What if competitive games (such as Rocket League) had a Stockfish-like accuracy system? [D]

I was wondering why dynamic competitive games like Rocket League don't have a decision-quality engine like chess has with Stockfish. Something that doesn't just measure your boost usage or speed, but actually evaluates whether your positioning or challenge was the mathematically optimal play.

I initially drafted a naive proposal based on slicing replays into 5-second physical rollouts in the cloud to test decision trees. However, after using AI models to strictly critique the physics and compute feasibility, Machine Learning seems to offer a much more viable path:

Decision Quality: Evaluates plays using Offline RL (Trajectory Transformers & Implicit Q-Learning) over a Sequential POMDP. Instead of chaotic physics simulations, it calculates decision probability based on pro dataset distributions.

Anti-Cheat & Smurf Detection: Uses frequency spectrum analysis (FFT) on input signals and kinematic limits (4th derivative/Snap) to catch bot scripts, along with NLL divergence to mathematically flag smurfs.

Disclaimer: I want to be 100% clear that I am not looking for any personal credit or clout for this. As a 1st-semester student, I don't even have the technical background to understand or build the advanced machine learning model proposed by the AI. I just thought this could be fascinating and wanted to share it with the community in case it sparks interesting discussions or research for the future.

You can read the full breakdown here: https://github.com/Pnlw/esports-performance-evaluation