1. A Quantitative Analysis of the Impact of
Competitiveness on Match Attendances
ResearchQuestion:
Have attendances at Scottish Premier League (SPL), English Premier League (EPL) and La
Liga football games been impacted by the competitiveness of the competitions over the
period 2003-2013?
3. Abstract:
The purpose of this study is to investigate whether attendances in SPL, EPL and La Liga are
effected by the competitiveness of the leagues. The study employs a Herfindahl-Hirschman
index to provide the competitiveness of the leagues. The analysis also includes various
econometric regressions used to estimate the effects competitiveness has on attendances,
while also controlling for other independent variables.
Keywords: Competitive Balance, Attendances
2. 1. Introduction
The following study will attempt to investigate if SPL, EPL, and La Liga football attendances
are effected by the competitiveness of the competitions from 2003 – 2013. The rationale for
conducting this research is to demonstrate the relationship between league competitiveness
and match attendances to investors and sponsors of the leagues and clubs. As football is a
multi-millionaire pound business, investor and sponsors should be interested in effects
competitiveness may have on attendances. From the reading of literature, the proposed
research is been taken quite seriously in sports institutions in many countries around the
world. For example this subject is constantly under review in North America where they
believe that competitions like Major League Baseball (MLB), National Basketball
Associations (NBA) etc. only reach equilibrium when all clubs are competitive and
monopolies are limited.
The proposed research will in effect form an extension of such existing research. The SPL,
EPL and La Liga are their countries only professional level of club football, and I believe
there is significant commercial value in researching matters that impact on aggregate match
attendances, whether positively or negatively.
The research will be investigated by measuring the leagues competitiveness with various
economic formulas which will be mentioned in the literature review. Published season
rankings from the seasons 2003-2013 will provide the data required to measure the
competitiveness of the league. Season by season average match attendances per club and
country is available at www.european-football-statistics.co.uk. Other independent variables
such as GDP per capita, ticket prices and UEFA coefficients will be included. The sources for
all variable information will be included in the Data section of this work.
3. The dependent and independent variables will be analysed using different econometric
models. Various models will be used until the model of ‘best fit’ is found. Factors such as
multicollinearity and heteroscedasticity will also be taken into account to provide the model
with more accuracy.
The dissertation will be structured as follows:
Section two will look in detail at the literature and previous research done related to my
chosen topic. This section will be divided into sub-sections according to suitable themes
relating to my research. Section three will look at the economic theory and concepts that I am
using in my research. These concepts and theories will be from modules and literature that
I’ve studied to date. Section four will provide full sources of the used data. This section will
also explain how the data was collected, what is not included in the data, if the chosen data is
reliable. Finally graphs and statistics of the data will be presented, explaining any patterns or
changes in the data they may have occurred over the time period. Section five will discuss the
method of analysis used for this piece of research. This will look specifically at the
econometric models used, given a brief history on each model and how they were applied to
this research. The final section will interpret the results and conclusions. These will entail
how my results may differ from other author’s results, what conclusions I can take out of this,
how the results differed to my expectations and how economic theory can be applied to my
results. This section will also give a brief reflection of being a researcher, looking at other
research questions that may have developed throughout this research that may be interesting
if I was to pursue further research in this area.
4. 2. Literature Review
The following sections details different literature that was studied related to this research
question. This section is divided into two sub-categories according to themes. The first theme
concentrates on the primary countries and areas in which competitive balance has been
researched, how they applied the concept to various sports and what may have affected the
balance of these sports. The second theme focuses on the different types of economic
formulas that were used in past literature relating to my chosen research topic.
2.1 Competitive Balance across Countries
The competitive balance concept is becoming highly popular in modern professional sports.
The primary reason this has become popular is because imbalanced leagues negatively affects
the demand for professional sports and, as a result, league revenues fall (Lee & Fort, 2005).
A large amount of sports economic literature in the past has focused on North American
professional sports and franchises, and part of this section shows how European economists
have applied the American research to their own domestic sports. The first part of this sub
section will look at the research that American sports economists have published on
competitive balance, while the second part of this sub section will look at how this theory has
been applied to European sports and leagues.
5. Large amounts of research on competitive balance in North America have been focused on
the MLB, NBA, NFL and NHL. All competitions employ a draft system, where the weaker
franchises have options to recruit the higher ranked college players and the stronger
franchises have options to recruit the lower ranked college players.
Due to the draft system, competitive balance is a major subject of research in North America
to see if the system is near equilibrium. A major issue in North America relates to the level of
attendances that professional sports franchises receive, Studies by Andrew Zimbalist
describes how gate revenues in the MLB were declining in 2002 and 2003 at a time when the
competition was experiencing a certain degree of competitive imbalance due to higher market
club dominance, with gate revenues declining more than 10% in these years. He described
this as an ‘Unregulated Legal Monopoly’ (Zimbalist, 2003). Competitive balance, or the
uncertainty of outcome in a league, should benefit the stronger franchises as much as the
smaller ones - if this is not the case fan interest will decline for both franchises (Humphreys,
2002). Studies by John Vrooman describe how to counteract higher market club dominance
in order to create a more competitively balanced market or league. His first method suggests
that increases in product market competition, which implies adding more teams to the
dominant leagues. This method may be difficult for the MLB to employ. The second method
he suggests involves the internalization of diseconomies of dominance in the large franchises
themselves. This involves higher costs placed on dominant teams (Vrooman, 2013). These
methods may be applicable to the chosen leagues if a lack of competitiveness is evident from
the results.
6. I have studied interesting research on how the geographical location of the league teams
would affect a team’s performance and therefore the competitiveness of the league. Research
has proven that many MLB teams win-percentage has increased when that particular team
geographically moved stadium or city (Lee & Fort, 2005). Other similar research was also
done mainly focused more specifically on stadium renovations of NFL teams and how
smaller cities became more powerful names in the NFL competition. For example, cities like
Nashville and Jacksonville, who built new stadia, believe they are stronger NFL cities due to
the positive economic returns that the stadium caused (Noll & Zimbalist, 1997).
7. Economic analysis of European sport was a largely under researched area until the 1990s
when satellite television began to broadcast a higher number of soccer games. In an article by
Stefan Szymanski, a description is given of why European soccer analysts needed to
implement the same policies as American ones. Due to the promotion and relegation system
which applies to European soccer leagues, competition is far more intense in European soccer
than American sports where promotion and relegation of franchises doesn’t exist. Alternative
competitions such as Champions League, where the top thirty-two European Clubs play
against each other, also increased the importance of greater research into competitive balance
into each of the European leagues. In particular, such research would help to see how the
comparative competitiveness of the various European leagues, which in turn would help
understand the domination of the Champions League by any particular country. (Syzmanski,
2008).
Research in the Southern Economic Journal again addressed the need for Competitive
balance to be further reviewed in English soccer. The authors outlined that the top clubs like
Chelsea, Arsenal, and Manchester United etc. have a larger amount of their games televised
than any other clubs, which implies that those clubs are earning a larger share of broadcast
revenues, this in turn increases their market size against smaller clubs and causes the league
to become more imbalanced (Buraimo, et al., 2007).
.
8. 2.2 Economic Formulas
In the study of relevant literature, the economic models that I encountered gave me useful
ideas on what type of regressions and formulas to use later in my research. This section will
explain in detail how the formulas measure my chosen variables: competitiveness and
attendances.
Competitive balance can be measured using various economic formulas such as the
Herfindahl-Hirschman Index (HHI) which is used to measure market share inequalities. The
indexes reflect the concentration of championships in a sports league over time in that they
reflect the distribution of the shares of championship. The diagram below shows an example
of one of these formulas (HHI) in use to estimate the competitive balance in a hypothetical
league.
Figure 1
Total points by every team are added together; each team’s total no. of points is divided by
the season’s total no. of points and then squared to show each team’s market share. Each
9. team’s market share is then added together to illustrate how competitive the hypothetical
league was in the given year. The closer the total of M.S^2 is to zero, the more competitive
the league is, while the closer it is to 1, the less competitive the league is.
The Standard Deviation Ratio formula is similar to HHI formula which was mentioned
previously in that the closer the ratio is to 1, the more competitive the league is. The formula
is as follows:
X1 = Win Percentage, X = Average Wins, while N = No. of teams. The fault with this formula
is that one country can’t be compared against another country in terms of competiveness if
those countries use different point systems (Goossens, 2006). In other research this formula
was applied to assess the effects competitive balance had on institutional changes in sports
leagues i.e. competitive balance was the controlled variable (H.Richardson, 2000).
In studies by Brad Humphreys, he uses relevant formulas that may be useful in my
investigation. The CBR which was mentioned earlier in this paper compares the average time
variation in win-loss percentage for teams in the league with the average variation in win-loss
percentages across seasons (Humphreys, 2002). The formula is as follows:
𝐶𝐵𝑅 =
𝜎 𝑇
𝜎 𝑁
10. T = Average time variation in won-loss %, N = Average variation in won-loss % across
seasons. The results of the Standard Deviation Ratio formula can complement the CBR
formula.
3. Economic Theory and Concepts
The economic framework behind this research focuses on competitive balance and incentives.
Both concepts scrutinize the two variables, competitiveness and attendance. Competitive
balance will be the primary concept behind analysis of the competitiveness of the SPL, EPL
and La Liga competitions, while incentives will be the primary concept behind the analysis of
the attendance at games i.e. investigating if supporters’ incentives change if the
competitiveness of the league changes, either positively or negatively.
3.1 Competitive Balance
Competitive balance is a sports economic concept which is an important determinant of
demand in any sporting events. Competitive balance focuses on the uncertainty of the
outcome of sporting occasions.
The uncertainty regarding the outcome is believed to be a primary factor in attendance at
sport events and also the incentive to tune into a broadcast (Humphreys, 2002). The origins of
competitive balance were developed in the mid-sixties and were called the ‘League Standing
Effects’ (Neale, 1964). Neale emphasized that if a league lacks competitive balance, fan
interest among the weaker clubs will decline followed by fan interest of stronger clubs. It is
believed in a perfectly balanced contest, each party starts with an equal chance of winning,
and therefore the outcome will be completely uncertain. Without a certain degree of
competitive balance, supporter’s interest will reduce along with match attendances
(Szymanski, 2001).
11. 3.2 Incentives
Incentives are one of the key principles in economic theory. When incentives change,
people’s behaviour changes. With regards to this research, the theory of incentives provides
us with a lens to view the relationship between the attendances of supporters and the
competitiveness of the league. The research will investigate whether fans are incentivised, or
not, to attend SPL, EPL or La Liga games if the leagues competitive balance changes over
time. Designing good incentives for economic agents is an important aspect of economics
today (Martimort D., Laffont J.J, 2001).
Competitive balance alone will not decide whether attendances have been positively or
negatively affected. As stated earlier there will be a number of different independent
variables included in this research. These variables should also give us a better understanding
of incentive changes towards supporters of these leagues. For example, if GDP per capita
increases we expect that attendances will increase as supporters can afford to do so.
12. 4. Data and Data analysis
This section will provide the full data sources for all data used in the research, it will also
explain how the data was collected, what is not included in the data, if the chosen data is
reliable. Finally graphs and statistics of the data will be presented, explaining any patterns or
changes in the data they may have occurred over the time period. Before providing the full
sources of data, the variables being used will be identified and discussed first.
4.1 Competitive Balance
The competitive balance or competitiveness for all leagues was calculated using the
Herfindahl-Hirschman index (HHI) which was discussed earlier. SPL, EPL and La Liga final
year standings from 2003-2013 were taken from a football website and then the HHI was
calculated. The graphs below give an indication of how the three leagues performed during
the proposed time frame:
Figure 2
As you can see from the first graph (SPL), the HHI is positioned at a higher base than the
second graph (EPL & La Liga) therefore the SPL competition is operating closer to 1,
meaning the competition is suffering from competitive imbalance, but in recent years the
competition is showing some signs of improvement.
