Conventional wisdom says control is the last thing to return for pitchers after Tommy John surgery, but the data isn't quite so sure.
Researching Tommy John surgery and its aftereffects is not an easy task. There are so many variables: the severity of the ligament tear, the health of the arm beforehand and the age of the pitcher it hangs off of, the skill of the doctor performing the procedure, the mutant healing factor of the elbow in question, the organizational philosophy toward rehab, the pitcher’s ability to pitch through pain or inability to disclose it, the small (yet burgeoning) sample size of those who’ve survived it. And luck.
It has become fashionable to bemoan the absence of novel, raw baseball data on which the next generation of would-be analysts can hone their skills. In the case of Statcast, that certainly describes both the status quo and the foreseeable future, as far as public analysis is concerned.
However, Statcast isn’t the only potential source of fresh baseball data. This week, we’d like to think we have made at least a small contribution along these lines: by reviewing our newly-released data bearing upon pitcher command, control, pitch tunnels, and pitch sequencing, both novice and seasoned analysts can unleash their creativity and hopefully teach the baseball community a thing or three.
That said, this data presents some rather unique challenges that might be overlooked in the rush to “see what Excel can do” or apply the trendiest machine-learning technique. So, while I encourage readers to do whatever they want with our new data, I will also start you off with a few words of advice.
Inference Versus Prediction
First, effectively using tunnels data will almost certainly require you to appreciate the distinction statisticians make between “inference” and “prediction.” By “inference,” statisticians describe the process of isolating predictors that tend to be associated with certain outcomes. This usually occurs by isolating certain coefficients in a regression or classification problem, and exploring whether they are consistently meaningful. Examples of inference would be comparing a new drug to a placebo in preventing disease, or in the baseball context, looking at the effect of ballparks on run-scoring. In both cases the outcome is important, but it is not the focus of the investigation.
“Prediction,” on the other hand, is not particularly concerned with the precise contribution each input makes to an outcome. Rather, prediction seeks to forecast the outcome as correctly as possible as often as possible. Many baseball models tend to focus on prediction, deriving an “expected” rate of some event or another, such as a batter’s home run rate or a pitcher’s strikeout rate. Prediction is right in the wheelhouse of your most advanced machine-learning algorithms, which tend to build the shiniest, blackest box imaginable in exchange for terrific results. You often don’t really know how the algorithm got there; all you know is that it did a great job—whatever the hell it did.
Kyle Hendricks might be a lot closer to Greg Maddux than he thinks.
One of the challenges of bringing BP's new pitching data to light is figuring out whether it’s useful and how we can leverage it to better understand what is happening on the field. As mentioned previously, we look at this in much the same way we look at pitch movement or velocity; we need to figure out how these tunnels data points interact with other components of a player’s performance to unlock a deeper understanding of what is happening.
Cubs right-hander Kyle Hendricks is a perfect subject to start with. As we mentioned in "Two Ways to Tunnel," Hendricks has some of the smallest pitch tunnels in all of baseball. Hendricks is often compared to Greg Maddux (including by us!), and we can see how he is in fact like Maddux in certain respects. It gives us an idea of how he’s successful, but only an abstract one. That is, we rationalize Hendricks’ success because we’ve seen Maddux do it before, but we don’t really know how all of the moving pieces come together.
In order to better understand how Hendricks is successful, we’ll have to dig into some of our new data to see what that can tell us about how he pitches.
Hendricks has steadily learned how to strike out opposing batters, increasing his K% by 55 percent from 2014 to 2015 and 2016, and it’s clear the effect that has had on his game. In fact, Hendricks’ new-found ability to strike batters out has resulted in him becoming one of the best pitchers in baseball as he has posted a sub-3.50 DRA over each of the past two seasons despite getting dinged for pitching (and winning an ERA title) in front of an elite defense.
Using our new pitching metrics to measure improved command.
As I discussed in my last post, one of the reasons we’re excited about our pitching metrics is that they allow us to answer new questions. On Monday, I looked at the pitchers who improved their control (using our CS Prob measure of Called Strike Probability) the most from 2015 to 2016 (minimum 60 innings in both years). This time I’m going to look at command.
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Raisel Iglesias and Zach Duke are extreme outliers, and possibly for good reason.
This week at BP has been a celebration of the pitchers with baseball’s best command and consistency. The new metrics we highlighted on Monday (control, as expressed by Called Strike Probability, and command, as expressed by Called Strikes Above Average) capture the degree to which pitchers are able to work the edges of the strike zone and help their catchers frame it. The new metrics rolled out since Tuesday focus on pitch tunnels, and what we’ve learned from them is that there’s a meaningful difference from one pitcher to another when it comes to the ability to disguise pitches.
