RESEARCH ARTICLE
Russians are the fastest 100-km ultra-
marathoners in the world
Beat Knechtle1,2*, Pantelis Theodoros Nikolaidis3, Fabio Valeri2
1 Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland, 2 Institute of Primary Care, University of
Zurich, Zurich, Switzerland, 3 Exercise Physiology Laboratory, Nikaia, Greece
* beat.knechtle@hispeed.ch
Abstract
Objectives
A recent study investigating the top 10 100-km ultra-marathoners by nationality showed that
Japanese runners were the fastest worldwide. This selection to top athletes may lead to a
selection bias and the aim of this study was to investigate from where the fastest 100-km
ultra-marathoners originate by considering all finishers in 100-km ultra-marathons since
1959.
Methods
We analysed data from 150,710 athletes who finished a 100-km ultra-marathon between
1959 and 2016. To get precise estimates and stable density plots we selected only those
nationalities with 900 and more finishes resulting in 24 nationalities. Histograms and density
plots were performed to study the distribution of race time. Crude mean, standard deviation,
median, interquartile range (IQR), mode, skewness and excess of time for each nationality
were computed. A linear regression analysis adjusted by sex, age and year was performed
to study the race time between the nationalities. Histograms, density and scatter plots
showed that some races seemed to have a time limit of 14 hours. From the complete dataset
the finishes with more than 14 hours were removed (truncated dataset) and the same
descriptive plots and analysis as for the complete dataset were performed again. In addition
to the linear regression a truncated regression was performed with the truncated dataset to
allow conclusion for the whole sample. To study a potential difference between races at
home and races abroad, an interaction term race site home/abroad with nationality was
included in the model.
Results
Most of the finishes were achieved by runners from Japan, Germany, Switzerland, France,
Italy and USA with more than 260’000 (85%) finishes. Runners from Russia and Hungary
were the fastest and runners from Hong Kong and China were the slowest finishers.
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
1 / 29
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Knechtle B, Nikolaidis PT, Valeri F (2018)
Russians are the fastest 100-km ultra-marathoners
in the world. PLoS ONE 13(7): e0199701. https://
doi.org/10.1371/journal.pone.0199701
Editor: Luca Paolo Ardigò, Universita degli Studi di
Verona, ITALY
Received: November 18, 2017
Accepted: June 12, 2018
Published: July 11, 2018
Copyright: © 2018 Knechtle et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
this study were collected from the ‘Deutsche
Ultramarathon Vereinigung’ (DUV) and are freely
accessible using the following link: http://statistik.
d-u-v.org/geteventlist.php?year=all&dist=
100km&country=all&Submit.x=17&Submit.y=
6&label=&surface=all&sort=1&from=&to=. The
authors used the following search criteria: ‘Year’–
‘all’, ‘Distance’–‘100 km’, and ‘Country’–‘All’. This
search leads to more than 4,500 races; all race
results were manually downloaded by the
investigators. The authors did not have any special
access privileges.
Conclusion
In contrast to existing findings investigating the top 10 by nationality, this analysis showed
that ultra-marathoners from Russia, not Japan, were the fastest 100-km ultra-marathoners
worldwide when considering all races held since 1959.
Introduction
Ultra-marathon running, such as 100-km race, is a sport of increasing popularity [1]. Particu-
larly, the number of finishers in 100-km ultra-marathon running increased exponentially,
both for women and men, from 1998 to 2011 [2]. Performance in this sport depends on physi-
ological, e.g. peak running velocity during a graded exercise test, anaerobic threshold, maximal
oxygen uptake (VO2max) and oxygen uptake (VO2) at 16 km/h [3] and psychological charac-
teristics, e.g. cognitive function [4].
Another performance-related characteristic is pacing as it has been shown that faster run-
ners exhibit smaller decrease in their speed during a race than slower runners [5]. With regards
to their training characteristics, ultra-marathoners have ~8 years of experience in ultra-run-
ning and show higher training volume and lower intensity than marathoners [1]. Training
characteristics, such as weekly training distance and training pace, are predictors of 100-km
ultra-marathon performance [6]. Also, anthropometric characteristics as age, body mass, body
mass index and body fat correlate with performance in this sport; however, they might be less
important than training characteristics [7, 8]. Furthermore, performance in 100-km ultra-mar-
athon running is influenced by nationality and origin of the runners [2].
It is well known that athletes of a certain origin are the fastest in certain sport disciplines.
For example in running, the fastest marathoners originate from East Africa, especially from
Kenya and Ethiopia [9]. In longer running distances such as 100-km ultra-marathon running,
most athletes at world class level originate from Japan [9]. When the top 10n athletes in
100-km ultra-marathon running between 1998 and 2011 were analysed, the 10 best race times
were achieved by Japanese runners for both women and men [2].
However, these results [2] might be biased since only the top athletes were considered and
not the whole number of athletes competing worldwide, and have not been verified by other
studies. The knowledge of the effect of nationality on 100-km ultra-marathon performance
would be of great practical importance for professionals working with ultra-marathoners.
Therefore, we analysed all 100-km ultra-marathon races held worldwide between 1959 and
2016 with the aim to identify the fastest runners by nationality. Based upon previous findings
we hypothesized to confirm that female and male Japanese runners would be the fastest world-
wide also when considering all finishers in 100-km ultra-marathon races held since 1959.
Materials and methods
Ethical approval
All procedures used in the study were approved by the Institutional Review Board of Kanton
St. Gallen, Switzerland with a waiver of the requirement for informed consent of the partici-
pants given the fact that the study involved the analysis of publicly available data.
Methods
We obtained from the website www.ultra-marathon.org/ of ‘Deutsche Ultramarathon Verei-
nigung’ (DUV). DUV has a large data record with race data from all ultra-marathons in
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
2 / 29
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
http://statistik.d-u-v.org/index.php. By using the link http://statistik.d-u-v.org/ each person
can access the publicly available database. We used http://statistik.d-u-v.org/geteventlist.php
and inserted in ‘Year’ the term ‘all’, in ‘Distance’ the term ‘100 km’ and in ‘Country’ the term
‘All’ when using the English version of the website. By clicking on ‘Go’, all 100-km ultra-mar-
athons held worldwide are presented. This search leads to more than 4,500 races; all race
results were manually downloaded by one of the investigators.
The original dataset contains the following variables: name, age at race, year of race, sex of
finisher, nationality and country, and speed in km per hours. We converted running speed to
time in hours by dividing 100 km by speed (km/h). To identify unique finisher we computed
date of birth with year of race minus age at race. After cleaning the variable name we identified
unique finisher if finisher has the same name, date of birth, sex and nationality. Finishes with
missing in age, year, sex and times were removed. Finishes out of the following ranges were
removed: date of birth between 1890 and 2000, age between 15 and 100, year between 1950
and 2016, and number if character in nationality and country is 3. After removing the finishes
from missing data and outliers we selected only finishes which nationality has equal or more
than 900 finishes. This dataset has 24 nationalities and we named it complete dataset. To study
the distribution of time we produced histograms and density plots with Gaussian kernel for
each of the selected nationality. Also we produced normal distribution for each nationality
defined by the crude mean and standard deviation of each nationality to compare with the
empirical distribution. Furthermore we computed crude mean, standard deviation, median,
interquartile range (IQR), mode, skewness and excess kurtosis of time for each nationality.
Excess kurtosis or shortened excess is defined as kurtosis minus 3. An excess of 0 means a
Gaussian-like kurtosis (mesokurtic), a positive excess has a slender form of curve (leptokurtic),
and negative excess has a broader curve (platykurtic).
Due to various kind of distribution of time we decided to cluster the nationalities according
to time density. The range of time (h) was segmented in 0 to 7, 7 to 8, 8 to 9, . . .,22 to 23, 23 to
24 and  24. For each of these 19 segments we computed the area under der density curve.
With that, we performed an agglomerative hierarchical clustering using the group average
clustering to analyse groups of similar distribution of time.
To study the time between the nationalities we performed a linear regression analysis
adjusting by sex, age and year:
time ¼ sex  ðyear þ year2Þ þ sex  ðage þ age2Þ þ sex  nationality
ð1Þ
We included a quadratic term for age and year and also an interaction term between sex
and age and age squared, sex and year and year squared and sex and nationality. This model
(1) is based on visual inspection of scatterplots of time against year and time against age for
each nationality (Figs 1 and 2). We included the following fitting curve to each panel: a b-
spline (solid) of age, respectively, time, and a quadratic term (dashed). Since both curves over-
laps for the large range using a quadratic term seems admissible. The variable age and year
were centered by the median with median year of 2009 and median age of 44. Reference level
of sex was male and reference level of nationality was Australia (AUS).
Histograms, density plots (Fig 3) and scatterplots (Figs 1 and 2) show that some races seem
to have a time limit: finishers who didn’t reach certain time limit were discarded. To account
for this limit we defined a time limit of 14 hours based on the plots and histograms of Japan,
Korea and Taiwan. From the complete dataset we removed the finishes which have more than
14 hours (Fig 4) which we called the truncated dataset and produced the same descriptive plots
and analysis as for the complete dataset (Figs 5 and 6). Additionally to the linear regression we
performed a truncated regression with the truncated dataset to allow conclusion to the whole
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
3 / 29
sample. The estimates from the regressions were used to compute the times of a reference fin-
isher: median of age, median of year and male. These times and resulting ranks were compared
between the various nationalities.
Furthermore, to study if there is a difference between races at home and races on abroad
we included in model (1) an interaction term race site (home/abroad) with nationality and sex
(2):
time ¼ sex  ðyear þ year2Þ þ sex  ðage þ age2Þ þ sex  nationality  site
ð2Þ
Since 64.6% of the finisher had only one race and 16.6% had two races we performed
regression analysis without including the cluster effect of finishers.
