Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction

Yu Zhao, Rennong Yang, Guillaume Chevalier, Rajiv C. Shah, Rob Romijnders

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Scopus)
1 Downloads (Pure)


Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories.

Original languageEnglish
Pages (from-to)266-272
Number of pages7
JournalOptik : International Journal for Light and Electron Optics
Publication statusPublished - 1 Apr 2018


  • Basketball trajectory
  • Bidiretional LSTM
  • Classification and prediction
  • Mixture density network
  • SportVu


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