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The Elite Imperfections of Mike Trout

It’s hard to tell exactly what’s going on with a player when their numbers get skewed. Sometimes its injury, others could be due to team/manager/front office resentment, more often than not it can be attributed to bad luck. However, when numbers begin to become conventional or eclipse career norms on a regular basis, under certain conditions, it behooves me as a curious self-proclaimed ‘baseball scientist’ to look into that.

Today’s subject is one Mike Trout of the Los Angeles Angels. Observe his monthly OPS through his seven seasons in Major League Baseball.


Before I proceed, this is not an indictment or deposition on Trout. This is a scrutinization that will attempt to answer why his OPS drops so sharply once we hit the dog days of summer.

Trout is a great player, no one can deny that. You ask just about any baseball player if they’d like to have numbers like Trout and they’d answer before you even finish the question.

A simple assumption through basic observation would be that it’s the fault of the three true outcomes; striking out more and walking less while his power remains the same or takes a dive as well. Since I don’t have a better explanation yet, we’ll stick with that.

But first, I wanted to see if Trout was any sort of outlier; does the average player peak mid-season, then drop off as Trout does? Sort of.


I see the same dip, somewhat as steep for Trout, in July but followed by a resurgence into August. OK, so nothing extreme; basically the same start with a disjunct finish.

Going back to his monthly performances, what also stood out is that as his at-bats increased, his OPS seemed to decrease. However, that only occurred once he surpassed 500 ABs. In the scatter plot below, the coefficient of determination reveals that just about 60% of Trout’s OPS change is attributed to his increase in ABs. That’s a pretty good interrelationship.


So far we know that Trout seems to fade in late summer and that his OPS plummets as his at-bats go up. Is it as simple as that? I can somewhat understand that as the season progresses, players get worn out and, sometimes but not always, their production drops. But the ABs situation makes it more intriguing; you’d think a great hitter is usually always great regardless of the number of times he comes to bat. It doesn’t always follow that the more chances you have the more likely you are to fail.

Remember my original supposition of K/BB/HR variation causing his OPS drop? That’s an invalid inference because we have the same thing happening; as ABs increased, his strikeouts and walks did also. Home runs bounced a little with no correlation to AB figures.


Trout’s strikeouts did jump quite a bit from June to July while his walks increased at the same rate as his ABs. However, his biggest OPS drop-off was from July to August, so we can’t parallel that to a conclusion. The following month (July to August), his ABs increased at the same 11% with both walks and strikeouts growing at the identical rates.

Not satisfied, I needed a couple of player comps to see if they showed any of the similar tendencies I see with Trout. Using his career wRC+ (the best all-inclusive offensive stat) of 169, I see Joey Votto, Miguel Cabrera, and David Ortiz in his range.

  • Votto- 162
  • Cabrera- 158
  • Ortiz- 151

Now, lets move back to their OPS. I took the quad’s career monthly average and created a comparison chart. Keep in mind we aren’t concerned about the numbers, only the trends.

And, because I’m a cheapskate, I have to use Google Sheets to create this chart which will not let me customize the labels.

 So you have: Trout, Cabrera, Votto, Ortiz


Cabrera dips about the same time as Trout but his trend line is much more stable. Votto seems to get better as the season goes on, while Ortiz seems to match pretty well except for his minor improvement in Sept/Oct.

So, is Trout and anomaly? Not really; Oritz has very similar tendencies, but also played twice as long as Trout has. To say for certain they match will take more playing time for Trout. In any case, for a player as good (and highly regarded) as Trout, that drop-off is still vexing.

So, I moved on to check and see if his hitting tendencies change. We can view Trout’s career monthly contact figures to determine if there are any obvious signs that could give any sort of explanation for the drop. Things like putting more balls on the ground instead of the air, contact type such as line drives which end up as hits more often, any infield pop-ups indicating a change in swing path, and directional hitting in regards to beating any sort of “shift” to his hitting proclivities (e.g. more balls are finding well-positioned fielders).

A couple of things stand out. The first being his line drive rate; dropping from 23.6 to 19.2 from June to Oct. Secondly his hard contact; while not a huge difference, we can see less potential for barreled contact. Lastly, as you would expect, his BABIP and OPS drop sharply from June on; .395 to .333 and 1.036 to .919 respectively.

Perhaps looking into what causes line drive as well as his hard contact regression will provide the answer; are there changes in exit velocity and/or launch angle? As a reminder, we only have the data that is available through the Statcast era (2015-2017), so take this with a grain of salt; I’m not sure we can glean much from it but its worth looking because it covers roughly half of his career.

  • June- 13.8 degrees/91.6 mph
  • July- 13.9 degrees/91.4 mph
  • August- 14.3 degrees/91.3 mph
  • Sept/Oct- 14.7 degrees/91.1 mph

There are drops but the change is slow; launch angle changes by nearly one degree and exit velocity declines by .5 MPH. Can we claim that as the cause? It’s hard to say because as I noted, it only covers his last three years.

To reinforce the lack of apparent swing path/tendencies, observe the gif that goes in chronological order from June through Sept. Do you see any pronounced change, because I don’t?


Perhaps I’m thinking about this too hard. Perhaps I’m asking the wrong question(s). Perhaps its just the way it is; sometimes you eat the ball and sometimes the ball eats you. As I said before, this isn’t a judgment or doubt on Trout’s ability; when he’s at his worst, he’s still better than most of the other hitters in the league.

This post and others like it can be found over at The Junkball Daily.

Let’s Strategize Under the Potential Extra Inning Rule

As I’m sure you know, Major League Baseball is toying with the idea of putting a runner on second base sometime around the 12th inning. While I’m not doing this to argue its validity or lack thereof, I’m going to discuss and evaluate some scenarios that could happen under those conditions. It won’t be anything groundbreaking; I’ll be demonstrating the metrics involved with a team under the various circumstances I induce.

The following scenarios are played out to score at least one run in a given inning. Top or bottom of the inning, I envisage the same sort of conditions will play out for both teams. And because there is never any telling what part of the order will start with this setup, I speak in generalizations.

I’ve thought about what would be the likeliest of moves under this arrangement and I’m going to guess it would come down to the most boring events in baseball; the offense bunts the runner to third or the pitcher intentionally walks the first batter attempting to set up the double play. Of course, there will be times when the managers decide to simply attack the situation as-is. That’s more of a volatile situation and therefore much harder to work with.

First, the basics. From 2010-2015, having a runner on second base with no one out produces the following:

  • The predicted number of runs scored is 1.100
  • The percent chance of scoring a run under those conditions is 61.4%

So from the get-go, the offense is expected to score a run in three out of every five chances.

Play the bunt or a standard defense?

Let’s start off with the first of two scenarios; the bunt to move the runner over to third. I feel like this is the most likely action but also the most difficult to work with because of varying defensive strategy. Will the defense make an anticipatory shift for a bunt or will they be in ‘straight up’ formation? In 2011, Bill James found out that bunting in sacrifice situations produced a .102 batting average. Not like we needed that because we could have guessed that you’re going to be out roughly 90% of the time.

To bunt or to swing away?

So assume the hitter lays down a bunt that moves the runner while making an out at first. Run expectancy is now 0.95 with a 66% chance of scoring a run. Your run expectancy went down 0.15 runs BUT you increase your chances of scoring by a little less than 5%. Would bunting make sense to you as a manager? Taking out any sacrifice-type contact, if your hitter produces an out and the runner has to stay at second, your run expectancy drops to 0.664 and the chance of scoring a run plummets to roughly 40%. Still feel the same way (regardless of the hitters bunting ability)?

Walk or pitch to the next hitter?

Keeping with the initial decision, we have a runner on third and one out. Pitch to the next hitter or put him on to set up the double play? Our strategy could be further altered because at this point the defense might be inclined to bring out a ground-ball pitcher or create a split situation (lefty vs lefty and vice versa). But again, let’s go with the assumption that the team will do the safest thing by having the next hitter walked. That puts runners on first and third with one out. That decision causes run expectancy to jump back up 0.18 to 1.13 and but the probability you’ll score at least one run drops to 63.4%. Would you make that same call (remember, we are in a vacuum)?

Runners on first and third with one out produce the following expectancy:

  • Average number of runs scored is 1.130
  • The chance of scoring a run under those conditions is 63%

One of a couple of outcomes will follow should you elect not to intentionally walk the hitter. He will drive in the run by putting the ball in play various ways (sacrifice fly, fielder’s choice, hit, etc) and accomplish what the offense set out to do; score at least once to put the pressure on the home team. Or, the hitter could strike out, ground out (which could turn into a double play, an out at home, etc) or fly out.  If contact is made, this could alter our base-out states: two outs and runners at various bases (first and third, second and third, second or first should the runner somehow get thrown out at home). Due to the randomness of contact in this event, we’ll stay with the intentional walk.

To bunt or to swing away, pt II?

So what about the offensive strategy for first and third, one out? The options are much more vast. You could sacrifice bunt to move a runner over to second (assuming the runner on third is held up), thereby dropping run expectancy to 0.580 and dropping your scoring chances to 26%. The risk here is having the batter somehow bunt into a double play; runner at third is tagged/thrown out and the batter is thrown out at first. Do you, as a manager, take the initial risk that set up this problem? It is challenging to turn a double play on a bunt but if the defense is ready, it makes it easier to do so.

This time, let’s assume the hitter botches the bunt to the first base side and the overeager runner is thrown out at home (or caught in a rundown), runner safe at first. Now, with two outs, there’s a runner on first and second, we sit at a very poor run expectancy of 0.429 and have just over a one in five chance of driving in that run.

Walk or pitch to the next hitter, pt. II?

At this point, again with neutral context, you can walk the batter to load the bases, (if the hitter is too good and the next isn’t great, etc.) or you can just pitch to the batter (maybe bringing in a bullpen specialist). Walking the batter gives the offense a 10%better chance of scoring and a .33 increase for run expectancy.

If you elect to pitch to the batter either the final out is made or runs score. Walking the batter loads the bases and forces the defense to hope for the best. The latter situation would actually produce the most excitement; a crucial decision would need to be made. Either way, my tangent baseball universe will end; three outs, inning over or the needed run(s) score.

While I don’t necessarily agree with or enjoy the thought of the game being altered in this way, it could produce some interesting strategical decisions and test the maneuvering skills of team managers.

This post and others like it can be found over at The Junkball Daily.

Another Thing Joey Votto is Great At

If you walked up to me and said “Joey Votto is the best player in baseball”, I’d have a hard time finding a good argument against that. Heading into his twelfth season, Votto has been one of the most consistent players in the league. From 2007 on he’s played at least 110 games each year, with the exception of his rookie year and the injury-shortened 2014 season.

Using a summary of his last three years, you’ll see he’s been at or near the top in almost every major statistical category used to evaluate players.

  • Fourth in overall WAR
  • Second in Batting Average
  • First in On Base Percentage
  • First in BB/K rate
  • Third in Win Probability Added
  • Second in Weighted Runs Created Plus

Does Votto leave anything to be desired? Well, of qualified first basemen, he ranks 12th in DRS (3) and 16th in UZR/150 (2.6) since 2015. So if you could get on him for anything, it would be his fielding.

Hitting a baseball is one of the hardest feats to accomplish in any sport. I would venture to guess, whether you’re the pitcher or hitter, that a full count creates the most tension on the baseball field. I don’t think it takes a Bill James-like brain to figure out that 0-2 is a very tough situation to be in at the plate; the scales tipped heavily in the pitcher’s favor. Game tension is a fun energy to experience in baseball, which leads me to stick with looking into the more balanced full count.

Would you be surprised if I told you that no one has performed better in recent seasons under those conditions than Votto? But first, behold the predictable OPS under all two-strike counts!

  • 0-2, .389
  • 1-2, .419
  • 2-2, .470
  • 3-2, .814

Digging into the specifics of a full count, 30% of hitters get walked and 46% reach base. Votto is one of those 46%-ers. In fact, since 2015, no other hitter had a better wOBA under a full count than Votto.

Pretty impressive at the plate to begin with, every aspect of Votto’s at-bats are above average; needless to say, you’re going to have your work cut out for you when he comes to bat.


Votto has an advanced eye, which you can tell by only looking at his swings out of the zone; at least 10% lower than league average. On the other hand, he seems to make contact more than average when offering at those pitches. But, only achieves a paltry .219 when putting the ball in play. Regardless, pitchers have to be pretty careful with what they do to get him out lest he ends up on base and/or putting crooked numbers on the scoreboard. We’ll get to that in a minute.

Getting back to his production with a full count, we have three other hitters within reach of Votto. The qualifying threshold is 200 at-bats (regular and postseason), of which 28 hitters qualified. The following are tops in wOBA when faced with a full count. After that foursome, there is a very sharp drop off.

  1. Votto- .481
  2. Matt Carpenter- .480
  3. Kris Bryant- .467
  4. Mike Trout- .461

Votto and Carpenter are very close, one one-thousandth of a point, but Votto has been in this position 111 times more. The averages keep them close but there is no way to be certain Carpenter could keep that number consistent as his at-bats go up.


No real correlation there but Votto and Bryant are the clear outliers; Carpenter and Trout are with them as well but are a bit further back in terms of pitches.

Votto also has the highest percentage of 3-2 counts in terms of pitches faced with 7.12% of his pitches being delivered in that situation; again with a minimum of 200 ABs. That’s an attribute to his plate discipline.

So how has he been so successful? This is where things get interesting. When Votto is faced with a 3-2 count, look where the pitches he has to work with are concentrated.


Furthermore, take a look at his career batting average based on zone location.


For whatever reason, pitchers seem to be content delivering a 3-2 pitch right into Votto’s butter zone. To be specific, the three pitches thrown at him the most in a full count are:

  • 34.8% Four-seam Fastballs
  • 17.2% Two-seam Fastballs
  • 17% Sliders
  • 9% Changeups

Almost half of the pitches thrown are fastballs of the two and four-seam variety. Guess what Votto eats up?


Those are the pitches Votto has seen the most since 2015. Coincidently, they are not only the four he sees most in 3-2 counts but also, with the exception of the slider, the pitches he has the most success against.

There are some pitchers don’t throw a slider but what I don’t understand is why they try to beat him with a fastball almost 50% of the time. It obviously doesn’t work. I’ll try to quantify as best I can, the situations Votto comes to bat under. Just going on what I have in the previous charts, maybe there aren’t many high-leverage situations when its Votto’s turn to hit.

For his career, he’s come to the plate 2,683 times with runners on base; 1,528 of those are with runners in scoring position. For the former, his OPS is 1.026 and the latter 1.079.

To give context to how much more/less Votto comes up with runners on base, I used the 2015-2017 average season numbers of total at-bats and divided that by 750. The 750 is 25 players per 30 teams. That’s a loose guesstimate but I would presume that through a given season a team holds at least that many (different) hitters on average, taking into consideration promotions/demotions/injuries/etc. That gave me 107 plate appearances per season with runners on base for the average hitter.

Then I used Votto’s career 2,683 PAs with runners on and divided it by his 11 seasons to get an average of 244 PAs per year. So, he has runners on about 44% more than the average hitter each season. Where he hits in the order DOES help but you have to remember he’s been playing on, for his career, a pretty mediocre Reds offense.

And, as a footnote, his worse performances are with runners on second and third followed by bases loaded; he excels with runners only on second, third, or first AND second.

That tangent we just went on only answered part of the question. We can’t know what the score was, the leverage index and other minor variables. All of those could change the way Votto is pitched to given the particulars. But, for whatever reason, the best hitter in baseball under a full count does not seem to be challenged much at all.

This post and others like it can be found over at The Junkball Daily.

What to Expect From J.D. Martinez’s Power in Fenway

Several days ago the Boston Red Sox acquired J.D. Martinez, presumably under the expectation of adding a lot of power to the lineup. Since 2015, he’s eighth in home runs with 105, a league-best .284 ISO (four-thousandths of a point ahead of Nolan Arenado), and his 147 wRC+ puts him at sixth in all of Major League Baseball.

Yes, he can hit for average as well but I’m not interested in that. What I’m curious about is whether or not the famed Green Monster in Fenway Park will be a hindrance to Martinez’s power.

He’ll now be playing 82 games each season in Fenway Park, where every time he comes to bat he’ll have the Green Monster in peripheral view; a 37.2-foot high wall 310 feet down the left field line and as far away as 380 feet at left center. There are dozens of hits every year at Fenway that could have ended up as home runs in other parks, but instead, are eaten up by the Green Monster and spit back out as (extra) base hits.

To attempt to approximate the minimum required launch angle and exit velocity to hit a home run over the Monster, I needed visual proof. Using Baseball Savant, I searched all the home runs hit in Fenway Park during the Statcast era.

I keyed in on home runs specifically hit to left/left center field, spanning the entire range of that monstrosity. Using the spray chart tool, I found any and all homers that were as close to the barrier of the GM (Green Monster) as possible. I came across one that seemed to fit perfectly and cleared the wall just enough.

That’s Steven Souza, Jr. driving a home run under (nearly) perfect metrics to breach the wall.

Just to be certain that this was as close as I could get, I wanted to know what the weather conditions were that day. I was able to find the barometric pressure and how mother nature’s influence could have affected this hit, in terms of exit velocity. Air pressure matters because when its low, baseballs go further due to less friction on the baseball and vice versa.

  • Game time: 1:35 PM
  • Game Duration: 4 hours and 32 minutes
  • Approximate time HR was hit 5:00PM
  • Conditions at time of HR: 50 degrees, light rain, wind blowing NW at roughly 16 MPH with gusts up to 27 MPH
  • Game barometric pressure: A consistent 29 inches

OK, so what jumps out at you? Wind speed, right? All Fenway Park’s contact to left (center) head in a northerly direction. The low barometric pressure and wind speeds give me two possible caveats for this examination.

However, as you see in the GIF, the trajectory was fairly high and it cleared the wall by a couple of feet. It’s impossible to tell if the wind was blowing (and how hard) during Souza’s homer, so keep those things in mind since they are variables that don’t make this investigation exact when applying it to Martinez.

Souza’s hit metrics on that homer were as follows:

  • Breaking ball at 80 MPH
  • 93 MPH exit velocity
  • 33.5-degree launch angle
  • Hit distance of 344 feet

We can use those measurements to get a guesstimate of what Martinez could or would have done hitting regularly in Boston. I produced the following spray chart using his last three seasons under the backdrop of Fenway Park.


J.D. Martinez(2)

Clearly he’s able to hit to all fields; you could suggest that a fair amount of his hard contact is concentrated in the area of the GM and that’s what I’m going to hone in on. Yet with the height of the wall, some of those home runs (hit in other ballparks) could have been inhibited.

I inspected all Martinez’s home runs since 2015, shifted focus to the launch angle and exit velocity using the Souza home run as my model, and ran a query of all his contact using the metrics it would take to clear the wall.

