Race Time Predictor
Predict your finish time at another race distance from a result you’ve already run, using Riegel’s formula.
Your Known Race Result
Target Race Distance
Full Breakdown
Predicted Times at Standard Race Distances
Race Time Predictor
Our Race Time Predictor estimates your finish time at a different race distance based on a result you’ve already run, using Riegel’s formula — a widely used race prediction model among runners and coaches. Enter a recent race distance and time, choose a target distance, and see a predicted finish time and pace, along with predictions for every standard race distance from 1 mile to the marathon.
How Race Time Prediction Works
Race time prediction uses your performance at one distance to estimate what you’re capable of at another, based on the well-documented pattern that runners slow down predictably as distance increases. This calculator uses Riegel’s formula, published by American research engineer and marathoner Peter Riegel in Runner’s World in 1977 and refined in 1981:
T₂ = T₁ × (D₂ / D₁)1.06
Where T₁ is your known finish time, D₁ is the distance you ran it over, D₂ is your target distance, and T₂ is the predicted finish time. The exponent 1.06 represents a “fatigue factor”: because it’s greater than 1, your average pace is predicted to slow gradually as distance increases, rather than staying constant. For example, doubling the distance increases predicted time by a factor of about 2.085, not exactly 2.
How Accurate Is Riegel’s Formula?
Riegel’s formula was developed from race and time-trial data across running, swimming and cycling, and remains a widely used race prediction method nearly 50 years after its publication. It works best when the input and target distances are reasonably close together (for example, predicting a 10K from a 5K, or a half marathon from a 10K), and when your training is broadly appropriate for the target distance. It tends to become less reliable for very large distance jumps — for example, predicting a marathon from a 1-mile time — because it can’t account for factors like fuelling strategy, pacing discipline and glycogen management that matter much more over longer distances. Riegel’s own analysis covered race durations of roughly 3.5 minutes to 3 hours 50 minutes; predictions based on times outside that range should be treated with more caution.
Other prediction methods exist, including the Cameron formula (which varies the exponent by distance pair) and VDOT-based methods developed by exercise physiologist Jack Daniels. These can give noticeably different answers, particularly for large distance jumps, which is a useful reminder that any single-formula prediction is an estimate rather than a guarantee.
Standard Race Distances
| Race | Kilometres | Miles |
|---|---|---|
| 1 Mile | 1.609 km | 1.000 mi |
| 5K | 5.000 km | 3.107 mi |
| 5 Mile | 8.047 km | 5.000 mi |
| 10K | 10.000 km | 6.214 mi |
| 15K | 15.000 km | 9.321 mi |
| 10 Mile | 16.093 km | 10.000 mi |
| Half Marathon | 21.098 km | 13.109 mi |
| Marathon | 42.195 km | 26.219 mi |
Using Your Prediction as a Training Goal
A Riegel prediction is a reasonable starting point for setting a goal time, but it works best alongside — not instead of — your own training history and race experience. If you’re stepping up to a much longer distance for the first time, treat the prediction as optimistic unless you’ve done the specific endurance training (long runs, fuelling practice, pacing strategy) that longer races demand. If you’re predicting a shorter distance from a longer one, the prediction can also undersell you, since shorter races reward a different kind of speed and lactate tolerance that a marathon training block doesn’t always build.
Frequently Asked Questions
How does a race time predictor work?
It uses a known race result (a distance and finish time you’ve already achieved) and applies a mathematical formula to estimate your finish time at a different distance. This calculator uses Riegel’s formula, T₂ = T₁ × (D₂/D₁)^1.06, which accounts for the fact that runners’ average pace slows predictably as race distance increases.
How accurate are race time predictions?
Riegel’s formula is a well-established mathematical estimate, not a guarantee. It tends to be most reliable when the target distance is reasonably close to your known result and your training is appropriate for the new distance. It becomes less reliable for very large distance jumps, such as predicting a marathon time from a 1-mile result, since it can’t account for factors like fuelling, pacing and glycogen management that matter more over longer distances.
Can I predict a marathon time from a 5K result?
Yes, this calculator can calculate it, but treat the result with more caution than a prediction between closer distances (like 5K to 10K). Marathon performance depends heavily on endurance-specific training, fuelling strategy and pacing discipline that a 5K result can’t fully capture, so a marathon prediction from a 5K is best treated as a rough starting point rather than a firm goal time.
What is the exponent 1.06 in Riegel’s formula?
It’s a “fatigue factor” that Peter Riegel fitted to observed race data. Because it’s greater than 1, the formula predicts that average pace slows gradually as distance increases, rather than staying constant. In practical terms, doubling the race distance increases predicted time by a factor of about 2.085, slightly more than exactly double.
Does Riegel’s formula work for all runners equally well?
Not perfectly. It’s a population-average model, so individual runners can deviate from it based on their specific strengths, muscle fibre composition and training background. Runners who fade more than average over long distances may find the formula overestimates their longer-distance performance, while well-trained endurance specialists sometimes outperform their Riegel prediction.
Should I use my most recent race result or my best-ever time?
Your most recent result is generally more useful, since it reflects your current fitness rather than a peak from months or years ago. Race time predictions assume the input performance reflects your present training state, so an old personal best can produce an overly optimistic prediction if your fitness has changed since then.
Why do different race predictors give different answers?
Different predictors use different formulas. Riegel’s formula uses a fixed 1.06 exponent, while alternatives like the Cameron formula vary the exponent by distance pair, and VDOT-based methods (developed by Jack Daniels) model the physiology differently again. These methods can diverge by several minutes for longer distances, which is a useful reminder that any single prediction is an estimate rather than a precise answer.