Archive for Player Analysis

Starter or Reliever: The Josh Hader Story

I’ve always wondered if certain players are aware of the comparisons floated with their names.

For one, it could be valuable to observe and learn from a player with similar mechanics. Struggle can be an unexpected teacher, and if their look-alike possesses a career with peaks and valleys, those turning points make invaluable late-night research material for a baseball nut. On the other hand, comparing can create unrealistic expectations.

Because I have not had the pleasure of speaking to Brewers pitcher Josh Hader, knowing whether he sees value in comparisons eludes me. What I do know is the most frequent comparison attached to Hader immediately creates those lofty expectations: Chris Sale.

Not as lanky, or elite, Hader’s sidearm-lefty slot causes Sale-like deception.

David Laurila of FanGraphs spoke with Hader about mechanics, and a few points resonated with me.

Hader is cognizant of the value biomechanical analysis can have, disclosing his run-in with motion-capture cotton balls affixing themselves to his body as he pops a glove with 95-mph heat. His max-effort delivery may cause worry for some, but reading about Hader’s confidence in his concoction of a motion is settling, even if it’s coming from the horse’s mouth. If you subscribe to the theory that past injury predicts future injury, Hader eclipsing 100 innings every year since 2013 should ease your concerns. (Thanks to Laurila for getting Hader’s thoughts in the column linked above.)

Hader also confirmed his awareness of the deception he creates when talking with Laurila. The less time a hitter has to pick up the ball out of his hand, the better. Left-handed hitters, in particular, have been decimated by Hader’s fastball-slider combo.

Lefties combined for a .158 slugging percentage against Hader last season. That was second in baseball, behind Pittsburgh Pirates closer Felipe Rivero (minimum 70+ total batters faced). Firmly inside the 99th percentile; when you drill down to how effective Hader’s slider was, I fear for any lefty who had to deal with this release point and horizontal bite (see gif above). Hader threw his slider 77 times last year to left-handed hitters and the resulting slugging percentage was .071. When they swung at this slider, 44% of the time they missed. Both metrics sit comfortably above average in relation to average slugging percentages and whiff rates for hitters, adding statistical backing to Hader’s dominance.

Unique about Hader is not only this slider, his hair, and his effectiveness, but his role heading into the offseason.

Since his move to Milwaukee from the Houston Astros in 2015’s Carlos Gomez swap, Hader was a starting pitcher for every one of his minor-league appearances. Craig Counsell & Co. entertained the reliever role for Hader only upon his promotion to the major leagues on June 10. Culprits for the switch could be situational — the Brewers were contending, and needed bullpen arms — but you could also convince me they were performance-based. A 13.6% walk rate over 52 Triple-A innings doesn’t inspire confidence.

This isn’t breaking news to Brewers fans.

Control issues have always been a problem for Hader, but as a reliever, the Wayne’s World look-alike had a good enough fastball to utilize it 75 percent of the time to lefties, upwards of 85 percent to righties, and net himself a shiny 36 percent strikeout rate (47 2/3 innings). In the process, Hader cut his walk rate to 11.7 percent in the majors, from north of 13 percent at Triple-A.

Unfortunately for Hader, even that improvement shouldn’t inspire confidence. We haven’t had a qualified pitcher at the major-league level, with a walk rate greater than 11.6%, since Francisco Liriano in 2014. I wouldn’t fault Hader for making a deal with the devil and taking Liriano’s 1,500-inning career, but my intentions are to consider a pitch vital to determining Hader’s 2018 role.


Considering everything” headlines an column from Brewers beat writer Adam McCalvy just over a week ago.

The vocalist of that quote was Craig Counsell, and the topic was our very own Josh Hader.

Indifference exists because Hader pitched so well in his 35 relief appearances and because of the smattering of question marks. The biggest of which is emerging ace Jimmy Nelson’s shoulder health. One depth chart has Hader as Corey Knebel’s set-up man. With an individual named “B. Suter” in the Brewers 2018 rotation. (Not “Bruce” Suter, just to confirm. Sorry, Brent.)

One question mark Hader can control is the development of his changeup. Stop me if you’ve heard this before, but a developed third pitch — so often the changeup — is how many minor-league arms get a chance to work for five-plus innings in the upper levels.

One of my favorite finds from 2017 has been the scout Chris Kusiolek (@CaliKusiolek on Twitter). In regards to changeups, Kusiolek mentioned on the Fantrax Baseball Show how much of a feel pitch it truly is. He detailed how he looks not at the present state of a pitcher’s changeup when determining the viability of the pitch’s future, but the athleticism of the pitcher, his arm action, fastball, and other aesthetics, to make that call. I’m nowhere near as seasoned of a scout as Kusiolek, but Hader hits a few of those points.

Even Hader will admit changeups are a feel pitch, and found in that same McCalvy column, the Brewers beat writer tweeted out the grip Hader was working on back in March of 2017.

“Messed up” can often prime one to think inconsistent, but that may apply to the resulting action Hader achieved on the pitch, rather than the results.

FanGraphs has Hader’s changeup just below 86 mph. This average velocity was the more common action on the pitch I observed watching tape of Hader. Other times, however, I’ve seen Hader’s change kick up to 88 mph. From my crude observation, the harder changeup only came spontaneously and later in counts. You’re about to see an 88-mph changeup on a two-strike pitch to Adam Duvall.

Harry Pavlidis has conducted extensive research on why some changeups are effective, noting those who generate elevated levels of ground balls and swinging strikes with the pitch are ideal (Stephen Strasburg is the poster-child).

Hader’s changeup hits one of those two criteria. Among starters and relievers with 50 or more changeups thrown, when Hader’s is put in play, it generates grounders at a 75-percent clip, sixth-highest in all of baseball (320 total starters and relievers). I understand it’s a pipe dream to ask Hader to replicate the arm action or grip that leads to the harder offering — if it is spontaneous — but if the structure of his general changeup leads to an elevated level of ground balls, this harder changeup might push him further into worm-killer territory.

Given Hader’s changeup has a sub-par whiff-per-swing rate in the bottom quarter of the league, playing to his strengths and embracing the harder version could make an interesting case for change.

You could argue Hader needs to continue mixing the two, but if the hittable, 86-mph changeup is thrown more as an early-count offering to righties, exploiting Hader’s attempt to pitch backwards could become an game plan. Or, in a perfect world, Hader can refine the swinging-strike rate on the slightly softer offering and turn into a two-changeup lefty. (A boy can dream, right?)


Considering Hader for a rotation spot is not a spontaneous decision, especially with Hader’s talent and polished, 23-year-old arm.

Both of his raw pitch count season-highs throwing his changeup came in consecutive appearances during late September. His usage with the pitch crept towards 19 percent, and both outings lasted north of two innings.

Hader can survive as a starting pitcher if his changeup becomes a legitimate weapon to right-handed hitters, especially if opposing managers understand Hader’s dominance against lefties and stack against his natural platoon split.

While Hader’s changeup is often knocked for being inconsistent, I counter that sentiment by saying he has a substantially better feel for the pitch than most, especially given the tendency of hitters to pound it into the ground, regardless of the velocity.

My gut tells me Hader will be utilized as a multi-inning reliever, and dominate both sides of the plate in 2018. My heart tells me to give Hader starts to further refine his feel for a pitch he’ll have to use effectively the second and third time through major-league lineups in order to survive.

In Craig Counsell and Derek Johnson I trust.

A version of this post can be found on my website,

Statistics all from BrooksBaseball, BaseballSavant, Baseball Prospectus, and FanGraphs, unless otherwise noted.

Ichiro Shot the Moon

Ichiro is one of the most bizarre players of the past 20 seasons. While many hitters have come over from Japan to the MLB, Ichiro has stuck in North America like no one else. The NPB is famous for its ground-ball-heavy approach — per DeltaGraphs, the NPB ran a GB% of 48% compared to 44% for the MLB last season — but that approach usually doesn’t work that well across the pond. That wasn’t the case for Ichiro. He made it work, and he made it work all the way to capturing the single-season hit record. And he did it in a really, really weird way.

How to Hit In Japan

To explain why it was so weird that Ichiro did what he did, we have to go all the way back to the beginning, back to Ichiro’s home country of Japan. Nippon Pro Baseball is the highest level of professional competition in Japan, and it’s where MLB superstars (and future superstars) like Ichiro, Shohei Ohtani, and Hideki Matsui started their careers.

The NPB is traditionally referred to as a ‘AAAA league’ — its level of competition is below that of the MLB, but above that of typical AAA team, which is why players who could mash in AAA but couldn’t hang on in the majors usually end up in the land of the rising sun (guys like Álex Guerrero and Casey McGehee were among the best hitters in the NPB in 2017).

The NPB’s style of baseball, however, is unique. It exists as some strange mesh of dead-ball play and modern baseball, where ground ball machines can thrive.

Earlier this year, Ben Lindbergh took a look at the biggest ground-ball-machine in the world, Nippon-Ham Fighter Takuya Nakashima, who ran an astonishing 74.4% GB% in 2016. Nakashima’s batted-ball profile looks like something of a caricature of the rest of the league, a gross exaggeration of the way the rest of the league plays.


League-wide, the NPB GB% year to year falls between 47% and 48%, which is quite a bit more than the 44%-45% that the MLB posts every season. Japanese players also traditionally reach base more frequently on grounders too, posting a BABIP of .245 on ground balls in 2017 compared to the MLB’s .241 figure.

NPB vs. MLB wRC+ on GB

But the biggest difference between MLB and NPB grounders? Ground balls are generally worth 30% more in Japan as they are in North America. MLB batters posted a 29 wRC+ on grounders, but NPB grounders were worth 42 wRC+. That’s a huge difference, especially for a league-wide figure. While it’s still not technically beneficial to hit ground balls, in Japan, hitters are rewarded for doing so more frequently than their North American counterparts.

How does such a huge difference exist between NPB and the MLB? Lindbergh, in the above article, suggests that the spongy Japanese turf is to blame, causing ground balls to have more life on them. In addition, Lindbergh suggests that the NPB, which has been slow to adopt many sabermetric and modern ideas, is shift averse, meaning many pull-happy hitters can run higher BABIPs. It’s also possible that since NPB has a lower skill level than the MLB, NPB infield defense could allow more hits than MLB infields.

Whatever the reason, hitters who came to the MLB from the NPB while relying on the ground ball as a means of production generally saw their production suffer. Tsuyoshi Nishioka, for example, hit .346/.423/.482 the season before coming to the MLB, but managed only a paltry .215/.267/.236 with the Twins in two seasons. Nishioka relied heavily upon the ground ball in both leagues but was punished more heavily for doing so in the MLB than in the NPB, and that, coupled with the difficulty of facing MLB pitchers, doomed him to mediocrity.

