the economics of the fifa football worldcup

KYKLOS, Vol. 57 – 2004 - Fasc. 2, 287–300

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The Economics of the FIFA Football Worldcup

Benno Torgler*

I. INTRODUCTION

The expansion of economics to other spheres of life, including politics, war,crime, religion, or sports, was according to Hirshleifer (2002), ‘like a breath offresh air’ (p. ix). With standard economic analyses new insights in these ‘non-market topics’ have been developed. Several Nobel Prizes indicate that econo-mists have successfully entered other territories.

The economic analysis of football (soccer) follows this path, showing howeffective traditional economic tools are to analyse this sport. First pioneeringworks in the economics of football have been made in the 70s (e.g., Sloane1971). Gärtner and Pommerehne (1978), for example, empirically analysed thedemand for football games with the German club Hamburger Sportverein be-tween 1969 and 1975. They found, for example, that the current league posi-tions of the home and the away team and the reputation of the away team havea strong impact on the demand for football games. Thus, the team qualitystrongly influences football attendance.

However, most research papers in the economics of sports are ‘America ori-ented’, where football is hardly treated (see, e.g., Kern 2000). It is surprisingthat economists do not have written more papers on that topic, as football offersa splendid field for empirical studies, thanks to large and well registered datasets. Until now, the main focus has been on the commercial structure and thecompetitive balance and uncertainty of the outcome1 in the football market, mi-

* Georgia State University, Andrew Young School of Policy Studies, Atlanta (USA); CREMA –Center for Research in Economics, Management and the Arts (Switzerland); University of Ba-sel, Wirtschaftswissenschaftliches Zentrum (WWZ), Basel (Switzerland). For advice and sug-gestions thanks are due to Doris Aebi and the editors Bruno S. Frey and René L. Frey. E-mails:[email protected], [email protected].

1. It is interesting to notice that a recent empirical study done with data from the German first footballdivision over two seasons (1996/97 and 1997/98), analysing football attendance, found that reputation(considering the performance of a given team over the last twenty years) is more important than out-come uncertainty (not statistically significant), using a generalized Tobit estimator to take into consid-eration that in over 25% of the cases the event was sold out (see Czarnitzki and Stadtmann 2002).

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gration tendencies of players (consequences of the Bosman ruling), the super-star effect (players’ wages), player discriminations, or the effects of manage-ment changes (see, e.g., Dobson and Goddard 2001, Feess and Muehlheusser2003, Preston and Szymanski 2000, Gius and Johnson 2000).

However, the literature mostly analyses football at the club level. Events asthe World Cup, UEFA Cup, Champions League are not treated empirically veryoften. Some studies at the international level use the FIFA World Ranking toanalyse soccer performance (see, e.g., Hoffmann et al. 2002, Houston and Wil-son 2002). In this paper we are going to use data from the FIFA World Cup2002 to analyse what determinants have an impact on the success in such tour-naments. It will be very interesting to analyse whether, e.g., the home advan-tage matters. Furthermore, it has often been argued that a World Cup tourna-ment is not free of surprises. Thus, it might be worthwhile to see whether thestrength of a team has a strong impact on the probability of winning. Finally, asan economic analysis of football should integrate all actors, we also integratean analysis of the referee. Empirical results might be especially interesting asto the author’s knowledge referees have not been investigated so often in thepast. The whole statistical data is available under http://fifaworldcup.yahoo.com and www.fifa.com.

II. TO WIN OR NOT TO WIN

The FIFA World Cup is one of the world’s biggest sport events. The FIFA WorldCup was broadcast to 213 countries and regions around the world, with over41 100 hours of dedicated programming and 28.8 billion estimated televisionviewers (see FIFA Media Information, November 21, 2002, http://fifaworld-cup.yahoo.com). In our analysis we are going to focus on this tournament heldin Korea and Japan in 2002 (May 31 through June 30). A total of 32 countriesrepresenting each continent are competing in this tournament, playing 64matches (see countries and their classification in the tournament in the Appen-dix). Interestingly, empirical investigations of the FIFA World Cup tournamentare still lacking, which is a surprise considering the amount of publicity and thedetailed data collected.

