Cricket Team Prediction Using Machine Learning Techniques

Player selection is an essential task for any sport and similarly for the game of cricket as well. The players’ performance varies on various factors. The team management and the captain selects eleven players for each match from the entire squad. Review of different attributes and players’ results is considered to pick the best eleven players. By scoring runs each batsman contributes and each bowler contributes by taking wickets and awarding minimum runs. This project aims to predict team success based on the player's past records. Acquisition of the players 'results individually and their contribution to the team i.e. Best batting performance among the batsmen available, best bowling performance among the available bowlers and best all-rounder performance will be a great help in selecting the eleven players. We used the Random Forest Algorithm and Decision Tree classifiers to produce the problem's prediction models. It was found that the Random Forest classifier is the most reliable for the problems proposed.

Suggested Citation: Suggested Citation

Patil, Nilesh M. and Sequeira, Bevan H. and Gonsalves, Neil N. and Singh, Abhishek A., Cricket Team Prediction Using Machine Learning Techniques (April 10, 2020). Available at SSRN: https://ssrn.com/abstract=3572740 or http://dx.doi.org/10.2139/ssrn.3572740