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Calculator · Prediction

Race Time Predictor

Give the calculator one honest recent race result and it estimates your finish time at every other standard distance, using Peter Riegel’s endurance model.

Written by Michael · Reviewed August 2026 · Formula published in the methodology

Your recent race

Predicted times

Half marathon to full marathon

Marathon estimate

The model behind the numbers

Peter Riegel was an engineer and a course measurer, and in 1977 he published a relationship that has outlived most of the sports science of its era because it is simple and it holds up. His observation was that when you plot finish time against distance on logarithmic axes for a single runner, you get something very close to a straight line.

T₂ = T₁ × (D₂ ÷ D₁)^k

T₁ = time you actually ran
D₁ = distance you actually ran
D₂ = distance you want predicted
k = fatigue exponent, 1.06 by default

The exponent is the whole model. If k were exactly 1.0, doubling the distance would exactly double the time and pace would never decay. Riegel found 1.06 fit his data across a wide range of runners, which means each doubling of distance costs about four percent of pace. That is why a 20-minute 5K predicts roughly 41:40 for 10K rather than 40:00.

A worked example

Say you race 10K in 44:30 — 2,670 seconds. To predict the half marathon: D₂ ÷ D₁ is 21.0975 ÷ 10, or 2.10975. Raise that to the power 1.06 and you get 2.2064. Multiply by 2,670 and the prediction is 5,891 seconds, or 1:38:11.

Run the same input out to the marathon and the ratio is 4.2195, raised to 1.06 gives 4.6008, and the prediction is 3:24:43. Hold that figure lightly. It is the time your aerobic engine could support if fuelling, pacing, weather and long-run preparation were all perfect. In practice, most runners predicting a first marathon from a 10K should add somewhere between five and fifteen minutes.

Choosing your exponent honestly

The default of 1.06 describes an average runner. You are probably not average in this specific respect, and you can find out which way you lean by testing the model against two races you have already run.

Self-calibration

Take two genuine efforts at different distances. Solve for your own exponent with k = ln(T₂ ÷ T₁) ÷ ln(D₂ ÷ D₁). If your 5K and half marathon give you 1.03, you are an endurance-biased runner and the standard model has been underselling your long races. If you get 1.09, your speed outruns your stamina and your training probably needs volume rather than intervals.

This is more useful than any generic prediction, because it turns the calculator from a fortune-teller into a diagnostic. The exponent is a measurable property of your current training, and it moves when your training moves.

Half to full: two models that disagree

The half marathon is the most common launching point for a marathon prediction, and it is where the gap between models is most visible. Riegel gives a multiplier of 2.085 — two raised to the power 1.06. The coaching convention is to double the half and add ten to twenty minutes.

For a 1:45 half, Riegel says 3:38:56 and the coaching rules say 3:40 to 3:50. The spread is not noise; it is a disagreement about what limits you. Riegel is modelling aerobic decay and assumes you are trained for the distance. The coaching cushion is pricing in the things Riegel omits: glycogen depletion, the musculoskeletal cost of an extra ninety minutes on your feet, and the fact that most people preparing for a first marathon have not run one.

Use Riegel if you are running high volume with long runs beyond 30 kilometres. Use double-plus-twenty if this is your first marathon or your weekly mileage is modest. The honest answer for most amateurs sits closer to the second.

Where the model fails

Riegel’s relationship assumes the same runner, similarly trained for both distances, on comparable courses in comparable conditions. Break any of those assumptions and the prediction drifts.

Alternative models exist. The Cameron formula adjusts more conservatively at marathon distance, and Jack Daniels’ VDOT tables approach the problem from oxygen cost rather than pure regression. All three agree closely in the 5K to half marathon range and diverge at the extremes, which tells you something honest about how much certainty is available.

Turning a prediction into a plan

A predicted time is only useful if it changes what you do on Tuesday. Feed the result into the training pace calculator to get the specific paces your intervals, tempo runs and easy days should sit at, then use the pace calculator to build the split sheet for race day.

Common questions

How accurate is a race time prediction?

For a well-trained runner predicting across a moderate distance jump, within one to two percent is typical. Predicting a marathon from a 5K is a different matter: the model routinely produces times that only runners with substantial long-run volume can actually hold, because it has no way of knowing whether you have built the aerobic base to support the distance.

Why does the marathon prediction look too fast?

Because the marathon is the one distance where fuel, not fitness, usually decides the outcome. Riegel's formula extrapolates cardiovascular capacity. It knows nothing about glycogen depletion at 32 kilometres, and that is precisely where most marathons are lost.

What is the fatigue exponent actually doing?

It sets how steeply your pace decays as distance grows. An exponent of 1.0 would mean you hold the same pace forever. At 1.06 a doubling of distance costs you roughly four percent of pace. Runners with high weekly volume decay more slowly and are better modelled at 1.04 or 1.05.

Should I use my best-ever race or my most recent one?

Most recent, provided it was a genuine effort within the last six to eight weeks. A personal best from three years ago predicts the fitness you had three years ago.