Technical efficiency in the English Football Association Premier League with a stochastic cost frontier

Transcription

1 Applied Economics Letters, 2007, 14, Technical efficiency in the English Football Association Premier League with a stochastic cost frontier Carlos Pestana Barros a, * and Stephanie Leach b a Instituto Superior de Economia e Gesta o, Technical University of Lisbon, Rua Miguel Lupi, 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 specification 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 In this article, we measure the technical efficiency of the clubs playing in the English Premier League with a Cobb Douglas cost frontier model, using data obtained in the Deloitte & Touche reports on English football from 1998/99 to 2002/03. Previous research into the English Premier League has made use of data envelopment analysis (DEA), e.g. Haas (2003b) and of the stochastic frontier model, Dawson et al. (2000). However, none of the articles adopted the stochastic frontier model with contextual variables, known as the technical efficiency effects model (Coelli et al., 1998). This article analyses the efficiency of the English Premier League with the use of a technical efficiency model, allowing for contextual variables which may affect the clubs performance. In fact, based on the sports economics literature (El-Hodiri and Quirk, 1971; Fort and Quirk, 1995) it is expected that the clubs fan base will have a major effect on their performance. As the club base is composed of the population and income in the club area, these contextual variables have to be included in the cost function. The motivation for the present article is derived from stylised facts observed in the English football industry, such as the clubs which overspend in order to achieve sporting success, but then fail to do so, e.g. Leeds United. In this case, the failure may be due to uneven playing fields in the Premier League, in which the market leaders in terms of turnover appear to be virtually guaranteed sporting success. In this case, the clubs playing in sub-championships of their own, and with very different objectives from the few elite clubs, sometimes start overspending in an attempt to achieve the elite position, but usually fail to do so. Alternatively, failure may be due to technical inefficiency, since when a club starts overspending it expects to overcome the uneven status quo, but may lack the managerial skills to do so. Additional reasons are exogenous contextual effects, such as the population and income of the club s fan base, which defines an inescapable environment that condemns clubs with small bases to life outside the top. Finally, there are exogenous shocks such as the Abramovich effect, presently observed at Chelsea, which translates into changes in the relative efficiency of the clubs in a league, circumventing the club base. *Corresponding author. cbarros@iseg.utl.pt Applied Economics Letters ISSN print/issn online ß 2007 Taylor & Francis DOI: /

2 732 C. P. Barros and S. Leach This article extends previous research into football efficiency, adopting a stochastic frontier model, alongside Hoeffler and Payne (1997) and Dawson et al. (2000) to evaluate the technical efficiency of the English Premier League football clubs. However, this article adopts the technical efficiency effects model, found in Coelli et al. (1998), which allows for contextual variables in the cost function. A sample of the clubs that played consecutively in the league in the years under analysis (1998/99 to 2002/03) is used. The use of such clubs ensures balanced panel data and is needed to obtain similar average scores over the period at club level. The article is organized as follows: in Section II, we describe the institutional setting; in Section III, we survey the literature on the topic; in Section IV, we present the theoretical framework; in Section V, the data and results are presented; in Section VI, the efficiency rankings are presented; in Section VII, we discuss the results and, finally, in Section VIII we draw conclusions. II. Institutional Setting The English Premier League is the most profitable football league, not only in Europe but also worldwide, and contains the world s richest club: Manchester United. Prior to 1992, there were four divisions grouped under one league in England. However, during the late 1980s and early 1990s, the top teams in England sought to improve their share of television broadcasting revenue and became less willing to subsidise smaller teams through the redistribution of this television income. However, even though the English Premier League is the most prosperous, it is not uncommon to find genuine concern for individual clubs financial health. Not only does the threat and subsequent effect of relegation to the smaller and less lucrative First Division sometimes leave former Premiership clubs (e.g. Derby County and Bradford City) near financial ruin, but they also run the risk of gambling and missing out on lucrative European championships such as the Champions League and UEFA Cup. Most notably, Leeds United invested heavily in playing talent only to miss out on qualification for the Champions League in 2002, thus eliminating a large sum of expected income and leading to a large-scale sell-off of playing talent. During the turmoil, Leeds were ultimately relegated. So, achieving success requires spending, but with that comes risk and it is not uncommon to see many Premiership teams spending heavily and incurring operating losses in the hope of achieving a certain position guaranteeing qualification for European competition. In order to compete for playing talent with other major teams in Europe (e.g. Real Madrid and Juventus), English teams have had to increase their spending on wages in order to attract the best players. Furthermore, the biggest teams in England, notably Manchester United, Arsenal, Liverpool and more recently Chelsea, have increasingly imported players (and in the case of the latter three, managers) from overseas with the result that perhaps only a handful of English players have a place in the starting 11. Currently the starting 11 at Arsenal includes two English players. Two of the world s top ten transfer records belong to Manchester United, and, over the past two seasons, Chelsea has spent over 200 million on new players. Not only do these big clubs have to compete in Europe, but also in traditional cup competitions (such as the League Cup and the FA 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 players competing in less prestigious games, whilst the superstars play in the more celebrated and lucrative matches. Hence, the finance and performance of these leading clubs can be very complex indeed. Some clubs have started expanding into new markets and entered into sponsorship deals. Manchester United has entered into a marketing agreement with the New York Yankees, and Arsenal recently signed a deal with Emirates Airlines amounting to 100 million for stadium and shirt sponsorship. Football in England is big business, and vital to the success of this business is success not only on the pitch, but also financial success and the development of an efficiently produced product. In Table 1, we present 12 English football clubs that remained in the Premiership throughout the seasons analysed. Table 1 shows that Manchester United ranks first in terms of points, which translates into the team s position at the end of the season, followed by Arsenal and Newcastle. Leeds ranks first in the ratio of wages/points, followed by Manchester. Finally, Liverpool ranks first in turnover, followed by Manchester United. These rankings establish a positive correlation between turnover, wages and position, signifying that sports results and financial results are closely related.

3 Technical efficiency in the EPL 733 Table 1. Figures in 2002/03 season Football club Points Wages ( m) Ratio wages/points Turnover ( m) Arsenal Aston Villa Chelsea Everton Leeds United Liverpool Manchester United Middlesbrough Newcastle United Southampton Tottenham Hotspur West Ham United Source: Deloitte & Touche (2004). III. Literature Survey There are two contemporary approaches to measuring efficiency: first, the econometric or parametric approach, and, second, the nonparametric or DEA approach. Unlike the econometric stochastic frontier approach, DEA permits the use of multiple 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 the fully efficient decision-making unit is known. In practice, this is not the case and the efficient isoquant must be estimated from the sample data. Under such conditions, the frontier is relative to the sample considered in the analysis. An important advantage of the econometric frontier is that there are a number of well-developed statistical tests available for investigating the validity of the model specification tests of significance for the inclusion or exclusion of factors, or for verifying the functional form. The accuracy of these hypotheses depends to some extent on the assumption of normality of errors, which is not always fulfilled. A second advantage of the econometric frontier is that if a variable which is not relevant is included, it will have a low or even zero weighting in the calculation of the efficiency scores, so that its impact is likely to be negligible. This is an important difference from DEA, where the weights for a variable are usually unconstrained. A third advantage of the econometric frontier is that it permits the decomposition of deviations from efficient levels into noise (or stochastic shocks) and pure inefficiency, while DEA classifies the whole deviation as inefficiency. Table 2 lists the characteristics of the articles reviewed. Nine articles using DEA, three articles using a deterministic econometric frontier approach and two articles using the stochastic econometric frontier are, in our view, clearly insufficient for analysing such an important issue in the sports market context. With the present article, we seek to widen the scope of sports economics in this specific respect and to draw the attention of other researchers to this neglected aspect of sports management. IV. Theoretical Framework In this article, we adopt the stochastic cost econometric frontier approach. The frontier approach, first proposed by Farrell (1957), was based on cost functions and came to prominence in the late 1970s as a result of the work of Aigner et al. (1977), Battese and Corra (1977) and Meeusen and van den Broeck (1977). The adequacy of a cost or production function depends on the environment in which the units analysed operate. In an environment where the ultimate objective is to maximise sales and profits, the producers face exogenously determined input prices and output prices and attempt to allocate inputs and outputs so as to maximise sales. Assuming this is the main strategy at football clubs, the production frontier is the most adequate model for analysing efficiency (Kumbhakar, 1987). However, when we have several outputs, it is better to adopt a cost frontier approach, relying on the duality theory (Cornes, 1992). The general frontier cost function, which is dual to the production function proposed by Aigner et al. (1977) and Meeusen and Van den Broeck (1977), is as follows: Cost it ¼ 0t þ it P it þ it Y it þðv it þ U it Þ i ¼ 1, 2,..., N; t ¼ 1, 2,..., N ð1þ

