DataStadium

class Tracking_data('soccer').process_tracking_data(game_id, data_path, event_data_name="play.csv", player_data_name="player.csv", tracking_data_name1="tracking_1stHalf.csv", tracking_data_name2="tracking_2ndHalf.csv") → tuple

Process and load tracking data for a specific game, including tracking data for home and away teams, as well as jersey numbers.

Parameters:

  • game_id (int) – Index of the game folder in the dataset to process.

  • data_path (str) – Path to the folder containing the game data.

  • event_data_name (str, optional) – Name of the event data file. Default is “play.csv”.

  • player_data_name (str, optional) – Name of the player data file. Default is “player.csv”.

  • tracking_data_name1 (str, optional) – Name of the first-half tracking data file. Default is “tracking_1stHalf.csv”.

  • tracking_data_name2 (str, optional) – Name of the second-half tracking data file. Default is “tracking_2ndHalf.csv”.

Returns:

Tuple containing processed tracking data for the home and away teams, and a DataFrame with jersey numbers of players.

Return type:

tuple - tracking_home (pd.DataFrame): Processed tracking data for the home team. - tracking_away (pd.DataFrame): Processed tracking data for the away team. - jerseynum_df (pd.DataFrame): DataFrame containing jersey numbers of players.

Example for single match

import pandas as pd
from preprocessing import Tracking_data

data_path = 'path/to/data/folder'
game_id = 0

tracking_home, tracking_away, jerseynum_df = Tracking_data('soccer').process_datadium_tracking_data(game_id,data_path)

print(tracking_home.head())
print(tracking_away.head())
print(jerseynum_df.head())

Example for multiple matches

import pandas as pd
from preprocessing import Tracking_data

data_path = 'path/to/data/folder'

for game_id in range(len(os.listdir(data_path))):
    tracking_home, tracking_away, jerseynum_df = Tracking_data('soccer').process_datadium_tracking_data(game_id,data_path)
    print(tracking_home.head())
    print(tracking_away.head())
    print(jerseynum_df.head())

Details

This function performs the following steps:

  1. Loads the event, player, and tracking data from the specified CSV files.

  2. Sorts the game data by date and processes the event and player data.

  3. Splits the tracking data into home and away team data for both halves of the game.

  4. Extracts unique player jersey numbers for both home and away teams.

  5. Prepares the tracking data for both teams by calculating frame information and periods.

  6. Aligns the tracking data with the corresponding game period and time.

  7. Iterates over the tracking data to fill in player and ball positions at each frame.

  8. Returns the processed tracking data for the home and away teams, as well as a DataFrame with the jersey numbers.

The returned tuple includes:

  • tracking_home: DataFrame with processed tracking data for the home team, including columns like Home_1_x, Home_1_y, etc.

  • tracking_away: DataFrame with processed tracking data for the away team, including columns like Away_1_x, Away_1_y, etc.

  • jerseynum_df: DataFrame with player jersey numbers for both teams, including columns like Player_ID, Team, Jersey_Number.