‘We’ Are the Champions: Examining the Effects of Same-League Rival Clubs in the European Football Industry by Mohamed Kerbek
A Thesis presented to The University of Guelph
In partial fulfillment of requirements for the degree of Master of Science in Marketing and Consumer Studies
Guelph, Ontario, Canada © Mohamed Kerbek, July, 2015 ABSTRACT
‘WE’ ARE THE CHAMPIONS: EXAMINING THE NETWORK EFFECTS OF SAME- LEAGUE RIVAL CLUBS IN THE EUROPEAN FOOTBALL INDUSTRY
Mohamed Kerbek Advisor: University of Guelph, 2015 Sergio Meza
In business, rivalry is associated with exchanging blows with a competitor in order to win customers. Rarely does a rival profit from the presence of another, and in such instances the benefits are based on a production side benefit, rather than increased marketability. In the sporting industry, the business between rivals seems to defy that logic. Rivals seem to profit from the presence of another without any locational antecedent. This paper aims to examine the impact of sports rivalry on club financial performance from a spatial and general rivalry perspective. It utilizes multi-linear regressions to determine the impact of having a rival on a team’s year-end finances. The results found significant support for rivalry in general and competitive balance. No support was found for geographic rivalries. Important implications for competitive strategy theories in the sporting industry, and decision-making criteria for practitioners. Dedication To my dad, May God Bless his soul, who I wish was here for my defense and my graduation. You will never be forgotten. To my mom and aunt, the strongest most enduring humans I have ever known, thank you for being so patient, understanding, and forever giving. To my friends who stuck by me in what I can only describe as one of the most challenging periods of my life, I cannot sufficiently voice my appreciation. This is for all of you. Acknowledgements I would like to thank my advisor, Professor Sergio Meza for his support and help in shaping my passion for this beautiful game into a discernable thesis topic. Thank you for providing me with the direction and know-how to proceed with this paper, and thank you for your patience with and belief in me. Also I would like to take this opportunity to express my sincere gratitude towards Professor Paul Anglin and Professor Vinay Kanetkar, the esteemed members of my advisory committee who helped me infinitely in structuring this paper and refining my thought to make this paper what it is today. I thank you for your passion, your valuable insight and your thought provoking conversation. I would like to thank Professor Towhidul Islam and Professor Timothy Dewhirst for their role in guiding me before I set foot in the University of Guelph, and their valuable advice and belief in my ability to fulfill the requirements of this program. Equally, I want to extend my gratitude to Professor Karen Finlay and Professor Theodore Noseworthy, both who encouraged me when I felt the challenge was too great, and for their constant advice and guidance. Thank you to soon to be Dr. Aseel Al-Ghamdi, who offered me valuable insight into my work as a fellow sports researcher and football enthusiast, and an ever-supportive friend. To Professor Tanya Mark, who chaired my Seminar and contributed valuable insight into the conversation at both the seminar and the Brown Bag presentation where I first presented this idea at the University of Guelph, thank you very much for your efforts, they are genuinely and wholeheartedly appreciated. To the esteemed staff in the Marketing and Consumer Studies Office, Rita Raso, Dominica Alderton and Cori Wells, I would not have gotten here without your constant help in my time here. I cannot tell you how much I appreciate it, or how much I am indebted to you. It has been a long ride, and I have been very fortunate to encounter so many remarkable people along the way. For those who are not mentioned in this section, pardon my spatial constraints but believe me when I say that I am every bit as grateful, and I definitely would not have been able to do this without you. iii Table of Contents
Chapter 1.0 Introduction...... 1 1.1 Preface and Motivation………………………………………………………...……………...1 1.2 Introduction to Football...... 7 Chapter 2.0 Literature Review ...... 12 2.1 Competition & Competitive Cooperation in Business ...... 12 2.2 The Curious Case of the Sporting Industry ...... 17 2.3 The Impact of Sports Rivalries on Demand ...... 19 2.4 Bridging the Gap ...... 22 2.5 How to Measure the Impact of Rivalry on Club Financial Performance ...... 23 Chapter 3.0 Empirical Framework ...... 25 3.1 Financial Success Metrics ...... 25 3.2 Revenue Streams ...... 26 3.2.1 Classifying Revenue Streams ...... 26 3.2.2 The Scope of Revenue Streams ...... 27 3.3 Demand Drivers ...... 29 3.3.1 Classifying Demand Drivers ...... 29 3.3.2 Scope of Demand Drivers ...... 35 Chapter 4.0 Methodology ...... 38 Chapter 5.0 Results ...... 48 Chapter 6: Conclusion & Discussion ...... 78 6.1 Summary of Findings ...... 78 Geographic Rivalry Hypotheses ...... 78 6.2 Contributions/Implications ...... 79 Managerial ...... 80 6.2 Limitations ...... 81 6.3 Future Research Considerations ...... 83 Bibliography ...... 85 APPENDICES ...... 89 Appendix A: Stakeholder Analysis: Consumers of the Football Product ...... 90 Appendix B: On Sources of Revenue ...... 91 Appendix C: Descriptive Statistics ...... 93 Appendix D: Study Results ...... 94 iv List of Tables
Table 1.1 Same city clubs represented in Market Value and Operating Revenue Ranks………………………………………………………………………………………………….P. 2
Table 3.1 Revenue Stream Scope ………………………………………………… P.27 Table 3.2 Classification of Demand Drivers……………………………………….P.23 Table 3.3 Demand Drivers & Their Corresponding Revenue Streams…………….P.36 Table 4.1 Variable Class & Operational IV………………………………………..P.48 Table 5.1 Numerical Impact of Each IV on DVs…………………………………..P.38 Table 5.2: Adjusted R-Square Values across models for all DVs…………………P.50 Table 5.3: Impact of 1,000 change in population total & match-day revenues……P.51 Table 5.4: Impact of Change of £1,000 in per capita GDP on total & match-day revenue …………………………………………………………………….………………..P.52 Table 5.5: League Rank Coefficient values & impact levels across models for all DVs ……………………………………………………………..……………………….P.54 Table 5.6: Impact of League Rank Coefficients on Commercial Revenue………..P.54 Table 5.7: UCL Participation Coefficient values & impact levels across models for all DVs…………………………………………………………………………………P.55 Table 5.8: EL participation coefficient values & impact levels across models for all DVs ………………………………………………………………………………………P.57 Table 5.9: Games played coefficient values across models for all DVs…………...P.57
Table 5.10: Games Played Coefficient Values and impact levels across Total & Match-day Revenue DVs ……………………………………………………………………….P.58 Table 5.11: Games Played Coefficient Values and impact levels across Television Revenue DV………………………………………………………………………...... P.59 Table 5.12: Transfer Expenditure Coefficient Values………………………………P.60
Table 5.13: Impact of a £1 mil change in transfer expenditure on total revenue…....P.60 Table 5.14: Transfer expenditure impact on Television and Commercial Revenue..P.61
Table 5.15: Impact of 1,000 change in average attendance on total and match-day revenue…………………………………………………………………………….....P.62
Table 5.16: Impact of 1,000 change in stadium capacity on commercial revenue….P.63 Table 5.17: Presence of a Local Rival coefficient value on all DVs……………..…P.64 v
Table 5.18 Distance to Nearest Rival coefficient value on all DVs…………………….....P.65 Table 5.19 Presence of a FIFA-determined Rival coefficient value on all DVs….…….....P.66 Table 5.20 Presence of a FIFA-determined Rival impact on Total and Match-day Revenue …………………………………………………………………………………..………….P.68 Table 5.21 Presence of a FIFA-determined Rival impact on Television & Commercial Revenue…………………………………………………………………………………...... P.79 Table 5.22 3Yr Avg Goal Difference with nearest competitor from 1 coefficient value on all DVs………………………………………………………………………………...... ,,,,,,,,,,,P.70 Table 5.23 3-Yr Avg Goal Difference with FIFA-determined rival from 1 coefficient value on all DVs………………………………………………………………………………...... P.71 Table 5.24: Rank Difference from FIFA-determined Rival coefficient on all DVs……...... P.72 vi List of Figures
Figure 3.1 Comprehensive Empirical Model…………………………………….P.37 1
Chapter 1.0 Introduction
1.1 Preface and Motivation
How should we feel about rivalry, and just how healthy is a little competition? Well, the
answer to that depends on what you are competing for. Traditional business literature
emphasized that in markets where competing companies offer the same products to the same
consumers, the more intense the rivalry between competitors, the greater the constrain on market
share and profitability (Porter, 1985). Companies competing for the same customers are in a
fixed ‘pie’ scenario, where the market size remains relatively the same, and consumer choice will
deviate towards one or the other.
Interestingly enough, the same intuition does not seem to apply to the sporting industry.
Worth an estimated $80 billion per year (Kearney, 2012), the dynamics this industry subscribes
to appear to be different. As a matter of fact, the financial metrics data for top European soccer
(football) clubs shows an overrepresentation of clubs with same-city competitors (or rivals):
Table 1.1 Same city clubs represented in Market Value and Operating Revenue Ranks
Year % Club in Same City % Club in Same City in % Club in the Same City (UEFA) Forbes Market Value in Deloitte Operational Rank Rev. Rank 2013 21% 35% 35% 2012 22% 35% 35% 2011 24% 45% 47% 2010 19% 35% 43% 2009 18% 36% 35% 2008 15% 40% 35% 2007 21% 44% 30% 2006 20% 36% 40% 2005 17% 40% 55% 2004 20% 35% 40% AVG. 20% 38% 41% 2
The factors affecting this disparity were not found to be demographic in nature, with cities like Manchester and Dortmund (populations of sub 1,000,000) are overrepresented, while cities like Paris and Berlin rarely feature over the past 10 years. It is also not a function of income that cities with rivals tend to thrive; Milan’s GDP per capita is lower than Rome’s and yet Milan consistently features two clubs, while Rome features one. The effect is also independent of production-side benefits, where limited numbers of clubs on the list share facilities that maybe allow them to focus elsewhere and thus generate higher ROIs.
In sports, the presence of same-city competitor is known as a cross-town rivalry. In the absence of exogenous explanations for this phenomenon, the data summarized in the table above seems to suggest that the presence of a sporting rival actually improves the financial performance of teams. It seems to be the case then, that rivalry increases the total ‘pie’ in a market that is not a zero-sum game. That the size of the total market is increasing in itself is not subject to doubt,
Deloitte’s data shows significant growth year after year in total revenues earned by football clubs, and not just because of the increase in ticket prices; match-day revenues have been comprising a declining portion of a club’s average total revenue in the past ten years (Deloitte,
2013). How significant then, is the role of rivalry on the income of a sporting institution, and how much does it contribute to this growth? Is this effect unique to cross-town rivals, or is it applicable to all forms or rivalry?
When the characteristics of the sporting industry are analyzed, it seems that traditional business views are not necessarily applicable (Neale, 1964). In the unique situation where two competing firms must compete in order to offer the end product to the customer, the demand for that product will impact both said firms (Neale, 1964). Known as the inverted product joint, this influenced academics to argue for the treatment sports leagues (formalized competitions between 3
sporting firms or clubs) as a singular firm rather than a set of separate firms (in keeping with the
NBA, NFL and NHL franchising models, for example), albeit that these companies have
different appeals to consumers and largely acquire their own customers or fans (Sloane, 1971). The
quality of the product on offer to the customer is contingent on the performance of both teams.
Sporting literature characterises the quality of the sporting product through examining
consumer motivations to watch sports. The uncertainty of the outcome, and the excitement that
comes with that uncertainty are significant factors, as is the amount that is at stake; it is logical to suggest that a championship game will generate greater interest than a regular season encounter, and data supports that intuition (Nielsen, 2011). Adding to the stakes and increasing fan affect is
rivalry, which in sporting literature is defined as a state of intense “affective disposition” from
the players, staff and/or fans of one sporting institution towards another (Szymanski, 2008). With
more emotion hanging on the outcome, a rivalry game is more likely to generate consumer
demand (Mason, 1999) (Szymanski, 2008) (Brugging and Roosma, 2011).
However, while examples have demonstrated that rivalry games are in greater demand,
the data presented in the table above suggests a greater; that the significance of these games
(which are traditionally two games at most out of a total 34 games at the very least) is enough to
impact financial performance across the entire year. Do top performing teams with rivals then
draw greater attention that carries forward across the season, or is it purely a coincidence that
teams with rivals are overrepresented in Europe’s football elite? The research questions that this
begets are more specifically posited below:
Do clubs derive financial benefits from having a rival club in the same city?
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In itself, this question posits interesting follow-ups. Rivalries vary in type, as there are
various antecedents to a rivalry (those include historical, political and religious – for more information, refer to section 2.3 in the literature review). The questions posited are whether this effect, if existent at all, is unique to cross-town rivals:
Is this effect present for cross-town rivals, or is it generalizable across rivalries as a whole?
The existing literature pointed the virtues of rivalry’s impact on clubs in terms of
marketability and ticket sales for rivalry games, and highlighted the impact of rivalry on fan
enjoyment and involvement from a consumer standpoint. Much of what makes the rivalry what it
is also embedded in a state of competitive balance; the two rivals have to be performing within
proximity to each other, at least in rivalry matches (Mason, 1999) (Szymanski, 2008). Two
critical factors were lacking: 1. The impact demonstrated was for one particular encounter; in
other words there was no literature pointing to the fact that clubs who have rivalries are perform
better financially 2. There was no specific reference given to cross-town rivalry that leads us to
believe that there is an effect unique to same city rival clubs as opposed to rival clubs as a whole.
One particular challenge to this notion is that the there has not been a method to quantify
rivalry in sports; there is no rivalry metric or measurement, rather it is a state present in fans’
mindset where no measurable correlation between any particular antecedent, proxy or outcome
and that mindset has been found. Therefore, defining what a rivalry is in terms of football would
either require extensive ethnographic research that is beyond the time and budgetary
considerations for this research, and therefore, there was a reliable, albeit limited measure that
can still operationalize this research to test for the impact of rivalry on year end financial
performance. The world governing football body, FIFA, presents rivalries within each country’s
league (for more information, refer to section 1.1). This is limiting from the perspective of
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identifying particular types of antecedents, as the research is now restricted to testing for
geographic and cross-town rivalries, and other forms of rivalry bundled as one. If the type of
rivalry were indeed significant, then other rivalry forms would run the risk of diluting the results.
However, since there is little from the literature to suggest that one particular form of rivalry is
the one that can have this sort of impact on financial performance, the research methodology still remains acceptable.
If the observations in Table 1.1 indeed pointed to a greater picture, and there was a correlation between the presence of a rival (or the presence of a closely performing rival), then the impact certainly extends beyond what is two games in a season that would consist of no less than 38 games for most clubs (for more information on leagues and fixtures, refer to section 1.1).
To address the aspect of competitive balance, it would also be expected that how the rivals perform relative to the other rival (rather than just overall) would be a factor that must be incorporated into the analysis. This points to an obvious gap that this research is trying to fill, and the hypotheses pertain to the states of rivalry and competitive balance:
H1. Geographic rivals will have a positive impact on year-end financial performance
H2. FIFA classified rivals will have a positive impact on year-end financial performance.
The hypotheses above break down into more specific aspects pertaining to competitive balance (refer to Section 4), and geographic rivalry takes on two forms: cross-town rivalry where teams are located in the same city, and rivalry by distance (nearest team, another rivalry antecedent – refer to section 2.3), and the rivals as pre-classified by an authority on the matter.
In order to assess the impact of sporting rivalry on a club’s financial performance, it is important to put rivalry within context to other factors proven to impact a club’s financial
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success. Literature has identified a group of factors that include socio-demographic elements,
aspects of a clubs operational strategy including stadium capacity, ticket pricing, the type of
personnel recruited as players or staff, and aspects of the club’s on field performance (for more information, refer to the empirical framework in section 3). These factors can only corroborate to particular revenue streams (refer to empirical framework in section 3), with the scope of socio- demographics for example only relevant to revenue streams accrued from local sources. When direct variables could not be found, proxies were developed to measure aspects fundamental to a club’s financial success.
The methodology of research selected was a multiple linear regression (refer to section
4), which has been used previously in sports to measure the impact of aspects pertaining to clubs’ financial success on year-end metrics such as ticket sales or revenue (Sloane, 1971)
(Staudahar, 1986). The regression would have to incorporate the aforementioned success factors and a club financial performance metric (year-end) as a dependent variable.
The measurement of impact on financial performance was restricted to a club’s operating revenue, with data provided from Deloitte’s Money League, an annual survey that presents
Europe’s top 20 clubs by revenue from football related operations. The selection of what is a non-random dataset (i.e. The elite institutions or the top performers in the area) posits a restriction on the generalizability of the results; should the data show an effect on revenue, it will be only applicable to the elite performing clubs of the European football industry rather than across all conditions for all countries. The way Deloitte breaks down revenue streams (refer to section 4) means that the impact of rivalry can be tested on every operational facet of clubs’ revenue generating activities: commercial income, television income and match-day income
(Deloitte, 2003).
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There were six regressions deployed for each revenue stream DV; one that included FIFA
FIFA-determined rivalry elements, another that incorporated geographic rivalry, and one that
incorporated both. Those three combinations were deployed with or without their respective competitive balance variables (the results between the rival teams – for more information, refer
to section 4).
