Identifying a Typology of Players Based on Longitudinal Game Data

Abstract

This study describes an approach to identify a typology of players based on longitudinal game data. The study explored anonymous user log data of 1854 players of EverQuest II (EQII)—a massively multiplayer online game (MMOG). The study tracked ten specific in-game player behavior including types of activities, activity related rewards, and casualties for 27 weeks. The objective of the study was to understand player characteristics and behavior from longitudinal data. Primary analysis revealed meaningful typologies, differences among players based on identified typologies, and differences between individual and group related gaming situations.