Open Access

NHL expansion and fan allegiance: a mathematical modelling study

Mathematics-in-Industry Case Studies20167:7

DOI: 10.1186/s40929-016-0009-0

Received: 27 April 2016

Accepted: 29 August 2016

Published: 4 October 2016

Abstract

Expansion of the NHL hockey league is of current interest. Success of an expansion team will rely on approved geographic locations, and most importantly fan support. Fan allegiance can be likened to infectious disease transmission, whereby individuals are susceptible, infected and recovered from a current team’s hockey season. We employ an infectious disease model to assess the viability of certain Canadian and American cities that have been discussed as potential locations for NHL expansion in the past decade. We find that Quebec City presents an ideal location for expansion, where as, Las Vegas is suboptimal.

Introduction

Expansion of the National Hockey League (NHL) has been a major conversation topic of team owners, players and fans over the past decade [3, 26, 31, 34]. Many cities in Canada and the USA have been featured in local and national media outlets as potential locations. Expansion to add another team to the Greater Toronto Area has been discussed widely [3, 16], as well as expansions to Las Vegas, Seattle and Kansas City (among others) [3]. The 2011 relocation of the Atlanta Thrashers to Winnipeg (the team was renamed the Winnipeg Jets, a team that had been moved out of Winnipeg in the year 1996 to become the Phoenix Coyotes) has also led to speculation that Quebec City, a city that lost its NHL team in 1995 (the Quebec Nordiques were moved to form the Colorado Avalanche), could provide an ideal location for relocation or expansion [26].

Success of a new NHL team will depend on the locational quality of a city or geographic region that will be able to support a team through thick and thin. Currently, several existing NHL teams are struggling financially, which may be attributed to poor performance. However, it is possible for a team to struggle financially, despite performing well on the ice. For example, while NHL teams in Canada and the northeastern United States have fairly steady revenues, teams in the southern US regularly rank in the bottom of league revenue and attendance lists [711, 2124, 29] - hockey may not be of interest to individuals living in cities in the sun-belt. Poor performance and poor locational quality simply lead to a lack of fans. NHL teams rely heavily on fan support to bring in revenue from ticket sales and merchandise purchases (note that the NHL is much more heavily reliant on its gate revenue than the NFL because of significantly less lucrative national television deals [2, 5, 19]). Thus fan allegiance is needed for an NHL team to ultimately survive.

We propose that the spread of fan allegiance can be likened to transmission of an infectious disease. An individual can be ‘infected’ through contact with fans in the population - typically due to the influence of family or friends, but individuals may also lose interest and recover or become immune to all future exposures. Also, in situations where there are multiple teams close by, a multi-strain model of infectious disease spread can be considered. We thus present a mathematical modelling study of fan allegiance using an infectious disease dynamical system. The model is then used to assess locations in Canada and the USA where NHL expansion may be successful.

In the sections that follow, we apply a Susceptible-Infected-Recovered modelling framework of fan allegiance so as to evaluate the locational quality of cities interested in housing new (or relocated) NHL teams. Model parameters are defined by the location attributes that have been shown to affect team financial success [17, 18]. The Basic Reproductive Ratio (\(\mathcal {R}_{0}\)), the number of new fans produced by one fan that is introduced into a totally susceptible population, is determined. Locations associated with \(\mathcal {R}_{0}\) values greater than unity are deemed acceptable for NHL expansion.

Note that in the course of this work, the NHL announced a bidding process for expansion [32]. Two cities filed applications: Quebec City and Las Vegas [30]. These cities were previously included in our analysis, so no modifications to the modelling study were needed.

Methods

Model

We propose that fan allegiance to a sports team spreads mainly through contact with other fans. As a result, it is appropriate to use an epidemiological model to explain the transmission of fan allegiance in the NHL.

Standard SIR model

The basic Susceptible-Infected-Recovered model of infectious disease dynamics is given by:
$$\begin{array}{@{}rcl@{}} \dot{S} & = & - \beta SI \end{array} $$
(1)
$$\begin{array}{@{}rcl@{}} \dot{I}\; & = & \beta SI - \gamma I \end{array} $$
(2)
$$\begin{array}{@{}rcl@{}} \dot{R} & = &\gamma I \end{array} $$
(3)
$$\begin{array}{@{}rcl@{}} N & =& S + I + R \end{array} $$
(4)
with the basic reproductive ratio:
$$\begin{array}{@{}rcl@{}} R_{0} = \frac{\beta N}{\gamma}\,. \end{array} $$
(5)

Here, β>0 represents the mean transmission rate of the disease, and 1/γ,γ>0 represents the mean infectious period. It should be noted that for S, I, and R to represent proportions of the population, we set N=1 in the last equation.

Expanding the model

In a 2014 survey, The Forum Poll asked respondents in Canada to rate their dedication to NHL hockey as ‘part-time,’ ‘regular,’ ‘enthusiastic,’ or ‘extreme’ [13]. Combining the part-time and regular fans in one group, and the enthusiastic and extreme fans in another, we expanded the classic SIR model to include a chronically infected compartment (C). Essentially, once a person is infected by the ‘disease’ of fan allegiance, it is possible for this infected person to become a chronic fan of one team, beyond recovery. This compartment represents enthusiastic and extreme fans who are very devoted to their respective teams of choice and are unlikely to recover from this ‘disease’.

The National Hockey League includes thirty teams, each of which draws fans at varying rates. For most NHL teams there exists little to no competition for fan allegiance among locals, as most teams are located far from each other geographically, in different and distinct cites. In New York City and Los Angeles the situation is different, however, with the LA Kings and Anaheim Ducks less than 100 km from each other, and the NY Rangers, NY Islanders and NJ Devils residing within a 50 km radius. We thus expand the model to consider one, two or three teams so that competition for fans can be realized. In the case of two teams the model is as follows:
$$ \begin{array}{rcl} \dot{S} & =& - S[\!\beta_{1}(I_{1} + C_{1}) + \beta_{2}(I_{2} + C_{2})] \\ \dot{I_{1}} & =& \beta_{1} S(I_{1} + C_{1}) - \gamma I_{1} -\alpha_{1} I_{1} + \delta_{21}[\!(I_{1} + C_{1})I_{2}] -\delta_{12}[\!I_{1}(I_{2} + C_{2})] \\ \dot{I_{2}} & = &\beta_{2} S(I_{2} + C_{2}) - \gamma I_{2} -\alpha_{2} I_{2} + \delta_{12}[\!I_{1}(I_{2} + C_{2})] -\delta_{21}[\!(I_{1} + C_{1})I_{2}] \\ \dot{C_{1}} & = & \alpha_{1} C_{1} \\ \dot{C_{2}} & =& \alpha_{2} C_{2} \\ \dot{R} & = & \gamma (I_{1}+I_{2}) \\ N &=& S+I_{1}+I_{2}+C_{1}+C_{2}+R \end{array} $$
(6)
where N is the total number of susceptible individuals at the time an NHL expansion team is located in the geographic region of the city. A schematic of the model is provided in Fig. 1. Briefly, β and γ are as above; α represents the mean rate at which infection becomes chronic (i.e. the rate of movement from compartment I to compartment C); and δ represents the mean rate at which an individual infected with strain i becomes infected with strain j instead (i.e. ‘switches’ teams).
Fig. 1