The results found that FIFA appointed rivalries (or thus, rivalry in general), had a
significant effect on revenue. The competitive balance variables signified a significant effect of
past rivalry games with a closely performing rival on revenue, where the greater the proximity of
the result between the teams, the greater the year-end revenue accrued.
What these results point towards is that rivalry does indeed have an impact on clubs’
financial performance, at least with respect to Europe’s top performing football clubs. The
competitive balance variable in particular suggests that investing in a rival’s well being could
return better results than investing in one’s own if the disparity in the performances is too great,
given that competitive balance is a healthy contributor to revenue.
Theoretically, these results show a greater need to investigate rivalry in sports on much
greater scale. While it supports the intuition that rivalry does indeed contribute to market growth
of the sporting industry, more research is required to validate the fact and ascertain conditions in
which this is applicable. They point to indicators that rivalry in sports can lead to counter-
intuitive results that are in contrast to business strategy literature, and further demonstrate an
extension of rivalry literature into the sporting arena. More than anything however, this points to
the urgent need to measure or quantify sporting rivalry in order to best operationalize it.
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Pragmatically, such a finding would leverage club investment decisions and operational
policy, as well as governance policy from the league – where disproportionate fixture allocation can be viewed as favourable in revenue-sharing leagues (refer to section 1.1). It also leverages investment decisions on the behalf of consortium takeovers (a common feature in the football business landscape in recent years), and further informs sponsor and broadcaster investment decisions.
The section below introduces football as a sport in the context of regulations and governing bodies, and provides further insight into its league structures and regulations, which the empirical framework for this research considered in its application. The literature review
(Chapter 2) previews the relevant academic work on the subject matter, and extends into the empirical framework (Chapter 3) that previews factors affecting the financial success of football clubs. The theoretical framework also explicates the inclusion of rivalry as a testable hypothesis, and qualifies the methodology used in Chapter 4. Chapter 5 previews and explicates the study results, while Chapter 6 analyzes the contributions and limitations of this dissertation, along with recommendations for future research.
1.2 Introduction to Football
Football, or soccer as it is known in North America, is a team sport that is played with two competing teams with eleven on-field players that play against one another over the course of ninety minutes with the target of scoring more goals into the opponents’ net in order to win the game (FIFA, 2013). It is the most popular sport in the world, with 3.3-3.5 billion fans worldwide (Stølen, Chamari, Castagna, & Wisløff, 2005) (Deloitte, 2014).
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Football events regularly feature on the top of the list of viewed television broadcasts
(Deloitte, 2010), soccer clubs and players frequent the most valuable sporting brands and highest
earning athletes lists (Forbes, 2014) (Forbes, 2013), and the FIFA World Cup, a once every four
years competition where national soccer teams compete against each other for what is the
world’s most renowned and prestigious team soccer award is the most anticipated and popular
sporting event in the world (FIFA.com, 2014). Football’s major competitions are comprised of
national competitions, which include global competitions such as the World Cup and the
Confederations Cup and continental competitions such as Europe’s EURO competition and
Africa’s AFCON (FIFA, 2014). However, soccer also has club competitions that are exclusive to
the domestic area, and continental club competitions that group top performing club teams from
each country to face their continental counterparts (UEFA, 2014).
Brief History and Background
The set of laws that the most defining in the formation of association football as we see it
today were primarily influenced through the efforts of the London-based Football Association in
1863 (FA, 2007). The formation of association football, where clubs would play one another in
either one-off exhibition games, or compete in tournaments with diverse competitive regulations
and structures ensued (FA, 2007).
The first structured local league was developed in England, widely known as the home of
football. The league format entailed that teams would play one another home and away with
varying points awarded to wins, draws, and typically none to losses. The rankings of the clubs
and the league winners would be determined by the points accrued at season’s end.
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Today, almost every country or region has its own professional soccer league
competition. They typically operate under the umbrella of a continental governing body and the
international football governing body, Fédération Internationale de Football Association
(commonly known as FIFA) (FIFA, 2009). Each competition has its own governing regulations,
varying tiers and structures, but most conform to the international standards of awarding points
based on results and measuring rankings subsequently.
League Structures and Models
Most sporting associations or leagues utilize a league structure or model in which teams
compete against each every opponent in the same league multiple times (at ‘home’; whereby the
one team plays host to the other and the event is held in the stadium in which the team is based
and ‘away’ which is held at the opposing team’s turf) and rankings are made as a function of the
wins and draws accrued, with differentiating factors on points levels including head to head
records and goal differentials (goals scored minus goals conceded). Various forms stem from that
basic model, where teams either proceed to a knockout round format of varying types; opponents
can either play a best-of series, where the first team to accrue a pre-agreed number of wins
proceeds to the next round, a one-time game with the winner proceeding, or a home and away
format where the aggregate score-line is computed to determine who progresses – and knockout
formats are typically ultimately determined by the final game(s), where the last remaining two
teams in the competition are paired together with the opportunity of winning the honors in
question (Sloane, 1971).
The relegation and non-relegation structures are another point of difference; in relegation
league models bottom ranking teams at each season end are relegated to a lower tier of the
league (the England based Football Association, for example, has 4 tiers of professionally
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competitive football) and top performing teams in the lower tier are promoted to take their place.
This model is more popular in European sports leagues and less utilized in North America
(Zimbalist, 2003).
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Chapter 2.0 Literature Review
2.1 Competition & Competitive Cooperation in Business
Competition in an industry continually works to drive down the rate of
return on invested capital toward the competitive floor rate of return, or the
return that would be earned by the economist's "perfectly competitive"
industry. (Michael E. Porter,1985)
The majority of business strategy literature that touches on competition suggests that
competition is a negative influence on company’s financial performance. In contrast to the
situation of a monopoly, where a supplier can sell a product at a much higher price than their
marginal cost (Colander, 2004), competitive businesses will comparatively have their
profitability. Competition is one of Porter’s five forces that influence strategy, with Porter
postulating that competitive intensity varies negatively with profitability (Porter, 1979). This
claim is upheld and treated as an assumption (Schmidt, 1996). Competitors are viewed as a
constraint on the revenue of a business and its probability of survival, and a dynamic equilibrium
between competitors is constrained for the individual competitor by the presence of competition
(Henderson, 1983). Competitors typically ‘jockey’ and venture into exchanging strategic blows
to one another, with strategies and tactics such as price wars, entry barriers and advertising
campaigns deployed in order to gain an edge over the competition (Porter, 1979).
The state of the competitive system has it such that an individual competitor thrives by
creating and sustaining advantages over the competition (Henderson, 1983). The principle of
sustainable competitive advantage, introduced and forwarded by Michael J. Porter, is based on
the ability to provide a unique and sustainable offering utilizing a variety of resources. The
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objective of this offering is to ensure that the competitor cannot match it, and therefore it
provides a unique market access to a customer segment that desires that very offering.
Competitive business practices are generally geared with the aim of nullifying the relative
weaknesses of the firm to its competitor and creating and sustaining comparative strengths that
provide unique value to the customer, or by providing lowest possible cost (Porter, 1985).
While literature on competition in business is abundant, there is also a significant stream
of literature on cooperation between business rivals. Facility sharing and cooperation along the
value chain was found to assist companies from a cost cutting perspective. Sharing facilities for manufacturing, for example, reduces overhead and step costs in the manufacturing process, thereby increasing the profit margin (Ghoshal, 1987). Cooperating between competitors along the value chain has been proven to improve revenue generation. Co-opetition, a term coined to describe competitive cooperation, is a developing concept that encompasses competing business entities sharing their resources in order to generate a better bottom line. Airline companies have formed alliances centered on customer loyalty programs, where rewards points are transferrable from one airline to another; this is done with the purpose of expanding the reach of flight offerings and incentivizing consumer loyalty towards the participating companies when selecting a service provider for a particular destination (Zea & Feldman, 1998) (Lederman, 2007). In similar vein, hotel chains and restaurants aligned their interests through the usage of joint reward systems that incentivize consumer choice towards a particular set rather than the entirety of the market (Al Khattab, 2007).
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Spatial Competition in Business
The existence of a same-market competitor has been found to constrain profit by virtue of
increasing competitive intensity (Porter, 1979). It is viewed that a competitor that is providing
the same offering within a given market will negatively impact profitability because it provides
consumers with multiple alternatives at similar transit costs for the producer (Henderson, 1983).
Early spatial models in business, such as the Hoteling model (Hoteling, 1929), introduce the idea
that businesses will take competitive measures in terms of location when they are catering to a
market in which a competitor exists. Traditionally the Hoteling model is a 2-player model, but
extensions have been made to simulate Hoteling’s model with more than two players. The result
is the same, with an equilibrium price point impossible. Porter’s logic on the competitive forces
and subsequent work with regards to business competition is in good agreement with these
findings (Porter, 1979).
However, an entire stream of research focuses on the cooperative and network benefits of
businesses in the same industry locating within proximity of one another. The concept of
companies in the same industry locating within proximity was first discussed by Alfred Marshall
in his book Principles of Economics (Marshall, 1890). Marshall demonstrates that businesses that
are involved in the production of a similar product were located in geographic clusters (Marshall,
1890). He argued that in conditions where companies are then able to optimize efficiency and
reduce transaction costs, with the recruitment of skilled labor and the exchange of knowledge
becoming significantly easier and faster (Marshall, 1890). The underlying assumption behind
this was that companies were small, produced minimally differentiated products, and were highly
competitive (Marshall, 1890). The principal of minimal differentiation was forwarded by
Boulding (Boulding, 1963), but the context focused on competitive intensity rather than the
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network benefits gained. However, a subsequent stream of literature in geographic economics
and business strategy were in good agreement with Marshall’s findings.
From a surplus theory perspective, agglomeration literature finds that there is significant
network output merit to industry players locating within proximity (Papageorgiou, 1979).
Advantages of agglomeration include lower transaction costs and an increased rate of knowledge transfer, with externalities external to the firm including an increased production-side network benefit from locating within proximity to same-industry players (Henderson J. V., 2001)
(Gordon & McCann, 2000). The scale externalities vary directly with the number of industry
players within the area (Krugman, 1993).
Key to the assumptions behind agglomeration economies and cluster theories is that the
markets being competed for extend beyond the immediate market place in question – which is
similar in the sporting context (Baimbridge, Cameron and Dawson, 1996). With the growth of international economies, and the transit costs decreasing rapidly, the location of a business has become less impactful its profitability (Porter, 1990). In another sense, spatial competition’s
downward pressure on profit was playing a less prominent role for bigger businesses in particular
(Porter, 1990). Businesses along the same industry cluster together (Porter, 1990) and derive
substantial benefits in doing so. Clusters, or agglomerations in geographic economics tend to
profit from the end of the production function. The network effect of adding a company to the
cluster implies a reduction in transit costs, a downward pressure on other companies along the
value chain and the gravitation of other areas of the production function (Porter, 1990).
Porter cites the California wine industry as an example of a cluster, where companies
along the value chain of production locate within proximity and extrapolate an increased value
from being located within proximity together (Porter, 1990). Krugman demonstrates the network
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effects gained from the US manufacturing belt, which was concentrated in the Northeast and the
Midwest, and still maintained approximately 65% of manufacturing employment in the United
States (Krugman, 1991).
Given that they would be competing for the same markets irrespective of where they
locate, locating the same area allows businesses to exploit pre-existing infrastructure that better
allows for the production function (Porter, 1990). Consider the example of Silicon Valley, where
the tech industry personnel are gathered in close conjunction along with several companies,
investors, relevant media outlets and research institutions. Technology giants will gravitate
towards the area given the increased access to production inputs and collective cooperation with
their competitors over the value chain (i.e. applying pressure on value chain producers).
The extensive work demonstrating the benefits of competitors locating within a same
market space (especially when the market they are catering to expands beyond the spatial area in
which they are located) is focused on value chain exploitation and production side network
benefits. The sport industry it appears, subscribes to different network dynamics. The
disproportionate representation of same city clubs in the Deloitte Money League Revenue rank
indicates that the benefit gained is not from a cost saving or transactional efficiency perspective.
Clubs recruit players and scout competitively, sell tickets at different price points through their
own vendor systems, have separate endorsement deals with global sportswear manufacturers and
recruit personnel from a global marketplace. While there is some evidence of facility sharing, it
does not account for revenue increase (it would primarily point towards cost savings, and while it
can be argued that the increased efficiency allows for greater investment in revenue generating
activities, but less than 10% of Deloitte’s money league clubs with same-market competitors
actually share facilities with that competitor).
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The positive network effect of having an additional supplier, therefore, appears to be
independent of value chain gains; the cluster benefit derived in this industry is not one derived
from the presence of infrastructure that supports the production function. If there exists a positive
network effect from having a club operate in the same market, it would be derived from the
demand side. This becomes a more feasible explanation upon examining how sporting rivals
operate.
2.2 The Curious Case of the Sporting Industry
Unlike most industries, the product offered by the sporting firm is co-dependent on its
competitors. Given that most sporting events involve a match-up between teams or individuals,
the quality of the product on offer is also dependent on what the rival offers. It is for this reason
that the principle of the inverted joint production, where two separate processes are combined in
order to produce a singular product (Neale, 1964). The nature of this intertwined connection
means that a club cannot deliver a quality offering without its rival, and implies that demand
would then be contingent on the joint success of the clubs in their respective processes. In
leagues that size from 16-20 teams (in addition to competition in continental competition), there
exists the possibility that every two competing teams are treated as rivals. It is important to
distinguish rivals in this context, where a special animosity is derived between two teams and fans feel a particular hatred towards the opposing team and its fans (Lee, 1985).
It follows intuition then, that the presence of rivalries, whether in a spatial context or a
rivalry context in general, would increase consumer utility. The following sections detail what
the sporting institution offers by way of value to customers, and the role that rivalry plays in that
value offering.
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The Value Offering of the Sports Organization
Like other forms of entertainment, sport offers a utopia, a world where everything is
simple, dramatic and exciting, and euphoria is always a possibility ... Sport
entertains, but can also frustrate, annoy and depress. But it is this very uncertainty
that gives its unpredictable joy their characteristic intensity. (Whannel, 1992
p.134)
The unique value proposition of the sporting product is entertainment (Whannel, 1992), which is derived from the unpredictability of the outcome of the events, and fans derive utility from the excitement that is produced from watching the competitive matches (Euchner, 1993).
The stream of psychological literature on the matter suggests that utility can be derived from
identification with a team, sport, and an affiliation with the consumption community that is
comprised of the other fans (Mason, 1999) (Guschwan, 2011) (Sanford & Scott, 2014). The
value offering of a sports club can also apply to those who identify with a nationalistic or
regional loyalty, whereby one derives utility from the support of their local club or national team
in sporting events (Guschwan, 2011). Given that the intensity of emotional responses is an
important component of how fans derive utility from sports, games of substantial importance
increase the utility of the viewing consumer (Sanford & Scott, 2014).
From the above, it can be inferred that the greater the intensity of the competitive state or
rivalry between the two teams (firms), the greater the consumer utility, and existing literature
support this conclusion (Whannel, 1992) (Osborne, 2008). The role of rivalry then, is to add to
the excitement and the uncertainty of the outcome, and what is at stake.
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2.3 The Impact of Sports Rivalries on Demand
There is a vast body of literature that attempts to capture the role of rivalry in the experiential utility derived by the consumer. A major part of the literature attempts to examine the rivalry from a demand perspective, and argues that rivalry stimulates local demand. Sloane argues that rivalries have a positive impact on the demand of the product, and cites empirical evidence that at least suggests that football clubs in multi-rival cities are “at least well accounted for in terms of population heads” (Sloane, 1971). It has also been suggested that firms invest in what is known as “rivalry capital” of their athletes, in an attempt to nurture and organizational culture and subsequent behaviors that would increase the intensity of sporting rivalries, which is believed to increase demand for the sporting product (Osborne, 2008). Research assessing the intensity of rivalries has also been conducted with independent variables comprising of ticket sales (indicating that rivalries would stimulate demand) (Sanford & Scott, 2014), and has found that rivalries have a positive impact on ticket sales (Furtonato, 2006) (Paul & Weinbach, 2013)
(Lemke, Leonard, & Tlhokwane, 2010) (Bruggink & Roosma, 2011) .
There is a significant stream of literature aiming to understand, measure and quantify sporting rivalries. The reasons behind the creation of rivalry vary significantly; rivalries have
formed with respect to performance success (Dreyer, 2013). The following section aims to
capture this literature in the context relevant to what this paper seeks to contribute; the role of
rivalry in sports enhances a firm’s financial performance.
The utility derived by spectators from rivalry is further increased by media amplification, the point where the matter becomes a ‘chicken and egg’ scenario; fans identify with rivalries without apportioning significant attention to their causes or nature. Consider the example of Real
Madrid and Barcelona FC, where the two football clubs share a deep ideological split; Barcelona
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is a club synonymous with the region of Catalunya, an area seeking independence from Spain, while Real (Spanish for Royal) Madrid is synonymous with General Franco’s regime and derives much of its historical successes to his preference for the team. El Clasico, the famous derby
between these two teams, is now centered on the fact that Real Madrid and Barcelona are age old
enemies in constant competition for titles (Szymanski et al, 2008). This is particularly important in the context of the modern sports era, as the antecedent to a century old rivalry may be an important component for a broadcaster’s decision to allocate air time (Refer to Appendix A), but the affiliations of the international fan base – which comprise the bulk of television revenue
(Baimbridge, Cameron & Dawson, 1996) are made independently of their sympathies to such antecedents (Weis, 1986). Nevertheless, the presence of a rivalry, and a network effect of the fan
base that projects its intensity, is of significance in determining the broadcaster’s decision as a consumer (Refer to Appendix A).
