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

Jo\~ao Moreira; Tom\'as Alves

arxiv: 2512.09802 · v3 · submitted 2025-12-10 · 💻 cs.HC

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

Tom\'as Alves , Jo\~ao Moreira This is my paper

Pith reviewed 2026-05-16 23:27 UTC · model grok-4.3

classification 💻 cs.HC

keywords dashboard designMagic: The Gatheringgame analyticsvisualization comprehensionuser testingCommander formatdata visualization

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The pith

Magic: The Gathering players understand gameplay data better with simple outcome-focused charts than with complex visualizations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes the early design of a dashboard for Commander-format Magic: The Gathering match data. It reports results from a user-task analysis and a structured comprehension test showing that players value metrics tied directly to outcomes and perform better with familiar chart types. The work identifies localized views, customization options, and progressive disclosure as practical requirements. These findings matter because they supply concrete rules for turning raw game logs into tools that players can actually use for post-match review and improvement. If the patterns hold, similar dashboards could raise the quality of data-driven decisions across other competitive games.

Core claim

The paper establishes that, in a tested dashboard prototype for Magic: The Gathering Commander data, players assign higher priority to contextually relevant outcome-driven metrics and achieve better comprehension with canonical charts such as heatmaps and line charts than with scatterplots or icicle plots. The evaluation further shows that localized data views, user-driven customization, and progressive disclosure improve perceived usefulness and reduce cognitive load when players analyze their own matches.

What carries the argument

Structured user test that measures comprehension and preference for specific metrics and visualization types within a proposed dashboard design.

Load-bearing premise

The small structured test sample and chosen analysis tasks accurately represent the needs and preferences of the broader Magic: The Gathering player population.

What would settle it

Re-running the same comprehension test with a larger, demographically broader sample of players and observing that a majority now prefers scatterplots or icicle plots, or that peripheral metrics receive equal or higher priority.

Figures

Figures reproduced from arXiv: 2512.09802 by Jo\~ao Moreira, Tom\'as Alves.

**Figure 2.** Figure 2: Screenshot of Archidekt’s data visualizations for collections. We found two resources that are aligned our objectives. First, Pacheco’s Commander dashboard (www.github.com/gabi-p acheco/MTG-Commander-Analysis) analyzes 53 Commander matches played between four players from June 2023 to August 2024 ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Screenshots of the dashboard developed by Pacheco. (a) Deck dashboard. (b) Player dashboard. (c) Playgroup dashboard [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Screenshots of the dashboards developed by Playgroup.gg. 4.1. Learn As we mentioned, we focus on Commander (https://magi c.wizards.com/en/formats/commander) (formerly known as Elder Dragon Highlander or EDH), one of the most popular and distinctive formats in MTG. Unlike competitive one-onone formats, Commander emphasizes creativity, social interaction, and long-term strategy, making it particularly appe… view at source ↗

**Figure 5.** Figure 5: Mockup of the proposed dashboard for Commander data analysis for Playgroup.gg. This suggests that scatterplots supported broad pattern recognition but demanded higher cognitive effort for precise lookups. The icicle plot showed a similar trend, with an overall mean accuracy of 75.30%. Participants were able to identify structural features such as color groupings and relative saltiest categories (above 75%… view at source ↗

read the original abstract

This paper presents the initial stages of a design study to develop a dashboard for visualizing gameplay data in the Commander format of Magic: The Gathering. We conducted a user-task analysis to identify requirements for such a dashboard, followed by a design proposal addressing players' needs and common analysis tasks. We then carried out a structured user test to evaluate comprehension and preferences. Results show that players prioritize contextually relevant, outcome-driven metrics over peripheral ones, and that canonical charts (e.g., heatmaps, line charts) support higher comprehension than more complex visualizations like 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 critical as accuracy in effective dashboard design. Overall, this study contributes practical guidelines for data visualization in gaming and broader insights for engagement-driven dashboards.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a straightforward design study that turns up some usable preferences for an MTG Commander dashboard from a user test, but the missing sample details limit how far the findings can be trusted.

read the letter

Hey, the main point on this paper is that the authors ran a user-centered design process for a Magic: The Gathering Commander dashboard and came back with two concrete results from their test: players want metrics that tie directly to game outcomes rather than side stats, and they read familiar charts like heatmaps and line graphs more accurately than scatterplots or icicle plots. They also note that localized views, customization, and progressive disclosure matter for keeping users engaged. That is the actual new content here, since the abstract says these specific priority and comprehension findings are not already in the cited literature for this domain. The work follows the expected steps of task analysis, design proposal, and structured testing, which produces practical guidelines that could help someone building a similar game-data tool. The methods are standard and applied honestly to a niche area, so the incremental contribution is real even if it does not introduce new theory. The soft spot is the user test itself. The abstract gives no participant count, recruitment method, demographics, or check that the chosen tasks cover the full range of analysis needs most players have. If the tested group was small or skewed, the reported ordering of metrics and chart types could be specific to that cohort rather than a general rule. That is the part that needs more evidence before the guidelines can be treated as robust. This paper is for HCI researchers or practitioners working on dashboards for games or other engagement-focused domains. A reader who is actually building something similar would get starting points on what to prioritize; someone looking for broad advances in visualization or HCI will not find them. I would send it to peer review. The core process is clear and the topic has practical value, so referees can request the missing test details and help tighten the claims without starting from scratch.

Referee Report

2 major / 2 minor

Summary. This paper presents the initial stages of a design study for a data dashboard visualizing gameplay data in Magic: The Gathering's Commander format. It describes a user-task analysis to identify requirements, followed by a design proposal and a structured user test evaluating comprehension and preferences for metrics and visualizations. Key results indicate that players prioritize contextually relevant, outcome-driven metrics over peripheral ones, and that canonical charts (e.g., heatmaps, line charts) yield higher comprehension than complex options like scatterplots or icicle plots. The work emphasizes localized views, customization, progressive disclosure, and the importance of adaptability in dashboard design, offering practical guidelines for gaming visualizations.

Significance. If the evaluation results hold under broader scrutiny, the paper contributes practical, user-centered guidelines for data dashboards in recreational gaming domains, underscoring that contextual relevance and customization can be as important as raw accuracy for engagement. It adds to HCI literature on visualization for non-expert users by identifying specific preferences in a popular game format. The preliminary nature limits broader impact, but the focus on outcome-driven metrics and simpler charts provides actionable insights for similar engagement-driven systems.

major comments (2)

[§4 (Structured User Test)] §4 (Structured User Test): The evaluation lacks any description of participant numbers, recruitment method, demographic or skill-level breakdown, exclusion criteria, or statistical analysis. Without these, the reported preferences for outcome-driven metrics and canonical charts over complex visualizations cannot be reliably generalized beyond the tested cohort, undermining the central claims about player priorities.
[§3 (Design Proposal) and §4] §3 (Design Proposal) and §4: The paper does not detail how the chosen analysis tasks were elicited or validated against a broader set of player needs (e.g., via prior survey or observation). This leaves open whether the tested tasks exhaustively cover common dashboard use cases, making the preference ordering potentially specific to the selected tasks rather than a general design principle.

minor comments (2)

[Abstract] The abstract would be strengthened by briefly noting the scale of the user test (e.g., approximate participant count) to give readers an immediate sense of the evidence base.
[Figures] Figure captions and axis labels in the visualization examples could be clarified to explicitly link each chart to the corresponding task and metric it was meant to support.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address each major comment below, indicating the revisions we will make to strengthen the paper while preserving its focus as an initial design study.

read point-by-point responses

Referee: [§4 (Structured User Test)] §4 (Structured User Test): The evaluation lacks any description of participant numbers, recruitment method, demographic or skill-level breakdown, exclusion criteria, or statistical analysis. Without these, the reported preferences for outcome-driven metrics and canonical charts over complex visualizations cannot be reliably generalized beyond the tested cohort, undermining the central claims about player priorities.

Authors: We agree that the current manuscript omits critical methodological details in Section 4. This is a valid point, as the absence of this information limits the ability to assess the scope and reliability of the reported preferences. In the revised version, we will add a dedicated subsection describing the participant recruitment process, the number of participants, demographic and skill-level information, exclusion criteria, and any statistical or qualitative analysis methods employed. We will also explicitly note the preliminary character of the study and avoid implying broad generalizability beyond the tested cohort. revision: yes
Referee: [§3 (Design Proposal) and §4] §3 (Design Proposal) and §4: The paper does not detail how the chosen analysis tasks were elicited or validated against a broader set of player needs (e.g., via prior survey or observation). This leaves open whether the tested tasks exhaustively cover common dashboard use cases, making the preference ordering potentially specific to the selected tasks rather than a general design principle.

Authors: We concur that the process by which the analysis tasks were identified and validated requires clearer exposition. The tasks originated from the user-task analysis phase described at the beginning of the paper, which drew on interviews and observations with Commander players. In the revision, we will expand the relevant portions of Sections 3 and 4 to specify the elicitation methods, how feedback was incorporated, and the steps taken to align the tasks with typical player needs. We will also acknowledge that the task set is not exhaustive and discuss this as a limitation of the initial study. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on independent user testing

full rationale

The paper reports a design study consisting of user-task analysis followed by a structured user test to evaluate comprehension and preferences. No mathematical derivations, equations, fitted parameters, or predictions appear in the provided text. Central claims about metric prioritization and chart-type comprehension are presented as direct outcomes of the user test rather than reductions to prior definitions or self-citations. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results are present. The derivation chain is self-contained against the external benchmark of the conducted user study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical HCI design study with no equations, fitted parameters, or new postulated entities; it rests on standard assumptions about user testing validity.

axioms (1)

domain assumption Structured user tests can reliably reveal player preferences for dashboard metrics and visualizations
Invoked to support the reported results on prioritization and chart comprehension.

pith-pipeline@v0.9.0 · 5440 in / 1150 out tokens · 62220 ms · 2026-05-16T23:27:58.992974+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages

[1]

Magic: The Gathering

[ABRG∗21] ALVINC., BOWLINGM., RIVERS-GREENS., SIGLIND., ALVINL.: Toward a competitive agent framework for Magic: The Gathering.The International FLAIRS Conference Proceedings 34(Apr. 2021).doi:10.32473/flairs.v34i1.128416. 1 [BFM21] BERTRAMT., FÜRNKRANZJ., MÜLLERM.: Predicting hu- man card selection in Magic: The Gathering with contextual preference ranki...

work page doi:10.32473/flairs.v34i1.128416 2021
[2]

Y., KALROA., CHAUD

2 [KAKC18] KAHNGM., ANDREWSP. Y., KALROA., CHAUD. H.: ActiVis: Visual exploration of industry-scale deep neural network mod- els.IEEE Transactions on Visualization and Computer Graphics 24, 1 (2018), 88–97.doi:10.1109/TVCG.2017.2744718. 2 [KBR25] KALLABISL., BERTRAMT., RUPPF.: Deceptive game de- sign? Investigating the impact of visual card style on playe...

work page doi:10.1109/tvcg.2017.2744718 2018
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2 [Ohm23] OHMJ.: From Magic to Gwent.Beyond the Deck: Critical Essays on Magic: The Gathering and Its Influence(2023), 273 –

