Archive for the ‘Pitching Strategy’ Category

There’s an article up on Fangraphs by Eno Saris that talks about whether the pitch to Justin Turner in the bottom of the 9th inning in Game 2 of the 2017 NLCS was the “wrong” pitch to throw in that count (1-0) and situation (tie game, runners on 1 and 2, 2 outs) given Turner’s proclivities at that count. I won’t go into the details of the article – you can read it yourself – but I do want to talk about what it means or doesn’t mean to criticize a pitcher’s pitch selection – on one particular pitch, and how pitch selection even works, in general.

Let’s start with this – the basic tenet of pitching and pitch selection: Every single situation calls for a pitch frequency matrix. One pitch is chosen randomly from that matrix according to the “correct” frequencies. The “correct” frequencies are those which result in the exact same “result” (where result is measured by the win expectancy impact of all the possible outcomes combined).

Now, obviously, most pitchers “think” they’re choosing one specific pitch for some specific reason, but in reality since the batter doesn’t know the pitcher’s reasoning, it is essentially a random selection as far as he is concerned. For example, a pitcher throws an inside fastball to go 0-1 on the batter. He might think to himself, “OK, I just threw the inside fastball so I’ll throw a low and away off-speed to give him a ‘different look.’ But wait, he might be expecting that. I’ll double up with the fastball! Nah, he’s a pretty good fastball hitter. I’ll throw the off-speed! But I really don’t want to hang one on an 0-1 count. I’m not feeling that confident in my curve ball yet. OK, I’ll throw the fastball, but I’ll throw it low and away. He’ll probably think it’s an off-speed and lay off of it and I’ll get a called strike, or he’ll be late if he swings.”

As you can imagine, there are an infinite number of permutations of ‘reasoning’ that a pitcher can use to make his selection. The backdrop to his thinking is that he knows what tends to be effective at 0-1 counts in that situation (score, inning, runners, outs, etc.) given his repertoire, and he knows the batter’s strengths and weaknesses. The result is a roughly game theory optimal (GTO) approach which cannot be exploited by the batter and is maximally effective against a batter who is thinking roughly GTO too.

The optimal pitch selection frequency matrix is dependent on the pitcher, the batter, the count and the game situation. In that situation with Lackey on the mound and Turner at the plate, it might be something like 50% 4-seam, 20% sinker, 20% slider, and 10% cutter. The numbers are irrelevant. Then a random pitch is selected according to those frequencies, where, for example, the 4-seamer is chosen twice as often as the sinker and slider, the sinker and slider twice as often as the cutter, etc.

Obviously doing that even close to accurately is impossible, but that’s essentially what happens and what is supposed to happen. Miraculously, pitchers and catchers do a pretty good job (really you just have to have a pretty good idea as to what pitches to throw, adjusted a little for the batter). At least I presume they do. It is likely that some pitchers and batters are better than others at employing these GTO strategies as well as exploiting opponents who don’t.

The more a batter likes (or dislikes) a certain pitch (in that count or overall), the less that pitch will be thrown. In order to understand why, you must understand that the result of a pitch is directly proportional to the frequency at which it is thrown in a particular situation. For example, if Taylor is particularly good against a sinker in that situation or in general, it might be thrown 10% rather than 20% of the time. The same is true for locations of course, which makes everything quite complex.

Remember that you cannot tell what types and locations of pitches a batter likes or dislikes in a certain count and game situation from his results! This is a very important concept to understand. The results of every pitch type and location in each count, game situation, and versus each pitcher (you would have to do a “delta method” to figure this) are and should be exactly the same! Any differences you see are noise – random differences (or the result of wholesale exploitative play or externalities as I explain below). We can easily prove this with an example.

Imagine that in all 1-0 counts, early in a game with no runners on base and 0 outs (we’re just choosing a ‘particular situation’ – which situation doesn’t matter), we see that Turner gets a FB 80% of the time and a slider 20% of the time (again, the actual numbers are irrelevant). And we see that Turner’s results (we have to add up the run or win value of all the results – strike, ball, batted ball out, single, double, etc.) are much better against those 80% FB than the 20% SL. Can we conclude that Turner is better against the FB in that situation?

No! Why is that? Because if we did, we would HAVE TO also conclude that the pitchers were throwing him too many FB, right? They would then reduce the frequency of the fastball. Why throw a certain pitch 80% of the time (or at all, for that matter) when you know that another pitch is better?

You would obviously throw it less often than 80% of the time. How much less? Well, say you throw it 79% and the slider 21%. You must be better off with that ratio (rather than 80/20) since the slider is the better pitch, as we just said for this thought exercise. Now what if the FB still yields better results for Turner (and it’s not just noise – he’s still better versus the FB when he knows it’s coming 79% of the time)? Well, again obviously, you should throw the FB even less often and the slider more often.

