According to Baseball Prospectus’s set of Run Expectations matrices, when Evan Gattis stepped up to the plate in the fifth inning of Houston’s June 27 game against Oakland, the Astros were expected to score an average of 2.26 runs (taking the three-year mean from 2014-2016). The bases were loaded for Houston, which was down 1-0, and in over four innings of work thus far, opposing pitcher Sean Manaea had already walked a trio of batters, including the previous hitter. Against Gattis, Manaea had gotten himself into even more trouble, with the first three pitches missing outside. On the fourth pitch, the Astros’ mighty slugger, evidently with the green light to swing, did exactly that, with the following result:

By the time Houston’s next batter, Brian McCann, stepped to the plate with two outs and a runner on third, the Astros were only projected to add an average of 0.352 runs to the one that had already crossed home plate. As it turns out, McCann grounded out to shortstop, and the ‘Stros ended up scoring only one total run from a bases loaded, no-out situation. As calculated by Baseball Reference’s wWPA metric, Gattis’s run-scoring double play actually decreased the team’s chances of winning by ten whole percentage points, and they’d eventually drop the game to Oakland, 6-4.

To an innocent MLB.TV subscriber who happened to see the preceding events play out, it seemed an odd scenario for Gattis to get a 3-0 green light. After all, Manaea had been relatively wild up to that point in the game, and he’d even walked the previous batter, Carlos Correa. It got me thinking about league-wide trends on 3-0 swings, and thanks to Baseball Savant, there’s a wealth of 3-0 count-related data to pore through.

First, there are some interesting trends involving the overall league frequency of 3-0 pitches and swings. Using R’s ggplot data visualization package, I graphed the frequency of pitches in a 3-0 count, relative to pitches in other counts, as well as the swing rates on those pitches:

Batters are swinging more and more at 3-0 pitches, even though those pitches are steadily becoming less common. In a league that’s been increasingly prioritizing power, it’s possible that batters are responding accordingly to the fact that they know, with a high degree of accuracy, what pitch they’re about to see. Of the 4,721 3-0 pitches through the 2017 All-Star break, nearly 87% of them were categorized as some type of fastball, as categorized by Baseball Savant.

But which batters are most frequently given the go-ahead to swing away in a 3-0 count? Common perception is that the more powerful the batter, the more likely he is to be given a green light from his manager; this would certainly fit the Gattis anecdote above, and a 2014 Beyond the Box Score article noted that “the guys who swing 3-0 are sluggers,” citing Albert Pujols and Ryan Howard’s high swing numbers as evidence.

I graphed the number of each batter’s 2016 3-0 swings against his 2015 SLG, limiting the data set to batters who saw at least ten 3-0 pitches to avoid outliers (among these outliers: pitchers Jose Fernandez, Jake Arrieta, and Madison Bumgarner, each of whom swung at at least one 3-0 pitch). If powerful hitters really do swing more often on a 3-0 count, we’d expect to see a positive relationship in the data. Of course, this analysis does come with the caveat that batters, once given the freedom to swing, still can choose not to, and pitchers are likely less inclined to groove a 3-0 fastball to hitters they know are more likely to punish them for doing so.

As it turns out, the five batters with the highest number of swings were all notable sluggers — Joey Votto (17), David Ortiz (14), Mike Napoli (14), Giancarlo Stanton (14), and Josh Donaldson (12). Take a look at the below graph:

Evidently, there is some sort of relationship between the number of 3-0 swings a batter takes, and that batter’s power. It might, however, make more sense to look at the rate of 3-0 pitches a batter swings at, rather than the absolute amount. After all, one might expect a batter with a high slugging percentage to have (a) more at-bats that reach 3-0, as the pitcher would be more likely to pitch around him; and (b) more at-bats total, as his high slugging percentage would warrant more frequent appearances in the starting lineup.

As illustrated below, I charted each batter’s 2016 3-0 swing rate against his 2015 SLG:

Interestingly, there doesn’t seem to be much of a correlation between a batter’s prior-year slugging percentage and his current-year 3-0 swing rate, although we should acknowledge the small sample sizes of the pitches driving each individual batter’s swing rate. (For what it’s worth, I performed the same analysis using ISO, rather than SLG, and limiting the data to batters who saw at least twenty 3-0 pitches, rather than ten, and got very similar results.) A list of the top batters by swing rate, again including only those batters facing at least ten 3-0 pitches, doesn’t exactly comprise an All-Star team, either — while Stanton and Pujols are numbers four and five, respectively, the swing leaders also include Rickie Weeks Jr. (1), Wilson Ramos (2), and Hernan Perez (7).

There’s also not much reason to believe that the batters who do swing most often at 3-0 pitches tend to make any better contact than those who don’t. The following chart compares batters’ 3-0 swing rates with their 3-0 swings’ expected wOBA, and, as the R^2 indicates, their relationship is nonexistent:

Finally, let’s observe the relationship, if there is one, between 3-0 swing rates and player age. Sam Miller, now an ESPN writer, penned an excellent 2014 article for Baseball Prospectus in which he listed a few managers’ respective 3-0 strategies. Ned Yost, for example, did only grant the green light to the most powerful members of his lineup — but only with one out, and only in certain game situations. On the other hand, Davey Johnson, then in charge of Washington, was far more liberal. My hunch, though, is that managers are generally most inclined to let their veteran players swing away. What follows is a plot of 2016 3-0 swing rates against player age:

As it turns out, there’s not much of a reason to suspect a relationship here, either. But again, each analysis comes with the caveat that batters don’t have to swing when given a 3-0 green light, and some batters may not even need an explicit green light signal to know that they’re allowed to swing.

Even so, we can conclude the following: although 3-0 counts are occurring less often, relative to other counts, batters are swinging at a steadily increasing rate — perhaps to take advantage of the grooved fastball they’re virtually guaranteed to see. Pitchers, therefore, shouldn’t necessarily take for granted that the hitter won’t swing at their 3-0 pitch, and shouldn’t necessarily expect younger and/or less powerful hitters to refrain from swinging. And while batters’ wOBA on 3-0 is significantly higher than on any other pitch, in a stark contrast to common perception, it surprisingly doesn’t appear that powerful batters make any better contact than weaker hitters. I may eventually replicate this analysis with a focus on different game scenarios — for example, whether sluggers behave differently in blowouts, or in high-leverage situations — but for now, I’ll definitely be paying extra attention next time I see a power hitter or veteran up with a 3-0 count.

I will lead this off by saying that I am both a fan of the Cleveland Indians and frustrated by the 2017 season. I generally try to write about things I have less of a stake in, but a weird stat caught my eye, and it merits discussion, bias be damned.

On the year, Cleveland has performed in a manner that has not reached their lofty expectations. They stand at 48-45, just a half game ahead of the inversely surprising Twins. You could point to a lot of reasons why this is true. Corey Kluber, excellent while on the field, has spent time on the DL. Young Francisco Lindor has caught the weak fly-out bug. After a slow start, Edwin Encarnacion has continued to put up his worst year since his breakout. Of the field, Jason Kipnis is still the same man, but you would not be able to tell by watching him play baseball this year. And I could continue with the players who have not quite met their external expectations that were placed upon them when the season started. Still, the Indians have the seventh-best run differential in baseball, and an even better BaseRuns differential, so the underlying stats still look good. How is this team only three games above .500?

