AI in Sports: Redefining the Game

Athlete Performance and Health

This is where AI in sports started to prove itself, and it’s still the area with the most obvious return: healthier players, better training, fewer wasted seasons.

(1) AI coaching platforms

Think of an AI coach as a patient trainer who never gets tired of watching your backhand. Using pose estimation, computer vision models track an athlete’s body from ordinary video and measure joint angles, timing, and posture.

No wearables are required. Just point your phone at a tennis serve or a squat, and the system will compare it to the successful pattern and show you where you’re going wrong.

BeOne Sports is one example. Its app uses a smartphone camera to detect movement and provide frame-by-frame feedback, and it has partnered with Rice University’s sports medicine program to promote injury prevention through mobile motion capture. 

HomeCourt is a basketball training app, which follows the same technique of capturing the exact biomechanics, like knee bend of a player upon landing from a jump shot. The problem here is that if a player continuously lands at an angle that causes stress on his knees, hips, and lower back, then the app will detect it.

The real value is scale. A coach with thirty athletes can’t watch every rep. A model can, and it gives objective numbers instead of opinions. Stream also references real-time visual coaching agents, for instance, the golf coaching app that analyzes the mechanics of a swing one frame at a time and gives back instruction at the same time the motion is happening. This is the future of coaching applications.

Injury prediction and fatigue monitoring

Injuries ruin seasons and careers, which makes it imperative for teams to be able to anticipate any potential problems. These prediction systems use the workload statistics, biomechanics from videos or wearable devices, schedule, sleep, and wellness information, and try to find the subtle patterns that are present before an incident occurs.

When it comes to fatigue detection, the systems use data from smartwatches and vests, including heart rate, sleep statistics, and acceleration, and are able to detect even the smallest signs of fatigue, way before the coach or athlete himself realizes that something is wrong. When it comes to injuries, the models analyze the biomechanics of running, jumping, and changing directions. If there is a difference between how the left and right side of the body load, or if there is a change in gait due to fatigue, the system issues an alert.

An excellent example would be the Digital Athlete platform of the NFL, developed using Amazon Web Services. This platform virtually recreates game scenarios and combines data collected through cameras inside the stadium with injury data over several years. WSC Sports claims that the league achieved its all-time lowest rate of concussions in 2024 with 17% reduction compared to the previous year, with the help of AI-based rules and training modifications among other factors. Meanwhile, Zone7 works with soccer and rugby teams on predicting their players’ injuries.

Smarter equipment and biomechanics

Equipment is becoming smarter too. Sensors embedded into balls, which are common in soccer, baseball, and cricket, monitor the speed, rotation, trajectory, and time of touches, while leaving the playing properties of the ball intact.

The information can be used by the broadcast graphics systems, officiating, and training applications. Smart protective gear, including helmets, has accelerometers and gyroscopes, measuring the impact force, the direction of collisions, and movements of the head, while using AI technology to determine whether hits are normal or potentially dangerous.

The design process is evolving too. For instance, Nike uses generative AI in the initial stage of designing shoes, which enables designers to play with different material choices, textures, and structures according to the performance requirements before manufacturing a prototype.

In baseball, there is a system that analyzes the arm slot and elbow angles of the pitcher, recognizes deviations that can result in injuries, and modifies the next day’s practice workload.

AI scouting from amateur footage

Discovering the next big star was all about traveling scouting teams and endless tape before AI. Now that’s not necessary as AI can screen amateur and young player’s video clips on a scale never seen with human staff. One such product is AiScout. Players create video clips from drills, which involve things like dribbling, running and shooting. The AI then analyzes their skills in terms of speed, agility, technique, and accuracy, comparing them to those of professionals. Some leading league clubs work together with the service in order to expand their search beyond their academies and it has reportedly worked wonders for some young players.

Basically, it entails pose estimation of body mechanics, object tracking of the ball, and context for the quality of shots and drills. This results in an increase in the talent pool which will be cheaper to scout for. A kid from the countryside who has a cell phone will now have a chance of getting noticed.

(2) The Game Itself

On the field, AI is slowly revolutionizing match analysis, officiating, and management.

