upeppe/fantabeto

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

fantabeto's Issues

Some clarifications about the project

Hi @uPeppe ,

I've read your article on Medium and I have a few questions:

• Did you use the project for both Fantacalcio Classic and Mantra?
• For the Mantra version, how did you handle players with multiple roles and select the best formation among the various available modules for each matchday?
• Did you apply the sin-arcsinh probability distribution to goalkeepers as well?
• Which bonuses and penalties did you consider? (e.g., goalkeeper clean sheets, defense modifier)
• For each matchday, did you consider validating the results by calculating the delta compared to the actual best formation?

Thanks!

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?

Missing requirements.txt

Here is the translated text:

"The requirements.txt file is missing, so it is necessary to inspect each file to understand which libraries are required."

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.

Adding a section In README to illustrate project Installation and Startup

Hi @uPeppe ,

Hi UPeppe,

I have been reviewing your project, and I find it highly interesting. To assist users who may not be very familiar with TensorFlow, I think it would be beneficial to add a section to the README that outlines the installation and setup process of the project.

Would you be willing to include such a section?

Thank you!

IndexError running 1_scraping_fbref.ipynb

Hi, when I run the 3rd cell of "1_scraping_fbref.ipynb" I receive this error, can you help me?

IndexError Traceback (most recent call last)
Cell In[3], line 1
----> 1 df_outfield = get_outfield_data('https://fbref.com/en/comps/11/','/Serie-A-Stats')
3 df_outfield.to_csv('fbref_data/outfield_players.csv', index=False)
5 df_outfield

Cell In[2], line 152
151 def get_outfield_data(top, end):
--> 152 df1 = frame_for_category('stats',top,end,stats)
153 df2 = frame_for_category('shooting',top,end,shooting2)
154 df3 = frame_for_category('passing',top,end,passing2)

Cell In[2], line 136
134 def frame_for_category(category,top,end,features):
135 url = (top + category + end)
--> 136 player_table, team_table = get_tables(url,'for')
137 df_player = get_frame(features, player_table)
138 return df_player

Cell In[2], line 45
42 soup = BeautifulSoup(comm.sub("",res.text),'lxml')
43 all_tables = soup.findAll("tbody")
---> 45 team_table = all_tables[0]
46 team_vs_table = all_tables[1]
47 player_table = all_tables[2]

IndexError: list index out of range