This is a review of the NBA referee bias research conducted by Christian Deutscher using frequencies and logic approach.
The National Basketball Association hires referees to be impartial judges. However, refereeing is by its nature subjective, which means it is possible that there is some bias involved. Biases may be based on favorite players or teams, hometown fans cheering for their team, or in some cases, bribes.
In an effort to ensure that referees are making fair calls, the league spends a great amount of money to monitor their actions. Promotions and job security is based on this monitoring, providing an incentive to the referees to remain objective. Referees have a difficult job in that the expectation of the league, players, and fans are all different.
In order to investigate the possible presence of any bias in play calling, differences between actual calls and league judgement is analyzed. These differences are compared for each individual call made on the court, which can be sorted by each player involved. Knowing which players are involved allows for studying any possible biases regarding individual differences between the players.
The NBA reviews all calls made by referees during crucial game situations, which is defined as games in which the point differential is at most five points with less than two minutes to play, or overtime. A senior referee manager or basketball operations manager reviews each call and these reviews are posted the day after the game. 1229 calls from the 2014-2015 NBA regular season are examined in this study.
The first step for assessing referee bias is finding the frequencies. In this case, 496 out of 619 fouls the league identified were called correctly by the referees and 593 of 610 no-foul situations the league identified were not called by the referees. However, the variations between players in the no-foul situations were too small so this data was excluded from the remainder of the study.
In order to look for possible biases additional information is included regarding each occurrence. This includes whether the call was against the home or away team, if the player committing the foul or the player being fouled is considered a superstar, if the player is American or not, and which team is considered to be the underdog. Also included is the time remaining in the game when the call is made and crowd attendance. Crowd attendance is included as ardent fans cheering for their team could possibly affect a referee’s decision-making process.
Classifying the fouls by these controls resulted in 19.7 percent of the fouls being classified as including superstars and 38.1 percent including a non-American player.
To test for referee bias a logic approach is applied in which the independent control variables deals with potential biases towards home teams, superstar players, players of US origin and favorite teams. The time remaining in the game and crowd attendance were determined to be insignificant and thus left out.
The results indicate that there is little referee bias in the NBA with the only resulting bias being referees showing a weak preference for the underdog.
This study is not infallible as it relies on the judgments of league employees who may be unwilling to release information that shows the league and its referees in a bad light. In addition, limiting the analysis to crucial game situations provides a bias in itself. As the NBA allows video reviews during the later part of the games, referees would naturally be more prone to ensure their calls were not biased.
Refining this information to include all calls made in a game would provide the league with information regarding possible biases of the referees. Knowing this, they could implement training specific to each individual referee.
Find out how Sports Analytics Expert Victor Holman can give your team the competitive advantage.
How mature is your team’s analytics program? Take the Sports Analytics Maturity Assessment.
Discover the Groundbreaking Sports Analytics Application and Framework coaches and sports analysts are talking about!
Learn all about sports analytics in Victor Holman’s Sports Analytics Blog.