + }, + { + "data": { + "text/html": [ + "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
team_idx
AtalantaAtalanta24.048.419.0209.01710.036.025.06.08.0...209.0218.025.01.08.01.01165.0239.0279.046.1
BolognaBologna24.052.620.0220.01800.025.019.03.03.0...252.0229.033.03.03.01.01081.0224.0180.055.4
CremoneseCremonese29.044.019.0209.01710.014.06.02.04.0...205.0247.029.03.04.00.01052.0348.0267.056.6
EmpoliEmpoli26.046.319.0209.01710.017.09.00.00.0...251.0214.031.02.00.00.0999.0217.0189.053.4
FiorentinaFiorentina27.057.219.0209.01710.021.017.02.04.0...259.0229.052.01.04.00.0942.0249.0289.046.3
VeronaHellas Verona29.043.419.0209.01710.016.013.00.00.0...192.0276.022.01.00.01.01038.0345.0388.047.1
InterInter23.052.919.0209.01710.037.025.02.02.0...242.0214.018.02.02.01.0869.0198.0232.046.0
JuventusJuventus26.049.519.0209.01710.030.023.02.03.0...210.0210.025.00.03.00.0947.0230.0230.050.0
LazioLazio21.051.219.0209.01710.034.025.03.04.0...261.0185.042.01.04.01.01021.0183.0181.050.3
LecceLecce26.042.520.0220.01800.018.012.01.02.0...263.0265.043.03.02.01.01072.0373.0294.055.9
MilanMilan27.054.519.0209.01710.033.028.02.02.0...234.0226.020.03.02.02.0964.0224.0275.044.9
MonzaMonza29.055.119.0209.01710.022.013.03.03.0...279.0242.034.00.03.01.0958.0197.0215.047.8
NapoliNapoli24.061.319.0209.01710.046.037.04.05.0...258.0167.027.01.04.00.0933.0201.0231.046.5
RomaRoma25.049.419.0209.01710.025.016.03.05.0...266.0214.010.00.05.00.0981.0184.0225.045.0
SalernitanaSalernitana28.044.820.0220.01800.024.016.01.01.0...226.0233.049.07.01.01.01084.0257.0240.051.7
SampdoriaSampdoria29.049.419.0209.01710.08.07.00.00.0...299.0266.055.03.00.00.01017.0300.0318.048.5
SassuoloSassuolo27.048.719.0209.01710.018.013.03.04.0...249.0182.062.02.04.00.0986.0216.0173.055.5
SpeziaSpezia30.046.720.0220.01800.015.010.02.02.0...207.0261.046.01.02.02.01182.0322.0281.053.4
TorinoTorino23.052.819.0209.01710.019.014.01.01.0...209.0265.021.03.01.00.0998.0323.0295.052.3
UdineseUdinese22.050.119.0209.01710.026.022.00.00.0...250.0227.029.02.00.01.0950.0189.0237.044.4
\n", + "

20 rows × 313 columns

\n", + "
" + ], + "text/plain": [ + " team team_players_used team_possession team_games \\\n", + "team_idx \n", + "Atalanta Atalanta 24.0 48.4 19.0 \n", + "Bologna Bologna 24.0 52.6 20.0 \n", + "Cremonese Cremonese 29.0 44.0 19.0 \n", + "Empoli Empoli 26.0 46.3 19.0 \n", + "Fiorentina Fiorentina 27.0 57.2 19.0 \n", + "Verona Hellas Verona 29.0 43.4 19.0 \n", + "Inter Inter 23.0 52.9 19.0 \n", + "Juventus Juventus 26.0 49.5 19.0 \n", + "Lazio Lazio 21.0 51.2 19.0 \n", + "Lecce Lecce 26.0 42.5 20.0 \n", + "Milan Milan 27.0 54.5 19.0 \n", + "Monza Monza 29.0 55.1 19.0 \n", + "Napoli Napoli 24.0 61.3 19.0 \n", + "Roma Roma 25.0 49.4 19.0 \n", + "Salernitana Salernitana 28.0 44.8 20.0 \n", + "Sampdoria Sampdoria 29.0 49.4 19.0 \n", + "Sassuolo Sassuolo 27.0 48.7 19.0 \n", + "Spezia Spezia 30.0 46.7 20.0 \n", + "Torino Torino 23.0 52.8 19.0 \n", + "Udinese Udinese 22.0 50.1 19.0 \n", + "\n", + " team_games_starts team_minutes team_goals team_assists \\\n", + "team_idx \n", + "Atalanta 209.0 1710.0 36.0 25.0 \n", + "Bologna 220.0 1800.0 25.0 19.0 \n", + "Cremonese 209.0 1710.0 14.0 6.0 \n", + "Empoli 209.0 1710.0 17.0 9.0 \n", + "Fiorentina 209.0 1710.0 21.0 17.0 \n", + "Verona 209.0 1710.0 16.0 13.0 \n", + "Inter 209.0 1710.0 37.0 25.0 \n", + "Juventus 209.0 1710.0 30.0 23.0 \n", + "Lazio 209.0 1710.0 34.0 25.0 \n", + "Lecce 220.0 1800.0 18.0 12.0 \n", + "Milan 209.0 1710.0 33.0 28.0 \n", + "Monza 209.0 1710.0 22.0 13.0 \n", + "Napoli 209.0 1710.0 46.0 37.0 \n", + "Roma 209.0 1710.0 25.0 16.0 \n", + "Salernitana 220.0 1800.0 24.0 16.0 \n", + "Sampdoria 209.0 1710.0 8.0 7.0 \n", + "Sassuolo 209.0 1710.0 18.0 13.0 \n", + "Spezia 220.0 1800.0 15.0 10.0 \n", + "Torino 209.0 1710.0 19.0 14.0 \n", + "Udinese 209.0 1710.0 26.0 22.0 \n", + "\n", + " team_pens_made team_pens_att ... vs_team_fouls \\\n", + "team_idx ... \n", + "Atalanta 6.0 8.0 ... 209.0 \n", + "Bologna 3.0 3.0 ... 252.0 \n", + "Cremonese 2.0 4.0 ... 205.0 \n", + "Empoli 0.0 0.0 ... 251.0 \n", + "Fiorentina 2.0 4.0 ... 259.0 \n", + "Verona 0.0 0.0 ... 192.0 \n", + "Inter 2.0 2.0 ... 242.0 \n", + "Juventus 2.0 3.0 ... 210.0 \n", + "Lazio 3.0 4.0 ... 261.0 \n", + "Lecce 1.0 2.0 ... 263.0 \n", + "Milan 2.0 2.0 ... 234.0 \n", + "Monza 3.0 3.0 ... 279.0 \n", + "Napoli 4.0 5.0 ... 258.0 \n", + "Roma 3.0 5.0 ... 266.0 \n", + "Salernitana 1.0 1.0 ... 226.0 \n", + "Sampdoria 0.0 0.0 ... 299.0 \n", + "Sassuolo 3.0 4.0 ... 249.0 \n", + "Spezia 2.0 2.0 ... 207.0 \n", + "Torino 1.0 1.0 ... 209.0 \n", + "Udinese 0.0 0.0 ... 250.0 \n", + "\n", + " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", + "team_idx \n", + "Atalanta 218.0 25.0 1.0 \n", + "Bologna 229.0 33.0 3.0 \n", + "Cremonese 247.0 29.0 3.0 \n", + "Empoli 214.0 31.0 2.0 \n", + "Fiorentina 229.0 52.0 1.0 \n", + "Verona 276.0 22.0 1.0 \n", + "Inter 214.0 18.0 2.0 \n", + "Juventus 210.0 25.0 0.0 \n", + "Lazio 185.0 42.0 1.0 \n", + "Lecce 265.0 43.0 3.0 \n", + "Milan 226.0 20.0 3.0 \n", + "Monza 242.0 34.0 0.0 \n", + "Napoli 167.0 27.0 1.0 \n", + "Roma 214.0 10.0 0.0 \n", + "Salernitana 233.0 49.0 7.0 \n", + "Sampdoria 266.0 55.0 3.0 \n", + "Sassuolo 182.0 62.0 2.0 \n", + "Spezia 261.0 46.0 1.0 \n", + "Torino 265.0 21.0 3.0 \n", + "Udinese 227.0 29.0 2.0 \n", + "\n", + " vs_team_pens_conceded vs_team_own_goals \\\n", + "team_idx \n", + "Atalanta 8.0 1.0 \n", + "Bologna 3.0 1.0 \n", + "Cremonese 4.0 0.0 \n", + "Empoli 0.0 0.0 \n", + "Fiorentina 4.0 0.0 \n", + "Verona 0.0 1.0 \n", + "Inter 2.0 1.0 \n", + "Juventus 3.0 0.0 \n", + "Lazio 4.0 1.0 \n", + "Lecce 2.0 1.0 \n", + "Milan 2.0 2.0 \n", + "Monza 3.0 1.0 \n", + "Napoli 4.0 0.0 \n", + "Roma 5.0 0.0 \n", + "Salernitana 1.0 1.0 \n", + "Sampdoria 0.0 0.0 \n", + "Sassuolo 4.0 0.0 \n", + "Spezia 2.0 2.0 \n", + "Torino 1.0 0.0 \n", + "Udinese 0.0 1.0 \n", + "\n", + " vs_team_ball_recoveries vs_team_aerials_won \\\n", + "team_idx \n", + "Atalanta 1165.0 239.0 \n", + "Bologna 1081.0 224.0 \n", + "Cremonese 1052.0 348.0 \n", + "Empoli 999.0 217.0 \n", + "Fiorentina 942.0 249.0 \n", + "Verona 1038.0 345.0 \n", + "Inter 869.0 198.0 \n", + "Juventus 947.0 230.0 \n", + "Lazio 1021.0 183.0 \n", + "Lecce 1072.0 373.0 \n", + "Milan 964.0 224.0 \n", + "Monza 958.0 197.0 \n", + "Napoli 933.0 201.0 \n", + "Roma 981.0 184.0 \n", + "Salernitana 1084.0 257.0 \n", + "Sampdoria 1017.0 300.0 \n", + "Sassuolo 986.0 216.0 \n", + "Spezia 1182.0 322.0 \n", + "Torino 998.0 323.0 \n", + "Udinese 950.0 189.0 \n", + "\n", + " vs_team_aerials_lost vs_team_aerials_won_pct \n", + "team_idx \n", + "Atalanta 279.0 46.1 \n", + "Bologna 180.0 55.4 \n", + "Cremonese 267.0 56.6 \n", + "Empoli 189.0 53.4 \n", + "Fiorentina 289.0 46.3 \n", + "Verona 388.0 47.1 \n", + "Inter 232.0 46.0 \n", + "Juventus 230.0 50.0 \n", + "Lazio 181.0 50.3 \n", + "Lecce 294.0 55.9 \n", + "Milan 275.0 44.9 \n", + "Monza 215.0 47.8 \n", + "Napoli 231.0 46.5 \n", + "Roma 225.0 45.0 \n", + "Salernitana 240.0 51.7 \n", + "Sampdoria 318.0 48.5 \n", + "Sassuolo 173.0 55.5 \n", + "Spezia 281.0 53.4 \n", + "Torino 295.0 52.3 \n", + "Udinese 237.0 44.4 \n", + "\n", + "[20 rows x 313 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rcsv = pd.read_csv('fbref_data/teams.csv') \n", + "team = pd.DataFrame(rcsv)\n", + "\n", + "rcsv = pd.read_csv('fbref_data/teams_vs.csv') \n", + "vs_team = pd.DataFrame(rcsv)\n", + "\n", + "for i in range(1, team.shape[1]):\n", + " team = team.rename(columns = {team.columns[i] : 'team_' + team.columns[i]})\n", + " \n", + "for i in range(1, vs_team.shape[1]):\n", + " vs_team = vs_team.rename(columns = {vs_team.columns[i] : 'vs_team_' + vs_team.columns[i]})\n", + " \n", + "vs_team.pop('team')\n", + "\n", + "team_data = pd.concat([team, vs_team], axis = 1)\n", + "\n", + "team_data['team_idx'] = team_data['team']\n", + "for i in range(team_data.shape[0]):\n", + " team_data['team_idx'][i] = team_data['team_idx'][i].split(' ')[-1]\n", + "\n", + "team_data = team_data.set_index('team_idx')\n", + "\n", + "team_data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b6a3db98", + "metadata": {}, + "outputs": [], + "source": [ + "team_data.to_excel('mid_outputs/team_data.xlsx')" + ] + }, + { + "cell_type": "markdown", + "id": "cc50dde9", + "metadata": {}, + "source": [ + "Load data generated from players_dataset_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c34eb398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0idrteamsurnameinitialfb_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
name
Meret0572PNapoliMeretNaN52925-31219970...20.227.1197.06.03.023.01.2117.26.2631580.496525
Provedel12814PLazioProvedelNaN53828-31719940...34.834.8256.07.02.737.01.9618.16.2368420.339055
Vicario24964PEmpoliVicarioNaN54726-11319960...45.641.8399.025.06.38.00.4210.56.4736840.379532
Szczesny3453PJuventusSzczesnyNaN54432-28519900...41.039.2155.04.02.67.00.5614.26.0384620.364878
Falcone42134PLecceFalconeNaN52427-29119950...75.051.4304.013.04.320.01.0012.56.3421050.364642
..................................................................
Kaio Jorge5425505AJuventusJorgeNaN-1000...0.00.00.00.00.00.00.000.06.0922800.894197
De Luca5435512ASampdoriaLucaNaN12824-19519981...0.00.00.00.00.00.00.000.06.2849270.532298
Lazetic5445785AMilanLazeticNaN25819-00620041...0.00.00.00.00.00.00.000.06.0940040.625570
Montevago5456113ASampdoriaMontevagoNaN31819-31620036...0.00.00.00.00.00.00.000.05.7119340.225193
Krollis5466143ASpeziaKrollisNaN-1000...0.00.00.00.00.00.00.000.05.7488950.595450
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547 rows × 165 columns

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" + ], + "text/plain": [ + " Unnamed: 0 id r team surname initial fb_ID age \\\n", + "name \n", + "Meret 0 572 P Napoli Meret NaN 529 25-312 \n", + "Provedel 1 2814 P Lazio Provedel NaN 538 28-317 \n", + "Vicario 2 4964 P Empoli Vicario NaN 547 26-113 \n", + "Szczesny 3 453 P Juventus Szczesny NaN 544 32-285 \n", + "Falcone 4 2134 P Lecce Falcone NaN 524 27-291 \n", + "... ... ... .. ... ... ... ... ... \n", + "Kaio Jorge 542 5505 A Juventus Jorge NaN -1 0 \n", + "De Luca 543 5512 A Sampdoria Luca NaN 128 24-195 \n", + "Lazetic 544 5785 A Milan Lazetic NaN 258 19-006 \n", + "Montevago 545 6113 A Sampdoria Montevago NaN 318 19-316 \n", + "Krollis 546 6143 A Spezia Krollis NaN -1 0 \n", + "\n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "name ... \n", + "Meret 1997 0 ... 20.2 \n", + "Provedel 1994 0 ... 34.8 \n", + "Vicario 1996 0 ... 45.6 \n", + "Szczesny 1990 0 ... 41.0 \n", + "Falcone 1995 0 ... 75.0 \n", + "... ... ... ... ... \n", + "Kaio Jorge 0 0 ... 0.0 \n", + "De Luca 1998 1 ... 0.0 \n", + "Lazetic 2004 1 ... 0.0 \n", + "Montevago 2003 6 ... 0.0 \n", + "Krollis 0 0 ... 0.0 \n", + "\n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", + "name \n", + "Meret 27.1 197.0 6.0 \n", + "Provedel 34.8 256.0 7.0 \n", + "Vicario 41.8 399.0 25.0 \n", + "Szczesny 39.2 155.0 4.0 \n", + "Falcone 51.4 304.0 13.0 \n", + "... ... ... ... \n", + "Kaio Jorge 0.0 0.0 0.0 \n", + "De Luca 0.0 0.0 0.0 \n", + "Lazetic 0.0 0.0 0.0 \n", + "Montevago 0.0 0.0 0.0 \n", + "Krollis 0.0 0.0 0.0 \n", + "\n", + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "name \n", + "Meret 3.0 23.0 \n", + "Provedel 2.7 37.0 \n", + "Vicario 6.3 8.0 \n", + "Szczesny 2.6 7.0 \n", + "Falcone 4.3 20.0 \n", + "... ... ... \n", + "Kaio Jorge 0.0 0.0 \n", + "De Luca 0.0 0.0 \n", + "Lazetic 0.0 0.0 \n", + "Montevago 0.0 0.0 \n", + "Krollis 0.0 0.0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 \\\n", + "name \n", + "Meret 1.21 \n", + "Provedel 1.96 \n", + "Vicario 0.42 \n", + "Szczesny 0.56 \n", + "Falcone 1.00 \n", + "... ... \n", + "Kaio Jorge 0.00 \n", + "De Luca 0.00 \n", + "Lazetic 0.00 \n", + "Montevago 0.00 \n", + "Krollis 0.00 \n", + "\n", + " gk_avg_distance_def_actions vote_avg vote_std \n", + "name \n", + "Meret 17.2 6.263158 0.496525 \n", + "Provedel 18.1 6.236842 0.339055 \n", + "Vicario 10.5 6.473684 0.379532 \n", + "Szczesny 14.2 6.038462 0.364878 \n", + "Falcone 12.5 6.342105 0.364642 \n", + "... ... ... ... \n", + "Kaio Jorge 0.0 6.092280 0.894197 \n", + "De Luca 0.0 6.284927 0.532298 \n", + "Lazetic 0.0 6.094004 0.625570 \n", + "Montevago 0.0 5.711934 0.225193 \n", + "Krollis 0.0 5.748895 0.595450 \n", + "\n", + "[547 rows x 165 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rx = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3) \n", + "players = pd.DataFrame(rx)\n", + "\n", + "players\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae37dfe3", + "metadata": {}, + "source": [ + "Function for merging match data (player, team, opponent team)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "71c8804e", + "metadata": {}, + "outputs": [], + "source": [ + "def player_match_data(player, pteam, oppteam):\n", + " if(not(player in players.index)):\n", + " return None\n", + " \n", + " pdata = players.loc[[player]]\n", + " \n", + " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", + " \n", + " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", + " \n", + " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", + " \n", + " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", + " \n", + " return(out)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "a6fdd15b", + "metadata": {}, + "source": [ + "Output to excel file data for a player. \n", + "\n", + "The file is copied and there the desired features are manually selected and printed." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "7eb4e666", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0idrteamsurnameinitialfb_IDagebirth_yeargames...opp_vs_team_foulsopp_vs_team_fouledopp_vs_team_offsidesopp_vs_team_pens_wonopp_vs_team_pens_concededopp_vs_team_own_goalsopp_vs_team_ball_recoveriesopp_vs_team_aerials_wonopp_vs_team_aerials_lostopp_vs_team_aerials_won_pct
Frattesi2762848CSassuoloFrattesiNaN18423-098199919...209.0218.025.01.08.01.01165.0239.0279.046.1
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1 rows × 791 columns

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" + ], + "text/plain": [ + " Unnamed: 0 id r team surname initial fb_ID age \\\n", + "Frattesi 276 2848 C Sassuolo Frattesi NaN 184 23-098 \n", + "\n", + " birth_year games ... opp_vs_team_fouls opp_vs_team_fouled \\\n", + "Frattesi 1999 19 ... 209.0 218.0 \n", + "\n", + " opp_vs_team_offsides opp_vs_team_pens_won \\\n", + "Frattesi 25.0 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 1165.0 239.0 \n", + "\n", + " opp_vs_team_aerials_lost opp_vs_team_aerials_won_pct \n", + "Frattesi 279.0 46.1 \n", + "\n", + "[1 rows x 791 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = player_match_data('Frattesi', 'Sassuolo', 'Atalanta')\n", + "test.to_excel('tmp/playermatchdata.xlsx')\n", + "\n", + "test" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "6b4c1b00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Sassuolo'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pteam = 'Sassuolo'\n", + "pteam_stats = team_data.loc[[pteam]]\n", + "pteam_stats.index[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6ea6df1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['r',\n", + " 'fb_ID',\n", + " 'games',\n", + " 'games_starts',\n", + " 'minutes',\n", + " 'goals',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red',\n", + " 'xg',\n", + " 'npxg',\n", + " 'shots_on_target',\n", + " 'shots_on_target_pct',\n", + " 'goals_per_shot',\n", + " 'goals_per_shot_on_target',\n", + " 'passes_completed',\n", + " 'passes_pct',\n", + " 'passes_into_final_third',\n", + " 'passes_into_penalty_area',\n", + " 'progressive_passes',\n", + " 'passes_live',\n", + " 'passes_dead',\n", + " 'through_balls',\n", + " 'passes_switches',\n", + " 'crosses',\n", + " 'corner_kicks',\n", + " 'dribble_tackles',\n", + " 'dribbles_vs',\n", + " 'dribble_tackles_pct',\n", + " 'dribbled_past',\n", + " 'blocks',\n", + " 'blocked_shots',\n", + " 'blocked_passes',\n", + " 'interceptions',\n", + " 'clearances',\n", + " 'errors',\n", + " 'touches',\n", + " 'touches_def_pen_area',\n", + " 'touches_def_3rd',\n", + " 'touches_mid_3rd',\n", + " 'touches_att_3rd',\n", + " 'touches_att_pen_area',\n", + " 'touches_live_ball',\n", + " 'dribbles_completed',\n", + " 'dribbles',\n", + " 'dribbles_completed_pct',\n", + " 'passes_received',\n", + " 'miscontrols',\n", + " 'dispossessed',\n", + " 'fouls',\n", + " 'fouled',\n", + " 'aerials_won',\n", + " 'aerials_lost',\n", + " 'aerials_won_pct',\n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rx = pd.read_excel('tmp/playermatchdata_sel.xlsx', index_col = 0, header = None) \n", + "\n", + "out = list()\n", + "\n", + "for i in range(1, rx.shape[1]):\n", + " if(rx[i][2] == \"x\"):\n", + " out.append(rx[i][0])\n", + " \n", + "out" + ] + }, + { + "cell_type": "markdown", + "id": "d4097382", + "metadata": {}, + "source": [ + "Define and select the dataset features: abs features and rel features.\n", + " \n", + "Rel features are divided by the player minutes played.\n", + "\n", + "Rel features (game corrected) are divided by the player number of games instead of minutes. \n", + "For example, a player who plays 20 minutes and scores, would be to match for stats averaging. It is better to consider the goals made per match." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "47bc651f", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs = ['r',\n", + " 'games',\n", + " 'games_starts', \n", + " 'minutes',\n", + " 'shots_on_target_pct',\n", + " 'goals_per_shot',\n", + " 'goals_per_shot_on_target',\n", + " 'passes_pct',\n", + " 'dribble_tackles_pct',\n", + " 'dribbles_completed_pct',\n", + " 'aerials_won_pct',\n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " 'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " 'opp_vs_team_dribble_tackles_pct',\n", + " 'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel = [\n", + " 'goals',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red',\n", + " 'xg',\n", + " 'npxg',\n", + " 'shots_on_target',\n", + " 'passes_completed',\n", + " 'passes_into_final_third',\n", + " 'passes_into_penalty_area',\n", + " 'progressive_passes',\n", + " 'passes_live',\n", + " 'passes_dead',\n", + " 'through_balls',\n", + " 'passes_switches',\n", + " 'crosses',\n", + " 'corner_kicks',\n", + " 'dribble_tackles',\n", + " 'dribbles_vs',\n", + " 'dribbled_past',\n", + " 'blocks',\n", + " 'blocked_shots',\n", + " 'blocked_passes',\n", + " 'interceptions',\n", + " 'clearances',\n", + " 'errors',\n", + " 'touches',\n", + " 'touches_def_pen_area',\n", + " 'touches_def_3rd',\n", + " 'touches_mid_3rd',\n", + " 'touches_att_3rd',\n", + " 'touches_att_pen_area',\n", + " 'touches_live_ball',\n", + " 'dribbles_completed',\n", + " 'dribbles',\n", + " 'passes_received',\n", + " 'miscontrols',\n", + " 'dispossessed',\n", + " 'fouls',\n", + " 'fouled',\n", + " 'aerials_won',\n", + " 'aerials_lost']\n", + "\n", + "features_rel_gamecorr = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg',\n", + " 'cards_yellow',\n", + " 'cards_red'\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "ced70285", + "metadata": {}, + "source": [ + "Function for merging match data (player, team, opponent team) and select only the desired features, applying the desired corrections." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3f43d509", + "metadata": {}, + "outputs": [], + "source": [ + "def player_match_data_ext(player, pteam, oppteam):\n", + " pdata = player_match_data(player, pteam, oppteam)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", + " \n", + " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", + " \n", + " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", + " \n", + " return out\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "959bf152", + "metadata": {}, + "source": [ + "Columns variable for the match data, to be then added to votes data." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "930d00c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rgamesgames_startsminutesshots_on_target_pctgoals_per_shotgoals_per_shot_on_targetpasses_pctdribble_tackles_pctdribbles_completed_pct...touches_live_balldribbles_completeddribblespasses_receivedmiscontrolsdispossessedfoulsfouledaerials_wonaerials_lost
FrattesiC1919156048.60.110.2477.954.554.5...0.4820510.0153850.0282050.3224360.0166670.0173080.0147440.0230770.0102560.007051
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1 rows × 121 columns

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" + ], + "text/plain": [ + " r games games_starts minutes shots_on_target_pct \\\n", + "Frattesi C 19 19 1560 48.6 \n", + "\n", + " goals_per_shot goals_per_shot_on_target passes_pct \\\n", + "Frattesi 0.11 0.24 77.9 \n", + "\n", + " dribble_tackles_pct dribbles_completed_pct ... touches_live_ball \\\n", + "Frattesi 54.5 54.5 ... 0.482051 \n", + "\n", + " dribbles_completed dribbles passes_received miscontrols \\\n", + "Frattesi 0.015385 0.028205 0.322436 0.016667 \n", + "\n", + " dispossessed fouls fouled aerials_won aerials_lost \n", + "Frattesi 0.017308 0.014744 0.023077 0.010256 0.007051 \n", + "\n", + "[1 rows x 121 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = player_match_data_ext('Frattesi', 'Sassuolo', 'Atalanta')\n", + "\n", + "add_columns = test.columns\n", + "\n", + "test" + ] + }, + { + "cell_type": "markdown", + "id": "8d95c9ef", + "metadata": {}, + "source": [ + "Load Fantacalcio votes data for each match of the season." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "3ae718d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote
01MussoAtalantaSampdoria06.0000.55.5
11ToloiAtalantaSampdoria07.0100.010.0
21DjimsitiAtalantaSampdoria06.0000.06.0
31HateboerAtalantaSampdoria06.0000.55.5
41OkoliAtalantaSampdoria05.5000.55.0
.................................
542319IlicVeronaLecce17.0010.08.0
542419Sulemana I.VeronaLecce16.0000.06.0
542519LasagnaVeronaLecce15.5000.05.5
542619KallonVeronaLecce16.0000.06.0
542719DjuricVeronaLecce16.0000.06.0
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5428 rows × 10 columns

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" + ], + "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", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", + "\n", + " cards_malus fantavote \n", + "0 0.5 5.5 \n", + "1 0.0 10.0 \n", + "2 0.0 6.0 \n", + "3 0.5 5.5 \n", + "4 0.5 5.0 \n", + "... ... ... \n", + "5423 0.0 8.0 \n", + "5424 0.0 6.0 \n", + "5425 0.0 5.5 \n", + "5426 0.0 6.0 \n", + "5427 0.0 6.0 \n", + "\n", + "[5428 rows x 10 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "votes = pd.read_excel('mid_outputs/players_votes.xlsx', index_col = 0)\n", + "votes" + ] + }, + { + "cell_type": "markdown", + "id": "8cf677e4", + "metadata": {}, + "source": [ + "Merge votes data with match data for the player." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "ec0bc56b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "100\n", + "200\n", + "300\n", + "400\n", + "500\n", + "600\n", + "700\n", + "800\n", + "900\n", + "1000\n", + "1100\n", + "1300\n", + "1400\n", + "1500\n", + "1600\n", + "1700\n", + "1800\n", + "1900\n", + "2000\n", + "2100\n", + "2200\n", + "2300\n", + "2400\n", + "2500\n", + "2600\n", + "2700\n", + "2800\n", + "2900\n", + "3000\n", + "3100\n", + "3200\n", + "3300\n", + "3400\n", + "3500\n", + "3600\n", + "3700\n", + "3800\n", + "3900\n", + "4000\n", + "4100\n", + "4200\n", + "4300\n", + "4400\n", + "4500\n", + "4600\n", + "4700\n", + "4800\n", + "4900\n", + "5000\n", + "5100\n", + "5200\n", + "5300\n", + "5400\n" + ] + } + ], + "source": [ + "db = votes.copy()\n", + "\n", + "delta = pd.DataFrame(columns = add_columns)\n", + "\n", + "for i in range(db.shape[0]):\n", + " ext_data = player_match_data_ext(db['player'][i], db['team'][i], db['oppteam'][i])\n", + " \n", + " if(not isinstance(ext_data, pd.DataFrame)):\n", + " continue\n", + " \n", + " ext_data = ext_data.rename(index = {db['player'][i] : i})\n", + " \n", + " delta = pd.concat([delta, ext_data], axis = 0)\n", + " \n", + " if(i % 100 == 0):\n", + " print(i)\n", + " \n", + "db = pd.concat([db, delta], axis = 1)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b292f9f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...touches_live_balldribbles_completeddribblespasses_receivedmiscontrolsdispossessedfoulsfouledaerials_wonaerials_lost
01MussoAtalantaSampdoria06.0000.55.5...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
11ToloiAtalantaSampdoria07.0100.010.0...0.6842110.0008490.0050930.4074700.0076400.0008490.0067910.0050930.0161290.011885
21DjimsitiAtalantaSampdoria06.0000.06.0...0.7535410.0000000.0028330.5212460.0028330.0084990.0028330.0028330.0056660.008499
31HateboerAtalantaSampdoria06.0000.55.5...0.5860900.0033930.0067850.3570820.0067850.0042410.0127230.0016960.0152670.011874
41OkoliAtalantaSampdoria05.5000.55.0...0.6071010.0000000.0011830.2792900.0130180.0035500.0153850.0082840.0473370.027219
..................................................................
542319IlicVeronaLecce17.0010.08.0...0.5722680.0070510.0188010.2773210.0199760.0058750.0105760.0105760.0117510.015276
542419Sulemana I.VeronaLecce16.0000.06.0...0.4779660.0101690.0305080.2067800.0169490.0135590.0203390.0067800.0203390.027119
542519LasagnaVeronaLecce15.5000.05.5...0.3012820.0074790.0256410.1912390.0363250.0256410.0192310.0170940.0074790.032051
542619KallonVeronaLecce16.0000.06.0...0.4093100.0080260.0337080.2937400.0337080.0240770.0176570.0272870.0112360.025682
542719DjuricVeronaLecce16.0000.06.0...0.5161810.0016180.0048540.4514560.0275080.0064720.0242720.0210360.1666670.043689
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5428 rows × 131 columns

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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...touches_live_balldribbles_completeddribblespasses_receivedmiscontrolsdispossessedfoulsfouledaerials_wonaerials_lost
11ToloiAtalantaSampdoria07.0100.010.0...0.6842110.0008490.0050930.4074700.0076400.0008490.0067910.0050930.0161290.011885
21DjimsitiAtalantaSampdoria06.0000.06.0...0.7535410.0000000.0028330.5212460.0028330.0084990.0028330.0028330.0056660.008499
31HateboerAtalantaSampdoria06.0000.55.5...0.5860900.0033930.0067850.3570820.0067850.0042410.0127230.0016960.0152670.011874
41OkoliAtalantaSampdoria05.5000.55.0...0.6071010.0000000.0011830.2792900.0130180.0035500.0153850.0082840.0473370.027219
51ZorteaAtalantaSampdoria06.0000.55.5...0.6486490.0450450.0945950.3648650.0270270.0090090.0180180.0000000.0225230.004505
..................................................................
542319IlicVeronaLecce17.0010.08.0...0.5722680.0070510.0188010.2773210.0199760.0058750.0105760.0105760.0117510.015276
542419Sulemana I.VeronaLecce16.0000.06.0...0.4779660.0101690.0305080.2067800.0169490.0135590.0203390.0067800.0203390.027119
542519LasagnaVeronaLecce15.5000.05.5...0.3012820.0074790.0256410.1912390.0363250.0256410.0192310.0170940.0074790.032051
542619KallonVeronaLecce16.0000.06.0...0.4093100.0080260.0337080.2937400.0337080.0240770.0176570.0272870.0112360.025682
542719DjuricVeronaLecce16.0000.06.0...0.5161810.0016180.0048540.4514560.0275080.0064720.0242720.0210360.1666670.043689
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4906 rows × 131 columns

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" + ], + "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", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", + "\n", + " cards_malus fantavote ... touches_live_ball dribbles_completed \\\n", + "1 0.0 10.0 ... 0.684211 0.000849 \n", + "2 0.0 6.0 ... 0.753541 0.000000 \n", + "3 0.5 5.5 ... 0.586090 0.003393 \n", + "4 0.5 5.0 ... 0.607101 0.000000 \n", + "5 0.5 5.5 ... 0.648649 0.045045 \n", + "... ... ... ... ... ... \n", + "5423 0.0 8.0 ... 0.572268 0.007051 \n", + "5424 0.0 6.0 ... 0.477966 0.010169 \n", + "5425 0.0 5.5 ... 0.301282 0.007479 \n", + "5426 0.0 6.0 ... 0.409310 0.008026 \n", + "5427 0.0 6.0 ... 0.516181 0.001618 \n", + "\n", + " dribbles passes_received miscontrols dispossessed fouls fouled \\\n", + "1 0.005093 0.407470 0.007640 0.000849 0.006791 0.005093 \n", + "2 0.002833 0.521246 0.002833 0.008499 0.002833 0.002833 \n", + "3 0.006785 0.357082 0.006785 0.004241 0.012723 0.001696 \n", + "4 0.001183 0.279290 0.013018 0.003550 0.015385 0.008284 \n", + "5 0.094595 0.364865 0.027027 0.009009 0.018018 0.000000 \n", + "... ... ... ... ... ... ... \n", + "5423 0.018801 0.277321 0.019976 0.005875 0.010576 0.010576 \n", + "5424 0.030508 0.206780 0.016949 0.013559 0.020339 0.006780 \n", + "5425 0.025641 0.191239 0.036325 0.025641 0.019231 0.017094 \n", + "5426 0.033708 0.293740 0.033708 0.024077 0.017657 0.027287 \n", + "5427 0.004854 0.451456 0.027508 0.006472 0.024272 0.021036 \n", + "\n", + " aerials_won aerials_lost \n", + "1 0.016129 0.011885 \n", + "2 0.005666 0.008499 \n", + "3 0.015267 0.011874 \n", + "4 0.047337 0.027219 \n", + "5 0.022523 0.004505 \n", + "... ... ... \n", + "5423 0.011751 0.015276 \n", + "5424 0.020339 0.027119 \n", + "5425 0.007479 0.032051 \n", + "5426 0.011236 0.025682 \n", + "5427 0.166667 0.043689 \n", + "\n", + "[4906 rows x 131 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db" + ] + }, + { + "cell_type": "markdown", + "id": "ab69d09e", + "metadata": {}, + "source": [ + "Save outfield players database to file." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "df26c8e0", + "metadata": {}, + "outputs": [], + "source": [ + "db.to_excel('mid_outputs/database_entries.xlsx')" + ] + }, + { + "cell_type": "markdown", + "id": "38b564d2", + "metadata": {}, + "source": [ + "Do the same process for goalkeepers, generating a different database." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "4d06576a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - r\n", + "11 - games\n", + "12 - games_starts\n", + "13 - minutes\n", + "14 - shots_on_target_pct\n", + "15 - goals_per_shot\n", + "16 - goals_per_shot_on_target\n", + "17 - passes_pct\n", + "18 - dribble_tackles_pct\n", + "19 - dribbles_completed_pct\n", + "20 - aerials_won_pct\n", + "21 - team_possession\n", + "22 - team_goals_assists_per90\n", + "23 - team_goals_pens_per90\n", + "24 - team_goals_assists_pens_per90\n", + "25 - team_xg_per90\n", + "26 - team_gk_goals_against_per90\n", + "27 - team_gk_save_pct\n", + "28 - team_gk_clean_sheets_pct\n", + "29 - team_passes_pct\n", + "30 - team_passes_pct_medium\n", + "31 - team_passes_pct_long\n", + "32 - team_sca_per90\n", + "33 - team_gca_per90\n", + "34 - team_dribble_tackles_pct\n", + "35 - team_aerials_won_pct\n", + "36 - vs_team_possession\n", + "37 - vs_team_goals_per90\n", + "38 - vs_team_assists_per90\n", + "39 - vs_team_xg_per90\n", + "40 - vs_team_gk_save_pct\n", + "41 - vs_team_gk_clean_sheets_pct\n", + "42 - vs_team_gk_pct_passes_launched\n", + "43 - vs_team_gk_crosses_stopped_pct\n", + "44 - vs_team_shots_on_target_per90\n", + "45 - vs_team_passes_pct\n", + "46 - vs_team_passes_pct_short\n", + "47 - vs_team_passes_pct_medium\n", + "48 - vs_team_passes_pct_long\n", + "49 - vs_team_sca_per90\n", + "50 - vs_team_gca_per90\n", + "51 - vs_team_dribble_tackles_pct\n", + "52 - vs_team_dribbles_completed_pct\n", + "53 - vs_team_aerials_won_pct\n", + "54 - opp_team_possession\n", + "55 - opp_team_goals_assists_per90\n", + "56 - opp_team_goals_pens_per90\n", + "57 - opp_team_goals_assists_pens_per90\n", + "58 - opp_team_xg_per90\n", + "59 - opp_team_gk_goals_against_per90\n", + "60 - opp_team_gk_save_pct\n", + "61 - opp_team_gk_clean_sheets_pct\n", + "62 - opp_team_passes_pct\n", + "63 - opp_team_passes_pct_medium\n", + "64 - opp_team_passes_pct_long\n", + "65 - opp_team_sca_per90\n", + "66 - opp_team_gca_per90\n", + "67 - opp_team_dribble_tackles_pct\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_dribble_tackles_pct\n", + "85 - opp_vs_team_dribbles_completed_pct\n", + "86 - opp_vs_team_aerials_won_pct\n", + "87 - vote_avg\n", + "88 - vote_std\n", + "89 - goals\n", + "90 - assists\n", + "91 - cards_yellow\n", + "92 - cards_red\n", + "93 - xg\n", + "94 - npxg\n", + "95 - shots_on_target\n", + "96 - passes_completed\n", + "97 - passes_into_final_third\n", + "98 - passes_into_penalty_area\n", + "99 - progressive_passes\n", + "100 - passes_live\n", + "101 - passes_dead\n", + "102 - through_balls\n", + "103 - passes_switches\n", + "104 - crosses\n", + "105 - corner_kicks\n", + "106 - dribble_tackles\n", + "107 - dribbles_vs\n", + "108 - dribbled_past\n", + "109 - blocks\n", + "110 - blocked_shots\n", + "111 - blocked_passes\n", + "112 - interceptions\n", + "113 - clearances\n", + "114 - errors\n", + "115 - touches\n", + "116 - touches_def_pen_area\n", + "117 - touches_def_3rd\n", + "118 - touches_mid_3rd\n", + "119 - touches_att_3rd\n", + "120 - touches_att_pen_area\n", + "121 - touches_live_ball\n", + "122 - dribbles_completed\n", + "123 - dribbles\n", + "124 - passes_received\n", + "125 - miscontrols\n", + "126 - dispossessed\n", + "127 - fouls\n", + "128 - fouled\n", + "129 - aerials_won\n", + "130 - aerials_lost\n" + ] + } + ], + "source": [ + "for i in range(db.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db.columns[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "01bb7413", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['r',\n", + " 'fb_ID',\n", + " 'gk_games',\n", + " 'gk_games_starts',\n", + " 'gk_minutes',\n", + " 'gk_goals_against_per90',\n", + " 'gk_shots_on_target_against',\n", + " 'gk_saves',\n", + " 'gk_save_pct',\n", + " 'gk_clean_sheets_pct',\n", + " 'gk_free_kick_goals_against',\n", + " 'gk_corner_kick_goals_against',\n", + " 'gk_own_goals_against',\n", + " 'gk_psxg',\n", + " 'gk_psnpxg_per_shot_on_target_against',\n", + " 'gk_psxg_net',\n", + " 'gk_psxg_net_per90',\n", + " 'gk_passes_completed_launched',\n", + " 'gk_passes_launched',\n", + " 'gk_passes_pct_launched',\n", + " 'gk_passes',\n", + " 'gk_passes_throws',\n", + " 'gk_pct_passes_launched',\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", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rx = pd.read_excel('tmp/playermatchdata_sel_gk.xlsx', index_col = 0, header = None) \n", + "\n", + "out = list()\n", + "\n", + "for i in range(1, rx.shape[1]):\n", + " if(rx[i][2] == \"x\"):\n", + " out.append(rx[i][0])\n", + " \n", + "out" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "02f961d8", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs_gk = [\n", + " 'gk_games',\n", + " 'gk_games_starts',\n", + " 'gk_minutes',\n", + " 'gk_goals_against_per90', \n", + " 'gk_save_pct',\n", + " 'gk_clean_sheets_pct',\n", + " 'gk_psxg_net_per90',\n", + " 'gk_passes_pct_launched',\n", + " 'gk_pct_passes_launched',\n", + " 'gk_passes_length_avg',\n", + " 'gk_pct_goal_kicks_launched',\n", + " 'gk_goal_kick_length_avg',\n", + " 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area_per90',\n", + " 'gk_avg_distance_def_actions',\n", + " \n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " 'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " 'opp_vs_team_dribble_tackles_pct',\n", + " 'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel_gk = [\n", + " 'gk_shots_on_target_against',\n", + " 'gk_saves',\n", + " 'gk_free_kick_goals_against',\n", + " 'gk_corner_kick_goals_against',\n", + " 'gk_own_goals_against',\n", + " 'gk_psxg',\n", + " 'gk_psnpxg_per_shot_on_target_against',\n", + " 'gk_psxg_net',\n", + " 'gk_passes_completed_launched',\n", + " 'gk_passes_launched',\n", + " 'gk_passes',\n", + " 'gk_passes_throws',\n", + " 'gk_goal_kicks',\n", + " 'gk_crosses',\n", + " 'gk_crosses_stopped',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "5e3694a0", + "metadata": {}, + "outputs": [], + "source": [ + "def player_match_data_ext_gk(player, pteam, oppteam):\n", + " pdata = player_match_data(player, pteam, oppteam)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " if(pdata['gk_games'][0] <= 0):\n", + " return None\n", + " \n", + " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", + " \n", + " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", + "\n", + " return out\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "9b671646", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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gk_gamesgk_games_startsgk_minutesgk_goals_against_per90gk_save_pctgk_clean_sheets_pctgk_psxg_net_per90gk_passes_pct_launchedgk_pct_passes_launchedgk_passes_length_avg...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
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1 rows × 98 columns

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" + ], + "text/plain": [ + " gk_games gk_games_starts gk_minutes gk_goals_against_per90 \\\n", + "Consigli 17.0 17.0 1530.0 1.53 \n", + "\n", + " gk_save_pct gk_clean_sheets_pct gk_psxg_net_per90 \\\n", + "Consigli 56.6 29.4 -0.26 \n", + "\n", + " gk_passes_pct_launched gk_pct_passes_launched \\\n", + "Consigli 41.3 30.9 \n", + "\n", + " gk_passes_length_avg ... gk_psxg \\\n", + "Consigli 34.4 ... 21.6 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "Consigli 0.37 -4.4 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "Consigli 100.0 242.0 621.0 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "Consigli 87.0 157.0 220.0 13.0 \n", + "\n", + "[1 rows x 98 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = player_match_data_ext_gk('Consigli', 'Sassuolo', 'Atalanta')\n", + "\n", + "add_columns_gk = test.columns\n", + "\n", + "test" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "33f805a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "100\n", + "200\n", + "300\n", + "400\n", + "500\n", + "600\n", + "700\n", + "800\n", + "900\n", + "1000\n", + "1100\n", + "1200\n", + "1300\n", + "1400\n", + "1500\n", + "1600\n", + "1700\n", + "1800\n", + "1900\n", + "2000\n", + "2100\n", + "2200\n", + "2300\n", + "2400\n", + "2500\n", + "2600\n", + "2700\n", + "2800\n", + "2900\n", + "3000\n", + "3100\n", + "3200\n", + "3300\n", + "3400\n", + "3500\n", + "3600\n", + "3700\n", + "3800\n", + "3900\n", + "4000\n", + "4100\n", + "4200\n", + "4300\n", + "4400\n", + "4500\n", + "4600\n", + "4700\n", + "4800\n", + "4900\n", + "5000\n", + "5100\n", + "5200\n", + "5300\n", + "5400\n" + ] + } + ], + "source": [ + "db_gk = votes.copy()\n", + "\n", + "delta = pd.DataFrame(columns = add_columns_gk)\n", + "\n", + "for i in range(db_gk.shape[0]):\n", + " ext_data = player_match_data_ext_gk(db_gk['player'][i], db_gk['team'][i], db_gk['oppteam'][i])\n", + " \n", + " if(i % 100 == 0):\n", + " print(i)\n", + " \n", + " if(not isinstance(ext_data, pd.DataFrame)):\n", + " continue\n", + " \n", + " ext_data = ext_data.rename(index = {db_gk['player'][i] : i})\n", + " \n", + " delta = pd.concat([delta, ext_data], axis = 0)\n", + " \n", + " \n", + " \n", + "db_gk = pd.concat([db_gk, delta], axis = 1)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "4d484846", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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01MussoAtalantaSampdoria06.0000.55.5...10.40.22-2.678.0167.0321.093.086.0147.08.0
11ToloiAtalantaSampdoria07.0100.010.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21DjimsitiAtalantaSampdoria06.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31HateboerAtalantaSampdoria06.0000.55.5...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41OkoliAtalantaSampdoria05.5000.55.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
542319IlicVeronaLecce17.0010.08.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542419Sulemana I.VeronaLecce16.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542519LasagnaVeronaLecce15.5000.05.5...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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542719DjuricVeronaLecce16.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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01MussoAtalantaSampdoria06.0000.55.5...10.4000000.220000-2.60000078.000000167.000000321.00000093.00000086.0147.08.000000
151SkorupskiBolognaLazio06.5-200.04.5...28.7000000.2800000.70000087.000000219.000000545.000000116.000000141.0280.018.000000
441VicarioEmpoliSpezia05.5-100.04.5...24.0000000.2700002.00000088.000000277.000000690.000000108.000000103.0399.025.000000
571GolliniFiorentinaCremonese15.0-200.03.0...15.6000000.2700001.60000034.00000092.000000425.00000069.000000114.0197.06.000000
711HandanovicInterLecce06.5-100.05.5...10.6000000.300000-1.40000027.00000052.000000266.00000046.00000045.079.02.000000
..................................................................
536219PegoloSassuoloMonza06.5-100.05.5...15.4666670.373333-2.86666769.666667167.333333434.33333361.333333110.0156.09.333333
537419DragowskiSpeziaRoma16.0-200.04.0...26.0000000.280000-3.00000085.000000255.000000479.00000087.000000106.0235.09.000000
538819Milinkovic-Savic V.TorinoFiorentina07.0000.07.0...20.3000000.2400000.300000138.000000493.000000746.00000085.000000142.0232.018.000000
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541419Montipo'VeronaLecce16.5000.06.5...25.2000000.250000-4.800000176.000000358.000000447.00000062.000000153.0261.010.000000
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375 rows × 108 columns

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" + ], + "text/plain": [ + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "15 1 Skorupski Bologna Lazio 0 6.5 \n", + "44 1 Vicario Empoli Spezia 0 5.5 \n", + "57 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "71 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "5362 19 Pegolo Sassuolo Monza 0 6.5 \n", + "5374 19 Dragowski Spezia Roma 1 6.0 \n", + "5388 19 Milinkovic-Savic V. Torino Fiorentina 0 7.0 \n", + "5401 19 Silvestri Udinese Sampdoria 0 6.0 \n", + "5414 19 Montipo' Verona Lecce 1 6.5 \n", + "\n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 10.400000 \n", + "15 -2 0 0.0 4.5 ... 28.700000 \n", + "44 -1 0 0.0 4.5 ... 24.000000 \n", + "57 -2 0 0.0 3.0 ... 15.600000 \n", + "71 -1 0 0.0 5.5 ... 10.600000 \n", + "... ... ... ... ... ... ... \n", + "5362 -1 0 0.0 5.5 ... 15.466667 \n", + "5374 -2 0 0.0 4.0 ... 26.000000 \n", + "5388 0 0 0.0 7.0 ... 20.300000 \n", + "5401 0 0 0.0 6.0 ... 22.100000 \n", + "5414 0 0 0.0 6.5 ... 25.200000 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.220000 -2.600000 \n", + "15 0.280000 0.700000 \n", + "44 0.270000 2.000000 \n", + "57 0.270000 1.600000 \n", + "71 0.300000 -1.400000 \n", + "... ... ... \n", + "5362 0.373333 -2.866667 \n", + "5374 0.280000 -3.000000 \n", + "5388 0.240000 0.300000 \n", + "5401 0.320000 1.100000 \n", + "5414 0.250000 -4.800000 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 78.000000 167.000000 321.000000 \n", + "15 87.000000 219.000000 545.000000 \n", + "44 88.000000 277.000000 690.000000 \n", + "57 34.000000 92.000000 425.000000 \n", + "71 27.000000 52.000000 266.000000 \n", + "... ... ... ... \n", + "5362 69.666667 167.333333 434.333333 \n", + "5374 85.000000 255.000000 479.000000 \n", + "5388 138.000000 493.000000 746.000000 \n", + "5401 76.000000 200.000000 406.000000 \n", + "5414 176.000000 358.000000 447.000000 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 93.000000 86.0 147.0 8.000000 \n", + "15 116.000000 141.0 280.0 18.000000 \n", + "44 108.000000 103.0 399.0 25.000000 \n", + "57 69.000000 114.0 197.0 6.000000 \n", + "71 46.000000 45.0 79.0 2.000000 \n", + "... ... ... ... ... \n", + "5362 61.333333 110.0 156.0 9.333333 \n", + "5374 87.000000 106.0 235.0 9.000000 \n", + "5388 85.000000 142.0 232.0 18.000000 \n", + "5401 72.000000 153.0 253.0 4.000000 \n", + "5414 62.000000 153.0 261.0 10.000000 \n", + "\n", + "[375 rows x 108 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db_gk" + ] + }, + { + "cell_type": "markdown", + "id": "8dbb7476", + "metadata": {}, + "source": [ + "Save goalkeepers players database to file." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d8b8b6da", + "metadata": {}, + "outputs": [], + "source": [ + "db_gk.to_excel('mid_outputs/database_entries_gk.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "54519b24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - gk_games\n", + "11 - gk_games_starts\n", + "12 - gk_minutes\n", + "13 - gk_goals_against_per90\n", + "14 - gk_save_pct\n", + "15 - gk_clean_sheets_pct\n", + "16 - gk_psxg_net_per90\n", + "17 - gk_passes_pct_launched\n", + "18 - gk_pct_passes_launched\n", + "19 - gk_passes_length_avg\n", + "20 - gk_pct_goal_kicks_launched\n", + "21 - gk_goal_kick_length_avg\n", + "22 - gk_crosses_stopped_pct\n", + "23 - gk_def_actions_outside_pen_area_per90\n", + "24 - gk_avg_distance_def_actions\n", + "25 - team_possession\n", + "26 - team_goals_assists_per90\n", + "27 - team_goals_pens_per90\n", + "28 - team_goals_assists_pens_per90\n", + "29 - team_xg_per90\n", + "30 - team_gk_goals_against_per90\n", + "31 - team_gk_save_pct\n", + "32 - team_gk_clean_sheets_pct\n", + "33 - team_passes_pct\n", + "34 - team_passes_pct_medium\n", + "35 - team_passes_pct_long\n", + "36 - team_sca_per90\n", + "37 - team_gca_per90\n", + "38 - team_dribble_tackles_pct\n", + "39 - team_aerials_won_pct\n", + "40 - vs_team_possession\n", + "41 - vs_team_goals_per90\n", + "42 - vs_team_assists_per90\n", + "43 - vs_team_xg_per90\n", + "44 - vs_team_gk_save_pct\n", + "45 - vs_team_gk_clean_sheets_pct\n", + "46 - vs_team_gk_pct_passes_launched\n", + "47 - vs_team_gk_crosses_stopped_pct\n", + "48 - vs_team_shots_on_target_per90\n", + "49 - vs_team_passes_pct\n", + "50 - vs_team_passes_pct_short\n", + "51 - vs_team_passes_pct_medium\n", + "52 - vs_team_passes_pct_long\n", + "53 - vs_team_sca_per90\n", + "54 - vs_team_gca_per90\n", + "55 - vs_team_dribble_tackles_pct\n", + "56 - vs_team_dribbles_completed_pct\n", + "57 - vs_team_aerials_won_pct\n", + "58 - opp_team_possession\n", + "59 - opp_team_goals_assists_per90\n", + "60 - opp_team_goals_pens_per90\n", + "61 - opp_team_goals_assists_pens_per90\n", + "62 - opp_team_xg_per90\n", + "63 - opp_team_gk_goals_against_per90\n", + "64 - opp_team_gk_save_pct\n", + "65 - opp_team_gk_clean_sheets_pct\n", + "66 - opp_team_passes_pct\n", + "67 - opp_team_passes_pct_medium\n", + "68 - opp_team_passes_pct_long\n", + "69 - opp_team_sca_per90\n", + "70 - opp_team_gca_per90\n", + "71 - opp_team_dribble_tackles_pct\n", + "72 - opp_team_aerials_won_pct\n", + "73 - opp_vs_team_possession\n", + "74 - opp_vs_team_goals_per90\n", + "75 - opp_vs_team_assists_per90\n", + "76 - opp_vs_team_xg_per90\n", + "77 - opp_vs_team_gk_save_pct\n", + "78 - opp_vs_team_gk_clean_sheets_pct\n", + "79 - opp_vs_team_gk_pct_passes_launched\n", + "80 - opp_vs_team_gk_crosses_stopped_pct\n", + "81 - opp_vs_team_shots_on_target_per90\n", + "82 - opp_vs_team_passes_pct\n", + "83 - opp_vs_team_passes_pct_short\n", + "84 - opp_vs_team_passes_pct_medium\n", + "85 - opp_vs_team_passes_pct_long\n", + "86 - opp_vs_team_sca_per90\n", + "87 - opp_vs_team_gca_per90\n", + "88 - opp_vs_team_dribble_tackles_pct\n", + "89 - opp_vs_team_dribbles_completed_pct\n", + "90 - opp_vs_team_aerials_won_pct\n", + "91 - vote_avg\n", + "92 - vote_std\n", + "93 - gk_shots_on_target_against\n", + "94 - gk_saves\n", + "95 - gk_free_kick_goals_against\n", + "96 - gk_corner_kick_goals_against\n", + "97 - gk_own_goals_against\n", + "98 - gk_psxg\n", + "99 - gk_psnpxg_per_shot_on_target_against\n", + "100 - gk_psxg_net\n", + "101 - gk_passes_completed_launched\n", + "102 - gk_passes_launched\n", + "103 - gk_passes\n", + "104 - gk_passes_throws\n", + "105 - gk_goal_kicks\n", + "106 - gk_crosses\n", + "107 - gk_crosses_stopped\n" + ] + } + ], + "source": [ + "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": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/5_scraping_match_probable_players-checkpoint.ipynb b/.ipynb_checkpoints/5_scraping_match_probable_players-checkpoint.ipynb new file mode 100644 index 0000000..d35380d --- /dev/null +++ b/.ipynb_checkpoints/5_scraping_match_probable_players-checkpoint.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fd8a6cb9", + "metadata": {}, + "source": [ + "Scraping for playing probability for the current Serie A matchday.\n", + "\n", + "Data from http://fantacalcio.it" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c7b7d26", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import sys, getopt\n", + "import csv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "91897dbb", + "metadata": {}, + "outputs": [], + "source": [ + "res = requests.get('https://www.fantacalcio.it/probabili-formazioni-serie-a')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7ba89027", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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playerstarterpercentage
0Skorupski1100.0
1Posch1100.0
2Soumaoro1100.0
3Lucumi'1100.0
4Cambiaso1100.0
............
435Verdi055.0
436Kallon055.0
437Piccoli055.0
438Ngonge025.0
439Braaf040.0
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440 rows × 3 columns

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" + ], + "text/plain": [ + " player starter percentage\n", + "0 Skorupski 1 100.0\n", + "1 Posch 1 100.0\n", + "2 Soumaoro 1 100.0\n", + "3 Lucumi' 1 100.0\n", + "4 Cambiaso 1 100.0\n", + ".. ... ... ...\n", + "435 Verdi 0 55.0\n", + "436 Kallon 0 55.0\n", + "437 Piccoli 0 55.0\n", + "438 Ngonge 0 25.0\n", + "439 Braaf 0 40.0\n", + "\n", + "[440 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "soup = BeautifulSoup(res.text)\n", + "\n", + "groups = soup.findAll('ul', attrs = {'class' : 'player-list'})\n", + "\n", + "probables = pd.DataFrame(columns = ['player', 'starter', 'percentage'])\n", + "\n", + "for i in range (len(groups)):\n", + " players = groups[i].findAll('a', attrs = {'class' : 'player-name'})\n", + " bars = groups[i].findAll('div', attrs = {'class' : 'progress-bar'})\n", + " \n", + " starter_string = groups[i]['class'][1]\n", + " if(starter_string == 'starters'):\n", + " starter = 1\n", + " else:\n", + " starter = 0\n", + " \n", + " for j in range(len(players)):\n", + " player = players[j].contents[0].replace('\\n \\n ', '')\n", + " perc = float(bars[j]['aria-valuenow'])\n", + " \n", + " row_df = pd.DataFrame([[player, starter, perc]], columns = probables.columns)\n", + " \n", + " probables = pd.concat([probables, row_df], ignore_index = True)\n", + " \n", + "probables " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "29e5b389", + "metadata": {}, + "outputs": [], + "source": [ + "probables = probables.set_index('player')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d711726e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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player1player2percentage
0ColomboCeesay55.0
1Pezzella Giu.Gallo65.0
2BandaDi Francesco F.60.0
3LovatoBronn55.0
4Nicolussi CavigliaBohinen65.0
5DiaBonazzoli65.0
6HaasAkpa Akpro60.0
7SatrianoBajrami55.0
8EbuehiStojanovic60.0
9BuongiornoAdopo60.0
10LinettyLukic65.0
11SanabriaSeck55.0
12SanabriaRadonjic55.0
13DessersCiofani D.55.0
14ChirichesBianchetti60.0
15AsllaniGagliardini55.0
16DarmianD'ambrosio60.0
17DumfriesD'ambrosio55.0
18MaehleRuggeri60.0
19BogaPasalic55.0
20RinconVieira55.0
21GabbiadiniQuagliarella55.0
22GiroudOrigi60.0
23De KetelaereDiaz B.55.0
24SaelemaekersMessias60.0
25Lopez M.Obiang55.0
26DefrelAlvarez A.60.0
27Traore' Hj.Thorstvedt65.0
28MckennieChiesa60.0
29KeanMilik60.0
30FagioliMiretti55.0
31RovellaMachin55.0
32Mari'Caldirola60.0
33PetagnaMota60.0
34MarusicHysaj60.0
35CataldiMarcos Antonio65.0
36IgorMartinez Quarta60.0
37Kouame'Jovic55.0
38Gonzalez N.Saponara55.0
39ElmasLozano55.0
40ElmasPolitano55.0
41Mario RuiOlivera55.0
42SpinazzolaEl Shaarawy60.0
43MaticBove55.0
44ArslanMakengo60.0
45SamardzicLovric55.0
46LasagnaKallon60.0
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" + ], + "text/plain": [ + " player1 player2 percentage\n", + "0 Colombo Ceesay 55.0\n", + "1 Pezzella Giu. Gallo 65.0\n", + "2 Banda Di Francesco F. 60.0\n", + "3 Lovato Bronn 55.0\n", + "4 Nicolussi Caviglia Bohinen 65.0\n", + "5 Dia Bonazzoli 65.0\n", + "6 Haas Akpa Akpro 60.0\n", + "7 Satriano Bajrami 55.0\n", + "8 Ebuehi Stojanovic 60.0\n", + "9 Buongiorno Adopo 60.0\n", + "10 Linetty Lukic 65.0\n", + "11 Sanabria Seck 55.0\n", + "12 Sanabria Radonjic 55.0\n", + "13 Dessers Ciofani D. 55.0\n", + "14 Chiriches Bianchetti 60.0\n", + "15 Asllani Gagliardini 55.0\n", + "16 Darmian D'ambrosio 60.0\n", + "17 Dumfries D'ambrosio 55.0\n", + "18 Maehle Ruggeri 60.0\n", + "19 Boga Pasalic 55.0\n", + "20 Rincon Vieira 55.0\n", + "21 Gabbiadini Quagliarella 55.0\n", + "22 Giroud Origi 60.0\n", + "23 De Ketelaere Diaz B. 55.0\n", + "24 Saelemaekers Messias 60.0\n", + "25 Lopez M. Obiang 55.0\n", + "26 Defrel Alvarez A. 60.0\n", + "27 Traore' Hj. Thorstvedt 65.0\n", + "28 Mckennie Chiesa 60.0\n", + "29 Kean Milik 60.0\n", + "30 Fagioli Miretti 55.0\n", + "31 Rovella Machin 55.0\n", + "32 Mari' Caldirola 60.0\n", + "33 Petagna Mota 60.0\n", + "34 Marusic Hysaj 60.0\n", + "35 Cataldi Marcos Antonio 65.0\n", + "36 Igor Martinez Quarta 60.0\n", + "37 Kouame' Jovic 55.0\n", + "38 Gonzalez N. Saponara 55.0\n", + "39 Elmas Lozano 55.0\n", + "40 Elmas Politano 55.0\n", + "41 Mario Rui Olivera 55.0\n", + "42 Spinazzola El Shaarawy 60.0\n", + "43 Matic Bove 55.0\n", + "44 Arslan Makengo 60.0\n", + "45 Samardzic Lovric 55.0\n", + "46 Lasagna Kallon 60.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "groups = soup.findAll('ul', attrs = {'class' : 'ballot-list'})\n", + "\n", + "ballots = pd.DataFrame(columns = ['player1', 'player2', 'percentage'])\n", + "for i in range (len(groups)):\n", + " players = groups[i].findAll('a', attrs = {'class' : 'player-name'})\n", + " percs = groups[i].findAll('strong', attrs = {'class' : 'percentage'})\n", + " \n", + " player1 = players[0].contents[0].replace('\\n \\n ', '')\n", + " player2 = players[1].contents[0].replace('\\n \\n ', '')\n", + " perc = float(percs[0].contents[0].replace('%', ''))\n", + " \n", + " row_df = pd.DataFrame([[player1, player2, perc]], columns = ballots.columns)\n", + " \n", + " ballots = pd.concat([ballots, row_df], ignore_index = True)\n", + " \n", + "ballots" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0171c61d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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starterpercentage
player
Skorupski1100.0
Posch1100.0
Soumaoro1100.0
Lucumi'1100.0
Cambiaso1100.0
.........
Verdi055.0
Kallon0.455.0
Piccoli055.0
Ngonge025.0
Braaf040.0
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440 rows × 2 columns

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" + ], + "text/plain": [ + " starter percentage\n", + "player \n", + "Skorupski 1 100.0\n", + "Posch 1 100.0\n", + "Soumaoro 1 100.0\n", + "Lucumi' 1 100.0\n", + "Cambiaso 1 100.0\n", + "... ... ...\n", + "Verdi 0 55.0\n", + "Kallon 0.4 55.0\n", + "Piccoli 0 55.0\n", + "Ngonge 0 25.0\n", + "Braaf 0 40.0\n", + "\n", + "[440 rows x 2 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range(ballots.shape[0]):\n", + " p1 = ballots['player1'][i]\n", + " p2 = ballots['player2'][i]\n", + " \n", + " perc = float(ballots['percentage'][i])\n", + " \n", + " probables.at[p1, 'starter'] = perc/100\n", + " probables.at[p2, 'starter'] = 1 - perc/100\n", + " \n", + "probables" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8b5468aa", + "metadata": {}, + "outputs": [], + "source": [ + "probables.to_excel('mid_outputs/match_probable_players.xlsx') " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb b/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb new file mode 100644 index 0000000..6c88d60 --- /dev/null +++ b/.ipynb_checkpoints/6_neural_network_training-checkpoint.ipynb @@ -0,0 +1,10029 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a867836", + "metadata": {}, + "source": [ + "Bayesian Neural Network model traning and prediction data generation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "210da263", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPRegressor\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import r2_score\n", + "\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "edbf3b27", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_probability as tfp\n", + "\n", + "tfk = tf.keras\n", + "tf.keras.backend.set_floatx(\"float32\")\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import IsolationForest\n", + "\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "markdown", + "id": "9dadf6ec", + "metadata": {}, + "source": [ + "Load the training databases, generated in player_match_database_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "fa098fa4", + "metadata": {}, + "outputs": [], + "source": [ + "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", + "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", + "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f71fa9a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...touches_live_balldribbles_completeddribblespasses_receivedmiscontrolsdispossessedfoulsfouledaerials_wonaerials_lost
01ToloiAtalantaSampdoria07.0100.010.0...0.6842110.0008490.0050930.4074700.0076400.0008490.0067910.0050930.0161290.011885
11DjimsitiAtalantaSampdoria06.0000.06.0...0.7535410.0000000.0028330.5212460.0028330.0084990.0028330.0028330.0056660.008499
21HateboerAtalantaSampdoria06.0000.55.5...0.5860900.0033930.0067850.3570820.0067850.0042410.0127230.0016960.0152670.011874
31OkoliAtalantaSampdoria05.5000.55.0...0.6071010.0000000.0011830.2792900.0130180.0035500.0153850.0082840.0473370.027219
41ZorteaAtalantaSampdoria06.0000.55.5...0.6486490.0450450.0945950.3648650.0270270.0090090.0180180.0000000.0225230.004505
..................................................................
2413138TamezeVeronaLazio05.5000.05.5...0.6293710.0132090.0233100.3581970.0198140.0101010.0128210.0132090.0236990.021368
2413238HonglaVeronaLazio07.0100.59.5...0.5821810.0015360.0076800.3410140.0184330.0122890.0230410.0076800.0230410.026114
2413338LasagnaVeronaLazio07.0100.59.5...0.3699320.0084460.0160470.2626690.0464530.0228040.0160470.0084460.0261820.041385
2413438CaprariVeronaLazio06.0000.06.0...0.6024100.0237310.0434470.4538150.0339540.0197150.0153340.0270170.0029210.009858
2413538SimeoneVeronaLazio07.0100.010.0...0.4070860.0086690.0229930.3203920.0512630.0301550.0211080.0218620.0226160.040709
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24136 rows × 131 columns

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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...10.4000000.220000-2.60000078.000000167.000000321.00000093.00000086.0147.08.000000
11SkorupskiBolognaLazio06.5-200.04.5...28.7000000.2800000.70000087.000000219.000000545.000000116.000000141.0280.018.000000
21VicarioEmpoliSpezia05.5-100.04.5...24.0000000.2700002.00000088.000000277.000000690.000000108.000000103.0399.025.000000
31GolliniFiorentinaCremonese15.0-200.03.0...15.6000000.2700001.60000034.00000092.000000425.00000069.000000114.0197.06.000000
41HandanovicInterLecce06.5-100.05.5...10.6000000.300000-1.40000027.00000052.000000266.00000046.00000045.079.02.000000
..................................................................
112019PegoloSassuoloMonza06.5-100.05.5...15.4666670.373333-2.86666769.666667167.333333434.33333361.333333110.0156.09.333333
112119DragowskiSpeziaRoma16.0-200.04.0...26.0000000.280000-3.00000085.000000255.000000479.00000087.000000106.0235.09.000000
112219Milinkovic-Savic V.TorinoFiorentina07.0000.07.0...20.3000000.2400000.300000138.000000493.000000746.00000085.000000142.0232.018.000000
112319SilvestriUdineseSampdoria06.0000.06.0...22.1000000.3200001.10000076.000000200.000000406.00000072.000000153.0253.04.000000
112419Montipo'VeronaLecce16.5000.06.5...25.2000000.250000-4.800000176.000000358.000000447.00000062.000000153.0261.010.000000
\n", + "

1125 rows × 108 columns

\n", + "
" + ], + "text/plain": [ + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "1 1 Skorupski Bologna Lazio 0 6.5 \n", + "2 1 Vicario Empoli Spezia 0 5.5 \n", + "3 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "4 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "1120 19 Pegolo Sassuolo Monza 0 6.5 \n", + "1121 19 Dragowski Spezia Roma 1 6.0 \n", + "1122 19 Milinkovic-Savic V. Torino Fiorentina 0 7.0 \n", + "1123 19 Silvestri Udinese Sampdoria 0 6.0 \n", + "1124 19 Montipo' Verona Lecce 1 6.5 \n", + "\n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 10.400000 \n", + "1 -2 0 0.0 4.5 ... 28.700000 \n", + "2 -1 0 0.0 4.5 ... 24.000000 \n", + "3 -2 0 0.0 3.0 ... 15.600000 \n", + "4 -1 0 0.0 5.5 ... 10.600000 \n", + "... ... ... ... ... ... ... \n", + "1120 -1 0 0.0 5.5 ... 15.466667 \n", + "1121 -2 0 0.0 4.0 ... 26.000000 \n", + "1122 0 0 0.0 7.0 ... 20.300000 \n", + "1123 0 0 0.0 6.0 ... 22.100000 \n", + "1124 0 0 0.0 6.5 ... 25.200000 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.220000 -2.600000 \n", + "1 0.280000 0.700000 \n", + "2 0.270000 2.000000 \n", + "3 0.270000 1.600000 \n", + "4 0.300000 -1.400000 \n", + "... ... ... \n", + "1120 0.373333 -2.866667 \n", + "1121 0.280000 -3.000000 \n", + "1122 0.240000 0.300000 \n", + "1123 0.320000 1.100000 \n", + "1124 0.250000 -4.800000 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 78.000000 167.000000 321.000000 \n", + "1 87.000000 219.000000 545.000000 \n", + "2 88.000000 277.000000 690.000000 \n", + "3 34.000000 92.000000 425.000000 \n", + "4 27.000000 52.000000 266.000000 \n", + "... ... ... ... \n", + "1120 69.666667 167.333333 434.333333 \n", + "1121 85.000000 255.000000 479.000000 \n", + "1122 138.000000 493.000000 746.000000 \n", + "1123 76.000000 200.000000 406.000000 \n", + "1124 176.000000 358.000000 447.000000 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 93.000000 86.0 147.0 8.000000 \n", + "1 116.000000 141.0 280.0 18.000000 \n", + "2 108.000000 103.0 399.0 25.000000 \n", + "3 69.000000 114.0 197.0 6.000000 \n", + "4 46.000000 45.0 79.0 2.000000 \n", + "... ... ... ... ... \n", + "1120 61.333333 110.0 156.0 9.333333 \n", + "1121 87.000000 106.0 235.0 9.000000 \n", + "1122 85.000000 142.0 232.0 18.000000 \n", + "1123 72.000000 153.0 253.0 4.000000 \n", + "1124 62.000000 153.0 261.0 10.000000 \n", + "\n", + "[1125 rows x 108 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", + "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", + "\n", + "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", + "\n", + "db_gk" + ] + }, + { + "cell_type": "markdown", + "id": "04df0936", + "metadata": {}, + "source": [ + "Load player stats from current season and past seasons" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "bc9dae87", + "metadata": {}, + "outputs": [], + "source": [ + "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", + "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", + "\n", + "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", + "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", + "\n", + "players = players_orig" + ] + }, + { + "cell_type": "markdown", + "id": "397babf2", + "metadata": {}, + "source": [ + "Load team data from current season and add an average Serie A team row" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "493b0495", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_19096\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" + ] + }, + { + "data": { + "text/html": [ + "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0048.4019.0209.01710.036.025.06.08.00...209.00218.025.001.008.01.001165.00239.00279.0046.100
BolognaBologna24.0052.6020.0220.01800.025.019.03.03.00...252.00229.033.003.003.01.001081.00224.00180.0055.400
CremoneseCremonese29.0044.0019.0209.01710.014.06.02.04.00...205.00247.029.003.004.00.001052.00348.00267.0056.600
EmpoliEmpoli26.0046.3019.0209.01710.017.09.00.00.00...251.00214.031.002.000.00.00999.00217.00189.0053.400
FiorentinaFiorentina27.0057.2019.0209.01710.021.017.02.04.00...259.00229.052.001.004.00.00942.00249.00289.0046.300
VeronaHellas Verona29.0043.4019.0209.01710.016.013.00.00.00...192.00276.022.001.000.01.001038.00345.00388.0047.100
InterInter23.0052.9019.0209.01710.037.025.02.02.00...242.00214.018.002.002.01.00869.00198.00232.0046.000
JuventusJuventus26.0049.5019.0209.01710.030.023.02.03.00...210.00210.025.000.003.00.00947.00230.00230.0050.000
LazioLazio21.0051.2019.0209.01710.034.025.03.04.00...261.00185.042.001.004.01.001021.00183.00181.0050.300
LecceLecce26.0042.5020.0220.01800.018.012.01.02.00...263.00265.043.003.002.01.001072.00373.00294.0055.900
MilanMilan27.0054.5019.0209.01710.033.028.02.02.00...234.00226.020.003.002.02.00964.00224.00275.0044.900
MonzaMonza29.0055.1019.0209.01710.022.013.03.03.00...279.00242.034.000.003.01.00958.00197.00215.0047.800
NapoliNapoli24.0061.3019.0209.01710.046.037.04.05.00...258.00167.027.001.004.00.00933.00201.00231.0046.500
RomaRoma25.0049.4019.0209.01710.025.016.03.05.00...266.00214.010.000.005.00.00981.00184.00225.0045.000
SalernitanaSalernitana28.0044.8020.0220.01800.024.016.01.01.00...226.00233.049.007.001.01.001084.00257.00240.0051.700
SampdoriaSampdoria29.0049.4019.0209.01710.08.07.00.00.00...299.00266.055.003.000.00.001017.00300.00318.0048.500
SassuoloSassuolo27.0048.7019.0209.01710.018.013.03.04.00...249.00182.062.002.004.00.00986.00216.00173.0055.500
SpeziaSpezia30.0046.7020.0220.01800.015.010.02.02.00...207.00261.046.001.002.02.001182.00322.00281.0053.400
TorinoTorino23.0052.8019.0209.01710.019.014.01.01.00...209.00265.021.003.001.00.00998.00323.00295.0052.300
UdineseUdinese22.0050.1019.0209.01710.026.022.00.00.00...250.00227.029.002.000.01.00950.00189.00237.0044.400
AvgAvg25.9550.0419.2211.21728.024.217.52.02.65...241.05228.533.651.952.60.651011.95250.95250.9549.855
\n", + "

21 rows × 313 columns

\n", + "
" + ], + "text/plain": [ + " team team_players_used team_possession team_games \\\n", + "Atalanta Atalanta 24.00 48.40 19.0 \n", + "Bologna Bologna 24.00 52.60 20.0 \n", + "Cremonese Cremonese 29.00 44.00 19.0 \n", + "Empoli Empoli 26.00 46.30 19.0 \n", + "Fiorentina Fiorentina 27.00 57.20 19.0 \n", + "Verona Hellas Verona 29.00 43.40 19.0 \n", + "Inter Inter 23.00 52.90 19.0 \n", + "Juventus Juventus 26.00 49.50 19.0 \n", + "Lazio Lazio 21.00 51.20 19.0 \n", + "Lecce Lecce 26.00 42.50 20.0 \n", + "Milan Milan 27.00 54.50 19.0 \n", + "Monza Monza 29.00 55.10 19.0 \n", + "Napoli Napoli 24.00 61.30 19.0 \n", + "Roma Roma 25.00 49.40 19.0 \n", + "Salernitana Salernitana 28.00 44.80 20.0 \n", + "Sampdoria Sampdoria 29.00 49.40 19.0 \n", + "Sassuolo Sassuolo 27.00 48.70 19.0 \n", + "Spezia Spezia 30.00 46.70 20.0 \n", + "Torino Torino 23.00 52.80 19.0 \n", + "Udinese Udinese 22.00 50.10 19.0 \n", + "Avg Avg 25.95 50.04 19.2 \n", + "\n", + " team_games_starts team_minutes team_goals team_assists \\\n", + "Atalanta 209.0 1710.0 36.0 25.0 \n", + "Bologna 220.0 1800.0 25.0 19.0 \n", + "Cremonese 209.0 1710.0 14.0 6.0 \n", + "Empoli 209.0 1710.0 17.0 9.0 \n", + "Fiorentina 209.0 1710.0 21.0 17.0 \n", + "Verona 209.0 1710.0 16.0 13.0 \n", + "Inter 209.0 1710.0 37.0 25.0 \n", + "Juventus 209.0 1710.0 30.0 23.0 \n", + "Lazio 209.0 1710.0 34.0 25.0 \n", + "Lecce 220.0 1800.0 18.0 12.0 \n", + "Milan 209.0 1710.0 33.0 28.0 \n", + "Monza 209.0 1710.0 22.0 13.0 \n", + "Napoli 209.0 1710.0 46.0 37.0 \n", + "Roma 209.0 1710.0 25.0 16.0 \n", + "Salernitana 220.0 1800.0 24.0 16.0 \n", + "Sampdoria 209.0 1710.0 8.0 7.0 \n", + "Sassuolo 209.0 1710.0 18.0 13.0 \n", + "Spezia 220.0 1800.0 15.0 10.0 \n", + "Torino 209.0 1710.0 19.0 14.0 \n", + "Udinese 209.0 1710.0 26.0 22.0 \n", + "Avg 211.2 1728.0 24.2 17.5 \n", + "\n", + " team_pens_made team_pens_att ... vs_team_fouls \\\n", + "Atalanta 6.0 8.00 ... 209.00 \n", + "Bologna 3.0 3.00 ... 252.00 \n", + "Cremonese 2.0 4.00 ... 205.00 \n", + "Empoli 0.0 0.00 ... 251.00 \n", + "Fiorentina 2.0 4.00 ... 259.00 \n", + "Verona 0.0 0.00 ... 192.00 \n", + "Inter 2.0 2.00 ... 242.00 \n", + "Juventus 2.0 3.00 ... 210.00 \n", + "Lazio 3.0 4.00 ... 261.00 \n", + "Lecce 1.0 2.00 ... 263.00 \n", + "Milan 2.0 2.00 ... 234.00 \n", + "Monza 3.0 3.00 ... 279.00 \n", + "Napoli 4.0 5.00 ... 258.00 \n", + "Roma 3.0 5.00 ... 266.00 \n", + "Salernitana 1.0 1.00 ... 226.00 \n", + "Sampdoria 0.0 0.00 ... 299.00 \n", + "Sassuolo 3.0 4.00 ... 249.00 \n", + "Spezia 2.0 2.00 ... 207.00 \n", + "Torino 1.0 1.00 ... 209.00 \n", + "Udinese 0.0 0.00 ... 250.00 \n", + "Avg 2.0 2.65 ... 241.05 \n", + "\n", + " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", + "Atalanta 218.0 25.00 1.00 \n", + "Bologna 229.0 33.00 3.00 \n", + "Cremonese 247.0 29.00 3.00 \n", + "Empoli 214.0 31.00 2.00 \n", + "Fiorentina 229.0 52.00 1.00 \n", + "Verona 276.0 22.00 1.00 \n", + "Inter 214.0 18.00 2.00 \n", + "Juventus 210.0 25.00 0.00 \n", + "Lazio 185.0 42.00 1.00 \n", + "Lecce 265.0 43.00 3.00 \n", + "Milan 226.0 20.00 3.00 \n", + "Monza 242.0 34.00 0.00 \n", + "Napoli 167.0 27.00 1.00 \n", + "Roma 214.0 10.00 0.00 \n", + "Salernitana 233.0 49.00 7.00 \n", + "Sampdoria 266.0 55.00 3.00 \n", + "Sassuolo 182.0 62.00 2.00 \n", + "Spezia 261.0 46.00 1.00 \n", + "Torino 265.0 21.00 3.00 \n", + "Udinese 227.0 29.00 2.00 \n", + "Avg 228.5 33.65 1.95 \n", + "\n", + " vs_team_pens_conceded vs_team_own_goals \\\n", + "Atalanta 8.0 1.00 \n", + "Bologna 3.0 1.00 \n", + "Cremonese 4.0 0.00 \n", + "Empoli 0.0 0.00 \n", + "Fiorentina 4.0 0.00 \n", + "Verona 0.0 1.00 \n", + "Inter 2.0 1.00 \n", + "Juventus 3.0 0.00 \n", + "Lazio 4.0 1.00 \n", + "Lecce 2.0 1.00 \n", + "Milan 2.0 2.00 \n", + "Monza 3.0 1.00 \n", + "Napoli 4.0 0.00 \n", + "Roma 5.0 0.00 \n", + "Salernitana 1.0 1.00 \n", + "Sampdoria 0.0 0.00 \n", + "Sassuolo 4.0 0.00 \n", + "Spezia 2.0 2.00 \n", + "Torino 1.0 0.00 \n", + "Udinese 0.0 1.00 \n", + "Avg 2.6 0.65 \n", + "\n", + " vs_team_ball_recoveries vs_team_aerials_won \\\n", + "Atalanta 1165.00 239.00 \n", + "Bologna 1081.00 224.00 \n", + "Cremonese 1052.00 348.00 \n", + "Empoli 999.00 217.00 \n", + "Fiorentina 942.00 249.00 \n", + "Verona 1038.00 345.00 \n", + "Inter 869.00 198.00 \n", + "Juventus 947.00 230.00 \n", + "Lazio 1021.00 183.00 \n", + "Lecce 1072.00 373.00 \n", + "Milan 964.00 224.00 \n", + "Monza 958.00 197.00 \n", + "Napoli 933.00 201.00 \n", + "Roma 981.00 184.00 \n", + "Salernitana 1084.00 257.00 \n", + "Sampdoria 1017.00 300.00 \n", + "Sassuolo 986.00 216.00 \n", + "Spezia 1182.00 322.00 \n", + "Torino 998.00 323.00 \n", + "Udinese 950.00 189.00 \n", + "Avg 1011.95 250.95 \n", + "\n", + " vs_team_aerials_lost vs_team_aerials_won_pct \n", + "Atalanta 279.00 46.100 \n", + "Bologna 180.00 55.400 \n", + "Cremonese 267.00 56.600 \n", + "Empoli 189.00 53.400 \n", + "Fiorentina 289.00 46.300 \n", + "Verona 388.00 47.100 \n", + "Inter 232.00 46.000 \n", + "Juventus 230.00 50.000 \n", + "Lazio 181.00 50.300 \n", + "Lecce 294.00 55.900 \n", + "Milan 275.00 44.900 \n", + "Monza 215.00 47.800 \n", + "Napoli 231.00 46.500 \n", + "Roma 225.00 45.000 \n", + "Salernitana 240.00 51.700 \n", + "Sampdoria 318.00 48.500 \n", + "Sassuolo 173.00 55.500 \n", + "Spezia 281.00 53.400 \n", + "Torino 295.00 52.300 \n", + "Udinese 237.00 44.400 \n", + "Avg 250.95 49.855 \n", + "\n", + "[21 rows x 313 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", + "\n", + "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", + "avg_row['team']['Avg'] = 'Avg'\n", + "\n", + "team_data = pd.concat([team_data, avg_row])\n", + "\n", + "team_data" + ] + }, + { + "cell_type": "markdown", + "id": "cc1cd13d", + "metadata": {}, + "source": [ + "Data processing functions copied from player_match_dataset_creation" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f4017b2f", + "metadata": {}, + "outputs": [], + "source": [ + "features_abs = ['r',\n", + " 'games',\n", + " 'games_starts', \n", + " 'minutes',\n", + " 'shots_on_target_pct',\n", + " 'goals_per_shot',\n", + " 'goals_per_shot_on_target',\n", + " 'passes_pct',\n", + " 'dribble_tackles_pct',\n", + " 'dribbles_completed_pct',\n", + " 'aerials_won_pct',\n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " 'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " 'opp_vs_team_dribble_tackles_pct',\n", + " 'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel = [\n", + " 'goals',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red',\n", + " 'xg',\n", + " 'npxg',\n", + " 'shots_on_target',\n", + " 'passes_completed',\n", + " 'passes_into_final_third',\n", + " 'passes_into_penalty_area',\n", + " 'progressive_passes',\n", + " 'passes_live',\n", + " 'passes_dead',\n", + " 'through_balls',\n", + " 'passes_switches',\n", + " 'crosses',\n", + " 'corner_kicks',\n", + " 'dribble_tackles',\n", + " 'dribbles_vs',\n", + " 'dribbled_past',\n", + " 'blocks',\n", + " 'blocked_shots',\n", + " 'blocked_passes',\n", + " 'interceptions',\n", + " 'clearances',\n", + " 'errors',\n", + " 'touches',\n", + " 'touches_def_pen_area',\n", + " 'touches_def_3rd',\n", + " 'touches_mid_3rd',\n", + " 'touches_att_3rd',\n", + " 'touches_att_pen_area',\n", + " 'touches_live_ball',\n", + " 'dribbles_completed',\n", + " 'dribbles',\n", + " 'passes_received',\n", + " 'miscontrols',\n", + " 'dispossessed',\n", + " 'fouls',\n", + " 'fouled',\n", + " 'aerials_won',\n", + " 'aerials_lost']\n", + "\n", + "features_rel_gamecorr = [\n", + " 'goals',\n", + " 'xg',\n", + " 'npxg',\n", + " 'assists',\n", + " 'cards_yellow',\n", + " 'cards_red'\n", + "]\n", + "\n", + "DEL_G = False\n", + "\n", + "features_to_del = [\n", + " 'goals',\n", + " 'assists',\n", + " 'xg',\n", + " 'npxg'\n", + "]\n", + "\n", + "def player_match_data(player, pteam, oppteam, oldseason = False):\n", + " if(not(player in players.index)):\n", + " return None\n", + " \n", + " if(oldseason):\n", + " pdata = players_old.loc[[player]]\n", + " else:\n", + " pdata = players.loc[[player]]\n", + " \n", + " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", + " \n", + " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", + " \n", + " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", + " \n", + " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", + " \n", + " return(out)\n", + "\n", + "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " assert pdata['games'][0] > 0\n", + " \n", + " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", + " \n", + " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", + " \n", + " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", + " \n", + " if(DEL_G):\n", + " out[features_to_del] = 0\n", + " \n", + " return out\n", + "\n", + "\n", + "\n", + "\n", + "features_abs_gk = [\n", + " 'gk_games',\n", + " 'gk_games_starts',\n", + " 'gk_minutes',\n", + " 'gk_goals_against_per90', \n", + " 'gk_save_pct',\n", + " 'gk_clean_sheets_pct',\n", + " 'gk_psxg_net_per90',\n", + " 'gk_passes_pct_launched',\n", + " 'gk_pct_passes_launched',\n", + " 'gk_passes_length_avg',\n", + " 'gk_pct_goal_kicks_launched',\n", + " 'gk_goal_kick_length_avg',\n", + " 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area_per90',\n", + " 'gk_avg_distance_def_actions',\n", + " \n", + " 'team_possession',\n", + " 'team_goals_assists_per90',\n", + " 'team_goals_pens_per90',\n", + " 'team_goals_assists_pens_per90',\n", + " 'team_xg_per90',\n", + " 'team_gk_goals_against_per90',\n", + " 'team_gk_save_pct',\n", + " 'team_gk_clean_sheets_pct',\n", + " 'team_passes_pct',\n", + " 'team_passes_pct_medium',\n", + " 'team_passes_pct_long',\n", + " 'team_sca_per90',\n", + " 'team_gca_per90',\n", + " 'team_dribble_tackles_pct',\n", + " 'team_aerials_won_pct',\n", + " 'vs_team_possession',\n", + " 'vs_team_goals_per90',\n", + " 'vs_team_assists_per90',\n", + " 'vs_team_xg_per90',\n", + " 'vs_team_gk_save_pct',\n", + " 'vs_team_gk_clean_sheets_pct',\n", + " 'vs_team_gk_pct_passes_launched',\n", + " 'vs_team_gk_crosses_stopped_pct',\n", + " 'vs_team_shots_on_target_per90',\n", + " 'vs_team_passes_pct',\n", + " 'vs_team_passes_pct_short',\n", + " 'vs_team_passes_pct_medium',\n", + " 'vs_team_passes_pct_long',\n", + " 'vs_team_sca_per90',\n", + " 'vs_team_gca_per90',\n", + " 'vs_team_dribble_tackles_pct',\n", + " 'vs_team_dribbles_completed_pct',\n", + " 'vs_team_aerials_won_pct',\n", + " 'opp_team_possession',\n", + " 'opp_team_goals_assists_per90',\n", + " 'opp_team_goals_pens_per90',\n", + " 'opp_team_goals_assists_pens_per90',\n", + " 'opp_team_xg_per90',\n", + " 'opp_team_gk_goals_against_per90',\n", + " 'opp_team_gk_save_pct',\n", + " 'opp_team_gk_clean_sheets_pct',\n", + " 'opp_team_passes_pct',\n", + " 'opp_team_passes_pct_medium',\n", + " 'opp_team_passes_pct_long',\n", + " 'opp_team_sca_per90',\n", + " 'opp_team_gca_per90',\n", + " 'opp_team_dribble_tackles_pct',\n", + " 'opp_team_aerials_won_pct',\n", + " 'opp_vs_team_possession',\n", + " 'opp_vs_team_goals_per90',\n", + " 'opp_vs_team_assists_per90',\n", + " 'opp_vs_team_xg_per90',\n", + " 'opp_vs_team_gk_save_pct',\n", + " 'opp_vs_team_gk_clean_sheets_pct',\n", + " 'opp_vs_team_gk_pct_passes_launched',\n", + " 'opp_vs_team_gk_crosses_stopped_pct',\n", + " 'opp_vs_team_shots_on_target_per90',\n", + " 'opp_vs_team_passes_pct',\n", + " 'opp_vs_team_passes_pct_short',\n", + " 'opp_vs_team_passes_pct_medium',\n", + " 'opp_vs_team_passes_pct_long',\n", + " 'opp_vs_team_sca_per90',\n", + " 'opp_vs_team_gca_per90',\n", + " 'opp_vs_team_dribble_tackles_pct',\n", + " 'opp_vs_team_dribbles_completed_pct',\n", + " 'opp_vs_team_aerials_won_pct',\n", + " \n", + " 'vote_avg',\n", + " 'vote_std']\n", + "\n", + "features_rel_gk = [\n", + " 'gk_shots_on_target_against',\n", + " 'gk_saves',\n", + " 'gk_free_kick_goals_against',\n", + " 'gk_corner_kick_goals_against',\n", + " 'gk_own_goals_against',\n", + " 'gk_psxg',\n", + " 'gk_psnpxg_per_shot_on_target_against',\n", + " 'gk_psxg_net',\n", + " 'gk_passes_completed_launched',\n", + " 'gk_passes_launched',\n", + " 'gk_passes',\n", + " 'gk_passes_throws',\n", + " 'gk_goal_kicks',\n", + " 'gk_crosses',\n", + " 'gk_crosses_stopped',\n", + "]\n", + "\n", + "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", + " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", + " \n", + " if(not isinstance(pdata, pd.DataFrame)):\n", + " return None\n", + " \n", + " if(pdata['gk_games'][0] <= 0):\n", + " return None\n", + " \n", + " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", + " \n", + " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", + "\n", + " return out\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "21fef3ae", + "metadata": {}, + "source": [ + "Players stats rework:\n", + "the current season stats are averaged (according to a calculated weight) with the past season data.\n", + "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", + "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", + "\n", + "These modified stats are used only for prediction, not for model traning.\n", + "\n", + "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", + "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "6f8707b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Averaging players stats with past seasons:\n", + "Meret 1\n", + "Provedel 1\n", + "Vicario 1\n", + "Szczesny 1\n", + "Falcone 1\n", + "Silvestri 1\n", + "Rui Patricio 1\n", + "Milinkovic-Savic V. 1\n", + "Sepe 1\n", + "Maignan 0.7353365384615383\n", + "Audero 1\n", + "Musso 1\n", + "Montipo' 1\n", + "Tatarusanu 1\n", + "Consigli 1\n", + "Dragowski 1\n", + "Terracciano 1\n", + "Sportiello 1\n", + "Skorupski 1\n", + "Perin 1\n", + "Handanovic 0.7268191268191269\n", + "Zoet 1\n", + "Pegolo 1\n", + "Mirante 0.7353365384615383\n", + "Ujkani 1\n", + "Berisha 1\n", + "Marchetti 1\n", + "Padelli 1\n", + "Bardi 1\n", + "Cordaz 0.9993762993762995\n", + "Pinsoglio 1\n", + "Fiorillo 1\n", + "Cragno 1\n", + "Sirigu 1\n", + "Rossi F. 1\n", + "Berardi A. 1\n", + "Gemello 1\n", + "Ravaglia 1\n", + "Boer 1\n", + "Adamonis 1\n", + "Marfella 1\n", + "Zovko 1\n", + "Piana 1\n", + "Dimarco 0.9979567307692307\n", + "Smalling 1\n", + "Doig affine to Lazovic\n", + "Doig 0.6426470588235293\n", + "Hernandez T. 0.8403846153846155\n", + "Udogie 0.7203296703296703\n", + "Parisi 1\n", + "Romagnoli 1\n", + "Di Lorenzo 0.9677156177156175\n", + "Danilo 1\n", + "Mazzocchi 1\n", + "Mario Rui 0.7415158371040723\n", + "Bastoni S. 0.7048387096774194\n", + "Rodrigo Becao 0.6242857142857142\n", + "Tomori 1\n", + "Juan Jesus 0.5602564102564102\n", + "Ibanez 0.8898190045248868\n", + "Demiral 0.9604395604395606\n", + "Scalvini 1\n", + "Bijol affine to Rodrigo Becao\n", + "Bijol 0.7203296703296703\n", + "Toloi 1\n", + "Depaoli 1\n", + "Mancini 0.9677156177156175\n", + "Bremer 0.8326340326340327\n", + "Ebuehi 0.9038461538461539\n", + "Dumfries 0.8658508158508158\n", + "Darmian 0.941230769230769\n", + "Kalulu 1\n", + "Vojvoda 0.7534482758620689\n", + "Maehle 1\n", + "Bastoni 0.8132754342431762\n", + "Gosens 1\n", + "Milenkovic 0.6920814479638008\n", + "Rodriguez R. 0.8403846153846154\n", + "Reca 1\n", + "Rrahmani 0.6111888111888112\n", + "Kyriakopoulos 0.6375331564986738\n", + "Martinez Quarta 1\n", + "Perez N. 1\n", + "Calabria 0.7110946745562131\n", + "Skriniar 0.9124175824175823\n", + "Marusic 0.9677156177156175\n", + "Lazzari 0.8674937965260546\n", + "Augello 0.8403846153846154\n", + "Ampadu 0.888262599469496\n", + "Ismajli 1\n", + "Cambiaso 1\n", + "Hysaj 0.8693633952254641\n", + "Izzo 1\n", + "Biraghi 0.8176715176715176\n", + "Medel 0.8658508158508158\n", + "Bonucci 0.6302884615384615\n", + "Acerbi 0.6869230769230769\n", + "Spinazzola 1\n", + "Lykogiannis 0.9813186813186815\n", + "Djidji 1\n", + "Singo 0.6723076923076923\n", + "Mari' 1\n", + "Casale 0.5724358974358974\n", + "De Vrij 0.7283333333333333\n", + "Patric 0.7003205128205129\n", + "Luperto 1\n", + "Faraoni 0.42019230769230775\n", + "Ceccherini 0.6426470588235293\n", + "Aina 0.8003663003663003\n", + "Soppy 0.6133241758241759\n", + "Ferrari A. 0.2986622073578596\n", + "Zappacosta 0.24717194570135745\n", + "Ferrari G. 0.8403846153846154\n", + "Fazio 1\n", + "Hateboer 1\n", + "Rogerio 1\n", + "Buongiorno 1\n", + "Gunter 0.7283333333333333\n", + "Colley 0.787860576923077\n", + "Terzic 1\n", + "Toljan 1\n", + "Zortea 0.5329575596816976\n", + "Soumaoro 0.9004120879120879\n", + "Amian 0.6828124999999999\n", + "Gyomber 0.7590570719602977\n", + "Alex Sandro 0.9604395604395606\n", + "Pezzella Giu. 0.7359890109890109\n", + "Bereszynski 0.0\n", + "Venuti 0.6576923076923077\n", + "Palomino 0.24717194570135745\n", + "Nuytinck 0.25441595441595444\n", + "Nikolaou 0.8403846153846154\n", + "Igor 0.7843589743589743\n", + "Dawidowicz 1\n", + "Bellanova 0.5539702233250621\n", + "Erlic 0.6869230769230769\n", + "Ballo-Toure' affine to Calabria\n", + "Ballo-Toure' 0.19393491124260354\n", + "Stojanovic 0.763986013986014\n", + "Zima 0.7563461538461539\n", + "Ghiglione 0.7926035502958579\n", + "Rugani 0.4201923076923077\n", + "De Sciglio 0.6723076923076924\n", + "Djimsiti 0.37952853598014885\n", + "Caldara 0.7201612903225807\n", + "Murru 0.5347902097902096\n", + "Karsdorp 0.4201923076923077\n", + "Bonifazi 0.5347902097902096\n", + "Kjaer 1\n", + "Magnani 1\n", + "Walukiewicz 0.9813186813186815\n", + "Walukiewicz 0.3666039729501268\n", + "Ayhan 0.7307692307692307\n", + "Ruggeri 0.9813186813186815\n", + "De Winter 1\n", + "Ostigard 1\n", + "Radovanovic 1\n", + "D'ambrosio 0.3361538461538462\n", + "De Silvestri 0.48796526054590567\n", + "Sala 0.5602564102564103\n", + "Chiriches 0.35530503978779837\n", + "Gabbia 1\n", + "Kumbulla 0.395475113122172\n", + "Lovato 0.6439903846153846\n", + "Tuia 0.1908119658119658\n", + "Ferrer 0.12450142450142451\n", + "Antov 1\n", + "Vasquez 0.49065934065934075\n", + "Zanoli 0.42932692307692305\n", + "Conti 0.24010989010989015\n", + "Conti 0.8679180434949665\n", + "Marrone 0.16355311355311355\n", + "Tonelli 0.0\n", + "Radu 0.0\n", + "Florenzi 0.14006410256410257\n", + "Fares 0.0\n", + "Fares 0.0\n", + "Marchizza 0.1807692307692308\n", + "Romagna 0.0\n", + "Romagna 0.0\n", + "Ranieri L. 0.06360398860398861\n", + "Muldur 0.054218362282878414\n", + "Carboni 0.228974358974359\n", + "Amey 0.0\n", + "Vina 0.19393491124260354\n", + "Ruan 1\n", + "Coppola D. 1\n", + "Cacace 0.5042307692307693\n", + "Zaccagni 0.9852785145888594\n", + "Milinkovic-Savic 0.8176715176715176\n", + "Barella 0.8870726495726495\n", + "Zielinski 0.9124175824175823\n", + "Luis Alberto 0.790950226244344\n", + "Felipe Anderson 0.8403846153846153\n", + "Diaz B. 0.921712158808933\n", + "Koopmeiners 1\n", + "Calhanoglu 0.8898190045248868\n", + "Frattesi 0.8870726495726495\n", + "Zambo Anguissa 1\n", + "Elmas 0.8176715176715176\n", + "Pereyra 1\n", + "Miranchuk 1\n", + "Samardzic 1\n", + "Politano 0.9167832167832167\n", + "Rabiot 0.787860576923077\n", + "Lobotka 1\n", + "Tonali 0.7470085470085471\n", + "Lazovic 0.7415158371040723\n", + "Bonaventura 0.8674937965260546\n", + "Pellegrini Lo. 0.9604395604395606\n", + "Pessina 1\n", + "Ferguson affine to Svanberg\n", + "Ferguson 0.6536324786324785\n", + "Ikone' 1\n", + "Candreva 0.9063568376068376\n", + "Bennacer 1\n", + "El Shaarawy 0.8092592592592592\n", + "Pasalic 0.7722453222453222\n", + "Orsolini 0.9852785145888594\n", + "Bandinelli 0.9124175824175823\n", + "Mkhitaryan 0.8863523573200994\n", + "Sensi 1\n", + "Chiesa 0.7203296703296703\n", + "Lukic 0.7203296703296703\n", + "Fagioli affine to Henderson L.\n", + "Fagioli 0.4423076923076923\n", + "Messias 0.8403846153846153\n", + "Arslan 1\n", + "Brozovic 0.48021978021978023\n", + "Verdi 1\n", + "Barak 0.888262599469496\n", + "Soriano 0.9124175824175823\n", + "Dominguez 1\n", + "Ranocchia F. affine to Henderson L.\n", + "Ranocchia F. 0.4423076923076923\n", + "Cristante 0.9392533936651583\n", + "Bajrami 0.8643956043956044\n", + "Ricci S. 1\n", + "Saponara 0.8693633952254641\n", + "Vecino 1\n", + "Locatelli 0.8674937965260546\n", + "Zaniolo 0.7803571428571429\n", + "Traore' Hj. 0.5421836228287841\n", + "Maldini 1\n", + "Coulibaly L. 1\n", + "De Roon 0.9524358974358974\n", + "Mandragora 1\n", + "Haas 1\n", + "Bourabia 1\n", + "Sottil 0.49022435897435895\n", + "Agudelo 1\n", + "Makengo 0.8149184149184149\n", + "Zalewski 1\n", + "Aebischer 1\n", + "Ederson D.s. 1\n", + "Miretti 1\n", + "Cataldi 0.8929086538461539\n", + "Djuricic 1\n", + "Linetty 1\n", + "Walace 0.8870726495726495\n", + "Marin 0.8586538461538462\n", + "Mckennie 1\n", + "Pobega 0.5724358974358975\n", + "Volpato 1\n", + "Cuadrado 0.763986013986014\n", + "Amrabat 1\n", + "Tameze 0.7961538461538461\n", + "Gyasi 0.8403846153846154\n", + "Ilic 0.5777644230769232\n", + "Harroui 0.9167832167832167\n", + "Duncan 0.662121212121212\n", + "Miguel Veloso 1\n", + "Ekdal 0.9247041420118343\n", + "Nicolussi Caviglia 1\n", + "Schouten 1\n", + "Rovella 0.8177655677655677\n", + "Crnigoj 0.050509049773755664\n", + "Romero L. 1\n", + "Basic 0.8114058355437664\n", + "Sabiri 1\n", + "Grassi 0.6296794871794873\n", + "Krunic 0.4802197802197803\n", + "Rincon 1\n", + "Vieira 1\n", + "Henderson L. 0.6192307692307691\n", + "Obiang 1\n", + "Lopez M. 0.6723076923076923\n", + "Saelemaekers 0.5135683760683761\n", + "Maggiore 0.4906593406593407\n", + "Akpa Akpro 0.0\n", + "Akpa Akpro 0.0\n", + "Kovalenko 0.6464497041420117\n", + "Maleh 0.30666208791208793\n", + "Matheus Henrique 0.7395384615384616\n", + "Asllani 0.7466555183946488\n", + "Ceide 1\n", + "Gagliardini 0.7470085470085471\n", + "Bove 1\n", + "Bianco 1\n", + "Benassi 0.14310897435897438\n", + "Kastanos 0.6847578347578348\n", + "Vignato 0.5602564102564103\n", + "Zurkowski 0.049065934065934076\n", + "Castrovilli 0.21923076923076923\n", + "Bohinen 1\n", + "Capezzi 0.0\n", + "Molina S. 0.29608753315649866\n", + "Bakayoko 0.0\n", + "Demme 0.2653846153846154\n", + "Askildsen 1\n", + "Darboe 0.0\n", + "Darboe 0.3361538461538462\n", + "Urbanski 0.0\n", + "Hongla 0.8403846153846154\n", + "Yepes 1\n", + "Osimhen 0.9337606837606838\n", + "Dybala 0.7698275862068965\n", + "Martinez L. 0.9124175824175823\n", + "Rafael Leao 0.8898190045248868\n", + "Immobile 0.7590570719602977\n", + "Vlahovic 1\n", + "Arnautovic 0.7130536130536129\n", + "Dzeko 0.8870726495726495\n", + "Nzola 1\n", + "Beto 1\n", + "Abraham 0.863097713097713\n", + "Giroud 0.9852785145888594\n", + "Deulofeu 0.790950226244344\n", + "Correa 1\n", + "Pedro 0.8929086538461539\n", + "Lozano 1\n", + "Simeone 0.4906593406593407\n", + "Cabral 1\n", + "Rebic 0.7703525641025643\n", + "Bonazzoli 0.9454326923076923\n", + "Berardi 0.5602564102564103\n", + "Caprari 0.9322527472527472\n", + "Di Maria affine to Chiesa\n", + "Di Maria 1\n", + "Piatek 1\n", + "Sanabria 0.8114058355437664\n", + "Zapata D. 0.8403846153846154\n", + "Kean 0.8929086538461539\n", + "Pinamonti 0.8109508547008547\n", + "Gonzalez N. 0.45839160839160836\n", + "Muriel 0.6847578347578348\n", + "Okereke 1\n", + "Alvarez A. affine to Raspadori\n", + "Alvarez A. 0.6536324786324785\n", + "Barrow 0.6920814479638008\n", + "Caputo 0.19081196581196586\n", + "Petagna 1\n", + "Di Francesco F. 1\n", + "Boga 1\n", + "Djuric 1\n", + "Henry 0.8326340326340327\n", + "Success 1\n", + "Gabbiadini 1\n", + "Origi affine to Rebic\n", + "Origi 0.8403846153846154\n", + "Lammers 0.457948717948718\n", + "Kallon 1\n", + "Lasagna 0.9004120879120879\n", + "Nestorovski 1\n", + "Pellegri 1\n", + "Belotti 1\n", + "Raspadori 0.6201388888888889\n", + "Verde 0.5093240093240092\n", + "Destro 0.636039886039886\n", + "Sansone 0.6225071225071225\n", + "Pjaca 0.6439903846153846\n", + "Strelec 1\n", + "Seck 1\n", + "Quagliarella 0.662121212121212\n", + "Piccoli affine to Lasagna\n", + "Piccoli 0.36016483516483516\n", + "Shomurodov 0.36016483516483516\n", + "Cancellieri 1\n", + "Antiste 0.19081196581196586\n", + "Afena-Gyan 1\n", + "Defrel 0.35384615384615387\n", + "Ibrahimovic 0.0\n", + "Pussetto 0.30666208791208793\n", + "Edera 0.0\n", + "Oddei 0.0\n", + "Oddei 0.6723076923076924\n", + "Braaf 0.0\n", + "Raimondo 0.0\n", + "Kaio Jorge 0.0\n", + "Lazetic 1\n", + "Players with low quantity of games:\n", + "Troost-Ekong 0.16666666666666663\n", + "Masina 0.6666666666666667\n", + "Aiwu 0.0\n", + "Thiaw 0.6666666666666667\n", + "Zeefuik 0.0\n", + "Ostigard 0.6666666666666667\n", + "Wisniewski 0.0\n", + "Dermaku 0.16666666666666663\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Donati 0.6666666666666667\n", + "Adopo 0.8333333333333334\n", + "Antov 0.6666666666666667\n", + "Gatti 0.8333333333333334\n", + "Gila 0.6666666666666667\n", + "Moutinho J. 0.6666666666666667\n", + "Paletta 0.0\n", + "Romagna 0.0\n", + "Cassandro 0.0\n", + "Amey 0.16666666666666663\n", + "Ferrarini 0.0\n", + "Zanotti 0.0\n", + "Bayeye 0.0\n", + "Ebosele 0.5\n", + "Buta 0.0\n", + "Abankwah 0.0\n", + "Guessand A. 0.0\n", + "Cabal 0.5\n", + "Sosa 0.6666666666666667\n", + "Guarino 0.0\n", + "Carboni F. 0.6666666666666667\n", + "Pogba 0.0\n", + "Wijnaldum 0.16666666666666663\n", + "Volpato 0.6666666666666667\n", + "Nicolussi Caviglia 0.6666666666666667\n", + "Romero L. 0.8333333333333334\n", + "Machin 0.0\n", + "Esposito Sa. 0.33333333333333337\n", + "Akpa Akpro 0.0\n", + "Bianco 0.5\n", + "D'andrea 0.8333333333333334\n", + "Tahirovic 0.6666666666666667\n", + "Winks 0.33333333333333337\n", + "Gaetano 0.5\n", + "Iling-Junior 0.6666666666666667\n", + "Capezzi 0.8333333333333334\n", + "Scozzarella 0.0\n", + "Darboe 0.6092307692307692\n", + "Urbanski 0.16666666666666663\n", + "Bertini 0.0\n", + "Yepes 0.8333333333333334\n", + "Trimboli 0.0\n", + "Pafundi 0.0\n", + "Milanese 0.33333333333333337\n", + "Adli 0.6666666666666667\n", + "Vignato S. 0.5\n", + "Bondo 0.6666666666666667\n", + "Samek 0.0\n", + "Zerbin 0.6666666666666667\n", + "Ilkhan 0.6666666666666667\n", + "Degli Innocenti 0.16666666666666663\n", + "Acella 0.0\n", + "Garbett 0.0\n", + "Carboni V. 0.33333333333333337\n", + "Cipot 0.16666666666666663\n", + "Solbakken 0.33333333333333337\n", + "Ngonge 0.0\n", + "Voelkerling Persson 0.33333333333333337\n", + "Edera 0.0\n", + "Oddei 0.4971794871794871\n", + "Braaf 0.6666666666666667\n", + "Raimondo 0.16666666666666663\n", + "De Luca 0.16666666666666663\n", + "Lazetic 0.16666666666666663\n", + "Krollis 0.0\n" + ] + } + ], + "source": [ + "#for i in range(players.columns.shape[0]):\n", + "# print(str(i) + ' - ' + players.columns[i])\n", + "\n", + "cols_toadapt = players.columns[9:]\n", + "\n", + "players = players_orig.copy()\n", + "\n", + "min_games = 6\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", + "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", + "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", + "WEIGHT_mul_gk = 2\n", + "\n", + "rcsv = pd.read_csv('config/affine_players.txt') \n", + "affine_players = pd.DataFrame(rcsv)\n", + "affine_players = affine_players.set_index('player')\n", + "\n", + "\n", + "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", + " if(same_team):\n", + " weight_0 = WEIGHT_0_same_team\n", + " else:\n", + " weight_0 = WEIGHT_0_different_team\n", + "\n", + " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "print(' ')\n", + "print('Averaging players stats with past seasons:')\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + "\n", + " if(p in players_old.index or p in affine_players.index):\n", + " p_ = p\n", + " affine = 0\n", + " \n", + " if(p in affine_players.index):\n", + " affine = 1\n", + " p_ = affine_players.loc[p]['alike']\n", + " \n", + " print(p + ' affine to ' + p_)\n", + " \n", + " if(players.loc[p]['r'] == 'P'):\n", + " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + " weight *= WEIGHT_mul_gk\n", + " weight = min(weight, 1)\n", + " else:\n", + " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", + "\n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight)) \n", + " \n", + " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", + " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", + " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", + " \n", + " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "# handle players with low quantitites of games\n", + "\n", + "print('Players with low quantity of games:')\n", + "\n", + "def calc_weight_low(current_games, min_games = min_games):\n", + " weight = 1 - (min_games - current_games)/min_games\n", + " \n", + " weight = min(weight, 1)\n", + "\n", + " return abs(weight)\n", + "\n", + "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", + "\n", + "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", + "count = 0\n", + "\n", + "for i in range(players_orig.shape[0]):\n", + " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", + " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", + " count = count + 1\n", + " \n", + "mean_players_stats /= count\n", + "\n", + "for i in range(players.shape[0]):\n", + " p = players.index[i]\n", + " \n", + " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", + " weight = calc_weight_low(players.loc[p]['games'])\n", + " \n", + " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", + " \n", + " print(p + ' ' + str(weight))\n", + " \n", + " \n", + "players_out = players.copy()\n", + "players_out = players_out.set_index(players_out.columns[0])\n", + "players_out.insert(2, 'name', players_out.index)\n", + "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "49c28b07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", + " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", + " ...\n", + " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", + " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", + " 'gk_def_actions_outside_pen_area',\n", + " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", + " 'vote_avg', 'vote_std'],\n", + " dtype='object', length=156)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "players.columns[9:]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d29102e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - r\n", + "11 - games\n", + "12 - games_starts\n", + "13 - minutes\n", + "14 - shots_on_target_pct\n", + "15 - goals_per_shot\n", + "16 - goals_per_shot_on_target\n", + "17 - passes_pct\n", + "18 - dribble_tackles_pct\n", + "19 - dribbles_completed_pct\n", + "20 - aerials_won_pct\n", + "21 - team_possession\n", + "22 - team_goals_assists_per90\n", + "23 - team_goals_pens_per90\n", + "24 - team_goals_assists_pens_per90\n", + "25 - team_xg_per90\n", + "26 - team_gk_goals_against_per90\n", + "27 - team_gk_save_pct\n", + "28 - team_gk_clean_sheets_pct\n", + "29 - team_passes_pct\n", + "30 - team_passes_pct_medium\n", + "31 - team_passes_pct_long\n", + "32 - team_sca_per90\n", + "33 - team_gca_per90\n", + "34 - team_dribble_tackles_pct\n", + "35 - team_aerials_won_pct\n", + "36 - vs_team_possession\n", + "37 - vs_team_goals_per90\n", + "38 - vs_team_assists_per90\n", + "39 - vs_team_xg_per90\n", + "40 - vs_team_gk_save_pct\n", + "41 - vs_team_gk_clean_sheets_pct\n", + "42 - vs_team_gk_pct_passes_launched\n", + "43 - vs_team_gk_crosses_stopped_pct\n", + "44 - vs_team_shots_on_target_per90\n", + "45 - vs_team_passes_pct\n", + "46 - vs_team_passes_pct_short\n", + "47 - vs_team_passes_pct_medium\n", + "48 - vs_team_passes_pct_long\n", + "49 - vs_team_sca_per90\n", + "50 - vs_team_gca_per90\n", + "51 - vs_team_dribble_tackles_pct\n", + "52 - vs_team_dribbles_completed_pct\n", + "53 - vs_team_aerials_won_pct\n", + "54 - opp_team_possession\n", + "55 - opp_team_goals_assists_per90\n", + "56 - opp_team_goals_pens_per90\n", + "57 - opp_team_goals_assists_pens_per90\n", + "58 - opp_team_xg_per90\n", + "59 - opp_team_gk_goals_against_per90\n", + "60 - opp_team_gk_save_pct\n", + "61 - opp_team_gk_clean_sheets_pct\n", + "62 - opp_team_passes_pct\n", + "63 - opp_team_passes_pct_medium\n", + "64 - opp_team_passes_pct_long\n", + "65 - opp_team_sca_per90\n", + "66 - opp_team_gca_per90\n", + "67 - opp_team_dribble_tackles_pct\n", + "68 - opp_team_aerials_won_pct\n", + "69 - opp_vs_team_possession\n", + "70 - opp_vs_team_goals_per90\n", + "71 - opp_vs_team_assists_per90\n", + "72 - opp_vs_team_xg_per90\n", + "73 - opp_vs_team_gk_save_pct\n", + "74 - opp_vs_team_gk_clean_sheets_pct\n", + "75 - opp_vs_team_gk_pct_passes_launched\n", + "76 - opp_vs_team_gk_crosses_stopped_pct\n", + "77 - opp_vs_team_shots_on_target_per90\n", + "78 - opp_vs_team_passes_pct\n", + "79 - opp_vs_team_passes_pct_short\n", + "80 - opp_vs_team_passes_pct_medium\n", + "81 - opp_vs_team_passes_pct_long\n", + "82 - opp_vs_team_sca_per90\n", + "83 - opp_vs_team_gca_per90\n", + "84 - opp_vs_team_dribble_tackles_pct\n", + "85 - opp_vs_team_dribbles_completed_pct\n", + "86 - opp_vs_team_aerials_won_pct\n", + "87 - vote_avg\n", + "88 - vote_std\n", + "89 - goals.1\n", + "90 - assists.1\n", + "91 - cards_yellow\n", + "92 - cards_red\n", + "93 - xg\n", + "94 - npxg\n", + "95 - shots_on_target\n", + "96 - passes_completed\n", + "97 - passes_into_final_third\n", + "98 - passes_into_penalty_area\n", + "99 - progressive_passes\n", + "100 - passes_live\n", + "101 - passes_dead\n", + "102 - through_balls\n", + "103 - passes_switches\n", + "104 - crosses\n", + "105 - corner_kicks\n", + "106 - dribble_tackles\n", + "107 - dribbles_vs\n", + "108 - dribbled_past\n", + "109 - blocks\n", + "110 - blocked_shots\n", + "111 - blocked_passes\n", + "112 - interceptions\n", + "113 - clearances\n", + "114 - errors\n", + "115 - touches\n", + "116 - touches_def_pen_area\n", + "117 - touches_def_3rd\n", + "118 - touches_mid_3rd\n", + "119 - touches_att_3rd\n", + "120 - touches_att_pen_area\n", + "121 - touches_live_ball\n", + "122 - dribbles_completed\n", + "123 - dribbles\n", + "124 - passes_received\n", + "125 - miscontrols\n", + "126 - dispossessed\n", + "127 - fouls\n", + "128 - fouled\n", + "129 - aerials_won\n", + "130 - aerials_lost\n" + ] + } + ], + "source": [ + "for i in range(db.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db.columns[i]))" + ] + }, + { + "cell_type": "markdown", + "id": "089690d6", + "metadata": {}, + "source": [ + "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", + "\n", + "For outfield players: X -> y = [vote, fantavote]\n", + "\n", + "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "f19304f6", + "metadata": {}, + "outputs": [], + "source": [ + "npdb = np.array(db)\n", + "\n", + "y = npdb[:, [5,9]] # vote, fantavote\n", + "\n", + "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", + "\n", + "f_start = 14\n", + "\n", + "X = npdb[:, f_start:]\n", + "\n", + "if(DEL_G): \n", + " del_g_idx = [\n", + " list(db.columns).index('goals.1') - f_start,\n", + " list(db.columns).index('assists.1') - f_start,\n", + " list(db.columns).index('xg') - f_start,\n", + " list(db.columns).index('npxg') - f_start,\n", + " list(db.columns).index('shots_on_target') - f_start]\n", + " \n", + " X[:, del_g_idx] = 0\n", + "\n", + "\n", + "# add role and home factor\n", + "toadd = np.zeros((X.shape[0], 4))\n", + "toadd[:, 0] = npdb[:, 4] # home\n", + "\n", + "toadd[:, 1] = npdb[:, 10] == 'D'\n", + "toadd[:, 2] = npdb[:, 10] == 'C'\n", + "toadd[:, 3] = npdb[:, 10] == 'A'\n", + "\n", + "X = np.concatenate((X, toadd), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "370d41d2", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "scaler.fit(X)\n", + "\n", + "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", + "\n", + "X_train = scaler.transform(X_train_)\n", + "X_test = scaler.transform(X_test_)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a7b1fb52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - matchday\n", + "1 - player\n", + "2 - team\n", + "3 - oppteam\n", + "4 - home\n", + "5 - vote\n", + "6 - goals\n", + "7 - assists\n", + "8 - cards_malus\n", + "9 - fantavote\n", + "10 - gk_games\n", + "11 - gk_games_starts\n", + "12 - gk_minutes\n", + "13 - gk_goals_against_per90\n", + "14 - gk_save_pct\n", + "15 - gk_clean_sheets_pct\n", + "16 - gk_psxg_net_per90\n", + "17 - gk_passes_pct_launched\n", + "18 - gk_pct_passes_launched\n", + "19 - gk_passes_length_avg\n", + "20 - gk_pct_goal_kicks_launched\n", + "21 - gk_goal_kick_length_avg\n", + "22 - gk_crosses_stopped_pct\n", + "23 - gk_def_actions_outside_pen_area_per90\n", + "24 - gk_avg_distance_def_actions\n", + "25 - team_possession\n", + "26 - team_goals_assists_per90\n", + "27 - team_goals_pens_per90\n", + "28 - team_goals_assists_pens_per90\n", + "29 - team_xg_per90\n", + "30 - team_gk_goals_against_per90\n", + "31 - team_gk_save_pct\n", + "32 - team_gk_clean_sheets_pct\n", + "33 - team_passes_pct\n", + "34 - team_passes_pct_medium\n", + "35 - team_passes_pct_long\n", + "36 - team_sca_per90\n", + "37 - team_gca_per90\n", + "38 - team_dribble_tackles_pct\n", + "39 - team_aerials_won_pct\n", + "40 - vs_team_possession\n", + "41 - vs_team_goals_per90\n", + "42 - vs_team_assists_per90\n", + "43 - vs_team_xg_per90\n", + "44 - vs_team_gk_save_pct\n", + "45 - vs_team_gk_clean_sheets_pct\n", + "46 - vs_team_gk_pct_passes_launched\n", + "47 - vs_team_gk_crosses_stopped_pct\n", + "48 - vs_team_shots_on_target_per90\n", + "49 - vs_team_passes_pct\n", + "50 - vs_team_passes_pct_short\n", + "51 - vs_team_passes_pct_medium\n", + "52 - vs_team_passes_pct_long\n", + "53 - vs_team_sca_per90\n", + "54 - vs_team_gca_per90\n", + "55 - vs_team_dribble_tackles_pct\n", + "56 - vs_team_dribbles_completed_pct\n", + "57 - vs_team_aerials_won_pct\n", + "58 - opp_team_possession\n", + "59 - opp_team_goals_assists_per90\n", + "60 - opp_team_goals_pens_per90\n", + "61 - opp_team_goals_assists_pens_per90\n", + "62 - opp_team_xg_per90\n", + "63 - opp_team_gk_goals_against_per90\n", + "64 - opp_team_gk_save_pct\n", + "65 - opp_team_gk_clean_sheets_pct\n", + "66 - opp_team_passes_pct\n", + "67 - opp_team_passes_pct_medium\n", + "68 - opp_team_passes_pct_long\n", + "69 - opp_team_sca_per90\n", + "70 - opp_team_gca_per90\n", + "71 - opp_team_dribble_tackles_pct\n", + "72 - opp_team_aerials_won_pct\n", + "73 - opp_vs_team_possession\n", + "74 - opp_vs_team_goals_per90\n", + "75 - opp_vs_team_assists_per90\n", + "76 - opp_vs_team_xg_per90\n", + "77 - opp_vs_team_gk_save_pct\n", + "78 - opp_vs_team_gk_clean_sheets_pct\n", + "79 - opp_vs_team_gk_pct_passes_launched\n", + "80 - opp_vs_team_gk_crosses_stopped_pct\n", + "81 - opp_vs_team_shots_on_target_per90\n", + "82 - opp_vs_team_passes_pct\n", + "83 - opp_vs_team_passes_pct_short\n", + "84 - opp_vs_team_passes_pct_medium\n", + "85 - opp_vs_team_passes_pct_long\n", + "86 - opp_vs_team_sca_per90\n", + "87 - opp_vs_team_gca_per90\n", + "88 - opp_vs_team_dribble_tackles_pct\n", + "89 - opp_vs_team_dribbles_completed_pct\n", + "90 - opp_vs_team_aerials_won_pct\n", + "91 - vote_avg\n", + "92 - vote_std\n", + "93 - gk_shots_on_target_against\n", + "94 - gk_saves\n", + "95 - gk_free_kick_goals_against\n", + "96 - gk_corner_kick_goals_against\n", + "97 - gk_own_goals_against\n", + "98 - gk_psxg\n", + "99 - gk_psnpxg_per_shot_on_target_against\n", + "100 - gk_psxg_net\n", + "101 - gk_passes_completed_launched\n", + "102 - gk_passes_launched\n", + "103 - gk_passes\n", + "104 - gk_passes_throws\n", + "105 - gk_goal_kicks\n", + "106 - gk_crosses\n", + "107 - gk_crosses_stopped\n" + ] + } + ], + "source": [ + "for i in range(db_gk.columns.shape[0]):\n", + " print(str(i) + \" - \" + str(db_gk.columns[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "5a7cf079", + "metadata": {}, + "outputs": [], + "source": [ + "npdb_gk= np.array(db_gk)\n", + "\n", + "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", + "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", + "\n", + "f_start_gk = 13\n", + "\n", + "X_gk = npdb_gk[:, f_start_gk:]\n", + "\n", + "# add home factor\n", + "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", + "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", + "\n", + "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "0bc0568b", + "metadata": {}, + "outputs": [], + "source": [ + "scaler_gk = StandardScaler()\n", + "scaler_gk.fit(X_gk)\n", + "\n", + "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", + "\n", + "X_gk_train = scaler_gk.transform(X_gk_train_)\n", + "X_gk_test = scaler_gk.transform(X_gk_test_)" + ] + }, + { + "cell_type": "markdown", + "id": "ebd27493", + "metadata": {}, + "source": [ + "MLP Regressor , to see performance of a simple neural network" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "04564bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1569906306642661\n", + "0.19152381866355805\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", + "\n", + "regr.fit(X_train, y_train)\n", + "\n", + "\n", + "y_train_predict = regr.predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = regr.predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f9513b25", + "metadata": {}, + "source": [ + "Train neural network for outfield players.\n", + "\n", + "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "8aad9652", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "76/76 [==============================] - 4s 10ms/step - loss: 12.8341 - distribution_lambda_12_loss: 8.9192 - distribution_lambda_13_loss: 3.9149 - val_loss: 8.0652 - val_distribution_lambda_12_loss: 4.9531 - val_distribution_lambda_13_loss: 3.1121\n", + "Epoch 2/1000\n", + "76/76 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val_distribution_lambda_13_loss: 1.3380\n", + "Epoch 216/1000\n", + "76/76 [==============================] - 0s 3ms/step - loss: 2.1396 - distribution_lambda_12_loss: 0.8319 - distribution_lambda_13_loss: 1.3077 - val_loss: 2.1937 - val_distribution_lambda_12_loss: 0.8543 - val_distribution_lambda_13_loss: 1.3393\n" + ] + } + ], + "source": [ + "load_model_of = False# load scaler and model weights for outfield player predictor\n", + "refit_model_of = True\n", + "\n", + "if(load_model_of):\n", + " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", + " \n", + " X_train = scaler.transform(X_train_)\n", + " X_test = scaler.transform(X_test_)\n", + "\n", + "\n", + "n_epochs = 1000\n", + "\n", + "n_samples = X_train.shape[0]\n", + "\n", + "batch_size = 256\n", + "\n", + "X_len = X_train.shape[1]\n", + "y_len = y_train.shape[1]\n", + "\n", + "\n", + "#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.2\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", + "x = tfk.layers.Dropout(0.2)(inputs)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "x = tfk.layers.Dropout(0.2)(x)\n", + "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", + "\n", + "\n", + "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", + "\n", + "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_of):\n", + " modelb.load_weights('saves/modelb')\n", + " \n", + "if( (not load_model_of) or refit_model_of):\n", + " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", + " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "4e2bf9dc", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict(X, iterations = 100):\n", + " y = np.zeros((2, X.shape[0]))\n", + " \n", + " dist = modelb(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " \n", + " return y.transpose() / iterations\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "c2674211", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.14844599198900188\n", + "0.17323732717963714\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train_predict = sample_predict(X_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", + "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_test_predict = sample_predict(X_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", + "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f574ffdb", + "metadata": {}, + "source": [ + "Train neural network for goalkeepers.\n", + "\n", + "For clean sheet probability prediction, a Bernoulli distribution is used." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "41e7e1ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2000\n", + "8/8 [==============================] - 4s 102ms/step - loss: 86.2533 - distribution_lambda_14_loss: 76.3453 - distribution_lambda_15_loss: 8.9897 - distribution_lambda_16_loss: 0.9182 - val_loss: 74.3172 - val_distribution_lambda_14_loss: 65.4215 - val_distribution_lambda_15_loss: 7.9738 - val_distribution_lambda_16_loss: 0.9220\n", + "Epoch 2/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 68.8012 - distribution_lambda_14_loss: 60.2621 - distribution_lambda_15_loss: 7.6220 - distribution_lambda_16_loss: 0.9171 - val_loss: 59.5450 - val_distribution_lambda_14_loss: 51.7760 - val_distribution_lambda_15_loss: 6.8508 - val_distribution_lambda_16_loss: 0.9182\n", + "Epoch 3/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 55.4490 - distribution_lambda_14_loss: 47.9629 - distribution_lambda_15_loss: 6.5739 - distribution_lambda_16_loss: 0.9123 - val_loss: 48.1461 - val_distribution_lambda_14_loss: 41.3038 - val_distribution_lambda_15_loss: 5.9274 - val_distribution_lambda_16_loss: 0.9148\n", + "Epoch 4/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 45.1109 - distribution_lambda_14_loss: 38.4840 - distribution_lambda_15_loss: 5.7175 - distribution_lambda_16_loss: 0.9094 - val_loss: 39.7642 - val_distribution_lambda_14_loss: 33.6357 - val_distribution_lambda_15_loss: 5.2195 - val_distribution_lambda_16_loss: 0.9090\n", + "Epoch 5/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 37.5347 - distribution_lambda_14_loss: 31.5482 - distribution_lambda_15_loss: 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[==============================] - 0s 6ms/step - loss: 2.3165 - distribution_lambda_14_loss: 0.4942 - distribution_lambda_15_loss: 1.3920 - distribution_lambda_16_loss: 0.4303 - val_loss: 2.2325 - val_distribution_lambda_14_loss: 0.4762 - val_distribution_lambda_15_loss: 1.3452 - val_distribution_lambda_16_loss: 0.4110\n", + "Epoch 1023/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.3142 - distribution_lambda_14_loss: 0.5055 - distribution_lambda_15_loss: 1.3888 - distribution_lambda_16_loss: 0.4199 - val_loss: 2.2509 - val_distribution_lambda_14_loss: 0.4902 - val_distribution_lambda_15_loss: 1.3501 - val_distribution_lambda_16_loss: 0.4107\n", + "Epoch 1024/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.3222 - distribution_lambda_14_loss: 0.4993 - distribution_lambda_15_loss: 1.3978 - distribution_lambda_16_loss: 0.4251 - val_loss: 2.2493 - val_distribution_lambda_14_loss: 0.4903 - val_distribution_lambda_15_loss: 1.3486 - val_distribution_lambda_16_loss: 0.4104\n", + "Epoch 1025/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.3127 - distribution_lambda_14_loss: 0.4995 - distribution_lambda_15_loss: 1.3896 - distribution_lambda_16_loss: 0.4235 - val_loss: 2.2356 - val_distribution_lambda_14_loss: 0.4782 - val_distribution_lambda_15_loss: 1.3475 - val_distribution_lambda_16_loss: 0.4099\n", + "Epoch 1026/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.3140 - distribution_lambda_14_loss: 0.4901 - distribution_lambda_15_loss: 1.3903 - distribution_lambda_16_loss: 0.4335 - val_loss: 2.2321 - val_distribution_lambda_14_loss: 0.4762 - val_distribution_lambda_15_loss: 1.3464 - val_distribution_lambda_16_loss: 0.4094\n", + "Epoch 1027/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.2689 - distribution_lambda_14_loss: 0.4915 - distribution_lambda_15_loss: 1.3664 - distribution_lambda_16_loss: 0.4110 - val_loss: 2.2370 - val_distribution_lambda_14_loss: 0.4829 - val_distribution_lambda_15_loss: 1.3453 - val_distribution_lambda_16_loss: 0.4088\n" + ] + } + ], + "source": [ + "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", + " \n", + " X_gk_train = scaler_gk.transform(X_gk_train_)\n", + " X_gk_test = scaler_gk.transform(X_gk_test_)\n", + " \n", + " \n", + "n_epochs = 2000\n", + "\n", + "n_samples = X_gk_train.shape[0]\n", + "\n", + "batch_size = 128\n", + "\n", + "X_gk_len = X_gk_train.shape[1]\n", + "y_gk_len = y_gk_train.shape[1]\n", + "\n", + "\n", + "#tailweight_param = 1.1\n", + "\n", + "tailweight_min = 0.5\n", + "tailweight_range = 1.5\n", + "\n", + "\n", + "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 30)\n", + "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", + "\n", + "\n", + "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", + "x = tfk.layers.Dense(16, activation=\"sigmoid\") (inputs)\n", + "x = tfk.layers.Dropout(0.3)(x)\n", + "x = tfk.layers.Dense(16, activation=\"sigmoid\") (x)\n", + "\n", + "\n", + "prob_dist_params = 4\n", + "\n", + "def prob_dist(t): \n", + " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", + " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", + " allow_nan_stats = False)\n", + "\n", + "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", + "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", + "\n", + "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", + "\n", + "x22 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", + "out_2 = tfp.layers.DistributionLambda(prob_dist)(x22)\n", + "\n", + "x23 = tfk.layers.Dense(1, activation=\"sigmoid\")(x2)\n", + "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x23)\n", + "\n", + "\n", + "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", + "\n", + "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", + " loss=neg_log_likelihood)\n", + "\n", + "if(load_model_gk):\n", + " modelb_gk.load_weights('saves/modelb_gk')\n", + "\n", + "if( (not load_model_gk) or refit_model_gk): \n", + " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", + " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", + " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "39a9bdc6", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predict_gk(X, iterations = 100):\n", + " y = np.zeros((3, X.shape[0]))\n", + " \n", + " dist = modelb_gk(X)\n", + " \n", + " for i in range(iterations):\n", + " y[0, :] += dist[0].sample()\n", + " y[1, :] += dist[1].sample()\n", + " y[2, :] += dist[2].sample()\n", + " \n", + " return y.transpose() / iterations\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "c41cf448", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2959033040611633\n", + "0.4342943294016782\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", + "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", + "\n", + "plt.show()\n", + "\n", + "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", + "\n", + "plt.plot([0, 20], [0, 20])\n", + "\n", + "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", + "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", + "\n", + "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", + "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "91869883", + "metadata": {}, + "source": [ + "Use the following codes to save the scalers and the model weights" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "cecf5392", + "metadata": {}, + "outputs": [], + "source": [ + "save_model_of = True\n", + "save_model_gk = True\n", + "\n", + "if(save_model_of):\n", + " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", + " modelb.save_weights('saves/modelb')\n", + " \n", + "if(save_model_gk):\n", + " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", + " modelb_gk.save_weights('saves/modelb_gk')\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "32635a0e", + "metadata": {}, + "source": [ + "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", + "\n", + "Estimate prediction mean and sigma (using a custom definitions).\n", + "\n", + "Generate a plot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "ddf433f9", + "metadata": {}, + "outputs": [], + "source": [ + "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", + " if(players['r'][player] == 'P'):\n", + " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 3:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 1))\n", + " xadd[0, 0] = home\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler_gk.transform(x_ptest)\n", + "\n", + " dist = modelb_gk(x_scaled)\n", + " \n", + " clean_shoot_prob = dist[2].probs.numpy()[0]\n", + " else:\n", + " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", + "\n", + " x_ptest = np.array(ptest)[:, 4:]\n", + " r = np.array(ptest)[0, 0]\n", + "\n", + " # add home and role\n", + " xadd = np.zeros((1, 4))\n", + " xadd[0, 0] = home\n", + " xadd[0, 1] = r == 'D'\n", + " xadd[0, 2] = r == 'C'\n", + " xadd[0, 3] = r == 'A'\n", + "\n", + " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", + "\n", + " x_scaled = scaler.transform(x_ptest)\n", + "\n", + " dist = modelb(x_scaled)\n", + " \n", + " \n", + " x = np.arange(0, 40, 0.002)\n", + "\n", + " px1 = dist[0].prob(x);\n", + " px2 = dist[1].prob(x);\n", + "\n", + " \n", + " #sample1 = dist[0].sample(10000)\n", + " #sample2 = dist[1].sample(10000)\n", + " \n", + " m1 = np.average(x, weights = px1)\n", + " m2 = np.average(x, weights = px2)\n", + " \n", + " #m1 = np.mean(sample1)\n", + " #m2 = np.mean(sample2)\n", + " \n", + " #s1 = np.std(sample1)\n", + " #s2 = np.std(sample2)\n", + " \n", + " # not standard deviation, but expected range extimated by quantile \n", + " \n", + " if(players['r'][player] == 'P'):\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", + " else:\n", + " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", + " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", + " \n", + "\n", + " \n", + " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", + " y_pred_m = np.array([m1, m2]).flatten()\n", + " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", + " y_pred_s = np.array([s1, s2]).flatten()\n", + " \n", + " clean_sheet_text = ''\n", + " if(players['r'][player] == 'P'):\n", + " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", + " \n", + " if(plot):\n", + " ax = plt.gca()\n", + " \n", + " plt.plot(x, px1, \n", + " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", + " color = 'b')\n", + " plt.plot(x, px2, \n", + " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", + " color = 'g')\n", + " \n", + " plt.fill_between(x, px1, color = 'lightblue')\n", + " plt.fill_between(x, px2, color = 'lightgreen')\n", + " \n", + " plt.legend()\n", + " \n", + " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", + " \n", + " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", + " \n", + " plt.xlim([0, 15])\n", + " \n", + " if(players['r'][player] == 'P'): \n", + " plt.ylim([0, 2.5])\n", + " else:\n", + " plt.ylim([0, 1.5])\n", + " \n", + " plt.show()\n", + " \n", + " if(log):\n", + " print(player + ': ' + \n", + " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", + " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", + " return [y_pred_m, y_pred_s, dist]\n" + ] + }, + { + "cell_type": "markdown", + "id": "98817aa0", + "metadata": {}, + "source": [ + "Load Serie A calendar. " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "d31bad38", + "metadata": {}, + "outputs": [], + "source": [ + "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", + "\n", + "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", + "\n", + "matchday = 0\n", + "\n", + "for i in range(cal.shape[0]):\n", + " if(cal[i, 0][0].isnumeric()):\n", + " matchday = matchday + 1\n", + " continue\n", + " \n", + " teams = cal[i, 0].split('-')\n", + " \n", + " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", + "\n", + " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "62b9f588", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matchdayteam1team2
01FiorentinaCremonese
11VeronaNapoli
21JuventusSassuolo
31LazioBologna
41LecceInter
............
37538LecceBologna
37638SassuoloFiorentina
37738MilanVerona
37838TorinoInter
37938UdineseJuventus
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380 rows × 3 columns

