This article describes the movement tasks (Rink, 1985) in which students engaged during a 14-lesson volleyball unit in an eighth-grade physical education class, and the differential motor skill responses of high- and low-skilled target students during the practice of these tasks. Audio and videotaped records were made of each lesson. Analysis focused on the identification of the movement tasks that were verbally presented by the teacher during the lessons, the determination of students’ level of engagement in these tasks, and the frequency and rate of motor skill responses/successful motor skill responses during task practice for three high- and three low-skilled students. Thirteen major movement tasks were identified that formed a simple to complex progression of activities. A high level of consistent student engagement in tasks was observed, as well as differential performance outcomes for students of high/low skill levels. The results reveal the complexity of providing appropriate instruction for different skill levels in a class. Implications for research and teacher education programs are discussed.
Helen T. Douda, Argyris G. Toubekis, Alexandra A. Avloniti and Savvas P. Tokmakidis
To identify the physiological and anthropometric predictors of rhythmic gymnastics performance, which was defined from the total ranking score of each athlete in a national competition.
Thirty-four rhythmic gymnasts were divided into 2 groups, elite (n = 15) and nonelite (n = 19), and they underwent a battery of anthropometric, physical fitness, and physiological measurements. The principal-components analysis extracted 6 components: anthropometric, flexibility, explosive strength, aerobic capacity, body dimensions, and anaerobic metabolism. These were used in a simultaneous multiple-regression procedure to determine which best explain the variance in rhythmic gymnastics performance.
Based on the principal-component analysis, the anthropometric component explained 45% of the total variance, flexibility 12.1%, explosive strength 9.2%, aerobic capacity 7.4%, body dimensions 6.8%, and anaerobic metabolism 4.6%. Components of anthropometric (r = .50) and aerobic capacity (r = .49) were significantly correlated with performance (P < .01). When the multiple-regression model—y = 10.708 + (0.0005121 × VO2 max) + (0.157 × arm span) + (0.814 × midthigh circumference) - (0.293 × body mass)—was applied to elite gymnasts, 92.5% of the variation was explained by VO2max (58.9%), arm span (12%), midthigh circumference (13.1%), and body mass (8.5%).
Selected anthropometric characteristics, aerobic power, flexibility, and explosive strength are important determinants of successful performance. These findings might have practical implications for both training and talent identification in rhythmic gymnastics.
Federico Y. Fontana, Alessandro Colosio, Gabriela F. De Roia, Giorgio Da Lozzo and Silvia Pogliaghi
Anthropometric evaluation of athletes is necessary to optimize talent identification and player development.
To provide a specific anthropometric reference database of senior male rugby players competing at different levels in the southern European region.
In 362 professional players (25 ± 4 y; 138 Italian national team, 97 first-division, and 127 second-division national championships) the authors measured mass, stature, and percentage body fat (plicometry). Mean, SD, and coefficient of variation were calculated for forwards and backs and for positional subgroups. Binomial logistic regression and receiver-operating-characteristic curve were performed to assess which variables best predicted level assignment (international vs national level).
For all competitive levels forwards were significantly heavier and taller and had a larger percentage body fat and fat-free mass than backs. The lower the competitive level, the higher the within-role variability observed; furthermore, players in a specific positional subgroup were lighter, shorter, and fatter and had less fat-free mass. Fat-free mass is the variable that best predicts the likelihood of being classified as an international or national player (cutoff value 79.54 kg).
The data confirm the specificity in the physical requirements of rugby in individual playing positions at all competitive levels and document significant differences among elite and 1st- and 2nd-division players in the same positional role. These differences may reflect the variable technical abilities, selection, training practices, and requirements of the game among these categories.
