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Like last month’s rankings, this month’s updates of my crude catcher defense rankings once again are reassuring that these ratings are measuring something real, even if that is somewhat boring. But if we look more closely at the components, some interesting contrasts stand out. We will troll around the middle a bit, too, to see what interesting stuff might come up. Read on, catcher defense fans.

[Imagine the usual qualifications about the simplicity, unoriginality, and limitations of this method here. Look after the table for more about the method.]

The overall leaders are basically the same as last month. Yadier Molina and Matt Wieters are way ahead of everyone at about nine runs above average each. Molina, well, what is there to say about one of the best players in baseball? Is he still underrated overall? I don’t know. He’s pretty amazing on both sides of the ball at this point. Even with his power down a bit, low strikeouts and a high BABIP keep his offensive production good at any position, and combined with outstanding defense being the plate, he looks to be headed to the beast season of his career.

Wieters is almost as good behind the plate, even if his bat has been down this year. His offense is coming back as well. After hitting poorly in April and May, and disastrously in June, pretty much everything turned up Wieters in July, putting him at about league average at the moment. League average hitting for any catcher makes him an above-average player. Add in excellent defense, and you have a player who is at least very good, and maybe a superstar.

Joe Mauer is having a good season behind the plate in a return to near-MVP form this year, but Russell Martin is really doing his thing, too, an underrated part of the Pirates run to credibility and maybe the playoffs. Whatever one made of the decision at the time, the Yankees are surely regretting letting Martin go now, as he’s been good on offense and great on defense. A.J. Ellis is also playing good defense, and with his good OBP and meh power, he is sort of a very poor man’s Mauer, and another underrated player.

Perhaps I’m in too good a mood to make fun of the players at the bottom, but I won’t belabor the point. Carlos Santana is on the very bottom, and seems to be having trouble with all aspects of catching this season. He has never been quite this bad behind the plate before, so some of it is random variation. His bat makes up for it, though. Chris Iannetta is just sufficient with the bat, I guess, and the, uh, “limitations” of J. P. Arencibia’s overall game are well known to readers of Getting Blanked.

For me, Alex Avila‘s general decline since 2011 has been depressing. It is not that I am Jon Paul Morosi (who probably has Avila as a dark horse MVP candidate anyway), it is just surprising. Avila had a monster 2011 out of nowhere, not only killing the ball and taking walks, but playing at least okay-ish defense according to my 2011 ratings (other sources have him worse, though he hit the ball well enough that it didn’t matter. He regressed further to the mean on offense than I expected, but still hit well enough (and played better defense) that he was still fine as a catcher. This year, though, his defense is worse than it has ever been. While he still takes his share of walks, the rest of Avila’s offensive game has fallen apart. In the past, he could make up for the strikeouts with at least decent power, but that is gone this year (.114 ISO), and his strikeouts are nearing 30 percent, anyway. It isn’t all his bad luck on BABIP (.244 this year).

Some players are getting most of their value (or lack thereof) from one component or another. Martin and Ellis are both about average at pitch-blocking so far, but are the two best in the league at throwing out runners so far this year (in terms of observed value). On the other side of things, Kurt Suzuki is about average overall. He has been poor at stopping the running game, but pretty much makes up for it by not letting pitches get by him, being second only to Molina.

Finally, a little memo for Royals fans and followers and any of the other myriad baseball watchers who are on the “Salvy Bench” bandwagon. As always, I acknowledge the deliberate crudity of these rankings. Moreover, they are not going to capture everything about true talent and observed performance — they do not replace scouting, blah blah blah. Nonetheless, with Molina and Wieters continually at the top, which most would agree with, maybe it is worth toning down the rhetoric about Perez’s defensive awesomeness a bit given that he is around average according to these ratings? There is plenty to like about Perez both now and in terms of his future potential, but although I think he is above-average overall, that is a far cry from the near-superstar status he often seems to be accorded by some. Maybe he’ll be another Yadier Molina down the road — it’s crazy to saddle any young player with that sort of expectation, but it is not totally insane for Perez — but he isn’t close in any respect at this point, so let’s not act like he’s worthy of that status at this point.

Rank Player Tm PA FERuns TERuns PBWPRuns CSruns Total
1 Yadier Molina STL 3132 -0.7 1.3 5.0 3.5 9.1
2 Matt Wieters BAL 3229 -0.7 1.4 3.9 4.0 8.5
3 Russell Martin PIT 2719 0.7 1.1 -0.2 6.1 7.7
4 Joe Mauer MIN 2308 -0.1 0.9 3.0 3.7 7.3
5 A.J. Ellis LAD 2489 -0.1 1.0 -0.1 5.4 6.2
6 Jeff Mathis MIA 1176 0.3 0.2 1.1 4.0 5.6
7 Brian McCann ATL 1841 0.5 0.1 2.6 0.7 3.9
8 John Buck NYM 2968 0.0 0.8 2.8 -0.1 3.4
9 Chris Stewart NYY 2209 -0.2 1.3 -0.1 2.4 3.3
10 Ryan Hanigan CIN 1568 0.4 0.4 -0.5 2.9 3.3
11 Yan Gomes CLE 1432 0.4 0.3 -1.2 3.6 3.1
12 David Ross BOS 773 0.2 0.0 0.7 1.2 2.1
13 Rob Brantly MIA 2008 -0.2 -0.3 0.3 2.2 2.0
14 Guillermo Quiroz SFG 773 0.2 0.4 -0.1 1.5 2.0
15 Carlos Ruiz PHI 1551 0.4 0.9 1.5 -0.8 2.0
16 Yorvit Torrealba COL 1186 0.3 -0.3 0.9 0.9 1.8
17 Tim Federowicz LAD 879 -0.5 -0.9 0.2 2.7 1.5
18 Erik Kratz PHI 1505 0.4 0.4 1.6 -1.2 1.2
19 Austin Romine NYY 1009 0.3 0.6 0.6 -0.3 1.2
20 John Baker SDP 443 0.1 0.3 -0.6 1.3 1.1
21 Evan Gattis ATL 1081 -0.5 0.6 -0.6 1.3 0.9
22 Ryan Lavarnway BOS 387 0.1 0.2 0.1 0.5 0.9
23 Tony Cruz STL 488 0.1 0.3 0.7 -0.2 0.8
24 Gerald Laird ATL 816 0.2 0.5 -0.8 1.0 0.8
25 Brayan Pena DET 1388 0.4 -0.2 0.9 -0.4 0.8
26 A.J. Pierzynski TEX 2568 0.7 1.5 0.4 -1.9 0.7
27 Carlos Corporan HOU 1220 0.3 -0.7 0.7 0.4 0.7
28 Mike Zunino SEA 1022 0.3 0.6 -0.2 0.0 0.6
29 Chris Herrmann MIN 155 0.0 0.1 0.5 -0.1 0.5
30 Kelly Shoppach SEA 1222 0.3 0.2 -1.5 1.4 0.4
31 Robinson Chirinos TEX 109 0.0 0.1 0.3 0.0 0.4
32 Chris Snyder BAL 180 0.0 0.1 -0.3 0.5 0.4
33 Martin Maldonado MIL 944 0.2 0.5 0.1 -0.5 0.4
34 Salvador Perez KCR 2869 0.8 -0.7 -0.9 1.3 0.4
35 Nick Hundley SDP 2378 0.6 -1.0 -0.2 0.9 0.3
36 Taylor Teagarden BAL 443 -0.6 0.3 0.5 0.2 0.3
37 Geovany Soto TEX 1164 0.3 -0.3 -0.6 0.8 0.3
38 Stephen Vogt OAK 139 0.0 0.1 0.4 -0.3 0.3
39 Buster Posey SFG 2828 0.7 0.7 1.5 -2.7 0.2
40 Rene Rivera SDP 103 0.0 0.1 0.0 0.0 0.1
41 Hank Conger LAA 1212 -0.4 -1.2 -1.0 2.7 0.1
42 Jordan Pacheco COL 20 0.0 0.0 0.1 0.0 0.1
43 Omir Santos CLE 17 0.0 0.0 0.1 0.0 0.1
44 Josh Phegley CHW 465 0.1 0.3 -1.1 0.7 0.0
45 Blake Lalli MIL 11 0.0 0.0 0.0 0.0 0.0
46 Kyle Skipworth MIA 10 0.0 0.0 0.0 0.0 0.0
47 Rob Johnson STL 6 0.0 0.0 0.0 0.0 0.0
48 Jesus Sucre SEA 289 0.1 0.2 0.1 -0.3 0.0
49 Adam Moore KCR 108 0.0 -0.4 0.3 0.1 0.0
50 Jonathan Lucroy MIL 2857 -0.8 1.2 0.7 -1.3 -0.1
51 Miguel Olivo MIA 600 0.2 -1.1 0.7 0.1 -0.1
52 Brandon Bantz SEA 34 0.0 0.0 0.1 -0.3 -0.2
53 Bryan Holaday DET 174 0.0 -0.4 0.5 -0.4 -0.2
54 Miguel Montero ARI 3274 0.1 0.5 -1.6 0.8 -0.2
55 Humberto Quintero PHI 718 0.2 -1.5 0.5 0.4 -0.4
56 Lou Marson CLE 51 0.0 0.0 0.2 -0.6 -0.4
57 Yasmani Grandal SDP 974 -0.5 0.1 1.1 -1.1 -0.5
58 Derek Norris OAK 2129 0.6 -0.2 0.4 -1.3 -0.5
59 Tyler Flowers CHW 2467 -0.1 0.0 -0.5 0.1 -0.5
60 Josh Thole TOR 404 0.1 -0.2 0.4 -0.8 -0.5
61 Jhonatan Solano WSN 345 0.1 -0.3 0.5 -0.9 -0.5
62 Steven Lerud PHI 68 0.0 0.0 -0.3 -0.3 -0.6
63 Ramon Hernandez LAD 412 0.1 -0.2 -0.4 -0.1 -0.6
64 Kurt Suzuki WSN 2438 -0.1 -0.5 4.2 -4.4 -0.8
65 Hector Gimenez CHW 779 0.2 -0.5 -0.9 0.3 -1.0
66 Corky Miller CIN 293 0.1 -0.3 0.1 -0.9 -1.0
67 Francisco Cervelli NYY 572 -2.1 -0.1 0.9 0.2 -1.1
68 Anthony Recker NYM 844 0.2 -1.0 0.7 -1.1 -1.1
69 Devin Mesoraco CIN 1977 0.5 -1.3 0.5 -1.1 -1.3
70 Henry Blanco TOR 443 0.1 0.3 -1.4 -0.4 -1.5
71 Henry Blanco SEA 355 0.1 -0.8 -1.1 0.2 -1.6
72 Ryan Doumit MIN 1278 -0.4 0.3 -0.5 -1.0 -1.6
73 Jose Lobaton TBR 1791 0.5 0.1 0.0 -2.3 -1.8
74 Hector Sanchez SFG 274 0.1 0.2 -0.8 -1.4 -2.0
75 Jarrod Saltalamacchia BOS 2771 0.7 -1.3 0.8 -2.5 -2.3
76 Welington Castillo CHC 2671 -0.8 -1.3 -0.1 -0.3 -2.6
77 George Kottaras KCR 678 0.2 -1.5 -1.3 -0.3 -2.9
78 Dioner Navarro CHC 1079 0.3 -0.8 -2.5 0.0 -3.1
79 Wilson Ramos WSN 1003 -1.2 -0.9 0.0 -1.1 -3.1
80 Jason Castro HOU 2711 -0.8 0.6 -2.0 -1.0 -3.2
81 Wil Nieves ARI 653 0.2 -0.6 -1.9 -0.9 -3.2
82 Jesus Montero SEA 928 0.2 0.1 -0.8 -2.8 -3.3
83 Jose Molina TBR 2010 -0.2 -0.3 -3.0 -0.6 -4.1
84 John Jaso OAK 1510 0.4 -0.6 -0.9 -3.1 -4.2
85 Wilin Rosario COL 2695 -0.8 -0.4 -3.4 0.1 -4.4
86 Michael McKenry PIT 1000 0.3 -0.4 -0.8 -3.6 -4.5
87 J.P. Arencibia TOR 3076 -0.7 0.3 -1.9 -2.6 -4.9
88 Alex Avila DET 2227 -0.9 0.3 -2.1 -3.3 -5.9
89 Chris Iannetta LAA 2652 -1.6 0.6 -0.5 -6.1 -7.5
90 Carlos Santana CLE 2310 -0.1 0.4 -4.6 -3.2 -7.6

