Estimation of mitt movement trajectory in catcher framing from baseball video

Catcher framing technique in baseball has recently gained attention in game analysis. This technique involves a catcher adjusting their catching motion to increase the likelihood of an umpire calling a pitch a strike. Its success is typically evaluated based on the strike rate at the boundary of the strike zone, calculated from pitch trajectory data obtained through a tracking system. However, evaluating catcher framing in games without a tracking system is challenging, and alternative methods based on different types of information are needed. This research proposes a method to detect the catcher's mitt movement trajectory during catcher framing, which is considered useful information apart from pitch trajectory. The method applies object detection, pose estimation and deep learning to videos of baseball pitching scenes.