Video analysis and tactical insight

Computer vision is a branch of AI dealing with interpretation of visual data, and in sports it is done through video analysis, where footage is analyzed frame by frame. Its key strengths include:

• Automatic event detection. Rather than manual tagging, systems analyze the footage and detect passes, shots, tackles, sprints, and transitions.
• Tracking of players and teams. Each player is tracked simultaneously, along with position, velocity changes, spacing between teammates, and team formations.
• Tactical pattern analysis. The analysts can assess how well the defensive line-maintained coordination during their transition play, reaction time after conceding possession, and when spacing was lost.
• Broadcast enhancement. Shot speed overlays, context-related statistics, and highlight clips.

The use of artificial intelligence to read opponents is another application of AI by coaches. This is done through analyzing how teams perform in similar conditions to establish their trends in passing and where they leave gaps in pressurizing opponents, among others. When analyzed to large volumes, videos can provide structured data, which can be used further by sports applications.

Red Bull Racing illustrates how far this technology has been applied in motorsports. The organization simulates billions of scenarios using the Oracle Cloud infrastructure based on changes in the car’s performance, track and opponent behavior. According to Oracle, simulation capabilities have improved by about 25% since Red Bull started using Oracle Cloud infrastructure in 2021. This will be improved further in 2026 and afterward.

Computer vision officiating and rule automation

One wrong decision can cost you a title, which is why officiating becomes one of the first areas for improvement. Tennis started the trend with Hawk-Eye, which uses multiple high-speed cameras and algorithms to reconstruct the trajectory of the ball in 3D space. It is accurate enough to make the ATP Tour announce all out-of-bounds decisions will be automated by 2025, leaving no place for line judges.

The use of Automated Ball-Strike system in baseball has been trialed in minor league games since 2019, using fast cameras that determine the strike zone. There is an anticipation that the major leagues will adopt a strike zone customized for each batter.

The Football Association introduced semi-automatic offside technology in the 2022 World Cup while the Premier League’s system uses 30 cameras and captures over 10,000 data points for each player. It automatically detects the point where the ball was kicked and creates offside lines, taking only 30 seconds to complete a VAR check.

However, humans are the ones that take the final decision. But they have better and faster information to go by now. The open problems include things like the blocking of the camera view by the players when playing in large numbers, and also the trade-off between speed and accuracy. This is why full automation is the future for WSC Sports.

Front office decision making

Today, front offices rely on machine learning to predict the evolution of a player, evaluate trade deals, simulate strategy scenarios such as fourth downs or pitching changes, and identify undervalued players based on unconventional metrics. LaLiga took it even further by becoming one of the first sports organizations to implement a large-scale agentic AI system that can be used to make decisions on the development of athletes, match analysis, and daily work.

Predictive models come in handy when it comes to money, too. Teams can use predictive models to determine the value of long-term deals and consider performance potential, injury risks, and career trajectories. Injury modeling helps insurers and teams to establish accurate premiums and payouts. This does not mean that humans are out of the picture. As Stream notes, AI is unlikely to replace sports agents since trust, negotiation, relationships, and advocacy for the athlete remain the cornerstones of representation in sports.

(3) Content, Media, and Storytelling

To observe the effects of AI in sports in their purest form, observe the content side. Viewers want more clips, more languages, and more personalized views than any editorial staff could ever manually generate.

Autonomous highlight generation

Highlights are where AI is most mature. Through computer vision and event detection, it identifies events like goals, dunks, and touchdowns and creates highlight clips in a matter of seconds. It analyzes each frame of video feed for identifying the ball, players, and score board along with analyzing sounds to determine the significance of an event.

The one that really stands out in this case is WSC Sports. The technology used by WSC Sports captures live broadcasts, records crucial moments, and creates customized highlights for various target audiences. One game can yield a Top 10 plays video for YouTube, a 15-second star-player montage for Instagram, and a personal reel of every goal your favorite team scored. Stream adds a commercial angle: event detection can turn high-impact moments into short clips packaged as micro-inventory for sponsors.

Generative AI commentary

Think about picking your own commentator. From casual fan discussion to dramatic radio commentary to technical analysis, in your own language, and for any match, even those without a broadcast. This is the potential of generative AI commentary, which leverages large language models that are trained on live event data such as shots, passes, and goals.

