Big Data Analytics Framework for Decision-Making in Sports Performance Optimization
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...
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MDPI AG
2025-07-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/10/7/116 |
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| author | Dan Cristian Mănescu |
| author_facet | Dan Cristian Mănescu |
| author_sort | Dan Cristian Mănescu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-18148ff87b4e4b83960e5e21d1e2aef7 |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-18148ff87b4e4b83960e5e21d1e2aef72025-08-20T03:36:11ZengMDPI AGData2306-57292025-07-0110711610.3390/data10070116Big Data Analytics Framework for Decision-Making in Sports Performance OptimizationDan Cristian Mănescu0Academy of Economic Sciences Bucharest, 010374 Bucharest, RomaniaThe 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.https://www.mdpi.com/2306-5729/10/7/116big datareal-time analyticsdecision-makingsportperformance optimization |
| spellingShingle | Dan Cristian Mănescu Big Data Analytics Framework for Decision-Making in Sports Performance Optimization Data big data real-time analytics decision-making sport performance optimization |
| title | Big Data Analytics Framework for Decision-Making in Sports Performance Optimization |
| title_full | Big Data Analytics Framework for Decision-Making in Sports Performance Optimization |
| title_fullStr | Big Data Analytics Framework for Decision-Making in Sports Performance Optimization |
| title_full_unstemmed | Big Data Analytics Framework for Decision-Making in Sports Performance Optimization |
| title_short | Big Data Analytics Framework for Decision-Making in Sports Performance Optimization |
| title_sort | big data analytics framework for decision making in sports performance optimization |
| topic | big data real-time analytics decision-making sport performance optimization |
| url | https://www.mdpi.com/2306-5729/10/7/116 |
| work_keys_str_mv | AT dancristianmanescu bigdataanalyticsframeworkfordecisionmakinginsportsperformanceoptimization |