A collection of wrappers over football [*] data from various websites / APIs. You get: Pandas dataframes with sensible, matching column names and identifiers across datasets. Data is downloaded when needed and cached locally. Example Jupyter Notebooks are in the Github repo.
| [*] | Soccer, if you're a heathen |
(https://projects.fivethirtyeight.com/soccer-predictions)
Season 2016-17 predictions and results for the top European and American leagues.
- Data no longer available on fivethirtyeight.com, I've added it to the repository
(http://www.football-data.co.uk/)
Historical results, betting odds and match statistics for English, Scottish, German, Italian, Spanish, French, Dutch, Belgian, Portuguese, Turkish and Greek leagues, including a number of lower divisions. Level of detail depends on league.
First team relative strengths, for all (?) European leagues. Recalculated after every round, includes history.
Add player stats, transfers, injuries and suspensions.
$ pip install footballdataimport footballdata as foo
# Create class instances
five38 = foo.FiveThirtyEight()
elo = foo.ClubElo()
mhist = foo.MatchHistory('ENG-Premier League', '2016-17')
# Create dataframes
matches = five38.read_games()
forecasts = five38.forecasts()
current_elo = elo.read_by_date()
team_elo_history = elo.read_team_history('Barcelona')
epl_2016 = mhist.read_games()See the Jupyter Notebooks here for more elaborate examples: https://github.com/skagr/footballdata/tree/master/notebooks
Tested against Python 2.7 and 3.4-3.6
MIT