J.L. PARTNERS 2024: The most accurate forecast

We were the most accurate pollster and modeling firm for the 2024 US Election, adding to our success in being the third-most accurate pollster in the UK General Election. 

We are continuing to conduct political research for clients as they look to engage with lawmakers, think ahead to 2026, and for corporates and the financial sector as they try to divine the priorities of the Trump administration.

A huge slice of our business is also corporate work, including reputation tracking and message testing. 

Whether political or corporate, be ahead of your audience and the curve: contact us using the form below for a free meeting with our team. 

OUR SUCCESS

  • Our final model, which you can see below, projected a 287-251 win, the highest projected margin for Trump of all models and only one of two to forecast a Trump victory.

  • We have called a Trump win since September 24th, and for the last 25 of 30 days have had a forecast of 312-226, where Trump is set to end up. 

  • Our final national poll also called Trump winning the popular vote, with a lead of 3 points. A Trump popular vote win was seen as an outlier and the final national polling average had a Harris lead of 1.2 points.

  • J.L. Partners also had the most accurate poll of Ohio, the main state we polled in due to our client base. NYT predicts this race will be a Trump win by 11 points and that Bernie Moreno wins the Senate with a margin of 6 points. Our final poll had Trump leading by 9 points and Moreno leading by 6 points, while the polling average showed a win for Sherrod Brown. This is the second cycle in a row that J.L. Partners is the most accurate pollster in Ohio, with our 2022 poll calling the Senate race to within 0.2 percentage points. 

  • Finally, we called that the Senate would be more Red than anyone expected in the Wall Street Journal because of our model predicting the behavior of undecided voters.

THE PRESIDENTIAL MODEL

Please use a larger screen or non-mobile device to view the "Routes to the White House" graphic.

Methodology

Our model of the Presidential election is a two stage, Bayesian process that begins by examining historical data in both the long term and short term

  • We analyze elections from the past 80 years and use economic data, approval ratings as well as incumbency effects and term effects – this forms the basis of our Long-Term Fundamentals model

  • We then analyze the election since 1996 using similar sorts of economic data, but also include information on Consumer Confidence and Congressional Approval – this forms the basis of the Short-Term Fundamentals model

  • These two models are then mixed together to yield our Hybrid-Fundamentals model that gives us our initial Presidential Prior – the vote share each candidate would be predicted to win if we had no polling data.

Then we gather polls from the state and national level and filter out any data that only polled questions involving direct head-to-heads – this helps account for some of the third-party effects in the model

  • Taking our prior for the Presidential vote share we produce priors for the states using historical data and then run an autoregressive Bayesian algorithm to fit a function to each of the states and the entire country

  • Some states aren’t polled as frequently as others, so we calculate how similar states are based off their 2020 vote, demographics, house prices, mortgage payments as well as other factors. This allows us to use data from one state to infer how other states will perform.

  • For example, Pennsylvania and Illinois are quite similar based on our measures so the more frequently polled Pennsylvania can be used to inform the state level polling of Illinois. 

  • These state level estimates are then combined to give an initial country-wide vote share

  • Note that we include adjustments to account for who is on the ballot in each state in order to properly account for voter flows. As such, we give more weight to full-ballot test polls and head-to-head polls are down-weighted in our model to reflect this.

We then add national polling data which mixes with this state level data and updates it. After this we produce the final estimates and probabilities