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Tutorial3_PitchControl.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 11:34:26 2020
Script for lesson 6 of "Friends of Tracking" #FoT
Data can be found at: https://github.com/metrica-sports/sample-data
Accompanying video tutorials can be found here: https://www.youtube.com/watch?v=5X1cSehLg6s
GitHub repo: https://github.com/Friends-of-Tracking-Data-FoTD/LaurieOnTracking
@author: Laurie Shaw (@EightyFivePoint)
"""
import Metrica_IO as mio
import Metrica_Viz as mviz
import Metrica_Velocities as mvel
import Metrica_PitchControl as mpc
import numpy as np
# set up initial path to data
DATADIR = '/PATH/TO/WHERE/YOU/SAVED/THE/SAMPLE/DATA'
DATADIR = '/Users/laurieshaw/Documents/Football/Data/TrackingData/Metrica/sample-data-master/data'
game_id = 2 # let's look at sample match 2
# read in the event data
events = mio.read_event_data(DATADIR,game_id)
# read in tracking data
tracking_home = mio.tracking_data(DATADIR,game_id,'Home')
tracking_away = mio.tracking_data(DATADIR,game_id,'Away')
# Convert positions from metrica units to meters (note change in Metrica's coordinate system since the last lesson)
tracking_home = mio.to_metric_coordinates(tracking_home)
tracking_away = mio.to_metric_coordinates(tracking_away)
events = mio.to_metric_coordinates(events)
# reverse direction of play in the second half so that home team is always attacking from right->left
tracking_home,tracking_away,events = mio.to_single_playing_direction(tracking_home,tracking_away,events)
# Calculate player velocities
tracking_home = mvel.calc_player_velocities(tracking_home,smoothing=True)
tracking_away = mvel.calc_player_velocities(tracking_away,smoothing=True)
# **** NOTE *****
# if the lines above produce an error (happens for one version of numpy) change them to the lines below:
# ***************
#tracking_home = mvel.calc_player_velocities(tracking_home,smoothing=True,filter_='moving_average')
#tracking_away = mvel.calc_player_velocities(tracking_away,smoothing=True,filter_='moving_average')
""" **** pitch control for passes leading up to goal 2 **** """
# get all shots and goals in the match
shots = events[events['Type']=='SHOT']
goals = shots[shots['Subtype'].str.contains('-GOAL')].copy()
print(goals)
# plot the 3 events leading up to the second goal
mviz.plot_events( events.loc[820:823], color='k', indicators = ['Marker','Arrow'], annotate=True )
# first get pitch control model parameters
params = mpc.default_model_params()
# find goalkeepers for offside calculation
GK_numbers = [mio.find_goalkeeper(tracking_home),mio.find_goalkeeper(tracking_away)]
# evaluated pitch control surface for first pass
PPCF,xgrid,ygrid = mpc.generate_pitch_control_for_event(820, events, tracking_home, tracking_away, params, GK_numbers, field_dimen = (106.,68.,), n_grid_cells_x = 50)
mviz.plot_pitchcontrol_for_event( 820, events, tracking_home, tracking_away, PPCF, annotate=True )
# evaluated pitch control surface for second pass
PPCF,xgrid,ygrid = mpc.generate_pitch_control_for_event(821, events, tracking_home, tracking_away, params, GK_numbers, field_dimen = (106.,68.,), n_grid_cells_x = 50)
mviz.plot_pitchcontrol_for_event( 821, events, tracking_home, tracking_away, PPCF, annotate=True )
# evaluated pitch control surface for third pass
PPCF,xgrid,ygrid = mpc.generate_pitch_control_for_event(822, events, tracking_home, tracking_away, params, GK_numbers, field_dimen = (106.,68.,), n_grid_cells_x = 50)
mviz.plot_pitchcontrol_for_event( 822, events, tracking_home, tracking_away, PPCF, annotate=True )
""" **** calculate pass probability for every home team succesful pass **** """
# get all home passes
home_passes = events[ (events['Type'].isin(['PASS'])) & (events['Team']=='Home') ]
# list for storing pass probablities
pass_success_probability = []
for i,row in home_passes.iterrows():
pass_start_pos = np.array([row['Start X'],row['Start Y']])
pass_target_pos = np.array([row['End X'],row['End Y']])
pass_frame = row['Start Frame']
attacking_players = mpc.initialise_players(tracking_home.loc[pass_frame],'Home',params, GK_numbers[0])
defending_players = mpc.initialise_players(tracking_away.loc[pass_frame],'Away',params, GK_numbers[1])
Patt,Pdef = mpc.calculate_pitch_control_at_target(pass_target_pos, attacking_players, defending_players, pass_start_pos, params)
pass_success_probability.append( (i,Patt) )
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
ax.hist( [p[1] for p in pass_success_probability], np.arange(0,1.1,0.1))
ax.set_xlabel('Pass success probability')
ax.set_ylabel('Frequency')
# sort the passes by pitch control probability
pass_success_probability = sorted( pass_success_probability, key = lambda x: x[1] )
# identify the events corresponding to the most risky passes (pitch control < 0.5)
risky_passes = events.loc[ [p[0] for p in pass_success_probability if p[1]<0.5 ] ]
# plot the events
mviz.plot_events( risky_passes, color='k', indicators = ['Marker','Arrow'], annotate=True )
# Print events that followed those risky passes
print("Event following a risky (completed) pass")
for p in pass_success_probability[:20]:
outcome = events.loc[ p[0]+1 ].Type
print( p[1], outcome )