Earlier this week, 538 put out an article on Ryan Howard, arguing the shift had killed his career…

Rather than the fact he was 37 years old and could not hit or field.

The article paints a picture of a stubborn player who refused to adapt when the league had figured him out:

While some hitters try to overcome the shift with well-timed bunts or tactical changes, Howard always stubbornly refused. “All you can do is continue to swing,” Howard said in a 2015 interview with MLB.com.

Howard’s stubbornness is contrasted with a link to an ESPN article about how a similar slugger (David Ortiz) learned to adjust, and imagines an alternate shift-free universe where Howard remains an MVP threat and HoF material.

This is crap.

Ortiz did not “figure out” the shift. He is a good hitter, who ran a 13% strikeout rate last year. Howard’s is over 28% for his career. I’m sure that the shift hurt him to some extent, but Ortiz and him both had BABIPs around .300 for their careers. He could make that work when he was hammering 40-plus homers, but take that away and there’s not much left. My guess, old age is what did him in. But this lead me to wonder, how does the shift actually work?

Many people treat the shift like some mystic boogeyman, out there to either ruin the game, or certain players in particular unless they “adjust.” As a Twins fan, I know many people who blame Joe Mauer’s decline on the shift.

Personally, I would like to just throw this chart out there:

This is the MLB BABIP on groundballs over the last 16 years. Notice how it didn’t go down at all. I don’t have the numbers to prove it, but I think we all know shift usage has exploded since 2002. Not a huge change in ground-ball outcomes. So where has it changed the game? A decline in line-drive BABIP over time. However, counteracting that’s the fact that fly-ball BABIP has gone up. Again, to the charts!

I wondered if some “line drives” of the past were simply fly balls that landed for hits, while outs were labeled “flies.” I don’t actually know if that’s true, if the process where line drives/fly balls are defined has been altered, but I decided to take a look at combined “air-ball” BABIP to see if it has changed over time. So here is the BABIP on all non-ground balls:

2017 is pretty clearly an outlier, but considering less than half the season’s in the books so far, and I have no idea how “air-ball” BABIP moves over the course of a season (more hits find grass when weather is warmer? no idea), I wouldn’t put too much stock in that just yet. Another option I had considered was that maybe the breakdown of line drives vs fly balls has changed over time. Since 2002, 36% of air balls have been line drives, and while some years are higher and some lower, there doesn’t seem to be any particular “trend” with respect to that number; the first eight years average 36% and the last eight have as well.

I know the shift has an impact on run scoring in aggregate. But in my opinion, skyrocketing strikeouts and the home-run explosion are the markers of the modern version of this nation’s pastime, not on which side of second base the shortstop stands.

Bartolo Colon has not been good. There is no way to spin things to say that he has been good. Conversely, it is pretty easy to spin things to say he is bad. After another bad outing on Monday, his ERA is 7.78 in 59 innings of work. Masahiro Tanaka and Bronson Arroyo are second- and third-worst among qualified starters, at 6.34 and 6.24 respectively.

However, if you look at other statistics, they are not so bad. By FanGraphs’ measure of WAR, he is a tick above replacement level. His K% and BB% have both trended in the wrong direction by a couple points when compared to recent three-year stint with the Mets. His HR rate is up, though some of that may be attributable to what might be a very homer-friendly home park. Colon has also suffered from some bad batted-ball luck, with a BABIP of .353, only .004 points lower than his 2007 season that ended his tenure with the Angels and made many question if he was finished.

However, above all else, what is hurting Colon is probably his strand rate. As of right now, his LOB% is 48.5%. This is terrible. This is pretty much without precedent. And here is a table to show exactly how unprecedented this is:

When you see charts like this and statistical points like this, one thing that should always pop into your mind is that the 2017 figure represents about one-third of a season. Regression to the mean should make Colon’s LOB% go up over the course of the next year. Unfortunately for Colon, he is on the wrong side of 40 and often times when older players struggle, whether fair or not, it spells the end of the road. However, there is evidence that in cases like this, pitchers do not get the opportunity to play their ways out of struggles, regardless of age.

What I wanted to do here was look up pitchers who had similar LOB% to Colon through a comparable amount of the season, and to see what happened to those players. To me, that would have been ideal. However, I get an error message when I try to do that on the leaderboards, so I’ll have to present some less ideal numbers and invite anyone else who has access to look into this further.

Going back to 2002, using a minimum of 50 innings pitched, Colon still has the very worst LOB%, just ahead of a guy you might have heard of, Roy Halladay, who clocked a 49.4% rate in what was a truly dreadful 2000 campaign. Looking through the bottom 50 LOB% list, you will find a couple interesting trends. First, a lot of these players played for terrible teams. The early-2000 Tigers and mid-2000 Devil Rays have a few entries. Colon joins Williams Perez’ extremely forgettable 2016 season as the recent Braves on this list. Second, aside from Derek Lowe in 2004, none of these pitchers came close to pitching a full season. Lowe, who checks in at #26 on this list, had 10 more starts and 45 more innings than the second-highest total.

What this list does not account for, however, is that there could be pitchers like Colon that do very poorly in the LOB% department early in the season, but then turn things around due to better luck and thus do not end up on this list. In order to do this, I wanted to look at players that were similar to Colon’s 2017 season. Colon sports a 123 FIP-, which is worse than league average by a decent amount, but not close to his 184 ERA-.

Looking at the next 10 worst LOB% ranked players, you see that they were not having good seasons. Here are the players:

And here is how they compare to Colon’s 2017 (numbers as starting pitcher only):

Finally, a quick rundown of what happened to each of these players during their unfortunate seasons:

Boof Bonser: Bonser was demoted after May 31^st to the bullpen, and finished the year there. Bonser was victimized by a horribly unlucky May, where his LOB% was 33.3%. Despite a lack of actual good pitching, the Twins did give Bonser a chance to improve his luck despite him being only 26. He had surgery in the offseason and barely played in the majors after that.

Dallas Braden: Braden actually did a pretty good job of keeping runners from scoring in his 2007 rookie season when he was coming out of the bullpen, but he was awful as a starter. Still, it seems as though the going-nowhere A’s did not hold Braden back as he finished the year as a starter. Braden was fairly successful until injuries cut his career short, most notably pitching a perfect game in 2010. It’s possible that Braden, 23 at the time, was helped by the A’s decision to let him continue in his starting role at the major-league level.

Charlie Morton: After starting with a 9.35 ERA, Morton was disabled, sent to sports psychiatrist, and demoted to the minor leagues on May 27^th. He was able to return on August 29^th, and had a decent rest of the season. Morton, who was 26 that year, has bounced around as a fringe starter ever since.

Jose Lima: Lima was a bad pitcher in 2000 and 2001 and somehow managed over 50 innings as a starter in 2002 to make this list. He struggled to a 7.77 ERA and famously responded to his release by Detroit by claiming he was “the worst pitcher on Earth.” Twenty-nine at the time, he managed to start 74 more games in the majors after that.

Brian Duensing: Duensing was 29 in 2012 and he makes this list because he managed to make just enough spot starts, despite the fact he was mostly a bullpen guy. For what it’s worth, Duensing’s 11 starts in 2012 were his last, though he still pitches in the majors. The fact that he went into the season thought of as a bullpen guy means you cannot make much out of his trajectory.

Taylor Buchholz: In his 24-year-old rookie season, Buchholz was not bad by peripheral stats when he was demoted to AAA on July 29^th. The Astros, with nothing to play for, had given up on him and traded him to the Rockies, where he has two adequate years mostly pitching in relief. While it would be a stretch to say that he could have been wildly successful had he been given a chance, even a team with a new, forward-thinking GM was unwilling to look past the painful on-field results.

Justin Germano: At 29, Germano got a shot to end the year with the Cubs, and performed okay based on peripheral stats. But Germano was a journeyman player who recorded 23 of his career 48 starts in 2007. He was demoted and released, but honestly it would be the toughest sell job to say that he had any real potential.

Charlie Furbush: Furbush was a mediocre reliever in his age-25 rookie season when he was traded to Seattle in the Doug Fister trade, and for some reason the Mariners let him finish the season as a starter. He wasn’t good, but he was also very unlucky in the stranding-runners department. The Mariners held onto him, but put him back in the bullpen, where he was okay.

Yohan Flande: Flande was a 28-year-old rookie in 2014 that only made two starts after mid-August thanks to his struggles. He has barely been heard from since.

Josh Fogg: Fogg was an old man compared to the rest of this list (except of course for Colon) at the age of 33. I can’t find any indication that Fogg was demoted due to his struggles, and he finished the season in the rotation. The Reds were not playing for anything. After the 2008 season, he barely played.

Conclusion: Players with poor LOB% generally are not pitching very well, and generally are not given a chance to recover. It is likely that extremely poor strand rates are correlated with pitching poorly. Colon’s stint with the Braves and his time in baseball may be coming to an end, and he has likely been the victim of some historically bad luck. But the bad luck can only explain so much. Most of the pitchers who have pitched like Colon in the past were young guys who ended up converting to relievers or guys that were on their way out of the game. Only Braden and Morton remained starters for a significant amount of time afterwards, and Morton has been below average. They were also both almost 20 years younger than Colon is now. In other words, considering all of the bad numbers Colon has, even when taking into consideration his bad luck, there is probably not a good case to be made for giving a fat guy a chance. And he probably won’t get one.

Suppose I tell you that a baseball team has just started the season 10-0. You literally know nothing about the team besides this information. What is a reasonable expectation for the number of games this team will win? Even if you don’t know the answer offhand, you probably know that the answer is not “162.” Tom Tango has been taking to Twitter recently to mock these “on-pace” numbers, and for good reason — saying the above hypothetical team is “on pace” for 162 wins has no real meaning in reality. So how do we fix it? I’m going to proceed in a way that a Bayesian statistician might, but mostly explaining the logic behind the reasoning, rather than going through any complicated math. So follow me if you want to see how a statistician thinks.
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How valuable is a first-round draft pick?

The draft is one of the most important resources for teams to add players that allow their organization to move in the right direction. But how quickly do these top-rated amateur players make a splash, and is it with the team who selected them?

Objective

My goal with this project was to analyze the type of overall impact first-round draft picks have on the organization that drafted them and observe how quickly an impact was made. Many first-round prospects are expected to move successfully through the ranks of the minor-league system, with the idea that they will impact their big-league affiliate in the near future. This of course isn’t always the case even for can’t-miss amateur prospects, as it is well known that only 10% of players in the minor leagues will make it to “The Show.” All players have different ways of developing and adapting based on the level of baseball they are drafted out of (High School/College), as well as what type of minor-league development systems they become part of moving forward.

In this analysis, I looked at the first-round draft classes from 2006-2010, which gave me a sample size of five different draft classes. The value of a prospect, especially in the first round, is in his potential to produce at the Major League level during his first six seasons of service time. This of course is based on Major League Baseball’s salary system that pays players very poorly, most of the time (relative to their market value), for their first six years of service time before becoming eligible for free agency. This is the reason why I chose five draft classes, with the last class just finishing up their sixth possible year of service time and becoming eligible for free agency following the 2017 season.

Method

The sabermetric stat that I used to analyze these five first round draft classes was WAR (Wins Above Replacement). I chose WAR because it is an analytical way to look at a player’s overall value to their team, while also being able to compare players from different timeframes in baseball, such as the first-round draft class from 2006 to the first-round class in 2010. The values are expressed in a format of wins so I can look see pick A is worth 5.2 wins to his team, while pick B is worth 7.8 wins to his team in that given season.  As a measure of their success, I looked at the full first-round draft classes from 2006-2010 and calculated the WAR of each class through the first six years of possible service time. For the 2010 class I calculated their WAR heading into the 2017 season. Calculating the WAR ranking for each class gave me a better understanding of just how impactful certain first-round picks have been for their team within their first six years of club control. My analysis also revealed the large number of highly-touted prospects drafted in the first round (outside of the Top 10) who failed to make substantial contributions to their team on the field. When calculating the WAR through the first six seasons of possible ML service time, I was also interested in looking at whether these picks were selected out of High School or College and the total amount of service time they had within through the 2016 season.

By The Numbers

2006: HS – 13, College – 17

Avg. ML Service Time – 3.66

Avg. WAR – 4.05

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2007: HS – 17, College – 13

Avg. ML Service Time – 2.60

Avg. WAR – 3.00

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2008: HS – 9, College – 21

Avg. ML Service Time – 3.35

Avg. WAR – 2.83

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2009: HS – 17, College – 15

Avg. ML Service Time – 2.06

Avg. WAR – 3.68

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2010: HS – 17, College – 15

Avg. ML Service Time – 1.04

Avg. WAR – 3.65

Conclusions

• First and foremost, there is no exact science as to whether a first-round draft class will be comprised of more high-school players or more college players. It depends on the stock each year. In 2006-2010 the most skewed first-round draft class between the two levels of play was in 2008 when there were 21 players drafted out of college and just 9 out of high school. This class also owns the lowest average WAR at 2.83 through their sixth season of service. The class is carried, far and away, by Buster Posey (#4 out of FSU) who owned a combined 22.8 WAR rating through his sixth season with 6.161 seasons of service time through 2016 (1^st in class). The remaining 29 picks in the 2008 draft combined for just a 2.14 WAR through their six team controlled seasons, led by Brett Lawrie (12.2 WAR), who is currently out of Major League Baseball.

• The class with the highest average WAR through their first six seasons is the class who has been around the longest; the 2006 1^st round draft class with a 4.05 WAR. The class production within their first six seasons also went more with the stereotypical draft script as four players within the top 12 picks exceeded a 10.0 combined WAR through their first six seasons (College #3 Longoria 29.8,  HS #7 Kershaw 24.3, College #10 Lincecum 23.9, College #11 Scherzer 11.4). Picks 12-30 combined for a minuscule 0.97 WAR.

• Although it seems that all we hear about when it comes to top 10 picks in drafts are those who failed to perform up to the expectations, there is something to be said for the production a top-5 pick can bring to an organization. In my WAR calculations, the #1 and #4 picks from the 2006-2010 draft classes owned the top two average WAR rankings, with the top pick averaging out to 10.98, and the fourth pick averaging out to a 11.68 WAR ranking. Picks 1-5 from 2006-2010 combined for an average WAR of 7.51 through six seasons.

• The numbers show that teams who are in rebuilding modes have a distinct advantage at developing their farm system, and in turn their big-league clubs, with a top-10 pick. The 50 players selected in the top 10 picks from 2006-2010 combined for an average WAR of 6.236. While picks 11-32 (104 total players) combined for just a 1.84 average WAR across their first six seasons of service time.

• There’s an argument to be made for the average player drafted out of High School taking a bit longer to develop into a big-league player than that of a player who has been drafted out of college. The WAR numbers of the first-round draft picks from 2006-2010 speaks to this theory as well. First-round college draft picks produced a higher WAR than those drafted out of high school in four out of the five draft classes I analyzed. First-round selections out of college produced an average WAR of 4.21, while players drafted out of high school produced an average WAR of 2.59.

Wins above replacement isn’t a tell-all story, and neither are the first six years of a professional baseball player’s career. It is, however, a nice way to analyze the overall contribution and impact a player can have for his team, and the first six years gives us a glimpse at just how quickly a team’s investment might pay off.

Draft Analysis Data Sheet

2006

2007

2008

2009

2010

Pick by Pick (#1-#32, 2006-2010)

There is always a lot of talk about the AL being better and the interleague record usually supports that, but this year it seems to be especially severe. The AL is once again dominating IL play and there might be some scheduling and market-size reasons for this, but also when looking at other factors the AL seems to be much better.

The number of very bad teams:

KC and Oakland have been quite bad, but still the three worst records belong to NL teams.  If you look at below .450 teams you have only the two mentioned teams in the AL, but six teams in the NL.  And that is with the Brewers as one total rebuild team actually over-performing. If you look at the teams that even try to compete you have the Braves, Padres, Phillies, Reds and Brewers as full rebuilders while in the in the AL only the White Sox are fully committed to rebuilding. Now you could say that the A’s and Royals should do a full rebuild but the same could be said for the Marlins. However you slice it, there are way more non-competitive teams in the NL than in the AL.

The WC Contenders:

There is a weak division too in the AL with the West, but there are still at least five somewhat credible WC contenders including all AL East teams and probably one of the Twins or Tigers.

In the NL that field has been thinned out to the Cards and the two overperforming West teams (although the Cards, like the Tigers and Twins, are basically projected as .500 teams now).

Now the Dodgers and Nats are really good but even the third supposedly great team, the Cubs, has been mediocre, albeit they should win the division rather easily considering the abysmal state of their division.

Overall the AL seems to be in a much better state as both the East and the Central division of the NL are in a really bad state.

There is hope of course as the Braves,  Brewers, Phillies and even Padres have some good young players and minor league prospects and the Reds have some big league success with position players that were somewhat unlikely prospects, but all of those teams still have ways to go.

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Marco Estrada has a .302 BABIP. If you don’t know, Estrada has been one of the best pitchers at limiting batting average on balls in play. Of the 41 qualified pitchers who have at least 750 innings pitched throughout their career, Estrada has the sixth-worst BABIP difference this season relative to his career.

Despite this increase, Estrada has managed a 3.86 ERA. It’s not great but it ranks 43^rd among qualified pitchers (90) this season. Marco’s 3.59 FIP ranks 25^th, one of the more intriguing developments of this season. From 2015-2016, Estrada had the second-largest difference between his FIP and ERA, behind only Dan Haren, who did not pitch in 2016.

One of the game’s better contact managers, Marco Estrada looks to be adapting. The Blue Jays ace has the best strikeout-to-walk ratio of his career thus far. Estrada has the thirteenth-best strikeout percentage this season, sandwiched between Cardinals ace Carlos Martinez and Nationals pitcher Stephen Strasburg. There are 45 pitchers who qualified for the leaderboards for the past two seasons, only six of which had a greater K/9 increase. Driving this increase looks to be the change.

