Deep Learning Techniques for Explainable Resource Scales in Collectible Card Games

A Conservative Metric of Power Creep

“…The concept of associating a score to game pieces from which to rank them is not new (Chen et al, 2018; Fancher, 2015; Karsten, 2015; Zuin and Veloso, 2019; Zuin et al, 2020). While selecting cards by how often they win may identify powerful ones, it may be insufficient to address power creep in a game as a whole (Chen et al, 2018; Fancher, 2015; Karsten, 2015).…”

“…Thus, win-ratio is insufficient to encapsulate power creep for a game as a whole. Zuin and colleagues take a fundamentally different approach by attempting to tabulate the resource cost for a card’s effect (Zuin and Veloso, 2019; Zuin et al, 2020). Conceptually, comparing the resource cost for an effect could allow one to check to see if the cost has changed over time.…”