Type a boulder name, get a V-grade.
Which is harder?
Guess which boulder is the harder grade. Pick an opponent:
Vibe Grader predicts a bouldering V-grade from nothing but the route's name.
Three reasons, in roughly honest order:
It's trained on ~80,000 real boulder problems from OpenBeta (open-licensed climbing data sourced from Mountain Project), each with a name and a community grade. Each name becomes TF-IDF features — character chunks like cri, eath and whole words like crimp, slab — and a Ridge regression maps those to a number. Nothing to do with hold size or wall angle, just which letters and words tend to show up on hard vs. easy problems. At its most accurate it lands within about ±2 V-grades on average (see the numbers below), so treat it as a vibe, not a verdict.
Both grades come from the exact same algorithm and data. The only difference is how the training examples are weighted:
Measured on the held-out 20% test set, by mean absolute error (average distance from the real grade — lower is better):
Climbing is a pyramid, theres tons of easy boulders, very few elite ones. OpenBeta (≈ Mountain Project, US-centric) has almost nothing above V15. We patched the very top (V16–V17, plus the proposed V18 Exodia) from a small open list of famous hard problems, but V14–V15 stays thin. So predictions up high are extrapolation from a handful of examples.
The model only ever saw names that already had grades, so it's really learning the naming conventions of climbs that have been around a while — not anything physical. Type a brand-new boulder and you're betting that whoever named it followed the same herd instincts as everyone before them. Name your V2 "Burden of Sharma Roof Low" and the model will happily believe you. Garbage in, gnarly out.
The bar chart isn't a real probability. Ridge gives a single number; we draw a bell curve around it using the model's typical error. It's a picture of uncertainty, not a calibrated distribution.