Introduction
As the only game in the world that is played in every country and by people of every race and religion football is one of the few institutions that is as exceptional as the United Nations.
-Former U.N. Secretary General Kofi Annan

The rules in the contemporary game of footballi as we know it nowadays have been formalized by the English Football Association in 1863 and spread around the globe via the British Empire in the very same fashion as rugby and cricket. As football became a operating class sport around 1870 Skelton 2000 it also grew increasingly well-liked in the rest in the planet and is right now thought to be to be probably the most well known sport in the world. Throughout the last World Cup held in Germany inside the summer time of 2006 32 nations qualified out on the 207 members of FIFA Federation Internationale de Football Association. Wesley market A lot more than one.1 billion tv viewers watched the last between Italy and France globally and in terms of participation you will discover right now a lot more than 200 million active players worldwide as football is a single of some sports that happen to be played on all five continents www.fifa.com.

Despite the apparent world wide appeal of football youll find only two continents which will display proof of international achievement. The Planet Cups are absolutely dominated by European and South American national teams as the eighteen World Cups currently being held considering that 1930 only had South American or European winners. The truth is there have only been seven one of a kind nations to ever lift the trophy some far more than once this kind of as Brazil who have won it an impressive five instances table five.
Table one- International World Cup Success

Group
Titles
Runners-up

Brazil
five 1958 1962 1970 1994 2002
2 1950 1998

Italy
four 1934 1938 1982 2006
two 1970 1994

Germany
three 1954 1974 1990
4 1966 1982 1986 2002

Argentina
2 1978 1986
2 1930 1990

Uruguay
two 1930 1950
-

France
1 1998
one 2006

England
one 1966
-

Netherlands
-
two 1974 1978

Czechoslovakia
-
two 1934 1962

Hungary
-
2 1938 1954

Sweden
-
one 1958
hosts
contains final results representing West Germany in between 1954 and 1990
states that have because split into numerous independent nations

But the Globe Cup is just not the only way of measuring overall performance. In 1993 FIFA began publishing a ranking of how every one of the national teams inside the globe compare against one another on a month-to-month basis. Immediately after going by means of two enormous alterations above the years considering that its introduction the FIFA ranking technique is at this time primarily based around the final results every group has achieved through the final 4 years where value of match strength of opposition and the result with the match ascertain how lots of points each and every team obtains. But more investigation of the best 20 on the FIFA ranking only confirms the predicament described above- With the prime 20 countries in the globe as of 15th of February 2007 only one country was African Cameroon 1 Central American Mexico whilst the rest have been either South American or European table 2.

Why is this Why are extremely populated nations example- China ranked as 79 not ready to outperform scarcely populated countries example- Portugal ranked as 10 thinking about China has got 124 times far more people to select from www.imf.org in regards to picking their very best eleven players. In other words- What can make a nation very good at football This query was the authors initial determination for writing this paper and by using an econometric strategy based mostly on previous operate on the topic an attempt will probably be created to search out clues that may possibly assist answering the query.

Table 2- FIFA ranking as of 15th of February 2007
Ranking
Country
Points

1
Italy
1562

two
Brazil
1540

3
Argentina
1535

four
France
1496

5
Germany
1359

6
England
1330

7
Netherlands
1312

eight
Portugal
1262

9
Czech Republic
1193

10
Spain
1161

11
Ukraine
1018

12
Croatia
987

13
Greece
926

14
Switzerland
913

15
Romania
912

16
Sweden
894

17
Cameroon
893

18
Denmark
876

19
Mexico
857

20
Scotland
854
Supply- www.fifa.com
2Literature review
Only not long ago have economists taken an academic interest in football. Regardless of the large public interest in each international and club football the quantity of literature on this topic is surprisingly scarce and for that purpose the following section will give a brief outline on which topics academics have focused on. The investigation that has been conducted has typically had monetary motivation but this need to come as no surprise looking at the massive commercialisation the sport has gone through over the last 20 years Haugen and Hervik 2002. The primary focus in the literature hitherto has been how clubs frequently European clubs have transformed from attempting to win football matches into large-scale firms holding some of the worlds largest trademarks–such as True Madrid and Manchester United worth 186.2mill and 166.4mill respectively Deloitte 2007.
2.1Sport- Micro level
Economic theory on football is largely based around the earlier theory written on sport generally and for that cause a brief outline of this literature will also be integrated inside the following.

The demand for sport in general has been completely investigated one of the earliest staying Neale 1964 which concerned the demand for baseball in USA. Neale illustrates the -firm- in specialized sports as the league and suggests the -products- the -firm- is selling is one the match and two the league standings. He claims that the closer the league standings the greater is the item plus the higher will be the demand for the item.

The demand for football has also been meticulously studied. Hart et al 1975 discovered that attendance to football matches generally was explained by ticket costs distance to travel for away fans the calibre with the opposing team and very own team efficiency. Bird 1982 makes use of time series data to investigate how attendance for English football matches have created with time and finds that demand for football is revenue inelastic producing football an inferior beneficial. He also reports that the findings of ticket value inelasticity is predicted to become exploited by the market place an insight that certainly materialized over time as ticket prices for leading division English football pretty much have quadrupled considering 1982 Dobson and Goddard 1999 pp75.
The demand for televised football has also been investigated Baimbridge et al. 1996 and Forrest Simmons 2002. The TV revenue for the 2006 Globe Cup reached 525 million and recent news of a new TV deal for the right to broadcast English football outside Britain worth 625 million for the subsequent three seasons exemplifies how much money is going into and in turn is expected to become created around the sport of football from a commercial point of view.

