Admittedly, this is a bit of a stupid topic. These distinctions are often thrown around with an air of importance that is far from earned. Nobody ever mentions that Hank Aaron had the most RBIs in the 1960s. You don’t need to talk about arbitrary endpoints with the Hammer. Mentioning that a player holds one of these “records” is a bit like saying a guy you are trying to set a friend up with has a great personality. It’s likely that this is covering up for something else like a hat covers a balding head.

(*before you head to the comments to blast me, yes, RBI is an incredibly useless statistic)

But they are fun! Hank Aaron did lead in RBI in the 1960s, but he finished second to Harmon Killebrew in home runs. Growing up into a baseball fan in the 1990s, one of the bigger surprises in this genre of record was the hit leader of that decade: Mark Grace. I’d imagine every Cub fan knows this. In addition, anyone who knows a chatty Cubs fan probably knows this, too. Looking at the most recent decade, there are a few surprises: Miguel Tejada had the most games played and at-bats. Andy Pettitte edged out Randy Johnson for most wins. Johnson, along with Alex Rodriguez, dominated most of the categories.

Moving to the point of this article, here is a quick rundown of compelling and not so compelling races to have the most X of this decade, with two seasons remaining:

WAR

You, the Fangraphs reader that goes into the depths of the Community Research section, probably know who leads this category, despite spending 2010 in A ball. But it is a bit closer than you might have thought. While Trout could conceivably win the position player award while sitting out the next two seasons (Joey Votto is the only player that might topple him in this scenario, and he is 9.1 WAR behind), this is the one major statistical category in which position players and pitchers compete with each other.

If I just jogged your memory of that, you probably know the name that is coming: Clayton Kershaw. Trout leads with 54.4 WAR. Kershaw has 52.1 WAR on the pitching side, but has also accrued 1.8 WAR as a batter. That should count. So Kershaw, at 53.9 WAR, is directly on Trout’s heals. Trout is still the heavy favorite, but Kershaw has a puncher’s chance, especially if another injury befalls Trout.

Totally made up odds: Trout, 98%; Kershaw, 2%

Hits

Jose Altuve has put up four straight 200-hit seasons, but he is 251 off the pace and in 15^th place. No, this title will most likely go Robinson Cano. Cano leads with 1501, and the only players within sniffing distance are similarly on the downside of their primes. Miguel Cabrera is second at 1416, and then there is a slew of players, including Fangraphs favorite Nick Markakis, in the low to mid 1300s.

Cano should clear 150 hits the next two seasons, and if he does, he will not be passed. Cabrera, as bad as he looked at times last season, would be the likely beneficiary of some unforeseen collapse by Cano. Elvis Andrus is the end of that slew of players behind Cabrera. He has 1329 hits, but recorded 191 last season and is significantly younger than everyone in front of him. I’m going to give him sleeper status for this title.

Totally made up odds: Cano, 93%; Cabrera, 5%; Andrus, 2%

Home Runs

Currently, this is an incredibly close race, with four players within five homers of each other at the top of the list. Jose Bautista is first with 272. Edwin Encarnacion and Nelson Cruz are second and third, and then you get to Giancarlo Stanton in fourth place with 267. Other than Miguel Cabrera and the remnants of Albert Pujols, no one else is close. Stanton has to be the favorite, here, but his status is extremely tenuous. First, let’s just get Buatista out of the way. He’s unemployed and several steps below the other players even if he does try to gut out two more seasons.

Without a doubt, it would be shocking if a healthy Stanton didn’t win this. But a healthy Stanton would be at least a little bit of a shock. The once-oft-injured Cruz and Encarnacion are 37 and 35, respectively, but are still mashing and project for mid-to-high 30-something homers apiece. Cruz has played four straight full seasons and E5 has three straight under his prodigious belt. Stanton is projected by Steamer for a literally—but not really literally—bananas number of 53 home runs. The Fans of Fangraphs are more modest, pegging him with only 48. But Stanton is injury prone. You all know that. There is no argument that he is not. So this is a fairly open race.

Totally made up odds: Stanton, 55%; Encarnacion, 25%; Cruz, 20%

RBI

Again, I do know this is a stupid statistic. But artificial endpoints of decades are pretty stupid, too, so this is fitting. This is Miguel Cabrera’s title to lose, and as long as he plays, he should easily win. And guys making the cash Cabrera is due for the next thousand years generally get every opportunity to play. Sitting behind Cabrera’s 860 RBI are Albert Pujols at 806 and Robinson Cano at 789. The aforementioned Edwin Encarnacion and Nelson Cruz round out the top five with 763 and 756 respectively.

If Cabrera falters, this looks like it would be a wide-open race. Pujols achieved the remarkable 100+ RBI season while losing 2.0 WAR last year. He likely will do much worse, but as long as he is playing, he will continue to accrue a decent number of RBI. E5’s Indians outscored the M’s by 68 runs last year and seem to be a better offensive team, but Cano does have a 26 RBI lead. Honestly, this looks like a virtual toss-up if Cabrera doesn’t win, but the idea of Edwin Encarnacion or Nelson Cruz leading the decade in home runs and RBI is rather delicious.

Totally made up odds: Cabrera, 80%; Cano, 7%; Encarnacion, 6%; Cruz, 4%; Pujols, 3%

Stolen Bases

Be honest, which would you rather be known for: a surprise answer to the question “who stole the most bases in the second decade of the new millennium?” or hitting an epic World Series Game Seven home run… for the losing team. Rajai Davis might say porque no los dos? Davis has the most stolen bases this decade with 301. However, he is actually a longshot to keep this title. Davis just signed a minor league deal with the Indians that includes a non-roster invitation to spring training. He will likely struggle to ever get regular playing time again. He’s 37 years old.

This will likely come down to a race between the two guys behind him. Dee Gordon has 278 stolen bases, had 60 last year, and only turns 30 in April. He has a 35 stolen base lead on 3^rd place, which would seem more insurmountable if that person was not arguably a full tick or two faster. Billy Hamilton has 243 stolen bases since coming into the league in 2013, and has been remarkably consistent, stealing one more base each year than the year before. The fans think he’ll do that again this year, hitting 60 stolen bases. Hamilton is over two years younger than Gordon, and might be faster, but the 35 stolen base edge Gordon enjoys makes him the clear favorite.

Totally made up odds: Gordon, 66%; Hamilton 33%; Davis, 1%

Wins

This is likely a two-person race between Max Scherzer and Clayton Kershaw who have 132 and 131 wins respectively. Justin Verlander and Zack Greinke sit enough off the pace at 123 and 122 to make a comeback very improbable absent an injury, but close enough to make a comeback very possible if both players in front of them miss significant time.

