I agree, of course, that a bad prediction can perform better than a good prediction by luck. That means if you were already sufficiently sure your prediction was good, you can continue to believe it was good after it performs badly. But your belief that the prediction was good then comes from your model of the sources of the competing predictions prior to observing the result (e.g. “PredictIt probably only predicted a higher Trump probability because Trump Trump Trump”) instead of from the result itself. The result itself still reflects badly on your prediction. Your prediction may not have been worse, but it performed worse, and that is (perhaps insufficient) Bayesian evidence that it actually was worse. If Nate Silver is claiming something like “sure, our prediction of voter % performed badly compared to PredictIt’s implicit prediction of voter %, but we already strongly believed it was good, and therefore still believe it was good, though with less confidence”, then I’m fine with that. But that wasn’t my impression.
edit:
Deviating from the naive view implicitly assumes that confidently predicting a narrow win was too hard to be plausible
I agree I’m making an assumption like “the difference in probability between a 6.5% average poll error and a 5.5% average poll error isn’t huge”, but I can’t conceive of any reason to expect a sudden cliff there instead of a smooth bell curve.
I agree, of course, that a bad prediction can perform better than a good prediction by luck. That means if you were already sufficiently sure your prediction was good, you can continue to believe it was good after it performs badly. But your belief that the prediction was good then comes from your model of the sources of the competing predictions prior to observing the result (e.g. “PredictIt probably only predicted a higher Trump probability because Trump Trump Trump”) instead of from the result itself. The result itself still reflects badly on your prediction. Your prediction may not have been worse, but it performed worse, and that is (perhaps insufficient) Bayesian evidence that it actually was worse. If Nate Silver is claiming something like “sure, our prediction of voter % performed badly compared to PredictIt’s implicit prediction of voter %, but we already strongly believed it was good, and therefore still believe it was good, though with less confidence”, then I’m fine with that. But that wasn’t my impression.
edit:
I agree I’m making an assumption like “the difference in probability between a 6.5% average poll error and a 5.5% average poll error isn’t huge”, but I can’t conceive of any reason to expect a sudden cliff there instead of a smooth bell curve.
I think I agree with all of that.