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Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model

Zachary Zimmer

出版	Virginia Commonwealth University, 2006
URL	http://books.google.com.hk/books?id=Sd3ujwEACAAJ&hl=&source=gbs_api

註釋

The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.