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Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms

Sarkar, Arkoprovo (2023) Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Local twenty20 cricket leagues such as the Indian Premier League and others have seen an increase in the amount of money franchise owners are willing to spend on quality players between the ages of 20 and 30. In cricket, the most important and time-consuming job is player recruitment since it determines the team’s chances of winning. If a team overpays for the incorrect player, they risk losing the championship and maybe millions of dollars. Because of this, there has been a lot of work put into developing machine learning models that can forecast a cricket player’s performance. This research is being done with the intention of determining whether it is possible to utilize machine learning to pick out potential young and middle aged players aged 20 to 30, on the basis of their past statistics. In this particular study, two distinct approaches to machine learning have been used. In the course of research, machine learning algorithms performance of Random Forest and Naive Bayes has been measured using a variety of metrics like accuracy, precision, and so on. Both of these models have been used to make predictions about the number of runs scored by the batsmen and the number of wickets taken by bowlers. It was discovered that the Random Forest Classifier was the one that was the most accurate for predicting both runs scored and wickets taken.

Item Type: Thesis (Masters)
Anant, Aaloka
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 25 May 2023 15:56
Last Modified: 25 May 2023 15:56

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