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.
Kachmarek A
Iemmi L
Kokot B
Chlodnicki J
Plunger M
Amarandei D A
Chepelev A
Parenti L
Vives Marcos P
Baibars A
Yesypchuk D
Tran L
Senic N
De Stefano S
Kawano Cho G
Bayerlova M
Mettraux M
Mpetshi Perricard D
Toth A K
Alvisi E
Kavaguti N. (Iapo)
Nugroho P. M. (Inz)
Barreira Bonzom V
Casas Blasi J
All stats and model conclusions
Model vs market
Glicko 1 / 2: 31.9% / 68.1%
Market 1 / 2: 28.9% / 71.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Cap d'Agde (France), Clay
De Schepper K
Pizzigoni M
All stats and model conclusions
Model vs market
Glicko 1 / 2: 65.2% / 34.8%
Market 1 / 2: 67.5% / 32.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M15 Cap d'Agde (France), Clay
Baumgartner S. (Shva)
Bieldiugin T
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.7% / 70.3%
Market 1 / 2: 28.1% / 71.9%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M25 Lausanne (Switzerland), Clay
Carboni L
Bellegy A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 88.3% / 11.7%
Market 1 / 2: 91.4% / 8.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Men - Singles: M25 Lausanne (Switzerland), Clay
Guntsiker A. (Shva)
Sakellaridis D
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.4% / 80.6%
Market 1 / 2: 16.9% / 83.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Lausanne (Switzerland), Clay
Sawant M. (Ind)
Okutoii A. (Ken)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 12.8% / 87.2%
Market 1 / 2: 8.6% / 91.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Guth M
Komano M. (Fra)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 88.0% / 12.0%
Market 1 / 2: 91.1% / 8.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Ryser V
Ivankovic I. (Shva)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 61.0% / 39.0%
Market 1 / 2: 86.6% / 13.4%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Singles: W35 Verbier 2 (Switzerland), Clay
Kotliar Y
Steur J L S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 38.5% / 61.5%
Market 1 / 2: 36.4% / 63.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W50 Oldenzaal (Netherlands), Clay
Tsakarevich S./Zaitseva K.
Aleksandrova R./Garnevska V.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 80.3% / 19.7%
Market 1 / 2: 82.8% / 17.2%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Doubles: W50 Kursumlijska Banja (Serbia), Clay
Morderger T / Morderger Y
Du Pree B./Van Emst S.
All stats and model conclusions
Model vs market
Glicko 1 / 2: 40.4% / 59.6%
Market 1 / 2: 18.6% / 81.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Women - Doubles: W50 Oldenzaal (Netherlands), Clay
McCormick T
Lopes Morilo I. (Isp)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 60.8% / 39.2%
Market 1 / 2: 45.9% / 54.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: ITF Men - Singles: M25 Oviedo (Spain), Clay
Aunion P. (Isp)
Demin Y
All stats and model conclusions
Model vs market
Glicko 1 / 2: 34.3% / 65.7%
Market 1 / 2: 31.0% / 69.0%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Oviedo (Spain), Clay
Parisca I
Perez Contri S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 51.4% / 48.6%
Market 1 / 2: 50.8% / 49.2%
Favourite trap: no
Data completeness: 29%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M25 Oviedo (Spain), Clay
Turini V
Steiner V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 45.2% / 54.8%
Market 1 / 2: 45.2% / 54.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: ITF Women - Singles: W35 Trieste (Italy), Clay
Argyrokastriti M
Kolmenia M. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.8% / 63.2%
Market 1 / 2: 34.9% / 65.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Women - Singles: W35 Trieste (Italy), Clay
Hejtmanek A. (Chekh)
Antici A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 87.0% / 13.0%
Market 1 / 2: 89.6% / 10.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Women - Singles: W35 Trieste (Italy), Clay
Fumagalli F
Petrovic Drazen
All stats and model conclusions
Model vs market
Glicko 1 / 2: 69.3% / 30.7%
Market 1 / 2: 70.2% / 29.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: ITF Men - Singles: M15 Kursumlijska Banja 13 (Serbia), Clay
Spiridon D C
La Vela G. (Ita)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 29.6% / 70.4%
Market 1 / 2: 27.4% / 72.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Maribor (Slovenia), Clay
Campana Lee, Gerard
Cizek, Jiri
All stats and model conclusions
Model vs market
Glicko 1 / 2: 79.0% / 21.0%
Market 1 / 2: 81.1% / 18.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Poznan (Poland), Clay
Sadzik J
Filar K
All stats and model conclusions
Model vs market
Glicko 1 / 2: 36.4% / 63.6%
Market 1 / 2: 34.9% / 65.1%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 4 · total 0
Tournament: ITF Men - Singles: M25 Poznan (Poland), Clay
Wazny A. (Pol)
Pieczonka F
All stats and model conclusions
Model vs market
Glicko 1 / 2: 59.4% / 40.6%
Market 1 / 2: 59.4% / 40.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M25 Poznan (Poland), Clay
Loffagen Dzh. (Vel)
Gray A
All stats and model conclusions
Model vs market
Glicko 1 / 2: 42.9% / 57.1%
Market 1 / 2: 49.1% / 50.9%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: Challenger Men - Singles: Roehampton 2 (United Kingdom), Hard
Bonding, Oliver
Durasovic V
All stats and model conclusions
Model vs market
Glicko 1 / 2: 53.8% / 46.2%
Market 1 / 2: 54.5% / 45.5%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Roehampton 2 (United Kingdom), Hard
Deckers, Alec
Potenza L
All stats and model conclusions
Model vs market
Glicko 1 / 2: 54.8% / 45.2%
Market 1 / 2: 58.6% / 41.4%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 1 · total 0
Tournament: Challenger Men - Singles: Roehampton 2 (United Kingdom), Hard
Haupt, Henri
Gulin S
All stats and model conclusions
Model vs market
Glicko 1 / 2: 11.1% / 88.9%
Market 1 / 2: 7.8% / 92.2%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Augsburg (Germany), Clay
Sanches-Martines B. (Ger)
Dellen Velasko M. (Bol)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 62.7% / 37.3%
Market 1 / 2: 63.3% / 36.8%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 3 · total 0
Tournament: Challenger Men - Singles: Augsburg (Germany), Clay
Mena F
Rehberg M H
All stats and model conclusions
Model vs market
Glicko 1 / 2: 19.7% / 80.3%
Market 1 / 2: 17.4% / 82.6%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 5 · total 0
Tournament: Challenger Men - Singles: Augsburg (Germany), Clay
Lopes N. (Shva)
Held J. (Ger)
All stats and model conclusions
Model vs market
Glicko 1 / 2: 71.0% / 29.0%
Market 1 / 2: 72.9% / 27.1%
Favourite trap: no
Data completeness: 18%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Allershausen (Germany), Clay
Mazdrashki A
Petre S I B
All stats and model conclusions
Model vs market
Glicko 1 / 2: 77.9% / 22.1%
Market 1 / 2: 79.7% / 20.3%
Favourite trap: no
Data completeness: 24%
Match context
Bookmaker coverage: 1×2 2 · total 0
Tournament: ITF Men - Singles: M15 Bucharest 3 (Romania), 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.