Tremont Director on Predicitve Modeling of Voter Behavior in Race for U.S. Senate

Thursday, September 12, 2013

Crossing Party Lines with Predictive Modeling

With the rise of Nate Silver and the emergence of mainstream data science, we've seen many uses for predictive analytics, including the entrance of predictive modeling into the political arena. Actually, although predicting election results is a booming business now, it has been around for quite some time. 

I recently got the chance to talk to Matt Hennessy, Managing Director at Tremont Public Advisors, about a campaign he worked on for Joe Lieberman in 2006, and how they implemented predictive modeling for a successful Senate election. For those who are interested, we'll be discussing this and other examples of predictive modeling in action in a webinar on Tuesday, September 17th. 

Can you give us some background on the 2006 Senate election?

In 2006 in Connecticut, Joe Lieberman was up for reelection to the Senate as a Democrat. He had been the Vice Presidential nominee in the 2000 election and had taken a position supporting the Iraq war which upset a lot of the Democratic base. He wound up losing the Democratic primary to Ned Lamont who won on a big anti-war push. Once Lieberman lost the primary election, he lost access to a considerable amount of infrastructure – union support, door to door field workers, and all of the other boots on the ground that he would have had were all gone. He lost most of his staff except for the people who had been there for a decade or two. He needed to figure out how to replace some of the advantages he’d had with other resources out there.

As someone advising him, I saw that we had a problem: without a field operation and all of those bodies, we didn’t know exactly who we wanted to get out the vote and who the likely voters for Lieberman were. We had a very expensive polling operation going which  was using the conventional method to reach some conclusions about which demographics were most likely to vote, but we decided that we needed something more.