Moving to who I give the edge to: there just isn’t a lot separating these two. Scherzer is older, but Kershaw has had a bit more in the way of nagging injuries lately. If it truly were a push going forward, I could just go with Scherzer since he is one ahead at the moment. But I’m going to give Kershaw the ever-so-slight edge because the Dodgers are almost assuredly going to be one of the best teams in baseball the next two years while the Nationals might only have that status for the next season. Verlander gets the nod as more likely spoiler for a similar reason: the Astros are ballers.

Totally made up odds: Kershaw, 45%; Scherzer 43%; Verlander, 7%; Greinke, 5%

Saves

Craig Kimbrel should put this away by midseason. At 291, he ranks 61 ahead of Kenley Jansen and Fernando Rodney, who are tied for second with 230. Aroldis Chapman sits in 5^th with 204. Kimbrel’s consistency and consistently light usage should ensure that he continues to rack up saves the next two seasons. Even a repeat of his comparatively modest 66 saves over the last two seasons would give him a realistic lock on this honor.

If Kimbrel does fall apart, the 30-year-old Jansen would be the likely beneficiary, as he has a much stronger hold on his 9^th inning role than the 40-year-old Rodney. While Kimbrel might have this decade locked down, he will likely fall short in his quest to surpass Rivera’s total from the last decade. Rivera saved 397 games that decade. It should be noted, however, that Kimbrel barely pitched in the majors in 2010 and recorded only one save. On the other hand, he’s already blown one more save than Mariano did all of last decade.

Totally made up odds: Kimbrel, 97%; Jansen, 2%; Rodney, 1%

Strikeouts

Stop me if you’ve heard this before, but this is a two-man battle between Max Sherzer and Clayton Kershaw. Only this time, there is a much clearer favorite. Scherzer leads Kershaw 1909 to 1835. He essentially built that entire lead last season when he recorded 66 more strikeouts than the limited Kershaw. But Kershaw’s innings shortfall was not the only thing at play here. Scherzer struck out 1.63 more batters per nine innings. For the decade, Scherzer has a 74 strikeout lead in only 14 1/3 more innings. The only realistic path for Kershaw to overtake Scherzer is injury. Of course, with pitchers, injury is always a legitimate and significant risk.

Behind Scherzer and Kershaw is Justin Verlander with 1670. No one past Verlander has any legitimate shot barring a mass retirement of the some of the game’s best starting pitchers. At the end of the day, this is really a question about health. But for Kershaw to overtake Scherzer, he’d not only need Scherzer to get hurt, but he’d have to stay healthy himself.

Totally made up odds: Scherzer, 79%; Kershaw, 19%; Verlander 2%

Runs

This has been a very positive article, but let’s get a bit negative for a second. Which pitcher will give up the most runs in this decade? A big factor in this, of course, is that giving up a lot of runs is bad, and playing bad usually leads to not playing. You have to be good enough to get the ball on a regular basis, but bad enough to rack up the runs allowed. Our frontrunners are honestly not terrible pitchers. Rick Porcello leads the way with 789 runs allowed. James Shields is just behind with 778 runs allowed. Porcello is 29, started a playoff game last year, and is owed a lot of money through the end of the decade. He also won the Cy Young Award in 2016 while accruing 5.1 WAR on the mound. He should have opportunities to add to this total. Porcello underperforms his peripherals, but only by a little. He is basically good enough to never get moved out of the rotation, and durable enough to throw over 1500 innings this decade, but has a suddenly-not-great-for-the-era ERA of 4.29 over the last eight seasons.

James Shields is slated to maybe start opening day 2018. Unlike Porcello, Shields has been dreadful the past two seasons. Yeah, the White Sox starting pitching this year might be awful. Shields is owed a lot of money in 2018, but 2019 is an option that will only be picked up if Shields has a dramatic turnaround. Thus, there is a bit of a catch-22 here. If Shields plays well enough to keep getting the ball regularly into 2019, it seems unlikely that he’ll chase down Porcello. Of course, this could also come down to injury. If either player gets hurt, the other will very likely take this notorious (dis)honor.

Ubaldo Jimenez sits in 3^rd with 734 runs allowed. He will thankfully have a hard time adding to that total. If Porcello and Shields find themselves with quick hooks and no jobs, there are a few possible dark horses, including Ervin Santana and Jon Lester, who at the very least should get two full seasons of starts barring injury. For this one, I’m just going to put the field as a third choice rather than trying to single out who might suck, but play.

Totally made up odds: Porcello, 60%; Shields, 30%; Field, 10%

Other Interesting Battles

(My favorites in italics)

Games: Robinson Cano, 1264; Alcides Escobar, 1250 (that would be something)

Runs: Ian Kinsler, 785; Miguel Cabrera, 741; Andrew McCutchen, 740; Robinson Cano, 738

Strikeouts: Chris Davis, 1266; Mark Reynolds, 1250; Justin Upton, 1249

HBP: Shin-Soo Choo, 98; Anthony Rizzo, 98; Chase Utley, 92

Games: Tyler Clippard, 576; Luke Gregerson, 551

Innings Pitched: Justin Verlander, 1705; Max Sherzer, 1670.2; Clayton Kershaw, 1656.1

HBP: Charlie Morton, 82; Justin Masterson, 77 (23 in AAA in 2017)

Balks: Clayton Kershaw, 17; Franklin Morales, 15; Johnny Cueto, 13

Bonus Trout Stuff

You will notice that, apart from WAR, Mike Trout was not mentioned at all in this article. Of course, Trout played zero MLB games in 2010 and only 40 in 2011. But he is also an all-around performer. He doesn’t even show up in the top 10 for most counting categories. So for the lazy, here is where Trout ranks in the decade (if among top 30, must be qualified for rate stats):

Triples: T-8^th (40)

Home Runs: T-16^th (201)

Runs: 8^th (692)

RBI: 30^th (569)

Walks: 7^th (571)

Intentional Walks: T-14^th (61)

HBP: T-27^th (55)

Sac Flies: T-16^th (40)

Stolen Bases: 17^th (165)

Batting Average: 6^th (.306)

OBP: 2^nd (.410) (not close to first, Joey Votto, .438)

Slugging%: 1^st (.566) (biggest threat is probably Giancarlo Stanton, at .554)

Trout is about to play his age 26 and 27 seasons to round out the decade. He’ll be an “old 27” with his August birthday. We don’t know how he’ll age. But it is possible that he plays his whole career, a career of an inner-inner-circle Hall of Famer, and never leads a decade in any traditional counting stat. This on top of his frustratingly low MVP totals. If nothing else does, perhaps that should tell you how stupid this whole exercise is, and how stupid rigid benchmarks for greatness are in general. If Trout were born three years earlier, he could have dominated the counting stat leaderboards of this decade. If he played for a better team, he could have 2-3 more MVP awards.

