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A macro to micro analysis 
to understand performance 
in 100‑mile ultra‑marathons 
worldwide
Mabliny Thuany 
1, Katja Weiss 2,3, Elias Villiger 4, Volker Scheer 5, Nejmeddine Ouerghi 
6,7, 
Thayse Natacha Gomes 
8,9 & Beat Knechtle 
2,3*
The purposes of this study were (i) to describe differences in participation in 100‑mile ultra‑marathons 
by continent; (ii) to investigate differences in performance between continents; and (iii) to identify 
the fastest runners by continent and country. Data from 148,169 athletes (119,408 men), aged 
18–81 years, and finishers in a 100‑miles ultra‑marathon during 1870–2020 were investigated. 
Information about age, gender, origin, performance level (top three, top 10, top 100) was obtained. 
Kruskal–Wallis tests and linear regressions were performed. Athletes were mostly from America and 
Europe. A macro‑analysis showed that the fastest men runners were from Africa, while the fastest 
women runners were from Europe and Africa. Women from Sweden, Hungary and Russia presented 
the best performances in the top three, top 10 and top 100. Men from Brazil, Russia and Lithuania 
were the fastest. The lowest performance and participation were observed for runners from Asia. 
In summary, in 100‑miles ultra‑marathon running, the majority of athletes were from America, but 
for both sexes and performance levels, the fastest runners were from Africa. On a country level, the 
fastest women were from Sweden, Hungary and Russia, while the fastest men were from Brazil, 
Russia and Lithuania.
The athletes’ performance is influenced by both individual (e.g., genetic, morphological, training) and environ-
mental factors (e.g., coach, family, social characteristics)1,2. Moving beyond the athlete-centered approach, recent 
studies were developed to understand the role of the environment in the athlete’s  performance3–5. The ‘birthplace 
effect’ has been largely studied in team sports, such as  soccer6, ice  hockey7,  basketball8,  volleyball9, and  handball10.
Furthermore, among individual sports such as running, the interest in understanding the link between the 
environment and the athletes’ performance has increased in the last  years11,12. These interests were associated 
with the increasing numbers of both runners and running events across the  world13, especially after the 1970’s 
in North America and after the 1980’s in  Europe14.
There is ample evidence that the fastest long-distance runners, such as marathoners, originate from the Afri-
can continent, particularly from Kenya and  Ethiopia15,16. This representation is related to a plethora of factors, 
which include–but are not limited to–physiological characteristics, training, lifestyle behaviors, and motivational 
 factors17,18. Considering Brazil, the Southeast region as the richest region of the country is the region with the 
highest number of elite long-distance runners in the  country19.
However, in the context of ultra-marathon running, little is known about where the fastest ultra-marathoners 
come from. One of the few studies found that Russian and Japanese were the fastest for the 100-km ultra-
marathon race  distance16,20. Similar results were found by Cejka et al.21, where most of the finishers in 100-km 
ultra-marathons were from Europe, but Japanese runners were the fastest. On the other hand, data covering 
OPEN
1Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, 
Porto, Portugal. 2Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001 St. Gallen, Switzerland. 3Institute 
of Primary Care, University of Zurich, Zurich, Switzerland. 4Klinik Für Innere Medizin, Kantonsspital St. Gallen, 
St. Gallen, Switzerland. 5Ultra Sports Science Foundation, 109 Boulevard de L’Europe, 69310 Pierre-Benite, 
France. 6University of Jendouba, High Institute of Sport and Physical Education of Kef, UR13JS01, 7100 Kef, 
Tunisia. 7Faculty of Medicine of Tunis, University of Tunis El Manar, Rabta Hospital, LR99ES11, 1007 Tunis, 
Tunisia. 8Post-Graduation Program of Physical Education, Department of Physical Education, Federal University 
of Sergipe, São Cristóvão Sergipe 49100-000, Brazil. 9Department of Physical Education and Sport Sciences, 
University of Limerick, Limerick V94T9PX, Ireland. *email: beat.knechtle@hispeed.ch
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96,036 athletes (88,286 men and 7,750 women) finishing the oldest 100-km ultra-marathon in the world (‘100 km 
Lauf Biel’ in Switzerland), showed that Switzerland, Germany, and France were the countries with the highest 
number of participants throughout the history of the  race22. These initial insights suggest that the place of the 
competition and the countries’ economic indicators are related to these results.
