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Framing the Debate

Or perhaps, framing, the debate.

The defensive influence of the catcher has long been debated, with sabermetric circles generally valuing it less than baseball insiders. It's possible to identify seven separate components as places where a catcher can contribute defensively: stolen bases, blocking pitches, blocking the plate, fielding bunts (and psuedo-bunts), game calling, pitch framing and pitcher preparation. Many people have made attempts at quantify different pieces of the overall catcher contribution. Ability to prevent stolen bases and fielding ability are included in a number of measures, such as UZR and Win Shares. Keith Woolner from Baseball Prospectus has examined the topic of game calling a few times and has placed an upper limit of the effect at .60 runs of ERA. I've attempted to measure how well catchers block pitches in the dirt. Blocking the plate is very difficult to measure since it occurs relatively rarely and success can be quite subjective (the runner might be out, but the catcher did nothing to impede him from reaching the plate). Determing a catcher's skill at blocking the plate is probably best left to visual analysis. Pitcher preparation, which I'm using to mean any interaction the catcher has with his staff, from going over game plans, to calming the pitcher during visits to the mound, is another area that would be nearly impossible to objectively measure, but almost certainly has some effect on the game. One area that hasn't been explicitly covered (I suspect many would roll it into game calling) is pitch framing.

I've attempted a study to measure the effects of framing pitches. I say attempted because, to be honest, the results seem wrong to me, but I can't figure out where the issue lies. I present the rest of this article in the hopes that someone can take the information and make sense of it. Some may say I shouldn't publish this until I'm more comfortable with the outcome, but I'm a big fan of collaboration and knowledge sharing, so I'm going ahead even though I don't know the answer. Ok, enough honesty, let's move on.

Star-divide

Back around Thanksgiving, Jonathan Hale broke down the strike zones of various umpires over at The Hardball Times. He used the PITCHf/x data and the strike zones figured by John Walsh to determine how many strikes above or below average an umpire called in the course of a game (for his purposes, 150 called pitches). The results ranged from -5.2 strikes for Gerry Davis to +4.81 strikes for Jeff Nelson, where negative numbers implies a smaller strike zone.

A similar methodology can be used to examine whether catchers have any effect on whether the pitch is called a ball or strike. Using Walsh's strike zones, empirically defined as the areas where at least 50% of pitches are called strikes, I determined for each catcher how many balls should have been strikes and vice versa. This allowed me to calculate an average rate and then figure out how many strikes above or belowe average each catcher was. The results for those catchers with more than 2000 called pitches (roughly 120 innings using Hale's 150 called pitches per game number) can be seen below. The run value is calculated using the run value for changing a ball to a strike - .161 runs, which I explain here.

Catcher Called Pitches SAA SAA / 150 Pitches Runs / 150 Pitches
Gregg Zaun 4518 159.70 5.30 0.85
Jeff Mathis 3430 93.43 4.09 0.66
Yadier Molina 3246 84.49 3.90 0.63
Russell Martin 7061 183.16 3.89 0.63
Yorvit Torrealba 2867 58.03 3.04 0.49
John Buck 3443 64.93 2.83 0.46
Brian McCann 5762 105.98 2.76 0.44
Javier Valentin 2013 36.79 2.74 0.44
Jose Molina 2313 41.28 2.68 0.43
Brian Schneider 2119 31.72 2.25 0.36
Ivan Rodriguez 3881 55.13 2.13 0.34
Brad Ausmus 2576 35.19 2.05 0.33
Johnny Estrada 3072 36.16 1.77 0.28
Carlos Ruiz 2471 11.22 0.68 0.11
Benji Molina 3909 14.06 0.54 0.09
Jorge Posada 2616 3.66 0.22 0.03
Paul LoDuca 2569 -0.54 -0.03 -0.01
A.J. Pierzynski 5932 -3.54 -0.09 -0.01
Victor Martinez 3233 -2.01 -0.09 -0.02
Josh Bard 5701 -15.68 -0.41 -0.07
Kurt Suzuki 4269 -15.75 -0.55 -0.09
Jason Varitek 3543 -14.90 -0.63 -0.10
Jason Kendall 5770 -25.32 -0.66 -0.11
Michael Barrett 2499 -15.86 -0.95 -0.15
Chris Snyder 3342 -23.19 -1.04 -0.17
Ronnie Paulino 2343 -16.87 -1.08 -0.17
Rob Bowen 2057 -26.90 -1.96 -0.32
Mike Napoli 3106 -45.14 -2.18 -0.35
Jamie Burke 2270 -35.07 -2.32 -0.37
Ramon Hernandez 2361 -39.56 -2.51 -0.40
Jarrod Saltalamacchia 2467 -41.63 -2.53 -0.41
Joe Mauer 2434 -44.36 -2.73 -0.44
Dioner Navarro 2436 -64.44 -3.97 -0.64
Kenji Johjima 7120 -201.11 -4.24 -0.68
Gerald Laird 6129 -317.09 -7.76 -1.25

