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Game Analytics

Game Analytics. Interactive Digital Entertainment. Digital storytelling, online behavior. Persuasion, value, learning. User behavior, data mining. Development, game economics . Communication in games. Play experience, design. Personal background . MSc. In Natural Sciences

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Game Analytics

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  1. Game Analytics

  2. Interactive Digital Entertainment Digital storytelling, online behavior Persuasion, value, learning User behavior, data mining Development, game economics Communication in games Play experience, design

  3. Personal background • MSc. In Natural Sciences • Large-scale trends and evolutions in time/space, Geographic Information Systems • PhD in Computer Science • Empirical evaluation of games, HCI, user testing, game telemetry • Post doc. At the Center for Computer Games Research, IT University Copenhagen • Play experience, biometrics, game data mining, game development • RA/project lead, Department of Informatics, Copenhagen Business School • Game piracy, behavioral economics, co-creation – more game telemetry data mining • Assistant Prof., Department of Communication, Aalborg University • Yet more game data mining, more game development, more innovation • Co-Founder & Lead Game Analyst, GameAnalytics • Tools and consulting on application of game telemetry to development

  4. Research breakdown • 90% applied research • 10% theory (play experience, play personas) • Collaboration with industry – real needs • Collaboration with international colleagues • 1 single-authored publication ...

  5. Game user research

  6. Game User Research • Focus: How users interact with IDE applications and each other + the business side • Game User Research – answering e.g.: • Who are the users of interactive digital entertainment products? • What do they do and where, with whom and why? • How do we develop products for different users?

  7. Game User Research • Multi-disciplinary ”field” • Researchers from CS, HCI, communication, design, media, psychology, AI, art, economics, development ... • Emergent field – lack of established theory • Exponential growth in research publications • Backed by a growing industry where users are central

  8. Game User Research • Four main lines of investigation in GUR: • Usability: Can the user operate the controls? • Playability: Is the user having a good experience? • Behavior: What is the user doing while playing? • Development: Integrating GUR in business practices

  9. Game User Research • Whyinteresting? • New field of research • Emergingmethodologies + theories • Plenty of tough problems • Collaboration • Broadrelevance • Multi-disciplinary • Affects millions of people • Industry interest • Latesttechnologies New field Multi-disciplinary Impact

  10. Interactive Digital Entertainment Digital storytelling, online behavior Persuasion, value, learning User behavior, data mining Development, game economics Communication in games Play experience, design

  11. User behavior

  12. Game metrics

  13. What are game metrics? • Metrics = Business Intelligence [BI] • BI is derived from computer-based methods for identifying, extracting and analyzing business data • for strategic or operational purposes • Across market-, geographic- and temporal distance • Supports decision making (Decision Support Systems)

  14. What are game metrics? • Quantitative measures about any aspect of games • Players: gameplay, customers, monetization, • Production: team size, pipeline, milestones, markets • Technical performance: servers, infrastructure • Any other relevant quantitative measure (e.g. management) • Analysis of game metrics = game analytics • [No accepted definition (working on a standard)]

  15. What are game metrics? • Metrics are measures, e.g.: • Average playtime per player • Number of ”Swords of Mayhem +5” sold • Daily Active Users • % server uptime/stability • Avg. network latency • Bugs reported/bugs resolved /day • Customer support call avg. length Players Performance Process

  16. Players

  17. Players

  18. Performance

  19. Process

  20. Process

  21. Why game metrics analysis? • Big data: populations not samples • Understanding all players • Research/development out of the lab and into the real world • Big depth: Detailed recording of all aspects of play • Includes communication, navigation, cross-games ... • Combining GUR data sources for in-depth research

  22. Challenges • Behavioral telemetry inform what players are doing, only by inference why • Finding the right features to track is not obvious • Managing the allure of numbers

  23. Game Data Mining

  24. Game data mining • Game data mining = data mining of game metrics • Gartner Group: “the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques”

  25. Game Data Mining: approaches • Common approaches in game data mining: • Description • Characterization • Discrimination • Classification • Estimation • Prediction • Clustering • Association

  26. Description • Simple description of patterns in data • Accomplished using Explorative Data Analysis • Example: how rapidly does the ”warrior” class advance through levels? • Answers many questions from designers and producers

