Predict football match outcomes and identify positive-EV value bets.
npx skills add https://github.com/Cortella/prevision-python --skill football-predictor
Please help me install this Agent Skill. Skill: football-predictor Source: https://github.com/Cortella/prevision-python/tree/main Command: npx skills add https://github.com/Cortella/prevision-python --skill football-predictor
Football analytics for match prediction and betting decisions often require coordinating data ingestion, model development, and ROI-focused recommendations. This skill provides a structured pipeline that combines public data sources, multiple predictive models, and clear EV-based guidance for actionable bets across leagues and seasons.
Run the data collector to fetch today’s matches and generate the ML-ready dataset, then train and compare models to generate actionable predictions
High-intent search queries and answers about installing and using this skill.
Football match prediction integrates data ingestion with probabilistic models like Dixon-Coles Poisson, ELO, and Bayesian enhancements to generate actionable match outcome probabilities across leagues and seasons.
EV calculation identifies positive expected value bets by comparing model probabilities against closing lines, while Kelly criterion determines optimal stake sizing to maximize long-term betting ROI.
Football prediction model comparison uses evaluation metrics including AUC, Brier score, and RPS to assess probabilistic accuracy and calibration across different leagues and seasons.
Yes, the pipeline performs deterministic data ingestion from public football data sources to fetch matches, generate ML-ready datasets, and produce dashboard-ready outputs for betting analysis.
Yes, the requests library is required as a dependency to execute the data collector component, which fetches match data from public sources and generates the ML-ready dataset for predictions.
The best way to backtest football betting models is by running the model suite across historical leagues and seasons, evaluating Brier and RPS metrics, and checking closing-line considerations to validate positive EV.
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