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The increasing demand for analyzing the insights in sports has stimulated aline of productive studies from a variety of perspectives, e.g., health statemonitoring, outcome prediction. In this paper, we focus on objectively judgingwhat and where to return strokes, which is still unexplored in turn-basedsports. By formulating stroke forecasting as a sequence prediction task,existing works can tackle the problem but fail to model information based onthe characteristics of badminton. To address these limitations, we propose anovel Position-aware Fusion of Rally Progress and Player Styles framework(ShuttleNet) that incorporates rally progress and information of the players bytwo modified encoder-decoder extractors. Moreover, we design a fusion networkto integrate rally contexts and contexts of the players by conditioning oninformation dependency and different positions. Extensive experiments on thebadminton dataset demonstrate that ShuttleNet significantly outperforms thestate-of-the-art methods and also empirically validates the feasibility of eachcomponent in ShuttleNet. On top of that, we provide an analysis scenario forthe stroke forecasting problem.

ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton