Trading away your team’s best players is never going to make you popular. You’ve probably read plenty about how the return for Marcell Ozuna was pretty good for the Marlins, while the return for Stanton was pretty thin. But savvy baseball fans understand that when you trade players, you’re not only trading their production, but also their contracts – so offloading an insane 13-year $325M contract might not return as much as a team-friendly contract for a lesser player. Add in the fact that Stanton had a no-trade clause (thus, a ton of leverage over to whom he was traded) the fact that the Marlins got anything in return for Stanton is actually impressive. The Yankees took on practically all of Stanton’s remaining contract; so in context, this was a fine deal for the Marlins. Dee Gordon, though contact-and-speed types typically don’t sustain a lot of value into their 30’s (as Gordon enters this year at 30), has put together 3.8 WAR/162 across his last 4 seasons, so maybe they could’ve gotten a little more out of that deal, but again – they were able to get rid of Gordon’s entire contract, which is guaranteed until his age-33 season of 2020.

The trade that stuck out most to me was the one for Christian Yelich. Yelich is an established star in the league who is still very young and has lots of upside, won’t be a free agent until 2023 (accounting for a team-friendly option in 2022), and seems like the type of player you might want to keep, even in a rebuild. They did receive top prospect Lewis Brinson and others in return, but of all the deals they made this one was, to me, the most indicative of “holy crap Jeter has no idea what he’s doing.”

And then, I realized, maybe he’s a genius.

Well, it doesn’t take a genius to recognize that Yelich is a future star, if he isn’t rightfully considered one already. It takes some genius (and perhaps a few gift baskets for your fans?) to say tear it all down. The Marlins could’ve kept any or all of Yelich, Ozuna, and even Stanton, but they’d still have been bad for the foreseeable future. The past four seasons they won 77, 71, 79, and 77 games. It’d have been easy to continue to toil in mediocrity, maybe even make a wildcard or two. But mediocrity is pointless in a business that overtly rewards losing.

You’re saying you want us to lose? No, we’ve BEEN losing. What I want is for us to finish dead last.
-Derek Jeter (probably).

It’s not a secret that tanking is now an actual strategy employed by “rebuilding” teams. I was surprised to learn in my research that tanking is probably not a new phenomenon (the percentage of teams who win 70 or fewer games is fairly consistent over the past several decades) but the game has changed so significantly in the era of free agency, “service time,” and revenue sharing, that the financial benefits of tanking should probably not be legal (but that’s for the CBA to determine). 2018 could be the worst year ever in terms of the number of teams not trying to compete.

Is that wrong? “Tank and bank” isn’t a purely theoretical exercise anymore. As you probably know, the past two World Series winners were responsible for some of the most blatant, disgusting, glorious middle-fingers-to-the-league you could ever imagine – and their paths coincide almost directly.

2008: the Cubs were an aging but solid team that led the NL in wins, with a dangerous lineup and a restored version of Kerry Wood, now a closer. They were bounced early in the playoffs however, in the same year Joe Maddon came up just short of an unlikely World Series title with the Rays. That same year, the Astros were competitive – winning 86 games – but came up short of a playoff birth.

Both teams achieved Marlins-esque mediocrity in 2009 and 2010, and that’s when the tanking rebuilding began. The Astros were the most aggressive and flagrant in their process, and many people forget just how bad they were. They won just 56 games in 2011, followed by campaigns of 55 and 51 wins (that’s three straight seasons of 106+ losses). Their payroll went from $77M in 2011 to $67M in 2012 to $25M in 2013 and then – somehow – cut it in half during the season by shedding even more salary. Notably, and not coincidentally, the Astros got a new owner in 2011. That historically bad 2013 for the Astros was actually historically great: they had the most profitable season in MLB history.

While the Cubs also lost a bunch of games during that same time period, they had a pretty big advantage over the Astros: they hired Theo Epstein (all due respect to Jeff Luhnow, whose roundabout career path is worthy of its own article). I’m not going to try and give Jeter or his staff a current/future grade as it pertains to winning lopsided trades but let’s just assume the Marlins are more like the 2011 Astros than the 2011 Cubs. Their “competitive advantage” over teams who may have better guys in analytics/baseball ops is that they can lose lots of games.

Currently, the Marlins are projected to win the fewest games in baseball which would of course net them the #1 overall pick. Picking first is certainly no guarantee of success (ahem, Kris Bryant went #2 to the Cubs in 2013 while the Astros picked up Mark Appel at #1) but it’s objectively better to pick in the top 2 or 3 than, say, outside of the top 5. There is also the correlated benefit of turning a bigger profit by fielding a lower payroll. To put it simply: if you’re going to miss the playoffs anyway, make as much money as possible while getting the best draft pick you can. It’s easy to say “I wouldn’t have traded Yelich/Ozuna/Stanton” in an attempt to appease your fan base (who aren’t coming to games anyway) while not having personally invested hundreds of millions of dollars into a team; but when your expensive team has little chance of even making the playoffs (never mind winning a World Series) the business side of things becomes even more important.

Based on the aggressive trades the Marlins have made to shed payroll, expect them to mirror the ’11-’13 Astros financially: they have about $80M committed this year, about $50M in 2019, but only $23M in 2020; 22M of that is to Wei-Yin Chen who I’m sure the Marlins hope can stay healthy long enough to generate a little interest from a contender. Righty-specialist and all-time home run preventer Brad Ziegler (making $9M) should have enough appeal to anyone who gets tired of giving up homers to the right-handed heavy Yankees or Angels lineups, and Junichi Tazawa (making $7M) might have a few buyers as well. Justin Bour (age 30, $3.4M, arb-eligible) should find a home with a competitor  – possibly best fit with the aforementioned Angels or even Yankees depending on how Greg Bird recovers, given their respective needs for some left-handed power options. Perhaps they can package the no longer desirable Martin Prado (2yr, $28.5M) with the very desirable J.T. Realmuto (age 27, $2.9M, arb-eligible) to shed some more salary.

By year 5 of their rebuild, both the Cubs and Astros blossomed into legitimate competitors, before winning their World Series in years 6 and 7 respectively (and being in great position to compete for years to come). Marlins fans probably don’t want to year “2022” as the best case scenario for their team to begin competing…but competing for a World Series doesn’t come easy. And as I’m sure Astros and Cubs fans could attest, it’s worth the wait.

During one of my recent visits to the Fangraphs home page, while scrolling across the leaberboards, I was confronted by a fact I had once known but had long ago forgotten over this slow and tired off-season. Aaron Judge led the league in WAR! as a rookie?! and by quite a wide margin. That happened last season? Shoot just over a year ago Judge was still relatively unknown and Jeff Sullivan was telling us not to underestimate his power.

This realization conjured up memories of last season’s AL MVP vote, how one of Sabermetrics’ patron saints shook the foundations of Sabermetrics’ most prominent statistical achievement, and how article after article were written about clutch hitting.

This, in turn, reminded me of another leaderboard Judge topped last season, this one more dubious. He led (lagged?) the league with the lowest Clutch score. He was fourth in WPA/LI with 5.85 Wins, trailing only this generation’s Mickey Mantle, Judge’s clone, and some guy who plays for the Reds and just a fractional win behind the leader. In contrast, he ranked just 38th in WPA tied with some guy who used to play in Korea. Add this up and he had by far the lowest Clutch score at -3.64 wins, a full win lower than the rest of MLB save for one blue-eyed Cub.

Which led me to ask the question: What would Aaron Judge have had to do to be a clutch batter? And I don’t mean the obvious answer, “Hit better in high leverage situations“. Duh! He batted an astounding 190 wRC+ in low leverage situations to just a 107 in high leverage at bats. But that’s not the answer I was looking for. I wanted to know specifically, what would Aaron Judge have had to do to be a clutch batter? as in what could we change from his epic near MVP season to bring his Clutch stat into the positive?

So I set to find out.

Using Fangraph’s own Play Log, and with plenty of assistance from BaseballSavant.com and Statcast, I decided to play as one of the “Baseball Gods” and see if I could tweak a few of Judge’s plays to make him more clutch. As a “Fair and Just Baseball God” I wouldn’t be aiming to increase Judge’s overall stat line. If I nudge a groundball a little to turn an out into a single in a high leverage situation, I’d do the opposite in a low leverage situation (Judge had nearly 50 PA’s with a Leverage Index, LI, of effectively 0) nudging another grounder into a fielder’s glove for an out.

Thus his overall stat line and his WPA/LI would remain effectively the same, and since in those low leverage situations no (or nearly no) WPA was added, we’ll only be looking at how the play’s I change increase Judge’s WPA.(And I’ll only be going through the plays I add not the ones I’d need to take away.) I also won’t worry about any of the time traveler unintended consequences stuff, I’ll assume that only the single event changes without it affecting other plays in the same game or others. (I’ll let some of the other “Baseball Gods” worry about that stuff…)

Recall the Equation for Clutch:

Clutch = (WPA)/(pLI) – (WPA/LI)

With my rule that Judge’s pLI (0.95) and WPA/LI (5.85) will remain fixed we are just looking to increase Judge’s WPA.

With that lengthy explanation out of the way, let’s begin!:

Judge Initial WPA = 2.10
_________________________________________________________
Situation #1:

July 27th, Bottom 9, 1 Out, Runner on Third, Yankees down 1.
LI = 5.81 – Actual Play – Judge Fly’s Out to Right. – WPA = -.252

We’ll start with a big one, in fact Judge’s second highest leverage play of his season!
With a chance to tie the game in the 9th, Judge just miss-hits the ball sending it not quite far enough to allow the speedy Brett Gardner to score from third. As you can see, similar hit balls all had the same result:

But as my first act as “Baseball God” I’m gonna adjust this hit ever so slightly, notching Judge’s bat up a millimeter to two to lower the Launch Angle of this hit and allow it to carry just a bit further. Something more like this:

That should be far enough out to score Gardner giving Judge a Sac Fly.

New Play – Sac Fly – New WPA = .112 – Net WPA Change = .364

Judge’s New WPA = 2.46
_________________________________________________________
Situation #2:

August 2nd, Bottom 8, No Outs, Runner on Second, Yankees down 2.
LI = 2.72 – Actual Play – Strike out swinging. – WPA = -.08

Sometimes the job of a “Baseball God” is rather easy. In this case I’ll just need to do some umpire convincing. In this at bat Judge struck out on a 3-2 slider, but earlier in the at bat, after three wild pitches, here was the 3-0 offering from Bruce Rondon:

Ok, sure, most umpires probably call this a strike on a 3-0 count, but I’m gonna go ahead and give this one to Judge. Ball Four!

New Play – Walk – New WPA = .087 – Net WPA Change = .167

Judge’s New WPA = 2.63
_________________________________________________________
Situation #3:

September 19th, Bottom 2, 2 Outs, Runners on Second and Third, Tie Game.
LI = 2.03 – Actual Play – Fly out to Center. – WPA = -.061

Judge crushed a Jose Berrios offering at 107 MPH:

Here’s what it looked like.

He was just a little under this one, wouldn’t take much more to send this ball out. So we’ll make the charge and turn this loud out into a bomb.

New Play – Three Run Home Run – New WPA = .249 – Net WPA Change = .310

Judge’s New WPA = 2.94
_________________________________________________________
Situation #4:

September 9th, Top 9, No Outs, Runner on First, Tie Game.
LI = 3.40 – Actual Play – Fielder’s Choice to third, out at second. – WPA = -.084

Judge grounds one to third, and nearly into a double play.
Here’s what it looked like.

Thing is, Rougned “De La Hoya” Odor is in such a hurry to turn two that it almost looks like he jumps off Second too early. Take a closer look:

Your guess is as good as mine, but here’s the thing: As a “Baseball God“, I don’t have to guess. I’ll just make the throw from third just a little higher and wider pulling Odor off the bag and leaving both runners safe on a throwing error. Did you know that errors count as positive WPA plays?!

New Play – Reach on Error, Throwing Error at Third, Runners safe at First and Second – New WPA = .109 – Net WPA Change = .193

Judge’s New WPA = 3.13
_________________________________________________________
Situation #5:

August 18th, Top 6, 2 Outs, Bases Loaded, Yankees down 1.
LI = 4.52 – Actual Play – Ground Out to Shortstop. – WPA = -.119

Judge hits a sharp ground ball at 103 MPH.

Here’s what it looked like.

Hit hard, but right into Xander Bogaerts‘ glove for a routine out. But per Statcast balls hit at that Velocity and at that Launch Angle become hits about half the time.

One can imagine Judge hitting this ball just a little closer to the pitcher’s mound, and seeing it get past a diving Bogaerts. With the runners going, that hit would easily score 2.

New Play – Ground Ball Single up the Middle Scoring 2, – New WPA = .275 – Net WPA Change = .394

Judge’s New WPA = 3.53
_________________________________________________________
Situation #6:

June 14th, Top 7, No Outs, Runners on First and Second, Tie game
LI = 2.89 – Actual Play – Fly Out to Left. – WPA = -.085

Judge ropes one into left field, where Eric Young Jr. makes an awkward dive for it.

Here’s what it looked like.

Young makes the out, but just barely. Imagine if his dive is just a little more awkward… That ball probably gets by him and clears the bases.

New Play – Bases Clearing Double to Left Field – New WPA = .219 – Net WPA Change = .304

Judge’s New WPA = 3.83
_________________________________________________________
Situation #7:

June 15th, Top 9, No Outs, Bases Empty, Yankees down 1.
LI = 2.88 – Actual Play – Strike Out Looking. – WPA = -.073

Were picking up steam now! And as a “Baseball God” I haven’t had to work very hard changing these last few plays. Now it’s time to work just a little harder.

Leading off a do or die ninth, Judge took three easy balls, then saw and fouled consecutive fast balls. This set up a full count pitch where Santiago Casilla froze him with a beautiful knuckle curve. Here’s what it looked like.

No doubt that’s a beautiful pitch. But guess what? Umpires sometimes miss calls, especially when they get some inadvertant dust in their eye…

New Play – Walk – New WPA = .110 – Net WPA Change = .183

Judge’s New WPA = 4.02
_________________________________________________________
Situation #8:

September 10th, Top 3, 1 Out, Bases Loaded
LI = 2.26 – Actual Play – Sac Fly to Right. – WPA = -.002

In an RBI situation, Judge blasts one.

Here’s what it looked like.

So Judge clearly gets under this pitch… but he still hit it over 300′ and scores a run.
The thing is the next two times up he did this and this!
I’m just gonna do a little rearranging on when these homers take place…

New Play – Grand Slam to Right – New WPA = .256 – Net WPA Change = .258

Judge’s New WPA = 4.27
_________________________________________________________
Situation #9:

April 18th, Bottom 9, 2 Outs, Bases Loaded, Yankees Down 3
LI = 3.86 – Actual Play – Fielder’s Choice to Shortstop, Out at Second. – WPA = -.100

Judge ends the game on a weakly hit grounder to shortstop.

Here’s what it looked like.

Looks like a routine grounder, but per Statcast similar balls become hits about a third of the time. And we don’t really need a hit here, Tim Anderson looks a little shaky fielding the grounder as it hops to his glove. In a critical situation like this who’s to say he doesn’t boot one? The answer is me, the “Baseball God“. I say he boots it…

New Play – Fielding Error at Shortstop, 1 Run Scores – New WPA = .090 – Net WPA Change = .190

Judge’s New WPA = 4.46
_________________________________________________________
Situation #10:

July 21st, Top 3, 1 Out, Runners on First and Third, Tie Game
LI = 2.12 – Actual Play – Sac Fly to Center. – WPA = +.016

Another well struck ball that just stays in the yard for a sac fly.

Here’s what it looked like.

But Judge would get one more try at Andrew Moore that game, and you may remember it. Judge’s next at bat was that time he broke Statcast!

I’m just gonna move that Statcast breaking smash up one AB if you don’t mind…

New Play – Three Run Home Run – New WPA = .216 – Net WPA Change = .200

Judge’s New WPA = 4.66
_________________________________________________________
Ok, awesome we’re 10 plays in, and as a “Baseball God” I don’t feel like I’ve had to work all that hard. But were still only at 4.66 WPA, nearly a win short of our target. It’s time to pull out the big guns. It’s time to perform a MIRACLE!

Situation #11:

July 30th, Bottom 9, 1 Out, Runners on First and Second, Yankees down 2.
LI = 4.78 – Actual Play – Foul out to First. – WPA = -.112

Representing the go ahead run, Judge pops up in foul ground to the first baseman. You can see his hit in blue in the image below.

(As to why this shows up as a -57° LA I think sometimes Miracle Work messes with Statcast…)

Just a lazy pop-up. Not much a “Baseball God” can do to affect this play without revealing myself to the world. So I’ll just void the play and blows this ball a little further to the right and into the seats where Trevor Plouffe can’t catch it!

So I’ve just given Judge a new lease on this particular at-bat. I hope he uses it wisely. I’ll just assume it goes something like this!

New Play – Walk Off Three-Run Home Run – New WPA = .793 – Net WPA Change = .905

What?! You don’t think that’s fair. Tough! I am Beerpope the Baseball God and this is my Miracle, don’t tell me what’s fair!

Judge’s New WPA = 5.57

And with that spectacular finish, we check Judge’s Clutch score:

5.57 / 0.95 – 5.85 = +.01 Wins

And there you have it. Aaron Judge – CLUTCH BATTER. My work here is done.

So what does this all mean? Really I’m not sure. Does the fact that it took 10 twists of fate and one walk-off miracle just to bring Judge barely into the positive show just how deeply un-clutch he was last season? Maybe. But it may also show us how futile it is to focus of how clutch or un-clutch a batter is if an ump call, miss hit, or bounce here or there in just 10 at bats can invalidate the other 600 plus plate appearances in a player’s season.

I’ll leave that determination to the readers.

Now enough with the 2017 Season. It’s time for me to begin contemplating what Miracles to perform thus upcoming season…

Cheers!

(Editor’s note: this post was submitted prior to the start of the season but it seems rather timely now)

I can’t shake myself from latching onto spring training hype trains. Even after all we’re taught about small sample sizes, I find myself watching games and wondering whether this could be the year for any number of players.

Watching the Braves and the Nationals last weekend, something about Dansby Swanson seemed different. I started digging and emerged on the other end of a rabbit hole that brought me from hitting guru Jason Ochart (@Jason_cOchart) to Coach Bobby Stevens Jr. (@StevieBobbins, BattersBoxChicago.com, GoWindyCityBaseball.com) to gif-ing up everything and more.

I’ll admit, I forgot Dansby Swanson was sent to the minor leagues in late July. The former number one pick relinquished his major league role after mustering only a .287 OBP in just under 400 plate appearances. Two weeks later he was recalled with little more than generalities to sift through in hopes of unearthing what the Braves wanted to change mechanically if anything at all.

After Swanson’s return to the major leagues tinkering began.

Video via MLB.com – 1, 2

It’s a relatively simple adjustment, but the ramifications and reasoning behind the alteration bubbles numerous points to the surface.

“Getting [your front foot] down too early can mess up timing and alter the kinematic sequencing of the swing.” Jason Ochart quickly summed up via Twitter what I speculated might be true.

For almost all of Swanson’s 2017, before his change in late August, his front foot was down earlier than your standard hitter (in the video on the left above).

“For most hitters, the pressure shifting onto the front foot is what initiates their swing. Force plate data shows that the forceful heel drop works as the trigger of the swing and works as a brake to send energy upward through the body… to accelerate the bat late in the swing arc, as all the best hitters do.”

Breaking down Orchart’s points make a complex explanation simple. A hitter’s front foot is used to initiate their swing. When this foot plants, it helps transfer energy from one’s lower body to upper body. Eventually, that energy affects a hitter’s bat.

“Force plate data” sounds complex, but it’s nothing more than a plate on the ground that measures exerted force. In this case, the force from a hitter’s front foot. (YouTube is always here to help as well).

Ochart went on to state research shows that shorter time between the peak of one’s front-foot force and contact with the baseball can lead to greater exit velocity. If your front foot peaks early, as a hitter’s might if they’re planting as early as Swanson was, the effects could be detrimental on the one variable most hitters are focused on.

Stats, however, have a tough time backing up a substantial performance boost solely through the hovering of Swanson’s front foot.

Upon Swanson’s return to the majors in August, there was a strong uptick productivity that lasted until the beginning of September. This correlates nicely with his front-foot alteration but doesn’t sustain through the end of the season, as one would hope a material adjustment would. A variety of other factors could counter the change: production uptick being artificial, fatigue, comfort with the new approach, etc.

But what about other components of Swanson’s swing that might have been affected by this change?

“Hitting is controlled all through the back hip in relation to controlling your weight and ‘staying back’ on pitches. The issue is in the explanation of ‘stay back’. Stay back in what position? With your foot off of the ground? With your front foot on the ground? In your stance? That is where understanding is lost in my opinion.” Stevens took a different route to a similar conclusion that buoys the case Swanson had beneficial intentions, even if stats cloud improvement.

“A hitter must ‘stay back’ in their hip with their foot off the ground or hovering. This does not mean that you lift the front foot off the ground and balance on your back leg, though. It means that we load or coil into our back hip, then as our lead leg begins to stride out towards the pitcher, we want to ‘stretch’, or use our back muscles, to hold our weight back until we decide it is time to launch the swing.”

Stevens’ broadening of terminology related to “staying back” unearths numerous other factors related to what Swanson did. Each of his points made me consider other aspects of Swanson’s kinetic chain, particularly how the most visible change – foot down early to hover – could be covering up other, more important changes to help the former college star, acting as the low-hanging fruit.

So why bring this front-foot change up now, six months late? Because Swanson’s lower body alteration was actually the second thing I noticed, behind another change that caught my eye on his long home run off Max Scherzer in spring’s first weekend of action.

Video via MLB.com – 1, 2

First his lower body, now his upper body. While the above camera perspective when comparing is slightly askew thanks to spring training parks and their uniqueness, Swanson is starting his hands lower and bringing them up into his load. In 2017, he started his hands higher and kept them there for the duration of his pre-swing rhythm. Now, his momentum is built up into the hitting position, yet the path and aesthetic of his swing after his load are nearly identical to the naked eye. This feels like a conscious attempt at relaxation in the box, with the foresight to alter the path to his load as opposed to how exactly he is loading. What could be invisible, however, to my untrained scouting eyes are the concepts Stevens talked about above relating to a hitter’s back hip and launch into his swing.

Swanson’s adjustment is similar in direction to Zack Cozart’s alteration from 2016 to 2017, one that brought Cozart a substantial uptick in power. Some might say Billy Eppler’s new third baseman’s breakout came demonstrably because of health, but Cozart admitted last Spring he wanted to start his bat on his shoulder to relax himself at the plate and come up into the hitting position. What Swanson is doing above mimics that concept – coming up into his load – even if the point at which the process begins is different. Swanson’s relaxation also reminds me of Anthony Rendon’s gradual adjustment, as the All-Star began to push his hands further south when comparing his swing at Rice University to that of later in his career.

Most relevant to my gracious sources, Ochart and Stevens, Swanson retains his front-foot hover from late in 2017 in the gif above.

While the stats seem doubtful a tangible change in the Braves shortstop, numbers can often be blind to progression mechanically that hasn’t manifested on the spectrum of production. My confidence in an improved Swanson is driven by the theory around adjustments he seems to have made, starting with the hover of his front foot to the repositioning of his hands preload. Add him to the list of players I’ll be watching closely in one month’s time.

A version of this column can be found on BigThreeSports.com.

You should do that thing where you follow me on Twitter – @LanceBrozdow.

Matt Davidson was not good last year. He got 443 plate appearances in his first full MLB year on a rebuilding White Sox club, and it didn’t go well as he posted a WAR of -0.9. That mark was seventh-worse in MLB for position players with at least 400 PA. There’s little mystery how he got there, as he combined DH-only caliber defense with a paltry 83 wRC+.

Davidson achieved that uninspiring number by hitting like a three-true-outcomes guy without the walks, more or less a poor man’s Chris Carter. Good news first: last year, he ran a pretty decent ISO of .232, putting him close to good-to-great hitters like Francisco Lindor, Anthony Rendon, and Anthony Rizzo, cracking 26 homers along the way. His raw strength is very real: he blasted a tape-measure 476-foot moonshot out of Wrigley with a 111MPH exit velocity in July. Big power is a good trait to have, but it’s been devalued in today’s game, where guys like Carter and Logan Morrison can hit 35+ homers in a year and then can’t find contracts of even $5M the following offseason.

Still, significant pop is necessary for a high offensive ceiling, so what’s holding Davidson back? In a word, strikeouts. He struck out a horrifying 37.2% of the time in 2017, second-most in the majors.  Unsurprisingly, his whiff rate was a scary 16.3%, sixth-highest among his peers; for reference, that’s identical to how often hitters swung and missed against Andrew Miller last year. The walk rate that keeps most K-prone sluggers’ OBP somewhat afloat wasn’t in evidence, as Davidson walked only 4.3% of the time. You won’t be shocked to find that he finished second-worst in K/BB with an ugly 0.12. Although he did hit the ball hard (we’ll come back to that), his flyball-heavy batted ball profile and below-average speed kept his BABIP suppressed to .285. That mark was in close agreement with his xBABIP of .283.

The astronomical K% and below-average BABIP held him to an ugly .220 AVG, which combined with the poor BB% led to a truly abysmal OBP of .260, second-worst among hitters with 400+ PAs. The only guy worse in that column was Rougned Odor, who has a similar offensive profile, but at least he can partially blame a particularly unlucky .224 BABIP.

Looking at last year’s stats, there appears to be approximately zero reason for optimism for Matt Davidson. He hit for power well, but was near the top of all the peripheral leaderboards that you really don’t want to be at the top of.  So why is this post being written at all? In short, Davidson seems to have turned over a new leaf this spring.

Now, I know the sabermetric kneejerk reaction to that last sentence: spring training means nothing and spring training stats mean less than that. But that’s not entirely true, as this excellent piece in the Economist way back in 2015 details. If you don’t want to read the whole piece, that’s fine, because it can be summed up very briefly: a hitter’s strikeout rate in spring training actually has a pretty high correlation with their strikeout rate in the regular season. Of course, one of the chief objections to drawing conclusions from spring training stats is the tiny sample sizes with which we’re working. Fortunately, strikeout rate is one of the fastest-stabilizing peripheral rates there is; Fangraphs itself puts the threshold for stabilization of strikeout rate at about 60 PA.

That piece was linked somewhere recently and I read it for the first time. A couple days later, being entirely starved for any form of baseball through this long winter, I reached the rock bottom of scouring the spring training stats of the team I supported, the White Sox. To my own surprise, there was actually something interesting buried there; as you might guess, it was in Matt Davidson’s stat line.

Luckily for us, and this piece, Davidson’s played the most of any White Sox this spring, totaling 60 PA as of March 20. He’s struck out twelve times, a K rate of 20%. He has walked seven times, for a walk rate of 11.7%. In this small sample, he’s almost halved his strikeout rate and nearly tripled his walk rate from 2017. On the one hand, that sounds like an insane improvement that cannot possibly be maintained; on the other, those rates from spring training are by themselves quite unremarkable for a major league hitter. Using BBRef’s summed 2017 stats to calculate league-wide rates, 20% K and 11% BB would have both been slightly better than average league-wide in 2017.

A significant walk rate improvement wouldn’t actually be terribly surprising. If you peruse Davidson’s player page, you’ll find that before last year he never posted a BB% worse than 9.1%, ranging up to 12.0%, from Double-A onwards, a total of five seasons spent mostly at Triple-A plus a month in the majors with Arizona. His walk rate at least doubling this coming year wouldn’t be coming out of left field; rather, it would be him returning to the player he has been in that sense for pretty much his entire professional career minus last year. It will probably come down from 11.7%, given that MLB pitchers likely have better control than those he’s faced this spring, but still, a big jump in walk rate seems likely for him this year.

That strikeout rate is a different animal, though. He’s always struck out a lot, never posting a K rate below 20% at any stop in the minors, and the whiff rate mentioned previously supports that. On the other hand, the sample size is now at the point where this being a complete fluke is pretty unlikely. Is this a real improvement or a mirage? I don’t know, and we don’t have plate discipline numbers in ST to see underlying patterns, but according to Davidson himself, making more contact is exactly what he’s trying to do. It sure seems like he’s succeeding in that thus far. As another small data point, he doesn’t seem to have a pattern of ST flukes in K rate, as in 58 PAs during last year’s spring training he struck out in 37.8% of his plate appearances, a number that echoes his full-season 37.2%.

This wouldn’t be as interesting a case if Davidson did nothing well offensively. He’s a large and very strong man, which is why he hasn’t just been released by the White Sox years ago. Take a look at his contact profile. Basically, last year, he pulled balls, hit more fly balls than ground balls, and vaporized balls in to play, with a quality-of-contact triple-slash line of 15.7% Soft/46.1% Med/38.2% Hard. His HR/FB% was a robust 22.0%, rubbing statistical shoulders with established sluggers like Nelson Cruz and Edwin Encarnacion. In short, when he actually did hit the ball, he looked for all in the world like a poster child for the fly ball revolution. Those underlying numbers hint at a lot more offensive potential than anyone outside of the White Sox organization sees in him, if he could just reduce that giant 32.9 K-BB%.

