Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing

Area of Science:

• Sports Analytics
• Machine Learning
• Decision Support Systems

Background:

• Professional car racing involves complex, real-time strategic decisions.
• Optimizing tire changes is critical for race performance and team rank.
• Existing sports analytics research lacks within-race prediction and decision-making models.

Purpose of the Study:

• To design a real-time prediction and decision system for professional car racing.
• To leverage domain expertise and statistical modeling for strategic race decisions.
• To enhance team strategy through optimized tire-change recommendations.

Main Methods:

• Infusing domain knowledge of racing into statistical modeling techniques.
• Developing a knowledge discovery process tailored for racing scenarios.
• Creating a real-time decision system specifically for tire changes during a race.

Main Results:

• Successfully developed a system integrating expert racing knowledge with statistical models.
• Demonstrated the capability to provide real-time strategic recommendations for tire changes.
• The system's forecasts can significantly impact team rank and race strategy optimization.

Conclusions:

• Expert knowledge is crucial for effective sports analytics in professional racing.
• The developed system represents a novel approach to within-race prediction and decision-making.
• This work advances sports analytics by providing a practical tool for optimizing race strategy.