Archive for June, 2017

Understanding Player Contracts from a Business Perspective

As statistics have become more advanced and public, we’ve gained myriad ways to understand baseball more in depth. We don’t just know that Aaron Judge smacks the crap out of the ball; we know that he can hit it out of the park at more than 120 miles per hour. We don’t just know that Yu Darvish’s pitches can dive all over the zone, but that they have an average spin rate of more than 2500 revolutions per minute.

While those stats represent single facets of a player’s game, there’s one that incorporates everything they do to give a sense of their overall value: Wins Above Replacement, or WAR. Depending where you find your stats — there’s fWAR from FanGraphs or bWAR from Baseball Reference — there will be subtle differences in how it’s calculated. But the point is the same: to tell you who the best and worst players are compared to anyone who could replace them.

WAR is the type of stat that enables us to react in real time, and with relatively sound reason, to newly-signed contracts. It’s how we can say Kevin Kiermaier’s deal is probably a notable win for the Rays and why Ryan Howard’s last extension was premature at best.

The reality we shape as observers and fans often looks at these contracts under a microscope, and only under a microscope. When a guy strikes out looking to end a rally, or gives up the hit that sparks one, that’s when we notice. And, fair or not, those moments craft the narratives we often carry throughout the life of a player’s contract.

Zooming out is helpful, though. In certain context, there might not be such a thing as a bad contract.


Owners have been raking in the money for a long, long time. They’ve pretty much always taken home more than the players and in recent years that difference has only grown. When you consider that there are only ever as many owners as there are teams, and that the players’ share is split hundreds more times, the disparity becomes emphasized.

If we want additional perspective, we can look at how the percent of overall revenue accounted for by player salaries has decreased almost annually like clockwork.


Revenue data goes beyond that which fans and analysts use to justify a point of view on a player’s worth to their team. Those trains of thought spur additional conversation about how a given contract can influence the team’s composition and ability to compete for championships. And these points may well hold water. But they probably don’t provide much influence on the business perspective.

No matter how good or bad a contract is, a team is likely still profitable and operating within a relatively certain margin of error that isn’t dramatically different than if they didn’t have that deal on the books.

That’s not to say owners don’t care about a bad contract. It’s just that, at an operational level, they have to concern themselves with the bottom line first and foremost because it’s what allows them to persist. Sure, the big deals that go sour are disappointing to them, but they’re not damning.

It’s Time to Stop Ignoring the Kershaw Home Runs

Clayton Kershaw is the best pitcher of his generation. He is a six-time All-Star and a three-time Cy Young winner. The Los Angeles Dodgers ace won’t turn 30 until next season, but he has already accumulated over 2,000 strikeouts and 135 wins. So, when we see Kershaw falter a little bit for a short period of time, it is justified that the struggles are written off as nothing. That’s what was done earlier this year, but, 14 starts into 2017, he has a problem that isn’t going away. And it’s time to really investigate the issue.

Kershaw has given up at least one home run in his last four starts, and is just three long balls away from tying his career-high 16 home runs allowed in 2012. Those 16 came in 33 starts. Let’s compare his HR/9 and HR/FB for each season:

Season HR/9 HR/FB
2008 0.92 11.6%
2009 0.37 4.1%
2010 0.57 5.8%
2011 0.58 6.7%
2012 0.63 8.1%
2013 0.42 5.8%
2014 0.41 6.6%
2015 0.58 10.1%
2016 0.48 7.5%
2017 1.21 15.9%

Both would easily be career highs, and aside from his rookie year in 2008, his current HR/9 of 1.21 would nearly double the next worst.

It’s not like this issue has destroyed him or trickled down into the rest of his game — he is still running a 2.23 ERA. His 23.8% K-BB% isn’t quite what it’s been the last few seasons, but it is still better than his career average. Kershaw has actually still been great, but he is held to a different standard than any other pitcher in the league. He just hasn’t been Kershaw great. So what’s behind the home runs?

Well, we are dealing with Kershaw here, so the first thing to investigate is whether he has had a little bad luck. Maybe a few balls that normally shouldn’t clear the fence did…

But that’s not the case, and that explanation is actually a lot further off than one might expect. Kershaw’s allowed home runs have been hammered. The average exit velocity on them is 105.5 mph, which ranks in the 12th percentile for pitchers who have allowed at least five home runs. Not a single one has been hit below 100 mph, and only two of the home runs had a home-run probability lower than 50%. One of those two is a home run 37% of the time, but it’s usually not an out, either. Similar balls in play to that home run have an .800 average. The other one under 50% is a home run 49% of the time. So, clearly he’s not suffering from bad luck, and it’s actually a little troubling how little luck has gone into the homers.

Strangely, while the home runs have been crushed, Kershaw isn’t giving up more hard contact overall. His hard-contact rate is nearly identical to last season’s, and he is actually sporting a career high in soft-contact rate. Hitters are turning on specific pitches, not hitting him harder overall.

The specific pitch they’re turning on is, surprisingly, his fastball. From 2011 (when Kershaw won his first Cy Young) to 2016, Kershaw’s fastball had a whopping 148.6 run value. That easily ranked first, and the next closest in that time frame was his teammate, Kenley Jansen, at 97.8. His fastball simply dominated guys. And while pitch values are not the most perfect metric to use, there is something to be said that his fastball ranks only 20th in run value in 2017. Yes, 20th out of 85 is far from poor, but remember who we are talking about here.

