Quantifying implicit biases in refereeing using NBA referees as a testbed

Abstract

Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a potential violation. In this work I revisit and extend some of the existing work on implicit biases in refereeing. In particular, I focus on refereeing in the NBA and examine three different types of implicit bias; (i) home-vs-away bias, (ii) bias towards individual players or teams, and, (iii) racial bias. For this study, I use play-by-play data and data from the Last 2 min reports the league office releases for games that were within 5 points in the last 2 min since the 2015 season. The results indicate that the there is a bias towards the home team—particularly pronounced during the playoffs—but it has been reduced since the COVID-19 pandemic. Furthermore, there is robust statistical evidence that specific players benefit from referee decisions more than expected from pure chance. However, I find no evidence of negative bias towards individual players, or towards specific teams. Finally, my analysis on racial bias indicates the absence of any bias.

Compensating Tendencies in Disciplinary Sanctions: The Case of Hurling

“…They point out that while the limited evidence of no referee bias toward home teams is contrary to most prior studies, some of these prior studies did find that such bias was reduced in the fourth quarter, for example, Price et al (2012). The use of different foul types can also be used with L2M reports as illustrated by Gong (2022) and Pelechrinis (2023). In all cases, the justification is that there is a different level of referee discretion in the calling of some fouls and that this discretion facilitates greater scope for biases to operate.…”

“…Gong (2022) examines the extent of potential home-bias using L2M reports including data from the Covid period where fans were absent from venues. Both Pelechrinis (2023) and Gong (2022) note the importance of the "correctness" of the data in L2M reports. Only in different subsets of errors do Gong (2022) and Pelechrinis (2023) find home bias, that is, loose-ball noncalls and technical personal fouls, respectively.…”

“…Both Pelechrinis (2023) and Gong (2022) note the importance of the "correctness" of the data in L2M reports. Only in different subsets of errors do Gong (2022) and Pelechrinis (2023) find home bias, that is, loose-ball noncalls and technical personal fouls, respectively. Gong (2022) finds almost no evidence that the decisions are related to the scoreboard.…”

“…Unlike baseball and cricket, there are many noncalls in other sports. The difficulties in classifying these noncalls are explained by Pelechrinis (2023) Peter Horgan is the Strategy, Insights, and Innovation Manager for the Gaelic Athletic Association, with responsibility to lead, develop, and implement a systematic approach to policy development and research within the Association.…”