Friday, November 10, 2006
Wins and Payroll
1. Use the player salaries table in the Lahman database (1992-2005), and figure a team payroll.
2. Figure an annual average team payroll
3. Divide 1 by 2, to get a payroll index
4. Get that team’s winning percentage
With me so far?
We’ve got, for all 406 teams, the year, winning percentage, and payroll index. The 2005 Yankee payroll is 2.86 times the average. The 1998 beloved Expos are 0.25 times the average.
5. Get each team’s winning record and payroll index in the following year.
6. Get the deltas of both
7. Correlate
We have 376 matches (2005 appears only in the “after” pool, 1992 in the “before”, all other years appear in both). The biggest delta in payroll index was Arizona, 1998/99, from 0.76 to 1.38. They also had the biggest change in winning percentage, from .401 to .618.
The correlation coefficient in the dataset was r=.08. What does this mean? That when teams change their payroll year-to-year, it does little to change their wins. How does this make sense?
First off, if a team keeps say 70% of the same players, those guys will each earn a little to alot more. But it’s the same players. Right away, we don’t expect the wins to change, even though more money is being spent.
Secondly, if you jettison an overpaid veteran, and bring in a “slave”, you save tons of money, and you may lose just a bit of wins, or maybe even gain a little.
Thirdly, if you bring in new players, the players will come disproportionately at slave-wages or overpriced freeagents (feast or famine). They won’t be drawn randomly from the normal distribution of players. Depending on the team, they can go either way, or both ways.
Finally, luck. A team’s record will regress toward .500 even if you don’t change any of your players (specifically, they will perform to the level of their talent).
All to say that the change in payroll spent doesn’t have to lead to wins.
Now, I looked at delta wins, but perhaps I should look at regressed wins in year 1 and compare that to actual wins in year 2. After all, maybe the reason teams made a change in spending is because they didn’t understand the role of luck in wins. So, let’s regress the wins in year 1 by 30%. Our r is now 0.15.
We could have achieved all this with an even simpler correlation. Wins year 1 to wins year 2 is r=.498 (r-squared = .248). Wins year 1 and delta in payroll to wins in year 2 is r=.528 (r-squared=.279). The difference in r-squared is .031, which means r=.176. That is the impact of CHANGING your payroll to impacting your wins (if we assume independence in wins in year 1 and change in payroll).
Finally, here is the overall average, 1992-2005, of team wins and payroll index. Correlation? r = .70. There is an extremely high correlation between wins and payroll.
Wins PyIndx WinsPaid Moneyball
0.608 1.374 0.541 0.067 ATL
0.590 1.766 0.585 0.005 NYA
0.541 1.024 0.502 0.040 CLE
0.541 0.941 0.492 0.049 HOU
0.539 1.373 0.541 (0.002) BOS
0.534 1.089 0.509 0.025 SLN
0.533 1.079 0.508 0.025 SFN
0.529 1.003 0.499 0.030 CHA
0.524 0.776 0.474 0.050 OAK
0.514 1.324 0.535 (0.022) LAN
0.512 1.094 0.510 0.002 SEA
0.503 1.178 0.519 (0.016) ARI
0.498 0.956 0.494 0.004 CIN
0.497 1.076 0.507 (0.010) TOR
0.496 0.987 0.497 (0.001) ANA
0.496 1.191 0.520 (0.024) TEX
0.492 1.193 0.521 (0.029) BAL
0.489 0.521 0.445 0.044 MON
0.486 1.238 0.526 (0.039) NYN
0.485 0.924 0.490 (0.006) PHI
0.482 0.685 0.463 0.019 MIN
0.481 1.103 0.510 (0.030) CHN
0.476 0.804 0.477 (0.001) SDN
0.472 0.704 0.465 0.006 FLO
0.465 0.948 0.493 (0.028) COL
0.454 0.677 0.462 (0.009) MIL
0.454 0.623 0.456 (0.003) PIT
0.443 0.768 0.473 (0.029) KCA
0.421 0.844 0.481 (0.060) DET
0.401 0.630 0.457 (0.057) TBA
Wins is average winning percentage per year.
PyIndx is the average Payroll index per year.
WinsPaid is the best-fit equation, using .113 * PayIndex + .386.
Moneyball is the difference between Wins and WinsPaid.
The WinsPaid shows this: if we assume the league average payroll is 75 million$, then every 75 million$ spent means an extra .113 wins per game, or +18.3 wins in a season. That is, the marginal $ per marginal win is 4.1 million$/win.
So, the relationship between payroll and wins is a little tricky and it depends on exactly what the question is. There’s no question that the driver to wins is the base talent level. That talent level is not necessarily going to be paid at the proper levels year in and year out.
To followup this thread:
http://www.insidethebook.com/ee/index.php/site/comments/competing_on_payroll/
You can calculate wins paid for as:
(P+1)/(P+3)
where P = payroll, indexed to league average
So, a team payroll of 150 million, where the league average is 75 million has a Payroll Index of 2, giving us an expecting winning percentage of:
(2+1)/(2+3)=.600
If their payroll is one-third the league average, then:
(0.333+1)/(0.333+3)=.400
In the first case, the extra 75 million$ bought then .100x162 wins, or 4.6 million$ per win. In the second case, saving 50 million$ cost then the same number of wins, or 3.1 million$ per win.
***
Note, this new equation is of the same form as the other one in the link, but it’s been best-fitted to the data in this new thread.