Phillies Attendance Analysis
Matt Groves
Introduction
The 2009 Major League Baseball season saw the Philadelphia Phillies make the
playoffs for the third straight year. They successfully defended their National League
pennant and returned to the World Series one year after winning ultimate title in baseball
for the first time since 1980. Coming into a season as the World “bleeping” Champions
and with virtually the same team intact and primed to make another run meant the
Phillies were going to be a hot selling ticket in the city of Philadelphia. The ball club was
not going to have trouble getting fans into the stands; but what aspects of each game are
going to affect the attendance that day?
During the off-season, teams set up promotional give-a-ways to attract fans to the
ballpark. Common sense would say that during a 162 game season a team will struggle to
draw big crowds on daily basis and promotions, in theory, should attract fans to the
ballpark. Bill Veeck, former owner of the Chicago White Sox and who is considered to
be the inventor of game day promotions, believed that each day should be like Mardi
Gras in order to enhance each fans experience at the ballpark. With that in mind, are the
Phils getting a positive return on their investment with promotional give-a-ways drawing
a bigger crowd? Do other variables, like the Phillies’ winning percentage, the opponent’s
winning percentage, whether the game is played at night or during the day, whether the
game is played on weekday or weekend, is the game in the first half or second half of the
season, importance of the game, and the Phillies winning streak heading into the game
affect attendance? Because certain variables did not change during the 2009 Phillies
season, factors like ballpark quality, tailgating options, and demographic data can be held
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constant. Economic factors such as ticket price, average income, and population are
considered constant in this study as well.
Prior Research
Early research concerning this topic goes back to 1989 when, Hansen and
Gauthier identified three categories into which most of the research falls. The first
category is economic factors like income, population characteristics, and price of tickets.
Next are demographic factors like race, sex, and age of the target audiences of the ball
clubs. The last category is the attractiveness of the game, using factors like opponent
quality, presence of star athletes, and promotions. The drawback of the economic and
demographic factors is that they are not easily controlled or acted upon by teams to boost
attendance.
In more recent times researchers have shifted their focus on factors that can be
controlled or manipulated by management to impact the attendance. The research
emphasized controllable aspects that make the game more attractive, with noncontrollable factors in the study to assure that the model is valid. Boyd and Krehbiel
(1999) were the first to study the effect of promotion timing on MLB attendance. They
used data for six teams over four seasons and studied the effect of running promotions on
weekend games versus weekday games and night games versus day games. Their results
showed that the timing of promotions can influence their effectiveness, showing that
management can make a difference with promotions. They found that the greatest impact
on attendance occurred when a promotion was run during a day game on a weekday. The
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Matt Groves
other main point of the research was that the data varied by team, so teams should not
rely on the aggregate data as their only source of information about the effectiveness of
promotions.
In 2000 McDonald and Rascher studied whether or not there were diminishing
marginal returns to additional promotions. To measure this they ran a regression testing if
the promotion variable is equal to 1one, rejection in the direction below one implies
diminishing returns. Using forty independent variables they examined data from twentynine MLB teams during the 1996 season, finding that promotions increased attendance by
14 percent on average. They did find evidence of diminishing marginal returns but they
were not negative for any team. Boyd and Krehbiel did not find any trace of diminishing
returns (differences within the samples could be the reason for this). In 2003 they argued
that diminishing marginal returns could be due to less expensive or less popular
promotions. The fact that promotions may be added to games that already have a high
attractiveness and would have a high attendance regardless of the promotion cannot be
ignored here. One would assume that if the game is highly attractive than the promotion
would have less of an impact on attendance.
McDonald and Rascher also added the idea that different promotions will not
have equal effect on attendance. They separated promotions by price and non price and
used the monetary value of the promotion to reflect attractiveness. Although monetary
value may not be the only factor in attractiveness, it is hard to put a value on a promotion
that would allow your ten year old to meet his favorite player or run the bases. Boyd and
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Krehbiel tried to answer this question by creating categories of different types of
promotions. Using multiple regression analysis and three categories of promotions (price
discounts, special events, and giveaways) they found that different promotions increased
attendance by different amounts. Their results for six teams in the 1999 season showed
that the effect of a price discount increased attendance by 1,347, a giveaway increased
attendance by 6,207, and a special event increased attendance by 5,563. The analysis also
showed that you must consider game attractiveness as well as promotions. They found
statistical significance for games played against rivals or on a weekend. The results also
showed that running a promotion with a highly attractive game (called stacking) still
increased attendance but the promotion did not have a big effect on the increased
attendance. The research done has yielded a general consensus that promotions do have a
positive impact on attendance and are the most effective when run on weekdays and for
day games.
Method
For my research I collected data from all 81 of the Phillies home games of the
2009 season. I got all of my information from the team website (www.phillies.com) and
looked up the box score from each game. Attendance is the dependent variable while the
independent variables I chose for the regression are Phillies winning percentage at the
time of the game, opponents winning percentage at the time of the game, day of the week
of the game, start time of the game, the importance of the game (also known as game
attractiveness), the Phillies win streak going into the game, the month of the year of the
game, and if the game had a promotion or not. Because it is a one season sample ticket
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price, population, stadium quality, tailgating options, and city demographics are all held
constant. Here is some information on each independent variable.
1) Phillies Winning Percentage (phillieswp) –The to-date winning percentage of the
team heading into each game. For example if the Phillies have a record of 20-20
heading into the game there win percentage for that game has been entered as
.500.
2) Opponents Winning Percentage (opponentwp) – Same as phillieswp but done
with the opponent of that day’s game.
3) Day of the Week (weekendgame) – Dummy variable depicting the day of the
week that the game is played. Weekend games are coded with a 1 and weekday
games are coded with a 0. So for example a game on a Wednesday would be
coded as 0.
4) Time of the Day (daygame) – Dummy variable based on the start time for each
game. Day games are coded with a 1 and night games are coded with a 0. Day
games are considered to start between the hours of 12 noon to 4 p.m. while night
games are any game starting from 5 p.m. to 12 midnight.
5) Importance (importance) – The importance of that day’s game. Can also be
referred to as game attractiveness. This graded on 1 thru 5 scale with 1 having no
importance and 5 being extremely important. Earlier season games are coded so
that they have lesser importance than games played later in the season. Also
games against division rivals are coded for being more important than other
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Matt Groves
games. Games against division leaders and interleague games are also given high
importance. Games in which the Phillies can clinch a playoff spot and/or the
division title are also given extreme importance. The rule followed is that games
on average increase in importance as the season progresses. For example a game
played against the New York Mets is given a higher importance than a game
against the Arizona Diamondbacks. Also, a game against the Mets in August or
September is given higher importance than a game against the Mets played in
April or May.
6) Phillies Win Streak (winstreak) – The win streak that the team has going into that
day’s game. Losing streaks are just coded as a 0.
7) Pre or Post All-Star Break (postallstar) – Dummy variable showing the part of the
season the game is played. All games played in the first half of season, before the
All-Star Break, are coded with a 0; all games played in the second half of the
season, after the All-Star Break, are coded with a 1. For example a game played
in May is coded a 0 and game played in August is coded a 1.
8) Promotions (promotion) – Dummy variable that measures whether that day’s
game had a promotional give-a-way or not. Games with a promotion are coded
with a 1 and games without a promotion are coded with a 0.
I selected these variables to try and be as consistent with the previous research as
possible. The winning percentage variables phillieswp and opponentwp are recorded
slightly different. The previous research uses the prior year’s winning percentages for the
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first series while I just went straight with the to date win percentage so Opening Day is
coded as .000 for both teams. Most of the variables are quantitative; they are indentified
on a numerical scale. Phillieswp, opponentwp, dayweek, timeday, winstreak and
monthyear are all based on numerical data that can be looked up. The qualitative
variables, data used to describe certain types of information, are importance and
promotion. Importance is more of an opinion but has a statistical backing while
promotion is based on the information of the day’s promotion of lack thereof.
Model
The equation for the regression is as follows:
attendance = constant term + phillieswp*X1 + opponentwp*X2 + importance*X3 +
winstreakX4 + weekendgame*X5 + daygame*X6 + postallstar*X7 + promotion*X8 +
error term
The regression than produced the following results:
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As you can see from the results three of the variables are statically significant on
two-sided test and one is significant on a one-sided test. The null hypotheses that
opponentwp = 0, importance = 0, and weekendgame = 0 can be rejected on a 95%
confidence interval. Opponentwp has a t-value of 3.81, greater than 2, and the probability
of obtaining a value greater is .000, less than .05. Importance has a t-value of 3.53,
greater than 2, and the probability of getting a greater value is .001, less than .05. We fail
to reject the null hypothesis that promotion = 0 on a 95% confidence interval but we can
reject the null on a 90% confidence interval. The t-value of promotion is 1.894, less than
2, and the probability of a greater value is .057, greater than .05 but less than .1. The
probability is much closer to .05 than .1. Therefore based on this data we can say that
promotions for the 2009 Philadelphia Phillies season did have a statistical significant
affect on attendance.
Based on the results, a 1% increase in the Phillies winning percentage would
cause attendance to increase by 2,133 people while the same increase in the opponents
winning percentage would cause increase attendance by 5,456 people. If the importance
of the game increased by 1, than the attendance will increase by 1,037 people. A 1 game
increase in a win streak will increase attendance by 104 people. A game played on a
weekend will bring 1,399 more people to the ballpark. Day games and games played
during the second half of the season actually have a negative effect on attendance. Based
on the regression a day game will decrease attendance by 178 people while a game
played after the All-Star Break will decrease attendance by 490 people. The R-squared
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value .4154 and the adjusted R-squared is .3504 showing that the regression line is a poor
approximation of the real data points.
We can see the relationship between the opponents winning percentage and
attendance with the following scatter plot:
The graph shows that for the most part the higher the opponents winning
percentage than the higher the attendance. There is a strong cluster of high attendance
right around the .500 win percentage mark. The outliers are due to the early season
games where attendance is not as likely to be affected by the opponent. The attendance
during the opening series is most likely to already be high regardless of the opponent’s
quality. Based on the graph games against an opponent with a better than .500 win
percentage will draw a high attendance figure.
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The following graph shows the relationship between importance of the game and
attendance:
The graph shows that the more important games, the games coded with a 4 or 5,
have a higher attendance on average. The games coded with a 2 or 3 have games that did
draw a high number but also have games with low attendance. One games coded with a 2
has an attendance figure at around 33,000 people. Based on the graph you can make the
claim that importance of the game isn’t the only factor in attracting fans to the ballpark
but games that are important will have a high attendance.
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The following graph shows the relationship between weekend games and
attendance:
The graph shows that weekend games will attract more fans to the ballpark. All
games that were played on a Friday, Saturday, or Sunday, have attendance figures
between 43,000 and 46,000 people. Some games played on weekdays still drew big
crowds but also had days with below 43,000 fans in attendance. Weekend games have a
low standard deviation of the mean, the range of the data is much smaller than the range
for weekday games. Based on the graph you can say that the day the game is played is
not the only factor affecting attendance, but games played on weekends will have a high
attendance.
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The relationship between promotions and attendance is shown on the following
graph:
The graph shows that games with promotions have high attendance figures. All
games with a promotion have an attendance figure between 40,000 and 46,000 people.
Games without a promotion still have high attendance numbers but also have low
attendance. Like with game importance and weekend games, promotions are not the only
factor that affects attendance but a game with promotion will most likely draw more fans
to the ballpark.
The normal regression done above shows the normal affects the independent
variables have on attendance. If we want to see the affects by percentage the given
variables have on attendance than we must run a regression of the log of attendance on
the logs of the same variables.
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Matt Groves
The equation for the regression of the logs is as follows:
logattendance = constant term + logphillieswp*X1 + logopponentwp*X2 +
logimportance*X3 + logwinstreak*X3 + error term. The dummy/binary variables get
dropped in this regression.
The regression than produced the following results:
The promotion, daygame, weekendgame, and postallstar variables get dropped in
the log form because they are dummy/binary variables. The regression shows that a 1%
increase in the winning percentage of the Phillies will increase attendance by 29%. A 1%
increase in the opponents winning percentage will increase attendance by 8%; a 1%
increase in importance of the game will increase attendance by 5%. Win streak actually
has a negative impact, although very small, on attendance in the log form. A 1% increase
in the teams win streak will decrease attendance by .07%. Only the opponents winning
percentage is rejected on a 95% confidence interval. The t-value to logopponentwp is
2.43, greater than 2, and the probability of a great value is .020, less than .05.
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The relationship between logattendance and logopponentwp is shown on the
following scatter plot:
The graph shows that larger logarithm of winning percentages is generally
associated with a higher logarithm of the attendance.
The summary of the data is shown on the following chart:
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The summary of the data of the logged variables is shown on the following chart:
Conclusion
The 2009 Major League Baseball season saw the Philadelphia Phillies follow up a
World Championship season with a return trip to the World Series. This time around they
lose 4 games 2 to the New York Yankees. Given the Phillies recent run of success and
the city of Philadelphia’s passion for sports, it is a no brainer that the team was going to
draw big crowds to the ballpark on a nightly basis. The team consistently drew crowds
over 40,000 fans and had plenty of games at capacity with over 45,000 fans. Despite this
the research shows that there were factors that had more significant contributions to the
attendance at each game.
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Works Cited
Boyd, Thomas C., and Timothy C. Krehbiel. "An Analysis of the Effects of Specific
Promotion Types on Attendance at Major League Baseball Games | American
Journal of Business." Ball State University - Home. Web. 16 Apr. 2010.
<http://www.bsu.edu/mcobwin/majb/?p=173>.
Browning, Aaron B., and Louisa S. DeBolt. "The Effects of Promotions on Attendance in
Professional Baseball | The Sport Journal." Volume 13 Number 2 | The Sport
Journal. Web. 16 Apr. 2010. <http://www.thesportjournal.org/article/effectspromotions-attendance-professional-baseball>.
The Official Site of The Philadelphia Phillies | Phillies.com: Homepage. Web. 25 Mar.
2010. <http://www.phillies.com>.
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