One of the biggest complaints I see about WPA is that it’s not predictive. The mere mention of it’s non-predictability seems to be enough for many to write it off as a mere toy used by some of stats community.
So let’s see how it actually correlates from year to year compared to the stats we all know, like AVG, OBP, SLG, and OPS. I’ll throw in Batting Runs Above Average for fun too.
Looking at the r-squared from 2005 to 2006 for batters with over 300 plate appearances, here’s how WPA stacks up against the regulars:
AVG: .12
WPA: .27
BRAA: .35
OBP: .36
OPS: .36
SLG: .38
Here’s the same deal, 2004 to 2005.
AVG: .14
WPA: .24
OBP: .27
OPS: .30
BRAA: .31
SLG: .33
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It’s true, WPA doesn’t correlate as well from year to year as OBP, SLG, or OPS, but it does have some correlation from year to year. In 2004, a players OBP was almost indicative of his 2005 OBP as his 2004 WPA was of his 2005 WPA. Yet, that wasn’t quite the case in 2005 to 2006. BRAA which is calculated by using Run Expectancy on a play-by-play basis (much like WPA uses Win Expectancy), holds its own against the regulars.
Anyway, the point is, let’s stop using the argument that WPA isn’t predictive as a crutch, because it does actually show some correlation from year to year.
David, how far back do you have WPA data available? If you have several years worth, you might try something like an auto-regressive (AR1) covariance matrix to find out what the intra-class correlation is. (For those who don’t know, this is a multiple observation technique that allows a more complete picture of how much correlation there is from year to year on an individual level.) If you like, e-mail me (I believe you can see my e-mail address from my post.)