Groups: Putting Data in Context

Making Comparisons That Matter

Sparta Science helps organizations understand the relationship between health and movement. Clients range from college athletics programs to the US Military. A core problem: all performance data was benchmarked against the full user population, which made comparisons meaningless. Stacking a veteran soldier's scores against a college athlete's produced no useful insight. I designed Groups, a feature for iPad and macOS that lets organizations categorize users by meaningful characteristics (age, sports position, occupation, injury history) and benchmark individuals against their actual peer group. Beta clients responded positively, follow-up testing showed a measurable increase in clarity and comprehension, and the visualization pattern I developed sparked conversations about becoming Sparta's new default interface.

How might we visually contextualize performance metrics for groups?

Understanding the Need

Major clients like the US Military and professional sports teams wanted performance context that actually made sense. Sparta's data scientists were already categorizing user data. The data was there, but the interface wasn't built to surface it. Due to HIPAA, I couldn't gather direct user feedback. Instead I worked closely with sales and customer success teams to interpret feedback from organization administrators and shape design requirements. Key objectives:

Enable comparisons both within specific groups and against a global population
Make it easy to switch between views without clutter or confusion
Ensure group comparisons were easy to interpret and provided meaningful context

Designing the Comparison Interface

To give users a way to switch between group views, I modified the existing dashboard to add a dropdown. Early designs used familiar formats: bell curves, bar charts, line graphs. UserBob.com testing surfaced a consistent problem: users struggled to understand score changes, interpret metadata, and read bell curves accurately. I went back to the drawing board with stakeholders and iterated toward a better approach.

From Bell Curves to Box Plots

Working closely with the data science team, I landed on a modified box and whisker plot that showed individual scores in context of both their peer group and the global population simultaneously. The final visualization used three layers:

A light background line representing the entire Sparta Science dataset, scaled 0–100
A blue line showing the distribution within the specific group
A white circle indicating the individual's actual score

The layered approach gave users a clear spatial reference for where an individual sits, without requiring them to interpret statistical terminology. Beta clients responded positively, follow-up testing confirmed improved clarity and comprehension, and the design's success opened conversations about extending this visualization pattern across Sparta's default interface. To ensure accessibility, I prioritized color contrast and screen reader compatibility throughout.

Groups: Putting Data in Context

Making Comparisons That Matter

How might we visually contextualize performance metrics for groups?

Understanding the Need

Designing the Comparison Interface

From Bell Curves to Box Plots

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