Where does this end? Every time we decrease the frequency of the FB, the batter gets worse at it since it’s more of a surprise. Remember the relationship between the frequency of a pitch and its effectiveness. At the same time, he gets better and better at the slider since we throw it more and more frequently. It ends at the point in which the results of both pitches are exactly equal. It HAS to. If it “ends” anywhere else, the pitcher will continue to make adjustments until an equilibrium point is reached. This is called a Nash equilibrium in game theory parlance, at which point the batter can look for either pitch (or any pitch if the GTO mixed strategy includes more than two pitches) and it won’t make any difference in terms of the results. (If the batter doesn’t employ his own GTO strategy, then the pitcher can exploit him by throwing one particular pitch – in which case he then becomes exploitable, which is why it behooves both players to always employ a GTO strategy or risk being exploited.) As neutral observers, unless we see evidence otherwise, we must assume that all actors (batters and pitchers) are indeed using a roughly GTO strategy and that we are always in equilibrium. Whether they are or they aren’t, to whatever degree and in whichever situations, it certainly is instructive for us and for them to understand these concepts.

Assuming an equilibrium, this is what you MUST understand: Any differences you see in either a batter’s results across different pitches, or as a pitcher’s, MUST be noise – an artifact of random chance. Keep in mind that it’s only true for each subset of identical circumstances – the same opponent, count, and game situation (even umpire, weather, park, etc.). If you look at the results across all situations you will see legitimate differences across pitch types. That’s because they are thrown with different frequencies in different situations. For example, you will likely see better results for a pitcher with his secondary pitches overall simply because he throws them more frequently in pitcher’s counts (although this is somewhat offset by the fact that he throws them more often against better batters).

Is it possible that there are some externalities that throws this Nash equilibrium out of whack? Sure. Perhaps a pitcher must throw more FB than off-speed in order to prevent injury. That might cause his numbers for the FB to be slightly worse than for other pitches. Or the slider may be particularly risky, injury-wise, such that pitchers throw it less than GTO (game theory optimally) which results in a result better (from the pitcher’s standpoint) than the other pitches.

Any other deviations you see among pitch types and locations, by definition, must be random noise, or, perhaps exploitative strategies by either batters or pitchers (one is making a mistake and the other is capitalizing on it). It would be difficult to distinguish the two without some statistical analysis of large samples of pitches (and then we would still only have limited certainty with respect to our conclusions).

So, given all that is true, which it is (more or less), how can we criticize a particular pitch that a pitcher throws in one particular situation? We can’t. We can’t say that one pitch is “wrong” and one pitch is “right” in ANY particular situation. That’s impossible to do. We cannot evaluate the “correctness” of a single pitch. Maybe the pitch that we observe is the one that is only supposed to be thrown 5 or 10% of the time, and the pitcher knew that (and the batter was presumably surprised by it whether he hit it well or not)! The only way to evaluate a pitcher’s pitch selection strategy is by knowing the frequency at which he throws his various pitches against the various batters in the various counts and game situations. And that requires an enormous sample size of course.

There is an exception.

The one time we can say that a particular pitch is “wrong” is when that pitch is not part of the correct frequency matrix at all – i.e., the GTO solution says that it should never be thrown. That rarely occurs. About the only time that occurs is on 3-0 counts where a fastball might be the only pitch thrown (for example, 3-0 count with a 5 run lead, or even a 3-1 or 2-0 count with any big lead, late in the game – or a 3-0 count on an opposing pitcher who is taking 100% of the time).

Now that being said, let’s say that Lackey is supposed to throw his cutter away only 5% of the time against Turner. If we observe only that one pitch and it is a cutter, Bayes tells is that there is an inference that Lackey was intending to throw that pitch MORE than 5% of the time and we can indeed say with some small level of certainty that he “threw the wrong pitch.” We don’t really mean he “threw the wrong pitch.” We mean that we think (with some low degree of certainty) he had the wrong frequency matrix in his head to some significant degree (maybe he intended to throw that pitch 10% or 20% rather than 5%).*

So, the next time you hear anyone say what a pitcher should be throwing on any particular pitch or that the pitch he threw was “right” or “wrong,” it’s a good bet that he doesn’t really know what he’s talking about, even if they are or were a successful major league pitcher.

* Technically, we can only say something like, “We are 10% sure he was thinking 5%, 12% sure he was thinking 7%, 13% sure he was thinking 8%, etc.” – numbers for illustration purposes only.


Richard Nichols (@RNicholsLV on Twitter) sent me this link. These are notes that the author, Lee Judge, a Royals blogger for the K.C. Star, took during the season. They reflect thoughts and comments from players, coaches, etc. I thought I’d briefly comment on each one. Hope you enjoy!

Random, but interesting, things about baseball – Lee Judge

▪ If a pitcher does not have a history of doubling up on pickoff throws (two in a row) take a big lead, draw a throw and then steal on the next pitch.

