American Journal of Oper ations Research, 2011, 1, 65-72
doi:10.4236/ajor.2011.12010 Published Online June 2011 (http://www.SciRP.org/journal/ajor)
Evaluating the Performance of Iranian Football Teams
Utilizing Linear Programming
Jahangir Soleimani-Damaneh, Mehrzad Hamidi, Nasrollah Sajadi
Faculty of Physical Education and Sport Science, University of Tehran, Tehran, Iran
Received May 24, 201 1; revised June 10, 2011; accepted June 16, 2011
Abstract
In this paper, we utilize Data Envelopment Analysis (DEA), which is a linear programming-based technique,
for evaluating the performance of the teams which operate in the Iranian primer football league. We use Ana-
lytical Hierarchy Process (AHP) technique for aggregating the sub-factors which involve in input-output fac-
tors, and then DEA is used for calculating the efficiency measures. Also, AHP is used to construct some
weight restrictions for increasing the discrimination power of the used DEA model. For calculating the effi-
ciency measures, input-oriented weight-restricted BCC model is utilized.
Keywords: DEA, Linear Programming, Sport Management, Football
1. Introduction
Awareness of the performance scores and analyzing the
effectiveness (productivity) of the units under the control
of a manager, using the scientific methods, is one of the
most important tools that could help the managers to make
better decisions and can be lead in optimal usage of the
current resources. Based upon the efficiency analysis, the
manager can decide about contracting or expanding the
units [1-3]. Furthermore, experiences show that measuring
and analyzing the efficiency of units can lead to a feeling
of competitiveness among the subsystems, and this would
have a positive effect on the overall performance of the
system [1,2]. Also, as a great advantage, the performance
analysis of the system can help the managers to sketch a
suitable plan for allocating the budget, common revenues,
rewards or shared costs to DMUs [1].
Data envelopment analysis (DEA) is a non-parametric
linear programming-based technique for measuring the
relative efficiencies of a set of decision making units
(DMUs) which consume multiple inputs to produce mul-
tiple outputs. This technique was initially proposed by
Charnes et al. (CCR model) [3] and was improved by
other scholars, especially Banker et al. (BCC model) [4].
More than 4000 journ al articles are now published in this
field [5]. DEA has allocated to itself a wide variety of
theoretical and applied research. See monographs [1,2]
and also the review paper [5] for more details about DEA
and its applications.
There are many publications that address the applica-
tions of DEA in football frameworks. Most of these pa-
pers are studying the English Football Premier League.
The problem of hidden action in organizations makes
direct measurement of managerial performance problem-
atic. As mentioned in [6], in Eng lish association football,
hidden action is unlikely to be as serious a problem be-
cause the owner observes the manager’s performance
each time the team plays. Barros et al. [7-10] utilized the
random stochastic frontier model, DEA models, and an
econometric frontier model to examine the (technical)
efficiency of the English football Premier League. In [8],
Barros and Garcia-del-Barrio considered a stochastic mo-
del for performance analysis from 1998/99 to 2003/04
which disentangles homogenous and heterogeneous va-
riables in the cost functio n.
Guzman and Morrow [11] used information from clubs’
financial statements as a measure of corporate perform-
ance. To measure changes in efficiency and productivity,
they utilized the Malm quist non-param etric technique.
Barros et al. [12] and Calôba and Lins [13] utilized
DEA for performance evaluation in Brazilian Football
League. Barros et al. [12] introduced a two-stage boot-
strapped DEA analysis model. Calôba and Lins [13] in-
corporated the value judgments, applying a method to
consolidate the results of the national and international
matches.
Tiedemann et al. [14] studied the assessing the per-
formance of German Bundesliga football players, cover-

