• Final Result: Obama 332, Romney 206

    by  • November 9, 2012 • 12 Comments

    The results are in: Obama wins all of his 2008 states, minus Indiana and North Carolina, for 332 electoral votes. This is exactly as I predicted on Tuesday morning – and as I’ve been predicting (albeit with greater uncertainty) since June. Not bad! The Atlantic Wire awarded me a Gold Star for being one of “The Most Correct Pundits In All the Land”. There were also nice write-ups in The Chronicle of Higher Education, BBC News Magazine, Atlanta Journal-Constitution and the LA Times, among others. Thanks to everyone who has visited the site, participated in the comments, and offered their congratulations. I really appreciate it.

    I’m still planning a complete assessment of the performance of the forecasting model, along the lines I described a few weeks ago. But in the meantime, a few quick looks at how my Election Day predictions stacked up against the actual state-level vote outcomes. First, a simple scatterplot of my final predictions versus each state’s election result. Perfect predictions will fall along the 45-degree line. If a state is above the 45-degree line, then Obama performed better than expected; otherwise he fared worse.

    Interestingly, in most of the battleground states, Obama did indeed outperform the polls; suggesting that a subset of the surveys in those states were tilted in Romney’s favor, just as I’d suspected. Across all 50 states, however, the polls were extremely accurate. The average difference between the actual state vote outcomes and the final predictions of my model was a miniscule 0.03% towards Obama.

    My final estimates predicted 19 states within 1% of the truth, with a mean absolute deviation of 1.7%, and a state-level RMSE of 2.3% (these may change slightly as more votes are counted). Other analysts at the CFAR blog and Margin of Error compared my estimates to those of Nate Silver, Sam Wang, Simon Jackman, and Josh Putnam, and found they did very well. All in all, a nice round of success for us “quants”.

    Unsurprisingly, my model made much better predictions where more polls had been fielded! Here I’ll plot the difference between Obama’s share of the two-party vote in each state, and my final prediction, as a function of the number of polls in the state since May 1. Again, positive values indicate states where Obama did better than expected.

    For minimizing the error in my forecasts, the magic number of polls per state appears to be around 25. That’s really not a lot; and I’m hopeful that we can get to at least this number in 2016. It’s a bit concerning, though, that there were about 25% fewer state-level presidential polls this year, compared to 2008.

    Recently there have been some complaints among pollsters – most notably Gallup’s Frank Newport – that survey aggregators (like me) “don’t exist without people who are out there actually doing polls,” and that our work threatens to dissuade survey organizations from gathering these data in the first place. My view is slightly different. I’d say that working together, we’ve proven once again that public opinion research is a valuable source of information for understanding campaign dynamics and predicting election outcomes. There’s no reason why the relationship shouldn’t be one of mutual benefit, rather than competition or rivalry. In a similar manner, our analyses supplement – not replace – more traditional forms of campaign reporting. We should all be seen as moving political expertise forward, in an empirical and evidence-based way.

    Election Day Forecast: Obama 332, Romney 206

    by  • November 6, 2012 • 91 Comments

    With the last set of polls factored into the model, my final prediction is Obama to win 332 electoral votes, with 206 for Romney. This is both the median and the modal outcome in my electoral vote simulation, and corresponds to Obama winning all of his 2008 states except Indiana and North Carolina.

    The four closest states – and therefore the most difficult to predict – are Florida, North Carolina, Virginia, and Colorado. Of these, my model only expects Romney to win North Carolina; but Florida is a true toss-up, with just a 60% chance of Obama victory. I would not be surprised if Florida ended up going for Romney. If that happens, Obama would win 303 electoral votes, which is the second-most likely scenario in my simulation. The third-most likely scenario is that Obama wins 347 electoral votes, picking up North Carolina in addition to Florida.

    It’s been interesting to watch the forecasts of other poll watchers converge on the 332 estimate. Sam Wang, at the Princeton Election Consortium, also sees 332 as the modal outcome. So does Simon Jackman at the Huffington Post, and Josh Putnam at FHQ. Nate Silver, at his FiveThirtyEight blog, reports the mean of his electoral vote simulation at 313 – effectively splitting the difference on Florida, which he currently rates a 50.3% chance of an Obama win. But his most likely outcome is still Obama 332, followed by 303 and 347, just like me. Update: both Wang and Jackman revised their forecasts slightly downward this afternoon, based on late-arriving polls.

    There will be plenty of opportunities to evaluate all of these forecasts once the election results are known. I’ve already laid out the standards I’ll be using to check my own model. This is how quantitative election forecasting can make progress, and hopefully work even better next time.

    I’ll add, though, that on the eve of the election, averaging the polls, or running them through any sort of sensible model, isn’t all that hard. We are all using the same data (more or less) and so it doesn’t surprise me that we’re all reaching similar conclusions. The real challenge is producing meaningful and accurate forecasts early in the campaign. My model is designed to be robust to short-term fluctuations in the polls, and converge in a stable and gradual manner to the final, Election Day estimates. It appears that in this regard, the model has worked as intended.

    But from a broader perspective, my model has been predicting that Obama will win 332 electoral votes – give or take – since June. If all of us are correct today, the next question to ask is when each model arrived at the ultimate outcome. That’s a big if, though. Let’s start with how the votes come in tonight, and go from there.