It is found that motivations for team rivalry vary from geographic/spatial to political and historical, to performance and competitiveness for honors and even playing styles. The literature classifying rivalry antecedents or conditions that facilitate a rivalry can be classified into three major streams.
Types of Rivalries
Political
Rivalries have formed between teams based on the historic identity of what they represent (Raspaud & Lachheb, 2014). Deeply imbued rivalries have emerged as a result of economic competition between their regions (Dreyer, 2013), a class-based comparison (Raspaud
& Lachheb, 2014), and even conflicting religious affiliations for the team and its respective fan
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base (Baimbridge, Cameron, & Dawson, 1996). Older clubs tend to be at the center of rivalries
due to the respective histories and encounters (FIFA, 2014).
Performance Rivalries
Contextualizing rivalries in comparative performance contexts has been a prevalent issue
in sports literature. However, the literature focuses on the comparative performance between the
two sides. A gulf in quality between one competitor and the other does not constitute an absence
of rivalry, but since quality is a central construct to the utility derived from the sporting product
it is important to understand that comparative quality is an endogenous construct to the intensity
of a rivalry. Results that are closer to each other, running on the assumption that fans seek a
slim/last-gasp victory where the outcome is uncertain (Whannel, 1992) (Euchner, 1993) (Lee,
1985), between teams ranked closer together are measures of quality, which is critical to the
relevance of a rivalry. The presence of a rivalry as FIFA and fans identify them, however (FIFA,
2015) is still central to this construct as it appears on the Deloitte observations noted in the
introduction.
Spatial Rivalries
Spatial rivalries are of particular interest to European football due to the differences in
fan preferences between Europe and North America. While North American sporting models
exhibit limited regional segments, European fans are more prone to regional affiliations and
loyalty as a result of their competitive leagues being divided by country (Fort & Fizel, 2004). As
a result, literature applying to rivalries in a spatial context is particularly relevant to European
sporting models.
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There is a growing body of literature identifying the role of rivalries in the construction
of self-identity and the positioning of what sports consumers call ‘home’ (Kraszewski, 2008)
(Guschwan, 2011). The embedded sense of regional loyalties and inter-regional discourse is
evident in sporting literature (Lee, 1985) (Mason, 1999) (Euchner, 1993). The exposition of fans
and identification with one team or the other is central to intensifying hostility between rivals, and therefore it follows intuition (and FIFA’s evaluation) that football clubs operating within the
same league are more subject to develop a rivalry (given the frequency of encounters).
Furthermore, there is another stream of literature found to demonstrate the role of spatial rivalries in the context of fan network effects. The greater the proximity of fans and the higher the potential for their interaction, the likelier it is for a rivalry to emerge (Krazewski, 2008).
2.4 Bridging the Gap
This research is attempting to fill an obvious gap. Extending Porter’s clusters and geo- economic models to the sporting industry, we find that there is limited production side benefit that could be gained by a sporting club (and while it is possible to increase that, the status quo is that the benefit derived is not a production side one). A demand-side network externality that positively impacts suppliers would be identified and would quantify the value of industry clustering for sports. Furthermore, the role of a rivalry in stimulating demand beyond its geographic scope would also be identified, which would be of particular value in conjunction with emerging economic literature around sport and its globalization. A measurement of the impact of club rivalry on financial performance would significantly contribute to sports and business strategy literature in that regard.
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2.5 How to Measure the Impact of Rivalry on Club Financial Performance
It therefore follows that a club having a rival will have a positive impact on its financial
performance. Given the breakdowns of rivalry constructs above, it also follows that spatial rivals
(by definition: teams competing in the same league within the same local market) would have a positive effect on financial performance. That this effect is independent of a value chain benefit, it would have to be on revenue rather than on profitability.
H1: Spatial rivals will have a positive effect on year-end revenue.
It also follows intuition that rivalries with precedent antecedents besides space (which
can be any collection of causes ranging from historical, economic, political, etc…) should have a
positive effect on economic performance. Therefore, a rivalry with a greater proximity in
performance over a past period (in more detail in Methodology) should have a positive effect on
club financial performance as well:
H2: Performance based rivals will have a positive effect on year-end revenue.
Basic Methodology
To predict the role of rivalries on financial performance, running regressions that model
the financial performance of clubs would be central to identify predictors of club success. For the
purposes of this paper, variables will be incorporated to measure the role of spatial rivalries as a predictor, and variables to measure the role of performance based rivalry. To isolate performance
from quality, we would compare Team A to its rival Team B and then to the most proximate on-
field performer on the list (Team C). Running variables simultaneously will satisfy two criteria:
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The quality construct will still be significant to rivalry. This is important because literature
identifies that a rivalry with a huge gulf in quality would be less salient to fans (especially global fans).
The empirical framework below models the financial success of a football club based on pre-existing literature and highlights predictor variables for revenues in sporting institutions.
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Chapter 3.0 Empirical Framework
This section builds a conceptual model based on the literature review that links the
different consumers of the sporting product to the revenue streams generated through the club’s
operations, and to the demand drivers that impact the revenue streams. The model specifies the
scope of each revenue stream (whether local or global) and its corresponding demand drivers.
This is done with the aim of isolating impact demand drivers into the specific revenue streams
that they impact, which consequently creates a framework that tests for presence of a positive
network effect derived from an existing local competitor, and the specific mechanism through
which it operates (i.e. which revenue stream it impacts and how it can overcome the revenue
constraints typically imposed by the presence of a competitor in the same local market).
3.1 Financial Success Metrics
Most major soccer clubs operate as either private or public limited companies (Section
1.1: Football as a Business), with the major notable exceptions being the MLS, which utilizes a
franchising system similar to that adopted by the NBA, MLB and NFL (Zimbalist, 2003).
Therefore, typical valuation methods such as Market Value, Operating Revenue and
Profitability are applicable when measuring the financial success of soccer institutions. A
number of corporations release reports measuring such variables; Forbes annually releases a
ranking of the top soccer clubs by market value measured in US Dollars (Forbes, 2013), Deloitte
ranks the top clubs by ‘Revenue from Football Operations’ (Deloitte, 2013) and Brand Finance
ranks clubs through Brand Value (BrandFinance, 2013).
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3.2 Revenue Streams
3.2.1 Classifying Revenue Streams
The metrics listed in the previous section are a reflection of business activities that feed
into cost and revenue, including combined off and on-field activities that moderate exposure and,
subsequently, brand value, merchandising strategies and agreements with affiliates.
Revenues amassed by sports clubs can be categorized into four major streams (Mason,
1999)
Fans: This is in reference to match-day revenue, which is amassed from ticket sales, the
on-site purchase of memorabilia and other complimentary products and club memberships.
Television: Comprises of the individual broadcasting agreements, in-house club
channels, and revenues accrued from collective broadcasting agreements (Mason, 1999).
Sponsorship: The endorsement and advertising agreements in place between the club
and entities that offer financial compensation in exchange for association with the club and
exposure through the club’s customer-facing operations – this includes and is not limited to: in-
stadium advertising, advertisements on jerseys, the standing position as the manufacturer of the
club’s training and match-day uniforms, advertising in press conferences and television.
Government/Community: The revenue accrued from government grant, tax credits and
co-operative projects to improve upon the local community and develop grass-roots sporting
projects.
There has been a call to incorporate modern media channels (specifically, the online
space) into a separate revenue stream (Ioakimidis, 2010). Raymond Boyle and Richard Haynes’ findings suggest that the facilitation of viewership in online spaces requires an incorporation of
27
an online medium into revenue streams generated from differing activities such as using the
online store to purchase memorabilia and fan-wear, online membership and access to exclusive
club content, viewing competitive events through online mediums, and the deployment of
sponsorship initiatives on club websites and fan pages (Boyle & Haynes, 2002). However, given
that these activities are ultimately sub-components of what is traditionally known as a revenue
aggregation process, adding the online space as a revenue stream of its own adds little to the
purpose of this paper, and is arguably unproductive. The activities will classify under the revenue
streams mentioned above, and subsequently are constituents of the overall empirical framework,
with the applicable scope (see below).
3.2.2 The Scope of Revenue Streams
As mentioned in the literature review, the revenue streams of football clubs are no longer
reliant on a local fan-base. The growth in media dispersion and the global nature of soccer as a
sport has created entirely new financing models for European soccer clubs (Andreff &
Staudohar, 2000). In fact, it was found that the emergence of satellite broadcasting has created
large revenue potential from international broadcasting that typically overshadows the role of the
local fan base (Baimbridge, Cameron, & Dawson, 1996). Deloitte’s research found that
sponsorship demands increase with televised exposure, and that the growing international
exposure has highly correlated with an increase of continental and global sponsors (Deloitte,
2010). Forbes reports similar findings in its analysis of the market value of soccer clubs, whereby it links the role of televised revenue and sponsorship closely to the market value of the sporting institution at hand (Forbes, 2011).
It is therefore important to classify revenue streams by the scope at which their contributing activities can achieve; fans’ purchases, for example, is a predominantly local
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activity (with match-day tickets, on-site purchases constituting a need for physical presence on
the location, at which point, international purchases are restricted to tourists and visitors, which
would not compose a significant portion of the revenue stream in question). The same applies to
government and community revenues; the governments that would grant the club are generally
of a local nature (in addition, that revenue stream would not be significant enough to validate the
materiality component of the Generally Accepted Accounting Practices). Therefore, the scope of
the above revenue streams is local in nature, and is comprised in the
However, with substantial research emphasizing the role of television revenue as a factor
in club income (Baimbridge, Cameron, & Dawson, 1996), and the subsequent global nature of
sponsorships that ensue, the scope of broadcasting revenue is global, as it translates into revenue
from global audience, and is mostly dependent on populations that reside outside the local area in
which clubs are located (Mason, 1999) (Baimbridge, Cameron and Dawson, 1996). Pragmatic examples include the fact that Fox Sports, a US-based sports channel that is part of the FOX conglomerate, broadcasts games from the Spanish La Liga, the European Champions League and the Italian Serie A and Al Jazeera, which broadcasts matches from all major European soccer competitions to subscribers in the Middle East (FOX) (AlJazeera BeIN Sports).
The same applies to sponsorships, which as postulated by Deloitte and Forbes’ findings, are increasingly global in their scope; Manchester United is affiliated with US-based sporting goods manufacturer Nike, Swiss watchmakers Hublot, Turkish Airlines, DHL, Chevrolet and
Aon Insurance to name a few, Barcelona advertise Qatar Airways on their jersey, Arsenal’s home stadium is known as the Emirates (the UAE based airliner also have their logo at the front of the jersey of Italian side AC Milan), to name a few examples (Deloitte, 2013). Therefore, it becomes evident that television and sponsorships are capable of generating revenue that is
29
beyond the local fan-base. Table 1 outlines the classification of revenue streams and the
subsequent scope under which they fall.
Table 3.1: Revenue Stream Scope Revenue Driver Market Scope 1. Fans Local 2. Broadcasting Local, National, Global 3. Corporate Sponsors Local, National, Global 4. Government, Community Local, National
3.3 Demand Drivers
As demonstrated by the literature review and the section above, the sporting product on offer for the customer can be classified into four major streams of revenue. Revenue is a function of demand (Colander, 2004) , and thus drawing a relationship where demand drivers (be it drivers to attract the consumer or to meet the demand in itself) are important constituents in determining revenue. Classifying demand drivers is important in providing a structure to the club revenue streams, and also in determining the role that is played by each demand driver and its corresponding revenue stream.
3.3.1 Classifying Demand Drivers
Not all demand drivers can be determined by the club. There are other influences at play that may impact demand that are not relevant to the club presenting the product/service; and identifying which ones are within the control of the club and which ones are not helps not only identify what actionable insight can be developed from this paper, but further determines what of can be ruled out as a cause behind the success of clubs that operate within the same local market.
Demand factors can be exogenous to the control of the institution, whereby the institution
has very little to no say in determining them. These factors can be a byproduct of the area in
30
which the club operates or due to the rules and regulations imposed upon the club by the
governing bodies of the competition(s) in which it competes, or contingent on the general
behavioral attitude towards the product or service offering.
External Demand Drivers
We define external demand drivers as givens that are outside the control of the club itself,
and cannot be directly changes as a result of management preferences. External demand drivers
are divided into several categories:
Psychographic/Demographic Demand Drivers
The demand drivers that can be classified under the psychographic and demographic
category are ones that pertain to the relevant area surrounding the club’s main offering, the
match-day events that are in the local area. Those drivers characterize the local market and
provide pointers to the presence of a demand for the product on offer by the club. The regional
demographic and psychographic demand drivers are: the population of the city in which the club
is located and the city’s average income.
The above drivers that act as determinants on demand for the club’s offering. These
factors cannot possibly be determined by the club, and while a big club in a small city can
possibly impact the average income (Siegfried & Zimbalist, 2000), and potentially skew it depending on the size of the city, it is impossible for a club to control for these factors, albeit that it can impact them. The support for the sport can be partially impacted by marketing strategies and over time (James & Ross, 2004), however once again this is not a simplistic decision mechanism available to the club’s directors in order to so, and thus cannot be within the control of the club.
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It furthermore stands to sense that a club would locate in an area with pre-existing
support for the sport as well as the population and income level that would uphold its facilities
and generate revenue that would comfortably offset the cost of running an institution of its size
(Kelly, Hoffman, & Carter, 1987), and therefore while these factors may change drastically due
to economic forces outside the control of the organization, this model will be tested in a uniform
manner that would account for such changes in the overall data set.
Competition-Dependent Demand Drivers
This section of the model refers to the actions undertaken by the clubs in competition
with the club and how they accordingly influence demand for the product or service offered by
the club in question. Clubs are granted the capacity to undertake executive actions that can
influence the well-being of the club, and yet it has an impact on the competition as well
(Whannel, 1992)
Operational Strategy of Competing Clubs
The operational strategy of the competing clubs will subsequently impact the level of on-
field success that they enjoy, and thus the level of competitiveness of the league overall (which
therefore partially determines the demand for the product, as it hinges on the uncertainty of the
outcome (Whannel, 1992)). The marketability of competing clubs will also determine how
desirable the end product (which is not only contingent on the offering of the individual club in
question, but the opponent(s)), and thus will also act as an according driver for demand.
League Dependent Demand Drivers
The last of the exogenous drivers of demand, demand drivers dependent on the league in
which the club participates in are factors that would influence overall demand for the individual
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club’s offering and yet also outside the control of the club (Sloane, 1971). As outlined in the
literature review, clubs participate in league/tournament style competitions that are governed by
an association which sets out rules and regulations for the competition, determines fixture dates,
and, depending on the level of involvement, can disburse revenue based on placement in the
competition or to incentivize clubs (Mason, 1999).
The literature review examined different types of league structures and the differences in
guidelines that can be imposed upon the participating clubs, which range from stipulating
competitive rules and arranging match fixtures, to a more comprehensive management strategy that governs mass broadcasting agreements and revenue allocation to the participating teams
(Refer to Appendix B). These factors are also outside the club’s individual scope of control and
are decided on by the participating club in the league’s tiered structure (Mason, 1999).
Demand factors pertaining to the clubs’ own activities can be viewed as endogenous
demand drivers. Such drivers are fully within the control of the club, and can be manipulated as
seen fit by the directors or managers. These drivers will subsequently influence the value of the product or service of the club to the consumer, and subsequently the willingness to buy the product.
Club-Controlled Demand Drivers
Demand drivers that are considered endogenous are within control of the club’s executive setup, and can be changed in order to influence the club’s well-being. Some of those factors have subsequent influences on competing clubs within the same league (refer to section 1.3.1.1.2).
These drivers are what the club essentially does in order to stimulate demand and create revenue streams. The endogenous demand drivers are the club’s operational strategy, merchandising and
33
memorabilia, the ticket prices charged by the club, and the stadium capacity (which arises from
the club’s decisions).
Operational strategy refers to expenditure on players and facilities, sporting and business
decisions that influence the well-being of the club. Merchandising and memorabilia refers to the
decisions on the marketing mix executed on the merchandising aspect of the club’s business,
which includes but is not limited to official jerseys and club wear, official match programs and
souvenirs, print and media items. Controlling the pricing of season and match-day tickets will
influence demand depending on the price elasticity of demand for the product, which is in part a
function of disposable income and support for the sport (Colander, 2004).
Co-Dependant Demand Drivers
There also exist demand drivers that are dependent on both the club and external forces
such as the clubs with which it competes and the rules and regulations of the
league/tournament/association under which it offers the products and services. These factors can
be partially influenced by the decisions of the individual clubs and yet are also contingent on the
decision making of the competitive clubs and the league association as a whole. The Co-
Dependent demand factors are as follows: On-field performance (where the club’s ranking and
achievments depend on both the club and its competition), the competitiveness of the league as a
whole, and the rivalries that emerge which are also a byproduct not just of the club’s affect, but
that of the competition.