Well, the Indians have a combined wRC+ of 102, a respectable total that is good for tenth in the MLB if you factor out pitchers. For a team expected to be above-average offensively coming into this season, that’s somewhere between disappointing and reasonable. It’s not as if they’re 25th in baseball, and randomness happens, so tenth is pretty good. In low-leverage situations, they are even better, putting up a Fonz-esque (cool) 111 wRC+. In medium leverage, they’ve been almost as good. In high-leverage situations, the Indians have a *twenty-eight* wRC+. For clarity’s sake, I’m going to call this HLwRC+.

If you read this website, I don’t need to explain how horrific that is, but I will anyway because it’s fun. In high-leverage situations, the Indians have basically batted like Kyle Freeland, a man who, before this year, likely had not held a baseball bat since high school. And it’s not particularly fluky either. FanGraphs’ batted-ball data shows that Cleveland has the lowest Hard% in these situations, and the fifth-highest Soft%. They’re definitely not an ideal gas, because they have not gotten hotter under pressure.

I really cannot overstate how ridiculously bad this is! Coming into the season, the lowest single-season HLwRC+  since 2002 was 50, by the 2003 Tigers. That team had zero(!) players worth 2+ wins, and had a 80 wRC+ in all situations. The lowest HLwRC+ by a team with a winning record was the 2006 Detroit Tigers, at 60. So much to say about this: that team won 95 games and went to the World Series! They must have been winning every game by scores like 10-3. Also, impressive turnaround by the Tigers. Their most valuable players were Carlos Guillen and Jeremy Bonderman. Wow.

This is a team-wide problem. Jose Ramirez, who’s quickly becoming one of those “so underrated, they’re properly rated” kind of players, has a wRC+ of 150, but an HLwRC+ of 34. Yan Gomes has some funny numbers: wRC+, 81, not great. HLwRC+: -81. In fact, only one member of the team with over 150 PA this year has a higher HLwRC+ than wRC+, and that is Roberto Perez, whose latter number is 37. Of course, all of these are small sample sizes.

At their current pace, Cleveland is on track to have about 218 more high-leverage at-bats. If from here on, their HLwRC+ equals 102, their current wRC+, they would still finish the season with an HLwRC+ of 60! Obviously, they’re still in first place, and have set themselves up to make the postseason for a second year in a row. But they’ve done so while being the anti-Freddie Mercury.

First, a caveat: what follows is assuredly too many words about middle relievers. When I set out to write this article I never could have guessed that it would occupy most of my leisure time this week. Nevertheless, something really interesting is happening in Anaheim and I hope I’m not the only one who thinks so.

The 2016 Angels were not a good team, and they had a terrible bullpen. When the 2017 Angels lost Mike Trout to a thumb injury at the end of May it seemed to ensure that they would miss the playoffs for the third season in a row. Instead of dropping completely out of the running, the team stayed afloat and at one point even improved their playoff odds without Trout. How did that happen? In addition to receiving surprising quality offensive performances from Andrelton Simmons and Cameron Maybin, they have quietly had one of the best bullpens in the Major leagues.

Looking at their five best relievers from last season, only Cam Bedrosian stands out as being any good. Other than not being particularly good, the rest are all completely unremarkable.

Astute readers will be quick to point out that 2.9 cumulative WAR isn’t that bad, as far as bullpens go. After all, the 2016 Cubs bullpen pitched to a 3.2 WAR total last year, and they won the World Series. The Giants and Rangers bullpens had worse totals and they were both playoff teams. These guys weren’t the whole problem. They were let down by their teammates, who dragged the whole team down to 0.3 WAR by pitching to -2.6 WAR. The whole bullpen was just ahead of the second-last place Rays (with the Reds’ bullpen in a class by itself with -3.9 WAR). These five pitchers were much better than their teammates, but they pale in comparison to the five best Angels this year.

These five guys have pitched to 4.4 WAR at the All-Star break. Isolating for the best five relievers, they are tied with the Dodgers for the second-best mark in baseball. Their walk rate has climbed up only slightly, they’re striking out more batters and their peripherals are way better.

In case you hadn’t noticed, there are four new names on the list. Those four new pitchers didn’t even pitch for the Angels last season and even the most ardent Angels fan could be forgiven for not noticing their signings. Nobody expected them to be any good whatsoever. Steamer projected them to collectively be worth 0.4 WAR. Depth Charts projected 0.2 WAR and didn’t project Hernandez to pitch at all (for the Angels or the Braves, with whom he spent spring training).

These five relievers are being paid a cumulative $5.65M for this season and have been worth $35.2M. They’ve already produced $30.35M in surplus value and we’re just past the halfway mark in 2017. For comparison’s sake, the Dodgers’ five best relievers have produced $16.16M of surplus value, the Yankees’ $10.04M and the Indians’ $4.05M. Among other leading bullpens, only the Blue Jays’ pitchers have produced more surplus value than the Angels and the Astros are the only other team within $10M. The Angels have have turned a group of cast-offs into the foundation of one of the best bullpens in the Major Leagues and are receiving an incredible return on their investment for that feat. Let’s dig a little into each of these pitchers to see what’s going on.

Blake Parker

Blake Parker took the most circuitous route to being the most valuable reliever in the Angels’ bullpen, which Neil Weinberg detailed in his article about Parker. He is playing on a deal worth $560,000 and won’t be eligible for free agency until 2021. His cumulative WAR total in his last three big-league seasons is 0.8. This year his 1.1 WAR through the All-Star break is tied for tenth among relievers.

How is he suddenly so good? I encourage you to read the entire Weinberg article for more detail, but in short: his pitch mix this season is markedly different from his previous two.

Perhaps more crucially, he’s gained velocity on all of his pitches and has been getting better results with his harder stuff, especially his splitter. After being completely forgettable last season, it has become a great pitch for him this year.

The uptick in velocity and change in pitch mix seems to be behind his improvement this year.

Bud Norris

Bud Norris was mostly ineffectual as a starter and reliever in the 2016 season for the Braves and Dodgers, providing 0.7 WAR after putting up 0.0 WAR as a reliever for the Padres in 2015. He signed a one-year minor-league deal for $1.7M dollars in January 2017 and has already been more valuable than last season.

The last article to appear on this website about Norris was on June 27, 2016, when Jeff Sullivan urge us to check out Bud Norris. In that piece, Sullivan extolled the virtues of the cutter that Norris had added to his repertoire. Well, look at him one year later:

Even more cutters, and half as many four-seamers! After spending most of his career as a fastball/slider guy, he’s totally transformed his approach. He’s throwing his fastball and slider less while using his cutter and sinker way more. His sinker has become an entirely different pitch from last year, gaining the most value of all of his pitches.

Given the massive increase in strikeout percentage and swinging-strike rate as well as the drop in zone percentage, it shouldn’t surprise you to learn that he’s locating the pitch much differently this year that last.

