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May 17, 2010

Ahead in the Count

The Cost of OPP

by Matt Swartz

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After remembering the 1981 hit "Should I Stay or Should I Go" by The Clash with last week’s title on the same topic, we move forward a decade to a 1991 Naughty by Nature hit—and we introduce the money to the equation this time (if you’re down with that).  In this article, I will show that players who re-sign with their clubs on multi-year deals provide far more bang for their buck than players who sign contracts with new teams.

To review what I have done so far, remember that I started by showing that players who signed with their own teams aged better, as evidenced by the fact that they provided more of their value late in their contracts.  In efforts to go through the data more finely for this article, I made some minor changes to the set of players for the sake of a consistent definition, but I reproduce these updated tables below.  For the sake of space and awesome old school rap references, OPP refers “Other People’s Players.”

 

Two-Year Deals

#

WARP3-yr1

WARP3-yr2

% of deal’s WARP3 in year 1

% of deal’s WARP3 in year 2

Re-signings

40

22.6

42.9

35%

65%

OPP

29

16.4

8.5

66%

34%

 

Three-Year Deals

#

WARP3-yr1

WARP3-yr2

WARP3-yr3

% of deal’s WARP3 in year 1

% of deal’s WARP3 in year 2

% of deal’s WARP3 in year 3

Re-signings

15

40.0

39.7

32.3

36%

35%

29%

OPP

33

38.4

39.1

14.5

42%

43%

16%

 

Four-Year Deals

#

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr4

% of deal’s WARP3 in year 1

% of deal’s WARP3 in year 2

% of deal’s WARP3 in year 3

% of deal’s WARP3 in year 4

Re-signings

6

11.2

18.6

16.2

9.8

20%

33%

29%

18%

OPP

13

36.2

31.2

26.2

29.4

29%

25%

21%

24%

Here we saw that players who sign two-year and three-year contracts appear to age much better, and are probably superior bets.  This is not true of four-year deals, but they are a small sample worth exploring, though difficult to draw conclusions.

Despite the evidence that re-signed players aged better, it was certainly plausible that re-signed players were doing better later in deals, but worse early in deals, making the net effect a wash.  I knew that Sean Smith found that those players were underperforming their CHONE projections by 16 percent, so I first checked whether PECOTA had the same problem, and also checked whether there was a difference between re-signed players and OPP.  As it turned out, both groups were underperforming their PECOTA projections by equal amounts.  This data set was different than the previous one, because I looked only at players who began their multi-year contracts in 2007-09, since this was the data for which I had good PECOTA projections.  This analysis compared projected and actual VORP, because WARP3 was recalculated since these projections were generated, and I needed to compare apples to apples.

 

Context

PECOTA

Actual

Difference

Re-Signings

1808.8

1572.6

-13%

OPP

1069.9

910.4

-15%

Combined

2878.7

2483.0

-14%

This topic became even more relevant, after I used the evidence that players who sign extensions with their current clubs as an indication that the Ryan Howard signing may turn out better than most people expect.  Many readers asked whether there was evidence that some of the above results may be biased by age, so I checked and found that even within age groups, the effect of extended players aging better remained strong.

 

Two-Year Deals, ages 26-31

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr1 per player

WARP3-yr2 per player

Re-signings

7

29.6

3.5

8.0

0.50

1.14

OPP

4

30.5

-3.1

-0.9

-0.78

-0.23

 

Two-Year Deals, ages 32-34

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr1 per player

WARP3-yr2 per player

Re-signings

15

33.3

4.3

6.3

0.29

0.42

OPP

8

33.1

3.2

3.5

0.40

0.44

 

Two-Year Deals, ages 35-37

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr1 per player

WARP3-yr2 per player

Re-signings

13

35.8

9.8

19.6

0.75

1.51

OPP

8

35.9

6.5

3.1

0.81

0.39

 

Two-Year Deals, ages 38-47

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr1 per player

WARP3-yr2 per player

Re-signings

5

41.0

5.0

9.0

1.00

1.80

OPP

9

40.0

9.8

2.8

1.09

0.31

 

Three-Year Deals, ages 27-30

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr1 per player

WARP3-yr2 per player

WARP3-yr3 per player

Re-signings

9

29.0

17.9

20.5

13.5

1.99

2.28

1.50

OPP

12

29.2

8.3

15.3

6.6

0.69

1.28

0.55

  

Three-Year Deals, ages 31-34

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr1 per player

WARP3-yr2 per player

WARP3-yr3 per player

Re-signings

4

32.5

16.5

14.9

14.7

4.13

3.73

3.68

OPP

15

32.3

17.4

21.2

3.9

1.16

1.41

0.26

  

Three-Year Deals, ages 35-38

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr1 per player

WARP3-yr2 per player

WARP3-yr3 per player

Re-signings

2

35.0

5.6

4.3

4.1

2.80

2.15

2.05

OPP

8

36.2

12.7

2.6

4.0

1.59

0.33

0.50


 

Four-Year Deals, ages 27-30

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr4

WARP3-yr1 per player

WARP3-yr2 per player

WARP3-yr3 per player

WARP3-yr4 per player

Re-signings

3

30.0

4.4

12.5

8.6

3.3

1.47

4.17

2.87

1.10

OPP

6

29.2

9.0

11.1

10.6

9.4

1.50

1.85

1.77

1.57

  

Four-Year Deals, ages 31-34

#

Avg. Age

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr4

WARP3-yr1 per player

WARP3-yr2 per player

WARP3-yr3 per player

WARP3-yr4 per player

Re-signings

3

32.7

6.8

6.1

7.6

6.5

2.27

2.03

2.53

2.17

OPP

7

32.4

27.2

20.1

15.6

20.0

3.89

2.87

2.23

2.86

Again, we see that with two-year and three-year contracts, players of similar ages continue to age differently.  They provide far more production throughout their deals, while players who sign with new teams instead drop in production rather suddenly.  Of course, these subgroups are small enough that they do not individually suggest evidence of aging differences, but together they do show that age is not an explanation for this effect.  Instead, players who are re-signed by their teams for two-year and three-year deals appear to age better than players signed for similar terms by other clubs.

