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Because the current rule of thumb is that ~9/10 startups fail. If you can reduce that to a 50/50 bet, you've made quite an improvement.

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Not really. What the article says is, the model would predict 50% of time whether a company will fail or not, which doesn't make sense, because 50% for a binary prediction (i.e. fail or not) is exactly nothing.

So maybe it's just bad or confusing wording in the article, the guy actually meant to say something else.

Yeah I'm going with confusing wording, I think he meant what I said above. Not to mention he could be referring to eliminating false positives, which is slightly different to finding true positives versus true negatives.

I think it's easier to relate to a coin flip if we use an "unfair coin".

90% of the time the coin flip returns tails (aka fail).

10% of the time it returns heads (aka win).

For a given coin flip, their algorithm can predict the results 50% of the time. At this point I don't remember the calculations off the top of my head, but it involves a Binomial distribution.

Hmm, I can predict that 90% of the time, so I don't follow.

That prediction doesn't improve over the prior. So its no better than the (biased) coin flip.

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