# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 3 Experiments
### Settings
Specifically, we compared our model with the models without audio feature \(\mathbf{x}_{t}^{\text{audio}}\) from  
Figure 4: The distribution of \(T_{\text{s}}\) in the positive samples. graph nodes, xG from graph edge weights, and both of them, which were defined as Ablations1-3, respectively. We also compared with Ablation4, which employed a simple neural network instead of a BNN. Furthermore, we adopted the following models for the comparison. * This is a method for traffic accident prediction from dashcam videos. This model learns temporal relationships through RNN and the importance of detected objects in the video through Dynamic Spatial Attention (DSA). * This is a video-based prediction model for traffic accidents, which uses GRU or LSTM to learn temporal relationships and Dynamic Spatial-Temporal Attention (DSTA) to learn the spatial and temporal importance of detected objects in the dashcam video. * This is a video recognition model known as a Video Vision Transformer (ViViT), which learns spatial and temporal dynamics by tokenizing videos into a series of image frames or patches. To the best of our knowledge, our previous studies [38; 39; 40; 41] are the only ones that use visual information to predict the shot event occurrence in soccer matches. Therefore, we employed DSA and DSTA as comparative methods although they are video-based traffic accident prediction models that consider spatial-temporal relationships between objects in the video. In addition, since ViViT has the ability to classify the existence of events by recognizing various factors from the entire video image, it is considered to have the same ability as an accurate prediction of event occurrences. Hence, we adopted ViViT as a valid standard of comparison for event prediction methods. In order to validate the prediction accuracy, we used Average Precision (AP) and F1-score (F1), which were defined as follows:  
\[\text{AP} =\sum_{th}(\text{Recall}_{th+1}-\text{Recall}_{th})\text{Precision }_{th}, \tag{20}\] \[\text{F1}_{th} =\frac{2\times\text{Recall}_{th}\times\text{Precision}_{th}}{ \text{Precision}_{th}+\text{Recall}_{th}}, \tag{21}\]  
where  
\[\text{Recall}_{th} =\frac{\text{TP}_{th}}{\text{TP}_{th}+\text{FN}_{th}}, \tag{22}\] \[\text{Precision}_{th} =\frac{\text{TP}_{th}}{\text{TP}_{th}+\text{FP}_{th}}. \tag{23}\]  
The prediction of event occurrence by the model is dependent on the threshold \(th\) being exceeded. At the threshold \(th\), \(\text{TP}_{th}\) denotes the count of video clips correctly predicted as events, \(\text{FP}_{th}\) indicates the number of video clips incorrectly predicted as events, and \(\text{FN}_{th}\) refers to the number of video clips that were events but not predicted as events. Additionally, to measure the prediction earliness for positive samples, we evaluated event prediction performance with mean Time to Event (mTTE) calculated by the following equation:  
\[\text{mTTE} =\mathbb{E}[\text{TTE}_{th}], \tag{24}\] \[\text{TTE}_{th} =\max\{T_{\text{s}}-t|\beta(y_{t})\geq th,1\leq t\leq T_{\text{s }}\}, \tag{25}\]  
where \(\beta(\cdot)\) represents the sigmoid function. TTE is a common metric in prediction tasks and is used in various studies [39; 58; 59; 61]. In this experiment, we verified the effectiveness of the proposed model in the above experimental settings.