To date, most studies regarding participation and performance trends have shown an increase in the number 
of finishers in the last decades’23,24, especially in events hosted in the USA, where most of the events were tak-
ing  place24,25. Notwithstanding the relevance of these studies, it is important to present a more generalized view 
by using a macro-level approach investigating the between-continents differences, followed by a micro-level 
analysis within-country. It has also been shown that athletes from specific countries improved their performance 
over years in specific races such as the ‘Spartathlon’26 whereas in other races such as the ‘100 km Lauf Biel’, the 
performance of athletes from specific countries  decreased22. Generally, the fastest athletes were able to improve 
their performance across years in the world’s most famous ultra-marathon races such as the ‘Spartathlon’26 and 
the ‘Comrades’27.
In this sense, the purposes of this study were (i) to verify the participation of athletes by continent and country 
in 100-mile ultra-marathons (161-km) performed between 1870 and 2020, (ii) to compare the athletes’ perfor-
mance between continents and countries, (iii) to identify the fastest athletes by country and by continent and, 
(iv) to investigate the trend in performance over years of the athletes from the fastest countries. Previous studies 
showed that North America and Europe presented higher numbers of ultra-marathon events worldwide (431 and 
455, respectively)25 compared to Asia (139), Africa (59), Australia (35), South America (18), as well as a higher 
participation in 161-km ultra-marathons28. For another way, Russian athletes were the fastest in long-distance 
running races such as the ‘Comrades Marathon’29, and in 100-km ultra-marathon running  races20. Based on it, 
we hypothesized that (i) the highest number of athletes would be found on the American continent, especially 
in USA and Canada, while Russian runners would be the fastest and (ii) the fastest runners would be able to 
improve their performance over time.
Methods
Ethical approval. 
The study was performed following the Declaration of Helsinki, and the institutional 
review board of St Gallen, Switzerland, approved this study (EKSG 01/06/2010). Since the study involved the 
analysis of publicly available data, the requirement for informed consent was waived. Participants were not iden-
tified during the data management and during all sections of the manuscript.
Design and sample. 
Data used in the present study was obtained from the website of the ‘Deutsche Ultra-
marathon-Vereinigung’ DUV (https:// stati stik.d- u-v. org/ getev entli st. php) and corresponded to the officially 
available results of the participants enrolled in 100-mile (161 km) ultra-marathon race events held during 1870–
2020 for both genders. The available information included the year of the event, race distance, year of birth, gen-
der, general ranking, country, team, mean running speed, and race time. Based on this information, we clustered 
the athletes by country and then by continent (e.g., Africa, America, Asia, Oceania, and Europe). Performance 
levels were categorized based on the general classification considering the top three, top 10 and top 100 for both 
genders. Exclusion criteria were: athletes aged below 18 years, athletes clustered in countries with less than 10 
participations (when comparison within a continent was made), mean running speed higher than 20 km/h, and 
missing information about the country of origin.
Statistical analysis. 
Descriptive information was presented in mean (standard deviation), minimum 
(min), maximum (max) values, and frequency (%). Data normality was formally tested using the Kolmogorov–
Smirnov test. Based on the athletes’ performance, runners were classified considering their ranking position 
(top 100, top 10, and top three), for both genders. Following, athletes were clustered based on their birthplace 
(e.g., country and continent). To compare the performance level according to the continent, we performed the 
Kruskal–Wallis test, followed by the post-hoc test adjusted by the number of comparisons.
