This paper focuses on the empirical derivation of regret bounds for mobile systems that can vary their locations within a spatiotemporally varying environment in order to maximize performance. In particular, the paper focuses on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including a maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches.