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- W3119254240 abstract "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." @default.
- W3119254240 created "2021-01-18" @default.
- W3119254240 creator A5035944575 @default.
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- W3119254240 date "2021-01-02" @default.
- W3119254240 modified "2023-09-25" @default.
- W3119254240 title "An Elo-like System for Massive Multiplayer Competitions" @default.
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- W3119254240 doi "https://doi.org/10.48550/arxiv.2101.00400" @default.
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