• William S. Mallios


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):
$$D(i,t){\text{ }} = {\text{ }}{f_{it}}[L(i,t),G(i,t - \tau )',x'(i,t)]$$
. 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.


Principal Component Regression Head Coach Team Model Home Team Game Outcome 
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References: Part 3

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