Maximizing Attendance at World Arena

MAXIMIZING ATTENDANCE AT WORLD ARENA

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

Alexander Krushelnyski

May 2013

MAXIMIZING ATTENDANCE AT WORLD ARENA

Alexander Krushelnyski

May 2013

Economics

Abstract

The Division 1 Men’s Ice Hockey Team for Colorado College sells out at their home arena, The World Arena, at 7,343. As one of two division 1 sports at Colorado College, this venue provides great entertainment for fans of the Colorado College Tigers. There have not been any studies to examine why and how the World Arena maintains such a successful rate of attendance. An Ordinary Least Squares Regression is used to determine which factors are significant in affecting attendance at Tiger Hockey Games. Ticket sales are used as a proxy for measuring attendance. Using two different models, results show that playing The Air Force Academy, being regular season champions, making it to the NCAA tournament and making it to the Frozen Four tournament are most significant in increasing attendance. Other variables that were also significant are penalty minutes.

KEYWORDS: (World Arena, Attendance, Ticket Sales, Colorado College Hockey)

TABLE OF CONTENTS

ABSTRACT ii ACKNOWLEDGEMENTS vii 1 INTRODUCTION 1

2 LITERATURE REVIEW 5 2.1 Determinants of Attendance Basic Breakdown……………………………….. 6 2.2.1 On Ice Factors…………….………………………………………………... 6 2.1.2 Opponent…………………………………………………………………... 8 2.1.3 Violence……………………………………………………………………. 8 2.1.4 Off Ice Factors……………………………………………………………... 9 2.1.5 Location and Substitutes………………………………………………….. 11 2.1.6 Accomplishments…………………………………………………………. 12

3 DATA COLLECTION 14 3.1 List of Variables……...... 16 3.2 Summary Statistics...... 20

4 REGRESSION RESULTS 21 4.1 Regression Results 4.1-4.4……………………………………………………. 22 4.2 Regression Results 4.5,4.6…………………………………………………… 23 4.3 Equation 4.1 Analyzed………………………………………………………... 24 4.4 Equation 4.2 Analyzed………………………………………………………... 26 4.5 Equation 4.3 Analyzed………………………………………………………... 27 4.6 Equation 4.5 Analyzed………………………………………………………... 29 4.7 Equation 4.6 Analyzed………………………………………………………... 30 4.8 Econometric Tests…………………………………………………………….. 31 4.8.1 Tests For Heteroskedasticity……………………………………………… 31 4.8.2 Distribution of Errors……………………………………………………... 32

5 CONCLUSION 33

6 REFERENCES 36

LIST OF TABLES

1.1 Division Men’s Hockey Attendance………………………………………………….2

3.1 Definition of Game by Game Variables………………...….….…………………...16

3.2 Definition of Variables (That Do Not Change Game to Game)…………………….17

3.3 Dependent Variable Definitions…………………………………………………….18

3.4 Summary Statistics………………………………………………………………….20

4.1 Regression Results Equations 4.1-4.4……………………………………………….22

4.2 Regression Results Equations 4.5, 4.6………………………………………………23

4.3 Heteroskedasticity Results…………………………………………………………..31

LIST OF FIGURES

2.1 Attendance at World Arena Basic Breakdown……………………………….. 6

ACKNOWLEDGEMENTS

This thesis would not have been possible without the help and support of many people. First, I would like to thank Jessica Bennett for her outstanding cooperation and brilliant insight. Your statistics and information provided are at the heart of this study and the reason why this was possible. Secondly, I would like to thank Professor Maroula Khraiche for her guidance and collaboration. Thirdly, I would like to thank my family for persuading me to write this thesis this year. I appreciate all the love and support. Finally, I would like to thank my roommates who also wrote their theses this year and the endless supply of sunflower seeds that help us make it through this process.

CHAPTER I

INTRODUCTION

Hockey is one of the most popular sports in North America today. There are 59

Division 1 Men’s Ice Hockey programs across the United States (“College Ice Hockey,

2013”), meaning that there are also 59 arenas that are typically filled with cheering students, parents and supporters. The state of Colorado alone possesses two big time

Division 1 hockey programs; University of Denver and Colorado College. Each school contains its own unique qualities. University of Denver contains many Division 1 sports other than hockey and is also located in a major city. The 12-county Denver-Aurora-

Boulder Combined Statistical Area had an estimated 2011 population of 3,157,520

(“Denver, 2013”). Colorado College on the other hand only contains two division 1 sports, Men’s Ice Hockey and Women’s Soccer. The school is located in the small

Colorado Springs containing a population of less than 500,000. The World Arena is also located in Colorado Springs 5 miles from the Colorado College campus. Yet Colorado

Springs World Arena, the home of Colorado College Hockey out seats and outsells the

Magnus Arena home of the Denver Pioneers of Denver University. How can it be possible that the small Colorado College community can fill the World Arena on such a regular basis? The highest grossing college event in the state of Colorado sells out at close to 7,500 people as the World Arena roars when the Colorado College hockey team hits the ice. With a student population of just over 2,000 attending Colorado College,

which is much less than other schools with division one programs, a crucial objective of

World Arena and Colorado College faculty and staff is maximizing attendance at hockey games. The question that must be answered is how to maximize attendance at an arena that seats close to 8,000 while only having a student population of 2,000 in a city with a population of just under 500,000 (“Colorado Springs, Colorado, 2013”). Colorado

College differs from most major universities that have a Division 1 Ice hockey team because most schools that have major Division 1 sports usually contain student populations of at least 10,000 or more.

TABLE 1.1

DIVISION 1 MENS HOCKEY ATTENDANCE

School Enrollment Avg. Attendance Capacity Wisconsin 30,367 11,773 15,237 North Dakota 11,522 11,155 11,634 Minnesota 51,853 9,539 10,000 Colorado College 2,026 6,754 7,343 Michigan 36,675 5,997 6,637 Michigan State 48,906 5,364 6,470 Denver 11,770 5,359 6,026 Ohio State 56,387 5,178 17,500 Western Michigan 24,598 3,444 3,667 SOURCE: www.ncaa.com/icehockey-men/d1

Table 1.1 shows 9 schools, including Colorado College, and their average attendance at their Division 1 hockey games for the 2011-2012 season as well as the maximum capacity the stadium holds and the student enrollment. Each of these schools is ranked in the top 20 in attendance for Division 1 Men’s Ice Hockey. Five out of the

nine schools contain student populations of over 30,000, the other three contain populations of over 10,000 while Colorado College only contains a student population of

2,026. The purpose of this study will be to understand why Colorado College has the sixth highest average attendance throughout all college hockey. This study will look at the many different factors that influence Colorado College Hockey games and attendance at World Arena and why Colorado College continues to be a top attendance event not only in Men’s Division 1 Ice Hockey, but in the state of Colorado and the city of

Colorado Springs. Motivations for this research arise because the World Arena is a main event holder in the city of Colorado Springs. Colorado College Hockey is a prominent source of entertainment in Colorado Springs and provides a large market. Not to mention the longstanding history of the program which has been a large part of the Colorado

College community for 72 years. Colorado College Hockey serves an large purpose in the Colorado College community, being one of two division one sports the school contains along with having the fourth largest stadium throughout all of division one hockey, research must be done to study the determinants of attendance in order to utilize the World Arena’s maximum potential, from a city perspective and also from a college perspective.

There are many challenges to filling a stadium. The staff and faculty must take many things into consideration when their main objective is to sell as many tickets to games as possible. The Colorado College student body is just over 2,000, that fills less than a third of the Colorado Springs World Arena. Marketing, advertising and performing are critical objectives for everyone involved with the World Arena to keep ticket sales as high as they are. The performance of the hockey team is very much

dependent on attendance, and attendance is very much dependent on the success of the team. The relationship the Colorado College Hockey team shares with fans is a very personal one. Through many player appearances, fundraisers and charity events the players and staff seek to develop more personal relationships with fans in order to sell tickets. However, ticket sales cannot simply be increased by only personal relationships.

The Colorado College Hockey team serves a large purpose for the school, but the World

Arena itself must maintain a successful attendance rate in order to help maintain the prestige of the program. It is important to utilize The World Arena to not only give the hockey team a good reputation but to also add to the outstanding reputation of Colorado

College itself. The success of school’s athletics add prestige and accomplishment to the academic institution.

The World Arena and Colorado College hockey games are an important economic opportunity for business and this study will examine how to take full advantage of what the World Arena provides to the Colorado Springs and The Colorado College population.

When the main objective is filling a stadium, it is essential to understand what factors attract fans to hockey games. As the number one facility used for entertainment in

Colorado Springs, research should be devoted into determining factors of attendance for this Arena in order to fully utilize its potential and support for the Colorado College

Hockey Team. This study will examine both “on ice” and “off ice” factors and their implications on ticket sales. This study will seek to better explain why the World Arena retains such a successful attendance rate and what factors possibly positively influence attendance and which factors might negatively affect attendance.

CHAPTER II

LITERATURE REVIEW

The purpose of this study is to look at factors that affect attendance and ticket sales at the World Arena for Colorado College hockey games. Previous studies have focused mainly on the impact of on ice factors and their influence in determining attendance in National Hockey League games. “On ice” being defined as factors during the game that could affect fans decisions to attend games, such as the score of games, the opponent, the outcome, and penalty minutes. Studies have also been conducted to determine what motivates fans to attend games, what do people like seeing at hockey games (sporting events). Other studies have concentrated their efforts on certain “off – ice” aspects and their importance in affecting attendance at sporting events. Competitive balance, location and marketing are just a few of the off-ice topics that have been examined to analyze their effect on attendance. There have been studies conducted on

NHL attendance but there have not been any that have focused their efforts on college hockey. As the table 1.1 above shows, college hockey is a very prominent sporting event in the United States, and each school listed contains their own markets specifically for college hockey games. Research should be put forth to analyzing economic implications of these markets. This study investigates written literature and the factors that could possibly influence attendance at Colorado College Hockey games. This study will explain

what on ice factors as well as important off ice factors specifically help the Colorado

College community fill the World Arena in Colorado Springs.

A number of factors can determine why people attend events they go to, this study will examine a number of things to understand why the Colorado Springs World

Arena has such a high attendance rate.

FIGURE 2.1

ATTENDANCE AT WORLD ARENA

Violence

Opponent On-Ice Factors

Attendance at World Arena

Accomplishments Off-Ice Factors

Marketing, economic, and athletic factors must be taken into consideration when determining factors of attendance. Figure 2.1 points out the basic breakdown of the factors that will be examined further when determining their influence in maximizing attendance.

On-Ice Factors

Two on ice factors that can greatly impact attendance are scoring, and as a result winning. It is reasonable to believe that fans like to see their teams win, and for teams to

win they must score. However, Paul (2008) found that for teams that had the same level of winning success, more scoring actually decreased attendance. Assuming scoring leads to wins, and wins lead to higher attendance it is rational to think scoring would increase attendance. Because of this discrepancy with Paul’s (2008) findings, certain scoring statistics will be examined in this study to determine their effects on attendance at the

Colorado Springs World Arena. That is why along with the simple win statistic, goals scored, and goals against will be examined to analyze their effect on attendance. Another study by Paul on attendance in the QMJHL (Quebec Major Junior Hockey League) found that “apart from valuing winning teams, fans of the QMJHL did not seem to be influenced by teams which play higher scoring games” (Paul and Weinbach, 2011). So for the QMJHL, scoring is not significant to prospective people in wanting to attend games. Paul found that scoring had two different effects in two different leagues.

