Archive for July, 2017

What Went Wrong With Chihiro Kaneko

In the 2014 offseason, many free agents changed teams, some even changed leagues. Hiroki Kuroda went back to Japan to pitch for his hometown team, the Hiroshima Toyo Carp, while the Yankees got an upgrade (when healthy) in Masahiro Tanaka on a seven-year, $155-million deal (with a $20-million posting fee that they spent to talk to him), which he can opt out of after this season.

There was a second pitcher who was almost as good as Tanaka, who had worse stuff but excellent command. He also had some injury concerns after his 2011 injury where he missed a few starts, and in 2012 where only pitched nine starts, albeit with 63 1/3 IP in those starts though. Heading into the 2014 offseason, he had two excellent seasons, with ERAs of around 2 in 2013 and 2014, pitching 223 1/3 IP, with 200 strikeouts and 58 walks allowed, then 191 IP with 199 K and only 42 BB respectively in those seasons. He had a 1.98 ERA in those 191 innings in 2014, and a 2.01 ERA in 2013, generating interest from big-league teams and making an appearance in Bradley Woodrum’s article as a pitcher of note that might come over. He ultimately re-signed with the Orix Buffaloes on a four-year deal.

The injury bug bit him again in 2015 as he pitched in 16 starts, throwing 93 IP, and he had a lower strikeout rate than he had in 2013 and 2014 (7.6 K/9) with an ERA of 3.19. He pitched in 2016 and had a mostly healthy season, save for a declining strikeout rate (6.9 K/9) and an increased walk rate (3.3 BB/9), with an ERA of 3.83 in 162 IP. This year his strikeouts (5.7 K/9) and walks (3.0 BB/9) have stayed bad, with a slightly better 3.57 ERA in 116 IP.

What has caused this drastic downturn in performance? It seems that some of his downturn is because he’s getting older, but that doesn’t explain his increased walk rate or his severe decrease in strikeouts. Most of this is likely due to injuries he sustained in the 2015 season. And given that he hasn’t gotten better, it seems as if he’s been pitching despite an injury which has been sapping his effectiveness. He went from being as good as Alex Cobb was in 2014 (considering the thought of the average active hitter in Japan being slightly better than AAA quality) to performing like Ervin Santana this year.

He was a great pitcher with some downside, like Jered Weaver was, but Kaneko hasn’t declined that far yet. Weaver is too bad to even be on an MLB team until he gets medical help to fix his hip and/or shoulder. Weaver is one of the other pitchers who had declined that quickly. So far, he hasn’t rebounded and has continued to get worse, worse than he was last year when he was the second-worst pitcher qualified for the ERA title. It appears that Weaver is virtually unfixable. I think that Kaneko’s issues can be fixed, though, and if they are fixed, he could be an interesting buy-low opportunity.

After the 2014 season, if I were Dayton Moore (armchair GM ideas away), I would’ve signed him to a three-year, $30-million deal with lots of incentives, which could’ve raised the value to $51 million if all were reached. And I think he would’ve done quite well; we might not have this article at all. I must digress, as what-ifs are all around us. (Look at Yordano Ventura, who died far too young with so much untapped potential left.)

He looks like a potential project for the Pirates if he can show signs of improvement in his performance and peripheral stats. The Pirates and Ray Searage could definitely turn Kaneko into something of value, like they did with A.J. Burnett, Edinson Volquez, JA Happ, Ivan Nova, Juan Nicasio, Joel Hanrahan, Mark Melancon, Tony Watson and more. There’s a good amount of upside in trying for this — some prospects that can help the team in the future.

Here is a link to his player page so you can see it for yourself and make your own conclusions about him, and what he can do to remedy himself.

I don’t own any stats used; all stats are from either FanGraphs or the NPB website linked above.

Follow-Up: Which Player Would You Rather Have For the Rest of the Season?

Last week I offered a poll in the Community Blog. The poll compared three anonymous players — Frank, Tom, and Dan, asking: which player would you rather have for the rest of the season?

The descriptions of each player provided a brief background of their performance in the first half of this season, some non-relevant details of how they have been described by others, and their history of performance, to the extent that there was any. Additionally, the poll provided the major-league averages of certain offensive statistics for the first half of this season. These stats were comparable to the stats given about the individual players.

The poll was not meant to take defense into account and the descriptions were quiet on any defensive characteristics of the players, including the position they played. There was also no indication that one player was more susceptible to injury than another. Therefore, the poll selection should have been focused solely on the player’s offensive potential for the second half of this season.

I came into the poll thinking that Dan is the player I would prefer to have for the rest of the season. I started leaning towards Tom as responses to the poll came in. I never considered Frank a viable option.

