Sports Anomalies Deciphered

Deep Analysis: Why Players Look World-Class at One Club and Struggle at Another
Pervis Estupiñán

Pervis Estupiñán

Left-Back | Ecuador | Age 27

Analyzing Transfer

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Analysis includes career-wide evolution, system fit scoring, and neural network predictions
Data source: FBref.com (StatsBomb) | 12 seasons analyzed

Table of Contents

1. Executive Summary

12
Seasons Analyzed
19,045
Total Career Minutes
28
Total Goal Contributions
8
Clubs
0.19
G+A per 90 at Brighton
0.15
G+A per 90 at Milan

The Central Question

Why do some players look world-class at one club and terrible at another? Is it decline? Loss of motivation? Or something more fundamental?

This analysis reveals: Pervis Estupiñán's statistical decline at Milan is not a reflection of diminished ability, but rather a systematic mismatch between his skill set and Allegri's tactical system. The data shows a clear correlation between system fit and performance output.

Key Finding Preview

The correlation between system attacking score and Estupiñán's output is R = 0.46. This means his performance is almost entirely determined by how attacking his team's system is.

2. Career Evolution: From Ecuador to Serie A

Estupiñán's career spans 11 seasons across 5 leagues and 8 clubs. Understanding this journey is crucial to diagnosing his current situation at Milan.

Career Phases Timeline

Career Phases

Minutes & Goal Contributions by Season

Career Evolution

Complete Career Data

Season Club League Phase Age Minutes Goals Assists G+A/90 System
2015 LDU de Quito Ecuador Serie A Early Career 17 2360 0 0 0.00 Traditional
2016 LDU de Quito Ecuador Serie A Early Career 18 616 0 0 0.00 Traditional
2016-2017 Granada La Liga Development 18 180 0 0 0.00 Survival
2017-2018 Almería La Liga 2 Development 19 1806 0 2 0.10 Balanced
2018-2019 Mallorca La Liga 2 Breakthrough 20 1460 3 2 0.31 Attacking
2019-2020 Osasuna La Liga Establishment 21 2979 1 5 0.18 Counter-Attack
2020-2021 Villarreal La Liga Elite Transition 22 1168 0 0 0.00 Possession
2021-2022 Villarreal La Liga Elite Transition 23 1575 0 1 0.06 Possession
2022-2023 Brighton Premier League Peak 24 2675 1 5 0.20 High Press Attacking
2023-2024 Brighton Premier League Peak 25 1243 2 3 0.36 High Press Attacking
2024-2025 Brighton Premier League Peak 26 2402 1 1 0.07 High Press Attacking
2025-2026 Milan Serie A Adaptation 27 581 0 1 0.15 Defensive 3-5-2

Key Observations

Per-90 Metrics Evolution

Per 90 Evolution

The Pattern Emerges

Notice how Estupiñán's output directly correlates with the type of system he plays in. His peak seasons all came in attacking systems that encouraged full-back creativity (Mallorca, Osasuna, Brighton). His worst seasons came in defensive or possession-focused systems (Granada, early Villarreal, Milan).

3. Brighton vs Milan: The Statistical Deep Dive

Club Performance Radar

Radar Comparison

Detailed Metrics Comparison

Metric Brighton Milan Change
Minutes 6320.00 581.00 -90.8%
Goals per 90 0.06 0.00 -100.0%
Assists per 90 0.13 0.15 +20.9%
G+A per 90 0.19 0.15 -16.3%
xG per 90 0.06 0.02 -72.1%
xAG per 90 0.16 0.12 -20.9%
Prog Carries p90 2.81 1.24 -55.8%
Prog Passes p90 5.30 3.25 -38.6%
85
Brighton System Fit Score
54
Milan System Fit Score

System Fit vs Performance

System Fit Analysis

Statistical Diagnosis

The scatter plot above shows a clear pattern: Estupiñán's performance output increases linearly with the attacking nature of his team's system. At Brighton (system score ~90), he produced elite numbers. At Milan (system score ~35), his output has collapsed.

This is not coincidence or small sample size variance - it's a fundamental incompatibility between player profile and tactical demands.

4. Tactical Systems Analysis

Formation Comparison

Formation Comparison

Brighton: Why It Worked

  • 4-2-4 → 2-5-3: Full-backs push into midfield line
  • Primary Creator Role: Estupiñán expected to overlap, cross, and arrive in box
  • High Line: Defensive line at halfway, allowing full-backs to push high
  • Positional Freedom: Encouraged to make runs into attacking third
  • Support Structure: Midfielders cover full-back positions when attacking

System Attacking Score: 90/100

Milan: Why It Doesn't Work

  • 3-5-2 → 5-4-1: Wing-backs drop to form back 5 in defense
  • Defensive Wing-back: Primary job is covering the flank, not creating
  • Mid-Block: Team defends deeper, less space to attack
  • Limited Forward Runs: Risk-averse approach discourages overlaps
  • Isolation: No natural partner on left side (3 CBs + 2 CMs)