While sporting decisions within the club’s control will have a direct influence on on-field
performance, similar decisions utilized by the opposition will influence the effectiveness of the said club’s strategies. This in turn has an impact on the competitiveness of the league, but the
34
competitiveness in itself (not just how well the club in question performs) is an important
constituent in the demand for the overall product, which as mentioned in the literature review,
hinges in large part on the uncertainty of the outcome (Whannel, 1992). Another important factor in the impact of demand for the sporting product, in conjunction with competitiveness and club performance, is rivalries with competing opponents (refer to literature review). This is important in investing an emotional output that combines with the uncertainty of the outcome in order to create a value offering (Whannel, 1992).
Table 1.2 summarizes the demand drivers incorporated into the model and their classification within the realm of the club’s control.
Table 3.2 Classification of Demand Drivers
Demand Driver Category Classification
Population Regional Demographic/Psychographic Socio-Demographic
Disposable Income Regional Demographic/Psychographic Socio-Demographic
Support for Sport Regional Demographic/Psychographic Socio-Demographic
Competitor Competition Dependent External Operational Strategy
Rules and Fixtures League Determined External Pre-Existing League Determined External Agreements Membership League Determined External Conditions Club Operational Club-Dependent External Strategy Matchday and Club-Dependent External Season Ticket Prices Stadium Capacity Club-Dependent Club-Dependent Club Performance Cross and Semi-Dependent Co-Dependent Competitiveness Cross and Semi-Dependent Co-Dependent Rivalry Cross and Semi-Dependent Co-Dependent
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3.3.2 Scope of Demand Drivers
The revenue streams generates by the sporting institutions are dependent on demand, and
thus subsequently the demand drivers or determinants. Therefore, it follows that demand drivers
would have their own scope, meaning that that each driver of demand has a sphere of influence
defined by the type of purchase that arises from the demand.
The demographic and psychographic demand drivers (refer to table 1.2) impact local
demand for the sporting product. Factors such as population of the region in which the club is
located, the average income of the residents of the region, and their support for the sport in
question is stipulated to impact revenue streams generated from the local population.
Competitor-dependent factors are likely to impact both local and global revenue streams,
considering that the demand for the product here is a function of willingness rather than ability –
and subsequently impacts a broader audience that is reached through televised and online media.
Logical deduction and the stipulation that corporations will attempt to act in their best interest
would imply that this would impact subsequent demand from sponsors, advertisers and
broadcasters. Thus, this category combines drivers that drive both local and global consumers in
terms of reach and attractiveness.
The same line of reasoning applies to the league regulations; fixtures and rules will
quantify the supply of the product available to meet the existing demand both in terms of on-site
operations and televised events. Membership conditions and revenue disbursement will impact
the quality of the facilities, the operational strategy deployed by the club and its marketability,
and consequently have impacts on both local global revenue streams such as match-day sales,
sponsorship and broadcasting revenue.
36
Endogenous demand drivers such as ticket prices, merchandising and stadium capacity
only are only tied to the local revenues of the club, while operational strategy and marketability
impact both local and global revenue streams.
Co-Dependent demand drivers mostly impact both local and global revenue streams, with
aspects such as performance impacting the support for the club both within the local area and
from a global perspective, rivalry generating attention on a local and global level. The competitiveness of the league is likely to have a greater impact on global revenue streams than it would on local (given that local revenue streams would be faced with a lack of an alternative for this particular driver).
Table 3.3 categorizes the demand drivers in terms of the level of control available to the
club (club-controlled vs external vs Co-Dependent), classifies them into the according market scope (local vs global) and lists the revenue streams that they impact.
37
Table 3.3 Demand Drivers and their Corresponding Revenue Streams
Demand Driver Scope Corresponding Revenue Streams Population Local Fans
Disposable Income Local Fans
Competitor Local and Global Fans, Television Operational Strategy Rules and Fixtures Local and Global Fans, Television
Membership Local and Global Fans, Television, Conditions Sponsorship,
Government
Club Operational Local and Global Fans, Television, Strategy Sponsorship, Government Matchday and Season Local Fans Ticket Prices Stadium Capacity Local Fans Club Performance Local and Global Fans, Television, Sponsorship Competitiveness Local and Global Fans, Television, Sponsorship Rivalry Local and Global Fans, Television, Sponsorship
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A diagrammatic representation of the conceptual model is presented below. It illustrates the categorization of demand drivers (endogenous vs exogenous vs Co-Dependent), the classification of demand drivers and revenue streams (local vs global) and the relationship between demand drivers and revenue streams and the financial success metrics. Figure 3.1 Comprehensive Empirical Model
External Demand Drivers Club – Dependent Demand Drivers
Co‐Dependent Demand Drivers
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Chapter 4.0 Methodology
The conceptual model explicated the relationships found between demand drivers (as
found by the literature) and total revenue. It further explained the potential role that rivalry can
play into determining club financial performance. Most importantly, developing market scopes
ensures that demand drivers correspond to the relevant revenue streams.
A regression equation on total revenue would incorporate the factors that speak to club
success; Table 4.1 summarizes the findings of the conceptual model below in a list of demand
drivers (variable classes) and the relevant IV/proxy IV to be utilized in a regression.
Table 4.1: Variable Class and Operational IV Variable Class Variable, Coefficient Socio-Demographic Population, β1 Income, β2 Performance League Rank, β3 UCL/EL Participation, β4, β5 Games Played, β6 Operational Strategy Stadium Capacity, β7 Average Attendance (000), β8 Transfer Expenditure (000), β9 Geographic Rivalry Presence of Local Rival 0/1, β10 Distance to Nearest Rival, β11 FIFA Rivalry Presence of App Rival 0/1, β12 3 Year Goal Difference with Rival distance from 1 (geographic and FIFA-determined), Competitive Balance β13 β14* Rank difference between rivals, (geographic and FIFA-determined), β15, β16 ** *The 3 year period was identified as optimal by literature (Talarico & Moore, 2012) ** FIFA experts classify rivalries based on all antecedents (FIFA, 2015) Isolating Comparative Quality Aspects in Rivalry from Quality
In section 2.1 we identified the importance of separating quality as a construct from the
comparative quality that is endogenous to the intensity of a rivalry. In order to ensure that there
would be no confusing the role of quality and rivalry (and since rivalry in part is a factor of
competitive quality) – the results between the club and its nearest national competitor (i.e. goal
40
difference over 3 years and rank difference) would be incorporated into the model as well. That
way, the comparative impact of quality and comparative quality in a rivalry would be measured;
if the significance of the impact only came from quality then it would render the variables of
goal difference insignificant.
Incorporating FIFA Rivalry Classifier
Literature has demonstrated that little by the way of successful attempts have been made
to measure rivalry empirically. Variables that capture rivalry from the on-the-pitch perspective
were not present (Tyler & Cobbs, 2009). The usage of proxies to measure what a rivalry yielded
no such success, and the majority of work to understand rivalry from the fan or personnel side
has been qualitative in nature.
Adding to that challenge is the antecedents to rivalry; the fact that rivalry or the unified
set of feelings that emerge from rivalry (Dalakas & Mancon, 2012) are based on a variety of
historical, political, religious and identity reasons that were mentioned in the literature review.
As a result, it would follow logic that it would be difficult to capture rivalry from data derived
from on-field performances (albeit that comparative performance would be a measure of the
intensity of a rivalry). Therefore, in order to avoid measuring a rivalry (given that no measure has
been developed), the utilization of an expert panel, amassing antecedents to determine rivals
becomes necessary. Referring to the section above, we can measure the intensity of this rivalry
and its impact on overall financial performance by isolating from most proximate performers
(whether they are rivals or not).
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The Dependent Variables
The Deloitte Money League and the Annual Review of Football Finance (to be utilized in
studies 1 and 2, respectively) present total revenue and break it down into components:
Match-day revenue: Ticket Sales and Hospitality Packages
Television Revenue: Revenue from Broadcasting Agreements
Commercial Revenue: Merchandising and Sponsorship
As a result, by tracing each club activity (and thus demand driver) to its corresponding revenue stream on the list, a more coherent set of regressions becomes apparent:
Regressing on television revenue would negate the need to incorporate local
demographics (given that it is a global revenue source)
Regressing on commercial income would incorporate stadium capacity (since sponsors
can be local and will be motivated by stadium size) but not include aspects such as
population or disposable income
Match-day revenue as a DV would only incorporate local demographic factors from
demand drivers.
The Selection of Revenue from Football Operations as the Dependent Variable
The cause behind selecting the Revenue from Football Operations as the Dependent
Variable is two-fold. Firstly, it is certainly available through the Deloitte Annual Review of
Football Finance for at least the top two tiers of clubs in English football for no less than the past five years, as aspect that was not readily available when concerning any of the other financial metrics at any of their respective sources. Secondly, Revenue from Football Operations is sufficiently volatile in order to accommodate the effects at hand, and yet sufficiently stable not to
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be substantially swayed by other uncertain metrics that cannot be immediately measured or
approximated with sufficient precision such as brand value. It also eliminates cost from the
equation which, for the sake of simplicity, conveniently circumvents the problems in evaluating cost structures, debt refinancing, and operates with the principle that the football club is a utility
maximizing organization that acts in its financial best interest.
The study is a linear regression model that tests the impact of independent variables that approximate demand for the football product on the operating revenue for the sporting institution in question. This is done for 20 clubs over a 10 year time period (2005-2014).
The study utilizes the Deloitte Money League as its primary data source. Demographic
information was obtained from Eurostat, the official European database that deals with
demographic data, and official league results to compute goal differences were obtained from the
respective league institutions. The Deloitte Money League is a publicly available annual ranking
of the 20 top performing European clubs by revenue from football operations (operating
revenue). The report breaks revenue down into three components: Match-day, Television and
Commercial.
The Regression Equations
Each revenue stream would have six regression equations:
1. Model 1: To study the impact of adding a FIFA-determined rival binary on the
revenue stream.
2. Model 2: To study the impact of adding a FIFA-determined rivalries on the
revenue stream, with variables of competitive balance to control for quality.
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3. Model 3: To study the impact of adding geographic (spatial and local) rivalries on
the revenue stream.
4. Model 4: To study the impact of adding geographic (spatial and local) rivalries on
the revenue stream, with variables of competitive balance to control for quality.
5. Model 5: To study the impact of adding both FIFA-determined and geographic
rivalries on the revenue stream.
6. Model 6: To study the impact of adding both FIFA-determined and geographic
rivalries on the revenue stream, with variables of competitive balance to control
for quality.
Models for Total and Match-day Revenue (y1,y2):
Model 1: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + , β12 (Presence of a FIFA appointed rival)
Model 2: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + β10 (Presence of a FIFA appointed rival) +
β14 (3 year GD with FIFA appointed rival)+ β16 (Rank difference with FIFA appointed rival)
Model 3: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + β10 (Presence of a local rival)+ β11 (Distance to nearest rival)
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Model 4: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + β10 (Presence of a local rival)+ β11 (Distance to nearest rival) + β13 (3 year GD with closest distance rival) + β15 (rank difference with closest
distance rival)
Model 5: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + β10 (Presence of a local rival)+ β11 (Distance to nearest rival)+ β12(Presence of a FIFA-appointed rival)
Model 6: y1, y2 = β 0 + β1 log10 (Population) + β2 log10 (GDP per capita) + β8log10 (Average
attendance) + β9log10 (transfer expenditure) + β3(League Rank) + β4 , β5(European Participation)+
β6(Number of Matches – Regular Season Matches) + β10 (Presence of a local rival)+ β11 (Distance to nearest rival) + β13 (3 year GD with closest distance rival) + β15 (rank difference with closest
distance rival) β10 (Presence of a FIFA appointed rival) + β14 (3 year GD with FIFA appointed
rival)+ β16 (Rank difference with FIFA appointed rival)
The regression models that employ television income (y3) as a dependent variable will
differ in the removal of three variables:
1. The socio-demographic variable of population will be removed given that it is not
relevant to the global scope of television income.
2. The socio-demographic variable of GDP per capita will be removed given that it is
not relevant to the global scope of television income.
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3. The match-day based variable of average attendance will be removed given that it
does not contribute to the revenue stream and is not relevant to the scope of television
income.
In similar vein to the regression models with television income as a DV, regressions with commercial income as a DV will omit the above variables. However, they will include stadium capacity across all six models in order to account for on-site merchandising activity and provide a proxy for real estate size and viewership numbers, which explain sponsorship and advertising revenue.
Hypotheses
Following the hypotheses developed in Section 2.5, it would be expected that:
H1. The presence of a local rival will positively impact total revenue.
H1.1 The presence of a local rival will positively impact television revenue.
The interest from broadcasters (refer to Appendix A: Stakeholder Analysis) and
subsequent willingness to pay is based on the attractiveness of the product to the market segment
in question. Therefore, given the identified role of rivalry in demand, television revenue is
expected to grow. In similar vein, the interest from sponsors should yield similar results, and thus
the expectation is that commercial revenue will be positively impacted by local rivalries:
H1.2 The presence of a local rival will positively impact commercial revenue.
Given the network effects of rivalry (refer to literature review), the expectation is that
there will be a negative relationship between revenue forms and the distance between rivals:
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H1.3 The distance between one rival and the other will have a negative relationship with
total revenue.
H1.3.1: The distance between one rival and the other will have a negative relationship
with television revenue.
H1.3.2: The distance between one rival and the other will have a negative relationship
with commercial revenue.
As demonstrated by the literature review, the rivalry intensity will increase with a greater
state of competitive balance (i.e. more at stake). Therefore, it is expected that variables that
denote competitive balance between rivals will have an effect on revenue:
H1.4 The closer the 3 year average goal difference between rivals to 1, the greater the impact on total revenue.
H1.4.1 The closer the 3 year average goal difference between rivals to 1, the greater the impact on television revenue.
H1.4.2 The closer the 3 year average goal difference between rivals to 1, the greater the impact on commercial revenue.
Based on the above, the an increase in the x value denoting how far a 3 year average goal difference is from 1 is expected to decrease revenue. Similar outcomes are expected for the difference in rank between the rivals (the closer the distance, the greater the competitive balance between the teams):
H1.5 The closer the rank difference between the rival teams, the greater the impact on total revenue.
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H1.5.1 The closer the rank difference between the rival teams, the greater the impact on
television revenue.
H1.5.2 The closer the rank difference between the rival teams, the greater the impact on
commercial revenue.
The cause behind having a negative relationship between rank distance and goal
difference revenue is because the smaller distance implies a greater rivalry intensity (Talarico &
Moore, 2012). The intensity impact of a result is also measured by its proximity to an absolute
value of 1 based on literature stipulating that proximity with a defined winner will escalate
rivalry from a positive and negative affect (Talarico & Moore, 2012) (Euchner, 1993).
Similarly to the geographic rivalry hypotheses, the hypotheses for FIFA-determined
rivalries are made with the expectation that rivalry and competitive balance will impact revenue:
H2. The presence of an appointed/ FIFA-determined rival will have a positive impact on total revenue.
H2.1 The presence of an appointed/FIFA-determined rival will have a positive impact on television revenue.
H2.2 The presence of an appointed/FIFA-determined rival will have a positive impact on commercial revenue.
H2.3 The closer the 3 year average goal difference distance with the nearest ranked rival is to 1, the greater the impact on total revenue. H2.3.1 The closer the average 3-year goal difference distance with the nearest ranked rival is to 1, the greater the impact on television
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revenue. H2.3.2 The closer the average 3-year goal difference distance with the FIFA-
determined rival is to 1, the greater the impact on commercial revenue.
H2.4 The closer the rank difference between the FIFA-determined rivals, the greater the
impact on total revenue.
H2.4.1 The closer the rank difference between the FIFA-determined rivals, the greater the
impact on television revenue.
H2.4.2 The closer the rank difference between the FIFA-determined rivals, the greater the
impact on commercial revenue.
No formal hypotheses are made for match-day revenue streams, which stems from a lack
of existing literature concerning the continual effect of two matches per season on attendance
figures for the remainder of the season. This is supplemented by the pragmatic finding that
attendance rates and season ticket holders are consistently high across the board for the clubs
involved in Deloitte’s Money League, and therefore, especially given the size of the sample, it
would be difficult to find differences for match-day revenue.
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Chapter 5.0 Results
The study contained a total of 24 regressions: six regressions were done for each dependent variable (total revenue, match-day revenue, television revenue and commercial revenue) with the independent variables changed according to what would have an impact on the revenue stream. The results are available in Appendix D, while the descriptive variables are in
Appendix C.
Of the six regressions conducted for each DV, the one selected is the sixth. The selection
of the regression as an emphasis for the analysis is due to the fact that it incorporates rivalry and
competitive balance predictors for both rivalry forms identified in this thesis, and thus it tests the
relative importance of all the rivalry forms in question and further controls for comparative
quality through the competitive balance variables. However, there is some important insight to
be generated through looking at the other regressions individually, and particularly when it
comes to assessing the role of geographic rivalries as a stand-alone (see study results below).
