A Bayesian approach for predicting match outcomes: FIFA World Cup 2026

Abstract

One of the biggest international football competitions, the FIFA World Cup provides teams with an exciting and unpredictable stage on which to display their skills. Predicting match outcomes isn't easy due to the numerous factors involved, like team strategy, player performance, and even unpredictable elements like weather or injuries. Traditional statistical methods in the frequentist framework (such as regression model, machine learning and Monte Carlo simulation) might not fully capture these complexities. This study applied the Bayesian logistics regression and gradient boosting model. to predict possible match outcomes in the forthcoming FIFA World Cup 2026. The Bayesian framework provides a probabilistic and adaptable base that adjusts to tournament dynamics and incorporates prior knowledge, while gradient boosting captures complex non-linear correlations. Key variables include player form, team dynamics, and strategic differences. Data were collected from FIFA's official site and Kaggle, covering historical match data, player statistics and team rankings. Data preprocessing, including median imputation for missing values and feature engineering were carried out. The dataset is split into train-test-validate sets, and the two models evaluated exhibited high predictive accuracy. The study identified top contenders, highlighted offensive and defensive strengths, noted feature importance. The findings emphasize the potential of machine learning in sports analytics. The results identified the leading contenders for the 2026 FIFA World Cup, listing them in order of superiority. Results aim to contribute to the field of sports analytics, offering valuable insights into the complex dynamics influencing success in high-stakes football tournaments. From the literatures, this study on the application of Bayesian logistics regression and gradient boosting model is one of the rare applications to sport analytic.