This repository contain the python code necessary for scraping NBA data (recap, box score,
play-by-play, and shot locations) from ESPN.  Current code requires a MongoDB install for 
dumping scrapes.  

No real "analytics" at this point.  Still in the works.  Just scraping at this point.

No play-by-play parsing functionality as of now (in progress).  Play by play data left
"unstructured" (i.e. the play statements are raw text, and not mapped onto a set of
players and actions), but I'm hoping to change this at some point.

Just as a note:  other than the shot data, nothing really "eye-opening" / amazing will
come from analyzing this data.  The play by play data recorded is known to be highly
"sparse" (e.g. no information about passing, player locations, etc., etc.), and box score
data is notoriously lacking in data for predictive / expalaniative (is that a word?) purposes.

This project is more meant as a fun way to get soem experience scraping and organizing web
data and developing an analytics pipeline.

###########################################################################################
General Resources:

data downloads:
http://basketballvalue.com/downloads.php

espn play-by-play w/ shot distance:   # not quite the correct form for this...
http://sports.espn.go.com/nba/gamepackage/data/shot?gameId=

phpbb:
http://apbr.org/metrics/

mit sloan bb papers:
http://www.sloansportsconference.com/?page_id=462

82Games.com
http://82games.com/index.htm
http://www.82games.com/comm30.htm

These seem useful too:
http://www.basketball-reference.com/
http://www.sportscity.com/

Cout Vision seems appropriate, since, you know, it deals with spatial analysis of NBA stuffs...
http://courtvisionanalytics.com/