Are you kidding me? Can framing be worth that much?

I created a bit of controversy on Twitter a few days ago (imagine that) when I tweeted my top 10 to-date 2018 projections for the total value of position players, including batting, base running, and defense, including positional adjustments. Four of my top 10 were catchers, Posey, Flowers (WTF?), Grandal, and Barnes. How can that be? Framing, my son, framing. All of those catchers in addition to being good hitters, are excellent framers, according to Baseball Prospectus catcher framing numbers. I use their season numbers to craft a framing projection for each catcher, using a basic Marcel methodology – 4 years’ weighted and regressed toward a population mean, zero in this case.

When doing this, the spread of purported framing talent is quite large. Among the 30 catchers going into 2018 with the most playing time (minors and majors), the standard deviation of talent (my projection) is 7.6 runs. That’s a lot. Among the leaders in projected runs per 130 games are Barnes at +18 runs, and Grandal and Flowers at +21. Some of poor framers include such luminaries as Anthony Recker, Ramon Cabrera, and Tomas Telis (who are these guys?) at -18, -15, and -18, respectively. Most of your everyday catchers these days are decent (or a little on the bad side, like Kurt Suzuki) or very good framers. Gone are the days when Ryan Doumit (terrible framer) was a full-timer and Jose Molina (great framer) a backup.

Anyway, the beef on twitter was that surely framing can’t be worth so much that 4 of the top 10 all-around players in baseball are catchers. To be honest, that makes little sense to me either. If that were true, then catchers are underrepresented in baseball. In other words, there must be catchers in the minor leagues who should be in the majors, presumably because they are good framers though not necessarily good hitters or in other arenas like throwing, blocking pitches, and calling games. If this beef is valid, then either my projection methodology for framing is too strong, i.e., not enough regression, or BP’s numbers lack some integrity.

As a good sabermetricians should be wont to do, I set out to find out the truth. Or at least find evidence supporting the truth. Here’s what I did:

I did a WOWY (without and with you – invented by the illustrious Tom Tango) to compare every catcher’s walk and strikeout rate with each pitcher they worked with to that of the the same pitchers working with other catchers – the without. I did not adjust for the framing value of the other catchers. Presumably for a good framing catcher they should be slightly bad, framing-wise, and vice versa for bad-framing catchers, so that there will be a slight double counting. I did this for each projected season 2014-2017, or 4 seasons.

I split the projected catchers into 3 groups, Group I were projected at greater than 10 runs per 150 games (8.67 per 130), Group II at less than -10 runs, and Group III, all the rest. Here is the data for 2014-2017 combined. Remember I am using, for example, 2017 pre-season projections, and then comparing that to a WOWY for that same year.

Total PA	Mean Proj per 130 g	W/ BB rate	WO/ BB rate	Diff	W/ SO rate	WO/SO rate	Diff
74,221	-12.6	.082	.077	.005	.197	.206	-..009
107,535	+13.3	.073	.078	-.005	.215	.212	.003
227,842	-.2	.078	.078	0	.213	.212	.001

 

We can clearly see that we’re on the right track. The catchers projected to be bad framers had more BB and fewer SO than average and the good framers had more SO and fewer BB. That shouldn’t be surprising. The question is how accurate are our projections in terms of runs. To answer that, we need to convert those BB and SO rates into runs. There are around 38 PA per game, so for 130 games, we have 4,940 PA. Let’s turn those rate differences into runs per 130 games by multiplying them by 4,940 and then by .57 runs which is the value of a walk plus an out, which assumes that every other component stays the same, other than outs. My presumption is that an out is turned into a walk or a walk is turned into an out. A walk as compared to a neutral PA is worth around .31 runs and an out around .26 runs.

Total PA	Mean Proj per 130 g	W/ BB rate	WO/ BB rate	Diff in runs/130	W/ SO rate	WO/SO rate	Diff
74,221	-12.6	.082	.077	+14.0	.197	.206	-.009
107,535	+13.3	.073	.078	-14.0	.215	.212	.003
227,842	-.2	.078	.078	0	.213	.212	.001

 

Let’s make sure that my presumption is correct before we get tool excited with those numbers. Namely that an out really is turning into a walk and vice versa due to framing. Changes in strikeout rate are mostly irrelevant in terms of translating into runs, assuming that the only other changes are in outs and walks (strikeouts are worth about the same as a batted ball out).

