Abstract
Professional bicycle racing is a popular sport that has attracted significant attention in recent years. The evolution and ubiquitous use of sensors allow cyclists to measure many metrics including power, heart rate, speed, cadence, and more in training and racing. In this paper we explore for the first time assignment of a subset of a team’s cyclists to an upcoming race. We introduce RaceFit, a model that recommends, based on recent workouts and past assignments, cyclists for participation in an upcoming race. RaceFit consists of binary classifiers that are trained on pairs of a cyclist and a race, described by their relevant properties (features) such as the cyclist’s demographic properties, as well as features extracted from his workout data from recent weeks; as well additional properties of the race, such as its distance, elevation gain, and more. Two main approaches are introduced in recommending on each stage in a race and aggregate from it to the race, or on the entire race. The model training is based on binary label which represent participation of cyclist in a race (or in a stage) in past events. We evaluated RaceFit rigorously on a large dataset of three pro-cycling teams’ cyclists and race data achieving up to 80% precision@i. The first experiment had shown that using TP or STRAVA data performs the same. Then the best-performing parameters of the framework are using 5 weeks time window, imputation was effective, and the CatBoost classifier performed best. However, the model with any of the parameters performed always better than the baselines, in which the cyclists are assigned based on their popularity in historical data. Additionally, we present the top-ranked predictive features.
Citation: Sagi M, Saldanha P, Shani G, Moskovitch R (2024) Pro-cycling team cyclist assignment for an upcoming race. PLoS ONE 19(3):
e0297270.
https://doi.org/10.1371/journal.pone.0297270
Editor: Jeremy B. Coquart,
Université de Lille: Universite de Lille, FRANCE
Received: September 5, 2023; Accepted: January 2, 2024; Published: March 4, 2024
Copyright: © 2024 Sagi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All STRAVA and PCS.csv source files are available from the [https://www.ise.bgu.ac.il/clusters/CyclistRaceData.zip] data download link. All relevant data are within the manuscript as well. Training Peaks data cannot be shared publicly because of discernment data of Israel Premier Tech data, but the data from STRAVA for the Israeli team is practically the same.
Funding: This study was financially supported by the Sylvan Adams Family Israel in the form of a donation. No additional external funding was received for this study.
Competing interests: The authors have declared that no competing interests exist.