MLB: Tampa Bay Rays at Detroit Tigers

As I have noted in past installments of these ratings, this season has been one of the more static in terms of which players are on top and bottom of these ratings, at least the way I remember it. It may be that my memory is getting faulty, or maybe just selective. I dunno. But in this (likely) final rating of the season, there is still some interesting stuff to discuss beyond simply the ratings, leaders, and trailers.

It is fitting that in this last (more on that in a bit) installment of these ratings that the two players on top are Yadier Molina and Matt Wieters at about 11 runs above average each. Molina, despite missing time with injury, is once again a dark horse MVP candidate, and Wieters’ arm and glove are enough to make him an above average player this year despite the BABIP-fueled problems with his bat.

The bottom of the rankings is still the same group is has been most of the year, and I do not feel like dwelling excessively on the negative today. There are not too many surprises down there: Carlos Santana, Bad Year Alex Avila, Chris Iannetta (who used to be at least acceptable), J.P. Arencibia, Wilin Rosario.

As for some other interesting cases, Joe Mauer again stands out. There has long been talk of if and when he should be moved off of catcher. Even with his rebound this season, his contract still is at least a bit scary for the Twins (though they can work around it if they are careful, smart, and a bit lucky), so this is an important issue. What his rating this year indicates is that he certainly still has the ability to play catcher well, The issue is the injury risk and attrition that comes with playing behind the plate.

Speaking of good-hitting catchers with injury issues, Brian McCann is heading into free agency. After a poor 2012, McCann has rebounded with the bat and also has played well behind the plate (about five runs above average according to these ratings). His injury history and age probably concern teams, and should, but he can still play. How much he gets and from which team is one of the big off-season questions.

Yasmani Grandal has had a rough year for a number for reasons, but the main reason I thought about the Padres this morning was because I happened upon Nick Hundley‘s name. I had forgotten all about Hundley, and he’s hardly a star (or really bad, another thing that grabs the attention). Hundley had a really nice (if BABIP-caused) 2011, then followed up with a horrific 2012 during which nothing went well at the plate. With Grandal’s various issues, Hundley has stepped back up in 2013, though, Sure, a .239/.297/.389 (92 wRC+) line is no great shakes, but he is hitting in San Diego and he is a catcher. He has only been about average with the glove this year, but it works. Nick Hundley, for some reason.

I discussed Salvador Perez‘s overrated-ness in last month’s installment, and while he is good behind the plate, he is not in the top tier according to these ratings. He did have a nice game against the Twins last night, going 4-4 with two home runs. But what about other former Royals catchers? Hey, Miguel Olivo is still in baseball! John Buck‘s bat fell to pieces after an insane start to the seasons, but after getting run out of Kansas City for an allegedly terrible glove, has been pretty good for the Mets (and that comforting hug he gave Matt Harvey… goosebumps). Smilin’ Brayan Pena, like Buck, one of my irrationally all-time favorite players, is pretty bad, usually, but he is hanging in there at just below average, and has hit well for a catcher this year, which the Tigers have needed with Avila having a disaster season.

In closing, I should note that this will be the last Fogging the Measure found at Getting Blanked. It has been a blast, and I would definitely do it again. I started writing for Getting Blanked prior during the 2011-2012 off-season, and then came back for the 2013 season. I started with a post about bloggers Playing GM, and feel like it is one of the better things I have posted. I have had a lot of freedom here, even to do stuff perhaps only I am interested in, like deriving linear weights from base runs. Revolutionary sabermetrics can also be found in my world-changing comparison of the Royals’ Alex Gordon to Judas Priest’s K.K. Downing. My appearance on the Getting Blanked show in 2012 was an all-time classic, full of my trademark rambling and weird grimacing.

My thanks to Dustin Parkes and Drew Fairservice for giving me the shot and putting up with me. I would work with them any time, even though Dustin and I never caught a game and Drew thought that trading Wil Myers for James Shields was a defensible move. And thanks to those of you who read my stuff, and perhaps even enjoyed it.

[As always, methodological remarks are found after the ratings.]

