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:

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-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.

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.

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:

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.

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.

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:

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:

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

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

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

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):

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):

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):

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):

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):

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):

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):

(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:

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.

As I stated in my earlier article, I would be posting data from my pitch-effectiveness measurement I introduced. Let’s start with the starting pitchers.

1. Top Ten Four-Seam Fastballs (Min 500):

The Stars: Chris Sale, Jacob deGrom, Max Scherzer, James Paxton

Young and Coming: Jose Berrios

Surprises: Rick Porcello, Chase Anderson, Jeff Samardzija

This group includes some bona-fide talent and some surprises. Porcello’s 1.90 Z-Score on the Sw+Whf% jumps out, considering his lack of stuff and general pitch to contact. Anderson is quietly putting together a solid season, with a 2.88 ERA in 122 innings of work. Samardzija’s incredible strikeout and walk peripherals have been well documented this year.

2. Top Ten Two-Seam Fastballs (Min 300):

The Stars: Sonny Gray, David Price, Chris Sale, Julio Teheran

Young and Coming: Jameson Taillon, Luis Castillo

Surprises: Jaime Garcia, Matt Garza

We see Sale again, which, considering what he has done this year, is not surprising. Garza has been generally terrible this year, so his inclusion in this list is unexpected. Castillo, a rookie for the Cincinnati Reds, has pieced together some quality starts out of the spotlight.

3. Top Five Cut Fastballs (Min 200):

(Only five because the small use of cutters)

The Stars: James Paxton, Corey Kluber

Young and Coming: Zach Godley

Surprises: Tyler Chatwood

We see Paxton again, who has established himself as a star this season. Godley has been great for the Arizona Diamondbacks, and Tyler Chatwood has been poor for the Colorado Rockies.

4. Top Five Sinker Fastball (Min 200):

(Only five because the small use of sinkers)

The Stars: Jake Arrieta, Jon Lester, Jose Quintana

Young and Coming: Trevor Williams

Surprises: None

An emerging starter for the Pittsburgh Pirates, an emerging ace for the Milwaukee Brewers, and…three Chicago Cubs. I gave the Cubs pitchers the benefit of the doubt and put them under “The Stars” category, but they may have pitched their way out of there this season.

5. Top Two Splitter Fastball (Min 200):

The splitter leaderboard included only nine starters, so this one is short. Kevin Gausman has rebounded from a horrendous start to be solid, and Ricky Nolasco has continued to provide what he always has: mediocrity.

6. Top Ten Curveball (Min 300):

The Stars: Corey Kluber, James Paxton, Carlos Carrasco

Young and Coming: Zach Godley

Surprises: James Shields, Alex Meyer, John Lester, Charlie Morton

We see Kluber again, and Godley again, and Paxton for a third time. No surprise considering the seasons they have put up. Shields’ days as a front-of-the-rotation starter are far behind him. Meyer has quietly put together some solid starts for the Los Angeles Angels as a complete unknown. Lester is a surprise here because this is his second leaderboard appearance, and he has not pitched well. Morton is mostly known for his injury problems, but he has developed some of the best “stuff” in the game in his first year in Houston.

7. Top Ten Slider (Min 300):

The Stars: Carlos Carrasco, Max Scherzer, Sonny Gray, Clayton Kershaw, Marcus Stroman, Zach Greinke

Young and Coming: Dylan Bundy, Mike Clevinger

Surprises: Patrick Corbin

Finally! The man we have been waiting to see, Kershaw, makes his first appearance. As does Scherzer. The star power of this group is by far the strongest. Bundy has been “Young and Coming” for decades it seems now, and no one knows if the flashes will become consistency ever. Still just 24 years old, though, so I will keep my hopes up. Clevinger has been a nice surprise for the Cleveland Indians, and Corbin has bounced back from a miserable 2016 to be solid for the Arizona Diamondbacks.

8. Top Ten Changeup (Min 300):

The Stars: Stephen Strasburg, Kyle Hendricks, Max Scherzer, Carlos Carrasco, Cole Hamels

Young and Coming: Luis Castillo, Eduardo Rodriguez

Surprises: Edinson Volquez

Scherzer again, which makes me feel better about the validity of this work. Carrasco for the third time in a row. His breaking and offspeed stuff are killer. Very few people outside of Cincinnati know Castillo, but this is the rookie’s second leaderboard appearance. Rodriguez has continued to flash this year, but injuries and inconsistency continue for the young Red Sock. Volquez is still embracing his mediocrity.

