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@article{196517,
        author = {Krish Uday Dakve and Arya Ashish Ganatra and Soham Anil Amburle and Prof. Pankaj Deshmukh},
        title = {TeamTrack: A SaaS-Based Sports Team Management and Player Performance Analytics Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4494-4500},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196517},
        abstract = {Sports team management in colleges and local academies continues to rely on manual registers and disconnected spreadsheets, resulting in fragmented player data, inconsistent performance tracking, and the absence of analytical insights needed for effective coaching decisions. This paper presents TeamTrack, a cloud-hosted multi-tenant Software as a Service (SaaS) platform that centralises team management, player profiling, match statistics, fitness monitoring, and performance analytics under a single, subscription-based system. Drawing on a review of seven research works spanning AI in sports, deep learning performance analysis, machine learning for cricket and basketball analytics, intelligent sports management systems, IoT-enabled monitoring, and big data frameworks, TeamTrack synthesises the state of the art into a practical, affordable platform for educational institutions. The system employs React.js for the frontend, Node.js/Django for the backend, PostgreSQL/MongoDB for data storage, and Scikit-learn for ML-based performance prediction using Random Forest and Support Vector Regression. Evaluation on synthetic multi-sport datasets demonstrates an ML classification accuracy of 82.4% and an R² of 0.76 for regression-based performance forecasting. TeamTrack addresses a significant gap in sports technology for colleges and academies, providing a scalable, data-driven foundation for talent identification and evidence-based coaching.},
        keywords = {SaaS; Sports Management; Player Performance Analytics; Machine Learning; Multi-Tenant Architecture; Random Forest; SVR; Cloud Computing; Fitness Tracking; Performance Prediction},
        month = {April},
        }