In that 2011-2016 time frame, Kershaw gave up 44 home runs on his fastball. 14 of those came on pitches that landed arm side of the plate and in the middle third. That was seven more than any other zone. That trend hasn’t changed this season, as half of his eight home runs allowed on fastballs have come in that zone. Obviously some of that is due to the frequency that he throws his fastballs there, but hitters still like to club his fastball there more than any other areas.

So, that zone, along with the middle of the plate (like with any pitcher), are the danger zones for Kershaw’s fastball. Well, look at Kershaw’s fastball location from 2011-2016. Kershaw likes to elevate his fastball on the outer third, so making the occasional mistake and bringing it too low is understandable. But, now, look at Kershaw’s fastball location this season. Yikes. Kershaw is throwing his fastball most often right in the middle of his weak zones.

The pitch is good enough that, even with the poor location this season, it’s still a great pitch. The average exit velocity on his fastball is 85.0 mph in 2017, compared to 84.8 mph in 2015-2016. Guys aren’t consistently hitting it any harder than they usually do. The issue is Kershaw is making more mistakes. In 2011-2016, hitters had a barrel (balls in play with at least .500 average, 1.500 slugging) rate of .11% on his fastball. In 2017, that number has skyrocketed to .94%. Kershaw is leaving it in the sweet spot of the zone too often.

The fly-ball rate on the pitch is also way up to 35.2% this season, which is much greater than recent years. We all know about the launch-angle obsession and how guys are trying to lift the ball out of the park more. If you hit the ball higher, it’s more likely to sail over the wall. Well, that is exactly what’s happening to Kershaw. The overall effectiveness of trying to raise one’s launch angle is yet to be determined, but it clearly leads to more home runs. It’s no surprise that if Kershaw is allowing more balls in the air, he’s allowing more home runs.

Kershaw seems to have lost some command on his fastball, and hitters are starting to tee off on it a little more than usual. If anyone could recover from this, it would be Kershaw. Obviously, with the way he is still pitching, the home runs are not a death sentence. But with the way these balls have been crushed, the issue is worrisome and it hasn’t been shrinking as the sample size increases.

What About Batted Ball Spin?

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:

Batter Name Number of batted balls Avg Statcast Distance (ft) Avg Estimated Distance (ft) Avg Deviation (ft)
Travis Jankowski 87 254 235 19
DJ LeMahieu 213 282 264 18
Carlos Gonzalez 226 293 276 17
Daniel Descalso 102 285 270 14
Max Kepler 150 285 271 14
Billy Burns 108 234 221 13
Rob Refsnyder 57 269 257 12
Jarrod Dyson 98 243 232 11
Martin Prado 256 262 251 11
Ketel Marte 154 250 239 11
Justin Morneau 73 278 268 11
Gary Sanchez 66 323 312 11
Tyler Saladino 107 270 260 10
Phil Gosselin 77 264 253 10
Jose Peraza 107 257 248 10
Mookie Betts 311 279 270 9
Melky Cabrera 280 271 261 9
Ichiro Suzuki 137 251 242 9
Omar Infante 68 269 261 9

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:

Batter Name Number of batted balls Avg Statcast Distance (ft) Avg Estimated Distance (ft) Avg Deviation (ft)
Colby Rasmus 136 285 306 -21
Tommy La Stella 72 273 294 -21
Brian McCann 195 273 294 -22
Todd Frazier 248 276 297 -22
Jorge Soler 88 278 300 -22
Brian Dozier 263 287 309 -22
Curtis Granderson 238 284 306 -22
Franklin Gutierrez 76 304 327 -23
James McCann 131 277 300 -23
Miguel Sano 158 301 324 -23
Khris Davis 213 303 326 -23
Freddie Freeman 269 289 312 -23
Mike Napoli 205 290 315 -25
Chris Davis 207 304 330 -26
Tyler Collins 54 270 296 -26
Ryan Howard 129 306 334 -28
Kris Bryant 284 281 309 -28
Jarrod Saltalamacchia 96 290 321 -31
Mike Zunino 63 295 327 -33
Ryan Schimpf 122 298 331 -33

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.

Mountain View

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.

Mountain View

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.

The Top Elevating Team in Baseball Is…

…the New York — not the mashing Yankees, but the Mets. Unfortunately I had a hardware crash so I currently can’t pull reports from Statcast and thus I now take ground-ball rate as a measure for elevation instead of launch angle. I prefer grounder rate over fly-ball rate because that tells you the “off the ground rate” (100 – gb%). Since liners are also very good I think they should be included.

The Mets have faced a lot of heat from sabermetric fans and sometimes for good reason, like their lowish OBP, neglecting defense and handling injuries.

But there is one thing they have done for a couple years now and that is elevate the ball.

In 2015 they had the third-lowest grounder rate in the majors at 41.9%, only trailing the Astros and Yankees. That means 58.1% of their balls were off the ground.

In 2016, after losing the poster boy of the fly-ball revolution, Daniel Murphy, they improved their grounder rate to a clearly league-leading 39.5% (almost 2 points on the second-place Rays). That improved their off-the-ground rate to over 60%.

In 2017, despite a lot of injuries, the Mets have even improved their GB rate to 38.2%, but they’ve been exceeded by the A’s.

Overall, the Mets clearly lead the Statcast era with a 40.3 GB%, almost 2 points ahead of the second-place Tigers.

The elevation also leads to power output, as they are 7th in ISO (only NL team ahead of them is the Rockies) and 6th in HR (top NL team, even ahead of the Rockies). Granted, they are only 21st in OBP, and negative in defense, so they are not without flaw, but there is no doubt they were built to elevate and mash, and that is by design.

Now did the Mets teach that or acquire elevation?

Looking at some long-time Mets:

Curtis Granderson

2013(Yankees): 33.8%, 2014: 34.2%, 2015: 30.8% , 2016: 36.4%, 2017: 31.3%

Granderson was a FB hitter when the Mets got him.

Daniel Murphy

We all know about him. 50% grounders in 2012 and improved that to 42% in 2013 and then more.

Lucas Duda

Always was a FB hitter with sub-40% grounder rates since the minors.

Yoenis Cespedes

Was a FB hitter when they got him (upper 30s grounder rate) but became a more extreme FB hitter in NY. This year he is running an insane sub-30% grounder rate.

Travis d’Arnaud

He started out in the mid-40s and then had some ups and downs with a very bad 50% rate last year, but this year he is down to 39%. We will have to wait to see whether that is sustainable.

Michael Conforto 

Sightly improved his grounder rate over his career from low-40s to now high-30s.

And then there is Jay Bruce who was acquired as a fly-ball hitter and became an extreme fly-ball hitter.

It seems like elevation was mostly acquired, but there are or were players who learned to lift more with the Mets. I assume it is at least encouraged by the Mets that hitters hit everything in the air.

The Mets have earned their share of criticism with some things they have done, but when it comes to the fly-ball revolution, it is they who deserve credit as the leaders of the fly-ball revolution, and probably moreso than the saber-darling teams like the Cubs or A’s, who are usually cited when talking about the fly-ball revolution. I’m not saying those teams did not target air balls, as the A’s have the 5th-lowest and the Cubs have the 7th-lowest grounder rates during the 2015 to 2017 to date time frame, but the leaders have clearly been the Mets.

The Value of Hitting the Ball Hard

There is value in the fly ball. That statement isn’t something that will surprise any fan. Even someone who knows very little about baseball could piece together the logic behind it. The most valuable individual outcome is a home run. How do you hit a home run? Hit a fly ball. As Travis Sawchik found for 2016, fly balls produced a wRC+ of 139, while ground balls put up a mark of 27 wRC+.

Of course, the sabermetrically inclined will quickly point out that it’s not that simple. Judging the value of a hit based on whether it is a fly ball or a ground ball is a futile exercise. You have to consider batted ball distance, launch angle, and exit velocity. Much has been made about the recent “fly ball revolution” occurring throughout the league. And while some believe hitting more fly balls really does increase the value of a player, data suggests that the fly ball revolution is hurting as many batters as it’s helped.

It’s possible that there are benefits to hitting more fly balls, but that doesn’t seem to correlate to an increased value.


There really is no correlation between fly ball % and wRC+. So, it seems that value is added not by hitting the ball higher, but by hitting the ball harder.


Now this is a pretty clear correlation. Hit the ball harder and a better outcome is more likely. A soft liner toward the second baseman will probably be an out. But, a laser to right-center field could be a triple.

This trend is not a new development or a new discovery. As far back as 2002, when batted-ball data became available, there has always been a positive correlation between Hard% and wRC+. In fact, the average correlation (R-squared value) between these two variables over the last 15 years is .475.

Hard% also has predictive value. Take a look at the data for 2017 thus far.


Although the correlation from past years isn’t there, it doesn’t need to be. We should no more expect the data to already have an R-squared value above .4 than we would expect an MVP to have a WAR higher than 6 at this point of the season. Because there are quite a few outliers that will come back to the mean, Hard%, based on its historical data, has considerable predictive value.

Ignoring the one point above the 200 wRC+ line (Mike Trout, whose entire career is an outlier), let’s examine a couple outliers. First, the point on the far right toward the bottom. Nick Castellanos is hitting the ball harder than Aaron Judge, who just set a Statcast record for hardest home run ever hit, but only has a wRC+ of 82 — well below average. Towards the top of the chart at the 175 wRC+ mark, we see that Zack Cozart is making hard contact only 32% of the time.

It is reasonable to expect, based on this chart, that Castellanos’s numbers will start to improve and Cozart’s will regress. As it turns out, Andrew Perpetua found the same outliers by looking at exit velocity and xOBA in a RotoGraphs article last week. These statistics all point toward the same thing — Castellanos has been very unlucky and Cozart has been just the opposite. The takeaway here is that Hard% can be used as a predictor for value even over a smaller sample size.

If Hard% is such a good indicator of success, what is the actual value of hitting the ball hard? Hitting the ball hard has been a hallmark of both HR leaders and batting champions. Over the last five years, the HR champion has an average Hard% of 40.12 and the batting champion has one of 35.16%. Although the almost five-point spread is a lot, a Hard% above 35% is nothing to laugh at — it’s still in the upper half of all players.

For the last full season (2016), increasing Hard% by even just 5% added 13 points to the wRC+ value. That is pretty significant. For context, 13 wRC+ is the difference between Aaron Judge and Yonder Alonso so far this year. But has it always been this way? Not exactly. In 2002, a 5% increase in Hard% increased a player’s wRC+ by 20 points. This points toward an interesting trend.


For the last 15 years, the correlation between Hard% and wRC+ has decreased. In other words, hitting the ball hard is not as valuable as it once was. My initial thought was that players aren’t hitting as many HRs as they did in 2002. But that is simply not true. 14.2% of flies result in HRs — the highest rate ever recorded. Perhaps this trend is a result of defenses shifting. Are batters hitting the ball harder than ever, but fielders are now better positioned? The shift is certainly a powerful tool — it kept Ryan Howard out of the Hall of Fame. Still, I’m not convinced the shift is solely responsible for this eerie trend.

