Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929278449352704 |
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| author | Baron, Ethan Janssens, Bram Bogaert, Matthias |
| author_facet | Baron, Ethan Janssens, Bram Bogaert, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10471 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races Baron, Ethan Janssens, Bram Bogaert, Matthias Machine Learning 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. |
| title | Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.10471 |