Statcast is slated for further improvements in the near future via Amazon Web Services, as Major League Baseball (MLB) has chosen AWS as its official provider for machine learning, artificial intelligence and deep learning workloads to upgrade experiences both on the field and off.
In extending its long-standing relationship, MLB will use AWS machine learning services to continue development of Statcast—the tracking technology that runs on AWS to analyze player performance for every game—and develop new technologies to support MLB clubs in driving innovative fan experiences and engagement across all 30 Major League ballparks.
In addition, MLB will work with the Amazon ML Solutions Lab to amplify game statistical data integrations within broadcasts, including MLB Network, and live digital distribution, such as MLB.com and the MLB At Bat app, using machine learning, creating more personalized viewer experiences tailored for each market and geographic region. In short: MLB and AWS will take the flood of data currently collected both on and off the field and attempt to organize it into a structure that produces actionable data.
“Incorporating machine learning into our systems and practices is a great way to take understanding of the game to a whole new level for our fans and the 30 clubs,” said Jason Gaedtke, Chief Technology Officer at Major League Baseball. “We chose AWS because of their strength, depth, and proven expertise in delivering machine learning services and are looking forward to working with the Amazon ML Solutions Lab on a number of exciting projects, including detecting and automating key events, as well as creating new opportunities to share never-before-seen metrics.”
AWS’s broad range of cloud-based machine learning services will enable MLB to eliminate the manual, time-intensive processes associated with record keeping and statistics, such as scorekeeping, capturing game notes, and classifying pitches. By using Amazon SageMaker, MLB is empowering its developers and data scientists to automate these tasks as they learn to quickly and easily build, train, and deploy machine learning models at scale.
For example, MLB and Amazon ML Solutions Lab are using Amazon SageMaker to test how well they can accurately predict pitches by evaluating the pitcher, batter, catcher, and situation to predict the type and location of the next pitch. MLB also intends to leverage Amazon SageMaker and the natural language processing service Amazon Comprehend to build a language model that would create scripts for live games in the tone and style of iconic announcers to capture that distinct broadcast essence baseball fans know and revere.
“MLB has been collecting statistical data on its players and clubs for decades, and turned to AWS in 2015 to take its game-day stats to the next level so that fans can dig deeper into advanced metrics that ultimately enhance enjoyment of the game,” said Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. “AWS has the broadest and deepest portfolio of cloud services with the best security and proven operational expertise, which is why MLB chose to work with the world’s leading cloud to build, run, and enhance Statcast. We are excited to work with MLB as they continue their journey in the cloud, leveraging AWS machine learning services to capture previously immeasurable aspects of the gameand find new ways to enhance the fan engagement model for baseball.”