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I’m going to summarize what I think about the model and break down the key states.
This model can’t really be that influenced by my opinion. I decided to code everything in terms of the Democratic candidate because the Democratic candidate won two out of the three elections I tested this model on. My predictions for Trump are just 1 – Biden’s prediction.
This is an non-interactive map for reference.
The median outcome (50th percentile) is 358 electors for Joe Biden.
Trump won 306 electors in 2016. To be re-elected, President Trump can’t have a net loss of more than 36 electors.
Overall, I trust the model’s output. The model is objective. There are very few parameters I chose subjectively and those mainly affect the uncertainty. The model starts out with assuming that the results will match 2016. Once the polling data comes in it focuses on the polling data over the past results. The model does “punish” large deviations from the past election. It’s going to be skeptical of large changes from 2016 to 2020. This is by design because polls tend to have more variation than election results.
One key thing that could go wrong is that if we have a scenario of 2016 where polling errors are skewed to towards one candidate the uncertainty estimates will be wrong. Honestly, it’s a 50-50 shot for Biden or Trump to be underestimated. And the level of error you would have to see from this model for Trump to win is about 1-2 points higher than in 2016. I would say it’s equally likely that Trump wins or Biden gets 400 electoral votes.
I’m a little skeptical of Iowa and Ohio flipping, and part of the reason they are tossups is because of the strong polling leads in Michigan and Wisconsin. It’s going to be a long night for watching the Midwest results.
I think Biden does have a significant advantage in the south over Clinton. Biden is up 1.5 points over Clinton’s prediction from the model. Arizona, Georgia, North Carolina, and Texas also have signs of a shift towards the Democrats. I think the model is being realistic about the change given the polling data. The predictions for Arizona, Georgia, and Texas were pulled towards Trump because of the 2016 results.
My model doesn’t predict the electors in Nebraska and Maine that are decided by congressional districts. My personal predictions are Nebraska-2 and Maine-2 will vote Democratic. Maine should be won by Biden, and Nebraska should be won by Trump.
One key thing to remember is the pandemic may slow counting down. We have more mail in ballots to process, in some states they can’t count the ballots until election day. This article gives a good explanation state by state about counting and when to expect results. It may not be possible to call every state on election night. It may not be necessary to call every state to determine the presidential winner. If it’s a landslide either way you may not need results everywhere because a candidate hit the magic 270 number. In particular, Pennsylvania and Nevada may not be called on election night. If we don’t know who won the 26 electors from Pennsylvania and Nevada, we may not know who won.
Do not react to early results. To call a race we need both mail in and in person votes that represent the entire state. It may take a while to have the information to call states. You might have temporarily leads for Trump and Biden that don’t hold. It appears that the method of voting depends on partisanship. So the early votes may not have the same partisan break down of the mail in votes or the election day votes. So just remember to breathe and wait. Honestly, if you are just a causal observer who hasn’t watched a live election night before don’t start looking at states until an hour after the polls close.
Now the breakdown of the key states. I am predicting only the vote for Biden and Trump. I ignore third party candidates because they have no chance and are hard to predict. I’ve going to mention the average error of this model for 2008, 2012, 2016 and compare it to the race’s projection. That’s what I mean when I say the model was within x points. The model could underestimate Trump, but it could also underestimate Biden. It is very possible to have above-average error, but if the lead is bigger than the average error, the candidate is more likely to win than lose. Also don’t take the decimal point in the model’s prediction that seriously. It’s just there for context. The model can only predict to within about one to two points of the actual result on average.
Arizona: I’m expecting a delay in results. I do think it is plausible for Arizona to flip given that the 2018 senate race was won by a Democrat. Arizona shifts a lot and the polling isn’t always accurate. On average, the model was within 3.7 points of the outcome in Arizona. Biden is predicted to get 52.2% of the vote. Arizona leans Democratic but is still uncertain.
Colorado: I don’t think Colorado is a battleground state for the presidency anymore. The population of Colorado has shifted in ways that make it more of a likely democratic state than the tossup it was in the past. On average, the model was within 1.8 points of the outcome. Biden is predicted to get 56.5% of the vote.
