Sports Science in Taiwan: From Trial and Error to Data-Driven AI Baseball
If you asked an experienced coach, “How did you determine whether an athlete’s movement was correct in the past?”
The answer would usually be simple: Look.
Watch for long enough, build experience, and rely on intuition.
Rich in Experience, but Short on Data: The Past
This approach is not necessarily bad. The problem is that it is difficult to replicate and difficult to verify. The same movement may receive completely different evaluations from different coaches.
This has long been one of the challenges facing Sports Science in Taiwan: there is plenty of experience, but limited data. Every coach has their own way of judging movement, yet it can be difficult to systematically pass that knowledge on to the next generation or compare performance across teams and organizations.
Three Common Challenges of Experience-Based Evaluation
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Different coaches may evaluate the same movement differently
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An athlete’s progress can be difficult to quantify and track with concrete numbers
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Knowledge transfer relies heavily on the coach-athlete apprenticeship model, making it difficult to scale
Sports Science Is Becoming More Systematic and Data-Driven
Things have started to change in recent years.
As sensors, high-speed imaging, and AI technologies continue to mature, Sports Science is no longer limited to the experience held by a small number of experts. It is gradually becoming a data asset that can be accumulated and shared.
What does that mean?
It means an athlete’s movement analysis no longer has to exist only in a coach’s notebook. It can be recorded, tracked, and compared over time. It also means that athletes training in northern and southern Taiwan may eventually be evaluated and compared using the same standards.
Three Observations on Taiwan’s Sports Science Transformation
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Training environments are beginning to adopt sensors and imaging analysis equipment instead of relying solely on visual observation
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Data is gradually becoming a common language between coaches and athletes
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Sports technology startups and research institutions are working together to bring technologies from laboratories into everyday training environments
AI Baseball Is a Concrete Example of This Transformation
Among many sports, baseball and softball are among the areas where Sports Science has been integrated into everyday training relatively early and comprehensively. From amateur batting cages to professional teams, AI Baseball analysis systems are bringing technologies once limited to elite competition into training environments accessible to more people.
This trend reflects the continued development of Sports Science in Taiwan: lower technical barriers, reduced equipment costs, and improved data-processing capabilities are gradually turning analytical capabilities that once belonged to a small group of experts into tools that more people can use.
From Trial and Error to Data-Driven Training
In the past, training adjustments often depended on a simple statement such as, “Something doesn’t feel right.” Today, systematically collected movement data can begin to break down that feeling into specific numbers and performance indicators.
This is a critical step in Taiwan’s transition from trial-and-error training to data-driven training—gradually turning scattered experience into a knowledge system that can be shared and validated.
Conclusion
The transformation of Sports Science in Taiwan did not happen overnight. It has been built one sensor and one data record at a time. AI Baseball is one of the most tangible examples of this transformation in everyday life, and it represents an important foundation for bringing Sports Science into practical use.