Hitting a ball hard is much more important than hitting it high, that is, if you can’t have it both ways. However, the value of hitting the ball hard has decreased for more than a decade. Looking at the data, is it possible that in 10 years we’ll see a sort of “v” shape, indicating a return to the value of hitting the ball hard? Maybe. But for now, this is an interesting trend with no clear indicator.

Why Is Nobody Talking About Adam Duvall?

I was planning on writing about Justin Smoak, but Jeff Sullivan stole my thunder and for some reason people like reading articles written by professional baseball analysts more than articles from college undergraduates (but I guess it’s still worth a read). So, I moved on to the next guy on my list.

First of all, if anyone is going to benefit from their environment in a lineup, it’s Adam Duvall. The Reds have turned out to be one of the most productive lineups in baseball (as a Cardinals fan, it hurts to write that). It starts with the best base-stealer in the MLB followed by the player about to overtake Mike Trout as the best of the 2017 season in terms of WAR, followed by one of the best hitters in baseball, followed by Duvall. He’s protected by a surging Eugenio Suarez, a breakout Scott Schebler (who many in baseball refer to affectionately as “this year’s Adam Duvall”), speedy Jose Peraza, and recently-discovered greatest player of all time, Scooter Gennett. Great American Ball Park has the best right-handed home-run factor in baseball. Overall, Adam Duvall has it good in Cincy.

We’ll start with the most obvious factor in what makes Adam Duvall such a force in the Reds lineup: the elite power. Duvall’s .530 slugging percentage and .258 isolated slugging are good for 26th (right behind Kris Bryant) and 28th (behind Paul Goldschmidt and ahead of George Springer) in the majors, respectively. By all accounts, he is one of the top 30 pure power hitters in the league. This much has not changed. What makes him interesting as a hitter is not a major change of swing plane or pitch selection like Alonso or Lowrie. He has always been near the top in FB/GB rate (20th this season with a 1.22 ratio).

The obvious “yes…but” to all of this is his plate discipline. Yeah…fair point. In 2017, he has a weak 24% K rate, and an even worse 6% walk rate, making a 0.26 BB/K ratio (ouch). We can hope for a Justin Smoak-esque transformation in the future where he starts making contact with two strikes without sacrificing any power, but in the meantime, what we should look for is what happens with the balls he does put in play.

Batted Ball Data

When I examined the batted-ball data, it doesn’t look like there’s a major change.

Year GB/FB LD% GB% FB% HR/FB Pull% Cent% Oppo%
2016 0.72 19.4% 33.8% 46.7% 17.8% 49.5% 31.1% 19.4%
2017 0.82 22.3% 34.9% 42.8% 19.7% 45.8% 33.1% 21.1%

There are very slight adjustments, some that might fall within the range of statistical noise, but interesting nonetheless. It looks like there’s a slight decrease in the number of fly balls, increasing his GB% by 1 and LD% by 2. It also looks like he’s becoming slightly less of a dead-pull hitter and hitting the ball more to center and opposite field. All of this resulted in a slight uptick in his HR/FB rate. This decrease in fly balls is confirmed by the difference in the two years’ launch-angle charts:

2017 Launch Angle Chart

2016 Launch Angle Chart

It seems clear that this year, in terms of launch angle, there’s a much larger difference between his home runs and fly balls. Last year, the majority of his hard-hit balls were square at 20 degrees. This could explain some of the jump in HR/FB rate.

Platoon Splits

One of the things that jumps out in Duvall’s stats from this year to last is the major transformation in results in his platoon splits.



2014 0.272 0.245
2015 0.374 0.233
2016 0.332 0.335
2017 0.338 0.455


What is the reason for this sudden transformation against left-handed pitching? Is it just luck?



2014 0.208 0.231
2015 0.273 0.273
2016 0.273 0.286
2017 0.287 0.353

It looks like there’s a combination of things at play. First, his BABIP in 2016 was right around league average for both right- and left-handed pitchers. His BABIP against righties basically followed the league average while against lefties it rose to almost .050 points higher than the average. It could be luck…or something has really changed for the rising power hitter.

He Goes Down Swinging…Hard

Here’s one of the coolest changes in Duvall’s performance the last few years.

Avg EV/


0-0 0-1 0-2 1-0 1-1 1-2 2-0 2-1 2-2 3-0 3-1 3-2
2016 83.5 81.3 81.4 86.2 84.1 82.2 85.9 90.5 83.0 NA 90.1 91.7
2017 88.0 87.1 91.9 90.7 89.6 87.1 93.7 86.6 88.4 NA 87.7 89.3

The 2016 data seems like what you would expect from a power hitter. Weak contact with two strikes, watch out when you fall behind in the count to him, and full-count with first base open, it might be worth walking him. However, the 2017 data shows a major difference. He’s averaging 92 mph exit velocity on 0-2?? He’s not getting cheated on any count. This explains some of the change in BABIP over the past two years. Instead of choking up and trying to make contact after falling behind in the count, he’s more consistently driving the ball. This comes with appreciable increases in exit velocity when ahead in the count 1-0 and 2-0.

