May 2006

The Determinants of TV audience for Spanish Football: A First Approach

Jaume García Department of Economics and Business

Universitat Pompeu Fabra

Plácido Rodríguez Department of Economics

Universidad de Oviedo

We are very grateful to Liga Nacional de Fútbol Profesional for providing us with the data used in this paper. Financial support from the grant SEJ2005-08783-C04-01 is gratefully acknowledged.

Abstract There are still not many papers analysing the determinants of the size of the TV audience for professional sports. In this paper, using match data for three seasons of the Spanish First Division Football League, we offer some evidence on this topic by estimating two equations related to broadcasting football matches in Spain: a broadcaster’s choice of the match equation and a size of audience equation. We control for the main determinants of demand in professional sports: the ex-ante attractiveness of the match and the recent performance of the teams (including outcome uncertainty). We find that ex-ante attractiveness of the match is the main determinant of both broadcaster’s choice and the size of the audience, whereas outcome uncertainty does not seem to matter on the choice the broadcaster makes. We also find some seasonality in the evolution of the size of the audience within the football season.

1

Introduction

In recent years the number of televised football matches has significantly increased

in Europe and Spanish football is not an exception. The public channels (national

and regional) broadcast one game per week, but a private channel by subscription

also broadcasts one game per week and in recent years the pay-per-view system

also offers the possibility of watching any First Division football game in Spain1. This

fact has had very important effects on the financial structure of the Spanish football

clubs (García and Rodríguez, 2003) but intervention by national or European

government can also have a significant impact on competition and governance of

professional sports. Cave and Crandall (2001) and Hoehn and Lancefield (2003)

analyse this issue by comparing the evidence from the United States and Europe.

In this paper we focus our attention on the analysis of the determinants of the size

of the audience for Spanish First Division football games. We follow the same

approach as Forrest et al (2005) when analysing the same issue for the English

Premier League. We estimate two models: one for the broadcaster’s choice of the

match to be televised and another one for the size of the audience, distinguishing

between public and private broadcasters. We use explanatory variables which try to

capture the demand determinants for professional sports: ex-ante attractiveness of

the match, recent performance (including outcome uncertainty), variables capturing

the television appearances of the teams and some time variables, capturing the

long run trend and some seasonal (within season) effects. We try to offer some

evidence to show how important these groups of variables are in explaining the

choices of the broadcasters and the size of the audience. In this sense, this study is

complementary to a previous one analysing the determinants of live attendance in

the Spanish football (García and Rodríguez, 2002).

The paper is organized as follows. In Section 2 we report on some evidence

showing how broadcasting affects the financial structure of the clubs and the

competitive balance of the league and also showing the evolution and distribution

between clubs of the size of the audience. In Section 3 we review the demand

literature for professional sports with special attention to the effect of televising

matches on live attendance and to the modelling of the size of the audience. The

empirical specification and the definition of the variables are discussed in Section 4,

and we report the empirical results in Section 5. The paper ends with a summary of

the main conclusions.

1 This increasing offer of televised football is complemented by an also increasing offer of Second Division football matches.

2

Broadcasting and football in Spain

Revenue generated by selling the TV rights of football clubs has played a crucial

role in the recent history of the financial situation of Spanish football clubs2. Back in

the 1986-87 season the Spanish football clubs’ association (LFP, Liga Nacional de

Fútbol Profesional) negotiated the contract for broadcasting First Division Spanish

Football League matches. Consequently, there was only one supplier (LFP) but also

only one potential purchaser: the Spanish public television company (TVE). With

the introduction of public regional television channels which were organized under

the aegis of FORTA (Federación de Organismos de Radio y Televisión Autonómicos),

TVE and FORTA shared the rights by the end of the eighties. At that time, total

football clubs TV revenue amounted to less than €7 million.

The appearance of a private television channel by subscription (Canal +) had a

substantial effect on the value of football TV rights. FORTA and Canal + paid around

€324 million for the TV rights corresponding to eight seasons (from 1990-91 until

1997-98). TV revenue jumped from €6.7 million in the 1989-90 season to €30.5

million in the next season, increasing at an average rate of 19% until the 1995-96

season, in which TV revenue was €72.7 million. As shown in Table 1, in that season

revenue from single and season tickets represented more than 46% of total

revenue, a percentage which had been bigger at the end of the eighties and the

beginning of the nineties, and TV revenue was almost 20% of total revenue. This is

a period where the SSSL (Spectators – Subsidies – Sponsors – Local) model by

Andreff and Staudohar (2000) seems to fit adequately.

The 1995-96 season is the starting point of a new era in Spanish football as a

consequence of the war between TV operators, although the contract signed by LFP

was valid until the end of the 1997-98 season. These private broadcasting

companies started to negotiate with individual clubs not with LFP. TV revenue

increased significantly with the new contracts. As shown in Table 1, in the 2004-05

season TV revenue was almost six times that of the 1995-96 season, representing

one third of the total revenue, more than the percentage corresponding to single

and season tickets. The model followed by the Spanish Football League since 1995-

96 fits better into the MCMMG (Media – Corporations – Merchandising – Marketing)

model considered by Andreff and Staudohar (2000).

2 See García and Rodríguez (2003) and García and Rodríguez (2006) for an exposition of the main features of the recent evolution of professional football clubs in Spain.

3

Table 1: Revenue of Spanish First Division football teams

1995-96 2004-05

Total revenue 368.8 1039.0

TV revenue 72.7 345.6

% TV revenue 19.7 33.3

% Gate revenue 17.0 9.6

% Season tickets revenue 29.2 21.6

Ratio top/bottom teams’ TV revenue 9.6 13.7

% Barcelona and Real Madrid in TV revenue 23.6 39.3

% Four top teams in TV revenue 42.3 53.3

On the other hand, in the 2004-05 season, compared to the 1997-98 season, there

is a higher level of concentration of TV revenue in the Spanish First Division, the

differences in TV revenue between top and bottom clubs being more significant.

Barcelona and Real Madrid share almost 40% of the total TV revenue and more

than 53% if we also include Deportivo and Valencia. Notice that TV revenue for the

top club is almost 14 times that of the team receiving the smallest payment for TV

rights.

Of course, one of the reasons for this increase in TV rights is the large number of

viewers of televised football. Table 2 shows some figures for the size of audience

for Spanish football in both public and private channels. Figures are substantially

different when we compare FORTA audience (public channels) with the Canal +

audience because the last one is a private subscription channel. The difference is

not very great when we compare the size of FORTA audience with that of Antena 3

(a free private channel), which during two seasons was broadcasting games on

Mondays, whereas FORTA did so on Saturdays. Additionally, we can observe a

significant decline in the size of the audience during the last years, probably as a

consequence of the appearance of the pay-per-view offer, which has had some

effect on the way the channels choose the matches to be televised, but also as a

consequence of the increasing offer of other types of entertainment by the digital

platforms.

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Table 2: Audience by television channel (thousands)

1994-95 1997-98 1999-00 2001-02

FORTA

Total audience 212,344 199,466 177,322 154,931 Average audience 5,588 5,249 4,666 4,077 Canal +

Total audience 25,004 - 24,167 18,207 Average audience 658 - 636 479 Antena 3

Total audience - 125,207 - - Average audience - 5,008 - -

The size of the audience exhibits a high degree of variability due to the differing

attractiveness of the matches. Figure 1 shows the size of the audience for all the

televised games during the three incomplete seasons of our sample (2000-01 to

2002-03). The peaks correspond to the games played by Barcelona against Real

Madrid, the two Spanish teams with the largest number of fans in Spain. This

differing attractiveness of the football clubs is also shown in Figure 2 and Figure 3,

where the average size of the audience is not uniformly distributed among clubs

and for some neither home nor away matches are televised. This is the case of

Numancia and Oviedo for FORTA and Recreativo for Canal + during the seasons in

our sample.

