पद्धति

1) Data Sources and League Scope

BetSigy ingests structured fixture feeds, historical match statistics (goals, shots, possession, form tables, head-to-head records), and market-specific feature sets. We prioritise leagues where the data pipeline is stable enough for repeatable pre-match analysis: top-division European football leagues, plus selected second-tier and international competitions with consistent data coverage.

• Fixture data: kickoff times, venues, referees, and competition context.
• Team form: recent results, goals scored/conceded, clean sheets, and both-teams-to-score frequency over rolling 6- and 10-match windows.
• Head-to-head: historical match outcomes between the two sides, weighted by recency.
• Market-specific features: over/under goal distributions, BTTS rates, half-time result patterns, and correct-score frequency tables.
• Feed availability, coverage gaps, and latency can change without notice. Missing data points are handled conservatively — a prediction with incomplete input is flagged or withheld rather than guessed.
• Fixture, lineup, injury, price, and settlement information should be verified against the relevant competition, club, and bookmaker rules before acting.

2) Model Architecture and Prediction Workflow

Predictions are produced through an ensemble approach that combines statistical modelling (Poisson-based goal expectation, Dixon-Coles style adjustments for attacking/defensive strength) with machine-learning classifiers (gradient-boosted trees and logistic regression for market-specific outcomes). No single model determines the final output.

• Statistical layer: expected goals (xG) models, Poisson simulations for scoreline distributions, and strength ratings derived from weighted historical performance.
• ML layer: gradient-boosted decision trees trained on structured match features; logistic regression for binary outcomes (BTTS yes/no, over/under thresholds).
• Ensemble agreement: where statistical and ML layers point in the same direction, model probability is higher; where they diverge, confidence is reduced.
• Outputs are grouped by market type (1X2, BTTS, Over/Under, Double Chance, Draw No Bet, Correct Score) so users can compare signals by category rather than relying on one blended number.
• For non-football sports (basketball, tennis, cricket, baseball, ice hockey, esports), a multi-sport ML pipeline is used where sufficient historical data exists; coverage is lighter and the model falls back to broader features when sport-specific history is thin.

3) Model Probability, Confidence, and Publishing Rules

• Model probability is a ranking signal (0–100) derived from ensemble agreement, historical accuracy for similar match profiles, and feature completeness. It is not a win percentage or a certainty score.
• Confidence tiers are used to prioritise picks within each market group; higher confidence does not remove variance or guarantee a result.
• Tips are refreshed throughout the UTC day as new fixture data, team news, and model runs complete.
• Fixtures are removed from live boards after their scheduled kickoff time and moved to the archive.
• Archive snapshots are designed to remain stable; we do not retrospectively alter settled records to improve apparent performance.
• Automation and AI assist data presentation and drafting. Every published article is reviewed by the BetSigy Editorial Desk for clarity, unsupported certainty, time sensitivity, internal consistency, and risk language before publication.

4) Performance Measurement and Limits

Performance is tracked by market, league, and time period using settled match outcomes. Key metrics include hit rate, return-on-investment proxy (flat-stake assumption), and correct-score exact-hit frequency. These metrics describe past process quality — they do not predict future results.

• Performance displays identify the time window, sample size, settlement assumptions, and any relevant exclusions.
• Losing outcomes are never removed to improve an apparent record.
• Short-term streaks (winning or losing) are not evidence of a change in model quality.
• Betting outcomes are uncertain even with strong models. Team news, lineup changes, weather, and late market moves can change edge quickly.

5) Governance and Accountability

We publish policy pages so users can review how content is written, corrected, and monetised. Nexa Vale is a disclosed fictional house persona used for a consistent editorial voice; it is not presented as a real expert, licensed adviser, or holder of fabricated credentials. Material policy updates receive a real revision date rather than an automatically refreshing timestamp.