Author Archive

Tarik Skubal Has Found a Groove

Chosen in the ninth round of the 2018 draft, Tarik Skubal had a meteoric rise in prospect pedigree, tearing through the minor leagues in just 145 innings. He allowed just one earned run in 22.1 innings in 2018, then compiled a 2.58 ERA in high A and a 2.13 ERA in Double-A, but what caught the eye of analysts and prospect hounds alike were the strikeout and walk rates. He punched out about 40% of the batters in 2018 versus a walk rate of about 5% and posted strikeout rates of 30.3% and 48.2% in high A and Double-A, respectively, against walk rates of 5.9% and 10.6% in ’19.

Those numbers earned Skubal a place on our 2019 Tigers list before the start of that season, with Eric Longenhagen and Kiley McDaniel noting that he “dominated in pro ball after signing by throwing about 80% fastballs. He’s a ground-up rebuild who had third-round stuff at his best in college.” After he continued to dominate in 2019, Skubal climbed the 2020 version of the list, placing fourth behind three excellent prospects in their own right.

Skubal missed the entirety of the July summer camp last year but showed enough at the alternate site to earn a call-up, making his major league debut on August 18. It didn’t go to plan, though: He struck out a healthy 27.9% of batters with an 8.2% walk rate but also allowed a 72.3% fly ball rate with a 20% HR/FB ratio. That led to an ugly 2.53 HR/9 and a 5.63 ERA over 32 innings.

Unsatisfied with his 2020, Skubal made the pilgrimage to Driveline Baseball over the offseason. There, he first tried to make improvements to his existing changeup, but nixed that in favor of working on a splitter after immediately feeling comfortable with the offering used by his highly-touted teammateCasey Mize.

Despite the new offspeed pitch, Skubal surrendered a 6.14 ERA in April despite allowing a miniscule .242 BABIP. His walk rate increased from 2020, and he was allowing even more contact in the air. And the splitter was not doing him any favors, with a pedestrian 10.2% SwStr% (the average for a left-handed pitcher’s splitter is 24.5%). He also had no control over the pitch, posting a 33.9% zone rate. Those two factors led to a .539 wOBA allowed on it.

About midway through the month, manager A.J. Hinch mentioned in a postgame press conference that Skubal would throw more breaking pitches going forward at the expense of his new splitter. That wasn’t all; he made a substantial change in terms of fastball usage after his start on the last day of April.

Skubal Pitch% by Start
Date CH CU FC FF FS SI SL
2021-04-04 0.0 5.7 0.0 59.8 4.6 0.0 29.9
2021-04-10 0.0 9.3 0.0 48.0 14.7 0.0 28.0
2021-04-15 0.0 6.8 3.4 50.0 13.6 0.0 26.1
2021-04-21 0.0 0.0 0.0 56.5 14.5 0.0 29.0
2021-04-25 0.0 6.6 0.0 45.9 21.3 0.0 26.2
2021-04-30 0.0 5.2 5.2 55.8 13.0 0.0 20.8
2021-05-07 21.9 8.3 0.0 58.3 0.0 0.0 11.5
2021-05-14 20.0 9.5 1.1 49.5 0.0 2.1 17.9
2021-05-19 13.3 3.3 0.0 54.4 0.0 0.0 28.9
2021-05-25 14.0 10.8 0.0 48.4 0.0 1.1 25.8
2021-05-30 15.8 9.5 1.1 36.8 0.0 18.9 17.9
2021-06-05 15.5 5.8 0.0 45.6 0.0 12.6 20.4
2021-06-11 11.5 9.4 0.0 44.8 0.0 9.4 25.0
2021-06-16 23.1 8.8 1.1 36.3 0.0 20.9 9.9
2021-06-22 11.3 8.2 1.0 51.5 0.0 11.3 16.5
2021-06-27 18.6 4.9 0.0 35.3 0.0 23.5 17.6
2021-07-03 21.3 5.3 1.1 43.6 0.0 7.4 21.3
2021-07-08 20.4 6.5 1.1 30.1 0.0 25.8 16.1
SOURCE: Baseball Savant

Read the rest of this entry »


What Happened to Zack Greinke’s Strikeouts?

Through 18 starts and 111.1 innings pitched, Zack Greinke has compiled a 3.64 ERA. To say those figures play at the top of a major league starting rotation would be an understatement. The mere fact that Greinke remains a good pitcher at the age of 37 in his 18th season is an accomplishment in itself. Arriving in Kansas City as a 20-year-old, just two years after being drafted sixth overall out of high school, Greinke has put together quite the career, accumulating 64.2 WAR (which ranks 42nd all-time and third among active players), winning a Cy Young Award in 2009, and finishing in the top-10 of Cy Young voting on four other occasions (’13, ’14, ’15, and ’17). His Cy Young campaign was 12 years ago and he is still an important cog on a club with World Series aspirations, a testament to Greinke’s greatness and longevity. And that’s to say nothing of what he has battled to become and remain one of the best pitchers in the sport for over a decade.

I prefaced this piece with a brief rundown of Greinke’s amazing career because I am going to be throwing up some red flags in regards to his performance thus far. Despite the excellent ERA I referenced to start, his strikeout rate is down to 18.5% (league average is 23.8%) after posting a rate of 24.5% in 2020 and 23.7% from when he signed in Arizona in 2016 through last season. His walk rate is up to 5%, still almost half the league average of about 9% but an increase compared to his 3.7% and 3.3% walk rates in 2019 and ’20, respectively. Greinke is inducing fewer groundballs than in any season since his Cy Young Award-winning 2009. From 2010-19, he allowed a groundball rate of 46.8%. In 2020, that plummeted to 41.2% and this year he is down to 40.9%. That means 59.1% of the contact he has allowed has been in the air. He has maintained his ERA with the help of a below-average HR/FB ratio and a HR/9 rate about 9% less than the rest of the league. His FIP remains at a solid 4.07, buoyed by his aforementioned home run fortune. Regress that HR/FB ratio towards league average and you get an xFIP of 4.14. Put a little more emphasis on his strikeout struggles and the types of batted balls allowed and you get a SIERA of 4.39, which is a tad less than 8% worse than league average.

So what gives? In his last four seasons, aging curve be damned, he has thrown 3.68 SIERA ball over a not insignificant sample of 685.2 innings, which placed him third in the majors behind only Jacob deGrom (690.1) and Gerrit Cole (688.2). Obviously Greinke is at the point in his career where we expect degradation in his performance, but this dip in his peripherals seems noteworthy given all his success, both in terms of surface-level numbers and those under-the-hood, in recent vintage. Read the rest of this entry »


How Should Pitchers Approach 0-2 Counts?

There is an interesting quote from Greg Maddux about the relative merits (or, if you’re Maddux, demerits) of “wasting” a pitch in a 0-2 count versus continuing to attack the hitter. Throwing a pitch outside of the zone and hoping for a hopeless swing in an 0-2 count is a baseball convention that’s ingrained in pitchers from the time they are adolescents. The idea is to not give the batter the chance to put the ball in play when the pitcher is in a supremely advantageous position. Maddux eschewed this notion. He said, “The hitter is most vulnerable when you get him in an 0-2 bind. My goal is to take him out immediately. I’m going right after him, not fooling around with wasting a pitch up high or throwing one in the dirt.”

Maddux’s impetus for questioning convention was twofold. First, a waste pitch is (wait for it) a waste. It is a waste of a pitcher’s time and energy and gets him out of rhythm. If you believe that on any given day a pitcher has a finite number of effective pitches in him, then throwing a pitch without the singular purpose of using that pitch to get the batter out is foolhardy. Maddux’s second gripe is that batters have the lowest batting average in 0-2 counts, so why would you fear throwing the ball in or around the strike zone? He also mentions the pitch is usually so far away from the strike zone that the hitter will lay off by default, giving the opposition the opportunity to see one more pitch out of the pitcher’s hand. Maddux is seemingly inferring that seeing this extra pitch assists the batter in timing up a pitcher’s motion, allowing them to gain a small edge in being able to better pick up the ball coming out of the hand.

The merits of a 0-2 waste pitch has been explored in the past. Earlier this yeah, Jim Albert used the same Maddux quote as a jumping off point for evaluating 0-2 pitches at his blog Exploring Baseball Data with R (as an aside, Jim is one of the coauthors of a must-have book if you are interested in getting into baseball analysis). Jim noted that pitchers don’t tend to use fastballs as waste pitches; when pitchers do waste pitches, they are more likely to bury breaking balls below the strike zone. He did note that 0-2 fastballs were located higher than fastballs in other counts, but they still were often in and around the strike zone, and thus were not waste pitches. Back in 2011, John Dewan at Bill James Online found that, in terms of the average plate appearance outcome, there was only a 10th of a run difference in favor of the pitcher between throwing in the strike zone versus outside of it, so there was no clear dominant strategy. Read the rest of this entry »


Jimmy Nelson Has Reinvented Himself as a Bullpen Ace

Jimmy Nelson has had a rough few years, facing the most harrowing tribulation of a professional pitcher: persistent injuries. In 2015 and 2016, Nelson tossed 177.1 and 179.1 innings, respectively, with a cumulative ERA of 4.37 and 2.5 WAR. That WAR total over 356.2 innings put him on about a 1.56 WAR per 180 innings pace, about what we might expect from a back-end starter. Given how many innings he was able to shoulder, he was a valuable contributor. The following season, Nelson broke out. He added almost a tick to his fastball and increased the usage of his two breaking balls with great results. He posted a 3.49 ERA in 2017. His strikeout rate increased from 18.5% in his prior two seasons to 27.3% and he trimmed his walk rate by about 33% while maintaining an above average groundball rate of 50.3%. These improvements added up to an astounding 4.8 WAR in 175.1 innings, a massive leap that placed him eighth in baseball among starters.

But the end of the 2017 season marked the start of a string of injuries that kept Nelson off the mound for much of the past three seasons. The first came in September of his superlative 2017. Running the bases following a single against the Cubs, Nelson took a hard turn around first and was forced to dive back to the bag after inducing a throw from the defense. Upon extending his arm to the base, he tore the labrum and rotator cuff in his right arm, comprising his shoulder capsule and incurring nerve damage; the injury kept Nelson out for all of 2018. He returned to the Brewers on June 5, 2019, made four appearances (three of them starts) and suffered an elbow injury that forced him off the mound until September of that same year when he then made six more appearances. In just 22 innings of work, he had a less-than-stellar 6.95 ERA and walked over 16% of the batters he faced. Milwaukee did not tender him a contract that offseason.

The Dodgers signed Nelson that winter, with the intent of rehabilitating someone who was among the most effective pitchers in the majors. But Nelson suffered a back injury in spring training prior to the COVID-19 shutdown, forcing him to have surgery on his back. That injury, according to Nelson himself, was the result of “mechanical compensations that developed over time going back from the shoulder.” He missed all of 2020 but the Dodgers brought him back on a minor-league contract, hoping he could rediscover some of his prior form and fortify a bullpen that lost its second, fifth, and ninth most-used arms, including Jake McGee, who was the team’s most valuable reliever by WAR. Read the rest of this entry »


Luis Garcia Is Two Pitchers in One

After losing Gerrit Cole in free agency after the 2019 World Series and Justin Verlander to a torn UCL just six innings into the season, the Astros found themselves in desperate need of pitching help last year. Zack Greinke helped fill the void left by Charlie Morton’s departure, and Lance McCullers Jr. returned after missing all of 2019, but besides those two pitchers, there were more questions than answers in Houston’s rotation.

Insert a quintet of young pitchers with varying degrees of experience: Framber Valdez (107.2 MLB innings to his name), José Urquidy (41 MLB innings and a stellar upper-minors track record), and three pitchers with a lot of blank space on their résumés in Cristian Javier, Enoli Paredes, and Luis Garcia. With the help of these five and some clever piggybacking, the Astros overcame a mediocre regular-season record to oust Minnesota in the wild-card round and Oakland in the ALDS before falling to Tampa Bay in the ALCS.

Of that group, I want to focus on Garcia, for two reasons. First, he is the team’s current leader in pitching WAR, narrowly edging out Greinke, with a 2.82 ERA supported by a 28.1% strikeout rate and 7.7% walk rate. Second, his rise to the big-league club was the most surprising of the five guys I mentioned above. He signed with Houston out of Venezuela at age 20 (which is old for an international amateur) for a mere $20,000. He had not pitched above high-A before his MLB call-up, though he did dominate the opposition at every level. In 2019, he saw a velocity bump and struck out almost 36% of the hitters he faced in 43 innings pitched at low-A, then whiffed 39.4% of batters he faced in 65.2 innings one level up.

Read the rest of this entry »


John Gant Has a Major Problem

Imagine for a moment that the sabermetric movement never took hold in baseball. Hitters would still be valued based on batting average and RBI; pitchers would be measured on their win total and ERA. In this context, John Gant would be considered among the more effective pitchers on the Cardinals’ staff and in all of MLB. His 4–5 record is not impressive, but his 3.50 ERA ranks second on the team and 45th among all starters who have thrown 50 innings — firmly in the territory of a solid No. 2 starter.

A fan who looks beyond ERA, though, knows Gant has not been a good pitcher in 2021. He has struck out only 16.5% of the batters he has faced, a rate about 33% below average, and walked 15.8% of the batters he has faced, close to double the league average. To put that in context, Gant’s walk rate is second worst among all starting pitchers who threw at least 50 innings since 2018; Tyler Chatwood walked 19.6% of batters in 96 innings that season. His K-BB% is third worst, after Chatwood in ’18 and Bryan Mitchell in that same year. The degree of his struggles with his control is almost unparalleled:

Gant’s FIP stands at 5.01, 1.51 runs worse than his ERA. Incorporate batted ball data, and the picture gets worse; his SIERA is 5.97. All in all, he has been worth just 0.1 WAR in 61 innings.

