2013 in Baseball: Without the Luck

DISCLAIMER: I know certain players are more likely to outperform/underperform the league-average BABIP based on their specific player profiles. This is just a fun exercise to consider if everyone’s “luck” was the same.

With that disclaimer out of the way, I began wondering who the best/worst hitters are in baseball if batted-ball luck didn’t figure into the equation. We hear analysis frequently about Player X who’s having a breakout year, and the refrain is consistently that he is having better luck on batted balls than he had been having in the past. For example, BABIP was one of the main reasons cited for how Chris Johnson batted .321 in 2013 after hitting .268 over the previous two seasons. Many people look for fantasy sleepers based on a much lower than normal BABIP. The effects of BABIP are undeniably real and have been well documented. If we take BABIP out of the equation though, who rises to the top?

Before we get to the results, let me go over my methodology. It’s extremely simple, and you can probably guess how this is done. If you don’t want the boring details, please skip ahead. The first step to these calculations was to keep all factors not included in the BABIP formula constant. Each player still hits the same number of home runs. Each player still walks at the same rate. Each player still strikes out the same amount. The only component that changes is hits that don’t leave the yard (1B, 2B, 3B). I took the denominator of the BABIP equation for each player (AB-K-HR+SF) and multiplied it by the league-average BABIP (.297). This gives us the number of non-HR hits a player would have tallied if luck was removed. To get the number of singles, doubles, and triples each player hit, I took the ratio of Actual Hit Type/Actual Total non-HR and multiplied by expected non-HR. For example, Mike Trout hit 115 singles, 39 doubles, and 9 triples in 2013. That means that 70.6% of his non-HRs were singles, 23.9% were doubles, and 5.5% were triples. When adjusted for BABIP, we would expect Trout to hit roughly 129 non-HRs this past season. Multiplying 129 by the component percentages gives us roughly 91 singles, 31 doubles, and 7 triples. Everything else remains the same.

Batting Average Leaders

To answer the question posed in the introduction, we can look at many different stats. We already discussed how much an effect BABIP can have on a batting average, so maybe we should start there. For what it’s worth, the MLB leader in BABIP in 2013 was Chris Johnson at .394, and the worst BABIP belonged to Darwin Barney at .222.

AL Adjusted Batting Average Leaders – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 AVG (Actual)

Difference

Edwin Encarnacion

.313

.272

+.041

Miguel Cabrera

.304

.348

-.044

Adrian Beltre

.295

.315

-.020

Coco Crisp

.294

.261

+.033

J.J. Hardy

.291

.263

+.028

 

NL Adjusted Batting Average Leaders – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 AVG (Actual)

Difference

Andrelton Simmons

.291

.248

+.044

Martin Prado

.290

.282

+.008

Norichika Aoki

.288

.286

+.002

Jonathan Lucroy

.287

.280

+.007

Yadier Molina

.283

.319

-.035

Looking at those tables, the first thing that jumps out to me is that only two players (Edwin Encarnacion and Miguel Cabrera) in all of Major League Baseball would have hit .300 last year if luck is removed. The American League seems to possess better luck-independent hitters as the NL “batting champ” would have finished tied for fifth in the AL. Also, if Andrelton Simmons could actually hit .291 each season, he’d be an MVP candidate. I also find it interesting to look at which players benefited and suffered the most from their respective BABIPs.

Most Positive Batting Average Changes – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 Average (Actual)

Difference

Darwin Barney

.273

.208

+.065

Andrelton Simmons

.292

.248

+.044

Dan Uggla

.220

.179

+.042

Edwin Encarnacion

.313

.272

+.041

Matt Wieters

.275

.235

+.040

 

Most Negative Batting Average Changes – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 Average (Actual)

Difference

Chris Johnson

.248

.321

-.073

Joe Mauer

.257

.324

-.067

Michael Cuddyer

.267

.331

-.064

Mike Trout

.265

.323

-.058

Freddie Freeman

.264

.319

-.055

On-Base Percentage Leaders

Perhaps we shouldn’t limit ourselves to just simply batting average. Isn’t it more important to avoid outs that it is to just get hits? Let’s look at the OBP results.

AL Adjusted On-Base Percentage Leaders – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Edwin Encarnacion

.406

.370

+.035

Miguel Cabrera

.404

.442

-.037

Mike Trout

.384

.432

-.047

Jose Bautista

.383

.358

+.025

David Ortiz

.379

.395

-.016

 

NL Adjusted On-Base Percentage Leaders – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Shin-Soo Choo

.399

.423

-.024

Joey Votto

.399

.435

-.037

Paul Goldschmidt

.373

.401

-.027

Matt Holliday

.372

.389

-.017

Troy Tulowitzki

.366

.391

-.025

Once again, only two hitters (Encarnacion and Cabrera) would have reached based at a .400 clip. A trend is definitely starting to emerge. The gap between the AL and the NL is much less pronounced here though. As for the biggest changes in the MLB, consider the following tables.

