Wimbledon wants to educate its audience about tennis. Luckily, there’s an app for that

This is not intended as a slight; not everyone can be an armchair pro.

“We did some research a few years ago, which showed us that most of the people who interact with Wimbledon are actually not year-round tennis fans,” said Alexandra Willis, director of marketing and communications at All England Club, which hosts the tournament .

“What we’ve heard anecdotally is, ‘I’ve heard of some top players, but I haven’t really heard of many others’ and ‘it all sounds confusing and confusing,'” he added.

It’s understandable. Tennis is experiencing an era where the men’s game and to a degree the women’s is defined by a small quota of dominant players with impressive career longevity.

To fill the knowledge gap, the All England Club teamed up 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, focused solely on the fans.

As part of the “Match Insights with Watson” feature on the Wimbledon app and Wimbeldon.com, a dynamic “IBM Power Index” ranking is assigned to each player, courtesy of IBM Watson, the AI ​​for business company.

The ranking is generated by analyzing athletes’ form, performance and momentum, explains Kevin Farrar, head of sports partnerships at IBM UK & Ireland. “Because it’s updated every day … you can see (players) to watch, (and) it can start to identify potential upset alerts — everything is interesting to the fans,” he explained he.

The idea is to help less-initiated fans find players to follow, “building their own fandom,” Willis said. Users can choose to track players and provide them with 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 on the 2022 tournament.

Watson’s party piece uses data to predict each match winner. Shown as a simple percentage probability, the AI ​​makes the call by drawing on millions of data points recorded before and during the tournament. Factors include past results between athletes, current form, and more details such as first serve win percentage, ace frequency and percentage of points won on first serve returns.

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Farrar explained that the tournament data was compiled by a team of “very good tennis players” — usually county level and above — watching every Wimbledon match, with three statistics on the show courts and one outside the courts. Hawk-Eye ball and player tracking is also used.

However, not all data sent to the predictor is based on hard statistics. Intriguingly, positive or negative media sentiment is also taken into account, scanning thousands of news articles about players.

“One of the markers of ‘who’s interesting?’ is ‘who’s excited about the media?'” Willis said. “A lot of members of the media, especially in a sport like tennis, where they’re with the players week in, week out, have a sense and understanding of how well people play — the types of soft factors that don’t necessarily come out of (structured data points).”

More than just a temporary hat: newspaper stories are analyzed by Watson to glean media sentiment among players.

Farrar reported that Watson predicted the results with “almost 100% accuracy” on the first day of the tournament, but the third day provided its first major upset when women’s number 2 seed and 66% match favorite Anett Kontaveit was defeated by unseeded Jule Niemeier in straight sets.

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Despite using one of the most popular AIs in the world, Willis insists that “it’s not intended to be exact or an exact science.”

And even if Watson loses, it’s still a win-win, Farrar insisted. “That’s an interesting talking point, and it engages the fans, which is the main goal.”

“Sports fans love to debate. So we give them something to debate about.”