A simple linear regression was performed to verify the performance trends. Running speed (miles/h) was the 
outcome variable, while the time (year) was considered as a predictor. The regression was performed considering 
both, the total sample and the ranking position (top three, top 10, and top 100), as well as the three best countries 
over time for both genders. For the total sample regression analysis, we considered data from the 1970s, since a 
running boom in high and middle-income countries from this decade was verified 30. For the three best countries 
according to the performance level, we considered data from 2010. Data analysis was performed in the SPSS 26, 
and the significance level was set at 0.05.
Results
A total of 148,169 athletes aged 18–81 years and of both genders (women = 28,761; men = 119,408), finished at 
least one 100-mile ultra-marathon during 1870–2020. Athletes came from 113 countries from all five continents. 
Most of them were from the American continent (72.7%), followed by Europe (15.9%), Asia (5.6%), Africa (3.7%), 
and Oceania (2.1%). Running speed distribution between athletes showed a similar pattern for all continents. 
Most of the runners presented mean values of 5–7 miles/h, and a small number of athletes presented running 
speeds higher than 10 miles/h. Mean running speed was 6.12 miles/h for the total sample (women = 5.96 miles/h; 
men = 6.21 miles/h).
Participation and performance by continent—a macro‑analysis. 
Figure 1 presents the distribu-
tion of the athletes, according to their continent of origin into different performance groups (top three, top 10, 
top 100, and all athletes) for both genders. Most women in all the performance levels were from the American 
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continent, followed by Europe. Visual differences are presented for the top three and top 10, where Oceania has 
a higher number of athletes compared to the African continent (e.g., top three and top 10).
Considering athletes clustering by continent, significant differences in performance were shown 
 (H(4) = 1766.22; p < 0.001). The descriptive analysis showed that African women achieved the highest 
mean running speed (6.89 ± 1.04 miles/h), followed by athletes from Europe (6.45 ± 1.38 miles/h), America 
(5.89 ± 1.01 miles/h), Oceania (5.85 ± 1.37 miles/h), and Asia (5.17 ± 1.49 miles/h). Significant differences were 
observed between most of the continents, except between America and Oceania (p-adjusted = 0.178). For men, 
for all the performance levels (top three, top 10, top 100 and all athletes), most of the athletes were from both 
the American and the European continent. The lowest percentages of participation were found for Oceania. 
Considering the performance by continent, athletes from Africa presented the highest mean values for running 
speed (7.42 ± 1.33 miles/h), followed by Europe (6.53 ± 1.53 miles/h), Oceania (6.39 ± 1.59 miles/h), America 
(6.13 ± 1.17 miles/h), and Asia (5.34 ± 1.47 miles/h). Differences in the performance were found between conti-
nents  (H(4) = 859.43; p < 0.001), with significant differences between all of them.
Participation and performance by country—a micro‑analysis. 
For the top three athletes, a total of 
36 and 65 countries were listed for both women and men, respectively. Most of the athletes were from the USA 
(62.1% and 56.9% for women and men, respectively). Similarly, the highest number of athletes from the USA 
were observed in the top 10 (62.6% and 58.4% for women and men, respectively) and top 100 (72.4% and 66% 
for women and men, respectively). Therefore, within the European continent, the majority of the athletes were 
from Germany and the Great-Britain for all performance levels.
Table 1 presents the results for the comparison between performance levels for both genders. Significant dif-
ferences were observed for both genders and all performance levels. For men athletes, the fastest runners were 
from the African continent. For the top three women, the fastest ones were from Europe, while for the top 10 
and top 100 the fastest were Africans.
Table 2 presents the descriptive results for women runners, considering athletes from the fastest country in 
each continent and the performance level (e.g., top three, top 10 and top 100). For the African continent, the 
fastest women were from South Africa, in the three performance groups. For the other continents, there was no 
verified pattern of association between the countries and performance levels, nonetheless, the highest running 
speeds were observed in the European continent when compared to the other continents. Among the top three, 
top 10 and top 100, women from Sweden, Hungary and Russia achieved the fastest running speeds.
