tag:blogger.com,1999:blog-7965585998403674176.post-46254460484626953502008-04-23T10:00:00.000-04:002008-04-23T10:00:00.000-04:002008-04-23T10:00:00.000-04:00"My only concern is that the PECOTA numbers already account for SOS, so teams that play hard schedules would be getting penalized twice."<BR/><BR/>That's a concern of mine, but it's not the only one. You are also using Pythag based on actual RS/RA, rather than AEQR/AEQRA (expected RS/RA via the batting lines produced, adjusted by SOS). Both of these concerns relate to #2 on my list of things needed to do a Monte Carlo sim. <BR/><BR/>Davenport's solution is to regress the AEQR/AEQRA Pythag to .500, then take a random sample from a normal distribution around the regressed WPCT. The flaw in that, IMHO, is that teams do not regress to .500. Your work above indicates that teams regress strongly to their PECOTA projections. And that's exactly what Davenport does in his PECOTA-adjusted Playoff Odds. Perhaps we should find out if he is using regression values similar to the ones you derived.skoormithttp://www.blogger.com/profile/14018318250262889801noreply@blogger.com