Building a Data Dashboard for Magic: The Gathering: Initial Design Considerations

This paper presents the initial stages of a design study aimed at developing a dashboard to visualize gameplay data of the Commander format from Magic: The Gathering. We conducted a user-task analysis to identify requirements for a data visualization dashboard tailored to the Commander format. Afterwards, we proposed a design for the dashboard leveraging visualizations to address players’ needs and pain points for typical data analysis tasks in the context domain. Then, we followed-up with a structured user test to evaluate players’ comprehension and preferences of data visualizations. Results show that players prioritize contextually relevant, outcome-driven metrics over peripheral ones, and that canonical charts like heatmaps and line charts support higher comprehension than complex ones such as scatterplots or icicle plots. Our findings also highlight the importance of localized views, user customization, and progressive disclosure, emphasizing that adaptability and contextual relevance are as essential as accuracy in effective dashboard design. Our study contributes practical design guidelines for data visualization in gaming contexts and highlights broader implications for engagement-driven dashboards.

💡 Research Summary

This paper reports the early phases of a design study aimed at creating a visualization dashboard for the Commander format of Magic: The Gathering (MTG). Leveraging Sedlmair et al.’s nine‑stage design study framework, the authors progress through the “learn,” “winnow,” and “cast” stages before presenting a concrete design prototype. In the learn phase, they detail the unique mechanics of Commander—100‑card singleton decks, a designated legendary commander that defines color identity, the commander‑damage rule, and the social, multi‑player nature of the format. They also survey existing community tools (e.g., Scryfall, MTGJSON, Archidekt, Playgroup.gg) and identify a gap: while these platforms provide basic statistics and static charts, they lack integrated, exploratory visual analytics for gameplay data such as synergy detection, temporal performance trends, or contextual win‑condition analysis.

During winnow, the team evaluates a range of life‑tracking and game‑logging applications (Gauntlet, Life‑Linked, Mythic Track, etc.) and determines that most capture only generic metrics (damage, win/loss) without exposing richer dimensions like card usage frequency, commander‑specific success rates, or inter‑player dynamics. By interviewing and observing a sample of Commander players, they extract core analytical tasks: (1) assessing win‑condition effectiveness, (2) tracking mana‑curve evolution across turns, (3) visualizing card‑to‑card synergies, (4) comparing deck performance over time, and (5) exploring social interactions such as alliances and rivalries.

In the cast stage, the authors formalize data requirements, proposing a hybrid storage model that combines relational tables for match logs with a graph database for card‑interaction networks. They then derive visualization requirements: outcome‑driven metrics, contextual cues (e.g., per‑turn, per‑player filters), interactive filtering, accessibility‑aware color encodings, and progressive disclosure to manage visual complexity.

The design prototype emphasizes “canonical” chart types—heatmaps for synergy matrices, line charts for turn‑by‑turn resource curves, bar charts for color distribution, and stacked bar charts for rarity breakdowns. More complex visualizations such as scatterplots or icicle plots are relegated to optional layers because a controlled user study (30 Commander players) showed significantly lower comprehension and preference for these forms. The user test measured both objective accuracy on task‑specific questions and subjective preferences via Likert‑scale surveys. Results indicated that heatmaps and line charts yielded the highest accuracy, while participants praised clear labeling, sufficient contrast, and the ability to progressively reveal detailed data. Customizable filters (by commander, player, time window) and a progressive‑disclosure UI were highlighted as critical for maintaining engagement without overwhelming users.

From these findings the authors distill four design guidelines: (1) prioritize outcome‑oriented metrics in chart selection, (2) ensure visual accessibility through contrast and labeling, (3) provide user‑driven filtering and layering for personalized analysis, and (4) employ progressive disclosure to balance depth and simplicity. They argue that these principles are transferable beyond MTG to other trading‑card, board, and e‑sports domains where high‑dimensional, temporally rich data coexist with strong community‑driven analysis needs.

The paper concludes by positioning the prototype as a foundation for a fully deployed Commander dashboard, outlining future work that includes longitudinal field studies, integration of AI‑based predictive models (e.g., win‑probability forecasts), and broader evaluation of the proposed guidelines across diverse gaming contexts.