Abstract:

This study explores the application of machine learning methods to predict football (soccer) match outcomes. Football, as a highly dynamic and data-rich sport, provides a valuable source of information for predictive modeling. The research focuses on evaluating and comparing the performance of several machine learning algorithms: a naïve baseline model, logistic regression, random forest, XGBoost, and an artificial neural network. The dataset used for training and testing consists of historical match statistics, team performance indicators, and situational variables such as home advantage. Feature engineering and data preprocessing steps, including normalization and handling of missing data, were applied to improve model performance and generalizability. Each model was assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score. The results indicate that while simple models like logistic regression provide solid baseline performance, ensemble methods such as random forest and XGBoost achieve higher predictive accuracy. The artificial neural network, although more computationally demanding, shows promising results in capturing complex, nonlinear relationships between match variables. The study highlights the challenges of modeling football outcomes, such as the inherent randomness of the sport and the influence of non-quantifiable factors, but demonstrates the potential of machine learning in enhancing predictive analytics in sports.