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Introduction IMGD 2905 Breakout Work What is data analysis for game development? Where does this data come from? What can game analysis do for game development? Icebreaker, Groupwork, Questions

  1. Introduction IMGD 2905

  2. Breakout Work • What is data analysis for game development? • Where does this data come from? • What can game analysis do for game development? • Icebreaker, Groupwork, Questions https://web.cs.wpi.edu/~imgd2905/d20/breakout/breakout-1.html

  3. What is data analysis for game development?

  4. What is data analysis for game development? • Using game data to inform the game development process • Where does this data come from? https://cdn0.iconfinder.com/data/icons/big-data-2-1/128/Data- Integration-process-database-collection-512.png

  5. What is data analysis for game development? • Using game data to inform the game development process • Where does this data come from? https://cdn0.iconfinder.com/data/icons/big-data-2-1/128/Data- Integration-process-database-collection-512.png  Players , actually playing game – Quantitative (instrumented) – Qualitative (subjective evaluation) – (But often lots more of the former!) https://cdn2.iconfinder.com/data/icons/sports-and-games-5-1/48/216-512.png

  6. What can game analysis do for game development?

  7. What can game analysis do for game development? • Improve level design – e.g., see where players are getting stuck • Focus development on critical content – e.g., see what game modes or characters are not used • Balance gameplay – e.g., tune parameters for more competitive and fun combat • Broaden appeal – e.g., hear if content/story is engaging or repulsing • Note: game data often informs players , too – Analytics not dissimilar

  8. Why is data analysis for game development needed?

  9. Why is data analysis for game development needed? • Challenge – Games gotten larger and more complex • Number of reachable states, characters  Game balance harder to achieve – Need for metrics to make sense of player behavior has increased • Opportunity – New technologies enable aggregation, access and analysis

  10. IMGD 2905 – Doing Data Analysis for Game Development • Data analysis pipeline – get data from games, through analysis, to stakeholders • Summary statistics – central tendencies of data • Visualization of data – how to display analysis, illustrate messages • Statistical tests – quantitatively determine relationships (e.g., correlation) – Probability needed as foundation (also used for game rules) • Regression – model relationships • More advanced topics (e.g., ML, For this class: Data management …) Described in lecture Read about in book Applied in projects and homework

  11. Foundations for Data Analysis @ WPI • Statistics classes – MA 2610 Applied Statistics for Life Sciences – MA 2611 Applied Statistics I – MA 2612 Applied Statistics II Note – other Stats and • Probability classes Probability classes are – MA 2621 Probability for Applications primarily geared for • Math majors Data Science (minor and major) – DS 1010 Introduction to Data Science – DS 2010 Modeling and Data Analysis – DS 3010 Computational Data Intelligence – DS 4433/CS4433 Big Data Management and Analytics • Data Mining – CS 4445 Data Mining and Knowledge Discovery in Databases • Other – CS 1004 Introduction to Programming for Non-Majors – CS 3431 Database Systems I

  12. Outline • Overview (done) • Game Analytics Pipeline (next) • Game Data Analysis Examples

  13. Sources of Game Data Quantitative (Objective) Qualitative (Subjective) • Internal Testing • Surveys • Reviews – Developers • Online communities – QA • Postmortems • External Testing – Usability testing – Beta tests – Long-term play data https://tinyurl.com/y3gaja4j How to get from data to dissemination?  Game analytics pipeline https://cdn.lynda.com/course/699344/699344-636703722500462287-16x9.jpg

  14. Game Analytics Pipeline Game Analysis Exploratory Graphs/Stats Extracted Data Statistical Tests Raw Data Charts and Tables Dissemination Report Presentation

  15. Game Analytics Pipeline – Example Analysis Track-o-Bot Dissemination Project 3!

  16. Game Analytics Tools • Games – breadth of experience with games, specific experience with game to be analyzed • Tools – import, clean, filter, format data so can analyze • Statistics – measures of central tendency, measures of spread, statistical tests • Probability – rules, distributions • Data Visualization – bar chart, scatter plot, histogram, error bars • Technical Writing and Presentation – white paper, technical talk; audience is peer group, developers, boss

  17. Outline • Overview (done) • Game Analytics Pipeline (done) • Game Data Analysis Examples (next)

  18. Example: Project Gotham Racing 4 K. Hullett, N. Nagappan, E. Schuh, and J. Hopson. “Data Analytics for Game Development”, International Conference on Software Engineering (ICSE ), May, 2011, Waikiki, Honolulu, HI, USA http://dl.acm.org/citation.cfm?id=1985952 • Publisher – Microsoft 2007 – 134 vehicles, 9 locations, 10 game modes • Analyzed data – (Authors worked at Microsoft) – 3.1 million log entries, 1000s of users

