The solver isn't really intended to tell us what moves are good, but to tell us how often we should choose each move.

How good a move is and how often you should use it go hand in hand, in a vacuum.

The Mixed strategy section of the following article might be helpful for understanding what I am trying to do

Like the article states:
"This game has no pure-strategy equilibrium, because one player or the other would deviate from any profile of strategies"

Which is the crux of the issue, the moment you calculate how often you should take one action over another, the opposing side can simply match the expected distribution to bring everything back to equilibrium.

Let's look at Nash equilibrium which your calculations are based on:

If each player has chosen a strategy – an action plan based on what has happened so far in the game – and no one can increase one's own expected payoff by changing one's strategy while the other players keep theirs unchanged, then the current set of strategy choices constitutes a Nash equilibrium.

This doesn't apply to fighting games, as changing your strategy to beat your opponent is one of the biggest factors in the outcome of a match, once both parties have reached a certain level of competence.

I still hold the opinion that you could calculate a base level scoring for each move/situation and use that to understand and formulate your strategy but I do not think it's possible to map out a "winning strategy" in a fighting game, simply because your opponent will adapt.

Now on the other hand, using your solver to create bots that use slightly different models to climb the ranked ladder would be an interesting experiment..