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.
Kompuesto B
Dodaj C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.8% / 54.2%
Market 1 / 2: 44.4% / 55.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Women. Australia
Gale L
McPhee K
Khan L
Santitto J
Cadwallader G
Yu Ramey
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.9% / 46.1%
Market 1 / 2: 53.3% / 46.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR Pro. Women. Australia
Hoole C
Ishikawa H
Andrews O
Girdler J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 43.0% / 57.0%
Market 1 / 2: 42.0% / 58.1%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR Pro. Men. Australia
Hazawa S / Mitsui S
Lomakin G / Tamm K
Ogura K / Saitoh K
Ichikawa T / Imamura M
Chung Yunseong / Kim Dong Ju
Fitriadi M R / Sarksian D
Guna R
Miletich I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 66.2% / 33.8%
Market 1 / 2: 67.4% / 32.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Romania. Doubles
Brown P / Miletich I
Andreescu S A / Schinteie G C
Seghetti S. (Ita)
Rottoli L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 30.9% / 69.1%
Market 1 / 2: 28.2% / 71.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: World Tennis. Men. Italy
Comino L
Basile P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 25.5% / 74.5%
Market 1 / 2: 22.2% / 77.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: World Tennis. Men. Italy
Rapagnetta D
Mazza M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.1% / 52.9%
Market 1 / 2: 45.7% / 54.3%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Italy. Doubles
Senn N
Bieldiugin T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.3% / 61.7%
Market 1 / 2: 37.3% / 62.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Men. Germany. Doubles
Petkovic M
Handel T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.0% / 35.0%
Market 1 / 2: 66.0% / 34.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Germany. Doubles
Zolotareva R
Zaar L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.5% / 41.5%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Women - Singles: W75 Kursumlijska Banja 3 (Serbia), Clay
Pieri J
Nagy A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.9% / 53.1%
Market 1 / 2: 45.7% / 54.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International World Tennis. Women. Romania. Doubles
Arzhankin A
Juhas K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.1% / 49.9%
Market 1 / 2: 48.8% / 51.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden
Mashtakov N
Zielinski M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 52.2% / 47.8%
Market 1 / 2: 53.4% / 46.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Mistelbach (Austria), Clay
Borg L
Hallquist Lithen J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.7% / 55.3%
Market 1 / 2: 44.2% / 55.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden. Doubles
Simonsson J
Guttau N
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.7% / 80.3%
Market 1 / 2: 16.4% / 83.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Sweden. Doubles
Fernandes R
Lagutin P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 27.6% / 72.4%
Market 1 / 2: 25.5% / 74.5%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ITF Men - Singles: M25 Idanha-a-Nova 2 (Portugal), Hard
Sciahbasi M. (Ita)
Madaras D. (Shve)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.9% / 54.1%
Market 1 / 2: - / -
Favourite trap: no
Data completeness: 12%
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: ITF Men - Singles: M15 Arad (Romania), Clay
Gao Xinyu
Kovackova J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.8% / 49.2%
Market 1 / 2: 50.5% / 49.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Women. Prague. Qualification
Giaccio J. (Isp)
Esquiva Banuls C. (Isp)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 22.7% / 77.3%
Market 1 / 2: 19.5% / 80.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Logrono (Spain), Hard
Planinsek F J
Gundacker J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.1% / 17.9%
Market 1 / 2: 84.8% / 15.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International World Tennis. Men. Slovenia. Doubles
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.