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.
Marozsan F
Duckworth J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.4% / 40.6%
Market 1 / 2: 59.8% / 40.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Uinston-Seylem
Tauson C
Parry D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.8% / 38.2%
Market 1 / 2: 62.0% / 38.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International WTA. Monterrey
Starodubtseva Y
Mertens E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.3% / 66.7%
Market 1 / 2: 32.7% / 67.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: International WTA. Monterrey
Tamm K. (Est)
Dellavedova M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 18.5% / 81.5%
Market 1 / 2: 17.8% / 82.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Men - Singles: M25 Taipei 2 (Taiwan), Hard
Chung Hyeon
Chen K. S. (Tvn)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.6% / 19.4%
Market 1 / 2: 81.6% / 18.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Taipei 2 (Taiwan), Hard
Samrej K
Chen Yan Cheng
All stats and model conclusions
Model vs market
Glicko 1 / 2: 84.1% / 15.9%
Market 1 / 2: 85.3% / 14.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Taipei 2 (Taiwan), Hard
Erel Y
Castelnuovo L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.9% / 25.1%
Market 1 / 2: 75.3% / 24.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Men - Singles: M25 Taipei 2 (Taiwan), Hard
Chon Khen
Chen Kuan-Shou
All stats and model conclusions
Model vs market
Glicko 1 / 2: 83.6% / 16.4%
Market 1 / 2: 84.8% / 15.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Taiwan
Aguiard E
Takahashi Yusuke
All stats and model conclusions
Model vs market
Glicko 1 / 2: 55.8% / 44.2%
Market 1 / 2: 56.0% / 44.0%
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)
Zhang T. (Kit)
Chzhao Lun-I (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 51.7% / 48.3%
Market 1 / 2: 51.9% / 48.1%
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)
Ferguson C
Jiang F. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.4% / 23.6%
Market 1 / 2: 76.9% / 23.1%
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)
Nugroho P. M. (Inz)
Chzhen U. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 69.9% / 30.1%
Market 1 / 2: 70.4% / 29.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 4 (China), Hard
Khan Zh. (Kit)
Lee Eunhye
All stats and model conclusions
Model vs market
Glicko 1 / 2: 22.5% / 77.5%
Market 1 / 2: 21.9% / 78.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 4 (China), Hard
Charlton J
Te Zhigele
All stats and model conclusions
Model vs market
Glicko 1 / 2: 41.0% / 59.0%
Market 1 / 2: 40.9% / 59.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. China. Doubles
Ferguson C
Jiang Fumin
All stats and model conclusions
Model vs market
Glicko 1 / 2: 78.5% / 21.5%
Market 1 / 2: 79.1% / 20.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. China. Doubles
Zhang Tianhui
Chzhao Linsi
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.7% / 49.3%
Market 1 / 2: 50.9% / 49.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. China. Doubles
Dzhons Aleks / Dzhons Mailz
Bar-Biriukov P./Fomin S.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.9% / 50.1%
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. Men. China. Doubles
Tszin Iuitsiuan / Sun Tsian
Inui Y / Sumizawa D
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 World Tennis. Men. China. Doubles
Jones Alex / Jones Miles
Bar-Biriukov P./Fomin S.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.2% / 50.8%
Market 1 / 2: 51.4% / 48.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Doubles: M15 Maanshan 8 (China), Hard (indoor)
Jin Yuquan / Sun Qian
Inui Y./Sumizava D.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.8% / 53.2%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Doubles: M15 Maanshan 8 (China), Hard (indoor)
Khanatani N./Kitahara Y.
Im H./Kim Eunchae
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.4% / 76.6%
Market 1 / 2: 22.6% / 77.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W15 Tianjin 4 (China), Hard
Plipuek P./Chzhen U.
Chipchandedzh P./Yodpetch K.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.0% / 32.0%
Market 1 / 2: 68.5% / 31.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Doubles: W15 Tianjin 4 (China), Hard
Bessonov D
Bakshi A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.8% / 67.2%
Market 1 / 2: 31.8% / 68.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Egypt. Qualification
Javia D
Krajci M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.5% / 67.5%
Market 1 / 2: 31.6% / 68.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Egypt. Qualification
Funk A
Lorusso L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.8% / 36.2%
Market 1 / 2: 64.1% / 35.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Egypt. Qualification
McKenzie N
Brune E-K
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. Egypt
Diatlova K
Ezzat Y
All stats and model conclusions
Model vs market
Glicko 1 / 2: 39.3% / 60.7%
Market 1 / 2: 39.0% / 61.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Egypt
Vaissaud D
Bosman C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 72.5% / 27.5%
Market 1 / 2: 73.0% / 27.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. Egypt
Ianin N
Smiej Y. (Mar)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.7% / 38.3%
Market 1 / 2: 56.9% / 43.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M15 Hurghada 6 (Egypt), Hard
Tkacheva M
Kujovic K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 83.3% / 16.7%
Market 1 / 2: 84.5% / 15.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Hurghada 6 (Egypt), 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.





