Post: Tennis Analytics: Pros Now Use Data to Improve Their Game

tennis player walking across a court with a line chart graphic overlaying it

Tennis Analytics: Pros Now Use Data to Improve Their Game

Most of the big-name players are using it, but few are talking about it.
Tennis is experiencing an analytics revolution. Top tennis players are paying six-figure retainers to analyst teams in hopes of gaining an edge over their competition.

At the start of the 2017 season, for example, world number one Novak Djokovic employed full-time strategy coach Craig O’Shannessy of Brain Game Tennis. Twelve-time French Open champion Rafael Nadal has been looking to analytics since adding Carlos Moya to his team.

Data analytics is also seeing early adoption by some of the best women’s tennis players, such as number-one ranked Ashleigh Barty, who has worked with Tennis Australia analyst Darren McMurtrie.

This concept isn’t new. Analytics has been used by professional sports teams for almost two decades, most notably when Billy Beane of the Oakland Athletics used statistics to field a winning team on a budget. But, unlike baseball or premiere league football, tennis analytics isn’t simply concerned with scouting a team, but also with optimizing performance of world-class players.

The surge in sports data has made this not only possible, but inevitable. In the past, it was far more difficult to get films of an opponent’s practices and games in order to prepare. Now virtually limitless amounts of footage enable analysts to discern patterns and predict behaviors to prepare a champion for a match against a specific opponent.

Where did this data come from? For years IBM has supplied IT services to tennis championships like the US Open and Wimbledon. This has allowed them to amass streams of analytics data from matches. Data was initially used to provide viewers with analytic visualizations like its SlamTracker. The data collected over the decades has amounted to over 63 million data points. It includes data from championships, dozens of high-level players, courtside data and more, in all a rich mine of analytics data which is available to players and their coaches.

It was in 2017 that tennis analytics was thrown into the limelight after Roger Federer’s dramatic comeback in the Australian Open. It was rumored that the pro was paying a significant fee to Golden Set Analytics for exclusive services. The company employs statisticians, economists and mathematicians who develop statistical models and algorithms to support tennis pros.

One example of this is the “stat tree,” analysis which reveals where a player is most likely to hit the ball from each position on the court. Using this data, coaches can devise two-shot patterns to exploit opponent’s weaknesses.

These data services give a competitive advantage to the players who can afford them, which keeps the best at the top of the rankings. Several leading male players are using or experimenting with high-level analysis, but it is a luxury, both financially and with the skill level required to use such insights effectively in actual game planning and play.

Ultimately, the game still belongs to the elite players, but millions of data points can provide insights into an opponent’s game beyond what the naked eye can see, allowing players to modify and adjust their game plans. As the technology develops, it is likely it may be used while matches are in progress for real-time coaching.

“Tennis is mostly mental,” said Venus Williams. “…You win or lose the match before you even go out there.” For fans of the game, the question remains: How will this impact the way players approach a match, when they know their opponent has an analytical view into their weaknesses and predictable patterns of play? Will we see ever-longer rallies because players can better predict their opponents’ volleys? Or will the game see greater volatility overall, as players try to disrupt the forecasts of their data models?

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