In this section we focus on the team performance during the whole tourna-ment. In our empirical analysis we take the main matches into consideration.Thus, we analyse 63 (126 team performances), instead of 64, excluding thematch for the third place (lack of relevance and tension).


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1. Model

The dependent variable in the empirical model is a dummy variable that meas-ures whether a team wins the game or not (1 = win). As already mentioned weare going to analyse the 126 team performances in the tournament. After thegroup games, only a victory allows to move on. As a tournament has only a re-stricted number of observations, it makes sense to evaluate all games together.Thus, a draw in a group game has been coded as 0. The team performance canbe modelled in an econometric win function: Wi = α + βXi + εi that is estimatedprobit or logit, where i catches the different team performances. In this subsec-tion we will first focus on the independent variables X = x 1 + x 2 +, . . ., xn. Inthe first estimations we include two different categories of independent varia-bles: strength of a team and the performance during the game. In a next step,we include two variables that try to catch the influence of a referee.

Strength of a team: 1. International rankingTo measure the strength of an international team just before the beginning ofthe tournament, the FIFA/Coca-Cola World Ranking might be one of the bestdata sources and thus a good proxy. We use the ranking of May 15, 2002, pub-lished on the FIFA website (www.fifa.com). This ranking is calculated from allinternational matches (game results (winning, drawing and losing), goalsscored, home or away match, importance and venue of the match (multiplica-tion factor) and regional strength (multiplication factor)) over the previouseight years on a monthly basis. The ranking of the participating teams can befound in Table A1 in the Appendix. Thus, it can be hypothesized that the lowerthe score (higher ranking), the higher the probability of winning the game.However, it has often been mentioned that such tournaments are not free of sur-prises. Therefore, it might be interesting to check how robust this variable is.

2. Hosting the TournamentBeing the hosting team might have a positive influence on the success of a na-tion. It can be assumed that a strong support of the public and the audience,high outside expectations, being more in the public eye, cultural closeness, be-ing used to performing in such geographic settings, and other home advantageswill result in better national performance in the football game. The World Cup2002 is useful to analyse as two countries instead of one have hosted the tour-nament. Thus, we get more observations and thus more robust results. Further-more, compared to other tournaments such an effect can be better analysed asKorea and Japan have low ranking values and not a strong football tradition as


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many previous hosting nations such as Brazil, Italy, France, Germany, England,Argentina, Spain or Mexico (see Table A1). Thus, our hypothesis is that beinga home team has a positive impact on the probability of winning a game.

Performance during the game: We are going to use several proxies that measure the performance of a teamduring the game:• Shots on Goal• Fouls• Corner Kicks• Free Kicks• Offsides• Cautions• Expulsions• Actual Playing Time (based on the ball possession)It can be supposed that the number of shots on goal has the strongest impact onthe probability of winning a game. But also an expulsion might have a strongimpact on the final game result. The team loses a player and thus its strengthand the team structure has to be reorganised which might reduce the players’comparative advantage, due to highly specialised skills. Thus, we hypothesizethat an expulsion has a negative impact on the probability of winning a game.The effects of other variables are less clear. The actual playing time can be seenas an indicator of the strength of team. On the other hand, it cannot be seen perse as an indicator of team efficiency. Teams might have different ‘playing cul-tures’, and having a score advantage during the game reduces the incentive ofcirculating the ball as much as possible. Free kicks can be carried out every-where in the field and the number of corners can be a proxy for strong offensivetendencies, but also a proxy for strong opponent defensive skills. Fouls mightbe a proxy for aggressiveness trying to win a tackle, but also an indicator of alower ability to use other instruments.

