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Abstract

The National Hockey League has endured several expansions and relocations

over the years, which has led to the current two-conference structure that exists

today. The Eastern Conference consists of 16 teams, while the Western Conference

contains only 14. For this reason, the NHL is exploring the possibility of one, or

potentially two, expansion teams in the Western Conference. However, the NHL

remains hesitant to go forward with the expansion as it searches for a market that

would be most beneficial for the league. To determine whether or not an NHL team

can thrive in a particular market depends on the demand for the sport in that city;

analyzing attendance regressions across various sports can provide a better

understanding of what drives the demand for sports. The information gathered from

these attendance regressions can be further used to estimate the attendance of NHL

teams and compare the results to the actual attendance recorded. I plan to use the

estimation results and predict the attendance of an NHL team in a city the league is

currently pondering. The importance of this thesis is to discuss how effective it is to

use attendance regressions to estimate the attendance of an NHL team in a

prospective city. Of the three most talked about current proposals, it was found that

Quebec City would be the front-runner if the league were to expand; however,

Quebec City would likely join the Eastern Conference, so this does not work into the

NHL’s plan, which would like to add two more teams to the Western Conference.

Table of Contents

i

Abstract........................................................................................................................................ i

CHAPTER TWO......................................................................................................................... 4

Attendance Regressions in Sports: A Review of the Literature................................5I. Introduction..................................................................................................................................... 5II. MLB.................................................................................................................................................... 6III. NBA.................................................................................................................................................. 7IV. NFL.................................................................................................................................................... 8V. NHL.................................................................................................................................................... 9VI. Conclusion................................................................................................................................... 10

CHAPTER THREE................................................................................................................... 12

Estimating Attendance in the NHL..................................................................................12I. Introduction.................................................................................................................................. 12II. Economic Model......................................................................................................................... 12III. Data............................................................................................................................................... 14

A. Dependent Variable:...............................................................................................................................14B. Explanatory Variables:...........................................................................................................................14C. The Data Set................................................................................................................................................17

IV. Estimation................................................................................................................................... 19V. Conclusion.................................................................................................................................... 22

CHAPTER FOUR...................................................................................................................... 24

Expansion and Relocation in the NHL............................................................................24I. Introduction.................................................................................................................................. 25II. History........................................................................................................................................... 26

A. 1970’s............................................................................................................................................................26B. 1980’s............................................................................................................................................................27C. 1990’s............................................................................................................................................................ 29D. 2000’s............................................................................................................................................................31

IV. Conclusion................................................................................................................................... 35

CHAPTER FIVE........................................................................................................................ 38

Current Proposals................................................................................................................. 38I. Introduction.................................................................................................................................. 38II. Las Vegas....................................................................................................................................... 39III. Seattle........................................................................................................................................... 41IV. Quebec City................................................................................................................................. 43V. Conclusion.................................................................................................................................... 44

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CHAPTER TWO

Attendance Regressions in Sports: A Review of the Literature

I. Introduction

The hot topic surrounding the business component of the National Hockey

League stems from the question: should the NHL expand, and if so, where?

Numerous authors have examined this topic through other major sports leagues in

North America. Lengthy discussions have taken place regarding candidates for

expansion teams in the NHL, but the question still remains: how successful will the

team be in a potential city?

In order to better understand the issue of expansion it is imperative that

researchers pinpoint the determinants that influence the demand for sports. In

depth analyses illustrates that spectator attendance provides the best

representation of sport demand. Attendance regressions provide the best

representation of demand for a particular sport within a city. The regressions

capture location, facility, and team characteristics, which incorporate vital

components of building and maintaining a successful franchise. Therefore, utilizing

these known elements in an economic regression enable researchers to adequately

predict the attendance of a sports team. Furthermore, the economic models used to

investigate this topic are evidently interchangeable between leagues; thus, analyses

of attendance in the National Basketball Association, Major League Baseball, and

National Football League is viable information for researchers interested in the

demand analysis of the NHL. In the literature review, Section II explores the

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research gathered on fan attendance in MLB; section III investigates the attendance

in the NBA; section IV examines attendance, mainly panel data, in the NFL; section V

assesses the significant results of attendance regressions for the NHL. Finally,

section VI offers conclusions about the results from previous literature and their

application to this thesis.

II. MLB

Much of the research on attendance regression stems from early exploration

of factors affecting MLB attendance. McEvoy et al. (2005) examine the impact of

facility age on seasonal attendance for a franchise. Using panel data between 1962

and 2001, the authors investigate the relationship between facility age and seasonal

attendance. Results of the study demonstrate a negative relationship between

facility age and seasonal attendance, whereby attendance is higher in the initial

years of the facilities existence and displays a steady decline as the facility ages.

Similarly, Clapp and Hakes’ (2005) panel data between 1950 and 2001 suggests that

the “honeymoon” effect of a new stadium increases attendance ranging from 32% to

37%. This result is determined once the effects from quality-of-play are removed

from the attendance regressions. Additionally, Lemke (2010) assesses MLB home-

team effects while incorporating a censored normal regression to determine

whether these in-game elements impact individual games in 2007. Ultimately, the

only significant result in the study responsible for attendance differences is the

outcome for the home team; the more probable it is for the home team to win the

higher the attendance. Intriguingly, Butler’s (2002) study yields noteworthy results

that capture the importance of interleague play on game-by-game MLB attendance

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during the 1999 season. The researcher uses a model examining daily attendance in

the MLB to reveal a 7% increase in attendance during an interleague game as

opposed to non-interleague play.

Other studies such as Baade and Tiehen (1990) and Rivers and DeSchriver

(2002) inspect the influence of having star players on the roster, whereby both

studies determine that there is a positive correlation between the number of star

players on a roster and attendance per game. Baade and Tiehen (1990) estimate

that one additional star player increases overall attendance by 18,711 fans over an

81 game time frame. Lastly, Denaux et al. (2011) analyze general factors of MLB

home games from 1979 to 2004 such as broadcasting, promotions, city

characteristics, team characteristics, fan interest, and time factors on long-run

demand of attendance in the MLB. Three forms of panel data estimation methods

are considered, but the most appropriate for estimating the long-run demand of

MLB attendance is the random effects model. The empirical results of this data

support that time factors, whether the game is an interleague rival, per capita

income of the city, and team’s winning percentage are all predictors of long-run

demand for attendance.

III. NBA

There has been limited investigation with regards to attendance regressions

in the NBA; however, the few studies provide evidence of determinants that affect

attendance in other leagues. As in McEvoy et al. (2005) and Clapp and Hakes’ (2005)

study on MLB attendance, Leadley and Zygmont (2005) obtain results, which

illustrate that the “honeymoon” effect exists in the NBA as well. Using a pooled

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cross-section time series sample from 1971 to 2000, their findings indicate the

demand for attendance increases 15% to 20% in the first four years of a facilities

existence. Burdekin and Idson (1991) explore the relationship between the racial

composition of the city’s demographic and the racial composition of an NBA team

using a pooled cross-section panel model. The authors hypothesize that fans are

more likely to attend home games, where the racial composition of the team caters

to the ethnic make up of the city. The results indicate a significant relationship exists

between teams located in a white dominant city and the above average number of

white players on the roster. However, Schollaert and Smith (1987) use a pooled

cross-section panel model with data from 1969 to 1983 to determine if the

percentage of African-American basketball players on a team influences attendance;

the empirical results do not yield a significant result. In fact, the authors of the

article find no correlation between racial composition of a team and fan attendance.

