Transformer-based neural marked spatio temporal point process model for analyzing football match events

Abstract

Predictive modeling plays a crucial role in machine learning, data analysis, and statistics. In sports, predictive modeling methods have emerged to provide insights and evaluate performances based on key performance metrics. However, most existing models tend to focus on predicting only partial aspects of an event, such as the outcome, action type, or location, while neglecting the temporal factors involved. To address this gap, this study introduces the Transformer-Based Neural Marked Spatio-Temporal Point Process (NMSTPP) model, specifically designed for football event data. The NMSTPP model predicts a comprehensive set of future event components, including inter-event time, zone, and action. Additionally, it features a dependent prediction layers architecture to enhance model performance. The Holistic Possession Utilization Score (HPUS) metric is also proposed to evaluate the effectiveness and efficiency of possession periods in football based on the NMSTPP model. With open-source football event data, the NMSTPP model successfully predicted the aforementioned three components of future events, with an improvement of up to 4% overall and 9% for individual components compared to baseline models. The HPUS demonstrated a 0.9 correlation with existing performance metrics, highlighting its utility in performance evaluation. The NMSTPP and HPUS were applied to the Premier League to demonstrate their practical feasibility.