In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 +/- 0.01 m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 +/- 7.1 degrees (7.2 +/- 1.5 degrees) for the outside leg, and -0.2 +/- 5.0 degrees (6.7 +/- 1.1 degrees) for the inside leg. For hip flexion, the corresponding values were -0.4 +/- 6.1 degrees (4.4 degrees +/- 1.5 degrees) and -0.7 +/- 4.7 degrees (3.7 +/- 1.0 degrees), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 +/- 0.01 m (0.01 +/- 0.00 m) for relative center of mass position, -0.1 +/- 3.8 degrees (3.4 +/- 0.9) for lean angle, 0.01 +/- 0.03 m (0.02 +/- 0.01 m) for center of mass to outside ankle distance, 0.01 +/- 0.05 m (0.03 +/- 0.01 m) for fore/aft position, and 0.00 +/- 0.01 m(2) (0.01 +/- 0.00 m(2)) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing.