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Trusted by 16 Champions & Europa League clubs, plus 200 more clubs

AI TRANSPARENCY

Complete
Transparency

Full visibility into our AI models, methodologies, and performance metrics. Open-source approach to building trust in artificial intelligence.

AUDITABLE
91.2% ACCURACY
REAL-TIME MONITORING
91.2%
Model Accuracy
Average accuracy across all AI models
+2.3%
2.1M+
Prediction Volume
Total predictions made this month
+15.7%
12 min
Data Freshness
Average data refresh interval
-3 min
99.97%
Model Uptime
AI system availability
+0.02%

AI Model Portfolio

active
94.7%

Player Valuation Engine

Advanced neural network analyzing 247 player attributes to predict market valuations

Type
Regression
Predictions
847,291
Updated 2 hours ago
12.4B data points
active
89.3%

Transfer Probability Model

Multi-modal AI predicting transfer likelihood based on performance, contract, and market factors

Type
Classification
Predictions
234,567
Updated 1 hour ago
8.7B data points
active
87.4%

Match Outcome Predictor

Real-time match outcome prediction incorporating team form, player availability, and historical data

Type
Probabilistic
Predictions
567,834
Updated 15 minutes ago
15.2B data points
active
92.1%

Investment Score Algorithm

Comprehensive club investment potential scoring across financial, sporting, and market dimensions

Type
Composite
Predictions
156,789
Updated 3 hours ago
6.8B data points
training
91.6%

Talent Scout AI

Early identification of promising talent in lower leagues before market recognition

Type
Discovery
Predictions
89,432
Updated 4 hours ago
9.3B data points

Player Valuation Engine

Performance Metrics

94.7%
Accuracy
96.2%
Confidence

Methodology

Advanced neural network analyzing 247 player attributes to predict market valuations

Technical Approach: Ensemble of gradient boosting, neural networks, and transformer models

Data Sources

Data Points
12.4B
Last Updated
2 hours ago

Recent Predictions

Viktor Tsygankov
correct
€45M market value
2024-01-15
Confidence:
94.2%

Open Source
AI Research

We believe in transparent AI development. Our research papers, model architectures, and evaluation methodologies are openly available to the scientific community.

Open Models
Source code available on GitHub
Public Datasets
Anonymized training data available
Research Papers
Peer-reviewed publications
MIT License • Full documentation • Community support