I would imagine most, if not all, the readers of this website assume that Gant is bound to regress, probably to the point where he will not be in the Cardinals’ rotation either at some point this season or next. To say he is walking a fine line would be an understatement. But I am not going to get into why Gant will most likely struggle the rest of the way. Instead, I want to dig into why he has struggled.

First, take a look at Gant’s arsenal:

John Gant’s 2021 Pitch Mix
CH CU FC FF SI SL
21.5 8.0 7.1 11.7 38.6 13.1
SOURCE: Baseball Savant

Gant has a broad array of offerings. Based on my research into pitcher repertoires and their reliance on two pitches, he is among the leaders in number of pitch types thrown and among the laggards in use of his top two pitches. He throws his two most used pitches, the sinker and changeup, 60.1% of the time. He also throws a slider, four-seamer, curveball, and cutter, all of which he uses enough that the batter at least has to think about the prospect of seeing any of them.

The diversity of pitches may not be doing him any favors, though. The league as a whole throws the ball in the zone 49.1% of the time, and batters swing a shade above 47% of the time. Gant’s zone rate is a little below the rest of the league at 46.6%, but he only induces swings on 42.3% of all pitches. Let me break it down by count:

Gant Zone% by Count Compared to League
Player 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
John Gant 48.6 45.5 43.2 44.7 45.8 35.9 53.4 47.8 43.8 69.6 45.7 53.3
League Average 51.9 45.5 34.0 54.0 49.5 38.9 57.1 56.0 47.1 60.0 60.4 57.4
SOURCE: Baseball Savant

 

Gant Swing% by Count Compared to League
Player 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
John Gant 25.9 50.4 59.5 35.0 52.1 53.3 27.6 49.3 60.0 8.7 37.0 63.3
League Average 30.1 49.3 52.3 42.2 53.6 57.5 41.9 58.0 65.0 10.5 54.5 70.8
SOURCE: Baseball Savant

Gant generally avoids the zone more than the rest of the league in most counts, with the largest deviations coming in hitters’ counts. He grooves pitches into the zone on 3–0 more than league average, but he lags by a noticeable amount in two-ball counts. His control struggles stand out most on 3–1 counts, though. Those are a bit different than 3–0 counts; batters swing so infrequently on 3–0 that you can be confident in throwing a pitch in the strike zone without major repercussions. In 3–1 counts, though, hitters become much more aggressive, hunting for a pitch they can hurt. For whatever reason, whether it is lack of control or lack of confidence in his stuff, Gant finds the zone a whopping 15% less than league average in those situations. Hitters have taken notice, swinging at only 37% of his offerings compared to 54.5% for all other pitchers. Batters also seem to have figured out that getting deep into counts against Gant is especially beneficial given his penchant for walks; they are only swinging at 35% of his 1–0 pitches (42.2% is average), 27.6% of his 2–0 pitches (versus 41.9% league-wide), and 49.3% of his 2–1 pitches (against the 58% average). Clearly the book is out on Gant and his passive approach.

Only one of Gant’s offerings, meanwhile, exceeds the league-average zone rate of 49.1%:

Gant Pitch Type Zone%
CH CU FC FF SI SL
32.8 34.4 44.3 48.9 56.9 45.6
SOURCE: Baseball Savant

His sinker, the most used pitch in his arsenal, is the lone one he can consistently throw for strikes; the changeup, his second favorite pitch, only finds the zone 32.8% of the time. That is a large percentage of total pitches that hitters know will not be competitive.

Though Gant does not throw his curveball very often (8% of the time), his usage of the pitch is notable for one particular reason.

Gant Pitch% by Count
Pitch Type 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
CH 12.6 21.1 18.9 31.7 29.2 25.0 20.7 26.9 27.5 13 15.2 23.3
CU 18.3 9.8 1.4 0.8 6.2 5.4 5.2 1.5 7.5 4.3 2.2 3.3
FC 5.8 11.4 4.1 8.9 10.4 5.4 6.9 6.0 5.0 8.7 8.7 3.3
FF 6.8 13.8 35.1 7.3 8.3 19.6 5.2 7.5 13.8 13.0 6.5 15.0
SI 46.0 29.3 29.7 41.5 30.2 35.9 39.7 44.8 31.2 47.8 45.7 38.3
SL 10.4 14.6 10.8 9.8 15.6 8.7 22.4 13.4 15.0 13.0 21.7 16.7
SOURCE: Baseball Savant

Gant throws the curveball on almost 20% of his first pitches in a plate appearance. Group that together with the changeup, and more than 30% of his first pitches are non-competitive. Focus on the counts where he struggles to find the strike zone (1–0, 2–0, 2–1, and 3–1) and on his pitch usage in them. Now look at how often he throws each pitch in the zone in those counts:

Gant Zone% by Pitch Type and Count
Pitch Type 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
CH 31.4 34.6 28.6 41.0 28.6 21.7 50.0 22.2 36.4 66.7 14.3 35.7
CU 37.3 25.0.0 100 0.0 16.7 0.0 66.7 100 33.3 100 0.0 50.0
FC 37.5 50.0 0.0 18.2 50.0 80.0 50.0 100 50.0 0.0 50.0 50.0
FF 36.8 58.8 53.8 55.6 50.0 38.9 33.3 20.0 36.4 100 66.7 66.7
SI 61.7 58.3 45.5 51.0 58.6 48.5 60.9 63.3 60.0 72.7 38.1 56.5
SL 44.8 33.3 37.5 50.0 60.0 12.5 46.2 33.3 33.3 66.7 80.0 60.0
SOURCE: Baseball Savant

He has yet to throw a curveball in the strike zone after starting a plate appearance out with a ball. The changeup’s zone rate on 1–0 counts is only 41%. In 2–2 counts, most of his repertoire finds the zone at a rate 25% worse than league average. For 3–1 counts, the situations where he struggles the most, his two favorite pitches, the sinker and changeup, are thrown in the zone just 38.1% and 14.3% of the time, respectively; the rest of the league fills up the zone 60% of the time! His avoidance of the strike zone and subsequent lack of swings are astounding.

Add all of this up and you can understand why Gant is struggling to strike hitters out and keep them off the bases:

Gant BB% in PAs Reaching Each Count
Player 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
John Gant 15.5 12.2 8.1 22.8 17.7 15.2 37.9 29.9 17.5 56.5 52.2 36.7
League Average 8.4 4.7 2.8 15.3 9.3 5.6 30.3 19.5 12.3 60.7 44.5 31.9
SOURCE: Baseball Savant

 

Gant K% in PAs Reaching Each Count
Player 0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
John Gant 16.5 26 36.5 11.4 21.9 30.4 6.9 11.9 23.8 4.3 8.7 13.3
League Average 24.0 32.4 47.8 19.8 29.1 45.0 14.5 23.4 39.2 8.9 13.6 28.5
SOURCE: Baseball Savant

Gant is walking batters nearly four times more often than the league average on plate appearances that reach 0–2 counts and almost three times more often on plate appearances that are 0–1 and 1–2. While not as drastic, the same trend persists across all counts except 3–0, where he is more likely to pitch in the zone. Correspondingly, he is failing to put batters away when he reaches advantageous counts because hitters feel comfortable leaving the bat on their shoulders.

What does this mean for Gant going forward? Unless he makes drastic changes to how he attacks the opposition, he will continue to give out free passes and fail to put batters away via strikeouts, leading to traffic on the base paths and balls in play — the perfect recipe for opposing teams to put up crooked numbers. Is there a fix? He could start with throwing the ball in the strike zone more often, but if it were that simple, he would be doing it already. Maybe he lacks confidence in his stuff and fears what will happen if he lives in the strike zone at even a league-average rate. Or maybe he just does not have the control to be a starting pitcher.

The latter explanation would put the fault more on the club than Gant. If he is not capable of turning over a lineup effectively due to a lack of control, the impetus is on the Cardinals to make an adjustment. Without Jack Flaherty for an extended stretch and Miles Mikolas for all but four innings, as well as injuries to Kwang Hyun Kim and the struggles of Carlos Martinez to find his velocity, St. Louis has had a hard time piecing together a viable rotation. The only consistent options have been Adam Wainwright and Kim when healthy, with the talented yet raw Johan Oviedo brought up from Memphis as a reinforcement. If the club hopes to make a playoff push, Gant either needs to improve substantially in the very near future, or St. Louis needs to turn to other options, whether it be from the farm or via trade.


Lance Lynn, the Same As He Ever Was, Just With a Twist

Lance Lynn has been among the best pitchers in the majors since the moment he signed with the Rangers back in 2019. Before that season, Lynn accumulated 16.9 WAR in 1,134.1 innings, good for a rate of 2.7 WAR per 180 IP, the epitome of a very good mid-rotation starter. He was remarkably consistent across those seasons, first for the Cardinals from 2011-17 and then for the Twins and Yankees in ’18. The winter after his partial season in New York, Lynn signed with the Rangers for a modest (by quality veteran standards) $30 million over three years. This was a perfectly reasonable contract given his output prior to 2019; if anything, it was a little light. Lynn had proven time and again that he could effectively eat innings for playoff-caliber clubs. From 2012-18, he threw 176.0, 201.2, 203.2, 175.1, 186.1, and 156.2 innings; again, a paragon of consistency.

Starting in 2019, Lynn found another gear. In his first season in Arlington, he posted 6.7 WAR on the back of a minuscule 66 FIP-. He has not looked back since: from 2019 through this season, Lynn is fifth overall in WAR, with 9.9 wins to his name, narrowly edging out Zack Wheeler. The only pitchers with better results have been Jacob deGrom, Gerrit Cole, Shane Bieber, and Max Scherzer. Much of that production can be attributed to continuing to soak up innings; Lynn is fourth in innings pitched in that time frame. But he has also been excellent on a rate basis. From 2019-21, he has posted the sixth lowest ERA- among starting pitchers and the ninth lowest FIP-. His production is the confluence of continuing to be a workhorse and upping the ante in terms of his per start effectiveness.

Lynn’s salary and the Rangers place in their rebuilding cycle made Lynn an obvious trade candidate this past winter. Lynn’s contract and the White Sox wanting to (let me be nice) maintain “payroll flexibility” while also making a playoff push made the player and club a perfect match. These factors led Chicago to send Dane Dunning and Avery Weems to the Rangers for the last year of Lynn (and his rib-smashing aesthetic). As one can imagine, given his place on the WAR leaderboard through the 2021 season, Lynn has continued to excel on the Southside. He is striking out 28.1% of the batters he faces while posting a walk rate of just 7.0% through 12 starts and a park adjusted ERA 64% better than league average. He has been everything the White Sox could ask for and more. His continued success might make you believe that Lynn is humming along, picking up right where he left off after dominant 2019 and ’20 showings. Read the rest of this entry »


Let’s Take a Closer Look at Hitter Swing Decisions

Swing decisions are generally evaluated with limited nuance. We consider whether the pitch was in the strike zone (as defined by your data provider of choice) and whether the batter swung. Over the course of hundreds or thousands of pitches, this provides an easy-to-comprehend method of effectively evaluating a player’s approach. With a sufficient sample, these binary classifications give us insight into how players approach their plate appearances relative to their peers, which hitters are better at discerning the strike zone and which are more aggressive.

I have a bone to pick, though: there is often no differentiation between pitches that just miss the defined strike zone versus those that miss by multiple feet, or pitches that just nick the strike zone as opposed to pitches right down the middle. A lot of swing decision analysis is done in the binary, but as many analysts have shown, looking at the gradations in the strike zone can be revealing. Granted, this distinction lacks meaning over many pitches; selective hitters with elite batting eyes will separate from their less fastidious peers with respect to chase rate over time. But in smaller samples, the lack of distinction between pitches and their proximity to the strike zone makes judging a player’s swing decisions difficult.

One method we can use is to group pitches by their probability of being called a strike. Similar to how pitches are evaluated for the purpose of studying catcher framing, I created a general additive model for gauging the probability that a given pitch would be called a strike. My model was trivial (relative to the research I linked above) in that I just considered pitch location and pitch movement; for the purpose of this exercise, I thought that would be enough to get the idea across. The model was trained on 80% of pitches called a ball or strike from the 2020 season, with the remaining 20% used as the test set. For the test set, the model was about 92.5% accurate, in that it correctly predicted whether a pitch was called strike 92.5% of the time.

I applied the model to all pitches from the 2019 and ’20 regular seasons, which yielded the probability of a called strike on every pitch. Pitches with higher probabilities of being a called strike if taken are toward the heart of the zone. Pitches at the edges of the zone have anywhere from a 40–60% chance of being called a strike. And pitches with expected probabilities closer to zero are nowhere near the strike zone.

I binned every pitch in increments of 10% of called strike probability. The following represents the swing rates in each of those bins:

Swing Rate by Called Strike Probability
CS Prob at Least (%) CS Prob at Most (%) Swing%
0 10 22.7
10 20 43.9
20 30 47.3
30 40 49.0
40 50 50.9
50 60 53.4
60 70 55.1
70 80 56.9
80 90 59.9
90 100 70.3
SOURCE: Baseball Savant
Data From 2019-20 Seasons

As one would imagine, the league as a whole swings at pitches that have higher called strike probabilities; the closer the pitch is to the heart of the zone, the higher that probability. Break those probabilities down even further, and you can see that the chance of a swing increases steadily with called strike probability.

Swing rates increase rapidly as the called strike probability approaches 0 and 100%. For the more competitive pitches, the changes in swing rate are much smaller. Intuitively, you would expect this relationship to be linear throughout the probability interval; for every 1% increase in called strike probability, the swing rate would also increase by some corresponding percent described by the slope of a line regardless of where you are along this interval. This is not the case.

My hunch is that once a pitch reaches a certain threshold of competitiveness (in terms of challenging the hitter to swing), the swing decision is not as tethered to the chance of the pitch being called a strike. Instead, the choice depends on the pitch type and what the hitter is guessing or picks up out of the pitcher’s hand. Addressing the rapid increase in swing rate on the lower end of the spectrum, I would imagine that many of these pitches are thrown in advantageous counts from the perspective of the pitcher — two-strike counts. While the lack of stigma surrounding strikeouts has been talked about ad nauseam in baseball circles, hitters still do not want to strike out. So if these less competitive pitches are often being thrown with two strikes, the swing rate increases are going to be more sensitive to any marginal change in called strike probability. Break it down by count, and you can see that that’s the case:

For the sharp increase on the higher end of the range, my theory is the same as the other end of the spectrum: Pitches approaching a 100% called strike probability are so enticing to swing at that batters will disregard the count to attack them. Murkier pitches will not really be swung at in 2–0, 3–0 or 3–1 counts, but if the pitch is close to an automatic strike, it must be toward the heart of the plate; a batter who has the green light will want to swing.

For context, league-wide swing rates have oscillated between 45–47% over the past decade. Swing rates on pitches with a called strike probability between 40–60% generally fell in this range in 2019 and ’20. It’s the extreme ends of the spectrum where hitter behavior changes most rapidly. We also saw that the count has a significant effect on the swing rates for any given pitch, especially those that were most and least competitive. So, we know the general league-wide trends and we understand why this is a more nuanced method in evaluating swing decisions. What about at the player level? I found a couple of interesting quirks. When you look at the players who are most aggressive on the pitches that are the most advantageous to swing at (those with a called strike probability of at least 90%), you get a mix of players who we think of as having good plate discipline and those who are more free swingers:

Most Aggressive Swingers on Most Enticing Pitches
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Ozzie Albies 27.8 49.1 54.2 58.6 61.8 67.8 76.5 62.1 71.1 83.4
Jorge Alfaro 44.3 62.4 72.6 69.2 70.2 66.7 77.8 74.6 68.4 82.8
Jay Bruce 28.3 57.1 73 68.2 59.5 57.5 64.7 54 75.4 82.9
Khris Davis 19.8 48 43.7 58.8 52.5 66.7 56.9 68.2 71.7 83.9
Freddie Freeman 20.2 45.3 49.3 63.7 62.4 59.1 65.5 66.9 71.1 84.1
Brandon Lowe 19.7 40.3 54 56.5 54.7 57.1 56.5 64.8 63.7 81.8
Jeff McNeill 27.5 67.3 62.3 69 77.5 80.9 69 78.9 76.2 87
Austin Reilly 28.1 54.7 59.6 61.4 67.6 62.7 77.1 73.1 82.1 81.2
Corey Seager 21.9 45.7 56.4 45.5 50.7 57.4 62.8 74.5 71 83.4
Luke Voit 19 52.6 49.5 48.3 56.5 49.3 56.3 67.3 67.3 81.2
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Freddie Freeman, Luke Voit, Brandon Lowe, and Corey Seager are all examples of players we generally understand as having good plate discipline. They lay off pitches that have very little chance of resulting in a called strike and attack pitches that can result in positive outcomes on contact. This list also includes Jeff McNeil, Jay Bruce, and Jorge Alfaro, all of whom swing at pitchers at rates higher than league average no matter the location. This type of strategy can work for a player like McNeill, who has displayed throughout his career he is among the league’s best at making contact. For players like Bruce and Alfaro, this is a recipe for either falling out of the league (in the case of Bruce) or finding more time on the bench as time goes on (in the case of Alfaro). On the other end of the spectrum, the analysis is more cut and dry:

Most Passive Swingers on Most Enticing Pitches
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Harrison Bader 19.3 34.7 45 53.8 49.1 53.2 59.7 56.6 53.3 59.7
David Fletcher 18.7 32.4 32.6 35.3 29.6 41.2 45 42.6 43.8 50.7
Greg Garcia 12.4 31.5 25.5 27.1 37.5 24 51.7 34.7 39.6 57.5
Brett Gardner 16.3 32.7 25.4 32.4 35.4 43 41.9 50 52.5 57.8
Mitch Garver 12.5 37.2 37.3 19.1 32.5 46.4 32.5 32.8 44.7 58
Yasmani Grandal 15.1 25.4 37.7 37.1 45.1 44.8 50.9 43.7 48.2 59.1
Tommy La Stella 15.2 31.9 53.7 33.3 41.1 38.9 50 58.2 51.1 59
Eric Sogard 16.6 31.2 27.9 45.9 36.2 49.2 51.5 49.3 49 56.7
Josh VanMeter 17.3 30.2 38.6 64.5 37 29.4 44.2 53.8 61.5 59.6
Daniel Vogelbach 14.3 34.8 28.3 36.6 31.1 38.4 39 36.7 44.7 53.7
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Here we have a list of players who we consider either disciplined or passive. These players do a good job of avoiding swinging at bad pitches, but it seems to be more of a product of just not swinging at all. It could also mean that these players are zeroing in on “their pitches to hit,” and can lead to very good seasons (see: Yasmani Grandal, Mitch Garver, and until this season David Fletcher, Brett Gardner, and Eric Sogard) but passing up good pitches can be problematic without either elite power or contact ability (see: Greg Garcia, Harrison Bader before 2021, and Josh VanMeter). This extreme passivity is a fine line to walk; as you can see after great combined 2019-20 seasons, Fletcher, Gardner, and Sogard have fallen off this season after posting very good lines previously. Fletcher especially is one of the best at putting the bat-head on the baseball, but his passivity may be catching up to him as the league has collected more data on his swing patterns.

Finally, here were the most aggressive and passive swingers on pitches with very little chance of becoming a strike:

Most and Least Aggressive Hitters on Likely Called Balls
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Jorge Alfaro 44.3 62.4 72.6 69.2 70.2 66.7 77.8 74.6 68.4 82.8
Hanser Alberto 42.4 60.4 77.6 67.3 71.8 64.9 71.9 67.1 68.9 76.6
José Iglesias 37.8 54.3 62.5 63.8 52.9 67.6 58.8 61.6 58.3 67.8
Kevin Pillar 37.2 63.7 67.8 69.4 69.2 58.1 58 65.6 72.2 73.4
Tim Anderson 36.7 62.2 66.7 65.3 57.1 67.6 62.8 64.5 73.5 77.4
Javier Báez 36 58.9 57.5 66.7 61.8 72.6 70.7 64.8 72.5 74.2
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
Juan Soto 11.2 29.9 38.1 32.4 37.1 43.2 47.2 55.3 54.4 71
Carlos Santana 10.8 34.9 30.8 35.4 39.1 42.9 51.3 39.8 55.5 68.2
Alex Bregman 10.7 28.6 25.7 38.6 39.6 34.5 42.4 42 43.1 61.9
Andrew McCutchen 10.7 25 28.9 30.2 45.6 38.9 41.3 48.4 43.9 61.5
Cavan Biggio 9.7 22.6 23.7 37.2 26.1 29.8 44.6 37.3 43.6 65.3
Tommy Pham 9.6 31.3 40.4 37.7 31.1 41.2 58 39.8 58.4 63.7
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Unsurprisingly, batters who avoid swinging at the worst pitches tend to post good results. The other end is a bit of a mixed bag. Tim Anderson has gotten away with what we would consider poor swing decisions because of his demonstrated ability to post high-end BABIPs the past few years, a combination of hitting the ball at angles that result in singles and his foot speed. Javier Báez has posted excellent lines (2020 notwithstanding) by slugging his way to success. Without outlier skills, this sort of approach leads to lackluster performance. Before 2020, José Iglesias was not a good hitter in the majors. Kevin Pillar and Hanser Alberto have mostly posted middling results, and I talked about Alfaro’s issues above.

