How to Use FBref, Understat, and Other Football Analytics Sites
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# How to Use FBref, Understat, and Other Football Analytics Sites: A Complete Guide
### ⚡ Key Takeaways
- FBref offers comprehensive StatsBomb data with advanced metrics like progressive carries, pressures, and shot-creating actions—all free
- Understat's xG models reveal shooting efficiency and chance quality, helping identify overperforming strikers and defensive vulnerabilities
- Combining multiple platforms gives you the complete picture: FBref for possession metrics, Understat for finishing quality, and Transfermarkt for context
- Understanding position-specific metrics is crucial—what matters for a defensive midfielder differs completely from what matters for a winger
- Sample size matters: wait for 900+ minutes before drawing conclusions, and always compare across multiple seasons
## Why Football Analytics Matter (And Why You Should Care)
Modern football is a data-driven sport. When Brighton signed Alexis Mac Allister for £7 million in 2019, they weren't guessing—they were analyzing his progressive passing numbers, ball retention under pressure, and defensive work rate at Argentinos Juniors. When he moved to Liverpool for £35 million in 2023, the data had proven them right.
You don't need a scouting department to access this information. The same data that clubs use is available for free. Here's how to actually use it.
## FBref (fbref.com): Your Primary Analytics Hub
FBref is powered by StatsBomb data, the same provider used by professional clubs. It covers 30+ leagues with granular metrics that go far beyond goals and assists.
### Understanding the Scouting Report
Navigate to any player's page and click "Scouting Report." You'll see a pizza chart (radar chart) comparing them to players in the same position across Europe's top 5 leagues.
**Example: Analyzing a Midfielder**
Let's say you're evaluating a central midfielder. Key metrics to examine:
- **Progressive Passes (90th percentile or higher)**: Indicates ability to break lines and advance play. Rodri averaged 9.8 progressive passes per 90 in 2023-24, putting him in the 95th percentile among defensive midfielders.
- **Progressive Carries (75th+ percentile)**: Shows willingness to dribble forward with the ball. This separates press-resistant midfielders from static passers.
- **Tackles + Interceptions (position-dependent)**: For defensive midfielders, you want 85th+ percentile. For attacking midfielders, 40th percentile is acceptable—they have different responsibilities.
- **Pass Completion % under pressure**: Not shown directly, but compare "Passes Completed %" with "Passes Attempted" in the detailed stats. Elite midfielders maintain 85%+ completion even when pressed.
**Red flags to watch for:**
- High percentile in "Miscontrols" or "Dispossessed" (suggests poor touch under pressure)
- Low percentile in "Aerials Won" for a physically imposing player (might indicate poor positioning)
- Declining percentiles across consecutive seasons (potential decline phase)
### Advanced FBref Features Most People Miss
**1. Squad Comparison Tool**
Go to any league page → "Squad Stats" → Select multiple teams. This reveals tactical patterns:
- Manchester City typically leads in "Passes into Final Third" and "Progressive Passes" (possession-dominant)
- Liverpool often tops "Pressures in Attacking Third" (high press system)
- Atletico Madrid usually ranks high in "Tackles in Defensive Third" (deep block defense)
**2. Match Report Deep Dive**
Every match has a detailed page with:
- **Shot maps with xG values**: Click on individual shots to see exact xG. A 0.45 xG chance is a very good opportunity; 0.15 xG is a low-quality shot.
- **Pass maps**: Shows average positions and passing networks. Look for isolated players (potential tactical issues) or overloaded zones (tactical strengths).
- **Defensive Actions map**: Reveals where teams win the ball back. High defensive actions in the attacking third = aggressive press. Concentrated actions in own half = deep block.
**3. Player Comparison Tool**
Compare up to 6 players side-by-side. Essential for:
- Scouting transfer targets
- Evaluating tactical fits (does this winger create chances or score goals?)
- Identifying value signings (similar stats, lower market value)
**Example comparison:**
Comparing two strikers with similar goal tallies:
| Metric | Striker A | Striker B |
|--------|-----------|-----------|
| Non-Penalty Goals | 15 | 15 |
| npxG | 12.3 | 16.8 |
| Shots per 90 | 2.8 | 4.2 |
| Shot-Creating Actions | 2.1 | 3.7 |
**Analysis**: Striker B is significantly outperforming their xG (15 goals from 16.8 xG is normal; 15 from 12.3 suggests overperformance). Striker B also creates more chances and shoots more often. Despite identical goal tallies, Striker B is the better player—Striker A is likely experiencing a purple patch.
## Understat (understat.com): The xG Specialist
Understat focuses exclusively on expected goals data for Europe's top 5 leagues. Its xG model considers shot location, assist type, shot pattern, and defensive pressure.
### Shot Maps: Reading Between the Lines
Click on any player to see their shot map. Each dot represents a shot, color-coded by xG:
- Dark red: 0.3+ xG (excellent chance)
- Orange: 0.15-0.3 xG (decent chance)
- Yellow: 0.05-0.15 xG (low-quality chance)
- Light yellow: <0.05 xG (very poor chance)
**What to look for:**
**Elite finishers**: Shots clustered in the 6-yard box with high conversion rates. Example: Erling Haaland's 2022-23 shot map showed 68% of his shots came from inside the box, with an average xG per shot of 0.21 (very high).
**Volume shooters**: Many shots from outside the box with low xG values. These players often have impressive goal tallies but are statistically inefficient. They're scoring despite poor shot selection, not because of it.
**Chance quality indicators**:
- Average xG per shot above 0.15 = getting into good positions
- Average xG per shot below 0.10 = poor positioning or shot selection
- Conversion rate 20%+ above xG = likely overperforming (regression coming)
- Conversion rate 20%+ below xG = likely underperforming (improvement expected)
### xG Timeline: Understanding Match Flow
The xG timeline shows how expected goals accumulated during a match. This reveals:
**Dominant performances**: Steady xG accumulation throughout the match. Example: Manchester City often shows consistent xG growth, reflecting sustained pressure.
**Counter-attacking success**: Flat xG for long periods, then sudden spikes. Teams like Atletico Madrid often show this pattern—absorbing pressure, then creating high-quality chances on the break.
**Smash-and-grab wins**: Team wins 1-0 but xG shows 0.8 vs 2.3. They got lucky. Expect regression in future matches.
**Unlucky defeats**: Team loses 0-1 but xG shows 2.5 vs 0.6. They dominated and were unlucky. Positive regression likely.
### League Tables by xG: Finding Value and Risk
Compare the actual league table to the xG-based table. Large discrepancies indicate:
**Overperforming teams** (actual position much better than xG position):
- Likely to decline unless they improve underlying performance
- Often have a goalkeeper in exceptional form or a striker overperforming xG
- Example: Newcastle's 2022-23 season (4th place finish with xG suggesting 6th-7th place)
**Underperforming teams** (actual position worse than xG position):
- Likely to improve with regression to the mean
- Often have poor finishing or goalkeeping
- Example: Chelsea's 2022-23 season (12th place with xG suggesting 6th place)
**Practical application**: If you're betting or playing fantasy football, target players from underperforming teams with good underlying numbers. They're likely to improve.
## Other Essential Analytics Sites
### Transfermarkt (transfermarkt.com)
Not strictly analytics, but essential for context:
- **Market values**: Understand a player's transfer value relative to their stats. A £15m player with elite numbers is a bargain; a £70m player with average numbers is overpriced.
- **Injury history**: Check "Injury History" tab. Players with recurring muscle injuries (hamstring, groin) are high-risk signings.
- **Contract situations**: Players in the final year of their contract are transfer targets. Their stats might improve as they play for a new deal.
### WhoScored (whoscored.com)
More accessible than FBref but less detailed:
- **Match ratings**: Algorithm-based ratings (6.0-10.0 scale). Useful for quick assessments but don't rely on them exclusively.
- **Heat maps**: Visual representation of where players operate. Essential for understanding tactical roles.
- **Strengths and weaknesses**: Automated analysis based on stats. Good starting point for deeper investigation.
