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Big Data Analytics Framework for Decision-Making in Sports Performance Optimization

Author

Listed:
  • Dan Cristian Mănescu

    (Academy of Economic Sciences Bucharest, 010374 Bucharest, Romania)

Abstract

The rapid proliferation of wearable sensors and advanced tracking technologies has revolutionized data collection in elite sports, enabling continuous monitoring of athletes’ physiological and biomechanical states. This study proposes a comprehensive big data analytics framework that integrates data acquisition, processing, analytics, and decision support, demonstrated through synthetic datasets in football, basketball, and athletics case scenarios, modeled to represent typical data patterns and decision-making workflows observed in elite sport environments. Analytical methods, including gradient boosting classifiers, logistic regression, and multilayer perceptron models, were employed to predict injury risk, optimize in-game tactical decisions, and personalize sprint mechanics training. Key results include a 12% reduction in hamstring injury rates in football, a 16% improvement in clutch decision-making accuracy in basketball, and an 8% decrease in 100 m sprint times among athletes. The framework’s visualization tools and alert systems supported actionable insights for coaches and medical staff. Challenges such as data quality, privacy compliance, and model interpretability are addressed, with future research focusing on edge computing, federated learning, and augmented reality integration for enhanced real-time feedback. This study demonstrates the potential of integrated big data analytics to transform sports performance optimization, offering a reproducible and ethically sound platform for advancing personalized, data-driven athlete management.

Suggested Citation

  • Dan Cristian Mănescu, 2025. "Big Data Analytics Framework for Decision-Making in Sports Performance Optimization," Data, MDPI, vol. 10(7), pages 1-29, July.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:7:p:116-:d:1701302
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    References listed on IDEAS

    as
    1. Daniel Link & Steffen Lang & Philipp Seidenschwarz, 2016. "Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-16, December.
    2. Alessio Rossi & Luca Pappalardo & Paolo Cintia & F Marcello Iaia & Javier Fernàndez & Daniel Medina, 2018. "Effective injury forecasting in soccer with GPS training data and machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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