# Skill Issues: An Analysis of CS:GO Skill Rating Systems

arXiv: https://arxiv.org/abs/2410.02831 · HTML: https://mlsanity.com/paper/2410.02831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [The Evaluation of Rating Systems in Online Free-for-All Games](https://mlsanity.com/paper/2008.06787/) (2020)

## Built on by (most cited first, 1 of 1)

- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)

## Abstract (arXiv)

The meteoric rise of online games has created a need for accurate skill rating systems for tracking improvement and fair matchmaking. Although many skill rating systems are deployed, with various theoretical foundations, less work has been done at analysing the real-world performance of these algorithms. In this paper, we perform an empirical analysis of Elo, Glicko2 and TrueSkill through the lens of surrogate modelling, where skill ratings influence future matchmaking with a configurable acquisition function. We look both at overall performance and data efficiency, and perform a sensitivity analysis based on a large dataset of Counter-Strike: Global Offensive matches.