Hopefully the thrill of All-Star Week — the selections, the Derby, the booing, the controversy, the boring game, and all the rest of the whatnot — has not drained all of your mental energy. Even if it has, too bad. Guys have been playing catcher since we last judged them a month ago. We can’t stop now! Things are starting to level out after a few early season anomalies, so there are fewer surprises to be found, but let’s see what we can find.

As always, the boring methodology stuff is found at the bottom.

Pitch Blocking (Passed Balls and Wild Pitches) Leaders and Trailers:

One of the big jumps from last month is that Matt Wieters leapt to the front in terms of the value above average he has provided in terms of pitch blocking. His bat has cooled off a lot since the beginning of the year, but he’s still bringing it behind the plate and having a good overall year. Carlos Ruiz and Ryan Hanigan (not a great hitter, but sort of underrated as a player) are right behind.

At the bottom of the pitch blocking rankings is Wilin Rosario. Rosario seems to be channeling Miguel Olivo: he has power, but has a bad time judging pitch locations whether he’s hitting (4.0% walk rate combined with a 26.4% strikeout rate) combined with bad pitch judgment whether he’s trying to hit the ball or catch it. Carlos Santana is second-worst, but his continuing problems with the bat are what is really troubling Cleveland. Third-worst is A.J. Pierzynski, and I’m sure the White Sox couldn’t care less, considering how he’s hitting so far this season.

Caught Stealing Leaders and Trailers:

The leader so far this season is Miguel Montero, who started the season slow at the plate but raked in June. He (and his new contract) have not been the problem for Arizona this year. Carlos Santana has had difficulties, but he’s actually been the second-best catcher (according to this method) so far this year, which is nice, given that he isn’t doing much else. Miguel Olivo has been third best, which totally makes his .237 wOBA palatable. Just kidding, he’s been horrible.

At the very bottom we (still) have the essence of Veteran Defensive Catchers: Rod Barajas. The Pirates are baffling. Gerald Laird is another member of that club, and he is right above Barajas. John Baker is third-worst, keeping Joe Mauer just out of the bottom three.

Overall Leaders and Trailers:

The top three are the same as last month. Carlos Ruiz is still the overall best, and already about seven runs above average overall. Somehow, his wOBA is still .422, as well. If it weren’t for him, the Phillies would be completely out of it and should be selling. Oh, wait. Montero has been just as good (although his team is floundering just as badly). Ryan Hanigan is this year’s Mystery Catcher thus far.

On the bottom — congratulations for Jason Castro for getting out of the bottom slot and moving into third-worst! His hitting has moved into the “not bad for a catcher” territory, so Houston has that going for them. The Yankees’ Russell Martin has had a defensive slump to match that he has been on with the bat, and he is now second-worst. At the bottom is Wilin “Olivo” Rosario.

