From 5K to 10K
- Input
- T1 = 20:00 (5 km) · D2 = 10 km
- Expected output
- T2 = 20:00 x (10/5)^1,06 ≈ 41:42
1:42 more than a simple linear doubling (40:00).
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riegel formula race time prediction
Published by Peter Riegel in 1977, it remains the most widely used reference formula for race prediction calculators today, nearly 50 years later.
1:42 more than a simple linear doubling (40:00).
The margin of error tends to grow the bigger the gap between distances.
It is the time needed to cover one unit of distance, usually expressed in minutes and seconds per kilometer (e.g. 5:30/km) or per mile. It is the inverse of speed: the lower the pace, the faster the runner is moving.
Accuracy is better between relatively close distances (for example, 10K to half marathon) than between very distant ones (for example, 5K to marathon), where endurance and fueling factors matter far more than the model captures.
Peter Riegel was an American engineer and runner who published the relationship between time and distance in endurance events in 1977, in a paper that became the basis for most race prediction calculators still used today.
It tends to overestimate performance, meaning it predicts a faster time than the athlete can actually sustain, when the target distance is much longer than the reference. That is why marathon predictions from a 5K result, or ultramarathon predictions from any shorter distance, tend to be too optimistic.
Yes. Other popular models include coach Jack Daniels' VDOT tables and the McMillan Running prediction calculators, all pursuing the same time-distance relationship, with small calibration differences from Riegel's exponent.
| Split | Cumulative time |
|---|---|
| 1 | 4:00 |
| 2 | 8:00 |
| 3 | 12:00 |
| 4 | 16:00 |
| 5 | 20:00 |
Estimate from the Riegel formula (T2 = T1 x (D2/D1)^1.06), which models the performance drop-off at longer distances. It is a statistical prediction, not a guarantee, factors like specific training, weather and course profile change the real result.
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