Tiaki Brett Smith

Variability and predictability of finals times of elite rowers.

PURPOSE Little is known about the competitive performance characteristics of elite rowers. We report here analyses of performance times for finalists in world-class regattas from 1999 to 2009. METHODS The data were official race times for the 10 mens and 7 womens single and crewed boat classes, each with ∼ 200-300 different boats competing in 1-33 of the 46 regattas at 18 venues. A linear mixed model of race times for each boat class provided estimates of variability as coefficients of variation after adjustment for means of calendar year, level of competition (Olympics, world championship, World Cup), venue, and level of final (A, B, C, …). RESULTS Mean performance was substantially slower between consecutive levels of competition (1.5%, 2.7%) and consecutive levels of finals (∼ 1%-2%). Differences in the effects of venue and of environmental conditions, estimated as variability in mean race time between venues and finals, were extremely large (∼ 3.0%). Within-boat race-to-race variability for A finalists was 1.1% for single sculls and 0.9% for crewed boats, with little difference between men and women and only a small increase in lower-level finalists. Predictability of performance, expressed as intraclass correlation coefficients, showed considerable differences between boat classes, but the mean was high (∼ 0.63), with little difference between crewed and single boats, between men and women, and between within and between years. CONCLUSIONS The race-to-race variability of boat times of ∼ 1.0% is similar to that in comparable endurance sports performed against water or air resistance. Estimates of the smallest important performance enhancement (∼ 0.3%) and the effects of level of competition, level of final, venue, environment, and boat class will help inform investigations of factors affecting elite competitive rowing performance.

Overtraining and the complexities of coaches’ decision making: Managing elite athletes on the training cusp.

Abstract In many elite sport performance contexts, overtraining is a critical issue for coaches to understand, recognise and respond to. While the indicators and effects of overtraining have attracted considerable research interest, there are no diagnostic tests that reliably predict impending overtraining. This study explores the practices and beliefs of three highly successful professional elite rowing coaches as they predict and manage the risk of athlete overtraining during crucial periods of intensive training. These coaches took part in a series of semi-structured interviews that explored their practices, philosophies and experiences. The research draws on sociological perspectives pertinent to coaching pedagogy and, more specifically, the tenets of naturalistic decision-making in exploring how the coaches define and make decisions about overtraining. In doing so it recognizes that coaching involves decision-making in complex and demanding situations. The coaches were found to monitor the fatigue of their rowers through observation, communication and measures of training pace. In general, the intuitions and cues that were used by coaches had little in common with overtraining markers or indicators that are promoted in the sport science literature. Coaches’ decision-making was based largely on subjective information and processes that were influenced by a range of stressors unique to their positions. We contend that the research provides an important new perspective on coaches’ definitions and management of overtraining and enables management of overtraining to be better understood as a key element of coaching pedagogy in elite performance settings. In this article we argue successful coaches have unique insights into this topical area and this is worthy of future exploration.

An examination of the jump-and-lift factors influencing the time to reach peak catch height during a Rugby Union lineout

ABSTRACT The goal of an offensive Rugby Union lineout is to throw the ball in a manner that allows your team to maintain possession. Typically, the player catching the ball jumps and is lifted upwards by two teammates, reaching above the opposing player who is competing for the ball also. Despite various beliefs regarding the importance of the jumper’s mass and attempted jump height, and lifters’ magnitude and point of force application, there is negligible published data on the topic. The squeeze technique is one lifting method commonly employed by New Zealand teams during lineout plays, whereby the jumper initiates the jump quickly and the lifters provide assistance only once the jumper reaches 20–30 cm. While this strategy may reduce cues to the opposition, it might also constrain the jumper and lifters. We developed a model to explore how changes in the jumper’s body mass and attempted jump height, and lifters’ magnitude and point of force application influence the time to reach peak catch height. The magnitude of the lift force impacted the time-to-reach peak catch height the most; followed by the jumper’s (attempted) jump height and body mass; and lastly, the point of lift force application.

A Novel Method to Reduce the Impact of Countermovement Jump Monitoring In Professional Rugby Athletes

The countermovement jump (CMJ) is widely used to monitor jump performance, with greater interest being demonstrated in the propulsive phase. When landing from a CMJ, high forces are produced; this can increase the risk of injury. The present study aimed to test the validity and reliability of a countermovement jump to a box (CMBJ) where the forces associated with the landing are reduced. Eighteen professional rugby athletes (age=22 ± 2 years; body mass=104.2 ± 13.0 kg; height=187.4 ± 7.1 cm) performed 3 CMJ’s and 3 CMBJ’s on 3 different occasions. Net impulse (N.s), peak and mean absolute and relative force (N; N/kg) were obtained from a force plate system. The kinetic validity of the CMBJ was assessed by calculating the intraclass correlation coefficient, Pearson product-moment correlation, Cohen’s effect sizes and statistical hypothesis testing (paired t-test) in comparison to the CMJ. Intraday and interday reliability was assessed for each variable for both jumping conditions by calculating typical error, within subject coefficient of variation and intraclass correlation coefficient. Nonsignificant, trivial differences between the CMJ and CMBJ were observed for all jump variables. Low within-subject variability was observed between the CMJ and CMBJ for all variables. Interday and intraday variability showed good reliability and an almost perfect interday agreement score. In conclusion, net impulse, peak and mean force and relative peak and mean force obtained from a CMBJ are valid and reliable to monitor jump performance. This data demonstrates that the CMBJ is a viable alternative to monitor jump performance in athletes.

Examining deceptive behaviours by attackers in rugby union: The influence of decoy runners on defensive performance

Background Deceptive tactics are commonly used in sport; however, the psychological mechanisms underpinning the effectiveness of these tactics are unclear. Increasingly, in rugby union, decoy runners are used to cause deception. Multiple players in an attacking team run option lines off the ball carrier, making it possible that any of the players could receive the ball. Runners who do not receive the ball are decoys. The aim of the tactic is for the decoys to attract attention from defending players, which creates confusion about which player will receive the ball. Aim To identify behaviours of option line runners that can be used to improve the effectiveness of this deceptive tactic. Method Observational analysis of completed scrums from the 2015 Super Rugby season (n = 260) during which decoy runners were used by the attacking side. Decoy characteristics, including Hands Up, Line Change and Hands Up with Line Change were noted following each scrum. Gainline success was used as a dependent measure of the efficacy of the decoy characteristics. Characteristics were included in a binary logistic regression analysis to develop a best-fit model for gainline success. Results Hands Up, Line Change and Hands Up with Line Change significantly improved the likelihood of gainline success seven, eight and six times, respectively. Discussion We discuss possible mechanisms that underpin the effectiveness of decoy runners and make recommendations for coaches.