Evolution-Based Performance Prediction of Star Cricketers

Cricket databases contain rich and useful information to examine forecasting patterns trends. This paper predicts Star Cricketers (SCs) from batting bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved using effective features that incorporate the standard measures of player their peers. Prediction performed applying Bayesian-rule, function decision-tree-based Experimental evaluations are validate applicability proposed approach. In particular, impact individual on prediction SCs analyzed. Moreover, category model-wise feature also conducted. A cross-validation mechanism applied our approach which further confirms incorporated statistically significant. Finally, leading extracted based scores standings cross-checked with those provided International Council.