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.
Wei Sijia / Yuan Chengyiyi
Lin Fang An / Yang Ya Yi
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: 57.1% / 42.9%
Market 1 / 2: 56.8% / 43.2%
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
Zanolini C
Ewald W
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.5% / 64.5%
Market 1 / 2: 33.7% / 66.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · 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: 66.4% / 33.6%
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: 33.4% / 66.6%
Market 1 / 2: 32.5% / 67.5%
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: 62.9% / 37.1%
Market 1 / 2: 64.1% / 35.9%
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: 42.1% / 57.9%
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: 70.8% / 29.2%
Market 1 / 2: 72.3% / 27.7%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Italy. Doubles
Henning P
Fernandes R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 83.3% / 16.7%
Market 1 / 2: 85.5% / 14.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M25 Idanha-a-Nova 2 (Portugal), Hard
Wagner A
Mashtakov N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.4% / 71.6%
Market 1 / 2: 26.7% / 73.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Austria
Wallechner D
Noce G M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.6% / 70.4%
Market 1 / 2: 26.8% / 73.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Men. Austria
Barroso Campos A
Colombo Andrea
All stats and model conclusions
Model vs market
Glicko 1 / 2: 55.2% / 44.8%
Market 1 / 2: 56.0% / 44.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Portugal
Kovackova A
Kovackova J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.5% / 49.5%
Market 1 / 2: 49.2% / 50.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Prague. Qualification
Strakhova V / Tikhonova A
Gavrila O / Sebestova I
Rechek D / Siniakov D
Grevelius E./Heinonen A.
Loge J
Paardekooper S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 70.8% / 29.2%
Market 1 / 2: 72.4% / 27.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Germany. Doubles
Cembranos P
DeFalco J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.0% / 60.0%
Market 1 / 2: 38.7% / 61.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Spain. 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.