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
Bruno Ferreira Viana, Flávio Oliveira Pires, Allan Inoue, and Tony Meireles Santos
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
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
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
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.
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
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
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.
Angela Heidenfelder, Thomas Rosemann, Christoph A. Rüst, and Beat Knechtle
To examine pacing strategies of ultracyclists competing in the Race Across AMerica (RAAM), the world’s longest ultracycling race, covering ~4860 km from the West to the East coast of America.
Age, cycling speed at and across time stations, race distance, relative difference in altitude between time stations, wind velocity, wind gradient, and temperature at each time station were recorded for women and men competing from 2010 to 2014. Changes in cycling speed and power output of elite and age-group finishers were analyzed using mixed-effects regression analyses.
Cycling speed decreased across time stations for women and men where men were faster than women. Power output decreased across time stations in women and men and was lower for women for all finishers, the annual 3 fastest, and age group 60–69 y but not for age groups 18–49 and 50–59 y. The change in temperature and altitude had an influence on cycling speed and power output in all finishers, the annual top 3, nonfinishers, and in all different age groups for both women and men but in the age group 50–59 y altitude had no influence on cycling speed.
Positive pacing (ie, decrease in speed throughout the race) seemed to be the adequate strategy in the RAAM. The top 3 finishers started faster and had a higher power output at the start than less successful competitors, achieved the highest peak cycling speeds and power output, and maintained peak cycling speed and power output longer before slowing down.
To investigate pacing strategy during the 1-km time trial (TT) and 3- and 4-km individual pursuit (IP), in elite cyclists.
Total times and intermediate times were obtained from the 2007 and 2008 cycling World Championships in the 1-km TT and 2006, 2007, and 2008 World Championships in the 3- and 4-km IP. Data were analyzed to examine the pacing-profiles employed and pacing strategies of “slow” and “fast” performances.
Similar pacing-profiles were evident in each event, which were characterized by an initial acceleration followed by a progressive decay in split times. In the 1-km TT, the first 250-m split time was a primary determinant of total time, whereas the rate of fatigue over the remainder of the race did not discriminate between performances. The first 250-m split time was also related to total time in the 3- and 4-km IP, although to a lesser extent than in the 1-km TT, whereas the ability to maintain a consistent pacing-profile was of increased importance. There were differences in the pacing strategies of slow and fast performances in the 3- and 4-km IP, with slow performances characterized by an overly quick start with a concomitant slowing at the finish.
The pacing profiles adopted were similar to the optimal pacing strategies proposed in simulation models of cycling performance. However, in the 3-km and 4-km IP small alterations in pacing strategy appear to be important, at the elite level.