Archive for September, 2017

Can Wobble Rob(ble) Hitters? Fly Ball Distance and Baseball Precession

In the chase to break the story of the “smoking gun” behind the recent surge in MLB home runs, many a gallon of digital ink hath been spilt exploring possible modifications to the MLB balls, home-run-optimized swing paths, and even climate change. In my field of Earth Science (atmospheric chemistry, to be more exact), it’s rare that a trend in observations can be easily attributed to a single causal factor. Air quality in a city is driven by emissions of pollutants, wind conditions, humidity, solar radiation, and more; this typically leads to a jumble of coupled differential equations, each with a different capacity to impact overall air quality. To my untrained eye, agnostic to the contents of the confidential research commissioned by MLB and others, this problem is no different: a complex mixture of factors, some compounding each other and some canceling others, is likely fueling the recent home-run spike.

This article will examine the potential for a change in the MLB ball minimally explored thus far: reduction of precession due to decreased internal mass anisotropy. What a mouth full! “Precession” and “anisotropy” don’t have the same ring as “juiced ball” or “seam height” (though they may be on par with “coefficient of restitution”). But these words can be replaced with a more familiar (though funny-sounding) word: wobble. This wobble can occur for many reasons, but the most probable explanation in baseball is that the internal baseball guts are slightly shifted from the center of the ball. This could be due to manufacturing imperfection, or in the course of a game, contact-induced deformation of the ball.

Precession, in general, occurs when the rotational axis of an object changes its own orientation, whether due to an external torque (such as gravity) or due to changes in the moment of inertia of the rotating object (torque-free). Consider a spinning top: the top spins about its own axis (symmetrically spinning about the “stem” of the top) while the rotational axis itself (as visualized by the movement of the stem) can trace out a coherent pattern. If imparted with the same initial “amount” of spin in different ways, the total angular momentum (from both rotation and precession) of the top will be the same whether it’s spinning straight-up or precessing (wobbling) in an elliptical path.

Figure 0: Perhaps the most hotly debated spinning top in the world

As with other potential explanations relating to a physical change in the ball, a change in mass distribution could have occurred unintentionally due to routine improvements in manufacturing processes. By getting the center of mass (approximately, the cork core of the baseball) closer to the exact geometric center of the ball, backspin originally “lost” to precession (in the form of wobble-inducing sidespin) could remain as backspin while conserving total angular momentum; increased backspin has been shown to increase the “carry” of a fly ball, therefore increasing the distance (potentially extending warning-track shots over the fence). A deeper discussion of angular momentum can be found in any mechanics textbook or online resource (such as MIT OCW handouts), but the key takeaway when considering a particular batted fly ball is that productive backspin gets converted to non-productive precession (roughly approximated as sidespin in one axis) when mass is not isotropically (uniformly from the center in every direction) distributed. This imparts a torque-free precession on the spinning ball, causing the rotational axis to trace out a coherent shape.

Precession in baseball has not been deeply studied; in fact, when explicitly mentioned in seminal baseball physics resources, it is noted as a potential factor that will be ignored to simplify the set of physical equations. Together, dear reader, we shall peek behind the anisotropic veil and explore how precession might impact fly-ball distance, and by extension, home-run rates.


For those of us with some experience throwing a football, even just in the park, we can picture the ideal “backyard Super Bowl” pass: a tight spiral that neatly falls into the outstretched hands of the intended receiver. The difficulty of executing such a perfect throw is evident in the number of nicknames for imperfect throws that wobble (precess) on their way up the field short of their intended target (see “throwing ducks” re: Peyton Manning). In football, the wobbly precession of a ball in flight is typically blamed on the passer or credited to a defender for deflecting it (or in some cases, allegedly, a camera fly wire). It’s not as easy to imagine such behavior in baseball: even in slow-motion video shots of fly balls, the net spin of the ball is dominated by backspin. In addition, the nearly-spherical shape of a spinning baseball has significantly different aerodynamics than the tapered ellipsoid used in football. However, even a small amount of precession has the potential to shave yards off the distance of a football pass; therefore, impacts of precession are certainly worth exploring in the game of baseball.

As a sometimes-teacher (I have taught two laboratory classes at MIT), I strongly believe in the power of simple physical models to qualitatively inform trends in the not-so-simple real world. Therefore, for the first step of exploring the effect of ball precession in the game of baseball, I have turned to the wonderful Trajectory Calculator developed by Dr. Alan Nathan. The Calculator numerically solves the trajectory of a batted ball by computing key physical properties in discrete time steps. While many physical attributes of the ball are calculated in the various colored fields, any of them can be overwritten with custom values.

Figure 1: Fly Ball Distance with Nathan Trajectory Calculator defaults, conversion of backspin to sidespin

In Figure 1, I use the Trajectory Calculator to explore the effect of sidespin conversion on a single fly ball with the same initial contact conditions as the default (100mph exit velocity, 30-degree launch angle, default meteorological conditions), with the total spin set to 240 radians per second. Backspin is not converted to sidespin in a one-to-one fashion: because of the Pythagorean relationship between these factors, total spin is equal to the square root of the sum of the squares of sidespin and backspin. Therefore, to conserve angular momentum, a 10% reduction in backspin (216 rad/s) yields 104.6 rad/s of sidespin, which together lead to a ~1% decrease in fly ball distance from 385.3 ft to 381.3 ft.

With all of the assumptions made here, notably that introduction of precession can be simulated as pure conversion to sidespin to conserve angular momentum, the effect of precession on the flight path is clear but rather modest in this simple approach. However, the Calculator results show that by reducing the “wobble” in a ball’s trajectory, it will carry further. A league-wide reduction in precession would mean that balls would, on average, travel further, leading to an uptick in home runs. If decreased precession would also decrease the effective drag the ball experiences in flight, the effect of increased fly-ball distance could be even further enhanced.

A more realistic exploration of precession will require further modification to the modeling tools at hand. Following Brancazio (1987), which studied the effects of precession on the trajectory of a football, and additional follow-on work, a precession-only physical model can be developed to explore more complex aspects of the problem posed here. Elements of this precession-only model can be fed back into the Nathan Trajectory Calculator, but without a full understanding of some unconstrained physical constants and mechanical aspects of the pitch-contact-trajectory sequence, a tidy figure in the style of Figure 1 will be difficult to produce.

Again, as I mentioned above, I find simple models to be effective tools for teaching concepts. Therefore, let’s consider a “perfect” baseball to be a completely uniform, isotropic sphere, as in Figure 2. This perfect ball is axially symmetric and should not have any precession in its trajectory due to changes in its moment of inertia (I). Now, let’s add a small “spot mass” (that doesn’t add roughness to the surface) on the surface of the ball along the axis of rotation corresponding to pure backspin (the x-axis here). This ball with a spot mass should approximately represent an otherwise-perfect sphere whose center of mass is slightly shifted in the x-direction.

Figure 2: (A) real baseball, (B) perfect sphere, (C) sphere with a point mass at the surface, and (D) sphere with slightly offset center of mass approximately equivalent to (C)

If the model ball has a mass m1 that is isotropically distributed through the entire sphere, and a point mass with mass m2 that is located on the surface along the x-axis, the moment of inertia can be calculated in each direction, summing the contributions from the bulk mass m1 and the point mass m2 (Figure 3).

Figure 3: Moments of inertia for isotropic ball (mass m1) with a point mass (m2) at the surface

Of course, the mass of a real baseball isn’t isotropically distributed, and there is no such thing as a “point mass” in reality; however, by exploring different combinations of m1 and m2 that sum to to mass of an actual MLB baseball (5.125 oz, as used in the Nathan Trajectory Calculator), the ball can be distorted in a controlled manner to explore the effects on precession and fly-ball distance.  Using a set of equations derived from Brancazio (1987) Equation #7, the initial backspin of a ball (omega_x0) can be calculated given an initial total spin (omega), the variable B (the “spin-to-wobble” ratio indicating the number of revolutions about the x-axis per precession-induced “wobble”, a function of the moments of inertia I_x and I_yz), and the angle of precession (built into the variable C, with theta being the angle between the x-axis and the vector of angular momentum when precessing, similar to the angle between a table and the “stem” of a spinning top).

Equation Block 1: Derivations from Brancazio (1987) used in a simple model of baseball precession

The limitation of this approach is that in order to explore the theta-m2 phase space, we must prescribe a priori an angle theta at which the precession occurs. By instead solving for theta from equation 5 above (Figure 4), we can get a sense of the possible values for theta by prescribing the fraction of omega that is converted to precession (the variable A, a mixture of omega_y and omega_z, also called “effective sidespin”).

Figure 4: Contour plot of theta (degrees) with respect to ranges of m2 and variable A (effective total sidespin)

Figure 4 shows that angles between 0 and 6 degrees are reasonable for the conditions explored using the approach from Brancazio (1987) as translated to baseball. So let’s turn to equation 6, using a range of angles from 0 to 6 degrees, to explore the effects of precession on backspin omega_x (Figure 5).

Figure 5: Contour plots of backspin (omega_x) and effective sidespin (variable A) with respect to m2 (as % of m) and theta (degrees)

Great, the effect of a point mass along the x-axis of the ball can be quantified in this model! The effect is modest, but has the potential to slightly decrease the distance of an identically struck isotropic ball. But there is one major limitation to the model as currently shown: when the angle theta is chosen a priori, there is no capacity of the model to correct to a more physically stable angle. In fact, along the entire x-axis of the plots in Figure 5, where m2 = 0, the ball should be completely isotropic and therefore no precession would occur; a small initial theta would likely be damped out over a small number of time steps. In addition, the contours of constant omega_x in Figure 5a curve in the opposite sense than might be expected: increasing m2 should lead to more pronounced procession. On the other hand, this very simple model does not take into account the possible effects of torque-induced precession caused by gravity (extending the effect of mass anisotropy alone), nor does it account for additional drag impacting a precessing ball. More study is needed to further elucidate the possibility of precession having a considerable impact on fly-ball distance; however, unlike the sometimes-empty calls for “further exploration” of minimally promising leads in academic journal articles, I intend to execute such investigation.

All of these limitations are inherent in the fact that, without outside data to constrain the physics of precession as it applies to baseball, the problem we are trying to solve with this simple model is an ill-posed problem in which there is not a unique solution for a given set of initial conditions. Luckily for us, we live in the Statcast age where position, velocity, and spin of the baseball are all continuously measured (if not fully publicly available). In addition to benefits gained from Statcast data, this problem can also be further constrained by experimental data on MLB balls. Finally, an opportunity to put my skills as an experiment-first, computational-modeling-second scientist, to use! Stay tuned to these pages for follow-up experiments and data analysis in this vein.

The conspiratorial allure of an intentional ball modification directly induced by Commissioner Rob Manfred is visible on online comment sections far and wide; however, many of the most credible explanations for ball changes are benign in Commissioner intent and perhaps attendant with improvements in ball-manufacturing processes. In any case, there are likely multiple facets to the current home-run surge. Ball trajectory effects due to precession have traditionally been ignored to simplify the problem at hand; this initial exploration shows that due to the difficulty of the problem, that was likely a good trade-off given the data available in the past. In the future, however, past work in diverse areas from planetary dynamics to mechanics of other sports can be used alongside new and emerging data streams to help determine the impact of precession on fly-ball distance.


Python code used to generate Figures 4-5 can be found at

Special thanks to Prof. Peko Hosoi (MIT) and Dr. Alan Nathan for providing feedback on early versions of this idea, which was born on a scrap of paper at Saberseminar 2017.

The Battle of the 240-Million-Dollar Men

Albert Pujols and Robinson Cano have a lot in common. They both play in the AL West. They both will one day have a plaque in Cooperstown. They’ve both played on World Series-winning teams. And they both signed 10-year, $240-million mega-contracts with their current teams.

In my office, we had a short debate about which player has come closer to justifying the cost of their deal, and it wasn’t hard to sell even the most ardent defenders of home runs and RBI that the answer was Robinson Cano. Still, one co-worker encouraged me to do a year-by-year comparison of these two players. Mostly to see just how badly Cano has outclassed Pujols since signing said mega-deals. Still, I was quite surprised at just how stark the difference has been.

So let us embark, then, on to the battle of $240-million men! For the players’ dollar-value figure, I’m using FanGraphs valuations. We’ll start off by comparing Pujols’ first four years with the Angels to Robbie’s first four with the Mariners.

Pujols year 1: .285 AVG/.343 OBP/.516 SLG, 133 wRC+, 30 HR, 3.6 WAR, $23.4m
Cano year 1: .314 AVG/.382 OBP/.454 SLG, 137 wRC+ 14 HR, 5.2 WAR, $39.3m

Pujols year 2: .258/.330/.437, 112 wRC+, 17 HR, 0.6 WAR, $4.1m
Cano year 2: .287/.334/.446, 116 wRC+ 21 HR, 2.1 WAR, $16.7m

Pujols year 3: .272/.324/.466, 123 wRC+, 28 HR, 2.8 WAR, $21.4m
Cano year 3: .298/.350/.533, 137 wRC+ 39 HR, 5.9 WAR, $47.5m

Pujols year 4: .244/.307/.480, 114 wRC+, 40 HR, 1.8 WAR, $14.7m
Cano year 4: .282/.341/.455, 113 wRC+ 22 HR, 3.3 WAR, $26.0m

Of course, Pujols came to the division two years before Cano. With just a few days left in the season, each player’s 2017 numbers are unlikely to change much — their total season numbers will be pretty close to where they are today on September 26th.

Pujols 2014: .272/.324/.466, 123 wRC+, 28 HR, 2.8 WAR, $21.2m
Cano 2014: .314/.382/.454, 137 wRC+ 14 HR, 5.2 WAR, $39.3m

Pujols 2015: .244/.307/.480, 114 wRC+, 40 HR, 1.8 WAR, $14.7m
Cano 2015: .287/.334/.446, 116 wRC+ 21 HR, 2.1 WAR, $16.7m

Pujols 2016: .268/.323/.457, 110 wRC+, 31 HR, 0.8 WAR $6.3m
Cano 2016: .298/.350/.533, 137 wRC+ 39 HR, 5.9 WAR, $47.5m

Pujols 2017: .240/.287/.388, 79 wRC+ 23 HR, -1.8 WAR, $-14.3m
Cano 2017: .282/.341/.455, 113 wRC+ 22 HR, 3.3 WAR, $26.0m

Basically, Cano has won in almost every category except for home runs every single year. Even without doing the math (which I’m going to do in a second, don’t worry), it’s pretty obvious that no matter which way you compare these contacts, Cano’s looks better.

However, even though both players are making $240 million over 10 years, they’re making it differently. I wondered if that might tip the scales a little more in Pujols’ favor. Cot’s Contracts shows Cano making exactly $24 million annually, while Pujols has a backloaded deal. According to Cot’s, after this season Pujols is owed $114 million over four seasons ($28.5m per season). At $24 million a pop over six seasons, Cano is still owed $144 million.

Using these numbers, I compared their total value since signing with their new teams against how much they’ve actually been paid:

Pujols: $55.6m (actually paid $126m, -$73.6m in surplus value)
Cano: $129.5m (actually paid $96m, $33.5m in surplus value)

Since we’re comparing two guys who are currently division rivals, it’s also fun to look at their value compared to their pay just since Cano signed with the Mariners:

Pujols: $28.1m (actually paid $98m, -$69.9 in surplus value)
Cano: $129.5m (actually paid $96m, $33.5m in surplus value)

So pretty much any way you slice it, Cano has totally dominated Pujols in the battle of 10-year, $240-million men. For the most part, even, Cano has been better even if you limit your criteria to just offensive production. The fact he plays second base while Pujols has spent about half of that contract DHing is just gravy for the Mariners, even if Cano is just average or a little worse than that at second now.

The Angels do have one advantage, though: Pujols has four years left on his deal after this season, and Cano has six. Cano would probably still be lucky to break even on the $240 million he’s actually being paid, considering his second-half slump and his age (he will be 35 in 2018). Still, largely because he had a huge head start, Cano could end up actually earning the total value of his contract.

It’s probably likely that Cano will have some negative-value seasons in his future, though. If he’s moved off of second base, his bat becomes less valuable; if he doesn’t, his defense may erode much of his offensive value at second base. But by then, he will have likely come close to putting up $240 million in value. It would take a miracle for Pujols to end up being worth even half of his contract.

Whenever a team signs a player to that big of a deal, they know they’re likely going to suffer at the end of it — most people assumed the instant they were signed that both of these contracts would look bad at the end. Unfortunately for the Angels, Pujols’ has looked pretty bad from the beginning. Cano, on the other hand, has given the Mariners perhaps even a little more than what they expected at the time.

So, the Mariners finally won something! They won the battle of $240-million men! Now, if only that translated to winning games…

Merrill Kelly: A Mid-Rotation Starter in Korea

How many teams are looking for a cheap starting pitcher to be a veteran presence for a young rotation? Looking for an upgrade over what they currently have for starting pitching? Or just need a warm body to fill the hole left by Joe Ross with someone not named Edwin Jackson? As far as I can tell, 10 teams are looking for a 3/4 starter such as Merrill Kelly, especially considering his stats that he has accumulated in this season (maybe he’ll get one more start to add to his excellent season so far) have been particularly impressive. All this when the Rays thought that Merrill Kelly was just a “AAA starter” who could be a bullpen guy in the big leagues.

Merrill Kelly in the minor leagues was a solid minor leaguer who would become a swingman with the Durham Bulls. In his age-25 season, he went 9-4 in 114 IP with a 2.76 ERA, a 3.74 FIP, and a 3.57 xFIP. Which looked good with his 8.53 K/9 and 2.92 BB/9, a .298 BABIP, and a 47.9% ground-ball rate as well. Perhaps he could a solid swingman/fifth starter in the big leagues. The Rays apparently thought otherwise and said either he’d be a bullpen pitcher for the MLB team or a starter in AAA. Merrill Kelly thought otherwise and went to South Korea to play for the SK Wyverns.

Merrill Kelly in South Korea was all right in his first season, with an 11-10 record in 30 games (29 starts), 181 IP, and an ERA of 4.13. With peripheral rates that weren’t as good (6.91 K/9 and 2.69 BB/9). His next season was similar, with a 9-8 record in 31 games, but a great 200 1/3 IP with similar rate stats: 3.68 ERA, 6.83 K/9, and 2.70 BB/9. This year has been very different for him, with a 15-7 record in 29 games and 185 IP with a 3.65 ERA; his rate stats are much more improved, at 8.90 K/9 and 2.14 BB/9.

What is he doing differently to get these improved stats? Why is his ERA as high as it is, despite getting more strikeouts and walking fewer batters? He is allowing more pesky little hits: that is, his defense is not getting as many outs made as it should (1.08 hits per inning this year, vs 1.03 hits per inning in 2015-2016 combined). He has also allowed one more homer and two more doubles than last year, in 15 1/3 fewer IP.

His repertoire:

-4 Seam Fastball – 92-94 MPH (back in 2015, he was throwing 88-91 MPH)

-2 Seam Fastball – couple of miles slower and has slight sink, and runs in an opposite direction. He mixes this pitch well with his fastball

-Cutter – He started to throw this pitch more once he got to Korea and has mixed it well with his other fastballs and change

-Slider – Has a good slider that can break sharply when he’s pitching well. About 83-87 MPH

-Curveball – Decent enough curve but probably not his best pitch. Up 78-80 MPH

-Circle Changeup – Good sinking and running movement. He throws it about 85 MPH. One of his top pitches

What has he improved? Velocity on his pitches, sharper movement to his fastballs and changeup, getting better with the cutter, and improving his control. (This quoted from this article on Reddit: Merrill Kelly scouting report and info, which I think explains his improvements, but I disagree with his assessment of Merrill Kelly’s talent.) Given the talent level of the average hitter in the KBO is around AAA level, he should be able to perform as around a low-3/high-4 starter, as I’d say he is better than the average starter. A funny thing of note is that the Rays have another version of Merrill Kelly named Ryan Yarbrough, who has pitched better than Kelly did at a similar age; hopefully they’ll give him a chance to prove the Rays wrong for letting Merrill Kelly go.

