Competitive Balance in Professional Football

Submitted by Harald Jani

Submitted at Department of Economics

Supervisor Dr. Mario Lackner

April 2017

Competitive Balance in Professional Football

Master Thesis to obtain the academic degree of Master of Science in the Master’s Program Economics

JOHANNES KEPLER UNIVERSITY LINZ Altenbergerstraße 69 4040 Linz, Osterreich¨ www.jku.at DVR 0093696 STATUTORY DECLARATION

I hereby declare that the thesis submitted is my own unaided work, that I have not used other than the sources indicated, and that all direct and indirect sources are acknowledged as references. This printed thesis is iden- tical with the electronic version submitted.

Linz, 26 April 2017

Harald Jani

2 Contents

1 Introduction 4 1.1 Why is Competitive Balance important? ...... 6 1.1.1 How to model sports leagues ...... 8 1.1.2 The meaning of Competitive Balance ...... 11 1.2 What drives changes in Competitive Balance? ...... 13 1.3 How to measure Competitive Balance? ...... 15 1.4 The illustrative Case of Austria ...... 16

2 Background 19 2.1 A short history of UEFA competitions ...... 20 2.1.1 From the European Cup to the UEFA Champions League ...... 21 2.1.2 The lower ranked UEFA tournament: UEFA Cup or Europa League ...... 23 2.2 Proﬁt streams for participants ...... 24 2.3 Exploring the UEFA country coeﬃcient ...... 25 2.3.1 Econometric implications ...... 26

3 Data and Sources 27 3.1 How leagues evolved over time ...... 30

4 The Empirical Approach 32 4.1 Linking the UEFA Ranking and CB ...... 36 4.2 A Diﬀerence in Diﬀerence-Appraoch ...... 38

5 Summary and Outlook 41

References 44

A Appendix 46

3 1 Introduction

In context of the current discussion1 about competitive structures in professional football it is necessary for decision makers as well as the general public to assess the ideas being considered as reforms. When oﬃcials discuss the demand side for the product ”football”, consumer preferences and long-term development of the market, it is important to keep track on established theory. The simple idea of translating economic thinking into the football world (and vice versa in as the subsequent step) was the motivation for this Master thesis. Analytic thinking about the status quo is a necessary condition for sustainable reforms in this emerging industry.

In order to proceed in a consistent way I will approach in the following steps: First in section 1.1 I will give the an idea on how competitive balance might be an essential feature of consumer demand for football. Several authors have tried to pin down this very relation. Essentially, we need to make some assumptions on how the ”product/service of professional football” shall be delivered. In contrast to a ordinary product like a notebook which has definable (even quantifiable) attributes ascribed to a certain level of utility by consumers (although this may be abstract as well) football can be perceived as ”thrilling” or ”interesting”. Hence descriptions are rather vague. This is why scholars spent many pages of work on how preferences (and ultimately demand) for it may look like. Within those loads of work one major concept has repeatedly occurred in findings: the concept of competitive balance. This terminology (with obvious economic background) was often used as proxy for ”thrill”. Therefore, section 1.1.2 will bring a definition for the context of sports. In an environment of high competitive balance (from now on CB) we see championship races being decided in the final phase of the season, teams being head-to-head on winning a title, matches being decisive for final positions. The combination of luck to play a role (which is also debated in literature) and long-term success determined by single events of 90 minutes time. People tend to infer from those decisions which managers, coaches or players took the right decision at the right point of time. This created many myths and legends about the ”beautiful game” (which are discussed using an economic approach e.g. in Kuper and Szymanski (2012)).

In this paper I will to apply econometric tools to the field of professional football. Due to this fact I will try to give an overview on how existing literature in the field has already dealt with the issues discussed. We will find that sports leagues have been of academic interest since the mid of the

1See e.g. UEFA http://uk.reuters.com/article/uk-soccer-uefa-ceferin- idUKKBN16T1QN retrieved on March 30, 2017

4 twentieth century. Authors thought about how to explain the behaviour of agents in the industry in terms of wage setting and incentive design (for players as well as for clubs). In section 1.3 I will pin down the arguments in favour of using the Herﬁndahl-Hirschman-Index to measure CB.

The task of examining sports leagues can be meaningful in two aspects: First the size of the sports industry (in Europe predominantly football) are motivation enough the deepen insights on it’s development. To provide suf- ﬁcient background knowledge on the matter of football chapter 2 will sum up the facts on the status and development of the subject as far as it is necessary to follow the ideas in this master thesis. As mentioned above there is a growing number of professional athletes crossing the border of seven digits in annual income. With growing economic potential within the industry it is also a secondary industry participating (or even accelerating) in the trend of growth. From international tourism to attend matches to merchandising the businesses complementary with sports are widening. Secondly, there are aspects in sports which might deliver insights on diﬀerent economic topic like wages setting (e.g. salary caps in US sports), international capital mo- bility (free movement of labour vs. limited amounts of foreign players) or incentive design (monetary and non-monetary remuneration, e.g. ”drafting schemes” or relegation rules).

Professional football is said to constitute unsustainable financial depen- dencies. There will be need for evaluation whether proposed remedies (the re-distribution from TV rights revenues) are able to counteract the criti- cised matter. This is where econometric tools come to play. They allow their applicants to analyse whether the claim of causality or its absence is justified. The following question shall be examined: ”Does participation in UEFA competitions influence competitive balance within European football leagues?” In a further stage I will evaluate whether links identified between aforementioned aspects could display a positive or negative influence.

To draw a connection between this topic and core topics of economic thinking we could slightly re-interpret the situation of companies competing internationally additional to their home market. Professional football is typically coordinated in national leagues. The ”daily business” of the regular seasons provides a relatively homogeneous task for the teams participating. The possibility of playing against successful teams on international level delivers an additional challenge for the teams who achieved to qualify for it. Those international duties are implemented in the regular schedule of the clubs. From August to May the teams play a certain number of extra matches (depending on their stage of entrance). As this displays a higher physical eﬀort to bear for the players it is rewarded with substantial monetary compensation. This reimbursement accounts for a crucial part in

5 the club’s annual budgets. In chapter 2.2 we will ﬁnd the numbers of the proﬁts spilt over international starters.

After providing a basic understanding chapter 3 discovers the dataset I collected and applied. From it’s historical range to descriptive statistics I will discuss the important aspects. This will finally lead to the models intended to examine the main research question. Combining the information on final tables and participation on UEFA held tournaments I try to establish a connection to the Competitive Balance in national championships over time. In chapter 4 I will derive my empirical approach step-by-step. Starting with rather simple OLS-estimations I will include fixed-effects and account for long-term endogeneity resulting in a more advanced model at the end. To finalize my research I conduct a Difference-in-Difference model which profits from a major regulatory change in the Champions League mode in the late 1990s.

Finally, I will try to find a conclusion and summarize my findings in chapter 5. This will be accompanied by some further ideas on what could be further done in research on the topic. This summary will also comment on the shortcomings of the models to give some glances on how to possibly improve research in the field. Additional to the main chapters I provided data in an appendix which adds detailed information on some of the described material. It includes some financial information on the UEFA’s reward scheme. To refer again to the starting point: I will initially describe the insights on Competitive Balance I collected in the following part.

1.1 Why is Competitive Balance important? Besides looking at the design of competitive environments an important branch of literature describes statistical methods to investigate relative playing strengths in football. The terminology which is used in scientific background is “competitive balance” (CB). This jargon originates from the background of markets. One of the core fields of economics, Industrial Organi- zation (see e.g. Tirole (1988)), uses the concept in order to explore Wel- fare effects in markets. In the presence of a small number of firms which dominate the industry researchers typically find that measures of business activity (e.g. relative revenues) are concentrated among those firms. Conse- quently, they face a certain degree of power since customers are used to their products (the term product can be interpreted here in a very broad sense). Changing from one product to another can induce switching costs2. As an example we might think of an operating system: We assume two operating systems not being perfect substitutes. If a consumer considers changing the

2Following Thompson and Cats-Baril (2002) switching costs are deﬁned as the costs of switching from the supplier of a good/service to another

6 OS on her computer because of a cheaper substitute, she might weigh the positive utility through lower costs against the disutility from getting used to another product (see e.g. Klemperer (1995)). This is one basic interpretation of how market power create externalities (“side eﬀects” of usage like learning eﬀects). These switching costs leave the initial supplier a tiny (in reality it does not have to be tiny at all) margin for setting a price above marginal costs.

In the context of sports, the usage of CB measures is applied in a similar way. In industrial organization we can argue that a high degree of market share is the starting point as well as the outcome of other variables like profit, revenue or price-setting ability. It can be outcome if profits in the past allow for harsh defence against market entrants via establishing net- works (and with them externalities). In football this circular relation is not completely analogous unless we apply some assumptions on how revenues are generated in football leagues. Therefore we can distinguish between several streams of income. Market-based streams, as I will label them from here onwards, are those which are realized by market-interactions like selling tickets, merchandising products, gastronomic services or transfer surpluses (which can abstractly be viewed as talent accumulation or development). If we accept the idea of immanent skills in players which can be exploited by the manager, this view makes intuitive sense. As opposed to those sources of profits, football teams typically earn income on an aggregate level as a professional league or association. Aggregate refers to the fact that a certain number of clubs is necessary to form a league. The discussion on how we can model leagues is highly debated. Authors argue about joint production or cartel features of football leagues. It has already been conducted in the previous chapter (see Neale (1964)).

We find that in most countries there are systems of revenue redistribution by league authorities. To consider a specific example, take the English Premier League. In Table 1 we can see that English officials similarly separate profit stream into different categories. We will discuss later on where those profits come from. Focusing on the categories we find that about 50 percent of total revenues are distributed equally among PL participants. The rest is assembled into an profit scheme accounting for sportive performance (merit based) and consumer demand (facility fee). This is the reason why income is dependent on success. On the other hand we can assume that players typically earn money for their marginal product. To simplify things we can make the assumption that higher playing skills are more expensive than lower ones (although this assumption is not as straightforward as it sounds at first glance). We will take those considerations further in the next section on basis of a formal model for sport leagues.

7 Szymanski (2001) tries to relate the appearance of reduced Competitive Balance with the major source of income: fan interest. His interpretation in which the rational expectation of fan concerning a games outcome is mainly influenced by percieved CB. Formally he states that 0 ≤ P (A) ≤ 1 where P(A) is the probability of team A winning. That is, we can assign a probability P(A) to each team. His model predicts that is not necessarily optimal to seek a state in which P(A)=P(B)=0.5 (perfect balance between team A and B), instead there are different mechanisms at work influencing optimality conditions. For chronological reasons this model will be examined after the early pieces of work in team sports literature.

Place Team Equal Share Merit Based Facility Fee Total 1 Leicester City 55.5 24.7 12.8 93 2 Arsenal 55.5 23.5 21.8 100.8 3 Tottenham 55.5 22.2 17.3 95 4 Manchester City 55.5 21 20.3 96.8 5 Manchester United 55.5 19.8 21 96.3 6 Southampton 55.5 18.5 10.5 84.5 7 West Ham 55.5 17.3 12.8 85.6 8 Liverpool 55.5 16.1 18.8 90.4 9 Stoke City 55.5 14.8 8.75 79.1 10 Chelsea 55.5 13.6 18 87.1 11 Everton 55.5 12.4 15 82.9 12 Swansea City 55.5 11.1 8.75 75.3 13 Watford 55.5 9.9 8.75 74.2 14 West Brom 55.5 8.7 8.75 72.9 15 Crystal Palace 55.5 7.4 9.5 72.4 16 Bournemouth 55.5 6.2 8.75 70.4 17 Sunderland 55.5 4.9 11.3 71.7 18 Newcastle United 55.5 3.7 13.5 72.7 19 Norwich City 55.5 2.5 8.75 66.8 20 Aston Villa 55.5 1.236 9.5 66.2

Table 1: Table of Premier League Prize Money for Season 2015/16 (all numbers in Mio.pound), source: http://www.totalsportek.com/football/premier-league-prize-money- table-2015/ retrieved on April 26, 2017

1.1.1 How to model sports leagues In the tradition of describing the formal modelling of team sports in social sciences the genesis is typically credited to the inﬂuential work of Rottenberg (1956). He analyzed the market for professional baseball players in the United States (e.g. see Dobson and Goddard (2001)). In the 1950s the baseball teams were in the favourable position of keeping the decision power over their players even when their contracts expired: The Reserve Clause gave them the option of renewing the contract for another year once it ended. This prevented players from being free agents and hence redistributed bar-

8 gaining power in a severe manner. Rottenberg (1956) therefore introduced the notion of monopsonies into the contractual affair of players in order to explain their worsened position on the market. The argumentation in favour in the Reserve Clause was the following: If teams were not given this option, smaller teams (i.e. teams with a smaller potential talent pool) would find it difficult to deter their best players from subscribing to the bigger clubs. So the idea behind this measure was to retain Competitive Balance in the league.

The argument which Rottenberg (1956) used to overcome the idea of protectionism in this sense was that team officials will acknowledge their di- minishing demand for a highly unbalanced league. This is why they would incorporate a (small) preference for balance into their decision making. Un- der the assumption over rational profit maximizing teams, a market inter- vention like a Reserve Clause will not change the overall distribution of talent. Players will be signed according to their productive use (under the assumption that this value can be estimated). Ultimately the article reflects on how player salaries evolve and what mainly influences them. He con- cludes on which measures alternatively might be able to ensure equal talent distribution among league participants. Those are measures like Revenue Sharing, Salary Caps or Team Franchises which are still applied in nowadays team sports. This shows that Rottenberg (1956) had a large impact on the field of economics in team sports.

The second important paper in this field of interest was the work of Neale (1964). From observations in Heavyweight Boxing he draw the conclusion that fights in which contenders were closer in terms of fighting skills or strength earnings for the stars of the branch were significantly higher. The working title ”Peculiar Economics” suggests that unlike standard economic theory would predict mechanisms lead to different and paradox outcomes in sports. In his investigation he separates competition into sporting and economic aspects. When he considers the sporting side of leagues he argues that fair, thrilling and close competition will be preferred by spectators over a monopoly situation. Furthermore he states that in an economic sense the term ”firm” to be used for a single sports team is a non-trivial labelling. The reason is that a single team would never be able to serve a whole market. To capture the characteristics of sports leagues we need to implement a kind of interaction between participants. This lead to the idea of coopera- tive or joint production of the product football. In this interpretation teams are rather ”plants” of the national association then separate enterprises. If we accept this the next question consequently arising is whether leagues compete with each other. In nowadays set-up of international tournaments (like the UEFA’s leagues) this might be a realistic approach to take. Op- posing this idea, Neale (1964) argues that economic characteristics make

9 reality more complex: In his evaluation of team sports in the United States costs structures are such that associations tend to favour a situation with a natural monopoly. For one league it will be efficient to supply the whole market.3 Following his reasoning we also observe interdependence between demand and supply of football talent: The larger the number of fascinated spectators, the more young players could enter a professional football career widening the pool of potential players for top clubs. Additionally he states the direct as well as indirect streams of utility are at play: directly through supporters in stadiums and indirectly through spectators enjoying a thrilling title race. Finally he argues that this peculiarity has to be considered by league officials and decision-makers in order to efficiently regulate competition in this industry.

Questioning Neale’s idea of the league-based decision making Sloane (1971) thinks that it is the clubs (i.e. their officials) setting the parameters within competition: they accumulate talent, build infrastructure and create ”brands” (as ideas and trends of well-established business branches enters sports industry). Although, he admits the team’s interdependencies with each other as typical for ”joint production” of the final product. In what he considers a cartel it is decision making under some constraints that decides precondition for CB. Furthermore, Sloane (1971) emphasizes the reduced validity for profit maximizing regimes in European football (as opposed to US sports). Rather we should see football clubs as utility maximizers facing a solvency constraint. He names several aspects of utility which can be important for clubs: • Profit

• Security: ensure the club’s existence in the future

• Attendance/Revenue: Attracting a large crowd (customers) to games

• Playing succes: a rather abstract goal since it can be success as well as an ”attractive” style of play which owners want to see from their teams

• Health of the league: accounts for mutual interdependence between teams Using those aspect Sloane (1971) formulates a utility function of the following form: U = u(P, A, X, πR − π0 − T ) with each of the arguments being one incorporated aspect of utility. P stands for playing succes, A captures attendances, X describes the league’s health, πR being realized proﬁt and π0 proﬁt after taxes and T accounting for tax burden. This work brought

3This trend might be a future scenario for Europe as well, if we take the dominance of a small number of major leagues with worldwide demand as evidence.

10 several new insights on sports league theory appreciatively incorporated by the scholars afterwards.

In another early piece of work on sports league theory, El-Hodiri and Quirk (1971) examine how sports leagues and their respective participants behave in terms of competitiveness. They construct a model on decision making on team basis, accounting for the peculiar character of a sportive competition. What motivates their research were the aspects of antitrust law in this context: Due to lower technological standards of sports broadcasting (at the time when El-Hodiri and Quirk published their work) consumers had a smaller set of alternatives when choosing to follow a team or a specific league. Typically spectators had a closer relationship to their local team and followed games in it’s stadium. From this starting point El-Hodiri and Quirk (1971) thought about antitrust law enforcement. When teams representatives affiliated to form leagues in which they can pool their market power. This lead the severe changes in market power. We can think of collective selling of TV rights, price setting behaviour for tickets or merchandise, wage setting or contract design for players. For this study it is important to dis- cover the interaction of profit-maximising behaviour and the convergence of playing strength. Their work can be seen as early examination of incentive design and effects on CB. This is a highly important and current issue as changes in regulation are constantly in the spotlight of media attention in sports.

As already mentioned in section 1.1 Szymanski (2001) conducts an analysis on how CB might be influential on long-term league development via fan-interest. Although he finds that financial disparities are increasing in English football Competitive Balance it is not deteriorating accordingly. So is attendance. In order to explore the demand side of football I assume it requires sufficient knowledge of CB. This is why I will try to keep close track on how the development of CB over time might manifested itself in the industry.

1.1.2 The meaning of Competitive Balance When scholars talk about CB in a sports context they typically distinguish between three kinds (see Szymanski (2003)): • Match CB

• Season CB

• Championship CB On a match level CB relates to the concept of outcome uncertainty (see Peel and Thomas (1988)). If there is imparity at this level the opposing

11 teams have contrasting playing strengths. This will occur mostly when teams of diﬀerent divisions face each other. For spectators those matches are usually not very thrilling as they expect the favourite to triumph. Seasonal competitive balance is what this paper mainly focuses on: the dominance of k teams within one season (where k can be chosen to analyse the group of interest). The third bullet-point widens the examined range of seasons. Championship CB research deals with the long-term development within a league.

Season Champion 1985 Real Madrid 1986 Real Madrid 1987 Real Madrid 1988 Real Madrid 1989 Real Madrid 1990 FC Barcelona 1991 FC Barcelona 1992 FC Barcelona 1993 FC Barcelona

Table 2: Period of low championship CB in Spain, source: own data

Typically we think of one team to win the title in a contest repeatedly. As an example may serve the superiority of Real Madrid in the Primera Divisiónin the late 1980s. They achieved to win the title 5 times in a row. Just in order to be dominated by FC Barcelona in the following 4 seasons. This kind of patterns can be found in many countries in European football, but there are hardly examples where this dominance lasted longer than 5 years. It leaves the impression of counter-forces which prevent teams from achieving successful periods longer than 5 years. Although the examination of mechanisms at work would be an interesting field of research they are not at the core of this work. Speaking about championship imbalance the Champions League is offering insights as well as domestic competition: Since the introduction of the UEFA Champions League in 1992 no team was able to defend the title at least once. This might indicate close competition at the peak of European football or the importance of randomness in the game on a top level. On the other hand reforms of the CL gave the major football leagues in Europe chance to establish their dominance: Changes in the qualifying mode might influence CB nationally as well as internationally. For econometric purpose those changes come in quite handy as the provide space for statistical analysis. This will be explained in all detail in chapter 4. In the following section I will introduce some ideas on how CB might change.

12 1.2 What drives changes in Competitive Balance? After defining CB in the context of football, we can take the next step and think about factors which are able to influence it. To deepen our understanding we can follow the research conducted by Partosch et al. (2014), who studied data on the German Bundesliga (which will also be part of the dataset for this work). Although his research design was similar to the paper at hand, it focusses on the impact of the UEFA Champions League on one league. In the case of Schalke 04 (a team from Gelsenkirchen) they argue that 20 percent of the club’s income are generated through Cham- pions League participation. If the importance of UEFA participation for a German first division club is that consequential, we can imagine that for smaller leagues’ teams it might even more influential. In Austria, Belgium or Switzerland (just to name those who appear in the dataset), teams competing internationally might find themselves with significantly higher budgets for the years following. As sports-economic literature (e.g. see Lehmann, Weigand, et al. (1997)) emphasizes: Monetary resources improve the competitive advantage for clubs. Nevertheless, there is no one-to-one relation between money invested and success. Partosch et al. (2014) continue by arguing that we have to consider to the role of randomness in competitive outcomes. The term ”randomness” has to be used in a rather colloquial sense: Decisions of referees, player’s injuries, significant tactical or playing errors, etc. have physical/psychological causes, but the causation might be due to such an enormous number of variables which makes anticipation impossible. In order not to lose the grasp for randomness in this context we will leave epistemological thoughts beside and follow the argumentation further. The concern of league officials is that frequent participation in highly rewarded international tournament will lead to something called ”Big Push”(see Vöpel (2007))4. I will briefly introduce the underlying model as described in Partosch et al. (2014).