13. However, the EPL and La Liga competitions are showing little fluctuations, meaning that the
competitions are remaining competitively balanced.
4.2 Ticket Prices
As ticket prices vary from game to game, average ticket price per game for each season is
difficult to measure for, therefore the highest ticket prices of 4 clubs from each league was
taken, averaged and deflated by each year’s inflation rate assigned to each country. Price
tickets from two clubs in the top half and second half of the leagues were taken to give a
more accurate spread of ticket prices. The example below shows how it was conducted:
Figure 3
After this was conducted, the prices were deflated by each year’s inflation rate assigned to
each country, as stated earlier. The example on the next page shows the deflation method in
use for the SPL competition:
SPL EPL LaLiga
Celtic 26 Chelsea 87 Barcelona 196
Dundee Utd 21 Liverpool 59 Sevilla 112
Kilmarnock 22 Stoke City 50 Celta Vigo 275
St.Mirren 22 Leicester City 48 Cordoba 179
£22.75 £61 € 190.50
€ 30.94 € 82.94
14. Figure 4
This method of allocating ticket prices is the main limitation in this research.
4.3 UEFA Coefficients
The UEFA club coefficient rankings are based on the match results of all European clubs in
UEFA club competitions. The club coefficient rankings take into account the results of each
club in UEFA club competitions and are used to determine a club’s seeding in club
competition draws (UEFA, 2015). The coefficients are changed after every UEFA match. We
expect that the position of the leagues on the UEFA ranking table should impact match
attendances.
4.4 GDP Per Capita
GDP per capita statistics were obtained from the ‘World bank’ website and was included
because as income increases we expect games to become more affordable for supporters. If
income decreases we expect a contrary result. Both SPL and EPL’s GDP per capita is the
same in this research as they are members of the United Kingdom.
2014 1.50% 1 101.50% € 30.94
2013 1.70% 1 101.70% € 30.48
2012 1.70% 1 101.70% € 29.97
2011 2.10% 1 102.10% € 29.47
2010 3.20% 1 103.20% € 28.87
2009 2% 1 102.00% € 27.97
2008 2.90% 1 102.90% € 27.42
2007 2.90% 1 102.90% € 26.65
2006 2.70% 1 102.70% € 25.90
2005 2.80% 1 102.80% € 25.22
2004 2.90% 1 102.90% € 24.53
2003 2.20% 1 102.20% € 23.84
15. 4.5 Attendance
Average attendance per game was collected for each season for all leagues. The data source
was recommend by (Syzmanski, 2008) and provided the relevant attendance data. The
Graphs below show the patterns in attendances for the three leagues in the purposed time
frame:
Figure 5
As you can see from the graphs above, attendances in the SPL competition is declining quite
considerably towards the end of the time period. A preliminary reason for this may be that the
HHI is operating at a relatively high base point as we seen earlier. EPL and La Liga
attendances have also been fluctuating over time but not to the worrying extent of the SPL
competition.
16. 4.6 Full Data Sources
The following table provides all data sources to all the variables which were identified and
discussed earlier:
Figure 6
Competitive Balance (HHI) Statto.com
Ticket Prices Club Websites*
Inflation Rates World Bank.org
UEFA Coefficients UEFA.com
GDP Per Capita World Bank.org
Attendances European-football-statistics.co.uk
4.7 Reliability
Statto.com:
The website was launched in 1998 and is a major internet resource for football statisticians
and betting enthusiasts (Statto, 2015). The website contains many quantities of information
regarding European and international leagues around the world.
Club Websites*:
Figure 7
Celtic Chelsea Barcelona
Dundee Utd Liverpool Sevilla
Kilmarnock Stoke City Celta Vigo
St.Mirren Leicester City Cordoba
17. All selected prices were found from the list of clubs above. These were the original websites
of the clubs.
World Bank.org:
The World Bank is a vital source of financial and technical assistance to developing countries
around the world (WorldBank, 2015). As the bank provides financial and technical support
around the world, their website provides statistics on each country they support. Fortunately
providing the necessary statistics to help this research.
UEFA.com:
UEFA is the administrative organisation behind European and Asian football. It runs
international and domestic competitions for the countries who are assigned to UEFA. Their
website holds all relevant statistics and figures in relation to all UEFA competitions.
European-Fooball-Statistics.co.uk:
As mentioned earlier in this paper, information on attendance statistics was recommended by
(Syzmanski, 2008). Syzmanski is one of the leading sports economists in the world,
publishing notable work such as Soccernomics.
18. 5. Method of Analysis
An econometric method of analysis was used for this research. A Fixed-effects model was
used followed by a Breusch Pagan test. After this regression a Linear ordinary least squares
(OLS) model was used including robust standard errors followed by a VIF test to see was
mulitcollinearity present in the model. The following sub sections explain the models and
tests mentioned above. The ‘Results and Conclusions’ section will identify any problems
found with the chosen econometric models.
5.1 Econometric Models
Fixed-Effects Model:
As the data being used is time series data, a panel fixed-effects model seemed like the
appropriate regression model. A fixed-effects regression model is used to analyse
longitudinal data with repeated measures on both independent and dependent variables. The
model has the attractive feature of controlling for all stable characteristics of the individuals,
whether measured or not (Allison, 2005). The basic fixed effects model is as follows:
Y = µ + βx + γz +α + ε
where µ is an intercept that may be different for each point in time, and β and γ are vectors of
coefficients. The two error terms are α and ε (Allison, 2005). Following the fixed-effects
regression, a Breucsh Pagan test was applied to see if the fixed-effects model suffered from
heteroskedasticity.
Breusch Pagan Test:
The Breusch Pagan test was discovered by (Breusch & Pagan, 1979). If the Breusch Pagan
test results in an insignificant result, than the fixed effects model will not be the model of
19. ‘best fit’ as heteroskedasticity will present in the model, therefore creating an inefficient
model. Heteroskedasticity can happen due to errors in the data such as outliers or incorrect
transformation of data.
Ordinary Least Squares Linear Regression Model (OLS):
OLS regression is a generalized linear modelling technique that may be used to model a
single response variable which has been recorded on at least an interval scale. The technique
may be applied to single or multiple explanatory variables and also categorical explanatory
variables. (Moutinho & Hutcheson, 2011). In simple terms, the relationship between a
continuous response variable (Y) and a continuous explanatory variable (X) may be
represented using a line of best-fit (Moutinho & Hutcheson, 2011). The equation below
represents the OLS regression:
Yi = BX + ui
Y = Regressand, X = Vector of regressors, and u is an error term. Also included in the OLS
regression is ‘robust standard errors’ which takes heteroskedasticity and other smaller failures
in into account.
Variance Inflation Factor (VIF) Test:
The VIF test is to check whether multicollinearity is present in the model. Multicollinearity
can occur if the regressors are correlated, defying the assumptions of the linear regression
model. The equation below represents how the VIF regression is calculated:
Multicollinearity may be identified in R-Square of the model, usually when the models R-
Square is too high i.e. too good to be true. A high VIF test will indicate that multicollinearity
20. is present in the model while a low VIF test will indicate very little multicollinearity in the
model.
5.2 Hypothesis and Expected Values of Coefficients
Hypothesis:
An increase in competitiveness should result in an increase in match attendances or vice
versa.
Expected Values of Coefficients:
Dependent Variable: Attendance
Independent Variables:
1. HHI – We expect a positive relationship (+)
2. Ticket Prices – We expect a negative relationship (-)
3. UEFA Co-efficient- We expect a positive relationship (+)
4. GDP per Capita- We expect a positive relationship (+0
21. 6. Results and Conclusions
6.1 Results
The first part of this section will look at the results of the econometric models mentioned
previously. This will entail interpreting the results, looking at any faults in the models and
what was the preferred econometric model. Note: Regressions were calculated using Stata
and all data was logged in excel prior to entering it into Stata.