Although the evidence appears to be that these players are no worse at reaching their PECOTA projections in the first year, and clearly better at outperforming their PECOTA projections in subsequent years, it may be that teams are not actually getting better deals.  If teams are re-signing their players for more money than their PECOTA projection suggests, than there is no difference in production.  In that case, it is an issue of the limits of computers.  Thus, I checked if teams were paying more for production from other team’s players than they are for their own players' production.

Below I list the players involved to give a sense of the talent that was available, and also allow further work on this pertinent and surprising issue.  First I list those players who signed two-year contracts.

 

Two-Year Deals

Re-sign

Years

Age

$MM

WARP3-yr1

WARP3-yr2

Andruw Jones

N

2008-09

31

36.20

-2.0

0.6

Dmitri Young

Y

2008-09

34

10.00

0.2

0.0

Gary Sheffield

N

2008-09

39

28.00

0.3

0.4

Geoff Jenkins

N

2008-09

33

13.00

0.2

0.0

Jermaine Dye

Y

2008-09

34

22.00

2.3

1.3

Joel Pineiro

Y

2008-09

29

13.00

-0.3

2.3

John McDonald

Y

2008-09

33

3.80

-1.5

-0.1

Jose Molina

Y

2008-09

33

4.00

1.6

0.3

Luis Vizcaino

N

2008-09

33

7.50

0.4

0.0

Marlon Anderson

Y

2008-09

34

2.20

-0.6

-0.1

Matt Stairs

Y

2008-09

40

3.25

0.1

0.2

Mike Lamb

N

2008-09

32

6.60

-1.1

0.0

Octavio Dotel

N

2008-09

34

11.00

1.2

1.5

Pedro Feliz

N

2008-09

33

8.50

0.7

2.2

Ramon Castro

Y

2008-09

32

4.60

0.9

0.9

Ron Mahay

N

2008-09

37

8.00

1.4

0.6

Ronnie Belliard

Y

2008-09

33

3.50

1.5

1.5

Ryan Franklin

Y

2008-09

35

5.00

1.7

5.4

Troy Percival

N

2008-09

38

8.00

-0.1

-0.3

Yorvit Torrealba

Y

2008-09

29

7.25

0.2

0.8

Alan Embree

N

2007-08

37

5.50

1.4

0.0

Alex Cora

Y

2007-08

31

4.00

1.2

0.7

Craig Counsell

N

2007-08

36

6.00

1.0

1.5

David Weathers

Y

2007-08

37

5.00

2.9

2.2

Dennys Reyes

Y

2007-08

30

2.00

0.3

1.4

Frank Thomas

N

2007-08

39

18.12

3.4

0.5

Gregg Zaun

Y

2007-08

36

7.25

1.3

1.2

Guillermo Mota

Y

2007-08

33

5.00

0.2

0.8

Henry Blanco

Y

2007-08

35

5.25

-0.3

1.1

Jamie Moyer

Y

2007-08

44

10.50

0.6

2.9

Jay Payton

N

2007-08

34

9.50

0.0

1.6

Jim Edmonds

Y

2007-08

37

19.00

1.6

1.7

Juan Castro

Y

2007-08

35

2.00

-1.3

-0.4

Mark Kotsay

Y

2007-08

31

15.00

-1.0

0.3

Mark Mulder

Y

2007-08

29

13.00

-1.0

0.0

Mike Mussina

Y

2007-08

38

23.00

1.1

5.0

Mike Redmond

Y

2007-08

36

2.00

1.6

0.1

Mike Stanton

N

2007-08

40

5.50

0.1

0.0

Nomar Garciaparra

Y

2007-08

33

18.50

0.1

1.2

Orlando Hernandez

Y

2007-08

41

12.00

3.0

0.0

Raul Ibanez

Y

2007-08

35

11.00

2.4

3.4

Ray Durham

Y

2007-08

35

14.50

-0.7

2.0

Rich Aurilia

N

2007-08

35

8.00

-0.2

0.8

Salomon Torres

Y

2007-08

35

6.50

-0.4

1.7

Scott Spiezio

Y

2007-08

34

4.50

-0.3

0.0

Steve Kline

Y

2007-08

34

3.50

0.0

0.0

Wes Helms

N

2007-08

31

5.45

-0.1

-0.6

Woody Williams

N

2007-08

40

12.50

1.0

0.0

Abraham Nunez

N

2007-08

30

3.35

-1.6

0.6

Brad Ausmus

Y

2006-07

37

7.50

0.5

1.8

Brett Tomko

N

2006-07

33

8.70

0.4

-1.0

Elmer Dessens

N

2006-07

35

3.40

0.8

-0.7

Hector Carrasco

N

2006-07

36

6.10

2.3

-0.4

Jason LaRue

Y

2006-07

32

9.10

1.1

0.0

Jay Witasick

Y

2006-07

33

2.75

-0.1

0.3

Julian Tavarez

N

2006-07

33

6.70

1.4

-0.8

Julio Franco

N

2006-07

47

2.20

0.0

0.0

Kenny Rogers

N

2006-07

41

16.00

2.8

0.3

Mark Sweeney

N

2006-07

36

1.80

0.2

0.1

Neifi Perez

Y

2006-07

33

5.00

-0.3

-0.7

Olmedo Saenz

Y

2006-07

35

2.00

1.2

-0.2

Orlando Palmeiro

Y

2006-07

37

1.90

-0.7

-0.4

Paul Byrd

N

2006-07

35

14.25

-0.4

1.2

Randy Johnson

Y

2006-07

42

32.00

0.2

0.9

Reggie Sanders

N

2006-07

38

10.00

1.3

0.7

Scott Elarton

N

2006-07

30

8.00

0.6

-1.5

Todd Jones

N

2006-07

38

11.00

1.0

1.2

Tony Clark

Y

2006-07

34

2.07

-0.8

0.9

Trevor Hoffman

Y

2006-07

38

13.50

4.1

2.5

Looking through the two-year contracts, we see a number of guys who seem to fit the mold of either risky players whose teams correctly determined that they would age well, and a number of players who seem to fit the mold of teams getting caught fooling around with OPP.  Andruw Jones at 31 years old seemed like a good bet to rebound from the outside.  After putting up matching TAvs of .301 in 2005 and 2006, Jones had fallen to a .257 TAv in 2007 on the back of a .242 BABIP while still hitting 26 home runs.  PECOTA saw him rebounding to .278, but the Braves did not like what they saw and opted to go with Mark Kotsay in center field instead.  Jones’ TAv fell all the way to .175 with the Dodgers in 2008.  Geoff Jenkins had played his entire career for the Brewers but they did not re-sign him after 2007.  His TAv had only gone down slightly from .274 in 2005 to .268 in 2006, and PECOTA saw him splitting the difference at .271 in 2007.  Instead, the Phillies saw him fall to .241, as he lost his role as the lefty half of their right-field platoon in 2008 and they were on the hook for $13 million either way.  Of course, when the Phillies signed Jamie Moyer to a two-year contract as a 44-year old in 2007, they were criticized far more, but his 3.71 ERA in 2008 helped them win a championship.  Relievers Ryan Franklin, Trevor Hoffman, and David Weathers were pretty great deals on the dollar when they re-signed with their teams as well.

Looking through these 69 contracts, we get the following data:

 

Two-Year Deals

Time Period & Type

#

$MM (net of league minimum)

Combined WARP3

$MM/WARP3

2008-09 Re-signed players

11

69.91

18.6

3.76

2008-09 OPP

9

119.69

6.0

19.95

2007-08 Re-signed players

20

168.10

36.6

4.59

2007-08 OPP

8

64.41

10.4

6.19

2006-07 Re-signed players

9

69.46

10.3

6.74

2006-07 OPP

12

83.02

8.5

9.77

All re-signed players

40

308.78

77.4

4.69

All OPP

29

280.81

26.7

10.73

Teams who sign other people’s players are paying 2.29 times as much for a win as teams who sign their own players!  This effect is similar in each of the three years tested.

Moving on to the players who signed three-year deals, we get the following players:

 

Three-Year Deals

Re-sign

Years

Age

$MM

WARP3-yr1

WARP3-yr2

WARP3-yr3

Adam Eaton

N

2007-09

29

24.51

-1.2

-0.4

-1.0

Adam Kennedy

N

2007-09

31

10.00

-1.6

0.8

1.7

Alex Gonzalez

N

2007-09

30

14.00

2.9

0.0

-0.4

Aubrey Huff

N

2007-09

30

20.00

0.6

4.6

-1.9

Bengie Molina

N

2007-09

32

16.00

2.7

4.2

2.0

Chad Bradford

N

2007-09

32

10.50

2.1

1.7

0.1

Danys Baez

N

2007-09

29

19.00

-0.1

0.0

1.7

Dave Roberts

N

2007-09

35

18.00

1.3

0.3

0.0

David Dellucci

N

2007-09

33

11.50

0.2

0.3

-0.8

Frank Catalanotto

N

2007-09

33

13.50

0.9

-0.1

0.9

Jamie Walker

N

2007-09

35

12.00

1.5

-0.4

0.1

Jason Marquis

N

2007-09

28

21.00

0.2

2.3

4.0

Jason Schmidt

N

2007-09

34

47.00

-0.6

0.0

-0.6

Kelvim Escobar

Y

2007-09

31

28.50

5.4

0.0

0.1

Mark DeRosa

N

2007-09

32

13.00

2.6

4.8

2.2

Melvin Mora

Y

2007-09

35

25.00

2.7

3.3

-0.2

Miguel Batista

N

2007-09

36

25.00

2.0

-2.4

0.7

Randy Winn

Y

2007-09

33

23.25

3.1

5.0

2.2

Scott Schoeneweis

N

2007-09

33

10.80

-0.1

0.9

-0.5

Vicente Padilla

Y

2007-09

29

33.75

-0.7

2.2

1.5

A.J. Burnett

N

2006-08

29

31.00

2.0

2.4

1.9

Bob Howry

N

2006-08

32

12.00

2.3

2.4

-0.2

Brad Penny

Y

2006-08

28

25.50

2.7

6.1

-1.7

Braden Looper

N

2006-08

31

13.50

2.0

1.4

1.9

Brian Giles

Y

2006-08

35

30.00

2.9

1.0

4.3

Chipper Jones

Y

2006-08

34

37.00

4.2

7.7

9.1

Esteban Loaiza

N

2006-08

34

21.38

0.2

-0.2

-0.1

Jacque Jones

N

2006-08

31

16.00

2.8

2.3

-1.5

Juan Encarnacion

N

2006-08

30

15.00

0.6

-0.5

0.0

Kyle Farnsworth

N

2006-08

30

17.00

1.1

0.5

0.6

Matt Morris

N

2006-08

31

27.00

1.3

0.4

-1.7

Rafael Furcal

N

2006-08

28

39.00

5.6

3.9

2.1

Ryan Dempster

Y

2006-08

29

15.50

-0.4

0.5

6.2

Scott Eyre

N

2006-08

34

11.00

1.2

0.4

0.6

Tom Gordon

N

2006-08

38

18.00

2.9

0.7

-0.8

Armando Benitez

N

2005-07

32

21.00

-0.4

1.7

-1.2

Carlos Guillen

Y

2005-07

29

14.00

2.8

5.6

3.0

Eric Milton

N

2005-07

29

25.50

-2.1

1.4

0.2

Freddy Garcia

Y

2005-07

30

27.00

3.1

2.5

-0.1

Geoff Jenkins

Y

2005-07

30

23.00

5.0

0.9

2.0

Jaret Wright

N

2005-07

29

21.00

-1.3

1.1

-0.6

Jason Isringhausen

Y

2005-07

32

25.75

3.8

2.2

3.3

Jon Lieber

N

2005-07

35

21.00

1.8

1.1

0.2

Livan Hernandez

Y

2005-07

30

21.00

3.3

2.1

2.5

Michael Barrett

Y

2005-07

28

12.00

2.1

1.4

-0.3

Odalis Perez

Y

2005-07

28

24.00

0.0

-0.8

0.4

Omar Vizquel

N

2005-07

38

12.25

3.2

3.3

3.8

Shawn Green

N

2005-07

32

32.00

1.8

0.2

1.1

The Braves kept up with the right Jonses—they dropped Andruw, but re-signed Chipper to a three-year deal for 2006-08 in which he produced 21.0 WARP3 at a cost of $37 million.  PECOTA had seen him starting the deal as a 34-year old with a .303 TAv, but he posted .334, .347, and .365 TAvs during the contract and provided the Braves a lot of bang for their buck.  Brian Giles and Carlos Guillen provided great values in extensions as well.  On the other hand, Jason Schmidt was paid $47 million over three years by the Dodgers to sit on the disabled list and not contribute at all.  Adam Eaton and Gary Sheffield were poor values as well.

This data is summarized by the following table, showing the same effect:

 

Three-Year Deals

Time Period & Type

#

$MM (net of league minimum)

Combined WARP3

$MM/WARP3

2007-09 Re-signed players

4

105.82

24.6

4.30

2007-09 OPP

16

267.09

38.2

6.99

2006-08 Re-signed players

4

103.61

42.6

2.43

2006-08 OPP

11

208.81

38.5

5.42

2005-07 Re-signed players

7

139.59

44.8

3.12

2005-07 OPP

6

126.61

15.3

8.28

All re-signed players

15

349.02

112.0

3.12

All OPP

33

602.51

92.0

6.55

