Archive for January, 2017

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.

Note: This post was edited to include some new data which leads us in the direction of a different conclusion. The addendum is at the end of the original post .

This is another one of my attempts at looking at “conventional wisdoms” that you hear and read about all the time without anyone stopping for a second to catch their breath and ask themselves, “Is this really true?” Or more appropriately, “To what extent is this true?” Bill James used those very questions to pioneer a whole new field called sabermetrics.

As usual in science, we can rarely if ever answer questions with, “Yes it is true,” or “No, it is not true.” We can only look at the evidence and try and draw some inferences with some degree of certainty between 0 and 100%. This is especially true in sports when we are dealing with empirical data and limited sample sizes.

You often read something like, “So-and-so pitcher had a poor season (say, in ERA) but he had a few really bad outings so it wasn’t really that bad.” Let’s see if we can figure out to what extent that may or may not be true.

First I looked at all starting pitcher outings over the last 40 years, 1977-2016. I created a group of starters who had at least 4 very bad outings and at least 100 IP in one season. A “bad outing” was defined as 5 IP or less and at least 6 runs allowed, so a minimum RA9 of almost 11 in at least 4 games in a season. Had those starts been typical starts, each of these pitchers’ ERA’s or RA9 would have been at least a run less or so.

Next I only looked at those pitchers who had an overall RA9 of at least 5.00 in the seasons in question. The average RA9 for these pitchers with some really bad starts was 5.51 where 4.00 is the average starting pitcher’s RA9 in every season regardless of the run environment or league. Basically I normalized all pitchers to the average of his league and year and set the average at 4.00. I also park adjusted everything.

OK, what were these pitchers projected to do the following season? I used basic Marcel-type projections for all pitchers. The projections treated all RA9 equally. In other words a 5.51 RA with a few really bad starts was equivalent to a 5.51 RA with consistently below-average starts. The projections only used full season data (RA9).

So basically these 5.51 RA9 pitchers pitched near average for most of the their starts but had 4-6 really bad (and short) starts that upped their overall RA9 for the season by more than a run. Which was more indicative of their true talent? The vast majority of the games where they pitched around average, the few games where they blew up, or their overall runs allowed per 9 innings? Or, their overall RA9 for that season (regardless of how it was created) plus their RA9 from previous seasons and then some regression thrown in for good measure – in other words, a regular, old-fashioned projection?

Our average projection for these pitchers for the next season (which is an estimate of their true talent that season) was 4.46. How did they pitch the next season – which is an unbiased sample of their true talent (I didn’t set an innings requirement for this season so there is no survivorship bias)? It was 4.48 in 10,998 TBF! So the projection which had no idea that these were pitchers who pitched OK for most of the season but had a terrible seasonal result (5.51 RA9) because of a few terrible starts, was right on the money. All the projection model knew was that these pitchers had very bad RA9 for the season – in fact, their average RA was 138% of league average.

Of course since we sampled these pitchers based on some bad outings and an overall bad ERA (over 5.00) we know that in prior seasons their RA9 would be much lower, similar to their projection (4.46) – actually better. In fact, you should know that a projection can apply just as well to previous years as it can to subsequent years. There is almost no difference. You just have to make sure you apply the proper age adjustments.

Somewhat interestingly, if we look at all pitchers with a RA9 above 5 (an average of 5.43) who did not have the requisite very bad outings, i.e. they pitched consistently bad but with few disastrous starts, their projected RA9 was 4.45 and their actual was 4.25, in 25,479 TBF.

While we have significant sample error in these limited samples, not only is there no suggestion that you should ignore or even discount bad ERA or RA that are the result of a few horrific starts, there is a (admittedly weak) suggestion that pitchers who pitch badly but more consistently may be able to outperform their projections for some reason.

The next time you read that, “So-and-so pitcher has bad numbers but it was only because of a few really bad outings,” remember that there is no evidence  that an ERA or RA which includes a “few bad outings” should be treated any differently than a similar ERA or RA without that qualification, at least as far as projections are concerned.

Addendum: I was concerned about the way I defined pitchers who had “a few disastrous starts.” I included all starters who gave up at least 6 runs in 5 innings or less at least 5 times in a season. The average number of bad starts was 5.5. So basically these were mostly pitchers who had 5 or 6 really bad starts in a season, occasionally more.

I thought that most of the time when we hear the “A few bad starts” refrain, we’re talking literally about “a few bad starts,” as in 2 or 3. So I changed the criteria to include only those pitchers with 2 or 3 awful starts. I also upped the ante on those terrible starts. Before it was > 5 runs in 5 IP or less.  Now it is >7 runs in 5 IP or less – truly a blowup of epic proportions. We still had 508 pitcher seasons that fit the bill which gives us a decent sample size.

