Implicit in a range like this one is that you are facing 'maximally exploitative opponents'. Since the game is zero-sum, over enough games the expected value of such a strategy is 0. However, since that expected value is against a maximally exploitative opponent any deviations of the opponent's strategy lead to an increase in your expected value. This is the game theory approach (GTO).

On the other hand, you can try to tailor your strategy to be maximally exploitative of the other players at the table, but doing this can be hard since often it can take a large number of hands to gauge what kind of player somebody is. On the other hand, GTO strategies are naive in the sense that you don't need to know anything about your opponents to guarantee that you Breakeven.

The data was generated in a Nash equilibrium with the sim playing against itself. So the opponent ranges are the ranges you see in the app.

its probably nash equilibrium ranges. so you break even if your opponent plays the equilibrium and are ahead if your opponent deviates. however, if you know your opponents ranges then you are leaving money on the table by using a nash strategy.

also, it's a bit more complex because its not heads up so i don't think there is a proper equilibrium. but i've seen a lot of preflop ranges that for these small stacks that claim to be solved using computers. i guess with multiway they use a lot of abstractions to make the problem simpler.