Estrada’s changeup is one of the best in the league. It’s not a hard change like Stephen Strasburg’s; rather the second-slowest in the league, ahead of only Jered Weaver. There are a couple of factors that make Estrada’s changeup one of the best. Foremost, it comes with an 11 MPH separation between his fastball, making it great for generating whiffs. Furthermore, his release points allow him to deceive batters. The pitch comes from a similar angle as the fastball but travels 10+ MPH slower, making it more difficult to pick up. If you’re thinking the fastball is coming, and a split-second later you realize it’s much slower, by then you’ve already swung as the ball goes right by you. Lastly, the pitch gets little drop. Estrada’s vertical movement on the changeup was 2.56 inches higher than the next right-hander, Chase Anderson. This is another problem for the hitter as the pitch barely drops relative to a major league pitcher’s average changeup.

In the end, you’ve got a pitch that might look like a fastball from the arm slot, is going quite slow, and doesn’t drop much. You can see how the batter faces a tough quandary. The fastball-changeup combo play off each other well. Deception is a key part of Estrada’s arsenal. To get even better, Estrada began to utilize his best pitch more. Using your best pitch isn’t a novel concept. We’ve seen Rich Hill and Lance McCullers Jr. have success in this mold.

Decreasing the usage of the cutter has brought better performance thus far. The cutter is inducing more swinging strikes, and less contact, as hitters have swung more often when he throws it. In 2016, Estrada threw 625 cutters leading to a .352 wOBA, the worst of his four pitches. In 2017, Estrada has thrown 93 cutters, to the tune of a .272 wOBA, currently the best of his repertoire. Why the change? The cutter has seen a massive drop in vertical movement, likely the reason for its reduced usage. While the results have been better, the process might not be. Marco has been unable to get sufficient rise on the cutter. Moreover, the increased effectiveness might simply be due to small sample size. Or perhaps throwing it less brings its own added value.

ACEstrada, as he is affectionately known as to Jays fans, has ramped up usage of his four-seam fastball as well. The pitch is still strong and it’s traveling a mile faster. It won’t keep a 31% strikeout rate but it should continue to induce lots of infield fly balls. On the downside, the average launch speed on the fastball on line drives and fly balls is up 1.5 MPH from last season, to 94.7 MPH. This would explain part of the .316 BABIP it currently sports, up 52 points compared to his career. With a first-pitch-strike rate the highest since his last season with the Brewers, and his best walk rate since 2013, Estrada’s not making it tougher than it has to be. Pitches inside the strike zone are at the highest rate of his career. Once again, it’s because of a changeup he’s commanding very well. It’s practically 50/50 whether the change will make it in the zone, up 8 percentage points relative to his career average.

Looking at Estrada’s batted-ball profile, the big one that jumps out is the decrease in popups. He’s inducing more than 50% fewer popups this season relative to last year. The main culprit: the changeup.  Given Estrada has an 18.2% popup rate on his changeup compared to the changeup generating popups at a 34% clip during his career, it’s likely this issue sorts itself out as the season progresses. With good command, Estrada is capable of finding those easy outs through strikeouts or pop-outs.

To counteract a cutter not moving like it usually does and some BABIP regression, Estrada turned to his two best pitches. The ERA should improve as the season progresses. Being a two-pitch pitcher isn’t an easy task; Estrada has the command of his two primary pitches to pull it off. The key during the rest of the season will be to hold his strikeout and walk gains while continuing to be one of the league’s better contact managers.  Combined, the Blue Jays ace might be getting better. Marco Estrada will play a key role down the stretch; whether it be with the Jays or for a contender in a contract year.

Dallas Keuchel has reemerged as an ace for the Houston Astros this season, as he has posted a 1.71 ERA thus far and is yet to lose a game. He has an absurd 67.4% ground ball rate while still maintaining an 8.21 K/9 innings. Keuchel’s performance has been impressive, but his brilliant pitch repertoire may be even more impressive. Starters in the MLB essentially need at least three pitches. However, a lot rely on two pitches, while sprinkling in a third out of necessity. Possessing confidence in three pitches can be a commodity. But not only does Keuchel have three weapons, he has four pitches that he can effectively use.

It all starts with the two-seam fastball for the bearded ace, which he is throwing almost exactly 50% of the time this year. Hitters are slashing .179/.252/.291 against the pitch, and it’s drawing a GB% of 80.8%. Watch the pitch in live action:

The pitch sinks at the last second, dropping from Joey Rickard’s knees as it crosses the plate to nearly hitting the dirt. Rickard may not be the poster child for hitting, but there isn’t much you can do with that pitch. Even if it doesn’t have the ridiculous late sink, it puts hitters in a bind. It’s perfectly located down and away, so hitters have to reach to get the ball. Maybe you can send it to the opposite field, but Keuchel’s two-seam generally comes in below 90 mph, so a hitter is gonna have to put a hard swing on that to get a solid line drive. And with they way it keeps guys off balance, hard swings usually aren’t finding that pitch.

But that is just one pitch, you say. Keuchel can’t replicate that perfection often. Well…

Keuchel rarely misses his spot with the two-seam, making it a dangerous ground ball/strikeout weapon for him. The two-seam is Keuchel’s most commonly seen fastball, but it is not his only one. He actually throws a cutter to accompany his fastball. The cutter is his least thrown pitch of the repertoire, but he still throws it 10.8% of the time. What’s rare here is the two-seam and cutter combo, as Keuchel is one of only four starters that throws the two-seam at least 25% of the time and the cutter at least 10% of the time.

His cutter is quite effective too, as hitters have a .174 average against it. Similar to the two-seam, Keuchel has great command of the pitch. He knows where he wants to throw it and, usually, he puts it right there. The cutter isn’t quite the ground-ball pitch that the two-seam is, but rather Keuchel uses it jam righties inside. The cutter has the highest infield fly-ball rate (20.0%) of his four-pitch arsenal.

Next is Keuchel’s slider, which he throws 22.2% of the time. Hitters are slashing just .125/.143/.208. His slider is incredibly effective, but it is also different than most sliders. In terms of vertical and horizontal movement, below is your average slider from a lefty:

Blake Snell’s slider breaks down and in, but now look at Keuchel’s slider:

Keuchel’s slider has a ton of horizontal movement, but has almost no downward break. It averages only half an inch of vertical movement. His command of the pitch isn’t nearly as pretty as the fastballs, but that makes sense considering it’s a breaking ball.

Last not but not least, Keuchel’s changeup, which he throws 12.7% of the time. The pitch has limited hitters to just a .233 slugging in 2017. And, like with any of his other pitches, Keuchel throws it where he wants to. Keuchel also kind of has a four-seam fastball, but the pitch is used very rarely and isn’t really part of repertoire.

But back to why I’m writing this in the first place. If you look back at the heat maps for all of Keuchel’s pitches, it’s pretty clear that, aside for elevating his changeup on occasion, Keuchel keeps everything low. All of his pitches consistently land across one plane at the bottom of the strike zone, covering every part of the plate from left to right. When you consider his slider is what it is, Keuchel essentially doesn’t have a true breaking ball. Why this is so odd is because every one of his pitches, in terms of vertical movement, moves in a straight line and lands in the same place every time.

However, every one of his pitches is moving side to side, so Keuchel never gives you anything straight up. They are always going to be cutting or fading. But Keuchel throws all of his pitches relatively slow, so they are not easy to discern based off velocity. If you combine that with the fact that all of his pitches are landing across the same plane and not breaking, it makes it incredibly hard to recognize his pitches.

Just to make it even harder on hitters, here is Keuchel’s pitch mix by count. It’s always going to be a heavy dose of two-seam fastballs, but any of his secondary pitches can be thrown at any time.

So, Keuchel can throw you four different pitches, that all look similar, at any time he wants and exactly where he wants to throw them. That sounds like a recipe for success. Keuchel’s pitch mix may be different, but it is about as effective as anybody’s. Despite extremely limited velocity and stuff, Keuchel remains one of the top pitchers in the game because his command and ability to mix pitches is truly beautiful.

Earlier this month, Eno Sarris posted an article called “Could Ichiro Have Been a Power Hitter?,” which began with a launch angle and exit velocity analysis of Ichiro himself, and developed into a wider examination which led to the interesting proposition that “players may have their own ideal launch angles based on where their own exit velocity peaks.”  In this article, I’ll look at a larger sample of players whose fly-ball rates increased from 2015 to 2016 and see if their peak exit velocity range changed or stayed constant.  First I’ll re-examine Elvis Andrus, then I’ll look at Jake Lamb, Xander Bogaerts and Salvador Perez.

Elvis Andrus

As mentioned by Eno, Andrus’ average launch angle went from 8.1 in 2015 to 8.6 in 2016, but his fly-ball rate actually decreased.  It seems like he started the change in 2015, but was only able to translate it into results (a 112 wRC+) in 2016.  Regardless, let’s look at the data again, and see what we can find.

Instead of just qualitatively looking at the distribution and giving an approximate range of maximum exit velocity, I split the data set into launch angle buckets, and found the bucket with the highest median exit velocity.  For example, if I set the bucket size at 5 degrees and applied it to Elvis Andrus in 2015, I got a range (-2°, 3°) (I’ll omit the degree symbol from now on).  If I set the size at 10 degrees, I got a range (-2, 8).  For the rest of the article, I’ll keep it set at a range of 5 degrees.

The peak range for Andrus’ 2016 was (-3, 2).

Using the method outlined, the peak range for 2015 was (-2, 3), and for 2016 it was (-3, 2), so Andrus’ peak exit velocity range did not change much from 2015 to 2016, just as Eno pointed out, and as we can see with the two years overlaid.

Jake Lamb

Comparing 2015 and 2016, Jake Lamb raised his average exit velocity from 89.7 to 91.3 MPH, and his fly-ball rate from 32.4% to 36.7%.  His adjustments were chronicled by August Fagerstrom during his breakout (http://www.fangraphs.com/blogs/jake-lambs-revamped-swing-made-him-an-all-star-snub/).

The peak 5 degree range for Jake Lamb’s 2015 was (3, 8).

The peak 5 degree range for Lamb’s 2016 was (15, 20)!

Unlike Andrus, Jake Lamb’s peak exit velocity range increased along with his launch angle distribution!  This seems to be the kind of effective swing change that players attempting to join the fly-ball revolution strive for.  Lamb managed to revamp his swing to not only elevate the ball more, but to hit the ball harder at high launch angles, and actually increase the angle at which he hit the ball the hardest.  However, as the next two cases show, this is far from a guaranteed outcome.

Salvador Perez

Perez’s peak 2015 range: (9, 14).

Perez’s peak 2016 range: (0, 5).

From 2015 to 2016, Perez increased his fly-ball rate from 37.4% to 47.1%, and increased his average exit velocity from 87.3 to 88.8 miles per hour.  He also increased his average launch angle from 13.7° to 19.1°.  But curiously, his peak exit velocity range actually went down from (9, 14) to (0, 5)!  When I saw this, I thought I’d have to change my methods, because it didn’t make sense to me at first.  But if you look at Perez’s exit velocity vs. launch angle graphs for 2015 and 2016, these ranges actually seem to qualitatively fit.  Somehow, the Royals backstop managed to hit the ball harder and higher, but become more effective at lower launch angles.  This could be a rising tide lifts all ships situation, whereby his swing adjustments let him hit tough low pitches hard at lower angles, or it could just be a sample size issue.  By splitting the data set into buckets, the sample size gets dangerously small, and prone to strange results.  But I think the results fit the picture, and either Sal Perez needed to hit more balls for us to get reliable results, or he just had a strange batted-ball distribution.  We have a similar, more extreme situation with Xander Bogaerts next.

Xander Bogaerts

Bogaerts’ peak 2015 range: (5, 10).

Bogaerts’ peak 2016 range: (-6, -1).

Bogaerts, like the other three players here, hit the ball harder in 2016 than in 2015.  He raised his fly-ball rate and his average launch angle, and was rewarded with a 113 wRC+, a slight improvement on his 109 wRC+ from 2015.  But his peak exit velocity range for 2016 was, like Perez, lower than in 2015.  Looking at his plots, it looks like he hit his ground balls harder in 2016, while not changing the exit velocity of his line drives and fly balls as significantly.  I’m not sure what else to say about Xander, other than that he’s kind of a weird player, as already noted by Dave Cameron (http://www.fangraphs.com/blogs/xander-bogaerts-is-a-very-weird-good-player/).

