Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games.
翻译:Win预测对于理解埃斯波特的技能建模、团队合作和匹配至关重要。 在本文中, 我们提议GCN- WP, 这是一种半监督的Esport半赢预测模型, 以图形革命网络为基础。 这个模型在一季( 一年) 中学习了 esport 联盟 的结构, 并在另一个类似的联盟上进行预测。 这个模型包含30多个关于比赛和玩家的特征, 并使用图形革命来根据周围的游戏进行分类 。 我们的模型比起机器学习或LoL 的技能评级模型, 实现了最先进的预测准确性。 这个框架可以普遍化, 从而可以很容易地推广到其他多人在线游戏 。