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
- Introduction to the NCAA men’s basketball prediction methods issue by Glickman, Mark E. and Sonas, Jeff
Risk management with tournament incentives
1Associate Professor, Department of Economics, San Diego State University, San Diego, CA 92182-4485, USA
Citation Information: Journal of Quantitative Analysis in Sports. Volume 9, Issue 4, Pages 301–317, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: 10.1515/jqas-2013-0036, September 2013
- Published Online:
The convex payoff structure in professional golf rewards scoring volatility, giving rise to player types who succeed in spite of higher average scoring. The same risk incentives should influence all players to adjust risk strategies at key moments in tournaments when payoffs either crystallize or become particularly convex. This paper develops a simple theoretical framework, then explores the empirical evidence for strategic risk adjustment by players over the 2003–2012 PGA Tour seasons. Findings suggest that players respond measurably on average to risk incentives around the cutline, but much less so (if at all) to leaderboard position on the closing holes of a tournament. Both the payoff to risk and the technical capacity of players to add or subtract risk are estimated. Analysis of individual players indicates that some elite players are more risk responsive. Bias (e.g., loss aversion) is discussed along with other possible explanations for the apparent lack of risk response over the closing holes of a tournament.