Pitch Breakdown

My next thought was: maybe this is the result of differences in his approach to certain pitches. This is where stuff gets interesting. I looked at the pitch breakdown for the past two years against Duvall and found major differences between years. More than half of the pitches he’s seen this year are fastballs, 138 of them two-seamers (pitchers around the league have decided low and outside sinkers are the only way to get him out). In those 138 pitches, he has a .481 average and is slugging .852…That’s not a typo. Around half of the time his at-bat ends with a two-seam, he gets a hit. Here’s the breakdown of his results against two-seams by year.

Year Pitches Hits AB AVG SLG Whiffs
2016 409 25 107 .234 .570 105
2017 (6/9) 138 13 27 .481 .852 4

He’s always hit them pretty hard when he makes contact (.570 SLG vs .234 AVG in 2016), but the biggest difference is apparent in the last column: he stopped whiffing on the two-seamer. Most of the change in slugging percentage can be explained by the massive .250 point increase in average against what used to be one of the most effective pitches against him. Because his underlying K rates haven’t changed that much, we can assume that it’s not just that he’s putting the sinker in play more, but that he’s driving it.

So we know he can hit the sinker now; what about other pitches? Below are his results on changeups.

Year Pitches Hits AB AVG SLG Whiffs
2016 208 15 49 .306 .612 41
2017 (6/9) 77 6 20 .300 .600 10

He’s whiffing slightly less and still getting on base more often than not, driving the ball a significant amount. While we can expect some of the spike in BABIP to be a result of batted-ball luck (and thus regress in the coming months), some of that change has come from an increase in exit velocity and above-average performance against the pitch that most lefties attempt to put him away with. The lesson here is if I were a DFS player and I saw the Reds facing…I don’t know…a Jason Vargas-type pitcher, it might be worth coughing up the money to buy one of the more-overlooked assets in the Reds lineup.

It’s Time to Revisit Eric Thames, Human Cyborg

Note: This article was originally published at The Unbalanced, with minor alterations

One of the best early stories of this season was that of Milwaukee Brewers first baseman Eric Thames. Thames, a former prospect who never developed into anything more than a journeyman (he was once traded for Steve Delabar, which is a rite of passage for all middling players bouncing around the league), decided to take his talents to the NC Dinos of the Korea Baseball Organization. The legend of Eric Thames begins there. He hit .345 in the Pacific, with 145 home runs in three years. After three years of doing his best Barry Bonds impression, he sought to return to Major League Baseball as a conquering hero this year. Based on what he did in April, that return went exactly as he intended.

Thames became the talk of baseball by that point, and universally praised by the online community. FanGraphs ran four articles and a podcast about him in one week, Baseball Prospectus declared that pitchers are as careful with him as they are Bryce Harper, and even our own Quinn Allen profiled the role his confidence plays in his game. April was a great comeback for Thames, but he has not been as hot since the calendar turned to May:

Everything that made Thames’ April so special dried up to his previous journeyman levels in May. His batting average dropped from “Barry Bonds” to “Mario Mendoza.” He only hit four home runs, three of which came in May. His on-base plus slugging (OPS), which measures how well a hitter can reach base, hit for average, and hit for power, was so low that it rivaled his .727 mark in the majors before leaving for Korea. Additionally, his Batting Average on Balls in Play (BABIP), which measures the role defense and luck plays in a batter’s success, went as south as one can go. This suggests that Thames was the recipient of luck in April, or that something went horribly wrong in May; for Thames, it was the latter.

In May, Thames dealt with a hamstring issue and a bout of strep throat. The hamstring is probably the injury to focus on, because it affected the physical approach Thames took at the plate. I believe that Thames, whom I consider something of an equal to Edwin Encarnacion, is not the player we saw in May and that he will return to his mashing ways after fully recovering from injury.

Normally, I would never bother writing an article in support of a struggling player by citing his injuries, but Thames is a special case because our data sample on him is so small. The idea that a journeyman in the MLB can come back from South Korea and hit like he did in April has drawn many skeptics. Reportedly, Thames has been drug tested five times already this season, and it’s easy to compare his May production to his early career production before going overseas. I want to point out some of the consequences Thames’ hamstring injury has had on his batted ball rates, and then point to the positives:

As you can see, Thames suffered drops in line-drive rate, pull-percentage, and hard-hit percentage; all of these are tell tale signs of a hamstring injury. Fellow writer Quinn Allen, who played college ball at Douglas College, talked to me about the direct causes and effects between the hamstring injury and those rate changes:

“A hamstring injury in Thames’ left leg, the loading leg, can inhibit his ability to pull the ball with power because he generates a lot of his power from the lower half — it’s the back leg in his stance, after all.”

He continues:

“Even though Thames has a very simple swing with minimal movement, not being able to fully use his lower half has affected his ability to turn on pitches for a high exit velocity on a consistent basis.”

Indeed, Thames has struggled to hit fastballs with the hamstring injury. In April, Thames posted a 90.8 MPH Average Exit Velocity (aEV) on fastballs, six of which accounted for his 11 home runs. Since May, that number has decreased all the way down to 86.2 MPH. While we can draw a direct line between Thames’ injury struggles and his struggles at the plate, there are more reasons to be optimistic that he will be back in form soon. There are some positives in Thames’ batted ball rates that I found very interesting:

Despite being limited by his hamstring troubles, Thames avoided rolling pitches over and hitting more ground-balls; in fact, it seems that he made a conscious effort to avoid just that. While decreasing his ground-ball rate, he posted a big uptick in fly-ball rate, all while continuing to avoid pop-ups. Additionally, his soft contact rate only increased minimally; this means that the drop in hard contact we saw earlier was distributed to his medium contact rate. In other words, Thames’ results may have been less productive, but he was never quite weak. There are more encouraging signs that Thames is maintaining the same solid approach that is conducive to generating power:

Thames may be getting less juice on his fly-balls, but he is certainly still hitting the snot out of his line drives. As Quinn alluded to, once the hamstring is fully healed, Thames will be able to transfer the power he is putting into his line-drives back to his fly-balls. David Cameron of FanGraphs noted in April that Thames produced a stellar 97.2 MPH FB/LD aEV. By combining the two batted ball types together, Cameron was able to point out that Thames hammers both fly-balls and line-drives. Even though he isn’t hammering his fly-balls with the hamstring injury, maintaining the damage on line-drives indicates that he will return to hitting fly-balls with authority.

We noted earlier that Thames is hitting fewer line-drives since May; conventional wisdom would conclude that he would probably have had better success while injured by not trying to hit as many soft fly-balls and instead concentrating on hard line-drives. This is an approach that Red Sox shortstop Xander Bogaerts took to deal with the cold weather in April:

“I mean in April it’s not easy to hit home runs,” Bogaerts said to WEEI. “You’re playing in Boston. I know the wall is right there but it’s pretty hard to hit in the cold in general. We’ll hit some home runs, especially when it starts warming up. Looking forward to a lot of home runs from a lot of guys.”

He continues:

“I mean the cold is good and bad for me,” he said. “The good part is that it helps me do a little bit less. My effort level goes down because it’s kind of cold. But when it warms up I start swinging a bit bigger. You feel stronger because of the sun and whatever. The cold is good because I just try to do more contact, don’t want to get jammed or off the end for my hands to feel pretty bad.”

Thames, as we can see in our chart above, is not taking that line-drive approach. His Average Launch Angle (aLA) has only increased (as has his fly-ball rate), which was par for the course for him, but not a player with a bad hamstring. While it’s easy to criticize Thames for not adjusting accordingly, it’s probable that keeping consistency is better for him in the long run. When the hamstring heals and the power returns, Thames will not have to adjust back to his April tendencies, because his swing plane is already where it needs to be. To me, that is a good sign that he will be back with a vengeance soon.

How the Astros Could Not Win the Division

98.4 percent is pretty good odds, correct? According to Baseball Prospectus, those are the current odds that the Houston Astros win the American League West. Houston has dominated the headlines and other teams thus far in the MLB season. The ‘Stros are 42-18, and 12 games up on the .500 Seattle Mariners, who are in second place. It looks like a lock that the Astros are going to win the AL West. I am here to explain to you how the Seattle Mariners can overtake the Astros. Let’s start by analyzing how some of the most important Astros may be due for regression.

Jose Altuve

Altuve is a flat-out stud, and looks to be well on his way to a fourth straight 4-WAR season. There is not that much to worry about looking at his stats this year, but I am going to nitpick. Altuve’s BB/K rate has plummeted this season to the lowest point in his career at 0.61, which is .25 lower than his mark a year ago. Also, take a look at his power numbers relative to some percentages over the last few years.

Home Runs Pull% Soft% GB%
2013 5 32.9 13.4 .49
2014 7 41.8 17.9 .48
2015 15 45.3 19.8 .47
2016 24 45.3 13.6 .42
2017 8 39.6 18.8 .53


Altuve is on pace for about the same amount of home runs as his career high 24 last year, but some numbers point to him hitting fewer balls out of the ballpark. Generally, those who pull the ball have more power, as has been the case with Altuve. This year though, Jose is pulling the ball much less, and is having more soft contact than any full year of his career other than 2015. Also, Jose is hitting a lot more ground balls, a sign of fewer home runs, which so far has not been the case. Additionally, it is not like the second baseman’s average is up with the decrease in fly balls, as it is down 12 points from a year ago. Not only is his average lower, but his BABIP is higher than it has been at any point in his career, a sign of luck. Jose is known for his ability to make contact at nearly anything, but his contact rate his dropped significantly to the lowest point in his career at 84.5%. Lastly, while a quick player, Altuve has been a below-average fielder as far as range is concerned over his career with the exception of 2015. This year, it looks a little unsustainable that his range runs above average is positive.

George Springer

Springer has had a very solid season thus far for Houston, and I had some trouble finding a reason not to believe it will not continue. I soon came across one stat that was very telling. Springer is on pace for over 43 home runs, which would shatter his career high. He is hitting about the same amount of ground balls, liners and fly balls, but his home run/fly-ball rate is an absurd 31.4%. Expect that to normalize and some of those wall-scrapers to be warning-track shots. Also, while a player can improve defensively, they usually do not improve as much as Springer has thus far this year. His UZR/150 in 2017 is almost twice as high as it ever has been in his career.

Carlos Correa

Correa has always been a player loaded with potential, drawing comparisons to Alex Rodriguez. Correa has lived up those expectations for the most part this season, but some of that may be due to luck. His BABIP is very high at .353, 30 points above his career average. Correa defensively has been interesting as well and has been better this year, but it may be unsustainable. The Houston shortstop has been below average as far as errors committed are concerned, but has shot up to above average this season.

Dallas Keuchel

The former Cy Young award winner has other-worldly stats this year. Keuchel was unlucky last year, but appears to be getting a little lucky this year. His ERA is an insane 1.67, but his FIP, a better measure of run prevention, is a much more realistic 3.02. His Left on Base rate is also much higher than it has been at 88.8, a tenth of a percent out of the highest in the majors. Both of these stats indicate luck. Another statistic that does the same is BABIP. Obviously Keuchel is inducing more weak contact this year, but not normally enough for his BABIP to drop over 80 points from a year ago.

