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.
Live win probability (market-implied) — how often it wins, not a profit forecast.
Glinka D
Rincon D
Montes-De la Torre I
Erhard M
Pucinelli de Almeida M
Hara Friend J D
Choinski J
Van de Zandschulp B
Kasatkina D
Badosa P
Molcan A
Bonzi B
Svajda Z
Altmaier D
Munar J
Rinderknech A
Merida Aguilar D
Rublev A
Marozsan F
Zheng Michael
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.2% / 59.8%
Market 1 / 2: 40.0% / 60.1%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Shelton B
Hurkacz H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 67.8% / 32.2%
Market 1 / 2: 68.3% / 31.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Lehecka J
Samuel T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 69.5% / 30.5%
Market 1 / 2: 70.3% / 29.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Duckworth J
Wu Yibing
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.4% / 57.6%
Market 1 / 2: 41.7% / 58.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Prizmic D
Paul T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.8% / 80.2%
Market 1 / 2: 19.0% / 81.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Bublik A
Mannarino A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.2% / 24.8%
Market 1 / 2: 75.6% / 24.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Harris L
Tsitsipas S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.3% / 66.7%
Market 1 / 2: 32.6% / 67.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Kostyuk, Marta
Stephens S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 84.5% / 15.5%
Market 1 / 2: 85.3% / 14.7%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Bartunkova N
Sherif M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 90.0% / 10.0%
Market 1 / 2: 91.4% / 8.6%
Favourite trap: no
Data completeness: 42%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Jovic, Iva
Frech M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.4% / 26.6%
Market 1 / 2: 73.9% / 26.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Boulter, Katie
Mukhova K. (Chekh)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 17.1% / 82.9%
Market 1 / 2: 16.3% / 83.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Selekhmeteva O
Rakhimova K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.1% / 63.9%
Market 1 / 2: 35.8% / 64.2%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Maria T
Ostapenko, Jelena
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.1% / 56.9%
Market 1 / 2: 42.6% / 57.4%
Favourite trap: no
Data completeness: 42%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Navarro E
McNally C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.2% / 45.8%
Market 1 / 2: 54.4% / 45.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Women
Wang Xinyu
Kalinskaya A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.5% / 64.5%
Market 1 / 2: 34.9% / 65.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Women
Jovic I
Frech M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 72.6% / 27.4%
Market 1 / 2: 73.1% / 26.9%
Favourite trap: no
Data completeness: 42%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Women
Linette M
Jones F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 25.8% / 74.2%
Market 1 / 2: 25.3% / 74.7%
Favourite trap: no
Data completeness: 42%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Women
Boulter K
Mukhova K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 17.1% / 82.9%
Market 1 / 2: 16.3% / 83.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International US Open. Women
Birrell K
Alexandrova E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.9% / 76.1%
Market 1 / 2: 23.2% / 76.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International US Open. Women
Li A
Vekic D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.7% / 40.3%
Market 1 / 2: 59.6% / 40.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International US Open. Women
Ivanov I / Sarksian D
Bertola R / Kym Jerome
All stats and model conclusions
Model vs market
Glicko 1 / 2: 14.4% / 85.6%
Market 1 / 2: 13.3% / 86.7%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International ATP Challenger. Manacor. Doubles
Berri Ch / Mazur D
Lok B / Lok K-Dzh
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.0% / 20.0%
Market 1 / 2: 80.9% / 19.1%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Manacor. Doubles
Shapovalov D
Van Assche L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.4% / 41.6%
Market 1 / 2: 58.6% / 41.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Berrettini M
Navone M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.9% / 38.1%
Market 1 / 2: 62.0% / 38.0%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Dreycopp I./Garay V.
Menzel T./Torrealba B.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.5% / 54.5%
Market 1 / 2: 45.3% / 54.7%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Doubles: M15 Trelew (Argentina), Hard (indoor)
Jara Lozano N. A./Lassaga E.
Aguilar Cardozo F./Del Pino M.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 21.5% / 78.5%
Market 1 / 2: 20.8% / 79.3%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Trelew (Argentina), Hard (indoor)
Bernardes B. (Bra)
Laron M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 9.7% / 90.3%
Market 1 / 2: 8.5% / 91.5%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Singles: W15 Porto Velho (Brazil), Hard
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.