Tyler Naquin’s Blossoming Power

Recently the Cleveland Indians were able to salvage their four-game series against the Seattle Mariners with a 5-3 victory, thanks to Tyler Naquin. In the top of the 8th inning with teammate Rajai Davis on first base, Naquin again found himself in an 0-2 count. Once again, it seemed that the rookie would strike out…especially because he was facing an excellent reliever in Joaquin Benoit. Going into the game, Benoit found himself with a respectable 3.27 ERA, 1.09 WHIP, and a BAA of just .154. But when Naquin came to the plate all of that was about to change. On an 0-2 pitch, Benoit threw Naquin a changeup down and in that he promptly golfed into the stands of Safeco Field giving the Tribe a 4-2 lead in the late innings. This advantage would end up sticking for the Tribe as they went on to split the four-game series and remain in first place in the AL Central.

Naquin is no stranger to hitting homers in the big leagues. In fact, at the time that was his fourth homer in his last six games. Before his most recent recall on June 1st, Naquin hadn’t yet hit one out of the park in the bigs. But now it appears that he has found his power stroke, and his team couldn’t be happier. Naquin always had a great swing; even looking back on his days at Texas A&M, that was more than apparent (he won two Big-12 batting titles). It appears now that he’s beginning to develop power. In the minors, Naquin managed just 22 homers in his 1542 plate appearances, a modest 70.1 PA/HR. In his short time in the majors this number has dropped significantly down to 22.3 PA/HR. In other words, around 27 HR in a 600 plate appearances. The power that he’s shown thus far has been quite impressive, and there’s a chance that it’s sustainable.

Naquin has shown the ability, throughout his minor and now major-league career, to possess a great swing with the ability to make good, solid contact which has translated well to this point. Naquin has a 41% hard-hit rate. Qualified players who have a hard-hit rate above 39% this season include the following list:

 # Player Team  PA  Hard%  HR  OPS  wRC+ wOBA
1 David Ortiz Red Sox 226 47.2 % 16 1.153 200 .470
2 Joey Votto Reds 248 43.5 % 11 .793 108 .338
3 Matt Carpenter Cardinals 255 43.2 % 9 .936 150 .394
4 Chris Carter Brewers 241 43.0 % 16 .803 105 .334
5 Trevor Story Rockies 258 43.0 % 16 .866 111 .362
6 Mike Napoli Indians 232 42.9 % 14 .799 115 .340
7 Chase Utley Dodgers 222 42.8 % 4 .748 110 .330
8 Michael Conforto Mets 211 42.8 % 9 .778 111 .330
9 Miguel Sano Twins 211 42.7 % 11 .799 116 .344
10 Yasmany Tomas Diamondbacks 208 41.1 % 7 .755 97 .324
11 Josh Donaldson Blue Jays 265 40.9 % 14 .890 139 .378
12 Victor Martinez Tigers 224 40.9 % 9 .925 149 .391
13 Khris Davis Athletics 215 40.8 % 14 .753 100 .316
14 Evan Longoria Rays 250 40.8 % 14 .865 134 .363
15 Curtis Granderson Mets 248 40.8 % 11 .742 102 .317
16 Buster Posey Giants 212 40.5 % 8 .766 108 .323
17 Giancarlo Stanton Marlins 214 40.4 % 12 .731 95 .315
18 Adam Duvall Reds 205 40.3 % 17 .902 135 .377
19 Jake Lamb Diamondbacks 225 40.3 % 11 .867 127 .368
20 Mike Trout Angels 263 39.8 % 13 .963 164 .405
21 Kris Bryant Cubs 257 39.8 % 14 .886 139 .380
22 Chris Davis Orioles 250 39.7 % 13 .795 114 .343
23 Corey Seager Dodgers 258 39.6 % 14 .869 135 .368
24 Mark Trumbo Orioles 251 39.0 % 20 .956 155 .403
25 Byung-ho Park Twins 201 39.0 % 11 .777 109 .334
26 Manny Machado Orioles 264 39.0 % 15 .968 155 .402

From the chart, 20 of the 26 players listed are in double digits in homers. If you take their ratio of HR/PA and multiply by 600 you find that they range anywhere from 27 HR to 48 HR potential. There’s no guarantee that any of these power hitters will keep their current pace, but one thing’s for sure, players who have a relatively high hard-hit rate are more likely to hit home runs and extra-base hits, and ultimately are more likely be more productive for their team. If we go back even further now, say the last three seasons (2013-2015), we get the following group:

 

# Name Team PA Hard% HR OPS wRC+ wOBA
1 Miguel Cabrera Tigers 1848 43.7 % 87 .981 168 .417
2 David Ortiz Red Sox 1816 43.7 % 102 .915 141 .382
3 Paul Goldschmidt Diamondbacks 1884 42.2 % 88 .968 159 .408
4 Giancarlo Stanton Marlins 1460 41.9 % 88 .915 150 .389
5 J.D. Martinez – – – 1447 40.9 % 68 .840 129 .359
6 Lucas Duda Mets 1534 40.6 % 72 .817 131 .355
7 Matt Kemp – – – 1537 40.0 % 54 .786 120 .341
8 Andrew McCutchen Pirates 2007 39.9 % 69 .917 157 .395
9 Chris Davis Orioles 1868 39.9 % 126 .891 140 .378
10 Jarrod Saltalamacchia – – – 1132 39.5 % 34 .746 104 .327
11 Pedro Alvarez Pirates 1550 39.1 % 81 .760 110 .327
12 Mike Trout Angels 2103 39.0 % 104 .973 172 .413

The chart says it all: the average HR% (HR/PA) of this group is 4.8%, or in other words about 29 HR per 600 PA. The average OPS of this group is an impressive .876, and even more impressive the average wOBA is .374. If Naquin can continue to make solid contact in his plate appearances, as he has proven throughout his career, he could be a very special player.

In the case of Tyler Naquin, he has: 99 PA, 41 Hard%, 4 HR, .870 OPS, 136 wRC+, and a .371 wOBA. His numbers correlate quite well to the rest of the group; in fact, his OPS, wRC+, and wOBA are all above or around the average in comparison. Obviously this is kind of a small sample size for Naquin. It’s nearly impossible to tell what kind of player Naquin will become with less than 100 major-league plate appearances, but there is definitely hope.


Success Rate of MLB First-Round Draft Picks by Slot

The MLB Rule 4 amateur draft was last week and fans will clamor for any sort of information regarding their team’s new, shiny, sometimes 18-year old future stars.  The draft gives fans a chance to dream on what will be in seasons to come, each team’s fans are hoping for their very own Mike Trout.  But for every Mike Trout, there are plenty of players like Hank Congers or Zack Cox who were also selected at pick number 25 and who aren’t exactly rewriting the record books.

In doing research for my latest post on the awful Jim Bowden, I found a concerning lack of recent research on draft success. We have plenty of anecdotes, and plenty of information on top prospects busting, but very little in the way of what to expect from a team’s first-round draft pick.  I found a good piece from 2012 from The View from the Bleachers on Success Rate of MLB Draft Picks by Slot and referenced that, but there’s definitely more here.

There have been nine drafts since the last draft referenced in that post.  Scouting, sabermetrics, and our general collective baseball knowledge feels like it has been increasing exponentially in that time.  Does draft success bear that out? Well, not exactly.

The first thing to set up here is to establish a “successful” player. I pondered it for a minute and settled on basically the same approach that Michael used way back in 2012. If the player hasn’t made the majors, or if they had a WAR of less than 1.5 per year when they got there, that first-rounder is a bust automatically. These players might be useful, but hardly the type that an organization should target in the first round. With that in mind, I established a simple calculation to assign a players success.

bWAR Per Season

(500 AB / 25 G for pitchers)

Under 1.5 Bust
1.5-2.5 Successful
Over 2.5 Superior

 

I likely should have built in a separate “World’s Best” category for those players who are averaging 8+ WAR.  Oh, that’s just Trout, OK.

The calculation feels like it makes sense on an anecdotal level, too.  Eric Hosmer, Yonder Alonso, and Wade Miley are labeled successful, but not superior.  That feels right.  These guys aren’t changing an organization.  They’re good major league players, but not great.

The trick comes in assigning busts, especially when considering players from more modern drafts.  Jameson Taillon has yet to achieve the mandatory 1.5 WAR, but he’s hardly a bust just yet. And what do we do with guys like Billy Butler? He’s officially a bust by my calculation, but that doesn’t feel quite right. Huston Street, James Loney, and Garrett Richards are all also busts.  Ike Davis, and Pedro Alvarez, too. But the formulas are sound.  A successful major leaguer should be able to produce 1.5 WAR per season. In 2015, Chase Headley, Nick Markakis, and Alcides Escobar all hit that threshold.  It shouldn’t be too much to expect a first-rounder to perform at that level.

Besides, this is baseball and statistics.  There’s no crying in baseball or statistics.

To the results!

First, how many of 1st rounders actually make the majors? That feels like some basic threshold of success. Is your organization capable of selecting a player in the first round that actually makes his way to the majors?