Another topic that has been given broad attention is how player contracts free agency and transfer payment have created and changed with time most significantly immediately after the Bosman Ruling in 1995. For examples see Sloane 1969 Speight and Thomas 1997 Charmichael and Thomas 1993 and 2002 Dilger 2001 and Fees and Muelhauser 2002. The main findings on this subject are that the contract length increases because the property rights of a player are transferred from the club to the player as was the legal consequence in the Bosman ruling.
Other aspects of club football that have been researched include evidence of discrimination Szymanski 2001 Reilly and Witt 1995 and Charmichael and Thomas 2000 and league reward systems this kind of as if the 3 points for winning increased the attacking play which has been investigated by Palmino et al 1999 and Guedes and Machado 2002 amongst others.
two.2Sport- Macro degree
Contrary to the economic and club degree referred to as the micro degree there may be the macro financial degree which could be the main concentrate from the following paper. The macro financial term is used when discussing how football plus the rest with the society are integrated. The book -France and the 1998 Globe Cup- Dauncey and Hare 1999 gives a thorough analysis of how hosting a Planet Cup affects the country as a whole both economically by means of an increase in tourism but also on aspects such as the -feel great factor- and how a well-organized tournament can influence politicians popularity as time passes. In other words the book explains how football has influenced the political and financial environment of a Globe Cup hosting nation.
Most from the literature on footballsports efficiency and economics at the macro degree modifications the causality from the Dauncey et al 1999 book into- Which countrys particular elements establish how well a nation performs in a given sport There are actually various articles contemplating which components establish sport efficiency implicitly and football efficiency explicitly. Most of the literature covering sport performance tends to make use of Olympic Games medalsparticipation as the dependant variable. The general idea throughout the literature is to use gross nationaldomestic item GNPGDP per capita and weathertemperature as explanatory variables with small variations.

Kuper and Sterken 2001 investigates Olympic good results employing data on winners of your winter Olympics exactly where the major finding was that in team sports in which a referee is required there will likely be proof for a home team advantage in addition to GDP per capita which is also found to become significant. Other articles on Olympic results are Johnston and Alis 2000 investigation of female participation rates inside the Olympics and an article by Hoffmann et al 2002b thinking of how political systems influence a countrys Olympic success.
2.3Football efficiency
The first article investigating determinants of football functionality to the authors finest knowledge was written by Hoffmann Lee and Ramasamy 2002a entitled -The socio financial determinants of international soccer performance- hereon termed HLR. HLR employs the FIFA World ranking of January 2001 as the dependent variable to investigate which country particular elements establish football functionality. They discover that population as a single explanatory variable has no impact on efficiency. To further investigate the idea of whether a larger population increases the efficiency of your national football team HLR introduced a variable intended to indicate football tradition which was named LATIN. This variable was used as a dummy variable for nations from Spain Portugal Central America and South America. The justification for working with this variable have been according to HLR underlying cultural components in the Luco- Hispanic culture that promotes male participation and support of sporting events in these countries. Even if this variable was insignificant when used on its own when multiplied with the population variable the benefits became significant. This is explained by claiming that a larger population does not necessarily mean superior football players unless the extra people actually play football. Other variables incorporated have been GNP per capita temperature the countrys share of globe population and a HOST dummy also intended to capture football traditions.

In another paper by Torgler 2004 the concentrate is on which variables determine female football overall performance. This paper is built upon the Hoffmann et al article. Torgler can make a couple of amendments however namely the change of your temperature variable is taken as an average temperature for the country as a whole rather than from the capital in the country investigated. He also changes the proxy from ranking points around the FIFA globe ranking to the actual ranking position each country has implicating a lower number equals better functionality. The key finding in this paper is that the principle determinants of female football overall performance are population football tradition and GDP per capita.Macmillan and Smith published a paper in December 2006 also seen as a response to HLR discussing econometric issues inside the article such as sample bias as well as the poor fit of your model.

Just before the Planet Cup final in 2006 Gelade wrote an article entitled -Academics uncover Formula for the finest international football team.- Utilizing five variables the paper claims to have worked out a new way of ranking international football teams but this ranking is ambiguous at most effective. Applying country certain variables this kind of as the number of men playing football number of years as a member of FIFA number of internationals playing abroad wealthand climate this formula ranks World Cup winners Italy as number 3 France as number four and Brazil as number 18 The cause given why these results are not realistic is because the formula just isnt able to quantify football passion a variable that if included is certain to boost poorer nations such as Brazils score significantly.
3Method
In this section an outline of the dependent and explanatory variables determining international football functionality might be given. As mentioned above this paper will take an econometric strategy based mostly on data comprehensively available on the internet. Substantial effort has been created to collect quite possibly the most recent information available mainly using official sources this kind of as FIFA Planet Bank and International Monetary Fund. The following regression will draw partly on the operate of HLR as this could be the first primary article around the subject but will also use others input e.g. Togler 2004 Macmillan and Smith 2006 and Gelade 2006. In addition to previous result to two new explanatory variables not previously tested are included ELITE and HEALTH.
three.1Sample
HLR uses the 76 medal winning nations from Summer Olympics in Sydney 2000 as their sample pool arguing that this would be a bias free sample. However MacMillan and Smith 2006 claim that this sampling method might cause sampling bias as the countries are not chosen randomly as well as the countries are much more likely to become chosen from the upper end of the ranking table. To avoid this Macmillan and Smith run a regression which includes 176 countries to avoid the problem of sample bias and inside the regression below 179 countries are integrated in an attempt to avoid sampling bias. The excluded countries are characterized by non-published information for the explanatory variables countries such as Cuba Iraq Somalia and North Korea. Further England because the biggest UK nation is chosen to represent United Kingdom excluding Northern Ireland Scotland and Wales.
three.2Dependent Variable
The dependent variable may be the FIFA ranking score as of 15th of February 2007 found on www.fifa.com and widely published by the planet press. The method for calculating the FIFA score was altered immediately after the 2006 World Cup and to the authors most effective knowledge has no paper been published making use of the new algorithm of determining the FIFA ranking. The ranking was first introduced in 1993 but has been heavily criticised for being the two extremely complicated and for getting unable to give a realistic ranking by means of the years. It was first altered in 1999 in an attempt to compensate for the substantial criticism but as there was still no evident outcome that the ranking mirrored the genuine functionality of international teams a entirely new algorithm have been created immediately after the Globe Cup in Germany in July 2006. The new simplified algorithm works as follows- For every match the national team plays three points is awarded for victory one point for draw and zero points if the team loses. Next the value of the match is weighted by awarding a single point for friendly two points for an intercontinental cup e.g. Euro Cup qualifying game three points playing in an intercontinental cup and 4 points for a Globe Cup match. Strength of opposition is calculated applying the oppositions ranking in the existing FIFA ranking. Subsequent each conference is weighted where UEFAii is weighted at one.00 followed by CONMEBOLiii weighted at 0.98 and all the other confederations being weighted 0.85. So for each match played these variables are multiplied to get a ranking score for each teams in any given international -A- match. Matches played inside the last 4 years contrary to eight years that had been the case before the change are taken into account when calculating the month-to-month FIFA score. More the latest matches are weighted as follows- The final twelve months count in full exactly where the prior year only count for half and the games played three and 4 years ago have decreasing significance only 30 and 20 respectively. Two things that have been removed in the new calculation method were the number of goals scored and homeaway advantage in each match.

To test for robustness on the specification the preferred regression will also be ran utilizing both Could 2006 information before the change in ranking method and on an alternative ranking system created by football enthusiasts on the Internet called the ELO ranking.
3.3Explanatory variables
The basic variables included inside the specification are drawn from HLR and we will initially test the robustness of their regression making use of updated data on both dependent and explanatory variables. We will then investigate more possible variables and we will also consider removing many of the original variables to allow for improved fit of your model.
3.three.1Population
The first explanatory variable is every countrys fraction of world population which theoretically should be positively correlated with football functionality as a larger pool of potential football players to draw from will need to increase the possibility of becoming successful within the sport. Population has been shown to become significant when testing Olympic good results Hoffmann et al 2002 even if the benefits have shown that this variable has become less significant as time passes Johnson Ali 2004.
3.three.2GNP and GNP2
The second explanatory variable hypothesizes initial development of football talent to be extremely relevant for the efficiency level of your national football team. Even if football is a low cost activity private access to equipment and available leisure time must be available and this is easiest to measure by making use of GNP per capita. However as revenue rises above a threshold level the marginal effect of extra earnings becomes negative Hoffmann et al 2002. As HLR note you will find two possible explanations of this relationship. First football is an inexpensive sport to engage in when compared to sports such as car racing golf sailing etc. For this cause it is possible to argue that poorer individuals will above invest in playing football simply because you will find couple of other sporting alternatives available to them. The second cause is similar in style to the first stating that as gnp per capita increases not only will other sporting activities act as substitutes for football but also other activities children get engaged in this kind of as video games DVD satellite TV and other indoor activities could compete with the time children spend playing football. To capture this we use gnp alone and as a quadratic function GNP2 expecting a u shaped curve and a negative coefficient exactly where there also will exist an optimal degree of gnp for developing football abilities. Information on population and gnp per capita have been gathered from the Globe Bank www.worldbank.org 3.three.3Temperature
Subsequent temperature is used as an indication of climatic conditions in each and every county. Prior studies regarding Olympic results Hoffmann et al 2002 have shown that the ideal yearly aggregate temperature is approximately 14C and any deviation from this temperature is expected to have negative impact on sporting performance. It is easy to imagine how nations with extreme temperatures will have difficulties performing any sporting activity at an optimal degree. To model this HLR use a variable that picks up any deviation from the preferred temperature given by Temp-14two expected to have a negative coefficient.

Other researchers have used climate as a variable to replace temperature Gelade 2006 but in the following the variable controlling for exogenous weather conditions is the deviations from the optimal 14C temperature described above.
Temperature information is collected from www.geographyiq.com and utilizes the yearly average in each and every countries capital. Employing temperature gives a precise estimate on the exogenous weather conditions even if the temperature is only estimated in the capital. The cause why this method is chosen is because a country can have huge domestic variations in yearly temperature. The capital is however normally the highest populated city in every single country plus the temperature here will affect always a significant proportion of your population.
3.3.4Latin
HLR also integrated two variables regarding cultural aspects promoting a countrys football achievement. The first variable is incorporated intending to capture how well-liked football is in a given nation each with regards to numbers of spectators and amount of active players in every nation. Directly popularity with regards to much more men and women watching football matches live or on TV increases economic and status incentives for players. Indirectly greater level of player rewards and increased popularity might as time passes increase the pool of potential national players.
To seek out a variable that may be capable to illuminate the cultural aspect which affects football functionality is a challenging task as this relationship has proved to be a complex one Archetti 1999 Giulianotti 1999 Lever 1995 as cited by HLR. Lacking alternative variables proven to display significant final results using the LATIN variable as reported by HLR as one of their primary findings appears as a decent proposal. The way HLR justifies the use of this variable is by investigate the leading 10 around the FIFA ranking as of January 2001 noting that eight of these nations have predominantly catholic population the only exceptions staying Germany and England. HLR more notes that a romantic language referred to as LATIN by the American Heritage Dictionary AHD is spoken in seven on the prime ten countries and only two earlier World Cup winners does not share these features again Germany and England. Even if religion does play an important role in shaping a countrys culture HLR employs language as a proxy for cultural attributes that increase international football overall performance. In turn the romantic languages differ internally and HLR chooses the Luco-Hispanic languages as defined by the US library of Congress- All Spanish and Portuguese speaking countries in the globe to become precise that is definitely the nations in Central and South America plus Spain and Portugal. According to HLR these nations share underlying cultural elements that support the high popularity of mens football each as spectator and participation within the sport. In the 2007 sample the countries within the prime ten have changed somewhat but as eight out of ten nations is still defined as LATIN according to AHD this variable is integrated inside the following specification as a single measure of football traditions.
3.3.5Host
HLR also employs a variable called HOST a dummy set to among the list of country has previously hosted a Globe Cup and zero otherwise. This variable is incorporated as a second variable for football culture. By indicating that a nation has hosted the Planet Cup it really should give an indication that this specific nation has long football traditions implicating that more men and women are likely to play football in that nation and therefore increase the chance of a increased FIFA ranking score.
The source of this information came from the www.fifa.com.

