{ "info": { "author": "Skag Rijsdijk", "author_email": "skag.rijsdijk@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Software Development :: Libraries" ], "description": "Football Data Analysis Toolkit\n==============================\n\n.. image:: https://img.shields.io/pypi/v/footballdata.svg\n :target: https://pypi.python.org/pypi/footballdata\n :alt: Latest PyPI version\n\n.. image:: https://travis-ci.org/skagr/footballdata.png\n :target: https://travis-ci.org/skagr/footballdata\n :alt: Latest Travis CI build status\n\n\nA 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.\n\n.. [*] Soccer, if you're a heathen\n\nData sources:\n-------------\n\nfivethirtyeight.com\n~~~~~~~~~~~~~~~~~~~\n(https://projects.fivethirtyeight.com/soccer-predictions)\n\nSeason 2016-17 predictions and results for the top European and American leagues.\n\nfootball-data.co.uk\n~~~~~~~~~~~~~~~~~~~\n(http://www.football-data.co.uk/)\n\nHistorical 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.\n\nclubelo.com\n~~~~~~~~~~~\n(http://clubelo.com)\n\nFirst team relative strengths, for all (?) European leagues. Recalculated after every round, includes history.\n\nRoadmap:\n--------\n\nAdd player stats, transfers, injuries and suspensions.\n\n\nInstallation\n------------\n\n.. code:: bash\n\n $ pip install footballdata\n\nDependencies\n~~~~~~~~~~~~\n\n- `Numpy `_\n- `Pandas `_\n- `Requests `_\n- `Unidecode `_\n\nUsage\n-----\n\n.. code:: python\n\n import footballdata as foo\n\n # Create class instances\n five38 = foo.FiveThirtyEight()\n elo = foo.ClubElo()\n mhist = foo.MatchHistory('ENG-Premier League', '2016-17')\n\n # Create dataframes\n matches = five38.read_games()\n forecasts = five38.forecasts()\n current_elo = elo.read_by_date()\n team_elo_history = elo.read_team_history('Barcelona')\n epl_2016 = mhist.read_games()\n\nSee the Jupyter Notebooks here for more elaborate examples: https://github.com/skagr/footballdata/tree/master/notebooks\n\nCompatibility\n-------------\n\nTested against Python 2.7 and 3.4-3.6\n\nLicence\n-------\n\nMIT", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/skagr/footballdata", "keywords": "football,soccer,metrics,sports,statistics", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "footballdata", "package_url": "https://pypi.org/project/footballdata/", "platform": "", "project_url": "https://pypi.org/project/footballdata/", "project_urls": { "Homepage": "https://github.com/skagr/footballdata" }, "release_url": "https://pypi.org/project/footballdata/0.3.1/", "requires_dist": null, "requires_python": "", "summary": "A collection of wrappers over football (soccer) data from various websites / APIs. 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