Ichiro was much the same — a ground-ball production machine. When he came over from Japan, perhaps in hindsight, he should have flopped for the same reasons that Nishioka, Kensuke Tanaka, Munenori Kawasaki, and Akinori Iwamura flopped. He fit the profile — speedy, high-contact ground-ball hitter coming over from Japan. Hell, Ichiro’s best-case scenario should have been what Nori Aoki turned out to be.

Instead, he thrived.

Ichiro Breaks the Mold

When Ichiro arrived in America, he was nothing short of a revelation, and a key factor in the Seattle Mariners posting the best record of the modern era in 2001 — and he was arguably the face of the franchise for close to a decade.

Ichiro’s high-contact, low walk/strikeout approach shouldn’t have worked. I ran Ichiro’s 2003 season through my similarity tool, and the best comps I generated were Jose Vizcaino’s 2004 season, Warren Morris’ 2003 season, and Brad Ausmus’ 2004 season (yes, that Brad Ausmus). None of these guys posted a wRC+ over 90 in those years, but Ichiro was at 112. How did Ichiro get by using a strategy that had failed so many hitters before him?

Career BABIP leaders by SLG

On paper, the answer is BABIP. For his first four seasons, Ichiro never posted a BABIP below .333. While the league average for BABIP is around .300, elite players generally have a BABIP skill above .300 as a result of making elite contact. If we make a rough and naive assumption that a high SLG means that a player made good contact, we see that the among the top 15 career BABIP leaders (with 10000 PA), most of them made good contact, except for Lou Brock … and Ichiro.

It gets weirder. Remember all that talk about ground balls? Ichiro hit a lot of them — since 2002, the earliest season for which we have batted-ball data, Ichiro has hit the most ground balls in the majors, almost 800 more than 2nd place (Derek Jeter). Here is a scatterplot of GB% versus BABIP for qualified single seasons since 2002.

GB% vs. BABIP, 2002-2017

There exists a weak, but roughly positive correlation between BABIP and GB%. Most everyone is hanging out somewhere around the 35%-50% GB% and .250-.350 range, but then there’s Ichiro, who consistently posts BABIPs well above what he should be getting. Ready? It gets even weirder.

GB% vs. BABIP vs. Age, 2002-2017

Here’s that same chart, but I’ve thrown in the ages of each hitter in a gradient color scale. There’s a good spread around here, but I’ve highlighted Ichiro’s 2004 season, and it should stand out in three big ways. First, he posted one of the highest GB% since 2002 (63.1%). Second, he posted the second highest single-season BABIP since 2002 (.399). And third, he was 30 when he did this! Many of the light blue values in the upper right of the column belong to Ichiro. Which is really unusual, since many of them are when he’s older than the median MLB player (29 years old).

GB vs. BABIP vs. Older or Younger than 29

In this chart, the red dots represent hitters 29 years old or younger, and the blue dots represent hitters 30 years old or older. Notice how there’s a roughly even mix in the middle, but older hitters tend towards the bottom left, and younger hitters tend towards the upper right (though there are exceptions to each).

GB vs. BABIP vs. Older or Younger than 29 without Ichiro

Here’s that same chart, but I’ve removed Ichiro’s seasons — look at the far upper right. See the difference?

Ichiro’s specialty is defying all aging curves and all logic by consistently posting these ridiculous BABIPs while acting like a ground-ball machine, and making contact that most hitters would be ashamed of.

Legs Don’t Fail Me Now

We’ve already identified that Ichiro makes sub-par contact, hits a lot of ground balls (not exactly a recipe for production), and doesn’t strike out or walk much. No, the biggest tool for Ichiro, as anyone who watched him play could tell you, was his speed.

August Fagerstrom previously found that Ichiro had elite speed in his younger days, estimating his time-to-first in his prime as just under 3.75 seconds, which would blow Billy Hamilton (3.95 seconds) out of the water. It’s no exaggeration to say that Ichiro could be one of the fastest men in MLB history.

So many hitters came over from Japan with profiles similar to Ichiro — speedy ground-ball hitters who make a lot of contact. But none of them had Ichiro’s generational speed, and so, none of them found the type of sustained success that he did.

One cannot help but feel a sense of wonder in looking at Ichiro’s career. Because his production relies almost solely on his ability to make contact and his speed, tools that decay slowly with age (I’m aware that speed tends to decrease with age, but exceptionally speedy runners such as Chase Utley and Rajai Davis can retain their prowess on the basepaths well into their late 30s), he was able to defy what we might expect from someone of his age and with his batted-ball profile.

Ichiro was shooting the moon with his approach the plate, in a way. Sabermetric wisdom tells hitters to elevate, draw walks, don’t be afraid to strike out, make solid contact, and don’t worry about speed. Ichiro did the exact opposite and was rewarded handsomely rewarded for it. I can think of no more unique player with such a storied career and legacy. Here’s hoping 2017 won’t be Ichiro’s last hurrah.

Juan Nicasio Has a New Slider, and He Needs His Old One Back

The Mariners recently inked Juan Nicasio to a 2-year/$17-million deal in their first significant addition to their pitching staff this offseason. After years as a middling starter, Nicasio emerged as a rock-solid relief option with the Rockies in 2014 before the Dodgers fully bought into his potential as a reliever the following year. The Pirates then acquired him and shifted him into the rotation a bit in 2016; however, he had more success in their bullpen and moved there full-time in 2017. He was again on the move last year, though — this time playing for two new teams — but he never started a game, posting a cumulative 2.61 ERA over 72.1 IP in 76 appearances.

He’s on the wrong side of 30, and breakout relievers tend to pop up and decline quickly, but it can be argued that Nicasio has done nothing but improve since moving into the bullpen.

Juan Nicasio as RP IP ERA AVG OBP SLG wOBA
2014 20.2 3.48 .227 .275 .400 .300
2015 56.1 3.83 .257 .359 .381 .320
2016 55.2 3.88 .249 .328 .387 .308
2017 72.1 2.61 .216 .277 .333 .265

As a reliever, Nicasio is largely a two-pitch pitcher, primarily throwing a four-seam fastball and a slider. He had occasionally mixed in a sinker and changeup in previous years, but 2017 saw Nicasio throw a four-seam fastball or slider 98.31% of the time. This pitch mix in combination with his K/9 dipping from slightly over 10 to just under 9 may raise a couple eyebrows, but Nicasio also improved his command considerably.

His 6.9% BB% in 2017 was his lowest since his debut season and marked a second straight year of improvement, and his 24.7% K% compares well to previous years. This would suggest that Nicasio is only getting more efficient with his outs, not striking guys out at a lesser rate. And sure enough, his 1.08 WHIP last year was by far the lowest it’s ever been.

A quick look at his splits from 2017 showed a distinct improvement against left-handed batters compared to previous years.

Juan Nicasio vs. LHH IP AVG OBP SLG wOBA
2015 14.1 .359 .494 .516 .427
2016 21.0 .241 .351 .476 .350
2017 33.0 .205 .252 .292 .235

In his largest sample yet, Nicasio made huge strides.

Since improvements against opposite-handed batters tend to suggest an improvement in a pitcher’s changeup or breaking ball, and given that Nicasio essentially throws just two pitches, his slider seemed like a good starting point. I found that (per Brooks Baseball) it had an entirely different shape in 2017.

Juan Nicasio Sliders Velocity HMov VMov
2015 86.92 1.94 1.86
2016 87.11 1.49 2.80
2017 88.92 0.47 4.04

While Nicasio’s slider was laterally less impressive in 2017, it made up for that with reduced drop.

Here is his slider in 2016 with a little frisbee action.

Slider 2016.gif

And here it is in 2017 a bit more tightly wound.

Slider 2017.gif

Nicasio’s slider was devastating to right-handers in 2015 and 2016 (cumulative .218 wOBA/.221 xwOBA), but it seemingly fell into the swing path of lefties, as they smashed it for a .369 wOBA/.272 xwOBA in the same period. In 2017, lefties floundered against it for the first time, posting just a .194 wOBA/.175 xwOBA. But his other slider disappeared.

Using this somewhat cutter-like breaking ball against RHB in 2017 yielded a .302 wOBA and .320 xwOBA. Considering the fastball didn’t play up (.298 wOBA/.334 xwOBA), that kind of performance is a slight concern, but righties’ triple slash against him was still an encouraging .225/.296/.367 (.287 wOBA).

On the surface, the Mariners seem to have gotten a quality reliever at about market rate for his talent, but I think there is still some upside here. Certainly, in this new slider, Nicasio has found a legitimate weapon against LHB, but the Mariners must hope his natural slider is not lost. In order to remain a high-quality, high-leverage setup man — the kind that posts sub-3 ERAs — he’s going to have to bring out both.

Another Weird Charlie Blackmon-ism

Charlie Blackmon is an atypical human being.

For one thing, he is a professional baseball player, meaning he is in the extreme upper echelon of athletic ability. But he is atypical even in his personal life, and his recent success has only highlighted his eccentric personality. He still drives a 2004 Jeep Grand Cherokee that he got in high school. He once had to be rescued on the side of the highway by DJ LeMahieu when he ran out of gas. He buys his clothes from Amazon. And of course, he is easily recognized by his impressive beard-and-mullet combo (the latter of which is pronounced “mu-lay” according to Blackmon).

Based on all his quirks, it should be no surprise just how unique his major-league career has been. He didn’t see regular playing time until his age-28 season, an age when some guys are already entering free agency. Despite this late start, he has steadily grown into an MVP candidate. In 2014, his first full season, Blackmon posted 2.0 fWAR, the exact threshold for a starting caliber player. In the three subsequent seasons, he posted an fWAR of 2.3, 4.1, and 6.5. I thought it seemed rather strange to have back-to-back seasons with ~2 WAR improvement, so I went to the leaderboards.

I searched for all batters with a minimum of 400 PAs in each of the past three seasons, producing a sample of 111 players. Then, I calculated the difference between each player’s 2015 WAR and 2016 WAR, and did the same for 2016 to 2017. This gave me two year-to-year improvements for each player, and I threw both values onto the scatter plot below, with Blackmon highlighted in purple.

2015 2017 WAR Improvements

Players generally don’t see improvements like this in back-to-back seasons; Blackmon is about as far to the top-right as you can get in this plot. Of course, value can come from many different places, and a player might make large defensive improvements one year and large offensive improvements the next. While Blackmon did see some improvement in his defensive metrics this season, the bulk of his improvements have come while batting. To get the following plot, I followed the same method as above, this time for wRC+.

2015 2017 w RC Improvements

Again, we see Blackmon floating towards the top right. Baseball is a game of adjustments, and if a batter enjoys a period of success, pitchers will generally approach him differently to gain an advantage. This is why players generally go through cycles, following the push and pull of the game. The past few years, Blackmon seems to be part of a small group of players who have been immune to this tug-of-war effect. He has stayed one step ahead of the pitchers, not only maintaining his gains but improving upon them as time goes on.

How has he found these improvements? Between 2015 and 2016, his walk rate and strikeout rate remained fairly constant, so he must have been getting much better results on balls in play. Sure enough, his batting average increased by 37 points and his ISO increased by 65 points, giving him 49 extra points of wOBA overall. At the time, Jeff Sullivan looked under the hood and found that Blackmon’s GB% was trending downward, and he had been attacking the low strike more so than ever before. Presumably, he realized that his swing path was conducive to driving low pitches into the air, and that balls in the air are more valuable, so he made the adjustment and enjoyed a power spike.

That all makes sense, but it begs the question: how did he improve even more in 2017? If he doubled down on the fly-ball revolution, he risked becoming Ryan Schimpf or Trevor Story.

Much to my surprise, the opposite happened – his GB% actually returned back to his career average. He increased his rate of ground balls, but he still managed raised his ISO by another 42 points. Before you cry BABIP or Coors Field, I’ll briefly note that in both years, his wOBA and xwOBA increased by approximately the same amount, so something real is going on here. In this case, I think he was finding more success on batted balls based on the pitches he didn’t put in play. Stay with me here.

In 2017, Blackmon’s strikeout rate rose by about 2.5%. This is what people in the industry call “not good,” but hold on, his walk rate also rose by…about 2.5%. This isn’t a player who suddenly developed a swing-and-miss problem to sell out for power, this is a player who is intentionally going deeper into counts. When a batter is more selective about the pitches he goes after, he is putting fewer balls in play in early counts, which leads to an increase in both walks and strikeouts simultaneously.

Let’s look at it a different way: Z-swing% measures the percentage of pitches inside the zone that a player swings at, and O-swing% measures the percentage of pitches outside the zone that a player swings at. Generally speaking, you want to swing at strikes and take balls, so you want your Z-swing% to be higher than your O-swing%; the larger the difference, the better your plate discipline.

In 2016, the difference between Blackmon’s Z-swing% and O-swing% ranked in the 9th percentile – he’s always been a bit of a free-swinging leadoff hitter. But in 2017, that difference increased by 4.7%, pushing him into the 26th percentile. While he’s still more aggressive than average, he has become decidedly less so, being more selective about the pitches he attacks and remaining comfortable in deep counts. By swinging at the right pitches, he’s able to avoid the easy outs that result from poor contact on pitches outside of the zone.

We have every reason to believe that Charlie Blackmon just had a career year, and he will never sniff an MVP race again in his career. But then again, we had every reason to believe the same thing last year. When it comes to Charlie, I have some advice: if you expect him to do something, he’s probably getting ready to do the exact opposite. It’s about time we stop trying to figure him out.

Identifying Impact Hitters: Proof of Concept

Earlier this season I set out to build a tool similar in nature to my dSCORE tool, except this one was meant to identify swing-change hitters. Along the course of its construction and early-alpha testing, it morphed into something different, and maybe something more useful. What I ended up with was a tool called cHit (“change Hit”, named for swing changers but really I was just too lazy to bother coming up with a more apt acronym for what the tool actually does). cHit, in its current beta form, aims to identify hitters that tend to profile for “impact production” — simply defined as hit balls hard, and hit them in the air. Other research has identified those as ideal for XBH, so I really didn’t need to reinvent the wheel. Although I’d really like to pull in Statcast data offerings in a more refined form of this tool, simple batted ball data offered here on FanGraphs does the trick nicely.

The inner workings of this tool takes six different data points (BB%, GB%, FB%, Hard%, Soft%, Spd), compares each individual player’s stat against a league midpoint for that stat, then buffs it using a multiplier that serves to normalize each stat based on its importance to ISO. I chose ISO as it’s a pretty clean catch-all for power output.

Now here’s the trick of this tool: it’s not going to identify “good” hitters from “bad” hitters. Quality sticks like Jean Segura, Dee Gordon, Cesar Hernandez, and others show up at the bottom of the results because their game doesn’t base itself on the long ball. They do just fine for themselves hitting softer liners or ground balls and using their legs for production. Frankly, chances are if a player at the bottom of the list has a high Speed component, they’ve got a decent chance of success despite a low cHit. Nuance needs to be accounted for by the user.

Here’s how I use it to identify swing-changers (and/or regression candidates): I pulled in data for previous years, back to 2014. I compared 2017 data to 2016 data (I’ll add in comparisons for previous years in later iterations) and simply checked to see who were cHit risers or fallers. The results were telling — players we have on record as swing changers show up with significant positive gains, and players that endured some significant regression fell.

There’s an unintended, possible third use for this tool: identifying injured hitters. Gregory Polanco, Freddie Freeman, and Matt Holliday all suffered/played through injury this year, and they all fell precipitously in the rankings. I’ll need a larger sample size to see whether injuries and a fall in cHit are related or if that’s just noise.


