Summary of Skill Issues: An Analysis Of Cs:go Skill Rating Systems, by Mikel Bober-irizar et al.
Skill Issues: An Analysis of CS:GO Skill Rating Systems
by Mikel Bober-Irizar, Naunidh Dua, Max McGuinness
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an empirical analysis of three popular skill rating algorithms used in online games, namely Elo, Glicko2, and TrueSkill. The authors employ surrogate modeling to evaluate the performance of these algorithms in terms of both overall accuracy and data efficiency, using a large dataset of Counter-Strike: Global Offensive matches. They also conduct a sensitivity analysis to examine how changes in the acquisition function impact the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at three skill rating systems used in online games like Counter-Strike: Global Offensive. It compares how well these systems do in predicting player skills and making fair teams for playing together. The researchers use special math tricks to test these systems and see which one does best. They also check if the systems are efficient with data and changeable based on different rules. |
