ArticlePloS one2023
Prediction of performance in a 100-km run from a simple equation.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Systematic Review of the Factors Associated with Performance in Non-Elite Runners.Journal of functional morphology and kinesiology · 2026Review
- Change in elevation predicts 100 km ultra marathon performance.Scientific reports · 2025Article
- Performance prediction equation for the Valencia Marathon based on time and pacing in the half marathon.Frontiers in physiology · 2025Article
- Prediction of half-marathon performance of male recreational marathon runners using nomogram.BMC sports science, medicine & rehabilitation · 2024Article
- Valid and accurate simple equation to predict 3,000-m steeplechase performance.Frontiers in sports and active living · 2024Article
- Win Your Race Goal: A Generalized Approach to Prediction of Running Performance.Sports medicine international open · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to identify predictive variables of performance for a 100-km race (Perf100-km) and develop an equation for predicting this performance using individual data, recent marathon performance (Perfmarathon), and environmental conditions at the start of the 100-km race. All runners who had performed official Perfmarathon and Perf100-km in France, both in 2019, were recruited. For each runner, gender, weight, height, body mass index (BMI), age, the personal marathon record (PRmarathon), date of the Perfmarathon and Perf100-km, and environmental conditions during the 100-km race (i.e., minimal and maximal air temperatures, wind speed, total amount of precipitation, relative humidity and barometric pressure) were collected. Correlations between the data were examined, and prediction equations were then developed using stepwise multiple linear regression analyses. Significant bivariate correlations were found between Perfmarathon (p<0.001, r = 0.838), wind speed (p<0.001, r = -0.545), barometric pressure (p<0.001, r = 0.535), age (p = 0.034, r = 0.246), BMI (p = 0.034, r = 0.245), PRmarathon (p = 0.065, r = 0.204) and Perf100-km in 56 athletes The, 2 prediction equations with larger sample (n = 591) were developed to predict Perf100-km, one including Perfmarathon, wind speed and PRmarathon (model 1, r² = 0.549; standard errors of the estimate, SEE = 13.2%), and the other including only Perfmarathon and PRmarathon (model 2, r² = 0.494; SEE = 14.0%). Perf100-km can be predicted with an acceptable level of accuracy from only recent Perfmarathon and PRmarathon, in amateur athletes who want to perform a 100 km for the first time.
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