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 e...

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Autores principales:	Mikael Jamil, Ashwin Phatak, Saumya Mehta, Marco Beato, Daniel Memmert, Mark Connor
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1d978cc071454fdbbfc4a042eb5e62b3
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spelling	oai:doaj.org-article:1d978cc071454fdbbfc4a042eb5e62b32021-11-28T12:19:16ZUsing multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football10.1038/s41598-021-01187-52045-2322https://doaj.org/article/1d978cc071454fdbbfc4a042eb5e62b32021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01187-5https://doaj.org/toc/2045-2322Abstract 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.Mikael JamilAshwin PhatakSaumya MehtaMarco BeatoDaniel MemmertMark ConnorNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mikael Jamil Ashwin Phatak Saumya Mehta Marco Beato Daniel Memmert Mark Connor Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
description	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.
format	article
author	Mikael Jamil Ashwin Phatak Saumya Mehta Marco Beato Daniel Memmert Mark Connor
author_facet	Mikael Jamil Ashwin Phatak Saumya Mehta Marco Beato Daniel Memmert Mark Connor
author_sort	Mikael Jamil
title	Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
title_short	Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
title_full	Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
title_fullStr	Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
title_full_unstemmed	Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
title_sort	using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1d978cc071454fdbbfc4a042eb5e62b3
work_keys_str_mv	AT mikaeljamil usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball AT ashwinphatak usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball AT saumyamehta usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball AT marcobeato usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball AT danielmemmert usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball AT markconnor usingmultiplemachinelearningalgorithmstoclassifyeliteandsubelitegoalkeepersinprofessionalmensfootball
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Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football

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