def preprocessing_tracking(tracking_df):
"""
Preprocesses the tracking data frame, by changing the time values of the second have,
splitting the DataFrame up in a home and away DataFrame. Changes the tracking data,
so that each team plays in one direction the entire game and calculates the velocities of the players.
Parameters
-----------
tracking_df: tracking DataFrame
Returns
-----------
tracking_home: tracking DataFrame for the Home team
tracking_away: tracking DataFrame for the Away team
"""
# change time values and split the tracking data
tracking_df = change_time_values_tracking(tracking_df)
tracking_home, tracking_away = split_tracking_data(tracking_df)
# reverse direction of play in the second half so that home team is always attacking from right->left
tracking_home = change_to_single_playing_direction(tracking_home)
tracking_away = change_to_single_playing_direction(tracking_away)
# calculate the player velocities
tracking_home = mvel.calc_player_velocities(tracking_home, smoothing=True)
tracking_away = mvel.calc_player_velocities(tracking_away, smoothing=True)
return tracking_home, tracking_away
def populate_tracking_dataframes():
tracking_home = mio.tracking_data(DATA_DIR, MATCH_ID, 'Home')
tracking_away = mio.tracking_data(DATA_DIR, MATCH_ID, 'Away')
events = mio.read_event_data(DATA_DIR, MATCH_ID)
tracking_home = mio.to_metric_coordinates(tracking_home)
tracking_away = mio.to_metric_coordinates(tracking_away)
events = mio.to_metric_coordinates(events)
tracking_home = mvel.calc_player_velocities(tracking_home, smoothing=True)
tracking_away = mvel.calc_player_velocities(tracking_away, smoothing=True)
return mio.to_single_playing_direction(tracking_home, tracking_away, events)
events = mio.read_event_data(DATADIR, game_id)
# Using Metricas function to prep tracking_data that will allow us to find: single playing direction and passing probabilities towards the end.
tracking_home = mio.tracking_data(DATADIR, game_id, 'Home')
tracking_away = mio.tracking_data(DATADIR, game_id, 'Away')
tracking_home = mio.to_metric_coordinates(tracking_home)
tracking_away = mio.to_metric_coordinates(tracking_away)
events = mio.to_metric_coordinates(events)
tracking_home, tracking_away, events = mio.to_single_playing_direction(
tracking_home, tracking_away, events)
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')
shots_map = events[(events['Type'] == 'SHOT') & (events['Team'] == 'Home')]
mviz.plot_events(goals_map, color='k', indicators=['Marker'], annotate=True)
# Defenders in front of ball for Home team shots
defenders = []
for i in shots_map.index:
ball = tracking_away.loc[i, 'ball_x']
players_count = [
player for player in tracking_away.columns
# region Laurie's code
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)
params = mpc.default_model_params(3)
# endregion
# Get GK numbers
GK_numbers = [
mio.find_goalkeeper(tracking_home),
mio.find_goalkeeper(tracking_away)
]