Flow diagram

At the time of expansion it is expected that there will be no chronically infected individuals in the population, and that switching between teams will be negligible. Therefore, we can determine that the basic reproductive ratio of a new team in a geographic region will be given by
$$\begin{array}{@{}rcl@{}} R_{0} = \frac{\beta N}{\gamma}\,. \end{array} $$
(7)

Note that demographics (birth and death) are ignored here. This permits the study of \(\mathcal {R}_{0}\) for one NHL season.

Parameters

The mean rate of transmission of infection for team t, β t , has several influencing factors. Firstly, team success is an important factor in fan allegiance since successful teams attract more fans, as seen during the 2015 playoff run of the Toronto Blue Jays [4]. Secondly, a team’s physical location may affect its ability to attract fans for several reasons: a city with a larger population means there are more potential fans nearby; the average income of the population has an effect; and because hockey is a winter sport, cities with colder climates have more inhabitants who are hockey fans. We propose that the last reason holds because in colder climates, inhabitants of the city are more likely to play the sport themselves, whether casually or in an organized league. It is worth mentioning that many children in Canada and the northeastern United States grow up playing shinny hockey in winter and road hockey in summer. We suggest that this early interest in the sport often leads to following an NHL team in adulthood.

Finally, following professional sports leagues tends to be a social phenomenon [15]: most hockey fans have either played the sport themselves or have been influenced by family and friends to become a fan. Thus, a susceptible individual’s social environment - their friends, family, and coworkers - is an important factor in determining the rate at which individuals become a fan of an NHL team. An individual whose social environment includes few hockey fans will be much less likely to become ‘infected’ than an individual whose close family and friends are ‘chronic’ hockey fans.

In order to take team success into account in determination of β, we focused on success in the NHL’s post-season. Called the Stanley Cup playoffs, the post-season consists of four rounds, each a best-of-seven-games series. Many NHL players readily admit that winning the Stanley Cup is the only important type of success (e.g., “I can’t hear what Jeremy says, because I’ve got my two Stanley Cup rings plugging my ears.” - Patrick Roy, four-time Stanley Cup winning NHL goaltender from 1985 to 2003, in response to a comment made by Jeremy Roenick, NHL forward from 1988 to 2009, during the 1996 NHL playoffs. (Roenick did not win a Cup during his career)). We considered only the seasons from 2000-011 to 2014-15, the most recent completed season.2

However, since only one Stanley Cup is awarded for each season, there is simply not enough data to draw meaningful conclusions. We considered separately either playoff series wins or playoff series appearances (which includes series won and lost), but neither of these metrics gives a complete picture on its own. Any useful measurement of team success rates must take into account Stanley Cup wins, playoff series wins, and also playoff series appearances (see Table 1 and Fig. 2). Simply adding the number of Cup wins, series wins, and series appearances, however, will also not suffice, since appearances should not influence fans as much as wins, and playoff wins will not influence fans as much as Stanley Cup championships. Also, such simple addition may rank teams with more Stanley Cups lower than a team with less Cups, but more playoff appearances (e.g., Chicago and Detroit, Fig. 2).
Fig. 2

A graph of playoff series appearances, playoff series wins, and Stanley Cup wins from 2001-15 for all thirty NHL teams (see Table 1). Note: success values given for the Winnipeg Jets were calculated only from results obtained since the franchise moved to Winnipeg for the 2011-12 season

Table 1

Revenue and playoff success for teams in the NHL

Financial and on-ice success of NHL teams

Team

Revenuea

Playoff appearancesb

Playoffwinsc

Stanley cup winsd

Anaheim ducks

107

21

13

1

Arizona coyotes

80

6

2

0

Boston bruins

164

19

10

1

Buffalo sabres

103

10

5

0

Calgary flames

122

10

4

0

Carolina hurricanes

91

12

9

1

Chicago blackhawks

172

22

16

3

Colorado avalanche

104

16

9

1

Columbus blue Jackets

86

2

0

0

Dallas stars

113

11

4

0

Detroit red wings

134

29

17

2

Edmonton oilers

119

6

3

0

Florida panthers

83

2

0

0

Los Angeles kings

146

16

11

2

Minnesota wild

111

10

4

0

Montreal canadiens

187

18

8

0

Nashville predators

98

10

2

0

New Jersey devils

111

21

12

1

New York islanders

83

6

0

0

New York rangers

217

19

10

0

Ottawa senators

117

18

8

0

Philadelphia flyers

136

21

10

0

Pittsburgh penguins

141

22

13

1

St. Louis blues

98

13

4

0

San Jose sharks

117

23

11

0

Tampa Bay lightning

97

16

10

1

Toronto maple leafs

190

9

4

0

Vancouver canucks

154

18

7

0

Washington capitals

117

13

4

0

Winnipeg jetse

102

1

0

0

Mean

123.00

14.03

7.00

0.47

Notes: ain millions of USD, for the 2013–2014 season [24]; bPlayoff series appeared in, 2001–2015 [14]; cPlayoff series won, 2001–2015 [14]; dStanley Cups won, 2001–2015 [14]; cSuccess values given for the Winnipeg Jets are calculated only from results obtained since the franchise moved to Winnipeg for the 2011-12 season

We therefore propose S t as the weighted per-season success rating of team t for the seasons 2000-01 to 2014-15, given by
$$ S_{t} = \frac{a \cdot PSA + b \cdot PSW + c \cdot SC}{N}, $$
(8)
where N=14 is the number of seasons in our analysis, SC represents Stanley Cup wins, SW is playoff series wins in any round, SA is playoff series appearances; and the coefficients a, b, c give the relative significance or ‘weight’ of each type of success, as follows:
  • a: Of the thirty teams in the NHL, sixteen play in the first round of the playoffs each season, so that each of these teams gains one playoff series appearance. Thus, \(a = \frac {30}{16} = 1.875\).

  • b: Half of the teams in the first round of the playoffs win their respective series. Thus \(b=2\cdot \frac {30}{16}= 3.75\), which is twice the value of a.

  • c: Only one NHL team wins the Stanley Cup each season, so we take \(c = \frac {30}{1} = 30\).

