## 6 Discussion  
The DxT model demonstrates competitive performance based on established validation metrics. A Brier Score close to 0 indicates high calibration quality, while a score approaching 1 signals poor predictive performance. A value below 0.1 is generally considered reliable. Our DxT model achieves a Brier Score of 0.0811, suggesting that its predictions are well-calibrated with actual outcomes. In addition, the model obtains an AUC-ROC of 0.7283, exceeding the typical acceptability threshold of 0.7. This indicates a reasonable capacity to discriminate between high- and low-threat actions. In contrast, the original xT model yields a significantly higher Brier Score of 0.4260, indicating poor calibration and limited reliability. Its AUC-ROC value of 0.6038 is only marginally above the baseline for random prediction, reflecting its limited ability to accurately distinguish between impactful and non-impactful actions. These shortcomings are particularly evident in situations where tactically valuable actions--such as backward or lateral passes--are systematically underestimated. To further contextualize DxT's performance, we conducted a direct comparison with VAEP, one of the few existing models that employs the same evaluation metrics. VAEP outperforms both xT and DxT in numerical terms, achieving a Brier Score of 0.01376 and an AUC-ROC of 0.7693. This result is not unexpected, given VAEP's design for comprehensive action valuation using large-scale datasets. However, the performance gap between VAEP and DxT can be narrowed by addressing some current limitations in the DxT implementation. Several avenues for improvement are identified. First, the transition probability matrices could be refined through more expressive mathematical transformations--for instance, using logarithmic scaling to better capture subtle but meaningful spatial transitions. Second, the shot and move probability matrices could benefit from calibration on a larger and more diverse dataset to improve generalization. Third, integrating synchronized tracking data would enable the model to consider the positions of all 22 players, significantly enriching its tactical awareness. At present, DxT relies on freeze-frame data, which limits the scope of spatial interpretation. Perhaps the most critical enhancement would involve coupling DxT with a more robust xG model. Since DxT's threat computation heavily depends on Expected Goal estimates, using an xG model trained on industrial-scale data would improve its reliability. Academic xG models often require class-balancing techniques to improve performance; however, these adjustments tend to distort the distribution of xG values by pushing them toward 0.5, thereby creating an artificial representation of scoring likelihood. Beyond performance metrics, DxT offers significant advantages in terms of interpretability and tactical insight. Unlike VAEP, which produces abstract probability estimates based on predefined action windows, DxT generates spatially explicit threat maps that reflect the real-time configuration of players on the field. These maps allow coaches and analysts to intuitively assess which zones are more or less dangerous based on opponent positioning and available space. For example, a zone may appear threatening not because of proximity to the goal but due to limited defensive coverage--something that DxT captures and visually conveys. By contrast, models like VAEP or those based on deep learning architectures tend to function as black boxes, offering little transparency regarding \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Brier Score** & **AUC-ROC** \\ \hline xT & 0.4260 & 0.6038 \\ DxT & 0.0811 & 0.7283 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance Comparison Between xT and DxT Models. how action values are derived in specific tactical contexts. Their reliance on latent feature representations makes them difficult to interpret, especially when concrete spatial decisions are required in the field.