There where a dozen or so models which took the polls as input (some added other inputs as well) and produced a probability of a candidate winning. Some of those models where garbage (Huffington post I had Trump at ~1.5-2%) and some where pretty good (fivethirtyeight had Trump at ~30% and trending upwards). The question about how you should interpret these numbers is a more open one. What does it mean to give numeric probabilities to events which are completely unique and will only occur once (cue discussion of Bayesian vs Frequentist inference here).

Admittedly the question about if the model was right or wrong is difficult to disentangle from the question about bad polling. No model can work correctly if you feed it garbage data. The only criticism you could make is that they should have been even more critical of data they where getting from certain polls than they where.

Now as to was there something wrong with the polls? Obviously. But the interesting question is what went wrong. They where pretty good at forecasting the national popular vote, while at the same time getting certain mid-western swing states dramatically wrong. So there is obviously something in their methodology which seems to works fine when looking at the country but fails when looking at certain states.

But like you I look forward to seeing more detailed investigations coming out in the next few month.