All data processing and analysis were performed with the statistical software R [10]. Trun-
cated regression was performed with function truncreg from package truncreg.
Results
Between 1959 and 2016, a total of 363,924 athletes finished a 100-km ultra-marathon. The vari-
able with the highest number of missing data is date of birth. There was no missing in variable
sex. Table 1 summarizes the exclusion criteria. Only nationalities with at least 900 finishes
were considered to allow precise estimates and robust histogram. To analyse which country
have the most missing data in variable date of birth nationalities with at least 1,000 finishes
and at least 10% of missing data are listed in Table 2 by descending order of missing data.
Malaysia, Korea, Portugal and Great Britain have the most missing data with 70.1%, 60.6%,
Fig 1. Scatterplots time against race year for each nationality based on the complete dataset. Year has been jittered.
https://doi.org/10.1371/journal.pone.0199701.g001
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
4 / 29
48.1%, 41.5%, respectively. Finally, a total of 150,710 finishers originating from 24 countries
with a total of 307,871 finishes could be considered for data analysis.
Table 3 presents the number of finishes by origin of the athletes. Most of the finishes were
achieved by runners from Japan, Germany, Switzerland, France, Italy and USA with more
than 260’000 (85%) finishes.
A total of 20 nationalities performed more than 50% of their races at their home country with
runners from Japan, Switzerland, Italy and Korea on the top whereas runners from Germany,
Great Britain, Belgium and Austria have performed less than 50% of the races abroad (Fig 7).
Runners from Finland, Germany, Switzerland, Italy, Netherlands Hungary, Belgium, Austria,
France and Russia have an average number of finishes per finisher of more than 2 (Fig 8).
A total of 64.6% of the finishers completed only one 100-km ultra-marathon (Table 4). On
average, the athletes were 43.7±11.1 years old (Table 5). A total of 88% of the finishers are men
and 12% are women (Table 6).
From the agglomerative hierarchical clustering 5 groups can be retrieved:
• Group 1 with China and Hong Kong which show a wide spread distribution of time.
• Group 2 with Russia which has the lowest mode and high excess (3.2).
• Group 3 with Korea, Japan and Taiwan which have a very high excess (7.1, 5.8 and 16.8). All
have a very step right curve at 18, 14 and 14 hours, respectively.
• Group 4 with Czech Republic, Spain, Great Britain, Australia, Switzerland, Italy and USA
with low excess and low skewness.
Fig 2. Scatterplots time against race age for each nationality based on the complete dataset. Age has been jittered.
https://doi.org/10.1371/journal.pone.0199701.g002
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
5 / 29
• Group 5 with Finland, Denmark, Nederland, Belgium, Hungary, Poland, Sweden, France,
Canada, Austria and Germany which have a higher skewness (1) compared to group 4
with skewness  1 (Fig 4).
Table 7 compares the number of finishes before and after truncation of the data set for
each nationality. Hong Kong and China have more than 90% truncated observation, Australia,
Czech Republic, Spain, Switzerland and USA have between 50% and 63% truncated observa-
tion and all others less than 50%. Table 8 presents the mean, SD, median, interquartiles, mode,
skewness and excess of time for each nationality of the complete dataset and Table 9 for the
truncated data set.
Estimates, standard errors and p-values from models (1) and (2) based on complete and
truncated dataset are given in Tables 10–14. These data were used to compute times at the ref-
erence sex = male, year of race = 2009 and age = 44 which are presented in Tables 15 and 16
and Figs 9 and 10.
The upper panel of Fig 9(A)–9(C) display the time for each nationality and confidence
intervals from the multivariable model (1) at reference sex = male, age = 44 and year = 2009.
(A) was computed with the complete data set, (B) with the truncated data set and (C) was per-
formed by truncated regression and truncated data set. The ranks from the results with com-
plete data set show that runners from Russia, Finland, and Hungary were the fastest and
Fig 3. Histograms, density plots and normal distributions based on mean and standard deviation for each country. The diagrams are
positioned according the hierarchical cluster analysis. Graphs are based on the complete dataset.
https://doi.org/10.1371/journal.pone.0199701.g003
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
6 / 29
runners from Hong Kong and China were the slowest finishers. The ranks from the results
from the truncated dataset changed to Russia, Hungary, Spain and Great Britain.
To visualize changes in ranks between these three methods the estimates were ordered by
descending time estimates and the nationality of the estimates were connected with lines. Fig 9
(D) shows the changes between results from linear regression with complete data set and trun-
cated data set and Fig 9(E) shows the results from linear regression with the truncated data set
and truncated regression. Comparing the regression of complete with truncated data set shows
that Russia, Canada, Hong Kong and China hold their position where all other nationalities
change their rank. Japan changed from rank 5 to rank 18. Hungary changed from third to sec-
ond and Finland from second to fourth. The time for Russia changed from 9.4 h (95%-CI: 9.1–
9.6) to 9.0 h (95%-CI: 8.9–9.1), Hungary from 10.7 h (95%-CI 10.4–11.0) to 9.9 h (95%-CI:
9.7–10.0), Japan from 11.1 h (95%-CI: 10.9–11.3) to 11.4 h (95%-CI: 11.3–11.5) and China
from 19.1 h (95%-CI: 18.9–19.3) to 11.9 h (95%-CI: 11.6–12.1).
There are only four nationalities which change ranks when ranks from linear regression are
compared with ranks from truncated regression both based on truncated dataset (Fig 9E). Fig
10(A) and 10(B) shows changes of ranks between running at home and abroad computed with
Fig 4. Scatterplot with excess against skewness. Groups of nation are distinguished by different colours.
https://doi.org/10.1371/journal.pone.0199701.g004
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
7 / 29
complete data set, respectively, truncated data set based on model (2). Both show many
changes in rank position. Again Russia remains at place 1 at home and abroad with 10.0 h
and 8.2 h, respectively, with complete data set and 9.7 h and 7.8 with truncated data set. Japan
changed from rank 10 (11.1 h, at home) to rank 20 (14.9 h, abroad) when complete dataset was
used and from rank 18 (11.4 h) to 3 (9.2 h) when truncated dataset was used (Table 16).
Table 17 shows the mean time of the top 10, 100 and 1000 finishers. Only the fastest finishes
of each finisher was considered to define the top. Japan is first at top 10 and top 100 and sec-
ond at top 1000 whereas Russia is second at top 10 and top 100 and ninth at top 1000. Table 18
shows the mean time of the top fastest finishes. Russia is first at top 10 and 100 with 5 finisher
and 10 finishes and 37 finishers with 100 finishes. Japan is second at top 10 and 100 with 7 fin-
ishers and 10 finishes and 50 finishes with 100 finishes.
Discussion
The aim of this study was to investigate the aspect of nationality of the fastest 100-km ultra-
marathons competing between 1959 and 2016 with the hypothesis that the fastest runners
would originate from Japan as it has been found for the top 10 runners worldwide competing
between 1998 and 2011. However, in contrast to previous findings, athletes from Russia
achieved the fastest race times, not athletes from Japan, when all athletes were considered by
nationality.
A first potential explanation could be the quote of finishes at the origin country. For exam-
ple, Russians have ~37% of the finishes outside the origin but Japanese less than 2%. Most
probably only the fastest Russian runners compete outside Russia on the fastest races (e.g.
Fig 5. Scatterplots time against race year for each nationality based on the truncated dataset. Year has been jittered.
https://doi.org/10.1371/journal.pone.0199701.g005
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
8 / 29
World Championships) or the fastest courses (e.g. completely flat course, track races) world-
wide. In contrast, Japanese runners competed preferably in races held in Japan where the
courses are most probably not fast (i.e. rather hilly courses than flat courses). The present
study is, however, not the first to show that Russian athletes are the fastest in an ultra-endur-
ance sport. Recently, an analysis of the ‘Engadin Ski Marathon’ showed that Russians were the
fastest cross-country skiers [11], which, combined with the findings of the present study, indi-
cated a general trend of excellence of Russians in ultra-endurance sports.
The two strongest factors which seems influence the population speed are the attitude to
participate and rules concerning time limits. Firstly, as the density plots and histograms show
it seems that there are countries where also very slow participants were allowed to compete in
races and who has been also considered in the ranking. Extreme examples of this kind of com-
petitions are athletes from the nationalities China, and Hong Kong, but also Czech Republic,
Great Britain, Spain, and Australia. Athletes from other countries like Denmark, Finland,
Fig 6. Scatterplots time against race age for each nationality based on the truncated dataset. Age has been jittered.
https://doi.org/10.1371/journal.pone.0199701.g006
Table 1. Total number, missing and out of range.
Criteria
N Finishes
N Finisher
N Nationality
% Finishes
% Finisher
% Nationality
[1] Total
363,924
195,983
128
100
100
100
[2] Exclude missing/incorrect hours
363,923
195,982
128
100
100
100
[3] Exclude missing age/date of birth
318,231
157,190
125
87.4
80.2
97.7
[4] Exclude unclear nationality
318,228
157,187
124
87.4
80.2
96.9
[5] Exclude nation < 900 finishes
307,871
150,710
24
84.6
76.9
18.8
https://doi.org/10.1371/journal.pone.0199701.t001
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
9 / 29
Table 2. Missing data in date of birth and/or age according to nationality. Only nationalities with at least 10% miss-
ing are shown.
Nationality
N
Missing
Missing (%)
MAS
1,507
1,057
70.1
KOR
6,539
3,963
60.6
POR
1,442
693
48.1
GBR
5,834
2,419
41.5
NZL
1,119
452
40.4
FIN
2,359
917
38.9
HKG
2,123
791
37.3
CHN
6,165
1,942
31.5
ESP
5,913
1,854
31.4
TPE
3,843
1,065
27.7
JPN
79,011
17,021
21.5
DEN
1,224
263
21.5
AUS
5,103
1,093
21.4
CAN
3,093
417
13.5
NED
2,261
234
10.3
BEL
2,896
294
10.2
https://doi.org/10.1371/journal.pone.0199701.t002
Table 3. Quantity structure of selected nationalities.
Nationality
N Finishes
N Finisher
Race at home (%)
Finishes per finisher
JPN
61’990
41’081
98.8%
1.51
GER
51’313
18’085
39.7%
2.84
SUI
49’596
17’609
98.8%
2.82
FRA
46’553
22’768
89.6%
2.04
ITA
38’177
14’766
96.2%
2.59
USA
14’356
9’627
91.6%
1.49
POL
5’472
3’112
79.4%
1.76
CHN
4’223
3’069
75.2%
1.38
ESP
4’059
2’785
78.8%
1.46
AUS
4’010
2’437
92.0%
1.65
GBR
3’414
2’301
38.7%
1.48
TPE
2’778
1’955
88.4%
1.42
CAN
2’676
1’401
69.6%
1.91
BEL
2’602
1’163
38.5%
2.24
KOR
2’576
1’486
95.9%
1.73
CZE
2’506
1’289
66.6%
1.94
AUT
2’082
969
19.5%
2.15
NED
2’027
805
62.0%
2.52
RUS
1’852
920
62.6%
2.01
FIN
1’442
479
86.8%
3.01
HKG
1’332
1’070
89.9%
1.24
DEN
960
544
70.6%
1.76
HUN
947
419
65.6%
2.26
SWE
928
570
73.0%
1.63
https://doi.org/10.1371/journal.pone.0199701.t003
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
10 / 29
Sweden, and Russia have a high skewness and excess which means that the bunch is concen-
trated over a narrow limit. This may due to attitudes within society (e.g. popularity of sports,
policy of furtherance) or socio-economic backgrounds of the individuals that only fast com-
petitors participates. It has been suggested also that a successful finish in this sport depends on
the motivation to train intensively [7].
Secondly, the density plots of athletes from Japan, Taiwan and Korea show a very steep
slope on the right side of the curve. This may due to time limits given by the organizer. These
time limits may not only performed in Japan or Taiwan but less frequently also in other coun-
tries. This could be the main source of bias which would explain why Japanese were the fastest
[1]. To counteract this bias we truncated the dataset and considered only finishes with lower
or equal 14 hours. This can cause bias like using top 10 finishers if we would conclude to the
whole population. So, we have to consider that using the complete dataset would give bias due
to policy rules and if we use the truncated dataset we have selection bias. We used also trun-
cated regression to allow conclusion to the whole running population but it seems that too
many observations have been truncated which changed the dataset in that way that it changed
completely the shape of the original distribution which does not anymore allow conclusion
on the complete data set but only on the truncated data set. That’s why the linear regression
and truncated regression of the truncated data set gives similar results. Nonetheless, the
Fig 7. Percentage of races which takes place at the origin of the finisher.
https://doi.org/10.1371/journal.pone.0199701.g007
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
11 / 29
assumption that in Japan is a time limit may be supported by the fact that the race time at
home is 11.1 h and on abroad is 14.9 h using model (2) and complete data set. This is an
increase of 3.1 hours. For athletes from Russia, the times are 10.0 h and 8.2 h, respectively, a
decrease of 1.7 hours. Assuming that only good and the best ultra-marathoners take the effort
Fig 8. Average number of finishes. This figure is based on the complete dataset.
https://doi.org/10.1371/journal.pone.0199701.g008
Table 4. Distribution of number of finishes per finisher.
N Finishes
N Finisher
Frequency in %
1
97,340
64.6
2
25,032
16.6
3
10,588
7
4
5,561
3.7
5
3,558
2.4
5–9
5,365
3.6
10–19
2,708
1.8
20–39
498
0.3
40–59
51
0
60–79
4
0
80–99
1
0
100–149
2
0
https://doi.org/10.1371/journal.pone.0199701.t004
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
12 / 29
Table 5. Baseline of continuous variables.
Variable
Mean
SD
Median
IQR
Min
Max
Age
43.7
11.1
44
36–51
15
92
Date of birth
1959
15.3
1960
1949–1970
1891
2000
Year
2003
14
2009
1993–2014
1959
2016
Time
13.7
3.82
12.9
11–15.7
6.17
46.8
https://doi.org/10.1371/journal.pone.0199701.t005
Table 6. Distribution of finishing according to categorical variables.
Variable
Level
N
Percent (%)
sex
Male
271,224
88
Female
36,647
12
nat
JPN
61,990
20
GER
51,313
17
SUI
49,596
16
FRA
46,553
15
ITA
38,177
12
USA
14,356
4.7
POL
5,472
1.8
CHN
4,223
1.4
ESP
4,059
1.3
AUS
4,010
1.3
GBR
3,414
1.1
TPE
2,778
0.9
CAN
2,676
0.87
BEL
2,602
0.85
KOR
2,576
0.84
CZE
2,506
0.81
AUT
2,082
0.68
NED
2,027
0.66
RUS
1,852
0.6
FIN
1,442
0.47
HKG
1,332
0.43
DEN
960
0.31
HUN
947
0.31
SWE
928
0.3
country
SUI
84,856
28
JPN
61,702
20
FRA
43,751
14
ITA
40,736
13
GER
21,943
7.1
USA
13,770
4.5
POL
4,404
1.4
AUS
4,223
1.4
ESP
3,922
1.3
NED
3,342
1.1
other (49)
28,564
9.3
https://doi.org/10.1371/journal.pone.0199701.t006
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
13 / 29
Table 7. Numbers of finishes before and after truncation and percentage of removed finishes.
Nationality
N finishes
before truncation
N finishes
after truncation
Removed (%)
AUS
4,010
1,702
57.6
AUT
2,082
1,417
31.9
BEL
2,602
1,810
30.4
CAN
2,676
1,728
35.4
CHN
4,223
287
93.2
CZE
2,506
940
62.5
DEN
960
826
14
ESP
4,059
1,965
51.6
FIN
1,442
1,324
8.2
FRA
46,553
29,815
36
GBR
3,414
1,871
45.2
GER
51,313
37,412
27.1
HKG
1,332
109
91.8
HUN
947
774
18.3
ITA
38,177
20,021
47.6
JPN
61,990
56,777
8.4
KOR
2,576
1,483
42.4
NED
2,027
1,612
20.5
POL
5,472
2,970
45.7
RUS
1,852
1,648
11
SUI
49,596
21,981
55.7
SWE
928
774
16.6
TPE
2,778
2,286
17.7
USA
14,356
5,819
59.5
https://doi.org/10.1371/journal.pone.0199701.t007
Table 8. Mean, SD, median, interquartiles, mode, skewness and excess of time for each nationality of the complete dataset.
Nationality
Number of finishes
Mean (SD)
Median (IQ)
Minimum
Maximum
Mode
Skewness
Excess
AUS
4,010
15.2 (4.17)
15 (12.2–17.8)
6.62
37.9
13.6
0.62
1.256
AUT
2,082
13 (3.69)
12.2 (10.3–15.2)
7.11
27.6
10.8
0.884
0.29
BEL
2,602
12.7 (4.21)
11.8 (9.48–14.8)
6.26
31.7
9.58
1.035
0.88
CAN
2,676
13.8 (4.39)
12.7 (10.7–16)
6.68
35.6
11.6
1.22
1.535
CHN
4,223
20.7 (4.4)
20.9 (17.4–23.9)
6.31
32.3
22.9
-0.086
-0.545
CZE
2,506
16 (5)
16 (11.7–20.3)
6.3
38.2
15.7
0.02
-0.879
DEN
960
11.6 (3.36)
10.7 (9.69–12.3)
6.96
29.8
10.2
2.225
6.055
ESP
4,059
14.8 (5.3)
14.3 (9.96–19.1)
6.33
33.1
9.42
0.377
-0.864
FIN
1,442
11 (2.52)
10.7 (9.49–12.1)
6.51
32.8
10.1
2.068
8.999
FRA
46,553
13.6 (3.88)
12.8 (10.9–15.4)
6.39
36.6
11.7
1.288
2.475
GBR
3,414
13.9 (4.92)
13.4 (9.8–16.8)
6.17
36.4
8.56
0.713
0.222
GER
51,313
12.5 (3.47)
11.7 (9.9–14.4)
6.41
33.9
9.78
1.004
0.728
HKG
1,332
20.1 (4.14)
20 (17.4–23)
8.09
33.4
22.6
-0.072
-0.103
HUN
947
11.2 (3.36)
10.4 (8.82–12.7)
6.53
26.6
9.91
1.46
2.744
ITA
38,177
14 (3.04)
13.8 (11.9–16.1)
6.31
35.7
13.4
0.412
0.489
JPN
61,990
12.4 (2.13)
12.5 (11.2–13.4)
6.23
31.7
12.8
1.262
5.774
KOR
2,576
13.8 (2.76)
13.7 (12.4–14.7)
7.2
28.2
14.5
1.905
7.052
NED
2,027
12 (3.43)
11.2 (9.72–13.4)
6.64
34.2
10.2
1.55
3.787
POL
5,472
14.9 (5.41)
13.4 (10.8–17.3)
6.3
32.4
10.8
1.028
0.292
RUS
1,852
9.92 (3.23)
8.94 (7.56–11.3)
6.31
28.1
7.47
1.644
3.245
SUI
49,596
15.1 (4.03)
14.8 (11.8–18.2)
6.63
33.8
11.8
0.187
-0.89
SWE
928
12 (3.61)
11.4 (9.85–13)
6.38
44.2
11.2
2.226
9.605
TPE
2,778
13.1 (2.7)
12.9 (11.7–13.8)
7.62
46.8
13.6
2.903
16.754
USA
14,356
15.1 (3.96)
14.8 (12.6–17)
6.46
41.8
14.7
1.019
2.522
https://doi.org/10.1371/journal.pone.0199701.t008
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
14 / 29
to go abroad the mean time should decrease which is not the case for Japanese runners. Never-
theless, both analyses with the complete and the truncated dataset show that Russian runners
were the fastest and athletes from China and Hong Kong were the slowest. All other nationali-
ties change their rankings reflecting the distribution of the running time.