### Sofascore (sofascore.com)
Mobile-friendly with real-time data:
- **Live xG during matches**: See xG accumulate in real-time
- **Player ratings**: Similar to WhoScored but with different algorithms
- **Head-to-head stats**: Compare players directly with visual charts
### FotMob (fotmob.com)
Best mobile app for match tracking:
- **Momentum charts**: Shows which team is dominating at any moment
- **xG race**: Visual representation of xG accumulation
- **Shot maps and pass maps**: Available for most matches
### StatsBomb IQ (statsbomb.com)
Free articles with advanced analysis:
- **Freeze frames**: See exact player positions during key moments
- **Pressure maps**: Understand pressing patterns
- **Passing networks**: Visualize team structure
## How to Actually Analyze a Player: Step-by-Step Process
Let's walk through a complete player evaluation using free data. We'll analyze a hypothetical winger you're considering for your fantasy team or scouting for your club.
### Step 1: Establish Context (5 minutes)
**Transfermarkt:**
- Age: 24 (prime years ahead)
- Market value: £25 million (mid-range)
- Contract: Expires 2026 (not desperate to leave)
- Injury history: 2 minor injuries in 3 years (acceptable)
- League: Bundesliga (strong league, but not Premier League)
### Step 2: Review Overall Stats (10 minutes)
**FBref - Standard Stats:**
- Appearances: 28 (2,340 minutes = 26 full matches)
- Goals: 8 (0.31 per 90)
- Assists: 6 (0.23 per 90)
- Non-penalty goals: 7 (one penalty)
**Initial assessment**: Decent output but not spectacular. Need to dig deeper.
### Step 3: Analyze Scouting Report (15 minutes)
**FBref - Scouting Report (compared to wingers in top 5 leagues):**
**Attacking metrics:**
- Non-penalty goals: 62nd percentile (above average)
- Assists: 58th percentile (above average)
- Shot-creating actions: 78th percentile (good)
- Progressive carries: 85th percentile (very good)
- Successful take-ons: 91st percentile (elite)
**Possession metrics:**
- Touches in attacking penalty area: 72nd percentile (good)
- Progressive passes received: 68th percentile (good)
- Pass completion %: 74% (45th percentile - below average)
**Defensive metrics:**
- Tackles: 35th percentile (acceptable for a winger)
- Interceptions: 28th percentile (low but not concerning)
- Pressures: 52nd percentile (average)
**Analysis**: This is a dribble-first winger who excels at carrying the ball forward and beating defenders (91st percentile take-ons). He creates chances for others (78th percentile shot-creating actions) but his passing completion is below average, suggesting he attempts risky passes. Defensively adequate but not exceptional.
### Step 4: Examine Shot Quality (10 minutes)
**Understat - Shot Map:**
- Total shots: 68 (2.6 per 90)
- Shots on target: 32 (47% accuracy)
- xG: 6.2
- Actual goals: 7
- Average xG per shot: 0.09 (low)
**Analysis**: He's slightly outperforming xG (7 goals from 6.2 xG), which is normal variance. However, his average xG per shot is low (0.09), meaning he takes many low-quality shots. Looking at the shot map, most shots are from outside the box or tight angles.
**Conclusion**: He's not a natural finisher. His value comes from dribbling and chance creation, not goalscoring.
### Step 5: Compare to Similar Players (10 minutes)
**FBref - Player Comparison:**
Compare to three similar wingers in the Bundesliga:
| Metric | Our Player | Player B | Player C | Player D |
|--------|------------|----------|----------|----------|
| Goals per 90 | 0.31 | 0.42 | 0.28 | 0.35 |
| Assists per 90 | 0.23 | 0.19 | 0.31 | 0.27 |
| Progressive carries | 85th % | 72nd % | 68th % | 79th % |
| Take-ons | 91st % | 65th % | 88th % | 71st % |
| Shot-creating actions | 78th % | 81st % | 85th % | 76th % |
| Market value | £25m | £35m | £30m | £28m |
**Analysis**: Our player has the best dribbling numbers (91st percentile take-ons, 85th percentile progressive carries) but lower goal output than Player B and D. However, he's also the cheapest. Players B and C have higher market values but our player's underlying numbers suggest he's undervalued.
### Step 6: Watch Video Confirmation (20 minutes)
Stats can't capture everything. Watch 3-4 full matches or extended highlights to verify:
- **Does he beat defenders with skill or just pace?** (Skill ages better)
- **How does he perform under pressure?** (Elite players stay calm)
- **What's his decision-making like?** (Does he pass when he should shoot?)
- **How's his work rate?** (Does he track back?)
- **Body language?** (Does he sulk when things go wrong?)
### Step 7: Final Verdict
**Strengths:**
- Elite dribbler (91st percentile)
- Excellent at progressing the ball (85th percentile)
- Creates chances for teammates (78th percentile)
- Undervalued relative to similar players
**Weaknesses:**
- Below-average passer (45th percentile completion)
- Takes low-quality shots (0.09 xG per shot)
- Not a natural goalscorer
- Minimal defensive contribution
**Best fit:**
- Teams that need width and dribbling
- Systems with a target striker who can finish chances
- Counter-attacking teams that value ball-carrying
**Not a good fit:**
- Possession-heavy teams requiring high pass completion
- Teams needing goalscoring wingers
- Systems requiring defensive work from wingers
**Fantasy football verdict**: Good for assists and bonus points from dribbles, but don't expect consistent goals.
**Transfer verdict**: At £25m, he's good value for a team needing creativity from wide areas. Not worth more than £30m given his limitations.
## Position-Specific Metrics: What Actually Matters
Different positions require different evaluation criteria. Here's what to prioritize:
### Goalkeepers
**Essential metrics:**
- **Post-shot xG minus goals allowed**: Measures shot-stopping ability. Positive numbers = good; negative = poor. Alisson typically posts +5 to +8 per season.
- **Save percentage**: Should be 70%+ for elite keepers
- **Pass completion %**: Modern keepers need 80%+ for possession teams
- **Crosses stopped %**: Important for Premier League keepers (more crosses)
- **Sweeper actions**: High numbers indicate proactive positioning
**Less important:**
- Total saves (depends on team quality)
- Clean sheets (team stat, not individual)
### Center Backs
**Essential metrics:**
- **Aerials won %**: Should be 65%+ for traditional center backs
- **Tackles + interceptions per 90**: 3.5+ indicates active defending
- **Pass completion %**: 88%+ for ball-playing center backs
- **Progressive passes**: 4+ per 90 for modern center backs
- **Errors leading to shots**: Should be near zero
**Context matters:**
- High-line defenders need pace (watch video)
- Deep-block defenders need positioning (check "Blocks" stat)
### Full Backs / Wing Backs
**Essential metrics:**
- **Progressive passes received**: Shows involvement in attack (5+ per 90)
- **Crosses into penalty area**: 3+ per 90 for attacking full backs
- **Tackles + interceptions**: 4+ per 90 (still need to defend)
- **Progressive carries**: 2+ per 90 indicates willingness to overlap
- **Aerials won**: Less important than for center backs
**Modern full backs need:**
- High percentile in both attacking and defensive metrics
- Trent Alexander-Arnold: 90th+ percentile in progressive passes, 60th+ in tackles
### Defensive Midfielders
**Essential metrics:**
- **Tackles + interceptions**: 5+ per 90 for pure destroyers
- **Progressive passes**: 6+ per 90 for deep-lying playmakers
- **Pass completion %**: 88%+ (they're the foundation of possession)
- **Pressures**: 15+ per 90 indicates active pressing
- **Aerials won**: Important for physical leagues
**Two types:**
- **Destroyers** (Casemiro type): High tackles, interceptions, aerials
- **Playmakers** (Rodri type): High progressive passes, pass completion, ball retention
### Central Midfielders (Box-to-Box)
**Essential metrics:**
- **Progressive passes + carries**: Combined 10+ per 90
- **Shot-creating actions**: 3+ per 90
- **Tackles + interceptions**: 3+ per 90 (need defensive contribution)
- **Touches in attacking penalty area**: 2+ per 90
- **Pass completion %**: 82%+ (lower than defensive mids, more risk-taking)
**Elite box-to-box midfielders:**
- 75th+ percentile in both attacking and defensive metrics
- Example: Kevin De Bruyne's 2022-23 season: 95th percentile in shot-creating actions, 65th percentile in tackles
### Attacking Midfielders / No. 10s
**Essential metrics:**
- **Shot-creating actions**: 5+ per 90 (their primary job)
- **Progressive passes received**: 6+ per 90 (shows involvement)
- **Key passes**: 2+ per 90
- **Touches in attacking penalty area**: 4+ per 90
- **Pass completion %**: 78%+ (lower than other midfielders, more risk)
**Less important:**
- Defensive stats (not their job)
- Progressive carries (usually receive the ball in advanced positions)
### Wingers
**Essential metrics:**
- **Successful take-ons**: 2+ per 90 (need to beat defenders)
- **Progressive carries**: 3+ per 90
- **Shot-creating actions**: 3+ per 90
- **Touches in attacking penalty area**: 4+ per 90
- **Goals + assists per 90**: 0.40+ combined
**Two types:**
- **Inverted wingers** (cut inside to shoot): High goals, shots, xG
- **Traditional wingers** (stay wide, cross): High crosses, assists, progressive carries
### Strikers
**Essential metrics:**
- **Non-penalty goals per 90**: 0.50+ for elite strikers
- **npxG per 90**: Should match or slightly exceed goals (0.45+)
- **Shots per 90**: 3+ indicates involvement
- **Shot-creating actions**: 2+ per 90 (even strikers should create)
- **Aerials won**: Important for target strikers (60%+ win rate)
**Finishing quality:**
- Goals minus npxG: +3 or more over a season = elite finishing
- Goals minus npxG: -3 or more = poor finishing or bad luck
**Two types:**
- **Poachers** (Haaland type): High goals, high xG, low shot-creating actions
- **Complete forwards** (Kane type): High goals, high shot-creating actions, high progressive passes received
## Common Pitfalls and How to Avoid Them
### 1. Comparing Across Leagues Without Context
**The mistake**: "Player A has 15 goals in the Eredivisie, Player B has 12 in the Premier League. Player A is better."
**Why it's wrong**: League quality varies dramatically. A goal in the Eredivisie is easier to score than a goal in the Premier League.
**How to fix it**:
- Use FBref's percentile rankings (compares across top 5 leagues)
- Apply a "league adjustment factor":
- Premier League: 1.0x (baseline)
- La Liga: 0.95x
- Bundesliga: 0.90x
- Serie A: 0.90x
- Ligue 1: 0.85x
- Eredivisie: 0.70x
- Championship: 0.75x
**Example**: 15 goals in Eredivisie × 0.70 = 10.5 "Premier League equivalent goals"
### 2. Ignoring Per-90 Minutes Stats
**The mistake**: "Player A scored 10 goals in 30 appearances. Player B scored 8 goals in 25 appearances. Player A is better."
**Why it's wrong**: Player A might have played 2,700 minutes (30 full matches), while Player B played 1,500 minutes (16.7 full matches).
**How to fix it**:
- Always use per-90 stats: Player A = 0.33 goals per 90, Player B = 0.48 goals per 90
- Player B is significantly more productive
**FBref automatically shows per-90 stats**—use them.
### 3. Small Sample Sizes
**The mistake**: "This striker scored 5 goals in his first 3 matches. He's going to score 60+ this season!"
**Why it's wrong**: Small samples are unreliable. Variance is huge over short periods.
**How to fix it**:
- Wait for at least 900 minutes (10 full matches) before drawing conclusions
- Compare across multiple seasons when possible
- Look for consistency: a player with 0.50 goals per 90 across three seasons is more reliable than one with 0.30, 0.70, 0.50
**Example**: A striker with 5 goals from 3.0 xG in 270 minutes is overperforming. Expect regression.
### 4. Ignoring Age Curves
**The mistake**: "This 32-year-old striker scored 25 goals last season. He'll do it again."
**Why it's wrong**: Players decline with age, especially those relying on pace.
**How to fix it**:
- Peak years by position:
- Strikers: 26-29
- Wingers: 25-28
- Midfielders: 26-30
- Center backs: 27-32
- Goalkeepers: 28-34
- Check for declining percentiles across consecutive seasons
- Players 30+ are higher risk for sudden decline
### 5. Overvaluing Goals and Assists
**The mistake**: "Player A has 20 goals and 10 assists. He's elite."
**Why it's wrong**: Goals and assists don't tell the full story. A player might be scoring penalties and tap-ins while contributing little to overall play.
**How to fix it**:
- Check npxG (non-penalty expected goals)
- Look at shot-creating actions (how much does he create?)
- Examine progressive passes and carries (does he advance play?)
- Review defensive contributions (does he press and tackle?)
**Example**: A striker with 20 goals (8 penalties, 12 open play) from 8.5 npxG is massively overperforming. Expect regression.
### 6. Ignoring Tactical Context
**The mistake**: "This center back has low passing numbers. He's not good on the ball."
**Why it's wrong**: He might play in a direct, long-ball system where passing numbers are naturally lower.
**How to fix it**:
- Check team stats: Does the whole team have low passing numbers?
- Look at pass completion % relative to teammates
- Watch video to understand tactical role
**Example**: A center back in a Burnley-style team will have lower passing numbers than one at Manchester City, even if they're equally skilled.
### 7. Confusing Correlation with Causation
**The mistake**: "This team has high possession and wins a lot. Therefore, high possession causes wins."
**Why it's wrong**: Winning teams often have high possession because they're leading and opponents sit back. Possession might be a result of winning, not a cause.
**How to fix it**:
- Look at xG, not just possession
- Check shot quality, not just shot quantity
- Examine progressive passes and carries (advancing play matters more than sideways passing)
### 8. Overreacting to Single Matches
**The mistake**: "This midfielder had 120 touches and 95% pass completion. He dominated!"
**Why it's wrong**: He might have made 100 safe sideways passes and contributed nothing to attacking play.
**How to fix it**:
- Check progressive passes and shot-creating actions
- Look at xG contribution
- Review multiple matches, not just one
**Example**: A midfielder with 120 touches, 95% pass completion, but 0 progressive passes and 0 shot-creating actions was invisible in attack.
### 9. Ignoring Opponent Quality
**The mistake**: "This striker scored a hat-trick. He's world-class!"
**Why it's wrong**: Was it against Manchester City or against a relegation-threatened team?
**How to fix it**:
- Check opponent quality on FBref (shows opponent's league position)
- Look at performance against top-6 teams separately
- Elite players perform consistently against all opponents
### 10. Trusting xG Blindly
**The mistake**: "xG says they should have won 3-1, but they lost 0-1. They were unlucky."
**Why it's wrong**: xG doesn't account for:
- Goalkeeper quality (elite keepers consistently outperform xG)
- Finishing ability (elite finishers consistently outperform xG)
- Defensive pressure (shots under pressure have lower conversion rates than xG suggests)
**How to fix it**:
- Use xG as a guide, not gospel
- Check post-shot xG (accounts for shot placement)
- Look at multiple matches to identify patterns
## Advanced Techniques for Serious Analysts
### 1. Building Your Own Database
**Why**: Tracking players over time reveals trends that single-season stats miss.
**How**:
- Use Google Sheets or Excel
- Export data from FBref (click "Share & Export" on any table)
- Track key metrics across multiple seasons
- Calculate year-over-year changes
**What to track**:
- Goals, assists, xG, xA per 90
- Key percentile rankings
- Minutes played
- Age
- Market value
**Example insight**: A 24-year-old winger whose progressive carries percentile increased from 65th to 85th over two seasons is improving rapidly—potential breakout candidate.
