Explaining the Command Disconnect

We only remember the most exciting plays. Diving catches, game-winning hits, high-pressure situations — these are the things that fill our memories and our imaginations. But baseball isn’t always exciting — often it’s far from it. I love baseball, but many of its events are boring. Headlines in the following morning never read, “Roy Halladay throws a two-seam fastball for a called strike to open third inning.”

Home runs are some of the most exciting events in baseball. Everyone — even the most apathetic towards baseball — can appreciate a baseball that’s hit really hard and really far. But home runs are, compared to more mundane baseball events, pretty rare. During the 2011 regular season, batters hit 4,552 homers. That’s a seems like a lot. But considering the fact that about 700,000 pitches were thrown, home runs make up less than 1% of all pitches. It seems pretty likely then that our impressions about what is important in baseball are disproportionately affected by home runs.

Not surprisingly, most homers come on pitches that are thrown down the middle:

So if you want to avoid giving up a home run, then announcers and conventional wisdom are absolutely right: don’t throw the ball down the middle. But given the spectacular nature of home runs, we tend to generalize this lesson to many other facets of pitching.

But what about the hundreds of thousands of pitches that weren’t put into play (including home runs)?

We can measure the quality of a pitch using linear weights, a tool that measures the change in run expectancy for a given event. The aforementioned home run obviously increases run expectancy — by an average of about 1.4 runs. But what about less-notable events, like balls and strikes? These affect run expectancy, too, just in much smaller capacities.

We calculate change in run expectancy (RE) with the equation: change in RE = final RE – initial RE + runs scored on event. Because run expectancy is lower in pitcher-favorable counts (0-2, 1-2, etc.), a single — or any hit — has a higher run value when the hit comes during a pitcher-favorable count. But you don’t even need a hit. A change from a 0-0 count to a 1-0 count also increases run expectancy, just by a small amount. We can use this method to look at all pitches with a unified metric.

As we discussed before, the above graph only covers a small portion of the events in baseball. While less than 1% of all pitches are home runs, just 19% are put into play. About 81% of pitches are a ball, a strike or a foul ball. On these pitches, run value is what you’d would expect:

The pitches that are most likely to be home runs — pitches thrown down the middle — are also the pitches with the lowest run values when not put into play, because they are most likely to be strikes. Keep in mind that negative run values are good for pitchers. If we compare the run value of pitches not put into play  with the pitches that are put into play, we find a nice juxtaposition:


On pitches down the middle, the balls that are put into play have, on average, about twice the magnitude of run value as pitches that aren’t put in play. That means for the two to come into equilibrium, you would need to have about 33% of pitches put into play and 66% not put into play. But as discussed earlier, far fewer than 33% of pitches are put into play. This means that, on average, pitches thrown down the middle are good for the pitcher, not the batter.

Merging everything together, we can see this visually:

Remember that everything with a negative run value is a positive event for a pitcher. And in this graph, I split up run value by batter handedness. The two don’t completely match because lefties and righties have different called strike zones, which I don’t show here to avoid cluttering the graph.

There is still so much more to look into. Last week I wrote about whether better pitchers have better command. Despite looking at the data in many different ways, I was unable to find any large differences in command between the best pitchers and everyone else.

One of the metrics I used was the percentage of pitches thrown to the horizontal borders of the strike zone. I defined a border pitch as one that was within half a foot of these horizontal borders. If we plot the pitches, they look like this:

There’s some overlap here because I accounted for the fact that a left-handed batter’s strike zone is shifted about a fifth of a foot left compared to a right-handed batter (from the catcher’s perspective), as found by Mike Fast. This measure of command came out of conventional baseball wisdom: Hit the corners, stay out of the middle of the plate, and you’ll be successful. By this definition, fewer than half of all pitches are border pitches.

The metric implicitly assumes that border pitches are better than other pitches. But it turns out that this isn’t really true. The average run value of a border pitch is nearly identical to the average run value of a non-border pitch. Of course this run value is distributed differently. The BABIP on border pitches is .279, while the BABIP on non-border pitches is .297. But this seems balanced out by the fact that border pitches are in the zone significantly less often.

I wondered if the metric was too inclusive. I altered the metric so that only pitches within a quarter of a foot of the strike zone were considered border pitches. As some of you suggested in the comments from my last post, I made the groups a little more granular and split up all pitchers into four groups (quartiles), again based on FIP. But despite making these changes, the results were basically the same. When ahead in the count, the best pitchers threw more border pitches. When they’re behind, better pitchers throw fewer border pitches. Still, the difference is very small.

It seems that the importance of not throwing pitches down the middle is overblown. On average, pitches thrown down the middle are actually pretty effective. The obsession with pitches thrown down the middle probably can be traced back in part to home runs. They disproportionately affect our perceptions because they’re so exciting. When they do happen, we need to explain the why behind them. According to the theory of cognitive dissonance, when reality and our mental attitudes diverge, we experience a mental tension. And we often resolve this tension through rationalizations.

Home runs are pretty random, but it’s hard to accept. Because of that, we rationalize their occurrences through a host of a factors, chief among them is pitch location. Throwing the ball down the middle is bad if the batter puts the ball in play — but that doesn’t happen often. And that might be why the command difference between the best pitchers and everyone else is much smaller than we might expect.

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23 Responses to “Explaining the Command Disconnect”

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  1. RMR says:

    It should be noted that pitches are not independent events. Hitter’s are not purely reactionary, but rather have some basis in expectation. That expectation is driven by many things, including the pitcher’s repertoire, the count and the base-out situation. From an overall analysis point of view, I assume these come out in the wash to some degree. However, we should be very careful about inferring the logic/value of making a given pitch based on these data.

    The RE of a fastball down the middle with the bases loaded, no outs and a 1-2 count (batter likely to swing) is presumably quite different than an identical pitch with the bases empty, 2 outs and a 3-0 count (batter likely to take).

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  2. Dan says:

    With the emphasis of trying to build pitch count by batters waiting more often for a first called strike, does this skew the results to make balls in the zone more likely to not be put into play? It would be interesting to remove all 1st unhit strikes and see the impact against working the corners. Good thought provoking article.

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    • Matt H says:

      Great article. A couple notes:

      First, it does seem like pitching towards the outside of the plate is much better than pitching down the middle or on the inside. Instead of bundling all “border pitches” together, I would be interested to see the difference in run values between outside border pitches, down the middle pitches, and inside border pitches for lefties and righties. Of course, it does seem that if you miss the strike zone, outside vs. inside doesn’t matter. Anyway, more analysis of that would be great.

      Second, let’s keep in mind that changing the distribution of pitch locations would likely also change the run values for the locations. In other words, if a pitchers starts throwing down the middle more often, batters will catch on, and cheat, leading to a higher run value for down the middle pitches. So although this research is very interesting, and does tell us fans a little more about what a pitcher is doing right and wrong, I’m not sure says anything about how a pitcher should pitch. (Not that you were saying that.)

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      • Matt H says:

        *FYI, that wasn’t supposed to be a reply to Dan.

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      • Yea, I’m definitely not saying that pitchers should just throw everything down the middle – batters would definitely adjust. The article is meant to discuss more our perceptions of baseball than what the best strategies are for pitchers.

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  3. CircleChange11 says:

    I think we “won’t see much” (i.e., variance) when we combine data of [1] pitch types, [2] pitch counts, [3] baserunner situations, [4] sequencing, etc.

    We’ve known pretty detailed data on hit quality by pitch location for ~70 years, and before that observational data led to common approaches of starting out over the plate and then moving to the corners.

    You mix too many colors together and you just keep getting “brownish”.

    I think you’ll continue to find “not much difference” and/or just general information unless/until we find a very good way of categorizing the data.

    That’s going to be a major task, especially considering count, type, sequencing, etc.

    Good Luck. *grin*

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    • Yea, it’s possible to break the data down into absurdly granular levels. But part of the problem with breaking down the information into these smaller units is that there’s just not that much current research on the effects of sequencing and things like that. I’d like to know the biases that I’m introducing by only looking at a certain type of pitch and situation, and that’s not known now. I do plan on looking into sequencing and things like that at some point though.

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    • baty says:

      (for the maps) I am really curious what the gradients look like in grayscale… The gradient between red and blue is tough to see because the red and blue you use have very similar value, and you do get a tricky kind of purple as a 50/50. Also, If you wanted to use color, using more of a difference in value between hues such as yellow and red or yellow and blue might be more visually descriptive.

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  4. baty says:

    “It seems that the importance of not throwing pitches down the middle is overblown. On average, pitches thrown down the middle are actually pretty effective. ”

    And maybe I’m still resisting this generalization, but if we’re looking at pitch type, situation, the batter, etc… doesn’t this throw in a bagillion factors that can influence this statement? When I look at this data, it seems to be influenced by the assumption that all pitchers are throwing with a similar objective per pitch situation, and all pitches are commanded to a similar degree.

