OpenSkill: A faster asymmetric multi-team, multiplayer rating system

Vivek Joshy
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Abstract

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.
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OpenSkill:更快的非对称多团队、多人对战系统
评估和比较在线多人游戏环境中玩家的技能对于公平匹配和玩家参与至关重要。Elo 和 Glicko-2 等传统排名模型是为双人游戏设计的,不足以应对复杂的多人非对称团队比赛。为了弥补这一不足,OpenSkill 库提供了一套复杂、快速、适应性强的模型,专为此类动态比赛量身定制。借鉴贝叶斯推理方法,OpenSkill 可以更准确地表示玩家的个人贡献,并加快排名计算速度。本文介绍了 OpenSkill 库,其中包括 Plackett-Luce 模型的 Python 实现,突出了其性能优势以及与 TrueSkill 等专有系统相比的预测准确性。OpenSkill 是游戏开发人员和研究人员的重要工具,它能根据游戏结果有效调整玩家排名,确保游戏体验的响应性和公平性。该库对时间衰减的支持和详尽的文档记录进一步帮助了其实际应用,使其成为适用于细微差别的多人游戏排名系统的强大解决方案。本文还指出了未来需要改进的地方,如部分游戏和贡献加权,强调了该库的持续发展,以满足在线游戏社区不断变化的需求。
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