Soccer match outcome prediction with random forest and gradient boosting models

Abstract

In order to accurately predict the results of soccer matches, this study introduces Machine Learning (ML) techniques in joint Random Forest (RF) and Gradient Boosting (GB) models. In order to forecast the results of the next World Cup, a model has been trained using past information from prior tournaments. The proposed model is evaluated using multiple performance criteria including precision and accuracy. The RF approach outperforms the GB approach in terms of both accuracy and precision, as concluded after the experiment. The most important features for predicting the outcome of football games are identified using feature importance scores. Football enthusiasts and analysts can use the proposed model to predict the outcome of football games with high accuracy. The implications of these findings for football teams are practical as they provide valuable insights for improving team performance and increasing their chances of winning the World Cup. By identifying the most important features for predicting the outcome of football games, teams can focus their efforts on improving these areas, increasing their chances of success. Football teams and football analysts can benefit from accurate predictions, which are enabled by machine learning techniques such as GB and RFs. Overall, this study presents a promising approach to predicting the outcome of football games, with practical implications for the field of sports analytics.