When you look at the sport of tennis, it actually does come across as a game that has maximum human influence.
When a layman looks at the sport, all they see is players practicing before tournaments, working on their strengths and playing out their strategies during a match. We all see Roger Federer play, he is easily the best server in the game.
He has the most devastating forehand on tour and that single-handed backhand is worth its weight in gold.
Did I just describe the perfect player? Well, maybe! However, all of his 100 tour level titles haven’t been because of how great he is at the game. Enter analytics!
Data has always been prevalent in the sport of tennis. Using it to their advantage is something that players and coaches have recently started doing.
Initially every opponent would be studied by means of watching the opponents old match tapes and notes would be written down to devise strategy.
Data was available in terms of video and analysis was done by watching the video. Today, tennis has become extremely analytics intensive where players depend on numbers and use data analytics to improve facets of their game to take down their opponents.
Coaches initially used to work with primitive data like forehand winners, backhand winners, unforced errors etc.
Today rally length has become a key metric that defines a players win-loss percentage.
In a typical tennis match, the winner is decided with the number rallies won that consist of 0-4 shots. Winning the longer rallies 5-8 shots or 9+ shots, does not determine the outcome of the match anymore.
The longer rallies tend to be split evenly between the players.
“When you watch a game, the 15+ or 20+ shot rallies are the ones you remember and are the ones that make the highlight reels, however, they aren’t the ones that win you the match.” – words from Novak Djokovic’s assistant coach and ATP top strategist Craig O’Shannessy.
O’Shannessy’s words make a lot of sense. Take the case of the kid wonder Nick Kyrgios – All his games are super exciting, and you see long rallies, tweeners, hot dogs and it is just pure entertainment.
The kid is box office no doubt. However, he ends up on the losing side many a time because of the short rallies that he fails to win.
His very recent match against Borna Coric in Miami where he lost 6-4 3-6 2-6 is the perfect example of this.
Analyzing performance on short rallies is extremely important for a player. O’Shannessy says the most common rally length is just one shot.
It can be a return error or an ace. Data suggests that if you win the short rallies, the match is yours.
The approach to matches and practice sessions is changing drastically as newly available data is proving to be a massive influence.
The new approaches have moved on from improving a player’s game to more about analysing the opponent’s game.
Never does a player have the ideal or perfect game, hence, it makes more sense to force the opponent to commit the error.
Making the opponent uncomfortable is a huge paradigm shift that is being witnessed in tennis now.
Initially the approach was ‘we had a bad day, we’ll get him next time!’ Today, we have head to head comparisons, forehand winner to error ration, weaker wing metrics, weaker serve, rally length comfort etc.
Take the case of Robin Soderling vs Rafael Nadal at the 2009 Roland Garros. Nadal’s weaker wing was his backhand.
On clay, Nadal could slide and retrieve almost anything, if the rally went beyond 6 shots it was Nadal’s be default. Soderling came armed with all this data and needed to play hard hitting tennis that was extremely flat.
He needed to keep the points short and needed to attack Rafa’s backhand. Soderling executed the plan like a dream and ended up beating King of Clay after 4 continuous Roland Garros titles from 2005-08.
Since 1991, data gathered by the ATP and Infosys on all no 1s till date in the men’s game proves that the best returners in the game like Rafa Nadal, Andy Murray and Novak Djokovic are a part of the golden era in the last 5 years.
The server was always the one known to hold serve and not get broken. Today, the importance given to returning serves is driven by the logic of wining more points aka return games.
By improving their return games and the percentage of points they win, players give themselves a statistical edge of winning more games and matches.
The percentage of points won affects a player emotionally as well. By winning 53% of the points you played, you could become world no.1 in 1991.
Today, it has come to the stage where you need to win 60% of the points consistently. If a coach tells a player that on even his best day, he can lose 40-45% of the points, it stops them from emotionally imploding during a match.
Coaches today cut up the court into segments for better positioning. The most common place that the ball is struck from is inside the court i.e. inside the baseline.
By aggregating data about how deep the opponent hits the ball and how much spin he puts on it, the player can analyze this data to position himself so that he plays from an area of strength.
Analyzing data on how far back or how far in the opponent makes contact with the ball and where the errors come from also allows the player to dictate play by playing to opponent’s weakness.
How you hit the balls definitely matters, but where you hit the ball from on the court matters more. A sound strategy can be devised by using advanced analytics to slice and dice this data.
A simple example is how Roger Federer reinvented his game and stormed back to world no.1. Roger Federer used to play 3-4 feet behind the baseline.
This allowed the opponent to dictate play. At his age of 36, it became difficult to chase down balls like he used to. He took 6 months off and returned at the 2017 Australian Open.
It was noticed that he started playing 3 feet in, i.e, on the baseline. He was the aggressor now as he was taking time away from the opponent.
He also started hitting balls early, meaning he was taking about 3 seconds of time away from his opponent. This led him to winning back to back Australian open titles and a Wimbledon.
With the aim of winning the highest number of points, coaches nowadays come up with patterns of play that are most effective.
Data analytics has come a long way in tennis from just saying that if a player serves down the T, he will win the point to looking at the next shot which is the serve plus one.
Nadal is easily the king of this. Statistics reveal that on hitting 78 percent forehands after the serve, Nadal wins 65% of the points. That right there is literally game set and match.
This allows for coaches to come up with specific patterns of play against particular opponents.
Take the case of the Federer-Nadal Australian open 2017 final. In the previous years, whenever Federer served and the plus one shot was a backhand slice, Nadal won 58% of the points and Federer’s plus one was a slice 61% of the time.
Ominous signs for Federer they were. Federer changed it up and started hitting his backhands flat or extremely deep with added topspin. Nadal hasn’t beaten Federer in their last 5 meetings spanning over 4 years now.
Identifying a pattern does not mean that the player will use it every point of the game. The primary pattern can be concealed using other patterns, however, in worst case scenarios where a point needs to be won, the players will use the percentage pattern almost always.
In this article, I have talked about players and coaches are capitalizing on the availability of data analytics services in tennis.
The above-mentioned examples are a few ways as to how analytics has influenced player strategy and how it is giving players the competitive edge today.
Analytics has seen a boom only very recently in tennis. It still has a very long way to go. At the on-set, if it has such a massive impact, the impact that it will have on the game in the next 5 years is unimaginable.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.