2 [NGCL18] NOBREC., GEHLENBORGN., COONH., LEXA.: Lineage: Visualizing multivariate clinical data in genealogy graphs.IEEE Trans- actions on Visualization and Computer Graphics 25, 3 (2018), 1543– 1558.doi:10.1101/128579. 2 [Ohm23] OHMJ.: From Magic to Gwent.Beyond the Deck: Critical Essays on Magic: The Gathering and Its Influence(2023), 273 –

work page doi:10.1101/128579 2018
[4]

doi:10.3983/twc.2024.2607. 1 [RLG∗24] RUANS., LIANGZ., GUANQ., GRIFFINP., WENX., LIN Y., WANGY.: VIOLET: Visual analytics for explainable quantum neu- ral networks .IEEE Transactions on Visualization & Computer Graphics 30, 06 (June 2024), 2862–2874.doi:10.1109/TVCG.2024.3388

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Sedlmair, M

2 [SMA∗15] SIGNORETTIA., MARTINSA. I., ALMEIDAN., VIEIRAD., ROSAA. F., COSTAC. M., TEXEIRAA.: Trip 4 All: A gamified app to provide a new way to elderly people to travel.Procedia Computer Science 67(2015), 301–311. 2 [SMM12] SEDLMAIRM., MEYERM., MUNZNERT.: Design study methodology: Reflections from the trenches and the stacks.IEEE Trans- actions on Visual...

work page doi:10.1109/tvcg.2012.213 2015

[1] [1]

Magic: The Gathering

[ABRG∗21] ALVINC., BOWLINGM., RIVERS-GREENS., SIGLIND., ALVINL.: Toward a competitive agent framework for Magic: The Gathering.The International FLAIRS Conference Proceedings 34(Apr. 2021).doi:10.32473/flairs.v34i1.128416. 1 [BFM21] BERTRAMT., FÜRNKRANZJ., MÜLLERM.: Predicting hu- man card selection in Magic: The Gathering with contextual preference ranki...

work page doi:10.32473/flairs.v34i1.128416 2021

[2] [2]

Y., KALROA., CHAUD

2 [KAKC18] KAHNGM., ANDREWSP. Y., KALROA., CHAUD. H.: ActiVis: Visual exploration of industry-scale deep neural network mod- els.IEEE Transactions on Visualization and Computer Graphics 24, 1 (2018), 88–97.doi:10.1109/TVCG.2017.2744718. 2 [KBR25] KALLABISL., BERTRAMT., RUPPF.: Deceptive game de- sign? Investigating the impact of visual card style on playe...

work page doi:10.1109/tvcg.2017.2744718 2018

[3] [3]

2 [Ohm23] OHMJ.: From Magic to Gwent.Beyond the Deck: Critical Essays on Magic: The Gathering and Its Influence(2023), 273 –

2 [NGCL18] NOBREC., GEHLENBORGN., COONH., LEXA.: Lineage: Visualizing multivariate clinical data in genealogy graphs.IEEE Trans- actions on Visualization and Computer Graphics 25, 3 (2018), 1543– 1558.doi:10.1101/128579. 2 [Ohm23] OHMJ.: From Magic to Gwent.Beyond the Deck: Critical Essays on Magic: The Gathering and Its Influence(2023), 273 –

work page doi:10.1101/128579 2018

[4] [4]

doi:10.3983/twc.2024.2607. 1 [RLG∗24] RUANS., LIANGZ., GUANQ., GRIFFINP., WENX., LIN Y., WANGY.: VIOLET: Visual analytics for explainable quantum neu- ral networks .IEEE Transactions on Visualization & Computer Graphics 30, 06 (June 2024), 2862–2874.doi:10.1109/TVCG.2024.3388

work page doi:10.3983/twc.2024.2607 2024

[5] [5]

Sedlmair, M

2 [SMA∗15] SIGNORETTIA., MARTINSA. I., ALMEIDAN., VIEIRAD., ROSAA. F., COSTAC. M., TEXEIRAA.: Trip 4 All: A gamified app to provide a new way to elderly people to travel.Procedia Computer Science 67(2015), 301–311. 2 [SMM12] SEDLMAIRM., MEYERM., MUNZNERT.: Design study methodology: Reflections from the trenches and the stacks.IEEE Trans- actions on Visual...

work page doi:10.1109/tvcg.2012.213 2015