Because previous research has shown that on ice factors, such as scoring, have different results on attendance in different leagues, this study will carefully focus on a few of those on ice factors such as goals for, goals against, as well as win percentage and determine the significance of their influence on attendance at World Arena. Other studies have been conducted analyzing the effects of win percentage on competitive balance and their effects on attendance in different sports. Meehan, Nelson, and Richardson (2007) studied how competitive balance effects attendance on Major League Baseball and found that the score as well as the competitiveness (closeness of score) actually affected attendance. To relate these efforts done on competitive balance to my study on Colorado College

Hockey I will simply have a point spread variable to investigate the effects the closeness of games on attendance at the World Arena.

Opponent

The Colorado College – Air Force rivalry (along with the Denver rivalry) seems to be a highlighted game in fans’ calendars in the Colorado College hockey season. The

Colorado College Hockey Team rotates with Air Force each year, one game is played at

Air Force, the following season the rivalry is played at the World Arena. An important concern for the staff of Colorado College and World Arena for the 2012-2013 season was the notion that season ticket holders would jump ship to Air Force after Colorado College lost to Air Force in the (previous) 2011-2012 season. Because this one game can possibly be an important influence on ticket sales this study will examine the effects of whether there is a game played against Air Force at the World Arena for a given season and the wins and losses against Air Force and the results it has on ticket sales the following year.

Violence

Many efforts have been put forth to studying how violence affects attendance and performance in NHL games. Jones (1996) and Paul (2003) devoted their research to the economics of violence in the NHL and its relationship with attendance and found results that confirm that there is a significant positive relationship between violence and attendance for both Canadian and American cities that have NHL teams. However studies have also shown that fighting has negative effects on player performance, more specifically points and goals which influences the outcome of the game which in turn influences attendance (Gee and Leith, 2007). Fights and penalties are a major part of the game of hockey, however, many fans have opinions on these aspects of the game that in turn affect their attendance rate. Paul (2008) devoted research to many factors and their

relationship with attendance, those of which included violence and scoring. His results showed that fans in certain locations actually liked to see low scoring, high violence games with many fights. However because there is no fighting in college hockey the results may differ for the case of Colorado College. Given that in College Hockey fights are a one game suspension and because of a short forty game season, we will assume in this study that fights do not occur in College Hockey because players do not wish to be suspended and will omit any violence variables from this examination. However, because two different studies have examined violence and the effect that penalty minutes has on attendance, penalty minutes will be examined in this study to determine if they have any relationship with attendance at Colorado College hockey games.

Off-Ice Factors

It is important to market and advertise correctly in order to fill stadiums across the nation. Whether it is for a professional, semi professional or an amateur sports team, each team plays at a certain venue. Each arena, venue, or stadium is a market, where profits must be maximized by trying to fill stadiums to their maximum capacities. Thus marketing and advertising play an important role in efforts of maximizing ticket sales.

Mumford, Kane, and Maina (2004) explain six different marketing strategies to increase attendance at sporting events. The six include, value of the event, student involvement in sports, event publicity, target markets, “fun” factors, and promotional give a ways

(Mumford, Kane, and Maina, 2004). Using these six different techniques, during the lockout season in 2005 the NHL launched a promotional campaign specifically designed to “send a central, unified message: the NHL, its players and corporate partners [were] ready to provide fans with a brand of hockey unlike any other” (NHL, 2005) in an

attempt to re-attract fans to the sport and help raise ticket sales. It is plausible to believe that certain promotional events involving players act as a branding/value technique in order to attract fans to games. By using this technique, the message to fans is that Tiger

Hockey is a unique experience and as (NHL, 2005) mentions, it “provides fans with a brand of hockey unlike any other.” Looking at appearances made by Colorado College hockey players done throughout Colorado Springs such as community service projects, autograph signings, cancer walks etc. this study will determine whether these interactive player events may in turn serve as promotions and help increase attendance.

There are numerous pieces of literature that focus on determining fan’s motivations to attend sporting events. A study done by James and Ross (2004) used 9 variables to measure motivations; entertainment, skill, drama, team effort, achievement, social interaction, family, team affiliation and empathy. However, of these 9 it was found that factors associating with the sport in general generate the most interest (Snipes,

Ingram 2007) (James and Ross 2004). This study will examine those 9 variables more closely and separate them into more specific categories such as goals per games, win percentage, player appearances etc. (using elements of the 9 categories studied by (James and Ross, 2004)) and attempt to understand their significance on ticket sales at the World

Arena box office. Another possible issue affecting attendance is sponsorship, and sponsors are also one way for the Tiger Hockey program to raise money. Attendance and sponsorships share a unique relationship because if attendance is high it will attract more sponsors. Sponsors also purchase a package of tickets along with their spot for advertisement which means the more sponsors might mean higher attendance.

This study will analyze the relationship between sponsorship numbers and its effect on attendance and ticket sales. Using both on ice factors and off ice factors we will hope to gain a more thorough understanding of why fans attend games and how to increase attendance by investigating which factors significantly affect ticket sales, whether it be negatively or positively.

Location/Substitutes

Location can greatly affect the attendance at sporting events. Previous research has shown that teams located closely to each other will not sell as many tickets simply due to competition (Carlton, Frankel, Landes, 2004). There is professional basketball, baseball and hockey in the state of Colorado. However each of these three professional sports teams is located in the city of Denver located 65 miles north of Colorado Springs.