5

Figure 1: Audience of televised matches (thousands)

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Figure 2: FORTA audience by team (thousands)

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Figure 3: Canal + audience by team (thousands)

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6

Modelling television demand for football

Demand studies in professional sports have mainly analysed the determinants of

attendance and, in particular, the impact of economic variables (ticket prices and

income), the quality of the teams and the uncertainty of the outcome, as the most

important variables. When using match-day data less attention has been devoted to

the effect of a match being televised on attendance3. In the case of football there is

some mixed evidence to show whether there is a significant impact of broadcasting

on attendance4. For the English Premier League Kuypers (1996) found no significant

effect whereas Baimbridge et al (1996) found a negative effect only for the matches

scheduled on Mondays. In some recent papers Forrest et al (2004) and Buraimo

and Simmons (2006) also found a negative effect of broadcasting on attendance

but, as Baimbridge et al (1996), Forrest et al (2004) conclude that revenue from

broadcasting more than compensates for loss of gate revenue for Premier League

teams. Czarnitzki and Stadtmann (2002) estimated a positive effect when

modelling attendance in the German premier football league, whereas no significant

effect is found by Falter and Pérignon (2000) for the French First Division. Finally,

negative effects are also found by García and Rodríguez (2002) for the Spanish

football league, with the effect differing depending on whether the match is

televised by a public channel or a private one by subscription.

In a recent paper, Forrest et al (2005) pointed out the advantages of modelling

television audience instead of live attendance when trying to analyse the

determinants of the demand for football, in particular, the effect of outcome

uncertainty. First, when studying attendance, the data usually refers to both season

ticket holders who attend the match and those purchasing a ticket for a single

match5, being required the purchase of season tickets not depending on the

characteristics of the matches in order to estimate consistently the effects of the

determinants of attendance. Second, apart from the capacity constraint issue, it

could be the case of observing more than true demand for those matches without

capacity constraints because of the way people can guarantee attendance for the

attractive matches. Third, since live attendance reflects attendance of home fans, it

3 The effect of broadcasting on attendance is usually captured by including a dummy variable reflecting whether a match is televised or not. No attention is paid to the potential endogeneity of this variable. An exception is the paper by Putsis and Sen (2000) where the dummy of whether there is a black-out or not is instrumented. 4 Mixed results are also obtained when analysing attendance for American team sports. For instance, Kaempfer and Pacey (1986) obtained that broadcasting improves attendance for college football. 5 García and Rodríguez (2002) is an exception since they estimate a model for attendance of purchasers of tickets for a single match.

7

is difficult to distinguish between outcome uncertainty and home success. Forrest et

al (2005) claim that the study of television audiences does not face these problems

since there is no division between season and non-season ticket holders, there is no

binding capacity constraint and there is no home team in terms of the viewer. They

estimate a model of the determinants of audience figures including among them

those which are usual in the attendance literature, paying special attention to the

effect of outcome uncertainty on attendance by using a new measure of the

closeness of the match which takes into account the effect of home advantage.

They conclude that, although outcome uncertainty has a significant effect on

audience, this effect is quite limited in terms of increasing the incomes of the clubs.

They also estimate a model for the broadcaster’s choice of the match where

outcome uncertainty also plays a significant role.

There are not many papers in the economics of sport literature which deal with the

estimation of a model to explain the determinants of audiences. Kuypers (1996)

estimates a model where the dependent variable is the proportion of Sky Sports

subscribers watching a football match (rating). He found that quality variables and

outcome uncertainty had a significant effect and, in general, team specific factors

are less important. For American professional sports we find some evidence based

on the use of Nielsen rating as the measure of the audience variable6. Hausman

and Leonard (1997) analyse the effect of certain players in the television ratings for

NBA games in order to estimate the value of a superstar for the other NBA teams.

They do not use a very complete specification of the audience equation in the sense

that variables capturing the uncertainty of the outcome are not included. This

limitation is also present in the paper by Kanazawa and Funk (2001) where, also for

the NBA, they try to estimate whether the race of the players has a significant

effect on audience, finding a positive effect for the presence of white players. In a

similar application to the NFL, Carney and Fenn (2004) emphasize the potential use

of audience estimated equations to forecast the number of viewers of a televised

match7.

6 The Nielsen rating is the proportion of households with televisions in a given ratings area which are actually watching a particular match. 7 There is an increasing literature devoted to identifying models to explain and predict television usage. See, for instance, Weber (2002)

8

Empirical specification: data and variables

In this paper we follow Forrest et al (2005) by modelling both the determinants of

the broadcaster’s choice of a match and of the audience for the matches of the

Spanish First Division Football League, noting that, as we mentioned above, the

Saturday games are televised by FORTA (national and regional public channels) and

Sunday games by Canal + (a private subscription channel). Consequently, we will

be estimating both equations for each of the broadcasters. The sample is composed

of those matches played (and televised in the case of the audience equations)

during the seasons 2000-01, 2001-02 and the first part of the 2002-03 season.

We model each broadcaster’s (FORTA and Canal +) choice by means of a Probit

model, including three potential types of explanatory variables. The precise

definitions of the variables are presented in Table 3. The first group includes those

variables which capture the ex-ante attractiveness of the match, i.e. those factors

which do not change through the season and are known in advance by both the

broadcasters and the potential viewers. Similarly to Forrest et al (2005) we include

a variable which is proxies the aggregate quality of both teams by means of the

predicted total spending of each club relative to the average value for all clubs

(sum of relative spending)8. By definition the mean of this value is 2, but the range

of variation goes from 0.45 to 7.65, a wider range than in the case of the English

Premier League. We also include a variable which is captures the ex-ante closeness

of the teams based on the absolute value of the difference of the relative spending

(difference in relative spending). We also control for the attractiveness of the match

in terms of the rivalry of the teams by means of a dummy variable for those

regional or historical derbies (derby). The last variable in this group of ex-ante

variables controls the potential size of the market in terms of the population in the

provinces of both teams (population in both teams’ provinces)9.

The second group of variables are those match-specific in the sense of reflecting

the recent performance of the teams. We use the same definition of the variable for

outcome uncertainty as in Forrest et al (2005). This definition takes into account

the home advantage to be added to the difference between the average points per

match of the home and the away teams. The definition of home advantage refers to

the difference between the average points per match for all home teams and all

8 Forrest et al (2005) use the wage bill to generate this quality variable. The definition of this variable as a ratio controls the fact that the variables are in nominal terms. 9 See Buraimo and Simmons (2006) for an attendance model using match-day data where the market size is controlled for by using Geographical Information System techniques.

9

away teams in the previous season. This difference is 0.76, 0.93 and 0.73 for the

three seasons of our sample, respectively. The league positions of both teams are

included separately as a measure of the recent performance of both teams.

Alternatively, we include a set of dummies indicating the situation of both teams in

terms of four groups of team-positions. To some extent this set of dummies also

captures some of the effect of outcome uncertainty (more uncertainty when both

teams are in the same group) and some of the effect of the league position

variables.

The third group of variables are what we call television variables because they

capture either a summary of the presence of the teams of a particular match in the

games televised previously during the season or the implications on the schedule of

a European competition match the following week. These variables proxy the virtual

and actual constraints faced by the broadcasters when choosing the matches to be

televised. In the first subgroup we include a dummy indicating whether one of the

teams were playing in the last televised game by the corresponding channel, the

number of games of the home team televised when playing at home and the

number of weeks since home team’s last televised match. The fact that there is a

European competition match the following week is relevant in terms of

broadcaster’s choice since usually teams are allowed to play on Saturday not on

Sunday when facing a European game the following week. This is captured by

means of a dummy variable indicating whether there are European competition

matches the following week.

In each audience equation the dependent variable is the log of the number of

viewers of a match. We use the same first two groups of variables (ex-ante

attractiveness of the match and recent performance of the teams) as in the

broadcaster’s choice model including also a dummy for either Barcelona or Real

Madrid being one of the teams in the first group. We also include a second order

polynomial for the number of the game to capture the behaviour of the audience

during the season10, a dummy for matches televised in mid-week and also dummy

variables to control for the seasonal effects. We do not include any variable of the

television variables group in the audience equations.

