Catching Up and Catching Down

Pitch-framing! Am I right? It’s still very much a fascinating subject, which is one of the reasons I write about it so often. But by this point we have a pretty good idea who’s good at it and who’s bad at it. That ground’s been covered. We know that Jose Molina is great. We know that Ryan Doumit was a problem. Yet we can break things down further still. Often, people don’t go beyond describing a guy as good, bad, or okay. But there are actually specific types of framers.

Which makes plenty of sense, doesn’t it? There are great hitters and there are bad hitters. Among them, there are guys with tremendous plate coverage, but there are also high-ball hitters and low-ball hitters. Every part of the zone area is different, and every player is different, so we should expect that different players respond differently to pitches in different parts of the zone. How this relates to framing is that some guys might be better with receiving high pitches, while other guys might be better with receiving low pitches. Intuitively, why not? And thanks to some awesome updates at Baseball Savant, this couldn’t be much easier to examine.

Of course, there are lots of ways you could go with this. Probably, different catchers are differently able to receive different pitch types. Catchers are differently able to receive inside pitches and outside pitches, and there are differing abilities inside and outside of the zone. What follows is just going to look at high and low pitches on the border of or within the PITCHf/x strike zone. This is one of many ways to slice up the information.

So, who looks best on should-be high strikes, and who looks best on should-be low strikes? I collected data from the entire reliable PITCHf/x era, spanning 2008-2013. I set a minimum of 10,000 pitches, which yielded a sample of 102 different catchers. For low pitches within the zone, I selected the lowest three zone boxes. For high pitches within the zone, I selected the highest three zone boxes. This study ignores the middle three boxes, and also the areas outside of the PITCHf/x strike zone. All that information would be great, when tackling other questions.

The sample gave more than 200,000 called pitches in the lower three zones, of which more than 77% were called strikes. It gave more than 130,000 called pitches in the higher three zones, of which more than 85% were called strikes. In the sortable table now, I’m going to show you everything. These are all 102 catchers, their performances, and the differences between low-strike rate and high-strike rate.

