Stacked-Based Ensemble Machine Learning Model for Positioning Footballer

Due to the high performance of machine learning (ML) methods in different disciplines, these methods have been used frequently in various sports fields, especially in the last decade. Researchers have used ML algorithms in football on various subjects such as match result prediction, estimation of factors affecting match results, prediction of league standings, and analysis of the performances of football players. However, there has not been enough work on determining the position of the football player, which is one of the leading problems for coaches in football. Therefore, this study aims to classify footballer positions employing a stacked ensemble ML model using the FIFA'19 game dataset. To achieve this aim, a two-stage application is followed. In the first stage, 10 features are selected using four different feature selection algorithms. In the second stage, Deep Neural Networks, Random Forest, and Gradient Boosting were used as single-based algorithms, and Logistic Regression was employed as a meta-learner in the stacked model. The results show that the combination of the Chi-square feature selection technique and the stacked-based ensemble learning model yielded the best accuracy (83.9%). The findings emphasize the validity and robustness of our stacked ensemble learning model to determine the positions of footballers.

Automated summary

Due to the high performance of machine learning methods, they have been widely used in various sports fields, including football. However, there has been insufficient work on determining the position of football players, which is an important problem for coaches. This study employs a stacked ensemble machine learning model and Chi-square feature selection technique to classify footballer positions and achieves good results.