If we look at the top 10, 100 and 1000 of the fastest finishers, Japanese ultra-marathoners
take the first place and the second place, respectively. The rank shifts to the rear the more par-
ticipants are included in the data set. It seems that there some very fast Japanese but as the
number of participants growths the mean time increase more than in other nationalities. A
look at the top 10, 100 of the finishes shows that Russian ultra-marathoners take the first
places. This is due to five runners with 10 finishes and 37 runners with 100 finishes. In this
case it seems that Russian ultra-marathoners take high ranks since some few runners achieved
some very fast finishes. This could be a limit of the linear regression if finishers are not consid-
ered in a multilevel regression as random variable. We also performed a linear regression with
finisher as random variable and we got similar results as in the linear regression with complete
and truncated data set (data not shown).
The role of nationality on 100-km ultra-marathon race times highlighted in the present
study was in agreement with previous research that identified sports as the most powerful
form of national performance [12]. An attempt to use sport to build a sense of national identity
has been reported [13]. Either biological or cultural heredity has been identified as a factor
associated with the dominance of a nationality in a sport [14]. For instance, certain genes have
been identified to relate to endurance performance [15]. In addition, an explanation of the
Table 9. Mean, SD, median, interquartiles, mode, skewness and excess of time for each nationality of the truncated dataset.
Nationality
N finishes
Mean (SD)
Median (IQ)
Minimum
Maximum
Mode
Skewness
Excess
AUS
1,702
11.4 (1.87)
11.7 (9.93–13)
6.62
14
13.4
-0.492
-0.782
AUT
1,417
10.9 (1.68)
10.9 (9.62–12.2)
7.11
14
10.5
-0.094
-0.896
BEL
1,810
10.4 (1.98)
10.3 (8.79–12.1)
6.26
14
9.58
0.015
-1.025
CAN
1,728
11.1 (1.71)
11.3 (9.84–12.6)
6.68
14
11.6
-0.249
-0.848
CHN
287
12.4 (1.29)
12.8 (11.6–13.4)
6.31
14
13.4
-1.152
1.482
CZE
940
10.6 (2)
10.6 (9.01–12.3)
6.3
14
9.56
-0.054
-1.053
DEN
826
10.5 (1.46)
10.4 (9.53–11.4)
6.96
14
10.1
0.119
-0.473
ESP
1,965
10.1 (1.88)
9.88 (8.66–11.5)
6.33
14
9.59
0.296
-0.801
FIN
1,324
10.5 (1.63)
10.5 (9.38–11.7)
6.51
14
10
0.009
-0.692
FRA
29,815
11.3 (1.64)
11.4 (10.1–12.6)
6.39
14
11.7
-0.352
-0.581
GBR
1,871
10.3 (2.14)
10.2 (8.41–12.1)
6.17
14
8.34
0.086
-1.231
GER
37,412
10.8 (1.69)
10.8 (9.48–12.1)
6.41
14
9.74
-0.027
-0.869
HKG
109
12.4 (1.39)
12.6 (11.4–13.6)
8.09
14
13.4
-0.882
0.019
HUN
774
9.91 (1.79)
9.84 (8.54–11.1)
6.53
14
10.1
0.244
-0.675
ITA
20,021
11.7 (1.6)
12 (10.6–13)
6.31
14
13.5
-0.643
-0.32
JPN
56,777
11.9 (1.5)
12.3 (11–13)
6.23
14
12.8
-0.846
0.155
KOR
1,483
12.3 (1.43)
12.7 (11.5–13.4)
7.2
14
13.5
-1.098
0.72
NED
1,612
10.6 (1.72)
10.5 (9.42–11.8)
6.64
14
10
-0.023
-0.696
POL
2,970
11 (1.64)
10.9 (10–12.2)
6.3
14
10.5
-0.312
-0.127
RUS
1,648
9.04 (1.93)
8.53 (7.39–10.5)
6.31
14
7.23
0.623
-0.647
SUI
21,981
11.3 (1.71)
11.5 (9.95–12.7)
6.63
14
11.7
-0.323
-0.814
SWE
774
10.7 (1.76)
10.9 (9.53–12)
6.38
14
11.3
-0.31
-0.627
TPE
2,286
12.3 (1.31)
12.6 (11.4–13.4)
7.62
14
13.6
-0.74
-0.135
USA
5,819
11.7 (1.79)
12 (10.5–13.1)
6.46
14
13.5
-0.776
-0.245
https://doi.org/10.1371/journal.pone.0199701.t009
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
15 / 29
Table 10. Results from linear regression with complete dataset time = sex×(year+year2)+sex×(age+age2)+sex×nationality and referenced to male, age 44, year 2009
and nationality Australia.
Coefficient
Standard error
P-Value
Intercept
13.879
0.0600
0.000
Sex (female)
0.892
0.1254
0.000
Age
0.012
0.0006
0.000
Age squared
0.0033
0.0000
0.000
Year
0.156
0.0013
0.000
Year squared
0.0062
0.0000
0.000
Female×Age
0.022
0.0019
0.000
Female×Age squared
-0.0006
0.0001
0.000
Female×Year
0.014
0.0038
0.000
Female×Year squared
0.0016
0.0001
0.000
AUT
-1.922
0.0976
0.000
BEL
-1.713
0.0905
0.000
CAN
-0.976
0.0967
0.000
CHN
5.199
0.0808
0.000
CZE
1.104
0.0927
0.000
DEN
-2.806
0.1301
0.000
ESP
-0.069
0.0801
0.389
FIN
-3.634
0.1126
0.000
FRA
-0.898
0.0621
0.000
GBR
-0.597
0.0867
0.000
GER
-2.075
0.0634
0.000
HKG
4.708
0.1152
0.000
HUN
-3.176
0.1334
0.000
ITA
-0.378
0.0629
0.000
JPN
-2.764
0.0613
0.000
KOR
-1.425
0.0895
0.000
NED
-2.503
0.0985
0.000
POL
0.220
0.0758
0.004
RUS
-4.524
0.1064
0.000
SUI
-0.320
0.0642
0.000
SWE
-2.678
0.1339
0.000
TPE
-1.956
0.0881
0.000
USA
0.079
0.0676
0.244
AUT×Female
-0.705
0.2708
0.009
BEL×Female
-0.245
0.2660
0.357
CAN×Female
0.330
0.1868
0.077
CHN×Female
-0.070
0.1939
0.718
CZE×Female
0.629
0.2280
0.006
DEN×Female
-0.638
0.3222
0.048
ESP×Female
1.444
0.2405
0.000
FIN×Female
0.149
0.2622
0.569
FRA×Female
-0.038
0.1341
0.778
GBR×Female
-1.098
0.1905
0.000
GER×Female
-0.441
0.1372
0.001
HKG×Female
0.476
0.2756
0.084
HUN×Female
-0.856
0.2987
0.004
ITA×Female
-0.418
0.1374
0.002
JPN×Female
-0.661
0.1293
0.000
KOR×Female
0.304
0.3260
0.351
NED×Female
-0.548
0.2645
0.038
POL×Female
0.473
0.2011
0.019
RUS×Female
-0.358
0.2195
0.102
SUI×Female
0.583
0.1439
0.000
SWE×Female
-1.207
0.3046
0.000
TPE×Female
-0.586
0.2747
0.033
USA×Female
0.028
0.1383
0.840
https://doi.org/10.1371/journal.pone.0199701.t010
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
16 / 29
Table 11. Results from linear regression with truncated dataset time = sex×(year+year2)+sex×(age+age2))+sex×nationality and referenced to male, age 44, year 2009
and nationality Australia.