For comparison between teams the S t values are divided by the mean team success rate \(\mu _{S_{t}}\) to obtain
$$\begin{array}{@{}rcl@{}} s_{t} = \frac{S_{t}}{\mu_{S_{t}}} \,. \end{array} $$
(9)
Table 2 lists the success ratings for all thirty NHL teams using the Stanley Cup playoff data provided in Appendix: Table 1. Chicago is ranked first in the league with three Stanley Cup wins in the last 14 years, followed by Detroit and then Los Angeles, each with two championships.
Table 2

Success ratings for teams in the NHL

Success ratings of NHL teams

Team

S t a

s t b

Anaheim ducks

8.4375

1.7722

Arizona coyotes

1.3393

0.2813

Boston bruins

7.3661

1.5471

Buffalo sabres

2.6786

0.5626

Calgary flames

2.4107

0.5063

Carolina hurricanes

6.1607

1.2940

Chicago blackhawks

13.6607

2.8692

Colorado avalanche

6.6964

1.4065

Columbus blue jackets

0.2679

0.0563

Dallas stars

2.5446

0.5345

Detroit red wings

12.7232

2.6723

Edmonton oilers

1.6071

0.3376

Florida panthers

0.2679

0.0563

Los Angeles kings

9.3750

1.9691

Minnesota wild

2.4107

0.5063

Montreal canadiens

4.5536

0.9564

Nashville predators

1.8750

0.3938

New Jersey devils

8.1696

1.7159

New York islanders

0.8036

0.1688

New York rangers

5.2232

1.0970

Ottawa senators

4.5536

0.9564

Philadelphia flyers

5.4911

1.1533

Pittsburgh penguins

8.5714

1.8003

St. Louis blues

2.8125

0.5907

San Jose sharks

6.0268

1.2658

Tampa bay lightning

6.9643

1.4627

Toronto maple leafs

2.2768

0.4782

Vancouver canucks

4.2857

0.9001

Washington capitals

2.8125

0.5907

Winnipeg jetsc

0.4688

0.0985

Mean

4.7612

1.0000

Notes: aSee Table 1 and Eq. (8); bSee Eq. (9); cSuccess values given for the Winnipeg Jets are calculated only from results obtained since the franchise moved to Winnipeg for the 2011-12 season

To quantify the second influencing factor, team location, we slightly modified part of a statistical model given in Jones and Ferguson [18] that defined H t , the locational quality of team t’s home city, as a function of the city population size, mean income, and country. Jones and Ferguson defined H t as:
$$\begin{array}{@{}rcl@{}} H_{t} = \frac{1}{4}\frac{{a_{t}^{2}}}{{b_{t}^{2}}} \end{array} $$
(10)
and
$$\begin{array}{*{20}l} \log{a_{t}} & = \alpha_{0} + \alpha_{1} CAN + \alpha_{2} \log{POP} + \alpha_{3} \log{INC} \end{array} $$
(11)
$$\begin{array}{*{20}l} \log{b_{t}} & = \beta_{0} + \beta_{1} CAN + \beta_{2} \log{POP} + \beta_{3} \log{INC} \end{array} $$
(12)
where CAN is a dummy variable with a value of 1 if the team is located in Canada and zero otherwise, POP is the city’s population, and INC is the mean income in the city. Before applying the model in Jones and Ferguson to the thirty current NHL teams using current data, we first made one modification to the CAN variable. After looking at climate data for all current NHL cities [6], we chose to give a value of 1 for this variable to cities with climates similar to those in Canadian cities. We considered the average high temperature for each city’s coldest month; in Vancouver, the mildest of the Canadian cities with an NHL team, this temperature is 6 °C. We took this value as our benchmark so that any team whose city had a coldest month average high temperature of 6 °C or less was given a CAN value of 1. We therefore changed the variable name from CAN to WINT. Parameter values a t and b t are listed in Table 3. Note that we used gross domestic product per capita as an approximation of income per capita, all in USD. The final modification made to the Jones and Ferguson model was to again normalize the locational qualities to obtain a mean value of 1, defining:
$$\begin{array}{@{}rcl@{}} h_{t} = \frac{H_{t}}{\mu_{H_{t}}}, \end{array} $$
(13)
Table 3

Locational qualities for teams in the NHL and selected other North American cities

