Optimising Cricket Team Selection for One Day International Series Based on Match Conditions

Abstract

This thesis focuses on predicting an optimal Sri Lankan cricket team for One Day International (ODI) matches by analysing player performance under different conditions, including weather conditions, opponents and venue. We try to maximise the overall team performance by predicting the best team combination from existing players. The selectors generally perform team selection considering recent performance, including batting and bowling averages of the players. These metrics provide limited insight into players’ potential performance, which leads to drop-ups of qualified. Therefore, consideration of more factors and robust machine learning is required. Our study considers overall performance, consistency, venue, opposition, and recent form of players to predict the players' performance using Random Forest Regression. Then, use the predicted performance to evaluate the player rating of each player towards the team by using Neural Networks. Previous studies have proved that Neural Networks can solve team selection problems successfully [1]. Then we select the team based on the predicted winning contributions to maximise the overall team winning probability. The study concludes by predicting the last 45 matches the Sri Lankan cricket team played during 2017-2019 with the actual playing 11 and the optimal playing 11 selected using our proposed system. We observed that the winning rate of the Sri Lankan cricket team could be improved from 37.77% to 77.77% (105% improvement) if teams were selected using our proposed system.