A Study of Factors Affecting the Demand for

X OPHΓIA CHOREGIA HOREGIA Sport Management International Journal Scientific Forum in Sport Management SMIJ VOL. 7, Number 2, 2011 D.O.I: http:dx.doi.org/10.4127/ch.2011.0057 Ehsan Javanmardi 1, Dr. Kazem Noghondarian 2 A Study of Factors Affecting the Demand for Watching Football in Stadius 1 Management Department, Shiraz University, Iran 2 College of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Abstract This paper is to find the factors effective on football matches watching demand in stadiums. The factors effective on the demand are divided into 4 categories; economical, environmental, appeal, and geographical/demographical factors which converted into 23 independent parameters by virtue of the device appropriate to gather related information. In this research, Iranian super league was selected as the subject of the study and We limited our study to three cities; Shiraz, Isfahan, and Tehran. Finally by virtue of estimating the regressions and estimating the Ordinary Least Square and Minitab software three equations were gained to foresee the number of the spectators. Validation of the models was conducted by lack of fit test, studies on the remnants such as Darling - Anderson test of normality, and Durbin Watson statistics for remnant independence test and the issue of their variance being fixed, and the study of lack of complex collinearity between independent variables using Variance Inflation Factor (VIF). We used step - by - step regression method and regression of all probable conditions. By virtue of the conclusions of the regression equations we found that there is a structural difference between capital and the cities and the factors creating attractions such as their recent successes, history and the quality of the teams have the most effects on the fans demand to attend in the stadiums. Key Words: for football. Regression analysis, attendance demand, demand

6 SMIJ VOL. 7, Number 2, 2011 These days, the role of economic factors in football is undeniable and it is the attendance of spectators and club fans which leads to the increase of the income of the clubs and improvement in their economic situation. A study of the revenue of 21 clubs in Brazil suggests that 8% of the income of the clubs comes directly through selling tickets [1]. Besides, 29% of the income was due to TV broadcasts and 17% came from the financial supporters of the clubs. According to a research conducted by Baranzini (2008) [2], TV channels, financial supporters, and sponsors mostly go to the clubs for which there is more demand on the side of the spectators. Literature Review Studying spectators demand in football was first introduced by Hart and his colleagues (1975) [3]. He studied the effect of human and geographical factors as well as the success of clubs on the spectators demand in four football leagues in England. In the same year, Rivett studied the effect of population factor and success on the average number of spectators in each season using a minimum square error estimator [4]. In 1982, Bird, using the data he had compiled from 1948 to 1975 from the league of England, examined the role of ticket price, time of matches, national revenues, and other variables including the success of England in the World Cup competitions [5]. In 1994, Welki and his colleague Zelatoper fitted the number of the spectators of each football match against 14 independent factors including victories of the host team in a season [6]. In 1995, Dobson and Goddard assumed the logarithm of the number of spectators as the dependent variable and introduced a regression model with 3 principle and 9 secondary variables [7]. Following their research, in 1999, Jones and his colleagues designated 36 variables and studied the effect of these variables on the spectators demand for the rugby matches in England [8]. After that, in 2002, Forrest and Simmons studied the effect of probabilities and the uncertainty of the result on the spectators demand in the football league of England and fitted a second degree model with two variables of the second degree and 8 variables of the first degree. In 2003, Price and Sen introduced a regression model based on the dependent variable of the number of spectators in each match and 36 other dependent variables including club championships during the last decade and the number of victories of each team in the last eleven matches [10]. In 2004, Owen and Widerson studied the effect of the uncertainty of score and quality of players on the number of spectators. They had considered 26 variables from New Zealand rugby league [11]. And spectators demand in Switzerland was the subject of another study by Baranzini and his colleagues. They also emphasized the effect of appeals of a match on the decision of the TV networks in broadcasting games [2]. In the following study, we first introduce the research methodology, considered factors,

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 7 and the structure of Iranian league. After that, we review the regressive analyses and, eventually, analyze the results. Research Methodology Following the studies on the literature of research, it was found out that most of the studies have used regression models and statistical assessments such as Tabit or Least minimum squares for the estimation and determination of effective factors on the demand for watching football matches in the stadiums. We, also, used statistical methods and regressive modeling in order to determine the effective factors on the demand for watching football matches in the stadiums. Using regressive modeling, we can determine the level of the effect of each variable and the impact of each factor on the spectaotrs demand. More importantly, by introducing a formula for predicting the number of the spectators in the stadiums, we can determine the role of each factor and estimate the number of spectators in each match. Validation of the model was conducted by lack of consistency test, studies on the remnants such as Darling - Anderson test of normality, and Durbin Watson statistics for remnant independence test and the issue of their variance being fixed, and the study of lack of complex collinearity between independent variables using Variance Inflation Factor (VIF). Among regression formulae, we are looking for the best model which can provide the most suitable combination of independent variables discovers the highest percentage of factors affecting the number of spectators demand. In order to find the best combination of the regression model, we used step - by - step regression method and regression of all probable conditions. We also selected Minitab 13 software for analyzing our results. In this research, Iranian super league was selected as the subject of the study. Since 2001, Iranian super league became a professional league. We limited our study to three seasons; 2005/2006, 2006/2007, 2007/2008 and only three cities; Shiraz, Isfahan, and Tehran. These cities were selected because they have attended the league for a long time and have more than one team which participates in the super league and, therefore, a better comparison can be drawn. In our study, we chose two teams from each city; Bargh and FajrSepasi from Shiaz, Sepahan and ZobAhan from Isfahan, and Esteghlal and Persepolis from Tehran. It is noteworthy that in Iranian super league matches stadiums are rarely filled and matches are held in stadiums which can still accommodate more spectators. Therefore, we do not have to deal with the problem of censored data with regard to dependent variables in the regression model. We divided factors affecting spectators demand into 4 categories; economical, environmental, appeal, and geographica/demographical factors. In total, we

8 SMIJ VOL. 7, Number 2, 2011 considered 26 independent variables against the dependent variable of spectaotrs demand in each match which is shown as (y) or, if necessary, its logarithm form as Log(y). The following are the independent variables: 1. The independent variable of time of the match (time): is a virtual variable and if the match starts an hour prior to or an hour after the sunset it is assumed 1 and otherwise it is assumed 0. 2. The independent variable of the day of the match (day): is a virtual variable and if the match is held on an official holiday or the day before an official holiday it is assumed 1 and otherwise 0. The first 13 days of Farvardin were considered holidays, too. 3. The independent variables of the month in which the match is held: Month variables were defined as virtual variables. We considered Farvardin as the base month. And in the months of Tir and Mordad no matches had been held during these years. If a match is held during a month the value of the variable is 1, if not, its value is 0. In sum, we considered 9 virtual variables for the months of the year which were represented by M1 through M12. 4. Variable of the population of the city where the match is held (POP): represents the population of the city where the match is held. In fact, this variable represents the potential population of the spectators. 5. Variable of the distance between the cities which hold a match (Dist): It can have two effects; first the effect of traditional competition factor between the two cities and, second, the problems distance can cause for the spectators of the guest team. This variable was considered as the land or road distance between the two cities. For the teams from the same city this variable was considered 0. 6. The variable of the points of the host team in the previous matches (Point): represents the points made by the host team in the last 3 matches during one month. These points include the points made by the host team in other official matches such as Asian Clubs Cup or Iranian omissive cup and can also model the short memory of the spectators or even the recent victories of the team. 7. Variable of the number of the matches held in the same place during the past 5 days (Num): represents the number of the matches held in the same place during the past 5 days. Using this variable, we intended to find out if people are bored by watching many matches within a short span of time and, consequently their demand decreases or are they compelled to follow the matches and their demand increases? 8. Variable of coincidence of a match with one of Esteghlal s or Persepolis s. (Same): If the match is held simultaneously with that of Esteghlal or Persepolis its value is considered 1, if not, it is assumed 0 and the variable is considered to be virtual.

