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Int J Sports Med 2019; 40(05): 344-353
DOI: 10.1055/a-0826-1955
Orthopedics & Biomechanics
© Georg Thieme Verlag KG Stuttgart · New York

A Preventive Model for Hamstring Injuries in Professional Soccer: Learning Algorithms

Francisco Ayala
1   Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
,
Alejandro López-Valenciano
1   Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
,
Jose Antonio Gámez Martín
2   Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Albacete, Spain
,
Mark De Ste Croix
3   School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom of Great Britain and Northern Ireland
,
Francisco J. Vera-Garcia
1   Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
,
Maria del Pilar García-Vaquero
1   Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
,
Iñaki Ruiz-Pérez
1   Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
,
Gregory D. Myer
4   The SPORT Center, Division of Sports Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States
5   Department of Pediatrics and Orthopaedic Surgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States
6   The Micheli Center for Sports Injury Prevention, Waltham, MA, United States
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accepted 19. Dezember 2018

Publikationsdatum:
14. März 2019 (online)

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Abstract

Hamstring strain injury (HSI) is one of the most prevalent and severe injury in professional soccer. The purpose was to analyze and compare the predictive ability of a range of machine learning techniques to select the best performing injury risk factor model to identify professional soccer players at high risk of HSIs. A total of 96 male professional soccer players underwent a pre-season screening evaluation that included a large number of individual, psychological and neuromuscular measurements. Injury surveillance was prospectively employed to capture all the HSI occurring in the 2013/2014 season. There were 18 HSIs. Injury distribution was 55.6% dominant leg and 44.4% non-dominant leg. The model generated by the SmooteBoostM1 technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score=0.837, true positive rate=77.8%, true negative rate=83.8%) and hence was considered the best for predicting HSI. The prediction model showed moderate to high accuracy for identifying professional soccer players at risk of HSI during pre-season screenings. Therefore, the model developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention.

Key words

injury prevention - injury risk - modeling - screening - decision-making

Supplementary Material

 

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