A free sports API written for python

stats, sports, api, sportsreference, machine, learning, nba, machine-learning, statistics, reference, nfl, artificial-intelligence, teams, ncaa, athletes, mlb, nhl, sports-stats, sports-data, sports-reference
pip install sportsreference==0.5.2


Sportsreference: A free sports API written for python

Documentation Status

Sportsreference is a free python API that pulls the stats from 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. Sportsreference also now supports Professional Football (or Soccer) for thousands of teams from leagues around the world. 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.


The easiest way to install sportsreference is by downloading the latest released binary from PyPI using PIP. For instructions on installing PIP, visit 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
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 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


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 import Teams

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

Get a specific NFL team's season information

from 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 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')

Get a Pandas DataFrame of all stats for a MLB game

from import Boxscore

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

Find the number of goals a football team has scored

from import Team

tottenham = Team('Tottenham Hotspur')


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 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.


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.