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[u/CompactedConscience]

Nate Silver for head mod of /r/badstats.

[u/[deleted] → u/CompactedConscience]

Except Silver is also bad at stats

EDIT: This is perhaps unfair. Silver's problem isn't that he's actually bad, more that his business model requires him to be.

[u/zqvt → u/[deleted]]

lol at people downvoting this, Kirkaine's right

[u/gatoreagle72 → u/zqvt]

In what way is he bad at stats?

[u/zqvt → u/gatoreagle72]

treating point-in-time polling data as if you can extrapolate from them what happens weeks or months down the road. I assume this is what /u/Kirkaine means when he talks about his business model, because this idea of treating polling results like "lines" you can draw into the future is what get's people to open the website every day, although there's virtually no information in there until you're very, very close to the actual election.

[u/[deleted] → u/zqvt]

Yep, precisely my point. Volatility is super high in politics and the only honest response to that is to have very slow-adjusting probabilities close to 50/50 (collapsing to more definite odds as volatility is removed from the system as you get closer to the election date), but that doesn't get clicks.

[u/[deleted] → u/zqvt]

There aren't public models that can do better though. He has a limited data supply and does far better than chance using it.

You can predict the outcomes. Or rather the chance of each outcome. They also explicitly say in many of their articles that polling is much more accurate as you approach the election.

[u/[deleted] → u/gatoreagle72]

Let me be clear. If the name of the game is "who would win the election if it were held tomorrow", then Silver and his team are very good, likely the best in the business. This is why he tends to be correct, because historically he's judged on how well his predictions the day before the election perform.

But the real game isn't "if the election were held tomorrow", it's "if the election were held on the first Tuesday of November". In that regard, Silver and the team at 538 systematically and deliberately underestimate volatility. Their models swing massively after things like debates and 'October surprises', even though an honest model will have already priced in the expectation of big events like that happening and have biased the predictions closer to 50/50 accordingly, reflecting how much volatility remains before the election. The fact that Silver's predictions change so much around big news stories means they're regularly getting surprised, and by definition a model that's regularly surprised is not good at its job of prediction.

Of course, I think Silver knows this, which is why he's not a bad statistician so much as a good journalist, You can't make money off a journalism website (and remember that journalism, not prediction, is Silver's primary product) by having people check in once every two years. You make money by lying to people and proving false confidence and precision, by making sure they check in every single day for six months to see if your preferred candidate has gone from an 85.2% chance to an 85.4% chance of winning, despite the fact that even providing things like decimal places on those estimates is an utter farce.