If I had a laptop that only worked 1/4th of the time, rather than 1/20th of the time, would that make it a reliable laptop? I don't think so.
Also, it doesnt make sense to look at a single prediction to evaluate a model.
Out of all the predictions they have made (did you look at individual state predictions?), how many were correct (and how confident were they?) - how many were wrong (and how close to 50% were they?).
That is how you evaluate a model (aka cross entropy)
For example: anyone paying attention to the Rust Belt ±1980-2016 would have dramatically upped Trump's chances in Pennsylvania and Michigan. FiveThirtyEight had Hillary with 70%+ chance of winning both, which to me, shows a deep ignorance of actual cultural factors.
There was a very decent chance that Clinton could have won in 2016 (if any factor had gone slightly better for her), and if that had happened, nobody would be saying this now. This is literal hindsight bias.
My view is simple: the media completely, totally got 2016 wrong, mostly for sociological reasons. The people making the predictions simply had a huge blind spot. Brexit is another similar situation. The fact that Hillary almost won or Brexit almost didn't happen isn't really the point, because both things were never expected to be even remotely that close. Had the predictions been "Pennsylvania will be close", it would be relevant, but those weren't the predictions.
How else would one predict the outcome of a die roll, specifically?
It's unfortunate we can't just run the election again a few times, and actually find the rate at which Trump is elected given the polls.
And it's not empty signalling if 538 assigned Trump a higher chance of winning; they were pretty much the only ones saying he has a chance. That is why people think the models are useful.