He predicted Clinton would win. That’s the only reasonable prediction if her win probability was over 50%

Presidential elections are tricky because there is only one prediction.

Suppose your model says Trump has a 28% chance of winning in 2024, and mine says Trump has a 72% chance of winning in 2024.

There will only be one 2024 election. And suppose Trump loses it.

If that outcome doesn’t tell us anything about the relative strength of our models, then what’s the point of using a model at all? You might as well write a single line of code that spits out “50% Trump”, it is equally useful.

The point of a model is to make a testable prediction. When the TV predicts a 25% chance of rain, that means that it will rain on one fourth of the days that they make such a prediction. It doesn’t have to rain every time.

But Silver only makes a 2016 prediction once, and then he makes a new model for the next election. So he has exactly one chance to get it right.

I or Silver might point out there’s a 75% chance anything besides two heads in a row happening (which is accurate.)

Is it?

Suppose I gave you two coins, which may or may not be weighted. You think they aren’t, and I think they are weighted 2:1 towards heads. Your model predicts one head, and mine predicts two heads.

We toss and get two heads. Does that mean the odds I gave are right? Does it mean the odds you gave are wrong?

In the real world, your odds will depends on your priors, which you can never prove or disprove. If we were working with coins, then we could repeat the experiment and possibly update our priors.

But suppose we only have one chance to toss them, and after which they shatter. In that case, the model we use for the coins, weighted vs unweighted, is just a means to arrive at a prediction. The prediction can be right or wrong, but the internal workings of a one-shot model - including odds - are unfalsifiable. Same with Silver and the 2016 election.

If the weather forecast said there was a 28% chance of rain tomorrow and then tomorrow it rained would you say the forecast was wrong?

Is it possible for the forecast to be wrong?

I think so. If you look at all the times the forecast predicts a 28% chance of rain, then it should rain on 28% of those days. If it rained, say, on half the days that the forecast gave a 28% chance of rain then the forecast would be wrong.

With Silver, the same principle applies. Clinton should win at least 50% of the 2016 elections where she has at least a 50% chance of winning. She didn’t.

If Silver kept the same model over multiple elections, then we could look at his probabilities in finer detail. But he doesn’t.

Person B’s predicted outcome was closer to the truth.

Perhaps person A’s prediction would improve if multiple trials were allowed. Perhaps their underlying assumptions are wrong (ie the coins are not unweighted).

You are describing how to evaluate polling methods. And I agree: you do this by comparing an actual election outcome (eg statewide vote totals) to the results of your polling method.

But I am not talking about polling methods, I am talking about Silver’s win probability. This is some proprietary method takes other people’s polls as input (Silver is not a pollster) and outputs a number, like 28%. There are many possible ways to combine the poll results, giving different win probabilities. How do we evaluate Silver’s method, separately from the polls?

I think the answer is basically the same: we compare it to an actual election outcome. Silver said Trump had a 28% win probability in 2016, which means he should win 28% of the time. The actual election outcome is that Trump won 100% of his 2016 elections. So as best as we can tell, Silver’s win probability was quite inaccurate.

Now, if we could rerun the 2016 election maybe his estimate would look better over multiple trials. But we can’t do that, all we can ever do is compare 28% to 100%.