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 26^th (right behind Kris Bryant) and 28^th (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 (20^th 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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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)