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Annual Hogg and Craig Lecture Series: David A. Harville, Iowa State University

Apr 25, 2019

03:30 PM

Van Allen Hall, LR2

30 North Dubuque Street, North Liberty, IA 52317

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David A. Harville

The Department of Statistics and Actuarial Science and its Annual Hogg and Craig Lecture Series present David A. Harville, professor emeritus in the Department of Statistics at Iowa State University.

David A. Harville served for 10 years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories (at Wright-Patterson AFB, Ohio), for 20 years as a full professor in Iowa State University’s Department of Statistics (where he now has emeritus status), and for 7 years as a research staff member of the Mathematical Sciences Department of IBM’s Thomas J. Watson Research Center. He is the author of more than 80 research articles and of 3 books: Matrix Algebra From a Statistician’s Perspective, Matrix Algebra: Exercises and Solutions, and Linear Models and the Relevant Distributions and Matrix Algebra. He has served as an associate editor of Biometrics and of the Journal of the American Statistical Association. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics and is an elected member of the International Statistical Institute.

Dr. Harville will present “Ranking/Rating Basketball or Football Teams: The NCAA Way and the 'Right' Way”.

Systems for ranking or rating high school or college basketball or football teams take many forms, ranging from polls to so-called computer rankings. Such systems are sometimes used in a way that affects the teams being ranked or rated, as in the case of the NCAA’s use of the RPI (Rating Percentage Index) or more recently the NET (NCAA Evaluation Tool) in the selection and seeding of college basketball teams for “March Madness.” Then, ideally, the ranking/rating system should have certain attributes, including accuracy, appropriateness, impartiality, unobtrusiveness, nondisruptiveness, verifiability, and comprehensibility. The RPI and the NET lack some of these attributes. A system possessing all of the attributes, except for unobtrusiveness, can be achieved by applying least squares to a statistical model in which the expected difference in score in each game is modeled as a difference in team effects plus or minus a home court/field advantage. The potential obtrusiveness of this approach can be largely eliminated by introducing modifications to reward “winning per se” and to limit or do away with any incentive for “running up the score” or for extending a game into overtime.

This is Lecture 1 of 2. Lecture 2 is on Friday, April 26, at 3:30 p.m. in W151 Pappajohn Business Building.

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