Starters Top Fifteen Overall:

Best Pitch: Chris Sale, Boston Red Sox, 4-Seam Fastball

Best Repertoire: Corey Kluber, Cleveland Indians

Biggest Surprise: Luis Castillo, Cincinnati Reds, Changeup

This list is almost all household names. In first and second, we have the AL Cy Young frontrunners. Jeff Sullivan recently wrote an article about Kluber’s curveball, and how it may be the best pitch in baseball. It isn’t number one here, but second place is not too shabby. His cutter also appears here, so his dominance is not hard to explain. Sonny Gray’s stuff is well known, and he shows up twice on this table, but his numbers are not spectacular this year. Lastly, watch out for Castillo. He’s a no-name rook, but he has been solid for the Reds, and the ranking of his changeup may be the evidence to support his success.

Next up is relievers.

We are yet to establish a truly effective method of measuring the effectiveness of a pitch and comparing pitches. One of the main problems with attempting to evaluate a pitch on it’s own is nearly impossible. If a pitcher has an exceptional fastball, that is going to elevate his slider. Another pitcher may have a slider with more “stuff,” but it will not rank as well without the other effective pitches in his repertoire.

I will attempt to create a method here that will allow us to measure effectiveness in an improved way, although there is no guarantee here that I will succeed.

Let’s start with the main things a pitch has to be effective. In theory, a perfect pitch would be thrown in the zone and generate whiffs, while allowing weak contact when it is put in play. Obviously, it is unreasonable to expect a pitcher to generate lots of whiffs inside the zone against major-league hitters, so throwing outside the zone is a necessity in baseball to get swings and misses.

So, I used two evaluators for pitch effectiveness. I formed my own measurement, Swing%+Whiff% (Sw+Whf%). Since many pitches are meant not to be thrown in the strike zone, I did not include Zone% or Strike%. The Sw+Whf% should evaluate the ability of a pitch to get swings, and swings and misses. That evaluator covers the “stuff” component of the pitch. For the second evaluator, I used xwOBA (expected OBA), which gives a “true” wOBA based off exit velocity and launch angles. This covers the contact management component of the pitch. These are not all weighted for the part they play in run scoring, so it is not perfect, but they should give us a solid idea of what a pitch could do.

Obviously, different pitches will have different average values for these evaluators. A breaking ball is going to create more whiffs than a fastball. Fastballs are easier to hit, and thus will have a higher xwOBA. These evaluators themselves can not be used to compare different types of pitches. This is where the Z-Scores come in to play.

A Z-Score measure how much something deviates from average. First, we take the standard deviation and mean of a data sample. Then, for the Sw+Whf%, we subtract the mean from the individual’s Sw+Whf%, and divide that number by the standard deviation. We have our Z-Score! If the Sw+Whf% is higher than average, the Z-Score will be above zero, which means it is better than average. If the Sw+Whf% is lower than average, the Z-Score will be below zero, indicating the pitch is worse than average. It is the same thing for xwOBA, except for xwOBA lower is better. So instead of subtracting the mean from the individual’s xwOBA, we subtract the individual’s xwOBA from the mean. Add the two Z-Scores together, and we have our total Z.

Example:

The average Sw+Whf% on four-seam fastballs, for pitchers with a minimum of 500 four-seamers thrown, is 63.78. The average xwOBA allowed on these is .347. Chris Sale owns an 85.89 Sw+Whf% and .238 xwOBA on his four-seam fastball. The Sw+Whf% STD is 7.14 and the xwOBA STD is .038.

Sw+Whf% Z-Score: (85.89-63.78)/(7.14) = 3.08

xwOBA Z-Score: (.347-.238)/(.038) = 2.86

Total Z-Score: 3.08 + 2.86 = 5.94

Sale’s 5.94 is an incredible score, with second-place Jacob deGrom sitting at 4.81, over 1 below Sale. After that, no one else even reaches 3.5. The Z-Score has no unit, so it can be slightly confusing. It is a measurement of how many standard deviations above or below average something is.