Most Positive On-Base Percentage Changes – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Darwin Barney

.325

.266

+.059

Andrelton Simmons

.337

.296

+.041

Matt Wieters

.323

.287

+.036

Edwin Encarnacion

.406

.370

+.036

Dan Uggla

.344

.309

+.035

 

Most Negative On-Base Percentage Changes – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Chris Johnson

.289

.358

-.069

Joe Mauer

.345

.404

-.059

Michael Cuddyer

.331

.389

-.058

Allen Craig

.323

.373

-.050

Freddie Freeman

.347

.396

-.049

As you might expect, these tables don’t look all that much different from the batting average change tables. Other than some reordering, the only difference here sees Allen Craig replace Mike Trout on the most negative change table.

On-Base + Slugging Leaders

Getting on base a lot is a promising start, but you win baseball games by scoring runs. What hitters were best at driving the ball while avoiding outs? Let’s look at the OPS results.

AL Adjusted On-Base + Slugging Leaders – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Edwin Encarnacion

.993

.904

+.088

Miguel Cabrera

.988

1.078

-.090

Chris Davis

.953

1.004

-.051

David Ortiz

.918

.959

-.041

Jose Bautista

.918

.856

+.062

 

NL Adjusted On-Base + Slugging Leaders – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Paul Goldschmidt

.883

.952

-.069

Troy Tulowitzki

.871

.931

-.060

Jayson Werth

.839

.931

-.092

Matt Holliday

.837

.879

-.042

Domonic Brown

.835

.818

+.017

Once again, our Top 2 are Encarnacion and Cabrera, with a considerably gap between Cabrera and third place Chris Davis. The AL/NL split is at its most pronounced here. To see if our trend in the biggest changes tables continues, consider the following tables.

Most Positive On-Base + Slugging Changes – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Darwin Barney

.712

.569

+.143

Andrelton Simmons

.790

.692

+.098

Edwin Encarnacion

.993

.904

+.089

Dan Uggla

.759

.671

+.088

Matt Wieters

.790

.704

+.086

 

Most Negative On-Base + Slugging Changes – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Chris Johnson

.657

.816

-.159

Joe Mauer

.736

.880

-.144

Michael Cuddyer

.779

.919

-.140

Mike Trout

.863

.988

-.125

Allen Craig

.712

.830

-.118

The trend continues as expected. Also, the negative regressers are harder hit than the positive regression candidates.

Weighted Runs Created Plus Leaders

This is FanGraphs though, so we can’t simply look at traditional stats. We need something that’s park-adjusted and comparative to league average. Let’s look at wRC+. (NOTE: These numbers aren’t adjusted for individual leagues as is normally done with wRC+. I’m lazy and didn’t take the time to do that extra step, so the wRC+ values won’t make up exactly with what is listed elsewhere on this site.)

AL Adjusted wRC+ Leaders – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Edwin Encarnacion

161

137

+24

Miguel Cabrera

155

180

-25

David Ortiz

155

167

-12

Coco Crisp

154

132

+22

Chris Davis

154

168

-14

 

NL Adjusted wRC+ Leaders – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Paul Goldschmidt

148

168

-20

Hunter Pence

142

148

-6

Andrew McCutchen

141

170

-29

Shin-Soo Choo

141

158

-17

Buster Posey

140

149

-9

As you might expect, Encarnacion and Cabrera top the charts again. Paul Goldschmidt is once again the National League leader. As for the biggest movers, they look very similar as well as you might expect.

Most Positive wRC+ Changes – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Darwin Barney

79

40

+39

Andrelton Simmons

127

97

+30

Dan Uggla

122

97

+25

Matt Wieters

111

86

+25

Edwin Encarnacion

161

137

+24

 

Most Negative wRC+ Changes – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Chris Johnson

85

135

-50

Joe Mauer

105

147

-42

Allen Craig

113

150

-37

Michael Cuddyer

88

125

-37

Mike Trout

147

183

-36

Since we looked at the leaders in each category, let’s look at those who failed to meet such lofty standards in 2013.

Batting Average Laggards

AL Adjusted Batting Average Laggards – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 AVG (Actual)

Difference

Chris Carter

.216

.223

-.007

Mike Napoli

.219

.259

-.040

Mark Reynolds

.230

.220

+.010

Michael Bourn

.232

.263

-.031

Stephen Drew

.237

.253

-.016

 

NL Adjusted Batting Average Laggards – 2013 (min. 500 PA)

Player

2013 AVG (Adjusted)

2013 AVG (Actual)

Difference

Dan Uggla

.220

.179

+.042

Starling Marte

.234

.280

-.046

Chase Headley

.235

.250

-.015

Giancarlo Stanton

.240

.249

-.009

Gregor Blanco

.241

.265

-.024

The most startling thing I notice from these tables is that Dan Uggla gained .042 points in his batting average and still finished last in the league. Now, that’s impressive.

