For many years, sports analysis was limited to simple box scores and reviewing game tapes.
While modern computers brought better statistics, AI has truly transformed how teams use information.
Today, AI connects sensor data from athletic wearables, video feeds from venue cameras, and behavioral patterns from fans. Together, these streams create a comprehensive and real-time view of every game. This changes how athletes train, how teams build strategy, and what audiences expect when they watch sports.
That shift is already clear. Eighty percent of surveyed fans believe technology, specifically AI, will have the greatest influence on how they follow sports by 2027. More than half (56%) of these fans also want AI-powered insights and commentary for past, current, and future events.
In this post, we cover nine essential AI applications in the sports field that are improving performance and enhancing fan experiences.
How Is AI Used in Sports?
Modern sports organizations rely on multiple AI systems that often work together and draw from the same underlying data. Information about player health, movement, and workload can inform both training decisions and in-game strategy.
AI systems analyze immense data streams to reveal patterns for parameters like athlete stamina and skills. They can identify tendencies, such as when a player is more likely to pass instead of shoot, helping teams anticipate opponents’ behavior and adjust tactics.
LaLiga is among the first sports organizations to use a large-scale agentic AI model to support internal decision-making across areas like athlete development, match analysis, and everyday workflow management.
9 Examples of AI in Sports
Here are several examples of how AI is being applied to make sports smarter, safer, and more engaging.
Player Performance and Athlete Health
AI plays a growing role in helping teams protect player health while maintaining peak performance.
These systems continuously analyze physical, biometric, and movement data that support coaches and medical staff in the following ways:
- Fatigue Monitoring: AI systems constantly track data from players' smartwatches and vests for heart rate, sleep quality, acceleration patterns, and more. This lets them spot the smallest signs of a player being tired or slightly off their game, such as slower reaction times, long before a coach or player even notices.
- Injury Risk Detection: Advanced models assess movement patterns during running, jumping, and direction changes. By comparing how a player loads their left and right sides, or how their gait shifts under fatigue, the system can flag elevated injury risk, allowing medical staff to adjust training plans early.
- Real-Time Decision Support: AI-powered tools collect this data in real-time dashboards used by coaches and training staff. These dashboards show current fatigue levels, injury risk indicators, and performance consistency, helping staff decide when a player needs rest, lighter training, or workload adjustments.
If you’re building a product for coaches or performance teams, the goal is to turn complex player data into signals they can act on quickly.
You can take cues from apps like HomeCourt, which track specific biomechanical measurements. For example, your app might record a player’s knee angle during landing, such as 129 degrees after a jump shot. For certain players, consistently operating at those angles or ranges can place additional stress on the knees, hips, or lower back. The player can then adjust their form based on this data.
Video Analysis with Computer Vision
Computer vision is a branch of artificial intelligence focused on understanding visual information. In sports, it’s applied through video analysis systems that examine game footage frame by frame to interpret what’s happening on the field.
Here are a few ways AI-driven video analysis is used in sports:
- Automated Event Detection: Instead of relying on manual tagging or human observation, these systems automatically scan game footage to identify events, such as passes, shots, tackles, sprints, and transitions.
- Player and Team Tracking: Systems track every player on the field at the same time. Throughout the match, they measure player positions, speed changes, spacing between teammates, and overall team shape.
- Tactical Pattern Analysis: By analyzing movement and positioning data, coaches and analysts can study patterns that are difficult to catch in real time. These make post-match reviews more concrete and help compare similar situations across multiple games. For example, they can confirm:
- Whether a defensive line stays coordinated during transitions.
- How quickly players react after losing possession.
- When and how spacing breaks down under pressure.
- Broadcast Enhancement: Broadcasters also use AI-driven video analysis to enhance live viewing experiences, including:
- On-screen graphics, such as shot-speed overlays.
- Contextual stats tied to key moments, like performance from specific areas of the field.
- Automatic generation of highlight clips in real time.
At scale, visual data processing systems can help convert raw match footage into structured data that sports applications can build on, enabling faster analysis, richer context, and more engaging product experiences.
Smarter Coaching, Strategy, and Training Programs
AI-based tools help coaches make better decisions by identifying opponent patterns and likely actions, such as pressing triggers or weak-side gaps, based on how teams behave in similar situations over time.
Here are some other key ways AI systems can support coaching and training:
- Individualized Training Plans: Coaches can analyze how each athlete performs across drills, matches, and game scenarios, then adjust focus areas based on strengths and weak spots.
- Adaptive Daily Training Structure: Trainers can adjust training structure using wearable and performance data to decide when sessions should emphasize intensity, technique, or tactical work.
- Data-Informed Nutrition Planning: Performance teams align nutrition planning to training demands by mapping workload patterns, hydration signals, and stress markers to recommend intake and timing that support upcoming sessions.
- Biomechanics Analysis: AI can also help monitor biomechanics in real time. For example, a platform can analyze a baseball pitcher’s arm slot and elbow angle, flag deviations that could lead to injury, and adapt the next day’s throwing volume accordingly.
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AI-Powered Movement Analysis: Coaches leverage AI-powered vision agents to track player movements from live or recorded video streams, turning low-latency visual and sensor data into actionable insights that can feed apps or coaching platforms.
For example, in this real-time golf coaching demo, a vision agent tracks swing mechanics frame by frame, monitoring posture, club path, and timing. It then responds with contextual guidance as the motion unfolds.
Red Bull Racing runs billions of simulations on Oracle Cloud Infrastructure to optimize race strategy and react to changes in car performance, track conditions, and competitor actions.
Since moving to the cloud in 2021, simulation speed has increased by 25%, and new compute upgrades for the 2026 season and beyond will boost speeds by 10% further, allowing more scenario testing and faster race-day decisions.
If you’re building a sports analytics or coaching app, your product should integrate multiple data sources (like wearables, video feeds, and historical performances) to create a real-time dashboard. This could feed other features to help coaches make faster, more informed decisions, such as a scenario simulator.
Sports Equipment Innovation
Sports equipment design has moved well beyond lab tests and post-game feedback. Teams and manufacturers now use data collected directly from equipment, combined with predictive and analytical models, to improve safety, performance, and fit under real playing conditions.
In practice, this shows up in two key areas:
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Smart Ball Technology: This technology, common in sports like soccer, baseball, and cricket, uses lightweight motion and position sensors embedded inside the ball to track speed, spin, trajectory, and touch timing.
AI models interpret this data in real time, allowing analysts to study ball movement patterns without changing how the ball behaves in play. This data can also feed broadcast graphics, officiating systems, or training apps.
- Intelligent Protective Equipment: Accelerometers, gyroscopes, and other sensors measure impact force, collision direction, and head movement in protective equipment, such as smart helmets. AI analyzes these signals to distinguish routine contact from potentially dangerous impacts, helping teams monitor risk over time.
In footwear design, companies like Nike use generative AI early in the design process. Designers prompt AI systems to explore variations in materials, textures, and structures based on performance goals. This speeds up experimentation and helps narrow down designs before physical prototypes are built.
Fair Play
Machine learning and computer vision solutions play a critical role in modern officiating by turning raw visual and sensor inputs into fast and consistent decision support.
Here are two examples:
- Hawk-Eye: This is a ball-tracking system widely used in tennis and cricket. Multiple high-speed cameras placed around the venue capture the ball’s position across successive frames. Predictive models then reconstruct the ball’s three-dimensional trajectory, so officials can make calls even when the ball moves too quickly for the human eye to track reliably.
- Video Assistant Referee: In soccer, Video Assistant Referee (VAR) systems rely on AI-assisted video analysis to synchronize multiple camera feeds, isolate relevant frames, and highlight potential infringements, such as offsides or fouls.
The Premier League’s Semi-Automated Offside Technology uses up to 30 cameras and tracks 10,000 data points per player. It automatically identifies the kick point and generates offside lines, cutting the time for a close VAR review by about 30 seconds.
Broadcasting and Fan Engagement
Modern AI systems are transforming the way fans follow and interact with sports in these three ways:
- Smarter Livestream Sports Broadcasting: Computer vision systems analyze player movement, ball location, and game context in real time to determine where the action is most likely to unfold next. This allows broadcasters to switch camera angles and surface the right replay faster.
- Rapid Content Creation at Scale: Models detect key moments by combining signals, such as sudden movement changes, scoring events, crowd reactions, and commentary spikes. This makes it possible to generate highlight clips within seconds of an event, while interest is still high.
- Enhanced Fan Experiences: More than half of fans now rely on generative AI tools for game explanations, context, and personalization, and nearly six in ten say they trust the content these systems produce. AI-based solutions enhance fan engagement by:
- Adding contextual graphics such as speed, distance, or momentum indicators
- Powering AI avatars that explain rules, plays, or game situations for new or casual fans
- Enabling conversational assistants that answer match-specific questions
Wimbledon uses an AI-powered assistant that allows fans to ask questions about live matches, explore detailed stats, and view win-probability insights as momentum shifts without needing expert commentary.
Revenue Optimization
Beyond the field, AI plays a growing role in how sports organizations generate and protect revenue.
Here are a few ways AI supports revenue optimization:
- Dynamic Ticket Pricing: Predictive models adjust prices based on demand signals, such as opponent strength, timing, seat location, historical sales, weather, or recent player performance. This helps maximize attendance and revenue without manual repricing.
- Venue Operations Optimization: Forecasting models predict crowd flow, concession demand, and staffing needs. By anticipating peak periods, organizations can reduce waste and improve the customer experience.
- Athlete Contract Valuation: Predictive modeling assesses long-term performance potential, injury risk, and career trajectory. This helps teams evaluate contract value, especially in franchise-based sports.
- Injury Insurance Modeling: AI systems help combine injury likelihood, recovery timelines, and usage patterns to support more accurate insurance premiums and payout structures for teams and insurers.
- Sponsorable Moment Creation: Event detection models identify high-impact moments such as goals, dunks, celebrations, or viral plays, which can be converted into short clips packaged as micro-inventory for sponsors.
