The Next Great Manager: How Data is Changing Football’s Most Important Hiring Decision
How Data-Driven Clubs Recruit the Right Manager: The New Science of Coaching Appointments
📢 Introduction: Why Clubs Keep Getting Managerial Hires Wrong
Hiring a football manager is the most important decision a club can make—but also the most misunderstood.
Clubs spend millions on scouting, signing, and developing players, using cutting-edge analytics to maximize return on investment. Yet, when it comes to hiring a manager, many teams abandon data-driven logic and rely on:
❌ Reputation – “He’s coached at big clubs before, so he must be good.”
❌ Recent Form – “His team won the league last year; he’s a guaranteed success.”
❌ Personal Connections – “Our CEO knows him well, let’s bring him in.”
💥 The Result? More than half of all managers in Europe’s top leagues are sacked within two years.
📉 The Cost of Getting It Wrong
When clubs rush into hiring the wrong manager, the consequences are brutal:
🚨 Expensive Sackings – Chelsea paid £13M to fire Graham Potter after just 7 months.
🚨 Squad Instability – Different managers = different tactical systems = wasted transfers.
🚨 Lost Time & Momentum – The wrong hire can set a club back years in its progress.
And yet, some clubs keep making the same mistakes.
So how do elite teams like Liverpool, Brighton & Man City hire the right manager? Let's dive into the analytics revolution behind managerial recruitment.
📊 The Problem With Traditional Managerial Hiring
Most football clubs spend months scouting players but rush their managerial appointments based on:
❌ Past trophies – Success at one club doesn’t guarantee success elsewhere.
❌ Win percentage – A PSG coach will naturally have a higher win rate than one at Brighton.
❌ Big names & reputation – Hiring a coach based on their profile rather than tactical fit.
🔍 The result? A revolving door of sackings, wasted transfer budgets, and stalled progress.
Let’s look at three case studies that illustrate why traditional hiring often fails—and how data-driven clubs are changing the game.
📉 Case Study: Graham Potter to Chelsea – A Tactical Mismatch
Chelsea’s decision to hire (and then sack) Graham Potter in just seven months is a textbook example of poor managerial recruitment.
✅ What Made Potter Successful at Brighton?
🔹 Possession-based football with intricate build-up play.
🔹 A slow, methodical approach designed to create high-quality chances.
🔹 A well-defined recruitment strategy that fit his tactical system.
❌ Why He Struggled at Chelsea?
🔹 Chelsea’s squad wasn’t built for his patient, tactical philosophy.
🔹 Key playmakers were missing, forcing him to abandon his usual system.
🔹 No time to implement his ideas, as Chelsea needed instant results.
💡 Lesson: Just because a manager succeeds at one club doesn’t mean they’ll fit everywhere. Squad compatibility and tactical alignment matter more than reputation.
📉 Case Study: Julian Nagelsmann to Bayern – The Right Coach at the Wrong Time?
Bayern Munich paid €25M to hire Julian Nagelsmann—only to sack him 19 months later.
📊 The Problem?
🔹 He was a progressive coach expected to evolve Bayern’s playstyle.
🔹 His tactical ideas clashed with the club’s senior players.
🔹 Bayern panicked when results dipped and fired him mid-season.
🔍 Where Did Bayern Go Wrong?
❌ Hired him for long-term vision, but didn’t give him time to implement it.
❌ Ignored squad dynamics—senior players resisted his tactical shifts.
❌ Focused on reputation rather than tactical compatibility.
💡 Lesson: Even the most talented coaches need the right environment to succeed. Hiring a manager without considering squad buy-in and transition time is a recipe for failure.
📈 Case Study: Liverpool’s Data-Driven Masterstroke with Jurgen Klopp
Liverpool’s hiring of Klopp in 2015 is one of the best examples of data-driven managerial recruitment.
❌ The Narrative:
Klopp’s final season at Dortmund was a disaster. His team finished 7th in the Bundesliga, leading some to question if he had lost his edge.
✅ The Data Said Otherwise:
🔹 Dortmund’s expected goals (xG) suggested they had been extremely unlucky.
🔹 Klopp’s pressing system fit Liverpool’s existing squad perfectly.
🔹 His history of improving young players aligned with Liverpool’s long-term strategy.
📊 The Result?
🔹 Klopp transformed Liverpool from mid-table to Premier League & Champions League winners.
🔹 Liverpool became a global powerhouse without overspending like rivals.
🔹 Klopp’s tactical system remained consistent, creating long-term stability.
💡 Lesson: Ignore short-term results. The right hire is about tactical fit, long-term impact, and underlying data.
💡 So How Do Elite Teams Like Liverpool, Brighton & Man City Get It Right?
While many clubs rely on instinct and reputation, the smartest teams use:
📊 Advanced Analytics – Measuring managerial impact beyond win percentage.
🔍 Tactical Compatibility Models – Ensuring a coach’s system fits the squad.
📈 Long-Term Tracking – Identifying potential hires years before they become famous.
🚀 In the next section, we’ll break down the analytics revolution that’s changing managerial recruitment forever.
📌 Step 1: Measuring True Managerial Impact
Most clubs judge managers based on trophies and win percentage—but that’s an incomplete picture.