There is not a one-size-fits-all method of approaching plate appearances. A player’s ability to make contact and his power are the driving forces behind how often he should swing and which pitches he should choose to offer at. This conclusion is nothing revelatory but distinguishing swing decisions based on its chance of being a strike if taken gives additional insight into certain players’ plate discipline profiles. Freddie Freeman or Juan Soto, how swing clearly can track the ball very well and we know they have great discipline. But their plate discipline is different than a player like Yasmani Grandal, who has also displayed discipline throughout his career, though that discipline manifests itself in a much more passive approach. When parsing swing decisions by the quality of a pitch on a granular level, players can get by either through aggression or selectivity. This also shows that free-swingers are free-swingers, no matter the pitch. Baseball players and their skills contain multitudes. When we deal with samples in terms of pitches faced, it helps to further parse the information at hand to get a better understanding of how players struggle or perform well.


Evaluating Two-Pitch Pitchers

About a month ago, I wrote about Jack Flaherty and looked at his increased reliance on both his fastball and his slider. I posited that through his first seven starts, Flaherty had effectively been a two-pitch pitcher, with the aforementioned combination of pitches making up about 80% of his total pitches. (His curveball was his third-most used pitch, thrown sparingly at about a 13% clip.)

To investigate if this constituted a negative development and could account for Flaherty’s reduction in strikeouts relative to his career norms, I conducted a series of analyses. I grouped pitcher seasons from 2010-20 and looked at the number of pitches each pitcher had with a usage over 15%. This was somewhat arbitrary; I chose the 15% cutoff so pitchers with mixes like Flaherty’s in 2021 would appear in the bucket with two pitches. I then took each bucket and looked at the group’s strikeout rate, walk rate, FIP-, and WAR per 180 innings pitched. I found that between two, three, and four pitches, there was virtually no difference in any of the measures; the strikeout and walk rates were within a percentage point, as were the FIP- figures, while the prorated WAR numbers were within hundredths of a win. Next, I calculated the third time through the order (TTO) effect for pitchers in each bucket. To my surprise, there again was little difference between the pitcher buckets. My hypothesis was that two-pitch pitchers would struggle to get through the order as effectively as their peers who utilized more pitches. But based on my cutoffs for a relevant pitch (15% usage), this did not seem to be the case.

From there I concluded that Flaherty leaning on his fastball and slider more was not inherently bad; there seemed to be no evidence that being a two-pitch starter was inherently detrimental to striking out batters, preventing runs, and turning over a lineup on more than one, two, or three occasions.

But upon further reflection, I was dissatisfied with my process in arriving at this conclusion. The basis for my dissatisfaction was that my criteria for determining whether a pitcher was a two-pitch pitcher or a pitcher with three to four credible offerings. I chose the criteria, as I explained above, based on the tendencies of a single player I was interested in and in a way that would fit the narrative I was trying to tell. I also felt (anecdotally) there had been an influx of pitchers in the majors who have found success by primarily relying on two pitches; some of those pitchers happened to represent clubs the public deems “smart.” Thus, two-pitch starters were not actually more flawed than their peers with more diverse repertoires.

I will address the latter part of this line of thinking later (spoiler: it is extremely flawed) but this is just how I was trying to rationalize my findings. I have seen the performances of Luis Patiño and Shane McClanahan in 2021 and Tyler Glasnow last year (he added a slider this season) in Tampa with two pitches and thought the Rays may be on to something. Same for the Astros and Framber Valdez, Cristian Javier, and Lance McCullers Jr. (until this year, when he also added a slider). Two of the most surprising break-through pitchers of the past two-plus seasons have been Kevin Gausman and Lucas Giolito, both of whom rely primarily on a fastball/offspeed combination (for Gausman, the pitch is a splitter; for Giolito, it is a changeup). Dinelson Lamet is another pitcher with exceptional results (when healthy) relying only on a four-seamer and slider. As I mentioned above, this is all anecdotal evidence backing up a potentially faulty conclusion. There is no empirical support here. This is not the most rigorous approach to research.

That led me to redo my analysis, this time with more rigor in classifying the “two-pitchedness” of a player. Before I get into my methodology for this determination, I would be remiss if I did not at least introduce the main concept I am trying to measure: the third time through the order effect (which I will denote as TTO for the remainder of this piece). This is a phenomenon that has played a massive part in determining pitching roles and deployment in this era of major league baseball. It consists of the degradation of pitcher performance as he moves through the opposing lineup. No matter how you measure it — wOBA allowed, RA9, ERA — the pitcher population pitches worse the second time through the order compared to the first and the third time through the order versus the second. Generally, the effect is measured relative to the first time through the order. Since I will be using wOBA allowed in this piece, that means the second time through the order effect is the difference in wOBA allowed for the second and first time through the order and the third time through the order effect is the difference in wOBA allowed between the third and first time through the order. For some more background on the subject, I would recommend this piece at Baseball Prospectus by Mitchell Lichtman, which was my introduction to the phenomenon. More recently, Rob Mains did a multi-part series on the TTO penalty for BP. I would also recommend these two articles from Chris Teeter at Beyond the Boxscore; the first link measures the TTO for groundball versus fly ball pitchers and the second gauges the TTO by the type of secondaries a pitcher employs.

Now, onto to my analysis. First, let’s walk through how I grouped pitcher seasons this time around. For every pitcher season from 2010-19 (I threw out the shortened 2020 season) where the pitcher in question threw at least 100 innings, I looked at the percentage of pitches he threw for each pitch type. All the pitches were ranked in descending order based on their usage. I pulled the top two most used pitches for each pitcher and added their usage together. The sum of the usage of the top two pitches was my gauge of the “two-pitchedness” of that pitcher season. To give an example, Walker Buehler’s two most used pitches in 2019 were his fastball and slider. The former he threw 53.2% of the time and the latter he threw 14.2% of the time. Add those two figures together and you get 67.4%. That combined number was the figure I was concerned with for each pitcher season. A pitcher who only has two credible offerings will have a value close to 90%. Pitchers with the most egalitarian mixes will be down towards 50%. So instead of using an arbitrary cutoff to gauge whether a pitcher was a two-pitch pitcher, I used a continuous number that gives us a spectrum that’s not biased in any way (unlike my analysis in the Flaherty piece).

