Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League
Research output: Journal contributions › Journal articles › Research › peer-review
Authors
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.
Original language | English |
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Journal | Journal of Science and Medicine in Sport |
Volume | 21 |
Issue number | 4 |
Pages (from-to) | 410-415 |
Number of pages | 6 |
ISSN | 1440-2440 |
DOIs | |
Publication status | Published - 04.2018 |
- Data visualisation, Performance analysis, Sport analytics, Team sports
- Physical education and sports
- Sustainability Science