66 J. SOLEIMANI-DAMANEH ET AL.
ing the playing seasons 2002/03 to 2008/09, utilizing a
non-parametric meta-frontier approach. Haas et al. [15]
utilized some basic DEA models for evaluating the per-
formance of German football teams. The efficiency
scores obtained by Haas et al. are not correlated with rank
in the league. Also, th ey studied the sources of the ineffi-
ciency and utilized some tests for sensitivity analysis of
the results with respect t o di ff erent i nput-output factors.
Some scholars used DEA to evaluate the performance
of Spanish football teams. Escuer and Cebrian [16] stud-
ied this league, comparing the results that they actually
obtain with those that they should have obtained on the
basis of their potential. Barros and Garcia-del-Barrio [8]
analyzed the efficiency drivers of a representative sample
of Spanish football clubs, in the period 1996–2004, by
means of a two-stage DEA procedure. Guzman [17]
studied the Spanish league from a financial point of view.
In another study, Gomez and Tadeo [18] performed a
more comprehensive study on the Spanish league. They
assessed the performance of Spanish professional foot-
ball teams at various competition levels, namely, League,
King’s Cup and European competitions (Champions
League and UEFA Cup), using DEA and directional dis-
tance functions. They used the gap between the result
obtained by a team in a given competition and that ex-
pected according to its potential as a proxy of the degree
of satisfaction that fans should feel; the narrow er the gap,
the greater the level of satisfaction.
Jardin [19] studied the efficiency of the French foot-
ball clubs (Ligue 1) from 2004 to 2007 using weight-
restricted DEA m odel s.
There is a comparative research work on performance
assessing in Greece and Portugal football league. Douvis
and Barros [20] estimated changes in total productivity,
by means of DEA and Malmquist index, applied to a
representative sample of football clubs in Portugal and
Greece. They ranked the football clubs according to their
change in total productivity for the period 1999/2000 to
2002/2003, seek ing out tho se best p ractices that will lead
to improved performance in the market, and concluding
that some clubs experienced productivity growth while
others experienced a decrease in productivity. As another
comparative research study, Boscá [21] analyzed the
technical efficiency of Italian and Spanish football, using
basic DEA, during three recent seasons, to shed light on
the sport performance of professional football clubs.
The only Asian country which we found a research
work on its football league is Korea. Lee [22] studied
three Korean sport league, including football.
In addition to the above mention ed papers, which dealt
with the assessing in football leagues in different coun-
tries, there is a paper studying the UFEA league. Pa-
pahristodoulou [23] used basic DEA to estimate the per-
formance of all 32 participated football teams in the
UEFA Champions League (CL) tournament 2005-2006,
based on official match statistics from all 125 matches.
The main aim of the paper is utilizing DEA for meas-
uring the efficiency of the teams operating in the Iranian
primer football league. In fact, we provide a hybrid tool
consisting of Analytical Hierarchy Process (AHP) and
DEA. The AHP instrument is used for aggregating the
sub-factors which involve in input-output factors, and
then DEA is used for calculating the efficiency measures.
One of other techniques which are used in the present
study is AHP. The concept of AHP was developed by
Saaty [24] in the 1970s and is very useful when the deci-
sion-making process is complex. AHP is an approach to
decision making that invo lves stru cturing multiple ch o ice
criteria into a hierarchy, assessing the relative impor-
tance of these criteria, comparing alternatives for each
criterion, and determining an overall ranking of the al-
ternatives. Indeed, AHP allows a better, easier, and more
efficient identification of selection criteria, their weight-
ing and analysis [25].
The rest of the paper unfolds as follows: Section 2
deals with the basic DEA models; Section 3 is devo ted to
the main results and; Section 4 contains conclusions.
2. Basic DEA Models
Let us assume that we have n DMUs, that DMU
j
;
1, 2,,jn

 uses input levels ij
x
; to
produce output levels rj , . 1, 2, ,rs
,im
y1, 2,


t ,Le
j
j
x
y
denote the input-output vector of DMU
j
. Considering
DMUo


,
oo
x
y, which as the unit un-
der assessment, the following model contains both CCR
and BCC models in input orientation. These two models
were provided by Charnes et al. [3] and Bank er et al. [4],
respectively, as the first DEA models.

,

n1, 2o
M
inimiz

1
.. 0,
n
ojj
j
st xx 



1
,
n
j
jo
j
yy 





in which

:0
CCR



and
1
:1,
n
BCC j
j


0.


