Predict running times at any distance using the Riegel formula. Based on one known performance, estimate times for other race distances.
Last updated: March 2026 | By Patchworkr Team
| Known Performance | 5K Prediction | 10K Prediction | Marathon Prediction |
|---|---|---|---|
| 1 Mile: 5:30 | ~17:50 | ~37:10 | ~2:12:00 |
| 5K: 20:00 | ~20:00 | ~41:30 | ~2:45:00 |
| 10K: 42:00 | N/A | ~42:00 | ~3:01:00 |
| Half Marathon: 1:50:00 | N/A | N/A | ~4:10:00 |
💡 Pro Tip: Riegel is most accurate 3-4 distance categories apart. Predicting marathon from 5K is more accurate than from 1 mile. Account for ±3-8% variance based on training status, course, and weather conditions.
The Riegel formula is an empirical method for predicting running performance across different distances. Developed by Jack Riegel in the 1980s through analysis of thousands of race performances, it estimates how fast a runner can complete a race of distance D₂ based on their known performance over distance D₁.
The formula accounts for the fact that fatigue increases exponentially with distance. A runner who completes 5K in 20 minutes will likely be slower per kilometer in a marathon because endurance capacity differs from speed. The exponent of 1.06 was derived empirically and has proven remarkably accurate for distances ranging from 1 mile to 50+ miles.
The Riegel formula is most accurate within 3-4 distance categories and less reliable when extrapolating from very short distances (sprints) to very long ones (ultras). It assumes equivalent training and effort levels across all predicted races, which can vary in real life.
A runner just completed 5K in 22:30. What's their marathon time?
This prediction assumes equivalent training and effort levels
No formula is perfect. Riegel predictions are typically within 3-8% for most runners. Accuracy depends on recent training, course difficulty, weather, and the runner's experience at the target distance.
Jack Riegel analyzed thousands of race results and found that 1.06 empirically best fit the relationship between distances and performance times. This accounts for increasing fatigue effects over longer distances.
Yes, but predictions can be inaccurate. If you ran 5K slowly due to injury or poor conditions, predictions will be pessimistic. Use a representative recent race for best results.
Generally, predict from shorter distances if possible. Speed is more consistent short-term. Predicting marathons from 5K gives better estimates than predicting 5K from marathons.
The formula assumes fitness remains constant. If you're improving rapidly, predictions may be conservative. If you're fatiguing or trained at different intensity levels, accuracy decreases.
Track racing follows standard terrain. Predictions for road and trails are similar. Ultra-trail running (mountains, primitive terrain) has higher variability—predictions are less accurate.
The formula assumes normal distance-related performance curves. Some runners are exceptional at sprints or ultras. If you specialize, predictions may not apply to distant races.
Use your actual recent best effort at the known distance. The formula predicts based on demonstrated capability, not potential. Gaming the input won't improve prediction accuracy.
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