This paper examines the determinants of aggressiveness on the soccer pitch in 463 matches from FIFA (World Cup) and UEFA (Euro Cup) tournaments spanning from 1994 to 2012. We highlight the role of several measures of international rivalry between countries on the players’ aggressive behaviou

Caruso, Raul

Di Domizio, Marco

Savage, David A.

This paper examines the determinants of aggressiveness – proxied alternatively by the number of penalties issued by
the referee and by a related measure- on the soccer pitch in 463 matches from FIFA (World Cup) and UEFA (Euro
Cup) tournaments spanning from 1994 to 2012. We highlight the role of several measures of international rivalry
between countries on the players' aggressive behaviour

DI DOMIZIO, Marco

Savage, David

Archivio della Ricerca - Università degli Studi di Teramo

Determinants of aggressiveness in soccer: evidence from FIFA and UEFA tournaments

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Determinants of aggressiveness on the
soccer pitch: evidence from FIFA and
UEFA tournaments
Raul Caruso and Marco Di Domizio and David A. Savage
2015
Online at https://mpra.ub.uni-muenchen.de/61459/
MPRA Paper No. 61459, posted 20 January 2015 09:29 UTC
Determinants of aggressiveness on the soccer pitch:
evidence from FIFA and UEFA tournaments
Raul Caruso
Catholic University of the Sacred Heart, Milan, Italy
Marco Di Domizio
University of Teramo, Teramo, Italy
David A. Savage
Bond University, Robina, Australia.
ABSTRACT
This paper examines the determinants of aggressiveness on the soccer pitch in 463
matches from FIFA (World Cup) and UEFA (Euro Cup) tournaments spanning from 1994
to 2012. We highlight the role of several measures of international rivalry between countries
on the players’ aggressive behaviour
Keywords: soccer tournaments; aggressiveness; international rivalry.
JEL Classifications: L83, D74
INTRODUCTION
“Serious sport has nothing to do with fair play. It is bound up with hatred, jealousy,
boastfulness, disregard of all rules and sadistic pleasure in witnessing violence: in other
words it is war minus the shooting” (Orwell, 1945). Soccer matches often go beyond
pure sport competition, particularly when national teams take part in major international
tournaments. The idea that soccer is the stylisation of war is often used as a touchstone
to interpret the associated violence. Recently the focus has investigated the role that
socio-political and economic factors play in determining player aggression. This paper
extends previous contributions by examining additional factors that may better explain
player aggression, such as the roles of: stadium atmosphere, prize money incentivisation
and referees. We generate two proxy variables for player aggressiveness: the weighted
number of yellow/red cards issued and the number of fouls sanctioned by referees.
Our reference model estimations show that match variables alone are not exhaustive in
determining player aggression and factors such as trade and power gaps, match attendance
and prize money are significant. Finally, we attempt to control for the influence that referees’
may impose on the match through fixed effects.
Corresponding author Raul Caruso. Contact via Email: raul.caruso@unicatt.it.
2Violence and soccer
The hypothesis that violence is an innate part of sport is widely accepted, such
that Elias and Dunning (1986) claimed that soccer matches stylise and miniaturise war.
Where the associated aggression is related to overt masculinity, territorial struggle and
excitement. This is one of the reasons that the sporting environment is considered an odd
choice for an outlet of nationalistic tensions or as a political instrument for building trust
between rival countries. Particularly since the basic nature of sport is one of competition,
which can lead to extremely aggressive behaviour, coercion and threats of violence. This is
consistent with Caruso (2011), where sport is interpreted as a market and relational good,
and an expression of threat, power and coercion. Unfortunately, violence is viewed as a
negative but ineluctable component of sport, both on and off the pitch. The latter has been
the focus of the hooliganism phenomenon and its counter strategies (Domizio & Caruso,
2014), while the former is being used to empirically disentangle the effect of culture,
institutions and poverty in determining violent player behaviour. The Miguel, Saiegh, and
Satyanath (2008) analysis of the role of civil conflict in a players home provenance and the
number of yellow/red cards awarded by referees, showed a strong relationship between the
two. Supporting the idea that culture and identity can influence player’s attitudes towards
