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.
Mochizuki, Kotaro
Higashi, Riku
Kawanak, Haru
Shen Dzh-K
Macababbad, Diordan
Inagawa, Ryosuke
Imai S
Mitsui S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.0% / 58.0%
Market 1 / 2: 41.8% / 58.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Kazakhstan
Pleshivtsev E
Paparkar A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.1% / 43.9%
Market 1 / 2: 57.5% / 42.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Kazakhstan
Sornlaksup P
Sasikumar M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.1% / 56.9%
Market 1 / 2: 41.3% / 58.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Kazakhstan
Zhalgasbay D
Kesharvani M
Chkhun Yunson / Khan Son-En
Lay Chin-Kuan / Sarksyan D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.5% / 49.5%
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
Chun Yu./Han Seon Yong
Lai C. K./Sarksian D.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.7% / 50.3%
Market 1 / 2: 49.3% / 50.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Doubles: M15 Tianjin (China), hard
Lin Yuy-Tszyun / Chzhan Zhuyen
Chen Men-I / Van Yuykhan
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.4% / 45.6%
Market 1 / 2: 54.4% / 45.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. China
Lin Y. J./Zhang R.
Chen M./Van Ya.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.6% / 50.4%
Market 1 / 2: 54.4% / 45.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Doubles: W15 Tianjin (China), hard
Milovanova V
Yoshioka K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 76.6% / 23.4%
Market 1 / 2: 78.0% / 22.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International World Tennis. Women. Kazakhstan
Arifullina A
Chan Su-Chon
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.5% / 71.5%
Market 1 / 2: 26.0% / 74.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International World Tennis. Women. Kazakhstan
Arifullina A. (World)
Jang Su Jeong
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.8% / 53.2%
Market 1 / 2: 26.7% / 73.3%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: World Tennis. Women. Kazakhstan
Arystanbekova A
Pushkareva A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 25.0% / 75.0%
Market 1 / 2: 23.1% / 76.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: World Tennis. Women. Kazakhstan
Mamedova E
Kurt I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.0% / 20.0%
Market 1 / 2: 82.6% / 17.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: World Tennis. Women. Kazakhstan
Rovai, Samuel
McDonnell, Cian
All stats and model conclusions
Model vs market
Glicko 1 / 2: 63.5% / 36.5%
Market 1 / 2: 65.9% / 34.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: International UTR PTT Hamburg Men 01, 9-12 Playoff
Trklja, Armin
Wiger Nordas, Baltazar
Kalina, Vit
Coats, Theo
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.9% / 39.1%
Market 1 / 2: 61.5% / 38.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR PTT Hamburg Men 01, 9-12 Playoff
Novakova, Erika
Nizetic, Leandra
All stats and model conclusions
Model vs market
Glicko 1 / 2: 52.7% / 47.3%
Market 1 / 2: 53.2% / 46.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR PTT Hamburg Women 01, 17-20 Playoff
เฟเทคูว, อิรินา
Vasilesku A-G
วอจซินาโควา, อินกริด
ราเด็นโควิค, มายา
Dorofeeva-Rybas F
Paganetti V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.5% / 46.5%
Market 1 / 2: 53.8% / 46.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Women. Serbia
Herazo M
Glushkova D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.5% / 80.5%
Market 1 / 2: 17.1% / 82.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. Serbia
Pisarich A / Radzhenovich V
Khattsiavraam N / Kunturakis I
Misasi G
Ghetu G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.4% / 76.6%
Market 1 / 2: 19.6% / 80.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Romania
Pieleanu R T
Andreescu S A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.2% / 66.8%
Market 1 / 2: 31.9% / 68.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Romania
Hejtmanek A J
Pohle V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 48.8% / 51.2%
Market 1 / 2: 47.8% / 52.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Finland
Pisaric A./Radjenovic V.
Chatziavraam N./Kontorakis I.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.4% / 49.6%
Market 1 / 2: 70.1% / 30.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Doubles: M25 Kursumlijska Banja 2 (Serbia), 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.