Starting point is the simple idea of the indicator of success (SUCC - which determines the position in a ﬁnal table or tournament) being the aggregate of a market value (MW) and a random component (RC). RC refers to function normally distributed around zero. The market value itself is a function of structural conditions (SC), e.g. the economic strength of a club’s location and cyclical factors (CF) (e.g. through competing in the Champions League). Formally we can state that

SUCCi = MWi + RCi (1)

MWi = f(SCi,CFi) (2)

4Not to be confused with the terminology ”Big Push” from Economic Growth Theory, see Rosenstein-Rodan (1961)

13 The same framework is depicted in Figure 1. We can see that the most probable outcome (i.e. final position) at the end of the season is close to the intrinsic market value of the team i. Nevertheless for team j (where MWj < MWi) there might be random factors influential enough to overcome the difference in playing strength between those teams such that j reaches a higher position in the final table. This would be the static examination of what might happen in one particular season.

Figure 1: Success model, Source: Partosch, 2014

In the dynamic approach of Partosch et al. (2014), they argue that if we observe team A being on a higher position than team B, it might be that only A will reach a international qualification spot. Team A participating in this additional competition generates additional income. Furthermore, we can assume that additional income can be invested into structural improvements (players, staff, infrastructure, etc.). Reconsidering equation 2 those improvements induce further strengthening of the market value. If team A manages to establish itself in international competition, it might generate a gap large enough for the random component not to overcome the same. Scholars introduced the expression ”big push” for this case in reference to the more advanced team being pushed further away from its competitors. In the absence of any counteracting mechanism this could pave a gap between clubs within a league over time. The question of chapter 4 will be to examine if this is evidently the case in the dataset. Gaps between teams of different initial strength could be indicated by reduced championship CB (mid- and long-term CB). Furthermore, Partosch et al. (2014) mentions that CL participation significantly reduces the volatility in the performance of clubs. He compared the team performance three years before and after CL participation. This could support the theory of an increase in MWi. Although reduced volatility does not include a positive trend in performance.

14 1.3 How to measure Competitive Balance? The measurement of CB will can be conducted through a method describing certain statistical characteristics of the league. In Industrial Organisation, researchers in market analysis use the Herfindahl-Hirschman-Index (HHI) to characterise market concentration. The advantage which the HHI offers is the fact that large, dominant market participants are weighted higher as compared to functions of linear character. In a market (or sports league) L with n market participants the measure takes relative market share of firm i ai and sums up their respective squares to build an Index. Formally the HHI of league L can be written as in equation 3. Naturally, we find that this index has a lower bound given by 1/n (a perfect balance like in the two 1 1 team case would be denoted by Hlb = n = 2 = 0.5).

n X 2 HHIL = (ai) (3) i=1 where

wi ai = Pn i=1 wi gives the relative share of ﬁrm i to the total market.

Owen, Ryan, and Weatherston (2007) applied the HHI to sports leagues. Instead of market shares they suggest to use the number of wins per season. They emphasize that a simple HHI version would face the problem of being highly dependent of the number of teams n in a league. As mentioned above there exists a natural lower bound of the index given by 1/n. Additionally, they state that we can also describe a relationship between n and the maximum number of wins possible within one season (upper bound). To account for this inﬂuence Owen et al. (2007) postulate a slightly modiﬁed version of the HHI: the normalized HHI. As equation 4 states the normalized HHI will be given by an index ranging from 0 to 1. At its core the normalized HHI uses the basic concept of the HHI and additionally accounts for the conceptual upper and lower bounds.

HHI − HHI HHI∗ = lb (4) HHIub − HHIlb The lower bound originates from the fact that in football in each game there will be a gain of points for at least one team. Once again we reconsider the fact of league containing 20 teams: While team A facing team B can make sure B gains no points, at least nine of the remaining 18 teams will gain points as well. So if team A is able to win all of its games (38 x 3 points) the second placed team can still win 38 - 2 games (the two losses

15 against team A) and hence collect 36 x 3 points overall. Owen et al. (2007) derive a short-cut formula for this minimum of points.

HHIlb = 1/n (5) The nature of sports leagues require a natural limit of points (the object of interest and proxy for market domination) which can be collected by a n certain team. 2 matches are scheduled per match-day. For one team this means it can only take a maximum of one win from the round. Consequently, n 2 −1 wins cannot be achieved. This is where the upper boundary originates from. A total dominance in one league would be the to gain 3 points of every game in one season. In a league of 20 teams there will be 10 games per matchday. If one team faces each opponent two times per season this gives 20 teams - 1 = 19 games x 2 games per opponent = 38 matchdays x 10 matches per day = 380 games. Once again, a formal reproduction of this fact will give us a short-cut on calculating the upper limit 5. 2(2n − 1) HHI = (6) ub 3n(n − 1) Combining the information on the leagues structures leads to equation 4 stated above. Normalisation accounts for league size and the possibilities through a specific compensation scheme in football. This describes the basic unit of measurement in this piece of work. With the normalized HHI as workhorse, I will conduct further calculation on league data. We will see that the measures will not serve as a perfect proxy for CB. Indeed, it is a demanding task to consolidate all dimensions of unbalancedness into one ratio. When we consider different ways of capturing CB statistically like Michie and Oughton (2004) do in their work we will come over the pros and cons of each specific tool applied.

1.4 The illustrative Case of Austria To illustrate the calculation of the normalized HHI under different league setting I chose to explore an interesting period in Austrian football. Figures 2 and 3 show what normalizing the HHI does in an graphical analysis. I use data of the Austrian Bundesliga where several changes in the league mode have been made in the past decades. In Figure 2 I put emphasis on the effect of changes in n. On the graph beneath the normalized HHI (red line) was allowed to fluctuate after being corrected by the boundaries. Now we can observe higher volatility in the development over time. The range of the HHI is widened in the normalized case. From this I deduce the following question: Do these deviations reflect changes in what we would interpret as less balanced competition?

5For the deviation see Owen et al. (2007)

16 w data own 3: Figure

HHI 2: Figure HHI 0 .1 .2 .3 .4 0 .1 .2 .3 1970 1970 H ihbudre n omlzdfrteAsra udsia Source: Bundesliga, Austrian the for normalized and boundaries with HHI H ihbudre o h utinBnelg,Suc:ondata own Source: Bundesliga, Austrian the for boundaries with HHI 1975 1975 1980 1980 HHI forAustrianBundesliga H Upperbound Numberofteams Lower bound HHI HHI forAustrianBundesliga 1985 H Upperbound HHInormalized Lower bound HHI 1985 1990 season 1990 17 season 1995 1995 2000 2000 2005 2005 2010 2010 2015

0 2 4 6 8 10 12 14 16 18 20 2015 number of teams Rank Team Games Win Draw Lose Points Goal Diﬀ 1 SK Rapid Wien 36 18 11 7 47 26 2 Austria Wien 36 18 8 10 44 22 3 Grazer AK 36 16 6 14 38 -7 4 FC Admira Wacker 36 14 8 14 36 -7 5 FC Wacker Innsbruck 36 14 7 15 35 8 6 Sturm Graz 36 14 5 17 33 -9 7 Wiener Sport-Club 36 12 9 15 33 -12 8 FC Blau-Weiss Linz 36 12 8 16 32 -3 9 SV Salzburg 36 11 9 16 31 -7 10 Linzer ASK 36 12 7 17 31 -11

Rank Team Games Win Draw Lose Points Goal Diﬀ 1 SK Rapid Wien 30 20 8 2 48 54 2 Austria Wien 30 22 4 4 48 49 3 FC Wacker Innsbruck 30 13 12 5 38 19 4 Sturm Graz 30 16 5 9 37 17 5 SV Salzburg 30 14 6 10 34 11 6 Austria Klagenfurt 30 13 6 11 32 3 7 Grazer AK 30 12 8 10 32 0 8 FC Blau-Weiss Linz 30 12 8 10 32 -1 9 SC Eisenstadt 30 8 13 9 29 -7 10 FC Admira Wacker 30 9 9 12 27 -5 11 Wiener Sport-Club 30 10 7 13 27 -16 12 Linzer ASK 30 9 7 14 25 -7 13 SC Neusiedl/See 1919 30 7 7 16 21 -20 14 Union Wels 30 6 8 16 20 -19 15 First Vienna FC 30 7 5 18 19 -36 16 1. Simmeringer SC 30 2 7 21 11 -42

Rank Team Games Win Draw Lose Points Goal Diﬀ 1 Austria Wien 30 21 5 4 47 56 2 SK Rapid Wien 30 19 9 2 47 53 3 Linzer ASK 30 17 8 5 42 29 4 FC Wacker Innsbruck 30 13 11 6 37 23 5 Sturm Graz 30 15 7 8 37 9 6 FC Admira Wacker 30 12 12 6 36 11 7 Austria Klagenfurt 30 12 10 8 34 17 8 Grazer AK 30 13 6 11 32 8 9 Wiener Sport-Club 30 10 7 13 27 1 10 SV Salzburg 30 10 7 13 27 -7 11 SC Eisenstadt 30 9 7 14 25 -10 12 FC Blau-Weiss Linz 30 8 9 13 25 -12 13 Favoritner AC 30 8 9 13 25 -17 14 SV Sankt Veit 30 7 7 16 21 -22 15 Union Wels 30 4 6 20 14 -47 16 SC Neusiedl/See 1919 30 1 2 27 4 -92

Table 3: Final tables for the seasons 1981, 1982 and 1983 of the Austrian Bundesliga: Those shall display the underlyings of the normalized HHI. In the corresponding years the index performed a jump from 0.05 to 0.27 and 0.36 in 1983. * Points follow 2-point-rule ** For simplifying identiﬁcation team names were updated to current names

18 Indeed, this is what Table 3 suggests: The low concentration of 0.05 in 1981 shows the close standings in the final table. From 3 to 10 rank only seven points lay between the competitors. As opposed to this close outcome we find that in the following years (after the league expanded to 16 clubs) the less successful teams did not manage to keep up with the top teams. In 1983 the team finishing 16th had only 4 points, in contrast to the Austrian champion gaining 47 points. This leads to a large value in the normalized Herfindahl-Hirschman-Index. This example illustrates what a league expansion can cause if the additional teams might not be able to compete accordingly.

Whereas the HHI gives an overall statistical summary of what happened within one season the second measure for CB suggested in literature contains some degree of arbitrariness in its formulation. The Concentration Ratio (CR, see Manasis, Avgerinou, Ntzoufras, and Reade (2011)) relates the dominance of a particular subgroup to the size of the total market. It is arbitrary how many teams are included into this subgroup. Still there would be several examples where this measure might be interesting in terms of consumer preferences: The current situation in Spanish La Liga sees three teams dominating the rest of the league. With the CR we could calculate the relative share of points (wins) of those top 3 in contrast to the aggregate points of the following 17 clubs. This would deliver interesting insights into the divergence between the best and the rest. on the other hand, we would have to deﬁne the size of the top group consistently over time and leagues in order to compare ﬁndings.

2 Background

When we think about the UEFA competition affecting CB it is necessary to declare which underlying mechanisms could be able to influence competitive balance. To draw an analogy to economic theory it could be the case that it requires a certain firm size to engage in international trade. The term ”trade” is in this case a re-interpretation for supplying the good ”football” on an international basis. Trade could also mean the trade of playing talent in the football specific background which is basically possible for every participant of professional football association subject to national regulatory framework. Sticking to the interpretation of Vöpel (2007) the intrinsic MWi has to lie above a certain threshold depending on to which degree randomness plays a role in the game.

In order to evaluate the eﬀects of international competitions in Euro- pean professional football, it is necessary to receive fundamental knowledge about the proﬁt streams which can be expected from it. The following sec-

19 tions provide those basics. The Union of European Football Associations (abbr. UEFA) is the association governing international tournaments within Europa and partly Asia. On it’s homepage it describes itself in the following way:

”UEFA – the Union of European Football Associations – is the governing body of European football. It is an association of associations, a representative democracy, and is the umbrella organisation for 55 national football associations across Europe. Its objectives are, among other things, to deal with all questions relating to European football, to promote football in a spirit of unity, solidarity, peace, understanding and fair play, without any discrimination on the part of politics, race, religion, gender or any other reason, to safeguard the values of European football, maintain relations with all stakeholders involved in European football, and support and safeguard its member associations for the overall well-being of the European game.”6

In terms of competitions held by the UEFA one has to distinguish between club competition and tournaments of national teams. There are two competitions which are held on annual basis: the Champions League and the Europa League. For those, teams qualify in their national championships or cups (in each country association have some freedom of setting the mode according to their preferences). Typically the country champions are allowed to participate in the Champions League or it’s qualiﬁcation prequel. The detailed setting for the countries’ qualiﬁcation regulatory will be discussed in section 2.3.

• Club competition

– UEFA Champions League – UEFA Europa League

• UEFA European Championship

Euro 2016 in France; oﬃcial logo, Source: http://de.uefa.com/uefaeuro

2.1 A short history of UEFA competitions The idea of cross-border competitions in European football has already been conducted in the late 1920s. In several tournaments organizers invited national champions to match each other in international games. After various

6Source: http://www.uefa.org/about-uefa/index.html retrieved on December 18, 2016

20 forms of tournaments with participation restricted by political struggles and warfare in Europe the season of 1955/56 exhibited the ﬁrst opportunity to invite several national champions to participate in an international competition. Historically those two tournaments evolved diﬀerently. This is why their development shall be highlighted in separate sections.

2.1.1 From the European Cup to the UEFA Champions League The ﬁrst season of the so-called European Cup started with 16 teams playing in a knockout tournament. The cup became a prestigious trophy as national champions were invited to compete for it. According to the Union of Eu- ropean Football Associations (2004) the ﬁrst edition had on average 28,000 spectators per game. What followed was a story of success for the popular tournament. The UEFA’s important task was to establish the competition as an attractive spot for clubs as well as for spectators. The mode has only been marginally changed until the major reforms of the 1990s. In it’s early stages the rule setting of the European Cup allowed only one club per country to participate. Exception was made for the winner of the previous season: If this club had conquered the championship in it’s home country, the runner-up was allowed to join the exclusive UEFA tournament.

This mode has been established until the 1990s where the UEFA decided to re-label it’s main product on club-level. The denotation ”Champions League” was introduced for the season 1992/93. The reason being changes in marketing strategy by the host organisation. The increasing popular- ity was reinforced by a higher degree of professionalism in marketing efforts. The seasons 1991 to 1993 used a different approach to determine the participants of the final match. Two group stages were introduced with the winners of the second phase competing in a decisive game. Before advancing to the group stages teams entered a knockout qualifying match determining participants out of a larger pool of starters7.

How the changes aﬀected certain countries’ clubs can be examined by taking some examples: As an example, I chose to use the data on the En- glish Premier League in Table 4. With beginning of the dataset (which corresponds to the year 1963) we can see English champions regularly qualifying for international competition. Following the mode of participation at least one English team managed to qualify for the tournament except for two occasions and in the aftermath of the Heysel Stadium Disaster in 19858.

7It is important to mention that in the time from 1955 to the 1990s the UEFA expanded heavily incorporating several national associations in Europe and Western Asia 8In this disaster 39 people died at the European Cup ﬁnal in the Heysel Stadium in Brussels between Liverpool FC and Juventus Torino. This led to a ﬁve year ban of English clubs in the European Cup and one additional year for Liverpool FC, see

21 Season CL EL Season CL EL Season CL EL 1963 1 2 1980 2 3 1997 2 4 1964 1 2 1981 2 4 1998 2 4 1965 1 3 1982 2 4 1999 3 5 1966 1 2 1983 1 4 2000 3 3 1967 1 3 1984 1 5 2001 3 4 1968 2 4 1985* 0 0 2002 4 5 1969 1 4 1986* 0 0 2003 3 5 1970 1 5 1987* 0 0 2004 4 2 1971 0 5 1988* 0 0 2005 4 3 1972 0 3 1989* 0 0 2006 4 4 1973 1 4 1990** 0 1 2007 4 4 1974 1 4 1991 1 1 2008 4 5 1975 1 4 1992 1 2 2009 4 3 1976 1 4 1993 1 2 2010 4 2 1977 2 4 1994 1 3 2011 4 5 1978 2 4 1995 1 4 2012 4 4 1979 2 4 1996 1 3 2013 4 1 2014 4 3

Table 4: International participation of Premier League clubs English participants were sanctioned with a 5-year-ban after the Heysel Stadium Disaster in the Final of FC Liverpool-Juventus Turin in the season 1984/85 *A one year additional ban for Liverpool FC for the ﬁnal of 1985 caused another absence of Premier League clubs

With the reform of 1997 we see that English football receives increased international presence in the Champions League. Since 2004 each of the four Premier League starters attended the prestigious tournament.

From the example of the league with the highest economic prosperity we can draw the conclusion that dominant players in European football faced a improved situation in international participation after the regulatory changes of 1997. As Figure 4 indicates the five best performing leagues in European football managed to significantly increase the amount of teams starting in the Champions League group stage. The picture shows a significant increase in the percentage of Big Five representatives in the aftermath of the Champions League reform of 1996/97. The terminology ”Big Five” is often used in sports-economics related to football 9. Historically, the Big Five provide the predominant force in international football. Analysing this graph, we should account for the fact that the major five leagues are outnumbered by the remaining countries in the sample (5 vs 7). They dominate the field of starters from the observed data. This indicates that the changes which occurred after the rule modifications of 1997 would reshuffle the origins of participants towards already dominant players. As already http://www.bbc.com/news/uk-england-merseyside-32898612, retrieved on April 26, 2017 9Big Five leagues in European football: England, France, Germany, Italy and Spain; see e.g. Vrooman (2013), Goossens (2006) or Szymanski (2007)

22 mentioned in the history of the UEFA CL the tournament was originally intended to include all European national champions to face each other for the crown of European football. Oﬃcials deviated from this idea as they realized that the second, third or forth ranked teams of the biggest leagues in Europe could substitute national champions of lower sportive strength in order to increase attractiveness of their product.

Big Five starters in CL over time 1 35 .9 30 .8 25 .7 .6 20 .5 # teams 15 .4 % of total starters .3 10 .2 5 .1 0 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 season

Big 5 leagues Total number of teams

Figure 4: Relative share of Big Five representatives in total starting ﬁeld of the UEFA Champions League Tournament: ”Big Five”: ENG, GER, FRA, ITA, ESP

2.1.2 The lower ranked UEFA tournament: UEFA Cup or Eu- ropa League The UEFA Europa League which was previously called UEFA Cup is the second most important UEFA held tournament in professional club football. The tournament started out from previous international competitions and has been fundamentally reformed in 1971. It’s preceding version the ”Inter-cities Fairs Cup” has been established as counterpart to the more prestigious European Cup. Additionally, in 1999 the Cup Winners’ Cup which was aimed at the cup winners of the national associations has been integrated into the UEFA Cup. These national cup tournaments are usually played parallel to the regular championship. However, there are also championship spots rewarded with a participation in the Europa League. The distribution of these starting places is according to the same scheme as for the Champions League. For many mid-table clubs, this competition is

23 therefore the only way to compete against the dominance of regular Cham- pions League participants strengthened by their additional revenues.

In the course of the last budgeting of the prize money the UEFA has significantly improved the EL’s position in comparison to the CL: while the price pool for Champions League participants has been increased by 25% the volume for Europa League starters grow by 65%) Distributed). Concerning the income side the contests contribute rather different shares to total revenue: Whereas the Champions League earnings (together with to Super Cup, which exhibits a one time event) gains e1,496.7m, the EL contributes only e299m.10 The effect produced by this factual redistribution is also to be taken into account in later models of this work.

2.2 Profit streams for participants To further examine the monetary incentives are available for the starting field of UEFA competitions we need to take a close look at the prize money. The UEFA gains an increasing amount of revenues for collectively selling TV-rights of it’s two major club events. Since it detected the rising demand for it’s premium product numbers in earnings have grown constantly. The monopoly situation in international top tier football could be exploited to an high extent. To comprehend what this growth trend in economic potential meant for each participant we have to consider the amounts of money awarded for certain sportive achievement. There exists a certain difference in the status of the Champions League compared to the Europa League. The UEFA states that the ratio of 3.3:1 in the volume of prize money (see Appendix A) is appropriate for the two tournaments. This will be specifically interesting when it comes to modelling effects of participation on different kinds of competition. In the season 2015/16 the aggregate prize money adds up to e1.345bn for Champions League group starters plus ten Playoff-competitors. Table 5 provides an overview. The Europa League was endowed with e411.1m.11 This includes a fixed amount of 761.9 for the CL and 239.8 for the Europa League, respectively. Additionally, a variable amount (the so-called market pool made up to e577.7m and e183.1 and was allocated over participating associations according to the size of their televi- sion market. Those funds are again split into two halves: one accounting for previous domestic performance reward, the other compensating for matches played in the previous. season.

10The numbers concern the ﬁnancial year of 2015. Source: http://www.uefa.org/MultimediaFiles/Download/OﬃcialDocument/uefaorg/Finance/02/33/53/52/ 2335352 DOWNLOAD.pdf, retrieved on 2017-02-04 11All numbers retrieved from http://www.uefa.com/uefaeuropaleague/news/newsid=2398584.html as well as http://www.uefa.com/uefachampionsleague/news/newsid=2398575.html, retrieved on 2017-02-04

24 Distribution 2015/16: (numbers in TEUR) Europa League Champions League Fixed amounts 239,800 761,900 Allocation to all group stage participants 2,600 12,700 Group stage performance bonus: per win 360 1,500 Group stage performance bonus: per draw 120 500 Group winners bonus 600 Group runners-up bonus 300 Round of 32 appearance 500 Round of 16 appearance 750 6,000 Quartel-ﬁnal appearance 1,000 6,500 Semi-ﬁnal appearance 1,600 7,500 Winners 6,500 15,500 Runners-up 3,500 11,000

Table 5: Distribution of UEFA revenues over participants, example for the season 2015/16, Source: www.uefa.com, see footnote 11

When we look at the numbers set for diﬀerent achievements in the UEFA’s contests. If we consider the initial prize for group stage participation we can already see that this represents a substantial part of most clubs outside the ”Big Five” (although lower ranked teams in those might also ﬁnance a good share of their expenses with the starting money). If a club is than expected to reach further stages in the tournament, we can calculate the rewards with which it will be compensated. The total prize money for clubs can achieve can been seen in Appendix A for the season of 2015/16: Top earners in this years where Manchester City with e83.9m and the winners Real Madrid with e80.1m. Those sums represent a major source of income for the top teams of European football.

2.3 Exploring the UEFA country coefficient For organizers of the international tournaments, in case of European professional football the UEFA, it was necessary to find an acceptable mode of qualification for each of the participating countries. In order to fulfil this challenging task the UEFA invented a regulatory scheme called UEFA country coefficient. In this ranking each UEFA member association col- lects points through representation by it’s clubs. The starting slots for the UEFA’s tournaments are distributed via the UEFA country coefficient. In each year the total number of points are divided by the number of initial participants. Once again we recall that initial starting spots are given by the rank of the association, hence are fixed ex-ante. The points gained are collected in the following way: Within competition each win is rewarded with two points, each draw with one. Play-off rounds are compensated with the half. Starting with the knock-out phase in the CL each additional round brings a club one extra point. In the Europa League the same applies starting with the quarter-finals. The Champions League’s group stage as well as reaching the round of 16 brings each participant an extra amount of four

25 points. Following this rules the points of each club are aggregated by associations total and divided by the number of teams. The current position will then be decided through the moving sum of the last ﬁve years. This eponymous system than accounts for mid-term performances on international stage to distribute qualiﬁcation spots.

Rank Member association Coeﬀ. CL EL total 1 Spain 105.713 4 3 7 2 Germany 80.177 4 3 7 3 England 76.284 4 3 7 4 Italy 70.439 3 3 6 5 Portugal 53.082 3 3 6 6 France 52.749 3 3 6 7 Russia 51.082 2 3 5 8 Ukraine 44.883 2 3 5 9 Belgium 40.000 2 3 5 10 Netherlands 35.563 2 3 5 11 Turkey 34.600 2 3 5 12 Switzerland 33.775 2 3 5 13 Czech Republic 32.925 2 3 5 14 Greece 29.700 2 3 5 15 Romania 25.383 2 3 5 16 Austria 25.100 1 3 4

Table 6: UEFA Ranking ﬁnal standings 2015/16, Source: http://de.uefa.com/memberassociations/uefarankings/country/season=2016/index.html retrieved on 2017-01-28

In order to visualize Table 6 states the standings of December 2016 in the ranking. At the top of it Spain established a signiﬁcant gap to the runners- up. The lowest ranked division which still appears in the applied dataset is Austria on the 16th place. To understand the importance of those standings we have to consider their application for distributing starting spots in the seasons to follow.

2.3.1 Econometric implications As the concept of the UEFA country coefficient has now been explained in detail we can now take on the next analytic step and think about paths of causality between the observed matter. What should be immediately obvious is the econometric problem behind the link between international qualification and the competitive balance. It may be endogenous through the set-up of the UEFA country coefficient: The mechanism is able to rein- force the positive effect of participation for representing teams of a league. Through better performance relative to teams from countries with a lower UEFA coefficient international starters increase the likelihood of qualifying again for the subsequent period. It is not possible to trace back the starting point of a low CB inducing a high UEFA country coefficient amplifying

26 the low CB and so on. To be concrete: It is difficult to argue whether a club becomes dominant in it’s home league because of previous success or it becomes successful because of prior dominance. The link between CB and the impact on the international qualification spots can be formulated in the following way: Low balance in national leagues can indicate high concentration of playing talent at a small number of clubs. If those clubs in the other hand perform well internationally they serve as dray-horse for their countries association. The prize money collected in UEFA competitions can then be used for further talent accumulation at one dominant club. This shall be summarized graphically in Figure 5. As starting point we consider a certain level of the position in the UEFA country coefficient.

Figure 5: Circular scheme of possible underlying endogeneity, Source: own illustration

Nevertheless it is not the only factor that is relevant in this analysis. A contradicting interpretation would be the increased number of games which teams participating internationally have to face. For the lowest ranked countries (according to the UEFA ranking) there are three rounds of qualification prior the a Play-Off Round to be played. All of those fixtures are played in two games, one at each club’s home ground. The current Champions League tournament design adds six additional matches in a group phase from September to December to the team’s calenders. Those games are played on Tuesdays or Wednesday within the season of national championships. This reduces the number of possible training (or regeneration) sessions for participants.

3 Data and Sources

After discussing theoretical thoughts and concepts on how the question at hand can be examined, we continue by looking at the data available to test with. The retrieval of data on the the ﬁnal tables in football leagues is a

27 simple and trivial task when using online sources. In order to compare the biggest European football leagues (in terms of sportive dominance), I chose to add some smaller leagues with lower ﬁnancial resources. As can be seen in Figure 6, the total number of countries which the sample includes is twelve. As opposed to the top ﬁve leagues according to the UEFA Ranking (see previous Chapter), choosing the smaller leagues was rather arbitrary. Some of their interesting characteristics will be discussed in the following paragraphs. Beforehand some general features of the dataset shall be highlighted.

Seasons included in total sample TUR RUS POR NLD ITA GER FRA ESP ENG CHE BEL AUT 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Seasons observed

Figure 6: Total sample overview, Source: own data

Figure 6 indicates that the dataset covers a period for about 50 years for eight out of the twelve countries examined. In order to facilitate data collec- tion I chose to use two sources (mainly because my primary source fussball- daten.de had shortcomings on some seasons). Hence I used a complementary source: weltfussball.de (worldfootball.net) Due to data retrievement restric- tions the samples for Belgium, Switzerland and Russia were available only to a smaller extent. Nevertheless they were included in the total sample since the eﬀects of Champions-League participation might be interesting as well for those leagues. The example of the Austrian Bundesliga adds some variation in leagues sizes and modes in the observed period which might be interesting in terms of CB.

For the case of English Premier League the status of professional football has been expanded to 4 leagues with 92 teams. In general league size reduction typically was forced by the number of clubs that could handle the challenge of professional football with adequate infrastructure. This is

28 why league reduced teams when clubs struggled ﬁnancially or face severe limitations concerning infrastructure.

In order to understand the full effects of UEFA competition we also have to consider the increase of professionalism in football over the past decades: players are training their physical abilities in order to cover a larger amount of games. This amount of matches that increased with establishing an growing number of professional clubs may actually be the concrete downside of UEFA participation. Teams which have the privilege of participating might have an additional burden of putting higher physical effort in order to perform well in all competitions they achieved to enter. Clubs hire larger squads to ensure their competitiveness in the face of more exhausting fixtures.

After analysing the competition formal and composition of national leagues the next step is to look at particular levels of dominance. Table 7 shows seasons where teams managed to signiﬁcantly dominate competition. Obviously from those 13 examples only three concern ”Big 5” representatives, namely Bayern Munich and Juventus Turin in Germany and Italy. The remaining ten examples are from Portugal (5), the Netherlands (4) and Austria (1). The examples of Portugal and the Netherlands are interesting if we look at the development of CB over time. This is an important aspect of the initial research question: Has CB reduced in national competition over time? And if yes, can we identify a causal relation to additional income of the UEFA tournaments.

Team Season Win percentage

Austria Wien 1985 86.4% FC Bayern München 2012 85.3% FC Bayern München 2013 85.3% Juventus Turin 2013 86.8% Ajax Amsterdam 1971 88.2% Ajax Amsterdam 1972 88.2% Ajax Amsterdam 1997 85.3% PSV Eindhoven 2014 85.3% Benfica Lissabon 1971 86.7% Benfica Lissabon 1972 93.3% FC Porto 1984 86.7% FC Porto 1994 85.3% FC Porto 2010 90.0%

Table 7: Maximum dominance in particular leagues, source: own data

29 3.1 How leagues evolved over time To understand how influential participation on international tournaments might be, we have to consider the characteristics of certain leagues. The competitive situation in those is a manifold phenomenon. To find effects and extract causal relationship from them we have to consider the situation in each country. Historically we find that certain clubs consistently perform better then the rest of their competitors and gain a dominant status in their home leagues. In some leagues this might be due to economic dominance (e.g. capital clubs with a prosper urban location) or political privilege. In Figure 7 we see the specific development of our CB indicator the normalized HHI. We can see that in nine out of the twelve leagues we face a positive trend in long-term concentration of winning percentages. If we conduct the limited sample of the top leagues we find that a positive trend is common across them (compare places 1-5 in Table 11). Over time, the number of wins is increasingly concentrated in the top tier of European football. In terms of quantitative measurement we can see that on average leagues lie between an HHI interval of 0.1 to 0.3. There are certain outliers which are not particularly persistent over time: e.g the Dutch league in the late 1960s, the Austria Bundesliga under the dominance of the clubs from Vienna (1980s, see Table 7). If we consider the development in the Turkish league, we can observe a transition from a period of low concentration to two centuries of increased volatility with peaks of about 0.35 in the normalized HHI.

