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.
Ichikawa T
Vu Tun-Lin (Tvn)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.0% / 39.0%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Taipei (Taiwan), hard
Samrej K
Chung Hyeon
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.6% / 39.4%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Taipei (Taiwan), hard
Tamm K. (Est)
Jasika O
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.5% / 61.5%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Taipei (Taiwan), hard
Hsu J. (Tvn)
Dellavedova M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.9% / 62.1%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M25 Taipei (Taiwan), hard
Moriya H
Ogura K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 41.2% / 58.8%
Market 1 / 2: 67.0% / 33.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M25 Taipei (Taiwan), hard
Sun Yingqun
Yang Ya Yi
Matsuda Ryuki
Lan X. (Kit)
Choi O. (Kor)
Chanta A. (Tai)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.9% / 56.1%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 3 (China), hard
Li Y. (Kit)
Chon S. (Kor)
Wu R. X. (Kit)
Uchijima M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.5% / 36.5%
Market 1 / 2: 65.1% / 34.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 3 (China), hard
Yoshimoto N
Park E. (Kor)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.3% / 40.7%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 6%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 3 (China), hard
Becroft I
Jiang F. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.9% / 19.1%
Market 1 / 2: 82.6% / 17.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Isomura K
Jones M. (Ssha)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.6% / 54.4%
Market 1 / 2: 45.8% / 54.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Park Uisung
Zhao Z. (Kit)
Lu H. (Kit)
Oh Chan-Yeong
All stats and model conclusions
Model vs market
Glicko 1 / 2: 21.2% / 78.8%
Market 1 / 2: 16.8% / 83.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Van Herzeele J
Pan W. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.8% / 25.2%
Market 1 / 2: 77.6% / 22.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Charlton J
Takahashi Yusuke
Kim Dong Ju
Kang Ku K. (Kor)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 77.6% / 22.4%
Market 1 / 2: 79.2% / 20.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Chzhou Siao (Kit)
Alhogbani A. F. (Sau)
Kim Geun Jun
Fitriadi M R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.5% / 67.5%
Market 1 / 2: 31.8% / 68.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 7 (China), hard
Kong Weiyi
Trongcharoenchaikul W
Rocha H
Hong Seong Chan
All stats and model conclusions
Model vs market
Glicko 1 / 2: 72.1% / 27.9%
Market 1 / 2: 74.2% / 25.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger Cancun, Mexico Men Singles
Zanolini C
Bole S. (Fra)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.9% / 53.1%
Market 1 / 2: 53.5% / 46.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W35 Bistrita (Romania), clay
Amarlei I. (Rum)
Ewald W
All stats and model conclusions
Model vs market
Glicko 1 / 2: 64.4% / 35.6%
Market 1 / 2: 42.1% / 57.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W35 Bistrita (Romania), clay
Szabo B
Simionescu D-I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.0% / 51.0%
Market 1 / 2: 50.0% / 50.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W35 Bistrita (Romania), clay
Popa M. S. (Rum)
Bojica S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.6% / 50.4%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Women - Singles: W35 Bistrita (Romania), clay
Cherie Ligniere E. (Ita)
Andreescu S A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.5% / 71.5%
Market 1 / 2: 27.2% / 72.8%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Arad (Romania), clay
Enache S. (Rum)
Garbero F. F. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.9% / 36.1%
Market 1 / 2: 9.6% / 90.4%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M15 Arad (Romania), clay
Paolini A
Mazdrashki A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.6% / 70.4%
Market 1 / 2: 26.8% / 73.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Arad (Romania), clay
Lan X./Zeng Z.
Kim D./Kim N.
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.