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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63024
Volume:
10 |
5 |
May 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
department of computer science and engineering
oriental institute of science and technology
Professional sports analytics has evolved from rudimentary statistical tracking to a sophisticated computational field dominated by machine learning and artificial intelligence [6]. This paper presents an extensive, integrated framework the "Integrated Python Sports Stack" designed to unify disparate domains of sports science. By synthesizing data from multiple sources, including real world spatio temporal logs from the German Bundesliga and synthetic event data from game engines (SoccER), we address two primary pillars: predictive injury prevention and descriptive tactical analysis [2, 3]. We utilize SMOTE (Synthetic Minority Over Sampling Technique) to resolve the class imbalance inherent in injury datasets and employ Retrieval Augmented Generation (RAG) powered by LangChain and FAISS for natural language querying [8, 11, 12]. Our results demonstrate that Python’s flexibility allows for a submillisecond retrieval of tactical events with 70% accuracy in spatial temporal contexts, while Monte Carlo simulations effectively forecast injury risk with an 85.2% accuracy rate in lowrisk profiles. This research establishes a scalable blueprint for the transition from manual scouting to automated, AI driven performance optimization.
KEYWORDS
This paper has presented an integrated Python driven framework that successfully synthesizes predictive injury modeling with descriptive tactical intelligence. By utilizing SMOTE for data balancing and RAG for conversational retrieval, we offer a blueprint for the next generation of sports analytics [2, 8, 12]. Future Directions: 1. Multi Modal RAG: Integrating video frames directly into the vector database to allow queries like "Show me why this goal was conceded." 2. Real time Computer Vision: Using YOLO and OpenPose to automate the event tagging process, removing the reliance on manual providers [13]. 3. GraphRAG: Exploring Knowledge Graphs (GraphRAG) to improve the "Identity" query accuracy (currently at 50%).
[1] Suresh, Y., Avakian, R., Carpitella, S., et al. (2025). "Sports analytics optimizing injury prevention and player availability in professional soccer." Procedia Computer Science 274, 477 485. [2] Tzikas, I., Didovic, S., Gerschner, F., et al. (2025). "Dynamic Descriptive Analytics in Football: A Case Study with Retrieval Augmented Generation for Structured Data." Procedia Computer Science 270, 1886 1895. [3] Morra, L., Manigrasso, F., & Lamberti, F. (2020). "SoccER: Computer graphics meets sports analytics for soccer event recognition." SoftwareX 12, 100612. [4] Moreno, S. C., Escudero, E. C., & Navarro, A. R. (2026). "Towards the automation of sports scouting using artificial intelligence." Engineering Applications of Artificial Intelligence 176, 114795. [5] Jang, W., & Hwang, J. (2025). "Analysing perception of Over The Top (OTT) sports broadcasting: focus on social media bigdata analytics." Heliyon 11, e41679. [6] Nambron, A., Sreedharan, S. C., et al. (2026). "Evolving landscapes of artificial intelligence in sports: Insights from scientometric and altmetric analyses." Intelligent Sports and Health 2, 117 128. [7] Everett, G., Beal, R., et al. (2024). "The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer." arXiv preprint arXiv:2402.04898. [8] Chawla, N. V., Bowyer, K. W., et al. (2002). "SMOTE: Synthetic Minority Over sampling Technique." Journal of Artificial Intelligence Research 16, 321 357. [9] Wang, X., Salmani, M., et al. (2024). "Beyond the limits: A survey of techniques to extend the context length in large languagemodels." arXiv preprint arXiv:2402.02244. [10] Dvorak, J., Junge, A., et al. (2000). "Risk factor analysis for injuries in football players." The American Journal of Sports Medicine 28, S69 S74.
Publication Date
5/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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