Episode 21 – Does Starting Slower Actually Make You Faster in a Gran Fondo? (interview with Dr Fran Oficial-Casado)

Every sportive starts the same way. The road tilts up for the first time, a group comes past, and you decide in about three seconds whether to go with them. Nearly 6,600 finishers of one of Europe’s hardest Gran Fondos suggest that letting them go is the right move, though not for the reason most riders assume.

In this episode Professor Richard Davison speaks with Dr Fran Oficial-Casado of the Universitat de València, where he works in the Research Group in Sports Biomechanics (GIBD). He is a co-author of a 2026 study that took the public chip-timing data from the 2025 Quebrantahuesos (197.2 km and 3,190 m of Pyrenean climbing, seven timing mats, 6,589 finishers) and asked whether the fast riders pace it differently from everyone else.

The largest pacing dataset in cycling

Most of what we know about pacing comes from time trials, laboratory protocols and small field studies. This one is different in scale. Each rider’s speed in each of the seven segments was expressed as a percentage of that rider’s own average speed for the day, so the measure describes how evenly somebody covered the course rather than how quickly. The gap between a rider’s fastest and slowest segment becomes their pacing range.

The headline is clean. Riders finishing inside six hours had a pacing range of 116.7%. Every slower finishing band sat between 124% and 131%. The faster group also opened the first climb, the Somport, at a lower fraction of their own average speed, spent proportionally less of their total time on the two steepest mid-race ascents, and pulled away most decisively in the closing split, where the separation between finishing groups was very large indeed.

The finding that complicates it

Then there is the result that stops this being a simple story. A clustering analysis produced three distinct pacing shapes that cut across the finishing-time bands. The cluster with the flattest profile of the lot, at 112.5%, was also the slowest home, averaging 8 hours 54. The fastest cluster, averaging 7 hours 02, sat mid-range at 126.8%.

So an even speed is not the target. On a course with 3,000 m of climbing it is not physically available anyway. Low variability is a signature of good pacing rather than a cause of it, a distinction that matters if you are tempted to ride to a flat number on a head unit.

Description or strategy?

The conversation spends real time on what this kind of observational data can and cannot support, which is unusual for a study with a headline this quotable. Because relative speed on a mountainous course is mechanically tied to how much better a rider climbs than they ride the flat, and fitness expresses far more on a gradient than in a bunch where drafting compresses everybody, so a conservative opening climb cannot be read as proof of a deliberate plan. Durability is a live alternative: the faster riders may simply have held their power better into the fifth and sixth hour.

Two other numbers deserve their place next to the headline. The overall group effect on pacing range explained under 2% of the variance, with only the sub-six-hour band separating cleanly. And the field was 3.5% women, 229 riders out of 6,589, so the sex comparisons will not bear weight.

What to do at Sabiñánigo

The practical version survives the caveats. On a long mountainous day, pace the opening climb by your own effort (power or perceived exertion) and not by the wheels going past. Aim to be riding strongly in the final hour rather than defending a gap you took early. Do not chase an even speed as a goal in itself. Fran also talks about the road that led him here, from swimming and triathlon coaching into research on how endurance athletes distribute effort, and where the group’s marathon-prediction work goes next.

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Listen to the full episode on Spotify or watch on YouTube.

The paper is open access and the dataset is freely available: Sánchez-Jiménez, J.L., Priego-Quesada, J.I. & Oficial-Casado, F. (2026). Pacing strategies in Gran Fondo cycling: a large-scale analysis of the Quebrantahuesos race. Sport Sciences for Health, 22(3), 236. https://doi.org/10.1007/s11332-026-01828-0. Data deposited on Mendeley (CC-BY): 10.17632/NKZ3GW978S.

We also covered this study in the research blog: The Riders Who Start Slowest Finish Fastest.