1000 W at the outset of an XCO race, 3 thus supporting the suggestion that a fast-start pacing strategy is the most-used strategy in XCO races. 2 , 4 Because of this fast-start pacing, XCO athletes usually decrease power output as the race progresses so that the time to complete each lap increases. 1
Pacing Strategy During Simulated Mountain Bike Racing
Bruno Ferreira Viana, Flávio Oliveira Pires, Allan Inoue, and Tony Meireles Santos
Pacing Strategy and Tactical Positioning During Cyclo-Cross Races
Arthur H. Bossi, Ciaran O’Grady, Richard Ebreo, Louis Passfield, and James G. Hopker
field. Among the factors underpinning endurance cycling performance, pacing strategy is one of the most studied. 6 , 7 Descriptive 8 – 10 and experimental 11 – 13 studies have been published across disciplines, and, interestingly, a variable gradient course has been considered an extra challenge to
The Influence of Muscle Fiber Typology on the Pacing Strategy of 200-m Freestyle Swimmers
Adam Mallett, Phillip Bellinger, Wim Derave, Katie McGibbon, Eline Lievens, Ben Kennedy, Hal Rice, and Clare Minahan
In order to be successful in racing sports such as swimming, running, and cycling at the international level, an athlete’s pacing strategy is a key determinant of performance. 1 Pacing strategy refers to the distribution of energy expenditure over the duration of the event, 2 where the power
The Influence of Pleasure and Attentional Focus on Performance and Pacing Strategies in Elite Individual Time Trials
Theo Ouvrard, Alain Groslambert, and Frederic Grappe
, cyclists must sustain a mean PO ranging from 350 to 450 W for up to 60 minutes. 2 , 5 , 6 It is now well known that maintaining a high PO over a prolonged period of time involves a good regulation of exercise intensity throughout the event, generally called pacing strategy. 7 , 8 Results of the literature
Effect of the Pacing Strategies on the Open-Water 10-km World Swimming Championships Performances
Luis Rodriguez and Santiago Veiga
known as pacing strategies. 5 This is a popular topic for endurance and ultraendurance events, and different strategies have been described for a variety of race durations to minimize the effects of fatigue. 6 In short-duration events (≤30–60 s), the best race strategies are generally characterized by
Pacing Strategy in Short Cycling Time Trials
Jelle de Jong, Linda van der Meijden, Simone Hamby, Samantha Suckow, Christopher Dodge, Jos J. de Koning, and Carl Foster
To reach top performance in cycling, optimizing distribution of energy resources is crucial. The purpose of this study was to investigate power output during 250-m, 500-m, and 1000-m cycling time trials and the characteristics of the adopted pacing strategy.
Nine trained cyclists completed an incremental test and 3 time trials that they were instructed to finish as quickly as possible. Preceding the trials, peak power during short sprints (PPsprint) and gross efficiency (GE) were measured. During the trials, power output and oxygen consumption were measured to calculate the contribution of the aerobic and anaerobic energy sources. After the trial GE was measured again.
Peak power during all trials (PPTT) was lower than PPsprint. In the 250-m trial the PPTT was higher in the 1000-m trial (P = .008). The subjects performed a significantly longer time at high intensity in the 250-m than in the 1000-m (P = .029). GE declined significantly during all trials (P < .01). Total anaerobically attributable work was less in the 250-m than in the 500-m (P = .015) and 1000-m (P < .01) trials.
The overall pacing pattern in the 250-m trial appears to follow an all-out strategy, although peak power is still lower than the potential maximal power output. This suggests that a true all-out pattern of power output may not be used in fixed-distance events. The 500-m and 1000-m had a more conservative pacing pattern and anaerobic power output reached a constant magnitude.
Improved 1000-m Running Performance and Pacing Strategy With Caffeine and Placebo: A Balanced Placebo Design Study
Philip Hurst, Lieke Schipof-Godart, Florentina Hettinga, Bart Roelands, and Chris Beedie
reasonable to suggest that after ingesting caffeine, for example, athletes may anticipate an offset in fatigue and alter their exercise behavior. Thus, athletes’ pacing strategy may depend on their belief regarding the effect of a substance and their subsequent decisions during performance. Pacing strategies
Cross-Country Skiers With a Fast-Start Pacing Pattern Increase Time-Trial Performance by Use of a More Even Pacing Strategy
Thomas Losnegard, Ola Kristoffer Tosterud, Kasper Kjeldsen, Øyvind Olstad, and Jan Kocbach
challenges athletes’ ability to prescribe their exercise intensity and thereby their pacing strategy. In XC skiing, no objective internal or external markers (such as heart rate [HR], speed, and power output) can be used continuously during a race to plan, adjust, or evaluate exercise intensity and thereby
The Effect of Maximal Speed Ability, Pacing Strategy, and Technique on the Finish Sprint of a Sprint Cross-Country Skiing Competition
Pål Haugnes, Per-Øyvind Torvik, Gertjan Ettema, Jan Kocbach, and Øyvind Sandbakk
pacing strategy would influence the finish sprint have not yet been investigated. Therefore, the primary aim of this study was to investigate the contribution from V max and %V max to the speed obtained in the finish sprint of XC sprint competitions in classical and skating XC skiing, as well as the
Pacing Strategy During 24-Hour Ultramarathon-Distance Running
Arthur H. Bossi, Guilherme G. Matta, Guillaume Y. Millet, Pedro Lima, Leonardo C. Pertence, Jorge P. de Lima, and James G. Hopker
To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes’ performance group, and race edition.
Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses.
Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp 2 = .04) and race edition (F = 3.01, P < .001, ηp 2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp 2 = .01). Pearson product–moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = –.58, P < .001) but not in the last 2 h (r = .03, P = .480).
While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The “herd behavior” seems to interfere with pacing strategy across editions, but not sex or age group of runners.