I bucketed the combined usage of the top two pitches in increments of 10 percentage points. All players with a combined usage of their top two pitches greater than 50% and at most 60% were grouped together, then greater than 60% and at most 70%, etc. Note that we are dealing with pitchers who threw at least 100 innings in a season. This means we are considering starters and, in recent seasons, “bulk” guys or pitchers who appear after openers and are tasked with starter-level workloads without the designation of pitching as a starter.

With the pitchers bucketed I went to pitch-by-pitch data from Baseball Savant. Each plate appearance in each regular season game was given the designation of how many times that pitcher faced that spot in the batting order. I appended the information about the pitch usage bucket the pitcher fell into and then collected the data for each bucket.

Before I get to the TTO figures, let me show you the information I described towards the beginning of this article about the performance of pitchers in each bucket, now with the refined pitcher designations:

Performance by Reliance on Top Two Pitches
Top Two % No. of Pitchers K% BB% FIP- WAR per 180
10-20 9 17.4 7.1 107.9 1.73
20-30 1 15.3 6.2 100.0 2.33
30-40 1 19.4 8.7 90.0 3.34
40-50 41 18.6 6.9 104.1 1.94
50-60 322 19.6 7.2 99.0 2.35
60-70 487 20.1 7.3 97.6 2.44
70-80 349 20.9 7.5 97.1 2.49
80-90 179 20.9 7.1 96.2 2.61
90-100 28 21.5 7.5 97.0 2.58

For the rest of the piece, I am going to neglect the bins with so few players because the generalized results in those bins lack any signal given the size of the sample of pitchers in those buckets. Interestingly, it seems pitchers up to 90% combined usage of their top two pitchers performed best. They tied for the highest strikeout rates and posted the lowest walk rates, lowest park and league adjusted FIPs, and the highest WAR accumulation rates of all the relevant bins. All of these figures steadily decrease as the pitch mixes become less concentrated in the top two pitches.

Case closed! We shouldn’t care if a pitcher throws a useful third and/or fourth pitch, right? I will point out that I made this point in my Flaherty piece. But this is the incorrect conclusion. The pitchers in the 80% and up to 90% bucket faced the fewest batters per appearance, followed by the pitchers in the next lowest bucket. This means that these pitchers are being pulled earlier and do not have to combat the second or TTO penalty as often as the rest of their peers and suffer a degradation in performance. Managers and front offices have realized this effect and naturally have made a conscious effort to pull these types of pitchers before the opposition gets too comfortable in the batter’s box.

So pitchers with only two heavily used pitches post better results than those who leverage more offerings, but we know those performance indicators are biased in favor of those two-pitch pitchers. This performance bias presents itself with the TTO effect, which I calculated for the buckets in the table above.

TTO Effect by Top Two Pitch Usage
Top Usage Bin First Time wOBA Second Time wOBA Third Time wOBA Second Penalty Third Penalty
40-50 .319 .331 .337 .012 .018
50-60 .312 .323 .335 .011 .022
60-70 .307 .318 .332 .011 .025
70-80 .303 .319 .332 .016 .029
80-90 .308 .316 .340 .008 .033
SOURCE: Baseball Savant

The second time penalty is the wOBA allowed difference between the first and second time through the order and the last column is the TTO penalty. From the pitcher’s perspective, positive wOBA figures are disadvantageous because this indicates hitters are performing better.

The results here are stark. There seems to be no signal in how well a pitcher performs the second time he pitches through a lineup based on his propensity to throw his top two pitches. The TTO penalty, on the other hand, steadily increases from the lowest bucket in this set to the highest bucket. For pitchers who only use their top two pitches up to 50% of the time, the TTO penalty is worth just 18 points of wOBA. By the time we get to pitchers who are effectively throwing two pitches, the TTO penalty almost doubles relative to the lowest bucket, ballooning to 33 points of wOBA. The magnitude of the TTO penalty increases steadily among the buckets. The penalty for the second bucket (more than 50%, at most 60%) is four points higher than the lowest. The third is three points higher than that, while the fourth is four points higher than the third, and finally the last bucket is four points higher than the third. This is almost a perfectly linear trend. Adding pitches clearly gives pitchers more viable options to eat up innings and go deeper into games. That is not to say pitchers with broader repertoires do not suffer the consequence of the TTO penalty; instead the magnitude of the penalty is muted relative to their peers with arsenals concentrated in just a couple of pitches.

Along these lines and with the TTO penalty results on hand, I tried to determine if adding a pitch in a given season would improve a pitcher’s ability to get through a lineup by dampening the TTO penalty. I took two approaches. The first was more restrictive, where the new pitch in question could not be thrown at all in the season prior. This meant that I took every pitcher season from 2010-19 (with the same 100 innings minimum restriction as before) and for every pitch that pitcher threw, I cross-checked with their prior season and noted if they threw the pitch at all. If the answer to that query was yes, then the pitcher was not marked with utilizing a new pitch. Correspondingly, if the answer to the query was no, I marked the pitcher as having a new pitch. The restrictive nature of this querying and flagging of pitchers and pitches made me skeptical that the results would be relevant on account of the small group of pitchers who add a completely new pitch after not using it the prior year. My skepticism was borne out in the results (Note: a previous version of this table was the exact same as the table you will see later in the article. That mistake has been rectified and the following has the updated results).

Changes in TTO Penalty When Adding New Pitch
New Pitch Second Penalty Previous Second Penalty Change in Second Penalty Third Penalty Previous Third Penalty Change in Third Penalty
No .012 .013 -.001 .027 .024 .003
Yes .013 .012 .001 .025 .023 .001
SOURCE: Baseball Savant

In the cases of the second time through the order penalty and the TTO penalty, there is basically no change across seasons when adding a new pitch from scratch, with changes on the scale of single points of wOBA, which is noise. There is also no discernible difference between those who add a new pitch and those who do not, based on this criterion. However, the population of pitchers who truly add a new pitch, one they did not throw prior to the season at hand, is very small.

So I changed the definition of what constituted a new pitch. For the second go around, a new pitch was one the pitcher threw at least 10 percentage points more than the season prior. Yes, 10 percentage points is arbitrary and yes, I talked about arbitrary cutoffs at the start of this piece. But I would offer that the cutoff had to be set somewhere and my choosing of the cutoff was not influenced by the pool of pitchers I was analyzing. Also, I realize that my new criterion does not technically denote a “new” pitch like the first. But the spirit of this portion of the investigation is to flag pitchers who add a pitch the opposing hitter must account for differently in a plate appearance compared to how they would have approached the pitcher in a prior season. So, if a pitcher goes from throwing a pitch 5% of the time in year n-1 to 20% of the time in year n, that is a fundamental change in their repertoire that will have massive ripple effects on how they are scouted and what a hitter is looking for in any count.

The results of my second query were more promising but hardly groundbreaking.