Sports Journalism and Content Workflows
AI tools are increasingly used to speed up how sports content is created, enriched, and distributed, especially in fast-moving news environments where timing and context matter as much as accuracy. Here are some of the ways they’re utilized:
- Rapid Match Reporting: AI-powered solutions can help reporters generate early story starters and quick match summaries within minutes of a game ending. They scan play-by-play feeds to surface scorelines and standout performances, giving journalists a solid foundation to build on rather than starting from scratch.
- Clip Discovery: Instead of having to watch full matches, vision models can help journalists and content teams scan footage to locate key clips, such as fouls, errors, or wickets.
- Historical Context and Insights: LLMs that use natural language processing techniques can scan decades of historical data to surface interesting patterns. For example, they can flag achievements like “fastest inning at this venue in the last 20 years,” helping writers add depth without manual research.
- Content Production Automation: Intelligent automation tools can help handle many production tasks that slow teams down, like:
- Generating captions, transcripts, and subtitles for video content
- Translating content for regional or global audiences
- Tagging articles and clips for search and discovery
For sports news and media apps, these capabilities make it easier to deliver context-rich content at scale, even with lean editorial teams.
Stadium Operations
Inside venues, AI systems help operators manage large crowds, complex logistics, and real-time decision-making more effectively. Some examples include:
- Entry Point Management: At entry points, biometric verification and mobile-based access systems speed up check-ins by reducing manual scanning and bottlenecks. AI models monitor entry patterns and predict peak congestion windows, allowing operators to adjust gate staffing before queues build up.
- Crowd Movement Guidance: By combining camera feeds, ticketing data, and historical patterns, intelligent systems can dynamically guide fans toward less crowded concessions, restrooms, or walkways in real time.
- Parking and Traffic Optimization: By integrating parking, transit, and traffic data, platforms can suggest optimal arrival times, alternate routes, or post-event exit paths, helping reduce congestion around the stadium.
- Operations Dashboards for Staff: AI models predict spikes in food, beverage, or merchandise demand based on factors like opponent, weather, start time, and in-game momentum. This reduces waste, lowers costs, and ensures better service during peak moments.
Product teams building stadium operations dashboards can display AI predictions alongside live data from devices, such as turnstiles, smart cameras, or point-of-sale systems that track concession sales. This will help staff respond immediately to changing crowd sizes and concession demand.
Frequently Asked Questions
1. Which Sports Use AI The Most?
In 2024, soccer captured the highest market share in AI adoption among sports. Basketball is expected to grow at a notable CAGR in the coming years.
2. How Can AI Be Used in Sports Training?
AI is used in sports training to design, adjust, and evaluate training programs using performance, video, and sensor data. Training systems analyze how athletes respond to different workloads, drills, and game situations, helping coaches decide where to focus, when to increase intensity, and when to scale back.
3. How Is AI Transforming Fan Engagement?
AI is changing fan engagement by making sports products more responsive during live moments. Instead of showing the same updates to every fan, platforms can react to what is happening in the game and adjust what they show in real time based on the team being followed or the fan’s past behavior. For example, fans can ask a chatbot questions or see explanations triggered by a specific play.
4. How Is AI Being Used in Sports Broadcasts?
AI is used in sports broadcasting to help production teams keep up with live play. Systems track game footage and match signals to detect moments like goals, fouls, or sudden shifts in action. This helps crews quickly decide when to cut to a replay, surface a highlight, or add on-screen graphics during live coverage.
5. How Is the NFL Using AI?
The NFL uses AI in multiple ways, including player health and safety tools that help predict injuries and adjust training plans based on data from games and practices. It also uses advanced camera and tracking technologies to support officiating and provide data-rich analytics.
6. How Is the NBA Using AI?
The NBA uses AI to help with player tracking, team performance analysis, and, in some cases, creative content for broadcasts. For example, NBC Sports used an AI‑generated version of legendary narrator Jim Fagan’s voice for select promos and opening sequences in its 2025 NBA coverage.
7. How Does ESPN Use AI?
ESPN uses generative AI to produce quick game recaps and summaries for many leagues, including underserved ones, freeing up journalists to focus on deeper analysis. This AI content is reviewed by editors before it appears on ESPN’s platforms.
8. Will AI Replace Sports Agents?
AI is unlikely to replace sports agents. It can support research, valuation, and contract analysis, but it cannot replace trust-building, negotiation psychology, personal relationships, crisis management, or athlete advocacy, which remain central to elite representation.
Conclusion
AI is now embedded in how modern sports systems operate, from officiating and coaching to media workflows and day-to-day operations.
Whether minimizing injury downtime, guiding tactical adjustments, or accelerating content production, AI helps turn complex data into actions teams can rely on under pressure. These systems work because they connect fast-moving inputs like video, sensor data, and historical records into a single decision layer.
For teams building sports products, the key lesson is not to add AI for its own sake. AI matters most when it fits cleanly into existing workflows and delivers insight at the moment it’s needed.