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Player Radar Chart Design (Percentile Benchmarking)

Player EvaluationLevel 2 — Intermediate

What It Is

A player radar chart (spider chart) plots multiple metrics simultaneously as a polygon — the further a point extends from the center on each axis, the better the player on that metric. The key design choice is what the outer and inner boundaries mean. The StatsBomb convention: the outermost ring represents the top 5th percentile in Europe for that metric and position; the innermost represents the bottom 5th percentile. This means a player whose shape fills the radar is elite; a player with minimal color has almost no area where they outperform league-average peers.

Correct Execution

Design requirements: (1) normalize each metric to a percentile rank within position peer group (not all players); (2) set outer ring = 95th percentile, inner ring = 5th percentile; (3) select metrics that are independent (avoid pairs that are near-perfectly correlated); (4) include a mix of attacking and defensive metrics appropriate to the position; (5) display alongside a comparison player using a contrasting color. The radar shape should be immediately readable — "lots of color = complete player; gaps = weaknesses."

Progression Levels

Diagnostic Tree

Coaching Cues

  • "Outermost ring is top 5% in Europe. Innermost is bottom 5%. Lots of color = elite. Gaps = weaknesses." — WFS 2019
  • "The shape tells the story faster than any table."
  • "What are you solving for? That determines the reference frame." — Seth Partnow, 2019

Common Errors

  1. Not normalizing within position group: A center-back with 40 progressive carries looks amazing against all players but mediocre against CB peers.
  2. Mixing per-90 and total metrics on the same radar: A player with high total metrics but low per-90 looks better than they are.
  3. Comparing players who play in different positions on the same radar: Position peer group is the normalization anchor — cross-position radars are meaningless.

Edges

🔑 Hidden Causal Lever

The Same Data With Different Reference Frames Recommends Opposite Actions

Two perfectly valid visualizations of identical data can lead to opposite conclusions depending on the reference frame. A player who shoots above average from mid-range RELATIVE TO THAT LOCATION looks good on a player-vs-location chart (useful for scouting where they're dangerous). But the same shots are below average RELATIVE TO LEAGUE-AVERAGE EFFICIENCY because mid-range shots are inherently less efficient than other shot types (useful for deciding whether to allow those shots).

What most people do
Choose one reference frame and present it as the truth, unaware that a different baseline would tell a different story.
What the best do
Always ask "What question am I answering?" before choosing the reference frame. Explicitly state the baseline in every visualization. When the stakes are high, show both reference frames and explain why they disagree.
Why it's an edge: Analytical disagreements that seem data-driven are often reference-frame disagreements. A scouting department and a coaching staff can look at the same player's shot chart and reach opposite conclusions — both correctly — because they're answering different questions. Making the reference frame explicit eliminates this source of organizational friction.
How to exploit: For every visualization you build, document the reference frame. When presenting to stakeholders, show the alternative frame briefly: "Against location average, he's elite from here. Against league-average efficiency, this zone is still low-value. Both are true — which question are we answering?"
Seth Partnow, StatsBomb Innovation in Football Conference, 2019-10-28. D'Angelo Russell shot chart example showing opposite recommendations from identical data.

Sources

  • Ted Knutson & Siqur Arshad, WFS 2019 StatsBomb presentation, YouTube, 2019-10-02 — described StatsBomb's radar design using 5th/95th percentile rings; showed Benzema 18/19 vs. 19/20 radars and Benzema vs. Firmino comparison; noted box cross percentage as an example of a metric that "doesn't necessarily lead to xG"
  • Seth Partnow, StatsBomb Innovation in Football Conference, YouTube, 2019-10-28 — added visualization reference frame concept: same data with different baselines tells different stories; D'Angelo Russell shot chart example