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Multisensory assessment and machine learning for athlete classification in talent identification.

Stephen MacGabhann1, Gordon Waddington2, Jeremy Witchalls2

  • 1Research Institute for Sport and Exercise, Faculty of Health, University of Canberra, Australia; New South Wales Institute of Sport (NSWIS), Australia.

Journal of Science and Medicine in Sport
|April 4, 2026
PubMed
Summary

Elite divers show better visual-vestibular-somatosensory (VVS-A) function. Machine learning accurately identified podium-level athletes based on VVS-A measures, aiding talent identification in sports.

Keywords:
Athlete classificationAutonomic functionMachine learning (ML)Olympic divingSensorimotor assessmentTalent identification (TID)

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Area of Science:

  • Sports Science
  • Biomechanics
  • Neuroscience

Background:

  • Elite sport talent identification faces challenges with maturation and limited objective tools.
  • This study investigates visual-vestibular-somatosensory and autonomic (VVS-A) measures for distinguishing elite divers.

Purpose of the Study:

  • Identify VVS-A features differentiating podium-level divers from beginners using statistical analysis.
  • Evaluate machine learning models for classifying elite diving athletes.
  • Analyze classification probabilities using lift-curve analysis.

Main Methods:

  • Cross-sectional study employing machine learning classification.
  • Sixty participants from an Olympic diving program underwent VVS-A assessments.
  • Somatosensory (ankle proprioception), visual, vestibular, and autonomic functions were measured; machine learning models were trained and validated.

Main Results:

  • Podium-level divers exhibited superior ankle proprioception and visual-vestibular smooth pursuit.
  • No significant group differences were found in voluntary saccades or autonomic metrics.
  • A Ridge Logistic Regression model achieved 94.4% accuracy in classifying podium-level athletes (AUC=0.889).

Conclusions:

  • Specific VVS-A measures correlate with current performance levels in Olympic diving.
  • The study's cross-sectional design and sample limitations prevent definitive conclusions on predictive validity.
  • Longitudinal, sport-specific validation is required before applying these findings to talent identification.