Let’s get fired up! Let’s get fired up! That’s right sports fans … it’s that’s time of year again—Super Bowl time! And Super Bowl 50 promises to be even bigger and better than ever. This year’s game takes place Feb. 7, 2016, at the new tech-enabled Levi’s Stadium in Santa Clara, California, where 75,000 people are expected to cram in for the game. But you can bet that even more fans will be watching via broadcast and online.
Last year’s epic game between the Patriots and the Seahawks was the most watched broadcast in U.S. TV history with an average audience of 114.4 million viewers. Even the halftime show by Katy Perry during the 2015 Super Bowl earned the ‘most-watched’ performance in Bowl history. So how do we know all of this? Because big data is starting to have a profound impact on how the Super Bowl is planned for, managed and discussed in several ways.
Product Demand Data Helps Retailers Scale
As living rooms pack out with casual and die-hard fans alike, it’s estimated that over a BILLION chicken wings, 11.2 million pounds of potato chips, 8.2 million pounds of tortilla chips, 3.8 million pounds of popcorn and three million pounds of nuts will be consumed during Super Bowl 50. With numbers like those, retailers better be sure to stock up on their supplies in time for kick-off—a key area where big data can help.
Big Data Helps Predict the Winning Team
One of America’s other favorite pastimes is not just watching the game, but trying to predict the winner. For weeks before kick-off, sports experts and NFL enthusiasts alike spend hours on end pontificating on the minutiae of the tiniest details happening both on and off the field, in an effort to determine which team will win. For the third straight season, the top seeds from each conference—the Denver Broncos and the Carolina Panthers—will face off against one another in the Super Bowl. Prior to 2016, this has happened only three times since 1990. A victory in Super Bowl 50 would give Peyton Manning his 200th career win, including playoffs. Manning would be the only QB in NFL history with 200+ wins, surpassing Brett Favre (199 wins, including playoffs) for the greatest number of wins overall. Predicting a Super Bowl winner is far from an easy task, especially when both teams are so evenly matched. This is why many experts have turned to big data to help provide added insights on the potential game outcomes.
In the average football game, there are numerous data points that sports analysts track—from individual player performance to overall team stats. This is an area where big data can take insights to the next level—measuring things like total distance each team will travel to get to the game, the impact of weather conditions on individual plays, and comparisons between different player matchups. Yet another way today’s NFL uses data: equipping each player with sensors in shoulder pads so that coaches can access detailed location data on each player and from that data, analyze metrics like player acceleration and speed.
From all these different stats and figures, big data algorithms can be created to come up with an eventual winner in any game. The challenge to create the most accurate algorithm is one that a handful of businesses and institutions have already looked at in the past. For example, one company, Varick Media Management, created their own Prediction Machine that boasted a 69 percent accuracy rating during the 2013-2014 NFL regular season as well as an impressive record for other championship games. Facebook also tries to predict a winner each year from an analysis of social media data. Speaking of, in 2015 over 28.4 million tweets related to the game and halftime show were sent during the live telecast—making it the most tweeted Super Bowl ever.
Even for the non-sports elements of the game, big data has an impact. Yes, we’re talking about the commercials. Super Bowl ads cost millions of dollars and research seems to show that only about 20 percent of those ads lead to more products sold. Additionally, with big data collected through social media listening tools, companies can potentially get a picture of what people talk about most before, during, and after the game. Hence, using big data analysis tools, companies can potentially create more targeted advertising campaigns to drive more engagement, allowing their Super Bowl ad to have a better return on investment.
The Super Bowl is an exciting game that tens of millions of people around the world will enjoy, but many aspects of the game are changing in the era of big data. Whether it be in terms of predicting the most likely winner of the game or how advertising is handled, big data is changing the game.
Contributed by: Isabelle Nuage, Director of Product Marketing, Talend.
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