Radar Plots Must Die
A radar chart draws each of several independent metrics as a spoke and closes the points into a polygon. It compresses many numbers into one memorable shape, which is why it shows up whenever a team hands multi-attribute results to an executive: a causal experiment with several attributes, claims, or segments tested at once. The problem: the shape a reader perceives depends more on the analyst's arbitrary choices, spoke order, angular spacing, than on the numbers themselves.
For a buyer reporting causal effects across several attributes, the choice comes down to two chart families: a compressed polygon, or a linear, order-independent encoding such as bars or distributions on a shared scale. This isn't cosmetic. A polygon can amplify or suppress an effect purely through chart geometry, not the underlying estimate, and a leadership team can greenlight a claim, a price, or an audience based on a distorted read rather than the causal result.
What the polygon actually encodes
A radar plot maps a vector of percentiles onto a closed polygon in two dimensions. What the reader actually takes in, area, aspect ratio, pointiness, balance, maps back to nothing true about the underlying measurements.
Two problems compound:
- Spoke order carries no inherent meaning. Independent metrics don't come with a built-in sequence, yet a radar is inherently spatial, and spatial layouts push readers toward spatial inference: two adjacent spokes read as related, even though that proximity is only a layout choice.
- The radial encoding distorts magnitude through the polygon's own geometry. The radial axis encodes each percentile linearly, so an 80th-percentile spoke runs twice as long as a 40th-percentile spoke. But the polygon's area, what a reader actually perceives, follows a different rule.
For a polygon with n spokes at equal angular steps:
A = (1/2) * sin(2π/n) * Σ(r_i * r_(i+1))
Where: A = polygon area, n = number of spokes, r_i = the length (percentile value) of spoke iEvery term in that sum is a product of adjacent spoke values: each spoke's visual weight is proportional to its own percentile times the sum of its neighbors' percentiles, not to its percentile alone. A high value flanked by other high values gets visually amplified; a low value flanked by other low values gets visually suppressed beyond what its percentile would suggest. In one worked example from the source dataset, two metrics landed at nearly identical percentiles, but the polygon suppressed one of them by 11.6 percentage points of area relative to the other, purely because of which spokes sat next to it.
A third cost stacks on top: comparing two non-adjacent spokes requires mentally rotating one value onto the other before judging which is larger, and that gets more expensive as the spokes sit farther apart on the wheel.
Why linear position beats angle and area
None of this argues against polar coordinates generally. A wind rose, an hourly clock-face plot, a shot map broken out by attack angle: each belongs on a circle, since the radial axis stands for an actual direction or time, so adjacency on that circle reflects a real relationship in the data. The problem is specific to closing a polygon over independent metrics with no natural angular structure.
The visual-perception literature is direct on what to use instead: positions along a single quantitative scale, laid out linearly on one axis, are the easiest encoding for people to compare accurately. Length on a shared baseline outperforms angle, area, and radial position, which rank near the bottom of the same accuracy scale (Cleveland & McGill, Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods, Journal of the American Statistical Association).
A percentile bar strip follows directly: each metric gets a horizontal bar on a shared axis, named on the left, extending to its percentile on the right. Run the radar's failure modes back through this design and each one disappears. Reordering the rows changes nothing, since each row's encoding is (label, length), and its apparent size has nothing to do with its neighbors. Length scales linearly: a bar at the 90th percentile runs exactly double one at the 45th, rather than reading as four times heavier because of what sits beside it.
| Failure mode | Radar polygon | Percentile bar strip |
|---|---|---|
| Depends on spoke/row order | Yes, area term binds each value to its neighbors | No, each row is an independent `(label, length)` pair |
| Magnitude scales linearly with the underlying percentile | No, area amplifies or suppresses adjacent values | Yes, a 90th-percentile bar is exactly double a 45th-percentile bar |
| Comparing two non-adjacent items | Requires mental rotation around the wheel | Direct vertical read, same effort as neighboring rows |
The same test explains why other visualizations hold up: shot charts, where the 2D space is the physical court, so coordinates don't need reordering, and area deliberately encodes density rather than emerging incidentally from a closed shape; heatmaps, where rink or field coordinates are the chart's real space. The rule: a chart's axes have to mean something real. Field coordinates and literal physical quantities work. An arbitrary angular assignment connected into a polygon does not.
Reading grouped estimates without losing distribution
A bar strip solves ordering and area distortion, but still reduces each metric to a single point estimate. A grouped percentile-ridge layout carries that further: instead of one bar per metric, each row shows the full peer distribution as a shape, with the specific value marked as a dot inside it. Groups of related metrics are labeled, with each group's mean percentile shown at the header. This keeps everything that makes the bar strip work, no polygon, no adjacency effect, linear encoding, while adding what a single-number chart drops: whether a percentile gap is genuinely extreme inside a long-tailed distribution, or nominal inside a distribution with little real spread.
Where this applies beyond sports analytics
The design problem in football radar charts, several independent quantitative dimensions, no natural ordering, no angular interpretation, is the same problem a causal experiment produces once it estimates effects across several attributes. Subconscious's causal experiments return per-attribute effect estimates with uncertainty across many independent dimensions: which claim moved intent, which price point changed conversion, which audience segment responded differently. Handing those estimates to a decision-maker as a compressed polygon risks the same failure: an attribute's visual weight depends on its neighbors, not its estimated effect. Reporting effects on a shared linear scale, with confidence intervals attached to each bar, keeps the chart's geometry from editorializing a result the estimate doesn't support.
This is a data-visualization design principle, not a Subconscious product or shipped feature. The specific case above, and its 11.6-percentage-point distortion, comes from sports-analytics data and generalizes as a chart-design argument. Applying it to causal effect estimates means keeping the display linear and order-independent; it does not mean the underlying experiment design or effect estimates changed. How a result is estimated and how it is displayed are separate problems, and treating a distorted chart as new evidence about the underlying effect is exactly the mistake this argument is against.
The practical takeaway for reporting causal results
Before a team sends multi-attribute results into a deck, ask the same questions that apply to a polygon: does the chart's shape depend on attribute order, does one attribute's visual size depend on its neighbors rather than its own estimate, and can a reader compare any two attributes without mentally rotating between them. A bar strip or a grouped-ridge layout on a shared axis answers yes to none of those. A closed polygon over independent attributes answers yes to all three. Teams that want a closer look at how these estimates get produced can review the method Subconscious uses to run its experiments.
Limitations
This argument is drawn from sports-analytics visualization work, not a Subconscious case study or benchmark. Nothing here implies Subconscious sells a composite-score or player-card product, and no accuracy or speed claim attaches to Subconscious from this material. The principle, use a linear, order-independent encoding for independent multi-attribute estimates, applies to how results are displayed; it says nothing about how those estimates were produced or how accurate any specific model is.