Coefficient
Standard error
P-Value
Intercept
11.110
0.0427
0.000
Sex (female)
0.047
0.0969
0.628
Age
0.021
0.0004
0.000
Age squared
0.0011
0.0000
0.000
Year
0.070
0.0008
0.000
Year squared
0.0024
0.0000
0.000
Female×Age
0.007
0.0013
0.000
Female×Age squared
-0.0004
0.0001
0.000
Female×Year
0.015
0.0025
0.000
Female×Year squared
0.0000
0.0001
0.857
AUT
-0.319
0.0614
0.000
BEL
-0.705
0.0575
0.000
CAN
-0.094
0.0615
0.128
CHN
0.758
0.1064
0.000
CZE
-0.561
0.0690
0.000
DEN
-0.764
0.0730
0.000
ESP
-1.073
0.0559
0.000
FIN
-0.880
0.0636
0.000
FRA
0.058
0.0438
0.182
GBR
-0.740
0.0589
0.000
GER
-0.316
0.0441
0.000
HKG
0.787
0.1674
0.000
HUN
-1.241
0.0762
0.000
ITA
0.515
0.0443
0.000
JPN
0.261
0.0431
0.000
KOR
0.579
0.0595
0.000
NED
-0.553
0.0596
0.000
POL
-0.137
0.0522
0.009
RUS
-2.124
0.0616
0.000
SUI
0.242
0.0449
0.000
SWE
-0.620
0.0759
0.000
TPE
0.678
0.0545
0.000
USA
0.353
0.0484
0.000
AUT×Female
0.222
0.1713
0.195
BEL×Female
0.148
0.1704
0.386
CAN×Female
0.393
0.1282
0.002
CHN×Female
0.127
0.3268
0.698
CZE×Female
0.450
0.1899
0.018
DEN×Female
0.436
0.1792
0.015
ESP×Female
0.693
0.1996
0.001
FIN×Female
0.754
0.1505
0.000
FRA×Female
0.304
0.1015
0.003
GBR×Female
-0.434
0.1321
0.001
GER×Female
0.521
0.1015
0.000
HKG×Female
-0.137
0.4468
0.759
HUN×Female
0.068
0.1701
0.691
ITA×Female
0.134
0.1040
0.196
JPN×Female
0.164
0.0979
0.094
KOR×Female
0.022
0.2418
0.928
NED×Female
0.385
0.1582
0.015
POL×Female
0.600
0.1476
0.000
RUS×Female
0.534
0.1325
0.000
SUI×Female
0.536
0.1085
0.000
SWE×Female
0.051
0.1715
0.767
TPE×Female
-0.158
0.1617
0.327
USA×Female
0.108
0.1081
0.317
https://doi.org/10.1371/journal.pone.0199701.t011
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
17 / 29
Table 12. Results from truncated regression with truncated dataset time = sex×(year+year2)+sex×(age+age2) + sex×nationality and referenced to male, age 44, year
2009 and nationality Australia.
Coefficient
Standard error
P-Value
Intercept
11.490
0.067
0.000
Sex (female)
0.003
0.153
0.985
Age
0.038
0.001
0.000
Age squared
0.002
0.000
0.000
Year
0.109
0.001
0.000
Year squared
0.004
0.000
0.000
Female×Age
0.017
0.002
0.000
Female×Age squared
-0.000
0.000
0.123
Female×Year
0.035
0.004
0.000
Female×Year squared
0.001
0.000
0.010
AUT
-0.443
0.093
0.000
BEL
-0.912
0.086
0.000
CAN
-0.087
0.095
0.358
CHN
1.672
0.215
0.000
CZE
-0.717
0.102
0.000
DEN
-0.985
0.107
0.000
ESP
-1.343
0.083
0.000
FIN
-1.138
0.094
0.000
FRA
0.109
0.069
0.115
GBR
-0.935
0.088
0.000
GER
-0.405
0.069
0.000
HKG
1.454
0.330
0.000
HUN
-1.538
0.109
0.000
ITA
0.856
0.070
0.000
JPN
0.497
0.068
0.000
KOR
1.226
0.106
0.000
NED
-0.698
0.090
0.000
POL
-0.174
0.081
0.031
RUS
-2.464
0.089
0.000
SUI
0.389
0.071
0.000
SWE
-0.933
0.112
0.000
TPE
1.390
0.096
0.000
USA
0.614
0.078
0.000
AUT×Female
0.377
0.259
0.146
BEL×Female
0.676
0.255
0.008
CAN×Female
0.625
0.201
0.002
CHN×Female
0.245
0.701
0.726
CZE×Female
0.496
0.284
0.081
DEN×Female
0.672
0.268
0.012
ESP×Female
0.871
0.296
0.003
FIN×Female
0.852
0.232
0.000
FRA×Female
0.570
0.161
0.000
GBR×Female
-0.414
0.196
0.034
GER×Female
0.858
0.160
0.000
HKG×Female
-0.442
0.839
0.599
HUN×Female
0.267
0.242
0.270
ITA×Female
0.364
0.167
0.029
JPN×Female
0.368
0.156
0.018
KOR×Female
0.074
0.468
0.875
NED×Female
0.613
0.243
0.011
POL×Female
1.219
0.241
0.000
RUS×Female
0.699
0.194
0.000
SUI×Female
0.950
0.173
0.000
SWE×Female
0.311
0.258
0.228
TPE×Female
-0.190
0.294
0.517
USA×Female
0.218
0.173
0.208
https://doi.org/10.1371/journal.pone.0199701.t012
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
18 / 29
Table 13. Interaction with race site, results from truncated regression with complete data set time = sex×(year+year2)+sex×(age+age2) + sex×nationality×site and
referenced to male, age 44, year 2009 and nationality Austria.
Coefficient
Standard error
P-Value
Intercept
14.043
0.0615
0.000
Sex (female)
1.029
0.1296
0.000
Age
0.013
0.0006
0.000
Age squared
0.0032
0.0000
0.000
Year
0.157
0.0013
0.000
Year squared
0.0062
0.0000
0.000
0.157
0.0013
0.000
Female×Age
0.021
0.0019
0.000
Female×Age squared
-0.0007
0.0001
0.000
Female×Year
0.009
0.0039
0.015
Female×Year squared
0.0016
0.0001
0.000
AUT
-3.621
0.1796
0.000
BEL
-3.480
0.1234
0.000
CAN
-1.300
0.1076
0.000
CHN
5.201
0.0869
0.000
CZE
2.448
0.1055
0.000
DEN
-3.661
0.1475
0.000
ESP
-0.399
0.0851
0.000
FIN
-3.773
0.1179
0.000
FRA
-1.332
0.0637
0.000
GBR
-2.477
0.1188
0.000
GER
-2.968
0.0673
0.000
HKG
4.780
0.1187
0.000
HUN
-3.101
0.1529
0.000
ITA
-0.511
0.0643
0.000
JPN
-2.961
0.0626
0.000
KOR
-1.707
0.0907
0.000
NED
-3.523
0.1151
0.000
POL
0.632
0.0801
0.000
RUS
-4.060
0.1229
0.000
SUI
-0.424
0.0656
0.000
SWE
-3.181
0.1519
0.000
TPE
-2.170
0.0910
0.000
USA
-0.012
0.0693
0.867
AUT×race abroad
3.994
0.2913
0.000
BEL×race abroad
4.684
0.2605
0.000
CAN×race abroad
2.645
0.2770
0.000
CHN×race abroad
1.420
0.2545
0.000
CZE×race abroad
-2.328
0.2665
0.000
DEN×race abroad
4.545
0.3366
0.000
ESP×race abroad
2.878
0.2568
0.000
FIN×race abroad
1.972
0.3570
0.000
FRA×race abroad
4.657
0.2278
0.000
GBR×race abroad
4.787
0.2561
0.000
GER×race abroad
3.308
0.2240
0.000
HKG×race abroad
-0.484
0.4050
0.232
HUN×race abroad
1.293
0.3396
0.000
ITA×race abroad
1.596
0.2406
0.000
JPN×race abroad
5.831
0.2636
0.000
KOR×race abroad
5.142
0.4134
0.000
NED×race abroad
4.362
0.2723
0.000
(Continued)
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
19 / 29
Table 13. (Continued)
Coefficient
Standard error
P-Value
POL×race abroad
-0.624
0.2490
0.012
RUS×race abroad
0.267
0.2873
0.353
SUI×race abroad
2.078
0.2632
0.000
SWE×race abroad
3.296
0.3443
0.000
TPE×race abroad
2.539
0.3055
0.000
USA×race abroad
1.300
0.2495
0.000
AUT×female
-1.319
0.5884
0.025
BEL×female
-0.744
0.4024
0.064
CAN×female
0.792
0.2117
0.000
CHN×female
-0.483
0.2139
0.024
CZE×female
0.357
0.2593
0.169
DEN×female
-0.611
0.3860
0.113
ESP×female
0.849
0.2644
0.001
FIN×female
-0.351
0.2769
0.205
FRA×female
-0.068
0.1385
0.626
GBR×female
-0.251
0.2474
0.310
GER×female
-0.913
0.1526
0.000
HKG×female
0.650
0.2918
0.026
HUN×female
-1.180
0.4018
0.003
ITA×female
-0.491
0.1413
0.001
JPN×female
-0.781
0.1331
0.000
KOR×female
-0.311
0.3419
0.363
NED×female
-0.730
0.3210
0.023
POL×female
0.025
0.2143
0.906
RUS×female
-0.436
0.2716
0.109
SUI×female
0.442
0.1477
0.003
SWE×female
-1.042
0.3398
0.002
TPE×female
-0.522
0.3117
0.094
USA×female
0.092
0.1427
0.518
AUT× race abroad×female
-1.319
0.5884
0.092
BEL× race abroad×female
-0.744
0.4024
0.043
CAN× race abroad×female
0.792
0.2117
0.053
CHN× race abroad×female
-0.483
0.2139
0.000
CZE× race abroad×female
0.357
0.2593
0.291
DEN× race abroad×female
-0.611
0.3860
0.771
ESP× race abroad×female
0.849
0.2644
0.000
FIN× race abroad×female
-0.351
0.2769
0.000
FRA× race abroad×female
-0.068
0.1385
0.556
GBR× race abroad×female
-0.251
0.2474
0.335
GER× race abroad×female
-0.913
0.1526
0.004
HKG× race abroad×female
0.650
0.2918
0.982
HUN× race abroad×female
-1.180
0.4018
0.030
ITA× race abroad×female
-0.491
0.1413
0.356
JPN× race abroad×female
-0.781
0.1331
0.004
KOR× race abroad×female
-0.311
0.3419
0.011
NED× race abroad×female
-0.730
0.3210
0.189
POL× race abroad×female
0.025
0.2143
0.000
RUS× race abroad×female
-0.436
0.2716
0.031
SUI× race abroad×female
0.442
0.1477
0.005
SWE× race abroad×female
-1.042
0.3398
0.816
TPE× race abroad×female
-0.522
0.3117
0.870
USA× race abroad×female
0.092
0.1427
0.005
https://doi.org/10.1371/journal.pone.0199701.t013
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
20 / 29
Table 14. Interaction with race site, results from linear regression with truncated dataset time = sex×year+year2)+sex×(age+age2) + sex×nationality×site and refer-
enced to male, age 44, year 2009, site at home and nationality Australia.