2016:

2017:

He’s still locating his sinker off the plate but has also started throwing it below the zone this year, which I’m sure is what has contributed to the spike in his swinging-strike rate and strikeout percentage.

Yusmeiro Petit

Yusmeiro Petit is the most expensive of the bunch, signing a $2.25M minor-league deal after Washington declined to pick up his $3.0M option for 2017 and paid him $500,000 instead. After back-to-back seasons of negative totals, Petit is on pace to surpass his career high of 1.8 WAR that he set back in 2014 as a starter.

Petit doesn’t have an obviously different approach from previous years but that doesn’t mean that there haven’t been important changes under the hood. His cutter has improved remarkably, becoming his most valuable pitch this year.

Like Norris, he’s had a dramatic increase in strikeout percentage and swinging-strike rate, but he’s also given up much less contact this year. He’s actually throwing the ball in the zone slightly more frequently while missing more bats. His cutter heatmaps show the difference in approach this season.

2016:

2017:

Just like Norris, he’s added a new location. His new spot is way out of the zone down and off the plate and I’m sure it is contributing to his increase in swinging-strike rate.

David Hernandez

David Hernandez went to spring training with the Giants but was released when he didn’t make the Opening Day roster. After signing a minor-league deal with the Braves he was traded to the Angels for a PTBNL in late April after their bullpen was decimated by injuries. Hernandez had a positive WAR in 2016 but has been worth -0.3 WAR over his last three MLB seasons.

Something funny happened after he arrived in Los Angeles though:

For the first time ever, Hernandez is throwing a cutter. He’s not just experimenting with it, either. After throwing his fastball more than 60% of the time for his entire career, he’s throwing it less than half of the time this year. He’s using his cutter almost 25% of the time and it has been really good.

That 66.7% ground-ball rate is his highest on any pitch since he had a 71.4% mark with his changeup in 2013; that rate, however, came on only 36 pitches. He has never had a ground-ball rate this high on a pitch that he throws regularly, and adding the cutter has turned him into a much better pitcher.

Cam Bedrosian

The only holdover from last season, Bedrosian would surely rank higher in terms of WAR if he hadn’t been hurt this year. Even with the missed time, he has still almost matched his WAR total from 2016 in almost half the innings. He’s also doing something differently in 2017:

He’s still a two-pitch guy, but he’s throwing his slider more and his fastball less. The results for both pitches haven’t been much different this year compared to last, but his slider was the better pitch in 2016. This could very well just be a matter of throwing his best pitch more often to get more favourable results.

All of these guys have changed something in 2017, either the usage or location of a particular pitch or both. This suggests to me that the bullpen improvements in Anaheim are not only from changing personnel but also from coaching. Charles Nagy joined the team before the 2016 season and perhaps after presiding over one of the worst bullpens in baseball last year decided that a change in approach was in order. Besides Bedrosian, three other pitchers from last year’s most valuable list are still with the team and all three have tried something different this year as well. That’s not to say that they’ve been good, but fortunately this year’s Angels team doesn’t need them to be.

Kevin Gausman has been a nightmare for the Baltimore Orioles this year. That actually may be an understatement as he currently sports a 6.11 ERA. The peripherals don’t paint a much brighter picture with a 5.04 FIP, 4.71 xFIP, and 4.74 SIERA. His strikeout rate has dropped from 23% last year to a below-average 19.6%, while his walk percentage has increased to 9.4% from 6.2% last year. Kevin Gausman has been bad this year by just about any metric. But even in the increasingly warm weather (and high run environment) of Baltimore, there remain a few slivers of hope for the 26-year old.

The first case for improvement comes from the fact that he is still pitching every fifth day. He leads the Orioles in innings, despite having the worst ERA of all qualified pitchers. Jason Collette and Paul Sporer brought this up on their FanGraphs podcast, The Sleeper and the Bust, in regards to Mike Fiers, who has turned his season around after allowing all of the homers to start the year. Paul even mentioned this in regards to Gausman in an article about a month ago that you can read here. The case with Fiers was a simply unsustainable HR/FB%. With Gausman, he owns a .367 BABIP to this point in the year. That is gonna come down and at least a marginal decrease in ERA should come with it. However, a lower BABIP doesn’t help with strikeouts and walks, both areas he needs to improve on to have a solid second half of the season.

Thankfully for Gausman, there are signs that those might be coming around. He seems to have made an adjustment in the last month. Up until he took the mound against the Cleveland Indians on June 21st, Gausman’s horizontal release on all of his pitches was mostly between -3.00 and -2.75. Since the Indians start, his average horizontal release point is about -2.30. The chart below, taken from BrooksBaseball.com, illustrates this sharp change.

It seems to be quite a significant difference, so let’s take a look at some of the results since that start.

On the surface, Gausman has allowed run totals of 3, 0, 0, 5, 8, and 1 to give him an ERA of 4.94 in the last 30 days. That’s still bad, but there are good outings there. More promise comes with his strikeout totals in those games (9, 4, 9, 7, 5, and 8). That is good for a 31.6% strikeout rate. Only Chris Sale (36.4%), Max Scherzer (35.7%), and Corey Kluber (34.5%) have a higher K rate than that this season. I’m not trying to say that Gausman is in their company or that he will maintain that rate going forward, but hey, six starts with an elite strikeout rate isn’t nothing. The extra strikeouts have come along with an increase in whiff rate on his slider. In the next graph, you can see this increase paired with a continued strong whiff rate on his splitter. Gausman has also started to throw his four-seam less in favor of the splitter, throwing it 28% of the time so far in July.

Throw in a walk rate of 6.8% that is more in line with the rest of his career, some solid peripherals (3.91 FIP and 3.17 xFIP) plus a big decrease in xwOBA (taken from baseballsavant.com) from 0.384 to 0.309, and we might be seeing a turnaround from the right-hander. The Orioles probably wished it came sooner (or never got this bad), but with the mess of the AL wild-card race, they only sit 3.5 games back of the last AL playoff spot. As a team, the Orioles rank fourth in wRC+ in the last two weeks, partly thanks to Manny Machado starting to get out of his funk. Baltimore will need Gausman to pitch like he did last year if they want to stick around in the wild-card hunt. Another possibility is Gausman is dealt before the deadline. According to mlbtraderumors.com, the Rockies have reportedly inquired about him. Either way, it will be interesting to see if these improvements can push Gausman to a solid finish, although that may be even more difficult if half of his starts were to take place in Coors Field.

The HBL is what many around here would call a “home league”, though I generally take that comment to mean the level of skill is lower than that of an expert level league, which in this case would do a disservice to describe the talented owners we have. Over the course of 18+ years this group has been together, we’ve honed something I now refer to as “Hampshire-style dynasty”. The key components to this style of fantasy baseball are: 25 man roster (1C, 3OF, DH, 9P, 7 Bench), $217 salary cap (25-man only), 10 man minor league roster and an annual “prospect draft” each year during the All-Star break. While many of you fantasy players are getting twitchy, accidentally clicking on your live scoring 6-10 times a day for the four day break, we’re enjoying a glorious, leisurely-paced live draft for our future man-crushes.