Similar to Baade and Tiehen (1990) and Rivers and DeSchriver (2002), Berri et al.’s

(2004) study indicates the positive correlation between the number of star players

and annual attendance. Furthermore, the number of stars increases annual winning

percentage; winning percentage proves to have a significant effect of attendance; an

increase of stars correlates to an increase in attendance.

IV. NFL

Thorough research on the NFL provides various factors to explain

attendance. Jannetty (2014) tests a previously developed pooled cross-section panel

model on modern day NFL to determine game-day attendance through the 2010,

2011, and 2012 seasons. Tobit regressions are used to reveal that previous home

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game attendance, average ticket prices, if either team made the playoffs the

previous year, and home team win percentage at that point in the season are all

significant factors responsible for attendance of a particular game. Additionally,

Spenner et al. (2004) use a pooled data set from 1985 to 2002 and the idea of

rational theory, whereby past and future consumption play a role in current

consumption; the data collected are annual figures of each team and its respective

city. Spenner et al.’s (2004) results correspond to the findings of McEvoy et al.

(2005), Clapp and Hakes’ (2005), and Leadley and Zygmont (2005) studies which

indicate a significant outcome for age of facility. Moreover, the study reveals that

past attendance is also a significant element in determining game-day attendance.

As in previous studies, Welki and Zlatoper (1994) discover that winning percentage

is significant when examining cross-sectional game-day data of attendance during

the 1991 NFL season. However, the findings also suggest that ticket prices had an

effect on the demand for football. Using a Tobit analysis, the study reveals that

higher ticket prices reduced game-day attendance while maintaining an inelastic

demand for consumption.

V. NHL

Lastly, but most relative to this thesis, is the examination of attendance in the

NHL. As a preliminary exploration, Whitehead et al. (2008) are interested in

elements affecting the demand for NHL tickets in Alberta, Canada. The results of the

telephone survey are based upon using the stated preference method, which

determines that lower ticket prices, higher team quality, and additional stadium

capacity boosts attendance. Additionally, Coates and Humphreys (2011) investigate

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the effect of competitive balance and team characteristics on game attendance

between 2005 and 2010. Using a reduced form model the researchers find that

attendance increases as the expected home teams margin of victory becomes

greater. The study reveals that the more goals scored per game and the lower

number of goals against per game increases game day attendance.

Furthermore, Jones and Ferguson (1988) are interested in determining the

main factors affecting sports teams profit maximization; however, during their

study of the 1978-79 season the authors stumble upon data indicating the immense

effect location of a franchise has on attendance. Most notably, Cocco and Jones

(1997) assess whether or not the relocation of a small market NHL team would be

beneficial for the NHL. The intriguing information of this study stems from the

theoretical model using game-by-game data in the 1989/90 season to determine

attendance per game. The researchers break down the estimation model into two

sections: the first section captures the demand for hockey in the home city’s market

by using location specific characteristics such as population and income per capita;

the second section incorporates team characteristics such as wins, losses, goals for,

goals against and rank amongst the 26 teams in the NHL. Cocco and Jones (1997)

utilize Jones and Fergusons’ (1988) theory of profit maximization to interpret the

influence game day attendance has on the success of a franchise.

VI. Conclusion

The present study will fuses elements of previous attendance regression

literature along with modern assumptions presumed to capture the demand for

hockey in a particular market. Earlier work indicates a heavy influence of facility age

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on game day attendance from all four major sports teams. Furthermore, the number

of all-stars on a team’s roster demonstrates a positive correlation on attendance

throughout various major sports leagues. Additionally, the breakdown of Cocco and

Jones’ (1998) theoretical model appears to be the most effective in terms of

organization and significance when analyzing game day attendance. The two

categories the authors incorporate into their model are: city characteristics and

team characteristics. In this study, the location and city characteristics cover

population of the city, facility age, and income per capita of the city; the team

characteristics contain winning percentage, goals for, goals against, number of

playoff rounds won the previous year, the number of stars on the team’s roster, and

whether the team won the Stanley Cup the previous year. This study uses annual

data and therefore does not consider factors such as rivalry games, day of the week

and position of opponent in the standings.

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CHAPTER THREE

Estimating Attendance in the NHL

I. Introduction

Previous literature describes the relationship between demand and

consumption for hockey in a particular market. Several factors have been illustrated

to alter the demand such as performance, economic and demographic variables. The

remainder of this chapter is outlined as follows: Section II will discuss the economic

model behind attendance in the NHL; section III will examine the data and variables

affecting attendance in the NHL; section IV will illustrate the estimation equations

using the variables predicted to affect attendance in the NHL.

II. Economic Model

Graph 1 illustrates the supply and demand curve for an NHL game. This

model contains a fixed, or perfectly inelastic supply curve since each arena is can

accommodate a maximum amount of occupants. As a result, it can be suggested that

the determinants of average annual attendance in the NHL is largely driven by

demand and less by supply, subject to a supply constraint corresponding to the

capacity of the stadium. This study focuses on annual attendance, which is the sum

of attendance at games during the season.

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Graph 1: Demand and supply curve for an NHL game

TicketPrice

In theory, economic factors such as income, price, population, and taste

preferences generally tend to affect the demand curve. However, in this study I

choose to capture demand characteristics in four categories to remain consistent

with previous literature; the four categories are: economic (such as unemployment

rate) demographic (such as population), quality (such as team goal differential) and

residual variables (such as facility age).

Referring back to Graph 1, theoretically, any factor other than ticket price

reflects a shift in the demand curve. Furthermore, an increase in the unemployment

rate for a given area would result in a decrease in real per capita income, which

ultimately leads to a decrease in attendance. Similarly, the demand for NHL gamesis

affected by demographic and quality variables. All else being equal, an increase in

population or team goal differential causes a right shift of the demand curve

resulting in higher attendance. Lastly, residual factors such as facility age are

expected to alter the demand for NHL; previous literature suggests that the older

Attendance

D1

S1

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the facility the lower the demand, while newer facilities tend to increase the

demand.

III. Data

A. Dependent Variable:

Attendance (ATTENDANCE) – the average attendance at a home game for a specified

NHL team in a specific year. This variable is calculated by the total season

attendance divided by the number of home games played for each team.

B. Explanatory Variables:

Arena Capacity (ARENA_CAP) – the maximum number of attendees to view an NHL

game in a particular arena. This number includes all seats, luxury boxes and suites

in the arena. The coefficient for this variable is expected to be positive.

