About the talk
In this talk, we’ll explore how we approached the challenge of determining the attack range of NPCs in the latest Assassin’s Creed title. The complexity of animation systems, procedural adjustments, and varied environments made manual measurement unreliable and unscalable. To solve this, we developed a data-driven approach that captures real gameplay animations in a controlled environment, processes the data through rigorous cleaning, and analyzes it using data science techniques. This method allows us to validate and monitor attack ranges across a wide variety of NPCs and animations, ensuring consistency and catching regressions early. The talk walks through the full pipeline—highlighting lessons learned, future opportunities for automation, and why we chose data science over pure machine learning.
Takeaway
– Scaling validation across large content sets Learn how to move beyond manual data entry by building a scalable, automated system to validate gameplay behaviors across hundreds of assets – saving time while improving consistency.
– Designing reproducible environments for gameplay data collection See how creating a controlled, testable world can help teams gather high – quality animation data that reflects real gameplay conditions, enabling more reliable analysis.
– Choosing data science over machine learning – on purpose Understand when simpler, interpretable data science methods are more effective than complex models, especially when clarity, validation, and team collaboration are priorities.
– Building a pipeline for continuous monitoring and regression detection Discover how to implement a lightweight system that flags unexpected changes in gameplay behavior, helping teams catch bugs early and maintain design intent over time.
Experience level needed: Intermediate