Thesis- Final

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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 NHLs plan, which would like to add two more teams to the Western Conference.Table of ContentsAbstractiCHAPTER TWO4Attendance Regressions in Sports: A Review of the Literature5I. Introduction5II. MLB6III. NBA7IV. NFL8V. NHL9VI. Conclusion10CHAPTER THREE12Estimating Attendance in the NHL12I. Introduction12II. Economic Model12III. Data14A. Dependent Variable:14B. Explanatory Variables:14C. The Data Set17IV. Estimation19V. Conclusion22CHAPTER FOUR24Expansion and Relocation in the NHL24I. Introduction25II. History26A. 1970s26B. 1980s27C. 1990s29D. 2000s31IV. Conclusion35CHAPTER FIVE38Current Proposals38I. Introduction38II. Las Vegas39III. Seattle41IV. Quebec City43V. Conclusion44

CHAPTER TWOAttendance 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 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, Butlers (2002) study yields noteworthy results that capture the importance of interleague play on game-by-game MLB attendance 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 teams 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 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 citys 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 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