Pacing Strategy During 24-Hour Ultramarathon-Distance Running

Abstract

Purpose: To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes' performance group and race edition. Methods: Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cut-off. Mean running speed from each hour was normalised to the 24-h mean speed for analyses. Results: Mean overall performance was 135.6 ± 33.0 km with a mean effectiverunning 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 last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athletes' performance group (F = 7.01; P < 0.001; p 2 = 0.04) and race edition (F = 3.01; P < 0.001; p 2 = 0.02), but not between racing time and both sex (F = 1.57; P = 0.058; p 2 < 0.01) and age group (F = 1.25; P = 0.053; p 2 = 0.01). Pearson's product-moment correlations showed an inverse moderate association between performance and normalised mean running speed in the first 2 h (r = -0.58; P < 0.001) but not in the last 2 h (r = 0.03; P = 0.480). Conclusions:While the general behaviour represents a rough, reverse J-shaped pattern, fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The 'herd behaviour' seems to interfere with pacing strategy across editions, but not sex or age group of runners.

The Impact of Sex and Performance Level on Pacing Behavior in a 24-h Ultramarathon

“…Results presented in mean and standard deviation. (Bossi et al, 2017), the present study showed that runners adopted a reverse J-shaped pacing strategy during the VI Rio 24-h Marines Ultramarathon running, regardless of sex and performance level. Thus, runners slowed down the pace during most of the race, after performing a faster start and before spurting at the last 10% of the race.…”

“…We used the total distance covered within the 24 h as a performance indicator. In addition, pacing strategy analysis during this open-loop race was based on the distance completed within the 24 h. Thus, in accordance with previous works investigating the influence of the performance level on running pacing strategy (Lima- Silva et al, 2010;Bossi et al, 2017), we ranked runners according to tercile so that those runners within the lowest and highest tercile were designed as low (LP) and high performance (HP) groups, respectively. We discarded those runners within the intermediate tercile, as this ensured a comparison of pacing strategy profiles between distinguished different performance level groups.…”

“…For pacing strategy analysis, we used the individual time elapsed recorded every 400 m to calculate the individual running speed within each 10 min interval, being expressed as absolute. Thereafter, based on previous literature (Bossi et al, 2017) describing a reverse J-shaped pacing strategy in ultramarathons, we analyzed the runners pacing strategy according to three different epochs : (1) the initial epoch, defined as the mean speed over the first 10% of the 24-h race (0 to 120 min); (2) the intermediate epoch, defined as the mean speed over the following 80% of the 24-h race (121 to 1,300 min) and; (3) the final epoch, defined as the mean speed over the last 10% of the 24-h race (1,301 to 1,440 min).…”

“…New insights regarding ultramarathon pacing behavior may be obtained from the analysis of the VI Rio 24-h Marines Ultramarathon dataset. For example, Bossi et al (2017) analyzed data from 398 male and 103 female participants over five editions of this event, showing that athletes frequently use a reverse J-shaped pacing strategy in this 24-h running race. Using grouped data, the authors found that athletes used a fast start pace and then reduced the speed during the intermediate part of the race, before spurting during the final few hours.…”

“…Therefore, the use of high-frequency speed data rather than broad section averages may be needed to reveal how athletes incorporate breaks and walking periods in their pacing strategy. In fact, previous ultramarathon studies (Takayama et al, 2016;Bossi et al, 2017) have analyzed the 24h running pacing strategy through 1 h mean data, thus likely disregarding speed variations in intervals lower than 1 h. In this perspective, it could be argued that 1 h time intervals are not long enough to accommodate important speed variations that may reflect either running or walking speeds. Moreover, another critical factor is the presence of breaks or substantial speed reductions (SSR) in ultramarathons, since it has been shown that a lower time spent with active/passive recovery periods was related to the best ultramarathon performance (Kerherve et al, 2015).…”