Coefficient
Standard error
P-Value
Intercept
11.319
0.0447
0.000
Sex (female)
0.139
0.1045
0.182
Age
0.020
0.0004
0.000
Age squared
0.0011
0.0000
0.000
Year
0.068
0.0008
0.000
Year squared
0.0024
0.0000
0.000
Female×Age
0.005
0.0013
0.000
Female×Age squared
-0.0004
0.0001
0.000
Female×Year
0.006
0.0025
0.013
Female×Year squared
-0.0002
0.0001
0.152
AUT
-1.092
0.0953
0.000
BEL
-1.228
0.0701
0.000
CAN
-0.128
0.0668
0.056
CHN
0.601
0.1196
0.000
CZE
-0.314
0.0984
0.001
DEN
-0.831
0.0792
0.000
ESP
-1.188
0.0601
0.000
FIN
-0.994
0.0667
0.000
FRA
-0.136
0.0458
0.003
GBR
-1.322
0.0737
0.000
GER
-1.040
0.0472
0.000
HKG
0.841
0.2060
0.000
HUN
-0.986
0.0867
0.000
ITA
0.336
0.0463
0.000
JPN
0.074
0.0451
0.102
KOR
0.432
0.0609
0.000
NED
-0.856
0.0658
0.000
POL
-0.105
0.0567
0.063
RUS
-1.672
0.0706
0.000
SUI
0.001
0.0469
0.986
SWE
-0.661
0.0835
0.000
TPE
0.510
0.0568
0.000
USA
0.273
0.0507
0.000
AUT×race abroad
2.484
0.1627
0.000
BEL×race abroad
2.343
0.1501
0.000
CAN×race abroad
0.957
0.1663
0.000
CHN×race abroad
1.571
0.2566
0.000
CZE×race abroad
1.002
0.1705
0.000
DEN×race abroad
1.025
0.1950
0.000
ESP×race abroad
1.297
0.1558
0.000
FIN×race abroad
0.860
0.1944
0.000
FRA×race abroad
1.484
0.1350
0.000
GBR×race abroad
2.418
0.1524
0.000
GER×race abroad
2.584
0.1308
0.000
HKG×race abroad
1.065
0.3543
0.003
HUN×race abroad
0.197
0.1876
0.295
(Continued)
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
21 / 29
Table 14. (Continued)
Coefficient
Standard error
P-Value
ITA×race abroad
0.735
0.1427
0.000
JPN×race abroad
-0.399
0.1659
0.016
KOR×race abroad
0.012
0.2824
0.965
NED×race abroad
2.018
0.1583
0.000
POL×race abroad
0.864
0.1450
0.000
RUS×race abroad
-0.102
0.1583
0.520
SUI×race abroad
0.884
0.1622
0.000
SWE×race abroad
1.021
0.1956
0.000
TPE×race abroad
1.460
0.1714
0.000
USA×race abroad
0.567
0.1488
0.000
AUT×female
-0.564
0.3079
0.067
BEL×female
0.207
0.2213
0.349
CAN×female
0.324
0.1445
0.025
CHN×female
0.293
0.3774
0.437
CZE×female
-0.191
0.2767
0.489
DEN×female
0.408
0.2037
0.045
ESP×female
0.377
0.2155
0.081
FIN×female
0.567
0.1591
0.000
FRA×female
0.296
0.1089
0.007
GBR×female
-0.032
0.1626
0.845
GER×female
0.371
0.1127
0.001
HKG×female
0.798
0.8050
0.321
HUN×female
-0.139
0.2253
0.536
ITA×female
0.161
0.1113
0.149
JPN×female
0.144
0.1053
0.171
KOR×female
0.088
0.2539
0.729
NED×female
0.278
0.1813
0.125
POL×female
0.191
0.1639
0.244
RUS×female
0.284
0.1617
0.079
SUI×female
0.454
0.1155
0.000
SWE×female
0.024
0.1890
0.897
TPE×female
-0.121
0.1845
0.512
USA×female
0.234
0.1167
0.045
AUT× race abroad×female
0.658
0.4180
0.115
BEL× race abroad×female
-0.506
0.3768
0.179
CAN× race abroad×female
-0.052
0.3147
0.868
CHN× race abroad×female
-0.906
0.7296
0.214
CZE× race abroad×female
0.692
0.4175
0.098
DEN× race abroad×female
-0.199
0.4214
0.637
ESP× race abroad×female
1.056
0.5267
0.045
FIN× race abroad×female
0.958
0.4499
0.033
FRA× race abroad×female
-1.505
0.2853
0.000
GBR× race abroad×female
-1.157
0.3115
0.000
GER× race abroad×female
-0.119
0.2609
0.648
HKG× race abroad×female
-1.186
0.9940
0.233
HUN× race abroad×female
0.672
0.3816
0.078
ITA× race abroad×female
-1.355
0.3009
0.000
(Continued)
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
22 / 29
differences in participation among nationalities might be the differences in their attitudes
towards physical activity [16]. Participation in running might be influenced by economic
and cultural factors, e.g. those without a migration background are more likely to participate
in running than those with a migration background [17]. Another aspect affecting sport
Table 14. (Continued)
Coefficient
Standard error
P-Value
JPN× race abroad×female
-0.214
0.3048
0.482
KOR× race abroad×female
0.116
0.7603
0.878
NED× race abroad×female
-0.144
0.3698
0.697
POL× race abroad×female
0.900
0.3611
0.013
RUS× race abroad×female
0.585
0.3127
0.061
SUI× race abroad×female
-0.592
0.3508
0.091
SWE× race abroad×female
-0.452
0.4260
0.289
TPE× race abroad×female
-0.285
0.3870
0.461
USA× race abroad×female
-0.878
0.2887
0.002
https://doi.org/10.1371/journal.pone.0199701.t014
Table 15. Comparing times in hours with finishes performed. Times were computed based on the model time = sex×(year+year2)+sex×(age+age2) + sex×nationality
and referenced to male, age 44, year 2009 and nationality Australia. In parentheses: 95%-CI.
A
B
% Difference
C
Data: complete
Linear regression with fixed effects
Data: truncated at 15 hours
Linear regression with fixed effects
Between A and B
(B-A)/A
Data: truncated at 15 hours
Truncated linear regression with
fixed effects
AUS
13.9
(13.8–14.0)
11.1
(11.0–11.2)
-20.0%
11.5
(11.4–11.6)
AUT
12.0
(11.7–12.2)
10.8
(10.6–10.9)
-9.8%
11.0
(10.8–11.3)
BEL
12.2
(12.0–12.4)
10.4
(10.3–10.5)
-14.5%
10.6
(10.4–10.8)
CAN
12.9
(12.7–13.1)
11.0
(10.9–11.2)
-14.6%
11.4
(11.2–11.6)
CHN
19.1
(18.9–19.3)
11.9
(11.6–12.1)
-37.8%
13.2
(12.7–13.6)
CZE
15.0
(14.8–15.2)
10.5
(10.4–10.7)
-29.6%
10.8
(10.5–11.0)
DEN
11.1
(10.8–11.4)
10.3
(10.2–10.5)
-6.6%
10.5
(10.3–10.8)
ESP
13.8
(13.6–14.0)
10.0
(9.9–10.2)
-27.3%
10.1
(9.9–10.4)
FIN
10.2
(10.0–10.5)
10.2
(10.1–10.4)
-0.1%
10.4
(10.1–10.6)
FRA
13.0
(12.8–13.2)
11.2
(11.0–11.3)
-14.0%
11.6
(11.4–11.8)
GBR
13.3
(13.1–13.5)
10.4
(10.2–10.5)
-21.9%
10.6
(10.3–10.8)
GER
11.8
(11.6–12.0)
10.8
(10.7–10.9)
-8.6%
11.1
(10.9–11.3)
HKG
18.6
(18.3–18.8)
11.9
(11.6–12.2)
-36.0%
12.9
(12.3–13.6)
HUN
10.7
(10.4–11.0)
9.9
(9.7–10.0)
-7.8%
10.0
(9.7–10.2)
ITA
13.5
(13.3–13.7)
11.6
(11.5–11.7)
-13.9%
12.3
(12.2–12.5)
JPN
11.1
(10.9–11.3)
11.4
(11.3–11.5)
2.3%
12.0
(11.8–12.2)
KOR
12.5
(12.2–12.7)
11.7
(11.5–11.8)
-6.1%
12.7
(12.5–13.0)
NED
11.4
(11.2–11.6)
10.6
(10.4–10.7)
-7.2%
10.8
(10.6–11.0)
POL
14.1
(13.9–14.3)
11.0
(10.8–11.1)
-22.2%
11.3
(11.1–11.5)
RUS
9.4
(9.1–9.6)
9.0
(8.8–9.1)
-3.9%
9.0
(8.8–9.2)
SUI
13.6
(13.4–13.7)
11.4
(11.2–11.5)
-16.3%
11.9
(11.7–12.1)
SWE
11.2
(10.9–11.5)
10.5
(10.3–10.7)
-6.3%
10.6
(10.3–10.8)
TPE
11.9
(11.7–12.1)
11.8
(11.7–11.9)
-1.1%
12.9
(12.7–13.1)
USA
14.0
(13.8–14.1)
11.5
(11.3–11.6)
-17.9%
12.1
(11.9–12.3)
https://doi.org/10.1371/journal.pone.0199701.t015
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
23 / 29
performance is doping, for which no accurate rates exist due to its undisclosed practice; how-
ever, its prevalence has been estimated as 14–39% in adult elite athletes and has been shown to
vary by performance level and nationality [18].
