How to Build a Data Department in Football: A Blueprint for Success in the 5th Division

A data-driven approach to recruitment, performance analysis, and long-term success in the National League

📢 Introduction: Setting the Scene

Imagine this: You’ve just been hired as the Head of Data & Performance at a National League club (England’s 5th division). The club has big ambitions—promotion to League Two—but financial resources are tight, and traditional scouting methods haven’t yielded consistent success.

Your task? Build a data department from scratch to:
📊 Improve recruitment by finding undervalued talent
⚽ Maximize team performance with tactical insights
📈 Use advanced analytics to gain an edge over wealthier clubs

But how do you make data actionable for a club at this level? How do you build a strategy that works immediately while laying a foundation for long-term success?

This post outlines a step-by-step blueprint inspired by elite clubs and Ian Graham’s principles from How to Win the Premier League—tailored for the realities of the National League.

From recruitment models to tactical performance tracking, here’s how we’d structure a smart, data-driven department to transform a club’s fortunes.

Let’s dive in. 🚀📊

📌 Recruitment Strategy: Finding the Right Players

Building a competitive squad in the National League requires a smart, data-driven approach to recruitment. Given budget constraints, scouting inefficiencies, and the challenge of balancing short-term performance with long-term sustainability, clubs need a system that identifies undervalued talent and maximizes efficiency.

1️⃣ Comprehensive Squad Analysis

Before making any signings, we need to understand what we already have. A thorough squad evaluation helps identify:

✔️ Tactical gaps & weaknesses – Where does the team struggle? Do we lack creativity, defensive solidity, or physicality?
✔️ Player profiles – Who fits the manager’s style? Who thrives in our system, and who doesn’t?
✔️ Key areas for improvement – Where do we need reinforcements based on National League promotion benchmarks?

📊 What does it take to go up?
After analyzing the last five seasons, successful promotion teams in the National League consistently hit these targets:

🔹 70–75 points – The typical threshold needed to secure promotion or a playoff spot.
🔹 70–75 goals scored – A strong attack is crucial to staying competitive over a full season.
🔹 Fewer than 55 goals conceded – A solid defense ensures consistency, as teams that leak goals rarely sustain promotion form.

By comparing our current squad to these benchmarks, we can pinpoint where we must improve—whether it’s upgrading our frontline, tightening the defense, or strengthening depth in key areas.

This data-driven approach ensures that every recruitment decision is made with promotion in mind. 🚀⚽

2️⃣ League Benchmarking & Player Targeting

Recruitment isn’t just about signing the best players—it’s about finding the right players who can thrive in the league’s demands. To do this, we analyze leagues with similar tactical and physical profiles, focusing on:

🔹 National League North/South – Many quality players at this level are ready to step up.
🔹 League One & League Two – The gap between the 3rd, 4th, and 5th divisions in England is not as big as many think. Smart clubs can find free agents or loan deals for young talents overlooked by bigger sides.
🔹 Lower-tier European leagues – Leagues like the Dutch Eerste Divisie, Belgian Challenger Pro League, and Scandinavian leagues produce technically solid, physically capable players who often adapt well to English football.

📌 Why does this matter?
By studying which player archetypes succeed in these leagues—whether it’s aggressive ball-winners, clinical strikers, or aerially dominant defenders—we create a recruitment blueprint that ensures every signing aligns with our tactical approach.

This strategy allows the club to outsmart the market, signing players with the potential to thrive at this level while keeping costs low. 🔍⚽

3️⃣ Player Classification Model

Instead of simply scouting by traditional positions (strikers, midfielders, defenders), we use a data-driven classification model to categorize players based on their playing style and impact on the game.

📌 Why is this important?
Not all strikers, midfielders, or defenders play the same way. As Ian Graham pointed out, there are three main types of strikers:
⚽ Target Men – Strong in aerial duels, link-up play, and holding the ball.
🎯 Out-and-Out Goalscorers – Poachers who thrive in the box, relying on movement and finishing.
🔄 Hybrids – Players who blend both attributes, contributing to build-up and scoring.

🔍 How does this work?
Using clustering techniques from event data, we can:
⚡ Identify different playing styles within each position.
⚡ Find underrated players who fit the manager’s tactical needs.
⚡ Build custom shortlists based on the club’s specific requirements.

📊 From Research to Application
I’ve already explored this methodology in my GitHub project (A Data-Driven Clustering Approach in Football), using FBref data. However, this can be expanded with event data or even tracking data to analyze how players move on the pitch, their pressing tendencies, and how they influence possession sequences.

