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.
Daems J
Giza L
Loge J
Vankan M
Longueville R
Andrienko M
Rapagnetta D
Lokoli L
Segetti S
De Marchi A
Herbert P-H
Miyoshi K
Balshaw F
Pow L
Frantzen C / Haase R
Granollers M / Zeballos H
Burnett E
Fullerton M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.0% / 53.0%
Market 1 / 2: 47.5% / 52.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Men. East Lansing
Solheim F K
Almasi A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.8% / 54.2%
Market 1 / 2: 44.7% / 55.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International UTR Pro. Men. East Lansing
Wells C
Battle L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.5% / 46.5%
Market 1 / 2: 53.5% / 46.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR Pro. Men. East Lansing
Corwin F
Corwin O
Shaya P
Baltazor N
All stats and model conclusions
Model vs market
Glicko 1 / 2: - / -
Market 1 / 2: - / -
Favourite trap: no
Data completeness: -
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: International UTR Pro. Men. East Lansing
Lamchinniah H
Spurling N
All stats and model conclusions
Model vs market
Glicko 1 / 2: - / -
Market 1 / 2: - / -
Favourite trap: no
Data completeness: -
Match context
Bookmaker coverage: 1×2 0 · total 0
Tournament: International UTR Pro. Men. East Lansing
Nelson T
Gordon E
Loureiro M
Maunupau O
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.0% / 46.0%
Market 1 / 2: 54.6% / 45.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Women. Ist-Lansing
Blackford E
Ivanova E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.0% / 65.0%
Market 1 / 2: 34.2% / 65.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR Pro. Women. Ist-Lansing
Reynoldson E
Kovalcik J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 49.5% / 50.5%
Market 1 / 2: 49.5% / 50.5%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Women. Ist-Lansing
Nys H / Roger-Vasselin E
Luz O / Matos R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 50.9% / 49.1%
Market 1 / 2: 51.5% / 48.5%
Favourite trap: no
Data completeness: 61%
Match context
Court:Lindner Femili Tennis Senter, Grandstend
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Doubles. Cincinnati. Hard
Tu Li
Vandecasteele Q
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.3% / 52.7%
Market 1 / 2: 47.1% / 52.9%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International ATP. Uinston-Seylem
Gorzny S
Polmans M. (Avs)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.7% / 45.3%
Market 1 / 2: 54.1% / 46.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP. Uinston-Seylem
Heck H
Rottgering M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 21.6% / 78.4%
Market 1 / 2: 19.0% / 81.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP. Uinston-Seylem
Shelbayh A
Seggerman R
All stats and model conclusions
Model vs market
Glicko 1 / 2: 68.1% / 31.9%
Market 1 / 2: 69.0% / 31.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: International ATP. Uinston-Seylem
Zapp L
Treacy A
Duran B
Mayoral C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 52.0% / 48.0%
Market 1 / 2: 51.4% / 48.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Men. Neshvill
Jacques H
Ushizima H H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.9% / 61.1%
Market 1 / 2: 38.2% / 61.8%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International UTR Pro. Men. Neshvill
Vassar A
Vasilescu L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.6% / 39.4%
Market 1 / 2: 60.8% / 39.2%
Favourite trap: no
Data completeness: 33%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International UTR Pro. Men. Neshvill
Candiotto A / Mi Lan
Fullana L./Souza M.
Cobolli F
Fils A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.4% / 67.6%
Market 1 / 2: 30.7% / 69.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 7 · total 0
Tournament: ATP Cincinnati, USA Men Singles
Hipfl N./Oradini Dzh.
Bosio G / Martin Manzano J C
Pascual Ferra R
Suresh D. (Ind)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.4% / 63.6%
Market 1 / 2: 35.5% / 64.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP - Singles: Uinston-Salem (USA) - Qualification, Hard
Pegula J
Swiatek I
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.0% / 66.0%
Market 1 / 2: 32.6% / 67.4%
Favourite trap: no
Data completeness: 72%
Match context
Court:Lindner Femili Tennis Senter, Stadium
Bookmaker coverage: 1×2 5 · total 0
Tournament: International WTA Cincinnati, USA Women Singles
Villanueva G
Aguilar Cardozo J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.7% / 54.3%
Market 1 / 2: 45.6% / 54.4%
Favourite trap: no
Data completeness: 44%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International World Tennis. Men. Paraguay. Doubles
Sakellaridis S
Daniel T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 47.6% / 52.4%
Market 1 / 2: 47.8% / 52.3%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International ATP Challenger Quebec City, Canada Men Singles
Werner C
Vidmanova D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.4% / 80.6%
Market 1 / 2: 17.0% / 83.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Monterrey (Mexico) - Qualification, Hard
Jeanjean L
Rakhimova K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.2% / 61.8%
Market 1 / 2: 36.6% / 63.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: WTA - Singles: Monterrey (Mexico) - Qualification, Hard
Udvardy P
Arango E
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.2% / 54.8%
Market 1 / 2: 43.6% / 56.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Monterrey (Mexico) - Qualification, Hard
Liang En Shuo
Kraus S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 33.1% / 66.9%
Market 1 / 2: 32.1% / 67.9%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Monterrey (Mexico) - 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.