# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance
#### 5.3.7 Feature Importance  
To gain insights into our model's decision-making process, we employed the SHAP (SHapley Additive exPlanations) method. This approach helps identify which features most significantly influence the model's predictions. Figure 4 shows the SHAP summary plot to illustrate these features.  
Figure 4 shows that the SHAP (SHapley Additive exPlanations) feature importance plot provides valuable insights into the relative importance of different features in predicting shot accuracy in badminton. Salient points are as follows:  
QE_Duration (Quiet Eye Duration)  
This is by far the most important feature, with the highest mean |SHAP value|. This indicates that the duration of the Quiet Eye period has the strongest impact on the model's predictions for shot accuracy. This aligns with previous research emphasizing the importance of Quiet Eye in sports performance.  
Figure 4: SHAP summary plot.  
Figure 3: ROC curve.  
QE Onset  
The second most important feature is the onset of the Quiet Eye period. The substantial importance of this feature indicates that the timing of when a player initiates their Quiet Eye fixation is crucial for shot accuracy.  
Racket_Speed  
Racket speed at impact is the third most important feature. While less influential than the Quiet Eye metrics, it still plays a significant role in determining shot accuracy.  
Elbow_Angle, Shoulder_Rotation, and Wrist_Angle  
These biomechanical factors have relatively lower importance compared to the Quiet Eye metrics and racket speed, though they still contribute to the model's predictions, albeit to a lesser extent.  
Key Observations  
1. Dominance of Quiet Eye metrics: The two most important features (QE_duration and QE_onset) are both related to the Quiet Eye phenomenon. This strongly supports the hypothesis that Quiet Eye is a critical factor in badminton shot accuracy.
2. Importance of timing: Both the duration and onset of Quiet Eye are crucial, suggesting that not only how long a player maintains the Quiet Eye but also when they initiate it are key to accuracy.
3. Technique vs. Perception: While biomechanical factors (elbow angle, shoulder rotation, wrist angle) do play a role, they are less influential than perceptual-cognitive factors (Quiet Eye) and the dynamic factor of racket speed.
4. Racket speed significance: The importance of racket speed suggests that the execution of the shot itself remains a crucial factor, even if not as dominant as the Quiet Eye metrics.
5. Holistic approach: The presence of both perceptual-cognitive and biomechanical factors in the model suggests that a comprehensive approach considering both aspects is necessary for understanding and improving shot accuracy.  
The SHAP analysis provides evidence for the importance of Quiet Eye in badminton performance and offers a nuanced understanding of how various factors contribute to shot accuracy. It suggests that training programs focusing on improving Quiet Eye duration and timing, alongside traditional technique and speed training, could be particularly effective in enhancing players' performance.  
The use of SHAP values in our analysis offers an innovative way to interpret complex neural network models in sports science by quantifying the relative importance of each feature. This approach provides valuable insights into the factors driving shot accuracy, enhancing the interpretability of our model--an essential aspect for training players. The SHAP analysis highlights QE duration and onset as the most significant predictors of shot accuracy, followed by biomechanical factors like racket speed and wrist angle at impact. This hierarchy of feature importance serves as a roadmap for targeted training interventions and technique refinement in badminton.