Matchboard
AI tennis predictions: professional match breakdown
The page gathers the upcoming matches of the selected sport, model probabilities, odds, results and AI breakdowns in a single match center.
Kym J
Smith Colton
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.0% / 35.0%
Market 1 / 2: 65.5% / 34.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Schoolkate T
Hong Seong Chan
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.8% / 34.2%
Market 1 / 2: 66.4% / 33.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Draxl L
Johnson Andrew
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.3% / 31.7%
Market 1 / 2: 69.5% / 30.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Cina F
Dellien H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.8% / 24.2%
Market 1 / 2: 77.3% / 22.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Rincon D
Barrios Vera M T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 41.5% / 58.5%
Market 1 / 2: 40.4% / 59.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Dougaz A
Guerrieri A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.4% / 49.6%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Fearnley J
Rodionov J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 71.5% / 28.5%
Market 1 / 2: 72.7% / 27.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Virtanen O
Dimitrov G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.5% / 45.5%
Market 1 / 2: 33.4% / 66.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Samuel T
Garin K. (Chil)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.4% / 36.6%
Market 1 / 2: 64.1% / 35.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Analytics
Why are PropickAI tennis predictions effective?
Tennis is a sport of individual matchups, so the model considers more than just player ranking. It factors in court surface, form in recent tournaments, head-to-head records, serve and return quality, draw density and possible fatigue. AI tennis predictions help you see where ATP and WTA statistics match the market line and where there is a discrepancy.