Predicting the Winner of a Twenty20 International Cricket Match: Classification and Explainable Machine Learning Approach

Abstract

We present a supervised machine learning approach to predict the winner of a Twenty20 (T20) international match. The prediction dynamically changes as the match progresses. We also use explainable machine learning techniques (SHAP scores) to understand the importance of various features in making the decision at various stages of the T20 match. We present results on a dataset of 808 men's T20 international matches. The dynamic accuracy increases from about 55% in the initial stages of the T20 match to a maximum of about 85% in the final stages of the match (with an overall accuracy of about 63% in innings 1 and 74% in innings 2). SHAP scores reveal that team strength is an important feature in making the prediction in initial stages of the match; however, in the final stages, match situation plays the dominant role in the decision making process. Our work may help team coaches and captains to assess their chances of winning and/or chart a course towards winning in the ongoing T20 match, as well as be useful for sports analytics and gambling websites and apps.