Total PA	Mean Proj	W/ HR	WO/HR	Diff	W/ Hits	WO/Hits	Diff	W/ Outs	WO/ Outs	Diff
74,221	-12.6	.028	.028	0	.204	.203	.001	.675	.681	-.006
107,535	+13.3	.029	.029	0	.200	.198	.002	.689	.685	.004
227,842	-.2	.029	.029	0	.199	.200	-.001	.685	.683	.002

 

So, HR is not affected at all. Interestingly, both good and bad framers give up slightly more non-HR hits. This is likely just noise. As I presumed, the bad framers are not only allowing more walks and fewer strikeouts, but they’re also allowing fewer outs. The good framers are producing more outs. So this does in fact suggest that the walks are being converted into outs, strikeouts and/or batted ball outs and vice versa.

If we chalk up the difference in hits between the with and the without to noise (if you want to include that, that’s fine – both the good and bad framers lose a little, the good framers losing more), we’re left with outs and walks. Let’s translate each one into runs separately using .31 runs for the walks and .26 runs for the outs. Those are the run values compared to a neutral PA.

Total PA	Mean Proj per 130 g	W/ BB rate	WO/ BB rate	Diff in runs/130	W/ Outs	WO/ Outs	Diff
74,221	-12.6	.082	.077	+7.7	.675	.681	+7.7
107,535	+13.3	.073	.078	-7.7	.689	.685	-5.1
227,842	-.2	.078	.078	0	.685	.683	-2.6

 

So our bad framers are allowing 15.4 runs more per 130 games than the average catcher or than their others at least, in terms of fewer outs and more BB. The good framers are allowing 12.8 fewer runs per 130 games. Compare that to our projections, and I think we’re in the same ballpark.

It appears from this data that we have pretty strong evidence that framing is worth a lot and our four catchers should be in the top 10 players in all of baseball.

Do Catchers (and Pitchers) Really Know What the Hell They’re Doing?

Maybe.

In this article, Tuffy Gosewisch, the new backup catcher for the Braves, talks catching with Fangraphs David Laurilia. He says about what you would expect from a catcher. Nothing groundbreaking or earth-shattering – nothing blatantly silly or wrong either. In fact, catchers almost always sound like baseball geniuses. They do have to be one of the smarter ones on the field. But…

Note: This is almost verbatim from my comment on that web page:

I have to wonder how much better a catcher could be if he understood what he was actually doing (of course they do, they get paid millions, they’ve been doing it all their lives, and are presumably the best in the world at what they do. Who the hell are you, you’ve never put on the gear in your life?).

All catchers talk about how they determine the “right” pitch. I’m waiting for a catcher to say, “There is no ‘right’ pitch – there can’t be! There’s a matrix of pitches and we choose one randomly. Because you see, if there were a ‘right” pitch and that was the one we called, the batter would know or at least have a pretty good idea of that same pitch and it would be a terrible pitch, especially if the batter were a catcher!”

If different catchers and pitchers have different “right” pitches and that’s why batters can’t guess them then there certainly isn’t a “right” pitch – it must be a (somewhat) random one.

When I say “random” I mean from a distribution of pitches, each with a pre-determined (optimal) frequency, based on the batter and the game situation. Rather than it be the catcher and pitcher’s job to come up with the “right” pitch – and I explained why that concept cannot be correct – it is their responsibility to come up with the “right” distribution matrix, for example, 20% FB away, 10% FB inside, 30% curve ball, 15% change up, etc. In fact, once you do that, you can tell the batter your matrix and it won’t make any difference! He can’t exploit that information and you will maximize your success as a pitcher, assuming that the batter will exploit you if you use any other strategy.

If a catcher could come up with the “right” single pitch that the batter is not likely to figure out, without randomly choosing one from a pre-determined matrix, well….that can’t be right, again, because whatever the catcher can figure, so can (and will) the batter.

We also know that catchers don’t hit well. If there were “right” pitches, catchers would be the best hitters in baseball!

Tuffy also said this:

“You also do your best to not be predictable with pitch-calling. You remember what you’ve done to guys in previous at-bats, and you try not to stay in those patterns. Certain guys — veteran guys — will look for patterns. They’ll recognize them, and will sit on pitches.”

Another piece of bad advice! Changing your patterns is being predictable! If you have to change your patterns to fool batters your patterns were not correct in the first place! As I said, the “pattern” you choose is the only optimal one. By “pattern” I mean a certain matrix of pitches thrown a certain percentage of time given the game situation and participants involved. Any other definition of “pattern” implies predictability so for a catcher to be talking about “patterns” at all is not a good thing. There should never be an identifiable pattern in pitching unless it is a random one which looks like a pattern. (As it turns out, researchers have shown that when people are shown random sequences of coin flips and ones that are chosen to look random but are not, people more often choose the non-random ones as being random.)

Say I throw lots of FB to a batter the first 2 times through order and he rakes (hits a HR and double) on them. If those two FB were part of the correct matrix I would be an idiot to throw him fewer FB in the next PA. Because if that were part of my plan, once again, he could (and would) guess that and have a huge advantage. How many times have you heard Darling, Smoltz or some other ex-pitcher announcer say something like, “After that blast last AB (on a fastball) the last thing he’ll do here is throw him another fastball in this AB?” Thankfully, for the pitcher, the announcer will invariably be wrong, and the pitcher will throw his normal percentage of fastballs to that batter – as he should.

What if I am mixing up my pitches randomly each PA but I change my mixture from time to time? Is that a good plan? No! The fact that I am choosing randomly from a matrix of pitches (each with a different fixed frequency for that exact situation) on each and every pitch means that I am “somewhat” unpredictable by definition (“somewhat” is in quotes because sometimes the correct matrix is 90% FB and 10% off-speed – is that “unpredictable?”) but the important thing is that those frequencies are optimal. If I constantly change those frequencies, even randomly, then they often will not be correct (optimal). That means that I am sometimes pitching optimally and other times not. That is not the overall optimal way to pitch of course.

The optimal way to pitch is to pitch optimally all the time (duh)! So my matrix should always be the same as long as the game situation is the same. In reality of course, the game situation changes all the time. So I should be changing my matrices all the time. But it’s not in order to “mix things up” and keep the batters guessing. That happens naturally (and in fact optimally) on each and every pitch as long as I am using the optimal frequencies in my matrix.

Once again, all of this assumes a “smart” batter. For a “dumb” batter, my strategy changes and things get complicated, but I am still using a matrix and then randomizing from it. Always. Unless I am facing the dumbest batter in the universe who is incapable of ever learning anything or perhaps if it’s the last pitch I am going to throw in my career.

There are only two correct things that a pitcher/catcher have to do – their pitch-calling jobs are actually quite easy. This is a mathematical certainty. (Again, it assumes that the batter is acting optimally – if he isn’t that requires a whole other analysis and we have to figure out how to exploit a “dumb” batter without causing him to play too much more optimally):

One, establish the game theory optimal matrix of pitches and frequencies given the game situation, personnel, and environment.

Two, choose one pitch randomly around those frequencies (for example, if the correct matrix is 90% FB and 10% off-speed, you flip a 10-side mental coin).

Finally, it may be that catchers and pitchers do nearly the right thing (i.e. they can’t be much better even if I explain to them the correct way to think about pitching – who the hell do you think you are?) even though they don’t realize what it is they’re doing right. However, that’s possible only to an extent.

Many people are successful at what they do without understanding what it is they do that makes them successful. I’ve said before that I think catchers and pitchers do randomize their pitches to a large extent. They have to. Otherwise batters would guess what they are throwing with a high degree of certainty and Ron Darling and John Smoltz wouldn’t be wrong as often as they are when they tell us what the pitcher is going to throw (or should throw).

So how is that catchers and pitchers can think their job is to figure out the “right” pitch (no one ever says they “flip a mental coin”) yet those pitches appear to be random? It is because they go through so many chaotic decision in their brain that for all intents and purposes the pitch selection often ends up being random. For example, “I threw him a fastball twice in a row so maybe I should throw him an off-speed now. But wait, he might be thinking that, so I’ll throw another fastball. But wait, he might be thinking that too, so…” Where they stop in that train of thought might be random!

Even if pitchers and catchers are essentially randomizing their pitches, two things are certain. They can’t possibly be coming up with the exact game theory optimal (GTO) matrices, and trust me there IS an optimal one (although it may be impossible for anyone to determine it, but I guarantee that someone can do a better job overall – it’s like man versus machine). Two, some pitchers and catchers will be better at pseudo-randomizing than others. In both cases there is a great deal of room for improvement on calling games and pitches.