The abovementioned variables had been the ones incorporated inside the specification of HLR. For the remainder of this paper we will investigate the robustness of these variables applying 2007iv data. New variables will also be added to investigate how this effects the regression and its benefits regarding to fit and significance.
3.3.6History
As mentioned by most authors on this subject one of the most tricky variable to search out a proxy for could be the so called -football tradition- proxy. HLR chose the variables LATIN and HOST and whilst the additional use of LATIN has been justified above hosting the Planet Cup as a measure of football tradition is possibly not the preferred indicator of football traditions. As there has only been 14 one of a kind hosts out of the 207 member nations including this variable is bound to reject valuable information. This is also mentioned by Macmillan and Smith as well as the solution they discovered was to drop the variable HOST and replace it with a variable where there is data available for all of the nations within the sample namely the number of years the country happen to be an affiliate of your FIFA. This variable is named HISTORY and is expected to have a positive effect on international football functionality as longer membership really should mean longer traditions. Again data were collected from the FIFA webpage.
three.three.7Republic
Macmillan et al do however mention one problem in working with the history variable referring to the number of -new- states appearing in Eastern Europe soon after the fall in the Soviet Union the splitting of Czechoslovakia and post war adjustments to former Yugoslavia inside the early nineteen nineties. The problem this elevates is that the new nations will only have a short FIFA history. 1 way of addressing this problem is to create a dummy variable called REPUBLIC set equal to 1 for all of the countries that have been a former republic. This variable is intended to reduce the point loss occurred to the affected Eastern European nations of only having approximately 15 years of history when in fact these nations do have long football traditions as part of other nations.
3.3.8Elite
A absolutely new variable intended to become integrated is a single measuring football skills utilizing the percentage of internationals currently playing inside the best division inside the top rated 5 leagues in Europe. According to UEFA ranking www.uefa.com prior to UEFA Champions League 2006-2007 these were Spain La Liga England Premier League Italy Serie A France Ligue one and Germany Bundesliga This variable is called ELITE. A different version of this variable is incorporated in Gelade et al 2006 however that was a variable of how lots of players playing abroad. Merely playing abroad does not necessarily increase the quality of the national group as there could be many reasons why a player is playing club football in a different nation than his native 1. 1 cause why players play abroad could simply be because of previous generations emigration or players going abroad to a lower league club for the -experience-.

By only counting the players inside the top rated five leagues inside the world the players simply playing abroad can be distinguished from the players in demand from the very best clubs inside the planet- Specialist football clubs are ran like any other enterprise and a team in among the list of leagues mentioned above spends huge resources on scouting networks intended to bring in the worlds greatest talent from all more than the globe. As having a lot more players playing around the highest level really should make the overall national team functionality superior this variable is seen as extremely relevant. Another purpose why high profile international footballers can increase the overall performance in the national group over time would be the increased interest in this player in his home nation giving young players inspiration to become specialist footballers.
The information on how a lot of players playing abroad had been found at www.national-football-teams.com.
3.3.9Health
The last variable is a new proxy intended to quantify how the level of health in a country affects football performance. The arguably very best indicator of a countrys health is to gather information on healthy life expectancy Robine Romieu and Cambois 1999. A healthier population really should increase the pool of potential football players which in turn theoretically will need to increase the overall football overall performance of that nation. Information on healthy life expectancy have been gathered from the web pages with the Planet Health Organization www.who.org.
4Results four.1Robustness check of initial regression
First applying 2007 data we will replicate the original regression from HLR employing equation one and investigate and evaluate the results to check how robust the model is. Overall lower coefficients for the HLR study are anticipated because the score points obtained under the old technique had been lower than it is at this time Italy is number one in 2007 with 1562 points although Brazil only had 821 points as number one in 2001.

Y 1GNPi 2GNPi2 TEMPi-142 POPi x LATINiHOSTi ione
Table 3- Robustness of HLR estimates standard errors in brackets
Personal estimates
HLR estimates
Sample size- 179
Sample size- 76

Variable
Estimate 2007
t-value
Estimate 2001
t-value

Constant
408.2982
10.34616
492.5865
19.2582
39.4637

25.578
GNP
0.016409
3.624019
0.0107
two.3742
0.0043

0.0045
GNP2
-2.7210-8
-2.957301
-2.4510-7
-1.6875
6.8710-8

1.451910-7
TEMP-14two
-1.292244
-4.759471
-0.4895
-1.9848
0.2715

0.2466
POP x LATIN
20286.55
2.406350
8587.4616
2.1828
8430.42

3934.1495
HOST
492.5931
5.492097
81.0510
1.8238
89.6913

44.4407
Adjusted R2
0.4707

0.3180

Note- and denote significance at ten five and 1 respectively

As can be observed from the left hand side regression is that every one of the variables are significant at the 5 degree most even at a 1 degree and that the signs are as expected. The GNP coefficient is only 0.0164 and can be interpreted as a 1000 dollar increase in GNP per capita will increase the FIFA score by 16 points. The sign for the quadratic relationship of GNP2 is negative as anticipated and also significant which indicates that there is an optimal degree of GNP per capita to increase football performance. To locate the optimal level of GNP per capita the first derivative with respect to GNP is taken and set equal to zero. The optimal degree is according to this regression 30163 which is considerably greater than the results from HLR 21836.

The temperature coefficient is negative as expected and also highly significant. The variable measures the deviations from the optimal level of 14C and can be interpreted within the following manner- A deviation from the optimal temperature degree decreases the FIFA score plus the more you get away from the optimal temperature level the larger would be the decrease in football performance. The relationship is quadratic and a high deviation from the optimal temperature could possible mean a substantial decrease in estimated FIFA ranking points.

Whilst LATIN and POPULATION were tested individually only significant benefits can be discovered for the LATIN variable. However the variable LATIN POPULATION proves to become significant just as in HLR. This means that the size of a population really should not increase football functionality significantly unless the nation is of Latin origin as defined above. The coefficient of your LATIN POPULATION variable is significant in both studies but has a much lager impact in our study compared to HLR. If a countrys population relative to the rest on the planet grows by 1 86 points is going to be added in HLR specification working with 2001 data while in our new specification 203 points might be added for a 1 increase in relative globe population utilizing 2007 information.

The Host variable according to this regression have a significant impact on football efficiency along with the fact that a country have hosted the Globe Cup inside the past must increase the points gained by that respective nation by 492.six points. This is a lot larger than the outcome obtained by HLR but this is as expected with background inside the relative difference in point calculation.

