Introducing an expert system for prediction of soccer player ranking using ensemble learning

Abstract

Soccer is one of the most played sports in the world with many individuals involved. Recognizing talented players and team selection is a challenging task for coaches. Coaches need to employ different methods in order to rank soccer players and select them by their corresponding rank. In this paper, we propose a new web-based approach for ranking soccer players by using information available from online sources. The first step to do this task is collecting information about players. This information is fetched from the Internet and will be preprocessed or augmented by professional users at a web-based expert system. Information is highly dynamic in a sense that data change constantly. To build a ranking system for players, machine learning approaches are employed. We use different classification algorithms on prepared data and choose the best model from applied methods to rank new players in each state of the dataset. To improve classification results, a weighted ensemble method using a genetic algorithm for optimizing weights is proposed. We used this model to predict players’ rank. The ranking is done separately for different types of ranks with two, three, or four number of rankings. Experiments were done in the Persian premier league and have shown promising results for predicting player ranks with improvement in accuracy for four-, three-, and two-class predictions. The results show that (1) achieving higher performance will be harder with each level of granularity that is added to ranking classes of system. (2) A web-based system can be useful in order to develop a ranking system in sports. (3) The new ensemble method is able to improve classification models by improving the best model. We believe that using our innovative system, challenges for team selection and talent recognition can be solved. This assumption is proved with final results of the system and feedbacks from professionals.