  We extract and use player position time-series data, tagged along with the
action types, to build a competent model for representing team tactics
behavioral patterns and use this representation to predict the outcome of
arbitrary movements. We provide a framework for the useful encoding of short
tactics and space occupations in a more extended sequence of movements or
tactical plans. We investigate game segments during a match in which the team
in possession of the ball regularly attempts to reach a position where they can
take a shot at goal for a single game. A carefully designed and efficient
kernel is employed using a triangular fuzzy membership function to create
multiple time series for players' potential of presence at different court
regions. Unsupervised learning is then used for time series using triplet loss
and deep neural networks with exponentially dilated causal convolutions for the
derived multivariate time series. This works key contribution lies in its
approach to model how short scenes contribute to other longer ones and how
players occupies and creates new spaces in-game court. We discuss the
effectiveness of the proposed approach for prediction and recognition tasks on
the professional basketball SportVU dataset for the 2015-16 half-season. The
proposed system demonstrates descent functionality even with relatively small
data.

Machine Learning;Artificial Intelligence