Draft Year 2000-2010
Overall Pick Average bWAR Number to Reach Majors Number Still in Minors
1-5 12.8 48 7
6-10 9.5 41 14
11-15 8.7 45 10
16-20 4.9 43 12
21-25 6.5 36 19
26-30 4.5 32 23

 

A few things jump out from the chart above. Of the 55 players selected in the top five between 2000 and 2010, 48 reached the major leagues. That seems like a really good rate. Teams were able to more or less successfully identify the best five players available in a given draft. Of course, there’s probably some bias here as teams are more likely to promote players they took at the very top of the draft to save face, even if they might not be perfectly qualified.

The pattern pretty much holds for the rest of the first round too. There’s more uncertainty as you get later and later in the draft but scouts seem to hit more than they miss. That’s a pretty low bar though. You would hope that scouts would be a bit better than 32/55 (58%) on picks 26-30, considering that there are hundreds and hundreds of players chosen.

Next, let’s look at the chance to find a successful player, as we defined it earlier, in the first round of the draft.

Chance to Find a Successful Player in the Draft
 Year pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 2 5 5 3 4 1
06-10 4 3 1 0 2 2
All 6 8 6 3 6 3
Percentage 11% 15% 11% 5% 11% 5%

 

That’s pretty low. Our definition of a successful player was pretty narrow, to be sure, but it seems like 1.5 -2.5 WAR guys should be pretty prevalent. Guess not. Let’s see how front offices do on picking up superior players.

Chance to Find a Superior Player in the Draft
pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 9 5 5 2 4 5
06-10 7 6 5 3 3 1
All 16 11 10 5 7 6
Percentage 29% 20% 18% 9% 13% 11%

 

Pretty well actually! Superior players should be pretty rare, at least if we set the criteria correctly, but more than a quarter of top five picks are in that category. That seems pretty good.

I’m starting to wrap my head around a theory, let’s see if this next chart bears it out…

Chance to Find a Bust in the Draft
pick 1-5 pick 6-10 pick 11-15 pick 16-20 pick 21-25 pick 26-30
00-05 19 20 20 25 22 24
06-10 14 16 19 22 20 22
All 33 36 39 47 42 46
Percentage 60% 65% 71% 85% 76% 84%

 

OK, here’s what I’ve got. It’s more likely than not that a first-round selection will be a bust. If he’s not a bust, though, it’s more likely than not that he’ll be a superior player. It seems like the chances of a first-rounder being merely successful — just a decent big-league player — are actually pretty small.

A reasonable conclusion then, is that scouts go for the proverbial home run in first-round selections. They take a bit more risk in order to try and unearth a truly unique talent. They then aim to fill out their system with more average players in the later rounds.

My research gives fans and scouts all the more reason to dream on their first-round picks from last week.

A last little bit of fun.  For the recent draft, I wanted to point out which organizations were selecting in a spot that may not yield quite the results that they are hoping for. Yankees fans, shield your eyes.

Overall Pick Who has it this year? Busts Successful Players Superior Players
1 Phillies 5 0 6
2 Reds 5 3 3
3 Braves 8 1 2
4 Rockies 9 1 1
5 Brewers 6 1 4
6 Athletics 8 0 3
7 Marlins 4 4 3
8 Padres 8 3 0
9 Tigers 9 0 2
10 White Sox 7 1 3
11 Mariners 7 0 4
12 Red Sox 8 1 2
13 Rays 8 2 1
14 Indians 10 0 1
15 Twins 6 3 2
16 Angels 9 1 1
17 Astros 9 0 2
18 Yankees 11 0 0
19 Mets 9 1 1
20 Dodgers 9 1 1
21 Blue Jays 9 2 0
22 Pirates 9 2 0
23 Cardinals 8 1 2
24 Padres 8 0 3
25 Padres 8 1 2
26 White Sox 11 0 0
27 Orioles 9 1 1
28 Nationals 8 0 3
29 Nationals 8 2 1
30 Rangers 10 0 1

 

So before you go getting all excited about the draft picks in the books, keep in mind that a majority of them are simply going to be busts. The ones that aren’t, though — they’ll probably be stars.


Hardball Retrospective – What Might Have Been – The “Original” 1975 Astros

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

 

Assessment

The 1975 Houston Astros 

OWAR: 50.0     OWS: 291     OPW%: .535     (87-75)

AWAR: 28.7      AWS: 192     APW%: .398     (64-97)

WARdiff: 21.3                        WSdiff: 99  

The “Original” 1975 Astros fell six games short of the National League Western Division title as the Big Red Machine tallied 93 victories. Joe L. Morgan produced a .327 BA with 17 dingers, 94 ribbies and 107 runs scored to secure the NL MVP Award. “Little Joe” succeeded on 67 of 77 stolen base attempts and coaxed a League-leading 132 bases on balls. First-sacker John Mayberry racked up personal-bests in doubles (38), home runs (34), RBI (106), runs (95) and bases on balls (119). Rusty Staub swatted 19 big-flies and knocked in 105 baserunners. Cesar Cedeno swiped 50 bags and batted .288 while Bob “Bull” Watson delivered a career-high BA (.324) for the “Original” and “Actual” ‘Stros.

Joe L. Morgan is ranked as the top second baseman according to Bill James in “The New Bill James Historical Baseball Abstract.” “Original” Astros teammates listed in the “NBJHBA” top 100 rankings include Cesar Cedeno (21st-CF), Rusty Staub (24th-RF), Bob Watson (33rd-1st), John Mayberry (49th-1B), Doug Rader (64th-3B) and Jerry Grote (66th-C). “Actual” Astros outfielder Jose Cruz places 29th among left fielders.

 

  Original 1975 Astros                                    Actual 1975 Astros

LINEUP POS OWAR OWS LINEUP POS AWAR AWS
Greg Gross LF 1.91 14.4 Greg Gross LF 1.91 14.4
Cesar Cedeno CF 4.25 19.87 Cesar Cedeno CF 4.25 19.87
Rusty Staub RF 2.34 24.89 Jose Cruz RF 2.69 10.54
John Mayberry 1B 6.1 32.3 Bob Watson 1B 2.63 20.01
Joe L. Morgan 2B 9.44 43.74 Rob Andrews 2B 1.15 5.3
Enzo Hernandez SS -0.33 7.01 Roger Metzger SS 0.49 8.2
Doug Rader 3B 0.93 9.34 Doug Rader 3B 0.93 9.34
Jerry Grote C 2.15 17.24 Milt May C 0.6 7.5
BENCH POS OWAR OWS BENCH POS AWAR AWS
Bob Watson 1B 2.63 20.01 Cliff Johnson 1B 2.72 15.09
Derrel Thomas 2B 1.55 16.73 Wilbur Howard LF 1.52 9.93
Cliff Johnson 1B 2.72 15.09 Enos Cabell LF 0.34 7.12
Walt Williams DH 0.34 4.12 Jerry DaVanon SS 0.87 4.19
Fred Stanley SS -0.98 3.78 Ken Boswell 2B -0.11 3.51
Glenn Adams LF 0.61 3.63 Larry Milbourne 2B -0.25 1.31
Jack Lind SS -0.2 0.26 Tommy Helms 2B -0.32 1
Jesus de la Rosa 0.04 0.16 Skip Jutze C -0.5 0.88
Art Gardner RF -0.28 0.08 Jesus de la Rosa 0.04 0.16
Danny Walton 1B -0.55 0.07 Art Gardner RF -0.28 0.08
Ed Armbrister LF -0.46 0.03 Rafael Batista -0.01 0.07
Mike Easler -0.06 0 Mike Easler -0.06 0

Houston hurlers failed to generate much excitement during the ’75 campaign. Larry Dierker completed 14 of 34 starts and fashioned a record of 14-16 with a 4.00 ERA. Pat Darcy posted an 11-5 mark with a 3.58 ERA in his inaugural season. Dave Giusti furnished a 2.95 ERA and saved 17 contests despite accruing more walks than strikeouts.

 

  Original 1975 Astros                                    Actual 1975 Astros

ROTATION POS OWAR OWS ROTATION POS AWAR AWS
Larry Dierker SP 0.33 8.85 Larry Dierker SP 0.33 8.85
Pat Darcy SP 1.38 7.76 Ken Forsch SP 1.02 5.89
Ken Forsch SP 1.02 5.89 J. R. Richard SP -0.38 5.77
J. R. Richard SP -0.38 5.77 Dave Roberts SP -0.08 5.74
Roric Harrison SP -0.51 5.5 Doug Konieczny SP -0.92 3.17
BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS
Dave Giusti RP 0.55 9.94 Joe Niekro RP 1.03 6.53
Tom Burgmeier RP 0.77 7.4 Mike Cosgrove RP 0.96 5.05
Mike Cosgrove RP 0.96 5.05 Jim Crawford RP 0.09 4.27
Jim Crawford RP 0.09 4.27 Wayne Granger RP -0.71 2.96
Bill Greif RP -1.04 3.26 Jose Sosa RP 0.26 2.12
Doug Konieczny SP -0.92 3.17 Jim York SW -0.04 2.07
Wayne Twitchell SP -1.37 3.05 Paul Siebert SP 0.17 1.09
Jose Sosa RP 0.26 2.12 Mike T. Stanton SP -0.55 0
Paul Siebert SP 0.17 1.09 Tom Griffin SP -1.38 0
Mike T. Stanton SP -0.55 0 Fred Scherman RP -0.41 0
Tom Griffin SP -1.38 0

 

Notable Transactions

Joe L. Morgan

November 29, 1971: Traded by the Houston Astros with Ed Armbrister, Jack Billingham, Cesar Geronimo and Denis Menke to the Cincinnati Reds for Tommy Helms, Lee May and Jimmy Stewart.

