Journal of Quantitative Analysis in Sports
An official journal of the American Statistical Association
Editor-in-Chief: Glickman, PhD, Mark
4 Issues per year
SCImago Journal Rank (SJR) 2014: 0.265
Source Normalized Impact per Paper (SNIP) 2014: 0.513
Impact per Publication (IPP) 2014: 0.452
Volume 11 (2015)
Volume 10 (2014)
Volume 9 (2013)
Volume 5 (2009)
Volume 1 (2005)
Most Downloaded Articles
- Creating space to shoot: quantifying spatial relative field goal efficiency in basketball by Shortridge, Ashton/ Goldsberry, Kirk and Adams, Matthew
- Building an NCAA men’s basketball predictive model and quantifying its success by Lopez, Michael J. and Matthews, Gregory J.
- Predicting the draft and career success of tight ends in the National Football League by Mulholland, Jason and Jensen, Shane T.
- A generative model for predicting outcomes in college basketball by Ruiz, Francisco J. R. and Perez-Cruz, Fernando
- A new approach to bracket prediction in the NCAA Men’s Basketball Tournament based on a dual-proportion likelihood by Gupta, Ajay Andrew
Analysis of the NCAA Men’s Final Four TV audience
1Department of Statistics, Brigham Young University, Provo, UT 84602, USA
2BYU Broadcasting, Brigham Young University, Provo, UT 84602, USA
Citation Information: Journal of Quantitative Analysis in Sports. Volume 9, Issue 2, Pages 115–126, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: 10.1515/jqas-2013-0015, June 2013
- Published Online:
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.