Analysis of the worst-case scenarios in an elite football team: Towards a better understanding and application.
- Publisher:
- TAYLOR & FRANCIS LTD
- Publication Type:
- Journal Article
- Citation:
- Journal of sports sciences, 2021, 39, (16), pp. 1850-1859
- Issue Date:
- 2021-08
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
Analysis of the worst case scenarios in an elite football team Towards a better understanding and application.pdf | Published Version | 1.83 MB | Adobe PDF |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Novak, AR | |
dc.contributor.author | Impellizzeri, FM | |
dc.contributor.author | Trivedi, A | |
dc.contributor.author | Coutts, AJ | |
dc.contributor.author |
McCall, A https://orcid.org/0000-0003-3780-8153 |
|
dc.date.accessioned | 2021-09-09T01:44:48Z | |
dc.date.available | 2021-09-09T01:44:48Z | |
dc.date.issued | 2021-08 | |
dc.identifier.citation | Journal of sports sciences, 2021, 39, (16), pp. 1850-1859 | |
dc.identifier.issn | 0264-0414 | |
dc.identifier.issn | 1466-447X | |
dc.identifier.uri | http://hdl.handle.net/10453/150416 | |
dc.description.abstract | This study investigated the variability in the worst-case scenario (WCS) and suggested a framework to improve the definition and guide further investigation. Optical tracking data from 26 male players across 38 matches were analysed to determine the WCS for total distance, high-speed running (>5.5 m.s<sup>-1</sup>) and sprinting (>7.0 m.s<sup>-1</sup>) using a 3-minute rolling window. Position, total output, previous epoch, match half, time of occurrence, classification of starter vs substitute, and minutes played were modelled as selected contextual factors hypothesized to have associations with the WCS. Linear mixed effects models were used to account for cross-sectional observations and repeated measures. Unexplained variance remained high (total distance R<sup>2</sup> = 0.53, high-speed running R<sup>2</sup> = 0.53 and sprinting R<sup>2</sup> = 0.40). Intra-individual variability was also high (total distance CV = 4.6-8.2%; high-speed CV = 15.6-37.8% and Sprinting CV = 21.1-76.4%). The WCS defined as the maximal physical load in a given time-window, produces unstable metrics lacking context, with high variability. Furthermore, training drills targetting this metric concurrently across players may not have representative designs and may underprepare athletes for complete match demands and multifaceted WCS scenarios. Using WCS as benchmarks (reproducing similar physical activity for training purposes) is conceptually questionable. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | TAYLOR & FRANCIS LTD | |
dc.relation.ispartof | Journal of sports sciences | |
dc.relation.isbasedon | 10.1080/02640414.2021.1902138 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | 1106 Human Movement and Sports Sciences, 1302 Curriculum and Pedagogy | |
dc.subject.classification | Sport Sciences | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Athletic Performance | |
dc.subject.mesh | Cross-Sectional Studies | |
dc.subject.mesh | Football | |
dc.subject.mesh | Geographic Information Systems | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Male | |
dc.subject.mesh | Retrospective Studies | |
dc.subject.mesh | Running | |
dc.subject.mesh | Young Adult | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Retrospective Studies | |
dc.subject.mesh | Cross-Sectional Studies | |
dc.subject.mesh | Running | |
dc.subject.mesh | Football | |
dc.subject.mesh | Geographic Information Systems | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Male | |
dc.subject.mesh | Athletic Performance | |
dc.subject.mesh | Young Adult | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Athletic Performance | |
dc.subject.mesh | Cross-Sectional Studies | |
dc.subject.mesh | Football | |
dc.subject.mesh | Geographic Information Systems | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Male | |
dc.subject.mesh | Retrospective Studies | |
dc.subject.mesh | Running | |
dc.subject.mesh | Young Adult | |
dc.title | Analysis of the worst-case scenarios in an elite football team: Towards a better understanding and application. | |
dc.type | Journal Article | |
utslib.citation.volume | 39 | |
utslib.location.activity | England | |
utslib.for | 1106 Human Movement and Sports Sciences | |
utslib.for | 1302 Curriculum and Pedagogy | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Health | |
pubs.organisational-group | /University of Technology Sydney/Strength - CHSP - Health Services and Practice | |
pubs.organisational-group | /University of Technology Sydney/Strength - CHT - Health Technologies | |
pubs.organisational-group | /University of Technology Sydney/Centre for Health Technologies (CHT) | |
utslib.copyright.status | closed_access | * |
pubs.consider-herdc | false | |
dc.date.updated | 2021-09-09T01:44:47Z | |
pubs.issue | 16 | |
pubs.publication-status | Published | |
pubs.volume | 39 | |
utslib.citation.issue | 16 |
Abstract:
This study investigated the variability in the worst-case scenario (WCS) and suggested a framework to improve the definition and guide further investigation. Optical tracking data from 26 male players across 38 matches were analysed to determine the WCS for total distance, high-speed running (>5.5 m.s-1) and sprinting (>7.0 m.s-1) using a 3-minute rolling window. Position, total output, previous epoch, match half, time of occurrence, classification of starter vs substitute, and minutes played were modelled as selected contextual factors hypothesized to have associations with the WCS. Linear mixed effects models were used to account for cross-sectional observations and repeated measures. Unexplained variance remained high (total distance R2 = 0.53, high-speed running R2 = 0.53 and sprinting R2 = 0.40). Intra-individual variability was also high (total distance CV = 4.6-8.2%; high-speed CV = 15.6-37.8% and Sprinting CV = 21.1-76.4%). The WCS defined as the maximal physical load in a given time-window, produces unstable metrics lacking context, with high variability. Furthermore, training drills targetting this metric concurrently across players may not have representative designs and may underprepare athletes for complete match demands and multifaceted WCS scenarios. Using WCS as benchmarks (reproducing similar physical activity for training purposes) is conceptually questionable.
Please use this identifier to cite or link to this item:
Download statistics for the last 12 months
Not enough data to produce graph