\n", + "
" + ], + "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": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_df" + ] + }, + { + "cell_type": "markdown", + "id": "62872819", + "metadata": {}, + "source": [ + "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "861a06ce", + "metadata": {}, + "outputs": [], + "source": [ + "matchday = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c58ba41d", + "metadata": {}, + "outputs": [], + "source": [ + "def PlayerMatch(player, match = matchday):\n", + " team = players.loc[player]['team']\n", + " \n", + " for i in range (cal_df.shape[0]):\n", + " if(cal_df['matchday'][i] == match):\n", + " if(cal_df['team1'][i] == team):\n", + " home = 1\n", + " oppteam = cal_df['team2'][i]\n", + " elif(cal_df['team2'][i] == team):\n", + " home = 0\n", + " oppteam = cal_df['team1'][i]\n", + " \n", + " return [player, team, oppteam, home]\n", + "\n", + "def predict_player(player, match = matchday, plot = 0, log = 0, oldseason = False):\n", + " [player, team, oppteam, home] = PlayerMatch(player, match)\n", + " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" + ] + }, + { + "cell_type": "markdown", + "id": "e8b63a98", + "metadata": {}, + "source": [ + "Load current matchday playing probabilities for Serie A players." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "f79792b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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starterpercentage
player
Skorupski1.0100
Posch1.0100
Soumaoro1.0100
Lucumi'1.0100
Cambiaso1.0100
.........
Verdi0.055
Kallon0.455
Piccoli0.055
Ngonge0.025
Braaf0.040
\n", + "

440 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " starter percentage\n", + "player \n", + "Skorupski 1.0 100\n", + "Posch 1.0 100\n", + "Soumaoro 1.0 100\n", + "Lucumi' 1.0 100\n", + "Cambiaso 1.0 100\n", + "... ... ...\n", + "Verdi 0.0 55\n", + "Kallon 0.4 55\n", + "Piccoli 0.0 55\n", + "Ngonge 0.0 25\n", + "Braaf 0.0 40\n", + "\n", + "[440 rows x 2 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", + "\n", + "probables" + ] + }, + { + "cell_type": "markdown", + "id": "e4751014", + "metadata": {}, + "source": [ + "Generate prediction data for each Serie A player for the current matchday.\n", + "\n", + "Output to excel file, using a template made for data elaboration." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "5e63c2b7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Provedel: MV 6.31 ± 0.77; FV 5.74 + 1.23 (69.4% cs)\n", + "Vicario: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Szczesny: MV 6.23 ± 0.72; FV 5.70 + 1.25 (68.3% cs)\n", + "Falcone: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Silvestri: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Rui Patricio: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Milinkovic-Savic V.: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Sepe: MV 6.39 ± 0.82; FV 5.10 + 1.40 (10.3% cs)\n", + "Maignan: MV 6.37 ± 0.77; FV 5.79 + 1.16 (55.4% cs)\n", + "Onana: MV 6.24 ± 0.80; FV 5.69 + 1.31 (68.2% cs)\n", + "Audero: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Musso: MV 6.31 ± 0.75; FV 5.76 + 1.24 (70.2% cs)\n", + "Carnesecchi: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Montipo': MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Di Gregorio: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Tatarusanu: MV 6.17 ± 0.65; FV 5.09 + 1.24 (21.7% cs)\n", + "Consigli: MV 6.22 ± 0.75; FV 4.18 + 1.75 (5.4% cs)\n", + "Dragowski: MV 6.11 ± 0.70; FV 3.95 + 1.76 (4.1% cs)\n", + "Terracciano: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Sportiello: MV 6.19 ± 0.72; FV 5.65 + 1.28 (68.7% cs)\n", + "Skorupski: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Perin: MV 6.33 ± 0.81; FV 5.75 + 1.31 (71.8% cs)\n", + "Handanovic: MV 6.20 ± 0.84; FV 5.65 + 1.39 (67.0% cs)\n", + "Zoet: MV 6.30 ± 0.77; FV 5.48 + 1.26 (31.3% cs)\n", + "Gollini: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Ochoa: MV 6.45 ± 0.87; FV 5.39 + 1.33 (16.6% cs)\n", + "Pegolo: MV 6.23 ± 0.79; FV 4.63 + 1.65 (10.1% cs)\n", + "Mirante: MV 6.37 ± 0.77; FV 5.79 + 1.16 (55.4% cs)\n", + "Sarr M.: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Lamanna: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Ujkani: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Berisha: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Marchetti: MV 6.11 ± 0.70; FV 3.95 + 1.76 (4.1% cs)\n", + "Perilli: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Padelli: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Perisan: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Bardi: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Cordaz: MV 6.24 ± 0.80; FV 5.69 + 1.31 (68.2% cs)\n", + "Pinsoglio: MV 6.23 ± 0.72; FV 5.70 + 1.25 (68.3% cs)\n", + "Fiorillo: MV 6.39 ± 0.82; FV 5.10 + 1.40 (10.3% cs)\n", + "Cragno: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Sirigu: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Cerofolini: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Rossi F.: MV 6.31 ± 0.75; FV 5.76 + 1.24 (70.2% cs)\n", + "Ravaglia F.: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Brancolini: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Bleve: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Berardi A.: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Russo A.: MV 6.22 ± 0.75; FV 4.18 + 1.75 (5.4% cs)\n", + "Gemello: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Ravaglia: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Boer: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Adamonis: MV 6.31 ± 0.77; FV 5.74 + 1.23 (69.4% cs)\n", + "Marfella: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Zovko: MV 6.09 ± 0.73; FV 3.89 + 1.87 (3.9% cs)\n", + "Piana: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Bagnolini: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Luis Maximiano: MV 6.15 ± 0.75; FV 5.59 + 1.31 (68.2% cs)\n", + "Svilar: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Sorrentino A.: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Ciezkowski: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Chiesa M.: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Saro: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Vasquez D.: MV 6.43 ± 0.83; FV 5.85 + 1.20 (61.9% cs)\n", + "Turk: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Dimarco: MV 6.30 ± 1.04; FV 6.94 + 2.36\n", + "Smalling: MV 5.86 ± 1.14; FV 5.99 + 1.37\n", + "Doig: MV 6.12 ± 1.01; FV 6.58 + 1.83\n", + "Carlos Augusto: MV 5.86 ± 1.01; FV 5.96 + 1.24\n", + "Kim: MV 6.31 ± 1.07; FV 6.88 + 2.14\n", + "Hernandez T.: MV 6.29 ± 1.13; FV 6.95 + 2.44\n", + "Udogie: MV 6.27 ± 1.10; FV 7.07 + 2.72\n", + "Parisi: MV 6.17 ± 0.85; FV 6.47 + 1.43\n", + "Romagnoli: MV 6.21 ± 0.99; FV 6.54 + 1.63\n", + "Di Lorenzo: MV 6.23 ± 0.88; FV 6.62 + 1.67\n", + "Danilo: MV 6.30 ± 0.98; FV 6.82 + 2.00\n", + "Mazzocchi: MV 6.09 ± 0.94; FV 6.45 + 1.58\n", + "Mario Rui: MV 6.23 ± 0.90; FV 6.54 + 1.52\n", + "Bastoni S.: MV 6.07 ± 0.96; FV 6.42 + 1.58\n", + "Valeri: MV 6.05 ± 0.75; FV 6.25 + 1.22\n", + "Posch: MV 6.27 ± 1.14; FV 7.15 + 2.93\n", + "Rodrigo Becao: MV 6.23 ± 0.90; FV 6.57 + 1.57\n", + "Tomori: MV 6.15 ± 0.88; FV 6.43 + 1.36\n", + "Baschirotto: MV 6.30 ± 1.01; FV 6.89 + 2.17\n", + "Juan Jesus: MV 6.18 ± 0.76; FV 6.47 + 1.34\n", + "Ibanez: MV 5.69 ± 1.01; FV 5.72 + 1.04\n", + "Demiral: MV 6.24 ± 0.86; FV 6.63 + 1.64\n", + "Scalvini: MV 6.30 ± 1.02; FV 6.91 + 2.26\n", + "Bijol: MV 6.13 ± 1.02; FV 6.62 + 1.94\n", + "Toloi: MV 6.25 ± 0.85; FV 6.57 + 1.52\n", + "Depaoli: MV 5.83 ± 1.00; FV 6.15 + 1.51\n", + "Mancini: MV 5.78 ± 0.81; FV 5.77 + 0.68\n", + "Bremer: MV 6.00 ± 1.16; FV 6.44 + 1.91\n", + "Ebuehi: MV 6.07 ± 0.76; FV 6.30 + 1.14\n", + "Dumfries: MV 6.19 ± 1.13; FV 6.88 + 2.49\n", + "Schuurs: MV 6.05 ± 0.67; FV 6.10 + 0.65\n", + "Darmian: MV 6.21 ± 0.95; FV 6.75 + 2.04\n", + "Kalulu: MV 5.97 ± 1.07; FV 6.23 + 1.41\n", + "Vojvoda: MV 5.97 ± 0.82; FV 6.13 + 0.99\n", + "Maehle: MV 6.17 ± 0.78; FV 6.45 + 1.42\n", + "Holm: MV 5.96 ± 0.83; FV 6.15 + 1.12\n", + "Bastoni: MV 6.08 ± 0.78; FV 6.19 + 0.87\n", + "Gosens: MV 6.07 ± 0.71; FV 6.31 + 1.23\n", + "Milenkovic: MV 5.73 ± 1.13; FV 5.73 + 1.28\n", + "Rodriguez R.: MV 5.92 ± 0.68; FV 5.96 + 0.55\n", + "Reca: MV 5.96 ± 0.92; FV 6.19 + 1.26\n", + "Sernicola: MV 5.85 ± 0.78; FV 5.97 + 1.06\n", + "Rrahmani: MV 6.25 ± 1.03; FV 6.75 + 1.96\n", + "Kyriakopoulos: MV 5.84 ± 0.94; FV 6.01 + 1.18\n", + "Martinez Quarta: MV 5.79 ± 1.07; FV 5.79 + 1.17\n", + "Perez N.: MV 6.17 ± 0.88; FV 6.30 + 1.03\n", + "Olivera: MV 6.10 ± 0.77; FV 6.44 + 1.46\n", + "Calabria: MV 6.19 ± 1.05; FV 6.69 + 1.97\n", + "Skriniar: MV 5.94 ± 0.68; FV 5.94 + 0.55\n", + "Marusic: MV 6.00 ± 0.73; FV 6.04 + 0.68\n", + "Lazzari: MV 6.02 ± 0.75; FV 6.07 + 0.67\n", + "Augello: MV 5.72 ± 0.95; FV 5.90 + 1.21\n", + "Ampadu: MV 5.80 ± 0.91; FV 5.71 + 0.81\n", + "Ismajli: MV 6.05 ± 0.79; FV 6.06 + 0.72\n", + "Cambiaso: MV 5.96 ± 0.70; FV 6.03 + 0.63\n", + "Hysaj: MV 6.00 ± 0.68; FV 6.03 + 0.62\n", + "Izzo: MV 5.86 ± 0.77; FV 5.82 + 0.62\n", + "Biraghi: MV 5.71 ± 0.84; FV 5.68 + 0.73\n", + "Medel: MV 5.96 ± 0.68; FV 5.96 + 0.54\n", + "Bonucci: MV 6.24 ± 1.02; FV 6.77 + 2.00\n", + "Acerbi: MV 6.09 ± 0.71; FV 6.16 + 0.75\n", + "Spinazzola: MV 5.68 ± 0.78; FV 5.75 + 0.83\n", + "Lykogiannis: MV 5.97 ± 0.63; FV 6.04 + 0.61\n", + "Djidji: MV 5.86 ± 0.85; FV 5.95 + 0.94\n", + "Lazaro: MV 5.96 ± 0.79; FV 6.04 + 0.78\n", + "Gallo: MV 5.97 ± 0.69; FV 5.95 + 0.59\n", + "Singo: MV 5.98 ± 0.75; FV 6.09 + 0.81\n", + "Mari': MV 5.75 ± 1.19; FV 5.77 + 1.34\n", + "Casale: MV 5.98 ± 0.75; FV 5.99 + 0.62\n", + "Dodo': MV 5.56 ± 1.01; FV 5.48 + 0.87\n", + "De Vrij: MV 5.94 ± 0.75; FV 6.06 + 0.82\n", + "Patric: MV 6.05 ± 0.91; FV 5.98 + 0.82\n", + "Luperto: MV 5.96 ± 0.97; FV 5.90 + 0.83\n", + "Faraoni: MV 5.86 ± 0.76; FV 5.99 + 1.07\n", + "Ceccherini: MV 5.77 ± 0.99; FV 5.85 + 1.22\n", + "Umtiti: MV 6.19 ± 0.95; FV 6.30 + 1.12\n", + "Aina: MV 5.98 ± 0.80; FV 6.16 + 1.00\n", + "Caldirola: MV 5.71 ± 0.94; FV 5.61 + 0.80\n", + "Soppy: MV 6.08 ± 0.79; FV 6.19 + 0.93\n", + "Gendrey: MV 5.92 ± 0.68; FV 5.92 + 0.58\n", + "Ferrari A.: MV 5.71 ± 1.10; FV 5.72 + 1.25\n", + "Zappacosta: MV 6.20 ± 0.81; FV 6.49 + 1.46\n", + "Bianchetti: MV 5.63 ± 0.93; FV 5.63 + 1.00\n", + "Ferrari G.: MV 5.66 ± 1.16; FV 5.67 + 1.32\n", + "Fazio: MV 5.77 ± 1.20; FV 5.80 + 1.38\n", + "Hateboer: MV 6.13 ± 0.94; FV 6.64 + 1.95\n", + "Rogerio: MV 5.63 ± 0.95; FV 5.66 + 0.93\n", + "Buongiorno: MV 5.85 ± 0.73; FV 5.79 + 0.62\n", + "Gunter: MV 5.67 ± 0.97; FV 5.68 + 1.08\n", + "Colley: MV 5.71 ± 1.14; FV 5.74 + 1.35\n", + "Troost-Ekong: MV 5.88 ± 0.84; FV 5.92 + 0.84\n", + "Terzic: MV 5.90 ± 0.56; FV 5.88 + 0.41\n", + "Toljan: MV 5.58 ± 0.95; FV 5.53 + 0.86\n", + "Zortea: MV 6.13 ± 0.83; FV 6.50 + 1.54\n", + "Soumaoro: MV 5.98 ± 0.82; FV 5.92 + 0.73\n", + "Amian: MV 5.75 ± 0.81; FV 5.68 + 0.76\n", + "Pongracic: MV 6.03 ± 0.80; FV 5.97 + 0.75\n", + "Birindelli: MV 5.75 ± 0.76; FV 5.73 + 0.80\n", + "Lucumi': MV 5.88 ± 0.70; FV 5.86 + 0.58\n", + "Hien: MV 5.68 ± 0.86; FV 5.57 + 0.77\n", + "Ehizibue: MV 6.06 ± 0.84; FV 6.42 + 1.57\n", + "Masina: MV 6.13 ± 0.84; FV 6.64 + 1.81\n", + "Gyomber: MV 5.79 ± 0.80; FV 5.71 + 0.68\n", + "Alex Sandro: MV 5.76 ± 1.06; FV 5.69 + 0.94\n", + "Pezzella Giu.: MV 5.95 ± 0.65; FV 5.98 + 0.54\n", + "Bereszynski: MV 5.74 ± 0.79; FV 5.69 + 0.72\n", + "Venuti: MV 5.59 ± 0.87; FV 5.54 + 0.82\n", + "Palomino: MV 6.20 ± 0.80; FV 6.44 + 1.27\n", + "Nuytinck: MV 5.79 ± 1.06; FV 5.74 + 1.08\n", + "Marlon: MV 5.64 ± 0.78; FV 5.53 + 0.66\n", + "Nikolaou: MV 5.70 ± 0.81; FV 5.58 + 0.65\n", + "Igor: MV 5.56 ± 1.02; FV 5.45 + 0.81\n", + "Okoli: MV 5.87 ± 0.68; FV 5.86 + 0.55\n", + "Dawidowicz: MV 5.64 ± 0.99; FV 5.65 + 1.07\n", + "Celik: MV 5.59 ± 0.85; FV 5.59 + 0.86\n", + "Bellanova: MV 5.93 ± 0.76; FV 6.02 + 0.82\n", + "Erlic: MV 5.71 ± 1.10; FV 5.66 + 1.11\n", + "Ballo-Toure': MV 6.20 ± 0.77; FV 6.44 + 1.27\n", + "Dest: MV 5.85 ± 0.80; FV 5.84 + 0.67\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stojanovic: MV 5.76 ± 0.87; FV 5.67 + 0.80\n", + "Bradaric: MV 5.65 ± 0.89; FV 5.57 + 0.90\n", + "Daniliuc: MV 5.73 ± 1.03; FV 5.67 + 0.99\n", + "Zima: MV 5.85 ± 0.75; FV 5.83 + 0.67\n", + "Quagliata: MV 5.80 ± 0.69; FV 5.84 + 0.66\n", + "Ebosse: MV 5.81 ± 0.66; FV 5.81 + 0.52\n", + "Aiwu: MV 5.78 ± 0.95; FV 5.83 + 1.06\n", + "Lochoshvili: MV 5.67 ± 0.85; FV 5.59 + 0.88\n", + "Bronn: MV 5.75 ± 0.71; FV 5.70 + 0.60\n", + "Thiaw: MV 6.04 ± 0.79; FV 6.11 + 0.79\n", + "Zeefuik: MV 5.73 ± 0.92; FV 5.74 + 0.99\n", + "Ghiglione: MV 5.70 ± 0.87; FV 5.72 + 1.02\n", + "Rugani: MV 6.00 ± 0.65; FV 6.02 + 0.52\n", + "De Sciglio: MV 5.92 ± 0.69; FV 5.99 + 0.63\n", + "Djimsiti: MV 6.13 ± 0.75; FV 6.22 + 0.87\n", + "Caldara: MV 5.70 ± 0.99; FV 5.60 + 0.82\n", + "Murru: MV 5.59 ± 0.94; FV 5.62 + 1.05\n", + "Karsdorp: MV 5.61 ± 0.90; FV 5.63 + 0.83\n", + "Bonifazi: MV 5.85 ± 0.70; FV 5.81 + 0.56\n", + "Kjaer: MV 5.94 ± 0.67; FV 5.93 + 0.52\n", + "Magnani: MV 5.59 ± 0.87; FV 5.47 + 0.71\n", + "Walukiewicz: MV 5.78 ± 0.85; FV 5.69 + 0.75\n", + "Ayhan: MV 5.63 ± 1.22; FV 5.61 + 1.28\n", + "Amione: MV 5.55 ± 0.90; FV 5.47 + 0.91\n", + "Ruggeri: MV 6.05 ± 0.66; FV 6.07 + 0.63\n", + "De Winter: MV 5.89 ± 0.84; FV 5.83 + 0.68\n", + "Ostigard: MV 5.92 ± 0.62; FV 5.94 + 0.52\n", + "Wisniewski: MV 5.82 ± 0.89; FV 5.81 + 0.87\n", + "Radovanovic: MV 5.61 ± 0.74; FV 5.50 + 0.62\n", + "Dermaku: MV 6.06 ± 0.85; FV 6.12 + 0.92\n", + "D'ambrosio: MV 6.13 ± 0.73; FV 6.24 + 0.91\n", + "De Silvestri: MV 5.99 ± 0.84; FV 6.18 + 1.06\n", + "Sala: MV 5.85 ± 0.80; FV 5.86 + 0.77\n", + "Chiriches: MV 5.67 ± 1.11; FV 5.58 + 0.98\n", + "Donati: MV 5.76 ± 1.03; FV 5.68 + 0.98\n", + "Gabbia: MV 5.87 ± 0.68; FV 5.80 + 0.55\n", + "Kumbulla: MV 5.60 ± 0.76; FV 5.60 + 0.75\n", + "Adopo: MV 5.95 ± 0.69; FV 6.00 + 0.59\n", + "Pirola: MV 5.55 ± 0.93; FV 5.44 + 0.74\n", + "Lovato: MV 5.63 ± 0.94; FV 5.54 + 0.77\n", + "Tuia: MV 5.90 ± 0.89; FV 6.01 + 1.07\n", + "Ferrer: MV 5.81 ± 0.87; FV 5.77 + 0.81\n", + "Antov: MV 5.61 ± 1.07; FV 5.53 + 0.97\n", + "Vasquez: MV 5.70 ± 0.89; FV 5.62 + 0.91\n", + "Zanoli: MV 5.82 ± 0.70; FV 5.83 + 0.56\n", + "Gatti: MV 5.88 ± 0.90; FV 5.81 + 0.78\n", + "Gila: MV 6.00 ± 0.91; FV 5.97 + 0.81\n", + "Sambia: MV 5.89 ± 0.79; FV 5.89 + 0.66\n", + "Moutinho J.: MV 5.84 ± 0.98; FV 5.80 + 0.89\n", + "Conti: MV 5.89 ± 1.03; FV 6.00 + 1.28\n", + "Marrone: MV 5.55 ± 0.96; FV 5.44 + 0.81\n", + "Tonelli: MV 5.67 ± 0.88; FV 5.55 + 0.75\n", + "Murillo: MV 5.53 ± 0.76; FV 5.44 + 0.69\n", + "Radu: MV 5.74 ± 1.03; FV 5.69 + 0.94\n", + "Paletta: MV 5.77 ± 0.93; FV 5.75 + 0.98\n", + "Florenzi: MV 6.15 ± 0.86; FV 6.39 + 1.27\n", + "Fares: MV 5.87 ± 0.74; FV 5.87 + 0.66\n", + "Marchizza: MV 5.64 ± 0.85; FV 5.64 + 0.85\n", + "Romagna: MV 5.76 ± 0.97; FV 5.84 + 1.03\n", + "Ranieri L.: MV 5.61 ± 0.82; FV 5.59 + 0.86\n", + "Cassandro: MV 6.07 ± 0.86; FV 6.16 + 0.96\n", + "Muldur: MV 5.65 ± 0.88; FV 5.62 + 0.81\n", + "Carboni: MV 5.64 ± 0.87; FV 5.53 + 0.80\n", + "Amey: MV 6.10 ± 0.80; FV 6.23 + 0.92\n", + "Ferrarini: MV 5.77 ± 0.93; FV 5.75 + 0.98\n", + "Vina: MV 5.62 ± 0.93; FV 5.62 + 0.89\n", + "Zanotti: MV 6.03 ± 0.78; FV 6.16 + 0.91\n", + "Ruan: MV 5.56 ± 1.03; FV 5.44 + 0.73\n", + "Coppola D.: MV 5.62 ± 0.83; FV 5.53 + 0.80\n", + "Cacace: MV 5.91 ± 0.72; FV 5.91 + 0.61\n", + "Bayeye: MV 5.93 ± 0.80; FV 6.01 + 0.81\n", + "Ebosele: MV 6.05 ± 0.82; FV 6.13 + 0.82\n", + "Buta: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Abankwah: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Guessand A.: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Cabal: MV 5.85 ± 0.85; FV 5.91 + 0.84\n", + "Sosa: MV 5.72 ± 0.85; FV 5.63 + 0.70\n", + "Guarino: MV 5.94 ± 0.86; FV 6.00 + 0.87\n", + "Carboni F.: MV 5.78 ± 0.93; FV 5.75 + 0.95\n", + "Zaccagni: MV 6.49 ± 1.38; FV 8.19 + 5.10\n", + "Kvaratskhelia: MV 6.52 ± 1.50; FV 8.50 + 5.86\n", + "Milinkovic-Savic: MV 6.37 ± 1.30; FV 7.62 + 3.90\n", + "Barella: MV 6.36 ± 1.24; FV 7.47 + 3.54\n", + "Zielinski: MV 6.34 ± 0.96; FV 6.90 + 2.14\n", + "Luis Alberto: MV 6.38 ± 1.14; FV 7.22 + 2.91\n", + "Strefezza: MV 6.41 ± 1.17; FV 7.50 + 3.48\n", + "Felipe Anderson: MV 6.29 ± 1.30; FV 7.52 + 3.86\n", + "Diaz B.: MV 6.27 ± 1.32; FV 7.49 + 3.83\n", + "Koopmeiners: MV 6.49 ± 1.29; FV 7.84 + 4.20\n", + "Vlasic: MV 6.21 ± 1.16; FV 6.94 + 2.59\n", + "Calhanoglu: MV 6.35 ± 1.07; FV 7.05 + 2.53\n", + "Frattesi: MV 6.01 ± 1.11; FV 6.49 + 1.94\n", + "Zambo Anguissa: MV 6.30 ± 1.01; FV 6.89 + 2.22\n", + "Elmas: MV 6.24 ± 1.00; FV 6.96 + 2.49\n", + "Pereyra: MV 6.33 ± 1.09; FV 7.11 + 2.68\n", + "Miranchuk: MV 6.20 ± 1.24; FV 7.04 + 2.89\n", + "Samardzic: MV 6.31 ± 1.09; FV 7.13 + 2.78\n", + "Politano: MV 6.25 ± 0.91; FV 6.74 + 1.91\n", + "Rabiot: MV 6.21 ± 1.08; FV 6.87 + 2.34\n", + "Radonjic: MV 6.16 ± 1.04; FV 6.74 + 2.17\n", + "Lobotka: MV 6.18 ± 0.75; FV 6.43 + 1.21\n", + "Tonali: MV 6.31 ± 1.11; FV 6.98 + 2.46\n", + "Lazovic: MV 6.07 ± 1.10; FV 6.60 + 2.05\n", + "Bonaventura: MV 5.88 ± 0.96; FV 6.04 + 1.20\n", + "Pellegrini Lo.: MV 5.88 ± 1.12; FV 6.09 + 1.49\n", + "Pessina: MV 5.93 ± 0.97; FV 6.18 + 1.40\n", + "Kostic: MV 6.19 ± 1.02; FV 6.68 + 1.91\n", + "Lovric: MV 6.25 ± 0.89; FV 6.71 + 1.82\n", + "Ferguson: MV 6.24 ± 1.04; FV 6.88 + 2.25\n", + "Ciurria: MV 5.98 ± 1.08; FV 6.39 + 1.75\n", + "Ikone': MV 5.83 ± 1.05; FV 6.07 + 1.40\n", + "Baldanzi: MV 6.25 ± 1.04; FV 6.98 + 2.53\n", + "Candreva: MV 6.05 ± 1.09; FV 6.36 + 1.69\n", + "Bennacer: MV 6.18 ± 0.79; FV 6.40 + 1.17\n", + "El Shaarawy: MV 5.90 ± 0.85; FV 6.07 + 1.08\n", + "Pasalic: MV 6.31 ± 1.24; FV 7.44 + 3.58\n", + "Orsolini: MV 6.27 ± 1.34; FV 7.49 + 3.84\n", + "Bandinelli: MV 6.01 ± 0.85; FV 6.23 + 1.19\n", + "Mkhitaryan: MV 6.16 ± 1.04; FV 6.73 + 2.09\n", + "Colpani: MV 5.94 ± 0.83; FV 6.10 + 1.22\n", + "Pogba: MV 6.06 ± 0.93; FV 6.21 + 1.15\n", + "Sensi: MV 5.88 ± 1.05; FV 5.99 + 1.28\n", + "Chiesa: MV 6.24 ± 1.07; FV 6.82 + 2.22\n", + "Lukic: MV 6.07 ± 1.00; FV 6.45 + 1.63\n", + "Fagioli: MV 6.21 ± 1.02; FV 6.77 + 2.08\n", + "Messias: MV 6.15 ± 1.21; FV 6.97 + 2.88\n", + "Arslan: MV 6.03 ± 0.70; FV 6.12 + 0.77\n", + "Brozovic: MV 6.18 ± 0.87; FV 6.56 + 1.59\n", + "Verdi: MV 6.03 ± 0.87; FV 6.35 + 1.48\n", + "Barak: MV 5.71 ± 0.90; FV 5.81 + 1.03\n", + "Matic: MV 5.86 ± 0.85; FV 5.98 + 1.05\n", + "Soriano: MV 6.07 ± 0.86; FV 6.27 + 1.14\n", + "Dominguez: MV 6.19 ± 1.10; FV 6.78 + 2.22\n", + "Ranocchia F.: MV 5.88 ± 0.84; FV 6.01 + 1.11\n", + "Cristante: MV 5.70 ± 0.91; FV 5.75 + 0.96\n", + "Bajrami: MV 5.94 ± 0.94; FV 6.18 + 1.28\n", + "Ricci S.: MV 6.04 ± 0.71; FV 6.11 + 0.70\n", + "Thorstvedt: MV 5.86 ± 0.86; FV 6.01 + 1.13\n", + "De Ketelaere: MV 5.97 ± 0.73; FV 6.03 + 0.73\n", + "Saponara: MV 5.85 ± 1.02; FV 6.09 + 1.37\n", + "Vecino: MV 6.05 ± 0.91; FV 6.24 + 1.15\n", + "Locatelli: MV 6.09 ± 0.74; FV 6.20 + 0.82\n", + "Zaniolo: MV 5.66 ± 0.88; FV 5.75 + 1.00\n", + "Traore' Hj.: MV 5.89 ± 0.99; FV 6.19 + 1.48\n", + "Maldini: MV 5.97 ± 1.00; FV 6.32 + 1.68\n", + "Coulibaly L.: MV 5.93 ± 1.13; FV 6.23 + 1.75\n", + "Gonzalez J.: MV 6.11 ± 1.03; FV 6.62 + 1.98\n", + "Vilhena: MV 5.78 ± 0.91; FV 5.96 + 1.19\n", + "De Roon: MV 6.08 ± 0.68; FV 6.13 + 0.68\n", + "Mandragora: MV 5.76 ± 0.87; FV 5.76 + 0.93\n", + "Haas: MV 5.98 ± 0.81; FV 6.19 + 1.20\n", + "Wijnaldum: MV 5.70 ± 0.95; FV 5.74 + 1.00\n", + "Bourabia: MV 5.86 ± 0.81; FV 5.87 + 0.78\n", + "Sottil: MV 5.87 ± 1.02; FV 6.10 + 1.39\n", + "Agudelo: MV 5.80 ± 0.72; FV 5.83 + 0.69\n", + "Makengo: MV 6.00 ± 0.64; FV 6.04 + 0.58\n", + "Zalewski: MV 5.78 ± 0.75; FV 5.81 + 0.79\n", + "Aebischer: MV 6.03 ± 0.90; FV 6.40 + 1.51\n", + "Ederson D.s.: MV 6.12 ± 0.95; FV 6.45 + 1.55\n", + "Miretti: MV 6.00 ± 0.75; FV 6.09 + 0.81\n", + "Blin: MV 6.03 ± 0.60; FV 6.03 + 0.49\n", + "Hjulmand: MV 6.15 ± 1.00; FV 6.30 + 1.37\n", + "Cataldi: MV 5.97 ± 0.67; FV 5.97 + 0.54\n", + "Paredes: MV 5.83 ± 0.74; FV 5.80 + 0.60\n", + "Djuricic: MV 5.66 ± 1.04; FV 5.86 + 1.30\n", + "Linetty: MV 5.90 ± 0.88; FV 6.06 + 1.08\n", + "Walace: MV 5.96 ± 0.67; FV 5.99 + 0.55\n", + "Marin: MV 5.85 ± 1.00; FV 5.89 + 1.06\n", + "Mckennie: MV 5.89 ± 0.86; FV 6.11 + 1.29\n", + "Pobega: MV 6.15 ± 0.92; FV 6.50 + 1.61\n", + "Volpato: MV 5.69 ± 1.13; FV 5.69 + 1.19\n", + "Camara Ma.: MV 5.93 ± 0.78; FV 5.97 + 0.73\n", + "Cuadrado: MV 6.05 ± 0.98; FV 6.20 + 1.24\n", + "Ndombele': MV 5.88 ± 0.64; FV 5.90 + 0.49\n", + "Amrabat: MV 5.73 ± 0.93; FV 5.65 + 0.86\n", + "Tameze: MV 5.75 ± 0.86; FV 5.81 + 0.99\n", + "Gyasi: MV 5.86 ± 1.14; FV 6.22 + 1.76\n", + "Ilic: MV 5.84 ± 0.86; FV 5.88 + 0.94\n", + "Harroui: MV 5.81 ± 0.80; FV 5.93 + 1.07\n", + "Pickel: MV 5.74 ± 0.77; FV 5.80 + 0.94\n", + "Moro N.: MV 5.96 ± 0.65; FV 6.05 + 0.64\n", + "Duncan: MV 5.72 ± 0.82; FV 5.74 + 0.89\n", + "Miguel Veloso: MV 5.84 ± 0.73; FV 5.84 + 0.72\n", + "Ekdal: MV 5.77 ± 0.69; FV 5.72 + 0.62\n", + "Nicolussi Caviglia: MV 5.83 ± 0.95; FV 6.10 + 1.37\n", + "Schouten: MV 5.93 ± 0.69; FV 5.98 + 0.59\n", + "Rovella: MV 5.81 ± 1.04; FV 5.75 + 1.02\n", + "Crnigoj: MV 5.89 ± 0.82; FV 6.07 + 1.05\n", + "Romero L.: MV 6.27 ± 0.98; FV 6.84 + 2.13\n", + "Basic: MV 6.00 ± 0.66; FV 6.03 + 0.59\n", + "Sabiri: MV 5.69 ± 1.01; FV 5.89 + 1.32\n", + "Terracciano F.: MV 5.98 ± 0.63; FV 5.99 + 0.59\n", + "Castagnetti: MV 5.75 ± 0.67; FV 5.77 + 0.61\n", + "Oudin: MV 6.03 ± 0.63; FV 6.07 + 0.57\n", + "D'alessandro: MV 5.96 ± 0.65; FV 5.93 + 0.62\n", + "Grassi: MV 5.94 ± 0.67; FV 5.95 + 0.57\n", + "Krunic: MV 6.00 ± 0.64; FV 6.03 + 0.57\n", + "Rincon: MV 5.65 ± 0.92; FV 5.66 + 0.98\n", + "Machin: MV 5.81 ± 0.91; FV 5.80 + 1.00\n", + "Leris: MV 5.67 ± 0.83; FV 5.78 + 1.07\n", + "Meite': MV 5.73 ± 0.86; FV 5.66 + 0.87\n", + "Vieira: MV 5.64 ± 0.89; FV 5.72 + 1.08\n", + "Esposito Sa.: MV 5.82 ± 0.85; FV 5.77 + 0.80\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Henderson L.: MV 5.89 ± 0.73; FV 5.94 + 0.78\n", + "Obiang: MV 5.85 ± 0.68; FV 5.87 + 0.74\n", + "Lopez M.: MV 5.80 ± 0.84; FV 5.83 + 0.81\n", + "Saelemaekers: MV 6.03 ± 0.81; FV 6.14 + 0.97\n", + "Maggiore: MV 5.91 ± 0.76; FV 6.02 + 0.90\n", + "Akpa Akpro: MV 5.95 ± 0.89; FV 6.02 + 0.94\n", + "Kovalenko: MV 5.89 ± 0.76; FV 5.99 + 0.85\n", + "Maleh: MV 6.10 ± 0.89; FV 6.46 + 1.67\n", + "Matheus Henrique: MV 5.74 ± 0.77; FV 5.77 + 0.77\n", + "Asllani: MV 5.99 ± 0.64; FV 6.02 + 0.57\n", + "Ceide: MV 5.75 ± 0.81; FV 5.79 + 0.77\n", + "Gagliardini: MV 5.93 ± 0.65; FV 5.99 + 0.67\n", + "Bove: MV 5.62 ± 0.68; FV 5.64 + 0.75\n", + "Bianco: MV 5.75 ± 0.86; FV 5.71 + 0.89\n", + "Vranckx: MV 5.94 ± 0.69; FV 5.96 + 0.61\n", + "Marcos Antonio: MV 5.90 ± 0.59; FV 5.88 + 0.42\n", + "Fazzini: MV 5.79 ± 0.66; FV 5.70 + 0.59\n", + "D'andrea: MV 5.94 ± 0.77; FV 6.07 + 1.10\n", + "Sulemana I.: MV 5.78 ± 0.62; FV 5.77 + 0.62\n", + "Tahirovic: MV 5.69 ± 0.73; FV 5.72 + 0.77\n", + "Benassi: MV 5.74 ± 0.70; FV 5.77 + 0.78\n", + "Barberis: MV 5.73 ± 0.85; FV 5.68 + 0.78\n", + "Kastanos: MV 5.78 ± 0.66; FV 5.82 + 0.60\n", + "Vignato: MV 5.93 ± 0.71; FV 6.03 + 0.68\n", + "Valoti: MV 5.75 ± 0.69; FV 5.69 + 0.63\n", + "Winks: MV 5.71 ± 0.87; FV 5.67 + 0.87\n", + "Gaetano: MV 5.95 ± 0.82; FV 6.06 + 0.90\n", + "Zurkowski: MV 6.01 ± 1.11; FV 6.45 + 1.92\n", + "Castrovilli: MV 5.79 ± 0.92; FV 5.85 + 1.04\n", + "Bohinen: MV 5.74 ± 0.59; FV 5.76 + 0.49\n", + "Hrustic: MV 5.53 ± 0.66; FV 5.56 + 0.61\n", + "Iling-Junior: MV 6.00 ± 0.77; FV 6.07 + 0.78\n", + "Capezzi: MV 5.85 ± 0.71; FV 5.91 + 0.69\n", + "Molina S.: MV 5.80 ± 0.88; FV 5.85 + 1.04\n", + "Bakayoko: MV 5.86 ± 0.70; FV 5.77 + 0.60\n", + "Scozzarella: MV 5.81 ± 0.91; FV 5.80 + 1.00\n", + "Demme: MV 6.03 ± 0.80; FV 6.29 + 1.31\n", + "Askildsen: MV 5.71 ± 0.62; FV 5.75 + 0.52\n", + "Darboe: MV 5.73 ± 1.01; FV 5.75 + 1.00\n", + "Urbanski: MV 6.05 ± 0.78; FV 6.17 + 0.87\n", + "Bertini: MV 6.07 ± 0.88; FV 6.17 + 0.97\n", + "Hongla: MV 5.62 ± 0.69; FV 5.66 + 0.80\n", + "Yepes: MV 5.60 ± 0.94; FV 5.51 + 0.88\n", + "Trimboli: MV 5.78 ± 0.93; FV 5.78 + 1.00\n", + "Pafundi: MV 6.06 ± 0.80; FV 6.18 + 0.88\n", + "Milanese: MV 5.76 ± 0.91; FV 5.76 + 0.99\n", + "Helgason: MV 5.84 ± 0.64; FV 5.88 + 0.50\n", + "Adli: MV 5.95 ± 0.81; FV 5.98 + 0.83\n", + "Vignato S.: MV 5.77 ± 0.93; FV 5.72 + 0.91\n", + "Bondo: MV 5.81 ± 0.86; FV 5.75 + 0.85\n", + "Samek: MV 6.08 ± 0.88; FV 6.20 + 1.04\n", + "Zerbin: MV 5.99 ± 0.73; FV 6.07 + 0.81\n", + "Ilkhan: MV 5.85 ± 0.84; FV 5.84 + 0.77\n", + "Degli Innocenti: MV 5.97 ± 0.92; FV 6.04 + 0.99\n", + "Acella: MV 5.83 ± 0.93; FV 5.90 + 1.09\n", + "Garbett: MV 5.94 ± 0.81; FV 6.04 + 0.86\n", + "Carboni V.: MV 5.99 ± 0.75; FV 6.08 + 0.79\n", + "Cipot: MV 5.87 ± 0.86; FV 5.89 + 0.89\n", + "Osimhen: MV 6.55 ± 1.57; FV 8.64 + 6.25\n", + "Dybala: MV 6.39 ± 1.26; FV 7.60 + 3.82\n", + "Martinez L.: MV 6.44 ± 1.50; FV 8.14 + 5.20\n", + "Rafael Leao: MV 6.45 ± 1.47; FV 8.26 + 5.32\n", + "Immobile: MV 6.45 ± 1.53; FV 8.33 + 5.60\n", + "Lookman: MV 6.58 ± 1.53; FV 8.60 + 6.09\n", + "Vlahovic: MV 6.34 ± 1.50; FV 7.90 + 4.79\n", + "Arnautovic: MV 6.40 ± 1.42; FV 7.98 + 4.76\n", + "Dia: MV 6.07 ± 1.39; FV 6.92 + 3.09\n", + "Dzeko: MV 6.32 ± 1.46; FV 7.72 + 4.44\n", + "Milik: MV 6.29 ± 1.18; FV 7.33 + 3.31\n", + "Nzola: MV 6.09 ± 1.43; FV 6.99 + 3.28\n", + "Beto: MV 6.26 ± 1.40; FV 7.56 + 4.11\n", + "Abraham: MV 6.03 ± 1.26; FV 6.74 + 2.63\n", + "Giroud: MV 6.29 ± 1.39; FV 7.67 + 4.22\n", + "Deulofeu: MV 6.43 ± 1.30; FV 7.80 + 4.18\n", + "Correa: MV 6.22 ± 1.21; FV 7.14 + 3.12\n", + "Pedro: MV 6.22 ± 1.09; FV 6.83 + 2.28\n", + "Lozano: MV 6.22 ± 1.16; FV 7.12 + 3.00\n", + "Lukaku: MV 6.16 ± 1.33; FV 7.09 + 3.20\n", + "Lauriente': MV 5.99 ± 1.09; FV 6.34 + 1.75\n", + "Simeone: MV 6.28 ± 1.40; FV 7.64 + 4.23\n", + "Cabral: MV 5.88 ± 1.07; FV 6.20 + 1.62\n", + "Rebic: MV 6.25 ± 1.37; FV 7.46 + 3.87\n", + "Bonazzoli: MV 5.97 ± 1.12; FV 6.42 + 1.92\n", + "Berardi: MV 6.21 ± 1.36; FV 7.32 + 3.63\n", + "Caprari: MV 5.93 ± 1.08; FV 6.29 + 1.71\n", + "Di Maria: MV 6.29 ± 1.36; FV 7.60 + 4.01\n", + "Piatek: MV 5.91 ± 1.22; FV 6.37 + 2.09\n", + "Kouame': MV 5.93 ± 1.10; FV 6.32 + 1.75\n", + "Hojlund: MV 6.35 ± 1.40; FV 7.76 + 4.39\n", + "Sanabria: MV 6.05 ± 1.24; FV 6.74 + 2.59\n", + "Zapata D.: MV 6.27 ± 1.29; FV 7.45 + 3.75\n", + "Kean: MV 6.12 ± 1.26; FV 6.96 + 2.90\n", + "Ceesay: MV 6.18 ± 1.24; FV 7.09 + 3.07\n", + "Pinamonti: MV 5.79 ± 1.06; FV 6.12 + 1.59\n", + "Gonzalez N.: MV 6.08 ± 1.17; FV 6.67 + 2.30\n", + "Colombo: MV 6.22 ± 1.38; FV 7.41 + 3.81\n", + "Mota: MV 5.91 ± 1.14; FV 6.29 + 1.76\n", + "Dessers: MV 5.94 ± 0.99; FV 6.33 + 1.76\n", + "Muriel: MV 6.39 ± 1.38; FV 7.83 + 4.39\n", + "Okereke: MV 5.82 ± 1.07; FV 6.15 + 1.61\n", + "Alvarez A.: MV 5.92 ± 1.06; FV 6.29 + 1.65\n", + "Jovic: MV 5.72 ± 1.11; FV 5.98 + 1.45\n", + "Barrow: MV 6.15 ± 1.32; FV 7.10 + 3.22\n", + "Caputo: MV 6.01 ± 1.23; FV 6.63 + 2.43\n", + "Banda: MV 6.08 ± 0.96; FV 6.48 + 1.77\n", + "Ciofani D.: MV 6.05 ± 1.08; FV 6.63 + 2.25\n", + "Petagna: MV 5.82 ± 0.92; FV 5.99 + 1.25\n", + "Di Francesco F.: MV 6.17 ± 1.26; FV 7.04 + 2.97\n", + "Boga: MV 6.53 ± 1.38; FV 7.97 + 4.49\n", + "Cambiaghi: MV 6.07 ± 0.91; FV 6.38 + 1.42\n", + "Djuric: MV 5.97 ± 0.78; FV 6.16 + 1.27\n", + "Henry: MV 5.79 ± 1.06; FV 6.17 + 1.72\n", + "Success: MV 6.05 ± 0.83; FV 6.23 + 1.16\n", + "Gabbiadini: MV 5.72 ± 1.08; FV 5.98 + 1.46\n", + "Origi: MV 6.05 ± 1.15; FV 6.64 + 2.32\n", + "Lammers: MV 5.80 ± 0.91; FV 6.03 + 1.40\n", + "Satriano: MV 5.86 ± 0.94; FV 6.13 + 1.44\n", + "Zirkzee: MV 6.13 ± 1.18; FV 6.96 + 2.83\n", + "Kallon: MV 5.81 ± 0.85; FV 6.05 + 1.42\n", + "Solbakken: MV 5.73 ± 1.00; FV 5.77 + 1.03\n", + "Lasagna: MV 5.71 ± 0.90; FV 5.98 + 1.50\n", + "Nestorovski: MV 6.15 ± 0.93; FV 6.78 + 2.38\n", + "Pellegri: MV 5.85 ± 0.99; FV 6.18 + 1.67\n", + "Botheim: MV 5.85 ± 1.02; FV 6.20 + 1.64\n", + "Gytkjaer: MV 5.75 ± 0.80; FV 5.87 + 1.11\n", + "Belotti: MV 5.57 ± 0.69; FV 5.65 + 0.87\n", + "Raspadori: MV 6.01 ± 0.92; FV 6.46 + 1.84\n", + "Buonaiuto: MV 5.97 ± 0.73; FV 6.03 + 0.80\n", + "Verde: MV 6.00 ± 1.07; FV 6.44 + 1.88\n", + "Destro: MV 5.94 ± 1.22; FV 6.39 + 2.09\n", + "Sansone: MV 6.11 ± 1.12; FV 6.77 + 2.42\n", + "Pjaca: MV 5.89 ± 0.80; FV 5.99 + 0.83\n", + "Zanimacchia: MV 5.80 ± 0.74; FV 5.84 + 0.83\n", + "Strelec: MV 5.90 ± 0.65; FV 5.90 + 0.61\n", + "Seck: MV 6.00 ± 0.62; FV 6.00 + 0.55\n", + "Quagliarella: MV 5.75 ± 0.88; FV 5.95 + 1.33\n", + "Piccoli: MV 5.79 ± 0.85; FV 6.07 + 1.53\n", + "Tsadjout: MV 5.92 ± 0.83; FV 6.11 + 1.26\n", + "Shomurodov: MV 5.85 ± 1.00; FV 6.13 + 1.47\n", + "Cancellieri: MV 5.87 ± 0.60; FV 5.84 + 0.45\n", + "Antiste: MV 5.76 ± 0.89; FV 5.93 + 1.20\n", + "Afena-Gyan: MV 5.60 ± 0.72; FV 5.65 + 0.82\n", + "Soule': MV 6.01 ± 0.99; FV 6.18 + 1.24\n", + "Ngonge: MV 5.77 ± 0.91; FV 5.81 + 1.06\n", + "Defrel: MV 5.72 ± 0.77; FV 5.82 + 1.05\n", + "Karamoh: MV 5.88 ± 0.67; FV 5.90 + 0.57\n", + "Ibrahimovic: MV 6.43 ± 1.41; FV 8.05 + 4.89\n", + "Pussetto: MV 5.79 ± 0.97; FV 6.05 + 1.48\n", + "Voelkerling Persson: MV 6.09 ± 0.83; FV 6.16 + 0.90\n", + "Valencia D.: MV 5.65 ± 0.66; FV 5.69 + 0.79\n", + "Edera: MV 5.91 ± 0.83; FV 5.97 + 0.85\n", + "Oddei: MV 5.91 ± 0.85; FV 6.03 + 1.06\n", + "Braaf: MV 5.89 ± 1.08; FV 6.22 + 1.59\n", + "Raimondo: MV 6.11 ± 0.83; FV 6.25 + 1.00\n", + "Kaio Jorge: MV 6.00 ± 0.75; FV 6.06 + 0.75\n", + "De Luca: MV 5.81 ± 0.94; FV 5.82 + 1.02\n", + "Lazetic: MV 6.04 ± 0.88; FV 6.14 + 1.03\n", + "Montevago: MV 5.62 ± 0.70; FV 5.70 + 0.99\n", + "Krollis: MV 5.83 ± 0.90; FV 5.83 + 0.93\n" + ] + }, + { + "data": { + "text/html": [ + "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
MussoPAtalantaSampdoria11.0906.3056400.3774085.7575360.6199936.1804760.3949400.2323101.2101176.3057230.762046-0.5034791.19027270.224476
Rossi F.PAtalantaSampdoria10.016.3056400.3774085.7575360.6199936.1804760.3949400.2323101.2101176.3057230.762046-0.5034791.19027270.224476
SportielloPAtalantaSampdoria10.056.1931150.3585485.6508500.6378856.0949470.3877890.1864541.2211286.2292890.770664-0.5227611.19812068.716019
ScalviniDAtalantaSampdoria11.0806.2977600.5080606.9087791.1318256.2526110.5927020.0563761.1037865.8462351.1220850.5724021.9998180.000000
HateboerDAtalantaSampdoria11.0806.1338400.4715916.6352500.9762416.0115080.5152270.1748931.1530115.7474611.0471800.5281171.9997840.000000
............................................................
DjuricAVeronaUdinese01.0905.9726280.3884656.1630570.6349405.8338130.3964620.2558971.2751095.6613060.8501970.3968841.9996880.000000
PiccoliAVeronaUdinese00.0555.7918790.4271786.0671330.7644885.6358270.4358090.2615411.2372025.4011850.8926260.4792681.9996860.000000
KallonAVeronaUdinese00.4555.8051680.4232216.0472780.7098775.6634240.4408570.2356091.2340815.4522990.8813910.4429271.9996810.000000
LasagnaAVeronaUdinese00.6605.7092090.4486415.9820990.7492685.5540410.4652920.2441181.2105995.3381850.8951070.4658371.9996740.000000
NgongeAVeronaUdinese00.0255.7704540.4528625.8095380.5280655.7445370.5381300.0357481.1977535.5227810.9059100.2279521.9994110.000000
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547 rows × 19 columns

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" + ], + "text/plain": [ + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Sampdoria 1 1.0 90 6.305640 0.377408 \n", + "Rossi F. P Atalanta Sampdoria 1 0.0 1 6.305640 0.377408 \n", + "Sportiello P Atalanta Sampdoria 1 0.0 5 6.193115 0.358548 \n", + "Scalvini D Atalanta Sampdoria 1 1.0 80 6.297760 0.508060 \n", + "Hateboer D Atalanta Sampdoria 1 1.0 80 6.133840 0.471591 \n", + "... ... ... ... ... ... ... ... ... \n", + "Djuric A Verona Udinese 0 1.0 90 5.972628 0.388465 \n", + "Piccoli A Verona Udinese 0 0.0 55 5.791879 0.427178 \n", + "Kallon A Verona Udinese 0 0.4 55 5.805168 0.423221 \n", + "Lasagna A Verona Udinese 0 0.6 60 5.709209 0.448641 \n", + "Ngonge A Verona Udinese 0 0.0 25 5.770454 0.452862 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Musso 5.757536 0.619993 6.180476 0.394940 0.232310 \n", + "Rossi F. 5.757536 0.619993 6.180476 0.394940 0.232310 \n", + "Sportiello 5.650850 0.637885 6.094947 0.387789 0.186454 \n", + "Scalvini 6.908779 1.131825 6.252611 0.592702 0.056376 \n", + "Hateboer 6.635250 0.976241 6.011508 0.515227 0.174893 \n", + "... ... ... ... ... ... \n", + "Djuric 6.163057 0.634940 5.833813 0.396462 0.255897 \n", + "Piccoli 6.067133 0.764488 5.635827 0.435809 0.261541 \n", + "Kallon 6.047278 0.709877 5.663424 0.440857 0.235609 \n", + "Lasagna 5.982099 0.749268 5.554041 0.465292 0.244118 \n", + "Ngonge 5.809538 0.528065 5.744537 0.538130 0.035748 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Musso 1.210117 6.305723 0.762046 -0.503479 1.190272 \n", + "Rossi F. 1.210117 6.305723 0.762046 -0.503479 1.190272 \n", + "Sportiello 1.221128 6.229289 0.770664 -0.522761 1.198120 \n", + "Scalvini 1.103786 5.846235 1.122085 0.572402 1.999818 \n", + "Hateboer 1.153011 5.747461 1.047180 0.528117 1.999784 \n", + "... ... ... ... ... ... \n", + "Djuric 1.275109 5.661306 0.850197 0.396884 1.999688 \n", + "Piccoli 1.237202 5.401185 0.892626 0.479268 1.999686 \n", + "Kallon 1.234081 5.452299 0.881391 0.442927 1.999681 \n", + "Lasagna 1.210599 5.338185 0.895107 0.465837 1.999674 \n", + "Ngonge 1.197753 5.522781 0.905910 0.227952 1.999411 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Musso 70.224476 \n", + "Rossi F. 70.224476 \n", + "Sportiello 68.716019 \n", + "Scalvini 0.000000 \n", + "Hateboer 0.000000 \n", + "... ... \n", + "Djuric 0.000000 \n", + "Piccoli 0.000000 \n", + "Kallon 0.000000 \n", + "Lasagna 0.000000 \n", + "Ngonge 0.000000 \n", + "\n", + "[547 rows x 19 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matchday_out = 20\n", + "\n", + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " try:\n", + " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + " \n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", + " \n", + " role = players['r'][player] \n", + " \n", + " starter = 0\n", + " voteperc = 0\n", + " \n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + " \n", + " if(player in probables.index):\n", + " starter = probables['starter'][player]\n", + " voteperc = probables['percentage'][player]\n", + " \n", + " row = [player, role, team, oppteam, home, \n", + " starter, voteperc, \n", + " mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs]\n", + " \n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + " \n", + " output = pd.concat([output, row_df])\n", + " \n", + " except:\n", + " print(players.index[i] + ' no data')\n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "6befd611", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "eed7a7ac", + "metadata": {}, + "source": [ + "Predict average Serie A performance for each player" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "2b637a15", + "metadata": {}, + "outputs": [], + "source": [ + "gk_starters = ['Maignan', 'Sepe', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", + " 'Terracciano', 'Handanovic', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Radu I.', 'Rui Patricio',\n", + " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "60d73507", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret (6.03, 0.43); (5.52, 0.72)\n", + "Provedel (6.26, 0.37); (5.68, 0.61)\n", + "Vicario (6.45, 0.42); (5.69, 0.59)\n", + "Szczesny (6.19, 0.38); (5.60, 0.67)\n", + "Falcone (6.33, 0.38); (5.04, 0.64)\n", + "Silvestri (6.38, 0.40); (5.28, 0.63)\n", + "Rui Patricio (5.95, 0.36); (5.14, 0.73)\n", + "Milinkovic-Savic V. (6.32, 0.38); (5.23, 0.61)\n", + "Sepe (6.40, 0.40); (5.11, 0.68)\n", + "Maignan (6.36, 0.39); (5.42, 0.62)\n", + "Onana (6.21, 0.36); (4.73, 0.75)\n", + "Audero (6.33, 0.38); (4.48, 0.77)\n", + "Musso (6.25, 0.38); (5.53, 0.63)\n", + "Carnesecchi (6.39, 0.42); (5.37, 0.67)\n", + "Montipo' (6.31, 0.37); (4.50, 0.75)\n", + "Di Gregorio (6.23, 0.37); (4.28, 0.83)\n", + "Tatarusanu (5.99, 0.33); (4.03, 0.84)\n", + "Consigli (6.23, 0.35); (4.79, 0.70)\n", + "Dragowski (6.29, 0.37); (4.85, 0.69)\n", + "Terracciano (6.02, 0.37); (3.84, 0.97)\n", + "Sportiello (6.07, 0.38); (5.43, 0.69)\n", + "Skorupski (6.19, 0.36); (4.36, 0.78)\n", + "Perin (6.33, 0.44); (5.76, 0.66)\n", + "Handanovic (6.04, 0.37); (4.27, 0.89)\n", + "Zoet (6.35, 0.40); (5.74, 0.61)\n", + "Gollini (6.03, 0.43); (5.52, 0.72)\n", + "Ochoa (6.41, 0.41); (5.09, 0.70)\n", + "Pegolo (6.31, 0.39); (5.34, 0.68)\n", + "Mirante (6.36, 0.39); (5.42, 0.62)\n", + "Sarr M. (6.39, 0.42); (5.37, 0.67)\n", + "Lamanna (6.23, 0.37); (4.28, 0.83)\n", + "Ujkani (6.45, 0.42); (5.69, 0.59)\n", + "Berisha (6.32, 0.38); (5.23, 0.61)\n", + "Marchetti (6.29, 0.37); (4.85, 0.69)\n", + "Perilli (6.31, 0.37); (4.50, 0.75)\n", + "Padelli (6.38, 0.40); (5.28, 0.63)\n", + "Perisan (6.45, 0.42); (5.69, 0.59)\n", + "Bardi (6.19, 0.36); (4.36, 0.78)\n", + "Cordaz (6.21, 0.36); (4.73, 0.75)\n", + "Pinsoglio (6.19, 0.38); (5.60, 0.67)\n", + "Fiorillo (6.40, 0.40); (5.11, 0.68)\n", + "Cragno (6.23, 0.37); (4.28, 0.83)\n", + "Sirigu (6.02, 0.37); (3.84, 0.97)\n", + "Cerofolini (6.02, 0.37); (3.84, 0.97)\n", + "Rossi F. (6.25, 0.38); (5.53, 0.63)\n", + "Ravaglia F. (6.19, 0.36); (4.36, 0.78)\n", + "Brancolini (6.33, 0.38); (5.04, 0.64)\n", + "Bleve (6.33, 0.38); (5.04, 0.64)\n", + "Berardi A. (6.31, 0.37); (4.50, 0.75)\n", + "Russo A. (6.23, 0.35); (4.79, 0.70)\n", + "Gemello (6.32, 0.38); (5.23, 0.61)\n", + "Ravaglia (6.33, 0.38); (4.48, 0.77)\n", + "Boer (5.95, 0.36); (5.14, 0.73)\n", + "Adamonis (6.26, 0.37); (5.68, 0.61)\n", + "Marfella (6.03, 0.43); (5.52, 0.72)\n", + "Zovko (6.28, 0.38); (4.77, 0.73)\n", + "Piana (6.38, 0.40); (5.28, 0.63)\n", + "Bagnolini (6.19, 0.36); (4.36, 0.78)\n", + "Luis Maximiano (5.48, 0.37); (5.14, 0.81)\n", + "Svilar (5.95, 0.36); (5.14, 0.73)\n", + "Sorrentino A. (6.23, 0.37); (4.28, 0.83)\n", + "Ciezkowski (6.39, 0.42); (5.37, 0.67)\n", + "Chiesa M. (6.31, 0.37); (4.50, 0.75)\n", + "Saro (6.39, 0.42); (5.37, 0.67)\n", + "Vasquez D. (6.38, 0.41); (5.58, 0.64)\n", + "Turk (6.33, 0.38); (4.48, 0.77)\n", + "Dimarco (6.23, 0.51); (6.79, 1.05)\n", + "Smalling (6.23, 0.50); (6.73, 0.97)\n", + "Doig (6.23, 0.50); (6.75, 1.00)\n", + "Carlos Augusto (6.04, 0.53); (6.47, 0.94)\n", + "Kim (6.31, 0.50); (6.92, 1.13)\n", + "Hernandez T. (6.19, 0.54); (6.76, 1.11)\n", + "Udogie (6.15, 0.54); (6.74, 1.13)\n", + "Parisi (6.18, 0.44); (6.52, 0.77)\n", + "Romagnoli (6.19, 0.49); (6.52, 0.82)\n", + "Di Lorenzo (6.25, 0.45); (6.67, 0.88)\n", + "Danilo (6.25, 0.49); (6.75, 0.98)\n", + "Mazzocchi (6.08, 0.47); (6.46, 0.79)\n", + "Mario Rui (6.25, 0.47); (6.63, 0.84)\n", + "Bastoni S. (6.10, 0.48); (6.50, 0.86)\n", + "Valeri (6.04, 0.36); (6.21, 0.50)\n", + "Posch (6.09, 0.57); (6.72, 1.16)\n", + "Rodrigo Becao (6.11, 0.43); (6.35, 0.64)\n", + "Tomori (6.15, 0.50); (6.52, 0.82)\n", + "Baschirotto (6.21, 0.50); (6.71, 0.99)\n", + "Juan Jesus (6.22, 0.41); (6.61, 0.82)\n", + "Ibanez (6.04, 0.50); (6.25, 0.72)\n", + "Demiral (6.13, 0.48); (6.52, 0.83)\n", + "Scalvini (6.07, 0.53); (6.49, 0.92)\n", + "Bijol (5.98, 0.55); (6.22, 0.84)\n", + "Toloi (6.15, 0.49); (6.45, 0.74)\n", + "Depaoli (6.04, 0.51); (6.50, 0.96)\n", + "Mancini (6.04, 0.35); (6.08, 0.32)\n", + "Bremer (5.97, 0.57); (6.26, 0.86)\n", + "Ebuehi (6.06, 0.38); (6.29, 0.57)\n", + "Dumfries (6.04, 0.52); (6.52, 0.99)\n", + "Schuurs (6.09, 0.35); (6.16, 0.36)\n", + "Darmian (6.11, 0.45); (6.54, 0.87)\n", + "Kalulu (5.91, 0.57); (6.12, 0.80)\n", + "Vojvoda (5.97, 0.43); (6.13, 0.52)\n", + "Maehle (6.02, 0.38); (6.23, 0.57)\n", + "Holm (5.99, 0.41); (6.18, 0.58)\n", + "Bastoni (6.03, 0.47); (6.10, 0.49)\n", + "Gosens (5.98, 0.35); (6.14, 0.50)\n", + "Milenkovic (5.96, 0.56); (6.23, 0.85)\n", + "Rodriguez R. (5.93, 0.36); (5.94, 0.31)\n", + "Reca (5.99, 0.45); (6.25, 0.65)\n", + "Sernicola (5.92, 0.39); (6.08, 0.52)\n", + "Rrahmani (6.24, 0.48); (6.72, 0.95)\n", + "Kyriakopoulos (5.89, 0.45); (6.03, 0.59)\n", + "Martinez Quarta (6.01, 0.50); (6.24, 0.66)\n", + "Perez N. (6.04, 0.48); (6.07, 0.52)\n", + "Olivera (6.11, 0.40); (6.52, 0.80)\n", + "Calabria (6.08, 0.51); (6.48, 0.89)\n", + "Skriniar (5.75, 0.43); (5.66, 0.35)\n", + "Marusic (5.95, 0.37); (5.97, 0.35)\n", + "Lazzari (5.98, 0.38); (5.99, 0.35)\n", + "Augello (5.82, 0.43); (6.01, 0.53)\n", + "Ampadu (5.81, 0.46); (5.74, 0.41)\n", + "Ismajli (6.02, 0.40); (6.03, 0.37)\n", + "Cambiaso (5.89, 0.39); (5.91, 0.37)\n", + "Hysaj (5.96, 0.34); (6.00, 0.31)\n", + "Izzo (5.98, 0.36); (6.01, 0.31)\n", + "Biraghi (5.94, 0.37); (6.01, 0.33)\n", + "Medel (5.88, 0.39); (5.83, 0.33)\n", + "Bonucci (6.16, 0.49); (6.59, 0.88)\n", + "Acerbi (6.05, 0.39); (6.15, 0.38)\n", + "Spinazzola (5.95, 0.34); (6.01, 0.32)\n", + "Lykogiannis (5.92, 0.34); (6.01, 0.34)\n", + "Djidji (5.77, 0.47); (5.85, 0.55)\n", + "Lazaro (5.91, 0.45); (5.94, 0.44)\n", + "Gallo (5.88, 0.36); (5.83, 0.30)\n", + "Singo (5.99, 0.40); (6.12, 0.44)\n", + "Mari' (5.87, 0.58); (6.01, 0.78)\n", + "Casale (5.95, 0.38); (5.92, 0.34)\n", + "Dodo' (5.70, 0.49); (5.62, 0.43)\n", + "De Vrij (5.78, 0.44); (5.81, 0.48)\n", + "Patric (6.01, 0.41); (5.97, 0.37)\n", + "Luperto (5.90, 0.52); (5.85, 0.47)\n", + "Faraoni (5.99, 0.40); (6.24, 0.66)\n", + "Ceccherini (5.90, 0.50); (6.03, 0.64)\n", + "Umtiti (6.02, 0.47); (6.00, 0.46)\n", + "Aina (5.96, 0.44); (6.15, 0.59)\n", + "Caldirola (5.81, 0.45); (5.72, 0.38)\n", + "Soppy (5.76, 0.42); (5.76, 0.45)\n", + "Gendrey (5.83, 0.35); (5.81, 0.30)\n", + "Ferrari A. (5.82, 0.53); (5.87, 0.64)\n", + "Zappacosta (6.04, 0.39); (6.24, 0.56)\n", + "Bianchetti (5.72, 0.47); (5.76, 0.57)\n", + "Ferrari G. (5.77, 0.56); (5.86, 0.72)\n", + "Fazio (5.70, 0.61); (5.69, 0.65)\n", + "Hateboer (5.83, 0.44); (5.98, 0.60)\n", + "Rogerio (5.70, 0.45); (5.69, 0.45)\n", + "Buongiorno (5.85, 0.40); (5.78, 0.35)\n", + "Gunter (5.76, 0.48); (5.81, 0.56)\n", + "Colley (5.78, 0.56); (5.81, 0.66)\n", + "Troost-Ekong (5.81, 0.47); (5.78, 0.47)\n", + "Terzic (5.94, 0.28); (5.90, 0.21)\n", + "Toljan (5.65, 0.44); (5.56, 0.40)\n", + "Zortea (5.93, 0.42); (6.18, 0.66)\n", + "Soumaoro (5.85, 0.50); (5.77, 0.44)\n", + "Amian (5.75, 0.40); (5.67, 0.39)\n", + "Pongracic (5.87, 0.40); (5.82, 0.34)\n", + "Birindelli (5.80, 0.38); (5.82, 0.41)\n", + "Lucumi' (5.77, 0.40); (5.70, 0.35)\n", + "Hien (5.78, 0.40); (5.67, 0.34)\n", + "Ehizibue (5.89, 0.40); (6.05, 0.57)\n", + "Masina (6.04, 0.43); (6.40, 0.74)\n", + "Gyomber (5.72, 0.46); (5.62, 0.38)\n", + "Alex Sandro (5.76, 0.52); (5.69, 0.48)\n", + "Pezzella Giu. (5.89, 0.33); (5.91, 0.27)\n", + "Bereszynski (5.76, 0.42); (5.68, 0.40)\n", + "Venuti (5.72, 0.40); (5.64, 0.37)\n", + "Palomino (6.11, 0.50); (6.40, 0.75)\n", + "Nuytinck (5.85, 0.49); (5.83, 0.49)\n", + "Marlon (5.73, 0.37); (5.61, 0.31)\n", + "Nikolaou (5.73, 0.40); (5.62, 0.33)\n", + "Igor (5.70, 0.47); (5.59, 0.38)\n", + "Okoli (5.67, 0.45); (5.55, 0.39)\n", + "Dawidowicz (5.74, 0.49); (5.74, 0.52)\n", + "Celik (5.77, 0.40); (5.65, 0.38)\n", + "Bellanova (5.78, 0.42); (5.78, 0.46)\n", + "Erlic (5.79, 0.49); (5.74, 0.48)\n", + "Ballo-Toure' (6.11, 0.38); (6.38, 0.63)\n", + "Dest (5.72, 0.43); (5.66, 0.39)\n", + "Stojanovic (5.73, 0.45); (5.67, 0.45)\n", + "Bradaric (5.62, 0.46); (5.55, 0.45)\n", + "Daniliuc (5.70, 0.56); (5.65, 0.54)\n", + "Zima (5.81, 0.41); (5.77, 0.37)\n", + "Quagliata (5.91, 0.31); (5.95, 0.28)\n", + "Ebosse (5.69, 0.37); (5.58, 0.34)\n", + "Aiwu (5.85, 0.46); (5.86, 0.48)\n", + "Lochoshvili (5.74, 0.40); (5.66, 0.40)\n", + "Bronn (5.67, 0.39); (5.57, 0.33)\n", + "Thiaw (5.98, 0.42); (6.05, 0.43)\n", + "Zeefuik (5.86, 0.44); (5.89, 0.44)\n", + "Ghiglione (5.75, 0.43); (5.72, 0.48)\n", + "Rugani (5.96, 0.32); (5.97, 0.26)\n", + "De Sciglio (5.89, 0.35); (5.96, 0.33)\n", + "Djimsiti (5.95, 0.41); (6.01, 0.39)\n", + "Caldara (5.71, 0.48); (5.61, 0.41)\n", + "Murru (5.66, 0.45); (5.66, 0.50)\n", + "Karsdorp (5.85, 0.39); (5.80, 0.34)\n", + "Bonifazi (5.74, 0.42); (5.65, 0.35)\n", + "Kjaer (5.91, 0.36); (5.86, 0.30)\n", + "Magnani (5.68, 0.42); (5.56, 0.33)\n", + "Walukiewicz (5.75, 0.44); (5.67, 0.41)\n", + "Ayhan (5.70, 0.57); (5.67, 0.58)\n", + "Amione (5.60, 0.44); (5.49, 0.43)\n", + "Ruggeri (5.93, 0.35); (5.99, 0.30)\n", + "De Winter (5.83, 0.45); (5.76, 0.38)\n", + "Ostigard (5.93, 0.31); (5.97, 0.27)\n", + "Wisniewski (5.83, 0.44); (5.83, 0.45)\n", + "Radovanovic (5.55, 0.41); (5.43, 0.34)\n", + "Dermaku (5.88, 0.42); (5.89, 0.39)\n", + "D'ambrosio (6.10, 0.37); (6.22, 0.44)\n", + "De Silvestri (5.85, 0.45); (6.03, 0.61)\n", + "Sala (5.89, 0.39); (5.95, 0.40)\n", + "Chiriches (5.75, 0.50); (5.64, 0.42)\n", + "Donati (5.87, 0.50); (5.82, 0.48)\n", + "Gabbia (5.78, 0.37); (5.70, 0.31)\n", + "Kumbulla (5.77, 0.34); (5.69, 0.30)\n", + "Adopo (5.95, 0.37); (5.98, 0.32)\n", + "Pirola (5.52, 0.51); (5.41, 0.39)\n", + "Lovato (5.60, 0.53); (5.51, 0.44)\n", + "Tuia (5.74, 0.47); (5.82, 0.56)\n", + "Ferrer (5.81, 0.44); (5.77, 0.42)\n", + "Antov (5.68, 0.52); (5.62, 0.49)\n", + "Vasquez (5.77, 0.43); (5.69, 0.42)\n", + "Zanoli (5.92, 0.32); (5.94, 0.25)\n", + "Gatti (5.82, 0.44); (5.78, 0.39)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gila (5.93, 0.43); (5.91, 0.38)\n", + "Sambia (5.81, 0.48); (5.71, 0.40)\n", + "Moutinho J. (5.86, 0.49); (5.81, 0.44)\n", + "Conti (5.94, 0.54); (6.09, 0.71)\n", + "Marrone (5.61, 0.47); (5.51, 0.41)\n", + "Tonelli (5.64, 0.46); (5.54, 0.42)\n", + "Murillo (5.58, 0.36); (5.47, 0.32)\n", + "Radu (5.72, 0.48); (5.65, 0.45)\n", + "Paletta (5.83, 0.47); (5.82, 0.52)\n", + "Florenzi (6.08, 0.42); (6.29, 0.56)\n", + "Fares (5.80, 0.38); (5.77, 0.36)\n", + "Marchizza (5.71, 0.40); (5.65, 0.41)\n", + "Romagna (5.85, 0.46); (5.89, 0.51)\n", + "Ranieri L. (5.74, 0.42); (5.79, 0.53)\n", + "Cassandro (5.88, 0.43); (5.90, 0.40)\n", + "Muldur (5.71, 0.42); (5.63, 0.39)\n", + "Carboni (5.70, 0.43); (5.58, 0.41)\n", + "Amey (5.96, 0.42); (6.04, 0.44)\n", + "Ferrarini (5.83, 0.47); (5.82, 0.52)\n", + "Vina (5.80, 0.43); (5.71, 0.40)\n", + "Zanotti (5.86, 0.45); (5.91, 0.51)\n", + "Ruan (5.64, 0.45); (5.55, 0.32)\n", + "Coppola D. (5.70, 0.40); (5.57, 0.36)\n", + "Cacace (5.83, 0.39); (5.79, 0.34)\n", + "Bayeye (5.88, 0.45); (5.92, 0.45)\n", + "Ebosele (5.87, 0.46); (5.87, 0.46)\n", + "Buta (5.89, 0.45); (5.90, 0.46)\n", + "Abankwah (5.89, 0.45); (5.90, 0.46)\n", + "Guessand A. (5.89, 0.45); (5.90, 0.46)\n", + "Cabal (5.98, 0.39); (6.06, 0.37)\n", + "Sosa (5.68, 0.47); (5.58, 0.42)\n", + "Guarino (5.87, 0.47); (5.90, 0.50)\n", + "Carboni F. (5.85, 0.48); (5.84, 0.49)\n", + "Zaccagni (6.46, 0.68); (8.04, 2.39)\n", + "Kvaratskhelia (6.53, 0.76); (8.54, 2.98)\n", + "Milinkovic-Savic (6.33, 0.65); (7.57, 1.92)\n", + "Barella (6.30, 0.58); (7.24, 1.55)\n", + "Zielinski (6.36, 0.51); (7.00, 1.18)\n", + "Luis Alberto (6.35, 0.56); (7.16, 1.40)\n", + "Strefezza (6.33, 0.55); (7.21, 1.47)\n", + "Felipe Anderson (6.23, 0.64); (7.32, 1.75)\n", + "Diaz B. (6.17, 0.63); (7.09, 1.54)\n", + "Koopmeiners (6.37, 0.63); (7.58, 1.88)\n", + "Vlasic (6.21, 0.58); (7.00, 1.37)\n", + "Calhanoglu (6.30, 0.53); (6.97, 1.20)\n", + "Frattesi (6.11, 0.58); (6.79, 1.24)\n", + "Zambo Anguissa (6.32, 0.54); (7.02, 1.26)\n", + "Elmas (6.27, 0.53); (7.04, 1.33)\n", + "Pereyra (6.23, 0.54); (6.90, 1.20)\n", + "Miranchuk (6.19, 0.62); (7.06, 1.47)\n", + "Samardzic (6.21, 0.53); (6.87, 1.20)\n", + "Politano (6.29, 0.49); (6.88, 1.10)\n", + "Rabiot (6.15, 0.55); (6.73, 1.08)\n", + "Radonjic (6.18, 0.53); (6.83, 1.18)\n", + "Lobotka (6.22, 0.40); (6.54, 0.72)\n", + "Tonali (6.22, 0.53); (6.82, 1.11)\n", + "Lazovic (6.17, 0.55); (6.76, 1.11)\n", + "Bonaventura (6.15, 0.55); (6.78, 1.18)\n", + "Pellegrini Lo. (6.18, 0.62); (7.05, 1.50)\n", + "Pessina (6.05, 0.51); (6.45, 0.86)\n", + "Kostic (6.10, 0.49); (6.51, 0.84)\n", + "Lovric (6.17, 0.44); (6.52, 0.76)\n", + "Ferguson (6.09, 0.49); (6.58, 0.94)\n", + "Ciurria (6.13, 0.58); (6.81, 1.24)\n", + "Ikone' (6.00, 0.55); (6.47, 0.98)\n", + "Baldanzi (6.25, 0.53); (6.99, 1.27)\n", + "Candreva (5.97, 0.59); (6.31, 0.93)\n", + "Bennacer (6.13, 0.40); (6.39, 0.62)\n", + "El Shaarawy (6.19, 0.47); (6.77, 1.07)\n", + "Pasalic (6.12, 0.59); (6.88, 1.35)\n", + "Orsolini (6.18, 0.64); (7.09, 1.55)\n", + "Bandinelli (5.97, 0.44); (6.23, 0.67)\n", + "Mkhitaryan (6.08, 0.48); (6.50, 0.87)\n", + "Colpani (6.02, 0.46); (6.36, 0.81)\n", + "Pogba (5.94, 0.46); (6.03, 0.53)\n", + "Sensi (6.02, 0.55); (6.41, 0.95)\n", + "Chiesa (6.15, 0.52); (6.62, 0.96)\n", + "Lukic (6.05, 0.52); (6.44, 0.89)\n", + "Fagioli (6.13, 0.49); (6.59, 0.90)\n", + "Messias (6.04, 0.55); (6.58, 1.07)\n", + "Arslan (5.97, 0.36); (6.06, 0.38)\n", + "Brozovic (6.20, 0.47); (6.62, 0.86)\n", + "Verdi (6.13, 0.45); (6.51, 0.80)\n", + "Barak (5.90, 0.47); (6.20, 0.74)\n", + "Matic (6.09, 0.39); (6.31, 0.56)\n", + "Soriano (6.00, 0.39); (6.10, 0.41)\n", + "Dominguez (6.10, 0.50); (6.51, 0.87)\n", + "Ranocchia F. (5.96, 0.45); (6.25, 0.73)\n", + "Cristante (5.92, 0.45); (6.07, 0.60)\n", + "Bajrami (5.86, 0.50); (6.14, 0.73)\n", + "Ricci S. (6.06, 0.37); (6.14, 0.38)\n", + "Thorstvedt (5.92, 0.43); (6.14, 0.65)\n", + "De Ketelaere (5.88, 0.39); (5.98, 0.49)\n", + "Saponara (6.01, 0.51); (6.46, 0.92)\n", + "Vecino (5.94, 0.46); (6.06, 0.55)\n", + "Locatelli (6.03, 0.37); (6.13, 0.40)\n", + "Zaniolo (5.87, 0.48); (6.09, 0.74)\n", + "Traore' Hj. (5.98, 0.52); (6.38, 0.91)\n", + "Maldini (6.00, 0.50); (6.39, 0.90)\n", + "Coulibaly L. (5.94, 0.59); (6.25, 0.94)\n", + "Gonzalez J. (5.99, 0.48); (6.33, 0.78)\n", + "Vilhena (5.78, 0.46); (5.99, 0.65)\n", + "De Roon (5.98, 0.37); (6.02, 0.35)\n", + "Mandragora (5.93, 0.44); (6.09, 0.58)\n", + "Haas (5.91, 0.40); (6.05, 0.56)\n", + "Wijnaldum (5.93, 0.45); (5.98, 0.50)\n", + "Bourabia (5.89, 0.40); (5.92, 0.38)\n", + "Sottil (6.05, 0.53); (6.56, 1.01)\n", + "Agudelo (5.83, 0.35); (5.85, 0.35)\n", + "Makengo (5.98, 0.34); (6.01, 0.32)\n", + "Zalewski (6.00, 0.33); (6.04, 0.31)\n", + "Aebischer (5.95, 0.45); (6.24, 0.72)\n", + "Ederson D.s. (5.87, 0.45); (6.04, 0.60)\n", + "Miretti (5.92, 0.38); (5.99, 0.42)\n", + "Blin (5.97, 0.30); (5.97, 0.23)\n", + "Hjulmand (5.97, 0.51); (6.01, 0.55)\n", + "Cataldi (5.93, 0.34); (5.91, 0.28)\n", + "Paredes (5.81, 0.37); (5.76, 0.32)\n", + "Djuricic (5.72, 0.51); (5.92, 0.67)\n", + "Linetty (5.90, 0.45); (6.06, 0.59)\n", + "Walace (5.88, 0.37); (5.82, 0.33)\n", + "Marin (5.78, 0.54); (5.82, 0.61)\n", + "Mckennie (5.77, 0.43); (5.92, 0.58)\n", + "Pobega (6.04, 0.42); (6.29, 0.67)\n", + "Volpato (5.99, 0.56); (6.40, 0.95)\n", + "Camara Ma. (6.10, 0.34); (6.14, 0.33)\n", + "Cuadrado (5.90, 0.51); (5.98, 0.57)\n", + "Ndombele' (5.87, 0.32); (5.89, 0.25)\n", + "Amrabat (5.77, 0.48); (5.66, 0.40)\n", + "Tameze (5.82, 0.41); (5.88, 0.44)\n", + "Gyasi (5.83, 0.54); (6.11, 0.77)\n", + "Ilic (5.93, 0.41); (5.99, 0.41)\n", + "Harroui (5.86, 0.40); (6.02, 0.59)\n", + "Pickel (5.79, 0.40); (5.90, 0.52)\n", + "Moro N. (5.93, 0.35); (6.00, 0.32)\n", + "Duncan (5.88, 0.41); (6.02, 0.52)\n", + "Miguel Veloso (5.95, 0.35); (5.97, 0.32)\n", + "Ekdal (5.79, 0.34); (5.74, 0.31)\n", + "Nicolussi Caviglia (5.84, 0.49); (6.16, 0.76)\n", + "Schouten (5.92, 0.38); (5.91, 0.34)\n", + "Rovella (5.87, 0.50); (5.82, 0.47)\n", + "Crnigoj (5.92, 0.42); (6.14, 0.63)\n", + "Romero L. (6.22, 0.49); (6.81, 1.06)\n", + "Basic (5.97, 0.33); (5.99, 0.29)\n", + "Sabiri (5.72, 0.48); (5.85, 0.58)\n", + "Terracciano F. (6.01, 0.31); (6.01, 0.28)\n", + "Castagnetti (5.83, 0.31); (5.87, 0.25)\n", + "Oudin (5.96, 0.31); (6.00, 0.26)\n", + "D'alessandro (6.02, 0.32); (6.03, 0.30)\n", + "Grassi (5.92, 0.35); (5.93, 0.30)\n", + "Krunic (5.99, 0.33); (6.02, 0.31)\n", + "Rincon (5.70, 0.43); (5.68, 0.44)\n", + "Machin (5.86, 0.47); (5.89, 0.55)\n", + "Leris (5.70, 0.41); (5.80, 0.53)\n", + "Meite' (5.76, 0.43); (5.67, 0.41)\n", + "Vieira (5.68, 0.44); (5.76, 0.55)\n", + "Esposito Sa. (5.83, 0.42); (5.81, 0.41)\n", + "Henderson L. (5.87, 0.38); (5.95, 0.46)\n", + "Obiang (5.90, 0.31); (5.92, 0.28)\n", + "Lopez M. (5.86, 0.40); (5.85, 0.40)\n", + "Saelemaekers (5.91, 0.39); (5.99, 0.46)\n", + "Maggiore (5.91, 0.38); (6.01, 0.47)\n", + "Akpa Akpro (5.86, 0.48); (5.90, 0.53)\n", + "Kovalenko (5.91, 0.37); (6.00, 0.42)\n", + "Maleh (5.96, 0.38); (6.05, 0.46)\n", + "Matheus Henrique (5.81, 0.36); (5.83, 0.35)\n", + "Asllani (5.97, 0.34); (5.99, 0.30)\n", + "Ceide (5.84, 0.38); (5.87, 0.35)\n", + "Gagliardini (5.86, 0.34); (5.87, 0.39)\n", + "Bove (5.74, 0.33); (5.71, 0.32)\n", + "Bianco (5.96, 0.40); (6.03, 0.43)\n", + "Vranckx (5.89, 0.37); (5.96, 0.36)\n", + "Marcos Antonio (5.88, 0.30); (5.86, 0.22)\n", + "Fazzini (5.77, 0.33); (5.69, 0.31)\n", + "D'andrea (5.97, 0.37); (6.06, 0.44)\n", + "Sulemana I. (5.88, 0.30); (5.89, 0.26)\n", + "Tahirovic (5.93, 0.32); (5.95, 0.28)\n", + "Benassi (5.79, 0.34); (5.81, 0.34)\n", + "Barberis (5.82, 0.41); (5.77, 0.39)\n", + "Kastanos (5.80, 0.33); (5.82, 0.32)\n", + "Vignato (5.90, 0.38); (5.96, 0.38)\n", + "Valoti (5.77, 0.35); (5.73, 0.33)\n", + "Winks (5.77, 0.42); (5.73, 0.42)\n", + "Gaetano (5.99, 0.41); (6.11, 0.47)\n", + "Zurkowski (6.05, 0.55); (6.54, 1.01)\n", + "Castrovilli (5.95, 0.49); (6.25, 0.74)\n", + "Bohinen (5.68, 0.31); (5.69, 0.25)\n", + "Hrustic (5.60, 0.31); (5.65, 0.26)\n", + "Iling-Junior (5.92, 0.39); (5.96, 0.40)\n", + "Capezzi (5.87, 0.35); (5.88, 0.34)\n", + "Molina S. (5.81, 0.46); (5.94, 0.60)\n", + "Bakayoko (5.74, 0.40); (5.61, 0.36)\n", + "Scozzarella (5.86, 0.47); (5.89, 0.55)\n", + "Demme (6.04, 0.41); (6.32, 0.64)\n", + "Askildsen (5.65, 0.31); (5.69, 0.27)\n", + "Darboe (5.96, 0.46); (5.97, 0.46)\n", + "Urbanski (5.93, 0.42); (6.00, 0.45)\n", + "Bertini (5.98, 0.43); (6.05, 0.47)\n", + "Hongla (5.66, 0.34); (5.70, 0.36)\n", + "Yepes (5.66, 0.45); (5.55, 0.41)\n", + "Trimboli (5.83, 0.45); (5.83, 0.48)\n", + "Pafundi (5.92, 0.45); (5.98, 0.50)\n", + "Milanese (5.79, 0.45); (5.76, 0.46)\n", + "Helgason (5.81, 0.32); (5.83, 0.25)\n", + "Adli (5.76, 0.44); (5.82, 0.53)\n", + "Vignato S. (5.82, 0.49); (5.76, 0.48)\n", + "Bondo (5.88, 0.42); (5.87, 0.40)\n", + "Samek (5.90, 0.42); (5.95, 0.41)\n", + "Zerbin (6.01, 0.37); (6.11, 0.42)\n", + "Ilkhan (5.82, 0.47); (5.79, 0.42)\n", + "Degli Innocenti (5.88, 0.50); (5.92, 0.53)\n", + "Acella (5.86, 0.46); (5.90, 0.50)\n", + "Garbett (5.90, 0.45); (5.97, 0.48)\n", + "Carboni V. (5.84, 0.45); (5.85, 0.50)\n", + "Cipot (5.89, 0.42); (5.96, 0.46)\n", + "Osimhen (6.56, 0.79); (8.66, 3.15)\n", + "Dybala (6.50, 0.68); (8.10, 2.42)\n", + "Martinez L. (6.37, 0.74); (7.97, 2.43)\n", + "Rafael Leao (6.40, 0.70); (7.96, 2.34)\n", + "Immobile (6.43, 0.76); (8.22, 2.68)\n", + "Lookman (6.47, 0.75); (8.27, 2.72)\n", + "Vlahovic (6.28, 0.74); (7.66, 2.18)\n", + "Arnautovic (6.28, 0.69); (7.60, 2.05)\n", + "Dia (6.13, 0.69); (7.16, 1.72)\n", + "Dzeko (6.27, 0.70); (7.59, 2.07)\n", + "Milik (6.22, 0.58); (7.09, 1.46)\n", + "Nzola (6.13, 0.71); (7.13, 1.74)\n", + "Beto (6.16, 0.68); (7.19, 1.74)\n", + "Abraham (6.30, 0.69); (7.59, 2.02)\n", + "Giroud (6.21, 0.65); (7.29, 1.74)\n", + "Deulofeu (6.35, 0.64); (7.55, 1.89)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correa (6.12, 0.55); (6.85, 1.33)\n", + "Pedro (6.17, 0.56); (6.81, 1.17)\n", + "Lozano (6.22, 0.62); (7.23, 1.64)\n", + "Lukaku (6.06, 0.60); (6.78, 1.30)\n", + "Lauriente' (6.14, 0.57); (6.74, 1.15)\n", + "Simeone (6.28, 0.72); (7.66, 2.16)\n", + "Cabral (6.08, 0.61); (6.80, 1.34)\n", + "Rebic (6.15, 0.65); (7.07, 1.57)\n", + "Bonazzoli (5.97, 0.52); (6.40, 0.92)\n", + "Berardi (6.30, 0.70); (7.68, 2.14)\n", + "Caprari (6.07, 0.59); (6.72, 1.24)\n", + "Di Maria (6.22, 0.66); (7.34, 1.78)\n", + "Piatek (5.97, 0.60); (6.53, 1.15)\n", + "Kouame' (6.12, 0.61); (6.87, 1.37)\n", + "Hojlund (6.17, 0.67); (7.15, 1.67)\n", + "Sanabria (6.05, 0.61); (6.72, 1.27)\n", + "Zapata D. (6.07, 0.60); (6.79, 1.34)\n", + "Kean (6.03, 0.61); (6.70, 1.24)\n", + "Ceesay (6.04, 0.58); (6.61, 1.15)\n", + "Pinamonti (5.89, 0.54); (6.31, 0.95)\n", + "Gonzalez N. (6.24, 0.63); (7.23, 1.66)\n", + "Colombo (6.12, 0.65); (6.99, 1.54)\n", + "Mota (6.03, 0.59); (6.58, 1.13)\n", + "Dessers (5.97, 0.56); (6.46, 1.05)\n", + "Muriel (6.23, 0.65); (7.25, 1.69)\n", + "Okereke (5.90, 0.56); (6.36, 0.98)\n", + "Alvarez A. (6.02, 0.56); (6.55, 1.05)\n", + "Jovic (5.95, 0.57); (6.45, 1.04)\n", + "Barrow (6.02, 0.62); (6.63, 1.22)\n", + "Caputo (6.01, 0.62); (6.65, 1.24)\n", + "Banda (5.94, 0.43); (6.11, 0.58)\n", + "Ciofani D. (6.07, 0.60); (6.75, 1.29)\n", + "Petagna (5.86, 0.49); (6.13, 0.74)\n", + "Di Francesco F. (6.03, 0.58); (6.53, 1.06)\n", + "Boga (6.44, 0.68); (7.87, 2.22)\n", + "Cambiaghi (6.07, 0.48); (6.43, 0.78)\n", + "Djuric (6.07, 0.42); (6.39, 0.76)\n", + "Henry (5.89, 0.56); (6.31, 0.96)\n", + "Success (5.96, 0.38); (6.03, 0.42)\n", + "Gabbiadini (5.81, 0.56); (6.12, 0.84)\n", + "Origi (5.89, 0.52); (6.23, 0.83)\n", + "Lammers (5.83, 0.47); (6.07, 0.72)\n", + "Satriano (5.81, 0.47); (6.04, 0.69)\n", + "Zirkzee (6.02, 0.54); (6.57, 1.08)\n", + "Kallon (5.87, 0.45); (6.15, 0.74)\n", + "Solbakken (5.95, 0.50); (6.03, 0.58)\n", + "Lasagna (5.76, 0.48); (6.06, 0.79)\n", + "Nestorovski (6.06, 0.42); (6.41, 0.85)\n", + "Pellegri (5.86, 0.48); (6.21, 0.84)\n", + "Botheim (5.87, 0.50); (6.27, 0.87)\n", + "Gytkjaer (5.82, 0.44); (6.05, 0.69)\n", + "Belotti (5.66, 0.35); (5.72, 0.40)\n", + "Raspadori (6.04, 0.52); (6.61, 1.09)\n", + "Buonaiuto (5.95, 0.36); (6.02, 0.33)\n", + "Verde (6.04, 0.53); (6.54, 1.02)\n", + "Destro (5.92, 0.62); (6.39, 1.06)\n", + "Sansone (5.93, 0.50); (6.31, 0.86)\n", + "Pjaca (5.82, 0.43); (5.96, 0.51)\n", + "Zanimacchia (5.83, 0.37); (5.86, 0.35)\n", + "Strelec (5.92, 0.32); (5.92, 0.31)\n", + "Seck (6.05, 0.32); (6.07, 0.29)\n", + "Quagliarella (5.78, 0.45); (5.97, 0.65)\n", + "Piccoli (5.84, 0.46); (6.14, 0.79)\n", + "Tsadjout (5.90, 0.47); (6.17, 0.75)\n", + "Shomurodov (6.03, 0.56); (6.60, 1.13)\n", + "Cancellieri (5.81, 0.30); (5.77, 0.24)\n", + "Antiste (5.82, 0.44); (6.05, 0.68)\n", + "Afena-Gyan (5.61, 0.35); (5.69, 0.37)\n", + "Soule' (5.86, 0.48); (5.97, 0.54)\n", + "Ngonge (5.87, 0.44); (5.91, 0.46)\n", + "Defrel (5.75, 0.40); (5.90, 0.59)\n", + "Karamoh (5.89, 0.35); (5.89, 0.33)\n", + "Ibrahimovic (6.35, 0.67); (7.70, 2.08)\n", + "Pussetto (5.82, 0.50); (6.10, 0.77)\n", + "Voelkerling Persson (5.91, 0.40); (5.92, 0.36)\n", + "Valencia D. 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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
MussoPAtalantaAvg111006.2506220.3784115.5302090.6295296.1310170.3999600.2193811.2040456.0269300.821359-0.4323021.11303743.711811
Rossi F.PAtalantaAvg1006.2506220.3784115.5302090.6295296.1310170.3999600.2193811.2040456.0269300.821359-0.4323021.11303743.711811
SportielloPAtalantaAvg1006.0729750.3807525.4300700.6916475.9765730.4163460.1706811.2279546.0594330.839207-0.5246991.15570948.146455
DemiralDAtalantaAvg11806.1326700.4841496.5179650.8305366.0595690.5538850.0977961.1391265.8194251.0246320.4458161.9997450.000000
ScalviniDAtalantaAvg11756.0682070.5271716.4926870.9178966.0198910.6143020.0581501.1005395.6968411.0777940.4745661.9997290.000000
............................................................
HenryAVeronaAvg11805.8874870.5599786.3071550.9575405.6305450.5534240.3360221.0777265.4318861.0148790.5345261.9997420.000000
KallonAVeronaAvg10805.8711920.4507606.1458010.7379645.7018550.4593090.2690801.1973835.5215180.9037410.4511431.9997000.000000
PiccoliAVeronaAvg10305.8402130.4550196.1359790.7917625.6580570.4556410.2909131.2004155.4409440.9118310.4872741.9997010.000000
LasagnaAVeronaAvg11755.7644750.4837056.0596230.7936675.5786510.4926070.2748591.1664235.3669970.9235460.4810541.9996930.000000
NgongeAVeronaAvg1005.8706720.4413425.9067400.4608755.9010170.545786-0.0412911.2123175.7249160.8636730.1539651.9994430.000000
\n", + "