aggressive or violent behaviour. However,Cuesta and Bohórquez (2012) reached different
conclusions through the investigation of the Copa Libertadores. Using factors such as
dictatorship length, homicides rates and years of armed conflict within each country,
the results showed that the violent behaviour of players depended exclusively on soccer
characteristics. In this vein, we extend the previous empirical investigations proposed in
Caruso and Di Domizio (2013), where we found that the differences in political, diplomatic,
education and economic factors between nations significantly affected the sanctions issued
by the referees. Specifically, we:
1. extend data set by 147 observations1
2. include fouls sanctioned as alternative dependent variable.
3. build innovative measures for the bilateral gap in trade and power.
4. include attendance and prize money as explanatory variables.
5. control for referees’ effect.
Data set and empirical strategy
The dataset contains 463 final phase matches from FIFA (World Cup) and UEFA
(Euro Cup) tournaments spanning from 1994 to 2012 and includes 61 countries. We
investigate players’ aggressiveness by means of two variables: (i) WINT - a weighted
measure of cards issued per match, (ii) FOULS - the count of sanctioned fouls. We
include a set of independent control variables divided into three groups: Tournament,
political-economic (Politec), and Match specific variables. From which we estimate a
regression equation, by means of negative binomial using maximum likelihood techniques.
Table 1 presents the summary statistics for the non-dummies variables and discussed below.
1This includes FIFA World Cup 1994 and 1998, and UEFA European Cup 1996 tournaments.
3[TABLE 1 ABOUT HERE]
The dependent variable WINT is a weighted measure of the penalties issued on the
pitch, calculated as follows:
WINT = [1stY ellow] + 2× [2ndY ellow] + 3× [DirectRed]
The weighting process distinguishes between a single direct red card (usually issued
after an breach of the rules) and an indirect red card (issued as the sum of two lesser fouls).
Eventually we also consider the count of fouls committed, FOULS. Additionally, we use the
absolute difference of log2 in FIFA World ranking between teams at the date of the match to
estimate relative team closeness (see Krumer, Rosenboim, & Shapir, 2014). The remaining
dummies capture match-specific information, such as Knockout Stage, the Hosting Country,
Over Time finishes and matches with at least one Penalty2. The second group of variables
relates to imbalances from international trade and status. Trade Imbalance is defined as:3
Trade Imbalance = 1− min[
Import A from B
Import A ;
Import B from A
Import B ]
max[ Import A from BImport A ;
Import B from A
Import B ]
where Import A from B are the gross imports (c.i.f.) of country A from B (and vice
versa), and Import A (B) are total imports (c.i.f.) of country A (B). The index ranges
between 0-1, such that at 0 countries have equal share of trade exchanges and as the index
approaches 1 there are asymmetric gains from trade in the bilateral relationship. Power
Imbalance, based on the Composite Index of National Capability (CINC)4, is defined as:
Power Imbalance = 1− min[CINC A/B]
max[CINC A/B]
The index ranges between 0-1, such that there is no recognised difference in strength
at 0 and at 1 they differ greatly on population, iron/steel production, energy consump-
tion, military personnel/expenditure. The third group refers to match-specific variables:
Attendance, used to control for external source of aggression (Savage & Torgler, 2013)
since a more passionate environment may induce more aggressive behaviour from players.
Additionally, we test the relevance of economic factors in determining player aggression,
by including the monetary stakes awarded by UEFA and FIFA5. We control for top league
players, as their monetary incentives may differ, by generating an index of the proportion of
players coming teams in the top five European leagues on national rosters. We then convert
2We expect a positive sign for all the associated coefficients.
3Trade data from IMF (2013), Direction of Trade Statistics (Edition: June 2013), Mimas, University of
Manchester (retrieved on October 2014).
4See http://www.correlatesofwar.org/ and Singer, Bremer, and Stuckey (1972).
5See FIFA World Cup Statistical Kit 6, 2012. We are grateful to Sara Williams - National Association
Development - for data on UEFA competitions.
4monetary prizes into Swiss Francs (CHF) at 2012 constant prices and divide this amount
by the index to obtain Adjusted Prize Money.
RESULTS
We utilise a Negative Binomial II regression model, as the dependent variables are
