Cycling Race Time Prediction
A personalized ML approach using route topology and training load.
Summary
This paper studies how to predict cycling duration for a specific rider using route topology and current fitness state, avoiding heavy dependence on hard-to-measure physics parameters.
The approach is trained on personal historical rides and evaluated as an N-of-1 study. A Lasso-based model using topology plus fitness features provides strong predictive performance, and checkpoint predictions support practical pacing decisions during a ride.
Quick Info
- Theme: Personalized endurance performance prediction.
- Data: Multi-year personal cycling data and route-derived features.
- Use case: Race planning, pacing support, and training decisions.