论文标题
关于ELO的局限性：现实世界游戏，是及时的，不是加性的
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
论文作者
论文摘要
现实世界中的竞争游戏，例如国际象棋，GO或Starcraft II，依靠ELO模型来衡量玩家的力量。由于这些游戏不是完全传递的，因此使用ELO隐含地假设它们具有可以正确识别和提取的强及其及时效应组件。在这项研究中，我们调查了确定游戏中及时成分强度的挑战。首先，我们证明ELO模型即使在基本的透明游戏中也无法提取此传递组件。然后，基于此观察，我们提出了ELO分数的扩展：我们最终获得了一个圆盘排名系统，该系统分配了每个播放器两个分数，我们将其称为技能和一致性。最后，我们提出了关于机器人和人类玩的现实游戏的回报矩阵的经验验证。
Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.
