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Today, there is a strong partnership between technology and sports. Almost all professional sports teams use technology in some capacity to help them win games. In the same vein, almost all professional sports leagues use technology in some capacity to help referee and manage the games. In baseball specifically, there has been video instant replay in use since the 2008 season, with the most current system (with manager challenges) in place since the 2014 season. Some people have proposed taking things to the next level, and using computers to replace some or all on-field umpires. Here is an article that surveys the current state of electronic umpires, and also includes opinions of some players and coaches, and here is an article where MLB Commissioner Rob Manfred discusses his views on the topic.
This post will explore building a robot umpire that calls balls and strikes based on actual game video, using a technique known as Deep Learning.

Background on Deep Learning
Before talking specifically about Deep Learning, let's first discuss Artificial Intelligence and Machine Learning. Artificial Intelligence (AI) is a broad field that attempts to build machines that can mimic cognitive functions typically associated with the human brain, such as learning and problem solving. Machine Learning (ML) is a subset of Artificial Intelligence that attempts to use statistical techniques to allow the machine to learn with data without being explicitly programmed. Although AI and ML have existed for over 50 years, their usage and importance have exploded in recent years, as the range of data and have access to it have increased.
https://tht.fangraphs.com/building-a-robot-umpire-with-deep-learning-video-analysis/
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