A wrapper for the SportMonks football API.
SportMonks Football API documentation: https://www.sportmonks.com/docs/football/2.0
NOT YET MADE IT IN TO A PACKAGE
Generate an API key from SportMonks: https://sportmonks.com/football-api?soccer
from football import Schedule
schedule = Schedule(api_key)
# returns all fixtures for the current day (JSON format)
fixtures_today = schedule.schedule_today()
# to return in pd.DataFrame format with specified columns (if possible)
fixtures_today = schedule.schedule_today(df=True, df_cols=None)
from football import Odds
odds = Odds(api_key)
# see the best odds offered across bookmakers for a given market
# use the filters keyword to specify which bookmakers to maximise
# if none given then all bookmakers will be considered
odds.best_odds(fixture_id, market_id, label, filters=None)
# we can also return the average odds
odds.average_odds(fixture_id, market_id, label, filters=None)
from football import Fixtures, FixtureStats
# the Fixtures class allows us to retrieve historical fixtures
# from 2005/2006 onwards by id, date or date range
fixts = Fixtures(api_key)
def by_date_range(start_date: str, end_date: str,
team_id: Optional[int] = None,
league_ids: Optional[Union[int, List[int]]] = None,
markets: Optional[Union[int, List[int]]] = None,
bookmakers: Optional[Union[int, List[int]]] = None,
includes: Optional[Union[str, List[str]]] = None,
filters: Optional[dict] = None,
df: bool = False, df_cols: Optional[Union[str, List[str]]] = None)
# see docstrings for more info regarding the arguments
# we can use the relevant includes to harvest statistics from the game
# returns DataFrame of match statistics for home and away team
fixt_stats = FixtureStats(api_key)
# cols keyword is used to specify what columns (stats) you want to keep
fixt_stats.by_date_range(start_date: str, end_date: str,
includes: Optional[Union[str, List[str]]] = "stats",
filters: Optional[dict] = None,
cols: Optional[Union[str, List[str]]] = None)
# we can even return player stats (goals, passes, fouls, cards etc)
fixt_stats.player_by_id(fixture_ids: Union[int, List[int]],
includes: Optional[Union[str, List[str]]] = "lineup",
filters: Optional[dict] = None,
cols: Optional[Union[str, List[str]]] = None):
# one row for each player, so, per fixture, 22 rowsThe file run.py is run every night to populate new data in to a SQL database
from the fixtures that day.
This includes all statistics from the fixtures, all player statistics from the fixtures
and also odds from certain markets.