count data. We build a set of model estimations by adding variable blocks one at a time, be-
ginning with Tournament (1), then successively add Politec (2), Match (3) Interactions (4)
and finally a Referee Fixed Effect (5) model. Given the subjectivity of referees issuing cards
or recognising fouls - which could potentially distort the WINT and FOULS distribution, we
control for the referee role in determining/limiting players’ aggressive attitude. We also in-
clude estimates with both WINT (Table 2) and FOULS (Table 3) as the dependent variable.
[TABLES 2 & 3 ABOUT HERE]
The results of the Wald test confirm that the sport variables are not exhaustive
in explaining the aggressive attitude of players (via WINT and Fouls), while the Politec
variables are significant both in the case of WINT and FOULS as dependent. We observe
that an increase of one standard deviation of Trade Imbalance results in an increase of 0.207
(WINT) and 0.517 (Fouls) and for Power Imbalance we observe a similar increase of 0.249
(WINT) and 0.451 (Fouls). The introduction of the Match and Interaction variables (3-4,
8-9), have a minor impact in the size of the Politec variables but they remain significant in
the WINT regressions, but we observe that Power Imbalance becomes insignificant in the
FOULS (9). Furthermore, we see the effect of Attendance and Adjusted Prize Money meets
with our expectation, such that both are significant and positive supporting the idea that
stadium atmosphere and expected monetary stakes may influence players’ behaviour.
As a robustness check we have included some interacted explanatory variables into
the regression, Adjusted Prize Money × Attendance and Adjusted Prize Money × Ranking
Difference. Given that it is possible that that the interaction of these variables may weaken
the over all aggression effect and it is possible that the monetary incentive effect is non-
linear (Adjusted Prize Money Squared), we include both. Results suggest that the prize
money effect on intenseness reduces when attendance and ranking difference increase. This
is reasonable since the ranking difference and the crowd effect may have a strong influence
on players’ intenseness. However, the interactions between dependent variables can be
read in the opposite direction; the significance and the negative sign of Adjusted prize
money × Ranking Difference and Adjusted prize money × Attendance indicate that the
ranking difference and the attendance effects are mitigated when prize money increases. The
(significant) negative sign of the coefficient associated to Adjusted Prize Money Squared
also supports that hypothesis.
Finally, we include the Referee FFX modelling (5 & 10) in order to evaluate their
impact on the game, the results of which are very interesting and observe in both models
that virtually all significance vanish for the Politec and Matches variables. Only Knockout
Stage and Penalty remain significant in WINT (5) and Over Time and Hosting Country in
the Fouls (10) model. This result suggests that the referees play a crucial role in keeping
the “potential” players’ aggressiveness inside the rules.
5CONCLUSION
The main purpose of this paper was to extend our previous investigations into the
determinants affecting player behaviour on the soccer pitch. We added 147 observations
from World (1994 and 1998), and Euro Cup (1996), refined variables summarising com-
mercial and leadership bilateral imbalance. We also included new variables associated with
stadium atmosphere (attendance) and incentives (prize money). We used negative binomial
estimation techniques to show that trade and power gaps are significant determinants for
the number of sanctions and fouls recorded during high-level international tournaments.
Moreover, the significance of attendance and prize money suggests that emotional and eco-
nomic factors directly affect the players’ decision-making and aggressive behaviour. Also
of relevance is the role of referees that vanish the significance of the majority of political,
economic, tournament and match variables. These results enrich the literature and provide
additional evidence that both international relations and economic factors are features of
all international matches. We capture how bigger crowds or a more passionate context
encouraged or influences the players’ behaviour on the pitch, and how the latter is also
stimulated by the opportunity of additional earnings distributed by FIFA and UEFA.
References
Caruso, R. (2011). Crime and sport participation: Evidence from italian regions over the period
1997–2003. The Journal of Socio-Economics, 40 (5), 455–463.
Caruso, R., & Di Domizio, M. (2013). International hostility and aggressiveness on the soccer
