Prediction of FIFA World Cup Match Outcomes Based on Random Forests

The prediction of ball game outcomes has emerged as a significant area in sports event forecasting, driven by the growing betting markets for football and basketball. Traditional methods, including human prediction analysis and statistical approaches, face limitations due to the complexity and numerous variables inherent in these games. The advent of machine learning has opened new avenues for enhancing prediction accuracy and efficiency in this field. The main work of this paper is based on FIFA rankings and international football match data from 1872 to 2018. It uses feature engineering techniques to process the data and applies the Random Forest algorithm to build a prediction model. The model shows high prediction accuracy through hyperparameter optimization using grid search techniques. Additionally, by simulating the FIFA World Cup match process and visualizing the knockout stage, the model's effectiveness in predicting actual match outcomes is validated.