Of course you can do that. But how many times can you get away with it? Once? If the pitcher or one of his teammates or coaches notices it, he’ll pick you off the next time by “doubling up.” Basically by exploiting the pitcher’s non-random and thus exploitable strategy, the runner becomes exploitable himself. A pitcher, of course, should be picking a certain percentage of the time each time he goes into the set position, based on the likelihood of the runner stealing and the value of the steal attempt. That “percentage” must be randomized by the pitcher and it “resets” each time he throws a pitch or attempts a pickoff.

By “randomize” I mean the prior action, pick or no pick, cannot affect the percentage chance of a pick. If a pitcher is supposed to pick 50% prior to the next pitch he must do so whether he’s just attempted a pickoff 0, 1, 2, or 10 times in a row. The runner can’t know that a pickoff is more or less likely based on how many picks were just attempted. In fact you can tell him, “Hey every time I come set, there’s a 50% (or 20%, or whatever) chance I will attempt to pick you off,” and there’s nothing he can do to exploit that information.

For example, if he decides that he must throw over 50% of the time he comes set (in reality the optimal % changes with the count), then he flips a mental coin (or uses something – unknown to the other team – to randomize his decision, with a .5 mean). What will happen on the average is that he won’t pick half the time, 25% of the time he’ll pick once only, 12.5% of the time he’ll pick exactly twice, 25% of the time he’ll pick at least twice, etc.

Now, the tidbit from the player or coach says, “does not have a history of doubling up.” I’m not sure what that means. Surely most pitchers when they do pick, will pick once sometimes and twice sometimes, etc. Do any pitchers really never pick more than once per pitch? If they do, I would guess that it’s because the runner is not really a threat and the one-time pick is really a pick with a low percentage. If a runner is not much of a threat to run, then maybe the correct pick percentage is 10%. If that’s the case, then they will not double-up 99% of the time and correctly so. That cannot be exploited, again, assuming that a 10% rate is optimal for that runner in that situation. So while it may look like they never double up, they do in fact double up 1% of the time, which is correct and cannot be exploited (assuming the 10% is correct for that runner and in that situation).

Basically what I’m saying is that this person’s comment is way to simple and doesn’t really mean anything without putting it into context as I explain above.

▪ Foul balls with two strikes can indicate a lack of swing-and-miss stuff; the pitcher can get the batters to two strikes, but then can’t finish them off.

Not much to say here. Some pitchers have swing-and-miss stuff and others don’t, and everything in-between. You can find that out by looking at…uh…their swing-and-miss percentages (presuming a large enough sample size to give you some minimum level of certainty). Foul balls with two strikes? That’s just silly. A pitcher without swing-and-miss stuff will get more foul balls and balls in play with two strikes. That’s a tautology. He’ll also get more foul balls and balls in play with no strikes, one strike, etc.

▪ Royals third-base coach Mike Jirschele will walk around the outfield every once in a while just to remind himself how far it is to home plate and what a great throw it takes to nail a runner trying to score.

If my coach has to do that I’m not sure I want him coaching for me. That being said, whatever little quirks he has or needs to send or hold runners the correct percentage of time is fine by me. I don’t know that I would be teaching or recommending that to my coaches – again, not that there’s anything necessarily wrong with it.

Bottom line is that he better know the minimum percentages that runners need to be safe in any given situation (mostly # of outs) – i.e. the break-even points – and apply them correctly to the situation (arm strength and accuracy etc.) in order to make optimal decisions. I would surely be going over those numbers with my coaches from time to time and then evaluating his sends and holds to make sure he’s not making systematic errors or too many errors in general.

▪ For the most part, the cutter is considered a weak contact pitch; the slider is considered a swing-and-miss pitch.

If that’s confirmed by pitch f/x, fine. If it’s not, then I guess it’s not true. Swing-and-miss is really just a subset of weak contact and weak contact is a subset of contact which is a subset of a swing. The result of a swing depends on the naked quality of the pitch, where it is thrown, and the count. So while for the most part (however you want to define that – words are important!) it may be true, surely it depends on the quality of each of the pitches, on what counts they tend to be thrown, how often they are thrown at those counts, and the location they are thrown to. Pitches away from the heart of the plate tend to be balls and swing-and-miss pitches. Pitches nearer the heart tend to be contacted more often, everything else being equal.

▪ With the game on the line and behind in the count, walk the big-money guys; put your ego aside and make someone else beat you.

Stupid. Just. Plain. Stupid. Probably the dumbest thing a pitcher or manager can think/do in a game. I don’t even know what it means and neither do they. So tie game in the 9th, no one on base, 0 outs, count is 1-0. Walk the batter? That’s what he said! I can think of a hundred stupid examples like that. A pitcher’s approach changes with every batter and every score, inning, outs, runners, etc. A blanket statement like that, even as a rule of thumb, is Just. Plain. Dumb. Any interpretation of that by players and coaches can only lead to sub-optimal decisions – and does. All the time. Did I say that one is stupid?

