See how AI on AWS gives baseball fans new insights into the game.

Stolen Base Success Probability

MLB and AWS used Amazon SageMaker to train and deploy a deep neural network to predict stolen base success by using numerous data including runner’s speed and burst, catcher’s pop time, pitcher’s velocity and handedness, lead-off distance, and the game situation.

Shift Impact

AWS and MLB employed machine learning to quantify the effectiveness of an infield position shift. Using Amazon SageMaker to train, evaluate, and deploy ML models, we can now predict the change in expected batting average as a batter steps up to the plate.

Pitcher Similarity

Often times, a batter stands across a pitcher he hasn’t encountered before. To shed light on these matchups, AWS worked with the MLB to group similar pitchers by training a deep learning architecture on Amazon SageMaker that examines pitch results.

Learn more about Statcast AI

David Ortiz stays sharp in retirement

See how Red Sox legend Big Papi maintains his competitive edge.

Featuring: David Ortiz (aka, “Big Papi”), Hall of Famer Joe Torre, and NESN sportscaster Dave O’Brien.

MLBAM re:Invent keynote

Watch Joe Inzerillo, former EVP and CTO of Major League Baseball Advanced Media (MLBAM), describe why the company has selected AWS to power its over-the-top (OTT) video streaming service, Bamtech.

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After 143 years of statistic tracking through manual processes, it was time for MLB to eliminate this time-intensive practice associated with record keeping, statistics, scorekeeping, game notes, and classifying pitches.

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Evolving the game

Major League Baseball (MLB) uses machine learning services on AWS to power Statcast AI — the tracking technology used by MLB to analyze player performance for every game on MLB.com and the MLB Network. In addition, Amazon ML Solutions Lab works with the MLB to continue to enhance viewer experiences with more personalized content for each market and geographic region.

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AWS can handle data streams from fluctuating game schedules across the country
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MLB can scale down during off days and in the off season
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Data can by used for broadcasts, MLB apps
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Can ingest, analyze and store 17+ petabytes of data per season
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Delivers new ways for fans, broadcasters, and clubs to analyze plays and players
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Smart statistics with machine learning

MLB turned to AWS due to the broad range of cloud-based machine learning services that enable efficiency and accuracy of managing data and analytics. MLB created Statcast AI Powered by AWS to empower 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.

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 has plans to leverage Amazon SageMaker and the natural language processing service Amazon Comprehend to build a language model that creates scripts for live games in the tone and style of iconic announcers, such as Vin Scully, to capture the essence of how they would call the game. Read the full press release here.

 

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Whether you're looking for compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability and reliability.