fbref2pandas 0.0.1

A scraper that directly gives football(not soccer) data from FBRef website directly to Pandas dataframe. Major teams and leagues supported.

FBREF2PANDAS - Export FBRef data to Pandas Dataframe

A scraper that directly gives football(not soccer) data from FBRef website directly to Pandas dataframe. Major teams and leagues supported.

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Disclaimer : This package in no way tries to take away from the work of FBRef.com I love that website and just needed a package that makes my life easier

To install the package(it just requires pandas !!) :

pip install fbref2pandas

After installation, create a MatchLogsLink object by providing it the following arguments(in str format) :

• team_id : The identifier of the team. Generally, as I have noticed, fbref uses a particular id for each of the team. It is a 8 character string that uniquely identifies a football team. See this table for id of some popular teams.

Team ID	Team Name
'206d90db'	'Barcelona'
'53a2f082'	'Real-Madrid'
'db3b9613'	'Atletico-Madrid'
'e31d1cd9'	'Real-Sociedad'
'2a8183b3'	'Villarreal'

Just copy the team_id and pass as the first argument.

• year : The year for which the data is required. The year is in the format 2022-2023(for 2022-23 season). Pass this as second argument in the MatchLogsLink object.

• comp_id : The comp_id is also one of the variables that fbref maintains internally, as far as I can deduce. The comp_id is of the form cXXX, and can be found from the fbref website. See this table below for some common comp_id

Comp ID	Competition Name
'c8'	'Champions-League'
'c12'	'La-Liga'
'c19'	'Europa-League'
'c122'	'UEFA-Super-Cup'
'c569'	'Copa-del-Rey'
'c646'	'Supercopa-de-Espana'
'c882'	'Europa-Conference-League'

• log_type : I love how many stats are available in the fbref website. These are just awesome for your next project. The log_type could be any of these values:

Log Type
'scores_and_fixtures'
'shooting'
'goalkeeping'
'passing'
'pass_types'
'goal_and_shot_creation'
'defensive_actions'
'possession'
'miscellaneous_stats'

After passing these 4 parameters to the MatchLogsLink object, most of the task is done. Just create a new Data object, and pass the above MatchLogsLink object. An example of this would be :

link = MatchLogsLink('206d90db', '2022-2023', 'c12', 'shooting')
# print(link)  

data = Data(link)

If the link is correct, there shouldn't be a problem. Now, to get the data as a DataFrame object, just call the function fbref2pandas() of the Data object. The functions returns the data as a pandas Dataframe. If the link is incorrect, an exception is raised. Just double check if the data from the table above. Enjoy the data.

To get the data as DataFrame :

df = data.fbref2pandas()

Note : I comply with all the rules given for the data use in the sports reference website, and I believe that it is in fair use. Not many requests will be taken from this package.

Note : Help would be really appreciated to expand this package. Create a PR and add whatever you could scrape from links of FBRef. Great PRs will be merged on small notices.