Module for scraping, parsing, and ingesting MLB GameDay data into a database

baseball, gameday, database, scraping
pip install pygameday==0.2



Pygameday scrapes Major League Baseball (MLB) GameDay data, parses it, and inserts it into a database of your choosing for later analysis.

Games, Players, At-Bats, Hits In Play, and Pitches are the data types captured.

One of the motivations behind creating pygameday was to make the database backend transparent; you should not have to worry about whether you're using SQLite, Postgres, or some other implementation. All you do is specify a URI to the database, and you're up and running.

Pygameday is build on SQLAlchemy, and should therefore be compatible with any database that SQLAlchemy supports. As of this writing, the following dialects are supported:

  • SQLite
  • PostgreSQL
  • MySQL
  • Oracle
  • Microsoft SQL Server
  • Firebird
  • Sybase

It should be noted that I've tested only SQLite and Postgres.


Install pygameday using pip:

pip install pygameday

Pygameday was developed and tested using Python 2.7. It may run with Python 3, but I need to do more testing.


Pygameday can be run in two modes: by instantiating a GameDayClient in your own Python code, or by using the command line tool.

Using the GameDayClient

First, instantiate the database client. All you need to do is specify the database URI. This example creates an SQLite database named gameday.db in the current directory, but you can substitute a URI for your database flavor of choice.

from pygameday import GameDayClient
database_uri = "sqlite:///gameday.db"
client = GameDayClient(database_uri)

Ingest games that occurred on a single day by specifying a date.

client.process_date("2015-05-01")  # Ingest games on May 1, 2015

You can also ingest games within a date range.

# Ingest games between May 1, 2015 and May 3, 2015
client.process_date_range("2015-05-01", "2015-05-03")

After ingesting data, use any tool you like to verify that the data is in the database. Here's an example using pandas.

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine(database_uri)

# Execute SQL queries against the database we just created
data = pd.read_sql_query("SELECT * FROM games LIMIT 5", engine)

Running from the command line

To run from the command line, cd to the top level pygameday directory. This directory contains files called and To run the GameDay client, execute python [yyyy-mm-dd], where yyyy is a four-digit year, mm is a two-digit month, and dd is a two-digit day. For example:

$ python 2015-05-30

Pygameday will ingest GameDay data for games played on the specified day, including information for games, atbats, hits in play, pitches, and players.

Alternatively, you can specify two dates on the command line, and pygameday will retrieve and ingest data for games on all days in the date range represented by the given dates. For example:

$ python 2015-05-31 2015-06-02

Database Configuration

You only need to specify the URI for the database for pygameday to work. Here are some example URIs.


  • "sqlite:///example.db" # File in the current directory
  • "sqlite:////absolute/path/to/example.db" # Absolute path to file (Unix/Mac)
  • "sqlite:///C:\absolute\path\to\example.db" # Absolute path to file (Windows)


  • "postgresql://user:password@host/database_name" # Standard Postgres dialect
  • "psycopg2+postgresql://user:password@host/database_name" # with psycopg2 driver

SQLAlchemy's engine documentation has additional details about the dialects it supports.


  • Better, more comprehensive unit testing
  • Ensure Python 3 compatibility
  • Enable multi-threaded processing