### 2. Creating Custom Metrics
**Why**: Standard metrics don't always capture what you care about.
**How**:
- Combine existing metrics into new ones
- Weight metrics based on importance
**Example custom metrics**:
**"Chance Creation Score" for attacking midfielders:**
- (Shot-creating actions × 2) + (Progressive passes × 1) + (Key passes × 1.5)
- Higher weight on shot-creating actions because they directly lead to shots
**"Defensive Impact Score" for midfielders:**
- (Tackles + Interceptions) × 1.5 + (Pressures × 0.5) + (Blocks × 1)
- Higher weight on tackles and interceptions because they directly win the ball
### 3. Identifying Breakout Candidates
**Why**: Finding players before they become expensive is valuable for fantasy football, betting, or scouting.
**How to identify**:
- Age 21-24 (prime breakout years)
- Increasing minutes year-over-year
- Improving percentile rankings across multiple metrics
- Playing in a strong league (top 5 European leagues)
- Market value under £20 million
- Underlying numbers better than output (high xG but low goals = likely to improve)
**Example process**:
1. Go to FBref → Bundesliga → Player Stats
2. Filter: Age 21-24, Minutes 1500+
3. Sort by "Progressive Carries" (high percentile)
4. Check if their goals/assists are lower than xG/xA (room for improvement)
5. Verify on Transfermarkt that market value is under £20m
6. Watch highlights to confirm eye test
### 4. Tactical Analysis Using Data
**Why**: Understanding team tactics helps predict player performance.
**How**:
**Identify pressing intensity:**
- FBref → Team Stats → "Pressures" per 90
- High pressures (180+ per 90) = aggressive press (Liverpool, Brighton)
- Low pressures (120- per 90) = deep block (Atletico Madrid)
**Identify possession style:**
- FBref → Team Stats → "Possession %"
- High possession (60%+) = possession-based (Manchester City, Barcelona)
- Low possession (45%-) = counter-attacking (Burnley, Getafe)
**Identify attacking patterns:**
- FBref → Team Stats → "Crosses" vs "Progressive Passes"
- High crosses, low progressive passes = direct, wide play
- Low crosses, high progressive passes = central, intricate play
**Application**: A winger joining a team with high crosses will likely get more assists. A striker joining a team with high progressive passes into the box will likely score more.
### 5. Injury Risk Assessment
**Why**: Injured players provide zero value.
**How**:
- Transfermarkt → Player → "Injury History"
- Red flags:
- 3+ injuries in the same area (hamstring, groin, knee)
- Injuries lasting 30+ days
- Increasing injury frequency year-over-year
- Age 30+ with recent muscle injuries
**Example**: A 28-year-old winger with three hamstring injuries in two years is high-risk. Hamstring injuries often recur and worsen with age.
### 6. Market Value vs. Performance Analysis
**Why**: Finding undervalued players is key for fantasy football and scouting.
**How**:
- Create a "value score": (Percentile ranking sum) / (Market value in millions)
- Higher score = better value
**Example**:
- Player A: 85th percentile average across key metrics, £40m value = 2.125 value score
- Player B: 75th percentile average, £20m value = 3.75 value score
- Player B is better value despite lower percentiles
### 7. Predicting Regression
**Why**: Players overperforming or underperforming their underlying numbers will likely regress to the mean.
**How to identify overperformers**:
- Goals significantly higher than npxG (3+ goals over a season)
- Shooting conversion rate 20%+ above league average
- Save percentage 5%+ above post-shot xG (for goalkeepers)
**How to identify underperformers**:
- Goals significantly lower than npxG (3+ goals under)
- Shooting conversion rate 20%+ below league average
- High xA but low assists (teammates missing chances)
**Application**: Avoid overperformers in fantasy football (regression coming). Target underperformers (improvement likely).
## Putting It All Together: Real-World Example
Let's analyze a real transfer decision using free data: Should Liverpool have signed Darwin Núñez for £85 million in 2022?
### Pre-Transfer Analysis (What Liverpool Saw)
**FBref - 2021-22 Benfica Season:**
- Age: 22 (prime years ahead)
- Minutes: 2,731 (30.3 full matches)
- Goals: