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MODEL PREDICTION · 2026-07-12

A. Li vs M. Timofeeva — prediction

Athens (Greece) - Qualification
Result pending
LIWIN PROBABILITYTIMOFEEVA
76%
model prob.
@1.66
odds · 60% impl.
H2H 2–0 LiRest 13d vs 10d🎾Serve 59%📈Form 5/10 · 3✗
THE MODEL'S REASONING

Ranking: #29 vs #153 (better ranked)

Recent form: 6/10 in recent matches

Head-to-head: 2-0 in favor

Model 76% vs market 60% → the model sees it as MORE likely than the odds

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.
@1.32
fair odds
+25.8%
expected value
HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Li●●●
Li is ranked #29 vs #153 and leads on Elo 1641-1531, a wide quality gap reinforced by a positive ranking trend.
Head-to-head▸ Li●●
Li has won both 2025 meetings in WTA singles, a clean 2-0 record that shows she has solved Timofeeva before.
Serve/return▸ Timofeeva●●
Serve marks are close (59% Li vs 57% Timofeeva), but Timofeeva returns far better (50% vs Li's 41%), threatening Li's own service games more.
Form▸ Timofeeva
Both are 6/10 in their last 10, but Li carries a 3-match losing streak while Timofeeva arrives off a 4-win run before one loss.
Rest▸ Li
Timofeeva played 2 matches in the last 14 days and has only 10 days of rest, versus Li's 1 match and 13 days.
Value▸ Li●●
Model prices Li at 76% against a 60% market-implied probability (odds 1.66), a +25.8% EV gap, though this is a soft qualification-level market.
RANKING GAP

The clearest structural edge here is quality: Li sits at #29 with an Elo of 1641, more than a hundred points above Timofeeva's 1531 and roughly 124 ranking spots better (#29 vs #153). A gap this size across two rating systems is unusual and typically reflects a real difference in week-to-week consistency, not just a single tournament run.

Li's ranking trend is also moving up (+1), while Timofeeva's is flat. Combined with the head-to-head sweep, this ranking gap is the single biggest reason the model leans heavily toward Li rather than treating this as a coin-flip qualifier match.

SERVE VS RETURN

The service numbers are much closer than the ranking gap suggests: Li wins 59% of her service points, Timofeeva 57% — only a 2-point difference. On paper this looks like two players who hold serve at a similar clip.

The bigger asymmetry is on return: Timofeeva returns at 50%, nine points above Li's 41%. That means Timofeeva is statistically more capable of pressuring Li's serve than Li is of pressuring hers, a detail that tempers how one-sided this match should be read purely from serve stats.

FORM AND HISTORY

Both players show an identical 6-10 record over their last ten matches, so recent win totals don't separate them. The shape of that form does differ, though: Li is mid a 3-match losing streak, while Timofeeva is coming off a 4-match win streak before dropping her most recent match.

History still favors Li directly — she has beaten Timofeeva in both of their 2025 WTA Singles meetings. That head-to-head record, even without surface or score detail, is a concrete data point in her favor that offsets some of the recent-momentum concerns.

RECOVERY

Rest slightly favors Li: she has had 13 days since her last match and played only once in the past two weeks, while Timofeeva has had 10 days off after playing twice in that span. In a three-set qualifying match this gap is modest but could matter if the contest goes the distance.

VALUE READ

The model rates Li's win probability at 76%, well above the 60% implied by the 1.66 odds, producing a +25.8% expected-value gap. That's a meaningful discrepancy, but this is a WTA qualification match, where market liquidity is thin and pricing is less efficient — a wide model-market gap here should be treated as an interesting signal, not a certainty.

Being the model's favorite is not the same as being a safe bet: the return-game numbers show Timofeeva can pressure Li's serve more than the reverse, and Li's current 3-match losing streak adds some uncertainty. The edge looks real on paper, but it comes with the caveats of a thin market and a closer underlying serve/return battle than the headline probability implies.

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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