How Did Nate Silver (And Everyone Else) Get Trump So Wrong: The Flip-Flopping Polster

Submitted by Salil Mehta Of Statistical Ideas

The Flip-Flopping Pollster

How poor have the election forecasters been this year?  It is a topic many are discussing given the large number of upsets we've had during the Primaries.  For example, statistician Nate Silver (who started the campaign season proclaiming Trump had <2% chance of being nominated) by March 1 predicted with 94% probability that Trump would win Alaska (he lost). 

Silver then predicted on March 8 with >99% probability that Clinton would win Michigan (she lost).  Silver again predicted on May 3 with 90% probability that Clinton would win Indiana (she lost).  But there is another issue besides being wrong, which is how much model flip-flopping is occurring just up to these elections.  

The most proximate example is Silver stating this past Sunday that Cruz had a 65% chance to win Indiana; the next day (Monday, the eve of the election) and with little new data, he "adjusts" that to Trump having a 69% chance to win!  That's horrible!  And this mistrust of forecasters going beyond Indiana, since we'll show below that in 14 of 63 state elections so far, likely voters and have had this sort of shoddy slip-flopping to contend with.  So an important question we all must have this year is given the more narrow polling margins for the general election, the degree to which these forecasters have not been able to make sense of their own models, and the large amount of flip-flopping anyway, how will we ever believe anyone's single forecast representation of who will win in November?  Whenever a forecaster repeatedly (here, here) and guardedly blames "special circumstances" for her or his deteriorating performance, that is further sign that trouble is brewing up and is being masqueraded as everything is awesome.

First we should note that Clinton's recent loss in Indiana is another horrifying loss of those states where a pollster suggested at least 90% accuracy (see our prequel article titled: The mercurial pollster results and which Columbia statistician Andrew Gelman thought was "cogent").  The probability of these many errors being simply due to "bad luck" is down to 3%, so highly unlikely. 

Now let's look at the 63 states where Silver has provided a time series of his forecasted winner.  We documented each of these states in the map below, separating out those which were/are Democratic races from those that are Republican races.  We show the Republican states, in green on the right map below.  For 7 of 32 states, Silver completely flipped his opinion on which Republican would win and who would lose that state's primary.  We instead color those red.  Only in Missouri did we color the state yellow, for a partial flip-flop.  Since the initial probability Silver gave to his predicted winner was beyond cut in half (from 14% advantage, to 6%).  For Republicans, Silver flip-flops in nearly 1 of 4 states!  Any general election poll without flip-flopping could have similar odds for the weaker of the two candidates, and now we cut this forecaster's reliability in half by flip-flopping.  That's the key to understanding the difference between "the signal and the noise".

For Democrats, things are about the same too, in the 31 states so far, suggesting that this year's difficulties are not a Trump-only phenomenon.  In 4 states (red) we see that Silver completely flip-flopped, and in 3 states (yellow) he at least partially flip-flopped.  For Democrats, Silver flip-flopped in nearly 1 of 5 states.  Different states too versus those on the Republican party, further suggesting there are serious problems at the core of how Silver and other forecasters are modeling and misleading the public as to their accuracy.

Something else to consider is whether these flip-flops are an excusable pattern where the country shifts its psyche fundamentally, over the course of the campaign season so far.  So we plot the states and how they have flip-flopped (0% for none, and 100% for full) throughout each day of the campaign season.  We show through larger data markers where we have more state forecasts behind them (such as recently near March's Super Tuesday).  We see the erratic level of flip-flopping (standard deviation of nearly 20%), through the uptick in the past month (including Indiana).


And for Democrats this flip-flopping timeline is only slightly better, but still significantly weak.  Note that the smoothed blue line gyrates, from a high at around the start of 2016, to more erratic in the past couple months.  Both of these charts suggest that anyone confidently knowing how the size up the Republicans and Democrats -6 months hence on Election Day- is simply being disingenuous about their own models.  You would fare better by taking a dice, painting some faces red (for Republican) and others blue (for Democrat); then rolling the dice!