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Pulling the odds, probabilities and the full engine breakdown for this fixture.
Pulling the odds, probabilities and the full engine breakdown for this fixture.
These are the last prices recorded before the match resolved, kept as a record. They are not current.
Recorded .
This is the pre-match model as it stood before the match, kept as a historical record.
Two-way probability model · tennis-surface-elo-v5 · generated · evidence to · provisional calibration
OddsPadi published no view for this match.
This fixture is not eligible for a new public pre-match decision.
This fixture is not eligible for a new public pre-match decision.
The engine passed on every market here, so these numbers are the model's read, not a recommendation.
| Selection | Model | Market | Best price |
|---|---|---|---|
| E. Slavikova | 28% | 26% | 3.79 |
| L. Petruzelova | 72% | 78% | 1.28 |
Fan votes and accountable tips sit beside the model, never inside it. Voting takes one tap and no account; publishing a tip needs one, because tips are an immutable record.
Selections lock 30 minutes before kickoff. Original tips cannot be rewritten after publication.
Model boundary: votes and community tips never change OddsPadi probability, edge, confidence, publication status, or model accuracy.
The full bar is 100% of the model's 1X2 distribution. Evidence quality is shown separately so confidence is not confused with probability.
No selected runtime probability is available for an empirical calibration interval.
This is the deterministic calculation path, not a generated explanation. Positive factors support the selected side; negative factors pull against it.
The engine did not produce a valid probability trace for this market.
Latest real-data backtest is available for shadow comparison only; live guardrails require an active model-bound calibration promotion.
Live outcome calibration: Probability calibration remains inactive until a real-data backtest receives explicit live-guardrail promotion.
Player-form coverage uses only provider-capable fixtures and requires acceptable or strong chronology-safe history in both training and holdout windows. Normalized Brier averages squared probability error across the market outcomes; lower is better. Calibration error measures the weighted gap between forecast probability and observed frequency.
Weighted evidence-risk index, not a statistical confidence level.
Data coverage uncertainty. Downgrade or block public trust until data coverage uncertainty is addressed. Confidence penalty -18.8%.
“Value edge” is our probability minus the bookmaker's fair probability (margin removed). Positive edge means the price is better than it should be. Current best edge: none found.
| Market | Selection | Odds | Bookmaker | Model | Raw implied | No-vig implied | Margin | No-vig edge | EV |
|---|---|---|---|---|---|---|---|---|---|
| Match winner | E. Slavikova | 3.79 | Pncl | 28% | 26% | 23% | +8.2% | +4.3% | +5.1% |
| Match winner | L. Petruzelova | 1.28 | Pncl | 72% | 78% | 77% | +8.2% | -4.3% | -7.5% |
Every market the model prices for this fixture, from one scoreline distribution. Selections without a live bookmaker quote show the model probability only — a probability is not a betting recommendation.
Decimal odds from stored pre-match provider snapshots. Lower odds mean the market has strengthened that outcome; higher odds mean it has weakened. This is observed movement, not proof that the move is correct.
OddsPadi estimates E. Slavikova at 28% and L. Petruzelova at 72%. The current prices do not show a clear positive value edge, so the responsible call is to avoid forcing a pick.
Sports outcomes are uncertain. Predictions are model estimates, not guarantees.
Probabilities are the model's own estimate; market probabilities remove the bookmaker margin where the market supports it. Full methodology and the promotion gates a model must pass are on the engine page.