product
Primarily theorycrafters and optimizer players who min-max Uma Musume builds, compare skill loadouts across race scenarios, and want data-driven decisions before spending in-game resources. They're comfortable with dense data, simulation parameters, and frame-level race analysis.
Secondarily, casual players who want a quick sense of how a build performs. The UI shouldn't intimidate newcomers, but it never dumbs down for them either. Progressive disclosure bridges the gap: power tools are always reachable, beginner-friendly defaults are always present.
Context: users arrive with a specific question ("Is this skill worth equipping?", "Which strategy wins on this course?") and want an answer fast. Sessions are short and goal-directed, not exploratory browsing.
Torena Sim is a race and skill simulation toolkit for Uma Musume: Pretty Derby (Global server). It lets players configure race scenarios, run repeatable simulations, compare skill loadouts, and inspect velocity/distance/stamina trends across many runs.
Success looks like: a player can set up a comparison, run it, and read the answer in under a minute. The tool replaces guesswork and in-game trial runs with reliable, reproducible data.
Precise, Playful, Approachable.
The voice is a knowledgeable friend who happens to love spreadsheets. Numbers are exact, labels are clear, but the tone never feels sterile or corporate. The warm parchment aesthetic and racing-program inspiration give the tool a physical, tactile quality that softens the analytical surface. It takes the data seriously and itself lightly.
- Generic SaaS dashboards. No bland card grids, hero-metric templates, navy-and-white corporate surfaces. This is a fan tool with character, not an enterprise product.
- Flashy gacha game UI. No neon glows, particle effects, or overwhelming visual noise borrowed from mobile game interfaces. The game's aesthetic is a reference point for warmth and personality, not for sensory overload.
- Raw spreadsheet dumps. No undesigned data tables with zero visual hierarchy. Every screen has a clear focal point and reading order.
- Game wiki aesthetic. No cluttered, ad-laden, poorly organized wiki pages. Information architecture is deliberate, not accumulated.
- Dense but never overwhelming. Pack information tightly, but use hierarchy, spacing rhythm, and tonal contrast so nothing feels cluttered. Every element has breathing room proportional to its importance.
- One-glance insight. The most important answer on any screen is visible without interaction. If the user has to click, scroll, or hover to find the primary result, the layout has failed.
- Playful precision. Exact numbers and data, presented with personality and warmth. Monospaced stat values sit next to friendly labels. The racing-program metaphor keeps things grounded and tactile.
- Respect the user's expertise. No hand-holding for power users. Defaults are smart, but every parameter is accessible. Progressive disclosure for newcomers: guided entry points that open into full control.
- Show, don't tell. Visualize data instead of describing it. Charts over paragraphs, inline previews over separate pages, color-coded comparisons over raw number columns.
- WCAG AA compliance (4.5:1 contrast minimum for text, 3:1 for UI components).
- Full keyboard navigation for all interactive elements.
- Screen reader support for simulation results and data tables.
- Japanese text (Uma names, skill descriptions) rendered with proper CJK font support via Noto Sans JP.
- Color-blind-safe chart palettes: never rely on color alone to distinguish data series.