Factors Affecting Minor League Baseball Attendance. League of AA minor league baseball. Initially launched as the Akron Aeros in 1997, the team

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1 Kelbach 1 Jeffrey Kelbach Econometric Project 6 May 2016 Factors Affecting Minor League Baseball Attendance 1 Introduction The Akron RubberDucks are an affiliate of the Cleveland Indians, playing in the Eastern League of AA minor league baseball. Initially launched as the Akron Aeros in 1997, the team rebranded themselves as the Akron RubberDucks in 2014 in an effort to become more family friendly and affordable. The data which this project is based upon covers their last year as the Aeros and the succeeding two as the RubberDucks. Hypothesis I will be testing whether minor league baseball teams have control over their attendance through different factors such as fireworks, bobbleheads, and game time, by performing a joint hypothesis test. Baseball organizations have control of these factors to an extent, but have no control over other attendance affecting factors such as seasonal and environmental effects. Motivation Minor league baseball is unlike many other sports in America. Whereas many other professional sports are centered around the product on the field, minor league baseball often attempts to focus on the fan experience. Major League Baseball will always dominate the market, but Minor League Baseball has still shown extreme popularity, drawing a total of over 42 million fans from 176 teams in 2014 ( MiLB, 2014). Many of these teams play a significant economic role in the cities they are located, as most are much smaller markets than their big

2 Kelbach 2 league counterparts. If minor league teams can be confident that the factors they can control will significantly boost consumer demand, they can better allocate labor and other resources to match the expected attendance. This could also lead to more efficient use of marketing and promotions across the baseball season. 2 Literature Review Cebula, Coombs, Lawson, Foley (2013) combine to find the key marketing factors which drive attendance at minor league baseball games. The paper s aim is to test the generally accepted theory that marketing and promotions lead to a higher demand for tickets. The model they use looks at how total revenue- which includes ticket, food, souvenir sales- is affected by variables such as general admission ticket price, population of metro area, and poverty level of the area, among others. Because this study examines eight different teams across the Carolina League, these factors are included to adjust for differences between the markets. Also included in the model were four other variables which the authors said captures the marketing efforts of each team, and they include low value giveaways (key chains, magnets, etc.), high value giveaways (hats, baseballs, shirts, etc.), fireworks, and discounted group tickets. Each of these were expected to have a positive impact on total revenue, as it would drive people to come to the game in addition to buying other merchandise and food. Control dummy variables such as day of the week, month, and weather were also included. Interestingly, they left out team performance due to insignificance. All eleven of the control variables followed their expected signs with eight showing significance at a 10% or higher. All four of the marketing variables were significant in the expected direction, the lowest t-value being The paper concludes that total revenue is

3 Kelbach 3 affected positively by all of the marketing measures, metro population, higher ticket prices, and certain months, and negatively affected by adverse weather and areas with higher poverty. Many studies of minor league baseball look at the promotional and environmental factors when examining team attendance or revenue. The Gitter and Rhoads (2010) paper strays from previous studies in that it looks at on field performance as a determinant of attendance. Minor league teams generally serve as development platforms for the Major League level and winning is not always an immediate goal, therefore teams try to engage fans with creative promotions. This econometric study looked at 2,076 teams per year from 1992 to 2006, from A, AA, and AAA. The model that is used here adds a few new factors which assume that Major and Minor League Baseball are substitutes, much like previous literature that shows college and professional football are substitutes. But they still include MiLB winning percentage because they assume that the nature of baseball fans to see a winner is similar across all levels. Also, included were the amount of minor league teams home runs, dummy variable of distance to closest MLB team, MLB team s winning percentage, and ticket prices. Minor league winning percentages were found to be significant at the A and AA levels, with a 10% increase in winning percentage resulting in a 1.9% and 2.3% increase in attendance, respectively. Home runs were only significant at the AA level, but MLB ticket prices were consistent with the substitute theory at all levels. Both of these pieces provide great insight as to what affects consumer demand at the minor league level. The first piece of literature describes some of the more obvious pieces which will be a part of my model. When you think of Minor League Baseball, often times you think of wacky promotions and an atmosphere that is geared toward providing the most enjoyable

