but it does mean that Boeing got something wrong.

Comparing it to Boeing shows you still misunderstand probability. If his model predicts 4 separate elections where each underdog candidate had a 1 in 4 chance of winning. If only 1 of those underdog candidates wins, then the model is likely working. But when that candidate wins everyone will say “but he said it was only a 1 in 4 chance!”. It’s as dumb as people being surprised by rain when it says 25% chance of rain. As long as you only get rain 1/4 of the time with that prediction, then the model is working. Presidential elections are tricky because there are so few of them, they test their models against past data to verify they are working. But it’s just probability, it’s not saying this WILL happen, it’s saying these are the odds at this snapshot in time.

Silver made a prediction. That’s the deliverable.

I see what you’re not getting! You are confusing giving the odds with making a prediction and those are very different.

Let’s go back to the coin flips, maybe it’ll make things more clear.

I or Silver might point out there’s a 75% chance anything besides two heads in a row happening (which is accurate.) If, as will happen 1/4 times, two heads in a row does happen, does that somehow mean the odds I gave were wrong?

Same with Silver and the 2016 election.

Silver made a prediction. That’s the deliverable. The prediction was wrong.

Would you mind restating the prediction?

It’s forecasting, not a prediction. 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? You could say that if you want, but the point isn’t to give a definitive prediction of the outcome (because that’s not possible) it’s to give you an idea of what to expect.

If there’s a 28% chance of rain, it doesn’t mean it’s not going to rain, it actually means you might want to consider taking an umbrella with you because there’s a significant probability it will rain. If a batter with a .280 batting average comes to the plate with 2 outs at the bottom of the ninth, that doesn’t mean the game is over. If a politician has a 28% probability of winning an election, it’s not a statement that the politician will definitely lose the election.

How about this:

Two people give the odds for the result of a coin flip of non-weighted coins.

Person A: Heads = 50%, Tails = 50%

Person B: Heads = 75%, Tails = 25%

The result of the coin flip ends up being Heads. Which person had the more accurate model? Did Person A get something wrong?

You do it by comparing the state voting results to pre-election polling. If the pre-election polling said D+2 and your final result was R+1, then you have to look at your polls and individual polling firms and determine whether some bias is showing up in the results.

Is there selection bias or response bias? You might find that a set of polls is randomly wrong, or you might find that they’re consistently wrong, adding 2 or 3 points in the direction of one party but generally tracking with results across time or geography. In that case, you determine a “house effect,” in that either the people that firm is calling or the people who will talk to them lean 2 to 3 points more Democratic than the electorate.

All of this is explained on the website and it’s kind of a pain to type out on a cellphone while on the toilet.