Month: October 2020

Model Update 10-30

Today I am introducing a new map.

My attempts to embed the map have failed so I’m just going to link to it here.

Mean: Average predicted support for Biden in that state

Standard Deviation: A measure of the uncertainty of the model.

95% Credible Interval: There is a 95% probability according to my model that the true support for Biden will be in this interval.

Probability Biden wins: The estimated probability Biden wins from my model

Electors: Number of Electors in the Electoral College in this state

Now I’m going to bring back the old map because I like how it adds up the electors. The median number of electors for Biden is 357.

Edit: Map had some mistakes

Analysis:

The race continues to look the same. Texas and Arizona seem to be trending a little more towards Biden than a week ago. Texas surpassed it’s 2016 turnout which probably is another sign Texas is competitive, although I think Trump is the slight favorite. I don’t see a single state that voted for Clinton that Trump has a decent chance of winning.

I did an experiment on what happens if the model as wrong as it is in the past. If the model underestimates Biden, Biden clearly wins in a landslide. But even if the model underestimates Trump at 2016 levels, the model still gives an average of 303 electors for Biden.

I think that maybe there is a 10-20% today that Trump wins. Now on election day that number would be more like 10%. This probability isn’t coming from my model. My model says there is a 99% probability Biden wins but that will only hold if my assumptions hold. So I think there is a 10% chance my assumptions fail and a 10% chance that a major event happens before election day. I’m concerned about some court cases that could invalidate lots of mail in ballots or a major revelation about Joe Biden. I also wonder if the high early turnout is going to increase election day turnout because people may realize that the race is close. While Democrats seemed to do better in early voting, the knowledge of the high early turnout could make more Republicans want to turn out too. 

Model Update 10/23

This is the fit of the new correlated model this map was made on Thursday. I ran the model again today. You may notice lots of changes from the other model. This is expected because what is happening is that this model better “learns” from similar states and uses the last election’s results as a starting point. There are lots of different forms of this model. I struggled to choose a single model and when this ends up in my dissertation, I’m going to be talking about multiple models.

Edit: Here is the google drive link for the daily model updates. This new model is labeled “correlated_fit” and then the date.

Scale: 0-.05 Safe Red (darkest) 0.05-0.15 Likely Red (second darkest) 0.15-0.25 Lean Red (light red) 0.25-0.75 Tossup (brown) 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Average electoral votes: 359

95% credible interval for electoral college: 290-416

Analysis:

I think there might be a slight underestimation of the uncertainty in the electoral college outcome. I’m reading about a 99% probability Biden wins if the election was held today. I think that’s probably high but Biden should still win provided there isn’t some new crazy event. When applied to 2016 data this model read about a 60% chance for Clinton. I am not putting a lot of faith in the electoral college probability because I can’t reliably vet it using past data. It’s really hard to model the correlation between states.

This model is a polling aggregation model and not a forecast. So this fit is like if the election was held today. Since early voting is common and the election is so close this model is now predictive.

There are some things I’m a little skeptical of. I compared this model to the Economist’s model because they have some similarities. I think the estimate for Iowa is too high for Biden, although I would not rule out a Biden win in Iowa. I am wondering if the model is being overconfident in Michigan, Pennsylvania, and Wisconsin.

Model Update 10/15

I am writing this week’s update a day early.

So I’ve developed a functional second model that allows for correlation between states. I am working on testing it. Next week I might base the post on the second model if I show that the second model is more accurate than the current model.

This week’s map:

Scale: 0-.05 Safe Red (darkest) 0.05-0.15 Likely Red (second darkest) 0.15-0.25 Lean Red (light red) 0.25-0.75 Tossup 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Biden/Trump is likely to lose about 1 in 4 of there lean states and 1 in 10 likely states. The expected number of electors is Biden 335, Trump 203. This adjusts for the uncertainty in winning a lean or likely state. Except for Texas, the likely and lean red states are labeled because of insufficient polling data. CA, WA, OR are likely blue because insufficient polling data.

Analysis:

Not much change in the model. The model is very stable. The number of polls are increasing which is nice. I think Biden remains the likely winner.

Model Update 10/9

This weeks map:

Scale: 0-.05 Safe Red (darkest) 0.05-0.15 Likely Red (second darkest) 0.15-0.25 Lean Red (light red) 0.25-0.75 Tossup 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Biden/Trump is likely to lose about 1 in 4 of there lean states and 1 in 10 likely states. The expected number of electors is Biden 339, Trump 199. This adjusts for the uncertainty in winning a lean or likely state. Except for Texas, the likely and lean red states are labeled because of insufficient polling data.

Analysis:

Some of the change for this week is due to me fine-tuning some model parameters and adding weights. Biden continues to be ahead.

Now let’s compare Biden’s lead to how accurate this model was in the past. If Biden’s lead is greater than the historical error this indicates a high probability Biden wins that state. The direction of the error could underestimate or overestimate Biden. Historically this model is equally likely to underestimate or overestimate a candidate. It’s equally likely Biden and Trump are underestimated. This comparison uses the models fit 28 days before the election in 2008-2016. We roughly assume that if Biden’s lead is bigger than the error he wins and the other states are split among the candidates.

Some interesting comparisons in the key states of AZ, CO, GA, FL, IA, NC, NH, NV, OH, PA, VA, WI :

If the model’s performance at 2008 levels, Biden’s lead is larger than the error in: IA, PA

In this scenario, Biden is likely to win but it is close. He should still pick up about half of the other key states since the error can go both ways.

If the model’s performance is at 2012 levels, Biden’s lead is larger than the error in: AZ,CO, FL, IA, NC, NH, NV, OH, PA, VA, WI

In this scenario Biden wins every state is he classified as likely to win. This leans to a blowout of approximately 374 electoral college votes.

If the model’s performance is at 2016 levels: , Biden’s lead is larger than the error in: AZ, CO, FL, GA, NV, IA, VA

In this scenario, Biden is likely to win.

If the model’s performance at the average compared to 2008-2016:

AZ, CO, FL, GA, IA, NC, PA, VA

In this scenario, Biden is likely to win.

Basically the lead we are seeing for Biden surpasses the historical error of this model. Now it is possible for the model to perform worse this year. But this along with the uncertainty estimates from within the model paints a picture that Biden is the likely winner. Trump has a chance, but it is smaller than Biden’s.

Model Update 10/2

Note that this update doesn’t account for the President’s positive covid test yesterday. I don’t what that means for the race.

This week’s map:

Scale (in terms of estimated win probability):

Scale: 0-.05 Safe Red (darkest) 0.05-0.15 Likely Red (second darkest) 0.15-0.25 Lean Read (light red) 0.25-0.75 Tossup 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Biden/Trump are likely to lose about 1 in 4 of his lean states and 1 in 10 likely states. So this means Biden will probably lose 22-46 electoral votes from this map, but him winning only 300 electoral votes would still be possible due to correlated errors. Except for Texas, the likely and lean red states are labeled because of insufficient polling data.

The 95% intervals should be accurate at this point in time.

This map changed from last week. The predicted vote share hasn’t changed, but a minor bug messed up the uncertainty estimates last week.

Analysis

I have no idea how Trump’s covid infection will play out. I think if he is lucky and has a mild case, then there wouldn’t be a lot of changes. But if he dies or has complications that prevent him from finishing his term or running for re-election then I think anything could happen. I hope he recovers, but his age and weight make him high risk. So for now I’m watching and waiting.