Predicting Outcomes in Limited-Overs Cricket Matches

Abstract

Cricket is a popular sport in the commonwealth countries, particularly the limited over formats. As with any sport, predicting the outcome of the game of cricket is of popular interest. For the first innings, the task is to predict the eventual score that the team batting first will reach. For the second innings, the task is to predict the match result. Existing algorithms for predicting the outcome of limited over cricket matches are simplistic and their performance leaves room for improvement. In this paper, we provide novel features including team strength indicators that capture the situation of the match more comprehensively and accurately. We use a collection of state-of-the-art supervised Machine Learning (ML) approaches for the prediction tasks. Further, we also present an approach based on Long-Short Term Memory (LSTM) Networks to incorporate the oft-mentioned concept of 'momentum' for predicting the outcomes. We show with real data that the mentioned ML models outperform the current state of art (WASP) in outcome prediction for cricket. Further, we show that incorporating the proposed features improves prediction accuracy. Finally, the LSTM model outperforms all other models with the same set of features, thereby confirming that 'momentum' indeed helps us in better prediction of outcomes.