The Rise of Artificial Intelligence in Major League Baseball
Data-Driven Decision Making in the Sports Industry
The rise of artificial intelligence (AI) has affected every industry, but the exploitation of data in Major League Baseball (MLB) is the definition of game-changing. New data sources are coming online all the time, and it’s the job of data engineers like Oliver Dykstra to turn this information into a competitive advantage.
5 Ways AI is Changing Baseball
1. Providing Better Predictions
Dykstra highlighted the importance of data-powered predictive matchups, "We can run those scenarios a lot faster and get a better sense of what’s out there. It’s about being able to toy with these matchups and run simulations to see how a game could go if we put in this guy or another or do particular pitch sequencing."
2. Creating New Partnerships
Internal data talent isn’t the only important resource. Successful MLB teams’ working relationships stretch beyond the enterprise. The Rangers collect data from disparate sources and use a combination of Apache Airflow and Astronomer’s orchestration and observability platform to ensure staff and players receive timely insights.
3. Removing Manual Tasks
Turning scout reports into useful data can be hard work, and that’s where generative AI (Gen AI) can help. "There are a lot of secret terms and codes that scouts use. It’s too much for one person to read through all that information, and it’s sometimes hard to understand. Extracting the value can be difficult. But with LLMs and generative AI, we can sort through these summaries, provide a great dictionary to translate key phrases, and summarize."
4. Monitoring Other Factors
Player data isn’t the only potential source of competitive advantage. The team also feeds its models with external information, including weather data. "This is a hot new source. Every five minutes, we’re getting data from all the different fields. The weather dynamics in a stadium are not quite what you would think they would be. You can’t just lift your finger. It’s not something you can necessarily intuitively get."
5. Building New Cultures
Industry experts say organizations must democratize data access to make the most of the insight created by emerging technologies. The Rangers have a data analyst embedded within the team to help ensure coaches and players make the most of data, "It’s always a conversation."
Conclusion
The rise of AI in MLB is revolutionizing the way teams make decisions, from predicting game outcomes to improving player performance. With the help of AI, teams can gain a competitive edge and improve overall performance.
FAQs
Q: What is the role of data engineers in MLB teams?
A: Data engineers like Oliver Dykstra are responsible for turning data into a competitive advantage.
Q: What is the importance of data-powered predictive matchups in MLB?
A: It allows teams to run scenarios faster and get a better sense of what’s out there, enabling them to make informed decisions.
Q: How do MLB teams collect and manage data?
A: Teams collect data from disparate sources and use a combination of Apache Airflow and Astronomer’s orchestration and observability platform to ensure timely insights.
Q: What is the role of generative AI (Gen AI) in MLB?
A: Gen AI helps turn scout reports into useful data, allowing teams to extract value from large amounts of information.
Q: How do MLB teams use external information, such as weather data?
A: Teams feed their models with external information, including weather data, to gain a competitive edge.
