Race Finish Time Predictor

Predicted Finish Time

{{ pace_km_str }} /km • {{ pace_mi_str }} /mi

Prev: {{ prev_distance_label }} in {{ prev_time_hms }} Target: {{ target_distance_label }} p = {{ exponent_display }} Adj {{ adjust_percent }} %

Previous race distance:

Use a recent all-out result: 5 km, 10 km, half marathon, marathon, or another preset.

Previous race time:

Enter h/m/s, e.g. 0 h 25 m 00 s for a 25:00 5K.

h m s

Target race distance:

Choose the race you are pacing next; the estimate and splits update together.

Model exponent (p):

Default 1.06; use 0.90-1.20 only when you know your distance profile.

Condition adjustment: {{ adjust_percent }}%

Keep 0% for comparable conditions; try +5% for heat/hills or -3% for a fast course.

Split table unit:

Choose kilometers for 1 km rows or miles for 1 mi rows plus any final partial split.

Metric	Value	Copy
{{ row.label }}	{{ row.value }}

#	Distance	Split	Cumulative	Copy
{{ row.label }}	{{ row.distance }}	{{ row.split }}	{{ row.cumulative }}

Scenario	p	Adj %	Finish	Δ vs input	Copy
{{ row.label }}	{{ row.exponent }}	{{ row.adjust }}	{{ row.finish }} {{ row.pace }}	{{ row.delta }}

Enter a previous effort and a target distance to render a chart.

Enter a previous effort and a target distance to render scenario comparisons.

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Race finish prediction estimates how one verified performance might translate to another event distance. A recent 5 km result can suggest a 10 km goal, and a strong half marathon can help frame a marathon plan, but the conversion has to account for the way pace usually slows as distance grows.

The useful question is not only the central finish time. Runners also need to know the average pace behind that estimate, how much the number changes under heat or hills, and whether the previous race is close enough to the target event to make the comparison fair. A 400 m time and a marathon goal sit so far apart that the estimate carries much more uncertainty than a 10 km to half-marathon comparison.

A curve showing finish time rising as race distance increases, with a prior result and target estimate marked.

A race prediction works best when the reference result was an honest all-out effort from the same general training phase. It becomes weaker when the previous race was a workout split, a tactical race, a course with unusual elevation loss, or an event run in very different weather. Road-course standards also matter because certified distances such as 10 km, half marathon, marathon, and 50 km are measured distances, while GPS watches often record a longer path after turns and crowd weaving.

Finish estimates should be read as planning numbers. They can help set a first target pace, choose a realistic race plan, or compare several goal distances, but they do not predict injuries, fueling mistakes, cramps, wind exposure, tactical decisions, or fitness changes after the reference race. For longer events, especially the marathon, training volume and long-run preparation can matter more than the simple distance relationship suggests.

The safest use is to treat the prediction as a starting point, then check whether the resulting pace matches recent workouts, course conditions, and the amount of specific preparation behind the goal.

Technical Details:

Race-time prediction here is built around a distance-power relationship often associated with Peter Riegel's endurance model. The model assumes that finish time grows with distance at a power slightly above one. If pace stayed perfectly constant, doubling distance would double time. With an exponent greater than one, doubling distance takes more than double the time, which matches the usual slowdown from shorter racing to longer racing.

The exponent is the main endurance-shape value. The default value is 1.06, while the input accepts values from 0.90 to 1.20. Lower values hold speed better as distance grows. Higher values add more slowdown, which can fit runners who are strong at shorter distances but fade when the target race gets longer.

t_{2} = round (t_{1} \times {(\frac{d_{2}}{d_{1}})}^{p} \times (1 + \frac{a}{100}))

The equation computes the predicted target finish in whole seconds. A positive condition adjustment slows the final estimate after the distance calculation has been made, while a negative value speeds it up.

Race prediction formula variables
Symbol	Meaning	Unit or source
`t1`	Previous race finish time	Hours, minutes, and seconds converted to total seconds
`d1`	Previous race distance	Selected preset distance in meters
`d2`	Target race distance	Selected preset distance in meters
`p`	Riegel exponent	Default `1.06`; editable from `0.90` to `1.20`
`a`	Condition adjustment	Percent change from `-15%` to `+30%`
`t2`	Predicted finish time	Whole seconds, also displayed as H:MM:SS or M:SS

Distance choices are stored in meters, including 400 m, 800 m, 1500 m, 1 mile, 3 km, 5 km, 10 km, 15 km, 10 miles, Half Marathon (21.0975 km), Marathon (42.195 km), and 50 km. Because the formula uses a distance ratio, the same previous time can produce very different uncertainty depending on how far apart the two distances are.

