CCC 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 CCC equivalent, accounting for the 101km distance, around 6,100m of gain and loss, and ultra fatigue scaling.

The opening climb shapes the whole race. CCC front-loads its difficulty: roughly 1,300m of gain in the first 10km up to the Tete de la Tronche (2,584m). Runners who bank time on this climb pay for it through the Swiss Val Ferret. The predictor gives you a whole-race estimate; pace the opening climb as if the race starts at Refuge Bertone.

Reference race selection. For an accurate CCC prediction, use a mountain race of at least 40km with sustained climbs - OCC, a 50-60km alpine race, or a hilly 80km. Flat road references underpredict CCC times because the fatigue profile of 6,100m of climbing is different.

Typical CCC finish times by category: Elite (sub-12h), strong amateur (12-16h), mid-pack (16-22h), completion-focused (22-26h30). The intermediate time barriers from Refuge Bertone onwards eliminate roughly the slowest tenth of the field before Chamonix.

Once you have a predicted finish time, use the CCC 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.