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  • The Astros are the best team at applying their data in a practical setting, and all teams should take notes from the World Series champions.
By Tom Verducci
November 09, 2017

The World Series champions are champions of technology, as well. Every team crunches numbers, but no team has been better at the practical application of data than the Houston Astros, especially on the pitching side.

Two years ago the Astros and the New York Yankees became the first teams to throw fewer than 50% fastballs. They knew the old pitching paradigm of “establish your fastball down” was broken. The modern hitter has adapted to the increase in velocity and, with the emphasis on hitting balls in the air, has a swing path geared toward the fastball down. This year the Indians, Rays and Angels joined Houston and New York in no longer believing fastballs should be thrown a majority of the time.

The data tell us that in particular the two-seam fastball/sinker is a dying pitch. It’s the easiest pitch to hit in baseball. Over the past three seasons the batting average on the pitch has been going up (.291, .293, .296) as well as the slugging percentage (.438, .452, .468).

Then, in the World Series, the Astros suddenly boosted their overall fastball percentage to 58%, with Brad Peacock (81%), Joe Musgrove (70), Justin Verlander (68) and Charlie Morton (67) leading the way, though they did so largely with high, hard fastballs. Maybe the slicker baseballs that pitchers complained about, particularly when throwing sliders, had something to do with the change. Sixth in MLB in slider usage during the regular season, the Astros decreased their slider percentage drastically in the World Series, from 19.6 to 12.8%.

Also, the matchup the Astros liked going into the World Series was their high-spin fastballs up in the zone against the Dodgers lineup. The Astros knew that the Dodgers, with so many hitters trying to launch balls in the air, were the worst team in baseball this year at hitting high fastballs (.204).

It wasn’t too long ago that the radar gun provided the only data point to evaluate pitching. Most of the evaluation was being done by scouts and pitching coaches based on what their eyes told them, which is how we came to accept phrases such as “good life on his fastball,” “the ball gets on you,” “12 to 6 curve,” “hides the ball well,” and so on. They were opinions, not facts.

Now we have actual data on how the ball moves and spins and how the pitcher releases the ball. The masters of the game no longer are those grizzled gurus with “an eye” for pitching, but the analysts who can interpret the data and combine it with a coach’s understanding of the craft.

Think about the role of Brian Bannister, Boston’s vice president of pitching development, an analyst/coaching job that didn’t exist two years ago. Bannister was about to start a private business based on pitching analytics, with centers around the country to provide data with “MRI level of precision” to train pitchers. Then the Red Sox called and asked him, “Can you do that for us?”

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“In any industry where there is a disruptive technology, in this case Pitch FX and Trackman, it creates non-traditional roles, sometimes you don’t even expect,” he said. “I never expected to do this when I broke into the big leagues in 2006, but I saw an opportunity. At the major league level it’s always about competitive advantages and giving your players better information. The next wave is personalizing things for the players.”

The next wave is here.

Coaching in sports has changed dramatically. Golfers hire swing coaches who never played on the tour, but through technology have gained a deeper understanding of the golf swing than those who played it at the highest level. Major league hitters such as Justin Turner, Josh Donaldson, Chris Taylor and J.D. Martinez have turned around their careers by seeking out hitting gurus who never played in the majors, but bring the same deep, analytical approach to hitting that swing coaches bring to pro golf.

The same fresh approach is an asset with pitching. Bannister, for instance, admits he failed as a big league pitcher in part because “I was a big trial and error guy. I eventually stumbled upon a lot of things that now I have a high level of confidence in, the things that make major league pitchers successful and allow them to stay successful, but my road was very rocky and I had a lot of failure.”

In Houston, pitching coach Brent Strom and manager A.J. Hinch have embraced new technology and thinking. Strom went 22–39 with three teams in his big league career. Hinch was a .219 hitter. Because they were not largely successful as players, they are not hidebound to “the way I did it”—the old school ways that no longer are as applicable in a changing modern game. They have the freedom to embrace new technology instead of relying on what worked for centuries: one generation passing on “the way I did it” to the next.

Let’s take four key pitchers as examples of how Houston has re-imagined pitching. Four times in the postseason Hinch let a reliever finish a game by getting 11 outs or more—the first time in any postseason a team had so many lengthy game-finishing outings. Those four outings were as many as managed by every other team combined in the previous 26 postseasons.

Bob Levey/Getty Images

What’s so interesting is that a different pitcher made each one, and the rise of each pitcher helps tell the story about how Houston has applied personalized data to pitching:

1. Collin McHugh (ALCS Game 3): McHugh was 0–8 with an 8.94 ERA when the Astros signed him after the 2013 season as a free agent. McHugh was failing in the traditional ways: the majority of his pitches were fastballs, including many hittable sinkers.

Despite the awful traditional stats, Houston liked McHugh because of the high spin rate on his curveball. The club figured McHugh should be throwing more breaking balls and fewer fastballs. He virtually eliminated the sinker (high fastballs work better in tandem with curveballs, because they work off the same “tunnel,” creating deception) and added a slider. The result: a guy who never won a game before is 48–28 since joining Houston, the 11th best winning percentage (.632) among starters over these past four seasons.

2. Lance McCullers (ALCS Game 7): The idea of throwing just 41% fastballs would have been heretical just five years ago. But McCullers did just that this year (sixth lowest among conventional pitchers) and thrived because his curveball is extraordinary. No other starter throws a curveball with a higher spin rate or at a greater velocity than does McCullers. Moreover, his fastball command is average at best, so why not throw more curveballs? He famously closed out the ALCS with 24 straight curveballs.

3. Brad Peacock (World Series Game 3): His place in the game was so tenuous this spring that Peacock told his wife that he wasn’t sure if he could make the club and was prepared to go find work pitching in Japan.

Thinking this might be his last shot, Peacock came to camp in better shape, and his pitches were crisper. The Astros also saw that Peacock had one of the highest spin rates on a slider (fourth highest among pitchers who threw at least 500 sliders), so they encouraged him to throw it more often—leading to career-high usage of 36%, which led to a career year (13–2, 3.00).

But it was his fastball that Houston liked against the flyball-hitting Dodgers. Peacock throws his fastball from an abnormally low release point—barely more than five feet off the ground. The normal release point is about the same height as the pitcher. Peacock is 6’ 1”.

Because of his long stride and because he has a low three-quarters delivery, Peacock “confuses” hitters with the angle he creates on his fastball. The ball seems to be “traveling up” to the hitters (it’s actually just dropping less than they expect), and they can’t get on top of it.

It’s the same illusion that works for Boston closer Craig Kimbrel. Peacock is a junior version of Kimbrel. That’s why the Dodgers hit .174 against Peacock’s fastballs in the World Series (4-for-23).

Rob Tringali/MLB Photos via Getty Images

4. Charlie Morton (ALCS Game 7): Many critics scoffed when the Astros quickly gave $14 million over two years to Morton, a free agent sinkerball pitcher with a 46–71 record, a long injury history and major problems against lefthanded hitters. The Astros knew, however, that Morton had one of the highest curveball spin rates and above-average velocity. In their estimation, he was throwing too many sinkers (62% in his last full season, with Pittsburgh in 2015) and not enough curveballs, especially to lefthanded hitters.

The change in Morton in Houston was dramatic. A career-high 29% curveballs led to a career season (14–7, 3.62). The biggest improvement came against lefthanded hitters. By increasing his curveball percentage to lefties compared to 2015 (25 to 35%) he eliminated his biggest weakness (the batting average by lefties against him dropped from .301 to .175).

With Houston, Morton transformed from a non-descript journeyman to a World Series star. He became only the fourth pitcher to win the seventh game of the World Series with at least four innings of game-ending relief, earning a place in history next to Bob Turley (1958), Joe Page (1947) and Walter Johnson (1924).

His turnaround is so stunning that it begs the question: who is the next Charlie Morton? Why can’t another club, using technology and data available today, identify better things in mediocre pitchers such as Morton, McHugh and Peacock?

