For Those Who Came In Late

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A number of people are asking “what are these numbers?” about the Votecastr data. First off, shame on you for not reading every post of mine voraciously in anticipation of the quiz we’ll be having tomorrow. I’m very disappointed.

Second, here’s what Slate and Votecastr are doing:

Here’s how the VoteCastr system operates. By combining proprietary, large-sample polls taken prior to Election Day with targeted, real-time tracking of voter turnout on Tuesday, VoteCastr will make rolling projections of how many ballots have been cast for each candidate in each of the states we’re tracking: Florida, Iowa, New Hampshire, Nevada, Ohio, Pennsylvania, and Wisconsin. If you visit Slate at 11 a.m. EST on Tuesday, you’ll see projections for how many votes have been cast for Hillary Clinton and Donald Trump in each of those states as of 11 a.m. (VoteCastr will also analyze the vote in Colorado, albeit using a different technique. More on that in a bit.)

It’s crucial to remember these projections are being made in real time. Even if we were to assume the VoteCastr models are perfect—and we won’t—they can’t tell us who will win a particular state, only who is winning that state at a specific moment in time and who might win if current trends continue. When it comes to who might win, the emphasis should be on might. There are too many unknowns for us to be able say with confidence that what we think is happening in the present will continue to happen in the future. It’s entirely possible, for instance, that Trump voters will be more likely to cast their ballots in the morning and that Clinton voters will be more likely to cast theirs in the evening—or vice versa. There just isn’t enough historical data to give us meaningful insight on that type of voter behavior. Over the course of the day, we expect Clinton’s and Trump’s respective shares of the total vote in each state to shift as turnout waxes in some areas and wanes in others.
Slate readers will be able to watch live as those vote totals update throughout Election Day. They’ll also be able to sort the data in a number of different ways. We’ll make it possible, for instance, to compare real-time turnout in Trump-leaning counties and Clinton-leaning counties, as well as to gauge turnout in counties grouped by age, income, and race. If our real-time trackers are seeing that turnout in Pennsylvania’s middle class or predominantly black counties has surpassed 2012 levels, you’ll know that. If turnout in Ohio counties that are predominantly white or lower class does the same, you’ll know that, too.
We also hope to use the VoteCastr model to bring some empiricism to all the anecdotes that pop up in the news on Election Day. If there’s rain in Cincinnati, a viral photo of long lines in Las Vegas, or an unplanned appearance by Tim Kaine in Philadelphia, we won’t have to speculate about whether those events will cause turnout to rise or fall. We’ll be able to look at the numbers and draw conclusions—albeit tentative ones—ourselves.

It’s not actual votes, it’s very, very educated estimates of how the votes actually cast are likely to break down. It’s never been done before, it’s being done in real time, and it’s like catnip and a ball of yarn all rolled up together.

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