Real-Time Player Engagement Measurement Using Nonintrusive Game Telemetry

Abstract

Player engagement is crucial for the success of modern video games, yet its real-time measurement remains challenging due to the intrusive nature of traditional measurement methods. In this article, we present a novel framework for nonintrusive, real-time, and indirect measurement of engagement in multiplayer online games based on flow theory. Our approach combines graph convolutional networks for modeling player interactions with Transformer networks for temporal processing, enabling indirect measurement of both player skill and game challenge, which in turn are used to classify player engagement. Using playerunknown’s battlegrounds (PUBGs) as a case study, we demonstrate that our framework can effectively measure phase-specific engagement using one minute of gameplay telemetry data. Our framework achieves 73% accuracy and 0.83 ROC-AUC in engagement classification, matching the performance of traditional survey-based methods while operating nonintrusively and in real time. Further cross-domain validation of the framework, as is and without transfer learning, with the games FIFA’23 and Street Fighter V, leads to 66% accuracy, demonstrating the model’s stable performance despite the significant differences in the test domains. Interestingly, our results suggest that objective gameplay metrics may better reflect engagement than subjective player assessments, with skill estimates showing significant correlation with self-reports.