We consider correlations between pairs of teams when deriving a win production function for professional baseball teams. Previous empirical studies derived win production estimates based on the assumption that the error terms were independent, but this is not reasonable given that a win is a relative output. The total number of wins per season is fixed: all teams share wins, in that a win for one team is a loss for the opponent. We applied spatial panel data models to a Major League Baseball dataset to control for cross-sectional dependence. A strong negative dependence was found; our estimates differ somewhat from previous estimates that assumed cross-sectional independence.
BatteseG. E.CoelliT. J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3(1–2), 153–169.
3.
BerriD.SchmidtM.BrookS. (2006). The wages of wins: Taking measure of the many myths in modern sport. Stanford University Press.
4.
BradburyJ. C. (2007). Does the baseball labor market properly value pitchers?Journal of Sports Economics, 8(6), 616–632.
5.
BradburyJ. C. (2013). What is right with Scully estimates of a player’s marginal revenue product. Journal of Sports Economics, 14(1), 87–96.
6.
ElhorstJ. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Springer.
7.
FortR.LeeY. H.BerriD. (2008). Race, technical efficiency, and retention: The case of NBA coaches. International Journal of Sport Finance, 3(2), 84–97.
8.
FortR.LeeY. H.OhT. (2019). Quantile insights on market structure and worker salaries: The case of Major League Baseball. Journal of Sports Economics, 20(8), 1066–1087.
9.
FreesE. W. (2004). Longitudinal and panel data: Analysis and applications in the social sciences. Cambridge University Press.
10.
FriedH. O.TauerL. W. (2011). The impact of age on the ability to perform under pressure: Golfers on the PGA tour. Journal of Productivity Analysis, 35(1), 75–84.
11.
GarrettT. A.MarshT. L. (2002). The revenue impacts of cross-border lottery shopping in the presence of spatial autocorrelation. Regional Science and Urban Economics, 32(4), 501–519.
12.
GlassA. J.KenjegalievaK.SicklesR. C. (2016). A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers. Journal of Econometrics, 190(2), 289–300.
13.
HausmanJ. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 46(6), 1251–1271.
14.
KahaneL. H. (2005). Production efficiency and discriminatory hiring practices in the National Hockey League: A stochastic frontier approach. Review of Industrial Organization, 27(1), 47–71.
15.
KrautmannA. C. (1999). What’s wrong with Scully-estimates of a player’s marginal revenue product. Economic inquiry, 37, 369–381.
16.
KrautmannA. C. (2012). The production technology of Major League Baseball. In ShmanskeS.KahaneL. H. (Eds.), The Oxford handbook of sports economics, Volume 2: Economics through sports (pp. 75–93). Oxford University Press.
17.
KrautmannA. C. (2013). What is right with Scully estimates of a player’s marginal revenue product: Reply. Journal of Sports Economics, 14(1), 97–105.
18.
KrautmannA. C.DonelyT. D. (2009). Shirking in Major League Baseball revisited. Journal of Sports Economics, 10(3), 292–304.
19.
KrautmannA. C.SolowJ. L. (2009). The dynamics of performance over the duration of Major League Baseball long-term contracts. Journal of Sports Economics, 10(1), 6–22.
20.
KumbhakarS. C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics, 46(1), 201–211.
21.
LeeY. H. (2011). Is the small-ball strategy effective in winning games? A stochastic frontier production approach. Journal of Productivity Analysis, 35(1), 51–59.
22.
LeeY. H. (2014). Stochastic Frontier Models in Sports Economics. International Journal of Sport Finance, 9(4), 360–374.
23.
LeeY. H.SchmidtP. (1993). A production frontier model with flexible temporal variation in technical inefficiency. In FriedH.LovellC. A. K.SchmidtS. (Eds.), The measurement of productive efficiency: Techniques and applications. Oxford University Press.
24.
LeedsE. M.LeedsM. A. (2017, July). Monopsony power in the labor market of Nippon Professional Baseball. Managerial and Decision Economics, 38(5), 689–696.
25.
LeSageJ. P.PaceR. K. (2009). Introduction to spatial econometrics. Taylor & Francis.
26.
NeweyW. K.WestK. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.
27.
PaulsenR. J. (2018). Contract options and performance: The case of Major League Baseball. Atlantic Economic Journal, 46(4), 379–388.
28.
PesaranM. H.TosettiE. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics, 161(2), 182–202.
ScullyG. W. (1974). Pay and performance in major league baseball. The American Economic Review, 64(6), 915–930.
31.
WangH. (2018). Pricing used books on Amazon.com: A spatial approach to price dispersion. Spatial Economic Analysis, 13(1), 99–117.
32.
ZimbalistA. (1992). Salaries and performance: Beyond the Scully model. In SommersP. (Ed.), Diamonds are forever: The business of baseball (pp. 109–133). Brookings Institution Press.