4 Kelbach 4 environment possible. The variables provided in the first paper show just that, especially through the different promotional measures. Some of the data provided will not apply, such as population and poverty of the area, because I am only looking at the Akron area. The control variables for weather and season will definitely apply to my model as well. One thing they did not include that I am curious about is the effect of team performance, and that is where the second piece of literature comes in play. Using this second piece of literature, I plan to include some of the performance variables into my model to see if a winning team drives more people into the stadium. They may not be significant in this model, given that I am only looking at three years of available data, and they may also suffer from the fact that the RubberDucks underwent a major brand makeover after the first season of available data. 3 Empirical Model The dependent variable in this model is Day of Sales but actually refers to the amount of tickets sold and distributed on the day of a game. Many professional teams, especially minor league teams where demand is not so great, use other methods to distribute tickets at little or no cost to various groups of people, in order to generate other in park sales. My model is as follows: Day_of_Sales= β1 + β2(60-69f) + β3(70-79f) + β4(80f+) + β5(cloudy) + β6(precip) + β7(night_game) + β8(fireworks) + β9(bobblehead) + β10(other_giveaway) + β11(june) + β12(july) + β13(wpct) + β14(f_sa) + β15(th_su) + e where,

5 Kelbach 5 β 2 > 0 Control variable where warmer temperatures should influence more people to attend but at different strengths for different ranges. The highest temperature range may actually deter people from attending, because of extreme heat. β 3 > 0 β 4 > 0 β 5 < 0 Variable where gloomier days would expect to keep people away, also perhaps showing a threat of precipitation. β 6 < 0 Variable where precipitation would expect to keep people from being outside in the elements. β 7 > 0 Variable where night games would allow more people to attend because of work or school not generally in session. β 8 > 0 Variable where fans are expected to demand more tickets to see fireworks than non-fireworks game. Free promotion. β 9 > 0 Variable where fans are expected to demand more tickets when there is a free bobblehead giveaway. β 10 > 0 Variable where fans are expected to demand more tickets when there are other giveaways such as t-shirts, magnets, baseballs, etc. β 11 > 0 Variable where children are off of school and weather is better for baseball. β 12 > 0 β 13 > 0 Variable where fans expected to demand more tickets to see a better team play. Better team play should lead to better fan experience. β 14 > 0 Variable where more fans are expected when playing on Friday or Saturday as opposed to other days. β 15 > 0 Variable where more fans are expected when playing on Thursday and Sunday as opposed to other days.

6 Kelbach 6 4 Data Table Variable Definition Source Day_of_Sales Tickets sold/distributed the day of a game Dependent variable 60-69F Dummy variable where 1 indicates game time start temperature in the 60-69F range / Weather 70-79F Dummy variable where 1 indicates game time start temperature in the 70-79F range 80F+ Dummy variable where 1 indicates game time start temperature above 80F Cloudy Dummy variable where 1 indicates cloudy game time conditions, as judged by box score reports Underground / Weather Underground / Weather Underground Box score reports Precip Night_Game Fireworks Bobblehead Other_Giveaway June July WPCT F_Sa Th_Su Dummy variable where 1 indicates precipitation immediately before or during game time Dummy variable where 1 indicates start time later than 6:00pm Dummy variable where 1 indicates free fireworks postgame promotion Dummy variable where 1 indicates free bobblehead giveaway to any number of fans Dummy variable where 1 indicates giveaways other than bobbleheads (shirts, magnets, baseballs, etc.) Dummy variable where 1 indicates game occurred in month of June Dummy variable where 1 indicates game occurred in month of July Variable which indicates winning percentage of home team (RubberDucks) at time of game (.667= win 66.7% of games; 0<x<1) Dummy variable where 1 indicates the game is on a Friday or Saturday Dummy variable where 1 indicates the game is on a Thursday or Sunday Box score reports Box score reports