Locational qualities of NHL teams and selected other North American cities

Team or City

Population (millions)a

GDP per capitaa

Mean cold tempb

WINTc

a t d

b t e

H t f

h t g

Anaheim ducks

5.552807

$65,052.00

21

0.00

23872.2091

1256.5751

90.2294

0.7639

Arizona coyotes

4.459692

$46,430.00

20

0.00

26569.6757

1330.5856

99.6843

0.8439

Boston bruins

4.725601

$76,204.00

2

1.00

27631.2531

1251.2492

121.9140

1.0321

Buffalo sabres

1.133666

$64,090.00

0

1.00

25899.9405

1241.9735

108.7209

0.9204

Calgary flames

1.401800

$69,826.00

-1

1.00

25566.3899

1229.4164

108.1137

0.9153

Carolina hurricanes

1.244021

$51,729.00

11

0.00

22711.8760

1258.0512

81.4797

0.6898

Chicago blackhawks

9.568133

$58,861.00

-1

1.00

32472.5174

1339.6617

146.8864

1.2435

Colorado avalanche

2.746768

$61,795.00

6

1.00

28460.1760

1281.8863

123.2299

1.0432

Columbus blue jackets

1.988002

$54,780.00

3

1.00

28918.0283

1299.4250

123.8155

1.0482

Dallas stars

6.937652

$59,483.00

14

0.00

25195.9012

1285.8766

95.9845

0.8126

Detroit red wings

4.286134

$48,421.00

0

1.00

32487.1820

1358.8221

142.9022

1.2098

Edmonton oilers

1.320300

$62,832.00

-6

1.00

26458.4329

1251.9393

111.6611

0.9453

Florida panthers

5.905918

$44,480.00

25

0.00

27701.5572

1351.9469

104.9613

0.8886

Los Angeles kings

7.668163

$65,052.00

20

0.00

24583.7917

1267.9831

93.9747

0.7956

Minnesota wild

3.491620

$60,544.00

-5

1.00

29313.3379

1295.4964

127.9965

1.0836

Montreal canadiens

4.011200

$38,867.00

-5

1.00

35078.3776

1413.4876

153.9696

1.3035

Nashville predators

1.785349

$53,193.00

8

0.00

23225.5464

1264.1947

84.3809

0.7143

New Jersey devils

5.419961

$69,915.00

4

1.00

28900.5599

1276.5556

128.1366

1.0848

New York islanders

4.014786

$69,915.00

4

1.00

28121.9798

1265.8737

123.3818

1.0445

New York rangers

10.639183

$69,915.00

4

1.00

30729.9067

1300.8921

139.5018

1.1810

Ottawa senators

1.318400

$44,149.00

-6

1.00

30215.1591

1337.7141

127.5449

1.0798

Philadelphia flyers

6.060560

$57,166.00

5

1.00

31495.4880

1329.9238

140.2113

1.1870

Pittsburgh penguins

2.361681

$58,615.00

2

1.00

28635.7681

1289.2109

123.3417

1.0442

San Jose sharks

1.945539

$82,414.00

14

0.00

19849.6403

1167.1598

72.3077

0.6121

St. Louis blues

2.807760

$50,070.00

4

1.00

30868.6490

1334.4001

133.7837

1.1326

Tampa Bay lightning

2.895946

$44,999.00

21

0.00

25848.9340

1322.3520

95.5281

0.8087

Toronto maple leafs

6.036800

$45,771.00

-1

1.00

34233.6201

1386.5012

152.4071

1.2902

Vancouver canucks

2.476600

$44,337.00

6

1.00

31948.2055

1360.4518

137.8690

1.1672

Washington capitals

6.056296

$73,017.00

6

1.00

28720.5782

1270.1267

127.8298

1.0822

Winnipeg jets

0.778500

$41,719.00

-11

1.00

29421.4851

1332.2235

121.9312

1.0322

Hamilton

0.765228

$35,315.00

-1

1.00

31278.1681

1373.9382

129.5652

1.0969

Kansas City

2.070981

$51,157.00

4

1.00

29783.5302

1317.7477

127.7109

1.0812

Las Vegas

2.051796

$45,744.00

14

0.00

24896.4995

1305.6199

90.9040

0.7696

Québec city

0.797500

$41,907.00

-7

1.00

29436.2287

1331.9975

122.0949

1.0336

Seattle

3.663399

$73,012.00

8

0.00

22007.7246

1215.3763

81.9726

0.6940

MEAN (NHL teams)

4.034628

$57,788.03

5.47

0.700

28171.2056

1298.3985

118.1226

1.0000

Notes: a[25]; bAverage coldest month high temperature [6]; cTeams with average coldest month high temperatures of 6 °Cor less given value of 1, and 0 otherwise; dSee Eq. (11); eSee Eq. (12); fSee Eq. (10); gSee Eq. (13)

where \(\mu _{H_{t}}\) is the mean locational quality for all thirty NHL teams. The locational quality for all thirty NHL teams is shown in Table 3 and Fig. 3. It is also shown for the five expansion cities being considered. It is interesting to note that little correlation exists between the city population size and the locational quality.
Fig. 3

Locational quality. The calculated locational quality for the thirty NHL team host cities is shown

We now need to quantify the final influencing factor, namely the effect of an individual’s social environment on their susceptibility to becoming a fan of an NHL team. Defining p as the ‘rating’ of an individual’s social environment, we chose three representative values in order to simplify our model, as follows:
  1. 1.

    p=0.05 represents a scenario where a susceptible person does not have any close friends or family who are hockey fans and does not play hockey at any level, on any surface.

     
  2. 2.

    p=0.25 represents a scenario where a susceptible person has some friends or family who are regular fans (i.e. they belong to class I t for team t)

     
  3. 3.

    p=0.75 represents a scenario where a susceptible person’s close friends or family are devoted fans (i.e. they belong to class C t for team t)

     
Bringing the three influencing factors together, we now define
$$\begin{array}{@{}rcl@{}} \beta_{t} = p \, s_{t} \, h_{t}. \end{array} $$
(14)

Other model parameters are easier to determine. We set the mean infectious period, 1/γ, at 9 months, the length of a typical NHL season including playoffs (approximately October through June). According to The Forum Poll [13], approximately 30 % of hockey fans are ‘enthusiastic’ or ‘extreme’ fans, combined. Thus, α could be calibrated so that 30 % of NHL teams are chronically infected. However, a casual fan is more likely to become chronically infected by fan allegiance to a well-located, winning team, and therefore α must also depend on locational quality and success rates. We thus assume that α is proportional to β, which is very small when β is small, properly reflecting that fact that the rate of becoming a chronically infected fan of a team will be very small (and may be almost zero) for expansion teams, especially in their first season. For the purposes of this study, we set α=0.3β.

Though not relevant to the results below, for the sake of thoroughness we will define \(\delta _{t_{1} t_{2}}\), the mean rate of ‘switching’ allegiance from team t 1 to team t 2. Switching team allegiance is not a common phenomenon. We suggest that it happens mainly among casual fans (class I), and that it depends on an individual’s social environment as well as on the success ratings of both the ‘old’ and ‘new’ teams. We further propose that this ‘switching’ only occurs when the new team’s success rating is higher than that of the old team. We therefore define \(\delta _{t_{1} t_{2}}\)
$$ \delta_{t_{1} t_{2}} = \left\{ \begin{array}{ll} p \cdot (s_{t_{2}} - s_{t_{1}})/ \mu_{s_{t}} \qquad \qquad & \text{if} \;\; s_{t_{2}} > s_{t_{1}}, \\ 0 \qquad \qquad & \text{otherwise}. \end{array} \right. $$
(15)

Results

The equilibria of Eq. (6) correspond to an uninfected equilibrium E 0={S,0,0,0,0,NS} and an infected equilibrium E 1={0,0,0,C 1,C 2,NC1−C2}. However, over one NHL season E 1 cannot be achieved, and thus every season ends with a population distribution across all compartments S, I 1,I 2,C 1,C 2,R; it is assumed that over the course of the summer the casually infected individuals may wane back to the susceptible class S. It is now possible to determine the circumstances under which a given team’s fanbase is likely to grow; in other words, when the ‘infection’ will spread. This corresponds to determining the point at which \(\mathcal {R}_{0}>1\) in an epidemiological model. Here,
$$\begin{array}{@{}rcl@{}} \mathcal{R}_{0,t} = \frac{\beta_{t} S}{\gamma} = \frac{p h_{t} s_{t} S}{\gamma} \end{array} $$
(16)

\(\mathcal {R}_{0}\) has a useful predictive property whereby we can expect that an infection will spread in a population only when \(\mathcal {R}_{0} > 1\). We will utilize this property of \(\mathcal {R}_{0}\) in order to investigate the ability of different NHL teams to grow their respective fanbases.

It is important to note that once the chronically infected compartment is populated, E 1 is always stable, since chronically infected fans will always infect others in the population. This, however, does not imply that a team will be financially viable, since a small number of chronically infected fans will not be able to support a team financially (through merchandise purchases and ticket sales). The chronically infected compartment must reach a threshold level for this to be true. Determination of this threshold is beyond the scope of this study. We are providing a first step towards this calculation, determining geographical locations where ‘infection’ will spread.

Current NHL teams

First, we look at the current NHL teams to determine \(\mathcal {R}_{0}\) with different success rates, and different locational qualities. Table 4 lists the \(\mathcal {R}_{0}\) values for all NHL teams when p=0.05, 0.25, and 0.75. The \(\mathcal {R}_{0}\) values are also shown in Fig. 4.
Fig. 4

Basic reproductive ratio \(\mathcal {R}_{0}\). \(\mathcal {R}_{0}\) is shown for all current NHL teams when p=0.05, 0.25, 0.75

Table 4

Basic reproduction ratios for teams in the NHL, calculated for p=0.05, 0.25, and 0.75

\(\mathcal {R}_{0}\) for p=0.05, p=0.25, p=0.75

Team

\(\mathcal {R}_{0}\), p=0.05

\(\mathcal {R}_{0}\), p=0.25

\(\mathcal {R}_{0}\), p=0.75

Anaheim ducks

0.6092

3.0458

9.1373

Arizona coyotes

0.1068

0.5341

1.6024

Boston bruins

0.7185

3.5927

10.7782

Buffalo sabres

0.2330

1.1651

3.4952

Calgary flames

0.2085

1.0427

3.1281

Carolina hurricanes

0.4016

2.0082

6.0247

Chicago blackhawks

1.6055

8.0277

24.0831

Colorado avalanche

0.6603

3.3014

9.9041

Columbus blue jackets

0.0265

0.1327

0.3980

Dallas stars

0.1954

0.9772

2.9315

Detroit red wings

1.4548

7.2740

21.8219

Edmonton oilers

0.1436

0.7179

2.1538

Florida panthers

0.0225

0.1125

0.3374

Los Angeles kings

0.7049

3.5247

10.5740

Minnesota wild

0.2469

1.2345

3.7034

Montreal canadiens

0.5610

2.8049

8.4148

Nashville predators

0.1266

0.6330

1.8989

New Jersey devils

0.8376

4.1881

12.5642

New York islanders

0.0793

0.3967

1.1900

New York rangers

0.5830

2.9151

8.7453

Ottawa senators

0.4647

2.3235

6.9706

Philadelphia flyers

0.6160

3.0802

9.2406

Pittsburgh penguins

0.8459

4.2296

12.6888

St. Louis blues

0.3011

1.5053

4.5160

San Jose sharks

0.3487

1.7434

5.2303

Tampa Bay lightning

0.5323

2.6616

7.9848

Toronto maple leafs

0.2776

1.3882

4.1647

Vancouver canucks

0.4728

2.3639

7.0917

Washington capitals

0.2877

1.4383

4.3150

Winnipeg jets

0.0457

0.2287

0.6860

As an example, consider the Chicago Blackhawks, 2015 Stanley Cup champions. The basic reproductive ratio in Chicago when p=0.05 is
$$\begin{array}{*{20}l} \mathcal{R}_{0} & = 9\cdot 0.05 \,h_{Chi}\,s_{Chi} \\ & = 0.45\cdot1.2435\cdot2.8692 \\ & = 1.6055 \end{array} $$

The Blackhawks have \(\mathcal {R}_{0}>1\) even if p is small. Winners of three Stanley Cups since the 2004-05 lockout, the Blackhawks have the highest \(\mathcal {R}_{0}\) values in the league. They are followed closely by the Detroit Red Wings, winners of two Cups since 2001. For all three p-values, both Chicago and Detroit have \(\mathcal {R}_{0}>1\).

Three teams in the league, the Florida Panthers, Columbus Blue Jackets, and Winnipeg Jets, have \(\mathcal {R}_{0}<1\) for all three values of p. This may be related to a warm geographic location (Florida), poor playoff success (Columbus), and the fact that a team is fairly new to the NHL (Winnipeg). A further five teams have \(\mathcal {R}_{0}<1\) for p≤0.25: the New York Islanders, Arizona Coyotes, Nashville Predators, Edmonton Oilers, and Dallas Stars - all teams with few playoff appearances in the timeframe of reference. It is interesting to note that the Toronto Maple Leafs, despite having similar playoff appearances to these teams, have \(\mathcal {R}_{0}>1\) when p=0.25.

Appendix: Figs. 5 and 6, show that a team’s success rate has a much stronger effect on \(\mathcal {R}_{0}\) than its locational quality. The outliers whose success rates are greater than their \(\mathcal {R}_{0}\) values (Los Angeles, Anaheim, Tampa Bay, Carolina, San Jose, Dallas, and Nashville) all have locational qualities well below the league average, and the outliers whose success rates are lower than their \(\mathcal {R}_{0}\) values all have high locational qualities. Thus locational quality does have an effect on \(\mathcal {R}_{0}\), but this is small compared to team success. It is interesting to note that all the teams that have a lower success rate than their corresponding \(\mathcal {R}_{0}\) are either Original Six teams,3 located in Canada, or have been in the NHL since the 1967-68 expansion.

Although the success rate is the most significant factor in determining a team’s \(\mathcal {R}_{0}\), a low locational quality is certainly a disadvantage for a team trying to grow its fanbase. Such a team requires a higher success rate to attract the same number of new fans compared to a team with a higher locational quality. According to this rationale, of the three teams that have \(\mathcal {R}_{0}\) values below 1 for all values of p, the Florida Panthers need a higher success rate than the Columbus Blue Jackets and Winnipeg Jets in order to be viable. In order to attract fans in the p=0.75 category, Florida must increase its success rate to approximately 6 playoff series appearances in 14 years, meaning that the team needs to reach the playoffs nearly every second year. Columbus and Winnipeg each need to increase their respective success rates to approximately 5 playoff series appearances in 14 years, which means reaching the playoffs at least every third year. In order to attract fans in the p=0.25 category, Florida must increase its success rate to one of the following: 10 series appearances and 4 series wins in 14 years (reaching the playoffs six times); 12 series appearances and 3 series wins in 14 years (reaching the playoffs 9 times); 14 series appearances and 2 series wins in 14 years (reaching the playoffs 12 times); or 16 series appearances and one series win in 14 years (reaching the playoffs every year). Columbus and Winnipeg, however, need increase their respective success rates to, for example, 9 series appearances and 3 series wins in 14 years (reaching the playoffs six times), or 11 series appearances and 2 series wins in 14 years (reaching the playoffs 9 times).

