Keep in mind that the pareto front is not a two-dimensional line, but a surface in a some high dimensional vector space. In every game there are many, many aspects to min/max. As others pointed out even Mario Kart doesn't boil down to speed and acceleration.
In a sufficiently complex game every build is on the pareto front as it optimizes some specific cost function.
I was refering here to playing competitively, that is, playing with the only goal of winning. Of course, it is perfectly acceptable to play for style or to manage a podium with the worst configuration or anything you fancy [].
However, if your one and only goal is winning I suspect that the high dimensional vector space will end up not looking so high dimensional once you account for the correlations between the different features you use. This is already clear from the very strong correlation between speed and accelaration.
[]I myself have played MK64 a lot and sometimes the goal was simply to see the world burn, standing on a corner with a shell waiting for the what would've been the winner of the race. Fond memories.
Even if you're only optimizing for race times, in the case of Mario Kart, the choice of track will have a huge impact on the optimal kart selection. Tool-assisted speedruns pick different karts for different tracks.
The min/max cost functions make a lot of sense(easiest to visualize). It also can do a lot when you do multi-objective optimization. But I have always wondered how you go about evaluating other cost functions. I mean mathematically the concept is intuitive. i.e. just swap it for a quadratic or exponential, but which cost functions are useful in the real world?
In a sufficiently complex game every build is on the pareto front as it optimizes some specific cost function.