Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League

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Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League. / Woods, Carl T.; Robertson, Sam; Sinclair, Wade H. et al.

in: Journal of Science and Medicine in Sport, Jahrgang 21, Nr. 4, 04.2018, S. 410-415.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{749bb4c91c0b4e2f8dc4815830aedc81,
title = "Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League",
abstract = "Objectives: Analysing the dissimilarity of seasonal and team profiles within elite sport may reveal the evolutionary dynamics of game-play, while highlighting the similarity of individual team profiles. This study analysed seasonal and team dissimilarity within the National Rugby League (NRL) between the 2005 to 2016 seasons. Design: Longitudinal. Methods: Total seasonal values for 15 performance indicators were collected for every NRL team over the analysed period (n = 190 observations). Non-metric multidimensional scaling was used to reveal seasonal and team dissimilarity. Results: Compared to the 2005 to 2011 seasons, the 2012 to 2016 seasons were in a state of flux, with a relative dissimilarity in the positioning of team profiles on the ordination surface. There was an abrupt change in performance indicator characteristics following the 2012 season, with the 2014 season reflecting a large increase in the total count of 'all run metres' (d = 1.21; 90% CI = 0.56-1.83), 'kick return metres' (d = 2.99; 90% CI = 2.12-3.84) and decrease in 'missed tackles' (d = -2.43; 90% CI = -3.19 to -1.64) and 'tackle breaks' (d = -2.41; 90% CI = -3.17 to -1.62). Interpretation of team ordination plots showed that certain teams evolved in (dis)similar ways over the analysed period. Conclusions: It appears that NRL match-types evolved following the 2012 season and are in a current state of flux. The modification of coaching tactics and rule changes may have contributed to these observations. Coaches could use these results when designing prospective game strategies in the NRL.",
keywords = "Data visualisation, Performance analysis, Sport analytics, Team sports, Physical education and sports, Sustainability Science",
author = "Woods, {Carl T.} and Sam Robertson and Sinclair, {Wade H.} and Collier, {Neil French}",
year = "2018",
month = apr,
doi = "10.1016/j.jsams.2017.06.014",
language = "English",
volume = "21",
pages = "410--415",
journal = "Journal of Science and Medicine in Sport",
issn = "1440-2440",
publisher = "Elsevier Australia",
number = "4",

}

RIS

TY - JOUR

T1 - Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League

AU - Woods, Carl T.

AU - Robertson, Sam

AU - Sinclair, Wade H.

AU - Collier, Neil French

PY - 2018/4

Y1 - 2018/4

N2 - Objectives: Analysing the dissimilarity of seasonal and team profiles within elite sport may reveal the evolutionary dynamics of game-play, while highlighting the similarity of individual team profiles. This study analysed seasonal and team dissimilarity within the National Rugby League (NRL) between the 2005 to 2016 seasons. Design: Longitudinal. Methods: Total seasonal values for 15 performance indicators were collected for every NRL team over the analysed period (n = 190 observations). Non-metric multidimensional scaling was used to reveal seasonal and team dissimilarity. Results: Compared to the 2005 to 2011 seasons, the 2012 to 2016 seasons were in a state of flux, with a relative dissimilarity in the positioning of team profiles on the ordination surface. There was an abrupt change in performance indicator characteristics following the 2012 season, with the 2014 season reflecting a large increase in the total count of 'all run metres' (d = 1.21; 90% CI = 0.56-1.83), 'kick return metres' (d = 2.99; 90% CI = 2.12-3.84) and decrease in 'missed tackles' (d = -2.43; 90% CI = -3.19 to -1.64) and 'tackle breaks' (d = -2.41; 90% CI = -3.17 to -1.62). Interpretation of team ordination plots showed that certain teams evolved in (dis)similar ways over the analysed period. Conclusions: It appears that NRL match-types evolved following the 2012 season and are in a current state of flux. The modification of coaching tactics and rule changes may have contributed to these observations. Coaches could use these results when designing prospective game strategies in the NRL.

AB - Objectives: Analysing the dissimilarity of seasonal and team profiles within elite sport may reveal the evolutionary dynamics of game-play, while highlighting the similarity of individual team profiles. This study analysed seasonal and team dissimilarity within the National Rugby League (NRL) between the 2005 to 2016 seasons. Design: Longitudinal. Methods: Total seasonal values for 15 performance indicators were collected for every NRL team over the analysed period (n = 190 observations). Non-metric multidimensional scaling was used to reveal seasonal and team dissimilarity. Results: Compared to the 2005 to 2011 seasons, the 2012 to 2016 seasons were in a state of flux, with a relative dissimilarity in the positioning of team profiles on the ordination surface. There was an abrupt change in performance indicator characteristics following the 2012 season, with the 2014 season reflecting a large increase in the total count of 'all run metres' (d = 1.21; 90% CI = 0.56-1.83), 'kick return metres' (d = 2.99; 90% CI = 2.12-3.84) and decrease in 'missed tackles' (d = -2.43; 90% CI = -3.19 to -1.64) and 'tackle breaks' (d = -2.41; 90% CI = -3.17 to -1.62). Interpretation of team ordination plots showed that certain teams evolved in (dis)similar ways over the analysed period. Conclusions: It appears that NRL match-types evolved following the 2012 season and are in a current state of flux. The modification of coaching tactics and rule changes may have contributed to these observations. Coaches could use these results when designing prospective game strategies in the NRL.

KW - Data visualisation

KW - Performance analysis

KW - Sport analytics

KW - Team sports

KW - Physical education and sports

KW - Sustainability Science

UR - http://www.scopus.com/inward/record.url?scp=85022029856&partnerID=8YFLogxK

U2 - 10.1016/j.jsams.2017.06.014

DO - 10.1016/j.jsams.2017.06.014

M3 - Journal articles

C2 - 28705436

AN - SCOPUS:85022029856

VL - 21

SP - 410

EP - 415

JO - Journal of Science and Medicine in Sport

JF - Journal of Science and Medicine in Sport

SN - 1440-2440

IS - 4

ER -

DOI