However, studies have also shown that peak power output is not the only important factor to success. 2 Indeed, a cyclist’s velocity is likely to be a much more important factor in the outcome of road cycling sprints. Cycling velocity is the result of power output, aerodynamic drag (CdA), road
Paul F.J. Merkes, Paolo Menaspà and Chris R. Abbiss
A computer model was developed of the aerodynamic drag forces acting to slow down a wheelchair. The model calculated drag forces over a range of wheeling speeds between 2 and 20 m/sec, and for wind conditions over the same range of speeds with wind direction varied between 0° (headwind) and 180° (tailwind). The computer model suggests that the large lateral area of a wheelchair adds considerably to the retarding drag forces at relative wind angles between 0 and 90°. It further suggests that three-wheeled wheelchairs have a considerable aerodynamic advantage over four-wheeled wheelchairs for a wide range of wind speeds and directions. In straight line races, the four-wheeled wheelchair has a slight aerodynamic advantage when the relative wind angle exceeds 90°, but under other speed and wind conditions in this study the three-wheeled wheelchair was more efficient.
Theo Ouvrard, Alain Groslambert, Gilles Ravier, Sidney Grosprêtre, Philippe Gimenez and Frederic Grappe
front of the main group, with their leader directly behind them. Although this strategy is now well established and used automatically by all teams seeking final victory during a Grand Tour, its impact on the main determinants of performance has not been studied yet. The reduction in aerodynamic drag
Levi Heimans, Wouter R. Dijkshoorn, Marco J.M. Hoozemans and Jos J. de Koning
, mechanical resistance of the drive system of the bike, and aerodynamic drag. At 10 m/s, more than 90% of the power loss in track cycling is due to aerodynamic drag. 1 , 2 Consequently, reduction of aerodynamic drag can lead to a significant decrease of the cyclist’s power requirements. With a given maximal
Jeanick Brisswalter and Christophe Hausswirth
Matt R. Cross, Matt Brughelli, Pierre Samozino, Scott R. Brown and Jean-Benoit Morin
To ascertain whether force-velocity-power relationships could be compiled from a battery of sled-resisted overground sprints and to clarify and compare the optimal loading conditions for maximizing power production for different athlete cohorts.
Recreational mixed-sport athletes (n = 12) and sprinters (n = 15) performed multiple trials of maximal sprints unloaded and towing a selection of sled masses (20–120% body mass [BM]). Velocity data were collected by sports radar, and kinetics at peak velocity were quantified using friction coefficients and aerodynamic drag. Individual force–velocity and power–velocity relationships were generated using linear and quadratic relationships, respectively. Mechanical and optimal loading variables were subsequently calculated and test–retest reliability assessed.
Individual force–velocity and power–velocity relationships were accurately fitted with regression models (R 2 > .977, P < .001) and were reliable (ES = 0.05–0.50, ICC = .73–.97, CV = 1.0–5.4%). The normal loading that maximized peak power was 78% ± 6% and 82% ± 8% of BM, representing a resistance of 3.37 and 3.62 N/kg at 4.19 ± 0.19 and 4.90 ± 0.18 m/s (recreational athletes and sprinters, respectively). Optimal force and normal load did not clearly differentiate between cohorts, although sprinters developed greater maximal power (17.2–26.5%, ES = 0.97–2.13, P < .02) at much greater velocities (16.9%, ES = 3.73, P < .001).
Mechanical relationships can be accurately profiled using common sled-training equipment. Notably, the optimal loading conditions determined in this study (69–96% of BM, dependent on friction conditions) represent much greater resistance than current guidelines (~7–20% of BM). This method has potential value in quantifying individualized training parameters for optimized development of horizontal power.
W. Lee Childers, Tim P. Gallagher, J. Chad Duncan and Douglas K. Taylor
The individual pursuit is a 4-km cycling time trial performed on a velodrome. Parathletes with transtibial amputation (TTA) have lost physiological systems, but this may be offset by the reduced aerodynamic drag of the prosthesis. This research was performed to understand the effect of a unilateral TTA on Olympic 4-km pursuit performance. A forward-integration model of pursuit performance explored the interplay between power loss and aerodynamic gains in parathletes with TTA. The model is calibrated to a 4-km pursuit time of 4:10.5 (baseline), then adjusted to account for a TTA. Conditions simulated were based on typical pedal asymmetry in TTA (AMP), if foot stiffness were decreased (FLEX), if pedaling asymmetries were minimized (ASYM), if the prosthesis were aerodynamically optimized (AERO), if the prosthesis had a cosmetic cover (CC), and if all variables were optimized (OPT). A random Monte Carlo analysis was performed to understand model precision. Four-kilometer pursuit performances predicted by the model were 4:10.5, 4:20.4, 4:27.7, 4:09.2, 4:19.4, 4:27.9, and 4:08.2 for the baseline, AMP, FLEX, ASYM, AERO, CC, and OPT models, respectively. Model precision was ±3.7 s. While the modeled time decreased for ASYM and OPT modeled conditions, the time reduction fell within model precision and therefore was not significant. Practical application of these results suggests that parathletes with a TTA could improve performance by minimizing pedaling asymmetry and/or optimizing aerodynamic design, but, at best, they will have performance similar to that of intact cyclists. In conclusion, parathletes with TTA do not have a net advantage in the individual pursuit.
Grégoire P. Millet, Cyrille Tronche and Frédéric Grappe
To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest.
Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3–6.3 km, slope 4.4–10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France.
Overall, the difference between Pmes and Pest was –0.95% (95%CI: –10.4%, +8.5%) for all trials and 0.24% (–6.1%, +6.6%) in conditions without wind (>2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial.
Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.
Paul F.J. Merkes, Paolo Menaspà and Chris R. Abbiss
with the hands in the drops. However, other variables like seated and standing, head high or low, or elbows tucked or not were not controlled. These small changes in riding position are likely to affect aerodynamic drag (product of drag coefficient [Cd] and frontal area [A]: CdA). 18 – 22 The Velocomp
Samuel Sigrist, Thomas Maier and Raphael Faiss
aerodynamic drag 15 in the slipstream, allowing partial recovery from the leading efforts. Practically, every transition during the race requires the lead rider to cover a longer distance while continuously pedaling to keep momentum when using the velodrome bankings to gain potential energy in the curve that