The fit of your model is fair with an adjusted R2 of 0.4707 and even if it is higher than the result HLR found 0.31 it does suggest there to be other potential aspects determining football functionality. To further test for specification errors the model has also been tested utilizing Ramsey RESET test 1969. This test makes use of additional variables called proxies intending to act as replacement for missing variables. If these proxies do certainly increase the fit on the model this means that there is a high possibility of omitted variables inside the model. The F score of your RESET test is one.45 and is lower than the critical value two.10 which mean we cannot reject the null hypothesis of no specification error. However as the R2 are relatively low we will make some amendments to the regression in an attempt to improve the fit in the model. Also the Durbin Watson test statistic DW testing for first order serial correlation in this case is one.798 which means that the test is inconclusive at the 5 percent level for 6 coefficients and a sample size of 179 1.69841 1.81311v which gives us another cause for doubting the specification.
4.2The new model explained
The first change we will make to the model is to substitute the HOST dummy variable by a new variable controlling for football traditions. The variable is inspired by Macmillan and Smith becoming the HISTORY variable measuring how lots of years every single nation has been a member of FIFA. The advantages of utilizing this variable compared to the HOST variable is that we have information on all the 179 nations for this variable plus the truth that this variable is also able to measure relative difference in football passion as is a continuous variable and not just a dummy. As mentioned above we also include the dummy variable REPUBLIC to measure the impact of beneficial football nations with a short football history in Eastern Europe. The sign of your HISTORY coefficient is expected to be positive as a longer affiliation with FIFA ought to mean that the country have stronger football traditions. The sign on the REPUBLIC variable is also predicted to be positive as a you will find various examples of beneficial -new- football nations this kind of as Ukraine Czech Republic Serbia etc.

To more investigate which other possible country specific indicators that determines football performance the above-mentioned variable called ELITE is included to investigate how footballers playing inside the best leagues within the world affect a countrys football overall performance.

As mentioned above we also wanted to test for healthy life expectancy to see if this has the hypothesised positive effect on football efficiency. Running test adding the HEALTH variable to the preferred specification above HEALTH was located to become insignificant at all levels and also to have an unexpected negative sign. This result suggests that healthy life expectancy according to the new model does not affect football overall performance to any greater degree. Another variable that has been tested will be the variable of how significant the fraction of GDP thats spent on health Services. The problem using this variable is that it has been investigated and identified to be a poor indicator on the degree of health in a country Filmer and Pritchett 1999. As you will find no information supporting other measures of healthy living without going into complicated calculations Filmer Hammer and Pritchett 1997 the following regression will not include a variable on health. The minimum degree of health needed to perform as a football nation really should anyway be captured by the GNP2 variable integrated above.
Equation 2 summarizes our new model of international football good results with the variables summarized in table 4. The regression is displayed in table five and also compares the new model to HLRs model with each regressions making use of the FIFA ranking score from 2007 as dependent variable.

The regression-
Y 1GNPi 2GNPi2 TEMPi-14two POPi x LATINi HISTi REP ELITEi i2

From table 5b observe that the many variables remain significant and that the sign for the two expected negative variables remain negative.The magnitude of each in the remaining variable does not change a great deal so this could indicate that the host variable is just not omitted within the new regression. The new variable HISTORY is very significant and has a positive relationship with the FIFA score. The interpretation of HISTORY is that 1 extra year as a member of FIFA increases the FIFA score by three.72 points. Currently being a former republic with only 15 years history is going to be compensated by the enormous lump increase in FIFA ranking points of 166 which would be the equivalent of approximately 44 years affiliation.

Following consider the effects of including the ELITE variable. Observe that the new variable ELITE is also extremely significant with a coefficient of 10.14. This is a rather large number and can be interpreted by saying that a one percentage point increase in the number of footballers exported to the worlds elite clubs will increase the FIFA ranking score by approximately ten points. Adjusted R2 increases and indicates that 74 of football functionality can be determined inside this model suggesting that this model is better specified than the one suggested by HLR. The optimal level of GNP per capita is in this model 18678 which is close to HLRs estimate.
Table four- Regression Variables

Variable
Description

Y
FIFA ranking points February 2007

GNP
GNP per capita of nation i

TEMP
Average annual temperature in nation Is capital

POP
Nation Is share of world population

LATIN
Latin dummy

HIST
Number of years nation I has been an affiliate of FIFA

REP
Republic dummy

elite
Number of players in national squad playing in an elite club

Error term

1 two
Parameters

Table 5- Regression outcomes standard error in parenthesis
a Old regression

b New regression

Sample size- 179

Sample size-179
Variable
Est. HLR07 information
t-value
Est. NEW07 information
t-value

Constant
427.9397
9.96
144.5484
3.0254
42.97

47.77751
GNP
0.015252
3.0941
0.0065
1.9768
0.00493

0.003267
GNP2
-2.5710-7
-2.5684
-1.74E-07
-2.6802
1.0010-7

6.5110-08
TEMP-142
-1.3075
-4.4231
-0.6575
-3.0808
0.2956

0.213417
POP x LATIN
20074.75
two.1871
16273.28
two.9527
9178.504

0.835036
HOST
504.3909
five.1653
97.65
HISTORY
three.729681
five.5093
0.676978
REP
165.6135
2.7829
59.51191
ELITE
10.141
12.2655
0.826782
Adjusted R2
0.4247

0.7400

Note- and denote significance at ten five and 1 respectively
4.3Testing the model
Running the RESET test on this specification increases the F score to three.63 which is above the critical value of two.10 indicating rejection in the null hypothesis of no specification error. This could potentially be a rather severe problem as we according to the RESET test is forced to assume this specifications isnt correct contrary to the regression applying HOST as an indication of football culture. Theory suggests however that when comparing two or much more specifications Akaikes criterion AIC and Schwartz criterion SC are recommended more than the RESET test to test for specification error Studenmund 2001. Although the RESET test ran above does suggest specification error inside the final regression econometric theory suggest that applying a variable that are available for the many nations within the sample will need to be a superior measure of football tradition than a dummy. Note that the HOST dummy affects less than 8 of the sample as only 14 out with the 179 nations have ever hosted the World Cup whilst there is data available on all countries regarding FIFA affiliation. Comparing the AIC and SC from the two specifications supports this view and it is observed that the AIC and SC falls from 13.97 to 13.26 and 14.07 to 13.41 respectively.