pitch: Evidence from european championships and world cups for the period 2000–2012.
International Area Studies Review, 16 (3), 262–273.
Cuesta, J., & Bohórquez, C. (2012). Soccer and national culture: estimating the impact of violence
on 22 lads after a ball. Applied Economics, 44 (2), 147–161.
Domizio, M. D., & Caruso, R. (2014). Hooliganism and demand for football in italy: Attendance
and counterviolence policy evaluation. German Economic Review.
Elias, N., & Dunning, E. (1986). Quest for excitement: sport and leisure in the civilizing process.
Basil Backwell.
Krumer, A., Rosenboim, M., & Shapir, O. M. (2014). Gender, competitiveness, and physical
characteristics evidence from professional tennis. Journal of Sports Economics.
Miguel, E., Saiegh, S. M., & Satyanath, S. (2008). National cultures and soccer violence (Tech.
Rep.). National Bureau of Economic Research.
Orwell, G. (1945). The sporting spirit. The collected essays, journalism and letters of George Orwell,
4 , 1945–50.
Savage, D. A., & Torgler, B. (2013). Nerves of steel? Stress, work performance and elite athletes.
Applied Economics, 44 (19), 2423-2435.
Singer, J. D., Bremer, S., & Stuckey, J. (1972). Capability distribution, uncertainty, and major
power war, 1820-1965. Peace, war, and numbers, 19 .
6Table 1
Descriptive Statistics
Variable Obs. Mean SD Min Max
WINT 463 5.052 2.97 0 24
FOULS 285 34.6 8.39 13 62
Ranking Diff. 463 1.648 1.264 0.046 6.714
Trade Imbalance 463 0.763 0.281 0 1
Power Imbalance 463 0.700 0.271 0 0.998
Attendance (’000) 463 46.984 16.638 16.002 94.194
Adjusted Prize Money 463 3.251 4.919 0 44.751
Dummies 0 1
Knockout Stage 463 348 115
World Cup 463 155 308
Hosting 463 400 63
Overtime 463 425 38
Penalties 463 382 81
7Table 2
NEGATIVE BINOMIAL II.
WINT (1) (2) (3) (4) (5)
Ranking Difference 0.535*** 0.085*** 0.035 0.057** 0.005
(0.029) (0.024) (0.025) (0.028) (0.024)
Knockout Stage 0.648*** 0.274*** 0.032 0.059 0.149**
(0.121) (0.086) (0.103) (0.093) (0.072)
Penalty 0.783*** 0.305*** 0.245** 0.215*** 0.183***
(0.116) (0.082) (0.079) (0.077) (0.066)
Over time 0.140 0.194 0.137 0.108 0.002
(0.199) (0.135) (0.129) (0.120) (0.100)
Hosting Country 0.628*** 0.201** -0.031 -0.025 0.104
(0.124) (0.101) (0.094) (0.081) (0.076)
Trade Imbalance 0.737*** 0.396*** 0.350*** -0.170
(0.102) (0.130) (0.116) (0.101)
Power Imbalance 0.918*** 0.655** 0.517*** 0.081
(0.109) (1.168) (0.128) (0.098)
Attendance 0.013*** 0.014*** -0.001
(0.002) (0.002) (0.002)
Adj. Prize Money 0.019** 0.153*** 0.018
(0.007) (0.026) (0.021)
Adj. Prize Money -0.002*** -0.0005
Squared (0.000) (0.0004)
Adj. Prize Money × -0.002*** -0.000
Attendance (0.000) (0.0003)
Adj. Prize Money × -0.014** -0.008
Ranking Difference (0.006) (0.005)
Referees FE NO NO NO NO YES
S.E. of regression 7.796 3.516 3.407 3.306 2.820
Akaike Info. Criterion 6.138 5.25 5.114 5.045 4.974
Log-pseudolikelihood -1414.95 -1205.90 -1171.44 -1152.48 -998.68
Likelihood ratio test χ2 817.54*** 205.16*** 163.20*** 134.13*** 0.63
Wald χ2 806.84*** 520.42*** 70.97*** 36.17*** na
Alpha 0.766*** 0.261*** 0.214*** 0.186*** 0.009
Observations 463 462 462 462 462
Standard errors in parenthesis. Statistical significance: ***>99%, **>95%, *>92%.
8Table 3
NEGATIVE BINOMIAL II.
FOULS (6) (7) (8) (9) (10)
Ranking Difference 2.081*** 0.338*** 0.119*** 0.125*** 0.0007
(0.107) (0.042) (0.035) (0.038) (0.012)
Knockout Stage 1.242*** 0.556*** -0.219* -0.132 -0.003
(0.304) (0.146) (0.128) (0.124) (0.037)
Penalty 1.209*** 0.775*** 0.390*** 0.318*** 0.013
(0.319) (0.154) (0.118) (0.106) (0.033)
Over time 0.516 0.529 0.455** 0.282 0.241***
(0.526) (0.243) (0.186) (0.171) (0.049)
Hosting Country 1.634*** 0.574** -0.113 -0.081 0.096***
(0.124) (0.158) (0.129) (0.116) (0.035)
Trade Imbalance 1.907*** 1.038*** 0.916*** 0.033
(0.137) (0.130) (0.123) (0.053)
Power Imbalance 1.664*** 0.938** 0.769 -0.010
(0.142) (0.132) (0.125) (0.047)
Attendance 0.038*** 0.041*** -0.001
(0.003) (0.003) (0.001)
Adjusted Prize Money 0.033** 0.259*** 0.009
(0.010) (0.026) (0.009)
Adj. Prize Money -0.003*** 0.0001
Squared (0.000) (0.0001)
Adj. Prize Money × -0.003*** 0.0001
Attendance (0.000) (0.0001)
Adj. Prize Money × -0.016** -0.001
Ranking Difference (0.007) (0.002)
Referees FE NO NO NO NO YES
S.E. of regression 74047.7 59.26 44.67 36.64 Na
Akaike Info. Criterion 12.34 10.11 9.43 9.18 Na
Log-pseudolikelihood -1752.17 -1427.28 -1329.30 -1290.72 -887.08
Likelihood ratio test χ2 1.08e04*** 4081.34*** 3150.34*** 2633.8*** 4.4e-236
Wald χ2 623.80*** 1049.85*** 220.54*** 98.35*** na
Alpha 0.766*** 0.827*** 0.474*** 0.370*** 1.29e-08
Observations 285 285 285 285 285
Standard errors in parenthesis. Statistical significance: ***>99%, **>95%, *>92%.