And now, for your viewing pleasure, the Big Table…

Rank Player Tm PA FERuns TERuns PBWPRuns CSruns Total
1 Carlos Ruiz PHI 2499 -1.4 1.6 3.6 2.9 6.7
2 Miguel Montero ARI 2541 0.1 -0.3 2.6 3.9 6.4
3 Ryan Hanigan CIN 1865 0.6 -0.3 3.5 2.3 6.2
4 Yadier Molina STL 2629 0.9 0.2 2.0 3.0 6.1
5 Kurt Suzuki OAK 2397 0.8 0.6 1.4 2.2 5.0
6 Matt Wieters BAL 2692 0.2 -1.7 3.9 2.1 4.5
7 Brian McCann ATL 2332 0.0 1.5 0.9 1.9 4.3
8 Tyler Flowers CHW 658 0.2 0.4 -0.1 3.0 3.5
9 Brian Schneider PHI 730 0.2 0.5 0.6 1.0 2.3
10 Devin Mesoraco CIN 1293 0.4 0.8 0.8 0.1 2.2
11 Nick Hundley SDP 1995 -0.1 -0.2 0.3 2.2 2.2
12 John Buck MIA 2368 0.8 -0.9 2.4 -0.3 2.0
13 A.J. Ellis LAD 2529 0.1 -0.3 -1.1 3.1 1.8
14 Bobby Wilson LAA 1336 0.5 -0.6 1.5 0.3 1.7
15 David Ross ATL 757 0.3 0.0 -0.7 1.6 1.2
16 Buster Posey SFG 2144 -0.8 -0.6 2.4 0.2 1.2
17 Welington Castillo CHC 362 0.1 -0.3 0.5 0.7 1.0
18 Jeff Mathis TOR 809 0.3 0.5 0.0 0.1 0.9
19 Koyie Hill CHC 373 0.1 -0.2 0.8 0.2 0.8
20 Salvador Perez KCR 454 0.2 0.3 -0.1 0.4 0.7
21 Jhonatan Solano WSN 345 0.1 0.2 -0.4 0.8 0.7
22 Anthony Recker OAK 339 0.1 0.2 -0.2 0.5 0.7
23 Miguel Olivo SEA 1513 -0.2 0.0 -2.2 3.1 0.7
24 Jonathan Lucroy MIL 1397 0.5 -0.6 0.8 -0.1 0.6
25 Henry Blanco ARI 601 -0.5 0.4 -0.3 1.1 0.6
26 Luis Exposito BAL 255 0.1 0.2 0.4 -0.1 0.5
27 J.P. Arencibia TOR 2479 0.1 0.1 -0.6 0.8 0.4
28 J.C. Boscan ATL 91 0.0 0.1 0.3 0.0 0.3
29 Chris Iannetta LAA 792 0.3 0.5 0.0 -0.4 0.3
30 Rob Johnson NYM 377 0.1 0.2 0.5 -0.6 0.3
31 John Jaso SEA 564 0.2 0.4 0.5 -0.8 0.2
32 Erik Kratz PHI 41 0.0 0.0 0.1 0.0 0.2
33 Humberto Quintero KCR 1529 0.5 -1.4 0.4 0.7 0.1
34 Yan Gomes TOR 22 0.0 0.0 0.1 0.0 0.1
35 Stephen Vogt TBR 20 0.0 0.0 0.1 0.0 0.1
36 Sandy Leon WSN 16 0.0 0.0 0.0 0.0 0.1
37 Yorvit Torrealba TEX 1434 0.5 0.4 -0.5 -0.4 0.1
38 Brandon Snyder TEX 3 0.0 0.0 0.0 0.0 0.0
39 Michael McKenry PIT 1190 0.4 0.3 0.5 -1.2 0.0
40 Bryan Holaday DET 135 0.0 0.1 0.1 -0.3 0.0
41 Derek Norris OAK 367 -0.6 0.2 0.7 -0.4 -0.1
42 Carlos Santana CLE 2075 0.7 -0.6 -4.0 3.8 -0.1
43 Carlos Maldonado WSN 118 0.0 0.1 0.0 -0.3 -0.1
44 Josh Thole NYM 1772 -1.6 0.6 1.0 -0.2 -0.1
45 Tony Cruz STL 645 0.2 -0.6 -0.2 0.3 -0.2
46 Jose Molina TBR 1619 0.5 -0.4 -1.6 1.3 -0.2
47 John Hester LAA 885 0.3 0.6 0.2 -1.3 -0.2
48 Kelly Shoppach BOS 1040 -0.4 0.2 0.1 -0.2 -0.3
49 Hank Conger LAA 185 -0.7 0.1 0.0 0.3 -0.3
50 Ryan Doumit MIN 1204 0.4 0.3 -0.8 -0.2 -0.3
51 Hector Sanchez SFG 1100 -0.4 0.2 -1.4 1.1 -0.5
52 George Kottaras MIL 794 0.3 0.5 0.3 -1.6 -0.5
53 Blake Lalli CHC 121 0.0 0.1 -0.2 -0.5 -0.6
54 Geovany Soto CHC 1519 -0.2 -1.0 2.0 -1.5 -0.6
55 Konrad Schmidt ARI 39 0.0 0.0 -0.7 0.0 -0.7
56 Ronny Paulino BAL 362 0.1 0.2 -0.4 -0.7 -0.7
57 Luke Carlin CLE 139 0.0 0.1 -0.5 -0.4 -0.7
58 Drew Butera MIN 669 0.2 0.4 -0.7 -0.8 -0.8
59 Martin Maldonado MIL 1133 0.4 -0.2 -0.8 -0.2 -0.8
60 Matt Treanor LAD 689 0.2 -0.5 0.0 -0.5 -0.8
61 Chris Stewart NYY 890 0.3 -1.4 -0.3 0.5 -0.9
62 Alex Avila DET 1977 0.7 -0.2 -1.5 0.1 -0.9
63 Josh Donaldson OAK 104 0.0 -0.4 0.0 -0.5 -0.9
64 Joe Mauer MIN 1392 0.5 0.4 0.3 -2.1 -1.0
65 Ramon Hernandez COL 986 0.3 -0.3 -0.6 -0.5 -1.1
66 Chris Snyder HOU 1387 -0.3 0.4 -1.2 -0.1 -1.1
67 Wilson Ramos WSN 900 -0.4 0.6 -0.3 -1.1 -1.2
68 Jesus Flores WSN 1756 -0.2 1.1 -1.0 -1.2 -1.3
69 Brett Hayes MIA 901 -1.2 -0.9 0.8 -0.2 -1.4
70 Yasmani Grandal SDP 317 0.1 -0.8 0.3 -1.1 -1.4
71 Brayan Pena KCR 1254 -1.1 -0.2 1.3 -1.5 -1.5
72 Steve Clevenger CHC 851 -0.5 0.5 0.1 -1.7 -1.5
73 A.J. Pierzynski CHW 2527 0.9 0.6 -3.3 0.3 -1.5
74 Lou Marson CLE 1065 -0.4 0.7 -0.1 -1.9 -1.7
75 Mike Napoli TEX 1782 -0.1 0.6 -0.3 -1.9 -1.7
76 Wil Nieves COL 472 0.2 -0.2 -0.1 -1.7 -1.8
77 Chris Gimenez TBR 776 -2.0 0.5 0.2 -0.8 -2.1
78 Jesus Montero SEA 1168 -0.4 0.3 -1.2 -0.9 -2.2
79 Omir Santos DET 122 -0.7 -0.4 -0.2 -0.9 -2.3
80 Mike Nickeas NYM 1083 -0.4 0.2 -1.2 -0.9 -2.3
81 Jose Lobaton TBR 831 -1.2 0.0 0.1 -1.3 -2.4
82 Gerald Laird DET 1011 -0.4 0.6 -0.3 -2.6 -2.7
83 Rod Barajas PIT 1947 0.7 0.3 0.4 -4.2 -2.8
84 Jarrod Saltalamacchia BOS 2247 -0.7 -0.5 0.4 -2.1 -3.0
85 John Baker SDP 991 0.3 0.1 -1.4 -2.5 -3.4
86 Jason Castro HOU 1911 0.6 -1.2 -1.9 -1.3 -3.8
87 Russell Martin NYY 2323 -0.7 0.0 -2.2 -0.9 -3.8
88 Wilin Rosario COL 1915 0.6 -3.1 -4.7 0.7 -6.5

Concluding Methodological Postscript 

I should make clear that for the purposes that I am not including such debated areas a 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.

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