Advertisement

Game Analytics pp 435-470 | Cite as

Visual Analytics Tools – A Lens into Player’s Temporal Progression and Behavior

  • Magy Seif El-Nasr
  • André Gagné
  • Dinara Moura
  • Bardia Aghabeigi
Chapter

Abstract

As argued in previous chapters, developing engaging interactive new media experiences, including virtual worlds, multi-player or single-player games, involves understanding the target market. Telemetry and metrics can provide a powerful method to enable designers and other industry professionals to gain insight about their users. Previous chapters have discussed telemetry analysis extensively (see  Chaps. 4,  5,  7,  9,  11,  12,  13,  14,  15,  16, and  17).  Chapter 18 introduced the Information visualization field and game visual analytics, specifically. In this chapter, we extend the arguments made in previous chapters, specifically investigating the use of visual analytics systems that reveal player behaviors over time, where we emphasize the temporal dimension as key to revealing information that indicate causes for specific behavior, and they may also give developers more insight about popular patterns of behaviors.

Keywords

Event Category Game Designer Player Behavior Visualization System Telemetry Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

References

  1. Aris, A., Shneiderman, B., Plaisant, C., Shmueli, G., Jank, W., Costabile, M., & Paternò, F. (2005). Human-computer interaction – INTERACT 2005. In M. F. Costabile & F. Paternò (Eds., Vol. 3585, pp. 835–846). Berlin/Heidelberg: Springer. doi: 10.1007/11555261
  2. Bartle, R. (1996). Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD Research, 1(1).Google Scholar
  3. Chittaro, L., Ranon, R., & Ieronutti, L. (n.d.). VU-flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1475–1485. doi: 10.1109/TVCG.2006.109
  4. Chittaro, L., Ranon, R., & Ieronutti, L. (2006). VU-flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1475–1485. doi: 10.1109/TVCG.2006.109.CrossRefGoogle Scholar
  5. Coulton, P., Bamford, W., Cheverst, K., & Rashid, O. (2008). 3D Space-time visualization of player behaviour in pervasive location-based games. International Journal of Computer Games Technology, 2008, 1–5. doi: 10.1155/2008/192153.CrossRefGoogle Scholar
  6. Drachen, A., & Canossa, A. (2009a). Towards gameplay analysis via gameplay metrics. In A. Lugmayr, H. Franssila, P. Näränen, O. Sotamaa, & J. Vanhala (Eds.), Proceedings of the 13th international MindTrek conference everyday life in the ubiquitous era on MindTrek 09 (pp. 202–209). New York: ACM Press. doi: 10.1145/1621841.1621878
  7. Drachen, A., & Canossa, A. (2009b). Analyzing spatial user behavior in computer games using geographic information systems. In International MindTrek conference (pp. 182–189). New York: ACM Press. doi: 10.1145/1621841.1621875
  8. Drachen, A., Canossa, A., & Yannakakis, G. N. (2009). Player modeling using self-organization in Tomb Raider: Underworld. In 2009 IEEE symposium on computational intelligence and games (pp. 1–8). IEEE: Milano. doi: 10.1109/CIG.2009.5286500
  9. Drachen, A., & Canossa, A. (2011). Evaluating motion: Spatial user behaviour in virtual environment. International Journal of Arts and Technology, 4(3), 294–314.CrossRefGoogle Scholar
  10. Drachen, A., Sifa, R., Bauckhage, C., & Thurau, C. (2012). Guns, swords and data: Clustering of player behavior in computer games in the wild. IEEE computational intelligence in games.Google Scholar
  11. Eccles, R., Kapler, T., Harper, R., & Wright, W. (2007). Stories in GeoTime. In W. Ribarsky & J. Dill (Eds.), IEEE symposium on VAST 2007, (pp. 19–26). Sacramento: IEEE. doi:  10.1145/1391107.1391109
  12. Gagné, A., Seif El-Nasr, M., & Shaw, C. (2011). A deeper look at the use of telemetry for analysis of player behavior in RTS games. International Conference on Entertainment Computing (ICEC 2011) Lecture Notes in Computer Science, 6972, 247–257.CrossRefGoogle Scholar
