Optimal selection of a probabilistic machine learning model for predicting high run chase outcomes in T-20 international cricket

Predicting high-run chases in cricket is a complex task influenced by various factors, including team rankings, match conditions, pitch behavior, and inning scores. This study evaluates the effectiveness of probabilistic machine learning models, namely Naïve Bayes (NB), Bayesian Network (BN), Bayesian Regularized Neural Network (BRNN), Hidden Naïve Bayes (HNB), Correlation Feature-Based Filter Weighting Naïve Bayes (CFWNB), and Class-Specific Attribute Weighted Naïve Bayes (CAWNB), in predicting high run chases in T20I cricket. Model performance was assessed using accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and entropy, while Monte Carlo simulations ensured robustness across multiple iterations. Non-parametric statistical tests were employed due to the non-normal distribution of performance metrics, with the Friedman test revealing significant ranking variations among models. The results demonstrate that CAWNB consistently outperforms other models in terms of accuracy, precision, AUC, and F1-score, making it the most reliable choice for high-run chase prediction. Future research should explore hybrid Bayesian deep learning approaches, real-time data adaptation, and the application of these models to other cricket formats and sports analytics to further enhance predictive performance.