Hardcore Gamer Profiling: Results from an unsupervised learning approach to playing behavior on the Steam platform

Based on a very large dataset of over 100 million Steam platform users we present the first comprehensive analysis of hardcore gamer profiles by over 700,000 hardcore players (users playing more than 20 hours per week) covering more than 3,300 games. Using an unsupervised machine learning approach we reveal the specific behavioral categories of hardcore players, i.e. First Person Shooter, Team Fortress 2 player, Action game player, Dota 2 player, Strategy and action combiner, Genre-switching player. Subsequently we derive individual patterns of hardcore gamers in the categories found, such as strategy-action games combiner or game switching players. Our results are useful for computer science and information systems scholars interested in individual differences in user behavior as well as practitioners interested in game-designing.