Tucker Barnhart has been an invaluable defensive catcher for the Reds recently, and his impressive 2017 season earned him a Gold Glove. After all, he had a .999 fielding percentage in 2017! He truly was a really important part of the Reds team last season and should be commended for his efforts.

Though there was another catcher last season, who was better than Barnhart. The player who was more impressive last season, who did not receive sufficient recognition for his efforts, is Austin Hedges. He took over the starting catching job for the Padres last season, and despite having a 71 wRC+, the team stuck with him behind the plate for the entirety of the season. The team is rebuilding, but traditionally when a player is producing so poorly on offense, they do not continue to receive everyday playing time. Unless they are so talented on defense, that it simply doesn’t really matter that they don’t hit very well. This is exactly the reason for Austin Hedges taking over the starting catcher role in San Diego last year.

Hedges should have won the Gold Glove at catcher last season, though obviously the reasoning behind him being superior to Barnhart must be explained. These are the likely reasons why Barnhart won the Gold Glove, and why they are flawed in being able to truly represent his individual performance as a catcher:

1. .999 Fielding Percentage – This hinges too significantly upon the official scorer’s rulings. Evidently Barnhart got very lucky this season in terms of  being held culpable for defensive miscues by the official scorers.
2. 44% Caught Stealing Percentage – Pitchers are much more significant to this statistic than people are often aware of. If a pitcher cannot hold runners on or is slow in his delivery of pitches to the plate, the catcher’s chance of throwing runners out can sometimes be eliminated altogether.

Miguel Montero echoed this sentiment perfectly in June last season, and despite his words resulting in his being released by the Cubs, it nonetheless holds true: “It really sucked because the stolen bases go to me. And when you really look at it, the pitcher doesn’t give me any time.”

Furthermore, Blue Jays outfielder Anthony Gose addressed base stealing, saying “It has nothing to do with the catcher,” and that “It’s on the pitcher and his times.” While this statement may be taking things too far, the idea here is that the pitcher is more significant to limiting baserunners than people often realize. As far as this concept is related to Barnhart and Hedges, the point is that Barnhart was simply helped out more by his pitchers, who were faster at pitching to the plate than the Padre pitchers Hedges was handling.

Barnhart also led the league in runners caught stealing, with 32, however, this statistic is flawed for the same reasons as Caught Stealing Percentage. Hedges’ fielding percentage was .990, and he had a 37% Caught Stealing percentage. Focusing on these simple statistics is not sufficient for evaluating catcher defense, which should be obvious in the context of how they were debunked earlier in the article. There are actually more advanced, as well as simple measurements that both indicate Hedges’ superiority over all other catchers in baseball defensively.

First of all, looking at the Baseball Prospectus defensive catcher data, reveals that Hedges lead the league in 2017 in multiple statistics:

Leading the league in Fielding Runs Above Average, and Adjusted Fielding Runs Above Average, is no small feat. Clearly, Hedges has a special defensive ability, that has not seemed to be recognized sufficiently. As far as the data above is concerned, he is a step ahead of even the second-best defensive catcher in the Major Leagues last season.

Recently Statcast released public data measuring catcher pop times, and Hedges topped the list in average pop time:

Also significant here is the fact that many catchers’ pop times on plays that resulted in runners being caught stealing, were slower than their average pop times. This confirms the earlier point that caught stealing statistics are influenced by the pitcher’s ability to get the ball to the plate quickly or not. Barnhart checks in at 22nd on the list of catcher pop times, recording pop times of 2 seconds flat across all three times being measured in the table above. Yet he led the league in runners caught stealing, which illustrates the effects of pitchers and obviously baserunners, on the caught stealing percentages of catchers. Hedges was objectively the fastest at getting the ball to second base last season among Major League catchers, which is yet another reason for his being the premier defensive catcher in Baseball.

Borrowing from Travis Sawchik’s piece last June, he says this regarding Pitchers and Catchers handling baserunners:

“Generally speaking, pitchers with times of 1.3 seconds and quicker to home are going to slow the run game, while anything above 1.5 seconds is going to entice teams to run. As such, a battery is generally looking to record a total time of 3.3 seconds or less. That should prevent most baserunners from stealing. Between 3.3 and 3.5 seconds is average. Above 3.5 seconds is likely to be a problem.”

Given that Hedges’ average pop time is 1.89 seconds, Padre pitchers can actually release the ball in 1.41 seconds to home plate, for the batteries to record a total time of 3.3 seconds. What is special about Hedges, is that he actually helps compensate for pitchers who are slow to the plate, because his release is lightning quick, and his throwing arm is so accurate. Watch him make up for Tyson Ross’ 1.59 second time in his pitch to the plate, and throw out Elvis Andrus with a pop time of 1.82 seconds:

Ross threw a slider at 85 mph, yet Hedges was still able to throw out the runner. Watch Jedd Gyorko’s glove here – He does not move it at all to catch the throw. It was not only lightning fast, but also a near-perfectly accurate throw. There are no catchers in Baseball who would have made that play, outside of Austin Hedges. The times of Ross and Hedges are self-timed by the writer of this article, and the average times were 1.59 and 1.82, though if one wants to they can time it themselves – The times will be quite similar to the numbers above.

Even more impressive than this play, however, was when Hedges threw out Billy Hamilton from his knees this past August. His Pitcher Luis Perdomo threw a low slider in 1.42 seconds to the plate, and Hedges actually has a pop time of 1.72 on this play to just get Hamilton. He was called safe initially but was determined to be out after replay review. Hedges didn’t even make a standing throw!

Hedges combined with Ross for a total time of 3.41 seconds, and with Perdomo for a time of 3.14 seconds. Both times making up for a pitcher who was slow in delivering his pitch to the plate.

Statistical analysis is the basis of the article, yet at times it really is valuable to just watch a player in action, in order to truly understand what makes him talented. In the case of Hedges, there are plays he makes as a catcher that others at his position simply can’t make.

Watch Hedges reach for a low and-in pitch, then quickly get out of the crouch and run in the opposite direction to make an incredible play here:

The Padres pitchers appreciate the effort, and ability Hedges brings to the table every game he plays, as Padre closer Brad Hand said very well:

“He does his homework. All the pitchers here have good trust in him. That’s good to see. He’s a young catcher, and you don’t really see that too often, a rookie catcher gaining that kind of trust from his pitching staff right out from the get-go. That’s a credit to him and the hard work he puts in.”

There are other talented defensive catchers in Baseball, yet there are none at the level of Austin Hedges. He knows how to handle the pitching staff, frames pitches as well as any catcher, blocks balls in the dirt with the best of them, and has a lightning quick release to throw out runners. The data presented earlier backs all of those claims. And the gifs help one visualize the true wonder in his defensive capabilities. The next time someone asks about the best defensive catcher in Baseball, the answer is now Austin Hedges.

Data used in this article was taken from Baseball Prospectus, Fangraphs, Statcast, and Video from MLB.com

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.

There’s been much criticism of “tanking” teams in this slow offseason, or at the very least, criticism of teams who are not aiming to win now. Most notably the Marlins, Pirates, and now Rays have been held up as examples of teams who are not aiming to win. Fans, players and commentators have raked the teams and their ownership/management over the coals for embracing a strategy of failure.

Let’s leave aside for a moment what is a tank and what is a rebuild. But from an empirical standpoint, I find it simply inaccurate to assert that most teams are not trying to win in 2018 based on their offseason moves. Rather, I think there is a strategy emerging in the non-super-team group of aiming for slightly above average and hoping for a Wild Card berth. Indeed, given the increased supply of teams on the low end of the win curve selling current assets for future ones and the smaller marginal returns for teams at the top end, the middle-tier teams actually can scoop up value here.

I guess the thing I don’t understand, particularly regarding the trades, is that the team on the other side of the ledger matters. That is, the seller’s loss in the current season is still the buyer’s gain.

Currently, 7 teams project for 90 wins or more according to Steamer (Astros, Dodgers, Cubs, Indians, Red Sox, Nationals, Yankees). We can safely say these teams are aiming to be division champs. But the next tier of roughly 10-12 teams has been quite active this offseason in aiming for wild card slots and hoping things break right for themselves (and wrong for the division favorites).

Consider the Marlins’ sell-off. Yes, Stanton went to the Yankees, so “the rich get richer,” but the other three “sell-off” players — Christian Yelich, Marcell Ozuna, and Dee Gordon — all arguably went to teams who are in the “middle.” The Brewers project for 78 wins in Fangraphs currently, Cardinals for 88 and the Mariners 80.

And many teams that could decide to sell off are actually holding steady or gearing up for another shot at the title.

Plenty of Mets commentators decried the Mets’ offseason as giving up on the season before it started, but the Mets have added $88M in salary obligations and roughly 6 extra projected WAR next season. The “middle class” of free agents is a value proposition for the Mets, who picked up Todd Frazier on the cheap. The Mets project 11 games back of the Nationals, but they’re still aiming for the middle, deciding that the possibility of even the one-game playoff is worth it and remembering that even superteams do fall apart.

Similarly, the Blue Jays, a team that won 76 games last season should have, if the “everyone’s tanking” narrative were correct, sold off Josh Donaldson. Instead, the team has re-loaded for another run in a division with two 90-win superteams with some tweaks to engineer a team capable of sneaking into the wild card race, adding roughly 5 WAR through a combination of free agency and trades.

And these moves are in keeping with what we might expect from Jesse Wolfersberger’s calculation of the second wild card win curve:

If you’re unlikely to beat out the top teams, maybe better to hover opportunistically around the middle and stumble into a playoff spot. The second wild card may make the value of the first wild card lower, but it stretches out the tail of value for teams in the 85-90 win range.

Even teams arguably outside the window of contention right now are aiming for the middle. The Padres just signed Eric Hosmer to a huge deal. They wouldn’t do that if they didn’t think they were close to contention. The Pirates, who were roundly condemned for trading Gerrit Cole, actually got good players in exchange who are ready to play in the majors, like Joe Musgrove (injury notwithstanding) and Colin Moran. (Indeed Musgrove and Moran currently combine to project for more 2018 WAR than Cole at the moment!)

It’s not sexy to aim to be number 2. To quote notable philosopher Ricky Bobby, “If you ain’t first, you’re last.” American culture and sports culture in particular places dominance and winning above all other goals. But given the randomness of the sport and the recent history of failure among projected “super teams,” there is a reasonable strategic position to embrace aiming for the middle. And as the offseason price of free agents fall, it seems that many teams are doing just that.

In the last week Ichiro, Tim Lincecum, Carlos Gonzalez, Jonathan Lucroy, Mike Moustakas, and Lance Lynn have all signed. On Sunday, Jake Arrieta joined them, agreeing to a three-year, $75-million contract with the Phillies. That’s an average of a signing a day! Of major leaguers, to major league contracts! The dominoes are certainly falling. Finally.

Arrieta’s signing comes with curiosities. Or maybe more accurately, concerns. He has more than 1,100 professional innings on his arm. From 2014-16 he had a nasty-good run. Toward the end of it, and through 2017, his velo started to dip. Pitch Info tells us he lost two mph off his sinker between 2016 and 2017. His Ks have slightly gone down and his walks have slightly risen. At 32, he’s at an age where it’s fair to begin wondering how much further he could fall, and how quickly.

How does he adapt? Arrieta might be past his peak prime while with the Phillies, but what will he be? What can he be, and what adjustments might it take to get there? The way hitters manufactured production off him last year could help us find a path to that answer.

Half of his actual weighted on-base averages were higher than what Statcast tells us we should have expected. Arrieta arguably has a skill of inducing weak contact, so what this would seem to suggest is that sometimes, when hitters put the ball in the air against him, he just gets beat. The overall numbers were lower during his run of dominance between 2014 and 2016, but the actual production similarly beat what could’ve been predicted based on the launch angle and exit velocity of balls in play against him.

Beyond that, though, we see a notable split in performance against lefties and righties last year. A single year of batter splits can be dubious, but consider this the New Arrieta; one whose age is revealing diminished skill. Lefties really went to town against his sinker and slider last year. The two pitches break in opposite directions, which makes them excellent sequencing buddies from the same tunnel, but things didn’t play that way for Arrieta last year.

One reason why could be because of the break on Arrieta’s slider. Per Brooks Baseball, he lost .7 inches of horizontal break and .53 inches of vertical break on it. What does that look like? I’m glad you asked.

Thanks to Statcast’s incredible, fantastic, super fun new 3d pitch visualizer, we can see how that loss of break on Arrieta’s slider could have impacted its performance against left-handed hitters.

The slider is in red circles. His sinker is in black squares. The ones closer to the mound mark the point at which batters could first recognize the pitch. The ones closer to the plate tell us when batters would have needed to commit to swinging. In 2017, lefthanders saw Arrieta’s slider sooner and were able to decide on swinging against it later than his sinker. Less movement, plus less velo, plus the same tunnel means hitters faced a pitch with very little bite. And that’s how an absurd .509 wOBA happens.