To smooth for sharp effects and normalize significant variability in the data, logarithms
were taken for all dependent variables, and for several predictor variables: population, average
income, stadium capacity, average attendance, transfer expenditure, wages and distance to
nearest rival. Therefore, the scale of the impact of each variable in dollar terms is quantified by
eliminating the logarithms from the equation. The table below summarizes the impact that each
variable would have on the regression:
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Table 5.1: Numerical impact of each IV on DVs
Variable Class Variable, Coefficient* Impact on DVs Socio- Population, β1 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β Demographic (x1/x0)
Income, β2 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β (x1/x0)
Performance League Rank, β3 y1,2,3,4 = Y1 – Y0 where: Y1/Y0 = β (x1/x0)
β4,5 UCL/EL Participation, β4, β5** y1,2,3,4 = 10
Games Played, β6 y1,2,3,4 = Y1 – Y0 where: Y1/Y0 = β (x1/x0)
Operational Stadium Capacity, β7 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β Strategy (x1/x0)
Average Attendance (000), β8 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β (x1/x0)
Transfer Expenditure (000), β9 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β (x1/x0)
β11 Geographic Presence of Local Rival 0/1, β11 y1,2,3,4 = 10 Rivalry Distance to Nearest Rival, β12 y1,2,3,4= Y1- Y0 where: Y1/Y0*** = β (x1/x0)
β13 FIFA-determined Presence of App Rival 0/1, β13 y1,2,3,4 = 10 rival
3 Year Goal Difference with Rival y = 10 β14 1,2,3,4 (geographic and appointed), β ** Competitive 14 β15, 16 Balance Rank difference between rivals, y1,2,3,4 = 10 (geographic and appointed), β15, β16 ** *The coefficient denotes coefficients assigned to exemplify the impact of a variable on the DV (column 3 of Table X). The order will differ depending on assigned IVs in each regression. **Denotes multiple variables that are operated in the same way. ***Y0 is the base mean, and Y1 is the new value with a change of x by one unit.
Data Fit
The R2 value was higher for both Total and Match-day revenue than it was for the other revenue streams. The difference can be attributed to the lack of information that would more accurately model revenue streams from television and commercial revenue, such as the number of televised matches on given days and the number of sponsors, respectively.
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Table 5.2: Adjusted R-Square Values across models for all DVs Model Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 0.711 (0.245) 0.594 (0.172) 0.536 (0.383) 0.405 (0.439) 2 0.721 (0.241) 0.613 (0.168) 0.574 (0.368) 0.402 (0.440) 3 0.635 (0.286) 0.675 (0.159) 0.459 (0.413) 0.426 (0.448) 4 0.639 (0.284) 0.711 (0.150) 0.458 (0.413) 0.442 (0.441) 5 0.729 (0.246) 0.709 (0.151) 0.527 (0.385) 0.441 (0.442) 6 0.736 (0.243) 0.745 (0.141) 0.579 (0.364) 0.459 (0.434)
* Number between brackets is the standard error.
There was a significant pairwise correlation (0.685) between stadium capacity and
average attendance, meaning that only one could have been used as a predictor variable for
revenue streams. Independent of rivalry variables, the best line of best fit results came from
average attendance for the total and match-day revenue streams (which is what average attendance is relevant to), and therefore average attendance was the choice predictor variable for said regressions. For commercial revenue, stadium capacity continued to be utilized (as it both acts as a predictor for commercial purchases made on the stadium grounds as well as the number of sponsor displays).
Socio-Demographic Variables
Using total revenue as a dependent variable meant that the socio-demographic variables that literature found to be significant for ticket sales (i.e. match-day revenue and revenues that are local in scope) would be used. The socio-demographic variables, population and average income, had differing effects on the dependent variables. Table 5.3 summarizes the findings for population:
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Table 5.3: Impact of 1,000 change in population total & match-day revenues Model Total Revenue Matchday Revenue Impact of 1,000 unit change on y (£) Total Rev Match-day Rev 1 0.044 (0.020) 0.064 (0.014) 5,283.10 1325.93 2 0.046 (0.019) 0.063 (0.013) 5,200.55 1305.21 3 0.084 (0.026) 0.045 (0.014) 3,714.66 932.29 4 0.076 (0.026) 0.037 (0.014) 3,054.27 766.55 5 0.037 (0.023) 0.042 (0.014) 3,467.02 870.13 6 0.037 (0.022) 0.026 (0.014)* 2,146.24 538.65* -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
For regressions that utilized FIFA-determined rivalries exclusively, population was
statistically significant. As can be seen for total revenue, population was significant in Models 1 through 4. The lack of significance in regressions 5 and 6 can be attributed to the addition of multiple variables that diminished the predictive power of population. In models that applied both geographic and spatial rivalries, population was not found to be statistically significant
(even at the 10% level).
For the statistically significant results, the impact a change in population would have would be the coefficient multiplied by 1,000 and then multiplied by y (given the notations). As can be seen above, a change of 1,000 in population would induce a change of up to £5,283.10 (or
£5.28 per citizen) for total revenue in Model 1 (refer to Table 5.1 for mathematical operation guide).
In the case of match-day revenue in particular, the effects were almost significant across
all six models; the notable exception being model 6 (that incorporates both rivalries and their
respective competitive balance predictors), where the result was significant at the p=0.1 level
(p=0.065). This is a clear indicator that population is a significant constituent in local revenue
streams, and the greater the population the greater the impact on finances. This is consistent with
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several findings in literature, which imply that socio-demographics are important for local
revenue streams.
Average income, the other socio-demographic predictor, did not have as significant an impact on the total and match-day revenue models. The table below demonstrates the coefficient values found for match-day income and sheds light on its lack of substance in this model:
Table 5.4: Impact of Change of £1,000 in per capita GDP on total & match-day revenue
Regression Total Revenue Matchday Impact of 1,000 unit change (£) on y Revenue Total Rev Match-day Rev 1 0.094 (0.056)* 0.030 (0.039) 168,726.51* 42,345.38 2 0.094 (0.055)* 0.031 (0.039) 174,353.17* 43,757.50 3 0.031 (0.068) 0.042 (0.038) 236,256.95 59,293.53 4 0.043 (0.068) 0.060 (0.098)* 337,595.34 84,726.47* 5 0.186 (0.065) 0.018 (0.040) 101,218.83 25,402.94 6 0.133 (0.060) 0.069 (0.035) 388,283.76 97,447.77 -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
As can be seen in Table 5.4, per capita GDP is only statistically significant for two models with total revenue as a dependent variable. The models that incorporate both rivalry forms are the ones where per capita GDP is statistically significant for total revenue, while it is only significant for model 6 when match-day revenue is the dependent variable (albeit that it is within the 10% confidence interval for model 4, which incorporates geographic/local rivalry with competitive balance). Without further analysis to rule out phenomena such as intercorrelation
(which is unnecessary, given that the variables that lend or detract support from the hypotheses are unaffected), the impact of per capita GDP cannot be verified, as the absence of parsimonious effects lends suggestion that there could be other causes behind the statistical significance in
Models 5 and 6 for total revenue, and Model 6 for match-day revenue.
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The absence of unanimous statistical significance on match-day revenue (and thus, total
revenue; there is no other stream where average income can have an impact) is not entirely
surprising (the reason for inclusion was that literature had conflicting views on the role of
income), given the fact that stadiums in this category of club almost always operate at near-
capacity (and the intuition that the club as a firm would offer seats and merchandise at rates that are affordable to the consumer).
What is surprising is the significant disparity in the coefficient values between total and match-day; while it is expected that the contribution to match-day income would not be greater than that towards total revenue, the fact that the contribution towards total revenue can be more than double that towards match-day income indicates that either there is another revenue stream that is impacted by average income (the likeliest being commercial income), or that there is a significant flaw within the GDP per capita variable as a proxy for average income (or that the requirement for an accurate proxy would be an average income within a particular radius, rather than the entire NUTS 3 region). Adding GDP per capita to commercial regressions would also run the risk of over-explanation, as there is a degree of correlation between the revenue streams and thus variables that are not relevant to how the revenue stream operates can be found significant (ex. Average attendance on television revenue) simply due to their accuracy as proxies of how the club would succeed across different revenue stream scopes.
Performance Variables
Aimed at quantifying the role of the club’s on-field performance on its revenue streams, several performance variables were deployed in order to most accurately capture this particular construct. A total of four variables that proxy how well the club has performed during that
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season. The results were mixed, but were largely within line of expectations for most variables operationalized.
League Rank: The league rank variable was only significant for commercial revenue, which is to be expected, given the rewards structure that each league disburses (refer to Figure A2.1 in
Appendix B), which Deloitte classes as commercial revenue (Deloitte, 2014).
Table 5.5: League Rank Coefficient values & impact levels across models for all DVs Model Total Revenue Matchday Television Commercial Revenue Revenue Revenue 1 -0.011 (0.006)* 0.001 (0.004) -0.001 (0.009) -0.034 (0.010) 2 -0.011 (0.006)* 0.001 (0.004) -0.002 (0.008) -0.034 (0.010) 3 -0.012 (0.007)* -0.000 (0.004) -0.004 (0.010) -0.039 (0.011) 4 -0.012 (0.008) -0.002 (0.004) 0.002 (0.011) -0.034 (0.012) 5 -0.012 (0.006)* 0.002 (0.004) 0.003 (0.009) -0.036 (0.011) 6 -0.007 (0.007) -0.001 (0.004) 0.011 (0.010) -0.030 (0.012) -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) *Starred values are within the 90% confidence interval
Table 5.6: Impact of League Rank Coefficients on Commercial Revenue
Model Commercial Impact of 1 unit Revenue change on y (%) 1 -0.034 (0.010) 14.4% 2 -0.034 (0.010) 14.4% 3 -0.039 (0.011) 16.4 % 4 -0.034 (0.012) 14.4% 5 -0.036 (0.011) 15.3% 6 -0.030 (0.012) 12.9%
-Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The table above demonstrates the average impact of a change in the team’s league ranking on their financial standing. Given that the significance was only found for Commercial
Revenue, the values were only computed for that particular stream. Given the negative sign, the smaller the league position (i.e. the higher), and the greater the revenue. Given merit portion of
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television rights disbursement (which is actually classified by Deloitte as commercial income) -
these values stand to sense, with an average commercial income of £68.9 million, a change in
league position should impact revenue by approximately £9.5-11 million.
The differences between the values across the 6 models imply that rivalry variables
impact the relationship between league placement and commercial revenue; a reference to Figure
A2.1 in Appendix B will demonstrate that while the compensation method for each differs, the
number of televised matches variability (with the idea that rivalry games are more likely to be
televised) is what explicates the variation in the impact of commercial revenue once rivalry
variables are introduced.
UCL Participation: Participation in the UEFA Champions’ League, Europe’s elite club
competition, yields significant financial rewards upon the participant. UEFA utilizes a tiered
reward system that remunerates participants based on qualifying to the competition and
subsequent accomplishments within it (UEFA, 2015).
Interestingly, the binary variable denoting participation in the UEFA Champions’ League
yielded no significant effect across any of the models; and was only within the 10% confidence
interval once.
Table 5.7: UCL Participation Coefficient values & impact levels across models for all DVs Model Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 0.062 (0.071) -0.078 (0.119) 0.155 (0.157) -0.033 (0.126) 2 0.077 (0.274) -0.092 (0.049) 0.191 (0.105)* -0.041 (0.128) 3 0.114 (0.207) 0.017 (0.050) 0.132 (0.128) -0.003 (0.139) 4 0.108 (0.090) 0.003 (0.047) 0.149 (0.129) -0.001 (0.138) 5 0.052 (0.078) 0.000 (0.048) 0.079 (0.120) -0.030 (0.138) 6 0.084 (0.077) -0.025 (0.045) 0.139 (0.115) -0.005 (0.137) -Number between brackets is standard error *Starred values are within the 90% confidence interval
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A deeper look at the data explicates this phenomenon: the descriptive statistics (refer to
Appendix C) indicate that the mean of the binary variable denoting UCL participation is 0.70
(meaning that 140 of the 200 clubs across the 10 years of data have participated in the
Champions’ League. Therefore, this large representation implies that participation in Europe’s
elite (and one its most financially lucrative) club competitions was not exclusive to the best
earners on the list, but it was almost a pre-requisite to be on the list (given that logic,
participation in Europe’s secondary competition, the Europa League, should create a negative
coefficient). The signs, for the vast majority of the coefficients, are positive (indicating a
positive correlation between participation in the UCL and revenue); the notable exception is
commercial revenue, where the signs are largely negative but not statistically significant.
Europa League Participation: Europe’s second tier continental competition is less viewed,
esteemed, and financially rewarded than the UEFA Champions’ League. It is therefore entirely plausible that, in a list of Europe’s top earning clubs, participation in the EL will actually cause a negative impact on revenue (i.e. when compared to clubs who play in the Champions’ League, the revenue for UEFA league participants should be smaller). While participation in the Europa
League would still be better than not participating in continental competition whatsoever, the assumption that Europe’s top financially performing clubs are most likely to be in European
competition (which is supported by data, a total of 172 clubs from the 200 strong sample have
participated either in the UCL or the EL, 140 of which were in the UCL – for more details refer
to Appendix C), and thus it is which competition the club participates in; in other words, if a club
is in the EL that means they are missing out on the extra revenue from the UCL, and thus being in the EL should have a negative impact on your earnings.
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Table 5.8: EL participation coefficient values & impact levels across models for all DVs Regression Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 -0.084 (0.076) -0.118 (0.053) -0.173 (0.116) 0.040 (0.133) 2 -0.072 (0.075) -0.128 (0.052) -0.140 (0.111) 0.034 (0.133) 3 -0.155 (0.097) -0.078 (0.054) -0.318 (0.138) -0.046 (0.150) 4 -0.166 (0.097)* -0.095 (0.051)* -0.305 (0.138) -0.054 (0.148) 5 -0.046 (0.085) -0.034 (0.052) -0.170 (0.132) 0.030 (0.151) 6 -0.049 (0.084) -0.051 (0.049) -0.128 (0.308) 0.033 (0.149) -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The data scarcely yields statistically significant results for EL participation, and in large part this is due to the small size of EL participants (32; the bottom 25% of the clubs alone comprise 50 clubs). Thus, any outlier (i.e. one of the big teams having an off-season on the field but still maintaining good financial performance for that year) that happened to participate in the
EL, or the many smaller-revenue clubs that made it to the UCL for the season (but are on unequal ground due to other factors) will mean that the impact of the EL variable is significantly diminished. However, for the few times that the variable did attain significance (Models 1 and 2
in Match-day revenue, Models 3 and 4 in Television Revenue), the coefficient effect was
substantial: for Model 1 in match-day revenue, participation in the EL would decrease income by
41.9%, for Model 2 participation in the EL would decrease match-day income by 44.5%.
In Models 3 and 4 for Television Revenue, participation in the EL would decrease
revenue by 76.8% and 75.5%, respectively. Of course, these numbers are not implying that not
participating in the EL would eliminate this decrease; rather they are signifying that participating
in the UCL (the overwhelming majority alternative in this dataset) is what would eliminate it.
Games Played over Regular Season Games: This variable was made to denote the progression of
each team into knockout competitions (including European competitions and domestic cups).
The subtraction of regular season games was put in place in order to ensure that there is equity
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across the teams (otherwise, German clubs, who play 34 games to the 38 that are played in
English, Spain, Italy – would be at a 2 game handicap, for example). The number of games played in a season was statistically significant across all DVs besides commercial income.
Table 5.9: Games played coefficient values across models for all DVs
Model Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 0.015 (0.004) 0.014 (0.003) 0.021 (0.006) 0.007 (0.007) 2 0.014 (0.004) 0.015 (0.003) 0.018 (0.006) 0.007 (0.007) 3 0.021 (0.005) 0.011 (0.003)* 0.032 (0.007) 0.013 (0.008) 4 0.023 (0.005) 0.014 (0.003) 0.030 (0.008) 0.015 (0.008)* 5 0.014 (0.005) 0.008 (0.003) 0.022 (0.007) 0.008 (0.008) 6 0.016 (0.005) 0.011 (0.003) 0.019 (0.007) 0.009 (0.008) -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
Table 5.10: Games Played Coefficient Values and impact levels across Total & Match-day Revenue DVs Model Total Revenue Impact of 1 unit Match-day Impact of 1 unit change on y Revenue change on y 1 0.015 (0.004) 7.15% 0.014 (0.003) 6.66% 2 0.014 (0.004) 6.66% 0.015 (0.003) 7.15% 3 0.021 (0.005) 10.15% 0.011 (0.003)* 5.20%* 4 0.023 (0.005) 11.17% 0.014 (0.003) 6.66% 5 0.014 (0.005) 6.66% 0.008 (0.003) 3.75% 6 0.016 (0.005) 7.64% 0.011 (0.003) 5.20% -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
As evident from table 5.10, the coefficient values for Total Revenue are statistically significant across all six models. The impact level of an extra game played varies from 6.66% to
11.17%, with Model 4 (local rivalry with competitive balance variables) demonstrating the greatest impact, while both models 2 and 5 (FIFA-determined rivalry, both rivalries) show the lesser impact of a unit change on overall revenue.
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Similarly, the coefficient values for Match-day Revenue are significant at p=0.05 for 5 of the 6 models; model 3 (geographic rivalry) is significant at the p=0.10 level. The effects on match-day revenue are less pronounced per unit change in games played, however, with the
range of coefficient impact going from 3.75% (Model 5) to 7.15% (Model 2).