    Final Estimates Tomorrow Morning

    by  • November 5, 2012 • 40 Comments

    I entered nearly 50 new state polls in the most recent model update, posted earlier today. There have been over 30 additional polls released since then. I’ll wait a few more hours to see if any more come out, then run the model one more time, overnight. My final estimates will be ready in the morning.

    In the meantime, you might have noticed that my EV forecast for Obama inched downward for the first time in weeks, from 332 to 326. That’s the median of my election simulations, but it doesn’t correspond to a particularly likely combination of state-level outcomes. Instead, it reflects the declining probability that Obama will win Florida (now essentially a 50-50 proposition), and Obama’s continuing deficit in North Carolina. I’ve updated the title on the chart in the banner to make this clear.

    Depending on how things go with the final run, I’ll keep updating the chart as I have been, using the median. But I’ll also create a more true-to-life forecast, based on assigning each state to its most likely outcome. With Florida (and its 29 electoral votes) right on the knife edge, this will either be Obama 332-206 if the model projects an Obama victory there, or Obama 303-235 if the model shows Obama behind. I’ll also have all sorts of other tables and charts ready to go for comparing the election results as they’re announced.

    Pollsters May Be Herding

    by  • November 5, 2012 • 26 Comments

    The accuracy of my election forecasts depends on the accuracy of the presidential polls. As such, a major concern heading into Election Day is the possibility that polling firms, out of fear of being wrong, are looking at the results of other published surveys and weighting or adjusting their own results to match. If pollsters are engaging in this sort of herding behavior – and, as a consequence, converging on the wrong estimates of public opinion – then there is danger of the polls becoming collectively biased.

    To see whether this is happening, I’ll plot the absolute value of the state polls’ error, over time. (The error is the difference between a poll’s reported proportion supporting Obama, and my model’s estimate of the “true” population proportion.) Herding would be indicated by a decline in the average survey error towards zero – representing no difference from the consensus mean – over the course of the campaign. This is exactly what we find. Although there has always been a large amount of variation in the polls, the underlying trend – as shown by the lowess smoother line, in blue – reveals that the average error in the polls started at 1.5% in early May, but is now down to 0.9%.

    How worried do we need to be? Herding around the wrong value is potentially much worse than any one or two firms having an unusual house effect. But even if the variance of the polls is decreasing, they might still have the right average. An alternative explanation for this pattern could be an increase in sample sizes (resulting in lower sampling variability), but this hasn’t been the case. Unfortunately, there weren’t enough polls to tell whether the pattern was stronger in more frequently-polled states, or if particular firms were more prone to follow the pack. Hopefully, this minor trend won’t mean anything, and the estimates will be fine. We’ll know soon.

    Another Look at Survey Bias

    by  • November 2, 2012 • 102 Comments

    Questions continue to be raised about the accuracy of the polls. Obviously, in just a few more days, we’ll know which polls were right (on average) and which were wrong. But in the meantime, it’s useful to understand how the polls are – at the very least – different from one another, and form a baseline set of expectations to which we can compare the election results on Tuesday. The reason this question takes on special urgency now is that there’s essentially no time left in the campaign for preferences to change any further: if the state polls are right, then Obama is almost certain to be reelected.

    In previous posts, I’ve looked at both house effects, and error distributions (twice!), but I want to return to this one more time, because it gets to the heart of the debate between right-leaning and left-leaning commentators over the trustworthiness of the polls.

    A relatively small number of survey firms have conducted a majority of the state polls, and therefore have a larger influence on the trends and forecasts generated by my model. Nobody disputes that there have been evident, systematic differences in the results of these major firms: some leaning more pro-Romney, others leaning more pro-Obama. As I said at the outset, we’ll know on Election Day who’s right and wrong.

    But here’s a simple test. There have been hundreds of smaller organizations who have released fewer than a half-dozen polls each. Most have only released a single poll. We can’t reliably estimate the house effects for all of these firms individually. However, we can probably safely assume that in aggregate they aren’t all ideologically in sync – so that whatever biases they have will all cancel out when pooled together. We can then compare the overall error distribution of the smaller firms’ surveys to the error distributions of the larger firms’ surveys. (The survey error is simply the difference between the proportion supporting Obama in a poll, and my model’s estimate of the “true” proportion on that state and day.)

    If the smaller firms’ errors are distributed around zero, then the left-leaning firms are probably actually left-leaning, and the right-leaning firms are probably actually right-leaning, and this means that they’ll safely cancel each other out in my results, too. On the other hand, if the smaller firms’ error distribution matches either the left-leaning or the right-leaning firms’ error distribution, then it’s more likely the case that those firms aren’t significantly biased after all, and it’s the other side’s polls that are missing the mark.

    What do we find? This set of kernel density plots (smoothed histograms) shows the distribution of survey errors among the seven largest survey organizations, and in grey, the distribution of errors among the set of smaller firms. The smaller firms’ error distribution matches that of Quinnipiac, SurveyUSA, YouGov, and PPP. The right-leaning firms – Rasmussen, Gravis Marketing, and ARG – are clearly set apart on the pro-Romney side of the plot.

    If, on Election Day, the presidential polls by Quinnipiac, SurveyUSA, YouGov, and PPP prove to be accurate, then the polls by Rasmussen, Gravis Marketing, and ARG will all have been underestimating Obama’s level of support by 1.5% consistently, throughout the campaign. Right now, assuming zero overall bias, Florida is 50-50. The share of Florida polls conducted by Rasmussen, Gravis Marketing, and ARG? 20%. Remove those polls from the dataset, and Obama’s standing improves.

    Four days to go.