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.
In many sports disciplines, the schedule of the competitions is undeniably an inherent yet crucial component. The present study modeled sports competitions schedules as networks and investigated the influence of network properties on the accuracy of predictive ratings and forecasting models in sports. Artificial networks were generated representing competition schedules with varying density, degree distribution and modularity and embedded in a full rating and forecasting process using ELO ratings and an ordered logistic regression model. Results showed that network properties should be considered when tuning predictive ratings and revealed several aspects for improvement. High density does not increase rating accuracy, so improved rating approaches should increasingly use indirect comparisons to profit from transitivity in dense networks. In networks with a high disparity in their degree distribution, inaccuracies are mainly driven by nodes with a low degree, which could be improved by relaxing the rating adjustment functions. Moreover, in terms of modularity, low connectivity between groups (i.e., leagues or divisions) challenges correctly assessing a single group’s overall rating. The present study aims to stimulate discussion on network properties as a neglected facet of sports forecasting and artificial data to improve predictive ratings.
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