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Football

  • William S. Mallios
Chapter

Abstract

NFL modeling is simplified by excluding S(i,t-τ), the vector of lagged statistical shocks, from the exploratory relation in (3.2.1: Part II):
$$D(i,t){\text{ }} = {\text{ }}{f_{it}}[L(i,t),G(i,t - \tau )',x'(i,t)]$$
(1.1.1)
. This simplification is necessary since lagged effects of unknown statistical shocks cannot be reliably estimated with such outcomes per team. Effects of S(i,t-τ) are examined in basketball and baseball where per team models are based larger sample sizes.

Keywords

Principal Component Regression Head Coach Team Model Home Team Game Outcome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References: Part 3

  1. 1.
    Reference 3 of Introduction.Google Scholar
  2. 2.
    Gunst RF, Mason RL. Advantages of examining multicollinearities in regression analysis. Biometrics, 1977; 33: 249–60.CrossRefGoogle Scholar
  3. 3.
    Needleman D. Multicollinearity in Linear Economic Models. Tilburg studies on economics, V.7, Tilburg University Press, 1973.Google Scholar
  4. 4.
    Dixon WJ., ed. BMDP Statistical Software, BMDP 2R, University of California Press, 1992.Google Scholar
  5. 5.
    ibid, BMDP 9RGoogle Scholar
  6. 6.
    ibid, BMDP 3RGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • William S. Mallios
    • 1
  1. 1.Craig School of BusinessCalifornia State UniversityFresnoUSA

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