A few things to be careful of here. These numbers are not predictive. They are simply meant to measure the effectiveness of a pitch and allow us to compare different types of pitch in a more simplified way than run values. It is just a fun statistic to look at, not something used to project the future. We also have the same problem as run values. It is impossible to look at a pitch by itself, as a good fastball will elevate a good slider. I am attempting to determine something that will allow us to better differentiate a player’s specific pitches from each other, but for now, we have this. I will be posting all the specific pitch data and tables for each pitch for starters and relievers and doing some analysis in the next few days. This is just the introduction.

Some say the sample size of Luis Castillo’s season is 15 starts; I say it’s only eight.

July 25th was the first game in which BrooksBaseball.net registered a sinker from the 24-year-old. While some other sites show blips of the pitch peering out from behind the curtain – misread changeups? – you’ll read elsewhere that he learned the pitch right as the August sun crept up on the city of Cincinnati. After adopting the sinker, his following eight starts showed a clearer picture of the pitcher he’ll be in 2018.

The issue that resonated most with analysts during his debut at the end of June was his fastball’s tendency to stay straight. An old adage you’ll hear in baseball circles revolves around a straight fastball’s velocity mattering less, because if it’s a straight 98-99 mph pitch, theoretically, a major-league hitter will have a better chance of squaring it up.

Castillo never got that memo.

Unless the Yankees’ ace Luis Severino concentrates some adrenaline to kick up his average fastball velocity by season’s end, Castillo will claim the “velo crown” for starting pitchers – 97.8mph is his number (min. 200 pitches). While velocity doesn’t tell the whole story – I’m looking at you, 2016 Nathan Eovaldi, and your 97.8mph average four-seamer – for Castillo it’s a catchy interlude; a hook that gets your undivided attention. Even with the pitch and its “straight” tendencies, aggregating all 15 of his starts, the pitch maintained a whiff-per-swing rate inside the 85th percentile among all starters – 22.6% (min. 200 pitches).

It may seem dubious that the hardest fastball among starters in all of baseball could get better during any stretch of time, but Castillo wove into his repertoire a sinker that allowed his four-seamer to change its attack.

Above we’re looking at Castillo’s four-seam fastball location pre-sinker adoption (before July 24th) and post-sinker adoption (July 25th forward). The former being a tight concentration towards the outside part of the plate, while the latter is the much larger area of dark red, up in the zone.

This philosophy makes sense; take a straight fastball, stop throwing it for strikes down in the zone, and put it at the letters, making it nearly impossible for hitters to muster success. It worked. Castillo wasn’t able to execute this move sooner because he didn’t have another fastball to establish the zone with early in starts.

Before Castillo’s sinker, hitters were teeing off on Castillo’s fastball to the tune of a .658 slugging percentage. All the while, his slider and changeup – which we’ll touch upon shortly – were nearly unhittable with slugging percentages that couldn’t edge past the fabled “Mendoza Line.” After Castillo learned his sinker, that cringe-worthy slugging percentage on his four-seamer fell to a manageable .368. His sinker, meanwhile, was his early-count pitch, and he located it unbelievably well. Castillo’s worm-killer was second among starters with a 77% grounder-per-ball-in-play rate, trailing only Jordan Zimmermann, who can’t sniff the whiff rates of our Dominican-born phenom (almost as good as Carrasco’s sinker). As Eno Sarris mentions in a FanGraphs column, Castillo is creating a duo of skills that most pitchers envy: choppers and whiffs.

Those whiffs come from his slider and changeup, two pitches that stood out before the sinker, becoming much easier to “arrive at” in terms of sequencing with a two-percent cut in walks and improved ability to get ahead of hitters. His changeup – like with most righties – is a put-away pitch to left-handers; 33% usage rate overall that kicked up to 43% when Castillo had two strikes on a lefty. The sinker we’ve discussed at length also seems to correlate with an uptick in slider usage to left-handed hitters. It ticks up 8% when Castillo has two strikes on a hitter. My speculation is this has a bit to do with gaining confidence in the pitch through understanding eye-level adjustments that hitters have to make after realizing Castillo is now living up in the zone with his four-seamer (see the GIF above). Of course, this is merely speculation; it could easily be Castillo becoming more comfortable with the break of the pitch, eliminating fears that he doesn’t follow-through and leave it up in the zone – essentially a meatball.

