sportowe_wizualizacja/11tegen11 position map.ipynb at 7afb40e3479a18ca4f7c98cece370ae8eb630a78

#edit only this tab
#give the folder path of the match
path = "/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/"
home_team = 'Espanyol'
away_team = 'Barcelona'

import json
import os
import codecs
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Arc, Rectangle, ConnectionPatch
from matplotlib.offsetbox import  OffsetImage
import StatsbombPitch as sb
from functools import reduce

Xg_req = pd.DataFrame(data=None)

filename = '69275.json' # remove the comment line to work for this match
with codecs.open("%s" % path + filename,encoding='utf-8') as data_file:    
    data = json.load(data_file)
    df = pd.DataFrame(data=None)
        
    df = pd.json_normalize(data, sep = "_")
        
        #df =  df[(df['type_name'] == "Shot")]
        #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]
        #print(df.shape)
Xg_req = Xg_req.append(df,ignore_index=True,sort=False)
        #df.drop(df.index, inplace=True)
        
print("done")
df = Xg_req

done

#type_id=30 is pass event AND type_id=19 is substitution event

pass_m = df.query('type_id == 30')
substitution = df.query('type_id == 19')
#pass_m = df.query('type_name == pass')

#this cell is WIP
substitution_home = set()
substitution_home = substitution[substitution.team_name == home_team]
substitution_home = substitution_home['substitution_replacement_name'].unique()
substitution_away = set()
substitution_away = substitution[substitution.team_name == away_team]
substitution_away = substitution_away['substitution_replacement_name'].unique()
print(substitution_away)
print(substitution_home)

['Javier Alejandro Mascherano' 'Seydou Kéita' 'Bojan Krkíc Pérez']
['Jesús Alberto Dátolo' 'Jordi Amat Maas' 'David García De La Cruz']

# to get 

home_player = pass_m[(pass_m.team_name == home_team)] 
home_team_list = set()
home_team_list = home_player['player_name'].unique()
#print(substitution_belgium)
#belgium_list = [player for player in belgium_list if player not in substitution_belgium]
#belgium_list.remove([x for x in substitution_belgium])#belgium_list - substitution_belgium
home_player =pass_m['player_name'].isin(home_team_list)
pass_home = pass_m[home_player] #contains 11 players of home team

away_player = pass_m[(pass_m.team_name == away_team)] 
away_list = away_player['player_name'].unique()
#away_list = away_list - substitution_brazil
away_player = pass_m['player_name'].isin(away_list)
pass_away = pass_m[away_player]

pass_home.head()

	id	index	period	timestamp	second	possession	duration	type_id	type_name	...	pass_cut_back	substitution_outcome_id	substitution_outcome_name	substitution_replacement_id	substitution_replacement_name	injury_stoppage_in_chain	foul_committed_offensive	pass_deflected	block_deflection	goalkeeper_punched_out
4	d6406089-778b-49e0-a70a-ade4788cf911	5	1	00:00:00.738	0	2	0.606100	30	Pass	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	73d8e43f-8e84-4b5d-88cc-9423278134d5	9	1	00:00:02.511	2	2	1.776163	30	Pass	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16	8e47d35d-ab38-4419-a991-efe7105dec56	17	1	00:00:09.581	9	2	1.242900	30	Pass	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
20	a449bfa2-7bd9-4f94-8e13-595620c9bcee	21	1	00:00:18.784	18	3	1.298500	30	Pass	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
24	77f45a53-2eab-4ee5-bfbd-ad39d20a36cb	25	1	00:00:20.122	20	3	0.950262	30	Pass	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 122 columns

players = home_team_list
#fig=plt.figure() #set up the figures
sb.sb_pitch("#195905","#faf0e6","horizontal","full")
plt.axis('on')
plt.gca().invert_yaxis()
for player in players:
    x_avg = 0
    y_avg = 0
    touches = 0
    if player not in substitution_home: 
        play_temp = pass_home[(pass_home.player_name == player)]
    #print(play_temp.location, players[player])
        for i in range(len(play_temp)):
            touches+=1
            
            #https://math.stackexchange.com/questions/1013230/how-to-find-coordinates-of-reflected-point
            x_avg = x_avg + play_temp.iloc[i]['location'][0]
            y_avg = y_avg + play_temp.iloc[i]['location'][1]
        x_avg = x_avg/len(play_temp)
        y_avg = y_avg/len(play_temp)
    #print(x_avg,y_avg,players[player])
        plt.scatter(x_avg, y_avg, s= (3 *touches ),color="red",edgecolors="black",zorder = 15)  
        plt.annotate(player, (x_avg, y_avg),zorder = 20,color = 'white')
plt.show()

players = away_list #['Alisson Ramsés Becker','Fágner Conserva Lemos','Fernando Luiz Rosa','Gabriel Fernando de Jesus','João Miranda de Souza Filho','José Paulo Bezzera Maciel Júnior','Marcelo Vieira da Silva Júnior','Neymar da Silva Santos Junior','Philippe Coutinho Correia','Thiago Emiliano da Silva','Willian Borges da Silva']
sb.sb_pitch("#195905","#faf0e6","horizontal","full")
plt.axis('on')
for player in players:
    x_avg = 0
    y_avg = 0
    touches = 0
    if player not in substitution_away:
    #print(players[player])
        play_temp = pass_away[(pass_away.player_name == player)]
    #print(play_temp.location, players[player])
        for i in range(len(play_temp)):
            touches+=1
            x_avg = x_avg + play_temp.iloc[i]['location'][0]
            y_avg = y_avg + play_temp.iloc[i]['location'][1]
        x_avg = x_avg/len(play_temp)
        y_avg = y_avg/len(play_temp)
    #print(x_avg,y_avg,players[player])
        plt.scatter(x_avg, y_avg, s= (3 *touches ),color="red",edgecolors="black",zorder = 15)  
        plt.annotate(player, (x_avg, y_avg),zorder = 20,color = 'white')
plt.show()

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