## Monday, June 15, 2009

### MLB: Wins and Payroll, a fast fast fast midseason look

Ok, so this is a really bad study (It took all of three minutes to do), but I found it interesting.

Data was sketchy at best and definitley would not hold up even in class let alone a paper, but like I said it took three minutes.

Y is number of wins right as of 1Am eastern today (June 15) (and yes I know some teams have played more games), X1 is payroll as reported by USA Today, X2 is number of players CURRENTLY on the disabled list.

Wins= 26.85 + (4.75E-08)Payroll
Payroll is significant at the 4% level (t=2.23)

Which is expected, pay more for talent get more wins.

What is interesting is to see how well this simple simple simple model predicts wins. (spreadsheet below). The best? The Red Sox. The Worst? The Nationals. The Mets? about 2 fewer ganes than would be expected (given they have one extra game to play than many teams AND won at least 8/9ths of the Friday game, I found this a good sign). LOL.

Then using a very noisy variable for injuries (Hey I am a Met fan, I get to use injuries as an excuse):

Y is number of wins right as of 1Am eastern today (June 15) (and yes I know some teams have played more games), X1 is payroll as reported by USA Today, X2 is number of players CURRENTLY on the disabled list.

Wins= 29.93 + (4.11E-08)(Payroll) + (-0.51627)(number of players CURRENTLY on DL)

t-stat for payroll 1.93, t stat for players on DL= -1.45

So point estimates are in the expected direction but not significant using normal levels. Tjis might be because of the failure to differentiate injury severity (15 vs 60 day DL), importance of player (a mop up player has the same weight as a Jose Reyes or Carlos Delgado), or the total number of games lost (literally this is just the number of players reported by Yahoo that are CURRENTLY) on the DL.

 Team payroll Injuries wins expected wins based off of payroll Wins - expected wins Yankees 201449189 5 36 36.414706 -0.41471 Mets 148373987 8 32 33.896147 -1.89615 cubs 134809000 3 30 33.252453 -3.25245 red sox 121745999 3 38 32.632579 5.367421 tigers 115085145 3 34 32.316504 1.683496 angels 113709000 3 32 32.251202 -0.2512 phillies 113004046 3 36 32.21775 3.78225 astros 102996414 4 29 31.742861 -2.74286 mariers 98904166 6 30 31.548673 -1.54867 braves 96726166 7 30 31.445322 -1.44532 white sox 96068500 2 30 31.414114 -1.41411 Giants 82616450 2 34 30.775778 3.224222 indians 81579166 7 29 30.726556 -1.72656 blue jays 80538300 5 34 30.677165 3.322835 brewers 80182502 2 34 30.660281 3.339719 cardianals 77605109 4 34 30.537977 3.462023 rockies 75201000 4 31 30.423895 0.576105 reds 73558500 3 31 30.345955 0.654045 diamondbacks 73516666 6 27 30.343969 -3.34397 royals 70519333 7 28 30.201738 -2.20174 rangers 68178798 8 35 30.090673 4.909327 orioles 67101666 4 27 30.039561 -3.03956 twins 65299266 3 32 29.954032 2.045968 rays 63313034 9 34 29.85978 4.14022 As 62310000 7 27 29.812183 -2.81218 nationals 60328000 7 16 29.718132 -13.7181 pirates 48693000 5 30 29.166021 0.833979 padres 43734200 7 28 28.930713 -0.93071 marlins 36834000 5 32 28.60328 3.39672

#### 1 comment:

John said...

You should try to re-run it with the payroll of the players on the injured reserve (if the data is available). I would expect that a player with a higher salary who is on the injured list has a greater negative effect on the record than a no-name player.