Player Classification in Games via Game Analytics

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