AI in Sports Performance

A snowboarder just used AI to win an Olympic medal. And most people have no idea how. The 2026 Winter Olympics in Milano Cortina aren't just a showcase of human performance. They've quietly become the world's biggest AI testing ground. Here's what's actually happening behind the scenes: → Google Cloud built a tool that turns a smartphone into a biomechanics lab. U.S. snowboarder Maddie Mastro used it to analyze her practice footage and adjust her body positioning mid-training. She and her teammates made the halfpipe final. → Team USA's speedskaters used AI to model ice conditions before ever stepping on the rink. Jordan Stolz went on to win gold. → USA Bobsled partnered with Snowflake's AI to analyze push-crew synchronization and determine the most efficient athlete pairings. The system tells them exactly how many steps each athlete should take before loading the sled. → An MIT researcher built an AI system called OOFSkate that analyzes figure skating jumps frame by frame, helping skaters chase the elusive quintuple jump. → Fourteen 8K cameras now capture every figure skater's movement and feed it into AI that builds a real-time 3D model of the athlete across all three axes. → AI is even being tested to assist Olympic judges, measuring body angles and rotation speeds with precision the human eye simply can't match. The pattern here isn't about sports. It's about what happens when AI meets any field where milliseconds and millimeters decide outcomes. That's healthcare. That's manufacturing. That's your business. The Olympics have always shown us what humans are capable of. In 2026, they're showing us what humans plus AI are capable of. What's been your favorite moment from these Games so far? 👇 #olympics #ai #artificialintelligence

Robots on the pitch....You better believe it. Will you be able to play with this one? No more standing cones or passive drills. Athletes today are dodging dynamic robots—machines that track, move, and react in real time. These aren’t gimmicks; they’re next-gen training partners. ⚽ In football, systems like SKILLSLAB, Rezzil, and Trailblazer Training Bots are already used by top clubs to simulate high-pressure situations, improve decision-making, and measure milliseconds of reaction time. 🏀 In basketball, robotic arms help perfect shooting arcs, while AI vision tools break down footwork frame by frame. 🎾 In tennis, smart ball machines adjust spin, speed, and placement in unpredictable sequences—training the brain as much as the body. Why it matters: + Athletes improve reaction speed by up to 20% using adaptive robotic drills. + Training bots allow 3x more touches per minute compared to traditional drills. + Machine-learning platforms track thousands of data points per session—customizing feedback instantly. This isn’t just tech—it’s transformation. Robots are helping players train faster, smarter, and with a grin on their face. #Innovation #Tech #Robots

>> 𝐓𝐡𝐞 𝐍𝐁𝐀 𝐛𝐮𝐢𝐥𝐭 𝐚𝐧 𝐀𝐈 𝐬𝐭𝐚𝐭 𝐟𝐨𝐫 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐡𝐮𝐦𝐚𝐧𝐬 𝐜𝐚𝐧’𝐭 𝐬𝐞𝐞. 𝐈𝐭 𝐦𝐢𝐠𝐡𝐭 𝐫𝐞𝐰𝐫𝐢𝐭𝐞 𝐭𝐡𝐞 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬. At AWS re:Invent 2025, I interviewed several leaders from Amazon Web Services (AWS), the National Football League (NFL), the PGA TOUR and the National Basketball Association (NBA). One theme repeated itself again and again. AI is transforming sports faster than most companies transform their business units. And the National Basketball Association (NBA) is leading the way. The league introduced a new AI metric called Gravity. It measures a player’s impact even without the ball by quantifying how much defensive attention they attract and the offensive opportunities it creates for teammates. It tracks 29 points on the body at 60 frames per second to deliver real-time insights that were previously impossible to measure. And this is not limited to basketball. The National Football League (NFL) uses AI to break down plays in milliseconds. The PGA TOUR processes every shot in real time. The trend is clear. People expect personalization, immediacy and intelligence. This is the new standard. Not only for sports. For business! 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐩𝐚𝐲 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐭𝐨: 1️⃣ 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞. Every NBA fan sees a different version of the game. The question for leaders is simple. How can your product adapt automatically to each customer in real time. 2️⃣ 𝐋𝐨𝐰 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. In sports, an insight delivered one minute late is worthless. The same is true in business. Value now depends on speed. 3️⃣ 𝐃𝐚𝐭𝐚 𝐚𝐬 𝐬𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠. Broadcasters use AI to explain why a moment matters, not just what happened. Companies can turn raw data into context in exactly the same way. 4️⃣ 𝐁𝐞𝐲𝐨𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐥𝐞𝐚𝐩. In my interview with Ishit Vachhrajani, we discussed AI systems that make independent decisions within defined guardrails. Not tools. Teammates. If AI can turn a basketball game into a personalized, real-time experience for millions of people, imagine what it can do for your business. Sports already learned this lesson. Now it is time for everyone else. ---- 👉 Love my content? ☑ Follow me on LinkedIn: Paul Storm 👉 Found this helpful? Share it! ♻️ Lenka Roedel Tom Rowland