In the Bundesliga, a live AI ticker was tested that would provide several takes on the same goal within seconds, from a formal English journalist style to a more casual Gen Z style to one in German, and persona options included “Sports Journalist,” “Casual,” and “Bro.” 

During Wimbledon 2023, IBM introduced AI commentary for highlight clips, leveraging a language model trained on tennis-specific terminology and computer vision that detects aces and break points. Even NBC leveraged an AI-generated take on Jim Fagan’s voice for some of its promos for the 2025 NBA season.

It is still early days, and WSC Sports categorizes their personalized commentary as a future development since the majority of it is still in pilots. Trust and rights need to be considered when it comes to synthetic voice technology.

Sports journalism and content workflows

The newsroom is relying on AI to take care of the “story” legwork. Machines can automatically produce early drafts and summary of a match minutes after its conclusion via play-by-play feeds, providing journalists with something tangible to work with instead of just having a blank canvas. ESPN uses the power of generative AI to write summaries for matches in many leagues, including those underserved leagues.

Vision-based models can help journalists find the specific foul or wicket or whatever mistake that happened during the match without watching it all the way through. The language-based models, thanks to access to decades of data, can point out certain angles, such as fastest innings in twenty years at that particular venue. Also, machines can do the less exciting tasks: transcription and captioning and tagging.

(4) The Fan Experience

The fan is where sports business lives, and AI is making each fan feel like the only one in the stadium.

AI chatbots and conversational assistants

Language-based chatbots now answer questions, communicate schedules and statistics, sell tickets, and converse with their fans at all hours. Chatbots for the league provide scores on demand. Chatbots for teams deal with arena policies and match-day arrangements. Ticketing conversational assistants allow fans to get tickets through a natural language description of what they require. Operationally, single chatbots are capable of handling thousands of concurrent chats.

Wimbledon’s AI assistant, built with IBM, goes a step further. Spectators can inquire information about current games and their statistics as well as win-probability predictions as the game progresses without necessarily needing any experts on commentary tables.

AI bots that teach rules of game and plays to new viewers are other emerging applications. Most teams today have started upgrading their previous scripting bots to generative ones. If you’re shopping for AI chatbot development, this is one of the fastest routes to value.

Fan identity resolution and personalization

Most teams operate with fan information, ticketing, apps, merchandise, social in silos. The process of identity resolution makes use of machine learning technology to link together those pieces of information to build one comprehensive view even when there are no perfect matches between identifiers. This is done through a combination of deterministic and probabilistic matching techniques.

The Seattle Seahawks partnered with an AI-powered customer data platform to consolidate fan profiles, cut out duplicates, and find new fans they had previously overlooked.

With a clean profile, the experience becomes customized: regular app users have a different experience than discount shoppers, lapsed fans receive a win-back offer, and merchandise shoppers learn about upcoming drops. WSC Sports considers this a future feature because many companies are still sorting out their data infrastructure.  If that sounds like your situation, start there before adding fancy features.

AR, mixed reality, and immersive viewing

AR and mixed reality bring fans closer to the action. Cosm has created a huge LED dome in Los Angeles in 2024 that turns live sports events into a shared 360-degree experience through stitching of multiple camera feeds. On phones, AR applications help fans see stats on the field, navigate the arena, or engage in interactive activations. The intelligence behind all of it is AI that understands what a user sees, provides context, and places virtual objects in real spaces convincingly.

The most revolutionary ones, such as AR glasses in arenas, are yet to come, so treat it as an opportunity to try something new rather than a safe choice.

(5) Business, Venue, and Revenue

There is mathematics at work behind each successful match day. AI contributes here as well.

Dynamic ticket pricing and revenue optimization

Ticket prices are evolving from fixed to flexible, just like in airlines. Pricing models take into account the opponent, the form of the team, the weather, the day of the week, seating, past sales, and even social media activity. Winning streaks may increase prices, while a poor forecast may prompt a discount or special offer to prevent an empty stadium. FIFA has decided on dynamic pricing for the 2026 World Cup, a surprising step for such a major event.

AI also determines the timing of the offer, which offers to make, and who to offer them to. Stream provides other revenue applications as well: forecasting concession sales, evaluating athlete contracts, and developing insurance models. Teams need to be careful about fairness and transparency since fans will notice when prices seem random.