Sitting just below Castillo’s changeup in terms of velocity is his preferred put-away pitch to right-handers, a compact slider that doesn’t jump out in terms of swinging strike rate, velocity, or even movement, but possesses an uncanny ability to avoid becoming line drives. A peculiar metric to stand out in, yes, but after understanding line drives go for hits three times more often than ground balls, limiting line drives becomes the best thing you can do if you’re offering a pitch devoid of Kershaw-territory whiff rates.

A 27% strikeout rate in just under 90 innings, with a ground-ball rate near 59%, is a combination very few pitchers possess; Castillo is one of them. With his sinker in play, those numbers became 25% and 61% respectively; just as dominant, yet more stable with the improved control I’d speculate the sinker brought about. I’ve heard a lot of Luis Severino and Michael Fulmer comparisons to Castillo because of the fastball-slider-changeup offerings, but one of the best young pitchers in baseball is becoming a unique beast with his sinker.

Castillo’s potential is the good kind of unbelievable, in contrast to another kind of unbelievable from this Cincinnati.com article titled, “Bryan Price: Luis Castillo is in the 2018 Rotation” which implies we needed to know one of the best young pitchers in all of baseball will be allowed to dominate as a starter come March 29th of 2018. March? Yes, March. Baseball and snow is quite possibly my second favorite pairing. Behind, of course, the whiffs and grounders Castillo generates.

A version of this post can be found on my site, BigThreeSports.com.

I’m on Twitter @LanceBrozdow

The Milwaukee Brewers have been a very entertaining team in 2017. Their early dominance of the NL Central over the struggling Cubs predictably came to an end after the Cubs decided to stop being bad and to start winning baseball games again. Now, thanks to some struggles, along with the surge of the Rockies and Diamondbacks, the Brewers are on the outside looking in during the playoff race. However, that doesn’t mean that the Brewers don’t have an interesting group of solid young pitching and an intriguing offensive core. Among all of these players, Zach Davies may be one of the most interesting. Davies was never a highly-touted prospect; he always lingered near the bottom of organizational top-10 lists during his time with the Baltimore Orioles and Milwaukee Brewers. In 2016, Baseball Prospectus had him ranked 8th in the Brewers system with a 45 FV, projecting him as a back-end starter with above-average command.

This year, Davies has sported a 4.38 xFIP and a 15.9 K%, which certainly profiles like a 4th starter’s stat line. However, Davies ranks in a tie for 27th (min. 100 IP) for WAR with 3.0 on the season up until September 16th, which definitely isn’t something to expect from a back-end starter. When you look at Davies’ numbers, you have to wonder how he has manged to succeed with such a low K rate and an inflated xFIP.  Even going back to last year, Davies had a higher K rate at 19.8%, along with a 3.94 xFIP in 163.1 innings, and posted a 2.7 WAR. These stat lines look fairly similar, except for the the fact that he reduced his K rate in 2017. What else changed that has allowed him to succeed with this decreased strikeout rate? One thing Davies has changed in 2017 is his HR rate, which decreased from 1.10 in 2016 to 0.87 in 2017. So, in essence, Davies traded strikeouts for home runs. How did he manage to do that?

Let’s revisit that scouting report from 2016, with an emphasis on the 55 FV for Davies’ command. In that same report, it’s said that he had battled control problems during his time in the Brewers system. Now, there has always been a little bit of confusion as to what exactly the difference is between command and control, but Baseball Prospectus did a great job of dissecting this issue. Basically, control is the ability to throw the ball in the strike zone, while command is the ability to throw the ball in precise locations in or out of the strike zone. Below is a very handy diagram from the aforementioned article.