 




These two models assess the DMUs under constant

J. SOLEIMANI-DAMANEH ET AL.
67
returns to scale (CRS) and variable returns to scale (VRS)
assumptions of technology, respectively. After introduc-
ing these basic DEA models, numerous applications of
this instrument have been reported in financial services,
sport frameworks, agricultural, health care services,
education, manufacturing, telecommunication, supply
chain management, and many more. See some bibliog-
raphies of DEA in [1-5].
3. Main Results
3.1. Novel Applications of AHP
Although AHP is usually used for ranking the prefer-
ences in an MCDM context, in this paper, we address
some novel applications of this technique.
One of the very important points which must be no-
ticed during using of DEA for applied purposes is the
number of input-output factors compared with the num-
ber of the DMUs. As it can be seen in the DEA literature
[1, 2, 26, 27], if the number of inputs and outputs is not
very less than the number of DMUs, then the efficiency
scores of the units increase and so the discrimination
power of DEA models decrease. To overcome this, we
must combine the input-output factors and reduce the
number of inputs and outputs. To do this, in this paper,
we estimate the value of the sub-factors which involve in
inputs/outputs, by using the AHP technique (utilizing a
group decision making manner), and then we calculate
inputs/outputs as a weighted sum of the sub-factors. We
do it through the following AHP steps:
Step 1: Constructing the hierarchy analysis tree: To
start the AHP approach, we need a hierarchy analysis
tree as depicted in Figure 1. The tree considered in the
present paper has three levels. The purpose of the study
i.e. assessing the performance of Iranian football Primer
League teams (P) is placed at the highest level. The sec-
ond level is then divided into the two categories of input
and output. In each category the decision criteria are
placed in the second level and the next level is allocated
to decision options (input-output sub-factors). As it can
be seen in Figure 1, in the category of inputs: (F) refers
to the fixed assets of each team and; (W) refers to the
amount of wages paid to the employees. This criterion in
turn is divided into three sub-factors: player wages (PW),
coach wages (CW), and staff wages (SW). In the cate-
gory of outputs, three criteria are also taken into account:
here (P) refers to the points received by the team, (S)
stands for the number of spectators watching the team’s
matches, and (R) refers to the team’s income at the end
of the season. The values of the above-mentioned factors
for considered clubs have been obtained from the infor-
mation sector of the Iranian Football Feder ation [2 8].
Step 2: Prioritizing the criteria through the use of
paired comparisons (weighing): Regarding the above
discussion, after obtaining the above mentioned factors
for each state, the second step is weighing these factors.
To this end, we used the pairwise comparison matrices
and a group decision making manner. Considering each
output, 50 top sports experts filled out these pairwise
comparison matrices. In fact, for each pair of the criteria,
50 experts in top sport management were asked to an-
swer the following question by making paired compari-
sons and to determine the relative value of a given prior-
ity as compared to the other priority by selecting a num-
ber from 1 to 9: “how do criterion A and B relatively
differ in importance?” In rating system, 1 indicates equal
importance and 9 denotes the maximum difference of
each criterion from another in importance. The values
were made normal for determining the average weight of
each criterion. After this, we used the geometric mean
for aggregating the experts’ opinions, see [24]. Tables
1-3 denote the pairwise comparison matrices after ag-
gregating the experts’ opinions.
Now, our aim is to conduct the proper measurements
for determining the priority of decision element by the
use of the information provided in paired comparisons
matrix (A). Here we normal each column of paired com-
parisons matrix using the following formula:
ij
jtj
t
A
Ni
A

The resulting matrix is called “normal comparisons
matrix”. Afterwards, the mean of the numbers on each
row of normal comparisons matrix was obtained as fol-
lows:
ij
j
i
N
mn 

