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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

4 Issues per year

CiteScore 2016: 0.44

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Analysis of the NCAA Men’s Final Four TV audience

Scott D. Grimshaw / R. Paul Sabin / Keith M. Willes
Published Online: 2013-06-19 | DOI: https://doi.org/10.1515/jqas-2013-0015


This is the first paper to investigate factors that affect the size of the TV audience for the NCAA Men’s Final Four. The model is based on Nielsen data for 54 markets for 10 years. One of the most interesting results is that college basketball teams have a measurable effect in their local markets, but even the biggest name programs do not have a national effect. However, the little known teams that succeed in the tournament, known as Cinderellas, have a national effect likely due to the media attention and success on the court. Broadcasters and advertisers are interested in maximizing the TV audience, and the model allows prediction to compare games between big market teams, big name teams, David vs. Goliath games, and a Championship game between two Cinderella teams.

Keywords: TV; Nielsen ratings; NCAA basketball; March Madness; demand for sport


  • Alavy, K., A. Gaskell, S. Leach, and S. Szymanski. 2010. “On the Edge of your Seat: Demand for Football on Television and the Uncertainty of Outcome Hypothesis.” International Journal of Sport Finance 5:75–95.Google Scholar

  • Aldrich, E. M., P. S. Arcidiacono, and J. L. Vigdor. 2005. “Do People Value Racial Diversity? Evidence from Nielsen ratings.” Topics in Economic Analysis & Policy 5(1):Article 4.Google Scholar

  • Balsdon, E., L. Fong, and M. A. Thayer. 2007. “Corruption in College Basketball? Evidence of Tanking in Postseason Conference Tournaments.” Journal of Sports Economics 8:19–38.CrossrefGoogle Scholar

  • Bauman, R., V. A. Matheson, and C. A. Howe. 2010. “Anomalies in Tournament Design: The Madness of March Madness.” Journal of Quantitative Analysis in Sports 6(2):Article 4.Google Scholar

  • Berkowitz, J. P., C. A. Depken, II, and D. P. Wilson. 2011. “When Going in Circles is Going Backward: Outcome Uncertainty in NASCAR.” Journal of Sports Economics 12:253–283.Web of ScienceCrossrefGoogle Scholar

  • Bojke, C. 2007. “The Impact of Post-Season Play-Off Systems on the Attendance at Regular Season Games.” pp. 179–202 in Statistical Thinking in Sports, edited by J. H. Albert and R. H. Koning. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar

  • Borland, J. and R. Macdonald. 2003. “Demand for Sport.” Oxford Review of Economic Policy 19:478–502.CrossrefGoogle Scholar

  • Brown, M. and J. Sokol. 2010. “An Improved LRMC Method for NCAA Basketball Prediction.” Journal of Quantitative Analysis in Sports 6(3):Article 4.Google Scholar

  • Buraimo, B. 2008. “Stadium Attendance and Television Audience Demand in English League Football.” Managerial and Decision Economics 29:513–523.CrossrefGoogle Scholar

  • Buraimo, B. and R. Simmons. 2009. “A Tale of Two Audiences: Spectators, Television Viewers and Outcome Uncertainty in Spanish Football.” Journal of Economics and Business 61:326–338.CrossrefGoogle Scholar

  • Buraimo, B., D. Forrest, and R. Simmons. 2007. “Outcome Uncertainty Measures: How Closely do they Predict a Close Game?” pp. 167–178 in Statistical Thinking in Sports, edited by J. H. Albert and R. H. Koning. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar

  • Carlin, B. P. 1996. “Improved NCAA Basketball Tournament Modeling Via Point Spread and Team Strength Information.” The American Statistician 50:39–43.Google Scholar

  • Coleman, J. and A. K. Lynch. 2009. “NCAA Tournament Games: The Real Nitty-Gritty.” Journal of Quantitative Analysis in Sports 5(3):Article 8.Google Scholar

  • Feddersen, A. and A. Rott. 2011. “Determinants of Demand for Televised Live Football: Features of the German National Football Team.” Journal of Sports Economics 12: 352–369.CrossrefWeb of ScienceGoogle Scholar

  • Forrest, D. and R. Simmons. 2002. “Outcome Uncertainty and Attendance Demand in Sport: the Case of English Soccer.” The Statistician 51:229–241.Google Scholar

  • Forrest, D. and R. Simmons. 2006. “New Issues in Attendance Demand: The Case of the English Football League.” Journal of Sports Economics 7:247–266.CrossrefGoogle Scholar

  • Forrest, D., R. Simmons, and B. Buraimo. 2005. “Outcome Uncertainty and the Couch Potato Audience.” Scottish Journal of Political Economy 52:641–661.CrossrefGoogle Scholar

  • Gray, K. L. and N. C. Schwertman. 2012. “Comparing Team Selection and Seeding for the 2011 NCAA Men’s Basketball Tournament.” Journal of Quantitative Analysis in Sports 8(1):Article 2.Google Scholar

  • Harville, D. A. 2003. “The Selection or Seeding of College Basketball or Football Teams for Postseason Competition.” Journal of the American Statistical Association 98:17–27.CrossrefGoogle Scholar

  • Kanazawa, M. T. and J. P. Funk. 2001. “Racial Discrimination in Professional Basketball: Evidence from Nielsen ratings.” Economic Inquiry 39:599–608.CrossrefGoogle Scholar

  • Mongeon, K. and J. Winfree. 2012. “Comparison of Television and Gate Demand in the National Basketball Association.” Sport Management Review 15:72–79.CrossrefGoogle Scholar

  • Morris, T. L. and F. H. Bokhari. 2012. “The Dreaded Middle Seeds – Are they the Worst Seeds in the NCAA Basketball Tournament?” Journal of Quantitative Analysis in Sports 8(2):Article 1.Google Scholar

  • Nüesch, S. and E. Frank. 2009. “The Role of Patriotism in Explaining the TV Audience of National Team Games – Evidence from Four International Tournaments.” Journal of Media Economics 22:6–19.Web of ScienceCrossrefGoogle Scholar

  • Paul, R. J. and A. P. Weinbach. 2007. “The Uncertainty of Outcome and Scoring Effects on Nielsen Ratings for Monday Night Football.” Journal of Economics and Business 59:199–211.CrossrefGoogle Scholar

  • Pew Forum on Religion & Public Life. 2008. “U.S. Religious Landscape Survey.” Technical report, Pew Research Center.Google Scholar

  • Schwertman, N. C., K. L. Schenk, and B. C. Holbrook. 1996. “More Probability Models for the NCAA Regional Basketball Tournaments.” The American Statistician 50:34–38.Google Scholar

  • Stekler, H. O. and A. Klein. 2012. “Predicting the Outcomes of NCAA Basketball Championship Games.” Journal of Quantitative Analysis in Sports 8(1):Article 3.Google Scholar

  • Tainsky, S. 2010. “Television Broadcast Demand for National Football League contests.” Journal of Sports Economics 11:629–640.CrossrefGoogle Scholar

  • Tainsky, S. and C. D. McEvoy. 2012. “Television Broadcast Demand in Markets Without Local Teams.” Journal of Sports Economics 13:250–265.Web of ScienceCrossrefGoogle Scholar

  • West, B. T. 2008. “A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament: Updated Results from 2007.” Journal of Quantitative Analysis in Sports 4(2):Article 8.Google Scholar

About the article

Corresponding author: Scott D. Grimshaw, Department of Statistics, Brigham Young University, Provo, UT 84602, USA, Tel.: +1-801-422-6251

Published Online: 2013-06-19

Published in Print: 2013-06-01

Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2013-0015.

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