2016 – 2019 Catcher Skills Spectrum Scores

Two weeks ago, I published Deconstructing the Catcher: Valuating the Five Tools of the Backstop. It was long. But it was actually even longer. In an effort to keep the piece from reaching “War and Peace” lengths, I edited out a lot. I was pleased to see that my article had a favorable response, not just from the Crawfish Boxes community, but outside the Astros fan community as well.

In one of the later sections of the article, “Reconstructing the Catcher”, I explored using each catcher’s skill spectrum scores to created weighted Overall Spectrum Scores. In the weighted overall score, each skill is weighted by their relative impact to team success to create an overall score for the catcher, roughly on a 0 to 100 scale. I reported the top 5 overall scores for 2017 thru 2019, and score breakdowns for Astros-related catchers.

Because the main point of the article was about the value of catcher skills in general, and not of individual players, I did not publish all catcher scores and breakdowns. Since I released my article, I have had some requests to publish all of the catchers’ scores breakdowns. So here are the catcher skill spectrum scores and weighted overall spectrum scores from the last three and a half years. 2019 has been updated through the All-Star Break.

This is simply an appendix to the original article. For full details on the methodology and exactly how these scores are calculated, please refer to the original article.

Briefly:

  • Data is taken from Baseball Prospectus.
  • Catchers who caught 500 innings and had 250 PA’s are used to create the skill spectrum.
  • Batting skill is derived from BRAA per PA, applied to 400 PA.
  • Baserunning skill is derived from BRR per PA, applied to 400 PA.
  • Pitch framing skill is derived from Framing Runs per Framing Chances, then applied to the number of Framing Chances the average catcher included in the spectrum would have over 825 innings.
  • Throwing skill is derived from Throwing Runs per Inning Caught, applied to 825 innings.
  • Blocking skill is derived from Blocking Runs per Blocking Chances, then applied to the number of Blocking Chances the average catcher included in the spectrum would have over 825 innings.
  • The 10th percentile catcher in a skill is set at 0. The 90th percentile catcher is set at 100.
  • For the weighted Overall Spectrum Score, the five skill scores are combined. Weighting is determined by the relative difference in impact between the five skills (illustrated in pie graphs)
  • The tighter a spectrum is (for example, baserunning does not have a very large differential between the 90th and 10th percentile catchers), the more volatile a catcher’s score may be in that skill from year to year. But the tightness of the spectrum also leads this skill score to be weighted less in the overall score.

2019

I have updated catcher data and scores through the 2019 All Star Break. As an average of only 90 of 162 games have been played, catchers required only 140 PA and 275 IC to qualify for inclusion in the spectrum. As I discussed in the original article, batting is weighted more heavily than in prior years. This is likely a function of the smaller sample sizes that come with using half season data. As catchers accumulate more plate appearances as the season goes on, the variance in batting should go down and the weighting should more closely resemble the pie graphs of 2016-2018. Even in recalculating the weightings from June 23 to July 7, with just two additional weeks of data, the weighting continued to shift towards previous year’s data.


2019 (Thru AS Break) Catcher Skills and Weighted Overall Spectrum Scores

Rank Name Team Inn PA Batting Baserunning Framing Thowing Blocking Overall
Rank Name Team Inn PA Batting Baserunning Framing Thowing Blocking Overall
1 Yasmani Grandal MIL 646.2 341 126 8 101 81 57 101
2 Mitch Garver MIN 326 176 145 41 56 57 22 96
3 Roberto Perez CLE 515 237 100 62 84 100 117 94
4 Jason Castro MIN 357 157 99 69 95 1 71 90
5 J.T. Realmuto PHI 659 338 81 92 78 215 100 86
6 Carson Kelly ARI 410.1 197 100 47 60 91 94 84
7 Willson Contreras CHC 605.1 302 131 37 0 34 41 75
8 Curt Casali CIN 352.1 160 71 73 77 0 66 71
9 Kurt Suzuki WSN 357.1 176 96 85 25 1 62 70
10 Robinson Chirinos HOU 590 269 87 90 16 17 107 69
11 Tyler Flowers ATL 387.2 177 57 23 130 17 13 69
12 Christian Vazquez BOS 493 272 72 82 84 82 0 68
13 James McCann CHW 471.1 251 95 56 21 147 49 67
14 Jorge Alfaro MIA 521.1 249 89 -8 69 52 19 67
15 Russell Martin LAD 284.2 147 77 62 69 25 22 66
16 Austin Barnes LAD 438.2 199 40 105 97 20 83 66
17 Buster Posey SFG 457.1 238 52 74 85 53 72 65
18 Brian McCann ATL 402 186 88 -10 61 -69 31 63
19 Tucker Barnhart CIN 404.1 187 37 40 100 30 108 62
20 Austin Hedges SDP 503.2 216 0 80 150 72 87 59
21 Omar Narvaez SEA 542.2 287 92 46 14 15 17 57
22 Gary Sanchez NYY 482.1 289 92 52 -3 42 38 56
23 Tony Wolters COL 514.1 231 58 153 23 81 36 54
24 Danny Jansen TOR 515.2 236 34 55 92 42 52 54
25 Wilson Ramos NYM 558.1 281 76 34 29 -180 29 49
26 Travis d’Arnaud 3TM 276.2 157 45 87 53 60 22 48
27 Martin Maldonado KCR 580.2 254 38 -24 47 59 93 42
28 Yan Gomes WSN 431 198 32 100 33 65 56 41
29 Yadier Molina STL 572.2 276 39 18 35 51 76 40
30 Josh Phegley OAK 551.1 227 59 11 29 96 -16 39
31 Pedro Severino BAL 390.2 190 71 0 5 80 -2 39
32 Mike Zunino TBR 414.2 182 12 21 73 114 76 38
33 Elias Diaz PIT 417.2 196 58 89 -46 30 50 31
34 Jonathan Lucroy LAA 533 264 32 19 15 79 -30 20
35 Jeff Mathis TEX 410.1 159 -31 114 69 -54 63 19
36 Grayson Greiner DET 381.2 169 -10 47 25 94 77 16
37 John Hicks DET 302 181 -1 60 -9 75 35 7

For those of you with nice big tablet or computer screens, here is the table in JPEG format, with hot and cold color shading:



2018




2017




2016




That is it for now. At the end of the season, I will update the 2019 data. If you have any questions not addressed in the original article, or if you have additional score breakdown requests for catchers not included in the table who may have fallen just short of the 500 IC or 250 PA requirements, let me know in the comments or by twitter @mhatter106 .

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