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.
Bergs Z
Pellegrino A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.4% / 24.6%
Market 1 / 2: 78.2% / 21.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP Challenger Quebec City, Canada Men Singles
Harris L
Llamas Ruiz P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.7% / 37.3%
Market 1 / 2: 62.9% / 37.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Safiullin R. (Mir)
Wawrinka S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.8% / 36.2%
Market 1 / 2: 64.4% / 35.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Swiatek I
Rybakina E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 57.2% / 42.8%
Market 1 / 2: 57.3% / 42.8%
Favourite trap: no
Data completeness: 78%
Match context
Court:Lindner Femili Tennis Senter. Stadium
Bookmaker coverage: 1×2 5 · total 0
Tournament: International WTA Cincinnati, USA Women Singles
Ugo Carabelli C
Echargui M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 67.5% / 32.5%
Market 1 / 2: 69.4% / 30.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger Cancun, Mexico Men Singles
Baez S
Pacheco Mendez R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.7% / 40.3%
Market 1 / 2: 60.8% / 39.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger Cancun, Mexico Men Singles
Kouame M
Burruchaga R A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.8% / 59.2%
Market 1 / 2: 39.9% / 60.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Fearnley J
Galarneau A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.7% / 34.3%
Market 1 / 2: 66.4% / 33.6%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP Challenger Quebec City, Canada Men Singles
Tirante T A
Fils A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.3% / 71.7%
Market 1 / 2: 25.8% / 74.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: ATP Cincinnati, USA Men Singles
Matsuda R
Yang Ya Yi
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.4% / 55.6%
Market 1 / 2: 43.0% / 57.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. China. Doubles
Chen Meng Yi
Wei Sijia
All stats and model conclusions
Model vs market
Glicko 1 / 2: 16.6% / 83.4%
Market 1 / 2: 12.3% / 87.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. China. Doubles
Desvignes E M
Jeong Sunam
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.1% / 65.9%
Market 1 / 2: 32.3% / 67.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. China. Doubles
Zhu Chenting
Plipuech Peangtarn
All stats and model conclusions
Model vs market
Glicko 1 / 2: 64.7% / 35.3%
Market 1 / 2: 66.1% / 33.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. China. Doubles
Wei Sijia / Yuan Chengyiyi
Lin Fang An / Yang Ya Yi
Becroft I
Fitriadi M R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.4% / 53.6%
Market 1 / 2: 45.0% / 55.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. China. Doubles
Isomura K
Takahashi Yusuke
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.0% / 58.0%
Market 1 / 2: 41.5% / 58.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. China. Doubles
Bar Biryukov P
Zhang Tianhui
All stats and model conclusions
Model vs market
Glicko 1 / 2: 71.3% / 28.7%
Market 1 / 2: 73.3% / 26.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Men. China. Doubles
Kang Ku Keon
Chung Yunseong
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.5% / 80.5%
Market 1 / 2: 16.8% / 83.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. China. Doubles
Cook E / Sach Tai
Alcantara F C / Matsuda K
Tiukaev R
Arzhankin A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 26.2% / 73.8%
Market 1 / 2: 23.1% / 76.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden
Tenti F
Miletich I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.3% / 53.7%
Market 1 / 2: 45.6% / 54.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Romania. Doubles
Guna R
Monzon I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.7% / 37.3%
Market 1 / 2: 63.0% / 37.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Romania. Doubles
Dominko S. (Sln)
La Vela G. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.9% / 36.1%
Market 1 / 2: 65.1% / 34.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M25 Slovenj Gradec (Slovenia), clay
Sorger S
Castagnola L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.3% / 36.7%
Market 1 / 2: 86.6% / 13.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M25 Slovenj Gradec (Slovenia), clay
Planinsek F J
Aboian V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.8% / 37.2%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Slovenj Gradec (Slovenia), clay
Astakhova D
Zolotareva R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.0% / 55.0%
Market 1 / 2: 44.6% / 55.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Singles: W75 Kursumlijska Banja 3 (Serbia), clay
Pieri J
Mettraux M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.6% / 36.4%
Market 1 / 2: 65.1% / 34.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Women. Romania. Doubles
Hallquist Lithen J
Tortora D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.5% / 54.5%
Market 1 / 2: 44.0% / 56.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden. Doubles
De Koning R
Cora-Bruneton C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.1% / 37.9%
Market 1 / 2: 63.2% / 36.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Belgium
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.