Wednesday, November 29, 2006

Team Run Production in 2006

Statistics such as runs created and OPS (on base percentage plus slugging percentage) are good for summarizing the offensive performance of a player. However, they don’t tell you much about what kind of hitter a player is. How to best break down offense into statistics that describe skills is always under debate and there is no one best way to do it. The combination of statistics most commonly used by analysts is batting average (BA), on base percentage (OBP) and slugging average (SLG). These are the statistics I typically use too but the combination does have some problems.


First, BA, OBP and SLG are highly correlated. For the more statistically inclined, a look at individual seasons of players in the American League between 2001 and 2005 shows that the correlations range from .63 to .68 with the highest correlation between BA and OBP. In short, this says that the three stats are measuring similar things. Logically, consider that batting average is essentially a subset of both OBP and SLG and thus the three of them are closely related.


Another problem (and this is related to the first issue described above) is that neither OBP nor SLG measures a pure skill. OBP combines the ability to get hits with the ability to draw walks. These are two very different skills which can be loosely defined as making contact (hits) and having a good eye (walks). Similarly, slugging combines the ability to get hits with the ability to get extra base hits. Again, these are two different skills – making contact and hitting for power.


BA can be subtracted from SLG to give us isolated power (ISO), a purer measure of slugging ability than SLG. Similarly, BA can be “subtracted” from OBP to give us extra on base percentage (EOBP). Because BA and OBP have different denominators (at bats and plate appearances respectively), it is best not to use the equation OBP-BA. Instead, we subtract hits from times reached based in the numerator and use plate appearances as the denominator. That is, EOBP=(BB+HBP)/PA. So we now have BA, ISO and EOBP. The correlations between these three statistics range from .01 to .42 with the highest correlation between ISO and EOBP. These correlations are much lower that those between BA, OBP and SLG. This is because BA, ISO and EOBP are more independent of one another than BA, OBP and SLG.


Furthermore, using AL team data from 2001 to 2005, I found that BA, ISO and EOBP explain 93% of the variation in team runs scored. I also found that BA, OBP and SLG explain 93% of the variation in runs scored. So the two sets of statistics have the same very strong explanatory power but the former trio is appealing because it more purely measures three separate skills.


Finally, BA in isolation, explains 63% of the variation in runs, ISO explains 59% and EOBP explains 49%. So neither of the three statistics explains a great deal by itself. All three must be used together to explain runs scored.


I have not included base running in run production because the only readily available statistic is stolen bases which is not highly correlated with runs scored. I can not analyze other base running statistics at this time without a lot of data entry and potential copyright issues. So base running will have to wait for another time. Even without it though, we are explaining most of what we need to know about runs scored as that 93% figure is very high.



Table 1 below presents the BA, ISO and EOBP for all American League teams in 2006. Table 2 shows the ranks for the three statistics. From the tables, we can see that the Tigers finished 5th in runs scored despite ranking 9th in BA and 13th in EOBP. This further illustrates how the Tigers had the most efficient offense in the American League in 2006. Much like last year and most other recent years, their biggest weakness was their inability to draw walks. We probably can not count on them being so efficient (lucky?) with their production next year. They will likely need to improve their walk rate if they are to rank as high as or higher than 5th in runs scored again in 2007.


Table 1: American League Team Run Production in 2006


Team

Runs

BA

ISO

EOBP

NY Yankees

930

.285

.177

.112

Cleveland

870

.280

.177

.097

Chicago Sox

868

.280

.184

.089

Texas

835

.278

.168

.087

Detroit

822

.274

.174

.077

Boston

820

.269

.166

.115

Toronto

809

.284

.179

.093

Minnesota

801

.287

.138

.087

Oakland

771

.260

.152

.112

Baltimore

768

.277

.146

.088

LA Angels

766

.274

.150

.085

Kansas City

757

.271

.140

.087

Seattle

756

.272

.153

.076

Tampa Bay

689

.255

.165

.081

Average

804

.275

.162

.092


Table 2: American League Team Run Production Ranks in 2006


Team

Runs Rank

BA Rank

ISO Rank

EOBP Rank

NY Yankees

1

2

3

2

Cleveland

2

4

4

4

Chicago Sox

3

5

1

6

Texas

4

6

6

8

Detroit

5

9

5

13

Boston

6

12

7

1

Toronto

7

3

2

5

Minnesota

8

1

14

9

Oakland

9

13

10

3

Baltimore

10

7

12

7

LA Angels

11

8

11

11

Kansas City

12

11

13

10

Seattle

13

10

9

14

Tampa Bay

14

14

8

12

Monday, November 27, 2006

Maybin, Miller Head BA Tiger Prospect List

Baseball America has released their annual Top Ten Tiger Prospects list. This is premium content at BA so you need a subscription to read the scouting reports but the top ten are listed below:

