This article was originally published at Birds on the Black, a St. Louis Cardinals blog. You can find the blog (@birdsontheblack), author (@zjgifford), and artist (@cardinalsgifs) on Twitter.

Back in November, the FanGraphs staff ranked Marcell Ozuna as the seventh-best available free agent. In that article, Kiley McDaniel and the FanGraphs crowd both expected Ozuna would receive a four-year deal, with the median crowdsource estimate coming in at $64 million ($16 million per year) while McDaniel was a little higher at $70 million ($17.5 million per year). Teams could dream on Ozuna’s potential and a return to his 2017 production with relatively minimal risk — since his 2013 debut, Ozuna has consistently produced as an average or better player. Coming into this offseason, he had produced more than 2 WAR in four straight seasons dating back to 2016. Free agents are never a sure bet, but Ozuna seemed pretty close to one at a reasonable price.

Ozuna ended up betting on himself by taking a one-year deal with Atlanta, which was a bit of a surprise. With that context, I wanted to see what happened during his breakout 2017 campaign and what might be holding him back from tapping back into that potential. We’ll start with some numbers: Read the rest of this entry »

When Statcast was launched, we were graced with incredible new stats such as Exit Velocity and Launch Angle, which revolutionized how we evaluate hitting. This new information confirmed obvious things like that Giancarlo Stanton hits missiles, but it also gave us a new breed of hitter. Daniel Murphy, Justin Turner, J.D. Martinez, and others looked at the data and made adjustments that started maximizing their power outputs. The standard evaluation method has become to look at EVs mixed with LAs to determine who is one tweak away from stardom. Hitting is a complex beast, with pitchers throwing 95-plus with nasty hooks to go with shifting defenses. Ultimately, a hitter is looking to produce solid contact regardless of where the ball goes. The goal of this analysis is to identify hitters who have an inefficient spray chart and see how they could optimize their profile by hitting more balls in a different direction to maximize production. Luckily with Statcast, we can now try to find these answers.

To do this analysis, I used Baseball Savant to gather 2018 Exit Velocity and xwOBA to Pull Side, Straight Away, and Oppo Side for all hitters with at least 50 plate appearances. I then used FanGraphs to pull the 2018 data for Pull%, Mid%, and Oppo% to discern how often a hitter attacks that field. I used 50 PAs as a filter since this is about where exit velocities become stable and helps weed out pitchers and other noise. This does create gaps in the data because some players didn’t register 50 PAs of a batted-ball direction. This dataset gives us the ability to look at how hard a hitter hits the ball to a field, what was their expected damage (xwOBA) to that field, and how often they went that way.

The first category I looked at was players who could use the opposite field more often. To do this, I looked at players who had an above average Oppo Side xwOBA and a below-average Oppo%. I used exit velocities to each field as a proxy to justify the directional swing change. Read the rest of this entry »

During one of my recent visits to the Fangraphs home page, while scrolling across the leaberboards, I was confronted by a fact I had once known but had long ago forgotten over this slow and tired off-season. Aaron Judge led the league in WAR! as a rookie?! and by quite a wide margin. That happened last season? Shoot just over a year ago Judge was still relatively unknown and Jeff Sullivan was telling us not to underestimate his power.

This realization conjured up memories of last season’s AL MVP vote, how one of Sabermetrics’ patron saints shook the foundations of Sabermetrics’ most prominent statistical achievement, and how article after article were written about clutch hitting.

This, in turn, reminded me of another leaderboard Judge topped last season, this one more dubious. He led (lagged?) the league with the lowest Clutch score. He was fourth in WPA/LI with 5.85 Wins, trailing only this generation’s Mickey Mantle, Judge’s clone, and some guy who plays for the Reds and just a fractional win behind the leader. In contrast, he ranked just 38th in WPA tied with some guy who used to play in Korea. Add this up and he had by far the lowest Clutch score at -3.64 wins, a full win lower than the rest of MLB save for one blue-eyed Cub.

Which led me to ask the question: What would Aaron Judge have had to do to be a clutch batter? And I don’t mean the obvious answer, “Hit better in high leverage situations“. Duh! He batted an astounding 190 wRC+ in low leverage situations to just a 107 in high leverage at bats. But that’s not the answer I was looking for. I wanted to know specifically, what would Aaron Judge have had to do to be a clutch batter? as in what could we change from his epic near MVP season to bring his Clutch stat into the positive?

So I set to find out.

Using Fangraph’s own Play Log, and with plenty of assistance from BaseballSavant.com and Statcast, I decided to play as one of the “Baseball Gods” and see if I could tweak a few of Judge’s plays to make him more clutch. As a “Fair and Just Baseball God” I wouldn’t be aiming to increase Judge’s overall stat line. If I nudge a groundball a little to turn an out into a single in a high leverage situation, I’d do the opposite in a low leverage situation (Judge had nearly 50 PA’s with a Leverage Index, LI, of effectively 0) nudging another grounder into a fielder’s glove for an out.

