medicina
Article
Pacing of Women and Men in Half-Marathon
and Marathon Races
Pantelis T. Nikolaidis 1,*
, Ivan ´Cuk 2
and Beat Knechtle 3
1
Exercise Physiology Laboratory, 18450 Nikaia, Greece
2
Faculty of Physical Education and Sports Management, Singidunum University, 11000 Belgrade, Serbia;
icuk@singidunum.ac.rs
3
Institute of Primary Care, University of Zurich, 8006 Zürich, Switzerland; beat.knechtle@hispeed.ch
*
Correspondence: pademil@hotmail.com; Tel.: +306977820298
Received: 4 November 2018; Accepted: 9 January 2019; Published: 14 January 2019


Abstract: Background and objective: Half-marathon is the most popular endurance running race in
terms of number of races and runners competing annually; however, no study has compared pacing
strategies for this race distance with marathon. The aim of the present study was to proﬁle pacing
in half-marathon, compare half-marathon and marathon for pacing, and estimate sex differences in
pacing. Materials and methods: A total of 9137 ﬁnishers in the half-marathon (n = 7258) and marathon
race (n = 1853) in Ljubljana 2017 were considered for their pacing in ﬁve race segments (0–23.7%,
23.7–47.4%, 47.4–71.1%, 71.1–94.8%, and 94.8–100% of the race. Results: Half-marathon runners
followed a positive pacing with every segment being slower than its previous one without the
presence of an endspurt. Compared to marathon (where the average percent of change in speed
(ACS) was 5.71%), a more even pacing was observed in half-marathon (ACS = 4.10%). Moreover,
women (ACS = 4.11%) had similar pacing as men (ACS = 4.09%) in half-marathons. Conclusions:
In summary, running a half-marathon followed a unique pattern that differentiated this race distance
from marathon, with the former showing a more even pacing with an absence of endspurt, and sex
difference compared to the latter. Consequently, runners should be advised to adopt a less variable
pacing when competing in a half-marathon, regardless of their sex. To the best of our knowledge,
the more even pacing in half-marathon, than in marathon, was a novel ﬁnding, as it was the ﬁrst
study to compare the two race distances for this characteristic.
Keywords: aerobic exercise; endurance; marathon; performance; running
1. Introduction
The half-marathon has evolved as a sport discipline of increasing popularity, documented by
the annual number of ﬁnishers and races taking place worldwide [1]. In general, performance in
endurance running, as well as in endurance sports of other modes of exercise (cycling, swimming,
cross-country skiing), has been shown to be associated with pacing, among other physiological and
psychological variables [2,3]. Despite the popularity of half-marathon, limited information about the
pacing, in this sport discipline, exists [4]. Since half-marathon has been characterized by an increased
woman participation compared to marathon [5], and sex differences in pacing in marathon have been
observed [2,3], estimating the pacing in half-marathon would be of great practical interest, especially
considering the aspect of sex.
Pacing has been well studied in many endurance and ultra-endurance sports, such as cycling [6,7],
swimming [8,9], and triathlon [10]. It has been observed that pacing in cycling varied depending on
whether cyclists performed exercise for a given time or distance, showing a faster start when competing
for distance [6]. Research on the world’s longest ultra-cycling race showed a decrease of speed across
Medicina 2019, 55, 14; doi:10.3390/medicina55010014
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the race [7]. The variation of speed, in 800 m for women [8] and 1500 m freestyle swimming for men,
followed a U shape [9]. In sprint triathlon, a comparison among three pacing strategies (positive,
negative, and even, where speed either decreased, increased, or remained stable across race) of
swimming indicated that a positive pacing in swimming induced a lower rate of perceived exertion
than a negative pacing [10]. The abovementioned studies found different pacing strategies among
endurance and ultra-endurance sports, which did not allow the generalization of their ﬁndings to
other sports. Adopting a pacing strategy might depend on athletes’ decision making [11], perception
of risk [12], and individual variability [13]. For instance, pacing might be inﬂuenced by internal bodily
state feedback, teleoanticipation, template formation of perceived exertion, and human-environment
interaction (e.g., interaction among competitors) [11]. In addition, it was shown that cyclists and
ultra-marathon runners with low perception of risk started the race faster than their counterparts with
high perception of risk [12]. Furthermore, inter-individual differences in the distribution of effort,
across a race, might be partially explained by athletes’ motivation [13].
