The Moneyball Effect

How MLB position player salaries are determined

Background

• How should we evaluate players?

• Triple Crown statistics—crude and flawed

• Bill James (pictured)—father of sabermetrics

• Sabermetrics is the empirical analysis of baseball in pursuit of objective knowledge about it

• Sabermetric research was still a niche aspect of the game until early 2000s

http://www.foxsports.com/mlb/story/Bill-James-the-man-who-changed-how-we-analyze-baseball-092111

Moneyball

• Published in 2003, focused on strategies of Oakland A’s GM Billy Beane

• Hugely successful—#1 New York Times bestseller and Oscar-nominated movie

• Popularized sabermetrics(OBP, walks) and exposed flaws in baseball groupthink

• Changed the way MLB front offices evaluate players

http://en.wikipedia.org/wiki/Moneyball

Current Market Conditions /Run-Scoring Environment

• Scarcity of offense: why?• Limited availability of free agents• Sport flooded with money from recent TV deals

http://www.hardballtimes.com/wp-content/uploads/2014/08/Judge-1.jpg

Data

• Used baseball statistics from the Lahman baseball database

• Spans 1985 season through 2013 season

• Mix of traditional stats (R, HR, RBI, Avg.) and sabermetrics (wOBA, SecA, ISO, RC) to measure different batting skills

• Combination of career statistics and most recent season stats

• Looking only at arbitration-eligible and free agent-eligible players

http://throughthefencebaseball.com/the-hit-list-most-useless-baseball-statistics-ever/38429

Process

• Basic model follows semi-log function

• Ln(salary) = B0 + B1GSavg + B2R + B3HR + B4RBI + B5RC + B6Avg + B7OPS + B8wOBA + B9SecA + B10BBKratio + B11ISO + B12YearsinMLB+ B13YearsinMLBsq + e

• Dummy variables; C, IF, OF, DH

• Separate regressions for pre-Moneyball and post-Moneyball eras

• Panel data is time-series and cross-sectional, used fixed effects to account for heteroskedasticity and autocorrelation (fixing standard errors in the process)

Aggregate Model ResultsCareer Most recent season

Variable Coefficient T-Stat Coefficient T-stat

constant 5.654496 12.72* 10.95082 95.59*

GSavg .0214179 11.70* .0038353 7.00*

R .0012259 -1.96* .0038035 3.48*

HR -.0029078 -2.04* .0017204 0.52

RBI .0009285 1.58 .0036076 4.04*

RC .0006847 0.79 -.002643 -1.81*

Avg 10.52837 1.75* -2.949597 -1.97*

OPS 8.260738 1.02 3.944636 2.11*

wOBA -12.11063 -0.82 -4.171996 -1.34

SecA .000551 1.47 -.0001244 -0.11

BBKratio .4054353 1.43 .0307032 0.74

ISO 2.483054 0.59 -2.59103 -3.07*

YearsinMLB .2306129 12.94* .4345581 25.12*

YearsinMLBsq -.0125622 -14.48* -.016977 -17.63*

observations 7,782 (1,475 clusters) 7,438 (1,453 clusters)

Regression estimates of ln(salary) aggregated 1985-2013

*means significant at the 10% level

• Most of the skills represented by these stats are significant determinants of salary or nearly so

• wOBA, ISO, runs, and home runs potentially undervalued

1985-2003 ResultsCareer Most recent season

Variable Coefficient T-stat Coefficient T-stat

constant 4.812548 10.16* 10.69811 53.51*

GSavg .0214313 12.95* .0037818 7.01*

R -.0010166 -1.77* .0042812 3.98*

HR .0003011 0.23 .0015807 0.48

RBI .0001063 0.21 .0034555 3.96*

RC .0018103 2.23* -.0031446 -2.17*

Avg 12.99415 2.40* -2.803983 -1.85*

OPS 1.197967 0.16 3.534443 1.77*

wOBA 2.956756 0.21 -3.194013 -0.95

SecA -.0001522 -0.44 .0002442 0.21

BBKratio .1613303 0.61 .0127604 0.30

ISO 5.84307 1.53 -2.40542 -2.81*

YearsinMLB .2368564 12.21* .443404 22.13*

YearsinMLBsq -.0125806 -14.58* -.0168725 -17.44*

observations 7,782 (1,475 clusters) 7,438 (1,453 clusters)

Regression estimates of ln(salary) aggregated 1985-2003

*means significant at the 10% level

• Heavy emphasis on batting average and power (ISO and RBI)

• Not much attention paid to walks or SecA

2004-2013 ResultsCareer Most recent season

Variable Coefficient T-stat Coefficient T-stat

constant 5.99605 13.71* 10.95749 95.14*

GSavg .0220074 12.38* .0039531 7.22*

R -.0012323 -2.04* .0036692 3.37*

HR -.0017564 -1.25 .0013545 0.41

RBI .0006166 1.09 .0035256 3.95*

RC .0011461 1.38 -.0027398 -1.88*

Avg 11.95485 1.96* -2.657011 -1.77*

OPS 7.819687 0.94 3.855477 2.03*

wOBA -14.04781 -0.93 -4.358033 -1.37

SecA .0003033 0.84 -.0000462 -0.04

BBKratio .4432352 1.59 .0415112 1.00

ISO 3.407737 0.83 -2.420432 -2.86*

YearsinMLB .2458825 14.12* .444569 25.40*

YearsinMLBsq -.0125346 -14.61* -.0169879 -17.64*

observations 7,782 (1,475 clusters) 7,438 (1,453 clusters)

Regression estimates of ln(salary) aggregated 2004-2013

*means significant at the 10% level

• Note increased emphasis on OPS, SecA, and BBK• Still a lot of weight given to batting average and

RBI

Conclusion

• The market has corrected somewhat since Moneyball, but traditional statistics still prominent

• Based on aggregate model, most of the statistics appear properly valued, suggesting front offices are competent

• Not surprisingly, batting average and RBI overvalued

• Power (home runs and ISO) and runs surprisingly undervalued

• Could also exploit wOBA