What does a chance feel like?
There is a wide range of possible outcomes across all 35 races. Click the button above to randomly simulate one possible election result.
How the odds have changed
The model works by first building a structural forecast for the national race and the race in each state, based on economic indicators, the president’s approval rating, past election results, the home races of each of the candidates, and incumbency and regional effects.
The model then uses state and national polls to estimate public opinion in every state and nationally, and makes a forecast for Election Day by simulating thousands of possible elections, taking into account the structural forecast based on the non-poll factors.
Of course, there is a lot of uncertainty in elections and polling. The model takes this into account, combining uncertainty across all of these factors to arrive at an overall distribution of the number of seats that each party will win. From this distribution, we can figure out the chances that the Democrats and Republicans will win at least 50 seats (or 51, if they don’t win the presidency) and control the Senate.
The model is updated regularly as new economic and polling data come in. The charts below show how the forecast has evolved over time.
This chart shows how the model’s estimate of the overall probability of winning the presidency has changed over time. Because the model is based on random simulations, these probabilities will naturally jump around a bit, so a shift of a few percentage points in either direction doesn’t usually reflect a change in the state of the race.
This chart shows the estimated range of seats that the Democrats will win, and how it has changed over time.
The range will narrow as we approach the election, because we will have more information, and there will be less time for the race to be upended by an economic or political development.
The outer band above is an 90% credible interval, meaning that based on the information available at the time the forecast was made, there was an 90% chance that the Democrats would win a number of seats somewhere in that interval. The inner band shows a 50% credible interval.
This chart shows the estimated margin in the national generic ballot (see more below), and how it has changed over time. The range will narrow as we approach the election, because we will have more information, and there will be less time for the race to be upended by an economic or political development.
The outer band above is an 90% credible interval, meaning that based on the information available at the time the forecast was made, there was an 90% chance that the Democrats would win a number of seats somewhere in that interval. The inner band shows a 50% credible interval.
Race forecasts
The table below summarizes every Senate race, including the forecasted vote share (excluding write-ins and third parties) and probability of winning. Seats that are more likely than not to flip parties are highlighted in gray. You can click on the column headers in order to sort the table, or click on a race to see more details.
| Race | Incumbent | Chances of winning |
Estimated vote share
Bands show 50% and 90% credible intervals
|
Margin | |
|---|---|---|---|---|---|
Select a race to see detailed forecasts and how they’ve changed over time.
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Possible scenarios
There is a chance of a 50-50 tie in the Senate, in which case control is decided by the party that wins the Vice Presidency. Our presidential model gives the Democrats a chance of winning the presidency.
The two charts below show the forecasted distribution of Democratic and Republican seat gains, and how they’ve changed over time. While the Democrats are currently only strongly favored to pick up a few seats, as the table above shows, there are many more races in which they have a medium-to-long shot than the Republicans, whose only best pickup opportunity after than Alabama is New Mexico.
What happens if… ?
There are many paths to a Senate majority. No one race is absolutely critical for a party to win. But races’ outcomes are correlated with one another, and winning one race can be a sign of strength elsewhere.
The buttons below are for the twelve closest races. Click them to cycle through hypothetical winners and play out different scenarios. What if the Democrats win Maine but the G.O.P. wins Alabama? You could even use these buttons on election night to provide live estimates of the race as each state is called.
Reset allModel components
The model works by combining various structural, non-polling factors with state and national polls to arrive at an overall forecast for the election. The sections below provide more detail on each of these components.
Prior model
One of the best predictors of the national Senate race is the so-called “generic ballot,” where polling firms ask voters which party they plan on voting for in their local House race.
The first component of the model is a so-called “prior” guess about what this generic ballot will be on Election Day. This prior model uses the president’s approval rating, earnings growth, GDP growth, the unemployment rate, which party controls the White House, and whether or not it is a midterm election to create a forecast of for the Democrats.
The second part of the model is a prior guess about how the vote in each race will differ from the national vote. The best predictor of this differential is the results in the last election, but the model also incorporates the average partisan lean (from presidential election results), and adjusts for incumbency effects, for both the individual candidate and his or her party.
National polling
With the initial prior guesses in hand, the model then combines them with polling data to arrive at an election-day forecast.
Support for the candidates in polls now is obviously not necessarily the same as their vote share on Election Day. The model adjusts and averages polls to estimate public opinion for each 3-day period of the race up to the present day, and then forecasts how it will change toward November.
Since mid–January, polls have been conducted. Polls conducted early on in the race (especially national polls) don’t have much impact on the overall forecast, since voters’ opinions will change a lot before November. But as we get closer to Election Day, the polls become more informative about the final result.
The chart below shows the model’s estimates of voters’ margin of support for the parties for each point in the race. The rightmost values on this chart are the election-day popular vote forecast.
In estimating the popular vote from polling data, the model also takes into account the tendency of certain kinds of polls to over- or under-estimate the support for each party. For example, polls of likely voters are generally more accurate than polls of all registered voters, and the latter tend to lean more Democratic.
In addition, certain polling firms have a pattern of producing polls that lean toward one party or another. These “house effects” are estimated by the model and used to make adjustments in estimating overall public opinion. Negative house effects (red) mean that the firm’s polls overestimate Republican support; positive effects (blue) mean that the firm overestimates Democratic support.
| Firm | Polls | House Effect | |
|---|---|---|---|
Methodology
The structure of this model is the same as the presidential model.
The above components section lays out the general overview of the model. All submodels operate on the logit scale. The prior models for the national vote and state differentials are linear models (with random effects for the state model); Cauchy(0, 2.5) priors on scaled and centered predictors were used.
The polling model is similar to a 2016 presidential election model by Pierre-Antoine Kremp, which in turn is built on Drew Linzer’s dynamic Bayesian forecasting model. In contrast to those models, this one incorporates a critical adjustment for the bias of registered-voter and all-adult polls, uses past election data to estimate polling errors, and uses more informative priors on national and race outcomes, built from the linear models described above.
Essentially, there is a latent national voter intent, and latent state differentials (how much more or less Democratic the state is than the national vote), which evolve as a random walk with Gaussian increments in 3-day and 3-week steps (the races’ walk has larger steps for computational reasons). The national and race priors become priors on the final step of the random walk, so prior information is percolated backwards in time. Student-t-distributed increments were also explored but the data were not very informative for the degrees of freedom parameter, and the model predictions changed very little.
Each poll is considered to be a binomial draw whose probability depends on the latent national and state (if a poll of an individual race) voter intent, adjusted for the polling firm’s house effects, the type of respondents (registered voters, likely voters, or all adults), poll-specific error, and state, regional, and national polling error.
Each of these adjustments is a parameter that the model estimates. Polling error cannot be estimated from polling data, and essentially just adds noise to the model. The distribution of these polling errors is estimated from past elections’ polling errors.
Election outcomes are simulated by looking at the Monte Carlo draws of the latent state and national intent parameters on Election Day.
Models were fit using Stan. Model code and data are available online. Email me with any questions.