A year ago the Raptors were victims of an unexpected 4-0 sweep in the first round of the NBA playoffs at the hands of the lower-ranked Washington Wizards. They’re now tied with the Miami Heat 2-2 in a grueling best-of-seven series, which will see the eventual winners play LeBron James and the Cleveland Cavaliers in the Eastern Conference finals.
Maple Leaf Sports & Entertainment, which owns the Raptors, announced in February that it would use cognitive analysis provided by IBM’s Watson technology platform, noting that Watson would be used mostly for talent acquisition.
Is cognitive technology one of the factors behind the improved performance?
It’s difficult to answer that question because the Raptors consider the IBM agreement to be a competitive advantage, and are therefore mum on this subject. With a head coach as tight lipped as Dwane Casey, this is hardly surprising.
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There’s only been one addition to the team since IBM and the Raptors announced the use of Watson, so the application of cognitive technology might not be a factor unless they’re using Watson’s analysis for more than recruitment.
Unstructured Data
Traditional analytics-based approaches require data to be tightly structured and presented in a predictable format. Watson is different in that it makes sense of large volumes of unstructured data — video footage, news articles, scientific journals, social media, as examples — which a few years ago would have required a human to interpret.
And the machine learns. The more data it’s fed, the better its predictions become. Each suggestion is even accompanied by a confidence level rating.
In professional sports, Watson can be used to determine, based on any number of parameters, if a player will be a good fit for a team’s social dynamics. Artificial intelligence can quickly identify, for example, a player who will fill the needs for a particular position, work within salary restrictions, and play well with teammates.
With two crucial contracts — Bismack Biyombo and DeMar DeRozan — on the table at the end of this season, a salary cap to manage and four first-round draft picks over the next two years, the next incarnation of the Raptors lineup is far from clear.
While it’s tough to gauge Watson’s impact on the current season, the platform is likely learning much as it analyzes players on and off the court. Whatever the case, fans should expect the big data addition to play a bigger role in the Raptors’ strategy going forward.
Frequently Asked Questions
How were the Raptors using IBM Watson in 2016?
Maple Leaf Sports and Entertainment announced in February 2016 that it would use cognitive analysis from IBM's Watson platform for the Raptors, mostly for talent acquisition.
Did Watson help the Raptors improve in the 2016 playoffs?
It was hard to say. The Raptors considered the IBM deal a competitive advantage and kept quiet, and only one player had been added since it was announced, so Watson may not have been a factor unless used beyond recruitment.
How is Watson different from traditional analytics?
Traditional analytics need tightly structured data. Watson makes sense of large volumes of unstructured data such as video, news articles and social media, learns as it is fed more data, and gives a confidence rating with each suggestion.
How can Watson help with team building in pro sports?
Watson can assess whether a player fits a team's social dynamics, and quickly identify players who fill a position's needs, work within salary limits and play well with teammates.
What roster decisions faced the Raptors after the 2016 season?
The Raptors faced two crucial contracts, Bismack Biyombo and DeMar DeRozan, a salary cap to manage and four first-round draft picks over the next two years, which the article expected Watson's analysis to inform.