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W60 Prague 3



W60 Prague 3 Women ITF Women - Singles - Czech Republic 2023

ITF Women - Singles W60 Prague 3 is a tennis tournament played in Czech Republic. There are 49 players competing in the 2023 Women season. Among the players competing in the W60 Prague 3 are Sonay Kartal, Aurora Zantedeschi, Reka-Luca Jani, Berfu Cengiz. Browse below for the W60 Prague 3 match results and fixtures, tennis trends or over/under number of games among other various statistics like 1st Set win sequence or set winning averages.

Final

Semi Finals

Quarter Finals

Round 3

Round 2

Round 1

🎾 Matches

Trends

Andreea Mitu has won 80.00% of sets played in all W60 Prague 3 matches
Anastasiya Soboleva has won all 1st sets in the last 4 matches played at W60 Prague 3

Players – Set Performance

Player NamePerformanceMatchesAverage
Andreea Mitu 96 2.50
Sara Bejlek 54 2.25
Anastasiya Soboleva 44 2.50
Camilla Rosatello 44 2.00
Seone Mendez 44 2.00
Alexia Todoni Anca 34 2.25
Pia Lovric 33 2.33
A. Kucmova 11 3.00
Ekaterine Gorgodze 12 2.50
Irina Bara 13 1.67
Data in above table is calculated for Set Performance as number of sets won minus number of sets lost along the tournament.

Over/Under

Matches of... Total games score (player + opponent)
Avg. over 22.5
A. Kucmova 32.00 100.00 %
Alexia Todoni Anca 20.25 25.00 %
Alice Rame 18.00 0.00 %
Amelie Smejkalova 24.00 100.00 %
Anastasiya Soboleva 20.75 25.00 %
Andreea Mitu 22.17 33.33 %
Andreea Prisacariu 18.50 0.00 %
Angelica Moratelli 22.00 50.00 %
Aurora Zantedeschi 17.00 0.00 %
Berfu Cengiz 25.50 50.00 %
Camilla Rosatello 19.00 0.00 %
Chiara Scholl 18.50 0.00 %
Dalma Galfi 16.00 0.00 %
Dominika Salkova 16.50 0.00 %
Ekaterine Gorgodze 19.00 50.00 %
Garbiela Vasilescu Arina 15.00 0.00 %
Irina Bara 15.00 0.00 %
Ivana Sebestova 23.00 100.00 %
J. Pastikova 27.00 100.00 %
Jana Vanik 27.00 100.00 %
Julie Struplova 18.50 0.00 %
Katarina Kuzmova 16.00 0.00 %
Katarina Stresnakova 28.00 100.00 %
Katharina Hobgarski 24.33 66.67 %
Kiara Toth Amarissa 22.00 0.00 %
Lea Boskovic 20.00 0.00 %
Linda Klimovicova 17.50 0.00 %
Luca Udvardy 20.67 0.00 %
Maja Chwalinska 19.50 0.00 %
Martina Okalova 26.00 100.00 %
Martyna Kubka 15.50 0.00 %
Oana Gavrila 17.00 0.00 %
Pia Lovric 22.00 33.33 %
Radka Zelnickova 20.00 0.00 %
Reka-Luca Jani 25.00 100.00 %
Rina Saigo 13.00 0.00 %
Sapfo Sakellaridi 22.50 50.00 %
Sara Bejlek 22.00 25.00 %
Sarah Iliev 20.00 0.00 %
Seone Mendez 19.75 0.00 %
Sofia Shapatava 25.00 100.00 %
Sonay Kartal 20.00 0.00 %
T. Krejcova 14.00 0.00 %
Tatiana Pieri 15.50 0.00 %
Tereza Smitkova 17.00 0.00 %
Valeriya Strakhova 21.00 0.00 %
Victoria Bervid 14.00 0.00 %
Zhibek Kulambayeva 32.00 100.00 %
Tournament Avg.   29.34%
Data in above table is calculated from finished matches only.