On-Base Percentage Laggards

AL Adjusted On-Base Percentage Laggards – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Alcides Escobar

.287

.259

+.028

Michael Bourn

.288

.316

-.028

Manny Machado

.294

.314

-.019

Leonys Martin

.297

.313

-.015

Torii Hunter

.300

.334

-.035

 

NL Adjusted On-Base Percentage Laggards – 2013 (min. 500 PA)

Player

2013 OBP (Adjusted)

2013 OBP (Actual)

Difference

Adeiny Hechavarria

.288

.267

+.021

Chris Johnson

.289

.358

-.069

Starlin Castro

.290

.284

+.006

Zack Cozart

.294

.284

+.010

Marlon Byrd

.300

.336

-.036

Michael Bourn is our only carryover from the batting average tables that appears on the OBP tables as well. Probably not a great sign for Cleveland.

On-Base + Slugging Laggards

AL Adjusted On-Base + Slugging Laggards – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Michael Bourn

.609

.676

-.066

Alcides Escobar

.621

.559

+.062

Elvis Andrus

.633

.659

-.026

Jose Altuve

.643

.678

-.035

Leonys Martin

.661

.698

-.037

 

NL Adjusted On-Base + Slugging Laggards – 2013 (min. 500 PA)

Player

2013 OPS (Adjusted)

2013 OPS (Actual)

Difference

Adeiny Hechavarria

.615

.565

+.050

Eric Young

.638

.645

-.007

Gregor Blanco

.639

.690

-.051

Starlin Castro

.644

.631

+.013

Chris Johnson

.657

.816

-.158

Uh-oh, Bourn is back again, and the only player relatively close to him is Adeiny Hechavarria. Hechavarria is a fine defensive shortstop who has noted offensive woes. Bourn was a big free agent signing for Cleveland expected to jump start the Indians offense. Those represent completely different expectations.

Weighted Runs Created Plus Laggards

AL Adjusted wRC+ Laggards – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Alcides Escobar

64

45

+19

Jose Altuve

70

80

-10

Ichiro Suzuki

75

68

+7

Michael Bourn

77

98

-21

Elvis Andrus

81

89

-8

 

NL Adjusted wRC+ Laggards – 2013 (min. 500 PA)

Player

2013 wRC+ (Adjusted)

2013 wRC+ (Actual)

Difference

Starlin Castro

62

58

+4

Adeiny Hechavarria

68

53

+15

Nolan Arenado

70

70

0

Darwin Barney

79

40

+39

Eric Young

80

82

-2

Nothing here is meant to be used to draw conclusions about any hitters. I’m not advocating for Edwin Encarnacion as the best regular in baseball or Starlin Castro as the worst. I just thought this would be an interesting simple exercise to consider. Just for fun though, let’s look at the AL MVP race one more (“luck-independent”) time.

Statistic

Miguel Cabrera

Mike Trout

AVG

.304

.265

SLG

.584

.479

OBP

.404

.384

OPS

.988

.863

wOBA

.418

.374

wRC+

155

147

If we take luck out of the debate, Cabrera is an 8% better hitter compared to league average than is Trout. I guess the BBWAA doesn’t think Trout is an 8% better fielder and base runner than Cabrera. Surely they know what they’re talking about though. I mean they do get to decide who belongs in the Hall of Fame after all. They’re the smartest baseball folks out there.




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Stats All Folks is a frustrated former Little League pitcher that knows if he could have only been taller, stronger, more athletic with more velocity on his fastball, better offspeed stuff, and improved control, he could have been the first overall pick in the MLB First-Year Player Draft. Alas, it was not in the cards for him.

6 Responses to “2013 in Baseball: Without the Luck”

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  1. olethros says:

    Are those NL/AL numbers generated using MLB average BABIP or each individual league’s? If the former, that might account for some of the gap between the two.

    Vote -1 Vote +1

    • Stats All Folks says:

      They are generated using MLB average. Using individual league averages could be interesting to look at as well. Thanks for the comment.

      Vote -1 Vote +1

  2. Casey says:

    This was some interesting stuff. I’d also be curious to see these numbers with each player normalized to their respective xBABIP. This could probably help to account for the fact that hitters probably have an adjusted normal BABIP.

    Vote -1 Vote +1

  3. eph says:

    shoot me a line eph_unit@yahoo.com
    I have a spreadsheet with all of this info neatly laid out

    Vote -1 Vote +1

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