This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R 2 = .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R 2 > .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.
James C. Martin, Douglas L. Milliken, John E. Cobb, Kevin L. McFadden and Andrew R. Coggan
Tammie R. Ebert, David T. Martin, Brian Stephens and Robert T. Withers
To quantify the power-output demands of men’s road-cycling stage racing using a direct measure of power output.
Power-output data were collected from 207 races over 6 competition years on 31 Australian national male road cyclists. Subjects performed a maximal graded exercise test in the laboratory to determine maximum aerobic-power output, and bicycles were fitted with SRM power meters. Races were described as fl at, hilly, or criterium, and linear mixed modeling was used to compare the races.
Criterium was the shortest race and displayed the highest mean power output (criterium 262 ± 30 v hilly 203 ± 32 v fl at 188 ± 30 W), percentage total race time above 7.5 W/kg (crite-rium 15.5% ± 4.1% v hilly 3.8% ± 1.7% v fl at 3.5% ± 1.4%) and SD in power output (criterium 250 v hilly 165 v fl at 169 W). Approximately 67%, 80%, and 85% of total race time was spent below 5 W/kg for criterium, hilly and fl at races, respectively. About 70, 40, and 20 sprints above maximum aerobic-power output occurred during criterium, hilly, and fl at races, respectively, with most sprints being 6 to 10 s.
These data extend previous research documenting the demands of men’s road cycling. Despite the relatively low mean power output, races were characterized by multiple high-intensity surges above maximum aerobic-power output. These data can be used to develop sport-specific interval-training programs that replicate the demands of competition.
John McDaniel, N. Scott Behjani, Steven J. Elmer, Nicholas A.T. Brown and James C. Martin
Previous authors have reported power-pedaling rate relationships for maximal cycling. However, the joint-specific power-pedaling rate relationships that contribute to pedal power have not been reported. We determined absolute and relative contributions of joint-specific powers to pedal power across a range of pedaling rates during maximal cycling. Ten cyclists performed maximal 3 s cycling trials at 60, 90, 120, 150, and 180 rpm. Joint-specific powers were averaged over complete pedal cycles, and extension and flexion actions. Effects of pedaling rate on relative joint-specific power, velocity, and excursion were assessed with regression analyses and repeated-measures ANOVA. Relative ankle plantar flexion power (25 to 8%; P = .01; R 2 = .90) decreased with increasing pedaling rate, whereas relative hip extension power (41 to 59%; P < .01; R 2 = .92) and knee flexion power (34 to 49%; P < .01; R 2 = .94) increased with increasing pedaling rate. Knee extension powers did not differ across pedaling rates. Ankle joint angular excursion decreased with increasing pedaling rate (48 to 20 deg) whereas hip joint excursion increased (42 to 48 deg). These results demonstrate that the often-reported quadratic power-pedaling rate relationship arises from combined effects of dissimilar joint-specific power-pedaling rate relationships. These dissimilar relationships are likely influenced by musculoskeletal constraints (ie, muscle architecture, morphology) and/or motor control strategies.
Yiannis Michailidis, Alexandros Tabouris and Thomas Metaxas
discriminate between a successful and an unsuccessful performance. Therefore, power training is very important in soccer. Plyometric training (PT) is an effective way of improving the rate of both force development and sprint performance. 5 It involves a variety of jumps and actions that are characterized by
Janet B. Parks, Patricia A. Shewokis and Carla A. Costa
Statistical power analysis involves designing and interpreting research with attention to the statistical power (probability) of the study to detect an effect of a specific size. Statistical power analysis, which is based on the interdependence of sample size, alpha, effect size, and power, is acknowledged by scholars of various disciplines as an indispensable component of high quality research. This paper reviews basic principles associated with power analysis and demonstrates its importance by comparing the meaningfulness of significant findings in two studies of job satisfaction. The perspective advanced in this paper is that the use of statistical power analysis will strengthen sport management research and will enable researchers to expand the body of knowledge in a systematic, coherent fashion.
Mayur K. Ranchordas, George King, Mitchell Russell, Anthony Lynn and Mark Russell
well supported in athletic populations as numerous studies have shown that caffeine can enhance performance of endurance ( Ganio et al., 2009 ); strength ( Timmins & Saunders, 2014 ); power ( Del Coso et al., 2012 ); agility ( Jordan et al., 2014 ); skill ( Russell & Kingsley, 2014 ); and reaction time
João Ribeiro, Argyris G. Toubekis, Pedro Figueiredo, Kelly de Jesus, Huub M. Toussaint, Francisco Alves, João P. Vilas-Boas and Ricardo J. Fernandes
To conduct a biophysical analysis of the factors associated with front-crawl performance at moderate and severe swimming intensities, represented by anaerobic-threshold (vAnT) and maximal-oxygen-uptake (vV̇O2max) velocities.
Ten high-level swimmers performed 2 intermittent incremental tests of 7 × 200 and 12 × 25 m (through a system of underwater push-off pads) to assess vAnT, and vV̇O2max, and power output. The 1st protocol was videotaped (3D reconstruction) for kinematic analysis to assess stroke frequency (SF), stroke length (SL), propelling efficiency (η P), and index of coordination (IdC). V̇O2 was measured and capillary blood samples (lactate concentrations) were collected, enabling computation of metabolic power. The 2nd protocol allowed calculating mechanical power and performance efficiency from the ratio of mechanical to metabolic power.
Neither vAnT nor vV̇O2max was explained by SF (0.56 ± 0.06 vs 0.68 ± 0.06 Hz), SL (2.29 ± 0.21 vs 2.06 ± 0.20 m), η P (0.38 ± 0.02 vs 0.36± 0.03), IdC (–12.14 ± 5.24 vs –9.61 ± 5.49), or metabolic-power (1063.00 ± 122.90 vs 1338.18 ± 127.40 W) variability. vV̇O2max was explained by power to overcome drag (r = .77, P ≤ .05) and η P (r = .72, P ≤ .05), in contrast with the nonassociation between these parameters and vAnT; both velocities were well related (r = .62, P ≤ .05).
The biomechanical parameters, coordination, and metabolic power seemed not to be performance discriminative at either intensity. However, the increase in power to overcome drag, for the less metabolic input, should be the focus of any intervention that aims to improve performance at severe swimming intensity. This is also true for moderate intensities, as vAnT and vV˙O2max are proportional to each other.
Hayley M. Ericksen, Caitlin Lefevre, Brittney A. Luc-Harkey, Abbey C. Thomas, Phillip A. Gribble and Brian Pietrosimone
concerned about the negative impact that reducing vGRF during landing may have on athletic performance (ie, vertical jump). Maximum vertical jump height (Vert max ) is commonly used to evaluate performance because of its ease of use as well as its ability to assess lower-extremity power. 19 Additionally
The purpose of the current study was to investigate the relationship between team sport coaches’ power and coaching effectiveness using French and Raven’s (1959) taxonomy of power bases as a theoretical framework. Coaching effectiveness (CE) was conceptualized as an umbrella concept and four different CE outcomes were used; athletes’ satisfaction with the coach, coaches’ general influence, adaptive training behaviours, and collective efficacy. Hypotheses were made on the specific relationships between the individual power bases and the effectiveness criteria. The total sample consisted of 820 athletes (47% females), representing 56 elite and nonelite teams from three team sports (soccer, floorball, and team handball). Data were analysed separately for adults and youths. Structural equations modelling showed that 30% (in the youth sample) and 55% (in the adult sample) of the proposed hypotheses was supported. Overall, coaches’ bases of power were strongly associated with coaching effectiveness, explaining between 13% and 59% of variance in the effectiveness outcomes used. Expert power was consistently positively related to coaching effectiveness; reward and coercive power had mixed relationships (positively, negatively, unrelated) as had legitimate power (negatively, unrelated) and reward power (positively, unrelated). The results are discussed in relation to coaching effectiveness, limitations, practical implications and future research.
William G. Thorland, Glen O. Johnson, Craig J. Cisar, Terry J. Housh and Gerald D. Tharp
This study assessed strength and muscular power of elite young male runners in order to determine the relationship of these characteristics to age and specialization in either sprint or middle distance events. Forty-eight national junior-level sprint and middle distance runners were evaluated for isokinetic peak torque for leg extension as well as muscular power and fatiguability. Peak torque values were greater for the older runners and for the sprinters when measured at higher velocities. However, when adjusted for body weight, the peak torque values of the sprinters became significantly greater at all testing velocities. Muscular power values were also greater for the older runners, but event-related differences only appeared for peak power and mean power measures (being greater in the sprinters).