We will assume, that not only because these are professional sports teams but are also located 65 miles north and target a different location and receive a different audience than the World Arena does that these three sports teams do not affect attendance at the World

Arena. One factor that may influence it is the close substitute Air Force. The Air Force

Academy located just over ten miles up the road may significantly impact attendance at

Colorado College hockey games. Especially because there is a heated rivalry between these two teams this could be an extremely influential factor in determining which team fans want to support. For simplicity and for the sake of this examination we will exclude the possibility of the Denver sports teams being possible competitors to Colorado College hockey games. As for the Air Force and Colorado College rivalry, this will be taken into account simply by looking at the result of the games and how they affect attendance at

The World Arena rather than attempting to analyze the decisions of fans in which team they choose to support and why.

Accomplishments

There are major accomplishments other than winning championships that measure the success of a College Hockey season. “In general, a newer stadium or recent championship led to higher attendance” (Domazlicky, 1990). This statement refers to attendance in Major League Baseball but is definitely relevant to this study. The possible achievements in college hockey are winning your league; there are currently five different leagues with different teams belonging to each league. Colorado College is a member of the Western Collegiate Hockey Association along with 11 other teams. One goal each and every year of the Colorado College Hockey Team is to finish first in the

WCHA (Western Collegiate Hockey Association). Another goal is to make the NCAA tournament. Out of the 59 Division I college hockey teams each team has an opportunity but only 16 teams actually make it to the NCAA tournament. An even more impressive accomplishment is making it to the semi-finals of the NCAA tournament. The semi- finals and finals are combined into an elimination round called the Frozen Four. It is an accomplishment in itself to make it to the NCAA, but to make it to the Frozen Four is considered to be an even bigger deal, and obviously winning the NCAA tournament

(which means winning the Frozen Four) is the most prestigious. Each of these accomplishments will be examined and taken into account while investigating their significance on ticket sales and attendance at Colorado College hockey games. As previously mentioned, Domazlicky (1990) considered the effects of winning a championship on attendance in Major League Baseball it would be plausible to believe

that a championship anywhere with a sizable market would have increased attendance as a result of a won championship. In this case, this study will look at the effects of each separate accomplishment and the effect of that accomplishment on ticket sales.

CHAPTER III

DATA COLLECTION

This study will seek to better explain why the World Arena retains such a successful attendance rate and what factors possibly positively influence attendance and which factors might negatively affect attendance. The purpose of this chapter is to describe the data set that will be used to test the model. The first part of this chapter will explain the dependent and independent variables that will be used for the model along with why they are relevant and important to this study. The OLS (Ordinary Least

Squares) regression method will be used as the estimation strategy, we will obtain a brief overview of the models that will be used and will gain a more detailed explanation in the results chapter.

A majority of the data was collected for this study and obtained by the Director of

Athletic Marketing at Colorado College. The data includes 7 seasons of home games played at the World Arena. This study focuses on attendance at the World Arena so data has been gathered and organized for each Colorado College home game played at the

World Arena starting in the 2004-2005 through the 2010-2011 season summing to a total number of 147 home games to use for observations. The Colorado College Hockey

Media Guide was used to obtain scores for each home game; thus acquiring the goals scored, goals allowed, result of the game, as well as opponent played and if the game

went into overtime or not. Because Colorado College hockey has been around for 72 years, it is part of its longstanding tradition and history to recognize the accomplishments achieved over the years. I will use the statistics provided by the Colorado College

Hockey Media Guide as well as the banners that hang in the rafters of the World Arena that prove the accomplishments I will examine; championships, NCAA appearances,

Frozen Four appearances. The site www.hockeydb.com has provided the amount of penalty minutes in a given season. Box office ticket sales, internet tickets, phone tickets, outlet tickets, net amount, capacity, kills, comps, opens, sponsors and player appearances are all provided for each home game for the last 7 seasons.

Now that we have a basic list of the sources of the data I will clarify what purpose the variables will serve in the regressions used in attempts to explain the data. The variables have been separated into two tables, those that differ game by game, and those that do not. Most of the variables that do not differ for each game are dummy variables that contain a numeric value representing the entire year. The following table, table 3.1 lists and describes the variables whose values differ for each game.

TABLE 3.1

DEFINITION OF GAME BY GAME VARIABLES

Independent Definition

Variables

Goals For number of goals scored in a given game

Goals Against number of goals scored against in a given game

Point Spread absolute value of goals scored minus goals against

Win whether or not Colorado College won on a given night

Overtimes whether or not the game went into overtime or not

Table 3.2, lists and describes each variable that do not differ from game to game.

The four variables, Play Air Force, Regular Season Champion, Lagged NCAA and

Frozen Four are used as dummy variables in this study. The reason why the accomplishments were measured as regular dummy variables and lagged variables is because it is plausible to believe that the accomplishments achieved in the previous season may affect attendance the following year. For example, it is reasonable to believe that more fans would attend games the following year if a team won a championship the previous year. Penalty Minutes are a season total, so the same number (the total number of penalty minutes for that season) is entered for each game for that season. Sponsors and appearances are not used in the final models and results however provide valuable insight into affecting attendance and will thus be included in the variable definitions.

TABLE 3.2

DEFINITIONS OF VARIABLES (THAT DO NOT CHANGE GAME TO GAME)

Independent Definition

Dummy Variables

Penalty Minutes Total number of penalty minutes for a given year

Play Air Force whether or not Colorado College played Air Force that

season at home

Regular Season Champ if Colorado College finished 1st in the WCHA that year

Lagged NCAA lagged variable for NCAA

Frozen Four if Colorado College made it to the Frozen Four

tournament that year

Appearances number of player appearances done in a given year

Sponsors number of sponsors for the given year

There are multiple regressions ran on the data using two different variables as dependent variables. Table 3.3 (below) lists the dependent variables that will be used.

