Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis

TL;DR

This paper introduces a transformer-based neural spatio-temporal point process model for football event analysis, improving prediction accuracy and proposing a new holistic possession metric that correlates with team performance.

Contribution

The paper presents a novel neural spatio-temporal point process model for football data and introduces the HPUS metric for comprehensive possession analysis.

Findings

Model outperforms baseline prediction models.

HPUS correlates with team rankings and scoring metrics.

HPUS provides insights into possession and match analysis.

Abstract

With recently available football match event data that record the details of football matches, analysts and researchers have a great opportunity to develop new performance metrics, gain insight, and evaluate key performance. However, most sports sequential events modeling methods and performance metrics approaches could be incomprehensive in dealing with such large-scale spatiotemporal data (in particular, temporal process), thereby necessitating a more comprehensive spatiotemporal model and a holistic performance metric. To this end, we proposed the Transformer-Based Neural Marked Spatio Temporal Point Process (NMSTPP) model for football event data based on the neural temporal point processes (NTPP) framework. In the experiments, our model outperformed the prediction performance of the baseline models. Furthermore, we proposed the holistic possession utilization score (HPUS) metric for a more comprehensive football possession analysis. For verification, we examined the relationship with football teams' final ranking, average goal score, and average xG over a season. It was observed that the average HPUS showed significant correlations regardless of not using goal and details of shot information. Furthermore, we show HPUS examples in analyzing possessions, matches, and between matches.