Modelling first innings totals in T20 cricket: applications in the Indian Premier League - Gilbert, Arlton (Department of Statistical Sciences, 2024)
In the game of cricket, teams batting first are faced with the question of how many runs are enough. This paper proposes a solution to this in the context of the Indian Premier League (IPL). The aim is to build a model that will allow teams to determine what scores they would need to score for any given confidence of avoiding defeat in regular time, viz. before any Super Overs. The following machine learning methods are considered for this purpose: logistic regression, classification trees, bagging, random forest, boosting, support vector machines, artificial neu- ral networks, and naive Bayes. Features are chosen that represent various key aspects of the game, including player strengths, stadium information, the winner of the toss, and which teams are involved. The results show that logistic regression is the best performing model, having a prediction accuracy of 70.27% and a Brier score of 0.2 for the 2022 season of the IPL. The majority of the incorrect predictions occurred in prediction ranges where the model itself suggested the game could have gone either way. The model is, therefore, fit for purpose and can allow teams to pace their innings and reduce unnecessary risks. The model can also be trained and used on other limited-over tournaments, including one-day matches.