Title

AWS re:Invent 2023 - NFL Next Gen Stats: Using AI/ML to transform fan engagement (PRO304)

Summary

Elena Ehrlich, a principal science manager at AWS, and Aarti Sahni, a Senior Practice Manager in AWS Professional Services, introduced the session.
Andrew Um, Director of Engineering for NFL's NextGenStats, discussed the evolution of player tracking data and its use in enhancing fan engagement and team strategies.
The NFL uses RFID technology to track player movements and has been doing so since the 2015 season.
AWS ProServe helped the NFL develop machine learning models to interpret the vast amount of tracking data, leading to the creation of new statistics and insights.
Elena detailed the creation of the QB passing score, a machine learning model that evaluates quarterback performance based on various factors, including game context and pressure.
Aarti highlighted the business impact of these innovations, including increased fan engagement, better decision-making tools for coaches, and new insights for broadcasters and fans.
The session concluded with a Q&A, addressing topics such as player safety, the impact of garbage time on stats, and the technical aspects of RFID tracking.

The NFL's partnership with AWS ProServe has led to significant advancements in the use of AI/ML for sports analytics, particularly in understanding and quantifying player performance.
The use of RFID technology for player tracking has been a game-changer, allowing for real-time data collection and analysis.
The QB passing score is a sophisticated metric that goes beyond traditional statistics to provide a more nuanced view of a quarterback's contribution to the game.
The NFL's use of machine learning models has not only enhanced fan experience but also provided teams with tools for better strategy and decision-making.
The NFL's approach to data analytics can serve as a model for other sports organizations looking to leverage technology to improve performance analysis and fan engagement.