10 A similar effect can be captured by including monthly dummies as in Forrest et al (2005).

10

Table 3: Variable definitions

Ex-ante attractiveness of the match Sum of relative spending The sum of the relative spending for both teams. Relative spending of a team in a season is the total spending of the team divided by the mean total spending in that season. Difference in relative spending Absolute value of the difference of the relative spending for the two teams in a match Derby Dummy variable equal to one if either both teams are from the same region or the teams are Barcelona and Real Madrid Population of both teams’ provinces The sum of the population of each team’s provinces (logs) Barcelona or Real Madrid Dummy equal to one if one the teams is either Barcelona or Real Madrid. Recent performance of the teams Outcome uncertainty Home advantage plus points per game to date of the home team minus points per game to date of the away team. Home advantage is the difference of the average points per game in the previous season of the home teams and that of the away teams. Home team’s (away team’s) league position Dummy variables reflecting both teams’ league positions All the interactions between four dummies for the home team and four dummies for the away team corresponding to the following group positions: champion, top two positions; European, positions 3-7; mid-table, positions 8-14; relegation, positions 15-20. The omitted dummy corresponds to relegation vs relegation. Television variables One of the teams televised in the previous week Dummy variable equal to one if one of the teams was playing in the last match televised by the corresponding channel. Televised games for the home team at home Number of televised games for the home team at home to date Weeks since home team’s last televised match Number of weeks since home team’s last televised match to date. Champions League following week Dummy variable equal to one if one of the teams is playing a Champions League game the following week Other variables Game (game squared) Second order polynomial for the number of the game Mid-week Dummy variable equal to one if the match is played in mid-week Season 2001-02 (Season 2002-03) Season dummies. The omitted dummy corresponds to the 2000-01 season.

11

12

Results

Broadcaster’s choice of the matches

As we mentioned in Section 2, over the sample period FORTA (TVE and the regional

channels) broadcasted live football matches on Saturdays, whereas the private

channel (by subscription) Canal + did so on Sundays. We analyse the determinants

of the choices made by the broadcasters by estimating a Probit model for each of

the broadcasters, where the explanatory variables correspond to the group of

variables: ex-ante attractiveness of the match, recent performance of the teams

and television variables, presented in the previous section. The estimation results

are reported in Table 4.

When comparing the results for both broadcasters we find substantial differences in

the effects of the explanatory variables. In both models the variable which captures

teams’ joint quality (sum of relative spending) has a significant positive effect on

the probability of a match being chosen by the broadcaster, the effect being

stronger for the FORTA model. Increasing the variable by one makes the probability

of choosing a match by more than 56% in the case of FORTA and by more than

30% in the case of Canal +. The effect does not change very much depending on

the way we specify the recent performance of the teams. On the other hand, when

looking at the other estimated coefficients for the variables in this group we can

observe some important differences. The condition of a match being a derby has a

positive significant effect on the probability of broadcasting a match in the case of

FORTA but, although positive, the effect is not significant for Canal +. The same

type of result is obtained for the estimated coefficient of the variable capturing the

ex-ante quality closeness of the teams (difference in relative spending), which has,

as expected, a negative effect on the probability of broadcasting a match in FORTA,

but the effect, although negative, is almost negligible in the case of Canal +.

Finally, the variable trying to capture the effect of the market size has a significant

positive effect on Canal + choices, but not in the case of FORTA. This can be

explained by the different economic objective functions which are faced by both

broadcasters. In the case of Canal +, if they try to maximize profits then the

number of the potential viewers, since they are fans of one the teams playing the

match, should matter when choosing the match, whereas in the case of FORTA, as

an association of the regional public channels, the choices are made in order to

satisfy the different preferences of the different channels in the group. Geographical

diversity matters more than the size of the potential market.

