Affiliations: [a] Department of Computer Science and Engineering, Techno International New Town, Kolkata, India | [b] Department of Computer Science and Engineering, JIS University, Kolkata, India | [c] Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India | [d] Department of Computer Science and Engineering, Meghnad Saha Institute of Technology, Kolkata, India
Correspondence:
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Corresponding author: Sanjay Chakraborty, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. E-mail: [email protected].
Abstract: In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting.
Keywords: Machine learning, prediction, cricket, T-20 format, classification
