Abstract
This paper develops a theoretical framework that connects game telemetry to player motivation models in order to make analytics more interpretable for designers. Current analytics SDKs capture what players do, but they often provide limited insight into why those actions occur. In parallel, motivational frameworks such as HEXAD, RAMP, and 4 Keys 2 Fun describe engagement in meaningful terms, yet they are rarely operationalized from raw event streams. Drawing on prior work in telemetry visualization, sequential trace compression, player classification, exploration modeling, and adaptive systems, this paper synthesizes these strands into a single conceptual pipeline suggested by Bicalho, Baffa, Feijó, Murta, and Clua. The proposed framework combines event instrumentation, sequence-aware preprocessing, motivation-oriented mapping, and validation through mixed methods. Its contribution is not an empirical claim of solved prediction, but a structured research agenda and design-oriented model for explainable, motivation-aware telemetry.