1. Designing Games based on Real World Data Gabriel Dzodom

2. Introduction Web of Data (e.g., data.gov) … … satellites sensors Organizations (e.g. NASA, NOAA, etc…) new contexts for engagement and entertainment

3. Visualization & Simulation (1)

4. Visualization & Simulation (2) Problems – user at mercy of designer’s view and understanding of data – design may lead to confusion, misinterpretations, or information overload – usually built on static dataset(s) – no incentive to reengage with visual artifact or simulation system after initial interaction How to encourage engagement with data through new online activities?

5. Games Advantages – Entertainment – Effective tool for motivating and engaging learners – Others?

6. Data Games Definition – Gameplay/game content based on real world data – Supports the exploration of and learning from this data Combines Sensemaking, visualization, and simulation in an entertaining context Approaches – content procedurally generated from data (PCG) – The data is the content

7. PCG Data Games – Example (1) OpenTrumps – Based on UN database of countries and demographic indicators – Software generate balanced deck of cards

8. PCG Data Games – Example (2) Flight Leader – Based on record flight paths or real-time path from flight24.com

9. PCG Data Games – Example (2) Flight Leader – Goal is to guide planes to their destination – “ghost flights” introduced with no destination or on collision path with real flights – Player must ensure that There is no collisions Planes land at the right airport – Knowledge of characteristics and constraints of flight traffic are important

10. PCG Data Games – Design Considerations Data Source – Data Type(text, number, image, etc…) – Data Topic(consequence of complexity) – Static vs Dynamic Data Selection(when, where, who and how) Data Transformation(depends on game genre and the nature of the data)

11. PCG Data Games – Open Issues Which domain(s) are interesting/important? Which domain(s) are usually misunderstood? What is the quality of data? And how do you curate it? What are the appropriate transformation?

12. Data is the content Fantasy Sports – What are they? – Very successful(33.5 million users in 2013, span across multiple sports) – Interaction model is being in other domains

13. Fantasy Scotus

14. Fantasy Forecaster (1)

15. Fantasy Forecaster (2)

16. Fantasy Forecaster (3)

17. Fantasy Forecaster (4)

18. Why are Fantasy Sports successful What design aspects make FS successful? What are the practices of FS users? – How do they select athletes for their teams? – What are their data collection and analysis practices – How do they scope the data they include in their decision process – What is their time commitment? – What are their perceived constraints and issues? And their recommendations?

19. Survey of Fantasy Sports Users Online survey on Amazon Mechanical Turk Questions regarded experiences, practices and suggesstions 160 Responses Demographics

20. Results How do players select athletes for their teams? – strategies that involve analyzing the athletes’ statistical data using a combination of tools built in to the game and external tools (Excel or paper).

21. Results What are players’ data collection and analysis practices? – Data Gathering Resources: on-line sources with preference for external data/information sources than the in-game sources. – Data Analysis Tools: a combination of in-game tools s and external tools (e.g. Excel or paper)

22. Results How do players scope the data they include in their decision process?

23. Results What is the time commitment and activities of players? – Social interaction is a major motivating factor to engage with the game during off season and post team submission

24. Results What aspects of fantasy sports are problematic? – Limitations User interface: lack of control over data presentation and organization, simpler interfaces for novice players Data limitations: missing historical data, slow data updates that undermine data analysis activities. – Recommendations: better data analysis support like data comparison/visualization tools and data export capabilities.

25. Design Implications Obvious ones? Accommodation for different classes of users, designs that foster community building through competition or collaboration support for communication at multiple levels(general level, contest level, and user level)

26. New Questions Are design implications transferable to another (non-entertainment) domain? Will the user behaviours stay the same? – Motivation & engagement – Data Scope – Resource & Tools preferences Is user’s knowledge of the domain improving? What are possible benefits to the users?(especially in an education setting)

27. Data Prediction Games Combines archival data and realtime real data Goal: make prediction(s) about a real world event based on historical data regarding the event Decision making – sensemaking tools, simulation, knowledge resource, and collaboration Fantasy Sports as a data prediction game

28. Approach – Domain Independent Data Prediction Engine Advantages? Three main components – Domain-independent (CMS, messaging, etc...) – Multi-domain(tools, scoring rules, etc…) – Domain-specific (data collectors, parsers, etc…)

29. Approach – Domain Independent Data Prediction Engine

30. Approach – The Climate Change Case Why climate change? – Data meets our requirements for data prediction games – Science still obscure to a good chunk of the public – Politically divisive Our approach – Neutral space that foster self-learning in an entertaining way

31. Approach – The Climate Change Case Climate-oriented game centered around prediction activities Based on historical and real time weather data (temperature, precipitation, etc…)

32. Approach – The Climate Change Case

34. Approach - Prediction Engine Implementation Storage [RDBMS] Data Access Module Content Management CMS LMS Scheduler Score Calculator Activity Manager Data Object Interface Resource Object Interface Toolkit Data Query Engine Simulation Interface Tools Manager Activity Interfaces Messaging Interfaces LMS/CMS Interfaces Toolkit Interfaces … Community-Built Interfaces Application Layer Service Layer Data Layer Messaging

35. Evaluation How would you evaluate such system?

36. For More Information Contact Pr Shipman Or Me (gabriel.dzodom@tamu.edu)gabriel.dzodom@tamu.edu Or come to our lab HRBB 232