Cycling Race Time Prediction: A Personalized Machine Learning Approach Using Route Topology and Training Load

• URL: http://arxiv.org/abs/2601.00604v2
• Date: Wed, 07 Jan 2026 11:15:05 GMT
• Title: Cycling Race Time Prediction: A Personalized Machine Learning Approach Using Route Topology and Training Load
• Authors: Francisco Aguilera Moreno,
• Abstract summary: This work presents a machine learning approach that predicts ride duration using route topology features combined with the athlete's current fitness state.<n>We evaluate the approach using a single-athlete dataset (N=96 rides) in an N-of-1 study design.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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