Rating systems play an important role in competitive sports and games. They provide a measure of player skill, which incentivizes competitive performances and enables balanced match-ups. In this paper, we present a novel Bayesian rating system for contests with many participants. It is widely applicable to competition formats with discrete ranked matches, such as online programming competitions, obstacle courses races, and some video games. The simplicity of our system allows us to prove theoretical bounds on robustness and runtime. In addition, we show that the system aligns incentives: that is, a player who seeks to maximize their rating will never want to underperform. Experimentally, the rating system rivals or surpasses existing systems in prediction accuracy, and computes faster than existing systems by up to an order of magnitude.

中文翻译：

---

类似Elo的大型多人比赛系统

评分系统在竞技体育和比赛中起着重要作用。他们提供了一种衡量球员技能的方法，可以激励比赛表现并实现平衡的比赛。在本文中，我们提出了一种新颖的贝叶斯评分系统，用于许多参与者的竞赛。它广泛适用于具有离散排名比赛的比赛形式，例如在线编程比赛，障碍赛和一些视频游戏。系统的简单性使我们能够证明鲁棒性和运行时的理论界限。此外，我们证明了该系统与激励措施保持一致：也就是说，寻求最大化其评分的玩家将永远不会表现不佳。在实验上，评级系统的预测准确性可与现有系统相媲美或超越现有系统，并且计算速度比现有系统快一个数量级。