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, 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 running37,38,39, 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 athletes 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, regarding 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 genders 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)46,47,48, 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-marathons 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, belonging to the HV cluster), characterizing athletes with the greatest endurance49. Normal resistance to pain is also 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 accuracy 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 sample 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, economical 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 consider 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.
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