Monday, January 12, 2009

How does a players ability and the team he signs withs payroll, performance and home team discount measure how much of a salary he will be offered/acc

Question: How does a players ability and the team he signs withs payroll, performance and home team discount measure how much of a salary he will be offered/accept?

Why I asked the question: I was wonder the amount that smaller teams have to pay more than better (more payroll and/or higher winning percentage) teams to get good talent to come to their team.

Analysis: I decided to look at the 2007-2008 free agent class when doing this initial study (will expand as time permits and suggestions are taken into account) The variables for most of the data was pretty strait forward.

  • Free Agents – I used the ESPN list of free agents, their salary and if they switched teams (home team discount). Only players that were given Major league contracts and that had a VORP from the previous season were used.

  • Total team salaries – I looked at the final 2008 value.

  • VORP – list both VORP for pitchers and hitters. The players VORP from the previous season were only used. Most free agent decisions are based on what the ball player has done recently.

Note: As you can tell the data was from several different sources, so if there happens to be any inconsistencies or transposition errors, please let me know.

After running a multiple regression for a predicted salary against payroll, VORP, wins over .500 and home team discount I got the following values:

Factor Salary Amount
Base Salary 108,500
Extra paid per win over 500 12,000
Hometown Discount -108,000
1 point of VORP above league average 209,000
Amount paid per 10,000,000 in total payroll 233,398

r-squared: 0.622

Standard Deviation of: 3,000,000

This wasn't what I expected. It seems that teams that are winning actually pay more for the same level of talent as teams that win less and those with higher payrolls over pay (i.e. Yankees). This is probably do to the bidding wars that teams get into for the top talent. Also, the home discount of ~1 million is much more of factor than the team's winning percentage that signs the player.

For example let's look at Mark Loretta's contract of 2.75 million. He starts with a base salary of 108,500 and almost gives it all up with his hometown discount of -108,000 since he stayed with Houston. His VORP of 10.5 leads to a salary of 2,000,000. While Houston being 8 games below .500 means they paid 95,000 less, but their payroll of 89 million means he should of another 2.1 million more. His estimated salary would be about 3.2 million or 450,000 less than he is currently making.

As I am a huge fan of charts, here are some of more of the results:

Top ten Overpaid Players:

Name Team Signed by Amount over paid
Andruw Jones LA Dodgers 13,933,330
Torii Hunter LA Angels 6,542,104
Mariano Rivera NY Yankees 6,132,745
Francisco Cordero Cincinnati 5,884,827
Jose Guillen Kansas City 4,969,959
Eric Gagne Milwaukee 4,956,344
Jason Jennings Texas 4,132,468
Alex Rodriguez NY Yankees 3,760,672
Andy Pettitte NY Yankees 3,755,589
Kip Wells Colorado 3,552,833

Top Ten Underpaid Players:

Name Team Signed by Amount under paid
Jorge Posada NY Yankees 5,919,700
Chad Durbin Philadelphia 4,702,272
LaTroy Hawkins NY Yankees 4,687,160
Shawn Chacon Houston 4,367,667
Sean Casey Boston 4,280,449
David Riske Milwaukee 3,521,124
Josh Fogg Cincinnati 3,356,606
Matt Wise NY Mets 3,310,750
Doug Brocail Houston 2,923,741
Chad Paronto Houston 2,868,081

Three players that projected to an actual negative salary (they should have paid the team that signed them to play):

Name Team Amount owed to team
Emil Brown Oakland -740,500
Jason LaRue St. Louis -326,622
Jason Jennings Texas -132,468

The amount underpaid (+ values) or overpaid (- values) per position

Position Amount Underpaid/Overpaid
1B 2,673,270
LF 1,063,523
C 617,522
RP 192,965
SP 79,669
3B -74,550
2B -256,537
SS -385,656
RF -1,898,907
CF -4,999,808

I looking at expanding the study to include more years. I have the data for 2007-2008 off season, but salaries and Free Agent signings before that are not as readily available. I would also like to incorporate any other factors that might help explain the data. Finally I might remove the games over .500 from the regression analysis since it has very little effect, but I want to wait to see if any other factors are added.

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