Alison Keogh, Barry Smyth, Brian Caulfield, Aonghus Lawlor, Jakim Berndsen and Cailbhe Doherty
Purpose: Despite the volume of available literature focusing on marathon running and the prediction of performance, no single prediction equations exists that is accurate for all runners of varying experiences and abilities. Indeed the relative merits and utility of the existing equations remain unclear. Thus, the aim of this study was to collate, characterize, compare, and contrast all available marathon prediction equations. Methods: A systematic review was conducted to identify observational research studies outlining any kind of prediction algorithm for marathon performance. Results: Thirty-six studies with 114 equations were identified. Sixty-one equations were based on training and anthropometric variables, whereas 53 equations included variables that required laboratory tests and equipment. The accuracy of these equations was denoted via a variety of metrics; r 2 values were provided for 68 equations (r 2 = .10–.99), and an SEE was provided for 19 equations (SEE 0.27–27.4 min). Conclusion: Heterogeneity of the data precludes the identification of a single “best” equation. Important variables such as course gradient, sex, and expected weather conditions were often not included, and some widely used equations did not report the r 2 value. Runners should therefore be wary of relying on a single equation to predict their performance.
Michael Wilkinson, Damon Leedale-Brown and Edward M. Winter
We examined the validity and reproducibility of a squash-specifc test designed to assess change-of-direction speed.
10 male squash and 10 male association-football and rugby-union players completed the Illinois agility run (IAR) and a squash change-of-direction-speed test (SCODS) on separate days. Tests were repeated after 24 h to assess reproducibility. The best time from three attempts was recorded in each trial.
Performance times on the IAR (TE 0.27 s, 1.8%, 90% CI 0.21 to 0.37 s; LOA -0.12 s ± 0.74; LPR slope 1, intercept -2.8) and SCODS (TE 0.18 s, 1.5%, 90% CI 0.14 to 0.24 s; LOA 0.05 s ± 0.49; LPR slope 0.95, intercept 0.5) were reproducible. There were no statistically significant differences in performance time between squash (14.75 ± 0.66 s) and nonsquash players (14.79 ± 0.41 s) on the IAR. Squash players (10.90 ± 0.44 s) outperformed nonsquash players (12.20 ± 0.34 s) on the SCODS (P < .01). Squash player rank significantly correlated with SCODS performance time (Spearman’s ρ = 0.77, P < .01), but not IAR performance time (Spearman’s ρ = 0.43, P = .21).
The results suggest that the SCODS test is a better measure of sport-specific capability than an equivalent nonspecific field test and that it is a valid and reliable tool for talent identification and athlete tracking.
Jorge E. Morais, António J. Silva, Daniel A. Marinho, Vítor P. Lopes and Tiago M. Barbosa
To develop a performance predictor model based on swimmers’ biomechanical profile, relate the partial contribution of the main predictors with the training program, and analyze the time effect, sex effect, and time × sex interaction.
91 swimmers (44 boys, 12.04 ± 0.81 y; 47 girls, 11.22 ± 0.98 y) evaluated during a 3-y period. The decimal age and anthropometric, kinematic, and efficiency features were collected 10 different times over 3 seasons (ie, longitudinal research). Hierarchical linear modeling was the procedure used to estimate the performance predictors.
Performance improved between season 1 early and season 3 late for both sexes (boys 26.9% [20.88;32.96], girls 16.1% [10.34;22.54]). Decimal age (estimate [EST] –2.05, P < .001), arm span (EST –0.59, P < .001), stroke length (EST 3.82; P = .002), and propelling efficiency (EST –0.17, P = .001) were entered in the final model.
Over 3 consecutive seasons young swimmers’ performance improved. Performance is a multifactorial phenomenon where anthropometrics, kinematics, and efficiency were the main determinants. The change of these factors over time was coupled with the training plans of this talent identification and development program.
Sergio Lara-Bercial and Clifford J. Mallett
In 2011, the Innovation Group of Leading Agencies of the International Council for Coaching Excellence initiated a project aimed at supporting the identification and development of the next generation of high performance coaches. The project, entitled Serial Winning Coaches, studied the personalities, practices and developmental pathways of professional and Olympic coaches who had repeatedly achieved success at the highest level of sport. This paper is the third publication originating from this unique project. In the first paper, Mallett and Coulter (2016) focused on the development and testing of a novel multilayered methodology in understanding a person through a single case study of a successful Olympic coach. In the second, Mallett and Lara-Bercial (2016) applied this methodology to a large sample of Serial Winning Coaches and offered a composite account of their personality. In this third instalment, we turn the focus onto the actual practices and developmental pathways of these coaches. The composite profile of their practice emerging from the analysis revolves around four major themes: Philosophy, Vision, People and Environment. In addition, a summary of the developmental activities accessed by these coaches and their journey to success is also offered. Finally, we consider the overall findings of the project and propose the concept of Driven Benevolence as the overarching operational principle guiding the actions and behaviours of this group of Serial Winning Coaches.
