- February 4, 2019
- Posted by: admin
- Category: Uncategorized
Analytics NFL- Fans will hopefully enjoy a ton of action on the field Sunday when the Los Angeles Rams line up against the New England Patriots in Super Bowl LIII. But what fans likely won’t see is how analytics is impacting football behind the scenes.
Up to this point, the adoption of analytics in the National Football League has been tepid at best. Compared to Major League Baseball and the National Basketball Association, where openly available player data and abundant analytics has energized fans and analysts alike, the NFL has lagged behind.
But by all accounts, that’s drought is on the wane as teams look for novel ways to apply big data analytics to improve their odds of winning. The NFL is also getting into the act, but can it do more?
Here are three ways analytics are being used in the NFL today.
Football is a team sport that’s unlike any other. Winning at football requires having the right roster of 53 men who not only are appropriately sized and physically skilled, but whose personalities fit well into the coach’s scheme and are also team players.
Teams spend millions to gather all available personnel data and develop insightful information — not only about their own players, but other teams’ players, as well as the most promising college players for the draft.
During the draft in April, there isn’t much difference in the intelligence collected about the top prospects, who are likely to go in the first few rounds. But the better teams are increasingly turning to analytics to make more informed picks in later rounds, as well as during free agency.
“We really let our analytics group lead us in sixth and seventh round – and especially undrafted free agency, they run it all,” Los Angeles Rams Chief Operating Officer and Executive Vice President of Football Operations Kevin Demoff said during a panel discussion last February at the 2018 MIT Sloan Sports Analytics Conference.
It’s all about spotting hidden traits that could indicate a good player down the line. “Your scouts may have seen these guys one time, but increasingly teams are looking to analytics to fill in the gaps,” Demoff said. “It still always comes down to human beings and developing them, but [analytics] gives you an even better chance.”
Starting in 2016, the NFL started placing sensors on players for the purpose of tracking player movement during games. The League kept the data to itself for the first two seasons, but last year it released the data to all 32 teams. The result has been somewhat of a data analytics arms race among the teams to see which ones can best utilize the data.
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Article Credit: Datanami
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