Selection of Best Players in ODI Cricket using Ranking Based Indexing Method

Yenuga, Bhaskar Reddy (2022) Selection of Best Players in ODI Cricket using Ranking Based Indexing Method. Masters thesis, Dublin, National College of Ireland.

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Abstract

In an effort to gain priority, cricket players are rated to show their superiority over rivals to acquire a position. Numerous scholars have proposed various statistical techniques to evaluate teams and their players in studies on this topic. The ability to compare a player's bowling and batting results have been established. In contrast to previous studies, it is difficult to achieve the goal of player and team selection. In the context of One-Day International (ODI) cricket, the research focuses on the prediction of the best player's selection for team-making using a ranking-based indexing method based on linear programming. Additionally, the dataset contains information from the ESPNCRICINFO website by web scrapping about seven important features, namely batting, bowling, fielding, partnership, all-rounder, batting from first innings, and bowling from first innings records. All the characteristics and several attributes are well analysed through the linear discriminate method to obtain the rank of each player and lastly the final rank for the selection of the 11 best players from a team bench of 15. Each rank of the cricketer's role for the selected players suggests which player plays which team's role along with their final rank estimation. In comparison, it has been found that 9 players from the predicted players from selection were selected for one ODI match played against Australia. It is calculated that the accuracy rate of the findings is close to 82%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Muntean, Cristina Hava UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	27 May 2023 11:59
Last Modified:	27 May 2023 11:59
URI:	https://norma.ncirl.ie/id/eprint/6682

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