Figure 1. AHP tree.
P
Input Output
F
CW
W P S R
PW
SW

68 J. SOLEIMANI-DAMANEH ET AL.
Table 1. Paired comparisons matrix for output criteria.
P R S
P 1 0.56243 2.091614
R 1.777997733 1 2.959909
S 0.478099721 0.337848 1
Table 2. Paired comparisons matrix for paired criteria of
inputs.
W F
W 1 1.20217726
F 0.831824084 1
Table 3. Paired comparisons matrix for the criterion of
wages.
CW PW SW
CW 1 0.503489 6.31888
PW 1.986142 1 6.973399
SW 0.158256 0.143402 1
in which n is the number of the rows. This mean refers to
the relative weight of decision elements against the rows
of the matrix.
Step 3: Determining the overall value of each option:
In the final step of the process, the obtained numbers
from each option were combined together in order to de-
termine the value of each criterion. For ranking the deci-
sion options, the relative weight of each element is multi-
plied by the weight of higher elements in order to deter-
mine the overall weight of that element. The weights ob-
tained from the above procedure for each sub-factor,
which can be interpreted as the value of them, have been
listed in the last column of Table 4. After obtaining the
weights of the sub-factors, we calculated the inputs as
weighted sum of these sub-factors. In Figure 1, it can be
seen that the outputs have been obtained without using of
AHP.
It is worth mentioning that, after using the AHP pro-
cedure, we calculated the consistency Ratio (CR) of each
used pairwise matrix too, and it was lees than 0.1 for
each matrix.
Table 4. The overall value of considered criteria.
Weight Sub criteria Weight Criteria
P.W 0.550676
C.W 0.361873
W 0.714286
S.W 0.08745
- -
- -
- -
Input
F 0.285714
- -
R 0.496231 - -
P 0.322138 - -
Output
S 0.181632 - -
In addition to the above-mentioned application of
AHP, as another application of this instrument, we use it
to construct some weight restrictions for imposing to
DEA models to increase the discrimination power of
these models [26,27]. It is worth mentioning that incor-
porating the value judgments and managers’ opinions in
efficiency analysis plays a crucial role to produce real
efficiency measures and to have a more realistic per-
formance analysis [1,2,26,27]. We have incorporated this
here, using a group decision making manner in an AHP
framework.
To evaluate the Efficiency of Iranian Football Primer
League teams, we selected the input-out put factors with a
reference to the literature and with respect to the avail-
ability of data. Accordingly, two inputs and three outputs
were selected.
The first input constitutes of the amount of the wages
paid to coaches, players and staff. Nowadays, sport clubs
invest huge amount of money in recruiting first grade
coaches to guarantee the team’s success. In their studies,
Haas et al. [16], Barros and Leach [7], and Barros et al.
[12] used this factor as an input for assessing the per-
formance of football teams. Furthermore, player wages
constitute a huge part of each team’s expenditure. The
results of the studies of Szymanski and Kuypers [29],
Szymanski and Smith [30] have indicated that there is a
relationship between the sporting success and the amount
of player wages in a team. Haas et al. [15] in their re-
search have taken this factor into account as an input in
assessing the performance of football teams. Staff such
as physician, massager, procurement manager, trainers is
considered as one of the factors effective in a team’s
success. Therefore, the wages paid to these people have
also been taken into account in this study.
The second input incorporates the teams’ fixed assets.
For any football team one of the deciding factors in its
achievement is no doubt the available facilities for that
team. Since the fixed assets of each team such as private
stadium, private practice camp, and administrative build-
ings to some extent reflect the teams’ available facilities,
this criterion has been considered as an input.
Three outputs have also been considered:
1) The points gained by each team at the end of the
season, 1: This variable assesses the sporting performance
of a football team. Guzman and Morrow [11], Haas et al.
[15], Douvis and Barros [20], Jardin [19], Lee [22], Bar-
ros et al. [12], have considered this indicator as an output.
2) Season total revenues: This variable indicates the
teams’ financial success [11]. In this study the club’s
incomes involve the incomes received from TV broad-
casting rights, sponsorship, trade, selling tickets, player
transfer and the other sources of income. Haas et al. [6],
Guzman [17], Douvis and Barros [20], Jardin [19], Bar-