So what does it all mean? Probably not much. If Albert Pujols squeaks out the most BRI of the decade, will that make it less of a disappointment? Does Nelson Cruz having the most home runs over an arbitrary 10-year period mean that he’ll one day be enshrined in Cooperstown? Well, no. However, I hope you had fun. I know that I did.

If you’re a baseball fan, you may have noticed you’ve been watching on average 10-15 minutes more baseball then you were 10 years ago.  Or maybe you are always switching between games like me and never stop to notice. If you’re not a fan, it’s probably why you don’t watch baseball in the first place: 3+ hour games, with only 18 minutes of real action. You are probably more of a football guy/gal right?  Believe it or not NFL games are even longer, and according to a WSJ study, deliver even less action.

The way the MLB is going, however, it may not be long before it dethrones the NFL as the slowest “Big Four” sport in America (and takes away one of my rebuttals to “baseball is boring”). Currently, the MLB is proposing pitch clocks and has suggested limiting privileges such as mound visits.

Before I get into the specific proposal and the consequences of these changes, let me give you some long winded insight into pace of play in the MLB.

A WSJ study back in 2013 broke down the game into about 4 different time elements:

1. Action ~ 18 minutes (11%)
2. Between batters ~ 34 minutes  (20%)
3. Between innings ~ 43 minutes (25%)
4. Between pitches ~ 74 minutes  (44%)

The time between pitches or “pace” is what everyone is focused on, and rightly so. It makes up almost twice as much time as any other time element and is almost solely responsible for the 11-12 minute increase in game length since 2008. Don’t jump to the conclusion that this is all the fault of the batter dilly-dallying or the pitcher taking his sweet time. This time also includes mound conferences, waiting for foul balls or balls in the dirt to be collected, shaking off signs and stepping off, etc. Even if we take all of those factors out, there are still two other integral elements that increase the total time between pitches: the total batters faced and the number of pitches per plate appearance (PA).  If either of these increase, the total time between pitches will increase by default. In the graph below, I separated the effects of each by holding the rest constant to 2008 levels to see how each factor would contribute to the total time added.

Any modest game time reduction due to declining total batters faced was made up by a surge in pitches per PA. Increasing pace between pitches makes up the rest.

As we have heard over and over again in the baseball world, the average game time has increased and is evident in the graph above. It’s not just that the number of long outlier games has increased; the median game time has actually crept up by about the same amount.

Plenty of players are at fault for the recent rise in game time. You can check out Travis Sawchik’s post about “Daniel Nava and the Human Rain Delays” or just check out the raw player data at FanGraphs. Rather than list the top violators here, I thought it would be amusing to make a useless mixed model statistic about pace of play.

A mixed model based statistic, like the one I created in this post, helps control for opposing batter/pitcher pace and for common situations that result in more time between pitches. Essentially, for the time between each pitch, we allocate some of the “blame” to the pitcher, batter, and the situation or “context”.

I derive the pace from PITCHf/x data, which contains details about each play and pitch of the regular season. I define pace as the time between any two consecutive pitches to the same batter excluding intervals that include pickoff throws, stolen bases, and other actions documented in PITCHF/x (This is very similar to FanGraphs’ definition, but they calculate pace by averaging over all pitches in the PA, while I calculate by pitch). For more specifics, as always, the code is on GitHub.

It’s a nice idea and all, but does context really matter?

The most obvious example comes from looking at the previous pitch. Foul balls or balls in the dirt trigger the whole routine involved in getting a new ball, which adds even more time. The graph below clearly shows that time lags when pitches aren’t caught by the catcher.

The biggest discrepancy comes with men on base. Even though pickoff attempts and stolen bases are removed from the pace calculation, it still doesn’t account for the game’s pitchers play with runners on base. This includes changing up their timing after coming set or stepping off the rubber to reset.

The remainder of the context I’ve included illustrates how pace slows with pressure and fatigue as players take that extra moment to compose themselves.

As the game approaches the last inning and the score gets closer, time between pitches rises (with the exception of a score differential of 0, since this often occurs in the early innings).

And similarly, as we get closer to the end of a PA from the pitcher’s point of view, pace slows.

Context plays a large part in pace meaning that some players who find themselves in notably slow situations, are not completely at fault. I created the mixed model statistic pace in context, or cPace, which accounts for all of the factors above. cPace can essentially be interpreted as the pace added above the average batter/pitcher, but can’t be compared across positions.

When comparing the correlation of Pace and cPace across years, cPace seems like a better representation of batters’ true tendencies. My guess is that, pitchers’ pace varies more than the average hitter, so many batters’ cPace values benefited from controlling for the pitcher and other context.

After creating cPace, I came up with a fun measure of overall pace: Expected Hours Added Per Season Above Average or xHSAA for short. It’s essentially what it sounds like: how many hours would this player add above average given 600 PA (or Batters Faced) in a season and league average pitches per PA (or BF).

The infamous tortoise, Marwin Gonzalez, leads all batters with over 3 extra hours per season more than the average batter.

That was fun. Now back to reality and MLB’s new rule changes. Here is the latest proposal via Ken Rosenthal:

MLB’s latest pace-of-play proposal, per sources: No pitch clock in ‘18. If games are 2:55 or longer, 18-second clock for ‘19 with no runners on base starting May 1, with ball-strike penalty. If in ‘19 games are 2:50 or longer, additional 20-second clock with runners on in ‘20.

— Ken Rosenthal (@Ken_Rosenthal) February 1, 2018

The MLB tried to implement pace of play rules in 2015, one of which required batters to keep one foot inside the box with some exceptions. The rules seemed to be enforced less and less, but an 18- or 20-second pitch clock is not subjective and will potentially have drastic consequences for a league that averages 24 seconds in-between pitches. Some sources say the clock actually starts when the pitcher gets the ball. Since my pace measure includes the time between the last pitch and the pitcher receiving the ball, the real pace relative to clock rules may be 3-5 seconds faster.

Let’s assume that it’s five seconds to be safe. If a pitcher takes 20 seconds between two pitches, we will assume it’s 15 seconds. To estimate the percentage of pitches that would be affected by these new rules I took out any pitches not caught by the catcher, assuming all the pitches left were returned to the pitcher within the allotted five seconds.

The 18-second clock results in about 14% of the pitches with no runners on in 2017 resulting in violations of the pitch clock. This doesn’t even include potential limits on batters times outside the box or time limits between batters, so we can safely say this is a lower bound. If both of the clocks are implemented in 2020, at least 23% of all pitches would be in violation of the pitch clock(excluding first pitch of PA). Assume it only takes three seconds to return the ball to the pitcher instead of five, and that number jumps to 36%!

And now we are on the precipice of the 2018 season, which could produce the longest average game time in MLB history for the second year in a row as drastic changes loom ahead. I don’t know who decided that 3:05 was too long or that 15 minutes was a good amount of time to give back to the fans. Most likely just enough time for fans to catch the end of a Shark Tank marathon.

Anyways, if game times keep going up, something will eventually have to be done. However, even I, a relatively fast-paced pitcher in college, worry that pitch clocks will add yet another element to countless factors pitchers already think about on the mound.

There are certainly some other innovative ideas out there: Ken Rosenthal suggests the possibility of using headsets for communication between pitchers and catchers, and Victor Mather of the NYT suggests an air horn to bring in new pitchers instead of the manager. Heck, maybe it’ll come down to limiting the number of batting glove adjustments per game. Whatever the league implements will certainly be a jolt to players’ habits and hardcore baseball fans’ intractable traditionalist attitude. The strategy, technology, and physicality of today’s baseball is changing more rapidly than ever. When the rules catch up, I have a feeling we will still like baseball.

With this article, I know that I’m walking into the fire, but I’m prepared.  I will craft my Cooperstown argument for Andruw Jones, the greatest defensive outfielder the game has ever seen.  Receiving 7.3% of the 2018 Hall of Fame votes is insulting to his career, and I hope that, upon reading this article, you’ll see why.

Tale of Two Andruws

30 and under Andruw Jones was on the fast-track to Cooperstown as the greatest defensive centerfielder of his time.  When he wasn’t patrolling the outfield for the Atlanta Braves, he was swatting home runs at a superhuman rate.

By age 31, that Andruw had disappeared, and he would play parts of 5 disappointing seasons with the Dodgers, Rangers, White Sox, and Yankees before retiring from Major League Baseball.  He gained some serious weight, and the athlete of his younger days was gone forever.

Putting Andruw Jones in Context

In my recent Mike Trout article, I brought up a plethora of stars with short careers to make the case for Mike Trout.  However, Andruw Jones is a very different type of player than Mike Trout, and Andruw deserves a different argument.

Too many baseball critics view Andruw Jones as a power hitter that failed to hit the 500 home run mark.  For reference, Andruw Jones hit 434 career home runs, still good for 47th all-time tied with Juan Gonzalez, but well short of the 500 mark.

Andruw Jones’ case will be created by framing his defensive ability and his home run ability in the context of history.  While he didn’t play a full season after age 30, what he did contribute to the game was Hall of Fame worthy, as he was only 19 when he joined the Majors.

The Greatest Defensive Outfielder, Ever

Seriously, Andruw Jones was not just the strongest defensive centerfielder of his time, or centerfielder of all-time.  He is undoubtedly the greatest defensive outfielder, ever.  And his defensive wins above replacement (dWAR) statistics prove it.

If we are looking at defensive skill and Andruw’s ability to perform run-saving, highlight reel plays on the daily, he is the best to ever do it in the outfield.

Baseball Reference has a nicely compiled list of the leaders in career dWAR.  Ozzie Smith sits atop as the greatest defensive player in the history of baseball, with 43.4 career dWAR.  Others high on the list are Brooks Robinson in 3rd (38.8 dWAR), Cal Ripken in 4th (34.6 dWAR), and catcher Ivan Rodriguez in 8th (28.7 dWAR).

These players, along with the top 19 in general, have one thing in common.  They all played infield.  It’s no secret that infielders are likely to have more defensive opportunities, thus having the ability to post higher dWAR each year.  Though playing infield requires quick reaction time and instinctual play, there is not much running involved.  Infielders can often play at an elite level for longer, since covering massive amounts of ground isn’t required.  If you don’t believe me, watch the below video and ponder how many 30+ year olds could chase that ball down.

That video displays the defensive patrolling that is reserved for the outfield.  Elite fielding outfielders above the age of 30 rarely exist because the sheer amount of running required just isn’t feasible on legs over 30 years old.

Now, I’ve held you in suspense long enough, and you’re still wondering where Andruw Jones ranks, and who number 20 is on the list of career dWAR.  That man, with 24.1 career dWAR, is Andruw Jones.  Okay.  Yay!  Cool.  So Andruw Jones has the highest dWAR of any outfielder, ever.  Where do the other great outfielders rank?

Before we can answer a question like that, let’s first keep scrolling down the list until we reach another outfielder.  Hmmm……

Of the top 50 career defensive WAR players, only Andruw Jones played in the outfield.  That’s strange.  It must be a mistake.  There’s no way he was this good and only received 7% of the vote.  Ozzie Smith was inducted first ballot as the best defensive infielder of all-time.  Brooks Robinson was inducted first ballot as the best defensive third baseman of all-time.

We have to move down to position number 60 to find the next highest outfielder on this list: Paul Blair, Orioles’ centerfielder in the 60s and 70s, recorded 18.6 career dWAR.

But Josh, Andruw Retired Early!

This is the unfortunate argument where we assume that because Andruw Jones didn’t play much as he got older, his career dWAR stayed in-tact, never declining with age.  While true that his dWAR dropped from a peak of 26.2 to 24.1 due to 5 sub-par half-seasons as he aged, Andruw Jones was still far better than other greats with longer careers.

Willie Mays played about a decade more full seasons than Jones, so you’d expect his 18.1 career dWAR to be more a factor of negative dWAR seasons as he grew older.  You’re correct in that Mays’ peak career dWAR was above 18.1, but sadly, Willie reached a peak career dWAR of only 19.3, according to Baseball Reference.  Even with almost a decade of additional playing exposure, Willie Mays was never even close to Andruw Jones’ 26.2 peak dWAR.  Even though Willie Mays was an above average outfielder into his mid 30s, his career of defensive excellence is still dwarfed by Andruw Jones’ decade of greatness.

Ken Griffey Jr. is another popular name to throw around as a defensive stalwart in center, but he maxed out at 11.1 dWAR at age 30, before injuries and age reduced his career dWAR to a paltry 1.3.  Jim Piersall reached a max of 16.0 career dWAR during the 50s and 60s.  Andre Dawson reached the 9.0 career dWAR mark before declining with age.  Jim Edmonds never crossed double digits either.  Defensive whiz Lorenzo Cain is currently at 12.1 career dWAR, though he is showing signs of defensive decline.

Andruw Jones’ career dWAR, along with his peak career dWAR is better than any other outfielder.  He is the greatest defensive outfielder in history.  No further questions.

