Poking Some More at the Effects of Receiving

Used to be the hipster thing was to talk about pitch-framing, or pitch-receiving, and how it’s more important than it’s been given credit for. That was all well and fun, but people have a pretty good idea now, as the concept has gone borderline mainstream. And it turns out we don’t actually know that much about the effects, since it’s not as simple as calculating the difference between a ball and a strike. Of course, all else being equal, a good receiver is more valuable than a bad one, but we don’t know how much more valuable. The new hipster thing is to talk about receiving realistically. To distrust the idea of a guy being worth something like 50 runs above average. I live in Portland so you can trust me on my evaluation of hipster things.

Over the rest of this post, not everything is figured out. You could argue that very little is figured out, and so much more research could be done. Research by people with more time and way better technical skills. But I’ve decided to mess around with some numbers, and I’ll try to make this as reader-friendly as possible. I’m not going to lay out for you the true effects of good or bad pitch-receiving. Hopefully this’ll just make you think a little, before you think about something else.

Central to this post will be a home-brewed statistic known as Diff/100. In the past, I’ve used Diff/1000, and this is just that divided by ten because why not? Diff/100 is derived from numbers readily available here at FanGraphs. It’s the difference, per 100 called pitches, between actual strikes and expected strikes, based on zone rate and out-of-zone swing rate. Diff/100 is adjusted to set the league average every year at zero. A positive Diff/100 means a pitcher or team got more strikes than expected. A negative Diff/100 means a pitcher or team got fewer strikes than expected. A catcher who’s bad at receiving, like Ryan Doumit, would contribute to a negative Diff/100. I’ve written this paragraph so many times that I don’t know how many more times it’ll need to be written. Probably all of the times.

The first thing I looked at was simple, and on the team level. I split seasons and looked at every team from between 2008 to 2012. For each team season, I calculated Diff/100, then I looked at the relationship between that and ERA-, FIP-, and xFIP-. This seems to call for a table:

Stat	r	Slope
ERA-	-0.2	-1.5
FIP-	-0.2	-1.2
xFIP-	-0.3	-1.2

Correlations exist, and as Diff/100 increases, ERA-, FIP-, and xFIP- decrease, slowly. Think of the slope as the gain or loss per one extra strike (per 100 called pitches). The highest Diff/100 in the sample belongs to the 2009 Braves, at +3.9. The lowest Diff/100 in the sample belongs to the 2011 Indians, at -3.9. Between extremes, that’s a difference of nearly eight strikes per 100 called pitches. But we don’t know why we might be seeing what we’re seeing. Pitchers, of course, have some effect on the way they’re received, and a staff with good command might come out looking better than a staff with worse command. I decided to dig into individuals, and now this gets a little more complicated. I promise I’ll be gentle.

I went to the pitcher leaderboards, split seasons between 2008 and 2012, and set a minimum of 100 innings pitched. For every individual pitcher season, I calculated Diff/100. Then, for every pitcher who threw at least 100 innings in consecutive seasons, I calculated the change in Diff/100, along with the changes in ERA-, FIP-, and xFIP-. This left me with a pool numbering 387. Then I sorted the numbers by change in Diff/100, looking for the biggest changes both positive and negative.

For example, between 2011 and 2012, Derek Lowe‘s Diff/100 dropped by an incredible 10.2. Between 2008 and 2009, Mark Hendrickson’s Diff/100 increased by an also incredible 8.2. The way I figure, individual pitchers will have roughly constant command. Command will, of course, vary, but this is the best I can do to isolate the receiving component. Let’s look now at another table, isolating the 20 pitchers with the biggest Diff/100 drops, and the 20 pitchers with the biggest Diff/100 gains. Shown are their average season-to-season changes in ERA-, FIP-, and xFIP-.

Pitchers	? Diff/100	? ERA-	? FIP-	? xFIP-
20 drops	-4.7	10	8	7
20 gains	4.7	-5	-3	-2
Average	0.1	4	3	2

The question marks are supposed to be delta symbols! Pretend that they’re delta symbols.

Unsurprisingly, the pitchers with the biggest drops in Diff/100 got worse. Meanwhile, the pitchers with the biggest gains in Diff/100 got better. On average, season-to-season ERA- increased by four points. For the 20 biggest drops, ERA- increased by ten points. For the 20 biggest gains, ERA- decreased by five points. And so on in that fashion. Sure enough, we don’t see no effect. Controlling for pitcher identity, it looks like receiving can make a real difference.

But the correlations are very, very weak. Here’s an example chart, plotting the change in xFIP- against the change in Diff/100. And the change in xFIP- has the strongest correlation with the change in Diff/100. The r value is -0.12.

Look at the line and you see a trend. Look at the points and you see a bunch of points. It’s not that there’s no effect. It’s that the effect is small, and there’s a lot that goes on with pitchers. According to the slope, for each additional strike per 100 called pitches, xFIP- changes by about 0.6. But this is noisier than Motörhead at an airport.

There’s no question in my mind that there’s a big gap between the best receivers and the worst ones. I don’t see much reason to believe a good receiver can save a pitcher season, or a bad receiver can cripple one. Presumably, if a pitcher has a bad receiver, he’ll find a means of compensating. If a pitcher has a good receiver, he might end up throwing more pitches out of the zone. Receiving matters. Of course it matters. It can’t not matter. But there’s a lot of work to do on figuring out how much it matters, and so as exciting as it is to look at, we should probably proceed with caution, just to be safe.