John Mayberry

December 2, 1971: Traded by the Houston Astros with David Grangaard (minors) to the Kansas City Royals for Lance Clemons and Jim York.

Rusty Staub

January 22, 1969: Traded by the Houston Astros to the Montreal Expos for Jesus Alou and Donn Clendenon. Donn Clendenon refused to report to his new team on April 8, 1969. The Montreal Expos sent Jack Billingham (April 8, 1969), Skip Guinn (April 8, 1969) and $100,000 (April 8, 1969) to the Houston Astros to complete the trade.

April 5, 1972: Traded by the Montreal Expos to the New York Mets for Tim Foli, Mike Jorgensen and Ken Singleton.

Honorable Mention

The 2013 Houston Astros 

OWAR: 26.6     OWS: 218     OPW%: .427     (69-93)

AWAR: 8.3       AWS: 151      APW%: .315    (51-111)

WARdiff: 18.3                        WSdiff: 67

Following a transfer to the American League West prior to the start of the 2013 campaign, the “Original” Astros finished dead last in the division. Nonetheless it represents a WSdiff of 67 and 18 additional wins compared to the “Actual” Astros from the same season. Hunter Pence established career-highs with 27 round-trippers and 22 stolen bases. Ben Zobrist laced 36 doubles and earned his second All-Star nod. Chris Johnson produced personal-bests in batting average (.321) and two-base hits (34). Jason Castro drilled 35 two-baggers and posted a .276 BA. Jose Altuve batted .283 and pilfered 35 bags.

On Deck

What Might Have Been – The “Original” 1984 Giants

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive


Elvis Andrus Is Trying to Become a Power Hitter

In his seven-plus seasons as a major-leaguer, Elvis Andrus has never been considered an offensive dynamo. And for good reason! Over nearly 5,000 plate appearances, Andrus owns an 83 wRC+ and measly .079 ISO. However, even a defensive-minded shortstop can change the game with one swing of the bat. While not an overly impressive blast, it exposes something different in his approach at the plate. Early in his career, Rangers fans and followers held out hope that Andrus could develop into a 15-20 homer a year guy. While that may feel like a lost cause, Andrus has recently displayed some newfound power. Last season, Andrus smacked a career-high seven home runs and tied his career-high with 43 extra-base hits. So far this season, he is on track for a career-high ISO and running a near league-average offensive line. Andrus’ Speed Score sits right at his career average, hence it does not appear he has bulked up significantly and traded in speed for power. Rather, he has altered his approach at the plate.

This current approach began last season, and to this point has continued over into his 2016 campaign. From his rookie season in 2009 to 2014, Andrus ran a 57.4 GB% and a 21.3 FB%. This past season and a third, those metrics have shifted to 46.5% and 31.1%, respectively. In context, Andrus has gone from the 2nd percentile in FB% to the 30th percentile. While no one will ever confuse him for Chris Carter, Andrus’ new batted-ball profile closely resembles that of in-state slugger George Springer. Perhaps even more indicative, Andrus has raised his Pull% from 33.9% over 2009-2014 to 43.6% since 2015; this represents a shift from the 12th percentile to the 81st percentile, placing him just ahead of renowned slugger Anthony Rizzo. Seeing as 27 of his 29 career home runs have landed to the left of center field, this seems a logical shift for a man in search of dingers.

Plate-discipline measures further reveal Andrus’ altered approach. Andrus has raised his O-Swing% from 21.8 to 25.8 in addition to raising his Z-Swing% from 53.3 to 61.2 over our familiar timeframes. In avoiding Simpson’s Paradox, these changes have increased his overall Swing% from 38.5 to 42.8. While still not a free swinger by any regards, Andrus’ new approach remarkably resembles fellow A.L. West shortstop Marcus Semien, albeit with superior contact rates. Known for providing impressive power from the six spot on the diamond, one could well view Semien as the ceiling of Andrus’ power dreams. Meanwhile, Andrus has held his contact rates largely steady, dispelling the notion that he has traded contact for power. Interestingly, his Zone% has steadily dropped since his rookie season but has held near 51% each of the past three full seasons. So far in 2016, that number has dropped further to 49%, so perhaps opposing pitchers have finally altered their approach in response. However, too little time has passed to determine whether this is by choice or simply small-sample variation. Indeed, Andrus will need to prove that these adjustments make him a “power” hitter before pitchers treat him differently.

That ultimately remains the question. Andrus has ostensibly made adjustments to improve his power, but do they truly make him a better overall hitter? To this point in the season, Andrus ranks 158th in average exit velocity on fly balls and 147th in average fly-ball distance among the 167 batters with 25 or more fly balls hit. Andrus pulling more fly-ball outs to left field doesn’t enhance his offensive output. However, if more of these balls turn into gap shots and home runs, Andrus could uncover another level to his game. With Jurickson Profar returning from the baseball grave in remarkable fashion and Rougned Odor forever cementing his place in Rangers lore, Andrus may be feeling the pressure to live up to his now ill-regarded contract extension. After three below-80-wRC+ seasons, something needed to change for Andrus at the plate. Whether this new approach works for the better remains to be seen, but right now Andrus remains a key cog on a surprising postseason contender.


The Curious Case of Carl Crawford

On December 8, 2010, the Boston Red Sox agreed to terms with Carl Crawford, inking the outfielder to a seven-year, 142-million-dollar deal, the largest ever signed by a position player that had never hit more than 20 home runs in a single season.  Although the majority of the population felt that the Red Sox had overpaid for his services, most considered it only a slight reach, and when factoring in Boston’s position on the win curve, their decision to splurge on a premium player could be justified.  Crawford was an established elite defensive outfielder coming off the best season of his career at the plate; an increase in power prior to the 2009 season had boosted Crawford to new heights just in time for his pay day, and in the final two years of his extension with the Rays, he posted WARs of 5.9 and 7.7, respectively.  In the immediate aftermath of his signing with Boston, FanGraphs’ own Dave Cameron declared him to be a true-talent 5-win player, and at the time, it was not difficult to imagine a scenario in which the 29 year-old Crawford continued to perform at peak levels before gradually declining in the final years of the contract.  If we assume that Crawford was in fact a 5-win player, then using the $/WAR figure accepted in the winter of 2010 (5 million dollars/win), 5% inflation, and a standard aging curve, the projection for Crawford’s contract would have looked something like this, with his 6-million-dollar signing bonus excluded from the analysis:

View post on imgur.com

Two things are striking when looking at this table.  First, on an unrelated note, league-wide inflation as a result of TV deals and increased revenue streams has far exceeded the expectations of 2010, actually surpassing 10% in order to reach the accepted value of 8-9 million dollars per win today in 2016.  Although the methodology did prove to be incorrect, I do still believe the results obtained here to be worthy of inspection, as they offer insight into teams’ valuations of Crawford as a player available in the free-agent market.  Second, the divergence between industry consensus and the arithmetic presented here is worth noting; most insiders felt that Boston had spent too much, while the data presented here suggests that the contract actually provided a bit of upside.  This disparity could perhaps be explained by a skepticism of defensive metrics in 2010, along with doubts about Crawford’s ability to age well, as a large portion of his value on the bases and in the outfield was tied up in his legs.  In order to account for this discrepancy, perhaps it is better to use a “worst-case scenario,” to subject Crawford’s performance to a more punitive aging curve over the life of the contract.  Instead of docking Crawford 0.5 WAR for his age-31 through -35 seasons, instead, he will lose 0.75 wins each year.  This steeper decline is forecast below:

View post on imgur.com

It appears that this table is a more accurate representation of front offices’ opinions about Carl Crawford, as shown by the deficit in the bottom right corner.  Using the more aggressive aging curve, the contract offered by the Red Sox does appear to be a slight overpay, and if they conformed to the opinion that the outfielder would decline more swiftly than other players of similar age, then they agreed to a contract in which there was no upside.  However, if Crawford’s performance fell anywhere between the standard aging projection and the “worst-case scenario,” as it was likely to, it seemed that both sides would be satisfied with the outcome.

As we all know now, this “worst-case scenario” projected in 2010 was a far cry from reality.  After suffering through a tumultuous year and a half in Boston and undergoing Tommy John surgery to repair a partially torn UCL, Crawford was unceremoniously dumped by the Red Sox and shipped to the Dodgers on August 25, 2012 as part of the infamous Nick Punto trade.  It appears that Crawford has finally hit rock bottom, with Los Angeles designating him for assignment on Sunday.  Crawford will almost certainly clear waivers, and assuming he asks to be released rather than assigned to the club’s AAA affiliate in Oklahoma City, the Dodgers will eat the remainder of his contract and essentially pay Crawford nearly 35 million dollars to disappear.  Rather than projecting future performance, let’s instead take a look back at Crawford’s production since signing with Boston:

View post on imgur.com

Although up-to-date $/WAR figures could have been used, I stuck with the estimates of 2010, in order to further emphasize how poorly this pact has been when compared to the organization’s expectations when they chose to sign Crawford.  Elbow injury notwithstanding, it is difficult to imagine how this contract could have soured so quickly.  Crawford has already cost his employers 85 million dollars more than his production would have warranted, and even if he latches on somewhere and plays out the remainder of the deal, it’s very likely that figure ends up more than 100 million dollars in the red.  So, how did this happen?  How did Carl Crawford, he of the 7.7 WAR in 2010, second in all of baseball, flop so badly, producing only 5.3 WAR since signing with the Red Sox?

