This Article Abstract Free Full Text (Free PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by W Bassett, G. Search for Related Content Social Bookmarking                         What's this?

Quantile regression for rating teams

Gilbert W Bassett Jr is Department of Finance, University of Illinois at Chicago, 601 South Morgan (MC168), Chicago, Illinois 60607–7121. E-mail: gib{at}uic.edu

Quantile regression is proposed for modeling game out comes and as the basis for rating teams. The model includes the standard location model for team strength as a special case, while allowing for a richer specification in which teams differ according to the quantiles of the out come distribution. Team ratings are defined as the handicap needed to equalize the out come of a contest. With teams differing by quantiles, this leads to a class of ratings that depend on where in the out come distribution the out come is equalized. Relation ships with betting games are discussed. The approach is illustrated by rating National Football League (NFL) teams based on game results for the 2005 season.

Key Words: handicaps • odds • pointspreads • quantile regression • sportsratings

References

• Bassett GW Jr (1997) Robust sports ratings based on least absolute values . American Statistician, 51, 99–105 .[CrossRef]
• Bassett GW (1981) Point spreads vs. odds . Journal of Political Economy, 80, 752–68 .
• David HA (1988) The method of paired comparisons, second edition, revised. London: Oxford University Press .
• Frey J (2005) A ranking method based on minimizing the number of in-sample errors . The American Statistician, 59, 207–16 .[CrossRef]
• Harville D (1977) The use of linear-model methodology to rate high school or college foot ball teams . Journal of the American Statistical Association, 72, 278–89 .[CrossRef][Web of Science]
• Koenker R (1984) Anoteon L-estimators for linear models . Statistics and Probability Letters, 2, 323–25 .[CrossRef]
• Koenker R (2005) Quantile Regression. Cambridge: Cambridge University Press .
• Koenker R and Hallock K (2001) Quantile regression . Journal of Economic Perspectives, 15, 143–56 .[Web of Science]
• Koenker R and Bassett G (1978) Regression quantiles . Econometrica, 46, 33–50 .[CrossRef][Web of Science]
• Massey K (2006) http://www.masseyratings.com (accessed 21 march 2006).
• Stern H (1995) Who's number one in college football?... And how might we decide? Chance, 8(3), 7–14 .
• Stern H (2004) Statistics and the college football championship . The American Statistician, 58, 179–85 .[CrossRef]

Statistical Modelling, Vol. 7, No. 4, 301-313 (2007)
DOI: 10.1177/1471082X0700700402


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This Article Abstract Free Full Text (Free PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by W Bassett, G. Search for Related Content Social Bookmarking                         What's this?