So far, with regards to endurance running sport disciplines of high popularity, pacing has been
well studied in marathon, where it has been found that runners adopted a positive pacing, i.e., their
speed decreased across race, accompanied by the presence of an end spurt, i.e., the speed increased
in the ﬁnal section [2,14–16]. A positive pacing has also been observed in shorter distances, such as
800 m [17], 5 km [18], and 10 km [19]. With regards to sex differences, women marathon runners
adopted a more even pacing in marathon [3,20] and in 100 km, than men [21]. Perceived effort and
physiological parameters have been identiﬁed as correlates of pacing, and their role might vary across
a 10 km race; e.g., perceived effort inﬂuenced speed at the start of the race, whereas mainly aerobic
capacity and muscle strength-to a lesser degree-inﬂuenced speed for the rest of the race [22]. Moreover,
with regards to the relationship of pacing with motivation, it has been shown that men with more even
pacing scored higher in psychological coping, self-esteem, life meaning, recognition, and competition,
than their counterparts with more variable pacing [23].
Although many studies have been conducted on the pacing of marathon [15,16,24–26], the limited
relevant information that existed for half-marathon [4] had focused on elite runners. Since these
two distance races differed for performance characteristics [5], it would be reasonable to assume
that knowledge from pacing in marathon could not be “transferred” to half-marathon. Knowledge
about pacing in half-marathon would have both theoretical and practical interest. From a theoretical
perspective, exercise physiologists would be interested in the patterns of energy distribution across a
21 km running race and on potential sex differences in these patterns. From a practical perspective,
coaches and ﬁtness trainers working with half-marathon runners would use such knowledge to assist
their athletes adopting sex-tailored pacing strategies. Therefore, the aim of the present study was
to (a) examine changes in speed and whether pacing would be positive, negative, or even across
half-marathon and marathon races, (b) investigate whether pacing would vary by race distance, and (c)
estimate sex differences in pacing for both race distances. It was hypothesized that half-marathon
would present a positive pacing with an endspurt, and women would adopt a more even pacing
than men.
2. Materials and Methods
2.1. Participants
This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland,
with a waiver of the requirement for informed consent of the participants as the study involved the
analysis of publicly available data. The study was conducted in accordance with recognized ethical
standards according to the Declaration of Helsinki adopted in 1964, and revised in 2013.
For the purpose of this study,
valid results and split times from 1853 participants
(age 41.7 ± 9.8 years, range 17–78 years; average race speed 3.03 ± 0.46 m/s, 2.13–5.47 m/s; mean ±
standard deviation) of the 2017 Ljubljana marathon and 7258 participants (age 40.3 ± 10.7 years, 12–86
Medicina 2019, 55, 14
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years; 2.99 ± 0.45 m/s, 1.61–5.15 m/s) of the 2017 Ljubljana half-marathon (total 9137 participants)
were analyzed. The obtained data presents publicly available, ofﬁcial results from the “Ljubljana
Marathon” website [27]. Participants who did not ﬁnish the race, or did not have any recorded
split times, were excluded from the study. Both marathon and half-marathon were held on the
same day and on the same track, whereas half-marathon race is entirely contained within marathon
race. This approach assisted in eliminating the potential inﬂuence of environmental conditions [28].
Moreover, both marathon and half-marathon were rather ﬂat, with an elevation difference of 29 m
(ranging from 295–324 m). The temperature on the race day ranged from 4.2–15.4 ◦C, without strong
wind or excess humidity.
2.2. Variables and Research Measures
From the available data, the average race speed 0–100% (0–21.0975 km for half-marathon and
0–42.195 km for marathon) was calculated. Moreover, average running speed in ﬁve race segments
was estimated for both marathon and half-marathon, that corresponded to:
Segment 1-Average running speed from 0–23.7% of the race (i.e., 0–5 km for half-marathon and
0–10 km for marathon)
Segment 2-Average running speed from 23.7–47.4% of the race (i.e., 5–10 km for half-marathon
and 10–20 km for marathon)
Segment 3-Average running speed from 47.4–71.1% of the race (i.e., 10–15 km for half-marathon
and 20–30 km for marathon)
Segment 4-Average running speed from 71.1–94.8% of the race (i.e., 15–20 km for half-marathon
and 30–40 km for marathon)
Segment 5-Average running speed from 94.8–100% of the race (i.e., 20–21.0975 km for
half-marathon and 40–42.195 km for marathon)
2.3. Process
Thereafter, the percentage of average change in speed for each segment (ACSS), with regards to the
average race speed, was calculated. The applied equation is as follows: ACSS = 100 − (100 × average
race speed/average segment speed). Finally, the average percentage of change in speed, through
the 5 race segments (average change in speed (ACS)), was estimated.
Note that absolute
values of ACS were presented and statistically tested.
The applied equation was as follows:
ACS = (ACSS1 + ACSS2 + ACSS3 + ACSS4 + ACSS5)/5. Changes in speed across a running race
have been previously used to study pacing in 800 m [17], 5 km [18], 10 km [19], half-marathon [4],
and marathon [15]. In the present study, the speed in each segment was expressed as the percentage
difference from the average race speed, in order to provide comparable data considering potential
differences in average race speed between races or sexes.
2.4. Data Analysis
Prior to all statistical tests, descriptive statistics were calculated as mean, standard deviation,
and minimum and maximum values. Since the Kolmogorov-Smirnov test is not sensitive in large
samples, data distribution normality was assessed by inspecting histograms and QQ plots. After careful
examination, the obtained data showed rather normal distribution. Mixed between-within analysis of
variance (ANOVA) was performed for ACSS to test differences between segments (i.e., Segments 1–5;
within-subject factor), race (i.e., marathon and half-marathon; between-subjects factor), as well as their
interaction (segment × race). To further assess race differences, four additional mixed between-within
ANOVAs for ACSS were performed. Two ANOVAs were performed to assess differences between
segments (i.e., Segments 1 to 5; within-subject factor), race (i.e., marathon and half-marathon;
between-subjects factor) as well as their interaction (segment × race), separately for men and women.
Another two ANOVAs were performed to assess differences between segments (i.e., Segments 1 to
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5; within-subject factor), sex (i.e., men and women), as well as their interaction (segment × sex),
separately for marathon and half-marathon. Finally, one two-way ANOVA was performed on ACS to
assess differences between races (i.e., marathon and half-marathon), sex (i.e., men and women), as well
as their interaction (race × sex). For all ANOVAs, a post hoc Bonferroni test was performed. Effect
size was presented via eta squared ( 2), where the values of 0.01, 0.06, and above 0.14 were considered
small, medium, and large, respectively [29]. Alpha level was set at p < 0.05. All statistical tests were
performed using Microsoft Ofﬁce Excel 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS
20 (IBM, Armonk, NY, USA).
3. Results
The average running speeds for ﬁve segments, as well as the average race speed of participants
by sex and race distance, are presented in Table 1. From the descriptive data in Table 1, a gradual
decrease in average speed through the race segments was observed for both sexes in both marathon
and half-marathon, e.g., from 3.26 ± 0.44 m/s in segment 1 to 2.99 ± 0.50 m/s in segment 5 in men
half-marathon runners. Moreover, the largest deviation of running speed was observed in marathon
men, i.e., decrease by 0.38 m/s from segment 1–5, whereas the smallest deviation of running speed was
observed in half-marathon women, i.e., decrease by 0.25 m/s from segment 1–5. Further examination
of participants’ speed and speed change is presented in Figures 1–3.
