Introduction to Tracking Data

Tracking data captures the x,y coordinates of every player and the ball at high frequency (typically 10-25 Hz), enabling sophisticated spatial and physical analysis of soccer matches. ## What is Tracking Data? Unlike event data which records discrete actions, tracking data provides continuous positional information: - Player positions at 10-25 frames per second - Ball position and possession status - Player identifiers and team assignments - Synchronized with match clock ## Data Structure Tracking data typically comes in CSV or JSON format: ```python import pandas as pd # Example tracking data structure tracking_df = pd.DataFrame({ 'frame': [1, 1, 1, 2, 2, 2], 'timestamp': [0.0, 0.0, 0.0, 0.04, 0.04, 0.04], 'player_id': ['P1', 'P2', 'Ball', 'P1', 'P2', 'Ball'], 'x': [52.3, 48.1, 60.2, 52.4, 48.3, 60.5], 'y': [34.2, 28.5, 35.1, 34.3, 28.6, 35.2], 'team': ['Home', 'Away', None, 'Home', 'Away', None] }) ``` ## Basic Analysis: Calculating Speed ```python def calculate_player_speed(tracking_data, player_id): player_data = tracking_data[tracking_data['player_id'] == player_id].copy() # Calculate distance between frames player_data['dx'] = player_data['x'].diff() player_data['dy'] = player_data['y'].diff() player_data['distance'] = np.sqrt(player_data['dx']**2 + player_data['dy']**2) # Calculate speed (distance per second) player_data['dt'] = player_data['timestamp'].diff() player_data['speed'] = player_data['distance'] / player_data['dt'] return player_data ``` ## Pitch Control Models Tracking data enables pitch control analysis, showing which team controls each area: ```python def calculate_pitch_control(frame_data, sigma=10): # Simplified pitch control using Gaussian influence x_grid, y_grid = np.meshgrid(np.linspace(0, 105, 50), np.linspace(0, 68, 32)) home_control = np.zeros_like(x_grid) away_control = np.zeros_like(x_grid) for _, player in frame_data.iterrows(): if player['team'] == 'Home': home_control += np.exp(-((x_grid - player['x'])**2 + (y_grid - player['y'])**2) / sigma**2) elif player['team'] == 'Away': away_control += np.exp(-((x_grid - player['x'])**2 + (y_grid - player['y'])**2) / sigma**2) pitch_control = home_control / (home_control + away_control) return pitch_control ``` ## Physical Performance Metrics Tracking data enables detailed physical analysis: - Total distance covered - High-speed running distance - Sprint counts and distances - Acceleration and deceleration events - Heat maps and position distributions ## Accessing Tracking Data Public tracking data is limited but available from: - Metrica Sports (sample matches on GitHub) - SkillCorner (limited open data) - Signality (research datasets) - Last Row/Friends of Tracking tutorials Tracking data opens up advanced analytics but requires significant computational resources and domain expertise to analyze effectively.