Well, the most obvious answer is the boring one: Carl Crawford got old.  Fast.  From 2008-2010, Crawford’s final three seasons in Tampa Bay, he was actually the best defensive player in the MLB, posting a UZR/150 of 20.6 in left field, two runs better than his nearest competitor on the leaderboard.  A bit of regression and decline were certainly expected, as it is incredibly difficult to sustain this level of performance, but nobody could have expected the utter evaporation of his defensive value upon arriving in Boston.  Crawford posted a negative UZR during his time patrolling the Green Monster; some criticize the Red Sox for wasting his defensive abilities in what is considered to be the smallest left field in all of baseball, suggesting that he should have been moved to Fenway’s right field in order to better leverage his extraordinary range.  However, even before his elbow injury, Crawford was known for having a weak throwing arm, and with a partially torn UCL, it would have been nearly impossible for him to play anywhere other than left.

Even so, his damaged elbow fails to explain the mysterious loss of range that sent him tumbling down the UZR leaderboards, and instead of providing value as an elite defensive player, Crawford instead resembled an average corner outfielder during his time in Boston.  After being dealt to Los Angeles, Crawford’s defensive numbers did improve slightly, perhaps indicating that he never felt comfortable playing in front of the 37-foot wall, but by the time he arrived in Chavez Ravine, Crawford had already lost a step or two, placing a ceiling on his future defensive contributions.

This loss of speed was evident on the base paths as well, with Crawford never again imposing his will upon opposing batteries like he did during his time with the Rays.  From the time of his promotion to Tampa Bay in 2002 until the end of 2010, Crawford had stolen 409 bases in 499 attempts, for a success rate of nearly 82% and the second-highest stolen base total in all of baseball during that timeframe.  However, after signing with Boston, it seems as if Crawford became more timid as a runner, never attempting more than 30 steals in a single season.  Since 2011, Crawford owns a 79% success rate, quite similar to his career average, yet he’s running far less frequently, stealing only 71 bases in 90 attempts.  Whether due to a loss of speed or a lack of aggression, or perhaps a combination of the two, Crawford never regained his form as a base-stealer, resulting in the loss of a huge chunk of his base-running value.

Unlike his collapse in the outfield and on the base paths, Crawford’s decline at the plate cannot be explained by a loss of speed simply chalked up to age.  This dilemma is a bit more perplexing.  After posting the two best seasons of his career at the plate in Tampa Bay immediately prior to hitting free agency, Crawford’s production in the batter’s box cratered after signing with Boston in 2011, falling to levels only experienced by the outfielder during his first full season in the majors in 2003.  Since joining the Red Sox, Crawford has sported a more aggressive approach leading to fewer walks and more strikeouts, has exhibited less power than he did during his time in Tampa Bay, and his problems against left-handed pitching have only been exacerbated.  In a vacuum, none of these changes themselves would be damning, but in conjunction with one another, this trio has formed a nasty combination, only hastening Crawford’s demise.

Starting in 2006, as Crawford entered his offensive prime and started to become a force at the plate, his Zone%, the number of pitches he saw in the strike zone, began to decline as pitchers decided to carefully pitch around him rather than challenging him, falling from a high of 57% to only 43% in 2010.  During his MVP-level campaigns in 2009 and 2010, Crawford adjusted to these changes appropriately, cutting his swing rate and accepting the free passes being handed to him by opposing pitchers, adopting what could be considered somewhat of a slugger’s profile.  However, in 2011, perhaps feeling the weight of his new contract and worrying that hits rather than walks were needed to justify the nine-figure deal and appease Boston fans, Crawford gave these gains back, as his O-Swing% jumped by nearly three points.

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Surprisingly, Crawford actually controlled the outside corner of the plate, but he expanded the zone in nearly every other direction.  By chasing balls rather than selectively punishing mistakes, Crawford effectively got himself out more than ever, posting a career-high K% and his lowest BB% since 2003.  Even when Crawford did make contact, the quality was often terrible, as his Soft% rose to a nearly unfathomable 26%, contributing to a 40-point drop in his BABIP and a subsequent, almost identical, fall in batting average.  Although some of the walks have returned since his horrendous 2011, Crawford’s strikeouts remain elevated, seriously limiting his offensive production.

The lack of quality contact has also affected Crawford’s power output, because although his fly ball and line drive tendencies have been in line with his career norms, Crawford is doing far less damage.  During his time in Tampa, Crawford had an ISO of .148, peaking at .188 in 2010.  In Boston and Los Angeles however, this number has fallen to .136, and he’s never posted a single-season ISO higher than .150.

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This loss of power is most obvious at the top of the strike zone and on the outside corner, where Crawford is now capable of doing little to no damage.  Even in his wheelhouse, down and in, Crawford’s strength has eroded, leaving him as a shell of his 2010 American League MVP candidate self at the plate.

Finally, and perhaps most troubling, since leaving Tampa Bay, it seems like Crawford has forgotten how to hit left-handed pitching.  Even in his prime, the lefty struggled against southpaws, boasting only a .308 wOBA, but since signing with Boston, his production against same-handed pitching has collapsed, with his wOBA falling nearly 30 points, leaving with him with a wRC+ of 73 against lefties.  And yes, we now have an answer, his platoon split absolutely matters.  The final straw came in 2013, when he posted an ISO 0f .084 and a wRC+ of 56 in 115 plate appearances against left-handers; since then, Crawford has become a platoon outfielder, almost never allowed to face lefties and failing miserably when he does, as evidenced by his -64 wRC+ against them this year (granted, in only 12 plate appearances).

So, there you have it.  Carl Crawford, the electric baserunner, phenomenal outfielder, and prodigious hitter of less than six years ago is soon to be unemployed, assuming he clears waivers and is released.  Does he have any baseball left in him, or is this 142-million-dollar man done?  In any other year, he might have been, but given the number of contenders that will need an outfielder and the limited supply, it’s very possible that a team will give him a chance.  However, it is unclear if Crawford even wants to continue playing, given that the team acquiring him will almost certainly place him in a platoon role, while he has stated that he doesn’t believe he is a platoon player.  If he does agree to play in a limited role, where could he land?  An obvious answer is Cleveland, yet during his time in Boston, Crawford didn’t get along well with current Indians’ skipper Terry Francona.  Somewhat comically, Boston is another obvious fit, as he could be a nice platoon partner for Chris Young, but we all know how his first stint with the Red Sox went.  Other teams that could be interested in the outfielder’s services include the Orioles, Nationals, Mariners, and White Sox, although they may look to make a bigger splash before settling upon Crawford.  Whether Crawford returns to the big leagues or not, his time as an impact player almost certainly ended years ago, and that’s a shame.

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No matter their team allegiance, fans of the game of baseball have to be disappointed by the outcome of Crawford’s career, as his prime was gone far too soon.  One of few players that could truly dominate the game in every phase, through a combination of injury, age, and perhaps a lack of mental toughness, Carl Crawford’s star was extinguished almost immediately after signing with Boston.


How Plate Discipline Impacts wRC+

Like many of you, I spend hours on FanGraphs trying to take in as much information as possible. One of the more fascinating statistics to me is the category of plate discipline. This includes how often a batter will swing on a pitch inside or outside of the zone, how often a batter swings and misses, and many other variables that affect a player’s approach at the plate. While these numbers alone are a good indication on how a player acts at bat, I wanted to know how these numbers affected performance. For instance, it would make sense that a higher O-Swing% could lead to less-than-average hitting. The 2015 season had Adam Jones second in O-Swing%, swinging at 47% of pitches outside of the strike zone. Pablo Sandoval led the league in O-Swing% with a 48% rate. Jones recorded a 109 wRC+ while the Kung Fu Panda had a 75 wRC+.

By breaking down these Plate Discipline statistics for the 2015 season, I believe that we can get a good answer on which statistic leads to the best performance. For my methodology, I used the wRC+ and Plate Discipline leaderboard for the 2015 season. After breaking down each statistic, I compiled a top 10 and bottom 10 wRC+. Additionally, I grouped percentages to get the number of batters and average wRC+ for certain percentages.

