IPL Team Winning Prediction using Machine Learning

Abstract: Cricket, being a data-intensive sport, offers a substantial opportunity for the application of machine learning in predictive analytics. This study employs data-driven machine learning methodologies to predict match outcomes in the Indian Premier League (IPL). To create the predictive models, data from the IPL from 2008 to 2024 was collected and prepared. This data included information about team stats, player performance, venue details, and toss results. We used and tested several algorithms, such as Logistic Regression, Random Forest, and XGBoost, to see how well they could predict the chances of a team winning. The XGBoost model did the best, with an accuracy rate of about 78%. This was better than traditional models, mostly because it was better at dealing with the non-linear relationships between match features. The system does more than just predict who will win a match; it also gives clear information about what factors have the biggest impact on how well a team plays. This study shows how machine learning could help analysts, coaches, and fans make strategic decisions, play fantasy sports, and comment on games.

Keywords: Machine Learning, IPL Prediction, Sports Analytics, XGBoost, Cricket Match Outcome, Data-Driven Decision Making.