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

Mikael Jamil, Ashwin Phatak, Saumya Mehta, Marco Beato, Daniel Memmert, Mark Connor

Publikation: Beitrag in FachzeitschriftZeitschriftenaufsätzeForschungBegutachtung

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, receiving passes successfully, and keeping clean sheets were all revealed to be common traits of GK’s performing at the elite level. Moderate to high accuracy was reported across all the MLA’s for the training data, LR (0.7), RFC (0.82) and GBC (0.71) and testing data, LR (0.67), RFC (0.66) and GBC (0.66). Ultimately, the results discovered in this study suggest that a GK’s ability with their feet and not necessarily their hands are what distinguishes the elite GK’s from the sub-elite.
OriginalspracheEnglisch
Aufsatznummer22703 (2021)
ZeitschriftScientific Reports
Jahrgang2021
Ausgabenummer11
Seitenumfang7
ISSN2045-2322
DOIs
PublikationsstatusVeröffentlicht - 2021

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