Understanding gender differences in professional European football through machine learning interpretability and match actions data

Abstract After the great success of the Women’s World Cup in 2019, several platforms have started identifying the reasons for gender inequality in European football. Even though these inequalities emerge from a variety of key aspects in the modern sport, we focused on the game and evaluated the main...

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Autores principales:	Marc Garnica-Caparrós, Daniel Memmert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ecce9f6ab75a40739fb30b00388e3250
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spelling	oai:doaj.org-article:ecce9f6ab75a40739fb30b00388e32502021-12-02T14:47:31ZUnderstanding gender differences in professional European football through machine learning interpretability and match actions data10.1038/s41598-021-90264-w2045-2322https://doaj.org/article/ecce9f6ab75a40739fb30b00388e32502021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90264-whttps://doaj.org/toc/2045-2322Abstract After the great success of the Women’s World Cup in 2019, several platforms have started identifying the reasons for gender inequality in European football. Even though these inequalities emerge from a variety of key aspects in the modern sport, we focused on the game and evaluated the main differential features of European male and female football players in match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female ( $$n_0 = 1511$$ n 0 = 1511 ) and male ( $$n_1 = 2703$$ n 1 = 2703 ) data points were collected from event data and categorized by game period and player position. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline included three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. The study was able to determine pivotal factors that differentiate each gender performance as well as disseminate unique patterns by gender involving more than one indicator. Data enhancement and critical variables analysis are essential next steps to support this framework and serve as a baseline for further studies and training developments.Marc Garnica-CaparrósDaniel MemmertNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marc Garnica-Caparrós Daniel Memmert Understanding gender differences in professional European football through machine learning interpretability and match actions data
description	Abstract After the great success of the Women’s World Cup in 2019, several platforms have started identifying the reasons for gender inequality in European football. Even though these inequalities emerge from a variety of key aspects in the modern sport, we focused on the game and evaluated the main differential features of European male and female football players in match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female ( $$n_0 = 1511$$ n 0 = 1511 ) and male ( $$n_1 = 2703$$ n 1 = 2703 ) data points were collected from event data and categorized by game period and player position. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline included three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. The study was able to determine pivotal factors that differentiate each gender performance as well as disseminate unique patterns by gender involving more than one indicator. Data enhancement and critical variables analysis are essential next steps to support this framework and serve as a baseline for further studies and training developments.
format	article
author	Marc Garnica-Caparrós Daniel Memmert
author_facet	Marc Garnica-Caparrós Daniel Memmert
author_sort	Marc Garnica-Caparrós
title	Understanding gender differences in professional European football through machine learning interpretability and match actions data
title_short	Understanding gender differences in professional European football through machine learning interpretability and match actions data
title_full	Understanding gender differences in professional European football through machine learning interpretability and match actions data
title_fullStr	Understanding gender differences in professional European football through machine learning interpretability and match actions data
title_full_unstemmed	Understanding gender differences in professional European football through machine learning interpretability and match actions data
title_sort	understanding gender differences in professional european football through machine learning interpretability and match actions data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ecce9f6ab75a40739fb30b00388e3250
work_keys_str_mv	AT marcgarnicacaparros understandinggenderdifferencesinprofessionaleuropeanfootballthroughmachinelearninginterpretabilityandmatchactionsdata AT danielmemmert understandinggenderdifferencesinprofessionaleuropeanfootballthroughmachinelearninginterpretabilityandmatchactionsdata
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Understanding gender differences in professional European football through machine learning interpretability and match actions data

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