Data-driven Support of Coaches in Professional Cycling using Race Performance Prediction

Karetnikov, A., Nuijten, W., & Hassani, M. (2021). Data-driven Support of Coaches in Professional Cycling using Race Performance Prediction. In P. Pezarat-Correia, J. Vilas-Boas, & J. Cabri (Eds.), icSPORTS 2021 – Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support (pp. 43-53). SciTePress Digital Library.


In individual sports, the judgment of which training activity will lead to the best performance is mostly based on the expert knowledge of the coach. Recent advances in data collection and data science have opened up new possibilities for performing a data-driven analysis to support the coach in improving the training programs of the athletes. In this paper, we investigate several methods to do such analysis for professional cyclists. We provide the coach with a framework to predict the Maximum Mean Powers (MMPs) of a cyclist in an upcoming race based on the recently performed training sessions. This way the coach can experiment with several planned alternatives to figure out the best way for preparing the athlete for a race. We conduct multiple prediction models through an extensive analysis of a real dataset collected recently about the performance of professional riders with varying physiologies and temporal performance peaks. We show that the application of the hybrid model using XGBoost and CatBoost has clear advantages. Additionally, we show that the accuracy of our general model can be further increased by filtering according to the mountain stages. We have additionally validated the proposed framework using an openly available real dataset and the results were consistent with those of the collected data. We offer an open source implementation of our proposed framework.

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