Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learning

TL;DR

This paper introduces a novel framework combining fuzzy and deep learning methods to analyze player position time-series data for predicting team movement outcomes in basketball.

Contribution

It presents a new encoding and modeling approach for short tactical sequences and space occupation patterns using fuzzy membership functions and deep neural networks.

Findings

Effective prediction of team movements using the proposed model

Model performs well even with limited data

Demonstrates applicability to professional basketball data

Abstract

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.