Summary

The following table summarizes the findings for each player.

It seems like Andrus improved by simply hitting the ball harder and staying within his peak exit velocity range of launch angles (which fits Eno’s hypothesis), whereas Jake Lamb improved by hitting the ball harder, raising his average launch angle, and shifting his peak exit velocity range (which runs contrary to Eno’s hypothesis).  Perez and Bogaerts didn’t really improve, and their Statcast data yielded some strange results, which suggests that this method is far from foolproof, and that there may have been better choices of players to investigate.

Many thanks to Eno for the inspiration for this article, and to Baseball Savant for all of the Statcast data.

Early on in what has been another exciting MLB season, we have been introduced to many new players, all while having the privilege of seeing some old ones as well.  Over the course of the last two months, we have been introduced to some breakout performers like Aaron Judge of the Yankees and Michael Conforto of the Mets.  Along with them, there have been a few players that haven’t lived up to their previous performances, like Matt Harvey of the Mets and Carlos Gonzalez of the Rockies (written up wonderfully here).  I will illuminate five cases of players exceeding expectations and five more of those falling short.  Along with that there will be an investigation on whether that performance could be sustainable long term.  Hopefully you can use some of this to learn more about some exiting major leaguers and glean some insights for your fantasy team!

For explanations for any of the stats below, look to http://www.fangraphs.com/library

5 Overperformers

Miguel Sano, Minnesota Twins 3B/DH

Relevant Statistics: .299/.408/.592, 13 HR, 41 RBI, 2.6 fWAR

One of the most dramatic over-performers of the new season is the massive man manning the hot corner in the Twin Cities.  The 6’4 260 lb. 24-year-old is absolutely tearing the cover off the ball and has fueled the Twins to a AL Central leading 28-24 record.  Sano is currently in seventh place on the leaderboard for fWAR (the FanGraphs version of Wins Above Replacement), already amassing a career high 2.6.  Driving this success is both his bat and his glove.  Sano is hitting for a 165 wRC+, signifying that his offense has been worth 65% more runs than league average (for more explanation on this stat look here).  Also, the young slugger’s glove work is behind his improvement.  In previous seasons, Sano has proven to be an average fielder, using his strong arm to make plays that many others could not, yet also losing nearly seven runs of value from errors. This year however, Sano has improved his defensive play, increasing his fielding percentage from .896 to .959.

Sustainability:

Sano has long been known for his prodigious power and strong arm.  Despite a down year in 2016, many were still high on his batting ability based on past performance.

Here is his prospect writeup from John Sickels of minorleagueball.com in 2014:

Power-mashing beast, comparable to a young Miguel Cabrera. He may not hit for the high averages that the mature Cabrera has produced, but power should be similar. Sano has made a lot of progress with the glove and a move to first base is not automatic.

Being compared to Miguel Cabrera is impressive for any hitter, let alone a minor leaguer like Sano was in 2014.  Sano was a consensus top ten prospect in baseball.  There are a few warning signs that some aspects of Sano’s play may slip, such as his .464 batting average on balls in play, while the MLB average hovers around .300.  He also has had very shaky defense in the past, and his rapid ascent into the top ten defensive third baseman could be taken with a grain of salt.  However, Sano hits the ball harder and more consistently than just about anyone, and he walks enough (15.5% BB) to mitigate some of the risk inherent with his absurd strikeout rate (37.4% K). He easily leads the majors with a 51.6% hard hit percentage, and a 98.7 mph average exit velocity, which both demonstrate that Sano is achieving insane consistency and power when putting bat to ball.

Verdict:

Look out for this young fella.  If you tune into a Twins game in the near future, do not be surprised if you see Sano send a ball into orbit.  Expect him to take pleasure in ruining ERAs for years to come.

Robbie Ray, Arizona Diamondbacks SP

Relevant Statistics: 69.0 IP, 5-3, 3.00 ERA, 3.27 FIP, 1.7 fWAR

Robbie Ray has been one of the most frustratingly inconsistent starters in recent memory.  Blessed with a fastball that can run up to 98 mph, and a slider that sits around 83 that accumulates a 40% strikeout rate and 20% swinging strike percentage, Ray has all the tools to be a dominant starter.  Last year, he dazzled with his outings against the Marlins on June 12th (7.2 IP, 0 ER, 3 H, 1BB, 6K), and the Padres on August 20th (7.0 IP, 1 ER, 1 H, 13 K, 1 BB).  He also had a few meltdowns, allowing 5 runs in three innings on April 24th to the Pirates and another 5 runs in 4.2 innings against the Padres on May 27th.  Ray has seemingly made adjustments, and is pitching like an ace this season.  He is top 12 in the majors among starters in fWAR and is sitting in sixth place in strikeouts per nine innings (10.96).  He has managed to avoid too many blowups and produced one of the most impressive starts of the season on May 30.  He spun a complete game shutout of the Pirates while only allowing four hits, no walks, and ten strikeouts.

Sustainability:

First, lets look at the issues that led to Ray’s disappointing performance (4.90 ERA) in 2016.  First off, he calls Chase Field home. Known as an extreme hitters park, Chase ranked third from the bottom for pitchers when considering the whole major leagues, behind only noted hitting havens Coors and Fenway.  Ray also led the majors in batting average on balls in play allowed.  Again, where the major league average is around .300, Ray’s was at .352, indicating a bit of bad luck.  He was no doubt hurt by the injury to his rangy center fielder A.J. Pollack.  Finally, Ray issued too many free passes, in the bottom ten among qualified starters in BB%.  This year, Ray once again has Pollack manning center, and his BABIP has plummeted nearly 100 points to .252.  Meanwhile, his strikeouts are marginally down, while his walks are up.  This points to Ray maybe getting a bit more lucky this year, or at least regressing to the median, and this stands while looking at his left on base rates.  Ray has allowed a much lower percentage runners to score after reaching base compared to last year.

