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Sport as a Window into Decision-Making in Real Life

“Football is played with the head. Your feet are just the tools.” 

-Andrea Pirlo (Former soccer player)

The clock is approaching full-time, and you have managed to find the ball at your feet; only the opposing goalkeeper stands between you and the glory of your team lifting the championship. You have a teammate in a position that should make it easier for that person to score, even though he/she/they have scored much fewer goals than you throughout the tournament. It is not just the opposing goalkeeper that stands in your way; the decision you make at this point defines victory or defeat. Now, imagine fast-forwarding to the last minute of the match. Every other person is out of purview. It is the last penalty shootout and you are staring at the goalkeeper. Where would you decide to kick the ball?

Sports can provide a useful lens for understanding decision-making in real life. In sports, decisions are made in real-time, under pressure, and with limited information, which mirrors many of the challenges individuals face in daily life. Some of these decisions have high stakes (e.g., the above example, where a championship is on the line), while others may not matter as much. Sports often involve complex decision-making scenarios, such as assessing risks, allocating resources, and teamwork, which can be analyzed and studied to understand how these decisions are made. Some examples of decision theories used in both sports and real-life decision-making include:

Game Theory

With its origin in economics, game theory has been applied to various settings, including sports. Proposed by von Neumann and Morgenstern in their seminal volume, The Theory of Games and Economic Behaviour (1938), this mathematical theory models decision-making in situations where the outcome depends on the decisions of multiple actors. In sports, game theory is often used to analyze strategies for individual players and teams, such as determining the optimal shot or strategy of play based on the opponent’s likely response. Many sports teams use data analysis to model the possible payoffs one may receive from choosing each available option in a given situation- such as in the case of penalty kicks in soccer (Azar and Bar-Eli, 2011; Chiappori, Levitt and Groseclose, 2002) or decisions of the bowler and the batsman in cricket. Whenever the decision is not independent between two people, we have some kind of joint strategy evolving as one’s actions are contingent on the other. Some of the strategies observed in sports often apply to high-stakes, real-world scenarios. For instance, a team of market strategists may use game theory to determine the best pricing strategy, taking into account the likely responses of other companies in the market. This of course also holds in sports when the players themselves are in the market for being signed up. 

Prospect Theory

This theory posits that individuals make decisions based on the perceived value of potential prospects that consist of the psychological value of the outcome and the psychological estimate of the probability of its occurrence. In sports, this theory can be seen in the decisions made by sportspersons and athletes, such as whether to take a risk and attempt a high-stakes play or to play it safely, as in the example discussed above. Berger and Pope (2011) analyzed thousands of basketball matches and suggested that when teams are slightly behind their opponent at halftime, their chances of winning often increase due to higher motivation stemming from the strong affective impact of losses. Investigations into these phenomena help inform decision-making in other fields as well. 

Expected Utility Theory

Expected utility theory is a decision-making model that predicts how people make choices based on the expected utility of each possible outcome. In other words, it helps to explain how people make decisions by considering the expected value of the outcomes and the uncertainty or risk associated with those outcomes. According to this theory, people make decisions by weighing the expected utility of each possible outcome and choosing the option that provides the highest expected utility. Expected utility is calculated by multiplying each outcome’s utility (the value or satisfaction) by the probability of that outcome occurring. 

In sports, various examples of the use of expected utility theory have been used by coaches, scouts, and players. Coaches can use expected utility theory to analyze different strategies for a game and determine the best approach for their team. Scouts can use the expected utility theory to analyze the potential performance of different players and determine the best player to draft or sign. Athletes can use the expected utility theory to optimize their performance by weighing the potential outcomes of different training and nutrition programs. The expected utility model also finds use in decisions such as career choices, health decisions, investment decisions, and policy decisions by lawmakers.

Overall, sports allow researchers to test and identify strategies and theoretical decision models to inform decision-making in much more critical situations. Sports provide rich and dynamic contexts for studying decision-making and can offer valuable insights into how decisions are made in real-life situations. So the next time you scream at your favorite footballer making a mistake or jump up in joy when you see a goal, it is probably time to appreciate the role of decision-making in sports. After all, it’s so much more than just a game!

References

Azar, O. H., & Bar-Eli, M. (2011). Do soccer players play the mixed-strategy Nash equilibrium?. Applied Economics, 43(25), 3591-3601.

Berger, J., & Pope, D. (2011). Can losing lead to winning?. Management Science, 57(5), 817-827.

Chiappori, P. A., Levitt, S., & Groseclose, T. (2002). Testing mixed-strategy equilibria when players are heterogeneous: The case of penalty kicks in soccer. American Economic Review, 92(4), 1138-1151.

Von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior, 2nd rev.


Author: Rishav Singh ; Edited by Sumitava Mukherjee

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