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.

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? 👇

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 quietly changing how football decisions will be made. Not just highlights. Not just data. But actual understanding. I came across a research paper introducing a multi-agent system that can: * Identify players from video * Retrieve their career data * Analyze match situations * Answer complex football questions All in one flow. In simple terms: It doesn’t just watch football, it understands it. Now here’s the real question: If a system can break down a player’s: * Tactical fit * Decision making * Match impact * Career trajectory Where does that leave: * Traditional scouting? * Agent-driven narratives? * “Eye test” evaluations? From my perspective, this is where things get interesting: The future won’t be: * Data vs scouts It will be: Data + context + human judgment For platforms like Soccer Circular this opens a different direction: Not just tracking players… But helping clubs understand them faster and better. Curious to hear from you: 1. Do you think AI can realistically evaluate a player beyond highlights? 2. What part of scouting can never be replaced? 3. Would you trust an AI-generated scouting report? #FootballBusiness #Scouting #AI #SportsTech #SoccerCircular FFAR Online

⚾️ Beyond the Dugout: How the Los Angeles Dodgers Are Operating Like an #AI Startup The Los Angeles Dodgers are proving that the future of competitive sports is deeply rooted in #DataScience and #ArtificialIntelligence (#AI). Their front office structure, brimming with quantitative analysts and data engineers, mirrors a high-growth tech company, demonstrating a commitment to gaining a quantifiable edge both on and off the field. Key AI & Data Applications Driving Success: Player Development & Injury Prevention: Leveraging systems like KinaTrax and Rapsodo, AI algorithms process biomechanical data to optimize training regimens and predict potential injuries before they occur—extending careers and performance. Game Strategy ("42" System): A sophisticated internal system processes real-time Statcast data to power in-game decisions: Optimizing Defensive Shifts based on historical spray charts. Determining effective Pitching and Batting Strategies for specific matchups. Using Machine Learning to run millions of game simulations for predictive modeling and tactical evaluation. Talent Scouting: Data analytics is key to their successful roster construction, helping to identify undervalued talent and predict future performance trends for drafting and acquisition. Enhancing the Fan Experience & Operations: Crowd Management: Through partnerships like WaitTime, AI uses cameras to provide real-time insights into crowd density and line lengths, reducing wait times and improving venue safety and efficiency. Personalized Engagement: Exploring AI for custom highlight reels, real-time statistical displays, and interactive analysis tools to enrich the fan experience. By integrating AI into nearly every facet of the organization—from the pitching mound to the business office—the Dodgers are shifting from assumptions to data-informed decisions to maintain their competitive advantage. What other industries are seeing the most dramatic shifts by adopting AI and data-centric organizational structures? Share your thoughts below! #SportsTech #AI #DataScience #MachineLearning #LosAngelesDodgers #Innovation #Analytics #CompetitiveAdvantage

I’ve been supporting so many founders over the past several months that are building AI Agents to support all sizes of businesses. I was recently watching a game where the announcers started talking about the use of AI in sports. I did a bit of research and found out that AI agents aren’t just helping businesses automate workflows; they’re now making moves in the sports world, too. We’re starting to see real use cases where AI is influencing how athletes train, how teams compete, and how fans engage. One of the coolest applications is in performance coaching. AI agents can now analyze game footage, track athlete movement, and provide personalized insights almost instantly. Think of it like having a 24/7 assistant coach who never misses a play and can break down exactly what went wrong or right during a match. They’re also being used in recruiting and scouting. Instead of sifting through hours of footage manually, teams can use AI to identify talent based on stats, playing style, and performance trends. Some agents even simulate how a player might fit into a specific team’s system. On the fan side, AI agents are helping create hyper-personalized content and experiences, from generating custom highlight reels to managing fantasy teams or answering game-day questions in real time. And then there’s strategy. Some teams are testing AI agents to simulate different in-game scenarios and make data-driven recommendations in real time. It’s still early, but the idea of AI in the coach’s ear—helping with substitutions, plays, or tactics- isn’t as far off as it sounds.