Verdict:

Ray still calls the desert his home, and will always have a challenging home park. Considering his penchant for striking out or walking seemingly every batter he faces, expect Ray to continue to be challenging into the future, with a few dud outings and a few masterpieces.

Zach Cozart, Cincinnati Reds SS

Relevant statistics: .335/.423/.574, 7 HR, 5 Defensive Runs Saved, 2.7 fWAR

Notice my surprise the other day, when I was browsing the WAR leaderboards on FanGraphs and saw who was in third place.  I was so astounded I sent a tweet to High Heat Stats (awesome account by the way, well worth the follow).  Please ignore the misspelling of the player’s name, I was excited!

This was my inspiration for this whole article.  For years, the only thing you heard when hearing about Zach Cozart was how mediocre he was.  It was simply incredible that such an unremarkable performer had ascended to such heights.  Under the surface though, Cozart has been making improvements for years.  First off, Cozart has always been a great fielder, peaking at 19 defensive runs saved in 2014.  After being a slap hitter for many years, Cozart has increased his isolated power from .079 in 2013 all the way to .239 here in 2017.  He also has gone from a career 5.9% BB to 13.9% BB this year.

Sustainability:

I hate to be the bearer of bad news, but everything in Cozart’s profile screams regression to something more like last years numbers.  His average exit velocity is about three miles per hour below the MLB average.  He has not dramatically increased his fly ball percentage or pull percentage, both indicators of swing transformations that can lead to increased power.   Zach Cozart is riding what seems to be a wave of good fortune to a batting line 61% better than league average.  I say these things, and as I am writing this article I have been tuned in to the Reds game, where he has hit another two home runs, and a triple to boot!  I’m already starting to regret this.

Verdict:

All signs point to Cozart slowing down here in the near future.  Despite this, Cozart remains a solid performer who the Reds may ship off at the trade deadline for some young talent to build around.

Jason Vargas, Kansas City Royals SP

Relevant Statistics: 69.1 IP, 7-3, 2.08 ERA, 3.16 FIP, 1.8 fWAR

Jason Vargas is one of the most puzzling players of 2017.  He was last seen prominently in 2015 pitching for the Royals in the regular season leading up to their World Series winning playoff run.  However, Vargas needed Tommy John surgery during the season, and along with the playoffs missed the entire 2016 season sans 12 innings.  Vargas, a 34 year old lefty, has never been anything close to an ace.  However, through a third of the MLB season, he is only trailing Dallas Keuchel of the Astros in ERA.  Call me crazy, but I never saw a breakout coming from a 34 year old coming off of Tommy John surgery that has never shown anything like this in his career.

Sustainability:

Vargas may have made some improvements, but there is no way that he continues to dominate major league hitting like this over the course of the year.  First, I’ll go over the improvements.  He is striking out an extra batter every nine innings over his career rate, and issuing .5 less walks.  He also is leaning on his changeup that is striking out batters over 30% of the time.  However, he is not inducing ground balls at an increased rate, and his overall swinging strike percentage outside his changeup (24%) is dismal.  His fielding-independent pitching indicates that his ERA should be around a run higher.  His BABIP is at .278, which is a bit low.  Vargas appears to be getting a bit lucky this year, but also seems to have improved a bit too.

Verdict: While Vargas may have made improvements on the player he was early on in his career, he also seems to be a bit lucky this year, benefiting from a home stadium that is kind to pitchers and some grace from the baseball gods.  I doubt Vargas will continue to run an ERA in the low 2’s.

Ryan Zimmerman, Washington Nationals 3B

Relevant Statistics: .368/.416/.695, 15 HR, 45 RBI, 2.1 fWAR

Ryan Zimmerman was once the face of the Washington Nationals.  Before their youth movement started and the #1 overall picks started rolling in, he was a rock for a new team.  In recent years however, Zimmerman has declined, finally bottoming out at -1.3 fWAR last year, signifying he was far worse than an average replacement player. Zimmerman has been on a tear this year however, capturing NL player of the month for April and hitting as many long balls as he did last year.  Zimmerman is one of the driving forces on one of the best offenses in baseball, and has a ton of RBI opportunities with former MVP Bryce Harper hitting in front of him.  Thus far, he has put up an offensive line 89% better than league average this season!

Sustainability:

Zimmerman is yet another MLB player who has undergone a significant swing change that has seemingly overnight turned him back into a slugger.  In 2016, Zimmerman just hit the ball on the ground too much, and in 2017 has reduced that percentage by 7%.  His average exit velocity was always high, so putting the ball into the air has maximized the damage that he can do with his prodigious power.  Zimmerman has a unsustainably high .404 BABIP, and that will fall back to earth, but he should remain a great hitter.

Verdict:

Zimmerman should no longer be the awful drag on the franchise that he was in 2016, yet going forward may suffer some regression to the mean. He should however be a dynamic cog in the Nationals lineup for the rest of the year, and remain an above average player.

5 Underperformers

Maikel Franco, Philadelphia Phillies 3B

Relevant Statistics: .223/.277/.359, 6 HR, 28 RBI, -.3 fWAR

Franco, like Sano, was also a top 3B prospect in 2014, usually falling in the top 30 or so prospects on most lists.

Here is John Sickel’s writeup of Franco that year:

Posted .926 OPS in Double-A at age 20 with a low strikeout rate, 31 homers on the season, 36 doubles with just 70 whiffs in 581 PA. Despite impeccable performance at young age, some observers still critique his swing and overall approach. Usually serious swing problems show up with an elevated strikeout rate and/or serious production slippage in the high minors, but so far that hasn’t occurred. We’ll see what happens in Triple-A, but overall I can’t see how Franco is anything but an elite prospect.

Franco is not trending well across the last three seasons.  His wRC+ has gone from 129, to 92, to 67.  He is now firmly a below average hitter this season.  His average is down to .223, and he on base percentage is only .277.  His exit velocity is still far above the MLB average, so he is still hitting the ball with authority.  Franco has been a disappointment, and as his prospect sheen wears off he will need to start to play better to stay in the long term plans of the Phillies front office.

