In this work we generate user profiles from the raw activity data of over 12000 marathon runners. We demonstrate that these user profiles capture accurate representations of the fitness and training of a runner, and show that they are comparable to current methods used to predict marathon performance - many of which require many years of prior experience or expensive laboratory testing. We also briefly investigate how these user profiles can be used to help marathon runners in their training and race preparation when combined with current recommender systems approaches.
© Copyright 2020 Machine Learning and Data Mining for Sports Analytics. KU Leuven. Published by Springer. All rights reserved.