Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
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©Journal of Sports Science and Medicine (2024) 23, 537 - 547   DOI: https://doi.org/10.52082/jssm.2024.537

Research article
Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis
Theodoros Tsilimigkras1,2, Ioannis Kakkos1, George K. Matsopoulos1, Gregory C. Bogdanis2,3, 
Author Information
1 Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece
2 Asteras Tripolis Football Club, Tripoli, Greece
3 School of Physical Education and Sport Science, National and Kapodistrian University of Athens, Greece

Gregory C. Bogdanis
‚úČ School of Physical Education and Sport Science, National and Kapodistrian University of Athens, 17237 Athens, Greece
Email: gbogdanis@phed.uoa.gr
Publish Date
Received: 27-05-2024
Accepted: 25-06-2024
Published (online): 01-09-2024
 
 
ABSTRACT

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

Key words: Soccer, Injury, Machine Learning, Classification, Exertion, training load


           Key Points
  • The machine learning model used seven key input training load variables that provided high accuracy classification of muscle injuries in soccer.
  • Three of the variables concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum), while the other four concerned cumulative load features (total distance, high speed and sprint running distance and training load score).
  • By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.
 
 
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