{ "info": { "author": "Bradley Fay", "author_email": "bradley.fay@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3" ], "description": "py-Goldsberry\n=============\n\nA Python Package for easily acquiring NBA Data for analysis\n\nWhat is ``py-Goldsberry``?\n--------------------------\n\n``py-Goldsberry`` is designed to give the user easy access to data\navailable from stats.nba.com in a form that facilitates innovative\nanalysis. With a few simple commands, you can have access to virtually\nany data available on the site in an easy to analyze format. In fact,\nsome of the data is in a less summarize form giving you the opportunity\nto work with the most raw data possible when you are attempting to\nanswer questions that interest you.\n\nWhy was it built?\n-----------------\n\nI attended the 2015 Sloan Sports Analytics conference and had the\nfortunate opportunity to listen to `Kirk Goldsberry`_ address the crowd\nregarding the state of analytics in sports (You can watch the talk\n`here`_). One of the questions he addressed at the end was related to\nthe availability of data (or lack thereof in some instances). Basically,\nhe concluded that the lack of availability of some of the newest data is\nactually hindering the progression of analytics in sports. Innovation is\nnow restricted to those with access to data instead of to the entire\ncommunity of interested parties. I wrote (am writing) this package in an\nattempt to help address this issue in whatever small way I can.\n\nThis package is a work in progress. As the NBA continues to make more\ndata available, I will do my best to update ``py-Goldsberry`` to reflect\nthese additions. Currently, there is almost a cumbersome amount of data\navailable from the NBA so dealing with what is there is a bit of a\nchallenge. \n\n*UPDATE:* The NBA has apparently masked some of the tables that were previously available. The log level data is no longer available. This is disappointing as there was a multitude of research opportunities availble with the use of the data. Hopefully, the NBA will make this data available again in the near future.\n\nGetting started\n---------------\n\nTo get started with ``py-Goldsberry``, you need to install and load the\npackage. From your terminal, run the following command:\n\n::\n\n pip install py-goldsberry\n\nOnce you have the package installed, you can load it into a Python\nsession with the following command:\n\n.. code:: python\n\n import goldsberry\n import pandas as pd\n\nThe package is designed to work with `pandas`_ in that the output of\neach API call to the NBA website it returned in a format that is easily\nconverted into a pandas dataframe.\n\nGetting a List of Players\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nOne of the key variables necessary to fully utilize ``py-Goldsberry`` is\n``playerid``. This is the unique id number assigned to each player by\nthe NBA. ``py-Goldsberry`` has a top-level class ``PlayerList()``\nbuilt-in to give you quick access to a list of players and numbers. \n\n.. code:: python\n\n\n players2010 = goldsberry.PlayerList(Season='2010-11')\n players2010 = pd.DataFrame(players2010.players())\n players2010.head()\n\nIf you want a list of every game during the current season use the ``GameIDs()`` class:\n\n.. code:: python\n\n games = goldsberry.GameIDs()\n games = pd.DataFrame(games.game_list())\n games.head()\n\nAs you get started with ``py-goldsberry``, TAB completion in either Jupyter or IPython is going to be your best friend. I'm working on documetation, but there is a great deal of it to do and I don't have that much time. \n\n.. _Kirk Goldsberry: http://twitter.com/kirkgoldsberry\n.. _here: https://www.youtube.com/watch?v=wLf2hLHlFI8\n.. _pandas: http://pandas.pydata.org/\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://github.com/bradleyfay/py-goldsberry", "keywords": "sports NBA basketball", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "py-goldsberry", "package_url": "https://pypi.org/project/py-goldsberry/", "platform": "any", "project_url": "https://pypi.org/project/py-goldsberry/", "project_urls": { "Homepage": "http://github.com/bradleyfay/py-goldsberry" }, "release_url": "https://pypi.org/project/py-goldsberry/1.1/", "requires_dist": [ "requests", "retrying", "python-dateutil", "future" ], "requires_python": "", "summary": "API interface for stats.nba.com", 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