A Comparative Evaluation of Machine Learning Algorithms for Predicting Match Outcomes in White Ball Cricket

Machine learning (ML) has become a powerful tool in sports analytics, especially for predicting cricket match outcomes. In this study, we compare the performance of five commonly used ML algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes—for predicting the results of white-ball cricket matches (ODIs and T20s). We used historical match data with features like team names, toss winner, toss decision, venue, and match outcome. After preprocessing and training the models, we evaluated their performance using metrics such as accuracy, precision, recall, F1-score, and AUC. Our results show that XGBoost performed the best overall, followed closely by Random Forest. This study helps in understanding which algorithms work best for cricket match prediction and could be useful for teams, analysts, and even fans.