Table 4: Estimation results for the broadcaster’s choice of game model

FORTA (Probit) Canal + (Probit) Multinomial Logit (1) (2) (1) (2) FORTA Canal +

Ex-ante attractiveness of the match

Sum of relative spending

0.420** 0.445** 0.244** 0.266** 0.908** 0.495**

Difference in relative spending -0.290** -0.325** -0.032 -0.069 0.625** -0.210

Derby 0.452** 0.446** 0.265 0.982**0.223 0.591

Population in both teams’ provinces 0.053 0.051 0.184** 0.203** 0.171 0.416**

Recent performance of the teams

Outcome uncertainty -0.082 -0.161 0.033 -0.071 -0.116 0.320

Home team’s league position -0.044** -0.037** -0.099** -0.073**

Away team’s league position -0.035** -0.028* -0.075** -0.086**

champion vs champion 1.082** 0.382

champion vs European 1.173** 1.143**

champion vs mid-table 1.143** 1.167**

champion vs relegation 1.281** 1.324**

European vs champion 1.018** 1.255**

European vs European 1.229** 1.087**

European vs mid-table 0.855* 0.996**

European vs relegation 1.008** 0.836*

mid-table vs champion 0.917** 0.975**

13

14

FORTA (Probit) Canal + (Probit) Multinomial Logit (1) (2) (1) (2) FORTA Canal +

mid-table vs European 1.054** 0.903**

mid-table vs mid-table

0.742* 0.515

mid-table vs relegation -0.121 0.482

relegation vs champion 1.137** 0.981**

relegation vs European 0.474 0.580

relegation vs mid-table -0.036 -0.030

Television variables

One of the teams televised in previous week -0.631** -0.664** -0.232 -0.323* -1.236** -0.252

Televised games for the home team at home -0.093 -0.104 -0.025 -0.076 -0.196* -0.105

Weeks since home team’s last televised match 0.016* 0.013 -0.007 -0.016 0.033* -0.012

Champions League week 0.333** 0.295* 0.247 -2.387**

Constant -1.364 -2.739** -3.221** -4.621** -3.000 -6.493**

Log-likelihood -270.34 -263.96 -250.58 -245.22 -528.94

Pseudo R2 0.185 0.204 0.162 0.179 0.178

Sample size 1020 1020 860 860 1020

Results for the effects corresponding to the performance of the teams during the

season are quite similar to those obtained by Forrest et al (2005) in the sense that

the current strength of the teams matters when the broadcaster makes the choice

of the match to be screened. The league position of both the home and the away

teams has a negative significant effect on the probability of choosing a match11, the

effects being slightly stronger for FORTA than Canal + choices. When using the

interactions between the dummies of the four groups of team positions in the

League we find that when one of the contenders is in one of the top two positions

the probability of choosing the match is significantly higher than in the other cases,

whereas those matches involving mid-table and relegation teams have smaller

probabilities. This specification in terms of the interactions seems to perform better

than the specification with league positions when looking at the pseuso-R2, but if

we use the Akaike Information Criterion based on the value of the (log) likelihood

function and the number of parameters to be estimated then the model with the

league positions seems better. Finally, in contrast to the results in Forrest et al

(2005) the uncertainty of outcome variable does not have a significant effect in

none of both models, although it has the correct sign in the FORTA models.

The television variables try to capture the effect of the presence of teams in a

particular match in previous televised games. The participation of one of the teams

in the last match televised by the broadcaster has a significant negative effect on

the probability of the match being televised in the case of FORTA, with almost a

50% decrease in this probability. This not the case for Canal + where, although the

coefficients have the expected sign, the corresponding coefficients are not

significant at a 5% level. Finally, the dummy capturing whether there is a

Champions League game in the following week has, as expected, a highly

significant effect on the probability of a match being chosen by FORTA. Teams

playing in Europe usually have the option of playing the league game on Saturday,

increasing the probability of the match being chosen by FORTA, not playing usually

on Sunday and even less likely in the late slot for Canal + games (9 p.m.). This is

why we have eliminated those matches from the choice set of Canal +, implying the

differences in the sample sizes in the models for both broadcasters.

To corroborate the results obtained by estimating the Probit models for both

channels, we have estimated a multinomial Logit model with three alternatives for

each match: televised by FORTA, televised by Canal + and not televised. The

results are shown in the last two columns of Table 4. Results are similar to those

11 Notice that the higher the number showing the league position the worse is this position.

15

for the separate models with the exception of the effect of the dummy for a

Champions League in the next week which has a negative and significant coefficient

for the Canal + alternative. This is so because those games played by teams

playing the Champions League are included in the estimation sample of the

multinomial Logit model.

We can conclude that the performance of the teams in the current season and the

ex-ante quality variables for both teams are the main determinants of the

probability of a match being televised, in particular for the FORTA choices. This is

also evident by looking at the figures in Table 5, where we show the changes in the

(log) likelihood value at the maximum when excluding one group of variables. In

fact, even when taking into account the degrees of freedom (a kind of Akaike

Information Criterion) for both FORTA and Canal + models, excluding the variables

in the ex-ante attractiveness of the match group represents the larger the change

of the (log) likelihood. On the other hand, the uncertainty of the outcome does not

seem to influence the choices made by the broadcasters. A channel does not seem

to decide what matches to televise in terms of the closeness of the match as

measured by the uncertainty of the outcome variable we used.

