The Problem
The Old Way
FOCUS ON INDIVIDUAL STATS WITHOUT INTERACTIONS
TOP PLAYERS ASSUMED TO MAKE TOP TEAM
NO GUIDANCE FOR PLAYER FIT OR TACTICS
RANDOM SIGNINGS WITHOUT FIT ANALYSIS
MANUAL TRIAL-AND-ERROR TEAM SETUP
The New Way
DATA-DRIVEN SYNERGY BETWEEN PLAYERS
TEAM BASED ON COLLABORATION
CHEMISTRY-BASED LINEUPS
STRATEGIC SIGNINGS BY COMPATIBILITY
ALGORITHMIC LINEUP OPTIMIZATION
Technical Description
Player Profiling
Cluster players based on normalized vectors using K-means to assign a profile c(i).
Synergy Calculation
Compute synergy coefficients spq between all profile pairs using historical joint performance.
Lineup Evaluation
For a given squad L, calculate total chemistry Chem(L) from interaction matrix W.
Optimization & Substitution
Search for optimal L* and evaluate impact of substitutes using ∆ij gain in chemistry.
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Details:
This image is a Player Interaction Synergy Map (w_ij)
What It Shows
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Nodes (P1–P11): Each represents a player in the lineup.
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Edges (lines): Measure interaction strength (w_ij) between players.
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Weights: Numbers on the lines quantify synergy (0–1 scale).
Color Coding
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Yellow/Orange: High synergy, strong collaboration.
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Purple/Red: Medium to low synergy, weaker interactions.
Key Insights
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Central Players (e.g., P9, P11): Act as synergy hubs for team chemistry.
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High Links (~1.0): Indicate strong mutual understanding.
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Low Links (0.1–0.3): Suggest pairs needing tactical adjustment.
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This image represents a Player Synergy Matrix (w_ij values),
Matrix Layout
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Rows & Columns: Represent team positions (GK, LB, CB1, etc.).
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Cells w_ij: Show synergy values between position pairs (0–1).
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Diagonal: Self-synergy, mainly for reference.
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Color Coding
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Red (0.7–1.0): Strong synergy and collaboration.
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White (~0.5): Moderate or average interaction.
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Blue (0.0–0.3): Weak synergy, poor on-pitch link.
Key Insights
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Strong Partnerships: e.g., GK & CB1 (0.87), LB & CB1 (0.77).
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Weak Links: e.g., RW & CM2 (0.14) need tactical adjustment.
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Team Optimization: Quickly shows where to reinforce or adjust lineups.
Football Is Not About Buying Players—It’s About Building Teams
In a game driven by dynamic, multi-variable interactions, WINNING’s Chemistry Model decodes the complex relationships that define true collective performance.
Other Objetives
Diagnose Key Interactions
Diagnose Key Interactions: Detect player pairs or groups whose collaboration creates added value.
Insightful
Collaborative
Tactical Recommendations
Tactical Recommendations: Suggest real-time formation changes to enhance on-field cohesion.
Adaptive
Practical
Optimize Signings
Evaluate player fit based on compatibility and synergy, not just individual metrics.
Strategic
Analytical
Track Evolution
Track Evolution: Monitor cohesion trends across the season to guide training and squad development.
Dynamic
Continuous
Team & Inverse Scouting
Team & Inverse Scouting: Recommend best-fit lineups for clubs, or suggest ideal clubs for a player’s development.
Personalized
Intelligent