M. Sakkari vs H. Dart — prediction
›Ranking: #43 vs #152 (better ranked)
›Recent form: 4/10 in recent matches
›Match-sharp: 3 matches in the last 2 weeks
›Model 70% vs market 77% → the model sees it as less likely than the odds
The headline numbers all point the same way: Sakkari's #43 ranking and 1686 Elo compare to Dart's #152 and 1503, a 183-point Elo gap that historically translates into a strong favorite edge. This is reinforced by the baseline metric, where Sakkari's 43% dwarfs Dart's 14%, suggesting a broad quality gap beyond just current ranking position.
Sakkari's recent form adds to this picture — a 6-4 record over her last 10 matches including a win over Noskova (Elo 1943), a result that shows she can still beat elite-level competition. Dart's 5-5 stretch with no listed quality wins does not offer the same evidence of upside.
The service numbers complicate the picture somewhat. Sakkari's 59% serve-points-won is the single best number in the match, but Dart's 46% return rate is unusually strong and outpaces Sakkari's own 38% return figure by 8 points. That means on the exchange of return games, Dart is the more dangerous returner of the two, which could keep her competitive in individual sets even if she is outclassed overall.
In practice, this suggests rallies on both serves are likely to be contested rather than routine — Sakkari's own service games are not automatic against a returner performing at Dart's listed level, even though Sakkari's greater overall serve percentage still gives her the mechanical edge across a full match.
Schedule load is a mild caution flag for the favorite: Sakkari has played 5 matches in the last 14 days compared to Dart's 2, even though both enter with a single day of rest. Over a three-set match this workload difference is unlikely to be decisive, but it is a real factor working against Sakkari's freshness.
Ranking trend also shows Dart moving up faster (+46) than Sakkari (+5), which points to some momentum on the underdog's side, though it is a longer-term signal and does not offset the current gap in ranking, Elo, or baseline quality.
The model sets Sakkari's win probability at 70%, below the market's implied 77% (odds of 1.30), producing a -9.4% expected value. This is a case where being the clear favorite does not translate into a betting edge — the market is already pricing in Sakkari's superiority, and arguably more aggressively than the model's own factors justify.
Based on the data, Sakkari remains the more probable winner given her ranking, Elo, baseline profile, and better recent form, but backing her at these odds is not a value play. This is a match where the favorite label and a positive-EV bet clearly diverge.
Impact and analysis from real match data (Elo, form, head-to-head, rest, surface vs baseline, weather, altitude). The model ≈ the market on average; the odds already capture almost all the edge. 18+ · gamble responsibly.