Anomaly Detection in Player Performances in Multiplayer Online Battle Arena Games
Xin Qian, Rafet Sifa, Xuefei Liu, Shreyashi Ganguly, Borchuluun Yadamsuren, Diego Klabjan, Anders Drachen, Simon Demediuk · 2022
Esports are digital video games that are played professionally. In recent years there has been a growing need to improve the broadcast experience by incorporating real-time data-driven analytics. In these same games, when played by the general public, there is a growing issue of cheating. Using the popular esport and video game DOTA 2 as a case study, we present a novel application of Archetype Analysis that can be used for anomaly detection in player performance. We show how these anomalies can be utilised for both esports broadcasting and cheat detection.