Advanced AI-Powered Betting Market Behavior Analysis
Budget: $1,500 – $3,000 AUD
Seeking highly advanced Python/AI pattern-detection specialist with experience in market microstructure, behavioural analytics, anomaly detection, and bookmaker odds movement analysis.
Project involves building a rerunnable AI-assisted analytics framework designed to detect and classify pre-match betting market behaviour across global football leagues.
Core objective is to distinguish between:
• genuine information-driven market movement
• liquidity/liability-based bookmaker gearing
• deceptive or behavioural drift patterns
• ambiguous market behaviour
The system must analyse:
• Moneyline markets
• Over/Under markets
• Multi-bookmaker movement concurrence
• League-specific behavioural fingerprints
• Provider-specific movement bias
• Margin expansion/contraction behaviour
• Lead/lag bookmaker relationships
• Time-based odds movement patterns prior to kick-off
Important:
This is NOT a generic sports betting model. The focus is behavioural pattern detection and microstructure analysis of bookmaker movement.
The framework should build a “corrective lens” per:
• league
• bookmaker/provider
• market type
The lens must calibrate automatically from historical data rather than manual weighting.
Deliverables:
• Cleaned and structured XLS dataset
• Rerunnable Python notebook/framework
• Signal vs Drift classifier with confidence scoring
• League/provider behavioural calibration engine
• Visualisations of movement patterns and provider behaviour
• Metrics workbook
• Future-ready framework capable of recalibrating on new data imports
Strong preference for candidates with experience in:
• anomaly detection
• quantitative pattern analysis
• sports trading models
• financial market microstructure
• behavioural AI systems
• machine learning classification
• time-series analytics
Please provide examples of similar pattern-detection or behavioural analytics work.
Project involves building a rerunnable AI-assisted analytics framework designed to detect and classify pre-match betting market behaviour across global football leagues.
Core objective is to distinguish between:
• genuine information-driven market movement
• liquidity/liability-based bookmaker gearing
• deceptive or behavioural drift patterns
• ambiguous market behaviour
The system must analyse:
• Moneyline markets
• Over/Under markets
• Multi-bookmaker movement concurrence
• League-specific behavioural fingerprints
• Provider-specific movement bias
• Margin expansion/contraction behaviour
• Lead/lag bookmaker relationships
• Time-based odds movement patterns prior to kick-off
Important:
This is NOT a generic sports betting model. The focus is behavioural pattern detection and microstructure analysis of bookmaker movement.
The framework should build a “corrective lens” per:
• league
• bookmaker/provider
• market type
The lens must calibrate automatically from historical data rather than manual weighting.
Deliverables:
• Cleaned and structured XLS dataset
• Rerunnable Python notebook/framework
• Signal vs Drift classifier with confidence scoring
• League/provider behavioural calibration engine
• Visualisations of movement patterns and provider behaviour
• Metrics workbook
• Future-ready framework capable of recalibrating on new data imports
Strong preference for candidates with experience in:
• anomaly detection
• quantitative pattern analysis
• sports trading models
• financial market microstructure
• behavioural AI systems
• machine learning classification
• time-series analytics
Please provide examples of similar pattern-detection or behavioural analytics work.