Title: Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games,
Abstract: Multiplayer Online Battle Arena (MOBA) games have received increasing
worldwide popularity recently. In such games, players compete in teams against
each other by controlling selected game avatars, each of which is designed with
different strengths and weaknesses. Intuitively, putting together game avatars
that complement each other (synergy) and suppress those of opponents
(opposition) would result in a stronger team. In-depth understanding of synergy
and opposition relationships among game avatars benefits player in making
decisions in game avatar drafting and gaining better prediction of match
events. However, due to intricate design and complex interactions between game
avatars, thorough understanding of their relationships is not a trivial task.
In this paper, we propose a latent variable model, namely Game Avatar
Embedding (GAE), to learn avatars' numerical representations which encode
synergy and opposition relationships between pairs of avatars. The merits of
our model are twofold: (1) the captured synergy and opposition relationships
are sensible to experienced human players' perception; (2) the learned
numerical representations of game avatars allow many important downstream
tasks, such as similar avatar search, match outcome prediction, and avatar pick
recommender. To our best knowledge, no previous model is able to simultaneously
support both features. Our quantitative and qualitative evaluations on real
match data from three commercial MOBA games illustrate the benefits of our
model.