Abstract
We study the feasibility of applying machine learning to predict the performance of road cyclists using publicly available data. The performance is investigated by predicting the presence or absence in the top places of next year’s ranking based on a rider’s characteristics and results in the current and previous years. We apply several classification algorithms and obtain that random forest is the best-performing model. Our work is a first step towards creating personalised performance models in professional road cycling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Hand, D.J., Yu, K.: Idiot’s Bayes: not so stupid after all? Int. Stat. Rev. 69(3), 385–398 (2001). https://doi.org/10.2307/1403452
Janssens, B., Bogaert, M., Maton, M.: Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents. Ann. Oper. Res. 325, 5570–588 (2022). https://doi.org/10.1007/s10479-021-04476-4
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)
Kholkine, L., et al.: A learn-to-rank approach for predicting road cycling race outcomes. Front. Sports Act. Living 3, 714107 (2021). https://doi.org/10.3389/fspor.2021.714107
de Leeuw, A.-W., Heijboer, M., Hofmijster, M., van der Zwaard, S., Knobbe, A.: Time series regression in professional road cycling. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds.) DS 2020. LNCS (LNAI), vol. 12323, pp. 689–703. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61527-7_45
de Leeuw, A.W., van der Zwaard, S., van Baar, R., Knobbe, A.: Personalized machine learning approach to injury monitoring in elite volleyball players. Eur. J. Sport Sci. 22(4), 511–520 (2022)
Longo, A.F., Siffredi, C.R., Cardey, M.L., Aquilino, G.D., Lentini, N.A.: Age of peak performance in Olympic sports: a comparative research among disciplines. J. Hum. Sport Exerc. 11, 31–41 (2016)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. B Stat. Methodol. 67(2), 301–320 (2005). http://www.jstor.org/stable/3647580
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kholkine, L., de Leeuw, AW., Verdonck, T., Latré, S. (2023). Towards Personalised Performance Prediction in Road Cycling Through Machine Learning. In: Baca, A., Exel, J. (eds) 13th World Congress of Performance Analysis of Sport and 13th International Symposium on Computer Science in Sport. IACSS&ISPAS 2022. Advances in Intelligent Systems and Computing, vol 1448. Springer, Cham. https://doi.org/10.1007/978-3-031-31772-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-31772-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31771-2
Online ISBN: 978-3-031-31772-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)