Since it has been shown that the role of nationality might vary by distance in endurance
and ultra-endurance running [9], the findings of the present study should not be generalized
to other distances. On the other hand, strength of the study was its methodological approach
to the research question: first, it used one of the larger samples of 100-km ultra-marathoners
ever studied, second, it considered a probably applied time limit barrier by using a truncated
data set, third, we compared adjusted times of a reference finisher to compare ranks, and
forth, we provide a time distribution classification which helps to interpret the results.
We found that ultra-marathoners from Russia were the fastest in this specific ultra-mara-
thon distance. Unfortunately, this kind of analysis is not able to explain the reason for this
dominance. The role of ethnicity in running is, however, well-known for other running dis-
tances. Running best performances are dominated by a few groups of athletes including run-
ners with West African ancestry for the sprint distances and East African runners for the long
Table 16. Comparing times in hours with finishes performed at home. Times were computed based on the model time = ex×(year+year2)+sex×(age+age2) + sex×natio-
nality×site and referenced to male, age 44, year 2009 and nationality Australia.
A
% Difference between
races abroad/at home
B
% Difference between races
at home/on abroad
C
% Difference between
races abroad/at home
Data: complete
Linear regression
with fixed effects
Data: truncated at 14
hours
Linear regression
with fixed effects
Data: truncated at 14
hours
Truncated linear
regression with fixed
effects
Races at
home
Races
abroad
Races at
home
Races
abroad
Races at
home
Races
abroad
AUS
14.0
12.0
11.3
11.3
9.6
-15.4%
11.7
9.6
-17.8%
AUT
10.4
12.4
10.2
10.2
11.0
7.2%
10.5
11.4
9.3%
BEL
10.6
13.2
10.1
10.1
10.7
5.9%
10.2
10.9
6.5%
CAN
12.7
13.3
11.2
11.2
10.4
-7.1%
11.7
10.6
-9.4%
CHN
19.2
18.6
11.9
11.9
11.7
-1.5%
13.1
13.2
0.7%
CZE
16.5
12.1
11.0
11.0
10.3
-6.8%
11.3
10.3
-8.8%
DEN
10.4
12.9
10.5
10.5
9.8
-6.9%
10.7
9.9
-7.2%
ESP
13.6
14.5
10.1
10.1
9.7
-4.4%
10.3
10.0
-3.2%
FIN
10.3
10.2
10.3
10.3
9.4
-8.6%
10.4
9.3
-10.6%
FRA
12.7
15.3
11.2
11.2
10.9
-2.4%
11.6
11.2
-3.4%
GBR
11.6
14.3
10.0
10.0
10.7
6.7%
10.1
10.9
8.1%
GER
11.1
12.3
10.3
10.3
11.1
8.1%
10.4
11.5
10.6%
HKG
18.8
16.3
12.2
12.2
11.5
-5.6%
13.1
11.2
-14.5%
HUN
10.9
10.2
10.3
10.3
8.8
-15.0%
10.5
8.9
-15.3%
ITA
13.5
13.1
11.7
11.7
10.6
-8.7%
12.4
10.8
-12.9%
JPN
11.1
14.9
11.4
11.4
9.2
-18.8%
12.0
9.4
-21.4%
KOR
12.3
15.4
11.8
11.8
10.0
-14.8%
12.8
10.3
-19.4%
NED
10.5
12.8
10.5
10.5
10.7
2.6%
10.6
11.0
4.0%
POL
14.7
12.0
11.2
11.2
10.3
-7.9%
11.6
10.5
-9.2%
RUS
10.0
8.2
9.6
9.6
7.8
-19.2%
9.5
7.9
-17.2%
SUI
13.6
13.7
11.3
11.3
10.5
-7.6%
11.8
10.4
-12.3%
SWE
10.9
12.1
10.7
10.7
9.9
-6.8%
10.7
9.8
-8.5%
TPE
11.9
12.4
11.8
11.8
11.5
-2.4%
12.9
12.2
-5.8%
USA
14.0
13.3
11.6
11.6
10.4
-10.2%
12.4
10.6
-14.1%
https://doi.org/10.1371/journal.pone.0199701.t016
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
24 / 29
distances [9, 19]. For marathoners, East-African runners from Kenya and Ethiopia dominate
since decades running distances up to the marathon [9, 20–22] while for other running dis-
tances such as the sprint distances, runners from Jamaica are dominating [23].
For elite Kenyan runners, it is well-known that they are from a distinctive environmental
background in terms of geographical distribution, ethnicity and run to school [20]. Interest-
ingly, the same findings were reported for elite Ethiopian runners, who also have a distinct
environmental background in terms of geographical distribution, ethnicity, and also often run
to school [21]. So for both Kenyan and Ethiopian runners both environmental and social fac-
tors are important for their athletic success. These aspects are, however, not only for East Afri-
can distance runners, but also for sprinters from Jamaica of importance. It has been shown
that a higher proportion of middle distance and both jump and throw athletes walked to
school and travelled greater distances to school [23].
Although different anthropometric, physiological, biomechanical and training characteristics
are of importance for the East African running dominance [22, 24, 25], a strong psychological
Fig 9. The upper panel shows the adjusted time for each nationality in ascending order at reference sex = male,
age = 44 and year = 2009. (A) is based on linear regression of the complete dataset, (B) on the truncated dataset and
(C) on the truncated regression of the truncated dataset. The lower panel with figures (D) and (E) shows the changes in
rank from (A) to (B) and (B) to (C).
https://doi.org/10.1371/journal.pone.0199701.g009
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
25 / 29
Fig 10. The rank of nationality computed from model 2 (interaction of nationality with races at home/abroad).
(A) shows rank changes from races at home to races abroad based on linear regression with complete dataset. (B)
shows rank changes from races at home to races abroad based on linear regression with truncated dataset.
https://doi.org/10.1371/journal.pone.0199701.g010
Table 17. Mean time of the top 10, 100 and 1000 of the fastest finishers for each nationality. Only the lowest time of a finishes was considered if one finisher had sev-
eral finishes.
Nationality
N of finishers
Mean time of top 10
Nationality
N of finishers
Mean time of top 100
Nationality
N of finishers
Mean time of top 1000
1
JPN
10
6.41
JPN
100
6.83
GER
1000
7.83
2
RUS
10
6.43
RUS
100
6.86
JPN
1000
7.97
3
FRA
10
6.52
GER
100
7.00
FRA
1000
8.19
4
GBR
10
6.55
FRA
100
7.01
SUI
1000
8.31
5
ESP
10
6.61
SUI
100
7.18
ITA
1000
8.81
6
BEL
10
6.62
GBR
100
7.29
USA
1000
9.46
7
USA
10
6.62
USA
100
7.31
POL
1000
10.24
8
GER
10
6.63
ITA
100
7.38
GBR
1000
10.80
9
POL
10
6.65
ESP
100
7.57
RUS
920
10.82
10
ITA
10
6.67
BEL
100
7.74
FIN
479
10.96
11
SUI
10
6.72
POL
100
7.96
ESP
1000
11.05
12
FIN
10
6.79
NED
100
8.06
TPE
1000
11.42
13
CZE
10
6.81
AUS
100
8.08
AUS
1000
11.60
14
HUN
10
6.81
HUN
100
8.18
DEN
544
11.62
15
NED
10
6.87
AUT
100
8.21
HUN
419
11.62
16
SWE
10
6.89
FIN
100
8.28
CAN
1000
11.86
17
AUS
10
6.95
CZE
100
8.34
NED
805
12.10
18
DEN
10
7.33
CAN
100
8.38
BEL
1000
12.14
19
AUT
10
7.34
SWE
100
8.40
SWE
570
12.18
20
CAN
10
7.36
DEN
100
8.61
KOR
1000
12.37
21
KOR
10
7.68
KOR
100
9.05
AUT
969
12.66
22
TPE
10
7.99
TPE
100
9.26
CZE
1000
14.81
23
CHN
10
8.85
CHN
100
11.14
CHN
1000
15.31
24
HKG
10
9.47
HKG
100
12.41
HKG
1000
19.22
https://doi.org/10.1371/journal.pone.0199701.t017
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
26 / 29
motivation to succeed athletically for the purpose of economic and social advancement is
known [26]. Elite Kenyan runners become a competitive athlete due to economic reasons. Typi-
cally, Kenyan athletes see athletics as a means of making money to help their families, parents
and siblings [20, 27].
Practical applications for athletes and coaches
The present study advances existing theoretical knowledge as scientists will improve their
understanding of participation and performance trends by nationality in 100-km ultra-mara-
thon running which is relatively less studied compared to shorter distances such as sprint and
marathon distances. Moreover, coaches and runners can use the findings to optimize their
preparation and participation in a 100-km ultra-marathon. Athletes from other countries than
the dominating nationalities (i.e. Russia, Hungary) must be aware that they will most probably
not be able to reach a world-class level in 100-km ultra-marathon running.
Conclusions
In summary, in contrast to existing findings investigating the top 10 by nationality, this analy-
sis of all runners showed that ultra-marathoners from Russia, not Japan, were the fastest
100-km ultra-marathoners worldwide when considering all races held since 1959. Since we
know for the best sprinters and marathoners in the world that specific anthropometric, train-
ing and environmental characteristics are prevalent, future studies need to investigate why
Russian ultra-marathoners dominate the 100-km ultra-marathon distance.