After doing some research, I think all three players are viable options. However, I think Tom stands above the rest and resembles the closest thing to an objective choice when faced with a decision to take only one of these players for the rest of the season. Before explaining why, the results of the poll can be found here. Here is a summary of the 62 responses:

Question 1: Which Player Would You Rather Have For The Rest of This Season?

Dan: 37.1% (23)

Frank: 32.3% (20)

Tom: 30.6% (19)

Question 2: What Best Describes You?

I am a professional. I get paid to assess baseball players for a team, media, or other company: 3% (2)

I am extremely knowledgeable in sabermetric analytics, but not a professional: 22% (13)

I am knowledgeable in sabermetric analytics: 53% (31)

I am familiar with sabermetric concepts: 22% (13)

No Response: (3)

The Analysis of Dan

There are likely three scenarios you have in mind if you would choose Dan for the rest of the season. They all revolve around the idea that he will likely perform at a level that he has over the course of his career or above that level, bringing his total season number closer to his career average.

Below are the results of the three likely scenarios you could play out in your mind when you choose Dan.

The “Good” result is Dan performing at career averages.

The “Better” result is Dan performing 50% better or worse than his under-/over-performance in the first half of the season, on top of his career averages. For example, Dan’s BABIP of .234 was .067 points lower than his career average. Therefore, his BABIP in this scenario is .0335 better than his career average of .301, bringing it to .334 in this scenario. Conversely, his BB% was 1.6% better in the first half, so in this projection it would be .08% worse than his career average, or 6.2%.

The “Best” result is Dan performing 100% better or worse than his under-/over-performance in the first half of the season, on top of his career average. For example, his .234 BABIP, .067 point lower than his career average, is reversed completely in this projection, where his BABIP is .368. His 1.6% improvement on his career BB% is reversed completely, and his BB% is projected to be 5.4%. 

Good 360 0.301 61 25 14 259 58 18 2 0.336
Better 360 0.334 56 22 13 269 67 21 2 0.358
Best 360 0.368 50 19 12 278 76 24 2 0.380

The Analysis of Tom

The analysis for Tom isn’t quite as complicated. That may be why you chose Tom.

Tom’s numbers are very close to his career averages. The three likely scenarios you have for Tom were probably one where he hits at his career averages, one where he hits as he did in the first half, or one where he performs as Dan did in the “best” case scenario, described above.

This is what those three scenarios look like:

Same 352 0.299 84 37 23 208 46 15 1 0.373
Career Average 352 0.320 99 39 26 188 44 14 1 0.383
Best 352 0.341 113 39 29 172 44 14 1 0.399

The Analysis of Frank

The analysis of Frank is the most difficult because we have very little information about what we should expect from him. You should be confident that, despite his first half, he will not go on to have one of the luckiest and best baseball seasons in history, only because those seasons are extremely rare.

The prospect of someone having something good happen over 50% of the time his bat touches the ball is untenable. So is Frank’s .427 BABIP, which you could have backed into or just ballparked by the numbers given. In light of the league averages, and our general knowledge of baseball, we know that these results are on the extreme of a spectrum and are a product of a great talent coupled with a large amount of luck.

So, these numbers tell us Frank is talented and that he has been really lucky, but we have no context of historical performance to place that talent and luck in. Therefore, I thought the following three scenarios would be most appropriate for Frank.

The “League Average” scenario, where Frank’s performance reverts to league average for the rest of the season. These numbers coupled with his first-half numbers still result in an impressive rookie season.

The “Towards Average” scenario, where Frank’s  performance comes back toward, but not all the way to the league average. In this scenario I have brought all his numbers back half-way. Therefore, his 30% strikeout rate, 8.6% above league average, is scaled back to 25.7%, which is 4.3% lower than it was during the first half of the season.

The “Best” case scenario, where Frank’s performance from the first half of the season continues.

League Average 352 0.301 76 30 12 234 51 16 1 0.314
Towards Average 352 0.334 91 45 21 195 49 15 1 0.377
Best 352 0.427 104 59 29 160 51 16 1 0.468

Which Player Would I Rather Have For the Rest of The Season?

I’d imagine everyone knew Frank was Aaron Judge. The other two may have been more mysterious, but Tom is Giancarlo Stanton and Dan is Manny Machado.

The one scenario that I didn’t account for in my analysis is things going very poorly for any of these players in the second half. That is a real possibility, but it’s unlikely things will get much worse than what I projected for these players (I’ll discuss that a little more for each player below).