2. Empirical Results

Table 1 presents the empirical results of the first estimations. In Eq. 2 and 3 weuse corrected standard error values, clustering the 63 games and the 32 teamsand thus taking into account game and team differences. Furthermore, in orderto get the quantitative effects for the probit estimation, we calculate the mar-ginal effects. The results indicate that a higher ranking leads to a higher prob-ability of winning the game. However, especially Eq. 3 indicates that the coef-



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ficient is on the border of significance. Interestingly, hosting nations have astrong advantage. Being at home increases the probability of winning by 45percentage points. This is remarkable, taking into consideration the low FIFAranking of Korea (40) and Japan (32) before this competition (see Table A1).

Not surprisingly, a higher number of shots on goal leads to a higher proba-bility of winning. Furthermore, expulsions have a strong negative impact on theprobability of winning with a marginal effect of 29.6 percentage points. Thus,an expulsion diminishes the strength of a team. Finally, based on previous the-oretical considerations it is not a surprise that the other coefficients are not sta-tistically significant.

Table 1

Determinants of Performance (Dummy Dependent Variable: Win = 1)

In a next step we are going to analyse the impact of the referee on the team per-formances. The idea is that a referee is not free of human errors and subjective

probit Coeff. z-value Marg. Coeff. z-value Marg. Coeff. z-value Marg.

Eq. 1 Eq. 2 Eq. 3

Estimation Methods

standard errors adjusted for clustering on games

Standard errors adjusted for clustering on teams

a) Strength

RANKING –0.024** –1.97 –0.009 –0.024* –1.90 –0.009 –0.024* –1.68 –0.009

HOME ADVANTAGE 1.222** 2.43 0.453 1.222** 2.10 0.453 1.222*** 3.58 0.453

b) Game Performance

SHOTS ON GOAL 0.187*** 3.47 0.071 0.187*** 3.38 0.071 0.187*** 3.76 0.071

FOULS –0.022 –0.76 –0.008 –0.022 –0.81 –0.008 –0.022 –0.82 –0.008

CORNER KICKS –0.048 –1.00 –0.018 –0.048 –0.94 –0.018 –0.048 –1.05 –0.018

FREE KICKS 0.000 0.01 0.000 0.000 0.01 0.000 0.000 0.01 0.000

OFFSIDES 0.026 0.49 0.010 0.026 0.45 0.010 0.026 0.50 0.010

CAUTIONS 0.003 0.04 0.001 0.003 0.04 0.001 0.003 0.03 0.001

EXPULSIONS –0.786* –1.82 –0.296 –0.786** –2.36 –0.296 –0.786** –2.44 –0.296

ACTUAL PLAYING TIME –0.021 –0.75 –0.008 –0.021 –0.02 –0.008 –0.021 –0.83 –0.008

Number of observations 126 126 126

Prob > chi2 0.002 0.030 0.000

Pseudo R2 0.170 0.170 0.170

Notes: In the reference group is NOT PLAYING AT HOME. Ranking variable: the higher the value, the lower the ranking. Significance levels: * 0.05 < p < 0.10, ** 0.01< p < 0.05, *** p < 0.01.


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influences. He is a key player in a game enforcing the law of the game, he con-trols the match, acts as a timekeeper, takes disciplinary action against playersetc. So we are going to analyze with two dummy variables whether culturalcloseness between the referee and a team has a positive impact on the probabil-ity of winning a game. Thus, we include the following two referee variablesseparately into the estimations:

1. The referee is/is not from the same (football) region. In the World Cup teams and referees from all football regions are participating(UEFA, AFC, CAF, CONCACAF, CONMEBOL and OFC). Referees might bemore strongly attached to the teams from the same region. On the one hand, astronger cultural closeness and identification might be observed; on the otherhand, referees have also more performances in these areas, not only at the in-ternational, but also at the local level. Certainly, it can be supposed that theFIFA tries to control for such possible biases, but our data set indicates that in17.5 percent of the cases the referee and one of the teams come from the samefootball region.

2. The referee has/has not the same mother tongue as the team. Instead of regional closeness, we can measure cultural closeness with lan-guage. It is an important and interrelated proxy for a shared culture. Contraryto the previous measurement, the referee has fewer contacts in areas outside hisfootball region, which might reduce the identification with a team. Further-more, it should be noticed that this variable has a lower variance compared tothe previous one (only in 6.3 percent of the cases the language and the team cor-relate). Thus, results should be treated with caution.

