Main Objetive
Develop a one-number metric that objectively and dynamically quantifies a footballer’s individual performance by integrating personal impact, team contribution, and contextual interactions — accurately reflecting their progression throughout the season.
Inspired by the ELO algorithm, adapted to the complexity of football.
The Problem
The Old Way
SCORES 1–10 CLUSTER IN 6.5–7.5 → HARD TO DIFFERENTIATE
ARBITRARY BASE SCORE (E.G. 6.0) REDUCES OBJECTIVITY
IGNORES DUELS TEAM SUCCESS AND OPPONENT STRENGTH
NOT COMPARABLE ACROSS LEAGUES OR POSITIONS
DRIVEN BY PERCEPTION AND REPUTATION
STATIC NOT ADAPTIVE TO THE GAME
The New Way
CONTEXTUAL ELO SCORE WITH PERFORMANCE &CONTEXT
DYNAMIC MATCH-BY-MATCH UPDATES
CONSIDERS OPPONENT ROLE IMPACT
FAIR SCALABLE CROSS-LEAGUE COMPARISONS
DATA AND STATISTICAL FOUNDATION
MAXIMIZES CORRELATION WITH EXPECTED POINTS
Technical Description
Metrics definition
The algorithm compares observed vs. expected performance in:
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Team impact
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Duel success
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Historical form
ELO Base Formula
The dynamism of the ELO
The dynamism expressed through coefficients. These coefficients are adjusted based on the playing styles of the players that maximize points earned and victories. In this way, the ELO becomes a dynamic system, adapted to shifts in playing styles and trends, distinguishing between good players and players who are currently optimal for today’s style of football.
Details:
ELO Panel – Performance Visualization:
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Current ELO: 1655 (+10 pts), including data on position, role, country, and league
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Chart: Performance evolution from September to April
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Key Factors: Team performance, direct duels, and consistency
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Comparative Ranking: ELO changes between players and overall impact
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Objective: Quantify performance dynamically and in context
Winning is the first sports tech company to build a modular solver system for decision-making in football, powered by AI, game theory, and optimization.
Other Objetives
Unified Performance Score
Unify performance into a global, objective, and cross-context metric.
Global
Objective
Comparable
Cross-Context Comparison
Enable comparisons across leagues, teams, and competitions.
Scalable
Neutral
Standardized
Decision Intelligence
Support decisions on transfers and playing time allocation.
Strategic
Actionable
Context-aware
Contextual Data Framework
Develop a contextualized measurement foundation for future metrics, talent tracking, and analysis.
Foundational
Scalable
Insight-driven
Trajectory-Based Sensitivity
Adjust system sensitivity based on the player's career trajectory.
Adaptive
Personalized
Dynamic