Do Pitchers Adjust to Their Receivers’ Strengths and Weaknesses?

Rob Arthur published a really interesting piece at Baseball Prospectus, where he presented evidence in favor of the idea that batters are aware of the relative framing ability of the receiver they’re facing. That’s really fascinating to me, because it suggests that this skill, which the baseball research community has only recently begun to quantify, has been understood by players for a long enough time to show up in the behavior of major leaguers.

If that were true, batters are not the first component of an at-bat I’d expect to adjust to the receiver. Quotes from pitchers in the past have suggested that they’re aware of when their catcher is helping them out, and how; I want to know if that awareness is reflected in their pitch tendencies. Specifically, I want to know if pitchers are aware of the particular framing skills of their receivers. This article, by Community Blog Overlord Jeff Sullivan, is a little old, but it was one of the first framing articles I read, and the first I remember suggesting that some catchers were not just better at framing than their counterparts, but framing in specific parts of the zone. This more recent article, where Dave Cameron discusses the possibility of voting for Jonathan Lucroy as NL MVP, does talk about pitcher tendencies based on receiver skill, but it’s one pitcher and one catcher. Additionally, I’m just as interested to see how pitchers react to bad receivers, which as far as I can tell, hasn’t been covered. Do pitchers throw to their receivers’ strengths, and do they avoid their weaknesses?

The first thing to do is to establish how catchers do in different sections of the strike zone. I’m using Pitch F/X data from the wonderful Baseball Savant, which splits the zone like so:

For the purposes of this article, I’m concerned with the relative ability of receivers to preserve and gain strikes in different parts of the zone. As such, I’m going to categorize all pitches as “high in-zone” (in zones 1, 2, and 3), “high out-zone” (11 and 12), “low in-zone” (zones 7, 8, and 9), and “low out-zone” (13 and 14). It is a little unfortunate that this doesn’t pick up the relative horizontal skill of receivers, but these divides should still allow for some real differentiation between catchers while also keeping our sample sizes large-ish. If we pick too narrow a slice of the zone, the results might get a bit iffy.

Calculating relative framing ability took a few steps. To begin with, I looked at receivers with at least 30 pitches in each of the four zones, which picks up 87 catchers. That’s might be way too small a sample size, but the least pitches caught by any of these receivers is 1,040, which is not terrible. For each receiver, I calculated their rate of strikes for each of the four zones, and took the ratio between their strike rate and the average strike rate for the sample, and averaged together that ratio for the two low zones and the two high zones. That left two ratios for each player, high and low, where a number greater than 100 indicated better than average framing ability and a number less than 100 indicated worse than average framing ability.

Now, this is not a very good framing metric, but it does allow for a zone-oriented measure. I then divided the high-zone ratio by the low-zone ratio to get a final ratio, where greater than 100 indicated a receiver relatively better at getting the high strike, and less than 100 indicated a receiver relatively better at getting the low strike. Catchers notably better in the lower part of the zone: George Kottaras (.68), Jeff Mathis (.72), and Travis d’Arnaud (.73). Jonathan Lucroy, mentioned as a good low-ball framer, had a score of .89, but as he was good in both parts of the zone, there was a limit to how extreme his ratio could be. Catchers notably better in the high part of the zone: A.J. Ellis (1.46), Adrian Nieto (1.33), and Brett Hayes (1.29), again, three catchers with pretty bad receiving reputations.

So we now have a rough indication of how much better catchers are in the bottom and top of the zone. What kind of relation does this have to how they were pitched to? To estimate that, I stayed simple – I ran a linear regression, with the high/low ratio as the independent variable and the percentage of low or high pitches the catcher was thrown as the dependent variable. This, again, is a very rough measurement, since different pitchers are throwing to these catchers, but looking on a battery-by-battery basis would make the sample sizes tiny. Additionally, sometimes a catcher is catching a given pitcher because he’s good at receiving in a certain part of the zone that pitcher throws to frequently. So while this might be picking up manager actions as well as pitcher actions, it should be picking up something.

Results! Two graphs.

     

Both graphs show the expected relationship, with this blunt measurement of relative framing ability doing a fairly good job of predicting the distribution of low and high pitches thrown to a given catcher. Obviously there’s more at play here, but clearly pitch selection is impacted by the strengths of the receiver behind the plate.

There’s another question that can potentially be answered using this metric: do pitchers react differently to strengths and weaknesses? If one catcher is 30% better at framing low pitches than high pitches, and very good at framing low pitches, and another catcher is also 30% better at framing low pitches than high pitches, but very bad at it (and apparently even worse at framing high pitches, I guess (he is a very good hitter)), is one of them more likely than the other to get an increased rate of low pitches? In other words, are pitchers more inclined to avoid the bad, or seek the good?

To answer this question, I split the receivers into above-average and below-average low pitch receivers (46 and 41 in each group) and above-average and below-average high pitch receivers (51 and 36 in each group), using the scale described above. I then plotted the rate of pitches in the appropriate zone against each group separately. Following: more graphs!

 

What we see here is a higher R² value in both of the below-average samples, indicating that the high vs. low ability of bad framers appears to influence pitcher decisions more than the high vs. low ability of good framers. The gap for low pitches isn’t huge, but the gap for high pitches is fairly substantial. While this analysis is way too rough to conclusively show anything, this would seem to suggest that pitchers behave differently when throwing to good and bad framers, and may be more inclined to avoid weaknesses than to seek out strengths.

As I said (several times), this is a rough analysis that relies on a rough metric, but I think it provides some evidence for some very interesting pitcher behavior. I’d love to hear about other ways of identifying receivers’ strengths and weaknesses in different parts of the zone, so if anyone knows of articles doing so, or has some different ideas, say so in the comments!