Thus his overall stat line and his WPA/LI would remain effectively the same, and since in those low leverage situations no (or nearly no) WPA was added, we’ll only be looking at how the play’s I change increase Judge’s WPA.(And I’ll only be going through the plays I add not the ones I’d need to take away.) I also won’t worry about any of the time traveler unintended consequences stuff, I’ll assume that only the single event changes without it affecting other plays in the same game or others. (I’ll let some of the other “Baseball Gods” worry about that stuff…)

Recall the Equation for Clutch:

Clutch = (WPA)/(pLI) – (WPA/LI)

With my rule that Judge’s pLI (0.95) and WPA/LI (5.85) will remain fixed we are just looking to increase Judge’s WPA.

With that lengthy explanation out of the way, let’s begin!:

Judge Initial WPA = 2.10
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Situation #1:

July 27th, Bottom 9, 1 Out, Runner on Third, Yankees down 1.
LI = 5.81 – Actual Play – Judge Fly’s Out to Right. – WPA = -.252

We’ll start with a big one, in fact Judge’s second highest leverage play of his season!
With a chance to tie the game in the 9th, Judge just miss-hits the ball sending it not quite far enough to allow the speedy Brett Gardner to score from third. As you can see, similar hit balls all had the same result:

But as my first act as “Baseball God” I’m gonna adjust this hit ever so slightly, notching Judge’s bat up a millimeter to two to lower the Launch Angle of this hit and allow it to carry just a bit further. Something more like this:

That should be far enough out to score Gardner giving Judge a Sac Fly.

New Play – Sac Fly – New WPA = .112 – Net WPA Change = .364

Judge’s New WPA = 2.46
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Situation #2:

August 2nd, Bottom 8, No Outs, Runner on Second, Yankees down 2.
LI = 2.72 – Actual Play – Strike out swinging. – WPA = -.08

Sometimes the job of a “Baseball God” is rather easy. In this case I’ll just need to do some umpire convincing. In this at bat Judge struck out on a 3-2 slider, but earlier in the at bat, after three wild pitches, here was the 3-0 offering from Bruce Rondon:

Ok, sure, most umpires probably call this a strike on a 3-0 count, but I’m gonna go ahead and give this one to Judge. Ball Four!

New Play – Walk – New WPA = .087 – Net WPA Change = .167

Judge’s New WPA = 2.63
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Situation #3:

September 19th, Bottom 2, 2 Outs, Runners on Second and Third, Tie Game.
LI = 2.03 – Actual Play – Fly out to Center. – WPA = -.061

Judge crushed a Jose Berrios offering at 107 MPH:

Here’s what it looked like.

He was just a little under this one, wouldn’t take much more to send this ball out. So we’ll make the charge and turn this loud out into a bomb.

New Play – Three Run Home Run – New WPA = .249 – Net WPA Change = .310

Judge’s New WPA = 2.94
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Situation #4:

September 9th, Top 9, No Outs, Runner on First, Tie Game.
LI = 3.40 – Actual Play – Fielder’s Choice to third, out at second. – WPA = -.084

Judge grounds one to third, and nearly into a double play.
Here’s what it looked like.

Thing is, Rougned “De La Hoya” Odor is in such a hurry to turn two that it almost looks like he jumps off Second too early. Take a closer look:

Your guess is as good as mine, but here’s the thing: As a “Baseball God“, I don’t have to guess. I’ll just make the throw from third just a little higher and wider pulling Odor off the bag and leaving both runners safe on a throwing error. Did you know that errors count as positive WPA plays?!

New Play – Reach on Error, Throwing Error at Third, Runners safe at First and Second – New WPA = .109 – Net WPA Change = .193

Judge’s New WPA = 3.13
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Situation #5:

August 18th, Top 6, 2 Outs, Bases Loaded, Yankees down 1.
LI = 4.52 – Actual Play – Ground Out to Shortstop. – WPA = -.119

Judge hits a sharp ground ball at 103 MPH.

Here’s what it looked like.

Hit hard, but right into Xander Bogaerts‘ glove for a routine out. But per Statcast balls hit at that Velocity and at that Launch Angle become hits about half the time.

One can imagine Judge hitting this ball just a little closer to the pitcher’s mound, and seeing it get past a diving Bogaerts. With the runners going, that hit would easily score 2.

New Play – Ground Ball Single up the Middle Scoring 2, – New WPA = .275 – Net WPA Change = .394

Judge’s New WPA = 3.53
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Situation #6:

June 14th, Top 7, No Outs, Runners on First and Second, Tie game
LI = 2.89 – Actual Play – Fly Out to Left. – WPA = -.085

Judge ropes one into left field, where Eric Young Jr. makes an awkward dive for it.

Here’s what it looked like.

Young makes the out, but just barely. Imagine if his dive is just a little more awkward… That ball probably gets by him and clears the bases.

New Play – Bases Clearing Double to Left Field – New WPA = .219 – Net WPA Change = .304

Judge’s New WPA = 3.83
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Situation #7:

June 15th, Top 9, No Outs, Bases Empty, Yankees down 1.
LI = 2.88 – Actual Play – Strike Out Looking. – WPA = -.073

Were picking up steam now! And as a “Baseball God” I haven’t had to work very hard changing these last few plays. Now it’s time to work just a little harder.

Leading off a do or die ninth, Judge took three easy balls, then saw and fouled consecutive fast balls. This set up a full count pitch where Santiago Casilla froze him with a beautiful knuckle curve. Here’s what it looked like.

No doubt that’s a beautiful pitch. But guess what? Umpires sometimes miss calls, especially when they get some inadvertant dust in their eye…

New Play – Walk – New WPA = .110 – Net WPA Change = .183

Judge’s New WPA = 4.02
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Situation #8:

September 10th, Top 3, 1 Out, Bases Loaded
LI = 2.26 – Actual Play – Sac Fly to Right. – WPA = -.002

In an RBI situation, Judge blasts one.

Here’s what it looked like.

So Judge clearly gets under this pitch… but he still hit it over 300′ and scores a run.
The thing is the next two times up he did this and this!
I’m just gonna do a little rearranging on when these homers take place…

New Play – Grand Slam to Right – New WPA = .256 – Net WPA Change = .258

Judge’s New WPA = 4.27
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Situation #9:

April 18th, Bottom 9, 2 Outs, Bases Loaded, Yankees Down 3
LI = 3.86 – Actual Play – Fielder’s Choice to Shortstop, Out at Second. – WPA = -.100

Judge ends the game on a weakly hit grounder to shortstop.

Here’s what it looked like.

Looks like a routine grounder, but per Statcast similar balls become hits about a third of the time. And we don’t really need a hit here, Tim Anderson looks a little shaky fielding the grounder as it hops to his glove. In a critical situation like this who’s to say he doesn’t boot one? The answer is me, the “Baseball God“. I say he boots it…

New Play – Fielding Error at Shortstop, 1 Run Scores – New WPA = .090 – Net WPA Change = .190

Judge’s New WPA = 4.46
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Situation #10:

July 21st, Top 3, 1 Out, Runners on First and Third, Tie Game
LI = 2.12 – Actual Play – Sac Fly to Center. – WPA = +.016

Another well struck ball that just stays in the yard for a sac fly.

Here’s what it looked like.

But Judge would get one more try at Andrew Moore that game, and you may remember it. Judge’s next at bat was that time he broke Statcast!

I’m just gonna move that Statcast breaking smash up one AB if you don’t mind…

New Play – Three Run Home Run – New WPA = .216 – Net WPA Change = .200

Judge’s New WPA = 4.66
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Ok, awesome we’re 10 plays in, and as a “Baseball God” I don’t feel like I’ve had to work all that hard. But were still only at 4.66 WPA, nearly a win short of our target. It’s time to pull out the big guns. It’s time to perform a MIRACLE!

Situation #11:

July 30th, Bottom 9, 1 Out, Runners on First and Second, Yankees down 2.
LI = 4.78 – Actual Play – Foul out to First. – WPA = -.112

Representing the go ahead run, Judge pops up in foul ground to the first baseman. You can see his hit in blue in the image below.

(As to why this shows up as a -57° LA I think sometimes Miracle Work messes with Statcast…)

Just a lazy pop-up. Not much a “Baseball God” can do to affect this play without revealing myself to the world. So I’ll just void the play and blows this ball a little further to the right and into the seats where Trevor Plouffe can’t catch it!

So I’ve just given Judge a new lease on this particular at-bat. I hope he uses it wisely. I’ll just assume it goes something like this!

New Play – Walk Off Three-Run Home Run – New WPA = .793 – Net WPA Change = .905

What?! You don’t think that’s fair. Tough! I am Beerpope the Baseball God and this is my Miracle, don’t tell me what’s fair!

Judge’s New WPA = 5.57

And with that spectacular finish, we check Judge’s Clutch score:

5.57 / 0.95 – 5.85 = +.01 Wins

And there you have it. Aaron Judge – CLUTCH BATTER. My work here is done.

So what does this all mean? Really I’m not sure. Does the fact that it took 10 twists of fate and one walk-off miracle just to bring Judge barely into the positive show just how deeply un-clutch he was last season? Maybe. But it may also show us how futile it is to focus of how clutch or un-clutch a batter is if an ump call, miss hit, or bounce here or there in just 10 at bats can invalidate the other 600 plus plate appearances in a player’s season.

