Predicting events and behavior in games is a crucial task of game analytics, both during gameplay, such as predicting wins, and after, like forecasting player churn. Game metrics have been widely adopted due to their human-readable and task-oriented nature. However, these metrics vary between games and require expert knowledge. Game analytics has evolved with techniques that primarily handle non-tabular and sequential data, like graphs and time series. However, existing methods still depend on traditional game metrics from extensive feature engineering. In parallel, graph representation learning has been applied to game provenance graphs — structures that log game events as nodes and link them through causal relationships. This approach may allow analysts to bypass traditional game metrics and learn relevant downstream features during training. This paper compares this metric-less graph-based representation learning with traditional metric-based machine learning in two predictive game analytics tasks: win prediction and hit prediction. Our results show that the same graph-based representation learning architecture achieves competitive results in win prediction and outperforms metric-based approaches in hit prediction, suggesting its suitability for predictive game analytics tasks. This method could significantly benefit as it does not require game-specific metric expertise, and its learned features are versatile across multiple tasks.