Race Time Predictor
This predictor estimates finish time for a target race distance using multiple established methods. Choose the method that best matches your data: simple performance scaling (power‑law) or physiology‑based scaling (VO2).
Predictions include configurable parameters for exponent and environmental/condition factors. Use recent validated performance inputs and review the uncertainty guidance before planning race goals.
Uses the established power‑law scaling (T2 = T1 * (D2/D1)^k) with a configurable exponent. Recommended when you have one recent race or time trial.
Inputs
Advanced inputs
VO2 inputs
Results
Predicted finish time (minutes)
50.0167
Predicted finish time (seconds)
3,001
| Output | Value | Unit |
|---|---|---|
| Predicted finish time (minutes) | 50.0167 | minutes |
| Predicted finish time (seconds) | 3,001 | seconds |
Visualization
Methodology
The tool implements three approaches: a default power‑law (Riegel-style) scaling, a custom power‑law that accepts a user exponent, and a VO2‑based approximation that converts aerobic capacity to sustainable speed using widely used metabolic approximations.
Inputs are validated to sensible ranges and the tool surfaces key assumptions: single-performance scaling assumes consistent training and fatigue status; VO2‑based estimates require a measured or well‑estimated VO2 value.
For numeric robustness and reproducibility this implementation follows general best practices for numerical calculation and validation as recommended by standards bodies for measurement and computation (see citations).
Worked examples
Example 1: Recent 5 km in 25 minutes, target 10 km, exponent 1.06 gives predicted time = 25 × (10 ÷ 5)^(1.06), about 52.6 minutes. Adjust with the condition factor if needed.
Example 2: VO2 method with VO2 = 50 ml·kg⁻¹·min⁻¹ for a 10 km. Speed ≈ (50 − 3.5) ÷ 12 ≈ 3.88 m/s; predicted time ≈ 10,000 metres ÷ 3.88 m/s ≈ 43.0 minutes.
Key takeaways
Use power‑law when you have a reliable recent performance and want a quick equivalency. Use VO2‑based predictions when you have a measured VO2 and prefer a physiological scaling.
All predictions are estimates with uncertainty. Adjust exponent and condition factor based on coaching input and multiple performances when available.
Further resources
Expert Q&A
How accurate are these predictions?
Accuracy depends on input quality, how well the athlete maintains fitness between performances, and environmental differences. Typical single‑performance prediction errors vary widely; use multiple data points or physiological measurements to reduce uncertainty. See the methodology section for error sources and recommended calibration steps.
What is the exponent (k) and how should I choose it?
The exponent controls how pace degrades with distance. The default (1.06) is a commonly used central value. Faster athletes or those specializing in longer distances may use a slightly lower exponent; shorter-distance specialists may use a slightly higher exponent. Prefer exponents derived from multiple recent performances when possible.
Can I use treadmill VO2 measurements?
Yes. VO2 values measured in laboratory or field testing can be used with the VO2 method. Ensure measurements are representative of running economy and that you apply the same metabolic model assumptions. See citations for recommended measurement and reporting standards.
How should I adjust for heat, hills, or race day conditions?
Apply the condition factor input to scale predicted time (values above 1 slow you down, below 1 indicate faster conditions). This is a heuristic; for critical planning use course‑specific data or multiple historical performances.
What are the limitations and recommended verification steps?
Limitations include single‑performance variability, pacing strategy differences, and physiological changes. Verify by comparing predictions against additional recent performances or lab tests. For formal use (research, coaching plans), document inputs and validate predictions against held‑out races.
Sources & citations
- NIST — Guidelines for computational numerical stability and measurement — https://www.nist.gov
- ISO — Standards on measurement accuracy and uncertainty — https://www.iso.org
- IEEE — Best practices for numeric and algorithmic reproducibility — https://www.ieee.org
- OSHA — Guidance on workplace exposure and safety for exercise testing settings — https://www.osha.gov