Concluding Methodological Postscript 

I should make clear that for reasons of simplicity I am not including such debated areas as pitch framing or the more amorphous “game calling.” I am not taking a position one way or the other on either of those, simply making clear the bounds of these rankings.  When I discuss “catcher defense,” like most others, I will be discussing preventing stolen bases, blocking pitches, etc.

One of the difficulties with evaluating catcher defense with regard to even these issues is that, much more than with other fielding positions, the catcher’s performance is dependent on another player — namely, the pitcher. No matter now strong or weak the catcher’s arm is, he can’t escape the reality that he depends on the pitcher’s skill with regard to holding runners, quickness to the plate, etc. While the catcher’s skill with regard to blocking pitches that are off the mark is clearly important, catching Tim Wakefield poses a unique challenge — just ask Josh Bard. And so on.

For these reasons, probably the best way of measuring catcher defense is Tom Tango’s WOWY (With or Without  You) method of defensive evaluation as detailed the 2008 Hardball Times Annual.  You can read about the details in the links provided. Versions of WOWY for catchers have also been done by Brian Cartwright and Dan Turkenkopf. I would do it that way if I could. The main issue is that 1) it’s pretty complicated, and beyond my present capabilities, and 2) it requires something like Retrosheet, which isn’t available until after the World Series is over, so even if I could do it, I couldn’t get the numbers during the season of even now…

While the method used here is neither terribly subtle nor original, I think when compared to things like the Fans’ Scouting Report and WOWY methods, it compares fairly well. Just keep in mind the acknowledged limits (e.g., not taking into account the pitchers’ contributions like WOWY does).

The Method Used Here

For non-WOWY catcher defense, the basic idea is to 1) choose what events you’re going to deal with, 2) determine each catchers performance with respect to league average, and 3) decide the run value of each event.

Stolen Bases/Caught Stealing (CSRuns): First, we figure out the league rate for caught stealing. One cool thing about the new Baseball Reference is that it separates out the catcher caught stealings from the pitcher pickoffs, so we can exclude the pickoffs (not under the catcher’s control) from the equation. So we total the CSctch +SB to get total stolen base attempts (SBA) and then to total CSctch/total SBA for the lgCS rate. We use the weight of .63 runs for each caught stealing, which represents the average linear weight of the caught stealing (.44 runs) plus the weight of the stolen base not achieved (.19 runs). The formula for runs above/below average for each catcher is thus (CS – (lgCSrate) * SBA) * 0.63.

Wild pitches/passed balls (WPPBRuns): The league rate is (WPlg + PBlg)/lgPA. The linear weight for each passed ball/wild pitch is 0.28 runs, which we make negative since the more WP/PBs a catcher has, the worse his defense is. The formula for each player is ((WP + PB) – (lgWPPBrate * PA)) * -0.28.

Errors (FcE and TE Runs): I deal with three different kinds of catcher error recorded by Baseball Reference: throwing errors, catching errors, and fielding errors. I’ve assimilated catching errors to fielding errors. There are separate linear weights for throwing (including catching) errors (-0.48) and fielding errors (-0.75). The method is the same as above. Get the league rate, then see how far over/under the player is. For throwing errors: (TE – (lgTErate * PA)) * -0.48. Fielding errors: (FE – (lgFErate * PA)) * -0.75.

Then you just add them all up to get the total runs above/below average. It’s not perfect, and hopefully, there will be some improved options soon, but the results do seem to reflect reality. I round to one decimal: I aware that gives an illusion of precision that isn’t there, I simply do it to expedite sorting and ranking.  I thought about coming up with a “rate” version like UZR/150, but that isn’t as simple as prorating for innings caught/PA — one needs to normalize each sort of event separately, the chart is confusing enough as it is. For now, this is just a value measurement of what each player did this season.