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Technical classification of professional cycling stages using unsupervised learning: implications for performance

Igor Garcia-Atutxa1, Ekaitz Dudagoitia Barrio2, Francisca Villanueva-Flores3

  • 1Escuela Politécnica Superior, Universidad Católica de Murcia (UCAM), Murcia, Spain.

Frontiers in Sports and Active Living
|October 31, 2025
PubMed
Summary

This study objectively classifies professional cycling stages using KMeans, revealing that challenging terrain like high elevation and unpaved surfaces increases performance variability. These findings offer data-driven insights for optimizing training and race strategies in professional cycling.

Keywords:
clusteringexternal loadperformance variabilityprofessional cyclingunsupervised learning

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