  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.
