Statistic analysis of IPL match score and winner inning wise using machine learning algorithms

Title

Statistic analysis of IPL match score and winner inning wise using machine learning algorithms

Creator

Preety; Ray S.K.; Yadav M.; Khare A.; Mishra V.

Description

This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning. 2024, Taru Publications. All rights reserved.

Source

Journal of Interdisciplinary Mathematics, Vol-27, No. 2, pp. 245-258.

Date

2024-01-01

Publisher

Taru Publications

Subject

Cricket analytics; Data analysis; IPL score prediction; Machine learning; Predictive modeling

Coverage

Preety, Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Ray S.K., Department of Mathematics and Statistics, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India; Yadav M., Department of Information Technology, Galgotias College of Engineering and Technology, Uttar Pradesh, India; Khare A., Department of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India; Mishra V., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India

Rights

Restricted Access

Relation

ISSN: 9720502

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.47974/JIM-1819

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188539125&doi=10.47974%2fJIM-1819&partnerID=40&md5=44273e83e4dd30c8839e4fb2646fd185