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.

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

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.

🏀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

📊 Max Speed Exposure Dashboard – built in the Fundamentals of Load Monitoring Course 📊 This visual is one of the key Power BI dashboards featured in our Fundamentals of Load Monitoring course—in collaboration with Jo Clubb for Sport Horizon UK - designed to help practitioners better understand and manage exposure to high-speed running. But beyond the dashboard… here’s a bit of real-life context 👇 When I was working as a sport scientist in elite football, this exact issue—exposure to max speed—was something we faced constantly: ⚽️ At QPR FC, at times we had players not hitting the required high-speed thresholds, especially those on the fringes or returning from injury. It was a challenge to balance rehab, rotation, and tactical work while ensuring the right physical stimulus. 🌍 At Kerala Blasters FC in India, it became even more complex. The climate, fixture congestion, and travel made it difficult to maintain consistent high-speed exposures. Monitoring was critical to managing fatigue, injury risk, and training load. 🟢 With the London Senior Gaelic football team, we faced a different issue—amateur athletes balancing jobs and travel with training. We had to be smarter with the limited time we had, using simple data tools to guide high-intensity exposures. 👴 And now—playing with the London Masters (Over-40s Gaelic football team)—I feel this challenge myself. We train less, we recover slower, and we’re still competitive. Even at this level, exposing the body safely to higher speeds is a real consideration! That’s why I believe dashboards like this one—tracking % of max speed and days since last high-speed effort—are so valuable. They help guide smarter, safer, and more effective decisions for athletes at every level. 📈 This dashboard is just one example from the course—bringing real-world monitoring issues to life through data and design. #Tableau #PowerBI #SportsAnalytics #DataVisualisation #SportHorizon #SportScience #BespokeInsights #PerformanceAnalysis #DataAnalytics #DataAnalysis #Football #Soccer #Excel #AthleteMonitoring #LoadMonitoring

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

This week's defining shift for me is that motion data is shifting from something systems measure to something they use to guide action. Instead of treating movement as a record to analyze after the fact, more platforms are turning perceptual motion data into live input for coaching, control, and interaction. This is not about better measurement. It is about using motion data to drive decisions and feedback in real time. This week’s news surfaced signals like these: ⛷️ U.S. Ski & Snowboard and Google Cloud are testing smartphone-based, markerless motion analysis to turn ordinary video into near real-time coaching guidance for elite athletes. 🤚 Meta and the University of Utah are studying how surface EMG can translate muscle signals into usable gesture control, including for people with limited hand mobility. Why this matters: This is the difference between watching a replay and having something help you in the moment. Instead of looking at motion after it happens, these systems are starting to use it while you’re still moving, to help decide what comes next. #physicalAI #perceptionsystems #motion #motioncapture #data #sports

AI sceptics, take note: The Paris Olympics are being heralded as the world’s first 'AI Olympics,' a testament to how deeply artificial intelligence is now woven into the fabric of our lives. The numbers speak for themselves: the International Olympic Committee (IOC) has identified over 180 potential use cases for AI in these Games, underscoring the rapid pace of technological integration.   Intel Corporation, at the forefront of this revolution, is deploying AI to generate dynamic highlight reels, drawing attention to lesser-watched sports like table tennis and speed climbing. By analysing thousands of hours of footage in real-time, AI ensures that every sport, no matter how niche, gets its moment in the spotlight. The future of work isn't about AI taking over; it's about leveraging AI to elevate what we do best. It’s estimated that the 2024 Olympics created 150,000 jobs worldwide, a vast majority of these relating to STEM, according to IOC.   The result?   📌 Global Reach: 2024 games are on track for more than half of the worldwide population to follow the Olympic Games Paris 2024. AI-driven algorithms have optimised content delivery across platforms, ensuring that live broadcasts and highlights reach audiences in real-time, no matter where they are.   📌 Widespread Engagement in France: An astounding 92% of the French population has watched the Paris 2024 Games coverage so far. AI-powered content recommendation systems have ensured that viewers receive tailored content, increasing engagement and making the Games more accessible to everyone.   📌 Massive European Audience: By Day 10, Warner Bros. Discovery (WBD) had already surpassed 140 million viewers in Europe across its platforms, driven by AI-enhanced content curation. AI’s role in analysing viewer behaviour and optimising content distribution has made it possible to capture such a large audience. On August 4th, WBD recorded 600 million minutes of streaming, showcasing AI’s capacity to manage and enhance the viewer experience.   📌 Surge in US Viewership: In the US, Paris 2024's total audience is up 80% on Tokyo 2020, with an average of 33 million viewers across NBCU platforms. AI has played a pivotal role in this increase, from personalizing content recommendations to optimizing streaming quality across diverse devices and networks.   📌 High Engagement in Japan: In Japan, 102.2 million viewers—over 87% of potential viewers—have watched the Olympics, comparable to the Tokyo 2020 Games. AI’s role in content distribution, language translation, and accessibility features has ensured that this massive audience can enjoy seamless coverage of the Games.   AI has helped to connect billions of people, enhance the viewing experience, and ensure that no sport or athlete goes unnoticed.   Now the Olympics has come to an end the Paralympics begins. Im sure we will see how STEM has played its part in the event but also the athlete’s lives. #STEMOlympics #Ai #Olympics #parisolympics2024 #STEM

