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Identifying Weekly Physical and Physiological Profiles in Professional Basketball Using Heart Rate Variability and

Marc Olmos1,2, Toni Caparros1,3, Victor Olmos4

  • 1National Institute of Physical Education of Catalonia (INEFC), University of Barcelona, Barcelona, Spain.

Research Quarterly for Exercise and Sport
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

Unsupervised machine learning identified three distinct weekly physiological load profiles in professional basketball players. These profiles, based on heart rate variability (HRV), cardiovascular (SHRZ), and movement load (ML), reveal player variability and aid in load management.

Keywords:
Cluster analyseselite basketballheart rate variabilityload monitoringunsupervised learning

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Area of Science:

  • Sports Science
  • Physiology
  • Data Science

Background:

  • Understanding physiological load is crucial for optimizing athletic performance and preventing injury in professional sports.
  • Individual responses to training load vary significantly among athletes, necessitating personalized monitoring strategies.

Purpose of the Study:

  • To apply unsupervised machine learning to characterize distinct weekly physiological load states in professional basketball players.
  • To identify and describe player profiles based on autonomic, cardiovascular, and mechanical load parameters over an 18-week season.

Main Methods:

  • Utilized K-means clustering on weekly standardized data of heart rate variability (zHRV), internal load (zSHRZ), and movement load (zML) from 11 professional male basketball players.
  • Determined optimal cluster number using the elbow criterion and assessed clustering quality with the silhouette coefficient.
  • Employed repeated-measures correlations to analyze associations between physiological variables.

Main Results:

  • Identified three significant physiological load profiles: High load-elevated HRV, Low load-near-average HRV, and High load-reduced HRV.
  • Observed substantial temporal and inter-individual variability in these profiles across the season.
  • Found a strong association between internal load (SHRZ) and movement load (ML) (r=0.87), but weak correlations between HRV and SHRZ/ML.

Conclusions:

  • Unsupervised clustering provides a data-driven framework for summarizing weekly physiological load states in elite athletes.
  • This approach has the potential to enhance individualized load management strategies.
  • Future research should explore the link between these identified profiles and performance, fatigue, and injury risk.