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 9 9. Variable of the week of the match (Fix): represents the week in which the match is held. It is a continuous variable which is represented as the current phase of the season. It may be assumed that, probably, this variable will have a linear relation for those teams for which the final weeks do not make a difference and a non - linear or even second rate relation for those teams whose fate is determined during the last weeks. This variable can represent the appeal factor. 10. Variable of team ranking: In the league tables, this variable is represented as Home Rank for the host and as Away Rank for the guest teams. They are measured as a team s ranking in the league table prior to a given match. 11. Variables of age or the number of matches for a club: These variables which are represented as Home Age for the host and Away Age for the guest teams indicate the age of the clubs. 12. Variables of the number of championships of the teams: These variables are represented as Home Champ for the host and Away Champ for the guest teams. And they represent the number of championships in domestic leagues since 1991. We omitted the variable of the difference between the rankings of the teams because it had a linear relationship with other variables; the variable of unemployment rate was also omitted since it was approximately equal for the three cities, and the variable of the area of the city was omitted from the model, too, because there was not a reliable source. The number of variables in the final model is 23. Variables 1, 2, and 3 are categorized as environmental factors, variables 4 and 5 as geographical/demographical factors, and the rest of the variables as appeal factors including success, psychological factors, and the quality of teams. In case we need to use the second or third degree variables in the regression model, the variables are followed by numbers 2 and 3. We gathered 197 observations out of which 101 were about Shiraz and Isfahan and 76 about Tehran and the rest were omitted due to various problems. The required information for this research were acquired from the results of the general census of people and buildings in 2006 published by the Presidential Strategic Planning and Supervision Deputy and the Iranian Statistics Centre, Keyhan Varzeshi weekly, Donyaye Varzesh weekly, the official website of the Iranian Football Clubs Association, and Atlas of Iran s Roads. Findings of the Research Following initial observations, we performed a simple statistical study on the independent variable in three modes of observations of the entire country, Tehran, and other cities. The results of this initial study can be seen in Table 1.

10 SMIJ VOL. 7, Number 2, 2011 Table 1. A simple statistical survey on dependent variable. Dependent variable Mean Standard error Median min max skirt Number of observations Whole country 17297.5 22809.3 5000 100 100000 99900 177 Townships 3065.84 2611.45 2000 100 15000 14900 101 Tehran 36210.5 24011.8 30000 5000 100000 95000 76 We fitted 3 regression models for the observations of the entire country, Tehran, and other cities. As a first step to determine the relationship between each independent and dependent variable, both linear and non -linear, we first fitted each variable separately against its dependent variable in order to determine the best regressive form of each independent variable on its dependent variable. Besides, due to the huge difference in the number of spectators between Tehran and other cities, we used the Chow test to prove that there is a structural difference between the observations of Tehran and other cities. According to line 10 which displays the result of Chow test and since the statistical value is f.05, 25, 116 = 1.601, with a minimum of 95 percent certainty, it can be said that a structural difference has occurred between the observations of Tehran and other cities. Therefore, we study observations of Tehran and other cities separately and provide regressive fits for each of them. This structural difference is due to population variable (POP) and, therefore, by omitting it from the overall equation we can get a better equation because it led to the inconstancy of error variances in relation to predicted values. This study showed that variables of the week in which the match is held (Fix), Home Rank, and Away Rank can be fitted into the overall model in a second degree form and the rest of the variables in a first degree form. In the model for Tehran, the variable of the week (Fix) is identified as one of a second degree, Away Age variable as third degree, and Num or the variable of the number of matches held over the past five days as second degree. In these observations, the ranking of the host and guest teams was not recognized as a second degree variable because these teams, constantly, appear in the upper half of the league charts. In the model for other cities, too, variables of scores made in three matches over the span of one month (Point), the week of the match (Fix), Home Rank, Away Rank, and Home Champ can enter the regression model as variables of the second degree. By performing step-by-step regression as well as regression of all possible conditions for each of the models, variables which had a less significant effect on the spectators demand were omitted from the model and we estimated 3 regression models using the minimum square error method for the overall and Tehran models, and the maximum validity method for the other cities

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 11 due to the specific form of the remnants and the distribution of the function of the dependent variable for other cities observation. The results of these estimations have been presented in Tables (3), (4), and (5). Table 2. Chow test. Analysis of variance for Whole county Analysis of Variance Source DF SS MS F P Regression 24 54926486899 2288603621 9.71 0.000 (1) Residual Error 141 33225865450 235644436 (2) Total 165 88152352349 (3) Analysis of variance for Townships Analysis of Variance Source DF SS MS F P Regression 24 347932385 14497183 3.54 0.000 (4) Residual Error 67 274304463 4094096 (5) Total 91 622236848 (6) Analysis of variance for Tehran Analysis of Variance Source DF SS MS F P Regression 24 25530165092 1063756879 3.13 0.000 (7) Residual Error 49 16654321394 339884110 (8) Total 73 42184486486 (9) SSEkol ( SSEshahrestan + SSE teh ) 33225865450 ( 274304463+ 16654321394) dfkol ( dfshahrestan + dfteh ) 141 ( 67+ 49) f= = SSEshahrestan + SSE teh 274304463+ 16654321394 df ahrestan + dfteh 67+ 49 sh = 4/ 4669 (10)

12 SMIJ VOL. 7, Number 2, 2011 Table 3. Ultimate results for whole county. Regression Analysis: Logy versus POINT; TIME;... The regression equation is Logy = 4.59 + 0.0205 POINT + 0.491 TIME 0.0894 AWAYRANK + 0.00500 AWAYRANK2 0.189 m9 0.234 m10 0.172 m11 + 0.0326 AWAYCHAMP 0.169 HOMERANK + 0.00696 HOMERANK2 Predictor Coef SE Coef T P VIF Constant 4.5940 0.1756 26.17 0.000 POINT 0.02048 0.01595 1.28 0.201 1.5 (1) TIME 0.49109 0.06488 7.57 0.000 1.2 (2) AWAYRANK 0.08943 0.02687 3.33 0.001 19.4 (3) AWAYRANK2 0.004998 0.001453 3.44 0.001 19.4 (4) HOMERANK 0.16893 0.02524 6.69 0.000 15.0 (5) HOMERANK2 0.006958 0.001572 4.43 0.000 14.6 (6) AWAYCHAM 0.03258 0.02382 1.37 0.173 1.2 (7) m9 0.18862 0.09940 1.90 0.060 1.0 (8) m10 0.2340 0.1734 1.35 0.179 1.1 (9) m11 0.17162 0.09871 1.74 0.084 1.0 (10) S = 0.3707 R Sq = 66.1% R Sq(adj) = 63.9% (11) Analysis of Variance Source DF SS MS F P Regression 10 41.4562 4.1456 30.16 0.000 (12) Residual Error 155 21.3029 0.1374 Lack of Fit 154 21.2874 0.1382 8.92 0.262 (13) Pure Error 1 0.0155 0.0155 Total 165 62.7591 Durbin - Watson statistic = 2.09 (14) No evidence of lack of fit (P 0.1) (15)

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 13 Table 4. Ultimate results for Tehran. Regression Analysis: log(y) versus FIX; HOMERANK;... The regression equation is Logy= 4.35 + 0.0101 FIX 0.0534 HOMERANK 0.0118 AWAYAGE = + 0.000223 AWAYAGE2 + 0.0588 AWAYCHAMP + 0.526 m + 0.421 m7 = + 0.341 m8 Predictor Coef SE Coef T P VIF Constant 4.3533 0.1669 26.08 0.000 FIX 0.010138 0.005700 1.78 0.080 3.6 (1) HOMERANK 0.05339 0.01042 5.13 0.000 1.0 (2) AWAYAGE 0.011791 0.006767 1.74 0.086 18.5 (3) AWAYAGE2 0.0002233 0.0001029 2.17 0.034 18.5 (4) AWAYCHAM 0.05879 0.02217 2.65 0.010 1.0 (5) m6 0.5260 0.1496 3.52 0.001 2.5 (6) m7 0.4214 0.1442 2.92 0.005 2.1 (7) m8 0.3412 0.1195 2.85 0.006 1.8 (8) S = 0.2507 R Sq = 45.6% R Sq(adj)S = 39.1% (9) Analysis of Variance Source DF SS MS F P Regression 8 3.53188 0.44148 7.02 0.000 (10) Residual Error 67 4.21166 0.06286 Total 75 7.74354 Durbin-Watson statistic = 2.04 (11) No evidence of lack of fit (P 0.1) (12)

14 SMIJ VOL. 7, Number 2, 2011 Table 5. Ultimate results for Township. Response Variable: Y Estimation Method: Maximum Likelihood Distribution: Weibull Standard 95.0% Normal CI Predictor Coef Error Z P Lower Upper Intercept 9.2320 0.3539 26.09 0.000 8.5384 9.9256 (1) POINT 0.07256 0.09297 0.78 0.435 0.25477 0.10966 (2) point2 0.012408 0.009836 1.26 0.207 0.006870 0.031685 (3) HOMERANK 0.20874 0.05735 3.64 0.000 0.32115 0.09634 (4) HOMERANK2 0.010792 0.003199 3.37 0.001 0.004522 0.017062 (5) SAME 0.2564 0.1141 2.25 0.025 0.4800 0.0329 (6) AWAYRANK 0.02930 0.01347 2.18 0.030 0.05569 0.00290 (7) AWAYCHAM 0.12667 0.04922 2.57 0.010 0.03020 0.22314 (8) AWAYAGE 0.001720 0.003288 0.52 0.601 0.008163 0.004724 (9) m11 0.3551 0.1762 2.02 0.044 0.7005 0.0098 (10) Shape 1.9709 0.1614 1.6787 2.3140 (11) Log - Likelihood = 850.417 Anderson - Darling (adjusted) Goodness - of - Fit Standardized Residuals = 1.2690 (12) Conclusion Taking into consideration the great effect of the variable of population and the considerable difference in the number of spectators in Tehran and other cities, we conducted the Chow test and concluded that the observations must be analyzed in two models; Tehran and other cities. According to Tables (3), (4), and (5) the following results were reached: 1. Variable of Point: which represents the score achieved in the past three matches over the period of a month had not been the subject of study in any of the previous researches and we incorporated it into the model as