Here’s my major prediction for the professional sports industry next year.   By the end of 2026, artificial intelligence will no longer be a fringe experiment in sports – it will be a foundational layer powering the industry’s growth, on and off the field. Any organization still relying on gut feel, spreadsheets, and siloed data will be structurally behind in both revenue and relevance.   It’s not just about performance. The integration of AI is reshaping every part of the sports business — from fan engagement and ticketing to media, commercial operations and player health. This is key to unlocking a new era of scalable value creation, sustaining the growth we’ve seen in recent decades.   AI is already bending the curve, and the growth potential looks a lot like a hockey stick:   💲 Spend is exploding: The global “AI in sports” market, estimated at nearly $9B in 2024, is forecast to reach $28B by 2030, a 21%+ CAGR. That’s not a side bet; it’s a signal of where leaders and operators see future value.   ⚕️ Performance & health are moving first: Teams working with specialized platforms have reported material outcomes. One AI system forecasts ~75% of potential athlete injury risks inside a seven-day window. Another is helping Major League Soccer teams cut total injuries by ~28% and reduce the salary paid to unavailable players by ~30% (equating to millions of dollars a season). Those are direct P&L and asset-protection gains, not just “innovation theatre”.   📣 Fan experience is being rewired in real time: The NBA’s work with Microsoft and AWS, for example, is pushing AI into games broadcasts: instant narrative-building, multilingual recaps, “Inside the Game” analytics feeds, and new experiences across apps, social media and even inside the stadium/arena. Formula 1 is also turning 1.1 million data points per second per car into predictive race insights and storytelling for a global audience.   By 2026, the true outliers won’t be the AI pioneers, they’ll be the organizations that failed to adapt. Here’s what’s becoming table stakes:   – A robust AI layer across ticketing, pricing, media, sponsorship, and performance – A single, integrated data spine replacing fragmented systems – The skills, talent, and culture to deploy AI tools with the same fluency as playbooks and scouting reports   The road to AI-based optimization won’t be clean. There will be bad models, governance clashes, and cultural pushbacks. But positive transformation never happens in straight lines. It requires bold experimentation. The difference now is that AI’s upside can be quantified in revenue growth, commercial yield and fan lifetime value.   As AI capabilities are adapted across the sports value chain, the industry’s ability to continue growing its overall value could accelerate dramatically.   #BigIdeas2026 – here on LinkedIn.

On Saturday, the Oakland Ballers became the first pro sports team to let AI manage in-game decision-making. AI set the lineup, decided when to pull pitchers, when to use pinch hitters, and how to position the defense. The experiment offers useful lessons for all organizations: 𝗨𝘀𝗲 𝗔𝗜 𝘁𝗼 𝘁𝗮𝗰𝗸𝗹𝗲 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱: The Ballers turned to AI, in part, because the data had outgrown human capacity. Every pitch, matchup, and defensive shift produces more signals than a manager can possibly process in real time. AI’s biggest value isn’t surfacing more information. It’s in parsing complexity so leaders can act with speed and confidence. And the advantage compounds: it’s rarely one big decision that wins the game (or transforms a business), but hundreds of small ones made swiftly and correctly. 𝗕𝗲 𝗱𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗱𝗶𝘃𝗶𝘀𝗶𝗼𝗻 𝗼𝗳 𝗹𝗮𝗯𝗼𝗿: The Ballers set clear roles for humans and AI. AI handled the data-heavy calls (lineups, pitching changes, defensive shifts), while humans kept the split-second judgments, like third-base coaching or waving runners home. Manager Aaron Miles also had override authority. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗻𝗲𝗲𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆: decide where AI should automate, where it should augment, and what should remain exclusively human. And always design the system so a human can step in to override. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗻𝗲𝘄 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗼𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: When the Ballers brought AI into the game, they weren’t just watching the scoreboard. They wanted to understand how AI’s decisions compared to a human manager’s, and what could be learned from the differences. Measuring AI performance isn’t just about whether the outcome was successful; it’s about the counterfactual: did the machine’s call actually beat the decision a human would have made? Congratulations to the Ballers for pioneering this experiment—and for winning the game to boot.

