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Dependence Relationships between On Field Performance, Wins, and Payroll in Major League Baseball

Listed author(s):
  • Stimel Derek S

    (Menlo College)

This article examines the dependence and direction of dependencies between on the field performance variables, winning percentage, and payroll in Major League Baseball using team data from 1985-2009. Particular focus is given to the relationship between winning and payroll. The method is to employ the PC algorithm, which is an implementation of graph theoretic methods in order to identify these dependence relationships. Results indicate that winning percentage directly depends on fielding percentage, on-base percentage, and saves while payroll directly depends on fielding percentage, strike outs against, and winning percentage. Using this results panel, models are estimated to assess the magnitudes of the relationships. Further, a system based on these relationships is estimated to examine the effects of winning and payroll on each other over time using impulse responses. Those responses show that payroll has a temporarily positive effect on winning but not permanently so. Finally, some cautions are offered in interpreting the results and some suggestions for future research.

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File URL: https://www.degruyter.com/view/j/jqas.2011.7.2/jqas.2011.7.2.1321/jqas.2011.7.2.1321.xml?format=INT
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Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

Volume (Year): 7 (2011)
Issue (Month): 2 (May)
Pages: 1-19

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Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:2:n:6
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  1. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
  2. Martin Schmidt & David Berri, 2002. "Competitive Balance and Market Size in Major League Baseball: A Response to Baseball's Blue Ribbon Panel," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 21(1), pages 41-54, August.
  3. Miceli Nicholas S & Huber Alan D., 2009. "`If the Team Doesn't Win, Nobody Wins:' A Team-Level Analysis of Pay and Performance Relationships in Major League Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-20, May.
  4. Martin B. Schmidt & David J. Berri, 2001. "Competitive Balance and Attendance," Journal of Sports Economics, , vol. 2(2), pages 145-167, May.
  5. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
  6. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, July.
  7. Hoover, Kevin D., 2003. "Some causal lessons from macroeconomics," Journal of Econometrics, Elsevier, vol. 112(1), pages 121-125, January.
  8. Stimel Derek, 2009. "A Statistical Analysis of NFL Quarterback Rating Variables," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-26, May.
  9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
  10. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
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