Wednesday, December 31, 2008

Ranking the first basemen - 2008

It's now time for the fourth annual Tiger Tales fielding data series. Today, I'll preview the series and then start by ranking the first basemen defensively. The table of contents for the remainder of the series is shown below:

Second basemen
Third basemen
Shortstops
Left fielders
Center fielders
Right Fielders
Adding Arms to the outfield

In this year's analysis, I am including the following measures:
These are not the only modern fielding measures but they are the ones for which I currently have data for 2008. I will try to add others later as they become available.

For each player in 2008, I converted each of the six measures to runs saved above average per 150 games. Last year, I covered in detail the algorithms for converting to runs saved. Rather than do that again this year, I will simply point you towards last year's articles which can be found in the Fielding Glossary. Many thanks go to Chris Dial, Tom Tango, Justin Inaz and others involved in developing these algorithms. I simply applied their work to this year's data.

There are a few purposes I can see for these data:
  • I think it's useful for people to see all the data in one place making it easier to analyze the differences between the systems.
  • It reinforces the idea that we should still not trust any one system and that there is is still a fair amount of disagreement between the systems for some players.
  • I came up with an aggregate measure by averaging the results of all the systems together. This helps to eliminate the affect of an outlier where one system does not work for a particular player.
I don't claim that the method described in the third bullet is the best way to aggregate the data. I admit there is possible bias in the averaging of four Baseball Info Solutions (BIS) based systems with one STATS based system (Zone Rating). However, it's clear that the STATS versus BIS issue is not the only source of variation between the systems. Thus, I included all the BIS systems in my measure.

Others may not want to see the fan fielding data as part of the aggregate measure but I decided to include it because I think the information is valuable given the limitations of the quantitative measures. I tried it both ways (with and without fan data) and it does not change the final results dramatically. Anyway, the data from all systems will be listed side by side which gives people the option to combine the systems as they see fit.

Table 1 presents the results for first basemen with 700 or more innings played in 2008. The first thing to notice on the table is that much of the +/- data are unavailable. This is because only the top ten and bottom five fielders at each position are freely available. To obtain complete +/- data, you would need to get the Fielding Bible or purchase a subscription to BillJamesonline. Each average is based on 5 or 6 values depending on the availability of +/- for a given player.

It seems that every new offensive statistic invented ranks Pujols on top. The fielding statistics are no different. Depending on the system,Pujols ranks anywhere from 10 runs saved above the average first baseman (UZR) to 30 runs saved above average (PMR). His Average Runs Saved (ARS) is 21. Other first basemen who rank positively on every statistic are Lance Berkman (15), Mark Teixeira (15) and Todd Helton (13).

First sackers who rank poorly on all statistics are Mike Jacobs (-24), Jason Giambi (-16), Prince Fielder (-12) and Ryan Garko (-8). Just a little further up the list is the Tigers Miguel Cabrera at -6 runs saved below average, although the Zone Rating system (+2) rates him a little more favorably than the BIS systems.

Just as interesting as the aggregate scores are the disagreements among systems for some players. James Loney, for example, fell anywhere from -8 on UZR to 15 on RZR. Another player with a lot of variation was Adrian Gonzalez who ran from -12 on UZR to 12 for the fan scouting report.

Table 1: Range statistics for first basemen in 2008

player

team

inn

ZR

RZR

PMR

+/-

UZR

Fans

ARS

Pujols

StL

1,215

15

28

30

18

10

22

21

Berkman

Hou

1,307

12

22

16

15

10

13

15

Teixeira

LAA/Atl

1,335

12

16

15

19

10

15

15

Helton

Col

715

7

26

9

9

10

15

13

Votto

Cin

1,223

4

18

12

17

8

-1

10

Pena

TB

1,168

1

8

20

13

5

11

9

Kotchman

LAA/Atl

1,210

4

13

8

11

6

12

9

Overbay

Tor

1,354

7

7

0

9

1

13

6

Lee

ChC

1,339

1

2

5

N/A

3

18

6

Youkilis

Bos

984

3

-4

6

6

5

15

5

Barton

Oak

1,121

10

4

3

7

7

-1

5

Loney

LA

1,362

-1

15

-0

N/A

-8

12

4

Gonzalez

SD

1,417

4

-12

5

N/A

-7

18

2

Millar

Bal

1,131

-0

0

12

N/A

-2

-4

1

LaRoche

Pit

1,135

-5

1

-2

N/A

-7

5

-2

Konerko

CWS

995

-3

-1

6

N/A

-2

-14

-3

Delgado

NYM

1,376

-2

1

0

-12

-4

-15

-5

Gload

KC

878

-2

-18

-11

N/A

2

0

-6

Cabrera

Det

1,204

2

-6

-13

N/A

-5

-8

-6

Morneau

Min

1,363

-1

-17

-15

N/A

-5

6

-7

Howard

Phi

1,402

4

-9

-11

N/A

1

-22

-8

Garko

Cle

1,058

-8

-8

-13

N/A

-6

-13

-10

Fielder

Mil

1,383

-13

-10

-11

-9

-8

-21

-12

Giambi

NYY

898

-13

-23

-16

-21

-3

-20

-16

Jacobs

Fla

927

-19

-34

-23

-31

-17

-23

-24

Friday, December 26, 2008

How much are Tigers hitters worth?

FanGraphs continues to come through with free and accessible advanced statistics. The latest addition to the rich database is replacement win and dollar values for all major league hitters. The values are based on total batting and fielding contributions with adjustments for position, ballpark and league. The system is similar to WARP created by Baseball Prospectus but the FanGraphs system has a better fielding component (UZR) and thus is probably more accurate.

In my first dip into these new data, I looked at the estimated dollar values for the current Tigers. The second column of Table 1 shows how much each Tiger was worth, on average, over the last three seasons. The third column is their 2009 salary. The table shows that the only Tiger whose estimated value ($3.7 million) is substantially less than his 2009 salary ($14 million) is Gary Sheffield. There are also a few bargains: Curtis Granderson ($18.5 versus $3.0), Placido Polanco ($12.2 versus $4.6) and Adam Everett ($4.6 versus $1.0).

There are a couple of caveats that must be mentioned though. First, if Cabrera had been a first baseman all of the last three years, his value would have been closer to $14 million. Plus, if you consider that his average salary over the rest of his contract is $17.3 million, his contract might not look as good in the future. It's good that they moved him to first though because that's where he belongs and I'm also happy that his bat will be in the Tigers line-up the next seven years.

The other issue is Carlos Guillen who, of course, has been moved to left field after playing various infield positions over the last three years. Had he been a left fielder from 2006-2008, his value would have been around $9 million which is close to his $10 million 2009 salary and less than the average salary of $12 million over the remainder of his contract. If he continues to regress and have health problems, this contract could become a problem.

Table 1: Dollar values for current Tigers

Player

Avg Value 2006-2008 (millions)

2009 salary (millions)

Miguel Cabrera

$20.1

$15.0

Curtis Granderson

$18.5

$3.5

Magglio Ordonez

$17.8

$18.0

Carlos Guillen

$14.5

$10.0

Placido Polanco

$12.2

$4.6

Brandon Inge

$8.0

$6.3

Adam Everett

$4.6

$1.0

Gary Sheffield

$3.7

$14.0

Gerald Laird

$3.4

$2.5 (est.)

Marcus Thames

$3.3

$3.0 (est.)

Ramon Santiago

$2.4

$0.8

Matt Treanor

$2.3

$0.8

Wednesday, December 24, 2008

Run Preventing Events - 2008

A couple of years ago, I developed a crude but I believe useful statistic called Run Preventing Event Percentage. Here is how it works: An at bat can result in any of the following events:

  • Strikeout
  • Base on balls
  • Hits batsman
  • Ground ball
  • Line drive
  • Outfield fly
  • Infield fly
Three of those events are generally favorable events for pitchers:

  • Strikeout
  • Ground ball
  • Infield fly
I call these run preventing events (RPE). Of course, a ground ball is not as easy an out as a strikeout or an infield fly and can have a negative result for a pitcher. However, inducing a lot of ground balls will help to prevent runs over the course of a season. On the other hand, it is good for pitchers to avoid, for the most part, the following events:

  • Base on balls
  • Hits batsman
  • Line drive
  • Outfield fly
Run Preventing Event percentage (RPE%) is calculated as follows: (SO + GB + IFF)/BFP. Striking out batters and inducing grounders have been shown to be repeatable skills. Getting batters to hit infield flies is not very stable from year to year. However, infield flies are relatively rare compared to other batted ball types and including them does not change the RPE% substantially in most cases. Plus, I suspect (without statistical evidence) that this is a real ability for some power pitchers.