I’ll leave that determination to the readers.

Now enough with the 2017 Season. It’s time for me to begin contemplating what Miracles to perform thus upcoming season…

Cheers!

Recently, for my job, I got to mess around with Statcast data for fly balls. I have a good job. As part of the task I was working on, I attempted to calculate the maximum heights and travel distances of fly balls using my extensive ninth-grade physics knowledge. Now, I was excellent at ninth-grade physics, especially kinematics, but my estimates, compared to the official Statcast numbers, were terrible. Figuring the discrepancies must be due to air resistance, I did my best to remember AP physics (with the help of NASA) and adjusted my calculations for drag. The results improved, but were still way off. There are many additional factors that affect the flight of a fly ball such as wind, air temperature and altitude, but I think the biggest factor causing the inaccuracy of my estimates is batted-ball spin. (If you disagree, let me know in the comments.) Exit velocity and launch angle get all the attention when discussing batted-ball metrics, but the data I was looking at suggested that batted-ball spin merits attention too. Are there batters who are consistently better at spinning the ball than others, and if so, is this a valuable skill?

We already know that balls hit with top-spin sink faster than normal while balls hit with back-spin stay in the air longer. It’s unclear, though, whether it’s better for the batter to hit the ball with more or less spin, and whether top-spin or back-spin is more beneficial. Back-spin would seem to be better if you are a home-run hitter while top-spin might be more beneficial if you are a line-drive hitter.

As far as I know, Statcast doesn’t measure batted-ball spin, and if it does, it’s not available on Baseball Savant. So to act as a proxy for spin, I calculated the estimated travel distance (adjusted for air resistance) from its launch angle and exit velocity for every line drive, fly ball and pop up hit in 2016 and subtracted this number from the distance estimated by Statcast. The bigger the deviation between these two numbers, the faster the ball was spinning, theoretically. Balls with positive deviations (actual distance > estimated distance) must have been hit with back-spin and balls with negative deviations (actual distance < estimated distance) must have been hit with top-spin.

The following table shows the 20 hitters (min. 50 fly balls hit) who gained the most distance on average in 2016 due to back-spin:

With a few exceptions, these are not home-run hitters. This group of 20 players averaged 8.25 home runs in 2016. The players who are getting the most added distance on their fly balls are not the ones who need it most. (Note: four players on this list and three of the top four players played their home games at Coors Field. Did you forget that Daniel Descalso played for the Rockies last year? Me too.)

What about the other end of the spectrum? The following are the 20 players who lost the most distance on average in 2016 due to top-spin:

Kris Bryant, Miguel Sano, Ryan Schimpf: this list is full of extreme fly-ball hitters with an average of 24 home runs last year. The scatter plot below with a correlation of -0.58 shows the relationship between batting spin and fly-ball percentage for all players in 2016.

And this isn’t just a one-year phenomenon. I was relieved to find out that the correlation between 2016 average distance deviations and 2015 average distance deviations is 0.75. Players who hit balls with a lot of spin in 2015 overwhelmingly did so again in 2016. Again, the plot below shows the strong relationship.

Mechanically, this is not such a surprising result. Players with a more dramatic uppercut swing (like a tennis swing) will impart more top spin onto the ball while the opposite should be true for players with a more level swing.

It remains to be seen whether this knowledge is useful in any way or if it falls more into the “interesting but mostly irrelevant” category of FanGraphs articles. There is essentially no relationship between a player’s average distance deviation and his wRC+ (correlation = -0.13), so we cannot say that spinning the ball more or in either direction leads to better results. And I imagine it is difficult to alter one’s swing to decrease top-spin while still trying to hit fly balls. At best, maybe this is a cautionary tale for players who want to be more hip and trendy and hit more fly balls like James McCann (FB% = 0.41), but don’t have the raw power to absorb a loss of 28 feet per fly ball (HR = 12, wRC+ = 66).

Let me know what you think in the comments.

For the last three years, Anthony Rizzo has been one of the most consistent hitters in baseball. His wRC+ from 2014-2016: 155, 145, 145. His wOBA: .397, .384, .391. He consistently draws a walk in about 11% of his plate appearances and strikes out in less than 20% of his plate appearances. So far this year? It has been a much slower start, as he’s slashing .231/.371/.448. Though the OBP and SLG aren’t bad, the batting average is tougher to stomach. He’s been just above average with a wRC+ of 114, hardly the numbers the Cubs were expecting from their perennial All-Star. Still, there’s some explanation for all this. For comparison’s sake, we will only be looking at 2016 and 2017. Here’s some charts from Brooks Baseball:

There isn’t an obvious change in approach. He’s swinging at about the same amount of pitches and really is staying inside the zone. In 2017 it seems like he’s swinging more at the low and in pitches but otherwise, same approach. The stats from Baseball Info Solutions and PITCHf/x back this up. He’s in line with his career swing% by both metrics; the difference is in the contact he’s making. By Baseball Info, his O-Contact% is 71.1% up from 68.1%. PITCHf/x also has him at 71.1% up from 66.1%.

