论文标题
团队运动中的竞争平衡
Competitive Balance in Team Sports Games
论文作者
论文摘要
竞争是玩家满意度和在多人在线游戏中的参与度的主要驱动力。传统的配对系统旨在创建涉及类似的个人技能水平的团队（例如ELO得分或Trueskill）的比赛。但是，不能仅使用此类线性预测指标捕获团队动力学。最近，已经表明，非线性预测因素将学习获胜的概率作为球员和团队的函数的概率明显优于这些基于线性技能的方法。在本文中，我们表明，使用最终分数差异为竞争平衡提供了更好的预测度量。我们还表明，在精心挑选的团队和个体功能上训练的线性模型几乎可以实现更强大的神经网络模型的性能，同时提供了两个变化级的推理速度提高。这显示了在线对接系统中实施的巨大希望。
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.
