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.
Fullana L
Bohrer Martins C
Claeys L
Andrienko M
Loge J
Faucon R
Deniel Dzh
Dang Yiming
Donald M W
Tabacco G
Marterer M
Hassan B
Rahmani K. (Irn)
Krutykh O
Petrovic Andreja
Echeverria J
Perego G
Nordquist A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 44.5% / 55.5%
Market 1 / 2: 29.2% / 70.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: International Rokhempton 2
Petkovic M
Iannaccone F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.2% / 37.8%
Market 1 / 2: 62.9% / 37.1%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International Augsburg
Frey A
Krueger A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 10.6% / 89.4%
Market 1 / 2: 6.0% / 94.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International WTA 125K. Filadelfiya
Seidel E
Jauffret C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 86.6% / 13.4%
Market 1 / 2: 90.1% / 9.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International WTA 125K. Filadelfiya
Wang Xiyu
Koike E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 86.4% / 13.6%
Market 1 / 2: 89.6% / 10.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International WTA 125K. Filadelfiya
Samsonova L
Preston T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.7% / 26.3%
Market 1 / 2: 75.9% / 24.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International WTA 125K. Filadelfiya
Wong H Y C
Volynets K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 14.6% / 85.4%
Market 1 / 2: 10.5% / 89.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International WTA 125K. Filadelfiya
Pohankova M
Reasco Gonzalez M E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.1% / 17.9%
Market 1 / 2: 86.2% / 13.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International WTA 125K. Filadelfiya
Kuzuhara B
Pirson K. (Avs)
Munoz E
Fancutt T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 12.9% / 87.1%
Market 1 / 2: 8.5% / 91.5%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Gore N
Martin D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 13.6% / 86.4%
Market 1 / 2: 9.3% / 90.7%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Britto L. (Bra)
Sheehy J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 12.4% / 87.6%
Market 1 / 2: 7.9% / 92.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Zhu Evan
Reymond A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.8% / 24.2%
Market 1 / 2: 77.6% / 22.4%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Bianchi J J
Chopra K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 23.9% / 76.1%
Market 1 / 2: 21.2% / 78.8%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Gonzalez Fernandez M
Mallory R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.3% / 17.7%
Market 1 / 2: 85.6% / 14.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Mayo A
Goncalves Ceolin J V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 84.9% / 15.1%
Market 1 / 2: 88.3% / 11.8%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger. Kingston 2. Qualification
Grenier H
Rottgering, Mees
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.5% / 61.6%
Market 1 / 2: 38.4% / 61.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP - Singles: Uinston-Salem (USA) - Qualification, Hard
Sakellaridis S
Van Assche L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.1% / 59.9%
Market 1 / 2: 38.7% / 61.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger Quebec City, Canada Men Singles
Shelbayh A
Suresh D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 48.5% / 51.5%
Market 1 / 2: 48.2% / 51.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP. Uinston-Seylem
Herbert P-H
Vandecasteele Q
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.1% / 41.9%
Market 1 / 2: 58.4% / 41.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Uinston-Seylem
Balshaw F
Gorzny S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 58.8% / 41.2%
Market 1 / 2: 58.8% / 41.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Uinston-Seylem
Mpetshi Perricard G
Brooksby J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.2% / 59.8%
Market 1 / 2: 39.9% / 60.1%
Favourite trap: no
Data completeness: 61%
Match context
Court:Veyk Forest Tennis Senter, Center court
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Uinston-Seylem
Prizmic D
Hewitt Cruz
Comesana F
Wu Yibing
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.3% / 61.7%
Market 1 / 2: 36.6% / 63.4%
Favourite trap: no
Data completeness: 61%
Match context
Court:Veyk Forest Tennis Senter, Tretiy court
Bookmaker coverage: 1×2 5 · total 0
Tournament: International ATP. Uinston-Seylem
Luz O / Matos R
Frantzen C / Haase R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.2% / 38.8%
Market 1 / 2: 61.5% / 38.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ATP Cincinnati, USA Men Doubles
Zakharova A
Blinkova A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 52.7% / 47.3%
Market 1 / 2: 52.0% / 48.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: Challenger Women - Singles: Philadelphia (USA), Hard
Prizmic D
Hewitt C. (Avs)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 74.2% / 25.8%
Market 1 / 2: 77.9% / 22.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ATP - Singles: Uinston-Salem (USA), Hard
Shimabukuro S
Kecmanovic M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.9% / 67.1%
Market 1 / 2: 31.0% / 69.0%
Favourite trap: no
Data completeness: 67%
Match context
Court:Veyk Forest Tennis Senter, Tretiy court
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP - Singles: Uinston-Salem (USA), Hard
Banthia S
Kukhar M. (Ssha)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.8% / 64.2%
Market 1 / 2: 33.7% / 66.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: Challenger Men - Singles: Kingston 2 (Jamaica) - Qualification, Hard
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.