speed is dependent on the maximal total energy expenditure relative to body mass (energy cost), which is associated with the intracyclic variations of the horizontal velocity ( dv ) of the body. 1 , 2 While opposing results have been presented regarding the link between intracyclic variations and
Pedro G. Morouço, Tiago M. Barbosa, Raul Arellano and João P. Vilas-Boas
Andrew Renfree, Louise Martin, Ashley Richards and Alan St Clair Gibson
This study examined individual contributions to overall pacing strategy during 2- and 5-km rowing trials in a coxless-4 boat.
A crew of 4 male rowers performed maximal-effort on-water trials over 2 and 5 km, and power output during every individual stroke was measured for each crew member. Mean overall boat and individual rower stroke power were calculated for each 25% epoch (25% of total strokes taken), and power for each individual epoch was calculated as a percentage of mean power maintained over the entire distance. The coefficient of variation was used to determine stroke-to-stroke and epoch-to-epoch variability for individual rowers and the overall boat.
In both trials, the overall pacing strategy consisted of a high power output in the initial 25% that decreased in the middle 50% and increased again in the final 25%. However, individual rower data indicate wide variation in individual power profiles that did not always mimic the overall boat profile.
This study demonstrates that overall boat power profiles during 2- and 5-km rowing trials are similar to velocity profiles previously reported for individual ergometry and on-water racing events. However, this overall profile is achieved despite considerable variation in individual rower profiles. Further research is warranted to determine the mechanisms through which individual contributions to overall pacing strategy are regulated and the effectiveness or otherwise of seemingly disparate individual strategies on overall performance.
Stephanie A. Hooker, Laura B. Oswald, Kathryn J. Reid and Kelly G. Baron
behaviors, such as physical activity and caloric intake. One recent study showed that day-to-day physical activity variability is very high, as 50% of the variability in physical activity is due to intraindividual variation. 9 To our knowledge, no studies have examined the associations among variability in
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.
Francisco J. Amaro-Gahete, Lucas Jurado-Fasoli, Alejandro R. Triviño, Guillermo Sanchez-Delgado, Alejandro De-la-O, Jørn W. Helge and Jonatan R. Ruiz
percentage at ventilatory threshold 2 (VT2), and running economy are considered important outcomes in endurance sports performance. 5 , 6 Endurance sport performance, specifically running and cycling performance, seems to present diurnal variation, being higher in the afternoon than in the morning. 7 This
Chris Brogden, Kelly Marrin, Richard Page and Matt Greig
of movement screening is fundamental to the subsequent design of prehabilitation and injury management strategies. Circadian rhythm is a term used to describe variations in many human physiological variables 8 and factors influencing athletic performance, relative to time of day. 9 , 10 It has been
Arthur H. Bossi, Cristian Mesquida, Louis Passfield, Bent R. Rønnestad and James G. Hopker
Although this laboratory protocol is appealing as a training session, it is not practical for the majority of athletes. Alternatively, a HIIT session in which the work intervals include power output variations might provide similar means to increase time at > 90 % V ˙ O 2 max . Previous research suggests
Gabriel Andrade Paz, Lohanne Almeida, Larissa Ruiz, Sabrina Casseres, Giovanna Xavier, João Lucas, Haroldo Gualter Santana, Humberto Miranda, Scott Bonnette and Jeffrey Willardson
competitions, and sport-specific training. 2 Although the squat, and its variations, are widely adopted to strengthen the lower-limb musculature, 3 , 4 its role in injury prevention and in whole-body movement patterns is not fully understood. The proper mechanics and muscle activation patterns during the
Claudio M. Rocha
the OG. To advance theory and practice of the OG organization, longitudinal studies are necessary because variations in popular support have important consequences for event organizers ( Kim, Gursoy, & Lee, 2006 ; Mihalik & Simonetta, 1999 ; Waitt, 2003 ). For example, when low popular support is
Andrew Renfree, Arturo Casado, Gonzalo Pellejero and Brian Hanley
also calculated for each individual athlete. The SDs of the time and position per 100-m segment indicate the variation in these variables, while skewness is a measure of the asymmetry of the distribution. A positive skewness means the right tail of the distribution is longer, and the mass of the