cHit 2017
Name Team Age AB cHit Score BB% GB% FB% Hard% Soft% Spd ISO
Joey Gallo Rangers 23 449 27.56 14.10% 27.90% 54.20% 46.40% 14.70% 5.5 0.327
J.D. Martinez – – – 29 432 23.52 10.80% 38.30% 43.20% 49.00% 14.00% 4.7 0.387
Matt Carpenter Cardinals 31 497 22.46 17.50% 26.90% 50.80% 42.20% 12.10% 3.1 0.209
Aaron Judge Yankees 25 542 21.56 18.70% 34.90% 43.20% 45.30% 11.20% 4.8 0.343
Lucas Duda – – – 31 423 19.69 12.20% 30.30% 48.60% 42.10% 14.50% 0.5 0.279
Cody Bellinger Dodgers 21 480 19.26 11.70% 35.30% 47.10% 43.00% 14.00% 5.5 0.315
Miguel Sano Twins 24 424 17.73 11.20% 38.90% 40.50% 44.80% 13.50% 2.9 0.243
Jay Bruce – – – 30 555 16.50 9.20% 32.50% 46.70% 40.30% 11.70% 2.6 0.254
Trevor Story Rockies 24 503 16.39 8.80% 33.70% 47.90% 40.30% 14.40% 4.7 0.219
Justin Turner Dodgers 32 457 16.16 10.90% 31.40% 47.80% 38.90% 9.80% 3.3 0.208
Khris Davis Athletics 29 566 15.64 11.20% 38.40% 42.30% 42.10% 13.50% 3.4 0.281
Brandon Belt Giants 29 382 15.38 14.60% 29.70% 46.90% 38.40% 14.00% 4.2 0.228
Nick Castellanos Tigers 25 614 14.94 6.20% 37.30% 38.20% 43.40% 11.50% 4.6 0.218
Eric Thames Brewers 30 469 14.52 13.60% 38.40% 41.30% 41.50% 16.00% 4.6 0.271
Justin Upton – – – 29 557 14.43 11.70% 36.80% 43.70% 41.00% 19.80% 4 0.268
Justin Smoak Blue Jays 30 560 14.38 11.50% 34.30% 44.50% 39.40% 13.10% 1.7 0.259
Wil Myers Padres 26 567 14.32 10.80% 37.50% 42.90% 41.40% 19.50% 5.3 0.220
Paul Goldschmidt Diamondbacks 29 558 14.31 14.10% 46.30% 34.90% 44.30% 11.30% 5.6 0.265
Chris Davis Orioles 31 456 14.28 11.60% 36.70% 39.80% 41.50% 12.80% 2.7 0.208
Kyle Seager Mariners 29 578 13.57 8.90% 31.30% 51.60% 35.70% 13.10% 2.2 0.201
Nelson Cruz Mariners 36 556 13.35 10.90% 40.40% 41.80% 40.70% 14.70% 1.7 0.261
Mike Zunino Mariners 26 387 13.31 9.00% 32.00% 45.60% 38.60% 17.50% 1.9 0.258
Mike Trout Angels 25 402 13.16 18.50% 36.70% 44.90% 38.30% 19.00% 6.2 0.323
Corey Seager Dodgers 23 539 13.08 10.90% 42.10% 33.10% 44.00% 12.90% 2.7 0.184
Logan Morrison Rays 29 512 12.74 13.50% 33.30% 46.20% 37.40% 17.50% 2.4 0.270
Randal Grichuk Cardinals 25 412 12.61 5.90% 35.90% 42.70% 40.20% 18.20% 5.2 0.235
Salvador Perez Royals 27 471 12.50 3.40% 33.30% 47.00% 38.10% 16.50% 2.4 0.227
Michael Conforto Mets 24 373 12.42 13.00% 37.80% 37.80% 41.60% 20.20% 3.6 0.276
Matt Davidson White Sox 26 414 12.19 4.30% 36.20% 46.50% 38.20% 15.80% 1.8 0.232
Mike Napoli Rangers 35 425 12.15 10.10% 33.20% 52.10% 35.50% 21.90% 2.7 0.235
Miguel Cabrera Tigers 34 469 12.03 10.20% 39.80% 32.90% 42.50% 9.90% 1.1 0.149
Brandon Moss Royals 33 362 11.83 9.20% 33.10% 44.50% 37.30% 13.60% 2.3 0.221
Curtis Granderson – – – 36 449 11.69 13.50% 32.60% 48.80% 35.30% 17.60% 4.8 0.241
Ian Kinsler Tigers 35 551 11.64 9.00% 32.90% 46.50% 37.00% 18.70% 5.6 0.176
Edwin Encarnacion Indians 34 554 11.01 15.50% 37.10% 41.80% 37.60% 15.50% 2.7 0.245
Manny Machado Orioles 24 630 10.79 7.20% 42.10% 42.10% 39.50% 18.50% 3.3 0.213
Freddie Freeman Braves 27 440 10.72 12.60% 34.90% 40.60% 37.50% 12.40% 4.3 0.280
Nolan Arenado Rockies 26 606 10.60 9.10% 34.00% 44.90% 36.70% 17.60% 4.1 0.277
Anthony Rendon Nationals 27 508 10.41 13.90% 34.00% 47.20% 34.30% 13.00% 3.5 0.232
Yonder Alonso – – – 30 451 10.34 13.10% 33.90% 43.20% 36.00% 13.20% 2.4 0.235
Kyle Schwarber Cubs 24 422 10.24 12.10% 38.30% 46.50% 36.40% 21.30% 2.8 0.256
Carlos Gomez Rangers 31 368 10.19 7.30% 39.10% 40.30% 39.00% 16.50% 5 0.207
Luis Valbuena Angels 31 347 9.81 12.00% 38.40% 47.30% 35.80% 22.00% 1.3 0.233
Dexter Fowler Cardinals 31 420 9.61 12.80% 39.40% 38.20% 38.10% 12.70% 5.9 0.224
Jed Lowrie Athletics 33 567 9.40 11.30% 29.40% 43.50% 34.50% 12.10% 2.7 0.171
Giancarlo Stanton Marlins 27 597 8.96 12.30% 44.60% 39.40% 38.90% 20.80% 2.3 0.350
Jose Abreu White Sox 30 621 8.95 5.20% 45.30% 36.40% 40.50% 15.80% 4.4 0.248
Josh Donaldson Blue Jays 31 415 8.92 15.30% 41.00% 42.30% 36.30% 17.30% 1.6 0.289
Joey Votto Reds 33 559 8.87 19.00% 39.00% 38.00% 36.30% 10.40% 2.8 0.258
Victor Martinez Tigers 38 392 8.75 8.30% 42.10% 34.20% 39.90% 12.40% 0.9 0.117
Charlie Blackmon Rockies 31 644 8.63 9.00% 40.70% 37.00% 39.00% 17.10% 6.4 0.270
Mitch Moreland Red Sox 31 508 8.43 9.90% 43.40% 36.20% 38.90% 13.50% 1.7 0.197
Scott Schebler Reds 26 473 8.29 7.30% 45.60% 38.20% 39.40% 19.30% 3.9 0.252
Paul DeJong Cardinals 23 417 8.19 4.70% 33.70% 42.90% 36.40% 21.40% 2.5 0.247
Ryan Zimmerman Nationals 32 524 8.18 7.60% 46.40% 33.70% 40.50% 14.10% 2.2 0.269
Mookie Betts Red Sox 24 628 7.76 10.80% 40.40% 42.80% 35.70% 18.20% 5.5 0.194
Rougned Odor Rangers 23 607 7.61 4.90% 41.50% 42.20% 36.80% 18.50% 5.6 0.193
Francisco Lindor Indians 23 651 7.42 8.30% 39.20% 42.40% 35.20% 14.30% 5.1 0.232
Brad Miller Rays 27 338 7.39 15.50% 47.40% 36.10% 38.40% 18.10% 4.6 0.136
Daniel Murphy Nationals 32 534 6.97 8.80% 33.50% 38.90% 35.70% 16.70% 3.8 0.221
Travis Shaw Brewers 27 538 6.87 9.90% 42.50% 37.60% 37.10% 15.80% 4.5 0.240
Jake Lamb Diamondbacks 26 536 6.86 13.70% 41.10% 38.30% 35.70% 12.90% 4.4 0.239
Todd Frazier – – – 31 474 6.75 14.40% 34.20% 47.50% 32.20% 23.20% 3.1 0.215
Yasmani Grandal Dodgers 28 438 6.63 8.30% 43.50% 40.00% 36.50% 17.60% 1.1 0.212
Brian Dozier Twins 30 617 6.60 11.10% 38.40% 42.60% 34.10% 15.90% 5.2 0.227
Adam Duvall Reds 28 587 6.55 6.00% 33.20% 48.60% 31.80% 17.50% 3.9 0.232
Hunter Renfroe Padres 25 445 6.52 5.60% 37.90% 45.40% 34.60% 23.50% 3.2 0.236
Justin Bour Marlins 29 377 6.40 11.00% 43.40% 33.60% 38.80% 19.60% 1.6 0.247
Carlos Correa Astros 22 422 6.33 11.00% 47.90% 31.70% 39.50% 15.00% 3.2 0.235
Marcell Ozuna Marlins 26 613 6.09 9.40% 47.10% 33.50% 39.10% 18.30% 2.3 0.237
Domingo Santana Brewers 24 525 5.85 12.00% 44.90% 27.70% 39.70% 11.70% 4 0.227
Kris Bryant Cubs 25 549 5.83 14.30% 37.70% 42.40% 32.80% 14.80% 4.4 0.242
Gary Sanchez Yankees 24 471 5.47 7.60% 42.30% 36.60% 36.90% 18.60% 2.6 0.253
Asdrubal Cabrera Mets 31 479 5.46 9.30% 43.50% 36.20% 36.80% 17.20% 2.5 0.154
Austin Hedges Padres 24 387 5.37 5.50% 36.60% 45.70% 33.10% 22.30% 2.7 0.183
Logan Forsythe Dodgers 30 361 5.33 15.70% 44.00% 33.10% 36.60% 13.20% 2.8 0.102
Yadier Molina Cardinals 34 501 5.25 5.20% 42.20% 37.40% 36.40% 16.50% 3.9 0.166
Bryce Harper Nationals 24 420 5.07 13.80% 40.40% 37.60% 34.30% 13.30% 3.7 0.276
Neil Walker – – – 31 385 5.01 12.30% 36.20% 41.70% 32.80% 17.70% 2.8 0.174
Aaron Altherr Phillies 26 372 5.01 7.80% 43.10% 37.50% 36.40% 20.10% 5.5 0.245
Andrew McCutchen Pirates 30 570 4.90 11.20% 40.70% 37.40% 35.20% 17.50% 4.3 0.207
Eduardo Escobar Twins 28 457 4.86 6.60% 33.70% 45.30% 31.40% 16.00% 5.1 0.195
Anthony Rizzo Cubs 27 572 4.79 13.20% 40.70% 39.20% 34.40% 19.80% 4.4 0.234
Ryan Braun Brewers 33 380 4.73 8.90% 49.20% 31.90% 39.00% 19.20% 5.3 0.218
Kendrys Morales Blue Jays 34 557 4.56 7.10% 48.40% 33.20% 37.90% 15.20% 1.1 0.196
Jose Ramirez Indians 24 585 4.54 8.10% 38.90% 39.70% 34.00% 16.70% 6 0.265
Mike Moustakas Royals 28 555 4.51 5.70% 34.80% 45.70% 31.90% 21.20% 1.1 0.249
Andrew Benintendi Red Sox 22 573 4.50 10.60% 40.10% 38.40% 34.30% 16.60% 4.5 0.154
Jose Bautista Blue Jays 36 587 4.47 12.20% 37.70% 45.80% 31.40% 21.70% 3.4 0.164
Jason Castro Twins 30 356 4.36 11.10% 41.90% 33.50% 36.00% 14.00% 1.5 0.146
Albert Pujols Angels 37 593 4.12 5.80% 43.50% 38.10% 35.10% 15.90% 2.1 0.145
Hanley Ramirez Red Sox 33 496 4.04 9.20% 41.80% 37.10% 35.30% 20.00% 1.5 0.188
Tommy Joseph Phillies 25 495 3.99 6.20% 41.70% 39.00% 35.00% 20.90% 2.2 0.192
Tim Beckham – – – 27 533 3.99 6.30% 48.80% 29.50% 39.10% 15.50% 4.4 0.176
Jonathan Schoop Orioles 25 622 3.90 5.20% 41.90% 37.20% 36.10% 23.00% 2.2 0.211
George Springer Astros 27 548 3.58 10.20% 48.30% 33.80% 36.70% 17.90% 3.1 0.239
Carlos Beltran Astros 40 467 3.54 6.50% 43.10% 40.40% 33.70% 17.50% 1.8 0.152
Alex Bregman Astros 23 556 3.52 8.80% 38.40% 39.90% 33.00% 18.00% 5.9 0.191
Carlos Santana Indians 31 571 3.49 13.20% 40.80% 39.30% 33.00% 18.40% 4 0.196
Eugenio Suarez Reds 25 534 3.33 13.30% 38.90% 37.10% 33.80% 20.70% 3.1 0.200
Scooter Gennett Reds 27 461 3.29 6.00% 41.30% 37.60% 34.40% 17.20% 4.3 0.236
Mark Reynolds Rockies 33 520 3.26 11.60% 42.10% 36.30% 34.50% 19.00% 2.7 0.219
Josh Reddick Astros 30 477 3.23 8.00% 33.60% 42.30% 31.10% 17.20% 4.8 0.170
Mitch Haniger Mariners 26 369 2.97 7.60% 44.00% 36.70% 34.70% 17.70% 4.3 0.209
Ian Happ Cubs 22 364 2.92 9.40% 40.20% 39.70% 32.80% 18.70% 5.7 0.261
Josh Harrison Pirates 29 486 2.90 5.20% 36.50% 40.80% 32.40% 18.70% 4.9 0.160
Keon Broxton Brewers 27 414 2.78 8.60% 45.10% 34.60% 35.30% 17.00% 7.4 0.200
Matt Joyce Athletics 32 469 2.69 12.10% 37.80% 42.80% 30.30% 16.30% 3.2 0.230
Derek Dietrich Marlins 27 406 2.65 7.80% 36.50% 40.70% 32.10% 20.50% 3.9 0.175
Ryon Healy Athletics 25 576 2.56 3.80% 42.80% 38.20% 33.90% 16.50% 1.4 0.181
Evan Longoria Rays 31 613 2.50 6.80% 43.40% 36.80% 34.30% 18.00% 3.8 0.163
Zack Cozart Reds 31 438 2.49 12.20% 38.20% 42.30% 30.80% 19.50% 5.3 0.251
Robinson Cano Mariners 34 592 2.48 7.60% 50.00% 30.60% 36.90% 12.80% 2 0.172
Max Kepler Twins 24 511 2.39 8.30% 42.80% 39.50% 32.90% 18.70% 4.2 0.182
Steven Souza Jr. Rays 28 523 2.22 13.60% 44.60% 34.30% 34.10% 16.50% 4.8 0.220
Michael Taylor Nationals 26 399 2.17 6.70% 42.90% 36.70% 34.00% 18.10% 5.9 0.216
Yulieski Gurriel Astros 33 529 2.12 3.90% 46.20% 35.20% 35.10% 15.90% 2.8 0.187
Corey Dickerson Rays 28 588 1.24 5.60% 41.80% 35.80% 33.60% 18.70% 4 0.207
Whit Merrifield Royals 28 587 1.01 4.60% 37.70% 40.50% 30.60% 15.40% 6.7 0.172
Chris Taylor Dodgers 26 514 0.88 8.80% 41.50% 35.80% 32.40% 15.80% 6.4 0.208
A.J. Pollock Diamondbacks 29 425 0.81 7.50% 44.60% 32.10% 35.00% 19.80% 7.5 0.205
Marwin Gonzalez Astros 28 455 0.71 9.50% 43.90% 36.20% 32.70% 18.60% 3.2 0.226
Yangervis Solarte Padres 29 466 0.62 7.20% 41.60% 42.10% 31.10% 25.20% 2.4 0.161
Shin-Soo Choo Rangers 34 544 0.57 12.10% 48.80% 26.20% 36.10% 12.20% 4.7 0.162
Buster Posey Giants 30 494 0.50 10.70% 43.60% 33.00% 33.00% 14.10% 2.8 0.142
Jedd Gyorko Cardinals 28 426 0.48 9.80% 40.50% 39.30% 30.80% 19.20% 3.8 0.200
Yasiel Puig Dodgers 26 499 0.30 11.20% 48.30% 35.60% 32.90% 18.30% 4.4 0.224
Eddie Rosario Twins 25 542 0.12 5.90% 42.40% 37.40% 31.70% 16.70% 3.9 0.218
J.T. Realmuto Marlins 26 532 -0.01 6.20% 47.80% 34.30% 33.30% 14.90% 5 0.173
Jorge Bonifacio Royals 24 384 -0.20 8.30% 39.30% 34.80% 32.20% 20.20% 2.9 0.177
Gerardo Parra Rockies 30 392 -0.27 4.70% 46.80% 30.30% 34.70% 14.40% 3 0.143
Willson Contreras Cubs 25 377 -0.34 10.50% 53.30% 29.30% 35.50% 17.00% 2.4 0.223
Kole Calhoun Angels 29 569 -0.37 10.90% 43.90% 35.00% 31.80% 17.00% 3.7 0.148
Robbie Grossman Twins 27 382 -0.43 14.70% 40.70% 34.40% 30.90% 16.00% 3.5 0.134
Matt Holliday Yankees 37 373 -0.46 10.80% 47.70% 37.50% 31.80% 21.20% 2.1 0.201
Mark Trumbo Orioles 31 559 -0.47 7.00% 43.30% 40.60% 30.40% 20.90% 2.5 0.163
Stephen Piscotty Cardinals 26 341 -0.80 13.00% 49.20% 33.20% 32.70% 17.90% 2.7 0.132
Tommy Pham Cardinals 29 444 -0.86 13.40% 51.70% 26.10% 35.50% 15.40% 6 0.214
Joe Mauer Twins 34 525 -0.92 11.10% 51.50% 23.60% 36.40% 12.80% 2.4 0.112
Jackie Bradley Jr. Red Sox 27 482 -0.94 8.90% 49.00% 32.60% 33.30% 17.50% 4.5 0.158
Brandon Crawford Giants 30 518 -0.98 7.40% 46.20% 34.40% 32.60% 19.30% 2.5 0.151
Nomar Mazara Rangers 22 554 -1.13 8.90% 46.50% 34.20% 32.60% 20.90% 2.6 0.170
Ben Zobrist Cubs 36 435 -1.35 10.90% 51.10% 33.30% 32.30% 14.90% 3.6 0.143
Javier Baez Cubs 24 469 -1.36 5.90% 48.60% 36.00% 32.40% 21.30% 5.3 0.207
Jorge Polanco Twins 23 488 -1.42 7.50% 37.90% 42.80% 27.70% 19.90% 4.9 0.154
Avisail Garcia White Sox 26 518 -1.70 5.90% 52.20% 27.50% 35.30% 15.70% 4.3 0.176
Matt Kemp Braves 32 438 -1.76 5.80% 48.50% 28.20% 34.70% 17.40% 1.7 0.187
Maikel Franco Phillies 24 575 -2.04 6.60% 45.40% 36.70% 30.90% 20.80% 1.5 0.179
Nick Markakis Braves 33 593 -2.17 10.10% 48.60% 29.20% 33.10% 15.60% 1.9 0.110
Tucker Barnhart Reds 26 370 -2.46 9.90% 46.00% 27.80% 33.20% 16.50% 3.4 0.132
Trey Mancini Orioles 25 543 -2.48 5.60% 51.00% 29.70% 34.10% 19.60% 3.2 0.195
Christian Yelich Marlins 25 602 -2.51 11.50% 55.40% 25.20% 35.20% 15.90% 5.2 0.156
Lorenzo Cain Royals 31 584 -2.79 8.40% 44.40% 32.90% 31.10% 18.70% 6.5 0.140
Josh Bell Pirates 24 549 -2.87 10.60% 51.10% 31.20% 32.60% 20.60% 3.5 0.211
Jose Reyes Mets 34 501 -3.00 8.90% 37.20% 43.10% 26.70% 26.10% 7.2 0.168
Carlos Gonzalez Rockies 31 470 -3.04 10.50% 48.60% 31.70% 31.90% 20.50% 3.2 0.162
Adam Jones Orioles 31 597 -3.27 4.30% 44.80% 34.30% 30.90% 20.10% 2.7 0.181
Byron Buxton Twins 23 462 -3.57 7.40% 38.70% 38.00% 27.60% 18.20% 8.2 0.160
Kevin Kiermaier Rays 27 380 -3.81 7.40% 49.60% 32.10% 31.80% 22.00% 5.9 0.174
Chase Headley Yankees 33 512 -3.90 10.20% 43.50% 31.70% 30.00% 17.10% 4.3 0.133
Xander Bogaerts Red Sox 24 571 -4.31 8.80% 48.90% 30.50% 31.40% 19.70% 6.7 0.130
Jordy Mercer Pirates 30 502 -4.33 9.10% 48.30% 30.90% 31.00% 19.00% 2.9 0.151
Brandon Drury Diamondbacks 24 445 -4.44 5.80% 48.80% 29.40% 31.70% 16.60% 2.4 0.180
Alex Gordon Royals 33 476 -4.69 8.30% 42.60% 33.00% 29.20% 19.40% 4.3 0.107
Ben Gamel Mariners 25 509 -4.84 6.50% 44.90% 33.30% 29.40% 18.70% 4.9 0.138
Hernan Perez Brewers 26 432 -4.85 4.40% 48.30% 33.50% 30.40% 21.20% 5.3 0.155
Matt Wieters Nationals 31 422 -4.94 8.20% 42.50% 36.40% 27.40% 18.10% 2 0.118
Brett Gardner Yankees 33 594 -5.07 10.60% 44.50% 33.20% 28.80% 20.00% 6 0.163
Odubel Herrera Phillies 25 526 -5.10 5.50% 44.10% 34.70% 29.40% 24.40% 4.3 0.171
Freddy Galvis Phillies 27 608 -5.11 6.80% 36.70% 39.20% 25.50% 18.10% 5.3 0.127
Elvis Andrus Rangers 28 643 -5.13 5.50% 48.50% 31.50% 30.50% 18.70% 5.7 0.174
Danny Valencia Mariners 32 450 -5.93 8.00% 47.90% 31.00% 29.80% 20.50% 3.3 0.156
Kevin Pillar Blue Jays 28 587 -6.25 5.20% 43.10% 36.40% 27.30% 22.50% 4.4 0.148
Dansby Swanson Braves 23 488 -6.35 10.70% 47.40% 29.40% 29.30% 18.00% 3.2 0.092
Jose Altuve Astros 27 590 -6.45 8.80% 47.00% 32.70% 28.20% 19.00% 6.4 0.202
Alcides Escobar Royals 30 599 -6.47 2.40% 40.80% 37.40% 26.80% 22.80% 4.3 0.107
Andrelton Simmons Angels 27 589 -6.62 7.30% 49.50% 31.50% 29.30% 20.60% 5 0.143
Didi Gregorius Yankees 27 534 -6.91 4.40% 36.20% 43.80% 23.10% 24.40% 2.7 0.191
Ryan Goins Blue Jays 29 418 -6.94 6.80% 50.30% 34.80% 27.70% 19.60% 2.7 0.120
Gregory Polanco Pirates 25 379 -7.00 6.60% 42.20% 37.50% 25.90% 22.80% 3.7 0.140
David Peralta Diamondbacks 29 525 -7.02 7.50% 55.10% 26.50% 31.80% 21.20% 4.6 0.150
Kolten Wong Cardinals 26 354 -7.11 10.00% 48.10% 31.80% 28.20% 20.80% 5.4 0.127
Orlando Arcia Brewers 22 506 -7.74 6.60% 51.60% 28.50% 30.20% 22.90% 4.1 0.130
Martin Maldonado Angels 30 429 -7.80 3.20% 48.50% 36.60% 26.70% 21.60% 2.3 0.147
Cory Spangenberg Padres 26 444 -7.85 7.00% 49.30% 27.80% 29.20% 16.90% 5 0.137
Joe Panik Giants 26 511 -7.96 8.00% 44.00% 34.10% 26.10% 20.10% 4.2 0.133
David Freese Pirates 34 426 -8.08 11.50% 57.00% 22.60% 31.90% 19.40% 1 0.108
Melky Cabrera – – – 32 620 -8.14 5.40% 48.90% 29.00% 28.90% 19.00% 2.3 0.137
Hunter Pence Giants 34 493 -8.28 7.40% 57.20% 29.40% 29.40% 18.50% 3.6 0.126
Manuel Margot Padres 22 487 -8.30 6.60% 40.50% 36.30% 25.40% 25.90% 6.1 0.146
Trea Turner Nationals 24 412 -8.61 6.70% 51.70% 33.50% 26.70% 18.00% 8.9 0.167
Jonathan Villar Brewers 26 403 -8.85 6.90% 57.40% 21.90% 33.20% 27.00% 5.4 0.132
Starlin Castro Yankees 27 443 -9.19 4.90% 51.80% 28.00% 29.20% 21.80% 3.5 0.153
Denard Span Giants 33 497 -9.30 7.40% 45.00% 33.60% 25.10% 18.60% 5.5 0.155
Jacoby Ellsbury Yankees 33 356 -9.73 10.00% 45.90% 31.00% 26.10% 22.70% 7.7 0.138
Delino DeShields Rangers 24 376 -9.93 10.00% 45.10% 34.80% 23.90% 20.10% 7.1 0.098
Adam Frazier Pirates 25 406 -9.98 7.90% 47.90% 26.80% 27.50% 17.90% 5.7 0.123
DJ LeMahieu Rockies 28 609 -10.42 8.70% 55.60% 19.70% 30.60% 15.40% 3.9 0.099
Yolmer Sanchez White Sox 25 484 -10.53 6.60% 44.50% 33.90% 24.00% 19.30% 5.3 0.147
Jason Heyward Cubs 27 432 -10.54 8.50% 47.40% 32.70% 25.50% 25.80% 4.3 0.130
Tim Anderson White Sox 24 587 -10.66 2.10% 52.70% 28.00% 28.30% 21.30% 6.2 0.145
Jean Segura Mariners 27 524 -10.79 6.00% 54.30% 26.40% 28.30% 19.70% 5.5 0.128
Cameron Maybin – – – 30 395 -10.88 11.30% 57.70% 27.90% 27.40% 20.10% 6.9 0.137
Dustin Pedroia Red Sox 33 406 -10.90 10.60% 48.80% 28.80% 25.90% 20.10% 2.2 0.099
Jose Iglesias Tigers 27 463 -10.91 4.30% 50.40% 26.40% 28.40% 23.40% 4.2 0.114
Eric Hosmer Royals 27 603 -11.30 9.80% 55.60% 22.20% 29.50% 21.80% 3.4 0.179
Eduardo Nunez – – – 30 467 -12.27 3.70% 53.40% 29.10% 26.70% 24.50% 4.8 0.148
Jon Jay Cubs 32 379 -12.53 8.50% 47.10% 23.90% 25.30% 11.50% 5.3 0.079
Brandon Phillips – – – 36 572 -12.97 3.50% 49.50% 28.30% 25.50% 21.70% 4.1 0.131
Guillermo Heredia Mariners 26 386 -15.19 6.30% 47.40% 34.90% 20.40% 23.80% 2.2 0.088
Ender Inciarte Braves 26 662 -15.36 6.80% 47.00% 29.10% 22.10% 20.90% 5.4 0.106
Jonathan Lucroy – – – 31 423 -16.18 9.60% 53.50% 27.90% 22.30% 20.50% 3.1 0.106
Jose Peraza Reds 23 487 -16.45 3.90% 47.10% 31.30% 21.40% 26.60% 5.8 0.066
Cesar Hernandez Phillies 27 511 -18.08 10.60% 52.80% 24.60% 22.10% 23.50% 6 0.127
Billy Hamilton Reds 26 582 -21.80 7.00% 45.80% 30.60% 16.00% 25.00% 9 0.088
Dee Gordon Marlins 29 653 -28.88 3.60% 57.60% 19.60% 16.10% 24.70% 8.5 0.067

