# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
###### Abstract  
This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players' shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.  
b 2020 2024 14 9906. 18 October 2024  
on discrete visual metrics isolated from biomechanical data, providing an incomplete understanding of athletic performance.  
In this study, we introduce a novel approach by leveraging neural networks to forecast shot accuracy in badminton. Our model integrates QE metrics, biomechanical data (such as body movement), and shuttlecock trajectory to generate predictions about shot outcomes. By processing this comprehensive dataset, we aim to create a tool that can enhance training strategies for players and coaches by providing actionable insights into the visual and motor components that drive performance.  
The current study introduces several innovative elements to the domain of sports science and badminton research. We uniquely integrate Quiet Eye metrics with biomechanical data using a neural network model, enabling the real-time predictive modeling of shot accuracy. Our approach goes beyond traditional analyses by employing advanced interpretability techniques, offering potential for adaptive learning, and providing insights across different skill levels. These innovations not only advance our understanding of visual-motor expertise in badminton but also open new avenues for personalized training and performance enhancement in fast-paced racket sports.