But Could He Hit?

Andruw Jones had a career .254 batting average, but once you get past that, a feared hitter takes shape.  Jones launched 434 career home runs in a very short career.  He also walked 10.3% of the time, which is no number to snuff at.  He had a career slugging percentage of .486, good enough for 170th all-time.  His career OPS+ sits at 111.

Before I dive into his career home run mark, let’s treat ourselves to game 1 of the 1996 World Series, where at 19 years old, Andruw Jones homered in his first two at-bats.  By all my fact-checking, he is the youngest player to hit a home run in the postseason, let alone a World Series, and he did it in back-to-back at-bats.

If we look at the players on the all-time home run list that are higher than Andruw Jones, we note only a handful of players that accomplished this feat in fewer at-bats.  These players are included below, with two objects to note.  In parentheses is the player’s career dWAR, along with an asterisk if this player took any sort of steroids or HGH at any point in their career.  While Juan Gonzales had the same amount of career HRs as Jones, I included him because he did it in fewer at-bats.

The short list:

• Mark McGwire* (-12.8 dWAR)
• Carlos Delgado* (-17.9 dWAR)
• Jason Giambi* (-20.5 dWAR)
• Dave Kingman (-17.1 dWAR)
• Adam Dunn* (-29.6 dWAR)
• Jose Canseco* (-14.5 dWAR)
• Juan Gonzalez* (-12.3 dWAR)

Note that all these players had career dWAR well below -10, and all but Dave Kingman were linked to steroid abuse.  While none of these players is enshrined in Cooperstown, these players were all one-way guys.  Clearly, none of them contributed from the field throughout their careers.

Andruw Jones had a career AB/HR of 17.51, placing him just ahead of Reggie Jackson on that list.  Jones had an innate ability to hit the ball out of the park, and his career HR/dWAR combination resembles that of only Adrian Beltre (462 HR, 27.8 dWAR), who is a likely first-ballot Hall of Famer himself.  Of course, it did take Beltre significantly more seasons to crush 400 home runs, along with much more time in the infield to reach 20 dWAR.

Defensive Specialists in Cooperstown

Cooperstown loves defensive specialists, and I too, believe they are often deserving.  There is no doubt that players like Ozzie Smith and Brooks Robinson should be in the Hall of Fame.  These guys were first ballot since there was no debate about their credentials.  However, if we look at Smith and Robinson, and notice their career OPS+, we get 87 and 104 respectively.   Both rank below Jones’ mark of 111.

In fact, if we look at the 19 players with more career dWAR than Andruw Jones, only three players have higher career OPS+: Adrian Beltre (117), Gary Carter (115), and Cal Ripken (112).  Carter and Ripken are already in the Hall, and Beltre, as previously mentioned, is a lock for the Hall whenever he finishes his incredible career.  Jones deserves to join them as well, and his abysmal 7.3% of the vote in 2018 worries me greatly.

The Whole Package

I used JAWS a lot in my Mike Trout article as an advanced way to look at Hall of Fame worthiness and included a snapshot of Andruw Jones’ JAWS stat (from Baseball Reference) compared to other CFs in the Hall.

His JAWS is barely below the average Hall of Fame CF, and his 7 year peak WAR is actually above this average.  If we look at 7 year peak WAR for all centerfielders, we find only 8 guys with a figure above Jones’ 46.4, and 7 of those 8 are already in Cooperstown.  And the 8th is… you guessed it, Mike Trout.

Below is a snapshot of Omar Visquel’s JAWS worthiness.  Visquel was a defensive specialist who snagged 37% of the vote in 2018.  Someone please tell me why his case for Cooperstown is better than Jones’.

Then we have the beloved Vladimir Guerrero, who entered the Hall this year, even though his 7-year peak WAR was below the average for RFs, and his JAWS score was about 8 points lower than the average Hall of Fame right fielder.

OK, rant over.  Andruw Jones should be in the Hall.  I’ll leave you with a final Andruw Jones moment, this one a broken bat home run to deep center field.  Anyone see this happen recently?  That’s what I thought.  The next time I’m in Cooperstown, I hope to see a plaque of Andruw Jones there.

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

First, who Hosmer is. Consider his WAR each season since 2011:
1.0
-1.7
3.2
0.0
3.5
-0.1
4.1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Prognosis

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

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

Amid the snaillike pace of the free agent and trade market this offseason, the argument that MLB owners are in collusion against the players does not hold water. It is, instead, the player’s failure to recognize the swing of the pendulum, and to fight back with some creativity in their thinking.

Owners throughout all of MLB, whether they operate in large or small markets have (finally) figured out they’ve been overpaying players at the top of the wage scale for more than a decade. Correction, the nerds with the analytical skills have figured it out for them, and they’re serving it up to owners on a silver platter.

Of all the stats out there today, the most powerful one is Wins Above Replacement, commonly known as WAR. WAR is a number arrived at utilizing a complicated algorithm which boils a player down to how many wins he represents his team if the team had to replace him.

Writing for Bleacher Report, Joel Reuter put together an impressive coalition of stats to demonstrate what he calls a player’s “Net Value,” which is derived from the player’s WAR value (1.0 = $8 million in salary (see FanGraphs), minus the player’s actual salary. Here’s an example of how a few of the Yankees and Mets rated in 2017.

New York Yankees Best

New York Mets Best

New York Yankees Worst

New York Mets Worst

Now, let’s take Reuter’s accounting one step further. If we total up the Net Values on the plus side benefiting the owners, the Yankees “saved” $43.7 million last season and the Mets saved $48.4 million. On the negative side where the owners took a hit, the Yankees “lost” a total of $28.8 million on those three players and the Mets $25.7 million.

Which, in sum, seems to show that owners are not taking as big a bath as they would like us to believe. And further, that it’s possible players are being underpaid instead of overpaid. To draw any firm conclusions, though, a comprehensive study encompassing the entire payroll of both the Yankees and  Mets would need to be executed, which is something the Major League Player’s Association might want to tackle in making their case to MLB and the public.

My thinking is drawn more to the number of years in contracts rather than the money per year that concerns owners and general managers throughout MLB. Giancarlo Stanton, as an example, will likely be worth every penny he is paid – until he reaches a point when he isn’t. At that point, Stanton will become a reincarnation of Teixeria and Alex Rodriguez, each of whom burdened the team financially as their careers faded.

And if we reach far back into MLB’s past, players were routinely issued one-year contracts based exclusively on last year’s performance. MLB will never go there again, but the point the owners and GM’s seem to be making is the need for the pendulum to swing back the other way, in which two and three-year deals represent the top of the pay stratosphere.