After the finish time is rounded, pace and speed are derived from that rounded target result. Pace per kilometer divides the predicted seconds by target kilometers. Pace per mile uses 1609.344 meters per mile. Average speed is the target distance divided by predicted hours, shown in kilometers per hour and miles per hour.

Pacing adjustment map bands
Map band	Boundary	Meaning
`Faster window`	`finish <= 97%` of the no-adjustment baseline	The selected adjustment has moved the estimate clearly faster than the neutral course baseline.
`Baseline window`	`97% < finish < 103%` of the no-adjustment baseline	The finish remains within a narrow range around the neutral baseline.
`Slower window`	`finish >= 103%` of the no-adjustment baseline	The selected adjustment has slowed the estimate enough to change pacing expectations.

The split table is not a fatigue simulation. It takes the predicted average pace and divides the target distance into 1 km or 1 mile rows, then adds a final partial row when the target is not an exact multiple of the selected unit. A half marathon shown in miles therefore has 13 full mile rows plus a short final segment.

The scenario rows compare fixed assumption changes against the current inputs. They include the current input row, a cooler or slightly aided row, humid or hot conditions, a hilly or trail course, and a late-race fade row. Each row changes the exponent, the condition adjustment, or both, then reports the finish, pace, and difference from the current input.

A browser-side calculation handles the prediction, tables, charts, JSON output, and export actions. No tool-specific race-prediction service is defined for these values.

Everyday Use & Decision Guide:

Use the most recent race that reflects current fitness. A chip-timed 10 km from two weeks ago is a better reference than a month-old workout split or a race where you jogged the first half. Keep Model exponent (p) at 1.06 and Condition adjustment at 0% for the first pass so the result starts from a neutral comparison.

Change one assumption at a time after the baseline looks believable. Move the exponent only when your own race history supports it. Use the condition slider for the course or day itself: heat, humidity, hills, altitude, trail footing, or a fast point-to-point course. If the prior race and target event were run in similar weather and terrain, leaving the slider at 0% is usually the clearer choice.

Start with Previous race distance, Previous race time, and Target race distance; the headline Predicted Finish Time should appear as soon as the previous time is greater than zero.
Read Predicted pace before committing to the finish time. A goal that looks good as H:MM:SS may still imply a pace that recent training does not support.
Use Pace Split Table for even-checkpoint planning, not for a race strategy with surges, walk breaks, or planned negative splits.
Check Condition Scenario Table when the target course differs from the reference course. Several minutes of spread is a reason to choose a more conservative goal.
Use Pacing Adjustment Map when you want to see whether the condition slider keeps the finish near baseline or pushes it into the faster or slower band.

A finish prediction does not prove race readiness. If the Model note, scenario spread, or chart bands suggest the estimate is sensitive to assumptions, compare the predicted pace with recent workouts and long-run evidence before using it as a race-day target.

Step-by-Step Guide:

Follow the baseline path first, then use the sensitivity views only if the neutral estimate makes sense.

Choose Previous race distance and enter Previous race time in hours, minutes, and seconds. If the time fields total zero seconds, no prediction is available.
Select Target race distance. The Predicted Finish Time panel should show a finish time plus pace per kilometer and per mile.
Open Prediction Metrics and check Previous pace, Predicted pace, Average speed, Exponent (p), and Condition adjustment together.
Leave Model exponent (p) at 1.06 unless you have enough race history to justify a lower or higher distance-fade value.
Set Condition adjustment only when the target course or weather differs from the reference race. Positive values slow the finish; negative values speed it up.
Choose Split table unit as kilometers or miles, then use Pace Split Table to read split and cumulative checkpoints.
Compare Condition Scenario Table, Split Pace Timeline, Scenario Finish Chart, and Pacing Adjustment Map when you need sensitivity checks before picking a goal pace.
If a field produces a strange result, reset the exponent, return the condition adjustment to 0%, and confirm the previous distance, target distance, and time units before changing anything else.

When comparing several target races, keep the reference race fixed and change only the target distance or one adjustment so the finish differences stay easy to explain.

Interpreting Results:

The headline finish time is the main estimate, but the average pace is the better test of whether the number is usable. If the projected pace is faster than your recent sustained workouts suggest, the finish may be too optimistic even when the formula is behaving correctly.

Use the scenario and map outputs as confidence checks. A small difference between the current input row and the condition rows means the target is not very sensitive to modest assumption changes. A wide difference means your goal depends heavily on the exponent or condition slider, so the middle estimate deserves caution.

