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Physical Workload Patterns in U-18 Basketball Using LPS and MEMS Data: A Principal Component Analysis by Quarter and

Sergio J Ibáñez1, Markel Rico-González2, Carlos D Gómez-Carmona1,3,4

  • 1Research Group in Optimization of Training and Sports Performance (GOERD), Department of Didactics of Music, Plastic and Body Expression, Faculty of Sport Science, University of Extremadura, 10003 Caceres, Spain.

Sensors (Basel, Switzerland)
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Summary

This study used principal component analysis (PCA) to analyze basketball players' physical workload. Key variables explained workload variations across game quarters and positions, informing individualized training strategies.

Keywords:
inertial measurement unitsmultivariate statisticsperformance analysisteam sportsultra-wideband positioningwearable sensorsworkload monitoring

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Area of Science:

  • Sports Science
  • Biomechanics
  • Basketball Performance Analysis

Background:

  • Basketball involves high-intensity, intermittent activity with fluctuating physical demands.
  • Previous research often analyzed these demands in isolation, lacking integrative approaches.

Purpose of the Study:

  • To identify key variables explaining physical workload in elite U18 male basketball players.
  • To analyze workload variations across game quarters and playing positions using principal component analysis (PCA).

Main Methods:

  • Utilized WIMU PRO™ multi-sensor wearable devices with local positioning systems (LPS) and microelectromechanical systems (MEMS).
  • Collected data from 94 elite U18 male players during the EuroLeague Basketball ANGT Finals.
  • Applied PCA to 31 selected variables for dimensionality reduction and workload analysis.

Main Results:

  • Five to eight principal components explained 61-73% of variance per quarter and 64-69% per position.
  • High-intensity variables decreased across quarters, while specific workload profiles emerged for guards, forwards, and centers.
  • Forwards showed highest explosive distance loading, while centers had concentrated power demands.

Conclusions:

  • Findings support situational and individualized training programs for basketball players.
  • Coaches can use these insights to optimize training, game rotations, and injury risk management.
  • Understanding position-specific and game-phase workload variations is crucial for performance enhancement.