Cricket Team Selection Based On Past Performance Using ML

The procedure for deciding the cricket team is based on the selectors' experience and personal opinion. This selection method will lead to biased judgments sometimes, and it does not always reflect the actual potentials of a player. Machine learning algorithms can easily overcome this issue because they analyse the historical performance data and predict the best composition for a team. Hence, this paper discusses various machine learning algorithms on applications such as Random Forest, XG Boost, Support vector machine and Linear Regression in the aim of improving practice cricket team selections. Therefore, some numbers of the machine learning model have been implemented on these cricketing selections using batting averages, bowling economy, and fielding efficiency past performance data to predict optimum team composition. This paper was able to predict the accuracy of team selection using Linear regression, Random Forests and SVM, obtaining a performance rate of 93.82%, 94%, and 93.83% respectively. ML algorithms can, therefore, improve significantly the fairness and efficiency with which cricket teams are selected. Further research is, however necessary to enhance the developed models and involve other important factors like player fitness and match conditions in the selection processes.