Race Time Predictor
Predict race time with an adjustable Riegel exponent, compare distance scenarios and see how endurance assumptions affect the result.
Endurance predictions inputs
Your race prediction
00:52:07 predicted hours:minutes:seconds
00:05:13 per km · model estimate
Full result table
| Distance (km) | Predicted hh:mm:ss | Decimal minutes | Predicted min/km |
|---|---|---|---|
| 1 | 00:04:32 | 4.539749 | 4.539749 |
| 3 | 00:14:33 | 14.547231 | 4.849077 |
| 5 | 00:25:00 | 25 | 5 |
| 10 | 00:52:07 | 52.123288 | 5.212329 |
| 15 | 01:20:07 | 80.110333 | 5.340689 |
| 21.0975 | 01:55:00 | 115.004983 | 5.451119 |
| 42.195 | 03:59:47 | 239.777515 | 5.682605 |
| 50 | 04:47:02 | 287.038405 | 5.740768 |
| 100 | 09:58:27 | 598.455419 | 5.984554 |
A performance estimate, not a guaranteed finish. Similar fitness, terrain and conditions are assumed. Longer-distance extrapolation, particularly to a marathon, can be optimistic. Scenario spread is method sensitivity, not a confidence interval.
Let’s work it out.
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How to use Race Time Predictor
- Use a completed race distance and time from your current fitness.
- Set the distance you want to explore.
- The main result is rounded to a clock display; pace appears separately.
Worked example
A 25-minute 5 km race with exponent 1.06 predicts 25 × 2^1.06 = 52.123288 minutes for 10 km, about 00:52:07.
Formula, accuracy & limitations
Target time = known time × (target distance / known distance)^exponent. Target pace = target minutes / target kilometres. Sensitivity uses exponent ±0.03, bounded to the supported 1.00–1.25 range.
A performance estimate, not a guaranteed finish. Similar fitness, terrain and conditions are assumed. Longer-distance extrapolation, particularly to a marathon, can be optimistic. Scenario spread is method sensitivity, not a confidence interval.
These outputs use the stated model and your supplied inputs. A mathematically consistent model can still differ from personal measurements. Scenario curves and formula comparisons are not statistical confidence intervals.
Read the research and method reference ↗Start with a recent representative race
Use a completed race distance and time from your current fitness. Enter decimal minutes, including seconds divided by 60. An old personal best, a downhill course or an intentionally easy run can produce a misleading anchor even if the arithmetic is correct.
Select the new distance and exponent
Set the distance you want to explore. The exponent controls how rapidly pace slows with distance. The default is a common modelling assumption, not your measured endurance coefficient. Compare nearby exponents before relying on a distant extrapolation.
Read time, pace and sensitivity together
The main result is rounded to a clock display; pace appears separately. The curve includes several distances from the same model. A marathon prediction from a short race can be too fast if endurance preparation does not match, so use it as a scenario rather than a pacing guarantee.
Frequently asked questions
Is this the same as a pace calculator?
A pace calculator keeps a chosen pace constant. This predictor changes pace across distances according to an endurance exponent.
Why is the marathon estimate uncertain?
Research on recreational runners found Riegel predictions could underestimate marathon finish times. Training volume, endurance preparation and conditions are not represented by one short race.
What if target and known distance are identical?
The distance ratio is one, so the result equals the entered race time for every supported exponent.
Are the faster and slower scenarios confidence limits?
They show different exponent assumptions. No statistical probability is attached to them.
