1. Fixture ingestion and identity
Every match starts as a normalized fixture record. Provider identifiers, kickoff timestamps, teams, competition data, and status are written into the internal match table. This lets MatchEdge track a fixture over time as it moves from scheduled to live to finished.
2. History and team information banks
Historical match results, head-to-head records, team form, and selected club metadata are stored in dedicated database tables. When a new fixture is shown to users, the app reuses those existing records instead of rebuilding history on demand. That is the reason a team page and a match history tab can reference the same underlying team and result bank.
3. Prediction layer
The model blends fixture-level context, recent form, historical results, and market-oriented transformations into probabilities for home win, draw, away win, BTTS, totals, and derivative markets. Where additional structured data is available, the system also materializes lineup, player, and underlying team metrics into the same database-backed flow.
4. Accuracy tracking
Resolved predictions are evaluated after final results land. MatchEdge uses Brier score as the primary calibration metric because it penalizes overconfident forecasts and makes it possible to track whether changes to the model are improving or degrading forecast quality over time.
Public history pages summarize resolved predictions, 1X2 hit rate, BTTS hit rate, totals hit rate, and average Brier score so users can inspect performance instead of relying on marketing claims.
5. Why some sections can be unavailable
Lineups, player-level numbers, injuries, and live clocks depend on provider coverage and timing. MatchEdge intentionally avoids inventing placeholder statistics when a provider does not return trustworthy data. If a section is unavailable, the goal is to say that explicitly rather than show fake precision.