Race Time Predictor
Enter one race result and predict your finish time at any other distance. Uses three prediction formulas with confidence ratings for every prediction.
Enter Your Race Result
Select a race distance and enter your finish time
Predicted Times for All Distances
Select a race distance and enter your time above to see predictions
Prediction Confidence Guide
High Confidence
Target distance is 1x to 4x your input distance. Predictions are typically accurate within 1 to 3 percent for trained runners. Example: predicting 10K from a 5K result.
Medium Confidence
Target distance is 4x to 8x your input distance. Predictions may be off by 3 to 8 percent. Training specificity matters more. Example: predicting a marathon from a 5K result.
Low Confidence
Target distance is more than 8x your input distance, or you are predicting a much shorter race from a longer one. Use these as rough estimates only. Your actual time will depend heavily on specific training.
Factors That Affect Prediction Accuracy
Training Volume
Runners with higher weekly mileage tend to outperform predictions at longer distances. If you run 20 miles per week, your marathon prediction from a 5K will likely be too optimistic. If you run 50+ miles per week, you may beat the prediction.
Course Terrain
Predictions assume a flat course with ideal conditions. A hilly race or trail race will be slower than predicted. Conversely, a downhill-net course (like the Boston Marathon) may produce faster results than predicted, though not always due to the quad-punishing descents.
Weather Conditions
Heat and humidity degrade performance significantly at longer distances. A 5K is short enough that heat has minimal impact, but a marathon in 80F weather could add 10+ minutes to your time. Wind, rain, and altitude also affect results.
Race Experience
First-time racers at a new distance often underperform predictions because they lack pacing experience and mental familiarity with the distance. After completing the distance once, subsequent attempts are typically much closer to predicted times.
How Race Prediction Formulas Work
The Riegel formula, published by Peter Riegel in 1977, is the most widely used race prediction model. The formula is T2 = T1 x (D2/D1)^1.06, where T1 is your known time, D1 is the known distance, D2 is the target distance, and 1.06 is the fatigue exponent. This exponent was derived from analysis of world records across distances and represents the rate at which performance declines as distance increases.
The Cameron formula, developed by Dave Cameron, uses a more complex approach with empirically derived coefficients for each distance. Instead of a single exponent, Cameron calculated specific conversion factors from large datasets of race results. This can produce slightly different predictions, especially at the extremes (very short or very long distances).
The adjusted prediction shown on this page is the average of both formulas. This averaging approach tends to smooth out the biases of each individual formula. Neither formula accounts for training specificity, weather, terrain, or individual physiology, which is why the confidence ratings are important context for interpreting your results.
Predict Your Time, Then Prove It on the Map
Your predicted race time tells you what is possible. Motera gives you a reason to go prove it on every training run. The faster you build fitness, the more territory you can claim before anyone else does.
MoteraWorked Example: The Math, Step by Step
This is an illustrative example so you can see exactly how the Riegel formula turns one result into a prediction. Say a runner posts a 22:30 5K (1,350 seconds over 5,000 meters). Here is how that becomes a 10K and half marathon estimate.
Start with the formula
T2 = T1 x (D2/D1)^1.06. T1 is the known time in seconds, D1 is the known distance in meters, D2 is the target distance in meters.
Plug in the 5K to 10K prediction
T2 = 1,350 x (10,000/5,000)^1.06 = 1,350 x 2^1.06 = 1,350 x 2.085 = 2,815 seconds, which is 46:55. That is the pace-decline-adjusted 10K time, not simply double the 5K.
Plug in the 5K to half marathon prediction
T2 = 1,350 x (21,097.5/5,000)^1.06 = 1,350 x 4.2195^1.06 = 1,350 x 4.622 = 6,240 seconds, which is 1:44:00. Notice the distance ratio is 4.22x but the time ratio is 4.62x, because the 1.06 exponent adds extra time for the additional fatigue.
Read the confidence rating alongside the number
The 10K prediction (2x the input distance) carries High confidence. The half marathon prediction (4.2x the input distance) sits at the edge of High confidence, so treat it as a target range rather than a guarantee.
Where These Formulas Come From
Both prediction models used on this page trace back to published research on endurance performance, not internal guesswork. Here is the sourcing behind each one.
Riegel Formula, 1977
Engineer and marathoner Pete Riegel proposed T2 = T1 x (D2/D1)^1.06 in Runner's World, then formalized it in a 1981 American Scientist paper covering endurance activities across running, swimming, and cycling.
View the sourceCameron Formula
Dave Cameron built a non-linear regression model from world-level performances spanning 400m to 50 miles, producing a distance-specific fatigue coefficient rather than a single fixed exponent.
View the source"More than two-fifths [of marathon runners] experience severe and performance-limiting depletion of physiologic carbohydrate reserves, a phenomenon known as hitting the wall."
Benjamin Rapoport, PLOS Computational Biology, 2010This is exactly why the further a target distance sits from your input race, the more the prediction assumes an endurance base and fueling strategy the formula cannot see.
Watch: How Race Predictor Algorithms Actually Work
A coaching walkthrough of how race calculators and predictor algorithms, including Riegel-style models and VDOT-based tools, arrive at their numbers and where they tend to go wrong.
When to Trust a Race Prediction, and When Not To
Trust the number when
- Your input race was a genuine all-out effort on a fair, standard course within the last 6 to 8 weeks.
