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Deconstructing Open-World Game Mission Design Formula: A Thematic Analysis Using an Action-Block Framework

Kaijie Xu, Yiwei Zhang, Brian Yang, Clark Verbrugge

Abstract

Open-world missions often rely on repeated formulas, yet designers lack systematic ways to examine pacing, variation, and experiential balance across large portfolios. We introduce the Mission Action Quality Vector (MAQV), a six-dimensional framework-covering combat, exploration, narrative, emotion, problem-solving, and uniqueness-paired with an action block grammar representing missions as gameplay sequences. Using about 2200 missions from 20 AAA titles, we apply LLM-assisted parsing to convert community walkthroughs into structured action sequences and score them with MAQV. An interactive dashboard enables designers to reveal underlying mission formulas. In a mixed-methods study with experienced players and designers, we validate the pipeline's fidelity and the tool's usability, and use thematic analysis to identify recurring design trade-offs, pacing grammars, and systematic differences by quest type and franchise evolution. Our work offers a reproducible analytical workflow, a data-driven visualization tool, and reflective insights to support more balanced, varied mission design at scale.

Deconstructing Open-World Game Mission Design Formula: A Thematic Analysis Using an Action-Block Framework

Abstract

Open-world missions often rely on repeated formulas, yet designers lack systematic ways to examine pacing, variation, and experiential balance across large portfolios. We introduce the Mission Action Quality Vector (MAQV), a six-dimensional framework-covering combat, exploration, narrative, emotion, problem-solving, and uniqueness-paired with an action block grammar representing missions as gameplay sequences. Using about 2200 missions from 20 AAA titles, we apply LLM-assisted parsing to convert community walkthroughs into structured action sequences and score them with MAQV. An interactive dashboard enables designers to reveal underlying mission formulas. In a mixed-methods study with experienced players and designers, we validate the pipeline's fidelity and the tool's usability, and use thematic analysis to identify recurring design trade-offs, pacing grammars, and systematic differences by quest type and franchise evolution. Our work offers a reproducible analytical workflow, a data-driven visualization tool, and reflective insights to support more balanced, varied mission design at scale.
Paper Structure (62 sections, 14 figures, 8 tables)

This paper contains 62 sections, 14 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Game Analytics and Player Behavior Visualization
  4. Quest and Mission Design Literature
  5. Methodological Background: Evaluating Creativity Support Tools and Thematic Analysis
  6. Study 1: Mission Action Quality Vector
  7. Methodology
  8. MAQV Framework
  9. Dataset
  10. Action Block Extraction
  11. Validation Study: Human-Labeled Gold Set
  12. Visualization Results
  13. Study 2: User Study
  14. Methodology
  15. Participants
  16. ...and 47 more sections

Figures (14)

  • Figure 1: End-to-end workflow of our study. Left-top (Data Acquisition & Structuring): community Fandom walkthroughs (text only, via MediaWiki API) are consolidated into a mission dataset and converted by an LLM-assisted parser into normalized action block sequences. Left-bottom (Frameworks & Tools): the MAQV six-dimensional lens (combat, exploration, narrative, emotion, problem-solving, uniqueness) and the action block grammar feed a Mission Analysis Dashboard with Browse/Compare views. Right-top (Quantitative Validation): extraction is assessed against a human-labeled gold set, complemented by participant data-validity checks and usability/utility evaluation (SUS, UEQ-S, SEQ). Right-bottom (Qualitative Insights): participants' exploration & reflection inform Reflexive Thematic Analysis to surface themes, trade-offs, and pacing heuristics.
  • Figure 2: Action view in Browse mode from our implemented tool. Actions are grouped by category; each row shows 0-1 MAQV scores: Uniqueness (U), Combat (C), Narrative (N), Exploration (E), Problem-Solving (P), and Emotion (A), plus a brief description. This figure and all other visualizations in the paper are direct screenshots of the working interface.
  • Figure 3: Quality-flow visualization tracking the six MAQV dimensions across normalized mission progress (not wall-clock time). Peaks highlight intense phases; valleys denote lulls; example shown is from Marvel's Spider-Man 2 (Main Quest: "A Second Chance").
  • Figure 4: Action-timeline visualization showing the ordered sequence of action categories, enabling rapid assessment of pacing and structural rhythm; example shown is from Marvel's Spider-Man 2 (Main Quest: "A Second Chance").
  • Figure 5: Normalized per-game radar charts highlighting each title's internal balance. U=Uniqueness, C=Combat, N=Narrative, E=Exploration, P=Problem-Solving, A=Emotional.
  • ...and 9 more figures