30 31 h aae a etitdt hre eido ie ore w data own Source: time. of period shorter a to restricted was dataset the 7: Figure

normalized HHI normalized HHI normalized HHI 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 1965 1965 1965 itrcldt ntenraie ismnHﬁdh-ne nteosre ege.I soc gi eakdta o oeleagues some for that remarked again once is It leagues. observed the in Hirshman-Heﬁndahl-Index normalized the on data Historical 1975 1975 1975 1985 1985 1985 Season Season Season ESP NLD AUT 1995 1995 1995 2005 2005 2005 2015 2015 2015

normalized HHI normalized HHI normalized HHI 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 1965 1965 1965 1975 1975 1975 1985 1985 1985 Season Season Season POR FRA BEL 1995 1995 1995 2005 2005 2005 2015 2015 2015

normalized HHI normalized HHI normalized HHI 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 1965 1965 1965 1975 1975 1975 1985 1985 1985 Season Season Season GER CHE RUS 1995 1995 1995 2005 2005 2005 2015 2015 2015

normalized HHI normalized HHI normalized HHI 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 0 .1 .2 .3 .4 .5 1965 1965 1965 1975 1975 1975 1985 1985 1985 Season Season Season ENG TUR ITA 1995 1995 1995 2005 2005 2005 2015 2015 2015 4 The Empirical Approach

After looking at the trend of each league’s evolution in concentration it is necessary to focus on the combination of competitive concentration and international football. As my research question suggests the participation provides a competitive advantage for clubs. By concentration of talented players, connected with a corresponding amount of transfer fees, dominant teams are able to strengthen their status. As chapter 1.3 describes in all detail, the measurement of concentration is conducted using the normalized Herﬁndahl-Hirschman-Index. As an abbreviation for concentration of games won in league i in season t, I use Hit. Seasons are always named after the year of their beginning. To measure participation on tournaments like the UEFA Champions or Europa League I simply took the total numbers of starters per season using the label CLi and ELi. Therefore, regression analysis will give us the correlation of a marginal increase of the HHI with the entrance of an additional team on European Cups.

Additionally, we have to consider characteristics which leagues bring with them: To be precise there is no unique system on how national associations have to determine their international starters. Most of the leagues play their season in the form of a regular group stages in which teams face two or more times per season. In contrast, there is the possibility to split the season into part: Starting a group phase where teams face every opponent twice in order to qualify for a final play-off phase. As an example we might look at the Belgian Pro League which has it’s season separated into different stages. A play-off scheme as applied in some countries tries to implement the best incentive design together with maximizing demand for the product. Some- times this may be conflicting targets to aim at in the scheme is not optimally set up. If we assume this could happen it makes sense to control for play-offs.

The introduction of the 3-point-rule depicted a incentive-based trial to make football more attractive. In 1994 the FIFA decided to implement this rule into professional football starting with the World Cup in the United States of America in the same year. The idea was to induce teams to play more attacking football (”to go for the win”) since it is considered more attractive for spectators. It is nowadays not uncontroversial whether the introduction succeeded in doing so. Hon and Parinduri (2014) argue that for the German Bundesliga the intended eﬀect did not arise from the reform. Nevertheless, if any other change in incentive despondence came up it will be captured by controlling for the 3-point-rule.

Before we can look at the outcome presented in full range in Tables 8 and 9 we will work through the set-up of the models. As already explained I regress the participation in Champions League (CLit) and Europa

32 Leauge/UEFA Cup (ELit) on the normalized HHI (Hit) for league i in season t. Additionally, I control for play-off (POit) and the appliance of the 3-point-rule (3PRit). To emphasize oncemore: We seek to find the effect of CL participation in the past on the current competitive balance. By decid- ing to do so, it is not clear what timespan is necessary for the hypothesized causal mechanism to take effect. This is why I choose to test different periods from which the average international presence was calculated. I did so by defining the variable

PT (CLit−j) ∅CL = j=1 (7) iT T as the average appearance of clubs from country i in periods T (where T = [1, 3, 5, 8]) in both competitions: CL, EL. Hence two independent12 variables are used per period. Together with the control variables mentioned above we come closer to our ﬁrst statement of a hypothesized relationship.

Hit = β0 + β1∅CLiT + β2∅ELiT + β3URt−1 + β4PO + β53PR (8)

As explained above, equation 8 was conducted four times in order to model differences in the periodic length of international appearance to unfold it’s outcome on CB. I used clustered SEs in order to prevent serial correlation from tampering the model specification. Table 8 shows that in this simple OLS framework we can see that CL participation has had an correlates pos- itively with Hit in the range of 0.4-1.9pp. Although it only gains statistical significance over a period of minimum five years. In the long term we can already see significance and a comparably large value of +1.9pp on win concentration. Over the same period we observe EL presence to have an even stronger negative (counteracting) coefficient on CB (-2pp). The obvious problem with those models is their low explanatory power. R-squared only reached the 10% for the long-term model OLS 8. This is why I chose to apply more sophisticated methods in order to increase this important category. The next step to take is to consider characteristic factors in each country affecting the CB. In order to deal with this issue I have applied fixed-effects for the different leagues in a second approach. The models FE1 to FE8 once again compare distinct periods with their influx on Hit. Considering the significant results the models show increased results for each of the periods. For the average of the past eight seasons we can extract an effect of 2.2pp on concentration in the league. This gives us an even higher increment than the naive OLS approach.

12The independence assumption for participating internationally will be relaxed later on, but for the basic model as starting point it will be meaningful to assume it. The participation is inﬂuenced by club performance, that is their playing strength.

33 Table 8: OLS and Fixed-eﬀect models

OLS1 OLS3 OLS5 OLS8 FE1 FE3 FE5 FE8 CL part. in t-1 0.004 0.016* (0.005) (0.008)

EL part. in t-1 -0.008** -0.003 (0.004) (0.002)

∅ CL part. to t-3 0.009 0.022** (0.006) (0.009)

∅ EL part. to t-3 -0.013** -0.006*

34 (0.005) (0.003)

∅ CL part. to t-5 0.012* 0.024** (0.006) (0.009)

∅ EL part. to t-5 -0.016** -0.006 (0.006) (0.004)

∅ CL part. to t-8 0.019** 0.029*** (0.007) (0.009)

∅ EL part. to t-8 -0.020** -0.006 (0.007) (0.006) N 420 420 420 420 427 427 427 427 R2 0.066 0.079 0.086 0.102 0.482 0.493 0.493 0.498 3-point Rule, Playoff, UEFA coefficient t-1 Yes Yes Yes Yes No No No No League Fixed-Effects No No No No Yes Yes Yes Yes Season Fixed-Effects No No No No Yes Yes Yes Yes Standard errors in parentheses Source: own data * p¡0.10, ** p¡0.05, *** p¡0.01 The conceptual idea applied in those estimations is that we can measure the average number of teams which qualified for UEFA tournaments. The rows differentiate over the time horizon for this measurement. Interestingly, we can observe statistical significance when we connect average participation with the constructed measure for concentration. The HHI shows significant movements over a longer timespan. The interpretation in the sense of this work is that participation on international tournaments affects the concentration of wins in domestic leagues.

As a next step I will try to account for the problems of endogenous relations between the variables in the model. As we already considered the UEFA Ranking is the mechanism to decide the number of starters in league i. I assume that the number of starter than feeds back the UEFA coefficient in season t. For illustration issues I used Figure 8. It shall highlight the idea behind the models formulated in the following sections. The question of interest is the effect of Champions League participation on the Competitive Balance (red arrow). As already considered there might be an effect of ”talent concentration” in financially dominant clubs in league i. Theoretically their dominant status can be transferred from domestic to international tournaments. Concentrated talent exhibits an advantage in collecting points for their country in the UEFA ranking. This will in turn improve the long-run UEFA ranking with the consequence that country i offers it’s clubs more starting spots for UEFA competition. Furthermore, I will argue that endogeneity might be at work in this cyclical relationship.

Figure 8: Illustration of circular relationship, Source: own illustration

35 4.1 Linking the UEFA Ranking and CB To oppose the problem described above I will slightly change the model. This time we account for rankings in the past which partly explain variation in CL. By looking at Figure 8 we can see the calculation of the UEFA ranking for season t-1 we take the sum of the past season t-2 until t-6. If we go back one additional year URit−7 will still give us a glance of country i’s relative strength but it does not directly affect the points for t-1. If we suppose one country being highly successful in season t-7 we would than see it’s representatives accumulating respective prize money. Additionally, for the following five years this success will increase the presence of those teams on international stage. The aim is to isolate those two effects as I am concerned about the first more than the latter. By including a necessary timespan between the points of measurement I applied this idea technically. Those thoughts adapt the model into the following form:

Hit = β0 + β1∅CLiT + β2∅ELiT + β3URt−7 + β4PO + β53PR (9)

By adding the term URit−7 I try to resolve the relationship indicated by the dashed vertical arrow in Figure 8. In other words it is the attempt to circumvent the simultaneity problem described in section 2.3.1. As I as- sumed, participation in previous periods would affect the UEFA Ranking in any direction13 For teams who were able to concentrate a higher degree of talent in their squads it will be more likely to achieve a further stage in the competition. Hence, the improvement of the UEFA Ranking is than percussed as well as the higher financial returns. From a theoretic point of view, this would mean a general trend towards clustering of playing talent. It would require further examination to strengthen such a hypothesis. Nevertheless, in a more general way I choose to use an index to describe distribution of final results per season to draw a picture of competitiveness.

Table 9 states the results of the estimations including a time lag variable. We can see that a significant effect immediately comes to play in the following season: One additional CL starter increase concentration by 1 pp (considering the FE model). The effect persists over the whole range of time intervals. In the long term this effect nearly doubles (+1.9pp for t+8). As theoretic considerations proposed, the EL participation operates in the op- posite direction. That is, it reduces concentration of wins by reducing gaps between different parts of the final table.

13Starting spots are limited by the UEFA regulatory. This induces a zero-sum between associations. Additionally there are different probabilities of improving the UEFA Ranking between various starters. Lower ranked league representatives would typically not be expected to reach an advanced state of the tournament. This is why effects depending on the countries’ playing strength movements in different directions could be expected.

36 Table 9: Models applying lagged values

OLS1 OLS3 OLS5 OLS8 FE1 FE3 FE5 FE8 CL part. in t-1 0.005 0.011 (0.005) (0.006)

EL part. in t-1 -0.006* -0.004** (0.003) (0.001)

∅ CL part. to t-3 0.009 0.016* (0.006) (0.008)

∅ EL part. to t-3 -0.011** -0.008**

37 (0.004) (0.003)

∅ CL part. to t-5 0.012* 0.017* (0.006) (0.009)

∅ EL part. to t-5 -0.013** -0.008* (0.005) (0.004)

∅ CL part. to t-8 0.017** 0.022** (0.006) (0.008)

∅ EL part. to t-8 -0.016** -0.007 (0.006) (0.006) N 414 414 414 414 414 414 414 414 R2 0.073 0.085 0.090 0.102 0.494 0.501 0.500 0.503 3-point Rule, Playoff, UEFA coefficient t-1 Yes Yes Yes Yes No No No No Includes lagged value for UR Yes Yes Yes Yes Yes Yes Yes Yes Fixed-Effects No No No No Yes Yes Yes Yes Standard errors in parentheses Source: own data * p¡0.10, ** p¡0.05, *** p¡0.01 4.2 A Difference in Difference-Appraoch As presented in Chapter 2.1 there have been severe changes in the regulatory framework of the UEFA tournaments. Now I will take them into account for econometric purposes. The first aspect is how starting spots shifted from one member association to another. A change in qualification can be taken as exogenous factor influencing the dynamics of the national competition. Before 1997 we saw that typically national champions had the opportunity the represent their countries in the former European Cup of Champions. Only exception has been the previous winner of the cup. They attended the following term as pre-qualified starter. Nevertheless, playing in the CL in more consecutive years meant that a club had to become champion each year. The reforms made it possible for the second, third and fourth placed team in a top league to participate internationally. This obviously increases the likelihood of qualification. A graphical representation of this change can be retrieved from Figure 9. Associations with a high UR come in favour of receiving additional starting spots for the CL. Hence, we can argue that teams of those leagues might have carried away an improvement in financial prerequisites. If this was used to strengthen their competitiveness by pur- chasing playing talent for their top performing teams it might then affect national CB.

Following this argumentation I tried to specify a model accounting for those historical changes in the Champions League. I will make use of the as- sumed exogeneity between changes and its implications for the CB in participating associations’ national leagues by applying a Difference-in-Difference model. The idea behind this model is that different leagues may respond distinctly to the treatment of gaining or losing starting places for international tournaments. To identify treatment and control group I chose to separate between the group of the ”Big Five” (as described in section 2.1) and the remaining seven nations observed. Graphically I took this approach in Figure 10. It contains two scatter-plots for the normalized Herfindahl- Hirschman-Index of each country in the corresponding subsample. In red, we find the representatives of Germany, England, France, Italy and Spain whereas in blue the set contains Austria, Belgium, the Netherlands, Switzer- land, Portugal, Russia and Turkey. We consider the fitted values as trend line. Although the levels are different between groups we can detect a stable, parallel development of CB in both. The initial level of concentration is larger in the countries of lower international dominance. After the reform of the Champions League format in 1997 the most obvious change is lying the CB of the ”Big Five”-Leagues. The plot shows an increase in the normalized concentration of win percentages from about 15 % to over 20 % in the five most competitive leagues. Before the HHI remained on a stable level over the years.

38 39 iue9: Figure

Normalized HHI cte fnraie H fo oa ape vrtm.Sprtdb r-adps-eomi 97 ore w data own Source: 1997, in post-reform and pre- by Separated time. over sample) total (from HHI normalized of Scatter 0 .1 .2 .3 .4 .5 1980 1985 HHI: Diff−in−DiffVisualisation 1990 omH Fitted values normHHI 1995 Season 2000 2005 2010 2015 40 o-i-v AT E,CE L,PR U,TR n ype n otrfr n19,Suc:ondata own Source: 1997, in post-reform and pre- by and TUR) RUS, POR, NLD, CHE, BEL, (AUT, non-big-five 10: Figure cte fnraie H fo oa ape vrtm.Sprtdit ruso i v EG E,FA T n S)and ESP) and ITA FRA, GER, (ENG, five big of groups into Separated time. over sample) total (from HHI normalized of Scatter Normalized HHI 0 .1 .2 .3 .4 .5 1980 1985 om H BgFv’norm.HHI’Non−Big Five’ norm. HHI’BigFive’ HHI: Diff−in−DiffVisualisation 1990 1995 Season 2000 2005 2010 2015 In addition to the graphical analysis I conducted a model separating the effects of the reforms between the groups of interest. It contains dummies for being part of the ”Big Five” as well as periodical dummies for a pre- and post-reform phase. The interaction term (”Post reform on Big 5”) is defined as the combined effect of ”Big5” and ”Post96”. It accounts for the effect the treatment has on the group of already dominant players compared to the lower ranked associations of the UEFA. Equation 10 shows the regression applied. Once again, I included dummies for playoff-systems and the application of the 3-point-rule.

Hit = β0 + β1B5 + β2P ost96 + [β3B5 ∗ P ost96] + β43PR + β5PO + uit (10)

Table 10: Diﬀ-in-Diﬀ

Model Big Five Dummies -0.076*** (0.007)

Post-CL-reform of 1997 -0.010 (0.011)

Post reform in Big 5 0.036*** (0.011) N 523 R2 0.243 3-point Rule, Playoﬀ dummy Yes Standard errors in parentheses Source: own data * p¡0.10, ** p¡0.05, *** p¡0.01

Table 10 sums up the results of the proposed model. Indeed, we see that there is positive and explicitly signiﬁcant relation between the Champions League reform of 1997 for representatives of the Big Five. We can see that the combined eﬀect will increase concentration of win percentages by 3.6%. This represents an interesting result as it tells us that dominant nations (which are overrepresented in the Champions League Tournament) did, in fact, face a non-neglectable change in concentration.

5 Summary and Outlook

In the difference-in-difference model we saw that under the assumptions made it is possible to find evidence for a connection. For the reasons already discussed there will be no ultimate claim for this connection being causal.

41 Scientific objectivity seems to be especially appropriate in consideration of the complex relations in such a matter. Nevertheless, this examination of professional football can be seen as another small step towards understanding the dynamics of concentration. In order to investigate the mechanisms more closely, a careful examination of the matter is a complex task. As it turns out, the measurement of the target variable, the HHI, is already difficult to perfectly execute. The problem mainly lies in the concrete definition of what inequality means in this context. Are the performance gaps between the teams significant? Or is it more a question of a discrepancy between a group of internationally active clubs and the mid-table teams, who seek to have such a presence. If we take a look at the field of the Champions League, it is evident that there have always been representatives of the mid- table class who were able to enter the European top. However, taking into account the frequency of these outstanding successes a trend is apparent.

It seems to be an obvious next step to shed light on the history in which this change is embedded. For this purpose the theories of other authors were presented. They range from the basic analysis of the participants of the football world as economic decision-makers to a correlation between outcomes in the UEFA competitions and the national championships. The Big Push theory (see Vöpel (2007)) portrays a very vivid way how a shift in balance can be explained within the competitions. The accumulation of additional funds seems to have a noticeable effect on the balance of the championships. Teams that take part regularly in the Champions League can upgrade their squad accordingly with a high density of talent. This affects Szymanski’s first dimension of the CB (see Szymanski (2003)): In the game of a regular CL participant against a team in the lower half of the table the betting odds show a difference in the respective probability of winning. The question of how this affects the perception of football as a product arises as a conclusion of this evidence. After this precise definition a research question could be: Which assumptions and statements can be derived from the demand behaviour of football consumers? A completely balanced competition with 1 equal probability for victory over teams (given by n ) would be an imagin- able extreme. Whether this is an ”ideal” condition with regard to demand, remains open at this point.

From the numerous sources on the subject of the competitive balance, I have chosen a measure that is able to provide a comparison across diﬀerent countries and periods without being distorted by the individual parameters such as league format or size. For this purpose, a normalized form of the Herﬁndahl-Hirschman-Index has proven its worth. This concentration measure was applied to the distribution of wins within a championship period. As an outcome we received a basically comparable measure of concentration within a season. In addition, I have expanded the data to include records

42 on the UEFA rankings, a point system that decides who is to participate in UEFA competitions. This was necessary to address the problem of endogeneity. If one chooses the simple approach to regress the international representatives of a league with their HHI, one runs the risk of not taking into account the fact that the participation in one year affects the following. This has been considered by the inclusion of an extended model incorporating a lagged value of the UEFA standings. As a result, the endogeneity should be acknowledged. Proper measures for circumventing problems of endogeneity in estimations were applied. The models were designed for different periods: from the impact of the participation in the following year to a long-term effect of eight years. As suggested in theory after this adjust- ment a negative effect of the CL participation is shown in the balance of the league. The model predicts an increase in concentration by one to two percentage points depending on the time of the measurement.

As final result I presented a Difference-in-Difference model that makes use of a reform of the participation mode in the 1996/97 season. This model attempts to estimate the effect between the group of profiteers and the under-represented leagues. Profiteers are those associations which were allowed to delegate more teams than under the previous set-up. In essence, these are the established football forces of Germany, England, France, Italy and Spain. Consequently, this group is called ”Big Five”. If the change within these Big Five is compared to that of the less important leagues, there is a clear increase in concentration in the larger leagues. The higher number of Champions League participants has a negative effect on the balance of the league.

43 References

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45 A Appendix

This appendix will provide some additional material which has been useful in the creation of this work. Table 11 provides the current standings in the UEFA ranking. This can give a glimpse of the picture of European club football at this moment. Spain is the most dominant force it club football nowadays followed by the German and English top leagues. The stars indicate inclusion in the dataset of this work. Further I chose to insert some ﬁnancials of the major UEFA competition: The following pages contain data on the distribution of the UEFA Champions League Season 2015/16 was retrieved from www.uefa.com.14 It shall indicate what quantities exactly have been distributed over participants for one speciﬁc season. The distribution mechanism is a complex system of payments/incentives and needs therefore be fully understood in order to analyse monetary streams for clubs. In total, a number of e1.35bn was spilt over Champions League starters in the observed season whereas the Europa League clubs received e411m for their past performances. This corresponds to the distribution ratio of 3.3/1 from CL to EL as referred by the UEFA.

