Tennis and Artificial Intelligence (AI): Three Powerful "Use Cases" with the Promise of Taking the Game to a New Level

 Photo Courtesy: promptshine.com

I. What is Artificial Intelligence or AI?

Artificial Intelligence or AI refers to computer systems that perform tasks of human intelligence such as visual perception, speech recognition, sensory intake and even decision-making. 

Sometimes called machine-learning, AI utilizes powerful semi-conductor chips called GPUs (Graphics Processing Units) matched with high-speed advanced software to produce accelerated computing - which is a computer system "learning from itself", and doing tasks at faster speeds and greater precision than what humans can do. 

Meanwhile, Generative AI is a methodology of harnessing and synthesizing complex data, and then generating some new information or idea, in the form of text, image or video. These new results are unbound by organic biochemistry and thus human imagination, and look astonishing. And promise to get even better. 

The purpose and promise of AI is to improve productivity and enhance performance in any field of work, business or human activity. 

Of course, most of us are familiar in our daily lives with the most rudimentary artificial intelligence - baby AI - such as Google and Amazon searches, iPhone Siri inquiries, and fitness tracking such as in Apple Watch or Fitbit. 

But AI technology has now reached a higher stage.  

II. What about Tennis and AI?
Let's take the sports arena of tennis. 

Tennis is among the most data-intensive of sports, generating tons of informational data in each player match or practice session. 

For example, the data consists of: groundstrokes, forehands, backhands, volleys, serves, returns, overheads, player movements, errors and winners, ball spin, pace, depth and placement, and much more. In short, millions of available data points are ripe for harnessing and analysis. 

AI applies the tools of motion analysis, statistical review and historical information retrieval. And generates patterns, trends and fresh insights. 

Let's see how AI data-driven technology is now being used and can further be developed in tennis. 

III. How is AI applied to Specific Uses in Tennis?

Use Case #1: PLAYER DEVELOPMENT

Player development can be improved with AI, from the recreational to the professional level. For example, Digital Video can capture strokes and movements on a granular, even individual pixel, scale. 

And AI motion analysis can spot patterns and deficiencies in movements and strokes. Individual player strengths and opponent weaknesses can be identified. 

Indeed, even injury potential in a player’s strokes can be revealed, to help prevent injury. 

Player video can be juxtaposed with video of correct and effective player technique consistent with natural human biomechanics movement. 

Thus, a clear and cogent visual illustration is generated to guide and develop player technique. 

A multitude of Apps are currently blazing a trail in these areas, and working to improve their offerings, including: Coach’s Eye, Baseline Vision, Tennis Plus, AI Tennis Trainer, Smashpoint, PlaySight, MotionCloud and others.

As part of motion detection, these Apps can also track and record metrics such as percentage of forehands vs. backhands, breakdown of topspin vs. slice vs. flat, down-the-line shots vs. cross-court, winning points vs. losing points and more. 

Motion analysis is even being incorporated into the next generation of player equipment. For example, racket manufacturer Babolat has engineered a line of rackets with built-in sensors which track data during play. And connects to a smart phone application via Bluetooth to provide feedback. 

Even racket dampeners and wrist bands are now are being modified with sensors to track data during play and practice. Qlipp is an example of the next generation of “smart” dampener. While Babolot Pop  is an example of the “smart” band. 

Tennis courts themselves can now be transformed into a "smart court", with specialized electronic netposts able to record data, collate statistics and make line calls. An example is: Wingfield (AI Powered Tennis).

Sensors capture and record information such as stroke speed, ball spin, match statistics, and even racket vibration, and reveal patters and discrepancies to improve player performance. 

Beyond the physical game, AI-inspired Apps now even seek to improve mental performance on court with skills training, such as with Apeak Tennis. 

Use Case #2: MATCH PLAY

AI can improve match play as well. Millions of data points are generated in every match, including about player styles, preferences, predilections and quirks. AI can then harness this information, and offer it up to help improve match play and spectator knowledge. 

Sense Arena for instance is an immersive “virtual reality”  platform enabling the user from the comfort of home to literally play against players with different player styles, such as an aggressive baseline or a heavy net player. It can also help injured players stay sharp as they recover. 

Researchers at Stanford and elsewhere are developing this type of virtual reality to a new level where you would play a simulation of Andy Roddick’s serve or Roger Federer’s net play or Serena Williams’s groundies. 

Meantime, apps such as Swing Vision and Sevensix allow you to capture video from your smart camera and replay your match forehands, backhands, volleys and so on. And they display metrics such as win percentage, ball placement and depth, and type of ball spin (flat, slice, topspin). 

Meanwhile, for the benefit of fans, IBM Watson technology offers Open Questions and Match Insights. Open Questions is a large language model which can moderate fan discussions such as “is Roger Federer or Rafael Nadal the best player in the history?”. 

Match Insights collects and analyzes millions of news and journalism articles and tennis videos about major players. And offers fans fresh and data-rich insights about players and matches. 

Examples include items such as a “tip sheet” on players and matches, a ranking of entertaining points based on crowd and commentator reactions, and even predictive information about what type of shot or play a player might likely use.  

AI algorithms are being developed to even include how variables such as environmental noise, crowd reaction or quiet, weather events or merely the psychological threat of bad weather might affect a given player’s game and results. 

As for game officiating, the technology of Electronic Line Calling (ELC), a familiar form of AI which the tennis public knows as Hawk-Eye, has radically altered how matches are conducted. 

Digital video coupled with computerized 3D instant replay on TV has revolutionized officiating and player disputes. In fact, the ATP has committed to full ELC in all official matches by 2025, doing away with human umpires. 

Use Case #3: COACHING & STRATEGIZING

An obvious and emerging outcome of AI development is how it can help in coaching and strategizing. AI plainly tracks player movements and their balls. As such, coaches can use AI information to quickly spot and correct their player weaknesses, or identify and fortify strengths. 

On the other side, an opponent player’s movements and their balls can also reveal subtle patterns and discrepancies. AI-tracked data for example can immediately spot how often a player runs around his backhand or what kind of second serve he typically falls back on. Players can then be coached to attack and exploit such opponent patterns.

AI can be also used to help optimize and implement winning game strategy. For example, AI would start by accumulating exhaustive data on each player. In particular, AI might focus on their match losses, and what type of winning strategies most often worked against them, and under what court or weather conditions. 

You could then scan that particular player on your smart phone or watch, and immediately see an “augmented reality” overlay displaying suggested actions against that player to consider in the match. 

AI suggestions might include: the best strategy to employ and under what court or weather conditions; the best ball spin and direction that could be used for optimal success; the best style of play to use to maximize probability of winning the point such as moving to net or aggressively slicing the ball and so on. 

Furthermore, in the same way, AI-tracked data can also be instrumental during a match, as play develops sometimes unpredictably. With the same historical information and player behavior, AI can suggest strategic adjustments and tactical changes in real time while match play is ongoing. Examples are: where to serve, which type of shot to play and when to attack the net. 

IV. Conclusion 
Artificial Intelligence (AI) has now undeniably emerged in the arena of tennis, and offers the promise of taking the sport to a whole new plateau. 

Players can be trained faster and developed more effectively. Matches can offer a higher level of play based on player access to better court knowledge. 

Officiating can be made more precise and accurate, thus making spectator viewing more satisfying. And coaches can strategize and advise their players more cogently and productively. 

A Whole New World of Tennis is here and growing bigger!