Trading cards are a fast-growing industry. However, previous research in sports merchandise has largely overlooked the role of cards’ visual appeal in online-commerce. This study addresses this gap by analyzing over 7000 samples from a leading sports card trading platform. Using computer vision algorithms (Mask R–CNN) and a machine learning algorithm (CatBoost), we unveil the importance of 12 image display attributes and their relationship with the card premium rate. Moreover, we identify inverted U-shaped relationships with attributes such as warm hue, saturation, and brightness. The findings offer valuable insights for card dealers to enhance product image display effectiveness.