Jeopardy-Winning Supercomputer Could Be Toronto Raptors’ MVP
Hungry for sponsorship dollars, pro sports teams turn to IBM’s Watson and StatCast to track and boost performance.

Big data and artificial intelligence have become game-changers for major league sports — an industry that drives more than $10 billion in economic value — by giving managers increasingly effective tools to bring their teams into the finals.

In “Big Data Analysis is Changing the Nature of Sports Science,” MIT Technology Review analyzed how those with the largest vested interests in sports are trying to use data “to gain a competitive advantage, whether in real time during the game or to help in training, preparation, or recruitment.”

As part of the data race in professional sports, the Toronto Raptors partnered with IBM to leverage the tech giant’s Watson technology platform. The computer – well known for its appearance on Jeopardy! – can parse huge amounts of unstructured data, and learn from these data sets, to answer questions accurately in a variety of fields. In the world of sports, Watson can collect statistics, medical records, video, and social network sentiment, and use them to help the team decide if a given player fits the team’s needs (physically or mentally), can stay healthy and looks likely to succeed.

Where scouts can analyze a player’s performance in the moment, Watson’s cognitive abilities can examine more of the intangibles — for example, if a player’s attitude aligns with the team’s competitive atmosphere. Big data can theoretically stop toxic player relations before they start.

Sponsorship Returns

Teams that perform well generate more viewer interest, and thus get a bigger cut of the $24 billion in TV broadcasting deals that the NBA clinched for the nine years starting in 2014. Sponsorship agreements with brands like Pepsi and Anheuser-Busch raise the stakes even higher.

The Raptors-IBM partnership is still in its early days, and Watson’s data may, in the future, include medical data — perhaps players will even use wearables to track their health in real time. The idea isn’t to completely replace coaching staff and other advisors; it’s to build on the human element.

Athletes, coaches and front office staff have in the past had difficulty communicating their needs, but big data seems to be changing that with easier-to-interpret and more meaningful data.

“The biggest transformation in the world of sports isn’t simply the fact that there is so much more data available — it’s the fact that it’s breaking down barriers between groups that were historically distinct and sometimes struggled to communicate,” sports analyst Dash Davidson wrote for VentureBeat.

Catch of the Year

Meanwhile, baseball fans now have more statistics to look at than ever, in a sport where statistics are king. In April 2015, MLB Advanced Media (MLBAM) launched StatCast, which allows for deeper looks at every hit, defensive play and pitch, using an array of radars and hi-res optical cameras.

To herald the launch, MLBAM demonstrated StatCast’s abilities by analyzing the Blue Jays’ most jaw-dropping catch of the year, courtesy of outfielder Kevin Pillar. Last year, the Tampa Bay Rays’ Tim Beckham hammered a pitch deep into left field. Running out of room, Pillar used the left-field wall as a brace and sprung himself high enough to catch the ball. StatCast tracked Pillar’s top running speed (15.2 miles an hour), his distance covered (81.3 feet) and even his route efficiency (97.9%).

Since then, StatCast has given fans an endless array of data to further measure performance. It can give a deeper look at home runs, for example. On September 6, 2015, the Chicago Cub’s Kris Bryant hit the longest home run of the season. StatCast revealed just how impressive it was: It left Bryant’s bat at a scorching 111.5 miles per hour at a launch angle of 33 degrees and was projected to fly 495 feet.

StatCast’s petabytes of data can help general managers compare hits and defensive plays against players’ previous records. Players themselves, in turn, have better video to learn from.

On September 16, 2015, former Jays pitcher David Price wanted more insight into how Ryan Goins got an out on what looked to be an infield single from the Atlanta Braves’ Nick Markakis. Price asked StatCast on Twitter to analyze the play, and eventually analysis showed Goins took his first step just 0.24 seconds after the ball left Markakis’ bat. He covered 24.8 feet and threw the ball at 66.5 mph to achieve the out at first base.

Just as with the Raptors-Watson partnership, StatCast will no doubt become an important tool for front offices in their draft-pick decisions. Digital enhancements look to become as important as home runs and three-point shots.

 

IBM BlueMix