An Elo-like System for Massive Multiplayer Competitions

TL;DR

This paper introduces a Bayesian rating system for large-scale competitions that is simple, theoretically robust, incentivizes honest performance, and outperforms existing systems in accuracy and speed.

Contribution

A novel Bayesian rating system designed for massive multiplayer contests, with proven theoretical bounds and improved computational efficiency.

Findings

Achieves comparable or better prediction accuracy than existing systems.

Computes ratings faster, up to ten times quicker than current methods.

Ensures players are incentivized to perform honestly.

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.