[su_pullquote align=”right”]The game as we’ve known it is over. And the players need to come up with some better ways to fight what is happening – other than saying, “I’m gonna take my ball and go home.”[/su_pullquote]

The players and their union (MLBPA) are balking mainly because they (now) realize they shot themselves in the foot when the signed the collective bargaining agreement (CBA) that is with us until 2021. There was talk of boycotting Spring Training, but that will never happen.

But, is it collusion on the part of the owners? Of course, it is. If you and I and ten other people decide to go to the movies tonight, is that collusion? It could be if we plan to rob the concession stand. But otherwise, it’s just twelve people who had the same idea at the same time, and they are acting on it.

The Players Need To Get Creative

The players can do themselves a favor by stepping up the analytics game themselves. They already have one bullet in their arsenal which is MLB Merchandise. For example, how many Matt Harvey t-shirts have been sold over the years? How many pairs of Yoenis Cespedes batting gloves, Noah Syndergaard headbands, and so on? We’re talking big money here, folks. Last year, according to Forbes, MLB Shop took in $9.5 billion (that’s billion with a B) in revenue, a $500 million increase from 2015. For being #1 in the T-shirt sales department, how much money do you think Aaron Judge collected?

Similarly, the players argue that fans come to the ballpark to watch them perform athletic feats that often challenge gravity. True enough, but why not use that as a means to demonstrate the value some of these players have for, as George Steinbrenner liked to say, putting asses in the seats?

Why not, for instance, issue a card to each fan entering the ballpark with a list of all players in uniform that day with one instruction. Check the names of five players on your ballot who you came to see play today. Total ’em up at the end of a season, and the players have their version of WAR. Only this stat can be called TAP, for Tickets Sold Above Replacement.

Individually and as a unit, MLB players are not political animals. They seek only to play the game they have grown up with and love to play. No one, these days, goes to the poorhouse playing major league baseball. No one needs a second job driving a milk truck during the offseason like Hank Bauer, and Yogi Berra did for years during their playing days.

At the time time, the MLBPA and the players themselves need to understand that owners and general managers are (indeed) drawing a line in the sand. In days to come, Albert Pujols and even, Giancarlo Stanton will be seen as dinosaurs, the topic of conversation in bars across America for the contracts they won.

The game as we’ve known it is over. And the players need to come up with some better ways to fight what is happening – other than saying, “I’m gonna take my ball and go home.”

Footnote: A good follow-up read if you have the time appeared in the New York Daily News, and features MLB player, Brandon Moss, who has some controversial views on the current stalemate (“We Screwed Up”)

It was difficult to exist as a baseball fan in 2017 without hearing the phrase “fly-ball revolution” and its family members “exit velocity” and “launch angle.” The idea that ground balls are not great and fly balls are pretty decent isn’t something that only major league batters have figured out and adjusted their approach accordingly. College hitters have taken notice, and purchased trips to Ding Dong City as well.

Trends in college baseball mirror trends in MLB: homers were up this year (from .61 per team per game to .75 per game); and strikeouts were up from 7.17 per game to 7.54 per game — a record high.

— Aaron Fitt (@aaronfitt) January 5, 2018

Even though Major League Baseball has vehemently shot down the idea of the baseball being juiced, the NCAA has been rather transparent when it comes to ways to improving offense in the game. Instead of reverting back from BBCOR bats to the rocket launchers used beforehand, a flatter-seamed baseball was introduced in 2015 after scoring had fallen to 5.08 runs per game and a record low of 0.39 home runs per game. Since then, scoring has jumped to 5.77 runs per game (still a far cry from the 6.98 runs per game the year before BBCOR bats were initiated ) and 0.75 home runs per game.

Home run totals have gone up 80.1% since 2014. Again, these totals are nowhere near the insane days when home runs per game was near 1, but

The increase in home runs isn’t just a product of changing the ball. It’s a systematic shift in how players across all levels of the game are approaching hitting. MLB teams have used Trackman data to change hitter’s swings to an optimal level, and now colleges and high school showcases have started to install Trackman systems in their stadiums. Trackman data at colleges certainly isn’t publically available, and all of my emails to coaches asking them to hand it over were not returned.

Without the sophisticated data, I was only able to track the number of ground balls, line drives, pop up, and fly balls that were hit when an out was made from play-by-play data.

View post on imgur.com

Line drives have been stable, but ground ball outs and fly ball outs have been slowly diverging over time. Even without the data to pinpoint launch angle changes amongst college players, it’s still no secret as to what the players are attempting to do; hit the ball in the air.

As any pulse-having FanGraphs reader will know, the surge in home runs has risen in line with the other two of the Three True Outcomes™, strikeouts, and walks. This has been no different at the collegiate level as well. K% has increased from 16.0% in 2014 to 18.3% in 2017, and BB% has increased from 9.0% to 9.9% in the same time. These numbers are understandably off from where the MLB was in 2017 (21.6% and 8.5%, respectively) given that the command of college pitchers isn’t as developed as it is among professional players, and anecdotally speaking, there is more effort to pitch around team’s star players in college than there is in the pros. The NCAA will never be able to perfectly recreate the conditions that exist in professional baseball in college, the days of college coaches instituting modern day dead-ball era philosophies are quickly coming to an end.

These changes to the ball and the way teams approach the game is part of what needed to be done to make baseball at the collegiate level more exciting for fans. It’s no secret that college baseball ranks well below the excitement of its basketball and football counterparts. It’s still to be determined whether a Three True Outcomes™ approach to the game is what’s best for baseball, but with Major League baseball looking to strengthen its relationship with the college game and record number of games appearing on television this year, interest in the game is only going the way of the baseballs, up.

A special thanks to Christopher D. Long and his godsend of a GitHub for supplying the data to make this research possible. 

Pitchers are fickle beings. Relief pitchers are really fickle beings. Edwin Diaz, for example, burst onto the scene in 2016. Jeff Sullivan detailed how he generated comical whiffs with both a 98 mile-an-hour fastball and a fwippy, drops-off-the-table slider. He also worked in the zone while doing it, which is pretty much the best combo you could ask for from a pitcher.

But in 2017, Diaz essentially laid an egg. His Ks were down. His walks were up. He couldn’t stay in the zone nearly as much, so batters swung less. When they did bite, they hit him much harder than in 2016. His manager talked about how his mechanics had become wonky. He went from being the game’s 13th best reliever to being its 54th.

What’s curious about those wonky mechanics is that they appear to have only burdened his fastball. Not his slider.