Since he is on the right side of 30 and will pitch the 2018 season at age 29, I’d offer him a three-year deal worth $6 million per year with incentives that could boost the value of the deal to around $24 million over three years, with an option for a fourth season at $7 million (buyout of $2 million) with incentives to boost the option value to $10 million. This is due to his risk, and likely lower than what Phil Hughes was offered after the 2013 season from the Twins.

Who are the 10 teams that could use Merrill Kelly as a starting pitcher? The answers might be more surprising at first glance than other answers. The best choice would be the Miami Marlins for the same reasons listed, but it could become a wild-card contender taking a chance for Kelly to make more money in a playoff cut. The second-best choice is one that is pretty questionable, depending on whether the Nationals are willing to take a risk on a player from the KBO and whether they want someone better than him. But he’d be great for them in place of Joe Ross, and would be an upgrade over their current options; plus he would be cheap enough to fit in their payroll. One issue is that the Nationals have a hitter-friendly park, but not having to face the Nationals would mitigate some of those concerns. The San Diego Padres would be the third-best choice due to the non-DH league, an extremely pitcher-friendly park according to MLB park factors, and multiple available rotation spots, but they are in a tough NL West and aren’t likely to be a playoff team.

The next one is questionable but they would certainly be able to make room for him — the Oakland A’s have always been unconventional, and the park is usually known for being pitcher-friendly. The Twins would be similar to the A’s in those respects and are in fact a playoff threat (I didn’t expect to be saying this about the Twins this year at all). The Royals are practically in a tie with the Twins and A’s due to a pitcher-friendly park, although their team is going to be worse due to many key players leaving (Cain, Hosmer, and Moustakas).

Despite the Rangers having a definite hole in the rotation (who would let Nick Martinez or A.J. Griffin start in an extreme hitter-friendly park?), they are the seventh-best option due to that park, the DH league, and just not having a great team in general. The White Sox are an even more extreme version of the Rangers, and are extremely bad as well; I doubt he’d want to play for such a poor team. Same with the Reds, except there is no DH, but the Reds might want to give younger options a try first. The Orioles have almost all the bad factors: A league with a DH, a hitter-friendly park, a tough division, a bad defensive team, and generally bad development staff that has done more harm than good for its pitchers.

I would love to see one of the top six teams sign Kelly to a contract, since those would be best for him getting another contract after the first one expires. Can’t wait for him to get his shot in the big leagues, to prove his previous doubters wrong, and to have a long and successful career in the MLB.

All stats are owned by their respective owners (ESPN, FanGraphs, KBO, Reddit), I own none of the stats used. All stats are as of 9-23-2017.

A Lost Member of the Fly-Ball Revolution

We’ve seen names like Francisco Lindor, Yonder Alonso, and, of course, Josh Donaldson associated with the fly-ball revolution constantly. But one of the most underrated breakouts of 2017 has come from the launch-angle craze: Marwin Gonzalez.

If you were familiar with him before 2017, it was almost certainly because of his defensive versatility. Gonzalez has never been much of a force, but the ability to play the entire infield and outfield corner spots earned him consistent playing time in the past. He’s no longer lacking in the offensive department.

Gonzalez is slashing .302/.371/.531 and has more than doubled his 2016 walk rate, while keeping his strikeout rates steady, en route to a 142 wRC+. Names like Jose Altuve, Carlos Correa, George Springer, and Josh Reddick are the first that come to mind with the Astros. But Gonzalez ranks 3rd in batting average, 2nd in wOBA, and 2nd in wRC+. He leads one of the best offenses in major-league history in runs batted in, despite six players having more plate appearances.

Here is his exit velocity and launch angle by year, stretching back to 2015 (when Statcast started measuring):

Year Avg. Exit Velo Launch Angle
2015 86.5 9.1
2016 86.3 6.5
2017 88.6 13.1

Gonzalez has seen a massive uptick in his launch angle this season, and, like the other successful members of the launch-angle increase, has seen a massive uptick in power. With 23 home runs, he has ten more than last season.

Interestingly, with the home-run increase, Gonzalez has increased his use of all fields. His Oppo% is up to 25% from 18% in 2016. But only two home runs have been to the opposite field (he’s a switch hitter, so a basic spray chart won’t show this). He’s displaying the ability to turn on the balls he can crush and adapting better to the ones he cannot.

Just Monday night, September 25th, Gonzalez put this on full display in a four-hit night against the Texas Rangers. Here’s a two-run single:

Gonzalez gets low and adapts to the pitch location, sending it straight back up the middle. Now here he is, a couple at-bats later:

Gonzalez sees a pitch he can hit hard, and does just that, obliterating this ball (sending it 443 feet at 108.1 mph, exactly).

Using all fields while maintaining pull power is a hard skill to master, but one Gonzalez has mastered no less. He has mastered hitting the balls well that he can hit well. His 19 line-drive base hits are tied for 24th in the league, but his 19.8% LD% ranks 98th. He is 287th in average ground-ball exit velocity at 82.3, but 110th in average line-drive/fly-ball exit velocity at 93.9 mph. This is likely what skews his xwOBA-wOBA numbers, which peg him for a much less impressive performance. He’s not hitting everything harder, but he is hitting what he should hit hard harder. And with the increased launch angle, he’s only hitting more and more line drives and fly balls.

Gonzalez has stayed out of the national spotlight despite being one of the most consistent and best players on the league’s best offense. Don’t be surprised to see him explode in the national spotlight.

Jonathan Lucroy, the Rockie, Is Baseball’s Best Contact Hitter

It’s no secret that Jonathan Lucroy is having a subpar season.

The two-time NL All Star was projected to be a top-three catcher in 2017.  Before the start of the season, Steamer pegged his value at 3.6 wins above replacement, while ZiPS had him at 3.2.  His .242/.297/.338 line and 66 wRC+ in 306 plate appearances as a member of the Texas Rangers produced 0.2 WAR.  No one really expected that.

Lucroy was eventually traded to the Colorado Rockies.  The Rockies, who had the worst catching tandem in baseball, instantly viewed Lucroy as an upgrade, while many other playoff-bound teams would have viewed him as a liability.  With the hitter-friendly environment of Coors Field and poor pitching staffs among the San Francisco Giants and San Diego Padres, the team figured that Lucroy would return to his All-Star form once again.  Although he has not returned to being the power threat that he once was, he has changed his game ever so slightly, such that he might have become the game’s best-hitting catcher.

His basic stat line is not reflective of his plate discipline as a member of the Rockies.  His slash line has gone back up to near his career average (.279/.384/.377), but what is most impressive about him is his actual hitting ability.  Always a good contact hitter, he has changed his game to be more selective, get more contact, and put the ball in play.  His 92 percent contact percentage ranks first in baseball since the trade, and his 88 percent contact percentage of pitches outside the strike zone also ranks first.  The result: a high walk rate (12.3 percent) and fewer swinging strikeouts (6.3 percent of plate appearances resulting in a strikeout).  All of this while swinging at fewer pitches outside the strike zone (18.6 percent) and fewer swings in general (38 percent).  You may be asking “Why isn’t he leading the league in hitting with numbers like that?”  Well, the answer is rather simple.

While he is making more contact than anyone in baseball, most of the balls in play are hit to the defense.  This season, he is hitting more ground balls than ever before.  As a Rockie, 50 percent of the balls he has hit in play have been ground balls, well above his career average of 42.8 percent.  As a result, he has hit fewer fly balls (28.7 percent) which has led to fewer home runs (3.2 percent HR/FB).  This explains his lack of power this year.

He has hit the ball in the wrong place more this season than any other.  For his career, Lucroy has had a tendency to drive the ball up the middle — that has not changed much this season — but this season he has hit the ball softer than in any previous season.  His average exit velocity (85.0 miles per hour) is more in line with middle infielders and outfielders than catchers.  In fact, he has the fourth-slowest average exit velocity among all qualified catchers.  His average exit velocity last season was 87.6 miles per hour, and it was 88.6 in 2015.  Without the wheels of a speedy outfielder or infielder capable of beating out a ground ball (or at the very least forcing the defense to rush the throw), a ground ball for Lucroy is as good as an out.  Just as the saying “baseball is a game of inches,” it’s a game of miles per hour, too.

Fewer ground balls are going through the holes in the infield, and fewer ground balls are becoming hits.  His batting average of balls in play as a Rockie is similar to his career average (.308 as a Rockie and .306 for his career), but his RBBIP — percentage of balls in play that go for a hit or an error — is .318.  While it is above league average, it is well below his RBBIP numbers of both his All Star seasons and 2012, when he hit .320.  Has Lucroy been entirely unlucky with his balls in play?  No; pitchers have pitched to him largely down and away, which has resulted in a horrible contact percentage on those pitches, and he has also regressed slightly in every season since 2015.  But if Lucroy can keep his contact percentage up, hit fewer ground balls, and stay selective at the plate, then he could be one of the best-hitting catchers in the game again.

Is Z-O Swing% a Better Indicator of Plate Discipline Than O-Swing%?

In some FanGraphs articles, Z-O swing percentage is thrown around as a measure of plate discipline. That makes sense because generally when a hitter swings at strikes, good things happen, and if he swings at balls, bad things happen.

To test if that stat is really better, I looked at the 2017 leaderboard. I looked at the wRC+ of the top 30 and bottom 30 hitters with Z-swing%, O-swing%, and Z-O swing%. Here is what I found:

z-o swing z swing o swing
top30 122 112 122
bot30 103 105 96
all qualified 110 110 110

There is a slightly positive effect of Z-swing, but a much stronger effect of both Z-O swing% and O swing%. At the top, the low-chaser and high-differential guys do about the same, while the bottom chasers do even worse than the bottom differential guys.

If you widen the search for top half and bottom half you get that picture:

z-o swing z swing o swing
119 110 117
102 110 103
110 110 110

Z-swing has no effect at all, and the differential is slightly better than the chase rate, but not by much.

Overall, the Pearson value for differential was a positive .42, for the chase rate it was .32 (used 100 – O-swing% to get positive value), and for Z-swing there was almost no effect (.07). So the differential is a bit better, but the effect isn’t huge; it is probably like with OPS+ and wRC+ where one is mathematically more elegant and correct but the actual values won’t differ much.

I also dissected the hitting into the components OPB, ISO and BABIP.


z-o swing z swing o swing
top30 .220 .210 .200
bot30 .170 .180 .163
all qualified .193 .193 .193
z-o swing z swing o swing
top30 .360 .335 .370
bot30 .324 .350 .317
all qualified .341 .341 .341
z-o swing z swing o swing
top30 .308 .309 .306
bot30 .304 .303 .306
all qualified .306 .306 .306

The result is quite interesting. The differential (+27 ISO points) does clearly better in the power department than chase rate (+7); in fact, even Z-swing had a more positive effect (+17) on power than a low chase rate.

With OBP, that is reversed. Here, the chase group does better than the differential group, while a high Z-swing rate has a negative effect.

With BABIP there was a very small positive effect of differential and Z-swing, and no effect of the chase rate, but the effects are almost non-existent.

So we seem to have two opposing effects here. Being more aggressive in the zone helps the power but seems to slightly hurt the OBP (of course there probably is a bias that aggressive hitters in the zone are often also aggressive outside, but still). And for OBP, chase rate clearly is king, while it doesn’t really have an effect on power.

Still, that might have an effect for certain hitters and especially pitchers, but overall the advantage doesn’t seem to be big, even though it is a bit due to coincidence due to the opposing effects.

The Mets Should Trade Noah Syndergaard

Thor’s lat tear was the team’s biggest disappointment in 2017, a season that’s been chock-full of frustration and futility.

It was more demoralizing than the poor winning percentage. More displeasing than a certain player’s disappearance. And even more disheartening than the injuries en masse.

The fall of the Mets’ burgeoning ace is so distressing because it raised alarms about the future of their starting rotation. It’s now uncertain whether Noah Syndergaard, the pitcher once dubbed the second coming of Nolan Ryan, can play a significant role – let alone become the successor to Tom Seaver and Dwight Gooden.

How should the Mets deal with this situation? Though unpopular, there’s only one pragmatic solution: hope for a full recovery, let him recoup value, and trade him before future injuries occur.

Wait – trade him? Wouldn’t it make more sense if they gave such a tremendous talent the opportunity to fix his problems before they press panic button?

Sadly, it’s not quite that simple.

Syndergaard’s issues are so deeply rooted that he’s probably going to get hurt again.

And again.

And again.

Two core components of his skill set are the likely culprits of these injuries, and both are difficult to cure – at least without harming his effectiveness.

The first is Thor’s throwing motion. In the GIF below, you can see how he relies heavily on his golden arm when delivering a pitch:

At first glance, these mechanics appear quick, straightforward, and minimalistic. But also arm-dependent. If you look more closely, you won’t spot a single movement that attempts to alleviate the immense stress placed on his right wing. Not one.

You don’t see Syndergaard use a high leg kick or take a long stride. Nor do you notice him place substantial weight on his right leg when pushing off the rubber. You can’t observe him rotate his hips fully. And you won’t find too much torque in his upper body.

In short, he utilizes none of the mechanics that generate substantial velocity from his legs, hips, and core. Instead, you witness Thor gain most of his power from a sudden, violent contortion of his back and a quick snap of his almighty arm.

Needless to say, this delivery taxes his right wing…exorbitantly. On every single pitch.

But that’s not all. You also glimpse a slight timing problem that’s already become a ticking time bomb:

Syndergaard’s throwing arm is practically parallel to the ground when he plants his left foot. Then – before raising it to the cocked position – he rotates his hips and accelerates all components of his upper body, actions that place additional stress on his elbow and shoulder.

