In today's sports tech innovation, one of the biggest breakthroughs is Computer Vision, a branch of AI that allows machines to interpret and analyze visual data. The technology elevates the experience for players, broadcasters and fans alike.
Image: Example of 3D visualization of tennis match analyzed by AI.
1. How does Computer Vision work in sports?
Computer Vision trains machines to recognize and interpret visual data, such as images and videos, using algorithms. In racquet sports, cameras capture real-time footage of matches or training sessions, which AI then analyzes to extract key data, including:
- Player movement: Tracking players, even if they leave and return to the court.
- Ball trajectory: 3D tracking of the ball’s path, speed, bounces, and hits.
- Court detection: Identifying court lines and bounce locations.
- Highlight generation: Capturing key moments like the longest rally, fastest shot,
or longest set/game.
The processed data provides actionable insights for players, coaches, and analysts, either in real-time or after the match. This makes the technology valuable for both performance improvement and broadcasting.
2. How Computer Vision is changing racquet sports
Improving player performance
One of the most important uses of computer vision is improving player performance. By analyzing things like technique, footwork, shot choices, and court positioning, players and coaches can spot both strengths and areas for improvement. For example, a tennis player might realize they lose efficiency by overreaching on backhands, or a padel player might find out they play defensive shots too close to the glass walls.
These insights help create focused training plans, allowing players to fine-tune their skills and achieve better results in matches.
Better coaching insights
Coaches gain a lot from the detailed data provided by computer vision. They can analyze their opponents' patterns, like where they place shots and how they move on the court. This helps coaches spot opportunities to adjust tactics during matches, such as targeting an opponent's weak side. Over time, they can build long-term strategies based on real data instead of relying on guesswork.
With this accurate information, coaching becomes more fact-based and less dependent on personal opinions.
Engaging broadcasting visuals
For fans and broadcasters, computer vision transforms the way matches are viewed and understood. Key improvements include:
- Real-time statistics: Displaying ball speed, and player movements during live matches.
- Heatmaps: Showing areas of the court where players spend the most time.
- Slow-motion replays: Highlighting critical moments with detailed data overlays, such as the exact angle of a winning shot.
- Technique comparisons: Comparing the body movement of players against each other or showing development over time.
This makes the game more exciting to watch and easier for fans to understand. Broadcasters can use these insights to share engaging stories that capture the audience's attention. Eye-catching 3D visuals can serve as great highlights for studio segments or to keep viewers engaged during breaks.
Injury prevention and recovery
Computer vision can help prevent injuries by analyzing how players move. For example, it can detect overuse of a player’s dominant arm or spot imbalances in their footwork, allowing coaches to address these problems early. During recovery, athletes can use data to monitor their progress and measure improvements.
Officiating and rule enforcement
Computer vision is already used at major tennis tournaments like Wimbledon, the Australian Open, and the US Open through tools like Hawk-Eye, which makes real-time line calls to check if a ball is in or out. But its potential doesn’t stop there. AI can also monitor rules, such as tracking how long players take between points to ensure they stay within the allowed time limits.
The future of AI in sports broadcasting
As AI technology advances, even more powerful uses are on the horizon. Imagine AI not just analyzing matches but also simulating future scenarios based on past data. Players could train by practicing against a virtual version of their next opponent, similar to how chess players study an opponent’s strategies. One thing is certain: the future of racquet sports isn’t just happening on the court—it’s being shaped and visualized through AI-powered computer vision
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