Canadian Team (CANTEAM) – a dummy variable indicating if the NHL team is in

Canada; ‘1’ indicates the team is from Canada, ‘0’ indicates the team is not. The

coefficient for this variable is expected to be positive.

Facility Age (FACILITY) – the age of an NHL team’s arena measured in years. This

measurement is used in previous attendance literature such as Leadley and

Zygmont (2005). The coefficient for this variable is expected to be negative.

Goals Against Per Game (GA_GAME) – the average number of goals an NHL team

scores against them per game. This variable is calculated by the total number of

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goals scored against a particular team divided by the total number of games in a

season. The coefficient for this variable is expected to be negative.

Goals For Per Game (GA_GAME) – the average number of goals scored by an NHL

team per game. This variable is calculated by the total number of goals scored by a

particular team divided by the total number of games in a season. The coefficient for

this variable is expected to be positive

Goals Per Game Differential (G_DIFF) – the difference between an NHL teams goals

for and against per game in a season. The coefficient for this variable is expected to

be positive

Population (POP) – the population of the metropolitan statistical area (MSA) where

the NHL team plays. This measurement is used in previous literature, such as

Cawley (2010), which finds a positive relationship between population and

spectator attendance. The population does not change over the sample tested. The


Playoff Round 1 (PR_1) – a dummy variable to capture whether an NHL team made

the first round of playoffs in a particular year; ‘1’ indicates a team made the playoffs,

‘0’ indicates the team did not. The coefficient for this variable is expected to be

positive.

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Playoff Round 2 (PR_2) – a dummy variable to capture whether an NHL team

advanced from the first round to the second round of the playoffs in a particular

year; ‘1’ indicates a team made the playoffs, ‘0’ indicates the team did not. The



advanced from the second round to the third round of the playoffs in a particular

year; ‘1’ indicates a team made the playoffs, ‘0’ indicates the team did not. The



advanced from the third round to the fourth and final round of the playoffs in a

particular year; ‘1’ indicates a team made the playoffs, ‘0’ indicates the team did not.

The coefficient for this variable is expected to be positive.

Stanley Cup (SC_PS) – a dummy variable to indicate whether a team won the Stanley

Cup (NHL championship) for a particular year; ‘1’ indicates a team won the Stanley

Cup, ‘0’ indicates the team did not. The coefficient for this variable is expected to be

positive.

Unemployment Rate (UNEM_R) – the unemployment rate of the metropolitan

statistical area (MSA) where the NHL team plays. The coefficient for this variable is

expected to be negative.

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Winning Percentage (WIN_PCT) – the total number wins for an NHL team in a season

divided by the total number of games in an NHL season, multiplied by 100. The


Note that teams performance variables are measured for the same year as

the dependent variable (contemporaneous) rather than lagged. This assumes that

fans adjust their attendance behavior during the year according to how the team is

doing.

C. The Data Set

The data set is pooled cross-sectioned from 2013-14 to 2008-09, omitting the

lockout year of 2012-13 due to a partial season. The total number of observations is

150. Each team for each year is considered a separate observation.

Table 1: Descriptive Statistics

Mean Median Maximum

Minimum

Std. Dev.

ATTENDANCE 17362.69 17565 22623 11059 2274.1

98ARENA_CAP 21735.11 18532 22623 15016 17962.

34CANTEAM 0.2 0 1 0 0.25FACILITY 14.04 13 47 0 11.66G_DIFF -0.0002 0.16 1.07 -1.13 1.15GA_GAME 2.20 2.64 3.5 1.89 1.08GF_GAME 2.23 2.61 3.52 1.83 1.04

POP 5048686. 2805184.

19900000 730018 546168

8.PR_1 0.42 0 1 0 0.49PR_2 0.22 0 1 0 0.41PR_3 0.21 0 1 0 0.35PR_4 0.60 0 1 0 1.11SC_PS 0.57 0 1 0 1.11UNEM_R 7.74 7.8 15 3.7 1.98

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WIN_PCT 0.55 0.567 0.738 0.319 0.23

Table 2: Correlation Coefficients for the Variables Included in Equation 1

ATTENDANCE ARENA CANTE

AMFACILITY POP PR_1 PR_2 PR_3 PR_4 SC_PS UNEM

_RWIN_PCT

GA_GAME GF_GAME

ATTENDANCE 1.00

ARENA_CAP 0.43 1.00

CANTEAM 0.36 -0.01 1.00

FACILITY 0.08 -0.24 0.13 1.00

POP -0.26 0.08 -0.19 0.11 1.00

PR_1 0.31 0.11 -0.18 -0.09 -0.03 1.00

PR_2 0.28 0.08 -0.13 -0.01 0.14 0.58 1.00

PR_3 0.19 0.11 -0.06 0.03 0.2 0.38 0.65 1.00

PR_4 0.14 0.01 -0.07 0.01 0.2 0.26 0.44 0.68 1.00

SC_PS 0.15 0.13 -0.09 0.01 -0.04 0.18 0.14 0.15 0.09 1.00

UNEM_R 0.02 0.28 -0.29 0.06 0.16 0.13 0.12 0.17 0.08 0.06 1.00

WIN_PCT 0.25 0.01 -0.17 -0.14 -0.15 0.65 0.45 0.28 0.21 0.12 0.05 1.00

GA_GAME -0.24 0.01 0.21 0.07 -0.06 -0.60 -0.44 -0.31 -0.26 -0.02 -0.11 -0.55 1.00

GF_GAME 0.27 0.08 -0.08 -0.08 -0.14 0.51 0.32 0.18 0.13 0.29 -0.03 0.59 -0.09 1.00

Table 2 illustrates the correlation matrix for the thirteen variables to determine if

there are any multicollinearity issues; any coefficients above .5 are to be noted.

Goals Against Per Game and Playoff Round 1; Goals For Per Game and Playoff Round

1; Winning Percentage and Playoff Round 1; Playoff Round 1 and Playoff Round 2;

Playoff Round 3 and Playoff Round 2; Playoff Round 3 and Playoff Round 4; Goals

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Against Per Game and Winning Percentage; and, Goals For Per Game and Winning

Percentage.

