Baseball Research Attendance in Baseball

Baseball Research

Attendance in baseball is directly related to team revenues. In addition to the league-wide television revenues, attendance is a critical source of revenues for baseball clubs. Because of the high number of games, shifts in average attendance of even 1000 customers can have a substantial impact on the bottom line of the baseball club. There are a number of different variables that can impact baseball attendance. As most teams have outdoor stadiums, weather is an important factor, especially in the early part of the season. Team performance is another major factor. For example, Tampa Bay saw its attendance increase early in the 2010 following its pennant win in 2009, while Toronto saw a steep decline in attendance as fans took a negative view of the team's prospects in the same division as Tampa Bay, Boston and the Yankees (Nightengale & Boeck, 2010).

The New York Yankees saw a fall in attendance this year as the result of moving to a new stadium, which supports the idea that part of the attendance equation is stadium location. The closer a team is to other teams, the lower the attendance will be. It is worth noting that ticket prices are not a major factor in baseball attendance demand -- demand is relatively price inelastic (Winfree, McCluskey, Mittelhammer & Fort, 2004). Promotions -- both the usage and the type -- are also a factor, as indicated in research by Browning & DeBolt (2010).

The purpose of this research is to test some of the other factors that may affect attendance at major league baseball games. Because there are so many factors that can potentially affect baseball attendance, the totality of academic work in the field should study the widest range of variables possible. This research will add to the growing body of work that studies the impacts that some of these variables have on baseball attendance. The work will be limited to Major League Baseball (MLB) as it is believed that there are substantive differences between the attendance drivers of major league teams compared to minor league teams, including television coverage, knowledge of players and fan information and opinions of team performance. Thus, conclusions about Major League Baseball can only be drawn from data about Major League Baseball, rather than data drawn from minor leagues. As a result, this study will focus on longitudinal data from MLB teams, gathered from official MLB sources.

The number of potential variables is vast, so this study will limit its scope to a handful of variables that are easily quantifiable and that have been subject to a lower amount of coverage in the previous literature. The variables that will be studied is the salary level of the team relative to other baseball clubs, the size of the stadium, the age of the stadium, and the number of wins that the team has that season. The null hypothesis is that the each of these independent variables will have a statistically significant impact on the dependent variable, which is attendance. For example, the New York Mets have seen their attendance drop in 2010 as the result of moving to a smaller stadium. The age of the stadium can be appealing if the stadium is rich with history, or if it is new and equipped with the most modern features. However, middle-aged stadiums may have neither appeal and could result in depressed attendance figures. The number of wins that the team has in the season impacts fan interest, as does the team's salary (which is a good way to operationalize a team's star power).

Each of these variables is already quantified, so is fully operationalized. This avoids the trap of using spurious proxies in a study. There are three possible outcomes that can result from this research. The null hypothesis can be proven within significant confidence limits or it may be disproved. The third option is that the null hypothesis may be proven, but there is reason to suspect that some of the variables are closely related to one another and that this has an impact. For example, it is possible that while both salary and wins are shown to be correlated with attendance, that this is only because they are strongly correlated with one another. Thus, the third option is that evidence may be found of cross-correlations that casts doubt about the findings that either support or fail to support the null hypothesis.

The level of measurement for the age of the stadium will be years. One year will be used as the scale, as partial years are significant in assets that could be decades old. The level of measurement for the teams' salaries will be millions of dollars and the scale will be to the $100,000 level. So for example, the salary of the Toronto Blue Jays in 2005 was $45.7 million. The size of the stadium will be measured in seats, and the level will be 1 seat. Thus, it will be recorded that in 2005 Shea Stadium had a capacity of 55,775. The level of measurement for wins will be one win.