# OpenSkill: A faster asymmetric multi-team, multiplayer rating system

arXiv: https://arxiv.org/abs/2401.05451 · HTML: https://mlsanity.com/paper/2401.05451/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2024.

## Built on by (most cited first, 9 of 9)

- [CalBench: Evaluating Coordination-Privacy Trade-offs in Multi-Agent LLMs](https://mlsanity.com/paper/2605.09823/) (2026)
- [Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet](https://mlsanity.com/paper/2603.08163/) (2026)
- [An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation](https://mlsanity.com/paper/2410.23730/) (2024)
- [Reliable, Reproducible, and Really Fast Leaderboards with Evalica](https://mlsanity.com/paper/2412.11314/) (2024)
- [Incentivizing Permissionless Distributed Learning of LLMs](https://mlsanity.com/paper/2505.21684/) (2025)
- [CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V](https://mlsanity.com/paper/2604.07733/) (2026)
- [Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models](https://mlsanity.com/paper/2412.18421/) (2024)
- [PandaSkill - Player Performance and Skill Rating in Esports: Application to League of Legends](https://mlsanity.com/paper/2501.10049/) (2025)
- [Superhuman AI for Generals.io Using Self-Play Reinforcement Learning](https://mlsanity.com/paper/2606.23348/) (2026)

## Abstract (arXiv)

Assessing and comparing player skill in online multiplayer gaming environments is essential for fair matchmaking and player engagement. Traditional ranking models like Elo and Glicko-2, designed for two-player games, are insufficient for the complexity of multi-player, asymmetric team-based matches. To address this gap, the OpenSkill library offers a suite of sophisticated, fast, and adaptable models tailored for such dynamics. Drawing from Bayesian inference methods, OpenSkill provides a more accurate representation of individual player contributions and speeds up the computation of ranks. This paper introduces the OpenSkill library, featuring a Python implementation of the Plackett-Luce model among others, highlighting its performance advantages and predictive accuracy against proprietary systems like TrueSkill. OpenSkill is a valuable tool for game developers and researchers, ensuring a responsive and fair gaming experience by efficiently adjusting player rankings based on game outcomes. The library's support for time decay and diligent documentation further aid in its practical application, making it a robust solution for the nuanced world of multiplayer ranking systems. This paper also acknowledges areas for future enhancement, such as partial play and contribution weighting, emphasizing the library's ongoing development to meet the evolving needs of online gaming communities.