Men descriptive results for athletes from the fastest countries in each continent are presented in Table 3. For 
the top three, the fastest athletes were from America (Brazil: 9.54 ± 1.75 miles/h). In the top 10 and top 100, 
athletes from Europe (e.g., Russia and Lithuania) presented the highest mean running speed compared to the 
other continents.
Figure 2 presents the performance trend results for both genders considering all the sample. For all perfor-
mance groups and both genders, performance decreased over time (p < 0.001).
Figure 1.  Percentage of women and men participation according to performance level.
Table 1.  Descriptive results (mean ± SD for miles/h) for performance (speed mean, mile/h) according to 
gender and performance level. SD standard deviation. a difference for Asia; bdifference for America; cdifference 
for Oceania; ddifference for Europe.
Men
Women
Top three
Top 10
Top 100
Top three
Top 10
Top 100
Africa
9.37 (1.7)abcd
8.64 (1.6)abcd
7.45 (1.3)abcd
7.52 (1.7)
7.39 (1.5)bc
6.91 (1.0)abc
America
7.93 (1.5)a
7.28 (1.4)a
6.29 (1.2)ab
7.37 (1.7)d
6.80 (1.4)a
6.04 (1.1)ab
Asia
7.10 (1.8)
6.59 (1.7)
5.76 (1.5)
7.29 (2.1)d
6.44 (1.8)a
5.71 (1.5)
Europe
8.29 (1.9)abc
7.64 (1.8)abc
6.74 (1.5)ac
8.29 (1.8)
7.38 (1.7)abc
6.72 (1.4)abcd
Oceania
8.05 (1.7)a
7.27 (1.6)a
6.45 (1.6)a
7.45 (1.5)
6.71 (1.4)
5.91 (1.4)a
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Figure 3 presents the linear regression results for performance across time considering athletes from the 
best countries in the top three, top 10, and top 100, respectively. For women, runners from Russia, Hungary, 
and Finland were the fastest, while runners from Brazil, Russia, and Lithuania were the fastest among men. The 
time frame considered was the last 10 years (from 2010), in which a significant performance decline was shown 
for women from Sweden (r2 = 0.10; p < 0.001; 95%CI =  − 0.18– − 0.08). Based on the r2 values, the magnitude of 
the performance decline was about 0.10miles/h across the years. For men, performance decline was showed for 
Brazilian (r2 = 0.27; p < 0.001; 95%CI =  − 0.47 – − 0.18) and Russian runners (r2 = 0.02; p = 0.017; 95%CI =  − 0.18 
– − 0.018), in which a decline of 0.27 miles/h and 0.02 miles/h was showed across the years, respectively. Athletes 
Table 2.  Women descriptive results (min, mean ± SD, max) for the best country in each continent, based on 
performance level. Min–Minimum value; Max–Maximum value. SD standard deviation. We only considered 
the best country for each continent. Countries with total athletes below 10 were not considered.
Country
Total athletes
Speed (miles/h)
(Min–Max)
Speed (miles/h)
(Mean ± SD)
Top three
Africa
South Africa
35
3.08–10.81
7.52 (1.72)
America
Canada
82
4.35–10.00
7.44 (1.23)
Asia
Philippines
13
5.14–6.36
5.71 (0.36)
Europe
Sweden
17
5.52–10.60
8.31 (1.47)
Oceania
Australia
33
4.46–10.44
7.55 (1.35)
Top 10
Africa
South Africa
150
2.41–10.81
7.39 (1.52)
America
USA
2658
2.80–19.42
6.82 (1.50)
Asia
Japan
26
4.08–11.59
8.13 (2.02)
Europe
Hungary
11
7.59–11.12
9.07 (1.06)
Oceania
New Zealand
48
3.41–9.64
6.87 (1.55)
Top 100
Africa
South Africa
713
2.41–10.81
6.92 (1.05)
America
El Salvador
11
5.47–8.06
6.65 (0.83)
Asia
Cyprus
67
3.38–10.37
7.21 (1.52)
Europe
Russia
55
5.19–10.29
8.05 (1.21)
Oceania
Australia
445
3.38–10.44
5.93 (1.36)
Table 3.  Men descriptive results (min, mean ± SD, max) for the best country in each continent, based on a 
performance level. Min–Minimum value; Max–Maximum value. SD standard deviation. We only considered 
the best country for each continent. Countries with total athletes below 10 were not considered.
