3. pandas -- module to work with dataframes.
4. FCPython -- module to create football pitch map
"""
import matplotlib.pyplot as plt
import json
from pandas.io.json import json_normalize
from FCPython import createPitch
## Note Statsbomb data uses yards for their pitch dimensions
pitch_length_X = 120
pitch_width_Y = 80
## calling the function to create a pitch map
## yards is the unit for measurement and
## gray will be the line color of the pitch map
(fig, ax) = createPitch(pitch_length_X, pitch_width_Y, 'yards', 'gray')
## match id for our El Clasico
match_id = 69249
home_team = 'Real Madrid'
away_team = 'Barcelona'
player_name = 'Xavier Hernández Creus'
## this is the name of our event data file for
## our required El Clasico
file_name = str(match_id) + '.json'
## loading the required event data file
my_data = json.load(
open('../Statsbomb/data/events/' + file_name, 'r', encoding='utf-8'))
pitchLengthX = 120
pitchWidthY = 80
OldRange = 1.0 - 0.0
NewRange_X = 120 - 0
NewRange_Y = 80 - 0
shots['X'] = shots['X'].apply(lambda x:
(((x - 0.0) * NewRange_X) / OldRange) + 0.0)
shots['Y'] = shots['Y'].apply(lambda x:
(((x - 0.0) * NewRange_Y) / OldRange) + 0.0)
#print(shots['result'].unique())
#Plotting two plots for goals scored and shots that were not goals
(plt1, fig1, ax1) = createPitch(pitchLengthX, pitchWidthY, 'yards', 'black')
(plt2, fig2, ax2) = createPitch(pitchLengthX, pitchWidthY, 'yards', 'black')
for i, shot in shots.iterrows():
x = shot['X']
y = shot['Y']
#circleSize = 1.5
circleSize = np.sqrt(shot['xG']) * 2
if shot['result'] == 'ShotOnPost':
shotCircle = plt.Circle((x, y), circleSize, color="cyan")
ax1.add_patch(shotCircle)
if shot['result'] == 'Goal':
if shot['shotType'] == 'Head':
shotCircle = plt.Circle((x, y), circleSize, color="green")
with open('/Users/lindelwemoyo/SoccermaticsForPython/Statsbomb/data/events/' +
file_name) as data_file:
#print (mypath+'events/'+file)
data = json.load(data_file)
#get nested structure into a dataframe
#store the dataframe in a dictionary with the match id as key (remove .json from string)
from pandas.io.json import json_normalize
df = json_normalize(data, sep="_").assign(match_id=file_name[:-5])
#A dataframe of shots
shots = df.loc[df['type_name'] == 'Shot'].set_index('id')
#Draw the pitch
from FCPython import createPitch
(fig, ax) = createPitch(pitchLengthX, pitchWidthY, 'yards', 'gray')
#PlotTheShots
for i, shot in shots.iterrows():
x = shot['location'][0]
y = shot['location'][1]
goal = shot['shot_outcome_name'] == 'Goal'
team_name = shot['team_name']
circleSize = 2
circleSize = np.sqrt(shot['shot_statsbomb_xg'] * 15)
if (team_name == home_team_required):
if goal:
shotCircle = plt.Circle((x, pitchWidthY - y),
data = json.load(data_file)
#get the nested structure into a dataframe
#store the dataframe in a dictionary with the match id as key (remove '.json' from string)
from pandas.io.json import json_normalize
df = json_normalize(data, sep="_").assign(match_id=file_name[:-5])
#A dataframe of shots
shots = df.loc[df['type_name'] == 'Shot'].set_index('id')
#A dataframe of passes
#passes = df.loc[df['type_name'] == 'Pass'].set_index('id')
#Draw the pitch
from FCPython import createPitch
(fig, ax) = createPitch(pitchLengthX, pitchWidthY, 'yards', 'black')
#Plot the shots
for i, shot in shots.iterrows():
x = shot['location'][0]
y = shot['location'][1]
goal = shot['shot_outcome_name'] == 'Goal'
team_name = shot['team_name']
#draw arrows on the pass to show direction
destinationx = shot['shot_end_location'][0] - x
destinationy = shot['shot_end_location'][1] - y
circleSize = 1