Also the DW test statistic is one.98 making it unreasonable to reject the null hypothesis of no serial correlation. These three tests Adjusted R2 AICSC and DW increase the confidence of improved ability in the new specification to estimate each countrys FIFA ranking score compared to HLR.

Equation three shows the new model including the coefficients.
Points 144.55 0.0065 gnp – 0.000000174 gnp two – 0.6575 temp- 142
16273.28 poplatin three.7230 history 165.6135 republic ten.141 elite ithree
5Discussion and Robustness checks five.1Discussion
So how well does the new model perform in regards to determining how several points any given country receives on the FIFA ranking To investigate this the regression will probably be run around the data available about every nation. When performing this test there might be various cases in which the chosen specification does not explain how well a nation performs and additional investigations are going to be produced to locate any patters that can be the basis for further studies to improve the model.
When testing the regression on genuine data it becomes clear that the point estimated by the regression did rather well overall estimating 125 of the 179 70 nations within one standard deviation from the real FIFA score as of February 2007. This is illustrated in figure one which shows the residual plot of all the countries included within the study. The two dotted lines indicate the standard deviation and simply by eyeballing the information we can see that a fair amount of the variables are within 1 standard deviation from the true value.
Figure 1- Residual plot

What seems striking is that Table 6 shows how inside the prime 20 only 8 nations 40 were predicted a score within a single standard deviation form the genuine score. This suggests that the specification is lacking elements which can emphasize how well the nations at the top on the ranking really perform.

Table six- Checking how the model performs

Country
Points
Est. points

Difference
A single SD from genuine
Two SD from real

Italy
1562
1457
105
1
one

Brazil
1540
1549
9
one
1

Argentina
1535
1430
105
one
1

France
1496
1557
62
1
1

Germany
1359
1550
191
0
1

England
1330
1533
204
0
one

Netherlands
1312
855
456
0
0

Portugal
1262
958
303
0
0

Czech Republic
1193
999
194
0
1

Spain
1161
1694
533
0
0

Ukraine
1018
406
611
0
0

Croatia
987
627
360
0
0

Greece
926
706
220
0
one

Switzerland
913
805
108
1
one

Romania
912
591
320
0
0

Sweden
894
703
190
0
one

Cameroon
893
565
327
0
0

Denmark
876
865
11
1
1

Mexico
857
792
65
one
one

Cte dIvoire
853
718
134
1
one

SD176.27

The Standard Deviation SD in case is 176.27 and if we look at nations such as Netherlands Portugal Spain Ukraine Croatia Romania and Cameroon we can see that these countries scores are far more than two SDs from the real score which shows that the model is hardly predicting these nations at all. Ukraine and Spain are the two outliers predicted 611 less and 533 points more than what they really have respectively. In the overall study working with all the countries these two countries were the highest outliers more than all and will later be the subject of a case study to examine exactly where the regression failed.

Comparing the actual points with the estimated points figure two display that the model predicts the lower scores improved than the larger scores.

Figure two- Comparing how several points needed for every ranking position
Later additional investigations is going to be made within the case of Brazil which has been top rated of the ranking for nearly four and a half years prior to the February ranking and which was estimated only 9 points away from the true score. Gambia will also be examined as it was the only nation estimated with the exact score 163 vs. 162.96 and discussions will probably be made to see if this was due to chance or if it can be contributed to the effectiveness in the new model.
5.1.1Ukraine-
Ukraine was essentially the most underestimated nation of the many nations inside the sample. As the model catches 70 of each of the countries scores what is it with Ukraine that can make the new model miscalculate this country so grossly

The GNP of Ukraine is only 1532 per capita which is far from the optimal level in our specification 18678. By inserting these values into the specification for GNP we locate that Ukraine only wins 9.52 points for their GNP per capita.

Up coming the average temperature in Ukraine is eight.2 degrees Celsius which is off the ideal temperature by nearly 6 degrees and actually creates a loss of 22 ranking points. Ukraine does not have a Latin population and are therefore not awarded any points for their 25 million inhabitants 0.36 of world population according to this model. Following Ukraine became independent from the Soviet Union in 1991 giving them only 17 years of membership to boost their score. This is far shorter than a lot of other nations that Ukraine can examine them selves too this kind of as Russia Former Soviet and Poland which has been an affiliate considering 1912 and 1923 respectively. As a former republic Ukraine does however benefit from the Republic dummy which adds 165 points to the total score in addition to the 63 points won for having 17 years of membership.The final coefficient would be the ELITE coefficient and our data shows that only 5 on the current national team plays their club football in on the list of worlds best leagues adding only 50 points generating it a total of 611 points.

In short Ukraines low wealth cold climate short independent history and few players playing in the leading leagues inside the globe are the key reasons for the low score they received calculated by the new specification.
5.1.2Spain-
Up coming investigations of how Spain managed to get the highest score of all countries employing our specification are performed. The mystery of Spain underperformance is well known in the football world and numerous have asked why a country which such a vast amount of talented footballers have not won any key trophies since their 1964 European Championship Ball 2003.

First a short look at the data The GNP per capita is 26458 which is well above the optimal degree yet it increase the FIFA score by 49 points. The subsequent variable is the temperature variable along with the average temperature of Spain is 14.3 Degrees Celsius which is very close to optimal and therefore does not deduct any points from the total.

Spain has a Latin population as defined by HLR and a population of 41 million 0.62 of planet population contributes to increase the overall score by 100 points. As Spain was one of the nations who established FIFA in 1904 they have the longest history as an affiliate of 103 years which adds a further 383 points to the total score. The final relevant variable could be the ELITE variable and this data confirms what mentioned above about the number of quality players the national group have at its disposal 100 from the players within the current national squad plays inside the prime division from the most important leagues in Europe in which a huge majority plays their football domestically in La Liga. This fact adds a large 1014 points to the total score and is probably the key reason for giving Spain the highest estimated score calculated by our regression. When investigating the Elite variable further by looking at table 3 England Germany and France all have had their scores overestimated by the formula. As these countries in addition to Spain and Italy have been the only countries to have 100 with the national players in the five top leagues it is possible that the Elite coefficient is overestimated and really should be adjusted down to achieve a much more precise specification. For the moment however no adjustments will probably be made to this variable.
5.one.3Brazil
Brazil has been ranked on best on the FIFA ranking for 55 months consecutively before they had been knocked down from the best spot by Italy in February 2007. This is a nation that the new regression managed to estimate exceptionally well with only a 9 point difference in between the real along with the estimated score. Why the specification seems to pick up so much on the cultural and financial components inside the Brazilian society are going to be inspected subsequent.

