  Vector embeddings have been successfully applied in several domains to obtain
effective representations of non-numeric data which can then be used in various
downstream tasks. We present a novel application of vector embeddings in
professional road cycling by demonstrating a method to learn representations
for riders and races based on historical results. We use unsupervised learning
techniques to validate that the resultant embeddings capture interesting
features of riders and races. These embeddings could be used for downstream
prediction tasks such as early talent identification and race outcome
prediction.
