Toronto's young closer has a potentially dominant cutter, if he can just figure out how to use it.
Earlier this week BP Toronto ran an excellent article by Kyle Matte about Roberto Osuna’s evolving array of breaking stuff. Specifically, Matte wrote about Osuna’s development of a cutter in 2016, and the way (as he observed, providing considerable evidence) it somewhat cannibalized his slider. Whenever a pitcher adds a new pitch to his arsenal there’s reason to hope that it will add a new dimension to his game, but there’s also cause to worry that it might eat into the effectiveness of one or more of his other pitches.
Last week, I wrote aboutDan Straily’s effort to flesh out his two-seam fastball this winter and about his expressed concern that doing so would compromise his changeup or slider. As I did with Straily’s sinker, though, I thought I'd dig into Osuna’s tunneling numbers to see whether the cutter offered a benefit that might make the tradeoffs worthwhile. What I found was pretty interesting, so I thought I would briefly share it here.
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Traded from the Reds to the Marlins, Dan Straily is an example of how new pitching data can help change a repertoire.
For nerdy baseball fans, the worst trade of the offseason was the Reds’ swap of Dan Straily to the Marlins. That’s not because there was an especially egregious mismatch in value in the deal; it was because the move separated Straily from the Reds’ beat reporters.
Just before being dealt, Straily spent almost an hour on a podcast with Zach Buchanan, one of the Reds writers for the Cincinnati Enquirer (and author of the Reds chapter in this year's Baseball Prospectus Annual). It was a delightful listening experience: wide-ranging but detailed, relaxed, smart. They talked about hunting and (ironically) what it’s like to be blindsided by a trade. My favorite discussion centered on the trip to Driveline Baseball from which Straily had returned just before the interview.
Taking a deep dive into the Cardinals right-hander's repertoire, sequencing, tunnels, and overall approach.
In one sense, Cardinals right-hander Carlos Martinez is an easy pitcher to understand. He can touch 100 miles per hour with his fastball. He throws both a four-seamer and a sinker, has a slider and a changeup to go with them, and all four pitches could be counted as above average. He’s fiercely competitive and a great athlete. Bob Gibson was a bigger guy than Martinez at a time when everyone else on the field was smaller. Gibson had only two dominant pitches, and rarely even bothered with others. He’s also a Hall of Famer. Still, it’s really hard not to compare Martinez to Gibson.
In another sense, though, there’s a whole lot we don’t know about Martinez. No, that’s not true. We know a ton about Martinez, far more than we would have known 10 years ago. Yet, we would have been much more confident in our assessments of Martinez then than we are now. Sometimes, even valuable new information only makes the essential truth about something feel further beyond our reach.
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.
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.