POLITECNICO  DI  TORINO    

Facoltà  di  ingegneria    

Corso  di  Laurea  Magistrale    in  Management  Engineering  

         

Tesi  di  Laurea  Magistrale      

Economic  value  in  football  industry        

     

Relatore    Dott.  Prof.  Luigi  Buzzacchi  

Candidato    

Dott.  Andrea  Donegà    

     

                                                                                   Luglio  2014  

  2  

Abstract

European football market is continuously increasing in terms of revenues and number of consumers

(followers), it is necessary to assess the value of its core assets, which are clubs and leagues. An

important value driver is revenue, as greater incomes, generally, translate in higher expenses and,

consequently, better sportive results because of higher winning probabilities, which satisfy existing

fans and are able to get new consumers, laying the foundation to further increase revenues. It is a

virtuous circle. With the help of SPSS statistics we’ll try to predict clubs’ revenues and values,

laying on variables mostly related on actual and potential consumers, past and recent sportive

results.

                         

  3  

 Index  

Introduction  ___________________________________________________________________________________________  4  

CHAPTER  1:  Football  market  ________________________________________________________________________  6  

     1.1  European  football  market  overview  ___________________________________________________________  6  

     1.2  Revenue  streams  _______________________________________________________________________________  11  

     1.3  Attendance   _____________________________________________________________________________________  13  

     1.4  Determinants  of  value  _________________________________________________________________________  17  

     1.4.1  UEFA  ranking  ______________________________________________________________________________  19  

     1.4.2  Brand  value  ________________________________________________________________________________  23  

CHAPTER  2:  Football  clubs  values  _________________________________________________________________  28  

     2.1  Firms  economic  value  _________________________________________________________________________  28  

     2.2  Market  capitalization  __________________________________________________________________________  31  

     2.3  Discounted  cash  flows  models  ________________________________________________________________  34  

     2.4  Specific  methodologies  to  value  football  clubs  ______________________________________________  37  

     2.4.1  Forbes  valuation   __________________________________________________________________________  38  

     2.4.2  Multivariate  model  ________________________________________________________________________  45  

     2.4.3  Revenue  multiples  approach.   ____________________________________________________________  52  

     2.4.4  Fans  Method  _______________________________________________________________________________  54  

CHAPTER  3:  SPSS  Statistics  and  factorial  models  ________________________________________________    65  

     3.1  Top  European  leagues  correlations  __________________________________________________________  65  

     3.2  Correlations  and  factor  analysis  in  Italian  leagues  __________________________________________  72  

     3.3  Multiple  regressions  ___________________________________________________________________________  82  

     3.3.1  Serie  A  ______________________________________________________________________________________  82  

     3.3.2  Italian  lower  categories  factor  analysis   _________________________________________________  86  

     3.3.3  Top  European  clubs  _______________________________________________________________________  88  

     3.4  Factorial  Fans  model   __________________________________________________________________________  96  

     3.5  Factorial  Italian  model  _______________________________________________________________________  100  

Conclusions  _________________________________________________________________________________________  108  

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Introduction  

The work would like to assess the value of football industry, in particular European and Italian

professional football, deepening the nature of its core assets’ value, which are the football

companies and related leagues. In this work we’ll not refer to national teams and competitions, only

club teams and related market.

Clubs and leagues are strongly related, as a club builds a product in order to gain an higher market

share with respect to its league’s competitors, thus gaining new consumers/fans, increase profit and

achieving better sportive results, but it contributes also to build another bigger product, which is the

whole league, as club’s revenues depend on league’s revenues, so it is allied with all the other clubs

participating to that championship in order to build a better league and, in a lesser extent, with the

club participating to the lower categories in order to build a better product and to retain new

consumers/followers, reaching new profits and improving sportive results.

If a league will increase its market share, then each club could get a bigger slice of revenues, getting

more visibility and reaching new consumers, thus increase investments in talent favouring sportive

results and, consequently, consumers’ satisfaction.

As main competitions per clubs, which guarantee the richest awards and visibility, are divided on

countries and, for top clubs, on continents (with few exceptions of worldwide matches), we can

suppose even a competition among continental confederations, in which leagues collaborate in

order to build a better competitions (e.g. Europa League, Champions League, Copa Libertadores)

and, on the opposite, a worldwide collaboration between continental confederations in order to

promote the whole product football.

The biggest clubs and leagues in the world are in Europe, getting fans from all over the world. In

this work I’ve concentrated on the old continent with a focus on Italy.

In this work we’ll not deepen the relationship between continents, because from a European club

perspective the main concerns are its value inside the league and the value of its league with respect

to others European leagues, because they have to share most of football industry resources.

In the first part I’ve identified the context in which a football club operates, focusing on revenues of

top European Leagues and revenues streams, with a special attention on attendance at the stadium, a

factor able to drive not only “matchday” direct incomes, but rather is a very important slice of

consumers (for many clubs the most important part), which could affect positively or negatively the

whole show with their enthusiasm, changing the image on media and also affect the other

consumers through direct chats, photos, opinions, thus influencing the value of the club.

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By continuing, I asked myself what determines the value of a football club and starting from

Szymanski & Kesenne (2004) and Barajas (2005), I’ve introduced the relationship between profits

and sportive results, talking about an important indicator of sportive results, able to drive directly

revenues, UEFA ranking, and introducing an intangible asset that affect directly club’s values,

which is their brand.

In second part, starting from Modigliani & Miller (1958) and the definition of firms’ economic

value, I’ve introduced the evaluation methods, trying to apply Market capitalization and Discounted

cash flow methods to football clubs, and then applying more specific methods like Multivariate

Model, Forbes evaluations, Multiplier methods. In all the cases there isn’t and absolute and

indisputable value, neither the better methodology to value a football firm, but I’ve identified some

strengths and weaknesses of these methodologies, when referring to football market, thanks to

comparison with evidence and real transaction prices. I’ve also made an attempt to find a

methodology, called “Fans method”, that starting from real revenues and having ad idea of actual

values, because of the knowledge of market share and sportive results, would like to catch which

are the more stable factors determining clubs’ value, we can call the sum of them intrinsic value,

and try to find differences among clubs in order to build an ordinal value rank.

In this way, factors’ weights are estimated starting from real revenues, market share etc., hence

knowing some results and trying to be consistent with them; in order to estimate real weights, one

way could be factor analysis and in the third part I’ve searched correlations and component weights

with the support of SPSS statistic software, in order to propose final evaluations starting from those

findings, as we will see in “Factorial fans and Factorial italian models”.

 

 

 

       

  6  

Chapter  1  –  Football  market  

1.1  European  football  market  overview  

A football club is an entertainment company, with the aim of give a show, get sportive results and

satisfy the fans and the community; reaching high levels, this translates in make economic results,

otherwise the aims are unreachable. Moreover, given the large number of consumers (followers), it

arises the opportunity to make profits.

Each club competes to excel in its market, which is the league where it plays, but it is also allied

with all the other clubs of the same league in trying to build the best product possible, to improve

their product quality with respect to other championships in order to improve the total league

market share, and then each team will compete for a most valiant slice of that with its rivals/allies.

Limiting ourselves to professional football, the context in which a football firm operates is large,

our first objective is to identify it in order to have an idea of the market value.

To determine size and potentiality of a football company, whether it is a single club or a league,

incomes are very significant, since a greater availability of money translates in grater probability to

provide better services to the customers/fans and to hire better talents, there is a correlation along

with cost of the team and results. In the next paragraphs, we’ll try to calculate how strong is it.

Moreover, new regulations of Financial fair play guarantee that a club should be in equilibrium

between what it gains and what it spends (with a tolerance range in these first years from

application), compliance with this law would mean no more exceptional investments in human

resources from willing owners, so even more focus on how to improve incomes; that’s why we are

starting talking about revenues which will remain a core indicator to value a club.

Let’s start with a general overview of the European football industry.

According to Deloitte researches’ results, football market is continuously increasing all around the

world. In this work we’ll consider only European clubs and leagues, because they are stronger

correlated to Italian leagues and clubs as they have to share incomes from the same competitions

and satisfy similar target of customers/supporters, moreover they include the biggest clubs and

leagues in the world, in terms of revenues and market share (in terms of number of customers/fans

served).

Just to know, the first non-European league in terms of aggregate revenues is the Brazilian one,

with approximately 800M€ of turnover in 2012, globally it was the 6th league in terms of incomes

after five Europeans.

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This doesn’t mean that the rest of the world will be out, because whenever we talk about consumers

or fans of a European club, they may come from all over the world.

We see now some initial numbers:

Aggregates revenues of European Leagues divisions, in 2012, were 19,4 Billions of €, since 2006

they have increased from 12,6 Billions, with an impressive growth of +54% in few years.

In the same period, Italian Serie A has grown more slowly, +25%. Also costs have increased,

resulting in net losses for many clubs, here we limit to first divisions:

Figure 1

 Figure  1:  Aggregate  profits

Source: Report Calcio FIGC 2013.

Figure 1 refers to aggregate European first divisions net profits, which is actually, on average, a

loss. Blue histograms are losses in billions of euros, percentages on the black line indicates the ratio

between net losses and revenues. Taxes are included in costs, in 2011 Italian professional football

have paid 1034 millions of euros in contribution, which is more than 35% of the whole incomes.

In Figure 2 we can see the improving revenues (on the vertical axis) of the “Top 5 Leagues”

(England, Spain, Italy, Germany, France) since 2002, whose are the five larger football market in

the world in terms of market share and incomes (Deloitte, 2013). Notice that whenever we talk

about revenues, they exclude player transfer fees, VAT and other sales related taxes. Because we

need a stable comparison among clubs, and incomes from players transfer are inhomogeneous and

discontinuous.

Looking at the graph, English Premier League is stable at the first place, German Bundesliga is the

second one, despite it’s the only one with 18 teams instead of 20. Spanish La Liga is that one which

has improved more in the last ten years, +132%, reaching the third place. It follows Italian Serie A

and French Ligue 1.

  8  

Figure 2

Figure  2:  Top  5  leagues  growth

Source: personal elaboration from Deloitte data

Football market has grown faster than European economy, even during the crisis periods. In Figure

3 it’s emphasized the difference between economic growths in Italy, France, Germany, Spain and

England, on average, and the correspondent growth of their top football teams, here in particular are

considered the top 20 clubs in terms of revenues, that all belonged to these leagues in 2012:

  9  

Figure 3

Figure  3:  Countries  economies

Source: Deloitte analysis, Eurostat.

Therefore, we’re talking about an expanding market, whose growth does not depend only on

available money, but rather there could be even a huge surge of interest from the followers

regardless of the economy. Do I add, reasonably and according to the theory, that in general poorer

consumers will spend less, so in terms of direct gain of money there is a correlation between

incomes and economic situation in that context. From Figure 3 we can guess that many other factors

affect consumers/fans in football, not only their economic situation.

A further consideration is that Figure 3 is related to the economy of the “top 5 Leagues” countries

and only them, while their clubs have followers from all over the world, so by considering only this

slice of fans, even if it’s the core market, result could be misleading.

Staying in the European football, the weight of the “Top 5” first divisions inside the whole market

is stable or a bit dropped in the last 7 years; by summing up their incomes, in 2006 they generated

around 52% of the total aggregate revenues of all European leagues (12,6 Billions€, estimated by

Deloitte), while in 2012 nearly 49% (out of 19,4 Billions€, Deloitte), which is still a huge share;

moreover, by considering the lower categories of English, Italian, Spanish, French and German

Leagues, which are included in the aggregate total, we don’t know their exact weight but the share

of the top five federations is widely over 50% of the total made from 54 UEFA federations, because

this share is reached by including only English second division, which earned 590M€ in 2012

(Statista.com).

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Anyway, looking at Report Calcio FIGC 2014, there are other first division championships to be

considered in order to have a better idea of this market, like Russian (896 Million€ in 2012 with 16

clubs), Turkish (551M€ with 18 clubs, 2012), Dutch (439M€, 18 clubs, 2012); meaningful

especially the Russian growth: from 2009 to 2012, earnings have gone from 368M€ (Deloitte 2011

review) to 896M€, increasing by 143%. An impressive growth, even if the origin of these incomes

is not always known, Russian league is widely the first league when considering only the stream

“other incomes” getting 42% of the total (Report Calcio FIGC 2014).

Until now we have reasoned in terms of leagues aggregate revenues, which are the sum of each

football team revenues; single clubs are one of the core assets of a league, probably the most

important. As there isn’t a standard, a fixed number of clubs forming a league, there exist different

formats and aggregate revenues could be distorted, even if in “Top 5” only German league has 18

clubs while all the other components have 20 football team in their first divisions.

Anyway, to not depend on the number of teams, in Table 1 there are the average incomes (again,

net of player transfers gains) per club of top 10 European Leagues, which are related to the season

2010-11 and 2011-12, and average net profit per club of season 2011-12 (Report Calcio FIGC

2014).

Table 1

League (first divisions) Revenues per club (M€) 2011 Revenues per club (M€) 2012 Net profit per club (M€) 2012

England 134,0 139,0 -11,3

Germany 100,5 106,0 +1,8

Spain 84,5 93,0 +0,2

Italy 81,5 85,7 -10,0

France 56,5 58,4 -4,3

Russia 39,5 56,0 -5,4

Turkey 24,5 30,6 -6,9

Holland 24,0 24,4 +1,2

Portugal 18,5 17,8 -7,1

Scotland 16,0 10,4 -1,2

Table  1:  Average  per  club  revenues

I add a little corollary: only some clubs have the right to play in European competitions, this

generates visibility, possibility to reach new customers/fans and earn new revenues.

A proper consideration should be done for top teams in terms of revenues earned per league, the

ones with greater probabilities to access at European competitions, because they have a different

  11  

“weight” for the whole league entries, also due to their higher visibility that translates in higher

league visibility and new customers achievement.

For example, by considering only top 4 clubs in terms of revenues, Spanish clubs are near to

English ones, on average 310M€ with respect to 332M€ per club in 2013, by detaching all the other

leagues; Spanish La Liga it the second one in this special rank even by considering only top 7 clubs.

This is an imbalance indicator but it also able to explain the great sportive results of Spanish clubs

in European competitions, despite the whole league revenues were relatively low in the past decade.

In the second and, especially, third part of the thesis, we will see how these sportive results could

affect positively incomes of participating clubs but also of the whole league.

1.2  Revenue  streams  

To understand the structure of revenues, let us review some core products of each single club.

A football company sells a show, the match, which can be seen either live at the stadium, paying

tickets, or through the media; this generates media rights, sponsorship and advertising. Moreover,

there are a lot of clubs’ products, like official clothing, gadgets or museums. In synthesis, we can

decompose revenues of football teams (at least professional clubs) in 3 macro factors: matchday,

related to stadium direct entrances, broadcasting, mostly related to TV rights but also other media

rights, and commercial side, comprehensive of gadgets, naming rights, sponsorship and advertising.

In Figure 4 and Figure 5, the slice “other”, consisting on all the incomes that do not come directly

from stadium and media, are mostly related to the commercial side.

  12  

Figure 4

Figure  4:  Top  5  revenue  streams

Source: KPMG, European stadium insight (2011)

Figure 5

Figure  5:  Europe  revenue  streams

Source: UEFA, KPMG analysis.

  13  

Figure 4 is related to revenue streams of Top 5 European Leagues in the season 2009/10: Italian and

French clubs take most of their revenues from broadcasting, while Germans get a huge amount

from commercial side. English and Spanish clubs are more balanced getting comparable incomes

from each stream.

Figure 5 gives a picture of nearly all the European market, ranked by matchday as percentage of

revenues; Serie A is rather backward, while it’s the first one in term of TV share percentage. About

broadcasting incomes, Premier League is at the top and it has huge rooms of improvement, because

it sells live in GB only 154 matches out of 380, while all the other top leagues are selling 100%

matches live, but gaining less (Teatino e Uva, 2012). Moreover, Serie A revenues in the last few

years have increased only from the commercial part, while we are the only league with a negative

trend in matchday in the last 10 years (Report Calcio FIGC 2012 and 2013).

One of the causes of this relatively very low and falling Serie A matchday is a negative trend in

attendance at the stadium, I think this problem deserves to be highlighted.

1.3  Attendance  

Following the data available on website “Stadiapostcards” (Their sources are all the Italian sportive

newspapers, FIGC and LegaserieA.it), the decline has been started in the ‘80s and is still

continuing, even if in the last few years it appears less marked, but we have to invert these numbers

not just keep them unchanged; not only for direct incomes from matchday, but also for

positive/negative externalities which could affect consumers from all over the world, able to change

significantly, in the long term, also commercial and broadcasting side, by changing the number of

followers/consumers; that’s why I think this is a critical point, worthy of investigations, able to pull

up or down highly significantly revenues in the long run. Only recently Italian clubs have started to

take into account this problem:

  14  

Figure 6

Figure  6:  Top  5  attendances

Source: statista.com

German championship has double attendance than Italian and French. Despite the difference with

England and Spain, Premier League (610M€) and La Liga (460M€) still gain more than Bundesliga

(385M€) from matchday, due to 2 more clubs but also to higher ticket prices (Teatino e Uva, 2012).

Serie A and Ligue 1 are far away (180M€ and 135M€), anyway the negative trend in our country

it’s not due to higher prices, there should be other relevant factors:

Figure 7

Figure  7:  Ticket  prices

Source: Report Calcio FIGC 2012

  15  

This index calculates the weight of average ticket prices of first divisions with respect to average

daily salaries; Italy is in the middle, behind England, Spain and Germany, despite the huge

difference in attendance.

The negative trend for our championships is even more marked in lower divisions; clubs belonging

to a lower division are not a core asset for Serie A clubs, anyway they are able to influence

significantly upper categories, directly because of promotions and relegations, indirectly by

influencing the image and the quality of the whole product “Italian football” and especially “Italian

professional football”, which is the same whole product build from Serie A teams. So finally, even

if they are not core assets of Serie A teams, there exists a relevant correlation.

In figure 8,9 and 10 we see the average attendance per match of 2nd, 3rd and 4th Italian divisions,

respectively; of course there are fluctuations, mostly due to changes in the catchment area, very

changeable at these levels because of promotions and relegations; however, the negative trend is

evident, average per game attendance on the vertical axis and related season on the horizontal one:

Serie B: Figure 8

Figure  8:  Attendance  Serie  B

Lega Pro (3rd division on the left, 4th on the right):

Figure 9 Figure 10

Figure  9:  Attendance  Lega  Pro  I                                                          Figure  10:  Attendance  Lega  Pro  II

  16  

Source: personal elaboration from Stadiapostcards.com data.

Another worrying indicator for our League is the percentage of stadiums filled, in Figure 11 I have

considered Top 5 first divisions season 2010-2011:

Figure 11

Figure  11:  Percentage  of  stadium  filling

The gap is accentuated by considering lower categories like Serie B and Lega Pro and especially the

national cup; our “Coppa Italia”, with the actual format, presents empty stadiums until the last

rounds (in 2011/12 only 20% of stadiums have been filled, despite the first round, with the lowest

attendance, it is not taken into account, (Report Calcio FIGC, 2013).

This, in addition to the lack of economic returns and waste of resources, is able to deteriorate the

show both for who attends directly (and then makes public what she has seen) and who watches the

match on a media, it could foster a loss of enthusiasm so finally it contributes to a decline in value.

In the next paragraphs we’ll deepen the relation with value.

Anyway, it is evident that the ambiance of a full stadium is different than seeing the show in a

“desert cathedral”, moreover the image on media it is different; to confirm these considerations,

I’ve asked to a sample of 72 football fans whether “it is important if there is much public and the

stadium is filled”. It is not a very significant number for a statistic, but the answer was given almost

by unanimity: 90% said yes, the remaining 10% declares herself disinterested, with only 1 person

which was contrary and prefers empty stadiums. I point out that the sample was not uniform (95%

men, on average 26 years old, 40% Emilians), anyway this accordance is very marked on this point.

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In order to point out the value of a football company, it is reductive to consider only economic

factors like revenues and respective components, we have mentioned some intangible factors like

consumers/fans enthusiasm and related externalities.

In the next paragraph I’ll try to figure out what determines the value of a football club and,

consequently, of a football league, talking about UEFA ranking and brand value, so that we can

complete this introductory part in which we are defining the context of European football market

and the position of Italian leagues, and begin with practical clubs value calculation methods.

1.4  Determinants  of  value  

A club playing in a football market builds a product which depends on the results with respect to

the other teams in the league, which are able to influence the value and increase or decrease revenue

streams; but a club also plays for a bigger product, which is the whole league, because if a league is

able to increase its value and its revenues, then each participant club will benefit from it. We can try

to find some numbers to better investigate this trade-off between inside competition and

collaboration.

Let’s start from Szymanski and Kesenne 2004 work on “Competitive balance and revenue sharing”;

they begin with the assumptions that the probability of winning the league championship depends

on the investment in playing talent and if all contestants invested the same amount of resources in

trying to win the contest, then each contestant would have an equal probability of winning.

[1]

 Equation  1:  Winning  probabilites

Where w is the probability of success and t the investment in playing talent. Then, in a two team

model where each club is a profit maximizing, and profit consists on gate revenues minus cost of

talent investment, this is the profit function they found for each team:

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[2]

 Equation  2:  Profit  function  

By assuming that talent can be hired in the market at a constant marginal cost c, and α is a fraction

(>50%) of income R generated from home matches. By differentiating with respect to t, the result

is that, in equilibrium, the marginal revenue from the hiring of talent is equalised across teams (this

is not the same as saying the marginal revenue of a win is equalised).

Following these assumptions, they finally derived the winning percentage ratios (w1/w2) with

respect to the fraction of sharing α, and they found that gate revenue sharing will not only reduce

total investment in talent by teams in a league but also diminishes the degree of competitive

balance, so there must be regulations in order to sustain competition and equilibrium.

In ‘Owner objectives and competition’ (Fort and Quirk, 2004), authors differentiate between 2

clubs’ objectives, profit and sportive results: they distinguish between owner profit maximizing and

owner winning percentage maximizing; of course, having different aims changes the market

approach and owner investments priorities.

Despite some evidence (for example, English Premier League has been more inclined to profit

objectives than Italian Serie A in the last decade), it’s difficult to translate into numbers those

attitudes, also because comparing currently existing leagues cannot distinguish owner objectives if

the existing leagues serve different markets with different revenue possibilities. However, they

demonstrate the initial insights that winning percentage teams are willing to bid higher prices in

talent and that if, in a market, the number of profit maximizing teams increase, then talent price

should fall.

Anyway, I think we cannot strictly separate between profit and winning objectives, as they are

related affecting each other.

Revenues are related to larger expenses and greater winning probability, on the opposite, sportive

results are able to influence significantly clubs value, because of direct incomes, rewords and also

the capture of new fans. Barajas and Crolley (2005) have found that there is a non-linear

relationship, with an explanation degree of 55.12%, between budget (expected income) of Spanish

football clubs and sports performance. This outcome is consistent with the findings of Szymanski

and Kuypers (1999), they state that the proportion of the change in income during the 1996/97

season is explained by 82% (R2=0.82) by the English league position in that season. They use the

  19  

logarithm of the revenues of each club divided by the yearly mean of the revenues of the all clubs as

a whole. The result raises to an R2 of 0.89 when they work with a longer period (1978-97).

So, sportive results are related to future incomes, and we know that actual incomes are related to

actual expenses (especially in talent) and, consequently, results.

For these reasons I introduce a rank, made by UEFA, which summarize clubs results in the

European competitions during the last 5 years.

1.4.1  UEFA  ranking  

Every year UEFA updates ranks in order to define participants to its competitions, and different

zones within them, which influence paths inside the game board. A country ranking is used for

invitations, while a team ranking defines groups and part of the route inside the competitions

(UEFA.com). For our aim, which is to identify clubs and Leagues’ values, they are both

meaningful. Anyway they are strongly related, as country ranking is build by adding teams ranking

coefficients as we are going to see in a while, and the same methodology is used to build these

ranks, a method which relies on “UEFA coefficients”.

UEFA country ranking is an indicator of the technical value of a League, as it reflects all the

sportive results in European competitions of the last five years of all the participants clubs. It is able

to influence leagues’ revenues by deciding the number of teams invited to European competitions

(UEFA Champions League and UEFA Europa League) every season.

UEFA coefficient is determined by the results of the clubs of the associations in the UEFA

Champions League and the UEFA Europa League games over the past five seasons. Technically, it

is build as follows:

Two points are awarded for each win by a club, and one for a draw (points are halved in the

qualifying and playoff rounds). Results determined by extra time do count in determining the

allocation of points, but results determined by penalty-shootouts do not affect the allocation of

points, other than for bonus points given for qualification into the latter rounds of the Champions

League or the Europa League (UEFA.com);

To determine a country or club coefficient for a particular season, the coefficients for the last five

seasons are added. Bonus points are added to the number of points scored in a season, they are

allocated in this way:

  20  

Table 2

Table  2:  UEFA  ranking  bonus

Source UEFA.

For team ranking, the total score is calculated by adding at the points scored by each team a fixed

coefficient, equal to 20% of the national coefficient on the current season. For example, 11.375

points of the Italian coefficient (country ranking), for the 2008/09 season, they secured additional

2.275 points per each Italian club.

About leagues, the number of points awarded each season by the team of that country is divided by

the number of teams that participated for that association in that season, for example in 2013/14 the

sum of points gained by Italian clubs is divided by six. This number is then rounded down to three

decimal places (ex. 2.⅔ would be rounded to 2.666, source UEFA.com).

Here is the country ranking 2013, which decides the number of clubs per country participating at

UEFA competitions in 2014/15.

  21  

Table 3

Table  3:  UEFA  country  ranking

Source: UEFA.

Briefly, the ranking is the sum of points gained in the last 5 years, hence for the next rank season

2008/09 will be completely deleted, and so on.

In the right column, “teams” refers to number of clubs participating at European competitions in the

current season, and this depends only on the rank 2012. There are few exceptions, for example a

“fair play” award, which guarantees one more club in Europa League for three countries, for

example in 2012/13 season, Nederland has won the premium having 7 teams instead of 6.

Finally UEFA rank is a good indicator of clubs (and consequently leagues) results in the European

competitions during the last five seasons.

Below the performances of top Italian teams since 1999 (actually SSC Napoli is 32nd in the 2014

projection, but it wasn’t in the rank until 2008), we have the UEFA team rank position on the ‘y’

axis and the related year on the ‘x’ axis.

  22  

Figure 12

Figure  12:  Top  Italian  teams  UEFA  ranking

Source UEFA.

Figure 12 well illustrates Serie A situation and related country ranking, as it is made mostly by

these teams, the ones which have been more present in European competitions during the last 15

years; if at the end of the nineties we had 6 teams in the top 30 and 3 tightly in the top 10, in the

following years our clubs have lost positions with few exceptions, but in actual 2012 and 2013 rank,

and 2014 projections, the gap has been reduced and we have recovered indeed 6 clubs in the top 50

(including SSC Napoli), anyway in 2013 only one Italian club is present in the top 10. I have put

this graph as an indicator of Top Italian teams sportive results, which is related to technical values

on the pitch but there exists also a correlation with economic results, as we will verify in the

following sections.

Economically speaking, it’s important not only the number of clubs per country, but in which

competition, because Champions League generates 75% of the whole pie despite is divided for a

lower number of clubs; nevertheless, relatively speaking, Europa League is acquiring more

importance within an expanding market, data in Millions of euros:

  23  

Figure 13

Figure  13:  UEFA  distributions

Source UEFA.

Moreover, if these direct incomes are very significant, and UEFA rankings provide the possibility

to access at these competitions and is able to change the path inside it, there are also intangible

factors which contribute in increasing the value of a club (and consequently of its league); consider

for example the image value which comes from team participation and performance, able to build

consumers (fans) loyalty, influence future incomes and, consequently, results.

For all these reasons I deem UEFA ranking a relevant factor for clubs’ and leagues’ value creation.

1.4.2  Brand  value  

Football clubs are made up of a mixture of fixed tangible assets (stadium, training ground) and

disclosed intangible assets (purchased players), internally developed players and goodwill, making

up the difference to provide the combined clubs value (Brandfinance.com).

An indicator of leagues value is Brand, as it considers actual awareness and attractiveness, but also

potentiality of future growth. In synthesis, it is “the trademark and associated intellectual property”

(Brandfinance, 2013).

Below, in Figure 14, I’ve put the brand rank of European leagues:

Here, after the richest English Premier League, it is emphasized the solid position of German

Bundesliga, which can rely on a strong economy (with respect to Spain and Italy), modern facilities,

and a huge catchment area (80 Million inhabitants), which have contributed to the impressive

  24  

increase in attendance during the last 10 years. Fans are important not only for direct incomes they

can assure through tickets, merchandising and media rights, but also for network externalities they

generate, that are able to get or lose other consumers.

Following those results of Brandfinance, England and Germany get 70% value of top 5 leagues in

2012, so by considering only this factor it would be expected that in the coming years they will

widen the gap with Spain, Italy and France in terms of revenues and, consequently, improve their

sportive results because of higher winning probabilities, assuming that higher revenues translates in

higher expenses in most talented players; indeed, the probability of winning the league

championship depends on the investment in playing talent and if all contestants invested the same

amount of resources in trying to win the contest, then each contestant would have an equal

probability of winning. (Szymanski & Kesenne, 2004)

Surely there are other factors to be considered before to try predicting future market, by remaining

on brand values, we should consider the weight of the top clubs of each league, the ones that

participate to European competitions, they can tow the whole leagues’ revenues, as it’s happening

in Spain. On the other hand, a balanced and richer league, should provide benefits to all underlying

clubs in the long run, including “towing” top clubs, as it is happening in England.

Below the rank:

  25  

Figure 14

Figure  14:  Leagues  brand  values

Source: Brandfinance.

These values have been calculated through ‘royalty relief method’, which determines the value of

the brand in relation to the royalty rate that would be payable for its use were it owned by a third

party. The rate is supported by a profit margin analysis of comparable companies. The methodology

uses discounted cash flow techniques to discount estimated future royalties at an appropriate rate to

arrive at a net present value, which is held to constitute the brand value (Brandfinance.com).

Calculations are not public, its reliability depends on the accuracy of rates’ calculation, which is

surely a complex process with many chances of errors, especially for an unpredictable company as

a football league.

In the right column there is the Brand rating, which benchmarks the strength, risk and future

potential of a brand relative to its competitors on a scale ranging from AAA+ to D, like a credit

rating.

  26  

According to the rating, Premier League is confirmed as very strong and it assures extensive

guarantees to investors, while Serbian and Romanian Leagues, despite a quite good value mostly

due to their popularity in these countries, are near to default, not being able to cover all their debts.

Italian Serie A, despite 1630M€ of debts in 2012 net of credits (Report Calcio FIGC, 2013), the

decline in attendance and the paucity of modern facilities with respect to top 10 leagues, it is quite

solidly the fourth rated league with Brandrating ‘A’ and 1122M€ of value, evidence that with some

targeted investment (e.g. stadiums), the recovery of fans enthusiasm and some positive results in

European competitions, we could, in few years, cover or reduce the gap with top three leagues. The

same goal could be reached by other leagues, for example Ligue 1 that with the organization of

2016 European championships and the arrival of rich owners (PSG, AS Monaco, RCD Lens) is

catching up Serie A. On our side, if we would like to underline a strength in a “leagues SWAT

analysis”, there is a hundred of rooted and unique football history, which have brought top

attendances until the ’90s, indeed during the ‘80s Italian leagues where the most-watched live, with

the pick of 38.872 average spectators per match during 1985’ Serie A (European-football-

statistics.com), numbers exceeded only recently from Bundesliga and approached by Premier

League and no one else (Ligue 1 pick is 23120, dating 2000/01, European-football-statistics.com).

That’s why I’ve spoken about “recovery of fans enthusiasm”, together with new facilities.

To complete the introduction at values calculation and closing this overview about the context of

the European football market, related size and Italian league position, I add this little corollary

about lower divisions, because as mentioned in the previous paragraphs, there is a strong correlation

among teams belonging to the same league, as they play for the same product in the same market,

and they have to share revenues of this whole product.

Moreover, it is shallow to consider strictly separated the values of different divisions belonging to

the same federation, not only because there are promotions and relegations, able to affect catchment

area and incomes, but also because of followers and supporters, which are able to influence the

image of the whole product changing the value; so the value of a first division depends also on the

value of the lower categories.

Hence, to define the value of a league it’s important the whole production of a federation, certainly

with a higher weight for upper categories. We have seen an overview of the European football

market mostly focusing on top first division in terms of revenues, followers and ranking, here I

show a simplified economic picture of “Top 5” second divisions, where the strong position of

England and Germany is confirmed, it’s emphasized the Spanish imbalance in terms of revenues

  27  

and it’s stressed the lack of public in Italy, despite the high capacity of our stadiums, e.g. Serie B

average stadiums utilization was 32% in season 2011/12 (Report Calcio FIGC, 2013):

Figure 15

Figure  15:  Top  5  second  divisions  economy

Source: Sportingintelligence, Rohlmann, 2013.

We see that even economically there are lower divisions with a relative high importance, for

example in terms of revenues, English second division earns more than many other first divisions

League (590M€ in 2012), considering all the leagues, so also the first divisions, it is the 7th League

in Europe in terms of incomes.

Beyond this coarse comparison between second divisions, many other factors should be considered

to have an idea of the value of a whole federation, (just think that in Italy there were 14450 football

clubs in 2012, playing in FIGC championships in all its categories), but this is beyond our aims,

which are to estimate the value of clubs and leagues, focusing on professional clubs, because at

higher levels the “optical of a firm”, resumable as selling a successful service reaching sportive

results to satisfy new consumers and make new profits, it is more evident.

I have tried to point out size and relevance of the football market.

At this point a question arises, by considering all the relevant factors of this market, how we can

determine the value of football clubs and leagues?

  28  

Chapter  2  –  Football  clubs  value  

2.1  Firms  economic  value  

Economic value is a measure of the benefit that an economic actor can gain from either a good or

service, comparable to the amount of money a specific actor is able to pay for that good or service

(Keen, 2001). By definition, it is a subjective value, because it cannot exist an absolute and

indisputable evaluation, indeed the only indisputable evaluation is the market price, but if a

customer is willing to buy a good, assuming a rational world, she places on it a value higher than

market price.

Anyway, it may exist a best way to estimate the economic value in each business, context and

situation, the difficulty is to identify it.

Despite a wide literature, I haven’t found an optimal or universally used way to value a football

club, and neither the same attention, in terms of attempts, data and methodologies, findable in other

sectors with similar size, which provides many estimation approaches.

Anyway, we are talking about an industry which generates 20 Billions of € in Europe, without

considering externalities in other sport sectors or related products (small tournaments, videogames,

balloons sold etc.). Hence I think it’s imperative to know the value of the clubs, as accurate as

possible, which are the core assets of the football business; both for those directly involved and for

investors.

Starting from these needs, I would like to go in deep with this subject.

In the next paragraphs, we will see some techniques used to value a firm, first some general

methods and then some specialized for football companies.

Before to start, we resume Modigliani and Miller (1958); they affirmed that managers cannot

change the value of a firm by repackaging the firm’s securities. In synthesis, a firm’s capital

structure is irrelevant.

The basic Modigliani and Miller proposition is based on the following key assumptions:

• No taxes.

• No transaction costs.

• No bankruptcy costs

• Equivalence in borrowing costs for both companies and investors.

  29  

• Symmetry of market information, meaning companies and investors have the same information.

• No effect of debt on a company's earnings before interest and taxes.

In a following paper, Miller (1977) demonstrated that in presence of corporate taxes, firm value is

positively related to its debts. Anyway, a too high level of debt increases financial distress. Debt

provides tax benefits to the firm but it puts pressure because obligations have to be serviced. So it’s

a trade-off, as we can see from Figure 16:

Figure 16

 Figure  16:  Wacc

‘rWACC’ is the rate that a company is expected to pay on average to all its security holders to finance its

assets (Miles, James A., Ezzell, John R., 1980), it falls initially because of the tax advantage to debt.

Beyond the optimal level of debt it rises because of financial distress costs.

In this work the assumption is that there is no difference in values with respect to capital structure,

in other words, the way a football club finances its assets, through some combination of equity,

debt, or hybrid securities, doesn’t affect its worth.

After Modigliani and Miller, there have been many attempts to value firms and, in particular, its

equity; in general, we can decompose evaluation approaches in:

1) Profitability methodologies: With these methods, firm value is only a function of future profits;

it actualizes future economics results and cash flows, to determine a present value. Strengths

  30  

are that this methodology is future looking, moreover profit is usually considered as one of the

most important companies’ performance indicators. Weaknesses are that by considering future

perspectives, it is introduced subjectivity, and assets and liabilities are not directly considered

in value calculation.

2) Capital (patrimonial) methodologies: It is an evaluative criterion closely anchored to a

patrimonial conception of the company, simple or complex, depending on whether or not you

take into account intangibles, often not emerging from the accounting records and the financial

statements, such as, for example, know-how, brand, goodwill, palatability of the product. It

consists on the analytical assessment of the assets and liabilities of the firm, items that are

valued at their current value, not the functioning value used for the preparation of the financial

statements. Strengths are the objectivity and analyticity of the process, evaluating separately

each element on the statement. Weaknesses are that it is completely backward looking, without

considering future flows, it is difficult to assign the right weight to the intangible assets and it

assigns a value to each balance item active or passive, irrespective of his belonging to the

complex, unitary and functional, business.

3) Mixed patrimonial and profitability methods: In order to combine the advantages of the first

two methods, some hybrids approaches were born; they take into account the objectivity of the

patrimonial size and future potentiality, by considering the concept of goodwill, identifiable as

the difference between firm value and capital structure value. Strengths and weaknesses of

these methods are the same of capital and profitability methodologies, combined.

4) Financial methodologies: they start from the assumption that a firm is comparable to any other

financial investment and hence its value depends on actualized future cash flows generated by

the firm to potential investors. Strengths are that these methods are rational, universally

applicable and future looking by considering financial cash flows; weaknesses are the

subjectivity of the process and the omission of firms’ assets and related profitability.

5) Multiples methods: they try to express the market value of an (or more) asset relative to a key

statistic that is assumed to relate to that value (e.g. Earnings). Advantages are usefulness and

relevance (they focus on key statistics), simplicity can be both advantage or disadvantage,

because their very simplicity and ease of calculation makes multiples an appealing and user-

friendly method of assessing value, on the other hand by combining many value drivers into a

point estimate, multiples may make it difficult to disaggregate the effect of different drivers,

such as growth, on value. Staticity, dependence on correctly valued peers and short term view

are generally disadvantages, also comparing relative values could be difficult.

  31  

6) Market capitalization (a particular market price methodology): Market capitalization is the

total value of the issued shares of a publicly traded company; it is equal to the share price times

the number of shares outstanding (Investopedia Market capitalization definition, 2013).

Strengths are the objectivity and theoretical precision of the value, which should reflect the cost

of buying all company’s shares, which is equity value, and knowing debts we should have a

good estimation of market value; weaknesses are that it is usable only for quoted public

companies and to provide a fair evaluation, in practice, it needs a completely liquid market with

perfect symmetric information, which are ideal conditions. It is meaningful only under some

circumstances. Another weakness is that the market value is given for minority shares, i.e. it

does not incorporate the value of control.

There isn’t an unquestionably better approach, the choice of the appropriate methodology depends

on the business and related relevant factors.

By referring to football industry, in the literature I have found some attempts mostly related to

financial methodologies rather than patrimonial approaches, variations of profitability

methodologies focusing on revenues and some hybrid of all the methods.

Market capitalization is a separate chapter that I would like to deepen in the next paragraph together

with another traditional financial evaluation method, Discounted cash flow, before to get into

methodologies more specific for football companies.

2.2  Market  capitalization  

One specific way in which firms can make use of financial markets is to list the firm on a public

exchange, allowing many types of investors the opportunity to purchase a share of the ownership of

the firm, and permitting the firm to source capital at the lowest available cost for investment in

productive projects. Firms undertake this change in ownership from a private, entrepreneur-driven

entity to a public firm via an Initial Public Offering (IPO). (Baur, Dirk G. and Conor McKeating,

2011).

An IPO is a public offering of company stocks, available for the first time on a securities exchange.

To find IPO, the issuer’s intermediating investment bank expends efforts and resources to discover

the price at which the firm’s shares can be sold (Qfinance.com). The market price will be a

weighted average of the many resulting value estimates; for the issuing company, it’s important to

know a range of the projected price, with an appropriate confidence interval, before to decide

  32  

whether to place or not into financial market.

An example of this is Manchester United IPO; in 2012, the IPO, led by New York City-based

investment bank Jefferies, predicted the 16.7 million shares available at an expected price of

between $16 and $20, raising approximately $300 million, and driving the club's total valuation to

$3 Billions, as the ownership of the club, Glazer family, decided to sell 10% of shares (Jolly, 2012).

Market capitalization is the total value of the issued shares of a publicly traded company; the

formula is trivial:

Value = Pstock * Nshares.

Market capitalization is equal to the share price times the number of shares outstanding.

As outstanding stock is bought and sold in public markets, capitalization could be used as a proxy

for the public opinion of a company's net worth and is a determining factor in some forms of stock

valuation (Griffin & Ebert, 2012).

Hence, only a company floated on an exchange where equities regularly change hands can be

valued reliably using market capitalization.

Modigliani and Miller (1958) said that companies with liquid shares in efficient market tend to be

valued appropriately as quoted share prices are realisable for investors. This is not the case of

football clubs, where shares have tended to be stagnant and illiquid resulting in share prices that do

not reflect the true value of clubs being quoted, like the cases above, and market sentiment

influences share volatility (Markham, 2013). Shareholder structure within publicly quoted

companies may impact a potential acquisition and valuation (Damodaran, 2012); in some instances

in football clubs, majority shareholders’ will have no interest in selling, but usually investors

looking to gain control of a company are likely to have to pay a significant premium on top of a

share’s current value in order entice shareholders to sell (La Porta et al., 2002).

This lack of liquidity around football clubs stocks is also due to the fact that most of them work

zero profit (also negative), and returns are mostly spent in player transfer fees and wages, so

investors are losing interest, shares are rather stagnant and owned by a dominant owner or

individual fans who had no interest in selling. Hence, many shares are illiquid, market capitalization

does not reflect the club’s worth and it is not significant to make comparisons. So we can conclude

that market capitalization is applicable to few football companies and in general it does not

represent the whole firm value, it only refers to equity and in particular it reports the value of little

part of stock exchanges made by minority shareholders, it is useful in that sense and by definition it

should be misleading when referring to a football club value.

To verify those considerations, we can look at those football clubs whose are actually quoted in the

financial market. In European football, including all the teams belonging to the UEFA associations,

  33  

there are only 25 clubs actually listed in stock exchange, because adherence to stringent stock

exchange rules and annual reporting requirements is a costly process (Brealey and Myers, 2002),

also problems around strategic objectives could arise because for example a listed club would have

to declare to the stock exchange if negotiations to sign a star new player were taking place whilst a

non-listed club would not; these constraints, for the majority of football clubs, dominate

advantages, like to have more liquidity. Hence, we cannot use this method as a standard to value

football companies. However, let’s have a look to a part of this minority, data taken from

Bloomberg, Dec 27th 2013:

Table 4

MARKET CAPITALIZATION

(M€)

FORBES 2013(M€)

Manchester United England 1815 2449

Arsenal England 1136 1034

Fenerbahce Turkey 229 /

Juventus Italy 223 541

Borussia Dortmund Germany 221 340

Ajax AFC Nederland 154 /

AS Roma Italy 154 265 (2012)

Besiktas Turkey 137 /

Galatasaray Turkey 97 /

SS Lazio Italy 33 /

O. Lyonnais France 27 287

Porto FC Portugal 5 / Table  4:  Market  cap

From this table, we can see the comparison between market capitalization (third column) and

Forbes magazine value (fourth) for those available. Forbes it can be consider as a benchmark, we’ll

deepen it in the next pages. Despite the poverty of data, it’s evident the inconsistency between

market capitalization and Forbes values, with the impressive difference between 287 and 27

Millions € of Olympique Lyonnais. Forbes methodology is not available but we may suppose they

include also assets in their evaluation, making the comparison with market capitalization

misguided.

  34  

They aim is to find the whole enterprise value (Equity plus Debt). Moreover, problems could arise

because control premium is not quantifiable on the market, we’ll deepen this aspect later.

Looking at the data, from this table it seems that Forbes gives significantly higher values with

respect to Market capitalization, and we could imagine the difference as debts, because Forbes

should consider equity plus debts while market capitalization reflects only equity value, but we

can’t affirm this not only for the paucity of data: for example, Fenerbahce is worth, following

Market cap, 229M€, while the yellow and blue Turkish are not present in the Forbes rank, it means

they are back to the 20th position of Newcastle United, valuing less than 210M€, and they where

never present in the rank even in past editions. Arsenal FC is valued less by Forbes. So there are at

least 2 cases out of 12 in which we can’t affirm strictly that. Surprising 5M€ of FC Porto, the

company has a ratio between total debts and annual revenues of 1,2 (Bloomberg), bad but not so

worrying for a football team of that size, it means they are indebted by around 110M€, but they own

a 52000 attendance modern stadium (Do Dragao), built with an estimated cost of 98M€ in 2003,

which is more than annual Porto turnover, near to 90M€ (Deloitte); moreover, they have got 85M€

of players value net of their annual salaries (Transfermarkt.com, 2014). It would be interesting to

deep where the evaluation comes out, because at first sight it seems a blatant underestimation, as

the case of Olympique Lyonnaise.

In any case, we conclude that comparing football clubs’ value through Market capitalization could

be useless as this method is appropriate to value only equity’s minority stocks exchange, anyway it

can be used as an indicator to compare the size of two quoted clubs in similar context, provided that

market is sufficiently liquid.

2.3  Discounted  cash  flows  models  

Discounted cash flow is defined as a valuation method used to estimate the attractiveness of an

investment opportunity. Discounted cash flow analysis uses future free cash flow projections and

discounts them (most often using the weighted average cost of capital) to arrive at a present value,

which is used to evaluate the potential for investment (Miles, James A.; Ezzell, John R., 1980). The

more general formula is this one, in which the discount factor is the rate that a company is expected

to pay on average to all its security holders to finance its assets, here “r” (WACC):

  35  

 [3]  Equation  3:  Discount  rate  

Using this technique, the value of an asset is calculated by obtaining the present value of the

expected future cash flows. These cash flows are discounted back to the present day using a

discount rate aligned with the perceived risk of the investment (Pratt, 2008).

This method is recognised as the most credible means to value assets or companies by both

academics and practitioners alike (Demirakos et al., 2004).

Discounted cash flow can be applied to any profitable firms, but its effectiveness depends on the

stability of future cash flows: it can value any company that has predominantly positive and

predictable flows going forward (Markham, 2013).

This is not the case of football clubs, many of them are perpetually loss making entities and

therefore do not have any positive cash flows to discount back to today’s value, but we should

consider also positive cash flows regardless from profits.

Notice that by concentrating only in profits and their present values sum, we are considering only

equity thus we need debts value in order to identify company’s value.

In an expanding market like football, some club is able to plan future profits and this could explain

great positive values for loss making entities, not detectable in a present value based on old balance

sheets. Moreover, usually investors bought them for other aims, whose values (e.g. politics reasons,

huge control premium value, visibility etc.) is difficult to quantify but it would be necessary when

applying correctly Discounted cash flow method, because otherwise we omit relevant flows’

present value, which are benefits related to clubs’ holders that compose club’s value.

The traditional way to calculate the discount rate comes from a fusion between Modigliani & Miller

and Capital Asset Pricing Model:

[4]

Equation  4:  Wacc  drivers

Where V is the sum of net worth plus net debts, Re and Rd are respectively cost of equity and cost

of debt, Tc is the tax rate; Capital Asset Pricing Model is used to determine the fair rate of return of

an asset compared to a market systematic risk and a risk free rate; here it could be used to estimate

Re.

  36  

Let us focus on this term which could be misleading for a football firm: we know from the theory

that the cost of equity is the return a firm theoretically pays to its equity investors, i.e., shareholders,

to compensate for the risk they undertake by investing their capital. Hence it presupposes to plain

some positive return. Moreover, as the risk free rate is theoretically available for all the investors,

which is an assumption of the Capital Asset Pricing Model, it should be greater than this amount.

In the richest English Premier League, only 8 clubs out of 20 made a profit in 2011 (Jones et al.,

2012), and few of them stably during years, it seems meaningless trying to apply the traditional

Discounted cash flow method.

The methodology should provide perfect evaluation when knowing future cash flow, but how can

we estimate them? Many owners have bought football clubs because of political aims, image,

personal benefits useful for other aims as, in same cases, industries. It seems utopian to find a fair

estimation of those values.

Anyway, there are different variations of this approach, like using a risk adjusted rate, with or

without considering debts, by estimating differently future entries.

A problem is that for a football club is hard to determine the correct rate of risk due to the instable

conditions in which most of the teams play, depending, among other factors, on own and

competitors sportive results and fans future enthusiasm, which are very changeable or difficult to

quantify, and highly unpredictable over time.

For these reasons, actualizing future cash flow, even for profit maker clubs, presents high

unpredictability and may cause inconsistency with the actual value of the company.

Take for example the case of Arsenal FC, which is a solid firm that typically generates profits every

year, playing in one of the best context for a football company, the Premier League, which

guarantees a relatively low dependence on results. This is shown in the table 5:

Table 5

Arsenal DCF (Markham, 2013)

M€

Forbes M€ Revenues (Deloitte)

M€

2012 791 806 290

2011 1022 802 251

2010 1044 718 274

2009 847 603 263

2008 730 598 264 Table  5:  Discounted  cash  flow

  37  

Despite fluctuations in results, Gunners revenues are quite stable with a positive trend, and great

future expectations due to the increasing market of Premier League and their solid position in terms

of market share inside it (Arsenal have been estimated as the third English club per number of

supporters after Manchester Utd and Chelsea FC in 2014, with 113 millions of fans worldwide,

(Teen’s Digest), and third even when considering only its core consumers, English fans, not far

from Manchester Utd and Liverpool).

We can discuss about Forbes values, anyway they respect the positive trend, while Discounted cash

flow method presents up and downs (DCF values are taken from Markham, 2013, calculations are

not available, anyway they are calculated taken into account the average cost of borrowing in

Premier League, actual free cash flow and an estimated grow rate, as is summarized on the paper).

Because of the high unpredictability of clubs’ profit and expected growth, we can conclude that

these values are not enough reliable even for a virtuous model like Arsenal, which makes profits

since 2003, so it’s an ideal case for a football club and this confirms the inconsistency in applying

DCF model, so structured, for the totality of football companies, most of which works in loss or

without making stable profits. DCF would provide theoretically a perfect evaluation by knowing

control premium value and future cash flows, most of them not related to clubs’ profit and loss but

to property’s benefits, but we don’t have the data to apply the method suitably.

Limiting ourselves to the companies able to plan future profits, it is still possible trying to find an

appropriate discount rate, taking into account the relevant factors and knowing the high

unpredictability of sportive results, able to affect significantly the rate.

I haven’t found any other attempt in this direction in the literature, except some single calculations

of discounted future cash flow, without claiming to be a football club evaluation methodology.

However, I’ve found some other approaches that I would like to deepen in the next paragraphs.

2.4  Specific  methodologies  to  value  football  clubs  

In addition to traditional techniques, in the literature there are other methods actually used to try to

assess football clubs’ value. Three of them are Forbes valuation, Multivariate model and Revenue

multiples approach. Let’s see how they work by discussing methodologies, looking for strengths

and weaknesses by exploiting the theory and the wide literature about firm evaluation methods, and

then comparing with the evidence and the real prices at which clubs were sold.

  38  

2.4.1  Forbes  valuation  

Forbes is an US magazine that ranks top 20 (or 25 in some cases) football teams since 2004.

Valuation methodology is not public, to do this the magazine throws available financial data, its

own research, and some expert opinions into a black box shakes it around and out come values

for clubs (Footyfinance.com).

“We began our valuations with the Football Money League report, published by the Sports Business

Group at Deloitte, which compiles vital figures for the 20 soccer teams with the most revenue. We

then use our own research, which includes reviewing financial documents and speaking to sports

bankers, to derive operating income, debt and values for each team.” (Forbes, 2012).

“If a club was scheduled to move into a new stadium that will potentially increase its annual

revenue, this is factored into the valuation estimate (Forbes, 2003). “

We deduce that some factors taken into account are assets, debts, transaction prices and multiples of

revenues, but it’s difficult to know how the included variables are used. We will try to understand

more by analyzing the data.

Here two other more illustrious deductions: The 2012 valuations are based on an even vaguer model

that starts with Deloitte’s Football Money League figures from which Forbes engages in its own

research, which includes reviewing financial documents and speaking to sports bankers, to derive

operating income, debt and values for each team (Ozanian, 2012). Forbes looked at the revenue

generated and each team’s earnings before interest and taxes, depreciation and amortization and

player trading. However, the evaluation completely overlooks player trading expenses, player

salaries and other expenses (Karan Popli, 2013), this is a strong assumption for a football team,

because player values in many cases is greater than incomes, consequently spending huge amounts

of money in player trading could affect significantly a club value.

Anyway, following those considerations, The Forbes model, starting from Deloitte, is heavily

biased toward revenues. To verify this assumption and better understand how Forbes evaluate, I

post here the Forbes rank compared with annual revenues (Deloitte 2013) and in the pictures below

(Figures 6, 7 and 8) is tabled the ratio between value (here indicated as market value or price) and

annual revenues:

  39  

Table 6 Table 7

Table  6:  Forbes  2013                                                                        Table  7:  Forbes  value/revenue  ratio  2013

Table 8

Table  8:  Forbes  value/revenue  ratio  (historical)

  40  

The club valuations are highly correlated with revenues, increases in revenues translate quite

directly into increases in valuation. Also, a club’s individual multiple stays roughly the same

throughout its history. A higher coefficient means a more aggressive valuation, where Forbes

considers other factors heavier with respect to incomes.

Looking at revenues alone is pretty meaningless, we try to include relevant costs by taking the

relation between values and EBITDA, a factor which is included in Forbes calculation (Forbes,

2012):

Table 9

Table  9:  Forbes  value/EBITDA

The same correlation and consistency we see in Value/Revenues is not found when looking at

Value/EBITDA, in most cases it fluctuates wildly from year to year which suggests it does not

factor heavily in the valuation calculation. Taking into account operating costs, it remains unclear

why some clubs have stably an high ratio between value and revenues (e.g. Manchester Utd) and

the opposite for others (e.g. Manchester City); by considering revenues and operating costs we are

omitting important factors like assets (also intangibles that have an high influence in value

especially for big clubs), and debts, factors which should be included in Forbes calculation, we

don’t know how.

  41  

Anyway values reflect revenues rank and potentiality of greater investments, indeed the ratio

between values and incomes (Table 7) do not change substantially the list, with some exception like

SC Corinthians, which has a high ratio (3,0) relatively to its revenues (119M$).

This is reasonable in an expanding market like football, the more you earn, the greater possibility to

further expand your market share you have.

The high values of Real Madrid, Manchester Utd and Barcelona are justified from their strong

position in their championships (in the case of Manchester Utd it is more the strong position of its

league, the Premier League, and its strong and wide share inside that market), new rich deals like

Adidas and Yamaha (Real Madrid), General Motors (Manchester Utd), Qatar Airways (Barcelona)

and increased market share worldwide; we have a rough approximation of that share by ranking the

number of fans on Facebook, that actually is the bigger social media, top 15 clubs per fans number,

2nd Jan 2014 :

Table 10

Table  10:  Number  of  fans

It is clear that those numbers are a superficial estimation of the real number of supporters rank,

which is of course an important indicator of clubs value, which should be included by Forbes; in

Table 10 we see the comparison between Facebook fans at the beginning of 2014 and a more

accurate research made from Sport + Markt (2010), with many top clubs which on Facebook have a

Club Number of fans

(millions) Facebook

(Jan 2014)

Real number of fans

estimation in millions

(Sport + markt 2010)

Barcelona 50,0 57,8

Real Madrid 46,7 31,3

Manchester Utd 38,5 30,6

Chelsea 21,0 21,4

Milan AC 18,9 18,4

Arsenal 18,0 20,3

Liverpool 14,2 16.4

Bayern M 11,1 20,7

Galatasaray 9,2 6,8

Juventus FC 9,0 13,1

Manchester C 7,9 /

Fenerbahce 7,0 /

Borussia D 6,3 /

PSG 6,1 /

Corinthians 5,0 /

  42  

similar Sport + Markt rank, after four years. Only after 10th position on actual Facebook rank we

find clubs that were not present in top 20 ranking of 2010 (Sport + Markt).

Social media have increased from 430 millions of users (2007) up to 1,2 Billions in 2012, (Teatino

& Uva, 2012), and the growing is still continuing, we will see how football clubs will exploit those

fans or sympathizers.

Anyway, we note a correlation between Forbes values rank and number of fans on Facebook (top

10 Forbes clubs are all included in top 15 Facebook number of fans rank), with the proper

considerations, for example the popularity of this social media in each country could influence

significantly the list, as we can see from the high position of Turkish teams (also Besiktas is above

4 Millions of fans), indeed Turkey was the 6th country per number of registered in 2011

(Wikipedia), and the first in Europe.

By considering these correlations, number of supporters seems a factor considered by Forbes in its

evaluation, even if we don’t know how the research have been done to estimate fans catchment

area.

Let’s discuss about Forbes values reliability.

Looking at real market values, discrepancies between transaction effective prices and Forbes values

were found by comparing Forbes North American sports franchises valuations, from 1998 to 2003,

and their actual transaction price during the same period. The result was that Forbes tend to

underestimate, indeed on average the transaction price of the franchises was 27% higher (Vine,

2004). To verify those assumptions, let have a look at this table:

Table 11

Date Club Transaction price (M£) Forbes (M£)

2005 Manchester Utd 800 690

2006 Aston Villa 75 76

2007 Manchester City 82 124

2007 Newcastle Utd 131 141

2008 Manchester City 233 103

2010 Liverpool 300 508

2013 Inter FC 350 M€ 313 M€  

Table  11:  Forbes  comparison

Sources: Markham, 2013; Tifosobilanciato.it; XE convertor.

  43  

We have few data to draw an indisputable conclusion, and an inhomogeneous sample formed 6/7 by

English teams; this is due to the lack of official transaction data (moreover only a minority of

football club has been sold in the last years), and the main problem of Forbes is that it has valued

only top 20 or 25 football companies; anyway, looking at these data, we can see a correlation

between actual transaction costs and Forbes one, with some discrepancies, both positives and

negatives.

We must consider that actual transaction prices do not necessarily reflect the real value of the clubs,

they are only the maximum amount investors are able to pay at that moment.

Moreover, sellers could be influenced by their passion and by fans, admitting new investors at

lower prices hoping in brighter future perspectives; on the opposite control premium encourages a

higher offer. We’ll deepen it in the next pages.

However, in general, we know that seller and buyer seek the maximum possible gain, and the

equilibrium point between supply and demand should be the club fair price.

Forbes’ estimates deviate from +69% (Liverpool 2010) to -126% (Manchester City 2008), in this

small sample results are worst than the ones on American franchisees. Looking specifically at

Manchester City which changed hands in 2007 and 2008, Forbes valued the club 52% higher than

the price in 2007 and -126% in 2008; this huge difference may be attributed to the large expenditure

in players during that year, that changed significantly the club value, that’s why I think that 2008

Forbes value is meaningless.

I’m reminded at Karan Popli affirmation about Forbes exclusion of player trading expenses, player

salaries and other expenses (Karan Popli, 2013).

Anyway, only 2 clubs out of 7 were valued by Forbes within 10% of the actual transaction price,

which brings us some doubts about the reliability of the method, even if in many cases it could be

considered as a valid benchmark.

At the end, we can affirm that Forbes list could be considered a good benchmark of football clubs

worth, but not enough reliable to be used as an universal accepted method, neither for the few clubs

it considers; indeed, not only there is a relatively high standard deviation between transaction prices

and Forbes estimation, but it seems to be a lagging indicator:

I begin with some considerations to justify this assumption, before to start I would like to highlight

that as we don’t know how Forbes actually includes basic and relevant economic factors like debts,

assets and equity, neither the real values of that indicators for some clubs, our estimation could

change significantly.

  44  

Anyway, there are some cases in which discrepancies between Forbes and actual economic and

sportive situation seem too big to justify any combination of these factors, this is the assumption we

have made.

Take for example the case of Manchester City, where the shopping spree effects, which

immediately increase a club value, enhancing winning probability and bringing more revenues,

seem to be considered in late:

In 2008, when the club was bought, it was worth by Forbes 191M$, 258M$ in 2010 when the

ambitious plans were already well known and implemented bringing the club back to European

competitions, and only now, 2013, the very higher estimate of 689M$, which takes into account big

potentiality compared with greater volatility of the club, with respect for example to Manchester

Utd rivals, which have a comparable winning probability in the next few years, but a very higher

value (3165M$) due to their indelible history and solid position.

The same is happening for Paris Saint Germain, which is out of 20th position of the rank, we are

talking about a club with total player values of 320M€ (Transfermarkt.com), with 220M€ of

turnover in 2012, a good history in the last 20 years of French football and a relevant catchment

area, it plays in a quite good league, the 5th in the world per incomes and it has ambitious plans for

the next years; “Le Parisien” published a paper about PSG business plans (2013), writing that they

aim to reach 500M€ of revenues within 2015, and the growth has already begun.

Despite the relatively high volatility of the property and few data mentioned, I think that the club

actually should be included in the top 15 values list, and the first club of its league.

With these personal considerations and following the previous case of Manchester City, in the next

Forbes ranks PSG will overtake home rivals Olympique Lyonnais and Olympique Marseille, which

have few possibility of winning and collect more than PSG in the next few years, and a market

share drop is predictable for them, as consumers, generally, would like to win. We can extend these

considerations to other companies, like German clubs position seems lower than their bright future

perspectives. For example, I expect that in the next few years the very broad gap between top

Spanish clubs and Bayern will be reduced, based only on the actual situation.

Again, how is it possible that Juventus FC dropped by 37M€ from 2011 to 2012, in a season in

which they inaugurated the new stadium, returned to win the championship after 9 years and they

increased annual revenues by 40M€, growth carried out despite it suffered the momentary absence

from European competitions? Moreover, during the same period, others Italian clubs present in the

rank (Inter FC, AS Roma, AC Milan) had improved their value, testifying that Juventus FC fall was

not due to a league crash.

  45  

From these results, by accepting the assumptions we have made with available data, the reasonable

conclusion is that Forbes methodology has a too limited viewing, and unless past trends can be

reasonably expected to continue into the future, they are useless for a valuation that is inherently

forward looking with great importance to last seasons.

Anyway, as we actually don’t know how important factors like assets and debts are included in the

calculation, we cannot state that in an absolute manner, we would need to see the used

methodology. Forbes evaluations remain the most credible values I have found, that’s why they are

a meter of comparison and a benchmark for clubs available. It remains some doubts when talking

about actual rank, which appears as one or two years old updated, and the same happened in the

previous ranks, as I have tried to report in the case of Juventus FC, Manchester City, PSG and top

German clubs.

2.4.2  Multivariate  model  

This model, (inspired from Markham, 2013) has been thought to find a reliable valuation for

English Premier League clubs. Multivariate model appears as a multiplier method where the

fundamental variable is revenue multiplied by some factors. It is a variation of Revenues multiple

approach we’ll deepen in the next pages.

The logical foundation of this method can be traced in many American researches; sports

franchise’s ability to make money in the future determines its valuation. Following Philips and

Krasner (2010), the most important factors in the US market are:

1) League, with its revenues streams and player salary cap.

2) Stadium and related capacity, corporate boxes, sponsorship and advertising.

3) Market, including corporate presence and demographic catchment area.

Multivariate model follows these assumptions, with the exception that in Premier League (but also

in European football in general) many issues related to revenue sharing policy and salary cap are

not applicable. As this approach aims to operate in the English reality, other important assumptions

are Premier League regulations and context, as the relevant number of owned stadiums and the

broadcasting revenues sharing policy, which guarantees 50% of the most important revenue stream

(Broadcasting was the 52% of the whole Premier League incomes, excluding player trading, in

2012) divided in equal parts, wage expenses that where 1,9 Billions of € in Premier League in 2013

(Ilsole24ore.com, 2013), getting 66% of revenues, and incentives to follow UEFA’s Financial Fair

Play rules which encourage clubs to ultimately operate within the revenue they generate and have

been followed up by the Premier League adopting similar financial controls.

  46  

Finally, following Markham (2013, p. 16), “If a club is profitable or at least breaking even, it shows

they are prudent and controlling costs.” Moreover, “The main assets of a club, typically a stadium,

training ground and player registrations, need to be weighted up versus the liabilities (normally

trade creditors and debt)”. Trying to answer to these questions, net profit and club’s net assets have

been included in this method.

The model reduces to this formula:

Club value = (Revenue + Net Assets) * [(Net Profit + Revenue) / Revenue] * (% stadium filled) /

(%wage ratio) [5] Equation  5:  Markham  formula

Let’s analyse this equation: it can be divided into three factors, multiplied for one another.

The first one is (Revenue + Net Assets), it takes into account actual potentiality but also future

perspectives, since a club’s ability to generate future revenues and consequently increase its value is

related to assets actually available.

This factor is multiplied by the club’s net profit (or loss) figure added to revenue and divided by

revenue [(Net Profit + Revenue) / Revenue]. Hence, the second factor examines a club’s

profitability in comparison to its revenue. So it could be either greater or lower than 1, depending if

the club is profitable, able to enhance or reduce the final valuation.

The third factor is the ratio between stadium utilization and club’s wage to revenue ratio (%

stadium filled) / (% wage ratio), the average stadium utilisation percentage illustrates how

effectively the club is using its core differentiating asset, divided by the club’s ability to control its

major expenditure. So, even this third factor could be either greater or lower than 1, the higher

player wages, the lower the whole ratio, the higher the percentage of stadium filled, the higher the

ratio. In this model revenues are taken net of gains on players selling, too.

Multivariate model gives a lot of importance to revenues in determining a football club’s value, but

it takes into account also costs (factor 2) and indicators of management accuracy and effectiveness.

As we can see, this model is very simplistic and there are possible incongruities, for example when

summing up revenues and net assets, it makes sense to add flows together with something related to

stocks, [€/year] + [€]? We still accept the equation in order to try to apply the methodology, but

we’ll resume this perplexity.

Basically, Multivariate model is applicable with these assumptions:

  47  

1) Revenues are the most important factor in determining football club’s value, in other words,

by doing a multifactor analysis, incomes should capture most of the variance.

2) Wage ratio and percentage of stadium filled are able, together, to affect one third of an

English club value.

3) Wage ratio and stadium utilization are the most relevant indicator for, respectively,

management ability to control costs and exploit assets.

They are very strong assumptions.

Another weakness of this method is precisely this difficulty to include all the relevant determinants

of value in these three indicators with their fair weight. Factors like debts, intangible assets as brand

and human resources are indirectly included with disputable weights.

On the other hand many importance is given to percentage of stadium filled and wage ratio, they are

able to influence one third of a club value.

To verify the model in practice, trying to prove its reliability, let’s see some results of this method,

related to English Premier League. Data taken from Markham, 2013:

Table 12

Transaction date Club Transaction cost (M£) Multiv. Method (M£) Variation on actual %

23rd Dec 2003 Bolton Wanderers 54 51 -4,7

28th Jun 2005 Manchester Utd 800 801 0,1

19th Jul 2006 Portsmouth 64 43 -32,8

14th Aug 2006 Aston Villa 75 82 8,6

21st Nov 2006 West Ham Utd 108 112 3,4

6th Feb 2007 Liverpool 219 260 18,6

6th Jul 2007 Manchester City 82 75 -8,6

18th Jul 2007 Newcastle Utd 131 133 1,8

28th Jan 2008 Derby County 20 21 4,4

23rd Sep 2008 Manchester City 233 241 3,4

28th May 2009 Sunderland 20 20 0,6

20th Aug 2009 Birmingham City 96 83 -14,4

19th Nov 2010 Blackburn Rovers 44 70 59,2

15th Oct 2010 Liverpool 300 333 10,9

18th Aug 2011 Queens Park Rangers 68 62 -9,0

AVERAGE +2,8

STANDARD DEVIATION 19,7

Table  12:  Multivariate  model

  48  

In all these cases, Multivariate results are close to transaction prices, some variation is congenital as

transaction prices not necessarily reflect market value, moreover there could be also unintended

discrepancies, for example Moores family who sold Liverpool in 2007 acknowledged that they sold

at a lower price as they felt they were selling to new custodians who would propel the club forward.

We know from the theory that whenever we are comparing a firm value calculation with effective

transaction prices we should take into account control premium, which is an amount that a buyer is

usually willing to pay over the current market price of a publicly traded company.

Control provides different benefits depending on sector, context and size of the firm. In public

companies the effect is evident because of direct gain of control through a higher shares’ percentage

or voting rights acquired from specific shares, but even in private firms there could be a huge value

of control. Let’s start with public companies, considering that in football market and sport sector in

general value of control is relevant because of the great visibility and decision power.

Many English clubs have been traded on the stock exchange from 2000 on, by alternating periods of

listing and de-listed (by remaining in the table above, 9 teams were present at least for one year).

Moreover, we should consider a control premium even for private firms, as in general when valuing

a firm, the value of control is often a key factor in determining value, even if in private companies,

with respect to public, there is often a discount attached to buying minority stakes in companies

because of the absence of control (Damodaran, 2005).

We have distinguished between public and private because control value is more evident for the

first ones, in which you pay directly the premium over the quoted shares, depending also on the

type of share you are going to buy. Moreover, in some sector the value is negligible for private

firms. This is not the case of professional football in which control assures great visibility and

influences, at least in the local area of the club, even if the club is not quoted.

In order to guarantee the applicability of Markham’s model, two assumptions are made:

1) There is no difference from public and private firms in terms of control premium value.

2) Control premium value enhances transaction price but adhesion to the club reduce it, as we

have seen in the Liverpool case. Here the assumption is that these two values cancel out

each other.

3) The probability of changing management and control in the future doesn’t affect the price.

With these strong remarks, looking only at the results on the table, this method seems to be the

optimal one to value a football club, at least in English Premier League, but we need more data to

verify the strong assumptions of the method.

  49  

Moreover, we have some doubts about the good results of the table, is it because of the great

reliability of the method in English Premier League or there have been some tricks, in the sense that

starting from the real transaction prices, some economic indicator has been taken in order to throw

out the best result possible? We have mentioned perplexities about summing revenues and net

assets, thus summing up an income statement flow with a patrimonial asset.

Even by admitting the possibility of doing so (which should be misleading as we are summing up

[€/year] and [€]), I would like to point out some consideration about the factors used in the method:

Net assets calculation is not visible in Markham paper, they could be taken directly from the

balance sheet for those available, looking at total assets and liabilities, but the problem is that they

not reduce to that, they include also all the intangible assets, as supporters and actual players value,

which are significant factors in determining club worth, difficult to evaluate on a balance sheet.

Moreover, with this method, problem could arises when considering a club with great attendance

and stadium not completely filled, because attendance it is not directly taken into account, also

wages and profit/loss could be a problem because they are highly variable, as well as players value.

Furthermore, using net profit in the evaluation could be misleading, in particular in the football

market: if a club makes a lot of investments in talent, by increasing wages and operating expenses

thus obtaining negative profits, this doesn’t mean necessarily a decrease in value, on the contrary it

can raise significantly. It depends on the accuracy of the investments.

Let’s verify these considerations in a particular situation taking the case of Chelsea FC.

Year 2011:

Revenues = 250M€; Wages/turnover = 0,84; net loss = -83M€; %stadium filled = 98%;

net assets =260M€* à club value = 340M€; (Forbes value =498M€).

2012:

Revenues = 323M€; Wages/turnover = 0,67; net profit = 2M€; %stadium filled = 98%;

net assets = 260M€* à club value = 858M€ (+153% of growth); (Forbes value = 570M€, +14% of

growth).

2013:

Revenues = 307M€; Wages/turnover = 0,7; net profit = -59M€; %stadium filled = 98%;

net assets = 260M€* à club value = 581M€ (-32% decline); (Forbes value =703M€, +23%

growth).

  50  

Here net assets are estimated considering data (current assets and liabilities) available on balance

sheet 2012 and adding players value from transfermarkt.com, this is our approximation, with the

aim to enable the comparison between years.

It’s a mere approximation but our aim is to emphasize the differences of the whole club value from

one year to another, and this is independent from net asset value since we have assumed it constant

over three years.

We keep it constant since by using our estimation the value is pretty stable during that period.

Probably because the club has not build new stadiums neither changed significantly the value of its

human resources from one year to another, anyway looking at the balance sheet I haven’t found

meaningful changes in financial structure, a part from an increase of 81M€ in credits, from 2011 to

2012, at the entry “amount falling due after one year”, which would further push up the evaluation

from 2011 to 2012, which has already had a very big jump (+153%) and with an increase in net

asset, following the equation, it would be further extended.

What I want to point out in the example is that this model goes out of sight when clubs face instable

condition; we don’t have all the data to draw an absolute conclusion on Chelsea value, but big

jumps seem not justified in a stable market like Premier League, knowing revenues and relevant

costs, without changing ownership and in a period in which the club faced stable results, reaching

top positions in the championship and thus qualifying stably for the richest Champions League

competition.

Looking at economic results above, when Chelsea profit from 2011 to 2012 has increased by 85M€

(Also due to great sportive results, they won Champions League which alone guaranteed 60M€,

UEFA.com), revenues rose by 73M€ too (keeping wages, the higher cost of the club, almost

unchanged), so following the equation of this model, and assuming net assets stable, the club value

has increased by 518M€, +153% in one year: an incredible jump, which is not seen for example

from Forbes, neither in the following years.

Probably because also the first value devaluated the company, while the 2012 overestimates it.

The confirmation comes from 2013 balance sheet, where there is another big drop of 277M€ in

club’s value, following Multivariate method, while in the same period the value has even grown in

Forbes rank by 23%.

To remedy at this weakness of the method it could be deleted, for the must unstable clubs, the factor

referred to net profit, thus finding a more reliable estimation, even if less accurate.

Despite these defects, looking only at results, this technique seems to be the more credible to value

a football club at least in English Premier League, the only market they pretend to evaluate.

  51  

Multivariate model has the peculiarity that is applicable to different club sizes, with some cases in

which it provides clearly busted results, but they can be easily detected.

Anyway, it remains our doubts about the multiplier factors, as the meaning of summing up revenues

and net assets and the weight given to profit, percentage of stadium filled and wages.

The fact that a simplistic model so structured provides very good values in Premier League bring us

some suspects about the results: are they thrown out by the model or, on the opposite, the model

was built knowing the transaction prices in order to fall in a limited range around them? This

second hypothesis seems more credible, this doesn’t mean that a model building in this way is

useless, we can get some relevant information and comparing clubs value, but it seems a ‘magic

formula’ able to provide great results in a particular context, well known and with many data

available on firms value, built starting from those data (ex. Transaction prices) by shaking around

the proper economic indicators, with the aim to reach a result near to the real values. This

consideration is supported by the fact that Multivariate model is not applicable outside English

Premier League.

Outside English championship problems could arise because of the assumptions of the method.

Stadium utilization it is considered as one fundamental indicator of ability to exploit assets, but in

Italian context and many others leagues most of the stadiums are not owned by the clubs; anyway,

the percentage of stadium filled remains an important performance indicator but in this model

problem could arise because of the higher weight given to this factor which is pretty low and

instable outside English reality, so able to change significantly the value. In Italian Serie A only

58% of stadiums have been filled in 2011, with some of the big clubs (for example A.S. Roma,

Inter FC, A.C. Milan and S.S. Lazio) with a filling percentage near to 50% but an average

attendance per match which is widely over the Serie A average of 23.000 per match, each of these

clubs have brought, on average, more than 30.000 spectators per match in 2011/2012 season. This is

due to bigger stadiums that with this method penalize significantly the evaluation, because a sold

out in 80.000 capacity stadium is considered exactly as a sold out in a 20.000 capacity one. This

lack could be solved by including an attendance indicator, it remains unsolved the problem of the

weight of this “stadium” factor. Other defects, outside English Premier League, are the instability of

net profit and wage ratio that we have, for example, in Italian leagues. We have seen what could

happen in the case of Chelsea FC, the same problem there should be for many Italian clubs,

precisely busted values due to variable net profits and wage ratios, whose decline and increase,

respectively, doesn’t mean necessarily a decrease or increase in club value.

  52  

In conclusion we deserve many doubts on this method that however could be used as a tool to

compare clubs value, but only in Premier League or similar contexts and without claim an absolute

evaluation.

From Multivariate model we keep the idea of revenue multiplier, now we are going to analyse the

more general and simplest method available, before to deepen the multiplier factors: Revenue

multiples approach.

2.4.3  Revenue  multiples  approach.  

It measures a company’s value relative to its turnover. This valuation is calculated by multiplying

the organisation’s annual revenues by the appropriate multiplier, merely like this:

Value = Annual revenues * ß.

Typically, this technique is used to value troubled or younger businesses, which cannot be valued

by more technical traditional means (Markham, 2013). This method is also suited to industries with

volatile earnings (Damodaran, 2012), so it seems appealing for football club valuation, even if very

simple, but this is not necessarily a weakness, ease of application is surely a strength.

We know from the theory that the value of a firm is defined to be the sum of the value of the firm’s

debt and the firm’s equity or, on the opposite, the value of its assets. Until now we have seen two

equity based method (Discounted cash flows and Market capitalization), whose have proven to be

difficult to apply in the football industry, and some assets method (or hybrid) as Forbes, for which

we don’t know the calculations but for sure they take into account assets value, as well as

Multivariate model.

Revenue multiples approach fully relies on revenues to valuing a firm, by implication they should

be able to catch the whole assets value. So here the assumptions are:

1) The value of a football company is directly proportional to its revenues.

2) It follows that incomes multiplied by an appropriate number reflect the sum of the value of

firm’s debts and equity.

3) We are able to estimate that number.

4) Similar firms have same multiplicative factors.

5) A multiple represents a snapshot of where a firm is at a point in time, but fails to capture the

dynamic and ever-evolving nature of business and competition. So we implicitly assume a

pretty stable value.

  53  

If we would like to be more flexible getting some future perspectives, maybe this is not the best

methodology to be applied, or a possible solution could be to use a variable multiplier, able to catch

trend and probability of revenues jumps.

Anyway, before to discuss other possibilities of improvement, let’s try to verify the method in

practice, starting from it’s basic formulation, which provide football firm value in function of

revenues multiplied by a constant. I would like to point out also that simplicity could bring both

disadvantages and advantages, because by combining many value drivers into a point estimate,

multiples may make it difficult to disaggregate the effect of different drivers, such as growth, on

value. On the other hand simplicity and ease of calculation makes multiples appealing and user-

friendly method of assessing value. Multiples can help the user avoid the potentially misleading

precision of other, more accurate approaches. (Suozzo & Cooper; Deng & Sutherland, 2001).

In order to apply this approach, the main problem is to find the fairest multiplier.

In the 2008 edition of its Annual Review of Football Finance, Deloitte reported that English

Premier League clubs have typically priced the equivalent of between 1.5 and 2.0 times annual

revenue (Many foreign investors bought English clubs in that period).

According to those numbers, it seems that this quick calculation may be effective, it would be

enough to find the appropriate multiplier. Let’s try with a fixed multiplier of 1,7 following Deloitte

considerations

Table 13

Transaction date Club Price (M€) Forbes (M€) Revenue

multiplier (M€)

2005 Manchester Utd 1192 1028 418

2007 Liverpool 304 342 343

2007 Newcastle Utd 183 197 219

2011 Queens Park

Rangers

82 / 34

2013 FC Internazionale 350 313 284

2013 Real Madrid / 2574 886

2013 AC Milan / 737 467 Table  13:  Revenue  fixed  multiplier

Sources: Deloitte; Markham, 2013. XE.com for money conversion.

  54  

The approximation of Revenue multiples approach gives comparable results only in few cases;

referring to top clubs with elevated incomes, it seems to underestimate heavily the actual value (like

Manchester Utd and Real Madrid cases), but the same happened for Queens Park Rangers (QPR),

which was sold for 82M€ while the multiplier estimation was only 34M€; because that revenues

was referred to a second division championship, but the coming property aimed to promoting and

increase incomes significantly because of that.

This approach does not consider future perspectives, as it is only a mere approximation based on

actual revenues; however, for some clubs, it is close to the real value stated.

The problem is, in synthesis, the lack of perspective view of accounting indicators.

A season with a drop in revenues doesn’t means, necessarily, a decrease in club value; causes may

be for example worst sportive results, but if the club has signed new deals, has captured new fans or

has built new facilities, its value may be increased in the same season. Anyway as revenues are a

good indicator of a football club future potentiality, at least in the near future, this approach with

some modification could be theoretically applicable with significant results. Some additional factors

are needed, able to capture probabilities of a big shock in revenues, for example probabilities of

promotion and relegation in other divisions or to expand market share. In the next pages, after doing

factor analysis, we’ll see a possible implementation of the method, using variable coefficients

depending on other factors (e.g. Sportive results, market share).

2.4.4  Fans  Method  

It remains the unresolved question, which is to find an effective methodology to value our leagues’

clubs, especially Serie A, and then seeing if it works even in other contexts.

Here the aim is not to find the exact amount of money that corresponds to clubs values, but to

provide a methodology able to give reliable comparisons among clubs value, and then seeing if the

results are consistent. To do this we’ll try to compare the value of some relevant assets, many of

which are intangibles (sportive results value, supporters value). With respect to Revenues multiple

approach, here revenues are not directly included inside calculation because they should be implied

in the factors used. Fans Method is a factor model in which components are assets multiplied by a

weight. Hence, it is an asset based value model, focusing on intangible assets, as we assume that

some of them, as history, fans, local context and the other intangible assets hardly replicable in a

short time, are part of an intrinsic value, and this is a very important factor to value a football clubs,

able to drive revenues and accordingly sportive results; in the next paragraph we’ll try to verify

these considerations and in particular how revenues, results and consumers are correlated.

  55  

I try to synthesize in the following general equation the personal initial assumptions:

€ = Ω(S, R) + ß(s, r) + e. [6] Equation  6:  General  value  formula  

 We can summarize the value of a club with this sum, Ω is a variable that considers league value and

ß is related to the value of our club inside the league. Ω depends on league followers all around the

world (S) and results of every league’s team over the world (R).

ß depends on the results of the team inside its championship and abroad (r) and on local supporters

(s). Where s < S, r < R, because local fans and single clubs’ results are also included in global fans

and whole leagues’ clubs results; both Ω and ß increase with S, s, R, r.

Externalities (e) can be positive or negative. Externalities originate from a fact (e.g. empty

stadium). They are difficult to evaluate, as externalities we refer to all the social factors able to

influence supporters’ attitudes and enthusiasm, thereby encouraging fans to invest in tickets, official

products, watching matches on media (positive externalities) or, at the opposite, in something else

not related to football or just moving to club or league’s competitors (negative externalities).

Comments, information, ratings, experiences, which thanks to globalization flow quickly.

Externalities can be both virtual or direct daily chats between fans, in any case they have a value for

clubs, even if it is difficult to quantify.

We assume all these factors affecting each other (we’ll search correlations using SPSS statistics)

and determinants of value.

Our problem now is to find the weight of each component of the equation. Before make use of

factor analysis, I’ve made an attempt to get a comparison among Serie A club’s value by placing

this table, where some factor attributable to what we have called intrinsic value is blended with

some more volatile intangible asset, now we are going to see each data and we are trying to evaluate

each factor. In this model, factors’ values have been thought starting from known revenues (and

related streams’ weight) in trying to build meaningful comparisons among values in Italian Serie A,

and not to provide a methodology universally applicable. With this method emphasis is on factor

used, then we could discuss about factors’ weights in order to build a general value method.

Club value = [bv + (pv – ps) + nf * fai + ca * cai + er] * t [7] Equation  7:  Fans  method  formula

  56  

Table 14

Serie A

2012/13

clubs

Brand

value

bv

(M€)

Players

value

01/01/2013

pv (M€)

Players’

salaries

ps (M€)

N° fans

(Millions)

nf

Fans

average

tv

incomes

fai (M€)

Catchment

area

ca

Catchment

Area

average

incomes

cai (M€)

European

Results

Premium

er

(M€)

League

trend

t

Fans

Method

(M€)

Fans

Method

without

players

(M€)

Atalanta 5 66,9 23,7 0,127 16,65 1,24 9,30 3,00 0,944 61,2 18,0

Bologna 11 57,6 28,4 0,331 16,65 1,83 9,30 7,50 0,944 66,3 37,1

Cagliari 4 63,5 15,9 0,446 16,65 0,86 9,30 3,00 0,944 66,0 18,5

Catania 2 54,6 18,0 0,229 16,65 1,67 9,30 0,00 0,944 54,7 18,1

Chievo 1 42,1 15,9 0,127 16,65 1,10 9,30 1,00 0,944 38,2 12,0

Fiorentina 39 124,6 38,8 0,719 16,65 1,79 9,30 28,15 0,944 171,3 85,6

Genoa 6 86,2 28,9 0,178 16,65 1,12 9,30 3,51 0,944 75,7 18,4

Inter 113 223,2 100,0 4,232 16,65 2,99 9,30 115,35 0,944 424,6 301,4

Juventus 135 307,7 115,0 7,087 16,65 2,11 9,30 126,54 0,944 558,8 366,0

Lazio 39 127,0 66,2 0,816 16,65 5,13 9,30 33,14 0,944 183,3 122,5

Milan 195 232,2 120,0 4,130 16,65 2,99 9,30 159,82 0,944 532,0 419,8

Napoli 75 187,6 53,2 2,345 16,65 5,07 9,30 34,03 0,944 311,2 176,9

Palermo 3 58,7 23,4 0,319 16,65 2,74 9,30 4,04 0,944 69,0 33,8

Parma 8 63,1 21,2 0,102 16,65 0,82 9,30 16,50 0,944 71,4 29,6

Pescara 1 38,9 10,8 0,076 16,65 0,59 9,30 0,00 0,944 33,9 5,7

Roma 63 145,7 95,0 1,526 16,65 5,13 9,30 44,76 0,944 218,7 167,9

Sampdoria 11 61,8 29,8 0,229 16,65 1,12 9,30 13,18 0,944 66,5 34,5

Siena 1 31,5 18,9 0,025 16,65 0,36 9,30 0,00 0,944 16,4 3,8

Torino 13 56,5 22,0 0,421 16,65 2,11 9,30 9,50 0,944 79,0 44,5

Udinese 15(2012) 96,4 21,2 0,217 16,65 0,72 9,30 13,73 0,944 107,8 32,6

Table  14:  Fans  method

All factors are in Million of €. I remark that annual revenues are not present, because I’ve assumed

incomes’ determinants already present inside the formula. Anyway, incomes of the whole market

are considered in the League trend “t” factor, as they are able to influence the total worth.

Let us examine all the factors of this methodology, in order to understand strengths and weaknesses.

Data and factors:

“Bv” is the Brand value, which takes into account actual awareness and attractiveness, but also

potentiality of future growth of the club, I have summed up Brand value because it is a direct

intangible contribute to firm value, able to getting or losing customers (fans). A problem of this

factor could be the reliability of the value and especially the paucity of clubs evaluated. Calculation

of Brandfinance is not an official balance data, it is an estimation that seeks to be as accurate as

possible, it has been calculated as follows (methodology from Brandfinance.com):

  57  

• Calculate brand strength on a scale of 0 to 100 based using a balanced scorecard of a

number of relevant attributes such as emotional connection, financial performance and

sustainability, among others. This score is known as the Brand Strength Index.

• Determine the royalty rate range for the respective brand sectors. This is done by reviewing

comparable licensing agreements sourced from Brand Finance’s extensive database of

license agreements and other online databases.

• Calculate royalty rate. The brand strength score is applied to the royalty rate range to arrive

at a royalty rate. For example, if the royalty rate’s range in a brand’s sector is 1-5% and a

brand has a brand strength score of 82 out of 100, then an appropriate royalty rate for the use

of this brand in the given sector will be 4.1%.

• Determine brand specific revenues estimating a proportion of parent company revenues

attributable to each specific brand and industry sector.

• Determine forecast brand specific revenues using a function of historic revenues, equity

analyst forecasts and economic growth rates.

• Apply the royalty rate to the forecast revenues to derive the implied royalty charge for use

of the brand.

The forecast royalties are discounted post tax to a net present value which represents current value

of the future income attributable to the brand asset. (Brandfinance.com).

We notice that this methodology mostly rely on revenues and there are subjective components, like

“emotional connection” which are difficult to evaluate, they are related to externalities “e” in our

equation [7].

This causes instability in club values, for example a very positive year in terms of sportive results

which affects positively incomes and “emotional connections”, could change significantly the

whole brand value, before declining the following years if results are not repeated (Inter FC

dropped from 263M$ in 2011 to 161M$ in 2013, Brandfinance.com). On the other hand, it is

necessary to consider sportive results and related “emotions” because they affect fans enthusiasm,

loyalty and the achievement of new consumers.

The main problem of “bv” is that it is not present for many Italian clubs, so I have recourse to a

rough estimation for those one, as explained below, anyway we are talking about a relatively little

slice of value for provincial clubs. Brand values are taken from Brandfinance (2013); they are

available only for 7 Italian clubs, which are the only present in the Top 50 rank, hence following the

  58  

model the remaining Italian clubs have a lower Brand worth, below 39M€ in 2013 (and below

15M€ in 2012, in which Udinese was included in the rank and we have taken this value). So for the

other clubs I’ve made an estimation as follows, to propose a final comparison: 1M€ per each 10

Serie A seasons participation, rounded at the nearest ten. Adding 1M€ per each quarter final

achievement in a European competition. Because of sportive results importance in affecting number

of consumers, image, awareness and attractiveness of a club. Of course recent results have a higher

weight as we will see in “er” factor.

“Players’ salaries” are related to season 2012/13 (tifosobilanciato.it).

“Players values” have estimated by Transfermarkt.com, related to season 2012/13, updating on 1st

January 2013. This amount should take into account players’ ages, by subtracting these two values

we obtain a sort of value added given by players; problems could arise when contracts are near to

expire: for the owner club there could be a loss but here values seem not discounted, probably

because a contract can be renewed anytime. It is a common problem for every football club, but in

some cases there could be a huge difference in value, this happen when a top player is going to

expire and the owner club doesn’t gain anything from him (e.g. Lewandowski for Borussia

Dortmund). Another weakness is that this factor is pretty volatile as player’s value could change

significantly in short time, for example because of a promotion or relegation. There is another

problem, as we are summing player value with a periodic flow like wages [€ - €/year], so we must

multiply the second factor times years, and as we are considering yearly salaries, we multiply times

one year in order to allow the comparison.

We don’t know the methodology used, anyway in general transfer fee paid is counted as an asset for

the buying club. The value of the player is normally amortized by the straight line method and

home grown players are activated up to the formation costs supported by the club. (Pujol, Barrio

2008).

I’ve added Players value “pv”, because this is a direct contribution to clubs’ actual value,

discounted by their direct expenses, Player salaries “ps”; about this factor, beyond how it has been

built, a weakness is the reliability of values, which are taken from Transfermarkt.com, so it depends

to their effectiveness as for Brandfinance; moreover it is not a stable value, it could change rapidly

in time so it is not part of the intrinsic value which is our aim.

Transfermarkt.com uses a kind of market value method, not available to the public, there could be

others more accurate evaluation methods, there isn’t an official and indisputable value for a player.

