abailey81/MatchOracle
Deep ensemble EPL match prediction engine — 13 base learners, Dixon-Coles statistical model, 376+ engineered features, 8 data sources, NLP sentiment analysis. 60.2% accuracy, outperforms market odds.
What's novel
Deep ensemble EPL match prediction engine — 13 base learners, Dixon-Coles statistical model, 376+ engineered features, 8 data sources, NLP sentiment analysis. 60.2% accuracy, outperforms market odds.
Code Analysis
0 files read · 4 roundsA project that claims to be an EPL match prediction system with ensemble models but whose actual implementation could not be read or verified.
Strengths
The project structure suggests a well-thought-out architecture with clear separation between data fetching, feature engineering, model training, and prediction components.
Weaknesses
Unable to verify any claims due to inability to access source code files; README claims about advanced ML pipelines, NLP sentiment analysis, and ensemble methods could not be validated against actual implementation.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
8
Contributors
1
Files
24
Active weeks
1
Repository
Language
Python
Stars
1
Forks
0
License
MIT