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.
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
Kovackova A
Kovackova J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.6% / 49.4%
Market 1 / 2: 49.3% / 50.7%
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: 70.9% / 29.1%
Market 1 / 2: 72.5% / 27.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Germany. Doubles
Vankan M
Majdandzic M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 66.2% / 33.8%
Market 1 / 2: 67.0% / 33.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Men. Germany. Doubles
Cembranos P
DeFalco J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.1% / 59.9%
Market 1 / 2: 38.8% / 61.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Spain. Doubles
Giaccio J. (Isp)
Ishii S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.3% / 53.7%
Market 1 / 2: 21.2% / 78.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W15 Logrono (Spain), Hard
Vujovic L
De Stefano S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.0% / 37.0%
Market 1 / 2: 58.2% / 41.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W75 Kursumlijska Banja 3 (Serbia), Clay
Pankin S
Haita S H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 48.0% / 52.0%
Market 1 / 2: 47.4% / 52.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Sweden
Batin T
Sciahbasi M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.5% / 53.5%
Market 1 / 2: 46.1% / 53.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Romania. Doubles
Simionescu D-I
Miron T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.1% / 41.9%
Market 1 / 2: 58.1% / 41.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Romania. Doubles
Michalski D
Zeuch T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 83.6% / 16.4%
Market 1 / 2: 87.1% / 12.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Poland
Yunis F
Behrmann T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.8% / 67.2%
Market 1 / 2: 30.2% / 69.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Austria
Derepasko T
Kisimov D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.1% / 46.9%
Market 1 / 2: 53.8% / 46.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Kursumlijska Banja 12 (Serbia), Clay
Blando Dzh / Vasser O
Petrovic Andreja / Slavic N
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
Seghetti S. (Ita)
Rottoli L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 30.9% / 69.1%
Market 1 / 2: 28.2% / 71.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · 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
Sanchez Jover C
Lopes Morilo I. (Isp)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.7% / 31.3%
Market 1 / 2: 70.1% / 29.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M25 Santander (Spain), Clay
Fita Juan S
Juan Mano A. (Isp)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.1% / 62.9%
Market 1 / 2: 35.5% / 64.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Santander (Spain), Clay
Lopez Montagud C
Perez Contri S
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 5 · total 0
Tournament: ITF Men - Singles: M25 Santander (Spain), Clay
Rahmani K. (Irn)
Weis A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 39.6% / 60.4%
Market 1 / 2: 39.5% / 60.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Italy. Doubles
Hipfl N
Reitano S. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 70.9% / 29.1%
Market 1 / 2: 72.4% / 27.6%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Italy. Doubles
Lagutin P
Scaglia M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 83.7% / 16.3%
Market 1 / 2: 85.8% / 14.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Men. Portugal
Park Sohyun
Giza L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.9% / 40.1%
Market 1 / 2: 60.6% / 39.4%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Germany. Doubles
McCormick T
Marques G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.7% / 45.3%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Idanha-a-Nova 2 (Portugal), Hard
Senn N. (Shva)
Bieldiugin T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.7% / 54.3%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Ueberlingen (Germany), Clay
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.