What head-to-head election polls tell us about November
These graphs show that during the year of the general election, polls gradually converge to a point that is close to the actual November outcome.
Wlezien and Erikson expressed their findings in terms of correlation coefficients. In early February (about 280 days from the election), the correlation between polls and November outcomes is +0.2, where 0.0 corresponds to no relationship and +1.0 indicates a perfect relationship. The correlation rises to +0.9 by October. However, this measure is not easily used by consumers of polls.
Instead, a more intuitive measure is how far polls tend to move over time
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The November outcome should be within 1 SD of current polls approximately two-thirds of the time. Hillary Clinton’s polling margin over Donald Trump is currently +8% (median of 19 pollsters since mid-March) – twice the standard deviation. Based on past years, how likely is it that Trump can catch up? It is possible to convert Clinton’s lead to a probability using the t-distribution*, which can account for outlier events like 1964 and 1980. Using this approach, the probability that Trump can catch up by November is 9%, and the probability that Clinton will remain ahead of Trump is 91%**. This probability doesn’t take into account Electoral College mechanisms. But since the bias of the Electoral College is quite small, it does not make a difference in the calculation.
I should note that the polls have been telling us this information for some time. In the first half of March, Clinton led Trump by a median of 9 percentage points. Using an SD of 4.5 percentage points, her win probability would come out as 93%. So today’s estimate has been knowable for several months.
http://election.princeton.edu/2016/05/01/what-do-head-to-head-general-election-polls-tell-us-about-november/
Pretty weighty stuff. But he has a stellar record for building political models.
