Solverly

Race Time Predictor (Riegel)

This tool projects finish times across different race distances from a single recent performance using the Riegel power-law relationship. It’s useful for setting realistic goals, dialing in target splits, and comparing how a 5K or half marathon effort might translate to another event without trial-and-error.

The Race Time Predictor converts one known result into estimated times and pacing guidance for other distances so we can plan training and race-day strategy with confidence. Use it to choose goal pace, structure workouts around the right intensity, and gauge whether your next target is ambitious yet achievable as your fitness improves.

Use a recent race result with the Riegel model to predict finish times, target pace, and splits for any distance.

Enter your reference effort

Target distance

Typical range 1.03–1.08 (lower = better endurance carryover).

Quick breakdown

Predicted time
3:54:59
Ref. pace (from your input)
7:53/mi • 4:54/km
Target pace
8:58/mi • 5:34/km
Training factor (k)
1.06

Pace table & splits

Target pace
  • per mile: 8:58
  • per km: 5:34
  • per 5K: 27:51
  • per 10K: 55:41
  • half marathon: 1:57:29
  • marathon: 3:54:59
Projected splits (mi)
Mark (mi)Cumulative time
2.6223:30
5.2447:00
7.871:10:30
10.491:34:00
13.111:57:29
15.732:20:59
18.352:44:29
20.983:07:59
23.603:31:29
26.223:54:59

Results interpretation

We project your target race time from a known performance using a power-law relationship between distance and time. A lower k (training factor) means endurance carries over better to longer distances; a higher ksuggests relatively sharper short-distance performance. Paces are even splits; conditions and course profile will change real pacing.

  • Within similar training: predictions are often close for neighboring distances (5K → 10K).
  • Long step-ups: errors increase when jumping from short to marathon without the matching long-run base.
  • Use it to plan: target a pace range, then refine with workouts and course specifics.

How it works

The model applies the Riegel relation: t₂ = t₁ × (d₂ / d₁)k. We compute pace by dividing the predicted time by the target distance, and we generate simple even splits. You can tune k to match your training response.

Formulas, steps, assumptions, limitations

Time prediction: t₂ = t₁ × (d₂/d₁)k.

Pace: pacekm = t₂ / d₂(km); pacemi = t₂ / d₂(mi).

Assumptions: comparable conditions, steady effort, adequate training for the target distance.

Limitations: hills, heat, altitude, turns, surfaces, fueling, and drafting can shift outcomes.

Use cases & examples

5K → 10K

5K in 24:30 with k=1.06 predicts ~51:02 for 10K. Use that pace to set workout targets.

10K → Half

10K in 45:00 (k=1.06) predicts ~1:39:28 for the half. Long runs and fueling strategy matter.

Half → Marathon

Half in 1:45:00 (k=1.06) predicts ~3:40:55. Adjust for course profile and planned gels.

Race time prediction FAQs

What value should we use for k?

Start near 1.06. If you carry endurance well, try 1.03–1.05. If you’re speed-leaning, 1.07–1.10 may fit better.

Do we need identical conditions?

Predictions assume broadly comparable conditions. Adjust for heat, hills, altitude, or sharp turns.

Why don’t negative splits show?

We show even splits for planning simplicity. On race day, pacing strategies may vary.

Can we use track distances?

Yes—pick Custom and enter meters as km (e.g., 1500 m → 1.5 km).

Is this a training plan?

No—it's a projection tool. Pair the prediction with workouts that build toward the target distance.

How accurate is it?

Close for adjacent distances with matched training. Less precise when the target requires capacities you haven’t practiced.

From One Finish Line to the Next: Using the Riegel Model to Plan Smart Pacing

We use a simple power-law to bridge the gap between two distances so we can plan with confidence. The idea is straightforward: your current performance anchors reality; the training factor adjusts how that performance scales as the distance changes. With a few careful inputs, we turn a personal best or recent race into a practical pacing plan for the next challenge.

Why a power-law works for racing

As distance grows, time rises faster than linearly—fatigue and fueling play bigger roles, and efficiency shifts with pace. A power-law captures that curvature without demanding lab data. It isn’t perfect, but it is consistent, which is the key to making good decisions across training blocks and courses.

Choosing a sensible training factor

The exponent k encodes how your fitness scales. Runners with strong endurance often sit on the low side (≈1.03–1.05), while speed-leaning profiles may live higher (≈1.07–1.10). We tune k with experience: if predictions for longer races are too fast, raise it slightly; if they’re conservative, lower it. Keep notes—your value tends to remain stable across a season with similar training volume.

Turning predictions into workouts

Once we have a target pace, we build repeatable workouts around it. For a 10K, cruise intervals at or just slower than race pace teach rhythm without excessive strain. For a half or marathon, long runs at portions of goal pace—layered with fueling practice—teach economy and gut readiness. The prediction isn’t a promise; it’s a north star for the next few weeks.

Course, conditions, and realism

A flat, cool course behaves differently from a rolling route in the sun. We consider elevation, surface, wind exposure, and congestion. If conditions look hot or hilly, we nudge the pace modestly. It’s better to finish strong than to chase a number that the day won’t allow.

Fueling and long-distance pacing

Beyond the half marathon, fueling and hydration dominate outcomes. Even pacing assumes a steady energy supply; under-fueling turns even plans into positive splits. We practice gel timing, electrolyte intake, and bottle handling in training so race day feels familiar. A small plan beats a big guess.

Adapting the model to you

The best projection is the one you iterate. After each key effort, compare predicted and actual results. Adjust k by a hundredth or two. Over a season, your personal factor will settle—and your pacing calls will become uncannily accurate.

Putting it all together

We anchor predictions in what we’ve already done, layer in what the course and weather demand, and keep the plan honest with recent workouts. The goal is not to be perfect; it’s to be prepared. With a clear target pace and simple split plan, race mornings feel calmer—and finishes, more satisfying.