Determinants of aggressiveness on the soccer pitch: evidence from FIFA and UEFA tournaments

Caruso, Raul (ORCID:0000-0002-9307-5460)

PubliCatt

Determinants of aggressiveness in soccer: Evidence from FIFA and UEFA tournaments

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveDeterminants of aggressiveness on thesoccer pitch: evidence from FIFA andUEFA tournamentsCaruso, Raul and Di Domizio, Marco and Savage, David A.2015Online at https://mpra.ub.uni-muenchen.de/61459/MPRA Paper No. 61459, posted 20 Jan 2015 09:29 UTCDeterminants of aggressiveness on the soccer pitch:evidence from FIFA and UEFA tournamentsRaul CarusoCatholic University of the Sacred Heart, Milan, ItalyMarco Di DomizioUniversity of Teramo, Teramo, ItalyDavid A. SavageBond University, Robina, Australia.ABSTRACTThis paper examines the determinants of aggressiveness on the soccer pitch in 463matches from FIFA (World Cup) and UEFA (Euro Cup) tournaments spanning from 1994to 2012. We highlight the role of several measures of international rivalry between countrieson the players’ aggressive behaviourKeywords: soccer tournaments; aggressiveness; international rivalry.JEL Classifications: L83, D74INTRODUCTION“Serious sport has nothing to do with fair play. It is bound up with hatred, jealousy,boastfulness, disregard of all rules and sadistic pleasure in witnessing violence: in otherwords it is war minus the shooting” (Orwell, 1945). Soccer matches often go beyondpure sport competition, particularly when national teams take part in major internationaltournaments. The idea that soccer is the stylisation of war is often used as a touchstoneto interpret the associated violence. Recently the focus has investigated the role thatsocio-political and economic factors play in determining player aggression. This paperextends previous contributions by examining additional factors that may better explainplayer aggression, such as the roles of: stadium atmosphere, prize money incentivisationand referees. We generate two proxy variables for player aggressiveness: the weightednumber of yellow/red cards issued and the number of fouls sanctioned by referees.Our reference model estimations show that match variables alone are not exhaustive indetermining player aggression and factors such as trade and power gaps, match attendanceand prize money are significant. Finally, we attempt to control for the influence that referees’may impose on the match through fixed effects.Corresponding author Raul Caruso. Contact via Email: raul.caruso@unicatt.it.2Violence and soccerThe hypothesis that violence is an innate part of sport is widely accepted, suchthat Elias and Dunning (1986) claimed that soccer matches stylise and miniaturise war.Where the associated aggression is related to overt masculinity, territorial struggle andexcitement. This is one of the reasons that the sporting environment is considered an oddchoice for an outlet of nationalistic tensions or as a political instrument for building trustbetween rival countries. Particularly since the basic nature of sport is one of competition,which can lead to extremely aggressive behaviour, coercion and threats of violence. This isconsistent with Caruso (2011), where sport is interpreted as a market and relational good,and an expression of threat, power and coercion. Unfortunately, violence is viewed as anegative but ineluctable component of sport, both on and off the pitch. The latter has beenthe focus of the hooliganism phenomenon and its counter strategies (Domizio & Caruso,2014), while the former is being used to empirically disentangle the effect of culture,institutions and poverty in determining violent player behaviour. The Miguel, Saiegh, andSatyanath (2008) analysis of the role of civil conflict in a players home provenance and thenumber of yellow/red cards awarded by referees, showed a strong relationship between thetwo. Supporting the idea that culture and identity can influence player’s attitudes towardsaggressive or violent behaviour. However,Cuesta and Bohórquez (2012) reached differentconclusions through the investigation of the Copa Libertadores. Using factors such asdictatorship length, homicides rates and years of armed conflict within each country,the results showed that the violent behaviour of players depended exclusively on soccercharacteristics. In this vein, we extend the previous empirical investigations proposed inCaruso and Di Domizio (2013), where we found that the differences in political, diplomatic,education and economic factors between nations significantly affected the sanctions issuedby the referees. Specifically, we:1. extend