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.
Live win probability (market-implied) — how often it wins, not a profit forecast.
Schueller A
Petkovic Anna
Perelygina E
Karatancheva A
Ricci B
Reami A
Ivanovic Gala
Lee Gyeong Seo
Lokoli L. (Fra)
Seghetti S. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.4% / 41.6%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: World Tennis. Men. Italy
Sciahbasi M. (Ita)
Miletich I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.5% / 59.5%
Market 1 / 2: 38.5% / 61.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Arad (Romania), Clay
Sorger S
Gundacker J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.4% / 39.6%
Market 1 / 2: 83.5% / 16.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M25 Slovenj Gradec (Slovenia), Clay
Nagy A
Miron T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.6% / 49.4%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: World Tennis. Women. Romania
Kisimov D
Juhas K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.2% / 62.8%
Market 1 / 2: 35.3% / 64.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden
Behrmann T
Mashtakov N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.0% / 39.0%
Market 1 / 2: 62.5% / 37.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Austria
Zolotareva R
Encheva L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 64.0% / 36.0%
Market 1 / 2: 65.1% / 34.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W75 Kursumlijska Banja 3 (Serbia), Clay
Grzegorzewski O / Sadzik J
Januchowski J / Romer K
Garcia Carbajal A
Ros Mesas M
Sanchez Jover C
Ritschard A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 39.3% / 60.7%
Market 1 / 2: 37.0% / 63.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Men. Spain. Doubles
Borg L
Guttau N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.3% / 43.7%
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. Sweden. Doubles
Fajta P
Gulin S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.7% / 66.3%
Market 1 / 2: 31.6% / 68.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International Augsburg
Strombachs R
Matic D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 88.6% / 11.4%
Market 1 / 2: 91.6% / 8.4%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International Augsburg
Wehnelt K
Siniakov D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.3% / 62.7%
Market 1 / 2: 35.7% / 64.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International Augsburg
Handel T
Nagel A. (Fra)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.5% / 64.5%
Market 1 / 2: 34.3% / 65.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Ueberlingen (Germany), Clay
Lagutin P
Marques G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.3% / 31.7%
Market 1 / 2: 69.6% / 30.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Idanha-a-Nova 2 (Portugal), Hard
Grammatikopoulou V
Lim J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.2% / 71.8%
Market 1 / 2: 25.3% / 74.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. Switzerland. Qualification
Montes-De la Torre I
Ponchet M
Paldanius O
Makk P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.6% / 56.4%
Market 1 / 2: 42.9% / 57.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger. Rokhempton 2. Qualification
Poullain L
Nordquist A
Cacao T
Andaloro F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.1% / 52.9%
Market 1 / 2: 45.7% / 54.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Rokhempton 2. Qualification
Albot R
Gannon C
Ayeni A
Brady Patrick
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.3% / 63.7%
Market 1 / 2: 35.5% / 64.5%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger. Rokhempton 2. Qualification
Petrovic Andreja
Echeverria J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 30.7% / 69.3%
Market 1 / 2: 28.0% / 72.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger. Rokhempton 2. Qualification
Walters M
Hurrion M
Rybakov A
Bonding O
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.0% / 40.0%
Market 1 / 2: 59.6% / 40.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International Rokhempton 2
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.


