U.S. Election Models Have the Same Flaws as in 2016
The day-to-day moves in probability are too small to support an almost 50 percentage-point drop since March in Trump’s chances of winning.
U.S. President Donald Trump re-election odds may be higher than thought.
Photographer: Chip Somodevilla/Getty Images
One of the major stories of the 2016 Presidential election was how Donald Trump won despite most quantitative analysts giving him anywhere from a 1% to a 15% chance in the weeks leading up to the election. Today the Economist has Trump at a 7% chance. Other sites are more generous, but I can’t find any statistical models giving Trump even a 20% chance. Betting markets are still around 40%, as is the estimate from Professor Philip Tetlock’s Good Judgment Project, which I consider the most reliable available.
I raised an objection before the 2016 election to the quantitative models that was similar to points made by Nassim Taleb, Dhruv Madeka and others. It’s a subtle statistical point, but an important one, and we’re seeing the same flaws in 2020.1 The day-to-day moves in probability are too small to support a 47 percentage-point drop in Trump’s probability of winning since mid-March, to 7% from 54%, at the same time the actual day-to-day moves in the models are implausibly large. Trump’s probability of winning has to be closer to 50% than the quantitative models suggest.