Okay, so here’s the breakdown. I pulled all 2017 hitters with 400 at-bats or more so I could capture some significant hitters that didn’t have qualifying numbers of ABs due to injury. Ball-bludgeon extraordinaire Joey Gallo is a pretty solid name to have heading up this list, as he’s pretty much the human definition of what this tool is trying to identify. JD Martinez, Aaron Judge, Cody Bellinger, Miguel Sano, Trevor Story, and Justin Turner all in the top 10 is pretty much all the proof-of-concept I needed.

Interesting notes:

Brandon Belt at 12 — Someone needs to tell the Giants to trade him to literally any other team, stat.

Giancarlo Stanton at 46 — Surprisingly, the MVP fell off from his stats in 2016. His grounders and soft contact rose by 3 or more percentage points, and shaved off the equivalent from hard and fly balls. His output was fueled by adding almost 200 ABs to his season — he could actually get better if he can stay healthy and add those hard flies back in!

Francisco Lindor at 58 — The interesting part of this is even though Lindor is still a decent way down the list, he actually was the biggest gainer from last season to this, adding 9.52 points to his cHit. We knew he was gunning for flies from the outset of the season, and it looks like his mission was accomplished.

Mike Moustakas at 87 — Frankly, being bookended by Jose Ramirez and Andrew Benintendi should, in a vacuum, should be great company. But this is a prime example of how cHit requires users to not take the numbers at face value. Ramirez and Benintendi aren’t slug-first hitters like Moose. They’ve got significantly better Speed scores, plus aren’t as prone to soft contact. I’d be very wary of Moose regressing, as he seems to rely on sneaking some less-than ideal homers over fences. If he goes to San Francisco I could see his value crater (see Belt, Brandon).

Eric Hosmer at 206 — Nope, negative, pass, I’m trying to sign quality hitters here <— Suggested responses for GMs when approached this offseason by Scott Boras on behalf of Hosmer.

Final Notes:

  •  Batted-ball distribution data is noticeably absent. In one of my iterations I added in those stats, and found that they actually regressed the accuracy of the formula. It doesn’t matter where you hit the ball, as long as you hit it hard.
  • Medium% and LD% are noisy stats. They also regressed the formula.
  • I may look to replace BB% in future iterations. For now though, it does a decent job of capturing plate discipline and selectivity.
  • K% doesn’t seem to have much of an impact on cHit (see Gallo, Joey).
  • R-squared numbers over the last four years of data hold pretty steady between .65 and .75, which is really encouraging. Also, the bigger the pool of data per year (number of batters analyzed), the higher R-squared goes; which is ultimately the most encouraging result of this whole endeavor.

Input is greatly appreciated! I’m not a mathematician in any stretch of the imagination, so if there’s a better way of going about this I’d love to hear it. I’ll do a writeup about my swing-change findings at a later date.

Yasiel Puig Was a Terrible, Terrible Baserunner

Yasiel Puig had an impressive rebound season in 2017. He responded to disappointing, injury-marred seasons in 2015-16 with a solid 2.9 WAR this year. Puig greatly improved his plate discipline, increasing his selectivity and his contact rates en route to an 11.2 BB% and 17.5 K%. He has been known for his free-swinging ways since entering the league, but he may have changed that reputation this past season. Puig was not the reckless hitter he had been in the past. However, he may have decided to channel that recklessness to the base paths.

Puig is a good athlete, but has never been much of a base-stealer. In his first two years, he converted a poor 22/37 of his steal attempts, and mostly quit trying to steal in 2015-16. Despite the failures of his base-stealing, he had actually been a slight positive on the bases in his career, accumulating 0.5 runs above average in 2013-16, per FanGraphs’ base-running metric. Puig reverted to his aggressive base-stealing in 2017, and his 15 stolen bases indicated success with the approach. His 71.4% conversion rate was not exceptional, but not horrible. But his base-running had no semblance of success.

Puig was the sixth-worst player on the bases in 2017, accumulating -7.6 runs. He was surrounded by names like Albert Pujols, Miguel Cabrera, and Edwin Encarnacion. Not exactly names you want to be grouped with when talking about base-running.

FanGraphs’ base-running metric encompasses three things: wSB, wGDP, and UBR. wSB measures the run value a player produced based off attempting steals. Puig produced a mediocre mark of 0.1, which lines up with his stolen-base numbers. wGDP measures the ability of a player to avoid double plays. Puig ranked 13th-worst in 2017 with -2.4 runs produced, but wGDP is more related to avoiding ground balls with men on base and beating out throws to first. UBR (Ultimate Base Running), measures the value of a player with respect to non-stealing base-running, like taking an extra base. Puig produced -5.3 runs per UBR, sixth-worst in the league.

Let’s focus on that UBR. Providing context, that figure is a whole lot worse than sixth-lowest in the league. Puig had a speed score of 4.4 in 2017, placing him 80th in the league, among players with at least 400 PA. The five players directly ahead of Puig in UBR had an average speed score of 2.3. That would rank 185th. Considering speed, Puig was likely the worst base-runner in the league. He did things like this, which you probably remember from the World Series:

Puig was probably the worst base-runner in 2017. But how bad was he on a historical level?

Of all individual seasons (min 400 PAs) since 2002, when UBR was introduced, Puig’s UBR ranks lower than the 3rd percentile out of 3393 seasons. Of those individual seasons with a speed score within one standard deviation of Puig’s, his UBR ranks lower than the 1st percentile.

Here is a plot of every one of those seasons, with each player’s stolen-base total versus their UBR. Puig in 2017 is highlighted in yellow.

Obviously, players with higher stolen-base totals are generally faster, and thus produce more value on the bases. As with anything, though, there are outliers. Puig is definitely an outlier. Only one player with as many stolen bases has produced an UBR lower than Puig: Juan Encarnacion in 2003. Here is another chart, with speed score plotted against UBR. Puig again is in yellow.

Puig is again an extreme outlier, even historically. Considering his athleticism, Puig had one of the worst base-running seasons of the last 15 years. This does not mean a ton. Puig has not always been a terrible base-runner, and he was still a quite effective player in 2017, woes on the basepaths aside. He can easily turn it around and produce a solid base-running season with the physical gifts he has. However, in 2017, Puig’s base-running was really, really terrible.

On Starling Marte and Steroids

Each baseball fan has a set of specific events throughout time they remember fondly. Some exist in said group because of their emotional impact on your fandom. Others remain on the peripheral of importance because of a random characteristic that still stands out.

Those peripheral events, for me, are often those I’ve seen on live television. I don’t think of these events often, nor do I keep a record of them, or have some strict guideline for what sticks in my head, but when a story in the present day sparks my memory, a picture often emerges. My teenage years watching baseball were done one of two ways: sitting on the ground in front of my laptop with fading in-and-out, or scouring local stations for a good matchup. These two primary settings allowed for many one-off memories to accumulate.

When I began to think about Pittsburgh Pirates’ outfielder Starling Marte — due to this offseason’s stagnation — I thought back to the first pitch he saw in his major-league career. Just over five years ago, 23-year-old Starling Marte took the first pitch Dallas Keuchel threw on July 26 out of Minute Maid Park. The rarity of that event — a prospect’s debut, leading off a game, first-pitch home run — forces me to remember that bomb whenever Marte steps into a batter’s box. Because I happened to see it live, that memory has stuck.

For the wider population of fans, what now supersedes that milestone is Marte’s run-in with performance-enhancing drugs.  Suspended for 80 games during the 2017 season, this mistake by Marte will couple itself with any other success he has.

Predicting how Marte would fare upon his return during this layoff in 2017 raised some interesting, PED-related questions. Would his power drop? Would his speed deteriorate? What about his overall durability?

Nestled within all those asks is what exactly the effect of PEDs on an athlete’s body is after stopping use. Much more intriguing is this question: does any use at all matter as much as stopping that use? In other words, do the effects of PED use in the first place help prolong success?

