Balance Analytics

Advanced · 10 min read · By Chitmunk Team · Updated May 11, 2026

Balance Analytics is Chitmunk's data-driven dashboard for analyzing the content and balance of your card game. It reads directly from your project's CSV data and surfaces patterns, outliers, and potential balance issues that are hard to spot by reading cards one at a time.

Accessing the Analytics Dashboard

Open Balance Analytics from the Game Home dashboard sidebar — look for Balance Lab in the game section, next to Playtest and AI Image Studio. The dashboard analyzes your current project's CSV data automatically.

The dashboard has three tabs:

1. Health Report — an overall grade, a snapshot of your cost curve and type balance, and your most critical findings.
2. Deep Dive — expandable sections covering cost curve, type balance, over/under-powered cards, and keyword frequency. Duplicate detection, correlation heatmaps, and keyword insights all live here.
3. Simulator — draw probability and Monte Carlo hand simulation for your deck.

A Sensitivity control (Low/Medium/High) in the toolbar tunes how eagerly the dashboard flags issues, and Remap Columns lets you correct which CSV columns it treats as cost, type, stat, or keyword data.

Health Report

The Health Report tab opens with an overall letter-grade health score and a plain-language headline, then breaks it down:

• Snapshot: A cost-curve histogram and a card-type donut chart, so you can see distribution shape at a glance.
• Design Space Coverage: A heatmap of how your cards cover the space you've defined.
• Critical Findings: The highest-severity insights from the full analysis, so you see the biggest issues first without digging into Deep Dive.

Keyword Frequency Analysis

Inside Deep Dive's "What keywords define the game?" section, Chitmunk counts how often each tag or keyword appears across your cards, using a column you've mapped as tags/keywords — it doesn't free-text-parse full card descriptions.

What It Shows

• Frequency bar chart: Each keyword with its card count and percentage — hover a bar for the exact numbers.
• Co-occurrence chart: Which keyword pairs show up together often (or never), useful for spotting missing synergy support.

Using Keyword Data

Keyword frequency tells you which mechanics dominate your design. If a keyword appears on 60% of cards, your game may over-rely on it as a resource. If a keyword only shows up on a couple of cards, it may not have enough support to function as a viable strategy — the Insights feed flags both cases automatically.

Duplicate and Near-Duplicate Detection

Duplicate detection runs on your mapped stat columns, not raw card text, and shows up as an insight inside Deep Dive rather than its own tab.

• Exact duplicates: Cards with identical values across every mapped stat. These are almost always data entry errors (copied rows that weren't updated).
• Near-duplicates: Cards whose stat profiles are close enough to flag, using a threshold Chitmunk adjusts automatically to your data's value range — there's no manual similarity slider to configure.

Reviewing Flagged Cards

Click the duplicates insight card to expand it in place and see the affected cards and their values — there's no separate jump to a spreadsheet editor. The Sensitivity control in the toolbar (Low/Medium/High) is the closest thing to a threshold dial, and it affects every kind of insight, not just duplicates.

Balance Heatmaps

Deep Dive auto-generates its charts from the column roles you've set (cost, type, stat, keywords) rather than a manual "pick any two columns" picker.

What You Get

• Cost vs. stat efficiency scatter: Your cost column plotted against each mapped stat, with a regression line and outlier cards called out — high-cost cards should land above the line, low-cost cards below it.
• Correlation heatmap: A full pairwise correlation matrix across all your numeric columns, so you can spot stats that move suspiciously in lockstep.
• Type balance donut: How many cards fall into each type or category, with an entropy score flagging when one type dominates.

If you want a different pairing analyzed, use Remap Columns to change which column plays which role (cost, stat, type) rather than picking chart axes directly.

Interpreting Insights

The Insights tab runs a set of automated checks and surfaces findings in plain language:

• Outlier cards: Cards with values more than 2 standard deviations from the mean in any numeric column. These may be intentionally powerful (rares, boss cards) or accidental errors.
• Underrepresented keywords: Keywords that appear fewer than 3 times in the entire set — too few to form a consistent strategy around.
• Category imbalance: If your cards have a Type/Faction column, flags any category with fewer than 15% of total cards as potentially underrepresented.
• Numeric column anomalies: Columns where the maximum value is more than 5x the mean (sharp outliers), or where many cells are empty (possible missing data).
• Power curve assessment: If a cost and power column are detected, grades the correlation as Strong, Moderate, or Weak. Weak correlation suggests cost and power are not well-calibrated.

Drill-Down Navigation

Insight cards are the drill-down mechanism in Balance Lab:

• Click an insight card to expand it in place and see the specific cards it's about, with the relevant columns called out.
• Use the tab bar to jump between Health Report, Deep Dive, and Simulator — each insight is tagged with a related page, so the highest-severity ones on Health Report point you toward the right Deep Dive section.

To actually edit a flagged card's data, switch to the editor's Data Mode yourself — Balance Lab doesn't jump you there automatically.

Tips for Using Analytics Effectively

• Run analytics at milestones: Analytics is most valuable after completing a full draft (50+ cards), not during early prototyping. Run it at version checkpoints to track how the design evolves.
• Define your intended power curve first: Before interpreting heatmaps, write down your intended cost-to-power ratio. Is a cost-2 card supposed to deal 3 damage? Having that baseline makes the scatter plot meaningful.
• Use near-duplicate detection before playtesting: Catching accidental duplicates before a playtest saves time. Run this check every time you add a batch of new cards.
• Track your game's health across versions: Use the toolbar's Export button to download a JSON snapshot of your current health score, insights, and correlations, and compare it to a snapshot from an earlier version. Seeing how the numbers shift over iterations reveals whether you're refining or drifting from your original design intent.
• Analytics works on one card type at a time: Balance Lab analyzes your project's active card type's CSV data. If your project has multiple card types, switch the active card in the editor before opening Balance Lab to analyze a different one.