How to Read a Football Radar Chart: Percentiles, Peer Groups and the Traps to Avoid

A football radar chart is a circular graphic that shows how a player ranks against a chosen peer group across several statistics at once, with each spoke representing one metric. Player statistics of the kind tracked by RubiScore (https://rubiscore.com) are the raw material these charts are built from. This guide explains, step by step, how to read one properly and how to avoid the most common misreadings.

What a Radar Chart Actually Shows

Most modern football radars do not plot raw numbers. They plot percentile ranks. If a striker sits at the 80th percentile for shots per 90 minutes, it means the player takes more shots per 90 than 80 percent of the players in the comparison group. The centre of the chart represents the bottom of the group and the outer edge represents the top.

Some versions, often called pizza charts, use wedge-shaped slices instead of a connected polygon. The principle is the same: each slice or spoke is a separate percentile, and the overall shape gives a fast impression of where a player stands out and where they lag behind.

That speed is the chart's great strength and its main weakness. A radar compresses a lot of information into a shape the eye reads instantly, but the shape depends entirely on choices made before the chart was drawn.

Radars are used in very different settings. Scouting departments use them to narrow long lists of candidates, analysts use them to illustrate a player's style in an article, and supporters share them to argue about who is better. The same chart can serve all three, but the standard of evidence required is not the same. A radar that is perfectly adequate for illustrating a style can be far too thin to justify a recruitment decision.

Before You Start: Three Questions to Ask

Every radar rests on three hidden decisions. Before interpreting any shape, find the answers:

• Who is the peer group? Percentiles are relative. A full-back compared against all outfield players will look very different from the same full-back compared only against other full-backs in the same league.
• What is the minutes threshold? Players with very few minutes can post extreme per-90 numbers by chance. Most charts exclude players below a cut-off, and the cut-off changes who is in the comparison pool.
• Which time period is covered? A single season, the last few months and a multi-season window can produce quite different shapes for the same player.

If a chart does not state its peer group, minutes threshold and time period, treat it as an illustration rather than evidence.

Step 1: Read the Metric Labels First

It is tempting to look at the shape before the labels. Resist that. The metrics chosen define what the chart can say. A radar built for strikers will typically include non-penalty expected goals, shots, touches in the box and conversion-related measures. A radar built for central midfielders may focus on progressive passes, passes into the final third, ball recoveries and pressures.

Ask whether the metrics fit the role. A defensive midfielder will look weak on a chart designed for attacking players, not because they are poor, but because the chart asks the wrong questions of them.

Step 2: Check How the Numbers Were Normalised

Before percentiles are calculated, raw counts are usually converted into rates. The two most common methods are:

• Per 90 minutes, which allows players with different playing time to be compared.
• Possession-adjusted, which scales defensive actions by how much of the ball the opponent had, so that defenders in dominant teams are not penalised simply because they had less to do.

These choices matter most for defensive metrics. A centre-back in a team that dominates possession will often have low raw tackle and interception numbers. Adjusting for possession can lift those figures considerably. If two radars use different normalisation methods, they cannot be compared directly.

Step 3: Look for Inverted Metrics

On some spokes, a lower raw number is better. Times dispossessed, fouls committed and failed dribbles are typical examples. Well-built charts invert these metrics so that a longer spoke always means "better". Poorly labelled charts do not, and the reader is left to guess.

Check the legend or labels for words such as "inverted" or "lower is better". Misreading one inverted spoke can reverse the meaning of part of the chart.

Step 4: Read Spokes Individually, Then as Groups

Only now is it worth looking at the shape. Start by noting which spokes reach towards the edge and which stay near the centre. Then group them. Many radars cluster their metrics by theme, such as shooting, creation, progression and defending, often with colour coding.

Reading by group turns a set of numbers into a profile. A player whose creation and progression spokes are long but whose shooting spokes are short is a facilitator rather than a finisher. A player with long defending spokes and short progression spokes may be a ball-winner who passes simply once possession is regained.

Step 5: Be Careful With Area

The eye naturally reads a radar as a filled area, and a bigger area looks like a better player. That impression is misleading for two reasons.

First, the area of a shape grows faster than the length of its spokes, so small differences in percentiles can look like large differences in overall quality. Second, the area depends on the order in which metrics are arranged around the circle. Placing two strong metrics next to each other creates a larger filled triangle than placing them on opposite sides. The same numbers in a different order can produce a visibly different shape.

The safest habit is to compare spoke by spoke rather than shape by shape.

Step 6: Remember That Percentiles Compress and Stretch

Percentiles describe rank, not distance. In the middle of a distribution, many players have similar raw values, so a small change in output can move a player many percentile points. At the extremes, the opposite happens: the gap between the 95th and 99th percentile can represent a large difference in raw numbers.

This means two players at the 50th and 60th percentile might be almost identical in practice, while two players at the 95th and 99th may be quite different. Where possible, check the underlying raw values alongside the percentile.

Step 7: Add the Context the Chart Leaves Out

A radar knows nothing about team style, tactical role, league strength or injuries. A winger in a counter-attacking side will usually record fewer touches and passes than a winger in a possession side, regardless of individual quality. A player moving between leagues may look different on the same radar because the peer group and the playing environment have changed.

Combine radar reading with basic context from match data: minutes played, positions played, team results and competition. Comparing a player's radar with their match-by-match statistics is also a quick check that the season-level shape is not driven by a handful of unusual games.

Common Mistakes When Reading Radars

• Comparing two radars built on different peer groups as if they were directly comparable.
• Treating a larger filled area as proof of a better player.
• Ignoring the minutes threshold and the size of the sample.
• Missing inverted metrics and reading "bad" spokes as strengths.
• Judging a player on a template designed for a different role.
• Forgetting that percentiles are rankings, not raw quantities.

For readers who build their own comparisons, the per-player statistics on RubiScore are a natural starting point for checking the raw numbers behind any percentile shown on a third-party chart.

Overlaid Radars: Comparing Two Players

Some charts overlay two players on the same radar. This works well when both players were measured against the same peer group, over a similar time span and with similar minutes. In that case, the comparison spoke by spoke is genuinely informative.

Overlaid radars become misleading when one player has far fewer minutes, or when they play in different leagues with different statistical environments. The overlap then shows two different populations, not two players on one scale.

A Quick Checklist

• Identify the peer group, minutes threshold and time period.
• Read the metric labels and confirm they match the role.
• Check whether values are per 90 or possession-adjusted.
• Find any inverted metrics.
• Compare spokes, not areas.
• Look up raw values when percentiles are close or extreme.
• Add team, league and role context before drawing conclusions.

Used this way, a radar chart becomes what it was designed to be: a fast, honest summary that points to the questions worth investigating, rather than a final verdict on a player.