I mention this because Marte joins Dee Gordon as the more prominent speed-first users of prohibited substances in the recent years. The drugs Gordon and Marte took were different from my understanding — nandrolone versus a stacked dose with clostebol — but maybe some intrigue exists in the stats before and after use?

The overall comparison doesn’t show us much. Even in what I highlighted with darkened gridlines — slugging percentage and wRC+ — has more noise within it than signal. Two main questions exist, among many others, that don’t have answers.

  • What portion of the “before” PED use window contains tainted statistics?
  • What portion of the drop is due specifically to the lack of steroids in the body?

But perhaps our intentions with those questions are incorrect. Think back to the question I asked before showing this dataset: do the effects of PED use in the first place help prolong success?

What if the muscle memory and learning that takes place while a player is under the influence of the drug extends beyond the window where a player can run a positive test?

With some high-level Googling, I found one instance where this idea might be a reasonable rabbit hole to dig into (BBC News). Certainty around this topic, however, is impossible, given all the variables. Some selection bias brings us the average fan to Nelson Cruz and Bartolo Colon as examples of this idea. But assuming two players with demonstrable skills outside of steroid use represent a wider population is not an appropriate assumption. We’re left in limbo regarding how much one positive test early on can affect one’s long-term production.


Let’s leave the uncertainty around long-term effects of Marte’s steroid use alone for now and focus on what has happened in Marte’s career.

The attribute his value has been tied to for most of his career, like Dee Gordon, is speed. But for Marte, age-induced deterioration of that attribute may be underway as he heads into his 29-year-old season with the Pirates.

It wasn’t too long ago we were concerned about the viability of McCutchen’s long-term impact, yet speed has a much greater weight on the impact of Marte as a player than McCutchen. I remain perplexed as to how Marte intends to turn around this decline in sprint speed as he starts to fall away from elite towards the 27.0 feet-per-second average the standard MLB player possesses.

Marte can still produce with his bat, but after seeing this decrease in peak sprint speed, I wonder if he becomes less reliant on his wheels to buoy his BABIP and the resulting average he’ll post. The Pirates’ outfielder might need to adjust.

To counteract this potential speed regression, Marte might want to adjust back to his approach from 2015, where he popped 19 home runs.

What we do know from that year presides in his tendency to pull the ball above his career average, which resulted in the majority of his home runs landing somewhere near the corner in a park’s left-field seats. He was also more aggressive than he had ever been in his career in 2015, but since, Marte has reverted to a contact-based approach, raising his zone-contact rate by two percent and overall contact rate by three percent.

With all this said, the form and substance of Marte’s swing has been largely the same since the early days of his career. Each of the four videos embedded within the GIF below are base hits to left field for Marte. Instead of focusing on the moments just before contact — where most hitters look identical — focus on his pre-pitch rhythm and timing.

Marte has a unique pulse when it comes to the timing mechanism in his hands, as his bat moves towards the first-base line twice prior to his load. The speed at which he executes this varies slightly based on the pitch, but his front foot’s inward turn and hip rotation remain unaltered from this selection of swing in our four-year sample.

My worry is that pushing Marte towards the 2015 version of himself, with pull-happy tendencies and a little bit more aggression, may not lead to the power result we want. With his speed possibly deteriorating, the balls he rolls over on with his sights set on the bleachers will turn into hits less often. We might want Marte to trade some of his contact for power, but my inclination is that such a trade, at present, is not one-for-one and would result in a net-negative effect.

This contact approach of Marte’s may be the new normal, and I remain worried about what the ceiling of productivity can be if he doesn’t find a second wind in the speed department. Marte can still be an asset to the Pirates, and isn’t a financial burden, but it might be too late to expect 2015’s power-speed combo that had the chance to nudge Marte towards the elite bracket of outfielders in baseball.

Bill Brink of Pittsburgh Post Gazette reports that Marte is making up his lost at-bats in the Dominican Winter League for Leones del Escogido. The results, so far, in a small sample have not been great:

.197/.244/.316 in 76 at-bats, with a 21:3 strikeout-to-walk ratio.

Marte’s evolution as a hitter will become clearer as our post-PED sample size increases. The Pirates’ outfield, once considered the best in baseball with McCutchen, Marte, and Polanco, now finds itself in a pickle, especially if Cutch is traded, Marte’s speed continues to trend south, and Polanco can’t stay healthy.

An Exercise in Generating Similarity Scores

In the process of writing an article, one of the more frustrating things to do is generate comparisons to a given player. Whether I’m trying to figure out who most closely aligns with Rougned Odor or Miguel Sano, it’s a time-consuming and inexact process to find good comparisons. So I tried to simplify the process and make it more exact — using similarity scores.

An Introduction to Similarity Scores

The concept of a similarity score was first introduced by Bill James in his book The Politics of Glory (later republished as Whatever Happened to the Hall of Fame?) as a way of comparing players who were not in the Hall of Fame to those who were, to determine which non-HOFers deserved a spot in Cooperstown. For example, since Phil Rizzuto’s most similar players per James’ metric are not in the HOF, Rizzuto’s case for enshrinement is questionable.

James’ similarity scores work as such: given one player, to compare them to another player, start at 1000 and subtract one point for every difference of 20 games played between the two players. Then, subtract one point for every difference of 75 at-bats. Subtract a point for every difference of 10 runs scored…and so on.

James’ methodology is flawed and inexact, and he’s aware of it: “Similarity scores are a method of asking, imperfectly but at least objectively, whether two players are truly similar, or whether the distance between them is considerable” (WHHF, Chapter 7). But it doesn’t have to be perfect and exact. James is simply looking to find which players are most alike and compare their other numbers, not their similarity scores.

Yes, there are other similarity-score metrics that have built upon James’ methodology, ones that turn those similarities into projections: PECOTA, ZiPS, and KUBIAK come to mind. I’m not interested in making a clone of those because these metrics are obsessed with the accuracy of their score and spitting out a useful number. I’m more interested in the spirit of James’ metric: it doesn’t care for accuracy, only for finding similarities.

Approaching the Similarity Problem

There is a very distinct difference between what James wants to do and I what I want to do, however. James is interested in result-based metrics like hits, doubles, singles, etc. I’m more interested in finding player similarities based on peripherals, specifically a batted-ball profile. Thus, I need to develop some methodology for finding players with similar batted-ball profiles.

In determining a player’s batted-ball profile, I’m going to use three measures of batted-ball frequencies — launch angle, spay angle, and quality of contact. For launch angle, I will use GB%/LD%/FB%; for spray angle, I will use Pull%/Cent%/Oppo%; and for quality of contact, I will use Soft%, Med%, Hard%, and HR/FB (more on why I’m using HR/FB later).

In addition to the batted-ball profiles, I can get a complete picture of a player’s offensive profile by looking at their BB% and K%. To do this, I will create two separate similarity scores — one that measures similarity based solely upon batted balls, and another based upon batted balls and K% and BB%. All of our measures for these tendencies will come from FanGraphs.

Essentially, I want to find which player is closest to which overall in terms of ALL of the metrics that I’m using. The term “closest” is usually used to convey position, and it serves us well in describing what I want to do.

Gettin’ Geometrical

In order to find the most similar player, I’m going to treat every metric (GB%, LD%, FB%, Pull%, and so on) as an axis in a positioning system. Each player has a unique “position” along that axis based on their number in that corresponding metric. Then, I want to find the player nearest to a given player’s position within our coordinates system — that player will be the most similar to our given player.

I can visualize this up to the third dimension. Imagine that I want to find how similar Dee Gordon and Daniel Murphy are in terms of batted balls. I could first plot their LD% values and find the differences.

1-D visualization of Daniel Murphy's and Dee Gordon's batted ball profiles

So the distance between Murphy and Gordon, based on this, is 4.8%. Next, I could introduce the second axis into our geometry, GB%.

2-D visualization of Daniel Murphy's and Dee Gordon's batted ball profiles

The distance between the two players is given by the Pythagorean formula for distance — sqrt(ΔX^2 + ΔY^2), where X is LD% and Y is GB%. To take this visualization to a third dimension and incorporate FB%…

3-d visualization of Daniel Murphy's and Dee Gordon's batted ball profiles

… I would add another term to the distance calculation — sqrt(ΔX^2 + ΔY^2 + ΔZ^2). And so on, for each subsequent term. You’ll just have to use your imagination to plot the next 14 data points because Euclidian geometry can’t handle dimensions greater than three without some really weird projections, but essentially, once I find the distance between those two points in our 10 or 12-dimensional coordinate system, I have an idea how similar they are. Then, if I want to find the most similar batter to Daniel Murphy, I would find the distance between him and every other player in a given sample, and find the smallest distance between him and another player.

If you’ve taken a computer science course before, this problem might sound awfully familiar to you — it’s a nearest-neighbor search problem. The NNS problem is about finding the best way to determine the closest neighbor point to a given point in some space, given a set of points and their position in that space. The “naive” solution, or the brute-force solution, would be to find the distance between our player and every other player in our dataset, then sort the distances. However, there exists a more optimized solution to the NNS problem, called a k-d tree, which progressively splits our n-dimensional space into smaller and smaller subspaces and then finds the nearest neighbor. I’ll use the k-d tree approach to tackling this.

Why It’s Important to Normalize

I used raw data values above in an example calculation of the distance between two players. However, I would like to issue caution against using those raw values because of the scale that some of these numbers fall upon.

Consider that in 2017, the difference between the largest LD% and smallest LD% among qualified hitters was only 14.2%. For GB%, however, that figure was 30.7%! Clearly, there is a greater spread with GB% than there is with LD% — and a difference in GB% of 1% is much less significant than a difference in LD% of 1%. But in using the raw values, I weight that 1% difference the same, so LD% is not treated as being of equal importance to GB%.

To resolve this issue, I need to “normalize” the values. To normalize a series of values is to place differing sets of data all on the same scale. LD% and GB% will now have roughly the same range, but each will retain their distribution and the individual LD% and GB% scores, relative to each other, will remain unchanged.

Now, here’s the really big assumption that I’m going to make. After normalizing the values, I won’t scale any particular metric further. Why? Because personally, I don’t believe that in determining similarity, a player’s LD% is any more important than the other metrics I’m measuring. This is my personal assumption, and it may not be true — there’s not really a way to tell otherwise. If I believed LD% was really important, I might apply some scaling factor and weigh it differently than the rest of the values, but I won’t, simply out of personal preference.

Putting it All Together

I’ve identified what needs to happen, now it’s just a matter of making it happen.

So, go ahead, get to work. I expect this on my desk by Monday. Snap to it!

Oh, you’re still here.

