Haseeb Ahmad1, Shahbaz Ahmad1, Muhammad Asif1, Mobashar Rehman2,*, Abdullah Alharbi3, Zahid Ullah4
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1215-1232, 2021, DOI:10.32604/cmc.2021.016659
Abstract Cricket databases contain rich and useful information to examine and forecasting patterns and trends. This paper predicts Star Cricketers (SCs) from batting and bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers. Prediction is performed by applying Bayesian-rule, function and decision-tree-based models. Experimental evaluations are performed to validate the applicability of the proposed approach. In particular, the impact of the individual features on the prediction of SCs are analyzed. Moreover, the category and model-wise feature evaluations… More >
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