Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms - MSc Research Project

Sep 17, 2026

Uploaded by Levi

Local Twenty20 cricket leagues, including the Indian Premier League, have increased investment in high-quality players aged 20 to 30, making recruitment a critical and time-consuming task that directly affects championship outcomes and financial risk. This MSc research evaluates whether machine learning can identify promising young and middle-aged players using past performance statistics, comparing Random Forest and Naive Bayes models on metrics such as accuracy and precision. Predictions target runs scored by batsmen and wickets taken by bowlers, with Random Forest delivering the highest accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Abstract

1 Introduction

1.1 Research Motivation & Project Background

Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms MSc Research Project Data Analytics Arkoprovo Sarkar Student ID: x19148038 School of Computing National College of Ireland Supervisor: Aaloka Anant National College of Ireland Project Submission Sheet School of Computing | Student Name: | Arkoprovo Sarkar | | --- | --- | | Student ID: | x19148038 | | Programme: | Data Analytics | | Year: | 2022-2023 | | Module: | MSc Research Project | | Supervisor: | Aaloka Anant | | Submission Due Date: | 15/12/2022 | | Project Title: | Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms | | Word Count: | 6335 | | Page Count: | 19 | I hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project. ALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action. | Signature: | | | --- | --- | | Date: | 31st January 2023 | PLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST: | Attach a completed copy of this sheet to each project (including multiple copies) . | □ | | --- | --- | | Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ | | You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ | Assignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office. | Office Use Only | | | --- | --- | | Signature: | | | Date: | | | Penalty Applied (if applicable): | | Identification and Selection of Rising Performers in T20 Cricket Using Machine Learning Algorithms Arkoprovo Sarkar x19148038 Abstract 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 alot 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. 1 Introduction Cricket is played between two teams of eleven players each. It was initially played in England in the sixteenth century. (Kamble et al.; 2021) . One team bats to score runs, while the other bowls to stop them from scoring. Team trying to score maximum amount of runs are known as batting team while the team trying to restrict the batting team from scoring runs by taking as many wickets as poss

FAQs

What problem does the project address in T20 cricket recruitment?

The project targets the difficulty of selecting the right players for T20 teams, since incorrect recruitment can lead to poor results and significant financial loss.

Which machine learning approaches are compared in this study?

The study measures model performance for Random Forest and Naive Bayes using evaluation metrics such as accuracy and precision.

What outputs do the models predict?

The models are used to predict the number of runs scored by batsmen and the number of wickets taken by bowlers.

Sep 17, 2026

Uploaded by Levi