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@article{201204,
        author = {Akash Pawar and Anushka  Karpe and Rutuja  Varpe},
        title = {IPL Match Prediction Winner Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3481-3488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201204},
        abstract = {Cricket is one of the most followed sports in India, and the Indian Premier League (IPL) has gained massive popularity due to its fast-paced T20 format and diverse team compositions. Predicting the outcome of IPL matches is valuable for analysts, sponsors, teams, and fans, as match results depend on multiple dynamic factors such as team form, venue conditions, toss decisions, and player performances. This study aims to develop a predictive framework that can estimate the winning team of an IPL match using machine learning techniques. The dataset used spans IPL seasons from 2008 to 2023 and includes match-level and team-performance attributes. The data preprocessing process involved cleaning incomplete entries, encoding categorical variables, and normalizing performance-based metrics. Feature engineering included calculating recent win performance, head-to-head strength, and batting and bowling impact indices. The study evaluates multiple machine learning models, with a focus on ensemble approaches including Gradient Boosting and Random Forest, along with Logistic Regression as a baseline classifier. The methodology incorporates feature selection, model training with cross-validation, and comparative evaluation based on standard performance metrics. This framework demonstrates how machine learning can be effectively applied to sports analytics to support strategic decision-making and enhance understanding of match dynamics.},
        keywords = {IPL Match Prediction, Machine Learning, Ensemble Learning, Gradient Boosting, Sports Analytics},
        month = {May},
        }