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One of the benefits of our recently released catching defense metrics is they’re essentially ready-to-project, thanks to the regression feature of the model (the "R" in RPM). RPM also gives us two ways to assign value to framing, one using context (the ball-strike count) and one using a flat value (recently adjusted* to ~.155 runs).

To date, we haven’t found an aging curve in the PITCHf/x-based framing metric we developed. This makes projecting framing performance a little easier, and for extreme simplicity I have created a set of projections for 2014 based on a 3-2-1 weighting of framing opportunities for the past three seasons. First, though, we wanted to generate a "retrojection" to test RPM’s success in predicting 2013’s framing performances.

Since RPM seasons are regressed to careers, and pitcher and umpire adjustments are derived from those actors’ “career” numbers, a retrojection requires recreating the 2008-2012 numbers as if 2013 had never occurred. Since the probabilistic portion of the model (the "P" in RPM) is specific to each season, there's no need to recreate the zone maps (see first footnote below).

The WOWY (With or Without You) corrections for pitchers and the umpire corrections were all recreated without including 2013, and new regressed “career” lines were created with 2008-2012 totals for each catcher. Those 2008-2012 seasons were then regressed to these new, abbreviated career lines. Finally, the 3-2-1 weighting was applied for 2010-2012 to arrive at retrojections for 2013.

The verdict? The retrojections did very well. Both context-dependent and generic run projections (as rate stats) correlated with actual 2013 performance at better than 0.81. That’s a lot of variance (>65 percent) captured by a simple model.

So with a fair amount of confidence in the crystal ball and the playing time assigned to each catcher in our own Depth Charts, we can project player and team framing contributions for 2014.

Yasmani Grandal is currently projected to top framing magician Jonathan Lucroy in 2014, based on playing time projections from March 30 (which may be optimistic for Mr. Grandal, as the Padres recently added a third catcher, Adam Moore). It’s a close race, perhaps too close to call.

Look down just a few spots, and you'll see some other young backstops, along with the usual suspects (Brian McCann, Russell Martin, Ryan Hanigan, the Molinas). Yan Gomes just got a contract extension that raised some eyebrows; maybe his framing projection will help lower a few of them.

name Team perc Generic Runs Context Runs
Yasmani Grandal SDN 0.7087 37.0 36.8
Jonathan Lucroy MIL 0.8643 35.7 34.2
Mike Zunino SEA 0.8119 28.1 26.7
Brian McCann NYA 0.7143 24.4 26.0
Yan Gomes CLE 0.7846 25.5 24.4
Russell Martin PIT 0.8116 25.1 24.3
Jose Molina TBA 0.4065 21.8 21.7
Buster Posey SFN 0.7635 20.3 19.2
Ryan Hanigan TBA 0.5935 17.8 18.6
Travis d'Arnaud NYN 0.7515 19.4 16.6
Miguel Montero ARI 0.8175 15.9 16.0
Hank Conger ANA 0.3978 19.4 14.3
Yadier Molina SLN 0.8717 13.2 12.0
Wilson Ramos WAS 0.7528 13.2 10.9
Evan Gattis ATL 0.5370 12.7 9.9
Jarrod Saltalamacchia MIA 0.8131 10.9 9.4
David Ross BOS 0.1831 8.6 8.8
Alex Avila DET 0.7511 8.7 8.3
Tyler Flowers CHA 0.6750 7.1 6.4
Francisco Cervelli NYA 0.1633 7.0 6.1
Erik Kratz TOR 0.1997 4.4 5.2
Carlos Corporan HOU 0.1686 6.0 5.2
Geovany Soto TEX 0.4452 6.5 4.7
Brayan Pena CIN 0.2448 4.0 4.4
Chris Stewart PIT 0.0920 4.4 3.9
Martin Maldonado MIL 0.1357 3.8 3.8
Tuffy Gosewisch ARI 0.1825 4.4 3.6
Tony Sanchez PIT 0.0964 3.5 3.1
Derek Norris OAK 0.3540 2.1 2.9
Josh Thole TOR 0.1498 2.2 2.9
Matt Wieters BAL 0.7769 1.9 2.8
Dioner Navarro TOR 0.6505 5.5 2.6
Jose Lobaton WAS 0.1483 2.3 2.3
Steve Clevenger BAL 0.2231 2.0 2.3
Wil Nieves PHI 0.1940 1.9 2.0
Jeff Mathis MIA 0.0935 2.2 2.0
Austin Romine NYA 0.1224 1.2 1.0
Josh Phegley CHA 0.1277 0.9 0.8
Ryan Lavarnway BOS 0.0998 1.3 0.6
Jhonatan Solano WAS 0.0989 0.5 0.3
J.P. Arencibia TEX 0.3864 0.8 0.2
James McCann DET 0.0995 0.0 0.0
Christian Bethancourt ATL 0.0705 0.0 0.0
Adrian Nieto CHA 0.1973 0.0 0.0
Max Stassi HOU 0.0663 -0.1 0.0
John Baker CHN 0.1927 0.1 -0.2
Brett Hayes KCA 0.0446 -1.2 -0.8
Tony Cruz SLN 0.1283 -1.3 -1.0
Tim Federowicz LAN 0.2548 -2.7 -1.8
Rob Brantly MIA 0.0935 -2.2 -1.9
Ramon Hernandez KCA 0.0923 -2.4 -2.1
Stephen Vogt OAK 0.1416 -1.8 -2.3
Michael McKenry COL 0.1226 -2.3 -2.3
Hector Sanchez SFN 0.2365 -2.8 -2.9
A.J. Pierzynski BOS 0.7171 -3.2 -3.8
John Buck SEA 0.1881 -3.9 -4.1
Robinson Chirinos TEX 0.1684 -5.0 -4.5
Nick Hundley SDN 0.2913 -5.5 -5.1
Jordan Pacheco COL 0.2401 -6.5 -6.1
Anthony Recker NYN 0.2485 -7.8 -6.5
Bryan Holaday DET 0.1493 -6.3 -6.5
Gerald Laird ATL 0.1468 -6.5 -6.6
Devin Mesoraco CIN 0.7552 -6.0 -6.6
Carlos Ruiz PHI 0.8060 -6.9 -7.0
Jason Castro HOU 0.7651 -5.6 -7.1
Matt Treanor CLE 0.2154 -5.9 -7.5
Josmil Pinto MIN 0.2686 -9.6 -7.7
Salvador Perez KCA 0.8631 -7.9 -8.2
Kurt Suzuki MIN 0.7314 -13.2 -12.0
Wilin Rosario COL 0.6373 -13.7 -13.5
A.J. Ellis LAN 0.7452 -12.5 -14.6
Chris Iannetta ANA 0.6022 -17.3 -15.0
Ryan Doumit ATL 0.2457 -16.2 -16.1
John Jaso OAK 0.5044 -15.8 -17.9
Welington Castillo CHN 0.8073 -18.9 -18.2

Grouping by team, we can see Tampa’s dynamic duo leading the way, with the Rockies' terrible trio bringing up the rear.

Team Generic Runs Context Runs
Tampa Bay Rays 39.6 40.3
Milwaukee Brewers 39.5 38.0
New York Yankees 32.6 33.1
San Diego Padres 31.5 31.7
Pittsburgh Pirates 33.0 31.3
Seattle Mariners 24.2 22.6
Arizona Diamondbacks 20.3 19.6
Cleveland Indians 19.6 16.9
San Francisco Giants 17.5 16.3
Washington Nationals 16.0 13.5
St. Louis Cardinals 11.9 11.0
Toronto Blue Jays 12.1 10.7
New York Mets 11.6 10.1
Miami Marlins 10.9 9.5
Chicago White Sox 8.0 7.2
Boston Red Sox 6.7 5.6
Baltimore Orioles 3.9 5.1
Detroit Tigers 2.4 1.8
Texas Rangers 2.3 0.4
Los Angeles Angels 2.1 -0.7
Houston Astros 0.3 -1.9
Cincinnati Reds -2.0 -2.2
Philadelphia Phillies -5.0 -5.0
Kansas City Royals -11.5 -11.1
Atlanta Braves -10.0 -12.8
Los Angeles Dodgers -15.2 -16.4
Oakland Athletics -15.5 -17.3
Chicago Cubs -18.8 -18.4
Minnesota Twins -22.8 -19.7
Colorado Rockies -22.5 -21.9

*Since first publishing RPM, we've reset the run expectancies used in the final calculations to vary by season, to reflect the drop in scoring we’ve seen during the PITCHf/x era. During that process, we realized that we were about .01 runs low on the generic framing factor. It’s a zero-sum** game, so you won’t notice a difference of any substance.

**Next, we'll have to ensure that the framing totals zero out by season. The run totals do zero out overall, but in 2008 everyone seems to be getting penalized an extra run, with things moving in the opposite direction in 2013. This is reflected in our 2014 projections, too.

Thank you for reading

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majnun
3/31
The Atlanta team based rating scared me, but I think it's largely driven by doumit. God willing he doesn't spend much time back there.

Very interesting.
harrypav
3/31
I'm a huge advocate for taking away Doumit's catching gear.
SixToolPlayer
3/31
I'm going to hold you to these predictions. Your credibility is on the line, Pavlidis.
devine
3/31
Cervelli beats Stewart. That makes me smile.