TABLE 3.3

DEPENDENT VARIABLE DEFINITIONS

Dependent Definition

Variable(s)

Total Tickets total number of tickets sold (non-monetary value,

Sold actual ticket count)

Total Tickets total amount of tickets sold + total amount of

Sold Plus complementary tickets

Complementary

Tickets

The dependent variables count the amount of tickets sold. It is a non-monetary value, it is an actual ticket count for how many people have purchased a ticket for the game. For example for sold out games, the variable total tickets sold would have a value of 7,750.

For one set of regressions the independent variable will be Total Tickets Sold and the model will go as follows.

Totalsoldtickets = β₀ + β₁goalsfor + β₂goalsagainst + β₃playairforce + β₄regseasonchamp

+ β₅laggedncaa + β₆frozenfour etc… (3.1)

For the other set of regressions, complementary tickets will be added to Total Tickets

Sold. A complementary ticket is defined as any ticket not paid for, which includes players who get free tickets, children who work at the arena for one night, visiting team’s players’ tickets and most importantly corporate sponsor tickets go into this category.

This set of regressions will analyze the effect of those who gain entrance upon free admission as well as those who pay to gain admission. The model below illustrates the second set of regressions that will be used.

Totalpluscomps = β₀ + β₁goalsfor + β₂goalsagainst + β₃playairforce + β₄regseasonchamp

+ β₅laggedncaa + β₆frozenfour etc… (3.2)

Table 3.3 summarizes the variables that will be used as a proxy for attendance. It is important to note the difference between the two because in the regressions being explored, tickets sold are being used in place of attendance. Because we did not have the statistic for total attendance per game, it is reasonable to substitute tickets sold for attendance. We assume the more tickets sold, the higher the attendance. Total Tickets

Sold includes box office tickets sold, internet tickets sold, phone tickets sold and outlet tickets sold. In another specification, attendance is proxied by Total Tickets Sold plus

Complementary Tickets. This additional specification is important because it will include not only tickets sold, but also fans who attend games for free which could possibly be affected by the independent variables differently than when running regressions against the dependent variable of Total Tickets Sold.

The table below presents the summary statistics for the variables that will be used in the different regressions. One problem in this study is that for most variables the data changes from game to game while other variables’ data only uses number for each game for the entire season. The results may have been more accurate and different if penalty minutes or appearances changed from game to game. Below are the summary statistics for the variables that were used in the regression analysis.

TABLE 3.4

SUMMARY STATISTICS

Variable Obs Mean Std. Dev. Min Max Goals For 166 3.4 1.83 0 9 Goals Against 166 2.5 1.75 0 9 Wins 166 0.61 0.48 0 1 Play Air Force 166 0.52 0.5 0 1 Regular Season Champion 167 0.18 0.39 0 1 Overtimes 167 0.13 0.34 0 1 Pointspread 166 0.9 2.64 -8 7 Penalty Minutes 145 591.87 67.88 522 724 Lagged NCAA 167 0.54 0.49 0 1 Frozen Four 167 0.05 0.23 0 1

The statistics above show the number of observations, the means, standard deviations, and minimums and maximums of each data variable used in the regression models.

An “off ice” variable is one that is defined as not having any relation to game statistics which means it has nothing to do with the performance in hockey games. Some of the “off ice” aspects such as player appearances and sponsors failed to show significant results on affecting attendance. It was reasonable to assume that player appearances, sponsors, and sponsor dollars may have significantly affected attendance.

CHAPTER IV

REGRESSION RESULTS

In this chapter I list the models that will be used during regression analysis and I will interpret the results of the statistics. The models used to estimate the effects of the independent variables on attendance are as follows. The first model used that was found to have the most significance is equation 4.1. Equations 4.1-4.4 each use Total Tickets

Sold as the dependent variable. Equations 4.1 and 4.2 are the exact same, except equation 4.2 the variable Penalty Minutes was added to measure the significance of the effects of violence on ticket sales. Equations 4.3-4.4 are very similar to equations 4.1 and

4.2. The difference between the two is easily noticeable. Equations 4.1 and 4.2 use the variables Goals For and Goals Against, equations 4.3 and 4.4 use Point Spread instead.

Both sets of equations are very similar except for minor changes made by adding and removing only a few variables. Below, table 4.1, includes the dependent variables as well as the independent variables used in each equations 4.1-4.4. To go along with the variables, the coefficients of each independent variable along with their standard errors

(directly below the coefficients) are also included. The r-square and the F score are also included for each regression, as well as the number of observations.

TABLE 4.1

REGRESSION RESULTS EQUATIONS 4.1, 4.2, 4.3, 4.4

Dependant Variable (Total Tickets Sold)

Independent Equation Variables Equation 4.1 Equation 4.2 Equation 4.3 4.4 Goals For -49.27* -44.66* (-31.35) (-30.94) Goals Against 37.91 36.88 (-33.3) (-32.97) Point Spread - 53.28* - 46.38 (-35.24) (-34.84) Win 69.39 41.44 (-204.01) (-202.19) Play Air Force 474.43*** 510.82*** 471.83*** 509.11*** (-133.34) (-140.66) (-132.99) (-140.11) Regular Season Champion 780.77*** 807.45*** 775.63*** 804.22*** (-197.67) (-205.19) (-198.69) (-206.32) Lagged NCAA 904.12*** 890.7*** 910.37*** 895.24*** (-134.37) (-150.45) (-134.62) (-130.96) Frozen Four 1467.32*** 1223.74*** 1479.417*** 1233.22*** (-306.36) (-412.2) (-307.37) (-414.63) Overtime -185.21 -51.23 -169.24 -42.05 (-173.83) (-180.07) (-180.4) (-185.13) Penalty Minutes 1.38 1.37 (-1.22) (-1.22)

Number of Observations 147 144 147 144 R-Sqaure 0.42 0.45 0.41 0.45 Adjusted R-Square 0.39 0.41 0.39 0.41 F-Score 14.13 13.6 14.17 13.61 Coefficients of the variables are given along with their standard errors in parentheses. Asterisk show statistical significance. *** if P value ≤ .05. ** if P value ≤ .10. * if P value ≤ .15.