I thought Machado would be the best answer when I created the poll. A lot of that was based on bias, not the information given. Machado’s most recent seasons have been much better than his career averages suggest. That probably shaded my thoughts about how he would perform for the rest of this season. In reality, the career numbers look right, particularly in light of the struggles Machado faced in the first half of the season, which is factored into those career numbers.

I mentioned the lack of exploration of a “worst” case scenario above. In my opinion, the projection for Machado is most vulnerable to this omission. I don’t think the vulnerability is that large, though. Machado’s .234 BABIP is on the opposite, yet nearly as extreme, end of the spectrum as Aaron Judge’s .427 BABIP. While it’s possible that the bad luck continues, it’s probable it does not. The BABIP number from the first half says a lot more about luck, not Machado’s talent level.

Machado’s main issue, in a comparison with these players, is that his best-case scenario is needed to get him in the conversation. The mean wOBA of his three scenarios is .358, which is very good, but it’s not on the level of the others. His wOBA in the best scenario is .380. It is a level where the risk is not worth the reward (in the context of this poll).

In actuality, Machado has another asset: he is a very good third baseman, but for purposes of this poll that is irrelevant. Based on this, Manny Machado is not the player I would want for the rest of the season.

I’m an Aaron Judge skeptic. I think he’s likely to remain an All-Star player, but I don’t think he is one of the best players ever.  The average wOBA of his three scenarios is .386, with a high of .468 in the “best” scenario, replicating his first-half performance. The potential of such high performance tempers the risk of Judge’s floor of a .314 wOBA laid out in the “League Average” scenario.

There are a lot of scenarios that I’m leaving out here. I have brought all of Judge’s numbers down to league average, or half-way to league average. That predicts regression in areas such as BABIP and power, but it also attributes a fake ability to not swing and miss to Judge.  However, even if we said that the “League Average” scenario has a 20% chance of happening, the “Towards Average” scenario has a 70% chance of happening, and the “Best” has a 10% chance of happening, Judge’s average wOBA would be .374. This does not necessarily eliminate the issue of attributing “fake” qualities to Judge, but those “fake” qualities run both ways, as the “League Average” scenario severely underestimates his ability to hit home runs and draw walks. Either way, I hesitantly will take Aaron Judge over Manny Machado for the rest of the year.

That leaves Stanton. Why is he the best bet? Because he is not much of a gamble at all. Stanton is performing very close to his career averages, if not a shade under many of them. His projected scenarios reflect this. Stanton is close enough to his career averages that it’s not unreasonable to believe he can perform above those averages in the second half of this season and create a season meeting his career averages. It’s certainly not an unreasonable thought that he will close out the year performing in line with his career averages, nor is it unreasonable to think that his first half represents a new, slightly lower level of baseline performance for Stanton. All of this adds up to very little uncertainty. The average wOBA of the three scenarios is .385. If you had to take one of these player for this second half of the season you would take Stanton. He’s much of the upside and none of the downside. You know what’s coming and it’s going to be very good to great.


  • These projections aren’t very scientific or complex. They are based on three scenarios that come to mind and then a basic application of standard baseball stats.
  • I used wOBA to measure the players projected success in the scenarios laid out. This version of wOBA does not account for the value of  a stolen base, caught stealing, hit by pitch, or sacrifice fly. I used the 2017 weights from FanGraphs’ GUTS to calculate wOBA. I used the weights that were available around July 21st.
  • I projected how many hits were singles, doubles, and triples by determining the percentage of non-home-run hits that were singles, doubles, and triples, respectively, between 2012-2016 and applying that percentage to each player’s overall hits (which is calculated using BABIP).
  • I projected home runs using HR/PA.

Thank you to everyone that voted in the poll!

Giancarlo Stanton Is on Fire

The millionaire slugger from Miami has had some misfortune in the past few seasons of his promising career, from common injuries to freak accidents. However, while these unfortunate happenings ended his season early each year, they are no testament to the achievements he accumulated during that time and the possibilities shown by his ability.

With a somewhat slow start to this 2017 season, the G-train seems to be picking up speed. In the month of July, he is hitting and fielding better than all three previous months of the season. For reference, I will put up his stat line that I am basing this interpretation off and add some more graphs later for easier visual interpretation.

April 23 100 7 27.0% 0.264 0.296 0.264 0.366 -1 28.0% 39.3%
May 27 115 7 20.0% 0.28 0.325 0.299 0.382 -1.1 23.3% 33.3%
June 27 112 7 25.9% 0.274 0.271 0.242 0.365 -1.1 29.2% 34.9%
July 15 67 9 20.9% 0.526 0.235 0.298 0.487 -0.7 45.0% 44.2%

The first thing that grabs my attention is his home-run numbers in the month of July. Compare them to the home-run totals from each previous month and it does not seem like much of a difference, but when you take into consideration the plate appearances the difference is more discernible. On average it took Stanton 109 at-bats throughout the first three months of the season to reach the seven-home-run mark. In July however, at only 67 at-bats, he has already passed his previous monthly home run total by two home runs. At that rate, by the time he reaches that 109th plate appearance he could have 14 home runs in total for the month of July. That is double the home-run production that he has given in any other previous month this season (after writing this he just put up another two home runs in one game!).

To speak more on his power, take a look at his ISO, which is a metric that basically measures just that, his power.

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As you can see, it has shot up tremendously in the month of July, far higher than any previous month (in which they were still very high. The .270 mark is still far above league average). And while it is almost certain that he will not be able to maintain an above .500 ISO for the rest of the season, it is still a remarkable achievement to obtain throughout the duration of a whole month, as July is almost over. Another stat to look at is his weighted On Base Average (wOBA). League-average wOBA consistently sits around .315 – .320 season to season, and Giancarlo’s is currently at .487 (at the time of writing this article). The explanation for that can be summed up in two words. He’s mashing.

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He is hitting the ball harder, higher and farther. More consistently too, and the proof is all in the numbers. His home-run-to-fly-ball ratio is up, along with the percentage of balls he hits hard.

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And while I, like most other people in the world, would attribute this surge of excellence to a lucky hot streak, this might not be the case. In fact, he might not be getting lucky at all. Batting Average on Balls in Play, or BABIP, is a statistic that is useful for getting a sense of how “lucky” or “unlucky” a position player has been in terms of offense. League-average BABIP usually sits around, again, .300. Anything far above or below that number could point to a batter being “lucky” or “unlucky,” respectively. Stanton’s BABIP is .235, far below the league average and even further below his career average (.318). This means that when he puts the ball in play, excluding home runs, he only gets on base roughly two out of ten times. Sounds pretty unlucky to me, especially for a player of his known caliber, which would explain his lackluster batting average that sits at .298. When his BABIP starts regressing back to the normal .300 area, who knows just how good he could be playing.

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I also thought it would be a good idea to take a look at some of the charts and heat maps that FanGraphs offers to see if I can gather some more information, and what I found was pretty interesting. I have seen a lot of Stanton’s at-bats, and through visual memory, I can recall that most of the bad ones end with him striking out on a breaking ball low and away. After taking a look at the heat maps for the percentage of pitches he gets in specific locations of the zone, my memory served me pretty well.

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As you can see, during the first three months of the season Stanton got a lot of pitches low and away. Pitchers would pitch him there because, well, that is a hard place to hit, and for the most part it worked for them. But during the month of July, they are no longer going after that weak spot. More of the pitches that Stanton has seen this month have been concentrated in the middle and upper part of the zone, the part of the zone that he thrives in. This serves to further explain his monstrous July!

The future for the Marlins slugger is beyond bright, and as a Marlins fan, I cannot wait to sit by and watch.


*Side note*

This is my second post in the FanGraphs community! And while I am very excited, I at the same time want to be sure to improve with each and every post and write about things that people want to hear. If you, the readers, do not have anything to say about the content of the articles but do have some constructive criticisms please feel free to leave a comment! Have a good one!

Do Sluggers Really Swing More on 3-0?

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.

Cleveland Is Incredibly Bad Under Pressure

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.

The Angels Have the Most Amazing Bullpen in Baseball

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.

Team Bullpen Leaders by WAR

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.

2016 Angels Five Best Relievers by WAR

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.

2017 Angels Five Best Relievers by WAR

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.

Blake Parker Pitch UsagePerhaps 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.

Blake Parker Pitch Results Comparison 2016-2017

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:

Bud Norris Pitch UsageEven 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.

Bud Norris Sinker Results 2016-2017

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.


Bud Norris Sinker Heatmap 20162017:

Bud Norris Sinker Heatmap 2017He’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.

Yusmeiro Petit Pitch UsagePetit 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.

Yusmeiro Petit Cutter 2016-2017

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.


Yusmeiro Petit Cutter Heatmap 20162017:

Yusmeiro Petit Cutter Heatmap 2017Just 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:

David Hernandez Pitch UsageFor 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.

David Hernandez Cutter Results 2017

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:

Cam Bedrosian Pitch UsageHe’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.

A Sign of Hope for Kevin Gausman

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, 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 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, 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.

2017 HBL Dynasty League Prospect Draft – So Deep You’ll Love It

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.

