Data in Sports Analytics

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.

Last quarter, we spent $1,404,619 on AI tokens - an all-time high - and the ROI wasn’t what we expected… Most of the ROI didn’t come from “flashy AI”, it came from boring AI doing boring work at scale. Here’s where our spend went and what actually moved the needle: 1. Telling reps who to call today (and why) We’re using AI to sift through millions of signals and tell reps who to talk to today and why. The signals that we’ve found matter: Job changes (new decision makers = new opportunities), buying committee changes and intent signals (active web research and pricing page visits). The big ROI driver is helping our customers with daily prioritization so they don’t have to go fishing for actionable info. At ZoomInfo, We’ve seen a 25-33% increase in meeting quality and opp creation when AEs are sourcing using our AI tools. Win rates also jump from 16-20% to 30%. 2. Writing outreach that doesn’t sound automated We’re moving from “20 segments of 1,000” to 20,000 segments of 1. Not “VP IT at enterprise insurance” messaging… but John at State Farm, who we talked to last year, who competes with three of our customers, with context pulled in automatically. Customer ROI here ultimately comes from better response rates and higher close rates by being more relevant. Buyers care when you show you care. 3. Turning sales calls into usable data Every sales call (ours and customers) is recorded using @Chorus and becomes structured data: objection patterns, competitor mentions, deal risk, coaching moments. We’ve found the benefits of this are huge - 25-30% faster ramp time for new reps, and 10-15% larger deal sizes through better discovery and value articulation. The average rep sells more like the best rep. 4. Speeding up low-value engineering work Every engineer at Zoominfo has Intellij and VS Code w/ Cline. AI handles the unglamorous stuff: Boilerplate code, refactors, test coverage. We’ve seen ~25–30% faster execution on these routine tasks, which frees senior engineers to focus on system design and real product innovation. Our biggest lesson so far has been that if your data foundation is garbage, AI just helps you move faster in the wrong direction. You won’t get AI “working” until you have contextual customer/prospect data centralized, and you can actually build on top of it. We’re still early and we’re trying a lot of things but these have been the highest ROI drivers by a mile. If you’re testing AI in your GTM stack, drop a comment with what’s actually working for you - I’m all ears.

Google Cloud is announcing an industry-first, AI athlete performance tool prototyped with U.S. Ski & Snowboard (USSS). Built by Google Cloud engineers, the tool was tested by U.S. Olympians ahead of the Winter Olympic Games to understand their high-performance needs. Using our full-stack AI, we are enabling athletes to train with better safety, more confidence and greater precision than ever before. Here’s what makes it unique:  - 3D Analysis: Our AI leverages spatial intelligence to "see" through bulky winter gear, mapping an athlete’s 3D skeletal points using only standard video—no sensors or wearable suits required. - Near Real-Time Physics: Using custom Google Cloud TPUs, Gemini reasoning engines, and research from Google DeepMind, we deliver complex biomechanical insights—like angular velocity and airtime—in near real-time. - Conversational Insights: Using the multimodal power of Gemini, coaches can interact with data using natural language. - Safety & Resilience: By identifying subtle biomechanical patterns, we can help coaches mitigate injury risks before they happen. Read more on our blog: https://lnkd.in/g9Fu2Ckd

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

🚨 University of Arizona Football | Performance Systems Overview (2022–2023) During the 2022–2023 season, we had the opportunity to build out a truly integrated performance system at Arizona — blending objective monitoring, individualized training, and collaborative decision-making to support player development and availability. Here’s what that looked like behind the scenes: 🔧 System Components – Developed a centralized Athlete Management System (AMS) – Integrated force plate CMJs on GD-2 and GD+1 – Mapped GPS data to every drill in practice by volume, intensity, and density – Built a stoplight readiness model (Red/Yellow/Green) based on force, asymmetry, and wellness inputs – Weekly 1080 Sprint profiling to target individual acceleration deficits and monitor trends 📊 In-Season Monitoring Strategy – Combined neuromuscular data (jump height, RSImod, asymmetry) with GPS and workload trends – Used CV% and SD thresholds to flag meaningful fatigue changes – Adjusted pre-practice prep, lifting intensities, and recovery based on G+1/G-2 trends – Created individual and positional reports shared daily with performance and coaching staff 📈 Results – Logged 31 new top speed records – Saw a 35% reduction in soft tissue injuries with minimal hamstring-related time-loss – Aligned training with the competitive calendar: Winter → Spring → Camp → Season → Postseason – Worked closely with the Performance Director to manage daily decisions around practice structure and player availability 🎯 Takeaway What made the difference wasn’t any one piece of tech or protocol — it was the ability to tie together force diagnostics, GPS load, sprint data, and on-field context into a unified decision-making system. Building that bridge between data and action is where the real impact happens. #SportsScience #AthleteMonitoring #PerformanceAnalytics #SpeedDevelopment #InjuryPrevention #CollegeFootball #ForcePlates #GPS #1080Sprint #SpellmanPerformance #ArizonaFootball

