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Prediction Equations for Marathon Performance: A Systematic Review.

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    No single marathon performance prediction equation is universally accurate. A systematic review found 114 equations, but variability means runners should be cautious when using any single prediction model.

    Keywords:
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    Area of Science:

    • Sports Science
    • Exercise Physiology
    • Running Performance Analysis

    Background:

    • Extensive literature exists on marathon running performance prediction.
    • Existing prediction equations vary in accuracy and utility for different runners.
    • A clear understanding of the strengths and weaknesses of current equations is lacking.

    Purpose of the Study:

    • To systematically review and collate all available marathon performance prediction equations.
    • To characterize, compare, and contrast these equations.
    • To assess the overall landscape of marathon performance prediction models.

    Main Methods:

    • A systematic review methodology was employed.
    • Identified observational research studies containing marathon performance prediction algorithms.
    • Analyzed identified studies for relevant data and equation characteristics.

    Main Results:

    • A total of 36 studies yielded 114 distinct prediction equations.
    • Equations were categorized based on input variables: 61 used training/anthropometric data, 53 used laboratory/equipment data.
    • Reported accuracy metrics varied, with R-squared values ranging from .10 to .99 for 68 equations and Standard Error of Estimate (SEE) from 0.27 to 27.4 minutes for 19 equations.

    Conclusions:

    • The heterogeneity of data prevents identifying a single optimal prediction equation.
    • Key performance-influencing factors like course gradient, sex, and weather were often omitted.
    • Many widely used equations lack essential reporting metrics (e.g., R-squared).
    • Runners are advised against relying solely on one prediction equation for their marathon performance.