Three changes that come with “The Rise of Poll Quants”

Nate Silver isn’t the only one making election predictions based on poll data; there are now a number of “poll quants” who are using similar techniques.

So what exactly do these guys do? Basically, they take polls, aggregate the results, and make predictions. They each do it somewhat differently. Silver factors in state polls and national polls, along with other indicators, like monthly job numbers. Wang focuses on state polls exclusively. Linzer’s model looks at historical factors several months before the election but, as voting draws nearer, weights polls more heavily.

At the heart of all their models, though, are the state polls. That makes sense because, thanks to the Electoral College system, it’s the state outcomes that matter. It’s possible to win the national vote and still end up as the head of a cable-television channel rather than the leader of the free world. But also, as Wang explains, it’s easier for pollsters to find representative samples in a particular state. Figuring out which way Arizona or even Florida might go isn’t as tough as sizing up a country as big and diverse as the United States.”The race is so close that, at a national level, it’s easy to make a small error and be a little off,” Wang says. “So it’s easier to call states. They give us a sharper, more accurate picture.”

But the forecasters don’t just look at one state poll. While most news organizations trot out the latest, freshest poll and discuss it in isolation, these guys plug it into their models. One poll might be an outlier; a whole bunch of polls are likely to get closer to the truth. Or so the idea goes. Wang uses all the state polls, but gives more weight to those that survey likely voters, as opposed to those who are just registered to vote. Silver has his own special sauce that he doesn’t entirely divulge.

Both Wang and Linzer find it annoying that individual polls are hyped to make it seem as if the race is closer than it is, or to create the illusion that Romney and Obama are trading the lead from day to day. They’re not. According to the state polls, when taken together, the race has been fairly stable for weeks, and Obama has remained well ahead and, going into Election Day, is a strong favorite. “The best information comes from combining all the polls together,” says Linzer, who projects that Obama will get 326 electoral votes, well over the 270 required to win. “I want to give readers the right information, even if it’s more boring.”

While it may not seem likely, poll aggregation is a threat to the supremacy of the punditocracy. In the past week, you could sense that some high-profile media types were being made slightly uncomfortable by the bespectacled quants, with their confusing mathematical models and zippy computer programs. The New York Times columnist David Brooks said pollsters who offered projections were citizens of “sillyland.”

Three things strike me from reading these “poll quants” leading up to the election:

1. This is what is possible when data is widely available: these pundits use different methods for their models but it wouldn’t be possible without accessible data, consistent and regular polling (at the state and national level), and relatively easy to use statistical programs. In other words, could this scenario have taken place even 20 years ago?

2. It will be fascinating to watch how the media deals with these predictive models. Can they incorporate these predictions into their typical entertainment presentation? Will we have a new kind of pundit in the next few years? The article still noted the need for these quantitative pundits to have personality and style so it their results are not too dry for the larger public. Could we end up in a world where CNN has the exclusive rights to Silver’s model, Fox News has rights to another model, and so on?

3. All of this conversation about statistics, predictions, and modeling has the potential to really show where the American public and elite stands in terms of statistical knowledge. Can people understand the basics of these models? Do they simply blindly trust the models because they are “scientific proof” or do they automatically reject them because all numbers can be manipulated? Do some pundits know just enough to be dangerous and ask endless numbers of questions about the assumptions of different models? There is a lot of potential here to push quantitative literacy as a key part of living in the 21st century world. And it is only going to get more statistical as more organizations collect more data and new research and prediction opportunities arise.

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