Technical–tactical differences between female and male elite football: A data mining approach through neural network analysis, binary logistic regression, and decision tree techniques

Author:

Iván-Baragaño Iyán1ORCID,Maneiro Rubén2,Losada José Luís3,Casal Claudio Alberto45,Ardá Antonio6

Affiliation:

1. Faculty of Sport Sciences, Universidad Europea de Madrid, Madrid, Spain

2. Faculty of Education and Sport, University of Vigo, Vigo, Spain

3. Department of Social Psychology and Quantitative Psychology, University of Barcelona, Barcelona, Spain

4. Department of Science of Physical Activity and Sport, Catholic University of Valencia, San Vte Mártir, Valencia, Spain

5. Department of Health Sciences, Isabel I University, Burgos, Spain

6. Department of Physical and Sport Education, University of A Coruña, Galicia, Spain

Abstract

The technical−tactical performance of women’s football has improved markedly in recent years. Despite this improvement, there are still differences between men’s football and women’s football. The objectives of this study were to know the technical and tactical key performance indicators (KPIs) that differentiate elite men’s and women’s football teams as well as to determine which statistical techniques demonstrate superior classification ability and interpretability in football terms. For this purpose, 768 matches corresponding to the latest editions of the UEFA Champions League, UEFA Euro and FIFA World Cup for men and women were analyzed. First, the differences at the bivariate level were analyzed using student’s t-test for independent sample ( p < 0.05) for the male and female teams. Secondly, three data mining classification algorithms were applied: (i) Artificial Neural Network (ANN), (ii) Binary Logistic Regression, and (iii) Decision Tree. Significant differences were found between men’s football and women’s football in variables related to technical elements such as lost balls (ES = 1.19), ball recoveries (ES = 1.00), and accurate passes (ES = 0.97), as well as regulatory aspects like fouls (ES = 0.59), successful tackles (ES = 0.46), and yellow cards (0.45). On the other hand, the classification models presented excellent or good predictive capability [Range AUC 0.774−0.982], with very small differences between the ANN’s and logistic regression models. This result justifies the use of simpler models as the linear regression model to understand the differences between men’s and women’s football. Moreover, the observed differences may offer insights for future efforts aimed at enhancing the performance of women’s football.

Publisher

SAGE Publications

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