This makes me think the quality of the contact is the issue. Here are two videos showing at bats in 2017 and 2016. The focus here is what Rizzo is doing with outside pitches. First 2016, then 2017:

https://baseballsavant.mlb.com/videos?video_id=730449083

https://baseballsavant.mlb.com/videos?video_id=1383639883

In 2016, Rizzo lets that outside pitch get deep to poke it to left field. The 2017 version is early and rolls it over into a shift. Baseball Savant has limited video for 2017 but I’ve seen the same thing and the numbers back it up. Here are two charts showing his exit velocities, 2016 is on the bottom, 2017 is on the top.

It would be easy to say Rizzo needs to do a better job going the other way with the outside pitch, but that’s the main difference I’m seeing this year. Overall, Rizzo’s hard contact is down to 30.4% from last year’s 34.3%, and from his career rate. His pull rate is also the highest in his career, at 53%, vs. 43.9%. Rizzo has been pulling a decent amount of grounders, specifically at a rate of 68.1% with about 78.2% being characterized as soft or medium contact, higher than in 2016. Rizzo faces a shift quite a bit, so pulling grounders isn’t going to help him. He’s hitting line drives at the lowest rate since he was first called up, and down to 15% from his career 20% rate. Take a look at the spray charts below. The first chart is 2017 and the second is 2016. It’s the classic small sample vs. large sample but you can definitely see that Rizzo is not using all fields like he has in the past.

This what confounds me. Despite all this, he still is producing better than average, because his walk rate and strikeout rate are the best rates of his career. So just imagine if his BABIP currently wasn’t .212? I don’t want to say that’s going to raise for sure, but I believe it will get closer to his career rate of .285. This is probably a long-winded way of saying small sample size, so here’s one last thing. This has happened with Rizzo before. In 2016 he had a similar start in March through May, but turned it on for the rest of the year.

Still, this isn’t a simple “It’s been 50 games and he’s been unlucky” that would imply that he’s the same player doing the same things but getting different results. The concern I have is that Rizzo’s doing things differently this year. He’s not using all fields, and he’s hurting his performance by trying to pull pitches and generating weaker contact (his EV is down this year). Using all fields might lead to more line drives and would drive his batting average up to his career norms. Maybe he’s putting pressure on himself after last year’s championship? He’s had success before and I believe he can get back to where he was.

For many years, the undisputed king of velocity in Major League Baseball has been Aroldis Chapman, with his fastball that averages around 100 mph and regularly reaches higher. Few pitchers have even been able to approach the level of Chapman’s fastball since he came into the league, and none have surpassed him. However, in 2016, one pitcher finally did it. Mauricio Cabrera of the Atlanta Braves averaged nearly 101 mph on his fastball in 2016 and he regularly touched 103; but yet there was still a major difference between Cabrera and the incredible Chapman. Chapman struck out over 40% of the batters he faced last year, while Cabrera struck out less than 20%. Strikeouts are intuitively related to fastball velocity. The faster that a pitcher can throw the ball, the less time a batter has to react, making it harder to make contact. So how does a pitcher such as Cabrera, who throws as hard as anyone in the game, strike batters out at a well below-average rate?

I first thought that maybe his perceived velocity is not as great as his actual velocity, and sure enough Cabrera does gets very little extension toward the plate when he delivers the ball. He only extends about six feet toward the plate before he releases the ball, which is a full foot shorter than fellow reliever, Zach McAllister, and several inches shorter than average for fastball-heavy relievers. This lack of extension means that the velocity that the batter perceives is slower than the actual velocity coming out of Cabrera’s hand, because it has farther to travel before it gets to the plate. However, this is only a minor difference, as Cabrera’s perceived velocity is still above 100 mph. This is not a huge drop, but it does bring him closer to the pack, as many relievers get good extension that increases their perceived velocities above their actual velocities. Chapman, for instance, gets great extension toward the plate on his already incredible fastball, which results in his excellent perceived velocity of over 101 mph. Cabrera’s lack of extension is likely a contributing factor to his low strikeout numbers, but it does not seem to be the main culprit.

Next, I wanted to see if there was something about the spin rate on his fastball that doesn’t lend itself to strikeouts. Spin rates correlate quite strongly with strikeout rates. Pitchers with high spin rates on their fastballs typically generate more swings and misses, and thus more strikeouts. It turns out that Mauricio Cabrera does have a low spin rate on his fastball. His fastball spin rate of 2300 rpm is well below average for fastball-heavy relievers, which is probably a major reason why he doesn’t miss many bats.

While it makes intuitive sense that something like the amount of spin on his fastball could be the reason for his low strikeout totals, it is still puzzling to see that his spin rate is so low, because spin rate is typically correlated with velocity. For most pitchers, the harder you throw, the more spin you will put on the ball. Aroldis Chapman, for example, has one of the highest spin rates in the sample. In order to single out the spin rate from the velocity, I divided the spin rate by the velocity to find the Bauer Unit, named after Indians pitcher Trevor Bauer. Cabrera’s average Bauer Unit of 22.85 is one of the lowest in the entire sample of fastball-heavy relievers. This means that he has some of the lowest spin per MPH in the game. There must be something inherent in how Cabrera throws a baseball that just doesn’t allow him to generate the amount of spin that is typically commensurate of how fast he throws.

Cabrera’s low spin is not all bad, though. Just as high spin rates lead to strikeouts, low spin rates lead to ground balls. An average spin rate is really where you don’t want to be, as those are the pitches that get squared up more often. While Cabrera actually has an above-average spin rate for the entire population of major-league pitchers, his spin rate is one of the lowest in the league compared to his velocity. This effectively makes him a low-spin pitcher, and last year’s batted-ball numbers bear that out. Nearly 50% of the batted balls Cabrera gave up last season were on the ground, and he didn’t surrender a single home run all season despite giving up the hardest average exit velocity in the game last year on his fastball. Cabrera got away with that extreme exit velocity by only allowing an average launch angle of 5.9 degrees, which was one of the lowest among the fastball-heavy relievers. It is hard to do much damage on balls hit on the ground, even if they are hit 95 mph. While the myth that the harder the ball is thrown the harder the ball can be hit has largely been disproved, it is interesting to see that the pitcher who throws the hardest also gave up the highest average exit velocity.

Of course, strikeouts aren’t just about swinging strikes; you have to get called strikes as well. Throughout Cabrera’s minor-league career, he struggled to throw strikes consistently. So much so that many thought his strike-throwing ineptitude might prevent him from ever even reaching the big leagues. However, once he started pitching in the majors, he suddenly discovered how to find the strike zone. Of course, walking four and a half batters per nine innings is still poor, but that mark represented his lowest walk rate since rookie ball in 2012. Even with the high walk rate last year, he actually threw strikes at an above-average rate. His Called Strike Probability, according to Baseball Prospectus, was 47%, which is slightly above league average. For a guy like Cabrera who has always struggled with control, it is probably a good thing to see him filling up the strike zone at an above-average clip. However, the tendency to pitch within the zone could result in more contact and thus bring his strikeout numbers down. Since he doesn’t command his pitches well, he cannot nibble at the corners or trust himself to throw his pitches just off the plate to generate swings and misses. This allows hitters to either lay off pitches that are safely outside, or lock in to the pitches that are squarely in the zone. This could be another significant cause for his lack of strikeouts.

Another reason Cabrera doesn’t strike out many batters is because he doesn’t possess a bat-missing secondary offering. His secondary pitches are all used primarily to get hitters off of his fastball. He throws the hardest change-up in baseball at 91 mph, and a mid-80s slider with good depth. The change-up got squared up pretty often in 2016, which makes sense, seeing that he throws the pitch with the velocity of a league-average fastball. The slider also does not get many whiffs, but hitters were not able to do much damage off of it in 2016. Batters only slugged .136 off of his slider last season, and the pitch generated the highest rate of fly balls of any slider in the game. Perhaps what is even more significant is that hitters had an average exit velocity against his slider of 85 mph and an average launch angle of 30 degrees. For reference, hitters that hit the ball with an exit velocity of 85 mph at a 30-degree launch angle went 4 for 72. His slider may not be a swing-and-miss offering, but it sure seems to be a good out pitch for him.

It looks like Cabrera’s low spin rate on his fastball relative to its velocity is the main reason for his lack of strikeouts. However, it is also likely that that same low spin rate allows him to induce an extreme amount of ground balls, which helps him limit the damage from the opposing batter. His lack of extension toward the plate and his tendency to live in the strike zone are also contributing factors. He also doesn’t have a secondary offering that gets many swings and misses. His slider, however, does produce a great deal of pop-ups, which is another way he limits damage on his batted balls. A major reason for his success last season despite his low strikeout totals and high walk numbers was that he didn’t give up any home runs. While a complete lack of dingers is very unlikely to persist, the types of batted balls he allows on his fastball and slider make it difficult for batters to hit it deep off of him.