In this same article, two new stats are introduced that help attempt to quantify command and control: CSAA (called strikes above average) and CSProb (called strike probability). While these stats were originally created to show how well a catcher frames pitches, they can also tell you a lot about a pitcher. CSAA attempts to quantify how many strikes a pitcher creates for his team solely on taken pitches, and quantifies command. For example, having a CSAA of 3.0% means that there is 3 percent better chance that pitcher’s pitches will be called a strike than your average pitcher. CSProb quantifies how likely a certain pitch is to be called a strike, and highlights control. If you have a 50% CSProb, then there is a 50% chance a pitch you throw will be called a strike. As it turns out, the 2016 scouting report on Davies was correct; he ranks at the top of the leaderboard in CSAA and is near the bottom in CSProb. In 2017, Davies ranks 6th among qualified pitchers in CSAA at 2.83%, and he has a CSProb of 43.8%.

Davies has always been good at working the corners with a low 90’s to high 80’s fastball that has a lot of heavy run/sink to it. Usually, pitchers like this have to ensure that they can nibble at the corners of the plate so that their “slow” fastballs don’t get completely crushed by the power hitters of the league (which are apparently Elvis Andrus and Didi Gregorius now). Davies does just that, as shown in his zone heat map from Brooks Baseball below. He stays low in the zone to the majority of batters and does a good job of working both sides of the plate.

A pitcher who lives on the corners like this usually tends to draw poor contact, and that’s exactly what Davies does, in whatever way you want to to quantify it. He is one of the better pitchers in a multitude of categories, as he ranks near the top in Baseball Savant’s barrels per plate appearance stat at 2.9, has one of the lowest average exit velocities at 85.0 mph, and only 28.3% of contact against him is classified as hard hit by FanGraphs. It seems like Davies has the ability to use his pitches to work the corners and manipulate contact in his favor, which explains how he started allowing fewer home runs in 2017.

You can see this in his numbers, like stated before, where he has worked to his strengths and traded strikeouts for weak contact. This is also supported by his ability to command the ball, and his 2.83% CSAA. Put both of these qualities together, and you get a pitcher who not only limits good contact but also excels in getting called strikes more often than the average pitcher. These qualities can also help show why Davies has an inflated xFIP. Davies’ contact rate has gone up 2.5% from 2016 to 2017, and since xFIP relies a lot on batted-ball events, it can help explain why his is fairly high despite his 3.0 WAR. Davies has shown that he has the ability to adapt to major-league hitting by identifying that he can be successful in limiting good contact while at the same time allowing more of it and striking fewer batters out. If he can keep it up, there’s no reason to believe he couldn’t be a mainstay in the Brewers rotation for a long time.

Earlier this season, Baseball Savant unveiled expected wOBA, which, around these parts of the Internet, has made some real waves. For those unfamiliar, expected wOBA, or xwOBA, predicts a batter’s wOBA from the launch angles and exit velocities of his in-play contact. Because certain speeds and angles are more conducive to hits — for instance, most consider an launch angle to be around 25 degrees — xwOBA is often interpreted as a rough measure of luck. In particular, the difference between a player’s expected and actual wOBA (referred to as xwOBA-wOBA) is often cited in discussions of just how “lucky” that player has been. If a hitter’s xwOBA is significantly higher than his actual wOBA, for example, one can deduce that he’s hit the ball far better than his actual results imply.

A few months ago, Craig Edwards wrote an excellent piece on the new statistic, and discussed the interaction between xwOBA-wOBA and player speed. He noted that most of the “luckiest” batters — those with negative xwOBA-wOBA figures — were generally some of the faster players in the league, and the least lucky batters were among the slowest. Intuitively, this makes sense, as faster players are more likely to beat out infield hits and take extra bases when given the opportunity.

Edwards also charted players’ xwOBA-wOBA against their BsR scores, producing a linear-looking graph (with an R-squared of 0.27) which confirmed at least a moderate link between the two statistics. He noted that because there was no “perfect metric” for player speed at the time, he chose to use BsR as a proxy. While BsR serves this purpose well enough, I do find it problematic that the statistic, by definition, includes runners “taking the extra base,” as this information is also reflected in the wOBA element of xwOBA-wOBA (i.e. when a batter stretches a would-be single into a double, his wOBA is that of a double, while his xwOBA remains at a single). I’d be more comfortable, therefore, comparing xwOBA-wOBA against a more “pure” form of player speed.

