<![CDATA[The FIFA World Cup is the most prestigious international football competition, bringing together the best national teams from various countries. The 2026 FIFA World Cup introduces a new format featuring 48 teams, thereby increasing the complexity of predicting the eventual champion. This study aims to predict the winning probabilities for the 2026 FIFA World Cup by combining the Random Forest algorithm with Monte Carlo simulation, based on national team performance statistics. The dataset comprises the 48 participating teams, analyzed using eight variables: FIFA Ranking, Elo Rating, win percentage, average goals scored per match, average goals conceded per match, goal difference, squad market value, and past World Cup performance. The data underwent a preprocessing stage—specifically normalization—before being used to construct the Random Forest model. Model evaluation results yielded an accuracy of 84.50%, precision of 82.30%, recall of 81.70%, and an F1-score of 82.00%. Subsequently, a Monte Carlo simulation was conducted over 10,000 iterations to estimate the winning probability for each national team. The results indicate that Morocco has the highest probability of winning at 14.82%, followed by France (13.97%), Argentina (12.76%), Egypt (12.03%), and Brazil (10.88%). The findings demonstrate that the combination of the Random Forest algorithm and Monte Carlo simulation can provide objective, data-driven predictions regarding championship probabilities for international football competitions.]]>
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