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First, a disclaimer: Morton’s turnaround is not so simple as just throwing more high-spin curveballs. It’s a metamorphosis that began in late 2015 when he decided to throw harder instead of pitching to contact. It continued that winter when, with former Pirates pitcher and current Dodger Tony Watson as his workout partner, Morton revamped his diet and training regimen, which led him to losing 15 pounds and helped him gain speed and flexibility in his delivery. Extra fuel came from the disappointment he felt by pitching poorly against St. Louis in a key game down the stretch for the Pirates. “I let everybody down,” he said.

So it’s a recipe with many ingredients, not just one.

But let’s start with the main ingredients to find the next Charlie Morton: an underachieving pitcher with high-spin rates who is not using his pitches in proper proportion, who suffers from serious platoon splits, and who is stuck in the old “fastball-first” paradigm.

Here are the three pitchers who could be breakout stars next season—the next Charlie Morton.

1. Chris Stratton, 27, Giants.

The pitcher: A former 2012 first-round pick, Stratton finally made an imprint in the big leagues last season. In his final eight starts he went 4­2 with a 2.27 ERA. He’s ticketed to be in the mix for the No. 5 spot in the San Francisco rotation next year.

The problem: Stratton has a mediocre four-seam fastball (91.8 mph) and, if you lower the bar to 100 curveballs thrown, the fastest-spinning curveball in baseball (3,105 rpm). Batters hit .292 against his fastball, but only .100 against his curveball. But he’s stuck in an old-school way of pitching: 61% fastballs and only 18% curves.

The symptoms: Lefthanded hitters crushed Stratton, lighting him up for a .811 OPS, while he held righthanded hitters to a .670 OPS. Stratton throws his curveball even less often to lefties (17%) than to righties (21).

The mechanics: They need work. Stratton has poor arm deceleration, meaning his arm and hand brake too soon after release. He can improve velocity by working on better deceleration. He also can throw harder by driving his head and torso more toward the plate; he has a tendency to drift toward the first-base side of the mound while releasing the ball. Bottom line: there’s more in there.

How to get Morton-ized: Increase curveball percentage to lefthanded hitters, work the high fastball/curveball tunnel more often, and tighten mechanics.

Justin Berl/Getty Images

2. Chad Kuhl, 25, Pittsburgh Pirates

The pitcher: A 2013 ninth-round pick out of Delaware, Kuhl learned the sinker while in the Pittsburgh minor league system—the Pirates and Cardinals love the sinker - and that pitch got him to the big leagues. He has added ridiculous velocity. In four games last year he hit 100 mph with his sinker!

Alas, after 45 big league starts, Kuhl is a good athlete with a power arm but a mediocre record: 13–15 with a 4.30 ERA.

The problem: Kuhl is trying to win in the big leagues as a sinker/slider pitcher. That was a great idea in the 90s; not so much now, especially since his fastball command can be spotty.

The parallels to Morton are almost uncanny. Kuhl’s exceptional spin rate on his curve (2,877) is almost identical to that of Morton (2,877). His average fastball velocity (95.6) is almost identical to Morton’s heater (95.7).

The symptoms: Lefties crush Kuhl (.893 OPS, fifth worst in baseball) because he doesn’t throw his curveball enough to them (7%). In fairness, Kuhl didn’t start throwing his knuckle-curve until late May, so confidence in the pitch may be lacking.

The mechanics: Terrific. His arm deceleration is among the best you can find. He’s a great athlete who repeats his mechanics well. Like some sinkerball pitchers, however, to “get on top” of the sinker he throws it with a release point two inches higher than his other pitches. Ideally, you want every pitch coming out of the same spot, so as not to give a hitter any early indication of what’s coming.

How to get Morton-ized: Trust the knuckle-curve more against lefthanders. Embrace the four-seam fastball up in the zone. De-emphasize the power sinker down.

3. Tyler Chatwood, 27, Rockies

The pitcher: He reached the big leagues just three years after the Angels drafted him out of high school in 2008. He has undergone two Tommy John surgeries and largely been mediocre: 40–46 with a 4.31 ERA. He led the league in losses last year (15).