From 2014-16, lefties only generated a .240 wOBA against Arrieta’s slider. Last year’s numbers are probably an outlier, but if the pitch continues to flatten out it could really threaten the viability of one of his weapons. He could consider turning the pitch into more of a true cutter to deliberately make it run further inside on lefties, or he could use it less in favor of the curveball. There’s also a chance he could take a little off the slider to widen the velocity gap with his fastball, but deliberately throwing slower in this context doesn’t seem ideal. 

Arrieta’s going to be an intriguing piece to watch on an increasingly intriguing team. The Phillies are showing they’re getting ready to contend, and his evolution as a pitcher could be key to making it happen.

—

Pitch mix and wOBA data from Statcast.

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.

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.

As spring hopes eternal so, too, do the annual Spring Training stories. Guys are in the best shape of their lives or feeling better than they have in years. Or futzing with new pitches. In fact, so many guys try new pitches that Jason Colette keeps an annual, running list of pitchers who are attempting to add to their arsenal. 

Edwin Diaz is among those attempting to do that this year by adding a changeup to his very fast fastball and exceptionally mean slider. Mariners General Manager Jerry DiPoto says that so far the changeup is “pretty firm.” He also adds that “it could be something in [Diaz’s] back pocket that he can introduce against an occasional lefty.” But does he even need it?

A glance at the top 10 relievers over the last three years tells us a few things. None of them threw any two pitches at a volume that would allow them to throw a third at a clip of 10% or more. Jansen’s and Britton’s numbers don’t even facilitate doing it for a second pitch! That 10% seems to be the tipping point at which an offering is actually useful to a pitcher. That’s when a hitter has to be accountable to it, or at least be aware of it in the back of his head. Less than that and they can take their chances focusing on what they know is coming more than 90% of the time.

The lone near-exception in this group is Roberto Osuna. After his fastball and slider, he’s thrown a cutter 9.4% of the time. He’s thrown a changeup slightly less than that (8.4) and a sinker slightly less than his change (7.4). While his repertoire might be an outlier compared to his peers, he still falls short of the 10%-per-offering threshold.

It’s important to acknowledge that each reliever’s primary and secondary pitch types aren’t listed above. They all throw different stuff. But what they use, they use similarly. In this sense, it’s kind of like taking different routes to the same destination, but each one takes the nearly same amount of time. Looking at each reliever’s individual splits shows us that almost all of them also faced a relatively even amount of right-handed and left-handed hitters. Only Miller, Chapman, and Britton had splits that tilted more distinctly one way, and that was against righties. None faced notably more lefties.

And that brings us back to Diaz. He, too, has faced more righties than lefties so far in his time in the bigs, about 14% more. Adding a pitch specifically to focus on hitters he’s seen less of, in anticipation that he might see them more, seems premature at best. Remember, the M’s moved Diaz to the bullpen because he couldn’t develop a third pitch to stick in the rotation. That’s how we get a lot of our power relief arms. As a starter, that third pitch is way more critical because of the volume of hitters per appearance. For relievers — especially the dominant ones, which Diaz is capable of being — the lack of volume is by design.

Odds are that Diaz stops fiddling with a changeup and just keeps throwing his fastball and slider as the season gets going. But nonetheless, the situation feels like trying to push a buoy underwater. It’ll just keep bobbing back up. And why the Mariners would advocate for it in this context, whether passively or actively, is very, very confusing to me.

In my day job, I’m an educator. For every lesson planned, there’s a constant inner monologue, a series of cascading questions. What’s the best way to approach the day’s goal? Does this lesson serve the unit? If not, does the lesson have enough value to still include or would it just be empty fun? What questions can I anticipate, and what answers could I have ready?

If I were the Mariners, I wouldn’t plan for Diaz to throw a changeup. If he asked to do it, I’d conference with him about why he thinks it would be effective. I’d speak to him, with evidence, about why it might be cool, but emphasize that it’s definitely not necessary to succeed. I’d map out why it makes sense for him to just throw that dang slider.

But alas, I’m not the M’s.

—

Data from Fangraphs.