Country
Total athletes
Speed (miles/h)
(Min–Max)
Speed (miles/h)
(Mean ± SD)
Top three
Africa
South Africa
411
3.14–13.47
9.35 (1.73)
America
Brazil
16
4.97–11.65
9.54 (1.75)
Asia
Taiwan
62
3.36–10.24
8.25 (1.47)
Europe
Hungary
32
5.73–11.95
9.48 (1.60)
Oceania
Australia
374
4.19–13.21
8.07 (1.72)
Top 10
Africa
South Africa
1166
2.52–13.47
8.63 (1.67)
America
Brazil
28
4.97–11.65
8.77 (1.91)
Asia
Taiwan
214
3.36–10.49
7.97 (1.22)
Europe
Russia
100
5.03–14.03
9.36 (1.51)
Oceania
New Zealand
239
3.38–12.39
7.31 (1.60)
Top 100
Africa
Zimbabwe
12
4.61–11.88
8.01 (2.60)
America
Brazil
64
3.43–11.65
7.61 (2.00)
Asia
Israel
23
4.01–10.49
7.50 (1.78)
Europe
Lithuania
13
4.31–12.53
8.41 (2.05)
Oceania
Australia
1850
3.42–1.21
6.51 (1.60)
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from Lithuania presented a visual increase in running speed over the last 10 years, however, a non-significant 
effect was shown.
Discussion
The purpose of this study was to identify where the fastest runners in 100 miles ultra-marathons come from, 
considering the performance level. The main results showed that (i) for both genders and all performance levels, 
most of the athletes were from the American and European continents; (ii) a macro-analysis showed that the 
fastest men were from Africa, while the fastest women were from Europe and Africa; (iii) women from Sweden, 
Figure 2.  Linear regression results, considering all sample (A) Women top three; (B) Women top 10; (C) 
Women top 100; (D) Men top three; (E) Men top 10; (F) Men Top 100).
Figure 3.  (A) Sweden women top three; (B) Hungary women top 10; (C) Russian women top 100; (D) Brazilian 
men top 3; (E) Russian men top 10; (F) Lithuanian men top 100.
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Hungary and Russia presented the best performance in top three, top 10 and top 100; (iv) men runners from 
Brazil, Russia and Lithuania were the fastest in top three, top 10 and top 100, respectively; and (v) the lowest 
performance and participation were achieved by athletes from Asia.
Participation and performance by continent—a macro‑analysis. 
The first important finding was 
that most of the finishers were from America, but African runners were the fastest when analysis was performed 
by continent. These findings confirm our hypothesis. For both genders, the highest runner’s frequency was from 
the American continent, especially from the USA. Similar results were reported by  Hoffman23 in 100 miles 
(161 km) ultra-marathon running competition in North America. The authors showed that from 1977 to 2008, 
the number of annual finish rates increased, but no improvements in performance were  verified23.
The results of the present study can be related to the American cultural and fitness revolutions of the 1960s 
and 1970s, which included a ‘running boom’13. Specifically for the ultra-marathon races, a historical perspective ˗ 
the USA was one of the main ultra-marathon birthplaces around the  world31—can influence the highest number 
of race events and athletes from these countries. In addition, the increase in participation among women and 
older athletes can be associated with this  result32. A previous report covering approximately 107 million race 
results from 1986 to 2018 showed that the USA was the country with the highest number of runners, but with 
the slowest  athletes33. Accordingly, the highest proportions of women participants were from USA and Canada, 
while Switzerland and Italy were the countries with the lowest women  participation33.