Diaz throws his fastball nearly 70% of the time. More than just impacting what was in the zone and what was out of it in 2017, though, his wild tendencies with the heat also appeared to influence his pitches on the edges of the zone. Hitters were more willing to take their chances holding off on a pitch on the paint, as evidenced by a nearly two percent drop in whiffs on those offerings from 2016. With the slider, it seemed to induce more swings.

If Diaz is going to throw the fastball so much, then the obvious tweak he needs appears to be with that offering. But what if the Mariners looked at what Diaz has done best in his time in the Majors, and tried to amplify it?

Overall, Diaz’s fastball hasn’t been terrible. But it hasn’t been good, either. By wOBA, it ranks 137th out of 354 pitchers in the last two years. It was beaten up by righties in 2016 and then lefties in 2017. Even if the year-to-year stickiness of those numbers isn’t necessarily reliable, the real hammer has always been the slider. It’s yielded a meager .187 wOBA. By expected wOBA, Statcast actually says it’s even been 22% better than that. Diaz simply upping its usage would likely bring more whiffs for him. The pitch generates a greater percentage of swings and misses (33.8) than the fastball gets misses and called strikes together (30.4).

There’s also this: Diaz throws the slider 15% less to lefties than to righties, who have also hit his fastball harder and more consistently. He has room to use it more against opposite-handed hitters, and doing so seems like a natural progression.

Beyond that, there might be two things Diaz could tinker with in regards to his breaking ball that could enhance his overall game. He primarily pounds the low, glove side corner of the zone with it. Commanding the pitch to additional parts of the zone — say, in the vein of Kenley Jansen’s cutter — would force hitters to attempt to be more accountable to it, while still being subjected to its devastating drop. This could pair really well with a more erratic fastball, too. If a batter has to be aware of the slider breaking in different portions of the plate, they could be coaxed to swinging at a wilder heater coming at them 10 mph faster.

While it would require more sophistication and time, Diaz could also adjust his arm slot for his slider depending on the handedness of a batter to give it a different look. This may come with more caveats than benefits at first. Max Scherzer has said this kind of approach takes years to master. Zack Greinke has suggested it provides one globby, less useful look more than two distinct ones. And of course, Diaz has already been cited as having control issues at times. But the fact of the matter is he’s young and immensely talented and finding ways to make his slider more of a weapon should be a priority. It could be what makes his potential dominance undeniable.

—

Data from Statcast; gif from PitcherList. 

I have wondered about this. Unlike my other articles this is going to be less analytical so don’t be mad at me and maybe discuss in the comments. There is a lot of talk about why middle ground teams are not investing to get better.

Now, of course, competitive baseball is better but we also can’t expect teams to fight a futile fight. We do now have better projections, aging curves and other stuff and we can’t teams to just act like this didn’t exist. Winning should be the goal but throwing away the future doesn’t make sense either.

In theory, the second Wild Card is another playoff spot but in reality, it is really only half a playoff spot. There is value in the Wild Card but teams are not really attacking it preseason, they will wait and see and then maybe make a small deadline move. It really isn’t worth to throw away the future for a 30% or so https://www.fangraphs.com/community/the-pirates-and-the-value-of-being-around-500/ chance of reaching a coinflip game if you are a .500 team.

The second Wild Card has mostly hurt the first Wild Card team and it has increased the incentive to be a super team especially in a weak division. IMO,  being a super team is too big of an advantage because there is also less risk to being in being kicked out by a weakened Wild Card team that has used its ace in a one-game playoff.  And at the same time there is too little reward for being the fourth best team.

That means teams either try to tank to become a super team or they try to stay a boring .500 team doing not much hoping to occasionally luck into a Wild Card like the pirates might want to do now.

We can’t just force teams to spend money foolishly, if we want teams to spend more and try to be competitive we need to actually increase the incentive to win as a non super teams and maybe also punish the super teams with a little more variance.

Now of course not anyone wants that. Some like the best team to win and baseball already has some of the more luck influenced playoffs but if you want teams to compete you need to change the rules.

One possibility would be doing away with the second Wild Card so that being the Wild Card really guarantees a playoff spot. Another thing you could do is doing away with the divisions and make it top 4 per league directly to the playoffs or maybe even use NBA-style 16 team playoffs (although that would be too much variance for me).

IMO we shouldn’t talk so much about punishing bad teams but about making good not great more lucrative. Currently, 2/3rd of each league just have little inventive to be buyers because the super teams have too much of an edge and the second Wild Card might have increased that division.

The second Wild Card was a good idea but teams have really voted with their feet and decided the second Wild Card is not a full playoff spot and thus not worth chasing with a lot of resources.

The grades on Cristian Pache’s Fangraphs page, reported on during 2017 are impressive: 70-grade speed, 70-grade arm, 60-grade future glove.

With 50 considered the average for a given tool, Pache is one of the few with discernible, impact tools that isn’t on two of the industry’s biggest top 100 prospect lists – Baseball America and MLB.com.

The reason for the omission is reasonable. As JJ Cooper (@JJCoop36) mentions in the comment section of Baseball America’s list, the projection, or assumption of future production in lieu of tangible results, regarding Pache’s bat prevent buzz from swelling. With zero home runs across 750 plate appearances in the minors, despite the majority of those chances coming in one of the worst parks for power in the minor leagues, State Mutual Stadium, it’s hard to disagree with Cooper’s point.

Projecting Pache (great sitcom title), is a task any player evaluator must deal with to really understand his bat’s viability to reach the major leagues; his defense and speed are already apparent. While I’m not a professional scout or player evaluator, tinkering with some video will hopefully present the case for Pache’s bat as it stands and whether you believe in the emergence of another plus tool.

July 2015

(Video from YouTube, Fangraphs)

Starting with Pache’s roots, this combination of videos in the gif above is from the year he was signed, 2015. What stood out to me was how Pache dealt with his lower body and front foot from swing to swing; the two swings in the gif above provide the most noticeable difference. Inconsistent isn’t poor terminology, per say, but I’d rather consider it raw. As these swings both look like they’re coming from live pitching, I immediately thought of a column written for the Collegiate Baseball Scouting Network. Nick Holmes, the author of this particular post, has deep roots in player development in Latin and South America and mentions how a lot of talents, like Pache, don’t receive ample exposure to in-game situations like amateurs in the United States do. This can cause muddying of skill perception from batting practice and drills to the actual games themselves.

While this variation in stride – toe tap on the left; modest leg kick on the right – was initially a knock in my eyes, my perspective evolved to consider it a feature that repetition could iron out. Pache’s ability to simply make contact gives me pause when critiquing an aspect of his game that might not be a detriment at all.

Keep in mind this video is from 2015.

Pache earned around 250 plate appearances in affiliated ball during 2016, and as we’re about to dive into, some of that smoothing I briefly entertained may have emerged.