IV. Estimation

Equation 1:

ATTENDANCE = Bo + B1 (ARENA_CAP) + B2 (FACILITY) + B3 (GA_GAME) + B4 (GF_GAME) + B5 (POP) + B6 (PR_1) + B7 (PR_2) + B8 (PR_3) + B9 (PR_4) + B10 (SC_PS) + B11 (UNEM_R) + B12 (WIN_PCT) + B13 (CANTEAM) error

Equation 2:

ATTENDANCE = Bo + B1 (ARENA_CAP) + B2 (CANTEAM) + B3 (FACILITY) + B4 (G_DIFF) + B5 (POP) + B6 (PR_1) + B7 (PR_2) + B8 (PR_3) + B9 (PR_4) + B10 (SC_PS) + B11 (UNEM_R) + B12 (WIN_PCT) + error

Equation 3:

ATTENDANCE = Bo + B1 (ARENA_CAP) + B2 (CANTEAM) + B3 (FACILITY) + B4 (G_DIFF) + B5 (POP) + B6 (PR_2) + B7 (PR_4) + error

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Table 3: Estimations of Factors Affecting AttendanceExplanatory Variables Equation 1 Equation 2 Equation 3Arena Capacity 0.04

(4.19)****0.89

(7.74)****0.89

(7.99)****Canadian Team 1899.03

(5.93)****1881.43

(5.87)****1855.38

(5.89)****Facility Age 25.29

(1.69)***44.47

(3.47)****45.13

(3.58)****Goal Differential 1668.93

(3.28)****1566.13

(4.88)****Goals Against -448.09

(-0.73)Goals For 1846.02

(2.85)****Population -9.14 x 10-5

(-2.89)****-0.00011

(-4.47)****-0.0001

(-4.60)****Playoff Round 1 688.04

(1.36)69.94(0.18)

Playoff Round 2 81.26(0.13)

698.38 (1.73)**

542.28 (1.61)**


-476.82(-0.84)


988.64(1.51)*

679.46 (1.28)*

Stanley Cup 737.36(1.10)

299.01(0.43)

Unemployment Rate -28.84(-0.31)

-13.62(-0.20)

Winning Percentage -145.24(-0.17)

-1344.33(-0.55)

Observations 150 150 150R2 0.32 0.58 0.58Adjusted R2 0.26 0.54 0.55Note: the t-statistics are presented in parentheses.

* indicates significant at the 20% level ** indicates significant at the 10% level*** indicates significant at the 5% level **** indicates significant at the 1% level.

In Equation 1, the R2 is .32 and the adjusted R2 is .26, which means 32%/26%

of the variation of attendance in the NHL is accounted for by the independent

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variables estimated in the regression. There are three independent variables whose

coefficients do not support the hypothesis: facility age, population and winning

percentage. Facility age reveals a positive correlation opposing Clapp and Hakes’

(2005) findings; for every year older the building becomes, attendance increase by

roughly 25 people. Furthermore, the coefficient for winning percentage is negative,

which contradicts Cawley (2010) and Humphreys and Coates’ (2011) findings that

both reveal positive coefficients. As winning percentage increases by one percent,

attendance decreases by roughly 145 people. Similarly, population’s negative

coefficient is a marginal effect, but it does not support the hypothesis. The t-statistic

indicates population is significant at the 1% level. Likewise, Arena Capacity,

Canadian Team and Goals For are all significant at the 1% level, and facility age is

significant at the 5% level. The estimation of Equation 1 has a low R2 and adjusted

R2, along with very few significant independent variables.

In Equation 2, the R2 and adjusted R2 are .58 and.54, respectively. This drastic

increase occurred following a merge of the independent variables Goals For and

Goals Against to create one variable: Goal Differential. This alteration was made due

to the high correlation between Goals For and Winning Percentage. One can assume

that a higher goals for would result in a greater winning percentage; therefore,

creating a variable that captures the difference between goals for and against

decreases the possibility for multicollinearity. Similar to Equation 1, there are three

independent variables whose coefficients do not support the hypothesis:

Population, Playoff Round 3 and Winning percentage. Population remains a

marginal negative effect however, the result is significant at the 1% level. Playoff

19

Round 3 reveals a large negative coefficient, which contradicts the hypothesis. One

would assume that advancing to the third round of the playoffs should have a

positive effect on attendance. Lastly, Winning Percentage presents a large negative

coefficient, which is not consistent with the hypothesis. The results for Playoff

Round 3 and Winning Percentage are not significant and will be dropped for the 3rd

and final equation. The results for Arena Capacity, Canadian Team, Facility, Goal

Differential and Population are all significant at the 1% level. The results for Playoff

Round 2 and Playoff Round 4 are significant at the 10% and 20% levels,

respectively.

Equation 3 contains only the remaining significant results from the previous

equation. The R2 is consistent with the previous equation however, the adjusted R2 is

now .55. The persisting seven variables in this equation contain significant results

from the 1% level to the 20% level. Arena Capacity illustrates a positive coefficient,

which indicates that as capacity increases by 1, attendance rises by roughly 1; this

variable is significant at the 1% level. Canadian Team is a dummy variable that

determines whether a team is located in a Canadian city or not. Equation 3 reveals

that a team in a Canadian city draws 1855 more people per game as opposed to a

team in the United States; this variable is significant at the 1% level. The Facility Age

coefficient is positive, which suggests that as a team’s arena increases by 1 year in

age, attendance increases by roughly 45 people per game; this variable is significant

at the 1% level. The positive Goal Differential coefficient indicates that when the

ratio of Goals For and Goals against increases by 1, attendance per game by 1566

fans; this variable is significant at the 1% level. The coefficient for Population is

20

marginally negative, which suggests that as population increases by 1 person, the

attendance per game decreases by .0001; the effect of Population on attendance per

game is evidently minute however, the result is significant at the 1% level. Playoff

Round 2 is a dummy variable that determines if a team advanced to the second

round of playoffs or not. Equation 3 reveals that a team advancing to the second

round of playoffs averages roughly 542 more fans per game; this variable is

significant at the 10% level. Lastly, Playoff Round 4 is a dummy variable that

determines if a team advanced to the fourth round of the playoffs or not. Equation 3

illustrates that a team advancing to the fourth and final round of the playoffs

averages roughly 679 more fans per game; this variable is significant at the 20%

level.

V. Conclusion

The information discovered in past literature regarding attendance

regressions provides an informed decision on which factors are considered most

important to attendance in sports. Unfortunately, not much research has been

conducted on NHL attendance specifically, however, the results from other

attendance regressions are easily applied to elements of the NHL; the variables

utilized for previous regressions are incorporated, to some extent, to this paper’s

estimations of NHL attendance.

The economic model in this paper is consistent with the previous research,

which indicates that attendance models contain a vertical supply curve; this fixed, or

perfectly inelastic supply curve, exists because it represents the maximum amount

of occupants an arena can accommodate. Evidently, attendance is determined by

21

determinants of demand rather than supply; economic, demographic, quality and

residual variables are incorporated in the data to capture the demand for NHL

games.

Thirteen variables are utilized in the estimation, many of which stem from

prior research on attendance regressions. Three equations are constructed in an

attempt to discover the significant variables affecting NHL attendance. The first

estimation includes all thirteen variables, which illustrates only a few significant

variables. However, it seemed Goals For and Winning Percentage are closely

correlated therefore, Goal Differential is created to eliminate this concern in the

second estimation. Once the second estimation is complete only seven of the

thirteen variables are found to be significant. Lastly, the third estimation includes

the seven significant variables from the previous estimation, which illustrates each

of the variables estimated were significant at the 20% level or greater. The seven

variables found to be affecting NHL attendance are: Arena Capacity, Canadian Team,

Facility Age, Goal Differential, Population, Playoff Round 2 and Playoff Round 4.