7 Kelbach 7 5 Results TABLE 1 Day of Game Sales Akron RubberDucks (Dependent variable: Day_of_Sales) [60-69F] β= [70-79F] β= [80F+] β= [Cloudy] β= [Precip] β= [Night_Game] β= [Fireworks] β= [Bobblehead] β= [Other_Giveaway] β= [June] β= [July] β= [WPCT] β= [F_Sa] β= [Th_Su] β= * (1.95) ** (4.74) ** (4.15) ** (-2.12) ** (-3.67) ** (2.55) * (1.71) * (1.95) ** (3.30) (1.23) * (1.97) ** (2.45) ** (2.40) ** (5.49) R-Squared Adjusted R-Squared Number of Observations 212 Note: The figures in parentheses are t-statistics expressed in absolute value; ** and *, respectively, denote statistical significance at the 5% (or better) and 10% levels. From the data shown above, I ran a linear regression model of all of the variables on day of game ticket sales, which includes distributed tickets. This predicted value could simply be

8 Kelbach 8 added to advance sale tickets, which would be available on game day, in order to get a total ballpark figure. From the ticket sales data given to me by the RubberDucks, I was able to classify them according to how long they occurred before the scheduled game. Knowing that the weather can be extremely variable especially in this region, I chose to only look at day of game sales. By doing this, I could account for temperature and other weather factors in my model. I also ran other models without the weather and winning percentage factors so that it could be used to estimate attendance several days in advance, but for the sake of this assignment I chose to focus on the day of game sales. Table 1 above shows the output from this model. All of the variables in question had significance at least at the 10% level except June, which had a t-value of I chose to keep this variable in because I believe it is necessary to show these two months in which children are completely free from school. While it might be more promising to have more than 9 of the variables significant at the 5% level, I think a lot of this stems from the fact there were only three years of data from one specific team. Following my regression analysis, I decided to run a joint hypothesis test to see if promotional factors actually mattered in increasing attendance on the day of the game. H(null): β(fireworks), β(bobblehead), β9(other_giveaway) H(alternative): At least one of the above variables matter I ran a test in SAS to restrict these variables, and the F-value came back as 4.58 with a Pr>F of This means that we can reject the null hypothesis that fireworks, bobbleheads, and other giveaways don t matter in determining day of game sales; therefore at least one of the factors listed matters in determining day of game sales.

9 Kelbach 9 Many of the statistics from the regression came back as expected, but others seemed very interesting. It is expected that nice temperatures will drive demand up for any given game, but there seems to be a plateau at which temperature ceases to be as favorable. Temperatures in the 70 s seem to be ideal baseball weather for consumers, but beyond that it may be reasonable to assume that consumers start to value the heat less. A game in the 70 s increases day of game sales by 476, whereas a game in the 80 s only increases it by 418. It seems to be a small difference, but nonetheless one worth noting. Also interesting is that detrimental weather conditions can drive so many people away. Precipitation immediately before or during a game can result in almost 262 less people in game day attendance. The same applies for cloudy conditions in that 141 less people will be in attendance. A potential problem with my model which I will mention in the limitations is that other giveaways, which are usually much cheaper and given out to less people, have more of an effect than bobbleheads and fireworks. I also thought that June and July would have a greater effect on attendance being that the weather is nicer and all students are out of school, but their respective parameters 84 and 142 seem marginal when compared to some of the other variables. I was also surprised to see that winning percentage is significant, especially when considering that minor league teams have very little control over the on-the-field product. From the literature review, though, it does make sense that human behavior would apply at all levels of baseball, just a much smaller impact in the minors. The parameter estimate for winning percentage may seem very large at 647, but as more games are played the winning percentage of a team changes only marginally so that the effect is actually very small. The parameter estimate from the model suggests that if the RubberDucks were undefeated, their day of game sales would increase by

