Germany, Brazil and Portugal lost. In the forecast these teams occupy the first three places. The analysis is done correctly, but the result is erroneous.

Hi All, Great to see interest in the world cup. I have two notebooks about the world cup. One is the history of the world cup displayed on a world map. The other is the results, calendar, stats and other interesting facets of the current world cup, also displayed on the world map. I think it is very compelling because so much info is available on one screen. I would love to share them but trying to do so using Wolfram Cloud seems to be a complete disaster. They wont even load and when they do, the controls are all out of whack. I'm just a hobbyist. If one of you has some time to look at the code to see if there is any hope of sharing it on the cloud, I would be delighted for your assistance. Here is my direct email in case you want to contact me directly. A link to the code and the code itself follow below. markstevenlawton@gmail.com

here is the code: https://drive.google.com/file/d/1r7AGWaUYd7j3xT0St7p5VUxrQd-LBPzE/view?usp=sharing

Attachments:

WorldCup2018V9.nb

Great post @Francisco thank you for sharing! Perhaps we can get a more accurate prediction if we include info about each player. Fifa provides some nice info about the 700+ players that will participate in the World Cup https://www.fifa.com/worldcup/players/ . So I created the following dataset (see it attached):

datasetWC18 = Import["WC18PlayersDataset.m"]

Age of the players might be a relevant feature. But the most relevant info is still missing .... players market value and achievements within their clubs (there are several places where this info is available, but I haven't had time to include it).

Anyway let's do some nice visualization with this players dataset as a start. For example I was curious about the correlation between "Height" / "Weight" and their "Field Position". The results are the following (Note that I'm using ListPointPlot3D to associate a 3rd dimension to players indicating their "Field Position") :

fieldPositions = {"GK", "DF", "MF", "FW"};

ListPointPlot3D[
    Table[
            Map[Append[#, "PositionN" -> i] &, 
                    datasetWC18[Select[#Position == fieldPositions[[i]] &], {"Height", "Weight"}]
                    ]
      , {i, 4}],
 ImageSize -> 800,
 PlotStyle -> PointSize[0.012],
 PlotLegends -> SwatchLegend@{Style["Goalkeepers", 18], Style["Defenders", 18],   Style["Midfielders", 18], Style["Forwards", 18]},
 AxesLabel -> {Style["cm", 18, Bold], Style["kg", 18, Bold]},
 PlotLabel -> Style["Players height vs weight", 20, Bold]]

As expected the Goalkeepers on average are the tallest and heaviest. What I wasn't expecting was that on average the Midfielders are the smallest. Perhaps it is because Midfielders best asset is their strategic mind to orchestrate the game from the middle with clever ball passes.

Projecting the flags of World Cup countries

Borrowing Francisco's code for countries entities one can easily get all their flags, except the one from England which is missing (so, I added it manually):

countries = {Entity["Country", "Russia"], 
   Entity["Country", "SaudiArabia"], Entity["Country", "Egypt"], 
   Entity["Country", "Uruguay"], Entity["Country", "Portugal"], 
   Entity["Country", "Spain"], Entity["Country", "Morocco"], 
   Entity["Country", "Iran"], Entity["Country", "France"], 
   Entity["Country", "Australia"], Entity["Country", "Peru"], 
   Entity["Country", "Denmark"], Entity["Country", "Argentina"], 
   Entity["Country", "Iceland"], Entity["Country", "Croatia"], 
   Entity["Country", "Nigeria"], Entity["Country", "Brazil"], 
   Entity["Country", "Switzerland"], Entity["Country", "CostaRica"], 
   Entity["Country", "Serbia"], Entity["Country", "Germany"], 
   Entity["Country", "Mexico"], Entity["Country", "Sweden"], 
   Entity["Country", "SouthKorea"], Entity["Country", "Belgium"], 
   Entity["Country", "Panama"], Entity["Country", "Tunisia"], 
   Entity["AdministrativeDivision", {"England", "UnitedKingdom"}], 
   Entity["Country", "Poland"], Entity["Country", "Senegal"], 
   Entity["Country", "Colombia"], Entity["Country", "Japan"]};

flags = EntityValue[countries, "Flag"];

flagsAll = (flags /. {x_Missing -> englandFlag})

Finally with GeoGraphics we can create an interactive map using Tooltip as follows:

GeoGraphics[
{EdgeForm[ Directive[Thin, Black]],
{GeoStyling[{"Image", #2}], Tooltip[Polygon[#1], CommonName[#1]]} & @@@ Transpose[{countries, flagsAll}]}, 
Background -> "CountryBorders", 
GeoProjection -> "Robinson", ImageSize -> 1200, 
PlotLabel ->  Style["Countries in the 2018 FIFA World Cup Russia", 24]]

Population of countries in the 2018 FIFA World Cup

In my opinion two other interesting features to look for in the participating countries are their Population and GDP. Which can be easily obtained using EntityValue :

valsPopulation = EntityValue[countries, "Population"];
GeoRegionValuePlot[Thread[countries -> valsPopulation], 
 GeoBackground -> "CountryBorders", GeoProjection -> "Robinson", 
 ImageSize -> 1200, PlotLabel ->  Style["Population of countries in the 2018 FIFA World Cup Russia",  24]]

GDP of countries in the 2018 FIFA World Cup

valsGDP = EntityValue[countries, "GDP"];

GeoRegionValuePlot[Thread[countries -> valsGDP], 
 GeoBackground -> "CountryBorders", GeoProjection -> "Robinson", 
 ImageSize -> 1200, PlotLabel -> Style["GDP of countries in the 2018 FIFA World Cup Russia", 24]]

Attachments:

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Very interesting link, and nice way to display the final output, I wonder if with the output of my functions we could generate also a complete set of the most possible fixture of matches.