Forecast compilation: Trump vs Biden

Why is it important?

In this article I’ll compile some forecasts for the 2020 US Presidential election. I used these forecasts at 11 AM, November 2nd, one day before the elections. I use maps without toss-ups when available.



Biden: 350 - Trump: 188


Biden: 351 - Trump: 187

It’s interesting to note that they got the same result fivethirtyeight got while having a much less complicated method:

The candidate that leads in the polls is shown as the winner of the state. The 2016 party winner is used where there are no polls.

(3) CNN

Biden: 290 - Trump: 163


Biden: 335 - Trump: 203


Biden: 350 - Trump: 188

(6) my own lazy forecast: averaging forecasts of other people

We have 4 forecasts without toss-ups which for three of them are quite the same. The final result could be 350/188 but because of realclearpolitics we have to adjust to a bit lower than that. Weighting, based on my estimation of the quality of their methods, fivethirtyeight 5/5, 270towin 1/5, realclearpolitics 3/5 and 4/5, we get 347/191 on average (which may not be possible because of how the election is done but it should minimize the error).

Biden: 347 - Trump: 191

I could also add a bias towards Trump because that’s how it went in 2016 but I’m trusting previous models as some of them claim to have corrected their algorithm to account for the previous presidential election.

The main problem is that all models seem to be deeply using polls and even complex models don’t seem to be far from what raw polls say. Maybe they’re all right and most polls were done appropriately, we’ll know that this week, but if not they should definitely try to base themselves on different metrics, and try to know why they weren’t able to predict the outcome in the State where they failed to have the great result.

The outcome will surely be interesting for next elections all over the world, to understand how far we’ve come in forecasting elections. 2016 had a surprising result so we hope that this time all people doing forecasts have corrected their algorithms.

Who was right / Who was wrong / Why?

Ok, the count is still not over but it’ll probably take a long time before it is. Let’s extrapolate the last results, Biden gets Georgia and Trump get NC and Alaska. Final result:

Biden: 306 - Trump: 232

Who was right?

What lessons can be learned?

Votes shouldn’t only be predicted based on what people say they’ll do, but also based on what they did and who they are. Knowing how people voted during previous elections is a great indicator on how they’ll vote for the next one. It appears that this time the truth was in between 2020 polls and 2016 election, while models mostly followed 2020 polls.

It’s a possibility that the next republican candidate will be less “pro-countryside” in order to win the next election. If models don’t account for that fact, they’ll put a big bias towards that candidate, not understanding it’s not Trump anymore. These models could predict a win for the republican candidate while this bias won’t be as much prominent in the public opinion, and the democrat could win in the end to the surprise of forecasters.

It’s important to not only learn the lesson, but also understand why people voted like they did. Has Trump invested much more in Florida? Was it because he voted in that state? Because he often goes to Florida and he has a special aura in that place? This should be accounted into the algorithms, but one data point isn’t enough and these phenomena should be analyzed quantitavely thanks to the data coming from other elections.



This article was written on Medium, by Elie D, for

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Date: 2020/11/02

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