  The application of visual tracking to the performance analysis of sports
players in dynamic competitions is vital for effective coaching. In doubles
matches, coordinated positioning is crucial for maintaining control of the
court and minimizing opponents' scoring opportunities. The analysis of such
teamwork plays a vital role in understanding the dynamics of the game. However,
previous studies have primarily focused on analyzing and assessing singles
players without considering occlusion in broadcast videos. These studies have
relied on discrete representations, which involve the analysis and
representation of specific actions (e.g., strokes) or events that occur during
the game while overlooking the meaningful spatial distribution. In this work,
we present the first annotated drone dataset from top and back views in
badminton doubles and propose a framework to estimate the control area
probability map, which can be used to evaluate teamwork performance. We present
an efficient framework of deep neural networks that enables the calculation of
full probability surfaces. This framework utilizes the embedding of a Gaussian
mixture map of players' positions and employs graph convolution on their poses.
In the experiment, we verify our approach by comparing various baselines and
discovering the correlations between the score and control area. Additionally,
we propose a practical application for assessing optimal positioning to provide
instructions during a game. Our approach offers both visual and quantitative
evaluations of players' movements, thereby providing valuable insights into
doubles teamwork. The dataset and related project code is available at
https://github.com/Ning-D/Drone_BD_ControlArea
