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.
Maio E. (Ssha)
Andrade, Andres
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.0% / 55.0%
Market 1 / 2: 45.1% / 54.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Kingston 2 (Jamaica), Hard
Parry D
Mertens E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.7% / 63.3%
Market 1 / 2: 36.0% / 64.0%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International WTA. Monterrey
Chen K. S. (Tvn)
Dellavedova M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.9% / 76.1%
Market 1 / 2: 23.1% / 76.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Taipei 2 (Taiwan), Hard
Zhang T. (Kit)
Hernandez Carles
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.3% / 67.7%
Market 1 / 2: 31.5% / 68.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Maanshan 8 (China), Hard (indoor)
Shi Han
Khan Zh. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 87.0% / 13.0%
Market 1 / 2: 87.7% / 12.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Tianjin 4 (China), Hard
Kong Weiyi
Leong M W K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 13.0% / 87.0%
Market 1 / 2: 11.9% / 88.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Palan D
Nam D. (Kor)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.6% / 55.4%
Market 1 / 2: 44.3% / 55.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Charlton J
Kumasaka T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.3% / 57.7%
Market 1 / 2: 42.1% / 57.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Lu H. (Kit)
Dev S D P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.5% / 70.5%
Market 1 / 2: 28.7% / 71.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Meng Fanming
Huang Tsung-Hao
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.6% / 57.4%
Market 1 / 2: 42.1% / 57.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Moriya H
Lomakin G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 86.1% / 13.9%
Market 1 / 2: 87.3% / 12.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Sultanov K
Jiang F. (Kit)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.8% / 41.2%
Market 1 / 2: 90.7% / 9.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Tang S. (Kit)
Peliwo F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.3% / 70.7%
Market 1 / 2: 28.7% / 71.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Aguiard E
Delaney Jake
All stats and model conclusions
Model vs market
Glicko 1 / 2: 86.4% / 13.6%
Market 1 / 2: 87.6% / 12.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Park Uisung
Isomura K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.6% / 62.4%
Market 1 / 2: 37.5% / 62.5%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Zhangjiagang (China) - Qualification, Hard
Kirov V
Parisca I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 13.0% / 87.0%
Market 1 / 2: 11.6% / 88.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Plovdiv 3 (Bulgaria) - Qualification, Clay
Vales A
Manukyan V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.3% / 26.7%
Market 1 / 2: 74.2% / 25.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Plovdiv 3 (Bulgaria) - Qualification, Clay
Jade D
Zhadun N. (Sen)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.1% / 34.9%
Market 1 / 2: 69.8% / 30.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M15 Monastir 28 (Tunisia), Hard
Pieri S
Dominko S. (Sln)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 48.6% / 51.4%
Market 1 / 2: 48.8% / 51.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M25 Maribor (Slovenia), Clay
Stankovich A. (Ser)
De Stefano S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.2% / 65.8%
Market 1 / 2: 33.4% / 66.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W50 Kursumlijska Banja (Serbia), Clay
Sperle J
Monzon I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.0% / 35.0%
Market 1 / 2: 65.3% / 34.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Pecs (Hungary), Clay
Turcanu R D
Ghetu G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.0% / 58.0%
Market 1 / 2: 41.7% / 58.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M15 Bucharest 3 (Romania), Clay
Wessels L
Vanshelboim E. (Ukr)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.8% / 50.2%
Market 1 / 2: 49.5% / 50.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Oldenzaal (Netherlands), Clay
Angelini L
Lokoli L. (Fra)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.7% / 76.3%
Market 1 / 2: 23.1% / 76.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Gulin S
Molleker R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 46.4% / 53.6%
Market 1 / 2: 46.4% / 53.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Nedic A
Garbero F. F. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 91.4% / 8.6%
Market 1 / 2: 92.2% / 7.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Okutoii A. (Ken)
Cvetkovic A. (Ser)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 48.3% / 51.7%
Market 1 / 2: 48.1% / 51.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Liutkemeier A. (Ssha)
Cervino Ruiz C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.7% / 43.3%
Market 1 / 2: 56.8% / 43.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Torello (Spain), Hard
Palicova B
Zoldakova D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.8% / 34.2%
Market 1 / 2: 66.1% / 33.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W75 Bytom (Poland), Clay
Chepelev A
Bernet, Henry
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.2% / 71.8%
Market 1 / 2: 27.5% / 72.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Lausanne (Switzerland), 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.