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“We did some research a few years ago, which showed us that most people who get involved with Wimbledon aren’t actually 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.

As part of the “Match Insights with Watson” feature on the Wimbledon app and Wimbeldon.com, an ever-changing “IBM Power Index” ranking has been assigned to each player, courtesy of IBM Watson, l Enterprise AI for Business.

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 is 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.

An IBM technician poses with screens showing AI-generated highlights during Wimbledon 2019. The tournament has partnered with IBM to educate fans during the 2022 tournament.

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.

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Farrar explains that tournament data is compiled by a team of “very good tennis players” – usually at county level and above – who watch every match at Wimbledon, with three statisticians on the show courts and one on the outdoor courts. Hawk-Eye ball and player tracking is also used.

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).”

More than just a fortune hat: newspaper articles are analyzed by Watson to glean media sentiment towards players.

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. .

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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 talking point, and it engages with the fans, which is the key focus.”

“Sports fans love debate, so we give them something to debate.”

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