Predicted finish is rounded to whole seconds, so one-second differences near a band boundary can change a chart label.
Predicted pace uses the target distance, not the previous race distance.
Condition adjustment changes the entire finish after the distance formula, so it is better for race-day conditions than for general fitness improvement.
Split table unit changes only the checkpoint display. It does not change the finish estimate.
Slower window on the map starts at 103% of the no-adjustment baseline, and Faster window starts at 97% or lower.

Worked Examples:

A 25:00 5 km used for a 10 km goal
Set Previous race distance to 5 km, enter 0 h 25 m 00 s, choose 10 km as the target, keep Model exponent (p) at 1.06, and leave Condition adjustment at 0%.

The Predicted finish is 52:07. The Predicted pace is about 5:13 /km and 8:23 /mi, so the split table gives roughly even 1 km checkpoints at that pace.

This is a close-distance comparison. The result is still an estimate, but it is much easier to defend than projecting from a very short race to a marathon.
Checking heat sensitivity before a half marathon
Use a 10 km result of 55:30 and target Half Marathon (21.0975 km). The neutral setup with p = 1.06 and 0% adjustment gives 2:02:27, or about 5:48 /km.

A humid-row style setup of p = 1.07 and +4% produces 2:08:18, around 6:05 /km. That is a meaningful pacing change, not a rounding detail.

When Condition Scenario Table changes the finish by several minutes, use the slower row as a planning warning and check whether the target day really matches the old race.
Reading the map boundary
For the 25:00 5 km to 10 km example, the neutral prediction is 52:07. In Pacing Adjustment Map, a +3% condition adjustment rounds to about 53:41.

That crosses the 103% threshold of the no-adjustment baseline, so the map treats it as Slower window. A smaller change may remain in Baseline window if the rounded finish stays between 97% and 103% of baseline.

The band is a sensitivity cue, not a moral label for the race. It tells you the course or weather assumption is large enough to change how the target pace should be discussed.
Fixing a missing prediction
If Previous race time is left at 0 h 0 m 0 s, the prediction panel does not have a valid previous effort to use. The target distance can be selected, but the calculation still needs positive seconds.

Enter the official finish time, then confirm that the Predicted Finish Time panel appears. If the result still looks wrong, check for swapped distances, a mistaken hour entry, or an exponent that was changed during an earlier comparison.

Returning Model exponent (p) to 1.06 and Condition adjustment to 0% gives a clean baseline for troubleshooting.

FAQ:

Which previous race should I use?

Use a recent official or well-measured all-out result. A verified 5 km, 10 km, half marathon, or marathon result is better than a workout interval because Previous race time is treated as the fitness baseline for every output.

Why does the marathon estimate look too fast?

A distance-power formula can be optimistic for marathons when the reference race is much shorter or when long-run preparation, fueling, heat, or hills are not reflected in the inputs. Check Condition Scenario Table before treating the marathon result as a goal.

Does changing kilometers to miles change the prediction?

No. Split table unit changes the checkpoint rows only. The Predicted finish, average pace, scenario rows, and map are still based on the target distance in meters.

Why is there a short final split?

Some official distances are not exact whole numbers of the chosen split unit. A half marathon in miles has a final partial row after 13 full miles because the target distance is 21.0975 km.

What should I do if no result appears?

Confirm that Previous race time is greater than zero and that both distance menus have valid selections. Minutes and seconds are treated as 0 to 59, and the exponent must be positive.

Are my race inputs sent to a prediction server?

No tool-specific prediction request is defined. The page loads its chart code as part of the interface, then computes the finish time, split table, scenarios, charts, and exports in your browser.

Glossary:

Riegel exponent: Value that controls how strongly finish time rises as distance increases.
Condition adjustment: Percent change applied after the distance formula to account for slower or faster race conditions.
Predicted pace: Average pace required to run the target distance in the predicted finish time.
Cumulative checkpoint: Total elapsed time shown at the end of a split row.
Baseline window: Pacing map band for finishes between 97% and 103% of the no-adjustment baseline.
Scenario row: A comparison row that changes the exponent, the condition adjustment, or both around the current inputs.

References:

Athletic records and human endurance, American Scientist, 1981.
An empirical study of race times in recreational endurance runners, BMC Sports Science, Medicine and Rehabilitation, 2016.
Competition and Technical Rules - 2026 Edition, World Athletics, 2026.
Half Marathon, World Athletics.
Impact of Environmental Parameters on Marathon Running Performance, PLOS ONE, 2012.