E. Jacquemot vs C. Burel — prediction
›Ranking: #80 vs #1486 (better ranked)
›Recent form: 2/10 in recent matches
›Model 72% vs market 44% → the model sees it as MORE likely than the odds
The headline number is the ranking gap: #80 for Jacquemot against #1486 for Burel, a gulf that alone would suggest an easy night. That gap is clearly the main driver behind the model moving from a 39% baseline figure to a final 72% probability for Jacquemot.
But the Elo ratings tell a different story — 1491 for Jacquemot against 1589 for Burel — meaning Burel's underlying match-quality metric is actually stronger. Add in Jacquemot's -16 ranking trend (she's been sliding) against Burel's flat 0, and the 'huge favorite' picture looks less one-sided once you look past the ranking number alone.
Recent results favor Burel clearly: she is 6-4 in her last 10 matches and currently on a 2-match winning streak, while Jacquemot is just 2-8 in her last 10, albeit with a 1-match win streak. This is a meaningful gap in current match sharpness.
Combined with her win in their only previous meeting, Burel arrives with more competitive rhythm than the ranking alone would suggest, even though she is over a thousand places lower.
The available serve/return numbers slightly favor Burel across the board: 55% serve points won versus Jacquemot's 53%, and 46% on return versus Jacquemot's 42%. Neither gap is large, but both point the same direction.
In practice this means Burel profiles as marginally better at both holding and breaking, which does not match a picture of Jacquemot dominating on pure output.
The model prices Jacquemot at 72% while the market implies only 44% (odds of 2.25), producing a large flagged edge of +62.6%. That gap is unusually wide and should be treated with caution rather than as a sure thing — this is a WTA factor model, not a market-calibrated Elo system, and the gap between Elo (favoring Burel) and ranking (favoring Jacquemot) shows the inputs are not fully aligned.
Being the favorite is not the same as being undervalued with certainty: Jacquemot's ranking is the strongest argument for her, but Burel's form, H2H win, and slightly better serve/return marks are real, data-backed counterpoints. Treat the market price as a reasonable anchor and the model's edge as a signal worth weighing, not a guarantee.
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.