The final attendance regressions used to understand past relocations and

expansions and to analyze current proposals.

22

CHAPTER FOUR

Expansion and Relocation in the NHL

I. Introduction

The National Hockey League officially formed in 1917, whereby it consisted

of 5 teams: 2 in Montreal, 1 in Ottawa, Toronto, and Quebec. Over the next several

years there was much movement between franchises; one of the Montreal teams

relocated to Quebec City; the original team in Quebec City headed west to Hamilton,

Ontario; and lastly, in 1923 the Boston Bruins became the first American team in the

NHL. The Hamilton franchise lasted two seasons and in 1926 all their contracted

players were transferred to the New York Americans. This relocation was the focal

point for the NHL to begin its expansion across the United States. Teams began

emerging in established cities such as Pittsburgh, Philadelphia and Detroit, but still

questions remained about their survival in these markets. Eventually in 1967 the

formation of the “Original Six” was created, 26 years after the launch of the NHL. The

“Original Six” began in 1942 and they are known as the first six teams to play

amongst each other for 25 consecutive years. The six teams consisted of the:

Toronto Maple Leafs, Detroit Red Wings, Boston Bruins, Montreal Canadians,

Chicago Blackhawks, and New York Rangers (Razulu’s Street: NHL Expansion

History).

Meanwhile, the Western Hockey League began developing around the 1950’s

in a questionable market. The league focused its attention on Californian cities and

23

experienced immediate success. The WHL had devised a plan of expansion eastward

in hopes of competing against teams in NHL for the prized Stanley Cup. The WHL

fought this strategy for several years, but in 1974 the league could not withstand the

NHL market competition and decided to fold the league. However, the NHL took

note of the WHL’s original success on the ice and the TV market. NHL administrators

determined it would be in their best interest to expand out west in order to benefit

from the high demand for the hockey market on the West Coast (The People’s

History: Ice Hockey Origins, Growth and Change in the Game).

In 1967, the NHL tried its luck further west and further east than its “Original

Six” teams. The Los Angeles Kings, California Seals, Minnesota North Stars,

Philadelphia Flyers, Pittsburgh Penguins, and St. Louis Blues were the six new teams

added to the NHL to compete against the “Original Six” franchises. At this time, the

league created two divisions, an east and a west, which ultimately opened the door

for continued expansion throughout North America (Razulu’s Street: NHL Expansion

History).

This chapter traces the history of the NHL. The attendance regressions

described in chapter three are tested against three selected cases.

II. History

A. 1970’s

The early 1970’s were a time in hockey history where the NHL became a

powerhouse across North America. Seven hockey leagues, either rival or minor pro

leagues to the NHL, across the continent shut down operations leaving the NHL to

stand-alone. However, as with the scenario of the WHL, the NHL took advantage of

24

the markets that these leagues left behind. The 12 teams of the NHL expanded to 14

teams in 1970 with the addition of the Buffalo Sabres and Vancouver Canucks,

which the NHL sought out once they folded from their original league of the World

Hockey Association (WHA). In 1972, the Atlanta Flames and New York Islanders

joined the NHL following their dispute with the WHA. At the time, a franchise

already existed in New York, which meant the Islanders had to pay a $6 million

expansion fee along with a $5 million location fee to the New York Rangers (The

Puck Report: History of NHL Expansion). In 1974, the last wave of teams to join the

NHL from the downward spiraling WHA were the Kansas City Scouts and

Washington Capitals. During the 1976-77 season, the NHL had 9 teams in each

division however, this was the first year NHL franchises began to relocate since

initial expansion in 1967. The California Seals struggled to thrive in Oakland, which

forced them to play the 1976 season in Cleveland and renamed the Barons;

unfortunately, in the summer of 1977 the Barons became financially unstable and

merged with the Minnesota franchise for the 1978 season (Razulu’s Street: NHL

Expansion History). The Kansas City Scouts developed financial troubles during

their short stint in the NHL, which opened doors for an owner from Denver to save

the franchise and relocate them to his hometown in Colorado. The 1979-80 season

saw the last wave of teams from the obsolete WHA join the NHL; the Winnipeg Jets,

Quebec Nordiques, Hartford Whalers and Edmonton Oilers would further increase

the number of teams in the NHL to 22 (The People’s History: Ice Hockey Origins,

Growth and Change in the Game).

25

B. 1980’s

The 80’s marked the first year since the early 1950’s, whereby the NHL did

not add a single franchise to the league. This era was a time for the relocation of

struggling teams and a revamp of the leagues divisions. The 21 teams in the league

had become established in their respective cities, but the finale of the 1981 season

saw a financially stressed team in Atlanta relocate to a hockey starved market in

Calgary. Similarly, in 1982 the NHL determined that the Colorado Rockies would

thrive in a market like New Jersey; the name of the team was altered from the

Rockies to the Devils, which continues today (Razulu’s Street: NHL Expansion

History).

In 1981, the NHL proposed to realign the divisions geographically into what

we know now as the Eastern and Western Conferences; the conferences were

named The Prince of Wales conference and The Clarence Campbell conference,

respectively. The conferences were named after the trophies given to the winner of

their individual conference. Within these conferences, divisions were assembled and

named after former players from the “Original Six” teams; the Norris and Adams

divisions (named after James Norris and Jack Adams) made up The Prince of Wales

conference and the Smythe and Patrick divisions (named after Conn Smythe and

Lester Patrick) comprised The Clarence Campbell conference. Prior to the alignment

proposal, there was no methodology behind the structure of both the divisions and

the conferences. This proposal enabled the NHL to organize the teams from the east

into the Wales conference and the teams from the west and Midwest into the

Campbell conference. Furthermore, the NHL’s playoff format was revamped; the top

26

8 teams from each conference advance into the playoffs competing for their

conference titles and the two remaining teams played for the championship trophy

of the league – Lord’s Stanley Cup (The People’s History: Ice Hockey Origins, Growth

and Change in the Game).

C. 1990’s

The league remained at 21 teams throughout the 80’s, but the

disproportionate number of teams in the Campbell conference became an apparent

segway for a future league expansion. The league began increasing revenue due to

the heightened market demand across North America; the high demand for hockey

enabled the league to charge heftier expansion fees to owners interested in entering

the league. In 1991, the first expansion team introduced to the league was the San

Jose Sharks, which would complete an even 22-team league. However, the Sharks

ownership group was required to pay a $45 million expansion fee, the most in

league history. Following the 1991 season, the Ottawa Senators and Tampa Bay

Lightning were granted access into the league under the condition that both teams

compensated the NHL with a $45 million fee (The Puck Report: History of NHL

Expansion). The league was operating at 24 teams, but with tremendous success of

teams located in Tampa Bay and San Jose the league took advantage of the demand

in these states by adding another team in each state. As a result, the beginning of the

1992-93 included two new franchises, the Florida Panthers and Anaheim Mighty

Ducks (Razulu’s Street: NHL Expansion History). The teams were obligated to pay

an increased expansion fee of $50 million to the league; however the Mighty Ducks,

27

similar to the Islanders, were required to pay a league mandated location fee of $25

million to the to Kings (The Puck Report: History of NHL Expansion).