Catcher Low# LowStr% High# HighStr% Diff
A.J. Ellis 2269 73.7% 1655 88.6% 15.0%
A.J. Pierzynski 4462 68.6% 3771 90.4% 21.8%
Adam Moore 564 70.4% 392 87.2% 16.9%
Alex Avila 3415 79.0% 2295 86.7% 7.7%
Bengie Molina 2336 69.6% 1844 85.1% 15.4%
Bobby Wilson 1063 82.7% 633 87.7% 5.0%
Brad Ausmus 749 76.8% 562 83.5% 6.7%
Brayan Pena 1792 78.7% 1175 89.5% 10.8%
Brett Hayes 658 78.3% 491 84.7% 6.5%
Brian McCann 4780 89.3% 2600 80.8% -8.4%
Brian Schneider 1767 72.5% 1149 85.5% 13.0%
Buster Posey 2285 89.4% 1721 88.1% -1.3%
Carlos Corporan 896 86.9% 500 83.8% -3.1%
Carlos Ruiz 4856 78.4% 3054 80.7% 2.4%
Carlos Santana 2161 71.6% 1643 89.5% 17.9%
Chris Coste 795 79.5% 570 77.9% -1.6%
Chris Iannetta 3577 68.7% 2456 88.5% 19.8%
Chris Snyder 2483 81.4% 1676 87.1% 5.7%
Chris Stewart 1520 87.1% 810 86.4% -0.7%
Corky Miller 556 80.2% 389 83.8% 3.6%
Craig Tatum 618 79.4% 432 85.9% 6.4%
David Ross 1986 87.1% 1205 82.3% -4.8%
Derek Norris 1133 86.8% 611 85.8% -1.0%
Devin Mesoraco 1090 80.3% 688 89.7% 9.4%
Dioner Navarro 2487 64.8% 2139 88.6% 23.8%
Drew Butera 1183 81.0% 778 86.4% 5.4%
Eli Whiteside 1243 86.1% 825 86.4% 0.3%
Erik Kratz 773 88.4% 494 88.1% -0.3%
Francisco Cervelli 1275 76.9% 785 84.5% 7.6%
George Kottaras 1536 77.0% 1030 85.6% 8.7%
Geovany Soto 3999 78.8% 2979 88.0% 9.2%
Gerald Laird 3055 68.5% 2028 85.1% 16.5%
Gregg Zaun 1245 81.8% 692 80.8% -1.0%
Guillermo Quiroz 568 70.8% 416 88.9% 18.2%
Hank Conger 996 88.9% 580 85.7% -3.2%
Hector Sanchez 633 80.3% 503 90.3% 10.0%
Henry Blanco 1650 80.4% 1037 81.5% 1.1%
Humberto Quintero 2403 79.4% 1538 82.5% 3.2%
Ivan Rodriguez 2485 76.1% 1634 80.2% 4.1%
J.P. Arencibia 2526 83.1% 1643 83.3% 0.2%
J.R. Towles 870 78.9% 478 75.9% -2.9%
Jarrod Saltalamacchia 3071 69.7% 2460 90.0% 20.2%
Jason Castro 1684 84.6% 1095 84.5% -0.1%
Jason Jaramillo 775 73.9% 475 81.1% 7.1%
Jason Kendall 3087 80.8% 1761 76.7% -4.1%
Jason LaRue 773 78.5% 493 75.1% -3.5%
Jason Varitek 2083 58.3% 1839 90.4% 32.0%
Jeff Mathis 3349 83.3% 1828 84.7% 1.4%
Jesus Flores 1396 78.2% 905 84.1% 5.9%
Jesus Montero 532 73.7% 438 85.2% 11.5%
Joe Mauer 3192 62.8% 2881 92.7% 29.9%
John Baker 1919 78.7% 1137 79.5% 0.8%
John Buck 4398 77.1% 2851 86.7% 9.6%
John Hester 562 82.0% 288 77.4% -4.6%
John Jaso 1614 78.4% 1075 83.5% 5.1%
Jonathan Lucroy 3342 91.4% 1638 83.9% -7.6%
Jorge Posada 1455 66.4% 976 84.7% 18.3%
Jose Lobaton 1207 86.5% 693 87.0% 0.5%
Jose Molina 2806 80.1% 2013 90.7% 10.6%
Josh Bard 1302 75.4% 772 82.8% 7.3%
Josh Thole 2036 84.2% 1402 87.0% 2.8%
Kelly Shoppach 2856 71.0% 1969 86.9% 15.9%
Kenji Johjima 1148 55.2% 848 88.6% 33.3%
Kevin Cash 642 67.9% 497 88.3% 20.4%
Koyie Hill 1602 75.3% 1108 82.7% 7.3%
Kurt Suzuki 5283 72.9% 3463 85.4% 12.5%
Landon Powell 757 71.7% 588 87.9% 16.2%
Lou Marson 1906 77.2% 1319 86.2% 9.0%
Martin Maldonado 960 89.9% 424 88.2% -1.7%
Matt Treanor 1806 68.2% 1315 85.6% 17.4%
Matt Wieters 4265 79.5% 3057 86.6% 7.2%
Michael McKenry 1137 79.1% 735 84.6% 5.6%
Miguel Montero 4518 88.7% 2543 85.3% -3.4%
Miguel Olivo 3487 74.9% 2177 86.1% 11.1%
Mike Napoli 2768 73.1% 1886 86.8% 13.7%
Mike Redmond 588 64.1% 440 89.1% 25.0%
Nick Hundley 3363 70.4% 2219 85.5% 15.2%
Omir Santos 660 68.0% 507 82.6% 14.6%
Paul Bako 941 79.0% 569 82.2% 3.3%
Ramon Castro 824 68.0% 689 87.8% 19.8%
Ramon Hernandez 2634 77.2% 1678 81.0% 3.8%
Raul Chavez 532 63.9% 424 85.1% 21.2%
Rob Brantly 743 78.1% 442 88.9% 10.9%
Rob Johnson 1618 66.4% 1138 88.3% 21.9%
Rod Barajas 3042 69.4% 2309 89.8% 20.4%
Ronny Paulino 1878 75.6% 1294 86.1% 10.5%
Russell Martin 5101 81.0% 3465 86.3% 5.2%
Ryan Doumit 3053 66.5% 1956 79.3% 12.9%
Ryan Hanigan 2854 82.8% 2115 89.1% 6.2%
Salvador Perez 1922 78.1% 1190 89.7% 11.6%
Taylor Teagarden 1134 73.3% 744 84.8% 11.5%
Tony Cruz 610 88.2% 382 84.8% -3.4%
Tyler Flowers 1170 83.3% 883 88.1% 4.8%
Victor Martinez 1645 71.2% 1184 84.6% 13.4%
Welington Castillo 1277 79.4% 715 84.6% 5.2%
Wil Nieves 1857 81.3% 1085 86.2% 4.9%
Wilin Rosario 1666 76.5% 995 88.0% 11.6%
Wilson Ramos 1438 80.8% 1023 91.2% 10.4%
Yadier Molina 5773 88.0% 3417 83.5% -4.5%
Yan Gomes 629 87.0% 454 89.2% 2.2%
Yasmani Grandal 565 92.4% 284 83.8% -8.6%
Yorvit Torrealba 3159 78.7% 1901 84.2% 5.6%