Table 5: Values of the (log) likelihood for different specifications

Coefficients FORTA (1) Canal + (1)

Base model -270.34 -250.58

Excluded group

Ex-ante attractiveness of the match 4 -291.36 -272.10

Recent performance of the teams 3 -281.98 -266.12

Television variables 4 -283.76 -257.30

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17

Audience

In Table 6 we show our estimation results by OLS to account for the variability in

the size of the audience12. The set of explanatory variables included in the different

specifications is composed of those variables in the groups: ex-ante attractiveness

of the match, recent performance of the teams and other variables. The models for

both channels show a significantly high explanatory power for the different

specifications, as indicated by the values of the adjusted R2, well above 0.56 as in

Forrest et al (2004) or in other American audience studies, such as Kanazawa and

Funk (2001), between 0.5 and 0.6, or Carney and Fenn (2004) (0.72).

The variable capturing the joint quality of both teams (sum of relative spending)

has a positive and significant effect on the size of audience, this effect being

stronger in the Canal + model. In this case there is a 59% increase in the audience

if the match is played by the teams with largest value of the aggregate relative

spending (7.65), Barcelona and Real Madrid, compared to a match with the average

value (2) for this variable. But the other variables capturing the ex-ante

attractiveness of the match have no significant effects, although in general they

have the expected sign13. The negligible effect of the variable controlling for local

rivalry is not surprising, given that it affects only a part of the population of

potential viewers (those living in the region). A similar explanation can be given for

the non-significant effect of the population variable. Additionally, we also included a

dummy measuring whether Barcelona or Real Madrid are among the teams playing

a match. As expected, its effect is highly significant. The presence of either

Barcelona or Real Madrid implies a 33% increase in the audience of FORTA

broadcasts and a 36% in the case of Canal +.

Outcome uncertainty has a significant effect on the audience of the public channels

(FORTA). Uncertainty increases the number of viewers of a match. The effect of this

variable is not significant for Canal +, although the coefficient is signed as

expected. The other variables of the recent performance of the teams (the league

position of both teams14 and the interaction dummies) have no significant effects.

12 Notice that the sample of televised matches is a selected sample and OLS estimates are potentially biased. We also estimated the audience equation by including the correction term (the inverse of Mills’ ratio) obtained from the corresponding Probit model for the broadcasters’ choice (Heckman, 1979). The coefficient of the correction term was not significant and in order to gain precision for the estimated coefficients we eliminated this correction term in the reported estimates. 13 The coefficient of the difference in relative spending variable is significant at a 10% significance level. 14 The home team’s league position has a expected and significant negative effect on the size of audience for FORTA broadcasts.

Table 6: Estimation results for the (log) audience model FORTA Canal + (1) (2) (1) (2)

Ex-ante attractiveness of the match

Sum of relative spending 0.057** 0.061** 0.082** 0.122**

Difference in relative spending -0.016 -0.019 -0.046 -0.069*

Derby

0.027 0.014 0.095 0.058

Population in both teams’ provinces -0.001 0.006 0.020 0.009

Barcelona or Real Madrid 0.285** 0.270** 0.310** 0.272**

Recent performance of the teams

Outcome uncertainty -0.086** -0.100** -0.032 -0.087

Home team’s league position -0.010** 0.002

Away team’s league position 0.000 -0.003

champion vs champion 0.035 -0.064

champion vs European 0.150 -0.263

champion vs mid-table 0.108 -0.005

champion vs relegation 0.073 0.045

European vs champion 0.024 -0.024

European vs European 0.066 0.017

European vs mid-table -0.016 -0.034

European vs relegation 0.062 0.089

mid-table vs champion -0.055 -0.126

18

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FORTA Canal + (1) (2) (1) (2)

