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ITF Women - Singles: W35 Florence, SC πŸ‡ΊπŸ‡Έ, hard
ITF Women - Singles: W35 Florence, SC πŸ‡ΊπŸ‡Έ, hard
28.07.2026 12:00 UTC

Fakih K. πŸ‡ΊπŸ‡Έ - Nguen A. πŸ‡ΊπŸ‡Έ

🎯 PropickAI prediction

Our model prediction (Glicko-2)

2 Nguen A. πŸ‡ΊπŸ‡Έ β€” 51%

Pure model vs market (2 Β· Nguen A. πŸ‡ΊπŸ‡Έ): model 34% Β· market 53% Ξ” βˆ’19 pp model estimates below the market

1 Β· Fakih K. πŸ‡ΊπŸ‡Έ49.1%
2 Β· Nguen A. πŸ‡ΊπŸ‡Έ50.9%

Model estimate: win Fakih K. πŸ‡ΊπŸ‡Έ 49.1%, win Nguen A. πŸ‡ΊπŸ‡Έ 50.9%. Model favourite β€” Nguen A. πŸ‡ΊπŸ‡Έ.

Informational estimate, not a betting recommendation.

AI agent prediction

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

2Nguen A. πŸ‡ΊπŸ‡Έ (odds1.75) Score 1:2 confidence 51%
  • • Fakih K
  • • πŸ‡ΊπŸ‡Έ β€” Nguen A
  • • πŸ‡ΊπŸ‡Έ (tennis)
  • • Fakih K
  • • πŸ‡ΊπŸ‡Έ: forma PVVVV (4-1)
  • • Nguen A
  • • πŸ‡ΊπŸ‡Έ: forma PPVVP (2-3)
  • • Model backs Nguen A
  • • πŸ‡ΊπŸ‡Έ: probability 51% vs 57% on the line (odds 1.75)
  • • no edge over the line (-6 pp) β€” informational only.

Analytical AI-agent assessment, not a betting recommendation.

Math-model prediction

AI match prediction Fakih K. πŸ‡ΊπŸ‡Έ - Nguen A. πŸ‡ΊπŸ‡Έ July 28, 2026

Tennis math model

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

Line favourite market consensus: Nguen A. πŸ‡ΊπŸ‡Έ. We cross-check with Glicko and the math model below.
  • Win odds (market consensus): 1 1.96 / 2 1.76

For reference

Fair probability (no margin)

Probability excluding the bookmaker margin β€” for reference, not a betting recommendation.
Winner bookmaker margin 7.8%
1 47.3%
2 52.7%

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

Tennis ITF Women - Singles: W35 Florence, SC πŸ‡ΊπŸ‡Έ, hard
ITF Women - Singles: W35 Florence, SC πŸ‡ΊπŸ‡Έ, hard hard 28.07.2026 12:00 UTC Match in progress
28.07.2026 12:00 UTC Match in progress
F
Fakih K. πŸ‡ΊπŸ‡Έ
N
Nguen A. πŸ‡ΊπŸ‡Έ
-:-
Odds
P1 1.96 47.3% BetBoom
P2 1.76 52.7% 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 12:00 UTC
Coverage hard
Form 6 matches
Match center

Fakih K. πŸ‡ΊπŸ‡Έ - Nguen A. πŸ‡ΊπŸ‡Έ

V baze uchteny nedavnie matchi oboikh uchastnikov.

Form 5/5 recent games
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 Form Match Participants Line Overview
More needed
H2H Championat, Flashscore
Tournament Championat, Flashscore
Movement Line history
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
Fakih K. πŸ‡ΊπŸ‡Έ 47.3% 49.1%
Nguen A. πŸ‡ΊπŸ‡Έ 52.7% 50.9% 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 49.1% / 50.9% probabilities
Rating 1,900 / 1,331 Fakih K. πŸ‡ΊπŸ‡Έ / Nguen A. πŸ‡ΊπŸ‡Έ
RD 208 / 184 rating uncertainty
r +/- 2RD 1,485-2,316 / 964-1,698 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 1.76 market consensus Β· no value Β· model 50.9%
1 1.96 market consensus Β· no value Β· model 49.1%

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
1.95 51.3% 1.96 51.0%
1.76 56.8% 1.75 57.1%
Match center
Fakih K. πŸ‡ΊπŸ‡Έ
L W W W W
25.07 16:45 UTC+0 ITF Women - Singles: W35 Santa Fe πŸ‡ΊπŸ‡Έ, hard
Fakih K. πŸ‡ΊπŸ‡Έ 1:2 Inue Khin. πŸ‡ΊπŸ‡ΈInue Khin. πŸ‡ΊπŸ‡Έ
03.07 17:00 UTC+0 ITF Women - Singles: W15 San Diego, CA πŸ‡ΊπŸ‡Έ, hard
Fakih K. πŸ‡ΊπŸ‡Έ 2:1 Aytoyan M. πŸ‡ΊπŸ‡Έ
02.07 18:30 UTC+0 ITF Women - Singles: W15 San Diego, CA πŸ‡ΊπŸ‡Έ, hard
Fakih K. πŸ‡ΊπŸ‡Έ 2:0 Keller K. πŸ‡ΊπŸ‡Έ
30.06 20:00 UTC+0 ITF Women - Singles: W15 San Diego, CA πŸ‡ΊπŸ‡Έ, hard
Fakih K. πŸ‡ΊπŸ‡Έ 2:0 Sahdiieva A. πŸ‡ΊπŸ‡¦
02.06 17:00 UTC+0 ITF Women - Singles: W15 Lakewood, CA 2 πŸ‡ΊπŸ‡Έ, hard
Fakih K. πŸ‡ΊπŸ‡Έ 2:0 Teylor L. πŸ‡¦πŸ‡Ί
4 Wins
1 Losses
1.8 Sets won
0.6 Sets lost
80% Win percentage
Nguen A. πŸ‡ΊπŸ‡Έ
L L W W L
15.07 16:50 UTC+0 ITF Women - Singles: W35 Dallas, TX πŸ‡ΊπŸ‡Έ, hard
Nguen A. πŸ‡ΊπŸ‡Έ 0:2 Gaylis R. πŸ‡ΊπŸ‡Έ
08.07 17:00 UTC+0 ITF Women - Singles: W15 Rancho Santa Fe, CA πŸ‡ΊπŸ‡Έ, hard
Nguen A. πŸ‡ΊπŸ‡Έ 0:2 Allegre C. πŸ‡ΊπŸ‡Έ
08.07 17:00 UTC+0 ITF Women - Singles: W15 Rancho Santa Fe, CA πŸ‡ΊπŸ‡Έ, hard
Nguen A. πŸ‡ΊπŸ‡Έ 2:0 Nguen K. πŸ‡ΊπŸ‡Έ
27.06 16:00 UTC+0 ITF Women - Singles: W15 Claremont, CA πŸ‡ΊπŸ‡Έ, hard
Nguen A. πŸ‡ΊπŸ‡Έ 2:1 Lyutkemeyer A. πŸ‡ΊπŸ‡Έ
11.06 16:00 UTC+0 ITF Women - Singles: W15 Los Angeles, CA πŸ‡ΊπŸ‡Έ, hard
Nguen A. πŸ‡ΊπŸ‡Έ 0:2 Chan Dzh. πŸ‡ΊπŸ‡Έ
2 Wins
3 Losses
0.8 Sets won
1.4 Sets lost
40% Win percentage