Saturday, January 14, 2006

Percentiles for Offensive Production Measures

I’ve been discussing quite a few measures of offensive production in the past few months. I’ve spent some time discussing how these statistics are constructed and what they mean but it’s probably still a little difficult for some people to interpret the values once they are calculated. For example, we all know that a .300 batting average is very good and a .220 batting average is bad. Most of us also have gotten used to the idea that a .380 OBP is very good and a .300 OBP is bad. However, what can we say about less familiar measures such as isolated power and runs created per game?


I’ve constructed a chart that shows the percentiles for various rate statistics for the 132 American League players with 295 or more plate appearances in 2005:


BA= Batting Average
ISO=Isolated Power
IWP=Isolated Walk Percentage
SLG=Slugging percentage
OBP=On Base Percentage
OPS=OBP+SLG
RC/G=Runs Created Per Game


The chart is read as follows. A .295 batting average falls at the 80th percentile meaning that 20% of the players hit .295 or better and 80% hit worse. Reading across the 80 percentile row, we can see that a value of .202 for isolated power is equivalent in magnitude to a .295 batting average. Similarly, 6.5 runs created per game is equivalent in magnitude to an .833 OPS.

From the chart, you will see that an ISO of .240 is outstanding, an ISO of .161 is average and that an ISO of .110 is poor. You can make similar conclusions for the other measures.


PCT

BA

IWP

ISO

OBP

SLG

OPS

RC/G

100

.338

.240

.304

.440

.610

1.031

9.7

90

.304

.134

.239

.378

.515

.885

7.1

80

.295

.120

.202

.366

.477

.833

6.5

70

.286

.104

.184

.352

.458

.803

6.0

60

.277

.099

.169

.343

.446

.786

5.6

50

.272

.091

.161

.332

.436

.768

5.2

40

.268

.086

.152

.325

.422

.749

5.0

30

.260

.079

.137

.320

.403

.729

4.7

20

.252

.072

.118

.309

.383

.705

4.4

10

.241

.061

.105

.301

.367

.677

3.8

0

.216

.029

.031

.254

.280

.556

3.0


The second chart lists all the Detroit Tigers and color codes them according to percentile. Those in the top 20 percent (8oth percentile or better) for a given statistic are in blue, those in the bottom 20 percent are in red and the rest are in black.


Player

PA

BA

IWP

ISO

OBP

SLG

OPS

RC/G

Inge

694

.261

.097

.158

.330

.419

.749

5.2

Monroe

623

.277

.075

.169

.322

.446

.768

5.4

Rodriguez

525

.276

.029

.168

.290

.444

.735

3.5

Young

509

.271

.087

.200

.325

.471

.796

5.0

Infante

434

.222

.042

.145

.254

.367

.621

3.5

Shelton

431

.299

.090

.211

.360

.510

.870

6.9

White

400

.313

.055

.176

.347

.489

.837

6.8

Polanco

378

.338

.077

.123

.386

.461

.846

7.6

Guillen

361

.320

.080

.114

.368

.434

.803

5.0

Logan

356

.258

.072

.077

.305

.335

.641

3.9

Ordonez

343

.302

.093

.134

.359

.436

.795

6.9

Pena

295

.235

.125

.242

.325

.477

.802

5.7

Granderson

174

.272

.058

.222

.314

.494

.808

6.3

Wilson

173

.197

.099

.086

.275

.283

.558

3.1

Thames

118

.196

.092

.215

.263

.411

.674

3.4

McDonald

78

.260

.064

.069

.308

.329

.636

3.1

Smith

63

.190

.017

.086

.203

.276

.479

2.2

Martinez

62

.268

.050

.018

.300

.286

.586

4.9

Giarratano

47

.143

.106

.071

.234

.214

.448

2.1

Higginson

27

.077

.037

.000

.111

.077

.188

-0.5

Gomez

18

.188

.111

.000

.278

.188

.465

2.7

3 comments:

  1. A chart for stat morons! Oh man. This stuff is so useful for people like me. I cuddle the computer screen in sheer joy... the kind of joy that comes from INSOMNIA and NUMERICAL EDUCATION!

    ReplyDelete
  2. Nice job. This chart succeeds in succintly presenting a lot of useful information is a form that is quickly digested.

    For example, who would you rather have in CF, Logan or Granderson?

    Or how overpaid was Pudge, even if he had been a saint in the clubhouse?

    ReplyDelete
  3. Good, I'm glad someone actually reads and gets something out of all these numbers! This exercise was actually interesting for me because I hadn't looked at the distributions for ISO and IWP that closely in a while.

    ReplyDelete

Sabermetrics Book

Sabermetrics Book
One of Baseball America's top ten books of 2010

Blog Archive

Subscribe

501 Baseball Books

501 Baseball Books
Recommended by Tiger Tales

Stat Counter

Site Meter