A machine learning approach for road cycling race performance prediction - Kholkine, L.; De Schepper, T.; Verdonck, T.; Latré, S. (Machine Learning and Data Mining for Sports Analytics, 2020)
Predicting cycling race results has always been a task left to experts with a lot of domain knowledge. This is largely due to the fact that the outcomes of cycling races can be rather surprising and depend on an extensive set of parameters. Examples of such factors are, among others, the preparedness of a rider, the weather, the team strategy, and mechanical failure. However, we believe that due to the availability of historical data (e.g., race results, GPX files, and weather data) and the recent advances in machine learning, the prediction of the outcomes of cycling races becomes feasible. In this paper, we present a framework for predicting future race outcomes by using machine learning. We investigate the use of past performance race data as a good predictor. In particular, we focus on the Tour of Flanders as our proof-of-concept. We show, among others, that it is possible to predict the outcomes of a one-day race with similar or better accuracy than a human.