Table 2 presents the results. The estimations are the same as in Table 1, but in-clude the effects of the referee. The results in Table 2 show in Eq. 4 that havinga referee from the same region has a positive impact on the probability of win-ning a game. The marginal effects are quite high, but the statistical significanceis not robust regarding the estimation method. The coefficient of the second ref-eree variable is not significant. Thus, it seems that speaking the same languageworks less than being from the same region. While the Pseudo R2 increasewhen integrating the first referee variable into the estimations, the second var-iable has no impact. To increase the number of observations we have also esti-mated regressions where the language variable has been more broadly defined2.In all estimations the coefficient was not significant.

2. Luso-Hispanic culture together (Spanish and Portuguese countries) and Latin countries together(French, Italian, Iberian culture, Turkey). Estimations are availabe upon request.


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Table 2

The Effects of the Referee (Dummy Dependent Variable: Win = 1)

probit Coeff. z-value Marg. Coeff. z-value Marg. Coeff. z-value Marg. Coeff. z-value Marg.

Eq. 4 Eq. 5 Eq. 6 Eq. 7

Estimation MethodsStandard errors adjusted for

clustering on gamesStandard errors adjusted for

clustering on teamsStandard errors adjusted for

clustering on gamesStandard errors adjusted for

clustering on teams

a) Strength

RANKING – 0.021 –1.62 – 0.008 –0.021 –1.44 –0.008 –0.024* –1.94 –0.009 –0.024* –1.71 –0.009

HOME ADV. 1.339** 2.22 0.487 1.339*** 3.71 0.487 0.239** 2.15 0.458 0.239*** 3.69 0.458

b) Game Performance

SHOTS ON GOAL 0.199*** 3.47 0.075 0.199*** 3.76 0.075 0.187*** 3.35 0.071 0.187*** 3.73 0.071

FOULS –0.032 –1.14 –0.012 –0.032 –1.09 –0.012 –0.022 –0.80 –0.008 –0.022 –0.81 –0.008

CORNER KICKS –0.050 –0.98 –0.019 –0.050 –1.14 –0.019 –0.048 –0.95 –0.018 –0.048 –1.05 –0.018

FREE KICKS –0.015 –0.24 –0.006 –0.015 –0.21 –0.006 0.000 0.00 0.000 0.000 0.00 0.000

OFFSIDES 0.034 0.58 0.013 0.034 0.67 0.013 0.028 0.48 0.011 0.028 0.55 0.011

CAUTIONS 0.011 0.13 0.004 0.011 0.10 0.004 0.003 0.04 0.001 0.003 0.03 0.001

EXPULSIONS –0.070** –2.02 –0.265 –0.070** –2.13 –0.265 –0.799** –2.34 –0.301 –0.799** –2.49 –0.301

ACTUAL PLAYING TIME –0.022 –0.76 –0.008 –0.022 –0.85 –0.008 –0.022 –0.77 –0.008 –0.022 –0.84 –0.008

c) Referee

SAME REGION 0.508* 1.77 0.197 0.508 1.44 0.197

SAME LANGUAGE 0.141 0.27 0.054 0.141 0.29 0.054

N 126 126 126 126

Prob > chi2 0.010 0.000 0.048 0.000

Pseudo R2 0.183 0.183 0.170 0.170

Notes: In the reference group are NOT PLAYING AT HOME, OTHER REGION, OTHER LANGUAGE. Ranking variable: the higher the value, the lower the ranking. Significance levels: * 0.05 < p < 0.10, ** 0.01< p < 0.05, *** p < 0.01.

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Interestingly, the coefficient of the variable RANKING loses its statisticalsignificance in Eq. 4 and 5, when the variable REFEREE SAME REGION isadded. This result might suggest a connection between both variables. It seemsthat the referee mediates the positive effect of the variable RANKING, espe-cially when the referee is from the same football region. Thus, we observe anindirect effect of a team’s international ranking on the probability of winning,through affecting the referee’s behaviour measured by the variable REFEREESAME REGION.