Now he’s showing signs of significant improvement in that fatal flaw of plate discipline. It doesn’t seem like the improvement in K% and BB% thus far in spring training has cost him much in power, considering that he’s demolished ST pitching to the tune of .358/.433/.679 (1.113 OPS & .321 ISO). Obviously, he’s not going to keep hitting quite that well, but the still-rebuilding White Sox aren’t about to outright bench or demote him either. Maybe it’s all a lot of noise, and he’ll be bad again this year. Or maybe Matt Davidson, at the age of 26, is about to be the Next Big Breakout™. Just as a reminder, it took J.D. Martinez until 26 to figure it out and become the “King Kong of Slug”; Justin Turner was 29-year-old replacement-level utility infielder who suddenly blossomed offensively in 2014; Jose Bautista was almost 30 before he turned into a nightmare for AL pitchers in 2010. So, here’s an prediction I would have laughed off for 2018: Matt Davidson is about to bust out in a big way.

UPDATE 3/29: Davidson hit three homers on a cold day in Kauffman Stadium, every single one of them with a 114+ MPH exit velocity. He also walked and did not strike out. Jump on the bandwagon now while there’s still room.

Another off-season has come and gone, and your favorite team practically sat out free agency citing some combination of small-market budget concerns or vague allusions to the luxury tax, hoping you won’t mind another season of mediocrity. Why couldn’t they just get some of those cost-controlled star players? You know, the kind [team you hate] has! What were they thinking in the draft when they passed on all those late round gems?!?

It may be tempting to think you could assemble a truly unbeatable team with the benefit of hindsight, but even with some form of crystal ball or time machine, many players will never be available to you. Maybe a bargain free agent doesn’t like the weather in your city, international free agents might not see your system as their best option, and depending on your draft position, dozens of the most highly regarded young players will already be taken.

What follows is a hypothetical team constructed from a pool of players any team would have access to: domestic players drafted outside the first 30 picks. Steamer projections based on a full season of playing time were used to control for the differences in playing time these players might be expected to get on their real teams. Signability may still be of some concern so draft bonuses (as listed on thebaseballcube.com) are included for reference and players are split into arbitration eligible and pre-arb categories if a slightly more expensive upgrade is available. In some cases, your team may have needed to pass up on a free agent signing to avoid losing a first round pick.

All pre-arb team:

C: Austin Barnes (2.2 WAR, LAD) Drafted 9^th round, 283^rd overall in 2011. $95,000 Bonus.

1B: Rhys Hoskins (3.4 WAR, PHI) Drafted 5^th round, 142^nd overall in 2014. $349,700 Bonus.

2B: Whit Merrifield (1.9 WAR, KC) Drafted 9^th round, 269^th overall in 2010. $100,000 Bonus.

3B: Travis Shaw (1.6 WAR, MIL) Drafted 9^th round, 292^nd overall in 2011. $110,000 Bonus.

SS: Paul DeJong (2.4 WAR, STL) Drafted 4^th round, 131^st overall in 2015. $200,000 Bonus.

LF: Cody Bellinger (2.7 WAR, LAD) Drafted 4^th round, 124^th overall in 2013. $700,000 Bonus.

CF: Chris Taylor (2.1 WAR, LAD) Drafted 5^th round, 161^st overall in 2012. $500,000 Bonus.

RF: Aaron Judge (3.9 WAR, NYY) Drafted 32^nd overall in 2013. $1,800,000 Bonus.

SP: Zack Godley (3.4 WAR, ARI) Drafted 10^th round, 288^th overall in 2013. $35,000 Bonus.

SP: Joe Musgrove (3.4 WAR, PIT) Drafted 46^th overall in 2011. $500,000 Bonus.

SP: Tyler Glasnow (3.2 WAR, PIT) Drafted 5^th round, 152^nd overall in 2011. $600,000 Bonus.

SP: Steven Matz (3.1 WAR, NYM) Drafted 2^nd round, 72^nd overall in 2009. $895,500 Bonus.

SP: Bryan Mitchell (2.6 WAR, SD) Drafted 16^th round, 495^th overall in 2009. $800,000 Bonus.

RP: Chad Green (1 WAR, NYY) Drafted 11^th round, 336^th overall in 2013. $100,000 Bonus.

RP: Edwin Diaz (0.9 WAR, SEA) Drafted 3^rd round, 89^th overall in 2012. $300,000 Bonus.

RP: A.J. Minter (0.9 WAR, ATL) Drafted 2^nd round, 75^th overall in 2015. $814,300 Bonus.

RP: James Hoyt (0.8 WAR, HOU) Undrafted, signed in 2013.

RP: Scott Alexander (0.7 WAR, LAD) Drafted 6^th round, 179^th overall in 2010. $125,000 Bonus.

RP: Drew Steckenrider (0.7 WAR, MIA) Drafted 8^th round, 257^th overall in 2012. $137,900 Bonus.

RP: Carl Edwards Jr. (0.7 WAR, CHC) Drafted 48^th round, 1464^th overall in 2011. $50,000 Bonus.

Even assuming a replacement level bench, this team is a wild card contender with a combined 41.6 WAR from the listed players. With a full replacement level team expected to win 48 games, our best guess for this team would be 90 wins. All for a payroll around $15,000,000 and a total of $8,211,900 in bonuses, although you might expect to pay higher bonuses if you had selected some players in higher rounds.

Add in some arbitration-eligible players and you’ve got a real contender;

All under 6 years service time team:

C: J.T. Realmuto (2.4 WAR, MIA) Drafted 3^rd round, 104^th overall in 2010. $600,000 Bonus. 2018 Salary: $2,900,000.

1B: Anthony Rizzo (4.4 WAR, CHC) Drafted 6^th round, 204^th overall in 2007. $325,000 Bonus. 2018 Salary: $7,000,000.

2B: Brian Dozier (3.5 WAR, MIN) Drafted 8^th round, 252^nd overall in 2009. $30,000 Bonus. 2018 Salary: $9,000,000.

3B: Josh Donaldson (5.8 WAR, TOR) Drafted 48^th overall in 2007. $652,500 Bonus. 2018 Salary: $23,000,000.

SS: Paul DeJong (2.4 WAR, STL) Drafted 4^th round, 131^st overall in 2015. $200,000 Bonus.

LF: Aaron Judge (3.9 WAR, NYY) Drafted 32^nd overall in 2013. $1,800,000 Bonus.

CF: Kevin Kiermaier (3.7 WAR, TB) Drafted 31^st round, 941^st overall in 2010. 2018 Salary: $5,500,000.

RF: Mookie Betts (5.3 WAR, BOS) Drafted 5^th round, 172^nd overall in 2011. $750,000 Bonus. 2018 Salary: $10,500,000.

SP: Noah Syndergaard (5.8 WAR, NYM) Drafted 38^th overall in 2010. $600,000 Bonus. 2018 Salary: $2,975,000.

SP: Corey Kluber (5.2 WAR, CLE) Drafted 7^th round, 134^th overall in 2007. $200,000 Bonus. 2018 Salary: $10,500,000.

SP: Jacob deGrom (4.6 WAR, NYM) Drafted 9^th round, 272^nd overall in 2010. $95,000 Bonus. 2018 Salary: $7,400,000.

SP: Robbie Ray (4.5 WAR, ARI) Drafted 12^th round, 356^th overall in 2010. $799,000 Bonus. 2018 Salary: $3,950,000.

SP: Chris Archer (4.2 WAR, TB) Drafted 5^th round, 161^st overall in 2006. $161,000 Bonus. 2018 Salary: $6,250,000.

RP: Dellin Betances (1.3 WAR, NYY) Drafted 8^th round, 254^th overall in 2006. $1,000,000 Bonus. 2018 Salary: $5,100,000.

RP: Ken Giles (1.1 WAR, HOU) Drafted 7^th round, 241^st overall in 2009. $250,000 Bonus. 2018 Salary: $4,600,000.

RP: Zach Britton (1 WAR, BAL) Drafted 3^rd round, 85^th overall in 2006. $435,000 Bonus. 2018 Salary: $12,000,000.

RP: Chad Green (1 WAR, NYY) Drafted 11^th round, 336^th overall in 2013. $100,000 Bonus.

RP: Justin Wilson (0.9 WAR, CHC) Drafted 5^th round, 144^th overall in 2008. $195,000 Bonus. 2018 Salary: $4,250,000.

RP: Edwin Diaz (0.9 WAR, SEA) Drafted 3^rd round, 89^th overall in 2012. $300,000 Bonus.

RP: A.J. Minter (0.9 WAR, ATL) Drafted 2^nd round, 75^th overall in 2015. $814,300 Bonus.

This youthful juggernaut combines for 62.8 WAR – 111 wins for the season – with a payroll just under $120,000,000 and a total of $9,300,000 in draft bonuses.

Surely most readers don’t need any additional convincing that drafting and player development is incredibly hard, but the presence of so many late-round picks on this list should be some cause for optimism for fans of teams that leak either the means or desire to sign from the top tier of free agent talent. If you do happen to come across a time machine, congrats on all your future World Series rings.

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.

Fluctuation of prospect value during the offseason is a mental exercise. Given the lack of activity to substantiate one’s changing opinion, hype can often be attributed to reputable names in the industry praising players, or the release of top prospect lists into the wild. Nate Pearson’s name has generated helium in the recent months, but instead of dismissing a storyline and citing our historically slow offseason for the surfacing of this hype, I wanted to understand the origin of praise surrounding our budding prospect.

Jim Czajkowski, the Vancouver Canadians pitching coach helped put into perspective how bullish the Blue Jays organization is on their first-round pick from 2017’s draft. Pearson carries a 6-foot-6, 240-pound frame onto the mound, his arm balancing out the offensive firepower Bo Bichette and Vladimir Guerrero Jr. bring to a system loaded with top-end talent.

Having groomed the likes of Aaron Sanchez, Marcus Stroman, and Noah Syndergaard, Czajkowski’s reps with advanced skill sets and assessment of their potential needs no introduction.

“[Nate] is better at his age than any of those guys were…. If I were to rank those guys, Sanchez probably had the best pure arm action and a good curveball, a good sinking fastball too, but Nate has all four [pitches].”

Pearson transferred from Florida International University (FIU) to Central Florida Junior College for the 2017 season for personal development reasons, and the gamble paid off as he posted 118 strikeouts in 81 innings with only 23 walks. Even with his stellar stats, one could assume Pearson may have been passed on last June due to his size.

“It’s a chunky 240 [pounds]. And in high school he was up to 300… he’s thinned down some… It was definitely his workout regiment; it was phenomenal.”

As his time at FIU was largely in a relief role, it was inevitable that discussion arose between Czajkowski and myself regarding how to condition the 6-foot-6 righty to shoulder a progressively larger workload. The focus was more on optimization – the sequencing of Pearson’s innings and coinciding off days – than sheer control of inning quantity.

“He probably pitched once a week [in college], and then he’d have six days to recover… we got him down to one less [recovery] day in Vancouver, and then wherever he goes next year, he’s going to be on a five-man rotation, so he’ll really need to adjust his regiment and take care of his arm care.”

Preparation for the next level is front of mind for Czajkowski and the Blue Jays. Focusing on routine and laying the groundwork to ease Pearson’s adaptation to higher levels lead to necessary and subtle tweaking.

“When we talked to him about his routine, we actually thought he might be overdoing it right after the game with his arm care. We wanted him to tone it down a little bit.”

This restructuring of Pearson’s off-day regiment and arm care was not suggested to his detriment. It became a vital step to eventually ease him into Lansing or Dunedin’s standard, five-man rotation, dealing with less off days in the process.

While any arm possesses the inherent risk of injury, Czajkowski admitted that himself and management are more optimistic with Pearson’s arm health knowing the primary generator of velocity comes from his lower half.

Adding audible intimidation to Pearson’s presence on the mound is a less statistical reason hitters struggled mightily against his offerings.

“There is not a lot of herky-jerky in [Pearson’s] motion, there are times where he pitches and he’ll grunt. And when he does that, he throws 100 [mph]. There are times early in counts where he grunts because he’s trying to make a statement, and he’ll overthrow a couple pitches… he was almost trying to strike guys out early in counts; trying to not let them touch the ball, that’s when he would lose a little bit of command and come out of his delivery a little bit.”

Pearson’s delivery is unique. His 6-foot-6 frame barrels downhill towards a hitter, as the harmony of his kinetic chain capitalizes on the energy stored in his lower half. A strong front leg allows him to stabilize after the energy released from his torso’s aggressive tilt forward finishes his motion. Exceptional is an understatement when describing the extension he achieves; the eye is tricked for seconds as one forgets the amount of mass supporting the big righty.

(Gif from YouTube, video credit to Niall O’Donohoe)

“If you watch him play long toss you know where he gets his power; his power is from his legs.” Czajkowski was quick to confirm what is visually consistent.

Pearson’s work ethic and natural ability, continually touted by Czajkowski in our talk, remain one reason why concerns over inconsistency fell to a simmer from the boil that eclipsed his potential pre-draft. An unusual detriment associated with this level of velocity is how advanced it can be for the pitcher’s level.

“At the lower levels they can’t catch up to his 100[-mph fastball]… The higher Nate goes, to Double-A and Triple-A, his changeup will be able to play because those guys will be able to catch up to his 100.”

Velocity differential between a pitcher’s fastball and changeup remains one of the key factors to predicting the value of the feel-dominant pitch and whether it behaves like a sinker, generating ground balls, or a true changeup, generating whiffs. While Czajkowski rated each of Pearson’s four pitches – fastball, slider, changeup, and curveball – above average, he was quick to disclose his high expectations for a pitch that was hit around for Pearson in his 19 innings with Vancouver.

Pearson’s arm speed is another reason why I’m bullish on his changeup. His body’s aggressive motion towards the plate can deceive hitters from an aesthetic standpoint. Add that to the fade he’ll be able to generate as he evelates his feel for a pitch and his mastery will quickly exceed the talents of his seniors.

But Pearson’s calling card is a two-plane slider; an unfair pitch when backed up with his command. He seamlessly changes the eye level against hitters, leaving most Class A Short Season hitters to guess if they stand a chance of hitting either pitch. The offering below is at this hitter’s belt, which gives a better idea of the pitch’s depth, rather than the late, “fall off the table” break noticeable when he buries the pitch at a hitter’s knees.

(GIF via YouTube, video credit to Blue Jays Prospects)

Is there a point where overuse of such an advanced pitch could hurt a young arm?

“If we think he is overusing his slider, just for strikeouts, we’ll talk about the percentage he throws his pitches. [Nate] gets a breakdown… and I think he did a very nice job this year in utilizing everything.”

Czajkowski reiterated the themes of our talk, bringing up a final thought that adds to his appreciation for the righty.

“He has four major league quality pitches, he has size, but the one thing he doesn’t have yet is stamina. He hasn’t built up the innings to be a starter at the major league level. Roberto Osuna pitched a couple years in the minor leagues as a starter and then became a reliever. So Nate Pearson as a closer at the major league level, I can see that too. Because of his regiment; the way that he throws, and the way that he bounces back tells me that he can handle a relief role, too.”

If the Blue Jays window of contention opens quicker than some anticipate, Pearson’s services may be needed at the major league level sooner than later. With Czajkowski’s suggestion that Pearson could reach Double-A New Hampshire by season’s end if the stars align, opportunity for Pearson to make an impact in 2019 isn’t off the table. His adaptation to higher levels and a five-man rotation are what I consider the largest factors dictating his future role.

Czajkowski’s final words to me on the record epitomize what we’re all thinking about Pearson.

“The sky is the limit for him.”

Special thanks to Jim Czajkowski for allowing me to steal some of his vacation time to chat Canadians baseball and Pearson. I wish the Blue Jays organization, and each pitcher he grooms, the best in the coming season.

I can be found on Twitter – @LanceBrozdow

A version of this post can be found on BigThreeSports.com

As I’d like to think I’m an aspiring sabermetrician, or saberist (as Mr. Tango uses), I decided to test my skills and explore this research question. How did starters, who had 25 or more starts in one season and an ERA of 6.00 or higher in their final 10 starts, perform in the following season? This explores whether past performance, regardless of intermediary performance, adequately predicts future performance. Mr. Tango proposed this question as a way to explore the concept of replacement level. From his blog: “These are players who are good enough to ride the bench, but lose some talent, or run into enough bad luck that you drop below ‘the [replacement level] line’.” Do these players bounce back to their previous levels of performance, or are they “replacement level” in perpetuity?

To explore this, I gathered game-level performance data for all starters from 2008 through 2017 from FanGraphs, grouped by season. I then filtered out pitchers who had fewer than 25 starts and had an ERA less than 6.00 in their final 10 starts. This left me with a sample of 78 starters from 2008 through 2016 (excluding 2017 as there is no next year data yet). I assumed that a starter with an ERA above 6.00 was at or below replacement level. Lastly, as some starters were converted to relievers in the following year, I adjusted the following year ERA according (assuming relievers average .7 runs over nine innings less than starters: see this thread).

Seems like the 10-game stretch to end each season is a bit of an aberration. The following year’s adjusted ERA is much closer to the first 15+ games than the final 10 games for pitchers in our sample. In fact, the largest difference between any first 15+ game ERA and its following year adjusted ERA is .58 runs, in 2011. The smallest difference between any last 10 games ERA and its following year adjusted ERA counterpart, for comparison, is 1.7 runs, in 2009.

Using adjusted ERA corrects for the potential slight downward bias in our following year totals. Following year games started fell by ~9%, while reliever innings increased from zero to each season’s value. Relievers, on average, have a lower ERA than starters. As mentioned above, I adjusted each season’s following year ERA by .3 runs per reliever inning pitched (my assumed difference in runs allowed between starters and relievers per inning pitched). Another source for potential downward bias is sample size – of the 78 pitchers who fit our sample qualifications, only 69 pitched in the majors the following season. A survivor bias could exist in that the better pitchers in the sample stayed pitching, while the worse pitchers weren’t signed by a team, took a season off or retired.

What is driving these final 10 game ERA spikes? It has been shown that pitchers don’t have much control over batted ball outcomes. Generally, it is assumed pitchers control home runs, strikeouts and walks – the basis of many defense-independent pitching stats. Changes in these three stats could explain what happens during our samples’ final 10 games. Looking at each stats’ rate per nine innings, however, would be misleading, as each season exhibits uniform change (such as the recent home run revolution, or the ever-growing increasing in strikeouts). I calculated three metrics for each subset (first 15+, last 10 and following year) to use in evaluation: HR/9–, K/9– and BB/9–. All three are similar to ERA– in interpretation – a value of 100 is league average, and lower values are better.

Further, not necessary math details: for example, a value of 90 would be read as the following. For HR/9– or BB/9–, a value of 90 means that subset’s HR/9 or BB/9 is 10% lower, or better, than league average.  For K/9–, a value of 90 means that the league average is 10% lower, or worse, than the subset’s K/9. To create these measures, I calculated HR/9, K/9 and BB/9 for each subset and normalized them to the league value for each season – including the next year’s value for the following year’s rates. Then, I normalized these ratios to 100. To do that, I divided HR/9 and BB/9 by the league averages and multiplied by 100. Because a higher K/9 is better (unlike HR/9 and BB/9), I had to divide the league average by K/9 and then multiply by 100, slightly changing its interpretation (as noted above).

As mentioned above, the issue of starters-turned-relievers within our sample likely influences our following year statistics. I was able to adjust the ERA, but I did not adjust the rate stats – HR/9, K/9 or BB/9 – as I have not seen research suggesting specific conversion rates between starters and relievers for these.

Interestingly, our sample of pitchers improved their K/9– across the three subsets, despite having fluctuating ERAs. They were below average, regardless, but improved relative to league average over time. Part of this could be calculation issues, as league K/9 fluctuates monthly, and I used season-level averages in calculations.

Both HR/9– and BB/9– drastically get worse during the 10 start end-of-season stretch. These clearly drive the ERA increase. In fact, despite seven of the nine seasons’ samples having better-than-average HR/9 in their first 15+ starts, every season’s sample has a much-worse-than-average HR/9 in their last 10 starts, where eight of the nine seasons’ samples HR/9 are 40%+ worse than league average. Likewise, though less drastically, our samples’ BB/9 are much worse than league average in the last 10 starts subset. Unlike HR/9–, though, our samples’ BB/9– is worse than league average in the first 15+ starts subset. The first 15+ games’ HR/9– and BB/9– are identical to the following year’s values, unlike K/9–.

It appears that starters with an ERA greater than or equal to 6.00 in their final 10 starts, assuming 25 or more starts in the season, generally return to close to their pre-collapse levels in the following year. This end of season collapse seems to be driven primarily by a drastic increase in home run rates allowed, coupled with an increase in walk rate. These pitchers performed at a replacement level (or worse) for a short period and bounced back soon after. Mr. Tango & Bobby Mueller, in their email chain (posted on Mr. Tango’s blog), acknowledge this conclusion: “they are paid 0.5 to 1.0 million$ above the baseline… At 4 to 8 MM$ per win, that’s probably an expectation of 0.1 wins to 0.2 wins.” We can debate the dollars per WAR, and therefore the expected wins, but one thing’s for sure – past performance is a better predictor of the future than most recent performance.

– tb

Special thanks to Mr. Tango for his motivation and adjusted ERA suggestion.

Roberto Osuna, the Blue Jays young star reliever, has put together a very impressive resume in his 3-year career. Last season Osuna ranked 3rd in RP WAR (3.0) only behind Craig Kimbrel and Kenley Jansen in his age 23 season, and has also posted the highest cumulative WAR among relievers aged 20-23 years old in the last 40 years, while also producing the 2nd best FIP (2.69) and the moves saves (95).

