In article <38398f87$0$...@nntp1.ba.best.com>, jfra...@planeteer.com says...
> Voros, your "Explanation Post" went along way to convince me of the
>validity of your method. In fact, I am now almost (very close) sold on
>DIPS. I know that some of these ideas/answers to questions/suggestions
>are only feasable on "the magical planet where everyone has access to
So tragically true. Intellectual property is tricky stuff.
>(maybe we really do need a project scoresheet II, or is
>Total Sports releasing the data for this years games?)
>-Wouldn't using zone rating take away the problem of pitchers who give
>up "sure hit" line drives. It seems that using all balls in play lends
>itself to putting them on equal ground.
Possibly, but the question again begs itself, are there actually any pitchers
who _consistently_ give up significantly more of these types of hits, and who
are these pitchers? Looking over the 1998 & 1999 seasons, the only pitcher
that jumps out is Shane Reynolds. Sele and the Rockies pitchers have plenty
of reasons to excuse their rates, but Reynolds' is a bit enigmatic. I went
back a few years in Reynolds career and found a few years not quite as high,
but it looks like it's _possible_ something is going on there.
>-Aren't groundball pitchers given a bit of an advantage. It seems that
>GBs are less likely to turn into outs than flyballs (although without
>the risk of turning into HR).
The top 10 Ground Ball pitchers in the AL in 1999
Name ER DPER DIFF
Erickson 123 129 -6
Pettite 100 105 -5
Sele 109 92 17
Nagy 111 104 7
Heredia 107 93 14
Moehler 110 98 12
Suppan 105 113 -8
Mussina 79 80 -1
Mays 83 95 -12
Colon 90 96 -6
I chose the AL because there was no league adjustments so they should balance
pretty well close to zero difference. 6 of the 10 top GBers were hurt by the
DIP stats, although the 4 helped were helped by a larger amount. These ten
were helped by a total combined amount of 2 runs. However, I don't think
you're wrong here. If the league average totals are adjusted to represent
league average totals for GB, FB, LH, RH, SP and RP we might gain some
accuracy in the DIP numbers. Also to night is that, anecdotally, I believe
pitchers with trick deliveries (e.g. Knuckleballers) might post consistently
lower $H numbers than other pitchers. I looked at Tim Wakefield's career and
that seems to bear out slightly.
>I'm not sure if this is true, but I remember my baseball coach saying: "Hit
>all linedrives and bat 1.000, all ground balls and hit .300, all flies and
>you'll never get on base."
Then you're stuck asking, "What's a line drive and what's a flyball?" On
balls where there seems to be some question as to that, the usual method is,
"If it falls in, it was a line drive, and if it's caught, it's a flyball."
>-The fact that hit prevention is so far out of the pitcher's control is
>surprising. That means that keeping the ball out of play is a lot more
>important than we think. Wouldn't this validate Bill James' Game Scores
>(which put heavy weight on K's) over the BBBA QMAX (which rely on Hit
Only in the sense that it uses strikeouts, but not in the sense that it also
uses hits, runs and earned runs (hits having a significant effect on the
other two). As a quick box score figure I do prefer it to QMAX or SNWL, which
basically ascribe everything that happens while he's on the mound to the
pitcher. Game scores do too, but they also give credit to a stat that is a
huge indicator of _ability_ but is often left out do to it's lack of direct
>-Although I used the wrong example, Kevin Millwood might be getting
>hitters out in easier ways than Martinez.
Of course that leaves explanation as to why Martinez was significantly better
at it in 1998.
>Although assuming that all chances are created equal might be a better
>alternative than what we have now, it seems that a more detailed breakdown
>of GB/FB and LineDrives would set the H rate better.
But if GB/FB correlates for pitchers from year to year, and $H correlates
with GB/FB, then $H should correlate from pitcher to pitcher. But it doesn't.
I believe that GB/FB adjustments to league average figures would make the
system more accurate. I wonder how much more accurate though and would the
improvements make up for the very severe decrease in simplicity. A long term
project would be to do this and see what the differences are.
>Thanks for the clarification, and keep up the excellent work,
>Still surprised that H rate isn't consistent,
I'll leave the topic for just a second here. It's also surprising that the
pattern exists for hitters as well (with some important differences). $SO
still correlates very highly, but $BB correlation increases a bit, and $HR
correlation increases above $BB. About .80, .72 and .75 respectively. But the
batters version of $H only correlates between .35 and .40. Now that's much
bigger than the pitchers $H and does indicate that there is some correlation
going on, but it also suggests that there's a lot of fluctuation in the stat.
A topic for another time, but for fun, look at the yearly (H-HR)/(AB-SO-HR)
figures for Paul O'Neill from his rookie year until now.
Thanks again for the input, James.
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