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

TL;DR

This paper introduces a personalized machine learning model that predicts cycling race times by combining route topology and athlete training load data, outperforming physics-based models in accuracy.

Contribution

It presents a novel approach that replaces complex physical parameters with training load metrics and route features, enabling personalized and accurate ride duration predictions.

Findings

Lasso regression with route and fitness features achieves MAE=6.60 minutes.

Inclusion of training load metrics reduces prediction error by 14%.

Model supports dynamic race planning through progressive checkpoint predictions.

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.