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Evolution-Based Performance Prediction of Star Cricketers

Haseeb Ahmad1, Shahbaz Ahmad1, Muhammad Asif1, Mobashar Rehman2,*, Abdullah Alharbi3, Zahid Ullah4
1 Department of Computer Science, National Textile University, Faisalabad, Pakistan
2 Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, 31900, Perak, Malaysia
3 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
4 Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
* Corresponding Author: Mobashar Rehman. Email:
(This article belongs to this Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)

Computers, Materials & Continua 2021, 69(1), 1215-1232. https://doi.org/10.32604/cmc.2021.016659

Received 07 January 2021; Accepted 01 March 2021; Issue published 04 June 2021

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 are also conducted. A cross-validation mechanism is applied to validate the performance of our proposed approach which further confirms that the incorporated features are statistically significant. Finally, leading SCs are extracted based on their performance evolution scores and their standings are cross-checked with those provided by the International Cricket Council.

Keywords

Online social databases; cricket; star cricketers; prediction; machine learning

Cite This Article

H. Ahmad, S. Ahmad, M. Asif, M. Rehman, A. Alharbi et al., "Evolution-based performance prediction of star cricketers," Computers, Materials & Continua, vol. 69, no.1, pp. 1215–1232, 2021.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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