Game design and development activities are increasingly relying on the analysis of gamer's behavior and preferences data. Various tools are available to the developers to track and analyze general data concerning acquisition, retention and monetization aspects of game commercialization. This is good enough to give hints on where problems are, but not to enable a precise diagnosis, which demands fine-grained data. For this kind of data, there is not enough support or guidance to decide which data to capture, to write the code to capture it, to choose the best representation of it and to allow an adequate retrieval and presentation of it. This paper introduces GameGuts (GG), a framework devoted to give further assistance to developers in choosing, representing, accessing and presenting game sessions fine-grained data. As a case study, GG recorded sessions of a game platform with over a hundred thousand users. The logs were analyzed using a Visual Domain Specific Language (as a query language) and an ensemble of rules (as a compliance test). The results are encouraging, since we could - among other results - find bugs and catch cheaters, as well as spot design flaws.