sportowe_wizualizacja/shot_mapping.ipynb

import pandas as pd
import numpy as np
from pandas.io.json import json_normalize #package for flattening json in pandas df

df = pd.read_json(r"C:\Users\Koushik\Downloads\open-data-master\open-data-master\data\events\7298.json")
df.head()

	bad_behaviour	ball_receipt	ball_recovery	block	dribble	duel	duration	foul_committed	foul_won	goalkeeper	...	possession_team	related_events	second	shot	substitution	tactics	team	timestamp	type	under_pressure
0	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	...	{'id': 746, 'name': 'Manchester City WFC'}	NaN	0	NaN	NaN	{'formation': 433, 'lineup': [{'player': {'id'...	{'id': 746, 'name': 'Manchester City WFC'}	2018-11-30 00:00:00.000	{'id': 35, 'name': 'Starting XI'}	NaN
1	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	...	{'id': 746, 'name': 'Manchester City WFC'}	NaN	0	NaN	NaN	{'formation': 352, 'lineup': [{'player': {'id'...	{'id': 971, 'name': 'Chelsea LFC'}	2018-11-30 00:00:00.000	{'id': 35, 'name': 'Starting XI'}	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	8.16	NaN	NaN	NaN	...	{'id': 746, 'name': 'Manchester City WFC'}	[c771a4d4-51cb-41de-83aa-7103cd199c92]	0	NaN	NaN	NaN	{'id': 971, 'name': 'Chelsea LFC'}	2018-11-30 00:00:00.000	{'id': 18, 'name': 'Half Start'}	NaN
3	NaN	NaN	NaN	NaN	NaN	NaN	7.96	NaN	NaN	NaN	...	{'id': 746, 'name': 'Manchester City WFC'}	[48b94b06-ebbd-47e9-958c-44bf63622f5e]	0	NaN	NaN	NaN	{'id': 746, 'name': 'Manchester City WFC'}	2018-11-30 00:00:00.000	{'id': 18, 'name': 'Half Start'}	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	...	{'id': 971, 'name': 'Chelsea LFC'}	[237cac8c-5cb0-4015-9d08-c7df9699a136]	0	NaN	NaN	NaN	{'id': 971, 'name': 'Chelsea LFC'}	2018-11-30 00:00:00.100	{'id': 30, 'name': 'Pass'}	NaN

5 rows × 33 columns

shot = df[(df.possession_team =={'id': 971, 'name': 'Chelsea LFC'}) & (df.type == {'id': 16, 'name': 'Shot'})] 
#change possession_team to get shots by a different team

df_2 = pd.DataFrame(shot.location)
df_2[['X_axis','Y_axis']] = pd.DataFrame(shot.location.values.tolist(), index= shot.location.index) #df_2 has the x,y coordinate

df_2

	location	X_axis	Y_axis
33	[115.0, 25.0]	115.0	25.0
186	[109.0, 51.0]	109.0	51.0
192	[99.0, 52.0]	99.0	52.0
196	[107.0, 40.0]	107.0	40.0
204	[108.0, 32.0]	108.0	32.0
583	[108.0, 32.0]	108.0	32.0
695	[87.0, 41.0]	87.0	41.0
749	[108.0, 36.0]	108.0	36.0
765	[105.0, 43.0]	105.0	43.0
1060	[112.0, 39.0]	112.0	39.0
1176	[115.0, 54.0]	115.0	54.0
1179	[102.0, 34.0]	102.0	34.0
1291	[108.0, 47.0]	108.0	47.0
1400	[94.0, 54.0]	94.0	54.0
1486	[108.0, 27.0]	108.0	27.0
1622	[114.0, 34.0]	114.0	34.0
1666	[109.0, 39.0]	109.0	39.0
1828	[117.0, 31.0]	117.0	31.0
2136	[91.0, 52.0]	91.0	52.0
2240	[118.0, 39.0]	118.0	39.0
2325	[111.0, 32.0]	111.0	32.0
2692	[107.0, 47.0]	107.0	47.0
2695	[110.0, 36.0]	110.0	36.0
2813	[113.0, 42.0]	113.0	42.0
2820	[109.0, 52.0]	109.0	52.0

x_axis = df_2.X_axis.values.tolist()
y_axis = df_2.Y_axis.values.tolist()

import matplotlib.pyplot as plt
from scipy.misc import imread
import matplotlib.cbook as cbook

img = plt.imread(r'C:\Users\Koushik\Downloads\football_field.png')
fig, ax = plt.subplots()

ax.imshow(img, extent=[0, 120, 0, 80])
ax.plot(x_axis,y_axis,'ro')

[<matplotlib.lines.Line2D at 0x1fb7567e630>]