Introduction not, there would clearly be no

Introduction

This project aims to establish an econometric model to show
the factors which determine a country’s national football team qualifying for
the FIFA World Cup.

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Clearly, there are many factors which are not easily
quantifiable, such as the quality of the current crop of players available to a
national team, the manager, as well as constantly changing variables like the
tactics employed, momentum, even things like the weather potentially being of
benefit to one team during the match. If a perfect model existed to determine
which teams qualified and which did not, there would clearly be no need to go
through the formalities of the lengthy qualification process in the first
place. However, research may reveal less direct ways of improving a team’s
chances of qualifying for a big tournament. For example, if it was found
definitively that having a higher average age of players in a match-day squad
made it more likely for a team to qualify, we may start to see managers
choosing older players for their squads, or vice versa. If average attendance
at home games was found to have an effect, nations who don’t normally play in
front of sell-out crowds may decide to opt for more aggressive marketing to
attract people to games, or reduce ticket prices.

We analyse ten different criteria across nations
participating in the qualification process for the 2018 FIFA World Cup. For the
purposes of this research, we have limited our analysis to the UEFA qualifiers,
and thus the fifty-four UEFA member nations (fifty-five minus Russia, who
automatically qualified as hosts for the 2018 edition).

The variables investigated were:

·        
Pot Draw

·        
World Ranking

·        
Average Attendance

·        
Population

·        
GDP per capita

·        
No. of players in top 5 leagues

·        
World Cup appearances

·        
Previous champions?

·        
Average Age of Squad

·        
Standard Deviation of Age

‘Pot Draw’ refers to the pots (1 through 6) each nation were
assigned to before groups for qualifying were drawn in St Petersburg on 25 July
2015.

‘World Ranking’ refers to each nation’s place in the FIFA
World Rankings as of July 2015.

‘Average Attendance’ refers to the average attendance at
home games during the group stages of the 2018 qualifying campaign.

‘Population’ refers to the nation’s population according to
the most recent census data available.

‘GDP per capita’ refers to the nation’s GDP per capita
according to the most recent data available.

‘No. of players across top 5 leagues’ refers to the number
of players in the nation’s most recent 23-man qualifying campaign squad who
play their club football for a team in one of Europe’s ‘big 5’ leagues (English
Premier League, Spanish La Liga, Italian Serie A, German Bundesliga, French
Ligue 1).

‘World Cup appearances’ refers to the number of World Cups
each nation appeared in up to and including the 2014 edition.

‘Previous Champions?’ refers to whether a nation has
previously won a World Cup.

‘Average age of squad’ was determined by the average age of
a nation’s most recent 23-man squad.

‘Standard Deviation of Age’ was determined by the standard
deviation of player’s ages in the nation’s most recent 23-man squad.

Introduction

This project aims to establish an econometric model to show
the factors which determine a country’s national football team qualifying for
the FIFA World Cup.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Clearly, there are many factors which are not easily
quantifiable, such as the quality of the current crop of players available to a
national team, the manager, as well as constantly changing variables like the
tactics employed, momentum, even things like the weather potentially being of
benefit to one team during the match. If a perfect model existed to determine
which teams qualified and which did not, there would clearly be no need to go
through the formalities of the lengthy qualification process in the first
place. However, research may reveal less direct ways of improving a team’s
chances of qualifying for a big tournament. For example, if it was found
definitively that having a higher average age of players in a match-day squad
made it more likely for a team to qualify, we may start to see managers
choosing older players for their squads, or vice versa. If average attendance
at home games was found to have an effect, nations who don’t normally play in
front of sell-out crowds may decide to opt for more aggressive marketing to
attract people to games, or reduce ticket prices.

We analyse ten different criteria across nations
participating in the qualification process for the 2018 FIFA World Cup. For the
purposes of this research, we have limited our analysis to the UEFA qualifiers,
and thus the fifty-four UEFA member nations (fifty-five minus Russia, who
automatically qualified as hosts for the 2018 edition).

The variables investigated were:

·        
Pot Draw

·        
World Ranking

·        
Average Attendance

·        
Population

·        
GDP per capita

·        
No. of players in top 5 leagues

·        
World Cup appearances

·        
Previous champions?

·        
Average Age of Squad

·        
Standard Deviation of Age

‘Pot Draw’ refers to the pots (1 through 6) each nation were
assigned to before groups for qualifying were drawn in St Petersburg on 25 July
2015.

‘World Ranking’ refers to each nation’s place in the FIFA
World Rankings as of July 2015.

‘Average Attendance’ refers to the average attendance at
home games during the group stages of the 2018 qualifying campaign.

‘Population’ refers to the nation’s population according to
the most recent census data available.

‘GDP per capita’ refers to the nation’s GDP per capita
according to the most recent data available.

‘No. of players across top 5 leagues’ refers to the number
of players in the nation’s most recent 23-man qualifying campaign squad who
play their club football for a team in one of Europe’s ‘big 5’ leagues (English
Premier League, Spanish La Liga, Italian Serie A, German Bundesliga, French
Ligue 1).

‘World Cup appearances’ refers to the number of World Cups
each nation appeared in up to and including the 2014 edition.

‘Previous Champions?’ refers to whether a nation has
previously won a World Cup.

‘Average age of squad’ was determined by the average age of
a nation’s most recent 23-man squad.

‘Standard Deviation of Age’ was determined by the standard
deviation of player’s ages in the nation’s most recent 23-man squad.

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