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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.02831 |
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| _version_ | 1866914964170604544 |
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| author | Bober-Irizar, Mikel Dua, Naunidh McGuinness, Max |
| author_facet | Bober-Irizar, Mikel Dua, Naunidh McGuinness, Max |
| contents | The meteoric rise of online games has created a need for accurate skill rating systems for tracking improvement and fair matchmaking. Although many skill rating systems are deployed, with various theoretical foundations, less work has been done at analysing the real-world performance of these algorithms. In this paper, we perform an empirical analysis of Elo, Glicko2 and TrueSkill through the lens of surrogate modelling, where skill ratings influence future matchmaking with a configurable acquisition function. We look both at overall performance and data efficiency, and perform a sensitivity analysis based on a large dataset of Counter-Strike: Global Offensive matches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skill Issues: An Analysis of CS:GO Skill Rating Systems Bober-Irizar, Mikel Dua, Naunidh McGuinness, Max Artificial Intelligence Machine Learning The meteoric rise of online games has created a need for accurate skill rating systems for tracking improvement and fair matchmaking. Although many skill rating systems are deployed, with various theoretical foundations, less work has been done at analysing the real-world performance of these algorithms. In this paper, we perform an empirical analysis of Elo, Glicko2 and TrueSkill through the lens of surrogate modelling, where skill ratings influence future matchmaking with a configurable acquisition function. We look both at overall performance and data efficiency, and perform a sensitivity analysis based on a large dataset of Counter-Strike: Global Offensive matches. |
| title | Skill Issues: An Analysis of CS:GO Skill Rating Systems |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.02831 |