4 734 C. P. Barros and S. Leach Table 2. Literature survey of frontier models on sports Articles Method Units Inputs Outputs Prices Barros and Santos (2005) DEA-CCR model and DEA-BCC model Haas (2003a) DEA-CCR and DEA- BCC model Haas (2003b) DEA-CCR and DEA- BCC model Soccer clubs in the first Portuguese league 12 USA soccer clubs observed in year English Premier League clubs observed in 1 year (2000/01) Barros and Santos (2003) DEA-Malmquist index 18 training activities of the sports federations, Barros (2003) DEA-allocative model 19 training activities of the sports federations, Fizel and D Itri (1997) DEA-CCR model in first stage and regression analysis in second stage 147 college basketball teams, 1984 to 1991 Supplies and services expenditures, wage expenditures, amortization expenditure, other costs Players wages, coaches wages, stadium utilization rate Total wages, coache s salary, home town population Number of trainers, trainers remuneration, number of administrators, administrators remuneration and physical capital Number of trainers, number of administrators, physical capital Player talent, opponents strength Fizel and D Itri (1996) DEA-CCR model Baseball managers Player talent, opponents strength Match receipts, membership receipts, sponsorship receipts, TV receipts, gains on players, financial receipts, points won, tickets sold Points awarded, number of spectators and total revenue Points, spectators and revenue Number of participants, number of courses, number of approvals Number of participants, number of courses, number of approvals Winning percentages Winning percentages Price of trainers, price of administrators, price of capital

5 Technical efficiency in the EPL 735 Porter and Scully (1982) A linear programming technique (probably DEA-CCR) Dawson et al. (2000) Stochastic Cobb Douglas frontier model Major league baseball teams, 1961 to 1980 Sample of English football managers, 1992 to 1998 Hadley et al. (2000) Deterministic frontier model National football league teams, 1969/70 to 1992/93 Audas et al. (2000) Hazard functions English professional soccer, 1972/73 to 1996/97, match level data Hoeffler and Payne (1997) Stochastic production frontier 27 NBA teams, Scully (1994) Deterministic and stochastic Cobb Douglas frontier model Zak et al. (1979) Cobb Douglas deterministic frontier model 41 basketball coaches, 1949/50 to 1989/90 National basketball association teams Team hitting and team pitching Player age, career league experience, career goals, number of previous teams, league appearances in the previous season, goals scored, player divisional status Twenty-four independent variables describing attack and defence. Match result, league position and manager age, manager experience, player experience Ratio of field goal percentage, ratio of free throw percentage, ratio of offensive rebounds, ratio of defensive rebounds, ratio of assists, ratio of steals, ratio of turnover and difference in blocked shots Team hitting and team pitching Ten variables of pitch performance such as ratio of steals, ratio of assists Team percentage wins Winning percentages Team wins Duration (measured by the number of league matches played) Actual number of wins Winning percentages Ratio of final scores