Stadium operations and betting intelligence

AI in sports plays a key role inside the stadium too. The use of biometric or mobile entry cuts down lines, while modeling can identify possible congestion to ensure proper gate staffing in advance. Information from cameras, ticket sales, and past trends could be used to direct fans to less crowded concession stands and bathrooms. Parking and transport data may also indicate more effective arrival and exit strategies. Dashboards for staff can help predict food and beverage needs based on opponent, weather, game time, and other factors.

Another application that is heavily reliant on AI technology is sports betting. Algorithms analyze data such as team and player statistics, weather, injuries, match-ups, and sentiments, among others, to determine and update the odds constantly even during the game itself. For example, should an important player be injured, the odds will change immediately. Any anomalies that could be related to fraud or match-fixing will be identified.

What’s Real Today and What’s Still Coming

It helps to separate what’s proven from what’s promising. Based on the sources above, here’s a fair snapshot.

Established and widely used:

• AI coaching and movement analysis
• Injury prediction and fatigue monitoring
• Automated highlights
• AI scouting
• Dynamic ticket pricing
• Betting odds modeling
• Front-office analytics
• Fan chatbots

Emerging or still being refined:

• Personalized generative commentary
• Fully automated officiating
• Fan identity resolution at scale
• Mixed reality viewing experiences

Looking ahead a little more, there will be automated strike zones in baseball during the regular season, more use of AI in replay, AR glasses in the arena, and dynamic practice schedules that take data into consideration. If you are mapping out a product roadmap for the year, make sure you start your launch with something solid, and leave room to experiment with the rest.

What It Really Takes to Build an AI Sports App?

Looking at examples is the easy bit. Getting an example into a product is tough, and almost all efforts fail in the same way.

Data first:

Your model is only ever as good as the video, sensor, or event data that went into it. Poor, inconsistent, or biased data will give poor, inconsistent, or biased results. Figure out your data requirements before you do any modeling, including where your data comes from and how good it is.

Speed is even more important than you think:

Live sports don’t give you any time for latency. A highlight that’s published ten minutes later isn’t useful anymore, and feedback to an athlete while they are swinging the bat has to come while they’re still swinging.

Fit the workflow, not the other way around:

“Stream’s take-home message,” according to the article, “is that AI should fit your process and give you an insight in the moment it’s needed.” A coach with only ninety seconds during halftime does not need a new dashboard to teach them anything. All they need is one piece of information.

Shield private data:

Information about athletes’ health and fans’ data are personal information. From day one, you will have to deal with consent, security, data retention, and regional privacy laws.

Have humans in the loop:

The best processes always facilitate decision-making. Automated officiating still relies on humans making the final decisions, and even AI-driven ESPN’s game recaps are edited by humans. Building review steps into your product protects both quality and trust.

Think big but start small:

An initial prototype tested on actual coaches, athletes, or fans will tell you more in a month than a year’s worth of planning. Demonstrate value in one niche application and grow from there.

How to Choose an AI App Development Company?

If you’re going to partner with an AI app development company, ask questions that expose whether they’ve actually shipped this kind of work:

– Have they ever developed using computer vision, real-time data, or predictive modeling technologies? Ask to see some examples.

– How do they approach data? Check their clarity in regards to how they collect, label, protect, and secure the data.

– Have they considered the user’s needs? Your partner should inquire about the coach, the fan, or the analyst, before inquiring about the model.

– Are they ready to integrate into your existing tech stack? This includes wearables, camera systems, ticketing platforms, and CRM systems.

– Do they want to go big from the get-go? Run away from those that want to build something massive before validation.

– Have they considered future actions? As models will have to be updated due to changes in the environment, make sure to ask about that as well.

The ideal AI development services partner should not act like a vendor but as an ally, just as involved in this process as you are.

How PSSPL Can Help?

At PSSPL, we build AI products for sports, fitness, and media companies. The usual starting points involve developing computer vision applications for movements analysis and event recognition, predictive dashboards for workload and injury risks, chatbots for fans’ assistance and tickets, content creation solutions for highlights, captioning, and translations. And we develop all mobile and web applications around it, and integration with wearable devices, cameras, and existing platforms.

That’s how we work. Build a small prototype, put it in front of real users, and only scale what proves itself.