对比三种电竞评分系统在实测中的表现差异。
Skill Issues: An Analysis of CS:GO Skill Rating Systems
- 用代理建模方法评估评分系统对匹配的影响
- 发现Glicko2在数据效率上优于其他系统
- 适合研究游戏匹配机制的开发者参考
在线游戏的迅猛发展催生了对精准技能评分系统的需求，以追踪进步并实现公平匹配。尽管已有多种评分系统部署，理论基础各异，但针对其实际表现的分析仍不足。本文通过代理建模视角，对Elo、Glicko2和TrueSkill进行实证分析，其中技能评分影响未来匹配，且可配置采集函数。我们评估了整体性能与数据效率，并基于大型《反恐精英：全球攻势》（Counter-Strike: Global Offensive）比赛数据集进行了敏感性分析。
原文摘要 · Abstract (English)
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.
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