6 736 C. P. Barros and S. Leach where C it represents a scalar cost of the i decisionmaking unit under analysis in the t-th period, P it is a vector of input prices, and Y it is a vector of output descriptors used by the i-th club in the t-th period. The error term V it is the traditional error term of econometric models, assumed to be independently and identically distributed, which represents the effect of random shocks (noise) and is independent of U it. The inefficient term U it represents technical inefficiencies and is assumed to be positive and distributed normally with zero mean and variance U 2. The U it positive disturbance is reflected in a half-normal independent distribution truncated at zero, Nðm it, U 2 Þ, signifying that each club s production must lie on or above its cost frontier, but above the level of one. This implies that the two effects, the V effect, which is a random shock, and the U effect, which is a management shock controlled by the office, cause any deviation from the frontier. The mean inefficiency of the technical efficiency effects model, in Coelli et al. (1998) is a deterministic function of p explanatory variables: m it ¼ z it ð2þ where is a p 1 vector of parameters to be estimated. Following Battese and Corra (1977), the total variance is defined as 2 ¼ V 2 þ 2 U. The contribution of the error term to the total variation is as follows: V 2 ¼ 2 =ð1 þ 2 Þ. The contribution of the inefficient term is as follows: U 2 ¼ 2 2 =ð1 þ 2 Þ, where V 2 is the variance of the error term V, 2 U is the variance of the inefficient term U and is defined as ¼ U = V, providing an indication of the relative contribution of U and V to ". The inefficiencies in U it in Equation 1 can be specified as: U it ¼ z it þ W it ð3þ where W it is defined by the truncation of the normal distribution with mean zero and variance 2. Using this parameterisation, a test can be constructed to determine whether the estimated frontier is actually stochastic; ¼ 0 implies that the variance associated with the one-sided (efficiency) errors, U 2, is zero, meaning that these deviations from the frontier are better represented as fixed effects in the production function. Therefore, a test of the null hypothesis that ¼ 0 against the alternative hypothesis that is positive is used to test whether deviations from the frontier are stochastic and whether one should proceed with the estimation of parameters related to the sources of inefficiency within the context of a stochastic production frontier. Failure to reject the null hypothesis suggests that the determinants of inefficiency, Z it should be included in the cost function. The parameters of the model (,, and ) are estimated using the maximum-likelihood estimator; the likelihood function can be found in Battese and Coelli (1988). Thus, the technical inefficiency of the i-th club at time t is: TE it ¼ exð U it Þ¼expð z it W it : ð4þ The conditional expectation of TE is defined under the half-normal assumption: " # U i E ¼ i þ i = i i " i1,..., " it ð5þ i = i where ip ¼ i þð1 i Þð " i Þ, i ¼ 1=ð1 þð=t i Þ and i ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U 2 =ð1 þ T iþ. is the mean value of the distribution and T is the time period of the panel, is the standard normal distribution, and is the respective cumulative distribution function (Coelli et al., 1998; Kumbhakar and Lovell, 2000). V. Data To estimate the cost frontier, we used balanced panel data on English Premier League Football clubs in the years 1998/99 to 2002/03 (12 clubs 5 years ¼ 60 observations). Frontier models require the identification of inputs (resources) and outputs (transformation of resources). Several criteria can be used. Firstly, one empirical criterion is the availability of data. It is important for the applicability of the model results that football clubs buy in to the process, that the measures of inputs and outputs are relevant, that the appropriate archival data are available and that more is better in the case of outputs. Usually the criterion of available archival data is used, since it encompasses all the previous criteria and therefore means that the availability of data is the first criterion in input and output selection. Secondly, the literature survey is a way of ensuring the validity of the research and therefore another criterion to be taken into account. The final criterion for measurement selection is the professional opinions of sports managers. In this article, we follow these three criteria. Based on the available data span, we estimated a generalized Cobb Douglas stochastic cost function. We transformed the variables in keeping with the description column of Table 3. We adopted the traditional log log specification to allow for the possible nonlinearity of the frontier.

7 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 constant price 1999 ¼ 100 Log PL Logarithm of price of players measured by dividing total wage by the number of players Log PK1 Logarithm of price of capital measured by dividing the amortisation of players by the number of players Log PK2 Logarithm of price capital measured by dividing stadium facilities expenditures by net assets and liabilities Log points Logarithm of the points obtained in the season Log attendance Logarithm of the number of tickets sold in the season Log turnover Logarithm of turnover in the season, pounds at constant price 1999 ¼ 100 Log population Logarithm of the population in the city of the club Log income Logarithm of the income of the city of the club, pounds at constant price 1999 ¼ 1000 European Dummy variable, which is one for clubs participating in the European cups in each season We noted that the range is narrow, indicating that the clubs in the sample are of a similar size in terms of inputs and outputs, but that there is a very wide difference in the population and income of the club bases. The rationale for using capital-players needs a justification. Football clubs use players as an active, tradable commodity in order to capitalise on their market value. Moreover, football clubs are allowed to amortise the value of the football player on the balance sheet. Results In this study, we estimated a Cobb Douglas stochastic cost function with three input prices (one price of labour and two prices of capital), three outputs (points, attendance and turnover) and contextual variables (population and income in the club area and whether or not the club is playing in the European leagues). C it PL it log ¼ 0 þ 1 log þ 2 log PK1 it PK2 it PK2it PK2 it þ 3 logðpointsþ it þ 4 LogðAttendanceÞ þ 5 logturnover it þ ðv it þ U it Þ U i ¼ 0 þ 1 log Population it þ 2 logðincome it Þþ 3 log European it ð6þ This is the cost frontier model, known as the technical efficiency effects model, found in Coelli et al. (1998), because it accounts for the causes Table 4. Stochastic Cobb Douglas panel cost frontier model Variables Coefficients (t-ratio) Constant ( 0 ) (2.473) Log PL ( 1 ) (3.185) Log PK1 ( 2 ) (2.777) Log points ( 3 ) ( 1.840) Log attendance ( 4 ) (2.085) Log Turnover ( 5 ) (3.064) Constant ( 0 ) (0.190) Log population ( 1 ) ( 1.260) Log income ( 2 ) (3.553) European ( 3 ) ( 1.172) 2 ¼ V 2 þ 2 U (4.939) ¼ U 2 = (3.868) Log(likelihood) Lagrange test Observations 60 Notes: Dependent variable log of total cost. t-statistics in parentheses are below the parameters, those followed by * are significant at the 1% level. of efficiency that are controlled by management (labour, capital, attendance and turnover) and for the contextual factors that are beyond managerial control (population, income, European). The variables were defined and characterized in Table 3. Table 4 presents the results obtained for the stochastic frontier using Frontier 4.1 from Coelli (1996), with a half-normal distribution specification. We can see that the Cobb Douglas cost function specified above fits the data well, as the R-squared from the initial ordinary least-squares estimation that

