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Machine learning model for predicting Serie A players performance in a match, in terms of Fantacalcio (italian fantasy football) scores.

License: MIT License

Jupyter Notebook 100.00%
ai fantacalcio fantasy-football machine-learning neural-network python serie-a tensorflow tensorflow-probability

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fantabeto's Issues

Selecting team at the start of the season and preparing for the January market auction

I've read carefully the code and your article on Medium: it's really a great work, no doubts.

When reading about the implementation of the model I haven't understood two concepts:

  1. How to select the team at the start of the season: I've read your reply to a comment on medium and you stated that you've made the initial team by applying the prediction using as opposing team one made through averaging all the stats of Serie A teams. I didn't find this reference in the github code.

  2. In the Medium article you say that by using the algorithm to predict selecting a statistically average Serie A team as an opponent, it was useful in preparing for the January market auction: where is this implemented in the code?

I'm trying to understand all the parts of the model better, I hope I haven't inconvenienced you.

Thanks for your time.

GKs performance estimation?

Hi @uPeppe,

In the accompanying blog post you stated how the performance estimation model was good on movement players, but subpar on goalkeepers (GKs). Have you further experimented on the root causes of this phenomenon?

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