Our model prediction (Glicko-2)
PropickAI model · comparison with the market line (no margin)
1 Rapolu M — 69%
Pure model vs market (1 · Rapolu M): model 55% · market 9% Δ +46 pp model estimates above the market
Model estimate: win Rapolu M 68.7%, win Collins Kylie 31.3%. Model favourite — Rapolu M.
Informational estimate, not a betting recommendation.
Independent AI-agent assessment from our data (model, market line, form, H2H)
Analytical AI-agent assessment, not a betting recommendation.
Math-model prediction
Based on the collected prematch data, the favourite looks like Collins Kylie.
For reference
Odds source: Fonbet. This is the fair probability after removing the bookmaker margin from the odds — reference information, not a prediction or a betting recommendation.
For reference
↓ odds shortened (money on this outcome), ↑ drifted (line weakened). For reference, not a betting recommendation.
Model consensus
Modeli dayut edinyy signal. Takie matchi my pomechaem kak bolee silnye.
AI overview
AI analysis:1/7 ready · Still preparing:DeepSeek, ChatGPT, Claude, Qwen, Kimi, GLM 5.2
| AI | 1 | 2 | Score | Total games | Bet |
|---|---|---|---|---|---|
| Google AI | 75% | 25% | 2:0 | Under 20.5 |
V baze uchteny nedavnie matchi oboikh uchastnikov.
We mark in green only strong signals where the model passed our publication filter. The other rows are for audit and training.
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.
Glicko-2 is a team-strength rating: it weighs the line, history and consistency, but is not a guarantee.
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 notes are built from the line, market and our math model: take a signal only when the model and market agree.
| Outcome | Baltbet | Betcity | Fonbet |
|---|---|---|---|
| — | — | 1.75 57.1% | |
| — | — | 1.73 57.8% | |
| 1.77 56.5% | 1.81 55.3% | — | |
| — | — | 1.82 55.0% | |
| — | — | 1.87 53.5% | |
| — | — | 1.70 58.8% | |
| — | — | 1.78 56.2% | |
| 1.94 51.6% | 1.90 52.6% | 1.35 74.1% | |
| — | — | 1.95 51.3% | |
| — | — | 2.00 50.0% | |
| — | — | 1.92 52.1% | |
| — | — | 1.60 62.5% | |
| — | — | 1.77 56.5% | |
| — | — | 1.52 65.8% | |
| — | — | 1.75 57.1% |
| Outcome | Baltbet | Betcity | Fonbet |
|---|---|---|---|
| Over 17.5 | — | — | 1.75 57.1% |
| Over 18.5 | — | — | 2.02 49.5% |
| Over 19.5 | 1.76 56.8% | — | 1.90 52.6% |
| Over 20.5 | 1.95 51.3% | 1.96 51.0% | 1.85 54.1% |
| Over 21.5 | — | — | 1.95 51.3% |
| Over 22.5 | — | — | 1.93 51.8% |
| Over 23.5 | — | — | 1.83 54.6% |
| Over 24.5 | — | — | 1.60 62.5% |
| Over 25.5 | — | — | 2.90 34.5% |
| Over 26.5 | — | — | 1.92 52.1% |
| Over 27.5 | — | — | 2.15 46.5% |
| Under 17.5 | — | — | 1.95 51.3% |
| Under 19.5 | 1.95 51.3% | — | — |
| Under 20.5 | 1.76 56.8% | 1.75 57.1% | — |
| Outcome | Baltbet | Betcity | Fonbet |
|---|---|---|---|
| 1.29 77.5% | 1.32 75.8% | 10.00 10.0% | |
| 3.27 30.6% | 3.10 32.3% | 1.02 98.0% |
Rapolu M
Collins Kylie
Full head-to-head historyRapolu M - Collins Kylie
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