An interesting attempt has been made by Pujol and Barrio, 2008, through “Media value”,

built as follows:

  59  

Figure 17

Figure  17:  Players  media  value

Source: Pujol & Barrio, 2008

Their aim was to explain players value through: media value, buying team media value, selling

team media value, media value rank of the player in his precedent team, age, position in the pitch,

nationality (Pujol, Barrio 2008). They found interesting results, like that an increase in media value

of 1% determines an increase of 0.5% in market value.

Anyway, whatever methodology to value a player we use, it remains an important weakness; these

two factors, Players and Brand values, are highly variable from one year to another, and this affects

the stability of the final valuation, they can be included for a comparison among clubs but not to

find what we have called intrinsic value of a football club. Indeed, in table 14 I’ve included both

values with and without this “players value” factor.

Afterwards, I have summed up two factors more related to customers. They are number of fans

times average tv fans incomes (nf*fai) and catchment area times catchment area average incomes

(ca*cai), these factors should consider the impact that number of real and potential consumers have

on clubs worth. The first one it is more related on actual incomes. Here, to consider also foreign

supporters value (variable “S” in equation [7]), the assumption is that proportionally, the number of

fans for Serie A teams in Italy is the same than abroad; there are many studies rather contradictory

each other, but at least the rank of top 7 in Italy (Juventus FC, AC Milan, Inter FC, AS Roma, SSC

Napoli, SS Lazio, AC Fiorentina) is generally respected, with AC Milan ahead Juventus FC in some

case when considering global supporters; when considering only Italian fans, Inter FC is very close

  60  

to AC Milan and SSC Napoli in the last years have exceeded AS Roma. Regarding catchment area,

this addend takes into account future potentiality of build fans loyalty.

The greater weakness of these two factors is the estimation of euros’ value, which is disputable,

indeed I’ve chosen an average of tv revenues depending on fans and matchday incomes, but the

strength is that whatever is the actual worth, differences between clubs are respected, so we can

compare them, and this is the aim of the method. I’ve included these contributions because I deem

number of consumers-followers and their worth as a fundamental value for a football club, the only

problem is to find the monetary contribution of these differences.

“Number of fans” is taken from the statistic made by Lega Calcio (Tifosobilanciato, 2013) on

people older than 14 years old, and it calculates the number of Italian supporters which has chosen

that club answering the question “Which is your favourite team”, total estimated fans were 25,493

millions, in the table there is the slice of each club, the total is lower than 25,493 millions because

of lower categories and neutral followers.

“Fans average tv incomes”, is the sum of two contributions; the average money per club received

from the slice of TV rights which depends on fans number, which is, with the actual laws, 25% of

the whole pie, totalling 216M€ (10,8M per club, tifosobilanciato.it 2013), and the average value per

club of abroad Serie A tv rights, which is 117M€ (Media partners and Silva limited, 2013), so

5,85M€ per club. Hence, by adding these two contributions, we obtain the factor value of 16,65M€.

Even for this factor there are the same strengths (comparison among clubs) and weaknesses (Actual

monetary value is an estimation).

“Catchment Area”: the coefficient is calculated as follows:

(City citizens/ Average 20 bigger municipalities) + (Province citizens / Average 20 bigger

provinces), the assumption is that fans are more concentrated in local cities and, to a lesser extent,

in local provinces; hence, in this sum, city inhabitants are included two times.

Notice that, both number of citizens and average factors, they are divided by two whenever I have

considered a city or province with two Serie A or Serie B teams. The average is on twenty because

this is actually the Serie A format: a team residing in a bigger Area should have greater possibility

of getting fans, gains and results, thus will receive a coefficient greater than one. The opposite for

small towns’ teams, most probably candidate to relegate losing money.

Example, number of inhabitants taken from Wikipedia:

Average top 20 Italian municipalities = 390121 Milan citizens = 1305479

Average top 20 Italian provinces = 1168194 Milan province = 3075083

Inter FC catchment area coefficient = (1305479 / 2 / 390121) + (3075083 / 2 / 1168194) = 2,99.

  61  

“Catchment Area average incomes” is the average per club annual matchday revenues, which was

9,3M€ in 2012 (Report Calcio FIGC 2013). So this value comes from the total Serie A matchday

incomes (186M€), divided by the number of participants (20), it has the same strengths and

weaknesses of the previous factor.

The last addend is the European results premium “er” contribution, which has the same strengths

and weaknesses, too.

Here the assumption is that last years results have a more significant impact on clubs value, with an

higher weight for the nearest years, that’s why I have included this addend, by summing up an

historical value build on past European results and trophies, which have created also new followers

all around the world, moreover in this way also past domestic results are included, as they

determine European cups qualification. This factor comprehends an estimation of both ‘r’ and ‘R’

of equation [7].

European results premium it is the more complicated addend, it has been calculated taking UEFA

team ranking and summing up the coefficient of each year, by weighting each season in order to

give more importance to the nearest one, 2008/09 coefficients were divided by 5, 2009/10

coefficient by 4, 2010/11 by 3, 2011/12 by 2, until the 2012/13 points which remain unchanged. In

addition, due to the higher visibility and incomes generated by Champions League, each Europa

League result has been divided by 2 (this is added to the little discounts already considered by

UEFA ranking). Below the values, as an example:

Inter coefficient = 16,88/2 + 20,27/2 +21.31/3 + 34,09/4 + 13,27/5 = 36,85

Table 15

UEFA team

rank

2008/09 2009/10 2010/11 2011/12 2012/13 Coefficient

Inter 13,27 34,09 21,31 20,27 16,88 36,85

Milan 14,27 19,09 18,31 22,27 19,88 43,32

Juventus 16,28 18,09 8,31 0,00 25,88 35,04

Roma 16,28 12,09 18,31 3,77 0,00 12,76

Napoli 4,27 0,00 9,31 21,27 8,83 17,03

Udinese 17,27 0,00 0,00 14,27 5,88 8,23

Fiorentina 11,28 24,09 0,00 0,00 0,00 7,15

Lazio 0,00 7,09 0,00 9,27 20,88 13,64

Sampdoria 11,28 0,00 6,31 0,00 0,00 2,18

Palermo 0,00 0,00 7,31 3,27 0,00 2,04

Genoa 0,00 8,09 0,00 0,00 0,00 2,01

Table  15:  First  euro  premium

  62  

Then, in order to consider also past results and build an historical value, other points have been

added, with a simple semi quantitative criteria that tries to be consistent with cups worth and UEFA

ranking; historical data taken from UEFA.com and legaseriea.it:

For each Champions League: qualification, 1 point; quarter final achievement 2 points, winning 8

points.

For each other European official Cup: qualification, 0,5 point; quarter final achievement 1 point,

winning 2 points.

Finally the two coefficients found have been added in the third column building The “European

results premium” indicator.

Table 16

Club 1st

Coefficient

2nd

Coefficient

Euro results

premium

Atalanta / 3,00 3,00

Bologna / 7,50 7,50

Cagliari / 3,00 3,00

Catania / / 0,00

Chievo / 1,00 1,00

Fiorentina 7,15 21,00 28,15

Genoa 2,01 1,50 3,51

Inter 36,85 78,50 115,35

Juventus 35,04 91,50 126,54

Lazio 13,64 19,50 33,14

Milan 43,32 116,5 159,82

Napoli 17,03 17,00 34,03

Palermo 2,04 2,00 4,04

Parma / 16,50 16,50

Pescara / / 0,00

Roma 12,76 32,00 44,76

Sampdoria 2,18 11,00 13,18

Siena / / 0,00

Torino / 9,50 9,50

Udinese 8,23 5,50 13,73

Table  16:  Euro  results  premium

These numbers are dimensionless but for our aim they are simply multiplied by 1M€, this is an

estimation of their value, which is of course questionable. I recall that here the aim is the

comparison among clubs.

  63  

All these addends are then multiplied times “League trend”, which is the revenue annual Serie A

growth rate, calculated as the average of last 5 years percentage growth (or decline), as follows:

Table 17

Revenues

(M€)

2008 2009 2010 2011 2012 Annual rate

Serie A 1421 1494 1530 1553 1639 +2,9%

European

football

14600 15700 16300 16900 19400 +5,9%

Table  17:  Serie  A  growth  rate

Data taken from Deloitte and Statista.com.

So in this case, the whole valuation of each Serie A club, will be multiplied by (1+0,029) /

(1+0,059) à League trend = 0,944: our championship is growing slower than the total European

football, this means that globally we are losing market share, and this affects the value of each club

belonging to the championship, as they are selling together the product “Serie A”.

The two last column of Table 14 are the application of the equation above to find club values,

penultimate considering all these factors and the last one without “pv” and “ps”. For the first club:

Atalanta 2013 value (M€) = (5 + 43,2 + 2,1 + 11,4 + 3) * 0,944 = 61,1 M€

As the aim is to find a comparison among clubs value, this model would rank the club with respect

to their value. Then, I’ve tried to be as realistic as possible in monetary terms, to be consistent with

actual values, knowing real revenues and related streams. Compared to all the other methodologies

above, “Fans method” would not to find the exact value of each club if today it would be sell,

neither the exacts monetary differences among clubs, but to point out the determinants of football

clubs value, most of them are intangible assets, in order to compare clubs in a more stable way and

provide an ordinal ranking.

This because we assume there is an intrinsic value made of environment and context, catchment

area, fans, historical results that doesn’t change quickly in time, so this is a core asset for a football

club, and we would like to identify and, possibly, quantify it. The weights of each factor have been

built knowing actual revenues and hypothesizing a related value, anyway we would like to have a

reasonable estimation of weights and for this purpose we are going to deal with correlations,

  64  

regressions and factor analysis. With respect to Fans method player values and salaries will not be

included, they are not part of the intrinsic value we want to determine, because of their high

changeability and because talent expense should reflect money availability, and we would like to

identify those factors able to drive revenues and, as a consequence, increase talent. We notice that

player values affect revenues, too, because talent determines results and consequently revenues and

consumers satisfaction, moreover, at the highest levels, “Star players” are able to drive some

revenues’ streams because of their popularity which affect both direct incomes, through

merchandising, and number of club’s and related league’s followers.

  65  

Chapter  3  SPSS  Statistics  and  factorial  models  

 

3.1  Top  European  leagues  correlations  

I have attempted to find the relationship between revenues and sportive results, to verify the

assumption that revenues positively affect sportive results and vice versa.

In the first table, the aim was to find if there is a linear relation between global leagues’ revenues

and sportive results of top clubs in the following year. To do this, I’ve taken Top 5 leagues annual

revenues from 2002 to 2012 (Variable “Leagerev”) and UEFA country ranking from 2003 to 2013

(Variable “ucrank”).

Below the results:

Table 18 Correlations

ucrank Leaguerev

ucrank Pearson’s correlation 1 ,511**

Sign. (two tails) ,000

N 55 55

Leaguerev Pearson’s correlation ,511** 1

Sign. (two tails) ,000

N 55 55

**. Level of correlation significance = 0,01 (two tails).

Table  18:  Pearson  UCR-­‐League  revenues

Pearson coefficient ‘r’ is 0.511, and it was calculated with 2 tailed significance p=0.01.

N is the number of observations (55, indeed we are considering 5 Leagues and 11 seasons). We

know from the theory that Pearson coefficient is defined as the covariance of the two variables

divided by the product of their standard deviations, so it varies between -1 (completely negative

correlation) and +1 (completely positive correlation), so lower than one in absolute value (Cauchy,

Schwarz inequality); if two variables are independent their r=0, but the opposite it is not true

because r is only able to capture linear relationships.

  66  

In this case r is pretty large, so it exists a positive correlation as we expected, with this estimated

value of 0.511.

Here we are sure that effectively a dependence exists, because significance= .000.

A consideration is needed: in this way we are considering global revenues per league, by summing

up all clubs’ incomes, but UEFA country ranking reflects only European competitions’ results, this

means that only 6 or 7 clubs per League are involved each year, so only their incomes should be

included in order to understand the linear relation between revenues and results. By considering

only participating clubs we would expect a higher value of correlation, because those incomes are

effectively the only revenues earned by the clubs participating. To estimate these numbers, I

proceeded as follows:

1) I have identified all the Top 5 leagues’ club participating at Champions League or Europa

League from season 2009/10 to season 2012/13, and the Top 5 leagues’ UEFA country

ranking for the same period, made from the results of those clubs.

2) I’ve summed up the revenues of the previous year (so from season 2008/09 to 2011/12) of

each participating club of the following year, because we want to verify whether exists a

correlation between incomes and sportive results of the following year.

3) I’ve divided these aggregate incomes for the number of participating clubs, building an

average of club’s incomes, because in that period each league has had different numbers of

clubs invited (actually the number varied only from 6 to 7 in these leagues). This is our first

variable, called “AverageRev”, to be compared with “UefaCrank”.

Take for example England UEFA country ranking 2012/13. In that season they have totalized

16.428 points, thanks to seven participating clubs (Manchester Utd, Chelsea FC, Liverpool FC,

Tottenham, Arsenal FC, Newcastle Utd, Manchester City). The aggregate revenues of these clubs,

related to previous season 2011/12, were 1821M€ (Deloitte). Then I’ve divided this amount for

seven, by obtaining “AverageRev”= 260.1 (M€), to be compared with “UefaCrank” = of 16.428. By

doing the same for 4 seasons and 5 leagues, thanks to SPSS statistics, I’ve obtained the following

results:

  67  

Table 19

Average Std deviation N

AverageRev 153,6900 46,07709 20

UefaCrank 15,5192 2,99234 20 Table  19:  Average  UCR  and  top  clubs  revenues

Average incomes of European participating clubs during the period, by considering only “Top 5

leagues”, was 153.69M€ with a standard deviation of 46.08 M€. The average UEFA Country

Ranking of these leagues was 15.519 points per year, with 2.99 points of standard deviation.

Table 20

AverageRev UefaCrank

AverageRev Pearson’s correlation 1 ,656**

Sign. (two tails) ,002

N 20 20

UefaCrank Pearson’s correlation ,656** 1

Sign. (two tails) ,002 N 20 20

**. Level of correlation significance = 0,01 (two tails). Table  20:  UCR  -­‐  Pearson  participating  clubs  revenues

The result confirms our expectations, by considering only UEFA Country Ranking participating

clubs, Pearson coefficient is higher (0.656, meaningful at 0.01, two tailed); we are almost sure

(0.002 significance) that revenues and sportive results are correlated, precisely there is a linear

relation between incomes and results in European competitions in the following year, revenues

positively affects results and correlation value is 0.656.

Questions could arise because of the sample number, N=20 could be a failing sample, but in support

of the result we have seen how these 20 variables values have been build: actually they include 132

annual clubs revenues as well as UEFA country ranking includes all the results of each single club’s

match in European competition, more than 2000 matches’ results during the reference period.

Let’s see the inverse correlation, precisely if sportive results affect revenues of the following year

and if there is a linear relationship among them.

  68  

In doing so, I’ve used the same variables of the opposite case, “AverageRev” and “UefaCrank”, but

for different periods, precisely I’ve compared UEFA country ranking from season 2008/09 to

2011/12 with revenues of the following year, from 2009/10 to 2012/13.

Below the results by SPSS:

Table 21

Average Std deviation N

UefaCrank 14,7763 3,10303 20

AverageRev 162,6950 46,22225 20 Table  21:  Average  UCR  and  leagues  revenues  2

Table 22

UefaCrank AverageRev

UefaCrank Pearson’s correlation 1 ,578**

Sign. (two tails) ,008

N 20 20

AverageRev Pearson’s correlation ,578** 1

Sign. (two tails) ,008

N 20 20

**. Level of correlation significance = 0,01 (two tails). Table  22:  Pearson  UCR  and  league  revenues  2

Even here the correlation exists, significance is a bit higher (0.008) than the inverse case (0.002),

anyway it is closeness to zero assuring that there is a relation. Pearson coefficient is similar than

before, a bit lower (0.578 vs 0.656) but still with a meaningful value which tells us that good results

in European competition positively affect revenues of the following year (of course they affect

positively even incomes of the current year because of results awards, but here we are referring only

to the following year).

In this way we have found a linear relation, in both directions, between UEFA country ranking and

leagues’ revenues. Through this method we can’t catch any kind of correlation that is not linear,

moreover UEFA country ranking and revenues are only two possible indicators of sportive and

economic results, different value of relation there could be by considering, together with revenues,

top championships results or lower categories results, anyway even by including only these

indicators we can suppose that incomes and sportive results are strongly related growing together,

  69  

this is an important assumption in determining clubs value. To further verify the assumption we

should consider the rates of change, but this could be misleading because we don’t know how the

causal link is split, positive or negative effects of revenues and sportive results are not reduced to

the following year but they could affect, with different weights, the future. So, why we assume they

grow up together affecting each other?

Because there are more customers/fans, in other words market share increases.

In general, on equal debts situation, when a club earned more incomes it has greater expense

possibility, so it can hire more talent and this translate in better sportive results.

In synthesis, Results translate in more visibility, better rankings for top divisions’ clubs (both

UEFA ranks and national, e.g. in TV rights systems or national cup participation), consumers’

satisfaction and new followers, so new future revenues, together with direct incomes from awards.

More revenues translate in greater possibility of better structures, initiatives and especially talent

expenses, increasing winning probabilities and thus, for the reasons above, probability of further

increasing revenues.

In conclusion, we assume there is a correlation between results and number of fans, so there is also

a correlation between supporters and revenues, both directly and indirectly, and all of this increases

club, and consequently league, value. It’s a virtuous circle:

⇑revenues à⇑ sports results à⇑ followers à⇑revenues

î ê ê í

club value.

To verify the assumption that what supports the positive correlation among revenues and results are

customers, a possible indicator of fans involvement and enthusiasm is attendance at the stadium.

  70  

Figure 18

 Figure  18:  Attendance  and  revenues  correlation

We notice a meaningful relation between revenues “Rev” and attendance “Attend”. The sample was

made by 35 different teams belonging to “top 5 leagues”, in particular 7 selected teams per each

league and related revenues and attendance of the last three seasons, globally N=105. For some

aspects, not surprising that there exists a meaningful relation between attendance and incomes. Big

clubs with higher revenues have also the highest attendence because most of them own big and

modern stadiums and stars players, able to attract new fans, moreover, in general, the biggest clubs

in the world are based in big cities with a greater possibility to reach more consumers.

In figure 18, points far away from the central line are special cases, below we find, among the

others, clubs owned by sheiks, like PSG and Manchester City, which in few years have quickly

increased revenues and the attendance has not undergone the same increment, also because of

stadiums capacities, resulting in a small attendance for clubs of that size (PSG in 2013 earned

399M€ bringing only 43.239 people, on average, per each home match). On the opposite, above the

line, Borussia Dortmund case stands out, they are represented by the three circles (seasons 2010/11,

2011/12, 2012/13) above the line with a stable attendance, near to 80.000 per match, and increasing

revenues, ranging approximately from 100€ to 300M€, not so much for a club with that attendance.

In the Case of Borussia Dortmund is evident the positive effect on fans and, especially, sportive

results on revenues, that are quickly improving together with great attendance, stable also because

the stadium is generally sold out. In would be interesting to understand the underlying reasons of

the great attendance of Dortmund. In terms of past results and catchment area is surely good,

moreover they have relatively low ticket prices, but a difference between other clubs with similar

  71  

size and results I think should be sought in fans enthusiasm, that manifests itself with beautiful

coreographies, colors and fans involvement. I recall here what I’ve called positive externality.

I’ve also embedded brand value, to verify its strong correlation with revenues, the higher coefficient

confirms us that Brandfinance’s values are strongly dependent on current revenues, which is a

driver indicator for the calculation.

Regression analysis made on the same 105 clubs (35 clubs taken from top 5 leagues, from season

2010/2011 to 2012/2013), with revenues as dependent variable and attendance, brand and UEFA

team ranking as predictors, confirms our results:

Table 23

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,921a ,849 ,844 47,87258

a. Predictors: (constant), Attend, UteamR, Brand Table  23:  Predictions  with  UTR,  brand,  attendance

Table 24

Coefficients

Model

Unstandardized coefficients

Standardized

coefficients

t Sign. T Std error Beta

1 (constant) 24,478 17,448 1,403 ,164

Brand ,383 ,038 ,667 10,045 ,000

UteamR 2,780 ,634 ,237 4,387 ,000

Attend ,001 ,000 ,114 1,839 ,069

a. Dependent variable: Rev Table  24:  Regression  -­‐  1st  revenues  prediction

Based on the fact that revenues, sportive results and consumers/fans are positively correlated, we

would like to estimate the weight that each of these factors has on club’s value, measured by a

multifactor model as a weighted sum of determinants variables, like results and number of

consumers, and related drivers (attendance, UEFA ranking, etc.), or, as alternative, by a Revenues

multiple approach in which some determinants of value affect a variable revenues’ multiplier. We

well see both, anyway in doing so factor analysis could help us.

  72  

3.2  Correlations  and  factor  analysis  in  Italian  leagues  

I have started by taking 16 variables, each of them refers to a Serie A team of 2012/13 season. As

we have 20 clubs in the league, the whole database consists on 320 data.

Table 25

Communalities

Inizial Extraction

ItaFans 1,000 ,942

Attendance 1,000 ,945

StadFilling 1,000 ,941

UteamR 1,000 ,882

Pticamp 1,000 ,666

Rev 1,000 ,975

Cityab 1,000 ,895

Provab 1,000 ,859

FBfans 1,000 ,861

PlayerWages 1,000 ,961

PartA 1,000 ,741

TitIta 1,000 ,906

TitEuro 1,000 ,955

CLfinals 1,000 ,986

PartEuro 1,000 ,923

PartCL 1,000 ,993

Table  25:  Italian  variables  communalities

By applying factor analysis, communalities (percentage of variable variance explained by common

factors) are high, meaning there is a large verisimilitude among variables. I recall that variables

with high communalities could imply a hidden factor (Rossi G., 2009).

There are variables that refer to historical results, participation and winning titles in Italy and

Europe, (number of Serie A participation “PartA” and official Italian titles “TitIta”, European cups

participations and titles “PartEuro” and “TitEuro”, Champions League participation “Part CL”,

Champions League quarter finals achievement plus winning “CLfinals”), variables that refer to last

results (UEFA team ranking “UteamR” and points gained during 2012/13 Serie A “Pticamp”),

variables related to consumers, both actual (Number of Italian fans (Tifosobilanciato, 2013)

“ItaFans”, attendance at the stadium “Attendance” and percentage of stadium filled “Stadfilling”)

  73  

and potential (Number of fans on Facebook “FBfans”, club city’s and province’s inhabitants

“Cityab” and “Provab”), variables related to incomes “Rev” and costs “PlayerWages”.

