Leveraging Tracking Data to Contextualize the Difficulty of an Outcome
Welcome to the new frontier of football analytics. Welcome to the Next Gen Stats revolution. Since 2015, RFID tracking devices have been embedded in the shoulder pads of every 国产外流网player. These devices track the player's location every tenth of a second, for every play of every game. The combination of Next Gen Stats player tracking data and traditional football statistics reveals unprecedented insights immeasurable until now.
Moving beyond the traditional box score, Next Gen Stats can be used to quantify metrics such as the difficulty of every throw and the expected yards gained by a receiver after the catch. We are excited to unveil our first machine-learning developed metrics, Completion Probability and Expected Yards After Catch.
New for the 2018 season, these Next Gen Stats metrics add context to passing plays and can be used to answer the elusive questions: How difficult was a pass to complete? How much credit should be given to a receiver for the yards created after the catch? In several follow-up articles, we will describe the features powering the Completion Probability & Expected Yards After Catch models to show how several in-play factors, available only through Next Gen Stats player tracking data, have an effect on the chances a pass is complete and how well a receiver created yards after the catch relative to expectations.
To read more about Completion Probability,
Learn more about the NFL's use of machine learning with .