8 738 C. P. Barros and S. Leach was used to obtain the starting values for the maximum-likelihood estimation is in excess of 85% and the overall F-statistic is We can also see that the variables have the expected signs, with the operating cost increasing with the price of labour and the price of capital-players. Moreover, the total cost decreases with points, attendance, population and European. Finally, the total cost increases with the price of labour, the price of capital, turnover and income. The frontier parameters are all statistically significant and the inefficient error term () is 0.6% of the total variance, which is a low value when compared with other industries, such as banking (Drake and Weyman-Jones, 1996; Ashton, 2001). VI. Efficiency Rankings Table 5 presents the results of the time-invariant efficiency scores computed from the residuals. Technical efficiency is achieved, in a broad economic sense, by the unit which allocates resources without waste, and thus the concept refers to a situation on the frontier. A unit with a score equal to one is on the frontier and those with a score lower than one are above the cost frontier of best practices. The value of waste is measured by the difference between one and the score, so that, for example, the waste of Arsenal is ¼ This is a small waste when compared with the values observed in other sectors of activity. We can see that the mean score is 96%. This score suggests that football clubs could reduce their output cost by 3% without decreasing their input, which, in this case, is the price of labour and the price of capital-players. The maximum football club score was naturally 1, which was achieved by Middlesbrough, while the minimum efficiency score was 96% and was achieved by Arsenal in the first three years and then by Chelsea. The median was 97%, which was smaller than the mean. Therefore, there are more clubs below the mean than above the mean. The SD was 1.3%. These efficiency scores are high in comparison with those found elsewhere in other activities, such as banking (Ashton, 2001). High efficiency scores are consistent with efficient and more competitive organisations, such as those observed in sports. VII. Discussion What is the meaning of these results? Firstly, it can be seen that the cost increases with all factors of production, with the exception of points and attendance. This means that it is costly for football clubs to generate turnover from their activity. However, such generation of turnover is independent of performance on the pitch, since points and attendance contribute negatively to costs. Moreover, the contextual variables play a role in this context, with the population of the club base contributing negatively to costs, reflecting support for the club both through the contributions of season ticket holders and through attendance. Finally, participation in European competitions also contributes negatively to costs, as a result of the bonuses earned in European competitions. 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 function is defined as the sum of the parameters and, in the present case, the cost elasticity is equal to at the sample mean, signifying decreasing returns to scale. Thus, a 10% increase in outputs leads to about a 4.29% increase in costs. The inverse of this is larger than unity, indicating increasing returns to scale on production. This result means that scale is a major issue in the football industry, a result confirmed by the DEA research (Haas, 2003b). Thirdly, we can see that the elite clubs (Manchester United, Arsenal and Chelsea) are the least efficient whenever we include contextual variables in the analysis. The meaning of this ranking, which includes sports and financial questions, is that despite being league champions, these clubs use too many resources to win, and therefore they have a tendency to perform very well in terms of sport but not in terms of finance. The second-tier clubs, those clubs which, despite not winning, represent the country in the European cups (e.g. Liverpool and Newcastle), are better positioned in the efficiency rankings. However, the best situation is obtained by the third-tier clubs, those clubs which are playing in sub-championships of their own, with very different objectives from the few elite clubs. Clubs such as Middlesbrough and Southampton manage their position in the league prudently. Therefore, the general conclusion is that the three clusters of clubs observed have different managerial objectives, and that scale, overspending and managerial skills are necessary to win the league. Fourthly, the mean efficiency of the clubs in the league is relatively high, when compared with other industries (Ashton, 2001). This signifies that, on the pitch, football clubs are scrutinized to the extreme. In the stock exchange, football clubs are scrutinised alongside the other quoted firms. Therefore, despite some failures observed, this is an industry that is much more closely scrutinised than the average organisation, which results in a high level of efficiency.

