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Player Classification in Games via Game Analytics
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Player Classification in Games via Game Analytics


We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data …

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The method can be applied directly in other games. For more info see: http://andersdrachen.files.wordpress.com/2011/01/tombraider_modeling_ieeecig.pdf

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  • Examples of usertelemetry


  • 1. Player Behavior andPlay ExperienceChristian Thurau, Anders Drachen
  • 2. Whoweare
    Christian Thurau, Fraunhofer IAIS
    Anders Drachen, Aalborg University
    Whatwe do
  • 3. Player behavior
    Player behavior - definition:
    Everything a playerdoes in the game
    Moving an avatar
    Interacting with otherplayers
    Exploring an environment
    Assigningorders to units
    Navigating a storyline
  • 4. Behavior and PX
    Behavior is relevant whenconsidering PX:
    Behavioranalysisinforms 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
  • 5. User behavior
    Behavioranalysis: Recent complement to GUR methods:
    Usabilitytesting: Can the useroperate the controls?
    Playabilitytesting: Is the userhaving a goodexperience?
    Behavioranalysis: What is the userdoingwhileplaying?
  • 6. User behavior
    Behaviortraditionallyexploredusingobservation and video capture.
    Games todaycanbecomplex -> challengestraditional GUR methods
    Enter: game telemetry
    Used in general IT sector for 20+ years – only a fewyearswidespreaduse in games (acrossdisciplines – AI, storytelling systems, design…)
  • 7. User behavior
    Game telemetry is anythingthatcanberecorded from a game by an application!
    Player movement
    Firing weapons & usingabilities
    Information flow betweenplayers
    Measures of revenue
    Social networkbetweenplayers
    GUI interaction
    Game economybehavior
  • 8. User behavior
    Game telemetry data:
    Large or small samples
    Answers ”what” and ”who” in game design
    Inferenceonly for ”why” – onlyindirect info on PX
    (usually … - smart people in AI arebuilding models for predicting PX and adapting games in real-time)
  • 9. Gettingtelemetry data
  • 10. Game telemetry
  • 11.
  • 12. User behavior
    Telemetry notably widelyused for online social games
    Facebook games
    Virtual worlds
    Casual games
    These games have a long lifetime = important to monitor usercommunity
    Evaluatedynamics in usercommunity
  • 13. User behavior
    Metricsuse in other game genres catchingup
    Industry racing to adoptmethods - companieshiring
    All major publishersrunninginitiatives
    250+ members in the IGDA GUR SIG
    2ndGUR summit: 70+ participants
    Specializedvendors (e.g. game analytics, kontagent)
    Exponentialincrease in research publications
    First book on the way (spring 2012)
  • 14. Userbehavior
    Implications for research and development: The promise ofBig Data -andBig Depth
    Populations not samples
    Wide range of applications
    Measuringhowusersinteract with games and eachother
    Combiningmetrics with other measures for in-depthuser studies – notably PX
  • 15. Player BehaviorClassification
  • 16. Patterns of play
    Player behaviorclassificationvia game telemetry– aims:
    Distillcomplex datasets to find patterns of behavior[data mining]
    Debugging the playingexperience
    Comparingbehavior with design intent
    Optimization of game design
    Adaptation: Real-time dynamic adaptation to player type
  • 17. Patterns of play
    Fundamental challenge: reducedimensionality
    Can have thousands of behavioral variables (features)
    Find the mostimportantbehavioral variables and classifyplayersaccording to these
    Multiple methodsfor doingthis – all require a human component (deciding the number of classes!)
    Lack ofcomparison of methods
  • 18. Patterns of play
    Non-Negative Matrix Factorization
    Principal Components Analysis
    Archetype Analysis
    Otherapproaches: e.g. self-organizingmaps
    Common - usedbefore
    in behavior
    New – from economics
  • 19. Patterns of play
    Evaluated70k playersof World of Warcraft
    Substantialvariationsin the resultsoffered by the differentmethods (!)
    Differentnumber of classes
    Differentproperty distribution in classes
    Clear challenges to behavioralclassification
    Data types vs. algorithm
    Potential temporal effects (time-series analysis etc.)
  • 20. Thank you