Clean Sheets

Player Name CS Pld Perc.
Andreea Mitu 3 6 50.00%
Camilla Rosatello 3 4 75.00%
Sara Bejlek 3 4 75.00%
Seone Mendez 3 4 75.00%
Anastasiya Soboleva 2 4 50.00%
Irina Bara 2 3 66.67%
Pia Lovric 2 3 66.67%
Alexia Todoni Anca 1 2 50.00%
Andreea Prisacariu 1 2 50.00%
Chiara Scholl 1 2 50.00%
Dominika Salkova 1 2 50.00%
Ekaterine Gorgodze 1 2 50.00%
Julie Struplova 1 2 50.00%
Lea Boskovic 1 2 50.00%
Linda Klimovicova 1 2 50.00%
Luca Udvardy 1 3 33.33%
Maja Chwalinska 1 2 50.00%
Martyna Kubka 1 2 50.00%
Tatiana Pieri 1 2 50.00%
Victoria Bervid 1 2 50.00%
A. Kucmova 0 1 0.00%
Alice Rame 0 1 0.00%
Amelie Smejkalova 0 1 0.00%
Angelica Moratelli 0 2 0.00%
Aurora Zantedeschi 0 1 0.00%
Berfu Cengiz 0 2 0.00%
Dalma Galfi 0 1 0.00%
Garbiela Vasilescu Arina 0 1 0.00%
Ivana Sebestova 0 1 0.00%
J. Pastikova 0 1 0.00%
Jana Vanik 0 1 0.00%
Katarina Kuzmova 0 1 0.00%
Katarina Stresnakova 0 1 0.00%
Katharina Hobgarski 0 3 0.00%
Kiara Toth Amarissa 0 1 0.00%
Martina Okalova 0 1 0.00%
Oana Gavrila 0 1 0.00%
Radka Zelnickova 0 1 0.00%
Reka-Luca Jani 0 1 0.00%
Rina Saigo 0 1 0.00%
Sapfo Sakellaridi 0 2 0.00%
Sarah Iliev 0 1 0.00%
Sofia Shapatava 0 1 0.00%
Sonay Kartal 0 1 0.00%
T. Krejcova 0 1 0.00%
Tereza Smitkova 0 1 0.00%
Valeriya Strakhova 0 1 0.00%
Zhibek Kulambayeva 0 1 0.00%
Data in above table lists the number of clean sheets (matches with no lost sets) out of total matches played by each player in this tournament. All finished and retired matches are counted towards these statistics.

Form – Win Sequences

Player NameWin 1st SetSet Win%
A. Kucmova0 66.67%
Alexia Todoni Anca0 66.67%
Alice Rame0 0.00%
Amelie Smejkalova1 33.33%
Anastasiya Soboleva4 70.00%
Andreea Mitu2 80.00%
Andreea Prisacariu0 50.00%
Angelica Moratelli0 40.00%
Aurora Zantedeschi0 0.00%
Berfu Cengiz0 40.00%
Camilla Rosatello0 75.00%
Chiara Scholl0 50.00%
Dalma Galfi0 0.00%
Dominika Salkova0 50.00%
Ekaterine Gorgodze2 60.00%
Garbiela Vasilescu Arina0 0.00%
Irina Bara0 60.00%
Ivana Sebestova1 33.33%
J. Pastikova1 50.00%
Jana Vanik1 33.33%
Julie Struplova0 50.00%
Katarina Kuzmova0 0.00%
Katarina Stresnakova0 33.33%
Katharina Hobgarski0 50.00%
Kiara Toth Amarissa1 50.00%
Lea Boskovic0 50.00%
Linda Klimovicova0 50.00%
Luca Udvardy0 50.00%
Maja Chwalinska0 50.00%
Martina Okalova0 50.00%
Martyna Kubka0 50.00%
Oana Gavrila0 0.00%
Pia Lovric0 71.43%
Radka Zelnickova0 0.00%
Reka-Luca Jani1 33.33%
Rina Saigo0 0.00%
Sapfo Sakellaridi0 40.00%
Sara Bejlek0 77.78%
Sarah Iliev0 0.00%
Seone Mendez0 75.00%
Sofia Shapatava0 33.33%
Sonay Kartal0 0.00%
T. Krejcova0 0.00%
Tatiana Pieri0 50.00%
Tereza Smitkova0 0.00%
Valeriya Strakhova0 0.00%
Victoria Bervid0 66.67%
Zhibek Kulambayeva1 33.33%
Data in above table lists consecutive 1st Set wins by each player. When 1st Set is lost, data is reset. Set Win percentage shows the winning percentage out of total sets played by each player in this tournament. All finished and retired matches are counted towards these statistics.

The statistics for the tennis tournament W60 Prague 3 are updated regularly, as games are played and results are processed. Data shown in these statistics is organized to make it easy to identify tournament trends and probabilities for future tennis games, something that TennisStats247 system uses to calculate the predictions for ITF Women - Singles W60 Prague 3. The Predictions section comes with updated daily tips as our algorithm calculates probabilities for various outcomes in tennis games and it improves as more tennis statistics are available from matches played.

Seasons