Changes in TTO Penalty When Adding 10% Usage to a Pitch
New Pitch Second Penalty Previous Second Penalty Change in Second Penalty Third Penalty Previous Third Penalty Change in Third Penalty
No .011 .013 -.002 .026 .021 .005
Yes .016 .014 .002 .029 .031 -.002
SOURCE: Baseball Savant

Pitchers who added a new pitch by this criterion shave about two points of wOBA from their TTO penalty while the rest of the population adds about five points year-over-year. One possible explanation for this seven-point wOBA discrepancy is that without making a fundamental shift to your repertoire, major league hitters can get a better handle on you the following season, yielding a more substantial TTO penalty. Another explanation, which goes hand in hand with the fact that pitchers who do not meaningfully add a new pitch actually perform slightly better the second time through the order, is that the population of pitchers who did not add a new pitch includes pitchers who decreased their usage of certain pitches. So this population includes pitchers who became more of a two-pitch pitcher season over season, thus choosing to lean into their best pitches more.

As I said at the top, these two-pitch pitchers perform better on a rate basis but do not pitch as deep into games and suffer harsher TTO penalties. This, at least to me, is the most likely explanation for pitchers who would fall under the designation of the first row of the table improving the second time they go through the order but feel the effects of a more robust TTO penalty. On the flip side, pitchers who make a pitch a more substantial part of their arsenals worsen when they go through the order the second time but make up for it by dampening the TTO penalty.

Is this a worthwhile tradeoff? Would you rather have a pitcher more dominant on a per plate appearance level but who taxes your bullpen more? Or would you want your starter/bulk guy to go deeper into the game? It obviously depends on your roster construction and how often your bullpen has been used leading up to a game, but this is a question front offices and field staff constantly juggle throughout the season and in the offseason when building their teams.

Close to 3,000 words later, what have we learned? First and foremost, when attempting to measure anything or test a hypothesis, upon the conclusion of the research it is important to reflect and ask critical questions of how you approached the problem at hand. After my initial study into the viability of two-pitch starting pitchers centered around Jack Flaherty, I concluded that two-pitch pitchers were just as effective on a per pitch basis and that they suffer no additional TTO penalty. Therefore, I surmised, rostering these types of starting pitchers should have no detrimental effects on how you build your roster and are not a reason to be skeptical of a pitcher as a viable option to churn through an opposing lineup. The issue I found was that my definition of a two-pitch pitcher was flawed, based on an arbitrary cutoff to try to diagnose Flaherty’s lack of strikeouts in the early going.

When I eliminated the arbitrary cutoff and used a more continuous definition of how much a pitcher relies on his top two pitchers, I found that pitchers with more limited repertoires were a little more effective than the rest of their peers, but did not go as deep into games. Furthermore, they suffered a much harsher TTO penalty, which is most likely the explanation for those pitchers not facing as many opposing hitters.

The idea that pitchers with only two viable pitches are better suited for short starts, bulk work, or high leverage innings is not a groundbreaking finding, but I hope putting some empirical justification behind this idea is useful and this approach relatively new (at least on the public side). This confirmation of what many evaluators believed to be true should help us ask critical questions about how players should be deployed and developed, and what sorts of pitchers a roster requires. If the Rays invest in pitchers like Shane McClanahan and Luis Patiño, how should they be used and how does that affect Tampa’s roster? Well, it seems they are following what the research demonstrates: roster a deep bullpen and use these pitchers in three to five inning stints. The same concept holds true for the Astros and Cristian Javier and Framber Valdez or the Padres with Adrian Morejon, Ryan Weathers, and Dinelson Lamet.

Another essential part of this calculus is how we should be evaluating players in the minor leagues or amateurs in the draft. The starting viability of players like Garrett Crochet and Max Meyer has been called into question in recent draft classes; the same goes for Sam Bachman in this upcoming draft. Binning these types of pitchers — with high-end fastball velocity, wipe-out breaking pitches, and a history of starting — as starters or relievers seems foolhardy. Instead, we know pitchers with this skillset can effectively get through a lineup twice but more than that and the manager is playing with fire. Given this breed of pitcher’s effectiveness per plate appearance, actively avoiding acquiring pitchers with only two viable pitches is narrow-minded. Instead, if they make it to the major leagues, teams should be trying to supplement these elite talents with other pitchers who mesh with the roles required to maximize the skills of a Max Meyer or Garrett Crochet type pitcher.

I do not believe this is lost on much of the league. I am merely suggesting two-pitch starting pitchers can be excellent players in the correct environment. But given a TTO penalty almost twice that of starting pitchers with more diverse arsenals, two-pitch pitchers need to be monitored closely. If the league allows teams to carry as many pitchers as they would like, two-pitchedness and flame throwing bullpens are here to stay. Until the rules on pitcher limit take affect, with the correct usage limited pitch mixes will continue to be valuable assets to major league clubs, provided those two pitches are high-end offerings.


On Pitch Sequences and Spin Mirroring

With the adoption of the Hawk-Eye tracking system before the 2020 season, analysts and fans alike can directly measure the orientation of the baseball’s spin axis as it heads towards the pitcher. Previously the readings we would see on Baseball Savant were based on the movement of the pitch; the spin axis was inferred. Tom Tango, Senior Data Architect over at MLBAM and author of The Book, delves into the nuances between the spin axis readings here. The differences are derived from the nature of the tracking system before (TrackMan radar) and after (the aforementioned technology from Hawk-Eye, which consists of a series of high-speed cameras placed around the ballpark) 2020. During the offseason, the good people at Baseball Savant rolled out some leaderboards with the new measured spin axis data and compared that to inferred spin axis by pitcher and pitch type. The deviation between the two quantities is the result of the seam-shifted wake effect, a new idea permeating the baseball analyst community. Christian Hook from Driveline has a good piece introducing the phenomenon, as do our very own Ben Clemens and The Athletic’s Eno Sarris; I’d also point you to Barton Smith, Alan Nathan, and Harry Pavlidis’ excellent piece at Baseball Prospectus, as well as Barton’s other work on the subject.

At some point in the future, I hope to add to the discourse regarding seam-shifted wake. For now, though, I want to look into another idea we can analyze with this new access to measured spin axis. Until recently the ability to dive deep into the new spin axis data has been limited. We, the public, only had access to data summarized by pitcher and pitch type. Now, thanks to the wonderful work of Bill Petti and his baseballR package (and MLBAM for deciding to release the information), we can extract the measured spin axis on the pitch level in 2020. With this influx of new data, I re-scraped and stored the pitch-by-pitch data in my Statcast database (which I could not have done without Bill’s tutorial).

With that being said, my first inclination was to look at how pitches paired together in the context of spin mirroring. The idea behind spin mirroring is to deceive the hitter. Two pitches that rotate about the same axis but in opposite directions are hard to discern by the batter. For insight into spin axis and how it differs for different pitch types, I recommend checking out this comprehensive piece from Dan Aucoin at Driveline where he explains the importance of understanding a pitch’s spin axis, how it explains pitch movement, and deviations between axis and expected movement based on the axis via the magnus force. Mike Petriello at MLB.com has also given good insight into how spin axis allows certain pairs of pitches and repertoire’s to yield better results than just velocity and movement would indicate. He specifically dug into Shane Bieber’s diverse repertoire, which lacks elite velocity and correspondingly elite spin. Read the rest of this entry »