  19. Project Gotham Racing 4: Results Game Mode Races % Total • Thoughts? OFFLINE_CAREER 1479586 47.63% PGR_ARCADE 566705 18.24% NETWORK_PLAY 584201 18.81% SINGLE_PLAYER_PLAY 185415 5.97% • What are some …. main NET_TOURNY_ELIM 2713 0.09% messages? Group Races % Total STREET_RACE 795334 25.60% NET_STREET_RACE 543491 17.50% ELIMINATION 216042 6.95% HOTLAP 195949 6.31% … TESTTRACK_TIME 7484 0.24% CAT_N_MOUSE_FREE 3989 0.13% CAT_N_MOUSE 53 0.00%

  20. Project Gotham Racing 4: Results • Mode Game Mode Races % Total OFFLINE_CAREER 1479586 47.63% – Offline career PGR_ARCADE 566705 18.24% dominates NETWORK_PLAY 584201 18.81% – Network SINGLE_PLAYER_PLAY 185415 5.97% tournament hardly …. used NET_TOURNY_ELIM 2713 0.09% • Events – Street race and Group Races % Total network street race STREET_RACE 795334 25.60% dominate NET_STREET_RACE 543491 17.50% – Cat and mouse ELIMINATION 216042 6.95% never used HOTLAP 195949 6.31% • Vehicles (not shown) … – 1/3 used in less TESTTRACK_TIME 7484 0.24% than 0.1% of races CAT_N_MOUSE_FREE 3989 0.13% CAT_N_MOUSE 53 0.00%

  21. Project Gotham Racing 4: Conclusion • Content underused - 30-40% of content in less than 1% of races • Use to shift emphases for DLC, next version – Asset creation costs significant, so even 25% reduction noticeable • Other (not shown) – Encouraging new players to play career mode • Increasing likelihood of continuing play – Encouraging new players to stay with F Class longer • Rather than move to more difficult to control A Class

  22. Example: Halo 3 B. Phillips. “Peering into the Black Box of Player Behavior: The Player Experience Panel at Microsoft Game Studios”, Game Developers Conference (GDC) , 2010. http://www.gdcvault.com/play/1012387/P eering-into-the-Black-Box • Publisher – Microsoft 2007 – Achievements: single player missions, challenges such as finding skulls, multiplayer accomplishments… • Analyzed data – (Author worked at Microsoft) – 18,0000 players

  23. Halo 3: Results • Thoughts? • What are some main messages?

  24. Halo 3: Results • 73% of players completed campaign – Can compare to other Xbox games • Took 26 days to accomplish • Double that time for all original content • DLC provides users up to 2 years of content Good Descriptive Example

  25. Example: League of Legends Mark Claypool, Jonathan Decelle, Gabriel Hall, and Lindsay O'Donnell. “Surrender at 20? Matchmaking in League of Legends,” In Proceedings of the IEEE Games, Entertainment, Media Conference (GEM) , Toronto, Canada, October 2015. Online at: http://www.cs.wpi.edu/~claypool/papers/lol-matchmaking/ • ??? Publisher – Riot Games 2009 – Rank: ~5 Tiers, 5 divisions each  25 • User study (52 players) – Play LoL in controlled environment Fun – Record objective data Sweet spot • (e.g., player rank and game stats) – Provide survey for subjective data • (e.g., match balance and enjoyment) Too hard! Just right! Too easy! Game Balance

  26. League of Legends: Results Main messages? Objective Main messages?

  27. League of Legends: Results Main messages? Most teams are balanced But about 10% more than 3 from mean Subjective Objective Main messages? Most games evenly matched But about 5% difference of 2 from mean

  28. League of Legends: Results Win? Game is balanced Most teams are balanced Lose? Game is But about 10% more than imbalanced 3 from mean Subjective Objective Win? Game is fun (70%), never not fun Most games evenly matched Lose? Game But about 5% difference of 2 is almost from mean never fun (90%)

  29. League of Legends: Results Fun Sweet spot Game Balance Sweet spot? Fun Game Balance Imbalance in player’s favor the most fun! Matchmaking systems may want to consider - e.g., balance not so important, so long as player not always on imbalanced side 29

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