The first step is to understand the simulator’s architecture: it aggregates historical match data, player ratings, and team statistics to generate probabilistic outcomes for every fixture. This baseline allows us to compare simulated results against actual tournament history, revealing where the model aligns and diverges.

Once the framework is clear, we examine how external variables—such as home advantage, weather, or managerial changes—are weighted. These factors often explain outlier simulations, but they must be contextualised within broader trends to avoid misinterpreting isolated anomalies.