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Global Positioning System-Derived Metrics and Machine Learning Models for Injury Prediction in Professional Rugby

Xiangyu Ren1,2,3, Simon Boisbluche4, Kilian Philippe5

  • 1Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, Sino-French Joint Research Center of Sport Science, College of Physical Education and Health, East China Normal University, Shanghai, China.

European Journal of Sport Science
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models effectively predict rugby player injuries using GPS data and workload metrics. This approach identifies key injury risk factors, enabling tailored training strategies for enhanced athlete safety and performance.

Keywords:
analysisdatainjury & preventionmodelingteam sport

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Area of Science:

  • Sports Science
  • Data Science
  • Biomechanics

Background:

  • Injury prevention is crucial for athletic success in professional rugby.
  • Predicting injuries remains challenging despite technological advancements.
  • Data science offers new insights into understanding and mitigating injury risks.

Purpose of the Study:

  • To predict injury risk in professional rugby union players using machine learning (ML) models.
  • To analyze the impact of GPS-derived workload metrics on injury occurrence.
  • To identify position-specific injury predictors.

Main Methods:

  • Analyzed data from 63 professional rugby players over three seasons.
  • Utilized GPS data and derived metrics (workload, ACWR, monotony, strain).
  • Applied five ML classification models (LR, NB, SVM, RF, XGBoost) for injury prediction across player positions.

Main Results:

  • Machine learning models achieved average F1 scores up to 0.66 (± 0.14).
  • Random Forest (RF) showed strong performance for overall forwards and inside backs.
  • XGBoost excelled in tight five forwards, SVM in back row, and Naïve Bayes (NB) in outside backs.

Conclusions:

  • ML models effectively predict rugby injuries, especially with combined GPS metrics.
  • Identified key injury-indicative characteristics specific to different player positions.
  • ML enhances injury prediction and informs personalized training strategies for athletes.