Big Data and Data-Driven Research in Sports

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1105

Special Issue Editor


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Guest Editor
Department of Education. Area of Physical Education and Sports, University of Cantabria, Los Castros Avenue, 50, 39005 Santander, Spain
Interests: big data; sports performance; physiology; technologies; physical education; individual sports; team sports; health

Special Issue Information

Dear Colleagues,

The use of big data via the use of various techniques and strategies has evolved in recent years, yielding numerous advantages in the sports field. Its use has led to the development of research and measures that target athletes: performance measurement, physiological and biomechanical enhancements, tactical and strategic improvements in team sports, and many more. New research and methodological improvements in these fields are being produced continuously.

This Special Issue aims to compile scientific evidence regarding the use of big data and decision making for the improvement of sports performance, athlete safety, and other sports-related areas.

In more detail, this Special Issue welcomes original research articles, dataset descriptors, communications, and systematic reviews. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Biomechanics;
  • Physiology;
  • Injury prevention;
  • Tactics and strategies in team sports;
  • Nutrition;
  • New technology applied to big data.

Dr. Oliver Ramos Álvarez
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • performance
  • analysis
  • technology
  • biomechanics
  • physiology
  • optimization
  • team sports

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Published Papers (1 paper)

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Research

29 pages, 2211 KiB  
Article
Big Data Analytics Framework for Decision-Making in Sports Performance Optimization
by Dan Cristian Mănescu
Data 2025, 10(7), 116; https://doi.org/10.3390/data10070116 - 14 Jul 2025
Viewed by 444
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Big Data and Data-Driven Research in Sports)
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