TABLE 4.2

REGRESSION RESULTS EQUATIONS 4.5, 4.6

Dependent Variable (Total Tickets Sold Plus Comps)

Equation Equation Independent Variables 4.5 4.6 Goals For -38.15 (-34.07) Goals Against 68.48** (-36.3) Point Spread - 73.49***

(-38.36)

Win -73.49

(-38.36)

Play Air Force 156.47

(-222.32)

Regular Season Champion 174.59*** 184.09*** (-134.89) (-134.2) Lagged NCAA 300.59** 875.99** (-163.64) (-227.29) Frozen Four 166.94 201.19 (-453.87) (-436.33) Overtimes 10.41 42.35 (-198.28) (-203.75) Penalty Minutes 3.17*** 3.09*** (-1.34) (-1.34) Number of Observations 144 144 R-Square 0.28 0.28 Adjusted R-Square 0.23 0.23 F-Score 6.4 6.43 Coefficients of the variables are given along with their standard errors in parentheses. Asterisk show statistical significant. *** if P value ≤ .05. ** if P value ≤ .10. * if P value ≤ .15.

I now discuss the results of each regression from equations 4.1-4.6. I discuss the progressions of which variables are significant, as well as which variables are dropped and added. Equation 4.1 will be examined first.

As table 4.1 shows, the results indicate that Goals For and Goals Against are insignificant in affecting attendance in this model. The following variable, Play Air

Force, is investigating how playing Air Force at the World Arena that season would affect attendance. The coefficient for the dummy variable, Play Air Force, is (+)

474.7503 and it is also significant at the 99% level. Playing Air Force at the World

Arena actually significantly affects attendance. This could be for a number of reasons, it could be a result of a local rivalry that specifically increases attendance tremendously for that game or it could also be that fans anticipate seeing the rivalry and become interested with Tiger Hockey leading them to attend more games throughout the season.

Regardless, it is apparent that playing Air Force at the World Arena significantly does increase attendance. Because the coefficient is 474.75, we can say that in this model, playing Air Force actually increases attendance by close to 500 people relative to the seasons where Colorado College does not play Air Force at the World Arena.

The next question is how does being a regular season champion affect attendance?

The coefficient for Regular Season Champions is a positive 780.7742 and it is also significant at the 99% level. This is a very understandable result. If the Tigers finished

1st in the WCHA, we can assume that many games are won, whether it be at home or away. This would mean that fans are noticing the success of the season the Tigers are having and wish to go to more games. Thus the value of the coefficient 780.77 would allow us to say that if the tigers were regular season champions, attendance was increased by 780 people per game. This result is logical because fans wish to see teams who succeed and this result proves that Tiger fans recognize the success of the Tigers which leads them to attend more games. The next variable, Lagged NCAA, was used as a lag

for this regression because it is an accomplishment that occurs at the end of the season.

My hypothesis was if the Tigers made it to the NCAA tournament in the previous year, it might spur interest among the community, the city, and the students and could ultimately increase attendance in the following year. The NCAA tournament games are all played away from home so they are not included in these models, however, the fact that the

Tigers may have made it to the NCAA’s are included in this model. Because the NCAA tournament games are not played at home it may make fans eager to see the successful

Tigers play at their home of The World Arena, thus the decision to lag it. The positive coefficient for the variable Lagged NCAA is positive and equals to 904.119. It is also significant at the 99% level. It is reasonable to say that attendance is increased by 904 people per game when the Tigers make the NCAA tournament the year before. This makes sense because fans obviously wish to see the winning team they were unable to during NCAA playoffs.

The last variable I will discuss for this regression is the Frozen Four variable. The

Frozen Four is the tournament name for the semi-finals and the final game played for the

NCAA tournament. A total of four teams participate in the Frozen Four, hence the name.

The reason why this variable is included along with making the NCAA tournament is because making the NCAA tournament does not guarantee having a Frozen Four appearance. Making it to the Frozen Four could have a different effect on attendance than simply making it to the NCAA tournament. The coefficient for Frozen Four is a positive1467.318, which means that if the Tigers make the Frozen Four in a given year it is extremely significant and affects attendance the most out of the variables that we have seen. The variable is also significant at the 99% level. There are a few possible reasons

for this result. If the Tigers make it to the frozen four they will have made it to the

NCAA tournament. If they have made it to the NCAA tournament and the Frozen Four we can assume it is a very good team that has had many wins on the season. Thus assuming that fans know the team may go far into the playoffs they attend more games.

Although the Colorado College hockey team has been to the NCAA tournament and the

Frozen Four several times they are still considered rare accomplishments. This data set includes each home game for each season starting with the 2004-2005 season. This was the only year the Tigers have made the Frozen Four in the last 8 years. Because of the results of the coefficient and the strength of the significance of the Frozen Four variable, we can deduce that success is essential to attracting support from fans.

The regression results shown in table 4.1 shows the r-squared as well as the F score. Because the r-square (coefficient of determination) is .42 and the adjusted r-square is .39 we can only conclude that about 40% of the data can be elegantly explained by the model, in other words, the variables explain total ticket sales (attendance) roughly 40% of the time. However the one particular test to asses if the model is actually applicable, the

F-test, calculates a noteworthy value of 14.15 and the fact that the prob. F is below .05 also means that the model is significant.