J. SOLEIMANI-DAMANEH ET AL.
69
ros et al. [12], have utilized this variable as an effectual
factor in team’s performance for assessing the perform-
ance of football teams.
3) The rate of attraction of spectators to stadium: In
their studies, Douvis and Barros [20], Haas et al. [15],
and Lee [22] have utilized this variable as one of the in-
puts in measuring the efficiency of football teams.
To obtain the inputs-outputs of the clubs, we have
used the AHP tool. AHP is on e of the most popular mul-
tiple criteria decision making (MCDM) approaches and
was first introduced by Saaty [24]. This technique is
based on paired comparisons. Because of the high poten-
tiality of AHP technique in solving the MCDM problems,
it is extensively used in various areas such as taking de-
cision about expanding oil fields, measuring the per-
formance, agriculture, resource allocation, and decision
making in general.
After obtaining the above mentioned sub-factors for
each state, we performed AHP for weighing these sub-
factors, as mentioned above. After obtaining the weights
of the sub-factors, we calculated the inputs as weighted
sum of these sub-factors. In Figure 1, it can be seen that
the outputs have been obtaine d witho ut using of AHP.
3.2. Using of DEA
For starting the DEA application, the first stage is se-
lecting a convenient model. Since we would like to let
the small units to be efficient too, we choose the variable
returns to scale assumption on technology [1-5]. Also,
for incorporating the value judgments and managers’
opinion in analysis, we have used the weight-restricted
models [26, 27]. For constructing the weight restriction
we applied AHP again, and the obtained values of inputs
and outputs have been listed in the third column of Table
4. Due to these values, we imposed the following weight
restrictions:
13
3,uu ,
12
1.5uu12 2.5 .uu
Regarding the above discussion, we selected weight-
restricted BCC model, as follows:
13
12
12
3
max
..
0,1,2,, ,
1,
30,
1.5 0,
2.5 0,
0.
oo
jo j
o
uy u
st
uy u vxjn
vx
uu
uu
vv
v

 






Then we used the got input-output values in this model
using the GAMS software. The obtained efficiency
scores have been listed in Table 5.
In fact, the considered models estimates the efficiency
score based upon a weight restricted PPS (WRPPS), and
so, it can be written as follows too:




min :,.
oo
x
yWRPPS  
3.3. Results and Discussions
A ranking based upon the obtained efficiency scores can
be seen in the last column of able 2. The results given in
Table shows seven teams (i.e. 39%) are efficient. Sepa-
han, Esteghlal TEH, Shahin, Abumoslem, Tirakhtor,
Moghavemat and Steelazin possessed the efficiency
score of 1 and Esteqlal AHV had the lowest score with
efficiency score of 0.51.
The average of the efficiency scores is 0.80 and 45%
of the teams has a score more than this average. Al-
though the average of the scores is approximately good
(though it is not fabulous), there is only one inefficient
team (Perspolis) having a score more than this average.
This shows that the difference distance between the per-
formances of the first-level teams (efficient ones) and
other ones is high.
The efficiency average in Iranian league is less than
the efficiency average in French League during 2004 to
2007 with the efficiency average of 0.93 [19], English
Premier League with the efficiency average of 0.85 [11],
and German Bundesliga with the efficiency average of
0.92 [15]; having said that, the above scores in other
leagues have been obtained using different models and
different efficiency criteria.
Table 5. Value of obtained efficiency.
Tea
m
Score Ran
k
Perspolis TEH0.819179 8
Foolad KHU 0.764021 10
Sepahan ESF 1 1
Esteghlal TEH 1 1
Shahin BUSH 1 1
Abumoslem MSH 1 1
Zobahan ESF 0.670614 14
Saba QOM 0.607351 15
Mes KRM 0.56504 17
Malavan ANZ 0.778423 9
Steelazin THE 1 1
Paas HMD 0.691093 13
Peykan GHZ 0.727029 11
Rahahan RAY 0.724115 12
Saipa KRJ 0.603627 16
Tirakhtor TAB 1 1
Moghavemat SHZ 1 1
Esteghlal AHV 0.509958 18

