Tampa Bay Rays v Boston Red Sox - Game One

It would be easy for me to complain about this “boring” and “predictable” update of the catcher defense rankings. I mean, the top and the bottom catchers are just as we expect. On the other hand, that sort of reassures me that these catcher defense rankings are getting at something like reality, you know. If, say, Yan Gomes led all year like he did last month, I’d be pretty worried. There is always interesting stuff to be found in these semi-regularly updated catcher defense ratings, so let’s see what we have.

I am trying to get away from discussing the same methodological stuff in every update. As usual, there is a methodological postscript after the rankings. Look at those for more details if you are interested of the admitted scope and limits of these ratings. I will offer a bit of commentary, instead.

What is boring about this installment? After the Yan Gomes anomaly from last month, Yadier Molina and Matt Wieters are on top. That is hardly surprising; I don’t think these basic ratings are the only ones that have them as the best defensive catchers in baseball. Molina might be one of the best overall players in baseball, and people finally seem to realize it. Wieters’ is still weirdly underrated, although his bat has actually been bad (rather than simply disappointing relative to expectations as in the past) this year, mostly due to a low BABIP. Joe Mauer is in third, which is a bit surprising. It is not that he is a bad defender, just that he hasn’t been quite this good in the past. Combined with a revived bat, Mauer is quietly working on a MVP campaign.

The bottom three contains at least one surprise. Two are not total surprises. Chris Iannetta, the third worst is probably not as bad as his numbers so far, but he has never been a defensive wizard. Luckily, a league average bat behind the plate is passable. Carlos Santana is the worst in the league so far, and that is hardly surprising, either. He is no Joe Mauer, but his bat still makes him one of the better catchers in baseball this year.

The surprise is that Jose Molina is the second-worst overall. At least, it may be surprising to some, given that he has a good defensive reputation. Most of that is due to his famed ability to frame pitches. That research is very important, and I like it, although I also think some of us (including myself) have been a bit uncritical about it. The Rays must believe in it strongly to allow Molina to get so many reps. He can’t hit (he has an 80 wRC+ so far this year about what you would expect), and he has not been impressive in the non-framing aspects of fielding for a while. I watched a game this year during which he repeatedly let balls get past him.

In other words, given his bat and glove, Jose Molina had better be a bad-ass pitch-framer.

So here, for your entertainment and irritation, are the rest of the rankings.