To be a great sales manager, you have to be a great coach. But coaching often slips through the cracks — especially when there are big deals to close. Now, AI is making it possible for every manager to not just coach more, but coach better. When I was in sales, I saw many new managers fall into the same trap: putting on their superhero cape to rescue deals instead of coaching their reps through them. I was guilty, too! We all knew coaching was important — but we had no time and no tools to scale. With AI, sales managers can now deliver highly targeted coaching at scale. It’s now possible to analyze multiple call transcripts in minutes and pull in unstructured data to understand what happens between calls. You can: 1. Review each rep’s recent calls, emails, and conversations to get a complete picture of how they’re selling — and give them targeted recommendations for improvement. 2. Analyze calls from a specific segment and compare what’s working in closed-won versus closed-lost deals to pinpoint the messaging and strategies that perform best. 3. Generate summaries of how top performers handle objections, communicate ROI, and build a business case — and share those insights with new reps as they ramp. Many HubSpot customers (and our own sales managers) are already using these insights to send regular, personalized coaching to reps — and improve the productivity of their teams. Being a sales manager used to feel like you’re a “super rep” — jumping between calls, rescuing deals, trying to fit in some coaching along the way. Now, it feels like you’re a “super coach” — spotting trends, sharing insights, and helping your whole team scale their impact. Exciting times!

Most managers focus on performance once a year. The best managers improve performance daily. After 25 years of managing teams, I've learned, The difference between good and great managers isn't: • Effort • Skill • Tools It's having the right information at the right time. But most people managers work from scattered and stale data: • Random, handwritten notes from 1-on-1s • Performance reviews from 6 months ago • Gut feelings about who's struggling • Occasional frustrated feedback And that's if they can make time to manage at all. Here's where AI comes in. AI won't manage your team.  But it will help you know them. Here's why you need an AI-powered employee dashboard: HIGH-FIDELITY PROFILES | See The Whole Person ↳ Combine resumes, assessments, and feedback into one view ↳ Understand strengths, gaps, and motivations ↳ Predict where they'll thrive and where they'll struggle CONNECTED TO EXPECTATIONS | Align Profile to Performance ↳ Link their capabilities to role requirements ↳ Identify natural fits and development needs ↳ Spot blind spots before they become problems WITH TARGETED DEVELOPMENT | Focus on What Matters Most ↳ Build 90-day plans for 2-3 key capabilities ↳ Adjust based on real data, not assumptions ↳ Track progress with specific milestones INSTANT PATTERN RECOGNITION | Spot What You'd Otherwise Miss ↳ Upload weekly updates, KPIs, and meeting notes ↳ Get early warnings on performance shifts ↳ Let AI identify trends across time PERSONALIZED COACHING | Tailor Your Approach ↳ Get AI-suggested coaching topics for each person ↳ Customize feedback delivery to their profile ↳ Make every 1-on-1 more impactful The AI advantage: It never forgets context.  It spots patterns across months of data.  It grounds your coaching with homework, not guesswork. The 7-step framework: 1. Create high-fidelity employee profiles 2. Connect profiles to role expectations 3. Build targeted development plans 4. Upload ongoing performance data 5. Get AI-suggested coaching 6. Tailor feedback to their profile 7. Track progress and iterate [Get my starter prompts from the carousel below] Better yet: Join our Free Lighting Lesson next week.  And we'll build one together.  In under 30 minutes. November 13th at 1 PM ET: https://lnkd.in/e3h3aRDa A few more tips: • Keep one AI thread per employee for context • Upload data weekly, not just when problems arise • Use AI insights to inform your coaching, not replace it The truth about great management: Most managers react to problems after they happen. Great managers predict and prevent them. Better information leads to better decisions. Better decisions lead to high-performing teams. 📕 Save in case you want to build this out later.  ♻️ Share to help other managers connect with their people. 🔔 Follow Dave Kline for more AI-powered management strategies.

