The 2024 Election is right around the corner. There is 1 day until Election Day.

Updated November 4

Thank you for visiting Partisan-Gravity.com and taking a look at our Presidential Election Forecast. Until recently, each daily update has been featured on a new blog post. To make things easier, we’re launching the dedicated page you’re reading now so that you only ever have to look at one page.

If you’re following any of the major election forecasts, you’ve probably reviewed Nate Silver’s model, Split Ticket’s Presidential Ratings, and 538’s Election Forecast. These are rigorous models and generally show a pretty similar picture. Each model works a little differently but generally follows a lot of the same procedures.

Even if the candidate percentages differ, they all show what we know to be an extremely close Presidential race. The Partisan-Gravity model introduced below is, we hope, another methodologically rigorous and useful addition to the discourse and should be seen as a way of understanding the race but not a definitive prediction. Even with so little time between now and the election, there is enough time for considerable uncertainty to shake things up. After all, the Democratic nominee is different than we expected it would be just a few months prior and the Republican nominee survived an assassination attempt.

In other words, there is ample time and precedent for surprises to rear their ugly heads.

So, let’s dive right in and talk about what the Partisan Gravity model shows before talking a bit about the methodology.

A Summary of the Race: TOSS-UP

With 10,000 simulations:

HARRIS WINS 5282 (52.82%)

TRUMP WINS 4578 (45.78%)

NO WINNER 140 (1.40%)

The race simulations above demonstrate that most of the 1,000 simulated election outcomes happen very close to the center of the x-axis. This signals that, most of the time, a candidate will win with just over the necessary amount of Electoral College votes needed: 270.

Of course, this model includes some allowance for uncertainty. There are outcomes on the tails of the bell curve. These would indicate that electoral landslides are possible, though unlikely. It wouldn’t take that much polling error to deliver such a result, but this is still not a likely sort of outcome.

Looking at the map:

Let me first share my appreciation for the folks over at 270towin for creating the tool that I’ve used above to showcase a state-by-state overview broken down into a few color-coded categories related to a candidate’s likelihood of winning.

If you haven’t seen one of these before, there are a few important things to understand:

1. Dark Blue or Dark Red = Safe Democrat or Safe Republican
2. Medium Dark Blue or Medium Dark Red = Likely Democrat or Likely Republican
3. Light Blue or Light Red = Lean Democrat or Lean Republican
4. No Shade = Toss-up

The race is a toss-up.

As of November 3rd, Harris and Trump have seen their national polling converge substantially, with a small but persistent lead for Harris. In the swing states, Pennsylvania has slowly drifted away from Lean Harris into the Toss-Up category. Certain priors that you might have could call this into question, but there is fundamentally no real difference between 54% and 56% in the real world, so try not to let this get under your skin too much. Harris has also seen her polling stubbornly behind Trump’s in Arizona, which looks to be her worst swing state in the polling landscape. At the same time, her polling in the “Blue Wall” in the upper Midwest has continued to demonstrate a small advantage for her campaign. In Michigan, where the polling is the closest in this category, she still hangs on, if slightly, to the “Lean ” advantage. Her lead is a bit larger in Wisconsin, and she is safely ahead in Minnesota.

Interestingly, the polls are demonstrating consistently some real toss-up races in North Carolina and Georgia. In the last few days, Harris has actually seen her numbers come up, if only a bit, in these two states that share some similar demographics.

By far the most interesting development in the final days leading up to election day was the Selzer & Co. polling bombshell released on November 2. The high-quality pollster showed a surprising 3-point lead for Harris in Iowa, a state that was previously seen as staunchly within Trump’s grasp. This is a state that used to be something of a bellwether, but has not been seen as a swing state for some time. This poll is an outlier, but it does conform with some stronger-than-expected polling for Harris in Ohio and Kansas, while also allowing us to infer that some of her polling strength in Wisconsin may not be a mirage.

Polling

Partisan-Gravity’s model utilizes polls from 538’s database – and only those that have ended and been published within the last 45 days (as of a recent change on October 16). Polls are a measure of public opinion at any given time, and although past measures are important, weighting of much older polls would include substantially more subjectivity and uncertainty.

Many states do not have recent polling. In these instances, the model uses a weighted national polling environment for both candidates (more on that weighting shortly) that is then adjusted alongside partisan leans from the Cook Political Report PVI for each state.

The model also assumes that the polls will have some error. This error has been set to be 4.3%: an average of polling errors for recent presidential elections. We do not assume that the direction of this error goes one way or the other in particular. This error is used as the standard deviation for the model’s distributions for states with recent polling.

