1. Mark Nelson mjas@itu.dk Metrics and adaptation Fall 2013 www.itu.dkwww.itu.dk

2. Game metrics  Anything that can be recorded about gameplay  Used to understand how the game works, and how players experience it  Range of goals and techniques  Qualitative, quantitative, game user experience, monetization

3. Some types of game metrics

4. Which player are the metrics about?

5. Basic process  Choose variables to record  Choose frequency of recording  Store to a database  Run query/visualization later  Aggregation, correlation, etc.

6. Simple example

7. Spatial, in-engine metrics  Record positions of interesting events  Visualize frequencies, relationships, etc.  Usually by drawing on the map  One of the most commonly used kinds of metrics  Easy to record, clear how to visualize

8. Heatmap: HL2 deaths

9. Heatmap: TR8 in-game help usage

10. More in-depth spatial analysis  Heatmaps are 2d density estimates from samples  Can do other kinds of density estimates  Or, combine with qualitative data

11. Binned heatmap

12. Using a GIS?  Geographic Information System (GIS)  Used for aggregating spatial data in geography, urban planning, etc.  Some recent experiments with using GIS to analyze ”game geography”

13. GIS overlays

14. ArcGIS overlay (Hitman: Blood Money)

15. In-engine/editor visualization

16. Analytical metrics  Analyzing the game to extract information without player data, or as a supplement to player data  Examples:  Reachability analysis  Game-theory analysis of a combat system  Recent area of research  M.J. Nelson (2011), ”Game Metrics Without Players”

17. More on metrics  Annual conferences/workshops: Game User Experience  Engines increasingly come with their own metrics/viz infrastructure  And extensions, e.g. in the Unity store

18. Adaptation  Can we use metric data automatically?  Offline or online

19. Dynamic difficulty adjustment  Adjust game based on player’s in-game behavior  But ”smarter” than classic rubber-banding style DDA  Information on what player does successfully/unsuccessfully, combined with a model of interestingness

20. Experience-driven PCG  Use metrics information to generate levels or other game content  Offline: a kind of partially automated game design  Online: game customization  Like a more ambitious DDA  Togelius & Yannakakis (2011). ”Experience-driven PCG”

21. Final project  Project can be done individually or in a group  Individually written reports in either case  Scope  There are about 6 weeks left in the semester  Depending on the project, leave ~2 weeks to write up a report

22. Kinds of projects  Implementation projects  Scientific-experiment projects  Engine investigation projects

23. Implementation projects  Examples  Expand your software 3d renderer to do solid surfaces and lighting  Implement a character AI/scripting API  Write a Unity plugin to do something not currently supported

24. Scientific investigation projects  Do an analysis/comparison of algorithms or techniques  Possibilities  Implement several standard algorithms from a textbook (AI, graphics, etc.) and compare them  Implement something proposed in a research paper and try to replicate or extend its results

25. Engine investigation projects  Take an existing game engine and dive into a specific part of it (physics system, AI system, navigation system, …) to understand and characterize how it works.  Possible topics of investigation  What were the design goals, if known?  What does this support well and poorly?  Benchmarks, etc.