Table 18. Mean time of the top 10, 100 and 1000 of the fastest finishes for each nationality.
Nationality
N of finishers
Mean time of top 10
Nationality
N of finishers
Mean time of top 100
Nationality
N of finishers
Mean time of top 1000
1
RUS
5
6.37
RUS
37
6.57
GER
332
7.35
2
JPN
7
6.37
JPN
50
6.68
FRA
358
7.52
3
BEL
3
6.44
FRA
35
6.74
JPN
557
7.60
4
POL
3
6.47
GER
34
6.80
RUS
351
7.70
5
GBR
7
6.47
GBR
30
6.86
SUI
393
7.74
6
ITA
4
6.49
BEL
23
6.88
ITA
362
8.02
7
FRA
7
6.50
ITA
30
6.94
GBR
402
8.54
8
ESP
4
6.50
ESP
28
6.96
USA
543
8.58
9
GER
5
6.54
SUI
47
6.96
ESP
365
8.60
10
CZE
3
6.55
USA
42
6.99
BEL
359
8.87
11
USA
7
6.60
POL
25
7.00
POL
503
9.25
12
SUI
4
6.68
HUN
29
7.25
NED
415
9.49
13
SWE
5
6.68
NED
29
7.32
FIN
358
9.83
14
HUN
5
6.70
CZE
24
7.33
CAN
463
9.94
15
FIN
7
6.72
AUS
42
7.55
AUT
532
10.04
16
NED
4
6.77
FIN
40
7.64
AUS
536
10.14
17
AUS
4
6.81
SWE
47
7.69
CZE
451
10.82
18
CAN
3
6.89
CAN
41
7.80
TPE
753
11.05
19
DEN
7
7.26
AUT
47
7.86
HUN
419
11.18
20
AUT
6
7.28
DEN
47
8.11
DEN
544
11.59
21
KOR
5
7.42
KOR
72
8.76
KOR
625
11.62
22
TPE
9
7.98
TPE
79
9.15
SWE
570
11.96
23
CHN
9
8.85
CHN
84
10.95
CHN
808
14.82
24
HKG
10
9.47
HKG
90
12.21
HKG
846
18.46
https://doi.org/10.1371/journal.pone.0199701.t018
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
27 / 29
Author Contributions
Conceptualization: Beat Knechtle, Pantelis Theodoros Nikolaidis.
Data curation: Beat Knechtle, Fabio Valeri.
Formal analysis: Fabio Valeri.
Methodology: Beat Knechtle, Fabio Valeri.
Writing – original draft: Beat Knechtle, Pantelis Theodoros Nikolaidis, Fabio Valeri.
References
1.
Knechtle B. Ultramarathon runners: Nature or nurture? Int J Sports Physiol Perform. 2012; 7:310–2.
PMID: 23197583
2.
Cejka N, Rust CA, Lepers R, Onywera V, Rosemann T, Knechtle B. Participation and performance
trends in 100-km ultra-marathons worldwide. J Sports Sci. 2014; 32:354–66. https://doi.org/10.1080/
02640414.2013.825729 PMID: 24015856
3.
Noakes TD, Myburgh KH, Schall R. Peak treadmill running velocity during the VO2 max test predicts
running performance. J Sports Sci. 1990; 8:35–45. https://doi.org/10.1080/02640419008732129 PMID:
2359150
4.
Cona G, Cavazzana A, Paoli A, Marcolin G, Grainer A, Bisiacchi PS. It’s a matter of mind! Cognitive
functioning predicts the athletic performance in ultra-marathon runners. PloS One. 2015; 10:e0132943.
https://doi.org/10.1371/journal.pone.0132943 PMID: 26172546
5.
Lambert MI, Dugas JP, Kirkman MC, Mokone GG, Waldeck MR. Changes in running speeds in a 100
km ultra-marathon race. J Sports Sci Med. 2004; 3:167–73. PMID: 24482594
6.
Tanda G, Knechtle B. Effects of training and anthropometric factors on marathon and 100 km ultramara-
thon race performance. Open Access J Sports Med. 2015; 6:129–36. https://doi.org/10.2147/OAJSM.
S80637 PMID: 25995653
7.
Knechtle B, Knechtle P, Rosemann T, Lepers R. Predictor variables for a 100-km race time in male
ultra-marathoners. Percept Mot skills. 2010; 111:681–93. https://doi.org/10.2466/05.25.PMS.111.6.
681-693 PMID: 21319608
8.
Knechtle B, Wirth A, Knechtle P, Rosemann T. Training volume and personal best time in marathon, not
anthropometric parameters, are associated with performance in male 100-km ultrarunners. J Strength
Cond Res. 2010; 24:604–9. https://doi.org/10.1519/JSC.0b013e3181c7b406 PMID: 20145568
9.
Nikolaidis PT, Onywera VO, Knechtle B. Running Performance, Nationality, Sex and Age in 10km, Half-
marathon, Marathon and 100km Ultra-marathon IAAF 1999–2015. J Strength Cond Res. 2017;
31:2189–207. https://doi.org/10.1519/JSC.0000000000001687 PMID: 28731980
10.
R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria:
The R Foundation for Statistical Computing; 2011.
11.
Nikolaidis PT, Heller J, Knechtle B. The Russians are the fastest in marathon cross-country skiing: The
“Engadin Ski Marathon”. BioMed Res Int. 2017;9821757. https://doi.org/10.1155/2017/9821757 PMID:
28904979
12.
Bairner A. Assessing the sociology of sport: On national identity and nationalism. Int Rev Sociol Sport.
2015; 50:375–9.
13.
Houlihan B. Politics and sport. In: Coakley J, Dunning E, editors. Handbook of sports studies. London,
UK: Sage publications; 2006.
14.
Rupert JL. The search for genotypes that underlie human performance phenotypes. Comp Biochem
Physiol A Mol Integr Physiol. 2003; 136:191–203. PMID: 14527640
15.
Collins M, Xenophontos SL, Cariolou MA, Mokone GG, Hudson DE, Anastasiades L, et al. The ACE
gene and endurance performance during the South African Ironman Triathlons. Med Sci Sports Exerc.
2004; 36:1314–20. PMID: 15292738
16.
Ntoumanis N, Biddle SJH. Affect and achievement goals in physical activity: A meta-analysis. Scand J
Med Sci Sports. 1999; 9:315–32. PMID: 10606097
17.
Hallmann K, Breuer C, Dallmeyer S. Germany: running participation, motivation and images. In:
Scheerder J, Breedveld K, Borgers J, editors. Running across Europe: the rise and size of one of the
largest sport markets. New York, USA: Palgrave Macmillan; 2015.
18.
de Hon O, Kuipers H, van Bottenburg M. Prevalence of Doping Use in Elite Sports: A Review of Num-
bers and Methods. Sports Med. 2014; 45:57–69
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
28 / 29
19.
Marc A, Sedeaud A, Schipman J, Jacquemin JA, Saulière G, Kryger KO, Toussaint JF. Geographic
enrolment of the top 100 in athletics running events from 1996 to 2012. J Sports Med Phys Fitness.
2017; 57(4):418–25. https://doi.org/10.23736/S0022-4707.16.06019-9 PMID: 26632850
20.
Onywera VO, Scott RA, Boit MK, Pitsiladis YP. Demographic characteristics of elite Kenyan endurance
runners. J Sports Sci. 2006; 24(4):415–22. https://doi.org/10.1080/02640410500189033 PMID:
16492605
21.
Scott RA, Georgiades E, Wilson RH, Goodwin WH, Wolde B, Pitsiladis YP. Demographic characteris-
tics of elite Ethiopian endurance runners. Med Sci Sports Exerc. 2003; 35(10):1727–32. https://doi.org/
10.1249/01.MSS.0000089335.85254.89 PMID: 14523311
22.
Marc A, Sedeaud A, Guillaume M, Rizk M, Schipman J, Antero-Jacquemin J, Haida A, Berthelot G,
Toussaint JF. Marathon progress: demography, morphology and environment. J Sports Sci. 2014; 32
(6):524–32. https://doi.org/10.1080/02640414.2013.835436 PMID: 24191965
23.
Irving R, Charlton V, Morrison E, Facey A, Buchanan O. Demographic characteristics of world class
Jamaican sprinters. ScientificWorldJournal. 2013 10; 2013:670217. https://doi.org/10.1155/2013/
670217 PMID: 24396303
24.
Scott RA, Pitsiladis YP. Genotypes and distance running: clues from Africa. Sports Med. 2007; 37(4–
5):424–7. PMID: 17465625
25.
Mooses M, Hackney AC. Anthropometrics and Body Composition in East African Runners: Potential
Impact on Performance. Int J Sports Physiol Perform. 2017; 12(4):422–30. https://doi.org/10.1123/
ijspp.2016-0408 PMID: 27631418
26.
Wilber RL, Pitsiladis YP. Kenyan and Ethiopian distance runners: what makes them so good? Int J
Sports Physiol Perform. 2012; 7(2):92–102. PMID: 22634972
27.
Onywera VO. East African runners: their genetics, lifestyle and athletic prowess. Med Sport Sci. 2009;
54:102–9. https://doi.org/10.1159/000235699 PMID: 19696510
The fastest 100-km ultra-marathoners worldwide
PLOS ONE | https://doi.org/10.1371/journal.pone.0199701
July 11, 2018
29 / 29

Russians are the fastest 100-km ultra-marathoners in the world.
Knechtle, Beat,Nikolaidis, Pantelis Theodoros,Valeri, Fabio