Pitch Framing Park Factors

Back in March, we introduced catcher framing numbers on FanGraphs. Not long after, Tom Tango noted in a blog post that pitch framing numbers should be park-adjusted since pitchers and catchers in some parks are getting more strike calls (relative to Trackman’s recorded locations) than others.

We can see this in the graph above, which is based on called pitches within a 3.5 x 3.5 inch area in and around the strike zone. There are, on average, 64 pitches per game that meet this criteria so this graph essentially shows how many extra “framing” strikes pitches and catchers were assigned in each park per game. Put another way, this tells us how many more strike calls they received than we’d expect based on the recorded locations of the pitches. We’d certainly expect some spread in the results for home team pitchers and catchers, since some teams have better framers than others, but we shouldn’t see such a large spread for road pitchers and catchers, whom we’d expect to have essentially average framing talent. We also see that there’s a strong positive correlation between extra strikes for the home team and extra strikes for road team, suggesting that the park itself plays a role. There are two big outliers here — Sun Trust Park and Coors Field, both in 2017. Something must be amiss at those parks and we should control for it when calculating our framing numbers.

Adjusting Pitch Framing Numbers for Park Effects

Just as when constructing other park factors, we need to be careful to account for the quality of the players playing in each park. We’ll need to account not only for the pitchers and catchers who played in each park but also for the batters, some of whom have fewer strikes called against them. What we need is essentially a WOWY (with or without you) calculation where we find each park’s tendency to yield strikes, controlling for the pitcher, catcher, and batting team. In practice, it’s easiest to do this with the help of a mixed effects model. We can take the mixed-effects model we used to estimate pitcher and catcher framing and simply add random effects for the ballpark and batting team.

After adjusting for the park and batter effects that we find, we can take another look at the graph that led us here and compare home and road framing at each park, but this time with park-adjusted numbers.

This looks much better! With park effects removed, we still have a significant spread in home-team framing but a relatively small spread in road-team framing.

New Pitch Framing Numbers

For most catchers, our park adjustments make little difference. The graph below plots the new framing runs for catcher-seasons against the old framing runs with 2017 performances shown in red.

The tables below show the team-seasons, catcher-seasons, and catcher careers most affected by the park adjustments.

Top 5 Team-Seasons in Framing Runs Gained
Team Season Old FRM New FRM Park Bias
Rockies 2017 -26.2 -9.6 -16.6
Rangers 2017 -25.8 -12.2 -13.6
Blue Jays 2010 -0.5 10.9 -11.4
Mariners 2017 -8.2 3.0 -11.2
Tigers 2017 -24.1 -13.1 -11.0

Bottom 5 Team-Seasons in Framing Runs Gained
Team Season Old FRM New FRM Park Bias
Braves 2017 29.3 9.4 19.9
Orioles 2017 13.2 -0.4 13.6
Braves 2009 47.0 38.2 8.8
Brewers 2010 44.4 35.9 8.5
Pirates 2008 -51.7 -59.9 8.2

Top 5 Player-Seasons in Framing Runs Gained
Player Season Old FRM New FRM Park Bias
Jonathan Lucroy 2017 -22.1 -10.1 -12
James McCann 2017 -16.2 -8.1 -8.1
A.J. Pierzynski 2010 -5.8 2.2 -8.0
Mike Zunino 2017 2.4 10.2 -7.8
John Buck 2010 -19.1 -11.7 -7.4

Bottom 5 Player-Seasons in Framing Runs Gained
Player Season Old FRM New FRM Park Bias
Tyler Flowers 2017 31.9 20.5 11.4
Austin Hedges 2017 21.8 12.8 9.0
Kurt Suzuki 2017 -2.9 -10.9 8.0
Welington Castillo 2017 1.6 -6.3 7.9
Yadier Molina 2017 8.7 1.8 6.9

Top 5 Player-Careers in Framing Runs Gained
Player Old FRM New FRM Park Bias
A.J. Pierzynski -41.9 -21 -20.9
A.J. Ellis -77.0 -59.9 -17.1
Joe Mauer 13.7 27.5 -13.8
Jonathan Lucroy 126.9 139.6 -12.7
Wilin Rosario -39.5 -29.3 -10.2

Bottom 5 Player-Careers in Framing Runs Gained
Player Old FRM New FRM Park Bias
Brian McCann 181.9 162.0 19.9
Welington Castillo -52.0 -66.0 14.0
Miguel Montero 127.0 113.6 13.4
Wilson Ramos 21.2 8.3 12.9
Ryan Doumit -156.7 -165.7 9.0

Jared Cross is a co-creator of Steamer Projections and consults for a Major League team. In real life, he teaches science and mathematics in Brooklyn.

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4 years ago

lol at Ryan Doumit getting actually worse

4 years ago
Reply to  timmer

Ryan Doumit’s 2008 (-3.4 fWAR) is literally unbelievable:

* 4th worst of all-time for a position player (no min. PA), and the worst following 1977.

* By far, the worst for a player with an above average wRC+ (123), followed by Doumit’s 2010 (-2.2 fWAR with a 102 wRC+). The next worst (2007 Jermaine Dye) was just -1.2 fWAR.

* The next worst season for a player with a wRC+ of 120 or higher was Brad Hawpe’s 2008 season (122 wRC+, -0.7 fWAR).

* The worst season by DEF ever, with the runner-up more than 15 runs better (Adam Dunn’s 2009, which resulted in 1 fWAR amazingly).

Pirates Hurdles
4 years ago
Reply to  vslyke

Truly unbelievable that one guy could be worth over 60 negative runs in a season on defense, especially in only 106 games. Makes me skeptical of the measures, even though I know he was a really bad C. His DRS was only -3 that year. Something tells me that we are still missing something with regards to framing values. At the same time, gotta feel bad for Maholm, Duke, and Snell.