Table 5.11: Games Played Coefficient Values and impact levels across Television Revenue DV Model Television Impact of 1 unit Revenue change on y 1 0.021 (0.006) 10.15% 2 0.018 (0.006) 8.64% 3 0.032 (0.007) 15.88% 4 0.030 (0.008) 14.82% 5 0.022 (0.007) 10.66% 6 0.019 (0.007) 8.64% -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The impact of games played on television revenue (as a percentage) is the greatest out of
all the statistically significant coefficients for this particular variable. This range varies from
8.64% (Models 2 and 6 – signifying that competitive balance for FIFA-determined rivalries has a downward effect on the impact of games played) to 15.88% (Model 3). The reason behind this impact is that the clubs in question are likelier to have their matches televised (Deloitte, 2005).
Therefore, with the greater number of games that they play, the greater number of televised games (and hence, the greater the impact on television revenue).
Operational Strategy Variables
Transfer Expenditure: Net transfer expenditure is best available proxy to estimate a club’s
willingness to recruit and retain talent (wages paid data was unavailable for most clubs). There is
a significant correlation between transfer expenditure and wages paid, with the amount paid to
extract a player from their contract usually an indicator of their worth to the selling team (and
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thus the wage that they would command). With the exception of models where IVs were regressed on match-day revenue (where transfer expenditure was not significant for any of the 6 models), transfer expenditure was significant across every model for all DVs.
Table 5.12: Transfer Expenditure Coefficient Values
Model Total Revenue Match-day Television Revenue Commercial Revenue Revenue 1 0.104 (0.016) 0.010 (0.011) 0.147 (0.025) 0.135 (0.029) 2 0.107 (0.016) 0.007 (0.011) 0.156 (0.024) 0.134 (0.029) 3 0.126 (0.019) 0.018 (0.011)* 0.171 (0.026) 0.164 (0.030) 4 0.124 (0.019) 0.016 (0.010) 0.177 (0.028) 0.158 (0.030) 5 0.120 (0.017) 0.007 (0.011) 0.151 (0.026) 0.148 (0.030) 6 0.109 (0.017) 0.005 (0.010) 0.155 (0.025) 0.146 (0.030) -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
Table 5.13: Impact of a £1 mil change in transfer expenditure on total revenue Model Total Revenue Impact of £1 mil. change on y 1 0.104 (0.016) 1,457,670.45 2 0.107 (0.016) 1,470,888.01 3 0.126 (0.019) 1,554,619.46 4 0.124 (0.019) 1,545,803.98 5 0.120 (0.017) 1,528,174.18 6 0.109 (0.017) 1,479,700.20 -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
Transfer expenditure is at its most impactful on total revenue in Model 3, and at its least impactful in Model 1. The differences are minor (max to min range is £100,000) as an absolute, but in relative terms, that is a 10% difference in ROI. As a general trend, geographic rivalry variables tend to increase the impact of transfer expenditure on the overall model, and FIFA- determined rivalry variables decrease it. An investment of £1 million in transfers will increase
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revenue by no less than £1,457,670, and up to £1,554,619 (meaning a return that ranges from
45.7% to 55.5%).
Table 5.14: Transfer expenditure impact on Television and Commercial Revenue
Model Television Impact of £1 mil. Commercial Impact of £1 mil. Revenue change on y Revenue change on y 1 0.147 (0.025) 1,261,681.33 0.135 (0.029) 1,204,855.79 2 0.156 (0.024) 1,277,730.12 0.134 (0.029) 1,203,336.11 3 0.171 (0.026) 1,304,485.15 0.164 (0.030) 1,248,941.21 4 0.177 (0.028) 1,315,189.63 0.158 (0.030) 1,239,817.78 5 0.151 (0.026) 1,268,813.74 0.148 (0.030) 1,224,614.75 6 0.155 (0.025) 1,275,946.76 0.146 (0.030) 1,221,574.54 -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The impact of transfer expenditure on television and commercial revenue is higher than it
is on total revenue (which, as previously seen with population, is the case due to other variables
having varying degrees of significance across models, and due to unobserved variables in
television and commercial – such as number of televised matches and number of sponsors – that
would have more accurately limited the scale its impact).
For television revenue, the impact ranged from £1,261,681 to £1,315,189 per million
spent. This constitutes a return on investment from 26.1 to 31.5%. As was the case with total
revenue, the lowest values are associated with Models 1 and 2 (where FIFA-determined rivalry
variables are in play), and the higher values were with geographic rivalry variables (Models 3
and 4).
While the effect of transfer expenditure on commercial revenue was not as high as that of
television, the range was still higher than that of total revenue; the lowest figure was a return of
£1,204,855.79 (20.5%) and the highest was £1,248,941.21 (24.9%). As was the case with total
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revenue and television revenue DVs, the highest impact transfer expenditure had on commercial
revenue came from geographic rivalry models 3 and 4, and the lowest impact came from FIFA-
determined rivalry models 1 and 2.
Average Attendance: This variable, which is the average attendance per game for the home
games of the season, is used in total revenue and match-day revenue models. Average attendance
captures the match-day revenue portion of results and has a large, statistically significant
explanatory impact (p value range from 0.00 to 0.02) on both total and match-day revenue DVs.
Table 5.15 in the following page summarizes the coefficient values and financial impact that
average attendance has on both total and match-day revenue.
Table 5.15: Impact of 1,000 change in average attendance on total and match-day revenue Model Total Revenue Impact of 1,000 Match-day Impact of 1,000 unit unit change on y Revenue change on y 1 1.032 (0.153) £4,053,767.99 1.096 (0.108) £1,081,144.05 2 1.063 (0.152) £4,176,800.89 1.070 (0.106) £1,055,228.80 3 1.289 (0.175) £5,075,994.80 1.199 (0.098) £1,183,937.14 4 1.260 (0.176) £4,960,390.43 1.178 (0.093) £1,162,962.61 5 1.303 (0.124) £5,131,827.13 1.350 (0.145) £1,335,006.37 6 1.017 (0.158) £3,994,262.55 1.093 (0.092) £1,078,153.16 -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
Statistically significant for every model across both total and match-day revenue DVs,
average attendance has the largest statistically significant monetary impact on those models. For
total revenue, the range of impact of a 1,000-person change in average attendance is £3.994
million to £5.075 million (a revenue change of £3,994 – £5,075 per attendee per season). For
match-day revenue, the average attendance change of 1,000 will impact from £1.055 million to
£1.335 million (£1,055 to £1,335 per attendee per season).
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Stadium Capacity: Due to its significant correlation with average attendance, stadium capacity
was not used in the regressions on total and match-day revenue. However, as previously
mentioned, it is a useful proxy for commercial revenue as it estimates amount of advertising real
estate and exposure (quantity of advertising space, number of viewers: both functions of how
much a sponsor would be willing to pay). In the regressions on commercial income as a
dependent variable, stadium capacity was statistically significant for all 6 models.
Table 5.16: Impact of 1,000 change in stadium capacity on commercial revenue
Model Commercial Impact of 1,000 Revenue unit change on y 1 0.498 (0.120) £557,943.81 2 0.510 (0.123) £687,047,99 3 0.829 (0.139) £1,120,265.81 4 0.832 (0.138) £1,124,352.72 5 0.759 (0.140) £1,024,972.10 6 0.696 (0.155) £939,318.58 -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The range of impact that a change of 1,000 in stadium capacity has on commercial
revenue is from £0.558 million to £1.124 million (a return of £558 – £1,124 per seat). Stadium
capacity’s coefficient is visibly lower for Models 1 and 2, and higher for models 3 and 4. As
previously mentioned, this impact is most probably due to the fact that the variation in
explanatory power (geographic rivalry has more limited explanatory power than FIFA-
determined rivalry, meaning variables will take on more significant roles in the absence of others
that would have statistical significance).
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Rivalry Variables
Geographic Rivalry: These variables (presence of a local rival, distance to nearest rival) were
deployed to test hypotheses 1 to 1.3. Across the board, the results are inconclusive, due to an
inconsistency in the significance and directional impact of the variables across all DVs.
Table 5.17: Presence of a Local Rival coefficient value on all DVs Model Total Revenue Matchday Television Commercial Revenue Revenue Revenue 1 n/a n/a n/a n/a 2 n/a n/a n/a n/a 3 -0.284 (0.127) 0.151 (0.071) -0.359 (0.160) -0.178 (0.174) 4 -0.326 (0.015) 0.072 (0.070) -0.295 (0.171)* -0.278 (0.183) 5 -0.014 (0.118) 0.260 (0.072) -0.071 (0.160) -0.028 (0.184) 6 0.026 (0.125) 0.197 (0.072) 0.099 (0.163) -0.107 (0.195) -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
For total revenue, geographic rivalry is only significant in models that only incorporate
geographic rivalry (Models 3 and 4). In those models, the impact of geographic rivalry is
statistically significant and the presence of a geographic rival decreases total revenue by 48% to
52.8%. This result contradicts Hypothesis 1, and in combination with the lack of significance
across the other conditions, means that the results do not lend support. When FIFA-determined
rivalry is inserted in conjunction with geographic rivalry, the presence of a local rival variable
loses its explanatory power.
For match-day revenue, the combination of geographic and FIFA-determined rivalries
gives the geographic rival significance that it does not attain on its own. The impact is positive,
where having a geographic rival can increase match-day revenue by 57.40% (Model 6) to
81.97% (Model 5). However, the results as stand-alones are not reliable, and would not be in
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support of any hypotheses regardless; the absence of a significant effect for geographic rivalry
without the FIFA-determined rivalry variable means that the results are inconclusive.
The only statistically significant coefficient for the presence of a local rival variable on
television revenue is in Model 3 (which is only geographic rivalry), it is at -0.359; the negative
sign casts doubt on Hypothesis 1.1, and the absence of significant effects in conjuncture with
competitive balance variables (Model 4 is only significant at p=0.10, and denotes that geographic
rivalry has a negative effect on revenue) and with FIFA-determined rivalry variables (Models
5,6) means that the television revenue models for geographic rivalry do not lend support to
Hypothesis 1.1.
There is no statistically significant coefficient for the geographic rivalry on commercial
revenue (at p=0.05 or p=0.10) and the negative signs of the coefficients indicate an opposing
relationship to revenue than the one stipulated in Hypothesis 1.2. Therefore, the results from the
commercial revenue models do not lend support to Hypothesis 1.2.
Distance to the Nearest Competitor: Deployed to test the network rivalry effect (refer to
Literature Review for more information), the geographic distance to the nearest competitor
(measured in km) should have an inverse relationship with revenue to support Hypotheses 1.3,
1.3.1 and 1.3.2.
Table 5.18 Distance to Nearest Rival coefficient value on all DVs Regression Total Revenue Matchday Television Commercial Revenue Revenue Revenue 1 n/a n/a n/a n/a 2 n/a n/a n/a n/a 3 -0.151 (0.070) 0.007 (0.039) -0.185 (0.092) -0.135 (0.101) 4 -0.180 (0.017) -0.044 (0.039) -0.143 (0.100) -0.195 (0.108)* 5 -0.065 (0.066) 0.083 (0.042) -0.021 (0.092) -0.040 (0.107) 6 0.068 (0.072) 0.022 (0.042) 0.150 (0.098) -0.055 (0.124) *Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval)
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-Starred values are within the 90% confidence interval
As can be seen from Table 5.18, the distance variable is statistically significant on total
revenue only in models that exclusively incorporate geographic rivalry variables. The addition of
FIFA-determined rivalry variables causes the distance variable to lose statistical significance and
explanatory power. For the models that it is significant (Models 3 and 4), every 100 km increase
in distance causes total revenue to drop from 15.1% to 18.0%. However, this result is not
reliable, and overall, the results from the Total Revenue regressions do not lend support to
Hypothesis 1.3.
There is only one statistically significant distance variable in the Match-day Revenue
regressions (Model 5). The coefficient is positive, implying that an increase in distance to the
nearest rival by 100 km will lead to 8.3% increase in revenue. This can be explained by the fact
that local fans will choose to support to a club closer to them, and the further away a club is from
the area in question, the more likely the fan is to support/attend games with a more proximate
club (in essence, it limits consumer choice and provides a geographic monopoly).
For television revenue, Model 3 (geographic rivalry) was the only model in which the
distance to nearest rival variable was statistically significant. The coefficient of -0.185 implies a
positive change of 18.5% in commercial revenue with every 100 km nearer the nearest
competing team is located. As the only significant outcome for the nearest competitor variable
for television revenue, this result does not lend support to Hypothesis 1.3.1.
The distance to nearest competitor variable does not have any statistically significant
effects on commercial revenue, albeit that Model 3 displays a negative coefficient (which would impact revenue by 19.5%) within the p = 0.10 interval. However, the results cannot lend support to Hypothesis 1.3.2.
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While there is no sufficient evidence to support any of the hypotheses pertaining to
distance to the nearest competitor, there is sufficient evidence to warrant further investigation
into this issue. The varying levels of significance and the consistent general vector of the
coefficients implies that there might be an effect pertaining to competitor proximity, but that perhaps the distance in km is not the best proxy to measure it. Alternative suggestions include commute time and cost.
FIFA-determined Rivalry: The variable operationalized to test for the significance of FIFA-
determined rivalry is the binary variable that addresses the presence of a rival as determined by
FIFA on the Deloitte Money League Top 20 for that year. The mean value is 0.5, meaning half
the teams have a FIFA-determined rival. Similar to the local rival variable, this variable is
deployed to test Hypotheses 2 – 2.3.
Table 5.19 Presence of a FIFA-determined Rival coefficient value on all DVs Model Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 0.276 (0.043) 0.107 (0.030) 0.302 (0.066) 0.290 (0.076) 2 0.266 (0.042) 0.115 (0.029) 0.279 (0.064) 0.293 (0.076) 3 n/a n/a n/a n/a 4 n/a n/a n/a n/a 5 0.254 (0.052) 0.138 (0.032) 0.390 (0.079) 0.215 (0.092) 6 0.258 (0.051) 0.138 (0.030) 0.319 (0.076) 0.195 (0.091) -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
As can be seen in Table 5.19, the FIFA-determined rivalry variable is statistically
significant for every across all DVs. The coefficient signs are consistently positive, meaning that
the presence of a rival on the Deloitte Money League ranking positively impacts revenue. This
lends support to Hypotheses 2 – 2.3.
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Table 5.20 Presence of a FIFA-determined Rival impact on Total and Match-day Revenue Model Total Revenue Impact of change Match-day Revenue Impact of change on on y y 1 0.276 (0.043) 27.6% 0.107 (0.030) 10.7% 2 0.266 (0.042) 26.6% 0.115 (0.029) 11.5% 3 n/a n/a n/a n/a 4 n/a n/a n/a n/a 5 0.254 (0.052) 25.4% 0.138 (0.032) 13.8% 6 0.258 (0.051) 13.8% 0.138 (0.030) 13.8% -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
As can be seen from table 5.20, the most conservative estimate for the impact of having a
rival on the Deloitte Money League ranking is 13.8% on total revenue (Model 6). Without
competitive balance elements, it can go as high as 26.6% (Model 5), and without local rivalry the
impact can range from 26.6% to 27.6% (Models 2 and 1, respectively). With a mean total
revenue value of £200 million (Refer to Appendix C for descriptive statistics), having a FIFA-
determined rival can increase revenue within an average range of £27.6 million to £55.2 million.
The competitive balance variables seem to diminish the magnitude of the FIFA-determined
rivalry coefficient’s impact on total revenue, and there is a significant discrepancy between the figures quoted in Model 6 and the other models. On the side of conservatism (assuming the competitive balance variables are appropriate), and due to the fact that Model 6 does have the highest adjusted R-square value across all DVs, the 13.8% figure is the one that is most appropriate. The results, namely the consistent statistical significance, lend strong support to
Hypothesis 2.
The impact of havingFIFA-determined rival on match-day revenue is smaller for match- day revenue than it is for total revenue, with the figure ranging from 10.7% (Model 1) to 13.8%
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(Models 5, 6). The addition of geographic rivalry and competitive balance variables seems to
increase the effect of the FIFA-determined rivalry variable on match-day revenue.
Table 5.21 Presence of a FIFA-determined Rival impact on Television & Commercial Revenue Model Television Impact of Commercial Impact of change Revenue change on y Revenue on y 1 0.302 (0.066) 30.2% 0.290 (0.076) 29.0% 2 0.279 (0.064) 27.9% 0.293 (0.076) 29.3% 3 n/a n/a n/a n/a 4 n/a n/a n/a n/a 5 0.390 (0.079) 39.0% 0.215 (0.092) 21.5% 6 0.319 (0.076) 31.9% 0.195 (0.091) 19.5% -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The impact of the FIFA-determined rivalry variable is greatest on television revenue. The
range of impact is from 27.9% (Model 2) to 39.0% (Model 5). With a mean value of £80 million
for television income, the range of impact is £22.32 million to £31.2 million. The introduction of
competitive balance variables seems to diminish the magnitude of the FIFA-determined rivalry
coefficient, thereby decreasing its effect on television revenue. Overall, while the 31.9% (having
a rival would increase commercial revenue in Model 6 by £25.52 million) obtained in Model 6
(best R-square, all variables included) may potentially be overstated (the adjusted R-square for
television income in Model 6 is at 0.579, and there are some unobserved variables such as
number of televised games that can mitigate the impact of the FIFA-determined rivalry variable),
the consistent significance of the rivalry variable across all Models regressed on television
income lends strong support to Hypothesis 2.1.
