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Jul 17, 2017 · pybaseball is a Python package for baseball data analysis. This package scrapes Baseball Reference, Baseball Savant, and FanGraphs so you don't have to. The package retrieves statcast data, pitching stats, batting stats, division standings/team records, awards data, and more. Data is available at the individual pitch level, as well as ...
- Overview
- Using the Package
- TV View and Display Ranges
- Surface Units
- Examples
- Adding Analyses and Plotting Data
- License
- Contributions
As the field of sports analytics evolves, there's a growing need for methods to both track and visualize players throughout the game. This package aims to make this easy regardless of sport needed to be plotted.
This repository contains code necessary to draw scale versions of playing surfaces to visualize play-by-play data for baseball, basketball, curling, football, hockey, soccer, and tennis in Python. For the R version of this package, click here.
Each league that is supported by sportypy has its own class in its respective sport under the surfaces submodule. As an example, you can instantiate an NHL rink like so:
If you want to see what this would yield, you may call the draw() method that belongs to each league surface, like so:
Each league's class may take on the following arguments:
•display_range: This automatically "zooms" in on the area of the plot you're interested in. Valid ranges here vary by sport, but can be found in the docs
•x_trans and y_trans: By default, the origin of the coordinate system always lies at the center of the plot. For example, (0.0, 0.0) on a basketball court lies along the division line and on the line that connects the center of each basket. If you want to shift the origin (and therefore the entire plot), use x_trans and y_trans to do so
•{surface_type}_updates: A list of updates to the parameters that define the surface
TV View
All of the draw() methods, by default, will draw the surface in what can be considered "TV view", or the way that the surface would typically be displayed on a television broadcast. This typically means the action moves from left to right across the screen (a basketball game that has the baskets on the left and right end of the court). As mentioned above, it is possible to change this to be in any orientation through the rotation parameter to each draw() method. There are a few exceptions to this: •{baseball league}Field plots are displayed from the high-home view by default, as baseball coordinates typically have the +y axis extending from the back tip of home plate towards center field •{tennis league}Court is displayed with the court diagrammed from the perspective of the chair umpire
Display Ranges
Related to the idea of custom displays of the plot is the concept of the display_range parameter in the league's class draw() method. This is unique to each sport, but globally supported across the package. This parameter allows the specification of a designated region of the surface to display. As an example, if you're trying to limit the analysis of NFL data to being inside of the red zone, you may want to do something similar to the following: Another use case would be to make the data you're viewing easier to see. Say you wanted to look at curling data, but only cared about stone positions in the house (and not the path as to how the stones got there). You might do this:
Each league's surface has a standardized unit of measure in which the plot is created, and is standardized by the primary units specified in their respective rule books. They are as follows (and any explanation is in parentheses):
However, since the data that is supplied may come in various units of measure, the plots are able to be generated in the data's units. This is done via the {surface_name}_updates attribute in the class constructor, using the {surface_name}_units key. The features themselves will look visually identical, but the underlying coordinate grid will change.
Some examples to help navigate the package:
In addition to just creating the baseline plots, sportypy has several methods to display analyses on top of the surfaces that the classes create. The syntax is meant to wrap matplotlib methods.
Due to the nature of the layering of the surface's features, it's recommended to use the zorder parameter in the range of 11 to 15 to display model outputs, and zorder >= 20 to display individual tracking data points. This allows the features of the surface to be displayed when plotting models while also representing a birds-eye view of any action on the surface (e.g. player positions).
This package is released under the GPL 3.0 License.
League Office
The package maintainers and functional engineers •Ross Drucker - sportypy Commissioner
General Managers
Contribute by adding a new sport and become its general manager. Current general managers (and their sports) are: •Ross Drucker - Baseball •Ross Drucker - Basketball •Ross Drucker - Curling •Ross Drucker - Football •Ross Drucker - Hockey •Ross Drucker - Soccer •Ross Drucker - Tennis •Ross Drucker - Volleyball
Coaching Staffs
Notice something for a sport that already exists, but isn't quite right? Join that sport's coaching staff!
Sep 10, 2021 · To get started, you can install via pip: pip install pybaseball. Next, we’ll go over a basic example using this library. Let’s say that ESPN is down and I want to check to see how my team, the St. Louis Cardinals, is doing in the NL Central race. In one of my practice notebooks, I have the following block of code:
Jun 12, 2024 · Using field.draw() inside update() it tries to create many plots which slows down all program. But in matplotlib you can create it only once - outside update() It creates two plots - one with fields, and second with plot and data. And even example on homepage sportypy (in section Adding Analyses and Plotting Data) uses
Sep 20, 2023 · This can be done using Pip, a package manager that allows certain programs to be installed using Python. The code should look like this: !pip install pybaseball. The output will likely spit a bunch of random repeating words and loading bars, but the only words that matter are “Successfully Installed.”.
Jul 27, 2017 · Introducing pybaseball: an Open Source Package for Baseball Data Analysis. Baseball and statistics go together like peanut butter and jelly; it’s almost hard to imagine following one without involving the other. In recent years, the data that make this game enjoyable for so many have only gotten better. With the introduction of the Statcast ...
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Mar 21, 2018 · The dashboard relies entirely on Bokeh. To run it, all you need to do is navigate to the folder the Python script is saved and run bokeh serve baseballmatchups.py. You will need to be using Python 3.6 for everything to run. PyBaseball. I ultimately only needed three modules from PyBaseball: playerid_lookup, statcast_pitcher, and statcast_batter.
