Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men's football.

ABSTRACT: This study applied multiple machine learning algorithms to classify the performance levels of professional goalkeepers (GK). Technical performances of GK's competing in the elite divisions of England, Spain, Germany, and France were analysed in order to determine which factors distinguish elite GK's from sub-elite GK's. A total of (n = 14,671) player-match observations were analysed via multiple machine learning algorithms (MLA); Logistic Regressions (LR), Gradient Boosting Classifiers (GBC) and Random Forest Classifiers (RFC). The results revealed 15 common features across the three MLA's pertaining to the actions of passing and distribution, distinguished goalkeepers performing at the elite level from those that do not. Specifically, short distribution, passing the ball successfully, rec

SUBMITTER: Jamil M 

PROVIDER: S-EPMC8609025 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature