Yannis Moudere - Enhancing Event Analysis at Scale: Leveraging Tracking Data in Sports

Data in football comes from two main sources: event data (discrete actions) and tracking data (continuous player/ball positions) at 25 frames per second

Event data requires context enhancement through tracking data analysis to provide meaningful insights about off-ball movements and player pressure

Key analytical models include:

• Pitch control (player influence based on position/velocity)
• Pass selection probability
• Pass success probability
• Expected position value (EPV)
• Pressure analysis

Technical architecture leverages:

• Horizontal scaling with spot instances for cost optimization
• Message queues for asynchronous processing
• Dead letter queues for error handling
• Continuous integration/deployment pipeline

Processing requirements:

• 250MB tracking data per game
• 150k frames per game
• 8GB RAM needed for 150 seconds per game
• Total season storage ~95GB
• Processing cost approximately $10 for 10,000 games

Data applications include:

• Pre-game analysis
• Post-game reports
• Scouting dashboards
• Performance metrics visualization
• Player comparison tools

Machine learning integrates:

• Convolutional neural networks
• Kernel density estimation
• Success probability modeling
• Position value calculations

System is designed to be:

• Cost-effective through spot instance usage
• Fault tolerant with message queue backup
• Scalable based on processing demand
• Asynchronous for efficient processing