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

Aleksei Karetnikov, Wim Nuijten, Marwan Hassani

Abstract: 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