Johanne Christine Svendsen vs Arina Kostina
Summary
Match Info
Analysis
Summary: No value at current prices: the underdog would need odds ≈14.29 to be +EV against our conservative 7% win estimate, so we recommend no bet.
Highlights
- • Implied away probability (10.5) = 9.52%
- • Our conservative estimate for the away is 7% → negative EV at current price
Pros
- + Svendsen has documented match experience in 2024–2025, so she is a plausible favourite
- + Large market favorite suggests most liquidity and predictable outcome in many cases
Cons
- - Svendsen's 10-21 record and recent losses undermine a 96% implied chance
- - No available data on Kostina increases uncertainty; market could be inefficient but we lack evidence to exploit it
Details
We compare the market prices (Home 1.04 / Away 10.5) to a conservative estimate of the underdog's chance. The implied probability for the away line is 1/10.5 = 9.52%. The only player data available is Svendsen's profile showing a 10-21 career record and poor recent results, which makes the market's ~96% implied chance for her unrealistic. However, we have no independent data on Arina Kostina to justify a probability materially above the market-implied 9.52%. Using a conservative estimated true probability for Kostina of 7.0% (reflecting uncertainty and Svendsen still being the clear favourite despite weak form), the EV on the away price is negative: EV = 0.07 * 10.5 - 1 = -0.265 (−26.5% ROI). That means the current away decimal odds of 10.5 do not provide value versus our estimate; to be positive EV at our 7% estimate Kostina would need odds ≥ 14.286. Given the limited data (no profile for Kostina), the extreme market skew toward Svendsen, and Svendsen's poor but available form data, we cannot justify a value wager here.
Key factors
- • Market-implied probability for the away player (10.5) is 9.52%
- • Svendsen's provided record is weak (10-21) and recent form poor, which argues against a 96% market certainty
- • No available profile or form data for Kostina in the research — high informational uncertainty