UTMB Finish Time Predictor

The predictor uses the Riegel performance prediction model, adjusted for trail elevation using TrailMath's km-effort formula. Enter a race you have completed (any distance, any terrain) and the tool translates that performance to a UTMB equivalent, accounting for the 171km distance, 10,000m of gain and loss, and ultra fatigue scaling.

Understanding the output. The tool returns an optimistic and conservative estimate. For UTMB specifically, the conservative figure is more meaningful - UTMB's difficulty is heavily influenced by conditions (weather, heat, night navigation) that the model cannot capture. A runner with a 24-hour optimistic prediction should target 27-28 hours to account for UTMB's specific demands.

Reference race selection. For accurate UTMB prediction, use a reference race that is similar in character: ideally a mountain ultra of at least 50km with significant elevation. A flat road 100km will underpredict your UTMB time because the fatigue profile is different. The most reliable reference races are other UTMB World Series events (OCC, CCC, MCC) or comparable mountain ultras in the Alps.

Typical UTMB finish times by category: Elite (sub-24h), strong amateur (24-30h), mid-pack (30-38h), completion-focused (38-46h). The 30-hour mark roughly corresponds to a sub-3:00 road marathon equivalent in trail fitness.

Once you have a predicted finish time, use the UTMB Aid Station Planner to translate it into checkpoint splits and brief your crew.

The classic Riegel formula (T2 = T1 × (D2/D1)^1.06) was derived from road race data. It assumes distance is the only factor in fatigue - ignoring elevation, terrain friction, and ultra-specific physiological degradation. For mountain trail races, these factors dominate.

ITRA km-effort normalises trail races by effort rather than distance. A 50km race with 3,000m of gain has a km-effort of 80 - equivalent in effort to an 80km flat race. Our version also accounts for descent cost (loss_m / 150), because steep descents cause significant eccentric muscle damage (Minetti, 2002) that isn't free.

Ultra fatigue: Millet et al. (2011) documented progressive neuromuscular degradation in ultra-marathons - central fatigue, GI distress, and eccentric muscle damage compound over time. The fatigue exponent in our prediction scales from 1.06 (≤marathon) to 1.15 (>100km), producing increasingly conservative predictions as distance grows. The stepped exponents beyond marathon distance are TrailMath heuristics, not published constants - they represent a conservative extrapolation of Millet's qualitative findings, not empirically derived values.

Terrain multipliers are practical heuristics, not peer-reviewed constants. Technical trail is approximately 1.30× slower than road pace for the same flat effort - accounting for footplacement, rocks, roots, and lateral stability demands. These multipliers are applied relative to your known race's terrain, so comparing trail-to-trail or road-to-road eliminates the terrain effect and leaves only effort differences.

How to use this: Choose a known result you're proud of and that reflects your current fitness. A race from 6 months ago still works if your training hasn't changed significantly. The prediction gives a range - aim for somewhere between optimistic and conservative based on your training specificity for the target race.

ITRA. km-effort formula. International Trail Running Association. Riegel PS. (1977). Athletic records and human endurance. American Scientist. Millet GY et al. (2011). Neuromuscular consequences of an extreme mountain ultra-marathon. PLoS ONE. Minetti AE et al. (2002). J Appl Physiol. 93(3):1039-46.