Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.