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.
Soderqvist, Filip
Wassermann, Miko
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.6% / 23.4%
Market 1 / 2: 79.2% / 20.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR PTT Hamburg Men 01, 9-12 Playoff
Hennemann C W
Burillo I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.3% / 46.7%
Market 1 / 2: 54.4% / 45.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W75 Leipzig (Germany), clay
Chazal M
Fix D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.7% / 26.3%
Market 1 / 2: 75.8% / 24.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M15 Frankfurt (Germany), clay
Dullinger V
Penzlin J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.4% / 43.6%
Market 1 / 2: 57.4% / 42.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Frankfurt (Germany), clay
Kheytmanek A-Yu / Urbanova L
Blomkvist K / Kheynonen I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.4% / 23.6%
Market 1 / 2: 79.1% / 20.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. Finland
卡羅爾楊瑟·李
加布里埃拉, 安德里亚·克努森
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.3% / 36.7%
Market 1 / 2: 64.3% / 35.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International Warsaw, Poland
安托万,吉博多
西马金, 伊利亚
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.6% / 57.4%
Market 1 / 2: 41.1% / 58.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Istanbul 2, Turkiye
凯姆,杰罗姆
莫勒,埃尔默
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.5% / 49.5%
Market 1 / 2: 63.0% / 37.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International Hagen, Germany
根茨施, 汤姆
皮罗什,蓉博尔
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.9% / 53.1%
Market 1 / 2: 36.3% / 63.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International Hagen, Germany
Torcq B
Papamalamis T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 16.4% / 83.6%
Market 1 / 2: 12.1% / 87.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Men. Belgium
Dzhinen V / Vassermann D
Forger A / Kublal E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.6% / 67.4%
Market 1 / 2: 30.9% / 69.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Belgium
Black B / Okutoyi A
Appleton E / McDonald E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.2% / 43.8%
Market 1 / 2: 44.0% / 56.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International World Tennis. Women. Great Britain. Doubles
Palicova B
Bertea E R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.4% / 38.6%
Market 1 / 2: 62.4% / 37.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W75 Koksijde (Belgium), clay
Dolehide C
Gorgodze E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.2% / 24.8%
Market 1 / 2: 77.5% / 22.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Women. Landisvill
Cho I-Syuan / Cho I-Tsen
Rodzhers A / Dzamarripa A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.7% / 43.3%
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. Women. Landisvill
Martins I / Ovcharenko E
Osborne A / Wong H Y C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 51.0% / 49.0%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Women. Landisvill
Akli A
Zakharova A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 22.2% / 77.8%
Market 1 / 2: 19.1% / 80.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Women. Landisvill
劳雷罗,乔奥 维克托 库托 / 里贝罗 ,爱德华多
Radovanovic T / Rolland de Ravel C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.2% / 38.8%
Market 1 / 2: 61.5% / 38.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International Plovdiv 2, Bulgaria, Doubles
Dyullinger V
Pentslin Yu
All stats and model conclusions
Model vs market
Glicko 1 / 2: 57.2% / 42.8%
Market 1 / 2: 58.3% / 41.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Germany
阿里巴奇 T / 奥利维蒂 A
施纳特 J / 瓦尔纳 M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 55.9% / 44.1%
Market 1 / 2: 57.3% / 42.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Montreal, Canada, Doubles
帕夫拉塞克 A / 里克尔 P
杜姆比亚 S / 雷布尔 F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.4% / 55.6%
Market 1 / 2: 44.4% / 55.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Montreal, Canada, Doubles
Kucmova A / Laboutkova A
McAdoo R / Sakellaridi S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.1% / 49.9%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W75 Leipzig (Germany), clay
波伦 ,卢卡斯
布劳尔,吉杰
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.4% / 53.6%
Market 1 / 2: 67.0% / 33.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International Istanbul 2, Turkiye
Cervantes I / Molchanov D
Lalami Laaroussi Y / Pieczonka F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.6% / 40.4%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: Grodzisk Mazowiecki, Poland, Doubles
Sanchez A S
Bosio V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.8% / 62.2%
Market 1 / 2: 36.3% / 63.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Argentina. Doubles
Pedone G
Perez Alarcon L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.1% / 57.9%
Market 1 / 2: 41.3% / 58.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Women. Argentina. 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.