Behind enemy lines: using oppositional data to measure relative match performance in elite women's rugby league
Author(s)
Primary Supervisor
Minahan, Clare L
Other Supervisors
Bellinger, Phillip M
Bourne, Matthew
Year published
2021-04-22
Metadata
Show full item recordAbstract
Background: Rugby league is a contact sport played by men, women, and wheelchair athletes. In 2020 there were forty-five member nations, with men’s professional leagues in both the northern and southern hemisphere. Research has primarily focused on the male athlete and examined the physical, technical, and tactical differences between playing level, starters vs nonstarters, playing position, and successful and non-successful teams. The research is utilised by practitioners (Coaches, S&C Coaches, Sports Scientists, Physiotherapists etc) to apply evidence-based approaches to optimise athlete performance. However, applying the ...
View more >Background: Rugby league is a contact sport played by men, women, and wheelchair athletes. In 2020 there were forty-five member nations, with men’s professional leagues in both the northern and southern hemisphere. Research has primarily focused on the male athlete and examined the physical, technical, and tactical differences between playing level, starters vs nonstarters, playing position, and successful and non-successful teams. The research is utilised by practitioners (Coaches, S&C Coaches, Sports Scientists, Physiotherapists etc) to apply evidence-based approaches to optimise athlete performance. However, applying the research of male athletes to women’s sport is inaccurate, due to differences in their physical ability, technical skills, and game tactics used. Female athletes make up the fastest growing contingent of rugby league players yet the research in female rugby league is scarce. It is therefore the goal of this project to provide new insight into women’s rugby league and utilise a novel analytical approach. This project examines the movement profiles (GPS metrics) and performance indicators of teams in the Australian women’s elite rugby league competition, the NRLW, to determine which GPS metrics and performance indicators can predict points scoring and match outcome. Traditionally performance analysis is done with absolute sums of data (data averaged over an 80 minute game), yet with the data made available to this project, the analysis of opposing teams relative data (data relative to the opposition team both on a per minute basis, and score line difference) was conducted to determine if this approach could offer unique insight compared to traditional absolute data analysis. Method: This study examined 117 players from the four NRLW clubs during the 2018 & 2019 NRLW seasons, with data collected using 10 Hz Optimeye S5 (Catapult) GPS units. The GPS metrics analysed were total distance (m); average speed (m.s); distance covered greater than 12 km∙h-1 (i.e. high-speed running (HSR); distance covered greater than 18 km∙h-1 (i.e. sprint distance; SD); and average acceleration load (total sum of accelerations performed). Technical performance indicators used were `All Running Metres’, ‘Tackles’, ‘Missed Tackles’, and ‘Tackle Breaks’. The technical performance indicators were analysed for a full match, and the GPS data analysed for the full match, and half by half. The analysis of oppositional data was separated into three separate steps: i) absolute sum of data vs absolute score (total points scored), ii) absolute sum of data vs relative score (difference in score line), iii) relative difference of metrics (% difference between teams per minute) vs relative score (difference in score line). Generalised linear mixed models (GLMM) were employed (R version 3.5.211). Results: ‘All Running Metres’ was found to be the only significant performance indicators, and was related to positive points scoring and match outcomes. Although not significant ‘Tackles’ and ‘Missed Tackles’ negatively impacted points scoring. There were three GPS metrics found to be significant predictors of points scoring and match outcome. ‘Average High Speed’ and ‘Average Sprinting Speed’ had a positive relationship, whereas ‘Accelerations’ had a negative relationship, with points scoring and match outcome. Total distance was not a significant indicator of match outcome. Discussion: The performance indicators of ‘All Running Metres’, ‘Average High Speed’, ‘Average Sprinting Speed’, and ‘Average Acceleration’ were found to be significant predictors of success. ‘All Running Metres’ was significant in the absolute and relative analysis, with the relative analysis finding ‘All Running Metres’ to be more influential than in the absolute analysis. This suggests having higher run metres relative to your opposition is more important than having a high total amount of ‘All Running Metres’. ‘Average Sprinting Speed’ was significant in the absolute and relative analysis, with ‘Average High Speed’ significant in the half by half relative analysis. These findings indicate that running at higher speeds relative to your opposition will contribute positively to match outcome. ‘Accelerations’ were significant in the half by half analysis of absolute values, and were negatively associated with match outcome. This suggests over the full duration of the game ‘Accelerations’ do not impact match outcome; however, within smaller periods of a match the disparity between oppositions ‘Accelerations’ may impact match outcome. Conclusion: This study was the first to examine performance indicators in women’s rugby league, and identify which GPS and performance indicators metrics could explain points scored and match outcome. ‘All Running Metres’, ‘Average High Speed’, and ‘Average Sprinting Speed’ had a positive relationship with points scoring and match outcome. The relative analysis approach was able to provide more inference than the absolute analysis. The relative analysis highlighted the increased influence of each significant metric in points scoring opportunities and match outcomes. By identifying the technical and physical qualities related to success, coaches and athletes in women’s rugby league can design training programs to improve player performance; devise game tactics to exploit the opposition; and aid in talent identification and player recruitment of athletes who exhibit qualities that will contribute positively to match outcome. Practical application: The findings support the training and development of attacking play and maximising possession; in addition to training and developing speed and identifying and recruiting players with these qualities. Defensive work should also be prioritised as our findings show that ‘Missed Tackles’ are negatively associated with match outcome.
