What question you are looking to answer?, Why does this question matter?

I am a sports fan, and while I am a triathlete who loves his sport I enjoy watching many other sports like tennis, F1, soccer and football. When you are among friends and family who are passionate about sports like me, it is inevitable to call winners for any match you see or expect to attend. Is like betting but without the downside of losing money. You actually gain respect if you are consistent at calling winners, and a reputation if you end up constantly calling losers. I would definitely like to be of those who are respected, so I decided to improve my chances to be the one who says: I told you so. With the difference that I may have something to support my claim rather that just call it.

What data did you use? Where you got the data?

I decided to use what I learn with the analysis of the NFL using the Elo rating. The data used to build the models was collected from basketball-reference.com, this guys have the data on a way that is quite easy to crawl for all match results since.

How was the data sampled? How was the data obtained?

The model uses the Elo rating as the primary attribute to predict a match result. It starts at the same value for all teams and after each game is completed the rating is updated, becoming part of the sample data. Predictions are made for each season. The data was pull from using Python crawlers. Games scores and result for the same period were collected

How did you fit the model?

Elo rating may be computed after each game is played taking into its value the performance of the team. The implementation of Elo rating came across when I was selecting my the benchmarks to the NFL model.

How did you validated the model? How you know the results make sense?

The model is tested for each game in 2016, a period in which more than 1,000 games were played. In addition, 1 benchmarks was selected. I included FiveThertyEight. So far the model did awsome at the bigining of the season and is comming goinck back to what otherwise may be seen as the power of the Elo rating.

All models were implemented in Python 3.4 using scikit-learn package to run the ML algorithms. Elo rating is also computed in Python with custom code.