Table 1. Segments and race speed for men and women, marathon, and half-marathon runners.
Segment 1
Speed (m/s)
Segment 2
Speed (m/s)
Segment 3
Speed (m/s)
Segment 4
Speed (m/s)
Segment 5
Speed (m/s)
Average Race
Speed (m/s)
Men 42.2 km
N = 1478
Median
3.24
3.18
3.06
2.80
2.88
3.04
Mean
3.29
3.22
3.09
2.82
2.91
3.08
SD
0.44
0.45
0.49
0.52
0.49
0.46
CV
0.13
0.14
0.16
0.18
0.17
0.15
Women 42.2 km
N = 375
Median
2.96
2.90
2.78
2.63
2.77
2.80
Mean
3.04
2.95
2.82
2.68
2.81
2.86
SD
0.36
0.38
0.43
0.44
0.41
0.39
CV
0.12
0.13
0.15
0.16
0.15
0.14
Men 21.1 km
N = 4406
Median
3.21
3.18
3.06
3.04
2.97
3.10
Mean
3.26
3.22
3.09
3.06
2.99
3.14
SD
0.44
0.44
0.46
0.49
0.50
0.45
CV
0.14
0.14
0.15
0.16
0.17
0.14
Women 21.1 km
N = 2852
Median
2.90
2.83
2.69
2.69
2.64
2.76
Mean
2.91
2.83
2.70
2.69
2.66
2.77
SD
0.32
0.35
0.36
0.39
0.39
0.35
CV
0.11
0.12
0.14
0.14
0.15
0.13
SD = standard deviation; CV = coefﬁcient of variation.
Figure 1. Percentage of speed change by split section in half-marathon and marathon. Error bars
present standard deviation. ** p < 0.01 for signiﬁcant difference between races.
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Figure 2. Percentage of speed change by segment and race distance in men (a) and women (b).
Error bars present standard deviation.
Figure 3. Absolute average change of speed by race distance and sex. Error bars present standard
deviation. ** p < 0.01 for differences between sexes; ˆˆ p < 0.01 for differences between races.
In regards to marathon and half-marathon runners of both sexes (Figure 1), the signiﬁcant main
effects of segment (F(4, 9106) = 5959.2,
2 = 0.36, large magnitude, p < 0.01), race (F(4, 9106) = 11.7,
2 < 0.01, trivial, p = 0.17), and segment × race interaction (F(4, 9106) = 723.1,
2 = 0.04, small, p < 0.01)
were observed. Speciﬁcally, in both marathon and half-marathon runners, each segment signiﬁcantly
differs (p < 0.01) in speed change compared to the others.
When only men runners were considered (Figure 2a), results indicated the signiﬁcant main effects
of segment (F(4, 5879) = 4392.3,
2 = 0.39, large, p < 0.01), race (F(4, 5879) = 64.2,
2 < 0.01, trivial,
p < 0.01), and segment × race interaction (F(4, 5879) = 609.4,
2 = 0.05, small, p < 0.01). Speciﬁcally,
for the ﬁrst two segments (2.99% and 2.16% respectively), and the last two segments (6.81% and 0.69%
respectively), men marathon runners showed greater changes in speed than half-marathon runners of
the same sex (p < 0.01). However, in the third segment, men marathon runners presented more stable
speed than half-marathon runners by 1.81% (p < 0.01).
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Similar results were obtained when only women runners were observed (Figure 2b). Signiﬁcant
main effects of segment (F(4, 3222) = 1279.2,
2 = 0.27, large, p < 0.01), race (F(4, 3222) = 57.3,
2 < 0.01,
trivial, p < 0.01), and segment × race interaction (F(4, 3222) = 100.0,
2 = 0.02, small, p < 0.01) were
observed. In particular, for the ﬁrst two segments (1.13% and 0.94% respectively), and the fourth
segment (3.90%), women marathon runners showed a greater speed change than women half-marathon
runners (p < 0.01). On the other hand, in the third (1.28%) and the ﬁfth segment (2.53%), women
marathon runners exhibited more stable speed than women half-marathon runners of the same sex
(p < 0.01).
