How (not) to forecast an election: Analysis via a hierarchical Bayesian model

I have an alternative explanation for this : people who buy newspapers and mainstream web sites are liberal. Pollsters and analysts who wish to make a living must get clicks and sell copy. The ones that create models that agree with the general zeitgeist of the liberal milieu get clicks and sell copy, other models are gradually ground to extinction.

Markets are not efficient mechanisms for creating insight.

While beyond the scope of this article, it would be great see more research done into the relative strengths of each bias, i.e. which biases are skewing polls the most, rather than just using lumped bias terms. Though I did see after Brexit forecasts got it wrong too the British Polling Council is "looking into it".

What's most interesting is there wasn't more of an effort by the media to get accurate vote forecasts before the election. Granted, it's more expensive and takes longer but it's not terribly difficult to do. It does involve a research team and statisticians spending time in person in the state and getting deeper data from people. But the time/costs aren't as outrageous as one would think because it only needs to be done in the handful of close swing states. And a sample size of just a few hundred people is enough to get significant results. Seems obvious and worthwhile, maybe in 2020 they figure it out.