Cabrera walks too many batters, and while I wouldn’t be surprised to see some progression in his strikeout rate, I don’t expect him to ever strike out batters at the same rate as someone like Chapman. He should be able to persist for several years as a good late-inning reliever, but he probably will never reach the elite levels that his fastball might suggest.

It seems that 2016 will be the year that Statcast begins to permeate Fantasy Baseball analysis. Recently there has been a wealth of articles exploring the possibilities of using these kinds of data. These pieces have provided relevant insights on how to improve our understanding of well-hit balls and launch angles. Also, they’ve facilitated access to information on exit velocity leaders and surgers, as well as provided thoughtful analyses to the possible workings behind some early-season breakouts.

However, there is still a lot we don’t know about Statcast data. For instance, we are uncertain of how consistent these skills are over time, both across seasons or within seasons. Also we don’t know what constitutes a relevant sample size or when rates are likely to stabilize. All in all, this makes using 2016 Statcast data to predict rest of season performance a potentially brash and faulty proposition. Having said that, we can’t help but to try; so here’s our attempt at using early-season 2016 Statcast data to partially predict future performance.

One of the early gospels of Statcast data analysis posits that the “sweet spot” for hitting homers comes from a combination of a launch angle in the range of 25 – 30 degrees and a 95+ MPH exit velocity. If this is indeed the ideal combination for hitting home runs, one could argue that players that have a higher share of fly balls that meet these criteria should perform better in other more traditional metrics such as HR/FB%.

Following this line of thought we dug up all the batted balls under the “sweet spot” criteria, and divided them by all balls hit at a launch angle of 25 degrees or higher (which MLB determines as fly balls) to come up with a Sweet Spot%. In an attempt to identify potential HR/FB% surgers, we compare Sweet Spot% and HR/FB% z-scores (to normalize each rate) for all qualified hitters with at least 25 fly balls and highlight the biggest gaps.  Here are the Top five gaps considering the games up to May 28^th:

Calhoun seems like a good candidate for a power uptick. He has the third-highest Sweet Spot% of 2016, and he has sustained similar Hard% and FB% to the previous two seasons. Yet somehow he has managed to cut his HR/FB% to less than half of what he put together in either 2014 or 2015.  More so, he has had some bad luck with balls hit in the “sweet spot”; his batting average in these kinds of balls is .500, whereas the league average is around .680. He is not killing fly balls in general, with an average exit velocity of 84.6 MPH, but if he keeps consistently hitting balls in the “sweet spot” range he should improve in the power department. Look out for a potential turnaround in the coming weeks and a return to 2015 HR/FB% levels.

Piscotty holds second place in the Sweet Spot% rankings. However, his FB% is very similar to what he did in 2015 whilst his Hard% is down from 38.5% to 32.5%. Lastly, he plays half of his games in Busch Stadium, which has a history of suppressing home runs. I would be cautious of expecting a major home-run surge, but in any case Piscotty is likely to at least sustain his performance in the power department, which would be welcome news to owners that got him at bargain prices.

Carpenter is another dweller of Busch Stadium, however his outlook might be a bit different. He is the absolute leader in Sweet Spot%. He is posting the highest Hard% and FB% marks of his career. Carpenter is also crushing his fly balls in general, with an average Exit Velocity of 93.7 MPH. Just as a point of reference Miguel Cabrera, Josh Donaldson and Giancarlo Stanton fail to reach an average of 93 MPH on their own fly balls. Lastly, he has had some tough luck with balls hit in the “sweet spot”, posting a batting average of just .420. Carpenter is already putting up the highest HR/FB% of his career, and he is a 30-year-old veteran of slap-hitting fame, but the power looks legit and perhaps there is more to come.

Denard Span and Yonder Alonso show up in this list not because of their Sweet Spot% prowess but rather due to their putrid HR/FB%. They barely crack the Top 50 in Sweet Spot%. They play half their games in two of the bottom three parks for HR Park Factor. Span is putting up his lowest FB% and Hard% rates since 2013, when he ended up with a HR/FB% of 3.4%. Meanwhile, Yonder’s rates most closely resemble those of 2012, when he had a HR/FB of 6.2%. Whilst their batting average of “sweet spot” batted balls is just .500, there is nothing to look here. In any case, their power situation looks to improve from bad to mediocre.

If you are interested in the perusing the Top 50 gaps between HR/FB% and Sweet Spot%, please find them below:

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com

Over the last few years, Alex Chamberlain has published a series of posts exploring the concept of xISO. Like the most commonly known xFIP, this metric is supposed to be an “expected” ISO, based on batted ball metrics. Nobly, Alex kept his model quite simple, using only statistics available on the FanGraphs player pages: Hard%, FB%, and Pull%.

