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plotting_xGmap_(17-18)_players.py
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plotting_xGmap_(17-18)_players.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
from scipy.misc import imread
from datetime import datetime
np.seterr(over='ignore', under='ignore')
def sigmoid(x):
return 1/(1+np.exp(-x))
def myprob(distance, angle):
x = distance*np.power((angle+1), 0.5)
result = 1.0646383882981121*np.exp(-0.0247111*x)
return result
def nn_prob(shot_data):
theta1 = np.load('./Neural Net/xG_theta1.npy')
theta2 = np.load('./Neural Net/xG_theta2.npy')
X_cv = shot_data.as_matrix(columns=['x', 'y', 'Header',
'Distance', 'Angle'])
X_cv = np.c_[np.ones(X_cv.shape[0]), X_cv]
m = X_cv.shape[0]
a_1 = X_cv.T
z_2 = np.dot(theta1, a_1)
a_2 = sigmoid(z_2)
# WILL ADD A BIAS UNIT SOON
z_3 = np.dot(theta2, a_2)
a_3 = sigmoid(z_3)
h_theta = a_3
return h_theta.T
def tidy_and_format_data(shot_data):
'''
takes shot data, adds distance, angle, colour and a probability
:param shot_data:
:return:
'''
for index, row in shot_data.iterrows():
if row['x'] > 240:
shot_data.set_value(index, 'x', 480 - row['x'])
shot_data['y'] = shot_data['y'] - 366/2
shot_data['y'] = shot_data['y'] * -1
# calculate the angle and the distance from the goal
shot_data['Angle'] = np.arctan((np.absolute(shot_data['y'])/shot_data['x']))
shot_data['Distance'] = np.sqrt(shot_data['y']*shot_data['y'] + shot_data['x']*shot_data['x'])
# assign colours and numbers based on whether the shots were scored or missed
for index, row in shot_data.iterrows():
if row['Scored'] == 'Scored':
shot_data.set_value(index, 'Colour', 'magenta')
shot_data.set_value(index, 'ScoredBinary', 1)
else:
shot_data.set_value(index, 'Colour', 'white')
shot_data.set_value(index, 'ScoredBinary', 0)
shot_data['Proba_exp'] = nn_prob(shot_data)
return shot_data
def flip_y_values_if_away_team(football_data, shot_data):
'''
IF the shot was taken when the player was playing for the away side then
the y value needs to be flipped now that all shots are being plotted on one side of the pitch
:param football_data:
:param shot_data:
:return:
'''
for index_sd, row_sd in shot_data.iterrows():
for index_fd, row_fd in football_data.iterrows():
if row_sd['Date'] == row_fd['Date'] and row_sd['Team'] == row_fd['AwayTeam']:
shot_data.set_value(index_sd, 'y', row_sd['y']*-1)
return shot_data
data = pd.read_csv('./All shots from 17-18/E0/shots.csv', index_col=0)
football_data = pd.read_csv('./Football-data.co.uk/E0/17-18.csv')
football_data['Date'] = pd.to_datetime(football_data['Date'], format='%d/%m/%y')
data['Date'] = pd.to_datetime(data['Date'], format='%d-%m-%Y')
mappings = pd.read_csv('./All shots from 17-18/E0/mappings.csv', index_col=1, header=None)
data.replace(mappings[0], inplace=True)
img = imread('./xG Plots/background3.jpg')
img = np.swapaxes(img, 0, 1)
data = tidy_and_format_data(data) # add xG values to each shot and a colour etc
print(data.groupby(['PlayerID', 'PlayerName'])[['Proba_exp', 'ScoredBinary']].sum().sort_values('ScoredBinary', ascending=False).head(20))
player_id = input('Enter player ID: ')
player_id = int(player_id)
print(data[data.PlayerID == player_id]['PlayerName'].head(1))
player_name = input('Enter player name: ')
data = data[data["PlayerID"] == player_id]
team = data.iloc[0]['Team']
try:
goals = data['Scored'].value_counts()['Scored']
except KeyError:
goals = 0
xG = np.round(sum(data['Proba_exp']), 2)
data = flip_y_values_if_away_team(football_data, data)
data.sort_values(by=['Scored'], ascending=True, inplace=True)
mean_xG = xG/len(data)
fig, ax = plt.subplots()
shots = ax.scatter(data[(data.Header == 0) & (data.Scored == 'Missed')]['y'],
data[(data.Header == 0) & (data.Scored == 'Missed')]['x'], marker="o",
s=data[(data.Header == 0) & (data.Scored == 'Missed')]['Proba_exp']*800,
facecolors=data[(data.Header == 0) & (data.Scored == 'Missed')]['Colour'],
edgecolors='black', linewidth=0.6, label='Shots', alpha=0.6)
headers = ax.scatter(data[(data.Header == 1) & (data.Scored == 'Missed')]['y'],
data[(data.Header == 1) & (data.Scored == 'Missed')]['x'], marker="^",
s=data[(data.Header == 1) & (data.Scored == 'Missed')]['Proba_exp']*800,
facecolors=data[(data.Header == 1) & (data.Scored == 'Missed')]['Colour'],
edgecolors='black', linewidth=0.6, label='Headers', alpha=0.6)
shots = ax.scatter(data[(data.Header == 0) & (data.Scored == 'Scored')]['y'],
data[(data.Header == 0) & (data.Scored == 'Scored')]['x'], marker="o",
s=data[(data.Header == 0) & (data.Scored == 'Scored')]['Proba_exp']*800,
facecolors=data[(data.Header == 0) & (data.Scored == 'Scored')]['Colour'],
edgecolors='black', linewidth=0.6, label='Shots', alpha=1.0)
headers = ax.scatter(data[(data.Header == 1) & (data.Scored == 'Scored')]['y'],
data[(data.Header == 1) & (data.Scored == 'Scored')]['x'], marker="^",
s=data[(data.Header == 1) & (data.Scored == 'Scored')]['Proba_exp']*800,
facecolors=data[(data.Header == 1) & (data.Scored == 'Scored')]['Colour'],
edgecolors='black', linewidth=0.6, label='Headers', alpha=1.0)
plt.xlim(-366/2, 366/2)
plt.ylim(-10, 250)
plt.imshow(img, zorder=0, extent=[-366/2, 366/2, -10, 490])
text = plt.text(-165, 192, player_name + '\n' + team + '\n' + 'Goals: ' + str(goals) + '\n' + 'xG: ' + str(xG)
+ '\n' + 'Mean xG: ' + str(np.round(mean_xG, 2)),
horizontalalignment='left',
color='white', alpha=0.8, fontsize=10, fontweight='bold')
text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='black'),
path_effects.Normal()])
plt.legend(handles=[headers], loc=3)
plt.subplots_adjust(left=0.01, bottom=0.01, right=0.99, top=0.99,
wspace=None, hspace=None)
plt.axis('off')
fig.savefig('./xG Plots/Players/' + player_name + ' ' + datetime.today().strftime("%Y-%m-%d") + '.png',
bbox_inches=0, pad_inches=0)