An Analysis of Elo Rating Systems via Markov Chains
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909220206542848 |
|---|---|
| author | Olesker-Taylor, Sam Zanetti, Luca |
| author_facet | Olesker-Taylor, Sam Zanetti, Luca |
| contents | We present a theoretical analysis of the Elo rating system, a popular method for ranking skills of players in an online setting. In particular, we study Elo under the Bradley--Terry--Luce model and, using techniques from Markov chain theory, show that Elo learns the model parameters at a rate competitive with the state of the art. We apply our results to the problem of efficient tournament design and discuss a connection with the fastest-mixing Markov chain problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Analysis of Elo Rating Systems via Markov Chains Olesker-Taylor, Sam Zanetti, Luca Probability Statistics Theory Machine Learning We present a theoretical analysis of the Elo rating system, a popular method for ranking skills of players in an online setting. In particular, we study Elo under the Bradley--Terry--Luce model and, using techniques from Markov chain theory, show that Elo learns the model parameters at a rate competitive with the state of the art. We apply our results to the problem of efficient tournament design and discuss a connection with the fastest-mixing Markov chain problem. |
| title | An Analysis of Elo Rating Systems via Markov Chains |
| topic | Probability Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2406.05869 |