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.
Cerundolo J M
Auger-Aliassime F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.3% / 76.7%
Market 1 / 2: 20.9% / 79.1%
Favourite trap: no
Data completeness: 72%
Match context
Court:Lindner Femili Tennis Senter, Grandstend
Bookmaker coverage: 1×2 5 · total 0
Tournament: ATP Cincinnati, USA Men Singles
Sabalenka A
Wang Xinyu
All stats and model conclusions
Model vs market
Glicko 1 / 2: 85.6% / 14.4%
Market 1 / 2: 88.2% / 11.8%
Favourite trap: no
Data completeness: 72%
Match context
Court:Lindner Femili Tennis Senter. Stadium
Bookmaker coverage: 1×2 5 · total 0
Tournament: International WTA Cincinnati, USA Women Singles
Svrcina D
Wawrinka S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.5% / 38.5%
Market 1 / 2: 62.0% / 38.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Harris L
Djere L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.8% / 39.2%
Market 1 / 2: 61.6% / 38.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Cancun 2 (Mexico) - Qualifying, hard
Falck R / Suarez B
Martinez P / Midon L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 52.4% / 47.6%
Market 1 / 2: 42.1% / 57.9%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International ATP Challenger. Kingston. Doubles
Rocha H
Khon S.-Ch. (Kor)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 72.8% / 27.2%
Market 1 / 2: 74.2% / 25.8%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Llamas Ruiz P
Comesana F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.5% / 50.5%
Market 1 / 2: 49.5% / 50.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ATP Challenger Cancun, Mexico Men Singles
Bonzi B
Boulais J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.3% / 24.7%
Market 1 / 2: 76.7% / 23.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger Quebec City, Canada Men Singles
Tien L
Tiafoe F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.9% / 46.1%
Market 1 / 2: 53.4% / 46.6%
Favourite trap: no
Data completeness: 72%
Match context
Court:Lindner Femili Tennis Senter, Stadium
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Cincinnati, USA Men Singles
Bouzkova M
Jovic I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 39.2% / 60.8%
Market 1 / 2: 37.7% / 62.3%
Favourite trap: no
Data completeness: 72%
Match context
Court:Lindner Femili Tennis Senter, Grandstend
Bookmaker coverage: 1×2 5 · total 0
Tournament: International WTA Cincinnati, USA Women Singles
Tien L
Tiafo F. (Ssha)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.0% / 46.0%
Market 1 / 2: 53.4% / 46.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ATP Cincinnati, USA Men Singles
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
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)
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
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)
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)
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
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.