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ITF Women - Singles: W35 Dallas, TX 🇺🇸, hard
ITF Women - Singles: W35 Dallas, TX 🇺🇸, hard
15.07.2026 16:50 UTC Match prediction

Gaylis R. 🇺🇸 - Nguen A. 🇺🇸

Tennis ITF Women - Singles: W35 Dallas, TX 🇺🇸, hard
ITF Women - Singles: W35 Dallas, TX 🇺🇸, hard hard 15.07.2026 16:50 UTC Finished
15.07.2026 16:50 UTC Finished
G
Gaylis R. 🇺🇸
N
Nguen A. 🇺🇸
2:0
Odds
P1 1.99 46.7% BetBoom
P2 1.74 53.4% 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
🎯 PropickAI prediction

Our model prediction (Glicko-2)

2 Nguen A. 🇺🇸 — 53%

Pure model vs market (2 · Nguen A. 🇺🇸): model 49% · market 53% Δ −4 pp model estimates below the market

1 · Gaylis R. 🇺🇸47.0%
2 · Nguen A. 🇺🇸53.0%

Model estimate: win Gaylis R. 🇺🇸 47.0%, win Nguen A. 🇺🇸 53.0%. Model favourite — Nguen A. 🇺🇸.

Informational estimate, not a betting recommendation.

AI agent prediction

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

2Nguen A. 🇺🇸 (odds1.74) confidence 52% ✗ outcome missed

Analytical AI-agent assessment, not a betting recommendation.

Math-model prediction

AI match prediction Gaylis R. 🇺🇸 - Nguen A. 🇺🇸 15 июля 2026

Tennis math model

Based on the collected prematch data, the favourite looks like Nguen A. 🇺🇸.

Line favourite market consensus: Nguen A. 🇺🇸. We cross-check with Glicko and the math model below.
  • Win odds (market consensus): 1 1.99 / 2 1.74
  • Glicko: Gaylis R. 🇺🇸 47.0% / Nguen A. 🇺🇸 53.0%

For reference

Fair probability (no margin)

Probability excluding the bookmaker margin — for reference, not a betting recommendation.
Winner bookmaker margin 7.7%
1 46.6%
2 53.4%

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.

Match center
Start 15.07.2026 16:50 UTC
Coverage hard
Form 5 matches
Match center

Gaylis R. 🇺🇸 - Nguen A. 🇺🇸

В базе учтены недавние матчи обоих участников.

Form 2/5 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
Post-match / PropickAI database
Score 2:0
Events 0
Statistics 0
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
Gaylis R. 🇺🇸 46.5% 47.0%
Nguen A. 🇺🇸 53.5% 53.0% market and model agree
1X2: 3 Bookies

Glicko-2 - рейтинговая модель силы: сравнивает линию, историю и стабильность, но не дает гарантий.

Tennis math model
P1 / P2 47.0% / 53.0% probabilities
Rating 1,434 / 1,310 Gaylis R. 🇺🇸 / Nguen A. 🇺🇸
RD 257 / 180 rating uncertainty
r +/- 2RD 921-1,948 / 949-1,671 strength interval
HFA: 0 HFA mu: 0.000 MoV: on N: N=1 P: two_way_glicko2_expected_score

Best value: value не найден

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 53.0%
1 1.98 market consensus · no value · model 47.0%
Total
Over 20.5 1.78 market consensus · line only
Under 20.5 1.90 market consensus · line only

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

Handicap

Outcome Betcity Leon
П1 +1.5 1.81 55.3% 1.78 56.2%
П2 -1.5 1.90 52.6% 1.85 54.1%

Total

Outcome Betcity Leon
ТБ 20.5 1.79 55.9% 1.76 56.8%
ТМ 20.5 1.92 52.1% 1.88 53.2%

Outcome (1X2)

Outcome Baltbet Betcity Leon
П1 1.99 50.3% 1.98 50.5% 1.97 50.8%
П2 1.73 57.8% 1.74 57.5% 1.69 59.2%
Match center
Gaylis R. 🇺🇸
L W
09.07 16:30 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W50 Columbus, OH (США), хард
Gaylis R. 🇺🇸 0:2 McNeil C. (Сша)
09.06 19:30 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Los Angeles, CA (США), хард
Gaylis R. 🇺🇸 2:1 Giribalan K. (Сша)
1 Wins
1 Losses
1.0 Sets won
1.5 Sets lost
50% Win percentage
Nguen A. 🇺🇸
L L W W L
11.07 16:30 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Rancho Santa Fe, CA (США), хард
Nguen A. 🇺🇸 0:2 Miroshnichenko V. (Мир)Miroshnichenko V. (Мир)
08.07 17:00 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Rancho Santa Fe, CA (США), хард
Nguen A. 🇺🇸 0:2 Allegre C. (Сша)
08.07 17:00 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Rancho Santa Fe, CA (США), хард
Nguen A. 🇺🇸 2:0 Нгуен К. (Сша)
27.06 16:00 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Claremont, CA (США), хард
Nguen A. 🇺🇸 2:1 Люткемейер А. (Сша)
11.06 16:00 UTC+0 ITF ЖЕНЩИНЫ - ОДИНОЧНЫЙ РАЗРЯД: W15 Los Angeles, CA (США), хард
Nguen A. 🇺🇸 0:2 Чан Дж. (Сша)
2 Wins
3 Losses
0.8 Sets won
1.4 Sets lost
40% Win percentage