O-Swing %

O-Swing% = Swings at pitches outside the zone / pitches outside the zone

Top 10 wRC+ Average: 97

Name O-Swing% wRC+
Pablo Sandoval 47.80% 75
Adam Jones 46.50% 109
Avisail Garcia 45.20% 83
Marlon Byrd 43.90% 100
Salvador Perez 42.50% 87
Kevin Pillar 40.10% 93
Starling Marte 39.40% 117
Gerardo Parra 39.40% 108
Freddy Galvis 39.20% 76
Nolan Arenado 38.50% 119

 

Bottom 10 wRC+ Average: 132

Name O-Swing% wRC+
Brett Gardner 22.90% 105
Ben Zobrist 22.60% 123
Matt Carpenter 22.50% 139
Paul Goldschmidt 22.40% 164
Jose Bautista 22.20% 148
Carlos Santana 21.10% 110
Francisco Cervelli 20.90% 119
Dexter Fowler 20.90% 110
Curtis Granderson 19.90% 132
Joey Votto 19.10% 172

 

Percentage Count Average wRC+
40%-48% 6 91
30%-39% 73 106
20%-29% 60 117
< 20% 2 152

O-Swing% gives us a pretty good indication of a player’s overall performance. It’s no surprise that patience and a good eye are part of a skill set that leads to a higher wRC+. For each 10-percent decrease of O-Swing percentage, batters see an increase of over 10 points for their wRC+. The top 10 wRC+ compared to the bottom 10 also tells a compelling story of what O-Swing tells us. In the top 10, we see a couple of above-average hitters like Starling Marte and Nolan Arenado. However, we also see five of the top 10 with a wRC+ under 100 and one hitter (Marlon Byrd) at 100. On the other side of the spectrum, there isn’t a hitter under 100 wRC+ in the bottom 10. The difference in wRC+ between the top and bottom 10 is 35, the biggest difference between all the statistics.

Let’s look at two very different extremes: Joey Votto and Pablo Sandoval. Sandoval had an O-Swing% of 48 percent while Votto had a 19 percent rate, which means that while Sandoval is swinging at almost half of the balls he faces, Votto is taking a little more than 80% of pitches out of the zone. Sandoval faced 1848 pitches (1287 strikes to 561 balls) while Votto faced 3020 pitches (1644 strikes to 1376 balls). Sandoval’s more than double strike-to-ball ratio and Votto leading the league in walks can both be explained by their O-Swing percentage.

Z-Swing %

Z-Swing%  = Swings at pitches inside the zone / pitches inside the zone

Top 10 wRC+ Average: 111

Name Z-Swing% wRC+
Marlon Byrd 83.20% 100
Brandon Belt 80.90% 135
Adam Jones 80.60% 109
Avisail Garcia 78.90% 83
Billy Burns 78.80% 102
Carlos Gonzalez 78.10% 114
Ryan Howard 77.80% 92
Starling Marte 77.50% 117
Kris Bryant 76.20% 136
Brandon Crawford 76.10% 117

Bottom 10 wRC+ Average: 115

Name Z-Swing% wRC+
Carlos Santana 57.90% 110
Logan Forsythe 57.70% 126
Joe Mauer 57.50% 94
Brock Holt 57.40% 98
Brett Gardner 55.80% 105
Brian McCann 55.80% 105
Mookie Betts 55.70% 119
Mike Trout 55.60% 172
Ben Zobrist 55.40% 123
Martin Prado 53.20% 100

 

Percentage Count Average wRC+
80%-83% 3 115
70%-79% 51 111
60%-69% 75 110
50%-59% 12 114

The first thing that I noticed when looking at the Z-Swing charts is the duplication of names from the O-Swing charts. Adam Jones, Avisail Garcia, Marlon Byrd, and Starling Marte showed up on both the O and Z Swing percentage top-10 while Ben Zobrist, Carlos Santana, and Brett Gardner appeared on both bottom-10 lists. This is a very mixed bag of players for both the top and bottom. Both have a 100 wRC+ hitter, the epitome of average. Both have seven hitters batting above 100 wRC+ meaning that both lists also have two hitters batting below 100. The top and bottom 10 averages are almost even. The one outlier that separates them is Mike Trout in the bottom 10 with a 172 wRC+. Seeing the same name on multiple lists can tell us a lot about a player. Someone like Marlon Byrd will swing at most of the pitches you send his way while Ben Zobrist will take a pitch outside of the zone about 77% of the time but will also take a strike 45% of the time as well.

O-Contact %

O-Contact% = Number of pitches on which contact was made on pitches outside the zone / Swings on pitches outside the zone

Top 10 wRC+ Average: 104

Name O-Contact% wRC+
Nick Markakis 86.10% 107
Michael Brantley 84.60% 135
Daniel Murphy 83.50% 110
Ender Inciarte 82.30% 100
Melky Cabrera 82.10% 91
Wilmer Flores 82.00% 95
Jose Altuve 81.70% 120
Ben Zobrist 80.90% 123
Angel Pagan 80.80% 81
Yadier Molina 80.20% 80

 

Bottom 10 wRC+ Average: 104

Name O-Contact% wRC+
Anthony Gose 55.00% 90
Avisail Garcia 55.00% 83
Nick Castellanos 53.20% 94
Ryan Howard 52.80% 92
Michael Taylor 52.10% 69
Justin Upton 51.50% 120
Addison Russell 51.10% 90
Chris Davis 50.90% 147
Kris Bryant 49.20% 136
Joc Pederson 49.00% 115

 

Percentage Count Average wRC+
80%-86% 10 104
70%-79% 46 103
60%-69% 60 118
50%-59% 23 105
< 50% 2 126

Similar to Z-Swing%, O-Contact doesn’t show much disparity between the top and bottom 10. In fact, they’re identical at 104 wRC+. A higher O-Contact gives a batter more balls in play, but doesn’t always lead to success. My initial thought was that swinging at a pitch way out of the zone can lead to weak contact, and usually an out. The fact the top and bottom are identical shows that this isn’t always the case.  It also makes sense why the middle of the pack (60%-69%) has the greatest wRC+ (besides the small sample size of < 50%). These batters are still able to make contact with pitches outside of the zone more than half of the time, but also miss the pitch enough of the time where they don’t make bad contact.

Z-Contact %

Z-Contact%  = Number of pitches on which contact was made on pitches inside the zone / Swings on pitches inside the zone

Top 10 wRC+ Average: 110

Name Z-Contact% wRC+
Daniel Murphy 97.50% 110
Ben Revere 96.70% 98
Michael Brantley 96.30% 135
Yangervis Solarte 95.50% 109
Martin Prado 95.40% 100
A.J. Pollock 94.60% 132
Jose Altuve 94.60% 120
Ian Kinsler 94.50% 111
Erick Aybar 94.30% 80
Ender Inciarte 94.20% 100

 

Bottom 10 wRC+ Average: 125

Name Z-Contact% wRC+
Mark Trumbo 80.90% 108
Brandon Belt 80.70% 135
J.D. Martinez 80.60% 137
Nelson Cruz 79.30% 158
Justin Upton 78.00% 120
Michael Taylor 77.40% 69
Joc Pederson 77.00% 115
Chris Davis 76.50% 147
Alex Rodriguez 76.50% 129
Kris Bryant 75.80% 136

 

Percentage Count Average wRC+
90%-98% 55 106
80%-89% 79 113
70%-79% 7 125

Z-Contact was the most surprising statistic in terms on its effect on wRC+, until you look at the names in the bottom 10. One would expect that hitters that hit more pitches in the zone would be the better performers. However, the bottom 10 is filled with power hitters, leading to the main difference in wRC+. Davis and Cruz were number one and two in terms of home-run leaders in 2015. In fact, besides Michael Taylor, the bottom 10 is all in the top 50 for home runs in the MLB. The list makes sense as players like Chris Davis are trying to “Crush” the ball out of the park and swing harder than someone in the top 10 like Martin Prado.

SwStrike %

SwStr% = Swings and misses / Total pitches

Top 10 wRC+ Average: 110

Name SwStr% wRC+
Avisail Garcia 17.30% 83
Marlon Byrd 17.20% 100
Ryan Howard 16.60% 92
Kris Bryant 16.50% 136
Michael Taylor 16.00% 69
Chris Davis 15.60% 147
Carlos Gonzalez 15.20% 114
J.D. Martinez 14.90% 137
Mark Trumbo 14.60% 108
Joc Pederson 14.00% 115

 

Bottom 10 wRC+ Average: 105

Name SwStr% wRC+
Ian Kinsler 5.20% 111
Ender Inciarte 4.90% 100
Andrelton Simmons 4.90% 82
Angel Pagan 4.40% 81
Martin Prado 4.30% 100
Ben Zobrist 4.20% 123
Nick Markakis 4.10% 107
Ben Revere 4.10% 98
Daniel Murphy 3.90% 110
Michael Brantley 3.10% 135

 

Percentage Count Average wRC+
15%-18% 7 106
12%-14.9% 20 107
9%-11.9% 36 117
6%-8.9% 50 114
3%-5.9% 17 106

Not surprisingly, the top 10 for SwStrike looks a combination of both the O-Contact and Z-Contact bottom 10. Obviously if your contact is low, you’re going to have more swings and misses. The main factor that stood out to me looking at the top and bottom 10 is the deviation of wRC+. The top 10 is all over the place, having players like Kris Bryant with a 136 wRC+, Michael Taylor with 69, and every level of player in between. The bottom 10 has less variation, providing a more consistent group of hitters.

Totals

Category Top 10 Bottom 10 Difference (Bottom to Top)
O-Swing% 97 132 35
Z-Swing% 111 115 4
O-Contact% 104 104 0
Z-Contact% 110 125 15
SwStr% 110 105 -5

 

As evidenced by the chart, the main statistic in regards to plate discipline to show a great change in performance that compares the bottom to the top level is O-Swing percentage. Z-Contact seems to also be relevant when evaluating and predicting a player’s performance.


The Tampa Bay Rays and the Advantages of Pulling the Ball

The Rays always seem to be at the forefront of sabermetric innovation. They employ an army of Ivy League baseball analysts in the front office, they fully embrace the shift, and they employ pitch-framing superstars. The Rays like to stay on top of the ball. For the Rays, sabermetric advancement is a means of survival. And for the Rays, in the powerhouse AL East, it is the only way to survive.

