A Computational Approach to the Study of Player and Group Performance in Massively Multiplayer Online Games.

The market for video games skyrocketed over the past decade. Massively Multiplayer Online Games (MMOGs) have become increasingly popular and have communities comprising over 47 million subscribers by the year 2008. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. Each action is logged, and the level of granularity and completeness with which information is collected is unmatched by any real-life experimental setup. They serve as unprecedented tools to theorize and empirically model the social and behavioral dynamics of individuals, groups, and networks within large communities. Virtual world applications usually have a thin-client architecture, practically all user actions are captured in the click-stream logged at the server. This dataset contains a comprehensive record of every user's in-network activities, accomplishments, interactions, economic status, etc. A brief record of the user's side information (i.e. profile data) is also stored. It is common for popular social networking and collaborative systems to have hundreds of thousands of users generating copious amounts of data based on the many different activities they are participating in at any given time. The data also has a temporal component which is often an integral part of the analysis and introduces further relationships that must be accounted for. Thus, while providing an exciting new tool for the social sciences, the virtual worlds also present a set of difficult and novel computational challenges. In the gaming community today, there is a growing interest in understanding player behaviors both inside and outside the gaming space. Game companies are interested in finding out how their games are played, if they are being played as intended, how the different game mechanics are being played out and how the different game playing patterns lead to a high level of satisfaction and entertainment for customers. Retrospective analyses after the game launch on existing game features can reveal information on which features enhance player's gaming experience and to which demographic segments they especially appeal to. Features negatively correlated with gaming experience can be considered for removal while those positively correlated with gaming experience can be further enhanced. For new game features, prospective analyses before the game launch can reveal information on which features might appeal to certain player population segments with a certain level of confidence and user-oriented testing can focus on these features for further validation. This thesis work presents the first comprehensive quantitative analysis of an important aspect of MMOG game play, namely player and group performance. While there are many different game genres (i.e. action, shooter, action-adventure, adventure, role-playing, and simulation) and many dimensions comprising players' game-play experience, in certain game genres such as MMOGs, close connection has been reported between player enjoyment and completing challenges and mastering skills. A systematic study of individual game player characteristics, group composition and characteristics, social interactions amongst the group members, and game environments can reveal a great deal about what are the recipes for success in achieving various objectives in the game. Broadly, this thesis work seeks to develop 1) Player performance metrics and prediction models, 2) Player activity prediction model, 3) Player enjoyment prediction model, and 4) Group performance metrics and prediction models. Lastly, we contribute a single, generic framework for player and group behavior analysis that is applicable to other MMOGs with minimal configuration changes.