작성일자 : 2023-09-30
Ver 0.1.1
경기 데이터 불러오기
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
pd.set_option('display.max_columns', 40) #최대로 보여지는 칼럼수 지정
current_dir = os.getcwd()
current_dir
'/Users/limjongjun/Desktop/JayJay/Growth/Python/soccer-analytics/Excercise'
new_dir = '/Users/limjongjun/Desktop/JayJay/Growth/Python/soccer-analytics'
os.chdir(new_dir)
print(current_dir)
/Users/limjongjun/Desktop/JayJay/Growth/Python/soccer-analytics/Excercise
match_id = 2499895
match_events = pd.read_pickle(f'data/refined_events/England/2499895.pkl') #pkl 파일을 Dataframe으로 불러오기
match_events
|
match_id |
event_id |
period |
time |
team_id |
team_name |
player_id |
player_name |
event_type |
sub_event_type |
tags |
start_x |
start_y |
end_x |
end_y |
0 |
2499895 |
215108367 |
1H |
1.784 |
1625 |
Manchester City |
8325 |
S. Agüero |
Pass |
Simple pass |
[Accurate] |
52.00 |
34.68 |
40.56 |
34.68 |
1 |
2499895 |
215108368 |
1H |
3.324 |
1625 |
Manchester City |
105339 |
Fernandinho |
Pass |
Simple pass |
[Accurate] |
40.56 |
34.68 |
29.12 |
8.16 |
2 |
2499895 |
215108369 |
1H |
6.406 |
1625 |
Manchester City |
8277 |
K. Walker |
Pass |
Simple pass |
[Accurate] |
29.12 |
8.16 |
44.72 |
10.20 |
3 |
2499895 |
215108370 |
1H |
7.124 |
1625 |
Manchester City |
38021 |
K. De Bruyne |
Pass |
Simple pass |
[Accurate] |
44.72 |
10.20 |
69.68 |
6.12 |
4 |
2499895 |
215108371 |
1H |
8.676 |
1625 |
Manchester City |
11066 |
R. Sterling |
Pass |
Simple pass |
[Accurate] |
69.68 |
6.12 |
58.24 |
12.24 |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
1607 |
2499895 |
215110122 |
2H |
2875.703 |
1624 |
Tottenham Hotspur |
210044 |
E. Dier |
Pass |
Simple pass |
[Accurate] |
36.40 |
26.52 |
48.88 |
29.92 |
1608 |
2499895 |
215110123 |
2H |
2876.142 |
1624 |
Tottenham Hotspur |
240070 |
H. Winks |
Duel |
Ground attacking duel |
[Anticipation, Lost, Not accurate] |
48.88 |
29.92 |
43.68 |
24.48 |
1609 |
2499895 |
215109959 |
2H |
2876.768 |
1625 |
Manchester City |
447205 |
P. Foden |
Duel |
Ground defending duel |
[Anticipated, Won, Accurate] |
55.12 |
38.08 |
60.32 |
43.52 |
1610 |
2499895 |
215109960 |
2H |
2878.046 |
1625 |
Manchester City |
11066 |
R. Sterling |
Duel |
Ground attacking duel |
[Free space right, Lost, Not accurate] |
60.32 |
43.52 |
60.32 |
43.52 |
1611 |
2499895 |
215110124 |
2H |
2878.216 |
1624 |
Tottenham Hotspur |
240070 |
H. Winks |
Duel |
Ground defending duel |
[Free space left, Lost, Not accurate] |
43.68 |
24.48 |
NaN |
NaN |
1612 rows × 15 columns
경기 내 선수별 기록 집계
(1) 이벤트 유형을 기준으로 추출되는 기록의 집계
#슈팅 횟수
shot_records = match_events[
(match_events['event_type'] == 'Shot') |
(match_events['sub_event_type'].isin(['Free kick shot', 'Penalty']))
]
shots = shot_records.groupby(['team_id', 'team_name', 'player_id', 'player_name'])['event_id'].count()
shots.name = 'total_shots'
shots
team_id team_name player_id player_name
1624 Tottenham Hotspur 48 J. Vertonghen 1
54 C. Eriksen 1
8292 D. Rose 1
8717 H. Kane 2
14911 Son Heung-Min 1
1625 Manchester City 8325 S. Agüero 5
11066 R. Sterling 6
14808 İ. Gündoğan 3
38021 K. De Bruyne 2
245364 L. Sané 2
265673 Bernardo Silva 1
340386 Gabriel Jesus 1
Name: total_shots, dtype: int64
#패스 횟수
pass_records = match_events[
(match_events['event_type'] == 'Pass') |
(match_events['sub_event_type'].isin(['Free kick', 'Free kick cross', 'corner']))
]
passes = pass_records.groupby(['team_id', 'team_name', 'player_id', 'player_name'])['event_id'].count()
passes.name = 'total_passes'
passes
team_id team_name player_id player_name
1624 Tottenham Hotspur 48 J. Vertonghen 54
54 C. Eriksen 34
8292 D. Rose 45
8717 H. Kane 15
8945 K. Trippier 46
11152 M. Dembélé 32
13484 D. Alli 26
14911 Son Heung-Min 17
20441 E. Lamela 4
25381 H. Lloris 32
25804 M. Sissoko 2
210044 E. Dier 51
240070 H. Winks 32
1625 Manchester City 8277 K. Walker 54
8325 S. Agüero 13
8464 F. Delph 54
11066 R. Sterling 35
14808 İ. Gündoğan 39
38021 K. De Bruyne 49
70085 E. Mangala 36
70086 N. Otamendi 41
71654 Ederson 31
105339 Fernandinho 36
245364 L. Sané 29
265673 Bernardo Silva 5
340386 Gabriel Jesus 8
447205 P. Foden 8
Name: total_passes, dtype: int64
#파울 횟수
foul_records = match_events[match_events['event_type'] == 'Foul']
fouls = foul_records.groupby(['team_id', 'team_name', 'player_id', 'player_name'])['event_id'].count()
fouls.name = 'fouls'
fouls
team_id team_name player_id player_name
1624 Tottenham Hotspur 48 J. Vertonghen 2
54 C. Eriksen 1
8292 D. Rose 3
8717 H. Kane 2
8945 K. Trippier 1
11152 M. Dembélé 5
13484 D. Alli 3
20441 E. Lamela 2
25804 M. Sissoko 1
1625 Manchester City 8464 F. Delph 2
38021 K. De Bruyne 3
70086 N. Otamendi 1
105339 Fernandinho 5
245364 L. Sané 1
447205 P. Foden 1
Name: fouls, dtype: int64
#오프사이드 횟수
offside_records = match_events[match_events['event_type'] == 'Offside']
offsides = offside_records.groupby(['team_id', 'team_name', 'player_id', 'player_name'])['event_id'].count()
offsides.name = 'offsides'
offsides
team_id team_name player_id player_name
1624 Tottenham Hotspur 8717 H. Kane 1
14911 Son Heung-Min 1
1625 Manchester City 8325 S. Agüero 2
11066 R. Sterling 1
340386 Gabriel Jesus 1
Name: offsides, dtype: int64
(2) 태그 정보를 기준으로 추출되는 기록의 집계
# 유효슈팅 횟수
acc_shot_records = shot_records[shot_records['tags'].apply(lambda x : 'Accurate' in x)]
acc_shots = acc_shot_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
acc_shots.name = 'shots_on_target'
acc_shots
team_id team_name player_id player_name
1624 Tottenham Hotspur 54 C. Eriksen 1
8717 H. Kane 1
1625 Manchester City 8325 S. Agüero 1
11066 R. Sterling 3
14808 İ. Gündoğan 3
38021 K. De Bruyne 2
245364 L. Sané 1
265673 Bernardo Silva 1
Name: shots_on_target, dtype: int64
# 신체 부위별 슈팅 횟수
rshot_records = shot_records[shot_records['tags'].apply(lambda x : 'Right foot' in x)]
rshots = rshot_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
rshots.name = 'rfoot_shots'
lshot_records = shot_records[shot_records['tags'].apply(lambda x : 'Left foot' in x)]
lshots = lshot_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
lshots.name = 'lfoot_shots'
hshot_records = shot_records[shot_records['tags'].apply(lambda x : 'Head/body' in x)]
hshots = hshot_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
hshots.name = 'hfoot_shots'
shot_stats_list = [shots, acc_shots, rshots, lshots, hshots]
shot_stats = pd.concat(shot_stats_list, axis =1).fillna(0).astype(int)
shot_stats
|
|
|
|
total_shots |
shots_on_target |
rfoot_shots |
lfoot_shots |
hfoot_shots |
team_id |
team_name |
player_id |
player_name |
|
|
|
|
|
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
1 |
0 |
1 |
0 |
0 |
54 |
C. Eriksen |
1 |
1 |
0 |
1 |
0 |
8292 |
D. Rose |
1 |
0 |
0 |
1 |
0 |
8717 |
H. Kane |
2 |
1 |
2 |
0 |
0 |
14911 |
Son Heung-Min |
1 |
0 |
0 |
1 |
0 |
1625 |
Manchester City |
8325 |
S. Agüero |
5 |
1 |
1 |
3 |
1 |
11066 |
R. Sterling |
6 |
3 |
2 |
4 |
0 |
14808 |
İ. Gündoğan |
3 |
3 |
2 |
0 |
1 |
38021 |
K. De Bruyne |
2 |
2 |
0 |
2 |
0 |
245364 |
L. Sané |
2 |
1 |
1 |
1 |
0 |
265673 |
Bernardo Silva |
1 |
1 |
0 |
1 |
0 |
340386 |
Gabriel Jesus |
1 |
0 |
1 |
0 |
0 |
# 득점,도움, 자책골 횟수
goal_records = match_events[match_events['tags'].apply(lambda x : 'Goal' in x)]
goals = rshot_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
goals.name = 'goals'
assist_records = match_events[match_events['tags'].apply(lambda x : 'Assist' in x)]
assists = assist_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
assists.name = 'assists'
own_goal_records = match_events[match_events['tags'].apply(lambda x : 'Own goal' in x)]
own_goals = own_goal_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
own_goals.name = 'own_goals'
goal_stats_list = [goals, assists, own_goals]
goal_stats = pd.concat(goal_stats_list, axis =1).fillna(0).astype(int)
goal_stats
|
|
|
|
goals |
assists |
own_goals |
team_id |
team_name |
player_id |
player_name |
|
|
|
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
1 |
0 |
0 |
8717 |
H. Kane |
2 |
0 |
0 |
1625 |
Manchester City |
8325 |
S. Agüero |
1 |
0 |
0 |
11066 |
R. Sterling |
2 |
0 |
0 |
14808 |
İ. Gündoğan |
2 |
0 |
0 |
245364 |
L. Sané |
1 |
2 |
0 |
340386 |
Gabriel Jesus |
1 |
0 |
0 |
265673 |
Bernardo Silva |
0 |
1 |
0 |
# 성공한 패스 횟수 및 패스 성공률
acc_pass_records = pass_records[pass_records['tags'].apply(lambda x : 'Accurate' in x)]
acc_passes = acc_pass_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
acc_passes.name = 'acc_passes'
pass_stats = pd.concat([passes, acc_passes], axis =1).fillna(0).astype(int)
pass_stats['pass_accuracy'] = (pass_stats['acc_passes'] / pass_stats['total_passes']).round(2)
pass_stats
|
|
|
|
total_passes |
acc_passes |
pass_accuracy |
team_id |
team_name |
player_id |
player_name |
|
|
|
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
54 |
47 |
0.87 |
54 |
C. Eriksen |
34 |
24 |
0.71 |
8292 |
D. Rose |
45 |
33 |
0.73 |
8717 |
H. Kane |
15 |
9 |
0.60 |
8945 |
K. Trippier |
46 |
35 |
0.76 |
11152 |
M. Dembélé |
32 |
28 |
0.88 |
13484 |
D. Alli |
26 |
17 |
0.65 |
14911 |
Son Heung-Min |
17 |
14 |
0.82 |
20441 |
E. Lamela |
4 |
2 |
0.50 |
25381 |
H. Lloris |
32 |
28 |
0.88 |
25804 |
M. Sissoko |
2 |
1 |
0.50 |
210044 |
E. Dier |
51 |
49 |
0.96 |
240070 |
H. Winks |
32 |
31 |
0.97 |
1625 |
Manchester City |
8277 |
K. Walker |
54 |
46 |
0.85 |
8325 |
S. Agüero |
13 |
11 |
0.85 |
8464 |
F. Delph |
54 |
46 |
0.85 |
11066 |
R. Sterling |
35 |
32 |
0.91 |
14808 |
İ. Gündoğan |
39 |
35 |
0.90 |
38021 |
K. De Bruyne |
49 |
38 |
0.78 |
70085 |
E. Mangala |
36 |
30 |
0.83 |
70086 |
N. Otamendi |
41 |
37 |
0.90 |
71654 |
Ederson |
31 |
25 |
0.81 |
105339 |
Fernandinho |
36 |
31 |
0.86 |
245364 |
L. Sané |
29 |
22 |
0.76 |
265673 |
Bernardo Silva |
5 |
3 |
0.60 |
340386 |
Gabriel Jesus |
8 |
6 |
0.75 |
447205 |
P. Foden |
8 |
7 |
0.88 |
# 경고, 퇴장 횟수
yellow_records = foul_records[foul_records['tags'].apply(lambda x : 'Yellow card' in x)]
yellows = yellow_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
yellows.name = 'yellow_cards'
red_records = foul_records[foul_records['tags'].apply(lambda x : 'Red card' in x)]
reds = red_records.groupby(['team_id', 'team_name','player_id','player_name'])['event_id'].count()
reds.name = 'red_cards'
foul_stats = pd.concat([fouls, offsides, yellows, reds], axis = 1).fillna(0).astype(int)
foul_stats
|
|
|
|
fouls |
offsides |
yellow_cards |
red_cards |
team_id |
team_name |
player_id |
player_name |
|
|
|
|
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
2 |
0 |
0 |
0 |
54 |
C. Eriksen |
1 |
0 |
0 |
0 |
8292 |
D. Rose |
3 |
0 |
0 |
0 |
8717 |
H. Kane |
2 |
1 |
1 |
0 |
8945 |
K. Trippier |
1 |
0 |
0 |
0 |
11152 |
M. Dembélé |
5 |
0 |
1 |
0 |
13484 |
D. Alli |
3 |
0 |
1 |
0 |
20441 |
E. Lamela |
2 |
0 |
0 |
0 |
25804 |
M. Sissoko |
1 |
0 |
1 |
0 |
1625 |
Manchester City |
8464 |
F. Delph |
2 |
0 |
1 |
0 |
38021 |
K. De Bruyne |
3 |
0 |
0 |
0 |
70086 |
N. Otamendi |
1 |
0 |
1 |
0 |
105339 |
Fernandinho |
5 |
0 |
0 |
0 |
245364 |
L. Sané |
1 |
0 |
0 |
0 |
447205 |
P. Foden |
1 |
0 |
0 |
0 |
1624 |
Tottenham Hotspur |
14911 |
Son Heung-Min |
0 |
1 |
0 |
0 |
1625 |
Manchester City |
8325 |
S. Agüero |
0 |
2 |
0 |
0 |
11066 |
R. Sterling |
0 |
1 |
0 |
0 |
340386 |
Gabriel Jesus |
0 |
1 |
0 |
0 |
(3) 선수별 통계 정리
player_stats = pd.concat([goal_stats, shot_stats, foul_stats, pass_stats], axis =1, sort = True)
player_stats
|
|
|
|
goals |
assists |
own_goals |
total_shots |
shots_on_target |
rfoot_shots |
lfoot_shots |
hfoot_shots |
fouls |
offsides |
yellow_cards |
red_cards |
total_passes |
acc_passes |
pass_accuracy |
team_id |
team_name |
player_id |
player_name |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
1.0 |
0.0 |
0.0 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
2.0 |
0.0 |
0.0 |
0.0 |
54 |
47 |
0.87 |
54 |
C. Eriksen |
NaN |
NaN |
NaN |
1.0 |
1.0 |
0.0 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
0.0 |
34 |
24 |
0.71 |
8292 |
D. Rose |
NaN |
NaN |
NaN |
1.0 |
0.0 |
0.0 |
1.0 |
0.0 |
3.0 |
0.0 |
0.0 |
0.0 |
45 |
33 |
0.73 |
8717 |
H. Kane |
2.0 |
0.0 |
0.0 |
2.0 |
1.0 |
2.0 |
0.0 |
0.0 |
2.0 |
1.0 |
1.0 |
0.0 |
15 |
9 |
0.60 |
8945 |
K. Trippier |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
1.0 |
0.0 |
0.0 |
0.0 |
46 |
35 |
0.76 |
11152 |
M. Dembélé |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
5.0 |
0.0 |
1.0 |
0.0 |
32 |
28 |
0.88 |
13484 |
D. Alli |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
3.0 |
0.0 |
1.0 |
0.0 |
26 |
17 |
0.65 |
14911 |
Son Heung-Min |
NaN |
NaN |
NaN |
1.0 |
0.0 |
0.0 |
1.0 |
0.0 |
0.0 |
1.0 |
0.0 |
0.0 |
17 |
14 |
0.82 |
20441 |
E. Lamela |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2.0 |
0.0 |
0.0 |
0.0 |
4 |
2 |
0.50 |
25381 |
H. Lloris |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
32 |
28 |
0.88 |
25804 |
M. Sissoko |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
1.0 |
0.0 |
1.0 |
0.0 |
2 |
1 |
0.50 |
210044 |
E. Dier |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
51 |
49 |
0.96 |
240070 |
H. Winks |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
32 |
31 |
0.97 |
1625 |
Manchester City |
8277 |
K. Walker |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
54 |
46 |
0.85 |
8325 |
S. Agüero |
1.0 |
0.0 |
0.0 |
5.0 |
1.0 |
1.0 |
3.0 |
1.0 |
0.0 |
2.0 |
0.0 |
0.0 |
13 |
11 |
0.85 |
8464 |
F. Delph |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
2.0 |
0.0 |
1.0 |
0.0 |
54 |
46 |
0.85 |
11066 |
R. Sterling |
2.0 |
0.0 |
0.0 |
6.0 |
3.0 |
2.0 |
4.0 |
0.0 |
0.0 |
1.0 |
0.0 |
0.0 |
35 |
32 |
0.91 |
14808 |
İ. Gündoğan |
2.0 |
0.0 |
0.0 |
3.0 |
3.0 |
2.0 |
0.0 |
1.0 |
NaN |
NaN |
NaN |
NaN |
39 |
35 |
0.90 |
38021 |
K. De Bruyne |
NaN |
NaN |
NaN |
2.0 |
2.0 |
0.0 |
2.0 |
0.0 |
3.0 |
0.0 |
0.0 |
0.0 |
49 |
38 |
0.78 |
70085 |
E. Mangala |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
36 |
30 |
0.83 |
70086 |
N. Otamendi |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
1.0 |
0.0 |
1.0 |
0.0 |
41 |
37 |
0.90 |
71654 |
Ederson |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
31 |
25 |
0.81 |
105339 |
Fernandinho |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
5.0 |
0.0 |
0.0 |
0.0 |
36 |
31 |
0.86 |
245364 |
L. Sané |
1.0 |
2.0 |
0.0 |
2.0 |
1.0 |
1.0 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
0.0 |
29 |
22 |
0.76 |
265673 |
Bernardo Silva |
0.0 |
1.0 |
0.0 |
1.0 |
1.0 |
0.0 |
1.0 |
0.0 |
NaN |
NaN |
NaN |
NaN |
5 |
3 |
0.60 |
340386 |
Gabriel Jesus |
1.0 |
0.0 |
0.0 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
0.0 |
1.0 |
0.0 |
0.0 |
8 |
6 |
0.75 |
447205 |
P. Foden |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
1.0 |
0.0 |
0.0 |
0.0 |
8 |
7 |
0.88 |
player_stats = player_stats.fillna(0).reset_index()
for col in player_stats.columns[4:]:
if col != 'pass_accuracy' :
player_stats[col] = player_stats[col].astype(int)
player_stats
|
team_id |
team_name |
player_id |
player_name |
goals |
assists |
own_goals |
total_shots |
shots_on_target |
rfoot_shots |
lfoot_shots |
hfoot_shots |
fouls |
offsides |
yellow_cards |
red_cards |
total_passes |
acc_passes |
pass_accuracy |
0 |
1624 |
Tottenham Hotspur |
48 |
J. Vertonghen |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
2 |
0 |
0 |
0 |
54 |
47 |
0.87 |
1 |
1624 |
Tottenham Hotspur |
54 |
C. Eriksen |
0 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
34 |
24 |
0.71 |
2 |
1624 |
Tottenham Hotspur |
8292 |
D. Rose |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
3 |
0 |
0 |
0 |
45 |
33 |
0.73 |
3 |
1624 |
Tottenham Hotspur |
8717 |
H. Kane |
2 |
0 |
0 |
2 |
1 |
2 |
0 |
0 |
2 |
1 |
1 |
0 |
15 |
9 |
0.60 |
4 |
1624 |
Tottenham Hotspur |
8945 |
K. Trippier |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
46 |
35 |
0.76 |
5 |
1624 |
Tottenham Hotspur |
11152 |
M. Dembélé |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
0 |
1 |
0 |
32 |
28 |
0.88 |
6 |
1624 |
Tottenham Hotspur |
13484 |
D. Alli |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
0 |
1 |
0 |
26 |
17 |
0.65 |
7 |
1624 |
Tottenham Hotspur |
14911 |
Son Heung-Min |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
17 |
14 |
0.82 |
8 |
1624 |
Tottenham Hotspur |
20441 |
E. Lamela |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
4 |
2 |
0.50 |
9 |
1624 |
Tottenham Hotspur |
25381 |
H. Lloris |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
32 |
28 |
0.88 |
10 |
1624 |
Tottenham Hotspur |
25804 |
M. Sissoko |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
2 |
1 |
0.50 |
11 |
1624 |
Tottenham Hotspur |
210044 |
E. Dier |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
51 |
49 |
0.96 |
12 |
1624 |
Tottenham Hotspur |
240070 |
H. Winks |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
32 |
31 |
0.97 |
13 |
1625 |
Manchester City |
8277 |
K. Walker |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
54 |
46 |
0.85 |
14 |
1625 |
Manchester City |
8325 |
S. Agüero |
1 |
0 |
0 |
5 |
1 |
1 |
3 |
1 |
0 |
2 |
0 |
0 |
13 |
11 |
0.85 |
15 |
1625 |
Manchester City |
8464 |
F. Delph |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
0 |
1 |
0 |
54 |
46 |
0.85 |
16 |
1625 |
Manchester City |
11066 |
R. Sterling |
2 |
0 |
0 |
6 |
3 |
2 |
4 |
0 |
0 |
1 |
0 |
0 |
35 |
32 |
0.91 |
17 |
1625 |
Manchester City |
14808 |
İ. Gündoğan |
2 |
0 |
0 |
3 |
3 |
2 |
0 |
1 |
0 |
0 |
0 |
0 |
39 |
35 |
0.90 |
18 |
1625 |
Manchester City |
38021 |
K. De Bruyne |
0 |
0 |
0 |
2 |
2 |
0 |
2 |
0 |
3 |
0 |
0 |
0 |
49 |
38 |
0.78 |
19 |
1625 |
Manchester City |
70085 |
E. Mangala |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
36 |
30 |
0.83 |
20 |
1625 |
Manchester City |
70086 |
N. Otamendi |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
41 |
37 |
0.90 |
21 |
1625 |
Manchester City |
71654 |
Ederson |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
31 |
25 |
0.81 |
22 |
1625 |
Manchester City |
105339 |
Fernandinho |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
36 |
31 |
0.86 |
23 |
1625 |
Manchester City |
245364 |
L. Sané |
1 |
2 |
0 |
2 |
1 |
1 |
1 |
0 |
1 |
0 |
0 |
0 |
29 |
22 |
0.76 |
24 |
1625 |
Manchester City |
265673 |
Bernardo Silva |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
5 |
3 |
0.60 |
25 |
1625 |
Manchester City |
340386 |
Gabriel Jesus |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
8 |
6 |
0.75 |
26 |
1625 |
Manchester City |
447205 |
P. Foden |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
8 |
7 |
0.88 |