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.
Breazu V. C. (Rum)
Turcanu R D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.5% / 64.5%
Market 1 / 2: 35.0% / 65.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M15 Bucharest 3 (Romania), Clay
Ghetu G
Mazdrashki A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.7% / 31.3%
Market 1 / 2: 69.0% / 31.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Bucharest 3 (Romania), Clay
Pieri S
Sorger S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.5% / 66.5%
Market 1 / 2: 33.2% / 66.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M25 Maribor (Slovenia), Clay
Planinsek F J
Dominko S. (Sln)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.1% / 53.9%
Market 1 / 2: 45.8% / 54.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M25 Maribor (Slovenia), Clay
Hallquist Lithen J
Monzon I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.8% / 39.2%
Market 1 / 2: 61.1% / 38.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Pecs (Hungary), Clay
Sperle J
Baum S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 66.7% / 33.3%
Market 1 / 2: 67.0% / 33.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Pecs (Hungary), Clay
De Stefano S
Golovina M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.6% / 26.4%
Market 1 / 2: 74.3% / 25.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W50 Kursumlijska Banja (Serbia), Clay
Dencheva R
Stankovich A. (Ser)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.8% / 17.2%
Market 1 / 2: 83.5% / 16.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W50 Kursumlijska Banja (Serbia), Clay
Chazal M
De Schepper K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 71.2% / 28.8%
Market 1 / 2: 71.6% / 28.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Men - Singles: M15 Cap d'Agde (France), Clay
Lanik T
Gschwendtner J./Tenti F.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.2% / 49.8%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M15 Pecs (Hungary), Clay
Paardekooper S./Van Sambeek F.
Al-Amin K./Vessels L.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.0% / 50.0%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M25 Oldenzaal (Netherlands), Clay
Chepelev A
Carboni L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.9% / 59.1%
Market 1 / 2: 40.3% / 59.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Lausanne (Switzerland), Clay
Oparnica A./Warik C.
Fumagalli F./Liusso M.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.6% / 65.4%
Market 1 / 2: 33.9% / 66.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Kursumlijska Banja 13 (Serbia), Clay
Garsiia A./Mansilia Dies M.
Aunion P./Palomar X.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 72.4% / 27.6%
Market 1 / 2: 72.7% / 27.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M25 Oviedo (Spain), Clay
Dencheva R./Golovina M.
Karatancheva L./Tkhombare P.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.8% / 53.2%
Market 1 / 2: 46.6% / 53.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W50 Kursumlijska Banja (Serbia), Clay
Shinikova I./Tran L.
Morderger T / Morderger Y
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.6% / 39.4%
Market 1 / 2: 60.6% / 39.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W50 Oldenzaal (Netherlands), Clay
Lene E
Cvetkovic A. (Ser)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.1% / 40.9%
Market 1 / 2: 59.4% / 40.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Ryser V
Okutoii A. (Ken)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 57.7% / 42.3%
Market 1 / 2: 57.7% / 42.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Michalski D
Mashtakov N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 79.0% / 21.0%
Market 1 / 2: 79.9% / 20.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M25 Poznan (Poland), Clay
Hodzic M
Palicova B
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.9% / 71.1%
Market 1 / 2: 28.2% / 71.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Women - Singles: W75 Bytom (Poland), Clay
Zoldakova D
Mikulskyte J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.9% / 37.1%
Market 1 / 2: 63.4% / 36.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W75 Bytom (Poland), Clay
Krechun Dzh. (Rum)
Amarlei I. (Rum)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.7% / 64.3%
Market 1 / 2: 35.5% / 64.5%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Singles: W15 Brasov (Romania), Clay
Rechek D / Siniakov D
La Serna J M / Ribeiro E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.0% / 50.0%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Augsburg. Doubles
Ebeling Koning L
Shunk N. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 39.3% / 60.7%
Market 1 / 2: 38.8% / 61.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Women - Singles: W50 Oldenzaal (Netherlands), Clay
Pace F
Steur J L S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 30.1% / 69.9%
Market 1 / 2: 29.8% / 70.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Women - Singles: W50 Oldenzaal (Netherlands), Clay
Cervino Ruiz C
Garcia Julia
All stats and model conclusions
Model vs market
Glicko 1 / 2: 55.4% / 44.6%
Market 1 / 2: 35.6% / 64.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W15 Torello (Spain), Hard
Ce Gabriela
Lovric P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 51.0% / 49.0%
Market 1 / 2: 51.1% / 48.9%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W35 Trieste (Italy), Clay
Richard A. (Shva)
Perez Contri S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.2% / 25.8%
Market 1 / 2: 74.6% / 25.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Oviedo (Spain), Clay
Ritschard A
Perez Contri S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.6% / 23.4%
Market 1 / 2: 77.2% / 22.8%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Spain. Doubles
Hands T / Summers M
Gray A / Rybakov A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.6% / 43.4%
Market 1 / 2: 56.4% / 43.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International Rokhempton 2. Pary
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.


