Football Odds/Prediction Python Engine
Budget: €250 – €750 EUR
Build Football Fair-Odds Engine + Edge/CLV Module
Project Overview
I’m looking for an experienced Python developer / data scientist with strong knowledge of sports modelling, probability calibration, and bookmaker markets to build a football fair-odds engine with an integrated edge & CLV (Closing Line Value) detection module.
This project will serve as the core of a sharp-market value betting system (BetInAsia Black: Pinnacle, SBO, ISN). The goal is to systematically identify small but repeatable edges (0.5–2%) in sharp markets before the close.
Scope of Work
Phase 1 — Fair-Odds Engine (MVP)
Build a football goals-based prediction model (Poisson, Dixon-Coles, or equivalent).
Integrate team strength ratings (Elo or Glicko with attack/defence splits + home advantage).
Add recency weighting/form adjustments (e.g., exponential decay).
Generate calibrated probabilities & fair odds for:
1X2
Asian Handicap (incl. quarter lines like -0.25, -0.75, etc.)
Totals / Over-Under (incl. split totals like 2.25, 2.75, etc.)
Apply probability calibration (isotonic regression / Platt scaling).
Validate with:
Out-of-sample time-based splits (no leakage).
Reliability plots.
Brier scores and log-loss.
Benchmarking vs Pinnacle closers (no-vig).
Outputs in CSV/JSON, with optional FastAPI endpoint.
Documentation: clear README with retraining and updating instructions.
Phase 2 — Edge & CLV Detection Module
Ingest odds from Pinnacle, SBO, ISN (via BetInAsia Black).
Compute value % (model fair odds vs bookmaker odds).
Implement CLV-tracking: compare model’s no-vig prices vs closers, report realized edges.
Add configurable filters: league, market, edge threshold, odds range, time-to-kickoff.
Generate reports:
CLV trends per league/market.
Log-loss delta vs closers.
ROI simulations (flat stakes, Kelly).
Outputs in CSV/JSON, optional FastAPI integration for external bots.
Documentation: guide on interpreting edge/CLV results.
Requirements
Strong Python (pandas, numpy, scipy, statsmodels, scikit-learn).
Experience building sports prediction models (football strongly preferred).
Solid grasp of Poisson/Dixon-Coles modelling and Elo/Glicko ratings.
Knowledge of probability calibration (isotonic, Platt) and reliability testing.
Familiarity with bookmaker markets, esp. sharp books (Pinnacle, SBO, ISN).
Understanding of Asian Handicap and Totals betting (incl. quarter lines & pushes).
Experience benchmarking against closing lines (Pinnacle).
Ability to deliver modular, well-documented code.
Nice-to-Haves
Experience with xG models and integration of player-level or advanced features.
Knowledge of Bayesian priors (e.g., closers as prior for λ).
Prior work on value betting, CLV, or expected value systems.
Familiarity with API integration for odds feeds.
Deliverables
Phase 1: Football fair-odds engine (MVP) with calibration and validation reports.
Phase 2: Edge/CLV detection module, generating signals for sharp-market inefficiencies.
Documentation for retraining, updating, and interpreting outputs.
Budget & Timeline
Phase 1: Estimated 3–4 weeks.
Phase 2: To follow after MVP validation, flexible timeline.
Budget: Negotiable depending on experience.
To Apply
Please include:
Examples of previous sports modelling work (football strongly preferred).
A brief explanation of your approach to goal modelling + calibration.
Which libraries/tools you plan to use.
Any past experience with CLV, fair odds, or betting analytics systems.
Project Overview
I’m looking for an experienced Python developer / data scientist with strong knowledge of sports modelling, probability calibration, and bookmaker markets to build a football fair-odds engine with an integrated edge & CLV (Closing Line Value) detection module.
This project will serve as the core of a sharp-market value betting system (BetInAsia Black: Pinnacle, SBO, ISN). The goal is to systematically identify small but repeatable edges (0.5–2%) in sharp markets before the close.
Scope of Work
Phase 1 — Fair-Odds Engine (MVP)
Build a football goals-based prediction model (Poisson, Dixon-Coles, or equivalent).
Integrate team strength ratings (Elo or Glicko with attack/defence splits + home advantage).
Add recency weighting/form adjustments (e.g., exponential decay).
Generate calibrated probabilities & fair odds for:
1X2
Asian Handicap (incl. quarter lines like -0.25, -0.75, etc.)
Totals / Over-Under (incl. split totals like 2.25, 2.75, etc.)
Apply probability calibration (isotonic regression / Platt scaling).
Validate with:
Out-of-sample time-based splits (no leakage).
Reliability plots.
Brier scores and log-loss.
Benchmarking vs Pinnacle closers (no-vig).
Outputs in CSV/JSON, with optional FastAPI endpoint.
Documentation: clear README with retraining and updating instructions.
Phase 2 — Edge & CLV Detection Module
Ingest odds from Pinnacle, SBO, ISN (via BetInAsia Black).
Compute value % (model fair odds vs bookmaker odds).
Implement CLV-tracking: compare model’s no-vig prices vs closers, report realized edges.
Add configurable filters: league, market, edge threshold, odds range, time-to-kickoff.
Generate reports:
CLV trends per league/market.
Log-loss delta vs closers.
ROI simulations (flat stakes, Kelly).
Outputs in CSV/JSON, optional FastAPI integration for external bots.
Documentation: guide on interpreting edge/CLV results.
Requirements
Strong Python (pandas, numpy, scipy, statsmodels, scikit-learn).
Experience building sports prediction models (football strongly preferred).
Solid grasp of Poisson/Dixon-Coles modelling and Elo/Glicko ratings.
Knowledge of probability calibration (isotonic, Platt) and reliability testing.
Familiarity with bookmaker markets, esp. sharp books (Pinnacle, SBO, ISN).
Understanding of Asian Handicap and Totals betting (incl. quarter lines & pushes).
Experience benchmarking against closing lines (Pinnacle).
Ability to deliver modular, well-documented code.
Nice-to-Haves
Experience with xG models and integration of player-level or advanced features.
Knowledge of Bayesian priors (e.g., closers as prior for λ).
Prior work on value betting, CLV, or expected value systems.
Familiarity with API integration for odds feeds.
Deliverables
Phase 1: Football fair-odds engine (MVP) with calibration and validation reports.
Phase 2: Edge/CLV detection module, generating signals for sharp-market inefficiencies.
Documentation for retraining, updating, and interpreting outputs.
Budget & Timeline
Phase 1: Estimated 3–4 weeks.
Phase 2: To follow after MVP validation, flexible timeline.
Budget: Negotiable depending on experience.
To Apply
Please include:
Examples of previous sports modelling work (football strongly preferred).
A brief explanation of your approach to goal modelling + calibration.
Which libraries/tools you plan to use.
Any past experience with CLV, fair odds, or betting analytics systems.