Mitchell Naughton1, Ben Jones2,3,4,5,6, Sharief Hendricks3,6,7, Doug King2,8,9, Aron Murphy2, Cloe Cummins2,3,10. 1. School of Science and Technology, University of New England, Armidale, NSW, Australia. mnaught2@myune.edu.au. 2. School of Science and Technology, University of New England, Armidale, NSW, Australia. 3. Carnegie Applied Rugby Research (CARR) centre, Institute for Sport Physical Activity and Leisure, Leeds Beckett University, Leeds, UK. 4. Leeds Rhinos Rugby League club, Leeds, UK. 5. England Performance Unit, The Rugby Football League, Leeds, UK. 6. Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. 7. Health through Physical Activity, Lifestyle and Sport Research Centre (HPALS), Faculty of Health Sciences, The University of Cape Town, Cape Town, South Africa. 8. Sports Performance Institute New Zealand (SPRINZ), Faculty of Health and Environmental Science, Auckland University of Technology, Auckland, New Zealand. 9. School of Sport, Exercise and Nutrition, Massey University, Palmerston North, New Zealand. 10. National Rugby League, Sydney, Australia.
Abstract
BACKGROUND: Collisions (i.e. tackles, ball carries, and collisions) in the rugby league have the potential to increase injury risk, delay recovery, and influence individual and team performance. Understanding the collision demands of the rugby league may enable practitioners to optimise player health, recovery, and performance. OBJECTIVE: The aim of this review was to (1) characterise the dose of collisions experienced within senior male rugby league match-play and training, (2) systematically and critically evaluate the methods used to describe the relative and absolute frequency and intensity of collisions, and (3) provide recommendations on collision monitoring. METHODS: A systematic search of electronic databases (PubMed, SPORTDiscus, Scopus, and Web of Science) using keywords was undertaken. A meta-analysis provided a pooled mean of collision frequency or intensity metrics on comparable data sets from at least two studies. RESULTS: Forty-three articles addressing the absolute (n) or relative collision frequency (n min-1) or intensity of senior male rugby league collisions were included. Meta-analysis of video-based studies identified that forwards completed approximately twice the number of tackles per game than backs (n = 24.6 vs 12.8), whilst ball carry frequency remained similar between backs and forwards (n = 11.4 vs 11.2). Variable findings were observed at the subgroup level with a limited number of studies suggesting wide-running forwards, outside backs, and hit-up forwards complete similar ball carries whilst tackling frequency differed. For microtechnology, at the team level, players complete an average of 32.7 collisions per match. Limited data suggested hit-up and wide-running forwards complete the most collisions per match, when compared to adjustables and outside backs. Relative to playing time, forwards (n min-1 = 0.44) complete a far greater frequency of collision than backs (n min-1 = 0.16), with data suggesting hit-up forwards undertake more than adjustables, and outside backs. Studies investigating g force intensity zones utilised five unique intensity schemes with zones ranging from 2-3 g to 13-16 g. Given the disparity between device setups and zone classification systems between studies, further analyses were inappropriate. It is recommended that practitioners independently validate microtechnology against video to establish criterion validity. CONCLUSIONS: Video- and microtechnology-based methods have been utilised to quantify collisions in the rugby league with differential collision profiles observed between forward and back positional groups, and their distinct subgroups. The ball carry demands of forwards and backs were similar, whilst tackle demands were greater for forwards than backs. Microtechnology has been used inconsistently to quantify collision frequency and intensity. Despite widespread popularity, a number of the microtechnology devices have yet to be appropriately validated. Limitations exist in using microtechnology to quantify collision intensity, including the lack of consistency and limited validation. Future directions include application of machine learning approaches to differentiate types of collisions in microtechnology datasets.
BACKGROUND: Collisions (i.e. tackles, ball carries, and collisions) in the rugby league have the potential to increase injury risk, delay recovery, and influence individual and team performance. Understanding the collision demands of the rugby league may enable practitioners to optimise player health, recovery, and performance. OBJECTIVE: The aim of this review was to (1) characterise the dose of collisions experienced within senior male rugby league match-play and training, (2) systematically and critically evaluate the methods used to describe the relative and absolute frequency and intensity of collisions, and (3) provide recommendations on collision monitoring. METHODS: A systematic search of electronic databases (PubMed, SPORTDiscus, Scopus, and Web of Science) using keywords was undertaken. A meta-analysis provided a pooled mean of collision frequency or intensity metrics on comparable data sets from at least two studies. RESULTS: Forty-three articles addressing the absolute (n) or relative collision frequency (n min-1) or intensity of senior male rugby league collisions were included. Meta-analysis of video-based studies identified that forwards completed approximately twice the number of tackles per game than backs (n = 24.6 vs 12.8), whilst ball carry frequency remained similar between backs and forwards (n = 11.4 vs 11.2). Variable findings were observed at the subgroup level with a limited number of studies suggesting wide-running forwards, outside backs, and hit-up forwards complete similar ball carries whilst tackling frequency differed. For microtechnology, at the team level, players complete an average of 32.7 collisions per match. Limited data suggested hit-up and wide-running forwards complete the most collisions per match, when compared to adjustables and outside backs. Relative to playing time, forwards (n min-1 = 0.44) complete a far greater frequency of collision than backs (n min-1 = 0.16), with data suggesting hit-up forwards undertake more than adjustables, and outside backs. Studies investigating g force intensity zones utilised five unique intensity schemes with zones ranging from 2-3 g to 13-16 g. Given the disparity between device setups and zone classification systems between studies, further analyses were inappropriate. It is recommended that practitioners independently validate microtechnology against video to establish criterion validity. CONCLUSIONS: Video- and microtechnology-based methods have been utilised to quantify collisions in the rugby league with differential collision profiles observed between forward and back positional groups, and their distinct subgroups. The ball carry demands of forwards and backs were similar, whilst tackle demands were greater for forwards than backs. Microtechnology has been used inconsistently to quantify collision frequency and intensity. Despite widespread popularity, a number of the microtechnology devices have yet to be appropriately validated. Limitations exist in using microtechnology to quantify collision intensity, including the lack of consistency and limited validation. Future directions include application of machine learning approaches to differentiate types of collisions in microtechnology datasets.
Entities:
Keywords:
Global Positioning system; Microtechnology; Rugby; Tackle
Authors: Sam McCormack; Kevin Till; Jessica Wenlock; Sarah Whitehead; Keith A Stokes; Mark Bitcon; James Brown; Matt Cross; Phil Davies; Éanna C Falvey; Sharron Flahive; Andrew Gardner; Sharief Hendricks; Rich Johnston; Stephen D Mellalieu; James Parmley; Gemma Phillips; Carlos Ramirez; Joshua Stein; Sean Scantlebury; Stephen W West; Ben Jones Journal: BMJ Open Sport Exerc Med Date: 2022-10-11
Authors: José Pino-Ortega; Daniel Rojas-Valverde; Carlos D Gómez-Carmona; Markel Rico-González Journal: Int J Environ Res Public Health Date: 2021-03-05 Impact factor: 3.390