sportsreference-sio 0.4.6

Description:

sportsreferencesio 0.4.6

Contents

Installation
Examples

Get instances of all NHL teams for the 2018 season
Print every NBA team’s name and abbreviation
Get a specific NFL team’s season information
Print the date of every game for a NCAA Men’s Basketball team
Print the number of interceptions by the away team in a NCAA Football game
Get a Pandas DataFrame of all stats for a MLB game


Documentation
Testing


Sportsreference is a free python API that pulls the stats from
www.sports-reference.com and allows them to be easily be used in python-based
applications, especially ones involving data analytics and machine learning.
Sportsreference exposes a plethora of sports information from major sports
leagues in North America, such as the MLB, NBA, College Football and Basketball,
NFL, and NHL. Every sport has its own set of valid API queries ranging from the
list of teams in a league, to the date and time of a game, to the total number
of wins a team has secured during the season, and many, many more metrics that
paint a more detailed picture of how a team has performed during a game or
throughout a season.

Installation
The easiest way to install sportsreference is by downloading the latest
released binary from PyPI using PIP. For instructions on installing PIP, visit
PyPA.io for detailed steps on
installing the package manager for your local environment.
Next, run:
pip install sportsreference
to download and install the latest official release of sportsreference on
your machine. You now have the latest stable version of sportsreference
installed and can begin using it following the examples below!
If the bleeding-edge version of sportsreference is desired, clone this
repository using git and install all of the package requirements with PIP:
git clone https://github.com/roclark/sportsreference
cd sportsreference
pip install -r requirements.txt
Once complete, create a Python wheel for your default version of Python by
running the following command:
python setup.py sdist bdist_wheel
This will create a .whl file in the dist directory which can be installed
with the following command:
pip install dist/*.whl


Examples
The following are a few examples showcasing how easy it can be to collect
an abundance of metrics and information from all of the tracked leagues. The
examples below are only a miniscule subset of the total number of statistics
that can be pulled using sportsreference. Visit the documentation on
Read The Docs for a
complete list of all information exposed by the API.

Get instances of all NHL teams for the 2018 season
from sportsreference.nhl.teams import Teams

teams = Teams(2018)


Print every NBA team’s name and abbreviation
from sportsreference.nba.teams import Teams

teams = Teams()
for team in teams:
print(team.name, team.abbreviation)


Get a specific NFL team’s season information
from sportsreference.nfl.teams import Teams

teams = Teams()
lions = teams('DET')


Print the date of every game for a NCAA Men’s Basketball team
from sportsreference.ncaab.schedule import Schedule

purdue_schedule = Schedule('purdue')
for game in purdue_schedule:
print(game.date)


Print the number of interceptions by the away team in a NCAA Football game
from sportsreference.ncaaf.boxscore import Boxscore

championship_game = Boxscore('2018-01-08-georgia')
print(championship_game.away_interceptions)


Get a Pandas DataFrame of all stats for a MLB game
from sportsreference.mlb.boxscore import Boxscore

game = Boxscore('BOS201806070')
df = game.dataframe



Documentation
Two blog posts detailing the creation and basic usage of sportsreference can
be found on The Medium at the following links:

Part 1: Creating a public sports API
Part 2: Pull any sports metric in 10 lines of Python

The second post in particular is a great guide for getting started with
sportsreference and is highly recommended for anyone who is new to the
package.
Complete documentation is hosted on
readthedocs.org. Refer to
the documentation for a full list of all metrics and information exposed by
sportsreference. The documentation is auto-generated using Sphinx based on the
docstrings in the sportsreference package.


Testing
Sportsreference contains a testing suite which aims to test all major portions
of code for proper functionality. To run the test suite against your
environment, ensure all of the requirements are installed by running:
pip install -r requirements.txt
Next, start the tests by running py.test while optionally including coverage
flags which identify the amount of production code covered by the testing
framework:
py.test --cov=sportsreference --cov-report term-missing tests/
If the tests were successful, it will return a green line will show a message at
the end of the output similar to the following:
======================= 380 passed in 245.56 seconds =======================
If a test failed, it will show the number of failed and what went wrong within
the test output. If that’s the case, ensure you have the latest version of code
and are in a supported environment. Otherwise, create an issue on GitHub to
attempt to get the issue resolved.