Every time a tennis ball hits the court, data is collected – much like in baseball or premier league football – that can give players an advantage.
Top men’s players and tour coaches regularly utilise this analysis, while some women’s tour coaches are beginning to utilize it too; however, most find this service out of reach both financially and skill-wise.
Match Data
Umpires use tablets to record every time the ball hits a tennis net during a match and relay this data in real-time to the scoreboard system, broadcasters and media channels – providing players, coaches and fans alike with invaluable insight into the game as they identify strengths and weaknesses, determine strategies of opponents, and build better strategies themselves.
Tennis analytics aims to provide objective analyses of player performances to aid them in improving. Furthermore, tennis analytics can also be used to uncover new talent or predict tournament outcomes. Historically this process required manually reviewing video clips of matches but thanks to technology it has become more efficient; today analysts can use specialized software programs such as Smashpoint that analyze point-by-point video footage and provide information on rally progression.
Match analytics can reveal hidden patterns that could alter the outcome of a match, such as an opponent’s tendency to serve wide on deuce points. They can also assist coaches and players in understanding which aspects of their matches are most essential in terms of winning; this enables them to focus their practice sessions accordingly and achieve better results.
Information like this can make or break a player’s career. It can provide insights into which shots to focus on and avoid; and may give a player insight into their weaknesses; for instance if they lack the speed needed to win long rallies they may require more work on fitness than they initially anticipated.
Analytics in tennis have transformed the way we experience and view the game, giving fans a more immersive experience while changing how players and coaches approach tennis. Unfortunately, analytics do have their limitations; for example, it cannot completely negate intrinsic factors like match day condition and mental approach of each individual player.
Career Data
Although tennis analytics is still relatively undeveloped in comparison with most other sports in terms of data collection, its growth is on the rise among coaches and players wanting to advance their game. Big data allows coaches to examine patterns within opponent behavior to predict what their next move may be in any given scenario.
An opponent might hit deep volleys into the court or up the line, enabling players to devise strategies to exploit this weakness. Furthermore, big data can reveal whether an opponent tends to serve wide or short, providing insights on how best to approach that server.
At present, top tour players pay six-figure retainers to analyst teams in order to get expert advice on their game. But thanks to technological innovations, it is possible for you to collect similar data and analyze it yourself.
One effective method of assessing player performances involves labelling match videos to evaluate them. This can be accomplished manually or using special software designed to recognize key factors like number of aces, court area spent time and type of shots played.
Use of a camera to track ball movement can also be utilized, either manually or with software such as SwingVision which uses artificial intelligence for automation of this process. After tracking each shot taken by each player, analysis software then analyzes these shots in detail in order to reveal both strengths and weaknesses within their play style.
Some players have utilized this data to enhance their own performances. Craig O’Shannessy, an ATP strategist who works with Novak Djokovic and other players, conducted research into how different strategies affect players. For instance, he has observed that rallies featuring four or more shots tend to occur less often; suggesting shorter rallies offer players more effective attacks while minimizing exertion and fatigue.
Scoring Data
As tennis is typically played on small courts with an unpredictable scoring system that defies convention and consistency, traditional statistical analysis has proven difficult. But that has begun to change with more efficient and affordable technologies available for measuring performance analysis.
As such, most ATP and WTA tour players rely on analytics. Some utilize it for opposition scouting while others like Novak Djokovic and Craig O’Shannessy use statistical information to inform their playing styles and maximize competitive advantages against opponents. Data points collected through various sensors – like Hawk-Eye camera systems installed on tennis balls as well as GIG and TennisViz companies with innovative technologies for processing and analyzing the information.
These technologies aim to assist coaches and players in recognizing patterns in opponents’ play, so that two-shot patterns can be devised to exploit any weaknesses and ensure success on tour. Tactics such as these play an integral part in player success.
Talent and fitness remain essential, but data analytics are opening up an entirely new sphere of strategic gameplay for world-class tennis players. By knowing their opponent tends to serve deep in fifth sets or rush the net more frequently than expected, this information could make a crucial difference between winning and losing a match.
As with the introduction of statistical innovations into sports (for instance, through outsiders like 19th-century English cricket reporter who became baseball’s father or night shift worker at pork and beans factory who wrote foundation texts for sabermetric societies), technological innovations could potentially bring an upheaval similar to Moneyball in tennis as well. Dismantling any barriers that stand in its way may prove challenging – just like what was required when sabermetrics came to baseball.
While it will take time before tennis players recognize data scientists as part of their training team, with technological innovations coming online soon enough, it may soon be that champion players express gratitude towards both.
Performance Data
Tennis may seem like a game for individuals, but data analytics make this sport particularly well suited to data collection and use. From helping players understand the best way to practice to better preparing against opponents, tennis data allows athletes and coaches to take their game further than ever.
As tennis has long been subjected to qualitative and quantitative analyses, but prior to the automation of data collection systems in modern sports. Recently however, this technology has begun gaining steam, quickly becoming an invaluable asset in player preparation and analysis.
Now it is increasingly common for fans watching tennis matches both at home and at the stadium to see visualisations of standard statistics following each set, including first service percentage, break points, unforced errors and opponent performance comparisons. While this approach is great way of engaging and educating fans alike, its value should not only be limited by this method.
Tennis analytics have the ability to enhance coaching decisions with ease. By understanding a team’s strengths and weaknesses, coaches can devise more successful strategies. Knowing that their competitor tends to aim more toward shooting towards the net instead of trying lobs in the back court could enable players to exploit this weakness by exploiting this weakness in his or her game plan.
Professional players stand to benefit even more from this data. By employing technology that analyzes shot length, players can better pinpoint both their own and opponents’ sweet spots – giving coaches insight into which drills may most help improve players’ game.
Data Driven Sports Analytics (DDSA), founded five years ago by Shane Liyanage – a Montreal-based former pro player – and located in Australia has led this field in terms of innovation. Their analysis helps players know whether crosscourt forehand or slice backhand down the line is most effective and which opponents they should avoid in longer rallies if endurance wears thin.