I set the minimum launch angle to 30 degrees, to give a little breathing room because it appears as though Souza’s homer cleared the wall by a foot or two; I did the same for exit velocity, starting it at 90 MPH. For minimum hit projection range, I used the shortest distance to the GM; 310 feet.

Breaking it down even further, I ensured that homers hit to left center had ample room and momentum to clear the wall; e.g. the 310-foot distance wouldn’t work for a ball he actually hit to left center, for example.

Altogether, Martinez had a total of 121 batted ball events under the conditions of my launch angle/exit velocity/distance figures. 24 of those 121 BBEs resulted in contact to left field; 11 would have ended up being GM-clearing home runs if hit in Fenway, but instead were recorded as outs.

So, taking events strictly within the region of left to left-center field in Fenway, Martinez could be expected to hit about 43% more home runs facing the GM over the next three years of his contract.

Remember, that doesn’t include contact to other parts of the field. If you look back to the spray chart, you’ll see several spots marked home runs that would fall short in Fenway.

Furthermore, using his home run total from 2015-2017, we could reasonably surmise that he’ll hit an average of about 35 home runs for the next couple of years. Adding in these 11 outs as home runs, Martinez will be expected to hit roughly 9% more home runs (3 per season) at Fenway, so long as he is a Red Sox.

So, the monster won’t be as problematic as I originally assumed upon hearing of this acquisition for Boston; it might actually improve Martinez’s power.

-This post and others like it can be found over at The Junkball Daily.

The Trickiest Third Strike Pitcher in MLB

I ran some queries over at Baseball Savant and came across this tidbit of information. Since 2015, no other pitcher froze hitters on strike three more than Cleveland Indians’ Corey Kluber.


I decided to write an article on Kluber’s caught looking data along with how he’s able to be the best at getting hitters held up on that third strike.

Sifting through the last three years of Statcast data, and filtering the results down to a 5000 pitch minimum, Kluber ranks second overall to Clayton Kershaw (2.38%) in called third strike ratio to total pitches (2.28%).

So, why am I not writing about Kershaw? Well, I’m not concerned with ratio because, in this case, the ratio is independent of the number of times Kluber is able to deal that third strike. Kershaw might be better at working over hitters (thereby throwing less) but that doesn’t necessarily lend itself to more swing-less third strikes.

Kluber has thrown with two strikes nearly 1500 more times than Kershaw has in the last 3 years. But, Kershaw his pitched much less (mainly due to injuries), so we’re not going to ‘punish’ Kluber for this. And, we’re talking about a difference in the ratio that’s a tenth of a percent.

Moving on, I wondered if there is any advantage pitching in the American League? First, I looked at the overall plate discipline numbers for the entirety of Major League Baseball from 2015-2017.


So we have a 3-1 ratio of swings, as well as contact, in verses out of the zone. Now I’ll compare the AL vs NL three-year average.


We’re talking about fractions of a percent difference, with the only real disparity (if you can call it that) is the out of zone contact where the AL has a nearly 1% difference. So, there is no advantage to pitching in either league in terms of the type of at-bat you’ll experience.

Using a minimum of 1000 pitches each year, I found that Kluber finished first in 2015, third in 2016, and 2nd in 2017 in strikeouts looking. Furthermore, in context of plate appearances with two strikes, Kluber is ahead in the count (1-2/0-2 count) 24% of the time, even at 45%, and behind (or, a 3-2 count) 31% in those three years. Nearly a quarter of every two-strike situation, hitters are forced to be aggressive at the plate; and just under a third of the time, the batter has to make a mandatory choice.

Before I proceed,  I need to point out that there is some discrepancy as to what Kluber actually throws. He uses something of a sinking fastball that is hard to classify; it goes either way but my main source of research indicates it’s basically a sinker. And with his breaking pitches, which some sites call it a slider, some call it a curve, but it may be a slurve.  For argument’s sake, we will refer to both of them as a sinker and a slider.

So what is it that Kluber is using that’s laying waste to hitters on strike three? His sinker, which he’s thrown for strike three 108 times (50%) since 2015.


The above graph is his pitch selection after strike two the last three seasons.

His sinker location when he throws regardless of the count. Good luck telling a hitter where to concentrate his swing when he throws it.

chart (21)

chart (22)

However, something changed in 2017; he cut back on his bat-confining sinker by 7% and increased his change-up and slider/curve/slurve usage 1.5% and 7.3% respectively.


Just for curiosity’s sake, Kluber’s release points are nearly identical on all three pitches. So the hitter may not know whats coming at him with the intention of ending up as strike three (until its too late).


OK, so he leaned more on his slider last year. What can we make of that using his last three years’ run values in the context of runs above average?

Screen Shot 2018-02-28 at 4.48.06 PM

The sinker, his bread and butter pitch for strikeouts, seems to hover around league average in terms of run value. Upping his change and slider usage appears to have paid dividends; Kluber seems to believe those are better suited to set the batter up for the strikeout. I would also venture to guess his sinker isn’t nearly as effective when thrown earlier in the count, hence the negative run value.

To note, Kluber’s two-strike stats: .136 BA/.392 OPS/10-1 K-BB

His sinker is clearly working when he needs it to.  Overall, it’s his least-effective pitch as hitters eat it up for a .300 average. Nevertheless, according to the data, it’s a tough pitch to gauge when used for that third strike.

Maybe Kluber will start using his slider more with two strikes. However, if he does so, that could cause him to be dethroned as the ‘King of Caught Looking’; his slider is swung at more than any other pitch he has, thereby causing a swinging strikeout.

Regardless, Kluber should still be able to put batters away with that devastating sinking fastball; opponents have 2-to-1 odds they’ll be dealing with it when the count has their backs are against the wall.  It usually doesn’t end well.

Analysis and Projection for Eric Hosmer

Eric Hosmer is one of those guys you either love or hate. His career, which includes one World Series championship and two American League pennants, has been just as polarizing.

First, who Hosmer is. Consider his WAR each season since 2011:

Interesting pattern; let’s look into that. The chart below is Hosmer’s career plate discipline (bolded data are positive WAR seasons).

Nothing appears to be out of sorts, no obvious clues to suggest a divergent plate approach.

Moving on, I noticed his BB/K rate did relate to his productive seasons; that alone can’t possibly explain his offensive oscillations. While his strikeout and walk rates did vary, the differences were a matter of two or three percentage points, at best.

So, I decided to look at his batted ball contact trends and found that his line drive rate directly correlated with his higher WAR seasons; 22%, 24%, 22% in 2013, 2015, and 2017 respectively with 19%, 17%, 17% in 2012, 2014, and 2016 accordingly.

OK, so his launch angle must be skewed. But, like his plate discipline, no outliers were demonstrated; his 2017 season should be easy to pick out. The below animation is a glance at Hosmer’s three-year launch angle charts, in chronological order.


How about his defense? Well, something seems off about that, too.

He’s won a Gold Glove at first base four out of the last five years. He looks great on the field, but unfortunately, his defense reflects the same way as a skinny mirror; his UZR/150 sits at -4.1 and his defensive runs saved are -21. Since 2013 (the first year he won the award) he ranks 13th in DRS and 12th in UZR/150 out of all qualifying first basemen. So, middle of the pack basically but worth four gold gloves? Probably not.

As we could have surmised, he’s simply an inconsistent player. Falling to one side of the fence yet?

One thing is a certainty; his best season was, oddly enough, his walk year with the Kansas City Royals in 2017. Now, I’m not about to speculate that Hosmer played up his last year with the Royals to get a payday (which he most certainly got). Looking back at his WAR in the first part of the article, you can see his seasonal fluctuations suggest he was due for a good year.

Keeping with the wavering support of Hosmer, is the contract he acquired to play first base with the San Diego Padres. His eight-year deal (with an opt-out in year five), will net him $21 million each season. He will draw 25.8% of team payroll. When his option year arrives in 2022, he’s due for a pay cut of $13 million in the final three years.

A soundly contructeed contract as, according to Sportrac’s evaluation, his market value is set at $20.6 million a year. To note, the best first baseman in baseball, Joey Votto, signed a ten-year deal in 2015 for $225 million dollars (full no-trade clause). Starting in 2018, Votto is slated to make just $4 million more than Hosmer will in the early portion of his deal. Did San Diego overspend? It all depends on what their future plans are for him.

In any case, Hosmer will join a team that, following his arrival, is currently 24th in team payroll. In 2019, they will hop to 23rd. It could go down further upon the arrival of their handful of prospects who look to be the core of the team.

So who will the Padres have going forward? Using wOBA, probably the most encompassing offensive statistic, I decided to forecast what the coming years will look like for Hosmer. It goes without saying that defense is nearly impossible to project. So, for argument’s sake, we’ll continue to assume Hosmer will be an average defender at first.

Since Hosmer’s rookie year in 2011, the league average wOBA is approximately .315. Hosmer should stay above that through the majority of the contract. But, let’s be more accurate. Using both progressive linear and polynomial trend line data (based on both Hosmer’s past performance and league average wOBA by age), I was able to formulate a projection for Hosmer through age 35 (no, I’m not going to lay out any of my gory math details).

OK, I lied. Here is the equation I used to come to my prediction :

{\displaystyle y_{i}\,=\,\beta _{0}+\beta _{1}x_{i}+\beta _{2}x_{i}^{2}+\cdots +\beta _{m}x_{i}^{m}+\varepsilon _{i}\ (i=1,2,\dots ,n)}

From age 28 on is what we want to look on from. Hosmer is expected to take a dive offensively in 2019 with a bounce-back year in 2020, sticking with his past trends. A year before his opt-out clause (where he’s slated to make $13 million), his wOBA is expected to regress at a stable rate. He’ll continue to be league average or better during the twilight years of his career.


Hosmer seems to be appropriately compensated. You could argue that he’s making too much, but the Padres had the money to give him and they are banking on Hosmer to be highly productive at Petco. But, chances are (according to his history), he won’t maintain (or exceed) his 4.1WAR in 2018. He’ll be labeled as a bust but ought to have a few good years in him during the $21 million salary period. And, as my forecast chart shows, his 2022 pay cut comes at just the right time.

*This posts and more like it can be found over at The Junkball Daily

Has Barreled Contact Reached Statistical Stability?

When making evaluations on player ability in terms of their quantifiable actions, there comes a point when you have to take into consideration sample size to determine the validity of the numbers you’re seeing.

Take a batter who comes up 100 times and gets 27 hits. That’s a .270 batting average. Not bad. Another batter comes up 1000 times and gets 270 hits for the same .270 average. So, are both hitters the same? On the surface, yes. However, can you expect the hitter who came up 100 times to continue to hit .270? Is that a reliable amount of at-bats to make an inference? Can we assume the batter with 1000 at-bats is more likely to continue to hit around .270 going forward? I believe we’d all agree, since this is pretty basic-level statistics, that the higher at-bats, the more reliable the batting average.

Statcast has a new-ish measurement of balls hit on the barrel of the bat, or ‘barrels’. This is useful because now we can see how well batters are squaring up on pitches.

Let’s say you have two different batters. One that bloops singles off end of the bat or sneaks grounders past the infield may have a similar batting average as a guy who regularly rips hits into the outfield. So how would you judge the better hitter? They both (with exceptions) produce the same result. Would you go with the guy who regularly squares up on pitches; a hitter that is likely to produce more ‘effective’ hits? Or a batter who tends to hit the ball off the end of the bat, in on the hands, etc. who tends to produce weak contact that could result in groundouts, pop-ups, etc?

If you have to pick one to pinch hit, who would you rather have walking to the plate?

Before I roll up my sleeves, glance below at the type of contact MLB hitters have been producing on average the past three years.


What I’m going to do is determine if three years of data is enough to make an inference on what we can reasonably expect an average hitter to produce in terms of barrels per contact; have we reached a point where the three-year sample size is reliable to make inferences going forward?

First, I looked at the collection of batted ball events since 2015. Each year had roughly 900 hitters with at least one batted ball event. All together it accumulated a total ‘population’ of about 2700 hitters. I decided it would be easier and more educative to try and break it down year by year.

Using the 900-something batters per year, I wanted to develop a sample size from that group with a confidence interval no higher than five. Using the entire three-year ‘population’ of hitters would show results all over the board; the data became very volatile as the batted ball events decreased.

By taking no less than 100 occurrences of contact, it’s more reasonable to scale. The average batted ball event (BBE) per qualified hitter (with at least one event) is roughly 40% of the overall average of 253 events per hitter. This is closer to the overall ratio of hitters that had several dozen BBEs instead of batters with a few events, which produced large fluctuations.

You could ask “Why didn’t you take ALL the data and average it out?” Well, I could have. The problem I had was the variation is incredibly high; too many of the 2700+ had a very small amount of events (and barrel rate) which cannot lend itself to fidelity. On a scatter plot, it tells us almost nothing.

