Before diving into part three, let me explain what I considered framing in those articles (and in this one, as well). I should have made this premise clear at the beginning of the first article of this series, but I neglected to do that, probably believing that the definition would emerge from the text. Some of the comments made me realize that was not the case, so here I’m trying to redeem myself.
What has been considered framing in this series?
The way a catcher receives a pitch can slightly change the likelihood that said pitch is declared a strike by the umpire. I suspect there are several ways to alter the odds, and sometimes even the catcher himself is not aware he is doing something on that effect.
So, when talking about framing in the past couple of articles, I did not have any specific action in my mind: Bringing the glove inside the zone (that, in my opinion, would indeed reduce the chances of a called strike), positioning the body so that the pitch arrives right to a motionless glove, catching the slider closer to the plate so that the breaking action had not brought the ball too far away from the plate, and so on.
What I had in mind is the following: A catcher receives 100 pitches. Given their characteristics (pitch type, count, location, pitcher throwing it, batter facing it, umpire judging it), 60 of them should have been called for strikes. If 75 of them are actually declared strikes, then the catcher has converted 15 balls into strikes by virtue of his framing skills, no matter how he physically achieved the result.
If you feel that “framing” is a misnomer for what I’m measuring, feel free to suggest something else in the comments section below.
From rate to count
Until part two, the runs saved by framing were calculated by prorating the increase (decrease) in probability of a called strike due to the catcher to an estimated number of borderline pitches.
For the purposes of this article, the catcher effect has been calculated on every single pitch. Also, while the average run value of turning a ball into a strike was previously used, now the count-specific differential is adopted. Finally, framing for high and low pitches delivered to the middle part of the plate has also been thrown into the mix.
Unfortunately there is a portion of the PITCHf/x database that does not get into this analysis yet, as the ultimate statistical model would combine a spatial component with the multilevel structure (this stuff is not available on the stat software I use). The pitches falling in the green area in the diagram shown below are part of the analysis.
Let’s go with an example.
According to the model employed, a 2-1 fastball from Roy Halladay to Albert Pujols delivered two inches outside the middle of the plate one foot and eight inches from the ground has a 44 percent chance of being declared a strike if Dana DeMuth is calling the game.
If such a pitch ends up being called for a strike, we assign the catcher (1 – .44) * .181 = .10 runs; otherwise he gets (0 – .44) * .181 = -.08 runs.
The first number inside the parenthesis is either zero (ball) or one (strike). Then you have the predicted probability of a called strikes given all the considered factors (.44 in our example). Finally, you multiply for the run value of converting a ball into a strike on the given count (.181 on a 2-1 count).
-------2008 top ten------- player G RV Brian McCann 138 23.6 Russell Martin 149 22.5 Jose Molina 97 20.3 Yadier Molina 119 19.2 Paul Bako 96 13.8 Chris Coste 78 10.8 Joe Mauer 139 10.6 David Ross 54 8.0 Brad Ausmus 77 7.2 Yorvit Torrealba 67 6.7
------2009 top ten------ player G RV Brian McCann 127 17.7 Ryan Hanigan 88 13.5 Jose Molina 49 12.7 Gregg Zaun 83 12.2 Miguel Montero 111 12.1 David Ross 52 10.1 Russell Martin 137 9.9 Yadier Molina 138 9.3 Bengie Molina 123 8.4 Joe Mauer 109 8.1
------2010 top ten------- player G RV Jose Molina 56 16.9 Jonathan Lucroy 75 16.9 Brian McCann 136 15.9 Yadier Molina 135 15.8 Russell Martin 93 12.0 Ryan Hanigan 68 11.0 Matt Wieters 126 10.2 Miguel Montero 79 9.8 Geovany Soto 104 9.6 Buster Posey 76 8.8
Brian McCann tops both the 2008 and 2009 lists and comes up third in 2010. Several other names recur in those short lists: Various Molinas, Russell Martin, Joe Mauer, David Ross, Miguel Montero and Ryan Hanigan.
As you can see, the best catchers are contributing over one win with their framing skill. Jose Molina unbelievably tops the 2010 list despite having played just 56 games. (Remember, we are now dealing with a counting stat!) While a player can put up extreme numbers in a small sample, the fact that Jose recorded similar numbers both in 2008 and 2009, should leave out the possibility that he got lucky and had all the calls come his way in 2010.
Sure, the prorated three to four wins he would produce in 130 games behind the dish would need to be regressed somewhat, but it’s hard to believe he does not countribute an extra win per season thanks to his framing ability.
-----2008 bottom ten----- player G RV J.R. Towles 53 -4.7 Brandon Inge 60 -4.8 Mike Rabelo 32 -5.6 Toby Hall 37 -6.0 Gerald Laird 88 -6.2 Kenji Johjima 100 -8.3 Chris Iannetta 100 -9.3 Dioner Navarro 117 -10.9 Nick Hundley 59 -11.8 Ryan Doumit 106 -29.8
-----2009 bottom ten----- player G RV Omir Santos 91 -6.9 Koyie Hill 79 -8.2 Mike Napoli 96 -8.4 Dioner Navarro 113 -8.6 Jorge Posada 100 -9.3 Rob Johnson 80 -9.4 Kurt Suzuki 135 -11.9 Kenji Johjima 70 -15.9 Ryan Doumit 71 -16.2 Gerald Laird 135 -16.2
----2010 bottom ten----- player G RV Nick Hundley 76 -4.4 John Hester 33 -5.6 John Jaso 96 -5.8 Lou Marson 87 -6.8 Adam Moore 59 -7.4 Jason Kendall 118 -7.7 Gerald Laird 87 -10.8 Ryan Doumit 100 -10.9 Rob Johnson 61 -11.8 Jorge Posada 83 -14.5
The bottom lists also display recurring names. The 2008 run values have Pearson’s correlation coefficients of .69 and .66 with the 2009 and 2010 values, respectively, while 2009 and 2010 have a coefficient of .82.
The proposed model does not take park effects into consideration. Considering the really small sample of catchers that changed teams during the 2008-09 and 2009-10 offseasons, there does not seem to be a substantial variation in their ratings.
----------Rating of players changing uniforms----------- Run Value Run Value/130 G player seasons seas1 seas2 seas1 seas2 Rod Barajas 2009-2010 4.7 3.4 5.1 4.6 Kelly Shoppach 2009-2010 -2.2 -0.2 -3.5 -0.5 David Ross 2008-2009 8.0 10.1 19.3 25.2 Ramon Hernandez 2008-2009 -2.3 0.1 -2.4 0.2 Bengie Molina 2009-2010 8.4 5.8 8.9 6.7 Chris Snyder 2009-2010 4.7 8.5 10.9 10.9 Victor Martinez 2009-2010 -5.7 -1.7 -8.7 -2.0 Jason Kendall 2009-2010 -3.2 -7.7 -3.1 -8.5 Miguel Olivo 2009-2010 -2.2 2.4 -2.8 2.8 Yorvit Torrealba 2009-2010 0.2 4.8 0.4 6.8 Gregg Zaun 2008-2009 5.4 12.1 8.9 19.0 Victor Martinez 2008-2009 2.6 -5.7 6.1 -8.7 Gerald Laird 2008-2009 -6.2 -16.2 -9.2 -15.6 Ivan Rodriguez 2009-2010 -2.2 8.6 -2.5 11.0
Note: Catchers above are listed from lowest to highest difference in Run Value. Fourteen catchers who changed uniforms and played more than 50 games in consecutive seasons sport a .67 correlation. For comparison, 53 catchers who didn’t move to another ballclub record a .82 correlation.
Also, catchers’ performances at home and on the road are highly correlated, sporting a .82 Pearson’s correlation coefficient. However, it should be noted that home Run Values are generally higher than away Run Values.
Given all the above, one would be tempted to dismiss the park as an important effect and state that somehow catchers are more adept at putting their framing skills into play at home. However, when digging deeper into this issue, the following cases emerged.
During the full period considered (2008 to May 2011), Brian McCann totaled 47.5 runs at home and 30.7 on the road. Is that all due to the possible familiarity effect mentioned above? During the same time span, the visiting teams recorded a composite +12.2 runs at Turner Field.
Meanwhile, Ryan Doumit has been -22.7 at home and -39.9 on the road, while opposite teams have scored +20.8 runs at PNC Park.
While the bottom line is that McCann is great and Doumit is poor at framing pitches, both have played half of their games in parks that seem to favor strike calls. Thus, adding the park to the model will likely improve the final ratings.
In the past few weeks we have tried to estimate the runs a catcher may contribute by inducing the umpire to call more strikes. We progressively refined our estimation, but the final value has not moved by much. A catcher can contribute more than one win (probably even two wins) just by his ability to frame pitches.
As we mentioned, there is room for further improvement of the model. The ballpark should be part of the model, and pitches at the four corners are still out of the equation. Thus, we might still be on the conservative side when we state one or two wins as the ceiling.
Several of the top catchers in the proposed lists are backups. This makes sense, since it’s so hard to find one good-hitting catcher, let alone two. So, when looking at replacements, teams hunt for some other assets, like defense and ability to handle pitchers. Teams have probably figured that out for a while, but this analysis show that if you pay attention, you can get one extra win just by selecting the right backup catcher.
So just how good is Yadier Molina on defense? FanGraphs credits him with five to eight fielding runs each year from 2005 to 2010. For the last three seasons, we can add another 19, nine and 16 runs on top of that due to his ability at framing pitches.
References & Resources
Table 2 of John Walsh’s Searching for the game’s best pitch is the source for the value (by count) of turning a ball into a strike.