@inproceedings{354b9c01649a42239d26a49735a7a0af,
title = "A Bayesian Dual-Skill Framework for Roster-Based Cycling Race Outcome Prediction",
abstract = "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{\textquoteright} 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{\textquoteright}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.",
keywords = "Machine Learning, Recommendation System, Sports Analytics",
author = "Denis Rize and Paulo Saldanha and Robert Moskovitch",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 2nd International Sports Analytics Conference and Exhibition, ISACE 2025 ; Conference date: 26-09-2025 Through 27-09-2025",
year = "2026",
month = jan,
day = "1",
doi = "10.1007/978-3-032-06167-6\_15",
language = "English",
isbn = "9783032061669",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "193--208",
editor = "Jin-song Dong and Jing Sun and Xiaofei Xie and Kan Jiang",
booktitle = "Sports Analytics - 2nd International Conference, ISACE 2025, Proceedings",
address = "Germany",
}