data set by 147 observations12. include fouls sanctioned as alternative dependent variable.3. build innovative measures for the bilateral gap in trade and power.4. include attendance and prize money as explanatory variables.5. control for referees’ effect.Data set and empirical strategyThe dataset contains 463 final phase matches from FIFA (World Cup) and UEFA(Euro Cup) tournaments spanning from 1994 to 2012 and includes 61 countries. Weinvestigate players’ aggressiveness by means of two variables: (i) WINT - a weightedmeasure of cards issued per match, (ii) FOULS - the count of sanctioned fouls. Weinclude a set of independent control variables divided into three groups: Tournament,political-economic (Politec), and Match specific variables. From which we estimate aregression equation, by means of negative binomial using maximum likelihood techniques.Table 1 presents the summary statistics for the non-dummies variables and discussed below.1This includes FIFA World Cup 1994 and 1998, and UEFA European Cup 1996 tournaments.3[TABLE 1 ABOUT HERE]The dependent variable WINT is a weighted measure of the penalties issued on thepitch, calculated as follows:WINT = [1stY ellow] + 2 × [2ndY ellow] + 3 × [DirectRed]The weighting process distinguishes between a single direct red card (usually issuedafter an breach of the rules) and an indirect red card (issued as the sum of two lesser fouls).Eventually we also consider the count of fouls committed, FOULS. Additionally, we use theabsolute difference of log2 in FIFA World ranking between teams at the date of the match toestimate relative team closeness (see Krumer, Rosenboim, & Shapir, 2014). The remainingdummies capture match-specific information, such as Knockout Stage, the Hosting Country,Over Time finishes and matches with at least one Penalty2. The second group of variablesrelates to imbalances from international trade and status. Trade Imbalance is defined as:3Trade Imbalance = 1 −min[ Import A from BImport A; Import B from AImport B]max[ Import A from BImport A; Import B from AImport B]where Import A from B are the gross imports (c.i.f.) of country A from B (and viceversa), and Import A (B) are total imports (c.i.f.) of country A (B). The index rangesbetween 0-1, such that at 0 countries have equal share of trade exchanges and as the indexapproaches 1 there are asymmetric gains from trade in the bilateral relationship. PowerImbalance, based on the Composite Index of National Capability (CINC)4, is defined as:Power Imbalance = 1 −min[CINC A/B]max[CINC A/B]The index ranges between 0-1, such that there is no recognised difference in strengthat 0 and at 1 they differ greatly on population, iron/steel production, energy consump-tion, military personnel/expenditure. The third group refers to match-specific variables:Attendance, used to control for external source of aggression (Savage & Torgler, 2013)since a more passionate environment may induce more aggressive behaviour from players.Additionally, we test the relevance of economic factors in determining player aggression,by including the monetary stakes awarded by UEFA and FIFA5. We control for top leagueplayers, as their monetary incentives may differ, by generating an index of the proportion ofplayers coming teams in the top five European leagues on national rosters. We then convert2We expect a positive sign for all the associated coefficients.3Trade data from IMF (2013), Direction of Trade Statistics (Edition: June 2013), Mimas, University ofManchester (retrieved on October 2014).4See http://www.correlatesofwar.org/ and Singer, Bremer, and Stuckey (1972).5See FIFA World Cup Statistical Kit 6, 2012. We are grateful to Sara Williams - National AssociationDevelopment - for data on UEFA competitions.4monetary prizes into Swiss Francs (CHF) at 2012 constant prices and divide this amountby the index to obtain Adjusted Prize Money.RESULTSWe utilise a Negative Binomial II regression model, as the dependent variables arecount data. We build a set of model estimations by adding variable blocks one at a time, be-ginning with Tournament (1), then successively add Politec (2), Match (3) Interactions (4)and finally a Referee Fixed Effect (5) model. Given the subjectivity of referees issuing cardsor recognising fouls - which could potentially distort the WINT and FOULS distribution, wecontrol for the referee role in determining/limiting players’ aggressive attitude. We also in-clude estimates with both WINT (Table 2) and FOULS (Table 3) as the dependent variable.[TABLES 2 & 3 ABOUT HERE]The results of the Wald test confirm that the sport variables are not exhaustivein explaining the aggressive attitude of players (via WINT and Fouls), while the Politecvariables are significant both in the case of WINT and FOULS as dependent. We observethat an increase of one standard deviation of Trade Imbalance results in an increase of 0.207(WINT) and 0.517 (Fouls) and for Power Imbalance we observe a similar increase of 0.249(WINT) and 0.451 (Fouls). The introduction of the Match and Interaction variables (3-4,8-9), have a minor impact in the size of the Politec variables but they remain significant inthe WINT regressions, but we observe that Power Imbalance becomes insignificant in theFOULS (9). Furthermore, we see the effect of Attendance and Adjusted Prize Money meetswith our expectation, such that both are significant and positive supporting the idea thatstadium atmosphere and expected monetary stakes may influence players’ behaviour.As a robustness check we have included some interacted explanatory variables intothe regression, Adjusted Prize Money × Attendance and Adjusted Prize Money × RankingDifference. Given that it is possible that that the interaction of these variables may weakenthe over all aggression effect and it is possible that the monetary incentive effect is non-linear (Adjusted Prize Money Squared), we include both. Results suggest that the prizemoney effect on intenseness reduces when attendance and ranking difference increase. Thisis reasonable since the ranking difference and the crowd effect may have a strong influenceon players’ intenseness. However, the interactions between dependent variables can beread in the opposite direction; the significance and the negative sign of Adjusted prizemoney × Ranking Difference and Adjusted prize money × Attendance indicate that theranking difference and the attendance effects are mitigated when prize money increases. The(significant) negative sign of the coefficient associated to Adjusted Prize Money Squaredalso supports that hypothesis.Finally, we include the Referee FFX modelling (5 & 10) in order to evaluate theirimpact on the game, the results of which are very interesting and observe in both modelsthat virtually all significance vanish for the Politec and Matches variables. Only KnockoutStage and Penalty remain significant in WINT (5) and Over Time and Hosting Country inthe Fouls (10) model. This result suggests that the referees play a crucial role in keepingthe “potential” players’ aggressiveness inside the rules.5CONCLUSIONThe main purpose of this paper was to extend our previous investigations into thedeterminants affecting player behaviour on the soccer pitch. We added 147 observationsfrom World (1994 and 1998), and Euro Cup (1996), refined variables summarising com-mercial and leadership bilateral imbalance. We also included new variables associated withstadium atmosphere (attendance) and incentives (prize money). We used negative binomialestimation techniques to show that trade and power gaps are significant determinants forthe number of sanctions and fouls recorded during high-level international tournaments.Moreover, the significance of attendance and prize money suggests that emotional and eco-nomic factors directly affect the players’ decision-making and aggressive behaviour. Alsoof relevance is the role of referees that vanish the significance of the majority of political,economic, tournament and match variables. These results enrich the literature and provideadditional evidence that both international relations and economic factors are features ofall international matches. We capture how bigger crowds or a more passionate contextencouraged or influences the players’ behaviour on the pitch, and how the latter is alsostimulated by the opportunity of additional earnings distributed by FIFA and UEFA.ReferencesCaruso, R. (2011). Crime and sport participation: Evidence from italian regions over the period1997–2003. The Journal of Socio-Economics, 40 (5), 455–463.Caruso, R., & Di Domizio, M. (2013). International hostility and aggressiveness on the soccerpitch: Evidence from european championships and world cups for the period 2000–2012.International Area Studies Review, 16 (3), 262–273.Cuesta, J., & Bohórquez, C. (2012). Soccer and national culture: estimating the impact of violenceon 22 lads after a ball. Applied Economics, 44 (2), 147–161.Domizio, M. D., & Caruso, R. (2014). Hooliganism and demand for football in italy: Attendanceand counterviolence policy evaluation. German Economic Review.Elias, N., & Dunning, E. (1986). Quest for excitement: sport and leisure in the civilizing process.Basil