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Integrating physical and tactical factors in football using positional data: a systematic review.

Authors :
Eduardo Teixeira, José
Forte, Pedro
Ferraz, Ricardo
Branquinho, Luís
Silva, António José
Monteiro, António Miguel
Barbosa, Tiago M.
Source :
PeerJ; Nov2022, p1-32, 40p
Publication Year :
2022

Abstract

Background: Positional data have been used to capture physical and tactical factors in football, however current research is now looking to apply spatiotemporal parameters from an integrative perspective. Thus, the aim of this article was to systematically review the published articles that integrate physical and tactical variables in football using positional data. Methods and Materials: Following the Preferred Reporting Item for Systematic Reviews and Meta-analyses (PRISMA), a systematic search of relevant English-language articles was performed from earliest record to August 2021. The methodological quality of the studies was evaluated using the modified Downs and Black Quality Index (observational and cross-sectional studies) and the Physiotherapy Evidence Database (PEDro) scale (intervention studies). Results: The literature search returned 982 articles (WoS = 495; PubMed = 232 and SportDiscus = 255). After screening, 26 full-text articles met the inclusion criteria and data extraction was conducted. All studies considered the integration of physical and tactical variables in football using positional data (n = 26). Other dimensions were also reported, such as psychophysiological and technical factors, however the results of these approaches were not the focus of the analysis (n = 5). Quasi-experimental approaches considered training sets (n = 20) and match contexts (n = 6). One study analysed both training and play insights. Small sided-games (SSG) were the most common training task formats in the reviewed studies, with only three articles addressing medium-sided (MSG) (n = 1) and large-sided games (LSG) (n = 2), respectively. Conclusions: Among the current systematic review, the physical data can be integrated by player's movement speed. Positional datasets can be computed by spatial movement, complex indexes, playing areas, intra-team and inter-team dyads. Futures researches should consider applying positional data in women's football environments and explore the representativeness of the MSG and LSG. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21678359
Database :
Complementary Index
Journal :
PeerJ
Publication Type :
Academic Journal
Accession number :
175537759
Full Text :
https://doi.org/10.7717/peerj.14381