# Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races

arXiv: https://arxiv.org/abs/2305.10471 · HTML: https://mlsanity.com/paper/2305.10471/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data](https://mlsanity.com/paper/1712.01199/) (2017)
- [Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model](https://mlsanity.com/paper/2108.07139/) (2021)
- [Coach2vec: autoencoding the playing style of soccer coaches](https://mlsanity.com/paper/2106.15444/) (2021)
- [Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League](https://mlsanity.com/paper/2205.02731/) (2022)

## Built on by (most cited first, 1 of 1)

- [Boulder2Vec: Modeling Climber Performances in Professional Bouldering Competitions](https://mlsanity.com/paper/2411.02343/) (2024)

## Abstract (arXiv)

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.