<![CDATA[Player retention is the primary economic driver in the Free-to-Play (F2P) gaming industry, yet traditional churn prediction methodologies often rely solely on binary classification, neglecting the critical temporal dimension of when a player is likely to leave. Furthermore, the scarcity of open-source behavioral datasets restricts the development of reproducible academic frameworks. This study addresses these gaps by proposing a hybrid analytical framework that integrates Ensemble Learning (XGBoost) for predictive precision and Survival Analysis (Cox Proportional Hazards) for time-to-event risk modeling, utilizing a realistically simulated dataset of 5,000 players. Experimental results indicate that while the XGBoost model achieves robust discriminative stability with an AUC of roughly 0.90, the Survival Analysis provides deeper explanatory insights, revealing that game progression (level_reached) is a significantly more dominant determinant of retention (Hazard Ratio < 1.0) than short-term recency metrics. These findings suggest that depth of commitment acts as a stronger buffer against churn than login frequency, offering game developers a quantifiable basis to shift retention strategies from generic daily incentives to progression-based milestones. By openly providing the simulated dataset and full analytical pipeline, this work also contributes a reproducible methodological template for future game analytics research in data-scarce environments.]]>
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