What this calculates
The Riegel finish time predictor answers the question every runner asks before signing up for a new distance: "Based on my current fitness, what could I run?" It uses a mathematical model of how race performance degrades as distance increases.
The formula, published by Robert Riegel in 1981 after analysis of world record progressions, is: T₂ = T₁ × (D₂/D₁)^1.06. Here, T₁ is your known time over distance D₁, and T₂ is the predicted time for distance D₂. The exponent 1.06 is the key — it's greater than 1, which means doubling the distance costs more than doubling the time. A runner who can run 10K in 45 minutes doesn't simply run a marathon in 90 × 4.2195 = 315 minutes. The Riegel formula predicts 3:20:18, accounting for the additional physiological toll.
This calculator lets you enter any known race time and distance, and it predicts times for the four standard road racing distances: 5K, 10K, half marathon, and marathon. It also calculates the required pace for each predicted time.
Alternative prediction formulas exist — Cameron (1998), Vickers-Vertosick (2016), Purdy — and they differ in how they model fatigue. This calculator uses Riegel as it's the most widely used and well-documented. For a comparison of all four formulas, see the guide on race prediction formulas.
How to use this calculator
Enter your recent race time in the format H:MM:SS (for times over an hour) or MM:SS. Enter the distance of that race in kilometres. Standard distances: 5K = 5, 10K = 10, half marathon = 21.0975, marathon = 42.195.
For the most accurate predictions, use a race run at full effort on a flat course in good conditions. Time trials on a measured route work well too. Avoid using training run times — they're usually run at sub-maximal effort and will produce optimistic predictions.
The calculator shows predicted times and required paces for 5K, 10K, half marathon, and marathon. If your input distance is the same as a predicted distance (e.g., you enter 10K), that row shows your actual time.
Methodology
Uses Riegel's 1981 formula: T₂ = T₁ × (D₂/D₁)^1.06. The 1.06 exponent was derived by Riegel from analysis of world record progressions across distances from 1 mile to 100 miles. The formula assumes a well-trained runner with consistent performance across distances. It underestimates times for runners who are poorly trained in endurance (fast over short distances, fall apart over long ones) and overestimates for runners with exceptional endurance relative to speed.
Full methodology and formula sources →
Frequently asked questions
How accurate is the Riegel formula?
For trained runners who have raced recent effort at both distances, the formula is typically accurate to ±5% for adjacent standard distances (e.g., 10K to half marathon). Accuracy decreases when predicting across larger distance gaps — marathon from 5K, for example — or when the runner is poorly matched for one discipline.
Why does the formula use the exponent 1.06?
Riegel fitted the exponent to world record data across distances from 1 mile to 100 miles. The exponent greater than 1 captures the fact that running a race twice as long is harder than twice the work — fatigue, fuelling, and physiological limits compound at longer distances. Different populations fit slightly different exponents; 1.06 is the best-known and most cited.
Can I use a training run time instead of a race time?
You can, but expect the predictions to be optimistic. Training runs are typically run at sub-maximal effort, even 'hard' training runs. Use a parkrun, a race, or a timed all-out effort on a measured flat course for reliable predictions.
The formula is predicting a time faster than I've ever run. Why?
This happens when your base race was run at a short distance where you have disproportionate speed (e.g., a fast 1K time trial). The formula assumes performance scales predictably. If you're a sprinter with limited endurance training, the Riegel prediction for a marathon will be unrealistically fast.
What other prediction formulas exist?
Cameron (1998), Vickers-Vertosick (2016), and Purdy all offer alternative approaches. Cameron modifies the Riegel exponent for specific distance pairs. The Vickers-Vertosick study used data from 2.5 million race results and found slightly different coefficients. See the guide comparing all four formulas.