The problem: Coors Field, for one. It’s hard to judge any pitcher in that ballpark. Chatwood suffered a 6.01 ERA at home, with a .302 average against, but posted a 3.49 mark on the road, with a .200 average against.

Chatwood has an extreme high-spinning curveball (2,980) but doesn’t use it much. He relies mostly on fastball and sliders. His two- and four-seam fastballs have above-average velocity (94-95 mph), but they get hit.

The symptoms: He’s too fastball dependent. The data:

Pitch

Pct.

Avg.

HR

Fastball

64%

.314

17

Offspeed

36%

.154

4

Chatwood threw 177 curveballs to lefthanded hitters—and gave up just two hits! Lefties batted .063 against his hook, but he threw it only 13% of the time. Denver’s altitude is notorious for taking the bite out of curveballs, so his home park could be leading him away from a pitch that’s been successful.

The mechanics: Don’t try this at home. Chatwood, a short righthander (6-feet), keeps his hands away from his body, pulls the ball out of his glove early, pulls his elbow behind the line of his shoulders and raises the elbow higher than the shoulder before the ball rotates up—and that’s all before he gets the baseball in the loaded position. He throws over the top, but because of a long stride and an extreme bend of the front knee, actually has a low release point in terms of height off the ground.

How to get Morton-ized: Get out of Denver. Short of that, reduce fastball percentage and increase curveball percentage, especially to lefthanded hitters.

Honorable mentions: Pirates righthander Trevor Williams, 25, who because of freakish extension has the second-highest difference between effective and actual velocity on his four-seam fastball (Jacob deGrom is first); Twins reliever Ryan Pressly, 28, who throws 96 and also has a ridiculous curveball spin rate (3,083), but has mechanical issues because of forearm flyout (the ball when loaded is too far from his head); and Rays reliever Austin Pruitt, 28, a strike-thrower and converted starter with a high-spin hook (2,946) and overhand delivery who needs a tick or two on his mediocre four-seam fastball (91.8).

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One of the most underrated factors in a ballplayers’ development is environment. Nolan Arenado hits in the same batting practice group as Troy Tulowitzki in Denver, and adopts the same foot shuffle in his setup. CC Sabathia extended his career with the cut fastball he learned from Andy Pettitte. After he was traded from Baltimore to Chicago, Jake Arrieta inherited a former crossfire pitcher as a pitching coach, Chris Bosio, who encourages a return to throwing across his body.

In Houston, Morton, who had always been interested in analytics, found the right place to emphasize his curveball. Stratton, Kuhl and Chatwood all pitch for organizations that largely work off the old paradigm of “fastball first.” If you include cut fastballs, the Rockies, Pirates and Giants ranked 5-6-8 in the highest percentage of fastballs thrown.

Dodgers pitcher Rich Hill tells the story of the day in 2015 when he had a conversation with Bannister in Pawtucket, where Hill was pitching in Triple-A at age 35. Bannister told Hill his high-spinning curveball was so good that he could throw it 50% of the time. Hill had been around pro ball 14 years and never heard such a thing. When Hill came home, his wife immediately saw the excitement on his face. “You’ve had a creative explosion,” she told him.

Since then, Hill is 24–13 in the majors and signed contracts worth $83 million.

Bannister also helped turn around the career of Joe Kelly, who was raised in the Cardinals system as a traditional sinkerball pitcher despite having a high-spin breaking ball and elite velocity. Bannister encouraged Kelly at the end of the 2016 season to emphasize his four-seam fastball, not his sinker. Kelly also changed his arm swing so as not to pull his arm behind the line of his shoulders. Kelly, 29, began this year with a career 3.93 ERA, but re-born as a power reliever he posted his best season (2.79 ERA, 3.49 FIP).

Many more will follow the likes of McHugh, Peacock, Morton, Hill and Kelly. There will be more as organizations accept this teaching in which data is not just collected, but also applied in highly personalized ways. Morton gave this revolution the high profile moment to scale it up. In ending the World Series, he started a movement. 

 

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