Sarah Kölling, Rob Duffield, Daniel Erlacher, Ranel Venter and Shona L. Halson
The body of research that reports the relevance of sleep in high-performance sports is growing steadily. While the identification of sleep cycles and diagnosis of sleep disorders are limited to lab-based assessment via polysomnography, the development of activity-based devices estimating sleep patterns provides greater insight into the sleep behavior of athletes in ecological settings. Generally, small sleep quantity and/or poor quality appears to exist in many athletic populations, although this may be related to training and competition context. Typical sleep-affecting factors are the scheduling of training sessions and competitions, as well as impaired sleep onset as a result of increased arousal prior to competition or due to the use of electronic devices before bedtime. Further challenges are travel demands, which may be accompanied by jet-lag symptoms and disruption of sleep habits. Promotion of sleep may be approached via behavioral strategies such as sleep hygiene, extending nighttime sleep, or daytime napping. Pharmacological interventions should be limited to clinically induced treatments, as evidence among healthy and athletic populations is lacking. To optimize and manage sleep in athletes, it is recommended to implement routine sleep monitoring on an individual basis.
Dionne A. Noordhof, Carl Foster, Marco J.M. Hoozemans and Jos J. de Koning
A meaningful association between changes (Δ) in push-off angle or effectiveness (e) and changes in skating velocity (v) has been found during 5000-m races, although no significant association was found between changes in knee (θ0) and trunk angle (θ1) and Δv. It might be that speed skating event, sex, and performance level influence these associations.
To study the effect of skating event, sex, and performance level on the association between Δe and Δv and between Δθ0 and Δθ1 and Δv.
Video recordings were made from frontal (e) and sagittal views (θ0 and θ1) during 1500- and 5000-m men’s and women’s World Cup races. Radio-frequency identification tags provided data of v.
Skating event influenced the association between Δe and Δv, which resulted in a significant association between Δe and Δv for the 5000-m (β = –0.069, 95% confidence interval [–0.11, –0.030]) but not for the 1500-m (β = –0.011 [–0.032, 0.010]). The association between Δθ0 and Δθ1 and Δv was not significantly influenced by skating event. Sex and performance level did not substantially affect the association between Δe and Δv and between Δθ0 and Δθ1 and Δv.
Skating event significantly influenced the association between Δe and Δv; a 1° change in e results in a 0.011-m/s decrease in v during the 1500-m and a 0.069-m/s decrease in v during the 5000-m. Thus, it seems especially important to maintain a small e during the 5000-m.
Scott R. Brown, Matt Brughelli and Lee A. Bridgeman
Muscle imbalances aid in the identification of athletes at risk for lower-extremity injury. Little is known regarding the influence that leg preference or playing position may have on lower-extremity muscle strength and asymmetry.
To investigate lower-extremity strength profiles in rugby union athletes and compare isokinetic knee- and hip-strength variables between legs and positions.
Thirty male academy rugby union athletes, separated into forwards (n = 15) and backs (n = 15), participated in this cross-sectional analysis. Isokinetic dynamometry was used to evaluate peak torque, angle of peak torque, and strength ratios of the preferred and nonpreferred legs during seated knee extension/flexion and supine hip extension/flexion at 60°/s.
Backs were older (ES = 1.6) but smaller in stature (ES = –0.47) and body mass (ES = –1.3) than the forwards. The nonpreferred leg was weaker than the preferred leg for forwards during extension (ES = –0.37) and flexion (ES = –0.21) actions and for backs during extension (ES = –0.28) actions. Backs were weaker at the knee than forwards in the preferred leg during extension (ES = –0.50) and flexion (ES = –0.66) actions. No differences were observed in strength ratios between legs or positions. Backs produced peak torque at longer muscle lengths in both legs at the knee (ES = –0.93 to –0.94) and hip (ES = –0.84 to –1.17) than the forwards.
In this sample of male academy rugby union athletes, the preferred leg and forwards displayed superior strength compared with the nonpreferred leg and backs. These findings highlight the importance of individualized athletic assessments to detect crucial strength differences in male rugby union athletes.