Some Broundgreaking Thoughts on $H and DIPS

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Eric M.Van

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Jan 2, 2001, 4:57:07 PM1/2/01
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(Original title: Some Thoughts . . .
Later title: Some Bold, Original Thoughts. . .
But heck, if you want attention, you've got to ask for it!)

(For newbies -- $H is the percentage of balls in play that are turned into
outs. This varies surprisingly widely from year to year for any given
pitcher, leading some to question whether the ability to get guys to hit easy
grounders and fly balls is a real ability or not. Voros McCracken has explored
this notion in detail as Defense Independent Pitching Statistics (at least I
think that's what the acronym stands for <g>)).


1) Much of the reason why $H varies so much from year to year, where K% and BB%
don't, is *because the sample size is smaller*. Too small. Note that HR%,
which has a very similar sample size, also has a large variance from year to
year. The additional lack of correlation in $H can be attributed to larger
team defense and park effects.

It's trivial to simulate multiple seasons of $H in Microsoft Excel for a pitcher
with any theorized innate level of $H. The results are eye-opening.

Put this formula in cell A1:

=IF(RAND()>0.271,0,1)

where the 0.271 is whatever $H rate you want to simulate (that happens to be an
estimate for Maddux).

Now copy the formula down to the next 650 cells (650 being a typical number of
balls in play for a pitcher with 150+ IP).

In cell B1, put:

=SUM(A1:A650)/650

Now hit F9 repeatedly to regenerate the random numbers. Cell B1 will report
each season's $H number. You will see quite a bit of variation. I'm doing
this in real time:

.280, .308, .298, .271, .278, .255, .275, .263, .274, .314, .257, .263, .266,
.286, .263.

Obviously this CyberGreg learned to pitch after those first three mediocre
seasons! And he must have come up with some adjustment after the .314 to go
back to .257, which started another string of vintage years.

(In case you miss the point, the previous paragraph is total nonsesnse, although
total nonsesnse of the sort our brain is hard-wired to come up with.)

(Lowering the sample size just a bit will increase the variance noticeably.)

Let's do Glavine at a theorized .282:

.252, .288, .292, .317, .258, .288, .298, .266, .285, .280, .285, .280, .297,
.298, .305.

He was "better" than CyberGreg in years 1, 2, 3, 5, and 10.

Any trained scientist would dismiss the notion that there were any difference
between these two pitchers. Over 15 seasons, CybeGreg averaged .277 +/- .018,
CyberTom .286 +/- .017. That has a 16% chance of happening at random. And yet
we *know* the pitchers *are* different!

(OTOH, the scientist would also dismiss the *obvious* fact -- fact! -- look,
it's obvious in the numbers! -- that CyberTom was the better pitcher for the
first 5 years, until CyberGreg developed his mastery. And the scientist would
be correct: CyberGreg was just *unlucky* for 5 years.)

In reality, of course, the different levels of team defense behind the pitcher
cause an even greater fluctuation from year-to-year. Two years ago the real
Greg and Tom had their $H's go up hugely, to .331 and .318. If you include '99
and ask, what are the odds that this string of $H numbers could happen by
chance, you basically get No for an answer (2 and 4% chance, respectively).
Removing '99 returns the odds on Maddux to 72%, and Glavine to 20%. IOW, with
the exception of one season, you don't have to invoke *anything* beyond random
variation to explain the fluctuation in their $H numbers in their career
together, and in Maddux's case the variation is particularly trivial.

So: because of small sample sizes, differences in pitcher $H are *very* hard to
detect statistically. But that doesn't mean they're non-existent or even
trivial in size. The apparent difference between Maddux and Glavine over their
joint career in Atlanta, in terms of ERA, is about 0.20. That's not trivial.
Give me that every day and I'll finish 3 games ahead of you in the strandings.
My rough guess / gut feeling is that there are pitchers who shave up to 0.25 off
their ERA and others who lose about 0.25, because of their innate $H ability.
Maybe even more.

2) I believe that my study of pitchers who changed teams may be the first to
show conclusively that there *is* an effect. I originally compared pitcher's
new $H to his old $H, and his old and new team's $H with him included in the
team totals. That was messy. Subtracting him out from the team totals (this
spreadsheet is 68 columns wide and growing!) gives us this formula for a traded
player's $H, once everything has been converted to z-scores (minimum 450 balls
in play, which gives us 443 pairs of seasons in the study):

New $H = (.201 * old $H) + (.290 * new team's $H) - .076

The significance of the old $H factor is immense -- 1 chance in 684,050 of
happening by chance!

Combined, the two factors account for just 22.4% of his new $H.

So, this says his new $H is based 13% on his new team $H, and 9% on his old $H.

What determines his new team's $H? It's the average of the innate $H of all the
other pitchers -- which will *tend* to be average -- plus the effect of the
defense per se, plus the manager, plus the park effect. Now, the quality of the
other pitchers on the team doesn't actually affect him, while the other factors
do. The fact that there must be good and bad staffs for $H will thus add noise
to the relationship. This additional noise will thus weaken the observable
relationship of new $H to new team $H. Thus, the true factor for team $H
contribution is larger than 13% -- it's something that reduces to 13% when you
add the noise effect. It may be possible later to estimate the size of this
effect. I'm guessing 15 or 16%.

The last term above, the apparent -0.76 fudge factor (that's .076 of a standard
deviation, about .0017 in raw $H terms) is very interesting. Pitchers who
change teams *unquestionably* have a reduction in $H, that is independent of
everything else, from an average of .280 to .276. It will be very interesting
to see if this varies by era. I have only one weak idea why this happens:
change-of-scene psychology? I'm not convinced.


3) So how should DIPS work? We shouldn't throw out $H entirely. We can do two
things: we can compare $H to team $H without that pitcher, and we can look at
lifetime $H. Combining both approaches, we can look at lifetime $H relative to
team. This can then be modified by park and manager effects (when known) to get
a true $H estimate. This might be very valuable for predicting future pitcher
performance.

Until we do this, we won't really know how large the range of innate $H skill
is, and we won't know how much trouble to go to in order to figure it out!

(And I can think of a heaping mess of trouble. Ask me about it if you dare.)


4) None of this abrogates the importance of Voros' work with DIPS. The
realization that there are very large utterly random fluctuations in ERA, and
the understanding of where exactly that comes from, so that we can say whether a
change in ERA was luck or skill -- that remains revelatory.


5) Pedro Martinez went from a true $H of .325 in 1999 to one of .237 in 2000.
His HR rate, however, nearly doubled. The odds of this all happening by chance
are about 1 in 286. Conclusion: if anyone could refine their pitching approach
in a way that reduced how often batters hit the ball hard, at the expense of
some extra homers, it's Pedro. I think his true $H actually did go down last
year.
--
----
Eric M. Van
em...@post.harvard.edu

". . . from that day forward she lived happily ever after. Except for the dying
at the end. And the heartbreak in between." - Lucius Shepard.

