Data in Sports Analytics

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

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.

🏀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

Roger Federer: "Staying the same means going backward. I always needed to find ways to improve my game" Interesting to hear Roger Federer discussing the need to continue improving whilst being ATP Tour world number 1, because maintaining the same standard would mean going backwards. What happens if you are an athlete, coach, or leader and want to improve your game, but are not sure what to work on? Performance profiling is an excellent tool. 🔷 Performance Profile Performance profiling is embedded in Kelly’s (1955: 1991) Personal Construct Theory (PCT). The major tenet of the PCT is that individuals continually strive to make sense of the world that they are in, and themselves, by constructing personal theories. The performance profile was developed to enhance an athlete’s self-awareness regarding the characteristics that facilitate successful performance and to enhance the coach’s understanding of the athlete’s viewpoint (Butler, 1989: Butler et al., 1993). As such, performance profiling is method of allowing coaches to understand how athletes rate themselves in the qualities that are needed to be successful in their sport. Coaches can use this information to help develop training schedules in the areas in that players feel they could improve (Butler, 1996). 🔹 Performance Profiling involves the following stages: 1️⃣ The coach explains what the performance profile is. 2️⃣ Identifying and rating characteristics (e.g., mental toughness, speed, tactical awareness) that contribute to elite performance. 3️⃣ Athlete and coach rates each characteristic. 🔹 Assessing athlete and coach discrepancies Once the player has completed his or her performance profile, Butler (1996a) and Gucciardi and Gordon (2009) suggested that the coach should rate the athlete in each characteristic to assess the discrepancy between the coach and the athlete. 🔹 Monitoring progress Coaches can monitor their athlete’s level of improvement by asking the athlete to complete a performance profile on a regular basis, such as once a month. Improvements in qualities that athletes are working towards can instil confidence and also illustrate to the coach that their training programme is effective, or whether it needs altering (Butler, 1996b). In terms of a monitoring guideline, Weston et al. (2011) found that when athletes completed a performance profile three times in a six-week period, there was a significant increase in motivation. I write more about how to complete performance profiling in the third edition of my book, which is available from: https://lnkd.in/d95MvAan

📊 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

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

As sports performance practitioners, we’re always asking if the data point is meaningful. Z-scores help us answer that by showing how far a score deviates from an individual or group norm, taking into account the variability in the data. It’s not just about whether a score is higher or lower. It’s about how much higher or lower, in a way that’s grounded in statistical significance. We regularly see Z-scores applied to flag meaningful changes in an athlete’s training load or a particular physical capacity, compare individuals to group benchmarks, and visualise trends over time in a way that’s interpretable for both coaches and athletes. Excel even has a built-in function, but understanding the why behind the calculation is far more important than simply knowing the formula!

“Context is everything: the mistake of training without knowing where you are.” A data-driven perspective from LaLiga EA Sports ⚽ In elite football, performance isn’t just about talent or effort. It’s about context. And that context is shaped by the physical profile of the competition you play in. A player who thrives in the Bundesliga may struggle in LaLiga—not because they’re less capable, but because the demands are different. 📊 From the Football Intelligence department at LaLiga, we’ve analyzed thousands of matches to build a scientifically validated profile of the “typical” LaLiga player. It’s not theory—it’s data. 👇 Here's what we’ve learned: 🔹 1️⃣ The average LaLiga physical profile (per player, per match): 🔸 10.5 km total distance 🔸 900 m at 21–24 km/h 🔸 500 m >24 km/h 🔸 25 sprint efforts >21 km/h 🔸 15 sprints >24 km/h 🔸 1,500–1,800 m HMLD (High Metabolic Load Distance) ➤ https://lnkd.in/dzWbYJDS 🔸 33.0 km/h average top speed ➤ https://lnkd.in/dHHXbKdD 🔸 54 min effective playing time ➤ https://lnkd.in/dPynWzw6 🔹 2️⃣ Your team isn’t the league Knowing the average isn't enough. Each team has its own identity based on: 🔸 Style of play 🔸 Ball possession 🔸 Match context (winning, losing, opponent strength) ➤ Teams with more possession don’t necessarily run less ➤ https://lnkd.in/d2ZBCsci ➤ Scoreline affects physical output ➤ https://lnkd.in/dHtUG7tw ➤ Transitions and play style shift intensity profiles ➤ https://lnkd.in/dih_T_ra 🔹 3️⃣ The FC Barcelona case Using aggregated 2024/25 data, FC Barcelona shows: ✔️ Higher-than-average total distance (118 km) ✔️ Exceptional frequency of sprint efforts (487.5 >21 km/h; 230.2 >24 km/h) ✔️ Top speed aligned with elite standard (33.5 km/h) ✔️ Outstanding effective play time (57.8 min) 🧠 Their physical profile reflects pressing, positional dominance, and high-frequency short sprints—tailored to their tactical identity. 🔹 4️⃣ Drill down to the player Beyond the team, performance is shaped by: 🔸 Position ➤ https://lnkd.in/dzWbYJDS 🔸 Age ➤ https://lnkd.in/e_bayvkZ 🔸 Genetics ➤ https://lnkd.in/dHgf3uuz 📌 Bottom line❓ Stop training in the dark. Start preparing with context. Know your league profile. Map your team. Then adapt to your players. That’s the path to sustainable, elite performance. 📎 Read the full breakdown, analysis, and all citations here 👉 🔗 https://lnkd.in/dbBci-_M #LaLiga #FootballScience #PerformanceAnalysis #PlayerDevelopment #CoachingExcellence #SportScience #FootballIntelligence #TrackingData #HighPerformance #EliteFootball #ContextMatters #PhysicalDemands #FCBarcelona #DataDrivenCoaching #Conditioning #AnalyticsInSport

🚨 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