Brazil has a GNP per capita of only 3448 which is far from optimal and only adds 20 points to the estimated score. Following the average temperature in Brazil is 21.8 degrees Celsius which also is about 8 degrees higher than the optimal temperature and decreases the estimated score by 40 points. Brazil is however the biggest Latin country within the planet with a staggering 187 million inhabitants two.9 of planet population. From this variable Brazil gains 460 points this is about 13 of their total score. Brazil has also got long football traditions being an affiliate of FIFA for 84 years which more increase the predicted score by 312.5 points. Even if the Brazilian national team normally consists of international superstars according to the database we used on the ELITE variable only 64 with the national squad plays their club football in the globe very best clubs which wins them another 640 points.

This confirms what mentioned within the criticism of Gelandes paper that Brazil do rely heavily on football traditions as few other factors identified can predict them to become among the worlds very best football nations.

As can be observed from table three all these things added up gives Brazil an estimated score of 1549 even though the actual score have been 1540. 5.1.4Gambia-
The only country estimated exactly employing the new specification of football efficiency have been Gambia ranked as number 126. The purpose for this might be examined briefly below- Gambia has an extremely low gnp of only 248 per capita per year. According our model that will only add one.6 points towards the FIFA score. The average temperature in Gambia is 28 degrees which is twice as much because the optimal temperature and will decrease the score Gambia gains in our estimation by 128 points as temperature is non linearly and negatively correlated with the dependent variable. Gambia is just not a Latin country so population does not matter in this case. Gambia has been an affiliate of FIFA for 39 years and this truth will add 145 points to the total score which counts for just about all of the points this country receives from the regression estimation in addition to the constant coefficient. Lastly Gambia does not have any players playing inside the top rated five leagues so nothing additional is added to the regression.
In other words the reason why Gambia isnt a excellent football team is because the wealth of the country is extremely low the climate just isnt ideal for football does not have any football culture and therefore the potential pool of football players total population does not affect the overall performance. Gambia does have some football tradition and an affiliate of FIFA considering that 1968 but no international starts does nothing to assist increase the interest and quality of your national football group.
5.1.5Overall
To illustrate the overall fit of the model Figure 3 shows which nations are estimated within a single standard deviation from the true value.
As figure three shows the model predicts nearly every one of the countries within the Americas correctly. The model also performs adequately for Africa and South East Asia. Reversely the estimation does not seem to be a model of Europe as the FIFA ranking is at the moment nor the Middle East. This can be really worth keeping in mind for further studies to try and pinpoint why the distribution the correct results are as indicated above.

Figure three- Globe map

The nations marked in red have been estimated one particular standard deviation or less from the true score
Figure created on 5.2Robustness tests
In this section the regression is tested for robustness working with the old FIFA ranking method and an alternative ranking method called the ELO ranking as dependent variables 5.two.1May 2006 data
One of the most recent data using the previous FIFA ranking method is from May possibly 2006 as there is no publication of FIFA ranking throughout World Cups.
The regression outcome utilizing May 2006 data as dependent variable is in table 7.
By eyeballing the information we observe that GNP and GNP2 are no longer significant at a 10 degree of significance. Compared to the 2007 data it is obvious from the adjusted R2 that the fit on the model has decreased. Most of your coefficients have also decreased but as mentioned above this is as expected. However for the best 20 teams we observe that 75 of your nations are within one particular standard deviation from the true score table 8-
Table 7- Regression results utilizing -old ranking method- standard error in parenthesis

Pre WC 2006 ranking method
Sample size- 179
Variable
Est. Might 2006 information
t-value

Constant
282.0300
6.759077
41.72612
GNP
0.0039
one.369397
0.002854
GNP2
-7.7110 -7
-1.3570
five.69E-08
TEMP-142
-0.386534
-2.0739
0.186386
POP x LATIN
9786.713
2.0333
4813.194
HIST
2.982407
five.0444
0.591233
REP
120.6496
2.321334
51.97427
ELITE
1.784541
2.471446
0.722064
Adjusted R2
0.39759

Note- and denote significance at ten 5 and 1 respectively

Table 8- Utilizing May well 2006 information to calculate ranking points

May possibly FIFA ranking
Country
Points
Estimated
One sd from the true value1

1
Brazil
827
913
one

2
Czech Republic
772
682
one

three
Netherlands
768
681
1

4
Mexico
758
705
one

5
Spain
756
879
one

six
United States
756
615
1

7
Portugal
750
665
1

8
France
749
810
one

9
Argentina
746
777
1

10
England
741
801
1

11
Denmark
736
663
one

12
Nigeria
736
428
0

13
Italy
728
742
1

14
Turkey
726
570
0

15
Cameroon
722
437
0

16
Sweden
709
646
1

17
Egypt
708
526
0

18
Japan
705
473
0

19
Germany
696
806
one

20
Greece
694
601
one

SD- 144.37

Again Spain is overestimated however this time the benefits are within a single SD from the genuine value. Additional inspection with the table above shows that the only nations that had been not correctly estimated were the ones from Asia and Africa. This is the opposite result compared to the ones we identified applying the new tanking formula but as two of your variables have been not significant in this latter regression we select not to put too much emphasise on these final benefits.
five.2.2ELO ranking
There is also an alternative ranking method called the ELO ranking published regularly on the Internet- This ranking is based mostly around the ELO rating technique developed initially for the ranking of Chess players. The ELO ranking for international football teams bases its benefits on the following criteria- Status of match number of goals scored results of every match multiplied with the expected outcome of your match to capture the opposition strength. The ranking method takes into account all matches played by each country from 1872 onwards when calculating the last score. The advantage of this compared to by far the most recent 4 years as could be the case within the FIFA ranking is that you will get the notion of football culture incorporated into the model. The problem is that the results are not likely to change much as time passes because the time span is extremely huge.