Given that success rates have a greater impact than location on a team’s ability to attract fans, it is likely that many cities in North America are capable of financially sustaining a successful NHL team. However, as evidenced by the outliers in Fig. 5, a high locational quality is advantageous, as it lowers the success rate needed to ensure a team’s financial viability. It is not a given that a city with a low locational quality will be unable to support an NHL team; however, a high locational quality is an advantage to a new team as it works to attract fans in the team’s first years. Since many expansion or relocation teams struggle on-ice for the first several years, this advantage may be quite significant in the long run.4
Fig. 5

Yearly success rates and \(\mathcal {R}_{0}\). The normalized yearly success rate and basic reproduction ratio \(\mathcal {R}_{0}\) are shown for all thirty NHL teams. It is assumed that p=0.75

Fig. 6

Locational quality and \(\mathcal {R}_{0}\). The normalized locational quality and basic reproduction ratio \(\mathcal {R}_{0}\) are shown for all thirty NHL teams. It is assumed that p=0.75

Potential expansion sites

Current NHL expansion considerations - Québec City and Las Vegas

Las Vegas and Québec City are involved in the current NHL expansion considerations [20]. In terms of locational quality, Québec City, at 122.1, is just higher than the league average of 118.1, and on par with Boston and Winnipeg, both at approximately 121.9. Las Vegas’ locational quality is much lower; at only 90.9, it is closest to Anaheim at 90.2, and is higher than the cities of only three other current teams: San Jose at 72.3, Raleigh at 81.5, and Nashville at 84.4 (See Table 3).

Table 5 lists the minimum success rates required to attain an R 0>1 for potential expansion sites.
Table 5

Minimum success rates required to attain a basic reproduction ratio greater than one for a theoretical team located in one of the listed cities

Minimum s t to attain \(\mathcal {R}_{0}>1\) for p=0.05, p=0.25, 0.75

Team location

H t

s t , p=0.05

s t , p=0.25

s t , p=0.75

Hamilton

129.5652

2.0260

0.4052

0.1351

Kansas city

127.7109

2.0554

0.4111

0.1370

Las Vegas

90.9040

2.8876

0.5775

0.1925

Québec city

122.0949

2.1499

0.4300

0.1433

Seattle

81.9726

3.2022

0.6404

0.2135

For a team in Québec City to be viable, it would need to have similar success rates to Boston (\(\mathcal {R}_{0}>1\) when p=0.25, 0.75), Colorado (\(\mathcal {R}_{0}>1\) when p=0.25, 0.75) or the New York Islanders (\(\mathcal {R}_{0}>1\) when p=0.75). We note that expansion teams, in general, do not have very good success rates [14] (see Footnote 4 above), so a success rate similar to the New York Islanders could be expected, but a large p is needed for viability. Québec City previously hosted the Nordiques in the NHL from 1979 until the team relocated to Denver, Colorado in 1995. Many inhabitants of the city have not forgotten their old team and would be eager to support a new Nordiques team if the league allows it [1]. It is also expected that season tickets would sell out quickly in Québec City, similar to what occurred in Winnipeg when the Jets returned to that city in 2011 [33]. Thus, it is not unreasonable to believe that p could be high, and thus, a team in Québec City could thrive. Drawbacks that must be considered include the low Canadian dollar [27] and the relatively small size of the city [25].

In Las Vegas, because of the city’s much lower locational quality, a team would need to make 7 series appearances in 14 years for a p=0.75. This means that the team must reach the postseason every second year. In the p=0.25 category, a team in Las Vegas would need 11 series appearances and 5 series wins in order to ensure that \(\mathcal {R}_{0}\) is greater than 1. Again, citing the low expected success rates of expansion teams [14], these success ratings are very unlikely.

Bill Foley, who leads the group in Las Vegas, successfully ran a season ticket drive that secured more than 13 200 deposits for season tickets [30]. Interestingly, Las Vegas does not currently host a professional sports team from any of the major sports leagues [30],5 which may help a potential NHL team gain fans in the city. Its status as a popular tourist destination means that the many visitors from colder climates can be expected to purchase single-game tickets, as is already the case among ‘snowbirds’6 who visit Miami and Tampa in Florida [12]. The main drawback to putting a team in Las Vegas is the hot, dry climate, which is not conducive to ice hockey. As a result, there is little local interest in the sport, as evidenced by the low participation levels in amateur hockey in the state of Nevada [29].

Other potential expansion cities - Hamilton, Kansas City and Seattle

Although applications for teams in Kansas City, Seattle, and Hamilton - or the Greater Toronto Area - were not submitted for consideration in the current expansion process, these cities remain interesting as possible future sites for an expansion or relocation team. Hamilton, approximately 60 kilometres outside of Toronto, has the highest locational quality of the three; at 129.6, its locational quality is higher than those of all current Canadian teams except Montreal, Toronto, and Vancouver. Kansas City is not far behind at 127.7, which is on a par with Newark, NJ at 128.1, Minneapolis-St. Paul, MN at 128.0, Washington, D.C. at 127.8 and Ottawa at 127.5. Seattle’s locational quality is low at 82.0, higher than only two current teams: Raleigh, NC and San Jose.

Hamilton and Kansas City have higher locational qualities than Québec City; thus, a team in each of these cities would need a slightly lower success rate than Boston, Colorado, and the New York Islanders: for potential fans in the p=0.25 category, teams in these cities would only need 8 series appearances and 3 series wins, or 10 series appearances and 2 series wins, to reach \(\mathcal {R}_{0}>1\).

Based on its high locational quality, Kansas City could be an excellent place in which to put an expansion team. No potential ownership group completed an application in the current NHL expansion application process, but this does not rule it out as a potential location for an expansion or relocation team in the future.

Based on its locational quality, Hamilton seems to be an excellent place to put a new NHL team; however, its situation is complicated. Due to its proximity to both Toronto and Buffalo, a team in Hamilton could potentially ‘steal’ fans and revenue from either of those two cities, which might affect the viability of those teams. This proximity would not be expected to harm the Toronto Maple Leafs’ viability since the Leafs remain the most financially successful team in the league despite reaching the playoffs only once in the last ten years [14, 16, 24]; however, the less successful Buffalo Sabres could potentially experience significant damage.

In order to gain more insight into Hamilton’s situation, we looked at the situation in New York City, where three teams - the New York Rangers in Manhattan, the New York Islanders in Long Island, and the New Jersey Devils in Newark - are located within metropolitan New York City. In order to divide up the population of metro New York for our calculations, we looked at how the city’s 2014 hockey revenue was split between the three teams. The revenue split was as follows: the Rangers had 53 %, the Islanders, 20 %, and the Devils, 27 %, of the city’s hockey revenue. While the situation in Toronto-Hamilton-Buffalo is not identical due to a greater distance between the teams, the revenue split in New York City provides insight into whether a team in Hamilton would be financially viable.