These pitchers overall had a normalized (4.00 is average) RA9 of 4.19 in the seasons in question, so 2 or 3 awful starts didn’t produce such a bad overall RA. Remember I am using a 100 IP minimum so all of these pitchers pitched at least fairly well for the season whether they had a few awful starts or not. (This is selective sampling and survivorship bias at work. Any time you set a minimum IP or PA, you select players who had above average performance, through luck and talent.)

Their next year’s projection was 3.99 and the actual was 3.89 so there is a slight inference that indeed you can discount the bad starts a little. This is in around 12,000 IP. A difference of .1 RA9 is only around 1 SD so it’s not nearly statistically significant. I also don’t know that we have any Bayesian prior to work with.

The control group – all other starters, namely those without 2 or 3 awful outings – had a RA9 in the season in question of 3.72 (compare to 4.19 for the pitchers with 2 or 3 bad starts). Their projection for the next season was 3.85 and actual was 3.86. This was in around 130,000 IP so 1 SD is now around .025 runs so we can be pretty confident that the 3.86 actual RA9 reflects their true talent within around .05 runs (2 SD) or so.

What about starters who not only had 2 or 3 disastrous starts but also had an overall poor RA9? In the original post I looked at those pitchers in our experimental group who also had a seasonal RA9 of > 5.00. I’ll do the same thing with this new experimental group – starters with only 2 or 3 very awful starts.

Their average RA9 for the experimental season was 5.52. Their projection was 4.45 and actual was 4.17, so now we have an even stronger inference that a bad season caused by a few bad starts creates a projection that is too pessimistic; thus maybe we should  discount those few bad starts. We only have around 1600 IP (in the projected season) for these pitchers so 1 SD is around .25 runs. A difference between projected and actual of .28 runs is once again not nearly statistically significant. There is, nonetheless, a suggestion that we are on to something. (Don’t ever ignore – assume it’s random – an observed effect just because it isn’t statistically significant – that’s poor science.)

What about the control group? Last time we noticed that the control group’s actual RA was less than its projection for some reason. I’ll look at pitchers who had > 5 RA9 in one season but were not part of the group that had 2 or 3 disastrous starts.

Their average RA9 was 5.44 – similar to the 5.52 of the experimental group. Their projected was 4.45 and actual was 4.35, so we see the same “too high” projection in this group as well. (In fact, in testing my RA projections based on RA only – as opposed to say FIP or ERC – I find an overall bias such that pitchers with a one-season high RA have projections that are too high, not a surprising result actually.) This is in around 7,000 IP which gives us a SD of around .1 runs per 9.

So, the “a few bad starts” group outperformed their projections by around .1 runs. This same group, limiting it to starters with an overall RA or over 5.00, outperformed their projections by .28 runs. The control group with an overall RA also > 5.00 outperformed their projections by .1 runs. None of these differences are even close to statistically significant.

Let’s increase the sample size a little of our experimental group who also had particularly bad RA overall by expanding it to starters with an overall RA of > 4.50 rather than > 5.00. We now have 3,500 IP, 2x as many IP, reducing our error by around 50%. The average RA9 of this group was 5.13. Their projected RA was 4.33 and actual was 4.05 – exactly the same difference as before. Keep in mind that the more samples we look at the more we are “data mining,” which is a bit dangerous in this kind of research.

A control group of starters with > 4.50 RA had an overall RA9 of 4.99. Their projection was exactly the same as the experimental group, 4.33, but their actual was 4.30 – almost exactly the same as their projection.

In conclusion, while we initially found no evidence that discounting a bad ERA or RA caused by “several very poor starts” is warranted when doing a projection for starters with at least 100 IP, once we change the criteria for “a few bad starts” from “at least 5 starts with 6 runs or more allowed in 5 IP or less” to “exactly 2 or 3 starts with 8 runs or more in 5 IP or less” we do find evidence that some kind of discount may be necessary. In other words, for starters whose runs allowed are inflated due to 2 or 3 really bad starts, if we simply use overall season RA or ERA for our projections we will understate their subsequent season’s RA or ERA by maybe .2 or .3 runs per 9.

Our certainty of this conclusion, especially with regard to the size of the effect – if it exists at all – is pretty weak given the magnitude of the differences we found and the sample sizes we had to work with. However, as I said before, it would be a mistake to ignore any inference – even a weak one – that is not contradicted by some Bayesian prior (or common sense).