  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.
