In today’s data-driven football landscape, statistics are a key component of effective player scouting. While the traditional eye test and live observation remain vital, clubs at all levels now integrate data into their scouting workflows to reduce risk, uncover hidden talent, and align player profiles with tactical needs.
But using statistics effectively requires more than just browsing numbers on a screen. It requires a structured approach, clear objectives, contextual awareness, and an understanding of what each metric really means. This guide offers a step-by-step framework to help scouts, analysts, and coaches incorporate statistics into their scouting processes in a practical and impactful way.
Why Statistics Matter in Modern Scouting
Increased Efficiency
Traditional scouting involves countless hours of live or video-based analysis. While still essential, this process can be time-consuming and costly. By integrating data, clubs can quickly narrow down large player pools, saving time and focusing resources where they matter most.
Greater Objectivity
Human biases are inevitable. Scouts may be influenced by reputation, nationality, or personal preferences. Statistics introduce objectivity into decision-making, helping validate (or question) subjective impressions with factual evidence.
Tactical Alignment
Every club plays differently. A high-pressing team needs different player qualities than one that prioritizes deep blocks and counterattacks. Statistical scouting allows teams to identify players who fit their tactical identity, not just those with standout highlight reels.
Key Concepts: Understanding Football Data
Before diving into metrics, it’s critical to understand the principles behind them. Misinterpreting stats can be more harmful than not using them at all.
Per 90 Metrics
These metrics adjust player outputs based on playing time. A striker who scores 6 goals in 600 minutes (0.9 goals per 90) may be more efficient than one who scores 10 in 2,000 minutes (0.45 per 90). This makes comparisons fairer across players with different amounts of game time.
Percentiles and Benchmarking
A player’s raw number (e.g., 2.1 tackles per 90) is less useful without context. Percentiles compare that number to others in the same league/position. For instance, being in the 90th percentile for interceptions means the player outperforms 90% of their peers in that area.
Tactical and League Context
Stats must always be interpreted in context:
- A winger in a dominant possession team will have more passes and touches than one playing in a counter-attacking side.
- A player in the Eredivisie may produce better offensive numbers than one in Serie A due to differences in playing style and defensive intensity.
Step-by-Step Process for Statistical Scouting
Step 1: Define the Scouting Profile
Before collecting data, you need a clear picture of what kind of player you’re looking for. This should include:
- Position
- Age range
- Tactical role
- Physical and technical attributes
Example:
“We’re looking for a left-footed centre-back under 24 who is comfortable in possession, aggressive in duels, and proactive in defending high up the pitch.”
Step 2: Create a Data-Based Shortlist
Use scouting platforms like Wyscout, InStat, FBref, SciSports, TransferLab, or open data sources (e.g., StatsBomb, WhoScored) to filter players.
Define statistical thresholds based on your profile:
- Defensive duels won % > 65
- Progressive passes per 90 > 5
- Aerial duels per 90 > 4
- Errors leading to shots = 0
This can reduce a list of thousands to a manageable pool of candidates.
Step 3: Evaluate in Detail Using Relevant Metrics
Once you have your shortlist, go deeper into the numbers. Analyze performance in multiple phases:
Possession Phase
- Passes per 90
- Progressive carries
- Long pass accuracy
- Passes under pressure
Defensive Phase
- Tackles and interceptions (adjusted for possession)
- Defensive duel success %
- Pressures per 90
- Defensive actions in final third
Offensive Contribution (if relevant)
- Expected goals (xG)
- Expected assists (xA)
- Key passes
- Shot-creating actions (SCA)
Step 4: Contextualize with Video and Tactical Role
Statistics show what a player does, but not how or why. Once a player stands out statistically, switch to video:
- Do they pass under pressure or in open space?
- Is their ball progression driven or passing-based?
- Do their interceptions result from anticipation or a reactive style?
Always cross-check: does the player’s tactical role explain their numbers?
Key Metrics by Position
Central Defenders
- Progressive passes / 90: Ability to break lines
- Defensive duels won %: Defensive reliability
- Aerial duels won / 90: Set-piece and physical presence
- Mistakes leading to shots: Risk factor
Fullbacks / Wingbacks
- Progressive carries and passes
- xA (Expected Assists)
- Cross completion %
- Pressures in middle and final third
Central Midfielders
- Passes into final third
- Ball recoveries
- Progressive passes received
- Defensive actions per 90
Wingers / Attacking Midfielders
- Dribbles completed / 90
- Shot-creating actions
- xA and key passes
- Touches in attacking third
Strikers
- xG per 90
- Shots on target %
- Touches in penalty area
- Pressures and defensive actions
Case Study – Data-Driven Shortlisting for a High-Pressing Forward
Let’s say a club playing a high-pressing 1-4-4-2 is looking for a young central striker. The profile might look like:
- Age: 18–23
- xG/90: > 0.4
- Pressures/90: > 18
- Pass completion under pressure > 70%
- Aerial duels won % > 50 (if target-man qualities are desired)
Using a data platform, the analyst filters a list of players across second divisions in Spain, France, and Germany. Out of 500+ strikers, 14 meet the criteria. These players are then assessed using video and subjective scouting methods to validate their suitability.
Common Pitfalls and How to Avoid Them
Relying Only on Volume Stats
A player with high passing numbers may simply be on a dominant team with lots of possession. Use per 90 and possession-adjusted stats where available.
Misreading League Strength
A 20-year-old scoring 15 goals in the Austrian Bundesliga is impressive, but how will that translate to the Eredivisie or Premier League? Use league strength coefficients or transfer success models to calibrate expectations.
Overlooking Role and Tactics
A fullback who doesn’t create many chances may be playing in a team that focuses play through the midfield. Always factor in team tactics, not just individual outputs.
Ignoring Sample Size
Don’t base conclusions on 5–6 games. Look for consistency over a full season, and if possible, across multiple years or clubs.
Final Thoughts – The Balance Between Data and Scouting
Statistics should enhance, not replace, traditional scouting methods. The best clubs combine data, video analysis, and live observation to build a 360-degree view of each player. Data helps you:
- Identify players others might overlook
- Confirm or challenge scout opinions
- Ensure a clear fit between the player and your tactical model
By understanding how to filter, interpret, and contextualize football data, scouts and analysts can gain a powerful edge in recruitment.