The second model I will discuss is equation 4.2. Equation 4.2 is the same as equation 4.1 with a variable for penalty minutes added in. The results are very similar to the results in equation 4.1. The regression results displayed in table 4.1 indicate a high F score, just as in equation 4.1. The r-square of .4463 and adjusted r-square of .4135 is also similar to the results of equation 4.1. The variables can explain the dependent variable roughly 40% of the time. As expected, because of the results from equation 4.1, playing

Air Force, being regular season champions (finishing 1st in the WCHA), appearing in the

NCAA tournament the year before and making it to the Frozen Four are all significant at a 99% level. Each significant variable’s coefficient is very similar to the results of that in equation 4.1. The variable Penalty Minutes is a total number of penalty minutes for that season that were entered in for each home game. This is one fault of the model; the effect of penalty minutes on ticket sales could have been measure more accurately if the penalty minutes for each home game were entered separately rather than using a season total for each game. As the results show, although Penalty Minutes actually has a positive coefficient, the variable is insignificant thus the precise implications that the number of penalty minutes has on attendance at home games cannot be specifically concluded from this model. However it could mean that violence does not matter for college hockey, unlike the NHL. It is also plausible to believe that not a lot of penalties occur because the punishment is much more severe at the college hockey level.

The third model explained (equation 4.3) is also a modification from equation 4.1.

In equation 4.3, the variable Point Spread is substituted for the two variables Goals For and Goals Against that are included in equation 4.1. The purpose of this was to examine the effect of the point spread, the difference in goals scored between teams, and its effect on ticket sales. For example if the Tigers scored 6 goals and the opponent scored 1 goal the point spread would be 5. The purpose of this was to see if fans cared about the closeness or competitiveness of games.

As previously stated in the models representing equations 4.1 and 4.2, the results are very similar to each other. Refer to table 4.1 for detailed results. The F score is high again affirming the models relevance and significance. The r-squared is .42 and the

adjusted r-square is .39 which insinuates that the variables can explain ticket sales roughly 40% of the time. The four significant variables are Playing Air Force, Lagged

NCAA, Regular Season Champions, and Frozen Four. Goals Scored is also significant but only at an 85% level, thus it does not have a significant impact on ticket sales. The variable examined most closely in this model was the Point Spread variable which showed results of being insignificant. The hypothesis that the point spread of games would actually affect the ticket sales was proved incorrect.

Equations 4.1-4.4 each use Total Tickets Sold as the dependent variable. The results were consistent and showed that four significant variables, Play Air Force,

Regular Season Champions, Lagged NCAA and Frozen Four all increase attendance at a positive rate and a significant level.

The next set of regressions uses a different dependent variable, Total Tickets Sold plus Complementary Tickets. The reason for changing the dependent variable was to proxy attendance differently. Total Tickets Sold only includes the amount of tickets actually sold for a specific price. Total Tickets Sold plus Complementary Tickets includes each ticket sold as well as those given for free. A complementary ticket is defined as any ticket not paid for, which could include tickets given to players, tickets given to the opposing team’s players, child volunteers for the night and tickets from corporate sponsors. This might measure attendance in a dramatically different way. It also brings up an interesting issue with the amount of tickets given and used by sponsors.

Variables could affect attendance in a different way if more people gain entrance to games free of charge. Thus the following regressions will analyze the effect of the same

variables and their impact on attendance including complementary tickets in the dependent variable.

Equation 4.5 includes each variable from the previous equations and also includes penalty minutes. Although penalty minutes were previously found to be insignificant it could be significant in this model because of the change in the dependent variable. Table

4.2 displays the regression results, the F score as well as the coefficients of determination. The r-squared is .27 and the adjusted r-squared is .23 which is lower than in the previous models stating that only roughly 25% of the dependent variable can be explained by the variables in the model. The variable Regular Season Champion is significant at the 99% level and has a positive coefficient of 907.13. We can conclude from this observation that finishing in first place has a tremendous effect on attendance.

The variable Lagged NCAA possesses a positive coefficient of 300.59 but is only significant at the 90% level. In this specific model penalty minutes is significant which provides very interesting results. The coefficient of 3.17 is not substantially large but nonetheless it is positive and significant which means the amount of penalty minutes has a different effect on attendance when complementary tickets are included. One plausible reason would be to believe that those who actually purchase their ticket for Tiger home games are not interested in seeing penalties or violence, and those who gain admission free of charge with a complementary ticket actually prefer to see penalties and violence.

This is an extremely interesting result because the majority of complementary tickets go to sponsors. Assuming that sponsor tickets are those who either own companies or work for those sponsoring Tiger hockey the age and interests of this group could be entirely different than the average ticket purchasing Tiger hockey fan. Also in this model Play Air

Force and Frozen Four were found to be insignificant. These results differ than those obtained in previous models.

A further investigation developed from equation 4.5 to examine the effects of variables on this new dependent variable which is expanded into equation 4.6. Goals For and Goals Against will once again be omitted and the variable Point Spread will be inserted instead.

In this model the point spread of games is actually significant at a 95% level.

This model differs from the previous models because the point spread now significantly affects attendance. As the results in table 4.2 show, Regular Season Champion is once again significant with a positive coefficient of 873.99. Lagged NCAA is also significant in this model and possesses a positive coefficient of 306.78. In this model, Penalty

Minutes are significant at the 95% level with a positive coefficient of 3.08. Penalty

Minutes has a significant effect on attendance when attendance is measured including complementary tickets.

