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Challenger Women - Doubles: Vancouver (Canada), hard
Challenger Women - Doubles: Vancouver (Canada), hard
28.07.2026 15:00 UTC

Kabrera L./Kuvata Kh. - Kinosita Kh./Sakatsume Kh.

🎯 PropickAI prediction

Our model prediction (Glicko-2)

2 Kinosita Kh./Sakatsume Kh. — 53%

Pure model vs market (2 · Kinosita Kh./Sakatsume Kh.): model 48% · market 54% Δ −6 pp model estimates below the market

1 · Kabrera L./Kuvata Kh.46.9%
2 · Kinosita Kh./Sakatsume Kh.53.1%

Model estimate: win Kabrera L./Kuvata Kh. 46.9%, win Kinosita Kh./Sakatsume Kh. 53.1%. Model favourite — Kinosita Kh./Sakatsume Kh..

Informational estimate, not a betting recommendation.

AI agent prediction

Independent AI-agent assessment from our data (model, market line, form, H2H)

1Kabrera L./Kuvata Kh. (odds2.00) Score 2:1 confidence 52%
  • • Kabrera L./Kuvata Kh
  • • — Kinosita Kh./Sakatsume Kh
  • • (tennis)
  • • Model backs Kabrera L./Kuvata Kh.: probability 52% vs 50% on the line (odds 2.00)
  • • edge over the line +2 pp — value pick.

Analytical AI-agent assessment, not a betting recommendation.

Math-model prediction

AI match prediction Kabrera L./Kuvata Kh. - Kinosita Kh./Sakatsume Kh. July 28, 2026

Tennis math model

The summary is built from our prematch context: line, form, lineups and Glicko.

Line favourite market consensus: Kinosita Kh./Sakatsume Kh.. We cross-check with Glicko and the math model below.
  • Win odds (market consensus): 1 2.00 / 2 1.72

For reference

Fair probability (no margin)

Probability excluding the bookmaker margin — for reference, not a betting recommendation.
Winner bookmaker margin 8.1%
1 46.2%
2 53.8%

Odds source: market consensus. This is the fair probability after removing the bookmaker margin from the odds — reference information, not a prediction or a betting recommendation.

Tennis Challenger Women - Doubles: Vancouver (Canada), hard
Challenger Women - Doubles: Vancouver (Canada), hard hard 28.07.2026 15:00 UTC Not started
28.07.2026 15:00 UTC Not started
K
Kabrera L./Kuvata Kh.
K
Kinosita Kh./Sakatsume Kh.
-:-
Odds
P1 2.00 46.2% BetBoom
P2 1.72 53.8% BetBoom
Coverage: hard
Preview Statistics Broadcast Comments

About the athletes

- Country -
- Rating -
- Age -
- Height (cm) -
- Weight (kg) -
- Hand -
- Seed -
hard Coverage hard
0 H2H 0
Match center
Start 28.07.2026 15:00 UTC
Coverage hard
Match center

Kabrera L./Kuvata Kh. - Kinosita Kh./Sakatsume Kh.

H2H 0 head-to-head matches
Market 2 model / bookmakers
Flashscore match data source checked
Odds match data source checked
Line depth match data source checked
Present in data
Market Match Participants Line
More needed
Form Championat, Flashscore
H2H Championat, Flashscore
Tournament Championat, Flashscore
Movement Line history
Overview Championat
Market and Glicko

Tennis math model. Tennis without draws: the base signal comes from player rating, surface, form, serve/return and tournament fatigue. For live logic the set state and who is serving matter more than the overall points score.

Outcome Line Glicko Signal
Kabrera L./Kuvata Kh. 46.2% 46.9%
Kinosita Kh./Sakatsume Kh. 53.8% 53.1% market and model agree

Glicko-2 is a team-strength rating: it weighs the line, history and consistency, but is not a guarantee.

Tennis math model
P1 / P2 46.9% / 53.1% probabilities
Rating 1,500 / 1,500 Kabrera L./Kuvata Kh. / Kinosita Kh./Sakatsume Kh.
RD 350 / 350 rating uncertainty
r +/- 2RD 800-2,200 / 800-2,200 strength interval
HFA: 0 HFA mu: 0.000 MoV: on N: N=1 P: two_way_glicko2_expected_score

Best value: no value found

Tennis without draws: the base signal comes from player rating, surface, form, serve/return and tournament fatigue. For live logic the set state and who is serving matter more than the overall points score.

Betting snapshot
1X2
2 1.72 market consensus · no value · model 53.1%
1 1.99 market consensus · no value · model 46.9%

Betting notes are built from the line, market and our math model: take a signal only when the model and market agree.

Bookmaker line

Match markets

bookmaker line and markets

Outcome (1X2)

Outcome Baltbet Betcity Leon
2.00 50.0% 2.00 50.0% 1.98 50.5%
1.72 58.1% 1.72 58.1% 1.71 58.5%