Abstract #302202

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JSM 2003 Abstract #302202
Activity Number: 51
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Sports
Abstract - #302202
Title: A Bivariate Normal Model for 2002 NFL Passing Data
Author(s): Philip J. Everson*+
Companies: Swarthmore College
Address: 405 Rogers Lane, Wallingford, PA, 19086-6028,
Keywords: Bayesian inference ; hierarchical model ; constrained Wishart
Abstract:

A bivariate-normal model is appropriate for outcomes of many competitions involving two players or teams. In NFL football games, for example, the passing-yard-totals for the two teams involved form vectors that appear very nearly bivariate-normal. I consider a model in which the mean home-team and road-team yardage totals depend on an offensive and defensive parameter from each of the two teams involved. Given these means, the outcome vector is assumed bivariate-normal with an unknown covariance matrix that is the same for all pairs of teams. The offensive and defensive strength parameters for each of the 32 NFL teams form two-dimensional vectors that are also assumed normal with an unknown mean vector and 2x2 covariance matrix. I fit this model for data from the first few weeks of the 2002 NFL season in an attempt to make appropriate projections for the season passing yardage totals of Rich Gannon and Drew Bledsoe, who were "on pace" early in the season to break Dan Marino's 1984 record of 5,084 yards. Using Bayesian updating, we can see how the projections evolve through the course of the season.

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