An Elo-like System for Massive Multiplayer Competitions
This system provides a more efficient and accurate method for skill assessment in massive multiplayer competitions, benefiting organizers and participants by enabling fairer matchups and better incentives.
This paper introduces a new Bayesian rating system for large-scale multiplayer competitions with discrete ranked matches. The system achieves comparable or superior prediction accuracy to existing methods while computing up to an order of magnitude faster.
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.