The impact of the FIFA-determined rivalry variable on commercial revenue ranges from
19.5% (Model 6) to 29.3% (Model 1). The addition of geographic rivalry variables and their
corresponding competitive balance predictors diminishes the impact of the FIFA-determined
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rivalry variable; with a mean value of £69 million, the average impact of having a rival ranges from £13.455 million to £20.27 million. The low value for the commercial revenue adjusted r- square (0.459 in Model 6) implies that the effect may be overstated, but its statistical significance across all 6 models still lends strong support to Hypothesis 2.2.
Overall, the impact of the FIFA-determined rivalry variable on all six models across all four dependent variables indicates that there is a strong relationship between rivalry and financial performance. The presence of a rival on the Deloitte rankings influences financial performance positively and significantly. It is important to note that the average combined monetary impacts of the sub-revenue streams (match-day, television and commercial) sums up to more than the impact on total revenue. This does not take away from the strength of the finding, and can be attributed to several reasons, including:
1. The intercorrelation of revenue streams: Revenue streams are correlated, and thus the
impact that is seen on each is not entirely down to that particular DV, rather it involves
endogeneity from other DVs.
2. The r-square for television and commercial income indicate that there are unobserved
variables that can be deployed to obtain better models. The inclusion of these variables
can mitigate the impact of the FIFA-determined rivalry variable to more accurate levels.
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Competitive Balance Variables
For Geographic Rivalry (Models 4 &6)
Table 5.22 3Yr Avg Goal Difference with nearest competitor from 1 coefficient value on all DVs Regression Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 n/a n/a n/a n/a 2 n/a n/a n/a n/a 3 n/a n/a n/a n/a 4 0.115 (0.062)* 0.142 (0.033) -0.020 (0.088) 0.240 (0.094) 5 n/a n/a n/a n/a 6 0.159 (0.055) 0.003 (0.003) -0.003 (0.080) 0.264 (0.096) -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
The 3-year average goal difference distance (absolute value) from 1 is supposed to denote
the recent state of competitive balance between two teams. In the case of Geographic rivalries,
the two teams are the two teams that are closest together from the Deloitte Money League Rank.
In the event that there is no nearby team, the value assigned was significantly higher than the
highest distance (almost twice as high) in order to ensure that it would not skew the results
favorably. This variable is deployed with the aim of supporting Hypotheses 1.4, 1.4.1 and 1.4.2.
As is evident from the results, there is no support for any of the aforementioned hypotheses. In total revenue, the only time the goal difference variable attains significance is in
Model 6, and the coefficient is positive (implying that the greater the goal difference distance from 1 or the less competitive the recent run of head to head fixtures, the greater the impact on revenue. As a matter of fact, for every 3-year average goal more than 1, total revenue would increase by 44.2%. The results on the total revenue regressions do not lend support to
Hypothesis 1.4. Model 4 is significant at p=0.10, but also has a positive sign and implies a relationship opposite to the one postulated by Hypothesis 1.4.
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Similarly, Model 4 for match-day revenue is statistically significant and suggests that an
increase in the average goal difference would increase revenue by 38.6%. No other models are
significant at p=0.05 or p=0.10. Television revenue models do not have any significance for the
goal difference variable, albeit that they carry the expected negative sign. The results lend no
support to Hypothesis 1.4.1
Commercial revenue regressions yield a significant result for goal difference in models 4 and 6, but both are positive and imply an inverse relationship between competitive balance and financial performance. Therefore, the results do not support Hypothesis 1.4.2.
In similar vein, the rank difference between a team and their nearest competitor yields no significant results for any of the dependent variables, and therefore offer no support to
Hypotheses 1.5, 1.5.1 and 1.5.2.
To suggest that competitive balance is unfavorable in sports based on the results of an inductively constructed variable would be ill-advised (and in disagreement with all sports literature on the matter as far as this author’s knowledge); a possible explanation is that the variable should operationalized over a longer time span (albeit that literature was in support of the 3-year time period). Another likely explanation, which is supported by the negative results generated from the geographic rivalry variable, is that a geographic rival is actually detrimental to demand (income), and thus a disparity between the rivals can have significant influences on the decisions of sponsors and fans. This explanation becomes likelier when evaluating that television income produced negative (albeit statistically insignificant) results; sponsors may want
to pick a clear winner, match-going fans may want to see blow-outs, but broadcasters will want
to appeal to the excitement that consumers feel (Whannel, 1993) (Baimbridge, Cameron &
Dawson, 1996).
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However, unlike the goal difference variable, the signs are consistently negative for all
four dependent variables across all the applicable models. A possible explanation for the absence
of a significant effect is that the rank number is static for season end, and does not carry into the
next season. Lagging the variable could help the rank difference variable attain explanatory
power, and measuring the effect of rank difference over the previous years could make it
statistically significant as well.
For FIFA-determined Rivalry (Models 2 & 6)
The goal difference variable for FIFA-determined rivalry is operationalized in the same
manner as the geographic rivalry goal difference variable, with the difference being that the 3
year goal difference average is measured against the rival FIFA-determined by FIFA (if
applicable). A large goal difference was assigned for teams that did not have a FIFA-determined
rival. This variable is deployed with the aim of testing Hypotheses 2.3, 2.3.1 and 2.3.2.
The variable behaved largely in the manner it was expected to, which is in contrast to the
geographic goal difference variable.
Table 5.23 3-Yr Avg Goal Difference with FIFA-determined rival from 1 coefficient value on all DVs Regression Total Revenue Match-day Revenue Television Commercial Revenue Revenue 1 n/a n/a n/a n/a 2 -0.083 (0.032) 0.069 (0.022) -0.199 (0.048) 0.028 (0.059) 3 n/a n/a n/a n/a 4 n/a n/a n/a n/a 5 n/a n/a n/a n/a 6 -0.149 (0.039) 0.040 (0.022)* -0.174 (0.054) -0.108 (0.070) -Number between brackets is standard error - Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
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The negative coefficient of the goal difference variable for both models 2 and 6 implies a
positive relationship between competitive balance and revenue for determined rivalries. Model 2
(FIFA-determined rivalry with competitive balance) implies that a change in the 3-year average
goal difference distance from 1 between FIFA-determined rivals would impact total revenue by
17.39%, while Model 6 stipulates that the impact is actually at 29.04%. The significant disparity between the two is caused by the introduction of the geographic rivalry variables (and their
respective competitive balance proxies), but the consistent significance means that the results
lend support to Hypothesis 2.3.
The match-day results show a negative relationship between competitive balance and
match-day revenue, which does not contradict any hypotheses but also implies that match going
fans (i.e. the higher involvement consumers) prefer to see games where rivals are heavily
defeated. One variable is statistically significant (Model 2), while Model 6 is significant at
p=0.10.
Television revenue was significant for both models 2 and 6, and signifies a positive
relationship between competitive balance and television revenue; Model 2 stipulates that the
impact of a change in the distance of the 3-year goal difference average from 1 on television
revenue would be 36.76%, while the coefficient in model 6 indicates that the difference would be
33.01% of television revenue. The statistical significance of both lends support to Hypothesis
2.3.1.
The average goal difference variable was not found to be statistically significant (or
within the 10% confidence interval) for the commercial revenue DVs; this means that the results
do not lend support to Hypothesis 2.3.2.
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In summary, the goal difference variable was found to be supportive for FIFA-
determined rivalries. Similar to the binary variables, the results are contrasting with geographic
rivalry results, and this suggests that it is not the geographic aspect of rivalry that is financially beneficial.
For total, television and commercial revenue, the values were not statistically significant for the rank difference from FIFA-determined rival variable. Therefore, rank difference variable for FIFA-determined rivals did not lend support to Hypotheses 2.4, 2.4.1 or 2.4.2.
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Table 5.24: Rank Difference from FIFA-determined Rival coefficient on all DVs
Regression Total Revenue Match-day Television Commercial Revenue Revenue Revenue 1 n/a n/a n/a n/a 2 -0.002 (0.003) -0.005 (0.002) -0.002 (0.005) -0.006 (0.006) 3 n/a n/a n/a n/a 4 n/a n/a n/a n/a 5 n/a n/a n/a n/a 6 -0.001 (0.004) 0.131 (0.032) -0.005 (0.006) 0.004 (0.007) -Number between brackets is standard error -Bolded values are statistically significant (at 95% confidence interval) **Starred values are within the 90% confidence interval
Match-day revenue yielded statistically significant results for both models 2 and 6,
however while Model 2 yielded a negative coefficient (indicating a positive relationship between competitive balance and revenue), Model 6 yielded a positive coefficient. The insertion of geographic rivalry variables (and/or their competitive balance predictors is what is responsible
for this result, and thus no postulations can be made about the rank difference variable for FIFA- determined rivals and its influence on match-day revenue.
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Chapter 6: Conclusion & Discussion 6.1 Summary of Findings
Geographic Rivalry Hypotheses
Ultimately, the Study results did not lend support to Hypothesis 1 or any of the sub-
hypotheses falling under Hypothesis 1. On the contrary, the findings, while inconclusive,
suggested that local rivalry had a negative impact on revenue (refer to Table 5.17), and the
findings were inconclusive on the spatial competition effects (the distance variable – refer to
Table 5.18). In similar fashion, the 3-year goal difference distance from 1 for geographic rivals,
one of the competitive balance variables, behaved in a manner opposite to Hypotheses 1.4, 1.41
and 1.42 – and the results suggest that a state of competitive imbalance between local rivals is
more favorable towards revenue. In connection to the literature, the presence of a rivalry and the
state of competitive balance’s effects on revenue are as Porter put them from a Geographic
perspective: the competitive balance harms revenue, and thus, the findings are concurrent with
what the literature has to say on other industries. The distance variable was not significant and
did not lend support to Hypotheses 1.3, 1.31, 1.32, but that is primarily due to the
operationalization of the variable rather than what the results modeled.
FIFA-determined Rivalry Hypotheses
The results lend strong support to Hypotheses 2 – 2.31 and no support towards
hypotheses 2.32, 2.4, 2.41 and 2.42. It was found that the presence of a FIFA-determined Rival
on the Deloitte Money League rankings in the same year positively influences financial
performance across all four dependent variables (refer to Appendix D), and there was partial
evidence to support that a state of equal competitive balance between rival clubs would
positively impact revenues as well (refer to Table 5.23). The goal difference variable was
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statistically significant for the total, match-day and television revenue, but was not significant for
commercial revenue.
6.2 Contributions/Implications
Theory
In similar vein to the remainder of industries, geographic market dynamics seem
to operate in the same manner in sports as they do in other industries. The presence of a
local competitor will constrain profitability, and a state of equal competitive balance will
also limit revenue. This is in line with how rivalry operates in business (Porter, 1985),
and there is no theoretical contribution in economics as far as geographic rivalry is
concerned.
However, interestingly enough, it seems like a state of competitive imbalance
between local rivals or geographically proximate competitors in Deloitte’s Money
League rankings can generate additional revenue. This is a counter-intuitive finding; the
premise of sport, as put by Whannel, is the very uncertainty of outcome and excitement
(Whannel, 1993).
From the general (FIFA classified) rivalry perspective, the results suggest that
having rivalries (just not local ones) makes a football club thrive financially, and that
implies that the business view on rivalry is fundamentally different from the sporting
industry perspective on rivalry: in sports, rivalry is good for business. This opens the door
to many possibilities with regards to rivalry literature extension into the sporting industry,
and an outright exception to Porter’s 5 Forces Model (Porter, 1979).
Furthermore, there is partial support towards competitive balance being positive
for rivals (refer to FIFA-determined Rivalry Hypotheses section), and that is also in
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contrast to how business strategy views competition, and a unique extension atop
literature concerning consumer and stakeholder utility from sports.
Managerial
This paper can potentially have several contributions that apply to broadcasters,
policymakers in leagues and sports governing bodies, executives in clubs, sponsors.
For Broadcasters: Though behavior already indicates that broadcasters are aware of this,
broadcasters may be now more inclined to negotiate broadcasting rights with individual clubs
rather than with leagues as a whole (thereby able to optimize cost through discarding or underpaying for games of lesser attractiveness to the fans), and further develop a more integrated role in the marketing activities of the clubs to which they are contractually tied to showcase the rivalry effects in order to guarantee greater fan pull.
For Policy Makers: There are several policy makers that would benefit from this information.
League policy makers would be more likely to consider imbalanced fixture schedules that favor
local rivalries (meaning that local rivals would play against each other more so than other clubs),
and emphasize points of sale in their negotiation agreements with broadcasters, and develop a
more cautious anti-trust policy when dealing with financials of multi-rival clubs. European
Football’s governing body, UEFA, is currently deploying Financial FairPlay rules, which are
rules governing the expenditure of clubs and restricting it to set proportions of their income in
order to prevent large clubs from utilizing their better credit to incur great amounts of debts that
sustain their competitive advantage and give everyone an opportunity to increase their off and on
field performance. This would be an aspect to consider in order to level the playing field; greater
allowances should be made for clubs who would opt to invest in their rivals (should this prove as
financially beneficial, as addressed by one of the research questions) as that would reduce the
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rivals spending to income proportion and elongate the period through which the investor club
would recoup their gains.
Another host of European clubs, an association known as the G18 have been aiming to break
away from their local leagues and UEFA by creating a league that combines Europe’s top teams;
it would be advisable that they consider local rivalries and their impact – especially with teams
performing at a similar level.
Furthermore, for policymakers aiming to stimulate and develop local leagues and grow the
industry in the country, the potential findings of this paper would suggest to create more than
once club per market, in order to ensure that the development of rivalry can generate the
attention of the fans, and consequently broadcasters and sponsors.
For Executives: Club executives may look at conditions where they can increase revenue streams
they generate by stimulating a rival, and may find a better ROI in doing so than they would had
they spent it on themselves. In 2013, German club Bayern Munich famously refunded their fans’
season tickets after a season where they were peerless winners in the Germany league, with
controversial club president Uli Hoeness declaring that ‘the entertainment factor was absent for
the fans because there was no competition’. Investing to make a rival more competitive could be
a route that improves the attractiveness of the product that the club itself is offering, and
consequently improves the bottom line.
6.2 Limitations
This research suffers from several limitations, including: 1. The data set is not representative: This dataset measures the financial performance of
Europe’s top 20 clubs by financial performance (heteroskedasticity). The findings
therefore cannot be generalized until more studies find similar results with other club
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distributions, particularly across entire leagues (so that the biggest and smallest clubs are
put to the same test).
2. Unobserved Variables impacting coefficients: In many instances, especially when
concerning television and commercial income (refer to Appendix D), there are some
unobserved variables that are influencing the magnitude of statistically significant
variables. For example, if the coefficients are to be taken at face value, average
attendance can financially influence match-day revenue more than it can total revenue (in
absolute terms). Given that match-day revenue is a sub-constituent of total revenue, that
is pragmatically impossible (assuming average attendance does not negatively influence a
revenue stream, which it does not), there are lacking variables that would give more
accurate coefficient values for the other variables. Variables such as the number of
televised games and number of sponsors would improve television and commercial
revenue respectively, and further put the weight of the other coefficients in a more
accurate scale. While the results show that rivalries positively impact profitability, the
coefficients are not necessarily the best indicators of that magnitude of that influence.
Furthermore, if data was available on travel costs or commute times to the nearest
geographic rival, it would have posited a better test of the spatial dimension of rivalry
(i.e. how close, near, conveniently accessible is a club’s nearest competitor). The number
of trophies won, a win ratio against a rival are better alternatives to league rank difference
in terms of what constitutes competitive balance (Neale, 1964).
3. Method does not allow for examining the influence of previous years: The MLR is static,
and thus cannot measure the impact of a previous year’s league standing, for example. A
method that allows for examining the impact of an event on subsequent years (lagging,
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EFA) may produce more accurate results on the exact role of rivalry, and shed more
insight into the role of competitive balance.
6.3 Future Research Considerations
The findings of this thesis open up some exciting research avenues when concerning rivalry in soccer, or sports in general. Most importantly however, the most important avenue for research to explore is the uniformity of this finding.
Given the concerns cited with regards to whether rivalry is positively related to income for all clubs, or just top performing clubs, is an important question to answer. The first future research consideration must be about testing the generalizability of this principle. Not only would this lend significant implications for investors, broadcasters and sponsors, but it will also
explicate the scale of the finding found, and whether it’s a unique case of elite clubs or
applicable irrespective of what pre-conditions are there.
Another important future research consideration is regarding rivalry antecedents. This
study took rivals as determined by FIFA and grouped them together, albeit that classifying with
antecedents (historical, political, religious, performance-based – see literature review) in order to
determine if there is one (or a group) that is driving this effect.
More research must be conducted with regards to geographic rivalry in order to
determine an impact on revenue streams. While the findings of this study lent strong support to
ruling out local rivalries as beneficial to revenue, spatial rivalries (nearest competitor) may be a
potential rivalry dimension that positively impact income (especially given the negative – albeit
statistically insignificant – coefficients produced by the distance to nearest competitor variable).
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Research that measures the impact of rivalry on subsequent years of financial
performance is also important to accurately capture the role of rivalry on financial results. A
more dynamic model than an MLR is required.
Measuring rivalry intensity should be an objective; literature currently says that rivalry is
a negative affective disposition towards a particular team. But the measurement of this
disposition on the playing staff is important in understanding what truly constitutes a rivalry. In-
game statistics should be used to proxy this: events such as shots on target, yellow and red cards,
help capture the intensity of a rivalry, and thus develop a better idea on the effect of rivalry
intensity, and determine an empirical measure for rivalry intensity.