If you want to compare answers, I went ahead and wrote up an R package containing the function that performs this search (as well as a few other dog tricks). I can do this in two ways, either using solely batted-ball data or using batted-ball data with K% and BB%. For the rest of this section, I’ll use the second method.

Taking FanGraphs batted-ball data and the name of the target player, the function returns a number of players with similar batted-ball profiles, as well as a score for how similar they are to that player.

For similarity scores, use the following rule of thumb:

0-1 -> The same player having similar seasons.

1-2 -> Players that are very much alike.

2-3 -> Players who are similar in profile.

3-4 -> Players sharing some qualities, but are distinct.

4+ -> Distinct players with distinct offensive profiles.

Note that because of normalization, similarity scores can vary based on the dataset used. Similarity scores shouldn’t be used as strict numbers — their only use should be to rank players based on how similar they are to each other.

To show the tool in action, let’s get someone at random, generate similarity scores for them, and provide their comparisons.

Here’s the offensive data for Elvis Andrus in 2017, his five neighbors in 12-dimensional space (all from 2017), and their similarity scores.

Elvis Andrus Most Similar Batters (2017)

The lower the similarity score, the better, and the guy with the lowest similarity score, J.T. Realmuto, is almost a dead ringer for Andrus in terms of batted-ball data. Mercer, Gurriel, Pujols, and Cabrera aren’t too far off as well.

After extensively testing it, the tool seems to work really well in finding batters with similar profiles — Yonder Alonso is very similar to Justin Smoak, Alex Bregman is similar to Andrew McCutchen, Evan Longoria is similar to Xander Bogaerts, etc.

Keep in mind, however, that not every batter has a good comparison waiting in the wings. Consider poor, lonely Aaron Judge, whose nearest neighbor is the second furthest away of any other player in baseball in 2017 — Chris Davis is closest to him with a similarity score of 3.773. Only DJ LeMahieu had a further nearest-neighbor (similarity score of 3.921!).

The HR/FB Dilemma

While I’m on the subject of Aaron Judge, let’s talk really quickly about HR/FB and why it’s included in the function.

When I first implemented my search function, I designed it to only include batted-ball data and not BB%, K%, and HR/FB. I ran it on a couple players to eye-test it and make sure that it made sense. But when I ran it on Aaron Judge, something stuck out like a sore thumb.

Aaron Judge Similarity Scores

Players 2-5 I could easily see as reasonable comparisons to Judge’s batted balls. But Nick Castellanos? Nick Castellanos? The perpetual sleeper pick?

But there he was, and his batted balls were eerily similar to Judge’s.

Aaron Judge Most Similar Batters (2017)

Judge hits a few more fly balls, Castellanos hits a few more liners, but aside from that, they’re practically twins!

Except that there’s not. Here’s that same chart with HR/FB thrown in.

Aaron Judge Most Similar Batters (2017)

There’s one big difference between Judge and Castellanos, aside from their plate discipline — exit velocity. Judge averages 100+ MPH EV on fly balls and line drives, the highest in the majors. Castellanos posted a meek 93.2 MPH AEV on fly balls and line drives, and that’s with a juiced radar gun in Comerica Park. Indeed, after incorporating HR/FB into the equation, Castellanos drops to the 14th-most similar player to Judge.

HR/FB is partially considered a stat that measures luck, and sure, Judge was getting lucky with some of his home runs, especially with Yankee Stadium’s homer-friendly dimensions. But luck can only carry you so far along the road to 50+ HR, and Judge was making great contact the whole season through, and his HR/FB is representative of that.

In that vein, I feel that it is necessary to include a stat that has a significant randomness component, which is very much in contrast with the rest of the metrics used in making this tool, but it is still a necessary inclusion nevertheless for the skill-based component of that stat.

Using this Tool

If you want to use this tool, you are more than welcome to do so! The code for this tool can be found on GitHub here, along with instructions on how to download it and use it in R. I’m going to mess around with it and keep developing it and hopefully do some cool things with it, so watch this space…

Although I’ve done some bug testing (thanks, Matt!), this code is still far from perfect. I’ve done, like, zero error-catching with it. If in using it, you encounter any issues, please @ me on twitter (@John_Edwards_) and let me know so I can fix them ASAP. Feel free to @ me with any suggestions, improvements, or features as well. Otherwise, use it responsibly!

Hack Wilson: The Most Interesting Player You’ve Sorta-Kinda Heard of Before

Lewis Robert “Hack” Wilson was an outfielder for the New York Giants, Chicago Cubs, Brooklyn Dodgers, and Philadelphia Phillies in the early 20th century. Wilson was a very good ballplayer, and was enshrined in Cooperstown in 1979.

As my title suggests, you have probably heard the name Hack Wilson before, but I’m guessing you probably don’t know much about him, because his most popular claim to fame is considered by many to be irrelevant today. This claim to fame is his record-setting 191 RBI in 1930. This remains the single-season record for the stat to this day, and it’s hard to believe that anyone will come along who can break it. In that 1930 campaign, Hack also slugged 56 home runs, walked 105 times, struck out 84 times, and slashed .356/.454/.723 with a 1.177 OPS and a 177 OPS+. These were all league highs, excluding average and OBP.

That’s a great season, but it gets a whole lot more interesting when you look a little closer. 56 home runs is a lot. That mark is tied with Ken Griffey Jr.’s pair of 56-home-run campaigns for 17th-most all-time in a single season, and was the best non-Ruth mark at the time (although this would last just two years, when Jimmie Foxx hit 58 home runs in 1932).

Just hitting home runs isn’t what makes Hack Wilson so interesting to me, though. It’s who he was. Hack Wilson stood at just 5’6. The same height as our favorite short player today, Jose Altuve. In fact, at 5’6, Altuve and Hack are both the shortest players to ever hit 20 or more home runs in a single season. Hack alone is the shortest player to ever slug 30, 40, or 50 in a single season. Hack also holds the single-season home-run record for anyone under 6’0. Hack, Mantle (5’11), Mays (5’10), and Prince Fielder (5’11) are the only men to hit 50 or more home runs while being less than 6’0.

However, with that enormous home-run total comes strikeouts. You may have noticed that he struck out just 84 times in that 56 home-run season, and he even walked more than he struck out. But 84 was a lot in 1930. In fact, Hack Wilson led the league in strikeouts.

In 2017, just 25 qualified hitters struck out 84 times or fewer. Of these 25, just one (Mookie Betts) matched or exceed Hack’s 709 plate appearances. This tidbit really speaks more to the two eras in discussion, but it’s interesting nonetheless.

Some other Hack Wilson fun facts:

Hack received MVP votes in five years. Amazingly, his monstrous 1930 season (undoubtedly his best) was not one of the five. However, this was due to the fact that the MVP was not awarded in 1930. Had it been, Wilson likely would have won in a landslide.

Despite having the single-season record for most RBI, he is tied for just the sixth-most seasons of 150 or more RBI with two, behind Lou Gehrig (7), Babe Ruth (6), Jimmie Foxx (4), Hank Greenberg (3), and Al Simmons (3), and tied with Sosa, DiMaggio, and Sam Thompson.

Despite the legendary 1930 season, Hack’s career was significantly below that of a typical Hall of Famer. His Gray Ink score is 110 (average HOF’s is 144), and his “Hall of Fame Standards” is 39 (average HOF’s is 50). His 38.8 career bWAR is nearly half of the average bWAR for center fielders, at 71.2.

That’s all I have on Lewis Wilson. He may still seem like a relatively mundane player, but imagine if Altuve came out in 2018 and kept up with Stanton and Judge in the home-run race. That is what Hack Wilson did in 1930, belting 56 homers as a man who stood 5’6″ tall (how can you not be romantic about baseball?).

Who Are the Top “Pound-for-Pound” Power Hitters?

We all know that Aaron Judge hit for more power this year than Jose Altuve. But, whose power was more impressive? Aaron Judge, who is 6’7 and 282 pounds, has a considerable size advantage over Jose Altuve, at 5’6 and 164 pounds. Perhaps Altuve is actually a better power hitter for his size than is Judge. Let’s expand this idea to the entire league: who is the pound-for-pound top power hitter?

Role of Height and Weight in Batter Power

Using simultaneous linear regression, I estimated the effects of two physical characteristics — height and weight — on batter power. Measures of batter height and weight were taken from For batter power, I used Isolated Power.

As shown in the figures below, weight and height have positive relationships with power.

Height and Weight

Weight has a stronger relationship with power than height, though it is difficult to see in the figures alone. (It’s also not intuitively clear exactly how height affects power.) In subsequent analyses, I consider both weight and height.

Who are the top pound-for-pound power hitters?

Using the model, one can predict a batter’s expected power (based on height and weight) and compare it to their actual power.

Who are the top pound-for-pound power hitters? See below for the results.

Top 10 hitters

Khris Davis, formerly the #9 top power hitter, emerges as the #1 pound-for-pound power hitter in baseball. In 2017, Davis, who is three inches and over 30 pounds below average for a Major League hitter, hit a remarkable 43 home runs in 2017, with an ISO of .281. Nolan Arenado and Josh Donaldson made similar jumps in the rankings, from #7 to #2, and #10 to #3, respectively.

Notable power hitters have fallen slightly on this list, though remain in the top 10. For example, Aaron Judge fell from the top spot to #8, while Giancarlo Stanton dropped three spots (#2 to #5). It is important to note here that these power hitters are still impressive – continuing to hold spots in the top 10, regardless of their size.

Biggest improvements in rankings

Which players showed the most improvement in the list? Below are results from the top 50 players on the list.

Top 3 improved rank players

Andrew Benintendi showed the largest increase in rankings (from 184 to 43). Jose Altuve nearly broke into the top 10, jumping from 132 to 12. Lastly, Eddie Rosario improved 68 spots (100 to 32). Altuve, in particular, has recently shown increases in power (from .146 to .194 to .202 in 2015-2017); as a result, his pound-for-pound status may continually increase in upcoming years.

Who was more impressive?

To reference the initial question in this article: was Jose Altuve’s or Aaron Judge’s power more impressive? Results from the above analyses were compiled from 2015 to 2017 seasons. To compare Altuve and Judge’s recent season, take a look below.

Altuve vs Judge

Aaron Judge tops Jose Altuve in the pound-for-pound hitter rankings – by a very thin margin – in 2017. Judge’s power performance exceeded expectations (as predicted by his height and weight) to a slightly higher degree than Altuve.

Full Rankings

If you want to see the full list of hitters for this dataset, including the worst pound-for-pound power hitters (poor Jason Heyward!), click here.


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