# Tag Archives: A Beginner’s Guide to Irrational Behavior

## Forecasting France Euro 2016

I have a work colleague who not only is a tremendous negotiator and aircraft seller but also has a great sense of humor and manages in his free time late in the night to set up a contest for office staff to try to guess winners, matches’ scores, top scorers, etc., of major international soccer competitions. The France Euro 2016 which starts this afternoon could not be missed. Nacho managed to set up the contest in time.

In this post I am going to explain how I went about forecasting the results of the UEFA Euro 2016.

“when in doubt, build a model”, Nate Silver.

The readers of this blog may already know how much I do like to build models to produce forecasts, guesstimates, etc. In relation to forecasting this UEFA Euro 2016 there is some background that has shaped my mind in relation to the subject in the recent years, let me give you some hints:

Having shared this background, you may understand that I tried to remove all the beauty of guessing and my football knowledge out of the forecasting process (1).

• ESPN Soccer Power Index (SPI) ranking, introduced by the economist Nate Silver. I used its offensive and defensive scores plus weight for each of the scores based on a tip indicating that in competitive matches the defensive factor tends to be slightly more important (see “A Guide to ESPN’s SPI rankings”) (2).
• The frequency of different scores in the group phases of the Euro 2012 and the World Cup 2010, the in the round of 16, quarter finals and semi-finals.

• A few simple rules about how to allocate results given the difference between SPI ratings of the two nations playing each match. (3)
• The total number of goals during group phases the latest Euro and World Cup. In order to cross check that the total numbers of goals that my forecast yielded was in check with previous competitions.

It may sound very complex. It is not. It requires a bit of reading (which most of it I did years ago), retrieving the latest ratings, giving it a bit of thought to set up the model and then, not even looking at the names of the teams, you go about allocating the scores based on raw figures. Let’s see how my forecast fares this time! (4)

Les grandes personnes aiment les chiffres” (5), the Little Prince.

(1) In fact I have not watched a single national team football match from any country since the World Cup in Brazil in 2014.

(2) See here the blog post I published yesterday in which I made a more thorough review of the ESPN SPI index.

(3) I set up rules like “if the difference of the combination of indices of the two nations is below this threshold, I take it as a draw, if it is between x and y as victory by 1 goal, if higher…”, etc.

(4) This way of forecasting allowed me to finish 4th out of 47 in 2010, 15th out of 87 in 2014. As it removes biases it allows to be better than the average, though it prevents you of guessing outliers, gut feelings, etc.

Note: In the blog post from yesterday I mentioned that the latest complete ranking from the ESPN SPI index that I could retrieve dated from October 2015. That is the one I have used, therefore, Germany results as winner. Of the latest ranking, covering the Top 25 nations, only 13 countries of the 24 competing at the Euro 2016 are included. I could have set up an hybrid ranking taking the latest rankings and ratings for the top 13 from June and using the October figures for the lower 11 teams. I decided to go on with a single set of data. If I had done so, the maing changes would have come from the semifinals onwards. France would have appeared as winner instead of Germany. We’ll see if that was a good decision.

## Forecasting 2014 FIFA World Cup Brazil

I have a work colleague who not only is a tremendous negotiator and contracts’ drafter but also has a great sense of humor and manages in his free time late in the night to set up a contest for office staff to try to guess winners, matches’ scores, top scorers, etc., of major international soccer competitions. The 2014 FIFA World Cup in Brazil, which will start tomorrow, could not be missed. Nacho managed to set up the contest in time.

To set up the background as to how I have approached the game of forecasting this World Cup:

• I had written a review of the book “Soccernomics“, which among other things advocates the use of data in order to make decisions in relation to football transfer market, forecasting, etc. This book relies somewhat heavily in “Moneyball” another book which I read some months ago with a similar scope but with baseball as the theme sport.
• When the draw of the World Cup took place last December, I wrote a couple of blog posts discussing what was the so-called “group of death” basing the analysis on FIFA and ESPN rankings.
• During the last year, I read a couple of books which approach how we make decisions and how to remove different kind of biases from the thought processes of making them: “Thinking Fast and Slow” (by the 2002 winner of the Nobel Prize in Economics Daniel Kahneman) and “Seeking Wisdom“.
• Finally, last year I followed the open course “A Beginner’s Guide to Irrational Behavior” by Dan Ariely (though I missed the last exam due to my honeymoon and could not get credit for it).

Having shared this background, you may understand that I tried to remove all the beauty of guessing and my football “knowledge” to the forecasting process. I rather made use of  ESPN Soccer Power Index (SPI) ranking, introduced by the economist Nate Silver. I used its offensive and defensive scores plus the tip indicating that in competitive matches the defensive factor tends to be slightly more important (see “A Guide to ESPN’s SPI rankings”).

Once I plugged in the numbers from the index and used the referred tip on the defensive side, I built a simple model to guess each of the World Cup matches. Once you take this approach you will find that the model gives you plenty of results such as Nigeria 1.32 – 1.53 Bosnia… What to do with it? When the result was very tight I resolved it as a draw, otherwise a victory for the team with the highest score.

In very few instances I forecast that a team would score 3 or more goals in a match. I bore in mind that in the 2010 World Cup 80% of the matches ended up with scores of 1-0 (26% of the matches), 2-1 (15%), 0-0, 1-1 or 2-0 (each 13%).  That a team scores more than 3 goals in a match will certainly happen in some games, but I did not bother to guess in which ones, the odds are against.

The prize pot of the game organized by this colleague is not particularly big (few hundreds euros). The main point of the game is enjoying the chit-chat with work colleagues. My second main point is putting this rational approach to work and see how it fares.

Finally, what did I forecast?

A World Cup won by Brazil against Argentina in the final. With Spain beating Germany for the third place (in the penalties). For my English readers: England defeated by Colombia in the 1/8 of final. For the ones from USA, it doesn’t make the cut from the group phase. We will see along this month how well do I fare.

2014 FIFA World Cup Brazil forecast.

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