Mike Fiers

Upon first glance, Mike Fiers has not a good season, with an ERA in the high-4s. Further research, though, makes it clear that his 2017 campaign may be getting a lot worse soon. His FIP is at a massive 6.53, the second-highest in all of baseball. Also, his BABIP is just .289, over 30 points lower than last year, leading us to think he is not getting that unlucky as far as balls dropping in that would not normally be hits. Fiers’s LOB% is higher than it has been in any full MLB season for him at 86.0 %. The veteran right-hander has had a bad year, but it could get worse soon.


Now let’s take a look at the Seattle Mariners. I actually picked the M’s to win the west preseason (I hope I did not just lose all my credibility). I’ll highlight five players in Seattle that could lead to some success in the Pacific Northwest.

Robinson Cano

The M’s simply will not succeed unless Cano is phenomenal. And while he has been good this year, there are some signs that could point to him being better. His walk rate and strikeout rate are both the best they have been since 2014. That combined with his highest hard-hit percentage of his career, should point to great offensive success. More good news for Cano comes when you look at his O-Swing%, as it is down from a year ago, meaning he is swinging at fewer balls. His contact rate too is the highest it has been since 2014. His BABIP is also the lowest it has been in his entire career, majors and minors included.

Kyle Seager

The Mariner third baseman has been one of the most consistent players in the majors, and had a career year in 2016. This year, though, he is struggling a little bit. His wRC+, a measure of how productive a player is relative to league average, is the lowest it has been since his rookie year. Seager’s baserunning this year has been the worst of his career already, as measure by UBR. This, like defense, is something that is subject to skewed numbers in small sample sizes, and his baserunning should improve to around league average. Another reason for optimism is Seager’s HR/FB%. It has dropped all the way to 8.5%, over 6% lower than last year. Also, his BB% is the same as it was last year, but it should soon rise as evident by his O-Swing%. Seager is swinging at by far the fewest amount of balls outside the strike zone in his career.

James Paxton

Time to brag. I picked Paxton to be in the top three of AL Cy Young voting this year. He has been injured, but him coming back for this Mariner club, and I want to explain just how dominant he has been and is capable of being. He has reached 2.0 WAR in just 48 innings this year. His FIP and ERA are both sub-2, a sign that this success is not all due to luck. His WHIP, a good indicator of future success is the lowest it has been in any full season of his. His Hard% is the lowest of his career, and Paxton’s LD% is by far the lowest it has ever been. To do that with his uptick in velocity is very impressive. Speaking of the rise in velocity, he has been able to keep relatively the same speed on his changeup, increasing the discrepancy between the speed of the pitches. Paxton has all the ability to perform like a true ace the rest of the way.

Felix Hernandez

Hernandez will be coming back soon from injury, but has not performed up to standards of one affectionately called ‘The King.’ There are reasons to think he may turn it around though. He is throwing a greater percentage of strikes than he ever has. The main portion of those pitches thrown are fastballs, and while his fastball velocity is down from his career average, it is up from last year. There are some signs of bad luck too, as his HR/FB% is by far the highest it has ever been, while he’s still inducing fewer hard-hit balls than a year ago. Also, his xFIP is well over a run lower than his actual ERA. Felix may not be the King that accumulated 5.8 WAR a year for a six-year span anymore, but he can still be very effective.

Yovani Gallardo

Gallardo is now on his fourth team in four years, and is having the worst statistical season of that span. His ERA is over six, which obviously is a cause for concern, but his xFIP is in the mid-fours. His HR/FB% is the highest of his career, and his BABIP is the second-highest it has ever been. Additionally, his LOB% is the lowest it has ever been. His stuff is not all bad, though, as his fastball in over 2 MPH faster than it was a year ago. He is also inducing the most swing-and-misses since 2012.

The Free Agent Value of Michael Pineda

Michael Pineda is having by far the best season of his career ever since he broke into the big leagues with Seattle in 2011. This is good news for Pineda who is in a contract year and looking to earn a huge payday on the open market this winter. However, this is bad news for teams, especially the Yankees, who have many questions surrounding their starting rotation with CC Sabathia also in a contract year and Masahiro Tanaka having the chance to opt out of his current contract after the season (although the latter seems unlikely at the moment). Pineda reminds me of one player in particular: former Yankee Ivan Nova.

Like Pineda, Nova has a fastball in the mid-90s and good secondary pitches, including a nasty curve and a change-up which he has begun to develop under Pittsburgh Pirates pitching coach Ray Searage, aka “the pitcher whisperer”. While Nova’s strikeout numbers have gone down, he has learned to pitch rather than just throw, which has resulted in fewer guys getting on base against him as well as his K/BB ratio going down, which I believe have been key contributing factors to his success in Pittsburgh. Also like Pineda, Nova hit the ground running, going 16-4 with a 3.70 ERA in 2011, and he was arguably the Yankees’ second-best starter behind Sabathia. However, as teams began to expose tendencies, combined with mounting injuries, Nova was never able to maintain the same level of success in New York.

The same could be said for Pineda, who missed two full seasons and most of 2014. Even after coming back in 2015, Pineda still struggled to maintain any level of consistency, after posting respectable numbers as a rookie. Now, Pineda has harnessed the power of his wipe-out slider and has become a ground ball pitcher (51.5%) to cope with the home-run haven that is Yankee Stadium. His K/BB ratio has gone down and his WHIP has dropped from 1.35 to 1.13 this season. The formula is simple: the fewer baserunners there are, the better a team’s chances are of winning. Also, like Nova, Pineda is using a change-up more in his pitching repertoire, to complement his slider. As a result, he has generated a 43.3% swing and miss percentage on pitches outside the zone, a 7% increase from last season. Additionally, they are close in age, since Nova was 30 when he signed his new contract, and Pineda will be 29.