Big news from two people I deeply respect, Laura Correnti at Deep Blue Sports + Entertainment and the team at Mondo Metrics Nick Cicero. They just launched the Women’s Sports Index, a real-time, AI-powered data platform that benchmarks social media value across women’s sports leagues, teams, and athletes across TikTok, Instagram, YouTube, and Facebook. And much more to come with 1st party data integrations, streaming video and podcast signals . This is a big deal. Here’s why it matters right now: The investment gap in women’s sports isn’t a passion problem, it’s a data problem. Brands, agencies, and rights holders have wanted to invest more deeply in women’s sports for years. What’s held many of them back isn’t lack of interest, it’s lack of defensible, real-time data to justify the buy, benchmark the value, and prove the ROI. The Women’s Sports Index fills that gap in a meaningful way. Here’s what I think this unlocks for each stakeholder: Teams & Leagues: Finally have a live benchmark to understand how their social presence and media value stacks up, spot trends in real time, and make smarter content and partnership decisions. Athletes: LOVB is already using early data to help athletes build their brands. That’s huge. For the first time, an athlete can see what’s actually resonating, benchmark themselves against peers, and walk into a brand negotiation with data, not just a follower count. Brands & Agencies: No more guessing or over-relying on vanity metrics. This gives media planners and strategists a fact-based foundation to make the case internally for women’s sports investment, and a measurement framework to prove it out post-campaign. Media & Rights Holders: Real-time insight into what content formats, athletes, and moments are driving engagement helps optimize editorial, programming, and distribution strategies at a speed that was simply not possible before. The timing is perfect. Women’s sports is no longer an “emerging” category, it’s a high-growth, high-engagement media category that’s outperforming. The WNBA, NWSL, college athletics, and international women’s leagues are all experiencing record viewership and fan engagement. What they’ve needed is the measurement infrastructure to match the moment. What Deep Blue and Mondo have built is exactly that ----> the data layer that turns enthusiasm into investment and investment into accountability. Congrats to Laura Correnti, the Deep Blue Sports + Entertainment team, and Mondo Metrics on building something the industry genuinely needed. #WomensSports #SportsBusiness #DataAndAnalytics #MeasurementMatters #MediaInvestment #WomensBasketball #WNBA #AdTech #SportsTech #DeepBlue #MondoMetrics https://lnkd.in/gBD6_B6Q

Sudden cardiac arrest (SCA) in athletes can come from both cardiac and non cardiac causes, and the risk is not the same for everyone. The most common cardiac causes include cardiomyopathies, electrical rhythm disorders, coronary artery anomalies, and other structural heart abnormalities. What stood out most is the shift toward action and personalization. Strong pre participation screening supports primary prevention, well practiced emergency action plans support secondary prevention, and survivors need a diagnostic workup similar to non athletes, but interpreted by experts who understand normal athletic heart adaptation. With the right diagnosis and disease specific treatment (from medications to ablation, ICD programming, or surgery), many athletes can safely return to play through shared decision making rather than blanket restrictions. This is also where AI can meaningfully strengthen cardiovascular care, especially in arrhythmia risk prediction and heart failure progression. We are moving toward a future where models can integrate ECG patterns, imaging, genetics, labs, and wearable data to detect subtle warning signs earlier, personalize follow up, and support smarter return to play decisions. The goal is not replacing clinical judgment, but reinforcing it with earlier signals, better stratification, and faster pathways to intervention when seconds truly matter. Review Article: https://nej.md/3ZaAxI7 Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. #SuddenCardiacArrest #SportsCardiology #Cardiology #AthleteHealth #ReturnToPlay #EmergencyActionPlan #AED #CPR #Arrhythmia #Cardiomyopathy #HeartFailure #CVHealth #PreventiveMedicine #DigitalHealth #WearableTech #AIinHealthcare #ArtificialIntelligence #PredictiveAnalytics #PrecisionMedicine #ClinicalInnovation