1. Competitive Balance and Attendance in the MLB
Ryan Jann, Chris Kennedy, Gary Koplik, and Daniel Meyer
Professor LaFave Econometrics Fall
Abstract
The goal of this study is to examine the effects of competitive balance in Major League Baseball attendance at a game-by-game level. Our hypothesis is that increased competitive balance will lead to higher league wide attendance totals.
Competitive balance is an important issue for Major League Baseball. If there is too much separation between the best and worst teams it is believed that fans become disinterested because the season-end result is predictable. If there is too little separation fans may be uninterested because the regular season result appears to be simply luck based. The MLB offices have made strides to increase competitive balance, for example they have implemented a revenue sharing system and added additional playoff spots. Our model will use gate attendance as the dependent variable, and examine how the competitive balance and other control variables affect the attendance on a game-by-game basis.
Prior research has shown that home fans are more likely to come to the park to see good teams play against the home team and less likely to come out to see a bad team, indicating that fans prefer a balanced league with small margins between opponents. Our model will use both the difference in winning percentage between the two teams and the league wide standard deviation of winning percentages in order to capture the effect of within game and league wide competitive balance.
The findings of this paper will be relevant in determining whether or not Major League Baseball, a multi-billion dollar industry, should try to influence competitive balance in order to maximize gate attendance.
Data Description
Our panel data consists of every regular season game between 2004 and For each of these more than 24,000 games our data set includes attendance, measures of competitive balance, and various control variables.  We include teams’ performance with their current winning percentage as well as the difference between the home team and visiting teams current winning percentages. We measured the competitiveness of the league for each game by calculating standard deviation at that point in the season. We also control for the previous season’s winning percentage and whether each team made the playoffs the prior year. Finally we control for other factors affecting attendance like the day of the week, the time of the game, and the game number during the season.
Fixed Effects
Major League Baseball teams, however, are not homogeneous. For example, cities have varying local economic conditions, populations, and ticket prices. For some cities with more of a tradition of attending baseball games, like Boston or Chicago, demand for home games may be particularly inelastic, in which case none of the variables we are measuring may have much of an effect with respect to changing attendance numbers. Figure 1 exemplifies this heterogeneity between teams. We controlled for time-invariant variables that are unique to each team with team-fixed effects such as quality of stadium, history, and market size. This captures the historic charm of Fenway Park as well as the lack of traditional interest in Miami and Montreal. Since our data span nearly a decade, we also controlled for team-invariant factors changing over time that could influence attendance, such as nationwide economic activity and population growth.
Results
In each model, our measures of competitive balance all had statistically significant estimates. Using the pooled regression model, we find that fans are more inclined to attend games when the teams that are playing each other are relatively equal and when there is more balance throughout the league.
Model 2 has fixed effects within years. This controls for various other factors that influence fan attendance across each season that we could not control for in the pooled regression. Model 3 has fixed effects within teams, which controls for factors that influence fan attendance across each team that we could not control for in the pooled regression. The conclusion from these models is the same as the conclusion from the pooled regression model.
Model 4 has both team and year fixed effects, which makes it our most unbiased model. The coefficient estimate for difference in winning percentages is , which means that an increase in difference of winning percentages of .010 relates to a predicted decrease of 114 fans at that game, holding all other variables in the model constant. This is equivalent to about a two win difference in end of season record between the two teams. The estimated coefficient for standard deviation of winning percentages is , which means that with a increase in league wide standard deviation of winning percentages, we would expect a 725 fan decrease in attendance at a single game holding the rest of the model constant. For reference a change in league wide standard deviation of equates to about the best team being better by and the worst team being worse by about 0.050, about 8 games in either direction.
Across all models the result stays almost the same. MLB fans prefer to go to games where the teams are relatively equal and where league wide standings have a tighter concentration of teams in the middle of the pack with few outliers at the extremes.
Olympic Stadium, Montreal, QC
Figure 1
Safeco Field, Seattle, WA
The American League was particularly unbalanced in 2001
Fenway Park, Boston, MA