Defensive shifts are designed to prevent hits. By placing fielders in spots of higher hit frequency, the logic follows, fewer batted balls will drop in as hits. Notably, though, as the number of shifts has drastically increased, the league-wide BABIP hasn’t changed. Since 2011, shift deployment has increased tenfold (though BABIP has actually increased 1.7% – .295 in 2011 to .300 in 2017). Better positioning could lead to teams utilizing fielders who have less range, as they’d be located closer to batted balls. Do teams who shift frequently employ worse-ranged fielders?

First, the recent MLB environment. Through a combination of enhanced analysis and deeper data, teams across MLB are increasing shift usage. Positioning fielders in locations of high hit density, for specific batters, allows them to field more batted balls. Every team is increasing their shift usage, driving the total shifts deployed up.

The intuitive result of this would be batters are recording fewer hits. As fielders field more balls, they should convert more of those previously-hits into outs. However, league-wide BABIP has actually increased as shift usage increased. Perhaps the quality of the batted balls has decreased, though – trading doubles and triples for singles. According to the league-wide wOBA, though, the overall quality of offense has increased.

Clearly, shifts aren’t having the effect one would expect them to have. Rather than explore what effect they do have (as if they had no effect, why would teams continue to shift?), I want to see if perhaps the defenders being used are worse. Perhaps shifts have allowed teams to mask poor defenders with better positioning.

After browsing the data, I thought it was best to compare year-to-year changes in range runs saved above average to changes in shift deployment, in attempts to analyze the effects of a large change in shift use on range runs above average (RngR). This variable doesn’t measure data for shifts — any shift-influenced batted balls are excluded. This exclusion is what makes RngR perfect for analysis — we can isolate plays which are standard and similar fielder-to-fielder and control for frequency of shifts.

To do this, I first prorated range runs above average to a 150 defensive game rate (RngR.150), as each team had slightly different innings totals. I then took the year over year difference in RngR.150 as RangeDiff, to analyze changes in range runs above average. Similarly, I took the year over year percentage changes in shift deployments. Due to the drastic increase in shift usage across the majors, comparing these absolute numbers would be meaningless here, so I scaled these percentage changes to each season’s average change in shift usage. This variable, ShiftScaleYOY, represents a team’s shift usage change as standard deviations above or below the season average change. All this data is from Fangraphs, 2011-2017 team defense statistics and shift deployment.

My hypothesis is that teams that have a drastic increase in shift usage between seasons, compared to league-average, would have worse defenders, as measured by range. The results:

First, notice the axes. Third basemen have a larger variance. Teams with larger increases in shift usage year-to-year, relative to the rest of the league during this same time periods, appear to have defenders at third with range values closer to zero. This is difficult to see through inspection, however. There doesn’t appear to be much of a relationship with 2nd basemen or shortstops.

When I regressed the between-year standard deviation measurement of shift changes on between-year range change, with dummies for position and season, the shift change variable was insignificant. In fact, there were no significant variables, and the R-Squared was merely .13%. Notice the symmetry in the above graphs, though. A team’s range values seem to converge as the team’s standard deviation of shift changes increases.

To explore this, I ran two regressions, with subsets where the dependent variable, Range.150, was positive and negative. The positive regression had an R-Squared of 9.2%, implying it poorly describes the variance in positive Range changes year-over-year. 2017, 3B and SS were all statistically significant, at the 99% confidence level. This implies that there is a 2.15 range per 150 defensive games decrease in 2017 versus the other seasons, that there is a 1.5 run increase for being a third baseman and a 1.4 run increase for being a shortstop over a second baseman. The negative regression had an R-Squared of 8.6%, again implying this model poorly describes the variance in the data. Here, however, 2017 and 3B both were statistically significant, at the 99.9% or greater confidence level. The values were greater, but the direction of implication was the same – 2017 implies a 2.7 run increase, and a third baseman has a 2.4 run decrease over second basemen. These analyses suggest that 2017 resulted in fewer outlier defenders and that third basemen were higher variance than second basemen.

There are a few issues or improvements with this analysis that could be made. First, publicly available data is limited – comparing shifted plays and non-shifted plays would be best for this analysis. What I did could be seen as cursory, at best an introduction. Secondly, the sample size of defensive shift data is small. Defense data for individual, full-time players is generally utilized in three-year samples, and I was using single-year measurements (albeit at the team level, slightly larger samples per position than individual players). Lastly, a deep analysis on shift impacts on player abilities would use individual players – comparing his or her defensive prowess on shifted and non-shifted plays. This would allow us to try to measure the impact of shifts on defensive performance, to better understand if teams would employ different-skilled players as they increase shift usage or if their players perform differently with shift usage.

