No, I'm estimating that, of the difference in ERA (2.43 vs. 3.25) since they've
been  teammates, about 0.24 (revised estimation) comes from $H.  That's a good
30% of the difference.

That Voros claimed, in trying to explain correlation, that lack of correlation
implies small variance, is the key to misunderstanding the error of his claim.
There is no signal, no matter how humongous, that can't be rendered difficult or
impossible to detect by adding enough noise (often in the form of the noise
inherent in small sample sizes).  *That doesn't make the signal smaller.*

$H has so much noise added to it that it's very hard to nail down.  That
absolutely, positively does not mean that differences between $H talents must be
tiny.

Here it is in a nutshell:

Maddux with Atlanta (excluding '79) is .271,  Glavine is .282.  Draw, for each
one, a bell curve whose peak is centered at that number.   The bell curve is a
probability graph for their *true* number.  

Stats like K% and BB& have very narrow bell curves.  Thus, we can say for
certain that Pedro's K ability is truly better than Toma Ohka's.  $H has a
fairly broad bell curve, so broad that they overlap quite a bit for Maddux and
Glavine, or for almost any two pitchers.  That makes it impossible to say *for
certain* that one is better than the other at $H.

HOWEVER, broadening the bell curves DOES NOT change the position of the peaks,
the *most likely* number.  For all our uncertainty, the *most likely* case for
Maddux remains .271, and for Glavine, .282.  

It's just a fact of the universe that there can be a PROFOUND difference between
A and B, and yet we can be unsure of that difference because of insufficient
sample size.  Our being unsure does not mean we should not try to make a best
estimate of the difference, howeever.

--
----
Eric M. Van
em...@post.harvard.edu

". . . from that day forward she lived happily ever after.  Except for the dying
at the end.  And the heartbreak in between." - Lucius Shepard.