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.
Lin Fang An
Kawagishi N
Okh Chiiun
Jang Soo Ha
Chzheniui Sun
Minakata R
Dzhons Aleks
Sia Iue
Ichikawa T
Roddick J
Chanta T. (Tai)
Masabayashi T. (Iapo)
O'Connell B. (Avs)
Suresh K
Wang JiaYi
Bai Zhuoxuan
Simakin I
Yevseyev D
Tomic B
Imamura M
Hsu Yu Hsiou
Ymer E
Osaka N
Zakharova A
Castelnuovo L
Dellavedova M
Okh Chonkha
Jang Gio
Vikeri L
Lin Hao-Yu
Chang Alexander
Borg L
Patton T
Rathi A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 41.7% / 58.3%
Market 1 / 2: 41.0% / 59.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Bali 3 (Indonesia), Hard
Ryan Ziegann S
Ahren Moonga E. (Shve)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.1% / 25.9%
Market 1 / 2: 74.6% / 25.4%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Bali 3 (Indonesia), Hard
Mehrotra A
Dembo J. (Avs)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.9% / 40.1%
Market 1 / 2: 59.7% / 40.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Bali 3 (Indonesia), Hard
Ellis B
Falck R. (Nzl)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 77.1% / 22.9%
Market 1 / 2: 77.7% / 22.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Bali 3 (Indonesia), Hard
Persell M./Van Tszen
Koyama H./Tomida Y.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.7% / 26.3%
Market 1 / 2: 74.4% / 25.6%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Chen X. D./Li V.
Choe J./Kim G. J.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 13.9% / 86.1%
Market 1 / 2: 12.7% / 87.3%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Chen X./Trongcharoenchaikul V.
Lai C. K./Shin M.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.7% / 61.3%
Market 1 / 2: 38.2% / 61.8%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Jones Alex / Jones Miles
Pan W./Xiao L.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.7% / 19.3%
Market 1 / 2: 81.3% / 18.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Talic Z./Vickery L.
Liu Siu/Yang X.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.7% / 59.3%
Market 1 / 2: 40.7% / 59.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Jin Yuquan / Sun Qian
Lu C./Yang Z.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 70.9% / 29.1%
Market 1 / 2: 71.3% / 28.7%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Doubles: M15 Wuning 7 (China), Hard
Tseng C H
Zhang T. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.5% / 23.5%
Market 1 / 2: 77.6% / 22.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China), Hard
Aguiard E
Matsuoka H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.4% / 65.6%
Market 1 / 2: 33.7% / 66.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China), Hard
Jasika O
Santillan A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.7% / 41.3%
Market 1 / 2: 58.5% / 41.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China), Hard
Kusuhara Y./Nakagawa S.
Inui Y./Ogura K.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 71.0% / 29.0%
Market 1 / 2: 71.3% / 28.7%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M25 Urayasu (Japan), Hard
Vans Dzh./Vans Dzh.
Saito K./Tanuma R.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.4% / 54.6%
Market 1 / 2: 57.9% / 42.1%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Doubles: M25 Urayasu (Japan), Hard
Van Herzeele J
Sureshkumar M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.4% / 57.6%
Market 1 / 2: 41.8% / 58.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Nonthaburi (Thailand), Hard
Dong Ch./Hasson J.
Blando M./Hashimoto H.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.1% / 52.9%
Market 1 / 2: 38.5% / 61.5%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Doubles: M15 Nonthaburi (Thailand), Hard
Arcon A./Bertran P.
Sharoenfon Iu./Srisen T.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 78.5% / 21.5%
Market 1 / 2: 79.3% / 20.7%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Nonthaburi (Thailand), Hard
Masabayashi T./Sumizava D.
Congcar C./Koaykul K.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.0% / 26.0%
Market 1 / 2: 74.6% / 25.4%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Nonthaburi (Thailand), Hard
Bar Biryukov P
Leong M W K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.7% / 43.3%
Market 1 / 2: 56.9% / 43.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International Chzhantszyagan
Matsuda K
Delaney Jake
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.8% / 54.2%
Market 1 / 2: 45.4% / 54.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International Chzhantszyagan
Samrej K
Moriya H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.9% / 52.1%
Market 1 / 2: 47.4% / 52.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International Chzhantszyagan
Ivashka I
Zhou Yi
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.3% / 41.7%
Market 1 / 2: 55.4% / 44.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International Chzhantszyagan
Sultanov K
Fomin S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 57.5% / 42.5%
Market 1 / 2: 57.8% / 42.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International Chzhantszyagan
Li Ch./Shi H.
Lan X./Sun Y.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.9% / 17.1%
Market 1 / 2: 83.8% / 16.2%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W75 Tianjin (China), Hard
Kobori M / Shimizu A
Li E./Sato Kh.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.8% / 24.2%
Market 1 / 2: 76.3% / 23.8%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W75 Tianjin (China), Hard
Hou Y./Van M.
Plipuek P./Simidzu E.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.8% / 67.2%
Market 1 / 2: 32.2% / 67.9%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Doubles: W75 Tianjin (China), Hard
Lin N. H./Wang K.
Nakamura R./Susanto A.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.3% / 38.7%
Market 1 / 2: 61.5% / 38.5%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Bali 3 (Indonesia), Hard
Siniakov D
Kravchenko G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.1% / 46.9%
Market 1 / 2: 53.0% / 47.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: Challenger Men - Singles: Plovdiv 3 (Bulgaria), 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.