This study examined whether changes in scrum engagement laws from the “crouch-touch-set” in 2013 to the “PreBind” engagement from 2014 onward have led to changes in scrum characteristics, specifically timing, in international rugby union. Duration and outcomes were identified for all scrums occurring in the 2013–16 Six Nations (N = 60 games) using video analysis. Scrum duration increased after the introduction of the PreBind engagement from 59 s in 2013 to 69 s in 2016 (P = .024, effect size = 0.93). A significant increase in mean contact duration per scrum occurred when prebinding was adopted (P < .05), moving from 7.5 s under the crouch-touch-set process to 8.5, 10.0, and 10.8 s with PreBind in 2014, 2015, and 2016 (effect size = 0.71, 2.05, and 3.0, respectively). The number of scrum resets and collapsed scrums, along with early engagement and pulling down infringements, was lower under the PreBind process. Overall, the PreBind engagement resulted in longer scrums with significant increases observed in overall and contact durations, with improved stability-related characteristics. The longer contact time is a consequence of increased stability with a shift from high-energy impact to a sustained push phase with a lower force that is a benefit to player welfare.
Edward J. Bradley, Bob Hogg and David T. Archer
Michael D. Bush, David T. Archer, Robert Hogg and Paul S. Bradley
To investigate match-to-match variability of physical and technical performances in English Premier League players and quantify the influence of positional and contextual factors.
Match data (N = 451) were collected using a multicamera computerized tracking system across multiple seasons (2005–06 to 2012–13). The coefficient of variation (CV) was calculated from match to match for physical and technical performances in selected positions across different match contexts (location, standard, and result).
Wide midfielders demonstrated the greatest CVs for total distance (4.9% ± 5.9%) and central midfielders the smallest (3.6% ± 2.0%); nevertheless, all positions exhibited CVs <5% (P > .05, effect size [ES] 0.1–0.3). Central defenders demonstrated the greatest CVs and wide midfielders the lowest for both high-intensity running (20.2% ± 8.8% and 13.7% ± 7.7%, P < .05, ES 0.4–0.8) and sprint distance (32.3% ± 13.8% and 22.6% ± 11.2%, P < .05, ES 0.5–0.8). Technical indicators such as tackles (83.7% ± 42.3%), possessions won (47.2% ± 27.9%), and interceptions (59.1% ± 37.3%) illustrated substantial variability for attackers compared with all other positions (P < .05, ES 0.4–1.1). Central defenders demonstrated large variability for the number of times tackled per match (144.9% ± 58.3%) and passes attempted and received compared with other positions (39.2% ± 17.5% and 46.9% ± 20.2%, P < .001, ES 0.6–1.8). Contextual factors had limited impact on the variability of physical and technical parameters.
The data demonstrate that technical parameters varied more from match to match than physical parameters. Defensive players (fullbacks and central defenders) displayed higher CVs for offensive technical variables, while attacking players (attackers and wide midfielders) exhibited higher CVs for defensive technical variables. Physical and technical performances are variable per se regardless of context.
Graham J. Mytton, David T. Archer, Alan St Clair Gibson and Kevin G. Thompson
To assess the reliability and stability of 400-m swimming and 1500-m running competitions to establish the number of samples needed to obtain a stable pacing profile. Coaches, athletes, and researchers can use these methods to ensure that sufficient data are collected before training and race strategies are constructed or research conclusions are drawn.
Lap times were collected from 5 world and European championship finals between 2005 and 2011, resulting in the capture of data from 40 swimmers and 55 runners. A cumulative mean for each lap was calculated, starting with the most recent data, and the number of races needed for this to stabilize to within 1% was reported. Typical error for each lap was calculated for athletes who had competed in more than 1 final.
International swimmers demonstrated more reproducible performances than runners in 3 of the 4 laps of the race (P < .01). Variance in runners’ lap times significantly decreased by 1.7–2.7% after lap 1, whereas variance in swimmers’ lap times tended to increase by 0.1–0.5% after lap 1. To establish a stable profile, at least ten 400-m swimmers and forty-four 1500-m runners must be included.
A stable race profile was observed from the analysis of 5 events for 1500-m running and 3 events for 400-m swimming. Researchers and athletes can be more certain about the pacing information collected from 400-m swimming than 1500-m running races, as the swimming data are less variable, despite both events being of similar duration.
Graham J. Mytton, David T. Archer, Kevin G. Thompson, Andrew Renfree and Alan St Clair Gibson
The collection of retrospective lap times from video footage is a potentially useful research tool to analyze the pacing strategies in any number of competitive events. The aim of this study was to validate a novel method of obtaining running split-time data from publically available video footage. Videos of the 1500-m men’s final from the 2004 and 2008 Olympics, 2005 and 2009 World Championships, and 2010 European Championships were obtained from the YouTube Web site, and split times were collected from all competitors using frame-by-frame playback. The typical error of video split times ranged between 0.02 s and 0.11 s for the 4 laps when compared with official split times. Video finishing times were also similar to official finishing times (typical error of 0.04 s). The method was shown to be highly reliable with a typical error of 0.02 s when the same video was analyzed on 2 occasions separated by 8 mo. Video data of track races are widely available; however, camera angles are not always perpendicular to the start/finish line, and some slower athletes may cross the line after the camera has panned away. Nevertheless, the typical errors reported here show that when appropriate camera angles are available this method is both valid and reliable.
Graham J. Mytton, David T. Archer, Louise Turner, Sabrina Skorski, Andrew Renfree, Kevin G. Thompson and Alan St Clair Gibson
Previous literature has presented pacing data of groups of competition finalists. The aim of this study was to analyze the pacing patterns displayed by medalists and nonmedalists in international competitive 400-m swimming and 1500-m running finals.
Split times were collected from 48 swimming finalists (four 100-m laps) and 60 running finalists (4 laps) in international competitions from 2004 to 2012. Using a cross-sectional design, lap speeds were normalized to whole-race speed and compared to identify variations of pace between groups of medalists and nonmedalists. Lap-speed variations relative to the gold medalist were compared for the whole field.
In 400-m swimming the medalist group demonstrated greater variation in speed than the nonmedalist group, being relatively faster in the final lap (P < .001; moderate effect) and slower in laps 1 (P = .03; moderate effect) and 2 (P > .001; moderate effect). There were also greater variations of pace in the 1500-m running medalist group than in the nonmedalist group, with a relatively faster final lap (P = .03; moderate effect) and slower second lap (P = .01; small effect). Swimming gold medalists were relatively faster than all other finalists in lap 4 (P = .04), and running gold medalists were relatively faster than the 5th- to 12th-placed athletes in the final lap (P = .02).
Athletes who win medals in 1500-m running and 400-m swimming competitions show different pacing patterns than nonmedalists. End-spurtspeed increases are greater with medalists, who demonstrate a slower relative speed in the early part of races but a faster speed during the final part of races than nonmedalists.