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Licensed Unlicensed Requires Authentication Published by De Gruyter June 19, 2013

Analysis of the NCAA Men’s Final Four TV audience

  • Scott D. Grimshaw EMAIL logo , R. Paul Sabin and Keith M. Willes

Abstract

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.


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

References

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.Search in 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 4.10.2202/1538-0653.1396Search in 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 8:19–38.10.1177/1527002505275095Search in Google 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 4.10.2202/1559-0410.1233Search in 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 12:253–283.10.1177/1527002511404778Search in Google 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.10.1201/9781584888697.ch11Search in Google Scholar

Borland, J. and R. Macdonald. 2003. “Demand for Sport.” Oxford Review of Economic Policy 19:478–502.10.1093/oxrep/19.4.478Search in Google Scholar

Brown, M. and J. Sokol. 2010. “An Improved LRMC Method for NCAA Basketball Prediction.” Journal of Quantitative Analysis in Sports 6(3):Article 4.10.2202/1559-0410.1202Search in Google Scholar

Buraimo, B. 2008. “Stadium Attendance and Television Audience Demand in English League Football.” Managerial and Decision Economics 29:513–523.10.1002/mde.1421Search in Google 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 61:326–338.10.1016/j.jeconbus.2008.10.002Search in Google 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.10.1201/9781584888697.ch10Search in Google Scholar

Carlin, B. P. 1996. “Improved NCAA Basketball Tournament Modeling Via Point Spread and Team Strength Information.” The American Statistician 50:39–43.Search in 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 8.10.2202/1559-0410.1165Search in 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: 352–369.10.1177/1527002511404783Search in Google Scholar

Forrest, D. and R. Simmons. 2002. “Outcome Uncertainty and Attendance Demand in Sport: the Case of English Soccer.” The Statistician 51:229–241.10.1111/1467-9884.00314Search in Google Scholar

Forrest, D. and R. Simmons. 2006. “New Issues in Attendance Demand: The Case of the English Football League.” Journal of Sports Economics 7:247–266.10.1177/1527002504273392Search in Google Scholar

Forrest, D., R. Simmons, and B. Buraimo. 2005. “Outcome Uncertainty and the Couch Potato Audience.” Scottish Journal of Political Economy 52:641–661.10.1111/j.1467-9485.2005.00360.xSearch in Google 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 2.10.1515/1559-0410.1369Search in 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 98:17–27.10.1198/016214503388619058Search in Google Scholar

Kanazawa, M. T. and J. P. Funk. 2001. “Racial Discrimination in Professional Basketball: Evidence from Nielsen ratings.” Economic Inquiry 39:599–608.10.1093/ei/39.4.599Search in Google Scholar

Mongeon, K. and J. Winfree. 2012. “Comparison of Television and Gate Demand in the National Basketball Association.” Sport Management Review 15:72–79.10.1016/j.smr.2011.09.001Search in Google 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 1.10.1515/1559-0410.1343Search in 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 22:6–19.10.1080/08997760902724472Search in Google 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 59:199–211.10.1016/j.jeconbus.2006.05.001Search in Google Scholar

Pew Forum on Religion & Public Life. 2008. “U.S. Religious Landscape Survey.” Technical report, Pew Research Center.Search in 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.Search in 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 3.10.1515/1559-0410.1373Search in Google Scholar

Tainsky, S. 2010. “Television Broadcast Demand for National Football League contests.” Journal of Sports Economics 11:629–640.10.1177/1527002509355636Search in Google Scholar

Tainsky, S. and C. D. McEvoy. 2012. “Television Broadcast Demand in Markets Without Local Teams.” Journal of Sports Economics 13:250–265.10.1177/1527002511406129Search in Google 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 8.10.2202/1559-0410.1099Search in Google Scholar

Published Online: 2013-06-19
Published in Print: 2013-06-01

©2013 by Walter de Gruyter Berlin Boston

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