Cycling Race Time Prediction: A Personalized Machine Learning Approach Using Route Topology and Training Load
This addresses the need for practical race time prediction for amateur cyclists, offering an incremental improvement over existing physics-based models.
The paper tackles the problem of predicting cycling race times by developing a personalized machine learning model that uses route topology and training load metrics, achieving MAE=6.60 minutes and R2=0.922 on a single-athlete dataset, with fitness metrics reducing error by 14% compared to topology alone.
Predicting cycling duration for a given route is essential for training planning and event preparation. Existing solutions rely on physics-based models that require extensive parameterization, including aerodynamic drag coefficients and real-time wind forecasts, parameters impractical for most amateur cyclists. This work presents a machine learning approach that predicts ride duration using route topology features combined with the athlete's current fitness state derived from training load metrics. The model learns athlete-specific performance patterns from historical data, substituting complex physical measurements with historical performance proxies. We evaluate the approach using a single-athlete dataset (N=96 rides) in an N-of-1 study design. After rigorous feature engineering to eliminate data leakage, we find that Lasso regression with Topology + Fitness features achieves MAE=6.60 minutes and R2=0.922. Notably, integrating fitness metrics (Chronic Training Load (CTL), Acute Training Load (ATL)) reduces error by 14% compared to topology alone (MAE=7.66 min), demonstrating that physiological state meaningfully constrains performance even in self-paced efforts. Progressive checkpoint predictions enable dynamic race planning as route difficulty becomes apparent.