Review: Difficulty Level For UEFA European Countries To Qualify For The World Cup

Discussion in 'UEFA and Europe' started by Abram Jones, Sep 6, 2017.

  1. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
    UEFA has 14 spots for the 2018 World Cup. Here are the major European countries with their economic determinant (a number that estimates countries potential based on gdp per capita and population amount). See this video for more commentary: https://www.youtube.com/watch?v=1FNh8DIltP0

    Germany: 32.4 million
    France: 26.5 million
    Italy: 21.3 million
    England: 20.1 million
    Russia: 15.2 million
    Spain: 14.5 million

    Netherlands: 8.4 million
    Turkey: 7.3 million
    Switzerland: 5.8 million
    Sweden: 4.9 million
    Belgium: 4.9 million
    Poland: 4.8 million
    Norway: 4.3 million
    Austria: 3.9 million
    Denmark: 3.2 million
    Greece: 3 million

    Finland: 2.5 million
    Portugal: 2.3 million
    Israel: 2.3 million
    Ireland: 2.2 million
    Czechia: 2.1 million
    Scotland: 2 million
    Romania: 1.7 million
    Kazakhstan: 1.5 million
    Ukraine: 1.4 million
    Hungary: 1.3 million
    Wales: 1.2 million

    Slovakia: 892,000
    Northern Ireland: 689,000
    Croatia: 597,000
    Belarus: 552,000
    Azerbaijan: 529,000
    Luxembourg: 524,000
    Bulgaria: 499,000
    Slovenia: 480,000
    Serbia: 395,000
    Lithuania: 371,000
    Cyprus: 336,000
    Latvia: 237,000
    Estonia: 195,000
    Bosnia Herzegovina: 172,000
    Iceland: 132,000
    Georgia: 132,000
    Albania: 117,000

    Conclusion: Though competition is extremely heavy in Europe, the largest group of European countries still should have a relatively easy time qualifying even if playing somewhat below par. The next group of countries is in a much more difficult situation as the 3rd group is close to them in economic size, and the 4th group is full of over achievers. Difficulty for the 3rd and 4th groups are extremely high... slightly harder than in Asia even if skill differences between continents are ignored. The only country belolw these groups that may qualify is Montenegro of the former Yugoslavia. How far they have gone is nothing short of amazing.
     
  2. EvanJ

    EvanJ Member+

    Manchester United
    United States
    Mar 30, 2004
    Nassau County, NY
    Club:
    Manchester United FC
    Country:
    United States
    You listed 44 countries, so you left out 11. Croatia is 30th on the list but much better than 30th on the field. Maybe later I'll calculate the correlation between those values and the FIFA Rankings and/or ELO Ratings.
     
  3. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
    #3 Abram Jones, Sep 11, 2017
    Last edited: Sep 11, 2017
    You're telling me what I already told yo :) I left out countries that have below 100,000 economic determinant (unless I missed a few).

    Yes, Croatia (and other former Yugoslav countries) seem to overachieve in many sports. Here are estimated true rankings of Croatia in several sports (though some of the sports are incomplete because I haven't had time to enter all data): http://internationalsports.nfshost.com/index.php/home/profile/12 (note: these include present and historic stats, so Ustashe flag is displayed... don't click if you are offended by historic flags)

    I have done this for 2010 and 2014 already, not sure if you had the same thing in mind (this uses an old method before Economic Determinant called Available Population to represent wealth and population ): https://docs.google.com/spreadsheets/d/16izHFOKdbiSzRqj-ykeVQaqNJZ1SY8uPYHo2TFO5vcM
     
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  4. EvanJ

    EvanJ Member+

    Manchester United
    United States
    Mar 30, 2004
    Nassau County, NY
    Club:
    Manchester United FC
    Country:
    United States
    That's a good spreadsheet. For 2014 the correlation between FIFA Ranking points and your variable was 0.3840. That's not that strong a correlation, but I don't know if other methods would produce a stronger correlation.
     
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  5. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
    Can you show me your data, what does .3840 mean and how are you defining "not strong"
     
  6. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
    Keep in mind that sheet only accounts for 1 sport in 1 year... this is not much data to work with at all. I was surprised that the correlation for wealth and population was so strong for individual sports when I originally compiled a few individual sports back in 2012 (I wrongly predicted to myself that it would only be obviously noticeable when you combine the results of many sports). It also doesn't take into consideration countries where football is a 2nd, 3rd, 4th or even a 5th favorite sport, which is even more amazing why there is a noticeable correlation.

    I have also made a spreadsheet compiling 20 different sports, olympic medals since 2010, and Greatest Sports Nation results. Perhaps these will give you a bigger number if you wish to experiment... the reason I think they will (at least some of them) is because of the law of large numbers, we are dealing with more relative data.

    https://docs.zoho.com/writer/open/1vl7ie0117b6d4cb74977aeed9870336f002e (scroll down)

    take your pick on which spreadsheets you want to examine. the one where you should receive the least correlation is Australian Rules football.. because it is played mostly in an area where there are very small countries. As we can probably assume, interest in a sport is the biggest factor... though can often be negated by wealth and population amount.
     
  7. EvanJ

    EvanJ Member+

    Manchester United
    United States
    Mar 30, 2004
    Nassau County, NY
    Club:
    Manchester United FC
    Country:
    United States
    https://en.wikipedia.org/wiki/Correlation_and_dependence is about correlation. Correlation ranges from -1 to 1. If all the data points are on a line with a positive slope, the correlation would be 1. For example (1, 4), (2, 5), and (3, 6) have a correlation of 1 because y = x + 3 goes through all the points. If all the data points are on a line with a negative slope, the correlation would be -1. (1, 6), (2, 5), and (3, 4) has a correlation of -1 because y = -x - 7 goes through all the points. In reality, correlations are rarely going to equal 1 or -1. The correlation between points and goal differential in soccer can be strong. For example, that correlation for the teams in CONMEBOL World Cup Qualifying with 1 game left is .9504. If you calculate the correlation between points and goals scored, it will be positive, but not as strong as between points and goal differential. The correlation between points and goals scored for CONMEBOL is .7609.
     
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  8. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
    Thanks, do you have a spreadsheet setup for this you could share, and have you viewed any of the other spreadsheets? The multiple sports spreadsheet would probably be best to use because there is more data to work with.
     
  9. EvanJ

    EvanJ Member+

    Manchester United
    United States
    Mar 30, 2004
    Nassau County, NY
    Club:
    Manchester United FC
    Country:
    United States
    I copied and pasted your spreadsheet into a spreadsheet on my computer so I could edit it and do calculations. I might work on it more, but not today.
     
  10. Abram Jones

    Abram Jones Member

    Jun 18, 2016
    Wisconsin (WI)
  11. EvanJ

    EvanJ Member+

    Manchester United
    United States
    Mar 30, 2004
    Nassau County, NY
    Club:
    Manchester United FC
    Country:
    United States
    How does https://docs.google.com/spreadsheet...eVQaqNJZ1SY8uPYHo2TFO5vcM/edit#gid=1446180174 define "available population"? For Brazil the available population is 30.5% of Brazil's population according to Wikipedia. For Germany the available population is 64.9% of Germany's population according to Wikipedia. You used the July 2014 FIFA Rankings, but you have Liberia with 115 points and they were 115th with 256 points. This changes the average available population for 126-150 from 1.3 to 1.4 and for 151-207 from 1.3 to 1.1.

    The linear regression equation is FIFA Ranking Points = 6.832*available population + 346.212
     

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