We needed more smart, empirical pundits to make sense of 2016. Not horse-race coverage, through data models or "source close to" narratives.
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Replying to @zeynep
Once again, proof how it can be done.
@NormOrnstein from Aug. 2015, explaining clearly why the models may not work. http://www.theatlantic.com/politics/archive/2015/08/maybe-this-time-really-is-different/401900/ …1 reply 3 retweets 4 likes -
Replying to @zeynep
For low frequency events like presidential elex, "models" are another version of conventional wisdom, condemned to miss changing dynamics.
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Replying to @zeynep
I had explained why
@NateSilver538 got#Bra vs#Ger so wrong. Same reasons are why models got 2016 nomination wrong.https://medium.com/message/learning-from-natesilver538s-omg-wrong-bra-vs-ger-prediction-9d47fa0611e#.mve8k2903 …2 replies 4 retweets 11 likes -
Replying to @zeynep @NateSilver538
For a single event, does 65/35% actually look like a major failure?
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Replying to @deaneckles @NateSilver538
Well, the result was 1-7. For soccer, that's a bit like an top NFL team playing a mediocre college team.
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Replying to @zeynep @NateSilver538
Would be a good ex. for you if they predicted games won. Recall close to even odds can just mean lots of uncertainty.
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Replying to @deaneckles @NateSilver538
Yeah, but my point is Brazil had, on paper, solid defense but did not. Data was misleading, and experts all knew.
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A US electoral expert, for ex., would raise eyebrows at, say, poll with too white likely voter scenario. Similar.
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Predictive analytics is better with high frequency events with loads of data. Presidential elections are rare.
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Which means data won't tell you when your model needed to shift. Expertise will alert you something is amiss.
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