This year marks the 50th year of the Super Bowl, and plenty has changed since the first time teams duked it out for the national title. Players are bigger and faster, replays more instant and from more angles, commercials cost the equivalent of a small nation’s GDP…
But this year, there’s something else that has the potential to change the game: big data.
Sports analysts have been collecting data on football games since the very beginning, but our data collection techniques and abilities have vastly improved. I have written before about how the NFL is using big data.
New sensors in stadiums and on NFL players’ pads and helmets help collect real-time position data, show where and how far players have moved, and can even help indicate when a player may have suffered a damaging hit to the head.
In 2013, Microsoft struck a $400 million deal with the NFL to make this data available to coaches and players via their Surface tablets. Coaches use the tablets to demonstrate and review plays on the sidelines, as well as access real-time data from the NFL’s databanks.
Of course, the partnership hasn’t been all smooth. At first, commentors kept referring to the tablets as “iPads,” and had to be reminded many times to call them by their brand name. Then, during the AFC championship game this year, the Patriots briefly had connectivity issues, causing the tablets to stop working.
And, of course, the commentors remembered to call them by their brand name that time just as, almost as unfortunately, the network ran a Microsoft ad during the break.
Still, connectivity issues aside, the advent of real-time data access is potentially changing the way coaches view games and call plays.
Predicting the outcome (and placing bets)
For the biggest football game of the year — and the biggest sporting event of any kind in the U.S. — you can bet that people are betting on their ability to predict the outcome of the game.
And as data collection has improved, people are turning more and more to computer algorithms to predict the outcome of the game.
Sports analysts have kept track of many statistics since Super Bowl I, including everything from individual player to team stats. But big data, IoT sensors, and new analytics abilities makes even more data available, including real-time distance traveled and position on the field, how weather conditions affect individual plays, and even predicting individual player matchups.
But it’s still a long way from a sure bet.
A company called Varick Media Management has created its own Prediction Machine to predict the outcome of all the games in the season. The site also offers its “Trend Machine” which can analyse many different matchups from more than 30,000 games over 35 years of play.
However, their accuracy is far from perfect; in the 2014-2014 regular season, they boasted a 69% accuracy rating. But, they were the only major predictor to foresee the Seattle Seahawks blowout win over the Denver Broncos in 2014. Facebook tried to use social data to predict the outcome, and predicted the Broncos would take home the trophy.
Even video games get it right some of the time. The football video game “Madden NFL” uses more than 60 data points on each individual player to power its game simulations, including information about injuries. At the end of the regular season, the engineers input the new data about the competing teams and run a final simulation.
And in 2015, Electronic Arts, the producer of “Madden NFL 15”, correctly predicted the outcome of the game — with almost eerie accuracy. It not only predicted the exact final score, but also that Patriots quarterback Tom Brady would throw 4 touchdown passes and that the winning touchdown pass would go to wide receiver Julian Edelman. It came within 7 yards of Brady’s total passing yards for the game and within 3 yards of Edleman’s reception total.
That’s pretty amazingly accurate for a game.
Another way big data could be changing the game is in the realm of advertising.
Everyone knows Super Bowl ads cost millions of dollars — a record $5 million for a 30-second spot this year — but what big data showed marketers last year is that most of the online chatter about Super Bowl XLIX took place after the game.
This could be a boon for advertisers who want to take advantage of the attention on the game, but can’t afford a TV spot. In fact, by using data to strategically target only certain consumers, online advertising could represent a much better investment with better engagement — and at a fraction of the cost.
That could mean that, for well targeted ads, online advertising could have a better return on investment than television, even ads aired during the most-watched event on live TV.
In addition, social listening in years past told us that social media users talked more about the brands, commercials, and half-time show than they did the game. That points to an important social opportunity for brands who can afford to advertise during the game.
As we all know, brands can make or break a big social opportunity like this depending on how well their social media is managed, and how well they’re watching their data. Take the Oreo blackout tweet during the 2013 Super Bowl — many said the Twitter ad “won” the Super Bowl marketing game that year, and they paid mere pennies compared to brands that bought TV ads.
This article was written by Bernard Marr from Forbes and was legally licensed through the NewsCred publisher network.