Board-to-Board: Evaluating Moonboard Grade Prediction Generalization
Daniel Petashvili Matthew Rodda
Bouldering is a sport where athletes aim to climb up an obstacle using a set
of defined holds called a route. Typically routes are assigned a grade to
inform climbers of its difficulty and allow them to more easily track their
progression. However, the variation in individual climbers technical and
physical attributes and many nuances of an individual route make grading a
difficult and often biased task. In this work, we apply classical and
deep-learning modelling techniques to the 2016, 2017 and 2019 Moonboard
datasets, achieving state of the art grade prediction performance with 0.87 MAE
and 1.12 RMSE. We achieve this performance on a feature-set that does not
require decomposing routes into individual moves, which is a method common in
literature and introduces bias. We also demonstrate the generalization
capability of this model between editions and introduce a novel vision-based
method of grade prediction. While the generalization performance of these
techniques is below human level performance currently, we propose these methods
as a basis for future work. Such a tool could be implemented in pre-existing
mobile applications and would allow climbers to better track their progress and
assess new routes with reduced bias.