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View more >Background: Rugby league is a contact sport played by men, women, and wheelchair athletes. In 2020 there were forty-five member nations, with men’s professional leagues in both the northern and southern hemisphere. Research has primarily focused on the male athlete and examined the physical, technical, and tactical differences between playing level, starters vs nonstarters, playing position, and successful and non-successful teams. The research is utilised by practitioners (Coaches, S&C Coaches, Sports Scientists, Physiotherapists etc) to apply evidence-based approaches to optimise athlete performance. However, applying the research of male athletes to women’s sport is inaccurate, due to differences in their physical ability, technical skills, and game tactics used. Female athletes make up the fastest growing contingent of rugby league players yet the research in female rugby league is scarce. It is therefore the goal of this project to provide new insight into women’s rugby league and utilise a novel analytical approach. This project examines the movement profiles (GPS metrics) and performance indicators of teams in the Australian women’s elite rugby league competition, the NRLW, to determine which GPS metrics and performance indicators can predict points scoring and match outcome. Traditionally performance analysis is done with absolute sums of data (data averaged over an 80 minute game), yet with the data made available to this project, the analysis of opposing teams relative data (data relative to the opposition team both on a per minute basis, and score line difference) was conducted to determine if this approach could offer unique insight compared to traditional absolute data analysis. Method: This study examined 117 players from the four NRLW clubs during the 2018 & 2019 NRLW seasons, with data collected using 10 Hz Optimeye S5 (Catapult) GPS units. The GPS metrics analysed were total distance (m); average speed (m.s); distance covered greater than 12 km∙h-1 (i.e. high-speed running (HSR); distance covered greater than 18 km∙h-1 (i.e. sprint distance; SD); and average acceleration load (total sum of accelerations performed). Technical performance indicators used were `All Running Metres’, ‘Tackles’, ‘Missed Tackles’, and ‘Tackle Breaks’. The technical performance indicators were analysed for a full match, and the GPS data analysed for the full match, and half by half. The analysis of oppositional data was separated into three separate steps: i) absolute sum of data vs absolute score (total points scored), ii) absolute sum of data vs relative score (difference in score line), iii) relative difference of metrics (% difference between teams per minute) vs relative score (difference in score line). Generalised linear mixed models (GLMM) were employed (R version 3.5.211). Results: ‘All Running Metres’ was found to be the only significant performance indicators, and was related to positive points scoring and match outcomes. Although not significant ‘Tackles’ and ‘Missed Tackles’ negatively impacted points scoring. There were three GPS metrics found to be significant predictors of points scoring and match outcome. ‘Average High Speed’ and ‘Average Sprinting Speed’ had a positive relationship, whereas ‘Accelerations’ had a negative relationship, with points scoring and match outcome. Total distance was not a significant indicator of match outcome. Discussion: The performance indicators of ‘All Running Metres’, ‘Average High Speed’, ‘Average Sprinting Speed’, and ‘Average Acceleration’ were found to be significant predictors of success. ‘All Running Metres’ was significant in the absolute and relative analysis, with the relative analysis finding ‘All Running Metres’ to be more influential than in the absolute analysis. This suggests having higher run metres relative to your opposition is more important than having a high total amount of ‘All Running Metres’. ‘Average Sprinting Speed’ was significant in the absolute and relative analysis, with ‘Average High Speed’ significant in the half by half relative analysis. These findings indicate that running at higher speeds relative to your opposition will contribute positively to match outcome. ‘Accelerations’ were significant in the half by half analysis of absolute values, and were negatively associated with match outcome. This suggests over the full duration of the game ‘Accelerations’ do not impact match outcome; however, within smaller periods of a match the disparity between oppositions ‘Accelerations’ may impact match outcome. Conclusion: This study was the first to examine performance indicators in women’s rugby league, and identify which GPS and performance indicators metrics could explain points scored and match outcome. ‘All Running Metres’, ‘Average High Speed’, and ‘Average Sprinting Speed’ had a positive relationship with points scoring and match outcome. The relative analysis approach was able to provide more inference than the absolute analysis. The relative analysis highlighted the increased influence of each significant metric in points scoring opportunities and match outcomes. By identifying the technical and physical qualities related to success, coaches and athletes in women’s rugby league can design training programs to improve player performance; devise game tactics to exploit the opposition; and aid in talent identification and player recruitment of athletes who exhibit qualities that will contribute positively to match outcome. Practical application: The findings support the training and development of attacking play and maximising possession; in addition to training and developing speed and identifying and recruiting players with these qualities. Defensive work should also be prioritised as our findings show that ‘Missed Tackles’ are negatively associated with match outcome.
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Thesis Type
Thesis (Masters)
Degree Program
Master of Medical Research (MMedRes)
School
School of Medical Science
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
Subject
performance indicators
women’s rugby league
rugby league