# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance  
#### 5.3.1 Neural Network Training Architecture  
Building upon the architecture described in Section 4.6.1, we implemented our neural network model using TensorFlow Version 2.17 and Keras Version 3.4.1. To combat overfitting, we added dropout layers (rate = 0.2) between the hidden layers. The model was compiled using the Adam optimizer (\(learning\ rate=0.001\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), epsilon = \(1~{}\times~{}10^{-7}\)) with binary cross-entropy as the loss function.  
For training, we split the dataset into training (70%), validation (15%), and test (15%) sets. The model was trained for 100 epochs with a batch size of 32. To prevent overfitting, we implemented early stopping with a patience of 10 epochs. To ensure the robustness of our results, we repeated the training process five times with different random seeds.  
The performance of our neural network was assessed based on precision, recall, F1 score, and overall accuracy. These metrics provide a comprehensive evaluation of the model's predictive capabilities across different aspects of performance.  
Precision: Precision measures how often the model correctly identifies accurate shots when it predicts a shot to be accurate. It is calculated as:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
where TPs (True Positives) are accurate shots correctly predicted by the model, and FPs (False Positives) are inaccurate shots mistakenly predicted as accurate. A high precision score reflects the model's ability to correctly identify accurate shots while minimizing false positives. In our model, precision was 88.3% (SD = 0.9%), which indicates strong predictive accuracy in classifying accurate shots.  
Recall: Recall, also known as sensitivity, measures the model's ability to capture all accurate shots. It is computed as:  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
where FN (False Negatives) are accurate shots that the model failed to identify. High recall indicates that the model is catching most of the accurate shots, even if it occasionally predicts inaccurate shots as accurate. Our model reached a recall of 83.1% (SD = 1.4%),indicating that it successfully identifies the majority of accurate shots, though some may still be missed.  
The F1 score provides a balanced evaluation by considering both precision and recall, making it especially useful when dealing with imbalanced classes. It helps to ensure that neither false positives nor false negatives disproportionately affect the model's performance. It is calculated as:  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]  
F1 Scores: The F1 score ranges from 0 to 1, with higher values reflecting stronger overall performance. Our model achieved an F1 score of 0.856 (SD = 0.011), signifying robust performance with a well-balanced trade-off between precision and recall.  
Overall Accuracy: The overall accuracy of our model represents the proportion of correct predictions (including both true positives and true negatives) out of the total cases evaluated. It is calculated as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
where TP is True Positives, TN is True Negatives, FP is False Positives, and FN is False Negatives. Our model achieved an overall accuracy of 85.7% (SD = 1.2%) on the test set, indicating its strong performance in predicting shot outcomes based on Quiet Eye metrics and biomechanical features.  
These metrics allow us to evaluate our model's performance in predicting shot accuracy based on Quiet Eye metrics and biomechanical features. The strong precision, recall, F1 score, and overall accuracy highlight the effectiveness of our neural network in accurately capturing the complex relationship between visual attention and motor performance in badminton. These metrics demonstrate the model's ability to consistently perform well in identifying key patterns and making accurate predictions.