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Designing Games based on Real World Data
Gabriel Dzodom
Introduction
• Web of Data
• (e.g., data.gov)
•…
•…
• satellites
• sensors
• Organizations(e.g. NASA, NOAA, etc…)
• new contexts for engagement and entertainment
Visualization & Simulation (1)
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?
Games
• Advantages
– Entertainment
– Effective tool for motivating and engaging learners
– Others?
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
PCG Data Games – Example (1)
• OpenTrumps– Based on UN database of
countries and demographic indicators
– Software generate balanced deck of cards
PCG Data Games – Example (2)
• Flight Leader– Based on record flight paths or real-time path
from flight24.com
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
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)
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?
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
Fantasy Scotus
Fantasy Forecaster (1)
Fantasy Forecaster (2)
Fantasy Forecaster (3)
Fantasy Forecaster (4)
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?
Survey of Fantasy Sports Users
• Online survey on Amazon Mechanical Turk• Questions regarded experiences, practices and
suggesstions• 160 Responses• Demographics
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).
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)
Results• How do players scope the data they include in
their decision process?
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
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.
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)
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)
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
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…)
Approach – Domain Independent Data Prediction Engine
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
Approach – The Climate Change Case
• Climate-oriented game centered around prediction activities
• Based on historical and real time weather data (temperature, precipitation, etc…)
Approach – The Climate Change Case
Approach – The Climate Change Case
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
Evaluation
• How would you evaluate such system?
