Discover how AI in basketball is transforming the game with player tracking, analytics, and AI-powered officiating, with the NBA leading the way.
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Thanks to technological advancements, fan engagement and player analytics have become an important part of the sports industry. Sporting events are increasingly being driven by data, and AI is playing a huge role in this shift.
Previously, we've seen how technologies like computer vision, which helps computers see and understand what’s happening on the field, have made a big impact in fields like Formula One and the Olympics. Similarly, the National Basketball Association (NBA) has recently been making headlines for using AI in new, innovative ways.
However, the NBA entered the AI conversation a while ago. Since the league started in 1949, it’s been quick to adopt new technologies to connect with fans and improve the game.
Today, computer vision models like Ultralytics YOLO11 are taking basketball performance analytics a step further by enabling real-time object detection and tracking. Vision AI makes it easier to analyze the game on the fly and get a better understanding of what's going on.
In this article, we take a closer look at how AI and computer vision are reshaping basketball. We'll discuss how these technologies help teams track players in real-time, analyze performance data more accurately, make smarter coaching decisions, and create a better experience for fans.
Before we dive into how AI is being used to improve basketball games, let's take a look at how AI in sports has evolved over the years.
In the early days, sports analytics mostly relied on basic statistics and manual record-keeping. That began to change in 1997, when AI-based player tracking systems, like Prozone, started capturing player movement data.
By 2009, the NBA took a big step forward with SportVU’s AI-powered ball and player tracking. It marked a new milestone that unlocked detailed, data-rich analysis that changed how teams looked at player performance and game strategy.
In the past few years, we’ve seen a wide variety of AI techniques being used in sports - from machine learning for predictive analytics to computer vision for real-time analysis and robotics that assist with training.
As these technologies continue to evolve, AI-driven analytics are becoming common at both sporting events and practices, helping teams gain a competitive edge and giving fans deeper insights into the games they love.
One of the most exciting ways that AI has been brought to the NBA this season is through robots. The Golden State Warriors are leading the way with their Physical AI initiative, a cutting-edge system of AI-powered robots that assist during practice sessions.
These robots help with everything from rebounding and passing drills to simulating defensive plays, letting the players get instant feedback on their performance.
In a clip released by the team, Golden State Warriors point guard Steph Curry commented that although it felt weird at first, the robots have quickly become an integral part of their training routine.
Here are some other fascinating ways the NBA is using AI:
The 2025 NBA All-Star Technology Summit was predominantly about AI innovations. In fact, in a recent podcast, Philadelphia 76ers President for Basketball Operations, Daryl Morey, explained how AI, especially large language models (LLMs), has become an integral part of the decision-making process.
Morey noted, "We absolutely use models as a vote in any decision," emphasizing that AI now plays a role in evaluating everything from draft picks to game strategies. These models combine real-time data, historical performance, and other insights to predict trends and outcomes, adding a new layer of precision to how teams plan for the future.
Morey went on to explain the role of LLMs in this process: "It turns out LLMs do fairly well at prediction. They still are not beating human, like, super forecasters ... They do add signal over just scouts and things like that. So we'll treat them almost like one scout."
Over time, as these models improve, they may play an even larger role in shaping the future of the NBA.
So, how do Vision AI applications like real-time player tracking in basketball work? Let's take a step back and walk through the technical details.
Models like YOLO11 support a range of computer vision tasks, such as object detection, instance segmentation, and object tracking. With these capabilities, YOLO11 can process each video frame of a basketball game in real-time.
For example, if we want to track when the ball goes through the hoop or when a slam dunk occurs, a computer vision system integrated with YOLO11 can detect and track the ball as it leaves a player's hand, travels through the air, and makes contact with the backboard and basket to score.
Another good example is using YOLO11's pose estimation capabilities. Pose estimation involves identifying and tracking key points on a player's body, like the elbows, knees, and hips, in each frame of the video. This can be used to create a detailed map of a player's movement, showing not only where they are on the court but also how they move during important moments. The gathered insights can then be used to analyze performance, fine-tune training techniques, and even help reduce the risk of injuries.
Beyond player tracking and ball movement analysis, YOLO11 can be used for AI-powered referee assistance, helping to detect fouls, out-of-bounds plays, and other violations in real-time.
By analyzing video footage frame by frame, Vision AI can provide referees with additional insights to reduce human error. It can also be integrated into instant replay systems to automatically flag moments that need review, making the process faster and more reliable.
For instance, if a player steps out of bounds, YOLO11 can detect the position of their feet in relation to the court lines and instantly alert officials. Also, the model can track excessive physical contact between players to help identify fouls.
Likewise, in situations where the ball is in motion, YOLO11 can analyze its trajectory to determine whether it fully crossed the three-point line before a shot or if a goaltending violation occurred. By automating these detections, AI-driven referee assistance can improve the accuracy of officiating, reduce controversial calls, and make the game fairer for players and teams.
The use of AI in basketball is transforming everything from player performance to fan engagement, opening up new ways to analyze the game and make smarter decisions. Here’s a quick glance at some of the advantages that AI offers to basketball teams and organizations:
While there are clear benefits, implementing AI solutions can come with its own set of challenges. Here are some of the limitations and key considerations to keep in mind:
AI is redefining basketball in exciting ways. From real-time player tracking with YOLO11 to predictive models that help coaches make smarter decisions, these technologies are giving teams new tools to analyze the game and improve performance.
The NBA is already using AI for everything from optimizing game schedules and creating automated highlight reels to refining coaching strategies and enhancing fan engagement. As AI continues to evolve, we can expect even more accurate analytics, better injury prevention, and deeper insights into player performance.
To learn more, visit our GitHub repository and engage with our community. Explore innovations in sectors like AI in self-driving cars and computer vision in agriculture on our solutions pages. Check out our licensing options and bring your Vision AI projects to life.
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