By: mulkowsky
My long-ago college stats courses taught me that adding any variable will always increase R squared. You need to also look at adjusted R squared and the p-stat of each of the variables separately. (As...
View ArticleBy: Mike Podhorzer
The study did look at ERA, but I didn’t publish the correlations because they were so low. My last sentence though stated “Last, I figured I would also test spring ERA one more time to see if we can...
View ArticleBy: Mike Podhorzer
This is a good question and would require a lot more work. I am pretty sure the stadiums in Ariz are much more hitter friendly, so park adjusting may help. But i’m not sure the effect they have on just...
View ArticleBy: Mike Podhorzer
I’ll try to get Matt in here to respond to the rest of the math related questions. So much for my college stats class…guess that knowledge disappeared from my brain soon after.
View ArticleBy: Matt Swartz
The standard errors are about .02, so the coefficients’ confidence intervals are something like (.14,.22) for Spring K% and something like (.08,.16) for Spring BB%.
View ArticleBy: Matt Swartz
Adjusted R^2 was decidedly up. The p-stats were <.001, so usually that increases Adj R^2.
View ArticleBy: Chris
Less interested in the normality of the errors, and more interested in the assumption that the observational data used is i.i.d. – more specifically, interested in statistics which test that assumption.
View ArticleBy: Ben Bishin
This is a very nice start. I have a couple of suggestions: 1. One potential problem is that essentially you are fitting these observational data and then trying to use them to predict future...
View ArticleBy: evo34
“So a 10% increase in Spring K% corresponds to a 1.8% increase regular season K%.” A 1.8% increase in expected K rate is huge — whether you are a baseball GM or a fantasy player.
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