It wasn’t until the late 90’s that the league began contemplating further

expansion from their 26-team league. Following a debate to move the New Jersey

Devil franchise to Nashville for $20 million, commissioner Gary Bettman granted

Predator Holding LLC ownership group a conditional franchise; the condition

required Nashville to sell 12, 000 season tickets before the 1999 season began – and

that they did (The People’s History: Ice Hockey Origins, Growth and Change in the

Game). At the time, the New Jersey Devils remained a franchise, but the league

entertained proposals from Columbus, Atlanta and Minnesota. The league believed

the addition of these three teams would benefit the league, yet none of the teams

had a suitable playing arena. It would take the Atlanta Thrashers until the 1999-00

season to arrange an arena lease deal and bring hockey back to the city since the

1980’s (The People’s History: Ice Hockey Origins, Growth and Change in the Game).

The 90’s era consisted of more than just expansion for the NHL – it was also a

time for relocation and work stoppage. In 1993, the Minnesota North Stars were

forced out of Minnesota and relocated to become the Dallas Stars. Similarly, the

Quebec Nordiques headed southwest in 1995 to become the Colorado Avalanche

following financial woes caused by the Canadian dollar and lack of fan support.

Furthermore, in 1996 the Winnipeg Jets endured similar financial struggles as a

result of the Canadian dollar forcing them to become the Phoenix Coyotes. Lastly,

the Hartford Whalers relocated to North Carolina in 1997 to become the Carolina

28

Hurricanes (The People’s History: Ice Hockey Origins, Growth and Change in the

Game).

In 1992, the NHL endured its first labor dispute after the league could not

negotiate a new collective bargaining agreement (CBA). The lockout lasted a mere

10 days, but the new CBA would expire in 1993 and upon failing to reach an

agreement the owners declared a lockout that would last for 90 days. The players

association and the owners came to an agreement on January 11, 1994 shortening

the season to 48 days (The People’s History: Ice Hockey Origins, Growth and Change

in the Game).

D. 2000’s

The Columbus Blue Jackets and Minnesota Wild eventually negotiated arena

leases for the 2000-01 season, which granted both franchises expansion into the

league. With the addition of these two teams, the NHL reached its highest team

capacity since its inauguration. The conferences were made up of 15 teams each,

and within these conferences are systematic divisions combining rivalries with

geographical sensibility. However, it did not take long for the Atlanta Thrashers to

experience ownership, fan support and arena hardships. Therefore, the NHL did it’s

best to return an NHL team to a city hungry for hockey. Twelve years following their

introduction into the league, the NHL Board of Governors announced the relocation

of the Atlanta Thrashers to Winnipeg; the franchise took the name of the previous

team – the Winnipeg Jets (The People’s History: Ice Hockey Origins, Growth and

Change in the Game).

29

Stability seemed to be in order for the next few years until the clock began to

run out on the current CBA agreement set to expire before the 2012-13 season.

September 15, 2012 marked the longest labor dispute in NHL history, which lasted

119 days. Finally, on January 12, 2013 the players association and owners came to

an agreement, and the NHL would save the season by reducing the number of games

from 82 to 48 (The People’s History: Ice Hockey Origins, Growth and Change in the

Game).

The most recent restructuring of the NHL took place prior to the 2013-2014

season, whereby the NHL Board of Governors realigned the league into a more

geographical appropriate two-conference and four-divisions set-up. The adjustment

saw Detroit and Columbus move to the Eastern Conference in order to play more

games within their time zone; furthermore, Winnipeg was allocated to the Western

Conference with the same justification. However, this reorganization made the total

number of teams in each conference uneven (NHL.com: Guide to 2013-14 NHL

realignment). The disparity between the East and West conferences enables the

NHL to entertain potential offers for franchise hosts, specifically in the West.

III. Selected Analysis

Using the final estimation equation in chapter II, it is possible to analyze

selected franchises to discover the difference between the actual average annual

attendances of an NHL team during a specific year versus their estimated average

annual attendance. Below is the examination of three teams’ average annual

attendances from their inaugural year and the subsequent four years.

30

A. San Jose Sharks

Table 4. Attendance Analysis of the San Jose Sharks’ First Five YearsYear Arena

CapacityCan

Team Facility Goal Diff Pop PR 2 PR 4 Actual

Avg. AttEstimated Avg. Att

% Change

1991-92 11089 0 50 -1.7 782248 0 0 10888 9385.06 14%1992-93 11089 0 51 -2.39 782248 0 0 11045 8349.56 24%1993-94 11089 0 52 -0.16 782248 0 0 16537 11887.16 28%1995-96 17562 0 1 -1.28 782248 1 0 17190 14134.72 18%1996-97 17562 0 2 -0.82 782248 0 0 17420 14357.99 18%

Note: The 1993-94 season was omitted due to a lockout. CanTeam, PR2 and PR4 are dummy variables; 0= No, 1=Yes.

The table above illustrates the attendance analysis of the NHL expansion

team, the San Jose Sharks. Using the estimations in chapter 2, it is possible to predict

the attendance for each year and compare the actual attendance to the estimated

recorded attendance. Furthermore, it is interesting to note the difference between

the estimation and the actual attendance; this difference is captured in the last

column titled: “% change.”

It is interesting to note that out of the first five years in the NHL, the percent

change is the smallest in the first year (14%). The teams third year shows the

highest percent change (28%); out of the five seasons, San Jose posted their lowest

goal differential during this season. The percent change declines drastically during

1995-96 (18%) and 1996-97 (18%); a reason for this decline could be the new

facility the team played in during those two seasons.

31

B. Ottawa Senators

Table 5. Attendance Analysis of the Ottawa Senators’ First Five Years


The table above presents the attendance analysis of the NHL expansion team,

the Ottawa Senators. The region of Ottawa had a team several decades before the

1992-93 however, the lack of demand in the capital of Canada forced the team to go

bankrupt. As in the analysis of Table 4, Table 5 uses the estimation from chapter 2 in

order to predict the attendance of Ottawa’s first five years in the league.

The “% Change” column shows much more fluctuation than the previous

table; the column contains negative percentages, which means the equation from

chapter 2 overestimated attendances. In 1995-96, Ottawa would play their final

season in the Ottawa Civic Center; the difference between actual and estimated

average attendance was quite similar and analyzing the column pattern indicates

the estimation the following year would have been much closer to the actual

average attendance barring that Ottawa continued to play in the Civic Center. Even

with the new arena and a very low goal differential, the “% Change” exhibits

substantial differences between estimation and actuality. The equation

overestimated the first year in the new building by 41%, while the second year in

the new building was overestimated by 22%; it would be interesting to further

32

YearArena

CapacityCan

Team FacilityGoal Diff Pop PR2 PR 4

Actual Avg. Att

Estimated Avg. Att

%Change

1992-93 10585 1 26 -2.35 1036836 0 0 10485 8665.32 17%1993-94 10585 1 27 -2.39 1036836 0 0 10300 8647.81 16%1995-96 10585 1 28 -1.22 1036836 0 0 9879 10525.31 -7%1996-97 19153 1 0 -0.1 1036836 0 0 13252 18641.25 -41%1997-98 19153 1 1 -0.09 1036836 0 0 15377 18702.04 -22%

analyze the subsequent years in the new building to determine if this steady decline

of “% Change” continues

C. Columbus Blue Jackets

Table 6. Attendance Analysis of the Columbus Blue Jackets’ First Five Years


The table above illustrates the analysis of the Columbus Blue Jackets’ first

five years in the NHL as an expansion team. Columbus was one of the first teams to

join the league in the new millennium, along with Minnesota and Nashville. The

market for hockey in Columbus was deemed to be strong enough to withstand an

NHL team, especially with the new rink being built for the Blue Jackets. As in the

previous two tables, Table 6 utilizes the equations from chapter 2 to predict the

attendance in Columbus for its first five years as an expansion.

The difference between estimated and actual attendance in Table 6 is

considerably lower than the previous two tables. During the first five years

Columbus maintained a fairly low, but negative goal differential; this means they

were being scored on more often than they were scoring, but the margin was very

small. The highest “% Change” over the five years came during the year where their

negative goal differential was the lowest (-1.11). The equation had underestimated

33

Year

2000-01

Arena Capacity

18500

Can Team

0

Facility Age

0

Goal Diff

-0.52

Pop

1540157

PR2

0

PR4

0

ActualAvg. Att

17457

Estimated Avg. Att

15496.6

%Change

11%

2001-02 18500 0 1 -1.11 1540157 0 0 18136 14617.71 19%

2002-03 18500 0 2 -0.61 1540157 0 0 17744 15445.91 13%

2003-04 18500 0 3 -0.74 1540157 0 0 17369 15287.44 12%

2005-06 18500 0 5 -0.75 1540157 0 0 16796 15362.04 9%

the average annual attendance for this particular year by 19%; in fact, during the

season the actual average attendance was the highest of the five seasons.

IV. Conclusion

The NHL has come a long way from the first Original Six teams; expansions

and relocations have assisted the league to flourish into a 30-team league. The

evolution of the NHL has been dictated by factors such as the North American

economy, bankrupt owners and a city’s lack of demand for hockey. As the decades

passed, expansion fees began to increase making it much more difficult for owners

to bring and NHL team to prospective cities. Locating a combination of the right city,

owner and arena became the toughest task for the NHL; much of the movement

during the earlier years could be attributed to the lack of one of these three factors.

Furthermore, work stoppages and labor disputes between the NHL and the players

association became more prevalent during the 1990’s and 2000’s. New collective

bargaining agreements saw a rearrangement of rulings that would help make the

league more profitable and increase the demand for the sport.

The three selected analyses were chosen at random to determine the percent

change between the estimated average annual attendances versus the actual

average annual attendances for the teams first five years in the league. Using the

estimation equation in chapter 2, the data illustrated that teams were quite

successful in their inaugural years. Both San Jose and Ottawa thrived following the

completion of their new arenas, while Columbus’ attendance numbers remained

relatively steady over the five years. It is interesting to view the impact goal

differential has on the estimated attendances. It is expected for a team to struggle in

34

its inaugural years of existence, but as a team’s goal differential negatively increases

– for the most part – attendance decreases.

35

CHAPTER FIVE

Current Proposals

I. Introduction

Currently, the NHL is revising several proposals for potential expansion

teams across North America. The list began with 10 teams however, by process of

elimination it has been narrowed down to roughly 5 teams. The NHL is trying to

discover a market where the demand is high for an NHL team; keep in mind, at this

point in time the conferences are uneven— the East has 16 teams, while the West

has 14 teams— so expansion seems to be the subsequent move for the league. The

conferences were recently restructured to bring Detroit back into the East following

the relocation of Winnipeg and placing them in the West. Logically, the NHL should

explore two western markets to balance the conferences. Two of the western cities

that are most intriguing to the NHL and those following the league closely are:

Seattle and Las Vegas. Furthermore, the Canadian market is always accepting of the

idea of a new NHL team, in which case Quebec City should be thoroughly examined

as well.

In this section, these three proposals will be examined and their average

annual attendance will be estimated to determine how successful each team will be

in their market. The estimations will consist of five different alterations to the

equation in chapter; each of the alterations corresponds to the results from the goal

differentials of the three selected analyses in chapter 3. The five goal differentials

that will be used are: 0; the average of the combined three teams goal differential;

36

and, each selected teams average goal differential (for a total of 3 goal differentials).

It can be assumed the teams did not make playoff rounds 2 and 4 according to the

select analysis. The remaining variables such as Arena Capacity and Facility Age will

depend on the information retrieved regarding the league’s plan for the potential

expansion to the new selected city.

II. Las Vegas

Recently, Las Vegas hosted a ticket drive to better understand the hockey

market in the “city that never sleeps.” During the first 36 hours the ticket drive the

NHL received 5,000 commitments, which is half of the leagues commitment goal

established by league management. However, over the next two weeks the

commitments came to a drastic halt; “There is interest, but the supposed ‘hockey

fans’ that are in Vegas simply aren't buying the tickets like in previous ticket drives”

(Bruno, SportsBlog). The previous ticket drive took place in Winnipeg prior to the

NHL relocating the Atlanta Thrashers back to a Canadian city. “The ticket drive

conducted sold out the season in a matter of minutes. Minutes. The site that was

selling the tickets crashed from all of the traffic” (Bruno, SportsBlog). Furthermore,

the NHL’s Commissioner Gary Bettman proposed a new 20, 000 seat stadium, which

will be an extension of the MGM Grand (Cait, The Hockey Stuff). Although the ticket

drive was unsuccessful, there is enough information to estimate the average annual

attendance for an NHL in Las Vegas.

Below are the estimations for average annual attendance that include five

varying goal differentials, capturing the possibilities of attendance depending on the

team’s success that year.

37

Table 7. Attendance Analysis for a Proposed NHL Team in Las VegasYear

Arena Capacity

Can Team

Facility Age

Goal Diff

Pop PR2 PR4Estimated Avg.

Att2016-17 20000 0 0 0 1902834 0 0 17790.69

2017-18 20000 0 1 -1.08 1902834 0 0 16144.40

2018-19 20000 0 2 -1.27 1902834 0 0 15891.96

2019-20 20000 0 3 -1.23 1902834 0 0 15999.74

2020-21 20000 0 4 -0.75 1902834 0 0 16796.61 Average: 16524.68

Note: CanTeam, PR2 and PR4 are dummy variables; 0= No, 1=Yes.

