Why ‘Moneyball’ stats have taken tennis by storm

In last year’s final against Novak Djokovic, Nadal “absolutely decimated Djokovic in the short rallies,” Raghavan Subramanian, associate vice president and head of the Infosys Tennis Platform, told rolandgarros.com in an interview.

During his 6-0, 6-2, 7-5 defeat of the Serb in the 2020 title match, Nadal won 53 points in rallies of up to four shots, compared with 25 for his opponent. The Spaniard also kept his error tally very low; on the third shot of the rallies, Djokovic committed 16 errors, compared with just one for Nadal.

Especially on break points, Nadal “plays a very high-pressure, but slightly low-risk game,” said Subramanian. “He doesn’t go for the jugular, not too soon. But he plays a very tight game in most of the crucial points.”

The statistical analysis by Infosys, whose data-driven, artificial intelligence platforms register and crunch every single point played at Roland-Garros, gives players, coaches, fans and media invaluable insights into tactics or player patterns.

For example, the redesigned Infosys Match Centre on rolandgarros.com and the official tournament app give fans multiple ways to visualise a match. Fans can use MatchBeats for point-by-point play, stroke-summary to understand the go-to-strokes of players and rally-analysis to look at how a player changed tactics. They can also go to 3D CourtVision to gain serve, return, and rally-based insights.

Infosys has built a special app for players and coaches during Roland-Garros, which allows them to watch their matches back on video, analyse what the player did on certain pressure points such as break points, or how effective they were with certain shots.

Compared to other sports, tennis has been somewhat slow in adopting evidence-based data analysis, which was first made famous by Billy Beane, the general manager of American baseball team the Oakland A’s at the turn of this century.

Beane used data analysis to spot undervalued players, which allowed his team to successfully compete with much richer competitors. Their story was turned into the book 'Moneyball: The Art of Winning an Unfair Game' by Michael Lewis.

But in the past decade or so, tennis has embraced big data, and most players wouldn’t want to compete without their own version of 'Moneyball statistics.

“Nowadays statistics are very important,” said Tunisian world No.26 Ons Jabeur, who has someone on her team who helps her crunch the numbers after matches. “I don't think there is a single player who doesn't work with that, and obviously it will help us a lot.”

Sometimes, a player’s perception of what happened, and what actually happened, differ, as Darren Cahill found out when he did a coaching trial with Roger Federer in 2009.

Their trial took place shortly after the Swiss lost in the final of the Australian Open to Nadal. Cahill, the current coach of former Roland-Garros winner Simona Halep, had just started using a new computer programme to analyse tennis matches.

The Australian said he asked Federer how he had felt he played on the big points against Nadal in Melbourne.

“And he said: ‘Well, I was really aggressive and went for it on the first return to see if I can get to his backhand. I don't think I did much wrong’" Cahill recalled.

“And then we went through the tape, went through those points, went through where he was standing and he goes, ‘Woah, it's a little bit different to how I remember it’.”

Jabeur agrees that that reality doesn't always match recollection.

“Sometimes some things we don't see, and statistics show otherwise, so we are glad to say we work with that,” she added.

Wim Fissette, the coach of four-time major winner Naomi Osaka, introduced data analysis to the Japanese player when they began working together in December 2019. They have won two majors since.

“Naomi had never worked with data before,” Fissette told rolandgarros.com two weeks before the start of the tournament.

The Belgian, the former coach of Grand Slam winners Kim Clijsters and Angelique Kerber, mostly uses it for post-match analysis.

“For example, after Osaka’s second match in Madrid, it turned out that whenever she hit a forehand after her first or second serve, she would win 60% of the points. But if she hit a backhand, that would be just 42% of the points.”

“This makes a lot of sense of course on clay, but for Naomi to see it on paper, was very interesting for her. This is of course very different on hard court, the return comes back, and whether she hits a forehand or a backhand, she will just give it a whack and often she will win the point. But on clay, that’s different.”

Although Fissette said he is careful not to overload his players with post-match stats, he said he will typically give them “two to three points” from his data analysis after matches.

“The stats are very clear: it is there in black-and-white and you cannot have a debate about it,” said Fissette. “The numbers don’t lie. It shows you what you need to progress.”