PandaSkill - Player Performance and Skill Rating in Esports: Application to League of Legends
This addresses the challenge of fair player evaluation in professional esports, particularly for cross-regional comparisons, though it is incremental as it builds on existing rating systems like Elo and TrueSkill.
The authors tackled the problem of assessing player performance and skill rating in esports by introducing PandaSkill, a framework that uses machine learning to estimate individual performance scores and updates skill ratings via a Bayesian approach; applying it to five years of League of Legends data showed it better predicts game outcomes and aligns with expert opinions compared to existing methods.
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.