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PLAY WITH DATA – AN EXPLORATION OF PLAY ANALYTICS AND ITS EFFECT ON PLAYER EXPEREINCES A Dissertation Presented to The Academic Faculty by Ben Medler In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in Digital Media in the School of Literature, Communication and Culture Georgia Institute of Technology August 2012 COPYRIGHT 2012 BY BEN MEDLER PLAY WITH DATA – AN EXPLORATION OF PLAY ANALYTICS AND ITS EFFECT ON PLAYER EXPEREINCES Approved by: Dr. Brian Magerko, Advisor Dr. John T. Stasko School of Literature, Communication School of Interactive Computing and Culture Georgia Institute of Technology Georgia Institute of Technology Dr. Michael Nitsche Michael John School of Literature, Communication Creative Director and Culture Electronic Arts Georgia Institute of Technology Dr. Carl DiSalvo School of Literature, Communication and Culture Georgia Institute of Technology Date Approved: June 13, 2012 To my family, friends and everyone who has supported me over the years. ACKNOWLEDGEMENTS I have been lucky to have Brian Magerko as my graduate adviser for my entire graduate carrier starting at Michigan State University and ending at Georgia Tech. This dissertation would not have been possible without his constant guidance both relating to the work presented in this document and how to navigate the academic world as a whole. He gave me a free hand to pursue my research and I am forever grateful for his support over the years. I am also fortunate to have had an excellent committee filled with a group of intellectuals and practitioners from many fields related to my work. John Stasko, Michael Nitsche and Carl DiSalvo have been my professors and have made my academic career a success. Michael John introduced me to how the games industry functions and this has altered my perspective on academic research. All of them have been supportive and made their mark on my work. There are also many fellow Ph.D. comrades that helped me with my work that I must thank. Paul Clifton, Simon Ferrari, Sergio Goldenberg, Thomas Lodato, Bobby Schweizer provided both excellent conversations and alternate perspectives on my work, in addition to being dear friends. There are also many other Georgia Tech Ph.D. and Master students I call dear friends as well and made my time at Georgia Tech invigorating and spectacular. Thank you: Mariam Asad, Thomas Barnwell, Hank Blumenthal, Ka Nin Chow, Jill Coffin, Clara Fernandez-Vara, Thomas Gibes, Thomas Jenkins, Tanyoung Kim, Friedrich Kirschner, Jonathan Lukens, Hye Yeon Nam, Andrew Quitmeyer, Adam Rice, Ozge Samanci, Beth Schechter, Brian Schrank, Daniel Upton, Chih-Sung (Andy) Wu, Jichen Zhu and Alex Zook. Additionally, big thanks to my CoC gaming group for giving me a place to escape from my work: Mike Helms, Josh Jones, Maithilee Kunda and Bryan Wiltgen. iv I have many thanks to give to the faculty at Georgia Tech for creating an exciting academic environment and to both Janet Murray and Ian Bogost for being wondering graduate directors. I would also like to mention and thank the Georgia Tech staff and administrative member who have helped me over the years: Grantley Bailey, Kenya Devalia, Matthew Mcintyre, Alison Nichols and Melanie Richard. Furthermore, I am grateful for the funding received from the National Science Foundation and Electronic Arts that helped me complete this dissertation. Thank you to all of my friends I made in Georgia during my Ph.D. carrier, both named above and those outside of Georgia Tech, the friends I made while interning at Electronic Arts and my friends I had to physically leave in Michigan where all of this started. I have been very lucky over the years to have such wonder, caring friends. Finally, I would like to thank my family for their unwavering support and devotion to helping me pursue my passions in life. None of this would have been possible without them. v TABLE OF CONTENTS Page ACKNOWLEDGEMENTS iv LIST OF TABLES ix LIST OF FIGURES x SUMMARY xiv PART 1 - CHAPTERS 1 INTRODUCTION 1 Defending Halo Wars 1 Play Analytics 4 The Playful Side of Data Analysis 6 The Player, The Analyst 10 Designing and Experiencing Data Analysis 19 Functional and Phenomenological Aspects of Play Analytics 21 Studying Play Analytics 37 Overview of Dissertation Chapters 41 2 DECONSTRUCTING DATA 44 Defining Data 45 Data Transition and Enrichment 48 Game Data 51 Functional Data and Data Phenomenology 56 Summary 58 3 ENVISIONING VISUALIZATION 60 Your Vision 61 vi Information Overload and Filter Failure 65 Visualization Domains 68 The Functional and Phenomenological Aspects of Visualization 99 Summary 112 4 ANALYZING GAME ANALYTICS 114 Development Analytics 119 Play Analytics 130 The Functional and Phenomenological Aspects of Play Analytics 139 Summary 146 PART 2 - CHAPTERS 5 PLAY ANALYTIC CONTENT ANALYSIS 148 Methodology for a Play Analytic Content Analysis 148 Leaderboards 156 Player Statistics 169 Group Statistics 187 Match Statistics 196 Global Statistics 206 Maps 217 Content Generation 228 Content Database 240 Discussion 251 Summary 262 6 PLAY ANALYTIC USER STUDY 265 Methodology and Study Design 267 Analysis of Study Data 273 vii Discussion 297 7 CONCLUSION 303 What Is Play Analytics? 306 What Play Analytics Can Be 309 Dissertation Contributions 318 The Future of Play Analytic Research 321 APPENDIX A: PLAY ANALYTIC SYSTEM INFORMATION 325 REFERENCES 335 viii LIST OF TABLES Table 6.1: User Study - Time Played 277 Table 6.2: User Study - Cases 281 Table 7.1: Analytic Tasks 310 Table A.1: Play Analytic Systems – 1 to 15 326 Table A.2: Play Analytic Systems – 16 to 30 327 Table A.3: Play Analytic Systems – 31 to 45 328 Table A.4: Play Analytic Systems – 46 to 60 329 Table A.5: Play Analytic Systems – 61 to 75 330 Table A.6: Play Analytic Systems – 76 to 81 331 ix LIST OF FIGURES Page Figure 1.1: Halo Wars 3 Figure 1.2: Properties of Play 8 Figure 1.3: J.S. Joust Exposures 17 Figure 1.4: Platform Studies 20 Figure 3.1: Visual Data Representation 63 Figure 3.2: Starcraft 2 Mini-Map 67 Figure 3.3: Visualization Domains 69 Figure 3.4: Google Analytics 71 Figure 3.5: MRI and fMRI 74 Figure 3.6: Jason Salavon’s Work 75 Figure 3.7: Minimalist Visualization 77 Figure 3.8: Tag Cloud 78 Figure 3.9: Google Book 80 Figure 3.10: Gapminder 81 Figure 3.11: Critical Visualization 84 Figure 3.12: Architecture and Justice 86 Figure 3.13: Infocanvas 87 Figure 3.14: Artifacts of the Presence Era 89 Figure 3.15: Social Collider 90 Figure 3.16: Visual Diary 92 Figure 3.17: Nicholas Felton’s Yearly Reports 93 Figure 3.18: Nike+ 94 x Figure 3.19: Fizz 95 Figure 3.20: Halo Waypoint 96 Figure 3.21: Cyclopath 97 Figure 3.22: Spore Skeleton 98 Figure 4.1: Mochibot 120 Figure 4.2: Tableau 121 Figure 4.3: Skynet 123 Figure 4.4: Data Cracker 125 Figure 4.5: Visual AI Debugging 127 Figure 4.6: Giant Bomb 132 Figure 4.7: The Sims Exchange 133 Figure 4.8: SC2Gears 134 Figure 5.1: Single Criterion Leaderboard 158 Figure 5.2: Multiple Criteria Leaderboard 159 Figure 5.3: Limited Leaderboard 160 Figure 5.4: Friend Leaderboard 161 Figure 5.5: Group Leaderboard 162 Figure 5.6: Accumulated Statistics 171 Figure 5.7: Accumulated Statistics 172 Figure 5.8: Game Mode Statistics 173 Figure 5.9: Demographic Data 174 Figure 5.10: Load-out 175 Figure 5.11: Reputation and Ranking 176 Figure 5.12: Compare Players 176 Figure 5.13: Trophy Rooms 177 xi Figure 5.14: Media Sharing and Storage 178 Figure 5.15: Activity Feed 179 Figure 5.16: Friend List 179 Figure 5.17: Acknowledgement Group 188 Figure 5.18: Collection Group 189 Figure 5.19: Integrated Group 190 Figure 5.20: Support Group 191 Figure 5.21: Accumulated Report 197 Figure 5.22: Detailed Report 198 Figure 5.23: Event Timeline 199 Figure 5.24: Match Leaderboard 199 Figure 5.25: Accumulated Statistics 208 Figure 5.26: Community Collection Event 209 Figure 5.27: Global War 210 Figure 5.28: Usage Statistics 211 Figure 5.29: Game Data Maps 218 Figure 5.30: Player Data Maps 219 Figure 5.31: Global Player Data Maps 220 Figure 5.32: Game Content Sharing 230 Figure 5.33: Game Content Creation 231 Figure 5.34: Non-game Content Creation and Sharing 232 Figure 5.35: Gameplay Analysis and Sharing 234 Figure 5.36: Static Databases 242 Figure 5.37: Dynamic Databases 243 Figure 6.1: Do I Have A Right? 269 xii Figure 6.2: User Study – Play Analytic System Part 1 270 Figure 6.3: User Study – Play Analytic System Part 2 271 Figure 6.4: User Study - Game-related Data Part 1 283 Figure 6.5: User Study - Game-related Data Part 2 284 Figure 6.6: User Study - In-game Information Formats 286 Figure 6.7: User Study - Manage Groups 287 Figure 6.8: User Study - Spectating 288 Figure 6.9: User Study - Web Browsing and Gaming 290 Figure 6.10: User Study - Play Analytics Part 1 291 Figure 6.11: User Study - Play Analytics Part 2 292 Figure 7.1: SC2Gears 311 Figure 7.2: Spore Skeletons 315 Figure 7.3: Just Cause 2 Visualization 316 Figure 7.4: Kingdom Hearts Visualization 317 Figure 7.5: J.S. Joust Visualization 318 xiii SUMMARY In a time of ‘Big Data,’ ‘Personal Informatics’ and ‘Infographics’ the definitions of data visualization and data analytics are splintering rapidly. When one compares how Fortune 500 companies are using analytics to optimize their supply chains and lone individuals are visualizing their Twitter messages, we can see how multipurpose these areas are becoming.

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