Summer 2017, Ronald Acuna

(Acuna video via YouTube, RKyosh007; Pache video via YouTube, The Minor League Prospect Video Page)

A baseline in swing evaluation often makes capturing the intended point clearer. While I shy away from one-to-one player comparisons, aesthetic comps can be valuable for descriptive purposes. These two points are key disclosures to justify my pairing up of the game’s top prospect, Ronald Acuna, with my topic of interest, Cristian Pache. I acknowledge up front this is an aesthetic comparison to help us understand Pache’s swing.

Acuna came to my mind when looking at Pache’s tape. (Whether that comparison arose because of bias from watching far too much Acuna tape, I cannot confirm or deny). Their pre-pitch setup and core motion towards the ball are eerily similar, despite a slew of differences from the variation in pre-load hand placement to Pache’s slightly open stance. On top of that, Acuna initiates his swing much earlier than Pache, building a substantial amount of momentum that results in a bigger stride and force moving towards the ball. I also love how throughout Acuna’s building of momentum his hands are on the verge of proceeding into his swing. The trigger Acuna has once he chooses to explode his hips is mesmerizing. This difference is noticeable when watching Pache’s hands drift back and up into their hitting position as he goes into his load. I don’t expect Pache to evolve into an exact replica of Acuna, but the difference allows for visualization of where Pache can adjust to focus on the biggest issue facing Pache’s bat: the plane on which he makes contact.

Launch angle, once a mysterious and complex point, has become basic-knowledge for most fans. As we see players tinker for the better with their bat path at the major league level, it’s only natural for similar trends to occur in the minor leagues. In this case, something I’d be very interested to see Pache entertain.

Working backward, watch where Acuna’s hands finish in his swing. The tip of Acuna’s bat finishes much higher in relation to his upper body than Pache’s, which stays somewhat level with his shoulders. Now start to focus on earlier and earlier parts of Acuna’s swing that lead to where his bat finishes. Applying the same exercise to Pache shows you why scouts are able to confidently project out Acuna’s power and why some may be hesitant to give Pache 15-home run power.

Acuna and Pache are almost polar opposites when it comes to their bat path through the zone. Yet even with this differences, we’re not looking at polar opposites in terms of the how and where each player is hitting the ball. Acuna has a much better ability to go the other way – something I’d love to see Pache do more of – but the most important thing is that Pache’s ability to get the ball off the ground might be improving. His ground-ball rate, once around 65 percent in 2016, is now closer to 50 percent. Comparing the gif of Pache from 2015 to his swing next to Acuna shows a subtle difference in the path of his bat, which could be a reason for this tendency to get balls off the ground.

Pache is trending in the right direction, towards Acuna. I don’t think he’ll ever possess the all-fields power Acuna holds, but he doesn’t need it to raise his offensive ability to average, allowing his other skills to flourish at the major league level.

…Twitter?

(Via Cristian Pache’s Twitter, @CristianPache25)

This brings us to Pache’s Twitter, where we can get the most recent look possible at the glove-first prospect’s swing. While these aren’t game-speed swings, I want to point out that Pache seems to be raising his leg slightly more, hovering on his back foot like he never did in 2015, or even in his swing next to Acuna. It’s not necessarily an improvement in Pache loading on his back hip like you’ll see with hitters like Josh Donaldson, but it’s an improvement over Pache’s early tendency in 2015 to generate power from aggressively shifting all his weight into his front foot to generate any resemblance of power.

This could induce even more building of momentum towards the ball, or it could be more of his batting practice-style swing that doesn’t translate into his game tendencies. The result, in a perfect world, could be the most valuable thing of all: more line drives. Or it could be nothing at all. Only Pache’s game-speed hacks in 2018 will provide an answer.

I can be found gif’ing up hitter adjustments on Twitter – @LanceBrozdow.

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

Most of what happens in a baseball game are influenced by chance. A ball hit on the screws can end up in the outstretched glove of a diving fielder. The outfield wall could be just six inches too tall, keeping a home run in the park. Strike three could be called ball four by the home plate umpire. Traditional statistics can’t account for all of this, hence why sabermetricians have developed context-specific statistics like DIPS (defense independent pitching statistics) or wRC (weighted runs created). These stats try to explain the outcomes of batted balls while controlling for defense and ballparks.

I sought out to try and create a model that controls for defense, but from the hitter’s perspective. A model that could predict batted ball outcomes could be used to better evaluate hitters and their quality of contact. Using 2017 MLB pitch-by-pitch Statcast data’s batted ball statistics (launch angle, exit velocity, outcome and spray angle), I used a random forest to model whether a batted ball would be a hit or an out. I trained my model on 20% of the data, and felt confident the training set and test set were identical, with similar means and standard deviations for launch angle, speed and spray angle.

I chose to use a random forest because it runs multiple decision trees on subsets of the training set and averages the results across the sets. A Random Forest model uses k-decision trees, or binary ‘decision’ or outcome model, to model the data. Random forest algorithms minimize variance and bias through averaging; a random forest helps prevent overfitting, something I was afraid of doing. Using the Random Forest provided much better accuracy than running a Logistic Regression, my alternative hypothesized model, due to the number of trees (10) and the nature of a decision tree versus a regression.

Without further ado, the results (in visual form):

There’s quite a bit going on in these plots. Let me break it down.

These plots are of every fair ball hit (with a few misclassifications) in 2017 and their landing (or caught) locations. The dark blue balls in play are hits, while the light blue balls are outs. On the left are the actual hits and outs, while on the right are the predicted hits and outs. There are almost a hundred thousand points on these plots, making it difficult to sift through. Here is an explanation of these plots in tabular form:

My model does a much better job at predicting outs than hits. It was correct almost 90% of the time at predicting outs, compared to merely 66% of the time predicting hits. From From the perspective of hits being good (the batter’s perspective), 10% of outs were false positives, and 34% of hits were false negatives. I believe my model did better with outs because there are many more outs than hits – league-average BABIP is .300, or 30% of the time a ball in play is a hit, 70% of the time it’s an out. The model was accurate 81.4% of the time. Despite the high accuracy, the model only ran a .1769 R-Squared. That is, the model was able to describe 17.7% of the variance in batted ball results.

Overall, I feel this model can help predict batted ball results. Two main drawbacks of the model are that it only predicts hits instead of the type of hit and that it requires more data to increase accuracy. I believe having fielder data, such as shifts and defensive capabilities, would greatly increase the accuracy of the model, though at the risk of overfitting (given the small samples of fielded balls in certain areas).

I plan to explore this model further and look at individual batters to compare their actual hits to the predicted ones.