The live BBA data will be messier because the competitive environment isn’t controlled like that.
Anyway, I got to thinking about how I might use the BBA data to look at catchers. Here’s what I came up with:
1) Find a team that uses two catchers for a reasonable amount of time
2) And whose catchers have very different defensive ratings, ideally one very good and one not so good.
3) Compare pitching staff results, both as a whole and in individuals, when each catcher is being used
This, I figured, should give me at least a one-shot look at how big of an advantage great catcher defense is in the wild. After picking around a bit, I focused on Phoenix, who uses a pair of catchers:
- Cisco Arreola: 36 yo, 3/6 Ability/Arm
- Vicente Diaz: 25 yo, 6/8 Ability/Arm
I then had to do a little dipsy-do with the spreadsheet data, identifying which games and innings the two actually caught, then transposing those into the pitch/PA data. Once that was done, the rest was easy. I should note, I suppose, that the data I'll present here is captured through the end of August.
OVERALL/COMPOSITE REVIEW
First, I looked simply at strikes and balls, regardless of the pitcher. This is probably mostly a silly academic study. My guess is that OOTP assigns these after the fact, but (1) I don’t know this, and (2) I was having fun trying to see if a better defender got more called strikes—which is what framing is, right?
The bottom line is: yes, Diaz got more strike calls and had fewer balls called on the whole.
Catcher | Pitches | StCa | StSw | Balls |
---|---|---|---|---|
Cisco Arreola | 14498 | 15.8% | 9.4% | 37.9% |
Vicente Díaz | 4079 | 17.0% | 10.5% | 37.3% |
Lets go the next step and look at results.
Are these distributions resulting in more strikeouts and fewer walks, and if so, how many?
The answer is: yes, a few fewer walks and considerably more strikeouts.
Catcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|
Cisco Arreola | 3895 | 7.8% | 20.7% | 5.0% | 15.8% | 3.3% |
Vicente Díaz | 1114 | 7.5% | 24.4% | 6.6% | 17.8% | 3.0% |
Again, though, this is composite data and could be influenced by several selection issues.
Note, also, that I’ve included HR/AB rates, though I have no idea why a catcher would influence those. Fun, remember?
A LOOK AT INDIVIDUAL PITCHERS
So, yeah, fun stuff. But there are so many degrees of freedom here, not the least is that a catcher’s performance in those numbers could probably be swayed heavily by which pitchers he caught most. One guy gets a fireballing strikeout guy, and the other guy catches the junk baller, and see these numbers go haywire.
So, instead, here’s a look at a few of Phoenix’s pitchers, focusing on the guys with bigger sample sizes. This will still be noisy for sample size purposes, of course, and will still be influenced on the fact that I haven’t put the results in context of what hitters comprised the sample. Guys who face more sluggers, for example, could have different numbers here than guys who faced a bunch of high-contact slap hitters. So when you look at the numbers, you have to decide yourself how far to trust them—including none at all.
Let’s look, for example at Charlie Iron-Knife. Iron-Knife. He’s a knuckleballing lefty with limited control who leads the team with over 700 batters faced.
Catcher | Pitcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|---|
Cisco Arreola | Charlie Iron-Knife | 596 | 9.9% | 21.1% | 4.6% | 16.5% | 2.1% |
Vicente Díaz | Charlie Iron-Knife | 137 | 11.7% | 30.5% | 8.5% | 22.0% | 1.7% |
Here’s 24-year-old power pitcher Jose Aguilar with 584 PA:
Catcher | Pitcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|---|
Cisco Arreola | César Aguilar | 427 | 7.0% | 19.1% | 3.6% | 15.5% | 2.8% |
Vicente Díaz | César Aguilar | 157 | 7.6% | 28.0% | 9.8% | 18.2% | 2.1% |
With Maxime Manceau, we come to a different profile though. In 700 AP:
Catcher | Pitcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|---|
Cisco Arreola | Maxime Manceau | 507 | 7.1% | 15.3% | 4.6% | 10.7% | 2.6% |
Vicente Díaz | Maxime Manceau | 193 | 3.6% | 14.9% | 5.5% | 9.4% | 3.9% |
Here’s Elwood Blues, whose data seems to support Diaz across the board. Note, however, that sample size. Arreola seems to be Blue’s primary receiver.
Catcher | Pitcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|---|
Cisco Arreola | Elwood Blues | 512 | 9.8% | 21.2% | 6.3% | 15.0% | 4.2% |
Vicente Díaz | Elwood Blues | 41 | 7.3% | 27.8% | 5.6% | 22.2% | 2.8% |
Hector Amaral, too, appears to fare a touch better with Diaz behind the plate, though the impact looks subtle based on this data.
Catcher | Pitcher | PA | BB/PA | K/AB | KL/AB | KS/AB | HR/AB |
---|---|---|---|---|---|---|---|
Cisco Arreola | Héctor Amaral | 497 | 5.6% | 22.1% | 4.8% | 17.2% | 3.7% |
Vicente Díaz | Héctor Amaral | 190 | 5.3% | 25.3% | 5.1% | 20.2% | 0.0% |
At this point, we start getting to guys with sample sizes down in the ranges that are so low as to be make-work to post them here.
SO WHAT’S THE ANSWER?
I hear you. I started this to get an idea of how big the impact of catching really is in the wilds of the BBA. And in this case, I’ve got a data set that sets a poor defender (Arreola) vs. a competent one (Diaz). What, you ask, do I think the answer is? How much of an advantage do Phoenix pitchers get when they throw to Vicente Díaz rather than Cisco Arreola?
The answer is: I don’t know.
I’d baseline my thoughts on the idea that they’ll walk a half-percent fewer walks is probably good—maybe low. Depends on the pitcher. I’d also say that it wouldn’t surprise me to see if a bigger study said pitchers throwing to Diaz might strike out an extra guy every 3 games or so. I note, also, that in this very small sample it almost appears that Diaz helps pitcher give up fewer homers—though that might be due to the pitcher just striking out more guys. Dunno.
And I’ll note that I didn’t get a sample size from pairing an elite level catcher and a mid-range or worse partner. It’s certainly possible that the influence is small on the low end, and ramps up when an elite level catcher is in there.
Regardless, as I noted to Tyler in our discussion, the most important thing about these studies is the degree to which they confirm or deny that you can use “normal GM logic” (whatever that is) when assessing values of players, and what I’m seeing here does seem to line up with Tyler’s study that shows catcher defense in OOTP is something that influences the game in more ways than just controlling the running game.
Which is pretty cool, right?