Over the past seven years, it seems the Rays have been on to something. Looking at FanGraphs team offensive data from 2009 to 2016, there is a clear pattern with the Rays. They are third in fly ball% at 37.5%. The team with the highest FB% during that time span is the Oakland Athletics. The A’s pursuit of fly-ball-happy hitters was pretty well documented. In a great article over at Deadspin from 2013, Andrew Koo (who now works for the Tigers) shows us the advantages of hitting fly balls. First, Koo highlights how fly ball rates have decreased in the league since 2009. With an increasing trend towards ground ball pitchers, Billy Beane made a clear effort to acquire fly ball hitters. Why? Because as Koo shows us, fly ball hitters are significantly better against ground ball pitchers compared to other batters.  Tom Tango, who is mentioned in the Koo article,  found that this platoon advantage is very minimal, and is really only realized and meaningful when the “advantage is multiplied through several hitters. This is exactly what the A’s and Rays have done over the past seven years. Both teams have stockpiled fly ball hitters.

The Rays have done something else too. They have stockpiled fly ball hitters that also have a knack for pulling the ball. Over the past seven years, they lead the league in Pull% at 42.8%. Looking at this year’s team, the strategy seems to be in full effect once again. Of all the Rays hitters with at least 100 PA this year, only three players (Miller, Forsythe, and Dickerson) are below the league average in Pull%. Now, it could be pure coincidence that the Rays pull the ball so much. But I think we all know this is no coincidence at all. They seem to be preaching the pull-happy approach.

When looking at offensive data on pulled balls vs. data on other batted ball directions, the strategy makes sense. Looking at league data from 2009 to 2015, the average wRC+ on balls hit to the pull side is about 157, compared to 112 on balls hit up the middle. Isolated power on balls hit to the pull side is over 100 points greater than on balls hit up the middle or to the opposite field. There is an offensive advantage to pulling the ball, when the ball is put in play. Given the clear advantage to hitting the ball to the pull side, one might ask why wouldn’t every team stockpile dead pull hitters?

One answer: conventional wisdom says dead pull hitters don’t have the right approach. From the time I started playing baseball, I have been told to hit the ball to all fields. And I don’t disagree with this philosophy. Staying back and being able to drive the ball to all fields definitely makes for a very productive hitter. But it also results in dead pull hitters being undervalued.

Another knock on pull hitters is that when they hit ground balls, they roll over the ball and commit easy outs.  Looking at the soft hit percentage vs ground ball percentage on balls that are pulled for all 30 teams from 2009-2016, I found this to be a valid concern about pull hitters. The data shows a positive correlation between ground balls and soft hit percentage. 

The Rays, however, have the fourth-lowest GB% on pulled balls. During that same time span, the Rays have the sixth-lowest Soft% on ground balls. They aren’t hitting weak ground balls. The Rays have made a concerted effort to pull the ball and they have avoided the weak contact that comes with pulling the ball on the ground.

Conclusion

The Rays have found and pounced on a market inefficiency. They have optimized their offense by targeting and developing players that consistently pull the ball in the air and avoid weak contact on the ground. Since these players aren’t the conventional hit-to-all-fields type of player, they can get these players for cheap. Simply put, the Rays have have capitalized on the offensive advantage of pulling the ball.

Food For Thought

Something to think about further is the trend of Pull% in the MLB from 2002-2016. It is down 5%. Intuitively, this makes sense, as velocity is way up over that time span. With velocity up, it is harder to pull the ball. This trend reminded me of a trend mentioned earlier in the article. As noted by Koo and Tango, ground balls are up around the MLB. As Tango found, fly ball hitters have an advantage against ground ball pitchers, and it is beneficial to utilize that advantage. What if there is a similar platoon advantage regarding pull hitters vs. power pitchers? In line with Tango’s logic, what if dead pull hitters have a platoon advantage against power pitchers? What if the Rays have figured out this advantage and have been exploiting it for years? The platoon advantage makes sense. Dead pull hitters, by nature, go up to the plate looking to pull the ball. Which means they are early on almost everything. As a result, they wouldn’t have as much trouble catching up to gas. This is definitely something to think about, and something I will be certainly researching in the coming weeks.


Making Heaven Great Again: The Angels’ Struggle for Redemption

There are good systems, there are poor systems, then there’s 50 pounds of effluence, and then there’s the Marlins. Add another 50 pounds, and you’ve finally reached the Angels.

Baseball Prospectus, 2016

Disclaimer: The side effects of reading through the entire Angels Top 30 may include drowsiness and an upset stomach.

– Baseball America Prospect Handbook, 2016

I’ve been doing these rankings for eight years now, and this is by far the worst system I’ve ever seen.

Keith Law, 2016

The practice of farming is prohibited. All right or claim of a major league club to a player shall cease when such player becomes a member of a minor league club, and no arrangement between clubs for the loan or return of a player shall be binding between the parties to it or recognized by other clubs.

National Agreement, Article VI, Section 4 (1903)

Sometimes the most important things are the things that aren’t there. Those words from the 1903 National Agreement, the peace treaty ending the brief but intense war between the National and American Leagues, were omitted from the revised agreement in 1921. And into that omission rushed Branch Rickey, who did not invent the practice of “farming” minor league players, but who perfected it with a ruthless efficiency that real farmers would only achieve much later with he generous application of pesticides. Rickey purchased not players, but teams, and in some cases entire leagues.

The 1903 farming ban codified, albeit temporarily, the American League’s declaration of independence from the National League, first issued in 1901. The farming ban was Ban Johnson’s announcement to the world that no one was going to treat his league’s players as farmhands. The ban also helped secure the loyalty of the Players’ Protective Association, an incipient union opposed to the practice (see pdf p.2), and was one factor encouraging star players to jump to the new league.

Major league owners routinely eluded the farming ban, however, and by 1920, baseball’s next crisis year, the ban was on the ropes. Wracked by gambling scandals, poor wartime attendance, and the ghastly death of Ray Chapman, organized baseball forged a new National Agreement in 1921. The new agreement omitted the farming ban, perhaps because the AL, having by that time firmly achieved major league status, lost interest in the cause of player liberty. Although Commissioner Landis despised the concept of farm systems, he was largely unable to prevent their development.

Landis failed because the economic logic of farm systems is unassailable: By owning most (though certainly not all) aspects of the production process, major league teams could greatly reduce the transaction costs inherent in developing major league-caliber players. Farm systems also limit the competition among teams for minor league player’s services. After the draft, the player is essentially under team ownership for several years, unable to work for any other team without the owning franchise’s consent.

Every major league team eventually developed a farm system, though (as Bill James has noted) laggards like the Cubs and Pirates paid a heavy price, suffering through years of mediocrity beginning in the 1940s. It is now impossible to imagine a major league team without a farm system. Or at least it was until this year. The Los Angeles Angels of Anaheim today stand on the threshold of an alternate future, a future in which Judge Landis won. Alone among MLB franchises, the Angels today entirely fail to benefit from the major league owners’ long twilight struggle to reduce minor league players to peonage.

I want players. Lots and lots of players.

Billy Eppler, 2016

The Angels’ recently minted general manager, Billy Eppler, will lead the team through the next phase of its dystopian journey. To be fair, it’s not exactly true that the Angels have no farm system at all — they have minor league affiliates in thrall to the major league club, just like other major league clubs do. And those affiliates may even win a few games (so far, just a few). But the system is bereft of impact talent at any level. A handful of these guys will turn out much better than now perceived, but the vast majority won’t. The next pennant winning Angels lineup and rotation is invisible without experimental pharmacological assistance.

One way to get “lots and lots of players,” or at least a relatively large haul of good players, would be to trade Mike Trout. The idea has been debated on these pages and elsewhere, and I don’t propose to rehash the details here. One thing Eppler might want to consider, however, if he contemplates such a drastic move is that nothing of the kind has ever happened in baseball history. Nothing even close.

Trout had a 9.4 bWAR last year; no player with that high a WAR has ever been traded in the following season. Connie Mack infamously sold Eddie Collins and his 9.1 bWAR to the White Sox after the 1914 season. The woeful Boston Braves traded Rogers Hornsby (8.8 bWAR) to the Cubs after the 1928 season for a clown car of substandard players and $200,000 in a classic salary dump.

Mike Piazza was traded twice in one year after his 8.7 bWAR in 1997. First, the Dodgers shipped him to the Marlins in exchange for a pile of good but expensive players; in this odd case the salary-dumping team received a superstar, although it also unloaded a superstar in Gary Sheffield. The Marlins then Marlined it up real good just one week after Piazza put on the teal, sending him to the Mets for Geoff Goetz, Preston Wilson, and Ed Yarnall. Centuries from now the Marlins will be viewed as we view the giant Moai of Easter Island: with a mixture of awe at the achievement and amazement that the people responsible failed to put their limited resources to better use.

And that’s about it for the top 200 player-seasons. So Eppler would be piloting the S.S. Anaheim into uncharted seas if he traded Trout; there is no comparable trade out there by which one could even vaguely assess his value.  That doesn’t mean Eppler shouldn’t try, but he shouldn’t try too hard. Trout is still just 24, and it is conceivable that the next pennant winning Angels lineup could still have him in it. No other GM in baseball history has seen fit to trade a player of Trout’s caliber; Eppler should be wary of being the first.