It’s fortunate, then, that in the time since Edwards’ piece, Baseball Savant has also released sprint speed, which captures a player’s feet traveled per second on a “maximum effort” play. Using a list of batters with at least 200 at-bats on the season, I’ve re-created the scatterplot used in Edwards’s article, replacing BsR with sprint speed:

As it turns out, the results are fairly similar — there is a link, albeit not an incredibly strong one, between a hitter’s speed and his xwOBA-wOBA. The trend is downward-sloping, meaning that faster batters are luckier, but there’s still a lot of scatter around the line of best fit. The highest point on the graph, belonging to Tigers slugger Miguel Cabrera, is particularly far from the trend line, as his 66-point xwOBA-wOBA is far above the expected difference of around zero.

I should also note that the above scatterplot, with an R-squared of 0.16, has a notably weaker correlation coefficient than did Edwards’s chart. The plot did get me wondering, however, how much stronger or weaker the correlation would be for different hit types. Common sense suggests that batter speed, as it relates to xwOBA-wOBA, plays a much more significant role on ground balls than on balls hit in the air. After all, a lazy fly ball to left field will be caught whether hit by Byron Buxton (tied for the fastest batter in the league) or Albert Pujols (the slowest), but Buxton will reach far more on a weak ground ball to the pitcher:

Again using the all-powerful Baseball Savant search tool, I gathered separate xwOBA-wOBA figures for fly balls, line drives, and grounders. Now, let’s see how the interaction between player speed and xwOBA-wOBA changes based on hit category:

There’s virtually no relationship at all for either fly balls or line drives — indeed, neither’s simple linear regression R-squared is significantly above zero — but ground balls are a different story. Not only is the smoothed line for grounders much steeper than for either of the other two hit types, but the R-squared was nearly 0.31. While this is by no means a high correlation coefficient, it does confirm a link between ground ball “luckiness” and player speed.

Because we now know that we should expect faster players to outperform their respective xwOBAs on ground balls (and vice versa), it may also be appropriate to adjust batters’ xwOBA-wOBA figures accordingly. Using the results of the simple linear regression for ground balls, I’ve calculated the difference between each major-league batter’s actual xwOBA-wOBA and his expected xwOBA-wOBA as per the regression. I’ve called the stat “Actual Less Expected xwOBA-wOBA” (It’s a mouthful, I know; let’s just agree to call it ALE xwOBA-wOBA), and while it’s a pretty rough measure, it provides us with a speed-neutral valuation of batters’ ground-ball “luckiness.” A high ALE xwOBA-wOBA indicates misfortune; Brandon Belt, for instance, has an actual xwOBA-wOBA 161 points higher than his sprint speed would suggest. Full lists of batters with the highest and lowest ALE xwOBA-wOBAs are as follows:

Finally, I multiplied each batter’s ALE xwOBA-wOBA figure by his ground-ball rate, as per FanGraphs (multiplied by 100 for aesthetic purposes). This should show us which batters have been the most and least lucky in the context of their own respective batted-ball profiles. As shown below, there are a lot of familiar names in these weighted ALE xwOBA-wOBA lists, but there are also a few differences:

As mentioned above, an R-squared of 0.31 isn’t big enough to draw any major conclusions. Even so, there’s value in controlling for player speed in any discussion of players outperforming or underperforming their expected wOBAs. By accounting for batters’ sprint speeds, we can get a purer look at which players have actually been the beneficiaries of good luck, and which batters’ negative xwOBA-wOBA on ground balls have resulted from their foot speed. Further, it helps to highlight players who actually have been unlucky; if a player has a ground-ball ALE xwOBA-wOBA close to zero, but a high overall xwOBA-wOBA, they’ve been hitting much higher-quality fly balls and line drives — neither of which are significantly impacted by player speed — than their results indicate. Miguel Cabrera, for instance, falls into that category; while his ground-ball ALE xwOBA-wOBA is relatively close to zero (indicating that he hasn’t benefited from any speed-neutral luck or unluck on grounders) his fly-ball xwOBA-wOBA is a whopping 0.166. So, even though Miggy isn’t one of the faster baserunners in the league, he’s still got a legitimate gripe against Lady Luck — and now, we can see which other batters do, too.