mid-table vs European -0.016 -0.068

mid-table vs mid-table

-0.064 -0.164

mid-table vs relegation -0.140 -0.065

relegation vs champion -0.033 0.028

relegation vs European -0.071 -0.062

relegation vs mid-table -0.107 -0.161

Other variables

Game 0.036** 0.036** 0.047** 0.043**

Game squared -0.001** -0.001* -0.001** -0.001**

Mid-week 0.037 -0.014 -0.028 -0.047

Season 2001-02 -0.015 -0.030 -0.130** -0.111**

Season 2002-03 0.033 0.008 0.357** 0.414**

Constant 7.907** 7.746** 5.477** 5.683**

Adjusted R2 0.816 0.805 0.810 0.842

Sample size 102 102 99 99

Forrest et al (2005) found that viewing is higher in the midwinter months, peaking

in January. Instead of monthly dummies, we use the number of the game (a

quadratic) which means fitting a kind of (weekly) trend. We also get an inverted U-

shape for the game profile for both broadcasters’ audiences, where the maximum is

around game 21 for the FORTA model and around game 18 for Canal +. These

games are usually in January or February. Although it has not been considered by

the football authorities, these results point out that a winter break in Spanish

league would have some negative effects on broadcasters given that in this period

the size of the audience is larger, probably as a consequence of a lower opportunity

cost of staying at home watching television. On the other hand, the mid-week

broadcasts do not have audiences significantly different from those in weekend

broadcasts. This mid-week effect is not the same as that (negative) found for

attendance in the Spanish football league (García and Rodríguez, 2002). Finally, we

include dummies to control potential seasonal effects. The coefficients are not

significant in the FORTA model but they are in the Canal + model, capturing the

pattern of audience evolution for Canal + broadcasts in the three seasons we

mentioned in Section 2.

As with the broadcasters’ choice model, we proceed to evaluate the importance of

each group of variables by comparing the values of the F statistics corresponding

to the null hypothesis of all the coefficients of the variables of a particular group

being equal to zero. From Table 7, we conclude that in both cases the ex-ante

attractiveness of the match is the most important explanatory factor. This can be

said for the Canal + model since most of the high value of the F statistic for the

group of other variables is due to the seasonal variability we mentioned in the

descriptive analysis15.

Table 7: F statistics for the null hypothesis of non significance of a group of variables

Excluded group Coefficients FORTA (1) Canal + (1)

Ex-ante attractiveness of the match 5 66.11 26.19

Recent performance of the teams 3 4.00 1.27

Other variables 5 13.02 31.71

15 Without the season dummies in the null hypothesis the F statistics are 20.91 and 15.99 for the FORTA and the Canal + models, respectively.

20

Conclusions

In this paper we have analysed the determinants of the demand for professional

football in Spain associated with broadcasting matches. We modelled both the

broadcaster’s choice of a game to be televised and the size of the audience for that

game.

Using data for three seasons (2000-01 to 2002-03) we made a distinction between

public and private broadcasters, since the objective functions they have are

probably not the same. Empirical results show that there are some different

patterns for both broadcasters. The ex-ante attractiveness of the match, proxyed

by the relative spending of the teams and the potential rivalry between the two

clubs playing a match, is the main determinant of both the broadcaster’s choice and

the size of the audience in both cases. On the other hand, outcome uncertainty

does not seem to matter on the choice the broadcaster makes, a result which

differs from that found by Forrest et al (2005) for the English Premier League using

a similar approach. Finally, the size of the market of potential viewers seems to be

relevant for the choices made by Canal +, a private subscription channel, whereas

there is a strong seasonal component within season for the size of the audience.

This last result is almost equivalent to that in Forrest et al (2005).

Since there are not many empirical papers analysing the determinants of the

audience for professional sports, there are some interesting extensions to the

results reported in this paper. In this sense, the use of the available audience data

at a regional level will allow us to identify regional effects associated with a club’s

proximity. The selectivity issues could be relevant in this type of analysis given that

the size of audience equations are estimated using selected samples (chosen

games). Additionally, it would be worth comparing the determinants of different

types of demand (audience, live attendance, season ticket holders’ attendance).

Finally, the estimation of the Probit models for the broadcaster’s choice could also

help to estimate consistently the effect of a match being televised on live

attendance by giving us the possibility of defining an instrument from the discrete

choice model.

21

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