▪ A pitcher should not let a hitter know what he’s thinking; if he hits a batter accidentally he shouldn’t pat his chest to say “my bad.” Make the hitter think you might have drilled him intentionally and that you just might do it again.

O.K. To each his own.

▪ Opposition teams are definitely trying to get into Yordano Ventura’s head by stepping out and jawing with him; anything to make him lose focus.

If he says so. I doubt much of that goes on in baseball. Not that kind of game. Some, but not much.

▪ In the big leagues, the runner decides when he’s going first-to-third; he might need a coach’s help on a ball to right field — it’s behind him — but if the play’s in front of him, the runner makes the decision.

Right, we teach that in Little League (a good manager that is). You teach your players that they are responsible for all base running decisions until they get to third. Then it’s up to the third base coach. It’s true that the third base coach can and should help the runner on a ball hit to RF, but ultimately the decision is on the runner whether to try and take third.

Speaking of taking third, while the old adage “don’t make the first or third out at third base” is a good rule of thumb, players should know that it doesn’t mean, “Never take a risk on trying to advance to third.” It means the risk has to be low (like 10-20%), but that the risk can be twice as high with 0 outs as with 2 outs. So really, the adage should be, “Never make the third out at third base, but you can sometimes make the first out at third base.”

You can also just forget about the first out part of that adage. Really, the two-out break-even point is almost exactly in between the first-out and one-out one. In other words, with no outs, you need to be safe at third around 80% of the time, with one out, around 70%, and with two outs around 90%. Players should be taught that and not just the “rule of thumb.” They should also be taught that the numbers change with trailing runners, the pitcher, and who the next batter or batters are. For example, with a trailing runner, making the third out is really bad but making the first out where the trailing runner can advance is a bonus.

▪ Even in a blowout there’s something to play for; if you come close enough to make the other team use their closer, maybe he won’t be available the next night.

I’m pretty sure the evidence suggests that players play at their best (more or less) regardless of the score. That makes sense under almost any economic or cognitive theory of behavior since players get paid big money to have big numbers. Maybe they do partially because managers and coaches encourage them to do so with tidbits like that. I don’t know.

Depending on what they mean by blowout, what they’re saying is that, say you have a 5% chance of winning a game down six runs in the late innings. Now say you have a 20% chance of making it a 3-run or less game, and that means that the opponent closer comes into the game. And say that him coming into the game gives you another 2% chance of winning tomorrow because he might not be available, and an extra 1% the day after that (if it’s the first game in a series). So rather than a 5% win expectancy, you actually have a 5% plus 20% * 3% or, 5.6% WE. Is that worth extra effort? To be honest, a manager and coach is supposed to teach his players to play hard (within reason) regardless of the score for two reasons: One, because it makes for better habits when the game is close and two, at exactly what point is the game a blowout (Google the sorites paradox)?

▪ If it’s 0-2, 1-2 and 2-2, those are curveball counts and good counts to run on. That’s why pitchers often try pickoffs in those counts.

On the other hand, 0-2 is not a good count to run on because of the threat of the pitchout. As it turns out, the majority of SB attempts (around 68%) occur at neutral counts. Only around 16% of all steal attempts occur at those pitchers’ counts. So whoever said that is completely wrong.

Of course pitchers should (and do) attempt more pickoffs the greater the chance of a steal attempt. That also tends to make it harder to steal (hence the game theory aspect).

That being said, some smart people (e.g., Professor Ted Turocy of Chadwick Baseball Bureau) believe that there is a Nash equilibrium between the offense and defense with respect to base stealing (for most players – not at the extremes) such that neither side can exploit the other by changing their strategy. I don’t know if it’s true or not. I think Professor Turocy may have a paper on this. You can check it out on the web or contact him.

▪ Don’t worry about anyone’s batting average until they have 100 at-bats.

How about “Don’t worry about batting average…period.” In so many ways this is wrong. I would have to immediately fire whoever said that if it was a coach, manager or executive.

▪ It’s hard to beat a team three times in a row; teams change starting pitchers every night and catching three different pitchers having a down night is not the norm.

Whoever said this should be fired sooner than the one above. As in, before they even finished that colossally innumerate sentence.

▪ At this level, “see-it-and-hit” will only take you so far. The best pitchers are throwing so hard you have to study the scouting reports and have some idea of what’s coming next.

If that’s your approach at any level you have a lot to learn. That goes for 20 or 50 years ago the same as it does today. If pitchers were throwing maybe 60 mph not so much I guess. But even at 85 you definitely need to know what you’re likely to get at any count and in any situation from that specific pitcher. Batters who tell you that they are “see-it-and-hit-it” batters are lying to you or to themselves. There is no such thing in professional baseball. Even the most unsophisticated batter in the world knows that at 3-0, no outs, no runners on, his team is down 6 runs, he’s likely to be getting 100% fastballs.

▪ If a pitcher throws a fastball in a 1-1 count, nine out of 10 times, guess fastball. But if it’s that 10th time and he throws a slider instead, you’re going to look silly.

WTF? If you go home expecting your house to be empty but there are two giraffes and a midget, you’re going to be surprised.

▪ Good hitters lock in on a certain pitch, look for it and won’t come off it. You can make a guy look bad until he gets the pitch he was looking for and then he probably won’t miss it.

Probably have to fire this guy too. That’s complete bullshit. Makes no sense from a game-theory perspective or from any perspective for that matter. So just never throw him that pitch right? Then he can’t be a good hitter. But now if you never throw him the pitch he’s looking for, he’ll stop looking for it, and will instead look for the alternative pitch you are throwing him. So you’ll stop throwing him that pitch and then…. Managers and hitting coaches (and players) really (really) need a primer on game theory. I am available for the right price.

▪ According to hitting coach Dale Sveum, hitters should not give pitchers too much credit; wait for a mistake and if the pitcher makes a great pitch, take it. Don’t start chasing great pitches; stick to the plan and keep waiting for that mistake.

Now why didn’t I think of that!

▪ The Royals are not a great off-speed hitting club, so opposition pitchers want to spin it up there.

Same as above. Actually, remember this: You cannot tell how good or bad a player or team is at hitting any particular pitch by looking at the results. You can only tell by how often they get each type of pitch. Game theory tells us that the results of all the different pitches (type, location, etc.) will be about the same to any hitter. What changes depending on that hitter’s strengths and weaknesses are the frequencies. And this whole, “Team is good/bad at X” is silly. It’s about the individual players of course. I’m pretty sure there was at least one hitter on the team who is good at hitting off-speed.

Also, never evaluate or define “good hitting” based on batting average which most coaches and managers do even in 2016. I don’t have to tell you, dear sophisticated reader, that. However, you should also not define good or bad hitting on a pitch level based on OPS or wOBA (presumably on contact) either. You need to include pitches not put into play and you need to incorporate count. For example, at a 3-ball count there is a huge premium on not swinging at a ball. Your result on contact is not so important. At 2-strike counts, not taking a strike is also especially important. Whenever you see pitch level numbers without including balls not swung at, or especially only on balls put into play (which is usually the case), be very wary of those numbers. For example, a good off-speed hitting player will tend to have good strike zone recognition (and not necessarily good results on contact) skills because many more off-speed pitches are thrown in pitchers’ counts and out of the strike zone.

▪ According to catcher Kurt Suzuki, opposition pitchers should not try to strike out the Royals. Kansas City hitters make contact and a pitcher that’s going for punchouts might throw 100 pitches in five innings.

Wait. If they are a good contact team, doesn’t that mean that you can try and strike them out without running up your pitch count? Another dumb statement. Someone should tell Mr. Suzuki that pitch framing is really important.

▪ If you pitch down in the zone you can use the whole plate; any pitch at the knees is a pretty good pitch (a possible exception is down-and-in to lefties). If you pitch up in the zone you have to hit corners.

To some extent that’s true though it’s (a lot) more complicated than that. What’s probably more important is that when pitching down in the zone you want to pitch more away and when pitching up in the zone more inside. By the way, is it true lefties like (hit better) the down-and-in pitch more than righties? No, it is not. Where does that pervasive myth come from? Where do all the hundreds of myths that players, fans, coaches, managers, and pundits think are true come from?

▪ If you pitch up, you have to be above the swing path.

Not really sure what that means? Above the swing “path?” Swing path tends to follow the pitch so that doesn’t make too much sense. “Path” implies angle of attack and to say “above” or “below” an angle of attach doesn’t really make sense. Maybe he means, “If you are going to pitch high, pitch really high?” Or, “If the batter tends to be a high ball hitter, pitch really high?”

▪ Numbers without context might be meaningless; or worse — misleading

I don’t know what that means. Anything might be misleading or worthless without context. Words, numbers, apple pie, dogs, cats…

▪ All walks are not equal: a walk at the beginning of an inning is worth more than a walk with two outs, a walk to Jarrod Dyson is worth more than a walk to Billy Butler.

Correct. I might give this guy one of the other guys’ (that I fired) jobs. Players, especially pitchers (but batters and fielders too), should always know the relative value of the various offensive events depending on the batter, pitcher, score, inning, count, runners, etc., and then tailor their approach to those values. This is one of the most important things in baseball.

▪ So when you look at a pitcher’s walks, ask yourself who he walked and when he walked them.