Georgia: Turnout is high for early voting and the 2018 senate race was competitive. It is probably a pure tossup. On average, the model was within 0.6 points of the outcome. Biden is predicted to get 50.2% of the vote.
Florida: Things look worse for Biden than they did for Clinton. This model put Clinton at 50.6%, but Biden is at .52. Florida usually has highly accurate polls and this model was on average within a point of the election result in Florida for 2008, 2012, 2016. This is the state I’m watching the most. If Biden can win Florida, he can probably win the presidency.
Iowa: Iowa is likely a complete tossup. It’s definitely not a state likely to predict the overall winner especially since it only has six electors. On average, the model was within 2.7 points of the outcome. Biden is predicted to win 50% of the vote.
Michigan: My guess was that Trump winning Michigan in 2016 was a fluke. I expect the 2020 results to look more like 2008 or 2012 than 2016.Biden visited more and invested more than Clinton. Turnout was low in 2016, but it looks like 2020 turnout will be higher. On average, the model was within 2.6 points of the outcome. Biden is predicted to win
North Carolina: This state seems to be trending a little towards Biden. On average, the model was within 1.4 points of the outcome. Biden is predicted to have 51.6% of the vote. This is a tossup that leans Democratic.
New Hampshire: I expect New Hampshire to be less competitive than 2016. I think it’s highly likely Biden wins New Hampshire. On average the model is within 1.6 points of the outcome. Biden is predicted to have 54.7% of the vote.
Nevada: Nevada should be safe. I’m unsure if they will have all the mail in ballots counted. Wait for results from Clark County (home of Las Vegas and most of the voters) before you make any judgements. On average, the model is within 2 points of the outcome. Biden is predicted to have 54.3% of the vote.
Ohio: Ohio is a tossup. I don’t buy the theory that Ohio is going to be a bell-weather state that predicts the outcome. Trump needs Ohio to win, but Biden could win without it. On average, the model is within 2 points of the outcome. Biden is predicted to have 50.2% of the vote.
Pennsylvania: Pennsylvania doesn’t count mail in ballots until at least election day. We may have no idea what the result is in Pennsylvania for hours or even days. Take the early returns with a huge grain of salt until the networks call it. I think that Trump winning in Pennsylvania in 2016 was a fluke. I expect the 2020 results to look more like 2008 or 2012 than 2016. On average, the model was within 1.5 points of the outcome. Biden is predicted to have 53.6% of the vote.
Texas: I debated about talking about Texas, but I decided to because I’m a Texan. You have to be very careful with the early results. You need to see results from the entire state. There were times when Beto O’Rouke was leading the senate race, but he didn’t win at the end. I’ll be talking a lot about Texas on Twitter because I have a good grasp of the political geography of the state. I’m very unsure about what the record turnout means, but if I had to guess the non-2016 voters would lean at least slightly more democratic. Texas is in play. We don’t have data on the partisanship of the early vote since you don’t register by party. On average, the model was within 1.1 points of the outcome. Biden is predicted to have 48.7% of the vote. I would consider Texas a plausible state to flip if Biden has a really good night. But I would not be surprised if Trump wins by 3 points, even though that is a significant loss compared to his 9 point victory in 2016.
Wisconsin: Like Michigan and Pennsylvania, I think Trump winning Wisconsin was a fluke. I expect the 2020 results to look more like 2008 or 2012 than 2016. I’ve read reporting (in the 538 article I shared) that we may find out Wisconsin results early Wednesday morning. The overall winner may or may not be called by then. Don’t freak out again about early results. On average, the model was within 2.2 points of the outcome. Biden is predicted to have 54.2% of the vote.
And it’s a wrap! Happy election eve y’all! Make sure you vote tomorrow if you haven’t already. My election night live tweeting fest will start at 7 PM tomorrow (and you can follow it on the blog!). I will be tweeting off and on until then. I’ll do a follow-up post once the results have settled and we know who won. I’ll do a more detailed post in December went the results are final.