It should be noted that this comparison rests on two assumptions: first, that Buffalo’s revenue would not decrease; and second, that a team in Hamilton would bring in new revenue. If a team in Hamilton has a 20 % share of the revenue in the region, similar to the Islanders, then such a team can expect a revenue of $73.25 million, which is significantly lower than any current team. Since several current teams struggle even with higher revenues, such a low amount does not point to financial viability. However, if a team in Hamilton has a 27 % share of the region’s revenue, it can expect a much more viable revenue of $108.37 million. If we relax the second assumption and allow the team in Hamilton to rely on revenue ‘stolen’ from Toronto and Buffalo, it will not be viable based on the above revenue split, and, furthermore, will greatly damage the Buffalo Sabres’ financial viability.

The NHL may be hesitant to approve a team in Hamilton because of the potential danger to the viability of the Buffalo Sabres, who have not reached the postseason since 2011. Unless the Sabres start to reach the playoffs more regularly, thereby stabilizing their fanbase and securing the team’s financial health, the NHL is unlikely to approve a team in Hamilton in the near future.

An expansion team in Seattle would need at least 8 series appearances in 14 years for p=0.75 and at least 13 appearances and 5 wins for p=0.25 for the team to be viable, even more than a team in Las Vegas. Thus, Seattle might not be a good place in which to put an expansion team. We note, however, that Seattle previously hosted a professional ice hockey team, the Seattle Metropolitans, which played in the Pacific Coast Hockey League from 1915-24 [14]. This history, along with its proximity to the Canadian border, may have a positive effect on the success rate needed in this city.

Conclusions

The applications for expansion teams Québec City, Québec, and Las Vegas, Nevada by potential ownership groups have passed the first round of the application process (started by the NHL in June 2015), and are currently being evaluated by the league. Our study suggests that NHL expansion in Québec City would be viable, but that expansion to Las Vegas would be very risky. Our study also found that other North American cities that have shown interest in hosting NHL teams could be worthy of consideration, in particular Hamilton and Kansas City. We conclude that Québec City represents a viable option for NHL expansion, with Hamilton and Kansas City deserving consideration as well.

Our results are supported by a locational quality and success rating evaluation of all current NHL teams. The locational quality was determined using the population, GDP per capita, and mean winter temperature of all NHL cities and potential expansion cities. The success ratings of all current NHL teams were determined from Stanley Cup playoff and championship data for the years 2001-15. Given the locational quality of the potential NHL expansion cities, we then determined the success rating that an expansion team in each city would need to achieve in order to attract new fans.

Model results show that most current NHL teams can expect to have trouble gaining fans with little connection to hockey (see Table 4 and Fig. 4). Among those susceptible individuals in the category p=0.25, some fanbases could grow but many others would not, including some Canadian teams. Stability analysis of the model equilibria shows that the existence of chronically infected fans will enable growth in fan allegiance; however, for this growth to occur, susceptible individuals must have exposure to devoted fans. Calculation of a threshold of devoted fans needed to guarantee team financial stability is a course for future work.

In our analysis, we included a parameter that involved average winter temperature as a variable in our evaluation of locational quality to reflect ‘fan interest’ in the sport. We note here that involvement in amateur hockey and local leagues may be a better indication of hockey interest. Data compilation on participation rates in ice hockey, roller hockey, and ball hockey leagues is a course for future work.

Along a similar argument to that above, fans are more likely to be found in places where a particular sport is of interest. We suggest that expansion of the NHL to a particular geographic location could be preceded by NHL sponsorship or investment in hockey activities at the local level. This could involve sponsorship of house leagues, encouraging NHL player involvement in community activities, or even financial bursaries to renovate or build hockey facilities.

Finally, our analysis did not consider future expansion in the Greater Toronto Area (GTA). Considering that Los Angeles and New York City each support more than one NHL team, perhaps metropolitan Toronto is capable of supporting a second team as well. Toronto has a high locational quality that is second in the league, behind only Montreal, and at 6 million, it has the largest metropolitan population in Canada. Los Angeles hosts two teams, thus a comparison of Toronto to Los Angeles could be warranted. Revenue in Los Angeles for 2014 was split 58–42 % between the Kings and Ducks. If putting a new team in the GTA does not bring in new revenue but rather splits the Toronto Maple Leafs’ current revenue of $190 million - with the Leafs retaining the larger share - the new team would have a revenue of only $79.80 million. Given that the Arizona Coyotes had the lowest 2014 revenue in the league at $80 million and are known to be struggling financially [24, 28], $79.8 million may not be a viable revenue for a new team. This is a very simple analysis, however. Consideration must also be given to the cost of seasons tickets, individual tickets sales, and the seating capacity of the Maple Leaf arena, all factors that limit the spending habits of Maple Leaf fans. Thus, it could be expected that a new NHL team in the GTA could bring in new revenue. A game theoretic analysis of fan allegiance and merchandise sales would be informative here, and is a course for future work.

Endnotes

1 Two teams, the Minnesota Wild and the Columbus Blue Jackets, joined the league for the 2000-01 season, bringing the total number of teams in the league to 30, the current number.

2 Since the entire 2004-05 season was lost to a labour dispute, or ‘lockout’, this timeframe includes only 14 seasons.

3 A term commonly used to refer to the six teams in the NHL from 1943-1967: Boston, Chicago, Detroit, Montreal, New York Rangers, Toronto.

4 None of the nine expansion teams added to the NHL since 1991 reached the playoffs in its first year in the league; only one team, the Carolina Hurricanes, reached the playoffs in its second year; and two other teams, the San Jose Sharks and the Minnesota Wild, reached the playoffs in their third year [14].

5 The major sports leagues in North America, often referred to as the ‘Big Four’, include the NHL, the National Football League, the National Basketball Association, and Major League Baseball.

6 Canadians who spend the winter months in Florida are often referred to as ‘snowbirds.’

Declarations

Acknowledgments

The authors thank Jon Forde for interesting discussions. JMH is funded by NSERC.

Authors’ contributions

JL, AC and JMH particpated in the development of the model, analysis and interpretation of results, and in the writing of the manuscript. All authors read and approved the final manuscript.

Competing interests

The author declares that that they have no competing interests.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Mathematics and Statistics, York University
(2)
Centre for Disease Modelling, York University

References

  1. Associated Press (2015) Quebec City group makes NHL expansion pitch Tuesday. http://www.cbc.ca/news/canada/montreal/quebec-city-nhl-presentation-1.3248049. Web. Accessed Apr 2016.
  2. Badenhausen K (2011) The NFL signs TV deals worth $27 billion. www.forbes.com/sites/kurtbadenhausen/2011/12/14/the-nfl-signs-tv-deals-worth-26-billion/#12a158252a67. Web. Accessed Apr 2016.
  3. Boylen R (2014) Looking at six potential NHL expansion/relocation destinations, from Las Vegas to Hamilton. The Hockey News, http://www.thehockeynews.com/news/article. looking-at-six-potential-nhl-expansionrelocation-destinations-from-las-vegas-to-hamilton. Web.
  4. Chidley-Hill J (2015) Jays chase Leafs on social media, followers drawn by post-season run. https://www.thestar.com/sports/bluejays/2015/10/14/jays-chase-leafs-on-social-media-followers-drawn-by-post-season-run.html. Web.
  5. Condor B (2011) NHL, NBC sign record-setting 10-year TV deal. https://www.nhl.com/news/nhl-nbc-sign-record-setting-10-year-tv-deal/c-560238. Web. Accessed Apr 2016.
  6. Current Results Nexus. Weather averages. www.CurrentResults.com. Web. Accessed Apr 2016.
  7. ESPN (2011) NHL Attendance Report - 2010-11. www.ESPN.com. Web. Accessed Apr 2016.
  8. ESPN (2012) NHL Attendance Report - 2011-12. www.ESPN.com. Web. Accessed Apr 2016.
  9. ESPN (2013) NHL Attendance Report - 2012-13. www.ESPN.com. Web. Accessed Apr 2016.
  10. ESPN (2014) NHL Attendance Report - 2013-14. www.ESPN.com. Web. Accessed Apr 2016.
  11. ESPN (2015) NHL Attendance Report - 2014-15. www.ESPN.com. Web. Accessed Apr 2016.
  12. Fialkov H (2015) Barkov lifts surging Panthers past Canadiens 3-1 and into first place. www.Sun-Sentinel.com. Web. Accessed Apr 2016.
  13. Forum Research (2014) The Forum Poll: One half of Canadians are NHL fans. http://poll.forumresearch.com/post/162/one-half-of-canadians-are-nhl-fans/. Web.
  14. HockeyDB.com (2015) National Hockey League history and statistics. http://www.hockeydb.com/ihdb/stats/leagues/141.html. Web.
  15. James JD, Kolbe RH (2000) An identification and examination of influences that shape the creation of a professional team fan. Int J Sports Mark Sponsorship 2(1): 14–28. Accessed Apr 2016.View ArticleGoogle Scholar
  16. Johnston M (2015) Second Toronto-area NHL team will have to wait. http://www.sportsnet.ca/hockey/nhl/second-toronto-area-nhl-team-will-have-to-wait/. Web. Accessed Apr 2016.
  17. Jones JCH (1984) Winners, Losers and Hosers: Demand and survival in the National Hockey League. Atl Econ J 12(3): 54–63.View ArticleGoogle Scholar
  18. Jones JCH, Ferguson DG (1988) Location and survival in the National Hockey League. J Ind Econ 36(4): 443–457.View ArticleGoogle Scholar
  19. NHL.com (2014) Rogers deal changes viewing landscape dramatically. https://www.nhl.com/news/rogers-deal-changes-viewing-landscape-dramatically/c-733428. Web.
  20. NHL Public Relations (2015) Update on NHL expansion application process. https://www.nhl.com/news/update-on-nhl-expansion-application-process/c-775295. Web. Accessed Apr 2016.
  21. Ozanian M (2011) The business of hockey: Team values hit all-time high. http://www.forbes.com/sites/mikeozanian/2011/11/30/the-business-of-hockey/#2369cd201310. Web. Accessed Apr 2016.
  22. Ozanian M (2012) NHL team values 2012: Toronto Maple Leafs are first hockey team worth $1 billion. http://www.forbes.com/sites/mikeozanian/2012/11/28nhl-team-values-2012-maple-leafs-first-hockey-team-worth-1-billion/#67faa2ae1c36. Web. Accessed Apr 2016.
  23. Ozanian M (2013) The NHL’s most valuable teams. http://www.forbes.com/sites/mikeozanian/2013/11/25/the-nhls-most-valuable-teams/#54db4e073d95. Web. Accessed Apr 2016.
  24. Ozanian M (2014) The most valuable teams in the NHL. http://www.forbes.com/sites/mikeozanian/2014/11/25/the-most-valuable-teams-in-the-nhl/#4a1c01c57e5b. Web.
  25. Parilla J, Trujillo JL, Berube A (2014) Global Metro Monitor. Technical report, Brookings Institution, Washington, D. C.
  26. Robillard A (2015) Harper says ‘no better place’ than Quebec City for NHL expansion team. http://www.theglobeandmail.com/sports/hockey/harper-says-no-better-place-than-quebec-city-for-nhl-expansion-team/article25113047/. Web. Accessed Apr 2016.
  27. Rosen D (2016) Bettman says NHL expansion process is ongoing. https://www.nhl.com/news/bettman-says-nhl-expansion-process-is-ongoing/c-800364. Web. Accessed Apr 2016.
  28. Shoalts D (2014) Arizona Coyotes owners pan for gold in river of red ink. http://www.theglobeandmail.com/sports/hockey/arizona-coyotes-owners-pan-for-gold-in-river-of-red-ink/article21489565/. Web. Accessed Apr 2016.
  29. USA Hockey (2015) 2014-2015 Season Final Registration Reports. Technical report, USA Hockey.
  30. Wawrow J (2015) Las Vegas, Quebec City enter final phase of NHL expansion process. http://www.theglobeandmail.com/sports/hockey/las-vegas-quebec-city-enter-final-phase-of-nhl-expansion-process/article26062487/. Web. Accessed Apr 2016.
  31. Whyno S (2014) Las Vegas season-ticket drive raises more questions about NHL expansion. http://www.theglobeandmail.com/sports/hockey/las-vegas-season-ticket-drive-raises-more-questions-about-nhl-expansion/article22010858/. Web. Accessed Apr 2016.
  32. Whyno S (2015) NHL board approves opening formal expansion process. https://www.thestar.com/sports/hockey/2015/06/24/nhl-board-set-to-approve-opening-formal-expansion-process.html. Web. Accessed Apr 2016.
  33. Whyno S (2015) Winnipeg’s ‘Drive to 13,000’ is gold standard for Las Vegas. https://www.nhl.com/news/winnipegs-drive-to-13000-is-gold-standard-for-las-vegas-season-ticket-drive/c-752834. Web.
  34. Woolsey G (2008) Sales pitch for NHL expansion in Canada. https://www.thestar.com/opinion/columnists/2008/01/21/sales_pitch_for_nhl_expansion_in_canada.html. Web. Accessed Apr 2016.

Copyright

© The Author(s) 2016