PandaSkill - Player Performance and Skill Rating in Esports: Application to League of Legends

TL;DR

PandaSkill is a machine learning-based framework for assessing player performance and skill ratings in esports, specifically League of Legends, by modeling individual contributions and incorporating regional skill levels.

Contribution

It introduces a novel dual-rating system and performance-based skill updates, improving prediction accuracy and cross-regional comparison over traditional methods.

Findings

Better prediction of game outcomes

More aligned with expert opinions

Effective cross-regional skill comparison

Abstract

To take the esports scene to the next level, we introduce PandaSkill, a framework for assessing player performance and skill rating. Traditional rating systems like Elo and TrueSkill often overlook individual contributions and face challenges in professional esports due to limited game data and fragmented competitive scenes. PandaSkill leverages machine learning to estimate in-game player performance from individual player statistics. Each in-game role is modeled independently, ensuring a fair comparison between them. Then, using these performance scores, PandaSkill updates the player skill ratings using the Bayesian framework OpenSkill in a free-for-all setting. In this setting, skill ratings are updated solely based on performance scores rather than game outcomes, hightlighting individual contributions. To address the challenge of isolated rating pools that hinder cross-regional comparisons, PandaSkill introduces a dual-rating system that combines players' regional ratings with a meta-rating representing each region's overall skill level. Applying PandaSkill to five years of professional League of Legends matches worldwide, we show that our method produces skill ratings that better predict game outcomes and align more closely with expert opinions compared to existing methods.