Explore how the trend of sports teams and marketers using data analytics to reduce risk has impacted sports and marketers who depend on game time ad space.
Brad Pitt stares at a computer screen as Jonah Hill explains the figures and codes. “It’s about getting things down to one number,” Hill intones. “Using the stats the way we read them, we’ll find value in players that no one else can see.”
In Moneyball, powerful scenes like this one helped fuel the hype that earned Hill an Oscar nomination. But in a movie about baseball, Hill isn’t swinging a bat or throwing a ball; he’s crunching numbers.
Moneyball hit the truth of the matter: For all the drama in sports, it always comes down to numbers. The real-life characters of Peter Brand and Billy Beane used this philosophy to take a losing, small-budget baseball franchise and turn it into the talk of the league.
Now, others in the sports world are taking this idea to heart in order to manage risk and better predict the outcome of recruiting decisions. With billions of dollars riding on the outcome of performance both on-field on on-screen, sports teams and marketers are using data analytics to leave as little as they can to chance.
For the 2013-2014 National Basketball Association season, every arena was outfitted with a camera system to track player movements and generate datasets based on them. “The data are changing the way the game is played,” says Stanford’s Loren Mooney, “shifting emphasis from how many total points a player scores to measures of player efficiency, productivity per touch, and defensive effectiveness.”
From the Gridiron to the Spreadsheet
The National Football League is taking a similar approach, outfitting players’ shoulder pads with RFID sensors. These sensors pick up locations, movements and subsequent telemetry data.
“Individual teams can create their own applications to mine the data to improve scouting, education, and preparation for meeting an opposing team,” Bernard Marr wrote in Forbes.
Teams like the New England Patriots have already been using this data and other statistics to make unconventional decisions. For example, New England Coach Bill Belichick has been making riskier offensive plays on fourth downs when many coaches would punt. Belichick’s decisions are not based on a desire to throw caution to the wind, but on consistent statistical results saying that a punt is not always as safe an option as it appears to be.
Other teams are looking to data models to improve their performance off the field. The Dallas Cowboys have used real-time merchandising monitoring to boost sales of branded clothing and goods.
Retail marketers similarly use data based approaches to target and personalize ads. Last year’s Super Bowl broke new ground with highly targeted simultaneous social and TV ad campaigns intending to promote conversations that increase brand affinity.
“Technological advances are giving brands new ways to engage in what appears to be spontaneous, intimate communication with fans on social platforms,” reveals NBC News.
As NFL teams finalize their league standings and prepare for the 2016 playoffs and subsequent Super Bowl 50, expect more numbers whizzing by to help coaches take to the field and retailers to take to the airwaves with more confidence than ever before.