http://tinyurl.com/3h6ou9
This is the first part of a multi-part series on how to estimate player value.
I've been doing an awful lot of reading, thinking, and discussing these issues
over the past several weeks, which is part of the reason that it's been
relatively quiet around here. Because writing things out is the best way that
I know to master a complicated topic like this, my hope is that this series
will help me crystallize my thinking on player valuation and get up to speed
on the most significant research to date. It will also serve as a nice set of
papers to which I can refer to justify my methods moving forward...and who
knows, maybe it'll be useful to others who are working through similar issues
as well.
To be clear, little of the big ideas that follow are based on my own work,
though I may supplement them with a small study here and there. Because this
is a supposed to be Reds blog, I will often use the Reds in case studies. In
general, though, you can think of this as a popular science review article ...
or maybe a college term paper, given that I'm still new to much of this info.
:)
以上是废话，可以跳过
General Principles of Player Valuation
Wins vs. Runs
What do we mean by value? The answer can be fairly nuanced, as evidenced by
essays like this or this. I'm going to be a bit generic about it, however: I
want to know how much players did to help their team win.
Based on that definition, the ultimate goal would be to have statistics that
quantify player value in terms of wins. There have been several efforts on that
front, including Win Shares, WARP, and WPA. At this point, however, I'm not
satisfied with how any of those stats handle fielding (among other things),
so I'm not ready to make the leap to those stats.
The alternative, then, is to use stats that give their units in runs, for
which we have many "good" stats that can quantify hitting, pitching, and
fielding. How much are we losing by going with runs instead of wins? To get
some handle on this, I ran a quick regression of all teams from 1996-2004 with
data pulled from the Lahman database and looked at one variable models that
predict wins:
Predictor of Wins R-Square MSE
Run Differential 0.90 14.78
Runs Allowed 0.43 86.32
Runs Scored 0.35 97.92
R-Square, in this case, indicates the proportion of variation in wins explained
by the different predictors. Therefore, this quick 'n dirty analysis indicates
that we can explain 90% of variation in the number of team wins by just knowing
a team's run differential (the difference between a team's runs scored and its
runs allowed). The remaining 10% is presumably due to the timing of when those
runs are scored, or variation in run environments (e.g. a run in Coors' field
is worth less in terms of wins than a run in PETCO Park, simply because more
runs are scored in Coors' than in PETCO, so each one contributes less to wins).
Research to date indicates that most, though not all, of timing-based events
tend to be associated with events that involve very little unique player
skill