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
› Author Affiliations
Further Information

Publication History



accepted 19 December 2018

Publication Date:
14 March 2019 (eFirst)

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.

Supplementary Material