Abstract
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.
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Acknowledgment
This work was partly funded by the DAIQUIRI project, cofunded by imec, a research institute founded by the Flemish Government. Project partners are Ghent University, InTheRace, Arinti, Cronos, VideoHouse, NEP Belgium, and VRT, with project support from VLAIO.
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Kholkine, L., De Schepper, T., Verdonck, T., Latré, S. (2020). A Machine Learning Approach for Road Cycling Race Performance Prediction. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2020. Communications in Computer and Information Science, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-64912-8_9
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DOI: https://doi.org/10.1007/978-3-030-64912-8_9
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