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mplsoccer is a Python library for plotting soccer/football charts in Matplotlib and loading StatsBomb open-data.

Use the package manager pip to install mplsoccer.

pip install mplsoccer

Read more in the docs and see some examples in our gallery.

Plot a StatsBomb pitch

from mplsoccer import Pitch
import matplotlib.pyplot as plt
pitch = Pitch(pitch_color='grass', line_color='white', stripe=True)
fig, ax = pitch.draw()
plt.show()

Plot a Radar

from mplsoccer import Radar
import matplotlib.pyplot as plt
radar = Radar(params=['Agility', 'Speed', 'Strength'], min_range=[0, 0, 0], max_range=[10, 10, 10])
fig, ax = radar.setup_axis()
rings_inner = radar.draw_circles(ax=ax, facecolor='#ffb2b2', edgecolor='#fc5f5f')
values = [5, 3, 10]
radar_poly, rings, vertices = radar.draw_radar(values, ax=ax,
                                               kwargs_radar={'facecolor': '#00f2c1', 'alpha': 0.6}, 
                                               kwargs_rings={'facecolor': '#d80499', 'alpha': 0.6})
range_labels = radar.draw_range_labels(ax=ax)
param_labels = radar.draw_param_labels(ax=ax)
plt.show()

In mplsoccer, you can:

• plot football/soccer pitches on nine different pitch types
• plot radar charts
• plot Nightingale/pizza charts
• plot bumpy charts for showing changes over time
• plot arrows, heatmaps, hexbins, scatter, and (comet) lines
• load StatsBomb data as a tidy dataframe
• standardize pitch coordinates into a single format

I hope mplsoccer helps you make insightful graphics faster, so you don't have to build charts from scratch.

I would love the community to get involved in mplsoccer. Take a look at our open-issues for inspiration. Please get in touch at [email protected] or @numberstorm on Twitter to find out more.

View the changelog for a full list of the recent changes to mplsoccer.

mplsoccer was inspired by:

• Peter McKeever heavily inspired the API design
• ggsoccer influenced the design and Standardizer
• lastrow's legendary animations
• fcrstats' tutorials for using football data
• fcpython's Python tutorials for using football data
• Karun Singh's expected threat (xT) visualizations
• StatsBomb's great visual design and free open-data
• John Burn-Murdoch's tweet got me interested in football analytics

MIT