Female ( \(n_0 = 1511\)) and male ( \(n_1 = 2703\)) data points were collected from event data and categorized by game period and player position. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. 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. 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.
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March 2023
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