Abstract Analytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

中文翻译：

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电子竞技Dota 2的贝叶斯校正正负分析

摘要过去几年来，分析与专业运动之间已经建立了联系，但是对于运动分析社区中不断发展的电子竞技领域却鲜有关注。我们力求将调整后减负（APM）模型（曲棍球和篮球等传统运动中使用的公认分析方法）应用于一种特定的电子竞技游戏：《远古防御2》（Dota 2）。与传统运动一样，我们展示了如何使用通过贝叶斯层次回归开发的APM指标来量化单个球员对其团队的贡献，并最终使用该球员级别的信息来预测比赛结果。特别是，我们首先提供证据证明金可以用作胜利的连续指标，以评估团队的表现，然后使用贝叶斯APM模型估算球员如何为球队的金牌差距做出贡献。我们证明了该APM模型优于基于常见团队级别统计信息（通常称为“盒子分数统计信息”）的模型。除了我们的建模方法的细节外，本文还作为将传统体育分析方法应用于电竞分析方法的潜在效用示例。