I'll be the first to admit this is a much larger effect than I expected to see. In fact it's so large that I have to think there's something wrong in the analysis. Over the course of 120 games (a reasonable estimate for the number of games caught in a season by a starting catcher), the difference between Gregg Zaun and Gerald Laird is over 250 runs or 25 wins. The per game numbers, though, are similar to what Hale found for umpires. So either this style of analysis is lacking, or my run values are off.

But what about the effect of the umpire on this? After all, my study is based on Hale's work with the umpires. I did a weak correction for the umpires by using his numbers and determining what the expected strike delta would be for each catcher / umpire pair and subtracting that effect from the catcher's individual numbers. Since he only published umpires with a sample size greater than 20 games, I assumed the other umpires had 0 effect. This obviously gets into more drastic sample size issues than does the initial study, but I thought it would be interesting to look at.

Catcher Opportunities SAA SAA / 150 Pitches Runs / 150 Pitches
Gregg Zaun 4518 167.81 5.57 0.90
Yadier Molina 3246 83.25 3.85 0.62
Russell Martin 7061 167.61 3.56 0.57
Jeff Mathis 3430 77.43 3.39 0.55
Jose Valentin 2013 42.55 3.17 0.51
Jose Molina 2313 46.95 3.04 0.49
John Buck 3443 63.28 2.76 0.44
Brian McCann 5762 98.41 2.56 0.41
Brad Ausmus 2576 41.80 2.43 0.39
Yorvit Torrealba 2867 44.57 2.33 0.38
Ivan Rodriguez 3881 55.67 2.15 0.35
Brian Schneider 2119 29.76 2.11 0.34
Johnny Estrada 3072 32.23 1.57 0.25
Jorge Posada 2616 21.64 1.24 0.20
Benji Molina 3909 22.23 0.85 0.14
Paul LoDuca 2569 2.11 0.12 0.02
Carlos Ruiz 2471 1.56 0.09 0.02
A.J. Pierzynski 5932 2.11 0.05 0.01
Victor Martinez 3233 -4.29 -0.20 -0.03
Kurt Suzuki 4269 -6.32 -0.22 -0.04
Jason Varitek 3543 -5.81 -0.25 -0.04
Jason Kendall 5770 -21.46 -0.56 -0.09
Josh Bard 5701 -31.55 -0.83 -0.13
Ronny Paulino 2343 -15.23 -0.98 -0.16
Michael Barrett 2499 -20.02 -1.20 -0.19
Chris Snyder 3342 -34.53 -1.55 -0.25
Mike Napoli 3106 -42.51 -2.05 -0.33
Jamie Burke 2270 -33.45 -2.21 -0.36
Rob Bowen 2057 -30.50 -2.22 -0.36
Ramon Hernandez 2361 -35.80 -2.27 -0.37
Jarrod Saltalamacchia 2467 -48.45 -2.95 -0.47
Joe Mauer 2434 -48.64 -3.00 -0.48
Dioner Navarro 2436 -63.65 -3.92 -0.63
Kenji Johjima 7120 -189.77 -4.00 -0.64
Gerald Laird 6129 -314.00 -7.68 -1.24

Adding the influence of the umpire seems to dampen the effect somewhat, which is what we'd expect. Still, the spread between best and worst is huge. We're no better off than we were before.