I have very little formal training in statistics, most of it is self-taught to help me in my day job, so I’m also going to keep things simple. Inspired by Alex’s work, I began to experiment with improving the xISO model. I started building linear models including more predictors, and even introduced higher order and interaction terms. While these all improved the model slightly, I didn’t feel that the added complexity was worth the slight improvement. Along the way, I noticed that, although Chamberlain makes mention of the correlation between first half xISO and end of season ISO, if I calculated first half xISO and compared to second half ISO, I would find the initial xISO model to be a worse predictor of second half ISO than the actual first half ISO.

As I was running these calculations, I also became acquainted with the publicly available Statcast data through Daren Willman’s Baseball Savant site. Although the gathering of input data becomes a bit more tedious, surely some combination of exit velocity and launch angle information would improve an xISO model, and perhaps produce something which produces a better correlation between first and second halves. Let us see!

First things first, since Statcast is so new, we only have one full season of data. Ideally, we could use multiple years of data to build the model, but for now, we’ll stick with 2015 full season to train the model. As it turns out, the Statcast parameter that correlates best with ISO is the average exit velocity for line drives and fly balls (LDFBEV). This makes sense, right? It also makes sense that we can exclude ground ball exit velocity in an ISO predictor. Launch angle seems to have some relationship with ISO, but it’s relatively weak.

So, we’ll hang our predictive hats on LDFBEV and see what else can help. After constructing various models, we can pretty quickly see that Pull%, Center%, and Oppo% don’t add much additional explained variance between model and data, nor do Soft%, Med%, and Hard%. This isn’t surprising, since we already have an objective hard contact measure. Ultimately, the one traditional batted ball statistic that helps is GB%. In fact, in the final regression, adding GB% nets us about 18% more explained variance between model and data. This also makes sense. It’s pretty hard to hit a ground ball double or triple, and really hard to hit a home run.

So we’re down to two predictors, GB% and LDFBEV. If we ran a regression with only these two predictors, we would undersell the players who hit the ball really hard. To solve this, we’ll simply include another term in the regression, simply the square of the exit velocity. Throw in a constant term, and we’re ready to run the regression using all 2015 qualified hitters (141 of them). Here’s what comes out:

First things first, we see an R-squared value of 0.75. This is pretty decent; it means our really simple model explains 75% of the variance of of the ISO data. The regression coefficients are as follows.

xISO = -0.358973*(GB) – 0.108255*(EV) + .00066305*(EV)^2 + 4.66285

With this equation, one can look up the relevant data on FanGraphs and Baseball Savant, and calculate the current xISO for any given player. We’ll get to that, but first, I think it’s important to check whether the new xISO model can do a better job predicting future performance than a player’s current ISO. One could also check how quickly xISO stabilizes, compared to ISO, but I won’t attempt that here. What I will do is produce the necessary splits for GB%, LDFBEV, and ISO from FanGraphs and Baseball Savant, calculate 2015 first half xISO for all qualified, and compare to second half ISO. Unfortunately, the number of qualifying players common to the first and second half in 2015 was only 109, but this is what we have:

It’s hard to see from the plot, but the R-squared values tell the story: first half xISO does a better job than actual first half ISO at predicting second half ISO. Interestingly, it seems that several players significantly increased second half ISO compared to first half xISO or ISO, and relatively fewer saw a large decrease. I don’t know why this is, but perhaps it is related to the phenomenon detailed by Rob Arthur and Ben Lindbergh on the sudden power spike in 2015.

Having roughly demonstrated the predictive power of our new xISO, let’s show its utility by looking at a few interesting 2016 performers, as of May 22nd:

Trevor Story: ISO = .327,  xISO = .272

Domingo Santana: ISO = .142,  xISO = .238

Troy Tulowitzki: ISO = .190,  xISO = .182

Chris Carter: ISO = .349,  xISO = .355

Christian Yelich: ISO = .205,  xISO = .201

One of the first half’s great surprises, Trevor Story has a slightly inflated ISO, but he does hit the ball pretty hard, and does not hit many ground balls. While he probably won’t sustain an ISO north of .300, he’s a good bet to beat his Steamer ROS projected ISO of .191. Santana and Yelich are two guys who hit the ball hard, but are are held back by their ground ball tendencies. Chris Carter currently leads the pack in LDFBEV, and is a deserved second in ISO. Troy Tulowitzki fans: sorry, but it appears his days of .250 ISOs are a thing of the past.

So that’s it! We’ve got a cool new tool to use. Perhaps not surprisingly, I’ll be mostly using it for fantasy. Dedicated FanGraphs readers will also note that Andrew Perpetua has been doing work with Statcast data on “these electronic pages” recently as well. His use of launch angles introduces more sophistication into the models, but also more complication. My intent here is to present something which can be evaluated by anyone with a few clicks and a calculator. Please reach out with any qualms, criticisms, or suggestions for improvement!