For the second two graphs, I don’t know how much the sample size is reduced, but in any case the variance is even greater, and correlation differences of 0.06 or even 0.1 are probably not significant.

Not that this retracts from the point. Basically it just says that it can’t be determined which is a better predictor in isolation–win% or pythag%–and that’s good to know. I feel like multiple regressions with some additional independent variables such as bullpen ERA (or FIP or whatever) and some indicator variable for “type of deadline moves” categorized as “seller”, “buyer” or “neutral,” could be enlightening.

Thanks!

]]>Things which seem logical to somebody are about as likely to be false as true, and somebody making assertion on evidence that he claims to exist but can not or will not cite is responsible to support it, not to ask somebody else to find it for him.

See the replies to me by Bill and Eric above to find a more helpful form of discourse. ]]>

Anyway, the sample sizes above seem both flawed AND not large enough, a deadly combo. It seems to me that teams only slightly over or under-performing relative to their Pythagorean winning percentages are not instructive here, due to the relatively large amount of statistical noise. I don’t think they tell us much about the Orioles, a team that is greatly outperforming its Pythag winning percentage.

So the Orioles are more likely to play like a 67-57 team than a 56-68 team from here on out? I’d bet against that.

]]>I got similar results to your calculations in the second part. Indeed, Pythag seems to have a larger influence. But I’m not sure the difference is so large.

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