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.