Mechanics of this sort, both arm-dependent and off-time, significantly increase his chances of getting hurt in the future…and that his afflictions will be far more severe than a torn lat.

The second cause of Thor’s injuries tilts those odds even further. That’s his max-effort pitching style. He looks to dominate batters with a repertoire of five overpowering pitches and, as you can see, holds nothing back:

Noah Syndergaard Average Velocity (MPH), 2015-2017
Pitch Type 2015 2016 2017 Avg MLB Avg
Four-Seam FB 97.72 98.64 98.70 98.23 93.17
Sinker 97.69 98.52 97.99 98.11 91.67
Slider 87.86 91.42 92.27 91.27 84.77
Changeup 88.83 90.31 90.06 89.61 84.13
Curveball 81.21 82.95 84.25 81.91 78.14
OVERALL 92.63 94.83 93.93 93.85 88.52
SOURCE: Baseball Savant/Statcast/PITCHf/x

Both fastballs routinely register around 98 MPH, and his slider and changeup hover about or above 90 MPH. Each one is at least 5 MPH faster than league average and is among the hardest thrown by any starting pitcher. Even his curveball, the “slowest” of the group, is well above the 78.14 MPH mean.

But something sinister lurks beneath these awe-inspiring averages. And that’s the not-so-subtle implication that Thor competes on stuff alone.

There’s neither an inkling that he paces himself nor an indication that he uses complex pitching strategies – at least not to any meaningful degree. Au contraire. From the looks of it, he throws as hard as physically possible all game long. Nothing more, nothing less.

Such an explosive approach requires Thor to exert himself fully on every single pitch he throws. This places additional strain on his elbow and shoulder, accelerates the damage inflicted by his delivery, and dramatically increases his chances of developing major arm problems.

Making matters worse, the two reasons for his injury are incredibly difficult to fix without breaking something else, namely his dominating performance.

Which is exactly why the Mets should move their star pitcher.

Noah Syndergaard is still a blue-chip asset with great trade value. You’d be hard-pressed to find another starter whose repertoire resembles that of an elite closer…let alone one who just turned 25 and won’t be a free agent until 2022.

That combination of unique ability, extraordinary upside, and relatively low financial risk makes him an attractive target despite his injury and its causes. As such, the team would probably acquire several top prospects in return for their ace.

If he’s able to put together a healthy season (or half-season), they should shop him around and pull the trigger on the best deal they find. Otherwise, it’s likely that a catastrophic arm injury will compromise his value; they’d never be able to swap him for anything meaningful again.

Should that day of reckoning arrive, the Mets will be forced to admit that they have another Matt Harvey on their hands: a supremely talented, though fundamentally flawed pitcher whom they should have traded before it was too late.

Reliever Buy-Low: Craig Stammen

Any team need a reliever who can pitch multiple-inning stints if you need? I think lots of teams would jump at the chance to acquire such a reliever considering Madison Bumgarner’s legendary five-inning relief appearance in Game 7 of the 2014 World Series. Andrew Miller became a dangerous bullpen weapon in the 2016 postseason with the Indians, which brought them within a game of winning the World Series in three consecutive games. And there’s some guy on the Astros called Chris Devenski, who could also spot start if you need a starter desperately. The Blue Jays acquired Tom Koehler from the Marlins, who I admittedly have some interest in as a starter or multi-inning reliever. Maybe you want someone like Raisel Iglesias or Michael Lorenzen.

Currently, most relievers are used in one-inning stints; some are even used against lefties or righties only. Christian Bethancourt, Chris Gimenez, and Jordan Schafer have been two-way players: a hitter and a reliever to give more bench depth and help keep Rule 5 draft picks. Some top prospects have been billed as two-way players such as Brandon McKay, Hunter Greene and most notably Shohei Otani, who has been fantastic in Japan.

The reliever who should be receiving more attention as a multiple-inning reliever is Craig Stammen, who used to be a part of the Nationals as a starter and was then converted into a reliever when he was called up from AAA in 2011. Stammen was doing pretty well from 2012-2014 as a setup reliever, but then he missed most of 2015 and didn’t make it back to the big leagues until this season. As a result of him previously having been a starter for much longer, he has more stamina than an average reliever, and can be used in multiple-inning relief stints, providing more bench depth for a team like the effect of having a two-way player (even if they aren’t very good).

This year, he has been getting back to what he was doing before in terms of his ERA, strikeout and walk rates, and innings per appearance. His home-runs, however, have gone up quite a bit despite his 52.2% ground-ball rate. This is due to an unsustainable 19.4% HR/FB ratio(!), which has overly inflated his FIP to 4.34, with a much more appealing 3.75 xFIP and a 3.60 SIERA, which suggest a solid middle relief/ setup type of reliever that he has been performing like. This and his ability to pitch multiple-inning stints create a higher value than his $900,000 contract. He has four pitches with positive values according to Pitch Info this year. Despite minute velocity drops for his pitches from his peak years of 2012-2014, he is still very effective with his pitches, with only one registering a slight negative according to Pitch Info.

Admittedly, his BABIP is a bit lower than it should be at .254, but it shouldn’t regress too badly (somewhere around .280 since he does generate quite a few ground balls). He is only getting about 6.7% pop-ups, which is not very good, compared to his peak seasons. Batters are getting more hard contact this year compared to the rest of his career (30.1% this year compared to 28.5% for his career). And his strand rate is at 85.7% this year, compared to just a 71.9% career mark. Additionally, he has allowed a .329 wOBA against lefties this year vs a .256 wOBA vs righties.

Overall, Stammen has been lucky and unlucky this year. Ultimately, he is a solid reliever who should be able to do quite well in almost any park except Coors Field or any extreme hitters park. He should receive a two-year deal worth around $4-5 million per year for how well he can pitch as a solid multiple-inning reliever, and how he can help increase bench depth for a team that wants to keep a Rule 5 talent, an extra bench player, a normal reliever, or maybe a specialized reliever such as a LOOGY (looking at you, Randy Choate, Brian Shouse, and so many more who have made careers out of being LOOGYs). The former two are much more likely than the latter two — particularly a LOOGY, as most aren’t as useful to teams anymore.

All stats and links are owned by FanGraphs, except for the link to Shohei Otani’s player page, which is owned by the NPB.

Overall Pitch Data

This is the final part of my pitch-ranking data. Let’s start with the top 25 overall pitches, starters and relievers combined.

