Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

João Gustavo Claudino,George P Nassis,Adriano C Machado Pereira,Julio Cerca Serrão,Daniel De Oliveira Capanema,Thiago Vieira De Souza

doi:10.1186/s40798-019-0202-3

Abstract

BackgroundThe application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.MethodsSystematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.ResultsFifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.ConclusionsThe results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.

Highlights

The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports
The main purpose of this study is to provide an overview of the current state of the application of AI in assessing the injury risk and predicting performance in team sports athletes
Our analysis showed that the AI techniques or methods for predicting injury risk and sporting performance mostly used in team sports were artificial neural networks, decision tree classifier, support vector machine, and Markov process

Summary

Introduction

The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. As in most other areas of society, increasing volume of data has been gathered in all kinds of sports, and automated data analysis became an important and fast developing field Careful analyses of these large data sets can enhance our knowledge in sport sciences while at the same time assist in the decision-making of the practitioners who work on the optimization of training and competition strategies [4, 5]. The algorithms can be tuned and be optimized in order to produce better results for supporting decisions and provide applied knowledge to athletes and sport professionals. These algorithms are applied as supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering). Because of its fast-moving environment, sport professionals combine data (e.g., physical, technical and tactical) with their expert opinion to inform decisions on the players [12]

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