Poll Aggregation Models
How poll aggregators like FiveThirtyEight combine multiple polls into election forecasts and why aggregation outperforms individual polls.
Why Averaging Beats Any Single Poll
Individual polls are snapshots with sampling error, methodological quirks, and potential biases. Averaging multiple polls cancels out random errors and reduces the impact of any single poll's bias. A simple average of the last 10 polls in a state is typically more accurate than any individual poll, including the best one.
Sophisticated aggregation models go further. FiveThirtyEight's model, developed by Nate Silver, weights polls by recency (newer polls get more weight), sample size, and pollster quality (based on historical accuracy). It adjusts for known pollster biases: if a firm consistently overestimates Democrats by 2 points, its polls are shifted accordingly. The model also accounts for correlation between states (a shift in Pennsylvania likely means a similar shift in neighboring states) and incorporates non-polling information like economic indicators.