IPL Match Win Predictor

Pawan S. Pakhare, Tejas D. Pangarikar and Sanika C. Joshi

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 201-208

Abstract: The Indian Premier League (IPL) is among the largest cricket leagues with extensive statistical data, where estimating match results is difficult because game conditions change continuously. In this study, a predictive learning framework is designed to estimate the winning chances of a team using ball-by-ball data. The proposed system uses important match features such as current run rate, required run rate, wickets remaining, balls remaining, and match phase to capture the real-time match situation. The dataset used for this study consists of historical IPL matches obtained from open cricket datasets collected across several IPL seasons. Multiple predictive learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, are implemented and compared. Among these models, XGBoost provides the highest accuracy of approximately 81%, as it efficiently captures complex feature dependencies and scoring patterns. The results show that the prediction performance improves during the final stages of matches when the outcome becomes more predictable. The proposed system can be used for real-time match prediction and provides useful insights for sports analytics applications.

Keywords: Machine Learning; IPL Match Prediction; XGBoost; Sports Analytics; Cricket Data Analysis; Win Probability (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST26133127 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST26133127/IJSRST26133127 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1589

DOI: 10.32628/IJSRST26133127

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().