Introducing Pybaseball
Overview
Pybaseball is a Python package designed specifically for baseball enthusiasts and analysts. It simplifies the process of obtaining a wide variety of baseball data, which can be a daunting task given the number of sources. By scraping popular baseball data websites like Baseball Reference, Baseball Savant, and FanGraphs, Pybaseball enables users to access rich datasets without manually browsing these platforms. Whether you're looking for detailed pitch data, seasonal stats, player performance, or team standings, Pybaseball offers a comprehensive suite of tools to get what you need.
Installation
Getting started with Pybaseball is straightforward. You can quickly install it using pip:
pip install pybaseball
Alternatively, you can clone the repository from GitHub for the latest updates:
git clone https://github.com/jldbc/pybaseball
cd pybaseball
pip install -e .
Community and Documentation
Engaging with the Pybaseball community is easy via the dedicated Discord server, where users can discuss usage and development. For assistance, the documentation provides a detailed guide on the package’s functionalities, including examples, found in their docs folder.
Key Functionalities
Statcast Data
Pybaseball offers access to Statcast data, which includes detailed information about every pitch. Users can retrieve pitch-level data such as pitch type, speed, spin, and more, thanks to Baseball Savant. For those interested in specific players like Clayton Kershaw, Pybaseball lets you perform player-specific queries using their MLB ID.
from pybaseball import statcast
data = statcast(start_dt="2019-06-24", end_dt="2019-06-25")
Aggregate Statistics
Pybaseball isn't just about individual games; it also provides comprehensive statistics aggregated over seasons or custom time periods. You can pull season-level data for pitching or batting, leveraging the metrics provided by FanGraphs and Baseball Reference.
from pybaseball import pitching_stats
data = pitching_stats(2014, 2016)
Game Schedules and Results
Using the schedule_and_record function, users can access historical game-by-game results, making it easy to track team performance across seasons:
from pybaseball import schedule_and_record
data = schedule_and_record(1927, 'NYY')
Standings and Records
For up-to-date or historical standings information, the standings function is invaluable. It allows users to see where teams stand at any point in the season or review end-of-season results across divisions:
from pybaseball import standings
data = standings(2016)
Caching for Efficiency
To enhance efficiency, Pybaseball offers a caching mechanism. This is particularly useful when repeatedly accessing the same datasets, reducing wait times and load on external servers.
from pybaseball import cache
cache.enable()
Common Issues and Solutions
Users might encounter issues like stale cache or multiprocessing errors. Clearing the cache can solve data discrepancies, while structuring scripts to handle multiprocessing issues ensures smoother execution. The community provides continuous support through GitHub for any persistent issues.
Contributing
Pybaseball thrives thanks to community contributions. Documentation on how to contribute is readily available for those interested in enhancing the package.
Credit
Pybaseball was developed by James LeDoux and maintained by Moshe Schorr, inspired by Bill Petti’s baseball data insights in R. It integrates data from multiple respected baseball data sources, offering an unparalleled tool for baseball data analysis.