P-ISSN: 2663-3582, E-ISSN: 2663-3590
Printed Journal | Refereed Journal | Peer Reviewed Journal
2023, Vol. 5, Issue 2, Part A
Win probability prediction for IPL match using various machine learning techniques
Author(s): Chandra Sekhar Sanaboina and Kalaparthi Vikram Kumar
Abstract: The paper's primary objective is to predict the win probability for both the batting and bowling teams in the IPL (Indian Premier League) match using various machine learning algorithms. This paper tackles the challenge of forecasting the result of a match determined by the Target, Net Run Rate (NRR), Current Run Rate (CRR), Score, Fall of Wickets, and the Technique each team employs during each game. For making predictions, three machine learning models Logistic Regression, Random Forest, and Naive Bayes were utilized. The Logistic Regression and Random Forest Algorithms provide the best results and have an accuracy rate of 82% and 96% respectively whereas Naïve Bayes has an accuracy rate of 63%.
DOI: 10.33545/26633582.2023.v5.i2a.94
Pages: 13-20 | Views: 393 | Downloads: 226
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How to cite this article:
Chandra Sekhar Sanaboina, Kalaparthi Vikram Kumar. Win probability prediction for IPL match using various machine learning techniques. Int J Eng Comput Sci 2023;5(2):13-20. DOI: 10.33545/26633582.2023.v5.i2a.94
