ORCID Profile
0000-0001-5070-2869
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Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 12-2022
DOI: 10.1186/S40798-022-00535-7
Abstract: In professional team sports, like Rugby League, performance analysis has become an integral part of operational practices. This has helped practitioners gain deeper insight into phenomena like team and athlete behaviour and understanding how such behaviour may be influenced by various contextual factors. This information can then be used by coaches to design representative practice tasks, inform game principles and opposition strategies, and even support team recruitment practices. At the elite level, the constant evolution of sports technology (both hardware and software) has enabled greater access to information, making the role of the performance analyst even more valuable. However, this increase in information can create challenges regarding which variables to use to help guide decision-making, and how to present it in ways that can be utilised by coaches and other support staff. While there are published works exploring aspects of performance analysis in team sports like Rugby League, there is yet to be a perspective that explores the various operational uses of performance analysis in Rugby League, the addition of which could help guide the practices of emerging performance analysts in elite organisations like the Australian National Rugby League and the European Super League. Thus, this narrative review—with accompanying case ex les—explores the various ways performance analysis can help address pertinent operational questions commonly encountered when working in high-performance sport.
Publisher: SAGE Publications
Date: 23-02-2021
Abstract: This study examined the effect of match location, score-line, team quality and match outcome on the expression of team playing styles in the National Rugby League (NRL) across the 2015–2019 seasons. Thirty-eight performance indicators (e.g. offloads, runs) from all NRL games (n = 2010) were collected. Match-related factors examined were location (home/away/neutral), match type (absolute score differential), team quality (end of season ladder position) and outcome (win/draw/loss). Factor analysis using principal component analysis (PCA) were run to identify team playing styles, which were inferred from the clustered dimensions (Factors) of team performance indicators. Discriminant analysis was then used to determine the effect of the match factors on team playing styles. PCA revealed nine Factors accounting for ∼54% of team performance variance. Discriminant analysis did not meaningfully resolve team playing styles for match type, team quality or location (∼34%, ∼46% and ∼58% classification accuracy, respectively). One discriminant function correctly classified ∼81% of matches based on outcome, including four team playing styles defined as ‘attacking play’, ‘linebreaks’, ‘handling errors’ and ‘conceded linebreaks’. Team playing styles characterised by ‘attacking play’ and ‘linebreaks’, coupled with relative defensive efficiency showed the greatest association with winning regardless of team quality, match location or match type. Using similar sport analytical techniques, additional insight into the importance of various team playing styles over the time-course of a match may allow teams to further extrapolate the likelihood of success in real-time.
Publisher: SAGE Publications
Date: 18-04-2023
DOI: 10.1177/17479541221092525
Abstract: To examine the effects of match-related contextual variables on positional groups and success in the National Rugby League (NRL). Data relating to match location, match outcome, quality of opposition and match type (absolute score differential) from all matches across the 2015–2019 NRL seasons were collected, in addition to 14 previously identified Factors (technical performance indicators). A decision tree, grown using the Exhaustive Chi-square Automatic Interaction Detector (CHAID) algorithm, was used to model the effect of each of these match-related contexts on positional contribution according to match outcome. The accuracy of the exhaustive CHAID model in explaining the influence of positional groups on match outcome was 66%. The model revealed four primary splits: interchange forwards, utility backs, adjustables and a group containing the remaining three positional groups (forwards, backs, and interchange). Results suggest that interchange forwards, utility backs and adjustables could have a definitive role within the team compared to the remaining positional groups in determining match outcome. In contrast to team-level research, there is a greater emphasis on the importance of defensive actions (e.g. try causes, tackles made) at a positional level than attacking performance indicators. The moderate classification accuracy justifies the use of this approach for examination of the interactions between match-related contextual variables, performance indicators and positional groups.
No related grants have been discovered for Corey James Wedding.