GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports

Abstract--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-theart 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. Summoner's Rift - the stage where each game of League of Legends occurs. The first skill based matchmaking algorithm was invented in the 1950s, and eponymous named by, Arpad Elo.