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Beyond BLS

August 2021

“There’s no crying in baseball”—but there is a lot of data

Nicholas A. Schaffer

On September 29, 2004, the Montreal Expos Major League Baseball team played its final home game. The next season, the franchise would relocate to Washington, DC, for economic reasons. Sports franchises continually use economics, but can the study of economics benefit from sports? In their article, “Ask not what economics can do for sports—ask what sports can do for economics” (Journal of Behavioral and Experimental Economics, December 2020), Michael Bar-Eli, Alex Krumer, and Elia Morgulev illustrate how sports data can support economic research. The authors cite instances in which sports data have supported fundamental economic theories or revealed market failures and biases in decision making, before concluding that the abundance and quality of sports data have resulted in their growing popularity in the study of economic behavior.

The authors cite several research articles in which data from sports is used to evaluate fundamental economic theories. A recent article used the event of pulling a pitcher, an important moment in any baseball game, to evaluate Bayesian decision making among coaches. Two articles observed professional soccer players: one tested signaling theories in the labor market and the other showed that players’ actions during a penalty kick were according to a minimax theory. Another article showed that tennis players served the ball in a mixed strategy equilibrium. A separate article showed that when a tennis player challenged an umpire, their actions were true to optimal decision making.

Bar-Eli and colleagues also discussed several articles that tested behavioral deviations from optimal performance. Sports data have been used to reveal biases in decision making that lead to suboptimal results. For example, sports data are used in one article to reveal discrimination and favoritism by presenting compelling evidence of discrimination against Black players in English soccer. A different article found that players in the National Basketball Association had fewer fouls called on them if the race of the referee crew was the same as their own. Suboptimal decision making is exhibited again in an article that showed National Football League managers’ behavior during rookie drafts to be out of line with rational thinking and efficient markets. Many articles also exist on the effect of the order of actions. The order of actions can create ahead–behind asymmetry that can affect performance. For example, an article looking at soccer penalty shoot-outs found that the team that kicked first had a higher probability of winning.

The authors observe that since the beginning of the century, a large amount of economic literature has taken advantage of sports data. The authors speculate that this increase is due to the excellence and availability of the data. They also speculate that the high stakes and professionalism of the participants are advantageous. Laboratory experiments can yield unreliable results because often the decisions people face in an experiment are not ones they have experience making. This problem is overcome by sports data because participants face high stakes and familiar decisions. However, using sports data may have some potential limitations. Most likely, professional athletes have different qualities from the general population in ways that limit the general applicability of articles studying them. Athletes are subject to frequent travel, high pressure, and long distance from family. Also, most players compete against athletes of the same gender, which is not the case in many real-world interactions. Because of these limitations, Bar-Eli, Krumer, and Morgulev recommend caution when incorporating findings from sports into economic thinking.

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