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Player Behavior andPlay ExperienceChristian Thurau, Anders Drachen
Who we are
Christian Thurau, Fraunhofer IAISAnders Drachen, Aalborg University
What we do
Player behavior
Player behavior - definition:
Everything a player does in the game Moving an avatar Interacting with other players Exploring an environment Assigning orders to units Navigating a storyline Etc.
Behavior and PX
Behavior is relevant when considering PX:
Behavior analysis informs PX evaluations Behavior analysis provides evidence on PX
problems Lack of progress Interference by other players No attention to surroundings GUI issues
Behavioral analysis can be carried out at multiple scales – from one player to millions Distilling behavior into classes provides the means to detect
unwanted behavior and address the root causes (e.g. archetype analysis)
Testing and refining game design
User behavior Behavior analysis: Recent complement to GUR
methods:
Usability testing: Can the user operate the controls?
Playability testing: Is the user having a good experience?
Behavior analysis: What is the user doing while playing?
User behavior
Behavior traditionally explored using observation and video capture.
Games today can be complex -> challenges traditional GUR methods
Enter: game telemetry
Used in general IT sector for 20+ years – only a few years widespread use in games (across disciplines – AI, storytelling systems, design…)
User behavior
Game telemetry is anything that can be recorded from a game by an application! Player movement Firing weapons & using abilities Information flow between players Measures of revenue Social network between players GUI interaction Game economy behavior ….
User behavior
Game telemetry data: Highly detailed Large or small samples Unobtrusive Can be combined with qualitative methods
Answers ”what” and ”who” in game design
Inference only for ”why” – only indirect info on PX (usually … - smart people in AI are building
models for predicting PX and adapting games in real-time)
Getting telemetry data
Game telemetry
User behavior
Telemetry notably widely used for online social games Facebook games MMOGs Virtual worlds Casual games
These games have a long lifetime = important to monitor user community Evaluate dynamics in user community Detect disruptive user behavior
User behavior
Metrics use in other game genres catching up Industry racing to adopt methods -
companies hiring All major publishers running initiatives 250+ members in the IGDA GUR SIG 2nd GUR summit: 70+ participants Specialized vendors (e.g. game analytics,
kontagent) Exponential increase in research
publications Strong industry-academia collaborations First book on the way (spring 2012)
User behavior
Implications for research and development: The promise of Big Data - and Big Depth Populations not samples Wide range of applications
Measuring how users interact with games and each other
Combining metrics with other measures for in-depth user studies – notably PX
Player Behavior Classification
Patterns of play
Player behavior classification via game telemetry – aims:
1. Distill complex datasets to find patterns of behavior [data mining]
2. Debugging the playing experience
3. Comparing behavior with design intent
4. Optimization of game design
5. Adaptation: Real-time dynamic adaptation to player type
Patterns of play
Fundamental challenge: reduce dimensionality Can have thousands of behavioral variables
(features)
Find the most important behavioral variables and classify players according to these
Multiple methods for doing this – all require a human component (deciding the number of classes!)
Lack of comparison of methods
Patterns of play
We compared: K-means clustering C-means, Non-Negative Matrix Factorization Principal Components Analysis Archetype Analysis
Other approaches: e.g. self-organizing maps
Common - used before in behavioranalysis
New – from economics
Patterns of play
Evaluated 70k players of World of Warcraft
Substantial variations in the results offered by the different methods (!) Different number of classes Different property distribution in classes
Clear challenges to behavioral classification Scaling effects Data types vs. algorithm Potential temporal effects (time-series analysis
etc.)
Thank you