2017 HBL Prospect Draft
# Owner Player Team Pos Highest Level
1 O’Connor Vladimir Guerrero Jr. Blue Jays 3B A+
2 Helmers Francisco Mejia Indians C AA
3 Vonderharr Mitch Keller Pirates SP A+
4 Duginske/Pelto Luis Robert White Sox OF R
5 Beyler Bo Bichette Blue Jays 2B R
6 Woody Walker Buehler Dodgers SP AA
7 Jabs Michael Kopech White Sox SP AA
8 Kummer Triston McKenzie Indians SP A+
9 Jabs Forrest Whitley Astros SP A+
10 Rogers Scott Kingery Phillies 2B AAA
11 Kummer Brendan McKay Rays 1B/SP 2017 Draftee
12 Biesanz Hunter Greene Reds SP/SS 2017 Draftee
# Owner Player Team Pos Highest Level
13 Kummer Rhys Hoskins Phillies 1B AAA
14 Rogers Mike Soroka Braves SP AA
15 Jabs Kolby Allard Braves SP AA

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.

2017 HBL Prospect Draft
# Owner Player Team Pos Highest Level
16 Jabs MacKenzie Gore Padres SP 2017 Draftee
17 Helmers Willie Calhoun Dodgers 2B AAA
18 Duginske/Pelto Chance Sisco Orioles C AAA
19 Helmers Kyle Wright Braves SP 2017 Draftee
20 Beyler Sixto Sanchez Phillies SP A
21 Woody Franklin Perez Astros SP A+
22 Melichar Derek Fisher Astros OF MLB
23 Melichar Ryan Mountcastle Orioles SS A+
24 Melichar Juan Soto Nationals OF A
25 Rogers Dominic Smith Mets 1B AAA
26 Todosichuk Royce Lewis Twins SS R
27 Beyler Carson Kelly Cardinals C AAA

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.

2017 HBL Prospect Draft
# Owner Player Team Pos Highest Level
28 Melichar Jhailyn Ortiz Phillies OF A-
29 Helmers Austin Beck Athletics OF R
30 Vonderharr Luis Ortiz Brewers SP AA
31 Kummer Estevan Florial Yankees OF A
32 Beyler Riley Pint Rockies SP A
33 Woody Jack Flaherty Cardinals SP AAA
34 Vonderharr Shane Baz Pirates SP R
35 Duginske/Pelto Leody Taveras Rangers OF A
36 Melichar Taylor Trammel Reds OF A
37 Rogers Dylan Cease White Sox SP A
38 Helmers Luiz Gohara Braves SP AA
39 Todosichuk Fernando Tatis Jr. Padres SS A

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.

2017 HBL Prospect Draft
# Owner Player Team Pos Highest Level
40 Rogers Jesus Sanchez Rays OF A
41 Helmers Thomas Szapucki Mets SP A
Vonderharr **PASS**
42 Kummer JB Bukauskas Astros SP 2017 Draftee
43 Biesanz Colton Welker Rockies 3B A
44 Woody Kyle Lewis Mariners OF A+
45 Melichar James Kaprielian Yankees SP A+
Duginske/Pelto **PASS*
46 Melichar Yordan Alvarez Astros 1B A+
47 Melichar Cole Tucker Pirates SS A+
48 Helmers Jeren Kendall Dodgers OF 2017 Draftee
49 Biesanz Michael Chavis Red Sox 3B AA

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.

Maikel Franco Is Adjusting

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:




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.

The Opportunity Baseball Organizations Are Missing: Part II

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

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.


High Pitch Low-Middle Pitch* Very Low Pitch
Bat Angle 20° 45° 65°
Swing Loft 11.7° 7.5° 4.2°
Timing Loft 3.3° 7.5° 10.8°
Total (Goal) Loft 15° 15° 15°


*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):

Player Avg MUD Avg Std Dev
Chris Davis -16.0 21.5
Freddie Freeman -14.3 20.3
Joe Mauer -13.2 21.4
Joey Votto -12.8 20.1
Brandon Belt -9.9 19.6
Miguel Cabrera -9.9 20.4
Paul Goldschmidt -8.7 21.5
J.D. Martinez -8.6 21.6
Nick Castellanos -7.8 19.4
Adrian Gonzalez -6.9 21.0
Matt Carpenter -6.6 20.0
Yan Gomes -4.5 22.0
Christian Yelich -3.9 21.1
Mike Trout -3.0 20.6
Logan Forsythe -2.7 21.0
Howie Kendrick -2.4 21.8
Daniel Murphy -2.0 21.9
Justin Turner -1.2 21.4

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.