At the close of 2025, the Future Perfect team at Vox revisited 25 forecasts made at the beginning of the year, finding 19 predictions came to fruition while four did not. The annual exercise, designed to gauge the accuracy of prospective analysis, assigns probabilities to each forecast, reflecting the team's confidence level. Predictions with over 50 percent probability that proved correct, or those under 50 percent that did not, were deemed accurate. Conversely, predictions above 50 percent that failed, or below 50 percent that succeeded, were marked as incorrect.

The forecasting method, according to Bryan Walsh, a lead member of the Future Perfect team, aims to provide a transparent assessment of their predictive capabilities. "By assigning probabilities, we're not just saying what we think will happen, but how confident we are in that assessment," Walsh stated. This approach allows for a more nuanced understanding of the factors influencing future events and the inherent uncertainty involved.

One key aspect of the team's methodology is the reliance on data-driven analysis and expert consultation. Dylan Matthews, another member of the team, emphasized the importance of consulting diverse sources and perspectives. "We strive to incorporate a wide range of viewpoints and data points into our forecasts," Matthews explained. "This helps us to mitigate bias and improve the accuracy of our predictions."

However, unforeseen circumstances, such as governmental delays in data release, occasionally rendered some forecasts unverifiable. In such instances, the team acknowledged the limitations of predictive modeling in the face of unpredictable events. The team acknowledged that the accuracy of predictions can be affected by unforeseen events, such as policy changes or unexpected technological advancements.

The Future Perfect team intends to refine its forecasting methodology based on the lessons learned from the 2025 review. Marina Bolotnikova, a data scientist on the team, highlighted the importance of continuous improvement. "We're constantly evaluating our methods and seeking ways to enhance our predictive accuracy," Bolotnikova said. "This includes exploring new data sources, refining our statistical models, and incorporating feedback from experts in various fields." The team plans to publish a detailed analysis of its forecasting performance, including a discussion of the factors that contributed to both successful and unsuccessful predictions.