🏀Imagine seeing the invisible forces that shape every basketball game - the data and insights that explain the flow of every play. With Amazon Web Services (AWS), the National Basketball Association (NBA) and its affiliate leagues are making that a reality. As part of a multi-year partnership, the NBA is launching ‘Inside the Game’ – powered by AWS, a platform that combines billions of data points with AI and machine learning to generate real-time insights, including: 🔸 Defensive Box Score: Tracks which defender is guarding each offensive player in real time. 🔸 Shot Difficulty: Evaluates the difficulty and likelihood of each shot by analyzing factors like player orientation and setup. 🔸 Gravity: Measures how much defensive attention a player draws to reveal patterns in how defenders react. 🔸 Play Finder: Lets fans and broadcasters instantly find similar plays, offering deeper insights from historical data. For fans, this means a new level of understanding of the game they love. And the applications go beyond sports—data like this can drive smarter decisions, reduce risk, and unlock new insights across industries. The possibilities are limitless. Read more here: https://lnkd.in/dnUuMJzd #awsforindustries #awsforsport

Arsenal’s coach said something important about AI. Arteta admitted the club is already using AI across multiple processes. But the interesting part is not that they use it. It’s how he framed it. “It’s a powerful tool if you ask the right questions.” “You still need sensitivity and intuition.” “Bad data can confuse you fast.” That is the real lesson. Football isn’t becoming “AI driven.” 👉 It’s becoming question driven. Clubs that win with AI won’t be the ones with the biggest models. They’ll be the ones that know what problems to solve. And that have people who can interpret data without losing the human element. As Arsène Wenger said, "what is dangerous is if the science dominates the decisions.” This is where the gap will open. Some clubs will use AI to see patterns earlier, act faster, and build real information advantages. Others will keep repeating “we have data” while nothing in their process actually changes. AI won’t replace decision makers. But it will expose the ones who don’t know how to decide. What’s your view? Sources: Goal // Bloomberg

𝐇𝐢𝐠𝐡 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐢𝐧 𝐬𝐩𝐨𝐫𝐭 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐭𝐚𝐥𝐞𝐧𝐭, 𝐭𝐚𝐜𝐭𝐢𝐜𝐬, 𝐨𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐥𝐨𝐚𝐝. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐛𝐮𝐢𝐥𝐭 𝐨𝐧 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. One of the biggest differentiators between good teams and consistently winning organisations is the quality of their medical and performance structure. The right medical framework doesn’t just “treat injuries”, it builds availability, resilience, and ultimately, a true competitive advantage. 𝐈𝐧 𝐞𝐥𝐢𝐭𝐞 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬, 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲. Here are key areas of medical structure that are often overlooked but crucial for gaining the competitive edge: 𝐂𝐥𝐞𝐚𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 & 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 Medical, S&C, coaching, and performance analysis must operate under a shared philosophy. Silos kill performance. Integrated decision-making protects athletes while maximising output. 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐋𝐨𝐚𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 (𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐑𝐞𝐡𝐚𝐛) Monitoring training load, recovery markers, and fatigue trends helps prevent issues before they become injuries. Injury prevention is performance enhancement. 𝐑𝐞𝐭𝐮𝐫𝐧-𝐭𝐨-𝐏𝐥𝐚𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 Objective criteria, progressive exposure, and match-demand benchmarking reduce re-injury risk and ensure true readiness...not just clearance. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Daily reporting structures, transparent status updates, and clear accountability reduce guesswork and improve trust across departments. 𝐈𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥𝐢𝐬𝐞𝐝 𝐀𝐭𝐡𝐥𝐞𝐭𝐞 𝐏𝐫𝐨𝐟𝐢𝐥𝐢𝐧𝐠 Movement screening, injury history mapping, strength asymmetries, and performance baselines allow targeted interventions instead of generic programs. 𝐃𝐚𝐭𝐚-𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠 GPS, force plate metrics, wellness scores, and medical records should guide strategy and not sit in spreadsheets unused. ⭐When medical structure is reactive, teams survive. When it’s strategic, teams win. ⭐The “competitive edge” isn’t one big innovation. It’s the accumulation of small, well-structured decisions made consistently. ⭐High performance is built on availability. Availability is built on structure. #HighPerformance #SportsMedicine #PerformanceTeam #Leadership #AthleteCare