A Python library for plotting hockey rinks with Matplotlib.

Overview

Hockey Rink

A Python library for plotting hockey rinks with Matplotlib.

Installation

pip install hockey_rink

Current Rinks

The following shows the custom rinks currently available for plotting.

from hockey_rink import NHLRink, IIHFRink, NWHLRink
import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 3, sharey=True, figsize=(12, 6), gridspec_kw={"width_ratios": [1, 98.4/85, 1]})
nhl_rink = NHLRink(rotation=90)
iihf_rink = IIHFRink(rotation=90)
nwhl_rink = NWHLRink(rotation=90)
axs[0] = nhl_rink.draw(ax=axs[0])
axs[1] = iihf_rink.draw(ax=axs[1])
axs[2] = nwhl_rink.draw(ax=axs[2])

The NWHL logo comes from the NWHL site.

Customization

There is also room for customization. The image at the top was created as follows:

rink = Rink(rotation=45, boards={"length": 150, "width": 150, "radius": 75})

Rinks also allow for additional features to be added. Custom features should inherit from RinkFeature and override the _get_centered_xy method. The draw method can also be overridden if the desired feature can't be drawn with a matplotlib Polygon, though _get_centered_xy should still provide the feature's boundaries. CircularImage provides an example of this by inheriting from RinkCircle.

If a custom feature is to be constrained to only display within the rink, the returned object needs to have a set_clip_path method.

Plots

There are currently wrappers available for the following Matplotlib plots:
- plot
- scatter
- arrow
- hexbin
- pcolormesh (heatmap in Hockey Rink)
- contour
- contourf

If you'd like to bypass the wrappers, you can convert coordinates to the proper scale with convert_xy:

rink = Rink()
x, y = rink.convert_xy(x, y)

When plotting to a partially drawn surface, the plot will be applied to the entire rink, not what's visible. This can be avoided by setting plot_range (or plot_xlim and plot_ylim) in the plotting functions where they're available.

It's also important to realize that the plotting functions only allow arguments to be passed without keywords for the coordinates.
ie) hexbin(x, y, values) will throw an error.

The correct call is hexbin(x, y, values=values)

Examples

Let's look at some NWHL data via the Big Data Cup.

The first game is Minnesota vs Boston, so we'll go with that and do a scatter plot of each team's shots.

from hockey_rink import NWHLRink
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/bigdatacup/Big-Data-Cup-2021/main/hackathon_nwhl.csv")
game_df = df.loc[(df["Home Team"] == "Minnesota Whitecaps") & (df["Away Team"] == "Boston Pride")]
shots = game_df.loc[(game_df.Event.isin(["Shot", "Goal"]))]
boston_shots = shots[shots.Team == "Boston Pride"]
minnesota_shots = shots[shots.Team == "Minnesota Whitecaps"]
rink = NWHLRink(x_shift=100, y_shift=42.5)
ax = rink.draw()
rink.scatter(boston_shots["X Coordinate"], boston_shots["Y Coordinate"])
rink.scatter(200 - minnesota_shots["X Coordinate"], 85 - minnesota_shots["Y Coordinate"])

Extending the example, let's look at all of Boston's passes.

boston_passes = game_df.loc[(game_df.Team == "Boston Pride") & (game_df.Event == "Play")]
ax.clear()
rink.draw()
arrows = rink.arrow(boston_passes["X Coordinate"], boston_passes["Y Coordinate"], 
                    boston_passes["X Coordinate 2"], boston_passes["Y Coordinate 2"], color="yellow")

For some of the other plots, let's look at some NHL shooting percentages.

To mix things up a little, binsize will take different values in each plot and the heatmap won't include shots from below the goal line. We'll also throw in a colorbar for the contour plot.

from hockey_rink import NHLRink
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

pbp = pd.read_csv("https://hockey-data.harryshomer.com/pbp/nhl_pbp20192020.csv.gz", compression="gzip")
pbp["goal"] = (pbp.Event == "GOAL").astype(int)
pbp["x"] = np.abs(pbp.xC)
pbp["y"] = pbp.yC * np.sign(pbp.xC)
shots = pbp.loc[(pbp.Ev_Zone == "Off") & ~pbp.x.isna() & ~pbp.y.isna() & (pbp.Event.isin(["GOAL", "SHOT", "MISS"]))]

fig, axs = plt.subplots(1, 3, figsize=(14, 8))
rink = NHLRink(rotation=270)
for i in range(3):
    rink.draw(ax=axs[i], display_range="ozone")
contour_img = rink.contourf(shots.x, shots.y, values=shots.goal, ax=axs[0], cmap="bwr", 
                            plot_range="ozone", binsize=10, levels=50, statistic="mean")
plt.colorbar(contour_img, ax=axs[0], orientation="horizontal")
rink.heatmap(shots.x, shots.y, values=shots.goal, ax=axs[1], cmap="magma",
             plot_xlim=(25, 89), statistic="mean", vmax=0.2, binsize=3)
rink.hexbin(shots.x, shots.y, values=shots.goal, ax=axs[2], binsize=(8, 12), plot_range="ozone", zorder=25, alpha=0.85)

Inspiration

This project was partly inspired by mplsoccer.

Hopefully, it can lower a barrier for someone looking to get involved in hockey analytics.

Contact

You can find me on twitter @the_bucketless or email me at [email protected] if you'd like to get in touch.

Create a table with row explanations, column headers, using matplotlib

Create a table with row explanations, column headers, using matplotlib. Intended usage was a small table containing a custom heatmap.

4 Aug 14, 2022
Define fortify and autoplot functions to allow ggplot2 to handle some popular R packages.

ggfortify This package offers fortify and autoplot functions to allow automatic ggplot2 to visualize statistical result of popular R packages. Check o

Sinhrks 504 Dec 23, 2022
Mattia Ficarelli 2 Mar 29, 2022
Learn Data Science with focus on adding value with the most efficient tech stack.

DataScienceWithPython Get started with Data Science with Python An engaging journey to become a Data Scientist with Python TL;DR Download all Jupyter

Learn Python with Rune 110 Dec 22, 2022
Smarthome Dashboard with Grafana & InfluxDB

Smarthome Dashboard with Grafana & InfluxDB This is a complete overhaul of my Raspberry Dashboard done with Flask. I switched from sqlite to InfluxDB

6 Oct 20, 2022
Python Data Validation for Humansβ„’.

validators Python data validation for Humans. Python has all kinds of data validation tools, but every one of them seems to require defining a schema

Konsta Vesterinen 670 Jan 09, 2023
Graphical visualizer for spectralyze by Lauchmelder23

spectralyze visualizer Graphical visualizer for spectralyze by Lauchmelder23 Install Install matplotlib and ffmpeg. Put ffmpeg.exe in same folder as v

Matthew 1 Dec 21, 2021
AB-test-analyzer - Python class to perform AB test analysis

AB-test-analyzer Python class to perform AB test analysis Overview This repo con

13 Jul 16, 2022
Create animated and pretty Pandas Dataframe or Pandas Series

Rich DataFrame Create animated and pretty Pandas Dataframe or Pandas Series, as shown below: Installation pip install rich-dataframe Usage Minimal exa

Khuyen Tran 92 Dec 26, 2022
2021 grafana arbitrary file read

2021_grafana_arbitrary_file_read base on pocsuite3 try 40 default plugins of grafana alertlist annolist barchart cloudwatch dashlist elasticsearch gra

ATpiu 5 Nov 09, 2022
Shaded 😎 quantile plots

shadyquant 😎 This python package allows you to quantile and plot lines where you have multiple samples, typically for visualizing uncertainty. Your d

Mehrad Ansari 13 Sep 29, 2022
1900-2016 Olympic Data Analysis in Python by plotting different graphs

πŸ”₯ Olympics Data Analysis πŸ”₯ In Data Science field, there is a big topic before creating a model for future prediction is Data Analysis. We can find o

Sayan Roy 1 Feb 06, 2022
Ana's Portfolio

Ana's Portfolio ✌️ Welcome to my Portfolio! You will find here different Projects I have worked on (from scratch) πŸ’ͺ Projects πŸ’» 1️⃣ Hangman game (Mad

Ana Katherine Cortes Sobrino 9 Mar 15, 2022
China and India Population and GDP Visualization

China and India Population and GDP Visualization Historical Population Comparison between India and China This graph shows the population data of Indi

Nicolas De Mello 10 Oct 27, 2021
A comprehensive tutorial for plotting focal mechanism

Focal_Mechanisms_Demo A comprehensive tutorial for plotting focal mechanism "beach-balls" using the PyGMT package for Python. (Resulting map of this d

3 Dec 13, 2022
GDSHelpers is an open-source package for automatized pattern generation for nano-structuring.

GDSHelpers GDSHelpers in an open-source package for automatized pattern generation for nano-structuring. It allows exporting the pattern in the GDSII-

Helge Gehring 76 Dec 16, 2022
Visualization Website by using Dash and Heroku

Visualization Website by using Dash and Heroku You can visit the website https://payroll-expense-analysis.herokuapp.com/ In this project, I am interes

YF Liu 1 Jan 14, 2022
A Python Binder that merge 2 files with any extension by creating a new python file and compiling it to exe which runs both payloads.

Update ! ANONFILE MIGHT NOT WORK ! About A Python Binder that merge 2 files with any extension by creating a new python file and compiling it to exe w

Vesper 15 Oct 12, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
A shimmer pre-load component for Plotly Dash

dash-loading-shimmer A shimmer pre-load component for Plotly Dash Installation Get it with pip: pip install dash-loading-extras Or maybe you prefer Pi

Lucas Durand 4 Oct 12, 2022