Empirical parameterization of the Elo Rating System
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914211757555712 |
|---|---|
| author | Maitra, Shirsa Banerjee, Tathagata De, Anushka Mukherjee, Diganta Mukherjee, Tridib |
| author_facet | Maitra, Shirsa Banerjee, Tathagata De, Anushka Mukherjee, Diganta Mukherjee, Tridib |
| contents | This study aims to provide a data-driven approach for empirically tuning and validating rating systems, focusing on the Elo system. Well-known rating frameworks, such as Elo, Glicko, TrueSkill systems, rely on parameters that are usually chosen based on probabilistic assumptions or conventions, and do not utilize game-specific data. To address this issue, we propose a methodology that learns optimal parameter values by maximizing the predictive accuracy of match outcomes. The proposed parameter-tuning framework is a generalizable method that can be extended to any rating system, even for multiplayer setups, through suitable modification of the parameter space. Implementation of the rating system on real and simulated gameplay data demonstrates the suitability of the data-driven rating system in modeling player performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empirical parameterization of the Elo Rating System Maitra, Shirsa Banerjee, Tathagata De, Anushka Mukherjee, Diganta Mukherjee, Tridib Applications This study aims to provide a data-driven approach for empirically tuning and validating rating systems, focusing on the Elo system. Well-known rating frameworks, such as Elo, Glicko, TrueSkill systems, rely on parameters that are usually chosen based on probabilistic assumptions or conventions, and do not utilize game-specific data. To address this issue, we propose a methodology that learns optimal parameter values by maximizing the predictive accuracy of match outcomes. The proposed parameter-tuning framework is a generalizable method that can be extended to any rating system, even for multiplayer setups, through suitable modification of the parameter space. Implementation of the rating system on real and simulated gameplay data demonstrates the suitability of the data-driven rating system in modeling player performance. |
| title | Empirical parameterization of the Elo Rating System |
| topic | Applications |
| url | https://arxiv.org/abs/2512.18013 |