When marathon runners only were observed (Figure 2a,b), the results conﬁrmed the signiﬁcant
main effects of segment (F(4, 1848) = 1131.2,
2 = 0.36, large, p < 0.01), sex (F(4, 1848) = 88.0,
2 < 0.01,
trivial, p < 0.01), and segment × sex interaction (F(4, 1848) = 57.0,
2 = 0.02, small, p < 0.01). Speciﬁcally,
for the ﬁrst two segments (0.69% and 1.38% respectively), and the last two segments (2.75% and
4.35% respectively), men marathon runners presented greater changes in speed than women marathon
runners (p < 0.01). For the third and fourth segments (1.63% respectively), men marathon runners
showed more stable speed than women marathon runners (p < 0.01).
Considering half-marathon results only (Figure 2a,b), the signiﬁcant main effects of segment
(F(4, 7253) = 4772.6,
2 = 0.38, large, p < 0.01), sex (F(4, 7253) = 46.9,
2 < 0.01, trivial, p < 0.01),
and segment × sex interaction (F(4, 7253) = 71.3,
2 = 0.01, trivial, p < 0.01) were observed. Particularly,
in the ﬁrst three segments (1.17%, 1.10%, and 0.17%, respectively), men half-marathon runners had
more stable speed than women (p < 0.01), whereas in the ﬁfth segment (1.13%) women had more
stable speed (p < 0.01). Regarding fourth segment (0.16%), no signiﬁcant differences between men and
women half-marathon runners were observed.
Finally, in men and women runners in both marathon and half-marathon, each segment
signiﬁcantly differences in speed change than the other (p < 0.01). The only exception is the lack
of difference between the third and ﬁfth segment in women marathon runners (p = 0.96).
Regarding ACS (Figure 3), the signiﬁcant main effects of race (F(3, 9107) = 300.8,
2 = 0.03, small,
p < 0.01), sex (F(3, 9107) = 55.6,
2 = 0.01, trivial, p < 0.01), and race × sex interaction (F(3, 9107) = 60.5,
2 = 0.01, trivial, p < 0.01) were observed. Men marathon runners had a greater average change of
speed of 2.34% than half-marathon runners (p < 0.01), whereas women marathon runners had a 0.89%
greater change of speed than women half-marathon runners (p < 0.01). Moreover, the difference
between men and women in marathon showed that there was a 1.41% greater average change of
speed in marathon men (p < 0.01). Finally, the average speed difference between men and women in
half-marathon was only 0.030% (p = 0.67).
4. Discussion
The main ﬁndings of the present study were that (a) half-marathon runners followed a positive
pacing with every segment being slower than its previous one without the presence of an endspurt;
(b) compared to marathon, a more even pacing was observed in half-marathon; and (c) women had
similar pacing as men in half-marathon.
The overall distribution of energy across the race in the half-marathon followed the so-called
“positive pacing” [30], that is, the speed decreased continuously across the race. This observation was
in agreement with a previous research on IAAF World Half Marathon Championships where slower
athletes had decreased speeds from the ﬁrst segment onwards [4]. It should be highlighted that no
endspurt was shown, which was in disagreement with the study of Hanley [4] who examined only elite
runners. The discrepancy between the two studies might be attributed to the different performance
level, as the abovementioned study focused on world championships, where the faster athletes showed
larger endspurt than their slower peers. This suggested that lack of an endspurt might appear in
half-marathon races with a large participation of recreational runners.
To the best of our knowledge, the more even pacing in half-marathon than in marathon was
a novel ﬁnding, as it was the ﬁrst study to compare the two race distances for this characteristic.
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It might be assumed that this difference was due to the additional fatigue induced in the marathon.
This assumption is supported by the observation that their difference reached its peak in the fourth
segment, i.e., close to the end of the race. On the other hand, both race distances adopted a positive
pacing, which was in line with the notion that the perception of effort scaled with the proportion of
exercise time that remained [31].
With regards to sex differences, surprisingly, women had similar pacing as men in a half-marathon.
In marathon, women had a more even pacing than men, which conﬁrmed the previous ﬁndings in
other marathon races [20,25,26]. This sex difference has been attributed to differences in physiology
and decision making between women and men [32].
Both half-marathon and marathon runners adopted a variable pacing instead of maintaining
a steady speed across race. Following a variable self-pacing has been shown to present certain
advantages-i.e., enhancement of critical power and high-intensity exercise performance compared to
constant work rate cycling exercise [33]. In addition, the rate of perceived exertion has been shown
to associate with pacing [22] and might vary across race [34]. Moreover, it was acknowledged that
head-to-head competition improved performance compared to running alone [35]. Although this
aspect was not examined in the present study, it would be assumed that head-to-head competition
exerted a similar inﬂuence in both race distances, since half-marathon and marathon races were
massive events. Overall, pacing should be considered as a complex system, where individual responses
interacted with environment [36]. In this context, athletes were requested to balance behavior and
thinking (self-regulation) to optimize their speed across the race [37].
A limitation of the present research was that it considered a sport event (Ljubljana) with relatively
small participation compared to other races [38].
Thus, the ﬁndings should be considered as
preliminary, and should be veriﬁed in future studies. On the other hand, strength was the novelty
as it added original information in the existing literature with regards to one of the most popular
race distances. Considering that half-marathon was the most popular running race event in terms of
annual number of races and participants [1,5], the ﬁndings of the present study would have practical
applications for a wide range of professionals working endurance runners, e.g., coaches, ﬁtness
trainers, nutritionists, and physicians. As it has been shown that endurance runners might compete to
both half-marathon and marathon [39], it would be of great practical relevance to know how these
two race distances differed in pacing. With regards to sex, men and women should be advised to
adopt similar pacing patterns in half-marathon, whereas women should aim to run more evenly
than men in marathon. Considering the race distance, runners should be guided to regulate their
pacing as less or more variable, depending on whether they intended to run a half-marathon or
marathon, respectively. Since recent studies reported an association of pacing with physiological and
psychological parameters in marathon [23] and 10 km run [22], future research should verify this
association in half-marathon, too.
5. Conclusions
In summary,
both half-marathon and marathon races presented a positive pacing,
i.e., speed decreased across the race; however, half-marathon runners did not show an endspurt,
which was observed in marathon runners of the present study, as well as of all previous research in
marathon. Furthermore, women and men adopted similar pacing pattern in half-marathon, whereas in
marathon, women had more even pacing than men that was in agreement with the existing literature
about marathon. Consequently, runners should be advised to adopt a less variable pacing when
competing in a half-marathon, regardless of their sex. Further research would be needed to shed light
on the physiological and psychological correlates of pacing in half-marathon.
Author Contributions: Conceptualization, P.T.N., I.C. and B.K.; methodology, I.C.; software, I.C.; validation,
P.T.N., I.C. and B.K.; formal analysis, I.C.; investigation, I.C.; resources, I.C.; data curation, I.C.; writing-original
draft preparation, P.T.N., I.C. and B.K.; writing-review and editing, P.T.N., I.C. and B.K.; visualization, I.C.;
supervision, P.T.N. and B.K.; project administration, P.T.N.
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Funding: This research received no external funding.
Acknowledgments: We thank reviewers for their constructive criticism that resulted in improvement of the
content of the paper.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Pacing of Women and Men in Half-Marathon and Marathon Races.
Nikolaidis, Pantelis T,Ćuk, Ivan,Knechtle, Beat