What is most interesting about the progression of these models is that when the dependent variable changed and was measured differently, different variables affected attendance. Penalty Minutes and the Point Spread do effect attendance when the amount of people measured by gaining entrance free of admission are included. The conclusions as to why Penalty Minutes and the Point Spread affect attendance cannot be explicitly explained and should be questioned when exploring future research however it is noteworthy to take consideration of these results. It could be an endogeneity problem.

During important games, that are typically close, competitive, games and have a lot of violence, many complementary tickets are given or people simply use their

complementary tickets for these games. So a reverse causality may be at work here.

Alternatively, those who use complementary tickets are a different type of audience who enjoy different aspects of the game.

The coefficients that were obtained while running regressions that included the number of sponsors and player appearances did not seem to have a reasonable explanation and were also insignificant so they have been removed from this study.

There is a positive correlation between the number of sponsors and the number of player appearances, which means the more appearances, the more sponsors. It may have been plausible to assume that the more player appearances would result in a higher number of sponsors gained. However there is a possibility that appearances and sponsors could be measuring something unexpected so they will be omitted from the regressions and the models used in regression analysis

A few simple tests were conducted to determine if any econometric problems existed. The first simple test was a test for heteroskedasticity. Using the white test the table below was generated.

TABLE 4.3

HETEROSKEDASTICITY TEST

Breusch - Pagan / Cook - Weisber test for heteroskedasticity Ho: Constant Variance Variables: Fitted Values of Total Tickets Sold

chi2 (1) = 1.64 Prob > chi2 = 0.199

Not only does the white test prove that heterskedasticity does not exist but a simple visual inspection of the residuals plotted against the fitted values will show that there is no evidence indicating that heteroskedasticity will be a problem for this model.

The second test ran was to test if the distribution of errors was normal. The equation e = y - represents the actual y values minus the predicted y values which equals the errors. By examining the errors I concluded that the distribution of errors is indeed normal. These two econometric tests were simply to test for minor problems that could occur in my model.

CHAPTER V

CONCLUSION

The variables that were found to be most significant in affecting ticket sales at

World Arena were playing Air Force, being regular season champions, making it to the

NCAA tournament and making it to the Frozen Four tournament. In equations 4.1 through 4.4 the when the dependent variable was simply measured by total ticket sales, those four variables were extremely significant in increasing ticket sales. Playing the Air

Force Academy is a powerful catalyst for ticket sales which could be attributed to the fact that it is a local rivalry. The other three significant variables, Lagged NCAA, Frozen

Four and Regular Season Champions are all significant achievements attained at the end of the season. It is interesting to note although wins was never found to be significant, it is essential to win to become regular season champions or to make it to the NCAA tournament. Although fans may not necessarily recognize individual wins, they must be aware of the larger scope of things because the accomplishment variables have a significant impact in affecting attendance. When attendance was simply measured by total tickets sold, penalty minutes and the point spread were found insignificant.

In the second set of regressions, when total tickets sold plus complementary tickets were used as the dependent variable, a different set of variables were found to be significant. As was previously found, Regular Season Champion was again found significant at the 99% level. Making it to the NCAA tournament the previous year was

also found significant at the 90% level. In this second set of regressions Penalty Minutes and the Point Spread were found significant. These numbers indicate very surprising results because when the dependent variable was measured differently, including complementary tickets, other variables were found significant. An exact explanation as to why Penalty Minutes and the Point Spread were significant in the second set of regressions cannot be precisely deduced. However, the majority of complementary tickets are those that go to sponsors and it could be plausible to believe that sponsors have different preferences when watching games than regular Tiger fans. Meaning that those who gain admission free of charge wish to see violence in the form of penalty minutes, and also prefer to see more competitive games. These deductions are reasonable, considering usually in close matches have a higher intensity which leads us to believe that the interests of those who receive complementary tickets are different than the regular Tiger fan.

The population, college population, ethnicity, average income etc. each plays a role in the markets of cities. This study did not seek to examine the demographics of fans who attend Colorado College hockey games yet future research could look into this. However the main concern of this study was to analyze which factors affect attendance and how to maximize it. There are a number of colleges in the area including

University of Colorado at Colorado Springs, The Air Force Academy and Colorado

College itself. For the purpose of this study we assumed that students of these other schools typically do not regularly attend Colorado College hockey games however future research could also look into this. This study was extremely successful in determining simple factors that greatly affect attendance at Colorado College Tiger Hockey games.

Although the majority of these variables are mainly controlled by the performance of the

Tigers it should be recognized that these are the reasons why ticket sales increases and decreases. For the Tigers to have more sellout games and high attendance rates they must keep having successful post seasons, however future research could look into how to increase ticket sales when accomplishments in the post season are low. The effect of sponsorships and appearances should also be looked at more closely and how they affect attendance. With more precise data concerning sponsors and appearances, I believe those could potentially have a significant effect on attendance rates. Future research could also look into military deployment. With the number of military personal entering and exiting

Colorado Springs this is another factor that could also greatly affect attendance.

Faculty and staff have been successfully filling the World Arena for many years, and now that research on this has begun, more efforts should be put forth to this matter.

The hockey program, as well as other Colorado College Sports, has a reciprocal relationship with the College. The academic success and prestige attract talented athletes and hockey players who wish to attend an outstanding institution. On the other hand, the athletic success (of any athletic program) adds to the reputation that represents the academic institution. It is important to maintain a good academic and athletic reputation and understand how to successfully utilize each potential and element of the college.

This study was tremendously successful in recognizing which factors significantly affect attendance. Now that this study has been conducted, along with future research,

Colorado College and World Arena personnel will no doubt utilize this information into their efforts of maximizing attendance. Hopefully soon we will be able to ask how winning an NCAA championship affects attendance, Go Tigers!

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