But the point here seems to be to use the group average, not to find outliers with good prediction ability per se.

That's exactly it, which is why I was so confused about the "team" of "superpredictors". While you might learn something by comparing their methods with everybody else, you still have to do the same amount of scrutiny on everyone in the crowd to learn anything useful.

With enough data, you can apply an automatic adjustment to even the most horrible predictors, to make their contribution to the final result more useful. If, for instance, someone always predicts exactly 10% more favorable results for Israel, independently of the facts, you can just reverse that for that one person on every question mentioning Israel, to produce a bias-adjusted result. That is tremenddously difficult, though, and not nearly as useful as just increasing the size of the crowd.

Which is the whole point. With a big enough crowd, you don't need to do that at all, because the stupid little biases unrelated to the facts tend to be noise, not signal.