A Quick Glance at Pitch-Framing and Command Extremes

Thursday afternoon, I took a quick glance at 2014 team-by-team pitch-framing projections. This afternoon, I’m taking a quick glance at something else within the pitch-framing field. Longer glances are, of course, superior to quicker glances, but I take quick glances for three primary reasons. One, I don’t have the time, really, to dedicate to longer, research-paper-level glances. Two, I don’t have the mathematical chops to really get into stuff in depth. And three, quick glances make for good starting points, and they usually end up being fairly accurate. If you can get to X in an hour, and if you can only get to 1.1X in ten hours, how valuable are the extra nine hours? Extremely valuable, in science. Less valuable, in casual baseball analysis.

For some years, we’ve had pitch-framing information, for catchers. We’ve been able to tell how many strikes they gain or cost, and we’ve been able to assign run values. A major complication, however, lies in trying to separate catchers from pitchers. It’s the pitchers, after all, who’re throwing the pitches getting caught, and it stands to reason different pitchers might be differently challenging to receive. This is far from a new idea, but it’s an idea worthy of further exploration.

From prior investigations, it seems like the key to good receiving is having a quiet body. If the catcher doesn’t move very much, then all else being equal, he’s got a good shot of getting a strike call. If the catcher does move a lot, a strike is less likely, even if the pitch being caught ends up within the zone borders. People have focused on catcher wrists, catcher heads, and catcher anticipation, among other factors.

But it follows that a catcher won’t have to move very much if his pitcher hits his spot. If his pitcher is more wild, the location will be more unpredictable, and the catcher will have to physically react. Two pitches that end up in the same place aren’t created equal, because it matters where the catcher was positioned, previously. Just thinking about it, it’s easy to see how a chunk of framing data could actually be related to pitcher and pitching-staff command.

What can we do about that? Unfortunately the public doesn’t have access to quality command data. My understanding is it is out there, in private circles, but we have to settle for proxies. Thankfully, the proxies should seldom mislead. It would be almost impossible for us to rank active pitchers by their command, but we can have a pretty good sense of who’s strong in the area, and who’s relatively weak. We can at least probably identify the extremes.

So the task I gave myself was identifying groups at either command extreme, and then looking at their pitch-framing numbers. My proxy for command was simple walk rate, because it seems like it should do just fine. I decided on groups of 25 — 25 pitchers with good command, and 25 pitchers with bad command. I was interested in 2013 pitch-framing data, so, operating under the assumption that command is pretty stable, I put my groups together using 2012 walk rates since 2013 walk rates would be influenced by framing results. I selected from pitchers who threw at least 50 innings in both 2012 and 2013. I also made the particular inclusion of Mariano Rivera, since he was hurt in 2012, but since he obviously has phenomenal command.

The good-command group includes guys like Rivera, Cliff Lee, Sergio Romo, Kris Medlen, and Bartolo Colon. The bad-command group includes guys like Garrett Richards, Matt Moore, Brandon League, Samuel Deduno, and Tim Lincecum. I acknowledge the groups aren’t perfect, and players change from season to season, but this is what I wound up with.

Framing data comes from StatCorner. zTkB% refers to the percent of called pitches within the PITCHf/x strike zone called balls. oTkS% refers to the percent of called pitches outside the PITCHf/x strike zone called strikes. So how did the groups differ, on average, in this past season? No corrections have been made or attempted for catcher identities. Another assumption of mine is that that effect should wash out.


  • Good-command group: 13.3%
  • Bad-command group: 14.9%


  • Good-command group: 8.4%
  • Bad-command group: 5.9%

There’s a difference, with the good-command group being better than average in both categories, and with the bad-command group being worse than average in both categories. The differences appear relatively small, but then most strikes are obvious strikes, and most balls are obvious balls, so the differences are magnified if you look only at pitches closer to the borders. Based on these numbers and these numbers only, pitchers with better command do end up with more strikes, beyond what you’d expect just throwing more strikes in general. And keep in mind that bad-command pitchers might be behind in the count more often, and in those cases the strike zone has been proven to expand.

On average, by StatCorner, about half of all pitches are thrown in the zone. About 36% of pitches in the zone are taken, and about 73% of pitches out of the zone are taken. Based on those averages, per 100 called pitches, the difference between these two groups was 2.2 strikes. Per 100 pitches overall, the difference was about 1.2 strikes. So you can think of it as being about a strike a start. And a strike, they say, is worth something in the area of 0.13 runs. With framing, it’s always about little advantages and disadvantages piling up over the course of one or six months.

The data is preliminary, and it compares only two groups at either command extreme. Most pitchers would fall in between either group, so most pitchers would experience a smaller effect. But it does seem like an effect probably exists, as you’d think it would. Framing isn’t all about the catcher. It’s also about a pitcher’s ability to throw a pitch as the catcher anticipates, which lets the catcher maximize his own receiving ability. A good framer will always be a good framer, but a good framer will look better catching Mariano Rivera than he will catching Brandon League, through basically no fault of his own. The less predictable a pitcher is, the less a catcher will be able to do.

Jeff made Lookout Landing a thing, but he does not still write there about the Mariners. He does write here, sometimes about the Mariners, but usually not.

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10 years ago

Did you do anything to see if the differences were actually significant? The oTkS% difference of around 2.5% sounds pretty huge, but what is league average, and what std dev does it have? The ZTkB% seems smaller in comparison, but I don’t really know.