Detecting optimal gaze behavior of successful basketball free throwing via machine learning system

Eye tracking in sport is an emerging field that explores the relationships between visual function and motor performance. However, research has shown that visual behaviors are distinct enough to detect superior performance; and serve as a suitable input for designing machine learning systems, few study has been tested yet the eye tracking machine learning in sport tasks. The current research investigated the eye movement behaviors for detecting successful performance using machine learning. The gaze behavior of 25 student basketball players during the hit and miss free- throwing's trials was collected and analyzed by statistical (JMP pro) and machine learning (Python) approaches. Results showed significant differences between saccade duration in hit and miss trials. In previous studies of free throwing, fixations were used as a measure of visual information processing, but our results showed that the metrics related to saccades were more important for successful performance than those related to fixations. These findings highlight the importance of eye tracking machine learning in sport domain and suggest that successful performance can be reliably predicted from performers' eye movement data. Our results provide primary insights as well as inspiration for future research focusing on developing eye-tracking machine learning systems to detect proficiency in motor skills.