Predicting Football Match Results Using Historical Data and Machine Learning

Abstract

Predicting football match outcomes has been a subject of considerable interest due to its implications in sports analytics and betting markets. This paper explores the application of machine learning techniques to forecast football match results using historical data. Various features such as team performance metrics, player statistics, match venue, and weather conditions are considered to train and validate predictive models. The study compares several machine learning algorithms, including logistic regression, decision trees, and neural networks, evaluating their efficacy in accurately predicting match outcomes. Results indicate promising performance in predicting wins, losses, and draws, demonstrating the potential of machine learning in enhancing predictive capabilities in football.