Journal of Quantitative Analysis in Sports
An official journal of the American Statistical Association
Editor-in-Chief: Mark, Glickman, PhD
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
- 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
- Building an NCAA men’s basketball predictive model and quantifying its success by Lopez, Michael J. and Matthews, Gregory J.
- openWAR: An open source system for evaluating overall player performance in major league baseball by Baumer, Benjamin S./ Jensen, Shane T. and Matthews, Gregory J.
New Insights on the Tendency of NCAA Basketball Officials to Even Out Foul Calls
1St. Olaf College
2St. Olaf College
Citation Information: Journal of Quantitative Analysis in Sports. Volume 8, Issue 3, ISSN (Online) 1559-0410, DOI: 10.1515/1559-0410.1402, October 2012
- Published Online:
This analysis revises and strengthens a study by Anderson and Pierce (2009) on referee bias in NCAA basketball. Using a logistic regression model, they determined that referees display a statistically significant tendency to even out the foul count between the two teams—every additional increase in the foul differential (home team fouls minus visiting team fouls) raises the odds of a foul on the home team. This study analyzes Anderson and Pierce’s data on the 2004-2005 season from the Big Ten, Big East and ACC conferences, as well as additional data on the 2009-2010 season. Generalized linear mixed modeling, which takes into account the correlation in the data by game and by home and visiting teams, was used to consider the effect of several variables on the odds of a foul on the home team. These included the same variables used by Anderson and Pierce as well as additional terms including the timing and type of the foul. We also used estimates of the random effects in our models to study the relative proneness of different teams to fouls.In Anderson and Pierce’s logistic regression model, every additional unit of foul differential was found to raise the odds of a foul on the home team by 12.5% in 2004-2005. Using a generalized linear mixed model with the same terms, along with random effects for game, home team, and visiting team, raised that estimate to 19.9% and improved the quality of the model. A more in-depth analysis of this data also found that a foul on the home team becomes less likely as the game progresses, particularly when the home team is winning. Similar results were found in an analysis of data from the 2009-2010 season. The 2009-2010 analysis also found evidence that the odds of more subjective offensive fouls were more affected by foul differential than personal or shooting fouls. The effect of individual referees on the amount of bias in each foul call was explored through a preliminary analysis but no significant results were found.A tendency to even out the number of foul calls on each team has the potential to lead to increased physicality in NCAA basketball if the referee tries to keep the foul count close even when one team is clearly playing more physically. These results strengthen the evidence that referees display this propensity significantly, consciously or unconsciously.