A narrative review for a machine learning application in sports: an example based on injury forecasting in soccer

Rossi A., Pappalardo L., Cintia P.

soccer  Soccer  Sports Therapy and Rehabilitation  artificial intelligence  Sport science  Orthopedics and Sports Medicine  sport science  Physical Therapy  training and testing  GV557-1198.995  Artificial intelligence  Training and testing  Sports 

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.

Source: Sports (Basel) 10 (2021). doi:10.3390/sports10010005

Publisher: Molecular Diversity Preservation International, Basel

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