Last July, Jeff Sullivan wrote a very compelling and in-depth article into the pure dominance Osuna was displaying on the mound; he was having a near perfect start to his season. He showed that across the board, Osuna ranked in the top 90 or 95 percentile in all of the major pitching statistics, proving that he had put it all together – matching his control to his skills. A few weeks before Jeff published his article (around June 25th), Osuna had missed some time for personal reasons, which was later disclosed as time away from the team to deal with anxiety issues. Roberto showed great courage speaking out to the public about his own internal struggles, but it was soon after that announcement that Osuna began to struggle on the mound.

It is both a difficult and a delicate analysis to undertake when analyzing the changes to Roberto’s performance last season. It is important to not read too much into certain trends and extrapolate that these derive from mental rather than physical, mechanical or strategic changes; however, this article will explore these changes to see why he suddenly began to struggle and how Roberto can strive to regain his top form for his 2018 season and beyond.

Roberto was at the top of his game in May and June and was putting up ridiculous numbers every time he took the mound. From July onward, Osuna began throwing his cutter and sinker much more frequently and threw fewer four-seam fastballs and sliders, as shown below:

The increase in his FC and SI usage and decrease in his SL and FA usage resulted in a change in his batted ball profile and strikeout potential. Osuna has a devastating slider with one of the best chase rates and swinging strike percentages in the league. He moved away from this pitch in favor of his sinker, which resulted in a lot more groundballs, as shown below. This change affected his BABIP, as it rose from .269 to .298.

Further, the large increase in his cutter usage resulted in a lot more hard-hit balls and he began to use it more often in high leverage situations with runners on base. His cutter usage increased from 15.7% to 37.4% with runners on base and this led to a plummeting left on base percentage. Last season Osuna posted the 2nd worst LOB% in the league among relievers at 59.5%. This is a statistics that jump off the page when juxtaposed with his fellow elite relievers who post metrics above 80 or even 90 percent. Below we can see just how drastic the drop was for him.

Considering that his LOB% was such an outlier compared to his peers, it is important to delve further into how this occurred. Recent history shows how rare it is for a pitcher with such great skills and control to have such trouble with runners on base. Since 2000, there has only been one other reliever who had a FIP under 2.00 who had a lower LOB%. A contributing factor to his struggles with runners on base was his aforementioned change in pitch composition. Increased usage of his sinker increased his balls in play and BABIP, his increased usage of his cutter resulted in harder hit balls and his decreased slider usage decreased his strikeout rate at times where he needed it most. Before June 25th, Osuna had a 2.41 FIP, 29.4% strikeout rate, 0% walk rate and a .304 BABIP with runners on base. After his temporary absence, his FIP actually dropped to 2.02, despite striking out fewer batters (24.1%) and walking more batters (1.8%) but his BABIP increased to .378. His xwOBA of .274 versus his wOBA of .311 with runners on suggests that he got a bit unlucky in the second half of the season, so his high BABIP is likely a combination of poor pitch command or selection, poor defense behind him and bad luck on balls in play.

Osuna enjoyed such great success when getting ahead of hitters (.189 wOBA after 0-1) and especially with 2 strikes (.130 wOBA), that hitters began to be more aggressive earlier the count looking for something to hit hard. A combination of a loss in fastball velocity and poor pitch location, Osuna began to get hit harder in high leverage situations. The top two heatmaps are Osuna’s fastball location and the bottom two are for his cutter. The heatmaps on the left are before June 25th while the ones on the right are after June 25th.

Osuna began to leave his fastball up over the plate in a hittable spot, as opposed to up and in, where he could tie-up right-handed hitters and produce weak contact. His cutter went from a setup pitch or even a waste/chase pitch to a pitch that he threw for strikes. Since Osuna started to throw so many more cutters, of course, he had to throw more of them for strikes, but the problem was he was unable to command the pitch to the better areas of the zone. A likely reason why Osuna began throwing more cutters was because the drop in his fastball velocity, as it was losing its effectiveness.

Pete Walker the pitching coach for the Toronto Blue Jays recently discussed with reporters Roberto Osuna’s offseason and reflected on his 2017 season. He acknowledged that Osuna had a drop in velocity during the season, had some mechanical issues, which impacted his fastball command, and that perhaps he threw his cutter too often during stretches of the season. All of this can be backed up with stats. Both the Jays coaching staff and Osuna are aware of where he can improve to regain with elite form. Walker also alluded that perhaps Osuna’s off of the field issues had an impact on his performance last season. By interpreting some statistics through this lens we can see how it can appear that Osuna lost of a lot of his confidence on the mound, especially in high-stress situations.

In particular, Osuna struggled away from the Rogers Centre as his road ERA was 5.10 versus only 1.85 at home in 2017. Further, Osuna had the 2nd best home wOBA while on the road it was only ranked 48th best.

Osuna was still good in the 2nd half (1.80 FIP and 4.24 ERA) and overall had a great 2017 season, but when the pressure started to grow and the wheels started to spin, they usually fell off (i.e. on the road with runners on base). It is hard to say whether this is the result of a lack of confidence, his decreased velocity on his fastball and his subsequent increased usage of his cutter or if it was a bit of bad luck with runners on base. It is likely a combination of these factors that led to Osuna’s declining second half, but we shouldn’t forget how dominant he can be when he’s at his best. According to Walker, Osuna has put on some muscle this offseason to help him with his durability in maintaining fastball velocity. Just like for most if not all other pitchers, being able to command his fastball is pivotal to Osuna being successful. At the end of last season, Osuna saw a small up-tick in fastball velocity and retired all 15 batters he faced in his last 5 appearances of the season, which is an encouraging sign, but how will he handle adversity, when batters reach base in 2018? With some minor tweaking to his game, Osuna should be on track to bounce back and have another dominant season as the Blue Jays closer.

Spring is a time for optimism, in baseball and in life. Teams are starting to think about their opening day starters and more broadly, their starting rotations. Some rotations look “set” while some have a “battle for the 5th spot”. Some are toying with the idea of a 6-man rotation.

But here’s the thing: we know that (almost) every team will end up using a 6-man rotation, whether they like it or not. Eventually, your favorite team will need to call in reinforcements. This can happen because of poor performance or injury. But hey, we’ll cross that bridge when we come to it, right?

… when do you think we might come to it?

We know, as do those in charge that teams use something like 11 starters per year (in 2017: 11.3). In a six-month season, how long does it take before the first reinforcements arrive?

In a few words, not very long. Some pitchers have injuries, some get moved to the bullpen, some sent to the minors. Either way, at least one of them will be gone pretty soon, so don’t name the puppy.

Of course, fate comes at different paces. In 2017, the Cardinals didn’t use a sixth starter until June 13th. And even then, Marco Gonzales only pitched because they had a double-header. In contrast, Junior Guerra, the Brewers’ opening day starter, was injured that same opening day. He wouldn’t pitch in the majors for another seven weeks (and it turns out, not very well either).

Half of teams used a sixth starter before April 25th. 90% of teams used a sixth starter before their 50th game.

Some of those sixth starters, along with their full-season WAR: Alex Wood (3.4), Mat Latos (-0.3), Mike Clevinger (2.2), Mike Pelfrey (-1.0).

We know that teams need depth. Not only that, but life comes at you fast.

Data: Baseball Savant