用机器学习精准评估电竞选手表现，突破传统评分局限。
PandaSkill - Player Performance and Skill Rating in Esports: Application to League of Legends
- 基于玩家数据建模各位置表现，独立计算贡献。
- 结合贝叶斯框架，用表现分而非胜负更新段位。
- 双轨评级支持跨赛区对比，适合职业战队分析。
为推动电竞发展，我们提出PandaSkill框架，用于评估选手表现与技能等级。传统评分系统如Elo和TrueSkill常忽略个体贡献，且在职业电竞中受限于数据稀疏与赛事碎片化。PandaSkill利用机器学习，从单个选手统计数据估算游戏内表现，对每个游戏位置独立建模，实现公平比较。随后，基于这些表现分数，采用贝叶斯框架OpenSkill在自由对战场景中更新选手技能评级，仅依据表现分而非胜负结果，凸显个体贡献。针对孤立评级池导致跨赛区难比较的问题，PandaSkill引入双评级系统：融合选手所在赛区的区域评分与代表该赛区整体实力的元评级。将PandaSkill应用于全球五年职业《英雄联盟》比赛数据，结果显示，其生成的技能评级更准确预测比赛结果，并更符合专家判断，优于现有方法。
原文摘要 · Abstract (English)
To take the esports scene to the next level, we introduce PandaSkill, a framework for assessing player performance and skill rating. Traditional rating systems like Elo and TrueSkill often overlook individual contributions and face challenges in professional esports due to limited game data and fragmented competitive scenes. PandaSkill leverages machine learning to estimate in-game player performance from individual player statistics. Each in-game role is modeled independently, ensuring a fair comparison between them. Then, using these performance scores, PandaSkill updates the player skill ratings using the Bayesian framework OpenSkill in a free-for-all setting. In this setting, skill ratings are updated solely based on performance scores rather than game outcomes, hightlighting individual contributions. To address the challenge of isolated rating pools that hinder cross-regional comparisons, PandaSkill introduces a dual-rating system that combines players' regional ratings with a meta-rating representing each region's overall skill level. Applying PandaSkill to five years of professional League of Legends matches worldwide, we show that our method produces skill ratings that better predict game outcomes and align more closely with expert opinions compared to existing methods.
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