Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis
This work addresses the problem of comprehensive performance analysis in football for analysts and researchers, though it is incremental as it builds on existing neural temporal point process frameworks.
The authors tackled the challenge of modeling large-scale spatiotemporal football match event data by proposing a Transformer-based neural marked spatio-temporal point process model, which outperformed baseline models in prediction performance, and introduced a holistic possession utilization score metric that showed significant correlations with team rankings and goal metrics without using shot details.
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.