547 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 6.250622 0.378411 \n", + "Rossi F. P Atalanta Avg 1 0 0 6.250622 0.378411 \n", + "Sportiello P Atalanta Avg 1 0 0 6.072975 0.380752 \n", + "Demiral D Atalanta Avg 1 1 80 6.132670 0.484149 \n", + "Scalvini D Atalanta Avg 1 1 75 6.068207 0.527171 \n", + "... ... ... ... ... ... ... ... ... \n", + "Henry A Verona Avg 1 1 80 5.887487 0.559978 \n", + "Kallon A Verona Avg 1 0 80 5.871192 0.450760 \n", + "Piccoli A Verona Avg 1 0 30 5.840213 0.455019 \n", + "Lasagna A Verona Avg 1 1 75 5.764475 0.483705 \n", + "Ngonge A Verona Avg 1 0 0 5.870672 0.441342 \n", + "\n", + " FV FV std MV loc MV scale MV skewness \\\n", + "player \n", + "Musso 5.530209 0.629529 6.131017 0.399960 0.219381 \n", + "Rossi F. 5.530209 0.629529 6.131017 0.399960 0.219381 \n", + "Sportiello 5.430070 0.691647 5.976573 0.416346 0.170681 \n", + "Demiral 6.517965 0.830536 6.059569 0.553885 0.097796 \n", + "Scalvini 6.492687 0.917896 6.019891 0.614302 0.058150 \n", + "... ... ... ... ... ... \n", + "Henry 6.307155 0.957540 5.630545 0.553424 0.336022 \n", + "Kallon 6.145801 0.737964 5.701855 0.459309 0.269080 \n", + "Piccoli 6.135979 0.791762 5.658057 0.455641 0.290913 \n", + "Lasagna 6.059623 0.793667 5.578651 0.492607 0.274859 \n", + "Ngonge 5.906740 0.460875 5.901017 0.545786 -0.041291 \n", + "\n", + " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", + "player \n", + "Musso 1.204045 6.026930 0.821359 -0.432302 1.113037 \n", + "Rossi F. 1.204045 6.026930 0.821359 -0.432302 1.113037 \n", + "Sportiello 1.227954 6.059433 0.839207 -0.524699 1.155709 \n", + "Demiral 1.139126 5.819425 1.024632 0.445816 1.999745 \n", + "Scalvini 1.100539 5.696841 1.077794 0.474566 1.999729 \n", + "... ... ... ... ... ... \n", + "Henry 1.077726 5.431886 1.014879 0.534526 1.999742 \n", + "Kallon 1.197383 5.521518 0.903741 0.451143 1.999700 \n", + "Piccoli 1.200415 5.440944 0.911831 0.487274 1.999701 \n", + "Lasagna 1.166423 5.366997 0.923546 0.481054 1.999693 \n", + "Ngonge 1.212317 5.724916 0.863673 0.153965 1.999443 \n", + "\n", + " Clean Sheet % \n", + "player \n", + "Musso 43.711811 \n", + "Rossi F. 43.711811 \n", + "Sportiello 48.146455 \n", + "Demiral 0.000000 \n", + "Scalvini 0.000000 \n", + "... ... \n", + "Henry 0.000000 \n", + "Kallon 0.000000 \n", + "Piccoli 0.000000 \n", + "Lasagna 0.000000 \n", + "Ngonge 0.000000 \n", + "\n", + "[547 rows x 19 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "tot_matches = 2 # home and not home\n", + "\n", + "current_season_games = max(players_orig['games'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " home = 0\n", + " \n", + " for k in range(tot_matches):\n", + " #matchday_out = k + 1\n", + " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + " \n", + " player = players.index[i]\n", + " team = players['team'][i]\n", + " oppteam = 'Avg'\n", + " home = not home\n", + "\n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", + "\n", + " role = players['r'][player] \n", + "\n", + " starter = 0\n", + " voteperc = 0\n", + "\n", + " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", + " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", + " \n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + " \n", + " starter = int( player in gk_starters )\n", + " if(starter):\n", + " voteperc = 100\n", + " else:\n", + " voteperc = 0\n", + " else:\n", + " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", + " voteperc = int( min( 1, games / current_season_games ) * 100) \n", + "\n", + " if(k == 0):\n", + " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", + " \n", + " numrow_ = [mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs] \n", + "\n", + " if(k == 0):\n", + " numrow = numrow_\n", + " else:\n", + " for j in range(len(numrow)):\n", + " numrow[j] += numrow_[j]\n", + "\n", + " for j in range(len(numrow)):\n", + " numrow[j] /= tot_matches\n", + " \n", + " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", + " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", + " \n", + " row += numrow # list concat\n", + " \n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + "\n", + " output = pd.concat([output, row_df])\n", + " \n", + " \n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", + "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", + "\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "b47cbd63", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "a9687e92", + "metadata": {}, + "source": [ + "Same code but considering previous season" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "89e1dc82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meret (5.79, 0.46); (5.22, 0.60)\n", + "Provedel (6.40, 0.38); (4.78, 0.71)\n", + "Maignan (6.38, 0.38); (5.01, 0.67)\n", + "Silvestri (6.40, 0.38); (4.53, 0.74)\n", + "Sepe (6.38, 0.37); (4.77, 0.71)\n", + "Szczesny (6.29, 0.39); (5.05, 0.66)\n", + "Consigli (6.31, 0.37); (4.42, 0.76)\n", + "Falcone (6.33, 0.41); (5.23, 0.64)\n", + "Musso (6.39, 0.37); (4.71, 0.72)\n", + "Vicario (6.40, 0.38); (4.57, 0.74)\n", + "Rui Patricio (6.36, 0.38); (4.57, 0.73)\n", + "Milinkovic-Savic V. (6.28, 0.36); (4.68, 0.72)\n", + "Audero (6.32, 0.36); (4.39, 0.77)\n", + "Montipo' (6.37, 0.36); (4.63, 0.73)\n", + "Sportiello (6.33, 0.41); (5.58, 0.59)\n", + "Skorupski (6.39, 0.39); (4.61, 0.73)\n", + "Di Gregorio (6.13, 0.40); (4.30, 0.76)\n", + "Perin (6.23, 0.41); (5.53, 0.59)\n", + "Handanovic (6.44, 0.39); (4.66, 0.73)\n", + "Tatarusanu (6.06, 0.43); (5.47, 0.59)\n", + "Gollini (6.03, 0.41); (5.40, 0.60)\n", + "Dragowski (5.66, 0.42); (3.79, 0.84)\n", + "Terracciano (6.22, 0.34); (4.44, 0.75)\n", + "Berisha (6.26, 0.43); (5.58, 0.59)\n", + "Radu I. (5.33, 0.40); (3.14, 0.99)\n", + "Cragno (6.36, 0.36); (4.65, 0.72)\n", + "Luis Maximiano (6.05, 0.46); (5.19, 0.62)\n", + "Onana (6.23, 0.37); (5.38, 0.62)\n", + "Carnesecchi (6.27, 0.45); (5.50, 0.60)\n", + "Mirante (6.38, 0.38); (5.01, 0.67)\n", + "Sarr M. (6.27, 0.42); (4.63, 0.72)\n", + "Lamanna (6.13, 0.40); (4.30, 0.76)\n", + "Ujkani (6.40, 0.38); (4.57, 0.74)\n", + "Pegolo (6.28, 0.36); (4.38, 0.76)\n", + "Perilli (6.22, 0.33); (4.29, 0.78)\n", + "Padelli (6.45, 0.40); (4.62, 0.73)\n", + "Perisan (6.45, 0.41); (5.30, 0.63)\n", + "Bardi (6.40, 0.38); (4.71, 0.72)\n", + "Cordaz (6.44, 0.39); (4.66, 0.73)\n", + "Pinsoglio (6.26, 0.40); (5.18, 0.64)\n", + "Fiorillo (6.33, 0.36); (4.62, 0.74)\n", + "Sirigu (6.36, 0.36); (4.96, 0.68)\n", + "Cerofolini (6.09, 0.35); (4.64, 0.72)\n", + "Rossi F. (6.38, 0.37); (4.78, 0.70)\n", + "Contini (6.18, 0.34); (4.22, 0.80)\n", + "Brancolini (6.40, 0.37); (4.83, 0.70)\n", + "Bleve (6.40, 0.37); (4.83, 0.70)\n", + "Berardi A. (6.33, 0.35); (4.49, 0.75)\n", + "Russo A. (6.24, 0.37); (5.24, 0.63)\n", + "Gemello (6.26, 0.43); (5.64, 0.59)\n", + "Ravaglia (6.37, 0.36); (4.57, 0.74)\n", + "Zoet (5.55, 0.33); (2.93, 1.10)\n", + "Boer (6.36, 0.38); (4.57, 0.73)\n", + "Adamonis (6.16, 0.40); (4.50, 0.73)\n", + "Marfella (6.37, 0.37); (5.05, 0.67)\n", + "Zovko (6.38, 0.37); (4.50, 0.75)\n", + "Piana (6.40, 0.38); (4.53, 0.74)\n", + "Bagnolini (6.04, 0.36); (3.94, 0.82)\n", + "Svilar (6.24, 0.40); (5.38, 0.61)\n", + "Sorrentino A. (6.13, 0.40); (4.30, 0.76)\n", + "Ciezkowski (6.27, 0.42); (4.63, 0.72)\n", + "Micai (6.41, 0.37); (4.91, 0.69)\n", + "Chiesa M. (6.22, 0.33); (4.29, 0.78)\n", + "Saro (6.27, 0.42); (4.63, 0.72)\n", + "Smalling (6.16, 0.46); (6.51, 0.80)\n", + "Hernandez T. (6.24, 0.60); (6.99, 1.34)\n", + "Kim (6.28, 0.45); (6.62, 0.73)\n", + "Bastoni S. (6.08, 0.49); (6.43, 0.82)\n", + "Udogie (6.04, 0.56); (6.54, 1.08)\n", + "Dumfries (6.18, 0.51); (6.86, 1.22)\n", + "Romagnoli (5.99, 0.48); (6.00, 0.39)\n", + "Rodrigo Becao (6.00, 0.52); (6.13, 0.63)\n", + "Parisi (5.87, 0.40); (5.88, 0.34)\n", + "Mazzocchi (5.81, 0.40); (5.83, 0.36)\n", + "Bremer (6.21, 0.47); (6.40, 0.55)\n", + "Demiral (6.02, 0.52); (6.06, 0.49)\n", + "Valeri (6.08, 0.35); (6.40, 0.67)\n", + "Dimarco (6.17, 0.46); (6.58, 0.85)\n", + "Di Lorenzo (6.15, 0.41); (6.31, 0.49)\n", + "Ibanez (5.81, 0.51); (5.87, 0.58)\n", + "Tomori (6.09, 0.39); (6.16, 0.36)\n", + "Toloi (6.03, 0.48); (6.15, 0.56)\n", + "Gosens (6.07, 0.34); (6.17, 0.40)\n", + "Rrahmani (6.23, 0.50); (6.63, 0.85)\n", + "Kalulu (6.20, 0.49); (6.56, 0.80)\n", + "Bijol (6.07, 0.51); (6.47, 0.91)\n", + "Doig (6.16, 0.51); (6.72, 1.05)\n", + "Mario Rui (6.03, 0.42); (6.13, 0.44)\n", + "Spinazzola (6.02, 0.32); (6.02, 0.34)\n", + "Lazzari (6.01, 0.51); (6.38, 0.91)\n", + "Mancini (5.76, 0.50); (5.72, 0.40)\n", + "Danilo (6.12, 0.42); (6.25, 0.48)\n", + "Kyriakopoulos (5.78, 0.52); (5.74, 0.45)\n", + "Vojvoda (5.95, 0.37); (6.00, 0.40)\n", + "Scalvini (5.98, 0.38); (6.05, 0.41)\n", + "Carlos Augusto (5.83, 0.45); (5.95, 0.58)\n", + "Skriniar (6.15, 0.46); (6.35, 0.59)\n", + "Schuurs (6.03, 0.36); (6.07, 0.34)\n", + "Juan Jesus (6.12, 0.44); (6.36, 0.63)\n", + "Bonucci (6.21, 0.56); (6.77, 1.10)\n", + "Calabria (6.10, 0.35); (6.36, 0.60)\n", + "Bastoni (6.16, 0.41); (6.32, 0.50)\n", + "Depaoli (5.74, 0.40); (5.78, 0.48)\n", + "Darmian (6.06, 0.37); (6.30, 0.64)\n", + "Sernicola (5.96, 0.41); (6.27, 0.77)\n", + "Mari' (6.02, 0.59); (6.27, 0.86)\n", + "Martinez Quarta (5.76, 0.50); (5.73, 0.49)\n", + "Maehle (6.03, 0.30); (6.07, 0.36)\n", + "Olivera (6.17, 0.38); (6.48, 0.66)\n", + "Biraghi (5.87, 0.57); (6.14, 0.87)\n", + "Medel (5.75, 0.54); (5.69, 0.42)\n", + "Patric (5.77, 0.56); (5.77, 0.56)\n", + "Rodriguez R. (5.88, 0.39); (5.89, 0.33)\n", + "Aina (5.88, 0.36); (5.89, 0.33)\n", + "Cambiaso (5.88, 0.51); (5.94, 0.54)\n", + "Perez N. (5.74, 0.41); (5.70, 0.35)\n", + "Holm (5.93, 0.41); (6.14, 0.65)\n", + "Baschirotto (6.15, 0.47); (6.56, 0.86)\n", + "Dodo' (5.72, 0.41); (5.70, 0.39)\n", + "Hysaj (5.70, 0.40); (5.70, 0.41)\n", + "Faraoni (6.07, 0.44); (6.44, 0.81)\n", + "Bianchetti (5.74, 0.41); (5.79, 0.47)\n", + "Milenkovic (5.80, 0.51); (5.79, 0.47)\n", + "Marusic (5.68, 0.48); (5.65, 0.46)\n", + "Colley (5.78, 0.53); (5.73, 0.39)\n", + "Toljan (5.81, 0.38); (5.80, 0.33)\n", + "Celik (5.90, 0.35); (5.89, 0.32)\n", + "Ampadu (5.77, 0.51); (5.71, 0.39)\n", + "Singo (6.14, 0.54); (6.63, 1.00)\n", + "Casale (5.82, 0.41); (5.79, 0.34)\n", + "Daniliuc (5.83, 0.55); (5.78, 0.46)\n", + "Pongracic (5.88, 0.42); (5.86, 0.33)\n", + "Soppy (5.87, 0.33); (5.84, 0.30)\n", + "Kiwior (5.78, 0.39); (5.76, 0.32)\n", + "Posch (5.96, 0.59); (6.46, 1.09)\n", + "Masina (5.98, 0.42); (6.23, 0.72)\n", + "Ghiglione (5.72, 0.37); (5.69, 0.32)\n", + "De Vrij (5.81, 0.36); (5.79, 0.31)\n", + "Alex Sandro (5.83, 0.34); (5.79, 0.29)\n", + "Ceccherini (5.76, 0.48); (5.78, 0.49)\n", + "Fazio (5.69, 0.58); (5.64, 0.55)\n", + "Rogerio (5.77, 0.40); (5.75, 0.35)\n", + "Reca (5.75, 0.38); (5.72, 0.32)\n", + "Gunter (5.73, 0.53); (5.67, 0.44)\n", + "Djidji (5.82, 0.46); (5.79, 0.36)\n", + "Augello (5.77, 0.40); (5.84, 0.46)\n", + "Bellanova (5.82, 0.42); (5.85, 0.43)\n", + "Erlic (5.84, 0.54); (5.95, 0.67)\n", + "Ismajli (5.64, 0.53); (5.58, 0.43)\n", + "Ebuehi (5.64, 0.38); (5.57, 0.32)\n", + "Kasius (5.96, 0.32); (5.95, 0.31)\n", + "Lucumi' (5.77, 0.39); (5.73, 0.32)\n", + "Hien (5.67, 0.45); (5.62, 0.37)\n", + "Acerbi (6.01, 0.53); (6.29, 0.80)\n", + "Zappacosta (6.08, 0.39); (6.28, 0.56)\n", + "Chiriches (5.71, 0.53); (5.65, 0.42)\n", + "Gyomber (5.68, 0.52); (5.62, 0.42)\n", + "Pezzella Giu. (5.95, 0.37); (5.98, 0.36)\n", + "Ferrari G. (5.69, 0.55); (5.63, 0.47)\n", + "Bereszynski (5.66, 0.42); (5.60, 0.36)\n", + "Hateboer (5.78, 0.34); (5.77, 0.31)\n", + "Karsdorp (5.96, 0.43); (6.01, 0.39)\n", + "Nuytinck (5.98, 0.52); (6.03, 0.50)\n", + "Lykogiannis (5.72, 0.35); (5.69, 0.31)\n", + "Buongiorno (5.87, 0.40); (5.85, 0.33)\n", + "Nikolaou (5.66, 0.44); (5.62, 0.35)\n", + "Lazaro (5.83, 0.42); (5.86, 0.41)\n", + "Gallo (5.86, 0.41); (5.82, 0.34)\n", + "Ayhan (5.71, 0.57); (5.77, 0.67)\n", + "Dest (5.84, 0.36); (5.83, 0.32)\n", + "Stojanovic (5.75, 0.51); (5.71, 0.47)\n", + "Birindelli (5.75, 0.32); (5.72, 0.28)\n", + "Gendrey (5.77, 0.35); (5.71, 0.30)\n", + "Ebosse (5.86, 0.33); (5.81, 0.29)\n", + "Lochoshvili (5.81, 0.37); (5.80, 0.32)\n", + "Bronn (5.85, 0.35); (5.81, 0.29)\n", + "Thiaw (6.00, 0.40); (6.06, 0.42)\n", + "Izzo (5.76, 0.35); (5.68, 0.30)\n", + "D'ambrosio (6.13, 0.38); (6.27, 0.46)\n", + "Rugani (5.94, 0.34); (5.93, 0.29)\n", + "De Sciglio (6.06, 0.50); (6.32, 0.74)\n", + "Luperto (5.74, 0.59); (5.66, 0.47)\n", + "De Silvestri (5.83, 0.52); (6.09, 0.81)\n", + "Djimsiti (5.89, 0.45); (5.93, 0.44)\n", + "Palomino (6.07, 0.47); (6.15, 0.43)\n", + "Bonifazi (5.64, 0.50); (5.62, 0.38)\n", + "Hristov (5.69, 0.47); (5.85, 0.69)\n", + "Donati (5.65, 0.42); (5.59, 0.35)\n", + "Umtiti (5.82, 0.48); (5.79, 0.39)\n", + "Kjaer (6.01, 0.31); (6.00, 0.31)\n", + "Igor (5.72, 0.53); (5.67, 0.42)\n", + "Okoli (5.65, 0.47); (5.62, 0.38)\n", + "Soumaoro (5.74, 0.52); (5.69, 0.40)\n", + "Caldirola (5.75, 0.48); (5.69, 0.37)\n", + "Ballo-Toure' (5.73, 0.32); (5.69, 0.28)\n", + "Bradaric (5.82, 0.40); (5.82, 0.34)\n", + "De Winter (5.77, 0.53); (5.70, 0.39)\n", + "Quagliata (5.94, 0.32); (5.96, 0.33)\n", + "Ferrari A. (5.75, 0.48); (5.82, 0.59)\n", + "Florenzi (6.11, 0.39); (6.34, 0.58)\n", + "Sala (5.85, 0.39); (5.99, 0.55)\n", + "Caldara (5.82, 0.55); (5.78, 0.49)\n", + "Murru (5.61, 0.40); (5.65, 0.54)\n", + "Marlon (5.62, 0.39); (5.55, 0.32)\n", + "Walukiewicz (5.69, 0.44); (5.65, 0.34)\n", + "Kumbulla (5.78, 0.34); (5.72, 0.29)\n", + "Amian (5.71, 0.52); (5.66, 0.46)\n", + "Ostigard (6.00, 0.38); (6.06, 0.36)\n", + "Sambia (5.70, 0.39); (5.64, 0.32)\n", + "Aiwu (5.93, 0.43); (5.98, 0.44)\n", + "Ehizibue (5.87, 0.30); (5.80, 0.27)\n", + "Hendry (5.98, 0.44); (6.03, 0.44)\n", + "Marrone (5.51, 0.45); (5.45, 0.38)\n", + "Radovanovic (5.77, 0.51); (5.84, 0.58)\n", + "Murillo (5.63, 0.35); (5.52, 0.29)\n", + "Venuti (5.81, 0.33); (5.78, 0.30)\n", + "Magnani (5.77, 0.46); (5.73, 0.35)\n", + "Terzic (5.94, 0.30); (5.91, 0.29)\n", + "Gabbia (5.61, 0.47); (5.56, 0.39)\n", + "Zortea (5.80, 0.38); (5.82, 0.42)\n", + "Dawidowicz (5.75, 0.39); (5.72, 0.32)\n", + "Pirola (5.82, 0.32); (5.73, 0.27)\n", + "Carboni (5.62, 0.45); (5.57, 0.36)\n", + "Lovato (5.82, 0.50); (5.78, 0.38)\n", + "Tuia (5.76, 0.45); (5.80, 0.48)\n", + "Amione (5.63, 0.35); (5.56, 0.30)\n", + "Vasquez (5.89, 0.43); (5.93, 0.40)\n", + "Zima (5.87, 0.35); (5.86, 0.31)\n", + "Coppola D. (5.99, 0.36); (6.00, 0.34)\n", + "Gatti (5.95, 0.45); (5.99, 0.38)\n", + "Gila (5.81, 0.49); (5.78, 0.40)\n", + "Cabal (5.82, 0.43); (5.83, 0.39)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sosa (5.64, 0.48); (5.57, 0.42)\n", + "Conti (6.10, 0.55); (6.68, 1.13)\n", + "Dermaku (5.90, 0.43); (5.93, 0.39)\n", + "Tonelli (5.61, 0.47); (5.58, 0.38)\n", + "Radu (5.65, 0.49); (5.59, 0.43)\n", + "Paletta (5.82, 0.42); (5.83, 0.39)\n", + "Fares (5.76, 0.36); (5.71, 0.32)\n", + "Marchizza (5.64, 0.38); (5.57, 0.32)\n", + "Romagna (5.85, 0.44); (5.87, 0.41)\n", + "Ranieri L. (5.71, 0.37); (5.70, 0.38)\n", + "Cetin (5.88, 0.52); (5.83, 0.40)\n", + "Muldur (5.65, 0.42); (5.60, 0.36)\n", + "Adopo (5.82, 0.44); (5.81, 0.38)\n", + "Ferrer (5.86, 0.44); (5.86, 0.36)\n", + "Ruggeri (5.85, 0.34); (5.84, 0.31)\n", + "Antov (5.61, 0.51); (5.54, 0.43)\n", + "Amey (5.95, 0.42); (6.01, 0.41)\n", + "Ferrarini (5.82, 0.42); (5.83, 0.39)\n", + "Kamenovic (6.19, 0.44); (6.40, 0.54)\n", + "Vina (5.73, 0.48); (5.68, 0.39)\n", + "Zanoli (5.93, 0.32); (5.91, 0.29)\n", + "Zanotti (5.89, 0.39); (5.93, 0.41)\n", + "Ruan (5.63, 0.56); (5.64, 0.39)\n", + "Motoc (5.88, 0.41); (5.91, 0.38)\n", + "Cacace (5.84, 0.35); (5.79, 0.29)\n", + "Bayeye (5.92, 0.41); (5.97, 0.42)\n", + "Ebosele (5.96, 0.44); (6.03, 0.42)\n", + "Buta (5.92, 0.40); (5.97, 0.39)\n", + "Ndiaye (5.93, 0.43); (5.98, 0.44)\n", + "Abankwah (5.92, 0.40); (5.97, 0.39)\n", + "Guessand A. (5.92, 0.40); (5.97, 0.39)\n", + "Guarino (5.90, 0.43); (5.93, 0.40)\n", + "Milinkovic-Savic (6.44, 0.62); (7.60, 1.86)\n", + "Barella (6.33, 0.53); (6.93, 1.12)\n", + "Kvaratskhelia (6.59, 0.70); (8.44, 2.82)\n", + "Zaccagni (6.31, 0.59); (7.24, 1.55)\n", + "Zielinski (6.23, 0.54); (6.91, 1.21)\n", + "Luis Alberto (6.25, 0.62); (7.24, 1.62)\n", + "Frattesi (6.03, 0.52); (6.46, 0.97)\n", + "Politano (6.13, 0.41); (6.47, 0.72)\n", + "Koopmeiners (6.24, 0.54); (6.86, 1.15)\n", + "Vlasic (6.28, 0.56); (7.41, 1.76)\n", + "Pereyra (6.12, 0.56); (6.67, 1.09)\n", + "Felipe Anderson (6.23, 0.62); (7.34, 1.76)\n", + "Calhanoglu (6.30, 0.64); (7.42, 1.80)\n", + "Strefezza (6.34, 0.51); (7.12, 1.38)\n", + "Diaz B. (6.12, 0.55); (6.74, 1.15)\n", + "Pellegrini Lo. (6.25, 0.66); (7.62, 2.06)\n", + "Zambo Anguissa (6.10, 0.37); (6.15, 0.36)\n", + "Lobotka (6.23, 0.43); (6.55, 0.68)\n", + "Malinovskyi (6.27, 0.59); (7.30, 1.67)\n", + "Candreva (6.16, 0.67); (7.20, 1.71)\n", + "Pasalic (6.24, 0.68); (7.77, 2.24)\n", + "Bennacer (6.21, 0.44); (6.50, 0.69)\n", + "Kostic (6.20, 0.49); (6.79, 1.07)\n", + "Radonjic (6.27, 0.48); (7.03, 1.30)\n", + "Samardzic (6.15, 0.47); (6.63, 0.95)\n", + "Chiesa (6.25, 0.52); (7.04, 1.35)\n", + "Lovric (6.19, 0.38); (6.53, 0.70)\n", + "De Ketelaere (5.90, 0.34); (5.91, 0.36)\n", + "Brozovic (6.17, 0.38); (6.38, 0.51)\n", + "Pogba (5.93, 0.41); (5.99, 0.39)\n", + "Bonaventura (6.10, 0.61); (6.68, 1.20)\n", + "Sensi (5.84, 0.36); (5.93, 0.50)\n", + "Bandinelli (5.84, 0.43); (5.94, 0.57)\n", + "Ikone' (5.88, 0.40); (6.08, 0.65)\n", + "Barak (6.20, 0.69); (7.64, 2.13)\n", + "Mkhitaryan (6.21, 0.58); (6.99, 1.34)\n", + "Pessina (5.85, 0.36); (5.86, 0.39)\n", + "Tonali (6.28, 0.54); (6.88, 1.12)\n", + "Arslan (5.93, 0.40); (5.99, 0.46)\n", + "Lazovic (6.13, 0.39); (6.47, 0.73)\n", + "Cristante (5.95, 0.48); (6.05, 0.53)\n", + "Elmas (6.11, 0.39); (6.37, 0.63)\n", + "Bajrami (6.24, 0.55); (7.04, 1.40)\n", + "Vecino (6.06, 0.40); (6.33, 0.67)\n", + "Paredes (5.72, 0.35); (5.66, 0.30)\n", + "Mandragora (5.97, 0.41); (6.04, 0.47)\n", + "Djuricic (5.78, 0.48); (6.05, 0.79)\n", + "Lukic (6.07, 0.51); (6.47, 0.90)\n", + "Soriano (5.86, 0.41); (5.85, 0.37)\n", + "Zaniolo (5.91, 0.53); (6.20, 0.83)\n", + "Sottil (6.01, 0.53); (6.53, 1.03)\n", + "Traore' Hj. (6.18, 0.57); (7.13, 1.57)\n", + "Messias (6.13, 0.60); (7.05, 1.52)\n", + "Thorstvedt (5.95, 0.45); (6.28, 0.84)\n", + "Cuadrado (6.25, 0.48); (6.63, 0.81)\n", + "Locatelli (6.20, 0.48); (6.57, 0.81)\n", + "Rabiot (5.77, 0.36); (5.74, 0.32)\n", + "Dominguez (5.96, 0.41); (6.00, 0.39)\n", + "Tameze (6.16, 0.44); (6.59, 0.87)\n", + "Miranchuk (6.06, 0.56); (6.79, 1.28)\n", + "Vilhena (5.73, 0.36); (5.76, 0.43)\n", + "De Roon (6.00, 0.46); (6.24, 0.70)\n", + "Verdi (6.25, 0.62); (7.47, 1.88)\n", + "Walace (5.86, 0.41); (5.87, 0.36)\n", + "Wijnaldum (5.89, 0.41); (5.91, 0.39)\n", + "Mckennie (6.18, 0.51); (6.77, 1.08)\n", + "Makengo (5.91, 0.48); (5.97, 0.49)\n", + "Ricci S. (5.88, 0.39); (5.92, 0.36)\n", + "Coulibaly L. (5.84, 0.45); (6.09, 0.75)\n", + "Sabiri (6.04, 0.61); (6.92, 1.49)\n", + "Miretti (6.06, 0.42); (6.14, 0.38)\n", + "Colpani (6.10, 0.40); (6.54, 0.90)\n", + "Haas (5.86, 0.37); (5.88, 0.36)\n", + "Orsolini (6.14, 0.57); (7.08, 1.50)\n", + "Matic (5.99, 0.32); (5.99, 0.32)\n", + "Ascacibar (6.00, 0.37); (6.04, 0.38)\n", + "Ederson D.s. (6.15, 0.54); (6.78, 1.19)\n", + "Ciurria (5.89, 0.41); (6.01, 0.56)\n", + "Gonzalez J. (6.05, 0.47); (6.48, 0.90)\n", + "El Shaarawy (6.02, 0.46); (6.38, 0.84)\n", + "Meite' (5.87, 0.36); (5.88, 0.34)\n", + "Bourabia (6.17, 0.52); (6.95, 1.34)\n", + "Ndombele' (5.92, 0.32); (5.91, 0.30)\n", + "Henderson L. (5.96, 0.43); (6.18, 0.69)\n", + "Maggiore (5.98, 0.45); (6.16, 0.63)\n", + "Ilic (5.99, 0.41); (6.13, 0.52)\n", + "Kovalenko (5.92, 0.44); (6.19, 0.72)\n", + "Zalewski (5.83, 0.34); (5.80, 0.31)\n", + "Pickel (5.88, 0.35); (5.97, 0.51)\n", + "Ferguson (6.07, 0.51); (6.59, 1.02)\n", + "Camara Ma. (6.10, 0.30); (6.18, 0.34)\n", + "Saponara (6.21, 0.48); (6.77, 1.05)\n", + "Rincon (5.77, 0.34); (5.76, 0.31)\n", + "Cataldi (6.09, 0.44); (6.23, 0.51)\n", + "Zurkowski (6.11, 0.52); (6.70, 1.11)\n", + "Rovella (5.96, 0.38); (6.03, 0.46)\n", + "Agudelo (5.93, 0.50); (6.31, 0.90)\n", + "Amrabat (6.00, 0.38); (6.17, 0.56)\n", + "Lopez M. (6.03, 0.46); (6.21, 0.60)\n", + "Gyasi (5.88, 0.56); (6.36, 1.05)\n", + "Pobega (6.15, 0.45); (6.50, 0.79)\n", + "Harroui (5.84, 0.32); (5.82, 0.29)\n", + "Ceide (5.96, 0.35); (5.97, 0.38)\n", + "Baldanzi (6.25, 0.47); (7.03, 1.36)\n", + "Blin (6.00, 0.30); (5.98, 0.30)\n", + "Hjulmand (5.93, 0.53); (5.88, 0.42)\n", + "D'alessandro (5.98, 0.30); (5.99, 0.36)\n", + "Grassi (5.89, 0.44); (5.86, 0.34)\n", + "Krunic (6.00, 0.32); (6.02, 0.35)\n", + "Linetty (5.77, 0.32); (5.75, 0.30)\n", + "Miguel Veloso (5.93, 0.48); (6.00, 0.51)\n", + "Ekdal (5.74, 0.48); (5.73, 0.47)\n", + "Schouten (6.01, 0.48); (6.24, 0.70)\n", + "Villar (5.78, 0.32); (5.72, 0.28)\n", + "Saelemaekers (6.00, 0.40); (6.15, 0.54)\n", + "Vranckx (5.95, 0.39); (6.01, 0.39)\n", + "Basic (5.83, 0.35); (5.84, 0.36)\n", + "Aebischer (5.85, 0.30); (5.84, 0.29)\n", + "Marcos Antonio (5.82, 0.32); (5.77, 0.28)\n", + "Ranocchia F. (5.96, 0.41); (6.20, 0.68)\n", + "Bistrovic (5.84, 0.31); (5.85, 0.32)\n", + "Verre (5.67, 0.37); (5.62, 0.32)\n", + "Gagliardini (6.10, 0.39); (6.38, 0.71)\n", + "Barberis (5.72, 0.38); (5.71, 0.33)\n", + "Leris (5.69, 0.35); (5.70, 0.41)\n", + "Vieira (5.70, 0.34); (5.69, 0.34)\n", + "Winks (5.85, 0.41); (5.90, 0.43)\n", + "Castrovilli (5.97, 0.42); (6.14, 0.61)\n", + "Maldini (6.11, 0.40); (6.71, 1.15)\n", + "Marin (5.87, 0.51); (5.89, 0.48)\n", + "Maleh (5.97, 0.37); (6.19, 0.65)\n", + "Matheus Henrique (5.88, 0.39); (5.90, 0.34)\n", + "Asllani (6.00, 0.39); (6.11, 0.49)\n", + "Moro N. (5.84, 0.34); (5.83, 0.31)\n", + "Oudin (6.01, 0.35); (6.09, 0.41)\n", + "Duncan (5.92, 0.43); (6.10, 0.62)\n", + "Molina S. (5.78, 0.38); (5.79, 0.37)\n", + "Kastanos (5.83, 0.45); (5.83, 0.40)\n", + "Valoti (5.78, 0.33); (5.72, 0.28)\n", + "Gaetano (6.03, 0.36); (6.09, 0.39)\n", + "Escalante (5.77, 0.42); (5.73, 0.35)\n", + "Bohinen (5.82, 0.38); (5.84, 0.38)\n", + "Terracciano F. (6.01, 0.30); (6.02, 0.34)\n", + "Milanese (5.88, 0.44); (5.93, 0.44)\n", + "Castagnetti (5.97, 0.33); (5.99, 0.34)\n", + "Listkowski (5.93, 0.32); (5.91, 0.30)\n", + "Adli (5.73, 0.33); (5.73, 0.32)\n", + "Hrustic (5.66, 0.42); (5.61, 0.36)\n", + "D'andrea (5.95, 0.34); (6.00, 0.43)\n", + "Benassi (5.77, 0.32); (5.77, 0.33)\n", + "Baez (5.92, 0.46); (5.97, 0.44)\n", + "Vignato (5.87, 0.33); (5.87, 0.32)\n", + "Obiang (5.86, 0.39); (5.84, 0.32)\n", + "Askildsen (5.84, 0.33); (5.78, 0.29)\n", + "Hongla (5.78, 0.37); (5.83, 0.45)\n", + "Yepes (6.04, 0.33); (6.10, 0.35)\n", + "Helgason (5.82, 0.33); (5.80, 0.29)\n", + "Bondo (5.91, 0.42); (5.94, 0.37)\n", + "Ellertsson (5.88, 0.38); (5.90, 0.34)\n", + "Capezzi (5.93, 0.33); (5.92, 0.30)\n", + "Jajalo (5.87, 0.33); (5.85, 0.30)\n", + "Machin (5.84, 0.41); (5.86, 0.39)\n", + "Bakayoko (5.80, 0.37); (5.74, 0.32)\n", + "Scozzarella (5.84, 0.41); (5.86, 0.39)\n", + "Fagioli (6.12, 0.46); (6.60, 0.94)\n", + "Demme (6.00, 0.37); (6.19, 0.57)\n", + "Akpa Akpro (5.90, 0.44); (5.95, 0.42)\n", + "Darboe (5.90, 0.43); (6.01, 0.43)\n", + "Bove (6.20, 0.36); (6.67, 0.90)\n", + "Urbanski (5.92, 0.41); (5.96, 0.41)\n", + "Bertini (5.94, 0.41); (5.99, 0.39)\n", + "Cortinovis (5.85, 0.39); (5.89, 0.42)\n", + "Romero L. (5.81, 0.31); (5.80, 0.31)\n", + "Bianco (5.92, 0.40); (5.98, 0.46)\n", + "Sher (5.96, 0.31); (5.94, 0.29)\n", + "Nguiamba (6.04, 0.37); (6.08, 0.34)\n", + "Volpato (6.23, 0.40); (7.16, 1.52)\n", + "Praszelik (6.00, 0.30); (5.98, 0.30)\n", + "Trimboli (5.85, 0.41); (5.90, 0.43)\n", + "Pafundi (5.93, 0.40); (5.98, 0.40)\n", + "Bjorkengren (5.94, 0.42); (6.00, 0.43)\n", + "Vignato S. (5.88, 0.43); (5.91, 0.41)\n", + "Samek (5.94, 0.42); (6.00, 0.43)\n", + "Zerbin (5.95, 0.35); (5.99, 0.38)\n", + "Ilkhan (5.75, 0.35); (5.71, 0.30)\n", + "Degli Innocenti (5.94, 0.46); (5.99, 0.44)\n", + "Fazzini (5.63, 0.48); (5.58, 0.38)\n", + "Acella (5.95, 0.43); (6.03, 0.48)\n", + "Tripi (5.92, 0.41); (5.95, 0.40)\n", + "Garbett (5.92, 0.41); (5.97, 0.43)\n", + "Iling-Junior (5.99, 0.50); (6.03, 0.45)\n", + "Immobile (6.49, 0.72); (8.44, 2.88)\n", + "Vlahovic (6.35, 0.70); (8.01, 2.47)\n", + "Rafael Leao (6.46, 0.68); (8.11, 2.48)\n", + "Martinez L. (6.32, 0.73); (8.24, 2.70)\n", + "Dybala (6.33, 0.61); (7.56, 1.93)\n", + "Arnautovic (6.23, 0.67); (7.74, 2.21)\n", + "Beto (6.20, 0.69); (7.68, 2.18)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Giroud (6.25, 0.68); (7.80, 2.27)\n", + "Osimhen (6.39, 0.71); (8.15, 2.58)\n", + "Deulofeu (6.42, 0.64); (7.86, 2.22)\n", + "Lukaku (6.47, 0.71); (8.41, 2.84)\n", + "Milik (6.18, 0.56); (7.02, 1.39)\n", + "Pedro (6.26, 0.61); (7.42, 1.81)\n", + "Abraham (6.23, 0.71); (7.87, 2.37)\n", + "Berardi (6.46, 0.68); (8.20, 2.60)\n", + "Dia (6.25, 0.69); (7.86, 2.33)\n", + "Lookman (6.44, 0.65); (8.01, 2.39)\n", + "Simeone (6.18, 0.73); (7.79, 2.32)\n", + "Correa (6.13, 0.59); (7.15, 1.63)\n", + "Zapata D. (6.39, 0.69); (8.22, 2.64)\n", + "Dzeko (6.30, 0.68); (7.94, 2.38)\n", + "Nzola (5.88, 0.43); (6.24, 0.84)\n", + "Sanabria (6.12, 0.58); (7.16, 1.62)\n", + "Muriel (6.36, 0.68); (7.99, 2.41)\n", + "Rebic (6.06, 0.49); (6.59, 1.03)\n", + "Bonazzoli (6.16, 0.60); (7.25, 1.71)\n", + "Caprari (6.36, 0.64); (7.86, 2.24)\n", + "Di Maria (6.08, 0.65); (6.81, 1.40)\n", + "Lozano (6.19, 0.58); (7.16, 1.57)\n", + "Ceesay (6.07, 0.55); (6.97, 1.44)\n", + "Pinamonti (6.11, 0.65); (7.38, 1.91)\n", + "Kouame' (6.19, 0.61); (7.36, 1.79)\n", + "Lauriente' (6.28, 0.69); (7.47, 1.95)\n", + "Jovic (6.02, 0.61); (6.88, 1.46)\n", + "Barrow (6.11, 0.61); (7.09, 1.58)\n", + "Henry (6.07, 0.63); (7.08, 1.64)\n", + "Piatek (6.03, 0.52); (6.87, 1.36)\n", + "Gonzalez N. (6.24, 0.64); (7.47, 1.89)\n", + "Dessers (6.09, 0.49); (6.91, 1.34)\n", + "Caputo (6.02, 0.64); (6.99, 1.58)\n", + "Raspadori (6.11, 0.67); (7.15, 1.70)\n", + "Lammers (5.92, 0.39); (6.09, 0.62)\n", + "Okereke (6.00, 0.55); (6.62, 1.19)\n", + "Cabral (6.08, 0.48); (6.86, 1.29)\n", + "Gabbiadini (6.10, 0.65); (7.24, 1.78)\n", + "Belotti (6.21, 0.66); (7.62, 2.11)\n", + "Origi (6.03, 0.56); (6.70, 1.22)\n", + "Botheim (5.97, 0.42); (6.39, 0.88)\n", + "Alvarez A. (6.13, 0.60); (7.13, 1.62)\n", + "Verde (6.25, 0.52); (7.14, 1.47)\n", + "Destro (6.06, 0.65); (7.23, 1.80)\n", + "Kean (6.02, 0.56); (6.70, 1.26)\n", + "Pellegri (5.84, 0.33); (5.91, 0.54)\n", + "Satriano (5.89, 0.45); (6.28, 0.89)\n", + "Success (6.24, 0.59); (7.14, 1.49)\n", + "Mota (5.84, 0.41); (6.25, 0.86)\n", + "Banda (5.98, 0.36); (6.06, 0.45)\n", + "Hojlund (6.01, 0.43); (6.54, 1.01)\n", + "Lasagna (5.89, 0.40); (6.19, 0.78)\n", + "Pjaca (5.99, 0.47); (6.45, 0.97)\n", + "Gytkjaer (5.85, 0.37); (6.14, 0.74)\n", + "Petagna (5.99, 0.54); (6.66, 1.23)\n", + "Nestorovski (6.07, 0.46); (6.53, 0.95)\n", + "Zanimacchia (5.97, 0.34); (6.06, 0.45)\n", + "Piccoli (5.70, 0.39); (5.67, 0.34)\n", + "Kallon (5.80, 0.36); (5.82, 0.40)\n", + "Ciofani D. (6.13, 0.41); (6.85, 1.25)\n", + "Di Francesco F. (6.13, 0.47); (6.73, 1.09)\n", + "Boga (5.98, 0.38); (6.02, 0.39)\n", + "Zirkzee (6.04, 0.53); (6.82, 1.31)\n", + "Quagliarella (5.88, 0.47); (6.24, 0.85)\n", + "Ibrahimovic (6.36, 0.65); (7.89, 2.28)\n", + "Shomurodov (6.04, 0.51); (6.80, 1.30)\n", + "Djuric (5.89, 0.30); (5.86, 0.30)\n", + "Strelec (5.77, 0.38); (5.95, 0.66)\n", + "Antiste (5.75, 0.37); (5.80, 0.49)\n", + "Seck (5.94, 0.33); (6.04, 0.51)\n", + "Buonaiuto (6.05, 0.36); (6.19, 0.46)\n", + "Sansone (6.00, 0.46); (6.40, 0.86)\n", + "Pussetto (5.99, 0.55); (6.67, 1.24)\n", + "Cambiaghi (5.91, 0.30); (5.89, 0.31)\n", + "Colombo (6.01, 0.49); (6.74, 1.24)\n", + "Afena-Gyan (5.87, 0.54); (6.22, 0.91)\n", + "Defrel (5.84, 0.42); (6.00, 0.63)\n", + "Karamoh (6.03, 0.33); (6.11, 0.39)\n", + "Cancellieri (6.19, 0.49); (6.76, 1.09)\n", + "Tsadjout (6.03, 0.42); (6.20, 0.57)\n", + "Rodriguez P. (5.92, 0.34); (5.94, 0.34)\n", + "Valencia D. (5.68, 0.32); (5.70, 0.29)\n", + "Edera (5.92, 0.43); (5.98, 0.47)\n", + "Oddei (6.12, 0.31); (6.11, 0.31)\n", + "Raimondo (5.95, 0.43); (6.02, 0.45)\n", + "Kristoffersen (5.89, 0.43); (5.94, 0.41)\n", + "Kaio Jorge (5.98, 0.36); (6.03, 0.39)\n", + "De Luca (5.86, 0.44); (5.92, 0.46)\n", + "Soule' (6.01, 0.45); (6.08, 0.39)\n", + "Lazetic (5.97, 0.41); (6.05, 0.46)\n", + "Voelkerling Persson (5.94, 0.44); (6.01, 0.45)\n", + "Sanca (5.91, 0.37); (5.94, 0.36)\n" + ] + } + ], + "source": [ + "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", + "\n", + "tot_matches = 2 # home and not home\n", + "\n", + "current_season_games = max(players_old['games'])\n", + "\n", + "for i in range(players.shape[0]):\n", + " home = 0\n", + " \n", + " for k in range(tot_matches):\n", + " #matchday_out = k + 1\n", + " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", + " \n", + " player = players.index[i]\n", + " team = players['team'][i]\n", + " oppteam = 'Avg'\n", + " home = not home\n", + " \n", + " oldseason_ok = True\n", + " \n", + " try:\n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, oldseason = True)\n", + " games = max( players_old['games'][i], players_old['gk_games'][i] )\n", + " mins = max( players_old['minutes'][i], players_old['gk_minutes'][i] )\n", + " except:\n", + " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", + " oldseason_ok = False\n", + " games = 0\n", + " mins = 0\n", + " \n", + "\n", + " role = players['r'][player] \n", + "\n", + " starter = 0\n", + " voteperc = 0\n", + " \n", + " cs = 0\n", + " if(role == 'P'):\n", + " cs = dist[2].probs.numpy()[0] * 100\n", + " \n", + " starter = int( player in gk_starters )\n", + " if(starter):\n", + " voteperc = 100\n", + " else:\n", + " voteperc = 0\n", + " else:\n", + " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", + " voteperc = int( min( 1, games / current_season_games ) * 100) \n", + " \n", + " if (not oldseason_ok):\n", + " starter = -1\n", + " voteperc = -100\n", + "\n", + " if(k == 0):\n", + " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", + " \n", + " numrow_ = [mean[0], std[0], \n", + " mean[1], std[1], \n", + " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", + " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", + " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", + " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", + " cs] \n", + "\n", + " if(k == 0):\n", + " numrow = numrow_\n", + " else:\n", + " for j in range(len(numrow)):\n", + " numrow[j] += numrow_[j]\n", + "\n", + " for j in range(len(numrow)):\n", + " numrow[j] /= tot_matches\n", + " \n", + " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", + " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", + " \n", + " row += numrow # list concat\n", + " \n", + " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", + "\n", + " output = pd.concat([output, row_df])\n", + " \n", + " \n", + "\n", + "output = output.set_index('player')\n", + "\n", + "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", + "\n", + "template_file = 'outputs/pred_matchday_base.xlsx'\n", + "dest_file = 'outputs/pred_avg_seriea_season2122.xlsx'\n", + "\n", + "shutil.copyfile(template_file, dest_file)\n", + "\n", + "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", + " output.to_excel(writer, sheet_name='data')" + ] + }, + { + "cell_type": "markdown", + "id": "cf9df3bb", + "metadata": {}, + "source": [ + "Various predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "7300f3c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Terracciano: MV 6.30 ± 0.71; FV 4.72 + 1.43 (5.7% cs)\n", + "Maignan: MV 6.27 ± 0.84; FV 5.66 + 1.16 (56.8% cs)\n", + "Tatarusanu: MV 6.22 ± 0.78; FV 5.53 + 1.19 (45.2% cs)\n", + "Handanovic: MV 5.89 ± 0.66; FV 3.40 + 1.88 (1.4% cs)\n", + "Onana: MV 6.32 ± 0.73; FV 5.05 + 1.33 (11.7% cs)\n", + "Sepe: MV 6.39 ± 0.74; FV 4.76 + 1.42 (6.4% cs)\n", + "Di Gregorio: MV 5.83 ± 0.80; FV 3.91 + 1.59 (4.5% cs)\n", + "Szczesny: MV 6.15 ± 0.77; FV 5.54 + 1.19 (47.8% cs)\n", + "Audero: MV 6.34 ± 0.77; FV 5.27 + 1.28 (17.9% cs)\n", + "Silvestri: MV 6.34 ± 0.71; FV 4.18 + 1.60 (2.7% cs)\n", + "Carnesecchi: MV 6.17 ± 0.82; FV 5.58 + 1.17 (56.1% cs)\n", + "Dragowski: MV 6.52 ± 0.82; FV 5.18 + 1.30 (13.1% cs)\n", + "Milinkovic-Savic V.: MV 6.27 ± 0.77; FV 4.90 + 1.38 (12.4% cs)\n", + "Sportiello: MV 6.32 ± 0.86; FV 5.06 + 1.31 (18.5% cs)\n", + "Falcone: MV 6.34 ± 0.79; FV 5.47 + 1.21 (34.2% cs)\n", + "Consigli: MV 6.12 ± 0.70; FV 4.86 + 1.35 (12.9% cs)\n", + "Vicario: MV 6.32 ± 0.85; FV 5.64 + 1.17 (51.2% cs)\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.31691631, 5.6403682 ]),\n", + " array([0.42497158, 0.58689165], dtype=float32),\n", + " [,\n", + " ,\n", + " ]]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Terracciano', log = 1, plot = 0)\n", + "predict_player('Maignan', log = 1, plot = 0)\n", + "predict_player('Tatarusanu', log = 1, plot = 0)\n", + "predict_player('Handanovic', log = 1, plot = 0)\n", + "predict_player('Onana', log = 1, plot = 0)\n", + "predict_player('Sepe', log = 1, plot = 0)\n", + "predict_player('Di Gregorio', log = 1, plot = 0)\n", + "predict_player('Szczesny', log = 1, plot = 0)\n", + "predict_player('Audero', log = 1, plot = 0)\n", + "predict_player('Silvestri', log = 1, plot = 0)\n", + "predict_player('Carnesecchi', log = 1, plot = 0)\n", + "predict_player('Dragowski', log = 1, plot = 0)\n", + "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", + "predict_player('Sportiello', log = 1, plot = 0)\n", + "predict_player('Falcone', log = 1, plot = 0)\n", + "predict_player('Consigli', log = 1, plot = 0)\n", + "predict_player('Vicario', log = 1, plot = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd126870", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", + "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", + "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", + "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", + "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", + "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", + "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", + "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", + "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", + "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", + "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", + "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", + "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.41176047, 8.29456946]),\n", + " array([0.70926785, 2.7289915 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Muriel', plot = 0, log = 1)\n", + "predict_player('Tonali', plot = 0, log = 1)\n", + "predict_player('Lobotka', plot = 0, log = 1)\n", + "predict_player('Politano', plot = 0, log = 1)\n", + "predict_player('Zapata D.', plot = 0, log = 1)\n", + "predict_player('Frattesi', plot = 0, log = 1)\n", + "predict_player('Gonzalez N.', plot = 0, log = 1)\n", + "predict_player('Abraham', plot = 0, log = 1)\n", + "predict_player('Pobega', plot = 0, log = 1)\n", + "predict_player('Mario Rui', plot = 0, log = 1)\n", + "predict_player('Cuadrado', plot = 0, log = 1)\n", + "predict_player('Skriniar', plot = 0, log = 1)\n", + "predict_player('Lukaku', plot = 0, log = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4b9f5a7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skriniar: MV 6.06 ± 0.88; FV 6.21 + 1.04\n", + "Cuadrado: MV 6.11 ± 0.82; FV 6.33 + 1.20\n", + "Bastoni: MV 6.10 ± 0.80; FV 6.23 + 0.95\n", + "Barak: MV 6.08 ± 1.33; FV 7.10 + 3.33\n", + "Politano: MV 6.08 ± 0.74; FV 6.35 + 1.22\n", + "Smalling: MV 6.18 ± 0.92; FV 6.51 + 1.54\n", + "Gosens: MV 6.02 ± 0.66; FV 6.08 + 0.75\n", + "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", + "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", + "Bastoni: MV 5.84 ± 0.91; FV 5.86 + 0.82\n", + "Barak: MV 5.76 ± 0.70; FV 5.75 + 0.72\n", + "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", + "Smalling: MV 6.17 ± 1.06; FV 6.60 + 1.91\n", + "Gosens: MV 5.75 ± 0.65; FV 5.80 + 0.85\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([5.75403311, 5.80380687]),\n", + " array([0.3249786 , 0.42722106], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Skriniar', log = 1, oldseason= True)\n", + "predict_player('Cuadrado', log = 1, oldseason= True)\n", + "predict_player('Bastoni', log = 1, oldseason= True)\n", + "predict_player('Barak', log = 1, oldseason= True)\n", + "predict_player('Politano', log = 1, oldseason= True)\n", + "predict_player('Smalling', log = 1, oldseason= True)\n", + "predict_player('Gosens', log = 1, oldseason= True)\n", + "\n", + "predict_player('Skriniar', log = 1, oldseason= False)\n", + "predict_player('Cuadrado', log = 1, oldseason= False)\n", + "predict_player('Bastoni', log = 1, oldseason= False)\n", + "predict_player('Barak', log = 1, oldseason= False)\n", + "predict_player('Politano', log = 1, oldseason= False)\n", + "predict_player('Smalling', log = 1, oldseason= False)\n", + "predict_player('Gosens', log = 1, oldseason= False)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "10c7ad3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" + ] + }, + { + "data": { + "text/plain": [ + "[array([6.3442238 , 7.94607029]),\n", + " array([0.71331024, 2.395806 ], dtype=float32),\n", + " [,\n", + " ]]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_player('Rafael Leao', plot = 1, log = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "0a5de8a1", + "metadata": {}, + "source": [ + "Code for predicting the probability distribution of total team points" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "65f9a572", + "metadata": {}, + "outputs": [], + "source": [ + "squad = ['Falcone',\n", + " 'Valeri',\n", + " 'Di Lorenzo',\n", + " 'Parisi',\n", + " 'Mario Rui',\n", + " 'Strefezza',\n", + " 'Frattesi',\n", + " 'Felipe Anderson',\n", + " 'Beto',\n", + " 'Abraham',\n", + " 'Nzola']\n", + "\n", + "\n", + "dist = [None] * len(squad)\n", + "\n", + "defenders = list([0]) * len(squad)\n", + "\n", + "\n", + "for i in range(len(squad)):\n", + " [X, y, dist[i]] = predict_player(squad[i], plot = 0, log = 0) \n", + " \n", + " if(players['r'][squad[i]] == 'D'):\n", + " defenders[i] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2e78f674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Avg Total Points = 75.96627577018738\n", + "Avg Mod Points = 1.478\n", + "Avg Clean Sheets = 0.364\n" + ] + } + ], + "source": [ + "ITERS = 500\n", + "\n", + "MOD = True\n", + "\n", + "total_points = np.zeros(ITERS)\n", + "clean_sheets = np.zeros(ITERS)\n", + "mod_points = np.zeros(ITERS)\n", + "\n", + "mv_samples = [None] * len(squad)\n", + "fv_samples = [None] * len(squad)\n", + "\n", + "for i in range(len(squad)):\n", + " mv_samples[i] = dist[i][0].sample(ITERS)\n", + " fv_samples[i] = dist[i][1].sample(ITERS)\n", + " \n", + "cs_samples = dist[0][2].sample(ITERS)\n", + "\n", + "for k in range(ITERS):\n", + " d_points = list([0]) * len(squad)\n", + " \n", + " cleansheet = float(cs_samples[k])\n", + " total_points[k] += cleansheet\n", + " clean_sheets[k] += cleansheet\n", + " \n", + " for i in range(len(squad)): \n", + " if(defenders[i] == 1):\n", + " d_points[i] = float(mv_samples[i][k])\n", + "\n", + " total_points[k] += float(fv_samples[i][k])\n", + " \n", + " d_points.sort(reverse = True)\n", + "\n", + " if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n", + " mod_avg = 0\n", + " for j in range(3):\n", + " mod_avg += round(d_points[j] * 2) / 2\n", + " mod_avg /= 3\n", + "\n", + " if(mod_avg >= 7):\n", + " mod_points[k] = 6\n", + " elif(mod_avg >= 6.5):\n", + " mod_points[k] = 3\n", + " elif(mod_avg >= 6):\n", + " mod_points[k] = 1\n", + "\n", + " total_points[k] += mod_points[k]\n", + " \n", + " \n", + "print('Avg Total Points = ' + str(total_points.mean()))\n", + "print('Avg Mod Points = ' + str(mod_points.mean()))\n", + "print('Avg Clean Sheets = ' + str(clean_sheets.mean()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c5d752d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loc_0 = total_points.mean()\n", + "\n", + "squad_model = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.Dense(3),\n", + " tfp.layers.DistributionLambda(\n", + " lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n", + " skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n", + " )\n", + " ]\n", + ")\n", + "\n", + "def negloglik(y, distr):\n", + " return -distr.log_prob(y)\n", + "\n", + "squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n", + "\n", + "dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n", + "squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "9754ec4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n", + "\n", + "\n", + "x = np.arange(start = 0, stop = 200, step = 0.001)\n", + "prb = squad_points_dist.prob(x)\n", + "\n", + "mn = total_points.mean()\n", + "\n", + "f, ax = plt.subplots(1, 2)\n", + "\n", + "ax[0].plot(x, prb)\n", + "ax[0].fill_between(x, prb, color = 'lightblue')\n", + "ax[0].vlines(x = mn, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n", + "\n", + "ax[0].set_xlim([40, 120])\n", + "ax[0].set_ylim([0, 0.12])\n", + "\n", + "ax[0].legend()\n", + "\n", + "#ax[0].hist(total_points, bins = 20, density = True)\n", + "\n", + "\n", + "ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + "text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n", + " 'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n", + " 'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n", + "\n", + "ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n", + "\n", + "ax[1].axis('off')\n", + "\n", + "\n", + "\n", + "plt.subplots_adjust(right=1.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "30744d7a", + "metadata": {}, + "source": [ + "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", + "\n", + "Here the code to generate the probability density function is reproduced." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "3ec6c3dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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36NZbbz3ttStWrJDT6QzcCgoKhlImEPFCuftqX0xiBRANhjWB1WLpP/vfMIwB951sw4YN2rx5s5588kmtXLlSL7zwwmmvve+++9Tc3By4HT58eDhlAhEr1Luv+rHXCIBoMKT/lmVlZclmsw3ogtTV1Q3olpysuLhYknT++efr6NGjevDBB/XVr371lNc6HA45HI6hlAZElVDvvupHZwRANBhSZyQ+Pl4lJSUqKyvrd39ZWZnmzZs36NcxDENut3sobw3ElJYwLO2VCCMAosOQ/1u2fPly3XTTTZozZ47mzp2rNWvWqLKyUkuWLJHkG2KpqqrSr3/9a0nSE088ocLCQk2ZMkWSb9+Rn/zkJ7rrrruC+DGA6OJf2puWGJ7OSF2LW53dHiXE2UL6fgAwHEP+Jly0aJEaGhr08MMPq6amRjNmzNC6detUVFQkSaqpqVFlZWXgeq/Xq/vuu08HDhyQ3W7XhAkT9IMf/EC33XZb8D4FEGVODNOEtjPiTIxTaoJdLZ09OtzYrom5qSF9PwAYDothGBG/NaPL5ZLT6VRzc7PS0tLMLgc4Z59Z+ZZ21bboN7d8QgsmZof0vT73iw3aVuXSLxfP0dXTzjy3CwCCabA/vzmbBjBBuOaMSNK4zGRJ0sH6tpC/FwAMB2EEMEE4toP3G5/lCyP7CSMAIhRhBAgzr9dQa1d49hmRpHG9YeRAfWvI3wsAhoMwAoRZi7tH/pla4eiMFGf5h2lY3gsgMhFGgDDzbwUfb7fKYQ/9Ult/GKl1daq9tyMDAJGEMAKEWTgnr0pSelK8RiX53ovuCIBIRBgBwiyw4VkYhmj8igPzRpjECiDyEEaAMPN3RsIxX8TPP4n1YANhBEDkIYwAYdbi9m8FH55hGqnP8t5jhBEAkYcwAoSZq8O8zgjLewFEIsIIEGb+1TThmsAq9Vne28AEVgCRhzAChJnLjDkjvVvCN7Z1qbm9O2zvCwCDQRgBwqwlTCf29pXssCs3zSFJOsAkVgARhjAChJkrsM9I+DojUt/lvcwbARBZCCNAmJ04JC98nRGpTxhhRQ2ACEMYAcLMjH1GJGlCdookad8xOiMAIgthBAizwGqaMO4zIkkTc1MlSXuPEkYARBbCCBBmZqymkaSJOb7OyIH6NnV7vGF9bwA4E8IIEGZm7DMiSaOdCUqOt6nHa+gQK2oARBDCCBBGXT1edXb7uhLhDiMWi0Xn9XZH9tUxVAMgchBGgDDyd0UkKSXMwzSSdF4O80YARB7CCBBG/pU0KQ67bFZL2N9/Yq6vM7KXzgiACEIYAcLIFdh9NfxdEenEJFbCCIBIQhgBwsisPUb8/HNG9h9rlcdrmFIDAJyMMAKEkVkrafzGjkqSw26Vu8erI8c5wRdAZCCMAGHk6jC3M2KzWgI7sTKJFUCkIIwAYeQyaffVvpjECiDSEEaAMDJr99W+JrLXCIAIQxgBwqil05wTe/vy7zWy+6jLtBoAoC/CCBBG/tU0Zk1glaSpo31hZM/RVvVwRg2ACEAYAcLI1WHuPiOSVDAqScnxNnX1eHWQM2oARADCCBBGZu8zIklWq0WT83zdkR01LabVAQB+hBEgjFrc5q+mkaQpo9MkSbtqmDcCwHyEESCM/PuMpJnYGZGkqb2dkV21dEYAmI8wAoSR2Tuw+k3t7YzspDMCIAIQRoAwMQyjzz4j5oaRSb2dkZrmTjW1d5laCwAQRoAw6ej2BA6nM3MCq+TrzIwdlShJ2skkVgAmI4wAYeJfSWOzWpQUbzO5mhNDNbtqGaoBYC7CCBAmffcYsVgsJlfTZxIrnREAJiOMAGESCefS9OVf3ruTzggAkxFGgDCJlJU0fieGaVrUzbbwAExEGAHCJNI6I0UZSUp12NXV49Xeo5zgC8A8hBEgTCKtM2K1WjRjjFOStLWqydxiAIxohBEgTPy7r5q9x0hfF4z1h5FmkysBMJIRRoAw8XdGImWYRtKJzsgRwggA8xBGgDBxdUbGIXl9+TsjO2tb1NXDJFYA5iCMAGHiH6ZxRlAYKcxIUlqCbxLrnqPsNwLAHIQRIEyaO/wTWCNnmMZiseh85o0AMBlhBAgTfxiJpM6IJJ0/Jl0SYQSAeQgjQJi4IjaMMIkVgLkII0CYBDojSZEVRvyTWHfVuuTu8ZhcDYCRiDAChIFhGCdW00TQPiOSNHZUotKT4tTtMbSTQ/MAmIAwAoRBR7dH3R5DUuQN01gsFs0qSJckfXDouLnFABiRCCNAGPiHaOxWi5LibSZXM1BJ0ShJ0geVhBEA4UcYAcKg70oai8VicjUDzS70hZEtlU3mFgJgRCKMAGHg3/AsknZf7evCgnRZLVJVU4dqmzvNLgfACEMYAcIgsOFZhIaRZIddU/LSJDFUAyD8CCNAGETqhmd9zS5Kl8QkVgDhRxgBwiAqwkghk1gBmIMwAoSBKwLPpTmZf0XNtio2PwMQXoQRIAyioTNSmJGkzOR4dXm82lblMrscACMIYQQIg0g9l6Yvi8Wi2b3dkfcPNppcDYCRhDAChEE0dEYk6eLiDEnSu/sbTK4EwEhCGAHCINKX9vpdMj5TkrT54HH1eLwmVwNgpCCMAGHgPyQv0jsjU0enKTXBrhZ3j3bUMG8EQHgMK4ysWrVKxcXFSkhIUElJiTZs2HDaa1988UVdffXVys7OVlpamubOnatXXnll2AUD0ShahmlsVkufoRrmjQAIjyGHkbVr12rp0qW6//77tWXLFi1YsEALFy5UZWXlKa9/6623dPXVV2vdunUqLy/Xpz71KV177bXasmXLORcPRItoCSOSdHGxb6jmHeaNAAgTi2EYxlCecPHFF2v27NlavXp14L6pU6fquuuu04oVKwb1GtOnT9eiRYv0ve99b1DXu1wuOZ1ONTc3Ky0tbSjlAqZz93g0+bt/lyR9+L1SOZMiO5BsPdKsax/fqFSHXRUPlMpmjbyD/QBEh8H+/B5SZ6Srq0vl5eUqLS3td39paak2bdo0qNfwer1qaWlRRkbGaa9xu91yuVz9bkC08h+SZ7FIqRG86ZnftPw0pTp880Z2Mm8EQBgMKYzU19fL4/EoNze33/25ubmqra0d1Gs8+uijamtr0/XXX3/aa1asWCGn0xm4FRQUDKVMIKL4h2hSHXZZo6DLYLNadFHvvBGGagCEw7AmsFos/b9QDcMYcN+pvPDCC3rwwQe1du1a5eTknPa6++67T83NzYHb4cOHh1MmEBEC80UifHimr0vG+8LIpo8JIwBCb0g946ysLNlstgFdkLq6ugHdkpOtXbtWt9xyi/7whz/oqquuOuO1DodDDodjKKUBEevEuTTRE0bmn5clydcZ6erxKt7OLgAAQmdI3zDx8fEqKSlRWVlZv/vLyso0b9680z7vhRde0Ne+9jU9//zzuuaaa4ZXKRClomWPkb6m5qUpK8Wh9i6Pyg9xii+A0Bryf3eWL1+up59+Ws8++6x27typZcuWqbKyUkuWLJHkG2JZvHhx4PoXXnhBixcv1qOPPqpLLrlEtbW1qq2tVXNzc/A+BRDBomlZr5/VatGCib7uyFt7j5lcDYBYN+QwsmjRIq1cuVIPP/ywZs6cqbfeekvr1q1TUVGRJKmmpqbfniNPPfWUenp6dMcdd2j06NGB29133x28TwFEsOb26AsjknTZpN4wsocwAiC0hrXO8Pbbb9ftt99+yseee+65fn9+8803h/MWQMyIlnNpTnbpedmSpO3VLtW3upWVwjwuAKHBrDQgxJp6w0h6FK2mkaTsVIemjfZtUrRxb73J1QCIZYQRIMSa2rskSaOS4k2uZOgum+TrjjBvBEAoEUaAEDveO2ckPcqGaSTpMv8k1j318nqHdHIEAAwaYQQIseO9nZH0KOyMzBmXoRSHXfWtbm2tYgUcgNAgjAAh5l9NMyo5+joj8XarLu8dqnlt51GTqwEQqwgjQAgZhhGYwBqNc0Yk6appvqMbynYQRgCEBmEECCFXZ488vXMtom2fEb9PTc6RzWrRrtoWHW5sN7scADGIMAKEkH+IJjHOpoQ4m8nVDE96UrzmFI2SxFANgNAgjAAhdDywrDc6uyJ+V0/zHYRJGAEQCoQRIIT8YcQZpfNF/K6c6gsj7+5vDOwoCwDBQhgBQqjJv5ImyjsjxVnJOi8nRT1eQ2/urjO7HAAxhjAChFA07756ss9Mz5Mk/e9HNSZXAiDWEEaAEArsvhrlnRFJuuaC0ZKkN/ccU0snQzUAgocwAoRQU2D31egPI1PyUjUhO1ldPV4msgIIKsIIEELH26N7w7O+LBaLrrkgXxJDNQCCizAChJB/99VoPJfmVD7XO1Tz1p56VtUACBrCCBBCgWGaKN199WSTclM1KTdFXR6vXmN7eABBQhgBQiiw6VkUHpJ3Otec7xuq+ctH1SZXAiBWEEaAEGpqi61hGkm69kLfUM2GvfU61uI2uRoAsYAwAoRIt8erFnePpNiYwOo3PjtFswrT5fEa+nNFldnlAIgBhBEgRPpO8ExLsJtYSfD9y+yxkqQ/lh+RYRgmVwMg2hFGgBDxT15NS7DLboutf2rXXpCveLtVu2pbtL3aZXY5AKJcbH1DAhEkcC5NcuwM0fg5k+J0de/hef/zwRGTqwEQ7QgjQIg0tsXWst6TfanEN1Tz54pqdfV4Ta4GQDQjjAAh4g8jmSkOkysJjQUTs5Sd6lBjW5de38WeIwCGjzAChEhDbxjJiMFhGkmy26yB7sh/v1tpcjUAohlhBAiRQGckRsOIJN3wiUJZLL49Rw7Ut5ldDoAoRRgBQqSh1bchWKx2RiSpICNJl0/KliQ9/+4hk6sBEK0II0CINMT4nBG/Gy8ukiT9ofyIOrs9JlcDIBoRRoAQGQnDNJL0qSk5GpOeqKb2bq3bWmN2OQCiEGEECJHGGJ/A6mezWvTVTxRIkn79T4ZqAAwdYQQIAcMwYn41TV+LLipUvM2qisNNKj/UaHY5AKIMYQQIgVZ3T2AjsMyU2A8j2akOXTcrX5L0y7cOmFwNgGhDGAFCwD9EkxhnU1J8bB2Sdzq3LhgvSXplR60ONbDMF8DgEUaAEBhJQzR+k3JT9cnJ2TIM6dmNdEcADB5hBAiBxlb/st6RE0Yk6Ru93ZHfbz4SOLUYAM6GMAKEQEObb8OzWF/We7J5EzI1dXSaOro9+g0rawAMEmEECIETwzSxveHZySwWi5Zc7uuOPPP2AbW6e0yuCEA0IIwAITBSh2kk6XMX5Gt8drKa2rv1638eNLscAFGAMAKEwEjZ8OxUbFaL7rriPEnS0xsOqI3uCICzIIwAIdAwQraCP51rL8jXuMwkNbZ16bfvMHcEwJkRRoAQCExgHYHDNJJkt1l15xUTJUlr3tqv9i66IwBOjzAChIB/zshIm8Da13Uz81WUmaSGti796u2DZpcDIIIRRoAgMwxD9a0je5hG8nVHll89SZL05JsfB+bRAMDJCCNAkLk6etTl8Z1Lk506cjsjkm/uyPT8NLW4e/T46/vMLgdAhCKMAEF2rLVTkuRMjFNCnM3kasxltVp078IpkqTfvHNQhxvbTa4IQCQijABBVufyTV4d6V0RvwUTs7VgYpa6PYZ+8upus8sBEIEII0CQHWvtDSMphBG/ez7j6478uaJaH1QeN7kaAJGGMAIE2bEWXxjJSSOM+M0Y49SXSsZKkh7483Z5vIbJFQGIJIQRIMj8YYTOSH/3fGaKUh12ba1q1u83Hza7HAARhDACBFldC3NGTiU71aGlvUt9f/T3XWpqZ6kvAB/CCBBkxwgjp7V4bpEm5aboeHu3Hn11j9nlAIgQhBEgyAJzRlITTK4k8sTZrHrw89MlSb999xCTWQFIIowAQVfX4ttnhM7Iqc2bkKUvzh4jw5Du+eNHcvd4zC4JgMkII0AQdfV4dby9WxJh5Ez+85ppykyO1966Vq1642OzywFgMsIIEET+03rjbBalJ8aZXE3kGpUcr4e+4BuuWfXmPu2ubTG5IgBmIowAQeSfL5KV4pDVajG5msh2zfmjdfW0XHV7DP2/P36ont7zfACMPIQRIIjYCn7wLBaL/uu6GUpNsOvDI836BQfpASMWYQQIIv9W8DmEkUHJTUvQf103Q5L0i9f3qvwQq2uAkYgwAgTRURcraYbqCzPH6LqZ+fIa0rK1FWp195hdEoAwI4wAQVTb7Asjo52JJlcSXR76wgyNSU9UZWO7Hnx5u9nlAAgzwggQRDW9YSTPyYZnQ+FMjNNj118oi0X6Y/kRvfjBEbNLAhBGhBEgiE50RggjQ3Xx+EzddcVESdJ//GmrdtW6TK4IQLgQRoAgqmnukEQYGa67r5yoBROz1Nnt1f/97Qdq6ew2uyQAYUAYAYKkzd0jV6dv8mVuGmFkOGxWi372lVnKdyboQH2b/t8fP5JhGGaXBSDEhhVGVq1apeLiYiUkJKikpEQbNmw47bU1NTW64YYbNHnyZFmtVi1dunS4tQIRrbZ3JU2Kw67UBHZfHa6M5Hg98a+zFWez6G/barV6PdvFA7FuyGFk7dq1Wrp0qe6//35t2bJFCxYs0MKFC1VZWXnK691ut7Kzs3X//ffrwgsvPOeCgUhVy+TVoJlVOErfu9a3XfyPX9mtV7bXmlwRgFAachh57LHHdMstt+jWW2/V1KlTtXLlShUUFGj16tWnvH7cuHH62c9+psWLF8vpdJ5zwUCkqmHyalDddEmRbrqkSIYhLf1dhbZVNZtdEoAQGVIY6erqUnl5uUpLS/vdX1paqk2bNgWtKLfbLZfL1e8GRLra3smrecwXCZoHrp2mBROz1NHt0Td+vVl1vUNhAGLLkMJIfX29PB6PcnNz+92fm5ur2trgtVFXrFghp9MZuBUUFATttYFQoTMSfHabVY/fMFsTspNV09ypW/6/zezQCsSgYU1gtVj6n0ZqGMaA+87Ffffdp+bm5sDt8OHDQXttIFT8W8HnsftqUDkT4/TMzRcpIzleW6uaddtvNsvd4zG7LABBNKQwkpWVJZvNNqALUldXN6Bbci4cDofS0tL63YBIR2ckdMZlJeu5f7tIyfE2vb2vQcvWVsjjZckvECuGFEbi4+NVUlKisrKyfveXlZVp3rx5QS0MiDb+MMIeI6Fxwdh0PXXTHMXZLFq3tVbf+/M29iABYsSQh2mWL1+up59+Ws8++6x27typZcuWqbKyUkuWLJHkG2JZvHhxv+dUVFSooqJCra2tOnbsmCoqKrRjx47gfAIgArS5e9TY1iVJGjOKYZpQuXRiln66aKYsFum/363UD/62i0ACxAD7UJ+waNEiNTQ06OGHH1ZNTY1mzJihdevWqaioSJJvk7OT9xyZNWtW4Pfl5eV6/vnnVVRUpIMHD55b9UCEqGryraRJS7DLmciGZ6H0uQvy1dTere++tE1PvbVfFotF93xmclDnrQEIryGHEUm6/fbbdfvtt5/yseeee27AffzPBbHuyPF2SdLYUUkmVzIy3HhJkTxeQw+8vF1Prv9YVov0nU8TSIBoxdk0QBAcbvR1RgoyGKIJl5vnjdOD106TJK1682P95NXd/McHiFKEESAI6IyY42vzi/VAbyB54o2P9dBfdsjLKhsg6hBGgCA4ctzXGRnL5NWw+7f5xXr4C75zbJ7bdFDLf1+hbo/X5KoADAVhBAiCw3RGTLV47jj97CszZbda9FJFtW77Tbk6utgYDYgWhBEgCPydEeaMmOcLM8dozeISOexWvb6rTjc9825guTWAyEYYAc5RS2e3mtq7JUlj0gkjZrpiSq5+e+vFSk2wa/Oh4/o/q97WvrpWs8sCcBaEEeAc+bsi6UlxSk1gjxGzXTQuQy/+33kaOypRhxra9cVVb2vTvnqzywJwBoQR4BwFhmiYLxIxJuam6qU75mt2YbpcnT1a/Ox7+t17lWd/IgBTEEaAc3SooU0S80UiTVaKQ89/4xJde2G+eryG7n1xq+7/01ZO/AUiEGEEOEcH6n1hpDgr2eRKcLKEOJt+/pWZWnbVpMB5Ntc/9Y6qe7fvBxAZCCPAOTrY2xkZl0kYiUQWi0V3XzVRz958kdIS7PrwcJM+94uNzCMBIghhBDhHB475wsj4bMJIJPvUlBz99a4FmjY6TY1tXbrxmXe18rU96mGDNMB0hBHgHHR2e1Td3CmJzkg0KMxM0ou3z9OXSsbKa0grX9urr/7yncB2/gDMQRgBzoF/iCYtwa6M5HiTq8FgJMTZ9JMvX6iVi2YqxWHX+wePa+HPNuivH1WbXRowYhFGgHNwsM/kVY6vjy7XzRqjdd9coFmF6Wrp7NGdz2/Rt37/oZp7N7ADED6EEeAc7GclTVQrzEzS72+bq29ecZ6sFul/Pjiiq3+6Xq/tOGp2acCIQhgBzsGJzkiKyZVguOJsVi0vnaw/LJmr8VnJqmtx69Zfb9aytRVqaudsGyAcCCPAOfDvMTIui91Xo11JUYbW3b1At102XlaL9KctVbrqsbf014+qZRiG2eUBMY0wAgyTYRjac9R3CNt5OXRGYkFCnE33fXaq/uf/ztPEnBTVt7p15/NbtPjZ97T/GAfuAaFCGAGGqa7FreaOblkt0oRswkgsmVU4Sn/95qVaetVExdut2rC3Xp9ZuUGPvrpbHV1sJw8EG2EEGKY9R1skSeOykpUQZzO5GgSbw27T0qsmqWzZZbp8Ura6PF794vV9uvqn6/X3bTUM3QBBRBgBhml3rS+MTMpJNbkShFJRZrKe+7eL9OSNs5XvTNCR4x1a8tsPtOipd/Th4SazywNiAmEEGCZ/Z2RSHmEk1lksFn1mxmi99q3LddcV5ykhzqr3DjbqC0+8rbt/t4UdXIFzRBgBhsk/eXVSLvNFRoqkeLu+VTpZb3z7k/ri7DGSpD9XVOuKR9drxbqdOt7GUmBgOAgjwDB4vYb29nZGJufSGRlpRjsT9dj1M/XXuy7VJeMz1NXj1VNv7delP3xdj726W80d7OIKDAVhBBiGqqYOtXV5FGezaBy7r45YM8Y49cI3LtEzN8/RtNFpauvy6Oev79OCH76uX/xjr1rdPWaXCEQFwggwDNurmyVJE3NSFWfjn9FIZrFYdOXUXP31rku1+l9na1JuilydPXq0bE8glHDeDXBmfIsCw7C1yhdGzh/jNLkSRAqr1aKF54/W3+6+TD/7ykyNz0rW8fZuPVq2R/N+8A99/3936Kir0+wygYhEGAGGYWuVS5I0YyxhBP3ZrBZ9YeYYvbrMF0qm5KWqrcujX244oAU/fEP3/s9H7OYKnMRudgFAtDEMQ9vojOAs7DarvjBzjD5/Yb7e3H1Mq9/8WO8dbNTv3j+stZsP68opOfravGLNPy9TFovF7HIBUxFGgCGqbu5UY1uX7FaLprDHCM7CYrHoU1Ny9KkpOdp8sFGr3/xY/9hVp9d2+m7n5aTo5nnj9MVZY5Ts4CsZIxPDNMAQ+bsiE3NT2QYeQzJnXIae+dpF+se3LtfX5o1TcrxN++pa9Z8vbdMlK/6h//rrjsBJ0MBIQhgBhmjrEf8QTZrJlSBaTchO0YOfn653/uNKPXDtNI3LTFJLZ4+e3nhAn/rJm1r01D/10pYqdXZzKB9GBnqCwBCVHzouyXeyK3AuUhPi9G/zi3Xz3HFav+eYfv3Pg3pzzzG9e6BR7x5olPPlOP2fWWO06KICTR1N+EXsIowAQ9Dt8WrLYV8YmVNEGEFwWK0n5pVUN3Xo95sP6w+bj6iqqUPPbTqo5zYd1IVjnfri7LH63AWjlZniMLtkIKgsRhScg+1yueR0OtXc3Ky0NP53APNUHG7SdU+8rfSkOH3w3atltbIKAqHh8RrasPeY1r5/WGU7jqrH6/uqtlktumxilq6bNUal0/KUGM+8JUSuwf78pjMCDMHmg42SpJLCUQQRhJTNatEnJ+fok5NzdKzFrb98WK2XKqr00ZFmvbH7mN7YfUzJ8TZ9enqevjBrjOZNyGQ3YEQtwggwBJsP9g7RjMswuRKMJNmpDn390mJ9/dJi7atr1Z8rqvRSRZUON3boxS1VenFLlZyJcSqdlquF5+dp/nlZctjpmCB6MEwDDJLXa+ii77+mhrYu/WHJXF1EIIGJDMPQB5XH9actVfrb1lo1tHUFHkt12HXl1BwtPH+0Lp+UzRJ0mGawP78JI8Agbatq1ud+sVHJ8TZt+V6p4u20xBEZPF5D7x1o1N+31ehv22pV1+IOPJYUb9NlE7N1xdQcfWpyjrJTmfyK8GHOCBBkG/bWS5LmTsgkiCCi2KwWzZ2QqbkTMvXAtdO15fBxrdtaq79vq1VVU4f+vr1Wf99eK0m6sCBdV0zO0ZVTczQ9P42t6BERCCPAIG3Ye0yStGBitsmVAKdntVpUUpShkqIMffeaqdpW5dI/dh3V67vq9NGRZn14uEkfHm7ST1/bo9w0h66YkqPLJmZr3oQsOZPizC4fIxTDNMAgtHf1aOZDZeryePX6ty7X+OwUs0sChqzO1ak3dtfpHzvrtHFfvdq7TuzwarX4Dn68dGKWLj0vW7OL0pkEi3PGMA0QRJv2NajL49WY9EQVZyWbXQ4wLDlpCVp0UaEWXVSozm6P3j3QqDd21WnD3mP6+FibPjzSrA+PNOuJNz5WYpxNF4/P0KXnZWnehCxNyUtlOTtChjACDMLftvnG26+elssYO2JCQpxNl0/K1uWTfMOONc0d2ri3Xhv31evtffWqb+3Sm7uP6c3dvuHJtAS7PlGc0XvL1PT8NPY1QdAQRoCz6PZ49drOo5Kkz8zIM7kaIDRGOxP15TkF+vKcAnm9hnYfbdHGvfXasK9e5Qcb5ers0Ws76/TazjpJvlU6JUWj9IlxvoByYUE6S4gxbIQR4Cze2d+g5o5uZaXEs7cIRgSr1aKpo9M0dXSavnHZePV4vNpe7dJ7vQf4vX+wUc0d3dqwtz6wyizOZtG00WmaVThKswrTNatglAoyEukkYlAII8BZrNtaI0m6elqebIyZYwSy26y6sCBdFxak6xuXjZfXa2hPXUsgnLx3oFHHWtyBOSfPbfI9LzM5XrMK0zWzIF2zCkfpgrFOpSawYgcDEUaAM+jo8ugvH/rCyOcvzDe5GiAyWK0WTclL05S8NC2eO06GYejI8Q5tOdykLZXHtaWySdurm9XQ1tVvaMdikYqzkjUj36kZY9I0I9+p6WOcciYSUEY6wghwBn/fXqNWd48KMhJ1cTFDNMCpWCwWFWQkqSAjKRDaO7s92lHj0pbKJlX0hpQjxzu0/1ib9h9r08sfVgeeX5iRpBlj0jQ936kZY5yakZ+mzBR2ih1JCCPAGfz+/SOSpC+XFLCsERiChDibZheO0uzCUYH76lvd2l7t0raqZt+tulmHGztU2diuysZ2rdtaG7g2O9Whybmpmpznu03JS9XEnFQlxjNJNhYRRoDT2F3bon/ub5DVIv1LyVizywGiXlaKo99yYklqbu/W9mpfMNlW5dK26mYdqG/TsRa3jrW4tXFffeBai0UqykjyBZTcVE3OS9PkvBQVZSazzDjKEUaA03h6w35JvuW8Y9ITTa4GiE3OpDjNOy9L887LCtzX5u7R3rpW7a51aVdti3bXtmjP0RbVt3bpYEO7Dja065XtRwPX260WFWYkaXx2iibkJGtClu/X8VkpGpUcb8bHwhARRoBTqHN16qWKKknSrQvGm1wNMLIkO+yaWeBbhdNXfatbu3vDye7aFu062qK9R1vU3uXR/vo27a9v02s7+79WRnK8JmT7gsmEnGQVZ6WoKDNJhRlJ7IsSQQgjwCk8/sY+dXsMzSnqP+YNwDxZKQ5lnefQ/D5dFMMwVOvq1P5jbfr4WKs+rmvV/vo2fVzXqurmTjW2damxrUvvHzw+4PVy0xwqykhWYWaSijKSfL9mJqsoI0npSXHskRJGhBHgJAfr2/T8u5WSpG+VTja5GgBnYrFYNNqZqNHOxH4hRfIdcBkIKb2/Hmpo06GGdrV09uioy62jLrfeO9g44HVTE+wqykxSUUayxoxK1Jj0ROWn+34dMyqR5chBRhgBTrLibzvV4zX0ycnZmjsh0+xyAAxTUrzdt1R4jLPf/YZhqKm9W4ca23WooU2VDe061Nje+2ubjrrcauns8U2orXKd8rVTHXZfOOkbVHp/PyY9UdmpDjZJHALCCNDH37fV6JXtR2W3WnTvwilmlwMgBCwWi0Ylx2tUcvyAeSmSb7PDw8fbdajBt+S46niHqpraVd3UqaqmDjW2danF3aPdR1u0+2jLKd/DZrUoJ9WhnLQE5aU5lJeW0Pv7BOU5E5Sb5lBuWgI70vYijAC9Glrd+t6ft0uSbrt8vKbkpZlcEQAzJMbbNCk3VZNyU0/5eHtXTyCYVB3vUHVTR+D3VU0dqnV1yuM1VNPcqZrmTn14hvdKjrcpNy2h9+ZQrjNB2SkOZabE++bI9N4ykuNjutNCGAEkebyG7v5dhepa3JqQnay7rphodkkAIlRSvF3n5aTovJyUUz7e4/GqvrVLR12dqnV1qq7319pmt+paOlXb3Kmjrk65OnvU1mcl0JlYLb6VQf5w0j+sxCsr1REIMaOS4qNupRBhBCOeYRj6r//doY376pUYZ9PqG0ui7h8ygMhht1mV5/QNx1x4huvau/yTaDv73Nyqb+29tXSpvtWtxvYueQ2pvrVL9a1dkk49NNRXYpxNGcnxGpUcp1FJ8b7fJ8X3/j5Oo5LjlZHkG6oaleS7zmE373uPMIIRzTAMrXxtr3719kFJ0g+/dMFpW7MAEExJ8XYVZ9lVnJV8xut6PF41tncFwsmJW5fqW9w65v99q1vH27rU4zXU0e3xDR01dQy6nh9/6QJ9eU7BuX6sYSGMYMTq8Xj10F926DfvHJIkPXDtNE7mBRBx7DarclITlJOacNZrDcNQi7tHx3v3Vzne3qXjbd063n7iz75fu3Xc/3h7tzxew9TlyoQRjEhHjrfrW7//UO8eaJTFIn33mmn6t/nFZpcFAOfEYrEoLSFOaQlxKso8c8fFz+s11NLZI0eceef7EEYwonR2e/Srtw/qiTf2qdXdo6R4mx798oVaeP5os0sDAFNYrRY5k8xdYjysGLRq1SoVFxcrISFBJSUl2rBhwxmvX79+vUpKSpSQkKDx48frySefHFaxwHDVtXTq5//Yq8t//IZ++PddanX3qKRolP529wKCCACYbMidkbVr12rp0qVatWqV5s+fr6eeekoLFy7Ujh07VFhYOOD6AwcO6LOf/ay+8Y1v6Le//a3efvtt3X777crOzta//Mu/BOVDACfr9ni1q6ZF7x5o0Ks7jmrzwUZ5Dd9j+c4EffvTk3XdzDGyxvC6fQCIFhbDMIyhPOHiiy/W7NmztXr16sB9U6dO1XXXXacVK1YMuP6ee+7Ryy+/rJ07TxyluGTJEn344Yf65z//Oaj3dLlccjqdam5uVloaG1HBFzaa2rt7J2d1qdbVqUMNvh0TD9S3anu1S+4eb7/nzCpM1+K5Rfrs+aNNXcIGACPFYH9+D6kz0tXVpfLyct1777397i8tLdWmTZtO+Zx//vOfKi0t7Xffpz/9aT3zzDPq7u5WXNzAcSq32y23293vw4TC/5Qf0daq5lM+1jejnZzW+sY346RHT452xhke6/vogOcN8j2GUpvO+LzBft7BP2/g2w/+83b1eOUO3Dxyd5/4fbvboxZ3z8nvNkBagl0zC0fpU5OzddXUXBVkJJ31OQCA8BtSGKmvr5fH41Fubm6/+3Nzc1VbW3vK59TW1p7y+p6eHtXX12v06IHj9StWrNBDDz00lNKGZf2eY3r5w+qQvw9Cw2KRnIm+DX2yUx0qykhSUWaSCjOTNSM/TcVZyRwBDgBRYFiraU7+gjcM44xf+qe6/lT3+913331avnx54M8ul0sFBcHfiKV0eq4KT/rfct+SBlTX58GTHzv5o1j6XDHwsdM/92w/PPvXZzntYye/z5nqO9Xj/R876dpBvufJzz3btX0vcNiscsRZ5bBb5bDben9vk8NuVULvzoLOxLiYPqsBAEaKIYWRrKws2Wy2AV2Qurq6Ad0Pv7y8vFNeb7fblZl56uPZHQ6HHA7HUEobls9dkK/PXRDytwEAAGcwpKW98fHxKikpUVlZWb/7y8rKNG/evFM+Z+7cuQOuf/XVVzVnzpxTzhcBAAAjy5D3GVm+fLmefvppPfvss9q5c6eWLVumyspKLVmyRJJviGXx4sWB65csWaJDhw5p+fLl2rlzp5599lk988wz+va3vx28TwEAAKLWkOeMLFq0SA0NDXr44YdVU1OjGTNmaN26dSoqKpIk1dTUqLKyMnB9cXGx1q1bp2XLlumJJ55Qfn6+fv7zn7PHCAAAkDSMfUbMwD4jAABEn8H+/DbvVBwAAAARRgAAgMkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUw15O3gz+DeJdblcJlcCAAAGy/9z+2ybvUdFGGlpaZEkFRQUmFwJAAAYqpaWFjmdztM+HhVn03i9XlVXVys1NVUWiyVor+tyuVRQUKDDhw+PmDNvRtpn5vPGNj5vbOPzRj/DMNTS0qL8/HxZraefGRIVnRGr1aqxY8eG7PXT0tJi5i9+sEbaZ+bzxjY+b2zj80a3M3VE/JjACgAATEUYAQAAphrRYcThcOiBBx6Qw+Ewu5SwGWmfmc8b2/i8sY3PO3JExQRWAAAQu0Z0ZwQAAJiPMAIAAExFGAEAAKYijAAAAFON6DCyatUqFRcXKyEhQSUlJdqwYYPZJYXEihUrdNFFFyk1NVU5OTm67rrrtHv3brPLCpsVK1bIYrFo6dKlZpcSMlVVVbrxxhuVmZmppKQkzZw5U+Xl5WaXFRI9PT367ne/q+LiYiUmJmr8+PF6+OGH5fV6zS4taN566y1de+21ys/Pl8Vi0UsvvdTvccMw9OCDDyo/P1+JiYn65Cc/qe3bt5tTbBCc6fN2d3frnnvu0fnnn6/k5GTl5+dr8eLFqq6uNq/gc3S2v9++brvtNlksFq1cuTJs9ZlhxIaRtWvXaunSpbr//vu1ZcsWLViwQAsXLlRlZaXZpQXd+vXrdccdd+idd95RWVmZenp6VFpaqra2NrNLC7n3339fa9as0QUXXGB2KSFz/PhxzZ8/X3Fxcfrb3/6mHTt26NFHH1V6errZpYXED3/4Qz355JN6/PHHtXPnTv3oRz/Sj3/8Y/3iF78wu7SgaWtr04UXXqjHH3/8lI//6Ec/0mOPPabHH39c77//vvLy8nT11VcHzvGKNmf6vO3t7frggw/0n//5n/rggw/04osvas+ePfr85z9vQqXBcba/X7+XXnpJ7777rvLz88NUmYmMEeoTn/iEsWTJkn73TZkyxbj33ntNqih86urqDEnG+vXrzS4lpFpaWoyJEycaZWVlxuWXX27cfffdZpcUEvfcc49x6aWXml1G2FxzzTXG17/+9X73ffGLXzRuvPFGkyoKLUnGn/70p8CfvV6vkZeXZ/zgBz8I3NfZ2Wk4nU7jySefNKHC4Dr5857Ke++9Z0gyDh06FJ6iQuh0n/fIkSPGmDFjjG3bthlFRUXGT3/607DXFk4jsjPS1dWl8vJylZaW9ru/tLRUmzZtMqmq8GlubpYkZWRkmFxJaN1xxx265pprdNVVV5ldSki9/PLLmjNnjr785S8rJydHs2bN0i9/+UuzywqZSy+9VP/4xz+0Z88eSdKHH36ojRs36rOf/azJlYXHgQMHVFtb2+/7y+Fw6PLLLx8R31+S7zvMYrHEbPfP6/Xqpptu0ne+8x1Nnz7d7HLCIioOygu2+vp6eTwe5ebm9rs/NzdXtbW1JlUVHoZhaPny5br00ks1Y8YMs8sJmd/97ncqLy/X5s2bzS4l5Pbv36/Vq1dr+fLl+o//+A+99957+uY3vymHw6HFixebXV7Q3XPPPWpubtaUKVNks9nk8Xj0/e9/X1/96lfNLi0s/N9Rp/r+OnTokBklhVVnZ6fuvfde3XDDDTF1mFxfP/zhD2W32/XNb37T7FLCZkSGET+LxdLvz4ZhDLgv1tx555366KOPtHHjRrNLCZnDhw/r7rvv1quvvqqEhASzywk5r9erOXPm6JFHHpEkzZo1S9u3b9fq1atjMoysXbtWv/3tb/X8889r+vTpqqio0NKlS5Wfn6+bb77Z7PLCZiR+f3V3d+srX/mKvF6vVq1aZXY5IVFeXq6f/exn+uCDD2L+77OvETlMk5WVJZvNNqALUldXN+B/G7Hkrrvu0ssvv6w33nhDY8eONbuckCkvL1ddXZ1KSkpkt9tlt9u1fv16/fznP5fdbpfH4zG7xKAaPXq0pk2b1u++qVOnxuRkbEn6zne+o3vvvVdf+cpXdP755+umm27SsmXLtGLFCrNLC4u8vDxJGnHfX93d3br++ut14MABlZWVxWxXZMOGDaqrq1NhYWHg++vQoUP61re+pXHjxpldXsiMyDASHx+vkpISlZWV9bu/rKxM8+bNM6mq0DEMQ3feeadefPFFvf766youLja7pJC68sortXXrVlVUVARuc+bM0b/+67+qoqJCNpvN7BKDav78+QOWau/Zs0dFRUUmVRRa7e3tslr7f3XZbLaYWtp7JsXFxcrLy+v3/dXV1aX169fH5PeXdCKI7N27V6+99poyMzPNLilkbrrpJn300Uf9vr/y8/P1ne98R6+88orZ5YXMiB2mWb58uW666SbNmTNHc+fO1Zo1a1RZWaklS5aYXVrQ3XHHHXr++ef15z//WampqYH/UTmdTiUmJppcXfClpqYOmA+TnJyszMzMmJwns2zZMs2bN0+PPPKIrr/+er333ntas2aN1qxZY3ZpIXHttdfq+9//vgoLCzV9+nRt2bJFjz32mL7+9a+bXVrQtLa2at++fYE/HzhwQBUVFcrIyFBhYaGWLl2qRx55RBMnTtTEiRP1yCOPKCkpSTfccIOJVQ/fmT5vfn6+vvSlL+mDDz7QX//6V3k8nsB3WEZGhuLj480qe9jO9vd7ctiKi4tTXl6eJk+eHO5Sw8fcxTzmeuKJJ4yioiIjPj7emD17dswudZV0ytuvfvUrs0sLm1he2msYhvGXv/zFmDFjhuFwOIwpU6YYa9asMbukkHG5XMbdd99tFBYWGgkJCcb48eON+++/33C73WaXFjRvvPHGKf/N3nzzzYZh+Jb3PvDAA0ZeXp7hcDiMyy67zNi6dau5RZ+DM33eAwcOnPY77I033jC79GE529/vyUbC0l6LYRhGmHIPAADAACNyzggAAIgchBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmOr/B4PQxIYKOZ8oAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# custom franction to calculate the probability density function\n", + "\n", + "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", + "\n", + " mul = np.sinh( np.arcsinh(2) * delta)\n", + " \n", + " mul = 2 / mul\n", + " \n", + " sigma_corr = sigma * mul\n", + " \n", + " z = (x - mu) / sigma_corr\n", + " \n", + " \n", + " \n", + " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", + " \n", + " f = np.exp(-0.5 * S * S)\n", + "\n", + " f /= np.sqrt(2 * np.pi)\n", + " \n", + " f *= 1 / ( sigma_corr * delta )\n", + " \n", + " f *= np.sqrt(1 + S * S)\n", + " \n", + " f /= np.sqrt(1 + z * z)\n", + " \n", + " return f\n", + " \n", + "\n", + "x = np.arange(start = 0, stop = 15, step = 0.001)\n", + "\n", + "\n", + "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605dc968", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/1_scraping_fbref.ipynb b/1_scraping_fbref.ipynb index 9eb9560..71e4d20 100644 --- a/1_scraping_fbref.ipynb +++ b/1_scraping_fbref.ipynb @@ -285,23 +285,23 @@ " eng ENG\n", " FW\n", " Roma\n", - " 25-040\n", + " 25-118\n", " 1997\n", - " 14.0\n", - " 11.0\n", - " 972.0\n", - " 3.0\n", + " 19.0\n", + " 15.0\n", + " 1338.0\n", + " 5.0\n", " ...\n", - " 13.0\n", - " 22.0\n", - " 6.0\n", + " 19.0\n", + " 29.0\n", + " 8.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 16.0\n", - " 24.0\n", - " 23.0\n", - " 51.1\n", + " 28.0\n", + " 35.0\n", + " 32.0\n", + " 52.2\n", " \n", " \n", " 1\n", @@ -309,23 +309,23 @@ " it ITA\n", " DF\n", " Inter\n", - " 34-274\n", + " 34-352\n", " 1988\n", - " 8.0\n", - " 7.0\n", - " 641.0\n", + " 12.0\n", + " 10.0\n", + " 930.0\n", " 0.0\n", " ...\n", - " 6.0\n", - " 5.0\n", + " 7.0\n", + " 7.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 34.0\n", - " 17.0\n", - " 3.0\n", - " 85.0\n", + " 52.0\n", + " 25.0\n", + " 18.0\n", + " 58.1\n", " \n", " \n", " 2\n", @@ -333,7 +333,7 @@ " fr FRA\n", " MF,FW\n", " Milan\n", - " 22-105\n", + " 22-183\n", " 2000\n", " 4.0\n", " 1.0\n", @@ -357,20 +357,20 @@ " ch SUI\n", " FW,MF\n", " Bologna\n", - " 25-309\n", + " 26-022\n", " 1997\n", - " 12.0\n", - " 6.0\n", - " 558.0\n", - " 0.0\n", + " 17.0\n", + " 8.0\n", + " 767.0\n", + " 1.0\n", " ...\n", - " 7.0\n", - " 13.0\n", - " 2.0\n", + " 10.0\n", + " 14.0\n", + " 5.0\n", " 1.0\n", " 1.0\n", " 0.0\n", - " 31.0\n", + " 42.0\n", " 5.0\n", " 8.0\n", " 38.5\n", @@ -381,23 +381,23 @@ " gh GHA\n", " FW,MF\n", " Cremonese\n", - " 19-296\n", + " 20-009\n", " 2003\n", - " 7.0\n", + " 10.0\n", " 2.0\n", - " 220.0\n", + " 296.0\n", " 0.0\n", " ...\n", " 7.0\n", - " 2.0\n", + " 3.0\n", " 4.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 12.0\n", - " 4.0\n", - " 13.0\n", - " 23.5\n", + " 14.0\n", + " 8.0\n", + " 14.0\n", + " 36.4\n", " \n", " \n", " ...\n", @@ -424,16 +424,16 @@ " ...\n", " \n", " \n", - " 487\n", - " Jeroen Zoet\n", - " nl NED\n", + " 514\n", + " Petar Zovko\n", + " ba BIH\n", " GK\n", " Spezia\n", - " 31-309\n", - " 1991\n", + " 20-309\n", + " 2002\n", " 1.0\n", - " 1.0\n", - " 90.0\n", + " 0.0\n", + " 74.0\n", " 0.0\n", " ...\n", " 0.0\n", @@ -442,42 +442,18 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " \n", " \n", - " 488\n", - " Nadir Zortea\n", - " it ITA\n", - " DF\n", - " Atalanta\n", - " 23-145\n", - " 1999\n", - " 8.0\n", - " 1.0\n", - " 189.0\n", - " 0.0\n", - " ...\n", - " 4.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 13.0\n", - " 5.0\n", - " 1.0\n", - " 83.3\n", - " \n", - " \n", - " 489\n", + " 515\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", " Fiorentina\n", - " 25-047\n", + " 25-125\n", " 1997\n", " 2.0\n", " 0.0\n", @@ -496,112 +472,136 @@ " 50.0\n", " \n", " \n", - " 490\n", + " 516\n", + " Szymon Żurkowski\n", + " pl POL\n", + " MF\n", + " Spezia\n", + " 25-125\n", + " 1997\n", + " 1.0\n", + " 0.0\n", + " 8.0\n", + " 0.0\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " 517\n", " Milan Đurić\n", " ba BIH\n", " FW\n", " Hellas Verona\n", - " 32-173\n", + " 32-251\n", " 1990\n", - " 10.0\n", + " 15.0\n", + " 6.0\n", + " 618.0\n", " 1.0\n", - " 224.0\n", - " 0.0\n", " ...\n", - " 5.0\n", - " 5.0\n", + " 15.0\n", + " 13.0\n", " 2.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 4.0\n", - " 40.0\n", - " 15.0\n", - " 72.7\n", + " 14.0\n", + " 103.0\n", + " 27.0\n", + " 79.2\n", " \n", " \n", - " 491\n", + " 518\n", " Filip Đuričić\n", " rs SRB\n", " MF,FW\n", " Sampdoria\n", - " 30-285\n", + " 30-363\n", " 1992\n", - " 14.0\n", - " 12.0\n", - " 955.0\n", + " 18.0\n", + " 15.0\n", + " 1201.0\n", " 2.0\n", " ...\n", - " 16.0\n", - " 27.0\n", + " 19.0\n", + " 32.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 52.0\n", + " 67.0\n", " 7.0\n", - " 9.0\n", - " 43.8\n", + " 11.0\n", + " 38.9\n", " \n", " \n", "\n", - "

492 rows × 120 columns

\n", + "

519 rows × 120 columns

\n", "" ], "text/plain": [ " player nationality position team age birth_year \\\n", - "0 Tammy Abraham eng ENG FW Roma 25-040 1997 \n", - "1 Francesco Acerbi it ITA DF Inter 34-274 1988 \n", - "2 Yacine Adli fr FRA MF,FW Milan 22-105 2000 \n", - "3 Michel Aebischer ch SUI FW,MF Bologna 25-309 1997 \n", - "4 Felix Afena-Gyan gh GHA FW,MF Cremonese 19-296 2003 \n", + "0 Tammy Abraham eng ENG FW Roma 25-118 1997 \n", + "1 Francesco Acerbi it ITA DF Inter 34-352 1988 \n", + "2 Yacine Adli fr FRA MF,FW Milan 22-183 2000 \n", + "3 Michel Aebischer ch SUI FW,MF Bologna 26-022 1997 \n", + "4 Felix Afena-Gyan gh GHA FW,MF Cremonese 20-009 2003 \n", ".. ... ... ... ... ... ... \n", - "487 Jeroen Zoet nl NED GK Spezia 31-309 1991 \n", - "488 Nadir Zortea it ITA DF Atalanta 23-145 1999 \n", - "489 Szymon Żurkowski pl POL MF Fiorentina 25-047 1997 \n", - "490 Milan Đurić ba BIH FW Hellas Verona 32-173 1990 \n", - "491 Filip Đuričić rs SRB MF,FW Sampdoria 30-285 1992 \n", + "514 Petar Zovko ba BIH GK Spezia 20-309 2002 \n", + "515 Szymon Żurkowski pl POL MF Fiorentina 25-125 1997 \n", + "516 Szymon Żurkowski pl POL MF Spezia 25-125 1997 \n", + "517 Milan Đurić ba BIH FW Hellas Verona 32-251 1990 \n", + "518 Filip Đuričić rs SRB MF,FW Sampdoria 30-363 1992 \n", "\n", " games games_starts minutes goals ... fouls fouled offsides \\\n", - "0 14.0 11.0 972.0 3.0 ... 13.0 22.0 6.0 \n", - "1 8.0 7.0 641.0 0.0 ... 6.0 5.0 0.0 \n", + "0 19.0 15.0 1338.0 5.0 ... 19.0 29.0 8.0 \n", + "1 12.0 10.0 930.0 0.0 ... 7.0 7.0 1.0 \n", "2 4.0 1.0 116.0 0.0 ... 3.0 1.0 0.0 \n", - "3 12.0 6.0 558.0 0.0 ... 7.0 13.0 2.0 \n", - "4 7.0 2.0 220.0 0.0 ... 7.0 2.0 4.0 \n", + "3 17.0 8.0 767.0 1.0 ... 10.0 14.0 5.0 \n", + "4 10.0 2.0 296.0 0.0 ... 7.0 3.0 4.0 \n", ".. ... ... ... ... ... ... ... ... \n", - "487 1.0 1.0 90.0 0.0 ... 0.0 0.0 0.0 \n", - "488 8.0 1.0 189.0 0.0 ... 4.0 0.0 0.0 \n", - "489 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", - "490 10.0 1.0 224.0 0.0 ... 5.0 5.0 2.0 \n", - "491 14.0 12.0 955.0 2.0 ... 16.0 27.0 0.0 \n", + "514 1.0 0.0 74.0 0.0 ... 0.0 0.0 0.0 \n", + "515 2.0 0.0 32.0 0.0 ... 1.0 0.0 0.0 \n", + "516 1.0 0.0 8.0 0.0 ... 0.0 0.0 0.0 \n", + "517 15.0 6.0 618.0 1.0 ... 15.0 13.0 2.0 \n", + "518 18.0 15.0 1201.0 2.0 ... 19.0 32.0 0.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 1.0 0.0 0.0 16.0 24.0 \n", - "1 0.0 0.0 0.0 34.0 17.0 \n", + "0 1.0 0.0 0.0 28.0 35.0 \n", + "1 0.0 0.0 0.0 52.0 25.0 \n", "2 0.0 0.0 0.0 5.0 0.0 \n", - "3 1.0 1.0 0.0 31.0 5.0 \n", - "4 0.0 0.0 0.0 12.0 4.0 \n", + "3 1.0 1.0 0.0 42.0 5.0 \n", + "4 0.0 0.0 0.0 14.0 8.0 \n", ".. ... ... ... ... ... \n", - "487 0.0 0.0 0.0 1.0 0.0 \n", - "488 0.0 0.0 0.0 13.0 5.0 \n", - "489 0.0 0.0 0.0 2.0 1.0 \n", - "490 0.0 0.0 0.0 4.0 40.0 \n", - "491 0.0 0.0 0.0 52.0 7.0 \n", + "514 0.0 0.0 0.0 2.0 0.0 \n", + "515 0.0 0.0 0.0 2.0 1.0 \n", + "516 0.0 0.0 0.0 3.0 0.0 \n", + "517 0.0 0.0 0.0 14.0 103.0 \n", + "518 0.0 0.0 0.0 67.0 7.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 23.0 51.1 \n", - "1 3.0 85.0 \n", + "0 32.0 52.2 \n", + "1 18.0 58.1 \n", "2 3.0 0.0 \n", "3 8.0 38.5 \n", - "4 13.0 23.5 \n", + "4 14.0 36.4 \n", ".. ... ... \n", - "487 0.0 0.0 \n", - "488 1.0 83.3 \n", - "489 1.0 50.0 \n", - "490 15.0 72.7 \n", - "491 9.0 43.8 \n", + "514 0.0 0.0 \n", + "515 1.0 50.0 \n", + "516 0.0 0.0 \n", + "517 27.0 79.2 \n", + "518 11.0 38.9 \n", "\n", - "[492 rows x 120 columns]" + "[519 rows x 120 columns]" ] }, "execution_count": 3, @@ -674,23 +674,23 @@ " it ITA\n", " GK\n", " Sampdoria\n", - " 25-297\n", + " 26-010\n", " 1997\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 25.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 32.0\n", " ...\n", - " 37.9\n", - " 91.0\n", - " 53.8\n", - " 43.3\n", - " 181.0\n", - " 9.0\n", - " 5.0\n", - " 12.0\n", - " 0.86\n", + " 38.8\n", + " 142.0\n", + " 57.7\n", + " 44.5\n", + " 243.0\n", " 15.0\n", + " 6.2\n", + " 20.0\n", + " 1.05\n", + " 15.4\n", " \n", " \n", " 1\n", @@ -698,23 +698,23 @@ " it ITA\n", " GK\n", " Cremonese\n", - " 22-133\n", + " 22-211\n", " 2000\n", - " 5.0\n", - " 5.0\n", - " 450.0\n", - " 5.0\n", + " 10.0\n", + " 10.0\n", + " 900.0\n", + " 14.0\n", " ...\n", - " 42.2\n", - " 28.0\n", - " 57.1\n", - " 50.8\n", - " 54.0\n", - " 5.0\n", + " 40.0\n", + " 66.0\n", + " 59.1\n", + " 49.3\n", + " 108.0\n", + " 10.0\n", " 9.3\n", - " 1.0\n", - " 0.20\n", - " 7.1\n", + " 7.0\n", + " 0.70\n", + " 13.0\n", " \n", " \n", " 2\n", @@ -722,23 +722,23 @@ " it ITA\n", " GK\n", " Sassuolo\n", - " 35-288\n", + " 36-001\n", " 1987\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 19.0\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 26.0\n", " ...\n", - " 34.7\n", - " 134.0\n", - " 32.1\n", - " 33.7\n", - " 189.0\n", + " 34.4\n", + " 157.0\n", + " 31.8\n", + " 33.3\n", + " 220.0\n", " 13.0\n", - " 6.9\n", - " 9.0\n", - " 0.64\n", - " 14.1\n", + " 5.9\n", + " 15.0\n", + " 0.88\n", + " 14.5\n", " \n", " \n", " 3\n", @@ -746,23 +746,23 @@ " it ITA\n", " GK\n", " Monza\n", - " 25-107\n", + " 25-185\n", " 1997\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 22.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 28.0\n", " ...\n", - " 32.3\n", - " 79.0\n", - " 22.8\n", - " 28.2\n", - " 169.0\n", - " 7.0\n", - " 4.1\n", + " 33.0\n", + " 115.0\n", + " 31.3\n", + " 31.3\n", + " 246.0\n", " 10.0\n", - " 0.71\n", - " 13.8\n", + " 4.1\n", + " 12.0\n", + " 0.63\n", + " 13.4\n", " \n", " \n", " 4\n", @@ -770,22 +770,22 @@ " pl POL\n", " GK\n", " Spezia\n", - " 25-084\n", + " 25-162\n", " 1997\n", - " 13.0\n", - " 13.0\n", - " 1170.0\n", - " 24.0\n", - " ...\n", - " 34.0\n", - " 82.0\n", - " 67.1\n", - " 50.6\n", - " 179.0\n", - " 8.0\n", - " 4.5\n", " 18.0\n", - " 1.38\n", + " 18.0\n", + " 1572.0\n", + " 29.0\n", + " ...\n", + " 34.4\n", + " 106.0\n", + " 63.2\n", + " 47.9\n", + " 235.0\n", + " 9.0\n", + " 3.8\n", + " 24.0\n", + " 1.37\n", " 15.6\n", " \n", " \n", @@ -794,23 +794,23 @@ " it ITA\n", " GK\n", " Lecce\n", - " 27-213\n", + " 27-291\n", " 1995\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 17.0\n", + " 20.0\n", + " 20.0\n", + " 1800.0\n", + " 24.0\n", " ...\n", - " 38.9\n", - " 106.0\n", - " 70.8\n", - " 49.8\n", - " 223.0\n", - " 10.0\n", - " 4.5\n", - " 11.0\n", - " 0.79\n", - " 11.2\n", + " 40.9\n", + " 156.0\n", + " 75.0\n", + " 51.4\n", + " 304.0\n", + " 13.0\n", + " 4.3\n", + " 20.0\n", + " 1.00\n", + " 12.5\n", " \n", " \n", " 6\n", @@ -818,7 +818,7 @@ " it ITA\n", " GK\n", " Fiorentina\n", - " 27-238\n", + " 27-316\n", " 1995\n", " 3.0\n", " 3.0\n", @@ -842,7 +842,7 @@ " si SVN\n", " GK\n", " Inter\n", - " 38-120\n", + " 38-198\n", " 1984\n", " 8.0\n", " 8.0\n", @@ -866,7 +866,7 @@ " fr FRA\n", " GK\n", " Milan\n", - " 27-131\n", + " 27-209\n", " 1995\n", " 7.0\n", " 7.0\n", @@ -890,7 +890,7 @@ " pt POR\n", " GK\n", " Lazio\n", - " 23-310\n", + " 24-023\n", " 1999\n", " 1.0\n", " 1.0\n", @@ -914,23 +914,23 @@ " it ITA\n", " GK\n", " Napoli\n", - " 25-234\n", + " 25-312\n", " 1997\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 10.0\n", " ...\n", - " 26.9\n", - " 92.0\n", - " 19.6\n", " 26.6\n", - " 142.0\n", + " 114.0\n", + " 20.2\n", + " 27.1\n", + " 197.0\n", " 6.0\n", - " 4.2\n", - " 10.0\n", - " 0.71\n", - " 15.1\n", + " 3.0\n", + " 23.0\n", + " 1.21\n", + " 17.2\n", " \n", " \n", " 11\n", @@ -938,23 +938,23 @@ " rs SRB\n", " GK\n", " Torino\n", - " 25-264\n", + " 25-342\n", " 1997\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 16.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 20.0\n", " ...\n", - " 40.2\n", - " 102.0\n", - " 95.1\n", - " 73.0\n", - " 160.0\n", - " 11.0\n", - " 6.9\n", - " 24.0\n", - " 1.71\n", - " 17.7\n", + " 40.8\n", + " 142.0\n", + " 94.4\n", + " 72.1\n", + " 232.0\n", + " 18.0\n", + " 7.8\n", + " 33.0\n", + " 1.74\n", + " 16.5\n", " \n", " \n", " 12\n", @@ -962,23 +962,23 @@ " it ITA\n", " GK\n", " Hellas Verona\n", - " 26-264\n", + " 26-342\n", " 1996\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 27.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 31.0\n", " ...\n", - " 42.2\n", - " 116.0\n", - " 48.3\n", - " 40.1\n", - " 177.0\n", - " 6.0\n", - " 3.4\n", - " 36.0\n", - " 2.57\n", - " 17.7\n", + " 43.5\n", + " 153.0\n", + " 58.2\n", + " 44.8\n", + " 261.0\n", + " 10.0\n", + " 3.8\n", + " 46.0\n", + " 2.42\n", + " 17.8\n", " \n", " \n", " 13\n", @@ -986,79 +986,127 @@ " ar ARG\n", " GK\n", " Atalanta\n", - " 28-189\n", + " 28-267\n", " 1994\n", - " 9.0\n", - " 9.0\n", - " 727.0\n", - " 5.0\n", + " 13.0\n", + " 13.0\n", + " 1087.0\n", + " 14.0\n", " ...\n", - " 35.0\n", - " 55.0\n", - " 70.9\n", - " 51.6\n", - " 98.0\n", - " 3.0\n", - " 3.1\n", - " 7.0\n", - " 0.87\n", - " 16.5\n", + " 33.0\n", + " 86.0\n", + " 65.1\n", + " 49.4\n", + " 147.0\n", + " 8.0\n", + " 5.4\n", + " 10.0\n", + " 0.83\n", + " 14.6\n", " \n", " \n", " 14\n", + " Guillermo Ochoa\n", + " mx MEX\n", + " GK\n", + " Salernitana\n", + " 37-199\n", + " 1985\n", + " 5.0\n", + " 5.0\n", + " 450.0\n", + " 14.0\n", + " ...\n", + " 43.3\n", + " 52.0\n", + " 78.8\n", + " 54.7\n", + " 78.0\n", + " 2.0\n", + " 2.6\n", + " 4.0\n", + " 0.80\n", + " 16.5\n", + " \n", + " \n", + " 15\n", " André Onana\n", " cm CMR\n", " GK\n", " Inter\n", - " 26-223\n", + " 26-301\n", " 1996\n", - " 6.0\n", - " 6.0\n", - " 540.0\n", - " 7.0\n", + " 11.0\n", + " 11.0\n", + " 990.0\n", + " 12.0\n", " ...\n", - " 29.6\n", - " 48.0\n", - " 33.3\n", - " 36.9\n", - " 81.0\n", - " 5.0\n", - " 6.2\n", - " 2.0\n", - " 0.33\n", - " 14.0\n", - " \n", - " \n", - " 15\n", - " Rui Patrício\n", - " pt POR\n", - " GK\n", - " Roma\n", - " 34-269\n", - " 1988\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 13.0\n", - " ...\n", - " 30.2\n", - " 104.0\n", - " 26.0\n", - " 30.8\n", - " 175.0\n", + " 31.0\n", + " 79.0\n", + " 35.4\n", + " 37.9\n", + " 150.0\n", " 6.0\n", - " 3.4\n", - " 8.0\n", - " 0.57\n", + " 4.0\n", + " 3.0\n", + " 0.27\n", " 13.0\n", " \n", " \n", " 16\n", + " Rui Patrício\n", + " pt POR\n", + " GK\n", + " Roma\n", + " 34-347\n", + " 1988\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 16.0\n", + " ...\n", + " 31.4\n", + " 139.0\n", + " 30.2\n", + " 32.8\n", + " 221.0\n", + " 6.0\n", + " 2.7\n", + " 13.0\n", + " 0.68\n", + " 13.8\n", + " \n", + " \n", + " 17\n", + " Gianluca Pegolo\n", + " it ITA\n", + " GK\n", + " Sassuolo\n", + " 41-309\n", + " 1981\n", + " 2.0\n", + " 2.0\n", + " 180.0\n", + " 3.0\n", + " ...\n", + " 29.7\n", + " 16.0\n", + " 31.3\n", + " 28.3\n", + " 28.0\n", + " 2.0\n", + " 7.1\n", + " 0.0\n", + " 0.00\n", + " 9.0\n", + " \n", + " \n", + " 18\n", " Mattia Perin\n", " it ITA\n", " GK\n", " Juventus\n", - " 30-001\n", + " 30-079\n", " 1992\n", " 7.0\n", " 6.0\n", @@ -1068,7 +1116,7 @@ " 30.3\n", " 41.0\n", " 39.0\n", - " 35.4\n", + " 35.3\n", " 99.0\n", " 2.0\n", " 2.0\n", @@ -1077,36 +1125,36 @@ " 17.8\n", " \n", " \n", - " 17\n", + " 19\n", " Ivan Provedel\n", " it ITA\n", " GK\n", " Lazio\n", - " 28-239\n", + " 28-317\n", " 1994\n", - " 14.0\n", - " 13.0\n", - " 1253.0\n", - " 8.0\n", + " 19.0\n", + " 18.0\n", + " 1703.0\n", + " 15.0\n", " ...\n", - " 33.1\n", - " 94.0\n", - " 37.2\n", - " 36.6\n", - " 201.0\n", - " 5.0\n", - " 2.5\n", - " 29.0\n", - " 2.08\n", - " 17.6\n", + " 32.5\n", + " 115.0\n", + " 34.8\n", + " 34.8\n", + " 256.0\n", + " 7.0\n", + " 2.7\n", + " 37.0\n", + " 1.96\n", + " 18.1\n", " \n", " \n", - " 18\n", + " 20\n", " Ionuț Radu\n", " ro ROU\n", " GK\n", " Cremonese\n", - " 25-167\n", + " 25-245\n", " 1997\n", " 9.0\n", " 9.0\n", @@ -1125,408 +1173,450 @@ " 14.5\n", " \n", " \n", - " 19\n", + " 21\n", " Luigi Sepe\n", " it ITA\n", " GK\n", " Salernitana\n", - " 31-187\n", + " 31-265\n", " 1991\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 21.0\n", + " 15.0\n", + " 15.0\n", + " 1350.0\n", + " 24.0\n", " ...\n", - " 36.6\n", - " 128.0\n", - " 48.4\n", - " 41.7\n", - " 187.0\n", - " 14.0\n", + " 36.1\n", + " 134.0\n", + " 46.3\n", + " 40.6\n", + " 199.0\n", + " 15.0\n", " 7.5\n", - " 9.0\n", - " 0.64\n", - " 13.7\n", + " 11.0\n", + " 0.73\n", + " 14.2\n", " \n", " \n", - " 20\n", + " 22\n", " Marco Silvestri\n", " it ITA\n", " GK\n", " Udinese\n", - " 31-254\n", + " 31-332\n", " 1991\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 14.0\n", + " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 21.0\n", " ...\n", - " 34.4\n", - " 110.0\n", - " 40.9\n", - " 38.2\n", - " 194.0\n", + " 33.5\n", + " 153.0\n", + " 39.9\n", + " 36.8\n", + " 253.0\n", " 4.0\n", - " 2.1\n", - " 5.0\n", - " 0.36\n", - " 11.3\n", + " 1.6\n", + " 9.0\n", + " 0.47\n", + " 13.5\n", " \n", " \n", - " 21\n", + " 23\n", " Łukasz Skorupski\n", " pl POL\n", " GK\n", " Bologna\n", - " 31-190\n", + " 31-268\n", " 1991\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", - " 25.0\n", + " 20.0\n", + " 20.0\n", + " 1800.0\n", + " 30.0\n", " ...\n", " 32.1\n", - " 104.0\n", - " 33.7\n", - " 31.2\n", - " 203.0\n", - " 10.0\n", - " 4.9\n", - " 13.0\n", - " 0.93\n", - " 14.0\n", + " 141.0\n", + " 37.6\n", + " 33.9\n", + " 280.0\n", + " 18.0\n", + " 6.4\n", + " 17.0\n", + " 0.85\n", + " 14.2\n", " \n", " \n", - " 22\n", + " 24\n", " Marco Sportiello\n", " it ITA\n", " GK\n", " Atalanta\n", - " 30-185\n", + " 30-263\n", " 1992\n", - " 6.0\n", - " 5.0\n", - " 533.0\n", " 7.0\n", - " ...\n", - " 28.6\n", - " 55.0\n", - " 74.5\n", - " 51.3\n", - " 77.0\n", " 6.0\n", - " 7.8\n", - " 11.0\n", - " 1.86\n", - " 18.9\n", + " 623.0\n", + " 9.0\n", + " ...\n", + " 28.5\n", + " 61.0\n", + " 68.9\n", + " 48.9\n", + " 86.0\n", + " 7.0\n", + " 8.1\n", + " 14.0\n", + " 2.02\n", + " 19.9\n", " \n", " \n", - " 23\n", + " 25\n", " Wojciech Szczęsny\n", " pl POL\n", " GK\n", " Juventus\n", - " 32-207\n", + " 32-285\n", " 1990\n", - " 8.0\n", - " 8.0\n", - " 672.0\n", - " 3.0\n", + " 13.0\n", + " 13.0\n", + " 1122.0\n", + " 11.0\n", " ...\n", - " 36.5\n", - " 36.0\n", - " 50.0\n", - " 43.0\n", - " 97.0\n", - " 2.0\n", - " 2.1\n", - " 6.0\n", - " 0.80\n", - " 15.9\n", + " 36.1\n", + " 61.0\n", + " 41.0\n", + " 39.2\n", + " 155.0\n", + " 4.0\n", + " 2.6\n", + " 7.0\n", + " 0.56\n", + " 14.2\n", " \n", " \n", - " 24\n", + " 26\n", " Ciprian Tătărușanu\n", " ro ROU\n", " GK\n", " Milan\n", - " 36-275\n", + " 36-353\n", " 1986\n", - " 7.0\n", - " 7.0\n", - " 630.0\n", - " 6.0\n", + " 12.0\n", + " 12.0\n", + " 1080.0\n", + " 16.0\n", " ...\n", - " 34.4\n", - " 33.0\n", - " 45.5\n", - " 41.5\n", - " 64.0\n", - " 1.0\n", - " 1.6\n", - " 7.0\n", - " 1.00\n", - " 18.1\n", + " 32.7\n", + " 51.0\n", + " 35.3\n", + " 34.9\n", + " 125.0\n", + " 8.0\n", + " 6.4\n", + " 10.0\n", + " 0.83\n", + " 15.1\n", " \n", " \n", - " 25\n", + " 27\n", " Pietro Terracciano\n", " it ITA\n", " GK\n", " Fiorentina\n", - " 32-248\n", + " 32-326\n", " 1990\n", - " 11.0\n", - " 11.0\n", - " 990.0\n", " 16.0\n", + " 16.0\n", + " 1440.0\n", + " 23.0\n", " ...\n", - " 34.2\n", - " 69.0\n", - " 31.9\n", - " 35.6\n", - " 104.0\n", - " 5.0\n", - " 4.8\n", - " 25.0\n", - " 2.27\n", - " 19.3\n", + " 33.3\n", + " 101.0\n", + " 43.6\n", + " 39.7\n", + " 149.0\n", + " 7.0\n", + " 4.7\n", + " 30.0\n", + " 1.88\n", + " 18.7\n", " \n", " \n", - " 26\n", + " 28\n", " Guglielmo Vicario\n", " it ITA\n", " GK\n", " Empoli\n", - " 26-035\n", + " 26-113\n", " 1996\n", - " 14.0\n", - " 14.0\n", - " 1260.0\n", " 19.0\n", + " 19.0\n", + " 1710.0\n", + " 22.0\n", " ...\n", - " 34.4\n", - " 76.0\n", - " 46.1\n", - " 42.4\n", - " 285.0\n", - " 20.0\n", - " 7.0\n", + " 33.9\n", + " 103.0\n", + " 45.6\n", + " 41.8\n", + " 399.0\n", + " 25.0\n", + " 6.3\n", " 8.0\n", - " 0.57\n", - " 11.1\n", + " 0.42\n", + " 10.5\n", " \n", " \n", - " 27\n", + " 29\n", " Jeroen Zoet\n", " nl NED\n", " GK\n", " Spezia\n", - " 31-309\n", + " 32-022\n", " 1991\n", - " 1.0\n", - " 1.0\n", - " 90.0\n", + " 3.0\n", + " 2.0\n", + " 154.0\n", " 1.0\n", " ...\n", - " 35.6\n", - " 2.0\n", - " 50.0\n", - " 34.5\n", + " 41.7\n", " 8.0\n", - " 0.0\n", - " 0.0\n", + " 87.5\n", + " 56.6\n", + " 24.0\n", " 1.0\n", - " 1.00\n", - " 13.5\n", + " 4.2\n", + " 3.0\n", + " 1.74\n", + " 15.7\n", + " \n", + " \n", + " 30\n", + " Petar Zovko\n", + " ba BIH\n", + " GK\n", + " Spezia\n", + " 20-309\n", + " 2002\n", + " 1.0\n", + " 0.0\n", + " 74.0\n", + " 2.0\n", + " ...\n", + " 35.1\n", + " 13.0\n", + " 76.9\n", + " 51.1\n", + " 24.0\n", + " 1.0\n", + " 4.2\n", + " 2.0\n", + " 2.47\n", + " 17.0\n", " \n", " \n", "\n", - "