For most of football’s history, much of what we watched on the field went unmeasured. Today, nearly every player and ball movement throughout the game is measured, modeled, and analyzed in real time. This data is improving fan experiences and giving them richer sport insights. It's also changing how professionals approach the game—from improving player safety to unlocking new training environments. The results speak for themselves: a 35% reduction in lower-extremity injuries from the redesigned kickoff format, informed by Next Gen Stats data. Innovations like completion probability and rush yards over expectation that make broadcasts more engaging. And now, pose-tracking technology that captures full skeletal data 60 times per second, is opening doors to VR training that could accelerate player development from years to months. I'm proud of how we've expanded our partnership with the NFL on Next Gen Stats, powered by AI tools like Amazon SageMaker and Amazon Quick. What started as a tracking experiment in 2015 has become a critical part of the NFL’s infrastructure that uses machine learning models on AWS to process data from 22 players, generating 500-1,000 stats per play, instantly. What a win for the Hawks last night! If you're still riding the excitement, take a few minutes to read through this deep dive into the science that powers the complex stats you see on screen throughout the season. Cool look at the history of our partnership with the NFL through Next Gen Stats! https://lnkd.in/gX8Mpe7T

One of the latest applications for artificial intelligence could be a game changer — literally. Scientists at Google DeepMind in London have teamed up with the UK's Liverpool Football Club team to create TacticAI, a model that can provide insights on corner kicks. The tool has been trained on a dataset of 7,176 corner kicks from Premier League matches and uses a technique called 'geometric deep learning' to identify key strategic patterns that could prove to be critical in tight matches. "Predicting the outcomes of corner kicks is particularly complex due to the randomness in gameplay from individual players and the dynamics between them," Colin Murdoch, DeepMind's chief business officer, explained on LinkedIn. "TacticAI can model how players interact on the pitch, offering coaches advanced strategies to improve game outcomes." The research was published in a paper in Nature this week. But AI isn’t exactly new to sport, writes Edith Cowan University lecturer Mark Scanlan in The Conversation Australia + NZ. He says it was used in the men’s and women’s World Cups in 2022 and 2023, in conjunction with advanced ball-tracking technology to produce semi-automated offside decisions, and is a powerful tool for organisations. “Professional football clubs have analytical departments using AI at every level of the game, predominantly in the areas of scouting, recruitment and athlete monitoring. Other research has also tried to predict players’ shots on goal, or guess from a video what off-screen players are doing,” he writes. But, while he says AI promises to “offer coaches a more objective and analytical approach to the game��, it cannot make decisions on the fly, which is often where matches are won and lost. What do you think of the use of tech in sport? Could AI assistants give some coaches an unfair advantage or is it the future of competitions? Comment below. By Sam Shead and Cathy Anderson #sport #ParisOlympics Sources:  The Conversation Australia + NZ: https://lnkd.in/g7y35qC4 Financial Times: https://lnkd.in/gY6UENpR Nature: https://lnkd.in/gVsUwJRy