Repeating one’s release point and throwing consecutive pitches such that they look alike for the first 60 percent of their flight to the plate (with regard to both location and velocity) does seem to be a skill. (We should note that, while a cursory look at the leaderboards will tell you it’s a valuable tool, we haven’t yet developed any stat that assigns value to this skill. For now, we’re just speaking qualitatively about the way pitchers pitch.)
Tunneling from Greg Maddux and Barry Zito to Kyle Hendricks and Rich Hill, and everything in between.
The new pitch tunnels data released by Baseball Prospectus gives us a new glimpse into the repertoires of pitchers across the major leagues. Of course, this data is only as useful as the analysis it helps produce. To showcase how pitch tunnels data can help us better understand the success, or lack thereof, of certain pitchers, we’ll need to better understand how pitch tunnels manifest themselves in the real world.
The title of this article— “Two Ways to Tunnel”—already signals that there isn’t a one-size-fits-all approach to this new data. While game theory might suggest that each individual pitcher has an optimal approach (or approaches), there can be dramatic differences in how different pitchers attack major-league hitters. As such, we should look at this tunnels data much like we would PITCHf/x data. It’s descriptive, and there are many ways to interpret and utilize the data.
We’ll use modern pitchers to explain these concepts with requisite data, but first it’s worth revisiting a historical example. Jeff Long's very first post for BP over two years ago included the following quote about Greg Maddux, the patron saint of tunneling (yes, we know the majority of this quote is included in the introductory post about pitch tunnels, but it’s so good that it merits inclusion once again):
Greg Maddux was on to something, whether he knew it or not.
One day I sat a dozen feet behind Maddux’s catcher as three Braves pitchers, all in a row, did their throwing sessions side-by-side. Lefty Steve Avery made his catcher’s glove explode with noise from his 95-mph fastball. His curve looked like it broke a foot-and-a-half. He was terrifying. Yet I could barely tell the difference between Greg’s pitches. Was that a slider, a changeup, a two-seam or four-seam fastball? Maddux certainly looked better than most college pitchers, but not much. Nothing was scary.
Afterward, I asked him how it went, how he felt, everything except “Is your arm okay?” He picked up the tone. With a cocked grin, like a Mad Dog whose table scrap doesn’t taste quite right, he said, “That’s all I got.”
Then he explained that I couldn’t tell his pitches apart because his goal was late quick break, not big impressive break. The bigger the break, the sooner the ball must start to swerve and the more milliseconds the hitter has to react; the later the break, the less reaction time. Deny the batter as much information—speed or type of last-instant deviation—until it is almost too late.
With the release of BP’s new data on pitcher tunnels, the idea is that one way a pitcher can fool the hitter is to make sure that his pitches all look the same as they fly through the air toward the batter, until they get to the point where the batter has to make his decision of whether he will swing, and if so, where he will aim his bat. If a pitcher can keep all the pitches going through the same “tunnel” and have the break (or lack thereof) be a surprise that reveals itself only after the batter has started his swing, then he’s going to get more swings and misses and weakly hit balls.
Introducing new tools to evaluate command and control through the lens of strikes.
About a year and a half ago, Baseball Prospectus revealed a suite of catching stats that formed the basis for our industry-leading valuation of catchers. These new stats would shape how we perceived and discussed catcher value, but they also opened the door to better understanding the performance of pitchers.
Two key statistics—CSAA and CS Prob—serve as the basis for the pitch framing portion of our catching metrics. Today, we’ll show how those same statistics can tell us a great deal about pitching as well. CS Prob was initially introduced in 2014 with Harry Pavlidis and Dan Brooks’ first catcher framing model. Early the next year, Jonathan Judge joined the effort and the team introduced CSAA, officially moving our framing models beyond WOWY.
Of the two, CS Prob—short for Called Strike Probability—is the more straightforward: the likelihood of a given pitch being a strike. CS Prob goes beyond what the strike zone ought to be and instead reflects what it is: a set of probabilities that depends on batter and pitcher handedness, pitch location, pitch type, and count. Good pitchers understand that while the strike zone is a dynamic construct, it nonetheless has some consistencies depending on which combinations of these factors are present. We calculate CS Prob for every pitch regardless of the eventual outcome.
The other statistic, CSAA, stands for Called Strikes Above Average; a measure of how many called strikes the player in question creates for his team. In the case of catchers, we isolate the effects of the pitcher, umpire, and other situational factors which allows us to identify how many additional called strikes the catcher is generating, above or below average. For catchers, this skill is commonly described as “framing” or, in more polite company, “presentation.”
For pitchers, we can apply a similar methodology—controlling for the catcher, umpire, etc. to identify the additional called strikes created by the pitcher. CSAA is calculated only on taken pitches, an important nuance. A pitch must be taken in order to be eligible to be called a strike by the umpire, so while CS Prob looks at all pitches, CSAA only takes into account pitches where the outcome is left up to the umpire.
What can these two statistics tell us about pitcher performance and skill? First, we should define a few important things: