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A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

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 sports 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 of injuries 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.

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Accessibility Both
AccessibilityMode Download
Availability On-Line
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CreationDate 2022-05-02 09:00
Creator Rossi, Alessio, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
External Identifier https://doi.org/10.3390/sports10010005
Field/Scope of use Any use
Group Health Studies
Owner Rossi, Alessio, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
Sublicense rights No
Territory of use World Wide
Thematic Cluster Other
system:type Method
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Author Rossi Alessio
Maintainer Rossi Alessio
Version 1
Last Updated 12 September 2023, 09:20 (CEST)
Created 25 May 2022, 09:48 (CEST)