Sample was found adequate:

Table 26 KMO and Bartlett test

Kaiser-Meyer-Olkin measurement of sample adequacy ,764

Bartlett’s sphericity test Chi-squared approx 537,207

gl 120

Sign. ,000

Table  26:  Bartlett  test  1

Through principal component analysis, setting eigenvalues greater that 0.9, SPSS has chosen three

components that together explain 90% of the variance:

Table 27 Total variance explained

Initial eigenvalues Uploads extraction’s sum of squared Uploads rotation’s sum of squared

Total % of variance % cumulative Total % of variance % cumulative Total % of variance % cumulative

11,385 71,157 71,157 11,385 71,157 71,157 8,205 51,284 51,284

2,057 12,856 84,013 2,057 12,856 84,013 4,439 27,741 79,025

,989 6,182 90,195 ,989 6,182 90,195 1,787 11,170 90,195

,472 2,952 93,147

,415 2,594 95,742

,250 1,560 97,302

,172 1,077 98,379

,108 ,674 99,054

,062 ,387 99,441

,033 ,207 99,647

,025 ,158 99,805

,015 ,091 99,897

,010 ,060 99,957

,005 ,031 99,988

,001 ,007 99,995

,001 ,005 100,000

Table  27:  Factor  analysis  1  Serie  A  

  74  

One eigenvalue, alone, explains more than 70% of the whole variance, let’s see how we can identify

that component looking at the rotated matrix:

Table 28

Rotated components matrix

Component

1 2 3

ItaFans ,793 ,317 ,462

Attendance ,605 ,747 ,145

StadFilling ,220 ,003 ,945

UteamR ,799 ,488 ,072

Pticamp ,439 ,558 ,403

Rev ,863 ,385 ,285

Cityab ,115 ,929 -,137

Provab ,088 ,920 ,070

FBfans ,910 ,145 ,104

PlayerWages ,789 ,556 ,172

PartA ,414 ,721 ,223

TitIta ,802 ,340 ,382

TitEuro ,967 ,115 ,081

CLfinals ,974 ,140 ,131

PartEuro ,737 ,559 ,258

PartCL ,933 ,247 ,249

Table  28:  Rotated  matrix  1  Serie  A

First component is more related to economics and sportive results, but with meaningful correlations

also with number of fans, it incorporates many determinants of values with different weights so it is

difficult to isolate as a single factor. Second component is more related to Italian football catchment

area and third one is mostly related to percentage of stadium filled, with some correlation with

number of fans and Serie A results. In general, we notice a huge interdependence among variables

that makes difficult the decomposition in “Principal components”, in this case we could call the first

component “Sports title”, with the highest weight, the second component “Catchment area” and the

third one “Assets effectiveness”, but the strong interdependence among all the factors make the

distinction enforced.

I have made another attempt using the same variables without considering the economics indicators

(revenues and wages), because they should include most of the other factors.

Adequacy of the sample:

  75  

Table 29

KMO and Bartlett test

Kaiser-Meyer-Olkin measurement of sample adequacy ,735

Bartlett’s sphericity test Chi-squared approx. 418,439

gl 91

Sign. ,000

Table  29:  Bartlett  test  2

Variance explained:

Table 30

Component

Initial eigenvalues

Uploads extraction’s sum of

squared Uploads rotation’s sum of squared

Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative

1 9,486 67,755 67,755 9,486 67,755 67,755 6,764 48,311 48,311

2 2,035 14,533 82,287 2,035 14,533 82,287 4,040 28,855 77,167

3 ,983 7,021 89,309 ,983 7,021 89,309 1,700 12,142 89,309

4 ,461 3,291 92,600 5 ,404 2,889 95,489 6 ,248 1,768 97,257 7 ,159 1,134 98,390 8 ,098 ,700 99,090 9 ,055 ,395 99,485 10 ,032 ,228 99,713 11 ,025 ,178 99,891 12 ,009 ,062 99,953 13 ,005 ,038 99,991 14 ,001 ,009 100,000

Table  30:  Factor  analysis  2  Serie  A

Setting eigenvalues greater than 0.9, three components have been extracted reaching 89% of

variance explained, a bit less than the previous iteration with revenues and wages. Let’s see the

correlation with the extracted components in the rotated matrix:

  76  

Table 31

Component

1 2 3

Attendanc ,598 ,751 ,150

StadFilling ,211 ,005 ,948

Cityab ,100 ,927 -,134

Provab ,079 ,924 ,067

PartEuro ,735 ,566 ,263

PartCL ,928 ,255 ,255

CLfinals ,972 ,149 ,137

TitEuro ,966 ,125 ,086

TitIta ,796 ,346 ,389

PartA ,413 ,725 ,224

FBfans ,905 ,154 ,108

Pticamp ,435 ,566 ,399

UteamR ,798 ,495 ,077

ItaFans ,785 ,324 ,466

Table  31:  Rotated  matrix  2  Serie  A

First component is strongly related to club’s results all around the world: Participations and

especially winning in European competitions have the highest correlation coefficient as well as

number of fans on Facebook, which should depend on clubs history and results. Also recent results

are strongly related with the component (UEFA team ranking has 0,798 of positive correlation).

Italian fans, history and results are in general less considered but with a meaningful value of

correlation when referring to Italian titles and number of Italian fans. At the end, first component

gets most of the whole variance and is strongly related to past and recent sportive results all around

the world and, probably as a consequence, on number of consumers. We can call this first factor

“worldwide value”. The second component has huge correlations with catchment area and a bit less

with attendance at the stadium. It is reasonable that a club residing in a big city comprehensive of a

large province has, generally, a greater aggregate attendance. We notice a meaningful positive

correlation also with “Serie A participation”, we can suppose that catchment area strongly affect

club size e potentiality, indeed, in general, Serie A clubs belong to the biggest Italian cities and

provinces, with few exceptions every year. We can call the second factor “catchment area” and we

observe that both first and second component are positively related with all the variables, this could

be a signal of positive interdependence among them which positively affect each other growing

together. The third component gets only 7% of the whole variance and it is mostly related with

percentage stadium filling, positively related (even if with small values) with all the variables,

  77  

especially “number of Italian fans”, except for “City inhabitants”; this could be explained by the

fact that many Serie A teams belonging to the biggest cities (e.g. Milan and Rome) play into very

big stadiums with a relatively high attendance and a relatively low percentage of stadium filling,

with respect, for example, to Catania, Siena and Pescara, which have had small attendance in that

season but with >60% of stadium filled. We could call the third component “Stadium usage”

Finally, let’s see the weight assigned to each component by SPSS:

Table 32

components’ weights matrix

Component

1 2 3

Attendance ,015 ,185 -,042

StadFilling -,184 -,063 ,816

Cityab -,093 ,347 -,178

Provab -,152 ,344 ,013

PartEuro ,059 ,086 ,032

PartCL ,173 -,068 -,016

CLfinals ,236 -,113 -,133

TitEuro ,252 -,120 -,177

TitIta ,083 -,014 ,139

PartA -,057 ,204 ,076

FBfans ,222 -,099 -,142

Pticamp -,068 ,133 ,236

UteamR ,139 ,053 -,153

ItaFans ,063 -,023 ,213

Extraction method: Principal component analysis

Rotation method: Varimax with Kaiser normalization.

Table  32:    Component  weights  1  Serie  A

In the first column there isn’t any coefficient greater than 0.3, anyway as we are talking about a

factor getting 70% of the variance, if we multiply the values times the variance explained, many of

them are relatively meaningful. The contribution of “catchment area” variables is almost negligible,

except for city and province inhabitants that weights, respectively, 0.347 and 0.344. “Stadium

usage” provides a significant contribution only with variable “Stadium filling”, 0.816, which is the

greatest coefficient in the matrix but if we multiply times the variance explained, only 7% for this

component, we get a not surprising contribution. Moreover it has a negative weight when referring

to the first, heaviest, component.

  78  

Until now I have included variables with very high communalities, able to imply a single o more

factors. I would like to introduce another attempt, with fewer variables, embedding the previous

ones. The variables used are:

1) “CatchA” calculated as follows, for each Serie A 2012/13 club:

(City citizens/ Average 20 bigger municipalities) * (Province citizens / Average 20 bigger

provinces). Hence, city inhabitants are included both in city and province. In this way we

want to emphasize the positive effect of big cities and provinces and the negative effect of

small towns in small provinces, but penalizing big cities in small provinces (e.g. Genova) or

small cities in big provinces (e.g. Bergamo), with respect, for example, to medium cities

residing in medium size provinces (e.g. Catania). An alternative could be adding the two

contributions, as we did in “Fans Method”, building in this way a linear indicator. In the

following calculations, I have used both.

2) “Fans” is the sum of number of fans of each Serie A clubs (Tifosobilanciato, 2013) divided

by the club with the highest number of fans (Juventus FC, 7.1 Millions), plus the number of

Facebook fans divided by the highest number of Facebook fans updated April 2014 (AC

Milan, 21Millions).

3) “Stadium” is the product between average attendance per match of the club multiplied times

percentage of stadium filling, divided by the average attendance (per match) of the

championship in that season.

4) “HistPart” is, for each club, the sum the number of participations to, respectively, Serie A,

European competitions and Champions League, each of them divided by the highest number

of participations (primacy belongs to, respectively, Inter FC (82), Juventus FC (50), AC

Milan and Juventus FC (28)).

5) “Titles” is calculated as “HistPart”, considering the weighted sum of three variables: Italian

winnings, European winnings and Champions League quarter finals participations plus

winnings.

6) “LastRes” is the weighted sum of UEFA team ranking 2013 and Serie A points gained

during the season, calculated as “HistPart” and “Titles”.

  79  

Table 33 Table 34

à Table  33:  Serie  A  variables  1                                                                                                                                                                                  Table  34:  Serie  A  variables  2

Notice that all of these variables are dimensionless and with different weights, because of diverse

calculation methods and number of components’ variations. We need to standardize.

Therefore, I have divided the values obtained for each club by the sum of the values of that column,

thus obtaining values between 0 and 1 for each data, and an amount of 1 per each variable when

summing the values of all the clubs considered. Finally, just to have a clearer vision and to avoid

that SPSS could go out of sight with many values very close to zero, I’ve multiplied all the values

times one hundred:

Table 35 Table 36

à Table  35:  Serie  A  variables  3                                                                                                                                                              Table  36:  Serie  A  variables  4

As we can see database consists on 120 data, barely sufficient to apply a factor analysis. Sample has

resulted adequate:

  80  

Table 37 KMO and Bartlett test

Kaiser-Meyer-Olkin measurement of sample adequacy ,754

Bartlett’s sphericity test Chi-squared approx. 144,947

gl 15

Sign. ,000

Table  37:  Bartlett  test  3

Table 38

Communality

Inizial Extraction

CatchA 1,000 ,975

Fans 1,000 ,941

Stadium 1,000 ,905

HistPart 1,000 ,937

Titles 1,000 ,962

LastRes 1,000 ,891

Extraction method: principal

components analysis

Table  38:  Serie  A  communalities  2

Communalities are still very high, greater than previous cases, we can suppose that they imply a

unique common factor.

Table 39 Total variance explained

Component

Initial eigenvalues

Uploads extraction’s sum of

squared Uploads rotation’s sum of squared

Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative

1 4,668 77,802 77,802 4,668 77,802 77,802 4,167 69,452 69,452

2 ,943 15,721 93,523 ,943 15,721 93,523 1,444 24,071 93,523

3 ,158 2,638 96,161 4 ,127 2,120 98,281 5 ,086 1,441 99,722 6 ,017 ,278 100,000

Extraction method: principal components analysis

Table  39:  Variance  explained  Serie  A

  81  

Setting eigenvalues greater than 0.9, the software has isolated 2 components explaining more than

93% of the whole variance.

Table 40

Rotated components matrix

Component

1 2

CatchA ,147 ,976

Fans ,965 ,091

Stadium ,835 ,455

HistPart ,908 ,336

Titles ,981 ,010

LastRes ,854 ,402

Rotation method: Varimax with

Kaiser normalization.

3 iterations to converge

Table  40:  Rotated  matrix  3  Serie  A

By using as “catchment area” indicator the sum of the contributions (City citizens/ Average 20

bigger municipalities) + (Province citizens / Average 20 bigger provinces), we obtain similar

results, with the difference that to find two components we should set eigenvalues greater than 0,8:

Table 41

Total variance explained

Component

Initial eigenvalues

Uploads extraction’s sum of

squared

Uploads rotation’s sum of

squared

Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative

1 4,760 79,325 79,325 4,760 79,325 79,325 4,004 66,732 66,732

2 ,845 14,090 93,415 ,845 14,090 93,415 1,601 26,683 93,415

3 ,163 2,712 96,127

4 ,129 2,144 98,270

5 ,087 1,448 99,718

6 ,017 ,282 100,000

Extraction method: principal components analysis Table  41:  Variance  explained  2  Serie  A

  82  

Table 42

Components’ weights matrix

Component

1 2

CatchA -,300 ,895

Fans ,326 -,222

Stadium ,125 ,196

HistPart ,189 ,078

LastRes ,157 ,129

Titles ,355 -,288

Rotation method: Varimax with

Kaiser normalization. Table  42:  Components  weights  2  Serie  A

In any case, there isn’t a separation between variables related to results and consumers, they are

both included in the first component with high correlation, indeed they explain most of the variance.

The second component in Table 40 is positively correlated with all the variables but it is strictly

related only to Catchment area, anyway separation appears enforced.

3.3  Multiple  regressions    

3.3.1  Serie  A  

We have not been able to distinguish between variables related to results and variables related to

consumers/fans; they seem highly related and interconnected.

We know they are positively related with revenues, to further understand these links I have applied

a multiple linear regression using the same variables of the last factor analysis as independent

(CatchmA, Titles, LastRes, Stadium, HistPart, Fans) and revenues as dependent variable:

  83  

Table 43

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,995a ,991 ,986 8,13689

a. Predictors: (constant), CatchmA, Titles, LastRes, Stadium, HistPart, Fans Table  43:  Serie  A  regression  1

Results seem great as these factors fully explain revenues, with R^2 proxy to one. Problems could

arise when considering coefficients in trying to linearly approximate revenues:

Table 44

Coefficients

Model

Unstandardized coefficients

Standardized

coefficients

t Sign. T Std error Beta

1 (Constant) 28,377 6,549 4,333 ,001

LastRes 2,301 2,085 ,077 1,103 ,290

Titles 1,002 1,221 ,119 ,820 ,427

HistPart ,997 1,989 ,057 ,501 ,625

Stadium ,378 1,713 ,017 ,220 ,829

Fans 5,565 1,006 ,705 5,529 ,000

CatchmA 2,013 ,821 ,103 2,451 ,029

a. dependent variable: Rev Table  44:  Serie  A  regression  coefficients  1

Rev = 28,4 + 2,3LastRes + Titles + HistPart + 5,6 Fans + 2,0CatchA [8] Equation  8:  Serie  A  club  Revenues  1

Significance of many factors is pretty high, among variables only “Fans” and “CatchmA” present

sure values, and some variables appear proxy to zero. It seems that knowing actual number of fans

from Lega Calcio research (Tifosobilanciato, 2013) we can mostly predict revenues, unfortunately

we cannot verify it in other context using real numbers because I haven’t found any research on

number of fans of football teams (except for top 20), neither for Italian lower divisions, so we have

to resort to approximations of real numbers, e.g. number of fans in social networks. Anyway with

those factors we are able to predict revenues with a very good approximation, we’ll recall Table 44

in the next pages.

  84  

In order to find more reliable coefficients I have tried to assemble all these variables in two

independent ones. The first related to consumers, precisely the sum of “CatchA”, “Fans” and

“Stadium”, each of them with the same weight (w=1), let’s call this variable “Share”. The second

related to Results “Res”, is the sum of “HistPart”, “Titles” and “LastRes”, weights (w=1).

Dependent variable is annual revenues.

Table 45

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,982a ,964 ,960 14,00778

a. Predictors (constant), Res, Share

b. Dependent variable: Rev Table  45:  Serie  A  regression  2

Table 46

Coefficients

Model

Unstandardized coefficients

Standardized

coefficients

t Sign.

B confidence interval: 95,0%

T Std error Beta Lower limit Upper limit

1 (Costante) 15,969 4,666 3,422 ,003 6,124 25,814

Share 1,407 ,425 ,308 3,313 ,004 ,511 2,303

Res 3,505 ,464 ,702 7,548 ,000 2,525 4,485

a. Dependent variable: Rev Table  46:  Serie  A  regression  coefficients  2  

Revenues are mostly explained by these two variables and even coefficients appear more reliable.

Precisely the equation:

Rev =15,969 +1,407Share + 3,505Res [9] Equation  9:  Serie  A  club  revenues  2

Provides a good approximation for Serie A teams. With this model, Revenues for a new team with

no history and no fans (Res = 0; Share = 0) would be 16M€. This is not so bad if we think that this

ideal and hypothetic case of a new team starting from Serie A would have had, by default, the slice

of 17.31M€ from TV rights divided in equal parts (Tifosobilanciato.it).

  85  

Figure 19

 Figure  19:  Revenues  prediction,  expected  and  observed  probality

Table 47

Correlations

Res Share Rev

Res Pearson’s correlation 1 ,869** ,970**

Sign. (two tails) ,000 ,000

N 20 20 20

Share Pearson’s correlation ,869** 1 ,918**

Sign. (two tails) ,000 ,000

N 20 20 20

Rev Pearson’s correlation ,970** ,918** 1

Sign. (two tails) ,000 ,000

N 20 20 20

**. Level of correlation significance = 0,01 (two tails). Table  47:  Revenues  linear  correlations

 We notice a very strong linear relation between revenues and the variable comprehensive sportive

results, a bit lower, but still very high, Pearson’s coefficient between revenues and variable

including market share. Moreover, “Share” and “Res” have 0.918 of Pearson’s coefficient among

them; as we expected, the three variables are strongly interconnected, as results create new

  86  

consumers, this could explain while in Table 44 revenues are mostly explained by number of fans

(which are included in “Share”) while in [9] “Res” variable have got most of the variance.

3.3.2  Italian  lower  categories  factor  analysis  

In order to enlarge the Italian analysis, in Tables 48, 49 and 50, I’ve included 7 variables

(attendance, percentage of stadium filling, city inhabitants, Serie A and Serie B participations,

number of Facebook fans, points gained in the last championship) related to Serie B and Lega Pro

2012/13 season (except for Facebook fans which are related to April 2014), precisely all the Serie B

and Lega Pro I (third division) clubs and fifteen clubs of Lega Pro II (fourth division), so globally

70 clubs and 490 data:

Table 48

Commonality

Initial Extraction

Attend 1,000 ,844

Filling 1,000 ,713

Cityab 1,000 ,783

Pticamp 1,000 ,445

PartA 1,000 ,793

PartB 1,000 ,834

Fb 1,000 ,676 Table  48:  Serie  B  and  Lega  Pro  communalities

Number of points gained during the season is the less connected variable, one cause is that we are

comparing different categories and there is a huge difference in gaining 60 points in fourth division

than in second one, and the same difference is not present among the other variables. By applying

factor analysis two factors have been extracted thus explaining 73% of the variance:

  87  

Table 49

Total variance explained

Component

Initial eigenvalues

Uploads extraction’ sum of

squared Uploads rotation’ sum of squared

Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative Total

% of

variance

%

cumulative

1 3,863 55,192 55,192 3,863 55,192 55,192 2,933 41,893 41,893

2 1,225 17,505 72,697 1,225 17,505 72,697 2,156 30,804 72,697

3 ,750 10,711 83,408

4 ,467 6,674 90,083

5 ,323 4,616 94,699

6 ,201 2,865 97,564

7 ,171 2,436 100,000

Extraction method: Principal components analysis Table  49:  Serie  B  and  Lega  Pro  variance  explained

Table 50

Components’ weights matrix

Component

1 2

attend ,063 ,299

filling -,294 ,556

cityab ,382 -,188

pticamp -,005 ,272

partA ,287 -,013

PartB ,369 -,144

fb ,028 ,300

Rotation method: Varimax with

Kaiser’s normalization

Table  50:  Serie  B  and  Lega  Pro  components  weights  

Looking at coefficients, we see a first component mostly related with history and catchment area,

while the second one presents the higher coefficients with variables related to supporters. The

presence of different divisions increase variability and variance, anyway results seem not so

different than the previous factor analysis made with Serie A data, in which variables were strongly

interdependent, making it hard to assign separated weights to each variables.

  88  

3.3.3  Top  European  clubs  

Before to introduce the last model, in which we’ll try to estimate football clubs’ value starting from

these SPSS results, I have made another calculation by considering 35 selected clubs coming from

top 5 leagues, among which are present top clubs in the world, in terms of incomes and market

share. Because of the huge interdependence that makes difficult a clear weights’ distinction by

factor analysis, I propose a multiple linear regression, using the following variables:

1) “Foreign”: this variable includes UEFA team ranking and foreign consumers; as UEFA team

ranking, it has been made a weighted sum of the last five seasons for each club, the current season

rank is weighted 1, the fifth it is worth 1/5, the fourth is weighted 1/4 and so long, because

consumers are more satisfied by the recent results and coefficients are an estimation of that. As an

example, in the case of Manchester Utd the variable related to UEFA team ranking is 304.58:

Table 51

2009/10 2010/11 2011/12 2012/13 2013/14 Weighted sum

28.586 [1/5] 36.674 [1/4] 16.050 [1/3] 21.287 [1/2] 26.357 304.580 Table  51:  Man  Utd  UTR  variable

Number of foreign consumers has been estimated according to Facebook; this social network has

already reached an almost homogeneous penetration, in terms of registered, in the top five leagues

countries, and a wide penetration all around the world (1.28 billions monthly active, Facebook

report, January 2014), that’s why I deem number of fans on Facebook a good indicator of fans

number, in the absence of a specific research, moreover it is available for most of the professional

football clubs. This estimation is going to be more accurate with the increase of registered and the

negligibility of difference in time among clubs (for example official pages born in different periods

have had different catch possibilities, but the difference is reduced along time). It is also possible to

see from which countries fans come from, in this variable I’ve included only foreign fans.

“Foreign” variable is the weighted sum of UEFA team ranking and number of foreign consumers on

Facebook, taking for each club the related value of UTR divided by the higher UTR coefficient, and

summing up the related number of foreign fans divided by the higher number. So finally 0 <

Foreign <= 2. (Notice that FC Barcelona has got the highest values of both UTR and foreign fans,

thus obtaining an evaluation of 2). It has been included these two contributions because they are

  89  

strictly related, in the sense that results in European competitions affect fans from all over the

world, because they assure visibility, important gains and talent.

2) “Local”: on the line of “Foreign” variable, “Local” is the weighted sum of number of local fans

according to Facebook and local league results, because championship results especially affect local

consumers, even if of course there could be followers from all over the world. To estimate local

league results, I have made an average of last five championships placements, then the number

obtained has been subtracted to twenty, thus favouring first places in the ranks. Notice that in our

sample two clubs have been relegated one year in second division during the analysis period, for

that clubs (Villareal CF and Newcastle Utd) a 21st placement has been considered in that season. To

standardize Facebook local fans and championship coefficient, each single value has been divided

by the highest of that column, thus obtaining the range 0 < Local <= 2.

3) “CatchA” takes into account city inhabitants, attendance at the stadium and percentage of

stadium filling; we have seen the importance of the city population, even if it appears less important

for big clubs able to get fans from the rest of the country or even the rest of the world, it remains the

core of the catchment area. City values have been standardized in the usual way, taking single clubs

values and dividing by the highest, which in our table is Paris with approximately 2.3 millions

inhabitants. Notice that for cities with more residing clubs in the top divisions, during 2013/14

season, the number of citizens has been divided by that number of clubs; this is a strong

approximation in some case, as fans are not uniformly distributed in the local city, for example

London citizens have been divided by six, ignoring clubs participating to lower categories and

assuming, for example, Arsenal and Crystal Palace have the same catchment area possibilities

inside the city, as we don’t know actual fans distribution. Anyway, city inhabitants affect 1/3 of

“CatchA” variable. The others components are attendance at the stadium, for which I have taken an

average of the last five seasons’ attendance, in order to have a clearer vision of attendance

potentiality and to avoid the selection of single picks. The value obtained, affecting 1/3 of the

variable, has been standardized dividing by the highest (Borussia Dortmund, 79.547); actually

attendance directly affect a bigger slice of the variable, as the last contribution is given by the

standardization of the same average attendance multiplied by the percentage of stadium filling, in

this component I would like to include the ability to exploit a core asset as its stadium and also a

better context to create enthusiasm, more than the possibility of future fillings. Before the

standardization, percentage of stadium filling is multiplied by the attendance to remark the

importance of the effective number of consumers actually present every week. The whole “CatchA”

  90  

variable evaluation is composed by the sum of these three contributions, thus ranging between 0 <

CatchA <= 3.

Below the database with 105 values, actually coming from 735 variables components, as we have

seen.

Table 52

Table  52:  Top  clubs  variables'  value

First, using these variables, I’ve tried to predict Brand value, as Brand I’ve taken an average of last

3 years values (Brandfinance 2012, 2013, 2014), in order to avoid annual picks:

Table 53

Model

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,897a ,805 ,786 79,31947

a. Predictors: (constant), Foreign, CatchA, Local Table  53:  Top  club  regression  1

Brand value is well predicted from our variables that get 80% of its variability.

  91  

Table 54

Coefficients

Model

Unstandardized coefficients

Standardized

coefficients

t Sign. T Std error Beta

1 (Constant) -157,116 44,119 -3,561 ,001

CatchA 89,974 33,504 ,290 2,685 ,012

Local 66,747 48,494 ,162 1,376 ,179

Foreign 205,267 46,401 ,550 4,424 ,000

a. Dependent variable: Brand Table  54:  Top  clubs  brand  prediction

Precisely the equation Brand = -157 +89,97CatchA + 66,75Local + 205,27Foreign. [10] Equation  10:  brand  prediction  linear  formula

It is a good approximation. Significance is relatively high only for Local (.179), which is the value nearest to zero taking standardized coefficients. I recall that prediction is based on Brandfinance values, so we rely on them. Let’s try to predict revenues which are real values; as for brand, I have taken the average of last three years in order to avoid seasonal picks: Table 55

Model

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,952a ,907 ,898 40,42842

a. Predictors: (constant), Foreign, CatchA, Local Table  55:  Top  clubs  revenues  regression

Approximation is very good, getting most of the variance. Moreover, an unappreciated part is congenital as we are not able to include all the relevant factors in our variables, for example different owners attitudes (think about PSG and Manchester City sheiks, clubs present in our sample), are only indirectly and thus, partially, included in our variables.

  92  

Table 56

Coefficients

Model

Unstandardized coefficients

Standardized

coefficients

t Sign. T Errore std Beta

1 (Constant) -80,428 22,487 -3,577 ,001

CatchA 57,949 17,077 ,254 3,393 ,002

Local 80,313 24,717 ,264 3,249 ,003

Foreign 151,135 23,650 ,549 6,391 ,000

a. Dependent variable: Rev Table  56:  Top  clubs  revenues  prediction

Coefficients are almost surely different from zero, presenting very low significance values. Looking

at standardized coefficients, our variables explain respectively 26% (Local), 25% (CatchA) and

55% (Foreign) of the whole variance. The sum is a bit higher than 100% due to approximations.

The equation:

Rev = -80,43 + 57,95CatchA + 80,31Local + 151,14Foreign. [11] Equation  11:  Revenues  prediction  linear  formula

Provides a good annual revenues’ estimation. We notice that the equation [11] is similar to [10], not

surprising as brand values taken from Brandfinance are strongly related with annual revenues. Both

for revenues and brands, constant coefficient is widely negative, so it seems we could have negative

values, and this is not possible as brand and revenues should have zero as lower bound. A first

consideration is that our independent variables are strictly greater than zero, as there is no existing

club with zero fans or catchment area, neither zero in UEFA rankings as the national coefficients

are the yearly lower bound for each club. Secondly, in this sample values are significantly higher

than zero as we are taking 35 important clubs, residing in big cities, participating to top 5 leagues

and having a history behind. To generalize the equation to all professional football clubs or at least

to top divisions clubs, we should take an homogeneous sample, made of hundreds of teams with

different sizes and contexts, but we don’t have the data as for example revenues and brand values

are available only for few clubs. Anyway, with respect to [9], [10] and [11] could theoretically

provide negative values when applied to small clubs, so with available data we can’t generalize.

To verify our results I propose a comparison between real revenues and equations [9] and [11] for

top Italian clubs and between [11] and revenues for some other top European clubs.

  93  

Table 57

Club Country Revenues

2011 M€

Revenues

2012 M€

Revenues

2013 M€

Revenues with

regression [9]

Revenues with

regression [11]

Juventus FC Italy 154 195 272 242 214

AC Milan Italy 235 257 264 252 259

Inter FC Italy 211 186 169 197 199

AS Roma Italy 144 123 124 135 148

SS Lazio Italy 75 81 106 111 86

SSC Napoli Italy 115 148 116 127 143

ACF Fiorentina Italy 79* 75* 73* 91 68

Manchester Utd England 367 396 424 / 414

Arsenal FC England 252 290 284 / 311

Liverpool FC England 203 233 241 / 219

Chelsea FC England 250 323 303 / 315

Tottenham England 181 178 172 / 162

Manchester City England 170 286 316 / 174

Newcastle Utd England 98 115 112 / 67

Bayern Munich Germany 321 368 431 / 374

Bayer Leverkusen Germany 90* 96* 94* / 100

Borussia Dortmund Germany 139 189 256 / 245

Schalke 04 Germany 202 176 198 / 182

Hamburger SV Germany 129 121 135 / 143

Stuttgart VFB Germany 96 103 117 / 103

Werder Bremen Germany 100 86* 87* / 102

Barcelona FC Spain 451 483 483 / 485

Real Madrid Spain 480 513 519 / 482

Atletico Madrid Spain 100 109 120 / 208

Valencia CF Spain 117 111 116 / 155

Villareal CF Spain 62* 70* 43** / 41

Sevilla FC Spain 86* 75* 69* / 108

Athletic Bilbao Spain 60* 68* 59* / 76

Paris SG France 100 221 399 / 237

Ol. Marseille France 150 136 104 / 197

Ol. Lyonnaise France 133 132 100* / 167

Bordeaux France 70 * 66* 68* / 79

Lille LOSC France 55* 80* 99* / 81

AS S. Etienne France 59* 52* 54* / 34

Toulouse FC France 48* 49* 47* / 16

Table  57:  Top  clubs  revenues  comparison

*Data taken from local press or official clubs’ websites

**Second division incomes

For the remaining clubs revenues’ source is Deloitte.

  94  

Let’s analyse Table 57 results: In general, they appear near to actual revenues’ values, so our

regressions seem a good approximation. Even for real values we see some relevant variation among

years, which could be caused, for example, to intermittent participations at top European

competitions or global league revenues’ changes (e.g., new deal for league’s TV rights). There are

very few large variations, we are going more in details in searching some partial explanation for the

apparently more wrong values.

With regard to Italian Serie A and German Bundesliga, regressions provide good revenues

estimations proxy to actual values of the club considered, with little variations, for which we can try

to identify partial causes; Hamburger SV and AS Roma, following [11], present higher values than

real incomes of last seasons, the two club never participated to UEFA Champions League and have

had only few matches in European competitions in the past 4 seasons, hence their real revenues

could have underestimated the actual potentiality of two clubs of that size and catchment area.

Borussia Dortmund presents a very high estimation with respect to 2011 and 2012 revenues, but the

value follow the growth of the club, thanks to last years great results, a similar reasoning can be

done for Spanish Atletico Madrid and, in a lesser extent, to Sevilla CF, that thanks to last European

performances that have improved their ranking and achieved new fans, laying the foundations to

improve their revenues in the next few years. Anyway, in general, medium/large Spanish clubs have

got from [11] an higher evaluation, if one explanation could be the great results of some clubs

which will push up all the league revenues, another factor is that La Liga is the only “Top 5 league”

which has got individual TV rights, thus assigning huge amounts of money to their top clubs,

having an high unbalanced distribution. This is not directly factored in our regression, indeed top

clubs Barcelona FC and Real Madrid haven’t exploit this advantage in the evaluation, and the other

clubs haven’t exploit their “disadvantage” with respect to other leagues’ clubs of similar size, but

this could be a strength of our regression in terms of previsions, as from 2015 La Liga will pass to a

collective distribution, thus favouring most of the Spanish clubs to push up their revenues. By

remaining in Spain, we have some doubts about Villareal CF value, which is lower than their actual

revenues during the momentary appearance in Segunda Division (2012/13). It’s a club residing in a

small town (little over 50.000 citizens) but able to reach great results during the last decade, which

are positively affecting clubs’ revenues still now, but they need some confirmations in the coming

years to maintain an important slice of consumers and thus revenues, as they haven’t a huge

catchment area neither several decades of great history, they strongly depend on results which are

never granted. This could explain a lower value. The recent appearance in second division has

surely affected our calculation, which should be good if the club will have some (relevant)

  95  

probability to relegate in the next years. Even if probability of relegation is not directly factored in

our regression, by considering each team catchment area, worldwide fans and past recent results it

is, in a lesser extent, indirectly included in the evaluation. More doubts arise for some English and

French revenues values: Manchester City is growing faster in the last years and our prevision seems

to not sufficiently considering the effects, probably because the presence of very reach owners is

not directly included in our calculation, a similar reasoning could be done for PSG: our estimation

could be good if the sheiks would sell the two clubs in the next few years to new “normal” owners,

we should verify the event probability, which in absence of private information, appear us

unknown. Also O. Marseille and O. Lyonnaise revenues are related to PSG future perspectives,

because with actual situation, a drop in share and results is predictable, making our regressions

predictions too optimistic, also because in the same league there are other two teams owned by rich

foreign owners (AS Monaco and RC Lens) which have already undergone huge investments in

talent despite the relatively low revenues. It emerges a lack in our regression, which is the

importance of ownership that should be factored, in particular their disposability in spending huge

amounts of money in talent in the coming years. Even if Financial Fair Play UEFA should mitigate

losses over incomes, with some commercial deals bounds can be bypassed (PSG earned 64% of

their revenues from commercial part in 2013, widely the greater slice of all the top clubs from this

stream, Deloitte), we’ll see UEFA applications of the regulations. The low values of AS S. Etienne

and, especially, Toulouse FC, can be explained by some more probability of relegation in the long

run, which is a risk as it would cut significantly revenues. The remaining English and French clubs

have got an evaluation in line with actual revenues, with the exception of Newcastle Utd, for which

the estimation is pretty lower than the actual incomes, one explanation could be that most of their

strength comes from the participation to the richest English Premier League, which provides great

revenues with respect to other leagues even if a club has not recently undergone great European

results (Each club participating have earned a minimum of 55M£ (approximately 70M€) of TV

rights by default, because divided in equal parts, from 2013/14 season, according to the new,

pharaonic, deal). Moreover, Newcastle Utd played in second division just in 2009, so the their

constant stay in the top League is not so granted as for top English clubs, while revenues from 2011

are only referred to a first division championship.

In conclusion, revenue prevision with linear regression provides good results, similar to actual

values, with the peculiarity that the formula is individually changeable when new results and new

consumers will be included. Anyway we have seen some case in which it provides busted results,

due the lack of relevant factors included, or partially included because related to results and number

of fans, as the generosity and the stability of the ownership, the league growth tendency and its

  96  

weight on football market (knowing actual growth and already concluded deals) and the league

internal regulations (as different TV rights rules, which could provides different incomes

distribution).

3.4  Factorial  Fans  model  

We have seen the relation between some revenues’ drivers, finding an estimation of incomes using

indicators related to number of fans and sportive results. To find clubs and leagues’ values, we can

start from revenues that as we have seen are a relevant indicator of value, especially in football

market. Factorial Fans model tries to evaluate assets value, starting from revenues, in this sense it is

an evolution of Revenues multiples approach, using Forbes values as benchmark. As incomes, I

have taken an average of 2012 and 2013, in order to avoid or reduce single picks and to consider

only recent incomes as the market is quickly changing. According to Forbes (Table 7), top clubs

have a ratio between value and revenues, on average, slightly higher than 2.5, and clubs with higher

incomes present, on average, the highest multiplier (with the pick of Manchester Utd that have got

6.3). Moreover, looking at Table 7 and previous Forbes ranks, ratios are, on average, very similar to

hundredths of revenues and proportional to them. Following those numbers, I’ve set a multiplier

proportional to revenues, merely m = (incomes/100). Because clubs with higher incomes have

further probabilities to exploit their revenues in an expanding market like football. Then, multiplier

has been little corrected including clubs and leagues average growth, thus adding to one the product

between league market share (MS) and annual growth of European first divisions (EG, average of

last five years), to consider the league contribution, and adding to one the annual club growth or

decline (CG, average of last five years) to include each single club trend. The period of 5 years has

been chosen according to UEFA ranks, as it is a sufficiently long time to amortize extraordinary

seasons defining a trend and sufficiently short to avoid or reduce the effects of past, negligible,

events. The final value of m is (we call it ß):

ß = (Average last 2ys Rev) /100 * (1 + MS*EG) * (1 + CG) [12]. Equation  12:  Top  clubs  multiplier  1

E.g. ß Man Utd = ((396 + 424) /2) /100 * (1 + 0,209 * 0,060) * (1 + 0,040) = 4,100 * 1,013 * 1,040

= 4,32.

  97  

From table 55, revenues have been estimated by [11] with approximately 52% “Foreign” (a variable

including UEFA ranking and foreign fans), 24,5% “Local” (including local fans and clubs’ results

in the league) and 23,5% “CatchA” (Comprehensive of attendance at the stadium and citizens).

Following those numbers, also potentiality of further exploit revenues can be decomposed by these

three variables, hence I’ve added to the multiplier 52% of ß and subtracted to it, respectively, 24,5%

and 23,5%, thus obtaining three different multiplier that refer to different “parts” of revenues, and

for which ß is a weighted average.

Foreign mult. (∂) = ß + 0,520ß. Local mult. (∆) = ß – 0,245ß. CatchA mult. (µ) = ß – 0,235ß. [13] Equation  13:  Top  clubs  multiplier  2

E.g. Man Utd à ∂ = 6,56. ∆ = 3,30. µ = 3,26. (ß = 4,32).

As the three multipliers ∂, ∆ and µ refer to revenues variance decomposition from [11], I’ve taken

the whole incomes estimations by [11], then dividing the value in three parts: 52% to “Foreign

part”, 24,5% to “Local” and 23,5% to “CatchA”. Then each slice has been multiplied by the related

multiplier, thus obtaining our estimation of value.

E.g. Man Utd revenues from [11] = 414,3M€. Foreign value = 215,4. Local value = 101,5. CatchA

value = 97,4. à Value = 215,4∂ + 101,5∆ + 97,4µ = 2067M€.

We can synthesize the whole value formula as:

0,520Rev∂ + 0,245Rev∆ + 0,235Revµ [14]. Equation  14:  Top  clubs  value  formula

Where Rev comes from [11] and multipliers from [12] and [13]. Notice that ß in equation [12] starts

from actual revenues, inspired by Forbes, searching a fair, approximated multiplier of the average

of last 2 years revenues, while the factorization of ß [13] and revenues in the formula [14] follow

estimations of regression [11], because the decomposition of incomes with these proportions is

related to [11]. We have seen the consistency of [11] with actual revenues, at least for top clubs. I

resume in Table 58 the whole database:

  98  

Table 58

Table  58:  Top  clubs  calculation  variables

To analyse the results I propose a comparison with Forbes values, as our multipliers are initially

inspired from Forbes values and as both methods focus on top clubs, indeed our methodology could

provide busted results (or at least the reliability will change) when applied to medium/small size

clubs, let’s say for clubs that earn less than 60-70 millions of euro per year, because it was set

starting from SPSS, using only medium/large clubs. Below the top 20 rank following our model:

  99  

Table 59

Club Forbes 2013 M€

0.78 $ à € conversion

Forbes 2014 M€

0.78 $ à € conversion

Factorial model 2014

M€

Real Madrid 2574 (1st) 2683 (1st) 3048 (1st)

Barcelona 2028 (3rd) 2496 (2nd) 2889 (2nd)

Manchester Utd 2449 (2nd) 2192 (3rd) 2067 (3rd)

Bayern M 1021(5th) 1443 (4th) 1831 (4th)

Chelsea FC 703 (7th) 677 (6th) 1229 (5th)

Paris SG <205 (>21st) 324 (15th) 1069 (6th)

Arsenal FC 1034 (4th) 1037 (4th) 1067 (7th)

AC Milan 737 (6th) 669 (8th) 832 (8th)

Manchester C 537 (9th) 673 (7th) 773 (9th)

Borussia D 340 (13th) 468 (11th) 724 (10th)

Liverpool 508 (10th) 539 (10th) 619 (11th)

Juventus FC 541 (8th) 663 (9th) 586 (12th)

Schalke 04 388 (12th) 453 (12th) 432 (13th)

Inter FC 313 (14th) 377 (14th) 409 (14th)

Tottenham 406 (11th) 401 (13th) 354 (15th)

Atletico Madrid <205 (>21st) 256 (17th) 280 (16th)

O. Marseille 222 (19th) <231 (>21st) 277 (17th)

SSC Napoli 257 (17th) 231 (20th) 246 (18th)

O. Lyonnaise 287 (15th) <231 (>21st) 223 (19th)

Valencia CF <205 (>21st) <231 (>21st) 219 (20th) Table  59:  Top  clubs  values  comparison

I recall that in our sample clubs outside top 5 leagues have not been included, neither top 5 leagues’

clubs not present in Table 58. Anyway, with respect to Forbes ranks, only 2 clubs are not present in

our database, SC Corinthians (16th in 2013 Forbes rank) and Galatasaray (16th in 2014 Forbes rank).

The remaining 4 empty Forbes places are of Hamburger SV (18th in 2013 and 2014 rank and 21st

with Factorial model), AS Roma (19th in 2014 Forbes rank and 22nd with Factorial) and Newcastle

Utd (20th in 2013 Forbes rank and 24th with Factorial). If in terms of clubs ranked the

methodologies provide similar results, with respect to values emerge some relevant differences.

Without considering single cases, for which results are generated by different methodologies

(Forbes one is not available), the sum of Top 20 Factorial rank (2014) is 19.174M€ with respect to

16.344M€ of 2014 Forbes rank, and this difference is mostly due to top teams, indeed in Top 10

  100  

Factorial rank, 9 clubs present an higher values than Forbes (only Manchester Utd has a lower

evaluation), it seems that with Factorial model potentiality of further expand market share, with

respect to Forbes, is even expanded.

3.5  Factorial  Italian  model  

If the Euro model provides top clubs’ values estimation, most of the Italian professional clubs are

not included; according to factorial analysis (focusing on Tables 41, 43, 44, 46 and 49), even

limiting to Italian leagues, revenues and consequently values, are almost explained by actual and

potential number of consumers and sportive results of each team, and variables related to consumers

and results are strictly related, making difficult the decomposition in single factors, we have to

consider that whenever we take one (ex. Attendance), we are already getting part of the variance of

another (e.g. UEFA ranking). From [9], we have got a relation which estimates revenues of Serie A

teams knowing attendance, catchment area, number of fans, past results (divided in recent and

historical). The equation [9] have a constant value of 15,969M€, which is approximately the amount

that each club participating to Serie A has received in the last seasons (2012/13) by default from TV

rights, because coming from equal parts distribution. Since, for lower divisions, results comparable

to Serie A factor analysis (Table 48 and 49) have been obtained, we can take the equation [9]

diminished by the constant part as a benchmark, but we cannot keep it unchanged because some

factor is not available. In that equation variable related to results get most of the variance (around

70%) while variable related to market share get 30%. Previously, in [8] we had well predicted Serie

A clubs’ revenues with number of fans and catchment area getting, together, almost 80% of the

variance, an opposite percentage of variance with respect to [9], which tells us how much share and

results predictors are interconnected. In [9] and [8] were included variables like revenues, actual

number of fans (2013) or UEFA team ranking, not available or useless (UTR) when including lower

categories. Hence, we need to predict Serie A revenues with variables available even for lower

categories, and then try to extend revenues’ prediction to lower categories’ clubs.

As we don’t know lower categories actual fans number, a possible indicator could be the number of

Facebook fans, we’ve seen the correlation between Facebook fans and actual number of consumers,

which allow us the approximation, because the great majority of professional football clubs has got

an official page and for those who haven’t, we can take the number of fans of the wider unofficial

page. Moreover, attendance at the stadium and city inhabitants are available, as well as historical

results (participation to championships and leagues and winning titles).

  101  

Following [9] I’ve made a first attempt building a “share” variable and a “result” variable, as we

have different variables with respect to [9], I’ve tried setting different weights, 50% per each

variable. In “Share” I’ve included, per each club, Facebook local fans (May 2014), Facebook

foreign fans (May 2014), city inhabitants and attendance (average of last 5 years). Each of these

variables has been standardized. In “result” I’ve included the standardization of number of

European cup, Serie A and B participation (Serie B have got half weight), Italian and European

winning titles; then I’ve summed 50% of share with 50% result, obtaining “Part” variable, which

we are going to analyse with SPSS:

Table 60

Model

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,959a ,920 ,916 20,4962145

a. Predictors: (costante), Part Table  60:  Italian  model  regression  1

Table 61

Coefficients

Model

Unstandardized coefficients Standardized coefficients

t Sign.

B confidence interval 95,0%

T Error std Beta Lower limit Upper limit

1 (Constant) -18,246 8,690 -2,100 ,050 -36,502 ,010

Part 74,757 5,185 ,959 14,418 ,000 63,864 85,650

a. Dependent variable: Rev Table  61:  Italian  model  coefficients  regression  1

Serie A 2013 revenues are mostly explained, but the widely negative constant coefficient could

provide some problem when valuing lower division clubs, because it can cause negative revenues.

Another attempt could be done using one more variable, following [11]. I’ve built three variables:

1- “Res”, including Italian and European winning titles (only official completion without

considering lower divisions titles), both standardized compared to the club with the highest

value.

2- “Share”, including both Facebook local and foreign fans (May 2014), Participation to Serie

A, B and European cup. The whole sum has been standardized to the highest values, in order

  102  

to allow the comparison. Notice that when summing up participations, each Serie B

presence has been halved.

3- “Loc”, including average attendance at the stadium and city inhabitants, standardized.

Table 62

Model

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,991a ,982 ,979 10,24139

a. Predictors: (constants), Res, Loc, Share Table  62:  Italian  model  regression  2

Table 63

ANOVAa

Model Sum of squared gl

Quadratic

average F Sign.