What to make of all this? There are obvious quality and sample size concerns from the PITCHf/x data, which calls some of this into question. Beyond that, the results just seem too outlandish to be correct. Plus, there was only a weak correlation between these values and the team's runs allowed per game (r=-.30 based on those catchers who caught for the same team all year). I find it hard to believe pitch framing can have this big an impact and not be more noticeable. I have the same concern about the results of Hale's analysis of umpires - where the impact is nearly as big. Two other possibilities are that my run value number is wrong (likely, but I think it's in the ballpark) or that there's some underlying issue that affects both of our studies. Finally, maybe we've been wrong the whole time and catcher defense is really that important.

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Great findings Dan

The player I find most interestingly ranked is Johnny Estrada, he doesn't have much of a reputation for defense yet somehow he's above average on strike calls.

by R.J. Anderson on Apr 5, 2008 12:28 PM EDT reply reply actions actions   0 recs

It's definitely possible

that going from the best catcher to the worst could make a difference of 2 runs a game. I wouldn't rule that out.

What might be missing is a way to balance the pitcher's control against the two studies (umpires and framing). A pitcher who is consistently around the strike zone gets the benefit of the doubt on close calls, as does the veteran.

Which pitching staffs are getting more called strikes than average?

"Have faith in the Yankees, my son. Think of the great DiMaggio."

by jscape2000 on Apr 6, 2008 5:04 PM EDT reply reply actions actions   0 recs

Taking a look at that...

I'm working up the numbers for the pitching staffs, but I don't expect too much of a difference from the list of catchers simply because the data set is so small.

I'll post a follow-up comment once I get the numbers together for the team as a whole.

by Dan Turkenkopf on Apr 7, 2008 7:57 AM EDT to parent up reply reply actions actions   0 recs

Not a whole lot of difference

Ok, this isn't letting me format the response, so it might be tough to read.

Basically though they're roughly the same numbers as the catchers regressed slightly to the mean. Note: this is not controlling for umpire at all.

Team CalledPitches Runs / 150
TOR 7795 0.81
LAN 8377 0.53
ATL 7546 0.46
CIN 4625 0.42
WAS 3172 0.37
SLN 6454 0.28
COL 5487 0.26
KCA 5541 0.23
ANA 8892 0.22
NYA 4192 0.21
CLE 5214 0.16
SFN 5799 0.14
MIL 5256 0.12
DET 6568 0.10
CHN 6265 0.04
ARI 5609 -0.00
PHI 4325 -0.02
HOU 5057 -0.03
BOS 5996 -0.06
SDN 9013 -0.11
CHA 8347 -0.16
NYN 4469 -0.18
PIT 3464 -0.22
FLO 3870 -0.24
OAK 9170 -0.27
BAL 3604 -0.27
MIN 4924 -0.38
TBA 3974 -0.43
SEA 9830 -0.56
TEX 9329 -1.06

by Dan Turkenkopf on Apr 7, 2008 6:51 PM EDT to parent up reply reply actions actions   0 recs

repeatable?

Great to see more work being done on catchers!

I'm not sure what to make of the effect size (I think you're right that it's crazy-huge). Putting methodological issues aside, I guess the first question I have is whether this is a "skill" that you're describing, or whether you're describing random variation.

It might be hard to check this with just one season of data, but how repeatable is it? Could you split up the data between the first half and the second half (or, even vs. odd games), and see how much of a correlation there is in catcher performance between the two halves? This might also inform you about how much would be appropriate to regress these numbers (my guess is quite a bit).

