Sportsreference: A free sports API written for python
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.dataframeDocumentation
Two blog posts detailing the creation and basic usage of sportsreference can be found on The Medium at the following links:
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.