28 rows × 47 columns

\n", + "

31 rows × 47 columns

\n", "" ], "text/plain": [ " player nationality position team age \\\n", - "0 Emil Audero it ITA GK Sampdoria 25-297 \n", - "1 Marco Carnesecchi it ITA GK Cremonese 22-133 \n", - "2 Andrea Consigli it ITA GK Sassuolo 35-288 \n", - "3 Michele Di Gregorio it ITA GK Monza 25-107 \n", - "4 Bartłomiej Drągowski pl POL GK Spezia 25-084 \n", - "5 Wladimiro Falcone it ITA GK Lecce 27-213 \n", - "6 Pierluigi Gollini it ITA GK Fiorentina 27-238 \n", - "7 Samir Handanović si SVN GK Inter 38-120 \n", - "8 Mike Maignan fr FRA GK Milan 27-131 \n", - "9 Luís Maximiano pt POR GK Lazio 23-310 \n", - "10 Alex Meret it ITA GK Napoli 25-234 \n", - "11 Vanja Milinković-Savić rs SRB GK Torino 25-264 \n", - "12 Lorenzo Montipò it ITA GK Hellas Verona 26-264 \n", - "13 Juan Musso ar ARG GK Atalanta 28-189 \n", - "14 André Onana cm CMR GK Inter 26-223 \n", - "15 Rui Patrício pt POR GK Roma 34-269 \n", - "16 Mattia Perin it ITA GK Juventus 30-001 \n", - "17 Ivan Provedel it ITA GK Lazio 28-239 \n", - "18 Ionuț Radu ro ROU GK Cremonese 25-167 \n", - "19 Luigi Sepe it ITA GK Salernitana 31-187 \n", - "20 Marco Silvestri it ITA GK Udinese 31-254 \n", - "21 Łukasz Skorupski pl POL GK Bologna 31-190 \n", - "22 Marco Sportiello it ITA GK Atalanta 30-185 \n", - "23 Wojciech Szczęsny pl POL GK Juventus 32-207 \n", - "24 Ciprian Tătărușanu ro ROU GK Milan 36-275 \n", - "25 Pietro Terracciano it ITA GK Fiorentina 32-248 \n", - "26 Guglielmo Vicario it ITA GK Empoli 26-035 \n", - "27 Jeroen Zoet nl NED GK Spezia 31-309 \n", + "0 Emil Audero it ITA GK Sampdoria 26-010 \n", + "1 Marco Carnesecchi it ITA GK Cremonese 22-211 \n", + "2 Andrea Consigli it ITA GK Sassuolo 36-001 \n", + "3 Michele Di Gregorio it ITA GK Monza 25-185 \n", + "4 Bartłomiej Drągowski pl POL GK Spezia 25-162 \n", + "5 Wladimiro Falcone it ITA GK Lecce 27-291 \n", + "6 Pierluigi Gollini it ITA GK Fiorentina 27-316 \n", + "7 Samir Handanović si SVN GK Inter 38-198 \n", + "8 Mike Maignan fr FRA GK Milan 27-209 \n", + "9 Luís Maximiano pt POR GK Lazio 24-023 \n", + "10 Alex Meret it ITA GK Napoli 25-312 \n", + "11 Vanja Milinković-Savić rs SRB GK Torino 25-342 \n", + "12 Lorenzo Montipò it ITA GK Hellas Verona 26-342 \n", + "13 Juan Musso ar ARG GK Atalanta 28-267 \n", + "14 Guillermo Ochoa mx MEX GK Salernitana 37-199 \n", + "15 André Onana cm CMR GK Inter 26-301 \n", + "16 Rui Patrício pt POR GK Roma 34-347 \n", + "17 Gianluca Pegolo it ITA GK Sassuolo 41-309 \n", + "18 Mattia Perin it ITA GK Juventus 30-079 \n", + "19 Ivan Provedel it ITA GK Lazio 28-317 \n", + "20 Ionuț Radu ro ROU GK Cremonese 25-245 \n", + "21 Luigi Sepe it ITA GK Salernitana 31-265 \n", + "22 Marco Silvestri it ITA GK Udinese 31-332 \n", + "23 Łukasz Skorupski pl POL GK Bologna 31-268 \n", + "24 Marco Sportiello it ITA GK Atalanta 30-263 \n", + "25 Wojciech Szczęsny pl POL GK Juventus 32-285 \n", + "26 Ciprian Tătărușanu ro ROU GK Milan 36-353 \n", + "27 Pietro Terracciano it ITA GK Fiorentina 32-326 \n", + "28 Guglielmo Vicario it ITA GK Empoli 26-113 \n", + "29 Jeroen Zoet nl NED GK Spezia 32-022 \n", + "30 Petar Zovko ba BIH GK Spezia 20-309 \n", "\n", " birth_year gk_games gk_games_starts gk_minutes gk_goals_against ... \\\n", - "0 1997 14.0 14.0 1260.0 25.0 ... \n", - "1 2000 5.0 5.0 450.0 5.0 ... \n", - "2 1987 14.0 14.0 1260.0 19.0 ... \n", - "3 1997 14.0 14.0 1260.0 22.0 ... \n", - "4 1997 13.0 13.0 1170.0 24.0 ... \n", - "5 1995 14.0 14.0 1260.0 17.0 ... \n", + "0 1997 19.0 19.0 1710.0 32.0 ... \n", + "1 2000 10.0 10.0 900.0 14.0 ... \n", + "2 1987 17.0 17.0 1530.0 26.0 ... \n", + "3 1997 19.0 19.0 1710.0 28.0 ... \n", + "4 1997 18.0 18.0 1572.0 29.0 ... \n", + "5 1995 20.0 20.0 1800.0 24.0 ... \n", "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 14.0 14.0 1260.0 10.0 ... \n", - "11 1997 14.0 14.0 1260.0 16.0 ... \n", - "12 1996 14.0 14.0 1260.0 27.0 ... \n", - "13 1994 9.0 9.0 727.0 5.0 ... \n", - "14 1996 6.0 6.0 540.0 7.0 ... \n", - "15 1988 14.0 14.0 1260.0 13.0 ... \n", - "16 1992 7.0 6.0 588.0 4.0 ... \n", - "17 1994 14.0 13.0 1253.0 8.0 ... \n", - "18 1997 9.0 9.0 810.0 19.0 ... \n", - "19 1991 14.0 14.0 1260.0 21.0 ... \n", - "20 1991 14.0 14.0 1260.0 14.0 ... \n", - "21 1991 14.0 14.0 1260.0 25.0 ... \n", - "22 1992 6.0 5.0 533.0 7.0 ... \n", - "23 1990 8.0 8.0 672.0 3.0 ... \n", - "24 1986 7.0 7.0 630.0 6.0 ... \n", - "25 1990 11.0 11.0 990.0 16.0 ... \n", - "26 1996 14.0 14.0 1260.0 19.0 ... \n", - "27 1991 1.0 1.0 90.0 1.0 ... \n", + "10 1997 19.0 19.0 1710.0 14.0 ... \n", + "11 1997 19.0 19.0 1710.0 20.0 ... \n", + "12 1996 19.0 19.0 1710.0 31.0 ... \n", + "13 1994 13.0 13.0 1087.0 14.0 ... \n", + "14 1985 5.0 5.0 450.0 14.0 ... \n", + "15 1996 11.0 11.0 990.0 12.0 ... \n", + "16 1988 19.0 19.0 1710.0 16.0 ... \n", + "17 1981 2.0 2.0 180.0 3.0 ... \n", + "18 1992 7.0 6.0 588.0 4.0 ... \n", + "19 1994 19.0 18.0 1703.0 15.0 ... \n", + "20 1997 9.0 9.0 810.0 19.0 ... \n", + "21 1991 15.0 15.0 1350.0 24.0 ... \n", + "22 1991 19.0 19.0 1710.0 21.0 ... \n", + "23 1991 20.0 20.0 1800.0 30.0 ... \n", + "24 1992 7.0 6.0 623.0 9.0 ... \n", + "25 1990 13.0 13.0 1122.0 11.0 ... \n", + "26 1986 12.0 12.0 1080.0 16.0 ... \n", + "27 1990 16.0 16.0 1440.0 23.0 ... \n", + "28 1996 19.0 19.0 1710.0 22.0 ... \n", + "29 1991 3.0 2.0 154.0 1.0 ... \n", + "30 2002 1.0 0.0 74.0 2.0 ... \n", "\n", " gk_passes_length_avg gk_goal_kicks gk_pct_goal_kicks_launched \\\n", - "0 37.9 91.0 53.8 \n", - "1 42.2 28.0 57.1 \n", - "2 34.7 134.0 32.1 \n", - "3 32.3 79.0 22.8 \n", - "4 34.0 82.0 67.1 \n", - "5 38.9 106.0 70.8 \n", + "0 38.8 142.0 57.7 \n", + "1 40.0 66.0 59.1 \n", + "2 34.4 157.0 31.8 \n", + "3 33.0 115.0 31.3 \n", + "4 34.4 106.0 63.2 \n", + "5 40.9 156.0 75.0 \n", "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 26.9 92.0 19.6 \n", - "11 40.2 102.0 95.1 \n", - "12 42.2 116.0 48.3 \n", - "13 35.0 55.0 70.9 \n", - "14 29.6 48.0 33.3 \n", - "15 30.2 104.0 26.0 \n", - "16 30.3 41.0 39.0 \n", - "17 33.1 94.0 37.2 \n", - "18 37.3 67.0 50.7 \n", - "19 36.6 128.0 48.4 \n", - "20 34.4 110.0 40.9 \n", - "21 32.1 104.0 33.7 \n", - "22 28.6 55.0 74.5 \n", - "23 36.5 36.0 50.0 \n", - "24 34.4 33.0 45.5 \n", - "25 34.2 69.0 31.9 \n", - "26 34.4 76.0 46.1 \n", - "27 35.6 2.0 50.0 \n", + "10 26.6 114.0 20.2 \n", + "11 40.8 142.0 94.4 \n", + "12 43.5 153.0 58.2 \n", + "13 33.0 86.0 65.1 \n", + "14 43.3 52.0 78.8 \n", + "15 31.0 79.0 35.4 \n", + "16 31.4 139.0 30.2 \n", + "17 29.7 16.0 31.3 \n", + "18 30.3 41.0 39.0 \n", + "19 32.5 115.0 34.8 \n", + "20 37.3 67.0 50.7 \n", + "21 36.1 134.0 46.3 \n", + "22 33.5 153.0 39.9 \n", + "23 32.1 141.0 37.6 \n", + "24 28.5 61.0 68.9 \n", + "25 36.1 61.0 41.0 \n", + "26 32.7 51.0 35.3 \n", + "27 33.3 101.0 43.6 \n", + "28 33.9 103.0 45.6 \n", + "29 41.7 8.0 87.5 \n", + "30 35.1 13.0 76.9 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 43.3 181.0 9.0 \n", - "1 50.8 54.0 5.0 \n", - "2 33.7 189.0 13.0 \n", - "3 28.2 169.0 7.0 \n", - "4 50.6 179.0 8.0 \n", - "5 49.8 223.0 10.0 \n", + "0 44.5 243.0 15.0 \n", + "1 49.3 108.0 10.0 \n", + "2 33.3 220.0 13.0 \n", + "3 31.3 246.0 10.0 \n", + "4 47.9 235.0 9.0 \n", + "5 51.4 304.0 13.0 \n", "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 26.6 142.0 6.0 \n", - "11 73.0 160.0 11.0 \n", - "12 40.1 177.0 6.0 \n", - "13 51.6 98.0 3.0 \n", - "14 36.9 81.0 5.0 \n", - "15 30.8 175.0 6.0 \n", - "16 35.4 99.0 2.0 \n", - "17 36.6 201.0 5.0 \n", - "18 46.3 107.0 6.0 \n", - "19 41.7 187.0 14.0 \n", - "20 38.2 194.0 4.0 \n", - "21 31.2 203.0 10.0 \n", - "22 51.3 77.0 6.0 \n", - "23 43.0 97.0 2.0 \n", - "24 41.5 64.0 1.0 \n", - "25 35.6 104.0 5.0 \n", - "26 42.4 285.0 20.0 \n", - "27 34.5 8.0 0.0 \n", + "10 27.1 197.0 6.0 \n", + "11 72.1 232.0 18.0 \n", + "12 44.8 261.0 10.0 \n", + "13 49.4 147.0 8.0 \n", + "14 54.7 78.0 2.0 \n", + "15 37.9 150.0 6.0 \n", + "16 32.8 221.0 6.0 \n", + "17 28.3 28.0 2.0 \n", + "18 35.3 99.0 2.0 \n", + "19 34.8 256.0 7.0 \n", + "20 46.3 107.0 6.0 \n", + "21 40.6 199.0 15.0 \n", + "22 36.8 253.0 4.0 \n", + "23 33.9 280.0 18.0 \n", + "24 48.9 86.0 7.0 \n", + "25 39.2 155.0 4.0 \n", + "26 34.9 125.0 8.0 \n", + "27 39.7 149.0 7.0 \n", + "28 41.8 399.0 25.0 \n", + "29 56.6 24.0 1.0 \n", + "30 51.1 24.0 1.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 5.0 12.0 \n", - "1 9.3 1.0 \n", - "2 6.9 9.0 \n", - "3 4.1 10.0 \n", - "4 4.5 18.0 \n", - "5 4.5 11.0 \n", + "0 6.2 20.0 \n", + "1 9.3 7.0 \n", + "2 5.9 15.0 \n", + "3 4.1 12.0 \n", + "4 3.8 24.0 \n", + "5 4.3 20.0 \n", "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 4.2 10.0 \n", - "11 6.9 24.0 \n", - "12 3.4 36.0 \n", - "13 3.1 7.0 \n", - "14 6.2 2.0 \n", - "15 3.4 8.0 \n", - "16 2.0 9.0 \n", - "17 2.5 29.0 \n", - "18 5.6 9.0 \n", - "19 7.5 9.0 \n", - "20 2.1 5.0 \n", - "21 4.9 13.0 \n", - "22 7.8 11.0 \n", - "23 2.1 6.0 \n", - "24 1.6 7.0 \n", - "25 4.8 25.0 \n", - "26 7.0 8.0 \n", - "27 0.0 1.0 \n", + "10 3.0 23.0 \n", + "11 7.8 33.0 \n", + "12 3.8 46.0 \n", + "13 5.4 10.0 \n", + "14 2.6 4.0 \n", + "15 4.0 3.0 \n", + "16 2.7 13.0 \n", + "17 7.1 0.0 \n", + "18 2.0 9.0 \n", + "19 2.7 37.0 \n", + "20 5.6 9.0 \n", + "21 7.5 11.0 \n", + "22 1.6 9.0 \n", + "23 6.4 17.0 \n", + "24 8.1 14.0 \n", + "25 2.6 7.0 \n", + "26 6.4 10.0 \n", + "27 4.7 30.0 \n", + "28 6.3 8.0 \n", + "29 4.2 3.0 \n", + "30 4.2 2.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \n", - "0 0.86 15.0 \n", - "1 0.20 7.1 \n", - "2 0.64 14.1 \n", - "3 0.71 13.8 \n", - "4 1.38 15.6 \n", - "5 0.79 11.2 \n", + "0 1.05 15.4 \n", + "1 0.70 13.0 \n", + "2 0.88 14.5 \n", + "3 0.63 13.4 \n", + "4 1.37 15.6 \n", + "5 1.00 12.5 \n", "6 0.00 9.3 \n", "7 0.63 14.6 \n", "8 0.86 12.9 \n", "9 0.00 19.0 \n", - "10 0.71 15.1 \n", - "11 1.71 17.7 \n", - "12 2.57 17.7 \n", - "13 0.87 16.5 \n", - "14 0.33 14.0 \n", - "15 0.57 13.0 \n", - "16 1.38 17.8 \n", - "17 2.08 17.6 \n", - "18 1.00 14.5 \n", - "19 0.64 13.7 \n", - "20 0.36 11.3 \n", - "21 0.93 14.0 \n", - "22 1.86 18.9 \n", - "23 0.80 15.9 \n", - "24 1.00 18.1 \n", - "25 2.27 19.3 \n", - "26 0.57 11.1 \n", - "27 1.00 13.5 \n", + "10 1.21 17.2 \n", + "11 1.74 16.5 \n", + "12 2.42 17.8 \n", + "13 0.83 14.6 \n", + "14 0.80 16.5 \n", + "15 0.27 13.0 \n", + "16 0.68 13.8 \n", + "17 0.00 9.0 \n", + "18 1.38 17.8 \n", + "19 1.96 18.1 \n", + "20 1.00 14.5 \n", + "21 0.73 14.2 \n", + "22 0.47 13.5 \n", + "23 0.85 14.2 \n", + "24 2.02 19.9 \n", + "25 0.56 14.2 \n", + "26 0.83 15.1 \n", + "27 1.88 18.7 \n", + "28 0.42 10.5 \n", + "29 1.74 15.7 \n", + "30 2.47 17.0 \n", "\n", - 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" 168.0\n", - " 156.0\n", - " 18.0\n", + " 236.0\n", + " 223.0\n", + " 30.0\n", " 1.0\n", - " 2.0\n", - " 0.0\n", - " 759.0\n", - " 205.0\n", - " 168.0\n", - " 55.0\n", + " 3.0\n", + " 1.0\n", + " 1015.0\n", + " 275.0\n", + " 224.0\n", + " 55.1\n", " \n", " \n", " 11\n", " Monza\n", " 29.0\n", - " 55.5\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 55.1\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 22.0\n", " 13.0\n", - " 8.0\n", - " 1.0\n", - " 1.0\n", + " 3.0\n", + " 3.0\n", " ...\n", - " 183.0\n", - " 196.0\n", - " 25.0\n", - " 1.0\n", + " 248.0\n", + " 262.0\n", + " 38.0\n", + " 3.0\n", " 0.0\n", " 1.0\n", - " 647.0\n", - " 160.0\n", - " 141.0\n", - " 53.2\n", + " 906.0\n", + " 215.0\n", + " 197.0\n", + " 52.2\n", " \n", " \n", " 12\n", " Napoli\n", " 24.0\n", - " 59.8\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 34.0\n", - " 28.0\n", - " 3.0\n", - " 3.0\n", + " 61.3\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 46.0\n", + " 37.0\n", + " 4.0\n", + " 5.0\n", " ...\n", - " 123.0\n", - " 182.0\n", - " 23.0\n", - " 3.0\n", + " 179.0\n", + " 240.0\n", + " 33.0\n", + " 4.0\n", " 2.0\n", " 0.0\n", - " 740.0\n", - " 181.0\n", - " 142.0\n", - " 56.0\n", + " 1012.0\n", + " 231.0\n", + " 201.0\n", + " 53.5\n", " \n", " \n", " 13\n", " Roma\n", - " 23.0\n", - " 50.8\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 17.0\n", - " 11.0\n", - " 2.0\n", + " 25.0\n", + " 49.4\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 25.0\n", + " 16.0\n", " 3.0\n", + " 5.0\n", " ...\n", - " 167.0\n", + " 229.0\n", + " 250.0\n", + " 32.0\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 1001.0\n", + " 225.0\n", " 184.0\n", - " 26.0\n", - " 2.0\n", - " 0.0\n", - " 0.0\n", - " 723.0\n", - " 159.0\n", - " 142.0\n", - " 52.8\n", + " 55.0\n", " \n", " \n", " 14\n", " Salernitana\n", + " 28.0\n", + " 44.8\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", " 24.0\n", - " 47.1\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 18.0\n", - " 12.0\n", + " 16.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 163.0\n", - " 134.0\n", - " 31.0\n", + " 242.0\n", + " 207.0\n", + " 42.0\n", " 0.0\n", - " 4.0\n", + " 7.0\n", " 0.0\n", - " 728.0\n", - " 175.0\n", - " 169.0\n", - " 50.9\n", + " 1034.0\n", + " 240.0\n", + " 257.0\n", + " 48.3\n", " \n", " \n", " 15\n", " Sampdoria\n", - " 24.0\n", - " 50.8\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 6.0\n", - " 6.0\n", + " 29.0\n", + " 49.4\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 8.0\n", + " 7.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 199.0\n", - " 217.0\n", - " 23.0\n", + " 278.0\n", + " 293.0\n", + " 39.0\n", " 0.0\n", - " 3.0\n", + " 5.0\n", " 2.0\n", - " 708.0\n", - " 217.0\n", - " 215.0\n", - " 50.2\n", + " 968.0\n", + " 318.0\n", + " 300.0\n", + " 51.5\n", " \n", " \n", " 16\n", " Sassuolo\n", - " 26.0\n", - " 48.1\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 15.0\n", - " 12.0\n", - " 1.0\n", - " 2.0\n", - " ...\n", - " 133.0\n", - " 167.0\n", + " 27.0\n", + " 48.7\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 18.0\n", " 13.0\n", - " 2.0\n", - " 2.0\n", + " 3.0\n", + " 4.0\n", + " ...\n", + " 199.0\n", + " 236.0\n", + " 16.0\n", + " 3.0\n", + " 4.0\n", " 0.0\n", - " 608.0\n", - " 130.0\n", - " 146.0\n", - " 47.1\n", + " 868.0\n", + " 173.0\n", + " 216.0\n", + " 44.5\n", " \n", " \n", " 17\n", " Spezia\n", - " 24.0\n", - " 46.9\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 30.0\n", + " 46.7\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 15.0\n", " 10.0\n", - " 6.0\n", - " 1.0\n", - " 1.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 190.0\n", - " 127.0\n", - " 32.0\n", + " 281.0\n", + " 193.0\n", + " 37.0\n", " 1.0\n", " 1.0\n", " 0.0\n", - " 748.0\n", - " 179.0\n", - " 212.0\n", - " 45.8\n", + " 1136.0\n", + " 281.0\n", + " 322.0\n", + " 46.6\n", " \n", " \n", " 18\n", " Torino\n", " 23.0\n", - " 51.3\n", + " 52.8\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 19.0\n", " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 15.0\n", - " 10.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 209.0\n", - " 137.0\n", - " 33.0\n", + " 277.0\n", + " 195.0\n", + " 46.0\n", " 1.0\n", - " 2.0\n", + " 4.0\n", " 0.0\n", - " 735.0\n", - " 215.0\n", - " 228.0\n", - " 48.5\n", + " 1002.0\n", + " 295.0\n", + " 323.0\n", + " 47.7\n", " \n", " \n", " 19\n", " Udinese\n", " 22.0\n", - " 50.6\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 21.0\n", + " 50.1\n", " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 26.0\n", + " 22.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 166.0\n", - " 166.0\n", - " 28.0\n", + " 234.0\n", + " 238.0\n", + " 33.0\n", " 0.0\n", " 2.0\n", " 0.0\n", - " 735.0\n", - " 187.0\n", - " 142.0\n", - " 56.8\n", + " 989.0\n", + " 237.0\n", + " 189.0\n", + " 55.6\n", " \n", " \n", "\n", @@ -2080,92 +2170,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 Atalanta 23.0 46.4 14.0 154.0 1260.0 \n", - "1 Bologna 23.0 51.1 14.0 154.0 1260.0 \n", - "2 Cremonese 27.0 43.2 14.0 154.0 1260.0 \n", - "3 Empoli 25.0 46.0 14.0 154.0 1260.0 \n", - "4 Fiorentina 25.0 58.7 14.0 154.0 1260.0 \n", - "5 Hellas Verona 29.0 43.9 14.0 154.0 1260.0 \n", - "6 Inter 23.0 54.6 14.0 154.0 1260.0 \n", - "7 Juventus 26.0 50.5 14.0 154.0 1260.0 \n", - "8 Lazio 21.0 50.0 14.0 154.0 1260.0 \n", - "9 Lecce 24.0 40.7 14.0 154.0 1260.0 \n", - "10 Milan 27.0 53.9 14.0 154.0 1260.0 \n", - "11 Monza 29.0 55.5 14.0 154.0 1260.0 \n", - "12 Napoli 24.0 59.8 14.0 154.0 1260.0 \n", - "13 Roma 23.0 50.8 14.0 154.0 1260.0 \n", - "14 Salernitana 24.0 47.1 14.0 154.0 1260.0 \n", - "15 Sampdoria 24.0 50.8 14.0 154.0 1260.0 \n", - "16 Sassuolo 26.0 48.1 14.0 154.0 1260.0 \n", - "17 Spezia 24.0 46.9 14.0 154.0 1260.0 \n", - "18 Torino 23.0 51.3 14.0 154.0 1260.0 \n", - "19 Udinese 22.0 50.6 14.0 154.0 1260.0 \n", + "0 Atalanta 24.0 48.4 19.0 209.0 1710.0 \n", + "1 Bologna 24.0 52.6 20.0 220.0 1800.0 \n", + "2 Cremonese 29.0 44.0 19.0 209.0 1710.0 \n", + "3 Empoli 26.0 46.3 19.0 209.0 1710.0 \n", + "4 Fiorentina 27.0 57.2 19.0 209.0 1710.0 \n", + "5 Hellas Verona 29.0 43.4 19.0 209.0 1710.0 \n", + "6 Inter 23.0 52.9 19.0 209.0 1710.0 \n", + "7 Juventus 26.0 49.5 19.0 209.0 1710.0 \n", + "8 Lazio 21.0 51.2 19.0 209.0 1710.0 \n", + "9 Lecce 26.0 42.5 20.0 220.0 1800.0 \n", + "10 Milan 27.0 54.5 19.0 209.0 1710.0 \n", + "11 Monza 29.0 55.1 19.0 209.0 1710.0 \n", + "12 Napoli 24.0 61.3 19.0 209.0 1710.0 \n", + "13 Roma 25.0 49.4 19.0 209.0 1710.0 \n", + "14 Salernitana 28.0 44.8 20.0 220.0 1800.0 \n", + "15 Sampdoria 29.0 49.4 19.0 209.0 1710.0 \n", + "16 Sassuolo 27.0 48.7 19.0 209.0 1710.0 \n", + "17 Spezia 30.0 46.7 20.0 220.0 1800.0 \n", + "18 Torino 23.0 52.8 19.0 209.0 1710.0 \n", + "19 Udinese 22.0 50.1 19.0 209.0 1710.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 19.0 14.0 4.0 5.0 ... 179.0 142.0 23.0 \n", - "1 17.0 12.0 3.0 3.0 ... 168.0 175.0 27.0 \n", - "2 11.0 5.0 1.0 3.0 ... 195.0 136.0 34.0 \n", - "3 10.0 5.0 0.0 0.0 ... 158.0 167.0 20.0 \n", - "4 17.0 15.0 1.0 3.0 ... 187.0 180.0 18.0 \n", - "5 10.0 7.0 0.0 0.0 ... 211.0 128.0 34.0 \n", - "6 31.0 22.0 2.0 2.0 ... 166.0 169.0 25.0 \n", - "7 21.0 17.0 1.0 2.0 ... 171.0 153.0 19.0 \n", - "8 25.0 20.0 1.0 2.0 ... 132.0 189.0 17.0 \n", - "9 12.0 8.0 1.0 2.0 ... 196.0 178.0 24.0 \n", - "10 26.0 21.0 2.0 2.0 ... 168.0 156.0 18.0 \n", - "11 13.0 8.0 1.0 1.0 ... 183.0 196.0 25.0 \n", - "12 34.0 28.0 3.0 3.0 ... 123.0 182.0 23.0 \n", - "13 17.0 11.0 2.0 3.0 ... 167.0 184.0 26.0 \n", - "14 18.0 12.0 1.0 1.0 ... 163.0 134.0 31.0 \n", - "15 6.0 6.0 0.0 0.0 ... 199.0 217.0 23.0 \n", - "16 15.0 12.0 1.0 2.0 ... 133.0 167.0 13.0 \n", - "17 10.0 6.0 1.0 1.0 ... 190.0 127.0 32.0 \n", - "18 15.0 10.0 1.0 1.0 ... 209.0 137.0 33.0 \n", - "19 21.0 19.0 0.0 0.0 ... 166.0 166.0 28.0 \n", + "0 36.0 25.0 6.0 8.0 ... 231.0 196.0 27.0 \n", + "1 25.0 19.0 3.0 3.0 ... 242.0 238.0 41.0 \n", + "2 14.0 6.0 2.0 4.0 ... 257.0 194.0 44.0 \n", + "3 17.0 9.0 0.0 0.0 ... 222.0 237.0 26.0 \n", + "4 21.0 17.0 2.0 4.0 ... 244.0 249.0 25.0 \n", + "5 16.0 13.0 0.0 0.0 ... 285.0 185.0 43.0 \n", + "6 37.0 25.0 2.0 2.0 ... 233.0 229.0 32.0 \n", + "7 30.0 23.0 2.0 3.0 ... 227.0 199.0 27.0 \n", + "8 34.0 25.0 3.0 4.0 ... 194.0 252.0 26.0 \n", + "9 18.0 12.0 1.0 2.0 ... 283.0 254.0 36.0 \n", + "10 33.0 28.0 2.0 2.0 ... 236.0 223.0 30.0 \n", + "11 22.0 13.0 3.0 3.0 ... 248.0 262.0 38.0 \n", + "12 46.0 37.0 4.0 5.0 ... 179.0 240.0 33.0 \n", + "13 25.0 16.0 3.0 5.0 ... 229.0 250.0 32.0 \n", + "14 24.0 16.0 1.0 1.0 ... 242.0 207.0 42.0 \n", + "15 8.0 7.0 0.0 0.0 ... 278.0 293.0 39.0 \n", + "16 18.0 13.0 3.0 4.0 ... 199.0 236.0 16.0 \n", + "17 15.0 10.0 2.0 2.0 ... 281.0 193.0 37.0 \n", + "18 19.0 14.0 1.0 1.0 ... 277.0 195.0 46.0 \n", + "19 26.0 22.0 0.0 0.0 ... 234.0 238.0 33.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 3.0 0.0 0.0 793.0 211.0 \n", - "1 3.0 3.0 2.0 734.0 123.0 \n", - "2 3.0 3.0 0.0 757.0 184.0 \n", - "3 0.0 3.0 0.0 649.0 135.0 \n", - "4 2.0 1.0 0.0 740.0 224.0 \n", - "5 0.0 1.0 1.0 828.0 229.0 \n", - "6 1.0 1.0 1.0 673.0 172.0 \n", - "7 2.0 2.0 0.0 692.0 166.0 \n", - "8 1.0 1.0 0.0 677.0 126.0 \n", - "9 2.0 3.0 1.0 702.0 188.0 \n", - "10 1.0 2.0 0.0 759.0 205.0 \n", - "11 1.0 0.0 1.0 647.0 160.0 \n", - "12 3.0 2.0 0.0 740.0 181.0 \n", - "13 2.0 0.0 0.0 723.0 159.0 \n", - "14 0.0 4.0 0.0 728.0 175.0 \n", - "15 0.0 3.0 2.0 708.0 217.0 \n", - "16 2.0 2.0 0.0 608.0 130.0 \n", - "17 1.0 1.0 0.0 748.0 179.0 \n", - "18 1.0 2.0 0.0 735.0 215.0 \n", - "19 0.0 2.0 0.0 735.0 187.0 \n", + "0 6.0 1.0 1.0 1117.0 279.0 \n", + "1 3.0 5.0 2.0 1104.0 180.0 \n", + "2 3.0 4.0 1.0 1042.0 267.0 \n", + "3 0.0 3.0 0.0 907.0 189.0 \n", + "4 2.0 2.0 1.0 1002.0 289.0 \n", + "5 0.0 1.0 1.0 1142.0 388.0 \n", + "6 1.0 2.0 2.0 960.0 232.0 \n", + "7 3.0 2.0 0.0 965.0 230.0 \n", + "8 2.0 1.0 0.0 972.0 181.0 \n", + "9 2.0 3.0 1.0 1097.0 294.0 \n", + "10 1.0 3.0 1.0 1015.0 275.0 \n", + "11 3.0 0.0 1.0 906.0 215.0 \n", + "12 4.0 2.0 0.0 1012.0 231.0 \n", + "13 4.0 0.0 0.0 1001.0 225.0 \n", + "14 0.0 7.0 0.0 1034.0 240.0 \n", + "15 0.0 5.0 2.0 968.0 318.0 \n", + "16 3.0 4.0 0.0 868.0 173.0 \n", + "17 1.0 1.0 0.0 1136.0 281.0 \n", + "18 1.0 4.0 0.0 1002.0 295.0 \n", + "19 0.0 2.0 0.0 989.0 237.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 181.0 53.8 \n", - "1 165.0 42.7 \n", - "2 242.0 43.2 \n", - "3 163.0 45.3 \n", - "4 193.0 53.7 \n", - "5 240.0 48.8 \n", - "6 126.0 57.7 \n", - "7 184.0 47.4 \n", - "8 151.0 45.5 \n", - "9 216.0 46.5 \n", - "10 168.0 55.0 \n", - "11 141.0 53.2 \n", - "12 142.0 56.0 \n", - "13 142.0 52.8 \n", - "14 169.0 50.9 \n", - "15 215.0 50.2 \n", - "16 146.0 47.1 \n", - "17 212.0 45.8 \n", - "18 228.0 48.5 \n", - "19 142.0 56.8 \n", + "0 239.0 53.9 \n", + "1 224.0 44.6 \n", + "2 348.0 43.4 \n", + "3 217.0 46.6 \n", + "4 249.0 53.7 \n", + "5 345.0 52.9 \n", + "6 198.0 54.0 \n", + "7 230.0 50.0 \n", + "8 183.0 49.7 \n", + "9 373.0 44.1 \n", + "10 224.0 55.1 \n", + "11 197.0 52.2 \n", + "12 201.0 53.5 \n", + "13 184.0 55.0 \n", + "14 257.0 48.3 \n", + "15 300.0 51.5 \n", + "16 216.0 44.5 \n", + "17 322.0 46.6 \n", + "18 323.0 47.7 \n", + "19 189.0 55.6 \n", "\n", "[20 rows x 157 columns]" ] @@ -2237,482 +2327,482 @@ " \n", " 0\n", " vs Atalanta\n", - " 23.0\n", - " 53.6\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 12.0\n", - " 9.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 150.0\n", - " 168.0\n", + " 24.0\n", + " 51.6\n", " 19.0\n", - " 0.0\n", - " 5.0\n", + " 209.0\n", + " 1710.0\n", + " 22.0\n", + " 16.0\n", " 1.0\n", - " 834.0\n", - " 181.0\n", - " 211.0\n", - " 46.2\n", + " 1.0\n", + " ...\n", + " 209.0\n", + " 218.0\n", + " 25.0\n", + " 1.0\n", + " 8.0\n", + " 1.0\n", + " 1165.0\n", + " 239.0\n", + " 279.0\n", + " 46.1\n", " \n", " \n", " 1\n", " vs Bologna\n", - " 23.0\n", - " 48.9\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 23.0\n", - " 13.0\n", - " 3.0\n", - " 3.0\n", - " ...\n", - " 182.0\n", - " 158.0\n", " 24.0\n", - " 2.0\n", + " 47.4\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 28.0\n", + " 16.0\n", + " 5.0\n", + " 5.0\n", + " ...\n", + " 252.0\n", + " 229.0\n", + " 33.0\n", " 3.0\n", - " 0.0\n", - " 731.0\n", - " 165.0\n", - " 123.0\n", - " 57.3\n", + " 3.0\n", + " 1.0\n", + " 1081.0\n", + " 224.0\n", + " 180.0\n", + " 55.4\n", " \n", " \n", " 2\n", " vs Cremonese\n", - " 27.0\n", - " 56.8\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 24.0\n", - " 16.0\n", - " 3.0\n", - " 3.0\n", - " ...\n", - " 145.0\n", - " 188.0\n", + " 29.0\n", + " 56.0\n", " 19.0\n", - " 2.0\n", + " 209.0\n", + " 1710.0\n", + " 32.0\n", + " 20.0\n", + " 4.0\n", + " 4.0\n", + " ...\n", + " 205.0\n", + " 247.0\n", + " 29.0\n", " 3.0\n", + " 4.0\n", " 0.0\n", - " 744.0\n", - " 242.0\n", - " 184.0\n", - " 56.8\n", + " 1052.0\n", + " 348.0\n", + " 267.0\n", + " 56.6\n", " \n", " \n", " 3\n", " vs Empoli\n", - " 25.0\n", - " 54.0\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 26.0\n", + " 53.7\n", " 19.0\n", - " 14.0\n", + " 209.0\n", + " 1710.0\n", + " 22.0\n", + " 16.0\n", " 1.0\n", " 3.0\n", " ...\n", - " 176.0\n", - " 151.0\n", - " 28.0\n", + " 251.0\n", + " 214.0\n", + " 31.0\n", " 2.0\n", " 0.0\n", " 0.0\n", - " 723.0\n", - " 163.0\n", - " 135.0\n", - " 54.7\n", + " 999.0\n", + " 217.0\n", + " 189.0\n", + " 53.4\n", " \n", " \n", " 4\n", " vs Fiorentina\n", - " 25.0\n", - " 41.3\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 27.0\n", + " 42.8\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 24.0\n", " 18.0\n", - " 13.0\n", - " 1.0\n", - " 1.0\n", + " 2.0\n", + " 2.0\n", " ...\n", - " 186.0\n", - " 176.0\n", - " 35.0\n", + " 259.0\n", + " 229.0\n", + " 52.0\n", " 1.0\n", - " 3.0\n", + " 4.0\n", " 0.0\n", - " 694.0\n", - " 193.0\n", - " 224.0\n", + " 942.0\n", + " 249.0\n", + " 289.0\n", " 46.3\n", " \n", " \n", " 5\n", " vs Hellas Verona\n", " 29.0\n", - " 56.1\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 26.0\n", - " 25.0\n", + " 56.6\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 30.0\n", + " 28.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 130.0\n", - " 205.0\n", - " 18.0\n", + " 192.0\n", + " 276.0\n", + " 22.0\n", " 1.0\n", " 0.0\n", " 1.0\n", - " 758.0\n", - " 240.0\n", - " 229.0\n", - " 51.2\n", + " 1038.0\n", + " 345.0\n", + " 388.0\n", + " 47.1\n", " \n", " \n", " 6\n", " vs Inter\n", " 23.0\n", - " 45.4\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 47.1\n", " 19.0\n", - " 18.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 182.0\n", - " 154.0\n", - " 14.0\n", - " 1.0\n", + " 209.0\n", + " 1710.0\n", + " 23.0\n", + " 20.0\n", " 2.0\n", - " 0.0\n", - " 596.0\n", - " 126.0\n", - " 172.0\n", - " 42.3\n", + " 2.0\n", + " ...\n", + " 242.0\n", + " 214.0\n", + " 18.0\n", + " 2.0\n", + " 2.0\n", + " 1.0\n", + " 869.0\n", + " 198.0\n", + " 232.0\n", + " 46.0\n", " \n", " \n", " 7\n", " vs Juventus\n", " 26.0\n", - " 49.5\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 7.0\n", - " 5.0\n", + " 50.5\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 15.0\n", + " 12.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 161.0\n", - " 156.0\n", - " 21.0\n", + " 210.0\n", + " 210.0\n", + " 25.0\n", " 0.0\n", - " 2.0\n", + " 3.0\n", " 0.0\n", - " 674.0\n", - " 184.0\n", - " 166.0\n", - " 52.6\n", + " 947.0\n", + " 230.0\n", + " 230.0\n", + " 50.0\n", " \n", " \n", " 8\n", " vs Lazio\n", " 21.0\n", - " 50.0\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 8.0\n", - " 7.0\n", + " 48.8\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 15.0\n", + " 11.0\n", " 1.0\n", " 1.0\n", " ...\n", - " 197.0\n", - " 124.0\n", - " 35.0\n", + " 261.0\n", + " 185.0\n", + " 42.0\n", " 1.0\n", - " 2.0\n", + " 4.0\n", " 1.0\n", - " 746.0\n", - " 151.0\n", - " 126.0\n", - " 54.5\n", + " 1021.0\n", + " 183.0\n", + " 181.0\n", + " 50.3\n", " \n", " \n", " 9\n", " vs Lecce\n", - " 24.0\n", - " 59.3\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 26.0\n", + " 57.6\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 23.0\n", " 16.0\n", - " 10.0\n", " 3.0\n", " 3.0\n", " ...\n", - " 184.0\n", - " 184.0\n", - " 27.0\n", + " 263.0\n", + " 265.0\n", + " 43.0\n", " 3.0\n", " 2.0\n", - " 0.0\n", - " 703.0\n", - " 216.0\n", - " 188.0\n", - " 53.5\n", + " 1.0\n", + " 1072.0\n", + " 373.0\n", + " 294.0\n", + " 55.9\n", " \n", " \n", " 10\n", " vs Milan\n", " 27.0\n", - " 46.1\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 14.0\n", - " 11.0\n", - " 1.0\n", + " 45.5\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 23.0\n", + " 17.0\n", " 2.0\n", + " 3.0\n", " ...\n", - " 164.0\n", - " 161.0\n", - " 15.0\n", + " 234.0\n", + " 226.0\n", + " 20.0\n", + " 3.0\n", " 2.0\n", " 2.0\n", - " 1.0\n", - " 690.0\n", - " 168.0\n", - " 205.0\n", - " 45.0\n", + " 964.0\n", + " 224.0\n", + " 275.0\n", + " 44.9\n", " \n", " \n", " 11\n", " vs Monza\n", " 29.0\n", - " 44.5\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 44.9\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 27.0\n", " 21.0\n", - " 17.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 207.0\n", - " 178.0\n", - " 24.0\n", + " 279.0\n", + " 242.0\n", + " 34.0\n", " 0.0\n", + " 3.0\n", " 1.0\n", - " 0.0\n", - " 692.0\n", - " 141.0\n", - " 160.0\n", - " 46.8\n", + " 958.0\n", + " 197.0\n", + " 215.0\n", + " 47.8\n", " \n", " \n", " 12\n", " vs Napoli\n", " 24.0\n", - " 40.2\n", + " 38.7\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 10.0\n", - " 6.0\n", + " 9.0\n", " 1.0\n", " 2.0\n", " ...\n", - " 195.0\n", - " 116.0\n", - " 18.0\n", + " 258.0\n", + " 167.0\n", + " 27.0\n", " 1.0\n", - " 3.0\n", + " 4.0\n", " 0.0\n", - " 687.0\n", - " 142.0\n", - " 181.0\n", - " 44.0\n", + " 933.0\n", + " 201.0\n", + " 231.0\n", + " 46.5\n", " \n", " \n", " 13\n", " vs Roma\n", - " 23.0\n", - " 49.2\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 13.0\n", - " 9.0\n", + " 25.0\n", + " 50.6\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 16.0\n", + " 12.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 198.0\n", - " 157.0\n", - " 7.0\n", + " 266.0\n", + " 214.0\n", + " 10.0\n", " 0.0\n", - " 3.0\n", + " 5.0\n", " 0.0\n", - " 709.0\n", - " 142.0\n", - " 159.0\n", - " 47.2\n", + " 981.0\n", + " 184.0\n", + " 225.0\n", + " 45.0\n", " \n", " \n", " 14\n", " vs Salernitana\n", - " 24.0\n", - " 52.9\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 21.0\n", - " 13.0\n", - " 2.0\n", + " 28.0\n", + " 55.3\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 38.0\n", + " 23.0\n", " 4.0\n", + " 7.0\n", " ...\n", - " 150.0\n", - " 157.0\n", - " 34.0\n", - " 4.0\n", + " 226.0\n", + " 233.0\n", + " 49.0\n", + " 7.0\n", " 1.0\n", " 1.0\n", - " 750.0\n", - " 169.0\n", - " 175.0\n", - " 49.1\n", + " 1084.0\n", + " 257.0\n", + " 240.0\n", + " 51.7\n", " \n", " \n", " 15\n", " vs Sampdoria\n", - " 24.0\n", - " 49.2\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 23.0\n", - " 18.0\n", - " 2.0\n", - " 3.0\n", + " 29.0\n", + " 50.6\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 30.0\n", + " 21.0\n", + " 4.0\n", + " 6.0\n", " ...\n", - " 220.0\n", - " 192.0\n", - " 42.0\n", - " 1.0\n", + " 299.0\n", + " 266.0\n", + " 55.0\n", + " 3.0\n", " 0.0\n", " 0.0\n", - " 742.0\n", - " 215.0\n", - " 217.0\n", - " 49.8\n", + " 1017.0\n", + " 300.0\n", + " 318.0\n", + " 48.5\n", " \n", " \n", " 16\n", " vs Sassuolo\n", - " 26.0\n", - " 51.9\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 27.0\n", + " 51.3\n", " 19.0\n", - " 15.0\n", - " 2.0\n", - " 2.0\n", + " 209.0\n", + " 1710.0\n", + " 29.0\n", + " 20.0\n", + " 4.0\n", + " 4.0\n", " ...\n", - " 177.0\n", - " 120.0\n", - " 47.0\n", - " 2.0\n", + " 249.0\n", + " 182.0\n", + " 62.0\n", " 2.0\n", + " 4.0\n", " 0.0\n", - " 710.0\n", - " 146.0\n", - " 130.0\n", - " 52.9\n", + " 986.0\n", + " 216.0\n", + " 173.0\n", + " 55.5\n", " \n", " \n", " 17\n", " vs Spezia\n", - " 24.0\n", - " 53.1\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 25.0\n", + " 30.0\n", + " 53.4\n", " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 32.0\n", + " 27.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 136.0\n", - " 177.0\n", - " 32.0\n", - " 1.0\n", + " 207.0\n", + " 261.0\n", + " 46.0\n", " 1.0\n", " 2.0\n", - " 776.0\n", - " 212.0\n", - " 179.0\n", - " 54.2\n", + " 2.0\n", + " 1182.0\n", + " 322.0\n", + " 281.0\n", + " 53.4\n", " \n", " \n", " 18\n", " vs Torino\n", " 23.0\n", - " 48.7\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 16.0\n", - " 11.0\n", - " 2.0\n", - " 2.0\n", + " 47.2\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 20.0\n", + " 12.0\n", + " 3.0\n", + " 4.0\n", " ...\n", - " 148.0\n", - " 199.0\n", - " 17.0\n", - " 2.0\n", + " 209.0\n", + " 265.0\n", + " 21.0\n", + " 3.0\n", " 1.0\n", " 0.0\n", - " 710.0\n", - " 228.0\n", - " 215.0\n", - " 51.5\n", + " 998.0\n", + " 323.0\n", + " 295.0\n", + " 52.3\n", " \n", " \n", " 19\n", " vs Udinese\n", " 22.0\n", - " 49.4\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 14.0\n", - " 8.0\n", + " 49.9\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 21.0\n", + " 15.0\n", " 2.0\n", " 2.0\n", " ...\n", - " 176.0\n", - " 162.0\n", - " 17.0\n", + " 250.0\n", + " 227.0\n", + " 29.0\n", " 2.0\n", " 0.0\n", " 1.0\n", - " 707.0\n", - " 142.0\n", - " 187.0\n", - " 43.2\n", + " 950.0\n", + " 189.0\n", + " 237.0\n", + " 44.4\n", " \n", " \n", "\n", @@ -2721,92 +2811,92 @@ ], "text/plain": [ " team players_used possession games games_starts minutes \\\n", - "0 vs Atalanta 23.0 53.6 14.0 154.0 1260.0 \n", - "1 vs Bologna 23.0 48.9 14.0 154.0 1260.0 \n", - "2 vs Cremonese 27.0 56.8 14.0 154.0 1260.0 \n", - "3 vs Empoli 25.0 54.0 14.0 154.0 1260.0 \n", - "4 vs Fiorentina 25.0 41.3 14.0 154.0 1260.0 \n", - "5 vs Hellas Verona 29.0 56.1 14.0 154.0 1260.0 \n", - "6 vs Inter 23.0 45.4 14.0 154.0 1260.0 \n", - "7 vs Juventus 26.0 49.5 14.0 154.0 1260.0 \n", - "8 vs Lazio 21.0 50.0 14.0 154.0 1260.0 \n", - "9 vs Lecce 24.0 59.3 14.0 154.0 1260.0 \n", - "10 vs Milan 27.0 46.1 14.0 154.0 1260.0 \n", - "11 vs Monza 29.0 44.5 14.0 154.0 1260.0 \n", - "12 vs Napoli 24.0 40.2 14.0 154.0 1260.0 \n", - "13 vs Roma 23.0 49.2 14.0 154.0 1260.0 \n", - "14 vs Salernitana 24.0 52.9 14.0 154.0 1260.0 \n", - "15 vs Sampdoria 24.0 49.2 14.0 154.0 1260.0 \n", - "16 vs Sassuolo 26.0 51.9 14.0 154.0 1260.0 \n", - "17 vs Spezia 24.0 53.1 14.0 154.0 1260.0 \n", - "18 vs Torino 23.0 48.7 14.0 154.0 1260.0 \n", - "19 vs Udinese 22.0 49.4 14.0 154.0 1260.0 \n", + "0 vs Atalanta 24.0 51.6 19.0 209.0 1710.0 \n", + "1 vs Bologna 24.0 47.4 20.0 220.0 1800.0 \n", + "2 vs Cremonese 29.0 56.0 19.0 209.0 1710.0 \n", + "3 vs Empoli 26.0 53.7 19.0 209.0 1710.0 \n", + "4 vs Fiorentina 27.0 42.8 19.0 209.0 1710.0 \n", + "5 vs Hellas Verona 29.0 56.6 19.0 209.0 1710.0 \n", + "6 vs Inter 23.0 47.1 19.0 209.0 1710.0 \n", + "7 vs Juventus 26.0 50.5 19.0 209.0 1710.0 \n", + "8 vs Lazio 21.0 48.8 19.0 209.0 1710.0 \n", + "9 vs Lecce 26.0 57.6 20.0 220.0 1800.0 \n", + "10 vs Milan 27.0 45.5 19.0 209.0 1710.0 \n", + "11 vs Monza 29.0 44.9 19.0 209.0 1710.0 \n", + "12 vs Napoli 24.0 38.7 19.0 209.0 1710.0 \n", + "13 vs Roma 25.0 50.6 19.0 209.0 1710.0 \n", + "14 vs Salernitana 28.0 55.3 20.0 220.0 1800.0 \n", + "15 vs Sampdoria 29.0 50.6 19.0 209.0 1710.0 \n", + "16 vs Sassuolo 27.0 51.3 19.0 209.0 1710.0 \n", + "17 vs Spezia 30.0 53.4 20.0 220.0 1800.0 \n", + "18 vs Torino 23.0 47.2 19.0 209.0 1710.0 \n", + "19 vs Udinese 22.0 49.9 19.0 209.0 1710.0 \n", "\n", " goals assists pens_made pens_att ... fouls fouled offsides \\\n", - "0 12.0 9.0 0.0 0.0 ... 150.0 168.0 19.0 \n", - "1 23.0 13.0 3.0 3.0 ... 182.0 158.0 24.0 \n", - "2 24.0 16.0 3.0 3.0 ... 145.0 188.0 19.0 \n", - "3 19.0 14.0 1.0 3.0 ... 176.0 151.0 28.0 \n", - "4 18.0 13.0 1.0 1.0 ... 186.0 176.0 35.0 \n", - "5 26.0 25.0 0.0 1.0 ... 130.0 205.0 18.0 \n", - "6 19.0 18.0 1.0 1.0 ... 182.0 154.0 14.0 \n", - "7 7.0 5.0 1.0 2.0 ... 161.0 156.0 21.0 \n", - "8 8.0 7.0 1.0 1.0 ... 197.0 124.0 35.0 \n", - "9 16.0 10.0 3.0 3.0 ... 184.0 184.0 27.0 \n", - "10 14.0 11.0 1.0 2.0 ... 164.0 161.0 15.0 \n", - "11 21.0 17.0 0.0 0.0 ... 207.0 178.0 24.0 \n", - "12 10.0 6.0 1.0 2.0 ... 195.0 116.0 18.0 \n", - "13 13.0 9.0 0.0 0.0 ... 198.0 157.0 7.0 \n", - "14 21.0 13.0 2.0 4.0 ... 150.0 157.0 34.0 \n", - "15 23.0 18.0 2.0 3.0 ... 220.0 192.0 42.0 \n", - "16 19.0 15.0 2.0 2.0 ... 177.0 120.0 47.0 \n", - "17 25.0 20.0 0.0 1.0 ... 136.0 177.0 32.0 \n", - "18 16.0 11.0 2.0 2.0 ... 148.0 199.0 17.0 \n", - "19 14.0 8.0 2.0 2.0 ... 176.0 162.0 17.0 \n", + "0 22.0 16.0 1.0 1.0 ... 209.0 218.0 25.0 \n", + "1 28.0 16.0 5.0 5.0 ... 252.0 229.0 33.0 \n", + "2 32.0 20.0 4.0 4.0 ... 205.0 247.0 29.0 \n", + "3 22.0 16.0 1.0 3.0 ... 251.0 214.0 31.0 \n", + "4 24.0 18.0 2.0 2.0 ... 259.0 229.0 52.0 \n", + "5 30.0 28.0 0.0 1.0 ... 192.0 276.0 22.0 \n", + "6 23.0 20.0 2.0 2.0 ... 242.0 214.0 18.0 \n", + "7 15.0 12.0 1.0 2.0 ... 210.0 210.0 25.0 \n", + "8 15.0 11.0 1.0 1.0 ... 261.0 185.0 42.0 \n", + "9 23.0 16.0 3.0 3.0 ... 263.0 265.0 43.0 \n", + "10 23.0 17.0 2.0 3.0 ... 234.0 226.0 20.0 \n", + "11 27.0 21.0 0.0 0.0 ... 279.0 242.0 34.0 \n", + "12 14.0 9.0 1.0 2.0 ... 258.0 167.0 27.0 \n", + "13 16.0 12.0 0.0 0.0 ... 266.0 214.0 10.0 \n", + "14 38.0 23.0 4.0 7.0 ... 226.0 233.0 49.0 \n", + "15 30.0 21.0 4.0 6.0 ... 299.0 266.0 55.0 \n", + "16 29.0 20.0 4.0 4.0 ... 249.0 182.0 62.0 \n", + "17 32.0 27.0 0.0 1.0 ... 207.0 261.0 46.0 \n", + "18 20.0 12.0 3.0 4.0 ... 209.0 265.0 21.0 \n", + "19 21.0 15.0 2.0 2.0 ... 250.0 227.0 29.0 \n", "\n", " pens_won pens_conceded own_goals ball_recoveries aerials_won \\\n", - "0 0.0 5.0 1.0 834.0 181.0 \n", - "1 2.0 3.0 0.0 731.0 165.0 \n", - "2 2.0 3.0 0.0 744.0 242.0 \n", - "3 2.0 0.0 0.0 723.0 163.0 \n", - "4 1.0 3.0 0.0 694.0 193.0 \n", - "5 1.0 0.0 1.0 758.0 240.0 \n", - "6 1.0 2.0 0.0 596.0 126.0 \n", - "7 0.0 2.0 0.0 674.0 184.0 \n", - "8 1.0 2.0 1.0 746.0 151.0 \n", - "9 3.0 2.0 0.0 703.0 216.0 \n", - "10 2.0 2.0 1.0 690.0 168.0 \n", - "11 0.0 1.0 0.0 692.0 141.0 \n", - "12 1.0 3.0 0.0 687.0 142.0 \n", - "13 0.0 3.0 0.0 709.0 142.0 \n", - "14 4.0 1.0 1.0 750.0 169.0 \n", - "15 1.0 0.0 0.0 742.0 215.0 \n", - "16 2.0 2.0 0.0 710.0 146.0 \n", - "17 1.0 1.0 2.0 776.0 212.0 \n", - "18 2.0 1.0 0.0 710.0 228.0 \n", - "19 2.0 0.0 1.0 707.0 142.0 \n", + "0 1.0 8.0 1.0 1165.0 239.0 \n", + "1 3.0 3.0 1.0 1081.0 224.0 \n", + "2 3.0 4.0 0.0 1052.0 348.0 \n", + "3 2.0 0.0 0.0 999.0 217.0 \n", + "4 1.0 4.0 0.0 942.0 249.0 \n", + "5 1.0 0.0 1.0 1038.0 345.0 \n", + "6 2.0 2.0 1.0 869.0 198.0 \n", + "7 0.0 3.0 0.0 947.0 230.0 \n", + "8 1.0 4.0 1.0 1021.0 183.0 \n", + "9 3.0 2.0 1.0 1072.0 373.0 \n", + "10 3.0 2.0 2.0 964.0 224.0 \n", + "11 0.0 3.0 1.0 958.0 197.0 \n", + "12 1.0 4.0 0.0 933.0 201.0 \n", + "13 0.0 5.0 0.0 981.0 184.0 \n", + "14 7.0 1.0 1.0 1084.0 257.0 \n", + "15 3.0 0.0 0.0 1017.0 300.0 \n", + "16 2.0 4.0 0.0 986.0 216.0 \n", + "17 1.0 2.0 2.0 1182.0 322.0 \n", + "18 3.0 1.0 0.0 998.0 323.0 \n", + "19 2.0 0.0 1.0 950.0 189.0 \n", "\n", " aerials_lost aerials_won_pct \n", - "0 211.0 46.2 \n", - "1 123.0 57.3 \n", - "2 184.0 56.8 \n", - "3 135.0 54.7 \n", - "4 224.0 46.3 \n", - "5 229.0 51.2 \n", - "6 172.0 42.3 \n", - "7 166.0 52.6 \n", - "8 126.0 54.5 \n", - "9 188.0 53.5 \n", - "10 205.0 45.0 \n", - "11 160.0 46.8 \n", - "12 181.0 44.0 \n", - "13 159.0 47.2 \n", - "14 175.0 49.1 \n", - "15 217.0 49.8 \n", - "16 130.0 52.9 \n", - "17 179.0 54.2 \n", - "18 215.0 51.5 \n", - "19 187.0 43.2 \n", + "0 279.0 46.1 \n", + "1 180.0 55.4 \n", + "2 267.0 56.6 \n", + "3 189.0 53.4 \n", + "4 289.0 46.3 \n", + "5 388.0 47.1 \n", + "6 232.0 46.0 \n", + "7 230.0 50.0 \n", + "8 181.0 50.3 \n", + "9 294.0 55.9 \n", + "10 275.0 44.9 \n", + "11 215.0 47.8 \n", + "12 231.0 46.5 \n", + "13 225.0 45.0 \n", + "14 240.0 51.7 \n", + "15 318.0 48.5 \n", + "16 173.0 55.5 \n", + "17 281.0 53.4 \n", + "18 295.0 52.3 \n", + "19 237.0 44.4 \n", "\n", "[20 rows x 157 columns]" ] @@ -3191,54 +3281,6 @@ " \n", " \n", " 627\n", - " David Zima\n", - " cz CZE\n", - " DF\n", - " Torino\n", - " 20\n", - " 2000\n", - " 20.0\n", - " 13.0\n", - " 1314.0\n", - " 0.0\n", - " ...\n", - " 16.0\n", - " 5.0\n", - " 1.0\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", - " 57.0\n", - " 45.0\n", - " 40.0\n", - " 52.9\n", - " \n", - " \n", - " 628\n", - " Jeroen Zoet\n", - " nl NED\n", - " GK\n", - " Spezia\n", - " 30\n", - " 1991\n", - " 7.0\n", - " 7.0\n", - " 630.0\n", - " 0.0\n", - " ...\n", - " 1.0\n", - " 3.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", - " 5.0\n", - " 2.0\n", - " 1.0\n", - " 66.7\n", - " \n", - " \n", - " 629\n", " Nadir Zortea\n", " it ITA\n", " DF,MF\n", @@ -3262,7 +3304,7 @@ " 32.0\n", " \n", " \n", - " 630\n", + " 628\n", " Petar Zovko\n", " ba BIH\n", " GK\n", @@ -3286,7 +3328,7 @@ " 0.0\n", " \n", " \n", - " 631\n", + " 629\n", " Szymon Żurkowski\n", " pl POL\n", " MF\n", @@ -3309,6 +3351,54 @@ " 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", + " ...\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", "

632 rows × 120 columns

\n", @@ -3322,11 +3412,11 @@ "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 David Zima cz CZE DF Torino 20 2000 20.0 \n", - "628 Jeroen Zoet nl NED GK Spezia 30 1991 7.0 \n", - "629 Nadir Zortea it ITA DF,MF Salernitana 22 1999 29.0 \n", - "630 Petar Zovko ba BIH GK Spezia 19 2002 1.0 \n", - "631 Szymon Żurkowski pl POL MF Empoli 23 1997 35.0 \n", + "627 Nadir Zortea it ITA DF,MF Salernitana 22 1999 29.0 \n", + "628 Petar Zovko ba BIH GK Spezia 19 2002 1.0 \n", + "629 Szymon Żurkowski pl POL MF Empoli 23 1997 35.0 \n", + "630 Milan Đurić ba BIH FW Salernitana 31 1990 33.0 \n", + "631 Filip Đuričić rs SRB MF,FW Sassuolo 29 1992 12.0 \n", "\n", " games_starts minutes goals ... fouls fouled offsides pens_won \\\n", "0 36.0 3084.0 17.0 ... 38.0 49.0 17.0 1.0 \n", @@ -3335,11 +3425,11 @@ "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 1314.0 0.0 ... 16.0 5.0 1.0 0.0 \n", - "628 7.0 630.0 0.0 ... 1.0 3.0 0.0 0.0 \n", - "629 13.0 1410.0 1.0 ... 14.0 6.0 0.0 0.0 \n", - "630 0.0 29.0 0.0 ... 0.0 1.0 0.0 0.0 \n", - "631 29.0 2307.0 6.0 ... 41.0 67.0 0.0 0.0 \n", + "627 13.0 1410.0 1.0 ... 14.0 6.0 0.0 0.0 \n", + "628 0.0 29.0 0.0 ... 0.0 1.0 0.0 0.0 \n", + "629 29.0 2307.0 6.0 ... 41.0 67.0 0.0 0.0 \n", + "630 23.0 2165.0 5.0 ... 24.0 45.0 6.0 1.0 \n", + "631 9.0 671.0 2.0 ... 10.0 14.0 2.0 0.0 \n", "\n", " pens_conceded own_goals ball_recoveries aerials_won aerials_lost \\\n", "0 1.0 0.0 68.0 78.0 80.0 \n", @@ -3348,11 +3438,11 @@ "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 1.0 0.0 57.0 45.0 40.0 \n", - "628 1.0 0.0 5.0 2.0 1.0 \n", - "629 0.0 0.0 72.0 8.0 17.0 \n", - "630 0.0 0.0 0.0 0.0 0.0 \n", - "631 1.0 0.0 162.0 24.0 32.0 \n", + "627 0.0 0.0 72.0 8.0 17.0 \n", + "628 0.0 0.0 0.0 0.0 0.0 \n", + "629 1.0 0.0 162.0 24.0 32.0 \n", + "630 0.0 0.0 40.0 242.0 83.0 \n", + "631 0.0 0.0 34.0 3.0 5.0 \n", "\n", " aerials_won_pct \n", "0 49.4 \n", @@ -3361,11 +3451,11 @@ "3 42.3 \n", "4 51.7 \n", ".. ... \n", - "627 52.9 \n", - "628 66.7 \n", - "629 32.0 \n", - "630 0.0 \n", - "631 42.9 \n", + "627 32.0 \n", + "628 0.0 \n", + "629 42.9 \n", + "630 74.5 \n", + "631 37.5 \n", "\n", "[632 rows x 120 columns]" ] diff --git a/2_votes_dataset_creation.ipynb b/2_votes_dataset_creation.ipynb index 1bc20af..9f1882f 100644 --- a/2_votes_dataset_creation.ipynb +++ b/2_votes_dataset_creation.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "id": "b63beb69", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "a3c39376", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "92d3c6fb", "metadata": {}, "outputs": [ @@ -121,32 +121,32 @@ " \n", " 375\n", " 38\n", - " Milan\n", - " Lazio\n", + " Lecce\n", + " Bologna\n", " \n", " \n", " 376\n", " 38\n", " Sassuolo\n", - " Monza\n", + " Fiorentina\n", " \n", " \n", " 377\n", " 38\n", - " Napoli\n", - " Salernitana\n", + " Milan\n", + " Verona\n", " \n", " \n", " 378\n", " 38\n", - " Udinese\n", - " Sampdoria\n", + " Torino\n", + " Inter\n", " \n", " \n", " 379\n", " 38\n", - " Roma\n", - " Spezia\n", + " Udinese\n", + " Juventus\n", " \n", " \n", "\n", @@ -154,23 +154,23 @@ "" ], "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 Milan Lazio\n", - "376 38 Sassuolo Monza\n", - "377 38 Napoli Salernitana\n", - "378 38 Udinese Sampdoria\n", - "379 38 Roma Spezia\n", + " 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": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -181,14 +181,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "0d4a330d", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(columns = ['matchday', 'player', 'team', 'oppteam', 'home', 'vote', 'goals', 'assists', 'cards_malus', 'fantavote'])\n", "\n", - "LAST_MATCH = 14\n", + "LAST_MATCH = 19\n", "\n", "for matchday in range(1, LAST_MATCH + 1):\n", " \n", @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "6ade957d", "metadata": {}, "outputs": [ @@ -361,11 +361,24 @@ " ...\n", " \n", " \n", - " 4010\n", - " 14\n", + " 5423\n", + " 19\n", + " Ilic\n", + " Verona\n", + " Lecce\n", + " 1\n", + " 7.0\n", + " 0\n", + " 1\n", + " 0.0\n", + " 8.0\n", + " \n", + " \n", + " 5424\n", + " 19\n", " Sulemana I.\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", @@ -374,60 +387,47 @@ " 6.0\n", " \n", " \n", - " 4011\n", - " 14\n", + " 5425\n", + " 19\n", " Lasagna\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " \n", " \n", - " 4012\n", - " 14\n", + " 5426\n", + " 19\n", " Kallon\n", " Verona\n", - " Juventus\n", - " 1\n", - " 5.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 5.5\n", - " \n", - " \n", - " 4013\n", - " 14\n", - " Djuric\n", - " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", " 0\n", - " 0.5\n", - " 5.5\n", + " 0.0\n", + " 6.0\n", " \n", " \n", - " 4014\n", - " 14\n", - " Henry\n", + " 5427\n", + " 19\n", + " Djuric\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " \n", " \n", "\n", - "

4015 rows × 10 columns

\n", + "

5428 rows × 10 columns

\n", "" ], "text/plain": [ @@ -438,11 +438,11 @@ "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", "... ... ... ... ... ... ... ... ... \n", - "4010 14 Sulemana I. Verona Juventus 1 6.0 0 0 \n", - "4011 14 Lasagna Verona Juventus 1 6.0 0 0 \n", - "4012 14 Kallon Verona Juventus 1 5.5 0 0 \n", - "4013 14 Djuric Verona Juventus 1 6.0 0 0 \n", - "4014 14 Henry Verona Juventus 1 6.5 0 0 \n", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -451,16 +451,16 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \n", - "4010 0.0 6.0 \n", - "4011 0.0 6.0 \n", - "4012 0.0 5.5 \n", - "4013 0.5 5.5 \n", - "4014 0.0 6.5 \n", + "5423 0.0 8.0 \n", + "5424 0.0 6.0 \n", + "5425 0.0 5.5 \n", + "5426 0.0 6.0 \n", + "5427 0.0 6.0 \n", "\n", - "[4015 rows x 10 columns]" + "[5428 rows x 10 columns]" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "230213fe", "metadata": {}, "outputs": [], diff --git a/3_players_dataset_creation.ipynb b/3_players_dataset_creation.ipynb index 7a7a861..50c1cfa 100644 --- a/3_players_dataset_creation.ipynb +++ b/3_players_dataset_creation.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "id": "7c65df92", "metadata": {}, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "id": "b2d7073e", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "id": "667970f6", "metadata": {}, "outputs": [ @@ -109,33 +109,33 @@ " ...\n", " \n", " \n", - " 515\n", - " Wojciech Szczęsny\n", - " Juventus\n", - " \n", - " \n", - " 516\n", + " 545\n", " Ciprian Tătărușanu\n", " Milan\n", " \n", " \n", - " 517\n", + " 546\n", " Pietro Terracciano\n", " Fiorentina\n", " \n", " \n", - " 518\n", + " 547\n", " Guglielmo Vicario\n", " Empoli\n", " \n", " \n", - " 519\n", + " 548\n", " Jeroen Zoet\n", " Spezia\n", " \n", + " \n", + " 549\n", + " Petar Zovko\n", + " Spezia\n", + " \n", " \n", "\n", - "

520 rows × 2 columns

\n", + "

550 rows × 2 columns

\n", "" ], "text/plain": [ @@ -146,16 +146,16 @@ "3 Michel Aebischer Bologna\n", "4 Felix Afena-Gyan Cremonese\n", ".. ... ...\n", - "515 Wojciech Szczęsny Juventus\n", - "516 Ciprian Tătărușanu Milan\n", - "517 Pietro Terracciano Fiorentina\n", - "518 Guglielmo Vicario Empoli\n", - "519 Jeroen Zoet Spezia\n", + "545 Ciprian Tătărușanu Milan\n", + "546 Pietro Terracciano Fiorentina\n", + "547 Guglielmo Vicario Empoli\n", + "548 Jeroen Zoet Spezia\n", + "549 Petar Zovko Spezia\n", "\n", - "[520 rows x 2 columns]" + "[550 rows x 2 columns]" ] }, - "execution_count": 3, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "id": "e1e64596", "metadata": {}, "outputs": [], @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "id": "3078d6f3", "metadata": {}, "outputs": [], @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "id": "ffd6091c", "metadata": {}, "outputs": [ @@ -259,477 +259,507 @@ "46 Raoul Bellanova Bellanova\n", "47 Andrea Belotti Belotti\n", "48 Marco Benassi Benassi\n", - "49 Ismaël Bennacer Bennacer\n", - "50 Domenico Berardi Berardi\n", - "51 Bartosz Bereszyński Bereszynski\n", - "52 Beto Beto\n", - "53 Matteo Bianchetti Bianchetti\n", - "54 Jaka Bijol Bijol\n", - "55 Cristiano Biraghi Biraghi\n", - "56 Samuele Birindelli Birindelli\n", - "57 Kristijan Bistrović Bistrovic\n", - "58 Alexis Blin Blin\n", - "59 Jeremie Boga Boga\n", - "60 Emil Bohinen Bohinen\n", - "61 Giacomo Bonaventura Bonaventura\n", - "62 Federico Bonazzoli Bonazzoli\n", - "63 Warren Bondo Bondo\n", - "64 Kevin Bonifazi Bonifazi\n", - "65 Leonardo Bonucci Bonucci\n", - "66 Erik Botheim Botheim\n", - "67 Mehdi Bourabia Bourabia\n", - "68 Edoardo Bove Bove\n", - "69 Domagoj Bradarić Bradaric\n", - "70 Gleison Bremer Bremer\n", - "71 Dylan Bronn Bronn\n", - "72 Marcelo Brozović Brozovic\n", - "73 Cristian Buonaiuto Buonaiuto\n", - "74 Alessandro Buongiorno Buongiorno\n", - "75 Juan Cabal Cabal\n", - "76 Liberato Cacace Cacace\n", - "77 Davide Calabria Calabria\n", - "78 Mattia Caldara Caldara\n", - "79 Luca Caldirola Caldirola\n", - "80 Hakan Çalhanoğlu Calhanoglu\n", - "81 Mohamed Camara Camara\n", - "82 Nicolò Cambiaghi Cambiaghi\n", - "83 Andrea Cambiaso Cambiaso\n", - "84 Matteo Cancellieri Cancellieri\n", - "85 Antonio Candreva Candreva\n", - "86 Gianluca Caprari Caprari\n", - "87 Francesco Caputo Caputo\n", - "88 Andrea Carboni Carboni\n", - "89 Valentin Carboni Carboni\n", - "90 Carlos Carlos\n", - "91 Marco Carnesecchi Carnesecchi\n", - "92 Nicolò Casale Casale\n", - "93 Michele Castagnetti Castagnetti\n", - "94 Danilo Cataldi Cataldi\n", - "95 Federico Ceccherini Ceccherini\n", - "96 Assan Ceesay Ceesay\n", - "97 Emil Ceide Ceide\n", - "98 Zeki Çelik Celik\n", - "99 Federico Chiesa Chiesa\n", - "100 Vlad Chiricheș Chiriches\n", - "101 Daniel Ciofani Ciofani\n", - "102 Patrick Ciurria Ciurria\n", - "103 Omar Colley Colley\n", - "104 Lorenzo Colombo Colombo\n", - "105 Andrea Colpani Colpani\n", - "106 Andrea Consigli Consigli\n", - "107 Andrea Conti Conti\n", - "108 Diego Coppola Coppola\n", - "109 Joaquín Correa Correa\n", - "110 Alessandro Cortinovis Cortinovis\n", - "111 Lassana Coulibaly Coulibaly\n", - "112 Bryan Cristante Cristante\n", - "113 Juan Cuadrado Cuadrado\n", - "114 Marco D'Alessandro DAlessandro\n", - "115 Danilo D'Ambrosio DAmbrosio\n", - "116 Luca D'Andrea DAndrea\n", - "117 Flavius Daniliuc Daniliuc\n", - "118 Danilo Danilo\n", - "119 Matteo Darmian Darmian\n", - "120 Paweł Dawidowicz Dawidowicz\n", - "121 Charles De Ketelaere Ketelaere\n", - "122 Manuel De Luca Luca\n", - "123 Mattia De Sciglio Sciglio\n", - "124 Lorenzo De Silvestri Silvestri\n", - "125 Koni De Winter Winter\n", - "126 Grégoire Defrel Defrel\n", - "127 Duccio Degli Innocenti Innocenti\n", - "128 Merih Demiral Demiral\n", - "129 Diego Demme Demme\n", - "130 Fabio Depaoli Depaoli\n", - "131 Fabio Depaoli Depaoli\n", - "132 Kastriot Dermaku Dermaku\n", - "133 Cyriel Dessers Dessers\n", - "134 Sergiño Dest Dest\n", - "135 Mattia Destro Destro\n", - "136 Gerard Deulofeu Deulofeu\n", - "137 Samuel Di Carmine Carmine\n", - "138 Federico Di Francesco Francesco\n", - "139 Michele Di Gregorio Gregorio\n", - "140 Giovanni Di Lorenzo Lorenzo\n", - "141 Ángel Di María Maria\n", - "142 Boulaye Dia Dia\n", - "143 Brahim Díaz Diaz\n", - "144 Federico Dimarco Dimarco\n", - "145 Koffi Djidji Djidji\n", - "146 Berat Djimsiti Djimsiti\n", - "147 Dodô Dodo\n", - "148 Josh Doig Doig\n", - "149 Nicolás Domínguez Dominguez\n", - "150 Giulio Donati Donati\n", - "151 Bartłomiej Drągowski Dragowski\n", - "152 Denzel Dumfries Dumfries\n", - "153 Alfred Duncan Duncan\n", - "154 Paulo Dybala Dybala\n", - "155 Edin Džeko Dzeko\n", - "156 Festy Ebosele Ebosele\n", - "157 Enzo Ebosse Ebosse\n", - "158 Tyronne Ebuehi Ebuehi\n", - "159 Éderson Ederson\n", - "160 Kingsley Ehizibue Ehizibue\n", - "161 Albin Ekdal Ekdal\n", - "162 Emmanuel Ekong Ekong\n", - "163 Mikael Ellertsson Ellertsson\n", - "164 Elif Elmas Elmas\n", - "165 Martin Erlic Erlic\n", - "166 Gonzalo Escalante Escalante\n", - "167 Nicolò Fagioli Fagioli\n", - "168 Wladimiro Falcone Falcone\n", - "169 Davide Faraoni Faraoni\n", - "170 Federico Fazio Fazio\n", - "171 Jacopo Fazzini Fazzini\n", - "172 Lewis Ferguson Ferguson\n", - "173 Alex Ferrari Ferrari\n", - "174 Alessandro Florenzi Florenzi\n", - "175 Davide Frattesi Frattesi\n", - "176 Matteo Gabbia Gabbia\n", - "177 Manolo Gabbiadini Gabbiadini\n", - "178 Gianluca Gaetano Gaetano\n", - "179 Roberto Gagliardini Gagliardini\n", - "180 Antonino Gallo Gallo\n", - "181 Federico Gatti Gatti\n", - "182 Valentin Gendrey Gendrey\n", - "183 Paolo Ghiglione Ghiglione\n", - "184 Mario Gila Gila\n", - "185 Olivier Giroud Giroud\n", - "186 Pierluigi Gollini Gollini\n", - "187 Joan Gonzàlez Gonzalez\n", - "188 Nicolás González Gonzalez\n", - "189 Robin Gosens Gosens\n", - "190 Alberto Grassi Grassi\n", - "191 Koray Günter Gunter\n", - "192 Emmanuel Gyasi Gyasi\n", - "193 Norbert Gyömbér Gyomber\n", - "194 Christian Gytkjær Gytkjr\n", - "195 Nicolas Haas Haas\n", - "196 Samir Handanović Handanovic\n", - "197 Abdou Harroui Harroui\n", - "198 Hans Hateboer Hateboer\n", - "199 Liam Henderson Henderson\n", - "200 Jack Hendry Hendry\n", - "201 Matheus Henrique Henrique\n", - "202 Thomas Henry Henry\n", - "203 Theo Hernández Hernandez\n", - "204 Isak Hien Hien\n", - "205 Morten Hjulmand Hjulmand\n", - "206 Emil Holm Holm\n", - "207 Martin Hongla Hongla\n", - "208 Petko Hristov Hristov\n", - "209 Ajdin Hrustic Hrustic\n", - "210 Elseid Hysaj Hysaj\n", - "211 Rasmus Højlund Hjlund\n", - "212 Roger Ibanez Ibanez\n", - "213 Igor Igor\n", - "214 Jonathan Ikone Ikone\n", - "215 Ivan Ilić Ilic\n", - "216 Samuel Iling-Junior Iling-Junior\n", - "217 Emirhan İlkhan Ilkhan\n", - "218 Ciro Immobile Immobile\n", - "219 Ardian Ismajli Ismajli\n", - "220 Armando Izzo Izzo\n", - "221 Mato Jajalo Jajalo\n", - "222 Juan Jesus Jesus\n", - "223 Þórir Jóhann Helgason Helgason\n", - "224 Luka Jović Jovic\n", - "225 Hamed Junior Traorè Traore\n", - "226 Yayah Kallon Kallon\n", - "227 Pierre Kalulu Kalulu\n", - "228 Yann Karamoh Karamoh\n", - "229 Rick Karsdorp Karsdorp\n", - "230 Denso Kasius Kasius\n", - "231 Grigoris Kastanos Kastanos\n", - "232 Moise Kean Kean\n", - "233 Jakub Kiwior Kiwior\n", - "234 Simon Kjær Kjr\n", - "235 Teun Koopmeiners Koopmeiners\n", - "236 Filip Kostić Kostic\n", - "237 Christian Kouamé Kouame\n", - "238 Viktor Kovalenko Kovalenko\n", - "239 Julian Kristoffersen Kristoffersen\n", - "240 Rade Krunić Krunic\n", - "241 Marash Kumbulla Kumbulla\n", - "242 Khvicha Kvaratskhelia Kvaratskhelia\n", - "243 Giorgos Kyriakopoulos Kyriakopoulos\n", - "244 Sam Lammers Lammers\n", - "245 Kevin Lasagna Lasagna\n", - "246 Armand Lauriente Lauriente\n", - "247 Valentino Lazaro Lazaro\n", - "248 Marko Lazetić Lazetic\n", - "249 Darko Lazović Lazovic\n", - "250 Manuel Lazzari Lazzari\n", - "251 Rafael Leão Leao\n", - "252 Mehdi Léris Leris\n", - "253 Karol Linetty Linetty\n", - "254 Marcin Listkowski Listkowski\n", - "255 Stanislav Lobotka Lobotka\n", - "256 Manuel Locatelli Locatelli\n", - "257 Luka Lochoshvili Lochoshvili\n", - "258 Ademola Lookman Lookman\n", - "259 Maxime Lopez Lopez\n", - "260 Matteo Lovato Lovato\n", - "261 Sandi Lovrić Lovric\n", - "262 Hirving Lozano Lozano\n", - "263 Jhon Lucumí Lucumi\n", - "264 Romelu Lukaku Lukaku\n", - "265 Saša Lukić Lukic\n", - "266 Sebastiano Luperto Luperto\n", - "267 Charalambos Lykogiannis Lykogiannis\n", - "268 Giulio Maggiore Maggiore\n", - "269 Giangiacomo Magnani Magnani\n", - "270 Mike Maignan Maignan\n", - "271 Jean-Victor Makengo Makengo\n", - "272 Daniel Maldini Maldini\n", - "273 Youssef Maleh Maleh\n", - "274 Ruslan Malinovskyi Malinovskyi\n", - "275 Gianluca Mancini Mancini\n", - "276 Rolando Mandragora Mandragora\n", - "277 Riccardo Marchizza Marchizza\n", - "278 Gian Marco Ferrari Ferrari\n", - "279 Pablo Marí Mari\n", - "280 Răzvan Marin Marin\n", - "281 Marlon Marlon\n", - "282 Luca Marrone Marrone\n", - "283 Lautaro Martínez Martinez\n", - "284 Lucas Martínez Quarta Quarta\n", - "285 Adam Marušić Marusic\n", - "286 Adam Masina Masina\n", - "287 Nemanja Matić Matic\n", - "288 Luís Maximiano Maximiano\n", - "289 Pasquale Mazzocchi Mazzocchi\n", - "290 Weston McKennie McKennie\n", - "291 Gary Medel Medel\n", - "292 Soualiho Meïté Meite\n", - "293 Alex Meret Meret\n", - "294 Yıldırım Mert Çetin Cetin\n", - "295 Junior Messias Messias\n", - "296 Tommaso Milanese Milanese\n", - "297 Nikola Milenković Milenkovic\n", - "298 Arkadiusz Milik Milik\n", - "299 Sergej Milinković-Savić Milinkovic-Savic\n", - "300 Vanja Milinković-Savić Milinkovic-Savic\n", - "301 Kim Min-jae Min-jae\n", - "302 Aleksei Miranchuk Miranchuk\n", - "303 Fabio Miretti Miretti\n", - "304 Henrikh Mkhitaryan Mkhitaryan\n", - "305 Salvatore Molina Molina\n", - "306 Daniele Montevago Montevago\n", - "307 Lorenzo Montipò Montipo\n", - "308 Nikola Moro Moro\n", - "309 Dany Mota Mota\n", - "310 Mert Müldür Muldur\n", - "311 Luis Muriel Muriel\n", - "312 Jeison Murillo Murillo\n", - "313 Nicola Murru Murru\n", - "314 Juan Musso Musso\n", - "315 Joakim Mæhle Mhle\n", - "316 Michel Ndary Adopo Adopo\n", - "317 Tanguy Ndombele Ndombele\n", - "318 Ilija Nestorovski Nestorovski\n", - "319 Dimitris Nikolaou Nikolaou\n", - "320 Bram Nuytinck Nuytinck\n", - "321 M'Bala Nzola Nzola\n", - "322 Pedro Obiang Obiang\n", - "323 David Okereke Okereke\n", - "324 Caleb Okoli Okoli\n", - "325 Mathías Olivera Olivera\n", - "326 André Onana Onana\n", - "327 Divock Origi Origi\n", - "328 Riccardo Orsolini Orsolini\n", - "329 Victor Osimhen Osimhen\n", - "330 Remi Oudin Oudin\n", - "331 Adam Ounas Ounas\n", - "332 Leandro Paredes Paredes\n", - "333 Fabiano Parisi Parisi\n", - "334 Mario Pašalić Pasalic\n", - "335 Patric Patric\n", - "336 Rui Patrício Patricio\n", - "337 Pedro Pedro\n", - "338 Pietro Pellegri Pellegri\n", - "339 Lorenzo Pellegrini Pellegrini\n", - "340 Pepín Pepin\n", - "341 Roberto Pereyra Pereyra\n", - "342 Nehuén Pérez Perez\n", - "343 Mattia Perin Perin\n", - "344 Matteo Pessina Pessina\n", - "345 Andrea Petagna Petagna\n", - "346 Giuseppe Pezzella Pezzella\n", - "347 Krzysztof Piątek Piatek\n", - "348 Roberto Piccoli Piccoli\n", - "349 Charles Pickel Pickel\n", - "350 Andrea Pinamonti Pinamonti\n", - "351 Lorenzo Pirola Pirola\n", - "352 Marko Pjaca Pjaca\n", - "353 Tommaso Pobega Pobega\n", - "354 Matteo Politano Politano\n", - "355 Marin Pongračić Pongracic\n", - "356 Stefan Posch Posch\n", - "357 Ivan Provedel Provedel\n", - "358 Ignacio Pussetto Pussetto\n", - "359 Fabio Quagliarella Quagliarella\n", - "360 Giacomo Quagliata Quagliata\n", - "361 Adrien Rabiot Rabiot\n", - "362 Nemanja Radonjić Radonjic\n", - "363 Ivan Radovanović Radovanovic\n", - "364 Ionuț Radu Radu\n", - "365 Luca Ranieri Ranieri\n", - "366 Andrea Ranocchia Ranocchia\n", - "367 Filippo Ranocchia Ranocchia\n", - "368 Giacomo Raspadori Raspadori\n", - "369 Giacomo Raspadori Raspadori\n", - "370 Ante Rebić Rebic\n", - "371 Arkadiusz Reca Reca\n", - "372 Panagiotis Retsos Retsos\n", - "373 Franck Ribéry Ribery\n", - "374 Samuele Ricci Ricci\n", - "375 Tomás Rincón Rincon\n", - "376 Pablo Rodríguez Rodriguez\n", - "377 Ricardo Rodríguez Rodriguez\n", - "378 Rogério Rogerio\n", - "379 Alessio Romagnoli Romagnoli\n", - "380 Luka Romero Romero\n", - "381 Marten de Roon Roon\n", - "382 Nicolò Rovella Rovella\n", - "383 Nicolò Rovella Rovella\n", - "384 Amir Rrahmani Rrahmani\n", - "385 Ruan Ruan\n", - "386 Daniele Rugani Rugani\n", - "387 Matteo Ruggeri Ruggeri\n", - "388 Mário Rui Rui\n", - "389 Abdelhamid Sabiri Sabiri\n", - "390 Alexis Saelemaekers Saelemaekers\n", - "391 Jacopo Sala Sala\n", - "392 Lazar Samardzic Samardzic\n", - "393 Junior Sambia Sambia\n", - "394 Antonio Sanabria Sanabria\n", - "395 Leandro Sanca Sanca\n", - "396 Alex Sandro Sandro\n", - "397 Nicola Sansone Sansone\n", - "398 Riccardo Saponara Saponara\n", - "399 Martin Satriano Satriano\n", - "400 Giorgio Scalvini Scalvini\n", - "401 Jerdy Schouten Schouten\n", - "402 Perr Schuurs Schuurs\n", - "403 Demba Seck Seck\n", - "404 Jacopo Segre Segre\n", - "405 Stefano Sensi Sensi\n", - "406 Luigi Sepe Sepe\n", - "407 Leonardo Sernicola Sernicola\n", - "408 Stephan El Shaarawy Shaarawy\n", - "409 Eldor Shomurodov Shomurodov\n", - "410 Marco Silvestri Silvestri\n", - "411 Giovanni Simeone Simeone\n", - "412 Wilfried Singo Singo\n", - "413 Leo Skiri Østigård stigard\n", - "414 Łukasz Skorupski Skorupski\n", - "415 Milan Škriniar Skriniar\n", - "416 Chris Smalling Smalling\n", - "417 Brandon Soppy Soppy\n", - "418 Brandon Soppy Soppy\n", - "419 Roberto Soriano Soriano\n", - "420 Joaquin Sosa Sosa\n", - "421 Riccardo Sottil Sottil\n", - "422 Matìas Soulé Soule\n", - "423 Adama Soumaoro Soumaoro\n", - "424 Leonardo Spinazzola Spinazzola\n", - "425 Marco Sportiello Sportiello\n", - "426 Petar Stojanović Stojanovic\n", - "427 Gabriel Strefezza Strefezza\n", - "428 Dávid Strelec Strelec\n", - "429 Isaac Success Success\n", - "430 Ibrahim Sulemana Sulemana\n", - "431 Wojciech Szczęsny Szczesny\n", - "432 Adrien Tameze Tameze\n", - "433 Ciprian Tătărușanu Tatarusanu\n", - "434 Filippo Terracciano Terracciano\n", - "435 Pietro Terracciano Terracciano\n", - "436 Aleksa Terzić Terzic\n", - "437 Malick Thiaw Thiaw\n", - "438 Kristian Thorstvedt Thorstvedt\n", - "439 Jeremy Toljan Toljan\n", - "440 Rafael Tolói Toloi\n", - "441 Fikayo Tomori Tomori\n", - "442 Sandro Tonali Tonali\n", - "443 Frank Tsadjout Tsadjout\n", - "444 Alessandro Tuia Tuia\n", - "445 Iyenoma Udogie Udogie\n", - "446 Samuel Umtiti Umtiti\n", - "447 Diego Valencia Valencia\n", - "448 Emanuele Valeri Valeri\n", - "449 Mattia Valoti Valoti\n", - "450 Johan Vásquez Vasquez\n", - "451 Matías Vecino Vecino\n", - "452 Miguel Veloso Veloso\n", - "453 Lorenzo Venuti Venuti\n", - "454 Daniele Verde Verde\n", - "455 Simone Verdi Verdi\n", - "456 Valerio Verre Verre\n", - "457 Guglielmo Vicario Vicario\n", - "458 Ronaldo Vieira Vieira\n", - "459 Emanuel Vignato Vignato\n", - "460 Samuele Vignato Vignato\n", - "461 Tonny Vilhena Vilhena\n", - "462 Gonzalo Villar Villar\n", - "463 Matías Viña Vina\n", - "464 Dušan Vlahović Vlahovic\n", - "465 Nikola Vlašić Vlasic\n", - "466 Mërgim Vojvoda Vojvoda\n", - "467 Cristian Volpato Volpato\n", - "468 Aster Vranckx Vranckx\n", - "469 Stefan de Vrij Vrij\n", - "470 Walace Walace\n", - "471 Sebastian Walukiewicz Walukiewicz\n", - "472 Georginio Wijnaldum Wijnaldum\n", - "473 Gerard Yepes Yepes\n", - "474 Mattia Zaccagni Zaccagni\n", - "475 Denis Zakaria Zakaria\n", - "476 Nicola Zalewski Zalewski\n", - "477 Andre-Frank Zambo Anguissa Anguissa\n", - "478 Luca Zanimacchia Zanimacchia\n", - "479 Nicolò Zaniolo Zaniolo\n", - "480 Alessandro Zanoli Zanoli\n", - "481 Duván Zapata Zapata\n", - "482 Davide Zappacosta Zappacosta\n", - "483 Alessio Zerbin Zerbin\n", - "484 Piotr Zieliński Zielinski\n", - "485 David Zima Zima\n", - "486 Joshua Zirkzee Zirkzee\n", - "487 Jeroen Zoet Zoet\n", - "488 Nadir Zortea Zortea\n", - "489 Szymon Żurkowski Zurkowski\n", - "490 Milan Đurić uric\n", - "491 Filip Đuričić uricic\n", - "492 Emil Audero Audero\n", - "493 Marco Carnesecchi Carnesecchi\n", - "494 Andrea Consigli Consigli\n", - "495 Michele Di Gregorio Gregorio\n", - "496 Bartłomiej Drągowski Dragowski\n", - "497 Wladimiro Falcone Falcone\n", - "498 Pierluigi Gollini Gollini\n", - "499 Samir Handanović Handanovic\n", - "500 Mike Maignan Maignan\n", - "501 Luís Maximiano Maximiano\n", - "502 Alex Meret Meret\n", - "503 Vanja Milinković-Savić Milinkovic-Savic\n", - "504 Lorenzo Montipò Montipo\n", - "505 Juan Musso Musso\n", - "506 André Onana Onana\n", - "507 Rui Patrício Patricio\n", - "508 Mattia Perin Perin\n", - "509 Ivan Provedel Provedel\n", - "510 Ionuț Radu Radu\n", - "511 Luigi Sepe Sepe\n", - "512 Marco Silvestri Silvestri\n", - "513 Łukasz Skorupski Skorupski\n", - "514 Marco Sportiello Sportiello\n", - "515 Wojciech Szczęsny Szczesny\n", - "516 Ciprian Tătărușanu Tatarusanu\n", - "517 Pietro Terracciano Terracciano\n", - "518 Guglielmo Vicario Vicario\n", - "519 Jeroen Zoet Zoet\n" + "49 Marco Benassi Benassi\n", + "50 Ismaël Bennacer Bennacer\n", + "51 Domenico Berardi Berardi\n", + "52 Bartosz Bereszyński Bereszynski\n", + "53 Beto Beto\n", + "54 Matteo Bianchetti Bianchetti\n", + "55 Alessandro Bianco Bianco\n", + "56 Jaka Bijol Bijol\n", + "57 Cristiano Biraghi Biraghi\n", + "58 Samuele Birindelli Birindelli\n", + "59 Kristijan Bistrović Bistrovic\n", + "60 Alexis Blin Blin\n", + "61 Jeremie Boga Boga\n", + "62 Emil Bohinen Bohinen\n", + "63 Giacomo Bonaventura Bonaventura\n", + "64 Federico Bonazzoli Bonazzoli\n", + "65 Warren Bondo Bondo\n", + "66 Kevin Bonifazi Bonifazi\n", + "67 Leonardo Bonucci Bonucci\n", + "68 Erik Botheim Botheim\n", + "69 Mehdi Bourabia Bourabia\n", + "70 Edoardo Bove Bove\n", + "71 Domagoj Bradarić Bradaric\n", + "72 Gleison Bremer Bremer\n", + "73 Dylan Bronn Bronn\n", + "74 Marcelo Brozović Brozovic\n", + "75 Cristian Buonaiuto Buonaiuto\n", + "76 Alessandro Buongiorno Buongiorno\n", + "77 Juan Cabal Cabal\n", + "78 Liberato Cacace Cacace\n", + "79 Davide Calabria Calabria\n", + "80 Mattia Caldara Caldara\n", + "81 Luca Caldirola Caldirola\n", + "82 Hakan Çalhanoğlu Calhanoglu\n", + "83 Mohamed Camara Camara\n", + "84 Nicolò Cambiaghi Cambiaghi\n", + "85 Andrea Cambiaso Cambiaso\n", + "86 Matteo Cancellieri Cancellieri\n", + "87 Antonio Candreva Candreva\n", + "88 Gianluca Caprari Caprari\n", + "89 Francesco Caputo Caputo\n", + "90 Francesco Caputo Caputo\n", + "91 Andrea Carboni Carboni\n", + "92 Valentin Carboni Carboni\n", + "93 Carlos Carlos\n", + "94 Marco Carnesecchi Carnesecchi\n", + "95 Nicolò Casale Casale\n", + "96 Michele Castagnetti Castagnetti\n", + "97 Gaetano Castrovilli Castrovilli\n", + "98 Danilo Cataldi Cataldi\n", + "99 Federico Ceccherini Ceccherini\n", + "100 Assan Ceesay Ceesay\n", + "101 Emil Ceide Ceide\n", + "102 Zeki Çelik Celik\n", + "103 Federico Chiesa Chiesa\n", + "104 Vlad Chiricheș Chiriches\n", + "105 Daniel Ciofani Ciofani\n", + "106 Tio Cipot Cipot\n", + "107 Patrick Ciurria Ciurria\n", + "108 Omar Colley Colley\n", + "109 Lorenzo Colombo Colombo\n", + "110 Andrea Colpani Colpani\n", + "111 Andrea Consigli Consigli\n", + "112 Andrea Conti Conti\n", + "113 Diego Coppola Coppola\n", + "114 Joaquín Correa Correa\n", + "115 Alessandro Cortinovis Cortinovis\n", + "116 Lassana Coulibaly Coulibaly\n", + "117 Bryan Cristante Cristante\n", + "118 Domen Črnigoj Crnigoj\n", + "119 Juan Cuadrado Cuadrado\n", + "120 Marco D'Alessandro DAlessandro\n", + "121 Danilo D'Ambrosio DAmbrosio\n", + "122 Luca D'Andrea DAndrea\n", + "123 Flavius Daniliuc Daniliuc\n", + "124 Danilo Danilo\n", + "125 Matteo Darmian Darmian\n", + "126 Paweł Dawidowicz Dawidowicz\n", + "127 Charles De Ketelaere Ketelaere\n", + "128 Manuel De Luca Luca\n", + "129 Mattia De Sciglio Sciglio\n", + "130 Lorenzo De Silvestri Silvestri\n", + "131 Koni De Winter Winter\n", + "132 Grégoire Defrel Defrel\n", + "133 Duccio Degli Innocenti Innocenti\n", + "134 Merih Demiral Demiral\n", + "135 Diego Demme Demme\n", + "136 Fabio Depaoli Depaoli\n", + "137 Fabio Depaoli Depaoli\n", + "138 Kastriot Dermaku Dermaku\n", + "139 Cyriel Dessers Dessers\n", + "140 Sergiño Dest Dest\n", + "141 Mattia Destro Destro\n", + "142 Gerard Deulofeu Deulofeu\n", + "143 Samuel Di Carmine Carmine\n", + "144 Federico Di Francesco Francesco\n", + "145 Michele Di Gregorio Gregorio\n", + "146 Giovanni Di Lorenzo Lorenzo\n", + "147 Ángel Di María Maria\n", + "148 Boulaye Dia Dia\n", + "149 Brahim Díaz Diaz\n", + "150 Federico Dimarco Dimarco\n", + "151 Koffi Djidji Djidji\n", + "152 Berat Djimsiti Djimsiti\n", + "153 Dodô Dodo\n", + "154 Josh Doig Doig\n", + "155 Nicolás Domínguez Dominguez\n", + "156 Giulio Donati Donati\n", + "157 Bartłomiej Drągowski Dragowski\n", + "158 Denzel Dumfries Dumfries\n", + "159 Alfred Duncan Duncan\n", + "160 Paulo Dybala Dybala\n", + "161 Edin Džeko Dzeko\n", + "162 Festy Ebosele Ebosele\n", + "163 Enzo Ebosse Ebosse\n", + "164 Tyronne Ebuehi Ebuehi\n", + "165 Éderson Ederson\n", + "166 Kingsley Ehizibue Ehizibue\n", + "167 Albin Ekdal Ekdal\n", + "168 Emmanuel Ekong Ekong\n", + "169 Mikael Ellertsson Ellertsson\n", + "170 Elif Elmas Elmas\n", + "171 Martin Erlic Erlic\n", + "172 Gonzalo Escalante Escalante\n", + "173 Salvatore Esposito Esposito\n", + "174 Nicolò Fagioli Fagioli\n", + "175 Wladimiro Falcone Falcone\n", + "176 Davide Faraoni Faraoni\n", + "177 Federico Fazio Fazio\n", + "178 Jacopo Fazzini Fazzini\n", + "179 Lewis Ferguson Ferguson\n", + "180 Alex Ferrari Ferrari\n", + "181 Alex Ferrari Ferrari\n", + "182 Salvador Ferrer Ferrer\n", + "183 Alessandro Florenzi Florenzi\n", + "184 Davide Frattesi Frattesi\n", + "185 Matteo Gabbia Gabbia\n", + "186 Manolo Gabbiadini Gabbiadini\n", + "187 Gianluca Gaetano Gaetano\n", + "188 Roberto Gagliardini Gagliardini\n", + "189 Antonino Gallo Gallo\n", + "190 Federico Gatti Gatti\n", + "191 Valentin Gendrey Gendrey\n", + "192 Paolo Ghiglione Ghiglione\n", + "193 Mario Gila Gila\n", + "194 Olivier Giroud Giroud\n", + "195 Pierluigi Gollini Gollini\n", + "196 Joan Gonzàlez Gonzalez\n", + "197 Nicolás González Gonzalez\n", + "198 Robin Gosens Gosens\n", + "199 Alberto Grassi Grassi\n", + "200 Koray Günter Gunter\n", + "201 Emmanuel Gyasi Gyasi\n", + "202 Norbert Gyömbér Gyomber\n", + "203 Christian Gytkjær Gytkjr\n", + "204 Nicolas Haas Haas\n", + "205 Samir Handanović Handanovic\n", + "206 Abdou Harroui Harroui\n", + "207 Hans Hateboer Hateboer\n", + "208 Liam Henderson Henderson\n", + "209 Jack Hendry Hendry\n", + "210 Matheus Henrique Henrique\n", + "211 Thomas Henry Henry\n", + "212 Theo Hernández Hernandez\n", + "213 Isak Hien Hien\n", + "214 Morten Hjulmand Hjulmand\n", + "215 Emil Holm Holm\n", + "216 Martin Hongla Hongla\n", + "217 Petko Hristov Hristov\n", + "218 Ajdin Hrustic Hrustic\n", + "219 Elseid Hysaj Hysaj\n", + "220 Rasmus Højlund Hjlund\n", + "221 Roger Ibanez Ibanez\n", + "222 Igor Igor\n", + "223 Jonathan Ikone Ikone\n", + "224 Ivan Ilić Ilic\n", + "225 Samuel Iling-Junior Iling-Junior\n", + "226 Emirhan İlkhan Ilkhan\n", + "227 Ciro Immobile Immobile\n", + "228 Ardian Ismajli Ismajli\n", + "229 Armando Izzo Izzo\n", + "230 Mato Jajalo Jajalo\n", + "231 Juan Jesus Jesus\n", + "232 Þórir Jóhann Helgason Helgason\n", + "233 Luka Jović Jovic\n", + "234 Hamed Junior Traorè Traore\n", + "235 Yayah Kallon Kallon\n", + "236 Pierre Kalulu Kalulu\n", + "237 Yann Karamoh Karamoh\n", + "238 Rick Karsdorp Karsdorp\n", + "239 Denso Kasius Kasius\n", + "240 Grigoris Kastanos Kastanos\n", + "241 Moise Kean Kean\n", + "242 Jakub Kiwior Kiwior\n", + "243 Simon Kjær Kjr\n", + "244 Teun Koopmeiners Koopmeiners\n", + "245 Filip Kostić Kostic\n", + "246 Christian Kouamé Kouame\n", + "247 Viktor Kovalenko Kovalenko\n", + "248 Julian Kristoffersen Kristoffersen\n", + "249 Rade Krunić Krunic\n", + "250 Marash Kumbulla Kumbulla\n", + "251 Khvicha Kvaratskhelia Kvaratskhelia\n", + "252 Giorgos Kyriakopoulos Kyriakopoulos\n", + "253 Sam Lammers Lammers\n", + "254 Sam Lammers Lammers\n", + "255 Kevin Lasagna Lasagna\n", + "256 Armand Lauriente Lauriente\n", + "257 Valentino Lazaro Lazaro\n", + "258 Marko Lazetić Lazetic\n", + "259 Darko Lazović Lazovic\n", + "260 Manuel Lazzari Lazzari\n", + "261 Rafael Leão Leao\n", + "262 Mehdi Léris Leris\n", + "263 Karol Linetty Linetty\n", + "264 Marcin Listkowski Listkowski\n", + "265 Stanislav Lobotka Lobotka\n", + "266 Manuel Locatelli Locatelli\n", + "267 Luka Lochoshvili Lochoshvili\n", + "268 Ademola Lookman Lookman\n", + "269 Maxime Lopez Lopez\n", + "270 Matteo Lovato Lovato\n", + "271 Sandi Lovrić Lovric\n", + "272 Hirving Lozano Lozano\n", + "273 Jhon Lucumí Lucumi\n", + "274 José Luis Palomino Palomino\n", + "275 Romelu Lukaku Lukaku\n", + "276 Saša Lukić Lukic\n", + "277 Sebastiano Luperto Luperto\n", + "278 Charalambos Lykogiannis Lykogiannis\n", + "279 Giulio Maggiore Maggiore\n", + "280 Giangiacomo Magnani Magnani\n", + "281 Mike Maignan Maignan\n", + "282 Jean-Victor Makengo Makengo\n", + "283 Daniel Maldini Maldini\n", + "284 Youssef Maleh Maleh\n", + "285 Youssef Maleh Maleh\n", + "286 Ruslan Malinovskyi Malinovskyi\n", + "287 Gianluca Mancini Mancini\n", + "288 Rolando Mandragora Mandragora\n", + "289 Riccardo Marchizza Marchizza\n", + "290 Gian Marco Ferrari Ferrari\n", + "291 Pablo Marí Mari\n", + "292 Răzvan Marin Marin\n", + "293 Marlon Marlon\n", + "294 Luca Marrone Marrone\n", + "295 Lautaro Martínez Martinez\n", + "296 Lucas Martínez Quarta Quarta\n", + "297 Adam Marušić Marusic\n", + "298 Adam Masina Masina\n", + "299 Nemanja Matić Matic\n", + "300 Luís Maximiano Maximiano\n", + "301 Pasquale Mazzocchi Mazzocchi\n", + "302 Weston McKennie McKennie\n", + "303 Gary Medel Medel\n", + "304 Soualiho Meïté Meite\n", + "305 Alex Meret Meret\n", + "306 Yıldırım Mert Çetin Cetin\n", + "307 Junior Messias Messias\n", + "308 Tommaso Milanese Milanese\n", + "309 Nikola Milenković Milenkovic\n", + "310 Arkadiusz Milik Milik\n", + "311 Sergej Milinković-Savić Milinkovic-Savic\n", + "312 Vanja Milinković-Savić Milinkovic-Savic\n", + "313 Kim Min-jae Min-jae\n", + "314 Aleksei Miranchuk Miranchuk\n", + "315 Fabio Miretti Miretti\n", + "316 Henrikh Mkhitaryan Mkhitaryan\n", + "317 Salvatore Molina Molina\n", + "318 Daniele Montevago Montevago\n", + "319 Lorenzo Montipò Montipo\n", + "320 Nikola Moro Moro\n", + "321 Dany Mota Mota\n", + "322 João Moutinho Moutinho\n", + "323 Mert Müldür Muldur\n", + "324 Luis Muriel Muriel\n", + "325 Jeison Murillo Murillo\n", + "326 Nicola Murru Murru\n", + "327 Juan Musso Musso\n", + "328 Joakim Mæhle Mhle\n", + "329 Michel Ndary Adopo Adopo\n", + "330 Tanguy Ndombele Ndombele\n", + "331 Ilija Nestorovski Nestorovski\n", + "332 Hans Nicolussi Caviglia Caviglia\n", + "333 Dimitris Nikolaou Nikolaou\n", + "334 Bram Nuytinck Nuytinck\n", + "335 Bram Nuytinck Nuytinck\n", + "336 M'Bala Nzola Nzola\n", + "337 Pedro Obiang Obiang\n", + "338 Guillermo Ochoa Ochoa\n", + "339 David Okereke Okereke\n", + "340 Caleb Okoli Okoli\n", + "341 Mathías Olivera Olivera\n", + "342 André Onana Onana\n", + "343 Divock Origi Origi\n", + "344 Riccardo Orsolini Orsolini\n", + "345 Victor Osimhen Osimhen\n", + "346 Remi Oudin Oudin\n", + "347 Adam Ounas Ounas\n", + "348 Flavio Paoletti Paoletti\n", + "349 Leandro Paredes Paredes\n", + "350 Fabiano Parisi Parisi\n", + "351 Mario Pašalić Pasalic\n", + "352 Patric Patric\n", + "353 Rui Patrício Patricio\n", + "354 Pedro Pedro\n", + "355 Gianluca Pegolo Pegolo\n", + "356 Pietro Pellegri Pellegri\n", + "357 Lorenzo Pellegrini Pellegrini\n", + "358 Pepín Pepin\n", + "359 Roberto Pereyra Pereyra\n", + "360 Nehuén Pérez Perez\n", + "361 Mattia Perin Perin\n", + "362 Matteo Pessina Pessina\n", + "363 Andrea Petagna Petagna\n", + "364 Giuseppe Pezzella Pezzella\n", + "365 Krzysztof Piątek Piatek\n", + "366 Roberto Piccoli Piccoli\n", + "367 Charles Pickel Pickel\n", + "368 Andrea Pinamonti Pinamonti\n", + "369 Lorenzo Pirola Pirola\n", + "370 Marko Pjaca Pjaca\n", + "371 Tommaso Pobega Pobega\n", + "372 Matteo Politano Politano\n", + "373 Marin Pongračić Pongracic\n", + "374 Stefan Posch Posch\n", + "375 Ivan Provedel Provedel\n", + "376 Ignacio Pussetto Pussetto\n", + "377 Niklas Pyyhtiä Pyyhtia\n", + "378 Fabio Quagliarella Quagliarella\n", + "379 Giacomo Quagliata Quagliata\n", + "380 Adrien Rabiot Rabiot\n", + "381 Nemanja Radonjić Radonjic\n", + "382 Ivan Radovanović Radovanovic\n", + "383 Ionuț Radu Radu\n", + "384 Luca Ranieri Ranieri\n", + "385 Andrea Ranocchia Ranocchia\n", + "386 Filippo Ranocchia Ranocchia\n", + "387 Giacomo Raspadori Raspadori\n", + "388 Giacomo Raspadori Raspadori\n", + "389 Ante Rebić Rebic\n", + "390 Arkadiusz Reca Reca\n", + "391 Panagiotis Retsos Retsos\n", + "392 Franck Ribéry Ribery\n", + "393 Samuele Ricci Ricci\n", + "394 Tomás Rincón Rincon\n", + "395 Pablo Rodríguez Rodriguez\n", + "396 Ricardo Rodríguez Rodriguez\n", + "397 Rogério Rogerio\n", + "398 Alessio Romagnoli Romagnoli\n", + "399 Luka Romero Romero\n", + "400 Marten de Roon Roon\n", + "401 Nicolò Rovella Rovella\n", + "402 Nicolò Rovella Rovella\n", + "403 Amir Rrahmani Rrahmani\n", + "404 Ruan Ruan\n", + "405 Daniele Rugani Rugani\n", + "406 Matteo Ruggeri Ruggeri\n", + "407 Mário Rui Rui\n", + "408 Abdelhamid Sabiri Sabiri\n", + "409 Alexis Saelemaekers Saelemaekers\n", + "410 Jacopo Sala Sala\n", + "411 Lazar Samardzic Samardzic\n", + "412 Junior Sambia Sambia\n", + "413 Antonio Sanabria Sanabria\n", + "414 Leandro Sanca Sanca\n", + "415 Alex Sandro Sandro\n", + "416 Nicola Sansone Sansone\n", + "417 Riccardo Saponara Saponara\n", + "418 Martin Satriano Satriano\n", + "419 Giorgio Scalvini Scalvini\n", + "420 Jerdy Schouten Schouten\n", + "421 Perr Schuurs Schuurs\n", + "422 Demba Seck Seck\n", + "423 Jacopo Segre Segre\n", + "424 Stefano Sensi Sensi\n", + "425 Luigi Sepe Sepe\n", + "426 Leonardo Sernicola Sernicola\n", + "427 Stephan El Shaarawy Shaarawy\n", + "428 Eldor Shomurodov Shomurodov\n", + "429 Marco Silvestri Silvestri\n", + "430 Giovanni Simeone Simeone\n", + "431 Wilfried Singo Singo\n", + "432 Leo Skiri Østigård stigard\n", + "433 Łukasz Skorupski Skorupski\n", + "434 Milan Škriniar Skriniar\n", + "435 Chris Smalling Smalling\n", + "436 Ola Solbakken Solbakken\n", + "437 Brandon Soppy Soppy\n", + "438 Brandon Soppy Soppy\n", + "439 Roberto Soriano Soriano\n", + "440 Joaquin Sosa Sosa\n", + "441 Riccardo Sottil Sottil\n", + "442 Matìas Soulé Soule\n", + "443 Adama Soumaoro Soumaoro\n", + "444 Leonardo Spinazzola Spinazzola\n", + "445 Marco Sportiello Sportiello\n", + "446 Petar Stojanović Stojanovic\n", + "447 Gabriel Strefezza Strefezza\n", + "448 Dávid Strelec Strelec\n", + "449 Isaac Success Success\n", + "450 Ibrahim Sulemana Sulemana\n", + "451 Wojciech Szczęsny Szczesny\n", + "452 Benjamin Tahirovic Tahirovic\n", + "453 Adrien Tameze Tameze\n", + "454 Ciprian Tătărușanu Tatarusanu\n", + "455 Filippo Terracciano Terracciano\n", + "456 Pietro Terracciano Terracciano\n", + "457 Aleksa Terzić Terzic\n", + "458 Malick Thiaw Thiaw\n", + "459 Kristian Thorstvedt Thorstvedt\n", + "460 Jeremy Toljan Toljan\n", + "461 Rafael Tolói Toloi\n", + "462 Fikayo Tomori Tomori\n", + "463 Sandro Tonali Tonali\n", + "464 William Troost-Ekong Troost-Ekong\n", + "465 Frank Tsadjout Tsadjout\n", + "466 Alessandro Tuia Tuia\n", + "467 Iyenoma Udogie Udogie\n", + "468 Samuel Umtiti Umtiti\n", + "469 Diego Valencia Valencia\n", + "470 Emanuele Valeri Valeri\n", + "471 Mattia Valoti Valoti\n", + "472 Johan Vásquez Vasquez\n", + "473 Matías Vecino Vecino\n", + "474 Miguel Veloso Veloso\n", + "475 Lorenzo Venuti Venuti\n", + "476 Daniele Verde Verde\n", + "477 Simone Verdi Verdi\n", + "478 Valerio Verre Verre\n", + "479 Guglielmo Vicario Vicario\n", + "480 Ronaldo Vieira Vieira\n", + "481 Emanuel Vignato Vignato\n", + "482 Samuele Vignato Vignato\n", + "483 Tonny Vilhena Vilhena\n", + "484 Gonzalo Villar Villar\n", + "485 Matías Viña Vina\n", + "486 Dušan Vlahović Vlahovic\n", + "487 Nikola Vlašić Vlasic\n", + "488 Joel Voelkerling Persson Persson\n", + "489 Mërgim Vojvoda Vojvoda\n", + "490 Cristian Volpato Volpato\n", + "491 Aster Vranckx Vranckx\n", + "492 Stefan de Vrij Vrij\n", + "493 Walace Walace\n", + "494 Sebastian Walukiewicz Walukiewicz\n", + "495 Georginio Wijnaldum Wijnaldum\n", + "496 Harry Winks Winks\n", + "497 Gerard Yepes Yepes\n", + "498 Mattia Zaccagni Zaccagni\n", + "499 Denis Zakaria Zakaria\n", + "500 Nicola Zalewski Zalewski\n", + "501 Andre-Frank Zambo Anguissa Anguissa\n", + "502 Luca Zanimacchia Zanimacchia\n", + "503 Nicolò Zaniolo Zaniolo\n", + "504 Alessandro Zanoli Zanoli\n", + "505 Alessandro Zanoli Zanoli\n", + "506 Duván Zapata Zapata\n", + "507 Davide Zappacosta Zappacosta\n", + "508 Alessio Zerbin Zerbin\n", + "509 Piotr Zieliński Zielinski\n", + "510 David Zima Zima\n", + "511 Joshua Zirkzee Zirkzee\n", + "512 Jeroen Zoet Zoet\n", + "513 Nadir Zortea Zortea\n", + "514 Petar Zovko Zovko\n", + "515 Szymon Żurkowski Zurkowski\n", + "516 Szymon Żurkowski Zurkowski\n", + "517 Milan Đurić uric\n", + "518 Filip Đuričić uricic\n", + "519 Emil Audero Audero\n", + "520 Marco Carnesecchi Carnesecchi\n", + "521 Andrea Consigli Consigli\n", + "522 Michele Di Gregorio Gregorio\n", + "523 Bartłomiej Drągowski Dragowski\n", + "524 Wladimiro Falcone Falcone\n", + "525 Pierluigi Gollini Gollini\n", + "526 Samir Handanović Handanovic\n", + "527 Mike Maignan Maignan\n", + "528 Luís Maximiano Maximiano\n", + "529 Alex Meret Meret\n", + "530 Vanja Milinković-Savić Milinkovic-Savic\n", + "531 Lorenzo Montipò Montipo\n", + "532 Juan Musso Musso\n", + "533 Guillermo Ochoa Ochoa\n", + "534 André Onana Onana\n", + "535 Rui Patrício Patricio\n", + "536 Gianluca Pegolo Pegolo\n", + "537 Mattia Perin Perin\n", + "538 Ivan Provedel Provedel\n", + "539 Ionuț Radu Radu\n", + "540 Luigi Sepe Sepe\n", + "541 Marco Silvestri Silvestri\n", + "542 Łukasz Skorupski Skorupski\n", + "543 Marco Sportiello Sportiello\n", + "544 Wojciech Szczęsny Szczesny\n", + "545 Ciprian Tătărușanu Tatarusanu\n", + "546 Pietro Terracciano Terracciano\n", + "547 Guglielmo Vicario Vicario\n", + "548 Jeroen Zoet Zoet\n", + "549 Petar Zovko Zovko\n" ] } ], @@ -749,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "id": "3e759b1b", "metadata": {}, "outputs": [ @@ -882,7 +912,7 @@ "10 Martinez Alvarez Sassuolo" ] }, - "execution_count": 7, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -911,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "id": "f96eaaa1", "metadata": {}, "outputs": [ @@ -919,22 +949,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_2484\\2263921821.py:17: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_2484\\2263921821.py:18: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_2484\\2263921821.py:14: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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_2484\\2263921821.py:15: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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", @@ -991,29 +1021,29 @@ " \n", " \n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Maignan\n", - " Milan\n", - " Maignan\n", + " Vicario\n", + " Empoli\n", + " Vicario\n", " \n", " \n", " \n", " 3\n", - " 2211\n", + " 453\n", " P\n", - " Silvestri\n", - " Udinese\n", - " Silvestri\n", + " Szczesny\n", + " Juventus\n", + " Szczesny\n", " \n", " \n", " \n", " 4\n", - " 159\n", + " 2134\n", " P\n", - " Sepe\n", - " Salernitana\n", - " Sepe\n", + " Falcone\n", + " Lecce\n", + " Falcone\n", " \n", " \n", " \n", @@ -1026,7 +1056,16 @@ " ...\n", " \n", " \n", - " 549\n", + " 542\n", + " 5505\n", + " A\n", + " Kaio Jorge\n", + " Juventus\n", + " Jorge\n", + " \n", + " \n", + " \n", + " 543\n", " 5512\n", " A\n", " De Luca\n", @@ -1035,16 +1074,7 @@ " \n", " \n", " \n", - " 550\n", - " 5734\n", - " A\n", - " Soule'\n", - " Juventus\n", - " Soule\n", - " \n", - " \n", - " \n", - " 551\n", + " 544\n", " 5785\n", " A\n", " Lazetic\n", @@ -1053,46 +1083,46 @@ " \n", " \n", " \n", - " 552\n", - " 5837\n", + " 545\n", + " 6113\n", " A\n", - " Voelkerling Persson\n", - " Lecce\n", - " Persson\n", + " Montevago\n", + " Sampdoria\n", + " Montevago\n", " \n", " \n", " \n", - " 553\n", - " 6063\n", + " 546\n", + " 6143\n", " A\n", - " Sanca\n", + " Krollis\n", " Spezia\n", - " Sanca\n", + " Krollis\n", " \n", " \n", " \n", "\n", - "

554 rows × 6 columns

\n", + "

547 rows × 6 columns

\n", "" ], "text/plain": [ - " id r name team surname initial\n", - "0 572 P Meret Napoli Meret \n", - "1 2814 P Provedel Lazio Provedel \n", - "2 4312 P Maignan Milan Maignan \n", - "3 2211 P Silvestri Udinese Silvestri \n", - "4 159 P Sepe Salernitana Sepe \n", - ".. ... .. ... ... ... ...\n", - "549 5512 A De Luca Sampdoria Luca \n", - "550 5734 A Soule' Juventus Soule \n", - "551 5785 A Lazetic Milan Lazetic \n", - "552 5837 A Voelkerling Persson Lecce Persson \n", - "553 6063 A Sanca Spezia Sanca \n", + " id r name team surname initial\n", + "0 572 P Meret Napoli Meret \n", + "1 2814 P Provedel Lazio Provedel \n", + "2 4964 P Vicario Empoli Vicario \n", + "3 453 P Szczesny Juventus Szczesny \n", + "4 2134 P Falcone Lecce Falcone \n", + ".. ... .. ... ... ... ...\n", + "542 5505 A Kaio Jorge Juventus Jorge \n", + "543 5512 A De Luca Sampdoria Luca \n", + "544 5785 A Lazetic Milan Lazetic \n", + "545 6113 A Montevago Sampdoria Montevago \n", + "546 6143 A Krollis Spezia Krollis \n", "\n", - "[554 rows x 6 columns]" + "[547 rows x 6 columns]" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1131,7 +1161,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "id": "9c50e4b5", "metadata": {}, "outputs": [ @@ -1139,12 +1169,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_2484\\1456862974.py:4: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\1456862974.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_2484\\1456862974.py:10: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\1456862974.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", @@ -1185,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, "id": "5a3e2771", "metadata": {}, "outputs": [ @@ -1193,13 +1223,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Berisha\n", - "Cragno\n", + "Gollini\n", "Mirante\n", "Sarr M.\n", "Lamanna\n", "Ujkani\n", - "Pegolo\n", + "Berisha\n", + "Marchetti\n", "Perilli\n", "Padelli\n", "Perisan\n", @@ -1207,10 +1237,11 @@ "Cordaz\n", "Pinsoglio\n", "Fiorillo\n", + "Cragno\n", "Sirigu\n", "Cerofolini\n", "Rossi F.\n", - "Contini\n", + "Ravaglia F.\n", "Brancolini\n", "Bleve\n", "Berardi A.\n", @@ -1220,62 +1251,55 @@ "Boer\n", "Adamonis\n", "Marfella\n", - "Zovko\n", "Piana\n", "Bagnolini\n", "Svilar\n", "Sorrentino A.\n", "Ciezkowski\n", - "Micai\n", "Chiesa M.\n", "Saro\n", - "Palomino\n", + "Vasquez D.\n", + "Turk\n", + "Bereszynski\n", "Aiwu\n", + "Zeefuik\n", + "Wisniewski\n", "Tonelli\n", "Radu\n", "Paletta\n", "Fares\n", "Romagna\n", - "Ferrer\n", + "Cassandro\n", "Amey\n", "Ferrarini\n", - "Kamenovic\n", "Zanotti\n", - "Motoc\n", "Bayeye\n", "Buta\n", - "Ndiaye\n", "Abankwah\n", "Guessand A.\n", "Guarino\n", "Pogba\n", - "Winks\n", - "Castrovilli\n", - "Capezzi\n", "Machin\n", + "Akpa Akpro\n", + "Capezzi\n", "Bakayoko\n", "Scozzarella\n", - "Akpa Akpro\n", "Darboe\n", "Urbanski\n", "Bertini\n", - "Bianco\n", - "Sher\n", - "Nguiamba\n", - "Praszelik\n", "Trimboli\n", "Pafundi\n", - "Bjorkengren\n", "Samek\n", "Acella\n", - "Tripi\n", "Garbett\n", + "Ngonge\n", "Ibrahimovic\n", "Edera\n", "Oddei\n", + "Braaf\n", "Raimondo\n", "Kaio Jorge\n", - "Voelkerling Persson\n" + "Krollis\n" ] } ], @@ -1287,7 +1311,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "id": "3b4a36af", "metadata": {}, "outputs": [ @@ -1330,7 +1354,7 @@ " Napoli\n", " Meret\n", " \n", - " 502\n", + " 529\n", " \n", " \n", " 1\n", @@ -1340,37 +1364,37 @@ " Lazio\n", " Provedel\n", " \n", - " 509\n", + " 538\n", " \n", " \n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Maignan\n", - " Milan\n", - " Maignan\n", + " Vicario\n", + " Empoli\n", + " Vicario\n", " \n", - " 500\n", + " 547\n", " \n", " \n", " 3\n", - " 2211\n", + " 453\n", " P\n", - " Silvestri\n", - " Udinese\n", - " Silvestri\n", + " Szczesny\n", + " Juventus\n", + " Szczesny\n", " \n", - " 512\n", + " 544\n", " \n", " \n", " 4\n", - " 159\n", + " 2134\n", " P\n", - " Sepe\n", - " Salernitana\n", - " Sepe\n", + " Falcone\n", + " Lecce\n", + " Falcone\n", " \n", - " 511\n", + " 524\n", " \n", " \n", " ...\n", @@ -1383,78 +1407,78 @@ " ...\n", " \n", " \n", - " 549\n", + " 542\n", + " 5505\n", + " A\n", + " Kaio Jorge\n", + " Juventus\n", + " Jorge\n", + " \n", + " -1\n", + " \n", + " \n", + " 543\n", " 5512\n", " A\n", " De Luca\n", " Sampdoria\n", " Luca\n", " \n", - " 122\n", + " 128\n", " \n", " \n", - " 550\n", - " 5734\n", - " A\n", - " Soule'\n", - " Juventus\n", - " Soule\n", - " \n", - " 422\n", - " \n", - " \n", - " 551\n", + " 544\n", " 5785\n", " A\n", " Lazetic\n", " Milan\n", " Lazetic\n", " \n", - " 248\n", + " 258\n", " \n", " \n", - " 552\n", - " 5837\n", + " 545\n", + " 6113\n", " A\n", - " Voelkerling Persson\n", - " Lecce\n", - " Persson\n", + " Montevago\n", + " Sampdoria\n", + " Montevago\n", + " \n", + " 318\n", + " \n", + " \n", + " 546\n", + " 6143\n", + " A\n", + " Krollis\n", + " Spezia\n", + " Krollis\n", " \n", " -1\n", " \n", - " \n", - " 553\n", - " 6063\n", - " A\n", - " Sanca\n", - " Spezia\n", - " Sanca\n", - " \n", - " 395\n", - " \n", " \n", "\n", - "

554 rows × 7 columns

\n", + "

547 rows × 7 columns

\n", "" ], "text/plain": [ - " id r name team surname initial fb_ID\n", - "0 572 P Meret Napoli Meret 502\n", - "1 2814 P Provedel Lazio Provedel 509\n", - "2 4312 P Maignan Milan Maignan 500\n", - "3 2211 P Silvestri Udinese Silvestri 512\n", - "4 159 P Sepe Salernitana Sepe 511\n", - ".. ... .. ... ... ... ... ...\n", - "549 5512 A De Luca Sampdoria Luca 122\n", - "550 5734 A Soule' Juventus Soule 422\n", - "551 5785 A Lazetic Milan Lazetic 248\n", - "552 5837 A Voelkerling Persson Lecce Persson -1\n", - "553 6063 A Sanca Spezia Sanca 395\n", + " id r name team surname initial fb_ID\n", + "0 572 P Meret Napoli Meret 529\n", + "1 2814 P Provedel Lazio Provedel 538\n", + "2 4964 P Vicario Empoli Vicario 547\n", + "3 453 P Szczesny Juventus Szczesny 544\n", + "4 2134 P Falcone Lecce Falcone 524\n", + ".. ... .. ... ... ... ... ...\n", + "542 5505 A Kaio Jorge Juventus Jorge -1\n", + "543 5512 A De Luca Sampdoria Luca 128\n", + "544 5785 A Lazetic Milan Lazetic 258\n", + "545 6113 A Montevago Sampdoria Montevago 318\n", + "546 6143 A Krollis Spezia Krollis -1\n", "\n", - "[554 rows x 7 columns]" + "[547 rows x 7 columns]" ] }, - "execution_count": 11, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1473,7 +1497,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "id": "1d73a312", "metadata": {}, "outputs": [ @@ -1481,49 +1505,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\2261782218.py:9: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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", @@ -1546,7 +1570,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "id": "7acb93e3", "metadata": {}, "outputs": [ @@ -1571,7 +1595,7 @@ " dtype='object')" ] }, - "execution_count": 13, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1582,7 +1606,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "eb9127a9", "metadata": {}, "outputs": [ @@ -1590,87 +1614,87 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\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_14056\\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_2484\\3664433845.py:11: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_14056\\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", @@ -1694,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "id": "da977789", "metadata": {}, "outputs": [ @@ -1751,21 +1775,21 @@ " Napoli\n", " Meret\n", " \n", - " 502\n", - " 25-234\n", + " 529\n", + " 25-312\n", " 1997\n", " 0\n", " ...\n", - " 26.9\n", - " 92\n", - " 19.6\n", " 26.6\n", - " 142\n", + " 114\n", + " 20.2\n", + " 27.1\n", + " 197\n", " 6\n", - " 4.2\n", - " 10\n", - " 0.71\n", - " 15.1\n", + " 3.0\n", + " 23\n", + " 1.21\n", + " 17.2\n", " \n", " \n", " 1\n", @@ -1775,93 +1799,93 @@ " Lazio\n", " Provedel\n", " \n", - " 509\n", - " 28-239\n", + " 538\n", + " 28-317\n", " 1994\n", " 0\n", " ...\n", - " 33.1\n", - " 94\n", - " 37.2\n", - " 36.6\n", - " 201\n", - " 5\n", - " 2.5\n", - " 29\n", - " 2.08\n", - " 17.6\n", + " 32.5\n", + " 115\n", + " 34.8\n", + " 34.8\n", + " 256\n", + " 7\n", + " 2.7\n", + " 37\n", + " 1.96\n", + " 18.1\n", " \n", " \n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Maignan\n", - " Milan\n", - " Maignan\n", + " Vicario\n", + " Empoli\n", + " Vicario\n", " \n", - " 500\n", - " 27-131\n", - " 1995\n", + " 547\n", + " 26-113\n", + " 1996\n", " 0\n", " ...\n", - " 33.5\n", - " 18\n", - " 38.9\n", - " 40.4\n", - " 65\n", - " 5\n", - " 7.7\n", - " 6\n", - " 0.86\n", - " 12.9\n", + " 33.9\n", + " 103\n", + " 45.6\n", + " 41.8\n", + " 399\n", + " 25\n", + " 6.3\n", + " 8\n", + " 0.42\n", + " 10.5\n", " \n", " \n", " 3\n", - " 2211\n", + " 453\n", " P\n", - " Silvestri\n", - " Udinese\n", - " Silvestri\n", + " Szczesny\n", + " Juventus\n", + " Szczesny\n", " \n", - " 512\n", - " 31-254\n", - " 1991\n", + " 544\n", + " 32-285\n", + " 1990\n", " 0\n", " ...\n", - " 34.4\n", - " 110\n", - " 40.9\n", - " 38.2\n", - " 194\n", + " 36.1\n", + " 61\n", + " 41.0\n", + " 39.2\n", + " 155\n", " 4\n", - " 2.1\n", - " 5\n", - " 0.36\n", - " 11.3\n", + " 2.6\n", + " 7\n", + " 0.56\n", + " 14.2\n", " \n", " \n", " 4\n", - " 159\n", + " 2134\n", " P\n", - " Sepe\n", - " Salernitana\n", - " Sepe\n", + " Falcone\n", + " Lecce\n", + " Falcone\n", " \n", - " 511\n", - " 31-187\n", - " 1991\n", + " 524\n", + " 27-291\n", + " 1995\n", " 0\n", " ...\n", - " 36.6\n", - " 128\n", - " 48.4\n", - " 41.7\n", - " 187\n", - " 14\n", - " 7.5\n", - " 9\n", - " 0.64\n", - " 13.7\n", + " 40.9\n", + " 156\n", + " 75.0\n", + " 51.4\n", + " 304\n", + " 13\n", + " 4.3\n", + " 20\n", + " 1.00\n", + " 12.5\n", " \n", " \n", " ...\n", @@ -1888,84 +1912,12 @@ " ...\n", " \n", " \n", - " 549\n", - " 5512\n", + " 542\n", + " 5505\n", " A\n", - " De Luca\n", - " Sampdoria\n", - " Luca\n", - " \n", - " 122\n", - " 24-117\n", - " 1998\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " \n", - " \n", - " 550\n", - " 5734\n", - " A\n", - " Soule'\n", + " Kaio Jorge\n", " Juventus\n", - " Soule\n", - " \n", - " 422\n", - " 19-210\n", - " 2003\n", - " 5\n", - " ...\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " \n", - " \n", - " 551\n", - " 5785\n", - " A\n", - " Lazetic\n", - " Milan\n", - " Lazetic\n", - " \n", - " 248\n", - " 18-293\n", - " 2004\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " \n", - " \n", - " 552\n", - " 5837\n", - " A\n", - " Voelkerling Persson\n", - " Lecce\n", - " Persson\n", + " Jorge\n", " \n", " -1\n", " 0\n", @@ -1984,17 +1936,89 @@ " 0.0\n", " \n", " \n", - " 553\n", - " 6063\n", + " 543\n", + " 5512\n", " A\n", - " Sanca\n", - " Spezia\n", - " Sanca\n", + " De Luca\n", + " Sampdoria\n", + " Luca\n", " \n", - " 395\n", - " 22-311\n", - " 2000\n", - " 3\n", + " 128\n", + " 24-195\n", + " 1998\n", + " 1\n", + " ...\n", + " 0.0\n", + " 0\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 544\n", + " 5785\n", + " A\n", + " Lazetic\n", + " Milan\n", + " Lazetic\n", + " \n", + " 258\n", + " 19-006\n", + " 2004\n", + " 1\n", + " ...\n", + " 0.0\n", + " 0\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 545\n", + " 6113\n", + " A\n", + " Montevago\n", + " Sampdoria\n", + " Montevago\n", + " \n", + " 318\n", + " 19-316\n", + " 2003\n", + " 6\n", + " ...\n", + " 0.0\n", + " 0\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " \n", + " \n", + " 546\n", + " 6143\n", + " A\n", + " Krollis\n", + " Spezia\n", + " Krollis\n", + " \n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", " 0.0\n", " 0\n", @@ -2009,92 +2033,92 @@ " \n", " \n", "\n", - "

554 rows × 163 columns

\n", + "

547 rows × 163 columns

\n", "" ], "text/plain": [ - " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 502 \n", - "1 2814 P Provedel Lazio Provedel 509 \n", - "2 4312 P Maignan Milan Maignan 500 \n", - "3 2211 P Silvestri Udinese Silvestri 512 \n", - "4 159 P Sepe Salernitana Sepe 511 \n", - ".. ... .. ... ... ... ... ... \n", - "549 5512 A De Luca Sampdoria Luca 122 \n", - "550 5734 A Soule' Juventus Soule 422 \n", - "551 5785 A Lazetic Milan Lazetic 248 \n", - "552 5837 A Voelkerling Persson Lecce Persson -1 \n", - "553 6063 A Sanca Spezia Sanca 395 \n", + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", "\n", - " age birth_year games ... gk_passes_length_avg gk_goal_kicks \\\n", - "0 25-234 1997 0 ... 26.9 92 \n", - "1 28-239 1994 0 ... 33.1 94 \n", - "2 27-131 1995 0 ... 33.5 18 \n", - "3 31-254 1991 0 ... 34.4 110 \n", - "4 31-187 1991 0 ... 36.6 128 \n", - ".. ... ... ... ... ... ... \n", - "549 24-117 1998 1 ... 0.0 0 \n", - "550 19-210 2003 5 ... 0.0 0 \n", - "551 18-293 2004 1 ... 0.0 0 \n", - "552 0 0 0 ... 0.0 0 \n", - "553 22-311 2000 3 ... 0.0 0 \n", + " birth_year games ... gk_passes_length_avg gk_goal_kicks \\\n", + "0 1997 0 ... 26.6 114 \n", + "1 1994 0 ... 32.5 115 \n", + "2 1996 0 ... 33.9 103 \n", + "3 1990 0 ... 36.1 61 \n", + "4 1995 0 ... 40.9 156 \n", + ".. ... ... ... ... ... \n", + "542 0 0 ... 0.0 0 \n", + "543 1998 1 ... 0.0 0 \n", + "544 2004 1 ... 0.0 0 \n", + "545 2003 6 ... 0.0 0 \n", + "546 0 0 ... 0.0 0 \n", "\n", " gk_pct_goal_kicks_launched gk_goal_kick_length_avg gk_crosses \\\n", - "0 19.6 26.6 142 \n", - "1 37.2 36.6 201 \n", - "2 38.9 40.4 65 \n", - "3 40.9 38.2 194 \n", - "4 48.4 41.7 187 \n", + "0 20.2 27.1 197 \n", + "1 34.8 34.8 256 \n", + "2 45.6 41.8 399 \n", + "3 41.0 39.2 155 \n", + "4 75.0 51.4 304 \n", ".. ... ... ... \n", - "549 0.0 0.0 0 \n", - "550 0.0 0.0 0 \n", - "551 0.0 0.0 0 \n", - "552 0.0 0.0 0 \n", - "553 0.0 0.0 0 \n", + "542 0.0 0.0 0 \n", + "543 0.0 0.0 0 \n", + "544 0.0 0.0 0 \n", + "545 0.0 0.0 0 \n", + "546 0.0 0.0 0 \n", "\n", " gk_crosses_stopped gk_crosses_stopped_pct \\\n", - "0 6 4.2 \n", - "1 5 2.5 \n", - "2 5 7.7 \n", - "3 4 2.1 \n", - "4 14 7.5 \n", + "0 6 3.0 \n", + "1 7 2.7 \n", + "2 25 6.3 \n", + "3 4 2.6 \n", + "4 13 4.3 \n", ".. ... ... \n", - "549 0 0.0 \n", - "550 0 0.0 \n", - "551 0 0.0 \n", - "552 0 0.0 \n", - "553 0 0.0 \n", + "542 0 0.0 \n", + "543 0 0.0 \n", + "544 0 0.0 \n", + "545 0 0.0 \n", + "546 0 0.0 \n", "\n", " gk_def_actions_outside_pen_area gk_def_actions_outside_pen_area_per90 \\\n", - "0 10 0.71 \n", - "1 29 2.08 \n", - "2 6 0.86 \n", - "3 5 0.36 \n", - "4 9 0.64 \n", + "0 23 1.21 \n", + "1 37 1.96 \n", + "2 8 0.42 \n", + "3 7 0.56 \n", + "4 20 1.00 \n", ".. ... ... \n", - "549 0 0.00 \n", - "550 0 0.00 \n", - "551 0 0.00 \n", - "552 0 0.00 \n", - "553 0 0.00 \n", + "542 0 0.00 \n", + "543 0 0.00 \n", + "544 0 0.00 \n", + "545 0 0.00 \n", + "546 0 0.00 \n", "\n", " gk_avg_distance_def_actions \n", - "0 15.1 \n", - "1 17.6 \n", - "2 12.9 \n", - "3 11.3 \n", - "4 13.7 \n", + "0 17.2 \n", + "1 18.1 \n", + "2 10.5 \n", + "3 14.2 \n", + "4 12.5 \n", ".. ... \n", - "549 0.0 \n", - "550 0.0 \n", - "551 0.0 \n", - "552 0.0 \n", - "553 0.0 \n", + "542 0.0 \n", + "543 0.0 \n", + "544 0.0 \n", + "545 0.0 \n", + "546 0.0 \n", "\n", - "[554 rows x 163 columns]" + "[547 rows x 163 columns]" ] }, - "execution_count": 15, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2113,7 +2137,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "id": "6a0e43cd", "metadata": {}, "outputs": [], @@ -2133,7 +2157,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 40, "id": "61fac91a", "metadata": {}, "outputs": [ @@ -2141,8 +2165,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "vote_avg 6.214464\n", - "vote_std 0.438749\n", + "vote_avg 6.198757\n", + "vote_std 0.428693\n", "dtype: float64\n" ] }, @@ -2174,163 +2198,157 @@ " \n", " \n", " 0\n", - " 6.214286\n", - " 0.557875\n", + " 6.263158\n", + " 0.496525\n", " \n", " \n", " 1\n", - " 6.321429\n", - " 0.305143\n", + " 6.236842\n", + " 0.339055\n", " \n", " \n", " 2\n", + " 6.473684\n", + " 0.379532\n", + " \n", + " \n", + " 3\n", + " 6.038462\n", + " 0.364878\n", + " \n", + " \n", + " 4\n", + " 6.342105\n", + " 0.364642\n", + " \n", + " \n", + " 5\n", + " 6.289474\n", + " 0.438772\n", + " \n", + " \n", + " 6\n", + " 5.973684\n", + " 0.443482\n", + " \n", + " \n", + " 7\n", + " 6.157895\n", + " 0.399099\n", + " \n", + " \n", + " 8\n", + " 6.366667\n", + " 0.426875\n", + " \n", + " \n", + " 9\n", " 6.357143\n", " 0.440315\n", " \n", " \n", - " 3\n", - " 6.321429\n", - " 0.485767\n", - " \n", - " \n", - " 4\n", - " 6.428571\n", - " 0.371154\n", - " \n", - " \n", - " 5\n", - " 6.125000\n", - " 0.216506\n", - " \n", - " \n", - " 6\n", - " 6.142857\n", - " 0.349927\n", - " \n", - " \n", - " 7\n", - " 6.428571\n", - " 0.371154\n", - " \n", - " \n", - " 8\n", - " 6.187500\n", - " 0.347985\n", - " \n", - " \n", - " 9\n", - " 6.428571\n", - " 0.416497\n", - " \n", - " \n", " 10\n", - " 6.000000\n", - " 0.500000\n", + " 6.136364\n", + " 0.308288\n", " \n", " \n", " 11\n", - " 6.178571\n", - " 0.358924\n", + " 6.315789\n", + " 0.464830\n", " \n", " \n", " 12\n", - " 6.321429\n", - " 0.485767\n", + " 6.125000\n", + " 0.297560\n", " \n", " \n", " 13\n", - " 6.214286\n", - " 0.489690\n", + " 6.300000\n", + " 0.640312\n", " \n", " \n", " 14\n", - " 6.166667\n", - " 0.471405\n", + " 6.263158\n", + " 0.522354\n", " \n", " \n", " 15\n", - " 6.035714\n", - " 0.480487\n", + " 6.210526\n", + " 0.521026\n", " \n", " \n", " 16\n", - " 6.250000\n", - " 0.590097\n", + " 5.958333\n", + " 0.246503\n", " \n", " \n", " 17\n", + " 6.117647\n", + " 0.322190\n", + " \n", + " \n", + " 18\n", + " 6.176471\n", + " 0.418020\n", + " \n", + " \n", + " 19\n", + " 6.000000\n", + " 0.395285\n", + " \n", + " \n", + " 20\n", + " 6.071429\n", + " 0.494872\n", + " \n", + " \n", + " 21\n", + " 6.105263\n", + " 0.475144\n", + " \n", + " \n", + " 22\n", " 6.428571\n", " 0.562429\n", " \n", " \n", - " 18\n", + " 23\n", " 6.062500\n", " 0.526634\n", " \n", - " \n", - " 19\n", - " 6.071429\n", - " 0.174964\n", - " \n", - " \n", - " 20\n", - " 6.153846\n", - " 0.455083\n", - " \n", - " \n", - " 21\n", - " 6.045455\n", - " 0.396264\n", - " \n", - " \n", - " 22\n", - " 6.111111\n", - " 0.566558\n", - " \n", - " \n", - " 23\n", - " 6.166667\n", - " 0.235702\n", - " \n", - " \n", - " 24\n", - " 6.200000\n", - " 0.812404\n", - " \n", " \n", "\n", "" ], "text/plain": [ " vote_avg vote_std\n", - "0 6.214286 0.557875\n", - "1 6.321429 0.305143\n", - "2 6.357143 0.440315\n", - "3 6.321429 0.485767\n", - "4 6.428571 0.371154\n", - "5 6.125000 0.216506\n", - "6 6.142857 0.349927\n", - "7 6.428571 0.371154\n", - "8 6.187500 0.347985\n", - "9 6.428571 0.416497\n", - "10 6.000000 0.500000\n", - "11 6.178571 0.358924\n", - "12 6.321429 0.485767\n", - "13 6.214286 0.489690\n", - "14 6.166667 0.471405\n", - "15 6.035714 0.480487\n", - "16 6.250000 0.590097\n", - "17 6.428571 0.562429\n", - "18 6.062500 0.526634\n", - "19 6.071429 0.174964\n", - "20 6.153846 0.455083\n", - "21 6.045455 0.396264\n", - "22 6.111111 0.566558\n", - "23 6.166667 0.235702\n", - "24 6.200000 0.812404" + "0 6.263158 0.496525\n", + "1 6.236842 0.339055\n", + "2 6.473684 0.379532\n", + "3 6.038462 0.364878\n", + "4 6.342105 0.364642\n", + "5 6.289474 0.438772\n", + "6 5.973684 0.443482\n", + "7 6.157895 0.399099\n", + "8 6.366667 0.426875\n", + "9 6.357143 0.440315\n", + "10 6.136364 0.308288\n", + "11 6.315789 0.464830\n", + "12 6.125000 0.297560\n", + "13 6.300000 0.640312\n", + "14 6.263158 0.522354\n", + "15 6.210526 0.521026\n", + "16 5.958333 0.246503\n", + "17 6.117647 0.322190\n", + "18 6.176471 0.418020\n", + "19 6.000000 0.395285\n", + "20 6.071429 0.494872\n", + "21 6.105263 0.475144\n", + "22 6.428571 0.562429\n", + "23 6.062500 0.526634" ] }, - "execution_count": 17, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2369,7 +2387,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "id": "c9312080", "metadata": {}, "outputs": [ @@ -2401,28 +2419,28 @@ " \n", " \n", " 0\n", - " 6.214286\n", - " 0.557875\n", + " 6.263158\n", + " 0.496525\n", " \n", " \n", " 1\n", - " 6.321429\n", - " 0.305143\n", + " 6.236842\n", + " 0.339055\n", " \n", " \n", " 2\n", - " 6.357143\n", - " 0.440315\n", + " 6.473684\n", + " 0.379532\n", " \n", " \n", " 3\n", - " 6.321429\n", - " 0.485767\n", + " 6.038462\n", + " 0.364878\n", " \n", " \n", " 4\n", - " 6.428571\n", - " 0.371154\n", + " 6.342105\n", + " 0.364642\n", " \n", " \n", " ...\n", @@ -2430,53 +2448,53 @@ " ...\n", " \n", " \n", - " 549\n", - " 5.877688\n", - " 0.485814\n", + " 542\n", + " 6.092280\n", + " 0.894197\n", " \n", " \n", - " 550\n", - " 6.067846\n", - " 0.581002\n", + " 543\n", + " 6.284927\n", + " 0.532298\n", " \n", " \n", - " 551\n", - " 6.170097\n", - " 0.204814\n", + " 544\n", + " 6.094004\n", + " 0.625570\n", " \n", " \n", - " 552\n", - " 6.041983\n", - " 0.689071\n", + " 545\n", + " 5.711934\n", + " 0.225193\n", " \n", " \n", - " 553\n", - " 5.991021\n", - " 0.262247\n", + " 546\n", + " 5.748895\n", + " 0.595450\n", " \n", " \n", "\n", - "

554 rows × 2 columns

\n", + "

547 rows × 2 columns

\n", "" ], "text/plain": [ " vote_avg vote_std\n", - "0 6.214286 0.557875\n", - "1 6.321429 0.305143\n", - "2 6.357143 0.440315\n", - "3 6.321429 0.485767\n", - "4 6.428571 0.371154\n", + "0 6.263158 0.496525\n", + "1 6.236842 0.339055\n", + "2 6.473684 0.379532\n", + "3 6.038462 0.364878\n", + "4 6.342105 0.364642\n", ".. ... ...\n", - "549 5.877688 0.485814\n", - "550 6.067846 0.581002\n", - "551 6.170097 0.204814\n", - "552 6.041983 0.689071\n", - "553 5.991021 0.262247\n", + "542 6.092280 0.894197\n", + "543 6.284927 0.532298\n", + "544 6.094004 0.625570\n", + "545 5.711934 0.225193\n", + "546 5.748895 0.595450\n", "\n", - "[554 rows x 2 columns]" + "[547 rows x 2 columns]" ] }, - "execution_count": 18, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2521,7 +2539,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "id": "b2570ce5", "metadata": {}, "outputs": [ @@ -2578,21 +2596,21 @@ " Napoli\n", " Meret\n", " \n", - " 502\n", - " 25-234\n", + " 529\n", + " 25-312\n", " 1997\n", " 0\n", " ...\n", - " 19.6\n", - " 26.6\n", - " 142\n", + " 20.2\n", + " 27.1\n", + " 197\n", " 6\n", - " 4.2\n", - " 10\n", - " 0.71\n", - " 15.1\n", - " 6.214286\n", - " 0.557875\n", + " 3.0\n", + " 23\n", + " 1.21\n", + " 17.2\n", + " 6.263158\n", + " 0.496525\n", " \n", " \n", " 1\n", @@ -2602,93 +2620,93 @@ " Lazio\n", " Provedel\n", " \n", - " 509\n", - " 28-239\n", + " 538\n", + " 28-317\n", " 1994\n", " 0\n", " ...\n", - " 37.2\n", - " 36.6\n", - " 201\n", - " 5\n", - " 2.5\n", - " 29\n", - " 2.08\n", - " 17.6\n", - " 6.321429\n", - " 0.305143\n", + " 34.8\n", + " 34.8\n", + " 256\n", + " 7\n", + " 2.7\n", + " 37\n", + " 1.96\n", + " 18.1\n", + " 6.236842\n", + " 0.339055\n", " \n", " \n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Maignan\n", - " Milan\n", - " Maignan\n", + " Vicario\n", + " Empoli\n", + " Vicario\n", " \n", - " 500\n", - " 27-131\n", - " 1995\n", + " 547\n", + " 26-113\n", + " 1996\n", " 0\n", " ...\n", - " 38.9\n", - " 40.4\n", - " 65\n", - " 5\n", - " 7.7\n", - " 6\n", - " 0.86\n", - " 12.9\n", - " 6.357143\n", - " 0.440315\n", + " 45.6\n", + " 41.8\n", + " 399\n", + " 25\n", + " 6.3\n", + " 8\n", + " 0.42\n", + " 10.5\n", + " 6.473684\n", + " 0.379532\n", " \n", " \n", " 3\n", - " 2211\n", + " 453\n", " P\n", - " Silvestri\n", - " Udinese\n", - " Silvestri\n", + " Szczesny\n", + " Juventus\n", + " Szczesny\n", " \n", - " 512\n", - " 31-254\n", - " 1991\n", + " 544\n", + " 32-285\n", + " 1990\n", " 0\n", " ...\n", - " 40.9\n", - " 38.2\n", - " 194\n", + " 41.0\n", + " 39.2\n", + " 155\n", " 4\n", - " 2.1\n", - " 5\n", - " 0.36\n", - " 11.3\n", - " 6.321429\n", - " 0.485767\n", + " 2.6\n", + " 7\n", + " 0.56\n", + " 14.2\n", + " 6.038462\n", + " 0.364878\n", " \n", " \n", " 4\n", - " 159\n", + " 2134\n", " P\n", - " Sepe\n", - " Salernitana\n", - " Sepe\n", + " Falcone\n", + " Lecce\n", + " Falcone\n", " \n", - " 511\n", - " 31-187\n", - " 1991\n", + " 524\n", + " 27-291\n", + " 1995\n", " 0\n", " ...\n", - " 48.4\n", - " 41.7\n", - " 187\n", - " 14\n", - " 7.5\n", - " 9\n", - " 0.64\n", - " 13.7\n", - " 6.428571\n", - " 0.371154\n", + " 75.0\n", + " 51.4\n", + " 304\n", + " 13\n", + " 4.3\n", + " 20\n", + " 1.00\n", + " 12.5\n", + " 6.342105\n", + " 0.364642\n", " \n", " \n", " ...\n", @@ -2715,84 +2733,12 @@ " ...\n", " \n", " \n", - " 549\n", - " 5512\n", + " 542\n", + " 5505\n", " A\n", - " De Luca\n", - " Sampdoria\n", - " Luca\n", - " \n", - " 122\n", - " 24-117\n", - " 1998\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " 5.877688\n", - " 0.485814\n", - " \n", - " \n", - " 550\n", - " 5734\n", - " A\n", - " Soule'\n", + " Kaio Jorge\n", " Juventus\n", - " Soule\n", - " \n", - " 422\n", - " 19-210\n", - " 2003\n", - " 5\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " 6.067846\n", - " 0.581002\n", - " \n", - " \n", - " 551\n", - " 5785\n", - " A\n", - " Lazetic\n", - " Milan\n", - " Lazetic\n", - " \n", - " 248\n", - " 18-293\n", - " 2004\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 0\n", - " 0.00\n", - " 0.0\n", - " 6.170097\n", - " 0.204814\n", - " \n", - " \n", - " 552\n", - " 5837\n", - " A\n", - " Voelkerling Persson\n", - " Lecce\n", - " Persson\n", + " Jorge\n", " \n", " -1\n", " 0\n", @@ -2807,21 +2753,21 @@ " 0\n", " 0.00\n", " 0.0\n", - " 6.041983\n", - " 0.689071\n", + " 6.092280\n", + " 0.894197\n", " \n", " \n", - " 553\n", - " 6063\n", + " 543\n", + " 5512\n", " A\n", - " Sanca\n", - " Spezia\n", - " Sanca\n", + " De Luca\n", + " Sampdoria\n", + " Luca\n", " \n", - " 395\n", - " 22-311\n", - " 2000\n", - " 3\n", + " 128\n", + " 24-195\n", + " 1998\n", + " 1\n", " ...\n", " 0.0\n", " 0.0\n", @@ -2831,97 +2777,169 @@ " 0\n", " 0.00\n", " 0.0\n", - " 5.991021\n", - " 0.262247\n", + " 6.284927\n", + " 0.532298\n", + " \n", + " \n", + " 544\n", + " 5785\n", + " A\n", + " Lazetic\n", + " Milan\n", + " Lazetic\n", + " \n", + " 258\n", + " 19-006\n", + " 2004\n", + " 1\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " 6.094004\n", + " 0.625570\n", + " \n", + " \n", + " 545\n", + " 6113\n", + " A\n", + " Montevago\n", + " Sampdoria\n", + " Montevago\n", + " \n", + " 318\n", + " 19-316\n", + " 2003\n", + " 6\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " 5.711934\n", + " 0.225193\n", + " \n", + " \n", + " 546\n", + " 6143\n", + " A\n", + " Krollis\n", + " Spezia\n", + " Krollis\n", + " \n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.00\n", + " 0.0\n", + " 5.748895\n", + " 0.595450\n", " \n", " \n", "\n", - "

554 rows × 165 columns

\n", + "

547 rows × 165 columns

\n", "" ], "text/plain": [ - " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 502 \n", - "1 2814 P Provedel Lazio Provedel 509 \n", - "2 4312 P Maignan Milan Maignan 500 \n", - "3 2211 P Silvestri Udinese Silvestri 512 \n", - "4 159 P Sepe Salernitana Sepe 511 \n", - ".. ... .. ... ... ... ... ... \n", - "549 5512 A De Luca Sampdoria Luca 122 \n", - "550 5734 A Soule' Juventus Soule 422 \n", - "551 5785 A Lazetic Milan Lazetic 248 \n", - "552 5837 A Voelkerling Persson Lecce Persson -1 \n", - "553 6063 A Sanca Spezia Sanca 395 \n", + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", "\n", - " age birth_year games ... gk_pct_goal_kicks_launched \\\n", - "0 25-234 1997 0 ... 19.6 \n", - "1 28-239 1994 0 ... 37.2 \n", - "2 27-131 1995 0 ... 38.9 \n", - "3 31-254 1991 0 ... 40.9 \n", - "4 31-187 1991 0 ... 48.4 \n", - ".. ... ... ... ... ... \n", - "549 24-117 1998 1 ... 0.0 \n", - "550 19-210 2003 5 ... 0.0 \n", - "551 18-293 2004 1 ... 0.0 \n", - "552 0 0 0 ... 0.0 \n", - "553 22-311 2000 3 ... 0.0 \n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "0 1997 0 ... 20.2 \n", + "1 1994 0 ... 34.8 \n", + "2 1996 0 ... 45.6 \n", + "3 1990 0 ... 41.0 \n", + "4 1995 0 ... 75.0 \n", + ".. ... ... ... ... \n", + "542 0 0 ... 0.0 \n", + "543 1998 1 ... 0.0 \n", + "544 2004 1 ... 0.0 \n", + "545 2003 6 ... 0.0 \n", + "546 0 0 ... 0.0 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 26.6 142 6 \n", - "1 36.6 201 5 \n", - "2 40.4 65 5 \n", - "3 38.2 194 4 \n", - "4 41.7 187 14 \n", + "0 27.1 197 6 \n", + "1 34.8 256 7 \n", + "2 41.8 399 25 \n", + "3 39.2 155 4 \n", + "4 51.4 304 13 \n", ".. ... ... ... \n", - "549 0.0 0 0 \n", - "550 0.0 0 0 \n", - "551 0.0 0 0 \n", - "552 0.0 0 0 \n", - "553 0.0 0 0 \n", + "542 0.0 0 0 \n", + "543 0.0 0 0 \n", + "544 0.0 0 0 \n", + "545 0.0 0 0 \n", + "546 0.0 0 0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 4.2 10 \n", - "1 2.5 29 \n", - "2 7.7 6 \n", - "3 2.1 5 \n", - "4 7.5 9 \n", + "0 3.0 23 \n", + "1 2.7 37 \n", + "2 6.3 8 \n", + "3 2.6 7 \n", + "4 4.3 20 \n", ".. ... ... \n", - "549 0.0 0 \n", - "550 0.0 0 \n", - "551 0.0 0 \n", - "552 0.0 0 \n", - "553 0.0 0 \n", + "542 0.0 0 \n", + "543 0.0 0 \n", + "544 0.0 0 \n", + "545 0.0 0 \n", + "546 0.0 0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", - "0 0.71 15.1 \n", - "1 2.08 17.6 \n", - "2 0.86 12.9 \n", - "3 0.36 11.3 \n", - "4 0.64 13.7 \n", + "0 1.21 17.2 \n", + "1 1.96 18.1 \n", + "2 0.42 10.5 \n", + "3 0.56 14.2 \n", + "4 1.00 12.5 \n", ".. ... ... \n", - "549 0.00 0.0 \n", - "550 0.00 0.0 \n", - "551 0.00 0.0 \n", - "552 0.00 0.0 \n", - "553 0.00 0.0 \n", + "542 0.00 0.0 \n", + "543 0.00 0.0 \n", + "544 0.00 0.0 \n", + "545 0.00 0.0 \n", + "546 0.00 0.0 \n", "\n", " vote_avg vote_std \n", - "0 6.214286 0.557875 \n", - "1 6.321429 0.305143 \n", - "2 6.357143 0.440315 \n", - "3 6.321429 0.485767 \n", - "4 6.428571 0.371154 \n", + "0 6.263158 0.496525 \n", + "1 6.236842 0.339055 \n", + "2 6.473684 0.379532 \n", + "3 6.038462 0.364878 \n", + "4 6.342105 0.364642 \n", ".. ... ... \n", - "549 5.877688 0.485814 \n", - "550 6.067846 0.581002 \n", - "551 6.170097 0.204814 \n", - "552 6.041983 0.689071 \n", - "553 5.991021 0.262247 \n", + "542 6.092280 0.894197 \n", + "543 6.284927 0.532298 \n", + "544 6.094004 0.625570 \n", + "545 5.711934 0.225193 \n", + "546 5.748895 0.595450 \n", "\n", - "[554 rows x 165 columns]" + "[547 rows x 165 columns]" ] }, - "execution_count": 19, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2934,7 +2952,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "id": "f7620abe", "metadata": {}, "outputs": [ @@ -2944,7 +2962,7 @@ "'gk_games'" ] }, - "execution_count": 20, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -2963,7 +2981,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 44, "id": "700b7a7d", "metadata": {}, "outputs": [ @@ -2971,16 +2989,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Gollini, 0.5\n", - "Berisha, 0.0\n", - "Cragno, 0.0\n", - "Luis Maximiano, 0.16666666666666663\n", - "Carnesecchi, 0.8333333333333334\n", + "Zoet, 0.5\n", + "Gollini, 0.0\n", + "Ochoa, 0.8333333333333334\n", + "Pegolo, 0.33333333333333337\n", "Mirante, 0.0\n", "Sarr M., 0.0\n", "Lamanna, 0.0\n", "Ujkani, 0.0\n", - "Pegolo, 0.0\n", + "Berisha, 0.0\n", + "Marchetti, 0.0\n", "Perilli, 0.0\n", "Padelli, 0.0\n", "Perisan, 0.0\n", @@ -2988,29 +3006,31 @@ "Cordaz, 0.0\n", "Pinsoglio, 0.0\n", "Fiorillo, 0.0\n", + "Cragno, 0.0\n", "Sirigu, 0.0\n", "Cerofolini, 0.0\n", "Rossi F., 0.0\n", - "Contini, 0.0\n", + "Ravaglia F., 0.0\n", "Brancolini, 0.0\n", "Bleve, 0.0\n", "Berardi A., 0.0\n", "Russo A., 0.0\n", "Gemello, 0.0\n", "Ravaglia, 0.0\n", - "Zoet, 0.16666666666666663\n", "Boer, 0.0\n", "Adamonis, 0.0\n", "Marfella, 0.0\n", - "Zovko, 0.0\n", + "Zovko, 0.16666666666666663\n", "Piana, 0.0\n", "Bagnolini, 0.0\n", + "Luis Maximiano, 0.16666666666666663\n", "Svilar, 0.0\n", "Sorrentino A., 0.0\n", "Ciezkowski, 0.0\n", - "Micai, 0.0\n", "Chiesa M., 0.0\n", - "Saro, 0.0\n" + "Saro, 0.0\n", + "Vasquez D., 0.0\n", + "Turk, 0.0\n" ] } ], @@ -3039,7 +3059,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 45, "id": "2d9eee99", "metadata": {}, "outputs": [ @@ -3096,21 +3116,21 @@ " Napoli\n", " Meret\n", " \n", - " 502\n", - " 25-234\n", + " 529\n", + " 25-312\n", " 1997\n", " 0\n", " ...\n", - " 19.6\n", - " 26.6\n", - " 142.0\n", + " 20.2\n", + " 27.1\n", + " 197.0\n", " 6.0\n", - " 4.2\n", - " 10.0\n", - " 0.71\n", - " 15.1\n", - " 6.214286\n", - " 0.557875\n", + " 3.0\n", + " 23.0\n", + " 1.21\n", + " 17.2\n", + " 6.263158\n", + " 0.496525\n", " \n", " \n", " 1\n", @@ -3120,93 +3140,93 @@ " Lazio\n", " Provedel\n", " \n", - " 509\n", - " 28-239\n", + " 538\n", + " 28-317\n", " 1994\n", " 0\n", " ...\n", - " 37.2\n", - " 36.6\n", - " 201.0\n", - " 5.0\n", - " 2.5\n", - " 29.0\n", - " 2.08\n", - " 17.6\n", - " 6.321429\n", - " 0.305143\n", + " 34.8\n", + " 34.8\n", + " 256.0\n", + " 7.0\n", + " 2.7\n", + " 37.0\n", + " 1.96\n", + " 18.1\n", + " 6.236842\n", + " 0.339055\n", " \n", " \n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Maignan\n", - " Milan\n", - " Maignan\n", + " Vicario\n", + " Empoli\n", + " Vicario\n", " \n", - " 500\n", - " 27-131\n", - " 1995\n", + " 547\n", + " 26-113\n", + " 1996\n", " 0\n", " ...\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", - " 6.357143\n", - " 0.440315\n", + " 45.6\n", + " 41.8\n", + " 399.0\n", + " 25.0\n", + " 6.3\n", + " 8.0\n", + " 0.42\n", + " 10.5\n", + " 6.473684\n", + " 0.379532\n", " \n", " \n", " 3\n", - " 2211\n", + " 453\n", " P\n", - " Silvestri\n", - " Udinese\n", - " Silvestri\n", + " Szczesny\n", + " Juventus\n", + " Szczesny\n", " \n", - " 512\n", - " 31-254\n", - " 1991\n", + " 544\n", + " 32-285\n", + " 1990\n", " 0\n", " ...\n", - " 40.9\n", - " 38.2\n", - " 194.0\n", + " 41.0\n", + " 39.2\n", + " 155.0\n", " 4.0\n", - " 2.1\n", - " 5.0\n", - " 0.36\n", - " 11.3\n", - " 6.321429\n", - " 0.485767\n", + " 2.6\n", + " 7.0\n", + " 0.56\n", + " 14.2\n", + " 6.038462\n", + " 0.364878\n", " \n", " \n", " 4\n", - " 159\n", + " 2134\n", " P\n", - " Sepe\n", - " Salernitana\n", - " Sepe\n", + " Falcone\n", + " Lecce\n", + " Falcone\n", " \n", - " 511\n", - " 31-187\n", - " 1991\n", + " 524\n", + " 27-291\n", + " 1995\n", " 0\n", " ...\n", - " 48.4\n", - " 41.7\n", - " 187.0\n", - " 14.0\n", - " 7.5\n", - " 9.0\n", - " 0.64\n", - " 13.7\n", - " 6.428571\n", - " 0.371154\n", + " 75.0\n", + " 51.4\n", + " 304.0\n", + " 13.0\n", + " 4.3\n", + " 20.0\n", + " 1.00\n", + " 12.5\n", + " 6.342105\n", + " 0.364642\n", " \n", " \n", " ...\n", @@ -3233,84 +3253,12 @@ " ...\n", " \n", " \n", - " 549\n", - " 5512\n", + " 542\n", + " 5505\n", " A\n", - " De Luca\n", - " Sampdoria\n", - " Luca\n", - " \n", - " 122\n", - " 24-117\n", - " 1998\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 5.877688\n", - " 0.485814\n", - " \n", - " \n", - " 550\n", - " 5734\n", - " A\n", - " Soule'\n", + " Kaio Jorge\n", " Juventus\n", - " Soule\n", - " \n", - " 422\n", - " 19-210\n", - " 2003\n", - " 5\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 6.067846\n", - " 0.581002\n", - " \n", - " \n", - " 551\n", - " 5785\n", - " A\n", - " Lazetic\n", - " Milan\n", - " Lazetic\n", - " \n", - " 248\n", - " 18-293\n", - " 2004\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 6.170097\n", - " 0.204814\n", - " \n", - " \n", - " 552\n", - " 5837\n", - " A\n", - " Voelkerling Persson\n", - " Lecce\n", - " Persson\n", + " Jorge\n", " \n", " -1\n", " 0\n", @@ -3325,21 +3273,21 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.041983\n", - " 0.689071\n", + " 6.092280\n", + " 0.894197\n", " \n", " \n", - " 553\n", - " 6063\n", + " 543\n", + " 5512\n", " A\n", - " Sanca\n", - " Spezia\n", - " Sanca\n", + " De Luca\n", + " Sampdoria\n", + " Luca\n", " \n", - " 395\n", - " 22-311\n", - " 2000\n", - " 3\n", + " 128\n", + " 24-195\n", + " 1998\n", + " 1\n", " ...\n", " 0.0\n", " 0.0\n", @@ -3349,97 +3297,169 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.991021\n", - " 0.262247\n", + " 6.284927\n", + " 0.532298\n", + " \n", + " \n", + " 544\n", + " 5785\n", + " A\n", + " Lazetic\n", + " Milan\n", + " Lazetic\n", + " \n", + " 258\n", + " 19-006\n", + " 2004\n", + " 1\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 6.094004\n", + " 0.625570\n", + " \n", + " \n", + " 545\n", + " 6113\n", + " A\n", + " Montevago\n", + " Sampdoria\n", + " Montevago\n", + " \n", + " 318\n", + " 19-316\n", + " 2003\n", + " 6\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 5.711934\n", + " 0.225193\n", + " \n", + " \n", + " 546\n", + " 6143\n", + " A\n", + " Krollis\n", + " Spezia\n", + " Krollis\n", + " \n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 5.748895\n", + " 0.595450\n", " \n", " \n", "\n", - "

554 rows × 165 columns

\n", + "

547 rows × 165 columns

\n", "" ], "text/plain": [ - " id r name team surname initial fb_ID \\\n", - "0 572 P Meret Napoli Meret 502 \n", - "1 2814 P Provedel Lazio Provedel 509 \n", - "2 4312 P Maignan Milan Maignan 500 \n", - "3 2211 P Silvestri Udinese Silvestri 512 \n", - "4 159 P Sepe Salernitana Sepe 511 \n", - ".. ... .. ... ... ... ... ... \n", - "549 5512 A De Luca Sampdoria Luca 122 \n", - "550 5734 A Soule' Juventus Soule 422 \n", - "551 5785 A Lazetic Milan Lazetic 248 \n", - "552 5837 A Voelkerling Persson Lecce Persson -1 \n", - "553 6063 A Sanca Spezia Sanca 395 \n", + " id r name team surname initial fb_ID age \\\n", + "0 572 P Meret Napoli Meret 529 25-312 \n", + "1 2814 P Provedel Lazio Provedel 538 28-317 \n", + "2 4964 P Vicario Empoli Vicario 547 26-113 \n", + "3 453 P Szczesny Juventus Szczesny 544 32-285 \n", + "4 2134 P Falcone Lecce Falcone 524 27-291 \n", + ".. ... .. ... ... ... ... ... ... \n", + "542 5505 A Kaio Jorge Juventus Jorge -1 0 \n", + "543 5512 A De Luca Sampdoria Luca 128 24-195 \n", + "544 5785 A Lazetic Milan Lazetic 258 19-006 \n", + "545 6113 A Montevago Sampdoria Montevago 318 19-316 \n", + "546 6143 A Krollis Spezia Krollis -1 0 \n", "\n", - " age birth_year games ... gk_pct_goal_kicks_launched \\\n", - "0 25-234 1997 0 ... 19.6 \n", - "1 28-239 1994 0 ... 37.2 \n", - "2 27-131 1995 0 ... 38.9 \n", - "3 31-254 1991 0 ... 40.9 \n", - "4 31-187 1991 0 ... 48.4 \n", - ".. ... ... ... ... ... \n", - "549 24-117 1998 1 ... 0.0 \n", - "550 19-210 2003 5 ... 0.0 \n", - "551 18-293 2004 1 ... 0.0 \n", - "552 0 0 0 ... 0.0 \n", - "553 22-311 2000 3 ... 0.0 \n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "0 1997 0 ... 20.2 \n", + "1 1994 0 ... 34.8 \n", + "2 1996 0 ... 45.6 \n", + "3 1990 0 ... 41.0 \n", + "4 1995 0 ... 75.0 \n", + ".. ... ... ... ... \n", + "542 0 0 ... 0.0 \n", + "543 1998 1 ... 0.0 \n", + "544 2004 1 ... 0.0 \n", + "545 2003 6 ... 0.0 \n", + "546 0 0 ... 0.0 \n", "\n", " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", - "0 26.6 142.0 6.0 \n", - "1 36.6 201.0 5.0 \n", - "2 40.4 65.0 5.0 \n", - "3 38.2 194.0 4.0 \n", - "4 41.7 187.0 14.0 \n", + "0 27.1 197.0 6.0 \n", + "1 34.8 256.0 7.0 \n", + "2 41.8 399.0 25.0 \n", + "3 39.2 155.0 4.0 \n", + "4 51.4 304.0 13.0 \n", ".. ... ... ... \n", - "549 0.0 0.0 0.0 \n", - "550 0.0 0.0 0.0 \n", - "551 0.0 0.0 0.0 \n", - "552 0.0 0.0 0.0 \n", - "553 0.0 0.0 0.0 \n", + "542 0.0 0.0 0.0 \n", + "543 0.0 0.0 0.0 \n", + "544 0.0 0.0 0.0 \n", + "545 0.0 0.0 0.0 \n", + "546 0.0 0.0 0.0 \n", "\n", " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", - "0 4.2 10.0 \n", - "1 2.5 29.0 \n", - "2 7.7 6.0 \n", - "3 2.1 5.0 \n", - "4 7.5 9.0 \n", + "0 3.0 23.0 \n", + "1 2.7 37.0 \n", + "2 6.3 8.0 \n", + "3 2.6 7.0 \n", + "4 4.3 20.0 \n", ".. ... ... \n", - "549 0.0 0.0 \n", - "550 0.0 0.0 \n", - "551 0.0 0.0 \n", - "552 0.0 0.0 \n", - "553 0.0 0.0 \n", + "542 0.0 0.0 \n", + "543 0.0 0.0 \n", + "544 0.0 0.0 \n", + "545 0.0 0.0 \n", + "546 0.0 0.0 \n", "\n", " gk_def_actions_outside_pen_area_per90 gk_avg_distance_def_actions \\\n", - "0 0.71 15.1 \n", - "1 2.08 17.6 \n", - "2 0.86 12.9 \n", - "3 0.36 11.3 \n", - "4 0.64 13.7 \n", + "0 1.21 17.2 \n", + "1 1.96 18.1 \n", + "2 0.42 10.5 \n", + "3 0.56 14.2 \n", + "4 1.00 12.5 \n", ".. ... ... \n", - "549 0.00 0.0 \n", - "550 0.00 0.0 \n", - "551 0.00 0.0 \n", - "552 0.00 0.0 \n", - "553 0.00 0.0 \n", + "542 0.00 0.0 \n", + "543 0.00 0.0 \n", + "544 0.00 0.0 \n", + "545 0.00 0.0 \n", + "546 0.00 0.0 \n", "\n", " vote_avg vote_std \n", - "0 6.214286 0.557875 \n", - "1 6.321429 0.305143 \n", - "2 6.357143 0.440315 \n", - "3 6.321429 0.485767 \n", - "4 6.428571 0.371154 \n", + "0 6.263158 0.496525 \n", + "1 6.236842 0.339055 \n", + "2 6.473684 0.379532 \n", + "3 6.038462 0.364878 \n", + "4 6.342105 0.364642 \n", ".. ... ... \n", - "549 5.877688 0.485814 \n", - "550 6.067846 0.581002 \n", - "551 6.170097 0.204814 \n", - "552 6.041983 0.689071 \n", - "553 5.991021 0.262247 \n", + "542 6.092280 0.894197 \n", + "543 6.284927 0.532298 \n", + "544 6.094004 0.625570 \n", + "545 5.711934 0.225193 \n", + "546 5.748895 0.595450 \n", "\n", - "[554 rows x 165 columns]" + "[547 rows x 165 columns]" ] }, - "execution_count": 22, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -3460,7 +3480,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "id": "8336c025", "metadata": {}, "outputs": [], diff --git a/4_player_match_dataset_creation.ipynb b/4_player_match_dataset_creation.ipynb index 7ff11c0..fbc323e 100644 --- a/4_player_match_dataset_creation.ipynb +++ b/4_player_match_dataset_creation.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "id": "3ecf3676", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "id": "8a6867a5", "metadata": {}, "outputs": [ @@ -40,7 +40,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_17612\\552672520.py:19: SettingWithCopyWarning: \n", + "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_7952\\552672520.py:19: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -119,482 +119,482 @@ " \n", " Atalanta\n", " Atalanta\n", - 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" 710.0\n", - " 228.0\n", - " 215.0\n", - " 51.5\n", + " 998.0\n", + " 323.0\n", + " 295.0\n", + " 52.3\n", " \n", " \n", " Udinese\n", " Udinese\n", " 22.0\n", - " 50.6\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 21.0\n", + " 50.1\n", " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 26.0\n", + " 22.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 176.0\n", - " 162.0\n", - " 17.0\n", + " 250.0\n", + " 227.0\n", + " 29.0\n", " 2.0\n", " 0.0\n", " 1.0\n", - " 707.0\n", - " 142.0\n", - " 187.0\n", - " 43.2\n", + " 950.0\n", + " 189.0\n", + " 237.0\n", + " 44.4\n", " \n", " \n", "\n", @@ -604,169 +604,169 @@ "text/plain": [ " team team_players_used team_possession team_games \\\n", "team_idx \n", - "Atalanta Atalanta 23.0 46.4 14.0 \n", - "Bologna Bologna 23.0 51.1 14.0 \n", - "Cremonese Cremonese 27.0 43.2 14.0 \n", - "Empoli Empoli 25.0 46.0 14.0 \n", - "Fiorentina Fiorentina 25.0 58.7 14.0 \n", - "Verona Hellas Verona 29.0 43.9 14.0 \n", - "Inter Inter 23.0 54.6 14.0 \n", - "Juventus Juventus 26.0 50.5 14.0 \n", - "Lazio Lazio 21.0 50.0 14.0 \n", - "Lecce Lecce 24.0 40.7 14.0 \n", - "Milan Milan 27.0 53.9 14.0 \n", - "Monza Monza 29.0 55.5 14.0 \n", - "Napoli Napoli 24.0 59.8 14.0 \n", - "Roma Roma 23.0 50.8 14.0 \n", - "Salernitana Salernitana 24.0 47.1 14.0 \n", - "Sampdoria Sampdoria 24.0 50.8 14.0 \n", - "Sassuolo Sassuolo 26.0 48.1 14.0 \n", - "Spezia Spezia 24.0 46.9 14.0 \n", - "Torino Torino 23.0 51.3 14.0 \n", - "Udinese Udinese 22.0 50.6 14.0 \n", + "Atalanta Atalanta 24.0 48.4 19.0 \n", + "Bologna Bologna 24.0 52.6 20.0 \n", + "Cremonese Cremonese 29.0 44.0 19.0 \n", + "Empoli Empoli 26.0 46.3 19.0 \n", + "Fiorentina Fiorentina 27.0 57.2 19.0 \n", + "Verona Hellas Verona 29.0 43.4 19.0 \n", + "Inter Inter 23.0 52.9 19.0 \n", + "Juventus Juventus 26.0 49.5 19.0 \n", + "Lazio Lazio 21.0 51.2 19.0 \n", + "Lecce Lecce 26.0 42.5 20.0 \n", + "Milan Milan 27.0 54.5 19.0 \n", + "Monza Monza 29.0 55.1 19.0 \n", + "Napoli Napoli 24.0 61.3 19.0 \n", + "Roma Roma 25.0 49.4 19.0 \n", + "Salernitana Salernitana 28.0 44.8 20.0 \n", + "Sampdoria Sampdoria 29.0 49.4 19.0 \n", + "Sassuolo Sassuolo 27.0 48.7 19.0 \n", + "Spezia Spezia 30.0 46.7 20.0 \n", + "Torino Torino 23.0 52.8 19.0 \n", + "Udinese Udinese 22.0 50.1 19.0 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", "team_idx \n", - "Atalanta 154.0 1260.0 19.0 14.0 \n", - "Bologna 154.0 1260.0 17.0 12.0 \n", - "Cremonese 154.0 1260.0 11.0 5.0 \n", - "Empoli 154.0 1260.0 10.0 5.0 \n", - "Fiorentina 154.0 1260.0 17.0 15.0 \n", - "Verona 154.0 1260.0 10.0 7.0 \n", - "Inter 154.0 1260.0 31.0 22.0 \n", - "Juventus 154.0 1260.0 21.0 17.0 \n", - "Lazio 154.0 1260.0 25.0 20.0 \n", - "Lecce 154.0 1260.0 12.0 8.0 \n", - "Milan 154.0 1260.0 26.0 21.0 \n", - "Monza 154.0 1260.0 13.0 8.0 \n", - "Napoli 154.0 1260.0 34.0 28.0 \n", - "Roma 154.0 1260.0 17.0 11.0 \n", - "Salernitana 154.0 1260.0 18.0 12.0 \n", - "Sampdoria 154.0 1260.0 6.0 6.0 \n", - "Sassuolo 154.0 1260.0 15.0 12.0 \n", - "Spezia 154.0 1260.0 10.0 6.0 \n", - "Torino 154.0 1260.0 15.0 10.0 \n", - "Udinese 154.0 1260.0 21.0 19.0 \n", + "Atalanta 209.0 1710.0 36.0 25.0 \n", + "Bologna 220.0 1800.0 25.0 19.0 \n", + "Cremonese 209.0 1710.0 14.0 6.0 \n", + "Empoli 209.0 1710.0 17.0 9.0 \n", + "Fiorentina 209.0 1710.0 21.0 17.0 \n", + "Verona 209.0 1710.0 16.0 13.0 \n", + "Inter 209.0 1710.0 37.0 25.0 \n", + "Juventus 209.0 1710.0 30.0 23.0 \n", + "Lazio 209.0 1710.0 34.0 25.0 \n", + "Lecce 220.0 1800.0 18.0 12.0 \n", + "Milan 209.0 1710.0 33.0 28.0 \n", + "Monza 209.0 1710.0 22.0 13.0 \n", + "Napoli 209.0 1710.0 46.0 37.0 \n", + "Roma 209.0 1710.0 25.0 16.0 \n", + "Salernitana 220.0 1800.0 24.0 16.0 \n", + "Sampdoria 209.0 1710.0 8.0 7.0 \n", + "Sassuolo 209.0 1710.0 18.0 13.0 \n", + "Spezia 220.0 1800.0 15.0 10.0 \n", + "Torino 209.0 1710.0 19.0 14.0 \n", + "Udinese 209.0 1710.0 26.0 22.0 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", "team_idx ... \n", - "Atalanta 4.0 5.0 ... 150.0 \n", - "Bologna 3.0 3.0 ... 182.0 \n", - "Cremonese 1.0 3.0 ... 145.0 \n", - "Empoli 0.0 0.0 ... 176.0 \n", - "Fiorentina 1.0 3.0 ... 186.0 \n", - "Verona 0.0 0.0 ... 130.0 \n", - "Inter 2.0 2.0 ... 182.0 \n", - "Juventus 1.0 2.0 ... 161.0 \n", - "Lazio 1.0 2.0 ... 197.0 \n", - "Lecce 1.0 2.0 ... 184.0 \n", - "Milan 2.0 2.0 ... 164.0 \n", - "Monza 1.0 1.0 ... 207.0 \n", - "Napoli 3.0 3.0 ... 195.0 \n", - "Roma 2.0 3.0 ... 198.0 \n", - "Salernitana 1.0 1.0 ... 150.0 \n", - "Sampdoria 0.0 0.0 ... 220.0 \n", - "Sassuolo 1.0 2.0 ... 177.0 \n", - "Spezia 1.0 1.0 ... 136.0 \n", - "Torino 1.0 1.0 ... 148.0 \n", - "Udinese 0.0 0.0 ... 176.0 \n", + "Atalanta 6.0 8.0 ... 209.0 \n", + "Bologna 3.0 3.0 ... 252.0 \n", + "Cremonese 2.0 4.0 ... 205.0 \n", + "Empoli 0.0 0.0 ... 251.0 \n", + "Fiorentina 2.0 4.0 ... 259.0 \n", + "Verona 0.0 0.0 ... 192.0 \n", + "Inter 2.0 2.0 ... 242.0 \n", + "Juventus 2.0 3.0 ... 210.0 \n", + "Lazio 3.0 4.0 ... 261.0 \n", + "Lecce 1.0 2.0 ... 263.0 \n", + "Milan 2.0 2.0 ... 234.0 \n", + "Monza 3.0 3.0 ... 279.0 \n", + "Napoli 4.0 5.0 ... 258.0 \n", + "Roma 3.0 5.0 ... 266.0 \n", + "Salernitana 1.0 1.0 ... 226.0 \n", + "Sampdoria 0.0 0.0 ... 299.0 \n", + "Sassuolo 3.0 4.0 ... 249.0 \n", + "Spezia 2.0 2.0 ... 207.0 \n", + "Torino 1.0 1.0 ... 209.0 \n", + "Udinese 0.0 0.0 ... 250.0 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", "team_idx \n", - "Atalanta 168.0 19.0 0.0 \n", - "Bologna 158.0 24.0 2.0 \n", - "Cremonese 188.0 19.0 2.0 \n", - "Empoli 151.0 28.0 2.0 \n", - "Fiorentina 176.0 35.0 1.0 \n", - "Verona 205.0 18.0 1.0 \n", - "Inter 154.0 14.0 1.0 \n", - "Juventus 156.0 21.0 0.0 \n", - "Lazio 124.0 35.0 1.0 \n", - "Lecce 184.0 27.0 3.0 \n", - "Milan 161.0 15.0 2.0 \n", - "Monza 178.0 24.0 0.0 \n", - "Napoli 116.0 18.0 1.0 \n", - "Roma 157.0 7.0 0.0 \n", - "Salernitana 157.0 34.0 4.0 \n", - "Sampdoria 192.0 42.0 1.0 \n", - "Sassuolo 120.0 47.0 2.0 \n", - "Spezia 177.0 32.0 1.0 \n", - "Torino 199.0 17.0 2.0 \n", - "Udinese 162.0 17.0 2.0 \n", + "Atalanta 218.0 25.0 1.0 \n", + "Bologna 229.0 33.0 3.0 \n", + "Cremonese 247.0 29.0 3.0 \n", + "Empoli 214.0 31.0 2.0 \n", + "Fiorentina 229.0 52.0 1.0 \n", + "Verona 276.0 22.0 1.0 \n", + "Inter 214.0 18.0 2.0 \n", + "Juventus 210.0 25.0 0.0 \n", + "Lazio 185.0 42.0 1.0 \n", + "Lecce 265.0 43.0 3.0 \n", + "Milan 226.0 20.0 3.0 \n", + "Monza 242.0 34.0 0.0 \n", + "Napoli 167.0 27.0 1.0 \n", + "Roma 214.0 10.0 0.0 \n", + "Salernitana 233.0 49.0 7.0 \n", + "Sampdoria 266.0 55.0 3.0 \n", + "Sassuolo 182.0 62.0 2.0 \n", + "Spezia 261.0 46.0 1.0 \n", + "Torino 265.0 21.0 3.0 \n", + "Udinese 227.0 29.0 2.0 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", "team_idx \n", - "Atalanta 5.0 1.0 \n", - "Bologna 3.0 0.0 \n", - "Cremonese 3.0 0.0 \n", + "Atalanta 8.0 1.0 \n", + "Bologna 3.0 1.0 \n", + "Cremonese 4.0 0.0 \n", "Empoli 0.0 0.0 \n", - "Fiorentina 3.0 0.0 \n", + "Fiorentina 4.0 0.0 \n", "Verona 0.0 1.0 \n", - "Inter 2.0 0.0 \n", - "Juventus 2.0 0.0 \n", - "Lazio 2.0 1.0 \n", - "Lecce 2.0 0.0 \n", - "Milan 2.0 1.0 \n", - "Monza 1.0 0.0 \n", - "Napoli 3.0 0.0 \n", - "Roma 3.0 0.0 \n", + "Inter 2.0 1.0 \n", + "Juventus 3.0 0.0 \n", + "Lazio 4.0 1.0 \n", + "Lecce 2.0 1.0 \n", + "Milan 2.0 2.0 \n", + "Monza 3.0 1.0 \n", + "Napoli 4.0 0.0 \n", + "Roma 5.0 0.0 \n", "Salernitana 1.0 1.0 \n", "Sampdoria 0.0 0.0 \n", - "Sassuolo 2.0 0.0 \n", - "Spezia 1.0 2.0 \n", + "Sassuolo 4.0 0.0 \n", + "Spezia 2.0 2.0 \n", "Torino 1.0 0.0 \n", "Udinese 0.0 1.0 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", "team_idx \n", - "Atalanta 834.0 181.0 \n", - "Bologna 731.0 165.0 \n", - "Cremonese 744.0 242.0 \n", - "Empoli 723.0 163.0 \n", - "Fiorentina 694.0 193.0 \n", - "Verona 758.0 240.0 \n", - "Inter 596.0 126.0 \n", - "Juventus 674.0 184.0 \n", - "Lazio 746.0 151.0 \n", - "Lecce 703.0 216.0 \n", - "Milan 690.0 168.0 \n", - "Monza 692.0 141.0 \n", - "Napoli 687.0 142.0 \n", - "Roma 709.0 142.0 \n", - "Salernitana 750.0 169.0 \n", - "Sampdoria 742.0 215.0 \n", - "Sassuolo 710.0 146.0 \n", - "Spezia 776.0 212.0 \n", - "Torino 710.0 228.0 \n", - "Udinese 707.0 142.0 \n", + "Atalanta 1165.0 239.0 \n", + "Bologna 1081.0 224.0 \n", + "Cremonese 1052.0 348.0 \n", + "Empoli 999.0 217.0 \n", + "Fiorentina 942.0 249.0 \n", + "Verona 1038.0 345.0 \n", + "Inter 869.0 198.0 \n", + "Juventus 947.0 230.0 \n", + "Lazio 1021.0 183.0 \n", + "Lecce 1072.0 373.0 \n", + "Milan 964.0 224.0 \n", + "Monza 958.0 197.0 \n", + "Napoli 933.0 201.0 \n", + "Roma 981.0 184.0 \n", + "Salernitana 1084.0 257.0 \n", + "Sampdoria 1017.0 300.0 \n", + "Sassuolo 986.0 216.0 \n", + "Spezia 1182.0 322.0 \n", + "Torino 998.0 323.0 \n", + "Udinese 950.0 189.0 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", "team_idx \n", - "Atalanta 211.0 46.2 \n", - "Bologna 123.0 57.3 \n", - "Cremonese 184.0 56.8 \n", - "Empoli 135.0 54.7 \n", - "Fiorentina 224.0 46.3 \n", - "Verona 229.0 51.2 \n", - "Inter 172.0 42.3 \n", - "Juventus 166.0 52.6 \n", - "Lazio 126.0 54.5 \n", - "Lecce 188.0 53.5 \n", - "Milan 205.0 45.0 \n", - "Monza 160.0 46.8 \n", - "Napoli 181.0 44.0 \n", - "Roma 159.0 47.2 \n", - "Salernitana 175.0 49.1 \n", - "Sampdoria 217.0 49.8 \n", - "Sassuolo 130.0 52.9 \n", - "Spezia 179.0 54.2 \n", - "Torino 215.0 51.5 \n", - "Udinese 187.0 43.2 \n", + "Atalanta 279.0 46.1 \n", + "Bologna 180.0 55.4 \n", + "Cremonese 267.0 56.6 \n", + "Empoli 189.0 53.4 \n", + "Fiorentina 289.0 46.3 \n", + "Verona 388.0 47.1 \n", + "Inter 232.0 46.0 \n", + "Juventus 230.0 50.0 \n", + "Lazio 181.0 50.3 \n", + "Lecce 294.0 55.9 \n", + "Milan 275.0 44.9 \n", + "Monza 215.0 47.8 \n", + "Napoli 231.0 46.5 \n", + "Roma 225.0 45.0 \n", + "Salernitana 240.0 51.7 \n", + "Sampdoria 318.0 48.5 \n", + "Sassuolo 173.0 55.5 \n", + "Spezia 281.0 53.4 \n", + "Torino 295.0 52.3 \n", + "Udinese 237.0 44.4 \n", "\n", "[20 rows x 313 columns]" ] }, - "execution_count": 13, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "b6a3db98", "metadata": {}, "outputs": [], @@ -818,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "id": "c34eb398", "metadata": {}, "outputs": [ @@ -899,21 +899,21 @@ " Napoli\n", " Meret\n", " NaN\n", - " 502\n", - " 25-234\n", + " 529\n", + " 25-312\n", " 1997\n", " 0\n", " ...\n", - " 19.6\n", - " 26.6\n", - " 142.0\n", + " 20.2\n", + " 27.1\n", + " 197.0\n", " 6.0\n", - " 4.2\n", - " 10.0\n", - " 0.71\n", - " 15.1\n", - " 6.214286\n", - " 0.557875\n", + " 3.0\n", + " 23.0\n", + " 1.21\n", + " 17.2\n", + " 6.263158\n", + " 0.496525\n", " \n", " \n", " Provedel\n", @@ -923,93 +923,93 @@ " Lazio\n", " Provedel\n", " NaN\n", - " 509\n", - " 28-239\n", + " 538\n", + " 28-317\n", " 1994\n", " 0\n", " ...\n", - " 37.2\n", - " 36.6\n", - " 201.0\n", - " 5.0\n", - " 2.5\n", - " 29.0\n", - " 2.08\n", - " 17.6\n", - " 6.321429\n", - " 0.305143\n", + " 34.8\n", + " 34.8\n", + " 256.0\n", + " 7.0\n", + " 2.7\n", + " 37.0\n", + " 1.96\n", + " 18.1\n", + " 6.236842\n", + " 0.339055\n", " \n", " \n", - " Maignan\n", + " Vicario\n", " 2\n", - " 4312\n", + " 4964\n", " P\n", - " Milan\n", - " Maignan\n", + " Empoli\n", + " Vicario\n", " NaN\n", - " 500\n", - " 27-131\n", + " 547\n", + " 26-113\n", + " 1996\n", + " 0\n", + " ...\n", + " 45.6\n", + " 41.8\n", + " 399.0\n", + " 25.0\n", + " 6.3\n", + " 8.0\n", + " 0.42\n", + " 10.5\n", + " 6.473684\n", + " 0.379532\n", + " \n", + " \n", + " Szczesny\n", + " 3\n", + " 453\n", + " P\n", + " Juventus\n", + " Szczesny\n", + " NaN\n", + " 544\n", + " 32-285\n", + " 1990\n", + " 0\n", + " ...\n", + " 41.0\n", + " 39.2\n", + " 155.0\n", + " 4.0\n", + " 2.6\n", + " 7.0\n", + " 0.56\n", + " 14.2\n", + " 6.038462\n", + " 0.364878\n", + " \n", + " \n", + " Falcone\n", + " 4\n", + " 2134\n", + " P\n", + " Lecce\n", + " Falcone\n", + " NaN\n", + " 524\n", + " 27-291\n", " 1995\n", " 0\n", " ...\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", - " 6.357143\n", - " 0.440315\n", - " \n", - " \n", - " Silvestri\n", - " 3\n", - " 2211\n", - " P\n", - " Udinese\n", - " Silvestri\n", - " NaN\n", - " 512\n", - " 31-254\n", - " 1991\n", - " 0\n", - " ...\n", - " 40.9\n", - " 38.2\n", - " 194.0\n", - " 4.0\n", - " 2.1\n", - " 5.0\n", - " 0.36\n", - " 11.3\n", - " 6.321429\n", - " 0.485767\n", - " \n", - " \n", - " Sepe\n", - " 4\n", - " 159\n", - " P\n", - " Salernitana\n", - " Sepe\n", - " NaN\n", - " 511\n", - " 31-187\n", - " 1991\n", - " 0\n", - " ...\n", - " 48.4\n", - " 41.7\n", - " 187.0\n", - " 14.0\n", - " 7.5\n", - " 9.0\n", - " 0.64\n", - " 13.7\n", - " 6.428571\n", - " 0.371154\n", + " 75.0\n", + " 51.4\n", + " 304.0\n", + " 13.0\n", + " 4.3\n", + " 20.0\n", + " 1.00\n", + " 12.5\n", + " 6.342105\n", + " 0.364642\n", " \n", " \n", " ...\n", @@ -1036,84 +1036,12 @@ " ...\n", " \n", " \n", - " De Luca\n", - " 549\n", - " 5512\n", - " A\n", - " Sampdoria\n", - " Luca\n", - " NaN\n", - " 122\n", - " 24-117\n", - " 1998\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 5.877688\n", - " 0.485814\n", - " \n", - " \n", - " Soule'\n", - " 550\n", - " 5734\n", + " Kaio Jorge\n", + " 542\n", + " 5505\n", " A\n", " Juventus\n", - " Soule\n", - " NaN\n", - " 422\n", - " 19-210\n", - " 2003\n", - " 5\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 6.067846\n", - " 0.581002\n", - " \n", - " \n", - " Lazetic\n", - " 551\n", - " 5785\n", - " A\n", - " Milan\n", - " Lazetic\n", - " NaN\n", - " 248\n", - " 18-293\n", - " 2004\n", - " 1\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.00\n", - " 0.0\n", - " 6.170097\n", - " 0.204814\n", - " \n", - " \n", - " Voelkerling Persson\n", - " 552\n", - " 5837\n", - " A\n", - " Lecce\n", - " Persson\n", + " Jorge\n", " NaN\n", " -1\n", " 0\n", @@ -1128,21 +1056,21 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 6.041983\n", - " 0.689071\n", + " 6.092280\n", + " 0.894197\n", " \n", " \n", - " Sanca\n", - " 553\n", - " 6063\n", + " De Luca\n", + " 543\n", + " 5512\n", " A\n", - " Spezia\n", - " Sanca\n", + " Sampdoria\n", + " Luca\n", " NaN\n", - " 395\n", - " 22-311\n", - " 2000\n", - " 3\n", + " 128\n", + " 24-195\n", + " 1998\n", + " 1\n", " ...\n", " 0.0\n", " 0.0\n", @@ -1152,117 +1080,175 @@ " 0.0\n", " 0.00\n", " 0.0\n", - " 5.991021\n", - " 0.262247\n", + " 6.284927\n", + " 0.532298\n", + " \n", + " \n", + " Lazetic\n", + " 544\n", + " 5785\n", + " A\n", + " Milan\n", + " Lazetic\n", + " NaN\n", + " 258\n", + " 19-006\n", + " 2004\n", + " 1\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 6.094004\n", + " 0.625570\n", + " \n", + " \n", + " Montevago\n", + " 545\n", + " 6113\n", + " A\n", + " Sampdoria\n", + " Montevago\n", + " NaN\n", + " 318\n", + " 19-316\n", + " 2003\n", + " 6\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 5.711934\n", + " 0.225193\n", + " \n", + " \n", + " Krollis\n", + " 546\n", + " 6143\n", + " A\n", + " Spezia\n", + " Krollis\n", + " NaN\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.00\n", + " 0.0\n", + " 5.748895\n", + " 0.595450\n", " \n", " \n", "\n", - "

554 rows × 165 columns

\n", + "

547 rows × 165 columns

\n", "" ], "text/plain": [ - " Unnamed: 0 id r team surname initial \\\n", - "name \n", - "Meret 0 572 P Napoli Meret NaN \n", - "Provedel 1 2814 P Lazio Provedel NaN \n", - "Maignan 2 4312 P Milan Maignan NaN \n", - "Silvestri 3 2211 P Udinese Silvestri NaN \n", - "Sepe 4 159 P Salernitana Sepe NaN \n", - "... ... ... .. ... ... ... \n", - "De Luca 549 5512 A Sampdoria Luca NaN \n", - "Soule' 550 5734 A Juventus Soule NaN \n", - "Lazetic 551 5785 A Milan Lazetic NaN \n", - "Voelkerling Persson 552 5837 A Lecce Persson NaN \n", - "Sanca 553 6063 A Spezia Sanca NaN \n", + " Unnamed: 0 id r team surname initial fb_ID age \\\n", + "name \n", + "Meret 0 572 P Napoli Meret NaN 529 25-312 \n", + "Provedel 1 2814 P Lazio Provedel NaN 538 28-317 \n", + "Vicario 2 4964 P Empoli Vicario NaN 547 26-113 \n", + "Szczesny 3 453 P Juventus Szczesny NaN 544 32-285 \n", + "Falcone 4 2134 P Lecce Falcone NaN 524 27-291 \n", + "... ... ... .. ... ... ... ... ... \n", + "Kaio Jorge 542 5505 A Juventus Jorge NaN -1 0 \n", + "De Luca 543 5512 A Sampdoria Luca NaN 128 24-195 \n", + "Lazetic 544 5785 A Milan Lazetic NaN 258 19-006 \n", + "Montevago 545 6113 A Sampdoria Montevago NaN 318 19-316 \n", + "Krollis 546 6143 A Spezia Krollis NaN -1 0 \n", "\n", - " fb_ID age birth_year games ... \\\n", - "name ... \n", - "Meret 502 25-234 1997 0 ... \n", - "Provedel 509 28-239 1994 0 ... \n", - "Maignan 500 27-131 1995 0 ... \n", - "Silvestri 512 31-254 1991 0 ... \n", - "Sepe 511 31-187 1991 0 ... \n", - "... ... ... ... ... ... \n", - "De Luca 122 24-117 1998 1 ... \n", - "Soule' 422 19-210 2003 5 ... \n", - "Lazetic 248 18-293 2004 1 ... \n", - "Voelkerling Persson -1 0 0 0 ... \n", - "Sanca 395 22-311 2000 3 ... \n", + " birth_year games ... gk_pct_goal_kicks_launched \\\n", + "name ... \n", + "Meret 1997 0 ... 20.2 \n", + "Provedel 1994 0 ... 34.8 \n", + "Vicario 1996 0 ... 45.6 \n", + "Szczesny 1990 0 ... 41.0 \n", + "Falcone 1995 0 ... 75.0 \n", + "... ... ... ... ... \n", + "Kaio Jorge 0 0 ... 0.0 \n", + "De Luca 1998 1 ... 0.0 \n", + "Lazetic 2004 1 ... 0.0 \n", + "Montevago 2003 6 ... 0.0 \n", + "Krollis 0 0 ... 0.0 \n", "\n", - " gk_pct_goal_kicks_launched gk_goal_kick_length_avg \\\n", - "name \n", - "Meret 19.6 26.6 \n", - "Provedel 37.2 36.6 \n", - "Maignan 38.9 40.4 \n", - "Silvestri 40.9 38.2 \n", - "Sepe 48.4 41.7 \n", - "... ... ... \n", - "De Luca 0.0 0.0 \n", - "Soule' 0.0 0.0 \n", - "Lazetic 0.0 0.0 \n", - "Voelkerling Persson 0.0 0.0 \n", - "Sanca 0.0 0.0 \n", - "\n", - " gk_crosses gk_crosses_stopped gk_crosses_stopped_pct \\\n", - "name \n", - "Meret 142.0 6.0 4.2 \n", - "Provedel 201.0 5.0 2.5 \n", - "Maignan 65.0 5.0 7.7 \n", - "Silvestri 194.0 4.0 2.1 \n", - "Sepe 187.0 14.0 7.5 \n", - "... ... ... ... \n", - "De Luca 0.0 0.0 0.0 \n", - "Soule' 0.0 0.0 0.0 \n", - "Lazetic 0.0 0.0 0.0 \n", - "Voelkerling Persson 0.0 0.0 0.0 \n", - "Sanca 0.0 0.0 0.0 \n", - "\n", - " gk_def_actions_outside_pen_area \\\n", - "name \n", - "Meret 10.0 \n", - "Provedel 29.0 \n", - "Maignan 6.0 \n", - "Silvestri 5.0 \n", - "Sepe 9.0 \n", - "... ... \n", - "De Luca 0.0 \n", - "Soule' 0.0 \n", - "Lazetic 0.0 \n", - "Voelkerling Persson 0.0 \n", - "Sanca 0.0 \n", - "\n", - " gk_def_actions_outside_pen_area_per90 \\\n", - "name \n", - "Meret 0.71 \n", - "Provedel 2.08 \n", - "Maignan 0.86 \n", - "Silvestri 0.36 \n", - "Sepe 0.64 \n", - "... ... \n", - "De Luca 0.00 \n", - "Soule' 0.00 \n", - "Lazetic 0.00 \n", - "Voelkerling Persson 0.00 \n", - "Sanca 0.00 \n", - "\n", - " gk_avg_distance_def_actions vote_avg vote_std \n", + " gk_goal_kick_length_avg gk_crosses gk_crosses_stopped \\\n", "name \n", - "Meret 15.1 6.214286 0.557875 \n", - "Provedel 17.6 6.321429 0.305143 \n", - "Maignan 12.9 6.357143 0.440315 \n", - "Silvestri 11.3 6.321429 0.485767 \n", - "Sepe 13.7 6.428571 0.371154 \n", - "... ... ... ... \n", - "De Luca 0.0 5.877688 0.485814 \n", - "Soule' 0.0 6.067846 0.581002 \n", - "Lazetic 0.0 6.170097 0.204814 \n", - "Voelkerling Persson 0.0 6.041983 0.689071 \n", - "Sanca 0.0 5.991021 0.262247 \n", + "Meret 27.1 197.0 6.0 \n", + "Provedel 34.8 256.0 7.0 \n", + "Vicario 41.8 399.0 25.0 \n", + "Szczesny 39.2 155.0 4.0 \n", + "Falcone 51.4 304.0 13.0 \n", + "... ... ... ... \n", + "Kaio Jorge 0.0 0.0 0.0 \n", + "De Luca 0.0 0.0 0.0 \n", + "Lazetic 0.0 0.0 0.0 \n", + "Montevago 0.0 0.0 0.0 \n", + "Krollis 0.0 0.0 0.0 \n", "\n", - "[554 rows x 165 columns]" + " gk_crosses_stopped_pct gk_def_actions_outside_pen_area \\\n", + "name \n", + "Meret 3.0 23.0 \n", + "Provedel 2.7 37.0 \n", + "Vicario 6.3 8.0 \n", + "Szczesny 2.6 7.0 \n", + "Falcone 4.3 20.0 \n", + "... ... ... \n", + "Kaio Jorge 0.0 0.0 \n", + "De Luca 0.0 0.0 \n", + "Lazetic 0.0 0.0 \n", + "Montevago 0.0 0.0 \n", + "Krollis 0.0 0.0 \n", + "\n", + " gk_def_actions_outside_pen_area_per90 \\\n", + "name \n", + "Meret 1.21 \n", + "Provedel 1.96 \n", + "Vicario 0.42 \n", + "Szczesny 0.56 \n", + "Falcone 1.00 \n", + "... ... \n", + "Kaio Jorge 0.00 \n", + "De Luca 0.00 \n", + "Lazetic 0.00 \n", + "Montevago 0.00 \n", + "Krollis 0.00 \n", + "\n", + " gk_avg_distance_def_actions vote_avg vote_std \n", + "name \n", + "Meret 17.2 6.263158 0.496525 \n", + "Provedel 18.1 6.236842 0.339055 \n", + "Vicario 10.5 6.473684 0.379532 \n", + "Szczesny 14.2 6.038462 0.364878 \n", + "Falcone 12.5 6.342105 0.364642 \n", + "... ... ... ... \n", + "Kaio Jorge 0.0 6.092280 0.894197 \n", + "De Luca 0.0 6.284927 0.532298 \n", + "Lazetic 0.0 6.094004 0.625570 \n", + "Montevago 0.0 5.711934 0.225193 \n", + "Krollis 0.0 5.748895 0.595450 \n", + "\n", + "[547 rows x 165 columns]" ] }, - "execution_count": 15, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1284,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "id": "71c8804e", "metadata": {}, "outputs": [], @@ -1319,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 34, "id": "7eb4e666", "metadata": {}, "outputs": [ @@ -1370,27 +1356,27 @@ " \n", " \n", " Frattesi\n", - " 270\n", + " 276\n", " 2848\n", " C\n", " Sassuolo\n", " Frattesi\n", " NaN\n", - " 175\n", - " 23-020\n", + " 184\n", + " 23-098\n", " 1999\n", - " 14\n", + " 19\n", " ...\n", - " 150.0\n", - " 168.0\n", - " 19.0\n", - " 0.0\n", - " 5.0\n", + " 209.0\n", + " 218.0\n", + " 25.0\n", " 1.0\n", - " 834.0\n", - " 181.0\n", - " 211.0\n", - " 46.2\n", + " 8.0\n", + " 1.0\n", + " 1165.0\n", + " 239.0\n", + " 279.0\n", + " 46.1\n", " \n", " \n", "\n", @@ -1399,27 +1385,27 @@ ], "text/plain": [ " Unnamed: 0 id r team surname initial fb_ID age \\\n", - "Frattesi 270 2848 C Sassuolo Frattesi NaN 175 23-020 \n", + "Frattesi 276 2848 C Sassuolo Frattesi NaN 184 23-098 \n", "\n", " birth_year games ... opp_vs_team_fouls opp_vs_team_fouled \\\n", - "Frattesi 1999 14 ... 150.0 168.0 \n", + "Frattesi 1999 19 ... 209.0 218.0 \n", "\n", " opp_vs_team_offsides opp_vs_team_pens_won \\\n", - "Frattesi 19.0 0.0 \n", + "Frattesi 25.0 1.0 \n", "\n", " opp_vs_team_pens_conceded opp_vs_team_own_goals \\\n", - "Frattesi 5.0 1.0 \n", + "Frattesi 8.0 1.0 \n", "\n", " opp_vs_team_ball_recoveries opp_vs_team_aerials_won \\\n", - "Frattesi 834.0 181.0 \n", + "Frattesi 1165.0 239.0 \n", "\n", " opp_vs_team_aerials_lost opp_vs_team_aerials_won_pct \n", - "Frattesi 211.0 46.2 \n", + "Frattesi 279.0 46.1 \n", "\n", "[1 rows x 791 columns]" ] }, - "execution_count": 17, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1433,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 35, "id": "6b4c1b00", "metadata": {}, "outputs": [ @@ -1443,7 +1429,7 @@ "'Sassuolo'" ] }, - "execution_count": 18, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1456,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "id": "6ea6df1f", "metadata": {}, "outputs": [ @@ -1552,7 +1538,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 19, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1584,7 +1570,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "id": "47bc651f", "metadata": {}, "outputs": [], @@ -1734,7 +1720,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 38, "id": "3f43d509", "metadata": {}, "outputs": [], @@ -1765,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 39, "id": "930d00c7", "metadata": {}, "outputs": [ @@ -1817,26 +1803,26 @@ " \n", " Frattesi\n", " C\n", - " 14\n", - " 14\n", - " 1129\n", - " 50.0\n", - " 0.13\n", - " 0.27\n", - " 75.6\n", - " 52.6\n", - " 57.6\n", + " 19\n", + " 19\n", + " 1560\n", + " 48.6\n", + " 0.11\n", + " 0.24\n", + " 77.9\n", + " 54.5\n", + " 54.5\n", " ...\n", - " 0.476528\n", - " 0.016829\n", - " 0.029229\n", - " 0.330381\n", - " 0.019486\n", - " 0.015943\n", - " 0.011515\n", - " 0.022143\n", - " 0.009743\n", - " 0.0062\n", + " 0.482051\n", + " 0.015385\n", + " 0.028205\n", + " 0.322436\n", + " 0.016667\n", + " 0.017308\n", + " 0.014744\n", + " 0.023077\n", + " 0.010256\n", + " 0.007051\n", " \n", " \n", "\n", @@ -1845,24 +1831,24 @@ ], "text/plain": [ " r games games_starts minutes shots_on_target_pct \\\n", - "Frattesi C 14 14 1129 50.0 \n", + "Frattesi C 19 19 1560 48.6 \n", "\n", " goals_per_shot goals_per_shot_on_target passes_pct \\\n", - "Frattesi 0.13 0.27 75.6 \n", + "Frattesi 0.11 0.24 77.9 \n", "\n", " dribble_tackles_pct dribbles_completed_pct ... touches_live_ball \\\n", - "Frattesi 52.6 57.6 ... 0.476528 \n", + "Frattesi 54.5 54.5 ... 0.482051 \n", "\n", " dribbles_completed dribbles passes_received miscontrols \\\n", - "Frattesi 0.016829 0.029229 0.330381 0.019486 \n", + "Frattesi 0.015385 0.028205 0.322436 0.016667 \n", "\n", " dispossessed fouls fouled aerials_won aerials_lost \n", - "Frattesi 0.015943 0.011515 0.022143 0.009743 0.0062 \n", + "Frattesi 0.017308 0.014744 0.023077 0.010256 0.007051 \n", "\n", "[1 rows x 121 columns]" ] }, - "execution_count": 22, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1885,7 +1871,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 40, "id": "3ae718d2", "metadata": {}, "outputs": [ @@ -2002,11 +1988,24 @@ " ...\n", " \n", " \n", - " 4010\n", - " 14\n", + " 5423\n", + " 19\n", + " Ilic\n", + " Verona\n", + " Lecce\n", + " 1\n", + " 7.0\n", + " 0\n", + " 1\n", + " 0.0\n", + " 8.0\n", + " \n", + " \n", + " 5424\n", + " 19\n", " Sulemana I.\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", @@ -2015,60 +2014,47 @@ " 6.0\n", " \n", " \n", - " 4011\n", - " 14\n", + " 5425\n", + " 19\n", " Lasagna\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " \n", " \n", - " 4012\n", - " 14\n", + " 5426\n", + " 19\n", " Kallon\n", " Verona\n", - " Juventus\n", - " 1\n", - " 5.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 5.5\n", - " \n", - " \n", - " 4013\n", - " 14\n", - " Djuric\n", - " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", " 0\n", - " 0.5\n", - " 5.5\n", + " 0.0\n", + " 6.0\n", " \n", " \n", - " 4014\n", - " 14\n", - " Henry\n", + " 5427\n", + " 19\n", + " Djuric\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " \n", " \n", "\n", - "

4015 rows × 10 columns

\n", + "

5428 rows × 10 columns

\n", "" ], "text/plain": [ @@ -2079,11 +2065,11 @@ "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", "... ... ... ... ... ... ... ... ... \n", - "4010 14 Sulemana I. Verona Juventus 1 6.0 0 0 \n", - "4011 14 Lasagna Verona Juventus 1 6.0 0 0 \n", - "4012 14 Kallon Verona Juventus 1 5.5 0 0 \n", - "4013 14 Djuric Verona Juventus 1 6.0 0 0 \n", - "4014 14 Henry Verona Juventus 1 6.5 0 0 \n", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", "\n", " cards_malus fantavote \n", "0 0.5 5.5 \n", @@ -2092,16 +2078,16 @@ "3 0.5 5.5 \n", "4 0.5 5.0 \n", "... ... ... \n", - "4010 0.0 6.0 \n", - "4011 0.0 6.0 \n", - "4012 0.0 5.5 \n", - "4013 0.5 5.5 \n", - "4014 0.0 6.5 \n", + "5423 0.0 8.0 \n", + "5424 0.0 6.0 \n", + "5425 0.0 5.5 \n", + "5426 0.0 6.0 \n", + "5427 0.0 6.0 \n", "\n", - "[4015 rows x 10 columns]" + "[5428 rows x 10 columns]" ] }, - "execution_count": 23, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2121,7 +2107,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 41, "id": "ec0bc56b", "metadata": {}, "outputs": [ @@ -2168,7 +2154,21 @@ "3700\n", "3800\n", "3900\n", - "4000\n" + "4000\n", + "4100\n", + "4200\n", + "4300\n", + "4400\n", + "4500\n", + "4600\n", + "4700\n", + "4800\n", + "4900\n", + "5000\n", + "5100\n", + "5200\n", + "5300\n", + "5400\n" ] } ], @@ -2196,7 +2196,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 42, "id": "b292f9f9", "metadata": {}, "outputs": [ @@ -2282,16 +2282,16 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.617096\n", - " 0.001171\n", - " 0.004684\n", - " 0.357143\n", - " 0.007026\n", - " 0.001171\n", - " 0.009368\n", - " 0.005855\n", - " 0.018735\n", - " 0.008197\n", + " 0.684211\n", + " 0.000849\n", + " 0.005093\n", + " 0.407470\n", + " 0.007640\n", + " 0.000849\n", + " 0.006791\n", + " 0.005093\n", + " 0.016129\n", + " 0.011885\n", " \n", " \n", " 2\n", @@ -2306,16 +2306,16 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.736842\n", + " 0.753541\n", " 0.000000\n", - " 0.002924\n", - " 0.511696\n", - " 0.002924\n", - " 0.008772\n", - " 0.002924\n", - " 0.002924\n", - " 0.005848\n", - " 0.008772\n", + " 0.002833\n", + " 0.521246\n", + " 0.002833\n", + " 0.008499\n", + " 0.002833\n", + " 0.002833\n", + " 0.005666\n", + " 0.008499\n", " \n", " \n", " 3\n", @@ -2330,16 +2330,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.574423\n", - " 0.002096\n", - " 0.005241\n", - " 0.339623\n", - " 0.006289\n", - " 0.005241\n", - " 0.013627\n", - " 0.002096\n", - " 0.015723\n", - " 0.012579\n", + " 0.586090\n", + " 0.003393\n", + " 0.006785\n", + " 0.357082\n", + " 0.006785\n", + " 0.004241\n", + " 0.012723\n", + " 0.001696\n", + " 0.015267\n", + " 0.011874\n", " \n", " \n", " 4\n", @@ -2354,16 +2354,16 @@ " 0.5\n", " 5.0\n", " ...\n", - " 0.598540\n", + " 0.607101\n", " 0.000000\n", - " 0.001217\n", - " 0.278589\n", - " 0.013382\n", - " 0.003650\n", - " 0.015815\n", - " 0.008516\n", - " 0.048662\n", - " 0.026764\n", + " 0.001183\n", + " 0.279290\n", + " 0.013018\n", + " 0.003550\n", + " 0.015385\n", + " 0.008284\n", + " 0.047337\n", + " 0.027219\n", " \n", " \n", " ...\n", @@ -2390,11 +2390,35 @@ " ...\n", " \n", " \n", - " 4010\n", - " 14\n", + " 5423\n", + " 19\n", + " Ilic\n", + " Verona\n", + " Lecce\n", + " 1\n", + " 7.0\n", + " 0\n", + " 1\n", + " 0.0\n", + " 8.0\n", + " ...\n", + " 0.572268\n", + " 0.007051\n", + " 0.018801\n", + " 0.277321\n", + " 0.019976\n", + " 0.005875\n", + " 0.010576\n", + " 0.010576\n", + " 0.011751\n", + " 0.015276\n", + " \n", + " \n", + " 5424\n", + " 19\n", " Sulemana I.\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", @@ -2402,116 +2426,92 @@ " 0.0\n", " 6.0\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.477966\n", + " 0.010169\n", + " 0.030508\n", + " 0.206780\n", + " 0.016949\n", + " 0.013559\n", + " 0.020339\n", + " 0.006780\n", + " 0.020339\n", + " 0.027119\n", " \n", " \n", - " 4011\n", - " 14\n", + " 5425\n", + " 19\n", " Lasagna\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " ...\n", - " 0.279255\n", - " 0.006649\n", - " 0.018617\n", - " 0.186170\n", - " 0.038564\n", - " 0.023936\n", - " 0.021277\n", - " 0.018617\n", - " 0.005319\n", - " 0.029255\n", + " 0.301282\n", + " 0.007479\n", + " 0.025641\n", + " 0.191239\n", + " 0.036325\n", + " 0.025641\n", + " 0.019231\n", + " 0.017094\n", + " 0.007479\n", + " 0.032051\n", " \n", " \n", - " 4012\n", - " 14\n", + " 5426\n", + " 19\n", " Kallon\n", " Verona\n", - " Juventus\n", - " 1\n", - " 5.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 5.5\n", - " ...\n", - " 0.417949\n", - " 0.007692\n", - " 0.038462\n", - " 0.289744\n", - " 0.038462\n", - " 0.025641\n", - " 0.017949\n", - " 0.023077\n", - " 0.010256\n", - " 0.028205\n", - " \n", - " \n", - " 4013\n", - " 14\n", - " Djuric\n", - " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", " 0\n", - " 0.5\n", - " 5.5\n", + " 0.0\n", + " 6.0\n", " ...\n", - " 0.473214\n", - " 0.000000\n", - " 0.000000\n", - " 0.401786\n", - " 0.022321\n", - " 0.000000\n", - " 0.022321\n", - " 0.022321\n", - " 0.178571\n", - " 0.066964\n", + " 0.409310\n", + " 0.008026\n", + " 0.033708\n", + " 0.293740\n", + " 0.033708\n", + " 0.024077\n", + " 0.017657\n", + " 0.027287\n", + " 0.011236\n", + " 0.025682\n", " \n", " \n", - " 4014\n", - " 14\n", - " Henry\n", + " 5427\n", + " 19\n", + " Djuric\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\n", - " 0.342424\n", - " 0.003030\n", - " 0.014141\n", - " 0.282828\n", - " 0.049495\n", - " 0.025253\n", - " 0.024242\n", - " 0.015152\n", - " 0.039394\n", - " 0.039394\n", + " 0.516181\n", + " 0.001618\n", + " 0.004854\n", + " 0.451456\n", + " 0.027508\n", + " 0.006472\n", + " 0.024272\n", + " 0.021036\n", + " 0.166667\n", + " 0.043689\n", " \n", " \n", "\n", - "

4015 rows × 131 columns

\n", + "

5428 rows × 131 columns

\n", "" ], "text/plain": [ @@ -2522,55 +2522,55 @@ "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", "4 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", "... ... ... ... ... ... ... ... ... \n", - "4010 14 Sulemana I. Verona Juventus 1 6.0 0 0 \n", - "4011 14 Lasagna Verona Juventus 1 6.0 0 0 \n", - "4012 14 Kallon Verona Juventus 1 5.5 0 0 \n", - "4013 14 Djuric Verona Juventus 1 6.0 0 0 \n", - "4014 14 Henry Verona Juventus 1 6.5 0 0 \n", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", "\n", " cards_malus fantavote ... touches_live_ball dribbles_completed \\\n", "0 0.5 5.5 ... 0.000000 0.000000 \n", - "1 0.0 10.0 ... 0.617096 0.001171 \n", - "2 0.0 6.0 ... 0.736842 0.000000 \n", - "3 0.5 5.5 ... 0.574423 0.002096 \n", - "4 0.5 5.0 ... 0.598540 0.000000 \n", + "1 0.0 10.0 ... 0.684211 0.000849 \n", + "2 0.0 6.0 ... 0.753541 0.000000 \n", + "3 0.5 5.5 ... 0.586090 0.003393 \n", + "4 0.5 5.0 ... 0.607101 0.000000 \n", "... ... ... ... ... ... \n", - "4010 0.0 6.0 ... NaN NaN \n", - "4011 0.0 6.0 ... 0.279255 0.006649 \n", - "4012 0.0 5.5 ... 0.417949 0.007692 \n", - "4013 0.5 5.5 ... 0.473214 0.000000 \n", - "4014 0.0 6.5 ... 0.342424 0.003030 \n", + "5423 0.0 8.0 ... 0.572268 0.007051 \n", + "5424 0.0 6.0 ... 0.477966 0.010169 \n", + "5425 0.0 5.5 ... 0.301282 0.007479 \n", + "5426 0.0 6.0 ... 0.409310 0.008026 \n", + "5427 0.0 6.0 ... 0.516181 0.001618 \n", "\n", " dribbles passes_received miscontrols dispossessed fouls fouled \\\n", "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.004684 0.357143 0.007026 0.001171 0.009368 0.005855 \n", - "2 0.002924 0.511696 0.002924 0.008772 0.002924 0.002924 \n", - "3 0.005241 0.339623 0.006289 0.005241 0.013627 0.002096 \n", - "4 0.001217 0.278589 0.013382 0.003650 0.015815 0.008516 \n", + "1 0.005093 0.407470 0.007640 0.000849 0.006791 0.005093 \n", + "2 0.002833 0.521246 0.002833 0.008499 0.002833 0.002833 \n", + "3 0.006785 0.357082 0.006785 0.004241 0.012723 0.001696 \n", + "4 0.001183 0.279290 0.013018 0.003550 0.015385 0.008284 \n", "... ... ... ... ... ... ... \n", - "4010 NaN NaN NaN NaN NaN NaN \n", - "4011 0.018617 0.186170 0.038564 0.023936 0.021277 0.018617 \n", - "4012 0.038462 0.289744 0.038462 0.025641 0.017949 0.023077 \n", - "4013 0.000000 0.401786 0.022321 0.000000 0.022321 0.022321 \n", - "4014 0.014141 0.282828 0.049495 0.025253 0.024242 0.015152 \n", + "5423 0.018801 0.277321 0.019976 0.005875 0.010576 0.010576 \n", + "5424 0.030508 0.206780 0.016949 0.013559 0.020339 0.006780 \n", + "5425 0.025641 0.191239 0.036325 0.025641 0.019231 0.017094 \n", + "5426 0.033708 0.293740 0.033708 0.024077 0.017657 0.027287 \n", + "5427 0.004854 0.451456 0.027508 0.006472 0.024272 0.021036 \n", "\n", " aerials_won aerials_lost \n", "0 0.000000 0.000000 \n", - "1 0.018735 0.008197 \n", - "2 0.005848 0.008772 \n", - "3 0.015723 0.012579 \n", - "4 0.048662 0.026764 \n", + "1 0.016129 0.011885 \n", + "2 0.005666 0.008499 \n", + "3 0.015267 0.011874 \n", + "4 0.047337 0.027219 \n", "... ... ... \n", - "4010 NaN NaN \n", - "4011 0.005319 0.029255 \n", - "4012 0.010256 0.028205 \n", - "4013 0.178571 0.066964 \n", - "4014 0.039394 0.039394 \n", + "5423 0.011751 0.015276 \n", + "5424 0.020339 0.027119 \n", + "5425 0.007479 0.032051 \n", + "5426 0.011236 0.025682 \n", + "5427 0.166667 0.043689 \n", "\n", - "[4015 rows x 131 columns]" + "[5428 rows x 131 columns]" ] }, - "execution_count": 25, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2589,7 +2589,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 43, "id": "1db9f77f", "metadata": {}, "outputs": [], @@ -2601,7 +2601,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "id": "f56df3ca", "metadata": {}, "outputs": [ @@ -2663,16 +2663,16 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.617096\n", - " 0.001171\n", - " 0.004684\n", - " 0.357143\n", - " 0.007026\n", - " 0.001171\n", - " 0.009368\n", - " 0.005855\n", - " 0.018735\n", - " 0.008197\n", + " 0.684211\n", + " 0.000849\n", + " 0.005093\n", + " 0.407470\n", + " 0.007640\n", + " 0.000849\n", + " 0.006791\n", + " 0.005093\n", + " 0.016129\n", + " 0.011885\n", " \n", " \n", " 2\n", @@ -2687,16 +2687,16 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.736842\n", + " 0.753541\n", " 0.000000\n", - " 0.002924\n", - " 0.511696\n", - " 0.002924\n", - " 0.008772\n", - " 0.002924\n", - " 0.002924\n", - " 0.005848\n", - " 0.008772\n", + " 0.002833\n", + " 0.521246\n", + " 0.002833\n", + " 0.008499\n", + " 0.002833\n", + " 0.002833\n", + " 0.005666\n", + " 0.008499\n", " \n", " \n", " 3\n", @@ -2711,16 +2711,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.574423\n", - " 0.002096\n", - " 0.005241\n", - " 0.339623\n", - " 0.006289\n", - " 0.005241\n", - " 0.013627\n", - " 0.002096\n", - " 0.015723\n", - " 0.012579\n", + " 0.586090\n", + " 0.003393\n", + " 0.006785\n", + " 0.357082\n", + " 0.006785\n", + " 0.004241\n", + " 0.012723\n", + " 0.001696\n", + " 0.015267\n", + " 0.011874\n", " \n", " \n", " 4\n", @@ -2735,16 +2735,16 @@ " 0.5\n", " 5.0\n", " ...\n", - " 0.598540\n", + " 0.607101\n", " 0.000000\n", - " 0.001217\n", - " 0.278589\n", - " 0.013382\n", - " 0.003650\n", - " 0.015815\n", - " 0.008516\n", - " 0.048662\n", - " 0.026764\n", + " 0.001183\n", + " 0.279290\n", + " 0.013018\n", + " 0.003550\n", + " 0.015385\n", + " 0.008284\n", + " 0.047337\n", + " 0.027219\n", " \n", " \n", " 5\n", @@ -2759,16 +2759,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.634921\n", - " 0.052910\n", - " 0.111111\n", - " 0.328042\n", - " 0.026455\n", - " 0.005291\n", - " 0.021164\n", + " 0.648649\n", + " 0.045045\n", + " 0.094595\n", + " 0.364865\n", + " 0.027027\n", + " 0.009009\n", + " 0.018018\n", " 0.000000\n", - " 0.026455\n", - " 0.005291\n", + " 0.022523\n", + " 0.004505\n", " \n", " \n", " ...\n", @@ -2795,11 +2795,35 @@ " ...\n", " \n", " \n", - " 4009\n", - " 14\n", - " Terracciano F.\n", + " 5423\n", + " 19\n", + " Ilic\n", " Verona\n", - " Juventus\n", + " Lecce\n", + " 1\n", + " 7.0\n", + " 0\n", + " 1\n", + " 0.0\n", + " 8.0\n", + " ...\n", + " 0.572268\n", + " 0.007051\n", + " 0.018801\n", + " 0.277321\n", + " 0.019976\n", + " 0.005875\n", + " 0.010576\n", + " 0.010576\n", + " 0.011751\n", + " 0.015276\n", + " \n", + " \n", + " 5424\n", + " 19\n", + " Sulemana I.\n", + " Verona\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", @@ -2807,175 +2831,151 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.477778\n", - " 0.000000\n", - " 0.008889\n", - " 0.271111\n", - " 0.015556\n", - " 0.006667\n", - " 0.008889\n", - " 0.022222\n", - " 0.020000\n", - " 0.024444\n", + " 0.477966\n", + " 0.010169\n", + " 0.030508\n", + " 0.206780\n", + " 0.016949\n", + " 0.013559\n", + " 0.020339\n", + " 0.006780\n", + " 0.020339\n", + " 0.027119\n", " \n", " \n", - " 4011\n", - " 14\n", + " 5425\n", + " 19\n", " Lasagna\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.0\n", + " 5.5\n", " 0\n", " 0\n", " 0.0\n", - " 6.0\n", + " 5.5\n", " ...\n", - " 0.279255\n", - " 0.006649\n", - " 0.018617\n", - " 0.186170\n", - " 0.038564\n", - " 0.023936\n", - " 0.021277\n", - " 0.018617\n", - " 0.005319\n", - " 0.029255\n", + " 0.301282\n", + " 0.007479\n", + " 0.025641\n", + " 0.191239\n", + " 0.036325\n", + " 0.025641\n", + " 0.019231\n", + " 0.017094\n", + " 0.007479\n", + " 0.032051\n", " \n", " \n", - " 4012\n", - " 14\n", + " 5426\n", + " 19\n", " Kallon\n", " Verona\n", - " Juventus\n", - " 1\n", - " 5.5\n", - " 0\n", - " 0\n", - " 0.0\n", - " 5.5\n", - " ...\n", - " 0.417949\n", - " 0.007692\n", - " 0.038462\n", - " 0.289744\n", - " 0.038462\n", - " 0.025641\n", - " 0.017949\n", - " 0.023077\n", - " 0.010256\n", - " 0.028205\n", - " \n", - " \n", - " 4013\n", - " 14\n", - " Djuric\n", - " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", " 6.0\n", " 0\n", " 0\n", - " 0.5\n", - " 5.5\n", + " 0.0\n", + " 6.0\n", " ...\n", - " 0.473214\n", - " 0.000000\n", - " 0.000000\n", - " 0.401786\n", - " 0.022321\n", - " 0.000000\n", - " 0.022321\n", - " 0.022321\n", - " 0.178571\n", - " 0.066964\n", + " 0.409310\n", + " 0.008026\n", + " 0.033708\n", + " 0.293740\n", + " 0.033708\n", + " 0.024077\n", + " 0.017657\n", + " 0.027287\n", + " 0.011236\n", + " 0.025682\n", " \n", " \n", - " 4014\n", - " 14\n", - " Henry\n", + " 5427\n", + " 19\n", + " Djuric\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.5\n", + " 6.0\n", " 0\n", " 0\n", " 0.0\n", - " 6.5\n", + " 6.0\n", " ...\n", - " 0.342424\n", - " 0.003030\n", - " 0.014141\n", - " 0.282828\n", - " 0.049495\n", - " 0.025253\n", - " 0.024242\n", - " 0.015152\n", - " 0.039394\n", - " 0.039394\n", + " 0.516181\n", + " 0.001618\n", + " 0.004854\n", + " 0.451456\n", + " 0.027508\n", + " 0.006472\n", + " 0.024272\n", + " 0.021036\n", + " 0.166667\n", + " 0.043689\n", " \n", " \n", "\n", - "

3717 rows × 131 columns

\n", + "

4906 rows × 131 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "1 1 Toloi Atalanta Sampdoria 0 7.0 1 \n", - "2 1 Djimsiti Atalanta Sampdoria 0 6.0 0 \n", - "3 1 Hateboer Atalanta Sampdoria 0 6.0 0 \n", - "4 1 Okoli Atalanta Sampdoria 0 5.5 0 \n", - "5 1 Zortea Atalanta Sampdoria 0 6.0 0 \n", - "... ... ... ... ... ... ... ... \n", - "4009 14 Terracciano F. Verona Juventus 1 6.0 0 \n", - "4011 14 Lasagna Verona Juventus 1 6.0 0 \n", - "4012 14 Kallon Verona Juventus 1 5.5 0 \n", - "4013 14 Djuric Verona Juventus 1 6.0 0 \n", - "4014 14 Henry Verona Juventus 1 6.5 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", + "5423 19 Ilic Verona Lecce 1 7.0 0 1 \n", + "5424 19 Sulemana I. Verona Lecce 1 6.0 0 0 \n", + "5425 19 Lasagna Verona Lecce 1 5.5 0 0 \n", + "5426 19 Kallon Verona Lecce 1 6.0 0 0 \n", + "5427 19 Djuric Verona Lecce 1 6.0 0 0 \n", "\n", - " assists cards_malus fantavote ... touches_live_ball \\\n", - "1 0 0.0 10.0 ... 0.617096 \n", - "2 0 0.0 6.0 ... 0.736842 \n", - "3 0 0.5 5.5 ... 0.574423 \n", - "4 0 0.5 5.0 ... 0.598540 \n", - "5 0 0.5 5.5 ... 0.634921 \n", - "... ... ... ... ... ... \n", - "4009 0 0.0 6.0 ... 0.477778 \n", - "4011 0 0.0 6.0 ... 0.279255 \n", - "4012 0 0.0 5.5 ... 0.417949 \n", - "4013 0 0.5 5.5 ... 0.473214 \n", - "4014 0 0.0 6.5 ... 0.342424 \n", + " cards_malus fantavote ... touches_live_ball dribbles_completed \\\n", + "1 0.0 10.0 ... 0.684211 0.000849 \n", + "2 0.0 6.0 ... 0.753541 0.000000 \n", + "3 0.5 5.5 ... 0.586090 0.003393 \n", + "4 0.5 5.0 ... 0.607101 0.000000 \n", + "5 0.5 5.5 ... 0.648649 0.045045 \n", + "... ... ... ... ... ... \n", + "5423 0.0 8.0 ... 0.572268 0.007051 \n", + "5424 0.0 6.0 ... 0.477966 0.010169 \n", + "5425 0.0 5.5 ... 0.301282 0.007479 \n", + "5426 0.0 6.0 ... 0.409310 0.008026 \n", + "5427 0.0 6.0 ... 0.516181 0.001618 \n", "\n", - " dribbles_completed dribbles passes_received miscontrols dispossessed \\\n", - "1 0.001171 0.004684 0.357143 0.007026 0.001171 \n", - "2 0.000000 0.002924 0.511696 0.002924 0.008772 \n", - "3 0.002096 0.005241 0.339623 0.006289 0.005241 \n", - "4 0.000000 0.001217 0.278589 0.013382 0.003650 \n", - "5 0.052910 0.111111 0.328042 0.026455 0.005291 \n", - "... ... ... ... ... ... \n", - "4009 0.000000 0.008889 0.271111 0.015556 0.006667 \n", - "4011 0.006649 0.018617 0.186170 0.038564 0.023936 \n", - "4012 0.007692 0.038462 0.289744 0.038462 0.025641 \n", - "4013 0.000000 0.000000 0.401786 0.022321 0.000000 \n", - "4014 0.003030 0.014141 0.282828 0.049495 0.025253 \n", + " dribbles passes_received miscontrols dispossessed fouls fouled \\\n", + "1 0.005093 0.407470 0.007640 0.000849 0.006791 0.005093 \n", + "2 0.002833 0.521246 0.002833 0.008499 0.002833 0.002833 \n", + "3 0.006785 0.357082 0.006785 0.004241 0.012723 0.001696 \n", + "4 0.001183 0.279290 0.013018 0.003550 0.015385 0.008284 \n", + "5 0.094595 0.364865 0.027027 0.009009 0.018018 0.000000 \n", + "... ... ... ... ... ... ... \n", + "5423 0.018801 0.277321 0.019976 0.005875 0.010576 0.010576 \n", + "5424 0.030508 0.206780 0.016949 0.013559 0.020339 0.006780 \n", + "5425 0.025641 0.191239 0.036325 0.025641 0.019231 0.017094 \n", + "5426 0.033708 0.293740 0.033708 0.024077 0.017657 0.027287 \n", + "5427 0.004854 0.451456 0.027508 0.006472 0.024272 0.021036 \n", "\n", - " fouls fouled aerials_won aerials_lost \n", - "1 0.009368 0.005855 0.018735 0.008197 \n", - "2 0.002924 0.002924 0.005848 0.008772 \n", - "3 0.013627 0.002096 0.015723 0.012579 \n", - "4 0.015815 0.008516 0.048662 0.026764 \n", - "5 0.021164 0.000000 0.026455 0.005291 \n", - "... ... ... ... ... \n", - "4009 0.008889 0.022222 0.020000 0.024444 \n", - "4011 0.021277 0.018617 0.005319 0.029255 \n", - "4012 0.017949 0.023077 0.010256 0.028205 \n", - "4013 0.022321 0.022321 0.178571 0.066964 \n", - "4014 0.024242 0.015152 0.039394 0.039394 \n", + " aerials_won aerials_lost \n", + "1 0.016129 0.011885 \n", + "2 0.005666 0.008499 \n", + "3 0.015267 0.011874 \n", + "4 0.047337 0.027219 \n", + "5 0.022523 0.004505 \n", + "... ... ... \n", + "5423 0.011751 0.015276 \n", + "5424 0.020339 0.027119 \n", + "5425 0.007479 0.032051 \n", + "5426 0.011236 0.025682 \n", + "5427 0.166667 0.043689 \n", "\n", - "[3717 rows x 131 columns]" + "[4906 rows x 131 columns]" ] }, - "execution_count": 27, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2994,7 +2994,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "id": "df26c8e0", "metadata": {}, "outputs": [], @@ -3012,7 +3012,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 46, "id": "4d06576a", "metadata": {}, "outputs": [ @@ -3161,7 +3161,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 47, "id": "01bb7413", "metadata": {}, "outputs": [ @@ -3236,7 +3236,7 @@ " 'vs_team_aerials_won_pct']" ] }, - "execution_count": 30, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3255,7 +3255,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "id": "02f961d8", "metadata": {}, "outputs": [], @@ -3368,7 +3368,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 49, "id": "5e3694a0", "metadata": {}, "outputs": [], @@ -3392,7 +3392,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 50, "id": "9b671646", "metadata": {}, "outputs": [ @@ -3443,26 +3443,26 @@ " \n", " \n", " Consigli\n", - " 14.0\n", - " 14.0\n", - " 1260\n", - " 1.36\n", - " 58.5\n", - " 35.7\n", - " -0.17\n", - " 41.0\n", - " 31.0\n", - " 34.7\n", + " 17.0\n", + " 17.0\n", + " 1530.0\n", + " 1.53\n", + " 56.6\n", + " 29.4\n", + " -0.26\n", + " 41.3\n", + " 30.9\n", + " 34.4\n", " ...\n", - " 16.6\n", + " 21.6\n", " 0.37\n", - " -2.4\n", - " 84.0\n", - " 205.0\n", - " 523.0\n", - " 77.0\n", - " 134.0\n", - " 189.0\n", + " -4.4\n", + " 100.0\n", + " 242.0\n", + " 621.0\n", + " 87.0\n", + " 157.0\n", + " 220.0\n", " 13.0\n", " \n", " \n", @@ -3472,30 +3472,30 @@ ], "text/plain": [ " gk_games gk_games_starts gk_minutes gk_goals_against_per90 \\\n", - "Consigli 14.0 14.0 1260 1.36 \n", + "Consigli 17.0 17.0 1530.0 1.53 \n", "\n", " gk_save_pct gk_clean_sheets_pct gk_psxg_net_per90 \\\n", - "Consigli 58.5 35.7 -0.17 \n", + "Consigli 56.6 29.4 -0.26 \n", "\n", " gk_passes_pct_launched gk_pct_passes_launched \\\n", - "Consigli 41.0 31.0 \n", + "Consigli 41.3 30.9 \n", "\n", " gk_passes_length_avg ... gk_psxg \\\n", - "Consigli 34.7 ... 16.6 \n", + "Consigli 34.4 ... 21.6 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "Consigli 0.37 -2.4 \n", + "Consigli 0.37 -4.4 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "Consigli 84.0 205.0 523.0 \n", + "Consigli 100.0 242.0 621.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "Consigli 77.0 134.0 189.0 13.0 \n", + "Consigli 87.0 157.0 220.0 13.0 \n", "\n", "[1 rows x 98 columns]" ] }, - "execution_count": 33, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -3510,7 +3510,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "33f805a9", "metadata": {}, "outputs": [ @@ -3529,7 +3529,50 @@ "800\n", "900\n", "1000\n", - "1100\n" + "1100\n", + "1200\n", + "1300\n", + "1400\n", + "1500\n", + "1600\n", + "1700\n", + "1800\n", + "1900\n", + "2000\n", + "2100\n", + "2200\n", + "2300\n", + "2400\n", + "2500\n", + "2600\n", + "2700\n", + "2800\n", + "2900\n", + "3000\n", + "3100\n", + "3200\n", + "3300\n", + "3400\n", + "3500\n", + "3600\n", + "3700\n", + "3800\n", + "3900\n", + "4000\n", + "4100\n", + "4200\n", + "4300\n", + "4400\n", + "4500\n", + "4600\n", + "4700\n", + "4800\n", + "4900\n", + "5000\n", + "5100\n", + "5200\n", + "5300\n", + "5400\n" ] } ], @@ -3559,17 +3602,405 @@ }, { "cell_type": "code", - 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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...10.40.22-2.678.0167.0321.093.086.0147.08.0
11ToloiAtalantaSampdoria07.0100.010.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21DjimsitiAtalantaSampdoria06.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31HateboerAtalantaSampdoria06.0000.55.5...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41OkoliAtalantaSampdoria05.5000.55.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
542319IlicVeronaLecce17.0010.08.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542419Sulemana I.VeronaLecce16.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542519LasagnaVeronaLecce15.5000.05.5...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542619KallonVeronaLecce16.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
542719DjuricVeronaLecce16.0000.06.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5428 rows × 108 columns

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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...10.4000000.220000-2.60000078.000000167.000000321.00000093.00000086.0147.08.000000
151SkorupskiBolognaLazio06.5-200.04.5...28.7000000.2800000.70000087.000000219.000000545.000000116.000000141.0280.018.000000
441VicarioEmpoliSpezia05.5-100.04.5...24.0000000.2700002.00000088.000000277.000000690.000000108.000000103.0399.025.000000
571GolliniFiorentinaCremonese15.0-200.03.0...15.6000000.2700001.60000034.00000092.000000425.00000069.000000114.0197.06.000000
711HandanovicInterLecce06.5-100.05.5...10.6000000.300000-1.40000027.00000052.000000266.00000046.00000045.079.02.000000
..................................................................
536219PegoloSassuoloMonza06.5-100.05.5...15.4666670.373333-2.86666769.666667167.333333434.33333361.333333110.0156.09.333333
537419DragowskiSpeziaRoma16.0-200.04.0...26.0000000.280000-3.00000085.000000255.000000479.00000087.000000106.0235.09.000000
538819Milinkovic-Savic V.TorinoFiorentina07.0000.07.0...20.3000000.2400000.300000138.000000493.000000746.00000085.000000142.0232.018.000000
540119SilvestriUdineseSampdoria06.0000.06.0...22.1000000.3200001.10000076.000000200.000000406.00000072.000000153.0253.04.000000
541419Montipo'VeronaLecce16.5000.06.5...25.2000000.250000-4.800000176.000000358.000000447.00000062.000000153.0261.010.000000
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375 rows × 108 columns

\n", + "
" + ], + "text/plain": [ + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "15 1 Skorupski Bologna Lazio 0 6.5 \n", + "44 1 Vicario Empoli Spezia 0 5.5 \n", + "57 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "71 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "5362 19 Pegolo Sassuolo Monza 0 6.5 \n", + "5374 19 Dragowski Spezia Roma 1 6.0 \n", + "5388 19 Milinkovic-Savic V. Torino Fiorentina 0 7.0 \n", + "5401 19 Silvestri Udinese Sampdoria 0 6.0 \n", + "5414 19 Montipo' Verona Lecce 1 6.5 \n", + "\n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 10.400000 \n", + "15 -2 0 0.0 4.5 ... 28.700000 \n", + "44 -1 0 0.0 4.5 ... 24.000000 \n", + "57 -2 0 0.0 3.0 ... 15.600000 \n", + "71 -1 0 0.0 5.5 ... 10.600000 \n", + "... ... ... ... ... ... ... \n", + "5362 -1 0 0.0 5.5 ... 15.466667 \n", + "5374 -2 0 0.0 4.0 ... 26.000000 \n", + "5388 0 0 0.0 7.0 ... 20.300000 \n", + "5401 0 0 0.0 6.0 ... 22.100000 \n", + "5414 0 0 0.0 6.5 ... 25.200000 \n", + "\n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.220000 -2.600000 \n", + "15 0.280000 0.700000 \n", + "44 0.270000 2.000000 \n", + "57 0.270000 1.600000 \n", + "71 0.300000 -1.400000 \n", + "... ... ... \n", + "5362 0.373333 -2.866667 \n", + "5374 0.280000 -3.000000 \n", + "5388 0.240000 0.300000 \n", + "5401 0.320000 1.100000 \n", + "5414 0.250000 -4.800000 \n", + "\n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 78.000000 167.000000 321.000000 \n", + "15 87.000000 219.000000 545.000000 \n", + "44 88.000000 277.000000 690.000000 \n", + "57 34.000000 92.000000 425.000000 \n", + "71 27.000000 52.000000 266.000000 \n", + "... ... ... ... \n", + "5362 69.666667 167.333333 434.333333 \n", + "5374 85.000000 255.000000 479.000000 \n", + "5388 138.000000 493.000000 746.000000 \n", + "5401 76.000000 200.000000 406.000000 \n", + "5414 176.000000 358.000000 447.000000 \n", + "\n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 93.000000 86.0 147.0 8.000000 \n", + "15 116.000000 141.0 280.0 18.000000 \n", + "44 108.000000 103.0 399.0 25.000000 \n", + "57 69.000000 114.0 197.0 6.000000 \n", + "71 46.000000 45.0 79.0 2.000000 \n", + "... ... ... ... ... \n", + "5362 61.333333 110.0 156.0 9.333333 \n", + "5374 87.000000 106.0 235.0 9.000000 \n", + "5388 85.000000 142.0 232.0 18.000000 \n", + "5401 72.000000 153.0 253.0 4.000000 \n", + "5414 62.000000 153.0 261.0 10.000000 \n", + "\n", + "[375 rows x 108 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "db_gk" ] @@ -3597,7 +4416,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "d8b8b6da", "metadata": {}, "outputs": [], @@ -3607,7 +4426,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 56, "id": "54519b24", "metadata": {}, "outputs": [ diff --git a/5_scraping_match_probable_players.ipynb b/5_scraping_match_probable_players.ipynb index 729b54d..d35380d 100644 --- a/5_scraping_match_probable_players.ipynb +++ b/5_scraping_match_probable_players.ipynb @@ -71,31 +71,31 @@ " \n", " \n", " 0\n", - " Vicario\n", + " Skorupski\n", " 1\n", " 100.0\n", " \n", " \n", " 1\n", - " Ebuehi\n", + " Posch\n", " 1\n", " 100.0\n", " \n", " \n", " 2\n", - " Ismajli\n", + " Soumaoro\n", " 1\n", " 100.0\n", " \n", " \n", " 3\n", - " Walukiewicz\n", + " Lucumi'\n", " 1\n", " 100.0\n", " \n", " \n", " 4\n", - " Parisi\n", + " Cambiaso\n", " 1\n", " 100.0\n", " \n", @@ -106,55 +106,55 @@ " ...\n", " \n", " \n", - " 444\n", - " Basic\n", + " 435\n", + " Verdi\n", " 0\n", " 55.0\n", " \n", " \n", - " 445\n", - " Bertini\n", - " 0\n", - " 10.0\n", - " \n", - " \n", - " 446\n", - " Marcos Antonio\n", - " 0\n", - " 50.0\n", - " \n", - " \n", - " 447\n", - " Romero L.\n", - " 0\n", - " 50.0\n", - " \n", - " \n", - " 448\n", - " Cancellieri\n", + " 436\n", + " Kallon\n", " 0\n", " 55.0\n", " \n", + " \n", + " 437\n", + " Piccoli\n", + " 0\n", + " 55.0\n", + " \n", + " \n", + " 438\n", + " Ngonge\n", + " 0\n", + " 25.0\n", + " \n", + " \n", + " 439\n", + " Braaf\n", + " 0\n", + " 40.0\n", + " \n", " \n", "\n", - "

449 rows × 3 columns

\n", + "

440 rows × 3 columns

\n", "" ], "text/plain": [ - " player starter percentage\n", - "0 Vicario 1 100.0\n", - "1 Ebuehi 1 100.0\n", - "2 Ismajli 1 100.0\n", - "3 Walukiewicz 1 100.0\n", - "4 Parisi 1 100.0\n", - ".. ... ... ...\n", - "444 Basic 0 55.0\n", - "445 Bertini 0 10.0\n", - "446 Marcos Antonio 0 50.0\n", - "447 Romero L. 0 50.0\n", - "448 Cancellieri 0 55.0\n", + " player starter percentage\n", + "0 Skorupski 1 100.0\n", + "1 Posch 1 100.0\n", + "2 Soumaoro 1 100.0\n", + "3 Lucumi' 1 100.0\n", + "4 Cambiaso 1 100.0\n", + ".. ... ... ...\n", + "435 Verdi 0 55.0\n", + "436 Kallon 0 55.0\n", + "437 Piccoli 0 55.0\n", + "438 Ngonge 0 25.0\n", + "439 Braaf 0 40.0\n", "\n", - "[449 rows x 3 columns]" + "[440 rows x 3 columns]" ] }, "execution_count": 3, @@ -235,395 +235,339 @@ " \n", " \n", " 0\n", - " Lozano\n", - " Politano\n", - " 60.0\n", - " \n", - " \n", - " 1\n", - " Zambo Anguissa\n", - " Ndombele'\n", - " 60.0\n", - " \n", - " \n", - " 2\n", - " Mario Rui\n", - " Olivera\n", - " 55.0\n", - " \n", - " \n", - " 3\n", - " Elmas\n", - " Raspadori\n", - " 55.0\n", - " \n", - " \n", - " 4\n", - " Beto\n", - " Success\n", - " 60.0\n", - " \n", - " \n", - " 5\n", - " Lovric\n", - " Samardzic\n", - " 55.0\n", - " \n", - " \n", - " 6\n", - " Ehizibue\n", - " Nuytinck\n", - " 60.0\n", - " \n", - " \n", - " 7\n", - " Rincon\n", - " Vieira\n", - " 60.0\n", - " \n", - " \n", - " 8\n", - " Augello\n", - " Murru\n", - " 55.0\n", - " \n", - " \n", - " 9\n", - " Djuricic\n", - " Leris\n", - " 65.0\n", - " \n", - " \n", - " 10\n", - " Caputo\n", - " Montevago\n", - " 60.0\n", - " \n", - " \n", - " 11\n", - " Di Francesco F.\n", - " Banda\n", - " 55.0\n", - " \n", - " \n", - " 12\n", " Colombo\n", " Ceesay\n", " 55.0\n", " \n", " \n", - " 13\n", - " Gonzalez J.\n", - " Bistrovic\n", - " 60.0\n", - " \n", - " \n", - " 14\n", - " Lykogiannis\n", - " Cambiaso\n", - " 55.0\n", - " \n", - " \n", - " 15\n", - " Barrow\n", - " Orsolini\n", - " 55.0\n", - " \n", - " \n", - " 16\n", - " Ferguson\n", - " Schouten\n", - " 55.0\n", - " \n", - " \n", - " 17\n", - " Traore' Hj.\n", - " Berardi\n", - " 55.0\n", - " \n", - " \n", - " 18\n", - " Rogerio\n", - " Kyriakopoulos\n", - " 60.0\n", - " \n", - " \n", - " 19\n", - " Djimsiti\n", - " Scalvini\n", - " 60.0\n", - " \n", - " \n", - " 20\n", - " Zapata D.\n", - " Hojlund\n", - " 60.0\n", - " \n", - " \n", - " 21\n", - " Pasalic\n", - " Malinovskyi\n", - " 55.0\n", - " \n", - " \n", - " 22\n", - " Bastoni\n", - " Acerbi\n", - " 55.0\n", - " \n", - " \n", - " 23\n", - " Brozovic\n", - " Mkhitaryan\n", - " 55.0\n", - " \n", - " \n", - " 24\n", - " Skriniar\n", - " D'ambrosio\n", + " 1\n", + " Pezzella Giu.\n", + " Gallo\n", " 65.0\n", " \n", " \n", - " 25\n", - " Lazovic\n", - " Depaoli\n", - " 55.0\n", - " \n", - " \n", - " 26\n", - " Verdi\n", - " Hongla\n", + " 2\n", + " Banda\n", + " Di Francesco F.\n", " 60.0\n", " \n", " \n", - " 27\n", - " Dawidowicz\n", - " Hien\n", - " 60.0\n", - " \n", - " \n", - " 28\n", - " Verde\n", - " Gyasi\n", - " 55.0\n", - " \n", - " \n", - " 29\n", - " Bastoni S.\n", - " Amian\n", - " 55.0\n", - " \n", - " \n", - " 30\n", - " Carlos Augusto\n", - " D'alessandro\n", - " 60.0\n", - " \n", - " \n", - " 31\n", - " Birindelli\n", - " Ciurria\n", - " 60.0\n", - " \n", - " \n", - " 32\n", - " Petagna\n", - " Gytkjaer\n", - " 55.0\n", - " \n", - " \n", - " 33\n", - " Caldirola\n", - " Carboni\n", - " 65.0\n", - " \n", - " \n", - " 34\n", + " 3\n", " Lovato\n", " Bronn\n", " 55.0\n", " \n", " \n", - " 35\n", - " Piatek\n", + " 4\n", + " Nicolussi Caviglia\n", + " Bohinen\n", + " 65.0\n", + " \n", + " \n", + " 5\n", + " Dia\n", " Bonazzoli\n", - " 55.0\n", + " 65.0\n", " \n", " \n", - " 36\n", - " Candreva\n", - " Bradaric\n", - " 55.0\n", - " \n", - " \n", - " 37\n", - " Zalewski\n", - " El Shaarawy\n", + " 6\n", + " Haas\n", + " Akpa Akpro\n", " 60.0\n", " \n", " \n", - " 38\n", - " Volpato\n", - " Dybala\n", + " 7\n", + " Satriano\n", + " Bajrami\n", " 55.0\n", " \n", " \n", - " 39\n", - " Matic\n", - " Camara Ma.\n", - " 55.0\n", - " \n", - " \n", - " 40\n", - " Vojvoda\n", - " Singo\n", + " 8\n", + " Ebuehi\n", + " Stojanovic\n", " 60.0\n", " \n", " \n", - " 41\n", + " 9\n", + " Buongiorno\n", + " Adopo\n", + " 60.0\n", + " \n", + " \n", + " 10\n", + " Linetty\n", + " Lukic\n", + " 65.0\n", + " \n", + " \n", + " 11\n", " Sanabria\n", - " Pellegri\n", + " Seck\n", " 55.0\n", " \n", " \n", - " 42\n", - " Miranchuk\n", + " 12\n", + " Sanabria\n", " Radonjic\n", " 55.0\n", " \n", " \n", - " 43\n", - " Kjaer\n", - " Gabbia\n", + " 13\n", + " Dessers\n", + " Ciofani D.\n", " 55.0\n", " \n", " \n", - " 44\n", + " 14\n", + " Chiriches\n", + " Bianchetti\n", + " 60.0\n", + " \n", + " \n", + " 15\n", + " Asllani\n", + " Gagliardini\n", + " 55.0\n", + " \n", + " \n", + " 16\n", + " Darmian\n", + " D'ambrosio\n", + " 60.0\n", + " \n", + " \n", + " 17\n", + " Dumfries\n", + " D'ambrosio\n", + " 55.0\n", + " \n", + " \n", + " 18\n", + " Maehle\n", + " Ruggeri\n", + " 60.0\n", + " \n", + " \n", + " 19\n", + " Boga\n", + " Pasalic\n", + " 55.0\n", + " \n", + " \n", + " 20\n", + " Rincon\n", + " Vieira\n", + " 55.0\n", + " \n", + " \n", + " 21\n", + " Gabbiadini\n", + " Quagliarella\n", + " 55.0\n", + " \n", + " \n", + " 22\n", + " Giroud\n", + " Origi\n", + " 60.0\n", + " \n", + " \n", + " 23\n", " De Ketelaere\n", " Diaz B.\n", " 55.0\n", " \n", " \n", - " 45\n", - " Bennacer\n", - " Pobega\n", + " 24\n", + " Saelemaekers\n", + " Messias\n", " 60.0\n", " \n", " \n", - " 46\n", - " Martinez Quarta\n", + " 25\n", + " Lopez M.\n", + " Obiang\n", + " 55.0\n", + " \n", + " \n", + " 26\n", + " Defrel\n", + " Alvarez A.\n", + " 60.0\n", + " \n", + " \n", + " 27\n", + " Traore' Hj.\n", + " Thorstvedt\n", + " 65.0\n", + " \n", + " \n", + " 28\n", + " Mckennie\n", + " Chiesa\n", + " 60.0\n", + " \n", + " \n", + " 29\n", + " Kean\n", + " Milik\n", + " 60.0\n", + " \n", + " \n", + " 30\n", + " Fagioli\n", + " Miretti\n", + " 55.0\n", + " \n", + " \n", + " 31\n", + " Rovella\n", + " Machin\n", + " 55.0\n", + " \n", + " \n", + " 32\n", + " Mari'\n", + " Caldirola\n", + " 60.0\n", + " \n", + " \n", + " 33\n", + " Petagna\n", + " Mota\n", + " 60.0\n", + " \n", + " \n", + " 34\n", + " Marusic\n", + " Hysaj\n", + " 60.0\n", + " \n", + " \n", + " 35\n", + " Cataldi\n", + " Marcos Antonio\n", + " 65.0\n", + " \n", + " \n", + " 36\n", " Igor\n", + " Martinez Quarta\n", + " 60.0\n", + " \n", + " \n", + " 37\n", + " Kouame'\n", + " Jovic\n", " 55.0\n", " \n", " \n", - " 47\n", - " Bonaventura\n", - " Mandragora\n", - " 55.0\n", - " \n", - " \n", - " 48\n", - " Dodo'\n", - " Venuti\n", - " 55.0\n", - " \n", - " \n", - " 49\n", - " Ikone'\n", + " 38\n", + " Gonzalez N.\n", " Saponara\n", " 55.0\n", " \n", " \n", - " 50\n", - " Locatelli\n", - " Paredes\n", - " 60.0\n", - " \n", - " \n", - " 51\n", - " Milik\n", - " Vlahovic\n", - " 65.0\n", - " \n", - " \n", - " 52\n", - " Miretti\n", - " Di Maria\n", + " 39\n", + " Elmas\n", + " Lozano\n", " 55.0\n", " \n", " \n", - " 53\n", - " Immobile\n", - " Cancellieri\n", + " 40\n", + " Elmas\n", + " Politano\n", + " 55.0\n", + " \n", + " \n", + " 41\n", + " Mario Rui\n", + " Olivera\n", + " 55.0\n", + " \n", + " \n", + " 42\n", + " Spinazzola\n", + " El Shaarawy\n", " 60.0\n", " \n", " \n", - " 54\n", - " Vecino\n", - " Luis Alberto\n", + " 43\n", + " Matic\n", + " Bove\n", " 55.0\n", " \n", + " \n", + " 44\n", + " Arslan\n", + " Makengo\n", + " 60.0\n", + " \n", + " \n", + " 45\n", + " Samardzic\n", + " Lovric\n", + " 55.0\n", + " \n", + " \n", + " 46\n", + " Lasagna\n", + " Kallon\n", + " 60.0\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " player1 player2 percentage\n", - "0 Lozano Politano 60.0\n", - "1 Zambo Anguissa Ndombele' 60.0\n", - "2 Mario Rui Olivera 55.0\n", - "3 Elmas Raspadori 55.0\n", - "4 Beto Success 60.0\n", - "5 Lovric Samardzic 55.0\n", - "6 Ehizibue Nuytinck 60.0\n", - "7 Rincon Vieira 60.0\n", - "8 Augello Murru 55.0\n", - "9 Djuricic Leris 65.0\n", - "10 Caputo Montevago 60.0\n", - "11 Di Francesco F. Banda 55.0\n", - "12 Colombo Ceesay 55.0\n", - "13 Gonzalez J. Bistrovic 60.0\n", - "14 Lykogiannis Cambiaso 55.0\n", - "15 Barrow Orsolini 55.0\n", - "16 Ferguson Schouten 55.0\n", - "17 Traore' Hj. Berardi 55.0\n", - "18 Rogerio Kyriakopoulos 60.0\n", - "19 Djimsiti Scalvini 60.0\n", - "20 Zapata D. Hojlund 60.0\n", - "21 Pasalic Malinovskyi 55.0\n", - "22 Bastoni Acerbi 55.0\n", - "23 Brozovic Mkhitaryan 55.0\n", - "24 Skriniar D'ambrosio 65.0\n", - "25 Lazovic Depaoli 55.0\n", - "26 Verdi Hongla 60.0\n", - "27 Dawidowicz Hien 60.0\n", - "28 Verde Gyasi 55.0\n", - "29 Bastoni S. Amian 55.0\n", - "30 Carlos Augusto D'alessandro 60.0\n", - "31 Birindelli Ciurria 60.0\n", - "32 Petagna Gytkjaer 55.0\n", - "33 Caldirola Carboni 65.0\n", - "34 Lovato Bronn 55.0\n", - "35 Piatek Bonazzoli 55.0\n", - "36 Candreva Bradaric 55.0\n", - "37 Zalewski El Shaarawy 60.0\n", - "38 Volpato Dybala 55.0\n", - "39 Matic Camara Ma. 55.0\n", - "40 Vojvoda Singo 60.0\n", - "41 Sanabria Pellegri 55.0\n", - "42 Miranchuk Radonjic 55.0\n", - "43 Kjaer Gabbia 55.0\n", - "44 De Ketelaere Diaz B. 55.0\n", - "45 Bennacer Pobega 60.0\n", - "46 Martinez Quarta Igor 55.0\n", - "47 Bonaventura Mandragora 55.0\n", - "48 Dodo' Venuti 55.0\n", - "49 Ikone' Saponara 55.0\n", - "50 Locatelli Paredes 60.0\n", - "51 Milik Vlahovic 65.0\n", - "52 Miretti Di Maria 55.0\n", - "53 Immobile Cancellieri 60.0\n", - "54 Vecino Luis Alberto 55.0" + " player1 player2 percentage\n", + "0 Colombo Ceesay 55.0\n", + "1 Pezzella Giu. Gallo 65.0\n", + "2 Banda Di Francesco F. 60.0\n", + "3 Lovato Bronn 55.0\n", + "4 Nicolussi Caviglia Bohinen 65.0\n", + "5 Dia Bonazzoli 65.0\n", + "6 Haas Akpa Akpro 60.0\n", + "7 Satriano Bajrami 55.0\n", + "8 Ebuehi Stojanovic 60.0\n", + "9 Buongiorno Adopo 60.0\n", + "10 Linetty Lukic 65.0\n", + "11 Sanabria Seck 55.0\n", + "12 Sanabria Radonjic 55.0\n", + "13 Dessers Ciofani D. 55.0\n", + "14 Chiriches Bianchetti 60.0\n", + "15 Asllani Gagliardini 55.0\n", + "16 Darmian D'ambrosio 60.0\n", + "17 Dumfries D'ambrosio 55.0\n", + "18 Maehle Ruggeri 60.0\n", + "19 Boga Pasalic 55.0\n", + "20 Rincon Vieira 55.0\n", + "21 Gabbiadini Quagliarella 55.0\n", + "22 Giroud Origi 60.0\n", + "23 De Ketelaere Diaz B. 55.0\n", + "24 Saelemaekers Messias 60.0\n", + "25 Lopez M. Obiang 55.0\n", + "26 Defrel Alvarez A. 60.0\n", + "27 Traore' Hj. Thorstvedt 65.0\n", + "28 Mckennie Chiesa 60.0\n", + "29 Kean Milik 60.0\n", + "30 Fagioli Miretti 55.0\n", + "31 Rovella Machin 55.0\n", + "32 Mari' Caldirola 60.0\n", + "33 Petagna Mota 60.0\n", + "34 Marusic Hysaj 60.0\n", + "35 Cataldi Marcos Antonio 65.0\n", + "36 Igor Martinez Quarta 60.0\n", + "37 Kouame' Jovic 55.0\n", + "38 Gonzalez N. Saponara 55.0\n", + "39 Elmas Lozano 55.0\n", + "40 Elmas Politano 55.0\n", + "41 Mario Rui Olivera 55.0\n", + "42 Spinazzola El Shaarawy 60.0\n", + "43 Matic Bove 55.0\n", + "44 Arslan Makengo 60.0\n", + "45 Samardzic Lovric 55.0\n", + "46 Lasagna Kallon 60.0" ] }, "execution_count": 5, @@ -688,27 +632,27 @@ " \n", " \n", " \n", - " Vicario\n", + " Skorupski\n", " 1\n", " 100.0\n", " \n", " \n", - " Ebuehi\n", + " Posch\n", " 1\n", " 100.0\n", " \n", " \n", - " Ismajli\n", + " Soumaoro\n", " 1\n", " 100.0\n", " \n", " \n", - " Walukiewicz\n", + " Lucumi'\n", " 1\n", " 100.0\n", " \n", " \n", - " Parisi\n", + " Cambiaso\n", " 1\n", " 100.0\n", " \n", @@ -718,51 +662,51 @@ " ...\n", " \n", " \n", - " Basic\n", + " Verdi\n", " 0\n", " 55.0\n", " \n", " \n", - " Bertini\n", - " 0\n", - " 10.0\n", - " \n", - " \n", - " Marcos Antonio\n", - " 0\n", - " 50.0\n", - " \n", - " \n", - " Romero L.\n", - " 0\n", - " 50.0\n", - " \n", - " \n", - " Cancellieri\n", + " Kallon\n", " 0.4\n", " 55.0\n", " \n", + " \n", + " Piccoli\n", + " 0\n", + " 55.0\n", + " \n", + " \n", + " Ngonge\n", + " 0\n", + " 25.0\n", + " \n", + " \n", + " Braaf\n", + " 0\n", + " 40.0\n", + " \n", " \n", "\n", - "

449 rows × 2 columns

\n", + "

440 rows × 2 columns

\n", "" ], "text/plain": [ - " starter percentage\n", - "player \n", - "Vicario 1 100.0\n", - "Ebuehi 1 100.0\n", - "Ismajli 1 100.0\n", - "Walukiewicz 1 100.0\n", - "Parisi 1 100.0\n", - "... ... ...\n", - "Basic 0 55.0\n", - "Bertini 0 10.0\n", - "Marcos Antonio 0 50.0\n", - "Romero L. 0 50.0\n", - "Cancellieri 0.4 55.0\n", + " starter percentage\n", + "player \n", + "Skorupski 1 100.0\n", + "Posch 1 100.0\n", + "Soumaoro 1 100.0\n", + "Lucumi' 1 100.0\n", + "Cambiaso 1 100.0\n", + "... ... ...\n", + "Verdi 0 55.0\n", + "Kallon 0.4 55.0\n", + "Piccoli 0 55.0\n", + "Ngonge 0 25.0\n", + "Braaf 0 40.0\n", "\n", - "[449 rows x 2 columns]" + "[440 rows x 2 columns]" ] }, "execution_count": 6, @@ -792,14 +736,6 @@ "source": [ "probables.to_excel('mid_outputs/match_probable_players.xlsx') " ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf992051", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/6_neural_network_training.ipynb b/6_neural_network_training.ipynb index 479f839..6c88d60 100644 --- a/6_neural_network_training.ipynb +++ b/6_neural_network_training.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 40, "id": "fa098fa4", "metadata": {}, "outputs": [], @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "id": "f71fa9a4", "metadata": {}, "outputs": [ @@ -134,16 +134,16 @@ " 0.0\n", " 10.0\n", " ...\n", - " 0.617096\n", - " 0.001171\n", - " 0.004684\n", - " 0.357143\n", - " 0.007026\n", - " 0.001171\n", - " 0.009368\n", - " 0.005855\n", - " 0.018735\n", - " 0.008197\n", + " 0.684211\n", + " 0.000849\n", + " 0.005093\n", + " 0.407470\n", + " 0.007640\n", + " 0.000849\n", + " 0.006791\n", + " 0.005093\n", + " 0.016129\n", + " 0.011885\n", " \n", " \n", " 1\n", @@ -158,16 +158,16 @@ " 0.0\n", " 6.0\n", " ...\n", - " 0.736842\n", + " 0.753541\n", " 0.000000\n", - " 0.002924\n", - " 0.511696\n", - " 0.002924\n", - " 0.008772\n", - " 0.002924\n", - " 0.002924\n", - " 0.005848\n", - " 0.008772\n", + " 0.002833\n", + " 0.521246\n", + " 0.002833\n", + " 0.008499\n", + " 0.002833\n", + " 0.002833\n", + " 0.005666\n", + " 0.008499\n", " \n", " \n", " 2\n", @@ -182,16 +182,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.574423\n", - " 0.002096\n", - " 0.005241\n", - " 0.339623\n", - " 0.006289\n", - " 0.005241\n", - " 0.013627\n", - " 0.002096\n", - " 0.015723\n", - " 0.012579\n", + " 0.586090\n", + " 0.003393\n", + " 0.006785\n", + " 0.357082\n", + " 0.006785\n", + " 0.004241\n", + " 0.012723\n", + " 0.001696\n", + " 0.015267\n", + " 0.011874\n", " \n", " \n", " 3\n", @@ -206,16 +206,16 @@ " 0.5\n", " 5.0\n", " ...\n", - " 0.598540\n", + " 0.607101\n", " 0.000000\n", - " 0.001217\n", - " 0.278589\n", - " 0.013382\n", - " 0.003650\n", - " 0.015815\n", - " 0.008516\n", - " 0.048662\n", - " 0.026764\n", + " 0.001183\n", + " 0.279290\n", + " 0.013018\n", + " 0.003550\n", + " 0.015385\n", + " 0.008284\n", + " 0.047337\n", + " 0.027219\n", " \n", " \n", " 4\n", @@ -230,16 +230,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 0.634921\n", - " 0.052910\n", - " 0.111111\n", - " 0.328042\n", - " 0.026455\n", - " 0.005291\n", - " 0.021164\n", + " 0.648649\n", + " 0.045045\n", + " 0.094595\n", + " 0.364865\n", + " 0.027027\n", + " 0.009009\n", + " 0.018018\n", " 0.000000\n", - " 0.026455\n", - " 0.005291\n", + " 0.022523\n", + " 0.004505\n", " \n", " \n", " ...\n", @@ -266,7 +266,7 @@ " ...\n", " \n", " \n", - " 22942\n", + " 24131\n", " 38\n", " Tameze\n", " Verona\n", @@ -290,7 +290,7 @@ " 0.021368\n", " \n", " \n", - " 22943\n", + " 24132\n", " 38\n", " Hongla\n", " Verona\n", @@ -314,7 +314,7 @@ " 0.026114\n", " \n", " \n", - " 22944\n", + " 24133\n", " 38\n", " Lasagna\n", " Verona\n", @@ -338,7 +338,7 @@ " 0.041385\n", " \n", " \n", - " 22945\n", + " 24134\n", " 38\n", " Caprari\n", " Verona\n", @@ -362,7 +362,7 @@ " 0.009858\n", " \n", " \n", - " 22946\n", + " 24135\n", " 38\n", " Simeone\n", " Verona\n", @@ -387,7 +387,7 @@ " \n", " \n", "\n", - "

22947 rows × 131 columns

\n", + "

24136 rows × 131 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", - "22942 38 Tameze Verona Lazio 0 5.5 0 0 \n", - "22943 38 Hongla Verona Lazio 0 7.0 1 0 \n", - "22944 38 Lasagna Verona Lazio 0 7.0 1 0 \n", - "22945 38 Caprari Verona Lazio 0 6.0 0 0 \n", - "22946 38 Simeone Verona Lazio 0 7.0 1 0 \n", + "24131 38 Tameze Verona Lazio 0 5.5 0 0 \n", + "24132 38 Hongla Verona Lazio 0 7.0 1 0 \n", + "24133 38 Lasagna Verona Lazio 0 7.0 1 0 \n", + "24134 38 Caprari Verona Lazio 0 6.0 0 0 \n", + "24135 38 Simeone Verona Lazio 0 7.0 1 0 \n", "\n", " cards_malus fantavote ... touches_live_ball dribbles_completed \\\n", - "0 0.0 10.0 ... 0.617096 0.001171 \n", - "1 0.0 6.0 ... 0.736842 0.000000 \n", - "2 0.5 5.5 ... 0.574423 0.002096 \n", - "3 0.5 5.0 ... 0.598540 0.000000 \n", - "4 0.5 5.5 ... 0.634921 0.052910 \n", + "0 0.0 10.0 ... 0.684211 0.000849 \n", + "1 0.0 6.0 ... 0.753541 0.000000 \n", + "2 0.5 5.5 ... 0.586090 0.003393 \n", + "3 0.5 5.0 ... 0.607101 0.000000 \n", + "4 0.5 5.5 ... 0.648649 0.045045 \n", "... ... ... ... ... ... \n", - "22942 0.0 5.5 ... 0.629371 0.013209 \n", - "22943 0.5 9.5 ... 0.582181 0.001536 \n", - "22944 0.5 9.5 ... 0.369932 0.008446 \n", - "22945 0.0 6.0 ... 0.602410 0.023731 \n", - "22946 0.0 10.0 ... 0.407086 0.008669 \n", + "24131 0.0 5.5 ... 0.629371 0.013209 \n", + "24132 0.5 9.5 ... 0.582181 0.001536 \n", + "24133 0.5 9.5 ... 0.369932 0.008446 \n", + "24134 0.0 6.0 ... 0.602410 0.023731 \n", + "24135 0.0 10.0 ... 0.407086 0.008669 \n", "\n", " dribbles passes_received miscontrols dispossessed fouls \\\n", - "0 0.004684 0.357143 0.007026 0.001171 0.009368 \n", - "1 0.002924 0.511696 0.002924 0.008772 0.002924 \n", - "2 0.005241 0.339623 0.006289 0.005241 0.013627 \n", - "3 0.001217 0.278589 0.013382 0.003650 0.015815 \n", - "4 0.111111 0.328042 0.026455 0.005291 0.021164 \n", + "0 0.005093 0.407470 0.007640 0.000849 0.006791 \n", + "1 0.002833 0.521246 0.002833 0.008499 0.002833 \n", + "2 0.006785 0.357082 0.006785 0.004241 0.012723 \n", + "3 0.001183 0.279290 0.013018 0.003550 0.015385 \n", + "4 0.094595 0.364865 0.027027 0.009009 0.018018 \n", "... ... ... ... ... ... \n", - "22942 0.023310 0.358197 0.019814 0.010101 0.012821 \n", - "22943 0.007680 0.341014 0.018433 0.012289 0.023041 \n", - "22944 0.016047 0.262669 0.046453 0.022804 0.016047 \n", - "22945 0.043447 0.453815 0.033954 0.019715 0.015334 \n", - "22946 0.022993 0.320392 0.051263 0.030155 0.021108 \n", + "24131 0.023310 0.358197 0.019814 0.010101 0.012821 \n", + "24132 0.007680 0.341014 0.018433 0.012289 0.023041 \n", + "24133 0.016047 0.262669 0.046453 0.022804 0.016047 \n", + "24134 0.043447 0.453815 0.033954 0.019715 0.015334 \n", + "24135 0.022993 0.320392 0.051263 0.030155 0.021108 \n", "\n", " fouled aerials_won aerials_lost \n", - "0 0.005855 0.018735 0.008197 \n", - "1 0.002924 0.005848 0.008772 \n", - "2 0.002096 0.015723 0.012579 \n", - "3 0.008516 0.048662 0.026764 \n", - "4 0.000000 0.026455 0.005291 \n", + "0 0.005093 0.016129 0.011885 \n", + "1 0.002833 0.005666 0.008499 \n", + "2 0.001696 0.015267 0.011874 \n", + "3 0.008284 0.047337 0.027219 \n", + "4 0.000000 0.022523 0.004505 \n", "... ... ... ... \n", - "22942 0.013209 0.023699 0.021368 \n", - "22943 0.007680 0.023041 0.026114 \n", - "22944 0.008446 0.026182 0.041385 \n", - "22945 0.027017 0.002921 0.009858 \n", - "22946 0.021862 0.022616 0.040709 \n", + "24131 0.013209 0.023699 0.021368 \n", + "24132 0.007680 0.023041 0.026114 \n", + "24133 0.008446 0.026182 0.041385 \n", + "24134 0.027017 0.002921 0.009858 \n", + "24135 0.021862 0.022616 0.040709 \n", "\n", - "[22947 rows x 131 columns]" + "[24136 rows x 131 columns]" ] }, - "execution_count": 4, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "id": "1d024554", "metadata": {}, "outputs": [ @@ -521,16 +521,16 @@ " 0.5\n", " 5.5\n", " ...\n", - " 5.900000\n", + " 10.400000\n", " 0.220000\n", - " 0.90\n", - " 59.0\n", - " 126.000000\n", - " 222.000000\n", - " 62.000000\n", - " 55.000000\n", - " 98.0\n", - " 3.000000\n", + " -2.600000\n", + " 78.000000\n", + " 167.000000\n", + " 321.000000\n", + " 93.000000\n", + " 86.0\n", + " 147.0\n", + " 8.000000\n", " \n", " \n", " 1\n", @@ -545,44 +545,20 @@ " 0.0\n", " 4.5\n", " ...\n", - " 21.000000\n", - " 0.270000\n", - " -2.00\n", - " 62.0\n", - " 162.000000\n", - " 408.000000\n", - " 81.000000\n", - " 104.000000\n", - " 203.0\n", - " 10.000000\n", + " 28.700000\n", + " 0.280000\n", + " 0.700000\n", + " 87.000000\n", + " 219.000000\n", + " 545.000000\n", + " 116.000000\n", + " 141.0\n", + " 280.0\n", + " 18.000000\n", " \n", " \n", " 2\n", " 1\n", - " Radu I.\n", - " Cremonese\n", - " Fiorentina\n", - " 0\n", - " 5.0\n", - " -3\n", - " 0\n", - " 0.0\n", - " 2.0\n", - " ...\n", - " 21.000000\n", - " 0.290000\n", - " 2.00\n", - " 38.0\n", - " 143.000000\n", - " 265.000000\n", - " 34.000000\n", - " 67.000000\n", - " 107.0\n", - " 6.000000\n", - " \n", - " \n", - " 3\n", - " 1\n", " Vicario\n", " Empoli\n", " Spezia\n", @@ -593,19 +569,19 @@ " 0.0\n", " 4.5\n", " ...\n", - " 20.400000\n", - " 0.300000\n", - " 1.40\n", - " 68.0\n", - " 213.000000\n", - " 505.000000\n", - " 85.000000\n", - " 76.000000\n", - " 285.0\n", - " 20.000000\n", + " 24.000000\n", + " 0.270000\n", + " 2.000000\n", + " 88.000000\n", + " 277.000000\n", + " 690.000000\n", + " 108.000000\n", + " 103.0\n", + " 399.0\n", + " 25.000000\n", " \n", " \n", - " 4\n", + " 3\n", " 1\n", " Gollini\n", " Fiorentina\n", @@ -617,16 +593,40 @@ " 0.0\n", " 3.0\n", " ...\n", - " 7.650000\n", + " 15.600000\n", " 0.270000\n", - " -1.35\n", - " 28.0\n", - " 79.500000\n", - " 207.000000\n", - " 28.500000\n", - " 40.000000\n", - " 64.0\n", - " 3.000000\n", + " 1.600000\n", + " 34.000000\n", + " 92.000000\n", + " 425.000000\n", + " 69.000000\n", + " 114.0\n", + " 197.0\n", + " 6.000000\n", + " \n", + " \n", + " 4\n", + " 1\n", + " Handanovic\n", + " Inter\n", + " Lecce\n", + " 0\n", + " 6.5\n", + " -1\n", + " 0\n", + " 0.0\n", + " 5.5\n", + " ...\n", + " 10.600000\n", + " 0.300000\n", + " -1.400000\n", + " 27.000000\n", + " 52.000000\n", + " 266.000000\n", + " 46.000000\n", + " 45.0\n", + " 79.0\n", + " 2.000000\n", " \n", " \n", " ...\n", @@ -653,200 +653,200 @@ " ...\n", " \n", " \n", - " 841\n", - " 14\n", - " Consigli\n", + " 1120\n", + " 19\n", + " Pegolo\n", " Sassuolo\n", - " Roma\n", - " 1\n", + " Monza\n", + " 0\n", " 6.5\n", " -1\n", " 0\n", " 0.0\n", " 5.5\n", " ...\n", - " 16.600000\n", - " 0.370000\n", - " -2.40\n", - " 84.0\n", - " 205.000000\n", - " 523.000000\n", - " 77.000000\n", - " 134.000000\n", - " 189.0\n", - " 13.000000\n", + " 15.466667\n", + " 0.373333\n", + " -2.866667\n", + " 69.666667\n", + " 167.333333\n", + " 434.333333\n", + " 61.333333\n", + " 110.0\n", + " 156.0\n", + " 9.333333\n", " \n", " \n", - " 842\n", - " 14\n", - " Zoet\n", + " 1121\n", + " 19\n", + " Dragowski\n", " Spezia\n", - " Udinese\n", + " Roma\n", " 1\n", " 6.0\n", - " -1\n", + " -2\n", " 0\n", " 0.0\n", - " 5.0\n", + " 4.0\n", " ...\n", - " 17.616667\n", - " 0.246667\n", - " -2.55\n", - " 51.5\n", - " 160.833333\n", - " 305.666667\n", - " 58.833333\n", - " 68.666667\n", - " 150.5\n", - " 6.666667\n", + " 26.000000\n", + " 0.280000\n", + " -3.000000\n", + " 85.000000\n", + " 255.000000\n", + " 479.000000\n", + " 87.000000\n", + " 106.0\n", + " 235.0\n", + " 9.000000\n", " \n", " \n", - " 843\n", - " 14\n", + " 1122\n", + " 19\n", " Milinkovic-Savic V.\n", " Torino\n", - " Sampdoria\n", - " 1\n", - " 6.0\n", - " 0\n", - " 0\n", - " 0.0\n", - " 6.0\n", - " ...\n", - " 16.800000\n", - " 0.270000\n", - " 0.80\n", - " 97.0\n", - " 335.000000\n", - " 507.000000\n", - " 55.000000\n", - " 102.000000\n", - " 160.0\n", - " 11.000000\n", - " \n", - " \n", - " 844\n", - " 14\n", - " Silvestri\n", - " Udinese\n", - " Spezia\n", + " Fiorentina\n", " 0\n", " 7.0\n", - " -1\n", + " 0\n", + " 0\n", + " 0.0\n", + " 7.0\n", + " ...\n", + " 20.300000\n", + " 0.240000\n", + " 0.300000\n", + " 138.000000\n", + " 493.000000\n", + " 746.000000\n", + " 85.000000\n", + " 142.0\n", + " 232.0\n", + " 18.000000\n", + " \n", + " \n", + " 1123\n", + " 19\n", + " Silvestri\n", + " Udinese\n", + " Sampdoria\n", + " 0\n", + " 6.0\n", + " 0\n", " 0\n", " 0.0\n", " 6.0\n", " ...\n", - " 16.000000\n", - " 0.280000\n", - " 2.00\n", - " 59.0\n", - " 148.000000\n", - " 287.000000\n", - " 57.000000\n", - " 110.000000\n", - " 194.0\n", + " 22.100000\n", + " 0.320000\n", + " 1.100000\n", + " 76.000000\n", + " 200.000000\n", + " 406.000000\n", + " 72.000000\n", + " 153.0\n", + " 253.0\n", " 4.000000\n", " \n", " \n", - " 845\n", - " 14\n", + " 1124\n", + " 19\n", " Montipo'\n", " Verona\n", - " Juventus\n", + " Lecce\n", " 1\n", - " 6.0\n", - " -1\n", + " 6.5\n", + " 0\n", " 0\n", " 0.0\n", - " 5.0\n", + " 6.5\n", " ...\n", - " 19.700000\n", + " 25.200000\n", " 0.250000\n", - " -6.30\n", - " 109.0\n", - " 241.000000\n", - " 329.000000\n", - " 47.000000\n", - " 116.000000\n", - " 177.0\n", - " 6.000000\n", + " -4.800000\n", + " 176.000000\n", + " 358.000000\n", + " 447.000000\n", + " 62.000000\n", + " 153.0\n", + " 261.0\n", + " 10.000000\n", " \n", " \n", "\n", - "

846 rows × 108 columns

\n", + "

1125 rows × 108 columns

\n", "" ], "text/plain": [ - " matchday player team oppteam home vote goals \\\n", - "0 1 Musso Atalanta Sampdoria 0 6.0 0 \n", - "1 1 Skorupski Bologna Lazio 0 6.5 -2 \n", - "2 1 Radu I. Cremonese Fiorentina 0 5.0 -3 \n", - "3 1 Vicario Empoli Spezia 0 5.5 -1 \n", - "4 1 Gollini Fiorentina Cremonese 1 5.0 -2 \n", - ".. ... ... ... ... ... ... ... \n", - "841 14 Consigli Sassuolo Roma 1 6.5 -1 \n", - "842 14 Zoet Spezia Udinese 1 6.0 -1 \n", - "843 14 Milinkovic-Savic V. Torino Sampdoria 1 6.0 0 \n", - "844 14 Silvestri Udinese Spezia 0 7.0 -1 \n", - "845 14 Montipo' Verona Juventus 1 6.0 -1 \n", + " matchday player team oppteam home vote \\\n", + "0 1 Musso Atalanta Sampdoria 0 6.0 \n", + "1 1 Skorupski Bologna Lazio 0 6.5 \n", + "2 1 Vicario Empoli Spezia 0 5.5 \n", + "3 1 Gollini Fiorentina Cremonese 1 5.0 \n", + "4 1 Handanovic Inter Lecce 0 6.5 \n", + "... ... ... ... ... ... ... \n", + "1120 19 Pegolo Sassuolo Monza 0 6.5 \n", + "1121 19 Dragowski Spezia Roma 1 6.0 \n", + "1122 19 Milinkovic-Savic V. Torino Fiorentina 0 7.0 \n", + "1123 19 Silvestri Udinese Sampdoria 0 6.0 \n", + "1124 19 Montipo' Verona Lecce 1 6.5 \n", "\n", - " assists cards_malus fantavote ... gk_psxg \\\n", - "0 0 0.5 5.5 ... 5.900000 \n", - "1 0 0.0 4.5 ... 21.000000 \n", - "2 0 0.0 2.0 ... 21.000000 \n", - "3 0 0.0 4.5 ... 20.400000 \n", - "4 0 0.0 3.0 ... 7.650000 \n", - ".. ... ... ... ... ... \n", - "841 0 0.0 5.5 ... 16.600000 \n", - "842 0 0.0 5.0 ... 17.616667 \n", - "843 0 0.0 6.0 ... 16.800000 \n", - "844 0 0.0 6.0 ... 16.000000 \n", - "845 0 0.0 5.0 ... 19.700000 \n", + " goals assists cards_malus fantavote ... gk_psxg \\\n", + "0 0 0 0.5 5.5 ... 10.400000 \n", + "1 -2 0 0.0 4.5 ... 28.700000 \n", + "2 -1 0 0.0 4.5 ... 24.000000 \n", + "3 -2 0 0.0 3.0 ... 15.600000 \n", + "4 -1 0 0.0 5.5 ... 10.600000 \n", + "... ... ... ... ... ... ... \n", + "1120 -1 0 0.0 5.5 ... 15.466667 \n", + "1121 -2 0 0.0 4.0 ... 26.000000 \n", + "1122 0 0 0.0 7.0 ... 20.300000 \n", + "1123 0 0 0.0 6.0 ... 22.100000 \n", + "1124 0 0 0.0 6.5 ... 25.200000 \n", "\n", - " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", - "0 0.220000 0.90 \n", - "1 0.270000 -2.00 \n", - "2 0.290000 2.00 \n", - "3 0.300000 1.40 \n", - "4 0.270000 -1.35 \n", - ".. ... ... \n", - "841 0.370000 -2.40 \n", - "842 0.246667 -2.55 \n", - "843 0.270000 0.80 \n", - "844 0.280000 2.00 \n", - "845 0.250000 -6.30 \n", + " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", + "0 0.220000 -2.600000 \n", + "1 0.280000 0.700000 \n", + "2 0.270000 2.000000 \n", + "3 0.270000 1.600000 \n", + "4 0.300000 -1.400000 \n", + "... ... ... \n", + "1120 0.373333 -2.866667 \n", + "1121 0.280000 -3.000000 \n", + "1122 0.240000 0.300000 \n", + "1123 0.320000 1.100000 \n", + "1124 0.250000 -4.800000 \n", "\n", - " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", - "0 59.0 126.000000 222.000000 \n", - "1 62.0 162.000000 408.000000 \n", - "2 38.0 143.000000 265.000000 \n", - "3 68.0 213.000000 505.000000 \n", - "4 28.0 79.500000 207.000000 \n", - ".. ... ... ... \n", - "841 84.0 205.000000 523.000000 \n", - "842 51.5 160.833333 305.666667 \n", - "843 97.0 335.000000 507.000000 \n", - "844 59.0 148.000000 287.000000 \n", - "845 109.0 241.000000 329.000000 \n", + " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", + "0 78.000000 167.000000 321.000000 \n", + "1 87.000000 219.000000 545.000000 \n", + "2 88.000000 277.000000 690.000000 \n", + "3 34.000000 92.000000 425.000000 \n", + "4 27.000000 52.000000 266.000000 \n", + "... ... ... ... \n", + "1120 69.666667 167.333333 434.333333 \n", + "1121 85.000000 255.000000 479.000000 \n", + "1122 138.000000 493.000000 746.000000 \n", + "1123 76.000000 200.000000 406.000000 \n", + "1124 176.000000 358.000000 447.000000 \n", "\n", - " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", - "0 62.000000 55.000000 98.0 3.000000 \n", - "1 81.000000 104.000000 203.0 10.000000 \n", - "2 34.000000 67.000000 107.0 6.000000 \n", - "3 85.000000 76.000000 285.0 20.000000 \n", - "4 28.500000 40.000000 64.0 3.000000 \n", - ".. ... ... ... ... \n", - "841 77.000000 134.000000 189.0 13.000000 \n", - "842 58.833333 68.666667 150.5 6.666667 \n", - "843 55.000000 102.000000 160.0 11.000000 \n", - "844 57.000000 110.000000 194.0 4.000000 \n", - "845 47.000000 116.000000 177.0 6.000000 \n", + " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", + "0 93.000000 86.0 147.0 8.000000 \n", + "1 116.000000 141.0 280.0 18.000000 \n", + "2 108.000000 103.0 399.0 25.000000 \n", + "3 69.000000 114.0 197.0 6.000000 \n", + "4 46.000000 45.0 79.0 2.000000 \n", + "... ... ... ... ... \n", + "1120 61.333333 110.0 156.0 9.333333 \n", + "1121 87.000000 106.0 235.0 9.000000 \n", + "1122 85.000000 142.0 232.0 18.000000 \n", + "1123 72.000000 153.0 253.0 4.000000 \n", + "1124 62.000000 153.0 261.0 10.000000 \n", "\n", - "[846 rows x 108 columns]" + "[1125 rows x 108 columns]" ] }, - "execution_count": 5, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -871,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 43, "id": "bc9dae87", "metadata": {}, "outputs": [], @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 44, "id": "493b0495", "metadata": {}, "outputs": [ @@ -903,7 +903,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_17524\\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_19096\\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", - " 23.0\n", - " 46.400\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 24.00\n", + " 48.40\n", " 19.0\n", - " 14.0\n", - " 4.0\n", - " 5.0\n", + " 209.0\n", + " 1710.0\n", + " 36.0\n", + " 25.0\n", + " 6.0\n", + " 8.00\n", " ...\n", - " 150.0\n", - " 168.00\n", - " 19.00\n", - " 0.0\n", - " 5.0\n", - " 1.0\n", - " 834.0\n", - " 181.0\n", - " 211.0\n", - " 46.200\n", + " 209.00\n", + " 218.0\n", + " 25.00\n", + " 1.00\n", + " 8.0\n", + " 1.00\n", + " 1165.00\n", + " 239.00\n", + " 279.00\n", + " 46.100\n", " \n", " \n", " Bologna\n", " Bologna\n", - " 23.0\n", - " 51.100\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 17.0\n", - " 12.0\n", - " 3.0\n", - " 3.0\n", - " ...\n", - " 182.0\n", - " 158.00\n", " 24.00\n", - " 2.0\n", + " 52.60\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 25.0\n", + " 19.0\n", " 3.0\n", - " 0.0\n", - " 731.0\n", - " 165.0\n", - " 123.0\n", - " 57.300\n", + " 3.00\n", + " ...\n", + " 252.00\n", + " 229.0\n", + " 33.00\n", + " 3.00\n", + " 3.0\n", + " 1.00\n", + " 1081.00\n", + " 224.00\n", + " 180.00\n", + " 55.400\n", " \n", " \n", " Cremonese\n", " Cremonese\n", - " 27.0\n", - " 43.200\n", + " 29.00\n", + " 44.00\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 11.0\n", - " 5.0\n", - " 1.0\n", - " 3.0\n", - " ...\n", - " 145.0\n", - " 188.00\n", - " 19.00\n", + " 6.0\n", " 2.0\n", - " 3.0\n", - " 0.0\n", - " 744.0\n", - " 242.0\n", - " 184.0\n", - " 56.800\n", + " 4.00\n", + " ...\n", + " 205.00\n", + " 247.0\n", + " 29.00\n", + " 3.00\n", + " 4.0\n", + " 0.00\n", + " 1052.00\n", + " 348.00\n", + " 267.00\n", + " 56.600\n", " \n", " \n", " Empoli\n", " Empoli\n", - " 25.0\n", - " 46.000\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 10.0\n", - " 5.0\n", - " 0.0\n", + " 26.00\n", + " 46.30\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 17.0\n", + " 9.0\n", " 0.0\n", + " 0.00\n", " ...\n", - " 176.0\n", - " 151.00\n", - " 28.00\n", - " 2.0\n", + " 251.00\n", + " 214.0\n", + " 31.00\n", + " 2.00\n", " 0.0\n", - " 0.0\n", - " 723.0\n", - " 163.0\n", - " 135.0\n", - " 54.700\n", + " 0.00\n", + " 999.00\n", + " 217.00\n", + " 189.00\n", + " 53.400\n", " \n", " \n", " Fiorentina\n", " Fiorentina\n", - " 25.0\n", - " 58.700\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 27.00\n", + " 57.20\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 21.0\n", " 17.0\n", - " 15.0\n", - " 1.0\n", - " 3.0\n", + " 2.0\n", + " 4.00\n", " ...\n", - " 186.0\n", - " 176.00\n", - " 35.00\n", - " 1.0\n", - " 3.0\n", - " 0.0\n", - " 694.0\n", - " 193.0\n", - " 224.0\n", + " 259.00\n", + " 229.0\n", + " 52.00\n", + " 1.00\n", + " 4.0\n", + " 0.00\n", + " 942.00\n", + " 249.00\n", + " 289.00\n", " 46.300\n", " \n", " \n", " Verona\n", " Hellas Verona\n", - " 29.0\n", - " 43.900\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 10.0\n", - " 7.0\n", - " 0.0\n", + " 29.00\n", + " 43.40\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 16.0\n", + " 13.0\n", " 0.0\n", + " 0.00\n", " ...\n", - " 130.0\n", - " 205.00\n", - " 18.00\n", - " 1.0\n", + " 192.00\n", + " 276.0\n", + " 22.00\n", + " 1.00\n", " 0.0\n", - " 1.0\n", - " 758.0\n", - " 240.0\n", - " 229.0\n", - " 51.200\n", + " 1.00\n", + " 1038.00\n", + " 345.00\n", + " 388.00\n", + " 47.100\n", " \n", " \n", " Inter\n", " Inter\n", - " 23.0\n", - " 54.600\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 31.0\n", - " 22.0\n", - " 2.0\n", + " 23.00\n", + " 52.90\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 37.0\n", + " 25.0\n", " 2.0\n", + " 2.00\n", " ...\n", - " 182.0\n", - " 154.00\n", - " 14.00\n", - " 1.0\n", + " 242.00\n", + " 214.0\n", + " 18.00\n", + " 2.00\n", " 2.0\n", - " 0.0\n", - " 596.0\n", - " 126.0\n", - " 172.0\n", - " 42.300\n", + " 1.00\n", + " 869.00\n", + " 198.00\n", + " 232.00\n", + " 46.000\n", " \n", " \n", " Juventus\n", " Juventus\n", - " 26.0\n", - " 50.500\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 21.0\n", - " 17.0\n", - " 1.0\n", + " 26.00\n", + " 49.50\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 30.0\n", + " 23.0\n", " 2.0\n", + " 3.00\n", " ...\n", - " 161.0\n", - " 156.00\n", - " 21.00\n", - " 0.0\n", - " 2.0\n", - " 0.0\n", - " 674.0\n", - " 184.0\n", - " 166.0\n", - " 52.600\n", + " 210.00\n", + " 210.0\n", + " 25.00\n", + " 0.00\n", + " 3.0\n", + " 0.00\n", + " 947.00\n", + " 230.00\n", + " 230.00\n", + " 50.000\n", " \n", " \n", " Lazio\n", " Lazio\n", - " 21.0\n", - " 50.000\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 21.00\n", + " 51.20\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 34.0\n", " 25.0\n", - " 20.0\n", - " 1.0\n", - " 2.0\n", + " 3.0\n", + " 4.00\n", " ...\n", - " 197.0\n", - " 124.00\n", - " 35.00\n", - " 1.0\n", - " 2.0\n", - " 1.0\n", - " 746.0\n", - " 151.0\n", - " 126.0\n", - " 54.500\n", + " 261.00\n", + " 185.0\n", + " 42.00\n", + " 1.00\n", + " 4.0\n", + " 1.00\n", + " 1021.00\n", + " 183.00\n", + " 181.00\n", + " 50.300\n", " \n", " \n", " Lecce\n", " Lecce\n", - " 24.0\n", - " 40.700\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 26.00\n", + " 42.50\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 18.0\n", " 12.0\n", - " 8.0\n", " 1.0\n", - " 2.0\n", + " 2.00\n", " ...\n", - " 184.0\n", - " 184.00\n", - " 27.00\n", - " 3.0\n", + " 263.00\n", + " 265.0\n", + " 43.00\n", + " 3.00\n", " 2.0\n", - " 0.0\n", - " 703.0\n", - " 216.0\n", - " 188.0\n", - " 53.500\n", + " 1.00\n", + " 1072.00\n", + " 373.00\n", + " 294.00\n", + " 55.900\n", " \n", " \n", " Milan\n", " Milan\n", - " 27.0\n", - " 53.900\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 26.0\n", - " 21.0\n", - " 2.0\n", + " 27.00\n", + " 54.50\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 33.0\n", + " 28.0\n", " 2.0\n", + " 2.00\n", " ...\n", - " 164.0\n", - " 161.00\n", - " 15.00\n", + " 234.00\n", + " 226.0\n", + " 20.00\n", + " 3.00\n", " 2.0\n", - " 2.0\n", - " 1.0\n", - " 690.0\n", - " 168.0\n", - " 205.0\n", - " 45.000\n", + " 2.00\n", + " 964.00\n", + " 224.00\n", + " 275.00\n", + " 44.900\n", " \n", " \n", " Monza\n", " Monza\n", - " 29.0\n", - " 55.500\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 29.00\n", + " 55.10\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 22.0\n", " 13.0\n", - " 8.0\n", - " 1.0\n", - " 1.0\n", + " 3.0\n", + " 3.00\n", " ...\n", - " 207.0\n", - " 178.00\n", - " 24.00\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", - " 692.0\n", - " 141.0\n", - " 160.0\n", - " 46.800\n", + " 279.00\n", + " 242.0\n", + " 34.00\n", + " 0.00\n", + " 3.0\n", + " 1.00\n", + " 958.00\n", + " 197.00\n", + " 215.00\n", + " 47.800\n", " \n", " \n", " Napoli\n", " Napoli\n", - " 24.0\n", - " 59.800\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 34.0\n", - " 28.0\n", - " 3.0\n", - " 3.0\n", + " 24.00\n", + " 61.30\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 46.0\n", + " 37.0\n", + " 4.0\n", + " 5.00\n", " ...\n", - " 195.0\n", - " 116.00\n", - " 18.00\n", - " 1.0\n", - " 3.0\n", - " 0.0\n", - " 687.0\n", - " 142.0\n", - " 181.0\n", - " 44.000\n", + " 258.00\n", + " 167.0\n", + " 27.00\n", + " 1.00\n", + " 4.0\n", + " 0.00\n", + " 933.00\n", + " 201.00\n", + " 231.00\n", + " 46.500\n", " \n", " \n", " Roma\n", " Roma\n", - " 23.0\n", - " 50.800\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 17.0\n", - " 11.0\n", - " 2.0\n", + " 25.00\n", + " 49.40\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 25.0\n", + " 16.0\n", " 3.0\n", + " 5.00\n", " ...\n", - " 198.0\n", - " 157.00\n", - " 7.00\n", - " 0.0\n", - " 3.0\n", - " 0.0\n", - " 709.0\n", - " 142.0\n", - " 159.0\n", - " 47.200\n", + " 266.00\n", + " 214.0\n", + " 10.00\n", + " 0.00\n", + " 5.0\n", + " 0.00\n", + " 981.00\n", + " 184.00\n", + " 225.00\n", + " 45.000\n", " \n", " \n", " Salernitana\n", " Salernitana\n", + " 28.00\n", + " 44.80\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", " 24.0\n", - " 47.100\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 18.0\n", - " 12.0\n", - " 1.0\n", + " 16.0\n", " 1.0\n", + " 1.00\n", " ...\n", - " 150.0\n", - " 157.00\n", - " 34.00\n", - " 4.0\n", + " 226.00\n", + " 233.0\n", + " 49.00\n", + " 7.00\n", " 1.0\n", - " 1.0\n", - " 750.0\n", - " 169.0\n", - " 175.0\n", - " 49.100\n", + " 1.00\n", + " 1084.00\n", + " 257.00\n", + " 240.00\n", + " 51.700\n", " \n", " \n", " Sampdoria\n", " Sampdoria\n", - " 24.0\n", - " 50.800\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 6.0\n", - " 6.0\n", - " 0.0\n", + " 29.00\n", + " 49.40\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 8.0\n", + " 7.0\n", " 0.0\n", + " 0.00\n", " ...\n", - " 220.0\n", - " 192.00\n", - " 42.00\n", - " 1.0\n", + " 299.00\n", + " 266.0\n", + " 55.00\n", + " 3.00\n", " 0.0\n", - " 0.0\n", - " 742.0\n", - " 215.0\n", - " 217.0\n", - " 49.800\n", + " 0.00\n", + " 1017.00\n", + " 300.00\n", + " 318.00\n", + " 48.500\n", " \n", " \n", " Sassuolo\n", " Sassuolo\n", - " 26.0\n", - " 48.100\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 15.0\n", - " 12.0\n", - " 1.0\n", - " 2.0\n", + " 27.00\n", + " 48.70\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 18.0\n", + " 13.0\n", + " 3.0\n", + " 4.00\n", " ...\n", - " 177.0\n", - " 120.00\n", - " 47.00\n", - " 2.0\n", - " 2.0\n", - " 0.0\n", - " 710.0\n", - " 146.0\n", - " 130.0\n", - " 52.900\n", + " 249.00\n", + " 182.0\n", + " 62.00\n", + " 2.00\n", + " 4.0\n", + " 0.00\n", + " 986.00\n", + " 216.00\n", + " 173.00\n", + " 55.500\n", " \n", " \n", " Spezia\n", " Spezia\n", - " 24.0\n", - " 46.900\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", + " 30.00\n", + " 46.70\n", + " 20.0\n", + " 220.0\n", + " 1800.0\n", + " 15.0\n", " 10.0\n", - " 6.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 136.0\n", - " 177.00\n", - " 32.00\n", - " 1.0\n", - " 1.0\n", " 2.0\n", - " 776.0\n", - " 212.0\n", - " 179.0\n", - " 54.200\n", + " 2.00\n", + " ...\n", + " 207.00\n", + " 261.0\n", + " 46.00\n", + " 1.00\n", + " 2.0\n", + " 2.00\n", + " 1182.00\n", + " 322.00\n", + " 281.00\n", + " 53.400\n", " \n", " \n", " Torino\n", " Torino\n", - " 23.0\n", - " 51.300\n", + " 23.00\n", + " 52.80\n", + " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 19.0\n", " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 15.0\n", - " 10.0\n", - " 1.0\n", " 1.0\n", + " 1.00\n", " ...\n", - " 148.0\n", - " 199.00\n", - " 17.00\n", - " 2.0\n", + " 209.00\n", + " 265.0\n", + " 21.00\n", + " 3.00\n", " 1.0\n", - " 0.0\n", - " 710.0\n", - " 228.0\n", - " 215.0\n", - " 51.500\n", + " 0.00\n", + " 998.00\n", + " 323.00\n", + " 295.00\n", + " 52.300\n", " \n", " \n", " Udinese\n", " Udinese\n", - " 22.0\n", - " 50.600\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 21.0\n", + " 22.00\n", + " 50.10\n", " 19.0\n", + " 209.0\n", + " 1710.0\n", + " 26.0\n", + " 22.0\n", " 0.0\n", - " 0.0\n", + " 0.00\n", " ...\n", - " 176.0\n", - " 162.00\n", - " 17.00\n", - " 2.0\n", + " 250.00\n", + " 227.0\n", + " 29.00\n", + " 2.00\n", " 0.0\n", - " 1.0\n", - " 707.0\n", - " 142.0\n", - " 187.0\n", - " 43.200\n", + " 1.00\n", + " 950.00\n", + " 189.00\n", + " 237.00\n", + " 44.400\n", " \n", " \n", " Avg\n", " Avg\n", - " 24.6\n", - " 49.995\n", - " 14.0\n", - " 154.0\n", - " 1260.0\n", - " 17.4\n", - " 12.9\n", - " 1.3\n", - " 1.8\n", + " 25.95\n", + " 50.04\n", + " 19.2\n", + " 211.2\n", + " 1728.0\n", + " 24.2\n", + " 17.5\n", + " 2.0\n", + " 2.65\n", " ...\n", - " 173.2\n", - " 164.15\n", - " 24.65\n", - " 1.4\n", - " 1.8\n", - " 0.4\n", - " 718.8\n", - " 178.3\n", - " 178.3\n", - " 49.955\n", + " 241.05\n", + " 228.5\n", + " 33.65\n", + " 1.95\n", + " 2.6\n", + " 0.65\n", + " 1011.95\n", + " 250.95\n", + " 250.95\n", + " 49.855\n", " \n", " \n", "\n", @@ -1463,170 +1463,170 @@ ], "text/plain": [ " team team_players_used team_possession team_games \\\n", - "Atalanta Atalanta 23.0 46.400 14.0 \n", - "Bologna Bologna 23.0 51.100 14.0 \n", - "Cremonese Cremonese 27.0 43.200 14.0 \n", - "Empoli Empoli 25.0 46.000 14.0 \n", - "Fiorentina Fiorentina 25.0 58.700 14.0 \n", - "Verona Hellas Verona 29.0 43.900 14.0 \n", - "Inter Inter 23.0 54.600 14.0 \n", - "Juventus Juventus 26.0 50.500 14.0 \n", - "Lazio Lazio 21.0 50.000 14.0 \n", - "Lecce Lecce 24.0 40.700 14.0 \n", - "Milan Milan 27.0 53.900 14.0 \n", - "Monza Monza 29.0 55.500 14.0 \n", - "Napoli Napoli 24.0 59.800 14.0 \n", - "Roma Roma 23.0 50.800 14.0 \n", - "Salernitana Salernitana 24.0 47.100 14.0 \n", - "Sampdoria Sampdoria 24.0 50.800 14.0 \n", - "Sassuolo Sassuolo 26.0 48.100 14.0 \n", - "Spezia Spezia 24.0 46.900 14.0 \n", - "Torino Torino 23.0 51.300 14.0 \n", - "Udinese Udinese 22.0 50.600 14.0 \n", - "Avg Avg 24.6 49.995 14.0 \n", + "Atalanta Atalanta 24.00 48.40 19.0 \n", + "Bologna Bologna 24.00 52.60 20.0 \n", + "Cremonese Cremonese 29.00 44.00 19.0 \n", + "Empoli Empoli 26.00 46.30 19.0 \n", + "Fiorentina Fiorentina 27.00 57.20 19.0 \n", + "Verona Hellas Verona 29.00 43.40 19.0 \n", + "Inter Inter 23.00 52.90 19.0 \n", + "Juventus Juventus 26.00 49.50 19.0 \n", + "Lazio Lazio 21.00 51.20 19.0 \n", + "Lecce Lecce 26.00 42.50 20.0 \n", + "Milan Milan 27.00 54.50 19.0 \n", + "Monza Monza 29.00 55.10 19.0 \n", + "Napoli Napoli 24.00 61.30 19.0 \n", + "Roma Roma 25.00 49.40 19.0 \n", + "Salernitana Salernitana 28.00 44.80 20.0 \n", + "Sampdoria Sampdoria 29.00 49.40 19.0 \n", + "Sassuolo Sassuolo 27.00 48.70 19.0 \n", + "Spezia Spezia 30.00 46.70 20.0 \n", + "Torino Torino 23.00 52.80 19.0 \n", + "Udinese Udinese 22.00 50.10 19.0 \n", + "Avg Avg 25.95 50.04 19.2 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", - "Atalanta 154.0 1260.0 19.0 14.0 \n", - "Bologna 154.0 1260.0 17.0 12.0 \n", - "Cremonese 154.0 1260.0 11.0 5.0 \n", - "Empoli 154.0 1260.0 10.0 5.0 \n", - "Fiorentina 154.0 1260.0 17.0 15.0 \n", - "Verona 154.0 1260.0 10.0 7.0 \n", - "Inter 154.0 1260.0 31.0 22.0 \n", - "Juventus 154.0 1260.0 21.0 17.0 \n", - "Lazio 154.0 1260.0 25.0 20.0 \n", - "Lecce 154.0 1260.0 12.0 8.0 \n", - "Milan 154.0 1260.0 26.0 21.0 \n", - "Monza 154.0 1260.0 13.0 8.0 \n", - "Napoli 154.0 1260.0 34.0 28.0 \n", - "Roma 154.0 1260.0 17.0 11.0 \n", - "Salernitana 154.0 1260.0 18.0 12.0 \n", - "Sampdoria 154.0 1260.0 6.0 6.0 \n", - "Sassuolo 154.0 1260.0 15.0 12.0 \n", - "Spezia 154.0 1260.0 10.0 6.0 \n", - "Torino 154.0 1260.0 15.0 10.0 \n", - "Udinese 154.0 1260.0 21.0 19.0 \n", - "Avg 154.0 1260.0 17.4 12.9 \n", + "Atalanta 209.0 1710.0 36.0 25.0 \n", + "Bologna 220.0 1800.0 25.0 19.0 \n", + "Cremonese 209.0 1710.0 14.0 6.0 \n", + "Empoli 209.0 1710.0 17.0 9.0 \n", + "Fiorentina 209.0 1710.0 21.0 17.0 \n", + "Verona 209.0 1710.0 16.0 13.0 \n", + "Inter 209.0 1710.0 37.0 25.0 \n", + "Juventus 209.0 1710.0 30.0 23.0 \n", + "Lazio 209.0 1710.0 34.0 25.0 \n", + "Lecce 220.0 1800.0 18.0 12.0 \n", + "Milan 209.0 1710.0 33.0 28.0 \n", + "Monza 209.0 1710.0 22.0 13.0 \n", + "Napoli 209.0 1710.0 46.0 37.0 \n", + "Roma 209.0 1710.0 25.0 16.0 \n", + "Salernitana 220.0 1800.0 24.0 16.0 \n", + "Sampdoria 209.0 1710.0 8.0 7.0 \n", + "Sassuolo 209.0 1710.0 18.0 13.0 \n", + "Spezia 220.0 1800.0 15.0 10.0 \n", + "Torino 209.0 1710.0 19.0 14.0 \n", + "Udinese 209.0 1710.0 26.0 22.0 \n", + "Avg 211.2 1728.0 24.2 17.5 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", - "Atalanta 4.0 5.0 ... 150.0 \n", - "Bologna 3.0 3.0 ... 182.0 \n", - "Cremonese 1.0 3.0 ... 145.0 \n", - "Empoli 0.0 0.0 ... 176.0 \n", - "Fiorentina 1.0 3.0 ... 186.0 \n", - "Verona 0.0 0.0 ... 130.0 \n", - "Inter 2.0 2.0 ... 182.0 \n", - "Juventus 1.0 2.0 ... 161.0 \n", - "Lazio 1.0 2.0 ... 197.0 \n", - "Lecce 1.0 2.0 ... 184.0 \n", - "Milan 2.0 2.0 ... 164.0 \n", - "Monza 1.0 1.0 ... 207.0 \n", - "Napoli 3.0 3.0 ... 195.0 \n", - "Roma 2.0 3.0 ... 198.0 \n", - "Salernitana 1.0 1.0 ... 150.0 \n", - "Sampdoria 0.0 0.0 ... 220.0 \n", - "Sassuolo 1.0 2.0 ... 177.0 \n", - "Spezia 1.0 1.0 ... 136.0 \n", - "Torino 1.0 1.0 ... 148.0 \n", - "Udinese 0.0 0.0 ... 176.0 \n", - "Avg 1.3 1.8 ... 173.2 \n", + "Atalanta 6.0 8.00 ... 209.00 \n", + "Bologna 3.0 3.00 ... 252.00 \n", + "Cremonese 2.0 4.00 ... 205.00 \n", + "Empoli 0.0 0.00 ... 251.00 \n", + "Fiorentina 2.0 4.00 ... 259.00 \n", + "Verona 0.0 0.00 ... 192.00 \n", + "Inter 2.0 2.00 ... 242.00 \n", + "Juventus 2.0 3.00 ... 210.00 \n", + "Lazio 3.0 4.00 ... 261.00 \n", + "Lecce 1.0 2.00 ... 263.00 \n", + "Milan 2.0 2.00 ... 234.00 \n", + "Monza 3.0 3.00 ... 279.00 \n", + "Napoli 4.0 5.00 ... 258.00 \n", + "Roma 3.0 5.00 ... 266.00 \n", + "Salernitana 1.0 1.00 ... 226.00 \n", + "Sampdoria 0.0 0.00 ... 299.00 \n", + "Sassuolo 3.0 4.00 ... 249.00 \n", + "Spezia 2.0 2.00 ... 207.00 \n", + "Torino 1.0 1.00 ... 209.00 \n", + "Udinese 0.0 0.00 ... 250.00 \n", + "Avg 2.0 2.65 ... 241.05 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", - "Atalanta 168.00 19.00 0.0 \n", - "Bologna 158.00 24.00 2.0 \n", - "Cremonese 188.00 19.00 2.0 \n", - "Empoli 151.00 28.00 2.0 \n", - "Fiorentina 176.00 35.00 1.0 \n", - "Verona 205.00 18.00 1.0 \n", - "Inter 154.00 14.00 1.0 \n", - "Juventus 156.00 21.00 0.0 \n", - "Lazio 124.00 35.00 1.0 \n", - "Lecce 184.00 27.00 3.0 \n", - "Milan 161.00 15.00 2.0 \n", - "Monza 178.00 24.00 0.0 \n", - "Napoli 116.00 18.00 1.0 \n", - "Roma 157.00 7.00 0.0 \n", - "Salernitana 157.00 34.00 4.0 \n", - "Sampdoria 192.00 42.00 1.0 \n", - "Sassuolo 120.00 47.00 2.0 \n", - "Spezia 177.00 32.00 1.0 \n", - "Torino 199.00 17.00 2.0 \n", - "Udinese 162.00 17.00 2.0 \n", - "Avg 164.15 24.65 1.4 \n", + "Atalanta 218.0 25.00 1.00 \n", + "Bologna 229.0 33.00 3.00 \n", + "Cremonese 247.0 29.00 3.00 \n", + "Empoli 214.0 31.00 2.00 \n", + "Fiorentina 229.0 52.00 1.00 \n", + "Verona 276.0 22.00 1.00 \n", + "Inter 214.0 18.00 2.00 \n", + "Juventus 210.0 25.00 0.00 \n", + "Lazio 185.0 42.00 1.00 \n", + "Lecce 265.0 43.00 3.00 \n", + "Milan 226.0 20.00 3.00 \n", + "Monza 242.0 34.00 0.00 \n", + "Napoli 167.0 27.00 1.00 \n", + "Roma 214.0 10.00 0.00 \n", + "Salernitana 233.0 49.00 7.00 \n", + "Sampdoria 266.0 55.00 3.00 \n", + "Sassuolo 182.0 62.00 2.00 \n", + "Spezia 261.0 46.00 1.00 \n", + "Torino 265.0 21.00 3.00 \n", + "Udinese 227.0 29.00 2.00 \n", + "Avg 228.5 33.65 1.95 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", - "Atalanta 5.0 1.0 \n", - "Bologna 3.0 0.0 \n", - "Cremonese 3.0 0.0 \n", - "Empoli 0.0 0.0 \n", - "Fiorentina 3.0 0.0 \n", - "Verona 0.0 1.0 \n", - "Inter 2.0 0.0 \n", - "Juventus 2.0 0.0 \n", - "Lazio 2.0 1.0 \n", - "Lecce 2.0 0.0 \n", - "Milan 2.0 1.0 \n", - "Monza 1.0 0.0 \n", - "Napoli 3.0 0.0 \n", - "Roma 3.0 0.0 \n", - "Salernitana 1.0 1.0 \n", - "Sampdoria 0.0 0.0 \n", - "Sassuolo 2.0 0.0 \n", - "Spezia 1.0 2.0 \n", - "Torino 1.0 0.0 \n", - "Udinese 0.0 1.0 \n", - "Avg 1.8 0.4 \n", + "Atalanta 8.0 1.00 \n", + "Bologna 3.0 1.00 \n", + "Cremonese 4.0 0.00 \n", + "Empoli 0.0 0.00 \n", + "Fiorentina 4.0 0.00 \n", + "Verona 0.0 1.00 \n", + "Inter 2.0 1.00 \n", + "Juventus 3.0 0.00 \n", + "Lazio 4.0 1.00 \n", + "Lecce 2.0 1.00 \n", + "Milan 2.0 2.00 \n", + "Monza 3.0 1.00 \n", + "Napoli 4.0 0.00 \n", + "Roma 5.0 0.00 \n", + "Salernitana 1.0 1.00 \n", + "Sampdoria 0.0 0.00 \n", + "Sassuolo 4.0 0.00 \n", + "Spezia 2.0 2.00 \n", + "Torino 1.0 0.00 \n", + "Udinese 0.0 1.00 \n", + "Avg 2.6 0.65 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", - "Atalanta 834.0 181.0 \n", - "Bologna 731.0 165.0 \n", - "Cremonese 744.0 242.0 \n", - "Empoli 723.0 163.0 \n", - "Fiorentina 694.0 193.0 \n", - "Verona 758.0 240.0 \n", - "Inter 596.0 126.0 \n", - "Juventus 674.0 184.0 \n", - "Lazio 746.0 151.0 \n", - "Lecce 703.0 216.0 \n", - "Milan 690.0 168.0 \n", - "Monza 692.0 141.0 \n", - "Napoli 687.0 142.0 \n", - "Roma 709.0 142.0 \n", - "Salernitana 750.0 169.0 \n", - "Sampdoria 742.0 215.0 \n", - "Sassuolo 710.0 146.0 \n", - "Spezia 776.0 212.0 \n", - "Torino 710.0 228.0 \n", - "Udinese 707.0 142.0 \n", - "Avg 718.8 178.3 \n", + "Atalanta 1165.00 239.00 \n", + "Bologna 1081.00 224.00 \n", + "Cremonese 1052.00 348.00 \n", + "Empoli 999.00 217.00 \n", + "Fiorentina 942.00 249.00 \n", + "Verona 1038.00 345.00 \n", + "Inter 869.00 198.00 \n", + "Juventus 947.00 230.00 \n", + "Lazio 1021.00 183.00 \n", + "Lecce 1072.00 373.00 \n", + "Milan 964.00 224.00 \n", + "Monza 958.00 197.00 \n", + "Napoli 933.00 201.00 \n", + "Roma 981.00 184.00 \n", + "Salernitana 1084.00 257.00 \n", + "Sampdoria 1017.00 300.00 \n", + "Sassuolo 986.00 216.00 \n", + "Spezia 1182.00 322.00 \n", + "Torino 998.00 323.00 \n", + "Udinese 950.00 189.00 \n", + "Avg 1011.95 250.95 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", - "Atalanta 211.0 46.200 \n", - "Bologna 123.0 57.300 \n", - "Cremonese 184.0 56.800 \n", - "Empoli 135.0 54.700 \n", - "Fiorentina 224.0 46.300 \n", - "Verona 229.0 51.200 \n", - "Inter 172.0 42.300 \n", - "Juventus 166.0 52.600 \n", - "Lazio 126.0 54.500 \n", - "Lecce 188.0 53.500 \n", - "Milan 205.0 45.000 \n", - "Monza 160.0 46.800 \n", - "Napoli 181.0 44.000 \n", - "Roma 159.0 47.200 \n", - "Salernitana 175.0 49.100 \n", - "Sampdoria 217.0 49.800 \n", - "Sassuolo 130.0 52.900 \n", - "Spezia 179.0 54.200 \n", - "Torino 215.0 51.500 \n", - "Udinese 187.0 43.200 \n", - "Avg 178.3 49.955 \n", + "Atalanta 279.00 46.100 \n", + "Bologna 180.00 55.400 \n", + "Cremonese 267.00 56.600 \n", + "Empoli 189.00 53.400 \n", + "Fiorentina 289.00 46.300 \n", + "Verona 388.00 47.100 \n", + "Inter 232.00 46.000 \n", + "Juventus 230.00 50.000 \n", + "Lazio 181.00 50.300 \n", + "Lecce 294.00 55.900 \n", + "Milan 275.00 44.900 \n", + "Monza 215.00 47.800 \n", + "Napoli 231.00 46.500 \n", + "Roma 225.00 45.000 \n", + "Salernitana 240.00 51.700 \n", + "Sampdoria 318.00 48.500 \n", + "Sassuolo 173.00 55.500 \n", + "Spezia 281.00 53.400 \n", + "Torino 295.00 52.300 \n", + "Udinese 237.00 44.400 \n", + "Avg 250.95 49.855 \n", "\n", "[21 rows x 313 columns]" ] }, - "execution_count": 7, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1652,7 +1652,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 45, "id": "f4017b2f", "metadata": {}, "outputs": [], @@ -1981,7 +1981,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 46, "id": "6f8707b8", "metadata": {}, "outputs": [ @@ -1993,505 +1993,474 @@ "Averaging players stats with past seasons:\n", "Meret 1\n", "Provedel 1\n", - "Maignan 0.9682692307692308\n", - "Silvestri 1\n", - "Sepe 1\n", - "Szczesny 1\n", - "Consigli 1\n", - "Falcone 1\n", - "Musso 1\n", "Vicario 1\n", + "Szczesny 1\n", + "Falcone 1\n", + "Silvestri 1\n", "Rui Patricio 1\n", "Milinkovic-Savic V. 1\n", + "Sepe 1\n", + "Maignan 0.7353365384615383\n", "Audero 1\n", + "Musso 1\n", "Montipo' 1\n", + "Tatarusanu 1\n", + "Consigli 1\n", + "Dragowski 1\n", + "Terracciano 1\n", "Sportiello 1\n", "Skorupski 1\n", "Perin 1\n", - "Handanovic 0.957053757053757\n", - "Tatarusanu 1\n", - "Dragowski 1\n", - "Terracciano 1\n", - "Berisha 1\n", - "Radu I. 1\n", - "Cragno 1\n", - "Mirante 0.9682692307692308\n", - "Ujkani 1\n", + "Handanovic 0.7268191268191269\n", + "Zoet 1\n", "Pegolo 1\n", + "Mirante 0.7353365384615383\n", + "Ujkani 1\n", + "Berisha 1\n", + "Marchetti 1\n", "Padelli 1\n", "Bardi 1\n", - "Cordaz 0.957053757053757\n", + "Cordaz 0.9993762993762995\n", "Pinsoglio 1\n", "Fiorillo 1\n", + "Cragno 1\n", "Sirigu 1\n", "Rossi F. 1\n", "Berardi A. 1\n", "Gemello 1\n", "Ravaglia 1\n", - "Zoet 1\n", "Boer 1\n", "Adamonis 1\n", "Marfella 1\n", "Zovko 1\n", "Piana 1\n", + "Dimarco 0.9979567307692307\n", "Smalling 1\n", - "Hernandez T. 0.829945054945055\n", - "Bastoni S. 0.7139312300602624\n", - "Udogie 0.6955729984301412\n", - "Dumfries 0.9389277389277388\n", - "Romagnoli 1\n", - "Rodrigo Becao 0.569105180533752\n", - "Parisi 1\n", - "Mazzocchi 1\n", - "Bremer 0.8351648351648351\n", - "Demiral 0.94850863422292\n", - "Dimarco 0.9682692307692308\n", - "Di Lorenzo 0.9389277389277388\n", - "Ibanez 0.9113122171945701\n", - "Tomori 0.9995037220843672\n", - "Toloi 1\n", - "Gosens 1\n", - "Rrahmani 0.5365301365301365\n", - "Kalulu 1\n", - "Bijol affine to Rodrigo Becao\n", - "Bijol 0.6955729984301412\n", "Doig affine to Lazovic\n", - "Doig 0.5207498383968971\n", - "Mario Rui 0.7160310277957336\n", - "Spinazzola 1\n", - "Lazzari 0.928110599078341\n", - "Mancini 0.9389277389277388\n", + "Doig 0.6426470588235293\n", + "Hernandez T. 0.8403846153846155\n", + "Udogie 0.7203296703296703\n", + "Parisi 1\n", + "Romagnoli 1\n", + "Di Lorenzo 0.9677156177156175\n", "Danilo 1\n", - "Kyriakopoulos 0.7631678666161424\n", - "Vojvoda 0.6105342932929139\n", + "Mazzocchi 1\n", + "Mario Rui 0.7415158371040723\n", + "Bastoni S. 0.7048387096774194\n", + "Rodrigo Becao 0.6242857142857142\n", + "Tomori 1\n", + "Juan Jesus 0.5602564102564102\n", + "Ibanez 0.8898190045248868\n", + "Demiral 0.9604395604395606\n", "Scalvini 1\n", - "Skriniar 0.8852747252747253\n", - "Juan Jesus 0.5269492412349555\n", - "Bonucci 0.829945054945055\n", - "Calabria 0.5958579881656805\n", - "Bastoni 0.7139312300602624\n", + "Bijol affine to Rodrigo Becao\n", + "Bijol 0.7203296703296703\n", + "Toloi 1\n", "Depaoli 1\n", - "Darmian 0.8852747252747253\n", - "Mari' 1\n", - "Martinez Quarta 1\n", + "Mancini 0.9677156177156175\n", + "Bremer 0.8326340326340327\n", + "Ebuehi 0.9038461538461539\n", + "Dumfries 0.8658508158508158\n", + "Darmian 0.941230769230769\n", + "Kalulu 1\n", + "Vojvoda 0.7534482758620689\n", "Maehle 1\n", - "Biraghi 0.7776061776061776\n", - "Medel 0.9389277389277388\n", - "Patric 0.829945054945055\n", - "Rodriguez R. 0.7811247575953458\n", - "Aina 1\n", - "Cambiaso 1\n", + "Bastoni 0.8132754342431762\n", + "Gosens 1\n", + "Milenkovic 0.6920814479638008\n", + "Rodriguez R. 0.8403846153846154\n", + "Reca 1\n", + "Rrahmani 0.6111888111888112\n", + "Kyriakopoulos 0.6375331564986738\n", + "Martinez Quarta 1\n", "Perez N. 1\n", - "Hysaj 0.8394846532777566\n", - "Faraoni 0.5532967032967032\n", - "Milenkovic 0.6509372979961215\n", - "Marusic 0.9389277389277388\n", - "Colley 0.8991071428571429\n", - "Toljan 1\n", - "Ampadu 0.7127699886320576\n", - "Singo 0.6323390894819467\n", - "Casale 0.4465811965811966\n", - "Soppy 0.8202511773940345\n", - "Kiwior 1\n", - "Ghiglione 0.8833474218089602\n", - "Ghiglione 0.5924716248138645\n", - "De Vrij 0.8115018315018314\n", - "Alex Sandro 1\n", - "Ceccherini 0.5207498383968971\n", - "Fazio 1\n", - "Rogerio 1\n", - "Reca 0.9738021978021977\n", - "Gunter 0.8852747252747252\n", - "Djidji 1\n", - "Augello 0.7992063492063493\n", - "Bellanova 0.5926976249556895\n", - "Erlic 0.7655677655677655\n", + "Calabria 0.7110946745562131\n", + "Skriniar 0.9124175824175823\n", + "Marusic 0.9677156177156175\n", + "Lazzari 0.8674937965260546\n", + "Augello 0.8403846153846154\n", + "Ampadu 0.888262599469496\n", "Ismajli 1\n", - "Ebuehi 0.6043956043956044\n", - "Kasius 1\n", - "Acerbi 0.6124542124542124\n", - "Zappacosta 0.1301874595992243\n", - "Chiriches 0.39598332701780975\n", - "Gyomber 0.7853243530662884\n", - "Pezzella Giu. 0.6562009419152276\n", - "Ferrari G. 0.7992063492063493\n", - "Bereszynski 0.8220408163265306\n", - "Hateboer 1\n", - "Karsdorp 0.5532967032967033\n", - "Nuytinck 0.4918192918192918\n", - "Lykogiannis 0.902276295133438\n", - "Buongiorno 1\n", - "Nikolaou 0.7992063492063493\n", - "Ayhan 0.8660296225513617\n", - "Stojanovic 0.8047952047952047\n", + "Cambiaso 1\n", + "Hysaj 0.8693633952254641\n", "Izzo 1\n", - "D'ambrosio 0.44263736263736264\n", - "Rugani 0.36886446886446883\n", - "De Sciglio 0.8852747252747253\n", + "Biraghi 0.8176715176715176\n", + "Medel 0.8658508158508158\n", + "Bonucci 0.6302884615384615\n", + "Acerbi 0.6869230769230769\n", + "Spinazzola 1\n", + "Lykogiannis 0.9813186813186815\n", + "Djidji 1\n", + "Singo 0.6723076923076923\n", + "Mari' 1\n", + "Casale 0.5724358974358974\n", + "De Vrij 0.7283333333333333\n", + "Patric 0.7003205128205129\n", "Luperto 1\n", - "De Silvestri 0.6425381070542361\n", - "Djimsiti 0.3569656150301312\n", - "Palomino 0.0\n", - "Bonifazi 0.7041958041958042\n", - "Hristov 1\n", - "Kjaer 1\n", - "Igor 0.663956043956044\n", - "Soumaoro 0.71138147566719\n", - "Ballo-Toure' affine to Calabria\n", - "Ballo-Toure' 0.25536770921386304\n", - "De Winter 1\n", - "Ferrari A. 1\n", - "Florenzi 0.18443223443223442\n", - "Sala 0.7377289377289378\n", - "Caldara 0.5926976249556895\n", - "Murru 0.502997002997003\n", - "Walukiewicz 0.6562009419152276\n", - "Walukiewicz 0.4495488812600273\n", - "Kumbulla 0.3905623787976729\n", - "Amian 0.4841346153846154\n", - "Ostigard 1\n", - "Marrone 0.21873364730507583\n", - "Radovanovic 1\n", - "Venuti 0.48112756808408985\n", - "Magnani 1\n", - "Magnani 0.35410989010989\n", + "Faraoni 0.42019230769230775\n", + "Ceccherini 0.6426470588235293\n", + "Aina 0.8003663003663003\n", + "Soppy 0.6133241758241759\n", + "Ferrari A. 0.2986622073578596\n", + "Zappacosta 0.24717194570135745\n", + "Ferrari G. 0.8403846153846154\n", + "Fazio 1\n", + "Hateboer 1\n", + "Rogerio 1\n", + "Buongiorno 1\n", + "Gunter 0.7283333333333333\n", + "Colley 0.787860576923077\n", "Terzic 1\n", + "Toljan 1\n", + "Zortea 0.5329575596816976\n", + "Soumaoro 0.9004120879120879\n", + "Amian 0.6828124999999999\n", + "Gyomber 0.7590570719602977\n", + "Alex Sandro 0.9604395604395606\n", + "Pezzella Giu. 0.7359890109890109\n", + "Bereszynski 0.0\n", + "Venuti 0.6576923076923077\n", + "Palomino 0.24717194570135745\n", + "Nuytinck 0.25441595441595444\n", + "Nikolaou 0.8403846153846154\n", + "Igor 0.7843589743589743\n", + "Dawidowicz 1\n", + "Bellanova 0.5539702233250621\n", + "Erlic 0.6869230769230769\n", + "Ballo-Toure' affine to Calabria\n", + "Ballo-Toure' 0.19393491124260354\n", + "Stojanovic 0.763986013986014\n", + "Zima 0.7563461538461539\n", + "Ghiglione 0.7926035502958579\n", + "Rugani 0.4201923076923077\n", + "De Sciglio 0.6723076923076924\n", + "Djimsiti 0.37952853598014885\n", + "Caldara 0.7201612903225807\n", + "Murru 0.5347902097902096\n", + "Karsdorp 0.4201923076923077\n", + "Bonifazi 0.5347902097902096\n", + "Kjaer 1\n", + "Magnani 1\n", + "Walukiewicz 0.9813186813186815\n", + "Walukiewicz 0.3666039729501268\n", + "Ayhan 0.7307692307692307\n", + "Ruggeri 0.9813186813186815\n", + "De Winter 1\n", + "Ostigard 1\n", + "Radovanovic 1\n", + "D'ambrosio 0.3361538461538462\n", + "De Silvestri 0.48796526054590567\n", + "Sala 0.5602564102564103\n", + "Chiriches 0.35530503978779837\n", "Gabbia 1\n", - "Zortea 0.6335733232284956\n", - "Dawidowicz 0.7811247575953458\n", - "Carboni 0.3062271062271062\n", - "Lovato 0.28708791208791207\n", - "Tuia 0.17012617012617012\n", - "Amione 1\n", - "Vasquez 0.5741758241758242\n", - "Zima 0.6639560439560439\n", - "Coppola D. 1\n", - "Conti 0.3161695447409733\n", - "Conti 1\n", + "Kumbulla 0.395475113122172\n", + "Lovato 0.6439903846153846\n", + "Tuia 0.1908119658119658\n", + "Ferrer 0.12450142450142451\n", + "Antov 1\n", + "Vasquez 0.49065934065934075\n", + "Zanoli 0.42932692307692305\n", + "Conti 0.24010989010989015\n", + "Conti 0.8679180434949665\n", + "Marrone 0.16355311355311355\n", "Tonelli 0.0\n", "Radu 0.0\n", + "Florenzi 0.14006410256410257\n", "Fares 0.0\n", "Fares 0.0\n", - "Marchizza 0.24175824175824173\n", + "Marchizza 0.1807692307692308\n", "Romagna 0.0\n", "Romagna 0.0\n", - "Ranieri L. 0.08506308506308506\n", - "Cetin 0.38278388278388276\n", - "Muldur 0.07139312300602622\n", - "Ferrer 0.0\n", - "Ruggeri 0.6562009419152276\n", - "Antov 1\n", + "Ranieri L. 0.06360398860398861\n", + "Muldur 0.054218362282878414\n", + "Carboni 0.228974358974359\n", "Amey 0.0\n", - "Kamenovic 0.0\n", - "Vina 0.25536770921386304\n", - "Zanoli 0.18443223443223442\n", - "Ruan 0.44263736263736264\n", - "Cacace 0.44263736263736264\n", - "Milinkovic-Savic 0.7776061776061776\n", - "Barella 0.8606837606837607\n", - "Zaccagni 0.9921182266009851\n", - "Zielinski 0.8852747252747253\n", - "Luis Alberto 0.7811247575953458\n", - "Frattesi 0.8606837606837607\n", - "Politano 0.8718614718614718\n", + "Vina 0.19393491124260354\n", + "Ruan 1\n", + "Coppola D. 1\n", + "Cacace 0.5042307692307693\n", + "Zaccagni 0.9852785145888594\n", + "Milinkovic-Savic 0.8176715176715176\n", + "Barella 0.8870726495726495\n", + "Zielinski 0.9124175824175823\n", + "Luis Alberto 0.790950226244344\n", + "Felipe Anderson 0.8403846153846153\n", + "Diaz B. 0.921712158808933\n", "Koopmeiners 1\n", - "Pereyra 1\n", - "Felipe Anderson 0.8153846153846154\n", - "Calhanoglu 0.846218487394958\n", - "Diaz B. 0.8567174760723147\n", - "Pellegrini Lo. 1\n", + "Calhanoglu 0.8898190045248868\n", + "Frattesi 0.8870726495726495\n", "Zambo Anguissa 1\n", - "Lobotka 1\n", - "Malinovskyi 0.959047619047619\n", - "Candreva 0.8931623931623932\n", - "Pasalic 0.7776061776061776\n", - "Bennacer 0.9995037220843672\n", + "Elmas 0.8176715176715176\n", + "Pereyra 1\n", + "Miranchuk 1\n", "Samardzic 1\n", - "Chiesa 0.15808477237048665\n", - "Brozovic 0.569105180533752\n", - "Bonaventura 0.8567174760723147\n", - "Sensi 1\n", - "Bandinelli 0.8852747252747253\n", - "Ikone' 1\n", - "Barak 0.8711633194391815\n", - "Mkhitaryan 0.814959234314073\n", + "Politano 0.9167832167832167\n", + "Rabiot 0.787860576923077\n", + "Lobotka 1\n", + "Tonali 0.7470085470085471\n", + "Lazovic 0.7415158371040723\n", + "Bonaventura 0.8674937965260546\n", + "Pellegrini Lo. 0.9604395604395606\n", "Pessina 1\n", - "Tonali 0.7377289377289378\n", - "Arslan 0.959047619047619\n", - "Lazovic 0.6509372979961215\n", - "Cristante 0.9113122171945701\n", - "Elmas 0.7776061776061776\n", - "Bajrami 0.8852747252747253\n", - "Vecino 1\n", - "Mandragora 1\n", - "Djuricic 1\n", - "Lukic 0.7588069073783359\n", - "Soriano 0.8220408163265306\n", - "Zaniolo 0.7904238618524334\n", - "Sottil 0.6455128205128206\n", - "Traore' Hj. 0.3569656150301312\n", - "Messias 0.9363482671174977\n", - "Cuadrado 0.8718614718614718\n", - "Locatelli 0.7853243530662884\n", - "Rabiot 0.6916208791208792\n", - "Dominguez 0.94850863422292\n", - "Tameze 0.7571428571428571\n", - "Miranchuk 0.9670329670329669\n", - "De Roon 0.959047619047619\n", - "Verdi 1\n", - "Walace 0.8606837606837607\n", - "Mckennie 1\n", - "Makengo 0.8047952047952047\n", - "Ricci S. 1\n", - "Coulibaly L. 1\n", - "Sabiri 1\n", - "Miretti 1\n", - "Haas 1\n", - "Orsolini 0.8394846532777566\n", - "Ederson D.s. 1\n", - "El Shaarawy 0.7377289377289378\n", - "Bourabia 1\n", - "Henderson L. 0.6989010989010989\n", - "Maggiore 0.590580847723705\n", - "Ilic 0.4841346153846154\n", - "Kovalenko 0.5958579881656805\n", - "Zalewski 1\n", "Ferguson affine to Svanberg\n", - "Ferguson 0.4918192918192918\n", - "Saponara 0.8394846532777566\n", - "Rincon 1\n", - "Cataldi 0.829945054945055\n", - "Zurkowski 0.13124018838304552\n", - "Rovella 0.9843014128728415\n", - "Agudelo 1\n", - "Amrabat 1\n", - "Lopez M. 0.8220408163265306\n", - "Gyasi 0.7377289377289378\n", - "Pobega 0.6263736263736264\n", - "Harroui 1\n", - "Ceide 1\n", - "Grassi 0.6124542124542124\n", - "Krunic 0.5532967032967033\n", - "Linetty 1\n", - "Miguel Veloso 1\n", - "Ekdal 0.8833474218089602\n", - "Schouten 1\n", - "Saelemaekers 0.43034188034188037\n", - "Basic 0.7631678666161424\n", - "Aebischer 1\n", - "Ranocchia F. affine to Henderson L.\n", - "Ranocchia F. 0.34945054945054943\n", - "Verre 1\n", - "Gagliardini 0.6147741147741148\n", - "Vieira 1\n", - "Castrovilli 0.0\n", - "Maldini 1\n", - "Maldini 1\n", - "Marin 0.8931623931623932\n", - "Maleh 0.5532967032967033\n", - "Matheus Henrique 0.70821978021978\n", - "Asllani 0.6989966555183946\n", - "Duncan 0.5365301365301365\n", - "Molina S. 0.39598332701780975\n", - "Kastanos 0.5737891737891738\n", - "Bohinen 1\n", - "Benassi 0.38278388278388276\n", - "Vignato 0.7377289377289377\n", - "Obiang 1\n", - "Obiang 0.26826506826506824\n", - "Askildsen 1\n", - "Hongla 1\n", - "Yepes 1\n", - "Capezzi 0.0\n", - "Jajalo 0.20119880119880118\n", - "Bakayoko 0.0\n", + "Ferguson 0.6536324786324785\n", + "Ikone' 1\n", + "Candreva 0.9063568376068376\n", + "Bennacer 1\n", + "El Shaarawy 0.8092592592592592\n", + "Pasalic 0.7722453222453222\n", + "Orsolini 0.9852785145888594\n", + "Bandinelli 0.9124175824175823\n", + "Mkhitaryan 0.8863523573200994\n", + "Sensi 1\n", + "Chiesa 0.7203296703296703\n", + "Lukic 0.7203296703296703\n", "Fagioli affine to Henderson L.\n", - "Fagioli 0.34945054945054943\n", - "Demme 0.34945054945054943\n", - "Akpa Akpro 0.0\n", - "Akpa Akpro 0.0\n", - "Darboe 0.0\n", - "Darboe 0.44263736263736264\n", - "Bove 1\n", - "Bove 1\n", - "Urbanski 0.0\n", - "Romero L. 0.5532967032967032\n", - "Bianco 0.0\n", - "Sher 0.0\n", - "Nguiamba 0.0\n", + "Fagioli 0.4423076923076923\n", + "Messias 0.8403846153846153\n", + "Arslan 1\n", + "Brozovic 0.48021978021978023\n", + "Verdi 1\n", + "Barak 0.888262599469496\n", + "Soriano 0.9124175824175823\n", + "Dominguez 1\n", + "Ranocchia F. affine to Henderson L.\n", + "Ranocchia F. 0.4423076923076923\n", + "Cristante 0.9392533936651583\n", + "Bajrami 0.8643956043956044\n", + "Ricci S. 1\n", + "Saponara 0.8693633952254641\n", + "Vecino 1\n", + "Locatelli 0.8674937965260546\n", + "Zaniolo 0.7803571428571429\n", + "Traore' Hj. 0.5421836228287841\n", + "Maldini 1\n", + "Coulibaly L. 1\n", + "De Roon 0.9524358974358974\n", + "Mandragora 1\n", + "Haas 1\n", + "Bourabia 1\n", + "Sottil 0.49022435897435895\n", + "Agudelo 1\n", + "Makengo 0.8149184149184149\n", + "Zalewski 1\n", + "Aebischer 1\n", + "Ederson D.s. 1\n", + "Miretti 1\n", + "Cataldi 0.8929086538461539\n", + "Djuricic 1\n", + "Linetty 1\n", + "Walace 0.8870726495726495\n", + "Marin 0.8586538461538462\n", + "Mckennie 1\n", + "Pobega 0.5724358974358975\n", "Volpato 1\n", - "Praszelik 0.0\n", - "Immobile 0.7853243530662884\n", + "Cuadrado 0.763986013986014\n", + "Amrabat 1\n", + "Tameze 0.7961538461538461\n", + "Gyasi 0.8403846153846154\n", + "Ilic 0.5777644230769232\n", + "Harroui 0.9167832167832167\n", + "Duncan 0.662121212121212\n", + "Miguel Veloso 1\n", + "Ekdal 0.9247041420118343\n", + "Nicolussi Caviglia 1\n", + "Schouten 1\n", + "Rovella 0.8177655677655677\n", + "Crnigoj 0.050509049773755664\n", + "Romero L. 1\n", + "Basic 0.8114058355437664\n", + "Sabiri 1\n", + "Grassi 0.6296794871794873\n", + "Krunic 0.4802197802197803\n", + "Rincon 1\n", + "Vieira 1\n", + "Henderson L. 0.6192307692307691\n", + "Obiang 1\n", + "Lopez M. 0.6723076923076923\n", + "Saelemaekers 0.5135683760683761\n", + "Maggiore 0.4906593406593407\n", + "Akpa Akpro 0.0\n", + "Akpa Akpro 0.0\n", + "Kovalenko 0.6464497041420117\n", + "Maleh 0.30666208791208793\n", + "Matheus Henrique 0.7395384615384616\n", + "Asllani 0.7466555183946488\n", + "Ceide 1\n", + "Gagliardini 0.7470085470085471\n", + "Bove 1\n", + "Bianco 1\n", + "Benassi 0.14310897435897438\n", + "Kastanos 0.6847578347578348\n", + "Vignato 0.5602564102564103\n", + "Zurkowski 0.049065934065934076\n", + "Castrovilli 0.21923076923076923\n", + "Bohinen 1\n", + "Capezzi 0.0\n", + "Molina S. 0.29608753315649866\n", + "Bakayoko 0.0\n", + "Demme 0.2653846153846154\n", + "Askildsen 1\n", + "Darboe 0.0\n", + "Darboe 0.3361538461538462\n", + "Urbanski 0.0\n", + "Hongla 0.8403846153846154\n", + "Yepes 1\n", + "Osimhen 0.9337606837606838\n", + "Dybala 0.7698275862068965\n", + "Martinez L. 0.9124175824175823\n", + "Rafael Leao 0.8898190045248868\n", + "Immobile 0.7590570719602977\n", "Vlahovic 1\n", - "Rafael Leao 0.846218487394958\n", - "Martinez L. 0.8852747252747253\n", - "Dybala 0.6335733232284956\n", - "Arnautovic 0.8047952047952047\n", - "Beto 1\n", - "Giroud 0.9158014399393709\n", - "Osimhen 0.8196988196988197\n", - "Deulofeu 0.9113122171945701\n", - "Lukaku 0.2459096459096459\n", - "Pedro 0.829945054945055\n", - "Abraham 0.8374220374220374\n", - "Berardi 0.40239760239760236\n", - "Simeone 0.5249607535321821\n", - "Correa 1\n", - "Zapata D. 0.7377289377289377\n", - "Dzeko 0.8606837606837607\n", + "Arnautovic 0.7130536130536129\n", + "Dzeko 0.8870726495726495\n", "Nzola 1\n", - "Sanabria 0.6868510799545282\n", - "Muriel 0.7377289377289378\n", - "Rebic 0.829945054945055\n", - "Bonazzoli 0.9682692307692308\n", - "Caprari 0.9186813186813187\n", - "Di Maria affine to Chiesa\n", - "Di Maria 0.94850863422292\n", - "Lozano 0.959047619047619\n", - "Pinamonti 0.8931623931623932\n", - "Barrow 0.6509372979961215\n", - "Henry 0.9743589743589743\n", - "Piatek 1\n", - "Gonzalez N. 0.40239760239760236\n", - "Caputo 0.8606837606837607\n", - "Raspadori 0.6379731379731379\n", - "Okereke 1\n", + "Beto 1\n", + "Abraham 0.863097713097713\n", + "Giroud 0.9852785145888594\n", + "Deulofeu 0.790950226244344\n", + "Correa 1\n", + "Pedro 0.8929086538461539\n", + "Lozano 1\n", + "Simeone 0.4906593406593407\n", "Cabral 1\n", - "Gabbiadini 1\n", - "Belotti 1\n", - "Origi affine to Rebic\n", - "Origi 0.829945054945055\n", + "Rebic 0.7703525641025643\n", + "Bonazzoli 0.9454326923076923\n", + "Berardi 0.5602564102564103\n", + "Caprari 0.9322527472527472\n", + "Di Maria affine to Chiesa\n", + "Di Maria 1\n", + "Piatek 1\n", + "Sanabria 0.8114058355437664\n", + "Zapata D. 0.8403846153846154\n", + "Kean 0.8929086538461539\n", + "Pinamonti 0.8109508547008547\n", + "Gonzalez N. 0.45839160839160836\n", + "Muriel 0.6847578347578348\n", + "Okereke 1\n", "Alvarez A. affine to Raspadori\n", - "Alvarez A. 0.5532967032967033\n", - "Verde 0.4694638694638694\n", - "Destro 0.8506308506308506\n", - "Kean 0.829945054945055\n", - "Pellegri 1\n", - "Success 1\n", - "Lasagna 0.94850863422292\n", - "Pjaca 0.6698717948717949\n", - "Petagna 0.8612637362637364\n", - "Nestorovski 1\n", - "Nestorovski 0.502997002997003\n", - "Piccoli affine to Lasagna\n", - "Piccoli 0.3161695447409733\n", - "Kallon 1\n", + "Alvarez A. 0.6536324786324785\n", + "Barrow 0.6920814479638008\n", + "Caputo 0.19081196581196586\n", + "Petagna 1\n", "Di Francesco F. 1\n", - "Boga 0.7377289377289378\n", - "Quagliarella 0.7377289377289377\n", - "Ibrahimovic 0.0\n", - "Shomurodov 0.47425431711146\n", + "Boga 1\n", "Djuric 1\n", + "Henry 0.8326340326340327\n", + "Success 1\n", + "Gabbiadini 1\n", + "Origi affine to Rebic\n", + "Origi 0.8403846153846154\n", + "Lammers 0.457948717948718\n", + "Kallon 1\n", + "Lasagna 0.9004120879120879\n", + "Nestorovski 1\n", + "Pellegri 1\n", + "Belotti 1\n", + "Raspadori 0.6201388888888889\n", + "Verde 0.5093240093240092\n", + "Destro 0.636039886039886\n", + "Sansone 0.6225071225071225\n", + "Pjaca 0.6439903846153846\n", "Strelec 1\n", - "Antiste 0.25518925518925517\n", "Seck 1\n", - "Sansone 0.6557590557590557\n", - "Pussetto 0.41012558869701726\n", - "Afena-Gyan 0.9457013574660633\n", - "Defrel 0.23296703296703294\n", + "Quagliarella 0.662121212121212\n", + "Piccoli affine to Lasagna\n", + "Piccoli 0.36016483516483516\n", + "Shomurodov 0.36016483516483516\n", "Cancellieri 1\n", + "Antiste 0.19081196581196586\n", + "Afena-Gyan 1\n", + "Defrel 0.35384615384615387\n", + "Ibrahimovic 0.0\n", + "Pussetto 0.30666208791208793\n", "Edera 0.0\n", "Oddei 0.0\n", - "Oddei 0.8852747252747253\n", + "Oddei 0.6723076923076924\n", + "Braaf 0.0\n", "Raimondo 0.0\n", "Kaio Jorge 0.0\n", "Lazetic 1\n", "Players with low quantity of games:\n", + "Troost-Ekong 0.16666666666666663\n", "Masina 0.6666666666666667\n", - "Dest 0.6666666666666667\n", - "Thiaw 0.5\n", - "Donati 0.6666666666666667\n", - "Umtiti 0.6666666666666667\n", - "Ostigard 0.5\n", - "Sambia 0.8333333333333334\n", "Aiwu 0.0\n", - "Hendry 0.33333333333333337\n", - "Pirola 0.8333333333333334\n", - "Amione 0.6666666666666667\n", - "Gatti 0.5\n", - "Gila 0.5\n", - "Cabal 0.5\n", - "Sosa 0.5\n", - "Conti 0.8504971219256934\n" + "Thiaw 0.6666666666666667\n", + "Zeefuik 0.0\n", + "Ostigard 0.6666666666666667\n", + "Wisniewski 0.0\n", + "Dermaku 0.16666666666666663\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Dermaku 0.16666666666666663\n", + "Donati 0.6666666666666667\n", + "Adopo 0.8333333333333334\n", + "Antov 0.6666666666666667\n", + "Gatti 0.8333333333333334\n", + "Gila 0.6666666666666667\n", + "Moutinho J. 0.6666666666666667\n", "Paletta 0.0\n", "Romagna 0.0\n", - "Cetin 0.681013431013431\n", - "Adopo 0.5\n", - "Antov 0.6666666666666667\n", + "Cassandro 0.0\n", "Amey 0.16666666666666663\n", "Ferrarini 0.0\n", - "Kamenovic 0.16666666666666663\n", "Zanotti 0.0\n", - "Motoc 0.0\n", "Bayeye 0.0\n", - "Ebosele 0.33333333333333337\n", + "Ebosele 0.5\n", "Buta 0.0\n", - "Ndiaye 0.0\n", "Abankwah 0.0\n", "Guessand A. 0.0\n", + "Cabal 0.5\n", + "Sosa 0.6666666666666667\n", "Guarino 0.0\n", + "Carboni F. 0.6666666666666667\n", "Pogba 0.0\n", "Wijnaldum 0.16666666666666663\n", - "Vranckx 0.5\n", - "Winks 0.0\n", - "Maldini 0.8333333333333334\n", - "Moro N. 0.6666666666666667\n", - "Valoti 0.8333333333333334\n", - "Gaetano 0.5\n", - "Bohinen 0.8333333333333334\n", - "Milanese 0.16666666666666663\n", - "Listkowski 0.8333333333333334\n", - "Adli 0.6666666666666667\n", - "Hrustic 0.8333333333333334\n", - "D'andrea 0.8333333333333334\n", - "Baez 0.33333333333333337\n", - "Yepes 0.6666666666666667\n", - "Bondo 0.6666666666666667\n", - "Capezzi 0.8333333333333334\n", + "Volpato 0.6666666666666667\n", + "Nicolussi Caviglia 0.6666666666666667\n", + "Romero L. 0.8333333333333334\n", "Machin 0.0\n", - "Scozzarella 0.0\n", + "Esposito Sa. 0.33333333333333337\n", "Akpa Akpro 0.0\n", - "Darboe 0.5382417582417582\n", - "Bove 0.8333333333333334\n", + "Bianco 0.5\n", + "D'andrea 0.8333333333333334\n", + "Tahirovic 0.6666666666666667\n", + "Winks 0.33333333333333337\n", + "Gaetano 0.5\n", + "Iling-Junior 0.6666666666666667\n", + "Capezzi 0.8333333333333334\n", + "Scozzarella 0.0\n", + "Darboe 0.6092307692307692\n", "Urbanski 0.16666666666666663\n", "Bertini 0.0\n", - "Cortinovis 0.16666666666666663\n", - "Romero L. 0.78003663003663\n", - "Bianco 0.0\n", - "Sher 0.16666666666666663\n", - "Nguiamba 0.5\n", - "Volpato 0.5\n", - "Praszelik 0.33333333333333337\n", + "Yepes 0.8333333333333334\n", "Trimboli 0.0\n", "Pafundi 0.0\n", - "Bjorkengren 0.0\n", - "Vignato S. 0.16666666666666663\n", + "Milanese 0.33333333333333337\n", + "Adli 0.6666666666666667\n", + "Vignato S. 0.5\n", + "Bondo 0.6666666666666667\n", "Samek 0.0\n", "Zerbin 0.6666666666666667\n", "Ilkhan 0.6666666666666667\n", "Degli Innocenti 0.16666666666666663\n", "Acella 0.0\n", - "Tripi 0.0\n", "Garbett 0.0\n", - "Iling-Junior 0.33333333333333337\n", - "Tsadjout 0.5\n", - "Rodriguez P. 0.6666666666666667\n", + "Carboni V. 0.33333333333333337\n", + "Cipot 0.16666666666666663\n", + "Solbakken 0.33333333333333337\n", + "Ngonge 0.0\n", + "Voelkerling Persson 0.33333333333333337\n", "Edera 0.0\n", - "Oddei 0.39069597069597073\n", + "Oddei 0.4971794871794871\n", + "Braaf 0.6666666666666667\n", "Raimondo 0.16666666666666663\n", - "Kristoffersen 0.16666666666666663\n", "De Luca 0.16666666666666663\n", - "Soule' 0.8333333333333334\n", "Lazetic 0.16666666666666663\n", - "Voelkerling Persson 0.0\n", - "Sanca 0.5\n" + "Krollis 0.0\n" ] } ], @@ -2607,7 +2576,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 47, "id": "49c28b07", "metadata": {}, "outputs": [ @@ -2625,7 +2594,7 @@ " dtype='object', length=156)" ] }, - "execution_count": 10, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2636,7 +2605,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 48, "id": "d29102e5", "metadata": {}, "outputs": [ @@ -2797,7 +2766,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 49, "id": "f19304f6", "metadata": {}, "outputs": [], @@ -2837,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 50, "id": "370d41d2", "metadata": {}, "outputs": [], @@ -2853,7 +2822,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 51, "id": "a7b1fb52", "metadata": {}, "outputs": [ @@ -2979,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 52, "id": "5a7cf079", "metadata": {}, "outputs": [], @@ -3003,7 +2972,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "id": "0bc0568b", "metadata": {}, "outputs": [], @@ -3113,13 +3082,488 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 54, "id": "8aad9652", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "76/76 [==============================] - 4s 10ms/step - loss: 12.8341 - distribution_lambda_12_loss: 8.9192 - distribution_lambda_13_loss: 3.9149 - val_loss: 8.0652 - val_distribution_lambda_12_loss: 4.9531 - val_distribution_lambda_13_loss: 3.1121\n", + "Epoch 2/1000\n", + "76/76 [==============================] - 0s 3ms/step - loss: 7.1683 - distribution_lambda_12_loss: 4.2146 - distribution_lambda_13_loss: 2.9537 - val_loss: 6.4110 - val_distribution_lambda_12_loss: 3.6377 - val_distribution_lambda_13_loss: 2.7734\n", + "Epoch 3/1000\n", + "76/76 [==============================] - 0s 3ms/step - loss: 6.0331 - distribution_lambda_12_loss: 3.4393 - distribution_lambda_13_loss: 2.5938 - val_loss: 5.5737 - val_distribution_lambda_12_loss: 3.1841 - 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scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", @@ -3187,7 +3631,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 55, "id": "4e2bf9dc", "metadata": {}, "outputs": [], @@ -3207,7 +3651,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "id": "c2674211", "metadata": {}, "outputs": [ @@ -3215,13 +3659,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.1422293477741312\n", - "0.16931391630989778\n" + "0.14844599198900188\n", + "0.17323732717963714\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3233,13 +3677,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.1431761226649988\n", - "0.16687106328443524\n" + "0.12129447943215599\n", + "0.14153111901273285\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3286,13 +3730,2326 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 57, "id": "41e7e1ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2000\n", + "8/8 [==============================] - 4s 102ms/step - loss: 86.2533 - distribution_lambda_14_loss: 76.3453 - distribution_lambda_15_loss: 8.9897 - distribution_lambda_16_loss: 0.9182 - val_loss: 74.3172 - val_distribution_lambda_14_loss: 65.4215 - val_distribution_lambda_15_loss: 7.9738 - val_distribution_lambda_16_loss: 0.9220\n", + "Epoch 2/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 68.8012 - distribution_lambda_14_loss: 60.2621 - distribution_lambda_15_loss: 7.6220 - distribution_lambda_16_loss: 0.9171 - val_loss: 59.5450 - val_distribution_lambda_14_loss: 51.7760 - val_distribution_lambda_15_loss: 6.8508 - val_distribution_lambda_16_loss: 0.9182\n", + "Epoch 3/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 55.4490 - distribution_lambda_14_loss: 47.9629 - distribution_lambda_15_loss: 6.5739 - distribution_lambda_16_loss: 0.9123 - val_loss: 48.1461 - val_distribution_lambda_14_loss: 41.3038 - val_distribution_lambda_15_loss: 5.9274 - val_distribution_lambda_16_loss: 0.9148\n", + "Epoch 4/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 45.1109 - distribution_lambda_14_loss: 38.4840 - distribution_lambda_15_loss: 5.7175 - distribution_lambda_16_loss: 0.9094 - val_loss: 39.7642 - val_distribution_lambda_14_loss: 33.6357 - val_distribution_lambda_15_loss: 5.2195 - val_distribution_lambda_16_loss: 0.9090\n", + "Epoch 5/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 37.5347 - distribution_lambda_14_loss: 31.5482 - distribution_lambda_15_loss: 5.0838 - distribution_lambda_16_loss: 0.9027 - val_loss: 33.6112 - val_distribution_lambda_14_loss: 28.0225 - val_distribution_lambda_15_loss: 4.6863 - val_distribution_lambda_16_loss: 0.9024\n", + "Epoch 6/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 32.0395 - distribution_lambda_14_loss: 26.5573 - distribution_lambda_15_loss: 4.5872 - distribution_lambda_16_loss: 0.8950 - val_loss: 28.9921 - val_distribution_lambda_14_loss: 23.8037 - val_distribution_lambda_15_loss: 4.2949 - val_distribution_lambda_16_loss: 0.8935\n", + "Epoch 7/2000\n", + "8/8 [==============================] - 0s 5ms/step - loss: 28.0116 - distribution_lambda_14_loss: 22.8873 - distribution_lambda_15_loss: 4.2382 - distribution_lambda_16_loss: 0.8861 - val_loss: 25.4246 - val_distribution_lambda_14_loss: 20.5564 - val_distribution_lambda_15_loss: 3.9849 - val_distribution_lambda_16_loss: 0.8833\n", + "Epoch 8/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 24.5654 - distribution_lambda_14_loss: 19.7377 - distribution_lambda_15_loss: 3.9525 - distribution_lambda_16_loss: 0.8752 - val_loss: 22.6632 - 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[==============================] - 0s 6ms/step - loss: 2.3127 - distribution_lambda_14_loss: 0.4995 - distribution_lambda_15_loss: 1.3896 - distribution_lambda_16_loss: 0.4235 - val_loss: 2.2356 - val_distribution_lambda_14_loss: 0.4782 - val_distribution_lambda_15_loss: 1.3475 - val_distribution_lambda_16_loss: 0.4099\n", + "Epoch 1026/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.3140 - distribution_lambda_14_loss: 0.4901 - distribution_lambda_15_loss: 1.3903 - distribution_lambda_16_loss: 0.4335 - val_loss: 2.2321 - val_distribution_lambda_14_loss: 0.4762 - val_distribution_lambda_15_loss: 1.3464 - val_distribution_lambda_16_loss: 0.4094\n", + "Epoch 1027/2000\n", + "8/8 [==============================] - 0s 6ms/step - loss: 2.2689 - distribution_lambda_14_loss: 0.4915 - distribution_lambda_15_loss: 1.3664 - distribution_lambda_16_loss: 0.4110 - val_loss: 2.2370 - val_distribution_lambda_14_loss: 0.4829 - val_distribution_lambda_15_loss: 1.3453 - val_distribution_lambda_16_loss: 0.4088\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", @@ -3363,7 +6120,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 58, "id": "39a9bdc6", "metadata": {}, "outputs": [], @@ -3383,7 +6140,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 59, "id": "c41cf448", "metadata": {}, "outputs": [ @@ -3391,13 +6148,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.45257318940839\n", - "0.5349007718848356\n" + "0.2959033040611633\n", + "0.4342943294016782\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3409,13 +6166,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.37645262592381834\n", - "0.43365123619886836\n" + "0.19144084876983292\n", + "0.3805971703037422\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -3460,13 +6217,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 60, "id": "cecf5392", "metadata": {}, "outputs": [], "source": [ - "save_model_of = False\n", - "save_model_gk = False\n", + "save_model_of = True\n", + "save_model_gk = True\n", "\n", "if(save_model_of):\n", " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", @@ -3492,7 +6249,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 61, "id": "ddf433f9", "metadata": {}, "outputs": [], @@ -3619,7 +6376,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 62, "id": "d31bad38", "metadata": {}, "outputs": [], @@ -3645,7 +6402,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 63, "id": "62b9f588", "metadata": {}, "outputs": [ @@ -3715,32 +6472,32 @@ " \n", " 375\n", " 38\n", - " Milan\n", - " Lazio\n", + " Lecce\n", + " Bologna\n", " \n", " \n", " 376\n", " 38\n", " Sassuolo\n", - " Monza\n", + " Fiorentina\n", " \n", " \n", " 377\n", " 38\n", - " Napoli\n", - " Salernitana\n", + " Milan\n", + " Verona\n", " \n", " \n", " 378\n", " 38\n", - " Udinese\n", - " Sampdoria\n", + " Torino\n", + " Inter\n", " \n", " \n", " 379\n", " 38\n", - " Roma\n", - " Spezia\n", + " Udinese\n", + " Juventus\n", " \n", " \n", "\n", @@ -3748,23 +6505,23 @@ "" ], "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 Milan Lazio\n", - "376 38 Sassuolo Monza\n", - "377 38 Napoli Salernitana\n", - "378 38 Udinese Sampdoria\n", - "379 38 Roma Spezia\n", + " 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": 24, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -3783,17 +6540,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 64, "id": "861a06ce", "metadata": {}, "outputs": [], "source": [ - "matchday = 15" + "matchday = 20" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 65, "id": "c58ba41d", "metadata": {}, "outputs": [], @@ -3827,7 +6584,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 66, "id": "f79792b6", "metadata": {}, "outputs": [ @@ -3863,27 +6620,27 @@ " \n", " \n", " \n", - " Vicario\n", + " Skorupski\n", " 1.0\n", " 100\n", " \n", " \n", - " Ebuehi\n", + " Posch\n", " 1.0\n", " 100\n", " \n", " \n", - " Ismajli\n", + " Soumaoro\n", " 1.0\n", " 100\n", " \n", " \n", - " Walukiewicz\n", + " Lucumi'\n", " 1.0\n", " 100\n", " \n", " \n", - " Parisi\n", + " Cambiaso\n", " 1.0\n", " 100\n", " \n", @@ -3893,54 +6650,54 @@ " ...\n", " \n", " \n", - " Basic\n", + " Verdi\n", " 0.0\n", " 55\n", " \n", " \n", - " Bertini\n", - " 0.0\n", - " 10\n", - " \n", - " \n", - " Marcos Antonio\n", - " 0.0\n", - " 50\n", - " \n", - " \n", - " Romero L.\n", - " 0.0\n", - " 50\n", - " \n", - " \n", - " Cancellieri\n", + " Kallon\n", " 0.4\n", " 55\n", " \n", + " \n", + " Piccoli\n", + " 0.0\n", + " 55\n", + " \n", + " \n", + " Ngonge\n", + " 0.0\n", + " 25\n", + " \n", + " \n", + " Braaf\n", + " 0.0\n", + " 40\n", + " \n", " \n", "\n", - "

449 rows × 2 columns

\n", + "

440 rows × 2 columns

\n", "" ], "text/plain": [ - " starter percentage\n", - "player \n", - "Vicario 1.0 100\n", - "Ebuehi 1.0 100\n", - "Ismajli 1.0 100\n", - "Walukiewicz 1.0 100\n", - "Parisi 1.0 100\n", - "... ... ...\n", - "Basic 0.0 55\n", - "Bertini 0.0 10\n", - "Marcos Antonio 0.0 50\n", - "Romero L. 0.0 50\n", - "Cancellieri 0.4 55\n", + " starter percentage\n", + "player \n", + "Skorupski 1.0 100\n", + "Posch 1.0 100\n", + "Soumaoro 1.0 100\n", + "Lucumi' 1.0 100\n", + "Cambiaso 1.0 100\n", + "... ... ...\n", + "Verdi 0.0 55\n", + "Kallon 0.4 55\n", + "Piccoli 0.0 55\n", + "Ngonge 0.0 25\n", + "Braaf 0.0 40\n", "\n", - "[449 rows x 2 columns]" + "[440 rows x 2 columns]" ] }, - "execution_count": 27, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -3963,7 +6720,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 67, "id": "5e63c2b7", "metadata": { "scrolled": true @@ -3973,572 +6730,565 @@ "name": "stdout", "output_type": "stream", "text": [ - "Meret: MV 6.33 ± 0.80; FV 5.10 + 1.31 (16.0% cs)\n", - "Provedel: MV 6.21 ± 0.81; FV 5.42 + 1.22 (41.5% cs)\n", - "Maignan: MV 6.27 ± 0.84; FV 5.66 + 1.16 (56.8% cs)\n", - "Silvestri: MV 6.34 ± 0.71; FV 4.18 + 1.60 (2.7% cs)\n", - "Sepe: MV 6.39 ± 0.74; FV 4.76 + 1.42 (6.4% cs)\n", - "Szczesny: MV 6.15 ± 0.77; FV 5.54 + 1.19 (47.8% cs)\n", - "Consigli: MV 6.12 ± 0.70; FV 4.86 + 1.35 (12.9% cs)\n", - "Falcone: MV 6.34 ± 0.79; FV 5.47 + 1.21 (34.2% cs)\n", - "Musso: MV 6.42 ± 0.88; FV 5.22 + 1.28 (23.7% cs)\n", - "Vicario: MV 6.32 ± 0.85; FV 5.64 + 1.17 (51.2% cs)\n", - "Rui Patricio: MV 6.22 ± 0.80; FV 5.60 + 1.18 (51.2% cs)\n", - "Milinkovic-Savic V.: MV 6.27 ± 0.77; FV 4.90 + 1.38 (12.4% cs)\n", - "Audero: MV 6.34 ± 0.77; FV 5.27 + 1.28 (17.9% cs)\n", - "Montipo': MV 6.44 ± 0.78; FV 5.19 + 1.29 (13.4% cs)\n", - "Sportiello: MV 6.32 ± 0.86; FV 5.06 + 1.31 (18.5% cs)\n", - "Skorupski: MV 6.34 ± 0.80; FV 4.68 + 1.42 (8.3% cs)\n", - "Di Gregorio: MV 5.83 ± 0.80; FV 3.91 + 1.59 (4.5% cs)\n", - "Perin: MV 6.21 ± 0.80; FV 5.55 + 1.19 (48.3% cs)\n", - "Handanovic: MV 5.89 ± 0.66; FV 3.40 + 1.88 (1.4% cs)\n", - "Tatarusanu: MV 6.22 ± 0.78; FV 5.53 + 1.19 (45.2% cs)\n", - "Gollini: MV 6.28 ± 0.79; FV 5.22 + 1.28 (22.2% cs)\n", - "Dragowski: MV 6.52 ± 0.82; FV 5.18 + 1.30 (13.1% cs)\n", - "Terracciano: MV 6.30 ± 0.71; FV 4.72 + 1.43 (5.7% cs)\n", - "Berisha: MV 6.27 ± 0.77; FV 4.90 + 1.38 (12.4% cs)\n", - "Radu I.: MV 6.22 ± 0.79; FV 5.35 + 1.23 (35.3% cs)\n", - "Cragno: MV 5.83 ± 0.80; FV 3.91 + 1.59 (4.5% cs)\n", - "Luis Maximiano: MV 6.22 ± 0.84; FV 5.19 + 1.26 (33.8% cs)\n", - "Onana: MV 6.32 ± 0.73; FV 5.05 + 1.33 (11.7% cs)\n", - "Carnesecchi: MV 6.17 ± 0.82; FV 5.58 + 1.17 (56.1% cs)\n", - "Mirante: MV 6.27 ± 0.84; FV 5.66 + 1.16 (56.8% cs)\n", - "Sarr M.: MV 6.22 ± 0.79; FV 5.35 + 1.23 (35.3% cs)\n", - "Lamanna: MV 5.83 ± 0.80; FV 3.91 + 1.59 (4.5% cs)\n", - "Ujkani: MV 6.32 ± 0.85; FV 5.64 + 1.17 (51.2% cs)\n", - "Pegolo: MV 6.12 ± 0.70; FV 4.86 + 1.35 (12.9% cs)\n", - "Perilli: MV 6.44 ± 0.78; FV 5.19 + 1.29 (13.4% cs)\n", - "Padelli: MV 6.34 ± 0.71; FV 4.18 + 1.60 (2.7% cs)\n", - "Perisan: MV 6.32 ± 0.85; FV 5.64 + 1.17 (51.2% cs)\n", - "Bardi: MV 6.34 ± 0.80; FV 4.68 + 1.42 (8.3% cs)\n", - "Cordaz: MV 5.89 ± 0.66; FV 3.40 + 1.88 (1.4% cs)\n", - "Pinsoglio: MV 6.15 ± 0.77; FV 5.54 + 1.19 (47.8% cs)\n", - "Fiorillo: MV 6.39 ± 0.74; FV 4.76 + 1.42 (6.4% cs)\n", - "Sirigu: MV 6.33 ± 0.80; FV 5.10 + 1.31 (16.0% cs)\n", - "Cerofolini: MV 6.30 ± 0.71; FV 4.72 + 1.43 (5.7% cs)\n", - "Rossi F.: MV 6.42 ± 0.88; FV 5.22 + 1.28 (23.7% cs)\n", - "Contini: MV 6.34 ± 0.77; FV 5.27 + 1.28 (17.9% cs)\n", - "Brancolini: MV 6.34 ± 0.79; FV 5.47 + 1.21 (34.2% cs)\n", - "Bleve: MV 6.34 ± 0.79; FV 5.47 + 1.21 (34.2% cs)\n", - "Berardi A.: MV 6.44 ± 0.78; FV 5.19 + 1.29 (13.4% cs)\n", - "Russo A.: MV 6.12 ± 0.70; FV 4.86 + 1.35 (12.9% cs)\n", - "Gemello: MV 6.27 ± 0.77; FV 4.90 + 1.38 (12.4% cs)\n", - "Ravaglia: MV 6.34 ± 0.77; FV 5.27 + 1.28 (17.9% cs)\n", - "Zoet: MV 6.51 ± 0.83; FV 5.20 + 1.29 (14.8% cs)\n", - "Boer: MV 6.22 ± 0.80; FV 5.60 + 1.18 (51.2% cs)\n", - "Adamonis: MV 6.21 ± 0.81; FV 5.42 + 1.22 (41.5% cs)\n", - "Marfella: MV 6.33 ± 0.80; FV 5.10 + 1.31 (16.0% cs)\n", - "Zovko: MV 6.52 ± 0.82; FV 5.18 + 1.30 (13.1% cs)\n", - "Piana: MV 6.34 ± 0.71; FV 4.18 + 1.60 (2.7% cs)\n", - "Bagnolini: MV 6.34 ± 0.80; FV 4.68 + 1.42 (8.3% cs)\n", - "Svilar: MV 6.22 ± 0.80; FV 5.60 + 1.18 (51.2% cs)\n", - "Sorrentino A.: MV 5.83 ± 0.80; FV 3.91 + 1.59 (4.5% cs)\n", - "Ciezkowski: MV 6.22 ± 0.79; FV 5.35 + 1.23 (35.3% cs)\n", - "Micai: MV 6.39 ± 0.74; FV 4.76 + 1.42 (6.4% cs)\n", - "Chiesa M.: MV 6.44 ± 0.78; FV 5.19 + 1.29 (13.4% cs)\n", - "Saro: MV 6.22 ± 0.79; FV 5.35 + 1.23 (35.3% cs)\n", - "Smalling: MV 6.17 ± 1.06; FV 6.60 + 1.91\n", - "Hernandez T.: MV 6.30 ± 1.06; FV 6.85 + 2.12\n", - "Kim: MV 6.25 ± 0.88; FV 6.57 + 1.40\n", - "Bastoni S.: MV 6.22 ± 1.06; FV 7.07 + 2.89\n", - "Udogie: MV 5.90 ± 1.03; FV 6.16 + 1.60\n", - "Dumfries: MV 5.93 ± 0.89; FV 6.14 + 1.36\n", - "Romagnoli: MV 6.05 ± 0.80; FV 6.12 + 0.81\n", - "Rodrigo Becao: MV 5.93 ± 0.87; FV 6.05 + 1.12\n", - "Parisi: MV 6.19 ± 0.83; FV 6.53 + 1.54\n", - "Mazzocchi: MV 6.21 ± 1.07; FV 6.85 + 2.38\n", - "Bremer: MV 6.06 ± 0.75; FV 6.14 + 0.83\n", - "Demiral: MV 5.95 ± 1.08; FV 6.28 + 1.90\n", - "Valeri: MV 6.10 ± 0.70; FV 6.41 + 1.30\n", - "Dimarco: MV 6.14 ± 1.04; FV 6.61 + 1.97\n", - "Di Lorenzo: MV 6.09 ± 0.70; FV 6.20 + 0.82\n", - "Ibanez: MV 6.01 ± 0.94; FV 6.07 + 0.90\n", - "Tomori: MV 6.16 ± 0.83; FV 6.42 + 1.28\n", - "Toloi: MV 6.17 ± 0.94; FV 6.60 + 1.78\n", - "Gosens: MV 5.75 ± 0.65; FV 5.80 + 0.85\n", - "Rrahmani: MV 6.13 ± 0.78; FV 6.24 + 0.89\n", - "Kalulu: MV 6.06 ± 0.71; FV 6.10 + 0.70\n", - "Bijol: MV 5.86 ± 0.96; FV 6.05 + 1.43\n", - "Doig: MV 6.32 ± 1.08; FV 7.19 + 2.96\n", - "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", - "Spinazzola: MV 5.98 ± 0.66; FV 5.98 + 0.65\n", - "Lazzari: MV 5.93 ± 0.75; FV 5.94 + 0.65\n", - "Mancini: MV 6.04 ± 0.76; FV 6.07 + 0.67\n", - "Danilo: MV 6.03 ± 0.90; FV 6.10 + 0.75\n", - "Kyriakopoulos: MV 5.90 ± 0.90; FV 6.09 + 1.36\n", - "Vojvoda: MV 5.93 ± 0.76; FV 5.98 + 0.83\n", - "Scalvini: MV 5.95 ± 1.08; FV 6.26 + 1.85\n", - "Carlos Augusto: MV 5.98 ± 0.97; FV 6.37 + 1.71\n", - "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", - "Schuurs: MV 5.98 ± 0.73; FV 6.02 + 0.66\n", - "Juan Jesus: MV 6.14 ± 0.71; FV 6.34 + 1.09\n", - "Bonucci: MV 5.98 ± 0.92; FV 6.18 + 1.28\n", - "Calabria: MV 6.10 ± 0.68; FV 6.27 + 0.97\n", - "Bastoni: MV 5.84 ± 0.91; FV 5.86 + 0.82\n", - "Depaoli: MV 6.12 ± 1.00; FV 6.95 + 2.72\n", - "Darmian: MV 5.85 ± 0.64; FV 5.85 + 0.63\n", - "Sernicola: MV 6.03 ± 0.82; FV 6.34 + 1.56\n", - "Mari': MV 5.91 ± 1.20; FV 6.44 + 2.26\n", - "Martinez Quarta: MV 5.80 ± 1.06; FV 5.79 + 1.06\n", - "Maehle: MV 5.97 ± 0.60; FV 5.98 + 0.75\n", - "Olivera: MV 6.11 ± 0.70; FV 6.37 + 1.20\n", - "Biraghi: MV 5.73 ± 0.79; FV 5.74 + 0.86\n", - "Medel: MV 5.93 ± 0.77; FV 5.93 + 0.64\n", - "Patric: MV 5.95 ± 0.78; FV 5.95 + 0.63\n", - "Rodriguez R.: MV 5.83 ± 0.86; FV 5.84 + 0.70\n", - "Aina: MV 5.90 ± 0.89; FV 6.02 + 1.13\n", - "Cambiaso: MV 5.92 ± 0.84; FV 5.94 + 0.71\n", - "Perez N.: MV 5.77 ± 1.20; FV 5.79 + 1.28\n", - "Holm: MV 6.06 ± 0.86; FV 6.55 + 1.99\n", - "Baschirotto: MV 6.24 ± 0.94; FV 6.64 + 1.69\n", - "Dodo': MV 5.61 ± 0.85; FV 5.54 + 0.82\n", - "Hysaj: MV 5.90 ± 0.63; FV 5.87 + 0.61\n", - "Faraoni: MV 6.08 ± 0.76; FV 6.42 + 1.51\n", - "Bianchetti: MV 5.81 ± 0.81; FV 5.86 + 0.92\n", - "Milenkovic: MV 5.72 ± 1.13; FV 5.85 + 1.43\n", - "Marusic: MV 5.86 ± 0.79; FV 5.87 + 0.71\n", - "Colley: MV 5.73 ± 1.02; FV 5.93 + 1.48\n", - "Toljan: MV 5.65 ± 0.80; FV 5.61 + 0.70\n", - "Celik: MV 5.95 ± 0.71; FV 5.94 + 0.65\n", - "Ampadu: MV 5.83 ± 0.87; FV 5.81 + 0.74\n", - "Singo: MV 5.89 ± 0.72; FV 5.92 + 0.74\n", - "Casale: MV 5.88 ± 0.79; FV 5.83 + 0.64\n", - "Daniliuc: MV 5.87 ± 1.05; FV 5.85 + 0.89\n", - "Pongracic: MV 5.96 ± 0.78; FV 5.96 + 0.65\n", - "Soppy: MV 5.77 ± 0.83; FV 5.80 + 0.88\n", - "Kiwior: MV 5.86 ± 0.81; FV 5.85 + 0.66\n", - "Posch: MV 6.06 ± 1.23; FV 6.76 + 2.64\n", - "Masina: MV 5.70 ± 0.80; FV 5.75 + 1.03\n", - "Ghiglione: MV 5.95 ± 0.82; FV 6.01 + 0.90\n", - "De Vrij: MV 5.69 ± 0.77; FV 5.66 + 0.72\n", - "Alex Sandro: MV 5.70 ± 1.12; FV 5.70 + 0.85\n", - "Ceccherini: MV 6.09 ± 0.95; FV 6.40 + 1.59\n", - "Fazio: MV 5.96 ± 1.11; FV 5.92 + 1.05\n", - "Rogerio: MV 5.65 ± 0.94; FV 5.61 + 0.91\n", - "Reca: MV 6.13 ± 0.95; FV 6.74 + 2.24\n", - "Gunter: MV 5.89 ± 0.78; FV 6.04 + 1.20\n", - "Djidji: MV 5.77 ± 0.93; FV 5.89 + 1.17\n", - "Augello: MV 5.80 ± 0.64; FV 5.80 + 0.64\n", - "Bellanova: MV 5.74 ± 0.75; FV 5.72 + 0.68\n", - "Erlic: MV 5.67 ± 1.02; FV 5.61 + 0.92\n", - "Ismajli: MV 6.01 ± 0.72; FV 6.02 + 0.62\n", - "Ebuehi: MV 5.91 ± 0.72; FV 5.87 + 0.60\n", - "Kasius: MV 6.03 ± 0.83; FV 6.13 + 0.83\n", - "Lucumi': MV 5.79 ± 0.78; FV 5.76 + 0.65\n", - "Hien: MV 5.84 ± 0.76; FV 5.83 + 0.67\n", - "Acerbi: MV 5.93 ± 0.79; FV 5.98 + 0.76\n", - "Zappacosta: MV 5.96 ± 0.75; FV 6.11 + 1.07\n", - "Chiriches: MV 5.81 ± 1.06; FV 5.75 + 0.78\n", - "Gyomber: MV 5.75 ± 0.96; FV 5.71 + 0.74\n", - "Pezzella Giu.: MV 6.00 ± 0.70; FV 6.02 + 0.67\n", - "Ferrari G.: MV 5.65 ± 0.99; FV 5.59 + 0.82\n", - "Bereszynski: MV 5.76 ± 0.63; FV 5.73 + 0.54\n", - "Hateboer: MV 5.66 ± 0.84; FV 5.71 + 1.19\n", - "Karsdorp: MV 5.84 ± 0.77; FV 5.81 + 0.66\n", - "Nuytinck: MV 5.85 ± 1.14; FV 5.84 + 1.00\n", - "Lykogiannis: MV 5.99 ± 0.73; FV 6.21 + 1.20\n", - "Buongiorno: MV 5.72 ± 1.00; FV 5.68 + 0.74\n", - "Nikolaou: MV 5.73 ± 0.81; FV 5.69 + 0.66\n", - "Lazaro: MV 5.76 ± 0.87; FV 5.75 + 0.84\n", - "Gallo: MV 5.99 ± 0.80; FV 5.99 + 0.67\n", - "Ayhan: MV 5.68 ± 1.13; FV 5.64 + 1.08\n", - "Dest: MV 5.84 ± 0.75; FV 5.82 + 0.65\n", - "Stojanovic: MV 5.82 ± 0.82; FV 5.80 + 0.66\n", - "Birindelli: MV 5.77 ± 0.65; FV 5.75 + 0.59\n", - "Gendrey: MV 5.88 ± 0.70; FV 5.87 + 0.62\n", - "Ebosse: MV 5.66 ± 0.70; FV 5.58 + 0.59\n", - "Lochoshvili: MV 5.84 ± 0.73; FV 5.84 + 0.62\n", - "Bronn: MV 5.86 ± 0.70; FV 5.83 + 0.59\n", - "Thiaw: MV 6.01 ± 0.82; FV 6.08 + 0.85\n", - "Izzo: MV 6.05 ± 0.80; FV 6.10 + 0.68\n", - "D'ambrosio: MV 6.00 ± 0.67; FV 6.04 + 0.69\n", - "Rugani: MV 5.97 ± 0.75; FV 5.98 + 0.63\n", - "De Sciglio: MV 5.80 ± 0.62; FV 5.74 + 0.54\n", - "Luperto: MV 5.81 ± 1.10; FV 5.75 + 0.78\n", - "De Silvestri: MV 5.82 ± 0.84; FV 5.86 + 0.87\n", - "Djimsiti: MV 5.81 ± 0.85; FV 5.84 + 0.88\n", - "Palomino: MV 5.96 ± 1.06; FV 5.99 + 1.06\n", - "Bonifazi: MV 5.69 ± 0.86; FV 5.65 + 0.68\n" + "Meret: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Provedel: MV 6.31 ± 0.77; FV 5.74 + 1.23 (69.4% cs)\n", + "Vicario: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Szczesny: MV 6.23 ± 0.72; FV 5.70 + 1.25 (68.3% cs)\n", + "Falcone: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Silvestri: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Rui Patricio: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Milinkovic-Savic V.: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Sepe: MV 6.39 ± 0.82; FV 5.10 + 1.40 (10.3% cs)\n", + "Maignan: MV 6.37 ± 0.77; FV 5.79 + 1.16 (55.4% cs)\n", + "Onana: MV 6.24 ± 0.80; FV 5.69 + 1.31 (68.2% cs)\n", + "Audero: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Musso: MV 6.31 ± 0.75; FV 5.76 + 1.24 (70.2% cs)\n", + "Carnesecchi: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Montipo': MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Di Gregorio: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Tatarusanu: MV 6.17 ± 0.65; FV 5.09 + 1.24 (21.7% cs)\n", + "Consigli: MV 6.22 ± 0.75; FV 4.18 + 1.75 (5.4% cs)\n", + "Dragowski: MV 6.11 ± 0.70; FV 3.95 + 1.76 (4.1% cs)\n", + "Terracciano: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Sportiello: MV 6.19 ± 0.72; FV 5.65 + 1.28 (68.7% cs)\n", + "Skorupski: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Perin: MV 6.33 ± 0.81; FV 5.75 + 1.31 (71.8% cs)\n", + "Handanovic: MV 6.20 ± 0.84; FV 5.65 + 1.39 (67.0% cs)\n", + "Zoet: MV 6.30 ± 0.77; FV 5.48 + 1.26 (31.3% cs)\n", + "Gollini: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Ochoa: MV 6.45 ± 0.87; FV 5.39 + 1.33 (16.6% cs)\n", + "Pegolo: MV 6.23 ± 0.79; FV 4.63 + 1.65 (10.1% cs)\n", + "Mirante: MV 6.37 ± 0.77; FV 5.79 + 1.16 (55.4% cs)\n", + "Sarr M.: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Lamanna: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Ujkani: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Berisha: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Marchetti: MV 6.11 ± 0.70; FV 3.95 + 1.76 (4.1% cs)\n", + "Perilli: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Padelli: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Perisan: MV 6.39 ± 0.80; FV 5.73 + 1.10 (43.4% cs)\n", + "Bardi: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Cordaz: MV 6.24 ± 0.80; FV 5.69 + 1.31 (68.2% cs)\n", + "Pinsoglio: MV 6.23 ± 0.72; FV 5.70 + 1.25 (68.3% cs)\n", + "Fiorillo: MV 6.39 ± 0.82; FV 5.10 + 1.40 (10.3% cs)\n", + "Cragno: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Sirigu: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Cerofolini: MV 5.92 ± 0.74; FV 3.28 + 2.35 (2.1% cs)\n", + "Rossi F.: MV 6.31 ± 0.75; FV 5.76 + 1.24 (70.2% cs)\n", + "Ravaglia F.: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Brancolini: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Bleve: MV 6.31 ± 0.75; FV 5.46 + 1.16 (25.4% cs)\n", + "Berardi A.: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Russo A.: MV 6.22 ± 0.75; FV 4.18 + 1.75 (5.4% cs)\n", + "Gemello: MV 6.34 ± 0.77; FV 5.32 + 1.19 (18.3% cs)\n", + "Ravaglia: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Boer: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Adamonis: MV 6.31 ± 0.77; FV 5.74 + 1.23 (69.4% cs)\n", + "Marfella: MV 6.26 ± 0.88; FV 5.69 + 1.33 (68.3% cs)\n", + "Zovko: MV 6.09 ± 0.73; FV 3.89 + 1.87 (3.9% cs)\n", + "Piana: MV 6.38 ± 0.81; FV 5.79 + 1.16 (52.9% cs)\n", + "Bagnolini: MV 6.23 ± 0.69; FV 5.18 + 1.19 (20.8% cs)\n", + "Luis Maximiano: MV 6.15 ± 0.75; FV 5.59 + 1.31 (68.2% cs)\n", + "Svilar: MV 6.03 ± 0.74; FV 3.50 + 2.20 (2.5% cs)\n", + "Sorrentino A.: MV 6.23 ± 0.74; FV 4.16 + 1.67 (4.4% cs)\n", + "Ciezkowski: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Chiesa M.: MV 6.29 ± 0.76; FV 4.09 + 1.72 (3.7% cs)\n", + "Saro: MV 6.36 ± 0.84; FV 5.39 + 1.41 (20.2% cs)\n", + "Vasquez D.: MV 6.43 ± 0.83; FV 5.85 + 1.20 (61.9% cs)\n", + "Turk: MV 6.21 ± 0.74; FV 3.71 + 1.97 (2.7% cs)\n", + "Dimarco: MV 6.30 ± 1.04; FV 6.94 + 2.36\n", + "Smalling: MV 5.86 ± 1.14; FV 5.99 + 1.37\n", + "Doig: MV 6.12 ± 1.01; FV 6.58 + 1.83\n", + "Carlos Augusto: MV 5.86 ± 1.01; FV 5.96 + 1.24\n", + "Kim: MV 6.31 ± 1.07; FV 6.88 + 2.14\n", + "Hernandez T.: MV 6.29 ± 1.13; FV 6.95 + 2.44\n", + "Udogie: MV 6.27 ± 1.10; FV 7.07 + 2.72\n", + "Parisi: MV 6.17 ± 0.85; FV 6.47 + 1.43\n", + "Romagnoli: MV 6.21 ± 0.99; FV 6.54 + 1.63\n", + "Di Lorenzo: MV 6.23 ± 0.88; FV 6.62 + 1.67\n", + "Danilo: MV 6.30 ± 0.98; FV 6.82 + 2.00\n", + "Mazzocchi: MV 6.09 ± 0.94; FV 6.45 + 1.58\n", + "Mario Rui: MV 6.23 ± 0.90; FV 6.54 + 1.52\n", + "Bastoni S.: MV 6.07 ± 0.96; FV 6.42 + 1.58\n", + "Valeri: MV 6.05 ± 0.75; FV 6.25 + 1.22\n", + "Posch: MV 6.27 ± 1.14; FV 7.15 + 2.93\n", + "Rodrigo Becao: MV 6.23 ± 0.90; FV 6.57 + 1.57\n", + "Tomori: MV 6.15 ± 0.88; FV 6.43 + 1.36\n", + "Baschirotto: MV 6.30 ± 1.01; FV 6.89 + 2.17\n", + "Juan Jesus: MV 6.18 ± 0.76; FV 6.47 + 1.34\n", + "Ibanez: MV 5.69 ± 1.01; FV 5.72 + 1.04\n", + "Demiral: MV 6.24 ± 0.86; FV 6.63 + 1.64\n", + "Scalvini: MV 6.30 ± 1.02; FV 6.91 + 2.26\n", + "Bijol: MV 6.13 ± 1.02; FV 6.62 + 1.94\n", + "Toloi: MV 6.25 ± 0.85; FV 6.57 + 1.52\n", + "Depaoli: MV 5.83 ± 1.00; FV 6.15 + 1.51\n", + "Mancini: MV 5.78 ± 0.81; FV 5.77 + 0.68\n", + "Bremer: MV 6.00 ± 1.16; FV 6.44 + 1.91\n", + "Ebuehi: MV 6.07 ± 0.76; FV 6.30 + 1.14\n", + "Dumfries: MV 6.19 ± 1.13; FV 6.88 + 2.49\n", + "Schuurs: MV 6.05 ± 0.67; FV 6.10 + 0.65\n", + "Darmian: MV 6.21 ± 0.95; FV 6.75 + 2.04\n", + "Kalulu: MV 5.97 ± 1.07; FV 6.23 + 1.41\n", + "Vojvoda: MV 5.97 ± 0.82; FV 6.13 + 0.99\n", + "Maehle: MV 6.17 ± 0.78; FV 6.45 + 1.42\n", + "Holm: MV 5.96 ± 0.83; FV 6.15 + 1.12\n", + "Bastoni: MV 6.08 ± 0.78; FV 6.19 + 0.87\n", + "Gosens: MV 6.07 ± 0.71; FV 6.31 + 1.23\n", + "Milenkovic: MV 5.73 ± 1.13; FV 5.73 + 1.28\n", + "Rodriguez R.: MV 5.92 ± 0.68; FV 5.96 + 0.55\n", + "Reca: MV 5.96 ± 0.92; FV 6.19 + 1.26\n", + "Sernicola: MV 5.85 ± 0.78; FV 5.97 + 1.06\n", + "Rrahmani: MV 6.25 ± 1.03; FV 6.75 + 1.96\n", + "Kyriakopoulos: MV 5.84 ± 0.94; FV 6.01 + 1.18\n", + "Martinez Quarta: MV 5.79 ± 1.07; FV 5.79 + 1.17\n", + "Perez N.: MV 6.17 ± 0.88; FV 6.30 + 1.03\n", + "Olivera: MV 6.10 ± 0.77; FV 6.44 + 1.46\n", + "Calabria: MV 6.19 ± 1.05; FV 6.69 + 1.97\n", + "Skriniar: MV 5.94 ± 0.68; FV 5.94 + 0.55\n", + "Marusic: MV 6.00 ± 0.73; FV 6.04 + 0.68\n", + "Lazzari: MV 6.02 ± 0.75; FV 6.07 + 0.67\n", + "Augello: MV 5.72 ± 0.95; FV 5.90 + 1.21\n", + "Ampadu: MV 5.80 ± 0.91; FV 5.71 + 0.81\n", + "Ismajli: MV 6.05 ± 0.79; FV 6.06 + 0.72\n", + "Cambiaso: MV 5.96 ± 0.70; FV 6.03 + 0.63\n", + "Hysaj: MV 6.00 ± 0.68; FV 6.03 + 0.62\n", + "Izzo: MV 5.86 ± 0.77; FV 5.82 + 0.62\n", + "Biraghi: MV 5.71 ± 0.84; FV 5.68 + 0.73\n", + "Medel: MV 5.96 ± 0.68; FV 5.96 + 0.54\n", + "Bonucci: MV 6.24 ± 1.02; FV 6.77 + 2.00\n", + "Acerbi: MV 6.09 ± 0.71; FV 6.16 + 0.75\n", + "Spinazzola: MV 5.68 ± 0.78; FV 5.75 + 0.83\n", + "Lykogiannis: MV 5.97 ± 0.63; FV 6.04 + 0.61\n", + "Djidji: MV 5.86 ± 0.85; FV 5.95 + 0.94\n", + "Lazaro: MV 5.96 ± 0.79; FV 6.04 + 0.78\n", + "Gallo: MV 5.97 ± 0.69; FV 5.95 + 0.59\n", + "Singo: MV 5.98 ± 0.75; FV 6.09 + 0.81\n", + "Mari': MV 5.75 ± 1.19; FV 5.77 + 1.34\n", + "Casale: MV 5.98 ± 0.75; FV 5.99 + 0.62\n", + "Dodo': MV 5.56 ± 1.01; FV 5.48 + 0.87\n", + "De Vrij: MV 5.94 ± 0.75; FV 6.06 + 0.82\n", + "Patric: MV 6.05 ± 0.91; FV 5.98 + 0.82\n", + "Luperto: MV 5.96 ± 0.97; FV 5.90 + 0.83\n", + "Faraoni: MV 5.86 ± 0.76; FV 5.99 + 1.07\n", + "Ceccherini: MV 5.77 ± 0.99; FV 5.85 + 1.22\n", + "Umtiti: MV 6.19 ± 0.95; FV 6.30 + 1.12\n", + "Aina: MV 5.98 ± 0.80; FV 6.16 + 1.00\n", + "Caldirola: MV 5.71 ± 0.94; FV 5.61 + 0.80\n", + "Soppy: MV 6.08 ± 0.79; FV 6.19 + 0.93\n", + "Gendrey: MV 5.92 ± 0.68; FV 5.92 + 0.58\n", + "Ferrari A.: MV 5.71 ± 1.10; FV 5.72 + 1.25\n", + "Zappacosta: MV 6.20 ± 0.81; FV 6.49 + 1.46\n", + "Bianchetti: MV 5.63 ± 0.93; FV 5.63 + 1.00\n", + "Ferrari G.: MV 5.66 ± 1.16; FV 5.67 + 1.32\n", + "Fazio: MV 5.77 ± 1.20; FV 5.80 + 1.38\n", + "Hateboer: MV 6.13 ± 0.94; FV 6.64 + 1.95\n", + "Rogerio: MV 5.63 ± 0.95; FV 5.66 + 0.93\n", + "Buongiorno: MV 5.85 ± 0.73; FV 5.79 + 0.62\n", + "Gunter: MV 5.67 ± 0.97; FV 5.68 + 1.08\n", + "Colley: MV 5.71 ± 1.14; FV 5.74 + 1.35\n", + "Troost-Ekong: MV 5.88 ± 0.84; FV 5.92 + 0.84\n", + "Terzic: MV 5.90 ± 0.56; FV 5.88 + 0.41\n", + "Toljan: MV 5.58 ± 0.95; FV 5.53 + 0.86\n", + "Zortea: MV 6.13 ± 0.83; FV 6.50 + 1.54\n", + "Soumaoro: MV 5.98 ± 0.82; FV 5.92 + 0.73\n", + "Amian: MV 5.75 ± 0.81; FV 5.68 + 0.76\n", + "Pongracic: MV 6.03 ± 0.80; FV 5.97 + 0.75\n", + "Birindelli: MV 5.75 ± 0.76; FV 5.73 + 0.80\n", + "Lucumi': MV 5.88 ± 0.70; FV 5.86 + 0.58\n", + "Hien: MV 5.68 ± 0.86; FV 5.57 + 0.77\n", + "Ehizibue: MV 6.06 ± 0.84; FV 6.42 + 1.57\n", + "Masina: MV 6.13 ± 0.84; FV 6.64 + 1.81\n", + "Gyomber: MV 5.79 ± 0.80; FV 5.71 + 0.68\n", + "Alex Sandro: MV 5.76 ± 1.06; FV 5.69 + 0.94\n", + "Pezzella Giu.: MV 5.95 ± 0.65; FV 5.98 + 0.54\n", + "Bereszynski: MV 5.74 ± 0.79; FV 5.69 + 0.72\n", + "Venuti: MV 5.59 ± 0.87; FV 5.54 + 0.82\n", + "Palomino: MV 6.20 ± 0.80; FV 6.44 + 1.27\n", + "Nuytinck: MV 5.79 ± 1.06; FV 5.74 + 1.08\n", + "Marlon: MV 5.64 ± 0.78; FV 5.53 + 0.66\n", + "Nikolaou: MV 5.70 ± 0.81; FV 5.58 + 0.65\n", + "Igor: MV 5.56 ± 1.02; FV 5.45 + 0.81\n", + "Okoli: MV 5.87 ± 0.68; FV 5.86 + 0.55\n", + "Dawidowicz: MV 5.64 ± 0.99; FV 5.65 + 1.07\n", + "Celik: MV 5.59 ± 0.85; FV 5.59 + 0.86\n", + "Bellanova: MV 5.93 ± 0.76; FV 6.02 + 0.82\n", + "Erlic: MV 5.71 ± 1.10; FV 5.66 + 1.11\n", + "Ballo-Toure': MV 6.20 ± 0.77; FV 6.44 + 1.27\n", + "Dest: MV 5.85 ± 0.80; FV 5.84 + 0.67\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Hristov: MV 5.79 ± 0.67; FV 5.75 + 0.57\n", - "Donati: MV 5.75 ± 0.81; FV 5.70 + 0.68\n", - "Umtiti: MV 5.95 ± 0.85; FV 5.95 + 0.72\n", - "Kjaer: MV 6.02 ± 0.64; FV 6.01 + 0.63\n", - "Igor: MV 5.62 ± 1.20; FV 5.55 + 1.11\n", - "Okoli: MV 5.57 ± 0.93; FV 5.52 + 0.82\n", - "Soumaoro: MV 5.71 ± 1.02; FV 5.68 + 0.80\n", - "Caldirola: MV 5.88 ± 0.86; FV 5.85 + 0.70\n", - "Ballo-Toure': MV 6.17 ± 0.80; FV 6.52 + 1.48\n", - "Bradaric: MV 5.82 ± 0.79; FV 5.84 + 0.70\n", - "De Winter: MV 5.87 ± 0.91; FV 5.81 + 0.69\n", - "Quagliata: MV 5.96 ± 0.63; FV 5.97 + 0.64\n", - "Ferrari A.: MV 5.65 ± 0.77; FV 5.60 + 0.64\n", - "Florenzi: MV 6.15 ± 0.82; FV 6.39 + 1.17\n", - "Sala: MV 6.04 ± 0.75; FV 6.17 + 0.95\n", - "Caldara: MV 5.82 ± 0.91; FV 5.80 + 0.77\n", - "Murru: MV 5.77 ± 0.66; FV 5.81 + 0.82\n", - "Marlon: MV 5.71 ± 0.74; FV 5.63 + 0.61\n", - "Walukiewicz: MV 5.86 ± 0.74; FV 5.84 + 0.61\n", - "Kumbulla: MV 5.92 ± 0.66; FV 5.89 + 0.59\n", - "Amian: MV 5.74 ± 0.84; FV 5.72 + 0.72\n", - "Ostigard: MV 5.96 ± 0.71; FV 5.99 + 0.68\n", - "Sambia: MV 5.74 ± 0.79; FV 5.68 + 0.65\n", - "Aiwu: MV 5.95 ± 0.81; FV 5.99 + 0.83\n", - "Ehizibue: MV 5.71 ± 0.63; FV 5.66 + 0.53\n", - "Hendry: MV 5.99 ± 0.82; FV 6.03 + 0.82\n", - "Marrone: MV 5.54 ± 0.90; FV 5.50 + 0.75\n", - "Radovanovic: MV 5.52 ± 0.91; FV 5.58 + 0.74\n", - "Murillo: MV 5.75 ± 0.64; FV 5.62 + 0.53\n", - "Venuti: MV 5.59 ± 0.70; FV 5.51 + 0.62\n", - "Magnani: MV 5.92 ± 0.76; FV 5.88 + 0.65\n", - "Terzic: MV 5.93 ± 0.59; FV 5.90 + 0.56\n", - "Gabbia: MV 5.81 ± 0.86; FV 5.75 + 0.67\n", - "Zortea: MV 5.75 ± 0.65; FV 5.74 + 0.66\n", - "Dawidowicz: MV 5.73 ± 0.67; FV 5.64 + 0.65\n", - "Pirola: MV 5.84 ± 0.64; FV 5.77 + 0.54\n", - "Carboni: MV 5.72 ± 0.91; FV 5.68 + 0.75\n", - "Lovato: MV 5.74 ± 0.89; FV 5.70 + 0.71\n", - "Tuia: MV 5.92 ± 0.85; FV 5.98 + 0.99\n", - "Amione: MV 5.72 ± 0.66; FV 5.68 + 0.58\n", - "Vasquez: MV 5.84 ± 0.81; FV 5.84 + 0.66\n", - "Zima: MV 5.63 ± 0.89; FV 5.58 + 0.72\n", - "Coppola D.: MV 5.84 ± 0.75; FV 5.84 + 0.69\n", - "Gatti: MV 5.89 ± 0.81; FV 5.91 + 0.72\n", - "Gila: MV 5.76 ± 1.01; FV 5.68 + 0.86\n", - "Cabal: MV 5.99 ± 0.78; FV 6.09 + 0.89\n", - "Sosa: MV 5.66 ± 0.92; FV 5.61 + 0.79\n", - "Conti: MV 6.03 ± 0.96; FV 6.42 + 1.74\n", - "Dermaku: MV 6.01 ± 0.80; FV 6.07 + 0.82\n", - "Tonelli: MV 5.72 ± 0.80; FV 5.66 + 0.65\n", - "Radu: MV 5.63 ± 1.01; FV 5.56 + 0.88\n", - "Paletta: MV 5.92 ± 0.82; FV 5.96 + 0.81\n", - "Fares: MV 5.74 ± 0.69; FV 5.67 + 0.59\n", - "Marchizza: MV 5.71 ± 0.72; FV 5.70 + 0.67\n", - "Romagna: MV 5.84 ± 0.85; FV 5.89 + 0.94\n", - "Ranieri L.: MV 5.66 ± 0.74; FV 5.61 + 0.72\n", - "Cetin: MV 6.01 ± 0.86; FV 6.01 + 0.72\n", - "Muldur: MV 5.62 ± 0.83; FV 5.55 + 0.72\n", - "Adopo: MV 5.71 ± 0.98; FV 5.66 + 0.84\n", - "Ferrer: MV 5.91 ± 0.82; FV 5.96 + 0.79\n", - "Ruggeri: MV 5.84 ± 0.72; FV 5.86 + 0.65\n", - "Antov: MV 5.66 ± 1.00; FV 5.61 + 0.83\n", - "Amey: MV 6.02 ± 0.83; FV 6.09 + 0.81\n", - "Ferrarini: MV 5.92 ± 0.82; FV 5.96 + 0.81\n", - "Kamenovic: MV 5.90 ± 0.79; FV 5.91 + 0.69\n", - "Vina: MV 5.78 ± 0.84; FV 5.74 + 0.68\n", - "Zanoli: MV 5.87 ± 0.63; FV 5.83 + 0.57\n", - "Zanotti: MV 5.79 ± 0.76; FV 5.80 + 0.73\n", - "Ruan: MV 5.65 ± 1.02; FV 5.69 + 0.74\n", - "Motoc: MV 5.90 ± 0.82; FV 5.94 + 0.79\n", - "Cacace: MV 5.92 ± 0.72; FV 5.87 + 0.60\n", - "Bayeye: MV 5.83 ± 0.87; FV 5.86 + 0.83\n", - "Ebosele: MV 5.77 ± 0.94; FV 5.77 + 0.99\n", - "Buta: MV 5.72 ± 0.89; FV 5.69 + 0.90\n", - "Ndiaye: MV 5.95 ± 0.81; FV 5.99 + 0.83\n", - "Abankwah: MV 5.72 ± 0.89; FV 5.69 + 0.90\n", - "Guessand A.: MV 5.72 ± 0.89; FV 5.69 + 0.90\n", - "Guarino: MV 5.97 ± 0.79; FV 6.01 + 0.78\n", - "Milinkovic-Savic: MV 6.31 ± 1.16; FV 7.17 + 2.90\n", - "Barella: MV 6.32 ± 1.16; FV 7.34 + 3.33\n", - "Kvaratskhelia: MV 6.58 ± 1.39; FV 8.36 + 5.47\n", - "Zaccagni: MV 6.34 ± 1.13; FV 7.34 + 3.25\n", - "Zielinski: MV 6.32 ± 0.96; FV 6.89 + 2.03\n", - "Luis Alberto: MV 6.19 ± 0.85; FV 6.61 + 1.65\n", - "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", - "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", - "Koopmeiners: MV 6.30 ± 1.23; FV 7.40 + 3.47\n", - "Vlasic: MV 6.21 ± 1.08; FV 7.12 + 2.96\n", - "Pereyra: MV 6.08 ± 1.07; FV 6.58 + 2.07\n", - "Felipe Anderson: MV 6.08 ± 0.90; FV 6.48 + 1.77\n", - "Calhanoglu: MV 6.27 ± 1.04; FV 6.89 + 2.30\n", - "Strefezza: MV 6.42 ± 1.09; FV 7.23 + 3.03\n", - "Diaz B.: MV 6.34 ± 1.25; FV 7.63 + 3.97\n", - "Pellegrini Lo.: MV 6.11 ± 1.15; FV 6.86 + 2.65\n", - "Zambo Anguissa: MV 6.35 ± 1.09; FV 7.08 + 2.55\n", - "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", - "Malinovskyi: MV 6.09 ± 0.86; FV 6.50 + 1.69\n", - "Candreva: MV 6.30 ± 1.02; FV 6.95 + 2.38\n", - "Pasalic: MV 6.06 ± 1.12; FV 6.85 + 2.67\n", - "Bennacer: MV 6.19 ± 0.73; FV 6.50 + 1.28\n", - "Kostic: MV 6.04 ± 0.81; FV 6.34 + 1.37\n", - "Radonjic: MV 6.20 ± 0.88; FV 6.79 + 2.11\n", - "Samardzic: MV 6.00 ± 0.93; FV 6.32 + 1.60\n", - "Chiesa: MV 6.04 ± 0.83; FV 6.35 + 1.44\n", - "Lovric: MV 6.10 ± 0.76; FV 6.40 + 1.43\n", - "De Ketelaere: MV 5.94 ± 0.69; FV 5.97 + 0.76\n", - "Brozovic: MV 6.13 ± 0.87; FV 6.38 + 1.33\n", - "Pogba: MV 5.88 ± 0.76; FV 5.90 + 0.73\n", - "Bonaventura: MV 6.00 ± 0.86; FV 6.29 + 1.48\n", - "Sensi: MV 6.14 ± 1.10; FV 6.79 + 2.41\n", - "Bandinelli: MV 6.10 ± 0.97; FV 6.66 + 2.12\n", - "Ikone': MV 5.96 ± 0.90; FV 6.27 + 1.60\n", - "Barak: MV 5.76 ± 0.70; FV 5.75 + 0.72\n", - "Mkhitaryan: MV 6.11 ± 0.81; FV 6.43 + 1.46\n", - "Pessina: MV 6.00 ± 0.87; FV 6.39 + 1.70\n", - "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", - "Arslan: MV 5.81 ± 0.76; FV 5.86 + 0.94\n", - "Lazovic: MV 6.14 ± 0.77; FV 6.50 + 1.41\n", - "Cristante: MV 6.04 ± 0.98; FV 6.30 + 1.46\n", - "Elmas: MV 6.08 ± 0.74; FV 6.40 + 1.32\n", - "Bajrami: MV 5.96 ± 0.85; FV 6.23 + 1.40\n", - "Vecino: MV 5.86 ± 0.86; FV 5.90 + 0.90\n", - "Paredes: MV 5.69 ± 0.67; FV 5.60 + 0.57\n", - "Mandragora: MV 5.82 ± 0.84; FV 5.94 + 1.10\n", - "Djuricic: MV 5.81 ± 0.88; FV 6.11 + 1.63\n", - "Lukic: MV 5.98 ± 1.08; FV 6.31 + 1.76\n", - "Soriano: MV 5.99 ± 0.69; FV 6.03 + 0.73\n", - "Zaniolo: MV 5.92 ± 1.01; FV 6.35 + 1.93\n", - "Sottil: MV 5.94 ± 0.93; FV 6.23 + 1.51\n", - "Traore' Hj.: MV 6.14 ± 1.13; FV 7.04 + 3.02\n", - "Messias: MV 6.12 ± 1.11; FV 6.92 + 2.70\n", - "Thorstvedt: MV 5.96 ± 0.92; FV 6.42 + 1.97\n", - "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", - "Locatelli: MV 6.02 ± 0.73; FV 6.08 + 0.79\n", - "Rabiot: MV 6.09 ± 1.01; FV 6.32 + 1.39\n", - "Dominguez: MV 6.07 ± 0.90; FV 6.33 + 1.35\n", - "Tameze: MV 6.00 ± 0.76; FV 6.17 + 1.08\n", - "Miranchuk: MV 6.08 ± 1.17; FV 6.89 + 2.76\n", - "Vilhena: MV 5.75 ± 0.75; FV 5.82 + 1.02\n", - "De Roon: MV 5.93 ± 0.71; FV 5.97 + 0.76\n", - "Verdi: MV 6.09 ± 0.63; FV 6.32 + 0.98\n", - "Walace: MV 5.82 ± 0.73; FV 5.84 + 0.71\n", - "Wijnaldum: MV 5.94 ± 0.85; FV 5.96 + 0.77\n", - "Mckennie: MV 5.77 ± 0.71; FV 5.83 + 0.94\n", - "Makengo: MV 5.84 ± 0.72; FV 5.87 + 0.78\n", - "Ricci S.: MV 6.05 ± 0.73; FV 6.15 + 0.88\n", - "Coulibaly L.: MV 6.08 ± 1.00; FV 6.51 + 1.92\n", - "Sabiri: MV 5.77 ± 0.81; FV 5.95 + 1.36\n", - "Miretti: MV 5.93 ± 0.68; FV 5.95 + 0.73\n", - "Colpani: MV 6.19 ± 0.89; FV 6.85 + 2.32\n", - "Haas: MV 6.02 ± 0.76; FV 6.29 + 1.42\n", - "Orsolini: MV 6.05 ± 1.23; FV 6.98 + 3.05\n", - "Matic: MV 6.00 ± 0.64; FV 5.99 + 0.64\n", - "Ascacibar: MV 6.00 ± 0.72; FV 6.03 + 0.70\n", - "Ederson D.s.: MV 5.84 ± 0.67; FV 5.85 + 0.70\n", - "Ciurria: MV 5.99 ± 0.88; FV 6.31 + 1.46\n", - "Gonzalez J.: MV 6.15 ± 0.90; FV 6.62 + 1.92\n", - "El Shaarawy: MV 6.18 ± 0.86; FV 6.59 + 1.75\n", - "Meite': MV 5.88 ± 0.70; FV 5.89 + 0.66\n", - "Bourabia: MV 5.97 ± 0.68; FV 5.99 + 0.66\n", - "Ndombele': MV 5.88 ± 0.63; FV 5.84 + 0.58\n", - "Henderson L.: MV 5.92 ± 0.71; FV 5.98 + 0.91\n", - "Maggiore: MV 5.99 ± 0.70; FV 6.05 + 0.86\n", - "Ilic: MV 6.01 ± 0.74; FV 6.15 + 1.00\n", - "Kovalenko: MV 6.02 ± 0.76; FV 6.21 + 1.17\n", - "Zalewski: MV 5.98 ± 0.67; FV 6.00 + 0.74\n", - "Pickel: MV 5.92 ± 0.71; FV 6.02 + 1.02\n", - "Ferguson: MV 6.15 ± 1.09; FV 6.82 + 2.42\n", - "Camara Ma.: MV 6.11 ± 0.61; FV 6.18 + 0.68\n", - "Saponara: MV 5.83 ± 0.89; FV 5.97 + 1.17\n", - "Rincon: MV 5.80 ± 0.68; FV 5.80 + 0.74\n", - "Cataldi: MV 5.92 ± 0.72; FV 5.92 + 0.68\n", - "Zurkowski: MV 5.95 ± 0.94; FV 6.23 + 1.54\n", - "Rovella: MV 5.86 ± 1.07; FV 5.81 + 0.83\n", - "Agudelo: MV 5.95 ± 0.68; FV 6.00 + 0.89\n", - "Amrabat: MV 5.76 ± 0.98; FV 5.79 + 1.09\n", - "Lopez M.: MV 5.86 ± 0.80; FV 5.88 + 0.84\n", - "Gyasi: MV 5.86 ± 0.99; FV 6.33 + 2.02\n", - "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", - "Harroui: MV 5.78 ± 0.72; FV 5.88 + 1.11\n", - "Ceide: MV 5.86 ± 0.71; FV 5.85 + 0.74\n", - "Baldanzi: MV 6.34 ± 1.02; FV 7.34 + 3.35\n", - "Blin: MV 6.06 ± 0.61; FV 6.08 + 0.62\n", - "Hjulmand: MV 6.02 ± 0.94; FV 5.99 + 0.77\n", - "D'alessandro: MV 6.05 ± 0.62; FV 6.11 + 0.81\n", - "Grassi: MV 6.01 ± 0.61; FV 5.96 + 0.57\n", - "Krunic: MV 6.01 ± 0.66; FV 6.04 + 0.74\n", - "Linetty: MV 5.78 ± 0.81; FV 5.79 + 0.76\n", - "Miguel Veloso: MV 6.05 ± 0.74; FV 6.21 + 0.98\n", - "Ekdal: MV 5.82 ± 0.77; FV 5.80 + 0.66\n", - "Schouten: MV 5.89 ± 0.77; FV 5.85 + 0.64\n", - "Villar: MV 5.84 ± 0.62; FV 5.80 + 0.55\n", - "Saelemaekers: MV 5.98 ± 0.75; FV 6.08 + 0.96\n", - "Vranckx: MV 5.97 ± 0.81; FV 6.03 + 0.79\n", - "Basic: MV 5.91 ± 0.60; FV 5.88 + 0.60\n", - "Aebischer: MV 5.87 ± 0.77; FV 5.90 + 0.74\n", - "Marcos Antonio: MV 5.80 ± 0.63; FV 5.74 + 0.54\n", - "Ranocchia F.: MV 6.07 ± 0.90; FV 6.56 + 1.85\n", - "Bistrovic: MV 5.95 ± 0.63; FV 5.95 + 0.71\n", - "Verre: MV 5.77 ± 0.63; FV 5.75 + 0.56\n", - "Gagliardini: MV 5.94 ± 0.76; FV 6.04 + 1.00\n", - "Barberis: MV 5.79 ± 0.76; FV 5.78 + 0.67\n", - "Leris: MV 5.71 ± 0.69; FV 5.72 + 0.88\n", - "Vieira: MV 5.72 ± 0.66; FV 5.69 + 0.65\n" + "Stojanovic: MV 5.76 ± 0.87; FV 5.67 + 0.80\n", + "Bradaric: MV 5.65 ± 0.89; FV 5.57 + 0.90\n", + "Daniliuc: MV 5.73 ± 1.03; FV 5.67 + 0.99\n", + "Zima: MV 5.85 ± 0.75; FV 5.83 + 0.67\n", + "Quagliata: MV 5.80 ± 0.69; FV 5.84 + 0.66\n", + "Ebosse: MV 5.81 ± 0.66; FV 5.81 + 0.52\n", + "Aiwu: MV 5.78 ± 0.95; FV 5.83 + 1.06\n", + "Lochoshvili: MV 5.67 ± 0.85; FV 5.59 + 0.88\n", + "Bronn: MV 5.75 ± 0.71; FV 5.70 + 0.60\n", + "Thiaw: MV 6.04 ± 0.79; FV 6.11 + 0.79\n", + "Zeefuik: MV 5.73 ± 0.92; FV 5.74 + 0.99\n", + "Ghiglione: MV 5.70 ± 0.87; FV 5.72 + 1.02\n", + "Rugani: MV 6.00 ± 0.65; FV 6.02 + 0.52\n", + "De Sciglio: MV 5.92 ± 0.69; FV 5.99 + 0.63\n", + "Djimsiti: MV 6.13 ± 0.75; FV 6.22 + 0.87\n", + "Caldara: MV 5.70 ± 0.99; FV 5.60 + 0.82\n", + "Murru: MV 5.59 ± 0.94; FV 5.62 + 1.05\n", + "Karsdorp: MV 5.61 ± 0.90; FV 5.63 + 0.83\n", + "Bonifazi: MV 5.85 ± 0.70; FV 5.81 + 0.56\n", + "Kjaer: MV 5.94 ± 0.67; FV 5.93 + 0.52\n", + "Magnani: MV 5.59 ± 0.87; FV 5.47 + 0.71\n", + "Walukiewicz: MV 5.78 ± 0.85; FV 5.69 + 0.75\n", + "Ayhan: MV 5.63 ± 1.22; FV 5.61 + 1.28\n", + "Amione: MV 5.55 ± 0.90; FV 5.47 + 0.91\n", + "Ruggeri: MV 6.05 ± 0.66; FV 6.07 + 0.63\n", + "De Winter: MV 5.89 ± 0.84; FV 5.83 + 0.68\n", + "Ostigard: MV 5.92 ± 0.62; FV 5.94 + 0.52\n", + "Wisniewski: MV 5.82 ± 0.89; FV 5.81 + 0.87\n", + "Radovanovic: MV 5.61 ± 0.74; FV 5.50 + 0.62\n", + "Dermaku: MV 6.06 ± 0.85; FV 6.12 + 0.92\n", + "D'ambrosio: MV 6.13 ± 0.73; FV 6.24 + 0.91\n", + "De Silvestri: MV 5.99 ± 0.84; FV 6.18 + 1.06\n", + "Sala: MV 5.85 ± 0.80; FV 5.86 + 0.77\n", + "Chiriches: MV 5.67 ± 1.11; FV 5.58 + 0.98\n", + "Donati: MV 5.76 ± 1.03; FV 5.68 + 0.98\n", + "Gabbia: MV 5.87 ± 0.68; FV 5.80 + 0.55\n", + "Kumbulla: MV 5.60 ± 0.76; FV 5.60 + 0.75\n", + "Adopo: MV 5.95 ± 0.69; FV 6.00 + 0.59\n", + "Pirola: MV 5.55 ± 0.93; FV 5.44 + 0.74\n", + "Lovato: MV 5.63 ± 0.94; FV 5.54 + 0.77\n", + "Tuia: MV 5.90 ± 0.89; FV 6.01 + 1.07\n", + "Ferrer: MV 5.81 ± 0.87; FV 5.77 + 0.81\n", + "Antov: MV 5.61 ± 1.07; FV 5.53 + 0.97\n", + "Vasquez: MV 5.70 ± 0.89; FV 5.62 + 0.91\n", + "Zanoli: MV 5.82 ± 0.70; FV 5.83 + 0.56\n", + "Gatti: MV 5.88 ± 0.90; FV 5.81 + 0.78\n", + "Gila: MV 6.00 ± 0.91; FV 5.97 + 0.81\n", + "Sambia: MV 5.89 ± 0.79; FV 5.89 + 0.66\n", + "Moutinho J.: MV 5.84 ± 0.98; FV 5.80 + 0.89\n", + "Conti: MV 5.89 ± 1.03; FV 6.00 + 1.28\n", + "Marrone: MV 5.55 ± 0.96; FV 5.44 + 0.81\n", + "Tonelli: MV 5.67 ± 0.88; FV 5.55 + 0.75\n", + "Murillo: MV 5.53 ± 0.76; FV 5.44 + 0.69\n", + "Radu: MV 5.74 ± 1.03; FV 5.69 + 0.94\n", + "Paletta: MV 5.77 ± 0.93; FV 5.75 + 0.98\n", + "Florenzi: MV 6.15 ± 0.86; FV 6.39 + 1.27\n", + "Fares: MV 5.87 ± 0.74; FV 5.87 + 0.66\n", + "Marchizza: MV 5.64 ± 0.85; FV 5.64 + 0.85\n", + "Romagna: MV 5.76 ± 0.97; FV 5.84 + 1.03\n", + "Ranieri L.: MV 5.61 ± 0.82; FV 5.59 + 0.86\n", + "Cassandro: MV 6.07 ± 0.86; FV 6.16 + 0.96\n", + "Muldur: MV 5.65 ± 0.88; FV 5.62 + 0.81\n", + "Carboni: MV 5.64 ± 0.87; FV 5.53 + 0.80\n", + "Amey: MV 6.10 ± 0.80; FV 6.23 + 0.92\n", + "Ferrarini: MV 5.77 ± 0.93; FV 5.75 + 0.98\n", + "Vina: MV 5.62 ± 0.93; FV 5.62 + 0.89\n", + "Zanotti: MV 6.03 ± 0.78; FV 6.16 + 0.91\n", + "Ruan: MV 5.56 ± 1.03; FV 5.44 + 0.73\n", + "Coppola D.: MV 5.62 ± 0.83; FV 5.53 + 0.80\n", + "Cacace: MV 5.91 ± 0.72; FV 5.91 + 0.61\n", + "Bayeye: MV 5.93 ± 0.80; FV 6.01 + 0.81\n", + "Ebosele: MV 6.05 ± 0.82; FV 6.13 + 0.82\n", + "Buta: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Abankwah: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Guessand A.: MV 6.05 ± 0.80; FV 6.14 + 0.83\n", + "Cabal: MV 5.85 ± 0.85; FV 5.91 + 0.84\n", + "Sosa: MV 5.72 ± 0.85; FV 5.63 + 0.70\n", + "Guarino: MV 5.94 ± 0.86; FV 6.00 + 0.87\n", + "Carboni F.: MV 5.78 ± 0.93; FV 5.75 + 0.95\n", + "Zaccagni: MV 6.49 ± 1.38; FV 8.19 + 5.10\n", + "Kvaratskhelia: MV 6.52 ± 1.50; FV 8.50 + 5.86\n", + "Milinkovic-Savic: MV 6.37 ± 1.30; FV 7.62 + 3.90\n", + "Barella: MV 6.36 ± 1.24; FV 7.47 + 3.54\n", + "Zielinski: MV 6.34 ± 0.96; FV 6.90 + 2.14\n", + "Luis Alberto: MV 6.38 ± 1.14; FV 7.22 + 2.91\n", + "Strefezza: MV 6.41 ± 1.17; FV 7.50 + 3.48\n", + "Felipe Anderson: MV 6.29 ± 1.30; FV 7.52 + 3.86\n", + "Diaz B.: MV 6.27 ± 1.32; FV 7.49 + 3.83\n", + "Koopmeiners: MV 6.49 ± 1.29; FV 7.84 + 4.20\n", + "Vlasic: MV 6.21 ± 1.16; FV 6.94 + 2.59\n", + "Calhanoglu: MV 6.35 ± 1.07; FV 7.05 + 2.53\n", + "Frattesi: MV 6.01 ± 1.11; FV 6.49 + 1.94\n", + "Zambo Anguissa: MV 6.30 ± 1.01; FV 6.89 + 2.22\n", + "Elmas: MV 6.24 ± 1.00; FV 6.96 + 2.49\n", + "Pereyra: MV 6.33 ± 1.09; FV 7.11 + 2.68\n", + "Miranchuk: MV 6.20 ± 1.24; FV 7.04 + 2.89\n", + "Samardzic: MV 6.31 ± 1.09; FV 7.13 + 2.78\n", + "Politano: MV 6.25 ± 0.91; FV 6.74 + 1.91\n", + "Rabiot: MV 6.21 ± 1.08; FV 6.87 + 2.34\n", + "Radonjic: MV 6.16 ± 1.04; FV 6.74 + 2.17\n", + "Lobotka: MV 6.18 ± 0.75; FV 6.43 + 1.21\n", + "Tonali: MV 6.31 ± 1.11; FV 6.98 + 2.46\n", + "Lazovic: MV 6.07 ± 1.10; FV 6.60 + 2.05\n", + "Bonaventura: MV 5.88 ± 0.96; FV 6.04 + 1.20\n", + "Pellegrini Lo.: MV 5.88 ± 1.12; FV 6.09 + 1.49\n", + "Pessina: MV 5.93 ± 0.97; FV 6.18 + 1.40\n", + "Kostic: MV 6.19 ± 1.02; FV 6.68 + 1.91\n", + "Lovric: MV 6.25 ± 0.89; FV 6.71 + 1.82\n", + "Ferguson: MV 6.24 ± 1.04; FV 6.88 + 2.25\n", + "Ciurria: MV 5.98 ± 1.08; FV 6.39 + 1.75\n", + "Ikone': MV 5.83 ± 1.05; FV 6.07 + 1.40\n", + "Baldanzi: MV 6.25 ± 1.04; FV 6.98 + 2.53\n", + "Candreva: MV 6.05 ± 1.09; FV 6.36 + 1.69\n", + "Bennacer: MV 6.18 ± 0.79; FV 6.40 + 1.17\n", + "El Shaarawy: MV 5.90 ± 0.85; FV 6.07 + 1.08\n", + "Pasalic: MV 6.31 ± 1.24; FV 7.44 + 3.58\n", + "Orsolini: MV 6.27 ± 1.34; FV 7.49 + 3.84\n", + "Bandinelli: MV 6.01 ± 0.85; FV 6.23 + 1.19\n", + "Mkhitaryan: MV 6.16 ± 1.04; FV 6.73 + 2.09\n", + "Colpani: MV 5.94 ± 0.83; FV 6.10 + 1.22\n", + "Pogba: MV 6.06 ± 0.93; FV 6.21 + 1.15\n", + "Sensi: MV 5.88 ± 1.05; FV 5.99 + 1.28\n", + "Chiesa: MV 6.24 ± 1.07; FV 6.82 + 2.22\n", + "Lukic: MV 6.07 ± 1.00; FV 6.45 + 1.63\n", + "Fagioli: MV 6.21 ± 1.02; FV 6.77 + 2.08\n", + "Messias: MV 6.15 ± 1.21; FV 6.97 + 2.88\n", + "Arslan: MV 6.03 ± 0.70; FV 6.12 + 0.77\n", + "Brozovic: MV 6.18 ± 0.87; FV 6.56 + 1.59\n", + "Verdi: MV 6.03 ± 0.87; FV 6.35 + 1.48\n", + "Barak: MV 5.71 ± 0.90; FV 5.81 + 1.03\n", + "Matic: MV 5.86 ± 0.85; FV 5.98 + 1.05\n", + "Soriano: MV 6.07 ± 0.86; FV 6.27 + 1.14\n", + "Dominguez: MV 6.19 ± 1.10; FV 6.78 + 2.22\n", + "Ranocchia F.: MV 5.88 ± 0.84; FV 6.01 + 1.11\n", + "Cristante: MV 5.70 ± 0.91; FV 5.75 + 0.96\n", + "Bajrami: MV 5.94 ± 0.94; FV 6.18 + 1.28\n", + "Ricci S.: MV 6.04 ± 0.71; FV 6.11 + 0.70\n", + "Thorstvedt: MV 5.86 ± 0.86; FV 6.01 + 1.13\n", + "De Ketelaere: MV 5.97 ± 0.73; FV 6.03 + 0.73\n", + "Saponara: MV 5.85 ± 1.02; FV 6.09 + 1.37\n", + "Vecino: MV 6.05 ± 0.91; FV 6.24 + 1.15\n", + "Locatelli: MV 6.09 ± 0.74; FV 6.20 + 0.82\n", + "Zaniolo: MV 5.66 ± 0.88; FV 5.75 + 1.00\n", + "Traore' Hj.: MV 5.89 ± 0.99; FV 6.19 + 1.48\n", + "Maldini: MV 5.97 ± 1.00; FV 6.32 + 1.68\n", + "Coulibaly L.: MV 5.93 ± 1.13; FV 6.23 + 1.75\n", + "Gonzalez J.: MV 6.11 ± 1.03; FV 6.62 + 1.98\n", + "Vilhena: MV 5.78 ± 0.91; FV 5.96 + 1.19\n", + "De Roon: MV 6.08 ± 0.68; FV 6.13 + 0.68\n", + "Mandragora: MV 5.76 ± 0.87; FV 5.76 + 0.93\n", + "Haas: MV 5.98 ± 0.81; FV 6.19 + 1.20\n", + "Wijnaldum: MV 5.70 ± 0.95; FV 5.74 + 1.00\n", + "Bourabia: MV 5.86 ± 0.81; FV 5.87 + 0.78\n", + "Sottil: MV 5.87 ± 1.02; FV 6.10 + 1.39\n", + "Agudelo: MV 5.80 ± 0.72; FV 5.83 + 0.69\n", + "Makengo: MV 6.00 ± 0.64; FV 6.04 + 0.58\n", + "Zalewski: MV 5.78 ± 0.75; FV 5.81 + 0.79\n", + "Aebischer: MV 6.03 ± 0.90; FV 6.40 + 1.51\n", + "Ederson D.s.: MV 6.12 ± 0.95; FV 6.45 + 1.55\n", + "Miretti: MV 6.00 ± 0.75; FV 6.09 + 0.81\n", + "Blin: MV 6.03 ± 0.60; FV 6.03 + 0.49\n", + "Hjulmand: MV 6.15 ± 1.00; FV 6.30 + 1.37\n", + "Cataldi: MV 5.97 ± 0.67; FV 5.97 + 0.54\n", + "Paredes: MV 5.83 ± 0.74; FV 5.80 + 0.60\n", + "Djuricic: MV 5.66 ± 1.04; FV 5.86 + 1.30\n", + "Linetty: MV 5.90 ± 0.88; FV 6.06 + 1.08\n", + "Walace: MV 5.96 ± 0.67; FV 5.99 + 0.55\n", + "Marin: MV 5.85 ± 1.00; FV 5.89 + 1.06\n", + "Mckennie: MV 5.89 ± 0.86; FV 6.11 + 1.29\n", + "Pobega: MV 6.15 ± 0.92; FV 6.50 + 1.61\n", + "Volpato: MV 5.69 ± 1.13; FV 5.69 + 1.19\n", + "Camara Ma.: MV 5.93 ± 0.78; FV 5.97 + 0.73\n", + "Cuadrado: MV 6.05 ± 0.98; FV 6.20 + 1.24\n", + "Ndombele': MV 5.88 ± 0.64; FV 5.90 + 0.49\n", + "Amrabat: MV 5.73 ± 0.93; FV 5.65 + 0.86\n", + "Tameze: MV 5.75 ± 0.86; FV 5.81 + 0.99\n", + "Gyasi: MV 5.86 ± 1.14; FV 6.22 + 1.76\n", + "Ilic: MV 5.84 ± 0.86; FV 5.88 + 0.94\n", + "Harroui: MV 5.81 ± 0.80; FV 5.93 + 1.07\n", + "Pickel: MV 5.74 ± 0.77; FV 5.80 + 0.94\n", + "Moro N.: MV 5.96 ± 0.65; FV 6.05 + 0.64\n", + "Duncan: MV 5.72 ± 0.82; FV 5.74 + 0.89\n", + "Miguel Veloso: MV 5.84 ± 0.73; FV 5.84 + 0.72\n", + "Ekdal: MV 5.77 ± 0.69; FV 5.72 + 0.62\n", + "Nicolussi Caviglia: MV 5.83 ± 0.95; FV 6.10 + 1.37\n", + "Schouten: MV 5.93 ± 0.69; FV 5.98 + 0.59\n", + "Rovella: MV 5.81 ± 1.04; FV 5.75 + 1.02\n", + "Crnigoj: MV 5.89 ± 0.82; FV 6.07 + 1.05\n", + "Romero L.: MV 6.27 ± 0.98; FV 6.84 + 2.13\n", + "Basic: MV 6.00 ± 0.66; FV 6.03 + 0.59\n", + "Sabiri: MV 5.69 ± 1.01; FV 5.89 + 1.32\n", + "Terracciano F.: MV 5.98 ± 0.63; FV 5.99 + 0.59\n", + "Castagnetti: MV 5.75 ± 0.67; FV 5.77 + 0.61\n", + "Oudin: MV 6.03 ± 0.63; FV 6.07 + 0.57\n", + "D'alessandro: MV 5.96 ± 0.65; FV 5.93 + 0.62\n", + "Grassi: MV 5.94 ± 0.67; FV 5.95 + 0.57\n", + "Krunic: MV 6.00 ± 0.64; FV 6.03 + 0.57\n", + "Rincon: MV 5.65 ± 0.92; FV 5.66 + 0.98\n", + "Machin: MV 5.81 ± 0.91; FV 5.80 + 1.00\n", + "Leris: MV 5.67 ± 0.83; FV 5.78 + 1.07\n", + "Meite': MV 5.73 ± 0.86; FV 5.66 + 0.87\n", + "Vieira: MV 5.64 ± 0.89; FV 5.72 + 1.08\n", + "Esposito Sa.: MV 5.82 ± 0.85; FV 5.77 + 0.80\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Winks: MV 5.92 ± 0.74; FV 5.98 + 0.89\n", - "Castrovilli: MV 5.83 ± 0.83; FV 5.94 + 1.11\n", - "Maldini: MV 6.15 ± 1.04; FV 7.10 + 3.02\n", - "Marin: MV 5.80 ± 0.75; FV 5.78 + 0.64\n", - "Maleh: MV 5.76 ± 0.69; FV 5.77 + 0.79\n", - "Matheus Henrique: MV 5.79 ± 0.70; FV 5.79 + 0.72\n", - "Asllani: MV 5.83 ± 0.63; FV 5.81 + 0.59\n", - "Moro N.: MV 5.88 ± 0.70; FV 5.89 + 0.65\n", - "Oudin: MV 6.09 ± 0.70; FV 6.25 + 0.91\n", - "Duncan: MV 5.75 ± 0.78; FV 5.79 + 0.90\n", - "Molina S.: MV 5.84 ± 0.77; FV 5.88 + 0.81\n", - "Kastanos: MV 5.74 ± 0.72; FV 5.72 + 0.65\n", - "Valoti: MV 5.83 ± 0.66; FV 5.79 + 0.59\n", - "Gaetano: MV 6.00 ± 0.69; FV 6.04 + 0.76\n", - "Escalante: MV 5.83 ± 0.80; FV 5.77 + 0.64\n", - "Bohinen: MV 5.96 ± 0.68; FV 5.97 + 0.64\n", - "Terracciano F.: MV 6.08 ± 0.59; FV 6.20 + 0.82\n", - "Milanese: MV 5.91 ± 0.85; FV 5.96 + 0.83\n", - "Castagnetti: MV 5.98 ± 0.65; FV 5.99 + 0.65\n", - "Listkowski: MV 6.02 ± 0.64; FV 6.05 + 0.67\n", - "Adli: MV 5.72 ± 0.67; FV 5.72 + 0.64\n", - "Hrustic: MV 5.86 ± 0.75; FV 5.87 + 0.73\n", - "D'andrea: MV 5.97 ± 0.68; FV 6.06 + 1.01\n", - "Benassi: MV 5.75 ± 0.64; FV 5.74 + 0.62\n", - "Baez: MV 5.93 ± 0.86; FV 5.98 + 0.81\n", - "Vignato: MV 5.86 ± 0.78; FV 5.90 + 0.84\n", - "Obiang: MV 5.84 ± 0.77; FV 5.83 + 0.66\n", - "Askildsen: MV 5.77 ± 0.63; FV 5.77 + 0.57\n", - "Hongla: MV 5.82 ± 0.63; FV 5.82 + 0.67\n", - "Yepes: MV 5.66 ± 0.73; FV 5.63 + 0.64\n", - "Helgason: MV 5.94 ± 0.66; FV 5.94 + 0.63\n", - "Bondo: MV 5.99 ± 0.84; FV 6.05 + 0.77\n", - "Ellertsson: MV 5.93 ± 0.74; FV 5.97 + 0.79\n", - "Capezzi: MV 5.94 ± 0.71; FV 5.96 + 0.65\n", - "Jajalo: MV 5.73 ± 0.67; FV 5.71 + 0.60\n", - "Machin: MV 5.92 ± 0.82; FV 5.98 + 0.85\n", - "Bakayoko: MV 5.75 ± 0.78; FV 5.69 + 0.65\n", - "Scozzarella: MV 5.92 ± 0.82; FV 5.98 + 0.85\n", - "Fagioli: MV 5.98 ± 0.80; FV 6.20 + 1.29\n", - "Demme: MV 5.90 ± 0.75; FV 5.99 + 0.98\n", - "Akpa Akpro: MV 5.98 ± 0.79; FV 6.02 + 0.83\n", - "Darboe: MV 5.97 ± 0.92; FV 6.00 + 0.77\n", - "Bove: MV 5.84 ± 0.78; FV 5.84 + 0.69\n", - "Urbanski: MV 5.98 ± 0.83; FV 6.04 + 0.81\n", - "Bertini: MV 5.87 ± 0.79; FV 5.88 + 0.71\n", - "Cortinovis: MV 6.02 ± 0.77; FV 6.20 + 1.09\n", - "Romero L.: MV 5.86 ± 0.86; FV 5.98 + 1.17\n", - "Bianco: MV 5.76 ± 0.83; FV 5.77 + 0.87\n", - "Sher: MV 5.98 ± 0.77; FV 6.10 + 0.97\n", - "Nguiamba: MV 6.07 ± 0.79; FV 6.22 + 0.97\n", - "Volpato: MV 6.10 ± 1.00; FV 6.44 + 1.64\n", - "Praszelik: MV 6.08 ± 0.75; FV 6.28 + 1.07\n", - "Trimboli: MV 5.92 ± 0.74; FV 5.98 + 0.89\n", - "Pafundi: MV 5.75 ± 0.84; FV 5.75 + 0.88\n", - "Bjorkengren: MV 6.05 ± 0.80; FV 6.17 + 0.93\n", - "Vignato S.: MV 5.97 ± 0.86; FV 6.04 + 0.87\n", - "Samek: MV 6.05 ± 0.80; FV 6.17 + 0.93\n", - "Zerbin: MV 5.92 ± 0.67; FV 5.93 + 0.74\n", - "Ilkhan: MV 5.72 ± 0.70; FV 5.66 + 0.59\n", - "Degli Innocenti: MV 6.01 ± 0.83; FV 6.07 + 0.86\n", - "Fazzini: MV 5.76 ± 0.85; FV 5.71 + 0.66\n", - "Acella: MV 5.96 ± 0.81; FV 6.02 + 0.89\n", - "Tripi: MV 5.96 ± 0.84; FV 6.00 + 0.79\n", - "Garbett: MV 5.85 ± 0.83; FV 5.89 + 0.82\n", - "Iling-Junior: MV 5.91 ± 0.86; FV 5.94 + 0.79\n", - "Immobile: MV 6.38 ± 1.41; FV 8.11 + 5.10\n", - "Vlahovic: MV 6.17 ± 1.44; FV 7.56 + 4.21\n", - "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n", - "Martinez L.: MV 6.27 ± 1.41; FV 7.99 + 4.91\n", - "Dybala: MV 6.48 ± 1.27; FV 7.95 + 4.58\n", - "Arnautovic: MV 6.33 ± 1.39; FV 8.06 + 5.00\n", - "Beto: MV 6.13 ± 1.12; FV 7.18 + 3.26\n", - "Giroud: MV 6.29 ± 1.35; FV 7.79 + 4.45\n", - "Osimhen: MV 6.44 ± 1.45; FV 8.31 + 5.52\n", - "Deulofeu: MV 6.16 ± 1.13; FV 7.00 + 2.77\n", - "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n", - "Milik: MV 6.04 ± 0.91; FV 6.39 + 1.55\n", - "Pedro: MV 6.12 ± 0.91; FV 6.45 + 1.57\n", - "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", - "Berardi: MV 6.34 ± 1.39; FV 8.04 + 4.97\n", - "Dia: MV 6.29 ± 1.40; FV 8.00 + 4.92\n", - "Lookman: MV 6.42 ± 1.32; FV 8.02 + 4.79\n", - "Simeone: MV 6.25 ± 1.39; FV 7.74 + 4.47\n", - "Correa: MV 6.17 ± 0.83; FV 6.84 + 2.34\n", - "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", - "Dzeko: MV 6.12 ± 1.32; FV 7.38 + 3.80\n", - "Nzola: MV 6.16 ± 1.31; FV 7.63 + 4.25\n", - "Sanabria: MV 6.02 ± 1.11; FV 6.73 + 2.53\n", - "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", - "Rebic: MV 6.23 ± 1.31; FV 7.63 + 4.14\n", - "Bonazzoli: MV 6.12 ± 0.98; FV 6.62 + 2.00\n", - "Caprari: MV 6.02 ± 0.84; FV 6.51 + 1.86\n", - "Di Maria: MV 5.93 ± 1.29; FV 6.29 + 2.06\n", - "Lozano: MV 6.24 ± 1.22; FV 7.32 + 3.42\n", - "Ceesay: MV 6.23 ± 1.13; FV 7.29 + 3.37\n", - "Pinamonti: MV 5.94 ± 1.12; FV 6.73 + 2.71\n", - "Kouame': MV 6.03 ± 1.06; FV 6.68 + 2.33\n", - "Lauriente': MV 6.30 ± 1.38; FV 7.60 + 4.13\n", - "Jovic: MV 5.89 ± 1.10; FV 6.38 + 2.06\n", - "Barrow: MV 6.12 ± 1.28; FV 7.17 + 3.37\n", - "Henry: MV 6.07 ± 1.15; FV 7.11 + 3.22\n", - "Piatek: MV 6.08 ± 1.35; FV 7.35 + 3.84\n", - "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", - "Dessers: MV 6.14 ± 1.00; FV 7.09 + 3.01\n", - "Caputo: MV 5.80 ± 0.75; FV 5.97 + 1.26\n", - "Raspadori: MV 5.94 ± 0.91; FV 6.32 + 1.64\n", - "Lammers: MV 6.02 ± 0.79; FV 6.31 + 1.45\n", - "Okereke: MV 6.03 ± 1.09; FV 6.74 + 2.58\n", - "Cabral: MV 5.94 ± 0.83; FV 6.32 + 1.69\n", - "Gabbiadini: MV 5.82 ± 0.79; FV 6.08 + 1.48\n", - "Belotti: MV 5.82 ± 0.62; FV 5.81 + 0.62\n", - "Origi: MV 6.05 ± 1.13; FV 6.79 + 2.57\n", - "Botheim: MV 6.04 ± 0.92; FV 6.64 + 2.15\n", - "Alvarez A.: MV 6.16 ± 1.23; FV 7.25 + 3.47\n", - "Verde: MV 6.23 ± 1.04; FV 7.19 + 3.12\n", - "Destro: MV 6.05 ± 1.22; FV 7.01 + 3.17\n", - "Kean: MV 5.84 ± 0.95; FV 6.11 + 1.54\n", - "Pellegri: MV 5.82 ± 0.79; FV 6.01 + 1.33\n", - "Satriano: MV 5.97 ± 0.96; FV 6.55 + 2.16\n", - "Success: MV 5.87 ± 0.68; FV 5.87 + 0.76\n", - "Mota: MV 5.92 ± 0.92; FV 6.61 + 2.27\n", - "Banda: MV 6.08 ± 0.73; FV 6.24 + 1.05\n", - "Hojlund: MV 5.90 ± 0.88; FV 6.40 + 1.92\n", - "Lasagna: MV 5.93 ± 0.80; FV 6.53 + 2.08\n", - "Pjaca: MV 5.98 ± 0.79; FV 6.14 + 1.12\n", - "Gytkjaer: MV 5.94 ± 0.83; FV 6.54 + 2.05\n", - "Petagna: MV 5.90 ± 0.89; FV 6.37 + 1.79\n", - "Nestorovski: MV 5.93 ± 0.77; FV 6.05 + 1.15\n", - "Zanimacchia: MV 5.97 ± 0.68; FV 6.05 + 0.84\n", - "Piccoli: MV 6.06 ± 0.88; FV 6.78 + 2.42\n", - "Kallon: MV 6.06 ± 0.70; FV 6.47 + 1.78\n", - "Ciofani D.: MV 6.16 ± 0.84; FV 6.94 + 2.66\n", - "Di Francesco F.: MV 6.01 ± 0.89; FV 6.40 + 1.77\n", - "Boga: MV 5.82 ± 0.65; FV 5.81 + 0.73\n", - "Zirkzee: MV 6.11 ± 1.15; FV 7.10 + 3.13\n", - "Quagliarella: MV 5.82 ± 0.68; FV 5.91 + 1.07\n", - "Ibrahimovic: MV 6.39 ± 1.31; FV 7.95 + 4.67\n", - "Shomurodov: MV 6.02 ± 0.94; FV 6.62 + 2.27\n", - "Djuric: MV 6.00 ± 0.59; FV 5.96 + 0.71\n", - "Strelec: MV 6.03 ± 0.59; FV 6.04 + 0.85\n", - "Antiste: MV 5.78 ± 0.81; FV 6.12 + 1.65\n", - "Seck: MV 6.03 ± 0.61; FV 6.03 + 0.70\n", - "Buonaiuto: MV 6.06 ± 0.72; FV 6.19 + 0.85\n", - "Sansone: MV 5.87 ± 0.79; FV 6.02 + 1.12\n", - "Pussetto: MV 5.86 ± 0.74; FV 6.10 + 1.51\n", - "Cambiaghi: MV 5.98 ± 0.60; FV 5.95 + 0.64\n", - "Colombo: MV 6.12 ± 1.03; FV 7.05 + 2.97\n", - "Afena-Gyan: MV 5.80 ± 0.67; FV 5.79 + 0.60\n", - "Defrel: MV 5.80 ± 0.82; FV 5.96 + 1.28\n", - "Karamoh: MV 5.98 ± 0.67; FV 5.99 + 0.74\n", - "Cancellieri: MV 5.85 ± 0.65; FV 5.77 + 0.57\n", - "Tsadjout: MV 6.03 ± 0.80; FV 6.16 + 1.01\n", - "Rodriguez P.: MV 6.01 ± 0.69; FV 6.06 + 0.73\n", - "Valencia D.: MV 5.67 ± 0.64; FV 5.71 + 0.65\n", - "Edera: MV 5.84 ± 0.88; FV 5.89 + 0.90\n", - "Oddei: MV 6.00 ± 0.80; FV 6.08 + 0.92\n", - "Raimondo: MV 6.00 ± 0.85; FV 6.09 + 0.89\n", - "Kristoffersen: MV 5.92 ± 0.85; FV 5.96 + 0.87\n", - "Kaio Jorge: MV 5.95 ± 0.68; FV 5.96 + 0.72\n", - "De Luca: MV 5.91 ± 0.77; FV 5.97 + 0.89\n", - "Soule': MV 5.97 ± 0.83; FV 6.01 + 0.72\n", - "Lazetic: MV 5.99 ± 0.84; FV 6.08 + 0.94\n", - "Voelkerling Persson: MV 6.04 ± 0.83; FV 6.17 + 0.96\n", - "Sanca: MV 5.99 ± 0.74; FV 6.08 + 0.83\n" + "Henderson L.: MV 5.89 ± 0.73; FV 5.94 + 0.78\n", + "Obiang: MV 5.85 ± 0.68; FV 5.87 + 0.74\n", + "Lopez M.: MV 5.80 ± 0.84; FV 5.83 + 0.81\n", + "Saelemaekers: MV 6.03 ± 0.81; FV 6.14 + 0.97\n", + "Maggiore: MV 5.91 ± 0.76; FV 6.02 + 0.90\n", + "Akpa Akpro: MV 5.95 ± 0.89; FV 6.02 + 0.94\n", + "Kovalenko: MV 5.89 ± 0.76; FV 5.99 + 0.85\n", + "Maleh: MV 6.10 ± 0.89; FV 6.46 + 1.67\n", + "Matheus Henrique: MV 5.74 ± 0.77; FV 5.77 + 0.77\n", + "Asllani: MV 5.99 ± 0.64; FV 6.02 + 0.57\n", + "Ceide: MV 5.75 ± 0.81; FV 5.79 + 0.77\n", + "Gagliardini: MV 5.93 ± 0.65; FV 5.99 + 0.67\n", + "Bove: MV 5.62 ± 0.68; FV 5.64 + 0.75\n", + "Bianco: MV 5.75 ± 0.86; FV 5.71 + 0.89\n", + "Vranckx: MV 5.94 ± 0.69; FV 5.96 + 0.61\n", + "Marcos Antonio: MV 5.90 ± 0.59; FV 5.88 + 0.42\n", + "Fazzini: MV 5.79 ± 0.66; FV 5.70 + 0.59\n", + "D'andrea: MV 5.94 ± 0.77; FV 6.07 + 1.10\n", + "Sulemana I.: MV 5.78 ± 0.62; FV 5.77 + 0.62\n", + "Tahirovic: MV 5.69 ± 0.73; FV 5.72 + 0.77\n", + "Benassi: MV 5.74 ± 0.70; FV 5.77 + 0.78\n", + "Barberis: MV 5.73 ± 0.85; FV 5.68 + 0.78\n", + "Kastanos: MV 5.78 ± 0.66; FV 5.82 + 0.60\n", + "Vignato: MV 5.93 ± 0.71; FV 6.03 + 0.68\n", + "Valoti: MV 5.75 ± 0.69; FV 5.69 + 0.63\n", + "Winks: MV 5.71 ± 0.87; FV 5.67 + 0.87\n", + "Gaetano: MV 5.95 ± 0.82; FV 6.06 + 0.90\n", + "Zurkowski: MV 6.01 ± 1.11; FV 6.45 + 1.92\n", + "Castrovilli: MV 5.79 ± 0.92; FV 5.85 + 1.04\n", + "Bohinen: MV 5.74 ± 0.59; FV 5.76 + 0.49\n", + "Hrustic: MV 5.53 ± 0.66; FV 5.56 + 0.61\n", + "Iling-Junior: MV 6.00 ± 0.77; FV 6.07 + 0.78\n", + "Capezzi: MV 5.85 ± 0.71; FV 5.91 + 0.69\n", + "Molina S.: MV 5.80 ± 0.88; FV 5.85 + 1.04\n", + "Bakayoko: MV 5.86 ± 0.70; FV 5.77 + 0.60\n", + "Scozzarella: MV 5.81 ± 0.91; FV 5.80 + 1.00\n", + "Demme: MV 6.03 ± 0.80; FV 6.29 + 1.31\n", + "Askildsen: MV 5.71 ± 0.62; FV 5.75 + 0.52\n", + "Darboe: MV 5.73 ± 1.01; FV 5.75 + 1.00\n", + "Urbanski: MV 6.05 ± 0.78; FV 6.17 + 0.87\n", + "Bertini: MV 6.07 ± 0.88; FV 6.17 + 0.97\n", + "Hongla: MV 5.62 ± 0.69; FV 5.66 + 0.80\n", + "Yepes: MV 5.60 ± 0.94; FV 5.51 + 0.88\n", + "Trimboli: MV 5.78 ± 0.93; FV 5.78 + 1.00\n", + "Pafundi: MV 6.06 ± 0.80; FV 6.18 + 0.88\n", + "Milanese: MV 5.76 ± 0.91; FV 5.76 + 0.99\n", + "Helgason: MV 5.84 ± 0.64; FV 5.88 + 0.50\n", + "Adli: MV 5.95 ± 0.81; FV 5.98 + 0.83\n", + "Vignato S.: MV 5.77 ± 0.93; FV 5.72 + 0.91\n", + "Bondo: MV 5.81 ± 0.86; FV 5.75 + 0.85\n", + "Samek: MV 6.08 ± 0.88; FV 6.20 + 1.04\n", + "Zerbin: MV 5.99 ± 0.73; FV 6.07 + 0.81\n", + "Ilkhan: MV 5.85 ± 0.84; FV 5.84 + 0.77\n", + "Degli Innocenti: MV 5.97 ± 0.92; FV 6.04 + 0.99\n", + "Acella: MV 5.83 ± 0.93; FV 5.90 + 1.09\n", + "Garbett: MV 5.94 ± 0.81; FV 6.04 + 0.86\n", + "Carboni V.: MV 5.99 ± 0.75; FV 6.08 + 0.79\n", + "Cipot: MV 5.87 ± 0.86; FV 5.89 + 0.89\n", + "Osimhen: MV 6.55 ± 1.57; FV 8.64 + 6.25\n", + "Dybala: MV 6.39 ± 1.26; FV 7.60 + 3.82\n", + "Martinez L.: MV 6.44 ± 1.50; FV 8.14 + 5.20\n", + "Rafael Leao: MV 6.45 ± 1.47; FV 8.26 + 5.32\n", + "Immobile: MV 6.45 ± 1.53; FV 8.33 + 5.60\n", + "Lookman: MV 6.58 ± 1.53; FV 8.60 + 6.09\n", + "Vlahovic: MV 6.34 ± 1.50; FV 7.90 + 4.79\n", + "Arnautovic: MV 6.40 ± 1.42; FV 7.98 + 4.76\n", + "Dia: MV 6.07 ± 1.39; FV 6.92 + 3.09\n", + "Dzeko: MV 6.32 ± 1.46; FV 7.72 + 4.44\n", + "Milik: MV 6.29 ± 1.18; FV 7.33 + 3.31\n", + "Nzola: MV 6.09 ± 1.43; FV 6.99 + 3.28\n", + "Beto: MV 6.26 ± 1.40; FV 7.56 + 4.11\n", + "Abraham: MV 6.03 ± 1.26; FV 6.74 + 2.63\n", + "Giroud: MV 6.29 ± 1.39; FV 7.67 + 4.22\n", + "Deulofeu: MV 6.43 ± 1.30; FV 7.80 + 4.18\n", + "Correa: MV 6.22 ± 1.21; FV 7.14 + 3.12\n", + "Pedro: MV 6.22 ± 1.09; FV 6.83 + 2.28\n", + "Lozano: MV 6.22 ± 1.16; FV 7.12 + 3.00\n", + "Lukaku: MV 6.16 ± 1.33; FV 7.09 + 3.20\n", + "Lauriente': MV 5.99 ± 1.09; FV 6.34 + 1.75\n", + "Simeone: MV 6.28 ± 1.40; FV 7.64 + 4.23\n", + "Cabral: MV 5.88 ± 1.07; FV 6.20 + 1.62\n", + "Rebic: MV 6.25 ± 1.37; FV 7.46 + 3.87\n", + "Bonazzoli: MV 5.97 ± 1.12; FV 6.42 + 1.92\n", + "Berardi: MV 6.21 ± 1.36; FV 7.32 + 3.63\n", + "Caprari: MV 5.93 ± 1.08; FV 6.29 + 1.71\n", + "Di Maria: MV 6.29 ± 1.36; FV 7.60 + 4.01\n", + "Piatek: MV 5.91 ± 1.22; FV 6.37 + 2.09\n", + "Kouame': MV 5.93 ± 1.10; FV 6.32 + 1.75\n", + "Hojlund: MV 6.35 ± 1.40; FV 7.76 + 4.39\n", + "Sanabria: MV 6.05 ± 1.24; FV 6.74 + 2.59\n", + "Zapata D.: MV 6.27 ± 1.29; FV 7.45 + 3.75\n", + "Kean: MV 6.12 ± 1.26; FV 6.96 + 2.90\n", + "Ceesay: MV 6.18 ± 1.24; FV 7.09 + 3.07\n", + "Pinamonti: MV 5.79 ± 1.06; FV 6.12 + 1.59\n", + "Gonzalez N.: MV 6.08 ± 1.17; FV 6.67 + 2.30\n", + "Colombo: MV 6.22 ± 1.38; FV 7.41 + 3.81\n", + "Mota: MV 5.91 ± 1.14; FV 6.29 + 1.76\n", + "Dessers: MV 5.94 ± 0.99; FV 6.33 + 1.76\n", + "Muriel: MV 6.39 ± 1.38; FV 7.83 + 4.39\n", + "Okereke: MV 5.82 ± 1.07; FV 6.15 + 1.61\n", + "Alvarez A.: MV 5.92 ± 1.06; FV 6.29 + 1.65\n", + "Jovic: MV 5.72 ± 1.11; FV 5.98 + 1.45\n", + "Barrow: MV 6.15 ± 1.32; FV 7.10 + 3.22\n", + "Caputo: MV 6.01 ± 1.23; FV 6.63 + 2.43\n", + "Banda: MV 6.08 ± 0.96; FV 6.48 + 1.77\n", + "Ciofani D.: MV 6.05 ± 1.08; FV 6.63 + 2.25\n", + "Petagna: MV 5.82 ± 0.92; FV 5.99 + 1.25\n", + "Di Francesco F.: MV 6.17 ± 1.26; FV 7.04 + 2.97\n", + "Boga: MV 6.53 ± 1.38; FV 7.97 + 4.49\n", + "Cambiaghi: MV 6.07 ± 0.91; FV 6.38 + 1.42\n", + "Djuric: MV 5.97 ± 0.78; FV 6.16 + 1.27\n", + "Henry: MV 5.79 ± 1.06; FV 6.17 + 1.72\n", + "Success: MV 6.05 ± 0.83; FV 6.23 + 1.16\n", + "Gabbiadini: MV 5.72 ± 1.08; FV 5.98 + 1.46\n", + "Origi: MV 6.05 ± 1.15; FV 6.64 + 2.32\n", + "Lammers: MV 5.80 ± 0.91; FV 6.03 + 1.40\n", + "Satriano: MV 5.86 ± 0.94; FV 6.13 + 1.44\n", + "Zirkzee: MV 6.13 ± 1.18; FV 6.96 + 2.83\n", + "Kallon: MV 5.81 ± 0.85; FV 6.05 + 1.42\n", + "Solbakken: MV 5.73 ± 1.00; FV 5.77 + 1.03\n", + "Lasagna: MV 5.71 ± 0.90; FV 5.98 + 1.50\n", + "Nestorovski: MV 6.15 ± 0.93; FV 6.78 + 2.38\n", + "Pellegri: MV 5.85 ± 0.99; FV 6.18 + 1.67\n", + "Botheim: MV 5.85 ± 1.02; FV 6.20 + 1.64\n", + "Gytkjaer: MV 5.75 ± 0.80; FV 5.87 + 1.11\n", + "Belotti: MV 5.57 ± 0.69; FV 5.65 + 0.87\n", + "Raspadori: MV 6.01 ± 0.92; FV 6.46 + 1.84\n", + "Buonaiuto: MV 5.97 ± 0.73; FV 6.03 + 0.80\n", + "Verde: MV 6.00 ± 1.07; FV 6.44 + 1.88\n", + "Destro: MV 5.94 ± 1.22; FV 6.39 + 2.09\n", + "Sansone: MV 6.11 ± 1.12; FV 6.77 + 2.42\n", + "Pjaca: MV 5.89 ± 0.80; FV 5.99 + 0.83\n", + "Zanimacchia: MV 5.80 ± 0.74; FV 5.84 + 0.83\n", + "Strelec: MV 5.90 ± 0.65; FV 5.90 + 0.61\n", + "Seck: MV 6.00 ± 0.62; FV 6.00 + 0.55\n", + "Quagliarella: MV 5.75 ± 0.88; FV 5.95 + 1.33\n", + "Piccoli: MV 5.79 ± 0.85; FV 6.07 + 1.53\n", + "Tsadjout: MV 5.92 ± 0.83; FV 6.11 + 1.26\n", + "Shomurodov: MV 5.85 ± 1.00; FV 6.13 + 1.47\n", + "Cancellieri: MV 5.87 ± 0.60; FV 5.84 + 0.45\n", + "Antiste: MV 5.76 ± 0.89; FV 5.93 + 1.20\n", + "Afena-Gyan: MV 5.60 ± 0.72; FV 5.65 + 0.82\n", + "Soule': MV 6.01 ± 0.99; FV 6.18 + 1.24\n", + "Ngonge: MV 5.77 ± 0.91; FV 5.81 + 1.06\n", + "Defrel: MV 5.72 ± 0.77; FV 5.82 + 1.05\n", + "Karamoh: MV 5.88 ± 0.67; FV 5.90 + 0.57\n", + "Ibrahimovic: MV 6.43 ± 1.41; FV 8.05 + 4.89\n", + "Pussetto: MV 5.79 ± 0.97; FV 6.05 + 1.48\n", + "Voelkerling Persson: MV 6.09 ± 0.83; FV 6.16 + 0.90\n", + "Valencia D.: MV 5.65 ± 0.66; FV 5.69 + 0.79\n", + "Edera: MV 5.91 ± 0.83; FV 5.97 + 0.85\n", + "Oddei: MV 5.91 ± 0.85; FV 6.03 + 1.06\n", + "Braaf: MV 5.89 ± 1.08; FV 6.22 + 1.59\n", + "Raimondo: MV 6.11 ± 0.83; FV 6.25 + 1.00\n", + "Kaio Jorge: MV 6.00 ± 0.75; FV 6.06 + 0.75\n", + "De Luca: MV 5.81 ± 0.94; FV 5.82 + 1.02\n", + "Lazetic: MV 6.04 ± 0.88; FV 6.14 + 1.03\n", + "Montevago: MV 5.62 ± 0.70; FV 5.70 + 0.99\n", + "Krollis: MV 5.83 ± 0.90; FV 5.83 + 0.93\n" ] }, { @@ -4610,110 +7360,110 @@ " Musso\n", " P\n", " Atalanta\n", - " Inter\n", + " Sampdoria\n", " 1\n", " 1.0\n", " 90\n", - " 6.423182\n", - " 0.437600\n", - " 5.224919\n", - " 0.641963\n", - " 6.163942\n", - " 0.330588\n", - " 0.535840\n", - " 1.357606\n", - " 5.735502\n", - " 0.856370\n", - " -0.429674\n", - " 0.651929\n", - " 23.657246\n", + " 6.305640\n", + " 0.377408\n", + " 5.757536\n", + " 0.619993\n", + " 6.180476\n", + " 0.394940\n", + " 0.232310\n", + " 1.210117\n", + " 6.305723\n", + " 0.762046\n", + " -0.503479\n", + " 1.190272\n", + " 70.224476\n", " \n", " \n", " Rossi F.\n", " P\n", " Atalanta\n", - " Inter\n", + " Sampdoria\n", " 1\n", " 0.0\n", " 1\n", - " 6.423182\n", - " 0.437600\n", - " 5.224919\n", - " 0.641963\n", - " 6.163942\n", - " 0.330588\n", - " 0.535840\n", - " 1.357606\n", - " 5.735502\n", - " 0.856370\n", - " -0.429674\n", - " 0.651929\n", - " 23.657246\n", + " 6.305640\n", + " 0.377408\n", + " 5.757536\n", + " 0.619993\n", + " 6.180476\n", + " 0.394940\n", + " 0.232310\n", + " 1.210117\n", + " 6.305723\n", + " 0.762046\n", + " -0.503479\n", + " 1.190272\n", + " 70.224476\n", " \n", " \n", " Sportiello\n", " P\n", " Atalanta\n", - " Inter\n", + " Sampdoria\n", " 1\n", " 0.0\n", " 5\n", - " 6.319522\n", - " 0.427780\n", - " 5.056067\n", - " 0.654843\n", - " 6.069346\n", - " 0.328821\n", - " 0.522352\n", - " 1.352637\n", - " 5.634987\n", - " 0.860918\n", - " -0.482947\n", - " 0.652458\n", - " 18.510461\n", + " 6.193115\n", + " 0.358548\n", + " 5.650850\n", + " 0.637885\n", + " 6.094947\n", + " 0.387789\n", + " 0.186454\n", + " 1.221128\n", + " 6.229289\n", + " 0.770664\n", + " -0.522761\n", + " 1.198120\n", + " 68.716019\n", " \n", " \n", - " Toloi\n", + " Scalvini\n", " D\n", " Atalanta\n", - " Inter\n", + " Sampdoria\n", " 1\n", " 1.0\n", - " 85\n", - " 6.173315\n", - " 0.469779\n", - " 6.596937\n", - " 0.888518\n", - " 6.098449\n", - " 0.536081\n", - " 0.103324\n", - " 1.153739\n", - " 5.817624\n", - " 1.020300\n", - " 0.492989\n", - " 1.922028\n", + " 80\n", + " 6.297760\n", + " 0.508060\n", + " 6.908779\n", + " 1.131825\n", + " 6.252611\n", + " 0.592702\n", + " 0.056376\n", + " 1.103786\n", + " 5.846235\n", + " 1.122085\n", + " 0.572402\n", + " 1.999818\n", " 0.000000\n", " \n", " \n", - " Demiral\n", + " Hateboer\n", " D\n", " Atalanta\n", - " Inter\n", + " Sampdoria\n", " 1\n", " 1.0\n", - " 70\n", - " 5.953753\n", - " 0.540473\n", - " 6.281766\n", - " 0.949985\n", - " 5.934339\n", - " 0.641027\n", - " 0.022431\n", - " 1.109773\n", - " 5.482247\n", - " 1.160922\n", - " 0.455021\n", - " 1.905340\n", + " 80\n", + " 6.133840\n", + " 0.471591\n", + " 6.635250\n", + " 0.976241\n", + " 6.011508\n", + " 0.515227\n", + " 0.174893\n", + " 1.153011\n", + " 5.747461\n", + " 1.047180\n", + " 0.528117\n", + " 1.999784\n", " 0.000000\n", " \n", " \n", @@ -4739,187 +7489,187 @@ " ...\n", " \n", " \n", - " Henry\n", + " Djuric\n", " A\n", " Verona\n", - " Spezia\n", - " 1\n", + " Udinese\n", + " 0\n", " 1.0\n", " 90\n", - " 6.068598\n", - " 0.576060\n", - " 7.114568\n", - " 1.608339\n", - " 6.014072\n", - " 0.671000\n", - " 0.060142\n", - " 1.108052\n", - " 5.492682\n", - " 1.263124\n", - " 0.715161\n", - " 1.910237\n", + " 5.972628\n", + " 0.388465\n", + " 6.163057\n", + " 0.634940\n", + " 5.833813\n", + " 0.396462\n", + " 0.255897\n", + " 1.275109\n", + " 5.661306\n", + " 0.850197\n", + " 0.396884\n", + " 1.999688\n", " 0.000000\n", " \n", " \n", " Piccoli\n", " A\n", " Verona\n", - " Spezia\n", - " 1\n", - " 0.0\n", + " Udinese\n", " 0\n", - " 6.059833\n", - " 0.441204\n", - " 6.784868\n", - " 1.209420\n", - " 6.014452\n", - " 0.514128\n", - " 0.065397\n", - " 1.152553\n", - " 5.620515\n", - " 1.123672\n", - " 0.616607\n", - " 1.933259\n", - " 0.000000\n", - " \n", - " \n", - " Lasagna\n", - " A\n", - " Verona\n", - " Spezia\n", - " 1\n", " 0.0\n", - " 60\n", - " 5.929344\n", - " 0.400060\n", - " 6.528470\n", - " 1.039774\n", - " 5.915677\n", - " 0.476940\n", - " 0.021249\n", - " 1.155244\n", - " 5.555768\n", - " 1.046513\n", - " 0.571223\n", - " 1.926864\n", + " 55\n", + " 5.791879\n", + " 0.427178\n", + " 6.067133\n", + " 0.764488\n", + " 5.635827\n", + " 0.435809\n", + " 0.261541\n", + " 1.237202\n", + " 5.401185\n", + " 0.892626\n", + " 0.479268\n", + " 1.999686\n", " 0.000000\n", " \n", " \n", " Kallon\n", " A\n", " Verona\n", - " Spezia\n", - " 1\n", - " 1.0\n", - " 70\n", - " 6.058185\n", - " 0.351964\n", - " 6.469973\n", - " 0.892355\n", - " 6.018703\n", - " 0.409773\n", - " 0.071437\n", - " 1.189220\n", - " 5.663049\n", - " 0.968627\n", - " 0.526584\n", - " 1.927580\n", + " Udinese\n", + " 0\n", + " 0.4\n", + " 55\n", + " 5.805168\n", + " 0.423221\n", + " 6.047278\n", + " 0.709877\n", + " 5.663424\n", + " 0.440857\n", + " 0.235609\n", + " 1.234081\n", + " 5.452299\n", + " 0.881391\n", + " 0.442927\n", + " 1.999681\n", " 0.000000\n", " \n", " \n", - " Djuric\n", + " Lasagna\n", " A\n", " Verona\n", - " Spezia\n", - " 1\n", + " Udinese\n", + " 0\n", + " 0.6\n", + " 60\n", + " 5.709209\n", + " 0.448641\n", + " 5.982099\n", + " 0.749268\n", + " 5.554041\n", + " 0.465292\n", + " 0.244118\n", + " 1.210599\n", + " 5.338185\n", + " 0.895107\n", + " 0.465837\n", + " 1.999674\n", + " 0.000000\n", + " \n", + " \n", + " Ngonge\n", + " A\n", + " Verona\n", + " Udinese\n", + " 0\n", " 0.0\n", - " 40\n", - " 5.996716\n", - " 0.297127\n", - " 5.960767\n", - " 0.356038\n", - " 5.971903\n", - " 0.350155\n", - " 0.052594\n", - " 1.209070\n", - " 5.781554\n", - " 0.607889\n", - " 0.214249\n", - " 1.891385\n", + " 25\n", + " 5.770454\n", + " 0.452862\n", + " 5.809538\n", + " 0.528065\n", + " 5.744537\n", + " 0.538130\n", + " 0.035748\n", + " 1.197753\n", + " 5.522781\n", + " 0.905910\n", + " 0.227952\n", + " 1.999411\n", " 0.000000\n", " \n", " \n", "\n", - "

