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.
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: 43.1% / 56.9%
Market 1 / 2: 42.7% / 57.3%
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: 73.4% / 26.6%
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
Gonsales S./Kho R.
Kiger M / Seggerman R
Alvarez Valdes L C / Magadan A
Heredia S / Sharan D
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: 46.8% / 53.2%
Market 1 / 2: 46.2% / 53.8%
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: 57.3% / 42.7%
Market 1 / 2: 57.0% / 43.0%
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
Schlossmann F. (Ger)
Gundacker J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.1% / 55.9%
Market 1 / 2: 42.3% / 57.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Slovenj Gradec (Slovenia), clay
Popa M. S. (Rum)
Nagy A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.3% / 46.7%
Market 1 / 2: 66.3% / 33.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: World Tennis. Women. Romania
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: 64.8% / 35.2%
Market 1 / 2: 66.5% / 33.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · 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: 50.1% / 49.9%
Market 1 / 2: 49.1% / 50.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden. Doubles
Edengren K
Guttau N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.5% / 65.5%
Market 1 / 2: 33.8% / 66.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden. Doubles
Simonsson J
Mridha J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 22.2% / 77.8%
Market 1 / 2: 18.3% / 81.7%
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: 61.9% / 38.1%
Market 1 / 2: 63.1% / 37.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Belgium
Shlossmann F
Gundacker J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.5% / 56.5%
Market 1 / 2: 42.2% / 57.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Slovenia. Doubles
Comino L
Basile P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 25.5% / 74.5%
Market 1 / 2: 22.2% / 77.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: World Tennis. Men. Italy
Martin Manzano J C
Oradini G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 57.7% / 42.3%
Market 1 / 2: 56.0% / 44.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: World Tennis. Men. Italy
Hipfl N
Reitano S. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 69.3% / 30.7%
Market 1 / 2: 70.6% / 29.4%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Italy. Doubles
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.