Talent scouting and standardizing fitness data in football club: systematic review
Talent scouting and fitness data standardization in professional football clubs have become central topics in recent research. This review aims to consolidate advancements in technology, big data, and data analytics, examining their roles in optimizing talent identification and fitness evaluation w...
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| Format: | Article |
| Language: | English |
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FEADEF
2024-12-01
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| Series: | Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación |
| Subjects: | |
| Online Access: | https://recyt.fecyt.es/index.php/retos/article/view/107766 |
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| author | Moch Yunus Ronal Surya Aditya Nanang Tri Wahyudi Daifallah M. Al Razeeni Reem Iafi AlMutairi |
| author_facet | Moch Yunus Ronal Surya Aditya Nanang Tri Wahyudi Daifallah M. Al Razeeni Reem Iafi AlMutairi |
| author_sort | Moch Yunus |
| collection | DOAJ |
| description |
Talent scouting and fitness data standardization in professional football clubs have become central topics in recent research. This review aims to consolidate advancements in technology, big data, and data analytics, examining their roles in optimizing talent identification and fitness evaluation within football clubs. A systematic search strategy was applied across academic databases, including PubMed, IEEE Xplore, and Scopus, using keywords like "football talent scouting," "fitness data standardization," "data analytics in sports," and "machine learning in football performance." Studies selected for review involved professional football players and interventions using digital technologies and data-driven methods within club settings, covering experimental, observational, and mixed-method designs in football environments. This review highlights the impact of integrating quantitative player statistics with advanced analytics to enhance recruitment precision and team performance, showing that data models—such as classification and regression—can predict performance scores with up to 94% accuracy for forward positions, underscoring the transformative role of data analytics in professional football.
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| format | Article |
| id | doaj-art-5bf206557a024cac8c51ea3be3a71171 |
| institution | OA Journals |
| issn | 1579-1726 1988-2041 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | FEADEF |
| record_format | Article |
| series | Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación |
| spelling | doaj-art-5bf206557a024cac8c51ea3be3a711712025-08-20T02:35:15ZengFEADEFRetos: Nuevas Tendencias en Educación Física, Deportes y Recreación1579-17261988-20412024-12-016110.47197/retos.v61.107766Talent scouting and standardizing fitness data in football club: systematic reviewMoch YunusRonal Surya Aditya0Nanang Tri Wahyudi1Daifallah M. Al Razeeni2Reem Iafi AlMutairi3Universitas Negeri MalangUniversitas Negeri MalangKing Saud UniversityHail University Talent scouting and fitness data standardization in professional football clubs have become central topics in recent research. This review aims to consolidate advancements in technology, big data, and data analytics, examining their roles in optimizing talent identification and fitness evaluation within football clubs. A systematic search strategy was applied across academic databases, including PubMed, IEEE Xplore, and Scopus, using keywords like "football talent scouting," "fitness data standardization," "data analytics in sports," and "machine learning in football performance." Studies selected for review involved professional football players and interventions using digital technologies and data-driven methods within club settings, covering experimental, observational, and mixed-method designs in football environments. This review highlights the impact of integrating quantitative player statistics with advanced analytics to enhance recruitment precision and team performance, showing that data models—such as classification and regression—can predict performance scores with up to 94% accuracy for forward positions, underscoring the transformative role of data analytics in professional football. https://recyt.fecyt.es/index.php/retos/article/view/107766FootballData ScienceBig DataDigital TechnologyAthletic PerformanceMachine Learning |
| spellingShingle | Moch Yunus Ronal Surya Aditya Nanang Tri Wahyudi Daifallah M. Al Razeeni Reem Iafi AlMutairi Talent scouting and standardizing fitness data in football club: systematic review Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación Football Data Science Big Data Digital Technology Athletic Performance Machine Learning |
| title | Talent scouting and standardizing fitness data in football club: systematic review |
| title_full | Talent scouting and standardizing fitness data in football club: systematic review |
| title_fullStr | Talent scouting and standardizing fitness data in football club: systematic review |
| title_full_unstemmed | Talent scouting and standardizing fitness data in football club: systematic review |
| title_short | Talent scouting and standardizing fitness data in football club: systematic review |
| title_sort | talent scouting and standardizing fitness data in football club systematic review |
| topic | Football Data Science Big Data Digital Technology Athletic Performance Machine Learning |
| url | https://recyt.fecyt.es/index.php/retos/article/view/107766 |
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