The regression is nevertheless compared to this ranking to test the robustness in the new model and below is a short outline of our results-

Running the same regression as above only substituting the dependent variable for the ELO ranking points are presented in table 9.
Table 9- Regression benefits ELO points standard error in parenthesis
ELO ranking method

Sample size- 179
Variable
Est. ELO information
t-value

Constant
1099.564
19.55087
56.2412
GNP
0.0067
1.746035
0.0038
GNP2
-1.6810 -7
-2.186437
7.6610-8
TEMP-14two
-0.4088
-1.627330
0.2512
POP x LATIN
9143.235
one.409353
6487.539
HIST
five.0920
six.389771
0.7969
REP
272.6909
3.892564
70.0543
ELITE
4.943897
5.079810
0.9732
Adjusted R2
0.5303

Note- and denote significance at 10 five and 1 respectively

Observe that the signs stay as expected but a number of the variables have become less significant particularly the POP x LATIN and TEMP coefficients which now are the two insignificant. The fit with the model has also decreased. The Durbin Watson score is 1.99 indication that there is no severe problem of serial correlation inside the model.

Additional tests on how the nations inside the sample rank making use of the new coefficients follows below.

Table 10 monitors the actual ranking according to the ELO program as of 9th of March 2007 compared to the estimated points obtained utilizing the regression above. As we observe in the top rated 20 ranking 80 of your nations are predicted within one standard deviation form the true score which is far superior in comparison to the final results using the 2007 FIFA points regression. Overall 125 nations had been estimated within one particular standard deviation form the true value which was exactly exactly the same value as in the FIFA ranking regression. Yet again we can observe that Spain is estimated with the highest score of all the nations which was the identical outcome because the two other models predicted. This shows that the model is rather robust ignoring the fact that two on the variables in the new regression became insignificant.

Table 10- Making use of ELO ranking information to calculate ranking points
ELO ranking
Nation
Points
Estimated
A single SD from the real value1

1
Brazil
2034
2099
one

two
France
2021
2150
1

three
Italy
1992
2090
1

four
Argentina
1971
2058
one

5
Netherlands
1965
1801
one

6
Germany
1955
2145
1

7
England
1917
2127
0

8
Portugal
1890
1809
one

9
Croatia
1873
1598
0

10
Spain
1872
2236
0

11
Czech Republic
1866
1870
1

12
Denmark
1856
1766
1

13
Russia
1839
1605
0

14
Uruguay
1819
1806
1

15
Switzerland
1811
1695
one

16
Sweden
1800
1713
one

17
Greece
1795
1673
one

18
Turkey
1795
1613
one

19
Mexico
1793
1711
one

20
United States
1781
1638
one

SD- 207
5.3Suggestions for more testing
Another interesting comparison would be to test the model using bookmakers odds of winning the Planet Cup because the dependent variable linking the method to that used on international stock markets. Odds are supposed to capture the many current information about team strength at any moment in time inside the identical style because the stock market predicts the value of a company at any given time by share rates and need to therefore always change their odds depending of every teams strength. 1 potential problem could be that lots of weaker football nations are given exactly the same or no odds of winning the Planet Cup because the possibility of them even qualifying is close to zero.

An explanatory variable that would be interesting to test would be to include information on how many foreigners play inside the best league of each and every country. Several pundits have blamed the big favourites failure to perform within the large tournaments among them England and Spain on the enormous fraction of foreign players playing in their national top division. There have already been claims that because of the massive import of foreign players younger national players are not given a chance to prove themselves at the highest level resulting in lower functionality from the national group over time. To my knowledge there has been no testing done on in this field which will need to make it even far more attractive for future studies.

If we again investigate figure 2 which is a globe map that indicates how well the model performs all over the world observations are made that the Middle East is poorly estimated. More studies might want to keep this in mind and try to pinpoint why this will be the case. A single suggestion if further investigation of the Middle East is performed is that cricket can be seen as a competitor to football because the most well-liked national sport in this area. As cricket shares several with the characteristics of football as a -sport for the masses- this kind of as low cost and high availability this could be an interesting notion to try and implement in any more studies.
6Conclusion
This paper investigates which nation particular factors determining a countrys international football performance. It has been demonstrated that per capita wealth is important however beyond a certain degree of income increase football performances actually declines. We have also shown how temperature affects football efficiency inside the sense that any deviation from the optimal temperature of 14C harms a countrys football performance.

Length of FIFA membership does also play a part when attempting to estimate any given countrys FIFA score. The purpose for this is that a longer affiliation with the FIFA indicates a larger degree of football culture which again gives a larger probability of additional active international football players. That quality of domestic players measured by their demand from the world best clubs extremely influences a countrys efficiency in the FIFA ranking which ought to come as no surprise. Following we have also tested the impact a countrys population plays on international football performance and located that population size only matters if the nation in query has a strong football culture indicated by a Latin population. No results were discovered to support any significant health variables.

Overall the model performs well even if youll find some concerns above the low estimation ability for the greater ranked countries.

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i Football in this context is what the rest in the English speaking world knows as soccer Moore 2006. Soccer is an abbreviation for Association Football. The rest of this paper will use the word football when referring to Association Football.

ii Union of European Football Assosiations

iii CONfederacin sudaMEricana de FtBOL South American Football Confederation

iv In which 2007 data were not available we incorporated the most recent information

v Durbin Watson 5 critical values table- Wesley market The time-value of information- a temporal qualification of knowledge its difficulties and role inside improvement of knowledge intensive business processes.
Peter Dalmaris1 William P. Hall2 Adam Philp3
1Futureshock Research Sydney PO Box 184 Broadway 2007 NSW Australia
2Tenix Defence and also University of Melbourne Nelson House Annex Nelson Place Williamstown Vic 3016Australia Monthly bill.
3 DSTO Land Operations Division PO Pack 1500 Edinburgh SA 5111 Australia Wayne.
Keywords- time-value expertise business process management knowledge management expertise epistemology
Abstract
In Dalmaris ainsi que al we offered a framework for your improvement of knowledge-intense business processes KBPI.