How Data Influences Sports Performance

In 2022, the NFL paid nearly $800 MILLION to injured players. But in 2024, they used AI to crunch 6.4 million data points—per game. And it’s slashing injuries by 29%. Here’s how it’s saving careers (and championships): As a former NFL agent and Wall Street vet, I've seen data transform sports. The physical toll is brutal, but the NFL's latest innovation changes everything. They're using AI to predict injuries before they happen. But here's what makes this revolutionary: The system processes 8TB of video weekly through computer vision. ML models analyze every tackle, cut, and sprint in real-time. AI runs 4.3M simulations per game to spot injury risks. It's trained on 10,000+ simulated seasons. The tech behind it? Mind-blowing: Players wear Zebra Tech sensors tracking: • Location in real-time • Speed variations • Impact forces • Distance covered • Acceleration patterns But the magic happens in the cloud: AWS processes data within 12 seconds. The system builds a "Digital Athlete" - your virtual twin. It uses 3D pose estimation for biomechanical analysis. This predicts injuries with unprecedented accuracy. Here's where it gets fascinating: Mouthguards capture data at 20,000 Hz, measuring: • Force of collision • Direction of impact • Velocity at contact When risks exceed thresholds, something remarkable happens: Teams get instant tablet alerts. These aren't generic warnings. Each alert considers: • Player's injury history • Position benchmarks • Current game demands The impact? Staggering: The Chiefs now average 3.2 proactive subs per game based on AI. These aren't random switches. They're data-driven decisions revolutionizing player management. And here's the biggest breakthrough: The system flags players hitting: • 85% of position-specific speeds • 90th percentile contact forces • 15+ high-intensity impacts But there's an irony in all this: While the NFL develops this amazing tech, they keep pushing for an 18-game season. They're using AI to protect players while adding more wear and tear. It's like installing airbags while removing seatbelts. From my NFL experience, I know what's at stake. This isn't just about preventing injuries. It's about extending careers and protecting legacies. Keeping our favorite players on the field longer. Football's future isn't just player safety. AI drives smarter decisions in: • Player management • Performance data • Injury prevention This tech changes how we analyze the game.

Robots won't be playing pro sports (at least not for a few more years!) but, honestly, what is our thumbnail designer supposed to do? Today's episode *is* all about how A.I. is transforming baseball ⚾️ (with lessons for all industries) BASEBALL'S DATA REVOLUTION • Baseball's analytical journey evolved from the "Moneyball" era of the early 2000s to today's A.I.-powered decision making. • Every Major League Baseball (MLB) team now employs data scientists, treating analytics as a competitive necessity. • MLB's Statcast system generates 7 terabytes of data per game, tracking everything from pitch spin rates to fielder movements. • Machine learning (MLB ML?? 😂) algorithms excel at finding patterns in this mountain of information that humans would miss. SCOUTING & PLAYER DEVELOPMENT • Modern scouting uses ML models to analyze vast arrays of player data beyond traditional stats. • Models analyze nuanced metrics like exit velocity, launch angle, and spin rates to make more accurate performance projections. • Teams can identify undervalued players by recognizing patterns that traditional scouting might miss. • Player development has become personalized through A.I. systems that analyze individual strengths and weaknesses. • A.I. can flag mechanical issues in swings or pitching motions before they become major problems. GAME STRATEGY • Managers now use predictive analytics to inform game decisions, from pitching changes to defensive positioning. • A.I. models simulate countless scenarios to recommend optimal strategies for specific matchups. • Defensive shifts evolved through deep learning analysis of where every ball lands. • Teams blend human intuition with machine predictions, using technology as a "high-tech co-pilot". • Players and coaches regularly consult iPads mid-game to study the latest analytics. FAN EXPERIENCE • A.I. now enriches broadcasts with real-time "win probability" graphs and advanced metrics like "catch probability". • Advanced statistics help fans gain deeper appreciation of player skills and game dynamics. • MLB's Automated Ball-Strike system ("robo-umpire") uses AI and vision technology to ensure consistent, fair calls. LESSONS FOR EVERY BUSINESS • If baseball—deeply rooted in tradition—can embrace A.I., any industry can be transformed by a data-driven approach. • Organizations that blend domain expertise with A.I. insights outperform competitors. • Early adopters gain significant advantages until others catch up. • A.I. augments human strategic decision-making rather than fully replacing it (like the robots... this will be true for at least a few more years!). • Success comes from experimenting with data, trusting analytics, and maintaining an open mind to change. You can hear more on the above in the most recent episode (#874) of the "Super Data Science Podcast with Jon Krohn" on any podcasting platform and YouTube. Link in comments ⬇️ #superdatascience #ai #aiinsports #machinelearning #baseball #sports

The National Football League (NFL)'s commitment to player safety and performance has reached new heights with the groundbreaking Digital Athlete program, an innovative AI-powered initiative that’s redefining how teams train and protect players. Working alongside Amazon Web Services (AWS) and sports science experts, the NFL has built a virtual platform that creates a digital avatar for each player, enabling coaches and health professionals to monitor, predict, and optimize player health and performance. With real-time data at their fingertips, teams can anticipate injury risks and fine-tune player workloads, keeping athletes in peak condition and extending their careers. Ben Peterson, VP of Health and Performance for the San Francisco 49ers, has leveraged AI to revolutionize team management strategies, running millions of simulations to assess potential impacts on players. AI-driven adjustments have allowed coaches to prevent injuries, as shown when the system flagged a quarterback's mechanics as potentially problematic, leading to minor tweaks that improved both accuracy and safety. By freeing up valuable time for coaches and trainers to engage directly with players, AI has strengthened communication and boosted on-field performance for the 49ers. Jeff Miller VP of Player Health and Safety, notes that the proactive use of AI is making the game smarter and safer. The Digital Athlete program’s predictive analytics detect early warning signs, enabling prompt interventions that could avert serious injuries. The initiative is setting a new industry standard in sports safety, offering a blueprint for how AI can be adopted by other sports to improve player well-being and performance. The NFL's Digital Athlete program is a true testament to how technology can drive positive change. Dive into this journey of innovation by reading the full article — click the link to explore how AI is shaping the future of sports! https://lnkd.in/ex7qBUng

That 2016 Leicester team was something else... They shocked everyone with a £72M team, beating rivals worth 10X more. The odds of them winning the title was basically none—1/5000. While Leicester's success was undoubtedly a "Moneyball" story, they did the impossible with this: Data-driven team design. Here's their underdog story and what we can all learn from it: While most elite teams were chasing superstars, Leicester used "Moneyball" principles and built a team of Premier League winners. How? Riyad Mahrez: They paid just £400k for him from French second division. Their scouts spotted metrics that others overlooked. Mahrez went on to become the Player of the Year. N'Golo Kanté: Cost £5.6M—ignored by others for his height (5'6"). Leicester's analysts focused on his interception stats—highest in Europe. Then Jamie Vardy: Rejected everywhere, working in a factory for £30/week. Leicester detected his exceptional acceleration data. The factory worker broke scoring records and became England's Player of the Year. But Leicester's true innovation wasn't individual talent. It was creating a system where complementary skills magnified each other. Their manager built a tactical structure perfectly suited to his players' strengths. While elite teams dominated possession (65%+), Leicester averaged just 42%—the lowest of any champion ever. They didn't need possession. They built a system around their strengths. The lessons transcend sports: 1. Data trumps biases Leicester didn't judge players by reputation—they identified undervalued skills through data. 2. Systems beat individual brilliance They created a framework where every player knew their exact role. 3. Simplicity creates clarity Clear strategy beats complexity every time. I see the same pattern in effective AI implementation. Successful AI transformations focus on creating "AI-native teams" where: • Tasks flow naturally between humans and AI • Each handles what they do best • The system adapts constantly based on data Just like Leicester built around unique talents... High-performing teams design workflows where AI handles routine analysis while humans apply judgment, creativity, and strategic thinking.

Scaling isn’t hard because of lack of ideas. It’s hard because growth requires motion, Across data, people, and capital. And most companies can’t see if that motion is actually happening. Revenue is essentially a lagging indicator.  The right Leading indicators will tell you if your growth, system, Is on track or under pressure. The same holds true in sports.  Below are five ways elite teams used data to achieve incredible results. They won because of: World-class talent,  Elite coaching,  Strong team culture,  And clear strategy. But they also had the infrastructure to identify exactly what to focus on ⬇️ ➡️ Liverpool (under Jürgen Klopp) Hired throw-in coach Thomas Grønnemark after analyzing throw-in retention rates.  Built a data science department led by Ian Graham. The effect: ↳ Massive increase in throw-in possession.  ↳ More chances. 🏆 One Champions League,  🏆 One Premier League. ➡️ Germany (Coached by Joachim Löw) Partnered with SAP to analyze opponent behavior and positional data.  Ran simulations. The effect: ↳ Exposed Brazil’s defensive gaps.  ↳ Result: 7–1 in the semifinal. 🏆 World champions 2014. ➡️ Spain (2008–2012, led by Vicente del Bosque) Refined Tiki-Taka using passing network analysis.  Tracked control zones and movement efficiency. The effect: 🏆 Euro 2008.  🏆 World Cup 2010.  🏆 Euro 2012. ➡️ France (Didier Deschamps) Used STATSports GPS tracking and analytics to optimize physical output per player.  Customized recovery and load management based on real-time metrics.  Analysts focused on transition data and defensive compactness. The effect: ↳ Maintained peak performance across a high-intensity tournament.  ↳ Controlled tempo.  ↳ Dominated transitions. 🏆 Won the 2018 World Cup. ➡️ AS Monaco (Post-2013 Rebuild) Built a world-class data scouting department under Luis Campos.  Used statistical models to find undervalued,  high-upside players like Bernardo Silva, Fabinho, and Thomas Lemar. The effect: ↳ Sold €400M+ in talent in five years.  ↳ Reached the Champions League semifinals in 2017. 🏆 Won Ligue 1 against PSG’s budget. With the right foundations in place, Teams can track the right KPIs and make faster decisions. And that's what you need to grow. Who do you think will win this year's Champion’s League? * * * 👉 Follow me for more insights on how to turn data into growth.