Roger Cheng uses deep learning methods to build a robot umpire, one that can watch video and call balls and strikes. It doesn’t do that well in just trying to learn how umpires call balls and strikes, but it’s a start in the right direction.
This project was borne of interest in an area of research I found lacking. I couldn’t find any published articles on this topic, so I decided to give it a try myself. The results suggest both that Deep Learning could call a strike zone, and that the results would benefit greatly from better quality videos and equipment. There have been some very good articles published which successfully used PITCHf/x or Trackman data to build a robot umpire (such as this article which chronicles using a robot umpire in an independent league game).
The results of those studies suggest that method is farther along. It would be interesting to see if a Deep Learning system with more videos, better videos, and better computing infrastructure could equal or perhaps outperform the performance of those systems. In the meantime, this method offers another possible means by which strike zone calls could be improved. I look forward to fine-tuning this method in the months to come.
Very nice work.
from baseballmusings.com https://ift.tt/2OgKMoP
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