Statistical discrimination is the concept of using the average behavior of a group to judge an individual representative of that group. Some forms of statistical discrimination are common, such as charging smokers and non-smokers different health insurance rates. However, statistical discrimination is harmful when it does not rely on sound statistics, veiled racism, or unobservable traits. Paul Heaton at the University of Chicago investigates the prevalence of statistical discrimination in fantasy football, basketball, and football among sports fans in his paper

White Men Can’t Jump?-Discrimination Evidence From Fantasy Sports Leagues.

Previous studies of statistical discrimination in sports usually focused on player salaries. This is not a sound measure of discrimination since there are many variables that influence a player’s salary including possible fan biases, executive biases, or hard to quantify player traits such leadership skills or media exposure. The study focuses on possible fan biases by using the data of hundreds of thousands of fans and, since a fantasy sports player’s success in the game is based on only athletes’ statistics, there is an objective measure of an athlete’s value. He defines discrimination as two players, controlling for individual ability, team, position, etc, who receive different salaries because of their race.

After analyzing the data from the sports league, he concludes that “there is little evidence discrimination against Blacks or Hispanics in football, basketball, or baseball leagues.” He also argues that his study is more successful than previous ones since he is able to use data from a broad cross-section of the population. And, furthermore, that he avoids a pitfall of previous studies by using a situation where unobservable characteristics play little or no role in player valuations, those traits can not influence the athlete valuations.