1 Regression 91066,374 3 30355,458 289,414 ,000b

Residual 1678,176 16 104,886 Total 92744,550 19

a. Dependent variable: Rev

b. Predictors: (constant), Res, Loc, Share Table  63:  Italian  model  ANOVA

Table 64

Coefficients

Modello

Unstandardized coefficients

Standardized

coefficients

t Sign. T Std error Beta

1 (Constant) 23,136 4,281 4,549 ,000

Share 99,443 8,316 ,715 11,957 ,000

Loc 13,218 6,278 ,101 2,106 ,051

Res 28,529 8,302 ,244 3,437 ,003

a. Dependent variable: Rev Table  64:  Italian  model  regression  coefficients  2

The approximation appears even better and the equation for Serie A clubs’ revenues is:

  103  

Rev = 23,14 + 99,44Share + 13,22Loc + 28,53Res [15] Equation  15:  Serie  A  clubs  revenues  3

The slightly positive constant is of course suitable only for Serie A clubs, but we keep the

proportion of independent variables for lower divisions club. The amount is a bit higher than the

slice divided in equal part, but if we consider that the lower gain from tv rights in 2013, obtained by

Pescara Calcio, was 21,38M€ (tifosobilanciato.it), we can assume that amount as a Serie A

premium, keeping the same formula without the constant coefficient (or with proportional splits,

depending on category) for lower divisions.

I’ve made a third attempt, again using “Share” and “Res” variables, but without considering

weights provided by [9] and [8]. In “Share” I’ve included average attendance, average attendance

multiplied by percentage of stadium filling, Facebook local (Italian) fans and Facebook total fans

(May 2014), city inhabitants. Hence, 5 standardized variables, in which Facebook local fans and

average (5 years) attendance have been included two times. “Res” includes 5 variables, too. They

are the standardization of the sum of Serie A participations, Serie B participations (with halved

weight), European cup participations, Italian and European winning titles. Problem could arise

because here recent results have not directly rewarded, anyway they are included, with disputable

weight, in “Share”, when considering attendance and Facebook fans. Let see the results:

Table 65

Model R R-squared

R-squared

adapted

Estimation

standard error

1 ,964a ,929 ,921 19,63800

a. Predictors: (constant), Res, Share Table  65:  Italian  model  regression  3

Table 66

ANOVA

Model Sum of squared gl

Quadratic

average F Sign.

1 Regression 86188,480 2 43094,240 111,744 ,000b

Residual 6556,070 17 385,651

Total 92744,550 19

a. Dependent variable: Rev

b. Predictors: (constant), Res, Share Table  66:  Italian  model  ANOVA  2

  104  

Table 67

Coefficients

Model

Unstandardized coefficients Standardized coefficients

t Sign.

B confidence interval 95,0%

T Error std Beta Lower limit Upper limit

1 (Constant) -1,191 7,613 -,156 ,878 -17,253 14,871

Share 27,603 8,725 ,459 3,164 ,006 9,195 46,012

Res 35,924 9,818 ,531 3,659 ,002 15,209 56,638

a. Dependent variable: Rev Table  67:  Italian  model  regression  coefficients  3

The equation:

Rev= -1,20 + 27,60Share + 35,92Res [16] Equation  16:  Serie  A  revenues  4

Despite is related only to Serie A clubs (the only ones with known incomes), it seems suitable even

for lower categories clubs, as we can use the same variables for lower categories in trying to

generalize the estimation, and constant coefficient is proxy to zero, actually we are absolutely not

sure it is different, indeed Significance is very high (.878), so it could be a negligible contribution in

the approximation.

At this point we should verify the results, comparing with actual revenues for Serie A clubs, and

then applying the same formulas for the remaining, to verify if a generalization is possible. We have

some doubts because driver factors should have different impacts depending on club sizes, but we

would like to provide a grouping formula to have at least a value approximation.

As it is difficult to homogenise revenues and consequently values of Serie A and Lega Pro (third

division) clubs in a single formula, I’ve applied different revenues approximations, following [9],

[15] and [16], and then we’ll see an average of the results. Equation [9] has been changed,

neglecting the constant coefficient and weighting 50% both the independent variables, “Share” and

“Res”; [16] is unchanged, but without considering the constant coefficient (-1.2), [15] is unchanged

except for the constant coefficient: for Serie A clubs it has remained 23.14 (I recall that the lower

gain from TV rights was 21.38M€ in that season), for Serie B clubs 5.14 and for Lega Pro clubs it is

negligible, as an attempt to exploit Serie A and, in a lesser extent, Serie B commercial and

especially TV rights advantages. In all these equations but especially following [9] and [16], as we

are predicting Serie A revenues trying to generalize to lower divisions’ clubs, what we are finding

  105  

are not the exact incomes but different revenues’ “values”, ranked by the drivers factors we have

mentioned, so for example a club currently militating in third division with a good catchment area,

great history and high attendance, for the current season would get a relatively low slice of incomes

with respect to its potentiality, because that division assures lower incomes from all the revenues’

streams, but with ours equations it will get an higher shares, as we assume it will have greater

probability to promote in the next years increasing its revenues. To find clubs’ values, following

multiples models, we have taken revenues’ “values” divided by 100 to find a multiplier ß, then we

have multiplied those revenues by ß to get a value estimation. I’ve analysed all the 2012/13 Serie A,

Serie B and Lega Pro I clubs, plus top 15 seasonal attendance Lega Pro II clubs. In Table 68 I have

posted top 60 clubs rank (from that sample) in terms of values (millions of euros):

Table 68

Club

Category

(2012/13)

Revenues

[9]

Values

[9]

Revenues

[15]

Values

[15]

Revenues

[16]

Values

[16]

Average

revenues

“value”

Multiplier ß

Average

values

Milan Serie A 223,0 497,3 297,1 882,7 230,0 529,0 250,0 2,500 625,2

Juventus Serie A 223,2 498,2 281,2 790,7 222,4 494,6 242,3 2,423 586,9

Inter Serie A 176,0 309,8 177,4 315,0 186,9 349,3 180,1 1,801 324,4

Roma Serie A 149,1 223,9 140,0 196,2 157,3 247,4 148,8 1,488 221,4

Napoli Serie A 134,9 183,5 135,0 182,2 142,7 203,4 137,5 1,375 189,2

Lazio Serie A 107,4 115,4 89,2 79,6 119,3 142,3 105,3 1,053 110,9

Fiorentina Serie A 88,1 77,6 79,0 62,3 96,9 93,9 88,0 0,880 77,4

Torino Serie A 73,2 53,6 67,9 46,6 78,9 62,0 73,3 0,733 53,8

Bologna Serie A 69,5 48,3 65,5 42,9 75,9 57,7 70,3 0,703 49,4

Genoa Serie A 65,9 43,5 65,8 43,4 72,7 52,8 68,1 0,681 46,4

Sampdoria Serie A 61,5 37,8 59,5 34,1 64,8 42,0 61,9 0,619 38,4

Palermo Serie A 59,7 35,7 59,2 35,1 64,8 42,0 61,2 0,612 37,5

Parma Serie A 51,8 26,8 54,1 29,2 56,9 32,4 54,3 0,543 29,4

Verona Serie B 53,3 28,4 44,4 19,6 58,5 34,2 52,1 0,521 27,1

Atalanta Serie A 47,6 22,6 51,9 26,8 53,2 28,3 50,9 0,509 25,9

Udinese Serie A 47,0 22,1 49,5 24,5 54,4 27,4 50,3 0,503 25,3

Bari Serie B 47,9 22,9 38,8 15,1 53,5 28,6 46,7 0,467 21,8

Cagliari Serie A 41,1 16,9 49,2 24,2 45,3 20,5 45,2 0,452 20,4

Catania Serie A 36,3 13,2 44,1 19,5 40,3 16,2 40,2 0,402 16,2

Brescia Serie B 34,0 11,5 30,8 9,4 36,3 13,2 33,7 0,337 11,4

Vicenza Serie B 31,7 10,1 28,7 8,2 34,6 11,9 31,7 0,317 10,0

Modena Serie B 29,4 8,7 26,1 6,8 31,1 9,7 28,9 0,289 8,3

Pescara Serie A 23,4 5,5 35,5 12,6 26,0 6,7 28,3 0,283 8,0

Padova Serie B 28,3 8,0 26,1 6,8 30,3 9,2 28,2 0,282 8,0

Venezia Lega Pro II 27,5 7,6 21,2 4,5 29,4 8,7 26,0 0,260 6,8

Cesena Serie B 25,2 6,4 25,9 6,7 26,3 6,9 25,8 0,258 6,7

Chievo Serie A 19,3 3,8 31,1 9,6 25,1 6,4 25,2 0,252 6,3

Livorno Serie B 23,9 5,7 21,9 4,8 26,3 6,9 24,0 0,240 5,8

  106  

Perugia Lega Pro I 23,0 5,3 18,7 3,5 26,2 7,0 22,6 0,226 5,1

Salernitana Lega Pro II 22,4 5,0 17,5 3,0 26,5 7,1 22,1 0,221 4,9

Novara Serie B 21,7 4,7 21,1 4,5 21,5 4,6 21,4 0,214 4,6

Siena Serie A 15,3 2,4 28,3 8,0 16,4 2,7 20,0 0,200 4,0

Lecce Lega Pro I 20,2 4,1 16,1 2,6 22,5 5,1 19,6 0,196 3,8

Pisa Lega Pro I 20,0 4,0 15,9 2,5 22,5 5,1 19,5 0,195 3,8

Reggina Serie B 18,5 3,4 18,9 3,6 19,2 3,7 18,9 0,189 3,6

Pro Vercelli Serie B 18,0 3,3 18,9 3,6 19,4 3,7 18,8 0,188 3,5

Como Lega Pro I 18,7 3,5 14,4 2,1 20,2 4,1 17,8 0,178 3,2

Reggiana Lega Pro I 19,5 3,7 14,3 2,1 17,4 3,0 17,1 0,171 2,9

Ascoli Serie B 16,1 2,6 16,7 2,8 17,9 3,2 16,9 0,169 2,9

Spezia Serie B 15,4 2,4 17,7 3,1 17,0 2,9 16,7 0,167 2,8

Ternana Serie B 15,1 2,3 16,5 2,7 17,4 3,0 16,3 0,163 2,7

Cremonese Lega Pro I 16,0 2,6 14,1 2,0 17,5 3,0 15,9 0,159 2,5

Varese Serie B 15,1 2,3 15,8 2,5 15,6 2,4 15,5 0,155 2,4

Catanzaro Lega Pro I 16,0 2,6 11,8 1,4 17,6 3,1 15,1 0,151 2,3

Monza Lega Pro II 15,9 2,5 12,1 1,5 16,6 2,8 14,9 0,149 2,2

Alessandria Lega Pro II 15,8 2,5 11,7 1,4 16,7 2,8 14,7 0,147 2,2

Avellino Lega Pro I 15,1 2,3 11,0 1,2 17,3 3,0 14,5 0,145 2,1

Empoli Serie B 13,2 1,8 14,5 2,1 14,6 2,1 14,1 0,141 2,0

Mantova Lega Pro II 12,4 1,6 10,9 1,2 13,9 1,9 12,4 0,124 1,5

Pro Patria Lega Pro II 12,5 1,6 9,2 0,9 13,3 1,8 11,7 0,117 1,4

Sassuolo Serie B 9,4 0,9 12,9 1,7 10,8 1,2 11,0 0,110 1,2

Crotone Serie B 9,3 0,9 11,6 1,4 9,9 1,0 10,3 0,103 1,1

Latina Lega Pro I 8,8 0,8 8,1 0,7 11,1 1,2 9,3 0,093 1,1

Trapani Lega Pro I 7,5 0,6 7,7 0,6 10,5 1,1 8,6 0,086 0,7

Prato Lega Pro I 9,3 0,9 6,0 0,4 9,7 0,9 8,3 0,083 0,7

Treviso Lega Pro I 8,5 0,7 6,2 0,4 9,1 0,8 7,9 0,079 0,6

Frosinone Lega Pro I 7,6 0,6 5,6 0,3 8,2 0,7 7,1 0,071 0,5

Rimini Lega Pro II 7,2 0,5 6,1 0,4 7,9 0,6 7,1 0,071 0,5

Forlì Lega Pro II 7,4 0,6 5,4 0,3 7,6 0,6 6,8 0,068 0,5

Cittadella Serie B 4,8 0,2 9,0 0,8 5,2 0,3 6,3 0,063 0,4

Serie A 1702,3 2234,2 1860,5 2865,2 1830,2 2457,0 1801,3 / 2496,4

Serie B 439,8 126,0 433,9 105,7 481,2 150,9 457,5 / 127,5

Lega Pro I 251,6 34,1 174,4 17,2 275,6 41,0 225,0 / 30,8

Aggregate* 2543,9 2416,7 2565,9 2999,5 2736,7 2678,9 2651,2 / 2698,4

*It includes also top 15 Lega Pro II clubs, Serie B and Lega Pro I aggregates include all the participating clubs, not only the ones

ranked in the table

Table  68:  Italian  leagues  values

Equations [9] and [16] provide close results, indeed they are very similar. [15] exploits advantages

to be, currently, in a higher division. A lack is the absence of recent results direct premiums,

anyway this weakness is partially covered by the inclusion of actual number of fans and last 5 years

attendance, which are mostly related to recent results. A factor not included in the calculation is the

economic power and ability of the ownership, which could change significantly a club value, for

example US Sassuolo has been promoted in Serie A despite its relatively low catchment area and

  107  

poor history, thanks mostly to a solid, capable and generous property, and if the club would be

currently sold it would probably be valued more than the fifty first position of our rank (with a

value of 1,2M€), that considers only a part of these positive ownership effects when including

results, number of fans and attendance. Anyway, this could be a strength of Italian model in the

long run in trying to predict as accurately as possible clubs values, as the continuity of a rich

ownership is never assured, while intangible assets like catchment area and historical results are

pretty stable factors. With respect to Table 58 it is more difficult to set a comparison with actual

values, because only 5 Italian clubs are present in Forbes ranks and a little minority of Italian clubs

has been sold in recent years.

 

 

 

 

 

 

 

 

 

       

  108  

Conclusions  

We have investigated European football market with emphasis on clubs and, consequently, leagues

revenues. As clubs are the main assets of this increasing market we have tried to value them.

Economic value is not an absolute and indisputable evaluation but our aim has been to find some

reasonable estimations. We have seen that values are strictly related to annual revenues, and as

clubs (and consequently leagues) with higher incomes will have more ways to further increase their

market share, we have concentrated on multiples approaches with variables multipliers, depending

on clubs and leagues shares, results and trends. We have verified the high dependence of revenues

on number of consumers and sportive results: Consumers/fans are able to push up revenues and

consequently values, assuring greater investments possibilities and thus enhancing winning

probabilities, improving sportive results. In general, there are fans concentrating in local catchment

area and attending directly at the stadium, others consuming only media products from local or

foreign countries depending on club size, anyway they have different “degrees” of loyalty; it could

be influenced by sportive results, which in general are positively related to number of fans, indeed

they satisfy the existing ones and they are able to reach new consumers, thanks to positive

externalities and direct visibility, so finally they are positively related to clubs (and leagues)

revenues not only for direct monetary premiums coming from wins, but also thanks to an increased

market share which will further push up incomes and consequently results. It’s a virtuous circle.

Based on these considerations, using SPSS statistics we have found some relations between

variables related to market shares and number of consumers and variables related to results, thus

verifying thy are positively related and strongly interconnected, moreover they are able to predict

with a good approximation clubs revenues. In the last paragraphs, starting from those outcomes, we

have seen an attempt to estimate top European clubs values and Italian leagues values, finding

consistent results in the first case (using Forbes values, despite some our perplexities we’ve seen in

the related paragraph, as a proxy to estimate the multiplier thus favouring a final comparison), more

subjective the judge for Italian values as we have very few data to allow a comparison, moreover it

is more difficult to compare clubs participating to different categories having very different sizes,

and we’ve had to resort to more variables approximations as we hadn’t actual values of many

relevant variables, each reader may judge the results looking at the last paragraph. In all of these

cases, the aim was to rank clubs and, consequently, leagues value, in the more stable way,

concentrating on those intangible assets that constitute an intrinsic value, difficultly changeable in

the short run. Anyway, a stable valuation is certainly impossible as each formula should be

continuously updated inserting new variables values, changing because of new situations (e.g. last

  109  

sportive results), each of them with different probabilities to occur and able to continuously affect

economic values.

  110  

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  113  

Szymanski, S., & Kesenne, S. (2004). Competitive balance and gate revenue sharing in team sports.

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Watson, Farley and Williams LLP (2013). Sport briefing. Online edition obtained from wfw.com.

Xe.com (2014). Money conversion. The World's Favorite Currency Site.

  114  

Tables index

Table 1: Average per club revenues _________________________________________________ 10

Table 2: UEFA ranking bonus _____________________________________________________ 20

Table 3: UEFA country ranking ____________________________________________________ 21

Table 4: Market cap _____________________________________________________________ 33

Table 5: Discounted cash flow _____________________________________________________ 36

Table 6: Forbes 2013; Table 7: Forbes value/revenue ratio 2013 __________________________ 39

Table 8: Forbes value/revenue ratio (historical) _______________________________________ 39

Table 9: Forbes value/EBITDA ____________________________________________________ 40

Table 10: Number of fans ________________________________________________________ 41

Table 11: Forbes comparison ______________________________________________________ 42

Table 12: Multivariate model ______________________________________________________ 47

Table 13: Revenue fixed multiplier _________________________________________________ 53

Table 14: Fans method ___________________________________________________________ 56

Table 15: First euro premium ______________________________________________________ 61

Table 16: Euro results premium ____________________________________________________ 62

Table 17: Serie A growth rate _____________________________________________________ 63

Table 18: Pearson UCR-League revenues ____________________________________________ 65

Table 19: Average UCR and top clubs revenues _______________________________________ 67

Table 20: UCR - Pearson participating clubs revenues __________________________________ 67

Table 21: Average UCR and leagues revenues 2 _______________________________________ 68

Table 22: Pearson UCR and league revenues 2 ________________________________________ 68

Table 23: Predictions with UTR, brand, attendance ____________________________________ 71

Table 24: Regression - 1st revenues prediction ________________________________________ 71

Table 25: Italian variables communalities ____________________________________________ 72

Table 26: Bartlett test 1 __________________________________________________________ 73

Table 27: Factor analysis 1 Serie A _________________________________________________ 73

Table 28: Rotated matrix 1 Serie A _________________________________________________ 74

Table 29: Bartlett test 2 __________________________________________________________ 75

Table 30: Factor analysis 2 Serie A _________________________________________________ 75

Table 31: Rotated matrix 2 Serie A _________________________________________________ 76

Table 32: Component weights 1 Serie A ____________________________________________ 77

Table 33: Serie A variables 1; Table 34: Serie A variables 2 _____________________________ 79

  115  

Table 35: Serie A variables 3; Table 36: Serie A variables 4 _____________________________ 79

Table 37: Bartlett test 3 __________________________________________________________ 80

Table 38: Serie A communalities 2 _________________________________________________ 80

Table 39: Variance explained Serie A _______________________________________________ 80

Table 40: Rotated matrix 3 Serie A _________________________________________________ 81

Table 41: Variance explained 2 Serie A _____________________________________________ 81

Table 42: Components weights 2 Serie A ____________________________________________ 82

Table 43: Serie A regression 1 _____________________________________________________ 83

Table 44: Serie A regression coefficients 1 ___________________________________________ 83

Table 45: Serie A regression 2 _____________________________________________________ 84

Table 46: Serie A regression coefficients 2 ___________________________________________ 84

Table 47: Revenues linear correlations ______________________________________________ 85

Table 48: Serie B and Lega Pro communalities ________________________________________ 86

Table 49: Serie B and Lega Pro variance explained ____________________________________ 87

Table 50: Serie B and Lega Pro components weights ___________________________________ 87

Table 51: Man Utd UTR variable __________________________________________________ 88

Table 52: Top clubs variables' value ________________________________________________ 90

Table 53: Top club regression 1 ____________________________________________________ 90

Table 54: Top clubs brand prediction _______________________________________________ 91

Table 55: Top clubs revenues regression _____________________________________________ 91

Table 56: Top clubs revenues prediction _____________________________________________ 92

Table 57: Top clubs revenues comparison ____________________________________________ 93

Table 58: Top clubs calculation variables ____________________________________________ 98

Table 59: Top clubs values comparison ______________________________________________ 99

Table 60: Italian model regression 1 _______________________________________________ 101

Table 61: Italian model coefficients regression 1 _____________________________________ 101

Table 62: Italian model regression 2 _______________________________________________ 102

Table 63: Italian model ANOVA __________________________________________________ 102

Table 64: Italian model regression coefficients 2 _____________________________________ 102

Table 65: Italian model regression 3 _______________________________________________ 103

Table 66: Italian model ANOVA 2 ________________________________________________ 103

Table 67: Italian model regression coefficients 3 _____________________________________ 104

Table 68: Italian leagues values ___________________________________________________ 106

  116  

Figures index

Figure 1: Aggregate profits ________________________________________________________ 7

Figure 2: Top 5 leagues growth _____________________________________________________ 8

Figure 3: Countries economies _____________________________________________________ 9

Figure 4: Top 5 revenue streams ___________________________________________________ 12

Figure 5: Europe revenue streams __________________________________________________ 12

Figure 6: Top 5 attendances _______________________________________________________ 14

Figure 7: Ticket prices ___________________________________________________________ 14

Figure 8: Attendance Serie B ______________________________________________________ 15

Figure 9: Attendance Lega Pro I; Figure 10: Attendance Lega Pro II ______________________ 15

Figure 11: Percentage of stadium filling _____________________________________________ 16

Figure 12: Top Italian teams UEFA ranking __________________________________________ 22

Figure 13: UEFA distributions _____________________________________________________ 23

Figure 14: Leagues brand values ___________________________________________________ 25

Figure 15: Top 5 second divisions economy __________________________________________ 27

Figure 16: Wacc ________________________________________________________________ 29

Figure 17: Players media value ____________________________________________________ 59

Figure 18: Attendance and revenues correlation _______________________________________ 70

Figure 19: Revenues prediction, expected and observed probality _________________________ 85

Equations index

Equation 1: Winning probabilites __________________________________________________ 17

Equation 2: Profit function ________________________________________________________ 18

Equation 3: Discount rate _________________________________________________________ 35

Equation 4: Wacc drivers _________________________________________________________ 35

Equation 5: Markham formula _____________________________________________________ 46

Equation 6: General value formula _________________________________________________ 55

Equation 7: Fans method formula __________________________________________________ 55

Equation 8: Serie A club Revenues 1 ________________________________________________ 83

Equation 9: Serie A club revenues 2 ________________________________________________ 84

Equation 10: brand prediction linear formula _________________________________________ 91

Equation 11: Revenues prediction linear formula ______________________________________ 92

  117  

Equation 12: Top clubs multiplier 1 ________________________________________________ 96

Equation 13: Top clubs multiplier 2 ________________________________________________ 97

Equation 14: Top clubs value formula _______________________________________________ 97

Equation 15: Serie A clubs revenues 3 _____________________________________________ 103

Equation 16: Serie A revenues 4 __________________________________________________ 104