70 J. SOLEIMANI-DAMANEH ET AL.
The results indicated that while Abumoslem, Mogh-
avemat, and Shahin achieved the efficiency score of 1,
Moghavemat and Shahin were ranked 16 and 13, respec-
tively, in the final ranking and Abumoslem due to its
weak results has been relegated to the lower league
(League 1). Concerning the results it should be pointed
out that the efficiency score of these two football clubs
does not reflect their achievement. What these efficiency
scores actually denote is that these three clubs have fully
exploited the available resources and regarding their
available facilities produced acceptable outputs, so it will
not be reasonable to expect more. During the period of
investigation, Abumuslem Football Club, in spite of sub-
stituting five coaches, was relegated to a lower league.
Such high frequency in substituting several coaches in-
dicates that the club managers have recognized the
weakness in this area as the leading factor in the failure
of the team and by frequent substitution of the coaching
board of the club has tried to improve the conditions.
However, with respect to the results of the study and the
position of Abumoslem among the clubs with the effi-
ciency score of 1, it is safe to say that this football club
has completely took the advantage of the available re-
courses and with respect these resources, one cannot ex-
pect more from the coaches. The actual solution for these
three clubs is more investment in recruiting players and
coaches and effective management of the resources for
achieving either financial or sportive success. In evalu-
ating the performance of their coaches, the club manag-
ers are also recommended to take the available resources
into account so that their expectation of the caching
board will be proportional to the club’s facilities.
As the winner of the championship, Sepehan Football
Club enjoys an acceptable efficiency ranking and was
efficient. By studying the conditions of this club in two
input indicators, some amazing results were obtained. In
the ranking of the clubs on the basis of the wages paid to
the coaches, this club was ranked the first. This club has
paid their coaches twice more than the second club and
21 times more than the last club in this ranking. Likewise,
in the part of the wages paid to the players, this club was
also ranked the first. Having the great output and achiev-
ing the highest score, this club has invested huge amounts
of money in recruiting players and caches. The efficiency
position of Sepahan, is due to the maximum value of its
first outputs, regarding Theorem 4.3 in [1]. And, this team
might not be efficient under other assumptions on the
production technology. In fact, this team can be consid-
ered a very big decision m aking unit in DEA analysis.
Zobahan, as the second team in the final season table,
obtained the 14th rank in our obtained ranking. It is due
to its outputs related to revenue and spectators. In fact,
this team was not successful from these two viewpoints.
The TV rights in many countries constitute a large part
of a club’s revenues. Accordingly, success or failure of
clubs acts as a deciding factor in attracting TV channels
to obtain the TV rights. Nevertheless, football league
broadcast in Iran is confined to public TV networks and
there is no market for purchasing the TV rights.
With respect to the aforementioned points, it is safe to
say that in Iran, a club’s championship by no means con-
tribute to raise the fiscal revenues and a club’s invest-
ment in recruiting players and coaches is no guarantee of
championship which as a result of that the increased in-
come offsets the costs. However, this should not be
overlooked that championship can be a major factor in
attracting spectators to the stadiums and accordingly by
selling tickets increasing the club’s fiscal revenue. Be-
cause of failure in attracting spectators, a club such as
Zobahan has earned a little amount of income from sell-
ing the tickets.
From Table 5, it can be seen that 10th to 17th p ositions
in the efficiency ranking are related to the governmental
clubs which are dependent on industries such as car
companies and steel production. This question might be
posed here that despite of being apparently successful
clubs what caused such clubs to be in the lower effi-
ciency ranking? Considering the wages paid by these
clubs, it is observed that these are among the most ex-
travagant clubs with paying wages. For instance, in the
ranking of the clubs on the basis of the wages paid to
players, four of these clubs is ranked in first till seventh.
The same story also appears in the wages paid to coaches.
Regarding these results, it is argued that by accessing the
rich state recourses, these clubs have invested huge
amounts of money in players and coaches in order to
reach sportive achievements. At the end, some of these
clubs have achieved success while the others have not.
As it was mentioned, however, th ese club s do not en jo y a
promising efficiency score. It is to be noted that in order
to achieve efficiency, sportive achievement is not enough
and those clubs are efficient which are successful in both
financial and sportive domain [12]. The too much wages
paid to players and coaches has been, in fact, one of the
major factors in the inefficiency of these c lubs . In Fr en ch
Leagues [19], American Leagues, and German Leagues
[15] too much wages paid to players and coaches has
also been identified as one of the inefficiency factors.
Furthermore, the infinite availability of state resources
for these clubs and, as a result, making no attempt to
attract other fiscal revenues has been the other factors in
the inefficiency of these clubs.
With respect to the low efficiency scores of govern-
mental clubs and also low average score in efficiency, it
seems that the privatization of the clubs and incorporat-
ing them into the stock market can be proposed as gen-

