Expected results Primera División Argentina (Fecha 13)

In this blog I give probabilities for games in the Argentine Primera División, show a predicted league table based on those probabilities and give a list of bets that corresponds to the given probabilities (note: you are free to use the ‘tips’, but I am not using them myself!).

I have replicated an existing prediction model and I am optimizing it for the Argentine league. The model is the so-called pi-ratings model by Constantinou and Fenton (2013). In this article they describe a system in which they assign probabilities to match outcomes (home win, draw or away win) based on historic results.

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Primera División Argentina: betting tips and results (Fecha 11)

Based on the prediction model described here, you can find betting tips and results in this blog:

(Note: you are free to use the ‘tips’, but I am not using them myself!).

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Expected goals model Argentina

For the last couple of months I have been (slowly) working on an expected goals model for the Argentine league and I am very happy to finally present a first version here. I will explain the basics of the model here and I will also try to show some of the underlying numbers, something I haven’t seen so much in posts from others.

For the ones that want to skip this blog and just see the results: click here.

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10 rising stars from the Primera División (Argentina)

The losing finalist of the 2014 World Cup, Argentina, is one of the biggest football nations in the world. And there are plenty of Argentines among the most expensive football players in history:

5. Di Maria: €75M
14. Crespo: €55M
22. Agüero: €45M
27. Veron: €43M
30. Pastore: €41M

Real Madrid has spent €114M on Argentinians since 2000 and last summer (2014) they signed James (€80M), with which he became the most expensive player ever to have played in the Argentine premier league (Banfield, 2008-10).

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Prediction model Argentine Primera División (weekly updated)

I am constantly dealing with statistics from the Primera División of Argentina, and last week I was comparing shot averages, shot effectivity and save percentages. Doing this I started thinking about some of those statistics and how to apply them to predict the upcoming gameday. As fond player of the local Proode (you pay $2 pesos (€0,15) to predict 13 games, and in case you correctly predicted all the scores (home win, draw or away win) you share a $500,000 peso price with the other winners) I wanted to see what the value of the historical ratios was when forecasting the future games.

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