Tuning mobile game design using data mining

摘要

We present an application of data mining to the analysis and tuning of Bad Blood, a video game for Windows Phone developed for the 2012 Microsoft's Imagine Cup competition. The game was developed on a very short time frame (four months) by a small student team (three programmers and one designer). Because of the limited development time available the game could not undergo extensive playtest. More importantly, since the game had to be submitted to the competition we could not leverage digital distribution to update the game, identify design flaws or fix bugs. Accordingly, before submitting the game, we decided to instrument the code to collect as much information as possible about the gameplay, and performed a rather limited playtest during two public events. Then, we applied data mining both to look for peculiar characteristics of the platforms employed, to discover interesting patterns, and to identify flaws and/or opportunities in our game design. Overall, we identified at least one major design flaw regarding the pace in one specific game mode which we solved by introducing new game elements. We also discovered some design opportunities to inherently modify the game difficulty by leveraging upon the way players tend to use the devices.