论文标题
在反击中估价球员的行动：全球进攻
Valuing Player Actions in Counter-Strike: Global Offensive
论文作者
论文摘要
电子竞技尽管兴趣不断扩大，但缺乏基本的体育分析资源，例如可访问的数据或可重复的分析框架。甚至是反恐道：全球攻势（CSGO），第二个最受欢迎的电子竞技，也遇到了这些问题。因此，很难对CSGO玩家进行定量评估，这对团队，媒体，投注者和球迷很重要。为了解决这个问题，我们介绍了（1）具有开源实现的CSGO数据模型； （2）定义CSGO距离的图形距离； （3）基于球队获胜的机会的变化，对参与者的行动进行了上下文感知的框架。与现有的估值框架相比，使用超过7000万个游戏中的CSGO活动，我们证明了框架的一致性和独立性。我们还提供了表明高影响识别和不确定性估计的用例。
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.