Might be something that has to wait until after the 2008 season, of course, but you're effect sizes are huge enough that it might be worth a good.

by JinAZ on Apr 7, 2008 3:09 AM EDT reply reply actions actions   0 recs

Good point

As you say, it's likely going to be tough to figure how much of this is random variation because of the data set. I'll try to break it down by first and second half and see what we get.

by Dan Turkenkopf on Apr 7, 2008 7:59 AM EDT to parent up reply reply actions actions   0 recs

Ok, I broke the data down by first half (pre July 1) and second half (post July 1). Sportsvision definitely ramped up their PITCH f/x installations throughout the year because there were 3 times as many called pitches recorded in the second half of the season. Because of that, I chose catchers who had at least 500 called pitches in the first half and 1500 in the second half. There were 18 such catchers.

Since I know this won't format well, let me try to lay out the high level results before pasting the table. Correlation (whatever I get from the Excel correl function - I think it's the r) is .51 which if I remember correctly is moderately strong. The average number of opportunities from these 18 catchers was 1446 in the first half and 2908 in the second half. The mean for these catchers was .08 runs above average in the first half and -.09 in the second half.

There were three catchers in this sample who changed teams during the season last year: Michael Barrett, Jason Kendall and Jarrod Saltalamacchia. Barrett went from .06 to -.24, Kendall went from -.16 to -0 (there's some decimals that make him negative) and Saltalamacchia went from .97 to -.95. Remember, these are just first and second half splits - not by team. But combined with Gerald Laird's misery, Saltalamacchia's huge drop suggest that either Texas' pitchers or their PITCH f/x setup has a whole lot to do with how their catchers perform.

Here are the results, hopefully they're legible.

Catcher 1st Half Opps 2nd Half Opps 1st Half R/150 2nd Half R/150
Josh Bard 2093 3608 0.46 -0.33
Michael Barrett 940 1559 0.06 -0.24
John Buck 591 2852 0.32 0.45
Ramon Hernandez 595 1766 -0.25 -0.46
Kenji Johjima 2824 4296 -0.70 -0.61
Jason Kendall 2387 3383 -0.16 -0.00
Gerald Laird 2925 3204 -1.40 -1.01
Russell Martin 2894 4167 0.22 0.97
Victor Martinez 520 2713 0.10 -0.07
Brian McCann 1778 3984 0.82 0.29
Benji Molina 655 3254 -0.04 0.08
Dioner Navarro 720 1716 -0.71 -0.60
A.J. Pierzynski 2657 3275 0.23 -0.13
Jorge Posada 847 1769 -0.11 0.12
Ivan Rodriguez 706 3175 0.66 0.24
Jarrod Saltalamacchia 706 1761 0.97 -0.95
Jason Varitek 1183 2360 0.10 -0.18
Gregg Zaun 1013 3505 0.83 0.84
Average Opps 1446 2908 0.08 -0.09

by Dan Turkenkopf on Apr 7, 2008 8:54 PM EDT to parent up reply reply actions actions   0 recs

With

only a tiny data set to work from, I don't know what to think. My impulse is that you're right about the Salty effect, and pitchers have the lead role.
Maybe looking at how the same staff performed with different catchers would let you control for the quality of the staff?

Maybe this is the kind of research you need to table until November.

"Have faith in the Yankees, my son. Think of the great DiMaggio."

by jscape2000 on Apr 8, 2008 1:55 AM EDT to parent up reply reply actions actions   0 recs

I tend to agree

I'd love to be able to do a WOWY study (see my first post on catcher block percentage), but one year of data just isn't enough. I'm not sure two will really be enough either, but it should take us further down the path.

I'm curious as to what would cause the pitcher effect, though. With catchers you can kind of explain it with "framing." But with pitchers, is it age, reputation, wildness that day (if you've established yourself as wild, you get fewer calls) or something else entirely? Some of those might begin to be answerable now - or at least partially answerable - because I'll be looking at aggregates versus individuals. Plus, what fun is it waiting a whole season for more information?

by Dan Turkenkopf on Apr 8, 2008 7:48 AM EDT to parent up reply reply actions actions   0 recs

I've started looking into this

And found that young pitchers are definitely hurt by missed calls - to the tune of .37 runs per game compared to average.

More details (although not many) here

by Dan Turkenkopf on Apr 10, 2008 8:36 PM EDT to parent up reply reply actions actions   0 recs

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