This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: As politics in America become more polarized and each voting cycle seems more fraught than the last, a common narrative in the media is that each election is unique and increasingly unpredictable. But new research shows that congressional elections follow strong statistical patterns, even when individual races produce surprises.
"There are a lot of generalities and systematic things happening in elections," says Jonathan N. Katz, Caltech's Kay Sugahara professor of social sciences and statistics. "It's not to say that there aren't interesting stories, but, overall, elections are very predictable, and I don't think the general public thinks about them that way.
Patterns help estimate the odds, but close races—and control of Congress—can remain uncertain." To help illustrate the effects of different data points on predictions and give people a practical tool with which they can build their own Election Day scenarios, Katz teamed up with former graduate student Danny Ebanks (Ph.D. '24), now a research associate at Harvard's Institute for Quantitative Social Science (IQSS), and political scientist Gary King, the Weatherhead University professor at Harvard University and director of IQSS, to build a forecasting website called ElectIt. The interactive tool draws on decades of election data that the team assembled—with the help of AI—covering every U.S. House election since 1946 and Senate election since 1954 as well as the current races.
Katz has been modeling election dynamics for more than three decades. The forecaster is one product of a larger project with Ebanks and King: building the first generative statistical model that can simulate whole elections and that accurately describes congressional elections. King, a longtime collaborator of Katz's (he was Katz's postdoctoral adviser at Harvard), had co-developed what became the canonical model for legislative elections in the 1990s with statistician Andrew Gelman.
Seven years ago, Katz, Ebanks and King tried to port that model to a modern programming language and noticed a major flaw. "The model actually didn't fit very well; results that it treated as less than one-in-10,000 events in individual races showed up in almost every other congressional election cycle," Katz explains. "For example, it badly underestimated how often popular incumbents lose their seats.
We tried lots of different models to make one that worked better and finally came up with a framework that fits the data. The forecaster is one application of it." Unlike many election-prediction models seen in popular media, ElectIt's core model uses no polls at all. Instead, it draws on decades of statistical data—such as past results; which candidates are running and whether one is an incumbent; and the midterm penalty, which is the tendency of the president's party to lose support in midterm elections.
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