Currently for balance recovery, humans outperform humanoid robots that used hand-designed controllers. This study aims to close this gap by finding control principles which are shared across all recovery strategies. We do this by formulating experiments to test human strategies and quantify criteria for identifying strategies. A minimum jerk control principle is shown to accurately recreate human CoM recovery trajectories. Using this principle, we formulate a Model-Predictive Control (MPC) for the use in floating base systems (eg legged robots). The feasibility of generated motions from the MPC for implementation on the real robot is then validated using an Inverted Pendulum Model. Finally, we demonstrate improved capability over humans by tuning the parameters for time-optimal recovery performance.