Sustainability:

Franco seems like one of those players, like Ryan Zimmerman, Josh Donaldson, and J.D. Martinez, that would benefit from a swing change.  His fly ball percentage is low and he hits to many balls on the ground.  His BABIP is extremely low right now, so he could creep back to an average hitting line with a dynamic hitting line with a bit of luck.  Alex Stumpf wrote a great piece about Franco’s struggles with sliders here, and iterated that Franco has a lot of developing still to do.  The projections seem to still hold Franco in high esteem, with ZiPS being the low man and still projecting a +.9 fWAR rest of season performance.

Verdict:

Franco may never live up to his high prospect billing, and has a few steps he should take to return to being an above average hitter.  Expect this to take time, and his numbers the rest of the year may not be what many expected coming into the season.

Julio Teheran, Atlanta Braves SP

Relevant Statistics: 61.2 IP, 4-4, 4.82 ERA, 5.51 FIP, -.2 fWAR

Before diving into Julio Teheran and the next guy, Jose Quintana, I would encourage anyone to read this, which is an article that delves into why Quintana has been the pitched credited with more fWAR over the years.  Teheran has always been someone with much better results than the underlying numbers dictate.  This can be attributed to many things, but overall Teheran has a career ERA of 3.49 and a career FIP of 3.97.  Things have fallen apart for Teheran this season, attributable to his BB/9 jumping up nearly two walks and his K/9 falling by over a strikeout.  FIP sees Teheran as someone deserving of a 5.5 ERA thus far, and Teheran has compiled -.2 fWAR and has had his worst season to date.

Sustainability:

Teheran has always been a man on the edge, outperforming his peripherals for the bulk of his career.  His fastball velocity is down almost a full mile per hour, and is he is striking out less batters and walking more than he has in recent years.  His skills seem to be demonstrably eroding, and he will need to make some sort of change to get back to his normal self.  This does not seem to be a slump, and the only thing that could be attributed to luck is his 14% HR/FB rate that has his HR/9 at a career high 1.75.

Verdict:

Teheran is a pitcher that seems to be regressing. He is still over-performing his fielding independent numbers and may be someone that the braves may not be able to rely on to be a part of their core long term.

Jose Quintana, Chicago White Sox SP

Relevant Statistics: 64.1 IP, 2-7, 5.60 ERA, 4.28 FIP, .9 fWAR

Quintana is the opposite side of the coin from Teheran.  He has never been someone to consistently over-perform his fielding independent numbers.  He pitches in a tough park, and has been notorious for racking up low win totals despite his efforts, in part due to languid offensive support.  Quintana has struggled this year, because of an increased walk rate (up to 3.36 in 2017 after 2.16 in 2016), and a sky high HR/9 rate (1.40, up from .95 last year).  He still is not getting his wins or racking up insane strikeout numbers either, and many outside the organization are pondering whether or not the White Sox should have sold high this summer on someone that is having a down year.

Sustainability:

To me, all signs point to Quintana bouncing back.  If he could get his homers and walks under control, he could once again establish himself as an above average option.  His velocity hasn’t plummeted, the projections are all still supporting him, and his BABIP is slightly higher than his career average.

Verdict:

If I had to guess, I would say that Quintana moves back into the role of an under appreciated quality option.  All the trade rumors this summer made the general public more aware of his quiet performance, and this may be enough for him to stay toiling in Guaranteed Rate Field a year longer.

Adrian Gonzalez, Los Angeles Dodgers 1B

Relevant Statistics: .262/.309/.356, 1 HR, 21 RBI, -.5 fWAR

Adrian Gonzalez was once an incredible player, posting a 6.1 fWAR season and racking up over 1100 career RBIs.  Yet Gonzalez has been dismal this season, posting an 80 wRC+ and being completely pushed out of the Dodgers long term plans by a certain someone. Gonzalez has also gone on the DL this year, part of the Dodgers plan so seemingly stick half of their roster on there at once.  Gonzalez is hitting for almost no power, as his .094 isolated power demonstrates.

Sustainability:

You would think someone hitting this poorly after such a storied career would have a low BABIP.  Nope, Gonzalez has a BABIP of .311, above the MLB average.  His average exit velocity is slightly higher than the MLB average, but there is not a lot of promise in Gonzalez’ profile.  His walk rate has never been this low in a full season.  His ground ball rate is above his career average, and his fly ball rate is below his career average.

Verdict:

Time catches up to all of us, and sometimes rookies that turn Dodger Stadium into launching pads do too.  Gonzalez will eventually be phased out of the Dodgers 1st base pecking order.  It seems I am more down on him than anyone else on this list.

Johnathan Villar, Milwaukee Brewers 2B

Relevant Statistics: .210/.284/.319, 5 HR, 24 RBI, -.5 fWAR

Villar was a godsend to fantasy teams across America last year, as his 62 stolen bases paced the majors.  However, Villar has gone from a 118 wRC+ in 2016 to a 59 wRC+ in 2017.  In 2016 he found his power stroke, but his ISO has dropped precipitously in 2017. His baserunning value was always overstated by his steals totals, but has already accumulated more than half of last year’s value on the bases this year, so that does not seem to be the problem.  Villar was expected to be a top fantasy option this year, and has thus far been an enormous disappointment.

Sustainability:

Villar’s regression seems to be completely tied to his bat.  His defense and base running are actually both improved on a rate basis from last year, impressive considering a position change.  With both his walk rate up, his strikeouts down, and his power down, the general trend is not positive.  His ground ball percentage is up around 8%, and his fly balls are down.  It seems like Villar is going against the changes being implemented in the league as a whole, and it is not going to well for him.

Verdict:

While Villar will always be good for some swipes, he has not done anything this year to indicate that his monster 2016 was anything but a flash in the pan.  If I were in a fantasy league, I would sell while he has some semblance of fantasy value.

Thanks for reading!  If you have any criticisms or , email me at stanonaj@miamioh.edu or contact me on twitter @Stanonis_

*all stats from fangraphs.com, updated as of June 4, 2017

*Average exit velocity courtesy of MLB’s Statcast

*All photos courtesy of Getty Images

Originally, this article was posted to a website that one of my friends made so we have an outlet for some of our thoughts on sports.  The original link is here.  This is the reason for some of the explanations on statistics that an average FanGraphs reader already knows.