xG Chain and Build-Up Analysis in Football

Introduction

xG Chain and xG Buildup are advanced expected goals metrics that reveal player contributions beyond goals and assists. While traditional stats overlook deep-lying playmakers and build-up specialists, these metrics quantify how players contribute to goal-scoring opportunities throughout possession sequences. This comprehensive guide explains xG chain analysis, xG buildup metrics, and their applications in modern football analytics.

Understanding xG Chain

Definition

xG Chain: The total xG value from all possessions in which a player is involved, from the moment they touch the ball until the possession ends (shot, loss of possession, or goal).

Simple Explanation: If a player touches the ball at any point in a sequence that leads to a shot, they receive credit for that shot's xG value.

Calculation Example

Possession Sequence:

1. Center Back → Pass to Midfielder (Player A touches ball)
2. Midfielder (Player A) → Pass to Winger
3. Winger → Cross to Striker
4. Striker → Shot (xG: 0.35)

xG Chain Credits:

• Center Back: +0.35 xG Chain
• Player A (Midfielder): +0.35 xG Chain
• Winger: +0.35 xG Chain
• Striker: +0.35 xG Chain (also gets 0.35 xG personal)

Interpretation: All four players contributed to creating a 0.35 xG chance, so all receive 0.35 xG Chain credit.

Real Player Example

Kevin De Bruyne (Manchester City 2022-23):

Personal xG: 7.2 (goals he shot)
xA (Expected Assists): 14.8
xG Chain: 28.5

Calculation:
xG Chain includes:
- His own shots: 7.2
- Passes that led to shots: 14.8
- Earlier passes in sequences: 6.5
Total: 28.5 xG Chain

Interpretation: De Bruyne was involved in creating 28.5 xG worth of chances, far beyond his personal goals and assists.

Understanding xG Buildup

Definition

xG Buildup: The xG Chain credit a player receives for possessions where they did NOT take the final shot or make the final assist.

Formula:

xG Buildup = xG Chain - (Personal xG + xA)

Calculation Example

Midfielder Analysis:

xG Chain: 18.5
Personal xG: 4.2 (shots taken)
xA: 7.1 (final passes before shots)
xG Buildup: 18.5 - (4.2 + 7.1) = 7.2

Interpretation: This midfielder contributed 7.2 xG through build-up play (earlier passes, dribbles) that didn't result in direct goals or assists.

Real Player Example

Rodri (Manchester City 2022-23):

xG Chain: 24.3
Personal xG: 5.8
xA: 6.5
xG Buildup: 24.3 - (5.8 + 6.5) = 12.0

Interpretation: Rodri contributed 12.0 xG through deep build-up play, nearly double his direct contributions (goals + assists). This reveals his true value as a deep-lying playmaker.

Who Benefits Most from xG Chain & Buildup?

1. Deep-Lying Playmakers

Traditional Stats Miss Them:

Midfielder X:
- Goals: 3
- Assists: 5
- Traditional view: "Average player"

xG Chain Reveals Value:

Midfielder X:
- xG Chain: 22.7
- xG Buildup: 14.5
- Reality: "Key playmaker, starts most attacks"

Examples:

• Rodri (Manchester City)
• Fabinho (Liverpool)
• Jorginho (Chelsea/Arsenal)

2. Progressive Passers

Player Type: Midfielders who play penetrative passes that break defensive lines, but not always the final pass.

Example:

Thiago Alcântara:
- Assists: 2 (low)
- xG Buildup: 9.3 (high)
→ Makes the pass before the assist ("pre-assist")

3. Ball Carriers

Player Type: Dribblers who advance the ball but don't necessarily finish or assist.

Example:

Adama Traoré:
- Goals + Assists: 6
- xG Buildup: 8.7
→ Dribbles create space/opportunities for others

xG Chain vs Traditional Metrics

Comparison Table

Player Type	Goals	Assists	xG Chain	xG Buildup	Revealed Value
Striker	25	5	32.0	2.0	High direct, low buildup
#10 CAM	8	12	28.5	8.5	Medium direct, high buildup
Deep Mid	3	4	22.0	15.0	Low direct, very high buildup
Winger	12	8	25.0	5.0	High direct, medium buildup
Defender	2	1	8.5	5.5	Low direct, medium buildup

Case Study: Fabinho vs Bruno Fernandes

Fabinho (Liverpool Defensive Midfielder):

Goals: 2
Assists: 3
Traditional Contribution: 5
xG Chain: 18.3
xG Buildup: 13.1
→ Hidden value: 13.1 xG from deep play

Bruno Fernandes (Manchester United Attacking Midfielder):

Goals: 10
Assists: 8
Traditional Contribution: 18
xG Chain: 26.5
xG Buildup: 8.5
→ Less hidden value (more direct involvement)

Interpretation:

• Fernandes: Direct creator (goals + assists dominant)
• Fabinho: Deep creator (buildup dominant)
• Both valuable, but in different ways

Applications of xG Chain & Buildup

1. Player Scouting

Identify Undervalued Players:

Scenario:

Midfielder from mid-table team:
- Goals: 4
- Assists: 6
- Market value: €20M
- xG Buildup: 16.2 (very high)
→ Undervalued! True contribution > traditional stats suggest

Scouting Decision: Buy at €20M, actual value closer to €35M based on complete contribution.

2. Tactical Analysis

Identify Build-Up Patterns:

Team Analysis:

Top 3 xG Chain players:
1. Rodri: 24.3
2. De Bruyne: 28.5
3. Grealish: 20.1

Interpretation: Manchester City's attacks flow through these 3 players
Tactical insight: Press these players to disrupt City's build-up

3. Player Role Evaluation

Determine True Position Value:

Example:

Player listed as "Defensive Midfielder":
- xG Buildup: 18.5 (very high)
- Progressive passes: 95th percentile
→ Actually more of a deep-lying playmaker than pure DM

Contract Negotiations: Player can argue for higher wages based on xG Chain/Buildup metrics showing true value.