AI is already having a drastic impact on sports... 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 10 biggest 𝐰𝐚𝐲𝐬 this is happening: 1) Performance analysis: AI can analyze vast amounts of player performance data, such as biometrics, movement patterns, and game statistics, to provide detailed insights into individual and team performance. 2) Injury prevention: By analyzing data from wearables and tracking devices, AI can help detect early warning signs of fatigue, overexertion, or injury, allowing for proactive measures to mitigate risks and optimize player health. 3) Fan engagement and personalization: AI algorithms can analyze fan preferences, behaviors, and social media interactions to deliver personalized content, recommendations, and offers. 4) Content creation: Computer vision and automated video editing, can streamline the process of capturing, analyzing, and creating highlight reels, recaps, and other video content. 5) Ticketing and pricing optimization: AI algorithms can analyze various factors, such as team performance, opponent strength, and historical ticket sales data, to optimize ticket pricing strategies. 6) Data-driven scouting and recruitment: AI can assist in player scouting and talent identification by analyzing performance data, comparing attributes, and identifying patterns indicative of future success. 7) Sponsorship and revenue generation: AI can analyze fan data, market trends, and brand partnerships to identify sponsorship opportunities that align with fan interests and demographics. 8) Sports betting and predictive analytics: AI algorithms can analyze vast amounts of historical and real-time data to provide accurate predictions and insights for sports betting. 9) Athlete performance and training: AI-powered wearables and tracking devices can monitor and analyze athlete performance metrics in real-time. 10) Venue operations and security: AI-powered systems can optimize venue operations by analyzing data on crowd flow, security risks, and facility management. #artificialintelligence #sportsbusiness #sportstech

🧠 🎾 Smarter tennis, savvier fans 👏 🙌 For over three decades, IBM has partnered with the (USTA) United States Tennis Association to transform the US Open into a cutting‑edge digital experience—engaging 14 million+ global fans through the official app and website. Key pillars of the innovation: ☁️ Hybrid Cloud: A flexible, scalable multicloud infrastructure via Red Hat OpenShift enables the USTA to handle traffic surges exceeding 5,000%, while keeping apps agile and resilient 🔢 Data & watsonx.data: Capturing over 7 million data points per tournament—from serve speed to shot placement—plus 20+ years of historical records and media. Centralizing it all in a hybrid data lakehouse fuels real‑time AI insights. 🧐 AI (watsonx.ai / Granite / Orchestrate): Empowering content creators with tools that summarize matches, generate commentary, and craft rich narratives. Example: Match Reports jumped from 20 to 64 in just the first round of 2024—a 300% productivity boost IBM+8 👀 Automation & Observability: Tools like IBM Instana, Terraform, and Apptio deliver near-perfect stability—99.999% uptime, 80% reduction in provisioning cycle time, and optimized cloud cost management IBM Latest innovations powered by watsonx (2025): 🎾 AI‑generated commentary with audio & captions on video highlights, using AI trained on match stats, rankings, and linguistic nuance 🎾 Match Insights include the Power Index, blending structured stats and sentiment analysis, plus AI Draw Analysis, which ranks how "favorable" each player's draw is and updates as the tournament advances ❓ Why this matters: By blending hybrid cloud, AI, data, and automation, IBM and the USTA aren't just reporting scores—they're crafting immersive, dynamic, and personalized fan experiences. As the tournament scales, innovation scales with it. This is a prime example of enterprise AI in action: strategic, scalable, and fan-first! https://lnkd.in/eb98QSmE #AI #watsonx #USOpen #FanExperience #IBMConsulting #DigitalInnovation

4 practical AI lessons from sport. Sport is one of the best stress tests for AI, because decisions are fast, public, and high stakes. Here are 4 AI lessons every executive can steal from elite sport 👇 4) Fan Engagement At Scale 🏟️ Broadcasters use AI to tag key moments and auto-clip highlights in near real time, tailored to the player or team you follow. Business takeaway: broad segmentation is blunt, build personalization that reacts to real behavior. 3) Real-Time Adjustments ⏱️ In the NFL, coaches can review AI-assisted breakdowns seconds after a play. Business takeaway: if dashboards lag, you are managing last week’s reality, push for live pulse views and adjust during the quarter. 2) Digital Twins 🧪 In Formula 1, teams run what-if scenarios on tires, weather, traffic, and rivals before committing to a pit strategy. Business takeaway: replace static planning with dynamic scenario testing, build a digital twin of your supply chain or customer base, then stress-test it to find the real performance levers. 1) The Co-Pilot Model 🤝 The strongest teams treat AI as a probability engine, humans add context, the accountability stays human. Business takeaway: use AI as a decision engine, when leaders override it, state the missing context and feed it back to improve the system. What other lessons should business leaders take from sport, and where have you seen these ideas work in the real world? 👇