Notebook

PCA-based model to find similar players¶

In [11]:

#RUN ALL THE CELLS TILL TO RUN THE APP

In [12]:

class color:
        PURPLE = '\033[95m'
        CYAN = '\033[96m'
        DARKCYAN = '\033[36m'
        BLUE = '\033[94m'
        GREEN = '\033[92m'
        YELLOW = '\033[93m'
        RED = '\033[91m'
        BOLD = '\033[1m'
        UNDERLINE = '\033[4m'
        END = '\033[0m'

#print(color.BOLD + 'PCA-based model to find similar players' + color.END)
print('by Parth Athale (@ParthAthale)\n')
print('Data credits to FBref/StatsBomb')
print('Read methodology here: https://xgpershot.wordpress.com/2020/08/06/pca-based-model-to-identify-similar-players/')
print('Read code here: https://github.com/parth1902/PCA_Player_Finder\n')
print('Some examples as a guide to do this:')
print('If you want to find out a Pierre-Emile Højbjerg replacement for Southampton, choose Højbjerg, Southampton, Overall')
print('If you want to find out a player like Leroy Sané for Barcelona, choose Sané, Barcelona, Overall')
print('If you want to find out a player with the defensive ability of Wilfried Ndidi without any team constraint, choose Ndidi, Overall, Defensive work')

by Parth Athale (@ParthAthale)

Data credits to FBref/StatsBomb
Read methodology here: https://xgpershot.wordpress.com/2020/08/06/pca-based-model-to-identify-similar-players/
Read code here: https://github.com/parth1902/PCA_Player_Finder

Some examples as a guide to do this:
If you want to find out a Pierre-Emile Højbjerg replacement for Southampton, choose Højbjerg, Southampton, Overall
If you want to find out a player like Leroy Sané for Barcelona, choose Sané, Barcelona, Overall
If you want to find out a player with the defensive ability of Wilfried Ndidi without any team constraint, choose Ndidi, Overall, Defensive work

In [3]:

from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import pandas as pd
import numpy as np
pd.set_option('expand_frame_repr', False)
pd.set_option('display.max_columns', 10)
from IPython.display import HTML, display

display(HTML('''<style>
    .widget-label { min-width: 20ex !important; }
</style>'''))
        
        

In [4]:

import pandas as pd
url = 'https://raw.githubusercontent.com/parth1902/test/master/games.csv'
df = pd.read_csv(url,sep = ',')

url = 'https://raw.githubusercontent.com/parth1902/test/master/players.csv'
df_players = pd.read_csv(url,sep = ',')

df_players = df_players[df_players['minutes'] > 500]
df_players = df_players[df_players['position'] != 'GK']

def f(df):
    if df['result'] == 'W':
        val = 3
    elif df['result'] == 'D':
        val = 1
    else:
        val = 0
    return val
df['Points taken'] = df.apply(f, axis = 1)

In [5]:

arr = ['minutes',
 'goals',
 'assists',
 'pens_made',
 'pens_att',
 'xg',
 'npxg',
 'xa',
 'shots_total',
 'shots_on_target',
 'shots_free_kicks',
 'xg_net',
 'npxg_net',
 'passes_completed',
 'passes',
 'passes_total_distance',
 'passes_progressive_distance',
 'passes_completed_short',
 'passes_short',
 'passes_completed_medium',
 'passes_medium',
 'passes_completed_long',
 'passes_long',
 'assisted_shots',
 'passes_into_final_third',
 'passes_into_penalty_area',
 'crosses_into_penalty_area',
 'progressive_passes',
 'passes_live',
 'passes_dead',
 'passes_free_kicks',
 'through_balls',
 'passes_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_offsides',
 'passes_oob',
 'passes_intercepted',
 'passes_blocked',
 'sca',
 'sca_passes_live',
 'sca_passes_dead',
 'sca_dribbles',
 'sca_shots',
 'sca_fouled',
 'gca',
 'gca_passes_live',
 'gca_passes_dead',
 'gca_dribbles',
 'gca_shots',
 'gca_fouled',
 'gca_og_for',
 'tackles',
 'tackles_won',
 'tackles_def_3rd',
 'tackles_mid_3rd',
 'tackles_att_3rd',
 'dribble_tackles',
 'dribbles_vs',
 'dribbled_past',
 'pressures',
 'pressure_regains',
 'pressures_def_3rd',
 'pressures_mid_3rd',
 'pressures_att_3rd',
 'blocks',
 'blocked_shots',
 'blocked_shots_saves',
 'blocked_passes',
 'interceptions',
 'clearances',
 'errors',
 'touches',
 'touches_def_pen_area',
 'touches_def_3rd',
 'touches_mid_3rd',
 'touches_att_3rd',
 'touches_att_pen_area',
 'touches_live_ball',
 'dribbles_completed',
 'dribbles',
 'players_dribbled_past',
 'nutmegs',
 'carries',
 'carry_distance',
 'carry_progressive_distance',
 'pass_targets',
 'miscontrols',
 'dispossessed']
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
for i in range (0,len(arr)):
    if arr[i]!= 'minutes':
        df_players[arr[i]] = (df_players[arr[i]]/df_players['minutes'])*90
df_players = df_players.drop(['goals_per90',
'cards_yellow',
'cards_red',
'assists_per90',
'goals_assists_per90',
'goals_pens_per90',
'goals_assists_pens_per90',
'xg_per90',
'xa_per90',
'xg_xa_per90',
'npxg_per90',
'npxg_xa_per90',
'minutes_90s',
'shots_total_per90',
'shots_on_target_per90',
'xa_net',
'sca_per90',
'gca_per90',
'passes_received',
'cards_yellow_red',
'fouls',
'fouled',
'offsides',
'pens_won',
'pens_conceded',
'own_goals',
'ball_recoveries',
'aerials_won',
'aerials_lost',
'aerials_won_pct'], axis = 1)
df_playersnew = df_players.drop(['player',
 'nationality',
 'position',
 'squad',
 'age',
 'birth_year',
 'games',
 'games_starts','minutes'], axis = 1)
arr2 = list(df_playersnew.columns.values)
for i in range(0,len(arr2)):
    df_playersnew[arr2[i]] = scaler.fit_transform(df_playersnew[[arr2[i]]])
    
df.rename(columns={'xg_for':'xg'}, inplace=True)    
df_playersnew1 = df_playersnew.copy()

In [6]:

def shoot(df_playersnew1):
    return df_playersnew1[['goals',
     'xg',
     'npxg',
     'shots_total',
     'shots_on_target',
     'shots_free_kicks',
     'shots_on_target_pct',
     'goals_per_shot',
     'goals_per_shot_on_target',
     'npxg_per_shot',
     'xg_net',
     'npxg_net']]

def create(df_playersnew1):
    return df_playersnew1[['sca',
     'sca_passes_live',
     'sca_passes_dead',
     'sca_dribbles',
     'sca_shots',
     'sca_fouled',
     'assisted_shots',
     'through_balls',
     'gca',
     'gca_passes_live',
     'gca_passes_dead',
     'gca_dribbles',
     'gca_shots',
     'gca_fouled',
     'gca_og_for','assists','xa']]

def passs(df_playersnew1):
    return df_playersnew1[['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',
     'passes_into_final_third',
     'passes_into_penalty_area',
     'crosses_into_penalty_area',
     'progressive_passes',
     'passes_live',
     'passes_dead',
     'passes_free_kicks',
     '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_offsides',
     'passes_oob',
     'passes_intercepted',
     'passes_blocked']]

def deff(df_playersnew1):
    return df_playersnew1[['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']]

def poss(df_playersnew1):
    return df_playersnew1[['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',
     'pass_targets',
     'passes_received_pct',
     'miscontrols',
     'dispossessed']]

In [7]:

pl = np.array(df_players['player'])
te = np.array(df_players['squad'].unique())
te = np.append('Overall',sorted(te))

In [8]:

def find(player_name,team,skill,number_of_results):
    
    global df
    global df_players
    global finalDf
    global df_playersnew
    global df_playersnew2
    global df_playersnew1
    global principalDf
    df_playersnew1 = df_playersnew.copy()
    df = df.loc[:,~df.T.duplicated(keep='first')]
    df = df.loc[:, ~df.columns.duplicated()]
    if team != "Overall":
        df_new = df[df['for'] == team]
        corrMatrix = df_new.corr()
    else:
        corrMatrix = df.corr()
    arr2 = list(df_playersnew1.columns.values)
    for i in range(0,len(arr2)):
        df_playersnew1[arr2[i]] = (df_playersnew1[arr2[i]]) * (corrMatrix['Points taken'][arr2[i]])
    if skill == 'Overall':
        df_playersnew2 = df_playersnew1
    elif skill == 'Possession':
        df_playersnew2 = poss(df_playersnew1)
    elif skill == 'Shooting':
        df_playersnew2 = shoot(df_playersnew1)
    elif skill == 'Passing':
        df_playersnew2 = passs(df_playersnew1)
    elif skill == 'Creating':
        df_playersnew2 = create(df_playersnew1) 
    elif skill == 'Defensive work':
        df_playersnew2 = deff(df_playersnew1)
        
    
    features = list(df_playersnew2.columns.values)
    #df_playersnew2 = df_playersnew2.fillna(0)
    from sklearn.preprocessing import StandardScaler
    # Separating out the features
    x = df_playersnew2.loc[:, features].values
    # Separating out the target
    y = df_players.loc[:,['player']].values
    # Standardizing the features
    #x = StandardScaler().fit_transform(x)
    x = np.nan_to_num(x)
    from sklearn.decomposition import PCA
    #pca = PCA(n_components=2)
    pca = PCA(.90)
    principalComponents = pca.fit_transform(x)
    print('Number of PCA components:',pca.n_components_)
    print('\n')
    principalDf = pd.DataFrame(data = principalComponents)
    global finalDf
    df_players = df_players.reset_index(drop=True)
    finalDf = pd.concat([principalDf, df_players[['player']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['squad']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['position']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['age']]], axis = 1)
    
    player = player_name
    class color:
        PURPLE = '\033[95m'
        CYAN = '\033[96m'
        DARKCYAN = '\033[36m'
        BLUE = '\033[94m'
        GREEN = '\033[92m'
        YELLOW = '\033[93m'
        RED = '\033[91m'
        BOLD = '\033[1m'
        UNDERLINE = '\033[4m'
        END = '\033[0m'

#print(color.BOLD + 'Hello World !' + color.END)
    a = (finalDf[finalDf['player'] == player])[(finalDf[finalDf['player'] == player]).columns.drop(['player','squad','position','age'])]
    b = finalDf[finalDf.columns.drop(['player','squad','position','age'])]
    finalDf['distance'] = (finalDf[list(b.columns.values)] - np.array(a)).pow(2).sum(1).pow(0.5)
    dist = finalDf['distance'].max()
    dist2 = finalDf['distance'].quantile(0.95)
    finalDf['% match'] = 100-(finalDf['distance']/dist2)*100
    final = ((finalDf.sort_values(['distance'], ascending=[True])))[1:number_of_results+1]
    final = final.reset_index(drop=True)
    print(color.BOLD + 'List of similar players:' + color.END)
    print('\n')
    print(final[['player','squad','position','age','% match']])
    

In [9]:

def find2(player_name,team,stats,number_of_results):
    
    print('Stats selected:',stats)
    print('\n')
    global df
    global df_players
    global finalDf
    global df_playersnew
    global df_playersnew2
    global df_playersnew1
    global principalDf
    df_playersnew1 = df_playersnew.copy()
    df = df.loc[:,~df.T.duplicated(keep='first')]
    df = df.loc[:, ~df.columns.duplicated()]
    if team != "Overall":
        df_new = df[df['for'] == team]
        corrMatrix = df_new.corr()
    else:
        corrMatrix = df.corr()
    arr2 = list(df_playersnew1.columns.values)
    for i in range(0,len(arr2)):
        df_playersnew1[arr2[i]] = (df_playersnew1[arr2[i]]) * (corrMatrix['Points taken'][arr2[i]])
    if not stats:
        print("Choose at least one stat to see output")
        return
    else:
        df_playersnew2 = df_playersnew1[np.array(stats)]
        
    
    features = list(df_playersnew2.columns.values)
    #df_playersnew2 = df_playersnew2.fillna(0)
    from sklearn.preprocessing import StandardScaler
    # Separating out the features
    x = df_playersnew2.loc[:, features].values
    # Separating out the target
    y = df_players.loc[:,['player']].values
    # Standardizing the features
    #x = StandardScaler().fit_transform(x)
    x = np.nan_to_num(x)
    from sklearn.decomposition import PCA
    #pca = PCA(n_components=2)
    pca = PCA(.90)
    principalComponents = pca.fit_transform(x)
    print('Number of PCA components:',pca.n_components_)
    print('\n')
    principalDf = pd.DataFrame(data = principalComponents)
    
    global finalDf
    df_players = df_players.reset_index(drop=True)
    finalDf = pd.concat([principalDf, df_players[['player']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['squad']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['position']]], axis = 1)
    finalDf = pd.concat([finalDf, df_players[['age']]], axis = 1)
    
    player = player_name
    class color:
        PURPLE = '\033[95m'
        CYAN = '\033[96m'
        DARKCYAN = '\033[36m'
        BLUE = '\033[94m'
        GREEN = '\033[92m'
        YELLOW = '\033[93m'
        RED = '\033[91m'
        BOLD = '\033[1m'
        UNDERLINE = '\033[4m'
        END = '\033[0m'

#print(color.BOLD + 'Hello World !' + color.END)
    a = (finalDf[finalDf['player'] == player])[(finalDf[finalDf['player'] == player]).columns.drop(['player','squad','position','age'])]
    b = finalDf[finalDf.columns.drop(['player','squad','position','age'])]
    finalDf['distance'] = (finalDf[list(b.columns.values)] - np.array(a)).pow(2).sum(1).pow(0.5)
    dist = finalDf['distance'].max()
    dist2 = finalDf['distance'].quantile(0.95)
    finalDf['% match'] = 100-(finalDf['distance']/dist2)*100
    final = ((finalDf.sort_values(['distance'], ascending=[True])))[1:number_of_results+1]
    final = final.reset_index(drop=True)
    print(color.BOLD + 'List of similar players:' + color.END)
    print('\n')
    print(final[['player','squad','position','age','% match']])

In [10]:

interact(find, player_name = sorted(pl),team = te,skill = ['Overall','Passing','Creating','Shooting','Defensive work','Possession'],number_of_results = (range(100))[20:100]);

interactive(children=(Dropdown(description='player_name', options=('Aaron Connolly', 'Aaron Cresswell', 'Aaron…

In [11]:

print(color.BOLD + 'Here you can customise the stats which are analysed ' + color.END + '(use ctrl/command button to select multiple stats)')

Here you can customise the stats which are analysed (use ctrl/command button to select multiple stats)

In [11]:

interact(find2, player_name = sorted(pl),team = te,stats = widgets.SelectMultiple(options = list(df_playersnew.columns.values),rows=10),number_of_results = (range(100))[20:100]);

interactive(children=(Dropdown(description='player_name', options=('Aaron Connolly', 'Aaron Cresswell', 'Aaron…