There is another way, pioneered by a team just a few hours north on the 5. In 2002 the San Francisco Baseball Giants made it to the World Series with a team that GM Brian Sabean had built around Barry Bonds. Bonds, for you youngsters out there, was the Oughties’ Mike Trout, though I suspect that both men would bristle at the comparison. Drug-fueled or not, Bonds dominated the game like few ever have, yet Sabean labored mightily to get Bonds into the World Series. Ultimately, Sabean achieved this not by tending crops in the blazing fields from dawn to dusk, interrupted only by a cholesterol-laced dinner at noon. Your 2002 Giants had exactly one (1) player with a bWAR over 1.o who had come up through the Giants farm system. That was Russ Ortiz, a pitcher many may remember as a failure because the red crystal in his palm began glowing right after his age 30 season, but up to that point he was a reliable innings eater with roughly a league average ERA.

So here’s the point:

Giants total 2002 bWAR: 50.6

Giants 2002 bWAR from home-grown players:  5.3

Yeesh. Tony Torcato. Damon Minor. Trey Lunsford. Yep, they’re in the 5.3, and they’ll be gleefully wielding flaming pitchforks in Scouting Hell. The news wasn’t all awful — Joe Nathan is in that 5.3, as is the aforementioned Ortiz. But it’s safe to say that the 2002 Giants are a team that Judge Landis might have liked. Well, you know, except the PED part.

So how did Sabean do it? If you haven’t guessed the answer, you probably should consider taking some of those self-paced training courses you’ve been blowing off. He signed him a passel o’ free agents (including Bonds himself, of course, as well as Reggie Sanders (3.5 bWAR in 2002) and Benito Santiago (2.6)). And he traded. Oh, did he trade. Jeff Kent was the most critical acquisition, amassing a 7.0 bWAR in 2002, which, as the alert reader will quickly grasp, exceeded the entire Giants farm produce by a wide margin. Here are the other significant guys Sabean dealt for:

David Bell (3.2)

Kirk Rueter (3.0)

Robb Nen (2.5)

Jason Schmidt (2.3)

Tsuyoshi Shinjo (1.9)

Kenny Lofton (1.7)

Tim Worrell (1.5)

That’s 15.9 bWAR for those of you keeping score at home, and adding in Kent brings the total to 22.9, or just under half of the Giants’ 2002 total. The best players traded away for those guys, by far, were Matt Williams (cumulative 12.5 bWAR after being traded for Kent) and Bill Mueller (11.8 cumulative bWAR after being traded for Worrell). Given that Williams brought Jeff Kent, the only clear mistake in hindsight was Mueller, an outstanding but aging and fragile player who put together some memorable late career seasons after being traded for Tim Worrell.  That trade may not have worked as Sabean would have hoped, but it was defensible at the time.

So the Chapter 7 condition of the Angels’ farm system doesn’t necessarily prevent Eppler from remaking his roster. But it does severely constrain his efforts; the players Sabean traded away for the most part didn’t pan out, but he was able to convince other baseball executives that they would, executives who get paid good coin to see through exactly this kind of B.S. (those are Sabean’s initials — it’s probably a coincidence). Eppler doesn’t even have enough talent on the farm to fake it.

But the current Angels major league roster has some useful bits in addition to Trout. Kole Calhoun, Andrelton Simmons, and Garret Richards (albeit currently with a UCL subject to manufacturer recall) aren’t exactly a “core,” but they’re not a bad franchise starter kit. Nick Tropeano and Andrew Heaney (albeit currently with a UCL subject to manufacturer recall) offer some hope that young Angels fans might see a quality start before they have their first legal beer. And Josh Hamilton’s $26 million of dead weight exits the ledger after this year. The Angels will be paying Albert Pujols until humans colonize the Alpha Centauri system, but other than that, their contracts aren’t awful.

So one plan might include trading some (though certainly not all) of the above-named players, especially Calhoun, who is developing into an advanced hitter at a somewhat advanced age. It will also include signing free agents in bunches, more than Sabean did. Harder to do now than in the past, given that teams seem to be locking up their top-tier young players with greater frequency, but this is why scouts get paid (or should get paid) the big dollars. Some franchise (do I smell fish?) will undervalue its own talent, and Eppler must be there to pick up the pieces.

Or he can trade Trout.

I’m glad I’m not Billy Eppler.


Identifying HR/FB Surgers Using Statcast

It seems that 2016 will be the year that Statcast begins to permeate Fantasy Baseball analysis. Recently there has been a wealth of articles exploring the possibilities of using these kinds of data. These pieces have provided relevant insights on how to improve our understanding of well-hit balls and launch angles. Also, they’ve facilitated access to information on exit velocity leaders and surgers, as well as provided thoughtful analyses to the possible workings behind some early-season breakouts.

However, there is still a lot we don’t know about Statcast data. For instance, we are uncertain of how consistent these skills are over time, both across seasons or within seasons. Also we don’t know what constitutes a relevant sample size or when rates are likely to stabilize. All in all, this makes using 2016 Statcast data to predict rest of season performance a potentially brash and faulty proposition. Having said that, we can’t help but to try; so here’s our attempt at using early-season 2016 Statcast data to partially predict future performance.

One of the early gospels of Statcast data analysis posits that the “sweet spot” for hitting homers comes from a combination of a launch angle in the range of 25 – 30 degrees and a 95+ MPH exit velocity. If this is indeed the ideal combination for hitting home runs, one could argue that players that have a higher share of fly balls that meet these criteria should perform better in other more traditional metrics such as HR/FB%.

Following this line of thought we dug up all the batted balls under the “sweet spot” criteria, and divided them by all balls hit at a launch angle of 25 degrees or higher (which MLB determines as fly balls) to come up with a Sweet Spot%. In an attempt to identify potential HR/FB% surgers, we compare Sweet Spot% and HR/FB% z-scores (to normalize each rate) for all qualified hitters with at least 25 fly balls and highlight the biggest gaps.  Here are the Top five gaps considering the games up to May 28th:

Name Team HR/FB  % HR/FB  %         Z-Score Sweet Spot % Sweet Spot % Z-Score Z-Score Diff
Kole Calhoun Angels 6% -1.15 26% 2.24 3.39
Stephen Piscotty Cardinals 11% -0.35 26% 2.33 2.68
Matt Carpenter Cardinals 16% 0.44 29% 2.73 2.29
Denard Span Giants 3% -1.66 15% 0.52 2.18
Yonder Alonso Athletics 3% -1.69 15% 0.43 2.12

Calhoun seems like a good candidate for a power uptick. He has the third-highest Sweet Spot% of 2016, and he has sustained similar Hard% and FB% to the previous two seasons. Yet somehow he has managed to cut his HR/FB% to less than half of what he put together in either 2014 or 2015.  More so, he has had some bad luck with balls hit in the “sweet spot”; his batting average in these kinds of balls is .500, whereas the league average is around .680. He is not killing fly balls in general, with an average exit velocity of 84.6 MPH, but if he keeps consistently hitting balls in the “sweet spot” range he should improve in the power department. Look out for a potential turnaround in the coming weeks and a return to 2015 HR/FB% levels.

Piscotty holds second place in the Sweet Spot% rankings. However, his FB% is very similar to what he did in 2015 whilst his Hard% is down from 38.5% to 32.5%. Lastly, he plays half of his games in Busch Stadium, which has a history of suppressing home runs. I would be cautious of expecting a major home-run surge, but in any case Piscotty is likely to at least sustain his performance in the power department, which would be welcome news to owners that got him at bargain prices.

Carpenter is another dweller of Busch Stadium, however his outlook might be a bit different. He is the absolute leader in Sweet Spot%. He is posting the highest Hard% and FB% marks of his career. Carpenter is also crushing his fly balls in general, with an average Exit Velocity of 93.7 MPH. Just as a point of reference Miguel Cabrera, Josh Donaldson and Giancarlo Stanton fail to reach an average of 93 MPH on their own fly balls. Lastly, he has had some tough luck with balls hit in the “sweet spot”, posting a batting average of just .420. Carpenter is already putting up the highest HR/FB% of his career, and he is a 30-year-old veteran of slap-hitting fame, but the power looks legit and perhaps there is more to come.

Denard Span and Yonder Alonso show up in this list not because of their Sweet Spot% prowess but rather due to their putrid HR/FB%. They barely crack the Top 50 in Sweet Spot%. They play half their games in two of the bottom three parks for HR Park Factor. Span is putting up his lowest FB% and Hard% rates since 2013, when he ended up with a HR/FB% of 3.4%. Meanwhile, Yonder’s rates most closely resemble those of 2012, when he had a HR/FB of 6.2%. Whilst their batting average of “sweet spot” batted balls is just .500, there is nothing to look here. In any case, their power situation looks to improve from bad to mediocre.

