Leveraging Data From the Itch.io Online Game Distribution Platform to Help Indie Game Developers

• Author / Creator

  Vu, Ngoc Quang

• In the game distribution world, Steam is often regarded as the most prominent digital platform for its many famous games made by large developers. On the other hand, the itch.io game distribution platform is praised for its friendliness toward small independent (indie) games developed by small teams or even a single developer. Itch.io allows game developers to participate in online game jams (hackathons during which games are built) or publish their games at no publishing cost. In this thesis, we study game data mined from itch.io to help indie game developers: (1) have a higher chance of winning a game jam and (2) increase the discoverability of their games.

  In the first part of the thesis, we study the game jams and their high-ranking submissions to better understand the characteristics of a popular game jam (i.e., a jam that receives many submissions) and the characteristics of high-ranking game submissions in these jams. We collected data of 1,290 past game jams and their 3,752 submissions for our analysis. We found that a quality description contributes positively to a jam's popularity and a game's ranking. Additionally, more manpower organizing a jam or developing a game increases their likelihood of being popular or high-ranking respectively. High-ranking games tend to support Windows or macOS, and belong to the Puzzle'',Platformer'', Interactive Fiction'', orAction'' genres. Finally, shorter competitive jams tend to be more popular. Our findings are useful for both future game jam organizers and participants.

  In the second part of the thesis, we study an approach to increase the discoverability of the indie games hosted on itch.io by recommending similar indie games to players of top-selling Steam games. We implemented a content-based recommendation technique that leverages the similarity in tags, genres, and game description between an indie game and a top-selling game using the metadata of 2,830 itch.io indie games and 326 top-selling Steam games. We then contacted the indie game developers for feedback and suggestion on our approach. We found that the majority (67.9\%) of them show positive support for our idea. We analyzed the downvoted recommendations to understand the reasons and lay out the important requirements for such an indie game recommendation approach. These requirements are useful for future research and development in indie game discoverability and recommendation.

• Subjects / Keywords

  • empirical software engineering
  • games
  • Steam
  • mining software repositories
  • itch.io

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-axz9-2z52

• License

  Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.