# An Elo-like System for Massive Multiplayer Competitions

arXiv: https://arxiv.org/abs/2101.00400 · HTML: https://mlsanity.com/paper/2101.00400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 24 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance](https://mlsanity.com/paper/2411.03562/) (2024)
- [am-ELO: A Stable Framework for Arena-based LLM Evaluation](https://mlsanity.com/paper/2505.03475/) (2025)

## Abstract (arXiv)

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.