Keep up with the innovative tech transforming business
Tech Brew keeps business leaders up-to-date on the latest innovations, automation advances, policy shifts, and more, so they can make informed decisions about tech.
IBM Watson’s got a new job—as a bookie.
The AI system may not be taking bets or paying out winnings, but for the first time ever, it’s making its own daily player rankings and predictions at the US Open. Watson has a longstanding presence at the national tennis tournament, but in the past, it has focused on more straightforward tasks, like generating factsheets about players and summaries of major tennis debates.
IBM’s choice to spin the work forward into public player rankings represents a step forward in the mainstreaming of natural language processing (NLP)...and the industry’s increasing confidence in the tools.
How the tech stacks up
Each day during the annual tournament, which runs from August 24 to September 12, Watson is churning out a new iteration of its leaderboard. The process, which the company introduced at Wimbledon last month, is fueled by both statistical analysis and NLP (part of Watson Discovery).
How it works: There are two main ingredients for determining who comes in first, last, and everything in between: structured data (e.g., player performance specs) and unstructured data (e.g., media commentary).
For the latter, Watson uses NLP to gauge whether trusted news sources—like ESPN, Tennis Channel, and USOpen.org—are publishing positive or negative sentiments about a player.
- Case study: Articles have mentioned that Ashleigh Barty, an Australian tennis player, is “weary” or “exhausted” after spending five months away from home, sentiments which likely affected Watson’s ranking.
As far as structured data, more than 100,000 different statistics go into determining a player’s exact ranking throughout the tournament, Tyler Sidell, a technical program manager at IBM, told Emerging Tech Brew.
Examples include win velocity (how quickly a player beats their opponent), margin of victory (how close the win was), injury status, and more. IBM declined to share how the factors are weighted by the algorithm.
- Case study: Ahead of Friday’s match between Japanese tennis pro Naomi Osaka—the defending US Open champ—and 19-year-old Canadian upstart Leylah Fernandez, Watson ranked Osaka #9 and Fernandez #32. But Fernandez stunned both the crowd and the algorithm, besting Osaka in the third round.—HF