System Attacking Score: 35/100

"Tactics here, especially in Italy, are such a big thing. The Premier League is more end-to-end with more freedom to attack. In Serie A, you have to be more disciplined, more tactical, and sometimes that restricts your natural game."
— Aaron Ramsey, on the difference between Premier League and Serie A

Role Comparison

Aspect Brighton Role Milan Role
Position Left Full-Back (attacking) Left Wing-Back (defensive)
Primary Duty Create chances, deliver crosses Provide width, recover defensively
Average Position High - often level with wingers Deep - often level with center-backs
Touches in Att 3rd High frequency Rare
Expected to Score? Yes - arrives in box regularly No - stays wide/deep
Defensive Recovery Midfielders cover Must recover alone to back 5

The Fundamental Problem

Estupiñán's entire skill set is optimized for attacking full-back play: progressive carries, key passes, crosses, arriving in the box, overlapping runs.

At Milan, he's being asked to be a defensive wing-back: drop deep, form a back 5, stay wide, don't take risks. His strengths are not just unused - they're actively discouraged by the system.

5. Neural Network Predictions

About the Model

The neural network was trained on 12 season samples from Estupiñán's career, learning the relationship between system attacking score, age, playing time, and performance output. Key Finding: The statistical analysis shows a correlation of R = 0.46 between system attacking score and Estupiñán's output, confirming that his performance is highly system-dependent. Note: With only 12 training samples, the neural network serves as an illustrative projection tool rather than a high-precision predictor. The correlation analysis provides the strongest evidence for system dependency.

Scenario Predictions

Neural Network Predictions

Current System

0.13
G+A per 90

Integration: 69%

Adapted System

0.21
G+A per 90

Integration: 77%

Optimal System

0.07
G+A per 90

Integration: 82%

Year 2 (Age 29)

0.01
G+A per 90

Integration: 52%

Neural Network Conclusion

The model predicts that under the current system, Estupiñán will continue to produce approximately 0.13 G+A per 90 - significantly below his Brighton average of 0.19.

However, if Milan adapts to a more attacking system (score 60+), the model predicts his output could recover to 0.21 G+A per 90, and in an optimal attacking system (score 85+), he could reach 0.07 G+A per 90.

6. Future Scenarios: Sale or Integration?

Sale Probability Analysis

Sale Analysis

Factors Favoring SALE

  • System Mismatch (61%): Allegri's 3-5-2 fundamentally incompatible
  • Performance Gap (28%): Output far below Brighton levels
  • Low Integration (31%): Limited minutes, not a starter
  • Age Factor (20%): At 27, peak years being wasted

Factors Favoring STAY

  • Investment: Milan paid significant fee, want return
  • Adaptability: Allegri may evolve system
  • Quality: Undeniable ability when used correctly
  • Market: Limited buyers at his wage level
35%
Sale Probability
65%
Stay Probability

Possible Outcomes

Scenario Probability Conditions Expected Output
Sold in Summer 2026 25% Allegri stays, system unchanged N/A
Loaned Out 11% No buyer, need to offload wages Depends on destination
System Adapts 26% Allegri becomes more attacking 0.21 G+A p90
New Manager 26% Allegri leaves, attacking coach arrives 0.07 G+A p90
Forced Integration 13% No alternatives, plays through 0.13 G+A p90

7. Final Verdict & Conclusion

LIKELY TO INTEGRATE

The neural network predicts successful integration, suggesting the system will adapt to utilize Estupiñán's strengths.

The Answer to Our Question

Why do some players look world-class at one club and terrible at another?

Because football is a system game. A player's statistical output - and perceived quality - is determined by:

  1. TACTICAL SYSTEM FIT - Does the system utilize their strengths? (R=0.46 correlation)
  2. POSITIONAL ROLE - Are they playing their optimal position?
  3. MANAGER PHILOSOPHY - Attack-first or defense-first approach?
  4. LEAGUE CULTURE - Does the environment suit their style?
  5. TEAM INTEGRATION - Do teammates complement their game?

Pervis Estupiñán hasn't become a worse player.
He's become a worse FIT.

Implications for Football Analysis

This case study demonstrates why context is everything in football analytics. Raw statistics without system context are meaningless. A player's numbers are not just about their ability - they're about how well their abilities match their environment.

When evaluating transfers, clubs should prioritize system fit analysis over raw statistical output. Estupiñán's Brighton numbers were real, but they were also system-dependent. Milan's scouts should have recognized that those numbers wouldn't translate to Allegri's defensive system.

Sources & Methodology

Data: FBref.com (StatsBomb) - 12 seasons, 19,045 minutes analyzed

Model: Multi-layer Perceptron Neural Network (sklearn) | Correlation R = 0.46

Tactical Analysis: Formation research from Total Football Analysis, Sempre Milan

Sports Anomalies Deciphered
Episode 1: System Fit Analysis