What's the evidence for them being wrong?
To be clear, I'm not arguing that they were correct. But I see a trend of attacking any prediction (including those of 538) that rated a Trump victory at less than 50% likelihood as wrong, based on the evidence that he won.
If I predict there's a less than 1% chance of you winning the lottery, you winning the lottery doesn't prove that I was wrong.
I have no idea what the likelihood of a Trump victory was before the election. Maybe it was 1%, maybe it was 99%. But I think discussion of predictions shouldn't be results-orientated for single results.
As for the rest of the forecaster that gave Clinton 99% to win, given a US presidential election, the priori of a candidate winning is 50% (historical data, roughly speaking). So you would need some quite extraordinary evidences to get the posterior to 90+% for a candidate.
In this case, the evidences were mostly the polls. To have 99% prediction for a candidate based on a poll, the poll would need have at least 98% accuracy (assuming that when they're wrong, the probability of they being wrong in either directions is the same). Personally, I don't think the polls to be anywhere near that level of accuracy in general. Unfortunately I don't got any number of poll accuracy on hand - I'd love to see it if anyone have a citation.
There are more factors that will affect an election, but with similar reasoning to above, to get a 98-99% prediction, those factors will have to be 90%+ reliable as well. That is some quite tall order.
This is a case where the onus is on the forecaster to show that their number (98% for example) is sound, since they're the one making the extraordinary claim.
Regardless, for something like an election where so many factors can influence an outcome (turnout, late breaking news, biased polls) and with so few previous events to base your model on (~12 elections worth of data), you should heavily discourage your model from outputting such a high number. It is irresponsible considering what kind of an impact those types of forecasts can have on voter apathy and decision making, and the only reason I can think they did it was so that they could award themselves the 'most accurate forecaster' title after the election. But instead, they now get to award themselves the 'worst forecaster award', and the rest of us are stuck with Trump for 4 years.
[1] http://www.huffingtonpost.com/entry/pollster-forecast-donald...
538 made the compelling (and in the end, sadly accurate) statistical point that modeling errors in polls are very likely to be correlated across states, not independent like a lottery.
Nate Silver's reputation went up again for me in this election.
[0] - http://www.economist.com/news/united-states/21710024-how-mid...
I've gotten the general impression that while he might have done not as badly as others in this last minute polling, in general he did not cover himself in glory in 2016, and here I rate it by an org's confidence in their numbers.
But I didn't follow this at all closely, for it was obvious to me all or almost all the public polls were getting it seriously wrong, see e.g. https://news.ycombinator.com/item?id=12930950
Or to question your logic another way, what would a poll mean in the context of a lottery?
1. Trump says something really bad 2. Check a poll aggregator (538) 3. Breath sigh of relief
For a good chunk of the election, Hillary had a really big lead in the polls. But after the Comey incident, things got tighter[1]. Nate Silver had some really good analysis on how tight the race was (a Trump win was within the polling error, and when state level polls are off, a lot of times they are all off in the same direction). But he was getting shouted down by the other aggregators. I was pretty on edge for this part of the race, but at the end, it looked like Hillary was picking up some momentum.
I think part of it was the bubble effect, but I also think Democrats put too much faith in these forecasts.
[1] At least on 538, a bunch of the other forecasters didn't budge. HuffingtonPost had a 98% chance of Hillary winning at the end. I really hope someone holds them accountable for that, as I wouldn't be surprised if such confident forecasts contributed to voter apathy.