Sportec

class Event_data(data_provider='sportec', event_path=event_path, tracking_path=tracking_path, meta_data=meta_path).load_data()

Load Sportec event, tracking, and meta data from XML files into a pandas DataFrame.

Parameters:

  • event_path (str) – Path to the event XML file.

  • tracking_path (str, optional) – Path to the tracking XML file. Defaults to None.

  • meta_path (str, optional) – Path to the meta XML file. Defaults to None.

Returns:

A DataFrame containing the combined event, tracking, and meta data.

Return type:

pd.DataFrame

Example usage:

import pandas as pd
from preprocessing import Event_data

event_path = 'path/to/event.xml'
tracking_path = 'path/to/tracking.xml'
meta_path = 'path/to/meta.xml'

# Load event data only
sportec_df = Event_data(data_provider='sportec', event_path=event_path).load_data()

# Load event data and merge with tracking and meta data
sportec_df = Event_data(data_provider='sportec', event_path=event_path, tracking_path=tracking_path, meta_data=meta_path).load_data()

print(sportec_df.head())

Details:

This function loads data from three types of XML files: event, tracking, and meta data. The event_path is mandatory, while tracking_path and meta_path are optional. The function processes these XML files and combines their data into a single pandas DataFrame through the following steps:

  1. Load XML files: Parse the event, tracking, and meta XML files.

  2. Create dictionaries for team and player information: - Extract team information from the meta XML file and store it in a dictionary. - Extract player information from the meta XML file and store it in a dictionary.

  3. Initialize an empty list to store event details.

  4. Iterate through each event: - Extract event details such as match ID, time, event type, outcome, team, player, start and end coordinates. - Replace team and player IDs with their names using the dictionaries created earlier. - If tracking data is provided, merge the event details with the corresponding tracking data based on the time.

  5. Define columns for the resulting DataFrame: - Basic columns for event data. - Additional columns for tracking data if provided.

  6. Convert the list of event details to a DataFrame and return it.

The returned DataFrame contains the following columns:

  • match_id: Identifier for the match.

  • time: Time of the event.

  • event_type: Primary type of the event.

  • event_type_2: Secondary type of the event, if available.

  • outcome: Outcome of the event.

  • team: Name of the team involved in the event.

  • player: Name of the player involved in the event.

  • start_x: X-coordinate of the event start position.

  • start_y: Y-coordinate of the event start position.

  • end_x: X-coordinate of the event end position.

  • end_y: Y-coordinate of the event end position.

If tracking data is provided, the DataFrame also includes:

  • h1_x, h1_y, h1_z, ..., h16_x, h16_y, h16_z: Coordinates for each player on the home team.

  • a1_x, a1_y, a1_z, ..., a16_x, a16_y, a16_z: Coordinates for each player on the away team.