J. SOLEIMANI-DAMANEH ET AL.
71
eral strategies to improve efficiency in Iranian Football
League. In the early 90s in England, due to increased
club owning expenditures (particularly player wages),
privatization of clubs was introduced as an effective
strategy to achieve both financial and sportive success
which proved to have desirable outcomes [31]. What
happens in privatization is cutting the level of state sub-
sidies. By doing so, the sport clubs should act as finan-
cial corporations, which in such conditions income-
earning is a fundamental strategy to survive. In other
words, the corollary of privatization will be cutting the
expenditures, an increase in incomes and consequently
club’s efficiency improvement.
In sequel, Pearson Test was used to determine the re-
lationship between some of the variables. The results of
the test showed that at a significance level of 0.05, there
was no significant relationship between the clubs’s
ranking in the Efficiency Table and its ranking in the
Champion League Table. These results denote that de-
spite of spectators’ belief, the club with the highest
scores is not the best club of the league. This means that
there is no relationship between the degree sportive suc-
cess of a club and the degree of its efficiency and the
champion of the league is not necessarily the most effi-
cient club. These results are consistent with the results of
Barros et al. [12] in Brazilian Football League, Jardin
[19] in France football League, Haas [15] in English
Football Premier League, Haas and Kocher [15] in Ger-
man Football League. The results were, however, incon-
sistent with the results of Barros and Leach [7].
The results of Pearson Test demonstrated that at a sig-
nificance level of 0.05, there is a relationship between
the efficiency degree of the clubs and the population of
the host city. It means that the clubs in the over popu-
lated cities have less efficiency compared with clubs
from small cities with less population density. The re-
sults of this part of the study are inconsistent with the
results of Barros et al [12] in Brazil. However, Jardin [19]
reported similar results in France.
Furthermore, the results of the Pearson Test demon-
strated that there is a significant relationship between the
efficiency degree of the clubs and the amount of the
wages paid to players and coaches. In other words, there
is no significant relationship between the degree of spor-
tive achievement and the amount of expenditure on re-
cruiting coaches and players. These results are similar to
the results of Kern and Sussm uth [32] in Ger m an League.
4. Conclusions
In this paper, we used a hybrid approach, consisting of
DEA and AHP, for analyzing the performance of Iranian
football primer league team. DEA models have been util-
ized for obtaining the efficiency scores. AHP helped us
for obtaining the output factors and also to obtain some
weight restrictions for imposing to DEA models. The
results of the study, demonstrated that in 2009-10 season,
seven teams (i.e. 39%) were efficient. Sepahan, Esteghlal,
Shahin, Abumoslem, Tirakhtor, Moghavemat and Stee-
lazin possessed the efficiency score of 1 and Esteqlal-
e-Ahvaz had the lowest score with efficiency score of
0.51. The average of the efficiency scores is 0.80 and
45% of the teams has a score more than this average.
Although the average of the scores is approximately
good (though it is not fabulous), there is only one ineffi-
cient team (Persplois) having a score more than this av-
erage. The results of the Pearson Test showed there was
no significant relationship between the team’s ranking in
the efficiency Table and its ranking in the Champion
League Table, however, there is a significant relationship
between the efficiency degree of the clubs and the amount
of wages paid to players and coaches and between the
efficiency degree of the clubs and the population of the
host city. Generally speaking, the deciding factor in inef-
ficiency of Iranian football clubs is attributed to the too
much wages paid to players and coaches. On the other
hand, the government-owning of most of the clubs and
ineffective management and as a result low profitability
of the clubs can be considered as one of the causes of
inefficiency. In general, regarding low average score in
efficiency of clubs in Iranian Football Primer League,
particularly the government-own clubs, privatization of
clubs is proposed as an effective strategy to increase effi-
ciency in Iranian Football Primer League clubs.
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