The highest participation of these countries can be related to the highest number of ultra-marathon events 
performed in these  countries25. Regarding continents, 443 ultra-marathon events were developed in America, 
where 431 are situated in North America. Following, Europe hosted 421 events. For data used in this study, more 
than half of the race events were performed in EUA (58.5%), with about 20% performed in Great Britain (5.6%), 
Australia (4.8%), South Africa (4.7%), Canada (4.6%), and Germany (3.4%). Besides the higher events performed 
in-locus, the athletes’ socioeconomic characteristics can also be related to running  participation34,35. Athletes 
from a high-income country can present a better contextual indicator for traveling and participating in remote 
 events36. In another way, the lowest performances showed for athletes from the USA can be related to changes 
in running motivation across the years. As shown in studies that include short to long-distance events, the 
psychological, social, and physical are the main reasons for  running37–39, especially in non-professional athletes.
The macro-analysis has shown that the African continent presented the best mean values for running speed. 
This is an interesting finding, considering that African athletes are the strongest in long-distance running such 
as half-marathon and  marathon15,40. However, these runners are from Kenya and Ethiopia, different from the 
present results, where most of them are from South Africa. These results are similar to findings in a previous 
report covering 85% of ultra-running events worldwide during 1996–2018, including trail runs, mountain runs 
and road runs. South Africa was the country with the fastest athletes, with a running pace of 10:36 min/mile, 
followed by Sweden (11:56 min/mile), Germany (12:01 min/mile), Netherlands (12:41 min/mile), and Great-
Britain (12:44 min/mile), while the slowest were from Argentina (15:20 min/mile), Mexico (15:30 min/mile) and 
Malaysia (15:55 min/mile). These results were also associated with findings that countries from Asia presented 
the poorest  performance36.
Participation and performance by country—a micro‑analysis. 
The micro-analysis showed that ath-
letes from Sweden, Hungary, and Russia presented the best performance in the top three, top 10, and top 100 for 
women, and those from Brazil, Russia, and Lithuania were the fastest in the top three, top 10, and top 100 for 
men. We hypothesized that the fastest runners would originate from Russia, but the results partially disagree. 
These differences can be related to the methodological approach for the present study where we present the 
data for performance level (i.e., top three, top 10, top 100). For example, Nikolaidis et al.16, in a study including 
athletes ranked in World Athletics (i.e., IAAF) during 1999–2015, showed that among women, athletes from 
Russia were faster than athletes from France and Germany in ultra-marathon events. Similar results were shown 
in athletes who finished a 100-km ultra-marathon between 1959 and  201620, when considering the top 10 by 
nationality, runners from Russia and Hungary were the fastest.
Men from Brazil in the top three are untypical considering previous  studies16,20,41. Notwithstanding, regard-
ing the increase in runner’s participants and race events across the  country42,43, few studies were developed to 
understand the participation and performance in ultra-running  events44,45. The country characteristics, which 
include variations in weather, altimetry, nutritional habits, cultural aspects, and lifestyle among the regions, 
should be investigated in future studies to understand the association with performance in ultramarathon events.
Considering both, the total sample and the fastest countries, performance decreased over time for both gen-
ders and performance levels. These results are similar to previous  findings23,32. These results can be linked to the 
changes in the runner’s profile (e.g., intrapersonal motivation, training background, previous experience)46–48, and 
event characteristics (weather, altimetry). Differently, athletes from Lithuania showed an increase in performance 
over the last few years. This increase was not statistically significant, however, factors that explain these results 
can be related to the low number of athletes over the years, which can bias the results. The generalization of the 
present findings need to be considered carefully. In another way, the decrease in performance in other countries 
is according to previous findings, showing that countries have slowed down over the last 10 years and that those 
with have slowed down most are among the slowest in the  rankings36.
