IBM and the United States Tennis Association are introducing a new real time artificial intelligence system at the 2026 US Open that tracks 21 spatial points across a player’s body 50 times every second. The technology is designed to provide a detailed view of serve mechanics across all 254 singles matches, giving broadcasters, analysts and fans a new way to see what happens during one of tennis’s most technically demanding movements.
A New Layer of Analysis for the US Open
A tennis serve happens in a fraction of a second. A player tosses the ball, bends the knees, rotates the torso, accelerates the shoulder and snaps the racket through contact before landing and recovering for the next shot. To the human eye, those movements can appear almost instantaneous.
The new AI system aims to make those movements measurable. By tracking 21 spatial body points at a rate of 50 observations per second, the technology can create a detailed representation of how players move during the serve.
For fans watching a match, that information could turn a familiar statistic such as serve speed into something much more descriptive. Instead of simply seeing how fast a serve traveled, viewers could gain insight into the physical mechanics that helped produce it.
How AI Serve Tracking Works
The system uses computer vision and advanced analytics to interpret movement captured during competition. Rather than treating the player as a single moving object, the technology examines multiple points across the body and follows their changing positions over time.
Tracking 21 points provides a much richer picture than conventional measurements based only on the ball or racket. The system can observe relationships between different parts of the body as a player moves through the service motion.
At 50 observations per second, the analysis can capture rapid changes that are difficult to perceive in normal broadcast footage. This creates a large stream of movement information that artificial intelligence can organize into useful insights.
Why 21 Spatial Points Matter
A serve depends on coordination. The legs generate force, the hips and torso rotate, the shoulder and arm accelerate, and the racket transfers that movement into the ball.
When those elements work together, a player can generate substantial speed while maintaining balance and control. When timing changes, even slightly, the result can be a slower serve, reduced accuracy or additional physical strain.
Tracking numerous body points allows analysts to examine these relationships more closely. The result is a form of tennis analysis that focuses not only on what happened but also on how the movement happened.
From Statistics to Movement Intelligence
Tennis has always been a data rich sport. Fans are accustomed to seeing serve speed, aces, double faults, break points and first serve percentages. Those figures tell us what happened during a match, but they do not always explain the physical reasons behind a player’s performance.
AI based movement analysis can add another layer. A serve may be successful because a player creates strong leg drive, achieves efficient rotation or maintains a consistent contact position. A player may also experience a sudden drop in serve effectiveness because the timing between different parts of the motion changes.
We can think of this as the difference between a scoreboard and a motion study. The scoreboard records the result. Movement analytics can provide clues about the process.
What Fans Could See During Matches
The most visible benefit may come through television and digital coverage. Advanced statistics can be difficult for casual viewers to interpret, but carefully presented visualizations can make complex information easier to understand.
A broadcast could potentially show a player’s movement sequence, highlight important phases of the serve and compare different serving patterns. Such information can give viewers a clearer appreciation of why elite players generate extraordinary pace and consistency.
For longtime tennis fans, this may provide a new way to examine familiar players. A champion’s serve can look effortless from the stands, yet the underlying movement may involve dozens of precisely coordinated actions.
All 254 Singles Matches Bring Scale to the Project
The fact that the system is being applied across all 254 singles matches gives the project an unusually broad competitive sample. Rather than focusing on a handful of featured players, the technology can be used across the tournament’s singles field.
This scale matters because tennis players have different physical builds, serving styles and technical preferences. Some rely heavily on speed, while others prioritize placement and disguise. Left handed and right handed players can also create different visual patterns.
A large dataset can therefore provide a broader picture of serving behavior at the highest level of professional tennis. It may reveal common movement characteristics while also showing how individual players depart from those patterns.
Artificial Intelligence Could Change Tennis Commentary
Commentators often explain technical aspects of tennis using experience and visual judgment. They may notice that a player is opening the shoulders too early, losing balance after contact or changing the height of the ball toss.
AI analytics can provide additional evidence for those observations. Instead of relying entirely on visual interpretation, analysts can use quantified movement information to support their explanations.
This could make commentary more informative without replacing the human expertise that makes sports broadcasting engaging. The most useful role for AI may be to provide information that experienced commentators can translate into language audiences understand.
The Technology Could Help Explain Performance Changes
Professional tennis matches can change rapidly. A player may serve brilliantly for one set and struggle in the next. Fatigue, pressure, tactical adjustments and physical changes can all contribute.
