Predict your hybrid race finish
Enter a run time and a few gym numbers. Get a predicted finish, a target pace for all eight runs, and a time budget for every station — with the fatigue curve already baked in.
Run splits
--/kmTarget pace for each 1 km leg. They get slower as fatigue builds — that’s expected.
Station time budget
How long each station should take in-race. Your biggest time sinks are flagged.
| Station | System | Budget | Share |
|---|
Estimates from a physiology-based model — not a guarantee. Real splits depend on pacing, heat, transitions and the day. Add your real test results above to narrow the range.
Not a calculator — a fatigue model
Project a fresh pace
We turn your 5 km (and 10 km) into a fresh, flat 1 km-equivalent pace using the Riegel endurance formula — the same maths coaches use to compare race distances.
Add the hybrid tax
Running broken into eight pieces, on tired legs, with indoor turns, costs far more than a clean road 1 km. We apply a calibrated multiplier so the prediction matches what real racers actually run.
Stack the fatigue
Two separate fatigue ladders make each later run and each later station slower — your worst run is usually number seven, and wall balls bite hardest because they’re dead last.
Budget every station
Your station times come from your own tests when you give them, or from sensible fallbacks tied to your run pace and strength. Add real ski / row / wall-ball numbers to tighten the estimate.
Simulator FAQ
How accurate is the prediction?
You get a likely range, not one guaranteed number. That range is wide with only a 5k and tightens as you add real station tests (ski, row, max wall balls). The band reflects how much you’ve told the model — it’s a planning tool, not a validated error margin against real finishers.
Why are my run splits slower than my 5 km pace?
Because you never run a hybrid race fresh. Every 1 km comes after a station that has spiked your heart rate and trashed your legs. A 4:00/km road runner often races nearer 5:30/km here — that’s normal, not a mistake.
I don’t have any station tests — is the result still useful?
Yes. With just a run time, division and a strength self-rating, the model uses calibrated fallbacks for every station. It’s less precise than with real numbers, but the shape — where your time goes — is still informative.