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SET

Data Pilot

Surface Labels

Instrumentation-first Experiment design Lower decision friction Early anomaly detection Live optimization
SET character

Data Pilot

A game should reliably produce the intended experience, and decisions should align through observation and verification.

Core Traits

SET treats development not as art by gut feel, but as a repeatable loop of testable hypotheses. Rather than arguing forever, they first define what counts as success (success criteria), what counts as failure (guardrails), and which signals to watch (metrics/logs).
Then they run experiments and learn fast. Teams with SET can explain "why we changed this", and even in live situations decisions tend to align around evidence instead of taste.
In short, SET focuses on finding and fixing problems quickly with experiments / logs / metrics so the game converges on the target experience.

SET treats planning as a feedback loop: Build, Measure, Learn. They ship small changes, instrument them, and update the next decision from evidence, while keeping guardrails that protect experience quality and trust.

For SET, the core of a game is controllability: making the intended experience actually happen. They don't deny the value of emotion, presentation, or lore, but they try to turn them into something observable and measurable in real play.
A strong SET doesn't worship metric optimization; they act as a cockpit that keeps speed and alignment without damaging the qualitative experience.

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Core Values

North Star

A game should reliably produce the intended experience, and decisions should align through observation and verification.

Situational Behavior

  • When requirements are vague, you first define what success looks like and what guardrails must not break, then you make the change measurable.
  • In technical trade-offs, you value observability; if you cannot explain a change in production, you cannot improve it under pressure.
  • Under schedule pressure, you prefer small, verifiable changes with rollout control (A/B, staged release) instead of a risky rewrite.
  • When feedback conflicts, you segment players and contexts; the same complaint can have different causes across cohorts.

Operational Style

  • You run balance as a hypothesis loop: measure, learn, adjust, while keeping qualitative experience and trust as guardrails.
  • You iterate fast but insist on minimal logging/metrics so the team can converge with evidence instead of debate.
  • In live ops, you track trust lines (fairness, monetization perception, UX) alongside retention and economy metrics.
  • You treat rollback/hotfix plans and monitoring as part of release quality, not an afterthought.
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Strengths

  • Lower the cost of decisions. You reduce time spent arguing by gut feel and run fast experiments to find an aligned direction.
  • Strong under live pressure. You detect anomalies early and iterate quickly through the cause-response loop.
  • Shines in: productization after a Vertical Slice, Polishing, live service operations/optimization, and improvements to events/economy/matchmaking.

Trade-offs

  • If your attention locks onto what's measurable, qualitative experience (emotional arc, atmosphere, memorable moments) can be undervalued.
  • Optimizing short-term metrics can dilute long-term identity or step on trust lines (monetization/matching/pay-to-win perception).

Team Chemistry

  • When working with Data Pilot (SET), align that metrics are a dashboard, not the goal. The north star (intended experience) comes first; metrics are tools to read the state.
  • With Emotional Director (CDT)/Feel Artist (CET), rather than directly mapping qualitative goals into metrics, reduce friction by designing indirect metrics (learning, completion, return) alongside user research.
  • With System Alchemist (SDM)/Meta Gardener (SEM), ops chemistry is strong: observe the health of emergence/meta, and adjust guardrails when risk signals appear.

Representative Games

Lost Ark

Lost Ark

FINAL FANTASY XIV Online

FINAL FANTASY XIV Online

Black Desert

Black Desert

New World

New World

Apex Legends

Apex Legends

PUBG: BATTLEGROUNDS

PUBG: BATTLEGROUNDS

References

Work Link
Hunicke, R., LeBlanc, M., & Zubek, R. (2004). MDA: A Formal Approach to Game Design and Game Research. https://aaai.org/papers/ws04-04-001-mda-a-formal-approach-to-game-design-and-game-research/
Juul, J. (2002). The Open and the Closed: Games of Emergence and Games of Progression. https://www.jesperjuul.net/text/openandtheclosed.html
Juul (DiGRA DOI record) https://dl.digra.org/index.php/dl/article/view/214
Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. https://doi.org/10.1037/0022-3514.76.6.972
Denisova, A., et al. (2024). Towards Democratisation of Games User Research. https://doi.org/10.1145/3677108
Isbister, K., & Hodent, C. (Eds.). (2018). Game Usability: Advice from the Experts for Advancing UX Strategy and Practice in Videogames. https://global.oup.com/academic/product/game-usability-9780198794844