The application of deep learning in tactical analysis of football matches

This paper presents a deep learning-based framework for tactical analysis in football, focusing on modeling structured tracking data rather than attempting full end-to-end perception. We introduce two modular components: Tactiformer, a transformer-based encoder that captures spatiotemporal player dynamics with role- and zone-aware attention, and StratGaze, a segment-level reasoning module that identifies recurring tactical motifs using contrastive sequence modeling. Tactiformer learns representations of coordinated multi-agent behavior at the player level, while StratGaze operates at the segment level to extract interpretable tactical abstractions over time. Our framework is fully data-driven, modular, and compatible with standard football analytics pipelines. We evaluate the approach on benchmark datasets including SoccerNet and PASS, demonstrating consistent improvements in event prediction accuracy, trajectory forecasting, and motif clustering quality over existing baselines. In addition to quantitative results, we provide qualitative visualizations of model attention maps and inferred tactical timelines, supporting the interpretability and usability of the system in real-world scenarios. Rather than solving the entire pipeline from detection to tactical inference, this work targets the reasoning layer and shows how structured spatiotemporal representations, combined with inductive priors, can support scalable, interpretable, and data-efficient football analytics tools suitable for analysts and coaching staff.