Textbook framing: A baseball decision almost never depends on one knob. A pitcher can gain a little velocity, but if release consistency drops the net run value can still fall. A hitter can improve attack angle, but if contact depth shifts too far out front the quality of contact may worsen against certain pitch families. Multivariable functions let us represent this coordinated reality directly, with each input carrying a clear baseball meaning and each output tied to a decision target such as expected runs allowed, hard hit probability, or chase rate. This matters for communication because coaches need to hear which combinations are useful, not just whether one variable has a positive trend on average. It also matters for model governance because feasibility limits, player specific ranges, and data support can be encoded into the domain from the start. The result is a stronger analysis loop where mathematical objects describe practical options, tradeoffs, and risk in language that can guide bullpens, cages, and game planning. Before optimizing anything, list which inputs the athlete can actually move together in one week of work—that list is your feasible region in plain English.