The integration of AI into the sports industry is experiencing significant growth, with projections indicating market expansion in the coming years. In 2022, AI in the sports market was valued at approximately $2.2 billion and is expected to reach $29.7 billion by 2032.
This growth is driven by various factors, including advancements in AI technologies, increased investments from sports organizations, and a growing demand for data-driven insights to enhance performance and fan engagement.
AI applications like real-time player tracking, predictive analytics, and automated highlight generation improve team execution and elevate sports experience. These innovations usher in a new era of competitive edge in professional leagues and recreational sports.
Diverse Directions in AI Sports Applications
AI’s footprint in sports spans multiple domains, each driving new possibilities:
- Performance Analytics: ML algorithms track player movements, analyze fatigue, and suggest personalized training plans. For example, wearable sensors with AI detect patterns that minimize injury risks while optimizing performance.
Whoop and Catapult Sports develop wearable devices that monitor athletes’ biometrics, including heart rate, sleep, and recovery metrics. Their data-driven insights help coaches design personalized training regimens, reducing injury risks and boosting performance.
Barcelona FC uses analytics to track players’ physical data, optimizing training loads and preventing fatigue-related injuries.
- Fan Engagement: Augmented reality (AR) and virtual reality (VR) technologies enrich the viewing experience. Fans can access real-time stats, alternative camera angles, and virtual replays, fostering more profound engagement.
The NBA uses augmented reality to deliver interactive fan experiences, such as virtual courtside seats, player stats overlays, and immersive replays.
- Broadcast Enhancements: Automated camera systems and computer vision precisely track live game events, offering multi-angle coverage and content tailored to viewer preferences.
Hawk-Eye: Used in tennis and cricket, this system combines computer vision and automated cameras to deliver real-time ball tracking and replay technology for broadcasts.
- Game Strategy Optimization: Predictive analytics use historical and real-time data to forecast match outcomes, player performance, and optimal formations, helping teams stay ahead.
Liverpool FC uses predictive analytics to assess player performance, scouting reports, and optimal formations, leading to smarter recruitment and match strategies.
Golden State Warriors leverages analytics from Second Spectrum to optimize game strategies, predict player tendencies, and craft defensive and offensive plays.
Every year AI’s influence extends beyond performance and broadcast enhancements. Features like personalized content, multi-angle viewing, and AR-powered real-time stats enrich how fans watch games. These technologies provide deeper insights and more immersive experiences.
Modern stadiums integrate AI for crowd monitoring, security, and seamless data transmission. AI-driven infrastructures support both in-stadium and at-home viewers with unmatched quality.
The Role of High-Quality Data Annotation
Behind every AI sports application lies meticulously annotated data. Data annotation transforms raw information into actionable insights by labeling images, videos, and sensor data to train machine learning models. For example, annotating player movements, crowd actions, or ball trajectories enables AI systems to analyze events rapidly and accurately.
Key Techniques in Sports Data Annotation:
- Video and Image Labeling: A cornerstone of sports data annotation is the tagging and segmentation of visual content, such as match footage, training videos, and practice sessions. Platforms like Keylabs.AI facilitate these processes through automated segmentation, which breaks down video frames into detailed, analyzable components. This process often includes creating bounding boxes for identifying and tracking objects in motion, enabling precise localization and movement analysis.
In soccer, bounding boxes can isolate individual players, track their movements across the field, and analyze their interactions with the ball or other players. Similarly, in tennis, bounding boxes are used to follow the ball’s trajectory and monitor player positioning, helping to evaluate serve speeds, shot accuracy, and court coverage.
- Sensor Data Labeling: Systems such as the NBA’s SportVU utilize a combination of on-court sensors and advanced analytics to capture real-time metrics, including player speed, distance covered, and positioning during a game. Annotating this sensor data involves labeling parameters like acceleration, deceleration, and directional shifts, which can then be used to enhance team strategies, improve individual athletic performance, and provide predictive insights for injury prevention.
- Text Classification: Beyond physical performance, understanding sentiment and context in sports commentary, social media reactions, and game reviews have become critical for fan engagement and brand management. AI-driven text classification tools analyze unstructured textual data to extract meaningful patterns and trends. These systems can classify sentiments in live game commentary, track fan sentiment across platforms like X and Reddit, and identify recurring themes in game reviews or post-match analyses.
How it Works: Keymakr Methods and Mechanics
As a data annotation provider, Keymakr often works with data for sports projects, including overcoming object tracking and identification challenges in dynamic videos.
In one case, a client required the ability to monitor individual players’ movements during a soccer match using object detection and tracking techniques, keeping stable bounding boxes around each player throughout the recording.
To address this, the Keymakr team implemented a pre-annotation strategy for tracking players in the videos. This approach decreased the time spent manually annotating each object frame by frame while also delivering an initial level of accuracy as a starting point.
This is what it looked like, step by step.
- Step 1: Automated Pre-Annotation. The team used the SAM2 model for automatic object annotation. It created masks and bounding boxes around each player using standard coordinates (e.g., the top left and bottom right points of the bounding box). This set initial boundaries around the players, significantly speeding up data preparation for the client.
- Step 2: Solving Bounding Box Jumping Issues. During testing, the team observed that bounding boxes occasionally “jumped” from one player to another, especially in close-up or overlapping scenes. It was crucial for the client that each player retained their on-screen identity. Coaches needed to track specific movements, not just if players were on the field.
- Step 3: Implementing Unique Real IDs for Each Object. To maintain player identity and eliminate bounding box jumping issues, Keymakr assigned unique IDs to each player. These IDs were linked to metadata. Despite some bounding box adjustments, each player’s identifier remained constant. This allowed accurate tracking of each specific player, regardless of bounding box adjustments.
- Step 4: Verification and Finalization of Results. In the verification stage, each annotation was double-checked to match the correct player. Bounding boxes were also ensured not to shift to another player in the frame. If a bounding box was changed or disappeared in a frame, tracking was paused, and a new box was recreated in the next frame while keeping the same real ID. This approach ensured that tracking accuracy was preserved throughout the video.
The client received high-quality, precisely annotated data, allowing detailed analysis of player movements during matches. Each player was uniquely identified, enabling the client to generate actionable insights for coaches and analysts.
The Future of AI Sports Solutions
AI in sports is set to transform the industry through advanced performance predictions, generative AI for personalized content, and next-gen wearable tech offering real-time insights. Virtual training environments powered by AI and AR will simulate competitive scenarios, while AI-enabled scouting tools will identify global talent, diversifying recruitment.
Sustainability will also benefit, with AI optimizing energy use and logistics in smart stadiums. Real-time emotional analytics will enhance fan engagement and player-coach interactions. As technologies advance, AI will redefine performance, operations, and experiences across the sports landscape.