Voros

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Jan 2, 2001, 7:33:05 PM1/2/01
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Eric M.Van <em...@post.harvard.edu> wrote:
> 3) So how should DIPS work? We shouldn't throw out $H entirely.

Yes we should, actually. It stands for defense independent pitching
stats. If we include hits in play, it's no longer defense independent, no
matter how we choose to estimate the defenses contribution. It goes from
DIPS to PS.

The point I'm making is that there's a price to be paid by multiplying
things by a series of weights and balances. The numbers cease to mean
anything other than a best guess as to how good the pitcher was.

IOW, even if pitchers had a meaningful ability to affect hits per balls
in play (which they don't), DIPS would be a solid concept (in fact I
started putting them together before I knew the first part), if only for
use in evaluating teams.


--
Voros McCracken
vo...@daruma.co.jp
http://www.baseballstuff.com/mccracken/

Dale Hicks

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Jan 2, 2001, 8:32:37 PM1/2/01
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Eric M.Van <em...@post.harvard.edu> wrote in article <3A524E87...@post.harvard.edu>...

>
> So: because of small sample sizes, differences in pitcher $H are *very* hard to
> detect statistically. But that doesn't mean they're non-existent or even
> trivial in size.

Do you concur, Voros? You said that any ability was below
the noise floor, IIRC. He says this, then says the effect
isn't trivial.

> The apparent difference between Maddux and Glavine over their
> joint career in Atlanta, in terms of ERA, is about 0.20. That's not trivial.

So their BB, K, and HR rates are equivalent, and only the $H
makes up that difference?

--
Cranial Crusader dgh...@bellsouth.net

Eric M.Van

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Jan 2, 2001, 9:17:28 PM1/2/01
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No, I'm estimating that, of the difference in ERA (2.43 vs. 3.25) since they've
been teammates, about 0.24 (revised estimation) comes from $H. That's a good
30% of the difference.

That Voros claimed, in trying to explain correlation, that lack of correlation
implies small variance, is the key to misunderstanding the error of his claim.
There is no signal, no matter how humongous, that can't be rendered difficult or
impossible to detect by adding enough noise (often in the form of the noise
inherent in small sample sizes). *That doesn't make the signal smaller.*

$H has so much noise added to it that it's very hard to nail down. That
absolutely, positively does not mean that differences between $H talents must be
tiny.

Here it is in a nutshell:

Maddux with Atlanta (excluding '79) is .271, Glavine is .282. Draw, for each
one, a bell curve whose peak is centered at that number. The bell curve is a
probability graph for their *true* number.

Stats like K% and BB& have very narrow bell curves. Thus, we can say for
certain that Pedro's K ability is truly better than Toma Ohka's. $H has a
fairly broad bell curve, so broad that they overlap quite a bit for Maddux and
Glavine, or for almost any two pitchers. That makes it impossible to say *for
certain* that one is better than the other at $H.

HOWEVER, broadening the bell curves DOES NOT change the position of the peaks,
the *most likely* number. For all our uncertainty, the *most likely* case for
Maddux remains .271, and for Glavine, .282.

It's just a fact of the universe that there can be a PROFOUND difference between
A and B, and yet we can be unsure of that difference because of insufficient
sample size. Our being unsure does not mean we should not try to make a best
estimate of the difference, howeever.

Eric M.Van

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Jan 2, 2001, 9:37:48 PM1/2/01
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Voros wrote:
>
> Eric M.Van <em...@post.harvard.edu> wrote:
> > 3) So how should DIPS work? We shouldn't throw out $H entirely.
>
> Yes we should, actually. It stands for defense independent pitching
> stats. If we include hits in play, it's no longer defense independent, no
> matter how we choose to estimate the defenses contribution. It goes from
> DIPS to PS.

Of course you're right. What I meant to say, was, what's the role of DIPS?
It's a starting place. But you don't get the complete picture until you look at
$H, and look at it as meaningful.

>
> The point I'm making is that there's a price to be paid by multiplying
> things by a series of weights and balances. The numbers cease to mean
> anything other than a best guess as to how good the pitcher was.

Right. DIPS separates the part we're fairly certain about from a part that we
have difficulty measuring. But what the hell is wrong about a best guess of
something we can't know for certain? Especially if it can add or subtract 0.25
or 0.40 or whatever to an ERA?

>
> IOW, even if pitchers had a meaningful ability to affect hits per balls
> in play (which they don't),

Ahh, Voros . . I just proved that they do, to a rigor 34,202 times as great as
I would need to get it published in a scientific journal. When a pitcher changes
teams, his new $H depends on his old $H. Period. End of story. No possibility
of other explanation. It's as cut and dried as possible.

It has *never* followed that because $H correlates poorly, differences between
it must be small.

Think about this: looking at 443 pitchers who changed teams, I was able to
determine, with 95% certainty, that their old $H accounts for between 5.7 and
12.4 % of their new $H (with 9.2% likeliest). Could I have found such a small
effect if the differences between pitchers were very small? Unlikely.


DIPS would be a solid concept (in fact I
> started putting them together before I knew the first part), if only for
> use in evaluating teams.
>

DIPS is a great concept. The notion of making a separation between that which
we're sure about and that which is vague is wonderful. But vagueness does not
and never has and never will equal smallness. We've only got a rough idea of
the size of the Andromeda Galaxy.

--

Russell Steele

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Jan 2, 2001, 9:40:10 PM1/2/01
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On Wed, 3 Jan 2001, Eric M.Van wrote:
>
> HOWEVER, broadening the bell curves DOES NOT change the position of the peaks,
> the *most likely* number. For all our uncertainty, the *most likely* case for
> Maddux remains .271, and for Glavine, .282.

Ugh... Actually the probability of Maddux achieving a $H of .271 is 0, or
very close to 0 if you take into account your discretization of the
statistic to 3 decimal places. I suggest you find a good statistics book
and read carefully the sections on hypothesis testing and confidence
intervals. Your observed samples come from a distribution that is probably
peaked, and MAY have come from somewhere near the peak, but just as likely
came from somewhere outside of the peak. IOW, there's a 50% chance that
your sample mean came from outside the middle 50% of the distribution of
that mean.

>
> It's just a fact of the universe that there can be a PROFOUND difference between
> A and B, and yet we can be unsure of that difference because of insufficient
> sample size. Our being unsure does not mean we should not try to make a best
> estimate of the difference, howeever.
>

Very true, but one must be honest with oneself concerning the accuracy of
your estimate.

Voros

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Jan 2, 2001, 9:59:58 PM1/2/01
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Dale Hicks <dgh...@bellspamlesssouth.net> wrote:
> Eric M.Van <em...@post.harvard.edu> wrote in article <3A524E87...@post.harvard.edu>...
>>
>> So: because of small sample sizes, differences in pitcher $H are *very* hard to
>> detect statistically. But that doesn't mean they're non-existent or even
>> trivial in size.

> Do you concur, Voros? You said that any ability was below
> the noise floor, IIRC.

Basically. Essentially I said that any observed differences in the stat
between two pitchers have thus far been difficult to ascribe to ability.

> He says this, then says the effect isn't trivial.

I suppose it depends on your definition of "trivial". It's kind of like
building a compass from cork, needle, water and magnet and then once built
arguing whether it's dead on north or 0.4 degrees off.

>> The apparent difference between Maddux and Glavine over their
>> joint career in Atlanta, in terms of ERA, is about 0.20. That's not trivial.

> So their BB, K, and HR rates are equivalent, and only the $H
> makes up that difference?

No. I think he was talking about $H affect on it, which is a tough
argument. Since joining the Braves Maddux' rate is .275 and Glavine's is
.282. If you count that whole difference as completely attributable to
each's ability (tough since over the whole career of each the numbers are
.278 for Maddux and .279 for Glavine), you get a little over 5 hits a
year. To make a .20 difference, you'd need a little over 5 runs.

If the difference between the two in $H giving full credit for $H doesn't
account for 0.20 in ERA, I don't see where you can make that statement.

Eric M.Van

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Jan 2, 2001, 10:45:33 PM1/2/01
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Russell Steele wrote:
>
> On Wed, 3 Jan 2001, Eric M.Van wrote:
> >
> > HOWEVER, broadening the bell curves DOES NOT change the position of the peaks,
> > the *most likely* number. For all our uncertainty, the *most likely* case for
> > Maddux remains .271, and for Glavine, .282.
>
> Ugh... Actually the probability of Maddux achieving a $H of .271 is 0, or
> very close to 0 if you take into account your discretization of the
> statistic to 3 decimal places.

Well, yes, from a philosophical point of view you could put a zero on it. But
(assuming my estimation of .271 is correct) the odds of it being .271 are
slightly higher than it being .270 or .272. And so on.

I suggest you find a good statistics book
> and read carefully the sections on hypothesis testing and confidence
> intervals. Your observed samples come from a distribution that is probably
> peaked, and MAY have come from somewhere near the peak, but just as likely
> came from somewhere outside of the peak. IOW, there's a 50% chance that
> your sample mean came from outside the middle 50% of the distribution of
> that mean.

That does not change the fact that the sample mean is the *likeliest* mean for
the population. Which is all I asserted.

>
> >
> > It's just a fact of the universe that there can be a PROFOUND difference between
> > A and B, and yet we can be unsure of that difference because of insufficient
> > sample size. Our being unsure does not mean we should not try to make a best
> > estimate of the difference, howeever.
> >
>
> Very true, but one must be honest with oneself concerning the accuracy of
> your estimate.

I've said all along that it's quite prone to error.

Let's say you're shopping for a car, and you've narrowed it down to two cars
which are *exactly* as desirable except for breakdown & repair rates. That's
gonna break the tie. The Saab has a breakdown rate of 5% +/- 4% and the Volvo
is 10% +/- 6%. We're not sure *at all* that the Saab is more reliable than the
Volvo. Not at all. But which car do you buy? All things being equal?


(Actually, the answer is, if you had a friend who owned a Saab and it was a
lemon, you buy the Volvo even if you read that the rates are 5% +/- 1% and 10 %
+/- 1%. But that's another issue entirely.)

Voros

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Jan 2, 2001, 11:10:33 PM1/2/01
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Eric M.Van <em...@post.harvard.edu> wrote:
> Voros wrote:
>>
>> Eric M.Van <em...@post.harvard.edu> wrote:
>> > 3) So how should DIPS work? We shouldn't throw out $H entirely.
>>
>> Yes we should, actually. It stands for defense independent pitching
>> stats. If we include hits in play, it's no longer defense independent, no
>> matter how we choose to estimate the defenses contribution. It goes from
>> DIPS to PS.

> Of course you're right. What I meant to say, was, what's the role of DIPS?
> It's a starting place. But you don't get the complete picture until you look at
> $H, and look at it as meaningful.

>>
>> The point I'm making is that there's a price to be paid by multiplying
>> things by a series of weights and balances. The numbers cease to mean
>> anything other than a best guess as to how good the pitcher was.

> Right. DIPS separates the part we're fairly certain about from a part that we
> have difficulty measuring. But what the hell is wrong about a best guess of
> something we can't know for certain? Especially if it can add or subtract 0.25
> or 0.40 or whatever to an ERA?

For starters, they don't mean anything. What would any ERA derived from
this mean?

Also I'm very much disputing that the difference for ERA is that high, and
if it does it's in extremly rare cases.

Finally I think the error of such estimates is much larger than the
predicted differences.


>>
>> IOW, even if pitchers had a meaningful ability to affect hits per balls
>> in play (which they don't),

> Ahh, Voros . . I just proved that they do, to a rigor 34,202 times as great as
> I would need to get it published in a scientific journal.

You really haven't. You've shown only and exactly that there's a small
correlation that is less likely to be explained by park and defensive
biases than a study including all pitchers. you haven't ruled out other
explanations, and when the correlation in seasons is around .10 I think
maybe you should. Hell as Russell pointed out, I get a correlation of .07
with 1000 samples between the ASCII code of the first letter of the city
the pitcher pitched in and his $H rate that year. That's certainly not
likely to happen from chance, but it sure as hell is likely to have
happened from something that is effectively chance.

The fact is you have a correlation where the the difference between the
highest predicted value and the lowest one is a good bit smaller than the
standard error of all of those predicitions.

When a pitcher changes
> teams, his new $H depends on his old $H. Period. End of story. No possibility
> of other explanation.

For starters, this is _never_ true. There's always a possibility of other
explanations.

> It's as cut and dried as possible.

Cut and dried as possible would be a correlation of say .98, not one of
.10.

In my $H figures for pitchers who changed teams, the correlation between
the $H rate the second year and the year of the first year was .142.

So for the 1000+ pitchers who changed teams, the year they pitched in was
a better indicator of $H the next year than $H was.

> It has *never* followed that because $H correlates poorly, differences between
> it must be small.

It's a data point. The fact that the difference in active career $H rates
among active pitchers resemble the same range as you'd expect from chance
is another. The fact that a study of similar pitchers (done three separate
times with different groups) for whom all the stats are similar excpet
hits produced a result in which the high $H group gave up the same number
of hits the next year as the low $H group is another. The fact that
forecasts from $H totals yield higher errors than the predicted difference
between the worst and best prediction is another.

Voros

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Jan 2, 2001, 11:12:15 PM1/2/01
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Excluding '79?

Voros

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Jan 2, 2001, 11:23:07 PM1/2/01
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Eric M.Van <em...@post.harvard.edu> wrote:
> That does not change the fact that the sample mean is the *likeliest* mean for
> the population. Which is all I asserted.

Well then I think you're begging the question. If researches over the
years have concluded that everyone in the world is equally as talented at
tossing widgets as everybody else, and they peg that talent at an average
score of 65, and one guy tosses five widgets for an average of 75, the
most likely level of his talents is _still_ 65, since his results could
easily be explainable by chance and there is as of yet no information
indicating that anyone has an ability of 75.

FM

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Jan 2, 2001, 11:42:00 PM1/2/01
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Eric M.Van <em...@post.harvard.edu> wrote:

>1) Much of the reason why $H varies so much from year to year, where K% and BB%
>don't, is *because the sample size is smaller*. Too small. Note that HR%,
>which has a very similar sample size, also has a large variance from year to
>year. The additional lack of correlation in $H can be attributed to larger
>team defense and park effects.

The statement should probably be qualified. The sample size
is not really small in absolute terms, but small because the
weak effect we're trying to detect is intermingled with other
more significant effects.

Right. You can go from a theorized notion of their actual
ability to actual numbers, but you need a lot more data to
go the other way.


>In reality, of course, the different levels of team defense behind the pitcher
>cause an even greater fluctuation from year-to-year. Two years ago the real
>Greg and Tom had their $H's go up hugely, to .331 and .318. If you include '99
>and ask, what are the odds that this string of $H numbers could happen by
>chance, you basically get No for an answer (2 and 4% chance, respectively).
>Removing '99 returns the odds on Maddux to 72%, and Glavine to 20%. IOW, with
>the exception of one season, you don't have to invoke *anything* beyond random
>variation to explain the fluctuation in their $H numbers in their career
>together, and in Maddux's case the variation is particularly trivial.

On top of that, one's true ability probably doesn't stay
constant throughout one's career.


>So: because of small sample sizes, differences in pitcher $H are *very* hard to
>detect statistically. But that doesn't mean they're non-existent or even
>trivial in size. The apparent difference between Maddux and Glavine over their
>joint career in Atlanta, in terms of ERA, is about 0.20. That's not trivial.
>Give me that every day and I'll finish 3 games ahead of you in the strandings.
>My rough guess / gut feeling is that there are pitchers who shave up to 0.25 off
>their ERA and others who lose about 0.25, because of their innate $H ability.
>Maybe even more.

I'm pretty sure that there are a lot of pitchers in the
NL whose hitting ability makes a far greater difference.
(which is also hard to quantify because of the small
sample size). Not many seem to pay much attention though.
And the kind of difference that isn't significant over a
10-year span is virtually undetectable. A very smart
projection system can probably incorporate this to some
level though.


>The last term above, the apparent -0.76 fudge factor (that's .076 of a standard
>deviation, about .0017 in raw $H terms) is very interesting. Pitchers who
>change teams *unquestionably* have a reduction in $H, that is independent of
>everything else, from an average of .280 to .276. It will be very interesting
>to see if this varies by era. I have only one weak idea why this happens:
>change-of-scene psychology? I'm not convinced.

I'm guessing that those who underperform in $H *tend*
to be traded.


>3) So how should DIPS work? We shouldn't throw out $H entirely. We can do two
>things: we can compare $H to team $H without that pitcher, and we can look at
>lifetime $H. Combining both approaches, we can look at lifetime $H relative to
>team. This can then be modified by park and manager effects (when known) to get
>a true $H estimate. This might be very valuable for predicting future pitcher
>performance.

For a projection system, how much difference from the
mean can be *expected* for any individual pitcher? It's
one thing to say that some pitchers are much better at
this than others. It's another to find such pitchers
with reasonable confidence. I'm sure it's possible to
incorporate this, but I doubt this will change anyone's
projected ERA by more than .05.


>5) Pedro Martinez went from a true $H of .325 in 1999 to one of .237 in 2000.
>His HR rate, however, nearly doubled. The odds of this all happening by chance
>are about 1 in 286. Conclusion: if anyone could refine their pitching approach
>in a way that reduced how often batters hit the ball hard, at the expense of
>some extra homers, it's Pedro. I think his true $H actually did go down last
>year.

Well, it's more of a conjecture than a conclusion.

I said this before in another NG but note that this
ability to hit balls hard differs significantly from
one batter to another. That means if two pitchers
allow balls in play at different rates to different
hitters (or different types of hitters), then you
will see a difference in their $H ability level.


--
Capitalism is the extraordinary belief that the nastiest of men, for
the nastiest of reasons, will somehow work for the benefit of us all.
-- John Maynard Keynes

Eric M.Van

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Jan 3, 2001, 12:16:27 AM1/3/01
to
Voros wrote:
>
> > Here it is in a nutshell:
>
> > Maddux with Atlanta (excluding '79) is .271, Glavine is .282.
>
> Excluding '79?
>

Oops, '99. I guess I wish I were 20 years younger!

Eric M.Van

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Jan 3, 2001, 12:18:33 AM1/3/01
to
Voros wrote:
>
> Dale Hicks <dgh...@bellspamlesssouth.net> wrote:
> > Eric M.Van <em...@post.harvard.edu> wrote in article <3A524E87...@post.harvard.edu>...
> >>
> >> So: because of small sample sizes, differences in pitcher $H are *very* hard to
> >> detect statistically. But that doesn't mean they're non-existent or even
> >> trivial in size.
>
> > Do you concur, Voros? You said that any ability was below
> > the noise floor, IIRC.
>
> Basically. Essentially I said that any observed differences in the stat
> between two pitchers have thus far been difficult to ascribe to ability.
>
> > He says this, then says the effect isn't trivial.
>
> I suppose it depends on your definition of "trivial". It's kind of like
> building a compass from cork, needle, water and magnet and then once built
> arguing whether it's dead on north or 0.4 degrees off.
>
> >> The apparent difference between Maddux and Glavine over their
> >> joint career in Atlanta, in terms of ERA, is about 0.20. That's not trivial.
>
> > So their BB, K, and HR rates are equivalent, and only the $H
> > makes up that difference?
>
> No. I think he was talking about $H affect on it, which is a tough
> argument. Since joining the Braves Maddux' rate is .275 and Glavine's is
> .282. If you count that whole difference as completely attributable to
> each's ability (tough since over the whole career of each the numbers are
> .278 for Maddux and .279 for Glavine), you get a little over 5 hits a
> year. To make a .20 difference, you'd need a little over 5 runs.

It's about 7.4 hits a year, and 7.4 hits turned into outs will turn into 6.17
runs.

>
> If the difference between the two in $H giving full credit for $H doesn't
> account for 0.20 in ERA, I don't see where you can make that statement.
>
> --
> Voros McCracken
> vo...@daruma.co.jp
> http://www.baseballstuff.com/mccracken/

--

Eric M.Van

unread,
Jan 3, 2001, 12:48:30 AM1/3/01
to

The likelihood of a correlation being real is *not the same* as its size. Yes,
they tend to track together. But when you look at a correlation to judge its
validity, you ignore the strength. You look at the odds of it happening by
chance. If they are very large, it's absurd to start looking for alternate
explanations.

To your confusion between the size of a correlation and the variance in the
measure being correlated, you are now adding an equally deadly confusion between
a correlation's size and its significance.

you haven't ruled out other
> explanations, and when the correlation in seasons is around .10 I think
> maybe you should.

The size of the correlation is meaningless here.

Hell as Russell pointed out, I get a correlation of .07
> with 1000 samples between the ASCII code of the first letter of the city
> the pitcher pitched in and his $H rate that year. That's certainly not
> likely to happen from chance, but it sure as hell is likely to have
> happened from something that is effectively chance.

I found a relationship among new $H, new teammates' $H, and old $H that has 1
chance in 1,549,029,624,821,830,000,000,000 of happening by chance. That's 1.55
septillion, for you folks scoring at home.

The correlation with teammates $H has 1 chance in 739,236,817,630,109,000 of
happening at random. 739 Quadrillion.

The correlation with old $H has once chance in 684,050 of happening by chance.

I'm not going to lose any sleep wondering what else might have caused that to
happen in *this* universe but not in the 684,049 alternate ones (in which not
one, mind you, did the ball go through Buckner's legs.)

>
> The fact is you have a correlation where the the difference between the
> highest predicted value and the lowest one is a good bit smaller than the
> standard error of all of those predicitions.
>
> When a pitcher changes
> > teams, his new $H depends on his old $H. Period. End of story. No possibility
> > of other explanation.
>
> For starters, this is _never_ true. There's always a possibility of other
> explanations.
>

> > It's as cut and dried as possible.
>
> Cut and dried as possible would be a correlation of say .98, not one of
> .10.
>

Wrong, wrong, wrong, wrong, wrong. A correlation of .98 can be totally
meaningless if the sample size is small enough (say, 2). A correlation of .02
can be the word of God if the sample size is large enough. And for some things
(contribution of a gene to a complex disease, for instance), it could be crucial
information.

> In my $H figures for pitchers who changed teams, the correlation between
> the $H rate the second year and the year of the first year was .142.

And did you test that correlation for significance?

> > So for the 1000+ pitchers who changed teams, the year they pitched in was
> a better indicator of $H the next year than $H was.
>

Who said that determining $H was a winner-take-all proposition?

> > It has *never* followed that because $H correlates poorly, differences between
> > it must be small.
>
> It's a data point. The fact that the difference in active career $H rates
> among active pitchers resemble the same range as you'd expect from chance
> is another. The fact that a study of similar pitchers (done three separate
> times with different groups) for whom all the stats are similar excpet
> hits produced a result in which the high $H group gave up the same number
> of hits the next year as the low $H group is another. The fact that
> forecasts from $H totals yield higher errors than the predicted difference
> between the worst and best prediction is another.
>

Well, those are all thought-provking (although I'm not sure I can parse the
last one). I'll be working on trying to put a number on the inter-pitcher
variation later in the month.

Eric M.Van

unread,
Jan 3, 2001, 12:54:11 AM1/3/01
to
FM wrote:
>
> Eric M.Van <em...@post.harvard.edu> wrote:
>
>
> >The last term above, the apparent -0.76 fudge factor (that's .076 of a standard
> >deviation, about .0017 in raw $H terms) is very interesting. Pitchers who
> >change teams *unquestionably* have a reduction in $H, that is independent of
> >everything else, from an average of .280 to .276. It will be very interesting
> >to see if this varies by era. I have only one weak idea why this happens:
> >change-of-scene psychology? I'm not convinced.
>
> I'm guessing that those who underperform in $H *tend*
> to be traded.

Of course. Duh. Have an unlucky year, you're slightly more likely to change
teams.

>
> >3) So how should DIPS work? We shouldn't throw out $H entirely. We can do two
> >things: we can compare $H to team $H without that pitcher, and we can look at
> >lifetime $H. Combining both approaches, we can look at lifetime $H relative to
> >team. This can then be modified by park and manager effects (when known) to get
> >a true $H estimate. This might be very valuable for predicting future pitcher
> >performance.
>
> For a projection system, how much difference from the
> mean can be *expected* for any individual pitcher? It's
> one thing to say that some pitchers are much better at
> this than others. It's another to find such pitchers
> with reasonable confidence. I'm sure it's possible to
> incorporate this, but I doubt this will change anyone's
> projected ERA by more than .05.

The impact on projections will come from understanding park, manager, and
teammate effects. Right now I'm agnostic about the maximum size of the
effect. But my simulation showed that we *couldn't tell the difference*, even
after 15 years, with a true effect that would be worth 0.25.

>
> >5) Pedro Martinez went from a true $H of .325 in 1999 to one of .237 in 2000.
> >His HR rate, however, nearly doubled. The odds of this all happening by chance
> >are about 1 in 286. Conclusion: if anyone could refine their pitching approach
> >in a way that reduced how often batters hit the ball hard, at the expense of
> >some extra homers, it's Pedro. I think his true $H actually did go down last
> >year.
>
> Well, it's more of a conjecture than a conclusion

I *always* present my conjectures as conclusions <g>. Otherwise they think
you're a wuss.


.
>
> I said this before in another NG but note that this
> ability to hit balls hard differs significantly from
> one batter to another. That means if two pitchers
> allow balls in play at different rates to different
> hitters (or different types of hitters), then you
> will see a difference in their $H ability level.
>

--

FM

unread,
Jan 3, 2001, 1:28:52 AM1/3/01
to

I think we are reaching two conclusions here:

#1. It appears that there's *some* variance in pitchers'
ability to prevent hits on balls in play.

#2. It also appears that it's practically impossible to tell
a pitcher's $H ability-level from his $H alone.


The next step, then, is to look for other factors that could
tell us the pitcher's true $H better than using his actual $H
alone. Batters faced, Batters to whom the pitcher allowed
contact but not a homerun, GB/FB, HR/FB, Linedrives/GB+FB,
Rate of hits on LD, on GB, on FB, etc.

--
VMS, n.:
The world's foremost multi-user adventure game.

Voros

unread,
Jan 3, 2001, 1:18:11 PM1/3/01
to
Eric M.Van <em...@post.harvard.edu> wrote:
> Voros wrote:
>>
>> > Here it is in a nutshell:
>>
>> > Maddux with Atlanta (excluding '79) is .271, Glavine is .282.
>>
>> Excluding '79?
>>

> Oops, '99. I guess I wish I were 20 years younger!

What's your justification for excluding the highest $H level Maddux has
had in 10 years?

Voros

unread,
Jan 3, 2001, 1:21:15 PM1/3/01
to

I get about 5 hits. Maddux gets about 650 balls in play in an average
season. 650*.007 = 4.55. The difference due to rounding was probably a
little higher than .007 so it was around five hits.

Cameron Laird

unread,
Jan 3, 2001, 8:10:47 AM1/3/01
to
In article <3A52BDDE...@post.harvard.edu>,
Eric M.Van <em...@post.harvard.edu> wrote:
.
.

.
>> I'm guessing that those who underperform in $H *tend*
>> to be traded.
>
>Of course. Duh. Have an unlucky year, you're slightly more likely to change
>teams.
Is it obvious? It's not obvious to me. What
analytics *are* available for propensity-to-
be-traded? Quick speculations: players for
whom there is a divergence of beliefs about
their abilities; players with specialized
abilities; mediocre players ("deal-fillers");
...

>I *always* present my conjectures as conclusions <g>. Otherwise they think
>you're a wuss.
Yeah, I know about them.
.
.
.
Enormous thanks for launching this discussion.
This is fascinating stuff.

Incidentally, I believe you should be more
severe in trimming follow-ups.
--

Cameron Laird <cla...@NeoSoft.com>
Business: http://www.Phaseit.net
Personal: http://starbase.neosoft.com/~claird/home.html

Eric M.Van

unread,
Jan 3, 2001, 7:33:32 PM1/3/01
to
Voros wrote:
>
> Eric M.Van <em...@post.harvard.edu> wrote:
> > Voros wrote:
> >>
> >> > Here it is in a nutshell:
> >>
> >> > Maddux with Atlanta (excluding '79) is .271, Glavine is .282.
> >>
> >> Excluding '79?
> >>
>
> > Oops, '99. I guess I wish I were 20 years younger!
>
> What's your justification for excluding the highest $H level Maddux has
> had in 10 years?

I explained that already. First, the odds of it happening by random chance,
along with all of his other seasons for the Braves, were 2%. Take it out, the
odds of his remaining seasons happening by chance rise to 77%. Second, the
exact same thing happens to Glavine, though less dramatically -- 4% chance with
1999, 22% without it. I may do a closer look at the whole Braves' staff in
these years, but at the guesstimate level (which is all I was doing with the
computer simulations) I thought it made sense to toss out the year as an
egregious team defense effect.

--

Eric M.Van

unread,
Jan 3, 2001, 7:34:36 PM1/3/01
to
Cameron Laird wrote:
>
> In article <3A52BDDE...@post.harvard.edu>,
> Eric M.Van <em...@post.harvard.edu> wrote:
> .
> .
> .
> >> I'm guessing that those who underperform in $H *tend*
> >> to be traded.
> >
> >Of course. Duh. Have an unlucky year, you're slightly more likely to change
> >teams.
> Is it obvious? It's not obvious to me.

Only obvious as the explanation for this effect, as one of many reasons players
are dealt.

What
> analytics *are* available for propensity-to-
> be-traded? Quick speculations: players for
> whom there is a divergence of beliefs about
> their abilities; players with specialized
> abilities; mediocre players ("deal-fillers");
> ...
> >I *always* present my conjectures as conclusions <g>. Otherwise they think
> >you're a wuss.
> Yeah, I know about them.
> .
> .
> .
> Enormous thanks for launching this discussion.
> This is fascinating stuff.
>
> Incidentally, I believe you should be more
> severe in trimming follow-ups.
> --
>
> Cameron Laird <cla...@NeoSoft.com>
> Business: http://www.Phaseit.net
> Personal: http://starbase.neosoft.com/~claird/home.html

--

King Tut

unread,
Jan 3, 2001, 10:30:07 PM1/3/01
to
This is pseudo stat-headedness gone wild. Get a clue! Give
up the calculator and get some real life on-the-field baseball
experience. It will get you much closer to the baseball
knowledge you seek than this garbage ever will.

Bow down to the King!


>In article <3A524E87...@post.harvard.edu>,


Sent via Deja.com
http://www.deja.com/

Cameron Laird

unread,
Jan 5, 2001, 9:23:24 AM1/5/01
to
In article <3A53C4F4...@post.harvard.edu>,
Eric M.Van <em...@post.harvard.edu> wrote:
>Cameron Laird wrote:
.
.

.
>> >Of course. Duh. Have an unlucky year, you're slightly more likely to change
>> >teams.
>> Is it obvious? It's not obvious to me.
>
>Only obvious as the explanation for this effect, as one of many reasons players
>are dealt.
.
.
.
Perhaps I'm taking this too seriously. It *does* interest
me, though. I quite agree we can quantify "have an unlucky
year". I don't trust my own impressions of quantitative
propensity-to-be-traded, though; I suspect it's likely to
be as imperfect as other subjective evaluations. Moreover,
records exist so that we can, at least in principle, measure
it.

As to "explanations" and "reasons", are we modelling the
public-relations level ("He knows how to win") or what we
speculate are more consequential factors (he was a filler
tossed in for contractual reasons)?

I admire your work with $H. I suspect there's at least
equal scope to improve our understanding of management be-
havior.

Eric M.Van

unread,
Jan 5, 2001, 11:44:59 AM1/5/01
to
Cameron Laird wrote:
>
> In article <3A53C4F4...@post.harvard.edu>,
> Eric M.Van <em...@post.harvard.edu> wrote:
> >Cameron Laird wrote:
> .
> .
> .
> >> >Of course. Duh. Have an unlucky year, you're slightly more likely to change
> >> >teams.
> >> Is it obvious? It's not obvious to me.
> >
> >Only obvious as the explanation for this effect, as one of many reasons players
> >are dealt.
> .
> .
> .
> Perhaps I'm taking this too seriously. It *does* interest
> me, though. I quite agree we can quantify "have an unlucky
> year". I don't trust my own impressions of quantitative
> propensity-to-be-traded, though; I suspect it's likely to
> be as imperfect as other subjective evaluations. Moreover,
> records exist so that we can, at least in principle, measure
> it.


If you do it after the fact of the trade, you have a perfect system.


>
> As to "explanations" and "reasons", are we modelling the
> public-relations level ("He knows how to win") or what we
> speculate are more consequential factors (he was a filler
> tossed in for contractual reasons)?

Exactly. And the database I have is of *all* the pitchers who changed teams
from one year to the next in MLB history (excluding seasons split between two
teams in either year). Even with a real low cut-off of a minimum 150 balls in
play with each team, you find the effect of $H going down. So we know that $H
goes down for all these pitchers. Therefore, as $H varies randonly from year to
year, when it's on the high side you're more likely to change teams. That just
follows as a fact. You could strengthen the argument by coding each transaction
-- I bet the effect disappears for FA in the modern era (which means it's
probably even larger for players who are actually dealt.)


>
> I admire your work with $H. I suspect there's at least
> equal scope to improve our understanding of management be-
> havior.
> --
>
> Cameron Laird <cla...@NeoSoft.com>
> Business: http://www.Phaseit.net
> Personal: http://starbase.neosoft.com/~claird/home.html

--

C Nick Beaudrot

unread,
Jan 10, 2001, 1:09:48 AM1/10/01
to

On Tue, 02 Jan 2001 21:57:07 GMT, Eric M.Van <em...@post.harvard.edu> wrote:
: (Original title: Some Thoughts . . .

: Later title: Some Bold, Original Thoughts. . .
: But heck, if you want attention, you've got to ask for it!)
:
: (For newbies -- $H is the percentage of balls in play that are turned
: into outs. This varies surprisingly widely from year to year for
: any given pitcher, leading some to question whether the ability to get
: guys to hit easy grounders and fly balls is a real ability or not.
: Voros McCracken has explored this notion in detail as Defense
: Independent Pitching Statistics (at least I think that's what the
: acronym stands for <g>)).

[more synopsis -- Eric M.Van posted some work in which he noticed a
correlation between a teams manager and it's $H (relative to the
league's $H?) he suggested that $H was influenced by
park effects
managerial tendencies -- positioning of fielders pitching around
batters, etc.
team defense -- how talented are your fielders
luck -- probably still the biggest component
pitcher skill -- probably the smallest component
If this sounds really obvious, it's because it is, except for the notion
that pitcher skill has minimal influence on the %age of batted fair
balls that fall for hits.
]

: 1) Much of the reason why $H varies so much from year to year, where


: K% and BB% don't, is *because the sample size is smaller*. Too small.
: Note that HR%, which has a very similar sample size, also has a large
: variance from year to year. The additional lack of correlation in $H
: can be attributed to larger team defense and park effects.

Why stop there? How do we know that, say, an entire *team's* $H aren't
subject to noise in the "league-wide" $H? IOW, isn't it possible luck
becomes a large influence on team $H, so large that we can't reliably
detect team defense on batted fair balls (especially considering park
effects)?

:
: It's trivial to simulate multiple seasons of $H in Microsoft Excel for


: a pitcher with any theorized innate level of $H. The results are
: eye-opening.

I'm not sure what this [what I did] shows, but I used this method to
simulate a *full league* of $H, and there's still a good bit of variance
(I hope I'm using that word right).

For the 2000 season, the observed NL $H was .28750. I then plugged this
"innate" $H into 4500 cells, and ran 16 trials.

On the left are the actual $H numbers from 2000; on the right are
simulated $H numbers with a league wide $H of .28750
CIN .27502 .27333
STL .27773 .28222
LOS .27860 .28222
SDG .28009 .28222
MIL .28261 .28311
NYM .28303 .28667
ATL .28357 .28733
PHI .28516 .28800
CHC .28739 .28933
SFO .28860 .29133
ARI .28958 .29244
FLA .29047 .29333
COL .29514 .29444
HOU .29803 .29511
PIT .30126 .29511
MON .30351 .29822

[apparently, my random number generator blows -- .28222 three times!?!]

I'm not at all sure what this proves, but it seems plausible that a good
bit of the impact on *team* $H (not just individual pitcher $H) is
simply luck.

[there are many interesting things about these numbers, but I'm hesitant
to draw conculsions from the fact that the expos give up 5% more hits on
batted fair balls than league average, and the reds give up 3% less]

I just realized that I haven't looked at one of the best pieces of
information we have on the subject: correlation between team $H in year
N and team $H in year N+1 (!). Of course, I don't have this data on me,
nor as much of a grasp on correlations, regressions and spreadsheets as
others who read r.s.bb or r.s.bb.f :-). Does anyone have any data to
support/refute the idea that "team $H has strong correlation from year
to year"?


=======
The rotisserie implications of "team $H is not a reliable indicator of
team $H in the coming year" (which might not be true!)
====

In the thread "Rotisserie implications of $H", Voros McCracken and Eric
M.Van both suggest that you can help yourself out by looking for
pitchers who have high $Hs relative to their team $H. The idea is that
these pitchers are likely to see a decrease in their $H (and therefore
WHIP & probably ERA) in the coming year. It doesn't mean that they're
getting "better" necessarily, it means they're getting "luckier".
Inversely, you should avoid pitchers who have low $Hs relative to their
team's. You're not going to fool anybody on Greg Maddux here, but it
might help you make the right pick (or, more likely, help you avoid making
the wrong pick) in later rounds of the draft/auction.

This is getting abstract, so lets pick a concrete example:

HInP InP $H
Rusch 178 587 .30324
NYM 1234 4360 .28303

Glendon Rusch quietly put together a pretty good year last year. He was
27th in WHIP in an ML universe, and 52nd overall. Of course, Rusch is
not a "name brand" pitcher (read: Proven Veteran (TM)) yet, so some
rotisserie managers will look at last year and say "he got lucky". To
the contrary, Rusch was *unlucky* and still turned out to be an
effective starter (there may be some bias in here, because for a while,
IIRC, BobbyV was using Rusch as the 5th starter, and he may have more
starts against low-slugging chump teams, thus lowering his ERA).

If he had gotten average luck, then he would have given up 10-12 fewer
hits, enough for about a 0.20 era difference (and a .04 WHIP
difference). Of course, nothing says that Mr. Rusch will have average
luck next year -- he might get shelled in his first few starts and be
sent to the pen -- but odds are that he will have better luck next year
(thought it might not be much better).


Let's put Rusch and the Mets up against the NL as a whole.
HInP InP $H
Rusch 178 587 .30324
NYM 1234 4360 .28303
NL 20624 71736 .28750

Rusch, it would seem, had bad luck even by league wide standards. So
he's probably a good bet to see his luck improve; thus he's likely to be
morevaluable in the coming year than in later ones.

Let's take another example: Andy Benes.

HInP InP $H
AnBenes 144 504 .28571
STL 1207 4346 .27773
NL 20624 71736 .28750

Hmmmm .... Alan Benes's brother got bad luck relative to his team, but
slightly good luck relative to the league. If the more important factor
is team defense, then there there are slight odds that Mr. Benes will
have better luck next year. If the more important factor is league
offense (really the only way to interpret the NL $H in this context),
then you're basically flipping a coin -- he might get better luck, he
might get worse, and a priori there's no way to figure out which is more
likely (if you can, go tell Tony LaRussa).

Let's try one more: Jeff C. D'Amico. I'll just put $H here:

DAmico .25344
MIL .28261
NL .28750

This one's a relative no-brainer. Mr. D'Amico is unlikely to have luck
this bad again. If he had gotten average luck for a Brewer, he would
have had 8-11 hits more, which likley bumps his ERA by about .3 (he
pitched fewer innings than Rusch), and gives him a .06 increase in WHIP.
If he had gotten NL average luck, then he would have looked even worse.
D'Amico is more likely to have worse luck than better luck.


The question remains: which metric is more important? The team $H, which
can somehow measure team defense/park effects, or the league $H, which
measure league wide "average on balls in play"? Or the pitchers career
$H (there seems to be little evidence for this onea unfortunately)?


Allright, I'm going to bed now. I'll figure out a better way to look at
this ... later.

Cheers,
Nick

PS Many thanks to Voros for suggestiong Glendon Rusch and Jeff D'Amico
as over/underrated candidates. Saved me lots of hunting!

Thanks also to Voros and Eric for this discussion; it's really neat.

--
ni-q
.
bomb president allah marx encryption revolution Pat Buchanan unabomber occult

Voros

unread,
Jan 10, 2001, 1:47:43 AM1/10/01
to

It's around .40, but that includes not only defense but also park effects.

In any event the correlation is greater though it's tough to say what the
reason for it is.

> The question remains: which metric is more important? The team $H, which
> can somehow measure team defense/park effects, or the league $H, which
> measure league wide "average on balls in play"? Or the pitchers career
> $H (there seems to be little evidence for this onea unfortunately)?

Well the pitcher's career $H presents a problem for a number of
reasons: One, many pitchers haven't pitched for very long, so it's useless
for them. Two, those that have pitched a while aren't showing very big
differences between each other in the stat. Three, the cause of those
differences is a topic for much debate.

As roto implications go, the point I keep trying to make is that we're
really just trying to assess pitcher ability and regardless of what stats
we use, we're going to run into some level of error and most likely a
greater level than we do for hitters.

The point I'm making is that even if we accurately assess that one
pitcher's ability level is .003 higher than another, that information is
pretty useless in anything but the longest of runs. The stat is
constructed as such to where such a difference may not even manifest
itself over a period of eight years much less one.

The key is not to assume you have a level of certainty on hits that in
actuality we don't have. The point is not that Glendon Rusch's $H total is
going to go down, it's that we really don't know what it's going to be and
so the best guess is somewhere around average (or maybe team average or
halfway between or whatever).

In any event, multiple seasons worth of data are important for pitchers
especially in the areas of Home Runs and (if you choose to count them
anyway) hits.

I wouldn't mark Glendon Rusch down as necessarily being better (though
that's certainly a very possible outcome) as by eyeballing things it looks
like his Home Run rate was a bit lower than his previous established
level.

In any event I think he should be at worst a slightly above average
pitcher and at best a very good one.

Eric M.Van

unread,
Jan 10, 2001, 2:39:34 AM1/10/01
to

My study looked at many seasons, with ballpark and manager as the variables, and
team $H, expressed as standard deviations from league $H, as the outcome. The
statistical dependence of team $H on manager and ballpark was significant at the
100% level of confidence, and the effects were not small -- the "best" and
"worst" managers adding or subtracting 40-50 hits in a season, ditto for the
ballparks (although the average manager effect was larger than the ballpark
effect).

This sort of statistical analysis has the noise filter built in. The most
certain result in the whole output is Fenway Park at an average +0.87 standard
deviations per year, which is typically 40 hits. In general, the results follow
common perceptions of park effects remarkably well.

--

C Nick Beaudrot

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Jan 10, 2001, 1:43:46 PM1/10/01
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On 10 Jan 2001 06:47:43 GMT, Voros <vo...@daruma.co.jp> wrote:
: > I just realized that I haven't looked at one of the best pieces of

: > information we have on the subject: correlation between team $H in year
: > N and team $H in year N+1 (!). Of course, I don't have this data on me,
: > nor as much of a grasp on correlations, regressions and spreadsheets as
: > others who read r.s.bb or r.s.bb.f :-). Does anyone have any data to
: > support/refute the idea that "team $H has strong correlation from year
: > to year"?
:
: It's around .40, but that includes not only defense but also park effects.
:
: In any event the correlation is greater though it's tough to say what the
: reason for it is.

If nothing else, a larger sample size?

: > The question remains: which metric is more important? The team $H, which


: > can somehow measure team defense/park effects, or the league $H, which
: > measure league wide "average on balls in play"? Or the pitchers career
: > $H (there seems to be little evidence for this onea unfortunately)?
:
: Well the pitcher's career $H presents a problem for a number of
: reasons: One, many pitchers haven't pitched for very long, so it's useless
: for them. Two, those that have pitched a while aren't showing very big
: differences between each other in the stat. Three, the cause of those
: differences is a topic for much debate.

Four: the pitcher is getting older, traded, having the defense aroun
him change, etc.

:
: As roto implications go, the point I keep trying to make is that we're


: really just trying to assess pitcher ability and regardless of what stats
: we use, we're going to run into some level of error and most likely a
: greater level than we do for hitters.
:
: The point I'm making is that even if we accurately assess that one
: pitcher's ability level is .003 higher than another, that information is
: pretty useless in anything but the longest of runs. The stat is
: constructed as such to where such a difference may not even manifest
: itself over a period of eight years much less one.
:
: The key is not to assume you have a level of certainty on hits that in
: actuality we don't have.

I hope I didn't sound like I had any certainty

: The point is not that Glendon Rusch's $H total is


: going to go down, it's that we really don't know what it's going to be and
: so the best guess is somewhere around average (or maybe team average or
: halfway between or whatever).

Yes. That's really all I'm trying to say. It's not that Rusch *will*
come out of next season with better roto stats (I'm talking mostly about
ERA and WHIP of course), but rather that he's *likely* to come out with
better roto stats (if we assume his walk and HR rate will stay roughly
constant).

: In any event, multiple seasons worth of data are important for pitchers


: especially in the areas of Home Runs and (if you choose to count them
: anyway) hits.
:
: I wouldn't mark Glendon Rusch down as necessarily being better (though
: that's certainly a very possible outcome) as by eyeballing things it looks
: like his Home Run rate was a bit lower than his previous established
: level.

Right: if HR rate is subject to this sort of sampling error as well,
then you should take any improvement in it w/ a grain of salt.

Cheers,
Nick

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