Movement tracking offers a potential method for identifying mechanical changes during a match. If the relationship between body points shifts over time, analysts may be able to identify differences in movement patterns associated with changes in serve performance.
That does not mean AI can automatically determine why a player is struggling. Human context remains essential. Fatigue, injury, confidence and tactical decisions are not fully represented by a collection of spatial coordinates.
Potential Value for Coaches and Players
Although the immediate focus is on tournament coverage, detailed movement data could also have value for coaching and player development. Coaches already study match footage extensively. Additional movement information could provide another tool for evaluating technique.
A player could potentially compare different serves and identify changes in body positioning, timing or balance. Over time, repeated analysis could help establish a clearer picture of which movement patterns are associated with consistent performance.
However, data should remain a tool rather than a substitute for coaching judgment. Tennis technique is highly individual, and a movement pattern that works for one athlete may not be appropriate for another.
AI and the Human Side of Professional Tennis
There is an understandable temptation to treat advanced sports technology as a replacement for human expertise. Tennis offers a useful reminder that the most meaningful insights usually come from combining both.
A coach can see a player’s body language after a missed serve. A commentator can recognize a tactical shift. A sports scientist can interpret physical demands. AI can contribute precise measurements that support those observations.
That combination is where the technology becomes most valuable. Numbers alone cannot capture the emotional pressure of serving at match point in a packed stadium. They can, however, help explain the physical sequence that produces the serve.
Data Privacy and Athlete Considerations
The use of detailed movement analytics also raises questions about how athlete data is collected, stored and used. Movement information can reveal more than simple performance statistics, particularly when it is captured repeatedly throughout competition.
Professional sports organizations increasingly need clear policies governing access to performance data. Players and teams may want to know who can use the information, how long it is retained and whether it can be incorporated into future commercial or analytical products.
These questions will become more relevant as computer vision systems become capable of extracting increasingly detailed information from sports footage.
IBM’s Broader Role in Sports Analytics
IBM has a long history of working with sports organizations to apply data analysis and artificial intelligence to competition and fan experiences. Its work with the USTA places advanced computing technology directly alongside one of the most visible tennis tournaments in the world.
The IBM sports technology platform illustrates how data and AI are increasingly being used to provide deeper analysis and digital experiences for sports audiences.
The US Open provides an especially useful environment for this type of technology because it combines a large international audience with a huge amount of competitive data generated over a concentrated tournament period.
Why This Matters Beyond One Tournament
The significance of the new system extends beyond the 2026 US Open. Professional sports are becoming increasingly interested in technologies that can convert video into structured performance information.
Computer vision can potentially be applied to many aspects of tennis, including movement around the court, recovery after shots, positioning and tactical patterns. Similar methods can also be adapted for other sports where body movement plays a central role.
The development of more sophisticated movement analytics could eventually change how athletes train, how commentators explain competition and how fans consume sports statistics.
A More Detailed View of the Serve
The serve has always been one of tennis’s great demonstrations of coordination. A player standing behind the baseline may appear still for a moment before suddenly launching into a complex sequence of movement.
AI analysis gives us a chance to slow that sequence down and examine it with a level of precision that ordinary viewing cannot provide. The 21 tracked body points become a kind of digital map, showing how the player’s physical position changes through the motion.
At 50 observations per second, that map becomes detailed enough to reveal subtle differences that could otherwise disappear between frames of conventional footage.
The Future of Data Driven Tennis
The 2026 US Open experiment represents another step toward a sport in which performance data is becoming increasingly visual, immediate and accessible. The challenge will be deciding which information genuinely helps audiences and athletes rather than overwhelming them with statistics.
The strongest applications of AI in sports are likely to be those that answer clear questions. Why did the serve become faster? How did the player generate more rotation? What changed between two successful points? Which movement pattern appeared consistently during a strong serving performance?
When technology can help answer those questions in a clear and understandable way, it adds genuine value to the viewing experience.
For fans sitting in the stands or watching from home, the result may be simple but powerful. A serve that once appeared as a blur of motion can become a sequence we can examine, compare and understand.
That is the promise of the IBM and USTA initiative at the 2026 US Open. By combining artificial intelligence, computer vision and real time sports analytics, the tournament is giving audiences a closer look at the mechanics behind elite tennis while creating a substantial new source of performance data. The technology does not replace the drama of competition. Instead, it gives us another way to see the extraordinary physical precision hidden inside every serve.
