Pose estimation models offer promising opportunities for automated feedback in cricket training, but their practical impact is limited by the lack of personalized and understandable explanations. This study investigates how explanation formats can be tailored to users’ expertise levels, focusing on beginner, intermediate, and expert levels, to improve the effectiveness of AI-generated feedback. Based on a literature review of explanation needs and generation methods, we propose a taxonomy linking expertise levels to suitable explanation modes: visual, comparative, and statistical. We implement a set of explanation prototypes aligned with this taxonomy and evaluate them through a user study involving 17 participants across the three expertise levels. Results show that participants rated explanations tailored to their skill level as more useful, trustworthy, and easier to interpret. Statistical validation using Kruskal-Wallis and Dunn’s tests confirmed significant differences in perception between user groups, especially between beginners and experts. These findings demonstrate the value of expertise-based explanation design in cricket analytics and offer design guidelines for future explainable pose estimation systems in sports