Smart clubs go deeper. They analyze whether a coach improves a team beyond expectations by looking at key data-driven metrics.
🔍 Key Metrics That Matter
Instead of focusing on raw results, elite clubs evaluate:
📊 1. Performance vs. Squad Quality
➡️ Did the manager overachieve with limited resources?
➡️ How does the team perform compared to their expected results based on squad value?
✅ Example: If a coach takes a mid-table squad and consistently competes for European spots, that’s a sign of elite coaching.
⚽ 2. Tactical Fit
➡️ Does the manager’s playing style match the club’s existing squad?
➡️ Will they require a full squad overhaul, or can they maximize current resources?
✅ Example: Brighton hires managers like De Zerbi and Potter who fit their possession-based, high-pressing philosophy.
❌ Mistake: Chelsea hired Graham Potter, but their squad wasn’t built for his slow, methodical build-up play.
📈 3. Development Impact
➡️ Do young players improve under their coaching?
➡️ Does the manager have a track record of increasing player value?
✅ Example: Klopp transformed Mohamed Salah, Sadio Mané, and Trent Alexander-Arnold into world-class players.
🚀 4. Longevity & Adaptability
➡️ Can they sustain success across multiple seasons?
➡️ Have they adapted to different squads, leagues, and challenges?
✅ Example: Pep Guardiola reinvented his tactics at Barcelona, Bayern, and Man City while still dominating.
💡 Lesson: The right data reveals hidden gems—managers who might struggle short-term but have the ability to build dynasties.
📢 Next Up: How clubs use Tactical Compatibility Models to find the perfect managerial fit. 🚀
📌 Step 2: Tactical Fit Matters More Than Reputation
Most clubs chase big-name managers, assuming success will follow. But without tactical alignment, even the best coaches can fail.
✅ Elite teams hire managers based on fit, not just reputation.
🔹 How Liverpool Identified Arne Slot (2024)
Liverpool didn’t just pick the biggest name available—they used data to find a perfect fit.
🔎 How They Evaluated Slot:
✅ Playing Style Analysis: Studied his tactical approach at Feyenoord using advanced data models.
✅ Tactical Compatibility: Scored highest in Liverpool’s model for a high-pressing, attacking system.
✅ Performance vs. Resources: Overachieved despite lower budgets, proving he could maximize talent.
📊 Why This Matters: Klopp’s Liverpool thrived on intensity, pressing, and direct attacks—Slot’s Feyenoord had the closest tactical resemblance among available managers.
💡 Lesson: Tactical Fit > Reputation
A manager’s philosophy must align with the squad and club identity.
❌ Wrong Approach: Hiring based on trophies and reputation alone.
✅ Right Approach: Matching tactics, squad strengths, and long-term vision.
📢 Next Up: How clubs use predictive analytics to hire managers before they become superstars. 🚀
📌 Step 3: Finding the Next Elite Coach—Before They’re Famous
In the past, clubs only looked for new managers when they were in trouble. Now, the smartest teams scout future coaches years in advance, just like they do with players.
🧠 The Future of Manager Scouting: "Coach ID" & Data-Driven Profiling
Elite clubs don’t wait for a crisis—they constantly track rising coaching talent using advanced data models.
🔍 Key Metrics Used in Coach Scouting
Instead of relying on win percentage and trophies, data-driven clubs focus on:
✅ Expected Threat (xT) – Measures how well a manager’s team creates dangerous chances.
✅ Pressing Intensity (PPDA) – Tracks how aggressively a coach’s team defends high up the pitch.
✅ Squad Age Profile – Analyzes if the manager develops young talent or relies on veterans.
✅ Game State Influence – Evaluates if they adapt their tactics based on the scoreline.
🔹 Example: Brighton’s Hiring Model
When Graham Potter left for Chelsea, Brighton didn’t panic—they already had a plan.
📊 How They Identified Roberto De Zerbi:
🔹 Used tactical similarity models to find a coach with a near-identical playing style.
🔹 Ensured minimal squad changes, allowing for a smooth transition.
🔹 Result? No drop-off in performance—Brighton stayed competitive despite a managerial change.
💡 Lesson: Always Have a Shortlist
🔎 Instead of scrambling when a manager leaves, clubs should always maintain a shortlist of data-backed candidates.
✅ The Best Teams Are Proactive, Not Reactive.
🔮 The Future of Managerial Hiring: Will Data Dominate?
Football is entering a new era—where data and analytics are shaping managerial recruitment just as much as player scouting.
📊 The Smartest Clubs Are Already Ahead
🔹 Brentford, Brighton, and Liverpool are leading the way—using data to identify managers who fit their club’s philosophy.
🔹 Man City is already using predictive models to shortlist Guardiola’s successor.
🔹 The best teams constantly scout managers, ensuring they’re never unprepared when a vacancy opens.
🏆 Final Thoughts: Can Data Solve Football’s Biggest Hiring Problem?
Despite all the advancements, hiring a manager will never be 100% predictable.
However, clubs that use data-driven methods will always have an edge over those relying on gut feeling and reputation.
What Do You Think?
Can analytics consistently identify the best managers before they’re famous?
Or will luck and instinct always play a role in football’s biggest decisions?