1. Cameron Maybin
2. Andrew Miller
3. Brent Clevlen
4. Jair Jurrjens
5. Jordan Tata
6. Eulogio DeLaCruz
7. Gorkys Hernandez
8. Dallas Trahern
9. Jeff Larish
10. Scott Sizemore

Maybin and Miller need no introduction. They are two of the elite prospects in the entire minor leagues and will probably be ranked near the top of the top 100 prospects list which BA releases in the spring. It's not likely that any other Tigers will make that list. Humberto Sanchez probably has an outside shot but he, of course, was traded for Gary Sheffield. Maybin and Miller probably won't play regularly for the Tigers until 2008 but they are two names to watch very closely all year. They will probably spend the greater part of the season at AA Erie.

The prospect who is most likely to be ready to help in 2007 is Tata. He pitched for the Tigers a little bit this year and might wind up in their pen again next year. If he does not relieve for the Tigers, he'll start in AAA. Clevlen saw time with the Tigers this season and Jim Leyland likes him a lot but he is still struggling offensively and likely needs a year in the minors. I'm a little surprised he is ranked as high as number 3 but BA is very tools oriented and he has some good ones. The problem is he struggles at new levels and that is often a bad sign for a prospect.

The rest of the prospects are not likely to see much action in Detroit this year. The 20 year old Jurrjens raced up the prospect list from #16 to #4 after a solid season pitching for One A Lakeland and Erie. He'll pitch for Toledo this year. One of his teammates will be DeLaCruz who is the hardest throwing prospect but also wild. They are still trying to figure out his ultimate role - reliever or starter.

Gorkys Hernandez might be a new name for some of you. He batted .327/.356/.463 as an 18 year old in the rookie level GCL last season. He is still very raw but could become a very exciting prospect in time. middle infielder Scott Sizemore had a strong year for short season Oneonta this year hitting .327/.394/.435. Hernandez and Sizemore may both play for low A Western Michigan this year.

I thought Jeff Larish would be ranked a little higher after showing good power and a high walk rate for Lakeland this year. He did have trouble making contact though and he'll need to have a big year at Erie in 2007 in order to solidify his spot as the Tiger first baseman of the future.

The biggest surprise on the list was Dallas Trahern. He had a low strikeout rate this year (86 k in 145 innings) which isn't usually a good sign for pitching prospects. He may have a future as a ground ball pitcher. He'll most likely pitch for Erie in 2007

Saturday, November 25, 2006

Tigers Rank Well in Fielding Bible Awards

Fielding statistics have come a long way over the past couple of decades and have now reached the point where they are fairly useful evaluation tools. Certainly the Probabilistic Model of Range (PMR) is better than range factor which in turn tells us more than fielding percentage. However, there are still many gaps. Fielding stats like PMR and Zone Rating don’t work as well as hitting stats such as On Base Percentage and slugging Percentage. Questions arise because systems such as Zone Rating, Ultimate Zone rating and PMR which are supposed to measure the same thing (how efficiently players turn balls in play into outs) sometimes disagree substantially on individual players.


It’s still clear that a fair amount of subjective input and interpretation of available data is needed to accurately evaluate fielding performance. With this in mind, John Dewan, owner of Baseball Info Solutions (BIS) and long time leader in the sabermetric community, has developed an interesting new approach to the evaluation of fielding performance. Rather than relying solely on statistics, he put together a “panel of experts” to select the best fielders at each position. He calls it the Fielding Bible Awards as he considers them a complement to the statistics in the Fielding Bible.


The way the Fielding Bible Awards work is each of 10 voters ranks 10 players at each position. A player gets 10 points for a first place vote, 9 points for a second place vote, etc. Among the voters were several prominent sabermetricians including Dewan, Bill James and Rob Neyer, BIS video scouts who studied every single game of the 2006 season in great detail, advisors employed by Major League baseball teams and knowledgable fans who participated in the Tom Tango Fan Poll.


You can see the final results including how each panelist voted (something you don’t see in the Gold Glove award voting) in The Bill James Handbook 2007. Table 1 below, shows how the Tiger players ranked. Four Tigers finished in the top ten in the Major Leagues including Pudge Rodriguez who finished first receiving 96 out of 100 possible points for catchers. Other Tigers in the top ten were Placido Polanco (ranked 8th among second baemen) , Brandon Inge (6th among third basemen) and Curtis Granderson (5th among center fielders).


Do the Fielding Bible Awards work better than stats? That’s hard to say as this is the first year of the awards and much work needs to be done to see how well these awards correlate with fielding stats and whether they close any gaps. At the very least, the awards are a nice complement to the available quantitative data. I think they are a significant contribution to the ongoing quest to more accurately assess fielding performance.


Table 1: How the Detroit Tigers Ranked on Fielding Bible Awards


Position

Player

Points (100 Max)

MLB Rank

Catcher

Rodriguez

96

1

First base

Casey

7

14

First base

Shelton

2

19

Second base

Polanco

31

8

Third base

Inge

56

6

Shortstop

Guillen

0

Unranked

Left field

Monroe

3

19

Center field

Granderson

35

5

Right field

Ordonez

2

22

Thursday, November 23, 2006

PMR and Carlos Guillen

David Pinto at Baseball Musings has started posting his Probabilistic Model of Range (PMR) tables for 2006. PMR may be the most sophisticated freely available fielding statistic you can find on the internet. It is based on detailed play by play data collected by Baseball Info Solutions.

Here is how it works: The difficulty of turning a ball in play into an out is determine by several parameters: location of ball, how hard the ball is hit (soft, medium, hard), type of ball hit (e.g. ground ball, fly ball, line drive), handedness of batter and pitcher and ballpark. The expectation that a particular ball in play is turned into an out is determined by aggregation of this data for all games played in 2006.

Pinto then determined, for each fielder, how may balls were in play when he was on the field, how many he should have been expected to turn into outs and how many he actually turned into outs. The idea is that good fielders will record more outs than expected and poor fielders will record fewer outs than expected. David Pinto, himself, explains the whole PMR system in more detail on YouTube.

For example, 3808 balls were in play when Carlos Guillen was on the field. Based on all the parameters and data for all fielders, it was determined that Guillen should have turned approximatelly 455 balls into outs. In actuality, he turned 465 balls into outs. So he made 10 more plays than would be expected by the typical shortstop.

The other stats you'll see on Table 1 below are DER or Defensive Efficiency Ratio which is the proportion of balls in play turned into outs (465/3808 = .122), predicted or expected DER (455.45/3808) and the difference between actual and expected DER (.00251 in this example). The players are ranked on the difference.

Among 30 Major League shortstops with 2,000 or more balls in play in 2006, Guillen ranked 12th. Your first reaction might be: "but he made so many throwing errors so that negates his range". However, PMR takes care of that because he does not get credit for making a play if he throws the ball away. Instead, he gets penalized as he should.

The best shortstops according to PMR were Adam Everett (who made 35 more plays than expected), Billy Hall (+28) and Yuniesky Betancourt (+27). Two interesting names near the bottom of the list were Derek Jeter (who made 14 fewer plays than expected) and Miguel Tejada (-12). This will anger Jeter fans no doubt but it's not the first time he has found himself near the bottom of a statistical fielding list.

Table 1: PMR for Shortstops in 2006

Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Adam Everett 3801 500 464.88 0.132 0.122 0.00924
Bill Hall 3311 404 375.73 0.122 0.113 0.00854
Craig Counsell 2274 310 290.98 0.136 0.128 0.00836
Yuniesky Betancourt 4225 501 474.18 0.119 0.112 0.00635
Jason A Bartlett 2570 348 333.97 0.135 0.130 0.00546
Julio Lugo 2103 253 241.59 0.120 0.115 0.00542
Ben T Zobrist 1395 173 165.55 0.124 0.119 0.00534
Khalil Greene 3007 352 335.93 0.117 0.112 0.00534
Clint Barmes 3411 404 386.61 0.118 0.113 0.00510
Juan Castro 1743 205 197.03 0.118 0.113 0.00457
Jhonny Peralta 4086 533 516.35 0.130 0.126 0.00408
Rafael Furcal 4257 538 525.08 0.126 0.123 0.00304
Omar Vizquel 3974 441 430.32 0.111 0.108 0.00269
Carlos Guillen 3808 465 455.45 0.122 0.120 0.00251
Jack Wilson 3485 454 447.27 0.130 0.128 0.00193
Juan Uribe 3553 429 424.28 0.121 0.119 0.00133
John McDonald 2024 237 235.34 0.117 0.116 0.00082
Bobby Crosby 2595 307 304.87 0.118 0.117 0.00082
Alex Gonzalez 2991 350 347.62 0.117 0.116 0.00080
David Eckstein 3222 385 383.63 0.119 0.119 0.00043
Orlando Cabrera 3903 433 432.08 0.111 0.111 0.00024
Edgar Renteria 3958 446 445.44 0.113 0.113 0.00014
Michael Young 4307 536 536.41 0.124 0.125 -0.00009
Jimmy Rollins 4206 499 500.05 0.119 0.119 -0.00025
Ronny Cedeno 3258 398 400.25 0.122 0.123 -0.00069
Hanley Ramirez 4016 466 470.25 0.116 0.117 -0.00106
Alex Cora 1338 163 164.76 0.122 0.123 -0.00131
Geoff Blum 1168 149 150.75 0.128 0.129 -0.00150
Royce Clayton 3338 400 405.01 0.120 0.121 -0.00150
Jose Reyes 3887 443 451.38 0.114 0.116 -0.00215
Angel Berroa 3670 412 420.32 0.112 0.115 -0.00227
Miguel Tejada 4027 465 477.25 0.115 0.119 -0.00304
Derek Jeter 4009 450 464.37 0.112 0.116 -0.00358
Stephen Drew 1475 161 170.45 0.109 0.116 -0.00640
Marco Scutaro 1773 207 218.44 0.117 0.123 -0.00645
Felipe Lopez 4245 438 469.73 0.103 0.111 -0.00747
Aaron W Hill 1273 140 152.71 0.110 0.120 -0.00999