For three-year contracts, teams are paying 2.1 times as much for other people’s players on a per win basis!  This is a shocking discrepancy, and should not be taken lightly.  It must be that either players are taking extraordinary hometown discounts (and it is hard to imagine that this is true on such a large scale), or there is a severe market inefficiency.

This inefficiency does not appear to exist for players who sign four-year contracts. There are only 19 such players, and only six re-signings, so most likely this is just a sample-size issue, but here are those players:

 

Four-Year Deals

Re-sign

Years

Age

$MM

WARP3-yr1

WARP3-yr2

WARP3-yr3

WARP3-yr4

Billy Wagner

N

2006-09

34

43.00

3.9

3.4

1.4

0.6

Hideki Matsui

Y

2006-09

32

52.00

1.4

3.5

1.3

2.7

Jarrod Washburn

N

2006-09

31

37.50

1.3

1.9

1.0

3.1

Johnny Damon

N

2006-09

32

52.00

4.6

2.4

4.1

4.9

Placido Polanco

Y

2006-09

30

18.40

3.0

5.1

2.4

3.4

Ramon Hernandez

N

2006-09

30

27.50

3.7

0.7

0.6

1.3

Tim Hudson

Y

2006-09

30

47.00

0.8

5.6

3.8

1.0

Carl Pavano

N

2005-08

29

39.95

-0.6

0.0

0.0

0.0

Carlos Delgado

N

2005-08

33

52.00

5.1

3.7

1.8

4.5

Cristian Guzman

N

2005-08

27

16.80

-2.2

0.0

1.2

5.0

Derek Lowe

N

2005-08

32

36.00

2.3

4.7

2.6

4.6

Edgar Renteria

N

2005-08

29

40.00

1.7

4.2

6.1

0.7

Garret Anderson

Y

2005-08

33

48.00

0.7

1.3

2.1

1.8

Jason Varitek

Y

2005-08

33

40.00

4.7

1.3

4.2

2.0

Jose Vidro

Y

2005-08

30

30.00

0.6

1.8

2.4

-1.1

Orlando Cabrera

N

2005-08

30

32.00

2.3

3.1

3.3

2.7

Pedro Martinez

N

2005-08

33

53.00

5.2

0.6

0.6

-0.8

Richie Sexson

N

2005-08

30

50.00

4.1

3.1

-0.6

-0.3

Ivan Rodriguez

N

2004-07

32

40.00

4.8

3.4

4.1

3.1

The four-year contract do not really show much of a clear pattern. Of course, with only six extensions in the set, it is difficult to discern much of a pattern.  Garret Anderson and Jose Vidro drag the extensions down, while the Johnny Damon and Ivan Rodriguez deals make you want to get down with OPP after all.

The summary statistics are as follows:

 

Four-Year Deals

Time Period & Type

#

$MM (net of league minimum)

Combined WARP3

$MM/WARP3

2006-09 Re-signed players

3

112.9

34.0

3.32

2006-09 OPP

4

154.0

38.9

3.96

2005-08 Re-signed players

3

113.8

21.8

5.22

2005-08 OPP

8

308.4

68.7

4.49

2004-07 Re-signed players

0

-

-

-

2004-07 OPP

1

38.7

15.4

2.51

All re-signed players

6

226.7

55.8

4.06

All OPP

13

501.1

123.0

4.07

Clearly, this is the exact same dollar cost of a win and there is no evidence of this effect among players who sign four-year deals.

Regardless, there is overwhelming evidence that re-signed players and newly signed players are not aging similarly and are being compensated very differently.  Please look through the list of players, and discuss in the comments any issues you would like to see addressed in future research.  It may be that the re-signed players have something else in common, but it does not appear to be age or anything that teams are aware of when they award heavy compensation to other teams’ free agents. 

When you know your own free agents better than other teams' free agents, you can better determine which players will be worth their market rate, and pay them.  For their part, players who are safe bets to age well should be asking more from their teams, all the while justifying this by singing “yeah, you know me.”

Matt Swartz is an author of Baseball Prospectus. 
Click here to see Matt's other articles. You can contact Matt by clicking here

32 comments have been left for this article. (Click to hide comments)

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TangoTiger

Great stuff, and thanks for posting all the research. This is just how I like to see this done, and this will be fun to go through.

May 17, 2010 07:23 AM
rating: 1
 
TangoTiger

Two pieces of information that I would like to see:
1. the number of PA or IP in the year prior to signing
2. the WARP in the year prior to signing

The reason is to see if there is a bias there, that perhaps the overpaid players are the scrub players, not the star players. This is critical.

Just for fun, I'll try to approximate that. I took your list of 48 players that signed 3 year deals. If in any of the three following season they had any season with at least 2 WAR, then I will *presume* they were one of the good players in the season prior to resigning (again, I'm ONLY using this as a proxy, until Matt can generate this critical piece of data).

We had 20 players who signed a 3-yr deal at the age of 30 or under. Of those 20, 13 were "good" (based on the above definition) and 7 were not.

Of these 13 good players aged 30 or under, 8 resigned with the same team, and 5 signed with new teams. The 8 same-teamers averaged a 21.5MM$ deal, for a total 3-year WARP of an average of 6.5 for each player. That's 3.3MM$ per win. The 5 new-teamers were paid an average of 25MM$ and generated an average of 6.0 WAR. That's 4.2MM$ per win.

Obviously, this is only 8 and 5 players, so, we've got huge uncertainty bands here. But, this is the way I'd like to see the analysis progressing.

The focus should be on players who are considered good and who are no older than 30 (maybe 31). Under this more focused view, though with limited sample, Matt might be showing something interesting.

More work needs to be done.

May 17, 2010 09:29 AM
rating: 0
 
BP staff member Matt Swartz
BP staff

Tango, it appears you've selected on the dependent variable. Put it this way-- if you remove the OPP guys who failed to produce after the deal was signed, you're eliminating a lot of data that could be the exact evidence of what we are looking for.

Why is the fact that Adam Eaton and Jason Schmidt were disastrous three-year deals a lack of evidence that the Rangers and Giants knew something that the Phillies and Dodgers didn't get????

The point here is that the money is a proxy for how good they were before the deal. Eventually, I can dig up the WARP in previous years (if anybody has added to the data sets I provided above, please let me know), but the money they were signed for is the real value. I'm in agreement that this work isn't done, but the cost per win is really strong evidence that these groups are dissimilar for the reasons mentioned.

What we have here is evidence that it cost more to get wins from OPP than your own re-signed guys. Regardless of how good or bad the players were beforehand, the players allowed to reach public bidding were real bad contracts on average.

May 17, 2010 10:07 AM
 
TangoTiger

"Tango, it appears you've selected on the dependent variable. "

Matt, I obviously didn't make myself clear. Of course you CANNOT do what I did. I said this:

"again, I'm ONLY using this as a proxy, until Matt can generate this critical piece of data"

I shouted that out! Therefore, there's no reason for your first two paragraphs, since I already conceded that you can't do what I did.

***

I'm trying to show if there's bias in the data. I would like to have the previous season (or PECOTA actually) WARP and PA or IP. You are suggesting that a good proxy of that is the salary signed. That's a good enough suggestion (though that is still a proxy for what we want).

If we break them up the 48 3-yr players in thirds, we can put the 16 guys who signed for more than 24MM in the "great player" pile, the 16 at under 16MM in the "average player" pile, and the other 16 in the "good player" pile.

Here's what we have:
GREAT players
8 same-teamers, 29MM$, 8.2 WARP, 3.5MM$ per win
8 new-teamers, 31MM$, 2.1 WARP, 14.8MM per win (!!!)

Six of the 8 new-teamers were pitchers, only 2 were position players.

GOOD players
4 same-teamers, 23MM$, 6.4 WARP, 3.6MM$ per win
12 new-teamers, 19MM$, 2.7 WARP, 7.0MM per win

Eight of the 12 new-teamers were pitchers. Three of the 4 new same-teamers were pitchers.

AVERAGE players
3 same-teamers, 14MM$, 7.0 WARP, 2.0MM$ per win
13 new-teamers, 12MM$, 3.2 WARP, 3.8MM per win

Yes, very damning, if the salary paid can be used as a proxy for talent.

***

It seems to me that pitcher v nonpitcher deserves its own breakdown. I count 28 pitchers, 20 non-pitchers.

PITCHERS
8 same-teamers, 25MM$, 5.5 WARP, 4.5MM$ per win
20 new-teamers, 20MM$, 1.8 WARP, 11.1MM per win (!!!!)

NON-PITCHERS
7 same-teamers, 23MM$, 9.7 WARP, 2.4MM$ per win
13 new-teamers, 18MM$, 4.4 WARP, 4.1MM per win

First thing we see is: pitchers are way overpaid. So, that's going to be a huge source of bias, and therefore, if you have any subsample that doesn't have the same pitcher to non-pitcher split, that's a bias.

Even so, we still show a huge split. So, this still goes toward Matt's point.

Chipper Jones is one of the 48 players, generating 21 wins and being paid only 37MM. Now, it's certainly not like the Braves knew something that everyone else didn't here. But, any sample that Chipper is a part of will end up looking really really good for that sample.

That said, the most striking thing in the sample, and this goes toward Matt's point is this: of the 8 pitchers who generated the most wins, 6 did it with the same team, and 2 with new teams. Of the 20 pitchers who generated the fewest wins, 18 did it with new teams, and 2 with the same team. Here's the list of pitchers, with an "x" meaning same-teamers:
x Jason Isringhausen
x Livan Hernandez
x Brad Penny
Jason Marquis
A.J. Burnett
x Ryan Dempster
x Kelvim Escobar
x Freddy Garcia
Braden Looper
Bob Howry
Chad Bradford
Jon Lieber
x Vicente Padilla
Tom Gordon
Kyle Farnsworth
Scott Eyre
Danys Baez
Jamie Walker
Miguel Batista
Scott Schoeneweis
Armando Benitez
Matt Morris
Esteban Loaiza
x Odalis Perez
Eric Milton
Jaret Wright
Jason Schmidt
Adam Eaton

Notice the clumping.

The non-pitcher list certainly looks much more random than this, though a bit of clumping at the bottom:
x Chipper Jones
Rafael Furcal
x Carlos Guillen
Omar Vizquel
x Randy Winn
Mark DeRosa
Bengie Molina
x Brian Giles
x Geoff Jenkins
x Melvin Mora
Jacque Jones
Aubrey Huff
x Michael Barrett
Shawn Green
Alex Gonzalez
Frank Catalanotto
Dave Roberts
Adam Kennedy
Juan Encarnacion
David Dellucci

So, I think Matt has a great point, at least as it pertains to pitchers. And, I think, it's easy to explain that. Pitchers get injured far more, their talent hinges on their arm, and the teams know far more on their own pitchers.

I will disagree with Matt with regards to Ryan Howard, for three reasons:
1. Howard is not a pitcher
2. Howard is young
3. Howard was signed two-years out

Therefore, his discovery here, which looks great, won't apply very much, if at all, to Ryan Howard. Basically, Howard's representative group is so extremely limited, that it's hard to look at these results to try to infer something about the Phillies and Howard.

Matt's research stands well so far, on its own, and it doesn't need any tie-in to Ryan Howard to carry weight.

May 17, 2010 11:00 AM
rating: 2
 
BP staff member Matt Swartz
BP staff

I like the idea to look into hitters and pitchers separately! I forgot about that. I had started with a few splits into this issue in the first article on this topic a couple months ago, but I should have checked it again when it came to money. Thanks for breaking it down.

I do agree that Howard isn't the best example of a player who is likely to age differently than others. I think the only reason that he would be an example of the "information asymmetry" issue is because of the supposed workout plan. However, Howard was the big extension contract du jour a couple weeks ago, and I mentioned it in the article as a reason. I hate hitching my analyst wagon to the "Howard deal is good" simply because I said "it might not really be that bad." I'm still not thrilled about it as a fan of the team. Still, the comment about not needing any tie-in to Howard isn't really relevant. The Howard article was a great way to re-broadcast this research which got burried without much comment until Howard was signed. I'm a practical guy, and I knew that had a good strong enough relationship to my result that it was worth re-stating clearly.

Chipper, on the other hand, is an injury-ridden superstar. Clearly that makes him a better candidate to be a bargain based on information asymmetry. This is all the more relevant because it's the Braves, who are by all reports a tremendous scouting team who does the due diligence on these type of issues. If the Braves think Chipper's unhealthy after his deal is up and don't re-sign him, I sure don't want him.

May 17, 2010 12:33 PM
 
TangoTiger

Focusing on the 19 guys with the 4-yr deals, 7 were pitchers, and 12 non-pitchers.

The 7 pitchers had only one pitcher resign (Hudson, 47MM$, 11 WARP, 4.3MM$ per win). The 6 pitcher who were new-teamers averaged 39MM$, 7 WARP, 5.6MM$ per win. Again, pitcher inefficiency once more.

The 12 nonpitchers had 5 same-teamers (38MM$, 8.9 WARP, 4.3MM$/win) and 7 new-teamers (40MM$, 11.6 WARP, 3.4MM$/win).

This last one goes against what Matt is saying.

***

Merging the three year and four year players, this is what we get:
35 pitchers, 25.5MM$, 3.8 WARP, 6.7MM$ per win
32 nonpits, 27.0MM$, 7.8 WARP, 3.5MM$ per win

So, a huge source of bias is how much pitchers are getting paid. This should come as no surprise to anyone.

The breakdown of pitchers:
9 same-teamers, 27.6MM$, 6.2 WARP, 4.5MM$ per win
26 new-teamers, 24.9MM$, 3.0 WARP, 8.3MM$ per win

The breakdown on non-pitchers:
12 same-teamers, 29.4MM$, 9.4 WARP, 3.1MM$ per win
20 new-teamers, 25.7MM$, 6.9 WARP, 3.7MM$ per win

11 same-teamers, 28.7MM$, 8.3 WARP, 3.5MM$ per win (excludes Chipper Jones)

***

The strongest conclusion is: pitchers are severely overpaid relative to non-pitchers.

The second strongest conclusion: teams signing someone other than their own pitchers to long-term deals are taking a huge risk.

Thanks Matt for the data. This second one really surprised me to the extent it's happened.

May 17, 2010 12:42 PM
rating: 0
 
BP staff member Matt Swartz
BP staff

Interesting. I didn't think about the four-year deals being biased that much, but the breakdown of pitchers and non-pitchers is perfect. It's the same result after all!

I think this only says that pitchers are overpaid if the replacement level is perfect for both. If the replacement level for pitchers is overestimated or if the replacement level for hitters is underestimated, it may be fine.

One thing that this makes me think is that maybe there is a rational basis for teams over-drafting pitchers. They seem to be such bad bets based on Sky Andrecheck's draft pick analysis, consistently producing less than hitters drafted in similar rounds. Hitters on the free agent market being better bets than pitchers might make reevaluate this. I guess it would depend on vacancy availability to sign hitters. i.e. how many lineup spots are replacement level for a given team vs. how many rotation spots are?

May 17, 2010 13:04 PM
 
Mr. Cthulhu

Great article Matt! The only downside is now I will have that song stuck in my head all day (although maybe that's not a downside).

May 17, 2010 07:27 AM
rating: 0
 
Jivas
(649)

I was wondering exactly how you were going to address what that last "P" stood for with respect to this article. :)

May 17, 2010 07:54 AM
rating: 0
 
Rowen Bell

I guess he did it, ahhh, sorta properly.

May 17, 2010 08:40 AM
rating: 0
 
BP staff member Joe Sheehan
BP staff
(17)

It stands for property.

May 17, 2010 08:55 AM
 
Richie

Just great stuff.

May 17, 2010 09:16 AM
rating: 0
 
Josh Sklar

Just thought I'd add it's really great to have full tables in the article itself.

May 17, 2010 10:57 AM
rating: 0
 
irichmon

Hi Matt,

I know this would take a lot more work, but is there enough information to do this study over a larger time span? I'm curious to know if this has always been true for free agent signings, if it's an anomaly, or if it's a recent trend.

May 17, 2010 11:00 AM
rating: 0
 
kantsipr

I agree with this. I'd be very interested in being able to break this out in a matrix of value (scrub through superstar) by age range. That would require doing this sort of analysis for a longer time period in order to get statistical significance. It would also be interesting to take this sort of analysis (or maybe just MORP) and trace the history of the free-agent marketplace to see if it is becoming more efficient or whether there are any transients after significant rule changes.

I wonder if draft pick compensation plays any role in this. Since I get an additional pick if I let a player go, I'll be more risk-averse in resigning players. On the flip side, the team who signs that player has the reverse problem, but it seems likely that, as Matt points out, the original team will be better able to balance the relative value.

May 17, 2010 11:16 AM
rating: 0
 
BP staff member Matt Swartz
BP staff

I definitely agree the opportunity cost of re-signing teams is larger because of the supplemental sandwich pick, which effectively costs them 2 picks for signing a player instead of 1. I'm not sure that would explain the difference though, given the magnitude. The extra pick is probably worth about $6MM per contract. Also, if there were perfect competition between teams, the hometown teams would simply be outbid because of the extra "tax" of the sandwich pick.

May 17, 2010 12:37 PM
 
BP staff member Matt Swartz
BP staff

I agree a longer time period is a good idea. I don't have service time information that goes back all that far, so it makes it somewhat difficult to discern which extensions cover arbitration years and which extensions cover free-agency eligible years. If I could get my hands on this data, that would be great. It took a long, long time to aggregate all this data already, though. I agree that more would be ideal to see this effect through time.

May 17, 2010 12:35 PM
 
uoduckfan33

In Tango's breakdown of Pitchers vs. position players, this sample size of 48 would suggest there is more market inefficiency with pitchers. Someone above mentioned that home teams should know more about pitcher injury potential, and it would seem that looking at some sort of "innings lost due to injury" from among the re-signers vs. the OPPs could shed some light on this.

If it is an injury issue among pitchers, then that would probably be the most plausible cause of the re-signers outperforming the OPPs. But if injury doesn't seem to have much of an effect, perhaps it is more that once a player opts for free agency, bidding wars from desperate clubs drive his price up above his true value. If a player resigns early, he probably didn't test the market much, and his price was not driven up.

I'm just fishing for some causation here, because while the data presented is interesting, it gains a lot value if we know WHY...

Thanks, Matt :-)

May 17, 2010 11:53 AM
rating: 0
 
uoduckfan33

when I said "home teams", i meant original teams that the player was already with...

May 17, 2010 11:54 AM
rating: 0
 
BP staff member Matt Swartz
BP staff

The players who signed deals with the hometown clubs still may not have given hometown discounts. Certainly this magnitude of hometown discount would appear unlikely.

Economically, the lack of testing the market shouldn't be an issue on its own, because the player and team should both have a good approximation of how high bidding wars go, and the player should want his hometown team to match this approximation. It seems unlikely that player agents would perpetually undervalue the effect of a bidding war. If they expected a bidding war, they would typically force the hometown team to bid up for it.

Really, I think this comes down to information asymmetry. That's the "why" in my opinion at least. The evidence that pitchers show a more extreme effect probably adds to this case.

May 17, 2010 12:39 PM
 
kantsipr

Well, if you characterize it as a "hometown discount," I agree it is unlikely that players would give this magnitude of one. But what if you characterize it as risk management? A player also has more information about the team he was on previously than the one he's going to. I don't think it's surprising that that would be reflected in a discount rate. Many if not most people will take a reduced payment to decrease risk.

May 17, 2010 13:00 PM
rating: 0
 
kantsipr

I also think it could be very interesting to compare this with MORP. A lot of the effects we're starting to discuss would be more pronounced in MORP. That should be where "hometown discounts" would show up most clearly.

May 17, 2010 13:04 PM
rating: 0
 
BP staff member Matt Swartz
BP staff

Very good point. I got rid of contracts that covered arbitration years in this round of analysis to make sure that the risk-aversion of that sort wasn't mixing in with the results, but I actually think that there could be an element of exactly what you're talking about. Players don't want to move their families, etc.

On the other hand, that probably isn't the whole issue, because otherwise pitchers must be far more risk-averse individuals than hitters are!

May 17, 2010 13:06 PM
 
Michael Bodell
(89)

Yeah, rather than aging issues and extra information it really all could boil down to home team discounts. I mean Chipper Jones is a good example of that from Tango's look at the data.

Another will be Halladay with his contract with the Phillies. In no way was his extension anything near what he would have got on the free market, but he was willing to do it to play for the team he wanted.

Some free agents might be more willing to take a discount to play where they are comfortable, and free agents taking a discount are thus more likely to be signed by their teams.

So the correlation might be more that the contracts by players who resign are cheaper than they would be on the free market, rather than the contracts are the same as they would be if someone else were to sign them but the value produced different.

May 17, 2010 15:33 PM
rating: 0
 
BP staff member Matt Swartz
BP staff

I have a hard time believing the hometown discounts are 50%, especially because the real difference in the $/win performance from these players is a result of performance late in the deal, not early. If it were only hometown discounts, you would expect the WARP3 values to decline at the same pace for both re-signed players and OPP, but instead you see the real production different late in the deal. That smells like something far more than a half-off discount given to the hometown team.

May 17, 2010 15:41 PM
 
TangoTiger

I took Matt's data for the 3-yr and 4-yr players, parsed it, added a few columns of my own (to match the discussion point above), and have posted it here.

If there are any errors or additions, please email me.

This is what sabermetrics is all about.

May 17, 2010 12:58 PM
rating: 0
 
BP staff member Matt Swartz
BP staff

NOTE: Jason LaRue shouldn't be there in the two-year deals. Just realized he was a few days short of free agency when he signed the two-year deal in the off-season of 2005. Doesn't really change much analysis wise, but should be change in the dataset.

May 17, 2010 18:42 PM
 
BP staff member Matt Swartz
BP staff

AND Olmedo Saenz too. He wasn't eligible for free agency when he re-signed either. Again, doesn't change the results but worth mentioning.

