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 mplsoccerOr install via Anaconda.
conda install -c conda-forge mplsoccerRead 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 rowlinsonandy@gmail.com 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