Analyzing Player Behavior in Online Games

While the ubiquity of บาคาร่าเว็บตรง ไม่ผ่านเอเย่นต์ playing and gaming as a form of play has made it a natural candidate for gamification, gaining insights into the players’ behaviors is difficult. Behavioral profiling has a long history based on information from user-testing, surveys and marketing data, but only recently did we start to correlate gameplay observations with these profiles (e.g., ).

The main challenge is to find patterns that are common across the large and diverse set of games that people play. Identifying these patterns may have profound implications for the design of games, for example helping to improve retention or engagement, or detecting and moderating abnormal behaviors (e.g., bad play or toxicity).

Analyzing Player Behavior in Online Games

Behavioral datasets from major commercial games typically feature high dimensionality, covering tens of millions of players over years and real time. This makes dimensionality-reduction methods, such as clustering, particularly useful for finding recurring behaviors and patterns in the data.

Several studies have analyzed patterns in gameplay data using machine learning techniques. For example, (Chambers and Saha 2005) found that session length distributions for World of Warcraft players fitted a Weibull distribution. Other studies have analyzed the reward structure of games, for instance demonstrating that being rewarded at 50% of the time maximizes human response (McSweeney and Murphy 2001). This is in stark contrast to gambling, where reward intervals are much lower.

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