213 KiB
213 KiB
!git clone https://github.com/statsbomb/open-data.gitCloning into 'open-data'... remote: Enumerating objects: 760, done.[K remote: Counting objects: 100% (760/760), done.[K remote: Compressing objects: 100% (367/367), done.[K remote: Total 9482 (delta 640), reused 504 (delta 384), pack-reused 8722[K Receiving objects: 100% (9482/9482), 991.55 MiB | 26.66 MiB/s, done. Resolving deltas: 100% (8387/8387), done. Checking out files: 100% (1648/1648), done.
#5503
#change this cell to change season and player name
season = '2009/2010'
ssn = '5503'
length = int(20)import os
import json
from pandas.io.json import json_normalize
import codecs
import pandas as pd
import codecs
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(r'/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 = json_normalize(data, sep = "_")
#for x in df.competition_country_name:
# if x == 'Spain':
# print(df.match_id)
for i in range(len(df)):
if df.iloc[i]['competition_country_name'] == 'Spain' and df.iloc[i]['season_season_name'] == season :
match_no = df.iloc[i]['match_id'] #gets match with Spain as country
match_no = str(match_no) # from int to str
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 = json_normalize(df_match,sep="_")
main_df = main_df.append(df_match2,ignore_index=True,sort=False)
print('Done')Done
messi_df = main_df.query('type_id == 30 & player_id == 5503')messi_df = messi_df[['location','pass_end_location','pass_height_name','pass_type_name','pass_body_part_name']]messi_df.head()| location | pass_end_location | pass_height_name | pass_type_name | pass_body_part_name | |
|---|---|---|---|---|---|
| 29 | [29.5, 55.8] | [33.1, 48.1] | Low Pass | NaN | Left Foot |
| 99 | [64.7, 72.8] | [57.2, 74.5] | Ground Pass | NaN | Left Foot |
| 111 | [64.4, 73.7] | [54.1, 42.1] | Ground Pass | NaN | Left Foot |
| 140 | [80.6, 33.0] | [73.3, 43.8] | Ground Pass | NaN | Right Foot |
| 155 | [97.9, 46.4] | [115.5, 53.8] | Ground Pass | NaN | Left Foot |
def draw_pitch(ax):
# focus on only half of the pitch
#Pitch Outline & Centre Line
Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)
#Left, Right Penalty Area and midline
LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)
RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)
midline = ConnectionPatch([60,0], [60,80], "data", "data")
#Left, Right 6-yard Box
LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)
RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)
#Prepare Circles
centreCircle = plt.Circle((60,40),8.1, fill = False)
centreSpot = plt.Circle((60,40),0.71)
#Penalty spots and Arcs around penalty boxes
leftPenSpot = plt.Circle((9.7,40),0.71)
rightPenSpot = plt.Circle((110.3,40),0.71)
leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50)
rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230)
element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle,
centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]
for i in element:
ax.add_patch(i)import matplotlib.pyplot as plt
from matplotlib.patches import Arc, Rectangle, ConnectionPatch
from matplotlib.offsetbox import OffsetImage
fig=plt.figure() #set up the figures
fig.set_size_inches(7, 5)
ax=fig.add_subplot(1,1,1)
draw_pitch(ax) #overlay our different objects on the pitch
plt.ylim(-2, 82)
plt.xlim(-2, 122)
#plt.plot(x_axis,y_axis,'ro')
#plt.plot(x,y,'bo')
#plt.axis('off')
for i in range(len(messi_df)):
# annotate draw an arrow from a current position to pass_end_location
ax.annotate("", xy = (messi_df.iloc[i]['pass_end_location'][0], messi_df.iloc[i]['pass_end_location'][1]), xycoords = 'data',
xytext = (messi_df.iloc[i]['location'][0], messi_df.iloc[i]['location'][1]), textcoords = 'data',
