A Glossary of Advanced Football Data Visualizations

Beyond the now-familiar heat map and shot map sits a second layer of football visualizations, built to answer questions those basic charts cannot. This glossary covers five of the more advanced chart types analysts use to read space, momentum, and player profile — tools that trade simplicity for a sharper answer to a narrower question.

Space-Reading Charts

Voronoi diagram. A voronoi diagram divides the pitch into regions based on which player is closest to every point on the grass at a given instant, so each region shows the space a player would reach first if the ball landed there. Stitched together across all twenty-two players, the diagram becomes a live map of spatial control rather than possession — a team can be "controlling" large voronoi regions even while out of possession, which is precisely what makes the chart useful for judging defensive shape and pressing coverage independent of who has the ball.

Pitch control map. A close relative of the voronoi diagram, a pitch control map goes a step further by weighting each region not just by nearest player but by a probability estimate of which team would actually win the ball there, accounting for player speed and reaction time. Where a voronoi diagram answers "whose zone is this," a pitch control map answers "who would likely reach the ball first if it were played here right now" — a subtler, model-driven version of the same underlying idea.

Average position map. An average position map plots each player's mean location over a match or a set of matches, usually shown as a single dot per player connected into a rough formation shape. It is a simplification — a central midfielder who spends half the match tucked in defensively and half pushed high will show up as one dot somewhere in between, hiding both extremes — but it remains a fast way to read a team's base structure and to compare how far a nominal formation drifts from its assigned shape once a match is actually played.

Distribution and Delivery Charts

Pass sonar. A pass sonar is a compact radial chart, usually anchored to a single player's average position, that shows the direction and typical distance of every pass they attempt from that spot. Each "petal" of the sonar points in a compass direction, with its length representing how far passes in that direction tend to travel and its color or shading often representing completion rate. Reading a full team's pass sonars side by side reveals distribution patterns that a plain passing-network diagram compresses away — whether a deep-lying midfielder favors long diagonals to one flank, for instance, rather than simply how many passes he completed.

Radar chart. Sometimes called a pizza chart because of its wedge-shaped segments, a radar chart plots a player's percentile rank across a chosen set of statistical categories on a single circular grid, with each spoke representing one metric scaled from center (low percentile) to edge (high percentile). The chart is built for fast cross-metric comparison — a single glance shows whether a player's radar leans toward creation, defending, or ball progression — but it is only as informative as the metric selection and the peer group used to calculate the percentiles, since the same player can look very different on a radar built against attacking midfielders than one built against the full outfield player pool.

Momentum and Flow Charts

Momentum chart. A momentum chart tracks a rolling measure of which team is dominating a match over time, typically built from a short window of possession share, territorial presence, or shot-generating actions rather than the cumulative match total. Unlike a cumulative statistic that only ever climbs, a momentum chart can swing back and forth within a single half, which is what makes it useful for identifying spells of pressure that never resulted in a converted chance — passages of play a scoreline or a single end-of-match stat line would otherwise erase entirely.

What These Charts Need Behind the Scenes

None of the five visualizations above can be built from a bare results sheet or even a standard event log alone. Voronoi diagrams and pitch control maps require positional tracking data — the continuous x-y coordinates of every player, captured many times per second — because their entire premise is spatial ownership of the pitch at a given instant, not a record of discrete actions like passes or shots. Pass sonars and radar charts, by contrast, can be built from event data alone, since they summarize a set of already-logged actions (passes, in the sonar's case; a chosen metric set, in the radar's) rather than modeling continuous space. A momentum chart sits in between: it can be approximated from event data using a rolling count of attacking actions, but a version built on tracking data — incorporating territorial presence and pitch control rather than just event frequency — tends to produce a smoother, more responsive line.

That distinction matters practically. A reader who understands which data layer a chart is built on has a better sense of what it can and cannot show. A pass sonar, however detailed, cannot tell you where a player was standing when he wasn't passing; only tracking-derived visualizations like a voronoi diagram or a pitch control map can speak to off-ball positioning at all. Knowing which category a chart belongs to is often the fastest way to judge whether it is answering the question a reader actually has in mind.

Why These Sit Apart From the Basics

• Heat maps and shot maps summarize what already happened in one dimension — location of touches, location of shots — while voronoi diagrams, pitch control maps, and pass sonars model relationships between players, not just single-player activity.
• Radar charts and momentum charts are comparative by design, built to be read against a baseline (a peer group or a match average) rather than in isolation, which makes context and methodology part of reading them correctly.
• All five depend more heavily on underlying model choices — which peer group, which time window, which speed assumption — than a basic heat map does, so two charts of the same type built on different assumptions can legitimately disagree.

Common Misreadings Worth Avoiding

A few habits cause these charts to be misread more often than the basics. The first is treating a voronoi diagram or pitch control map as a record of what happened rather than a model of what was likely — both are estimates built on tracking data and reasonable physical assumptions, not a ground-truth account of who "actually" controlled a given patch of grass. The second is comparing radar charts built against different peer groups as though they used the same baseline; a percentile is only meaningful relative to the population it was calculated from, so a striker's radar and a winger's radar are not directly comparable unless both were built against the same reference group. The third is reading a single frame of a momentum chart in isolation — a short window of low activity can simply reflect a natural lull in a match rather than a genuine shift in control, which is why momentum charts are generally read as trends over several minutes rather than snapshots at one instant.

A fourth, more subtle habit is assuming every advanced chart updates at the same speed. Event-driven visualizations like the pass sonar and radar chart can only refresh when a relevant action occurs, so a player who has gone several minutes without touching the ball will show a stale sonar even if he has been working hard off the ball — a gap that only a tracking-based chart like a pitch control map is actually built to fill.

Reading Them Together

None of these charts is meant to stand alone. A pitch control map explains why a pass sonar's long diagonal worked on one occasion and was intercepted on another; a momentum chart can flag the ten-minute spell that a match's shot map alone makes look evenly balanced. The value of this second layer of visualization is less about any single chart replacing the basics and more about triangulating a fuller picture once the simple version has already been read. RubiScore builds several of these advanced views — average position maps and momentum-style match trackers among them — on top of the same underlying event and tracking data used for its standard shot and heat maps, which is what allows a reader to move between the familiar charts and the more specialized ones without switching data sources. The fuller set of match visualizations is available at rubiscore.com.