Dynamic Winner Prediction in Twenty20 Cricket: Based on Relative Team Strengths

Predicting the outcome of a match has always been at the center of sports analytics. Indian Premier League (IPL), a professional Twenty20 (T20) cricket league in India, has established itself as one of the biggest tournaments in cricket history. In this paper, we propose a model to predict the winner at the end of each over in the second innings of an IPL cricket match. Our methodology not only incorporates the dynamically updating game context as the game progresses, but also includes the relative strength between the two teams playing the match. Estimating the relative strength between two teams involves modeling the individual participating players’ potentials. To model a player, we use his career as well as recent performance statistics. Using the various dynamic features, we evaluate several supervised learning algorithms to predict the winner of the match. Finally, using the Random Forest Classifier (RFC), we have achieved an accuracy of 65.79% 84.15% over the course of second innings, with an overall accuracy of 75.68%.