Volume 18, Issue 3

This article studies whether a recent victory impacts attendance at sports events. We apply a regression discontinuity design to estimate the local average treatment effect of a win on the attendance of subsequent games in professional basketball. Using National Basketball Association data from seasons 1980–81 to 2017–18, we find that home team fan bases react to recent outcomes, with an increase in attendance of approximately 425 attendants (a 3% boost) following a close win relative to a close loss. The increment is approximately one-eighth of a recent estimate of the superstar effect. We do not find an attendance effect when the visiting team has a recent victory, which provides evidence against the existence of externalities. The positive fan base response to narrow home wins relative to narrow losses suggests that recent luck is rewarded in sporting attendance. We discuss possible mechanisms and document a gradual decline in the attendance response that coincides with the rise of alternative means for viewing games and secondary markets for tickets.

Competitive balance in a football league is extremely important from the perspective of economic growth of the industry. Many researchers have earlier proposed different measures of competitive balance, which are primarily adapted from standard economic theory. However, these measures fail to capture the finer nuances of the game. In this work, we discuss a new framework which is more suitable for a football league. First, we present a mathematical proof of a theoretically optimal situation where a football league becomes perfectly balanced. Next, a goal based index for competitive balance is developed. We present relevant theoretical results and show how the proposed index can be used to formally test for the presence of imbalance. The methods are implemented on the data from the top five European leagues, and it shows that the new approach can be helpful in explaining the changes in the seasonal competitive balance of the leagues. Further, using panel data models, we show that the proposed index is more suitable to analyze the variability in total revenues of the football leagues. We also discuss how the methods can be easily extended to develop other goal-based indices under different modeling assumptions.

The FIFA Women’s World Cup tournament consists of a group stage and a knockout stage. We identify several issues that create competitive imbalance in the group stage. We use match data from all Women’s World Cup tournaments from 1991 through 2019 to empirically assess competitive imbalance across groups in each World Cup. Using least squares, we determine ratings for all teams. For each team, we average the ratings of the opponents in the group to calculate group opponents rating. We find that the range in group opponents rating varies between 2.5 and 4.5 goals indicating substantial competitive imbalance. We use logistic regression to quantify the impact of imbalance on the probability of success in the Women’s World Cup. Specifically, our estimates show that one goal less in group opponents rating can increase the probability of reaching the quarterfinal by 33%. We discuss several policy recommendations to reduce competitive imbalance at the Women’s World Cup.

Two new Bayesian methods for estimating and predicting in-game home team win probabilities in Division I NCAA men’s college basketball are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pregame win probability. The proposed methods are compared to existing methods, showing the new methods are competitive with or outperform existing methods for both estimation and prediction. The utility is illustrated via an application to the 2012/2013 through the 2019/2020 NCAA Division I Men’s Basketball seasons.