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As you can see, Trump is favored to win the election even if he loses the popular vote by 1-2 points, which is what our national polling average currently suggests. And if the national polls turn out to be underestimating him, with Trump winning the popular vote by 1-2 points, he would be favored to win in a blowout.

Meanwhile, our model reckons Harris needs to win the popular vote by 2.1 points to be favored to win the election because swing states are more Republican-leaning than the nation as a whole. And if she wins the popular vote by 4.5 points (Biden's popular-vote margin in 2020), she is favored to win in a blowout of her own.

Polls are inherently uncertain. This is why we model.

So far, we have said little about the actual polls themselves. And there actually is some reason to believe polls may be more accurate this year than in the past. While the share of polls conducted or sponsored by Republican-aligned organizations is up — something we have written about — the overall share of partisan polls is lower than in previous years, and the average 538 pollster rating of 2024's polls is higher than past years'. All else being equal, that should make for better polls than in 2016 and 2020. We have also seen fewer polls from the firms that overestimated Democrats the most in those years.

However, the news is not all good. In particular, pollsters are still reporting difficulty reaching voters at all, and Trump supporters may still be less likely to respond to polls — even high-quality ones. This means that pollsters are as reliant as ever (or maybe more!) on weighting and modeling to get good estimates of public opinion. But the decisions they make matter a lot, and in particular, there appear to be large differences between polls that try to use these techniques to balance their samples by party or past vote and those that do not.

And this is the big, fundamental problem with preelection polling: We don't know what the demographic and political composition of the actual electorate will be, so pollsters are just making the best guesses they can. Those guesses have always, and will always, come with error attached to them.

And that's where election models like 538's become really helpful. The point of creating election forecasting models isn't to provide a hyper-accurate, laser-like predictive picture of the election that removes all error from the polls. Rather, it's to give people a good understanding of how the polls could be wrong and what would happen if they are. By analyzing possible errors and uncertainties in the polls, these models help us approach the election with a clearer sense of how likely each side is to win (and by how much).

As we enter the final week of this election, it is a good time to remember that uncertainty is an inherent part of polling and elections. That is especially true this year, with deadlocked races across the swing states. Given that polls are imperfect, our expectation is for them to be off by some amount in either direction. And if the polls do end up being off, given the closeness of the election, there is a rather wide spread in the range of Electoral College outcomes.

In other words, we can sum up the current state of the race like this: Although Trump and Harris have roughly equal chances of winning the election, the final margin is not necessarily going to be close. In fact, there's a pretty high probability that it won't be.

Footnotes

*We simulate potential polling errors for future elections using a fat-tailed distribution — specifically, a Student's t distribution with five degrees of freedom (a parameter that increases or decreases the likelihood of surprise \"tail\" events in our simulations). This 3.8-point error is the spread, or sigma, of that distribution — analogous to the standard deviation of a normal distribution. 538's distributions are slightly wider than the ones used by other forecasting models. This is because our model accounts for the fact that polling misses have gotten bigger over the last decade. Therefore, our model anticipates more polling error than it would if we assumed a constant level of error over time, like most other forecasts do.

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