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Multiplayer Online Battle Arena (MOBA) games have received increasingworldwide popularity recently. In such games, players compete in teams againsteach other by controlling selected game avatars, each of which is designed withdifferent strengths and weaknesses. Intuitively, putting together game avatarsthat complement each other (synergy) and suppress those of opponents(opposition) would result in a stronger team. In-depth understanding of synergyand opposition relationships among game avatars benefits player in makingdecisions in game avatar drafting and gaining better prediction of matchevents. However, due to intricate design and complex interactions between gameavatars, thorough understanding of their relationships is not a trivial task.  In this paper, we propose a latent variable model, namely Game AvatarEmbedding (GAE), to learn avatars' numerical representations which encodesynergy and opposition relationships between pairs of avatars. The merits ofour model are twofold: (1) the captured synergy and opposition relationshipsare sensible to experienced human players' perception; (2) the learnednumerical representations of game avatars allow many important downstreamtasks, such as similar avatar search, match outcome prediction, and avatar pickrecommender. To our best knowledge, no previous model is able to simultaneouslysupport both features. Our quantitative and qualitative evaluations on realmatch data from three commercial MOBA games illustrate the benefits of ourmodel.

Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games