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 15 a variable for determining the effect of psychological factors on demand. This variable was incorporated into the model as a second degree variable and showed that when a team made 9 scores out of the past three matches, the spectators would come to the stadium due to the success of their favorite team. However, even when their favorite team made no scores in the past three matches, more spectators would come to the stadium compared with the situation when the team had made medium scores. It is perhaps because they are worried that their favorite team might go down in the ranking charts of the league. 2. Variable of Num: we incorporated this variable into our model to determine the effect of addiction to or boredom with watching football matches but the results showed that it was of no considerable importance to any of the final models and its effect on spectators, compared with those of other variables, was rather random. 3. Variable of Time: time of a match has been discussed in most articles. Although it has been considered insignificant in many articles but its index has been positive in most of them. For instance, in 2004, Owen suggested that this index is positive and in the hours of the day the number of the spectators increases but that this increase is random and insignificant [11]. This variable was important in the entire country model and indicated an increase in the number of the spectators in the evening or after that. 4. Variable of Same: being simultaneous with an Esteghlal/Persepolis match was only studied in the model for other cities and was considered insignificant. Introducing this variable was one of the innovations in our model. In fact, we proved that if a match is held at the same time as a match between Esteghlal and Persepolis the number of spectators decreases. 5. Variable of Day: holding a match on a holiday or a day before a holiday was determined to be significant in none of our models. Although its index was positive and led to an increase in the number of the spectators but its significance was not remarkable. One of our innovations with regard to this variable was considering the holidays and the day before a holiday. 6. Variable of Fix: for the first time we considered the week of the match as a second degree variable. So far all researchers had considered it as a first degree variable. This variable was determined to be insignificant but in the final model for Tehran indicated an increase in the number of the spectators towards the final weeks. 7. Variable of Home Rank: ranking of the host team can be considered as the most important variable in our models. In the model for other cities, this variable was significant as a second degree variable. Presence of a team in the high or low rankings of the league chart indicates an increase in demand. Yet, being in the higher ranks has a greater effect.

16 SMIJ VOL. 7, Number 2, 2011 8. Variable of Away Rank: ranking of the guest team was only significant in the final overall model as a second degree variable. But in the final model for other cities it was determined to be significant as a first degree variable. However, just as in the Forest s and Simmons model in 2002 [9], it was proved that the ranking of the host team has a greater effect on the number of the spectators even though we had considered it as a variable of the second degree. 9. Variable of Home Age: the age of the host team was determined to be significant in none of our models. This variable had been studied in only few researches and although we could not prove its significance, it was one of our innovations. 10. Variable of Away Age: age of the guest team was incorporated into Tehran model as a variable of the second degree for the first time. The effect of this variable on the spectators indicated that spectators pay lots of attention to old clubs and also new clubs which are founded in the midst of propaganda and advertisement and become the centre of gravity for the first few years after their foundation. In fact, during the first few years following the foundation of a new club, spectators are curious about them and pay special attention to them and the demand for watching their matches increases. As for old clubs, the attention is due to the quality of their teams. 11. Variable of POP: so far, this variable has been significant in all the studies. In 1975, Hart and his colleagues [3] and, in 1999, Jones and his colleagues [8] proved this variable to be significant. In our research, this variable was determined to be significant although it caused structural differences between Tehran and other cities and was omitted from the studies of these two cases. 12. Variable of Dis: in Forest s research, in 2002 [9], this variable was determined to be significant as a variable of the second degree. In 1975 [3], in Hart and his colleagues research it was considered significant as a variable of the first degree. But in our study it was significant in neither of the cases. The reason for the insignificance of this variable must be traced in the structure of the Iranian Football league. In Iran, there are no clubs or associations for the fans and they are seldom organized to travel and watch their favorite team s matches in other cities. 13. Variable of Home Champ: the number of championships of the host team since 1991 was determined to be insignificant in all our cases and models. In fact, fans pay little attention to this fact. 14. Variable of Away Champ: the number of championships since 1991 was incorporated into the models for Tehran and the final model for other cities and it was determined to be significant. In fact, spectators pay more attention to the background and quality of the guest team and it is more appealing to them when high quality teams go to other cities.