The king of low strikes has been Yasmani Grandal, although his sample is admittedly among the smallest. Following him are teammates Jonathan Lucroy and Martin Maldonado, and then Buster Posey’s a hair ahead of Brian McCann. At the other end, Kenji Johjima looks absolutely dreadful, although he hasn’t caught since 2009 and around then PITCHf/x had some more bugs. But Johjima was suspected to be a pretty lousy receiver. Jason Varitek and Joe Mauer are down there. Dioner Navarro’s still catching, and he’s had his issues with should-be low strikes.

And then you turn to high strikes, where suddenly Mauer reigns supreme. He’s got a big edge on Wilson Ramos, who has a smaller edge on Jose Molina, who has a smaller edge still on Varitek and A.J. Pierzynski. Turn this around, and Jason LaRue had some troubles. Carlos Ruiz looks the worst among prominent active types. Naturally, the bad end of the table features Ryan Doumit.

It’s interesting to see Mauer look so good up high and so bad down low. More generally, it’s interesting to observe that there doesn’t seem to be much of a relationship between low success and high success. The data isn’t entirely all over the map, but it’s pretty scattered about.


The general trend is that the better a catcher is at receiving low strikes, the worse he is at receiving high strikes, and vice versa. It isn’t the strongest trend in the world, but it also makes sense that something like that would exist, and though I haven’t looked at it yet there’s probably a relationship here with catcher height. And given the sensitivity of good receiving, it’s probably quite difficult to prepare to receive and stick low pitches and high pitches. The required movements are going to be different. Catchers, individually, might be better at one than the other.

Included in the table is each catcher’s high-strike rate minus his low-strike rate. The greatest difference belongs to Johjima, then Varitek, then Mauer. So while Mauer isn’t a catcher anymore, as recently as last season he had the greatest difference between his ability to receive high and his ability to receive low. The lowest difference belongs to Grandal, who’s been better with low strikes than high strikes, by almost nine percentage points. Within the sample, 21 catchers have higher low-strike rates than high-strike rates. 35 catchers are within five percentage points of being even.

Given that Mauer and Grandal have been very different catchers, then, might we be able to see anything in the video? The answer is: this isn’t enough video. We can’t learn anything from one-pitch samples. But just for the hell of it, here’s Mauer catching a high strike and Grandal catching a high ball, and Mauer catching a low ball and Grandal catching a low strike.





Mauer, we know, is the taller catcher, by a few inches. It also seems like he has a bit of vertical glove drift as the pitcher is in his delivery, and it might be easier for him to continue that upward than to suddenly reverse direction. Grandal seems adept at catching the low ball in his palm, which helps to minimize the required glove movement with a low pitch. Grandal, in general, is pretty quiet — simply averaging low-strike rate and high-strike rate, Grandal would be tied for fourth in the table, behind Maldonado, Posey, and Erik Kratz. Based on Grandal’s playing time, receiving is a somewhat surprising strength of his.

There’s more to be done with this kind of data. There are a lot of different things that we can try to isolate. For now, this is a beginning attempt, with thanks again to Baseball Savant for being such an outstanding and user-friendly resource. There are most assuredly good framers and bad framers. It’s time to pay a little more attention to the subgroups.

