# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 3 Experiments
### Results and Discussion
#### 3.2.2 Qualitative Results  
To qualitatively evaluate the prediction performance, visualized examples of the shot prediction results obtained by PM are shown in Figure 5. Each example result represents positive and negative outcomes of accurate predictions (true-positive and true-negative, respectively), followed by negative outcomes of incorrect predictions (false-positive). A threshold value \(th=0.5\) was set to determine whether each prediction result was correct. In the figure of each example, several characteristic frames in the video clip are displayed at the top. For the bottom part of the figure, the prediction probability at each time step (black lines) and associated aleatoric and epistemic uncertainties (orange and cyan regions, respectively) are depicted. The larger region of each uncertainty at each time step indicates greater uncertainty regarding the prediction outcome. The true positive example shown in Figure 5a is a scene that includes the sequences of pass to near goal and shot, where the prediction probability increases rapidly after the pass into the penalty area and reaches the target threshold value 1.3 s before the shot moment. In this example, as the player dribbling approaches the penalty area gradually, the model provides a rising prediction probability while maintaining uncertainty. The fact that the model continues to offer significant uncertainty right up to the moment before the shot suggests that it recognizes the situation as ambiguous regarding whether the pass will succeed or not. The following example of a true negative shown in Figure 5b is a scene where a cross is made from the side but no one touches the ball. The prediction result does not exceed the threshold throughout the video, yet it suggests significant uncertainty. This indicates that while there are few attacking players near the goal, which leads to a low probability of a shot occurring, there is a potentially high possibility of it happening. Finally, we observe a false positive scenario where an attack from the side culminates in a centering attempt within the penalty area shown in Figure 5c. The predictive outcomes of this scene frequently reach the threshold in the video, accompanied by considerable uncertainty. Although this particular scene does not result in an actual shot, thus categorizing it as a negative instance in our study, it can arguably be considered a scene with a highly probable chance of leading to a shot or creating a shot opportunity. In a real soccer match, the shot opportunity often exists even in  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **AP** & **Recall** & **Precision** & **F1** & **mTTE** \\ \hline Ablation1 & 0.934 & 0.807 & 0.882 & 0.839 & 3.46 \\ Ablation2 & 0.941 & 0.781 & 0.891 & 0.824 & 3.63 \\ Ablation3 & 0.927 & 0.797 & 0.849 & 0.801 & 3.46 \\ Ablation4 & 0.926 & 0.766 & 0.884 & 0.816 & 3.61 \\ DSA [58] & 0.920 & 0.786 & 0.859 & 0.820 & 3.71 \\ DSTA [59] & 0.925 & 0.820 & 0.856 & 0.836 & 3.47 \\ PM & **0.948** & **0.828** & **0.896** & **0.858** & **3.82** \\ \hline \hline \end{tabular}
\end{table}
Table 2: AP, Recall, Precision, F1, and mTTE for all methods except ViViT at time step \(T_{\text{s}}\). Note that Recall and Precision adopt the values at the threshold \(th\) when F1 achieves the highest accuracy. Best and second-best performances are highlighted in bold and underlined, respectively.  
situations where the player does not choose the shot. This observation suggests that future research may need to reconsider the definitions within the dataset.