May 17, 2010 18:47 PM
 
uoduckfan33

I want to look at an aging issue with this sample. Just looking at the 41 players who signed three-year contracts in your spreadsheet (26 new team, 15 re-signers), I ran a couple tests. First, the third-year WAR was significantly higher among the re-signers using a t-test for similar means at the 5% level.

However, I then looked at how much each player regressed (or improved) between year 1 and 3 (simply WAR3 - WAR1). Those two means were quite similar (-.7 vs -.5, respectively) and were not significantly different at the 5% level.

So I guess I might assert that re-signers in this sample provide significantly more WAR, but do not seem to age differently in terms of raw WAR decline, if you will.

Changing my metrics of interest to WAR/Salary, I found that the difference between the WAR/Salary of the re-signers vs. the new teamers was NOT significant in the third year, despite means of 0.28 vs. 0.9 WAR/(Salary/3), and that the regression of that metric from year 1 to year 3 was also not significantly different between the two subsets of players.

In my opinion, there IS a sample size problem here. Though I did use a 5% level test, which is quite strong.

May 19, 2010 11:30 AM
rating: -1
 
BP staff member Matt Swartz
BP staff

How can you run t-tests?? What did you do for a standard deviation??

I don't think sample standard deviation makes any sense at all in this case, and I don't think you can make the necessary assumptions about normality or whatever else would be required to even assume a sample standard deviation works.

Also, the means for the three-year deals are very different so any changes in expected average wins is particularly useless as a measure. The third year new signings lost half of their value...how can you compare that to the re-signings who lost only 20% of their value?

I don't see how you can run t-tests really at all on that.

Nextly, there are 33 new signings of 3-year deals, not 26. So right there I'm not sure what you did either.

Also, where are you getting 0.9 WAR/(Salary/3)? Maybe 0.09? Is that what you meant? Why did you use wins per dollar instead of dollars per win? I can't imagine wins per dollar had a normal distribution either, so you're really confusing a number of things I think.

The three-year re-signed had a 3.6 WARP3/salary in the third year, and the newly signed had a 13.9 WARP3/salary in the third year. That's different by nearly 400%. If whatever test you're running doesn't find that significant, you're not running a useful test.

It's possible that whatever tests you're trying to run, even with the standard deviations that you could even use to make sure, just require a larger sample size. Someone on on another website checked my thesis for 1990-2009 and found that the results were on a similar scale by slightly smaller for this period of time. So if your post is a way of saying "we need more data," that might be a good place to start.

May 19, 2010 14:34 PM
 
uoduckfan33

I'm sorry, I miscounted the 26, and also meant 0.09 WAR/$1M for the third year of the new teamers. In re-running the t-test (see below about standard devs and means*), with 33 as my group 2 n-value, the difference became significant at the 5% level. I had simply entered the wrong n, making the group size smaller than it really was and fudging the t-test.

*The means and standard deviations were from the sample of 48 3-year signers (I think I counted right this time :-))

Why don't you believe that this data will fall into a normal distribution? Looking at all player's WAR / $1M in year 3, the data seems to clump around the sample mean, 0.15 WAR/$M, and gets sparser and sparser as we move away. A histogram shows this. Now when looking at the data for the DIFFERENCE between WAR/$M in year 3 vs. year 1, the data was somewhat bell-shaped (from those 48 players), but didn't seem to clump around the mean, so I'm not sure about that distribution.

I also don't believe that changes in WARs from year1 to year3 are useless data. While the means are different, looking at how a player ages in his contract must be relative to his ability. Whether you choose to do that as a percent of WAR lost, or a raw difference is a matter of what you're trying to measure. Saying that Geoff Jenkins (5.0 WAR1, 2.0 WAR3) got 2.5 times worse from 2005 to 2007, or that he got 3 WAR worse are both reasonable things to look at, and I'm not sure that one is more telling than another. (Thoughts?)

Also does it really matter whether you look at WAR/salary, or salary/war? As long you you know what you're looking at. I wanted an idea of how many wins a player provided per million dollars he was paid, as a way of measuring his on-field value per dollar earned.

Looking at the difference in WAR/$M between the new teamers and the re-signers, the former averaged a -0.12 WAR/$M loss while the latter a -0.06 loss. Using the sample standard deviations I did my t-test. I'm not about to argue that a t-test is usable, but I'm not so sure that it isn't, either. I'd like to hear your take.

And thank you :-)


May 19, 2010 19:52 PM
rating: 0
 
BP staff member Matt Swartz
BP staff

I mean, the t-test definitely makes more sense to run if the data has a unimodal nonskewed distribution, because that will at least look normal enough to make a t-test relevant. The thing is that the data is so incredibly different that really any t-test will either tell you "there is enough data assuming that the samples aren't biased" or "there is not enough data". The only thing a t-test can tell you is if you're ready for a t-test, because it would be a problem that the sample size was too small if the t-test failed to reject the null. Failed to reject the null is all you could say...certainly not enough to say you accept the null.

$/WARP or WARP/$ seems like a judgment call but it would probably be based on what the data was shaped like.

I guess looking at changes in total WARP vs. % WARP change is useful too, but it won't grab the injury effect for mediocre players (because going from a 1.0 WARP to a 0.0 WARP injured player isn't that much of a change, but going from a 5.0 WARP to a 2.5 WARP player will seem much worse). I think that could actually be a good way to look at things, but I just think it's tricky with playing time. Maybe absolute changes could be good for looking at rate stats? I'd need to think about that. Absolute changes in counting stats relative to a replacement level just could be really biased, especially for two-year deals often given to players not that far above replacement level. You did look at three-year deals at least though.

I see a little more what you're doing now, but be very careful not to read too much into t-tests. The real question is (a) if we have biased samples, rather than (b) if we have enough data. The "is the difference large enough" question is obviously not legitimate. The question is really about the sample size being large enough to confirm the difference matters, because the difference in performance is huge.

May 20, 2010 13:34 PM
 
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