4m4n5 / fifa18-all-player-statistics Goto Github PK

Thanks again for the project!

I wonder if the image zip file will be released soon?

best regards

Hi, thanks for this data!! It is great. I tried to run your code in order to get updated data but it not works becouse few variables as age, club, club_icon, wage and special does not match or works.

are you going to uptadete player statistics database?

Hello,

I found the project very interesting and i would like to work on analyzing the dataset which is returned by executing the code.
but, unfortunately when i executed the code in pycharm i got the below error :
line 24, in
age = td[2].find('div').text.strip()
AttributeError: 'NoneType' object has no attribute 'text'

Could you please let me know why the error is occuring in the above case.
Please let me know if there is an updated code.

Hi, first of all, thanks for creating the crawler and collecting the info into an easily accessible file. While playing around with the 2019 csv file, I came across a couple of things that you might want to take into consideration.

1. convert the file to UTF-8 BOM encoding. When opening the file in Excel, a lot of the special characters are read incorrectly, leading into various kinds of gibberish. I fixed this on my end by converting the file into utf-8 BOM in notepad++.

2. Every 60 rows there is a duplicate entry. Following your offset method of iterating on each page, I think you'll notice that the last player from one page becomes the first player on the next. Perhaps ignoring the first player of every page after the first page would make cleaning up the data just a little easier.

3. Columns calculating each player's "FUT Card" stats. This allows us to more easily and quickly get an aggregated view on each player's abilities. Of course, the weightings on the calculations can be discussed and disputed but for quick exploratory purposes this might be useful to have.

**4. There are several players with positions listed as "RES" (i.e. Koscielny, Cech, Yarmolenko) or "SUB". Seems like sofifa decided to list the positions weirdly and if they are a reserve or sub on the team sheet they get listed as such (This one, maybe not so minor) The number of "RES" players is 3091 and the number of "SUB" players is 8037.

Would appreciate your thoughts on these suggestions! Also, are you going to be posting this on Kaggle like you did for the FIFA 18 dataset?

Is it possible to attach a league attribute to the data? Or do you have a mapping from clubs to the leagues?