If you really like sports and you’re really skilled at data analysis or machine learning, you might want to make that your profession.
On Thursday, private equity firm Vista announced it has acquired a natural-language processing startup called Automated Insights and will make it a subsidiary of STATS, a sports data company that Vista also owns. It’s just the latest example of how much money there is to be made when you combine sports, data and algorithms.
The most-popular story about Automated Insights is that its machine-learning algorithms are behind the Associated Press’s remarkably successful automated corporate-earnings stories, but there’s much more to the business than that. The company claims its algorithms have a place in all sorts of areas where users might want to interact with information in natural language — fitness apps, health care, business intelligence and, of course, sports.
In fact, someone from Automated Insights recently told me that fantasy sports is a potential cash cow for the company. Because its algorithms can analyze data and the outcomes of individual matchups, it can deliver everything from in-game trash-talk to post-game summaries. The better the algorithms are at mimicking natural language (i.e., not just regurgitating stats with some static nouns and verbs around them), the more engaging the user experience — and the more money the fantasy sports platform, and Automated Insights as a partner, make. Automated Insights already provides some of this experience for Yahoo Sports.
So it’s not surprising that STATS would acquire Automated Insights. STATS provides a lot of data products to broadcasters and and folks selling mobile and web applications, ranging from analysis to graphics to its SportVU player-tracking system. At our Structure Data conference next month in New York, STATS Executive Vice President of Pro Analytics Bill Squadron will be on stage along with ESPN’s vice president of data platforms, Krish Dasgupta, to discuss how the two companies are working together the sate an ever-growing sports-fan thirst for data. (We’ll also have experts in machine learning and deep learning from places such as Facebook, Yahoo and Spotify discussing the state of the are in building machines that understand language, images and even music.)
And Automated Insights isn’t even STATS’s first acquisition this week. On Tuesday, the company announced it had acquired The Sports Network, a sports news and data provider. In September, STATS acquired Bloomberg Sports.
More broadly, though, the intersection of sports and data is becoming a big space with the potential to be huge. Every year around this time, people in the United States start going crazy over the NCAA collegiate men’s basketball tournament (aka March Madness) and spend billions of dollars betting on it in office pools and at sports books. And every year for the past several, we have been seeing more and more predictive models and other tools for helping people predict who’ll win and lose each game.
Statistician superstar Nate Silver might be best known for his ability to predict elections, but he has been applying his trade to sports including baseball and the NCAA tournament for years, too. It’s no wonder ESPN bought him and his FiveThirtyEight blog and turned it into a full-on news outlet that includes a heavy emphasis on sports data.
The National Football League might present the biggest opportunity to cash in on sports data. Aside from the ability to predict games and player performance (gambling on the NFL — including fantasy football — is a huge business), we now see individuals making their livings with football-analysis blogs that turn into consulting gigs. There’s a growing movement to tackle the challenge of predicting play calling by applying machine learning algorithms to in-game data.
Even media companies are getting into the act. The New York Times dedicates resources to analyzing every fourth down in every NFL game and telling the world whether the coach should have punted, kicked a field goal or gone for it. In 2013, Yahoo bought a startup called SkyPhrase (although it folded the personnel into Yahoo Labs) that developed a way to deliver statistics in response to natural language queries. The NFL was one of its first test cases.
Injuries are also a big deal, and there is no shortage of thought, or financial investment, into new ways of analyzing measuring what’s happening with players’ bodies so teams can better diagnose and prevent injuries. Sensors and cameras located near the field or even on players’ uniforms, combined with new data analysis methods, provide a great opportunity for unlocking some real insights into player safety.
All of this probably only skims the surface of what’s being done with sports data today and what companies, teams and researchers are working for tomorrow. So while analyzing sports data might not save the world, it might make you rich. If you’re into that sort of thing.
