Big Data Analytics & Cognitive Business

How Data Analytics Is Transforming the NFL

IBM Journal Staff | Sep 2, 2016 8:00:00 AM


Some of the highest performing teams in the NFL have been using data analytics since the start of the 21st century.

More teams are starting to warm to the trend as they see the range of possible applications for data analytics.

The NFL as a whole made a big commitment towards using analytics when they hired a Chief Information Officer for the first time. 

The NFL is also investing in new equipment so they can generate more useful data to make decisions.

Recently, the NFL announced a deal to install RFID data sensors in shoulder gear to track location data, including acceleration and speed during certain plays. In the NFL, analytics can be used for everything from making draft picks to monitoring player health.

Looking at specific ways that football teams are using data analytics shows how it is changing the sport. 

Key Players in the Analytics Game

 

Many NFL teams are investing in the staff and technology they need to make the analytics strategy work.

The Baltimore Ravens have 2 analysts on staff, including Sandy Weil, who came on as Director of Football Analytics in 2012. Weill studies game trends and helps with scouting decisions. Eugene Shen works closely with the coaching staff, evaluating player performance. 

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The Jacksonville Jaguars have 3 data analysts who work under the son of owner, Shahid Khan. Tony Khan serves as the Senior Vice President of Football Technology and Analysis. The Kansas City Chiefs employ Mike Frazier as their statistical analysis coordinator. The Buffalo Bills hired Michael Lyons as their Director of Analytics in 2013.

One of the more high-profile hires was made by the Cleveland Browns who brought Paul DePodesta onto staff. DePodesta was profiled in the book MoneyBall and was played by Jonah Hill in the movie. 

Other teams that use data analytics in various ways are the New England Patriots, the Atlanta Falcons, the Dallas Cowboys, the Philadelphia Eagles, and the San Francisco 49ers. These teams are using analytics to find and keep better players and to develop game-winning strategies. 

Building a Better Team

 

Analytics can be used for scouting and during the draft to take the guesswork out of the selection process. In 2014, the Cleveland Browns spent $100,000 on an independent study to evaluate that year’s quarterback class. The Dallas Cowboys pioneered the use of statistics in the draft all the way back in the 1960s. 

Once a player is on the team, continual evaluations using analytics help coaches and managers assess overall talent and determine when trades need to take place. Analytics also predict the long-term effects of contracts and help with salary-cap management for teams—which is what the Philadelphia Eagles do. 

Tracking Player Health

 

One aspect of data analytics that the entire NFL can appreciate is the role it plays in protecting the health and welfare of the players. All 32 NFL teams maintain electronic health records that can be accessed immediately after an injury using tablets. If a player gets injured during a game, the 2x Concussion Assessment tool can draw on baseline information to determine if a player has received a concussion, as well as prescribe treatment. 

Using sensors on shoulder pads or footgear, team leaders can monitor exertion during practice. Predictive analytics can process this data to forecast and prevent season-ending injuries. Both the Jaguars and the Falcons use this type of data to prevent injuries. 

Transforming Game Strategy

 

One of the most dramatic ways the NFL uses analytics is to determine which strategies will work best during play. In the past, the NFL mostly used descriptive analytics to preserve a record of what was happening during a game. Today, predictive and prescriptive analytics are used to gain actionable insights. Further analytics quantify and verify the impact of changes in gameplay strategy. 

Coaches can test out scenarios for the statistical probability that a certain play will succeed. The Falcons use GPS to evaluate plays made during practice so they can make more efficient plays during a game. 

The New England Patriot’s coach, Bill Belichick, used analytics to decide that his team punted too frequently during the fourth down. Backed by analytics, the Patriots have chosen to take more risks in running offensive plays. On the flip side, analytics have led Andy Reid, coach of the Chiefs, to switch from a running game to a throwing game. 

All About the Fans

 

As valuable as analytics can be for planning a game-winning strategy, teams like the Jaguars aren’t letting that overshadow the potential it can have for designing winning fan experiences. Social media discussions can be analyzed to measure fan sentiment and engagement. Teams can use this insight to fuel their marketing decisions and promotional offers. In addition, statistics such as game attendance and merchandise purchases help teams improve ticketholder retention rates. 

When it comes to the data itself, fans want it just as much as the coaches do. Why? Because that data can be used to build fantasy football teams and make more informed decisions. 

The Future of NFL Analytics

 

As data analytics gains increasing acceptance in the NFL, team leaders may find themselves learning to think with their heads as well as with their hearts and guts. Greg Gabriel of NFPost feels analytics need to be combined with traditional methods like examining tape and may be useful for specific tasks like evaluating quarterbacks. 

The draft is one area where analytics may show a lot of growth in the NFL. Typically, it’s difficult to judge whether a new player will be able to adapt to the demands of the NFL. New technologies like GPS and RFID tags generate real-time performance statistics for current players that can be compared to new players to see if they stack up. Analytics can also be used to predict other team’s picks for a better drafting strategy. 

Increased NFL involvement in the future may depend on seeing striking results from analytics-based decision-making. If a team wins the Super Bowl based on analytics, that may be proof enough for anybody. 

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Topics: Analytics, Big Data

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