There are suggestions in the data that certain years or positions differ with respect to defensive range. Nothing suggested relative increases in shift usage impacts range or quality of defenders on the field. All in all, I think this study can be summarized by the wisdom of Albert Einstein: “the more I know, the more I realize how much I don’t know.”

– tb

Last season was the year of the three true outcomes specialist.  Aaron Judge’s dominant three true outcomes season was the most prominent example of this: he ranked second in home runs (52) and walks (127) and first in strikeouts (208).  In total, 57% of his plate appearances resulted in one of the three true outcomes.  He was the American League Rookie of the Year and in the running for the 2017 American League Most Valuable Player award, finishing second.  His performance helped the Yankees reach the American League Division Series.

We know that the three true outcomes rate has been increasing.  In part, this is due to the average player increasing his rate of home runs, strikeouts and walks.  But there is also the unusual player in the mold of Judge who takes an extreme approach at the plate resulting in dominant three true outcomes seasons.  The number of these hitters has been increasing over time.

Figure 1. Three True Outcomes Specialists per Season, 1960-2017

View post on imgur.com

Figure 1 shows the number of dominant three true outcomes player seasons over time.  To get here I examined all players since 1913 with at least 170 plate appearances in a season.  I considered a dominant season one with a three true outcomes rate of at least 49%.  There have been 132 player seasons with a three true outcomes rate of at least 49%.  All of them have taken place after 1960.

The graph shows that the number of dominant seasons has been increasing over time.  Since Dave Nicholson first did it in 1962, most years have had at least one player cross the threshold.  Since 1994, every season has had at least one.  From 2001 to 2010 there were four seasons with five three outcomes hitters.  There was six in 2012 and eight in 2014.  The trend is currently peaking with 13 in 2016 and 16 in 2017.  The trend is a bit more extreme but similar to the average increases in three outcomes rates over time.  It seems that more players pursue (and teams tolerate) an approach to hitting that includes extreme rates of the three outcomes.

It is worth pointing out that those 16 players in 2017 account for about 4% of all players with at least 170 at-bats.  Three true outcomes specialists are more common but still rare.  Who are those 16 players?  Table 1 lists them including the home run, walk and strikeout rates, and the combined three true outcomes rate for the year.

Table 1. Three True Outcomes Specialists, 2017

The list includes many of the unique player stories of the year.  Aaron Judge’s rookie year was historic.  Joey Gallo made waves, particularly for his extreme three true outcomes rates.  Miguel Sano was an All-Star who helped lead the Twins to a bounce back year and a wildcard spot.  Eric Thames was a surprise story of the year, returning from a year in Japan and sparking the Brewers to an early lead in the National League Central.

Notable about this list is the young cohort of hitters who have consistently taken the all or nothing approach of the three true outcomes specialist.  Judge, Olson, and Blash all made their MLB debut in 2017.  Gallo still qualified as a rookie despite making his debut in 2016.  Keon Broxton, Ryan Schimpf, and Kyle Schwarber are in their second year.  Sano has been a specialist for three years running.  Sure, there are old hands like Napoli and Carter, and Davis who take the all or nothing approach, but the record number of specialists the last couple years have been due to this young cohort of three true outcomes specialists.  A new record will come down to 2018 rookies who practice this all or nothing approach heading into their major league debuts, and the number of teams willing to tolerate the strikeouts that come with this approach.

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.

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?

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.

Mookie Betts went to an arbitration hearing. Marcus Stroman went to an arbitration hearing. George Springer and Jonathan Schoop did not. Other than the obvious differences between these players, there are others— related to the arbitration process itself— that may have affected these outcomes. Particularly, the differences and qualities of their filings.

To those unfamiliar with the arbitration process, eligible players and teams who are unable to come to a settlement ahead of the given deadline, submit salary filings which reflect either party’s evaluation of the player’s worth. Even after filing, teams and players are able to negotiate a one-year contract, but in some cases, a panel of arbitrators will decide a salary: either the player’s bid or the team’s bid, but not any number in between. This “final-offer arbitration” system is designed to create compromise and negotiation between bargaining parties as the threat of losing a large amount of money increases the incentive to settle early while a midpoint is still available. By extension, teams and players are encouraged to moderate their bids as an outlandish one is surely to be challenged and lost.

