Wallner, G., and S. Kriglstein. “Visualization-Based Analysis of Gameplay Data-A Review of Literature.” Entertainment Computing (2013).
This paper reviews literature on visualization-based analysis of game metric data in order to give an overview of the current state of this field of research. Exactly, this paper reviews and classifies the visualization methods for game play metrics. Gameplay metrics are automatically collected by logging user initiated events (events that occur when a player interacts with a game). Furthermore, Gameplay metrics are numerical data about players’ behavior and interactions with the game and have become a valuable source for the analysis of how a game is played.
The paper first introduced application areas of visualization methods and then its classification of these visualization methods based on different aspects. Finally authors discussed their results of their review.
The authors classified the visualization methods for gameplay metrics, which intended for the analysis of data internal to gameplay sessions. Table 1 summarizes this classification.
|Target audience||Game developers||Data Cracker
The Unreal Master Control Program
Flying Lab Software
|Players||Rockstar Games Social Club
|Field of application||–||Lithium (for Return to Castle Wolfenstein: Enemy Territory game)
Data Cracker(as a Dead Space 2 tool)
Tracking Real-Time User Experience (TRUE)
Microsoft Game Studios
Age of Empires II
Dragon Age: Origins
Sony Online Entertainment
Unreal Master Control Program.
|Representation||Charts and diagrams||
Half Life: Episode 2
Psychostats (for Half-Life 1 and 2
Tomb Raider: Underworld
World of Warcraft
World of Warcraft
|Self-organizing maps||Tomb Raider: Underworld|
In the discussion part of the paper, at first, authors mentioned some practical problems in collecting and analyzing gameplay metrics data. They also discussed some suggestion about how we should overcome the existing issues in data collection and analyzing. Then, they shortly discussed open problems and future directions for research which mainly evolve around six broad areas. Table 2 briefly mentioned these areas.
|Data selection||Which data should be tracked and how it should be analyzed||
Statistical techniques or aggregated data
Clustering, SOMs or graphs for data with unknown patterns
Frameworks or heuristics
Transforming, cleaning, analyzing and visualizing the very large amount of data
Ways to reduce the visual complexity of the visualizations
|Using clustering in visualization|
|Context||Misinterpretations of the data||
Including contextual data
Being combined with user research methods like playtesting, video capture, or thinking aloud protocols
|Automatic analysis||Automatic detection of patterns in the data to assist human analysis||
Landmarks in virtual environments
Automatic identification of roles that avatars take in a group
The development of unsupervised and supervised machine-learning systems
|Integration||Effective game analytical tools||Integrating game analytical tools in the development process|
|Causal relationships||Understanding causal relationships to enhance playability||–|
To be honest, I didn’t like the paper. Exactly the topic of the paper is really boring. Reading such a tedious paper took lots of time of me, and killed me. However, in the technical view, the paper is written well and includes what it should. The authors mentioned that they reviewed 42 papers in this context, but they provided lots of other examples and references. The number of references, 130 papers, shows their deep analysis.