  Identifying significant shots in a rally is important for evaluating players'
performance in badminton matches. While there are several studies that have
quantified player performance in other sports, analyzing badminton data is
remained untouched. In this paper, we introduce a badminton language to fully
describe the process of the shot and propose a deep learning model composed of
a novel short-term extractor and a long-term encoder for capturing a
shot-by-shot sequence in a badminton rally by framing the problem as predicting
a rally result. Our model incorporates an attention mechanism to enable the
transparency of the action sequence to the rally result, which is essential for
badminton experts to gain interpretable predictions. Experimental evaluation
based on a real-world dataset demonstrates that our proposed model outperforms
the strong baselines. The source code is publicly available at
https://github.com/yao0510/Shot-Influence.