RPE% is essentially a fielding independent statistic because, although the end result is not independent of fielders, getting a grounder or infield fly to happen in the first place has nothing to do with fielders. It is as stable or more stable from year to year as FIP ERA but it is not weighted and thus does not explain quite as much about runs allowed. The value of RPE% is its simplicity.

There were 53 American League starters with 125 or more innings pitched in 2008. Table 1 lists the RPE% rankings for Tigers starters. Table 2 lists the top 20 pitchers in the league. In general, the RPE% seems to be good for identifying effective pitchers but sometimes it gives surprising results.

From Table 1, we can see that Nate Robertson (RPE%=51.4) ranked a lot better on RPE% (28th in the AL) than he did on ERA (53rd or dead last). Robertson had a league average ground ball percentage and walked batters in only 8% of plate appearances (league average = 10%). However, when he did give up a line drive or a fly ball, it tended to end up really bad. According to The Hardball Times Baseball Annual 2009, he allowed more runs per line drive than the average American League pitcher (.43 versus a .39) and far more runs per outfield fly than average (.28 versus .18). Part of this was Robertson's relatively high homeruns per flyball (13%). It's also possible, he allowed a lot of his line drives and fly balls with runners on base.

Recently acquired Edwin Jackson posted a low 47.1% RPE% but still managed a 4.42 ERA. In his case, he had a very low strikeout rate (14% versus a league average of 18%) and ground ball rate (39% versus 44%). However, his line drives (.35 runs) and outfield flies (.17) did a lot less damage than Robertson's line drives and fly balls.

What does this all mean for the future? Well, run preventing events are more predictive than runs per line drive/fly ball. Thus, if Robertson and Jackson pitch the same way they did last year, we can expect Nate's ERA to go down and Edwin's to go up.

The raw data used in calculating RPE% were abstracted from The Hardball Times database.

Table 1: Run Preventing Events for Tigers Starters in 2008

Rank

Name

BFP

SO

GB

IF

RPE

RPE%

24

Galarraga

746

126

241

25

392

.525

28

Robertson

761

108

262

21

391

.514

38

Verlander

880

163

246

26

435

.494

45

Jackson

792

108

237

28

373

.471

46

Rogers

782

82

257

24

363

.464

**

Miner

509

62

180

8

250

.491

**

Bonderman

319

44

112

9

165

.517


Table 2: Top 20 AL Starters by RPE% in 2008

Rank

Name

Team

BFP

SO

GB

IF

RPE

RPE%

1

Halladay

TOR

987

206

392

17

615

.623

2

Burnett

TOR

957

231

306

24

561

.586

3

Hernandez

SEA

857

175

309

14

498

.581

4

Pettitte

NYA

881

158

340

12

510

.579

5

Mussina

NYA

819

150

306

13

469

.573

6

Lee

CLE

891

170

313

27

510

.572

7

Buehrle

CHA

918

140

358

19

517

.563

8

Shields

TB

877

160

308

20

488

.556

9

Lester

BOS

874

152

307

26

485

.555

10

Santana

LAA

897

214

244

37

495

.552

11

Hochevar

KC

566

72

229

9

310

.548

12

Beckett

BOS

725

172

208

16

396

.546

13

Greinke

KC

851

183

260

19

462

.543

14

Litsch

TOR

735

99

286

10

395

.537

15

Lackey

LAA

675

130

223

7

360

.533

16

Garza

TB

772

128

241

42

411

.532

17

Danks

CHA

804

159

250

19

428

.532

18

Sonnanstine

TB

819

124

275

36

435

.531

19

Marcum

TOR

630

123

194

17

334

.530

20

Garland

LAA

864

90

353

15

458

.530



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