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

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 known race result might translate to another distance. A recent 5 km can help frame a 10 km goal, and a half marathon can guide a marathon target, as long as the comparison respects the usual slowdown that comes with longer racing.

The estimate is strongest when the previous result was recent, measured accurately, and run as an honest best effort. It weakens when the reference was a workout split, a tactical race, an unusually fast downhill course, or a race held in very different weather from the target event.

Race finish time curve showing a prior result and target estimate across race distance

Distance conversion is a planning aid, not a full training assessment. The same 25-minute 5 km can imply a reasonable 10 km target and an overly aggressive marathon target if long-run preparation is missing. Longer target races need more caution because endurance, fueling, terrain, and pacing discipline matter more as duration rises.

A useful prediction gives a finish time, average pace, splits, and condition sensitivity. Read those together before turning the number into a race plan.

How to Use This Tool:

Start with the neutral estimate, then change assumptions only when the prior race and target race differ in a specific way.

Choose Previous race distance and enter the official or best-known Previous race time. The time must be greater than zero.
Select Target race distance. Presets range from track distances through 50 km, including 5 km, 10 km, half marathon, and marathon.
Leave Model exponent (p) at 1.06 for the first pass. Change it only when your own race history shows that you fade less or more than the default curve.
Keep Condition adjustment at 0% when course and weather are comparable. Use positive values for heat, humidity, hills, trails, or expected fade, and negative values only for clearly faster conditions.
Choose Split table unit as kilometers or miles. This changes the split rows, not the predicted finish.
Review Prediction Metrics, Pace Split Table, Condition Scenario Table, Split Pace Timeline, and Pacing Adjustment Map before choosing a target pace.
If the result looks wrong, reset the exponent to 1.06, set condition adjustment to 0%, and check for swapped distances or a mistaken hour, minute, or second entry.

Interpreting Results:

The predicted finish should be judged through its pace. A finish time can look reasonable in hours and minutes while the average pace is faster than recent workouts can support. This is especially common when a short reference race is projected to a long target race.

The scenario table and pacing map show sensitivity to assumptions. If the humid, hilly, or late-fade rows move the finish by several minutes, the middle estimate is not a fixed truth. It is a neutral point inside a range of plausible race-day outcomes.

How to interpret race finish prediction outputs
Output	Meaning	Caution
`Predicted finish`	Target time after distance conversion and condition adjustment.	It assumes similar fitness and a suitable endurance base.
`Predicted pace`	Average pace required across the target distance.	Use this as the first realism check against training.
`Condition Scenario Table`	Comparison rows for cooler, humid, hilly, and fade assumptions.	Wide spread means the target depends heavily on race-day conditions.
`Pacing Adjustment Map`	Finish sensitivity across condition-adjustment values.	Crossing the faster or slower window changes how the goal should be discussed.

Splits are even-pace checkpoints. They do not model surges, walk breaks, tactical racing, aid-station stops, or a planned negative split. Use them as a baseline and adapt for the actual race plan.

Technical Details:

Race-time prediction uses a distance-power curve. If pace stayed exactly constant, time would rise in direct proportion to distance. Endurance racing usually slows as distance grows, so the exponent is set above 1. A default exponent of 1.06 is the classic Riegel-style assumption for many running comparisons.

The exponent controls the shape of the slowdown. Lower values preserve speed better across longer distances, which can fit endurance-strong runners. Higher values add more slowdown, which can fit runners who perform well at shorter distances but fade as the target gets longer. The condition adjustment is applied after the distance conversion, so it represents race-day difficulty rather than a change in baseline fitness.

Formula Core

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

{pace}_{km} = \frac{t_{2}}{d_{2} / 1000}

Here t1 is the previous finish in seconds, d1 is previous distance in meters, d2 is target distance in meters, p is the exponent, a is the condition adjustment percentage, and t2 is the rounded target finish in seconds. Mile pace uses the same finish time with 1609.344 meters per mile.

For example, a 25:00 5 km has t1 = 1500 and d1 = 5000. With a 10 km target, p = 1.06, and a = 0, the estimate is round(1500 x (10000 / 5000)^1.06), or about 52:07.

Race prediction technical boundaries
Element	Range or rule	Effect
Exponent	Visible control range `0.90` to `1.20`, default `1.06`	Changes how quickly pace slows as target distance grows.
Condition adjustment	Visible control range `-15%` to `+30%`	Speeds or slows the converted finish after the distance formula.
Baseline window	Within `97%` to `103%` of the no-adjustment baseline	Marks estimates close to the neutral course assumption.
Faster window	At or below `97%` of the no-adjustment baseline	Shows a condition setting that meaningfully speeds the target.
Slower window	At or above `103%` of the no-adjustment baseline	Shows a condition setting that meaningfully slows the target.

Preset distances are stored as measured meter values, including 400 m, 800 m, 1500 m, 1 mile, 3 km, 5 km, 10 km, 15 km, 10 miles, half marathon, marathon, and 50 km. Because the formula depends on the distance ratio, uncertainty grows when the previous and target distances are far apart.

Split rows divide the target distance into 1 km or 1 mile chunks, then add a partial final 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.

Worked Examples:

5 km to 10 km. A 25:00 5 km with the default exponent and no condition adjustment predicts about 52:07 for 10 km. That close-distance comparison is easier to trust than projecting the same 5 km to a marathon.

10 km to half marathon in warm conditions. A 55:30 10 km can produce a neutral half-marathon estimate near two hours. Adding a positive condition adjustment for heat or humidity slows the finish and pace together, which can move the target by several minutes.

Changing the exponent. A runner whose race history shows strong endurance may test a lower exponent. A runner who fades sharply beyond 10 km may test a higher exponent. Change the exponent only after reading the neutral result.

Missing result. If no prediction appears, check that the previous time is greater than zero and both distances are selected. Then reset the exponent and condition adjustment before troubleshooting anything else.

FAQ:

Which previous race should I use?

Use a recent, well-measured, all-out result from a similar training phase. A chip-timed race is better than a workout split or a tactical event.

Why can marathon predictions look too fast?

Shorter races do not prove marathon durability. Long-run preparation, fueling, heat, hills, and pacing errors can make the marathon slower than a distance-power formula suggests.

Does switching split units change the prediction?

No. Kilometer and mile split units change only the checkpoint rows. The finish estimate still uses the target distance in meters.

What does a positive condition adjustment mean?

A positive adjustment slows the target finish after the distance conversion. It is useful for heat, humidity, hills, trail footing, or likely late-race fade.

Is the output an official qualifying prediction?

No. The result is a planning estimate. Race organizers, qualifying standards, and official timing rules decide actual eligibility.

Glossary:

Condition adjustment: A percentage change applied after distance conversion to reflect race-day difficulty or advantage.
Distance-power curve: A relationship where finish time grows by a distance ratio raised to an exponent.
Exponent: The value that controls how strongly the estimate slows as target distance increases.
Half marathon: A road race distance of 21.0975 km.
Predicted pace: The average pace required to cover the target distance in the predicted finish time.
Riegel model: A commonly used endurance prediction model based on a distance-power relationship.

References:

Athletic Records and Human Endurance, American Scientist.
Competition and Technical Rules 2026 Edition, World Athletics.
Half Marathon, World Athletics.
Impact of Environmental Parameters on Marathon Running Performance, PLOS ONE.