In general, the results indicate that a World Cup is full of surprises and thecompetitive balance is higher than supposed. This is important as it ensures un-certainty of outcome, which is the lifeblood of any sporting event (see Dobsonand Goddard 2001). The effects of the other variables remain robust.

III. WHAT SHAPES THE PERFORMANCE OF A REFEREE?

In a next step we are going to analyse what shapes the performance of a referee.36 referees have participated in the World Cup 2002, enough observations toconduct a multivariate analysis. It is very difficult to measure the performanceof a referee. We find some individual rankings in newspapers, but such evalua-tions are full of biases. Thus, we proceed differently. We observe a variation inthe number of refereed games per referee as they are nominated during the tour-nament for the round after the group games3. This makes sense, as the best twoteams of each group are not known before. For example, the FIFA RefereesCommittee appointed Pierluigi Collina, who refereed the final on Sunday, 30June, on June 27 (see FIFA World Cup media information, http://fifaworld-cup.yahoo.com). Thus, the number of performances in the tournament mightbe a good proxy for how the FIFA evaluated the performance of a referee. Asmatches are not equally important, we use in a further estimation a weightedvalue4. Table 3 presents the results. It is interesting to observe that the variableHAVING PARTICIPATED IN A PREVIOUS WORLD CUP has the strongesteffect on our dependent variable in both estimations. It seems that previousWorld Cup experiences are highly relevant. On the other hand, the coefficientthat measures the years someone has been a referee is only significant in thefirst estimation. One reason might be that this proxy does not measure how im-portant the matches were in the referee’s career. Not surprising, age per se hasnot an impact on the performance. Eq. 8 also indicates that referees of North,

3. 38.9 percent of the referees had one performance, 47.2 two, and 13.7 three performances.4. Points: value 1 = group game, 2 = 16, 3 = quarters, 4 = semi finals, 5 = final. Range from 1 to 8.



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Central and South America have a significantly higher performance than thereference group (UEFA referees). However, this result is not robust regardingthe way the performance is weighted. Certainly, the way the dependent variableis defined is not free of biases. For example, the decision by the FIFA RefereesCommittee which referee is going to act in the semifinals or the final might bebased on previous experiences with a referee and not the game performancesduring the cup.

Table 3

Performance of the Referee(Dependent Variable: Weighted and Unweighted Number of Games Played)

Least squares Coeff. t-value Coeff. t-value

Eq. 8 Eq. 9

Unweighted performance Weighted performance

a) Experiences

YEARS BEING A REFEREE 0.034* 1.81 0.020 0.42

HAVING PARTICIPATED IN A PREVIOUS WORLD CUP 0.889*** 3.29 3.118*** 4.63

b) Demographic

Age 0.003 0.18 –0.017 –0.41

c) Region

AFC 0.033 0.12 –0.741 –1.10

CAF –0.324 –1.13 –0.659 –0.92

CONCACAF 0.900*** 2.99 1.156 1.54

CONMEBOL 0.669** 2.54 0.919 1.40

OFC –0.162 –0.29 –0.823 –0.59

Number of observations 36 36

Prob > F 0.001 0.001

Adj R-squared 0.454 0.459

Notes: Dependent variable: Number of games unweighted and number of games played multiplied with the importance of the game. In the reference group are NOT HAVING PARTICIPATED PRE-VIOUSLY IN WORLD CUP, UEFA. Significance levels: * 0.05 < p < 0.10, ** 0.01< p < 0.05, *** p < 0.01.


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IV. CONCLUSIONS AND RECOMMENDATIONS

The expansion of economics to ‘non-market topics’, such as the economics ofsport, is a recent development. Empirical analysis is therefore lacking in theseareas. This surprises somehow as a huge set of data is available. The academicliterature is dominated by studies of the North American sports. Thus, empiri-cal investigations on the economics of football are still in their infancy. In ourpaper we focused on the FIFA World Cup. We report empirical evidence ofteam and referee performances in the FIFA World Cup 2002. We found that thestrength of a team measured with the FIFA World Ranking does not play theimportant role one would assume, which indicates that the element of uncer-tainty is working. Furthermore, the results reveal that being a hosting nationhas a significant impact on the probability of winning a game. Looking at theWorld Cups between 1930 and 2002, we observe that the tournaments havemostly been conducted in countries such as Italy (1934, 1990), France (1938,1998), Brazil (1950), England (1966), Germany (1974), Argentina (1978) orSpain (1982), which have strong football teams. However, two recent tourna-ments have been held in less developed football regions (USA 1994 and Japan/South Korea 2002). Organizing the World Cup in such areas might help in-crease the competitive balance among the nations, which ensures uncertaintyof the game outcome. The question is whether such teams remain competitivein the long run. Based on the lack of observations, this question cannot be an-swered satisfyingly. However, looking at the case of the USA, we can see thatthe team remained highly competitive after the World Cup 1998 (FIFA WorldRanking position before the tournament: 22 in December 1993; ten years later:11 in December 2003, see www.fifa.com).

The findings also indicate that the influence of a referee on the game resultshould not be neglected. Thus, the FIFA should avoid cultural closeness be-tween a referee and a team, reducing situations in which the referee and one ofthe teams come from the same football region, a case that has been observed in17.5 percent of the games. One problem might be the strong overrepresentationof referees (and teams) from the European football region UEFA. 14 out of 36referees (15 out of 32 teams) came from Europe. This increases the difficultyto avoid cultural closeness. The results also suggest that previous World Cupexperiences are highly important for the performance of a referee. It surprisesthat only 7 out of 36 referees in this World Cup had such an experience. TheFIFA could induce a higher referee performance giving more referees thechance to perform several times in a World Cup competition.

Contrary to the players, the referees are still amateurs. Our findings indicatethat referees should be professionalised and their market opened, in order to



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make sure that the referee’s nationality is not a criterion any more, neither inclub nor in international games. This would ensure more competition betweenthe referees. As a consequence, referees’ identification with a specific footballregion could probably be reduced. We observe a strong concentration of goodplayers in European leagues at the club level. Many of the best internationalplayers work for Spanish, Italian, English or German clubs. Maguire and Pear-ton (2000) report that European clubs employed 62 percent of the players in the1998 World Cup in France. Thus, in line with these globalization tendenciesamong the players, non-European referees could also perform in the Europeanfootball region or vice versa. The European cups, especially the ChampionsLeague, allow a referee to gain important experiences at a high level. Further-more, professional referee careers would last longer enhancing the incentive tooptimize physical, mental and technical preparation and creating the opportu-nity to participate several times in the World Cup. It can be supposed that thereis a market for referees, as clubs, national teams and the international organi-zations such as the FIFA should have an interest seeing referees performingbetter.

A multi-referee system would be a further strategy to reduce subjective in-fluences based on cultural closeness. For example, one main referee per teamzone in the football field would allow to increase the number of main referees.Another possibility would be to get the two assistant referees more involved inthe game decision process, a strategy that we observe in other team sports suchas ice hockey.

Finally, the introduction of high technology would help settle controversialcases during the matches. Cameras in the most important parts of the field, suchas the penalty box, would help make crucial decisions. Thus, subjective influ-ences could better be controlled.

In general, it might be interesting to carry on research and thus to developan ‘economics of football tournaments’. Compared to the club football, tour-naments have the deficit to generate less data, which reduces the effectivenessof empirical data analysis. However, it is certainly possible to include morethan one World Cup tournament in the empirical analysis. In general, especiallychanges in the rules (e.g., changing the rewards in contests as the three-pointrule5) might be an interesting empirical field for the future.

5. In the 90s the three-point rule has been introduced. It means that the winner of a match obtainsthree points and the loser zero points (one point for each team in case of a draw). Previously thewinner of a match obtained only two points.


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APPENDIX

Table A1

World Cup 2002 Teams

Team Ranking Points May 2002 Classification in the World Cup 2002

France 1 802 group games

Brazil 2 784 finals (1)

Argentina 2 784 group games

Portugal 5 726 group games

Italy 6 717 round of 16

Mexico 7 716 round of 16

Spain 8 713 quarterfinals

Germany 11 695 finals (2)

England 12 694 quarterfinals

USA 13 690 quarterfinals

Ireland 15 674 round of 16

Cameroon 17 672 group games

Paraguay 18 671 round of 16

Sweden 19 665 round of 16

Denmark 20 657 round of 16

Croatia 21 655 group games

Turkey 22 654 semifinals (3)

Belgium 23 653 round of 16

Uruguay 24 652 group games

Slovenia 25 649 group games

Russia 27 644 group games

Nigeria 27 644 group games

Costa Rica 29 643 group games

Tunisia 30 635 group games

Japan 32 634 round of 16

Saudi Arabia 34 627 group games

Ecuador 35 624 group games

South Africa 37 623 group games

Poland 38 615 group games

Korea Republic 40 603 semifinals (4)

Senegal 42 599 quarterfinals

China PR 50 566 group games

Source: www.fifa.com.



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Dobson, S. and J. Goddard (2001). The Economics of Football. Cambridge: Cambridge UniversityPress.

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SUMMARY

The expansion of economics to ‘non-market topics’ has received increased attention in recent years.The economics of sports (football) is such a sub-field. This paper reports empirical evidence of teamand referee performances in the FIFA World Cup 2002. The results reveal that being a hosting nationhas a significant impact on the probability of winning a game. Furthermore, the strength of a teammeasured with the FIFA World Ranking does not play the important role presumed, which indicatesthat the element of uncertainty is working. The findings also indicate that the influence of a refereeon the game result should not be neglected. Finally, the previous World Cup experiences seem to havethe strongest impact on referees’ performances during the game.

ZUSAMMENFASSUNG

Die Erweiterung der Ökonomie auf ‘aussermarktliche Bereiche’ hat in den letzten Jahren gesteigerteAufmerksamkeit erhalten. Die Ökonomik des Sports (Fussballs) ist solch ein Unterfeld. Das vorlie-gende Papier analysiert empirisch Team- und Schiedsrichterleistungen der FIFA Fussballweltmeister-schaft 2002. Die Ergebnisse weisen darauf hin, dass der Heimvorteil einen signifikanten Einfluss aufdie Wahrscheinlichkeit eines Sieges ausübt. Zudem spielt die Stärke einer Mannschaft gemäss demFIFA World Ranking nicht die Rolle, die man eigentlich erwarten würde, was darauf hindeutet, dassdas Element der Unsicherheit funktioniert. Die Resultate zeigen auch, dass der Einfluss einesSchiedsrichters auf das Spielresultat nicht zu vernachlässigen ist. Schliesslich scheinen die früherenWeltmeisterschaftserfahrungen die Leistung des Schiedsrichters während des Spiels am meisten zubeeinflussen.


BENNO TORGLER

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RÉSUMÉ

L’extension des sciences économiques à des domaines ‘hors du marché’ a attiré une attention crois-sante au cours de ces dernières années. L’économie du sport (du football) est un tel domaine. Cetarticle analyse de façon empirique la performance des équipes ainsi que des arbitres pendant laCoupe du Monde FIFA en 2002. Les résultats montrent que l’avantage de jouer sur son propre terraina une influence significative sur la probabilité de gagner un match. En outre, la force d’une équipeselon le classement mondial de la FIFA ne joue pas le rôle prépondérant que l’on pourrait supposer,démontrant ainsi que l’incertitude joue. Les résultats montrent également que l’influence de l’arbitresur le résultat du match ne doit pas être négligée. Enfin, les expériences préalables en Coupe duMonde semblent être le facteur le plus influent sur la performance des arbitres pendant le match.


the economics of the fifa football worldcup

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