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@article{179774,
        author = {Jeevam Chivate},
        title = {Real Time Sports-Analytics System using AI and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2132-2136},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179774},
        abstract = {In recent years, machine learning (ML) has revolutionized many fields, including sports analytics. The ability to predict player performance based on historical statistics has opened new opportunities for coaches, analysts, and fantasy sports participants. This paper introduces a machine learning model aimed at predicting basketball player performance by leveraging various in-game statistics, including field goals, assists, rebounds, and more. The model was implemented using a linear regression algorithm and deployed through a Flask-based API to allow for real-time predictions. Through a rigorous training and testing phase, the model's performance was evaluated, and the results were compared to existing methods in the sports analytics space. This study provides a comprehensive overview of the dataset, model construction, API deployment, challenges faced, and future avenues for enhancing the system. The model's predictive power was assessed and compared against real-world models used by major sports organizations.},
        keywords = {},
        month = {April},
        }