2019
DOI: 10.1186/s40798-019-0202-3
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Abstract: Background The 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. Methods Systematic… Show more

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Cited by 154 publications
(98 citation statements)
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References 92 publications
(134 reference statements)
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“…The contribution of this study to the literature and the differences from previous studies are clear, both in the field of machine learning applications and in the contributions to the field of performance analysis. According to an updated review published in 2019, only one of the studies found included physical variables for the discrimination of the results and the cut-off points, applied to basketball [26]. This study also confirms that one of the two most productive methods of machine learning are the decision trees [26].…”
Section: Introductionsupporting
confidence: 60%
“…The contribution of this study to the literature and the differences from previous studies are clear, both in the field of machine learning applications and in the contributions to the field of performance analysis. According to an updated review published in 2019, only one of the studies found included physical variables for the discrimination of the results and the cut-off points, applied to basketball [26]. This study also confirms that one of the two most productive methods of machine learning are the decision trees [26].…”
Section: Introductionsupporting
confidence: 60%
“…In this study, we aim to build a classifier tool based on Supervised ML algorithms with our data. In the literature, many Supervised ML algorithms have been used for classification tasks (Claudino et al, 2019). We chose Support Vector Machine (SVMs), Decision Tree, and…”
Section: Machine Learningmentioning
confidence: 99%
“…However, the more massive collected data, the more complex their managing. It is now acknowledged that machine learning methods applied to sport can provide accurate diagnostic and decision tools for training management and injury risk assessment but are not yet widely used in the latest scientific studies (see for a review Claudino et al [18]). One of the first investigations that tried to predict non-contact injuries in team-sports using machine-learning methods was conducted by Rossi et al [19].…”
Section: Introductionmentioning
confidence: 99%