Rank Player Tm PA FERuns TERuns PBWPRuns CSruns Total
1 Yadier Molina STL 3748 -0.6 1.6 6.4 3.8 11.3
2 Matt Wieters BAL 4141 -0.5 1.8 5.4 4.3 11.0
3 Russell Martin PIT 3680 0.9 1.1 -0.3 7.1 8.8
4 Joe Mauer MIN 2799 -0.1 1.1 3.4 3.9 8.3
5 A.J. Ellis LAD 3309 -0.7 1.4 0.5 5.6 6.8
6 Brian McCann ATL 2623 0.7 0.0 3.7 0.2 4.6
7 Yan Gomes CLE 2056 0.5 0.2 0.0 3.8 4.4
8 Chris Stewart NYY 2980 0.0 1.2 0.1 2.9 4.1
9 Ryan Hanigan CIN 1850 0.5 0.6 0.2 2.7 3.9
10 Jeff Mathis MIA 2014 0.5 0.7 0.1 2.2 3.5
11 Tony Cruz STL 928 0.2 0.0 1.8 0.6 2.7
12 John Buck NYM 3669 0.2 0.6 2.8 -1.1 2.5
13 David Ross BOS 852 0.2 0.0 1.0 1.2 2.4
14 Carlos Ruiz PHI 2460 -0.1 0.9 2.4 -1.0 2.1
15 Buster Posey SFG 3735 0.2 0.7 2.7 -1.7 1.8
16 Carlos Corporan HOU 1540 0.4 -1.5 1.2 1.8 1.8
17 Tuffy Gosewisch ARI 290 0.1 0.2 0.6 0.8 1.7
18 Gerald Laird ATL 988 0.2 0.6 -0.8 1.6 1.6
19 Guillermo Quiroz SFG 859 0.2 0.5 -0.4 1.3 1.6
20 Tim Federowicz LAD 1240 -0.4 -0.7 0.5 2.3 1.6
21 Salvador Perez KCR 3747 0.9 -1.3 -0.6 2.5 1.5
22 Rene Rivera SDP 474 0.1 0.3 0.1 1.1 1.5
23 Erik Kratz PHI 1857 0.5 0.6 2.2 -1.8 1.3
24 Stephen Vogt OAK 891 0.2 0.0 -0.6 1.6 1.3
25 Yorvit Torrealba COL 1519 0.4 -0.6 1.1 0.3 1.1
26 A.J. Pierzynski TEX 3378 0.8 1.9 -0.9 -0.7 1.1
27 Humberto Quintero PHI 718 0.2 -1.5 0.8 1.5 1.0
28 John Baker SDP 443 0.1 0.2 -0.6 1.2 1.0
29 Nick Hundley SDP 3228 0.1 -1.1 1.1 0.8 0.9
30 Chris Herrmann MIN 598 0.1 0.3 0.2 0.2 0.9
31 Cody Clark HOU 81 0.0 -0.4 0.0 1.3 0.9
32 Jonathan Lucroy MIL 3775 -1.3 1.6 1.1 -0.6 0.8
33 Martin Maldonado MIL 1218 0.3 0.7 -0.1 -0.1 0.8
34 Humberto Quintero SEA 652 0.2 -0.1 0.4 0.4 0.8
35 Devin Mesoraco CIN 2784 0.7 -0.8 2.0 -1.1 0.7
36 Robinson Chirinos TEX 109 0.0 0.1 0.3 0.0 0.4
37 Taylor Teagarden BAL 644 -0.6 0.4 0.3 0.3 0.4
38 Chris Snyder BAL 180 0.0 0.1 -0.3 0.5 0.4
39 Mike Zunino SEA 1041 0.3 0.6 -0.4 -0.1 0.4
40 Kelly Shoppach SEA 1222 0.3 0.2 -1.5 1.4 0.4
41 Drew Butera MIN 35 0.0 0.0 0.1 0.0 0.1
42 Anthony Recker NYM 1073 0.3 -0.8 1.4 -0.7 0.1
43 Jake Elmore HOU 18 0.0 0.0 0.1 0.0 0.1
44 Omir Santos CLE 17 0.0 0.0 0.1 0.0 0.1
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 Jesus Sucre SEA 289 0.1 0.2 0.1 -0.3 0.0
48 Adam Moore KCR 108 0.0 -0.4 0.3 0.0 0.0
49 Brandon Bantz SEA 34 0.0 0.0 0.1 -0.3 -0.2
50 Miguel Olivo MIA 600 0.1 -1.1 0.8 0.0 -0.2
51 Victor Martinez DET 59 0.0 0.0 -0.1 -0.1 -0.2
52 Austin Romine NYY 1472 0.4 -0.1 -0.2 -0.3 -0.2