Top Pitches:

Position Pitch Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
SP 4-Seam Chris Sale 85.89 3.08 0.24 2.86 5.94
SP Curveball Corey Kluber 109.61 3.16 0.12 2.26 5.42
SP Changeup Stephen Strasburg 104.30 2.31 0.15 2.76 5.07
SP 4-Seam Jacob deGrom 83.06 2.68 0.27 2.13 4.81
SP Slider Carlos Carrasco 108.62 2.51 0.15 2.06 4.56
RP 4-Seam Craig Kimbrel 94.74 2.34 0.23 1.80 4.15
RP 4-Seam Sean Doolittle 90.81 1.91 0.22 2.02 3.93
SP Slider Max Scherzer 104.66 2.10 0.17 1.79 3.89
RP 4-Seam Chad Green 85.35 1.30 0.20 2.57 3.87
SP Cutter James Paxton 89.03 1.81 0.20 2.03 3.84
SP Changeup Luis Castillo 97.25 1.46 0.18 2.27 3.73
SP Sinker Trevor Williams 68.72 1.87 0.30 1.73 3.61
SP 2-Seam Sonny Gray 72.12 2.18 0.30 1.39 3.57
RP Slider Roberto Osuna 108.02 1.97 0.16 1.52 3.49
SP 4-Seam Jose Berrios 74.74 1.51 0.27 1.97 3.48
SP 2-Seam Jaime Garcia 67.96 1.49 0.28 1.97 3.46
RP Slider Arodys Vizcaino 105.81 1.78 0.16 1.54 3.32
SP Cutter Corey Kluber 97.90 2.82 0.28 0.48 3.30
SP Slider Sonny Gray 97.27 1.35 0.16 1.87 3.22
RP Cutter Jacob Barnes 104.09 1.99 0.22 1.21 3.20
SP 2-Seam David Price 72.83 2.29 0.32 0.86 3.15
SP 4-Seam Jimmy Nelson 76.65 1.78 0.30 1.34 3.12
SP Changeup Danny Salazar 102.60 2.11 0.23 1.01 3.12
SP Cutter Tyler Chatwood 84.08 1.25 0.21 1.81 3.06
RP Slider Raisel Iglesias 98.47 1.13 0.14 1.93 3.06

We have two pitchers that show up twice — Corey Kluber and Sonny Gray. Kluber has arguably been the best pitcher in baseball in 2017, so that is unsurprising. However, Gray as his only two-pitch counterpart is unexpected. Gray is by no means a poor pitcher, but not the same level as Kluber. Jaime Garcia and Tyler Chatwood are the only guys on this list who jump out as poor pitchers, in 2017 at least. Luis Castillo and Jacob Barnes are probably the only guys on this list who are completely unfamiliar for most. Castillo’s future looks bright, where Barnes looks less significant.

I’m sure some have been wondering: What are the worst pitches?

Applying some context, these are certainly not the worst pitches in the game. Just the worst thrown consistently. Every pitch had to reach a minimum number of times thrown to reach this list. These are not the absolute worst pitches in the game, but make no mistake, they are still truly awful. The bottom ten of over 700 pitches. Anyway, here are the ten worst that I measured:

Position Pitch Player xwOBA xwOBA Z Sw+Whf% Sw+Whf% Z Z Total
RP 4-Seam Justin Grimm 0.457 -3.16 55.67 -1.98 -5.14
SP Slider Kevin Gausman 0.428 -3.23 68.95 -1.52 -4.75
SP Changeup Mike Leake 0.344 -1.47 61.34 -2.87 -4.33
RP Curveball Dellin Betances 0.405 -2.99 66.16 -1.33 -4.32
RP 4-Seam Warwick Saupold 0.397 -1.85 53.32 -2.24 -4.09
RP Slider Jason Grilli 0.355 -2.19 67.13 -1.65 -3.83
SP Curveball Jordan Zimmermann 0.401 -2.64 60.79 -1.19 -3.83
SP Slider Johnny Cueto 0.337 -1.49 61.61 -2.27 -3.76
SP 2-Seam Paul Blackburn 0.402 -1.21 44.28 -2.43 -3.64
RP 4-Seam Mike Montgomery 0.36 -1.04 50.43 -2.56 -3.60

Two names jump out immediately in that list. Dellin Betances and Johnny Cueto. However, considering the widely-known struggles of those two, it’s not nearly as shocking as it might have been last year. Justin Grimm has been downright atrocious, so it’s fitting to see him there. The same goes for Jason Grilli. And Jordan Zimmermann. Kevin Gausman was awful, but has turned it around. Mike Leake has done the exact opposite of that. This is the first time I have seen Warwick Saupold and Paul Blackburn on a list of any kind, good or bad. Blackburn has actually been solid in a small sample for the A’s in his rookie year. Montgomery has continued to provide quality long-relief innings and spot starts for the Cubs.

This was just my first trial run playing around with pitch values. I will continue to work towards a better formula and continue to post in the future. I will post the Excel file with all the pitches and data I used for calculations. Feel free to add, but please don’t change or delete any of the original information.

Pitch Data Excel File


Relief Pitcher Pitch Rankings

To follow the starting pitchers, we have the relief pitcher pitch rankings.

1. Top Ten Four-Seam Fastball (Min 300):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Craig Kimbrel 94.74 2.34 0.23 1.80 4.15
Sean Doolittle 90.81 1.91 0.22 2.02 3.93
Chad Green 85.35 1.30 0.20 2.57 3.87
Anthony Swarzak 78.77 0.58 0.20 2.37 2.95
Josh Fields 89.12 1.72 0.27 0.89 2.61
Pedro Baez 90.00 1.82 0.28 0.78 2.60
Tommy Kahnle 84.53 1.21 0.25 1.34 2.56
Drew Steckenrider 84.55 1.21 0.26 1.13 2.34
Seung Hwan Oh 80.80 0.80 0.24 1.50 2.30
Josh Hader 87.30 1.52 0.28 0.67 2.19

The Stars: Craig Kimbrel, Sean Doolittle, Pedro Baez

Young and Coming: Chad Green, Drew Steckenrider, Josh Hader

Surprises: Anthony Swarzak, Josh Fields, Tommy Kahnle

No surprise that Kimbrel, probably the most dominant reliever of the past few years, is at the top. Jeff Sullivan discussed Green’s immense success overall and of his fastball recently in his second year for the Yankees. Steckenrider is an unknown rookie for the Marlins, but he has been exceptional for them. Hader is a top prospect for the Brewers and future starter, but his stint in the bullpen has gone perfectly. Swarzak is having a career year, so much so that the Brewers traded for him in an attempt to contend. Kahnle has broken out with the White Sox and Yankees.

2. Top Five Two-Seam Fastball (Min 250):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Craig Stammen 67.73 0.49 0.25 1.95 2.44
Kelvin Herrera 81.71 2.71 0.36 -0.52 2.18
Edwin Diaz 75.76 1.76 0.32 0.42 2.18
Joe Kelly 72.95 1.32 0.30 0.79 2.11
Ryan Madson 68.80 0.66 0.28 1.23 1.89

The Stars: Kelvin Herrera, Ryan Madson

Young and Coming: Edwin Diaz

Surprises: Craig Stammen, Joe Kelly

Herrera has been mostly terrible this year, but his track record says he is still a star. And he clearly hasn’t lost anything from his two-seam fastball. Diaz dominated as a rookie, but has slowed down a lot this season. He’s still 23 — no reason to worry. Stammen didn’t even pitch in the MLB in 2016, but he is performing solidly for the Padres. Kelly is having a career year in Boston behind his high-heat fastball.

