Hostname: page-component-5447f9dfdb-2bw5x Total loading time: 0 Render date: 2025-07-30T01:57:48.608Z Has data issue: false hasContentIssue false
  • English
  • Français

The PollyVote Forecast for the 2024 US Presidential Election

Published online by Cambridge University Press:  15 October 2024

Andreas Graefe Open the ORCID record for Andreas Graefe [Opens in a new window]
Andreas Graefe*
Affiliation:
Macromedia University of Applied Sciences, Germany
Get access Rights & Permissions [Opens in a new window]

Abstract

Originally founded in 2004 to improve election forecasting accuracy through evidence-based methods, the PollyVote project applies the principle of combining forecasts to predict the outcome of US presidential elections. The 2024 forecast uses the same methodology as in previous elections by combining forecasts from four methods: polls, expectations, models, and naive forecasts. By averaging within and across these methods, PollyVote predicts a close race, giving Kamala Harris a slight edge over Donald Trump in both the two-party popular vote (50.8 vs. 49.2%) and the Electoral College (276 vs. 262 votes). The forecast gives Harris a 65% chance of winning the popular vote and a 56% chance of winning the Electoral College, making both outcomes toss-ups. Compared to the combined PollyVote, component forecasts that rely on trial-heat polls tend to favor Harris, whereas methods that rely on alternative measures are less optimistic about the Democratic candidate’s chances. The polls may be overestimating Harris’s lead.

Information

Type
Article
Information
PS: Political Science & Politics , Volume 58 , Issue 2: Forecasting the 2024 US Election , April 2025 , pp. 306 - 311
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of American Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Abramowitz, Alan I. 2016. “Will Time for Change Mean Time for Trump?” PS: Political Science and Politics 49 (4): 659–60. https://doi.org/10.1017/S1049096516001268.Google Scholar
Algara, Carlos, Gomez, Lisette, Headington, Edward, Liu, Hengjiang, and Nigri, Bianca. 2024. “Forecasting Partisan Collective Accountability during the 2024 US Presidential & Congressional Elections.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000854.CrossRefGoogle Scholar
Armstrong, J. Scott, and Cuzán, Alfred G.. 2006. “Index Methods For Forecasting: An Application to the American Presidential Elections.” Foresight: International Journal of Applied Forecasting 3: 10–13.Google Scholar
Armstrong, J. Scott, and Graefe, Andreas. 2021. “The PollyVote Popular Vote Forecast for the 2020 US Presidential Election.” PS: Political Science & Politics 54 (1): 96–98. https://doi.org/10.1017/S1049096520001420.Google Scholar
Armstrong, J. Scott, Green, Kesten C., and Graefe, Andreas. 2015. “Golden Rule of Forecasting: Be Conservative.” Journal of Business Research 68 (8): 1717–31. https://doi.org/10.1016/j.jbusres.2015.03.031.CrossRefGoogle Scholar
Bates, J. M., and Granger, C.W.J.. 1969. “The Combination of Forecasts.” Journal of the Operational Research Society 20 (4): 451–68. https://doi.org/10.1057/jors.1969.103.CrossRefGoogle Scholar
Cerina, Roberto, and Duch, Raymond. 2024. “The 2024 U.S. Presidential Election PoSSUM Poll.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000982.CrossRefGoogle Scholar
Clemen, Robert T. 1989. “Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting 5 (4): 559–83. https://doi.org/10.1016/0169-2070(89)90012-5.CrossRefGoogle Scholar
DeSart, Jay. 2024. “Long-Range State-Level 2024 Presidential Election Forecast: How Can You Forecast an Election When You Don’t Know Who the Candidates Are Yet?” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000805.CrossRefGoogle Scholar
Enns, Peter K., Colner, Jonathan, Kumar, Anusha, and Lagodny, Julius. 2024. “Understanding Biden’s Exit and the 2024 Election: The State Presidential Approval/State Economy Model.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000994.CrossRefGoogle Scholar
Erikson, Robert S., and Wlezien, Christopher. 2012. The Timeline of Presidential Elections. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Fair, Ray C. 2009. “Presidential and Congressional Vote-Share Equations.” American Journal of Political Science 53 (1): 55–72. https://doi.org/10.1111/j.1540-5907.2008.00357.x.CrossRefGoogle Scholar
Gelman, Andrew, Goel, Sharad, Rivers, Douglas, and Rothschild, David. 2016. “The Mythical Swing Voter.” Quarterly Journal of Political Science 11 (1): 103–30. http://doi.org/10.1561/100.00015031.CrossRefGoogle Scholar
Graefe, Andreas. 2014. “Accuracy of Vote Expectation Surveys in Forecasting Elections.” Public Opinion Quarterly 78 (S1): 204–32. https://doi.org/10.1093/poq/nfu008.CrossRefGoogle Scholar
Graefe, Andreas. 2018. “Predicting Elections: Experts, Polls, and Fundamentals.” Judgment and Decision Making 13 (4): 334–44. https://doi.org/10.1017/S1930297500009219.CrossRefGoogle Scholar
Graefe, Andreas. 2021. “Of Issues and Leaders: Forecasting the 2020 US Presidential Election.” PS: Political Science & Politics 54 (1): 70–72. https://doi.org/10.1017/S1049096520001390.Google Scholar
Graefe, Andreas. 2022a. “Combining Forecasts for the 2021 German Federal Election: The PollyVote.” PS: Political Science & Politics 55 (1): 69–72. https://doi.org/10.1017/S1049096521000962.Google Scholar
Graefe, Andreas. 2022b. “Combining Forecasts for the 2022 French Presidential Election: The PollyVote.” PS: Political Science & Politics 55 (4): 726–29. https://doi.org/10.1017/S1049096522000555.Google Scholar
Graefe, Andreas. 2023. “Embrace the Differences: Revisiting the PollyVote Method of Combining Forecasts for U.S. Presidential Elections (2004 to 2020).” International Journal of Forecasting 39 (1): 170–77. https://doi.org/10.1016/j.ijforecast.2021.09.010.CrossRefGoogle Scholar
Graefe, Andreas. 2024. “Limits of Domain Knowledge in Election Forecasting: A Comparison of Poll Averages and Expert Forecasts.” International Journal of Public Opinion Research 36 (1): edae002. https://doi.org/10.1093/ijpor/edae002.CrossRefGoogle Scholar
Graefe, Andreas, and Armstrong, J. Scott. 2012. “Predicting Elections from the Most Important Issue: A Test of the Take-the-Best Heuristic.” Journal of Behavioral Decision Making 25 (1): 41–48. https://doi.org/10.1002/bdm.710.CrossRefGoogle Scholar
Graefe, Andreas, Armstrong, J. Scott, Jones, Randall J., and Cuzán, Alfred G.. 2014. “Accuracy of Combined Forecasts for the 2012 Presidential Election: The PollyVote.” PS: Political Science & Politics 47 (2): 427–31. https://doi.org/10.1017/S1049096514000341.Google Scholar
Graefe, Andreas, Jones, Randall J., Armstrong, J. Scott, and Cuzán, Alfred G.. 2016. “The PollyVote Forecast for the 2016 American Presidential Election.” PS: Political Science & Politics 49 (4): 687–90. https://doi.org/10.1017/S1049096516001281.Google Scholar
Graefe, Andreas, Küchenhoff, Helmut, Stierle, Veronika, and Riedl, Bernhard. 2015. “Limitations of Ensemble Bayesian Model Averaging for Forecasting Social Science Problems.” International Journal of Forecasting 31 (3): 943–51. https://doi.org/10.1016/j.ijforecast.2014.12.001.CrossRefGoogle Scholar
Green, Kesten C., and Armstrong, J. Scott. 2015. “Simple versus Complex Forecasting: The Evidence.” Journal of Business Research 68 (8): 1678–85. https://doi.org/10.1016/j.jbusres.2015.03.026.CrossRefGoogle Scholar
Gruca, Thomas S., and Rietz, Thomas A.. 2024. “Iowa Electronic Markets: Forecasting the 2024 US Presidential Election.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000921.CrossRefGoogle Scholar
Holbrook, Thomas M., and DeSart, Jay A.. 1999. “Using State Polls to Forecast Presidential Election Outcomes in the American States.” International Journal of Forecasting 15 (2): 137–42. https://doi.org/10.1016/S0169-2070(98)00060-0.CrossRefGoogle Scholar
Lawrence, Michael, Goodwin, Paul, O’Connor, Marcus, and Önkal, Dilek. 2006. “Judgmental Forecasting: A Review of Progress over the Last 25 Years.” International Journal of Forecasting 22 (3): 493–518. https://doi.org/10.1016/j.ijforecast.2006.03.007.CrossRefGoogle Scholar
Lichtman, Allan J. 2008. “The Keys to the White House: An Index Forecast for 2008.” International Journal of Forecasting 24 (2): 301–9. https://doi.org/10.1016/j.ijforecast.2008.02.004.CrossRefGoogle Scholar
Lindsay, Spencer, and Allen, Levi. 2024. “A Dynamic Forecast: An Evolving Prediction of the 2024 Presidential Election.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000878.CrossRefGoogle Scholar
Lockerbie, Brad. 2024. “The Challenge of Forecasting the 2024 Presidential and House Elections: Economic Pessimism and Election Outcomes.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S104909652400091X.CrossRefGoogle Scholar
Mongrain, Philippe, Nadeau, Richard, Jérôme, Bruno, and Jérôme, Véronique. 2024. “State-Level Forecasts for the 2024 US Presidential Election: Trump Back with a Vengeance?” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S104909652400088X.CrossRefGoogle Scholar
Norpoth, Helmut. 2014. “The Electoral Cycle.” PS: Political Science and Politics 47: 332– 35. https://doi.org/10.1017/S1049096514000146.Google Scholar
Saeki, Manabu. 2024. “Forecasting Popular Vote and Electoral College Vote Results: Partisan-Bounded Economic Model.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000891.CrossRefGoogle Scholar
Soll, Jack B., and Larrick, Richard P.. 2009. “Strategies for Revising Judgment: How (and How Well) People Use Others’ Opinions.” Journal of Experimental Psychology: Learning, Memory, and Cognition 35 (3): 780–805. https://doi.org/10.1037/a0015145.Google ScholarPubMed
Tien, Charles, and Lewis-Beck, Michael S.. 2024. “The Political Economy Model: Presidential Forecast for 2024.” PS: Political Science & Politics (this issue). https://doi.org/10.1017/S1049096524000908.CrossRefGoogle Scholar