NFL enters next frontier of predicting injury with NFL Contact Detection Challenge – NFL.com

The NFL today announced the NFL Contact Detection Challenge, a new innovation challenge to predict player injuries through machine learning and computer vision.
Held in collaboration with NFL partner Amazon Web Service (AWS), the challenge invites experts to design new ways to measure and analyze the timing, duration and frequency of player contact during NFL games using artificial intelligence and related disciplines. Collecting this information will improve the NFL’s ability to quantify when contact occurs on the field, allowing the league to better predict — and ultimately prevent — player injuries.
Announced at the AWS re:Invent conference in Las Vegas, the challenge will be open for three months. The top finishers — who will be announced in March 2023 — will receive a total of $100,000 in prize money, with the first-place finisher receiving $50,000.
Starting Monday, Dec. 5, entries can be submitted on the challenge website hosted by Kaggle, a leading platform for data science competitions. Entrants will be featured on Kaggle’s dynamic live scoreboard, which will reflect the score and rank of each entry as that information becomes available.
“Quantifying the risk of injury that players face in every possible in-game scenario is a crucial step in understanding how we can reduce that risk, and ultimately prevent injuries” said Jennifer Langton, senior vice president of health and safety innovation at the NFL. “By engaging entrants with a wide range of expertise, this challenge will help us understand which game situations have elevated amounts of contact. This can inform rules changes to improve the game.”
The NFL Contact Detection Challenge is part of the Digital Athlete, a joint effort between the NFL and AWS to build a virtual, 360-degree representation of an NFL player’s experience that can generate a precise picture of what they need when it comes to preventing and recovering from injuries while performing at their best.
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