Hockey Rink

A Python library for plotting hockey rinks with Matplotlib.


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.


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.


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)


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("")
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")]
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("", 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)


This project was partly inspired by mplsoccer.

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