Political Prediction and the Wisdom of Crowds

Abstract

We evaluate the relative forecasting performance of three statistical models and a prediction market for several outcomes decided during the November 2024 elections in the United States—the winner of the presidency, the popular vote, fifteen competitive states in the Electoral College, eleven Senate races, and thirteen House races. We argue that conventional measures of predictive accuracy such as the average daily Brier score reward modeling flaws that result in predicable reversals, as long as such movements are in a direction that is aligned with the eventual outcome. Instead, we adopt a test based on the idea that the strength of a model can be measured by the profitability of a trader who believes its forecasts and bets on the market based on this belief. The results of this test depend on the risk preferences with which the trader is endowed, but we show that within a large parameter range this does not lead to ranking reversals. We find that all models failed to beat the market in the headline contract but some did so convincingly in contracts referencing less visible races.