A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results - Bendiaf, Messaoud; Khelifi, Hakima; Mohdeb, Djamila; Belazzoug, Mouhoub; Saifi, Abdelhamid (International journal of computer science in sport, 2025)
Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have been made by team managers, fans, and analysts based on their knowledge and experience. However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. Accordingly, forecasting plays a pivotal role in assisting managers and clubs in making well-informed decisions geared toward securing victories in leagues and tournaments. In this paper, we presented an approach, which is generally applicable in all areas of sports, to forecast football match results based on three stages. The first stage involves identifying and collecting the occurred events during a football match. As a multiclass classification problem with three classes, each match can have three possible outcomes. Then, we applied multiple machine learning algorithms to compare the performance of those different models, and choose the one that performs the best. As a final step, this study goes through the critical aspect of model interpretability. We used the SHapley Additive exPlanations (SHAP) method to decipher the feature importance within our best model, focusing on the factors that influence match predictions. Experiment results indicate that the Multilayer Perceptron (MLP), a neural network algorithm, was effective when compared to various other models and produced competitive results with prior works. The MLP model has achieved 0.8342 for accuracy. The particular significance of this study lies in the use of the SHAP method to explain the predictions made by the MLP model. Specifically, by exploiting its graphical representation to illustrate the influence of each feature within our dataset in predicting the outcome of a football match.