*Originally published on sonsofsamhorn.com April 6, 2015; scraped from archive.org in 2018, possibly with some missing images. *

**Nearly twenty-five thousand batter-pitcher confrontations take place each season, about eighteen inches from the catcher. The role of the receiver has evolved in the modern game, with catcher framing the newest component to deeper understanding and analysis of the game.**

Batters have four or five plate appearances per game. Prolific fielders will average 4 to 5 plays per game ‒ **Andrelton Simmons** had 4.3 assists, putouts, and errors per game in 2014. Successful starting pitchers will throw around 100 to 120 pitches per game; Relievers can throw another 30 or 50 pitches in those games. Meanwhile, catchers handle the ball 150 times or more each game. It should follow that a catcher has the opportunity to affect the game’s outcome more than any other player except the pitcher.

One of the ways he can do that is by stealing strikes ‒ converting marginal pitches that the umpire would normally call a ball, into a strike. Or, converting a potential strike into a ball. That is catcher framing.

**Framing and PITCHf/x**

The advent of PITCHf/x has helped quantify the effect of catcher framing, and it is much larger than anyone had suspected. (Unless some team analysts had already identified it and kept it a secret, of course.) Even conservative estimates of the impact of catcher framing conclude that a catcher with strong framing skills contributes the equivalent of dozens of runs ‒ and therefore multiple wins to the team over the course of a season ‒ through framing alone.

Catcher framing has been measured in **many ways**, with both **complicated and subtle** approaches being used to get a detailed understanding of the exact effect each catcher has on the strike zone. Still, even a fairly simple approach can help show trends, strengths, and weaknesses. Here, I am going to look at catcher framing as a difference in the probability that a pitch is called a strike.

On average, in any particular region around the plate, a pitch has a certain chance of being called a strike. In the center of the strike zone, that’s nearly 100%; way outside, it’s nearly 0%. There’s not much a catcher can do about those pitches.

At the edges of the *de facto* strike zone a catcher *can* influence the probability. For some catchers, a pitch just outside the top of the strike zone might be called a strike 90% of the time, even though baseball-wide that pitch might only have a 10% chance of being a strike. Other catchers might hurt their pitcher’s chances, causing the umpire to call a ball in a region that has a 90% chance of being called a strike.

**One Of The Best**

Let us start with the catcher acknowledged as one of the best, if not the best, at framing: here’s how **Jose Molina** influenced the probability of having a pitch called a strike in 2008 (the earliest year for which we have PITCHf/x data). As with all PITCHf/x graphs, this is from the umpire’s point of view. Across this plot, areas where Molina was more likely than the rest of baseball to have a strike called are shown in red. Areas where he was less likely to have a strike called are blue. The gray polygon shows the de facto strike zone in 2008 — the region in which a pitch had a 50-50 chance of being called a strike.

There’s not a lot of blue there. In 2008, Molina had more strikes called at the edges of the strike zone than any other catcher. For left-handed batters, he pushed the strike zone down and outward; for right-handed batters, up and in. Virtually nowhere was Molina less likely to get a strike than the league average.

Framing is a repeatable skill. Year after year, the same catchers push the strike zone outward for their (lucky) pitchers, and the same catchers shrink the zone for their (unfortunate) pitchers. Here are Molina’s differential probability maps from 2008 through 2014:

Every year has a lot of red, though by 2014 the intensity of the red is dropping off — the 39-year-old Molina in 2014 was still better than the rest of the league, but not by as much.

It’s worth pointing out that the strike zone (the grey polygon in each frame) changed significantly over that period, too. The strike zone that umpires called in 2014 was approximately 10% larger than the one they called in 2008, with the expansion happening almost entirely at the bottom of the zone. The extra strikes below the 2008 strike zone that Molina grabbed would be clearly in the strike zone in 2014, and would be unambiguous strikes for most catchers. Nevertheless, while the strike zone moved outward, Molina’s expanded strike zone moved outward with it.

**One Of The Worst**

What does a bad framing catcher look like? **Ryan Doumit** has consistently been a terrible framer. Here’s what his probability maps look like from 2008 through 2013. (In 2014, understandably, he only caught 2 games for Atlanta.)

Since at least 2008, Doumit has lost strikes for his pitchers everywhere around the zone. In 2013 he wasn’t completely hopeless at the top of the zone for right-handed batters, but there’s not enough there to compensate for the losses everywhere else.

**Average Catcher**

Here is league-average catcher **Alex Avila** in 2014:

There is a little misty color there, but in general Avila has no particular strengths, and no particular weaknesses. His pitchers get the strike zone: No more, no less.

But, interestingly, there are other ways to be average. **Evan Gattis** in 2014 was just about exactly as average as Avila (I’ll explain what I mean by that in a moment). Here’s Gattis’s 2014 map:

Gattis achieved averageness by being pretty good at expanding the inside part of the plate to righties while losing the outside.

**Quantifying The Skill**

**There are many ways of** trying to put an actual number on the extent to which framing increases strikes, but they mostly come up with very similar answers. Here’s how I identify great, terrible, average, and good framers: combine the probability of a particular catcher getting a called strike in each region, the actual number of pitches he received in that region, and the league probability of a called strike in that region, to estimate the amount of called strikes that the catcher influenced per game.

For example, a pitch just outside the bottom corner of the zone might be called a strike 10% of the time across the league. If, in four games, our catcher had 10 pitches in that region and got strikes on 9 of them instead of the expected 1, he is awarded 8 extra strikes; that means 2 extra strikes per game.

Obviously, this approach misses out on many, many subtleties. It doesn’t include the pitcher’s contribution; it doesn’t consider the umpire or the game situation ‒ the strike zone changes with different counts, something I will explore this season. It doesn’t take into account the fact different pitcher/catcher combinations throw pitches to different locations. But, reassuringly, this approach ends up with numbers very similar to **other approaches**, so it’s probably measuring the same general thing.

Here are some of those numbers, showing that framing is a fairly consistent skill year to year:

Extra strikes per game | ||||||

Year | Molina | Doumit | Avila | Gattis | Lucroy | McCann |

2008 | 3.19 | -4.55 | – | – | – | 2.75 |

2009 | 3.00 | -2.90 | -1.04 | – | – | 2.98 |

2010 | 2.85 | -1.20 | -0.10 | – | 3.35 | 1.73 |

2011 | 2.06 | -2.50 | 0.02 | – | 3.26 | 2.59 |

2012 | 2.10 | -3.12 | 0.14 | – | 2.84 | 1.92 |

2013 | 1.79 | -3.18 | 0.48 | 1.12 | 2.50 | 0.80 |

2014 | 1.67 | – | -0.37 | -0.14 | 1.41 | 1.26 |

Doumit is consistently awful, Avila is consistently average, Molina, Lucroy, and McCann are consistently good to excellent (as are some other catchers, but that is a story for another day). All three of the elite framers in this set do show a decrease in extra strikes per game over time, although they all remained very good. In the case of Molina and McCann, that could just be age catching up with them, but another possibility is that as the overall strike zone has expanded over time, there are fewer opportunities for framing.

**Turning pitches into runs**

In 2008, **Dan Turkenkopf** estimated that each switched call is worth about 0.13 runs on average. If that’s about right, then having elite framers like Molina or Lucroy on staff, instead of average catchers, adds between a quarter and a third of a run per game over an average catcher, or the equivalent of about an extra 20-35 runs of value over an entire season from framing alone.

Returning to the average framers: In 2014, Avila lost about 0.37 extra strikes per game and Gattis lost about 0.14 extra strikes per game. As I said, they reached that in different ways; Avila is neutral all over the zone while Gattis shifts the zone slightly left.

This is also a common pattern among good framers: They pick up strikes from one part of the zone, but lose them elsewhere. Aside from **Jose Molina**, not many framers add strikes all around the zone. Even Molina lost his ability to expand the strike zone in all directions as he declined from his peak. By 2014, he was not getting many extra strikes at the bottom or the sides of the zone, though he was still able to get them at the top.

For example, **Brian McCann** has ranged from a good to an elite framer over his career, but he gets there in a very different way than Jose Molina:

Rather than expanding the strike zone in all directions, as Molina did from 2008 through 2013, or expanding the zone at the top while being neutral at the bottom like 2014 Molina, McCann greatly expands the bottom of the strike zone and gets fewer than average calls at the top of the zone. And he has done this consistently, year after year, for the seven years that we have had PITCHf/x data.

Not only is catcher framing a reproducible skill in general, but particular catchers can focus on framing specific regions of the strike zone, and those regions, too, are generally reproducible year to year. This predictability might let some teams choose specific sets of pitchers and catchers, working to the strengths of each.

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