The Importance of Data in Sports Strategy

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.

From the (NBA) Files… 📂 When a Player Asked Me to Make Wellness Matter. On my third day with the team, one of our players stopped me in the hallway and said: “We fill these out every day and nothing changes.” It wasn’t attitude - it was honest. And it was fair. I told him: “Give me a week.” I wasn’t totally sure what I’d show him. But I knew I had to make the data do something. So we rebuilt the process with one rule: Every score had to connect to an action. What we changed: •Adjusted scoring - Each athlete was compared to their own trendline, not the team •Weighted inputs - Some metrics triggered deeper review (sleep, soreness), others added context (motivation, stress) •Simplified thresholds - If it didn’t call for action, it didn’t need attention We stopped trying to catch everything. We focused on catching the right things. Then we matched those with interventions: •Minor shifts = awareness •Repeated drops = a check-in •Sharp signals = cross-department response That same player? He started filling the form out before we even asked. Asked follow-ups. Flagged things on his own. Because now, wellness wasn’t a task. It was a trusted early warning system. If you want data to matter, make it impossible to ignore. #Highperformancehabits #SportScience #HumanPerformance #AthletePerformance #Biomechanics #StrengthAndConditioning #SportsAnalytics #ProfessionalGrowth #Leadership #CareerDevelopment #Read #AppliedSportScience #PerformanceOptimization

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.

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