3. Top Five Cutter Fastball (Min 200):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Jacob Barnes 104.09 1.99 0.22 1.21 3.20
Dominic Leone 99.80 1.62 0.24 0.81 2.43
Kenley Jansen 90.61 0.84 0.22 1.38 2.21
Alex Colome 85.15 0.37 0.20 1.80 2.17
Tommy Hunter 88.07 0.62 0.22 1.32 1.94

The Stars: Kenley Jansen, Alex Colome

Young and Coming: None

Surprises: Dominic Leone, Jacob Barnes, Tommy Hunter

The most infamous cutter in the game makes the top five, coming from Dodgers closer Jansen. Colome has continued a breakout from 2016 as the Rays closer. Leone had a great rookie season for the Mariners in 2014, but was knocked around in 2015/2016. He has come back nicely in 2017.

4. Top Five Sinker Fastball (Min 200):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Pat Neshek 70.87 1.06 0.25 1.66 2.72
Matt Albers 66.94 0.58 0.24 1.96 2.54
Tony Watson 73.58 1.40 0.28 1.10 2.50
Scott Alexander 76.57 1.77 0.30 0.47 2.24
Richard Bleier 65.97 0.46 0.25 1.68 2.14

The Stars: Pat Neshek

Young and Coming: None

Surprises: Richard Bleier

Neshek, a two-time All-Star, has been spectacular for the Phillies. Bleier, a 30-year-old second-year player, has been unexpectedly good in the majors the past two years.

5. Top Two Splitter Fastball (Min 200):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Blake Parker 101.30 1.30 0.18 1.48 2.78
Chasen Shreve 97.50 0.79 0.18 1.48 2.27

Only nine relievers heavily used the splitter, so this is a small leaderboard. Parker has broken out for the Angels in 2017. Shreve is the third Yankee to appear.

6. Top Five Curveball (Min 200):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
David Robertson 102.86 1.89 0.16 0.73 2.62
Jerry Blevins 95.85 1.28 0.16 0.71 1.99
Ryan Pressly 89.25 0.70 0.12 1.28 1.98
Cody Allen 90.94 0.85 0.15 0.85 1.70
Keone Kela 85.24 0.35 0.13 1.12 1.47

The Stars: David Robertson, Cody Allen

Young and Coming: Keone Kela

Surprises: None

Our fourth Yankee to appear on a leaderboard is Robertson. And none of those four have been Dellin Betances or Aroldis Chapman. Scary. Kela has been one of the only relievers holding the Rangers bullpen afloat.

7. Top Ten Slider (Min 250):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Roberto Osuna 108.02 1.97 0.16 1.52 3.49
Arodys Vizcaino 105.81 1.78 0.16 1.54 3.32
Raisel Iglesias 98.47 1.13 0.14 1.93 3.06
Blake Treinen 105.37 1.74 0.17 1.23 2.97
Pedro Strop 107.08 1.89 0.19 0.97 2.86
Ken Giles 97.17 1.01 0.16 1.57 2.59
James Hoyt 110.74 2.22 0.23 0.19 2.41
Edwin Diaz 99.11 1.18 0.18 1.12 2.31
Adam Morgan 108.19 1.99 0.23 0.16 2.15
Kyle Barraclough 88.13 0.21 0.15 1.67 1.88

The Stars: Roberto Osuna, Pedro Strop, Ken Giles

Young and Coming: Raisel Iglesias, Edwin Diaz

Surprises: James Hoyt

Osuna has been nothing short of excellent for the Blue Jays, manning the closer job for all three of his professional seasons. Still just 22 years old, the best is yet to come. Strop is widely under-appreciated, but he has been a consistent force out of the Cubs bullpen for years. Mariners young stud Edwin Diaz makes his second leaderboard appearance. Hoyt has been terrible for the Astros, so his inclusion is unexpected.

8. Top Three Changeup (Min 200):

Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
Tommy Kahnle 99.96 1.16 0.18 1.59 2.75
Felipe Rivero 105.68 1.86 0.22 0.47 2.33
Chris Devenski 100.35 1.21 0.20 0.89 2.10

(the changeup is not much of a reliever pitch, so this leaderboard is small)

The Stars: Chris Devenski

Young and Coming: Felipe Rivero

Surprises: None

Kahnle appears again. With much-improved stuff, he has been striking out everybody en route to a big breakout season. Devenksi is only in his second year, but also in his second year of excellence. The unheralded minor-league starter turned long reliever turned dynamic/versatile setup man has been a star in Houston’s bullpen. His changeup is nicknamed the “Circle of Death,” so no surprise seeing him here. Rivero has been dominant for the Pirates in his third year in the bigs.

Top Fifteen Overall:

Pitch Player Sw+Whf% Sw+Whf% Z xwOBA xwOBA Z Z Total
4-Seam Craig Kimbrel 94.74 2.34 0.23 1.80 4.15
4-Seam Sean Doolittle 90.81 1.91 0.22 2.02 3.93
4-Seam Chad Green 85.35 1.30 0.20 2.57 3.87
Slider Roberto Osuna 108.02 1.97 0.16 1.52 3.49
Slider Arodys Vizcaino 105.81 1.78 0.16 1.54 3.32
Cutter Jacob Barnes 104.09 1.99 0.22 1.21 3.20
Slider Raisel Iglesias 98.47 1.13 0.14 1.93 3.06
Slider Blake Treinen 105.37 1.74 0.17 1.23 2.97
4-Seam Anthony Swarzak 78.77 0.58 0.20 2.37 2.95
Slider Pedro Strop 107.08 1.89 0.19 0.97 2.86
Splitter Blake Parker 101.30 1.30 0.18 1.48 2.78
Changeup Tommy Kahnle 99.96 1.16 0.18 1.59 2.75
Sinker Pat Neshek 70.87 1.06 0.25 1.66 2.72
Curveball David Robertson 102.86 1.89 0.16 0.73 2.62
4-Seam Josh Fields 89.12 1.72 0.27 0.89 2.61

Best Pitch: Craig Kimbrel, Boston Red Sox, four-Seam

Biggest Surprise: Jacob Barnes, Milwaukee Brewers, Cutter

The leaderboard is run by four-seam fastballs and sliders at the top, which is unsurprising considering those are the favorite pitches of relievers. I’ve said this before, but three Yankees in the top 15. And neither of their alleged best two! That’s absurd. Seeing Kimbrel at the top is the exact opposite. Jacob Barnes, however, is crazy too. The unheralded second-year man hasn’t shown much yet, with a 4.00 FIP in 2017. But that cutter is doing something to hitters.

I will add one more, combining relievers and starters, and with some interesting tidbits.