mplsoccer: The Python Library That Powers Football Visualisations

Five core capabilities make mplsoccer the standard for football data visualisation:

• Pitch drawing. A single Python function call renders a regulation football pitch with all markings (penalty area, centre circle, etc.). Multiple pitch styles available (Opta, StatsBomb, Wyscout, generic).
• Shot map plotting. `pitch.scatter()` plots shots with size = xG, fill = goal/no-goal. The standard analyst output.
• Pass network rendering. Position-based player nodes with passing volumes as edges between them.
• Heatmap rendering. 2D density plots showing player or team positional concentration.
• Tactical diagram drawing. Arrows, zones, formation overlays — all with mplsoccer's pitch coordinate system.

Three reasons mplsoccer dominated:

• Open source + free. No subscription, no paywall. Anyone with Python can use it.
• Multiple coordinate systems supported. Opta, StatsBomb, Wyscout all use slightly different x/y coordinate systems. mplsoccer translates between them.
• Excellent documentation. Andrew Rowlinson wrote extensive worked examples and tutorials. Lower learning curve than rolling your own matplotlib code.
• Community contributions. Many features added by users via GitHub pull requests over the years.

A typical mplsoccer workflow:

• Install: `pip install mplsoccer`.
• Import: `from mplsoccer import Pitch`.
• Create a pitch: `pitch = Pitch(pitch_type="opta", pitch_color="grass")`.
• Draw it: `fig, ax = pitch.draw()`.
• Add data: `pitch.scatter(x, y, ax=ax, s=xg_sizes, c=colors)` for a shot map.
• Save: `fig.savefig("shot-map.png", dpi=300)`.

mplsoccer integrates seamlessly with StatsBomb's open-data project — free downloadable event data for women's World Cups and selected men's competitions. The combination is powerful:

• Download a match's event data via StatsBomb's open API or git repository.
• Filter to shots, passes, or other event types.
• Pass directly to mplsoccer's plotting functions.
• Render publication-quality visualisations in 30-50 lines of code.

Five user communities:

• Football analysts on Twitter. The vast majority of analyst Twitter pitch-based content uses mplsoccer.
• Academic researchers. Soccer-analytics academic papers commonly cite mplsoccer in methods sections.
• Bloggers + journalists. Free tier of football analytics — what you see on The Analyst is paid commercial; what you see on amateur blogs is mplsoccer.
• Students learning analytics. mplsoccer is the standard intro-to-football-analytics teaching tool.
• Some clubs. Some clubs use mplsoccer for internal reports; most prefer paid tools but open-source is no longer rare in pro clubs.

Three things mplsoccer doesn't do:

• Live in-match plotting. mplsoccer renders static images. For live updates, separate frameworks (Plotly, D3) are needed.
• 3D visualisations. Pitch is 2D. For tracking-data flythrough or 3D visualisation, separate tools required.
• Interactive web plots. Output is image files; for interactive web visualisations, the user must build their own front-end on top of mplsoccer's plotting.

mplsoccer dominates but has competitors:

• socceraction. Python package for VAEP and related advanced metrics; less plotting, more metrics.
• Soccermatics. A coursebook + library by David Sumpter; more academic-flavoured.
• Pitch.js (JavaScript). Web-native pitch rendering for interactive applications.
• Custom matplotlib. Some analysts roll their own pure matplotlib code; mplsoccer is essentially a wrapper that saves time.