This approach allows us to go beyond simple stats and truly understand the roles and impact of each player, ensuring smarter recruitment decisions. 📈⚽

4️⃣ Possession Value Model: Identifying Game-Changers

Not all passes and touches are created equal. A possession value (PV) model evaluates players not just by volume, but by how their actions:

✅ Increase goal probability – Who progresses the ball effectively and creates dangerous situations?
✅ Reduce defensive risks – Who retains possession under pressure and makes smart decisions?
✅ Directly impact results – Who contributes the most to points gained over a season?

This model serves multiple purposes:

🔹 Recruitment – Find undervalued players whose contributions drive results, not just highlight stats.
🔹 Squad Valuation – Assess our own players objectively to negotiate contracts and manage squad planning.
🔹 Internal Negotiation – Quantify a player’s importance to the team, helping inform lineup decisions and future transfers.

By applying this model, we ensure that every signing and contract decision is backed by data, creating a sustainable, competitive edge in the transfer market. 🚀📊

5️⃣ Set-Piece Recruitment Model

Set-pieces are one of the most underrated sources of goals, and a data-led recruitment strategy can add 10–15 extra goals per season—a difference-maker in a league as tight as the National League.

🔍 How do we use data to optimize set-piece recruitment?
📌 Targeting Players with Aerial Dominance – Identifying forwards and center-backs who win a high percentage of aerial duels to convert set-piece opportunities.
📌 Precision in Delivery – Recruiting midfielders and full-backs with high-quality crossing and dead-ball delivery.
📌 Defensive Set-Piece Specialists – Finding players who excel at defending corners and free-kicks to neutralize threats.

📊 Real-World Application

At AZ Alkmaar, I gained firsthand experience in data-driven set-piece analysis, helping to optimize player recruitment and execution. This approach can be adapted to a fifth-division club to gain an edge over the competition in an area where marginal gains win points.

Smart recruitment in this area isn’t just about adding goals—it’s about preventing them too. A well-structured set-piece strategy can be the difference between a promotion push and mid-table mediocrity.

6️⃣ Sustainable Squad Building

Balancing youth development & experience is key:
✅ Young players with resale potential for financial sustainability
✅ Experienced leaders for immediate impact

7️⃣ The Long-Term Vision

Advanced tracking data will eventually unlock deeper insights:
📊 Pitch control models to evaluate spatial influence
🎯 Dynamic movement analysis to refine player positioning

By using data & analytics, National League clubs can outsmart richer teams in recruitment and squad building.

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📊 Performance Analysis: Data-Driven Tactical Insights

Once the squad is built, the next step is optimizing team performance through real-time analytics & post-match evaluation.

1️⃣ Automated Data Pipeline

Data is only useful if it’s accessible and actionable. That’s why the first step in our performance analysis strategy is building an automated data pipeline that generates weekly dashboards for the coaching staff.

📊 How Does It Work?
At the start of each week, an automated service will process match data and update dashboards in Streamlit, Tableau, or Power BI, allowing the club to:

📌 Break Down Tactical Trends – Analyze how the team played in and out of possession.
📌 Track Strengths & Weaknesses – Identify areas where the team is excelling or struggling.
📌 Support Objective Decision-Making – Remove emotional bias and make data-backed coaching decisions.

🚀 Why Is This a Game-Changer?

Rather than relying on delayed manual reports, this automated system ensures that coaches and analysts receive up-to-date insights immediately after each match, making weekly planning and opposition scouting far more efficient.

For a small club, having a data pipeline that delivers clear, concise, and actionable insights gives a competitive edge—bringing an elite-level approach to the National League.

2️⃣ Game-by-Game Player Evaluation

Traditional stats like goals and assists only tell part of the story. To truly evaluate player performances, we’ll use a possession value (PV) model that assigns a score to every action based on its impact on goal probability and defensive stability.

📊 How Does It Work?
For each match, the PV model will analyze:

✔️ Contribution to Goal Probability – Did a player’s pass, dribble, or movement increase the chance of scoring?
✔️ Defensive Risk Reduction – Did their actions help retain possession and prevent dangerous turnovers?
✔️ Impact Against Different Opposition Strengths – How did they perform against weaker vs. stronger teams?

🚀 Why Is This Important?

✅ Uncover Hidden Performers – Some players influence games without scoring or assisting, and this model quantifies their impact.
✅ Objective Player Ratings – No more relying on subjective opinions—each player receives a data-driven match rating.
✅ Better Squad Rotation Decisions – Helps the manager select the best lineup based on tactical needs, not just stats.

This match-by-match evaluation system ensures that we reward performance, not just outcomes, giving us a clearer picture of which players are truly driving the team’s success.

3️⃣ Justice Table & Predictive Analysis

Football is a game of fine margins—results don’t always reflect performances. A team can dominate a match, create high-quality chances, and still lose due to bad luck or clinical finishing from the opponent. That’s where the Justice Table comes in.

⚖️ What Is the Justice Table?

Instead of ranking teams by actual points, the Justice Table uses Expected Goals (xG) and Expected Goals Against (xGA) to assess whether a team’s position in the league table truly reflects their performances.

✅ xG (Expected Goals) – How many goals a team should have scored based on the quality of their chances.
✅ xGA (Expected Goals Against) – How many goals a team should have conceded based on the quality of shots faced.
✅ xPTS (Expected Points) – A fairer ranking based on xG performance, rather than just final scores.

🔍 Why Does This Matter?

📊 Removes Emotional Bias – Results can be misleading due to luck, refereeing decisions, or one-off moments. The Justice Table focuses on performances rather than emotions.
📈 Helps Identify Overperforming & Underperforming Teams – Is our team lucky or unlucky? Are we creating enough chances to sustain our form?
🎯 Forecasting Future Performance – If we’re underperforming xG, we may expect better results soon. If we’re overperforming, we need to adjust before reality catches up.

By using the Justice Table & predictive models, we can set realistic league targets, track performance trends, and make data-driven decisions before they impact results.

4️⃣ Set-Piece Analysis: The Hidden Edge

Set-pieces win points—and at this level, small margins can make the difference between promotion and mid-table obscurity. With around 30% of all goals in a season coming from dead-ball situations, a data-driven approach to set-piece analysis can be a game-changer.

📊 Why Set-Piece Analysis Matters

🔹 High Impact, Low-Cost – Unlike open-play tactics that require complex buildup, set-piece routines can be designed, practiced, and optimized with minimal resource investment.
🔹 Turning Draws into Wins – A well-drilled set-piece team can add 10-15 extra goals per season, shifting tight games in their favor.
🔹 Defensive Stability – Tracking defensive set-piece efficiency can cut down goals conceded, preventing costly lapses.

🔍 How a Data-Driven Set-Piece Model Works

⚡ Tracking Efficiency – Post-match reports will assess how many chances & goals were created/conceded from set-pieces.
🎯 Identifying Key Roles – Who are the best deliverers, aerial threats, and blockers? We use player data to refine roles in attacking and defensive setups.
🔄 Game-to-Game Adjustments – If opponents have specific weaknesses in set-piece defending, we tailor routines to exploit them.

By analyzing set-piece performance game-to-game, we can make incremental improvements that translate into tangible results—one corner, one free-kick, or one long throw at a time.

5️⃣ Long-Term Vision: Incorporating Tracking Data

📊 Pitch Control Models – Understanding space occupation & passing lanes
⚡ Movement Analysis – Evaluating player roles & tactical impact

A data-driven approach ensures marginal gains are exploited for maximum efficiency.

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🚀 Phased Execution Plan: Implementing the System

Phase 1: Immediate Actions

🔹 Squad analysis aligned with National League benchmarks
🔹 Automated dashboard for weekly performance tracking
🔹 Player classification model to refine recruitment strategy
🔹 Set-piece recruitment for maximizing dead-ball efficiency

Phase 2: Mid-Term Enhancements

🔹 Refining justice table for unbiased evaluations
🔹 Possession value models to rank players more accurately
🔹 Scouting players from benchmarked leagues

Phase 3: Long-Term Integration

🔹 Implementing tracking data for tactical refinements
🔹 Establishing a player development monitoring system

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🏆 Conclusion: Turning Data into a Competitive Advantage

Success in modern football isn’t just about spending more—it’s about spending smarter. By integrating advanced analytics into recruitment, performance analysis, and tactical planning, even a National League club can:

✔️ Recruit smarter – Identify undervalued talent using data-driven scouting.
✔️ Optimize performance – Use real-time insights to refine tactics and player roles.
✔️ Maximize set-pieces – Gain an extra 10–15 goals per season through structured routines.
✔️ Forecast results – Utilize xG-based predictive modeling to stay ahead of the curve.

This is how a lower-league club can punch above its weight, outthink wealthier rivals, and build a sustainable, long-term footballing model.

But here’s the big question:

🔮 Can data and smart decision-making truly level the playing field against clubs with bigger budgets? Or does money always win in football?

Let’s discuss. ⚽📊⬇️