  27. Description • Drill down/across

  28. Prediction • Using a large number of known values to predict possible future values • How many players will an MMORPG have in 3 months? • When will a F2P break the 1 million player threshold? • When will people stop playing? • One of the most widely used data mining methods in game analytics • Persistent world games • MMOs • F2P

  29. Clustering • Orders data into classes, but the class labels are unknown (unsupervised) • Groups formed according to internal similarity vs. across-group dissimilarity • Subjectiveelement • Problems applying algorithms to game metrics

  30. Player Behavior in Tomb Raider: Underworldw/ Alessandro Canossa, Georgios Yannakakis, Julian Togelius, Hector Perez, Tobias Mahlman

  31. Behavior in TRU • Goal: Using gameplay (behavior) metrics to classify the behavior of users • Uses: • Comparingbehaviorwithdesign intent • Optimization of game design • Debugging of playingexperience • Adaptation: Real-time dynamic adaptation to player type

  32. Behavior in TRU • Tomb Raider: Underworld (2008) • AAA-level commercial title • Data from 1.5 million users via Square Enix • Hundreds of variables • Metricsshouldfitpurpose • Selected variables fittingkey game mechanics • Jumping, completion time, causes of death …

  33. Behavior in TRU • Analysis: • Clusteringalgorithms (PCA, k-means) • Self-Organizing Map (unsupervised) • Revealed a 4 distinctbehaviors (94% users) • Players use the entire design space • Behaviors translated into design terms

  34. Behavior in TRU • 8.68% (Veterans):Very few death events (environment). Fast completion times. Generally perform very well in the game. • 22.12% (Solvers): Die rarely, very rarely use the help system. Slow completion. Slow pace of play. • 46.18% (Pacifists):Largest group of players, dies from enemies. Fast completion time, minimal help requests. Good navigation skills, not experienced with FPS-elements in TRU. • 16.56% (Runners): Die often (enemies, environment), uses the help system, very fast completion time

  35. Behavior in TRU Towards big data: • 1st study: 1365 players • 2nd study: 30,000 players • 3rd study: 203,000 players • 4th study (in prep): 1.6 million players • 5th study (in prep): across games • From dozens to hundreds of variables

  36. Behavior in TRU • Can we predict when people stop playing? • Use: uncovering design problems; engagement • Approach • TRU: 7 levels + prologue • 10,000 randomly selected players • 7 groups of metrics (400+ variables) • Training data: lvl 1 • Simple logistic regression best fit: 77.3% (base: 39)

  37. Behavior in TRU • Decision trees (prediction) • Use: predicting player behavior; transparent models – ideal for communicating across stakeholders • Level-2 rewards • Rewards > 10 • Level-3 playtime • -> playtime > 43 minutes : 4 • -> playtime < 43 minutes : 7 • Rewards < 10 : 2 • Lvl 2 rewards and playtime lvl 3 predictors of quitting

  38. Fun Facts aboutCharacter Namesw/ Christian Thurau

  39. Character names • Do paladins always have names like ”Healbot”? • Do Warlocks always have names like ”Ûberslayer?” • Are mages always called ”Gandalf”? • Are there any kind of ?

  40. Character names • 7,938,335 WOW characters (5 years logging) • Name, Race, Class, Playtime, Guild, Server Type, Domain, etc. ...

  41. Some findings 3,803,819 unique names (a surprising lot) More diverse than real-world names - despite naming constrictions Looks like naming is important to players – only unique feature you have RP-characters most diverse (83% unique – rest ~58%)

  42. Any relationships between name and game features? Class

  43. Any relationships between name and game features? Race

  44. Races ”Pretty” ”Bestial”

  45. 2D-isomap projection (dimensionality reduction technique) • ”pretty” races named differently than ”bestial” races • Not due to differences in m/f character ratios

  46. = Gnomes and dwarfs named as ”bestial” races? =

  47. RP vs. PvP/PvE servers • Names on US servers different from EU servers • Except for RP realms (larger overlap btw. EU/US)

  48. Can we predict names? • What is the chance that ”Gimli” will be a dwarf? • Estimated conditional propabilities of a given class/race/server type given a particular character name • Class and Race best predictors, but server type and faction also hints at naming decisions • Some names are very good predictors, others are not -> so yes, Gimli will likely be a dwarf

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