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Competitive Balance in Team Sports Games
arXiv - CS - Multiagent Systems Pub Date : 2020-06-24 , DOI: arxiv-2006.13763
Sofia M Nikolakaki and Ogheneovo Dibie and Ahmad Beirami and Nicholas Peterson and Navid Aghdaie and Kazi Zaman

Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.

中文翻译:

团体运动比赛中的竞争平衡

竞争是多人在线游戏中玩家满意度和参与度的主要驱动因素。传统的匹配系统旨在创建涉及具有相似聚合个人技能水平的团队的比赛,例如 Elo 分数或 TrueSkill。但是,不能仅使用此类线性预测器来捕获团队动态。最近,已经表明,旨在学习作为球员和球队特征的函数的获胜概率的非线性预测器明显优于这些基于线性技能的方法。在本文中,我们表明使用最终得分差异为竞争平衡提供了更好的预测指标。我们还表明,在一组精心挑选的团队和个人特征上训练的线性模型几乎实现了更强大的神经网络模型的性能,同时提供了两个数量级的推理速度提升。这显示了在在线配对系统中实施的重大前景。
更新日期:2020-06-25
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