Monday, November 20, 2006

Runs Created by Position (Part 3)

Today, I will finish up my series of posts on runs created analysis with ranks of center fielders, right fielders and designated hitters. Here are the links to the earlier articles:

Detroit Tigers Runs Created Analysis
Runs Created by Position (Part1)
Runs Created by Position (Part2)

The center fielders are ranked in Table 7 below. Grady Sizemore (7.7 R/CG), Rocco Baldelli (6.9 R/G in 387 PA) and Vernon Wells (6.7 RC/G) were the league’s most productive center fielders. Curtis Grandeson was 7th in the league. Granderson was inconsistent this year but he should become more steady next year when he is hopefully better adjusted to Major League pitching. That should mean an increase in runs created per game.



Table 7: Runs Created by AL Center fielders


Rnk

Player

Team

PA

R/G

RC

1

Sizemore

CLE

751

7.7

137

2

Baldelli

TB

387

6.9

66

3

Wells

TOR

677

6.7

112

4

Damon

NYA

671

6.5

107

5

Matthews Jr.

TEX

690

6.3

104

6

Patterson

BAL

499

5.5

72

7

Granderson

DET

679

5.4

93

8

Hunter

MIN

611

5.4

86

9

Figgins

LAA

683

4.9

86

10

Crisp

BOS

452

4.4

52

11

Kotsay

OAK

558

4.3

63

12

Anderson

CHA

405

3.3

36




Table 8 below ranks the right fielders. The league leaders were Jermaine Dye (8.0 RC/G) and Vladimir Guerrero (7.6 RC/G). Magglio Ordonez finished only 8th among players at his position with 6.0 runs created per game. He did stay healthy this year though and that was a big boost for the Tigers. Ordonez averaged more than 7 runs per game for the White Sox from 2000-2003 but he’s not the same player he was before his knee injury. I would expect around the same production next year from Ordonez.



Table 8: Runs Created by AL Right fielders


Rnk

Player

Team

PA

R/G

RC

1

Dye

CHA

611

8.0

117

2

Guerrero

LAA

665

7.6

122

3

Norton

TB

335

6.9

56

4

Suzuki

SEA

752

6.8

121

5

Cuddyer

MIN

635

6.6

101

6

Blake

CLE

456

6.2

69

7

Rios

TOR

498

6.1

77

8

Ordonez

DET

646

6.0

97

9

Bradley

OAK

405

5.8

58

10

Pena

BOS

304

5.7

44

11

Markakis

BAL

542

5.5

75

12

Mench

TEX

349

5.3

46

13

DeRosa

TEX

572

5.3

75

14

Nixon

BOS

453

5.2

57

15

Williams

NYA

462

5.0

59

16

Sanders

KC

358

3.9

39

17

Hollins

TB

355

3.8

37


Table 9 shows that Travis Hafner was the most productive designated this year. He also led all players at all positions with 10.4 runs created per game. Jim Thome (8.9 RC/G) and David Ortiz (8.9 RC/G) finished well behind Hafner but still had outstanding years. No Tigers are listed here because they didn’t have a regular designated hitter. Next year, Sheffield will be their primary DH. He missed much of this year with a wrist injury and finished with just 5.5 runs created per game. However, he finished at 7.3 and 7.4 RC/G in 2004 and 2005 respectively. If he’s healthy, he should come close to that again in 2007.



Table 9: Runs Created by AL Designated Hitters


Rnk

Player

Team

PA

R/G

RC

1

Hafner

CLE

564

10.4

126

2

Thome

CHA

610

8.9

119

3

Ortiz

BOS

686

8.9

137

4

Giambi

NYA

579

8.2

107

5

Thomas

OAK

559

7.2

96

6

Gibbons

BAL

378

5.3

52

7

Hillenbrand

TOR

319

4.9

42

8

Gomes

TB

461

4.5

54

9

Everett

SEA

343

3.5

33

10

White

MIN

355

3.0

30

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