## 7 Conclusions  
This study introduced the Dynamic Expected Threat (DxT) model, a novel framework designed to overcome the limitations of traditional Expected Threat (xT) models. Unlike conventional xT approaches, which assign fixed threat values to pitch zones, DxT dynamically adjusts threat estimations by integrating real-time spatial configurations of players. This innovation allows for a more context-aware assessment of football actions.  
Our model outperforms the traditional xT model, as demonstrated by its superior Brier Score (0.0811 vs. 0.4260) and AUC-ROC (0.7283 vs. 0.6038). Furthermore, two case studies based on real match sequences--Albania's opening goal against Croatia in the Euro 2024 group stage and Portugal's final offensive sequence in the quarter-final against France--illustrate DxT's ability to capture both tactical nuances, such as flank switches and spatial manipulation, and to support the analysis of individual decision-making in high-stakes scenarios. Despite its advancements, the DxT model still has several limitations that future research should address. One key area for improvement is the expansion of training datasets to enhance the performance of the xG model underlying the DxT framework. Additionally, exploring alternative formulations for transition probability matrices--such as logarithmic scaling--could better capture subtle variations in xG values. Finally, integrating continuous tracking data alongside event data, rather than relying solely on freeze frames, would be a crucial step toward increasing the model's realism and robustness.  
Methodology, K.H.; Validation, K.H. and M.R.; Formal analysis, K.H.; Investigation, K.H. and M.L.; Data curation, M.L.; Supervision, M.R. All authors contributed equally to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets utilized in this study are open-source datasets provided by StatsBomb. They are accessible at: [[https://github.com/statsbomb/open-data/tree/master](https://github.com/statsbomb/open-data/tree/master)]([https://github.com/statsbomb/open-data/tree/master](https://github.com/statsbomb/open-data/tree/master)) (accessed on 3 April 2025). These datasets include various types of football-related data:  
* Matches Data: Contains information about matches, including date, time, stadium, and competition details.
* Competition Data: Includes competition names, unique identifiers, and season details.
* Event Data: Describes the type of events occurring during a match, including their location, minute, and half-time status.
* 360 Data Frames: Provide spatial information about player positions at the time of an event.  
**Acknowledgments:** This research was supported through computational resources of HPC-MARWAN provided by the National Center for Scientific and Technical Research (CNRST) Rabat, Morocco.  
**Conflicts of Interest:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.