    I think this study might start hinting into that idea of “mistake pitches”. I’m not sure exactly what they are, and how you can identify the possibility of a mistake pitch, but the phrase gets thrown around a lot, and it would be interesting to see if there is such a thing, and what that thing is.

    I would think you not only have to look at the actual pitch run value, but also begin looking into other studies that not only study the pitch itself, but as it’s perceived by ways of the pitch(es)that precede relative to the count… How a pitch is “set up”.

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    • baty says:

      Maybe an interesting next step could be to compare two extreme pitch situations:

      an 0-2 “pitcher’s count”
      a 2-0 “hitter’s count”

      and maybe define the study with a specific matchup such as RHPs vs RHHs…

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  5. bill says:

    Very cool article. The obvious flaw is of course, that the hitter quality very much determines the value of throwing a pitch down the middle.

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    • CircleChange11 says:

      Does it?

      Wouldn’t that be the one pitch location that LOTS of major league hitters could thrive on?

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    • brendan says:

      ditto pitcher quality. guys with good fastballs throw down the middle more often, and more often up in the zone, if IIRC.

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  6. Baltar says:

    This is a truly great article, even revolutionary.
    Many of the comments describe fruitful areas for future study but that doesn’t take away from the article as is.
    My own additional thought is that the value of attempting to throw down the middle of the plate is probably underestimated by your study because even the pitches that miss are likely to be better pitches than would be obtained by trying to “paint the corners.” I am not sure if that could be studied.
    Please, Dave and Dave, give us more articles of this importance and quality.

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    • Baltar says:

      Though I regularly read scientific magazines, I have not seen a better explanation of cognitive dissonance.
      Examples of cognitive dissonance abound in both the articles and comments in this forum, probably even my own, and constitute nearly 100% of most baseball forums.

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  7. Paul says:

    Very good article and kudos for using quartiles this time. However, I think using SIERA for any study like this should be the gold standard going forward, given Matt Schwartz’s excellent work also posted today. I think FIP could actually be skewing the results a bit, but hard to really say since you used quartiles. But it would be interesting to see if using SIERA makes any difference.

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  8. Jeff K says:

    Interesting article. Would like to see more articles like this which examine commonly accepted beliefs in baseball.

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  9. Scooter says:

    Upon reading this article, I of course tried to figure out why it was wrong. But then I remembered watching BP a few times this year: even with a grooved pitch — one they know is coming — major-league batters still often fly out lazily, top a grounder, or smack the ball right at a fielder.

    I seem to recall studies of pitch types, where the main lesson is that the pitcher should use his best pitch more often. While that pitch will become less effective per se, the pitcher will do better overall. Essentially, you keep “overusing” your good stuff till everything is equally effective.

    I fear I may have grossly misstated things. But if I had it kinda right, I guess that’s the conclusion here: while it’s true that no pitcher should throw everything down the middle (if only to keep batters off balance), pitchers as a group should do it more often than they do.

    Anyway, thanks for making me think.

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  10. praxspop says:

    I may have missed this, but when you say just 19% of pitches are put into play, that’s all pitches right? What percentage of pitches right down the middle are put into play (seems like it might be larger)?

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  11. Toby says:

    Ok, I’m not buying the conclusions here.
    1. What’s the contact rate for pitches down the middle?
    2. Unless I’m missing something in picture four, the graphs on the run values for both RHH and LHH have peaks near the middle of the plate. Pitchers do better throwing to the outside portion of the zone than over the middle of the dish?
    3. Count and expectation issues raised by other commenters seem very important.
    4. Of course, from a pitchers’ perspective, a pitch down the middle for a strike is better than one off the plate for a ball.

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  12. Jacob says:

    This is great. Using the same dataset could you put together the run value versus vertical pitch location for lefties and righties? I have always been curious if it is true that lefties are better low-ball hitters.

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  13. Luis says:

    When I pitched, and as a coach in High School, I know that LH hitters SEEM to prefer the ball down and away rather than up an in, while wi RH hitters it is the reverse…but I have no hard data to prove that theory. One reason I think it is so, is that from day one LH hitters learn that if they hit a GB to the SS’s right a bit they will be safe, whereas a RH hitter will be out. Positive reinforcement when you are starting to play…just a thought

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