What this result means
Uphill race prediction starts from a flat performance, optionally scales distance with Riegel when the target differs, then multiplies by a Minetti factor from average grade (gain ÷ distance).
Use elevation-adjusted finish when you already have a flat goal clock for the same distance. Use the ordinary race time predictor when the course is flat.
1:45 half, same distance, 250 m gain → finish slower than 1:45 by the average-grade multiplier.
A flat 20:00 5K toward a hilly 10K applies Riegel first, then the gain multiplier on the 10K distance.
Calculation method
Average grade % = (gain_m ÷ (distance_km × 1000)) × 100. Multiplier = Minetti cost(grade) ÷ cost(0). Finish = flat target time × multiplier. Flat target time uses Riegel when race and target distances differ.
Labeled estimate. Ignores downhills as relief, heat, and pacing strategy.
Net gain understates a course with big climbs and descents. This is not a segment-by-segment GAP integration.
Frequently asked questions
How is this different from the race time predictor?
Riegel alone is flat. This page adds a climb multiplier from course gain after any distance scaling.
Should I use elevation-adjusted finish instead?
Use that page when you already have a flat goal time for the target distance. Use this page when you start from another flat race result.
Does it model downhills?
Only through net gain. A course that climbs 400 m and descends 400 m has 0 m net gain here and will not look hard in the multiplier.
Is this good enough for a mountain marathon?
Treat it as a rough planning number. Technical terrain and huge cumulative climb need more than average grade from net gain.