More and more is known about the factors that predispose to achieve outstanding results in ultramarathon 
running, but without pointing to the most important  ones49. Gajda et al. considered the success in ultra-mara-
thons as a complex multifactorial cause and called them the “mosaic theory”. Among the factors that guarantee 
success they mention genetic factors such as the presence of haplogroup H mtDNA (subgroup HV0a1, belong-
ing to the HV cluster), characterizing athletes with the greatest  endurance49. Normal resistance to pain is also 
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 important50. However, none of these factors isolated guarantees success for an individual athlete or a particular 
nation in ultra-marathons. Additional investigations considering the environment (natural, built, and social) 
are necessary.
Limitations and strengths. 
Limitations of the study are related to the nature of the data used. The accu-
racy of the data (e.g., the race distance in each event, the accuracy of the information in the first years, and the 
information about the birthplace of the athletes), as well as the missing data in specific time frames, and sam-
ple size variability between countries need to be considered. These limitations are challenged to be solved. To 
reduce the bias, the countries with a total number of athletes below 10, as well as the regression analysis with 
data before the 1970s were not considered. In addition, information about individual (e.g., training volume and 
intensity, running experience, running strategies) and contextual factors (i.e., number of competitions, econom-
ical support, cultural aspects and race course, different elevation changes, weather) are unavailable. Individual 
and contextual characteristics are helpful to deeply understand runners’ profile, as well as to deeply understand 
the impact of hosting events’ effect, and the characteristics of the race course for runners’ performance. The 
role of the individual characteristics for ultramarathon performance was previously investigated, however, little 
information is available about the role of social, economic, cultural, and geographical characteristics to increase 
the participation, as well as the role of the participation in performance outcomes. Future studies need to con-
sider data triangulation, including the place in which competitions are performed, the participation and the 
performance outcomes, adopting different strategies regarding the performance level and sample size required 
within countries. Finally, we did not control for the migration or multiple events participation across the years, 
that is, an athlete can have moved to represent another country than his/her home country or take part in more 
than one event over the years. In another way, we presented a detailed analysis, considering both macro-and 
micro-level approaches. Since the highest number of ultramarathon events are performed in North America and 
Europe, even though considering mean values the fastest are from Africa and Europe, the practical application of 
the present study includes supports local sport police programs to increase the availability of events in countries 
which they are underrepresented. Athletes living in countries with a higher number of events present a higher 
participation rate, since the costs (travel, host) are  lower51. Additionally, being familiar with local characteristics 
(language, cultural habits, and weather) is associated with performance  improvement51.
Conclusion
For the 100-miles ultra-marathons, most of the athletes were from the American and European continent, despite 
the fastest being from Africa. A micro-analysis showed that European countries’ (Sweden, Hungary, and Russia) 
were the best for women, while for men, Brazil, Russia and Lithuania were the fastest in the top three, top 10, 
and top 100. Regarding the best countries, a decrease in performance was shown over time, except for athletes 
from Lithuania. These results can be used to public policies to provide the highest number of race events among 
countries, especially those in Asia and Oceania, which showed the lowest engagement.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding 
author on reasonable request.
Received: 24 May 2022; Accepted: 18 January 2023
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Author contributions
Conceptualization: M.T., B.K.; Data curation: B.K., E.V.; Formal analysis: M.T. Methodology: B.K.; Writing—
original draft: M.T., K.W., V.S., N.O., T.N.G., B.K.
Funding
This research has not received any kind of financial support. All the work was done voluntarily for the prepara-
tion of the manuscript.
Competing interests 
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to B.K.
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© The Author(s) 2023

A macro to micro analysis to understand performance in 100-mile ultra-marathons worldwide.
Thuany, Mabliny,Weiss, Katja,Villiger, Elias,Scheer, Volker,Ouerghi, Nejmeddine,Gomes, Thayse Natacha,Knechtle, Beat