  13. Hochheiser, H., & Shneiderman, B. (2002). A dynamic query interface for finding patterns in time series data. In CHI’02 extended abstracts on human factors in computing systems – CHI ’02 (p. 522). New York: ACM Press. doi: 10.1145/506443.506460
  14. Kapler, T., & Wright, W. (2005). Geo time information visualization. Information Visualization, 4(2), 136–146. doi: 10.1145/1103520.1103526.CrossRefGoogle Scholar
  15. Kapler, T., Harper, R., & Wright, W. (2007). Stories in GeoTime. Visual Analytics Science and Technology, 2007. VAST 2007. Google Scholar
  16. Lusk, E. J. (2006). Email: Its decision support systems inroads—An update. Decision Support Systems, 42(1), 328–332. doi: 10.1016/j.dss.2005.01.001.CrossRefGoogle Scholar
  17. Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., & Yannakakis, G. N. (2010). Predicting player behavior in Tomb Raider: Underworld. In Proceedings of the 2010 IEEE conference on computational intelligence in games (pp. 178–185). Piscataway: IEEE. Retrieved from http://www.itu.dk/∼yannakakis/cig10TRU.pdf
  18. Marsh, T., Smith, S., Yang, K., & Shahabi, C. (2006). Continuous and unobtrusive capture of user-player behaviour and experience to assess and inform game design and development. 1st world conference for fun ‘n games, Preston.Google Scholar
  19. Medler, B., John, M., & Lane, J. (2011). Data cracker. Proceedings of the 2011 annual conference on human factors in computing systems – CHI’11 (p. 2365). New York: ACM Press. doi: 10.1145/1978942.1979288
  20. Moura, D., & Seif El-Nasr, M. (n.d.). Visualizing and understanding players’ behavior in video games: Discovering patterns and supporting aggregation and comparison. GDC submit on games user research.Google Scholar
  21. Moura, D., Seif El-Nasr, M., & Shaw, C. D. (2011). Visualizing and understanding players’ behavior in video games/: Discovering patterns and supporting aggregation and comparison. SIGGRAPH, Los AngelesGoogle Scholar
  22. Nate Hoobler, G. H. (2004). Visualizing competitive behaviors in multi-user virtual environments. Visualization. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.8561
  23. Niwinski, T., & Randall, D. J. (2010). Using telemetry to improve Zombie killing. Game developers conference, Vancouver, CanadaGoogle Scholar
  24. Plourde, P. (2010). Designing assassin’s creed 2. Game developers conference, San Francisco, CaliforniaGoogle Scholar
  25. Rashid, O., Bamford, W., Coulton, P., & Edwards, R. (2006). PAC-LAN. Proceedings of the 2006 ACM SIGCHI international conference on advances in computer entertainment technology – ACE’06 (p. 33). New York: ACM Press. doi: 10.1145/1178823.1178864
  26. Schuh, E., Gunn, D. V., Phillips, B., Pagulayan, R. J., Kim, J. H., & Wixon, D. (2008). TRUE instrumentation: Tracking real-time user experience in games. In K. Isbister & N. Schaffer (Eds.), Game usability advancing the player experience (pp. 237–265). Burlington: Morgan Kaufmann.CrossRefGoogle Scholar
  27. Thompson, C. (2007). Halo 3: How Microsoft labs invented a new science of play. Wired Magazine.Google Scholar
  28. Thurau, C., & Bauckhage, C. (2010). Analyzing the evolution of social groups in world of warcraft. IEEE computational intelligence in games, Los Alamitos.Google Scholar
  29. Zoeller, G. (2010). Development telemetry in video games projects. Game developers conference, San Francisco.Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Magy Seif El-Nasr
    • 1
  • André Gagné
    • 2
  • Dinara Moura
    • 3
  • Bardia Aghabeigi
    • 4
  1. 1.PLAIT Lab, College of Computer and Information Science, College of Arts, Media and DesignNortheastern UniversityBostonUSA
  2. 2.THQAgoura HillsUSA
  3. 3.School of Interactive Arts and TechnologySimon Fraser UniversitySurreyCanada
  4. 4.College of Computer and Information SciencesNortheastern UniversityBostonUSA

Personalised recommendations