FEATURED AWS SPORTS PARTNERSHIPS
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WHY THE NFL CHOOSES AWS

“One of the key components of our success is having great partners and AWS exemplifies that. We like to partner with smart people who have great skills and we have that in our partnership with AWS.” 

- Roger Goodell, NFL Commissioner

“One of the key components of our success is having great partners and AWS exemplifies that. We like to partner with smart people who have great skills and we have that in our partnership with AWS.” 

- Roger Goodell, NFL Commissioner

The vast majority of machine learning (ML) being done in the cloud today is being done on AWS which is why AWS is the best choice for the NFL to leverage the power of its data through sophisticated analytics and ML. The NFL uses the power of AWS ML to create new stats and improve player health and safety, creating a better experience for fans, players, and teams—all in real time.

Machine Learning

The NFL uses machine learning and data analytics services to boost the accuracy, speed, and insights provided by its Next Gen Stats platform.

Quick Access

Using the business intelligence tool Amazon QuickSight, the NFL is able to gain greater insight while also opening a window for fans, broadcasters, and editorial to engage with data.

Speed

Using Amazon SageMaker to build, train, and run predictive models helped reduce the time to get results from as much as 12 hours to 30 minutes.

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ENGAGING THE FANS

The NFL has built several new stats on AWS, each of which relies on different data points. Here are just a few examples. To see more, visit nextgenstats.nfl.com

Expected Rushing Yards

The NFL partnered with AWS for its second annual sports analytics contest, the 2020 Big Data Bowl, to develop this new stat. Expected Rushing Yards is designed to show how many rushing yards a ball-carrier is expected to gain on a given carry based on the relative location, speed and direction of blockers and defenders.

Expected Yards After Catch

This new predictive model uses Amazon SageMaker to output the expected yards after catch, based on numerous factors using tracking data such as how open the receiver is, how fast they’re traveling, and how many defenders/blockers are in the space.

Route Classification

This next generation statistic helps fans contextualize the passing game in new ways, indicating what type of route the player executes – go, post, or out – and assigns an aggregate score of how teams play so fans can study league-wide trends to gain a new understanding of offensive strategy.

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HERE'S HOW IT WORKS

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Capture Data

Data is captured by placing RFID tags on player’s shoulder pads and the game ball. Ultra-wideband receivers track the players and ball movement down to the inch. The data collected leverages Amazon EC2, S3, and EMR in the capture and storage process.

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Analyze It

Next, this real-time data is combined with traditional box score/play-by-play data to calculate 100’s of metrics never captured before. These metrics are run through machine learning models built on Amazon SageMaker to output predictions, stats and more.

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Distribute Results

Finally, the NFL leverages AWS Lambda, Amazon ElastiCache, Quicksight, RDS, Route S3, API gateway, and DynamoDB to put the insights, predictions, and stats into broadcast analysis, scouting, and coaching tools.

NFL player tracking, also known as Next Gen Stats, is the capture of real-time location data, speed and acceleration for every player, every play on every inch of the field. Sensors throughout the stadium track tags placed on players' shoulder pads, charting individual movements within inches. Data is captured using RFID tags in player equipment and the football itself. Real-time data is then transmitted to receivers installed in NFL stadiums, which provides the NFL with data on every player for every play.

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APPLYING MACHINE LEARNING
TO THE DATA

By leveraging AWS’s broad range of cloud-based machine learning capabilities, the NFL is taking its game-day stats to the next level so that fans, broadcasters, coaches, and teams can benefit from deeper insights.

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Training data from traditional box score statistics, as well as data collected from the stadium, will run through hundreds of processes within seconds with the output fed into Amazon Sagemaker. From there, machine learning models built by the NGS team ingest the data, which continually train and refine the models. The machine learning models are then deployed in real-time during games to generate outputs such as formations, routes, and events.

In the news

NFL TACKLES PLAYER HEALTH AND SAFETY

By combining the NFL's vast trove of football data and insights with AWS’s expertise in machine learning, AI, and cloud computing, the NFL is expanding its partnership with AWS beyond its Next Gen Stats fan experience, generating new insights into player injuries, game rules, equipment, rehabilitation, and recovery.

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"We chose AWS because of its combination of advanced cloud offering, powerful machine learning capabilities, and experience operating at the scale we need. By powering Next Gen Stats with AWS, we’ll be able to kick off our [season] with even more impactful and meaningful content, uncovering deeper insights into the game of football than we’ve ever done before."

- Matt Swensson, Vice President, Emerging Products and Technology at the NFL

"We chose AWS because of its combination of advanced cloud offering, powerful machine learning capabilities, and experience operating at the scale we need. By powering Next Gen Stats with AWS, we’ll be able to kick off our [season] with even more impactful and meaningful content, uncovering deeper insights into the game of football than we’ve ever done before."

- Matt Swensson, Vice President, Emerging Products and Technology at the NFL

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GOING LONG ON MACHINE LEARNING
WITH THE NFL

Want to see the playbook? Read more about the NFL’s machine learning journey, with an introduction from NFL CIO Michelle R. McKenna—and hear from Matt Swensson firsthand how his Next Gen Stats team worked with the AWS Machine Learning Solutions Lab to build, train, and deploy their machine learning models on Amazon SageMaker.

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