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They're not exactly in rank-space .. they discretize to the binary variable whether or not an article made it into the top-20, then use logistic regression to model that. so the coefficients are in log-odds space of that indicator

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Yeah, but instead of looking at if something got into the top-20 (binary 1/0), he's saying that they should have modelled the absolute score of an article. This would give you a way of weeding out the cruft that hits the front page and disappears quickly.

Another way of looking at it would be the amount of time that a post spends on the front page.

We spent a little time modeling various transforms of absolute score. The top features are essentially the same, but the coefficient variance is a lot higher. We're also interested in modeling rank or mindshare "stickiness" -- some articles remain in higher spots longer than others.

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