554 rows × 19 columns

\n", + "

547 rows × 19 columns

\n", "" ], "text/plain": [ - " role team oppteam home starter vote% MV MV std \\\n", - "player \n", - "Musso P Atalanta Inter 1 1.0 90 6.423182 0.437600 \n", - "Rossi F. P Atalanta Inter 1 0.0 1 6.423182 0.437600 \n", - "Sportiello P Atalanta Inter 1 0.0 5 6.319522 0.427780 \n", - "Toloi D Atalanta Inter 1 1.0 85 6.173315 0.469779 \n", - "Demiral D Atalanta Inter 1 1.0 70 5.953753 0.540473 \n", - "... ... ... ... ... ... ... ... ... \n", - "Henry A Verona Spezia 1 1.0 90 6.068598 0.576060 \n", - "Piccoli A Verona Spezia 1 0.0 0 6.059833 0.441204 \n", - "Lasagna A Verona Spezia 1 0.0 60 5.929344 0.400060 \n", - "Kallon A Verona Spezia 1 1.0 70 6.058185 0.351964 \n", - "Djuric A Verona Spezia 1 0.0 40 5.996716 0.297127 \n", + " role team oppteam home starter vote% MV MV std \\\n", + "player \n", + "Musso P Atalanta Sampdoria 1 1.0 90 6.305640 0.377408 \n", + "Rossi F. P Atalanta Sampdoria 1 0.0 1 6.305640 0.377408 \n", + "Sportiello P Atalanta Sampdoria 1 0.0 5 6.193115 0.358548 \n", + "Scalvini D Atalanta Sampdoria 1 1.0 80 6.297760 0.508060 \n", + "Hateboer D Atalanta Sampdoria 1 1.0 80 6.133840 0.471591 \n", + "... ... ... ... ... ... ... ... ... \n", + "Djuric A Verona Udinese 0 1.0 90 5.972628 0.388465 \n", + "Piccoli A Verona Udinese 0 0.0 55 5.791879 0.427178 \n", + "Kallon A Verona Udinese 0 0.4 55 5.805168 0.423221 \n", + "Lasagna A Verona Udinese 0 0.6 60 5.709209 0.448641 \n", + "Ngonge A Verona Udinese 0 0.0 25 5.770454 0.452862 \n", "\n", " FV FV std MV loc MV scale MV skewness \\\n", "player \n", - "Musso 5.224919 0.641963 6.163942 0.330588 0.535840 \n", - "Rossi F. 5.224919 0.641963 6.163942 0.330588 0.535840 \n", - "Sportiello 5.056067 0.654843 6.069346 0.328821 0.522352 \n", - "Toloi 6.596937 0.888518 6.098449 0.536081 0.103324 \n", - "Demiral 6.281766 0.949985 5.934339 0.641027 0.022431 \n", + "Musso 5.757536 0.619993 6.180476 0.394940 0.232310 \n", + "Rossi F. 5.757536 0.619993 6.180476 0.394940 0.232310 \n", + "Sportiello 5.650850 0.637885 6.094947 0.387789 0.186454 \n", + "Scalvini 6.908779 1.131825 6.252611 0.592702 0.056376 \n", + "Hateboer 6.635250 0.976241 6.011508 0.515227 0.174893 \n", "... ... ... ... ... ... \n", - "Henry 7.114568 1.608339 6.014072 0.671000 0.060142 \n", - "Piccoli 6.784868 1.209420 6.014452 0.514128 0.065397 \n", - "Lasagna 6.528470 1.039774 5.915677 0.476940 0.021249 \n", - "Kallon 6.469973 0.892355 6.018703 0.409773 0.071437 \n", - "Djuric 5.960767 0.356038 5.971903 0.350155 0.052594 \n", + "Djuric 6.163057 0.634940 5.833813 0.396462 0.255897 \n", + "Piccoli 6.067133 0.764488 5.635827 0.435809 0.261541 \n", + "Kallon 6.047278 0.709877 5.663424 0.440857 0.235609 \n", + "Lasagna 5.982099 0.749268 5.554041 0.465292 0.244118 \n", + "Ngonge 5.809538 0.528065 5.744537 0.538130 0.035748 \n", "\n", " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", "player \n", - "Musso 1.357606 5.735502 0.856370 -0.429674 0.651929 \n", - "Rossi F. 1.357606 5.735502 0.856370 -0.429674 0.651929 \n", - "Sportiello 1.352637 5.634987 0.860918 -0.482947 0.652458 \n", - "Toloi 1.153739 5.817624 1.020300 0.492989 1.922028 \n", - "Demiral 1.109773 5.482247 1.160922 0.455021 1.905340 \n", + "Musso 1.210117 6.305723 0.762046 -0.503479 1.190272 \n", + "Rossi F. 1.210117 6.305723 0.762046 -0.503479 1.190272 \n", + "Sportiello 1.221128 6.229289 0.770664 -0.522761 1.198120 \n", + "Scalvini 1.103786 5.846235 1.122085 0.572402 1.999818 \n", + "Hateboer 1.153011 5.747461 1.047180 0.528117 1.999784 \n", "... ... ... ... ... ... \n", - "Henry 1.108052 5.492682 1.263124 0.715161 1.910237 \n", - "Piccoli 1.152553 5.620515 1.123672 0.616607 1.933259 \n", - "Lasagna 1.155244 5.555768 1.046513 0.571223 1.926864 \n", - "Kallon 1.189220 5.663049 0.968627 0.526584 1.927580 \n", - "Djuric 1.209070 5.781554 0.607889 0.214249 1.891385 \n", + "Djuric 1.275109 5.661306 0.850197 0.396884 1.999688 \n", + "Piccoli 1.237202 5.401185 0.892626 0.479268 1.999686 \n", + "Kallon 1.234081 5.452299 0.881391 0.442927 1.999681 \n", + "Lasagna 1.210599 5.338185 0.895107 0.465837 1.999674 \n", + "Ngonge 1.197753 5.522781 0.905910 0.227952 1.999411 \n", "\n", " Clean Sheet % \n", "player \n", - "Musso 23.657246 \n", - "Rossi F. 23.657246 \n", - "Sportiello 18.510461 \n", - "Toloi 0.000000 \n", - "Demiral 0.000000 \n", + "Musso 70.224476 \n", + "Rossi F. 70.224476 \n", + "Sportiello 68.716019 \n", + "Scalvini 0.000000 \n", + "Hateboer 0.000000 \n", "... ... \n", - "Henry 0.000000 \n", - "Piccoli 0.000000 \n", - "Lasagna 0.000000 \n", - "Kallon 0.000000 \n", "Djuric 0.000000 \n", + "Piccoli 0.000000 \n", + "Kallon 0.000000 \n", + "Lasagna 0.000000 \n", + "Ngonge 0.000000 \n", "\n", - "[554 rows x 19 columns]" + "[547 rows x 19 columns]" ] }, - "execution_count": 30, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "matchday_out = 15\n", + "matchday_out = 20\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", @@ -4969,7 +7719,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 68, "id": "6befd611", "metadata": {}, "outputs": [], @@ -4995,7 +7745,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 69, "id": "2b637a15", "metadata": {}, "outputs": [], @@ -5007,7 +7757,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 70, "id": "60d73507", "metadata": {}, "outputs": [ @@ -5015,572 +7765,565 @@ "name": "stdout", "output_type": "stream", "text": [ - "Meret (6.26, 0.41); (5.52, 0.60)\n", - "Provedel (6.34, 0.44); (5.62, 0.59)\n", - "Maignan (6.22, 0.38); (5.53, 0.60)\n", - "Silvestri (6.32, 0.41); (5.45, 0.61)\n", - "Sepe (6.41, 0.37); (4.91, 0.69)\n", - "Szczesny (6.25, 0.43); (5.73, 0.57)\n", - "Consigli (6.24, 0.37); (5.24, 0.63)\n", - "Falcone (6.40, 0.37); (4.83, 0.70)\n", - "Musso (6.27, 0.43); (5.73, 0.57)\n", - "Vicario (6.45, 0.41); (5.30, 0.63)\n", - "Rui Patricio (6.24, 0.40); (5.38, 0.61)\n", - "Milinkovic-Savic V. (6.27, 0.38); (5.08, 0.67)\n", - "Audero (6.18, 0.34); (4.22, 0.80)\n", - "Montipo' (6.22, 0.33); (4.29, 0.78)\n", - "Sportiello (6.27, 0.41); (5.63, 0.58)\n", - "Skorupski (6.04, 0.36); (3.94, 0.82)\n", - "Di Gregorio (6.13, 0.40); (4.30, 0.76)\n", - "Perin (6.36, 0.47); (5.81, 0.57)\n", - "Handanovic (5.93, 0.37); (4.22, 0.76)\n", - "Tatarusanu (6.17, 0.36); (5.40, 0.61)\n", - "Gollini (6.03, 0.41); (5.40, 0.60)\n", - "Dragowski (6.35, 0.37); (4.39, 0.76)\n", - "Terracciano (6.09, 0.35); (4.64, 0.72)\n", - "Berisha (6.27, 0.38); (5.08, 0.67)\n", - "Radu I. (6.27, 0.42); (4.63, 0.72)\n", - "Cragno (6.13, 0.40); (4.30, 0.76)\n", - "Luis Maximiano (6.05, 0.46); (5.19, 0.62)\n", - "Onana (6.23, 0.37); (5.38, 0.62)\n", - "Carnesecchi (6.27, 0.45); (5.50, 0.60)\n", - "Mirante (6.22, 0.38); (5.53, 0.60)\n", - "Sarr M. (6.27, 0.42); (4.63, 0.72)\n", - "Lamanna (6.13, 0.40); (4.30, 0.76)\n", - "Ujkani (6.45, 0.41); (5.30, 0.63)\n", - "Pegolo (6.24, 0.37); (5.24, 0.63)\n", - "Perilli (6.22, 0.33); (4.29, 0.78)\n", - "Padelli (6.32, 0.41); (5.45, 0.61)\n", - "Perisan (6.45, 0.41); (5.30, 0.63)\n", - "Bardi (6.04, 0.36); (3.94, 0.82)\n", - "Cordaz (5.93, 0.37); (4.22, 0.76)\n", - "Pinsoglio (6.25, 0.43); (5.73, 0.57)\n", - "Fiorillo (6.41, 0.37); (4.91, 0.69)\n", - "Sirigu (6.26, 0.41); (5.52, 0.60)\n", - "Cerofolini (6.09, 0.35); (4.64, 0.72)\n", - "Rossi F. (6.27, 0.43); (5.73, 0.57)\n", - "Contini (6.18, 0.34); (4.22, 0.80)\n", - "Brancolini (6.40, 0.37); (4.83, 0.70)\n", - "Bleve (6.40, 0.37); (4.83, 0.70)\n", - "Berardi A. (6.22, 0.33); (4.29, 0.78)\n", - "Russo A. (6.24, 0.37); (5.24, 0.63)\n", - "Gemello (6.27, 0.38); (5.08, 0.67)\n", - "Ravaglia (6.18, 0.34); (4.22, 0.80)\n", - "Zoet (6.24, 0.36); (4.14, 0.80)\n", - "Boer (6.24, 0.40); (5.38, 0.61)\n", - "Adamonis (6.34, 0.44); (5.62, 0.59)\n", - "Marfella (6.26, 0.41); (5.52, 0.60)\n", - "Zovko (6.35, 0.37); (4.39, 0.76)\n", - "Piana (6.32, 0.41); (5.45, 0.61)\n", - "Bagnolini (6.04, 0.36); (3.94, 0.82)\n", - "Svilar (6.24, 0.40); (5.38, 0.61)\n", - "Sorrentino A. (6.13, 0.40); (4.30, 0.76)\n", - "Ciezkowski (6.27, 0.42); (4.63, 0.72)\n", - "Micai (6.41, 0.37); (4.91, 0.69)\n", - "Chiesa M. (6.22, 0.33); (4.29, 0.78)\n", - "Saro (6.27, 0.42); (4.63, 0.72)\n", - "Smalling (6.16, 0.54); (6.65, 1.02)\n", - "Hernandez T. (6.28, 0.53); (6.83, 1.05)\n", - "Kim (6.28, 0.45); (6.62, 0.73)\n", - "Bastoni S. (6.09, 0.52); (6.62, 1.05)\n", - "Udogie (6.10, 0.57); (6.78, 1.26)\n", - "Dumfries (6.07, 0.50); (6.58, 1.02)\n", - "Romagnoli (6.20, 0.45); (6.41, 0.54)\n", - "Rodrigo Becao (6.13, 0.44); (6.41, 0.68)\n", - "Parisi (6.15, 0.44); (6.51, 0.80)\n", - "Mazzocchi (6.14, 0.51); (6.70, 1.06)\n", - "Bremer (6.15, 0.41); (6.30, 0.48)\n", - "Demiral (6.09, 0.50); (6.51, 0.92)\n", - "Valeri (6.08, 0.35); (6.40, 0.67)\n", - "Dimarco (6.25, 0.56); (6.95, 1.27)\n", - "Di Lorenzo (6.11, 0.36); (6.22, 0.41)\n", - "Ibanez (5.97, 0.49); (6.04, 0.50)\n", - "Tomori (6.12, 0.39); (6.33, 0.59)\n", - "Toloi (6.21, 0.45); (6.51, 0.68)\n", - "Gosens (5.84, 0.33); (5.92, 0.49)\n", - "Rrahmani (6.17, 0.41); (6.30, 0.47)\n", - "Kalulu (6.03, 0.34); (6.05, 0.34)\n", - "Bijol (6.07, 0.51); (6.47, 0.91)\n", - "Doig (6.16, 0.51); (6.72, 1.05)\n", - "Mario Rui (6.14, 0.41); (6.31, 0.52)\n", - "Spinazzola (5.95, 0.33); (5.95, 0.33)\n", - "Lazzari (6.03, 0.41); (6.10, 0.36)\n", - "Mancini (6.02, 0.38); (6.06, 0.34)\n", - "Danilo (6.14, 0.47); (6.24, 0.41)\n", - "Kyriakopoulos (5.88, 0.44); (5.95, 0.52)\n", - "Vojvoda (5.99, 0.38); (6.08, 0.45)\n", - "Scalvini (6.09, 0.53); (6.51, 0.95)\n", - "Carlos Augusto (5.83, 0.45); (5.95, 0.58)\n", - "Skriniar (5.81, 0.34); (5.78, 0.30)\n", - "Schuurs (6.03, 0.36); (6.07, 0.34)\n", - "Juan Jesus (6.18, 0.38); (6.40, 0.57)\n", - "Bonucci (6.05, 0.48); (6.37, 0.78)\n", - "Calabria (6.07, 0.33); (6.21, 0.45)\n", - "Bastoni (5.94, 0.44); (6.02, 0.43)\n", - "Depaoli (5.93, 0.46); (6.31, 0.86)\n", - "Darmian (5.94, 0.32); (5.96, 0.35)\n", - "Sernicola (5.96, 0.41); (6.27, 0.77)\n", - "Mari' (5.80, 0.58); (6.06, 0.86)\n", - "Martinez Quarta (6.00, 0.49); (6.09, 0.54)\n", - "Maehle (6.03, 0.30); (6.07, 0.36)\n", - "Olivera (6.17, 0.38); (6.48, 0.66)\n", - "Biraghi (5.88, 0.39); (5.98, 0.50)\n", - "Medel (5.89, 0.39); (5.88, 0.32)\n", - "Patric (6.02, 0.42); (6.06, 0.34)\n", - "Rodriguez R. (5.92, 0.40); (5.94, 0.34)\n", - "Aina (5.99, 0.46); (6.27, 0.73)\n", - "Cambiaso (5.86, 0.43); (5.86, 0.36)\n", - "Perez N. (5.99, 0.53); (6.08, 0.62)\n", - "Holm (5.93, 0.41); (6.14, 0.65)\n", - "Baschirotto (6.15, 0.47); (6.56, 0.86)\n", - "Dodo' (5.72, 0.41); (5.70, 0.39)\n", - "Hysaj (5.95, 0.33); (5.95, 0.33)\n", - "Faraoni (5.93, 0.38); (6.14, 0.61)\n", - "Bianchetti (5.74, 0.41); (5.79, 0.47)\n", - "Milenkovic (5.98, 0.57); (6.26, 0.88)\n", - "Marusic (5.90, 0.42); (5.94, 0.38)\n", - "Colley (5.65, 0.55); (5.75, 0.68)\n", - "Toljan (5.68, 0.41); (5.63, 0.35)\n", - "Celik (5.90, 0.35); (5.89, 0.32)\n", - "Ampadu (5.79, 0.51); (5.74, 0.41)\n", - "Singo (5.96, 0.35); (6.00, 0.39)\n", - "Casale (5.92, 0.43); (5.91, 0.34)\n", - "Daniliuc (5.83, 0.55); (5.78, 0.46)\n", - "Pongracic (5.88, 0.42); (5.86, 0.33)\n", - "Soppy (5.88, 0.41); (5.91, 0.39)\n", - "Kiwior (5.78, 0.50); (5.72, 0.38)\n", - "Posch (5.96, 0.59); (6.46, 1.09)\n", - "Masina (5.98, 0.42); (6.23, 0.72)\n", - "Ghiglione (5.95, 0.42); (6.04, 0.49)\n", - "De Vrij (5.78, 0.38); (5.80, 0.39)\n", - "Alex Sandro (5.75, 0.52); (5.73, 0.40)\n", - "Ceccherini (5.86, 0.51); (6.04, 0.73)\n", - "Fazio (5.93, 0.55); (5.90, 0.50)\n", - "Rogerio (5.69, 0.50); (5.63, 0.44)\n", - "Reca (6.01, 0.48); (6.38, 0.84)\n", - "Gunter (5.73, 0.41); (5.75, 0.43)\n", - "Djidji (5.85, 0.44); (6.00, 0.60)\n", - "Augello (5.72, 0.34); (5.70, 0.31)\n", - "Bellanova (5.81, 0.38); (5.82, 0.36)\n", - "Erlic (5.70, 0.52); (5.64, 0.43)\n", - "Ismajli (5.97, 0.40); (5.97, 0.32)\n", - "Ebuehi (5.82, 0.37); (5.77, 0.31)\n", - "Kasius (5.99, 0.42); (6.07, 0.44)\n", - "Lucumi' (5.77, 0.39); (5.73, 0.32)\n", - "Hien (5.67, 0.45); (5.62, 0.37)\n", - "Acerbi (6.02, 0.39); (6.09, 0.41)\n", - "Zappacosta (6.06, 0.38); (6.24, 0.54)\n", - "Chiriches (5.77, 0.57); (5.69, 0.44)\n", - "Gyomber (5.74, 0.51); (5.68, 0.38)\n", - "Pezzella Giu. (5.91, 0.35); (5.90, 0.32)\n", - "Ferrari G. (5.69, 0.51); (5.64, 0.41)\n", - "Bereszynski (5.69, 0.33); (5.61, 0.27)\n", - "Hateboer (5.77, 0.46); (5.97, 0.72)\n", - "Karsdorp (5.80, 0.37); (5.76, 0.32)\n", - "Nuytinck (6.01, 0.43); (6.08, 0.37)\n", - "Lykogiannis (5.90, 0.35); (6.04, 0.51)\n", - "Buongiorno (5.83, 0.45); (5.78, 0.35)\n", - "Nikolaou (5.68, 0.47); (5.65, 0.37)\n", - "Lazaro (5.83, 0.42); (5.86, 0.41)\n", - "Gallo (5.86, 0.41); (5.82, 0.34)\n", - "Ayhan (5.69, 0.58); (5.64, 0.53)\n", - "Dest (5.84, 0.36); (5.83, 0.32)\n", - "Stojanovic (5.68, 0.51); (5.62, 0.44)\n", - "Birindelli (5.75, 0.32); (5.72, 0.28)\n", - "Gendrey (5.77, 0.35); (5.71, 0.30)\n", - "Ebosse (5.86, 0.33); (5.81, 0.29)\n", - "Lochoshvili (5.81, 0.37); (5.80, 0.32)\n", - "Bronn (5.85, 0.35); (5.81, 0.29)\n", - "Thiaw (6.00, 0.40); (6.06, 0.42)\n", - "Izzo (5.98, 0.41); (5.99, 0.33)\n", - "D'ambrosio (6.06, 0.34); (6.12, 0.36)\n", - "Rugani (6.01, 0.39); (6.04, 0.32)\n", - "De Sciglio (5.83, 0.32); (5.79, 0.28)\n", - "Luperto (5.70, 0.58); (5.67, 0.44)\n", - "De Silvestri (5.78, 0.41); (5.81, 0.42)\n", - "Djimsiti (5.94, 0.40); (5.98, 0.38)\n", - "Palomino (6.07, 0.47); (6.15, 0.43)\n", - "Bonifazi (5.66, 0.44); (5.63, 0.34)\n", - "Hristov (5.75, 0.35); (5.67, 0.28)\n", - "Donati (5.65, 0.42); (5.59, 0.35)\n", - "Umtiti (5.82, 0.48); (5.79, 0.39)\n", - "Kjaer (6.01, 0.31); (6.00, 0.31)\n", - "Igor (5.65, 0.53); (5.61, 0.42)\n", - "Okoli (5.65, 0.47); (5.62, 0.38)\n", - "Soumaoro (5.70, 0.56); (5.64, 0.45)\n", - "Caldirola (5.75, 0.48); (5.69, 0.37)\n", - "Ballo-Toure' (6.13, 0.38); (6.46, 0.69)\n", - "Bradaric (5.82, 0.40); (5.82, 0.34)\n", - "De Winter (5.77, 0.53); (5.70, 0.39)\n", - "Quagliata (5.94, 0.32); (5.96, 0.33)\n", - "Ferrari A. (5.55, 0.47); (5.50, 0.39)\n", - "Florenzi (6.11, 0.39); (6.33, 0.55)\n", - "Sala (5.96, 0.38); (6.00, 0.39)\n", - "Caldara (5.77, 0.55); (5.70, 0.45)\n", - "Murru (5.70, 0.35); (5.69, 0.36)\n", - "Marlon (5.62, 0.39); (5.55, 0.32)\n", - "Walukiewicz (5.77, 0.39); (5.73, 0.32)\n", - "Kumbulla (5.87, 0.32); (5.84, 0.29)\n", - "Amian (5.72, 0.48); (5.66, 0.41)\n", - "Ostigard (6.00, 0.38); (6.06, 0.36)\n", - "Sambia (5.70, 0.39); (5.64, 0.32)\n", - "Aiwu (5.93, 0.43); (5.98, 0.44)\n", - "Ehizibue (5.87, 0.30); (5.80, 0.27)\n", - "Hendry (5.98, 0.44); (6.03, 0.44)\n", - "Marrone (5.51, 0.45); (5.45, 0.38)\n", - "Radovanovic (5.52, 0.44); (5.56, 0.36)\n", - "Murillo (5.63, 0.35); (5.52, 0.29)\n", - "Venuti (5.67, 0.34); (5.63, 0.30)\n", - "Magnani (5.76, 0.46); (5.72, 0.36)\n", - "Terzic (6.01, 0.29); (5.96, 0.28)\n", - "Gabbia (5.84, 0.41); (5.81, 0.33)\n", - "Zortea (5.85, 0.32); (5.84, 0.34)\n", - "Dawidowicz (5.58, 0.35); (5.48, 0.29)\n", - "Pirola (5.82, 0.32); (5.73, 0.27)\n", - "Carboni (5.65, 0.48); (5.60, 0.39)\n", - "Lovato (5.72, 0.45); (5.68, 0.35)\n", - "Tuia (5.76, 0.45); (5.80, 0.48)\n", - "Amione (5.63, 0.35); (5.56, 0.30)\n", - "Vasquez (5.77, 0.42); (5.75, 0.35)\n", - "Zima (5.70, 0.41); (5.66, 0.34)\n", - "Coppola D. (5.69, 0.43); (5.64, 0.36)\n", - "Gatti (5.95, 0.45); (5.99, 0.38)\n", - "Gila (5.81, 0.49); (5.78, 0.40)\n", - "Cabal (5.82, 0.43); (5.83, 0.39)\n" + "Meret (6.03, 0.43); (5.52, 0.72)\n", + "Provedel (6.26, 0.37); (5.68, 0.61)\n", + "Vicario (6.45, 0.42); (5.69, 0.59)\n", + "Szczesny (6.19, 0.38); (5.60, 0.67)\n", + "Falcone (6.33, 0.38); (5.04, 0.64)\n", + "Silvestri (6.38, 0.40); (5.28, 0.63)\n", + "Rui Patricio (5.95, 0.36); (5.14, 0.73)\n", + "Milinkovic-Savic V. (6.32, 0.38); (5.23, 0.61)\n", + "Sepe (6.40, 0.40); (5.11, 0.68)\n", + "Maignan (6.36, 0.39); (5.42, 0.62)\n", + "Onana (6.21, 0.36); (4.73, 0.75)\n", + "Audero (6.33, 0.38); (4.48, 0.77)\n", + "Musso (6.25, 0.38); (5.53, 0.63)\n", + "Carnesecchi (6.39, 0.42); (5.37, 0.67)\n", + "Montipo' (6.31, 0.37); (4.50, 0.75)\n", + "Di Gregorio (6.23, 0.37); (4.28, 0.83)\n", + "Tatarusanu (5.99, 0.33); (4.03, 0.84)\n", + "Consigli (6.23, 0.35); (4.79, 0.70)\n", + "Dragowski (6.29, 0.37); (4.85, 0.69)\n", + "Terracciano (6.02, 0.37); (3.84, 0.97)\n", + "Sportiello (6.07, 0.38); (5.43, 0.69)\n", + "Skorupski (6.19, 0.36); (4.36, 0.78)\n", + "Perin (6.33, 0.44); (5.76, 0.66)\n", + "Handanovic (6.04, 0.37); (4.27, 0.89)\n", + "Zoet (6.35, 0.40); (5.74, 0.61)\n", + "Gollini (6.03, 0.43); (5.52, 0.72)\n", + "Ochoa (6.41, 0.41); (5.09, 0.70)\n", + "Pegolo (6.31, 0.39); (5.34, 0.68)\n", + "Mirante (6.36, 0.39); (5.42, 0.62)\n", + "Sarr M. (6.39, 0.42); (5.37, 0.67)\n", + "Lamanna (6.23, 0.37); (4.28, 0.83)\n", + "Ujkani (6.45, 0.42); (5.69, 0.59)\n", + "Berisha (6.32, 0.38); (5.23, 0.61)\n", + "Marchetti (6.29, 0.37); (4.85, 0.69)\n", + "Perilli (6.31, 0.37); (4.50, 0.75)\n", + "Padelli (6.38, 0.40); (5.28, 0.63)\n", + "Perisan (6.45, 0.42); (5.69, 0.59)\n", + "Bardi (6.19, 0.36); (4.36, 0.78)\n", + "Cordaz (6.21, 0.36); (4.73, 0.75)\n", + "Pinsoglio (6.19, 0.38); (5.60, 0.67)\n", + "Fiorillo (6.40, 0.40); (5.11, 0.68)\n", + "Cragno (6.23, 0.37); (4.28, 0.83)\n", + "Sirigu (6.02, 0.37); (3.84, 0.97)\n", + "Cerofolini (6.02, 0.37); (3.84, 0.97)\n", + "Rossi F. (6.25, 0.38); (5.53, 0.63)\n", + "Ravaglia F. (6.19, 0.36); (4.36, 0.78)\n", + "Brancolini (6.33, 0.38); (5.04, 0.64)\n", + "Bleve (6.33, 0.38); (5.04, 0.64)\n", + "Berardi A. (6.31, 0.37); (4.50, 0.75)\n", + "Russo A. (6.23, 0.35); (4.79, 0.70)\n", + "Gemello (6.32, 0.38); (5.23, 0.61)\n", + "Ravaglia (6.33, 0.38); (4.48, 0.77)\n", + "Boer (5.95, 0.36); (5.14, 0.73)\n", + "Adamonis (6.26, 0.37); (5.68, 0.61)\n", + "Marfella (6.03, 0.43); (5.52, 0.72)\n", + "Zovko (6.28, 0.38); (4.77, 0.73)\n", + "Piana (6.38, 0.40); (5.28, 0.63)\n", + "Bagnolini (6.19, 0.36); (4.36, 0.78)\n", + "Luis Maximiano (5.48, 0.37); (5.14, 0.81)\n", + "Svilar (5.95, 0.36); (5.14, 0.73)\n", + "Sorrentino A. (6.23, 0.37); (4.28, 0.83)\n", + "Ciezkowski (6.39, 0.42); (5.37, 0.67)\n", + "Chiesa M. (6.31, 0.37); (4.50, 0.75)\n", + "Saro (6.39, 0.42); (5.37, 0.67)\n", + "Vasquez D. (6.38, 0.41); (5.58, 0.64)\n", + "Turk (6.33, 0.38); (4.48, 0.77)\n", + "Dimarco (6.23, 0.51); (6.79, 1.05)\n", + "Smalling (6.23, 0.50); (6.73, 0.97)\n", + "Doig (6.23, 0.50); (6.75, 1.00)\n", + "Carlos Augusto (6.04, 0.53); (6.47, 0.94)\n", + "Kim (6.31, 0.50); (6.92, 1.13)\n", + "Hernandez T. (6.19, 0.54); (6.76, 1.11)\n", + "Udogie (6.15, 0.54); (6.74, 1.13)\n", + "Parisi (6.18, 0.44); (6.52, 0.77)\n", + "Romagnoli (6.19, 0.49); (6.52, 0.82)\n", + "Di Lorenzo (6.25, 0.45); (6.67, 0.88)\n", + "Danilo (6.25, 0.49); (6.75, 0.98)\n", + "Mazzocchi (6.08, 0.47); (6.46, 0.79)\n", + "Mario Rui (6.25, 0.47); (6.63, 0.84)\n", + "Bastoni S. (6.10, 0.48); (6.50, 0.86)\n", + "Valeri (6.04, 0.36); (6.21, 0.50)\n", + "Posch (6.09, 0.57); (6.72, 1.16)\n", + "Rodrigo Becao (6.11, 0.43); (6.35, 0.64)\n", + "Tomori (6.15, 0.50); (6.52, 0.82)\n", + "Baschirotto (6.21, 0.50); (6.71, 0.99)\n", + "Juan Jesus (6.22, 0.41); (6.61, 0.82)\n", + "Ibanez (6.04, 0.50); (6.25, 0.72)\n", + "Demiral (6.13, 0.48); (6.52, 0.83)\n", + "Scalvini (6.07, 0.53); (6.49, 0.92)\n", + "Bijol (5.98, 0.55); (6.22, 0.84)\n", + "Toloi (6.15, 0.49); (6.45, 0.74)\n", + "Depaoli (6.04, 0.51); (6.50, 0.96)\n", + "Mancini (6.04, 0.35); (6.08, 0.32)\n", + "Bremer (5.97, 0.57); (6.26, 0.86)\n", + "Ebuehi (6.06, 0.38); (6.29, 0.57)\n", + "Dumfries (6.04, 0.52); (6.52, 0.99)\n", + "Schuurs (6.09, 0.35); (6.16, 0.36)\n", + "Darmian (6.11, 0.45); (6.54, 0.87)\n", + "Kalulu (5.91, 0.57); (6.12, 0.80)\n", + "Vojvoda (5.97, 0.43); (6.13, 0.52)\n", + "Maehle (6.02, 0.38); (6.23, 0.57)\n", + "Holm (5.99, 0.41); (6.18, 0.58)\n", + "Bastoni (6.03, 0.47); (6.10, 0.49)\n", + "Gosens (5.98, 0.35); (6.14, 0.50)\n", + "Milenkovic (5.96, 0.56); (6.23, 0.85)\n", + "Rodriguez R. (5.93, 0.36); (5.94, 0.31)\n", + "Reca (5.99, 0.45); (6.25, 0.65)\n", + "Sernicola (5.92, 0.39); (6.08, 0.52)\n", + "Rrahmani (6.24, 0.48); (6.72, 0.95)\n", + "Kyriakopoulos (5.89, 0.45); (6.03, 0.59)\n", + "Martinez Quarta (6.01, 0.50); (6.24, 0.66)\n", + "Perez N. (6.04, 0.48); (6.07, 0.52)\n", + "Olivera (6.11, 0.40); (6.52, 0.80)\n", + "Calabria (6.08, 0.51); (6.48, 0.89)\n", + "Skriniar (5.75, 0.43); (5.66, 0.35)\n", + "Marusic (5.95, 0.37); (5.97, 0.35)\n", + "Lazzari (5.98, 0.38); (5.99, 0.35)\n", + "Augello (5.82, 0.43); (6.01, 0.53)\n", + "Ampadu (5.81, 0.46); (5.74, 0.41)\n", + "Ismajli (6.02, 0.40); (6.03, 0.37)\n", + "Cambiaso (5.89, 0.39); (5.91, 0.37)\n", + "Hysaj (5.96, 0.34); (6.00, 0.31)\n", + "Izzo (5.98, 0.36); (6.01, 0.31)\n", + "Biraghi (5.94, 0.37); (6.01, 0.33)\n", + "Medel (5.88, 0.39); (5.83, 0.33)\n", + "Bonucci (6.16, 0.49); (6.59, 0.88)\n", + "Acerbi (6.05, 0.39); (6.15, 0.38)\n", + "Spinazzola (5.95, 0.34); (6.01, 0.32)\n", + "Lykogiannis (5.92, 0.34); (6.01, 0.34)\n", + "Djidji (5.77, 0.47); (5.85, 0.55)\n", + "Lazaro (5.91, 0.45); (5.94, 0.44)\n", + "Gallo (5.88, 0.36); (5.83, 0.30)\n", + "Singo (5.99, 0.40); (6.12, 0.44)\n", + "Mari' (5.87, 0.58); (6.01, 0.78)\n", + "Casale (5.95, 0.38); (5.92, 0.34)\n", + "Dodo' (5.70, 0.49); (5.62, 0.43)\n", + "De Vrij (5.78, 0.44); (5.81, 0.48)\n", + "Patric (6.01, 0.41); (5.97, 0.37)\n", + "Luperto (5.90, 0.52); (5.85, 0.47)\n", + "Faraoni (5.99, 0.40); (6.24, 0.66)\n", + "Ceccherini (5.90, 0.50); (6.03, 0.64)\n", + "Umtiti (6.02, 0.47); (6.00, 0.46)\n", + "Aina (5.96, 0.44); (6.15, 0.59)\n", + "Caldirola (5.81, 0.45); (5.72, 0.38)\n", + "Soppy (5.76, 0.42); (5.76, 0.45)\n", + "Gendrey (5.83, 0.35); (5.81, 0.30)\n", + "Ferrari A. (5.82, 0.53); (5.87, 0.64)\n", + "Zappacosta (6.04, 0.39); (6.24, 0.56)\n", + "Bianchetti (5.72, 0.47); (5.76, 0.57)\n", + "Ferrari G. (5.77, 0.56); (5.86, 0.72)\n", + "Fazio (5.70, 0.61); (5.69, 0.65)\n", + "Hateboer (5.83, 0.44); (5.98, 0.60)\n", + "Rogerio (5.70, 0.45); (5.69, 0.45)\n", + "Buongiorno (5.85, 0.40); (5.78, 0.35)\n", + "Gunter (5.76, 0.48); (5.81, 0.56)\n", + "Colley (5.78, 0.56); (5.81, 0.66)\n", + "Troost-Ekong (5.81, 0.47); (5.78, 0.47)\n", + "Terzic (5.94, 0.28); (5.90, 0.21)\n", + "Toljan (5.65, 0.44); (5.56, 0.40)\n", + "Zortea (5.93, 0.42); (6.18, 0.66)\n", + "Soumaoro (5.85, 0.50); (5.77, 0.44)\n", + "Amian (5.75, 0.40); (5.67, 0.39)\n", + "Pongracic (5.87, 0.40); (5.82, 0.34)\n", + "Birindelli (5.80, 0.38); (5.82, 0.41)\n", + "Lucumi' (5.77, 0.40); (5.70, 0.35)\n", + "Hien (5.78, 0.40); (5.67, 0.34)\n", + "Ehizibue (5.89, 0.40); (6.05, 0.57)\n", + "Masina (6.04, 0.43); (6.40, 0.74)\n", + "Gyomber (5.72, 0.46); (5.62, 0.38)\n", + "Alex Sandro (5.76, 0.52); (5.69, 0.48)\n", + "Pezzella Giu. (5.89, 0.33); (5.91, 0.27)\n", + "Bereszynski (5.76, 0.42); (5.68, 0.40)\n", + "Venuti (5.72, 0.40); (5.64, 0.37)\n", + "Palomino (6.11, 0.50); (6.40, 0.75)\n", + "Nuytinck (5.85, 0.49); (5.83, 0.49)\n", + "Marlon (5.73, 0.37); (5.61, 0.31)\n", + "Nikolaou (5.73, 0.40); (5.62, 0.33)\n", + "Igor (5.70, 0.47); (5.59, 0.38)\n", + "Okoli (5.67, 0.45); (5.55, 0.39)\n", + "Dawidowicz (5.74, 0.49); (5.74, 0.52)\n", + "Celik (5.77, 0.40); (5.65, 0.38)\n", + "Bellanova (5.78, 0.42); (5.78, 0.46)\n", + "Erlic (5.79, 0.49); (5.74, 0.48)\n", + "Ballo-Toure' (6.11, 0.38); (6.38, 0.63)\n", + "Dest (5.72, 0.43); (5.66, 0.39)\n", + "Stojanovic (5.73, 0.45); (5.67, 0.45)\n", + "Bradaric (5.62, 0.46); (5.55, 0.45)\n", + "Daniliuc (5.70, 0.56); (5.65, 0.54)\n", + "Zima (5.81, 0.41); (5.77, 0.37)\n", + "Quagliata (5.91, 0.31); (5.95, 0.28)\n", + "Ebosse (5.69, 0.37); (5.58, 0.34)\n", + "Aiwu (5.85, 0.46); (5.86, 0.48)\n", + "Lochoshvili (5.74, 0.40); (5.66, 0.40)\n", + "Bronn (5.67, 0.39); (5.57, 0.33)\n", + "Thiaw (5.98, 0.42); (6.05, 0.43)\n", + "Zeefuik (5.86, 0.44); (5.89, 0.44)\n", + "Ghiglione (5.75, 0.43); (5.72, 0.48)\n", + "Rugani (5.96, 0.32); (5.97, 0.26)\n", + "De Sciglio (5.89, 0.35); (5.96, 0.33)\n", + "Djimsiti (5.95, 0.41); (6.01, 0.39)\n", + "Caldara (5.71, 0.48); (5.61, 0.41)\n", + "Murru (5.66, 0.45); (5.66, 0.50)\n", + "Karsdorp (5.85, 0.39); (5.80, 0.34)\n", + "Bonifazi (5.74, 0.42); (5.65, 0.35)\n", + "Kjaer (5.91, 0.36); (5.86, 0.30)\n", + "Magnani (5.68, 0.42); (5.56, 0.33)\n", + "Walukiewicz (5.75, 0.44); (5.67, 0.41)\n", + "Ayhan (5.70, 0.57); (5.67, 0.58)\n", + "Amione (5.60, 0.44); (5.49, 0.43)\n", + "Ruggeri (5.93, 0.35); (5.99, 0.30)\n", + "De Winter (5.83, 0.45); (5.76, 0.38)\n", + "Ostigard (5.93, 0.31); (5.97, 0.27)\n", + "Wisniewski (5.83, 0.44); (5.83, 0.45)\n", + "Radovanovic (5.55, 0.41); (5.43, 0.34)\n", + "Dermaku (5.88, 0.42); (5.89, 0.39)\n", + "D'ambrosio (6.10, 0.37); (6.22, 0.44)\n", + "De Silvestri (5.85, 0.45); (6.03, 0.61)\n", + "Sala (5.89, 0.39); (5.95, 0.40)\n", + "Chiriches (5.75, 0.50); (5.64, 0.42)\n", + "Donati (5.87, 0.50); (5.82, 0.48)\n", + "Gabbia (5.78, 0.37); (5.70, 0.31)\n", + "Kumbulla (5.77, 0.34); (5.69, 0.30)\n", + "Adopo (5.95, 0.37); (5.98, 0.32)\n", + "Pirola (5.52, 0.51); (5.41, 0.39)\n", + "Lovato (5.60, 0.53); (5.51, 0.44)\n", + "Tuia (5.74, 0.47); (5.82, 0.56)\n", + "Ferrer (5.81, 0.44); (5.77, 0.42)\n", + "Antov (5.68, 0.52); (5.62, 0.49)\n", + "Vasquez (5.77, 0.43); (5.69, 0.42)\n", + "Zanoli (5.92, 0.32); (5.94, 0.25)\n", + "Gatti (5.82, 0.44); (5.78, 0.39)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Sosa (5.64, 0.48); (5.57, 0.42)\n", - "Conti (5.91, 0.56); (6.23, 0.88)\n", - "Dermaku (5.90, 0.43); (5.93, 0.39)\n", - "Tonelli (5.61, 0.47); (5.58, 0.38)\n", - "Radu (5.65, 0.49); (5.59, 0.43)\n", - "Paletta (5.82, 0.42); (5.83, 0.39)\n", - "Fares (5.76, 0.36); (5.71, 0.32)\n", - "Marchizza (5.75, 0.37); (5.72, 0.32)\n", - "Romagna (5.85, 0.44); (5.87, 0.41)\n", - "Ranieri L. (5.72, 0.37); (5.71, 0.38)\n", - "Cetin (5.88, 0.52); (5.83, 0.40)\n", - "Muldur (5.64, 0.43); (5.58, 0.36)\n", - "Adopo (5.82, 0.44); (5.81, 0.38)\n", - "Ferrer (5.86, 0.44); (5.86, 0.36)\n", - "Ruggeri (5.95, 0.35); (5.96, 0.32)\n", - "Antov (5.61, 0.51); (5.54, 0.43)\n", - "Amey (5.95, 0.42); (6.01, 0.41)\n", - "Ferrarini (5.82, 0.42); (5.83, 0.39)\n", - "Kamenovic (5.97, 0.43); (6.03, 0.38)\n", - "Vina (5.73, 0.41); (5.69, 0.34)\n", - "Zanoli (5.90, 0.32); (5.88, 0.29)\n", - "Zanotti (5.89, 0.39); (5.93, 0.41)\n", - "Ruan (5.66, 0.53); (5.71, 0.37)\n", - "Motoc (5.88, 0.41); (5.91, 0.38)\n", - "Cacace (5.83, 0.39); (5.78, 0.31)\n", - "Bayeye (5.92, 0.41); (5.97, 0.42)\n", - "Ebosele (5.96, 0.44); (6.03, 0.42)\n", - "Buta (5.92, 0.40); (5.97, 0.39)\n", - "Ndiaye (5.93, 0.43); (5.98, 0.44)\n", - "Abankwah (5.92, 0.40); (5.97, 0.39)\n", - "Guessand A. (5.92, 0.40); (5.97, 0.39)\n", - "Guarino (5.90, 0.43); (5.93, 0.40)\n", - "Milinkovic-Savic (6.45, 0.64); (7.83, 2.14)\n", - "Barella (6.40, 0.62); (7.64, 1.97)\n", - "Kvaratskhelia (6.59, 0.70); (8.44, 2.82)\n", - "Zaccagni (6.48, 0.65); (8.04, 2.38)\n", - "Zielinski (6.39, 0.54); (7.11, 1.27)\n", - "Luis Alberto (6.38, 0.55); (7.19, 1.43)\n", - "Frattesi (6.20, 0.59); (7.26, 1.69)\n", - "Politano (6.25, 0.43); (6.75, 0.91)\n", - "Koopmeiners (6.32, 0.59); (7.32, 1.64)\n", - "Vlasic (6.28, 0.56); (7.41, 1.76)\n", - "Pereyra (6.25, 0.56); (6.98, 1.28)\n", - "Felipe Anderson (6.29, 0.59); (7.38, 1.73)\n", - "Calhanoglu (6.36, 0.57); (7.21, 1.46)\n", - "Strefezza (6.34, 0.51); (7.12, 1.38)\n", - "Diaz B. (6.31, 0.63); (7.60, 1.98)\n", - "Pellegrini Lo. (6.10, 0.59); (6.88, 1.36)\n", - "Zambo Anguissa (6.39, 0.57); (7.23, 1.45)\n", - "Lobotka (6.20, 0.39); (6.48, 0.61)\n", - "Malinovskyi (6.16, 0.43); (6.45, 0.71)\n", - "Candreva (6.25, 0.49); (6.81, 1.05)\n", - "Pasalic (6.14, 0.56); (7.00, 1.44)\n", - "Bennacer (6.16, 0.35); (6.44, 0.59)\n", - "Kostic (6.20, 0.49); (6.79, 1.07)\n", - "Radonjic (6.27, 0.48); (7.03, 1.30)\n", - "Samardzic (6.21, 0.52); (6.87, 1.19)\n", - "Chiesa (6.21, 0.51); (6.97, 1.29)\n", - "Lovric (6.19, 0.38); (6.53, 0.70)\n", - "De Ketelaere (5.90, 0.34); (5.91, 0.36)\n", - "Brozovic (6.21, 0.47); (6.53, 0.76)\n", - "Pogba (5.93, 0.41); (5.99, 0.39)\n", - "Bonaventura (6.17, 0.49); (6.81, 1.16)\n", - "Sensi (5.99, 0.53); (6.42, 0.95)\n", - "Bandinelli (5.99, 0.48); (6.39, 0.92)\n", - "Ikone' (6.12, 0.50); (6.71, 1.10)\n", - "Barak (5.82, 0.35); (5.86, 0.42)\n", - "Mkhitaryan (6.19, 0.44); (6.62, 0.87)\n", - "Pessina (5.89, 0.41); (6.07, 0.64)\n", - "Tonali (6.19, 0.45); (6.61, 0.85)\n", - "Arslan (6.02, 0.39); (6.21, 0.60)\n", - "Lazovic (6.05, 0.38); (6.33, 0.65)\n", - "Cristante (6.00, 0.48); (6.26, 0.72)\n", - "Elmas (6.13, 0.40); (6.51, 0.76)\n", - "Bajrami (5.80, 0.40); (5.91, 0.56)\n", - "Vecino (5.95, 0.49); (6.07, 0.59)\n", - "Paredes (5.72, 0.35); (5.66, 0.30)\n", - "Mandragora (5.95, 0.42); (6.17, 0.66)\n", - "Djuricic (5.78, 0.48); (6.05, 0.79)\n", - "Lukic (6.08, 0.54); (6.56, 1.02)\n", - "Soriano (5.94, 0.34); (5.96, 0.36)\n", - "Zaniolo (5.87, 0.49); (6.28, 0.94)\n", - "Sottil (6.08, 0.54); (6.72, 1.17)\n", - "Traore' Hj. (6.11, 0.56); (6.92, 1.40)\n", - "Messias (6.07, 0.54); (6.80, 1.26)\n", - "Thorstvedt (5.95, 0.45); (6.28, 0.84)\n", - "Cuadrado (5.88, 0.54); (5.86, 0.51)\n", - "Locatelli (6.08, 0.39); (6.18, 0.43)\n", - "Rabiot (6.17, 0.55); (6.79, 1.16)\n", - "Dominguez (6.00, 0.43); (6.19, 0.60)\n", - "Tameze (5.86, 0.40); (5.91, 0.41)\n", - "Miranchuk (6.16, 0.61); (7.20, 1.65)\n", - "Vilhena (5.73, 0.36); (5.76, 0.43)\n", - "De Roon (6.01, 0.36); (6.04, 0.36)\n", - "Verdi (6.01, 0.32); (6.13, 0.46)\n", - "Walace (5.96, 0.35); (5.99, 0.34)\n", - "Wijnaldum (5.89, 0.41); (5.91, 0.39)\n", - "Mckennie (5.80, 0.39); (5.90, 0.55)\n", - "Makengo (5.97, 0.35); (6.00, 0.36)\n", - "Ricci S. (6.11, 0.37); (6.26, 0.47)\n", - "Coulibaly L. (6.01, 0.50); (6.35, 0.86)\n", - "Sabiri (5.74, 0.42); (5.80, 0.56)\n", - "Miretti (5.99, 0.36); (6.05, 0.41)\n", - "Colpani (6.10, 0.40); (6.54, 0.90)\n", - "Haas (5.90, 0.38); (6.12, 0.66)\n", - "Orsolini (5.96, 0.59); (6.61, 1.23)\n", - "Matic (5.99, 0.32); (5.99, 0.32)\n", - "Ascacibar (6.00, 0.37); (6.04, 0.38)\n", - "Ederson D.s. (5.93, 0.33); (5.93, 0.35)\n", - "Ciurria (5.89, 0.41); (6.01, 0.56)\n", - "Gonzalez J. (6.05, 0.47); (6.48, 0.90)\n", - "El Shaarawy (6.16, 0.43); (6.63, 0.93)\n", - "Meite' (5.87, 0.36); (5.88, 0.34)\n", - "Bourabia (5.90, 0.34); (5.90, 0.31)\n", - "Ndombele' (5.92, 0.32); (5.91, 0.30)\n", - "Henderson L. (5.79, 0.35); (5.81, 0.42)\n", - "Maggiore (5.92, 0.34); (5.95, 0.37)\n", - "Ilic (5.82, 0.37); (5.84, 0.38)\n", - "Kovalenko (5.92, 0.37); (5.99, 0.44)\n", - "Zalewski (5.96, 0.33); (5.98, 0.37)\n", - "Pickel (5.88, 0.35); (5.97, 0.51)\n", - "Ferguson (6.07, 0.51); (6.59, 1.02)\n", - "Camara Ma. (6.10, 0.30); (6.18, 0.34)\n", - "Saponara (5.96, 0.46); (6.25, 0.74)\n", - "Rincon (5.74, 0.36); (5.75, 0.36)\n", - "Cataldi (5.99, 0.38); (6.04, 0.37)\n", - "Zurkowski (6.12, 0.52); (6.72, 1.14)\n", - "Rovella (5.75, 0.55); (5.71, 0.44)\n", - "Agudelo (5.86, 0.33); (5.86, 0.34)\n", - "Amrabat (5.91, 0.47); (5.96, 0.48)\n", - "Lopez M. (5.88, 0.40); (5.92, 0.39)\n", - "Gyasi (5.80, 0.47); (5.95, 0.67)\n", - "Pobega (5.99, 0.33); (6.06, 0.43)\n", - "Harroui (5.79, 0.36); (5.87, 0.52)\n", - "Ceide (5.87, 0.35); (5.86, 0.34)\n", - "Baldanzi (6.25, 0.47); (7.03, 1.36)\n", - "Blin (6.00, 0.30); (5.98, 0.30)\n", - "Hjulmand (5.93, 0.53); (5.88, 0.42)\n", - "D'alessandro (5.98, 0.30); (5.99, 0.36)\n", - "Grassi (5.94, 0.32); (5.91, 0.29)\n", - "Krunic (5.97, 0.32); (5.98, 0.35)\n", - "Linetty (5.84, 0.40); (5.85, 0.40)\n", - "Miguel Veloso (5.92, 0.37); (5.96, 0.40)\n", - "Ekdal (5.77, 0.38); (5.73, 0.33)\n", - "Schouten (5.87, 0.39); (5.84, 0.32)\n", - "Villar (5.78, 0.32); (5.72, 0.28)\n", - "Saelemaekers (5.94, 0.36); (6.01, 0.45)\n", - "Vranckx (5.95, 0.39); (6.01, 0.39)\n", - "Basic (6.03, 0.31); (6.07, 0.38)\n", - "Aebischer (5.85, 0.37); (5.86, 0.36)\n", - "Marcos Antonio (5.82, 0.32); (5.77, 0.28)\n", - "Ranocchia F. (5.96, 0.41); (6.20, 0.68)\n", - "Bistrovic (5.84, 0.31); (5.85, 0.32)\n", - "Verre (5.71, 0.33); (5.67, 0.28)\n", - "Gagliardini (6.01, 0.40); (6.19, 0.56)\n", - "Barberis (5.72, 0.38); (5.71, 0.33)\n", - "Leris (5.69, 0.35); (5.70, 0.41)\n", - "Vieira (5.67, 0.34); (5.65, 0.32)\n", - "Winks (5.85, 0.41); (5.90, 0.43)\n", - "Castrovilli (5.97, 0.42); (6.14, 0.61)\n", - "Maldini (6.04, 0.50); (6.69, 1.15)\n", - "Marin (5.72, 0.37); (5.68, 0.32)\n", - "Maleh (5.87, 0.35); (5.95, 0.47)\n", - "Matheus Henrique (5.82, 0.35); (5.82, 0.34)\n", - "Asllani (5.90, 0.32); (5.89, 0.32)\n", - "Moro N. (5.84, 0.34); (5.83, 0.31)\n", - "Oudin (6.01, 0.35); (6.09, 0.41)\n", - "Duncan (5.86, 0.39); (5.95, 0.49)\n", - "Molina S. (5.78, 0.38); (5.79, 0.37)\n", - "Kastanos (5.72, 0.36); (5.68, 0.31)\n", - "Valoti (5.78, 0.33); (5.72, 0.28)\n", - "Gaetano (6.03, 0.36); (6.09, 0.39)\n", - "Escalante (5.77, 0.42); (5.73, 0.35)\n", - "Bohinen (5.92, 0.34); (5.92, 0.31)\n", - "Terracciano F. (6.01, 0.30); (6.02, 0.34)\n", - "Milanese (5.88, 0.44); (5.93, 0.44)\n", - "Castagnetti (5.97, 0.33); (5.99, 0.34)\n", - "Listkowski (5.93, 0.32); (5.91, 0.30)\n", - "Adli (5.73, 0.33); (5.73, 0.32)\n", - "Hrustic (5.66, 0.42); (5.61, 0.36)\n", - "D'andrea (5.95, 0.34); (6.00, 0.43)\n", - "Benassi (5.83, 0.32); (5.83, 0.33)\n", - "Baez (5.92, 0.46); (5.97, 0.44)\n", - "Vignato (5.82, 0.38); (5.86, 0.40)\n", - "Obiang (5.86, 0.39); (5.84, 0.32)\n", - "Askildsen (5.69, 0.32); (5.65, 0.27)\n", - "Hongla (5.70, 0.32); (5.70, 0.29)\n", - "Yepes (5.56, 0.42); (5.48, 0.37)\n", - "Helgason (5.82, 0.33); (5.80, 0.29)\n", - "Bondo (5.91, 0.42); (5.94, 0.37)\n", - "Ellertsson (5.88, 0.38); (5.90, 0.34)\n", - "Capezzi (5.92, 0.35); (5.93, 0.32)\n", - "Jajalo (5.88, 0.33); (5.86, 0.30)\n", - "Machin (5.84, 0.41); (5.86, 0.39)\n", - "Bakayoko (5.80, 0.37); (5.74, 0.32)\n", - "Scozzarella (5.84, 0.41); (5.86, 0.39)\n", - "Fagioli (6.12, 0.46); (6.60, 0.94)\n", - "Demme (5.97, 0.41); (6.14, 0.57)\n", - "Akpa Akpro (5.90, 0.44); (5.95, 0.42)\n", - "Darboe (5.96, 0.47); (5.99, 0.41)\n", - "Bove (5.78, 0.38); (5.77, 0.34)\n", - "Urbanski (5.92, 0.41); (5.96, 0.41)\n", - "Bertini (5.94, 0.41); (5.99, 0.39)\n", - "Cortinovis (5.85, 0.39); (5.89, 0.42)\n", - "Romero L. (5.98, 0.51); (6.35, 0.89)\n", - "Bianco (5.92, 0.40); (5.98, 0.46)\n", - "Sher (5.90, 0.40); (5.94, 0.39)\n", - "Nguiamba (5.98, 0.41); (6.05, 0.41)\n", - "Volpato (6.08, 0.51); (6.45, 0.86)\n", - "Praszelik (5.96, 0.39); (6.04, 0.46)\n", - "Trimboli (5.85, 0.41); (5.90, 0.43)\n", - "Pafundi (5.93, 0.40); (5.98, 0.40)\n", - "Bjorkengren (5.94, 0.42); (6.00, 0.43)\n", - "Vignato S. (5.88, 0.43); (5.91, 0.41)\n", - "Samek (5.94, 0.42); (6.00, 0.43)\n", - "Zerbin (5.95, 0.35); (5.99, 0.38)\n", - "Ilkhan (5.75, 0.35); (5.71, 0.30)\n", - "Degli Innocenti (5.94, 0.46); (5.99, 0.44)\n", - "Fazzini (5.63, 0.48); (5.58, 0.38)\n", - "Acella (5.95, 0.43); (6.03, 0.48)\n", - "Tripi (5.92, 0.41); (5.95, 0.40)\n", - "Garbett (5.92, 0.41); (5.97, 0.43)\n", - "Iling-Junior (5.99, 0.50); (6.03, 0.45)\n", - "Immobile (6.48, 0.72); (8.40, 2.84)\n", - "Vlahovic (6.32, 0.71); (8.05, 2.52)\n", - "Rafael Leao (6.34, 0.71); (7.84, 2.30)\n", - "Martinez L. (6.33, 0.72); (8.16, 2.62)\n", - "Dybala (6.47, 0.64); (7.99, 2.33)\n", - "Arnautovic (6.28, 0.68); (7.90, 2.35)\n", - "Beto (6.27, 0.66); (7.92, 2.37)\n" + "Gila (5.93, 0.43); (5.91, 0.38)\n", + "Sambia (5.81, 0.48); (5.71, 0.40)\n", + "Moutinho J. (5.86, 0.49); (5.81, 0.44)\n", + "Conti (5.94, 0.54); (6.09, 0.71)\n", + "Marrone (5.61, 0.47); (5.51, 0.41)\n", + "Tonelli (5.64, 0.46); (5.54, 0.42)\n", + "Murillo (5.58, 0.36); (5.47, 0.32)\n", + "Radu (5.72, 0.48); (5.65, 0.45)\n", + "Paletta (5.83, 0.47); (5.82, 0.52)\n", + "Florenzi (6.08, 0.42); (6.29, 0.56)\n", + "Fares (5.80, 0.38); (5.77, 0.36)\n", + "Marchizza (5.71, 0.40); (5.65, 0.41)\n", + "Romagna (5.85, 0.46); (5.89, 0.51)\n", + "Ranieri L. (5.74, 0.42); (5.79, 0.53)\n", + "Cassandro (5.88, 0.43); (5.90, 0.40)\n", + "Muldur (5.71, 0.42); (5.63, 0.39)\n", + "Carboni (5.70, 0.43); (5.58, 0.41)\n", + "Amey (5.96, 0.42); (6.04, 0.44)\n", + "Ferrarini (5.83, 0.47); (5.82, 0.52)\n", + "Vina (5.80, 0.43); (5.71, 0.40)\n", + "Zanotti (5.86, 0.45); (5.91, 0.51)\n", + "Ruan (5.64, 0.45); (5.55, 0.32)\n", + "Coppola D. (5.70, 0.40); (5.57, 0.36)\n", + "Cacace (5.83, 0.39); (5.79, 0.34)\n", + "Bayeye (5.88, 0.45); (5.92, 0.45)\n", + "Ebosele (5.87, 0.46); (5.87, 0.46)\n", + "Buta (5.89, 0.45); (5.90, 0.46)\n", + "Abankwah (5.89, 0.45); (5.90, 0.46)\n", + "Guessand A. (5.89, 0.45); (5.90, 0.46)\n", + "Cabal (5.98, 0.39); (6.06, 0.37)\n", + "Sosa (5.68, 0.47); (5.58, 0.42)\n", + "Guarino (5.87, 0.47); (5.90, 0.50)\n", + "Carboni F. (5.85, 0.48); (5.84, 0.49)\n", + "Zaccagni (6.46, 0.68); (8.04, 2.39)\n", + "Kvaratskhelia (6.53, 0.76); (8.54, 2.98)\n", + "Milinkovic-Savic (6.33, 0.65); (7.57, 1.92)\n", + "Barella (6.30, 0.58); (7.24, 1.55)\n", + "Zielinski (6.36, 0.51); (7.00, 1.18)\n", + "Luis Alberto (6.35, 0.56); (7.16, 1.40)\n", + "Strefezza (6.33, 0.55); (7.21, 1.47)\n", + "Felipe Anderson (6.23, 0.64); (7.32, 1.75)\n", + "Diaz B. (6.17, 0.63); (7.09, 1.54)\n", + "Koopmeiners (6.37, 0.63); (7.58, 1.88)\n", + "Vlasic (6.21, 0.58); (7.00, 1.37)\n", + "Calhanoglu (6.30, 0.53); (6.97, 1.20)\n", + "Frattesi (6.11, 0.58); (6.79, 1.24)\n", + "Zambo Anguissa (6.32, 0.54); (7.02, 1.26)\n", + "Elmas (6.27, 0.53); (7.04, 1.33)\n", + "Pereyra (6.23, 0.54); (6.90, 1.20)\n", + "Miranchuk (6.19, 0.62); (7.06, 1.47)\n", + "Samardzic (6.21, 0.53); (6.87, 1.20)\n", + "Politano (6.29, 0.49); (6.88, 1.10)\n", + "Rabiot (6.15, 0.55); (6.73, 1.08)\n", + "Radonjic (6.18, 0.53); (6.83, 1.18)\n", + "Lobotka (6.22, 0.40); (6.54, 0.72)\n", + "Tonali (6.22, 0.53); (6.82, 1.11)\n", + "Lazovic (6.17, 0.55); (6.76, 1.11)\n", + "Bonaventura (6.15, 0.55); (6.78, 1.18)\n", + "Pellegrini Lo. (6.18, 0.62); (7.05, 1.50)\n", + "Pessina (6.05, 0.51); (6.45, 0.86)\n", + "Kostic (6.10, 0.49); (6.51, 0.84)\n", + "Lovric (6.17, 0.44); (6.52, 0.76)\n", + "Ferguson (6.09, 0.49); (6.58, 0.94)\n", + "Ciurria (6.13, 0.58); (6.81, 1.24)\n", + "Ikone' (6.00, 0.55); (6.47, 0.98)\n", + "Baldanzi (6.25, 0.53); (6.99, 1.27)\n", + "Candreva (5.97, 0.59); (6.31, 0.93)\n", + "Bennacer (6.13, 0.40); (6.39, 0.62)\n", + "El Shaarawy (6.19, 0.47); (6.77, 1.07)\n", + "Pasalic (6.12, 0.59); (6.88, 1.35)\n", + "Orsolini (6.18, 0.64); (7.09, 1.55)\n", + "Bandinelli (5.97, 0.44); (6.23, 0.67)\n", + "Mkhitaryan (6.08, 0.48); (6.50, 0.87)\n", + "Colpani (6.02, 0.46); (6.36, 0.81)\n", + "Pogba (5.94, 0.46); (6.03, 0.53)\n", + "Sensi (6.02, 0.55); (6.41, 0.95)\n", + "Chiesa (6.15, 0.52); (6.62, 0.96)\n", + "Lukic (6.05, 0.52); (6.44, 0.89)\n", + "Fagioli (6.13, 0.49); (6.59, 0.90)\n", + "Messias (6.04, 0.55); (6.58, 1.07)\n", + "Arslan (5.97, 0.36); (6.06, 0.38)\n", + "Brozovic (6.20, 0.47); (6.62, 0.86)\n", + "Verdi (6.13, 0.45); (6.51, 0.80)\n", + "Barak (5.90, 0.47); (6.20, 0.74)\n", + "Matic (6.09, 0.39); (6.31, 0.56)\n", + "Soriano (6.00, 0.39); (6.10, 0.41)\n", + "Dominguez (6.10, 0.50); (6.51, 0.87)\n", + "Ranocchia F. (5.96, 0.45); (6.25, 0.73)\n", + "Cristante (5.92, 0.45); (6.07, 0.60)\n", + "Bajrami (5.86, 0.50); (6.14, 0.73)\n", + "Ricci S. (6.06, 0.37); (6.14, 0.38)\n", + "Thorstvedt (5.92, 0.43); (6.14, 0.65)\n", + "De Ketelaere (5.88, 0.39); (5.98, 0.49)\n", + "Saponara (6.01, 0.51); (6.46, 0.92)\n", + "Vecino (5.94, 0.46); (6.06, 0.55)\n", + "Locatelli (6.03, 0.37); (6.13, 0.40)\n", + "Zaniolo (5.87, 0.48); (6.09, 0.74)\n", + "Traore' Hj. (5.98, 0.52); (6.38, 0.91)\n", + "Maldini (6.00, 0.50); (6.39, 0.90)\n", + "Coulibaly L. (5.94, 0.59); (6.25, 0.94)\n", + "Gonzalez J. (5.99, 0.48); (6.33, 0.78)\n", + "Vilhena (5.78, 0.46); (5.99, 0.65)\n", + "De Roon (5.98, 0.37); (6.02, 0.35)\n", + "Mandragora (5.93, 0.44); (6.09, 0.58)\n", + "Haas (5.91, 0.40); (6.05, 0.56)\n", + "Wijnaldum (5.93, 0.45); (5.98, 0.50)\n", + "Bourabia (5.89, 0.40); (5.92, 0.38)\n", + "Sottil (6.05, 0.53); (6.56, 1.01)\n", + "Agudelo (5.83, 0.35); (5.85, 0.35)\n", + "Makengo (5.98, 0.34); (6.01, 0.32)\n", + "Zalewski (6.00, 0.33); (6.04, 0.31)\n", + "Aebischer (5.95, 0.45); (6.24, 0.72)\n", + "Ederson D.s. (5.87, 0.45); (6.04, 0.60)\n", + "Miretti (5.92, 0.38); (5.99, 0.42)\n", + "Blin (5.97, 0.30); (5.97, 0.23)\n", + "Hjulmand (5.97, 0.51); (6.01, 0.55)\n", + "Cataldi (5.93, 0.34); (5.91, 0.28)\n", + "Paredes (5.81, 0.37); (5.76, 0.32)\n", + "Djuricic (5.72, 0.51); (5.92, 0.67)\n", + "Linetty (5.90, 0.45); (6.06, 0.59)\n", + "Walace (5.88, 0.37); (5.82, 0.33)\n", + "Marin (5.78, 0.54); (5.82, 0.61)\n", + "Mckennie (5.77, 0.43); (5.92, 0.58)\n", + "Pobega (6.04, 0.42); (6.29, 0.67)\n", + "Volpato (5.99, 0.56); (6.40, 0.95)\n", + "Camara Ma. (6.10, 0.34); (6.14, 0.33)\n", + "Cuadrado (5.90, 0.51); (5.98, 0.57)\n", + "Ndombele' (5.87, 0.32); (5.89, 0.25)\n", + "Amrabat (5.77, 0.48); (5.66, 0.40)\n", + "Tameze (5.82, 0.41); (5.88, 0.44)\n", + "Gyasi (5.83, 0.54); (6.11, 0.77)\n", + "Ilic (5.93, 0.41); (5.99, 0.41)\n", + "Harroui (5.86, 0.40); (6.02, 0.59)\n", + "Pickel (5.79, 0.40); (5.90, 0.52)\n", + "Moro N. (5.93, 0.35); (6.00, 0.32)\n", + "Duncan (5.88, 0.41); (6.02, 0.52)\n", + "Miguel Veloso (5.95, 0.35); (5.97, 0.32)\n", + "Ekdal (5.79, 0.34); (5.74, 0.31)\n", + "Nicolussi Caviglia (5.84, 0.49); (6.16, 0.76)\n", + "Schouten (5.92, 0.38); (5.91, 0.34)\n", + "Rovella (5.87, 0.50); (5.82, 0.47)\n", + "Crnigoj (5.92, 0.42); (6.14, 0.63)\n", + "Romero L. (6.22, 0.49); (6.81, 1.06)\n", + "Basic (5.97, 0.33); (5.99, 0.29)\n", + "Sabiri (5.72, 0.48); (5.85, 0.58)\n", + "Terracciano F. (6.01, 0.31); (6.01, 0.28)\n", + "Castagnetti (5.83, 0.31); (5.87, 0.25)\n", + "Oudin (5.96, 0.31); (6.00, 0.26)\n", + "D'alessandro (6.02, 0.32); (6.03, 0.30)\n", + "Grassi (5.92, 0.35); (5.93, 0.30)\n", + "Krunic (5.99, 0.33); (6.02, 0.31)\n", + "Rincon (5.70, 0.43); (5.68, 0.44)\n", + "Machin (5.86, 0.47); (5.89, 0.55)\n", + "Leris (5.70, 0.41); (5.80, 0.53)\n", + "Meite' (5.76, 0.43); (5.67, 0.41)\n", + "Vieira (5.68, 0.44); (5.76, 0.55)\n", + "Esposito Sa. (5.83, 0.42); (5.81, 0.41)\n", + "Henderson L. (5.87, 0.38); (5.95, 0.46)\n", + "Obiang (5.90, 0.31); (5.92, 0.28)\n", + "Lopez M. (5.86, 0.40); (5.85, 0.40)\n", + "Saelemaekers (5.91, 0.39); (5.99, 0.46)\n", + "Maggiore (5.91, 0.38); (6.01, 0.47)\n", + "Akpa Akpro (5.86, 0.48); (5.90, 0.53)\n", + "Kovalenko (5.91, 0.37); (6.00, 0.42)\n", + "Maleh (5.96, 0.38); (6.05, 0.46)\n", + "Matheus Henrique (5.81, 0.36); (5.83, 0.35)\n", + "Asllani (5.97, 0.34); (5.99, 0.30)\n", + "Ceide (5.84, 0.38); (5.87, 0.35)\n", + "Gagliardini (5.86, 0.34); (5.87, 0.39)\n", + "Bove (5.74, 0.33); (5.71, 0.32)\n", + "Bianco (5.96, 0.40); (6.03, 0.43)\n", + "Vranckx (5.89, 0.37); (5.96, 0.36)\n", + "Marcos Antonio (5.88, 0.30); (5.86, 0.22)\n", + "Fazzini (5.77, 0.33); (5.69, 0.31)\n", + "D'andrea (5.97, 0.37); (6.06, 0.44)\n", + "Sulemana I. (5.88, 0.30); (5.89, 0.26)\n", + "Tahirovic (5.93, 0.32); (5.95, 0.28)\n", + "Benassi (5.79, 0.34); (5.81, 0.34)\n", + "Barberis (5.82, 0.41); (5.77, 0.39)\n", + "Kastanos (5.80, 0.33); (5.82, 0.32)\n", + "Vignato (5.90, 0.38); (5.96, 0.38)\n", + "Valoti (5.77, 0.35); (5.73, 0.33)\n", + "Winks (5.77, 0.42); (5.73, 0.42)\n", + "Gaetano (5.99, 0.41); (6.11, 0.47)\n", + "Zurkowski (6.05, 0.55); (6.54, 1.01)\n", + "Castrovilli (5.95, 0.49); (6.25, 0.74)\n", + "Bohinen (5.68, 0.31); (5.69, 0.25)\n", + "Hrustic (5.60, 0.31); (5.65, 0.26)\n", + "Iling-Junior (5.92, 0.39); (5.96, 0.40)\n", + "Capezzi (5.87, 0.35); (5.88, 0.34)\n", + "Molina S. (5.81, 0.46); (5.94, 0.60)\n", + "Bakayoko (5.74, 0.40); (5.61, 0.36)\n", + "Scozzarella (5.86, 0.47); (5.89, 0.55)\n", + "Demme (6.04, 0.41); (6.32, 0.64)\n", + "Askildsen (5.65, 0.31); (5.69, 0.27)\n", + "Darboe (5.96, 0.46); (5.97, 0.46)\n", + "Urbanski (5.93, 0.42); (6.00, 0.45)\n", + "Bertini (5.98, 0.43); (6.05, 0.47)\n", + "Hongla (5.66, 0.34); (5.70, 0.36)\n", + "Yepes (5.66, 0.45); (5.55, 0.41)\n", + "Trimboli (5.83, 0.45); (5.83, 0.48)\n", + "Pafundi (5.92, 0.45); (5.98, 0.50)\n", + "Milanese (5.79, 0.45); (5.76, 0.46)\n", + "Helgason (5.81, 0.32); (5.83, 0.25)\n", + "Adli (5.76, 0.44); (5.82, 0.53)\n", + "Vignato S. (5.82, 0.49); (5.76, 0.48)\n", + "Bondo (5.88, 0.42); (5.87, 0.40)\n", + "Samek (5.90, 0.42); (5.95, 0.41)\n", + "Zerbin (6.01, 0.37); (6.11, 0.42)\n", + "Ilkhan (5.82, 0.47); (5.79, 0.42)\n", + "Degli Innocenti (5.88, 0.50); (5.92, 0.53)\n", + "Acella (5.86, 0.46); (5.90, 0.50)\n", + "Garbett (5.90, 0.45); (5.97, 0.48)\n", + "Carboni V. (5.84, 0.45); (5.85, 0.50)\n", + "Cipot (5.89, 0.42); (5.96, 0.46)\n", + "Osimhen (6.56, 0.79); (8.66, 3.15)\n", + "Dybala (6.50, 0.68); (8.10, 2.42)\n", + "Martinez L. (6.37, 0.74); (7.97, 2.43)\n", + "Rafael Leao (6.40, 0.70); (7.96, 2.34)\n", + "Immobile (6.43, 0.76); (8.22, 2.68)\n", + "Lookman (6.47, 0.75); (8.27, 2.72)\n", + "Vlahovic (6.28, 0.74); (7.66, 2.18)\n", + "Arnautovic (6.28, 0.69); (7.60, 2.05)\n", + "Dia (6.13, 0.69); (7.16, 1.72)\n", + "Dzeko (6.27, 0.70); (7.59, 2.07)\n", + "Milik (6.22, 0.58); (7.09, 1.46)\n", + "Nzola (6.13, 0.71); (7.13, 1.74)\n", + "Beto (6.16, 0.68); (7.19, 1.74)\n", + "Abraham (6.30, 0.69); (7.59, 2.02)\n", + "Giroud (6.21, 0.65); (7.29, 1.74)\n", + "Deulofeu (6.35, 0.64); (7.55, 1.89)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Giroud (6.27, 0.67); (7.70, 2.15)\n", - "Osimhen (6.45, 0.73); (8.40, 2.85)\n", - "Deulofeu (6.35, 0.64); (7.68, 2.04)\n", - "Lukaku (6.47, 0.71); (8.41, 2.84)\n", - "Milik (6.18, 0.56); (7.02, 1.39)\n", - "Pedro (6.32, 0.57); (7.20, 1.49)\n", - "Abraham (6.20, 0.64); (7.60, 2.07)\n", - "Berardi (6.32, 0.69); (7.97, 2.41)\n", - "Dia (6.25, 0.69); (7.86, 2.33)\n", - "Lookman (6.44, 0.65); (8.01, 2.39)\n", - "Simeone (6.27, 0.70); (7.91, 2.39)\n", - "Correa (6.27, 0.48); (7.10, 1.43)\n", - "Zapata D. (6.16, 0.62); (7.38, 1.87)\n", - "Dzeko (6.19, 0.69); (7.69, 2.19)\n", - "Nzola (6.06, 0.63); (7.16, 1.71)\n", - "Sanabria (6.08, 0.59); (7.03, 1.54)\n", - "Muriel (6.21, 0.65); (7.30, 1.76)\n", - "Rebic (6.22, 0.66); (7.52, 1.99)\n", - "Bonazzoli (6.06, 0.48); (6.46, 0.89)\n", - "Caprari (5.90, 0.38); (6.11, 0.66)\n", - "Di Maria (6.08, 0.65); (6.81, 1.40)\n", - "Lozano (6.29, 0.65); (7.62, 2.03)\n", - "Ceesay (6.07, 0.55); (6.97, 1.44)\n", - "Pinamonti (5.95, 0.54); (6.66, 1.26)\n", - "Kouame' (6.19, 0.61); (7.36, 1.79)\n", - "Lauriente' (6.28, 0.69); (7.47, 1.95)\n", - "Jovic (6.02, 0.61); (6.88, 1.46)\n", - "Barrow (6.04, 0.62); (6.90, 1.45)\n", - "Henry (5.96, 0.54); (6.54, 1.14)\n", - "Piatek (6.06, 0.65); (7.22, 1.79)\n", - "Gonzalez N. (6.23, 0.61); (7.31, 1.71)\n", - "Dessers (6.09, 0.49); (6.91, 1.34)\n", - "Caputo (5.78, 0.40); (5.97, 0.64)\n", - "Raspadori (6.03, 0.51); (6.60, 1.08)\n", - "Lammers (5.92, 0.39); (6.09, 0.62)\n", - "Okereke (6.00, 0.54); (6.60, 1.17)\n", - "Cabral (6.08, 0.48); (6.86, 1.29)\n", - "Gabbiadini (5.81, 0.42); (6.07, 0.74)\n", - "Belotti (5.77, 0.31); (5.79, 0.32)\n", - "Origi (6.03, 0.56); (6.70, 1.22)\n", - "Botheim (5.97, 0.42); (6.39, 0.88)\n", - "Alvarez A. (6.13, 0.60); (7.13, 1.62)\n", - "Verde (6.10, 0.45); (6.67, 1.04)\n", - "Destro (5.92, 0.60); (6.60, 1.28)\n", - "Kean (5.96, 0.56); (6.62, 1.23)\n", - "Pellegri (5.89, 0.41); (6.21, 0.79)\n", - "Satriano (5.89, 0.45); (6.28, 0.89)\n", - "Success (5.98, 0.33); (5.99, 0.37)\n", - "Mota (5.84, 0.41); (6.25, 0.86)\n", - "Banda (5.98, 0.36); (6.06, 0.45)\n", - "Hojlund (6.01, 0.43); (6.54, 1.01)\n", - "Lasagna (5.76, 0.36); (5.90, 0.57)\n", - "Pjaca (5.90, 0.40); (5.99, 0.53)\n", - "Gytkjaer (5.85, 0.37); (6.14, 0.74)\n", - "Petagna (5.80, 0.39); (5.95, 0.58)\n", - "Nestorovski (6.06, 0.39); (6.37, 0.75)\n", - "Zanimacchia (5.97, 0.34); (6.06, 0.45)\n", - "Piccoli (5.89, 0.40); (6.17, 0.75)\n", - "Kallon (5.91, 0.34); (6.09, 0.64)\n", - "Ciofani D. (6.13, 0.41); (6.85, 1.25)\n", - "Di Francesco F. (5.89, 0.43); (6.12, 0.72)\n", - "Boga (5.88, 0.32); (5.86, 0.36)\n", - "Zirkzee (6.04, 0.53); (6.82, 1.31)\n", - "Quagliarella (5.79, 0.36); (5.90, 0.55)\n", - "Ibrahimovic (6.36, 0.65); (7.89, 2.28)\n", - "Shomurodov (5.97, 0.46); (6.55, 1.08)\n", - "Djuric (5.89, 0.30); (5.86, 0.30)\n", - "Strelec (5.94, 0.29); (5.91, 0.32)\n", - "Antiste (5.80, 0.40); (6.09, 0.77)\n", - "Seck (6.06, 0.31); (6.10, 0.37)\n", - "Buonaiuto (6.05, 0.36); (6.19, 0.46)\n", - "Sansone (5.81, 0.38); (5.88, 0.48)\n", - "Pussetto (5.83, 0.39); (6.08, 0.75)\n", - "Cambiaghi (5.91, 0.30); (5.89, 0.31)\n", - "Colombo (6.01, 0.49); (6.74, 1.24)\n", - "Afena-Gyan (5.75, 0.33); (5.73, 0.30)\n", - "Defrel (5.82, 0.41); (5.95, 0.60)\n", - "Karamoh (6.03, 0.33); (6.11, 0.39)\n", - "Cancellieri (5.93, 0.34); (5.91, 0.31)\n", - "Tsadjout (6.03, 0.42); (6.20, 0.57)\n", - "Rodriguez P. (5.92, 0.34); (5.94, 0.34)\n", - "Valencia D. (5.68, 0.32); (5.70, 0.29)\n", - "Edera (5.92, 0.43); (5.98, 0.47)\n", - "Oddei (5.99, 0.40); (6.03, 0.41)\n", - "Raimondo (5.95, 0.43); (6.02, 0.45)\n", - "Kristoffersen (5.89, 0.43); (5.94, 0.41)\n", - "Kaio Jorge (5.98, 0.36); (6.03, 0.39)\n", - "De Luca (5.86, 0.44); (5.92, 0.46)\n", - "Soule' (6.01, 0.45); (6.08, 0.39)\n", - "Lazetic (5.97, 0.41); (6.05, 0.46)\n", - "Voelkerling Persson (5.94, 0.44); (6.01, 0.45)\n", - "Sanca (5.91, 0.37); (5.94, 0.36)\n" + "Correa (6.12, 0.55); (6.85, 1.33)\n", + "Pedro (6.17, 0.56); (6.81, 1.17)\n", + "Lozano (6.22, 0.62); (7.23, 1.64)\n", + "Lukaku (6.06, 0.60); (6.78, 1.30)\n", + "Lauriente' (6.14, 0.57); (6.74, 1.15)\n", + "Simeone (6.28, 0.72); (7.66, 2.16)\n", + "Cabral (6.08, 0.61); (6.80, 1.34)\n", + "Rebic (6.15, 0.65); (7.07, 1.57)\n", + "Bonazzoli (5.97, 0.52); (6.40, 0.92)\n", + "Berardi (6.30, 0.70); (7.68, 2.14)\n", + "Caprari (6.07, 0.59); (6.72, 1.24)\n", + "Di Maria (6.22, 0.66); (7.34, 1.78)\n", + "Piatek (5.97, 0.60); (6.53, 1.15)\n", + "Kouame' (6.12, 0.61); (6.87, 1.37)\n", + "Hojlund (6.17, 0.67); (7.15, 1.67)\n", + "Sanabria (6.05, 0.61); (6.72, 1.27)\n", + "Zapata D. (6.07, 0.60); (6.79, 1.34)\n", + "Kean (6.03, 0.61); (6.70, 1.24)\n", + "Ceesay (6.04, 0.58); (6.61, 1.15)\n", + "Pinamonti (5.89, 0.54); (6.31, 0.95)\n", + "Gonzalez N. (6.24, 0.63); (7.23, 1.66)\n", + "Colombo (6.12, 0.65); (6.99, 1.54)\n", + "Mota (6.03, 0.59); (6.58, 1.13)\n", + "Dessers (5.97, 0.56); (6.46, 1.05)\n", + "Muriel (6.23, 0.65); (7.25, 1.69)\n", + "Okereke (5.90, 0.56); (6.36, 0.98)\n", + "Alvarez A. (6.02, 0.56); (6.55, 1.05)\n", + "Jovic (5.95, 0.57); (6.45, 1.04)\n", + "Barrow (6.02, 0.62); (6.63, 1.22)\n", + "Caputo (6.01, 0.62); (6.65, 1.24)\n", + "Banda (5.94, 0.43); (6.11, 0.58)\n", + "Ciofani D. (6.07, 0.60); (6.75, 1.29)\n", + "Petagna (5.86, 0.49); (6.13, 0.74)\n", + "Di Francesco F. (6.03, 0.58); (6.53, 1.06)\n", + "Boga (6.44, 0.68); (7.87, 2.22)\n", + "Cambiaghi (6.07, 0.48); (6.43, 0.78)\n", + "Djuric (6.07, 0.42); (6.39, 0.76)\n", + "Henry (5.89, 0.56); (6.31, 0.96)\n", + "Success (5.96, 0.38); (6.03, 0.42)\n", + "Gabbiadini (5.81, 0.56); (6.12, 0.84)\n", + "Origi (5.89, 0.52); (6.23, 0.83)\n", + "Lammers (5.83, 0.47); (6.07, 0.72)\n", + "Satriano (5.81, 0.47); (6.04, 0.69)\n", + "Zirkzee (6.02, 0.54); (6.57, 1.08)\n", + "Kallon (5.87, 0.45); (6.15, 0.74)\n", + "Solbakken (5.95, 0.50); (6.03, 0.58)\n", + "Lasagna (5.76, 0.48); (6.06, 0.79)\n", + "Nestorovski (6.06, 0.42); (6.41, 0.85)\n", + "Pellegri (5.86, 0.48); (6.21, 0.84)\n", + "Botheim (5.87, 0.50); (6.27, 0.87)\n", + "Gytkjaer (5.82, 0.44); (6.05, 0.69)\n", + "Belotti (5.66, 0.35); (5.72, 0.40)\n", + "Raspadori (6.04, 0.52); (6.61, 1.09)\n", + "Buonaiuto (5.95, 0.36); (6.02, 0.33)\n", + "Verde (6.04, 0.53); (6.54, 1.02)\n", + "Destro (5.92, 0.62); (6.39, 1.06)\n", + "Sansone (5.93, 0.50); (6.31, 0.86)\n", + "Pjaca (5.82, 0.43); (5.96, 0.51)\n", + "Zanimacchia (5.83, 0.37); (5.86, 0.35)\n", + "Strelec (5.92, 0.32); (5.92, 0.31)\n", + "Seck (6.05, 0.32); (6.07, 0.29)\n", + "Quagliarella (5.78, 0.45); (5.97, 0.65)\n", + "Piccoli (5.84, 0.46); (6.14, 0.79)\n", + "Tsadjout (5.90, 0.47); (6.17, 0.75)\n", + "Shomurodov (6.03, 0.56); (6.60, 1.13)\n", + "Cancellieri (5.81, 0.30); (5.77, 0.24)\n", + "Antiste (5.82, 0.44); (6.05, 0.68)\n", + "Afena-Gyan (5.61, 0.35); (5.69, 0.37)\n", + "Soule' (5.86, 0.48); (5.97, 0.54)\n", + "Ngonge (5.87, 0.44); (5.91, 0.46)\n", + "Defrel (5.75, 0.40); (5.90, 0.59)\n", + "Karamoh (5.89, 0.35); (5.89, 0.33)\n", + "Ibrahimovic (6.35, 0.67); (7.70, 2.08)\n", + "Pussetto (5.82, 0.50); (6.10, 0.77)\n", + "Voelkerling Persson (5.91, 0.40); (5.92, 0.36)\n", + "Valencia D. (5.69, 0.32); (5.70, 0.40)\n", + "Edera (5.83, 0.48); (5.85, 0.48)\n", + "Oddei (6.00, 0.39); (6.10, 0.44)\n", + "Braaf (6.01, 0.54); (6.37, 0.86)\n", + "Raimondo (5.94, 0.43); (6.01, 0.45)\n", + "Kaio Jorge (5.93, 0.37); (5.97, 0.38)\n", + "De Luca (5.85, 0.46); (5.86, 0.47)\n", + "Lazetic (5.91, 0.48); (5.98, 0.57)\n", + "Montevago (5.63, 0.34); (5.67, 0.41)\n", + "Krollis (5.80, 0.46); (5.79, 0.47)\n" ] }, { @@ -5656,19 +8399,19 @@ " 1\n", " 1\n", " 100\n", - " 6.273052\n", - " 0.433253\n", - " 5.729354\n", - " 0.572779\n", - " 6.020063\n", - " 0.333488\n", - " 0.521017\n", - " 1.352889\n", - " 5.950712\n", - " 0.803720\n", - " -0.200497\n", - " 0.651549\n", - " 63.398904\n", + " 6.250622\n", + " 0.378411\n", + " 5.530209\n", + " 0.629529\n", + " 6.131017\n", + " 0.399960\n", + " 0.219381\n", + " 1.204045\n", + " 6.026930\n", + " 0.821359\n", + " -0.432302\n", + " 1.113037\n", + " 43.711811\n", " \n", " \n", " Rossi F.\n", @@ -5678,19 +8421,19 @@ " 1\n", " 0\n", " 0\n", - " 6.273052\n", - " 0.433253\n", - " 5.729354\n", - " 0.572779\n", - " 6.020063\n", - " 0.333488\n", - " 0.521017\n", - " 1.352889\n", - " 5.950712\n", - " 0.803720\n", - " -0.200497\n", - " 0.651549\n", - " 63.398904\n", + " 6.250622\n", + " 0.378411\n", + " 5.530209\n", + " 0.629529\n", + " 6.131017\n", + " 0.399960\n", + " 0.219381\n", + " 1.204045\n", + " 6.026930\n", + " 0.821359\n", + " -0.432302\n", + " 1.113037\n", + " 43.711811\n", " \n", " \n", " Sportiello\n", @@ -5700,19 +8443,41 @@ " 1\n", " 0\n", " 0\n", - " 6.269399\n", - " 0.405868\n", - " 5.630367\n", - " 0.582774\n", - " 6.035743\n", - " 0.316196\n", - " 0.509242\n", - " 1.355847\n", - " 5.924196\n", - " 0.807376\n", - " -0.264365\n", - " 0.642069\n", - " 51.028043\n", + " 6.072975\n", + " 0.380752\n", + " 5.430070\n", + " 0.691647\n", + " 5.976573\n", + " 0.416346\n", + " 0.170681\n", + " 1.227954\n", + " 6.059433\n", + " 0.839207\n", + " -0.524699\n", + " 1.155709\n", + " 48.146455\n", + " \n", + " \n", + " Demiral\n", + " D\n", + " Atalanta\n", + " Avg\n", + " 1\n", + " 1\n", + " 80\n", + " 6.132670\n", + " 0.484149\n", + " 6.517965\n", + " 0.830536\n", + " 6.059569\n", + " 0.553885\n", + " 0.097796\n", + " 1.139126\n", + " 5.819425\n", + " 1.024632\n", + " 0.445816\n", + " 1.999745\n", + " 0.000000\n", " \n", " \n", " Scalvini\n", @@ -5721,41 +8486,19 @@ " Avg\n", " 1\n", " 1\n", - " 71\n", - " 6.085859\n", - " 0.532578\n", - " 6.514391\n", - " 0.948817\n", - " 6.029768\n", - " 0.618735\n", - " 0.067370\n", - " 1.124569\n", - " 5.677100\n", - " 1.077408\n", - " 0.500653\n", - " 1.907312\n", - " 0.000000\n", - " \n", - " \n", - " Toloi\n", - " D\n", - " Atalanta\n", - " Avg\n", - " 1\n", - " 1\n", - " 71\n", - " 6.211302\n", - " 0.449656\n", - " 6.511771\n", - " 0.680813\n", - " 6.129649\n", - " 0.508679\n", - " 0.118679\n", - " 1.165321\n", - " 5.982656\n", - " 0.917384\n", - " 0.391923\n", - " 1.931778\n", + " 75\n", + " 6.068207\n", + " 0.527171\n", + " 6.492687\n", + " 0.917896\n", + " 6.019891\n", + " 0.614302\n", + " 0.058150\n", + " 1.100539\n", + " 5.696841\n", + " 1.077794\n", + " 0.474566\n", + " 1.999729\n", " 0.000000\n", " \n", " \n", @@ -5787,41 +8530,19 @@ " Avg\n", " 1\n", " 1\n", - " 100\n", - " 5.963665\n", - " 0.544947\n", - " 6.537935\n", - " 1.137651\n", - " 5.940691\n", - " 0.645068\n", - " 0.026251\n", - " 1.108576\n", - " 5.481170\n", - " 1.165579\n", - " 0.561815\n", - " 1.903113\n", - " 0.000000\n", - " \n", - " \n", - " Piccoli\n", - " A\n", - " Verona\n", - " Avg\n", - " 1\n", - " 0\n", - " 28\n", - " 5.891238\n", - " 0.396026\n", - " 6.168426\n", - " 0.747763\n", - " 5.886265\n", - " 0.475034\n", - " 0.007750\n", - " 1.153088\n", - " 5.531199\n", - " 0.897426\n", - " 0.465556\n", - " 1.907219\n", + " 80\n", + " 5.887487\n", + " 0.559978\n", + " 6.307155\n", + " 0.957540\n", + " 5.630545\n", + " 0.553424\n", + " 0.336022\n", + " 1.077726\n", + " 5.431886\n", + " 1.014879\n", + " 0.534526\n", + " 1.999742\n", " 0.000000\n", " \n", " \n", @@ -5831,19 +8552,41 @@ " Avg\n", " 1\n", " 0\n", - " 78\n", - " 5.910843\n", - " 0.344760\n", - " 6.086345\n", - " 0.644135\n", - " 5.901302\n", - " 0.412704\n", - " 0.017207\n", - " 1.176790\n", - " 5.561029\n", - " 0.818982\n", - " 0.428502\n", - " 1.903613\n", + " 80\n", + " 5.871192\n", + " 0.450760\n", + " 6.145801\n", + " 0.737964\n", + " 5.701855\n", + " 0.459309\n", + " 0.269080\n", + " 1.197383\n", + " 5.521518\n", + " 0.903741\n", + " 0.451143\n", + " 1.999700\n", + " 0.000000\n", + " \n", + " \n", + " Piccoli\n", + " A\n", + " Verona\n", + " Avg\n", + " 1\n", + " 0\n", + " 30\n", + " 5.840213\n", + " 0.455019\n", + " 6.135979\n", + " 0.791762\n", + " 5.658057\n", + " 0.455641\n", + " 0.290913\n", + " 1.200415\n", + " 5.440944\n", + " 0.911831\n", + " 0.487274\n", + " 1.999701\n", " 0.000000\n", " \n", " \n", @@ -5853,109 +8596,109 @@ " Avg\n", " 1\n", " 1\n", - " 85\n", - " 5.756809\n", - " 0.363771\n", - " 5.895058\n", - " 0.570621\n", - " 5.780692\n", - " 0.445713\n", - " -0.039756\n", - " 1.153128\n", - " 5.467175\n", - " 0.790118\n", - " 0.372230\n", - " 1.893109\n", + " 75\n", + " 5.764475\n", + " 0.483705\n", + " 6.059623\n", + " 0.793667\n", + " 5.578651\n", + " 0.492607\n", + " 0.274859\n", + " 1.166423\n", + " 5.366997\n", + " 0.923546\n", + " 0.481054\n", + " 1.999693\n", " 0.000000\n", " \n", " \n", - " Djuric\n", + " Ngonge\n", " A\n", " Verona\n", " Avg\n", " 1\n", " 0\n", - " 71\n", - " 5.887746\n", - " 0.301317\n", - " 5.859897\n", - " 0.295813\n", - " 5.882544\n", - " 0.362606\n", - " 0.010815\n", - " 1.195072\n", - " 5.769265\n", - " 0.555778\n", - " 0.120673\n", - " 1.881025\n", + " 0\n", + " 5.870672\n", + " 0.441342\n", + " 5.906740\n", + " 0.460875\n", + " 5.901017\n", + " 0.545786\n", + " -0.041291\n", + " 1.212317\n", + " 5.724916\n", + " 0.863673\n", + " 0.153965\n", + " 1.999443\n", " 0.000000\n", " \n", " \n", "\n", - "

554 rows × 19 columns

\n", + "

547 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 6.273052 0.433253 \n", - "Rossi F. P Atalanta Avg 1 0 0 6.273052 0.433253 \n", - "Sportiello P Atalanta Avg 1 0 0 6.269399 0.405868 \n", - "Scalvini D Atalanta Avg 1 1 71 6.085859 0.532578 \n", - "Toloi D Atalanta Avg 1 1 71 6.211302 0.449656 \n", + "Musso P Atalanta Avg 1 1 100 6.250622 0.378411 \n", + "Rossi F. P Atalanta Avg 1 0 0 6.250622 0.378411 \n", + "Sportiello P Atalanta Avg 1 0 0 6.072975 0.380752 \n", + "Demiral D Atalanta Avg 1 1 80 6.132670 0.484149 \n", + "Scalvini D Atalanta Avg 1 1 75 6.068207 0.527171 \n", "... ... ... ... ... ... ... ... ... \n", - "Henry A Verona Avg 1 1 100 5.963665 0.544947 \n", - "Piccoli A Verona Avg 1 0 28 5.891238 0.396026 \n", - "Kallon A Verona Avg 1 0 78 5.910843 0.344760 \n", - "Lasagna A Verona Avg 1 1 85 5.756809 0.363771 \n", - "Djuric A Verona Avg 1 0 71 5.887746 0.301317 \n", + "Henry A Verona Avg 1 1 80 5.887487 0.559978 \n", + "Kallon A Verona Avg 1 0 80 5.871192 0.450760 \n", + "Piccoli A Verona Avg 1 0 30 5.840213 0.455019 \n", + "Lasagna A Verona Avg 1 1 75 5.764475 0.483705 \n", + "Ngonge A Verona Avg 1 0 0 5.870672 0.441342 \n", "\n", " FV FV std MV loc MV scale MV skewness \\\n", "player \n", - "Musso 5.729354 0.572779 6.020063 0.333488 0.521017 \n", - "Rossi F. 5.729354 0.572779 6.020063 0.333488 0.521017 \n", - "Sportiello 5.630367 0.582774 6.035743 0.316196 0.509242 \n", - "Scalvini 6.514391 0.948817 6.029768 0.618735 0.067370 \n", - "Toloi 6.511771 0.680813 6.129649 0.508679 0.118679 \n", + "Musso 5.530209 0.629529 6.131017 0.399960 0.219381 \n", + "Rossi F. 5.530209 0.629529 6.131017 0.399960 0.219381 \n", + "Sportiello 5.430070 0.691647 5.976573 0.416346 0.170681 \n", + "Demiral 6.517965 0.830536 6.059569 0.553885 0.097796 \n", + "Scalvini 6.492687 0.917896 6.019891 0.614302 0.058150 \n", "... ... ... ... ... ... \n", - "Henry 6.537935 1.137651 5.940691 0.645068 0.026251 \n", - "Piccoli 6.168426 0.747763 5.886265 0.475034 0.007750 \n", - "Kallon 6.086345 0.644135 5.901302 0.412704 0.017207 \n", - "Lasagna 5.895058 0.570621 5.780692 0.445713 -0.039756 \n", - "Djuric 5.859897 0.295813 5.882544 0.362606 0.010815 \n", + "Henry 6.307155 0.957540 5.630545 0.553424 0.336022 \n", + "Kallon 6.145801 0.737964 5.701855 0.459309 0.269080 \n", + "Piccoli 6.135979 0.791762 5.658057 0.455641 0.290913 \n", + "Lasagna 6.059623 0.793667 5.578651 0.492607 0.274859 \n", + "Ngonge 5.906740 0.460875 5.901017 0.545786 -0.041291 \n", "\n", " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", "player \n", - "Musso 1.352889 5.950712 0.803720 -0.200497 0.651549 \n", - "Rossi F. 1.352889 5.950712 0.803720 -0.200497 0.651549 \n", - "Sportiello 1.355847 5.924196 0.807376 -0.264365 0.642069 \n", - "Scalvini 1.124569 5.677100 1.077408 0.500653 1.907312 \n", - "Toloi 1.165321 5.982656 0.917384 0.391923 1.931778 \n", + "Musso 1.204045 6.026930 0.821359 -0.432302 1.113037 \n", + "Rossi F. 1.204045 6.026930 0.821359 -0.432302 1.113037 \n", + "Sportiello 1.227954 6.059433 0.839207 -0.524699 1.155709 \n", + "Demiral 1.139126 5.819425 1.024632 0.445816 1.999745 \n", + "Scalvini 1.100539 5.696841 1.077794 0.474566 1.999729 \n", "... ... ... ... ... ... \n", - "Henry 1.108576 5.481170 1.165579 0.561815 1.903113 \n", - "Piccoli 1.153088 5.531199 0.897426 0.465556 1.907219 \n", - "Kallon 1.176790 5.561029 0.818982 0.428502 1.903613 \n", - "Lasagna 1.153128 5.467175 0.790118 0.372230 1.893109 \n", - "Djuric 1.195072 5.769265 0.555778 0.120673 1.881025 \n", + "Henry 1.077726 5.431886 1.014879 0.534526 1.999742 \n", + "Kallon 1.197383 5.521518 0.903741 0.451143 1.999700 \n", + "Piccoli 1.200415 5.440944 0.911831 0.487274 1.999701 \n", + "Lasagna 1.166423 5.366997 0.923546 0.481054 1.999693 \n", + "Ngonge 1.212317 5.724916 0.863673 0.153965 1.999443 \n", "\n", " Clean Sheet % \n", "player \n", - "Musso 63.398904 \n", - "Rossi F. 63.398904 \n", - "Sportiello 51.028043 \n", + "Musso 43.711811 \n", + "Rossi F. 43.711811 \n", + "Sportiello 48.146455 \n", + "Demiral 0.000000 \n", "Scalvini 0.000000 \n", - "Toloi 0.000000 \n", "... ... \n", "Henry 0.000000 \n", - "Piccoli 0.000000 \n", "Kallon 0.000000 \n", + "Piccoli 0.000000 \n", "Lasagna 0.000000 \n", - "Djuric 0.000000 \n", + "Ngonge 0.000000 \n", "\n", - "[554 rows x 19 columns]" + "[547 rows x 19 columns]" ] }, - "execution_count": 33, + 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