arrowprops=dict(arrowstyle="->",connectionstyle="arc3", color = "blue"),)
"""
for i in range(len(assist)):
# annotate draw an arrow from a current position to pass_end_location
ax.annotate("", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',
xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',
arrowprops=dict(arrowstyle="->",connectionstyle="arc3", color = "red"),)
"""
plt.show()messi_df[['X','Y']] = pd.DataFrame(messi_df.location.values.tolist(), index= messi_df.index)
messi_df[['endX','endY']] = pd.DataFrame(messi_df.pass_end_location.values.tolist(), index= messi_df.index)messi_df| location | pass_end_location | pass_height_name | pass_type_name | pass_body_part_name | X | Y | endX | endY | |
|---|---|---|---|---|---|---|---|---|---|
| 29 | [29.5, 55.8] | [33.1, 48.1] | Low Pass | NaN | Left Foot | 29.5 | 55.8 | 33.1 | 48.1 |
| 99 | [64.7, 72.8] | [57.2, 74.5] | Ground Pass | NaN | Left Foot | 64.7 | 72.8 | 57.2 | 74.5 |
| 111 | [64.4, 73.7] | [54.1, 42.1] | Ground Pass | NaN | Left Foot | 64.4 | 73.7 | 54.1 | 42.1 |
| 140 | [80.6, 33.0] | [73.3, 43.8] | Ground Pass | NaN | Right Foot | 80.6 | 33.0 | 73.3 | 43.8 |
| 155 | [97.9, 46.4] | [115.5, 53.8] | Ground Pass | NaN | Left Foot | 97.9 | 46.4 | 115.5 | 53.8 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 127962 | [69.9, 56.5] | [75.1, 73.7] | Ground Pass | NaN | Left Foot | 69.9 | 56.5 | 75.1 | 73.7 |
| 127969 | [85.4, 60.7] | [90.3, 62.4] | Ground Pass | NaN | Right Foot | 85.4 | 60.7 | 90.3 | 62.4 |
| 128096 | [75.3, 47.8] | [93.0, 45.7] | Ground Pass | NaN | Right Foot | 75.3 | 47.8 | 93.0 | 45.7 |
| 128221 | [87.9, 26.6] | [81.4, 37.8] | Ground Pass | NaN | Left Foot | 87.9 | 26.6 | 81.4 | 37.8 |
| 128301 | [83.7, 43.2] | [91.3, 64.7] | Ground Pass | NaN | Left Foot | 83.7 | 43.2 | 91.3 | 64.7 |
1642 rows × 9 columns
from sklearn.cluster import KMeans
import numpy as npnp.random.seed(42)# Create training data for clustering
training_data = []
training_data = messi_df[['X','Y','endX','endY']].to_numpy()cluster_model = KMeans(n_clusters=25).fit(training_data)cluster_df = pd.DataFrame(data=cluster_model.cluster_centers_,columns= ['X','Y','endX','endY'])cluster_df.head()| X | Y | endX | endY | |
|---|---|---|---|---|
| 0 | 69.526437 | 73.794253 | 61.916092 | 70.040230 |
| 1 | 91.892045 | 33.001136 | 104.770455 | 24.712500 |
| 2 | 56.681333 | 40.832000 | 50.676000 | 40.288000 |
| 3 | 86.620000 | 48.557778 | 96.874444 | 34.186667 |
| 4 | 78.813235 | 36.172059 | 83.302941 | 49.801471 |
import matplotlib.pyplot as plt
from matplotlib.patches import Arc, Rectangle, ConnectionPatch
from matplotlib.offsetbox import OffsetImage
plt.style.use('classic')
fig=plt.figure() #set up the figures
fig.set_size_inches(16, 9)
ax=fig.add_subplot(1,1,1)
draw_pitch(ax) #overlay our different objects on the pitch
plt.ylim(-2, 82)
plt.xlim(-2, 122)
plt.axis('off')
y_cary_end = 0
y_loc = 0
ax.set_facecolor("grey")
for i in range(len(cluster_df)):
# annotate draw an arrow from a current position to pass_end_location
ax.annotate("", xy = (cluster_df.iloc[i]['endX'], cluster_df.iloc[i]['endY']), xycoords = 'data',
xytext = (cluster_df.iloc[i].X, cluster_df.iloc[i].Y), textcoords = 'data',
arrowprops=dict(arrowstyle="->",connectionstyle="arc3", color = "seagreen"),)
"""
for i in range(len(assist)):
# annotate draw an arrow from a current position to pass_end_location
ax.annotate("", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',
xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',
arrowprops=dict(arrowstyle="fancy",connectionstyle="arc3", color = "red"),)
"""
plt.show()