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Sports verified Verified Outcome TRL 9

Moneyball 2.0: Goal Probability Data Modeling

domain Client: A top-tier English Premier League club handshake Provider: Internal / StatsBomb schedule Deploy: Q2 2020 (Peak Success)
87 Impact
Enterprise Ready
Evidence Score: 4/10
Strength: Medium

Executive Summary

ANALYST: COI RESEARCH

Unable to outspend state-owned rivals, the club invested in a best-in-class data science department led by a physicist. They developed proprietary 'Pitch Control' and 'Goal Probability Added' models to value players based on their contribution to space creation and ball progression, not just goals/assists. This led to high-ROI transfers (e.g., Salah, Robertson).

rate_review Analyst Verdict

"The premier case of data analytics delivering competitive advantage (Alpha). The club's ability to quantify 'off-the-ball' value allowed them to arbitrage the transfer market. However, as these methods become commoditized, the 'edge' is eroding."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Traditional scouting relied on the 'eye test' and basic stats (goals/assists), leading to inflated prices for hype players. The club needed to identify undervalued talent that fit a specific high-intensity tactical system (Gegenpressing) without paying premium prices.

psychology The Solution

The team ingested event data (XY coordinates of every ball touch) and tracking data (position of all 22 players @ 25fps). They built models to simulate thousands of match outcomes to determine how a specific player's actions increased the probability of a goal, even if they didn't touch the ball (e.g., making a decoy run).

settings_suggest Technical & Deployment Specs

Integrations
Scouting Database
Deployment Model
On-Premise / Private Cloud
Data Classification
Proprietary IP
Estimated TCO / ROI
Medium (Salaries)
POC Summary (2012-01-01 to 2024-01-01)

"N/A (Ongoing operations)"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Model Drift High Continuous recalibration as league tactics evolve.
Brain Drain Critical Key staff (Ian Graham, Michael Edwards) departing requires knowledge retention systems.

trending_up Impact Trajectory

Audited value realization curve

Creation of proprietary 'Pitch Control' metrics Verified Outcome
Primary KPISignificant reduction in transfer 'flops'
Audit CycleHigh correlation between model prediction and player output

policy Compliance & Gov

  • Standards: GDPR (Player Data)
  • Maturity (TRL): 9
  • Evidence Score: 4/10
  • Data Class: Proprietary IP

folder_shared Verified Assets

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Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for A top-tier English Premier League club without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-783
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-783

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