53 Rob Brantly MIA 2191 -0.2 -0.7 -0.7 1.3 -0.4
54 Corky Miller CIN 382 0.1 -0.3 0.4 -0.5 -0.4
55 Evan Gattis ATL 1273 -0.4 0.2 -1.4 1.2 -0.4
56 Lou Marson CLE 51 0.0 0.0 0.2 -0.6 -0.4
57 Josh Phegley CHW 1409 0.3 0.3 -1.8 0.7 -0.4
58 Travis d’Arnaud NYM 348 0.1 0.2 -0.6 -0.1 -0.4
59 Geovany Soto TEX 1546 0.4 -0.1 -0.5 -0.2 -0.4
60 Miguel Montero ARI 3433 0.1 0.5 -2.2 1.1 -0.5
61 Kurt Suzuki OAK 113 0.0 -0.4 0.1 -0.1 -0.5
62 Yasmani Grandal SDP 974 -0.5 0.1 1.1 -1.1 -0.5
63 Jordan Pacheco COL 91 0.0 -0.4 0.0 -0.1 -0.5
64 Brett Hayes KCR 113 0.0 0.1 -0.2 -0.4 -0.6
65 Jhonatan Solano WSN 345 0.1 -0.3 0.5 -0.9 -0.6
66 Steven Lerud PHI 68 0.0 0.0 -0.3 -0.3 -0.6
67 Rob Johnson STL 312 0.1 0.2 -0.7 -0.1 -0.6
68 Ramon Hernandez LAD 412 0.1 -0.2 -0.4 -0.1 -0.6
69 Tyler Flowers CHW 2851 0.0 0.2 0.2 -1.0 -0.7
70 Derek Norris OAK 2439 0.6 -0.1 0.0 -1.3 -0.7
71 Welington Castillo CHC 3561 -1.4 -1.4 0.7 1.1 -0.8
72 Hector Gimenez CHW 779 0.2 -0.5 -0.9 0.2 -1.0
73 Ryan Lavarnway BOS 579 0.1 -0.2 -0.7 -0.4 -1.1
74 Francisco Cervelli NYY 572 -2.1 -0.2 0.9 0.2 -1.1
75 Kurt Suzuki WSN 2795 -0.1 -0.4 5.1 -5.8 -1.2
76 Henry Blanco SEA 933 0.2 -0.9 -1.6 0.9 -1.3
77 Jarrod Saltalamacchia BOS 3661 0.9 -0.8 1.6 -3.1 -1.4
78 Jose Molina TBR 2560 -0.1 0.0 -2.1 0.9 -1.4
79 Bryan Holaday DET 364 0.1 -0.8 0.6 -1.3 -1.4
80 Brayan Pena DET 1952 0.5 -0.3 -0.1 -1.5 -1.5
81 Henry Blanco TOR 443 0.1 0.2 -1.4 -0.4 -1.5
82 Tony Sanchez PIT 312 0.1 0.2 -1.8 -0.1 -1.7
83 Koyie Hill MIA 194 -0.7 0.1 -0.5 -0.7 -1.8
84 Wilson Ramos WSN 1830 -1.0 -0.9 -0.2 0.2 -1.9
85 Jason Castro HOU 3534 -1.4 1.0 -1.6 0.0 -1.9
86 Ryan Doumit MIN 1607 -0.4 0.4 -0.6 -1.7 -2.2
87 George Kottaras KCR 985 0.2 -1.4 -0.3 -1.2 -2.7
88 Josh Thole TOR 891 0.2 -0.5 -1.7 -1.0 -2.9
89 Dioner Navarro CHC 1509 0.4 -0.6 -2.3 -0.5 -3.0
90 Hank Conger LAA 1782 -0.3 -1.4 -2.3 1.0 -3.0
91 Jose Lobaton TBR 2322 0.6 -0.1 -0.6 -3.2 -3.3
92 Jesus Montero SEA 928 0.2 0.0 -0.7 -2.9 -3.4
93 Hector Sanchez SFG 485 -0.6 0.3 -2.1 -1.4 -3.9
94 John Jaso OAK 1510 0.4 -0.6 -0.9 -3.1 -4.2
95 Michael McKenry PIT 1044 0.3 -0.4 -0.7 -3.7 -4.5
96 Wil Nieves ARI 1454 0.4 -0.1 -5.0 -0.8 -5.6
97 Wilin Rosario COL 3522 -0.6 -0.9 -3.6 -0.9 -6.0
98 J.P. Arencibia TOR 3888 -0.5 -1.2 -1.3 -3.7 -6.8
99 Chris Iannetta LAA 3339 -1.4 0.9 -1.1 -5.2 -6.8
100 Alex Avila DET 2661 -0.8 0.5 -2.1 -4.7 -7.0
101 Carlos Santana CLE 2910 -0.8 0.7 -4.6 -3.8 -8.5

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.