Princeton Election Consortium

Innovations in democracy since 2004

Outcome: Biden 306 EV (D+1.2% from toss-up), Senate 50 D (D+1.0%)
Nov 3 polls: Biden 342 EV (D+5.3%), Senate 50-55 D (D+3.9%), House control D+4.6%
Moneyball states: President AZ NE-2 NV, Senate MT ME AK, Legislatures KS TX NC

About the Princeton Election Consortium

This blog’s mission is to provide informed analysis of US national elections by members of the Princeton academic community. It is open to scholars in the Princeton area from all disciplines, including (but not restricted to) politics, neuroscience, psychology, computer science, and mathematics.

For now, much of the site’s information is about polling. As the campaign season progresses we will expand to other interesting topics, and expect for diverse contributions. To write for us, please contact Sam Wang.

This blog began in 2004 as a meta-analysis directed at the question of who would win the Electoral College. Meta-analysis of state polls provides more objectivity and precision than looking at a single poll and gives an accurate current snapshot of the state of play. Over the course of the campaign, this site attracted over a million visits. In 2004, the median decided-voter calculation on Election Eve captured the exact final outcome (read this article and the follow-up). The 2008 calculation provided results based on decided-voter polling from all 50 states, and in the closing week of the campaign ended up within 1 electoral vote of the final outcome.

The Management

Prof. Sam Wang (email) has academic expertise in biophysics and neuroscience. In these fields he uses probability and statistics to analyze complex experimental data, and has published over eighty papers using these approaches. His research program concerns how the brain learns from experience in adulthood and development, with a special emphasis on autism. He is also the author of two popular books, the prizewinning¬†Welcome To Your Brain, and a second book on child brain and mental development, Welcome To Your Child’s Brain. These books have been translated into over 20 languages. In 2015 Sam Wang was appointed to the New Jersey Governor’s Council on the Medical Research and Treatment of Autism by Governor Chris Christie.

Prof. Wang originally developed the Meta-Analysis in 2004 to help readers think about how to allocate campaign contributions. He was motivated by the fact that in a close race, one can make the biggest difference by donating at the margin, where probabilities for success are 20-80%. The Meta-Analysis has subsequently been found to be a highly sensitive tracking tool over time, and the concept has become extremely popular thanks to the efforts of FiveThirtyEight and other sites. To read a discussion click here. In 2004, the Meta-Analysis of State Polls got tens of thousands of hits per day, and since that time the Princeton Election Consortium has recorded over five million visits. Prof. Wang’s Meta-Analysis has also been featured on NPR, Fox News, and in the Wall Street Journal.

Sam Wang has written numerous articles, including for the New York Times and the American Prospect (for examples click here and here) and regularly gives public lectures. He also cohosts a Princeton University-sponsored podcast, Politics and Polls, with Prof. Julian Zelizer.

He is also founder of the Princeton Gerrymandering Project, a nonpartisan project that uses law and statistics to understand and prevent partisan abuse of redistricting. His proposed standards were published in the Stanford Law Review, and have been recognized by Common Cause.

Sam can be contacted as sswang at princeton dot edu.

Lucas Manning (email) is a current Princeton undergrad (class of 2020) studying Computer Science with a research focus on computer graphics/vision. He is responsible for administrating the website, managing/creating the 2018 PEC redesign, and gathering data for analysis.


Mark Tengi is a former Princeton undergrad in the School of Engineering and Applied Science (class of 2016) who took over for Andrew during the 2014 Senate race. He is studying computer science and linguistics, with an emphasis on computer systems.

Andrew Ferguson is a 2008 Princeton graduate and was responsible for the site’s data processing and infrastructure for its first six years. While in college, he studied probability, statistics, and computer science. He is currently a computer science graduate student at Brown University, where he works on Software Defined Networks and frameworks for analyzing Big Data.