from Amazon
Friday, December 04, 2009
Game Score using FIP
No, I’m not talking about this recent BtB article, which does some good work.
I am instead talking about this article that came out a few months after the unveiling of FIP 5 years ago, back when DIPS was all the rage, and he shows you all his work:
What would a DIPS-based game score look like? Also, why not make the revised game score equivalent to a support neutral win probability? ... GSDIPS = 50 + IP + 2*K – 3*BB – 13*HR
And there you have it – a DIPS-based pitcher game score equivalent to support neutral win probability that is easier to calculate and with fewer terms than James’ original Game Score!
Notes: Did James originally intend the Game Score to approximate win probability? The (2*K – 3*BB – 13*HR) term is the same thing as FIPS that was independently and originally noted by TangoTiger as a DIPS-based pitcher measure
Kevin’s got alot of other great stuff on his site. I get the feeling that alot of the new guys coming into sabermetrics aren’t aware of the really cool stuff that’s already been done out there.
Thanks for the good word on the article, Tango. You’re right in that I had no idea this had already been considered, though it does make sense that it would have been given the DIPS phenomenon of the earlier 2000’s. I read through the article by Kevin and it is very well done. When I was thinking about doing (or I guess in this case, re-doing) a DIPS version of GS, I was more or less going through a similar process as Kevin already did. I do want to add batted ball types as an added factor, similar to how tRA does their runs/outs estimation.