But, two different theories exist as to how the difference in bids itself affects the likelihood of hearing. Some argue that higher differences between teams and players in valuation would increase the likelihood of an arbitration hearing as the difference in bids reflects differences in valuation. However, others— namely Carell and Manchise in Negotiator Magazine (2013)— argue that differences in bids increase the risk of heading to a hearing and incentivize teams and players to hammer out a settlement.

Using two separate probability models and data on all players that filed for arbitration between 2011 and 2017, I examined the likelihood that a player goes to an arbitration hearing based on the differences in bids between the player and the team. The models both control for the player performance— by incorporating the effect of WAR— and utilize a dummy-variable for Super-Two status— controlling for the effect of players granted a “bonus year” of arbitration eligibility. The only difference between the two models is the variable of interest. The first uses the ratio of the absolute bid differences to the midpoint between the two salaries in order to measure the effect of a growing gap between filings relative to the actual size of the filings. The latter model separates the two effects to understand whether absolute gaps and absolute filing size have an effect on arbitration hearings. The model specifications and regression results are shown below. The table below essentially shows the marginal effect on likelihood to go to hearing due to a 1 unit change in the corresponding variable.

Model 1:

Model 2:

Results:

Both models demonstrate highly significant coefficients indicating that players with large gaps in salary filings are less likely to enter hearings. In fact, in the aggregate sample of players an increase of $100,000 in bid differences reduces the likelihood of a hearing by 2.7% and a 1% increase in Bid Difference to Midpoint Ratio decreases the likelihood of a hearing by 1.1%. This stands as an incredibly significant effect considering only 16.73% of players in the sample even made it to a hearing. Quite evidently, teams and players are incredibly risk-averse and fear losing the arbitration hearing and being forced to agree to a suboptimal salary. Thereby, the incentive to settle is driven up by higher bid differences.

Another interesting result shows that in all samples, an increase in filing midpoint by $100,000 increases hearing likelihood by 0.56%. As such, all else equal, players with higher filing midpoints are more likely to head to a hearing. The intuition behind this is best explained considering this with the negative coefficient on WAR, as both WAR as Midpoint are highly related but have opposite and significant signs. While WAR indicates that better players are less likely to head to a hearing, the positive coefficient on Midpoint states that “better” players are more likely to head to a hearing.

Though these indicate opposite effects, considering the effect of a high midpoint with WAR constant and vice-versa, the theory provides explanatory qualities. A more aggressive salary bid— given an exogenous and fixed level of production— is easier to dispute for a low-value player than a high-value player. Thus, independent of the player’s production level, a higher Midpoint leads to a higher likelihood to enter an arbitration hearing. As such, the positive coefficient on Midpoint likely reflects bad players bargaining for extra money rather than good players— whose effects on hearing likelihood are captured by the WAR coefficient. Considering the WAR coefficient independent of the filing midpoint as well, teams are more likely to focus their negotiation efforts on their better players, thereby reducing the likelihood high WAR players end up in hearing.

The final variable of interest in these regressions is the dummy-control for Super-Two status. As mentioned earlier, Super-Twos represent young players with substantial playing times who are rewarded with an extra year of arbitration eligibility. The models predict that Super-Two status increases the likelihood of hearings by 14.3%-16.9% depending on the model. As such, these young players seem more likely to challenge their teams in salary evaluations. This too comes as no surprise since challenging a team in your first (and bonus) year of arbitration eligibility can lead to significant level effects in subsequent arbitration hearings. A salary increase from the league minimum to $545,000 to even $1M can snowball into much larger raises in the following years with an arbitration victory. As such, these players may have a higher incentive to enter hearings and capture these multiplicative effects.

Now, revisiting the four cases above— Betts, Stroman, Springer, and Schoop— some interesting cases do pop out. Betts may not have been the most likely candidate to head to an arbitration hearing, the $3M difference between Betts and the Red Sox was incredibly high and reflected an enormous risk for either party entering a hearing. The predicted path for Betts was likely closer to George Springer’s contract extension or Jonathan Schoop’s 1-year deal. By contract, Stroman may represent the classic arbitration case, a low-risk hearing for either party, bargaining over a small fraction of their bids. And while Stroman expressed his frustration— or lack thereof— following the hearing, history shows that the Stromans of the world will likely end up there again. Ultimately, the final offer arbitration system does its job: those who disagree significantly tend to work toward compromise, while those who disagree a little take a change and roll the dice.