PLAYER GAME DATA MINING FOR PLAYER CLASSIFICATION

Abstract

Analyzing and understanding the standard of players in virtual environments has been an activity increasingly used by digital game developers and producers. Players are the main reason that games are developed and knowing the main characteristics for each of your player is fundamental for game developers have a successful product. In the case of Massively Multiplayer Online Role Playing Games (“MMORPG”), the types of players vary, and, by classifying players’ behaviors, it is possible for developers to implement changes which satisfy players in targeted manners which may impact their level of interest and amount of time spent in the game environment. This study suggests that it is possible to identify and classify players via gameplay analysis by using consolidated theories such as Bartle's archetypes or Marczewski’s types of players, which group players with the k-means algorithm. Below, is presented a dedicated section describing the Game Analytics processes and a session with the results obtained from the analysis of a specific guild from World of Warcraft. Key-words: game analytics, taxonomy of Bartle, classification of players, MMORPG, k-means