4. Formation Optimization

Identify Key Creators:

Analysis:

Current formation: 4-3-3
Midfielder A xG Buildup: 14.2
Midfielder B xG Buildup: 6.1

Tactical adjustment: Give Midfielder A more freedom to advance
Result: Increase overall team xG

Advanced: xG Chain Networks

Pass Network Visualization

Concept: Visualize xG Chain flows between players using network graphs.

Example Visualization:

[Rodri] → (12.3 xG Chain) → [De Bruyne]
      ↓
    (8.7 xG Chain)
      ↓
  [Grealish]
      ↓
    (6.2 xG Chain)
      ↓
  [Haaland] → GOAL

Interpretation:

• Rodri initiates most high-xG sequences
• De Bruyne is the primary connector
• Haaland finishes (high personal xG, low buildup)

Limitations of xG Chain & Buildup

1. Doesn't Distinguish Impact

Issue: A 5-yard sideways pass receives same xG Chain credit as a 40-yard through ball in the same sequence.

Example:

Possession:
1. GK → 5-yard pass to CB
2. CB → 40-yard through ball to striker
3. Striker → Goal (xG: 0.70)

xG Chain:
- GK: +0.70 (minimal contribution)
- CB: +0.70 (major contribution)
→ Both credited equally, unfair

Solution: Use "xG Chain per Touch" or weight contributions by pass difficulty.

2. Penalizes High-Risk Players

Issue: Players who lose possession frequently get lower xG Chain.

Example:

Creative Winger:
- Attempts 15 dribbles (10 succeed, 5 fail)
- Successful dribbles lead to high xG
- Failed dribbles break possession
→ Lower xG Chain than safer player

3. Team Style Dependent

Issue: Counter-attacking teams have lower xG Chain (fewer passes per sequence).

Example:

Team A (Possession): Average xG Chain = 25.0 per player
Team B (Counter-attack): Average xG Chain = 15.0 per player
→ Team B players undervalued by xG Chain

Best Practices for Using xG Chain & Buildup

1. Combine with Traditional Stats

Don't Use in Isolation:

Wrong: "Player A has 18.0 xG Chain, they're better than Player B with 15.0"
Right: "Player A: 18.0 xG Chain (8 goals, 6 assists) vs
        Player B: 15.0 xG Chain (15 goals, 3 assists)
        → Player B more direct, Player A more creative"

2. Position-Specific Benchmarks

Expected xG Chain by Position:

Striker: 20-30 xG Chain (high personal xG)
Attacking Mid: 20-30 xG Chain (balanced)
Central Mid: 15-25 xG Chain (buildup > personal)
Defensive Mid: 10-20 xG Chain (mostly buildup)
Defender: 5-15 xG Chain (low involvement)

3. Per 90 Minutes Adjustment

Formula:

xG Chain per 90 = (xG Chain / Minutes Played) × 90

Example:

Player A: 24.0 xG Chain in 2,500 minutes = 0.86 xG Chain per 90
Player B: 18.0 xG Chain in 1,800 minutes = 0.90 xG Chain per 90
→ Player B more efficient despite lower total

Data Sources

Free Sources:

• FBref.com (limited leagues, StatsBomb data)

Paid Sources:

• StatsBomb (comprehensive)
• Wyscout (scouting platform)
• InStat (professional)

DIY: Requires event data + coding (calculate sequences manually).

Conclusion

xG Chain and xG Buildup reveal player contributions beyond traditional goals and assists, quantifying deep playmakers, progressive passers, and build-up specialists. These advanced metrics are essential for complete player evaluation, scouting, and tactical analysis.

Key Takeaways:

1. xG Chain measures total involvement in goal-scoring possessions
2. xG Buildup isolates non-goal, non-assist contributions
3. Deep midfielders benefit most from these metrics
4. Combine with traditional stats for complete evaluation
5. Position-specific context is crucial for fair comparison

Golden Rule: Use xG Chain/Buildup to discover undervalued players and quantify "hockey assists" and deep build-up contributions.

Frequently Asked Questions

What's a good xG Chain for a midfielder?

Attacking midfielder: 20-30 xG Chain per season. Central midfielder: 15-25. Defensive midfielder: 10-20. Context (team style, minutes played) matters significantly.

Is xG Buildup more important than assists?

Not necessarily. Assists are direct contributions (more valuable). xG Buildup reveals hidden value but shouldn't replace traditional metrics—use both together.

Which players have the highest xG Chain?

Top creators in possession-heavy teams: Kevin De Bruyne, Bruno Fernandes, Lionel Messi, Luka Modrić. Typically 25-35 xG Chain per season.

Can defenders have high xG Chain?

Yes, especially ball-playing center backs and fullbacks in attacking systems. Trent Alexander-Arnold, Virgil van Dijk, John Stones can reach 12-20 xG Chain.

Where can I find xG Chain data?

FBref.com (free, limited leagues), StatsBomb (paid, comprehensive), Wyscout (scouting, paid). DIY calculation requires event data and programming skills.

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Meta Description: xG Chain and xG Buildup explained: Advanced metrics revealing player contributions beyond goals and assists. Build-up analysis and deep playmaker evaluation.

Keywords: xg chain, xg buildup, build-up play analysis, xg chain football, expected goals chain, deep playmaker metrics

Category: Analysis

Word Count: ~1,500 words