391 KiB
391 KiB
%%time
!git clone https://github.com/statsbomb/open-data.gitCloning into 'open-data'... remote: Enumerating objects: 1133, done.[K remote: Counting objects: 100% (1133/1133), done.[K remote: Compressing objects: 100% (632/632), done.[K remote: Total 9855 (delta 925), reused 690 (delta 482), pack-reused 8722[K Receiving objects: 100% (9855/9855), 996.16 MiB | 25.43 MiB/s, done. Resolving deltas: 100% (8672/8672), done. Checking out files: 100% (1648/1648), done. CPU times: user 478 ms, sys: 94.4 ms, total: 572 ms Wall time: 2min 17s
#import all modules
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
from matplotlib.patches import Ellipse
from functools import reduce
import math%%time
comp = ['FIFA World Cup','La Liga']
main_df = pd.DataFrame(data=None)
path_match = "/content/open-data/data/events/" #location for play by play events
for root, dirs, files in os.walk('/content/open-data/data/matches/'):
for file in files:
with open(os.path.join(root, file), "r") as auto:
with codecs.open(root + str('/') + file,encoding='utf-8') as data_file:
data = json.load(data_file)
df = pd.DataFrame(data=None)
df = pd.json_normalize(data, sep = "_")
#for x in df.competition_country_name:
# if x == 'Spain':
# print(df.match_id)
#print(df['competition_competition_name'])
for i in range(len(df)):
if df.iloc[i]['competition_competition_name'] in comp :
match_no = df.iloc[i]['match_id'] #gets match with Spain as country
match_no = str(match_no) # from int to str
#print('match list \n',match_no)
with codecs.open(path_match + match_no + str(r'.json'),encoding="utf8") as event_file: #open the respective file
df_match = json.load(event_file)
df_match2 = pd.DataFrame(data=None)
df_match2 = pd.json_normalize(df_match,sep="_")
df_match2 = df_match2[(df_match2['type_name'] == "Shot")]
main_df = main_df.append(df_match2,ignore_index=True,sort=False)
#print('total matches ',len(match_no))
print('Done')Done CPU times: user 6min 28s, sys: 1.27 s, total: 6min 29s Wall time: 6min 30s
main_df.head()| id | index | period | timestamp | minute | second | possession | duration | type_id | type_name | possession_team_id | possession_team_name | play_pattern_id | play_pattern_name | team_id | team_name | tactics_formation | tactics_lineup | related_events | location | player_id | player_name | position_id | position_name | pass_recipient_id | pass_recipient_name | pass_length | pass_angle | pass_height_id | pass_height_name | pass_end_location | pass_type_id | pass_type_name | pass_body_part_id | pass_body_part_name | carry_end_location | under_pressure | pass_outcome_id | pass_outcome_name | ball_receipt_outcome_id | ... | pass_deflected | block_deflection | substitution_outcome_id | substitution_outcome_name | substitution_replacement_id | substitution_replacement_name | ball_recovery_recovery_failure | dribble_overrun | 50_50_outcome_id | 50_50_outcome_name | shot_aerial_won | shot_open_goal | bad_behaviour_card_id | bad_behaviour_card_name | pass_no_touch | block_offensive | foul_committed_offensive | shot_saved_off_target | goalkeeper_shot_saved_off_target | miscontrol_aerial_won | goalkeeper_punched_out | clearance_other | ball_recovery_offensive | shot_deflected | dribble_no_touch | shot_redirect | block_save_block | injury_stoppage_in_chain | half_start_late_video_start | player_off_permanent | goalkeeper_lost_out | goalkeeper_saved_to_post | shot_follows_dribble | shot_saved_to_post | goalkeeper_shot_saved_to_post | pass_backheel | goalkeeper_lost_in_play | goalkeeper_success_out | goalkeeper_success_in_play | half_end_early_video_end | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fb785612-71d3-44df-aae4-da6e005756de | 117 | 1 | 00:02:06.532 | 2 | 6 | 6 | 1.015179 | 16 | Shot | 222 | Villarreal | 1 | Regular Play | 222 | Villarreal | NaN | NaN | [1febb4f7-0e2c-43f7-96fe-51fbffaaf664] | [107.5, 27.2] | 11386.0 | Santiago Cazorla González | 16.0 | Left Midfield | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 2dfa84fe-3579-4705-8d27-44b8917907e1 | 499 | 1 | 00:09:49.110 | 9 | 49 | 17 | 1.302674 | 16 | Shot | 222 | Villarreal | 4 | From Throw In | 222 | Villarreal | NaN | NaN | [1af68944-ff3d-49a1-92a0-fe6198e73e78] | [88.3, 50.2] | 25921.0 | Rubén Gracia Calmache | 12.0 | Right Midfield | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | c9d92f30-2159-4a5a-a5bf-9d1163e4b33f | 587 | 1 | 00:12:58.407 | 12 | 58 | 24 | 0.521000 | 16 | Shot | 222 | Villarreal | 5 | Other | 222 | Villarreal | NaN | NaN | [61239f9b-052f-42ab-8a73-3c3c3841d419] | [108.0, 40.0] | 20135.0 | Marcos Antonio Senna da Silva | 15.0 | Left Center Midfield | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 26b5d67b-5fce-4a5a-8b31-b879adbb61d3 | 1113 | 1 | 00:24:01.266 | 24 | 1 | 42 | 0.647279 | 16 | Shot | 217 | Barcelona | 7 | From Goal Kick | 217 | Barcelona | NaN | NaN | [1d8506ef-3a55-45c7-aba9-e31204db051e, 354b60e... | [107.3, 36.1] | 4913.0 | Bojan Krkíc Pérez | 23.0 | Center Forward | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 20528610-c092-482c-8238-6b9679328680 | 1343 | 1 | 00:28:38.374 | 28 | 38 | 49 | 1.023005 | 16 | Shot | 217 | Barcelona | 1 | Regular Play | 217 | Barcelona | NaN | NaN | [06385378-493b-4bd8-8731-f983c4ac28d8] | [105.3, 29.4] | 5503.0 | Lionel Andrés Messi Cuccittini | 17.0 | Right Wing | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 147 columns
"""Distance of shot location to centre of goal"""
def distFormula(coordinate):
a =(math.sqrt(((coordinate.location[0] - 120)**2) + ((coordinate.location[1] - 36)**2)))
b =(math.sqrt(((coordinate.location[0] - 120)**2) + ((coordinate.location[1] - 44)**2)))
return ((a+b)/2)""" near x y (nx,ny) (120,44)and far x y (fx,fy) (120,36)"""
nx = 120
ny = 44
fx = 120
fy = 36
goalpostLength = 8
def shot_angle(points):
len1 = (math.sqrt(((points.location[0] - nx)**2) + ((points.location[1] - ny)**2)))
len2 = (math.sqrt(((points.location[0] - fx)**2) + ((points.location[1] - fy)**2)))
ang = (len1**2 + len2**2 - goalpostLength**2)/(2 * len1 * len2)
if ang > 1:
ang = 1
elif ang < -1:
ang = -1
angRad = math.acos(ang)
return( (angRad * 180)/math.pi) """If shot was taken under Pressure?"""
def under_pressure(coordinate):
if coordinate['under_pressure'] == True:
return 1
return 0"""The Shot type Id"""
def shot_type(coordinate):
if coordinate['shot_type_id'] == 61:
return 1
if coordinate['shot_type_id'] == 62:
return 2
if coordinate['shot_type_id'] == 87:
return 3
if coordinate['shot_type_id'] == 88:
return 4
return 5"""The Shot Body part"""
def shot_body_part(coordinate):
if coordinate['shot_body_part_id'] == 37:
return 1
if coordinate['shot_body_part_id'] == 38:
return 2
if coordinate['shot_body_part_id'] == 70:
return 3
return 4"""The Shot Technique Id"""
def shot_technique(coordinate):
if coordinate['shot_technique_id'] == 89:
return 1
if coordinate['shot_technique_id'] == 90:
return 2
if coordinate['shot_technique_id'] == 91:
return 3
if coordinate['shot_technique_id'] == 92:
return 4
if coordinate['shot_technique_id'] == 93:
return 5
if coordinate['shot_technique_id'] == 94:
return 6
return 7"""If shot was taken first time?"""
def shot_first_time(coordinate):
if coordinate['shot_first_time'] == True:
return 1
return 0""" To Find if a point is inside the triangle
https://www.geeksforgeeks.org/check-whether-a-given-point-lies-inside-a-triangle-or-not/"""
def Triarea(a,b,c):
#return abs((x1*(y2-y3) + x2*(y3-y1)+ x3*(y1-y2))/2.0);
return abs((a[0] * (b[1] - c[1]) + b[0] * (c[1] - a[1]) + c[0] * (a[1] - b[1]))/2.0)
def isInside(a,b,c,p):
A = Triarea(a,b,c)
A1 = Triarea(a,b,p)
A2 = Triarea(p,b,c)
A3 = Triarea(a,p,c)
if (round(A,2) == round((A1 + A2 + A3),2)):
return 1
return 0 """If shot was taken first time?"""
def shot_one_on_one(coordinate):
if coordinate['shot_one_on_one'] == True:
return 1
return 0def infronofShot(frame):
if str(type(frame['shot_freeze_frame'])) == '<class \'float\'>':
return 0
if not len(frame['shot_freeze_frame']):
return 0
#print(type(frame['shot_freeze_frame']),'\n')
loc = pd.DataFrame(frame['shot_freeze_frame'])
#loc = loc[['location']]
X = frame['location'][0]
Y = frame['location'][1]
countgoal = 0
for i in range(len(loc)):
if isInside((X,Y),(120,36),(120,44),(loc['location'].iloc[i][0],loc['location'].iloc[i][1])) == 1:
countgoal +=1
return countgoal main_df['Distance'] = main_df.apply(distFormula,axis = 1)
main_df['Angle'] = main_df.apply(shot_angle,axis = 1)
main_df['UnderPressure'] = main_df.apply(under_pressure,axis = 1)
main_df['ShotType'] = main_df.apply(shot_type,axis = 1)
main_df['ShotBodyPart'] = main_df.apply(shot_body_part,axis = 1)
main_df['ShotTechnique'] = main_df.apply(shot_technique,axis = 1)
main_df['ShotFirstTime'] = main_df.apply(shot_first_time,axis = 1)
main_df['ShotOneonOne']= main_df.apply(shot_one_on_one,axis = 1)
main_df['InFrontofGoal'] = main_df.apply(infronofShot,axis = 1)goals_lst = main_df[main_df['shot_outcome_id'] == 97].index.tolist()#if shot is a goal
main_df['isGoal'] = False
goals_lst
main_df.loc[main_df.index.isin(goals_lst),'isGoal'] = Trueimport statsbombpitch as sb
sb.sb_pitch("#195905","#faf0e6","horizontal","full")
ilocv = 3477
plt.scatter(main_df.iloc[ilocv]['location'][0],main_df.iloc[ilocv]['location'][1],color="#ee3e32",edgecolors="none",zorder=10,alpha=1,s = 40 )
#plt.plt.plot((main_df.iloc[0]['location'][0],120),(main_df.iloc[0]['location'][1],44),color = 'black',zorder = 10)
#(main_df.iloc[1]['location'][1],main_df.iloc[1]['location'][0],120,zorder = 10)
#plt.plot((main_df.iloc[0]['location'][0],120),(main_df.iloc[0]['location'][1],44),color = 'black',zorder = 10)
tri = np.array([[main_df.iloc[ilocv]['location'][0],main_df.iloc[ilocv]['location'][1]],[120,36],[120,44]])
t1 = plt.Polygon(tri, color = 'blue',zorder = 8)
plt.gca().add_patch(t1)
loc = pd.DataFrame(main_df.iloc[ilocv]['shot_freeze_frame'])
for i in range(len(loc)):
plt.scatter(loc['location'].iloc[i][0],loc['location'].iloc[i][1],color="#ee3e32",edgecolors="black",zorder=10,alpha=1,s = 20 )
plt.show()sortxg[50:100]| location | Distance | Angle | UnderPressure | ShotType | ShotBodyPart | ShotTechnique | ShotFirstTime | InFrontofGoal | ShotOneonOne | xG | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 10082 | [115.2, 43.6] | 6.902760 | 62.487997 | 0 | 3 | 4 | 3 | 1 | 1 | 0 | 0.593055 |
| 11940 | [118.0, 45.0] | 5.727806 | 50.906141 | 0 | 3 | 4 | 7 | 1 | 0 | 0 | 0.588634 |
| 5946 | [115.1, 37.6] | 6.607503 | 70.644874 | 0 | 3 | 4 | 7 | 1 | 2 | 0 | 0.588151 |
| 4032 | [114.3, 42.8] | 7.348970 | 61.917732 | 0 | 3 | 4 | 3 | 1 | 1 | 0 | 0.582283 |
| 10411 | [116.3, 40.1] | 5.449276 | 94.443109 | 0 | 3 | 1 | 5 | 0 | 2 | 0 | 0.578671 |
| 10059 | [117.9, 44.7] | 5.581727 | 57.994617 | 0 | 3 | 4 | 3 | 1 | 1 | 0 | 0.577080 |
| 5721 | [115.0, 40.0] | 6.403124 | 77.319617 | 0 | 3 | 1 | 5 | 0 | 0 | 0 | 0.573219 |
| 10216 | [114.8, 38.0] | 6.755564 | 70.123128 | 0 | 3 | 1 | 5 | 0 | 1 | 1 | 0.563681 |
| 4014 | [112.3, 38.2] | 8.824071 | 52.934164 | 0 | 3 | 4 | 3 | 1 | 0 | 0 | 0.561885 |
| 8422 | [113.1, 43.4] | 8.521922 | 51.972274 | 0 | 3 | 4 | 5 | 1 | 0 | 0 | 0.557730 |
| 6056 | [114.0, 37.0] | 7.651153 | 58.861028 | 0 | 3 | 4 | 6 | 1 | 1 | 0 | 0.552528 |
| 1551 | [114.4, 39.4] | 6.899202 | 70.664392 | 0 | 3 | 2 | 3 | 1 | 1 | 0 | 0.549695 |
| 250 | [113.1, 42.1] | 8.183296 | 56.874096 | 0 | 3 | 4 | 5 | 0 | 0 | 0 | 0.548621 |
| 3272 | [113.6, 40.1] | 7.547661 | 64.001725 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.542347 |
| 9799 | [113.2, 42.0] | 8.078323 | 57.813206 | 0 | 3 | 4 | 4 | 1 | 1 | 0 | 0.538975 |
| 10820 | [113.6, 41.8] | 7.702349 | 61.154851 | 0 | 3 | 2 | 5 | 1 | 0 | 0 | 0.533676 |
| 5077 | [114.1, 43.6] | 7.767437 | 56.055770 | 0 | 3 | 4 | 7 | 1 | 1 | 0 | 0.528097 |
| 12541 | [113.0, 44.0] | 8.815073 | 48.814075 | 0 | 3 | 4 | 7 | 0 | 1 | 1 | 0.527048 |
| 11843 | [113.0, 38.0] | 8.249827 | 56.546691 | 0 | 3 | 4 | 7 | 1 | 1 | 0 | 0.524932 |
| 3227 | [115.6, 41.8] | 6.099730 | 79.380345 | 0 | 3 | 1 | 5 | 0 | 1 | 0 | 0.522069 |
| 1710 | [113.3, 39.1] | 7.841507 | 61.008967 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.514230 |
| 1796 | [112.4, 39.7] | 8.592467 | 55.459494 | 0 | 3 | 4 | 5 | 1 | 1 | 0 | 0.512211 |
| 12751 | [115.0, 42.0] | 6.597707 | 71.995838 | 0 | 3 | 2 | 5 | 0 | 1 | 0 | 0.510614 |
| 12635 | [118.0, 36.0] | 5.123106 | 75.963757 | 0 | 3 | 1 | 5 | 0 | 1 | 0 | 0.509163 |
| 5094 | [115.6, 43.6] | 6.599972 | 65.125846 | 0 | 3 | 2 | 5 | 1 | 1 | 0 | 0.509059 |
| 12870 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 12363 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 12622 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 11356 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 11390 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 12918 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 11495 | [109.0, 41.0] | 11.742400 | 39.699073 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.509021 |
| 9319 | [114.0, 42.8] | 7.593725 | 59.886267 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.508912 |
| 5479 | [114.3, 42.5] | 7.269647 | 63.495292 | 0 | 3 | 1 | 5 | 0 | 1 | 1 | 0.503553 |
| 9820 | [116.5, 37.5] | 5.595149 | 84.897835 | 0 | 3 | 1 | 2 | 0 | 2 | 0 | 0.502442 |
| 5385 | [112.6, 42.2] | 8.634894 | 53.628856 | 0 | 3 | 4 | 5 | 1 | 1 | 0 | 0.496973 |
| 9100 | [115.8, 44.3] | 6.756425 | 59.073874 | 0 | 3 | 4 | 5 | 1 | 2 | 0 | 0.495486 |
| 1360 | [113.7, 36.1] | 8.202624 | 52.338128 | 0 | 3 | 4 | 5 | 1 | 1 | 0 | 0.493024 |
| 3664 | [116.4, 43.2] | 5.868831 | 75.963757 | 1 | 3 | 2 | 5 | 1 | 1 | 0 | 0.491836 |
| 8527 | [115.0, 43.3] | 6.948463 | 63.561138 | 0 | 3 | 2 | 5 | 1 | 1 | 0 | 0.491444 |
| 10394 | [112.4, 38.0] | 8.770864 | 53.033726 | 0 | 3 | 4 | 5 | 1 | 1 | 0 | 0.490224 |
| 3910 | [112.6, 39.5] | 8.423397 | 56.617007 | 0 | 3 | 2 | 5 | 1 | 0 | 0 | 0.486898 |
| 4346 | [108.3, 40.3] | 12.368128 | 37.728461 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.484093 |
| 11026 | [108.2, 40.1] | 12.459894 | 37.449331 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.480520 |
| 3793 | [112.3, 44.5] | 9.592653 | 44.111835 | 0 | 3 | 4 | 5 | 0 | 1 | 1 | 0.479089 |
| 6938 | [112.2, 42.0] | 8.946530 | 51.949987 | 0 | 3 | 4 | 5 | 1 | 1 | 0 | 0.478999 |
| 7928 | [113.3, 38.2] | 7.956833 | 59.059829 | 0 | 3 | 4 | 5 | 1 | 2 | 0 | 0.477739 |
| 554 | [114.8, 36.7] | 7.104802 | 62.203440 | 0 | 3 | 2 | 7 | 1 | 1 | 0 | 0.477342 |
| 1305 | [108.1, 40.0] | 12.554282 | 37.158541 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.476817 |
| 3704 | [108.1, 40.1] | 12.554640 | 37.156345 | 0 | 4 | 4 | 5 | 0 | 0 | 0 | 0.476794 |
countgoal = 0
#isInside((X,Y),(120,36),(120,44),(loc['location'].iloc[i][0],loc['location'].iloc[i][1])) == 1:
for i in range(len(loc)):
if isInside((100.7,25.6),(120,36),(120,44),(loc['location'].iloc[i][0],loc['location'].iloc[i][1])) == 1:
countgoal +=1
countgoal 9
%%time
main_df['InFrontofGoal'] = main_df.apply(infronofShot,axis = 1)CPU times: user 16.9 s, sys: 39.6 ms, total: 17 s Wall time: 16.9 s
main_df[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal','isGoal']]| location | Distance | Angle | UnderPressure | ShotType | ShotBodyPart | ShotTechnique | ShotFirstTime | ShotOneonOne | InFrontofGoal | isGoal | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [110.5, 36.2] | 10.896986 | 40.593846 | 1 | 3 | 4 | 5 | 1 | 0 | 1 | False |
| 1 | [114.2, 48.0] | 10.186866 | 29.611685 | 0 | 3 | 4 | 7 | 1 | 0 | 2 | False |
| 2 | [95.3, 48.3] | 26.332306 | 16.596593 | 0 | 2 | 2 | 5 | 0 | 0 | 4 | False |
| 3 | [103.3, 61.6] | 27.413807 | 10.378789 | 0 | 3 | 2 | 5 | 0 | 0 | 1 | False |
| 4 | [94.9, 55.2] | 29.543437 | 13.366677 | 0 | 2 | 2 | 5 | 0 | 0 | 4 | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 12952 | [111.0, 27.0] | 15.981653 | 17.102729 | 0 | 3 | 2 | 5 | 0 | 0 | 3 | False |
| 12953 | [114.0, 33.0] | 9.619084 | 34.824489 | 0 | 3 | 2 | 5 | 0 | 0 | 1 | True |
| 12954 | [107.0, 32.0] | 15.646638 | 25.606661 | 0 | 3 | 2 | 5 | 0 | 0 | 1 | False |
| 12955 | [97.0, 22.0] | 29.376742 | 12.398277 | 0 | 3 | 4 | 5 | 0 | 0 | 1 | False |
| 12956 | [109.0, 52.0] | 16.508979 | 19.464104 | 0 | 3 | 4 | 5 | 0 | 0 | 1 | False |
12957 rows × 11 columns
#xG Model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
from sklearn import svm
from sklearn import linear_modelxgModel = main_df[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal','isGoal']]X_train,X_test,y_train,y_test = train_test_split(xgModel[['location','Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','InFrontofGoal','ShotOneonOne']],xgModel[['isGoal']],test_size = 0.2,shuffle = True)Logistic Regression Model
clf = LogisticRegression(random_state=0,max_iter = 5000).fit(X_train[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal']], y_train)/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)
#model weights
clf.coef_[0]array([-0.05881247, 0.03193658, -0.45802499, 0.59881635, 0.21925344,
-0.00195693, 0.21331492, 0.50199851, -0.29032136])SGD Model
xG = clf.predict_proba(X_test[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']])[:,1]#SGD
sgdclf = linear_model.SGDClassifier(loss='log', alpha = 0.17)
sgdclf.fit(X_train[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal']], y_train)/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)
SGDClassifier(alpha=0.17, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='log', max_iter=1000,
n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,
random_state=None, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)Xg Boost Model
xgb_model = xgb.XGBClassifier(objective='binary:logistic', max_depth=4, n_estimators=100)
xgb_model.fit(X_train[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal']], y_train)/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) /usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=4,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)Predict Shot Probability
# change model here sgcclf(SGD) or clf(LR)
xG = xgb_model.predict_proba(X_test[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne','InFrontofGoal']])[:,1]X_test['xG'] = xG
#X_test.head()sortxg = X_test.sort_values(by = ['xG'],ascending=False)
sortxg| location | Distance | Angle | UnderPressure | ShotType | ShotBodyPart | ShotTechnique | ShotFirstTime | InFrontofGoal | ShotOneonOne | xG | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 646 | [118.8, 37.0] | 4.332081 | 120.077993 | 0 | 3 | 4 | 5 | 1 | 0 | 0 | 0.979459 |
| 10495 | [119.3, 41.4] | 4.068882 | 157.545469 | 0 | 3 | 4 | 5 | 1 | 0 | 0 | 0.979459 |
| 2681 | [119.1, 39.6] | 4.100949 | 154.403626 | 0 | 3 | 4 | 5 | 1 | 0 | 0 | 0.979459 |
| 5237 | [118.7, 39.4] | 4.210111 | 143.294745 | 0 | 3 | 4 | 3 | 1 | 0 | 0 | 0.969388 |
| 7452 | [117.7, 37.8] | 4.766741 | 107.693813 | 0 | 3 | 2 | 5 | 1 | 0 | 0 | 0.967390 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 505 | [58.1, 28.1] | 63.155775 | 7.132821 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.004881 |
| 11160 | [61.8, 39.8] | 58.337636 | 7.863251 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.004881 |
| 3568 | [63.0, 30.5] | 57.920804 | 7.813054 | 0 | 3 | 4 | 5 | 0 | 1 | 0 | 0.004881 |
| 5084 | [87.5, 67.5] | 42.683234 | 8.235003 | 0 | 3 | 4 | 3 | 0 | 1 | 0 | 0.004615 |
| 6085 | [58.9, 43.2] | 61.313999 | 7.470866 | 0 | 3 | 4 | 5 | 0 | 2 | 0 | 0.004594 |
2592 rows × 11 columns
import statsbombpitch as sb
sb.sb_pitch("#195905","#faf0e6","horizontal","full")
plt.gca().invert_yaxis()
for i in range(len(sortxg)):
xe = sortxg.iloc[i]['location'][0]
ye = sortxg.iloc[i]['location'][1]
if sortxg.iloc[i]['xG'] >= 0.75:
g = plt.scatter(xe,ye,color="#ee3e32",edgecolors="none",zorder=10,alpha=1,s = 40 )
elif sortxg.iloc[i]['xG'] < 0.75 and sortxg.iloc[i]['xG'] >=0.5:
o = plt.scatter(xe,ye,color="#f68838",edgecolors="none",zorder=8,alpha=0.75,s = 30 )
elif sortxg.iloc[i]['xG'] < 0.5 and sortxg.iloc[i]['xG'] >=0.25:
a = plt.scatter(xe,ye,color="#fbb021",edgecolors="none",zorder=6,alpha=0.5,s = 20 )
else:
b = plt.scatter(xe,ye,color="#1b8a5a",edgecolors="none",zorder=4,alpha=0.25,s = 10 )
plt.axis('off')
plt.legend((g,o,a,b),('>=0.75','>=0.5','>=0.25','<0.25'),scatterpoints=1,loc=2,title = 'xG Value',fontsize='small', fancybox=True)
#plt.title('xG SGD model')
plt.savefig('xgXGBmodelFreezeFrame.png')
plt.show()import StatsbombPitch as sb
sb.sb_pitch("#195905","#faf0e6","vertical","half")
#plt.gca().invert_xaxis()
for i in range(len(sortxg)):
xe = sortxg.iloc[i]['location'][0]
ye = sortxg.iloc[i]['location'][1]
if sortxg.iloc[i]['xG'] >= 0.75:
g = plt.scatter(ye,xe,color="#ee3e32",edgecolors="none",zorder=10,alpha=1,s = 40 )
elif sortxg.iloc[i]['xG'] < 0.75 and sortxg.iloc[i]['xG'] >=0.5:
o = plt.scatter(ye,xe,color="#f68838",edgecolors="none",zorder=8,alpha=0.75,s = 30 )
elif sortxg.iloc[i]['xG'] < 0.5 and sortxg.iloc[i]['xG'] >=0.25:
a = plt.scatter(ye,xe,color="#fbb021",edgecolors="none",zorder=6,alpha=0.5,s = 20 )
else:
b = plt.scatter(ye,xe,color="#1b8a5a",edgecolors="none",zorder=4,alpha=0.25,s = 10 )
plt.axis('off')
plt.legend((g,o,a,b),('>=0.75','>=0.5','>=0.25','<0.25'),scatterpoints=1,loc=3,title = 'xG Value',fontsize='small', fancybox=True,edgecolor = 'black',framealpha = 2
)
#ax = plt.subplot()
#plt.savefig('MessiValverdeEraScatter.png')
plt.show()import StatsbombPitch as sb
sb.sb_pitch("#195905","#faf0e6","vertical","half")
#plt.gca().invert_xaxis()
for i in range(len(sortxg)):
xe = sortxg.iloc[i]['location'][0]
ye = sortxg.iloc[i]['location'][1]
if sortxg.iloc[i]['xG'] >= 0.75:
g = plt.scatter(ye,xe,color="#ee3e32",edgecolors="none",zorder=10,alpha=1,s = 40 )
elif sortxg.iloc[i]['xG'] < 0.75 and sortxg.iloc[i]['xG'] >=0.5:
o = plt.scatter(ye,xe,color="#f68838",edgecolors="none",zorder=8,alpha=0.75,s = 30 )
elif sortxg.iloc[i]['xG'] < 0.5 and sortxg.iloc[i]['xG'] >=0.25:
a = plt.scatter(ye,xe,color="#fbb021",edgecolors="none",zorder=6,alpha=0.5,s = 20 )
else:
b = plt.scatter(ye,xe,color="black",edgecolors="none",zorder=4,alpha=0.25,s = 10 )
plt.axis('off')
plt.legend((g,o,a,b),('>=0.75','>=0.5','>=0.25','<0.25'),scatterpoints=1,loc=3,title = 'xG Value',fontsize='small', fancybox=True,edgecolor = 'black',framealpha = 2
)
#ax = plt.subplot()
#plt.savefig('MessiValverdeEraScatter.png')
plt.show()#[['Distance','Angle','UnderPressure','ShotType','ShotBodyPart','ShotTechnique','ShotFirstTime','ShotOneonOne']]
xgb_model.predict_proba(pd.DataFrame([12.55,37.156,0,4,2,2,0,0]))[:,1][0;31m---------------------------------------------------------------------------[0m [0;31mValueError[0m Traceback (most recent call last) [0;32m<ipython-input-43-9dff4c73a403>[0m in [0;36m<module>[0;34m()[0m [0;32m----> 1[0;31m [0mxgb_model[0m[0;34m.[0m[0mpredict_proba[0m[0;34m([0m[0mpd[0m[0;34m.[0m[0mDataFrame[0m[0;34m([0m[0;34m[[0m[0;36m12.55[0m[0;34m,[0m[0;36m37.156[0m[0;34m,[0m[0;36m0[0m[0;34m,[0m[0;36m4[0m[0;34m,[0m[0;36m2[0m[0;34m,[0m[0;36m2[0m[0;34m,[0m[0;36m0[0m[0;34m,[0m[0;36m0[0m[0;34m][0m[0;34m)[0m[0;34m)[0m[0;34m[[0m[0;34m:[0m[0;34m,[0m[0;36m1[0m[0;34m][0m[0;34m[0m[0;34m[0m[0m [0m [0;32m/usr/local/lib/python3.6/dist-packages/xgboost/sklearn.py[0m in [0;36mpredict_proba[0;34m(self, data, ntree_limit, validate_features)[0m [1;32m 832[0m class_probs = self.get_booster().predict(test_dmatrix, [1;32m 833[0m [0mntree_limit[0m[0;34m=[0m[0mntree_limit[0m[0;34m,[0m[0;34m[0m[0;34m[0m[0m [0;32m--> 834[0;31m validate_features=validate_features) [0m[1;32m 835[0m [0;32mif[0m [0mself[0m[0;34m.[0m[0mobjective[0m [0;34m==[0m [0;34m"multi:softprob"[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [1;32m 836[0m [0;32mreturn[0m [0mclass_probs[0m[0;34m[0m[0;34m[0m[0m [0;32m/usr/local/lib/python3.6/dist-packages/xgboost/core.py[0m in [0;36mpredict[0;34m(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features)[0m [1;32m 1282[0m [0;34m[0m[0m [1;32m 1283[0m [0;32mif[0m [0mvalidate_features[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [0;32m-> 1284[0;31m [0mself[0m[0;34m.[0m[0m_validate_features[0m[0;34m([0m[0mdata[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0m[1;32m 1285[0m [0;34m[0m[0m [1;32m 1286[0m [0mlength[0m [0;34m=[0m [0mc_bst_ulong[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0;32m/usr/local/lib/python3.6/dist-packages/xgboost/core.py[0m in [0;36m_validate_features[0;34m(self, data)[0m [1;32m 1688[0m [0;34m[0m[0m [1;32m 1689[0m raise ValueError(msg.format(self.feature_names, [0;32m-> 1690[0;31m data.feature_names)) [0m[1;32m 1691[0m [0;34m[0m[0m [1;32m 1692[0m [0;32mdef[0m [0mget_split_value_histogram[0m[0;34m([0m[0mself[0m[0;34m,[0m [0mfeature[0m[0;34m,[0m [0mfmap[0m[0;34m=[0m[0;34m''[0m[0;34m,[0m [0mbins[0m[0;34m=[0m[0;32mNone[0m[0;34m,[0m [0mas_pandas[0m[0;34m=[0m[0;32mTrue[0m[0;34m)[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m [0;31mValueError[0m: feature_names mismatch: ['Distance', 'Angle', 'UnderPressure', 'ShotType', 'ShotBodyPart', 'ShotTechnique', 'ShotFirstTime', 'ShotOneonOne'] ['0'] expected ShotOneonOne, ShotBodyPart, ShotTechnique, ShotType, ShotFirstTime, Distance, UnderPressure, Angle in input data training data did not have the following fields: 0