The Model Review: 9/25

After fitting the model for a week, it’s time to look at the model.

Every week, I’ll include a model release that has the historical accuracy of this model at this time in 2008-2016. These will be called: weekxsummary in the google drive. The first one is here.

Here is an approximate map:

First up a couple of cautions. We still have thirty-eight states with less than 5 polls. At that point, the model will be very uncertain due to a lack of data.

These are the states with less than 5 polls that should be taken with a grain of salt: AK, AL, AR, CO, CT, DC, DE, HI, ID, IL, IN, KS, KY, LA, MA, MD, MO, MS, MT, ND, NE, NH, NJ, NM, NV, NY, OK, OR, RI, SC, SD, TN, UT, VA, VT, WA.

DC is a very odd election to model. I’m going to come up with something special for it since it is incredibly partisan and not like any of the states. I would ignore it.

This map comes from estimating the proportion of stimulations the model had Biden above 50%. Right now there is uncertainty in the model since it’s not a forecast so my labels are very cautious.

Scale:

<0.05 Safe Red

0.05-0.15 Likely Red

0.15-0.25 Lean Read

0.25-0.75 Tossup

0.75-0.85 Lean Blue

0.85-0.95 Likely Blue

>.95 Safe Blue

The reason my tossup category is so large is that there is plenty of room for shifts of 1-2 points in the model over time and if that happens in certain directions these states could flip.

If we have at least 3-5 polls per state the probability estimates are generally reliable individually. When you have very few polls there is a lot of uncertainty because individual polls are random. Roughly for every 5 states that are lean for a candidate about one will be won by the other party. Roughly for every 10 states that are likely for a candidate, about one will be won by the other party.

Analysis:

This is a really good map for Biden. I think my uncertainty estimates are matching the historical error well. In most of the likely states for Biden, the historical error is less than Biden’s margin in the past three elections. I’m a bit worried about the craziness of 2020 affecting poll accuracy and the effects of mail-in voting. If enough mail-in ballots get thrown out, that could throw the election if it is close. Polls aren’t going to capture the effects of rejected ballots. We also don’t really know how many rejected ballots to expect. I’m cautiously optimistic that the model will do as well in that past. If this model does as well as even 2016, Biden has a high probability of winning, but it’s hard to accurately pin down that number. If I had to pick a number I would say probably in the .80-0.90 because you would need a massive polling failure probably combined with a real shift towards Trump. Both of those things aren’t that likely.