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.
Te Zhigele
Ferguson C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.8% / 39.2%
Market 1 / 2: 61.0% / 39.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. China. Doubles
Zhang Tianhui
Aguiard E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.3% / 64.7%
Market 1 / 2: 34.9% / 65.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. China. Doubles
Zhang T. (Kit)
Aguiard E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.6% / 64.4%
Market 1 / 2: 34.9% / 65.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 8 (China), Hard (indoor)
Chipchandedzh P./Yodpetch K.
Im H./Kim Eunchae
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 2 · total 0
Tournament: ITF Women - Doubles: W15 Tianjin 4 (China), Hard
Bakshi A
Funk A. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.3% / 50.7%
Market 1 / 2: 49.2% / 50.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M15 Hurghada 6 (Egypt), Hard
Javia D
Smiej Y. (Mar)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.8% / 26.2%
Market 1 / 2: 74.1% / 25.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Hurghada 6 (Egypt), Hard
Vaissaud D
Tkacheva M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 25.5% / 74.5%
Market 1 / 2: 24.7% / 75.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Hurghada 6 (Egypt), Hard
Diatlova K. (Ukr)
Brune E. C. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 64.1% / 35.9%
Market 1 / 2: 64.4% / 35.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W15 Hurghada 6 (Egypt), Hard
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: 66.7% / 33.3%
Market 1 / 2: 66.9% / 33.1%
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: 46.5% / 53.5%
Market 1 / 2: 46.2% / 53.8%
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: 62.2% / 37.8%
Market 1 / 2: 62.5% / 37.5%
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: 67.7% / 32.3%
Market 1 / 2: 68.1% / 31.9%
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: 73.7% / 26.3%
Market 1 / 2: 74.4% / 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
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: 47.1% / 52.9%
Market 1 / 2: 46.9% / 53.2%
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: 60.3% / 39.7%
Market 1 / 2: 60.3% / 39.7%
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: 61.0% / 39.0%
Market 1 / 2: 61.3% / 38.7%
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: 28.8% / 71.2%
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
Krechun Dzh. (Rum)
Amarlei I. (Rum)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.4% / 65.6%
Market 1 / 2: 34.2% / 65.8%
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: 42.5% / 57.5%
Market 1 / 2: 42.0% / 58.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · 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: 36.0% / 64.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W15 Torello (Spain), Hard
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.

