Physical characteristics of different professional rugby union competition levels

Hamstrings mechanical properties profiling in football players of different competitive levels and positions after a repeated sprint protocol

Purpose: This study compares the average speed, knee flexor peak torque and shear modulus of the hamstrings after a repeated sprint task, in football players of different competitive levels and playing positions. Methods: Fifty-four football field players without hamstring strain injury history participated, 15 being categorized as professional (2nd league) and 39 as semi-professional (17 in 3rd and 22 in 4th league). Muscle shear modulus was assessed using ultrasound-based shear wave elastography at rest and at 20% of maximal voluntary isometric effort before and immediately after the repeated sprint protocol. Results: No significant differences were seen in average sprint speed between competitive levels (p = 0.07; η2p = 0.28) and positions (p = 0.052; η2p = 0.29). Moreover, the sprint fatigue index showed no significant differences between competitive levels (p = 0.14; η2p = 0.08) and playing positions (p = 0.89; η2p = 0.05). No significant differences were observed in hamstring shear modulus changes between competitive levels (p = 0.94; η2p = 0.03) and positions (p = 0.92; η2p = 0.03). Peak torque changes also showed non-significant association with competitive levels (p = 0.46; η2p = 0.03) and positions (p = 0.60; η2p = 0.02). Conclusion: The results of this study suggest that the average sprint speed performance parameter and mechanical parameters are not able to distinguish football players of different competitive levels and positions.

Running demands in club, regional, national, and international provincial New Zealand rugby union competitions

The demands of national and international professional rugby union matches are well established, however, there has not been a comparative study investigating running demands in New Zealand teams playing in club (amateur), Heartland Championship (semi-professional Div 2), the Mitre 10 Cup (semi-professional Div 1) or Super Rugby (professional) competitions. This information could enable specific training and rehabilitation that programmes to be developed to meet the needs of players in the different competitions. Players wore 10 Hz GPS units during games for one rugby season to determine absolute (m) and relative (m.min^-1) measures for total distance, running volume (∼≥7 km·h^-1) and high intensity running (∼≥16 km·h^-1). There were typically minimal differences (1-2 m.min^-1) in running distance measures between amateur level front row forwards and inside backs compared to players in these positions at higher levels of competition. Therefore, amateur players in these positions may find the transition to higher competitions less challenging with respect to running load. In contrast, amateur outside backs and back row forwards may find the increased pace of higher levels of competition more challenging due to typically covering significantly less running and high intensity running distances in amateur games. Differences for half backs were more variable between the levels of competition. Based on our results, it cannot be assumed that amateur rugby has lower running demands than higher competitions or that there is a continuum of increased running demands with increasing competition levels, as some playing positions in the semi-professional (Div 2) (second lowest level of competition) team recorded the largest values for total distance, running and high intensity running. Therefore, the specificity of running demands in a position and competition need to be considered individually for each player when transitioning between competitions. The practice and perception of returning a professional player to amateur club rugby due to the belief that running loads being lower may also be flawed, as we found considerable positional variation in running demands within-and-between competitions.

The Utility of Mixed Models in Sport Science: A Call for Further Adoption in Longitudinal Data Sets

PURPOSE

Sport-science research consistently contains repeated measures and imbalanced data sets. This study calls for further adoption of mixed models when analyzing longitudinal sport-science data sets. Mixed models were used to understand whether the level of competition affected the intensity of women's rugby league match play.

METHODS

A total of 472 observations were used to compare the mean speed of female rugby league athletes recorded during club-, state-, and international-level competition. As athletes featured in all 3 levels of competition and there were multiple matches within each competition (ie, repeated measures), the authors demonstrated that mixed models are the appropriate statistical approach for these data.

RESULTS

The authors determined that if a repeated-measures analysis of variance (ANOVA) were used for the statistical analysis in the present study, at least 48.7% of the data would have been omitted to meet ANOVA assumptions. Using a mixed model, the authors determined that mean speed recorded during Trans-Tasman Test matches was 73.4 m·min-1, while the mean speeds for National Rugby League Women and State of Origin matches were 77.6 and 81.6 m·min-1, respectively. Random effects of team, athlete, and match all accounted for variations in mean speed, which otherwise could have concealed the main effects of position and level of competition had less flexible ANOVAs been used.

CONCLUSION

These data clearly demonstrate the appropriateness of applying mixed models to typical data sets acquired in the professional sport setting. Mixed models should be more readily used within sport science, especially in observational, longitudinal data sets such as movement pattern analyses.

Strength Deficit in Elite Young Rugby Players: Differences Between Playing Positions and Associations With Sprint and Jump Performance

ABSTRACT

Zabaloy, S, Giráldez, J, Fink, B, Alcaraz, PE, Pereira, LA, Freitas, TT, and Loturco, I. Strength deficit in elite young rugby players: Differences between playing positions and associations with sprint and jump performance. J Strength Cond Res 36(4): 920-926, 2022-The aims of this study were twofold: to compare the strength-related performance between young forwards and backs rugby players and to examine the correlations between strength deficit (SDef), strength parameters, and sprint and jump performance. Fifty-seven male rugby players (mean ± SD: age, 17.4 ± 1.3 years) performed anthropometric and body composition assessments, vertical jumps, 30-m sprint, and squat (SQ) and bench press (BP) 1-repetition maximum tests (1RM SQ and BP). The differences in the tested variables between positions were analyzed through an independent t-test. A Pearson's correlation coefficient was used to assess the relationships among the variables. Significant differences were observed for anthropometric and body composition measures and jump and sprint performance between positions (p < 0.05; effect size [ES]: 0.60-1.34), except for 5-m velocity (p = 0.080; ES: 0.57). Backs demonstrated higher relative 1RM than forwards in both exercises (p = 0.009 and p = 0.008; ES = 0.88 and 0.91, for SQ and BP, respectively). In addition, backs demonstrated lower SDef from 70 to 90% 1RM (p < 0.048) but small-to-moderate nonsignificant lower SDef against lighter loads compared with forwards (50-60% 1RM). Overall, SDef across all loads (r: -0.378 to -0.529) and 1RM SQ (r: 0.504 to -0.590) were significantly related to sprint performance. Therefore, young rugby players who present lower magnitudes of SDef and superior 1RM SQ performance tend to be faster in linear sprints.

Quantifying Collision Frequency and Intensity in Rugby Union and Rugby Sevens: A Systematic Review

Background

Collisions in rugby union and sevens have a high injury incidence and burden, and are also associated with player and team performance. Understanding the frequency and intensity of these collisions is therefore important for coaches and practitioners to adequately prepare players for competition. The aim of this review is to synthesise the current literature to provide a summary of the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology.

Methods

A systematic search using key words was done on four different databases from 1 January 1990 to 1 September 2021 (PubMed, Scopus, SPORTDiscus and Web of Science).

Results

Seventy-three studies were included in the final review, with fifty-eight studies focusing on rugby union, while fifteen studies explored rugby sevens. Of the included studies, four focused on training—three in rugby union and one in sevens, two focused on both training and match-play in rugby union and one in rugby sevens, while the remaining sixty-six studies explored collisions from match-play. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Most of the studies used video-based analysis (n = 37) to quantify collisions. In rugby union, on average a total of 22.0 (19.0–25.0) scrums, 116.2 (62.7–169.7) rucks, and 156.1 (121.2–191.0) tackles occur per match. In sevens, on average 1.8 (1.7–2.0) scrums, 4.8 (0–11.8) rucks and 14.1 (0–32.8) tackles occur per match.

Conclusions

This review showed more studies quantified collisions in matches compared to training. To ensure athletes are adequately prepared for match collision loads, training should be prescribed to meet the match demands. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended. The frequency and intensity of collisions in training and matches may lead to adaptations for a “collision-fit” player and lend itself to general training principles such as periodisation for optimum collision adaptation.

Trial Registration PROSPERO registration number: CRD42020191112.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-021-00398-4.