# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 2 Related Work
### Integration of QE with Biomechanical Data  
While machine learning models have made advances in sports analytics, many studies still overlook the value of combining Quiet Eye (QE) metrics with biomechanical data. Biomechanical analysis captures detailed data about an athlete's physical movements, such as joint angles, velocity, and muscle activation, while QE metrics provide insights into visual attention and cognitive control. Combining these two data streams would deepen our understanding of the impact of cognitive processes on motor performance in sports. Recent studies have begun addressing this gap by exploring the interaction between visual focus and physical movement.  
In their research on the effects of high-pressure situations on QE during basketball three-point shooting, [PERSON] et al. [3] found that elite players exhibited significantly longer QE durations under pressure compared to semi-elite players. This suggests that elite athletes rely on extended QE durations to suppress distractions and non-optimal movements, a mechanism that becomes critical during high-pressure situations. Integrating these visual focus data with detailed biomechanical metrics, such as hand positioning, shooting form, and body alignment, could further refine predictive models for performance outcomes.  
[PERSON], [PERSON], and [PERSON] [10] found that longer QE durations during a golf putting task were associated with greater cortical activation in the prefrontal, premotor, and parietal cortices, suggesting that QE aids in both the preparation and execution phases of motor tasks. These findings highlight the neurophysiological mechanisms by which QE enhances motor performance, which could be beneficial for integrating into models that combine biomechanical and QE metrics.  
[PERSON] et al. [11] demonstrated the value of integrating multiple measurement approaches by combining eye-tracking and EEG data during golf putting. Their research revealed that while QE durations might be similar between successful and unsuccessful putts, distinct patterns of neural activity differentiated performance outcomes. Specifically, successful putts were characterized by greater beta suppression in central regionsand lower frontal theta power, suggesting enhanced movement planning and reduced hesitation. These findings highlight how combining QE metrics with neurophysiological measurements can provide deeper insights into the mechanisms underlying successful motor performance.  
A critical challenge in fast-paced environments like badminton is the rapid alternation between offensive and defensive movements. While specific research on badminton is limited in the literature, recent studies in other sports suggest that combining multiple data streams could enhance predictive accuracy for performance outcomes. [PERSON] et al. [4] highlighted the benefits of combining eye-tracking data with physiological indicators such as heart rate in their study on golf putting performance in high-pressure situations.  
Extrapolating from these findings, one could argue that combining biomechanical data--such as shuttlecock trajectory, swing velocity, and footwork patterns--with QE metrics in real-time models could potentially enhance predictive accuracy for shot outcomes in badminton. By integrating these data streams, machine learning algorithms could potentially assess how an athlete's visual focus aligns with their physical movements, offering real-time feedback that optimizes both cognitive and motor performance.