Backwell.Krumer, A., Rosenboim, M., & Shapir, O. M. (2014). Gender, competitiveness, and physicalcharacteristics evidence from professional tennis. Journal of Sports Economics.Miguel, E., Saiegh, S. M., & Satyanath, S. (2008). National cultures and soccer violence (Tech.Rep.). National Bureau of Economic Research.Orwell, G. (1945). The sporting spirit. The collected essays, journalism and letters of George Orwell,4 , 1945–50.Savage, D. A., & Torgler, B. (2013). Nerves of steel? Stress, work performance and elite athletes.Applied Economics, 44 (19), 2423-2435.Singer, J. D., Bremer, S., & Stuckey, J. (1972). Capability distribution, uncertainty, and majorpower war, 1820-1965. Peace, war, and numbers, 19 .6Table 1Descriptive StatisticsVariable Obs. Mean SD Min MaxWINT 463 5.052 2.97 0 24FOULS 285 34.6 8.39 13 62Ranking Diff. 463 1.648 1.264 0.046 6.714Trade Imbalance 463 0.763 0.281 0 1Power Imbalance 463 0.700 0.271 0 0.998Attendance (’000) 463 46.984 16.638 16.002 94.194Adjusted Prize Money 463 3.251 4.919 0 44.751Dummies 0 1Knockout Stage 463 348 115World Cup 463 155 308Hosting 463 400 63Overtime 463 425 38Penalties 463 382 817Table 2NEGATIVE BINOMIAL II.WINT (1) (2) (3) (4) (5)Ranking Difference 0.535*** 0.085*** 0.035 0.057** 0.005(0.029) (0.024) (0.025) (0.028) (0.024)Knockout Stage 0.648*** 0.274*** 0.032 0.059 0.149**(0.121) (0.086) (0.103) (0.093) (0.072)Penalty 0.783*** 0.305*** 0.245** 0.215*** 0.183***(0.116) (0.082) (0.079) (0.077) (0.066)Over time 0.140 0.194 0.137 0.108 0.002(0.199) (0.135) (0.129) (0.120) (0.100)Hosting Country 0.628*** 0.201** -0.031 -0.025 0.104(0.124) (0.101) (0.094) (0.081) (0.076)Trade Imbalance 0.737*** 0.396*** 0.350*** -0.170(0.102) (0.130) (0.116) (0.101)Power Imbalance 0.918*** 0.655** 0.517*** 0.081(0.109) (1.168) (0.128) (0.098)Attendance 0.013*** 0.014*** -0.001(0.002) (0.002) (0.002)Adj. Prize Money 0.019** 0.153*** 0.018(0.007) (0.026) (0.021)Adj. Prize Money -0.002*** -0.0005Squared (0.000) (0.0004)Adj. Prize Money × -0.002*** -0.000Attendance (0.000) (0.0003)Adj. Prize Money × -0.014** -0.008Ranking Difference (0.006) (0.005)Referees FE NO NO NO NO YESS.E. of regression 7.796 3.516 3.407 3.306 2.820Akaike Info. Criterion 6.138 5.25 5.114 5.045 4.974Log-pseudolikelihood -1414.95 -1205.90 -1171.44 -1152.48 -998.68Likelihood ratio test χ2 817.54*** 205.16*** 163.20*** 134.13*** 0.63Wald χ2 806.84*** 520.42*** 70.97*** 36.17*** naAlpha 0.766*** 0.261*** 0.214*** 0.186*** 0.009Observations 463 462 462 462 462Standard errors in parenthesis. Statistical significance: ***>99%, **>95%, *>92%.8Table 3NEGATIVE BINOMIAL II.FOULS (6) (7) (8) (9) (10)Ranking Difference 2.081*** 0.338*** 0.119*** 0.125*** 0.0007(0.107) (0.042) (0.035) (0.038) (0.012)Knockout Stage 1.242*** 0.556*** -0.219* -0.132 -0.003(0.304) (0.146) (0.128) (0.124) (0.037)Penalty 1.209*** 0.775*** 0.390*** 0.318*** 0.013(0.319) (0.154) (0.118) (0.106) (0.033)Over time 0.516 0.529 0.455** 0.282 0.241***(0.526) (0.243) (0.186) (0.171) (0.049)Hosting Country 1.634*** 0.574** -0.113 -0.081 0.096***(0.124) (0.158) (0.129) (0.116) (0.035)Trade Imbalance 1.907*** 1.038*** 0.916*** 0.033(0.137) (0.130) (0.123) (0.053)Power Imbalance 1.664*** 0.938** 0.769 -0.010(0.142) (0.132) (0.125) (0.047)Attendance 0.038*** 0.041*** -0.001(0.003) (0.003) (0.001)Adjusted Prize Money 0.033** 0.259*** 0.009(0.010) (0.026) (0.009)Adj. Prize Money -0.003*** 0.0001Squared (0.000) (0.0001)Adj. Prize Money × -0.003*** 0.0001Attendance (0.000) (0.0001)Adj. Prize Money × -0.016** -0.001Ranking Difference (0.007) (0.002)Referees FE NO NO NO NO YESS.E. of regression 74047.7 59.26 44.67 36.64 NaAkaike Info. Criterion 12.34 10.11 9.43 9.18 NaLog-pseudolikelihood -1752.17 -1427.28 -1329.30 -1290.72 -887.08Likelihood ratio test χ2 1.08e04*** 4081.34*** 3150.34*** 2633.8*** 4.4e-236Wald χ2 623.80*** 1049.85*** 220.54*** 98.35*** naAlpha 0.766*** 0.827*** 0.474*** 0.370*** 1.29e-08Observations 285 285 285 285 285Standard errors in parenthesis. Statistical significance: ***>99%, **>95%, *>92%.

This paper examines the determinants of aggressiveness – proxied alternatively by the number of penalties issued by the referee and by a related measure- on the soccer pitch in 463 matches from FIFA (World Cup) and UEFA (Euro Cup) tournaments spanning from 1994 to 2012. We highlight the role of several measures of international rivalry between countries on the players' aggressive behaviour

di Domizio, Marco

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Determinants of aggressiveness on the soccer pitch: evidence from FIFA and UEFA tournaments

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