Fawad Nasim^1,2,*, Muhammad Adnan Yousaf¹, Sohail Masood^1,2, Arfan Jaffar^1,2, Muhammad Rashid³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2865-2877, 2023, DOI:10.32604/iasc.2023.034258

Abstract Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success. A good batsman not only scores run but also provides stability to the team’s innings. The most important factor in selecting a batsman is their ability to score runs. It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record. This hypothesis is based on the fact that a player’s batting average is generally considered to be a good indicator of their future performance. We proposed a data-driven probabilistic system… More >

Haseeb Ahmad¹, Shahbaz Ahmad¹, Muhammad Asif¹, Mobashar Rehman^2,*, 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 >

Raja Sher Afgun Usmani¹, Syed Muhammad Saqlain Shah^{1, *}, Muhammad Sher Ramzan², Abdullah Saad AL-Malaise AL-Ghamdi², Anwar Ghani¹, Imran Khan¹, Farrukh Saleem²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1113-1136, 2020, DOI:10.32604/cmc.2020.010789

Abstract There is an international cricket governing body that ranks the expertise of all the cricket playing nations, known as the International Cricket Council (ICC). The ranking system followed by the ICC relies on the winnings and defeats of the teams. The model used by the ICC to implement rankings is deficient in certain key respects. It ignores key factors like winning margin and strength of the opposition. Various measures of the ranking concept are presented in this research. The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics. The proposed approaches not only rank… More >