World Cup & Soccernomics, Day-by-Day

In this earlier post I mentioned my friend's basic World Cup template that users can modify to suit their own purposes. I have done so, blending the GDP, population, and international soccer experience statistics from an earlier post to integrate the Soccernomics model results for each World Cup group play match. Using a bunch of VLOOKUP functions and pivot tables, I can get a good snap shot of summary statistics - both from what the model predicts and how each team does against the model. My work-in-progress spreadsheet can be found here.

I will use this spreadsheet throughout the tournament to comment on which teams are over- and underachieving against their predicted, historical average. The first attribute I'd like to highlight is which teams have the most and least difficult group play in the tournament based upon the Soccernomics model. Predicting the goal differential for each match and then using a pivot table to derive the average value for each team produces the table below.


As I mentioned in this post, Slovenia will be giant killers in this year's tournament if they can hold their own against the USA and England in their final two matches. What also jumps out at me on this table is the shear number of African nations at the top of the list - those facing the toughest climb. It speaks volumes that Ghana has been able to win, while all other African nations have either lost or drawn their matches.

In the next day or two I will post on how I will evaluate how each team performs versus their Soccernomics model prediction.