If you are interested in the perusing the Top 50 gaps between HR/FB% and Sweet Spot%, please find them below:

Name Team HR/FB  % HR/FB  %          Z-Score Sweet Spot % Sweet Spot % Z-Score Z-Score Diff
Kole Calhoun Angels 6% -1.15 26% 2.24 3.39
Stephen Piscotty Cardinals 11% -0.35 26% 2.33 2.68
Matt Carpenter Cardinals 16% 0.44 29% 2.73 2.29
Denard Span Giants 3% -1.66 15% 0.52 2.18
Yonder Alonso Athletics 3% -1.69 15% 0.43 2.12
Kendrys Morales Royals 10% -0.61 21% 1.38 1.99
Addison Russell Cubs 12% -0.27 22% 1.67 1.94
Yadier Molina Cardinals 2% -1.72 13% 0.11 1.83
Adam Jones Orioles 11% -0.46 20% 1.29 1.75
Alcides Escobar Royals 0% -2.10 10% -0.44 1.66
Jose Abreu White Sox 11% -0.35 19% 1.11 1.46
Joe Mauer Twins 17% 0.56 24% 1.96 1.40
Chris Owings Diamondbacks 3% -1.59 11% -0.26 1.32
Jacoby Ellsbury Yankees 5% -1.28 12% -0.09 1.19
Justin Turner Dodgers 6% -1.20 12% -0.01 1.19
Victor Martinez Tigers 12% -0.19 18% 0.95 1.14
Daniel Murphy Nationals 10% -0.60 16% 0.54 1.14
Justin Upton Tigers 4% -1.43 11% -0.29 1.14
Josh Harrison Pirates 5% -1.37 11% -0.25 1.12
Anthony Rendon Nationals 6% -1.23 12% -0.11 1.12
Corey Dickerson Rays 16% 0.42 21% 1.50 1.07
Brandon Crawford Giants 11% -0.41 16% 0.66 1.07
Ian Desmond Rangers 16% 0.35 21% 1.41 1.06
Derek Norris Padres 12% -0.30 17% 0.74 1.04
Ryan Zimmerman Nationals 19% 0.78 23% 1.81 1.03
Gregory Polanco Pirates 14% 0.11 19% 1.11 1.00
Austin Jackson White Sox 0% -2.10 6% -1.13 0.97
Nick Markakis Braves 2% -1.79 7% -0.86 0.93
Corey Seager Dodgers 18% 0.66 22% 1.56 0.91
Michael Saunders Blue Jays 20% 1.00 24% 1.88 0.89
Mike Napoli Indians 23% 1.38 26% 2.27 0.88
Brandon Belt Giants 7% -0.97 11% -0.15 0.81
Matt Kemp Padres 17% 0.59 20% 1.36 0.77
Nick Ahmed Diamondbacks 8% -0.81 12% -0.05 0.77
Matt Duffy Giants 4% -1.45 8% -0.73 0.71
David Ortiz Red Sox 19% 0.90 21% 1.53 0.63
Joe Panik Giants 9% -0.69 12% -0.06 0.63
Elvis Andrus Rangers 2% -1.72 6% -1.10 0.63
Brandon Phillips Reds 11% -0.41 14% 0.21 0.62
Adam Eaton White Sox 8% -0.81 11% -0.20 0.62
Gerardo Parra Rockies 8% -0.87 11% -0.26 0.61
C.J. Cron Angels 6% -1.18 9% -0.58 0.61
Dexter Fowler Cubs 13% -0.04 16% 0.56 0.60
Jose Altuve Astros 17% 0.53 19% 1.11 0.58
Prince Fielder Rangers 4% -1.42 7% -0.90 0.51
Jose Ramirez Indians 7% -1.09 9% -0.58 0.51
Joey Rickard Orioles 8% -0.91 10% -0.42 0.48
Asdrubal Cabrera Mets 7% -1.00 9% -0.53 0.46
Mark Teixeira Yankees 10% -0.50 12% -0.05 0.46
Ben Zobrist Cubs 13% -0.12 14% 0.34 0.45

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com


Erick Aybar Needs Your Prayers

One would do well to recall that the last feature article written about Erick Aybar appeared in NotGraphs (#KeepNotGraphs), where he was pictured as the inept, rebel fleet commander Admiral Ackbar from the good section of Star Wars. Before, that there were articles that described him as, “Erick Aybar: Not as Bad as You Might Think,” and “Erick Aybar, Perennial Sleeper,” and “Erick Aybar: 2012 Sleeper.” Since then, Aybar hasn’t had an actual FanGraphs piece done on him. It looks as though people are still sleeping on him (but for good reason this time).

One of the most interesting parts of the novel 1984 is the concept of “Newspeak,” where the government twists and eliminates the meaning of certain words to serve its own purposes. In the novel, Winston, the protagonist, is educated by one his colleagues at the Ministry of Truth, Syme. He tells Winston, “A word contains its opposite in itself. Take ‘good,’ for instance. If you have a word like ‘good,’ what need is there for a word like ‘bad’? ‘Ungood’ will do just as well – better, because it’s an exact opposite, which the other is not.” In today’s society, particularly in the world of baseball, there is a great need for descriptors such as “ungood” so that people don’t feel bad.

There are numerous expletive-laden phrases that would aptly describe Erick Aybar’s season up to this point, but perhaps it’s best to just say he’s doubleungood. That’s the clearest way of saying that Aybar has been incredibly awful this season. This isn’t just about offense or just about defense. He has been mind-numbingly, historically bad offensively and pretty subpar defensively.

It’s lucky for Aybar that the Braves aren’t exactly their c. 1998 selves because he can hide relatively easily on this roster. The Braves have three of the league’s ten worst players by wRC+ (min. 100 plate appearances), so it’s not like he’s exceptional. Moreover, it doesn’t look like the Braves are terribly interested in winning, anyway, so at least he isn’t holding back a team with championship aspirations (you’re being glared at, Russell Martin).

This season, through 43 games (many of them started) and 161 plate appearances, he has amassed an unimpressive -1.7 WAR, worst in the league. Also absolute worst in the league is his wRC+, which is 11! That’s insane. It’s 89% worse than average! Even 90-year-old A.J. Pierzynski has a 39 wRC+. Consider this: Erick Aybar is running a .184/.222/.211 line. How can a major-league baseball player be this bad?

Well, it’s not terribly helpful to have a .223 BABIP, a number 78 points off of his career average (and basically league average) .301 BABIP. Just for fun, let’s say he has a .301 BABIP this season. That would add approximately nine hits to his total of 27 thus far, giving him a much more respectable .245 batting average. Now let’s say he maintains his ratio of hits to extra-base hits and see what that does to his slugging percentage (he ends up with one more double). This gets him to a much better .245/.279/.279 line. But that’s still probably not good enough to be a major-league player.

As you can probably guess, Aybar’s plate discipline and power numbers suck quite a bit. His four doubles and 23 singles have given him a .027 ISO, which is the worst in the league by 16 points. He has a K-BB% of 14.3%, a number that’s meritorious as a pitcher (hint: Aybar isn’t a pitcher). His O-Swing% increased by five percentage points this year and his contact rate on pitches outside the strike zone decreased by five percentage points, leading to more strikeouts and worse contact when he actually hits it. At least he’s only a slightly below-average baserunner.

Unfortunately, his defensive numbers have been subpar this year also, but at least he’s not the worst player in the league in this category. Instead, he’s eighth-worst, with a raw UZR of -4.9 and a UZR/150 of -22.9. He isn’t committing too many errors, but his range is a definite factor. Aybar hasn’t completed a single play in the remote to unlikely range per Inside Edge. He’s also seen a marked drop in even chance fielding opportunities (down 6.7%) and likely opportunities (down 3%).

There aren’t a whole lot of good reasons for this. He isn’t injured (although he did have to get a chicken bone removed from his throat) and he doesn’t look injured. I can’t find a way to press the videos onto the article, but his swing looks a lot different from last year, at least from the right-hand side of the plate. I’m not a swing expert, but it looks like he isn’t using his hips to turn on the ball like he has in years past, which would explain the lack of power. Additionally, Aybar looks off-balance this year as compared to last year, when he was much better. Another thing to consider is that it seems like his swing has less lift than before, resulting in more ground balls and less power. On the other hand, maybe Aybar is just getting old. He’s 33 and hasn’t missed a lot of time in his career.

On the other hand, he actually was a very good player for a long time, a sleeper even. From 2008 through 2014, he was worth 20.1 WAR, combining passable offense for a middle infielder with good baserunning and decent defense. In fact, he was 57th* in WAR during that time period, better than more highly esteemed names like Carlos Beltran, Nelson Cruz, and David Ortiz. He was a very good player for a very long time, making more money than most people ever dream of. And that’s cause for positivity.

It stands to reason that Aybar will regress back to the mean. No one can sustain those numbers for a full season, if only because they would definitely get benched. There’s a reason why sample size and past performance matters and Mr. Aybar embodies it. If we expected him to keep playing at this level with the same amount of playing time, then he’d end up with the worst season in baseball history with a little over -6 WAR (not that six fewer wins would make that big of a difference to the Braves). But that isn’t going to happen. He’s projected to finish around zero, which would make him an average player the rest of the way. Based on his past performance, I fully expect that to happen and I want it to happen. It’s terribly sad when one of the game’s great, unknown players spirals into oblivion. Nonetheless, what he’s doing right now is insane and not for the right reasons.  Just as Admiral Ackbar managed to right the rebel fleet, Aybar can do the same with his performance.

*Fun fact: Mike Trout is 19th on that list. Remember, WAR totals from 2008 through 2014.

All statistics current through 5/26/2016