It’s not designed as a lightweight; not everyone can be an armchair pro.
“We did some research a few years ago, which showed us that most people who get involved with Wimbledon are actually not year-round tennis fans,” says Alexandra Willis, director of marketing and communications at the All England Club, which hosts the tournament. .
“What we heard anecdotally was, ‘I’ve heard of a few top players, but actually haven’t heard many others’ and ‘it all sounds a bit confusing and confusing. “”, she adds.
It’s understandable. Tennis is experiencing a time when the men’s game, and to some extent the women’s game, was defined by a small quota of dominant players with astonishing career longevity.
To fill the knowledge gap, the All England Club has partnered with IBM to use artificial intelligence (AI) and big data to boost fan engagement – and try to predict every match winner in the process.
Think Moneyball, only for fans.
The rankings are generated by analyzing athletes’ form, performance and momentum, says Kevin Farrar, head of sports partnerships at IBM UK & Ireland. “Because it’s updated daily…you can see (players) watching, (and) it can start to identify potential upset alerts – all of which are great for fans,” he explains.
The idea is to help less-initiated fans find players to follow, “growing their own fandom,” says Willis. Users can choose to follow players and receive personalized highlights as the tournament progresses.
Watson’s party article uses data to predict every match winner. Displayed as a simple percentage probability, the AI makes the call based on millions of data points recorded before and during the tournament. Factors include previous results between athletes, current form, and finer details like first-serve winning percentage, ace frequency, and first-serve return percentage of won points.
However, not all data fed into the predictor is based on reliable statistics. Curiously, the positive or negative sentiment of the media is also taken into account, combing through thousands of press articles about the players.
“One of the markers of ‘who’s interesting?’ is ‘who is the media excited about?'” Willis said. “Many members of the media, especially in a sport like tennis, where they’re with the players week in and week out, have an idea and an understanding of how people play – those kind of soft factors that don’t necessarily appear in (structured data points).”
Farrar reported that Watson predicted the results with “almost 100% accuracy” on day one of the tournament, but day three provided her first big upset when women’s No. 2 seed and 66% match favorite Anett Kontaveit was beaten by Jule Niemeier in straight sets. .
Despite employing one of the world’s most famous AIs, Willis insists “it’s not meant to be exact or an exact science.”
And even if Watson loses, it’s still a win-win, insists Farrar. “It’s an interesting topic of discussion, and that’s the engagement with the fans, which is the key goal.”
“Sports fans love debate, so we give them something to debate.”