Fixed-Effects model results:
Figure 8
. xtreg attendance gdpcap hhi uefaco tickets, fe
Fixed-effects (within) regression Number of obs = 33
Group variable: country Number of groups = 3
R-sq: within = 0.2401 Obs per group: min = 11
between = 0.8341 avg = 11.0
overall = 0.8090 max = 11
F(4,26) = 2.05
corr(u_i, Xb) = -0.9773 Prob > F = 0.1162
------------------------------------------------------------------------------
attendance | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gdpcap | .0696289 .0559478 1.24 0.224 -.0453734 .1846312
hhi | 316871.4 118585.1 2.67 0.013 73116.3 560626.6
uefaco | -1.157036 89.30433 -0.01 0.990 -184.7247 182.4107
tickets | -13.7288 35.82407 -0.38 0.705 -87.36623 59.90863
_cons | 1635.582 8908.15 0.18 0.856 -16675.38 19946.55
-------------+----------------------------------------------------------------
sigma_u | 20769.022
sigma_e | 1108.3085
rho | .99716042 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(2, 26) = 22.99 Prob > F = 0.0000
22. 6.2 Interpretation of Fixed Effects Model Results
When interpreting the results of this econometric model we must look at the four following
areas:
1. F-Test
2. R-Square
3. T-Tests (P-Values)
4. Coefficients
The significance values must be within the following three levels:
99% = 0.01
95% = 0.05
90% = 0.l
If the variables do not fall under these levels than they will be classified as insignificant.
1. F- Test
F- Test = 0.1162 therefore the model does not fall into any of the categories mentioned above
and therefore the regression insignificant.
2. R-Square
R-Square = 0.8090, therefore 80.9% of the variation in Y is explained by X.
3. T-Tests
GDP per capita = 0.224 therefore the variable is insignificant.
HHI (competitiveness) = 0.013 therefore the variable is significant at the 95% level.
UEFA Coefficient = 0.990 therefore the variable is insignificant.
Ticket Prices = 0.705 therefore the variable is insignificant.
From the T-tests we can roughly estimate that the fixed effects model is not the model of
‘best fit’. An interesting the oberservation however is the fact the HHI (competitiveness)
variable is still significant.
23. 4. Coefficients
GDP per capita = 0.6962 therefore it has a positive relationship with attendance.
HHI = 316871.4 therefore it has a positive relationship with attendance.
UEFA Coefficient = -1.157036 therefore it has a negative relationship with attendance.
Ticket Prices = -13.7288 therefore it has a negative relationship with attendance.
After the fixed effects regression was ran a Breusch Pagan test was applied to test for
heteroskedasticity.
The following provides the results of the Breusch Pagan Test:
Figure 9
. xttest2
Correlation matrix of residuals:
__e2 __e3 __e4
__e2 1.0000
__e3 -0.4890 1.0000
__e4 0.0232 -0.0803 1.0000
Breusch-Pagan LM test of independence: chi2(3) = 2.707, Pr = 0.4390
Based on 11 complete observations over panel units
The Breusch Pagan Test results in an answer = 0.4390, therefore heteroskedasticity is
present. Due to the interpretation of the results and the Breusch Pagan test, it is obvious that
the fixed effects model is not the model of best fit. Due the fixed effects model being
insignificant, an OLS regression model was then used instead. As mentioned earlier, robust
standard errors were used to account for heteroskedasticity. Robust standard errors may also
be known as Huber/White estimators.
Ordinary Least Squares (OLS) Regression Results:
Figure 10
. reg attendance gdpcap hhi uefaco tickets, robust
Linear regression Number of obs = 33
F( 4, 28) = 218.62
Prob > F = 0.0000
R-squared = 0.9631
Root MSE = 1777.1
------------------------------------------------------------------------------
| Robust
attendance | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gdpcap | .0265209 .0860441 0.31 0.760 -.1497325 .2027742
hhi | -386301.9 19019.98 -20.31 0.000 -425262.6 -347341.3
uefaco | 265.2422 120.5332 2.20 0.036 18.34109 512.1433
tickets | -78.99582 11.18794 -7.06 0.000 -101.9133 -56.07837
_cons | 56581.32 3733.397 15.16 0.000 48933.8 64228.84
24. 6.3 Interpretation of OLS regression model results
We interpret the results of the OLS the same as the fixed effects regression.
1. F-Test
F-Test = 0.0000 therefore the regression is significant at the 99% level.
2. R-Square
R-Square = 0.9631 therefore 96.3% of the variation in Y is explained by X. This represents a
very strong regression.
3. T-Tests
GDP per capita = 0.760 therefore variable is insignificant.
HHI = 0.000 therefore the variable is significant at the 99% level. This is a very positive sign.
UEFA Coefficient = 0.036 therefore the variable is significant at the 95% level.
Ticket Prices = 0.000 therefore the variable is significant at the 99% level.
4. Coefficients
GDP Per capita (although insignificant) = 0.265209 therefore it has a positive relationship
with attendance
HHI = -386301.9 there it has a negative relationship with attendance.
UEFA Coefficient = 265.2422 therefore it has a positive relationship with attendance.
Ticket Prices = -78.99582 therefore it has a negative relationship with attendance.
25. After the OLS regression a VIF test was estimated to account for any multicollinearity that
may be in the model.
The following provides the results of the VIF test:
Figure 11
. vif
Variable | VIF 1/VIF
-------------+----------------------
hhi | 5.50 0.181717
uefaco | 5.50 0.181909
tickets | 4.99 0.200527
gdpcap | 2.23 0.448751
-------------+----------------------
Mean VIF | 4.55
The mean VIF = 4.55. This suggests that there is very little multicollinearity in the chosen
model.
26. 6.4 Conclusions
The results of the OLS regression differ quite a bit from my hypothesized results. I expected
that both competitiveness and attendances would have a positive relationship with each other.
We see from the results as competiveness decreases, attendances increases. This is quite an
interesting result as we expect that fans would be more intrigued to attend these competitions
if the level of competitiveness in rising as the winner is becoming more uncertain. It is quite
an extraordinary result as all leagues have not had a variety of winners in during the 2003 –
2013 seasons. The aggregate number of seasons between SPL, EPL and La Liga is thirty
three. Within these thirty three seasons there have been only ten different winners. We would
have thought that fans would not be incentivised to attend these games if the same winners
were reoccurring. My proposed reason for these results is as follows:
1. Fans want to see winners, they are interested in watching the top quality teams with the top
quality players. Fans in these leagues rather see the winning teams win.
Another interesting aspect of the results was that GDP per capita was insignificant. As
mentioned previously, we expected that a rise in income would have a substantial effect on
the attendances of these competitions games, but surprisingly not.
A very interesting observation was that the UEFA Coefficient was significant. Prior to this
research I thought that this variable would not have an effect at all on attendances, but
interestingly it does.
As expected ticket prices were significant in the regression. Seeing as ticket prices change
from game to game, it has an impact on supporter’s attendance to games.
27. As mentioned earlier one the key contributors to sports economic papers, Stefan Szymanski
said “without a certain degree of competitive balance, supporter’s interest will reduce along
with match attendances” (Szymanski, 2001). In other words, Szymanski is saying that a
decrease in competitiveness results in a decrease in attendances. This statement was made in
one of his papers about ‘the attractiveness of team sports’. Although my research has a
particular limitation, being that the ticket prices used are not the exact ticket prices for the
games, the result I received is different to Syzmanski’s, implying that competitiveness
increases doesn’t always increase attendances. Apart from (Szymanski, 2001) , similar
assumptions were made by (Humphreys, 2002) who stated that ‘the uncertainty regarding the
outcome is believed to be a primary factor in attendance at sport events and also the incentive
to tune into a broadcast’.
Although the results from this research is contrary to some sports economists views, the
variables that they used will definitely be taken into account if this piece of research is going
to continue. In (Szymanski, 2001) he used a ‘day of the week’ variable where he estimated
that attendances for matches changed considerably due to the day of the week that the match
was played on.
Applying economic theory to my results we see that when league competiveness decreases
fans are still incentivised to attend the SPL, EPL and La Liga games. The results of ticket
prices from this regression obeys the law of the demand, where prices goes up, quantity goes
down, meaning that fans are not as incentivised to attend games if the ticket prices are raised.
As mentioned above the sports economic theory ‘competitive balance’ is disobeyed in this
research. Fans will continue to support their teams even though the leagues competitiveness
is decreasing.
28. To refer back to the rationale for doing this research, investors and sponsors of clubs and
leagues would be very satisfied with the results of this research and have no reason not to
pursue anymore sponsorship deals with leagues and clubs across the SPL, EPL and La Liga
competitions.
Reflecting on this research, I would have included a ‘stadium capacity’ variable to counteract
for leagues that might have been experiencing over demands for tickets. As the best teams
attract the biggest audiences, there must be a scarcity of tickets for some supporters within
the three leagues. Another variable that I would have controlled for would have been a ‘day
of the week’ variable used by (Szymanski, 2001). If I do continue this research those
variables will undoubtedly be included and should aid to more accurate results.
While doing this research many other research questions emerged. An interesting paper by
(Noll & Zimbalist, 1997) discussed how new stadiums or renovations effect different clubs. If
I was to pursue this kind of research I would research have teams become more competitive
after a new stadium has been built, will the new stadium attract more supporters? Because an
increase in attendance, will clubs be able to afford new players to make them more
competitive? If so the underlying reason might have been due to the stadium renovations.
This is definitely one research question that has emerged throughout this piece of research.
Another possible research question that emerged from this research was the ‘role of place for
success in sport’. This evolved from both the research on sport and from the EC3138 module
‘the role of place for innovation. To engage in this research I would investigate how
successful football teams who are located in peripheral regions i.e. teams located far from the
main urban areas. This type of research would only be applicable to smaller nations as bigger
nations may have a large variety of cities.
29. Finally, to relate back to the research question, it is evident that attendances in Scottish
Premier League, English Premier League and Spanish La Liga have been impacted by the
competitiveness of the league. As the leagues are becoming less competitive, the results tell
us that attendances to these games have been increasing, meaning that fans do not care if a
game is uncertain, they just want to attend league games where past winners are more than
likely to win again.
31. 1. Table of Contents
1. Introduction............................................................................................................................2
2. Literature Review...................................................................................................................4
2.1 Competitive Balance across Countries .................................................................................. 4
2.2 Economic Formulas............................................................................................................. 8
3. Economic Theory and Concepts ..........................................................................................10
3.1 Competitive Balance.......................................................................................................... 10
3.2 Incentives ......................................................................................................................... 11
4. Data and Data analysis.........................................................................................................12
4.1 Competitive Balance.......................................................................................................... 12
4.2 Ticket Prices..................................................................................................................... 13
4.3 UEFA Coefficients ............................................................................................................ 14
4.4 GDP Per Capita................................................................................................................. 14
4.5 Attendance........................................................................................................................ 15
4.6 Full Data Sources.............................................................................................................. 16
4.7 Reliability......................................................................................................................... 16
5. Method of Analysis..............................................................................................................18
5.1 Econometric Models.......................................................................................................... 18
5.2 Hypothesis and Expected Values of Coefficients ................................................................. 20
6. Results and Conclusions ......................................................................................................21
6.1 Results.............................................................................................................................. 21
6.2 Interpretation of Fixed Effects Model Results...................................................................... 22
6.3 Interpretation of OLS regression model results .................................................................... 24
6.4 Conclusions ...................................................................................................................... 26
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