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Professional road cycling is a team sport where cyclists serve in different tactical roles, yet most predictive models focus solely on individual performance. This paper introduces VeloRost, a Bayesian dual-skill framework that separately models cyclists’ capabilities as leaders and supporting helpers. Using the TrueSkill rating system, we develop three methods for quantifying helper contributions and aggregate them into a roster strength score combined with each cyclist’s leader skill to predict race outcomes. We evaluated our framework through direct ranking using the skill estimation and statistically enhanced learning across seven seasons of cycling data. Results demonstrate that modeling helper skills significantly outperforms state-of-the-art method, achieving NDCG@10=0.443, highlighting the important role of helpers in race outcomes.

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A Bayesian Dual-Skill Framework for Roster-Based Cycling Race Outcome Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Denis Rize,
  • Paulo Saldanha,
  • Robert Moskovitch

摘要

Professional road cycling is a team sport where cyclists serve in different tactical roles, yet most predictive models focus solely on individual performance. This paper introduces VeloRost, a Bayesian dual-skill framework that separately models cyclists’ capabilities as leaders and supporting helpers. Using the TrueSkill rating system, we develop three methods for quantifying helper contributions and aggregate them into a roster strength score combined with each cyclist’s leader skill to predict race outcomes. We evaluated our framework through direct ranking using the skill estimation and statistically enhanced learning across seven seasons of cycling data. Results demonstrate that modeling helper skills significantly outperforms state-of-the-art method, achieving NDCG@10=0.443, highlighting the important role of helpers in race outcomes.