Highlights

• • On-paper matchup is essentially even (similar records and surfaces).
• • Quoted odds are extreme and likely erroneous rather than reflecting true probabilities.

Pros

• + Clear identification of a massive market discrepancy that would be exploitable if reliable prices existed.
• + Conservative probability estimate based on symmetric player data avoids overbetting on an outlier market quote.

Cons

• - We cannot verify whether the quoted 1.01/15.0 prices are widely available or reflect non-public factors (withdrawals, walkovers).
• - If the market price is correct due to external information not in the research, our assessment would be invalid.

Details

We find the published moneyline (Ena Shibahara 1.01 / Natalia Sousa Salazar 15.0) to be an extreme outlier relative to the available player data. Both players show near-identical career records (10-21) and similar surface experience (clay and hard) with poor recent form, so a near-certain outcome for the home player is not supported by the performance data. Using a conservative estimated true win probability of 52% for Ena (slight home/tournament edge) implies a fair decimal price of ~1.923; the offered 1.01 would only be positive EV if Ena’s true win probability exceeded ~99%, which is implausible. Conversely, the 15.0 on Natalia implies an implied win probability of ~6.67%, which is extremely low compared with the symmetrical evidence and therefore appears to be a pricing anomaly rather than genuine value. Given the mismatch between market prices and on-paper probabilities, we decline to recommend a bet despite the large numerical EV shown on the underdog at face value, because the market quote looks erroneous/outlier and not widely reliable.

Key factors

• • Both players show identical recorded career numbers (10-21) and similar surface histories
• • Recent form for both is poor with multiple losses; no clear performance edge evident
• • Market odds (1.01 vs 15.0) are extreme and inconsistent with the available data, indicating a probable pricing anomaly