Rank Player Team PA FERuns TERuns PBWPRuns CSruns Total
1 Yadier Molina STL 2570 -0.7 1.0 4.2 3.0 7.5
2 Matt Wieters BAL 2448 -0.8 1.0 1.6 3.5 5.3
3 Joe Mauer MIN 1764 -0.2 0.6 2.2 2.3 4.9
4 A.J. Ellis LAD 1824 -0.2 0.6 0.2 4.0 4.6
5 Russell Martin PIT 2094 0.6 1.2 -0.7 3.3 4.5
6 Jeff Mathis MIA 557 0.2 0.3 0.4 3.2 4.0
7 Ryan Hanigan CIN 1412 0.4 0.4 0.0 2.9 3.7
8 Chris Stewart NYY 1533 -0.3 0.9 -0.5 3.1 3.3
9 Brian McCann ATL 1198 0.4 0.7 1.8 0.3 3.2
10 Yan Gomes CLE 1074 0.3 0.2 -0.5 2.9 2.8
11 Nick Hundley SDP 1795 0.5 -0.9 0.6 2.4 2.7
12 Rob Brantly MIA 1756 -0.2 -0.4 0.5 2.6 2.5
13 John Buck NYM 2315 -0.1 0.4 2.3 -0.3 2.4
14 David Ross BOS 773 0.2 0.0 0.8 1.2 2.2
15 Brayan Pena DET 997 0.3 0.1 1.2 0.1 1.7
16 Buster Posey SFG 2285 0.7 0.9 1.6 -1.7 1.4
17 Carlos Ruiz PHI 794 0.2 0.5 1.1 -0.4 1.4
18 Erik Kratz PHI 1473 0.4 0.4 1.6 -1.2 1.2
19 John Baker SDP 443 0.1 0.3 -0.6 1.3 1.1
20 Devin Mesoraco CIN 1357 0.4 -0.6 1.2 0.1 1.1
21 Yorvit Torrealba COL 888 0.3 -0.4 0.3 0.9 1.1
22 Evan Gattis ATL 1018 -0.5 0.6 -0.1 1.0 1.0
23 Tony Cruz STL 288 0.1 0.2 0.6 0.1 0.9
24 Tim Federowicz LAD 631 -0.6 -0.1 0.0 1.6 0.9
25 Geovany Soto TEX 918 0.3 -0.4 -0.5 1.4 0.8
26 Taylor Teagarden BAL 332 -0.7 0.2 0.5 0.7 0.8
27 Miguel Montero ARI 2521 0.7 1.0 0.1 -1.1 0.7
28 Gerald Laird ATL 648 0.2 0.4 0.1 0.0 0.7
29 Kelly Shoppach SEA 1222 0.4 0.2 -1.5 1.4 0.6
30 Robinson Chirinos TEX 109 0.0 0.1 0.3 0.0 0.4
31 Chris Snyder BAL 180 0.1 0.1 -0.3 0.5 0.4
32 Mike Zunino SEA 355 0.1 0.2 0.0 0.1 0.4
33 Chris Herrmann MIN 74 0.0 0.0 0.2 0.0 0.3
34 Stephen Vogt OAK 41 0.0 0.0 0.1 0.0 0.2
35 Jonathan Lucroy MIL 2142 -0.9 0.8 0.1 0.2 0.2
36 Ryan Lavarnway BOS 155 0.0 0.1 -0.1 0.1 0.1
37 Martin Maldonado MIL 703 0.2 0.4 0.5 -1.0 0.1
38 Omir Santos CLE 17 0.0 0.0 0.1 0.0 0.1
39 Blake Lalli MIL 11 0.0 0.0 0.0 0.0 0.0
40 Jordan Pacheco COL 11 0.0 0.0 0.0 0.0 0.0
41 Jesus Sucre SEA 289 0.1 0.2 0.1 -0.3 0.0
42 Kyle Skipworth MIA 10 0.0 0.0 0.0 0.0 0.0
43 Adam Moore KCR 108 0.0 -0.4 0.3 0.1 0.0
44 Guillermo Quiroz SFG 408 0.1 0.2 -0.9 0.6 0.0
45 Humberto Quintero PHI 599 0.2 -1.1 0.5 0.4 -0.1
46 Miguel Olivo MIA 600 0.2 -1.1 0.8 0.1 -0.1
47 Brandon Bantz SEA 34 0.0 0.0 0.1 -0.3 -0.2
48 Derek Norris OAK 1796 0.5 0.6 0.6 -2.0 -0.3
49 Josh Thole TOR 145 0.0 -0.4 0.5 -0.4 -0.3
50 A.J. Pierzynski TEX 1909 0.6 1.1 0.7 -2.8 -0.4
51 Lou Marson CLE 51 0.0 0.0 0.2 -0.6 -0.4
52 Anthony Recker NYM 512 0.1 -0.7 0.8 -0.7 -0.4
53 Carlos Corporan HOU 951 0.3 -0.9 0.2 -0.1 -0.5
54 Hector Gimenez CHW 735 0.2 -0.5 -0.5 0.3 -0.5
55 Jhonatan Solano WSN 300 0.1 -0.3 0.4 -0.7 -0.5
56 Austin Romine NYY 757 0.2 0.5 -0.1 -1.1 -0.6
57 Steven Lerud PHI 68 0.0 0.0 -0.3 -0.3 -0.6
58 Ramon Hernandez LAD 412 0.1 -0.2 -0.4 -0.1 -0.6
59 Corky Miller CIN 197 0.1 -0.4 0.3 -0.7 -0.7
60 Bryan Holaday DET 140 0.0 -0.4 0.4 -0.9 -0.8
61 Jarrod Saltalamacchia BOS 2081 0.6 -0.7 0.1 -1.0 -1.0
62 Yasmani Grandal SDP 743 -0.5 0.0 0.4 -0.8 -1.0
63 Ryan Doumit MIN 957 -0.5 0.1 -0.1 -0.6 -1.0
64 Kurt Suzuki WSN 2026 -0.2 -0.2 3.0 -3.7 -1.0
65 Francisco Cervelli NYY 572 -2.1 -0.1 1.0 0.2 -1.1
66 Salvador Perez KCR 2102 0.6 -1.2 -1.8 1.1 -1.2
67 Jose Lobaton TBR 1416 0.4 -0.1 -0.3 -1.4 -1.4
68 Henry Blanco SEA 115 0.0 -0.4 -0.8 -0.3 -1.4
69 Henry Blanco TOR 443 0.1 0.3 -1.4 -0.4 -1.4
70 Hector Sanchez SFG 236 0.1 0.1 -0.7 -1.0 -1.5
71 Tyler Flowers CHW 2033 -0.2 -0.2 -1.1 0.0 -1.5
72 George Kottaras KCR 535 0.2 -1.6 -0.5 0.3 -1.7
73 Welington Castillo CHC 2122 -0.9 -1.6 0.3 0.4 -1.8
74 Wilson Ramos WSN 502 -0.6 -0.7 -0.4 -0.2 -1.9
75 Wil Nieves ARI 390 0.1 -0.2 -1.3 -0.6 -2.0
76 Alex Avila DET 1705 -0.3 0.5 -0.5 -2.0 -2.2
77 Jason Castro HOU 2115 -0.9 0.3 -0.9 -1.2 -2.6
78 Hank Conger LAA 811 -0.5 -1.4 -1.1 0.3 -2.8
79 Wilin Rosario COL 2089 -0.1 -0.7 -2.6 0.7 -2.8
80 John Jaso OAK 1152 0.3 -0.3 -1.1 -1.9 -3.0
81 Jesus Montero SEA 928 0.3 0.1 -0.7 -2.8 -3.2
82 Dioner Navarro CHC 680 0.2 -1.0 -3.2 0.8 -3.2
83 J.P. Arencibia TOR 2397 0.0 0.5 -1.4 -2.7 -3.6
84 Michael McKenry PIT 813 0.2 0.0 -1.1 -3.2 -4.0
85 Chris Iannetta LAA 2218 -0.9 1.3 -0.3 -4.6 -4.4
86 Jose Molina TBR 1532 -0.3 -0.5 -2.7 -1.7 -5.3
87 Carlos Santana CLE 1730 -0.2 0.5 -4.6 -2.2 -6.5

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.

Comments (7)

  1. JP being shit again

  2. Small sample size, I know, but if any Jays fan is interested in crying themselves to sleep, look at this: http://www.fangraphs.com/leaders.aspx?pos=all&stats=bat&lg=all&qual=0&type=8&season=2013&month=0&season1=2013&ind=0&team=0&rost=0&age=0&filter=&players=9627,697

    Then look at the defensive numbers….

    • But is Yan Gomes a leader? Didn’t think so.

    • Yan Gomes is complete shit at Twitter compared to JPA. You can’t compare the two.

    • Bro, that Kit Kat Chunky commercial he’s in…the ability to measure its’ impact on the club hasn’t yet been invented yet.

      Has Yan Gomes ever been in a commercial with Cabbie?

      Bro.

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