9 Technical efficiency in the EPL 739 Table 5. Efficiency scores Football clubs Efficiency scores in 1998/99 Football clubs Efficiency scores in 1999/2000 Football clubs Efficiency scores 2000/01 Football clubs Efficiency scores 2002/03 Middlesbrough Middlesbrough Middlesbrough Middlesbrough Southampton Newcastle Southampton Southampton Newcastle Southampton Newcastle Leeds Liverpool Everton Everton Newcastle Everton Aston Villa Aston Villa Everton Aston Villa Liverpool Tottenham Aston Villa Tottenham Chelsea West Ham Tottenham West Ham West Ham Chelsea West Ham Leeds Tottenham Leeds Liverpool Manchester United Leeds Liverpool Manchester United Chelsea Manchester United Manchester United Arsenal Arsenal Arsenal Arsenal Chelsea Mean Median SD Note: The efficiency scores for the 2001/02 season are not displayed, but are available under request from the authors.

10 740 C. P. Barros and S. Leach The emotional discourse that surrounds the game clouds the efficiency drive the industry has adopted. How do we explain the different strategies adopted by the identified cluster of football clubs? These different strategies stem from strategic-based groups and differences in terms of resources. Firstly, strategic-based groups (Caves and Porter, 1977) 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 results in terms of performance (Porter, 1979). While there are different strategic options among the sectors of an industry, because of mobility impediments, not all options are available to each industry, causing a spread of the efficiency scores in the industry. Secondly, the differences in resources available to clubs (Wernerfelt, 1984; Barney, 1991; Rumelt, 1991) mean that football clubs are heterogeneous in relation to the resources and capabilities on which they base their strategies. These resources and capabilities may not be perfectly mobile across the industry, resulting in a competitive advantage for the best-performing football clubs. Purchasable assets cannot represent sources of sustainable profits. Indeed, critical resources are not available on the market. Instead, they are built up and accumulated on the football club s premises, their nonimitability and nonsubstitutability being dependent on the specific traits of their accumulation process. The difference in resources thus results in barriers to imitation (Rumelt, 1991) and in the football clubs inability to alter their accumulated stock of resources over time. In this context, unique assets are seen as exhibiting inherently differentiated levels of efficiency; sustainable profits are ultimately a return on the unique assets owned and controlled 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 low levels of competitiveness and perform efficiently. The general conclusion is that sporting success is a main driver in cost control, together with scale, confirming the importance of the local fan base. Managerial skills are also important and explain part of the behaviour observed. The role played by managerial skills in sports is linked to sporting and financial performance in the football market, decreasing costs and increasing sporting performance in order to perform better on the financial side. The role played by the club s social base in terms of population, income and scale should also be taken into account. A policy for overcoming the identified inefficiencies should start with an analysis of the scale of activities and the adoption of a competitive sporting strategy, as the case of Leeds has shown in confirming the theoretical results, in which the population of the club base is a main driver in economic performance (El-Hodiri and Quirk, 1971; Fort and Quirk, 1995). VIII. Conclusion This article has proposed a simple framework for the evaluation of English Football Premier League Clubs and the rationalisation of their operational activities. The analysis is based on a stochastic frontier model. Benchmarks are provided for improving the operations of sub-optimal performing clubs. Several interesting and useful economic insights and implications are raised by the study. For the group with the lowest efficiency score, adjustment is needed in order to reach the efficiency frontier. Too much expenditure on factors adds to inefficiency, namely when such expenditure is not translated into points. Attendance and turnover increases translate into cost increases, so that managerial procedures to decrease the contribution of these outputs to costs might be a priority for English football managers. The general conclusion is that football clubs have different efficiency scores. Sporting success is a main driver in cost control, together with scale, confirming the importance of the local fan base. Managerial skills are also important and explain part of the behaviour observed. The role played by managerial skills in sports is linked to sporting and financial performance in the football market. More investigation is needed to address the limitations mentioned. References Ashton, J. K. 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