AI in Sports Performance

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

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.

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

I’m genuinely excited about the potential of AI to support sports science and rehabilitation, not because it can replace clinician or practitioner judgement, but because it can reduce some of the time-consuming work that sits around it. Reviewing the literature and existing frameworks, pulling together an athlete’s historic data, translating that into return-to-play targets, structuring a rehabilitation plan, and then compiling the latest information into an update for coaches or other key stakeholders can take a considerable amount of time. That is where I think AI can be especially useful. In my latest video with Action Apps, I demonstrate their new AI-assisted rehabilitation planning feature using a grade 2 hamstring injury as an example. The platform helps generate an evidence-informed plan with return-to-play targets, phased interventions, exit criteria, monitoring assessments and progress visualisation. The supporting evidence is referenced, and every part of the plan can be reviewed, edited and adapted by the practitioner. That last point is important. Rehabilitation is complex, individual and rarely linear, so the final decisions still sit with the practitioner. For me, the value is in using AI to organise and communicate the information more efficiently, giving practitioners more time to focus on interpretation, adaptation and the athlete in front of them. Watch the demonstration in full on the Global Performance Insights YouTube channel, here: https://lnkd.in/efxk4g3K

🧠 🎾 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? 👇

🎾 Breakthrough in my AI-Powered Padel Analytics After months of intensive development, I'm thrilled to share a major milestone in my AI-powered padel analytics project! This latest iteration showcases how we can analyze amateur padel games through cutting-edge computer vision. 💻 What you're seeing on screen 1. Backbone inference pipeline using open-source models: 1.1 Player detection and tracking using a custom tracker specifically optimized for padel which mixes kalman filter with re-identification 1.2. Player pose estimation 1.3. Ball detection 2. Upstream inference pipeline using custom transformer based time series models 2.1 Ball state classification 2.1.1 🔴 Floor bounces 2.1.2 🔵 Player hits 2.1.3 🟢 Wall bounces 2.1.4 ⚫ Net 2.2. Player stroke classification 2.3 Rally classification 🚀 Lightning-Fast Performance The entire inference pipeline runs at 70 FPS on an RTX 3090 – that's 2.3x real-time speed. 📊 Rich Data Collection 1. Player position and velocity in real-world coordinates 2. Distance covered during play 3. Time spent in strategic zones (back court, net, or transition) 4. Team attribution 5. Top view ball projections in real world coordinates As in previous iterations, court keypoints enable homography projection of player positions onto a 2D court representation for comprehensive analysis. 💡 Latest Innovations 1. Enhanced Court Mapping: Each ball state now has its own 2D court projection for deeper tactical insights 2. Smoother Tracking: Custom smoother algorithms eliminate position jitter for cleaner data I truly believe that this kind of scentific advancements can make professional-grade insights accessible for amateur players, democratizing the sport. The combination of real-time processing power and comprehensive data collection opens up exciting possibilities for player development and tactical analysis. What applications do you see for this technology in sports training and performance analysis? Alessandro Ferrari Ultralytics Piotr Skalski Roboflow Nicolai Nielsen #deeplearning #computervision #sportstech #sportsanalytics #padel

AI isn't just changing sports. It's rewriting the rulebook entirely. For 100 years, competitive advantage in sports came down to three things: talent, training, and coaching instinct. That era is over. Here's what's happening right now across every major sport: 🏃 Performance & Injury Prevention AI models now analyze thousands of micro-movements per second. NBA teams are predicting soft-tissue injuries before they happen. NFL franchises are optimizing load management in-season. The human body has become a data stream. 📊 Real-Time Decision Intelligence Baseball managers receive pitch recommendation overlays mid-at-bat. Soccer coaches get live formation heat maps. Formula 1 pit crews act on AI-generated tire degradation models — in milliseconds. 🎯 Scouting & Talent Acquisition The Moneyball era used statistics. This era uses multimodal AI that watches film, tracks biometrics, and surfaces overlooked athletes that human scouts would never find. Every front office is now a data science team. 📺 Fan Experience Personalized broadcasts. AI-generated highlight reels delivered your way, for your player, on your timeline. The passive fan is becoming extinct. The uncomfortable truth for team executives: The teams winning championships in 2030 are already building the data infrastructure today. Those who treat AI as a gadget will watch it become their competitor's weapon. The scoreboard still ends in a number. But the game is now played in the models, the margins, and the milliseconds. What's the most underrated AI use case in sports that nobody's talking about yet? Drop it below. 👇 #ArtificialIntelligence #SportsTech #AIinSports #DataScience #FutureOfSports #SportsAnalytics #Innovation

AI Is Moving From Data to Decisions in Scouting Marquee just raised $1.2M in pre-seed funding to build what it calls the AI-native decision layer for professional sports scouting. Led by AnD Ventures, with participation from Avishai Abrahami and Omer Shai of Wix, Ami Sirkis of 365Scores, and former Maccabi Netanya owner Eyal Segal, the round reflects growing conviction around AI infrastructure inside elite clubs. The pain point is clear. Clubs are flooded with tracking data, scouting reports, video tools, and proprietary models. Yet industry estimates suggest they effectively use as little as 4 percent of the data available to them. That disconnect is what Marquee describes as the “Data Paradox.” Rather than becoming another data vendor, the company is positioning itself above the stack. A unified intelligence layer that integrates third-party feeds with a club’s internal models and sporting DNA. Under the hood, the platform encodes players as high-dimensional vectors and applies Transformer-based models trained on hundreds of thousands of match sequences. It simulates team chemistry, evaluates tactical fit, and delivers explainable outputs through a natural language interface. The broader shift is significant. Scouting is moving from data accumulation to decision intelligence. If more than half of player acquisitions underperform expectations, the inefficiency is rarely about access to data. It is about synthesis, context, and speed. Marquee is already working with 20 professional clubs across Israel, Europe, and the United States, with pilots planned in top divisions in the Netherlands and the U.S. The market backdrop is substantial: • $60B+ spent annually on transfers and contracts • A $4.5B sports data market • An estimated $15B opportunity for AI-driven decision layers The team blends AI depth with real club-side experience, including 8200 alumni and former scouts. The ambition is clear. Not to replace scouting departments, but to augment them. To reduce manual workflows. To standardize evaluation. To make insights explainable and faster. In modern football and basketball, marginal gains are everything. Better scouting decisions can reshape balance sheets. #sportsbusiness #sportstech #AI #football #basketball #scouting #datadriven #sportsinnovation