Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

TL;DR

This paper introduces a deep learning model combining bidirectional LSTM and mixture density networks to predict and generate basketball trajectories, aiding coaching decisions with improved accuracy and sample diversity.

Contribution

The novel integration of BLSTM and MDN effectively predicts and generates basketball trajectories, outperforming existing models in accuracy and convergence speed.

Findings

Model outperforms others in hit-or-miss classification accuracy.

Generated trajectories closely match real NBA data.

Model converges faster than comparable approaches.

Abstract

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.