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.
Covato M
Tabakko F. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 12.1% / 87.9%
Market 1 / 2: 11.3% / 88.7%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Parizzia N
Hassan B
All stats and model conclusions
Model vs market
Glicko 1 / 2: 24.4% / 75.6%
Market 1 / 2: 23.5% / 76.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Chepelev A
Ciavarella, Niccolo
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.3% / 17.7%
Market 1 / 2: 82.8% / 17.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Komo (Italy) - Qualification, Clay
Vaissaud D
Brune E. C. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 73.2% / 26.8%
Market 1 / 2: 74.0% / 26.0%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W15 Hurghada 6 (Egypt), Hard
Spasov D. (Bol)
Ribero F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 13.1% / 86.9%
Market 1 / 2: 11.9% / 88.1%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Plovdiv 3 (Bulgaria) - Qualification, Clay
Tolev A. (Bol)
Khamza Reguig S. (Alzh)
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: Challenger Men - Singles: Plovdiv 3 (Bulgaria) - Qualification, Clay
Mashtakov N
Basel, Valentin
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.5% / 17.5%
Market 1 / 2: 83.2% / 16.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Plovdiv 3 (Bulgaria) - Qualification, Clay
Javia D
Funk A. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.1% / 46.9%
Market 1 / 2: 52.8% / 47.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Hurghada 6 (Egypt), Hard
Vrba J
Nakashima, Brandon
All stats and model conclusions
Model vs market
Glicko 1 / 2: 26.1% / 73.9%
Market 1 / 2: 25.6% / 74.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Champaign, IL (USA), Hard
Wu Yibing
Walton A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.9% / 54.1%
Market 1 / 2: 46.0% / 54.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Lehecka J
Carreno Busta P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 75.7% / 24.3%
Market 1 / 2: 76.3% / 23.7%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Majchrzak K
Medjedovic H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.7% / 46.3%
Market 1 / 2: 53.4% / 46.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Merida Aguilar D
Fucsovics M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 79.2% / 20.8%
Market 1 / 2: 79.6% / 20.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Prizmic D
Shevchenko A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 70.6% / 29.4%
Market 1 / 2: 71.2% / 28.8%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Van Assche L
Norrie C
All stats and model conclusions
Model vs market
Glicko 1 / 2: 35.0% / 65.0%
Market 1 / 2: 34.6% / 65.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Munar J
Atmane, Terence
All stats and model conclusions
Model vs market
Glicko 1 / 2: 37.9% / 62.1%
Market 1 / 2: 37.7% / 62.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Samuel, Toby
Machac T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 56.5% / 43.5%
Market 1 / 2: 56.4% / 43.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Harris L
Kennedi Dzh. (Ssha)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 82.3% / 17.7%
Market 1 / 2: 83.0% / 17.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: ATP - Singles: Open Championship USA (USA), Hard
Shimabukuro S
Rinderknech A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 27.4% / 72.6%
Market 1 / 2: 26.6% / 73.4%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Wong C
Paul T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.0% / 81.0%
Market 1 / 2: 17.8% / 82.2%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Men. Qualifying Wild Card Challenge
Gibson T
Vekic D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 51.1% / 48.9%
Market 1 / 2: 51.3% / 48.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Photiades P
Erjavec V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.8% / 40.2%
Market 1 / 2: 83.9% / 16.1%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Rakhimova K
Krejcikova B
All stats and model conclusions
Model vs market
Glicko 1 / 2: 26.9% / 73.1%
Market 1 / 2: 26.4% / 73.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Zarazua R
Iatcenko P
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.5% / 45.5%
Market 1 / 2: 54.4% / 45.6%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Pegula J
Ruse E G
All stats and model conclusions
Model vs market
Glicko 1 / 2: 86.2% / 13.8%
Market 1 / 2: 87.0% / 13.0%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 6 · total 0
Tournament: WTA - Singles: Open Championship USA (USA), Hard
Arango E
Wang Xinyu
All stats and model conclusions
Model vs market
Glicko 1 / 2: 28.7% / 71.3%
Market 1 / 2: 28.3% / 71.7%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Women
Zhang S
Fernandez L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 32.0% / 68.0%
Market 1 / 2: 31.5% / 68.5%
Favourite trap: no
Data completeness: 39%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: International US Open. Women
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.

