SPRING 2025
Stats & Stumps: Using Machine Learning to Predict T20I Matches with Player and Venue Data
AUTHOR
Archith Sharma, Texas Academy of Mathematics and Science ’25
ABSTRACT
Cricket is gaining popularity worldwide rapidly, and at the front is the newest format of the game, Twenty20 Internationals (T20I), and big data. This project attempts to predict cricket match outcomes using player-level performance metrics and machine learning models.
A dataset of 1,029 T20I matches was analyzed, with player-level features engineered from batting and bowling statistics such as runs, strike rate, boundaries, wickets, economy rate, and maiden overs.
About the Wharton Sports Analytics Journal
ISSN 3070-4065 (Online)
The Journal is published by the Wharton Sports Analytics and Business Initiative and features original student research at the intersection of sports, business, and analytics. Explore the Journal.
