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Updated: Nov 30, 2025

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Predictive Performance Models in Long-Distance Runners: A Narrative Review.

José Ramón Alvero-Cruz1, Elvis A Carnero2, Manuel Avelino Giráldez García3

  • 1Faculty of Medicine, University of Málaga, Andalucía TECH, 29071 Málaga, Spain.

International Journal of Environmental Research and Public Health
|November 13, 2020
PubMed
Summary

This review synthesizes mathematical models for predicting long-distance running performance, highlighting key physiological and anthropometric predictors. It identifies gaps in field test-based predictions and emphasizes the importance of aerobic metabolism and training variables for race outcomes.

Keywords:
long-distance runnersperformanceprediction equations

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Area of Science:

  • Exercise Physiology and Sports Science
  • Biomechanical Performance Analysis
  • Mathematical Modeling in Sports

Background:

  • Long-distance running performance is multifactorial, influenced by physiological markers like maximal oxygen uptake (VO2max) and running economy (RE).
  • Accurate prediction of race times in events from 5000m to marathons is crucial for training optimization and performance enhancement.
  • Existing literature presents various mathematical models, but a comprehensive review is needed to consolidate predictor variables across different long-distance events.

Purpose of the Study:

  • To systematically review and present mathematical models used to estimate performance in 5000m, 10,000m, half-marathon, and marathon events.
  • To identify and categorize the key physiological, anthropometric, and training-related variables associated with long-distance running performance.
  • To highlight the strengths and limitations of current predictive models, particularly concerning field tests versus laboratory assessments.

Main Methods:

  • A systematic literature search was conducted, identifying 88 relevant articles.
  • Inclusion criteria were applied, and 58 studies published from 1983 to the present were included after full-text review.
  • Studies were categorized by race distance (5000m, 10,000m, half-marathon, marathon) and predictor variables (demographic, anthropometric, physiological, training load, field tests).

Main Results:

  • A total of 136 independent variables were analyzed, with aerobic metabolism (43.4%), training load (26.5%), and anthropometric variables (20.6%) being the most considered.
  • For half and full marathons, laboratory-derived variables (VO2max, velocity at maximal oxygen uptake [vVO2max]), training variables (pace, load), and anthropometric data (fat mass, skinfolds) showed the strongest associations.
  • Significant gaps exist in predicting long-distance times using field tests, and physiological assessments are predominantly used for shorter distances (5000m, 10,000m).

Conclusions:

  • Predictive models for half-marathon performance rely heavily on anthropometric variables, though with moderate predictive power.
  • Marathon performance prediction is primarily driven by training-associated variables, physiological evaluations, and anthropometric parameters.
  • Further research is needed to develop robust field-test-based models for predicting long-distance running performance across all event categories.