85
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APPENDICES
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Appendix A: Stakeholder Analysis: Consumers of the Football Product Customer Description Motivation Fans The end-users who actually watch the The excitement that is derived from games and support the respective the uncertainty of the outcome. teams. Social construction and the Purchase memorabilia and club wear fulfillment of nationalistic/regionalist and various items that demonstrate affiliations. their affiliation and loyalty to the club/institution. Identity construction; that can either be tied to geographic spheres, the success of the institution or the “underdog” tag. Networks Television broadcasters who purchase The objective is to maximize revenue the rights to televise the competitive by gaining maximum possible games and broadcast them to a viewership, which subsequently specific region or lease them to others allows for the broadcasters to charge for a set period of time. premium pricing for the advertisers, sponsors and sub-leasers of the Can either negotiate deals with clubs product. individually or the league as a whole. They will therefore be attracted to the Can act as middlemen who sub-lease same features that attract the fans, as the broadcasting rights to regional per their efforts to generate fan broadcasters. viewership. Sponsors Corporations who aim to be affiliated The objective is to maximize with the clubs in order to gain exposure and therefore clubs with the exposure through the club’s activities highest marketability and attention . from the customers in certain Their range of agreements can vary geographic scopes will be targeted. from being an official sponsor or partner to having their logo within the They will be looking for some stadium or on the jersey(s), can also attributes that are similar to what the be the kit manufacturer and official fans look for: on-pitch performance transport partner, and can offer and brand ambassadors being critical products and services through the factors in their decision. club. Government Looking for clubs whose business activities can serve objectives. Their search will be based on a set of criteria that varies depending on the Can offer payment in the form of tax objective at hand. cuts, subsidies and contractual agreements.
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Appendix B: On Sources of Revenue
Gate Receipts and Match-Day Sales Historically a major source of income for all sporting clubs, gate receipts and fan commercial expenditures within the premises comprised as much as 85% of average club income annually in the 1970’s (Andreff & Staudohar, 2000). This particular source of revenue is comprised of ticket sales (that can either be done on a per-season basis at a discounted per-match rate or individually) and supplementary services offered within the site during sporting events (this includes food and beverage offerings, on-site item sales, catering and so forth). The tickets themselves adhere to a tiered pricing system that is typically determined by the proximity to the event and the ‘class’ of the ticket (with executive class tickets offering a more exclusive experience that also includes catering services, being in proximity to club personnel and brand ambassadors and ‘giveaways’). Memorabilia and Merchandising This includes items such as club official replica sportswear, branded items, media, collectibles and whatever a club chooses to dispense through its retail channels and on-site megastore. This source of revenue comprised an average 10% of club income in the late 1990’s (Andreff & Staudohar, 2000). Memorabilia sales can be done through what is typically known as the club megastore (on-site), through locations outside that belong to the club, or through vendors and retail channels that are within the club’s supply chain. Merchandising is directly tied to the marketability of the club and the present playing staff that act as the face of the organization; demand increases when marketable players play for the club, with income from jersey sales alone reaching up to 50 million sterling pounds per player per season at times (Rodriguez, 2014). Sponsorship and Advertising In the earlier days sponsorship and advertising revenue did not comprise a large portion of the club’s spending. In contrast, it is now one of the two most dominant sources of income for a top flight football club competing in one of Europe’s top five leagues (Baimbridge, Cameron & Dawson, 1996). Sponsorship and advertising revenue will utilize an integrated marketing communication strategy that utilizes the club events to present products or increase visibility and credibility through communication channels that capitalize on the clubs existing audience. Sponsorship revenue is comprised of several facets: 1) sponsors who appear on club wear (such as team jerseys; this typically includes one major sponsor whose logo is visible at the front of the team jersey and the sporting company that manufactures the jersey), 2) sponsors who appear on the clubs advertising billboards at match events, 3) partners that appear through public relations and media relations. The size of sponsorship packages is influenced by a multitude of factors, including the visibility of the sponsor on the club’s ‘real estate’ (be it the training ground, stadium, conference rooms, or even club attire), the role that the sponsor plays with the club and
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the nature of the exposure (local, continental or global).There exists a direct relationship, therefore, between the club’s brand appeal and the amount of sponsorship revenue that they incur. Television and Broadcaster Revenue Initially, television and revenue obtained from broadcasters in exchange for televising club matches was a small, if not non-existent source of income for European football clubs (Jenrennaud & Kesenne, 2006). In 1976, French Football Club Olympique De Marseilles famously turned down an offer of $50,000 per match in order to televise its matches (Baimbridge, Cameron & Dawson, 1996). However, this would not persist for long, and since the late 1980’s, television revenue has been comprising an increasing proportion of total club receipts, rising to comprise over 33% of the English Premier League’s turnover in 2004-2005 (Deloitte, 2005). There are two primary models that clubs employ when conducting transactions with television broadcasters. The first model, employed in Spain, implies an individual bargaining process for television rights – meaning that the each member club of the league would negotiate the rights to broadcast single or multiple games with the television providers. The other model employed is collective. In this model, the league association negotiates a television contract individually prior to the discussion and implementation of revenue distribution amongst the member clubs based on multiple criteria (including on-field merit, number of matches televised and home/away fixture allocation). Figure AB.1 below shows the differing redistribution of income agreements within the respective European football leagues:
Figure AB.1 Television Rights Redistribution in European Football Leagues (Jenrennaud & Kesenne, 2006)
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Appendix C: Descriptive Statistics
Descriptive Statistics N Minimum Maximum Mean Std. Deviation City Pop 199 256652.0 13854740.0 2422594.465 2948407.2575 log of population 199 12.46 16.44 14.0947 1.07610 Per Capita GDP (000s) 200 .00 85100.00 35082.0250 12416.27594 log of GDP per capita 199 9.14 11.35 10.4109 .35246 League Position 200 1 21 3.95 3.623 Champions League 200 0 1 .70 .459 Participation Europa League Participation 200 0 1 .16 .372 number of games played 200 1.00 31.00 14.6000 5.82750 minus regular season games Average Attendance (000s) 200 20.872 80.521 50.93747 14.569763 Log(attendance) 200 1.32 1.91 1.6883 .13110 Stadium Capacity 200 32115 115000 61291.34 17538.992 log of stadium capacity 200 10.38 11.65 10.9829 .28592 Transfer Expense (mil eur) 200 .00 257.40 45.0067 39.45130 log of transfer expenditure 195 -4.02 5.55 3.3882 1.17430
Presence of App Rival 0/1 200 0 1 .50 .501 Presence of Local Rival 0/1 200 0 1 .41 .494 distance to nearest team 200 .00 1000.00 222.2720 277.76379 rank difference from app 200 .00 20.00 7.7050 7.08725 rank difference from nearest 200 .00 20.00 4.1200 4.65445 Total Rev (mil eur) 200 63.10 518.90 200.0235 96.85934 log of total revenue 200 4.14 6.25 5.1932 .45375 Matchday Income (mil eur) 200 7.70 137.50 50.2000 32.83990 TV Income (mil eur) 200 11.30 199.20 80.8745 40.74102 log of telivision income 200 2.42 5.29 4.2516 .56379 Commercial Income (mil 200 16.00 254.70 68.9490 44.37580 eur) log of commercial income 200 2.77 5.54 4.0644 .56983 Valid N (listwise) 193
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Appendix D: Study Results DV: Total Revenue
Variable Dimension Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Socio-Demographic Population 0.044 (0.020) 0.046 (0.019) 0.084 (0.026) 0.076 (0.026) 0.037 (0.023) 0.037 (0.022) Income 0.094 (0.056)* 0.094 (0.055)* 0.031 (0.068) 0.043 (0.068) 0.186 (0.065) 0.133 (0.060)
Performance League Rank -0.011 (0.006)* -0.011 (0.006)* -0.012 (0.007)* -0.012 (0.008) -0.012 (0.006)* -0.007 (0.007) UCL (0/1) 0.062 (0.071) 0.077 (0.274) 0.114 (0.207) 0.108 (0.090) 0.052 (0.078) 0.084 (0.077) EL (0/1) -0.084 (0.076) -0.072 (0.075) -0.155 (0.097) -0.166 (0.097)* -0.046 (0.085) -0.049 (0.084) GamesPlayed 0.015 (0.004) 0.014 (0.004) 0.021 (0.005) 0.023 (0.005) 0.014 (0.005) 0.016 (0.005)
Transfer Exp. 0.104 (0.016) 0.107 (0.016) 0.126 (0.019) 0.124 (0.019) 0.120 (0.017) 0.109 (0.017) Avg Attendance 1.032 (0.153) 1.063 (0.152) 1.289 (0.175) 1.260 (0.176) 0.303 (0.124) 1.017 (0.158) Geographic Rivalry Local Rival (0/1) n/a n/a -0.284 (0.127) -0.326 (0.015) -0.014 (0.118) 0.026 (0.125) Dist. Rival (km) n/a n/a -0.151 (0.070) -0.180 (0.017) -0.065 (0.066) 0.068 (0.072)
Determined Rivalry FIFA Rival (0/1) 0.276 (0.043) 0.266 (0.042) n/a n/a 0.254 (0.052) 0.258 (0.051)
Competitive Balance Local Comp Rank Diff Local n/a n/a n/a -0.003 (0.006) n/a -0.005 (0.310) 3 Year GD Local n/a n/a n/a 0.115 (0.062)* n/a 0.159 (0.055) FIFA Comp Rank Diff FIFA n/a -0.002 (0.003) n/a n/a n/a -0.001 (0.004) 3 Year GD FIFA n/a -0.083 (0.032) n/a n/a n/a -0.149 (0.039)
Constant 1.210 (0.585) 1.181 (0.576) 1.153 (0.711) 1.193 (0.712)* -4.803 (1.329) 0.786 (0.202) Adjusted R Square 0.711 (0.245) 0.721 (0.241) 0.635 (0.286) 0.639 (0.284) 0.729 (0.246) 0.736 (0.243)
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DV: Match-day Revenue
Variable Dimension Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Socio-Demographic Population 0.064 (0.014) 0.063 (0.013) 0.045 (0.014) 0.037 (0.014) 0.042 (0.014) 0.026 (0.014)* Income 0.030 (0.039) 0.031 (0.039) 0.042 (0.038) 0.060 (0.098)* 0.018 (0.040) 0.069 (0.035)
Performance League Rank 0.001 (0.004) 0.001 (0.004) -0.000 (0.004) -0.002 (0.004) 0.002 (0.004) -0.001 (0.004) UCL (0/1) -0.078 (0.119) -0.092 (0.049) 0.017 (0.050) 0.003 (0.047) 0.000 (0.048) -0.025 (0.045) EL (0/1) -0.118 (0.053) -0.128 (0.052) -0.078 (0.054) -0.095 (0.051)* -0.034 (0.052) -0.051 (0.049) GamesPlayed 0.014 (0.003) 0.015 (0.003) 0.011 (0.003) 0.014 (0.003) 0.008 (0.003) 0.011 (0.003)
Operational Strategy Transfer Exp. 0.010 (0.011) 0.007 (0.011) 0.018 (0.011)* 0.016 (0.010) 0.007 (0.011) 0.005 (0.010) Avg Attendance 1.096 (0.108) 1.070 (0.106) 1.199 (0.098) 1.178 (0.093) 1.350 (0.145) 1.093 (0.092) Geographic Rivalry Local Rival (0/1) n/a n/a 0.151 (0.071) 0.072 (0.070) 0.260 (0.072) 0.197 (0.072) Dist. Rival (km) n/a n/a 0.007 (0.039) -0.044 (0.039) 0.083 (0.042) 0.022 (0.042)
Determined Rivalry FIFA Rival (0/1) 0.107 (0.030) 0.115 (0.029) n/a n/a 0.138 (0.032) 0.138 (0.030)
Competitive Balance Local Comp Rank Diff Local n/a n/a n/a 0.001 (0.003) n/a 3 Year GD Local n/a n/a n/a 0.142 (0.033) n/a 0.003 (0.003) FIFA Comp Rank Diff FIFA n/a -0.005 (0.002) n/a n/a n/a 0.131 (0.032) 3 Year GD FIFA n/a 0.069 (0.022) n/a n/a n/a 0.040 (0.022)*
Constant -1.668 (0.411) -1.644 (0.401) -1.747 (0.396) -1.757 (0.377) -0.331 (0.852) -1.715 (0.356) Adjusted R Square 0.594 (0.172) 0.613 (0.168) 0.675 (0.159) 0.711 (0.150) 0.709 (0.151) 0.745 (0.141)
96
DV: Television Revenue
Variable Dimension Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Socio- Demographic Population n/a n/a n/a n/a n/a n/a Income n/a n/a n/a n/a n/a n/a
Performance League Rank -0.001 (0.009) -0.002 (0.008) -0.004 (0.010) 0.002 (0.011) 0.003 (0.009) 0.011 (0.010) UCL (0/1) 0.155 (0.157) 0.191 (0.105)* 0.132 (0.128) 0.149 (0.129) 0.079 (0.120) 0.139 (0.115) EL (0/1) -0.173 (0.116) -0.140 (0.111) -0.318 (0.138) -0.305 (0.138) -0.170 (0.132) -0.128 (0.308) GamesPlayed 0.021 (0.006) 0.018 (0.006) 0.032 (0.007) 0.030 (0.008) 0.022 (0.007) 0.019 (0.007)
Transfer Exp. 0.147 (0.025) 0.156 (0.024) 0.177 (0.028) 0.151 (0.026) 0.155 (0.025) Avg Attendance n/a n/a n/a n/a n/a n/a Geographic Rivalry Local Rival (0/1) n/a n/a -0.359 (0.160) -0.295 (0.171)* -0.071 (0.160) 0.099 (0.163) Dist. Rival (km) n/a n/a -0.185 (0.092) -0.143 (0.100) -0.021 (0.092) 0.150 (0.098)
Determined Rivalry FIFA Rival (0/1) 0.302 (0.066) 0.279 (0.064) n/a n/a 0.390 (0.079) 0.319 (0.076)
Competitive Balance Local Comp Rank Diff Local n/a n/a n/a -0.010 (0.008) n/a -0.009 (0.007) 3 Year GD Local n/a n/a n/a -0.020 (0.088) -0.003 (0.080) FIFA Comp Rank Diff FIFA n/a -0.002 (0.005) n/a n/a -0.005 (0.006) 3 Year GD FIFA n/a -0.199 (0.048) n/a n/a n/a -0.174 (0.054)
Constant 3.341 (0.137) 3.443 (0.134) 3.586 (0.281) 3.535 (0.284) 3.213 (0.273) 3.095 (0.261) Adjusted R Square 0.536 (0.383) 0.574 (0.368) 0.459 (0.413) 0.458 (0.413) 0.527 (0.385) 0.579 (0.364)
97
DV: Commercial Revenue
Variable Dimension Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Socio-Demographic Population n/a n/a n/a n/a n/a n/a Income n/a n/a n/a n/a n/a n/a
Performance League Rank -0.034 (0.010) -0.034 (0.010) -0.039 (0.011) -0.034 (0.012) -0.036 (0.011) -0.030 (0.012) UCL (0/1) -0.033 (0.126) -0.041 (0.128) -0.003 (0.139) -0.001 (0.138) -0.030 (0.138) -0.005 (0.137) EL (0/1) 0.040 (0.133) 0.034 (0.133) -0.046 (0.150) -0.054 (0.148) 0.030 (0.151) 0.033 (0.149) GamesPlayed 0.007 (0.007) 0.007 (0.007) 0.013 (0.008) 0.015 (0.008)* 0.008 (0.008) 0.009 (0.008)
Operational Strategy Transfer Exp. 0.135 (0.029) 0.134 (0.029) 0.164 (0.030) 0.158 (0.030) 0.148 (0.030) 0.146 (0.030) Stadium Capacity 0.498 (0.120) 0.510 (0.123) 0.829 (0.139) 0.832 (0.138) 0.759 (0.140) 0.696 (0.155) Geographic Rivalry Local Rival (0/1) n/a n/a -0.178 (0.174) -0.278 (0.183) -0.028 (0.184) -0.107 (0.195) Dist. Rival (km) n/a n/a -0.135 (0.101) -0.195 (0.108)* -0.040 (0.107) -0.055 (0.124)
Determined Rivalry FIFA Rival (0/1) 0.290 (0.076) 0.293 (0.076) n/a n/a 0.215 (0.092) 0.195 (0.091)
Competitive Balance Local Comp Rank Diff Local n/a n/a n/a -0.009 (0.008) n/a -0.013 (0.009) 3 Year GD Local n/a n/a n/a 0.240 (0.094) n/a 0.264 (0.096) FIFA Comp Rank Diff FIFA n/a -0.006 (0.006) n/a n/a n/a 0.004 (0.007) 3 Year GD FIFA n/a 0.028 (0.059) n/a n/a n/a -0.108 (0.070)
Constant -1.906 (1.337) -2.060 (1.378) -5.243 (1.533) -5.270 (1.515) -4.701 (1.530) -4.023 (1.672) Adjusted R Square 0.405 (0.439) 0.402 (0.440) 0.426 (0.448) 0.442 (0.441) 0.441 (0.442) 0.459 (0.434)