You didn't do any meaningful inference. The "noticings" you made are meaningless. I don't want to call them observations since I will use this term formally in the following example... something you could/should have done.

What you need is a vector regression. Assumptions in addition to mean independence, IID draws, etc etc: all sequences of card draws within the 30 element subset, (each game drawing an undetermined number of cards per game) are equiprobable within a constant number of cards drawn.

There are M=535 unique cards in the complete Hearthstone collection. Your data set consists of N observations, which are each a vector of 30 elements. That vector of 30 elements will each have been recorded with a sequence of wins and losses (equivalently 0's and 1's), or alternatively a p.hat, an estimator for probability of victory of that deck.

The representation of that vector within your vector regression is a linear combination of each unique card in your 30 elements and all permutations of unique cards (not combinations, since multiple copies of a card also have interaction effects).

Main model is p.hat regressed on the 535 unique cards as exogenous variables + every permutation of 535 cards up to 30 interactions. Use some statistical software to do the regression (will require some computing power) and obtain coefficient estimates.

Rank the first 535 coefficient estimates. Each coefficient estimate is an unbiased and consistent estimator for that corresponding card's marginal effect on probability of victory.

Discard interaction effect coefficient estimates, but do include them in design matrix.

Discard non-significant coefficient estimates.

Rank remaining cards, and denote the ones that don't have statistically significant coefficients. Apply subjective knowledge to ONLY those.

Finally, apply nominal values to the 535 cards based on this inference combined with subjective knowledge ONLY for cards (exogenous variables) without statistically significant coefficients.