Women's Rugby League
Positional Groups and Peak Locomotor Demands
Bibliographic Data
| ID | 5285059 |
|---|---|
| Authors | Joanna K Fadyl (0000-0003-1960-8916, Australian Football League, corresponding author), Cloe Cummins, Glen Charlton (0000-0002-1482-9720, University of New England), David Paul (0009-0001-4022-0652, University of New England), Kath Shorter, Kathleen Shorter (0000-0002-1309-5884, University of New England), Simon Buxton (Australian Football League), Johnpaul Caia (0000-0002-7793-7067, Australian Football League), Aron Murphy, Aron J Murphy (0000-0002-8964-072X, The University of Notre Dame Australia) |
| Year | 2021 |
| Volume | 3 |
| Pages | 648126-648126 |
| Publication date | 2021-06-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2021.648126 |
| PMID | 34268492 |
| OpenAlex | W3176809291 |
| Language | EN |
| Citations received | 1 |
| References cited | 22 |
The aims of this study were to (a) use a data-based approach to identify positional groups within National Rugby League Women's (NRLW) match-play and (b) quantify the peak locomotor demands of NRLW match-play by positional groups. Microtechnology (Global Navigational Satellite System [GNSS] and integrated inertial sensors; n = 142 files; n = 76 players) and match statistics (n = 238 files; n = 80 players) were collected from all NRLW teams across the 2019 season. Data-based clustering of match statistics was utilized to identify positional clusters through classifying individual playing positions into distinct positional groups. Moving averages (0.5, 1, 2, 3, 5, and 10 min) of peak running and average acceleration/deceleration demands were calculated via microtechnology data for each player per match. All analysis was undertaken in R (R Foundation for Statistical Computing) with positional differences determined via a linear mixed model and effect sizes (ES). Data-based clustering suggested that, when informed by match statistics, individual playing positions can be clustered into one of three positional groups. Based on the clustering of the individual positions, these groups could be broadly defined as backs (fullback, wing, and center), adjustables (halfback, five-eighth, and hooker), and forwards (prop, second-row, and lock). Backs and adjustables demonstrated greater running (backs: ES 0.51-1.00; p < 0.05; adjustables: ES 0.51-0.74, p < 0.05) and average acceleration/deceleration (backs: ES 0.48-0.87; p < 0.05; adjustables: ES 0.60-0.85, p < 0.05) demands than forwards across all durations. Smaller differences (small to trivial) were noted between backs and adjustables across peak running and average acceleration/deceleration demands. Such findings suggest an emerging need to delineate training programs in situations in which individual playing positions train in positional group based settings. Collectively, this work informs the positional groupings that could be applied when examining NRLW data and supports the development of a framework for specifically training female rugby league players for the demands of the NRLW competition
Cluster analysis · League · Match play · Microtechnology · Simulation · Statistics · Computer Science · Engineering · Mathematics · Sport Psychology and Performance · Sports injuries and prevention · Sports Performance and Training
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |