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Tuesday, September 27, 2011

Catcher Framing Skill, part WOWY

By Tangotiger, 02:59 PM

Mike Fast generously posted his file on Catcher Framing in his tremendous article last week.  That file is the data source of everything I’m about to post here.

Also note that when I’m going to present a rate stat, it’ll be “per 75 called pitches”, because there’s around 75 called pitches per 9 innings.  Since runs allowed per 9 innings uses the notation RA9, I’ll use CP9 for called pitches per 9 innings.  (That it bears some resemblance to CP30 is either an unfortunate blemish, or a cool byproduct.)

Another note is that turning a called ball into a called strike is worth roughly +.12 runs.  However, the way that Mike has identified the pitches, it’s more like a “probably would have been called ball” turning into a called strike.  As a result, the run value, using this data source, is going to be more like .08 runs per extra call.

On to the data.


Brian McCann (id=435263) was behind the plate for 42,098 called pitches.  He ended up with +2.4 more called strikes than the average catcher did per 75 called pitches (CP9 of +2.4).  Among the 31 catchers with at least 20,000 called pitches, he’s #1 by far.  Getting 2.4 more called strikes per game than average doesn’t seem like much, but when one called strike is worth .08 runs, we’re talking about observing almost 0.20 runs per game of savings. 

(This is why you can’t really see it in catcher ERA: the difference is so small relative to the possible noise, that you can’t see it.  But 0.20 runs per game is actually huge, especially if you catch 125 games, as that comes out to 25 runs.  Observed, and unregressed anyway.  And of course, biased based on his pitchers, which I’ll talk about right now.)

Derek Lowe (id=117955) was on the mound for 7,944 called pitches.  He ended up with a CP9 of +6.0!  Among the 163 pitchers with at least 3000 called pitches, Lowe is by far #1.  Derek Lowe gets those called strikes that no one else gets.

Lowe and McCann are both on the Braves.

So, is McCann benefitting from Lowe, or Lowe benefitting from McCann?  Or both help each other?

Enter WOWY.  Several years back, I introduced a process called With Or Without You (WOWY).  It involved, not coincidentally, catchers, and their SB, CS, WP, PB, BK, PK.  Pitchers and catchers are a two-man team there.  WOWY is a good way to isolate the performance of one player, when you’ve got alot of paired dependencies.  While the idea is pretty cool as to how it works, and a bit complex in the coding, the lifeblood of the process is the data.  In that case, it was Retrosheet.  In this case, it’s PITCHf/x data, as processed by Mike Fast.  What I’m going to do is nothing without that data.

Anyway, let’s start with McCann.  Of his 42,098 called pitches, only 4212 involved Derek Lowe.  Together, as a pair, they got a +5.8 CP9.  McCann, WITHOUT Lowe, was a +2.0 CP9.  Therefore, this data point is telling us that Lowe was worth +3.8 CP9 above the other McCann-pitchers.

If you need some math (and who doesn’t?), it looks like this:

McCann + Lowe = +5.8
McCann + Others = +2.0

If you subtract one from the other, McCann cancels out, and we get:
Lowe - Others = +3.8

That is, Lowe is worth +3.8 above “Others” (which in this case, is all the other pitchers that McCann caught).  Given that there were 67 other pitchers, we’re going to presume that “Others” are fairly representative of the league average pitcher.

Lowe however was also caught by other catchers, notably Russell Martin, when both were on the Dodgers.  As it turns out, Russell Martin was #2 in CP9 among the regular 31 catchers.  How fortunate that Derek Lowe was caught by these two catchers!

Of Martin’s 42,186 called pitches, Derek Lowe threw 2622.  Martin, with Lowe, had a CP9 of +6.7!  Martin, without Lowe, had a CP9 of +1.3.  Therefore, Derek Lowe had an impact of +5.4 CP9.

Lowe was also caught by a few other catchers (total of 1110 pitches).  Going through the same process for each catcher, we end up with Lowe being +4.9 CP9 with each catcher, and each of those catchers being +2.2 CP9 without Lowe.  This gives us an impact for Lowe os +2.7 CP9.

To recap:
+3.8 CP9 for Lowe (weight of 4212 pitches; inferred via McCann’s other pitchers)
+5.4 CP9 for Lowe (weight of 2622 pitches; inferred via Martin’s other pitchers)
+2.7 CP9 for Lowe (weight of 1110 pitches; inferred via rest of catcher’s other pitchers)

Obviously, Lowe gets favorable calls.  The overall weighted average is +4.2 CP9 for Lowe.  Remember, his unadjusted observed CP9 was +6.0.  After adjusting out for his catchers’ impact, we see that he was instead estimated to be +4.2.  Lowe has been caught by good catchers.

We do this for all pitchers.  Derek Lowe is still the #1 pitcher.  #2, just a sliver behind, is Livan Hernandez.  #3 is Jamie Moyer.  These three stand out as easily the best in getting favorable calls.

Anyway, now that I have the CP9 for each pitcher, I can then use those CP9 as adjustments for each catcher.  For example, Brian McCann has had the good fortune of pitching with pitchers who get favorable calls.  Those pitchers have a CP9 of +0.4.  It’s not that huge of an impact, but it’s there.  So, McCann’s unadjusted +2.4 gets reduced to a +2.0 adjusted.  That is, after adjusting for the quality of his pitchers, McCann’s adjusted CP9 is +2.0.

He is still #1 in baseball.  We convert that figure to runs by multiplying by 0.08, to give us an impact of +0.16 runs per game (or +20 runs in 125 games).  This is all unregressed.  However, given the huge number of balls caught, and Mike’s initial estimate that you would regress very little to begin with, this figure will go down by perhaps 10-15% to get an estimate of true talent.

Last among the 31 regulars is Ryan Doumit, with an observed -3.0 CP9, and runs of -30 in 125 games.  Given the smaller sample for Doumit, the true talent will be about a 20-25% drop from -30, down to around -25 runs.

#1          (see all posts) 2011/09/27 (Tue) @ 15:26

Lowe’s an odd case: he throws in the standard strike zone the least of any pitcher in the majors and has done so for each of the last few years.  (See my post: http://www.beyondtheboxscore.com/2011/2/18/1994525/missing-the-strike-zone-effectively-the-odd-case-of-derek-lowe)

His pitching style would seem to lend itself quite well to benefiting from a good framing pitcher, as his pitches are consistently in specific areas out of the zone.


#2    MGL      (see all posts) 2011/09/27 (Tue) @ 15:29

Great stuff Tango!  Do you have a spreadsheet of all the results? How does your list compare to Mike’s?

Why not do a direct WOWY for the catchers rather than doing it directly for the pitchers and then adjusting the catchers?  Wouldn’t that come out exactly the same?

Again, anyone who says that sabermetrics is dead (Huckabee?), or that there are no more major breakthroughs to be had, well…

The funny thing is that most sabermetric breakthroughs are either antithetical to conventional wisdom or something that baseball insiders never thought of.  In this case, I think that many baseball insiders would respond, “No shizit!  We already know that these guys were great (or bad) framers.  We don’t need your spreadsheets to tell us that.” Similar to defensive metrics I guess, but not like DIPS…


#3    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 15:55

I was going to figure out the regression component, and then I was going to post the file.

***

The reason I didn’t want to do the catcher directly without adjusting for the pitchers first is the problem we have with paired catchers (Mathis / Napoli let’s say).  In those cases, you can never have both of them being plus catchers.

But, since their pitchers have pitched not only in Anaheim, but also with many other teams (Lackey/Redsox, etc), I get one extra order of comparisons.

The ideal scenario is that I continue this iteration process until I get stable data. 

Presumably, Max Marchi did this, through a more refined regression approach. However, if I do that, I lose the character of the data.  I can no longer pinpoint all the little things that I did above.  That’s why I like my process, because I can explain it in more detail.


#4    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 16:04

By the way, for those who aren’t following closely, this is exactly like anyone would compute “strength of schedule”. 

Instead of opponents, it’s “mates”. Same idea though.

So, McCann with Lowe.  Lowe with Martin.  Martin with Kershaw.  Kershaw with…


#5          (see all posts) 2011/09/27 (Tue) @ 16:35

Tango,

Interesting.  I’m curious--mainly because I’m trying to do some conversion in my own work--as to how many ‘Wins’ McCann is producing through that skill.  In other words, if he saves about 20-25 runs in a season (is this above replacement or average?), how would this translate at the team level and Wins?

The reason I ask is that I am trying to convert similar differences in run scoring based on switching umpire calls in different situations (for the batter, not catchers).

Any info you have would be enlightening, as I would like to get that portion of my analysis right (as to not be called out by Phil at Sabermetric Research Blog).

I know I had read somewhere like 10 runs created above replacement is about one win (through Dave Cameron at Fangraphs).  For example (leaving aside the regression issue for now), can we take the 20 runs saved and add it to something like McCann’s total “RAR” as presented on Fangraphs (which--if I do my math right--should add about 2 WAR to his total)?


#6    Devon      (see all posts) 2011/09/27 (Tue) @ 17:22

I wish we had the stats to figure this out for Nolan Ryan… or Johnny Bench.


#7    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 18:33

Millsy: yes, definitely.  10 runs to a win will shield you from the wrath of Phil.


#8          (see all posts) 2011/09/27 (Tue) @ 19:08

I’ve tried coding it up to solve for the pitchers and catchers simultaneously as a weighted linear least squares problem.  That is, the net performance of each pitcher/catcher pair should be the sum of the pitcher and catcher skills, times the number of pitches the pair had.  However, the matrix is ill-conditioned.  I could probably get around this by collecting all the pitchers and catchers who don’t have a lot of pitches with any particular other pitcher or catcher into an “other” bucket.  That’s proving to be a little more time consuming than I’d like.


#9          (see all posts) 2011/09/27 (Tue) @ 19:26

MGL/2—to be fair to Huckabay, he explicitly excluded the kind of pitch analysis that Mike did and that made Tango’s follow-up here possible, from “baseball analysis is dead” in the article you’re alluding to.

Anyway—really exciting stuff.


#10    Peter Jensen      (see all posts) 2011/09/27 (Tue) @ 20:02

Brian McCann (id=435263) was behind the plate for 42,098 called pitches.  He ended up with +2.4 more called strikes than the average catcher did per 75 called pitches (CP9 of +2.4).

Tango - How did you calculate “CP9 of +2.4” from Mike’s data?


#11    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 22:23

player(ExtS - ExtB)/Total - league(ExtS - ExtB)/Total

And then multiplied by 75.


#12          (see all posts) 2011/09/27 (Tue) @ 22:30

I added a constraint that the average catcher should have a framing skill of 0 as should the average pitcher.  That removed the ill-conditioning of the problem.  I was able to solve the simultaneous problem using all pitcher-catcher pairs with more than 75 pitches.  I solved the weighted least squares problem with weights proportional to the number of pitches seen by a particular pair.  The results for catchers with more than 20,000 pitches caught are below.  They seem pretty similar to the WOWY results, though the range is a little smaller.

[Editor’s note: I have removed the data in this post, as Larry has corrected it further down.]


#13          (see all posts) 2011/09/27 (Tue) @ 22:33

I’m going to post the pitcher results here, but Derek Lowe comes out at the bottom.  It turns out it makes a huge difference whether I used Net or Net-Adj.  I used Net-Adj, since I figured that was the most correct data.  Switching to Net gives very different results for the pitchers, though less so for the catchers.  Anyway, here are all pitchers with 4500 pitches or more.
[Editor’s note: I have removed the data in this post, as Larry has corrected it further down.]


#14    Richard Bergstrom      (see all posts) 2011/09/27 (Tue) @ 22:51

As a bit of an aside, but Lowe was a pitcher DePodesta coveted (and was ridiculed for acquiring). I do find it interesting that he acquired a pitcher who gets a high CP9 and that the two pitchers after him that you mentioned, Livan Hernandez and Jamie Moyer, were players that retained that skill for a long time.

Does all of this have enough effect on BABIP to make a difference? Could one (though not all) of a reason a pitcher’s BABIP fluctuates from year to year have to do with the catcher jobshare behind the plate? Could some pitchers have better or worse BABIP than expected because they have stayed with the same catcher? Is it possible that Cardinals pitchers do better not just because of Dave Duncan, but because of Yadier Molina?


#15    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 23:02

Larry/12 was edited to include my numbers for CP9.


#16    Tangotiger      (see all posts) 2011/09/27 (Tue) @ 23:05

If you want Huck’s thread, please make comments here:

http://www.insidethebook.com/ee/index.php/site/comments/baseball_prospectus_is_dead/


#17          (see all posts) 2011/09/27 (Tue) @ 23:11

Yikes!  The Net-AdjP column is already adjusted for the pitcher.  Since I’m tryintg to solve for that, I should be using Net.

Here are updated results, also accounting for the fact that the average Net value is -0.03 extra stikes per pitch. 

[Editor’s note: I have removed the data in this post, as Larry has corrected it further down.]


#18    MGL      (see all posts) 2011/09/27 (Tue) @ 23:49

Mike, are you planning on updating your work using 2011 pitch f/x data?


#19    Tangotiger      (see all posts) 2011/09/28 (Wed) @ 01:16

Data includes 2011.

http://www.baseballprospectus.com/article.php?articleid=15093


#20    MGL      (see all posts) 2011/09/28 (Wed) @ 04:22

So it does!


#21    Rally      (see all posts) 2011/09/28 (Wed) @ 09:21

"Is it possible that Cardinals pitchers do better not just because of Dave Duncan, but because of Yadier Molina?”

Molina was born in 1982.  Duncan and LaRussa have teamed up to get surprisingly good pitching performances since 1983.  I don’t know how much help they had from infant Yadier in the early days.


#22          (see all posts) 2011/09/28 (Wed) @ 09:50

I find it interesting that in Larry’s results, Ryan Doumit shows up on at the top of the catcher list while two of his current/former battery mates, Zach Duke and Paul Maholm, show up at the very bottom of the pitcher list.  Also interesting is that those two pitchers both have high career BABIPs.

Of course we shouldn’t really draw conclusions from cherry-picked results, but I just found those things interesting (speaking as a Pirate fan).


#23    Tangotiger      (see all posts) 2011/09/28 (Wed) @ 10:05

No offense to Larry, but I would ignore his pitcher list.  He did something wrong, either in assumptions or mechanically.

There’s no way that Derek Lowe is anything other than near the top of the list of getting most favorable calls.  His unadjusted CP9 was, whatever I said it was, 5.8 or something.  His catchers, at best, were 1.5 to 2.0 or something.  So, it would be impossibly for Lowe to be anything other than a huge plus.

To make him a negative is basically jsut to be the opposite of McCann+Martin.  So, I think Larry just looked at the wrong columns.


#24          (see all posts) 2011/09/28 (Wed) @ 14:48

Tango/23 No offense taken.  It was obviously wrong, it can only help to have it pointed out.

Yes, I noted in 17 that I used the Adjusted column, which already adjusted for the pitchers, instead of the Net column.  I thought the adjustment was the pitch-f/x adjustment Mike alluded to in the article.  Only after I saw Tango/11 did I realize my mistake.  I have updated pitcher numbers with Lowe at the top end, but I’m double checking some things and trying to get some error bars estimated.


#25          (see all posts) 2011/09/28 (Wed) @ 15:42

I now have some error bars to post along with the results.  The error bars are approximately 1 SD = 0.2 framed pitches per game for the catchers and 1 SD = 0.35 framed pitches per game for the pitchers.

One interesting thing I found is that the average framing ability of the most played catchers is a CP9 of -0.14.  That is the average starting catcher has a negative framing skill.  The opposite is true for the pitchers.  The average starting pitcher has a positive “framing” skill.  Anyway, the results.

Catchers:

Ryan Doumit                22861     -3.1 +/- 0.23
Nick Hundley               20827     
-1.4 +/- 0.22
Gerald Laird               30298     
-1.1 +/- 0.18
Jason Varitek              27075     
-0.9 +/- 0.23
Dioner Navarro             24980     
-0.9 +/- 0.22
Chris Iannetta             25308     
-0.8 +/- 0.22
Jason Kendall              35772     
-0.8 +/- 0.17
Victor Martinez            22864     
-0.7 +/- 0.21
John Buck                  31314     
-0.5 +/- 0.17
Kelly Shoppach             21402     
-0.5 +/- 0.22
Joe Mauer                  30260     
-0.5 +/- 0.26
Carlos Ruiz                32761     
-0.5 +/- 0.21
Miguel Olivo               30104     
-0.4 +/- 0.17
Mike Napoli                23939     
-0.3 +/- 0.22
Kurt Suzuki                43225     
-0.2 +/- 0.22
Ramon Hernandez            27917     
-0.2 +/- 0.23
Ronny Paulino              20239     
-0.2 +/- 0.22
Geovany Soto               32705     
-0.1 +/- 0.22
Rod Barajas                26858     
-0.1 +/- 0.18
A
.JPierzynski            40828     -0.1 +/- 0.21
Bengie Molina              30096      0.1 
+/- 0.24
Ivan Rodriguez             30558      0.1 
+/- 0.17
Chris Snyder               23394      0.2 
+/- 0.21
Jarrod Saltalamacchia      20065      0.7 
+/- 0.21
Matt Wieters               25008      0.8 
+/- 0.24
Yorvit Torrealba           26306      0.8 
+/- 0.18
Jeff Mathis                25731      0.9 
+/- 0.22
Miguel Montero             27053      1.1 
+/- 0.20
Brian McCann               42098      1.2 
+/- 0.21
Yadier Molina              39184      1.3 
+/- 0.24
Russell Martin             42186      1.6 
+/- 0.17


#26          (see all posts) 2011/09/28 (Wed) @ 15:46

The pitchers.  Jamie Moyer, Livan Hernandex and Derek Lowe are in another world from everybody else.  Note that very few pitchers can survive and be definitively bad at this skill.

Justin  Masterson        5113     -2.6 +/- 0.39
C
.J.    Wilson           5703     -1.8 +/- 0.34
Scott   Feldman          4897     
-1.8 +/- 0.36
Jonatha Sanchez          6208     
-1.7 +/- 0.39
Scott   Kazmir           4793     
-1.5 +/- 0.38
Max     Scherzer         5425     
-1.5 +/- 0.36
Gio     Gonzalez         5072     
-1.5 +/- 0.40
Roy     Oswalt           5953     
-1.3 +/- 0.34
David   Price            4860     
-1.3 +/- 0.38
Edwin   Jackson          7283     
-1.2 +/- 0.30
Felix   Hernandez        8489     
-1.0 +/- 0.30
Trevor  Cahill           5367     
-0.9 +/- 0.39
Ricky   Romero           4950     
-0.9 +/- 0.37
Matt    Cain             7609     
-0.9 +/- 0.36
Josh    Beckett          6411     
-0.9 +/- 0.36
Jason   Hammel           5273     
-0.8 +/- 0.36
Cliff   Lee              6612     
-0.8 +/- 0.31
Justin  Verlander        8459     
-0.7 +/- 0.30
Joe     Saunders         7369     
-0.7 +/- 0.31
Fausto  Carmona          6242     
-0.7 +/- 0.35
Clayton Kershaw          6127     
-0.7 +/- 0.33
Jorge   De La Rosa       4787     
-0.7 +/- 0.37
Mike    Pelfrey          6966     
-0.7 +/- 0.31
Luke    Hochevar         4878     
-0.7 +/- 0.37
Chad    Billingsle       7842     
-0.7 +/- 0.30
Ervin   Santana          7467     
-0.7 +/- 0.34
Ricky   Nolasco          5767     
-0.6 +/- 0.35
A
.J.    Burnett          8071     -0.6 +/- 0.29
John    Lannan           6116     
-0.6 +/- 0.34
Jeremy  Guthrie          7177     
-0.5 +/- 0.33
Johan   Santana          4807     
-0.4 +/- 0.37
Tim     Lincecum         7929     
-0.4 +/- 0.35
John    Lackey           7279     
-0.4 +/- 0.31
Gil     Meche            4511     
-0.4 +/- 0.38
Wandy   Rodriguez        7082     
-0.4 +/- 0.34
Gavin   Floyd            6838     
-0.4 +/- 0.35
Armando Galarraga        4551     
-0.4 +/- 0.38
Ubaldo  Jimenez          7848     
-0.4 +/- 0.30
Randy   Wolf             7422     
-0.4 +/- 0.31
John    Danks            7092     
-0.3 +/- 0.34
Zach    Duke             5226     
-0.3 +/- 0.37
Christo Volstad          5001     
-0.3 +/- 0.38
C
.C.    Sabathia         7884     -0.3 +/- 0.30
Aaron   Cook             4735     
-0.2 +/- 0.38
Kevin   Correia          5880     
-0.2 +/- 0.33
Scott   Baker            5468     
-0.2 +/- 0.40
Adam    Wainwright       5572     
-0.1 +/- 0.40
Kevin   Millwood         6367     
-0.1 +/- 0.32
Jon     Lester           7264     
-0.0 +/- 0.33
Ted     Lilly            6513     
-0.0 +/- 0.33
James   Shields          6976     
-0.0 +/- 0.33
Brad    Penny            5501      0.0 
+/- 0.34
Josh    Johnson          4591      0.1 
+/- 0.39
Miguel  Batista          4540      0.1 
+/- 0.40
Daisuke Matsuzaka        4677      0.1 
+/- 0.41
Carlos  Zambrano         6541      0.2 
+/- 0.35
Matt    Garza            7019      0.2 
+/- 0.32
Brian   Bannister        5018      0.2 
+/- 0.35
Hiroki  Kuroda           5310      0.2 
+/- 0.36
Chris   Carpenter        5096      0.2 
+/- 0.41
Joe     Blanton          6488      0.3 
+/- 0.32
Francis Liriano          4726      0.3 
+/- 0.42
Jered   Weaver           8357      0.4 
+/- 0.32
Dave    Bush             4979      0.4 
+/- 0.37
Barry   Zito             6532      0.4 
+/- 0.38
Johnny  Cueto            6003      0.4 
+/- 0.36
Zack    Greinke          7268      0.5 
+/- 0.30
Paul    Maholm           6455      0.5 
+/- 0.34
Kyle    Davies           5531      0.5 
+/- 0.33
Jon     Garland          6657      0.5 
+/- 0.30
Javier  Vazquez          7303      0.5 
+/- 0.30
Roy     Halladay         7886      0.6 
+/- 0.29
Tim     Wakefield        5364      0.7 
+/- 0.38
Brett   Myers            5966      0.9 
+/- 0.35
Ryan    Dempster         7224      0.9 
+/- 0.34
Cole    Hamels           6669      0.9 
+/- 0.35
Joel    Pineiro          5430      0.9 
+/- 0.36
Aaron   Harang           5779      1.0 
+/- 0.35
Carl    Pavano           4993      1.1 
+/- 0.41
Bronson Arroyo           7657      1.1 
+/- 0.34
Dan     Haren            8641      1.2 
+/- 0.28
Kyle    Kendrick         4557      1.2 
+/- 0.39
Shaun   Marcum           5429      1.2 
+/- 0.35
Nick    Blackburn        5702      1.3 
+/- 0.40
Yovani  Gallardo         6365      1.3 
+/- 0.33
Doug    Davis            5197      1.4 
+/- 0.36
Mark    Buehrle          7765      1.5 
+/- 0.33
Jason   Marquis          5775      1.7 
+/- 0.33
Jair    Jurrjens         5785      1.7 
+/- 0.36
Tim     Hudson           5745      1.7 
+/- 0.37
Jake    Peavy            5546      1.9 
+/- 0.35
Jeff    Suppan           4593      2.0 
+/- 0.39
Andy    Pettitte         5154      2.1 
+/- 0.38
Kyle    Lohse            5680      2.3 
+/- 0.37
Jamie   Moyer            4798      4.1 
+/- 0.39
Livan   Hernandez        7453      4.1 
+/- 0.30
Derek   Lowe             8080      4.6 
+/- 0.30


#27          (see all posts) 2011/09/28 (Wed) @ 16:40

I’m not sure I understand the last comment, Larry.  This “skill” is ultimately about umpires’ misperception of pitches.  It’s equally likely there is simply a limit to how bad the umpires can get.  Are there small sample size examples of pitchers who as negative as Lowe, Livan and Moyer are positive that support your assertion?  A number of the pitchers at the top of the list are hardly marginal.  Wilson, Oswalt, Price, Felix, Beckett, Lee, Verlander:  I’d take any five for a rotation.


#28    Tangotiger      (see all posts) 2011/09/28 (Wed) @ 16:51

Right, this is a “skill”, in that it is directly related to the identity of the pitcher.  We can call it a “characteristic” instead, but regardless, Lowe, Livan and Moyer are benefitting because of what they are actually doing, however they are doing it.  It doesn’t matter if they don’t technically deserve it.  They’re getting the calls that the other pitchers are not getting.

As for the flip side, I presume Larry means that you can’t be a bad pitcher AND not get favorable calls, because if that happens, you won’t be in MLB long.  Felix, et al, are getting results in spite of the umps not able to call the pitches the way PITCHf/x expects of them.


#29          (see all posts) 2011/09/28 (Wed) @ 17:07

The skill is that these pitchers get more (or fewer) called strikes.  The ability to get the umpire to call a pitch on the boundary of the strike zone a strike more often, if persistent, would certainly be a skill. 

As to the limit comment, given the error bars, only 11 pitchers exceed 3-sigma from the null hypothesis.  Also, the average pitch thrown by a regular starter has a positive bias toward being a strike, relative to the average pitch thrown overall.  So, there is an imbalance.  This leads me to think it is something noticed in the selection of starting pitchers, but it is speculation.  Guys who consistently fail to get pitches like these called strikes are more likely to struggle, I’d think.

The difference between pitchers and catchers is catchers can presumably hide this deficiency with hitting, baserunner control, or perhaps pitch calling.  Pitchers might be more exposed with respect to this skill. 

Of pitchers missing my pitch cutoff, Ronald Belisario was -5.2 +/- 0.79 at 989 pitches, Brandom League was -4.2 +/- 0.6 at 1784 pitches, and Jesse Carlson -4.2 +/- 0.78 at 1099 pitches.

One other notable, Tom Glavine is +6.9 +/- 0.76 on 1301 pitches, which is pretty amazingly spectacular.  That’s 3 SDs above Lowe (accounting for both uncertainties).


#30          (see all posts) 2011/09/28 (Wed) @ 17:14

Tango/28:

I’m suggesting that not getting the calls might make you a bad pitcher.  A pitcher needs to get pitches that are hard to hit either called strikes or swung at.  If you can’t get these calls, it might make it harder to get the swings.  Plus, if you can’t fool the ump, maybe you can’t fool the batter, either.

Your suggestion is equally plausible though.


#31          (see all posts) 2011/09/28 (Wed) @ 18:35

One more interesting name:

Mariano Rivera           1996      3.9 +/- 0.56

Is there anything he’s not good at?


#32          (see all posts) 2011/09/28 (Wed) @ 19:00

The “skill” that’s involved for pitchers who get extra strikes is that they pitch on the edge of the zone, particularly the outside edge of the zone, more than average.  There is good evidence that umpires tend to call the zone relative to the catcher target.

That’s also the reason we have to adjust for that location/target effect before we measure the ability of the catchers to get extra strikes.


#33    Tangotiger      (see all posts) 2011/09/28 (Wed) @ 20:34

http://www.tangotiger.net/files/FramingCatchers.csv
http://www.tangotiger.net/files/FramingPitchers.csv

Data is NOT regressed.  I didn’t have time to do it.  So, be careful when using the data.

I found it quite amusing that the players who got the most favorable calls from umpires are the non-pitchers!  The top of the list is filled with these mopup “pitchers”.

A neat little article can be written focusing on these guys.



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