In this study, we propose a method for visualizing athlete performance in snowboard competition videos by su-perimposing movement trajectories and speed information. To compensate for viewpoint changes caused by camera pan, tilt, and zoom operations, an affine transformation matrix is estimated using SIFT-based feature point matching. In addition, pixel-based measurements are converted into real-world distances by utilizing actual com-petition gate dimensions as reference points. Through these processes, temporally and spatially consistent data are computed and overlaid onto the original footage, enabling the generation of intuitively interpretable performance visualization videos. Experimental results demonstrate that the proposed method achieves high detection accuracy and stable performance.