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MODEL PREDICTION · 2026-07-12

C. Liu vs I. Oz — prediction

Iasi
✓ Correct
LIUWIN PROBABILITYOZ
66%
model prob.
@1.20
odds · 83% impl.
🎾Serve 60%📈Form 7/10
THE MODEL'S REASONING

Ranking: #182 vs #210 (better ranked)

Recent form: 3/10 in recent matches

Model 66% vs market 83% → the model sees it as less likely than the odds

WATCH FOR

!Coming off 4 losses in a row

!Returning from a long layoff (45d) — possible rustiness

Calibrated model probability (~64% out-of-sample accuracy, validated specifically on WTA). Not a guarantee: the model ≈ the market on average, so the odds already capture almost all the edge. 18+ · gamble responsibly.
@1.53
fair odds
−21.4%
expected value
HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Liu●●
Liu is ranked #182 with a rising trend of +32 spots, better positioned than the #210 opponent noted in the model.
Form▸ Oz●●
Liu's last10 shows 3 losses including the most recent match (streak -1), signaling shaky momentum heading into Iasi.
Serve/return▸ Liu
Liu wins 60% of her service points and 43% on return, a balanced profile, though no comparable numbers exist for Oz.
Rest= Even
Liu has had 9 days since her last match with 3 matches in the past 14 days — moderate workload, no clear edge either way.
Level (market vs model)= Even●●●
The model gives Liu 66% while the market prices her at 83% (odds 1.20), leaving a -21.4% expected value gap.
RANKING AND TREND

Liu's #182 ranking, paired with a +32 spot improvement over recent months, points to an upward trajectory that generally correlates with rising competitive form. The model factor sheet also references a #210 standing for the opponent, suggesting Liu holds a modest but real ranking edge going into this match.

This gap is not large enough to be decisive on its own, but combined with her positive ranking trend, it supports the case that Liu is the more live player on paper, even if her recent match results don't fully reflect that trajectory yet.

FORM WARNING SIGNS

Liu's last10 log (WWLLWWWWWL) includes 3 losses, one of which is her most recent match, giving her a current streak value of -1. The risk flags note a stretch of consecutive losses and a long layoff, both of which raise questions about sharpness and rhythm entering this contest.

No form data exists for Oz, so this cannot be framed as a comparative edge — it is purely a caution flag on Liu's side. A player en route to improving her ranking but dropping recent matches presents an inconsistent profile that the market's steep price does not seem to fully account for.

SERVE PROFILE

Liu's own numbers — 60% of service points won and 43% of return points won — describe a player who leans on her serve as the primary weapon while holding a moderate return game. Since no return or serve percentages exist for Oz, this cannot be turned into a head-to-head style comparison, but it does confirm Liu has a clear, serve-oriented method for building an advantage when she is playing well.

With no surface or altitude data provided, this serve/return snapshot is the closest thing to a style-based read available for this match, and it functions as a mild positive marker for Liu in isolation.

VALUE READ

The model assigns Liu a 66% win probability, well below the market's implied 83% at odds of 1.20. That 17-point gap translates into a -21.4% expected value, meaning the price does not compensate for the model's more conservative view of her chances, informed partly by her rocky recent form and the absence of a head-to-head or surface edge to lean on.

Being the favorite here does not equate to being a value play. With no Elo inputs and a form line that includes a live losing skid, this looks like a case where the market is pricing in more certainty than the data supports. A cautious read is warranted: favor recognized, edge not established.

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.

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