Over 1 million race records show East Africans as fastest in 50-km ultra-marathons

In a recent study published in the journal Scientific Reports, researchers investigated the age and race locations of elite ultra-marathon runners, as well as the country they were affiliated with.

Their results indicate that the fastest runners are from countries in Africa, like Ethiopia and Kenya, with the fastest races occurring in Middle Eastern countries and Europe, and the peak performance age is between 20 and 24 years.

Study: Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons. Image Credit: lzf / ShutterstockStudy: Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons. Image Credit: lzf / Shutterstock

Background

Ultra-marathons, defined as races exceeding the standard marathon distance (42.2 km) or lasting longer than six hours, include both distance-limited and time-limited formats. Among these, the 50 km race is a popular and accessible entry point for marathoners seeking greater challenges.

Prior research on ultra-marathons has examined performance trends, performance variations linked to age, sex differences, inflammatory responses, nutritional needs, cardiovascular impacts, and running biomechanics.

Despite extensive exploration, the national origins of the fastest 50 km runners remained unstudied. Known for their dominance in traditional marathons, East African runners' performance in ultra-marathons, particularly at the 50 km distance, lacked comparable investigation.

Studies on longer races, like the 100-km and 100-mile races, identified top performers from Japan, Russia, Brazil, Sweden, and Hungary, with potential influences from doping scandals.

Contrarily, younger runners (those between 18 and 24 years) were found to be slower in 100-km events, raising questions about peak performance ages in shorter ultra-marathons.

About the study

This study addressed existing research gaps by analyzing the age and country of affiliation of the fastest 50-km ultra-marathoners and identifying where the fastest races were located.

The study analyzed a dataset comprising 1,398,845 race records from 549,154 unique runners across 122 countries. Each record included participants' affiliated country, gender, age, event location, year, and mean race speed.

Data was cleaned to remove incomplete records and duplicates and categorized into age groups of five years. Records from nations with fewer than ten entries were excluded to minimize noise.

The analysis involved visualizing histograms for age groups and race speeds and ranking nations by their mean race speed. To predict race speed, a regression model was developed using encoded variables, such as athlete gender, age group, athlete country, and event country.

The model's performance was evaluated and validated using mean absolute error (MAE) and R² metrics, prediction distribution plots, and feature importance.

Findings

The study analyzed 1,398,845 race records from 549,154 unique runners, including 1,026,546 men and 372,299 women, from 122 nations competing in 50-km races held in 86 locations between 1894 and 2022.

The number of female racers participating increased over time, reducing the male-to-female ratio. Men averaged faster speeds (8.2 km/h) than women (7.4 km/h), with the 20-24 age group being the quickest (8.3 km/h).

The fastest runners were from Ethiopia (14.1 km/h), Lesotho (13.1 km/h), Malawi (12.4 km/h), and Kenya (12.3 km/h). In comparison, the fastest races occurred in Luxembourg (11.4 km/h), Belarus (11.3 km/h), Lithuania (11.2 km/h), Qatar (11.2 km/h), and Jordan (10.7 km/h).

The regression model indicated that the event country was the strongest predictor of race speed (66%), with athlete gender (23%), age group (7%), and athlete country (5%) following. The model achieved an R² of 0.36 and an MAE of 1.4 km/h.

A multivariate linear regression (MLR) model yielded a similar R² of 0.325, with all predictors being statistically significant. Univariate models showed event and athlete country as significant predictors individually (R² = 0.279 and R² = 0.260, respectively), indicating a high level of correlation.

Conclusions

The study aimed to identify elite ultra-marathoners in the 50 km category by country, the locations of the fastest races, and their age.

It found that the fastest racers were from African nations (Malawi, Lesotho, Kenya, and Ethiopia), the quickest racecourses were in Europe and Middle Eastern countries, and the fastest age group was between 20 and 24 years, contrary to expectations of older peak performance.

The study's findings highlight the influence of geographic and demographic factors on ultra-marathon performance. They align with previous research highlighting East African dominance in distance running, attributed to genetic predispositions, high-altitude training, traditional diets, and sociocultural factors.

However, unlike prior studies that often focused on marathon performance peaking around 35 years or older, this study found that younger athletes (20-24 years) excelled in the 50-km races.

The study's strengths include a large dataset and a comprehensive analysis of predictors influencing race performance. However, it has limitations, such as potential sample bias due to socioeconomic factors and the exclusion of environmental variables like temperature and humidity.

Future research should delve into the specific physiological and psychological traits that allow young runners to excel in 50-km races and consider a more granular analysis of participant demographics to account for socioeconomic influences.

Understanding these factors can better inform training and development programs for ultra-marathon runners.

Journal reference:
  • Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons. Weiss, K., Valero, D., Villiger, E., Thuany, M., Forte, P., Gajda, R., Scheer, V., Sreckovic, S., Cuk, I., Nikolaidis, P.T., Andrade, M.S., Knechtle, B. Scientific Reports (2024). DOI: 10.1038/s41598-024-58571-0, https://www.nature.com/articles/s41598-024-58571-0
Priyanjana Pramanik

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Priyanjana Pramanik

Priyanjana Pramanik is a writer based in Kolkata, India, with an academic background in Wildlife Biology and economics. She has experience in teaching, science writing, and mangrove ecology. Priyanjana holds Masters in Wildlife Biology and Conservation (National Centre of Biological Sciences, 2022) and Economics (Tufts University, 2018). In between master's degrees, she was a researcher in the field of public health policy, focusing on improving maternal and child health outcomes in South Asia. She is passionate about science communication and enabling biodiversity to thrive alongside people. The fieldwork for her second master's was in the mangrove forests of Eastern India, where she studied the complex relationships between humans, mangrove fauna, and seedling growth.

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