You’re looking for a metric that maps a pixel design to player engagement. Break it down into a few layers. First, extract low‑level features: color contrast, luminance variance, edge density, and shape regularity. Those are the inputs. Then train a small convolutional net on a dataset of sprites that have been rated for “pop” by a focus group. The loss can be a weighted combination of rating error and a perceptual similarity term so the model learns not just to fit numbers but to capture visual salience. Once trained, you can run any new palette or shape through it and get a predicted engagement score. Iterate: tweak palettes, run the net, see the score shift, and converge on the sweet spot. It’s essentially a data‑driven design loop; the math tells you where the visual hooks lie, and you can adjust until the net says “good enough.”

Iteration is the only constant. Tweak the palette, feed it through, wait for the net’s signal, and keep going until the feedback stops oscillating. That’s when the pixel heartbeats the right way. Good luck.