The table above illustrates the estimated average annual attendance based

on five different scenarios of the team’s success. The attendance increases as the

goal differential approaches zero. The estimation is not affected by the variables

Canadian Team, Playoff Round 2 and Playoff Round 4; these dummy variables

indicate that Las Vegas is not a Canadian team and they did not make it to round 2

or 4 of the playoffs for any of the five seasons. Diagram 1 below demonstrates the

average of Table 7’s estimated average attendances in relation to the maximum and

minimum average annual attendances for the NHL during the 2013-14 season. The

average attendance for Las Vegas during its first five seasons is just below the

average annual attendance of 17, 362 for the 2013-14 season. According to the

estimations, a team in Las Vegas seems to be a plausible idea given the notion that

its average annual attendance for 5 years remains higher than almost half of the

teams in the NHL currently.

Diagram 1. Las Vegas’ Average Annual Attendance Compared to all NHL Teams Average Annual Attendance During the 2013-14 Season

III. Seattle

22,62316, 52511,059MIN MAXAVG.LV

17, 362

38

A second option, which would suit the NHL’s plan to enhance the number of

teams in the western conference, would be the addition of an NHL team in Seattle.

Like Las Vegas, this possibility makes sense for two reasons: Seattle is a big sports

market and there are no professional winter sports. Football is recognized in the

sports world as an “extended fall sport,” which would not drastically conflict with

the market or demand for an NHL team. Don Levin, a prospective owner for the

expansion team in Seattle, views the city as, “San Jose on steroids. It’s a very good

market, a very good sports market and they do a great job with their sports teams”

(Whyno, The Hockey News). The only problem an NHL team in this city poses is the

lack of a functioning NHL area. Key Arena in Seattle was the home to former NBA

basketball team, the Seattle Supersonics; however, the structure of the building is

not compatable with NHL arena regulations. Levin is willing to fulfill the final

component to bring an NHL team to Seattle; Commissioner Gary Bettman has stated

that a new franchise requires three things: a willing owner, a state-of-the-art facility,

and a high demand for hockey. A website devoted to providing up-to-date facts on

bringing an NHL team to Seattle has indicated that a facility seating roughly 19, 000

fans would be ideal.




Table 8. Attendance Analysis for a Proposed NHL Team in SeattleYear Arena

CapacityCan

TeamFacility

AgeGoal Diff

Pop PR2 PR4 Estimated Avg. Att

39

2016-17 19000 0 0 0 3610105 0 0 16702.89

2017-18 19000 0 1 -1.08 3610105 0 0 15056.60

2018-19 19000 0 2 -1.27 3610105 0 0 14804.16

2019-20 19000 0 3 -1.23 3610105 0 0 14911.94

2020-21 19000 0 4 -0.75 3610105 0 0 15708.81

Average: 15436.88


The table above illustrates the estimated average annual attendance based

on five different scenarios of the team’s success. As in Table 7, the attendance

increases as the goal differential approaches zero. Evaluating the estimated average

attendances for the five seasons, it would seem as though the attendance is lower

than the estimations in Table 7; however, the arena capacity has dropped by 1,000

for the projected Seattle arena affecting the estimated average annual attendance.

The estimation is not affected by the variables Canadian Team, Playoff Round 2 and

Playoff Round 4; these dummy variables indicate that Seattle is not a Canadian team

and they did not make it to round 2 or 4 of the playoffs for any of the five seasons.

Diagram 2 below reveals the average of Table 7’s estimated average attendances in

relation to the maximum and minimum average annual attendances for the NHL

during the 2013-14 season. The average attendance for Seattle during its first five

seasons is just below both the average annual attendance of 17, 362 for the 2013-14

season and the estimated average annual attendance for Las Vegas.

Diagram 2. Seattle’s Average Annual Attendance Compared to all NHL Teams Average Annual Attendance During the 2013-14 Season

IV. Quebec City

22,62315,43611,059MIN MAXAVG.SEA

17, 362

40

The last proposal doesn’t quite fit the criteria for the NHL’s plan in the near

future, but it would be interesting to evaluate the impact of adding a seventh

Canadian team to the league. In order to complete the expansion, the league must

undergo some restructuring to balance out the conference alignment. Quebec City

was host to the Nordiques until they moved to Colorado in 1995; the drastic decline

of the Canadian dollar was a major factor in sending the franchise south of the

boarder. However, there demand for hockey remains strong in the capital of Quebec,

which is illustrated by the rise of viewership for the Quebec Major Junior Hockey

League. Finding an owner becomes the next task for the NHL, but the CEO for

Hydro-Quebec has expressed serious interest along the way to make the expansion

happen. Lastly, the issue of building an NHL regulation facility is long gone since the

construction of a new arena that seats 18, 432 in the heart of Quebec City will be

completed by the fall of 2015.




Table 9. Attendance Analysis for Proposed Team in Quebec CityYear

Arena Capacity

Can Team

Facility Age

Goal Diff

Pop PR2 PR4 Estimated Avg. Att

2016-17 18432 1 0 0 799600 0 0 18356.22

2017-18 18432 1 1 -1.08 799600 0 0 16709.93

2018-19 18432 1 2 -1.27 799600 0 0 16457.50

2019-20 18432 1 3 -1.23 799600 0 0 16565.27

2020-21 18432 1 4 -0.75 799600 0 0 17362.15

Average: 17090.22


The table above shows the estimated average annual attendance based on

five different scenarios of the team’s success. The estimations for Quebec City over

41

five seasons are much higher than the previous two proposed cities. The arena

capacity is slightly smaller than both Seattle and Las Vegas, but Quebec City’s

estimations are much closer to full capacity in each of the five seasons. Contrary to

the previous two cities, Quebec City’s estimations are positively affected by the

Canadian Team dummy variable. Diagram 3 below illustrates the average of Table

7’s estimated average attendances in relation to the maximum and minimum

average annual attendances for the NHL during the 2013-14 season. The average

attendance for Quebec City during its first five seasons is slightly below the average

annual attendance of 17, 362 for the 2013-14 season, but above the estimated

average annual attendance for Las Vegas and Seattle.

Diagram 3. Quebec City Average Annual Attendance Compared to all NHL Teams Average Annual Attendance During the 2013-14 Season

V. Conclusion

Using the estimation equations from chapter 2, it is possible to estimate the

average annual attendances for proposed NHL expansion teams such as Las Vegas,

Seattle and Quebec City. The estimations included random goal differentials that

corresponded to the three selected analyses from chapter 3. The estimations

concluded that Quebec City would average 17, 090 fans, the highest average

attendance annually out of the three proposed cities. It was estimated that Las Vegas

would have the second highest average annual attendance (16, 525), while Seattle

22,62317, 09011,059

MIN MAXAVG.QC

17, 362

42

would have the lowest (15, 436). It seems as though the Canadian Team dummy had

the greatest affect on the estimation and as expected, adding an additional team in

Canada would be the ideal location for the NHL to begin its expansion.

43

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