10 Kelbach , which is much more unreasonable as the season progresses. One final discussion of the results comes from the grouping of Friday and Saturday into one variable and Thursday and Sunday into another. I grouped them this way because it reflects the marketing efforts of the RubberDucks. The team s first goal is to attract people to and potentially sell out the Friday and Saturday games, and the next objective is to get people to come out on Thursdays and Sundays. The model reflects that the latter grouping has more of an effect, which I will address in my limitations. 6 Limitations This regression analysis in this study only looks at day of game sales as opposed total attendance, because the amount sold before the day of the game would already be known. Weather was a definitely a factor in only looking at day of game sales. First of all, the weather can change greatly in a span of 1-3 days, so I assumed that people who bought in advance were not taking weather variables into account, even though someone might have a decent idea of precipitation probability or the general temperature range. Also, if I were to look further than just game day, it would be hard to locate weather forecasts for each day looked at in advance. Because of these reasons, I thought looking at the day of game sales was the most accurate way to look at human behavior and demand. I also could have some multicollinearity issues in my regression. The cloudy and precipitation variables definitely have some relation, but I chose to keep them because many times a 70 degree clear day is also very different than a 70 degree dark and cloudy day. The downside is that most of the precipitation observations also included cloudy conditions, which could skew my results. Another potential occurrence of multicollinearity occurs with giveaways

11 Kelbach 11 and the day of the week groupings. Bobbleheads are often given away on Saturdays and fireworks are often shot off on Fridays, so this is one reason why I think that the other giveaways show more of an impact than fireworks and bobbleheads. Finally, the winning percentage variable may not be completely representative because early on in the season it will vary greatly, due to number of games played. I did not know of another way to account for this and other papers I looked at included it the same way that I did. Other variables that I began with and removed due to significance included the following: 40-49F, 50-59F, threat of rain, weekend, May, and August. I also looked at the impact on sales when playing the Trenton Thunder, an affiliate of the Yankees. My manager suspected that more people come to see them play because of the Yankees affiliation, but the results were insignificant, possibly due to a small sample size, or the fact that the Yankees can buy players instead of developing them. I also never included price in my model because there were no major price changes in the time period. There are different priced tickets at any given game, but 18 out of 27 sections are general admission where most people choose to sit. Something for the future I might want to consider would be whether or not the minor league team has a top prospect on the roster. This might take some time, but it also might show some interesting results. Another factor I would like to include would be concessions and in park purchases like the Cebula paper. This would give a better idea of the revenue stream if data was available. 7 Conclusion From the econometric analysis performed above, we see that a lot of different factors go into determining minor league baseball attendance. Some of these factors were very straightforward, whereas others, such as cloudiness had to be observed with some judgment.

12 Kelbach 12 While there were some limitations that stemmed from the data and the type of regression, I believe that this model does a good job explaining the factors involved. My findings are definitely consistent with some of the literature I discussed, and I think that an adjusted r-squared of.54 makes sense when also considering the randomness of human behavior. I ve been told that only a handful of other minor league baseball teams have tried regression analysis to predict attendance. From talking to different people with the RubberDucks, it sounds as if more teams will begin to adopt models similar to these. I will be interested to see how similar these predictions are to the actual values when looked at over the entire course of a season. Because the RubberDucks have only been tracking this type of data for a few years, I m sure it will only get more accurate with time. With these findings, minor league teams can better allocate their resources for specific game types. This includes trying to maintain high, weekend attendance but also trying to adjust promotions so that people will come on days when they otherwise normally wouldn t.

13 Kelbach 13 References Box Scores. Akron RubberDucks Website. Cebula, Coombs, Lawson, and Foley. The Impacts of Promotions/Marketing, Scheduling, and Economic Factors on Total Gross Revenues for Minor League Baseball Teams. 6 July International Atlantic Economic Society. Gitter, Seth and Thomas Rhoads. Determinants of Minor League Baseball Attendance Journal of Sports Economics. "MiLB Draws Over 42 Million." MiLB.com. Corpus Christi Hooks, 11 Sept Web. 05 May Weather Underground.