# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 3 Experiments
### Results and Discussion  
#### 3.2.1 Quantitative Results  
Tables 1 and 2 show the quantitative results for each method. Concretely, in Table 1, we show the prediction accuracy at different lengths of video clips, and the performance of PM is better than other models at almost all lengths of video clips. Note that in all video clips, the time \(T_{\text{s}}\) of the shot occurrence is situated in the second half of the video clip. Compared with PM and Ablations1-3, we confirm that Ablation3 has the lowest and PM the highest performance when the length of video clips is long, that is, video clips including around the shot occurrence time \(T_{\text{s}}\). Since spectators cheered loudly at important moments, it is considered that the audio feature is effective for shot prediction. xG can reflect the importance of the player's position in more detail, and thus, it is assumed that the introduction of xG into the graph edge weights is also useful by comparing PM with Ablation2. In comparison between PM and Ablation4, it is observed that although Ablation4 has a slightly higher AP for only the first half of the video, PM shows higher accuracy as the video length increases. From this result, we confirm that Ablation4 does not correctly capture the match situations, which indicates the effectiveness of using the BNN instead of a general neural network since the shot occurred in the second half of the video clip. Furthermore, the comparison of PM with DSA and DSTA shows that it is more effective in shot prediction in soccer matches than video-based shot prediction models, which achieved high performance in a different task. In the comparison between PM and ViViT, we confirm that PM significantly outperforms ViViT at all lengths of video clips. The above results show that PM achieves higher prediction accuracy than that of the state-of-the-art video prediction model. Here, DSA and DSTA achieve high prediction accuracy, although not as high as PM, in contrast to ViViT, which has a considerably less accurate performance than that of the other comparison methods. The reason for the low performance of ViViT could be attributed to the crucial difference that ViViT does not perform object detection. With the self-attention of the vision transformer, ViViT learns the interaction of each position in a video frame with the other positions. However, it is considered to be difficult to determine the focus points in a scouting video, which is captured from a bird's-eye view of the field, without prior information. On the other hand, DSA, DSTA, and PM utilize object detection results to learn the extent to which each detected object is attributable to the prediction results. This difference may have affected the prediction accuracy. Moreover, PM utilizes a graph that introduces more detail on the importance of each player, which is assumed to be one of the factors behind its higher performance than that of DSA and DSTA. Since the shot occurrence time \(T_{\text{s}}\) is different for each video clip, we evaluate the prediction performance at time step \(T_{\text{s}}\). In Table 2, we present the prediction accuracy at time step \(T_{\text{s}}\) and the prediction earliness for the positive sample. The values of Recall and