Technical efficiency in the English Football Association Premier League with a stochastic cost frontier
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Applied Economics Letters, 2007, 14, 731–741
Technical efficiency in the English
Football Association Premier League
with a stochastic cost frontier
Carlos Pestana Barrosa,* and Stephanie Leachb
a
Instituto Superior de Economia e Gestão, Technical University of Lisbon,
Rua Miguel Lupi, 20 1249-078 Lisbon, Portugal
b
Tanaka Business School, Imperial College, London
This article uses an econometric frontier model to evaluate the technical
efficiency of English Premier League clubs from 1998/99 to 2002/03
combining sport and financial variables. A Cobb–Douglas cost specifica-
tion of the technical efficiency effects model is used to generate football
club efficiency scores, allowing for contextual variables which affect
inefficiency. We conclude that the efficiency scores are mixed. A policy
is devised for the management of this sector.
I. Introduction The motivation for the present article is derived
from stylised facts observed in the English football
In this article, we measure the technical efficiency industry, such as the clubs which overspend in order
of the clubs playing in the English Premier League to achieve sporting success, but then fail to do so,
with a Cobb–Douglas cost frontier model, using e.g. Leeds United. In this case, the failure may be due
data obtained in the Deloitte & Touche reports on to uneven playing fields in the Premier League, in
English football from 1998/99 to 2002/03. Previous which the market leaders in terms of turnover appear
research into the English Premier League has made to be virtually guaranteed sporting success. In this
use of data envelopment analysis (DEA), e.g. Haas case, the clubs playing in sub-championships of their
(2003b) and of the stochastic frontier model, Dawson own, and with very different objectives from the few
et al. (2000). However, none of the articles adopted elite clubs, sometimes start overspending in an
the stochastic frontier model with contextual vari- attempt to achieve the elite position, but usually
ables, known as the technical efficiency effects model fail to do so. Alternatively, failure may be due to
(Coelli et al., 1998). technical inefficiency, since when a club starts over-
This article analyses the efficiency of the English spending it expects to overcome the uneven status
Premier League with the use of a technical efficiency quo, but may lack the managerial skills to do so.
model, allowing for contextual variables which may Additional reasons are exogenous contextual effects,
affect the clubs’ performance. In fact, based on the such as the population and income of the club’s fan
sports economics literature (El-Hodiri and Quirk, base, which defines an inescapable environment that
1971; Fort and Quirk, 1995) it is expected that the condemns clubs with small bases to life outside
clubs’ fan base will have a major effect on their the top. Finally, there are exogenous shocks such
performance. As the club base is composed of the as the Abramovich effect, presently observed at
population and income in the club area, these Chelsea, which translates into changes in the relative
contextual variables have to be included in the cost efficiency of the clubs in a league, circumventing
function. the club base.
*Corresponding author. E-mail: cbarros@iseg.utl.pt
Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online ß 2007 Taylor & Francis 731
http://www.tandf.co.uk/journals
DOI: 10.1080/13504850600592440732 C. P. Barros and S. Leach
This article extends previous research into Premiership teams spending heavily and incurring
football efficiency, adopting a stochastic frontier operating losses in the hope of achieving a certain
model, alongside Hoeffler and Payne (1997) and position guaranteeing qualification for European
Dawson et al. (2000) to evaluate the technical competition.
efficiency of the English Premier League football In order to compete for playing talent with other
clubs. However, this article adopts the technical major teams in Europe (e.g. Real Madrid and
efficiency effects model, found in Coelli et al. Juventus), English teams have had to increase their
(1998), which allows for contextual variables in the spending on wages in order to attract the best players.
cost function. A sample of the clubs that played Furthermore, the biggest teams in England, notably
consecutively in the league in the years under analysis Manchester United, Arsenal, Liverpool and more
(1998/99 to 2002/03) is used. The use of such clubs recently Chelsea, have increasingly imported players
ensures balanced panel data and is needed (and in the case of the latter three, managers) from
to obtain similar average scores over the period at ‘overseas’ with the result that perhaps only a handful
club level. of English players have a place in the starting 11.
The article is organized as follows: in Section II, we Currently the starting 11 at Arsenal includes two
describe the institutional setting; in Section III, English players. Two of the world’s top ten transfer
we survey the literature on the topic; in Section IV, records belong to Manchester United, and, over the
we present the theoretical framework; in Section V, past two seasons, Chelsea has spent over £200 million
the data and results are presented; in Section VI, the on new players. Not only do these big clubs have
efficiency rankings are presented; in Section VII, to compete in Europe, but also in traditional cup
we discuss the results and, finally, in Section VIII competitions (such as the League Cup and the FA
we draw conclusions. Cup) and in the domestic league, which tends to
cause some clubs to field ‘reserve’ sides in the less
glamorous competitions. Thus, it appears that some
clubs create alternative squads, with one set of
II. Institutional Setting
players competing in less prestigious games, whilst
the ‘superstars’ play in the more celebrated and
The English Premier League is the most profitable
lucrative matches.
football league, not only in Europe but also world-
Hence, the finance and performance of these
wide, and contains the world’s richest club:
leading clubs can be very complex indeed. Some
Manchester United. Prior to 1992, there were four
clubs have started expanding into new markets and
divisions grouped under one league in England.
However, during the late 1980s and early 1990s, the entered into sponsorship deals. Manchester United
top teams in England sought to improve their share has entered into a marketing agreement with the
of television broadcasting revenue and became less New York Yankees, and Arsenal recently signed
willing to subsidise smaller teams through the a deal with Emirates Airlines amounting to £100
redistribution of this television income. However, million for stadium and shirt sponsorship. Football in
even though the English Premier League is the most England is big business, and vital to the success of
prosperous, it is not uncommon to find genuine this business is success – not only on the pitch,
concern for individual clubs’ financial health. but also financial success and the development
Not only does the threat and subsequent effect of of an efficiently produced product. In Table 1,
relegation to the smaller and less lucrative First we present 12 English football clubs that
Division sometimes leave former Premiership clubs remained in the Premiership throughout the seasons
(e.g. Derby County and Bradford City) near financial analysed.
ruin, but they also run the risk of gambling and Table 1 shows that Manchester United ranks first
missing out on lucrative European championships in terms of points, which translates into the team’s
such as the Champions League and UEFA Cup. position at the end of the season, followed by Arsenal
Most notably, Leeds United invested heavily in and Newcastle. Leeds ranks first in the ratio of
playing talent only to miss out on qualification for wages/points, followed by Manchester. Finally,
the Champions League in 2002, thus eliminating Liverpool ranks first in turnover, followed by
a large sum of expected income and leading to a Manchester United.
large-scale sell-off of playing talent. During the These rankings establish a positive correlation
turmoil, Leeds were ultimately relegated. So, achiev- between turnover, wages and position, signifying
ing success requires spending, but with that that sports results and financial results are closely
comes risk and it is not uncommon to see many related.Technical efficiency in the EPL 733
Table 1. Figures in 2002/03 season
Football club Points Wages (£m) Ratio wages/points Turnover (£m)
Arsenal 78 60 569 777 103 801
Aston Villa 45 32 310 718 45 447
Chelsea 67 54 365 811 93 027
Everton 59 29 735 504 46 781
Leeds United 47 56 595 1204 64 005
Liverpool 64 54 431 850 1 013 981
Manchester United 83 79 517 958 174 936
Middlesbrough 49 29 428 601 40 229
Newcastle United 69 45 195 655 96 689
Southampton 52 26 666 513 48 875
Tottenham Hotspur 50 38 024 760 66 506
West Ham United 42 33 342 794 51 712
Source: Deloitte & Touche (2004).
III. Literature Survey two articles using the stochastic econometric frontier
are, in our view, clearly insufficient for analysing such
There are two contemporary approaches to measur- an important issue in the sports market context. With
ing efficiency: first, the econometric or parametric the present article, we seek to widen the scope of
approach, and, second, the nonparametric or sports economics in this specific respect and to draw
DEA approach. Unlike the econometric stochastic the attention of other researchers to this neglected
frontier approach, DEA permits the use of multiple aspect of sports management.
inputs and outputs, but does not impose any
functional form on the data, neither does it make
distributional assumptions for the inefficiency term.
Both methods assume that the production function of IV. Theoretical Framework
the fully efficient decision-making unit is known. In
practice, this is not the case and the efficient isoquant In this article, we adopt the stochastic cost econo-
must be estimated from the sample data. Under such metric frontier approach. The frontier approach, first
conditions, the frontier is relative to the sample proposed by Farrell (1957), was based on cost
considered in the analysis. functions and came to prominence in the late 1970s
An important advantage of the econometric as a result of the work of Aigner et al. (1977), Battese
frontier is that there are a number of well-developed and Corra (1977) and Meeusen and van den Broeck
statistical tests available for investigating the validity (1977). The adequacy of a cost or production
of the model specification – tests of significance for function depends on the environment in which the
the inclusion or exclusion of factors, or for verifying units analysed operate. In an environment where
the functional form. The accuracy of these hypotheses the ultimate objective is to maximise sales and profits,
depends to some extent on the assumption of the producers face exogenously determined input
normality of errors, which is not always fulfilled. prices and output prices and attempt to allocate
A second advantage of the econometric frontier is inputs and outputs so as to maximise sales. Assuming
that if a variable which is not relevant is included, it this is the main strategy at football clubs, the
will have a low or even zero weighting in the calcu- production frontier is the most adequate model for
lation of the efficiency scores, so that its impact is analysing efficiency (Kumbhakar, 1987). However,
likely to be negligible. This is an important difference when we have several outputs, it is better to adopt a
from DEA, where the weights for a variable are cost frontier approach, relying on the duality theory
usually unconstrained. A third advantage of the (Cornes, 1992).
econometric frontier is that it permits the decom- The general frontier cost function, which is dual to
position of deviations from efficient levels into ‘noise’ the production function proposed by Aigner et al.
(or stochastic shocks) and pure inefficiency, while (1977) and Meeusen and Van den Broeck (1977),
DEA classifies the whole deviation as inefficiency. is as follows:
Table 2 lists the characteristics of the articles
reviewed. Costit ¼ 0t þ it Pit þ it Yit þ ðVit þ Uit Þ
Nine articles using DEA, three articles using
a deterministic econometric frontier approach and i ¼ 1, 2, . . . , N; t ¼ 1, 2, . . . , N ð1Þ734
Table 2. Literature survey of frontier models on sports
Articles Method Units Inputs Outputs Prices
Barros and Santos (2005) DEA-CCR model and Soccer clubs in the first Supplies and services Match receipts, mem- –
DEA-BCC model Portuguese league expenditures, wage bership receipts,
expenditures, amorti- sponsorship receipts,
zation expenditure, TV receipts, gains on
other costs players, financial
receipts, points won,
tickets sold
Haas (2003a) DEA-CCR and DEA- 12 USA soccer clubs Players’ wages, coaches’ Points awarded, number –
BCC model observed in year 2000 wages, stadium utili- of spectators and total
zation rate revenue
Haas (2003b) DEA-CCR and DEA- 20 English Premier Total wages, coache’s Points, spectators and –
BCC model League clubs salary, home town revenue
observed in 1 year population
(2000/01)
Barros and Santos (2003) DEA-Malmquist index 18 training activities of Number of trainers, Number of participants,
the sports federations, trainers’ remunera- number of courses,
1999–2001 tion, number of number of approvals
administrators,
administrators’ remu-
neration and physical
capital
Barros (2003) DEA-allocative model 19 training activities of Number of trainers, Number of participants, Price of trainers, price of
the sports federations, number of adminis- number of courses, administrators, price
1998–2001 trators, physical number of approvals of capital
capital
Fizel and D’Itri (1997) DEA-CCR model in 147 college basketball Player talent, opponents’ Winning percentages –
first stage and regres- teams, 1984 to 1991 strength
sion analysis in
second stage
Fizel and D’Itri (1996) DEA-CCR model Baseball managers Player talent, opponents’ Winning percentages –
strength
C. P. Barros and S. LeachPorter and Scully (1982) A linear programming Major league baseball Team hitting and team Team percentage wins –
technique (probably teams, 1961 to 1980 pitching
DEA-CCR)
Dawson et al. (2000) Stochastic Cobb– Sample of English Player age, career league Winning percentages –
Douglas frontier football managers, experience, career
model 1992 to 1998 goals, number of pre-
vious teams, league
appearances in the
previous season, goals
scored, player divi-
sional status
Hadley et al. (2000) Deterministic frontier National football league Twenty-four indepen- Team wins –
model teams, 1969/70 to dent variables
Technical efficiency in the EPL
1992/93 describing attack and
defence.
Audas et al. (2000) Hazard functions English professional Match result, league Duration (measured by
soccer, 1972/73 to position and manager the number of league
1996/97, match level age, manager experi- matches played)
data ence, player
experience
Hoeffler and Payne (1997) Stochastic production 27 NBA teams, 1992– Ratio of field goal per- Actual number of wins
frontier 1993 centage, ratio of free
throw percentage,
ratio of offensive
rebounds, ratio of
defensive rebounds,
ratio of assists, ratio
of steals, ratio of
turnover and differ-
ence in blocked shots
Scully (1994) Deterministic and 41 basketball coaches, Team hitting and team Winning percentages –
stochastic Cobb– 1949/50 to 1989/90 pitching
Douglas frontier
model
Zak et al. (1979) Cobb–Douglas determi- National basketball Ten variables of pitch Ratio of final scores –
nistic frontier model association teams performance such as
ratio of steals, ratio of
assists
735736 C. P. Barros and S. Leach
where Cit represents a scalar cost of the i decision- inefficiency, Zit should be included in the cost
making unit under analysis in the t-th period, Pit is function.
a vector of input prices, and Yit is a vector of output The parameters of the model (, , and ) are
descriptors used by the i-th club in the t-th period. estimated using the maximum-likelihood estimator;
The error term Vit is the traditional error term the likelihood function can be found in Battese and
of econometric models, assumed to be independently Coelli (1988). Thus, the technical inefficiency of the
and identically distributed, which represents the effect i-th club at time t is:
of random shocks (noise) and is independent of Uit.
TEit ¼ exðUit Þ ¼ expðzit Wit : ð4Þ
The inefficient term Uit represents technical ineffi-
ciencies and is assumed to be positive and distributed The conditional expectation of TE is defined under
normally with zero mean and variance U2 . The the half-normal assumption:
Uit positive disturbance is reflected in a half-normal " #
independent distribution truncated at zero, Ui i =i
E ¼ i þ i ð5Þ
Nðmit , U2 Þ, signifying that each club’s production "i1 , . . . , "it i =i
must lie on or above its cost frontier, but above the
level of one. This implies that the two effects, the V where ip¼ i þ ð1 ffi i Þð"i Þ, i ¼ 1=ð1 þ ð=Ti Þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
effect, which is a random shock, and the U effect, and i ¼ U2 =ð1 þ Ti Þ. is the mean value of the
which is a management shock controlled by the distribution and T is the time period of the panel,
office, cause any deviation from the frontier. is the standard normal distribution, and is the
The mean inefficiency of the technical efficiency respective cumulative distribution function (Coelli
effects model, in Coelli et al. (1998) is a deterministic et al., 1998; Kumbhakar and Lovell, 2000).
function of p explanatory variables:
mit ¼ zit ð2Þ
where is a p 1 vector of parameters to be V. Data
estimated. Following Battese and Corra (1977),
the total variance is defined as 2 ¼ V2 þ U2 . The To estimate the cost frontier, we used balanced panel
contribution of the error term to the total variation is data on English Premier League Football clubs in the
as follows: V2 ¼ 2 =ð1 þ 2 Þ. The contribution of the years 1998/99 to 2002/03 (12 clubs 5 years ¼ 60
inefficient term is as follows: U2 ¼ 2 2 =ð1 þ 2 Þ, observations).
where V2 is the variance of the error term V, U2 is the Frontier models require the identification of inputs
variance of the inefficient term U and is defined (resources) and outputs (transformation of
as ¼ U =V , providing an indication of the relative resources). Several criteria can be used. Firstly, one
contribution of U and V to ". empirical criterion is the availability of data. It is
The inefficiencies in Uit in Equation 1 can be important for the applicability of the model results
specified as: that football clubs ‘buy in’ to the process, that the
Uit ¼ zit þ Wit ð3Þ measures of inputs and outputs are relevant, that
the appropriate archival data are available and that
where Wit is defined by the truncation of the normal ‘more is better’ in the case of outputs. Usually the
distribution with mean zero and variance 2. Using criterion of available archival data is used, since it
this parameterisation, a test can be constructed to encompasses all the previous criteria and therefore
determine whether the estimated frontier is actually means that the availability of data is the first criterion
stochastic; ¼ 0 implies that the variance associated in input and output selection. Secondly, the literature
with the one-sided (efficiency) errors, U2 , is zero, survey is a way of ensuring the validity of the research
meaning that these deviations from the frontier are and therefore another criterion to be taken into
better represented as fixed effects in the production account. The final criterion for measurement selec-
function. Therefore, a test of the null hypothesis that tion is the professional opinions of sports managers.
¼ 0 against the alternative hypothesis that is In this article, we follow these three criteria.
positive is used to test whether deviations from the Based on the available data span, we estimated
frontier are stochastic and whether one should a generalized Cobb–Douglas stochastic cost function.
proceed with the estimation of parameters related to We transformed the variables in keeping with
the sources of inefficiency within the context of a the description column of Table 3. We adopted
stochastic production frontier. Failure to reject the the traditional log–log specification to allow for the
null hypothesis suggests that the determinants of possible nonlinearity of the frontier.Technical efficiency in the EPL 737
Table 3. Descriptive statistics of the data
Variable Description Minimum Maximum Mean SD
Log cost Logarithm of operational cost in pounds at 4.23 5.16 4.741 0.188
constant price 1999 ¼ 100
Log PL Logarithm of price of players measured by 2.55 3.49 3.053 0.217
dividing total wage by the number of players
Log PK1 Logarithm of price of capital measured by 2.08 2.99 2.612 0.202
dividing the amortisation of players by the
number of players
Log PK2 Logarithm of price capital measured by divid- 0.30 1.89 0.886 0.499
ing stadium facilities expenditures by net
assets and liabilities
Log points Logarithm of the points obtained in the season 1.61 1.96 1.760 0.095
Log attendance Logarithm of the number of tickets sold in the 4.18 4.83 4.556 0.127
season
Log turnover Logarithm of turnover in the season, pounds 4.13 6.01 4.736 0.279
at constant price 1999 ¼ 100
Log population Logarithm of the population in the city of the 5.830 7.016 6.649 6.618
club
Log income Logarithm of the income of the city of the 4.144 4.507 4.342 3.742
club, pounds at constant price 1999 ¼ 1000
European Dummy variable, which is one for clubs 0 1 0.33 –
participating in the European cups in each
season
We noted that the range is narrow, indicating that Table 4. Stochastic Cobb–Douglas panel cost frontier model
the clubs in the sample are of a similar size in terms
Variables Coefficients (t-ratio)
of inputs and outputs, but that there is a very wide
difference in the population and income of the club Constant (0) 2.198 (2.473)
Log PL (1) 0.872 (3.185)
bases. The rationale for using capital-players needs
Log PK1 (2) 0.101 (2.777)
a justification. Football clubs use players as an active, Log points (3) 0.543 (1.840)
tradable commodity in order to capitalise on their Log attendance (4) 0.301 (2.085)
market value. Moreover, football clubs are allowed Log Turnover (5) 0.400 (3.064)
to amortise the value of the football player on the Constant (0) 0.899 (0.190)
Log population (1) 0.308 (1.260)
balance sheet. Log income (2) 0.303 (3.553)
European (3) 0.046 (1.172)
2 ¼ V2 þ U
2
0.369 (4.939)
Results ¼ U2 = 2 0.0006 (3.868)
Log(likelihood) 13.693
In this study, we estimated a Cobb–Douglas stochas- Lagrange test 0.366
tic cost function with three input prices (one price Observations 60
of labour and two prices of capital), three outputs Notes: Dependent variable log of total cost. t-Statistics in
(points, attendance and turnover) and contextual parentheses are below the parameters, those followed by *
variables (population and income in the club area and are significant at the 1% level.
whether or not the club is playing in the European
leagues).
Cit PLit PK1it of efficiency that are controlled by management
log ¼ 0 þ 1 log þ 2 log (labour, capital, attendance and turnover) and for
PK2it PK2it PK2it
the contextual factors that are beyond managerial
þ 3 logðPointsÞit þ 4 LogðAttendanceÞ
control (population, income, European). The vari-
þ 5 log Turnoverit þ ðVit þ Uit Þ ables were defined and characterized in Table 3.
Ui ¼ 0 þ 1 log Populationit Table 4 presents the results obtained for the
stochastic frontier using Frontier 4.1 from Coelli
þ 2 logðIncomeit Þ þ 3 log Europeanit ð6Þ
(1996), with a half-normal distribution specification.
This is the cost frontier model, known as the We can see that the Cobb–Douglas cost function
technical efficiency effects model, found in Coelli specified above fits the data well, as the R-squared
et al. (1998), because it accounts for the causes from the initial ordinary least-squares estimation that738 C. P. Barros and S. Leach
was used to obtain the starting values for the and attendance. This means that it is costly for
maximum-likelihood estimation is in excess of 85% football clubs to generate turnover from their
and the overall F-statistic is 282.02. We can also activity. However, such generation of turnover is
see that the variables have the expected signs, with independent of performance on the pitch, since
the operating cost increasing with the price of labour points and attendance contribute negatively to
and the price of capital-players. Moreover, the total costs. Moreover, the contextual variables play a role
cost decreases with points, attendance, population in this context, with the population of the club base
and European. Finally, the total cost increases with contributing negatively to costs, reflecting support
the price of labour, the price of capital, turnover and for the club both through the contributions of season
income. The frontier parameters are all statistically ticket holders and through attendance. Finally,
significant and the inefficient error term () is 0.6% participation in European competitions also contri-
of the total variance, which is a low value when butes negatively to costs, as a result of the bonuses
compared with other industries, such as banking earned in European competitions.
(Drake and Weyman-Jones, 1996; Ashton, 2001). The income of the club base is, however, positively
related to costs, reflecting congestion costs in the
big cities. Secondly, scale in a Cobb–Douglas func-
VI. Efficiency Rankings tion is defined as the sum of the parameters and,
in the present case, the cost elasticity is equal to
Table 5 presents the results of the time-invariant 0.429 at the sample mean, signifying decreasing
efficiency scores computed from the residuals. returns to scale. Thus, a 10% increase in outputs
Technical efficiency is achieved, in a broad economic leads to about a 4.29% increase in costs. The inverse
sense, by the unit which allocates resources without of this is larger than unity, indicating increasing
waste, and thus the concept refers to a situation returns to scale on production. This result means that
on the frontier. A unit with a score equal to one is on scale is a major issue in the football industry, a result
the frontier and those with a score lower than one confirmed by the DEA research (Haas, 2003b).
are above the cost frontier of best practices. The value Thirdly, we can see that the elite clubs (Manchester
of waste is measured by the difference between United, Arsenal and Chelsea) are the least efficient
one and the score, so that, for example, the waste whenever we include contextual variables in the
of Arsenal is 1 0.901 ¼ 0.099. This is a small waste analysis. The meaning of this ranking, which includes
when compared with the values observed in other sports and financial questions, is that despite being
sectors of activity. league champions, these clubs use too many resources
We can see that the mean score is 96%. This score to win, and therefore they have a tendency to perform
suggests that football clubs could reduce their output very well in terms of sport but not in terms of finance.
cost by 3% without decreasing their input, which, The second-tier clubs, those clubs which, despite not
in this case, is the price of labour and the price winning, represent the country in the European cups
of capital-players. The maximum football club (e.g. Liverpool and Newcastle), are better positioned
score was naturally 1, which was achieved by in the efficiency rankings. However, the best situation
Middlesbrough, while the minimum efficiency score is obtained by the third-tier clubs, those clubs which
was 96% and was achieved by Arsenal in the first are playing in sub-championships of their own, with
three years and then by Chelsea. The median was very different objectives from the few elite clubs.
97%, which was smaller than the mean. Therefore, Clubs such as Middlesbrough and Southampton
there are more clubs below the mean than above manage their position in the league prudently.
the mean. The SD was 1.3%. These efficiency scores Therefore, the general conclusion is that the three
are high in comparison with those found elsewhere clusters of clubs observed have different managerial
in other activities, such as banking (Ashton, 2001). objectives, and that scale, overspending and manage-
High efficiency scores are consistent with efficient rial skills are necessary to win the league. Fourthly,
and more competitive organisations, such as those the mean efficiency of the clubs in the league is
observed in sports. relatively high, when compared with other industries
(Ashton, 2001). This signifies that, on the pitch,
football clubs are scrutinized to the extreme. In the
VII. Discussion stock exchange, football clubs are scrutinised along-
side the other quoted firms. Therefore, despite some
What is the meaning of these results? Firstly, it failures observed, this is an industry that is much
can be seen that the cost increases with all factors more closely scrutinised than the average organisa-
of production, with the exception of points tion, which results in a high level of efficiency.Table 5. Efficiency scores
Technical efficiency in the EPL
Efficiency scores Efficiency scores Efficiency scores Efficiency scores
Football clubs in 1998/99 Football clubs in 1999/2000 Football clubs 2000/01 Football clubs 2002/03
Middlesbrough 1.0000 Middlesbrough 1.0000 Middlesbrough 1.0000 Middlesbrough 1.0000
Southampton 0.9924 Newcastle 0.9939 Southampton 0.9932 Southampton 0.9946
Newcastle 0.9924 Southampton 0.9927 Newcastle 0.9930 Leeds 0.9940
Liverpool 0.9911 Everton 0.9914 Everton 0.9910 Newcastle 0.9936
Everton 0.9911 Aston Villa 0.9876 Aston Villa 0.9879 Everton 0.9924
Aston Villa 0.9841 Liverpool 0.9750 Tottenham 0.9731 Aston Villa 0.9890
Tottenham 0.9726 Chelsea 0.9732 West Ham 0.9731 Tottenham 0.9741
West Ham 0.9726 West Ham 0.9731 Chelsea 0.9731 West Ham 0.9741
Leeds 0.9659 Tottenham 0.9730 Leeds 0.9648 Liverpool 0.9653
Manchester United 0.9657 Leeds 0.9653 Liverpool 0.9648 Manchester United 0.9651
Chelsea 0.9653 Manchester United 0.9652 Manchester United 0.9646 Arsenal 0.9648
Arsenal 0.9653 Arsenal 0.9648 Arsenal 0.9643 Chelsea 0.9648
Mean 0.9799 – 0.9796 – 0.9786 – 0.9810
Median 0.9784 – 0.9741 – 0.9731 – 0.9815
SD 0.0131 – 0.0126 – 0.0134 – 0.0141
Note: The efficiency scores for the 2001/02 season are not displayed, but are available under request from the authors.
739740 C. P. Barros and S. Leach
The emotional discourse that surrounds the game A policy for overcoming the identified inefficiencies
clouds the efficiency drive the industry has adopted. should start with an analysis of the scale of activities
How do we explain the different strategies adopted and the adoption of a competitive sporting strategy,
by the identified cluster of football clubs? These as the case of Leeds has shown in confirming the
different strategies stem from strategic-based groups theoretical results, in which the population of the club
and differences in terms of resources. Firstly, base is a main driver in economic performance
strategic-based groups (Caves and Porter, 1977) (El-Hodiri and Quirk, 1971; Fort and Quirk, 1995).
refer to differences in the structural characteristics
of units within an industry, which lead to differences
in performance. In football, clubs with similar asset
configurations pursue similar strategies with similar VIII. Conclusion
results in terms of performance (Porter, 1979). While
there are different strategic options among the sectors This article has proposed a simple framework for the
of an industry, because of mobility impediments, not evaluation of English Football Premier League Clubs
all options are available to each industry, causing and the rationalisation of their operational activities.
a spread of the efficiency scores in the industry. The analysis is based on a stochastic frontier model.
Secondly, the differences in resources available to Benchmarks are provided for improving the opera-
clubs (Wernerfelt, 1984; Barney, 1991; Rumelt, 1991) tions of sub-optimal performing clubs. Several inter-
mean that football clubs are heterogeneous in relation esting and useful economic insights and implications
to the resources and capabilities on which they base are raised by the study. For the group with the lowest
their strategies. These resources and capabilities may efficiency score, adjustment is needed in order to
not be perfectly mobile across the industry, resulting reach the efficiency frontier. Too much expenditure
in a competitive advantage for the best-performing on factors adds to inefficiency, namely when such
football clubs. expenditure is not translated into points. Attendance
Purchasable assets cannot represent sources of and turnover increases translate into cost increases,
sustainable profits. Indeed, critical resources are not so that managerial procedures to decrease the
available on the market. Instead, they are built up contribution of these outputs to costs might be a
and accumulated on the football club’s premises, priority for English football managers.
The general conclusion is that football clubs have
their nonimitability and nonsubstitutability being
different efficiency scores. Sporting success is a main
dependent on the specific traits of their accumulation
driver in cost control, together with scale, confirming
process. The difference in resources thus results in
the importance of the local fan base. Managerial
barriers to imitation (Rumelt, 1991) and in the
skills are also important and explain part of the
football clubs’ inability to alter their accumulated
behaviour observed. The role played by managerial
stock of resources over time. In this context, unique
skills in sports is linked to sporting and financial
assets are seen as exhibiting inherently differentiated
performance in the football market.
levels of efficiency; sustainable profits are ultimately
More investigation is needed to address the
a return on the unique assets owned and controlled
limitations mentioned.
by the club (Teece et al., 1997).
Accordingly, football clubs may achieve high levels
of competitiveness by using vast amounts of resources
and thus perform inefficiently, or they may achieve References
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