Evidence map›Paper›PMID 40157985›Full record

ArticleScientific reports2025

Race course characteristics are the most important predictors in 48 h ultramarathon running.

Beat Knechtle, David Valero, Elias Villiger, Katja Weiss, Pantelis T Nikolaidis, Lorin Braschler, Rodrigo Luiz Vancini, Marilia Santos Andrade, Ivan Cuk, Thomas Rosemann and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. A Systematic Review of the Factors Associated with Performance in Non-Elite Runners.Journal of functional morphology and kinesiology · 2026
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Beat KnechtleMedbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001, St. Gallen, Switzerland. beat.knechtle@hispeed.ch.ORCID http://orcid.org/0000-0002-2412-9103
David ValeroUltra Sports Science Foundation, Pierre-Benite, France.ORCID http://orcid.org/0000-0003-4133-4843
Elias VilligerInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-8371-1390
Katja WeissInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-1247-6754
Pantelis T NikolaidisSchool of Health and Caring Sciences, University of West Attica, Athens, Greece.ORCID http://orcid.org/0000-0001-8030-7122
Lorin BraschlerFaculty of Medicine, University of Bern, Bern, Switzerland.ORCID http://orcid.org/0009-0005-0131-2991
Rodrigo Luiz VanciniMoveAgeLab, Physical Education Sport Center of Federal University of Espirito Santo, Vitoria, ES, Brazil.ORCID http://orcid.org/0000-0003-1981-1092
Marilia Santos AndradeDepartment of Physiology, Federal University of Sao Paulo, São Paulo, Brazil.ORCID http://orcid.org/0000-0002-7004-4565
Ivan CukFaculty of Sport and Physical Education, University of Belgrade, Belgrade, Serbia.ORCID http://orcid.org/0000-0001-7819-4384
Thomas RosemannInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-6436-6306
Mabliny ThuanyFaculty of Sports, University of Porto, Porto, Portugal.ORCID http://orcid.org/0000-0002-6858-1871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants' rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45-49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.

Indexed as

Athletic PerformanceMarathon RunningRunningAdultAthletesFemaleHumansMachine LearningMaleMiddle AgedPhysical EnduranceMachine learningNationalityPerformance analysisUltra-endurance

Identifiers

PMID40157985
PMCPMC11954999

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.