Kickass

Chapter 2: Game Analytics – The Basics
Anders Drachen1,2,3, Magy Seif El-Nasr1, Alessandro Canossa1,4
1

Northeastern University
Aalborg University
3
Game Analytics
4
ITU University, Copenhagen
2

Take Away Points:

Overview of important key terms in game analytics

Introduction to game telemetry as a source of business intelligence.

In-depth description and discussion of user-derived telemetry and metrics. 
Introduction to feature selection in game analytics
 Introduction to the knowledge discovery process in game analytics
 References to essential further reading.

1

Analytics – a new industry paradigm

Developing a profitable game in today’s market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players’ time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other
IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment.
Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The methodological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predictive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007).
Analytics typically relies on computational

References: Harvard Business School Press, 2007. 2. Web Analytics Association. Web Analytics Definitions, August 16th, 2007. URL: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDe 4. Rud, O. (2009). Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy 5. H. P. Luhn (1958). A Business Intelligence System. IBM Journal 2 (4): 314. Nacke, L.; Drachen, A. (2011): Towards a Framework of Player Experience Research. In Proceedings of the 2011 Foundations of Digital Games Conference (Bordeaux, France), 7. Law, E., Vermeeren, A. P. O. S., Hassenzahl, M. and Blythe, M. 2007. Towards a UX manifesto People and Computers XXI: HCI ... but not as we know it. (2007), [insert City of Publication], 205-206. 8. Isbister, K. and Schaffer, N. 2008. Game Usability: Advancing the Player Experience. 9. Kim, J. H., Gunn, D. V., E., S., Phillips, B. C., Pagulayan, R. J. and Wixon, D. 2008. Italy, 2008), 443-451. 10. Medlock, M. C., Wixon, D., Terrano, M., Romero, R. L. and Fulton, B. 2002. Using the RITE method to improve products: A definition and a case study Usability Professionals Association (Orlando, Florida, 2002). Morgan Kauffman Publishers, 2011. 14. Drachen, A. and Canossa, A. 2009. Towards Gameplay Analysis via Gameplay Metrics. In Proceedings of the 13th MindTrek (Tampere, Finland, 2009). ACM-SIGCHI Publishers. 15. Bohannon, J.: Game-Miners Grapple With Massive Data. Science 330(6000) (2010) 30–31 16 games. In The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pages 883-903. L. Erlbaum Associates, 2003. 17. Han J., Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2005. 18. Laramee, F. E. 2005. Secrets of the Game Business. Charles River Media.