Game Data Analysis – Tools and Methods


Game Data Analysis – Tools and Methods
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Overview
Table of Contents
Author
Support
Sample Chapters
  • Familiarize yourself with the main key performance indicators for game data analysis
  • Understand the data mining environment used for game data analysis
  • Choose reporting tools available on the market according to your needs

Book Details

Language : English
Paperback : 86 pages [ 235mm x 191mm ]
Release Date : November 2013
ISBN : 1849697906
ISBN 13 : 9781849697903
Author(s) : Coupart Thibault
Topics and Technologies : All Books, Game Development, Other

Table of Contents

Preface
Chapter 1: Context and Themes in Games
Chapter 2: Common Key Performance Indicators
Chapter 3: Environment and Tools for Data Analysis
Chapter 4: Game Analytics and Generation of Content
Chapter 5: Advanced Analysis and Statistical Methods
Chapter 6: Data Visualization
Chapter 7: Limits of Game Data Analysis
Index
  • Chapter 1: Context and Themes in Games
    • The rise of game analytics
    • Themes in games
      • The desire for reward
        • Ownership
        • Reputation
        • Achievement and collection
      • The desire for challenge
        • Complexity and difficulty
        • Competition between players
      • Desire for imagination
        • Discovery
        • Emotions and sensations
        • Immersion, story, and universe
        • Desire for entertainment
        • Distraction
        • Romping
    • From themes to engagement
      • Video game as a service
      • Free-to-play and engagement
    • References
    • Summary
    • Chapter 2: Common Key Performance Indicators
      • Definition and framework of Key Performance Indicators
        • Criteria
        • Structure
          • Acquisition of new players
          • Retention of players
          • Monetization of players
      • Working regularly with KPIs
      • Summary
      • Chapter 3: Environment and Tools for Data Analysis
        • Typical programming environment for data mining and storage
          • MySQL
          • NoSQL
          • Hadoop and Hive
        • Tools available on the market for quick data mining
          • Available free tools
          • Facebook Insights
          • Google Analytics
        • Commercial solutions
          • Kontagent
          • Honey Tracks
          • Flurry Analytics
      • Tools available for analysis
        • Open source tools
          • R-Project
      • Summary
        • Case study – monetization pop up
          • Inventory of each feature
          • Concrete examples of versioning
            • First example
            • Second example
        • Summary
          • Chapter 5: Advanced Analysis and Statistical Methods
            • General statistical description
              • Central tendencies
              • Dispersion tendencies
              • Statistical distribution and laws
              • Correlation and regression between variables
              • Types of variables
              • Chi-squared test
              • Linear regression
              • Logistic regression
            • Machine learning
              • Definition
              • Supervised learning
              • Unsupervised learning
            • Summary
            • Chapter 6: Data Visualization
              • Recommendations for good practices
                • Basic recommendations
                • Typical data visualization tools
                  • Line chart
                  • Bar chart
                  • Round chart
                  • Heatmap
                • Graphic semiology
              • Typical traps of data visualization
                • The choice of the scale – values on axis
                • The choice of the scale – equivalence and units between variables
              • Summary
              • Chapter 7: Limits of Game Data Analysis
                • Which game analytics should be used
                  • Game analytics as a tool
                    • Game analytics must serve your team
                    • When starting, focus your attention on simple practices
                  • What game analytics should not be used for
                  • Keep away from numbers
                  • Practices that need to be avoided
                • Summary

                Coupart Thibault

                Coupart Thibault had been studying town planning and statistics for years before he developed an in in-game data analysis. Switching from the real world to the virtual world, he studied game design and completed his transformation with his first professional experience at Corexpert. Working on a database of more than 20,000 players, he has experience with the study and analysis of data in order to deliver various insights for the company.
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                Sample chapters

                You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

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                What you will learn from this book

                • Monitor key performance indicators for game data analysis
                • Understand the issues and benefits of different types of data mining environments, such as MySQL or Hadoop
                • Explore the issues and benefits of different reporting solutions, such as Facebook Insights, Kontagent, or Honey Track
                • Avoid some of the typical traps of data analysis
                • Express important game design topics of your products through numbers and indicators
                • Generate content for your game through A/B testing

                In Detail

                Publishing video games online has been gaining in popularity for a number of years, but with the advent of social networks and the use of in-game data analysis recently, its potential profitability has skyrocketed. The power of video game analytics is immensely beneficial if done well; it can provide a lot of information with a high level of relevancy.

                Game Data Analysis - Tools and Methods is a practical, hands-on guide that provides you with a large overview of the choices available performing video game data analysis. From the technical aspect of the field to its implications in terms of game design, you will be able to choose the right tools for your needs.

                This book looks at the most useful key performance indicators used in video games and then highlights the strengths and weaknesses of different solutions that are available in order to collect your data. The book will finally explain the kind of analysis you need to perform according the content of your game.

                You will learn how to generate content through the use of data analysis with A/B testing and multivariate testing. We will also take a look at the general rules of data visualization, and we will describe some of the typical traps that you should avoid when manipulating numbers. So, if you want to acquire all the basics of game data analysis, this book is ideal for you.

                Video Game Data Analysis - Tools and Methods will teach you everything you need to know in order to make the right choice when it comes to the technical solutions and methods available in the field.

                Approach

                This book features an introduction to the basic theoretical tenets of data analysis from a game developer’s point of view, as well as a practical guide to performing gameplay analysis on a real-world game.

                Who this book is for

                This book is ideal for video game developers who want to try and experiment with the game analytics approach for their own productions. It will provide a good overview of the themes you need to pay attention to, and will pave the way for success. Furthermore, the book also provides a wide range of concrete examples that will be useful for any game data analysts or scientists who want to improve their general knowledge of the topic.

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