Player Profiling in World of Warcraft Using CTGAN-Generated Synthetic Data and Bartle’s Archetypes
Resumo
Introduction: Player behavior analysis in MMORPGs is essential for game analytics, yet it remains constrained by limited access to large-scale player telemetry. Existing player classification approaches, such as Bartle’s taxonomy, typically assign players to a single discrete category, overlooking mixed motivations. Objective: This work proposes a methodology for profiling World of Warcraft players using synthetic data generated by Conditional Tabular GANs (CTGAN), extending Bartle’s archetypes to percentage-based representations that capture mixed motivations. Methodology: The World of Warcraft Avatar History (WoWAH) dataset was preprocessed and used to train four generative models (Gaussian Copula, CTGAN, TVAE, CopulaGAN) under identical conditions. CTGAN was selected based on superior correlation preservation (Δ = 0.056), which is particularly relevant for downstream profiling tasks that rely on combinations of gameplay metrics. A percentage-based scoring framework maps gameplay features to archetype scores using conceptually defined weights grounded in Bartle’s taxonomy and supported by empirical findings from prior work. Results: CTGAN preserved categorical distributions (Chi-Square p > 0.99) and inter-feature correlations with the highest fidelity among all evaluated models. The profiling methodology produced strong correlations between archetype scores and expected gameplay metrics (r > 0.85), with distributions consistent with known characteristics of the game. These results suggest that synthetic data combined with continuous player profiling enables behavioral analysis in data-scarce environments, reducing dependency on proprietary telemetry and supporting more accessible game analytics research.
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