Hampshire-style dynasty actually shares a few similarities with the Ottoneu-style keeper leagues, but the minor league portion of the roster is set to mimic real-life baseball. Minor league player salaries, upon promotion, are set based on the round they are selected. $4 for a first-rounder, $3 for a second, $2 for a third and $1 for a fourth. This ensures that you’ll be able to keep most players a minimum of 3 years before their salaries become a decision point, and for super-star players it’s common to see them kept for 6 to 9 years before being released back into the auction. The feel is something very similar to the arbitration salary escalation process.

I share the background because I feel this feature is a fantastic one for those of you playing or creating dynasty leagues. The reason I wrote the article, though, is because I’m hoping to share the names of some further-off prospects to drive discussion. Because we roster 120 minor league players, we’re pretty well clear of the Baseball America Top 100 list, but both The Dynasty Guru’s Top 300 Prospects and the FG Consensus prospect rankings list are a useful base from which to begin monitoring prospect names. Both midseason prospect updates from BA and BP come out the week before our draft, and the new MLB draft class along with the J2 signings make for a really interesting first two weeks of July for us prospect hounds.

The first round of the draft normally consists of a few staple “types”. The guys we missed on last year that were fast movers (Guerrero Jr., Kingery, Bichette, Mejia), the new high-profile international signees (Robert), and whichever pitchers we missed last year that progressed quickly and now have industry hype around them (Kopech, Keller, Buehler, McKenzie, et al). Depending on the year, the current MLB draft class may get some love in the first round as well (McKay, Greene)

Though I didn’t myself have a first round selection, I had Vlad Jr. as the number one player available. There are some players like Hoskins or Mejia who are closer to having a fantasy impact, but we’re generally drafting for ceiling here. Among the top arms available Mitch Keller has been my favorite for going on a year now. Both Guerrero Jr and Keller were on my list as possible fourth round selections last year, but I didn’t pull the trigger (Delvin Perez, SS, STL was my sole 4th round selection in 2016 because I’m a sucker for shortstop prospects).

The Competitive Finish Round, which are picks awarded to those teams who finish in 4th through 6th place (just outside “the money”) kicked off what forever shall be known as the “holy cow the Braves have a lot of starting pitching prospects” draft.

Round 2 featured a couple players I had pegged as first round talents on my board with Calhoun and Fisher. It didn’t hurt that their proximity to impact is < 1 year. I’m also biased against selecting many pitchers, especially prior to AA. The probability of them washing out, having arm/shoulder injuries, or taking the [insert pitcher name who flew through the minors, was called up, didn’t fare well for three years, but you owned him in parts of all three seasons only to see a league-mate hit the lotto after you dropped him for the fifth time name here] and having to live with that shame/guilt.

Juan Soto was an interesting case. I’d honestly not heard his name before the BP midseason list came out and they ranked him #12. Once I started hearing things like “Victor Robles” I took notice and decided he was likely worth the gamble. My other picks in this round included Derek Fisher who I concluded was a safer high-ish ceiling guy with both speed and power (and currently nowhere to play in Houston, nor a decent lineup slot if he did), and Ryan Mountcastle who by all scout accounts won’t actually stick at SS but I’m hoping for a Brad Miller type. Mountcastle can’t take a walk, but we use AVG and Total Bases, so you can guess how many [poops] I could give so long as he can make it to the Show. Maybe he can learn from Adam Jones . . . or really any Orioles player, they really don’t seem to value OBP in that organization, do they?

I was happy to see so many pitchers and catchers go, because my draft strategy basically has me ignoring them.

I kicked off Round 3 with a player I decided I couldn’t wait on, even though I really wanted to get him in the fourth round so that his starting salary could be $1 someday down the road.

I love Jhailyn Ortiz. A lot. Probably too much. You may or may not remember him from the Vladimir Guerrero J2 class. Well I did, and I just started seeing some hype articles on the kid this week. I got scared and jumped to grab him. This is the type of player, the “fast movers”, that we generally miss in our draft (myself included) and end up being #1 of 1 the following year. His power potential is unmatched and I’m glad to own his ceiling.

My leaguemates all seemed generally bummed when Florial went off the board. I hadn’t read much on him but when there’s that much chatter when a single player goes off the board that’s generally a good sign for the owner who took him.

The other player of note in this round is Austin Beck, who was dubbed “future hall-of-famer” by his team. This is the type of crazy prognostication smack-talk that becomes lore.

I chose another player in this round, Taylor Trammel from the Reds. He’s your typical toolsy prep kid with speed and power. We all draft these guys every year. Sometimes they’re Monte Harrison and sometimes they’re Andrew McCutchen.

The fourth round is where things really get fun. We’re really past all the highest ranked talent on the industry lists and now you’re just using your intuition to try and snag the guys who will become the 2018 1st round picks — a year early. I was lucky enough to have traded for a few extra fourth round picks and had 3 picks in a row near the back of the round.

One player, I was sitting on all draft was Yankees 55FV SP James Kaprielian. He’s had Tommy John surgery this year, but with any luck I was able to snag a 1st round talent at a 4th round price. Having elite $1 pitchers to call up and hitch your wagons to for 4-9 years is every owner’s dream in this league. I’m just hoping he can be another TJ success story. I believe that if he doesn’t get hurt he’s right up there with Keller and Buehler in the fist round this year.

My other two picks aren’t the most conventional draft choices, but sometimes you just have to go with your gut. Truth be told, Colton Welker was snagged infront of me, so I had to scramble for my last guy, who ended up being Cole Tucker. Tucker is ranked as the #5 Pirates prospect on MLB Pipeline and #7 here at FG by Longenhagen. I skipped rival Pirates SS prospect, Kevin Newman (#4 MLB, #5 FG) because I love the steals that Tucker has piled up in his time in the minors. I can see he doesn’t have 60 or 70 grade speed, but when you’re gambling on ceiling you have to throw up a few hail mary shots. I was able to watch some video on Tucker, and the scouting reports told me the same thing my eyes told me, which is that he’s awfully “slappy” for a 6’3 180lb former first round pick. Also, in the video I found, his hands are all over the place from the left side of the plate (he’s a switch hitter). I’m scared and excited all at once.

The last pick I made was Yordan Alvarez. I was so caught up hoping no one noticed him or was writing about him to notice that he made the Future’s Game roster. Much like Jhailyn Ortiz, Alvarez is a former J2 signee as well and also has big time power. He was just recently promoted to A+ after destroying baseballs in A to the tune of a .297 ISO. I like the sound of that.

I’ve had a great time writing up our league’s draft and I hope it’s given some of you dynasty league owners some more names to talk about. I’d love to see comments about who you think got great value in this draft, as well as anyone that wasn’t taken that you might have drafted.

I can tell you that the top players left from the BA Midseason list after we were done drafting were: Luis Urias, Anthony Alford, and pitchers Brandon Woodruff, Alex Faedo, Ian Anderson, Anthony Banda, Erick Fedde, Justus Sheffield, Tyler Mahle, Matt Manning, Beau Burrows, and Nick Niedert. I considered some of these arms with my fourth round picks, but as I said, I prefer to see most of them pitch at AA and spend a higher pick on them if I really like them.

