A Framework for Spatio-Temporal Graph Analytics In Field Sports

TL;DR

This paper introduces a framework for analyzing team dynamics in field sports by constructing spatio-temporal graphs from GPS data, enabling better understanding of player interactions and activity patterns.

Contribution

It presents a novel approach to transform GPS data into Time-Window Spatial Activity Graphs for detailed spatio-temporal analysis in sports.

Findings

Effective extraction of spatio-temporal features from GPS data

Demonstrated application on Gaelic Football matches

Enhanced understanding of team dynamics

Abstract

The global sports analytics industry has a market value of USD 3.78 billion in 2023. The increase of wearables such as GPS sensors has provided analysts with large fine-grained datasets detailing player performance. Traditional analysis of this data focuses on individual athletes with measures of internal and external loading such as distance covered in speed zones or rate of perceived exertion. However these metrics do not provide enough information to understand team dynamics within field sports. The spatio-temporal nature of match play necessitates an investment in date-engineering to adequately transform the data into a suitable format to extract features such as areas of activity. In this paper we present an approach to construct Time-Window Spatial Activity Graphs (TWGs) for field sports. Using GPS data obtained from Gaelic Football matches we demonstrate how our approach can be utilised to extract spatio-temporal features from GPS sensor data