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Kholkine L, Servotte T, de Leeuw AW, et al. A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes. Front Sports Act Living. 2021;3:714107doi: 10.3389/fspor.2021.714107.
Kholkine, L., Servotte, T., de Leeuw, A. W., De Schepper, T., Hellinckx, P., Verdonck, T., & Latré, S. (2021). A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes. Frontiers in sports and active living, 3714107. https://doi.org/10.3389/fspor.2021.714107
Kholkine, Leonid, et al. "A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes." Frontiers in sports and active living vol. 3 (2021): 714107. doi: https://doi.org/10.3389/fspor.2021.714107
Kholkine L, Servotte T, de Leeuw AW, De Schepper T, Hellinckx P, Verdonck T, Latré S. A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes. Front Sports Act Living. 2021 Oct 06;3:714107. doi: 10.3389/fspor.2021.714107. eCollection 2021. PMID: 34693282; PMCID: PMC8527032.
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Front Sports Act Living. 2021 Oct 06;3:714107. doi: 10.3389/fspor.2021.714107. eCollection 2021.

A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes.

Frontiers in sports and active living

Leonid Kholkine, Thomas Servotte, Arie-Willem de Leeuw, Tom De Schepper, Peter Hellinckx, Tim Verdonck, Steven Latré

Affiliations

  1. Department of Computer Science, University of Antwerp-IMEC, Antwerp, Belgium.
  2. Department of Mathematics, University of Antwerp, Antwerp, Belgium.

PMID: 34693282 PMCID: PMC8527032 DOI: 10.3389/fspor.2021.714107

Abstract

Professional road cycling is a very competitive sport, and many factors influence the outcome of the race. These factors can be internal (e.g., psychological preparedness, physiological profile of the rider, and the preparedness or fitness of the rider) or external (e.g., the weather or strategy of the team) to the rider, or even completely unpredictable (e.g., crashes or mechanical failure). This variety makes perfectly predicting the outcome of a certain race an impossible task and the sport even more interesting. Nonetheless, before each race, journalists, ex-pro cyclists, websites and cycling fans try to predict the possible top 3, 5, or 10 riders. In this article, we use easily accessible data on road cycling from the past 20 years and the Machine Learning technique Learn-to-Rank (LtR) to predict the top 10 contenders for 1-day road cycling races. We accomplish this by mapping a relevancy weight to the finishing place in the first 10 positions. We assess the performance of this approach on 2018, 2019, and 2021 editions of six spring classic 1-day races. In the end, we compare the output of the framework with a mass fan prediction on the Normalized Discounted Cumulative Gain (NDCG) metric and the number of correct top 10 guesses. We found that our model, on average, has slightly higher performance on both metrics than the mass fan prediction. We also analyze which variables of our model have the most influence on the prediction of each race. This approach can give interesting insights to fans before a race but can also be helpful to sports coaches to predict how a rider might perform compared to other riders outside of the team.

Copyright © 2021 Kholkine, Servotte, de Leeuw, De Schepper, Hellinckx, Verdonck and Latré.

Keywords: cycling race performance; learn-to-rank; machine learning; road cycling; winner prediction

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Sports Med Open. 2019 Jul 3;5(1):28 - PubMed
  2. Sports Med Open. 2020 Jun 3;6(1):23 - PubMed
  3. Am J Psychol. 1987 Fall-Winter;100(3-4):441-71 - PubMed
  4. Entropy (Basel). 2021 Jan 10;23(1): - PubMed
  5. Sensors (Basel). 2018 Nov 23;18(12): - PubMed
  6. Math Biosci Eng. 2017 Apr 1;14(2):511-527 - PubMed
  7. Eur J Sport Sci. 2021 Jan 31;:1-11 - PubMed

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