Baseball Prospectus, in their 2015 scouting report of Maikel Franco, had this to say:

“Extremely aggressive approach; will guess, leading to misses or weak contact against soft stuff; gets out in front of ball often—creates hole with breaking stuff away; despite excellent hand-eye and bat speed, hit tool may end up playing down due to approach…”

We saw early this year, and even last year, that exact prediction come to life. Franco seemed to be flailing about vs the soft stuff, beating too many pitches into the ground, and even popping too many up. He never really stopped hitting the ball hard, but we saw too many of those hit in non-ideal ways. For most of the first part of this year the slider gave him absolute fits, and Alex Stumpf wrote about that here. He’s striking out at a career-low rate (13% on the year), but he still isn’t really walking that much although it’s bounced up a percentage point from last year (7.3% in 2017).

Here’s a rundown of his career batted-ball profiles:

I was watching the Phillies game vs. the Marlins on the 18th, and Franco went 3-4 with the go-ahead HR off Dustin McGowan. His HR came on a slider middle-away — literally the exact pitch that’s done nothing but given him fits all year. I also noticed that his batting stance seemed to be different. More upright, quieter. I pulled up a highlight video of an at-bat from early May. Here’s a screencap of his stance just before the pitcher starts his delivery:

That AB ended in an RBI line drive to right. Here’s a screencap of the HR in question from Tuesday, at a similar point in the pitcher’s delivery:

Now if that’s not a mechanical change, I don’t know what is. He’s closed off his stance, eliminated a lot of the knee bend, and seems to have raised his hands juuuuuust a touch. It could be the difference in the camera angle though. Phillies hitting coach Matt Stairs mentioned they’d been trying to get Franco to cut down on his leg kick, so let’s look at that too:
Old leg kick:

New:

Shortly after contact, old:

and the recent HR, similar point:

The “leg kick” seems to be more of a toe tap, and hasn’t changed. What did change, though, is the quality of his follow-through. His head is on ball, he’s better transferred his weight to his front foot, and the results follow. The old AB was a line-drive single opposite field, which looks less of an intentional opposite-field hit and more of a product of bad mechanics. Being so open, he really could only go to right field with authority. If he tried to pull it he’d roll over the pitch. That also would cause him to struggle with the breaking pitch away, which he’d bounce to second. Closing off has allowed him to better get the bat head into a more ideal position to cover the whole plate with authority. He’s always had the bat control to make contact everywhere, but it looks now like he’s improved his chances of making quality contact all over the zone. Here’s the same look at his batted-ball profile since the start of July:

Here’s some assorted metrics, same time period:

vs. his career metrics:

He’s cut his grounders by over 10%, raised his liners by 3%, and turned the rest into fly balls (8%). He’s likely always going to have a pop-up issue, but his pull/center/oppo profile is back to where he was at in 15/16, and he’s hitting the ball hard at a higher rate than ever. Also, his strikeout rate is 6%(!!!!!!)!!!!! He’s making more contact than ever, and that contact is better than ever.

We’ve seen Franco get us hyped before, but never before has there been this type of major mechanical change to point to. Miguel Sano did something similar preseason by raising his hands and quieting his pre-swing load, and it’s paid dividends. Since I started this article, Franco went 2-4 with a single, double, and sac fly; and three of those batted-ball events were hit at 100+mph (the single and double; he was robbed by the 3B on a sharp liner as well).

Going back to his 2015 scouting report: Franco’s still aggressive, if not slowly becoming less aggressive the more he’s in the majors. By changing up his stance, however, he’s closed up the two major holes in his report: getting out in front of the breakers away, and bad contact on soft stuff. Keep an eye on this. One of the more frustrating hyped prospects seems to have made the transformation we all hoped he would, right in front of our eyes.

In Part I, I suggested the “How” and “Why” as to what organizations might be missing. In Part II, I will give you the “What” – the specific findings that underpin an unprecedented opportunity for an organization to capture a significant competitive advantage.

At the major league level, approximately 20-30% of players suffer from significant swing path issues. Since path issues are likely the single largest factor in player failure and underperformance, an organization with a systematic approach to “cure path” would be at significant advantage relative to the remaining teams. Not only would the club benefit directly through improved offensive production, but having an effective cure to path would provide superior insight into “true talent” as the largest remaining factor.  There are also logical extensions into how this could be further monetized in potential areas such as player arbitrage, draft selection, etc.

In summary, the comprehensive solution can be simply stated as follows:

1) The optimal swing paths that exist in the muscle memory of the best hitters can be quantified and visually represented to players performing below potential.

2) A systematic process can be built around the above for significantly improved performance.

Before getting into the key findings, I would like to talk about process using another parallel to investing. There is a popular view that all hitters are different and a “systematic process” cannot be utilized for fixing the ones performing below potential. Successful investors are also presented with a significant amount of uniqueness in their decision process. They have all developed a process that effectively deals with uniqueness, not avoid it all together. So if a process isn’t effective in getting hitters to potential, it’s not that you need to abandon process, you just need a different one. Swing path is a core mechanic that can be systematized and as you will see, there is plenty of room for customization within a systematic process.

The findings below are presented with an extremely high level of conviction based on several years of research including a patent filing in 2013. History will be the ultimate judge but based on communication with a handful of organizations, there is a strong possibility that many clubs will be unwilling to consider such non-traditional sources of value.

The Findings – Quantifying the Optimal Swing Path

Variables:
1) X Axis – Swing Loft
2) Y Axis – Bat Angle (vertical)
3) Z Axis – Swing Direction (Very small changes – Not considered here)
4) Timing (technically, horizontal Bat Angle but we’ll just call it timing for simplicity)
5) Ball Contact Point (Relative to ball equator – Not considered here in terms of loft)

Constraint – The Bat Angle for any given pitch height represents a straight line from the ball to the chest area such that the intersection between the body and the swing plane is a point in the chest area not the mid-section or waist.

Nothing major yet – just some visual representations of the Variables and the Constraint.

Although not a major finding, many are still of the opinion that the bat should be relatively level resulting in a much lower swing plane/body intersection as in the illustration above. The importance of Bat Angle will become more clear shortly. For now, I’ll use two extreme Infield Fly Ball Rates (IFFB%) as a general proxy for a path quality. Below, you will see the Bat Angle (and plane/body intersection) for one of the lowest IFFB rates (Joey Votto) and one of the highest (Kevin Kiermaier). Note the significant 10° difference between the Bat Angles for the same height pitch.

Looking more broadly, the average Bat Angle on low-middle pitches for the players with the five lowest IFFB rates (2015 and 2016) was 34° while the average Bat Angle on low middle pitches for the players with the five highest IFFB rates  was 26°. An example application is screening for players with high IFFB rates on low pitches. Since a relatively flat bat is required for an infield fly ball, this type of screen can highlight players with consistently insufficient Bat Angle.  However, this is only a small part of a more comprehensive approach to identifying players with poor paths as discussed below.

Timing is a Separate Loft Factor

To illustrate Timing Loft, consider the model below in which Swing Loft has been set to zero, thus 100% of the loft is a result of Timing Loft (Bat Angle set to theoretical maximum to illustrate the point).