# Country 2012/2013 2013/2014 2014/2015 2015/2016 2016/2017 Overall 1 Spain* 17.714 23.000 20.214 23.928 8.571 93.427 2 Germany* 17.928 14.714 15.857 16.428 8.857 73.784 3 England* 16.428 16.785 13.571 14.250 7.928 68.962 4 Italy* 14.416 14.166 19.000 11.500 6.916 65.998 5 France* 11.750 8.500 10.916 11.083 7.916 50.165 6 Portugal* 11.750 9.916 9.083 10.500 5.250 46.499 7 Russia* 9.750 1.0416 9.666 9.583 6.000 45.415 8 Ukraine 9.500 7.833 10.000 9.800 3.700 40.833 9 Belgium* 6.500 6.400 9.600 7.400 6.300 36.200 10 Turkey* 10.200 6.700 5.800 6.600 5.900 35.200 11 Czech Republic 8.500 8.000 3.875 7.300 4.100 31.775 12 Switzerland* 8.375 7.200 6.900 5.300 3.300 31.075 13 Netherlands* 4.214 5.916 6.083 5.750 5.100 27.063 14 Greece 4.400 6.100 6.200 5.400 3.800 25.900 15 Croatia 4.375 4.375 6.875 4.500 5.125 25.250 16 Austria* 2.250 7.800 4.125 3.800 6.375 24.350 17 Romania 6.800 6.875 5.125 2.250 2.100 23.150 18 Denmark 3.300 3.800 2.900 5.500 6.500 22.000 19 Belarus 4.500 1.750 5.500 5.125 3.000 19.875 20 Sweden 5.125 3.200 3.900 4.750 2.750 19.725

Table 11: Current standing in the UEFA country coeﬃcient, Source: http://www.worldfootball.net/uefa country coeﬃcients/ retrieved on April 26, 2017 * indicates nations in the examined dataset

14Data on Champions League: http://www.uefa.com//MultimediaFiles/Download/competitions/General/02/41/82/55/2418255 DOWNLOAD.pdf Data on Europa League: http://www.uefa.com/MultimediaFiles/Download/competitions/General/02/41/82/56/2418256 DOWNLOAD.pdf Both retrieved on April 26, 2017

46 FINANCIAL MATTERS

UEFA CHAMPIONS LEAGUE: DISTRIBUTION TO CLUBS 2015/16 IN EUROS Participation Performance Play-offs bonus bonus Market pool Round of 16 Quarter-finals Semi-finals Final Total Group A FC Shakhtar Donetsk 2,000,000 12,000,000 1,608,000 2,015,000 17,623,000 Paris Saint-Germain 12,000,000 6,932,000 40,371,000 5,500,000 6,000,000 70,803,000 Malmö FF 2,000,000 12,000,000 1,608,000 4,901,000 20,509,000 Real Madrid CF 12,000,000 8,540,000 26,027,000 5,500,000 6,000,000 7,000,000 15,000,000 80,067,000 Group B Manchester United FC 2,000,000 12,000,000 4,216,000 19,914,000 38,130,000 VfL Wolfsburg 12,000,000 6,432,000 20,360,000 5,500,000 6,000,000 50,292,000 PFC CSKA Moskva 2,000,000 12,000,000 2,108,000 3,103,000 19,211,000 PSV Eindhoven 12,000,000 5,324,000 11,423,000 5,500,000 34,247,000 Group C FC Astana 2,000,000 12,000,000 2,000,000 1,292,000 17,292,000 Galatasaray A.Ş. 12,000,000 2,608,000 17,881,000 32,489,000 Club Atlético de Madrid 12,000,000 6,932,000 21,733,000 5,500,000 6,000,000 7,000,000 10,500,000 69,665,000 SL Benfica 12,000,000 5,324,000 7,331,000 5,500,000 6,000,000 36,155,000 Group D VfL Borussia Mönchengladbach 12,000,000 2,608,000 12,556,000 27,164,000 Manchester City FC 12,000,000 6,432,000 46,921,000 5,500,000 6,000,000 7,000,000 83,853,000 Juventus Football Club 12,000,000 5,824,000 52,932,000 5,500,000 76,256,000 Sevilla FC 12,000,000 3,216,000 5,968,000 21,184,000 Group E FC Barcelona 12,000,000 7,432,000 25,620,000 5,500,000 6,000,000 56,552,000 Bayer 04 Leverkusen 2,000,000 12,000,000 3,108,000 9,567,000 26,675,000 FC BATE Borisov 2,000,000 12,000,000 2,608,000 1,473,000 18,081,000 AS Roma 12,000,000 3,108,000 47,853,000 5,500,000 68,461,000 Group F FC Bayern München 12,000,000 8,040,000 25,851,000 5,500,000 6,000,000 7,000,000 64,391,000 GNK Dinamo 2,000,000 12,000,000 1,608,000 2,629,000 18,237,000 Arsenal FC 12,000,000 4,824,000 31,099,000 5,500,000 53,423,000 Olympiacos FC 12,000,000 4,824,000 16,134,000 32,958,000 Group G Maccabi Tel-Aviv FC 2,000,000 12,000,000 2,683,000 16,683,000 FC Dynamo Kyiv 12,000,000 5,824,000 3,318,000 5,500,000 26,642,000 FC Porto 12,000,000 5,324,000 4,916,000 22,240,000 Chelsea FC 12,000,000 6,932,000 44,742,000 5,500,000 69,174,000 Group H Olympique Lyonnais 12,000,000 2,108,000 27,770,000 41,878,000 Valencia CF 2,000,000 12,000,000 3,216,000 9,776,000 26,992,000 Football Club Zenit 12,000,000 8,040,000 5,138,000 5,500,000 30,678,000 KAA Gent 12,000,000 5,324,000 5,119,000 5,500,000 27,943,000 Clubs eliminated in play-offs APOEL FC 3,000,000 3,000,000 KS Skënderbeu 3,000,000 3,000,000 Celtic FC 3,000,000 3,000,000 FC Basel 1893 3,000,000 3,000,000 FK Partizan 3,000,000 3,000,000 Lazio 3,000,000 10,810,000 13,810,000 Club Brugge KV 3,000,000 368,000 3,368,000 Sporting Clube de Portugal 3,000,000 954,000 3,954,000 SK Rapid Wien 3,000,000 3,000,000 AS Monaco FC 3,000,000 7,178,000 10,178,000

Total 32 clubs 50,000,000 384,000,000 144,032,000 577,726,000 88,000,000 48,000,000 28,000,000 25,500,000 1,345,258,000 Allocated to the European Club Association in accordance with the Memorandum of Understanding with UEFA 4,170,000 Total 1,349,428,000

UEFA DIRECT • November 2016 – 29 FINANCIAL MATTERS

UEFA EUROPA LEAGUE: DISTRIBUTION TO CLUBS 2015/16 IN EUROS Participation Performance bonus bonus Market pool Round of 32 Round of 16 Quarter-finals Semi-finals Final Total Group A Celtic FC 2,400,000 360,000 3,051,070 5,811,070 Fenerbahçe SK 2,400,000 1,419,200 9,460,830 500,000 750,000 14,530,030 Molde FK 2,400,000 1,953,800 1,914,271 500,000 6,768,071 AFC Ajax 2,400,000 884,600 1,540,378 4,824,978 Group B FC Rubin Kazan 2,400,000 764,600 1,358,280 4,522,880 FC Girondins de Bordeaux 2,400,000 480,000 3,549,583 6,429,583 Liverpool FC 2,400,000 1,789,200 26,406,398 500,000 750,000 1,000,000 1,500,000 3,500,000 37,845,598 FC Sion 2,400,000 1,419,200 962,974 500,000 5,282,174 Group C FC Krasnodar 2,400,000 2,238,400 1,604,615 500,000 6,743,015 FC Qäbälä 2,400,000 240,000 382,565 3,022,565 PAOK FC 2,400,000 884,600 2,227,839 5,512,439 Borussia Dortmund 2,400,000 1,583,800 8,040,307 500,000 750,000 1,000,000 14,274,107 Group D Club Brugge KV 2,400,000 644,600 1,041,032 4,085,632 FC Midtjylland 2,400,000 1,179,200 2,820,635 500,000 6,899,835 Legia Warszawa SA 2,400,000 524,600 1,434,771 4,359,371 SSC Napoli 2,400,000 2,927,600 6,690,425 500,000 12,518,025 Group E FC Dinamo Minsk 2,400,000 404,600 456,204 3,260,804 SK Rapid Wien 2,400,000 2,523,000 2,209,827 500,000 7,632,827 Villarreal CF 2,400,000 1,988,400 8,359,646 500,000 750,000 1,000,000 1,500,000 16,498,046 FC Viktoria Plzeň 2,400,000 524,600 518,422 3,443,022 Group F Olympique de Marseille 2,400,000 1,868,400 4,534,925 500,000 9,303,325 FC Slovan Liberec 2,400,000 929,200 551,821 3,881,021 SC Braga 2,400,000 2,238,400 1,636,256 500,000 750,000 1,000,000 8,524,656 FC Groningen 2,400,000 240,000 1,808,982 4,448,982 Group G Rosenborg BK 2,400,000 240,000 1,282,766 3,922,766 FC Dnipro Dnipropetrovsk 2,400,000 929,200 1,672,208 5,001,408 S.S. Lazio 2,400,000 2,358,400 9,357,556 500,000 750,000 15,365,956 AS Saint-Étienne 2,400,000 1,419,200 4,534,925 500,000 8,854,125 Group H Beşiktaş JK 2,400,000 1,169,200 6,512,286 10,081,486 Sporting Clube de Portugal 2,400,000 1,583,800 1,131,355 500,000 5,615,155 FC Lokomotiv Moskva 2,400,000 1,953,800 1,832,159 500,000 6,685,959 KS Skënderbeu 2,400,000 404,600 519,649 3,324,249 Group I CF Os Belenenses 2,400,000 644,600 916,934 3,961,534 ACF Fiorentina 2,400,000 1,583,800 6,690,425 500,000 11,174,225 FC Basel 1893 2,400,000 2,238,400 980,585 500,000 750,000 6,868,985 KKS Lech Poznań 2,400,000 644,600 1,169,470 4,214,070 Group J FK Qarabağ 2,400,000 524,600 383,703 3,308,303 RSC Anderlecht 2,400,000 1,583,800 1,244,828 500,000 750,000 6,478,628 AS Monaco FC 2,400,000 764,600 3,549,583 6,714,183 Tottenham Hotspur FC 2,400,000 2,238,400 14,967,056 500,000 750,000 20,855,456 Group K AC Sparta Praha 2,400,000 1,823,800 755,687 500,000 750,000 1,000,000 7,229,487 APOEL FC 2,400,000 404,600 619,600 3,424,200 FC Schalke 04 2,400,000 2,358,400 4,756,484 500,000 10,014,884 Asteras Tripolis FC 2,400,000 524,600 2,227,839 5,152,439 Group L FC Augsburg 2,400,000 1,463,800 4,756,484 500,000 9,120,284 FK Partizan 2,400,000 1,213,800 826,292 4,440,092 AZ 2,400,000 524,600 1,540,378 4,464,978 Athletic Club 2,400,000 2,238,400 7,374,361 500,000 750,000 1,000,000 14,262,761 Clubs from Champions League FC Shakhtar Donetsk 903,665 500,000 750,000 1,000,000 1,500,000 4,653,665 Manchester United FC 2,551,427 500,000 750,000 3,801,427 Galatasaray A.Ş. 952,497 500,000 1,452,497 Sevilla FC 3,598,973 500,000 750,000 1,000,000 1,500,000 6,500,000 13,848,973 Bayer 04 Leverkusen 1,413,903 500,000 750,000 2,663,903 Olympiacos FC 574,783 500,000 1,074,783 FC Porto 90,323 500,000 590,323 Valencia CF 861,760 500,000 750,000 2,111,760 Total 115,200,000 60,843,000 183,112,000 16,000,000 12,000,000 8,000,000 6,000,000 10,000,000 411,155,000

UEFA DIRECT • November 2016 – 31