The Pirates ended up giving Nova a three-year, $26-million contract last offseason. As long as Pineda continues to have success this season, he will also end up getting a similar deal. I predict he will end up staying with the Yankees for three years for somewhere in the range of$36-39 million simply because the Yankees will be desperate for starting pitching and may even pay a little bit over his market value to keep him. These types of deals are always risky, and many look to the Dodgers signing Rich Hill. However, Pineda has proven that he has always had the talent to pitch in New York and it seems that he finally has his head in the right place to help him reach his full potential. I believe that the Yankees will also re-sign Sabathia to a one-year deal in the range of $5-10 million, considering he will be 37 next season. If the Yankees manage to acquire another lefty or even sign Jake Arrieta, the Yankees starting rotation could be something to look out for in 2018.

Curveballs Are Underutilized Early in the Count

I got the idea for this article thinking about pitching strategy. It makes sense to me that getting to two strikes for a pitcher is an important strategy for good performance. With two strikes, pitchers can get a hitter to swing out of the zone and either make bad contact or miss completely, two of the best possible results for a pitcher. The problem is, how does a pitcher get there without getting knocked around? If a pitcher throws a meatball down the middle in order to get early strikes, good hitters may take advantage and hit the ball hard before the pitcher can get to that good situation. So if a pitcher can throw a strike early, and maximize the chance a hitter chooses not to swing, that seems like the most effective strategy to get to this situation. The research below suggests that if this is the case, throwing a curveball high in the zone early might be a great strategy that almost no one uses.

I initially looked at first pitches going back from the beginning of 2016. I wanted to see which pitches had the highest swing rates on 0-0 counts. I was fairly certain that we would see fastballs with the highest swing rate. To my surprise, changeups have the highest swing rate, despite the lowest zone rate. Curveballs had the lowest swing rate. Below is the breakdown.

Changeup: 34%

Fastball: 29%

Slider: 29%

Curveball: 18%

The changeup swing rate suggests a well-placed changeup on the edges or out of the zone can be a good pitch to throw on the first pitch on occasion. However, with a curveball, you can throw it in the zone and not get a swing a large amount of the time. Given a pitcher’s goal to get to two strikes, the most advantageous count state for him, throwing first-pitch curveballs seems like a smart idea. However, this is not the strategy we generally see from pitchers. Below is percent of pitches thrown on first strikes.

Fastball: 60%

Slider: 14%

Curveball: 9%

Changeups: 7%

These frequencies suggest why changeups are so effective at getting swings out of the zone on 0-0 counts. Pitchers overwhelmingly throw fastballs early in counts, so when the changeup comes, it is very hard to distinguish it from the fastball, which a hitter will expect most of the time.

There are some practical reasons why pitchers throw mostly fastballs on 0-0 counts. First off, they are much easier to command, and as stated earlier, throwing in the zone and getting to two strikes is the main goal for a pitcher early in the count. Offspeed pitches, on the other hand, tend to have much more movement and can be harder to locate. Second, swings and misses aren’t a big deal without two strikes. Fastballs tend to have higher contact rates than offspeed pitches, but contact rates are much more relevant when whiffs lead to strikeouts.

But there are a few reasons why it makes sense for curveballs to be a go-to pitch early in the count. Some pitchers do locate the curveballs very well. Rich Hill is a great example. He famously throws his curveball about 50% of the time, throwing in the zone about 55% of the time the past three seasons. Throwing his curveball so often is probably why hitters swing so little against Hill despite his incredibly high rate of throwing the ball in the zone. Throwing his curveball, especially early in the count, may be a big reason behind Hill’s resurgence.

My next piece of research was looking at pitches high in the zone. I hypothesized that when pitches are located in the part of the zone that moves opposite to the pitch’s movement, hitters would swing less. For example, curveball breaks sharply downward, so a curveball high in the zone will look out of the zone to the hitter, therefore garnering less swings. I think this is logical and probably a well-known concept, but it was something I had never looked into.

I looked at all pitches thrown in the upper third of the zone on non-two-strike counts. Separating out curveballs and non-curveballs, the swing rates were vastly different.

Curveball swing rate: 26%

Non-Curveball swing rate : 65%

The results were overwhelming. There is nearly a 40% difference in swing rate between curveballs and non curveballs high in the zone. Hitters swing a lot high in the zone in general, but with curveballs they barely swing at all.

Very few pitchers utilize high curveballs without two strikes. The ones that do are a mix of bad and good pitchers. Of all pitchers who threw more than 200 curveballs on non-two-strike counts, Carlos Martinez had the highest percentage in the upper third of the zone, 15.3%. Hill is up there as well at 12.7%. But so is Paul Clemens at 14.6%, one of the worst pitchers in baseball. Jake Arrieta was the lowest at 3%, and he’s one of the best.

Early in the count, changeups and fastballs tend to have high swing rates, while curveballs tend to have low ones, especially high in the zone. Pitchers mostly use fastballs early in the count, but sparsely curveballs. While it makes sense to throw curveballs low with two strikes in the count to get swings and misses, this research suggests that a high curveball is an underutilized pitch early in the count.