This thesis identifies and analyzes successful movement strategies for the completion of a complex dynamic task. In the past it has been shown that movement strategies correlate well to performance for simple tasks. Therefore, in this thesis I was motivated to find out if motion based metrics correlated well to performance for more complicated motor tasks. First, the Nintendo Wiimote was verified as a suitable gaming interface enabling gross human motion capture through experimental comparisons with other gaming interfaces and precision sensors. Then, a complex motor task was rendered in an open-source gaming environment. This environment enabled the design of a rhythmic task that could be controlled with the Wiimote while data were simultaneously recorded for later analysis. For the task, success and failure could be explained by high correlation between two motion based performance metrics, mean absolute jerk (MAJ) and average frequency (AVF) per trial. A logistic regression analysis revealed that each subject had a range of MAJ and AVF values for being successful, outside of which they were unsuccessful. Therefore, this thesis identifies motion based performance metrics for a novel motor control task that is significantly difficult to master and the techniques used to identify successful movement strategies can be used for predicting success for other such complex dynamic tasks.