States without polling that rely on their partisan leans adjusted by national polling are probably in need of greater levels of uncertainty. This is an area where subjectivity enters the conversation. The model assumes a 25% greater level of error in these simulations. NOTE: in the first few days of the model run, this uncertainty was higher and has been adjusted downward. Otherwise, many of these states have 100% probabilities for either candidate because no tail events occur, and that often seems unrealistic. The result is that, for example, a state like Missouri ends up with a 6-ish% chance for Harris instead of below 1%. This allows for some movement to occur and to still be within the model’s expectations.

Accounting for different Pollsters

Partisan-Gravity does not engage in any polling whatsoever. The model relies on polling and Pollster ratings from 538. The model weights polls more or less heavily depending on their pollster quality. Their quality score ranges from 0 to 3.

Polls with a score of 0 begin with a 30% weight and step toward 100% incrementally with improved ratings.

Let’s also get something out of the way now: we do not let the model account for polls from the following:

1. Trafalgar
2. Rasmussen
3. ActiVote
4. TIPP Insights*
   • We are specifically excluding a recent batch of TIPP Insights polls from Pennsylvania that have manipulation of the likely voter screen in a way that appears intrinsically partisan and unscietific.

There are major methodological and partisan concerns with these pollsters. Outliers are appreciated and encouraged (an early version of the model excluded those, but we took this out because poll herding is bad).

However, these pollsters are either potentially nefarious or lack transparency about their operations. So, they are not included. This list may or may not be updated.

Various Weighting Choices and Assumptions

Without getting too in the weeds, the following weighting decisions are incorporated into the model :

1. Weight Likely Voter screens more heavily than Registered Voter screens. (70% vs. 30%)
2. Weight head-to-head matchups less heavily than third-party inclusive matchups (20% difference)
3. Weight polls more if their sample sizes are larger
4. Weight pollsters more if they have a better 538 pollster rating (described above)
5. Weighting based on duplications
   1. Let’s say NYT/Siena releases two LV polls on the same day, one with multi-candidates and one with a head-to-head matchup. Both are same-day and same sample size. Each of these two will be weighted less so that it does not amount to more than one full poll.
6. Convention Bounce Adjustment
   1. The model adjusts polls within 3 weeks of either national convention by assuming something of a 0.5% bounce that will fade after those few weeks. There has been discourse about assuming a larger bounce, but we’re not seeing that many changes in polling after the convention (specifically after the DNC). Maybe it has already been factored in. With this in mind, we’ve kept this bounce pretty small.
7. Debate and Taylor Swift Bounce Adjustments
   • These are very similar adjustments to the Convention Bounce Adjustment previously described and are set to be incorporated for the 3-week period after these events. Both will result in a temporary reduction in Harris’ odds because the bounce is assumed to be temporary.
   • This may sound silly, but the debate was viewed by over 70 million people and Taylor Swift is one of the most well-known and powerful individuals on the planet. We do not know the degree to which voters will be impacted by either or both of these events. However, on net, Partisan-Gravity assumes that both would lead to some (even if small) temporary gains for Harris.
8. House Effects
   • Certain pollsters with partisan leanings are treated through regressions and weighting assumptions to discern their distance from the averages. This can mean that a very partisan pollster showing a R+1, for example, might actually be read as tied or even slightly favoring Harris.

Simulations

After the polling data is weighted and aggregated, the models runs 10,000 simulations for each and every state. In a few cases, this also extends to a couple of districts for the odd states that don’t provide a winner-take-all opportunity for the candidates. Nebraska and Maine split their votes by district, awarding an electoral vote for each district and some amount for the entire state. You could, for example, lose the state of Nebraska overall (as Harris likely will) and still net an electoral vote from its second district that she is likely to win.

The state-by-state probability is then determined and utilized for the 10,000 simulation election night run.

To account for variable election nights where states can influence one another, the model also uses Markov chains in each simulation. Thus, if a state is won or lost by either candidate, it will adjust the probability distribution for every other state, and this continues until the end of each state election is held in all of the 1,000 simulations. On September 18, the model was briefly halted and then resumed with a new method for updating probabilities throughout the simulation. The new method is very similar, but groups states by similar geography and applies a larger probability adjustment factor for their shared attributes than for those without.

Final Thoughts

This is going to be an evolving model with changes every day. As long as there are polling or other changes, the model will update the forecast every day from now until the election. There also may be minor tweaks to the modeling and assumptions, but when this occurs, there will be references to those updates in as transparent of a way possible.

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