NFL Teams Preparing for RFID Data Dump

Posted by on Apr 25, 2016 2:40:39 PM

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Let’s say you are managing an NFL team and are currently on the lookout for cornerbacks before free agency.

Your coach prefers to play press coverage, placing defensive backs closely opposite receivers on the line of scrimmage. This means that his strategy will probably need cornerbacks to run after receivers to make up for a failed or inadequate contact.

Data Driven Drafting is Coming 

Who would be the best pick from the available cornerbacks? You have to make your choice based on how fast you think he is from the video, plus the forty times from an old scouting. However, this may change soon, as the NFL’s more cunning managers will possess a fresh and probably game-changing pile of helpful data.

During May, after the draft’s completion and the free agency’s closure, the NFL is planning to offer their members access to their cutting-edge statistics deriving from the past 2 seasons’ games.

How teams use them depends largely on them, however, a hoard of possibilities opened up during the subject’s discussion in the MIT Sloan Sports Analytics Conference, last week. The trend is for analytics to become more mainstream.

Adam Beard, new director of high performance for the Cleveland Browns, thinks that this would answer many questions. “With the current situation, all we have is a bunch of opinions. They all are experts when the game ends and they all knew how things would turn up. This is going to change when we get our hands on the data, as we would be in place to objectively judge the trends that could lead us to victory”, he added.

Data Gathered by Zebra Will be Available Soon

From 2014 onwards, the NFL has teamed up with Zebra Technologies to equip its stadiums with RFID technology (radio frequency identification signals), which monitors how players move in the field by tracking them through a chip placed under the pads they wear on their shoulders. The NFL released a sneak preview of the data on television (for example, how long a receiver ran in appropriate graphics), however, it’s far more important to the teams than anyone else. Most of the data have been kept secret, until every potential influence they may have is better understood. Although the league will be providing the teams with its own electronic gateway to access the date, there are already many 3rd party providers building customized software and offering it to any interested parties.

To get back to our initial case study of a manager looking for seasoned cornerbacks, our hero would have the full RFID data at his disposal, with actual numbers of the cornerback’s speed, instead his objective view based on a video recording. By comparing the actual speed of all available cornerbacks, to every cornerback on the league or just the potential opponents, he would be able to make the best possible choice.

There are many things to learn from data on “game speed” alone. For example, how has your running back’s speed deteriorated over the years? And how to interpret the case of a player who was slow during combine speed testing 3 years ago, but his videos show him to be really fast?

TruMedia’s VP of data science and former ESPN analyst Dean Oliver thinks that RFID offers a reality check. Game speed and combine speed are two different things, which the platform can help distinguish. It may not completely settle the debate of what a player’s speed really is, but through the data it is possible to discover his speed characteristics in certain aspects of the game. Data can help provide robust information on aspects of the game that are easily misunderstood when only objective judgment is used, which can avert strategies heading the wrong way for too long.

Most professional leagues in Australia and Europe commonly employ player tracking technologies. In fact, many NFL teams are already using it in some form to track their players’ exhaustion during practice. Andrew Hawkins, receiver for the Cleveland Browns, had an interesting remark to offer to the Sloan conference, one he had also made last year as well: Although players support the notion of increased health alertness, they are also worried about the date being turned against them when they negotiate their contracts. According to Hawkins, players could be put into a situation where players would be handed a paper sheet showing them they are not as fast anymore, instead just telling them. That would be hard to argue.

In reality, though, most teams don’t know how to make best use of the data, which is why the league has kept from releasing them until the “player acquisition” period of the offseason passes. Will its use be restricted to player evaluation? Will coaches exploit them to devise new game plans and changes during the game? Perhaps they will start rotating players not based on their own and their players’ objective perception but on hard data on exertion instead?

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Potential of Data is Not Harnessed Yet

During the Sloan conference held last year, Sean Payton, the coach of New Orleans Saints, proposed that real-time RFID data might be useful in setting matchups between offensive linemen and pass-rushers or any other position, instead of just cornerbacks and receivers. If coaches have in-game access of real-time data, they will be able to set up matchups according to their players’ physical condition and speed.

The NFL’s head of media operations and chief digital officer, Perkins Miller, has undertaken the development of such application to be used by TV broadcasters. To do so, he set up a “Hackathon” and challenged college students to fish for football stories in data.

"The winners," said Miller, "were a group that managed to calculate the chance for a successful pass at the ball’s snap, according to how the eligible receivers and the defensive line were configured. It has the potential to be an astonishing step forward and may even provide the basis of interpreting the thought process of a quarter-back in real time.”

If TV viewers are able to see in real time if a quarterback’s play looks “good” or “bad”, is it possible for coaches to gain that insight as well? What is known as “pattern recognition” to data scientists, is something usually unseen by scouts and coaches, probably until they review the game the following day. These are the issues to be resolved. Substantial and interesting issues, which, if they are put into good use, have all the potential to further boost the game.

Images credit: Zebra Technologies 

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Topics: RFID

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