  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.