A STUDY OF FACTORS AFFECTING THE DEMAND FOR WATCHING FOOTBALL IN... 17 15. Variables of the Months of the Year: in 2002, Forest and Simmons [9] proved that in December the number of the spectators decreases. In the solar calendar, December coincides with autumn and early winter. In the same article, May and April are considered significant and lead to the increase of the spectators. In the solar calendar, these months coincide with spring. In our research, Bahman (m11) and Azar (m9) were determined to be significant and indicated a decrease in the number of the spectators which is the same result as the abovementioned research. According to the final results of this research, the ranking of the host team and the number of the championships of the guest team which are some of the appealing factors were determined to be significant but the ranking of the guest team has less effect on the number of the spectators compared with these two variables. We proved that to the spectators the ranking of their city team is the most important factor and in a condition when their team is successful and needs support to become the champion or in a situation when their team faces failure and needs support to avoid falling down on the league charts, more spectators go to the stadiums and the medium rankings are not interesting to them. Besides, the environmental factors such as the month in which the match is held affect the demand of the spectators and the spectators tend to go to the stadiums in months when the weather is more pleasant. The background of the guest team is also significant as a variable of the second degree. As for old teams, it can be said that people are attracted to them due to their background but some newly-founded clubs attract great numbers of spectators by astronomical investments and employing famous football players. Geographical factors are not considered important in the Iranian super league because there are no fan clubs and, besides, it is difficult to travel around. In the future studies the effect of other factors such as economical factors including the revenue of the clubs, and also the management systems of the clubs and stability of their management as an independent factor, facilities of the stadiums and comfort of the spectators in them, and also the role of the media and newspapers on the number of the spectators can be studied. References Aragaki, C. (July 2007). Result of the casual list of Brazilian football clubsexercise 2006. Casual auditores independents s/s Baranzini, A., Ramirez, J. and Webber, S. (2008). The demand for football in Switzerland: an empirical estimation, J. Heg, Vol. 08/1/1 Bird, P. (1982). The demand for league football, Int. J. applied economics, Vol. 14, pp 637-649

18 SMIJ VOL. 7, Number 2, 2011 Dobson, S.M. and Goddard, J.A. (1995). The Demand for Professional League Football in England and Wales, 1925-92, Int. J. The Statistician, Vol. 44, No. 2, pp. 259-277 Forrest, D. and Simmons, R. (2002). Outcome Uncertainty and Attendance Demand in Sport: The Case of English Soccer, Int. J. The Statistician, Vol. 51, No. 2, pp. 229-241 Hart, R.A., Hutton, J. and Sharot, T. (1975). A Statistical Analysis of Association Football Attendances, Int. J. Applied Statistics, Vol. 24, No. 1, pp. 17-27 Jones, J.C.H., Schofield, J.A. and Giles, D.E.A. (1999). Our fans in the north: the demand for british rugby league, University of Victoria, British Columbia Owen, P.D. (2004). Uncertainty of outcome, player quality and attendance at national provincial championship rugby union matches:an evaluation in light of the competitions review, Int. J. university of otago economics discussion papers, No. 0408, ISSN 0111-1760 Price, D.I. and Sen, K.C. (2003). The Demand for Game Day Attendance in College Football: An Analysis of the 1997 Division 1 -A Season, Int. J Managerial and Decision Economics, Vol. 24, No. 1, pp. 35-46 Rivett, P. (1975). The structure of league footballoperational research quarterly, Vol. 26, No. 4, part 2, pp. 801-812 Welki, M. Andrew and Zlatoper, J. Thomas (1994). US Professional Football: The Demand for Game-Day Attendance in 1991, Int. J. Managerial and Decision Economics, Vol. 15, No. 5, pp. 489-495 Address for correspondence: Ehsan Javanmardi Shiraz University Management Department Iran e-mail: javanmardi.ehsan@gmail.com