Conversely, for a high pitch (Bat Angle set to theoretical minimum), 100% of the loft comes from Swing Loft while Timing determines the direction of the hit – pull, center, or oppo (below).

Loft Goals, Angle Mix, and Loft Contribution

Given the principles previously illustrated, optimal angle combinations can be constructed for each pitch location. Each location will have different loft contributions from Timing and Swing Loft based on the height of the pitch. So ball height determines Bat Angle which determines the mix of loft contribution. We will discuss customization shortly. For now, let’s just assume a launch angle goal of 15 degrees for a particular player.

*Note – A middle to low pitch was used to illustrate a 50/50 mix of loft contribution which occurs at a 45° Bat Angle. A true middle pitch has approximately 30-35° of Bat Angle

Thus, the optimal Swing Loft for a low-middle-height pitch for a player with a 15 degree loft goal is only 7.5 degrees – the other 7.5 degrees comes from timing

AND Swing Loft is not the major loft factor for low pitches – which is a lot of them.

Extending these concepts, the optimal loft goal and loft contribution mix can and should be adjusted for different hitter types. Pull hitters will have a greater relative contribution from Timing Loft while opposite field hitters will have a greater relative contribution from Swing Loft. While adjusting the loft goal higher for more powerful hitters is an option, there are potential drawbacks that should be carefully considered and are discussed below.

Reconciling the Swing Up / Swing Down Views

It is interesting to consider the above in light of baseball ignoring Ted Williams for so many years. Usually, there is some rationale for significant movement in a particular direction. In hindsight, it is not too difficult to consider that the “swing down” movement came about (in part) because the bat path / ball path matching issue can’t be solved with a simple one-factor (i.e. Swing Loft) solution as Williams proposed (specifically, page 67 of The Science of Hitting where he shows front knee bend to hit low pitches). While Swing Loft should never be negative, one can certainly appreciate how minimal Swing Loft and relying on Timing Loft for low pitches might feel down to a player. A few takeaways:

1) I am convinced that the failure of Williams’ teachings to fully catch on as well as the highly variable success rates today in adding loft is because some players are able to intuitively arrive at the correct mix of loft contribution while others are forcing too much Swing Loft.

2) Since “timing adjustments” are clearly required to achieve high levels of loft using an optimal mix of angles, excessive loft goals are problematic for many hitters, particularly those who are not natural “pull” (early timing) hitters.

3) Given the significant number of consistent hitters in the 13-15 average LA range, this indicates a Swing Loft on average, of 6.5 to 7.5 degrees for a low-middle-height pitch – nowhere close to what many believe is required to become a card carrying member of the “Fly Ball Revolution.”

Visually Representing Optimal Swing Paths to Hitters

I am by no means an expert in neuroscience; however, it seems relatively straightforward that the more information hitters can transfer out of conscious thought into subconscious/muscle memory, the better they will perform. Hitters don’t want to (and shouldn’t) think about complex angle combinations. Consequently, a visual representation of the optimal combination of angles tailor made for each considering their power (i.e. to determine goal loft) and pull vs oppo tendencies can quickly correct a consistently poor swing path. Yes, “Keep it Simple” but solve the complexity first.

Below is the device that allows a hitter to train for optimal angle mixes through seeing and feeling the optimal paths for different pitch locations. The ball joint allows flexibility for any mix of angles while the angle guide provides compound angle settings based on a hitter’s customized loft goal and pull/oppo preference.

I will keep the product plug to a minimum. Additional information may be found here.

Finding Great Paths in the Data

Statcast data confirms that the best (most consistent) hitters hit the ball significantly flatter (in terms of bat/ball contact, not launch angle) than average. You can read more about the details of this here. I refer to estimated spin impact as Mean Unexpected Distance (MUD).

In addition to hitting the ball flat, one of the best indicators of a great swing path is low variability (Standard Deviation) of a player’s launch angles. After witnessing significant reduction in launch-angle variability through focused training, I had a significantly high level of conviction that this was a key indicator of “quality of path” several years ago. The availability of Statcast data through Baseball Savant changed everything. The data not only confirmed the benefits of flat contact previously discussed but also proved that data combined with video analysis can assess  “quality of path” with a very high degree of accuracy. Considering no other data than (low) MUD scores and (low) Standard Deviation,  the following hitters are returned (based on 2015 and 2016 data):

The average wRC+ and BABIP for the hitters above are 129 and .330, respectively. Given the return of Cabrera, Votto, Mauer, Trout, Freeman, J.D. Martinez, and several others considering no other performance factors, the benefits of low Standard Deviation of LA and flat contact seem relatively clear.

Putting It All Together – A Better Training Approach

I believe it would be a mistake to ignore the iterative training process that the best hitters have utilized up to this point. Hit a lot of balls, keep what works, discard what doesn’t and repeat the process over a very long period of time. In many cases, the only thing differentiating good from bad paths is that a player’s “filter” allowed something to get through that should have been discarded.

The drawback of the iterative approach is that it takes a very long time. If a player gets off track, it can take a frustratingly long time to go through the process to fix something that may have a very simple solution. Combining what we know from the discussion above, the variability of launch angles can be used in training sessions to quickly determine if a path is moving in the right direction or not before a player fully commits to a contemplated change.

In other words, path issues can be effectively addressed from opposite directions – static/device training  to improve path and dynamic (pitched balls)  training focused on reducing LA volatility. In training sessions with BP-type pitching, players with good paths are able to get down to a 13-14 degree standard deviation.

Implications From The Findings

Part I  pointed out the first step in the research process (as I have known it) is “Identify the Key Drivers”. I believe it is fairly safe to say that most, if not all organizations, believe that path is a key factor. This leads to the logical question of – Why didn’t MLB organizations “go deep” on one of the largest factors of player performance?  I believe there are two primary reasons:

1)  They assumed there was nothing of significance to be found – they concluded before they considered.

2)  It was outside the scope of responsibilities for employees on both the data/analytics side and the player development side of the organization.

Looking more broadly, it would be my guess that most organizations would say they do not believe significant “Moneyball-size opportunities” exist. It is this type of thinking that suggests that they likely do. I’m currently looking into another key driver of performance that could possibly be addressed through a similar systematic approach. Until baseball organizations change their thinking (and possibly their organizational structure), it’s likely that these opportunities will continue to exist. The source is the same – the “Gap in the Middle” that was outlined in Part I.

Clearly, all of the findings presented here are “known” in the muscle memory of the best hitters with great paths.  To this point, however, this muscle memory knowledge had not been understood or quantified in a way that could be systematically transferred to other players. By separating the loft factors, quantifying optimal paths for each location, and presenting a simplified visual representation of the optimal combination of angles, hitters can correct path issues with a very high rate of success.

Going forward, it will be interesting to see how organizations change in regard to considering non-traditional sources of value such as the “Gap in the Middle” previously discussed. At least one, the Houston Astros, announced in March (two days after Part I but likely just a coincidence) that they were moving their lead analyst, Sig Mejdal, to get “on-field experience.” This move, combined with his title of “Process Improvement,” indicates that they might be ahead of the pack in terms of considering new ideas. If you are aware of other clubs moving in this direction, please indicate in the comments. Based on my communication with a few organizations, I believe several are going to be late to the party as they appear unwilling to challenge existing assumptions.

Given the possibility of more “meat on the bone” for the findings above, I will likely take another short break before publishing the next article. However, for those interested in considering opportunities where data and mechanics intersect such as what has been presented above, there is considerably more material for your future consumption.

Aaron Judge is good.  Some might say he is great.  The front-runner for AL Rookie of the Year and MVP is the face of MLB for 2017, but the face of MLB for the future?  Unfortunately, maybe not.

It’s hard to find something negative to say about the New York Yankees right fielder, but in order to play devil’s advocate and not get our hopes up too high about Aaron Judge, just in the event that he has a down season, I was able to find some rather unflattering comps for the slugger.

First, there’s his minor-league career.  Aaron Judge was a pretty good prospect ranking first in the Yankees’ system in 2015 and 17th in baseball according to MLB Pipeline.  However, just because a prospect is ranked highly does not mean they are without flaws.  Judge would strike out in at least 21 percent of his plate appearances in all levels in the minor leagues.  This article from 2016 even identified Judge’s proficiency to strikeout:  

Judge’s Triple-A debut at the end of 2015 did not go well. He slashed .224/.308/.373, well below both his career levels and expectations. More alarming, he struck out a career high 28.5-percent of the time (74 times in 260 plate appearances). [The 2016 season] has been more of the same. His batting average is a bit deceiving sitting at .284 (heading into this weekend), considering he currently has a nice .354 BABIP compared to last seasons .289. His plate discipline is troubling.

Perhaps the lofty expectations of Judge have him pressing. You simply can’t overlook the fact that his strikeout rate is nearly identical to the small sample size of last season’s Triple-A numbers (27.2-percent). It has to be at least a slight bit worrisome that this is a trend and not a slump. His walk right is dropping daily to a new career low (6.8-percent or eight walks in 103 plate appearances).

The article seems to point to his plate discipline as his main flaw — as other evaluators have — but is overall positive with his prospect status.  But his strikeout tendency should not be overlooked.  He has failed to improve on that statistic in his short major-league career, where he has struck out in 32 percent of his plate appearances between his call-up in 2016 and now.  However, because he also takes his walks, his walk percentage is rather high, which puts him in exclusive company.

Since 2000, there have only been four players with at least 300 plate appearances who have struck out in over 29 percent of their plate appearances and walked in at least 16 percent of them: Jack Cust (2007, 2008, 2010, 2011), Ryan Howard (2007), Adam Dunn (2012), and Aaron Judge (2017).  All of these seasons resulted in wRC+ well above 100, which means that they were productive players; however, these player were known to be the embodiment of the “three-true-outcome” hitters.  Dunn had five consecutive seasons of 40 or more home runs, but also led the league in strikeouts four times; Cust led the league in walks once and strikeouts three times; and Howard led the league in home runs twice and strikeouts twice.  Admittedly, these comps are not encouraging.  Although these players were not horrible in the simplest definition, their careers were short-lived and their production sharply declined.  For Cust and Dunn, it forced an early retirement, and Howard a well-publicized and sad end to an illustrious career.

But it’s not just Aaron Judge’s strikeout and walk percentage — it’s also his raw strikeout numbers.  Judge is on pace to strike out over 200 times this season.  While it’s already been established that he is strikeout-prone, it does not serve him justice that the 200-strikeout threshold is upon him.  No player who has struck out 200 or more times in a season has had a very high average.  As the legendary Pete Rose noted, the highest single-season average for a player with 200 or more strikeouts was .262 (Chris Davis holds that honor).  The short list of 200 single-season strikeout players is a whopping five players long: Mark Reynolds, Adam Dunn, Chris Davis, Chris Carter, and Drew Stubbs.  Kris Bryant had 199 in his rookie season (he was called up late to the bigs due to service-time considerations, so it’s likely that he would have joined this club), and Ryan Howard had 199 twice and Jack Cust had 197 once.  Dunn, Howard, and Cust again…

I love Aaron Judge, and I love 500-plus foot home runs, but we also have to be realistic and rational in our love and praise for the slugger.  The worst thing that the New York sports world can do is rattle this kid if, and when, he goes from being an All-Star to the 25th man on a roster.  There is nothing I want to see more, as a Yankees fan and a baseball fan, than Judge succeed; it’s good for the sport.  But I also don’t want to get my hopes up too high, because nothing stings more than a player of his caliber going down the path of Adam Dunn, Jack Cust, or Ryan Howard.

Early this spring I did a writeup on dScore (“Dominance Score), an algorithm that aims to identify early on pitcher “true talent.” That article reviewed RP performance for 2016.

Here’s a quick review of dScore and how it works:

dScore takes each pitcher and divides them up into a bunch of stats (K-BB%, Hard/Soft%, contact metrics, swinging strikes; as well as breaking down each pitch in their arsenal by weights and movements). We then weight each metric based on indication of success–for relievers, having one or two premium pitches, missing bats, and minimizing hard contact are ideal; whereas starters tend to thrive with a better overall arsenal, minimizing contact, and minimizing baserunners. Below is a breakdown of the metrics we used in our SP evaluations:

Performance metrics: WHIP, K/BB%, Soft%, Hard%, GB%, Contact%, SwStk%, Z-Contact%, O-Contact%

Pitch metrics: wPitch, vPitch (where “Pitch”= FA, FT, CU, SL, CH)

Our current weighting for SPs is a bit more subjective and complex than our RP weighting system, but I’m looking to implement a similar weighting system to the way we weight RP metrics in this evaluation in the near future.

dScore has been around for a year or so now, and one thing I was asked when I initially posted was whether or not it has any “predictive” tendencies. The answer is a pretty clear “no”–BUT what it does do very, very well is validate performance. There’s a fine line between saying “the numbers say pitcher X’s going to stay good” and saying “pitcher X has been good, and this confirms he’s been good”. The problem with the metric is it uses per-pitch statistics, rather than Fielding-Independent metrics. What that means is at a technical level, dScore views the pitcher as directly responsible for everything that happened after a pitch is thrown. There’s been a few outside cases that I’ll get into in a later article; but generally if a pitcher’s been bad, he’s generally viewed as having been bad, or vice versa. It seems particularly bad at projecting regression from underperformance, although I haven’t been tracking pitcher movement as well as I should. I’ll look to implement some sort of evaluation by next year.

The Studs

The top eight guys are really a who’s-who. Scherzer, Wood, Kluber, Sale, Kersh, Keuchel, Syndergaard…Only guy I’m touching on here is Thor, who’s close to begin throwing again. Lat injuries are a whole lotta “?????” for pitchers, but he’s certainly worth a buy if someone is (stupidly) wanting to sell.

The Loaded Teams

Astros – Dallas Keuchel (6), Lance McCullers (8), Brad Peacock (24) / McCullers has broken out. Consider him a stud going forward.

Diamondbacks – Randall Delgado (9), Zack Godley (10), Zack Greinke (18) / Delgado is likely more of a bullpen option at this point. Godley had an awful first outing off the break, but dScore really believes in him.

Dodgers – Alex Wood (2), Clayton Kershaw (5), Kenta Maeda (17), Rich Hill (21) / Come on, really? Give some other team a chance!

The Young Breakouts

Zack Godley (10) – I touched on him above. Although I’m pretty sure he’s due for regression, dScore continues to think he’s got premium stuff. Continue to roll with him.

Luis Castillo (14) – He’s 29 innings into his big-league career, but that’s also 29 innings vs. the Nationals (twice), Rockies (once, in Coors), and the Diamondbacks (once, in Chase). All three teams rank in the top five in the NL in runs scored. BUY. / FUN FACT: The Rockies rank third in runs scored, but are tied with the Padres for dead last in the NL in wRC+ at 81.

James Paxton (16) – He is who we thought he is.

The Still Believin’

Kenta Maeda (17)

Masahiro Tanaka (22)

Danny Salazar (23)

Tanaka’s been god-awful. dScore agrees with his 3.73 xFIP though, and says he should’ve been significantly better than he is. Salazar has somehow been worse, but once again dScore sides with his 3.57 xFIP and says BUY when he comes back from the minors, although I feel like that’s what Salazar’s always been. Every metric says he should be significantly better than he actually is. In 10 years I feel like his career is going to spawn the ultimate sabermetric “what could have been” from FanGraphs.

The Just Missed

Jacob Faria (26)

Jose Berrios (28)

Mike Clevinger (29)

Jordan Montgomery (30)

Chris Archer (31)

A whole bunch of kids and Archer, aka the pitcher we all want Danny Salazar to be.

R.I.P

Nathan Karns (19) – Thoracic Outlet Syndrome. Well, it was a good idea for the Royals…

Notes From Farther Down

Newly-minted Cubs ace Jose Quintana is sitting at 76th. Remember how I said this metric was bad at projecting regression from underperformance? Quintana was sitting just inside the top 100 before his last start. Even though dScore agrees he’s been bad, I’m still buying Quintana in bulk. Old Cubs ace Jon Lester is still getting love from dScore, even after his absolute meltdown vs the Pirates. He’s at 39th. Fellow lefties Sean Manaea and Eduardo Rodriguez bookend him at 38th and 40th respectively. Manaea was sitting in the high-teens for most of the season, then seemed to lose feel for his slider and effectively stopped throwing it. That really hurt his hittability and K’s. It came back around last start vs. Cleveland. I’m continuing to buy him as a #2 ROS. Boston activated Rodriguez recently. Adam Wainwright (104), Julio Teheran (108), Jake Odorizzi (123), Matt Harvey (137), Aaron Sanchez (140), Cole Hamels (143) are a whole bunch of ughhhhh. I’m out on all but Hamels, who I’d argue to hold. His strikeouts disappeared before getting shelved with an oblique strain, then got shelled in his first start back vs. Cleveland. His last three starts have been vintage, and I’m anticipating dScore to catch back up.

Baseball is a very fickle sport; sometimes everything will be going your way, and sometimes it may be the complete opposite. There will always be guys who go through long stretches where they are seemingly doing everything right but the results just are not coming. With that being said, let’s take a look at who should improve after the All-Star break.

Miguel Cabrera

Cabrera is in the midst of one of the worst seasons of his career. His .264 average would be a career worst, the 20 home runs he’s on pace for would be the third worst. His 110 wRC+ is his worst since his rookie season.

All signs point to that coming to an end quickly, though. Cabrera’s .067 xwOBA – wOBA is the highest in the league and his BABIP has plummeted to .307. He is obviously a terrible baserunner with his age so one might expect those numbers, but the .037 xwOBA – wOBA he posted in 2015-2016 and his .346 career BABIP suggest it has been more than his age. Comerica Park is one of the more pitcher-friendly parks in the league, but still shouldn’t account for the bad luck.

Cabrera’s batted-ball profile also appears to be in great shape. He is hitting more line drives than ever before, while also utilizing all parts of the field at a career high. To go along with that, his Hard% and Soft% are career bests. His Hard% is second in the league and within 0.1% of the godly Aaron Judge. Cabrera’s contact rates are slightly down but right in line with the last couple of seasons, and his O-Swing% and Z-Swing% are also similar to his past.

The basic numbers suggest he’s having perhaps the worst season of his career, but Cabrera’s peripherals suggest one of his best. Expect bigger things from the two-time MVP in the second half.

Matt Carpenter 

Carpenter’s numbers have not disappointed quite to the extent of Cabrera’s. He is hitting only .237 and his 119 wRC+ are down, but he is also posting an absurd 17.5% BB% and just a 18.6% strikeout rate. His 14 home runs show a little bit of improved power. However, the numbers suggest he could be doing quite a bit better.

His xwOBA is .044 higher than his wOBA, which is tied for eighth in the league. Similar to Cabrera, he is not an exceptional baserunner and is not playing in a hitter’s park, but his 2015-2016 xwOBA was only .014 higher than his wOBA. He’s also experienced the same BABIP drop as Cabrera, as the .256 mark he’s running in 2017 is way off his career .322 BABIP.

Carpenter’s batted-ball profile doesn’t excite as much as Cabrera, as his line-drive rate is down and his Soft% is up. But his hard-contact rate is at a career-high 45.1%.

His season has not been a total disappointment to date, but expect it to improve in the second half.

Manny Machado 

Lastly, we have the player disappointing the most on this list. Without even looking at the numbers, Machado could easily be included on this list. Machado is still not even at the peak of his prime yet, as he turned 25 just over a week ago. The three time All-Star posted 6+ WAR in three of the last four seasons. The only other players to do the same were Mike Trout and Josh Donaldson.

So, even without digging into things, improvement in the second half is expected. Luckily, the peripherals also support an improvement from Machado. His xwOBA of .355 is far more impressive than his .319 wOBA, and Machado is actually a solid baserunner and plays in a generally neutral park at Camden Yards. The -.013 xwOBA – wOBA he had in 2015-2016 makes a lot more sense than the .036 he is running right now. The .239 BABIP in 2017, way off his .302 career mark, further suggests bad luck.

Just like Cabrera and Carpenter, Machado’s batted-ball profile is actually even a little more impressive than past seasons. His hard-hit rate of 40.2% would be a career high by a good amount and his soft-contact rate has seen a 3% decline from last year.

There may be more cause for concern with Machado than the others, though. He has basically forgotten how to hit line drives, as his LD% has cratered to 13.9% and his ground-ball rates are up. Along with that, his pull rates are creeping up. Luckily, Machado crushes his ground balls. His 89.4 average GB MPH ranks fifth in the league, which helps to offset his minuscule liner rates. But even with that, his Contact% of 76.3% would be the lowest since his rookie year, and his plate discipline is trending in the wrong direction.

It’s possible Machado is selling a bit of his contact skills for improved batted balls, but the GB/LD/FB tendencies don’t support that. Overall, considering Machado’s youth, talent, and most of the peripheral numbers, a large improvement should be expected. However, it does appear that something may be a little off with the Orioles’ franchise third baseman.