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Jeff made Lookout Landing a thing, but he does not still write there about the Mariners. He does write here, sometimes about the Mariners, but usually not.

28 Responses to “Catching Up and Catching Down”

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  1. Hank says:

    Jeff – don’t have a real feel for the sample size and distribution with each box here.

    With the three higher boxes, are the samples large enough that the relative distribution of pitches within each box are similar from catcher to catcher? I assume the “framability” of a given pitch within each box will vary some, so it is important that the catchers see similar distributions to compare them against each other.

    If it’s 130K and we just assume it’s evenly split over the 102 catchers (obviously it won’t be) that’s ~1300 per catcher. Then with 3 high zones that’s 433 per zone. Is that enough to get a pretty reasonable distribution (in terms of location, pitch type, pitcher handedness) within each box. I have no idea one way or the other, and would be interested to get your thoughts.

    Good stuff though!

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    • Hank says:

      And somehow I just noticed that you had the individual pitch counts in the table (Yikes, I don’t know how I missed that) and didn’t need to do all the ‘fancy’ math.

      Question still stands though – if you take these individual catcher samples and split them into 3 zones, is it large enough for each catcher to have similar distributions to the others within each zone.

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  2. marc w says:

    I was just looking at this today. The thing that looked intriguing to me was the possible correlation between catcher height and framing high strikes.

    Lucroy dominates on low pitches, but not on high ones, whereas even terrible framers like Jesus Montero are able to poach a few high strikes. The numbers don’t show this as much as the heat maps at baseballsavant do, and part of that may be because the out-of-zone, er, zones wrap around the edge, so that they’re not JUST “high” their “high and outside” or “low and inside.” This means part of what’s getting counted here as a “high strike” may just be a lefty strike that’s off the plate away, and just barely above the middle of the zone. Anyway, this is a fascinating area for someone to dig in on.

    Jonathan Lucroy’s framing heatmap is basically a U shape. He gets calls off the plate on either side, and a ton of low pitches. Wieters/Mauer/Montero etc. have heat maps that look a bit more like a box – there’s a clear, identifiable grouping of pitches that are over the middle of the plate, but up and out of the zone.

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    • tz says:

      I was thinking about catcher height when Mauer came up as a leader on high strikes. In addition to height though, notice how low Grandal crouches. This really helps the umpire get a clean view of low strikes.

      I remember Tony Pena catching from basically a spread-eagle split on the ground, and how many low strike calls he would get. Roger Clemens had a great run from 1990-93 in part because of those extra low strikes.

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      • Belloc says:

        I agree that Tony Pena was a fantastic pitch framer. But Clemens was even better from 1986 through 1989 with Rich Gedman and Rick Cerone. He had some great games when John Marzano caught. Gedman and Bill Haselman caught his 20-strike out games.

        Don Zimmer in a rocking chair could have caught Roger Clemens in his prime.

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        • tz says:

          True. I was thinking about Clemens’ falloff after Pena left, which could have been due to other reasons as well (such as the strike zone getting shorter and fatter, or Clemens himself for that matter).

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        • Mr Punch says:

          Clemens fell off from mid-’91, when he stopped getting the strike call on his little 0-1/1-1 curve. He didn’t get his mojo back till he went to Toronto and picked up another secondary pitch.

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    • Cameron says:

      It would definitely seem intuitively like taller catchers would tend to frame better in the top of the zone, there seems to be no correlation based on this data. Top 20 catchers by high-low% average 73.3 inches, bottom 20 are at 73 exactly.

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      • tz says:

        I wonder if that would change at all if we just focused on torso height. I’m exactly 6’0″, but that’s with the torso of a 6’4″ guy and the legs of a 5’8″ guy, so chances are I’d be setting up my glove a bit higher than another 6’0″ guy with longer legs and a shorter torso.

        Or maybe I’m the only person built this weirdly.

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        • Baltar says:

          You’re not the only one. I’m 5’6″ with 5’10″ torso and 5’2″ legs. Standing up, I look really short; sitting down, I look average.
          As you said, this would affect setting up as a catcher.

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        • jfree says:

          My guess is that most catchers are more likely to be built that way than they are to have long legs and short torso. Squatting behind the plate would be a lot tougher and create more knee/leg problems with giraffe legs unless the players legs bend in three places.

          Catchers have the lowest height differential (between shortest and tallest) of all positions – even after adjusting for being the shortest position overall – indicates a more similar body type at that position than at other positions. And C weight is also higher for any given height which would tend to indicate more torso less limbs.

          Joe Mauer and Matt Wieters are among the tallest catchers in MLB history. It’s fluky (true multi-year outlier) that they are both playing now.

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        • stevenam says:

          This is a very interesting question not only with regard to pitch framing, but also throwing out baserunners. Watching Mike Piazza for much of his career, it always occurred to me that his primary challenge was getting his long legs under him and in position to release the throw. Carlton Fisk, on the other hand, had a massive torso. On his knees throwing the ball back to the pitcher, he was almost as tall as many umpires behind him.

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        • EricR says:

          Chris Stewart also has to be as tall as Wieters. He towered over a lot of Giants when he played here.

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  3. David says:

    Interesting article. Could this have anything to do with the rotations that the catchers are working with? If a catcher is mainly working with guys who live down in the zone then one might expect them to be more proficient with dealing with low pitches.

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    • Dirck says:

      I was thinking the same thing David . It seems like the pitchers a catcher is catching and the types of pitches they throw would definitely have an effect on the high or low strikes called .

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  4. Peter Jensen says:

    Jeff – I looked at the BaseballSavant web site and I don’t see where he explicitly states how he is defining his pitch location zones. From the data it appears that the 1+2+3 zones may be spanning 24 inches horizontally or about 1 inch greater than the rule book strike zone, but there is no clue whether the vertical zone is adjusted by batter height, or by Pitch Fx upper and lower limits, or is hard coded to some absolute pitch height values. In any case it appears from the 85% and 77% percentages that you gave that these are zones where most if not all balls should be called strikes. If so most of the pitches in those zones are just going to be noise for any study of pitcher framing because they are going to be called strikes no matter how badly they are framed. And you also are going to be totally missing out on data of pitches just outside the zone that should rightfully be called balls but end up being called strikes when caught by the good framers. The zones are just not defined in such a way as to make them suitable for pitch framing analysis.

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  5. cass says:

    Could pitch f/x calibration issues be affecting the data? If the cameras are misaligned so that balls just above the strike zone in reality are treated as in the strike zone in pitch f/x, then it stands to reason that at the same park balls at the bottom of the zone in reality might be treated as below the zone in pitch f/x. And vice versa. This would explain why catchers who are generally good at one are bad at the other.

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  6. Frank says:

    This is a great video where Lucroy discusses what makes him so great at receiving in general and receiving low strikes in particular. Anyone who has watched Lucroy behind the plate knows he gets very low and very compact and he keeps his body exceptionally still. He doesn’t jab at the ball, he lets it come to him. He makes the point that he always keeps his target low because it’s better for him to come up on a ball then go down on it because gravity will naturally carry your glove towards the dirt. I also like his point about not liking the term ‘framing’ which implies moving the ball into the zone after you’ve caught it, because I think that’s a pretty negligible part of what’s happening with the guys who are grabbing extra strikes behind the plate.

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  7. AK7007 says:

    I wonder if this is something that’s exploitable by pitching coaches? Focus more on pitches along the edges that your catcher is good at framing. I guess though that it would be balanced out by game theory issues where your opponent knows to look to a specific zone.

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  8. Franco says:

    It’s probably impossible to quantify, but I wonder what the pitch framing numbers look like for catchers who shift over inside/outside pre windup to receive the ball vs sitting dead center and stabbing at them, last second moving over, etc.

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  9. JKB says:

    You should try removing games played at Fenway Park, where all close calls go to the home team, since that could bias the sample…

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  10. Robert J. Baumann says:

    How do you adjust the Baseball Savant search so that it shows catchers and not pitchers?

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  11. John Elway says:

    Special notice: Off to the Big Game. NEIGHHH!!!!!!!!!

    Now, back to Jeff’s column.

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  12. RC says:

    “The general trend is that the better a catcher is at receiving low strikes, the worse he is at receiving high strikes, and vice versa. ”

    The ‘trend’ looks like its entirely created by the 3 data points in the top left. It looks like there’s no slope what-so-ever otherwise.

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