A. Korneeva vs P. Kudermetova — prediction
›Ranking: #98 vs #113 (better ranked)
›Recent form: 4/10 in recent matches
›Model 54% vs market 70% → the model sees it as less likely than the odds
Korneeva holds a tangible edge in both Elo (1629 vs 1556) and ranking (#98 vs #113), the kind of gap that typically translates into a few extra percentage points of match win probability rather than a lopsided favorite status. That edge is reinforced by recent form: she is 7-3 over her last 10 matches, while Kudermetova has lost four in a row, arriving with visibly less rhythm and confidence.
Together these two factors point in the same direction — toward Korneeva — but neither is large enough on its own to explain a heavy favorite tag. They combine to a moderate, not overwhelming, edge.
The clearest mechanical advantage in this match is on return. Korneeva returns serve at 49%, well above Kudermetova's 41%, while their own service numbers are almost identical (57% vs 58%). This means that in exchanges where Korneeva is returning, she is statistically the stronger side, whereas Kudermetova's return game does not offer the same counter-punch against Korneeva's serve.
In practical terms, this suggests Korneeva should generate more break chances than she concedes, since the return disparity is the largest statistical gap in the data set.
Schedule congestion works against Korneeva here. She is playing on just 1 day of rest after 4 matches in the last 14 days, including a run to the semifinals in Athens qualifying only a day before this match. Kudermetova, by contrast, arrives with 6 days off and only 3 matches in the same window — a much fresher physical state.
This kind of workload difference can blunt a form and level advantage over the course of a match, particularly if the contest extends into a deciding set.
The model rates Korneeva's win probability at 54%, notably below the market's implied 70% at odds of 1.42. That gap produces an expected value of -23.9%, meaning the price does not compensate for the model's more conservative view of her chances.
Korneeva being the favorite is not the same as this bet having value. On this data, the market appears to be pricing in more certainty than the factors here support, so backing her at these odds is not justified by the model.
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.