True. Walks should be weighed towards bases open, 2 outs, sluggers, close games, etc. If not, and the sample is large, then the pitcher is likely either doing something wrong or he has terrible command/control or both. For example, Greg Maddux went something like 10 years before he walked his first pitcher.

▪ When a pitcher falls behind 2-0 or 3-1, what pitch does he throw to get back in the count? Can he throw a 2-0 cutter, sinker or slider, or does he have to throw a fastball down the middle and hope for the best?

All batters, especially in this era of big data, should be acutely aware of a pitcher’s tendencies against their type of batter in any given situation and count. One of the most important ones is, “Does he have enough command of his secondary pitches (and how good is his fastball even when the batter knows it’s coming) to throw them in hitter’s counts, especially the 3-2 count?”

▪ Hitters who waggle the bat head have inconsistent swing paths.

I never heard that before. Doubt it is anything useful.

▪ The more violent the swing, the worse the pitch recognition. So if a guy really cuts it loose when he swings and allows his head to move, throw breaking stuff and change-ups. If he keeps his head still, be careful.

Honestly, if that’s all you know about a batter, someone is not doing their homework. And again, there’s game theory that must be accounted for and appreciated. Players, coaches and managers are just terrible at understanding this very important part of baseball especially the batter/pitcher matchup. If you think you can tell a pitcher to throw a certain type of pitch in a certain situation (like if the batter swings violently throw him off-speed), then surely the batter can and will know that too. If he does, which he surely will – eventually – then he basically knows what’s coming and the pitcher will get creamed!

Yesterday, I posted an article describing how I modeled to some extent a way to tell whether and by how much pitchers may be able to pitch in such a way as to allow fewer or more runs than their components, including the more subtle ones, like balks, SB/CS, WP, catcher PB, GIDP, and ROE suggest.

For various reasons, I suggest taking these numbers with a grain of salt. For one thing, I need to tweak my RA9 simulator to take into consideration a few more of these subtle components. For another, there may be some things that stick with a pitcher from year to year that have nothing to do with his “RA9 skill” but which serve to increase or decrease run scoring, given the same set of components. Two of these are a pitcher’s outfielder arms and the vagueries of his home park, which both have an effect on base runner advances on hits and outs. Using a pitcher’s actual sac flies against will mitigate this, but the sim is also using league averages for base runner advances on hits, which, as I said, can vary from pitchers to pitcher, and tend to persist from year to year (if a pitcher stays on the same team) based on his outfielders and his home park. Like DIPS, it would be better to do these correlations only on pitchers who switch teams, but I fear that the sample would be too small to get any meaningful results.

Anyway, I have a database now of the last 10 years’ differences between a pitcher’s RA9 and his sim RA9 (the runs per 27 outs generated by my sim), for all pitchers who threw to at least 100 batters in a season.

First here are some interesting categorical observations:

Jared Cross, of Steamer projections, suggested to me that perhaps some pitchers, like lefties, might hold base runners on first base better than others, and therefore depress scoring a little as compared to the sim, which uses league-average base running advancement numbers. Well, lefties actually did a hair worse in my database. Their RA9 was .02 greater than their sim RA. Righties were -.01 better. That does not necessarily mean that RHP have some kind of RA skill that LHP do not have. It is more likely a bias in the sim that I am not correcting for.

How about number of pitches in a pitcher’s repertoire. I hypothesized that pitchers with more pitches would be better able to tailor their approach to the situation. For example, with a base open, you want your pitcher to be able to throw lots of good off-speed pitches in order to induce a strikeout or weak contact, whereas you don’t mind if he walks the batter.

I was wrong. Pitchers with 3 or more pitches that they throw at least 10% of the time are .01 runs worse in RA9. Pitchers with only 2 or fewer pitches, are .02 runs better. I have no idea why that is.

How about pitchers who are just flat out good in their components such that their sim RA is low, like under 4.00 runs? Their RA9 is .04 worse. Again, their might be some bias in the sim which is causing that. Or perhaps if you just go out and there “air it out” and try and get as many outs and strikeouts as possible, regardless of the situation, you are not pitching optimally.

Conversely, pitchers with a sim RA of 4.5 or greater shave .03 points off their RA9. If you are over 5 in your sim RA, your actual RA9 is .07 points better and if you are below 3.5, your RA9 is .07 runs higher. So, there probably is something about having extreme components that even the sim is not picking up. I’m not sure what that could be. Or, perhaps if you are simply not that good of a pitcher, you have to find ways to minimize run scoring above and beyond the hits and walks you allow overall.

For the NL pitchers, their RA9 is .05 runs better than their sim RA, and for the AL, they are .05 runs worse. So the sim is not doing a good job with respect to the leagues, likely because of pitchers batting. I’m not sure why, but I need to fix that. For now, I’ll adjust a pitcher’s sim RA according to his league.

You might think that younger pitchers would be “throwers” and older ones would be “pitchers” and thus their RA skill would reflect that. This time you would be right – to some extent.

Pitchers less than 26 years old were .01 runs worse in RA9. Pitchers older than 30 were .03 better. But that might just reflect the fact that pitchers older than 30 are just not very good – remember, we have a bias in terms of quality of the sim RA and the difference between that and regular RA9.

Actually, even when I control for the quality of the pitcher, the older pitchers had more RA skill than the younger ones by around .02 to .04 runs. As you can see, none of these effects, even if they are other than noise, is very large.

Finally, here are the lists of the 10 best and worst pitchers with respect to “RA skill,” with no commentary. I adjusted for the “quality of the sim RA” bias, as well as the league bias. Again, take these with a large grain of salt, considering the discussion above.

Best, 2004-2013:

Sean Chacon -.18

Steve Trachsel -.18

Francisco Rodriguez -.18

Jose Mijares -.17

Scott Linebrink -.16

Roy Oswalt -.16

Dennys Reyes -.15

Dave Riske -.15

Ian Snell -.15

5 others tied for 10th.


Derek Lowe .27

Luke Hochevar .20

Randy Johnson .19

Jeremy Bonderman .18

Blaine Boyer .18

Rich Hill .18

Jason Johnson .18

5 others tied for 8th place.

(None of these pitchers stand out to me one way or another. The “good” ones are not any you would expect, I don’t think.)

We showed in The Book that there is a small but palpable “pitching from the stretch” talent. That of course would effect a pitcher’s RA as compared to some kind of base runner and “timing” neutral measure like FIP or component ERA, or really any of the ERA estimators.

As well, a pitcher’s ability to tailor his approach to the situation, runners, outs, score, batter, etc., would also implicate some kind of “RA talent,” again, as compared to a “timing” neutral RA estimator.

A few months ago I looked to see if RE24 results for pitchers showed any kind of talent for pitching to the situation, by comparing that to the results of a straight linear weights analysis or even a BaseRuns measure. I found no year-to-year correlations for the difference between RE24 and regular linear weights. In other words, I was trying to see if some pitchers were able to change their approach to benefit them in certain bases/outs situations more than other pitchers. I was surprised that there was no discernible correlation, i.e., that it didn’t seem to be much of a skill if at all. You would think that some pitchers would either be smarter than others or have a certain skill set that would enable them, for example, to get more K with a runner on 3rd and less than 2 outs, more walks and fewer hits with a base open, or fewer home runs with runners on base or with 2 outs and no one on base. Obviously all pitchers, on the average, vary their approach a lot with respect to these things, but I found nothing much when doing these correlations. Essentially an “r” of zero.

To some extent the pitching from the stretch talent should show up in comparing RE24 to regular lwts, but it didn’t, so again, I was a little surprised at the results.

Anyway, I decided to try one more thing.

I used my “pitching sim” to compute a component ERA for each pitcher. I tried to include everything that would create or not create runs while he was pitching, like WP/PB, SB/CS, GIDP, roe, in addition to s,d,t,hr,bb, and so. I considered an IBB as a 1/2 BB in the sim, since I didn’t program IBB into it.

So now, for each year, I recorded the difference between a pitcher’s RA9 and his simulated component RA9, and then ran year-to-year correlations. This was again to see if I could find a “RA talent” wherever it may lie – clutch pitching, stretch talent, approach talent, etc.

I got a small year-to-year correlation which, as always, varied with the underlying sample size – TBF in each of the paired years. When I limited it to pitchers with at least 500 TBF in each year, I got an “r” of .142 with an average PA of 791 in each year. That comes out to a 50% regression at around 5000 PA, or 5 years for a full-time starter, similar to BABIP for pitchers. In other words, the “stabilization” point was around 5,000 TBF.

If that .142 is accurate (at 2 sigma the confidence interval is .072 to .211), I think that is pretty interesting. For example, notable “ERA whiz” Tom Glavine from 2001 to 2006, was an average of .246 in RA9 better than his sim RA9 (simulated component RA). If we regress that difference 50%, we get .133 runs per game, which is pretty sizable I think. That is over 1/3 of a win per season. Notable “ERA hack” Ricky Nolasco from 2008 to 2010 (I only looked at 2001-2010) was an average of .357 worse in his ERA. Regress that 62.5%, and we get .134 runs worse per season, also 1/3 of a win.

So, for example, if you want to know how to reconcile fWAR (FG) and bWAR (B-R) for pitchers, take the difference and regress according to the number of TBF, using the formula 5000/(5000+TBF) for the amount of regression.

Here are a couple more interesting ones, off the top of my head. I thought that Livan Hernandez seemed like a crafty pitcher, despite having inferior stuff late in his career. Sure enough, he out-pitched his components by around .164 runs per game over 9 seasons. After regressing, that’s .105 rpg.

The other name that popped into my head was Wakefield. I always wondered if a knuckler was able to pitch to the situation as well as other pitchers could. It does not seem like they can, with only one pitch with comparatively little control. His RA9 was .143 worse than his components suggest, despite his FIP being .3 runs per 9 worse than his ERA! After regressing, he is around .095 worse than his simulated component RA.

Of course, after looking at Wake, we have to check Dickey as well. He didn’t start throwing a knuckle ball until 2005, and then only half the time. His average difference between RA9 and simulated RA9 is .03 on the good side, but our sample size for him is small with a total of only 1600 TBF, implying a regression of 76%.

If you want the numbers on any of your favorite or no-so-favorite pitchers, let me know in the comments section.

People are notoriously bad at recollecting things they hear and see, especially when they happen a long time ago. For example, the Innocence Project reports eyewitness misidentification occurs in approximately 75% of convictions that are overturned.

I submit that people are also poor at understanding the things that they do, even if they are experts at it and did it successfully for a long time. Professional golfers were once asked whether the orientation of the club face on a golf club or the direction of the swing was the primary determinant of the initial direction of the ball. In other words, if you swing to the right, but your clubface is pointed to the left at impact, which way does the ball start out? I forgot the numbers, but a significant percentage of PGA golfers answered incorrectly. If you care, it is mostly the angle of the clubface which determines the initial direction of the ball.

Tonight in the Braves game, in an AB by McCann with Kershaw pitching, Ron Darling, an excellent pitcher during his career and a Yale graduate to boot, remarked when the count went to 2-2 and Kershaw had thrown several fastballs, “He is surely going to throw the curve ball (or slider) now.” On its face, that is an absurd statement. If Darling knows that to any degree, then surely so does the batter, who happens to be a catcher! So that can’t possibly be correct! Of course, Kershaw threw another fastball. Darling immediately said, “Well, he decided to go with one more fastball and then the off-speed pitch.” Are you kidding me? Same shizit, different day. If Darling is that certain now, then surely so is McCann.

All pitchers, and especially great ones like Kershaw, randomize their pitch selections precisely so the batter cannot figure out what is coming with any certainty. Now, if in a certain count and certain situation, a certain pitcher throws 80% fastballs, then obviously the batter can “look” fastball and be right 80% of the time. But, still he does not know any more than he has an 80% chance of getting a fastball. He does not know, or should not be able to deduce, anything other than that 80/20 split based on the previous pitch or pitches. That is what it means to randomize your pitches. That you cannot tell what is coming based on prior pitches.

The concept of “set-up pitches” is largely a fallacy other than the fact that they may change the percentages. For example, if (and that is a big IF) throwing a high inside fastball actually makes a breaking pitch more effective on the next pitch, even if the batter knows that it is more likely to be coming, then you might throw 30% breaking pitches whereas if the previous pitch were a low and away fast ball or another breaking pitch, then maybe you would throw only 20% breaking balls. Let me put it another way.  A pitcher throws a high inside fastball. Now the count is 2-2 and the pitcher normally throws 50% off-speed and 50% fastballs at a 2-2 count with this batter and this exact situation. Are we to believe that he can throw 60% or 70% curve balls now, and yet if the last pitch were something else, he would throw 30% or 40% curve balls? If that were the case, then the batter would now know which way the pitcher was leaning based on the last pitch. That can’t be correct unless somehow the curve ball is more effective after a high inside fastball than it is after another pitch, even at the same frequency. That might be the case (I am not saying that it isn’t), but the batter can surely neutralize that by simply forgetting about the last pitch. Plus, again if the pitcher now throws the curve ball more often, the batter has the luxury of knowing that and he can now look  for the curve ball, thus making it less effective, presumably.

I hope that was clear, because it is a very important point.

Anyway, the point is that Darling, as a successful pitcher, clearly randomized his pitches in all situations, as do all pitchers. All he can tell you, as an ex-pitcher, are the percentages at any given point. He cannot tell what a pitcher is going to throw with any certainty unless those percentages reflect that.

And more importantly, those percentages should almost never be predicated on what was previously been thrown – only the count, batter, game situations, etc. If those percentages are predicated on previous pitches, then the batter can more easily figure out what you are going to throw AND those percentages will become sub-optimal (again, with the caveat that it might be true that a certain pitch makes it harder or easier to hit a certain subsequent pitch even if the batter knows that, which he surely does). In other words, if a pitch is predicated on previous pitches, for example, if you throw 5 fastballs in a row, you are more likely to throw an off-speed pitch, even at the same count, then you are not randomizing your pitches. That IS the definition of randomization. Darling should know that, but somehow when words come out of his mouth, he doesn’t.

That is why when you think you are getting good analysis from ex-players, because they are ex-players, you often are not.