- The target distance is within 2x to 4x of your input distance, where confidence is highest.
- Your training already includes long-run mileage appropriate for the target distance, not just speed work.
- You are using it to set a starting target for a training block, not a race-day guarantee.
Treat it as a rough guess when
- You are jumping more than 6x in distance, such as predicting a marathon from a mile time trial.
- Your input race was hilly, hot, or run conservatively rather than as a true time trial.
- You have not run the target distance's long runs in training, so the endurance assumption behind the formula does not hold.
- Race day conditions differ sharply from your input race, especially heat, humidity, or elevation gain.
How This Differs From Motera's Other Race Tools
Motera has several race-math tools that sound similar but answer different questions. This one predicts a finish time for any distance from a single result. Here is how it compares to the others so you land on the right one.
| Tool | What it answers | Best for |
|---|---|---|
| Race Time Predictor (this page) | What is my finish time at any distance, from 1 mile to 50K, based on one result? | Runners who want a general cross-distance forecast, not just the marathon. |
| Marathon Predictor | What is my marathon time specifically, using Riegel, Cameron, and Tanda models tuned for 26.2 miles? | Runners whose only goal is the marathon number. |
| Race Equivalency Calculator | What performance level does my result represent at another distance, including an age-graded comparison? | Comparing effort quality across distances or age groups, not predicting a future race. |
| Race Pace Calculator | Given a known distance and time, what pace per mile or km does that require? | Converting a time goal into a per-mile pace target, no fatigue modeling involved. |
In one sentence: the Race Time Predictor forecasts a finish time at any distance from one input, the Marathon Predictor narrows that same math to the marathon specifically, and the Race Equivalency Calculator compares performance quality across distances rather than forecasting a future result.
Turning a Prediction Into a Training Plan
A predicted time is only useful if it changes what you do in training. Here is how to put the number to work instead of just staring at it.
Set training paces off the prediction, not your input race
Your predicted time at the target distance, not your current 5K or 10K pace, should anchor your interval, tempo, and long-run paces for that specific race. A VDOT-style training pace calculator can convert the prediction into daily paces.
Re-run the prediction every 4 to 6 weeks
As fitness changes, so does the prediction. Re-testing with a fresh time trial or tune-up race keeps your training paces honest instead of locked to a number from two months ago.
Treat Low confidence predictions as a training target, not a race target
If you are predicting a marathon from a 5K, use the number to set a training block goal (build the long runs, hit the weekly mileage) rather than writing it on your race bib.
Log the actual result and compare
After the target race, compare your actual time to the prediction. The gap tells you whether your training matched the distance, which is more useful feedback than the prediction itself.
Frequently Asked Questions
How do race time prediction formulas work?
Race prediction formulas use the mathematical relationship between distance and fatigue. The Riegel formula (T2 = T1 x (D2/D1)^1.06) assumes that for every doubling of distance, you slow down by about 6 percent. The Cameron formula uses empirically derived tables from real race data. Both assume you are equally well-trained for both distances, which is the main source of error.
Which race prediction formula is most accurate?
No single formula is universally most accurate. The Riegel formula works well for distances between 5K and the marathon for trained runners. The Cameron formula tends to be slightly more conservative for longer distances. Averaging both formulas usually gives the most realistic prediction. The key factor is not which formula you use, but whether your training matches the target distance.
Can I predict a marathon time from a 5K?
You can, but the prediction will be less reliable than predicting from a half marathon. The further apart the distances, the more assumptions the formula makes about your endurance. A 5K to marathon prediction assumes you have the aerobic base and long-run training for 26.2 miles. Many runners who are fast at 5K do not have the marathon-specific endurance, so the prediction is often optimistic.
Why are my race predictions different from my actual times?
Several factors cause predictions to differ from reality. Training specificity is the biggest one: a runner who trains mostly short intervals will outperform predictions at shorter distances and underperform at longer ones. Weather, course terrain, elevation, race experience, fueling strategy, and mental toughness all affect the outcome but are not accounted for in the formulas.
What is the Riegel exponent and why does it matter?
The Riegel exponent (1.06) represents the rate at which performance declines as distance increases. A higher exponent means more slowdown per unit of distance. The standard value of 1.06 was derived from world-class performance data. Some researchers suggest using 1.07 for recreational runners, since less-trained athletes fatigue more quickly at longer distances.
How recent should my input race be?
For the most accurate predictions, your input race should be from the last 6 to 8 weeks. Older results may not reflect your current fitness. The race should also be a genuine all-out effort on a standard course with fair weather. A time trial or a very hilly race will produce less reliable predictions.
Are predictions more accurate for shorter or longer distances?
Predictions are most accurate when the input and target distances are within a 2x to 4x ratio. Predicting a 10K from a 5K (2x) is more reliable than predicting a marathon from a 5K (8.4x). Predictions get progressively less reliable as the distance ratio increases. For best results, use the closest race distance you have as your input.
What is the difference between this tool and a race pace calculator?
A race pace calculator converts a known distance and time into a pace per mile or kilometer. A race time predictor takes a known race performance and uses formulas to estimate what you could achieve at a different distance. The predictor accounts for the natural fatigue that occurs as distance increases, while a pace calculator simply divides.
