The Influence of Data Analytics on Sports Performance

Year : 2026 | Volume : 3 | 01 | Page :
    By

    Arya Amit Sadvilkar,

  • Ritik Saha,

  • Rohini Bagul,

  1. Research Scholar, Department of MCA, Thakur Institute of Management Studies, Career Development and Research, Maharashtra, India
  2. Research Scholar, Department of MCA, Thakur Institute of Management Studies, Career Development and Research, Maharashtra, India
  3. Assistant Professor, Department of MCA, Thakur Institute of Management Studies, Career Development and Research, Maharashtra, India

Abstract

Data analytics has drastically changed how we evaluate, improve, and maintain athletic performance. Coaches used to use subjective observations as well as only limited numbers of statistics to consider player performance; however, tracking technology is now advancing at a fast pace. There are now very large amounts of real-time data available on athletes in regards to speed, movement patterns, fatigue, efficiency, etc. This enables all teams to more accurately make objective and data-driven decisions about an athlete’s performance. This study provides examples of how data analytics impacts athletic performance across several different sports disciplines, such as football, cricket, hockey, and basketball. It highlights both the theoretical aspects of data analytics and the practical side using the various tools and machine learning techniques available in Python. This research provides results on how successfully processed, visualized, and analyzed data can provide insights that can support the improvement of athletic performance, therefore, helping teams develop better performance processes. By applying various analytical models to identify patterns, predict future results, and optimize athlete training/game play strategies. Moreover, the research emphasizes how valuable data analytics can assist with injury prevention and rehabilitation management. By tracking workload and physical stress indicators on a regular basis, teams can lower the chance of sustaining an injury while helping to provide athletes with superior rehabilitation plans. With the use of data-driven approaches, it is also easier to create training programs that are tailored to each player’s unique requirements due to the incorporation of additional analytic data.

Keywords: Athlete monitoring, data analytics, injury prevention, machine learning, predictive modeling, Sports performance

How to cite this article:
Arya Amit Sadvilkar, Ritik Saha, Rohini Bagul. The Influence of Data Analytics on Sports Performance. Recent Trends in Sports. 2026; 03(01):-.
How to cite this URL:
Arya Amit Sadvilkar, Ritik Saha, Rohini Bagul. The Influence of Data Analytics on Sports Performance. Recent Trends in Sports. 2026; 03(01):-. Available from: https://journals.stmjournals.com/rts/article=2026/view=241715


References

  1. Li A. Real-time athlete fatigue monitoring using fuzzy decision support systems. International Journal of Computational Intelligence Systems. 2025 Feb 10;18(1):23.
  2. Cañellas ML, Casado CÁ, Nguyen L, López MB. Estimating Exercise-Induced Fatigue from Thermal Facial Images. InICASSP 2024–2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024 Apr 14 (pp. 2800–2804). IEEE. DOI:1109/ICASSP48485.2024.10447613
  3. Lee Y, Lee Y, Kim S, Kim S, Yoo S. Contactless Fatigue Level Diagnosis System Through Multimodal Sensor Data. Bioengineering. 2025 Jan 26;12(2):116.
  4. Hadžić V, Širok B, Malneršič A, Čoh M. Can infrared thermography be used to monitor fatigue during exercise? A case study. Journal of sport and health science. 2019 Jan 1;8(1):89–92.
  5. Rossi A, Pappalardo L, Cintia P, Iaia FM, Fernández J, Medina D. Effective injury forecasting in soccer with GPS training data and machine learning. PloS one. 2018 Jul 25;13(7):e0201264.
  6. Kakhi K, Jagatheesaperumal SK, Khosravi A, Alizadehsani R, Acharya UR. Fatigue monitoring using wearables and AI: Trends, challenges, and future opportunities. Computers in biology and medicine. 2025 Sep 1;195:110461. https://doi.org/10.1016/j.compbiomed.2025.110461
  7. Hu C, Du N, Liu Z, Song Y. Can infrared thermal imaging reflect exercise load? An incremental cycling exercise study. Bioengineering. 2025 Mar 11;12(3):280. https://doi.org/10.3390/bioengineering12030280
  8. Mitchell JH, Haskell W, Snell P, Van Camp SP. Task Force 8: classification of sports. Journal of the American College of Cardiology. 2005 Apr 19;45(8):1364–1367.
  9. Mehmood I, Li H, Umer W, Arsalan A, Anwer S, Mirza MA, Ma J, Antwi-Afari MF. Multimodal integration for data-driven classification of mental fatigue during construction equipment operations: Incorporating electroencephalography, electrodermal activity, and video signals. Developments in the Built Environment. 2023 Oct 1;15:100198. 1–16 https://doi.org/10.1016/j.dibe.2023.100198
  10. Lyubovsky A, Liu Z, Watson A, Kuehn S, Korem E, Zhou G. A pain free nociceptor: Predicting football injuries with machine learning. Smart Health. 2022 Jun 1;24:100262. https://doi.org/10.1016/j.smhl.2021.100262
Ahead of Print Subscription Original Research
Volume 03
01
Received 11/03/2026
Accepted 17/03/2026
Published 21/03/2026
Publication Time 10 Days
1

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