It is a rational behavior to question a prediction if an event that was deemed to have low probability actually happened, I don’t like the implication that it is stupid to do so.

In fact, after thorough investigation, most polling agencies did identify significant factors that let to bias, and almost all major pollsters have adapted their methods in later elections as a result: education weighting, compensating for nonresponse bias, diversified polling channels beyond phone calls (online surveys, text messages, in-app polling), likely-voter modeling…

I don’t think any professional pollster will tell you that the prediction you mentioned was reasonably accurate in retrospect, regardless of affiliation.

The problem is using statistical models at all for predicting the probability of one time events. (elections are every 4 years, but those future models will be different).

You can't validate such a model, and it's not useful since any upset victory was "accounted for" simply because its possibility was named.

But is your 10% chance of Trump winning model a better model than one that gave Trump a 40%, 50%, 60%, 70%, 80%, or 90% chance of winning?

If you can't show that why should anyone listen to you?

538 had Trump at 30.7% the night before, FWIW: https://web.archive.org/web/20161107224336/https://projects....

If someone says there is an x% chance of an event happening, is there a value of x other than 0 & 100 where they can be wrong?

What is the value that someone can derive out of such predictions ?

If someone predicts something will happen with a 90% probability, then they should be wrong roughly 10% of the time. We can't determine that from a single event, but we could look at 10 events predicted by the same model. In the case of election forecasts, we would expect 1/10 elections predicted at 90% probability to have the wrong estimate. So we would expect the average person to see it at least once, and probably twice, in their lifetime.