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Pro-cycling team cyclist assignment for an upcoming race.
Maor Sagi1, Paulo Saldanha2, Guy Shani1
1Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Plos One
|March 4, 2024
Summary
This study introduces RaceFit, a novel model for assigning cyclists to professional races using recent workout data and past assignments. RaceFit accurately predicts participation, outperforming baseline methods.
Area of Science:
- Sports Science
- Machine Learning in Sports
- Data Analytics in Professional Cycling
Background:
- Professional cycling utilizes sensor data for training and racing analysis.
- Cyclist assignment to races is crucial for team strategy but often relies on heuristics.
Purpose of the Study:
- To develop and evaluate RaceFit, a predictive model for cyclist assignment to professional races.
- To leverage recent workout data and historical assignments for optimized team selection.
Main Methods:
- Developed RaceFit, a model using binary classifiers trained on cyclist and race features.
- Incorporated demographic, recent workout, and race-specific data.
- Evaluated two approaches: stage-level and entire-race assignment.
Main Results:
- RaceFit achieved up to 80% precision@i on a large dataset from three professional cycling teams.
- Performance was consistent whether using TP or STRAVA data.
- The CatBoost classifier with a 5-week window and imputation performed best, outperforming popularity-based baselines.
Conclusions:
- RaceFit offers a data-driven approach to cyclist race assignment, significantly improving upon traditional methods.
- The model demonstrates the efficacy of machine learning in optimizing team selection in professional cycling.
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