Global Sports Science Trends: How AI Baseball and the World Are Speaking Through Data

Major League Baseball teams have long incorporated data analysis into their daily operations—not only traditional statistics such as batting average and ERA, but also details that were once invisible, including swing angle, rotational speed, and exit velocity. These measurements can become part of player development, scouting, and training decisions.

This represents an important shift: in international competition, coaches who are good at reading data are increasingly working alongside—or becoming—the coaches who are good at reading the game by feel.

• Using data to compare movement efficiency and potential during player scouting

• Continuously collecting swing data throughout a season to monitor changes in player condition

• Tracking recovery progress after injury through movement data and helping reduce the risk of reinjury

The evolution of Sports Science can broadly be divided into three stages:

1. Experience-Driven Stage: Coaches and athletes rely primarily on years of observation and intuition

2. Data-Assisted Stage: Basic statistics are introduced to support experience-based judgment

3. Data-Driven Stage: Sensors, high-speed imaging, and AI analysis make data a core part of training and player-selection decisions

Sports Science in Taiwan is moving from the second stage toward the third, with AI Baseball being one of the clearest drivers of this transition.

Another important global trend in Sports Science is the gradual expansion of technology beyond professional competition.

High-speed cameras and sensing equipment that were once affordable only to elite teams are increasingly appearing in university teams, amateur leagues, and even ordinary batting cages.

This expansion is also part of the commercialization and wider adoption of AI Baseball. As equipment costs decrease and algorithms become more mature, the barriers to accessing Sports Science naturally continue to fall.

As AI, sensors, and high-speed imaging technologies continue to mature, where will Sports Science go next? How will cross-industry applications emerge?

For example, movement analysis technologies originally developed for baseball are gradually extending into softball, track and field, and even health promotion and everyday sports facilities.

This is one of the directions worth watching in the global development of Sports Science.

As the world increasingly speaks through data, people and technologies that can understand both data and sports will become some of the most valuable resources in this transformation.

AI Baseball is at the forefront of this trend, which is one reason Sports Science continues to attract growing attention.