Instead, I cut the ‘population’ down and required at least 100 BBEs. That gave me a total of 1170 players, or a little more than half of the entire 2015-2017  hotter population.

This is the scatter plot, based upon BBEs (Y-axis, horizontal) and total barreled hits (X-axis, vertical) that was produced using that criteria.

chart (15)

In the above chart, the coefficient of determination (or, r2) equaled 0.161; not a great, but certainly not menial, expectation of correlation between BBE and total barrels.

In layman’s terms, the more events you produce, the higher the expectation of having more barrels becomes. You could have made that inference without the chart, however, I was curious to see if the increase was as sharp as I expected it to be (it wasn’t).

So I wanted a more reliable correlation, as it is logical to assume that the more you do something, the higher the amount of times you achieve your goal.

I took all of those BBEs and compared them to the percentage of barrels (X-axis) to BBEs (Y-axis). I feel that ratio produces a much more accurate relationship.

chart (16)

This time, the r2 equaled a much more stable 0.006 with several outliers present. The further you look down from those outliers, the more concentrated the chart. For the most part, roughly 80% of the plot points are 10% or below. The amount of hitters above that 10% mark would be baseball’s elite power hitters.

It appears we may have concrete proof of normalization.

So, for now, we can assume that your average batter can expect to have maybe 5%-7% barrels per contact; slightly more as your contact events increase.

But, let’s break it down a bit so we can say with certainty that this ratio is dependable for hitters going forward. I wanted to keep the sample size the same throughout the three years of collected Statcast data; 66%, or 395 batters.

We’ll start with 2015.

Below I took the total population of 915 batters in 2015 and used a confidence interval of 4.89 to get the sample size of 395. And, as with all subsequent charts, I worked with a 99% confidence level.

-With all remaining charts, the X-axis is the percent of BBEs to barrels and the Y-axis is the BBEs.

chart (17)

For 2015, the coefficient of determination is 0.032 with maybe nine outliers. There is a minor amount of regression but mostly a stable trend line. And, we see the line staying within a 7%-9% ratio of barrels to BBEs.

Here is 2016’s data; a population of 909 hitters with a 5.00 confidence interval.

chart (18)

Now, even with a similar r2 as 2015 (0.039) we are starting to get larger variation and a few more outliers. Yet the trend line again regresses, this time at a slightly sharper scale.

For 2017, 905 total hitters and a confidence interval of 4.88.

chart (19)

2017 comes across as a mess of variation with dozens of outliers. The trend line produced an r2 of 0.007. And, in contrast to the previous years, there wasn’t a regressive trend as BBEs became more frequent; it actually shows a slight increase.

What does that mean? No idea. Could it be, now we have this information available, that hitting coaches are working with batters to improve their contact? Shot in the dark but I can’t come up with a better inference.

Now, lets use each year sample size combined (1175), use a confidence interval of 4.9 (average CI of the three years of study) to come up with a sample size of 66%, or 552 batters.

chart (20)

Now we have a very stable (with a negligible increase) trend, 0.003 coefficient of determination, with some variation and exceptions at a rate of 10%.

Most of those outliers from the graphs are represented in the following chart. And, of those aberrations, several appear in all three groups.


So, the question is whether or not the available Statcast data on barrels is considered stabilized after three years; can we reliably scale a batter’s barrel rate? Do we have a reliable sample size for hitters?

It looks as though we do.

After three years, the overall trend line(s) appear to be somewhat stable in the 5-8% window for an average batter; we can expect most hitters to be at or below 10% barrels per batted ball event.

Miguel Cabrera and the Inevitable Decline

Miguel Cabrera had a tough 2017. Could his decline be due to regression? Age? Could it be the back problems he allegedly played through? Or, was he just plain unlucky?

Knee-jerk assumption is health issues. From Jon Tayler (Sports Illustrated):

…it’s clear that, at age 34, his body is breaking down. On (September 24th 2017), Detroit learned that Cabrera, who had to leave Saturday’s game early with back pain, has been diagnosed with two herniated discs in his lower back, with manager Brad Ausmus telling reporters that his star may not play again this year. Back issues have been a problem for Cabrera since he played for Venezuela in the World Baseball Classic back in March and are the latest in a litany of aches and pains he’s dealt with since turning 30; as Ausmus put it, “This has probably slowly been developing for years.

Baseball players break down. Some sooner (and more drastically) than others.  A player with Cabrera’s skill set can regress and still be above average.

So, let’s delve into regression and luck.

A quick overview of the last four seasons for Cabrera.


2017 was likely worse than anyone could have reasonably expected.

We’ll mostly work with Weighted On-Base Average. wOBA is a great tool that helps determine how productive a hitter has been.

It’s more informative than OBP as it uses weights to determine where a hitter ended up and what he accomplished when reaching base. OBP only tells us that the batter got on base. That might be enough for others, but some of us would like a little more context; no judgment on which you prefer.

For regression sake, let’s look at Cabrera’s career wOBA against the league average in terms of age.

chart (11)

As we can see, Cabrera had a wOBA well above average for a player his age. According to the chart data, at ages 29 and/or 32 is when wOBA seems to peak; .319 for 29 year olds, .320 for 32 year olds.

Once Cabrera hit 34, he crashed back to earth and managed a league average wOBA.

Perhaps 2017 was an anomaly; a result of bad luck? Here’s a glance at his batting average on balls put in play. His career BABIP is .344 and 2017 he posted a .292; slightly worse than league average.

chart (12)

Cabrera’s BABIP remained steady, save for a few fluctuations, then plunged into mediocrity. So he was just unlucky…right?

We can look at this graph and see from age 19 to 23 it continually climbed, then dove down at 25. The chart follows a similar trend, with a bit more volatility, from age 26 to 32. Can we infer it will trend upward again? Since it would be quite a feat for his BABIP to go on another positive run, I’d venture to guess that, at this point, it will stabilize.

So, can we blame the injury now?

Well, we can’t measure how much his back problems affected his hitting. We can take it into consideration but we don’t know how much it was actually bothering him. He managed to play in 130 games, so its hard to say it was that much of a problem for him. I would presume, depending on the pride of the player, that as you got older you’d want to protect your body more; give it more rest. Obviously, Cabrera is a tough guy as he averages something like 150 games a season. Knowing that the organization was sliding down into obscurity, maybe he felt it was his duty to keep playing for the fans.

Those are a lot of ‘maybe’s’.

Other than his rookie year, he’s never played less than 100 games each season. He had injuries in 2015; listed as day-to-day with a back soreness on September 23rd and ended up on the 15-day DL July 4th with a calf injury.

Regardless, the sharpness of the wOBA decline is what I find disconcerting. His biggest drop in wOBA occurred between the ages of 29 and 30; about a .070 drop. Then, going from age 33 to 34, it dropped .086 points. To note, the average wOBA actually increases two-hundredths of a point from 33 to 34.

So why did this happen? We’re going to investigate Cabrera’s wOBA versus his xwOBA for 2017.

To summarize xwOBA: Based upon the type of contact, it’s what was expected to happen versus what actually happened.

*Already know xwOBA? Skip down to the chart

Aaron Judge drives a ball into the left-field gap, under a certain launch angle and exit velocity. Let’s say he hits it into an average outfield and it drops in for a double. Alternately, Mike Trout drives a ball under the same conditions but Billy Hamilton is playing center field. Since Hamilton has elite speed and is a good defender, he caught the same type of hit Judge dropped between inferior defenders.

One other thing I want to point out about xwOBA. It takes speed into account. Albert Pujols is not a fast runner; much slower than average. That being said, he’s more inclined to hit into double plays and/or unable to leg out an infield single. A ball hit with the same trajectory by other players might be beaten out.

I understand that speed is a factor in a game but given the likelihood of that ground ball being hit for an infield single, xwOBA would adjust for a player like Pujols because it would be expected that he could leg it out. That aspect could be seen as a flaw depending on your point of view.

This might be oversimplifying the concept…or making it even more confusing. And it might not be an exact science, but its pretty darn close.

Here’s a chart of comparison to other hitters who saw a variance from xwOBA to (actual) wOBA in 2017.


We can see one thing standing out; Cabrera, by two-hundredths of a point, is well ahead of the other nine in terms of the difference. It took a little bit of a dip from Brandon Moss to Logan Forsythe but not as drastic.

Going back to 2016, Billy Butler had the biggest drop at -.058; Cabrera finished fourth with -.050 (.459 xwOBA/.409 wOBA).

So, two reasonably big differential drops over the course of two years. The caveat here is in 2016, Cabrera was much more productive; a 4.8 WAR with a 152 wRC+.

Consider this contact visual, from ‘16-‘17, of Cabrera’s xwOBA and wOBA.


Quite a distinction in contact as well as balls in play. Yet, his launch angles remained in the same sphere, between roughly 40 and -20 degrees. Cabrera clearly isn’t having a problem with his swing. Mechanically, anyway.

Let’s move onto contact and exit velocity during the drop-off years of ’16 and ’17. In 2016 Cabrera had a total of 238 ‘good contact’ hits (barrels/solid/flares) on 9.5% of pitches seen:

Miguel Cabrera (6)

In 2017, 161 with a 7.8% ratio:

Miguel Cabrera (5)


And how about Cabrera’s exit velocity?


Was he swinging in pain? How much and to what detriment isn’t quantifiable, especially because he still managed 500-plus plate appearances. Pain to you isn’t necessarily pain to someone else.

Cabrera is on the decline. His xwOBA data makes a case for that. I’m going to infer it was simply a coincidence that his injury occurred the same year. While he wasn’t hitting the ball as hard (humans do lose strength), he maintained his launch angles; something that would have changed (at least a little) if you’re burdened from back pain.

You can’t play at a high level, like Cabrera has, forever. Even during his not-so-great years, he was still so much better than an average player. Regression is inevitable. Last season appeared to be the year that it happened to one of the best hitters the game has ever seen.

*Statcast data courtesy of Baseball Savant

Who Obtains the Most Assistance in Pitcher Welfare?

Nobody’s perfect, especially umpires. This is the case at any level of the game. Be it softball, tee ball, or baseball, from Little League to the Big Leagues, you will have undeniably disagreed with a call that an ump has made.

Given the movement, velocity, and the newly anointed skill of pitch framing, it’s becoming more difficult for umpires to get the calls right. The robo ump has been discussed quite a bit but I’m not sure how I feel about a machine making decisions in lieu of accepting the concept of human error. We did it for decades before instant replay was instituted.

Umpires get balls and strikes wrong a lot. It’s the way it goes. Given that understanding, I wanted to know which pitcher has in recent years been the beneficiary of favorable calls.

And, like the umpires, not all (strike zone) charts are 100% accurate; leave a little room for error here.

I’ve parsed data on which pitchers have had the most declared strikes that were actually out of the zone. I decided to stop at 2014 because I felt that four years of information was sufficient for the study.

First, the accumulated data.

From 2014 to 2017, the amount of pitchers with phantom strikes has been increasing at fairly high rate; the biggest leap was from 2014 to 2015 (36 pitchers).

chart (4)

Interestingly, the pitchers with at least 100 ‘phantom strike’ calls has actually decreased.

chart (6)

And, despite the jump in total pitchers involved from ’14 to ’15, the pitchers with <=100 strikes called decreased at the highest rate.

Should we go tin foil hat and infer that umps are no longer favoring certain pitchers as much as they used to? Doubtful, but I’m not investigating integrity here.

So who is getting the most benefit from the perceptively visually impaired? First, I took the last four years of pitching data for our parameters. Then, I cut final the list down to a minimum of 10,000 pitches thrown. Lastly, I included only the top 20 pitchers in the group.


As we can see, Jon Lester of the Chicago Cubs has been the most aided overall; 562 non-strikes in four years.

For the optically minded, here is the pitch chart of Lester’s data.

Jon Lester

That’s A LOT of Trix!

Now, lets see if the percent of pitches has any impact on our leader(s).


Not a whole lot of variance, at least near the top. Lester clearly wins The MLB Umpires’ “Benefit of the Doubt Award”.

OK, so now we’ve got our man. Case closed, right?

Oh…that little caveat of ‘pitch framing’. Perhaps its that Lester has had great framing from his catchers. Let’s look into that.

For the moment, we are going to focus on Lester and his primary catcher from 2014-2016, David Ross.


Clearly 2014 was Lester’s most favorable year with Ross. That year, Lester ranked third in total pitches called favorably out of the zone (156) and 11th in ratio of calls (4.47).

The subsequent years with Ross are as follows:

2015- 6th (141), 10th (4.43)
2016- 5th (125), 7th (3.95)

Here’s where things get a bit intriguing. Recapping 2017, things appear to fall apart completely for the Cubs in the context of pitch framing.


The only catcher who was able to garner a positive framing rating was Kyle Schwarber, who caught just seven innings that year. But even his stats are far from impressive.

And how did Lester fair in terms of ‘phantom strikes’ that year? He ranked first in overall strikes called out of the zone (150) and fourth in total call ratio to pitches thrown (4.46).

He wasn’t all that far from the top under Ross, but was basically the frontman of the metrics in 2017.

Some things are hopelessly lost in the sphere of the unexplained. But, the research didn’t set out to find reasoning. In this case its more fun to be left with subjective theories. However, it’s a bit silly to think that there is actually an umpire conspiracy allowing Lester to succeed when he apparently shouldn’t.

My best guess is maybe they feel sorry for him since he can’t accurately throw the ball in the infield anywhere other than to the catcher (which did changed a bit in 2017)?

Regardless, Lester is our guy, here; receiving a sizable edge in terms of missed calls. It will be interesting to see if this trend continues this season.

Is ‘Tanking’ in Baseball Worth It?

With all the blabbing about the fire sale of the Miami Marlins, and less so with the Pittsburgh Pirates, does the philosophy of ‘tanking‘ in Major League Baseball work? Can it come to fruition the same way it does in the National Football League or the National Basketball Association?

The biggest and most obvious difference in those sports is the vast majority of players you’ll draft in the NFL or NBA are ready to play (even start) the following season. Not only that, players are more of a ‘sure thing’ in those leagues; you’re more likely to hit on a player since the pool is much more shallow than it is in baseball.

While in MLB, there are several levels to break through before you’re actually ready to play in the top-level.

Now, I understand the angle of ‘tanking’ to accumulate funds and eventually splurge on some free agents or wanting to make your team younger. I can follow that train of thought (sort of) but we are going to go on the premise that teams are doing it to grab top-level draft picks through each round.

Yes, the Houston Astros and Chicago Cubs, after many seasons of horrid baseball, are now World Champions thanks to patience and a great analytics department. Let’s just break even and apply this to your average front office.

According to research done by Cork Gains of Business Insider back in 2013:

“After three years, we will probably only see about 15% of this year’s draftees in the big leagues. And for most players, it will take 4-6 years to make it to the highest level”.

Let’s take a more recent peek at draft success within the first five rounds of the MLB draft. We only go that deep because, honestly, after that point (and even five rounds is a reach) it’s mostly a crap shoot and I’d venture to guess no team has any sort of advantage over the other.

I’m using five years as its reasonable to expect, even with a high schooler, to reach the big leagues within that amount of time.  Of players drafted in 2017, none have reached the majors; no surprise there. In 2016 just one player drafted, third-round pick Austin Hays of the Baltimore Orioles, has made it to the majors. We ought not to reference that year, either.

The 2015 draft is when we start seeing results.


Of first rounders in the 2015 draft, first overall pick Dansby Swanson debuted in 2017. Alex BregmanAndrew Benintendi, and Carson Fulmer all came up in 2016.

In the 2015 draft, out of 165 players picked in the first five rounds, 7% have made it to the big leagues.

It goes without saying the list grew considerably in 2014 and it follows that it would in subsequent drafts. Out of rounds one through five in 2014, we have 19% currently in the majors.


Let’s investigate the success rate by round, referencing research done by Mike Rosenbaumof Bleacher Report.



So the first round, you’re likely to get two out of every three players in the majors, then 50/50 in the second round. However, this chart is no reference for success or failure for the player once they do reach MLB.

The standard deviation of success rate drop-off is 4.96%, with a variance of 24.6%

Here, we’ll observe the first overall picks since 2006 and their yearly average WAR from all MLB seasons.


The first-overall pick in this era yielded about 50% averaging WAR over 3.2 (we’ll get to the context in a bit).

Lastly, let’s look at the cumulative WAR of rounds one through five, starting at 2011 to 2015. As mentioned before, there was just one player who reached MLB that was selected in the first five rounds during the last two years.


*Mookie Betts 24.1 WAR

So the chart lends itself to logic; the older the year, the higher the cumulative WAR. But, there is still a lot we don’t know yet as there are still players in the 2011 draft toiling in the minors, yet could break into the big leagues in the next year or so.

Yet, something funny happens. There is a spike in WAR once we get to round 5. As noted, the majority of WAR from that round in 2011 comes from Mookie Betts. Could we infer that later rounds will increase as well? Probably not, as the random variation would likely be all over the place player to player. But it’s not a stretch to assume that you can find just as much value in later rounds as you can in the first couple.

Obviously, the bigger success stories come from the first round. But, keep in mind that’s just one player. On a team of 25 guys, it’s less likely that this player can turn an entire franchise around by themselves. In the NBA its possible, or the NFL where a quarterback can pull a team out of mediocrity within a year or so.

I’ll average the first round pick WAR to get an idea of what a team who continually ‘tanks’, could expect to get out of first rounders for the next several years.

2011- 2.8
2012- 1.7
2013- 1.2

Again, this isn’t concrete information but it’s enough data to get a rough inference. If you ‘tank’ for several years, and get a high first round pick, you can expect to get a players who will average a WAR of about 3 (after about five years); the average WAR of a number one pick (using the data in the ’06-’16 chart) is a little better than 2.

So you’ve got a good shot to get a player considered decent or, at best, above average.

The following chart give some context on WAR for those unfamiliar.


Is it really worth ‘tanking’ in baseball? In, say, five years of mediocrity, how often can you expect to hit on a player in the early rounds (average 2.5+ WAR)? Again, in the first round (’06-’16) chart above, you’ve got a 50/50 shot. Is it worth driving your franchise into the ground with those kinds of odds?

Like I’ve said before, we are using a small amount of data that is on a sliding scale (the older the draft, the higher the WAR). Since it would take roughly 3-5 years for an organization to acquire draft picks that could break into the league and help push the team into championship contention, it’s not too far of a reach. Meaning you can expect your first couple of picks per year to start normalizing WAR after a couple of seasons…if they reach the majors at all at all.

So is ‘tanking’ worth it? Allegedly to the Marlins and Pirates, it seems to be. They have highly paid analysts and I’m a lowly blogger, so they know better than I do. But, with the information I’ve been able to acquire, it doesn’t seem as though stockpiling high picks will benefit an organization enough to risk losing fans, revenue, and respect in MLB in the short term.