Metadata-Version: 1.0
Name: statsnba-playbyplay
Version: 0.2.0
Summary: Package for parsing play-by-play data from stats.nba.com
Home-page: https://github.com/ethanluoyc/statsnba-playbyplay
Author: Yicheng Luo
Author-email: ethanluoyc@gmail.com
License: MIT
Description: ===================
        statsnba-playbyplay
        ===================
        
        .. image:: https://img.shields.io/pypi/v/statsnba-playbyplay.svg?maxAge=2592000
           :target: https://pypi.python.org/pypi?name=statsnba-playbyplay&version=0.1.0&:action=display
           :alt: PyPi Version
        
        .. image:: https://readthedocs.org/projects/statsnba-playbyplay/badge/?version=latest
           :target: http://statsnba-playbyplay.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
        NOTE: This project is still pretty much work in progress so it might
        introduce many breaking changes.
        
        - `Introduction`_
        - `Use the data`_
        - `Benefits of this package`_
        - `Installation`_
        - `TODOs`_
        
        Introduction
        ------------
        
        Basketball analytics using play-by-play data have been an shared
        interest for many people. However, the lack of processed play-by-play
        has prohibited such analysis by many.
        
        This project is intended to provide parsing functionality for the
        play-by-play data from http://stats.nba.com into more a comprehensive
        format like that on
        `NBAStuffer <https://downloads.nbastuffer.com/nba-play-by-play-data-sets>`__.
        It is intended to accompany our research: `Adversarial Synergy Graph
        Model for Predicting Game Outcomes in Human
        Basketball <http://www.somchaya.org/papers/2015_ALA_Liemhetcharat.pdf>`__.
        to prepare the data. If you are interested in more general statistics or
        player information, you should definitely check out
        `py-Goldsberry <https://github.com/bradleyfay/py-Goldsberry>`__.
        
        While there are still limitations with the current parsing strategy, it
        does not affect the tabulation of APM and other play-by-play based
        metrics.
        
        Use the data
        ------------
        
        If you just want to use the data that is processed with the package
        without touching it, you can find a copy of the data
        `from S3 <http://statsnba.s3-website-us-east-1.amazonaws.com/>`__. Under
        ``data/zip/`` you will find the gamelog and game files in JSON format.
        You may introspect into the JSONs for the fields that are included in
        them.
        
        Benefits of this package
        ------------------------
        
        -  The data is obtained directly from http://stats.nba.com, the parsed
           play-by-plays can be verified against the official boxscores.
        
        
        Installation
        ------------
        
        At the command line
        
          .. code:: shell                
                    
            $ pip install statsnba-playbyplay
        
        
        TODOs
        -----
        
        -  Documentation.
        -  Parse subtypes of events. (e.g. when there is a shot, is it a layup
           or jumpshot? the raw data provides different codes for these subtypes
           but I have not yet figured out a way to easily decrypt all of them.)
        -  More tests at all levels of the package.
        -  A Github Pages website for showcasing the package.
        -  Wiki pages on the schema of the parsed data on my S3 bucket.
        -  Daily updates of the data feed (cronjob or Lambda function on an EC2
           instance to track the gamelogs daily and make updates on S3?)
        
Keywords: statsnba
Platform: UNKNOWN
