Big data may not be enough to predict injuries in the NFL
0Reported today on The Verge
For the full article visit: https://www.theverge.com/2019/12/6/20999403/amazon-nfl-injuries-concussions-big-data-machine-learning
Reported today in The Verge.
Big data may not be enough to predict injuries in the NFL
The NFL is hoping big data tools can help bring down the number of concussions, ligament tears, and other injuries sustained in each game of professional football. Currently, the injury count per game is holding steady at an average of six or seven. League engineers are working with Amazon Web Services to apply machine learning and artificial intelligence tools to player data, with the hope of finding in-game situations that commonly lead to injury, The Wall Street Journal reported this week.
"Ultimately, we will be able to identify injury risk scenarios, and we will be able to predict injury risk scenarios, and we will be able to find innovations that will make the game safer for our athletes while maintaining high quality of play," Jeff Crandall, chair of the NFL's engineering committee, said during the program announcement.
The NFL and Amazon have vast resources at their disposal. But injuries, especially in chaotic sports like football, are incredibly hard to predict. "It's the holy grail. Everyone wants to do it, and no one can," says Zachary Binney, an epidemiologist and consultant who has worked with Major League Baseball and college sports teams on injury prevention. "I'm skeptical until I see results."
Predicting injuries is challenging because there are so many factors that could contribute to a possible injury, from an athletes physical characteristics on a particular day to slight divots on a field. One athlete might have five attributes that research shows puts them at risk for an injury and still not get hurt, but another might look perfectly fine and tear a ligament the next day. "It is just an incredibly difficult problem," Binney says.
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