在Counter-Strike：Global Offensive中评估玩家的行动
Valuing Player Actions in Counter-Strike: Global Offensive
摘要：
尽管电子竞技的兴趣越来越大，但它缺乏基本的体育分析资源，例如可访问的数据或经过验证和可复制的分析框架。即使是第二大最受欢迎的电子竞技《反恐精英：全球攻势》（CSGO）也存在这些问题。因此，对CSGO球员进行定量评估对于球队、媒体、投注者和球迷来说是一项重要的任务，但却很困难。为了解决这个问题，我们介绍（1）CSGO的数据模型与开源实现;（2）图距离测量定义CSGO中的距离;（3）上下文感知框架，以价值球员的行动的基础上，他们的球队获胜的机会的变化。使用超过7000万个游戏内CSGO事件，我们证明了我们的框架的一致性和独立性相比，现有的估值框架。我们还提供了演示高影响区带识别和不确定性估计的用例。
英文摘要：
Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
