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

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...

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Autores principales: Kholkine, Leonid, Servotte, Thomas, de Leeuw, Arie-Willem, De Schepper, Tom, Hellinckx, Peter, Verdonck, Tim, Latré, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527032/
https://www.ncbi.nlm.nih.gov/pubmed/34693282
http://dx.doi.org/10.3389/fspor.2021.714107
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author Kholkine, Leonid
Servotte, Thomas
de Leeuw, Arie-Willem
De Schepper, Tom
Hellinckx, Peter
Verdonck, Tim
Latré, Steven
author_facet Kholkine, Leonid
Servotte, Thomas
de Leeuw, Arie-Willem
De Schepper, Tom
Hellinckx, Peter
Verdonck, Tim
Latré, Steven
author_sort Kholkine, Leonid
collection PubMed
description 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.
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spelling pubmed-85270322021-10-21 A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes Kholkine, Leonid Servotte, Thomas de Leeuw, Arie-Willem De Schepper, Tom Hellinckx, Peter Verdonck, Tim Latré, Steven Front Sports Act Living Sports and Active Living 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. Frontiers Media S.A. 2021-10-06 /pmc/articles/PMC8527032/ /pubmed/34693282 http://dx.doi.org/10.3389/fspor.2021.714107 Text en Copyright © 2021 Kholkine, Servotte, de Leeuw, De Schepper, Hellinckx, Verdonck and Latré. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Sports and Active Living
Kholkine, Leonid
Servotte, Thomas
de Leeuw, Arie-Willem
De Schepper, Tom
Hellinckx, Peter
Verdonck, Tim
Latré, Steven
A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title_full A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title_fullStr A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title_full_unstemmed A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title_short A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes
title_sort learn-to-rank approach for predicting road cycling race outcomes
topic Sports and Active Living
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527032/
https://www.ncbi.nlm.nih.gov/pubmed/34693282
http://dx.doi.org/10.3389/fspor.2021.714107
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